Deliverable - ICT Adoption Study

The original document contains some material which was collected in commercial confidence To enable public access to the document the parts that contain commercially sensitive information have been masked out This document as shown represents the views or comments of the independent Consultant based on its findings during the consultancy study The publication of this document does not necessarily mean that the Government has adopted or endorsed or otherwise any views recommendations or conclusions of the Consultant

Study on ICT Adoption in Hong Kong SMEs

Final Report

Prepared for

Office of the Government Chief Information Officer The Government of Hong Kong Special Administrative Region

Prepared by

Ali Farhoomand PhD Professor of Innovation and Information Management

October 8 2009


Table of Content

EXECUTIVE SUMMARY 1

1 INTRODUCTION 11

2 BACKGROUND 12

3 ASSESSING ICT ADOPTION IN HONG KONG SMES 14

31 The annual survey conducted by the CampSD in 2008 14 32 Additional literature review of ICT adoption by Hong Kong SMEs 17

4 RESULTS LITERATURE REVIEW OF ICT ADOPTION BY SMES IN OTHER COUNTRIES 20

5 CONCEPTUAL FRAMEWORK 27

6 OVERVIEW OF THE RESEARCH METHODOLOGY AND HYPOTHESES 33

7 ANALYSIS amp RESULTS BASED ON THE CampSD DATA 35

71 Analysis based on the ldquoAnnual Survey on IT Usage amp Penetration in the Business Sectorrdquo (ASIT) report 35 72 Analysis based on the ASIT report and Value Added obtained from the CampSD 38 73 Conclusions 38

8 OVERVIEW OF THE SURVEYS CONDUCTED BY HKU 40

81 Survey questionnaires 40 82 Telephonic case study interviews 42 83 Literature review of similar experiences across the world 42 84 Selection of sectors 42

9 RESULTS ANALYSIS OF THE SURVEY QUESTIONNAIRES 44

91 Usage of Personal Computers (PCs) ndash Stage 1 47 911 Not Using PCs 47 912 Using PCs 50 913 Value Added from PC Usage 52 914 Relationship between Perceived Value and PC Usage 53 915 Summary of findings for Section 91 ndash Stage 1 54

92 Usage of the Internet ndash Stage 2 55 921 Not Using the Internet 55 922 Using the Internet 57 923 Value Added by Internet Usage 59 924 Relationship between Perceived Value and Internet Usage 60 925 Summary of findings for Section 92 ndash Stage 2 61

93 Usage of a Website ndash Stage 3 62 931 Not Using a Website 62 932 Respondents Having a Website 66 933 Value Added from Having a Website 66


934 Relationship between Perceived Value and Website Usage 67 935 Summary of findings for Section 93 ndash Stage 3 68

94 Integration of the Website 69 95 Perceptions of Overall ICT Usage and Value Added 69 96 Relationships between Overall Value and Extent of ICT Use 70 97 Stages of ICT Development 71

971 Government Support 71 972 Reasons for Not Using ICT 72 973 ICT Value 73

98 Factors Influencing ICT Adoption 75 981 Value Added as a Function of ICT Usage Size Age Stage and Sector 75 982 Revenue as a Function of ICT Usage Size Age Stage and Sector 75

99 Predictors of ICT Adoption 76 991 PC Adoption 76 992 Internet Adoption 76 993 Website Adoption 77

910 Performance Differences between ICT Adopters and Non-Adopters 77 911 Hypotheses 78

10 SELECTED INDUSTRIES CASE STUDIES AND COMPARISONS WITH OTHER COUNTRIES 81

101 WholesaleRetail Sector 81 102 Import and Export Trade 87

1021 Import and Export Trade - Consumer non-durable goods 87 1022 Import and Export Trade - Consumer durable goods 92 1023 Import and Export Trade ndash Miscellaneous 98

103 Business Services 103 104 Manufacturing Sector 107

11 FINDINGS amp RECOMMENDATIONS 114

111 Prior to Stage 1 PC Non-Adopters 114 112 Stage 1 PCs Adopters Who Have Not Yet Adopted the Internet 115 113 Stage 2 Internet Adopters who have not yet adopted the Web 117 114 Stage 3 Adopters of the Web 119 115 Overall ICT Usage and Value Added 120 116 Differences in ICT Adoption across Stages of ICT Development 120 117 Value added as a function of ICT usage size age stage and sector 121 118 Revenue and ICT usage size age stage and sector 121 119 Predictors differentiating ICT adopters from non-adopters 121

APPENDICES 123

Appendix 1 SMEs Stages of Growth 123

Appendix 4 Weights assigned to the questions selected for each stage of ICT Usage calculation from the

Appendix 5 Details of the questions selected for calculation of ICT Usage from the ASIT Report based

Appendix 2 Calculation of ICT Usage 125 Appendix 3 Analysis of the questions selected for calculation of ICT Usage from the ASIT Report 127

ASIT Report 141

on the Codes available at CampSD 148 Appendix 6 Number of sampled establishments [1] in 2008 ASIT with PC and Internet connection 155 Appendix 7 Analysis of CampSD data ndash ICT Usage across Industries 157 Appendix 8 Value Added of Small and Medium Enterprises (SMEs) by select industry sector 2007 164


Appendix 9 Mail survey in English 165

Appendix 17 Use of Personal Computers (PCs) - Not using PCs - How important are the following

Appendix 19 Use of Personal Computers (PCs) - Do you think that using PCs would help improve the

Appendix 20 Use of Personal Computers (PCs) - What would really motivate or push you to use PCs

Appendix 10 Mail survey in Chinese181 Appendix 11 Telephone interview guidelines 196 Appendix 12 Online survey in English 198 Appendix 13 Online survey in Chinese 208 Appendix 14 Data Merge and Transformation for further analysis 217 Appendix 15 RevenueSales in the last fiscal year ndash by industry size and age 219 Appendix 16 Use of Personal Computers (PCs) - Does your firm use Personal Computers (PCs) 221

reasons for your firm not to use PCs 222 Appendix 18 Use of Personal Computers (PCs) - Not using PCs - ANOVA 223

operations of your firm If so how 226

230 Appendix 21 Use of Personal Computers (PCs) - How useful would the following types of Government support be to your firm for installing and using computers 234 Appendix 22 Use of Personal Computers (PCs) - How useful would the following types of Government

Appendix 23 Use of Personal Computers (PCs) - Using PCs - Please mark the extent to which your firm

Appendix 24 Use of Personal Computers (PCs) - Using PCs - Please mark the extent to which your firm uses software for the following tasks ndash ANOVA across Industry Number of Employees Age of the firm

support be to your firm for installing and using computers - ANOVA 235

uses software for the following tasks 239

241 Appendix 25 Use of Personal Computers (PCs) - Using PCs - The extent to which the firm uses the following tailor-made computer systems of FinanceAccounting PurchasingSale and order processing HRMTraining 253 Appendix 26 Use of Personal Computers (PCs) - Using PCs - The extent to which the firm uses the following tailor-made computer systems of FinanceAccounting PurchasingSale and order processing

Appendix 27 Use of Personal Computers (PCs) - Using PCs - How much value do you think PCs have


Appendix 31 Use of the Internet - Internet Usage - Not using Internet How important are the following


Appendix 33 Use of the Internet - Internet Usage - Not using Internet Do you think that using the

Appendix 34 Use of the Internet - Internet Usage - Not using Internet What would really motivate or

Appendix 35 Use of the Internet - Internet Usage - Not using Internet How useful would the following


Appendix 37 Use of the Internet - Internet Usage - Using Internet Please mark the extent that your firm

HRMTraining ndash ANOVA 254

added to the following aspects of your business 259

added to the following aspects of your business - ANOVA 260 Appendix 29 Correlations of VA1 and ICTU1 268 Appendix 30 Use of the Internet - Internet Usage - Does your firm use the Internet 279

reasons for your firm not to use the Internet 280

reasons for your firm not to use the Internet - ANOVA 282

Internet would help improve your business If so how 287

push you to use the Internet 289

types of Government support be to your firm for using the Internet 290

types of Government support be to your firm for using the Internet - ANOVA 291

uses the Internet for the following purposes 295


Appendix 38 Use of the Internet - Internet Usage - Using Internet Please mark the extent that your firm uses the Internet for the following purposes - ANOVA 296 Appendix 39 Use of the Internet - Internet Usage - Using the Internet Do you think that using the

Appendix 40 Use of the Internet - Internet Usage - Using the Internet How much value do you think the


Appendix 44 Website Usage - Not using Website How important are the following reasons for your


Appendix 46 Website Usage - Do you think that creating a website would help improve your business

Appendix 48 Website Usage - Not using Website How useful would the following types of Government


Appendix 50 Website Usage - Using Website Please mark the extent that your firm uses its website for


Appendix 52 Website Usage - Using Website How much value do you think your website has added to


Appendix 55 Integration of the Website - Is your firms websitewebpage integrated with the firmrsquos

Appendix 56 Integration of the Website - Is your firms websitewebpage integrated with your business

Appendix 57 Overall ICT Usage - Overall to what extent do you feel that your firm currently uses ICT

Internet more would help improve the business of your firm If so how 308

Internet has added to the following aspects of your business 314

Internet has added to the following aspects of your business - ANOVA 315 Appendix 42 Correlation between VA2 and ICTU2324 Appendix 43 Website Usage - Website Usage - Does your firm have website 333

firm not having a website 334

firm not having a website ndash ANOVA 336

If so how 343 Appendix 47 Website Usage - What would really motivate or push you to create a website 350

support be to your firm for creating a website 357

support be to your firm for creating a website - ANOVA 358

the following purposes 362

the following purposes ndash ANOVA 364

the following aspects of your business 377

the following aspects of your business ndash ANOVA 379 Appendix 54 Correlation of VA3 and ICTU 3 393

database 398

partnersrsquo database399

400 Appendix 58 Overall Value Added ICT Usage - Overall to what extent do you feel that using more ICT would increase the value added to your firm 402 Appendix 59 Overall ICT UsageValue Added - Overall to what extent do you feel that your firm currently uses ICT Overall to what extent do you feel that using more ICT would increase the value added to your firm ndash ANOVA 404 Appendix 60 Overall ICT UsageValue Added ndash Regression Analysis 410 Appendix 61 Overall ICT UsageValue Added ndash Revenue Regression Analysis ndash Composite Indices 451 Appendix 62 Stages of ICT Development ndash Differences in Government Support 492 Appendix 63 Stages of ICT Development ndash Reasons for Not Using ICT 522 Appendix 64 Stages of ICT Development ndash Differences in Value added from ICT 554 Appendix 65 Regression between Value added and ICTU Size Age Stage and Sector 587 Appendix 66 Regression between Revenue and ICT Usage Size Age Stage and Sector 590 Appendix 67 Discriminant Analysis 592 Appendix 68 Performance Differences between ICT Adopters and Non-Adopters 632



EXECUTIVE SUMMARY

I Objective

This Study titled ldquoStudy on Information and Communications Technology Adoption in Hong Kong Small and Medium Enterprisesrdquo has been undertaken by the University of Hong Kong at the behest of the Office of Government Chief Information Officer (ldquoOGCIOrdquo) The study aims at providing a better understanding of the extent of ICT adoption in Hong Kong SMEs and the barriers and motivators both perceived and actual for the SMEsrsquo ICT adoption It also provides recommendations to the OGCIO the strategies and measures to encourage SMEs to use and benefit from ICT

II Conceptual Framework

Our review of existing research showed that most studies on the adoption and use of ICT by SMEs were piecemeal and examined only parts of the complex tapestry that composes the SMErsquos ecosystem In this Study we have drawn on our previous work done in this area and by refining a holistic and integrated framework of key issues facing SMEs we have tried to ensure that all the key issues that confront SMEs during different stages of their development at enterprise industry and national levels along with the various socio-economic dimensions were addressed in a systematic and comprehensive manner

We have defined the following three stages of ICT Usage and used this as a basis for our research methodology

Stage 1 Localised Exploitation (PCs) amp Internal Integration Stage 2 Web presence (Internet Connection) amp Online Transaction Stage 3 External Integration (Electronic value chain) amp Transformation

III Research Methodology

We have conducted a multi-pronged approach to undertake this study After carrying out a comprehensive review of literature and study we identified the factors facilitating or hindering the adoption and use of ICT among SMEs We then posited several hypotheses subsequent to which a synthesis of the literature combined with the relevant statistical data allowed us to modify our conceptual framework to develop a structural model of factors leading to the adoption or inhibiting the adoption of ICT

We first analysed the existing data available on ICT adoptions by Hong Kong SMEs which included statistical analysis based on the survey data (including IT usage data from

1


business IT survey and business operating data from annual economic surveys) provided by the ldquoCensus amp Statistics Department Hong Kong SARrdquo (ldquoCampSDrdquo)

We also conducted our own surveys through mail survey large-scale telephone survey and email survey

Based on the results of the CampSD analysis the preliminary results of our own survey and the economic contribution of an industry we selected some industry sectors for in-depth study and in-depth interviews These have been detailed as case studies and have been used to complement our quantitative findings

The advantage of this multi-pronged approach is that it provides a richer picture of the issues surrounding the adoption of ICT by SMEs in Hong Kong

IV Data Collection

We have conducted a multi-pronged approach to undertake this study

We first conducted our analysis at the CampSD where we found a correlation between ICT Usage and the number of employees at a firm More important we found a significant correlation between the Value Added and the ICT Usage in the WholesaleRetail sector

Then based on the literature review and our findings at the CampSD we commenced the administration of a survey questionnaire as follows

bull Mail questionnaire The mail questionnaire was prepared in both Chinese and English and a total of 10000 questionnaires were sent out

bull Telephone interviews Large-scale telephone interviews were conducted and a total of 1000 telephone interviews were made

bull Email surveys We also sent out 3900 e-mail surveys to the SMEs with email address in both English and Chinese

Over and above a detailed research of literature available for the adoption of ICT by SMEs across the world was carried out to see if there are similar experiences in other parts of the globe

Based on our analysis at the CampSD we had identified the WholesaleRetail sector as showing a significant correlation between the Value Added and the ICT Usage In addition we had also decided to focus on the ImportExport Trade sector (further sub-

2


divided into three parts) as this contributes almost half of the Value Added of SMEs to the Hong Kong economy Further preliminary analysis based on our analysis of the survey responses showed a significant correlation between revenue and extent of ICT use for Business Services sector Hence this sector was also selected for further investigation It was decided that an in-depth interview would be conducted for two SMEs in each of the above sectors as well as the manufacturing sector Hence in all there were twelve interviews conducted and the findings have been reported as qualitative case studies to complement our quantitative findings

V Results of the Analysis

One of the most significant questions raised by the Study was deciding where the Government should intervene to promote ICT Usage In other words we were looking for those industries where ICT usage has shown a significant correlation with the value addedrevenue for the industry This was found to be the WholesaleRetail sector and the Business Services sector Further as analysis showed that the ImportExport Trade sector contributed 48 of the value added of SMEs in Hong Kong this too was included in the survey

We next attempted to answer some key questions

Do ICT users believe ICT can add value

We wanted to know if the overall perceived value of ICT is dependent on the extent of ICT use We found that the respondents saw a very clear connection between the perceived overall value of ICT and the overall extent of ICT use

How is revenue related to ICT use size age stage and sector

To answer this question we performed regression analysis between revenue1 and ICT usage size age stage and sector We found as expected a significant relationship between revenue and size with the larger the firm having higher revenue We also found a significant relationship between revenue and stage of ICT usage with a higher stage showing higher revenue

There was however no significant relationship between revenue and ICT usage or with the firmrsquos age When using revenue as the dependent variable we found no significant relationship between revenue and sector with the only exception being the Import amp Export ndash Consumer durable goods

1 For the purpose of this report ldquoRevenuerdquo refers to the responses received to the survey question ldquoTotal revenuesales in the last fiscal yearrdquo

3


Are there different reasons for not adopting PCInternetWebsite

We checked to see if there are differences in responses across the three stages of ICT development with regard to issues related to reasons for not using different types of ICT (PCs the Internet Website) and government support that would facilitate and encourage ICT adoption at the three stages We also took into account the firmrsquos age size and sector and further examined the effect of the stage of ICT development on perceived value of ICT taking into consideration these variables

We found that by and large there are no differences between responses regarding the various types of Government support reasons for not using ICT and the value-adding effect of ICT at different stages of ICT development One exception though relates to the importance of providing tax incentive which is significantly different at the three different stages of development with non-PC users perceiving it to be more important than non-Website users than non-Internet users Another exception relates to difficulty in finding trained personnel at different stages of development with non-Website users perceiving this factor more important than non-PC users than non-Internet users

We also found that that by and large SMEs see different value in ICT at different stages of ICT development For instance the value of ICT in terms of improving operational efficiency and customer services is highest among PC users followed by Internet and website users Further the value of ICT in terms of increasing sales penetration into new markets and providing competitive advantage is highest among PC users followed by Website users and then Internet users Website users see higher value in ICT than Internet users in terms of creation of new productsservices

Is value added related to ICT usage size age stage and sector

We found that there was a significant and direct relationship between value added2 and ICT usage This was also true for the value added and stage of ICT usage with highest value added perceived in the Website stage There was also a significant relationship between value added and age with newer firms seeing greater value added than the older ones There was however no significant relationship between value added and size

Are there any key predictors differentiating ICT adopters from non-adopters

2 For the purpose of this report ldquoValue addedrdquo refers to the responses received to the survey question of the type ldquoHow much value do you think PCs have added to the following aspects of your businessrdquo This question was repeated for each stage that is ldquoHow much value do you think the Internet has added to the following aspects of your businessrdquo and ldquoHow much value do you think the Website has added to the following aspects of your businessrdquo

4


We tried to ascertain the influential predictors of ICT adoption at various stages of adoption

First for PC adoption we used revenue size and age as predictors of PC adoption We found that these three independent variables did not have the predicting power to discriminate between PC adoptersnon adopters However when we repeated the above analysis by including sector as an additional predictor we found a significant model where PC adoption is inversely related to revenue and size Further PC adoption is more likely among firms that are between 3-7 years old

We repeated the analysis for Internet adoption but we also included the level of PC usage as an additional predictor We found a significant model where Internet adoption is directly related to PC usage inversely related to revenue and size and firms that are between 3-7 years old

We repeated the analysis for Website adoption but we also included the level of PC and Internet usage in addition to revenue size age and sector We found a significant model where Website adoption is directly related to PC and Internet usage directly related to revenue and size and more likely among firms less than 3 years or those over 7 years old than those 3-7 years old

In sum when we used revenue size age sector and prior usage of ICT as predictors we found that

bull PC and Internet adoption is more likely among small firms while Website adoption more likely among large firms

bull PC and Internet adoption is more likely among firms 3-7 years old while Website adoption more likely among very young or old firms

bull Both Internet adoption and Website adoption are influenced by prior usage of ICT Internet adoption is directly related to PC Usage and Website adoption is directly related to PC and Internet Usage

Are there any missed opportunities

We tried to see if there are any performance differences between ICT adopters and non-adopters This would help answering the question of whether or not some SMEs missed opportunities by not adopting ICT

For each of the three stages of the adoption we used revenue as a proxy for the firmrsquos performance Beside one exception (ImportExport (Consumer durable goods)) with regard to Website adoption we found no significant difference in revenue between ICT

5


adopters and non-adopters indicating that by and large there doesnrsquot seem to be any ldquomissed opportunityrdquo among the non-adopters

Findings related at different stages of ICT adoption

(i) Findings related to those not using personal computers

We found that the most important reason for not adopting a PC appears to be SMEsrsquo perception that they do not really need it They feel that they have managed well without a PC so far and see no need to make such an investment that would be difficult and expensive to maintain These perceptions were similar across all sectors size and age of the firm Lack of awareness of the benefits of PCs is in reality a great barrier to these firms

For those respondents who agreed that the use of PCs could be advantageous the main perceptions of its benefits revolve around its expected speed accuracy and efficiency

The primary motivator to adopt a PC was stated to be a business needrequirement for it The other popular facilitator mentioned was addressing the expense of the ICT investment (providing financial support and an inexpensive PCpackage) while the third most important was technical support

In terms of government support subsidising training in the use of PCs (which addresses both the knowledge and cost barriers) and providing PC adoption consulting services were the most popular suggestions We also observed that the younger firms found subsidising training in using PCs providing PC adoption consulting services and providing tax incentives to be far more useful than the older firms

(ii) Findings related to those using personal computers

We found that companies that only use PCs and not the Internet largely restrict their use to word processing computer security and spreadsheet software The comparatively more technically advanced packages or custom-made software such as HRMtraining and presentation are the least popular Custom-based packages too are not very popular However the use of software packages and custom-based packages differs across industries

6


The size of the firm also appears to be an inhibitor to using advanced PC software such as finance sales and HRM packages We noted that the usage of these as also custom-based packages increases with an increase in the number of employees

Improving operational efficiency and customer service are the two top value additions made by the use of PCs Overall we found a correlation between perceived value of PCs to the business and the extent to which firms use them This has important policy implication as it highlights the importance of education among PC users about benefits of other types of ICT such the Internet

(iii) Findings related to those not using the Internet

The most popular responses for not using the Internet was that it was not needed that it is costly and that the firmrsquos business partners and customers are not using the Internet Further on the whole in response to our interviews and case studies we found that most respondents were disinterested in adopting the Internet as they thought that it would not help improve their business This would suggest that a lack of business need combined with a lack of awareness of the benefits of the Internet is the reason these respondents have not opted for the Internet

Addressing this lack of awareness and knowledge of the benefits and costs of the Internet is the greatest support requested from the Government and hence providing consulting services and subsidising training were the most popular suggestions of the types of support that Government can provide

(iv) Findings related to those using the Internet

We found that the most popular uses of the Internet are e-mail and on-line sourcing of general information applications that do not require a Website We also found that some companies especially the young ones use the Internet for e-banking The respondents were also asked if they perceived additional use of the Internet would further improve their business The responses are mixed with almost half stating that more Internet usage would not help improve their business while the other half felt that it would

Those who saw the potentials of the Internet identified speedy communication and ease of searching for information and improvement in marketingsales development as areas with greatest potential As in the case of PCs improving operational efficiency and enhancing customer service are perceived to be the two top values ensued by the use of the Internet Providing competitive advantage and prompt response to market changes are the next most popular perception of where Internet use adds value to the business

7


We noted significant differences in the perception of value added across sectors and according to the age of the firm across all dimensions For instance the perception that the use of the Internet had improved customer service was far more pronounced in the Business Services sectors as compared to the WholesaleRetail sectors Similarly the value added by improving operational efficiency due to Internet usage is perceived to be significantly higher by the newer companies as compared to the older ones

(v) Findings related to those not using a Website

The following reasons are mentioned as to why the firm do not create a Website lack of business need difficulty of maintenance costs lack of industry critical mass Through our interviews we further collaborated these findings as we noted that non-adoption of a Website had to do with the fact that the businessmarket had not yet evolved sufficiently for a great need of a Website and the expense associated with creating and maintaining a Website perceived to be high

Once again as in the case of PC and Internet use it appears that business needs would be the main driver for creating a Website Addressing the technical and financial constraints are the other two prime motivators

We observed no significant differences about the reasons for not having a Website across different industries or based on the firmrsquos size or age

In terms of the types of support offered by the Government to motivate these firms to create a Website subsidising ICT training providing ICT adoption consulting services and providing tax incentives for ICT investments were the most popular

(vi) Findings related to those using a Website

The most popular uses of the Website are for ldquoProviding information on the firms products and servicesrdquo followed by ldquoHandling of business enquiryrdquo

Similar to the usage of PCs and the Internet improving operational efficiency and customer service are the two top dimensions of having a Website

However unlike the PC and the Internet we found no evidence of any correlation between perceived value of a Website to the business and the extent to which firms use a Website

VI Recommendations

Related to the use of personal computers

8


bull We did not find any connection between PC adoption and revenue (as a proxy for the firmrsquos performance) indicating that doesnrsquot seem to be any missed opportunities with regard to ICT adoption However if the Government decides to embark on any assistance program we recommend that it should first focus on the WholesaleRetail Business Services and ImportExport Trade industries as they either contribute a large amount to the value added of SMEs in Hong Kong or show significant correlation of value addition with ICT Usage

bull Moreover for non-PC users we recommend that the Government assistance should focus on subsidising training in the use of PCs and providing PC adoption consulting services This group looks for Government support primarily in terms of building knowledge and awareness Providing tax incentives for ICT investment should also be helpful

bull We also recommend that while providing Government support the focus should be on smaller firms and the firms that are between 3-7 years old as these appear more likely to the idea of investing in PCs

bull While promoting the benefits of using PCs the Government must consider the specific needsbarriers and facilitators for the industry

Related to the use of the Internet

bull We did not find any connection between Internet adoption and revenue indicating that there doesnrsquot seem to be any missed opportunities with regard to ICT adoption However if the Government decides to embark on any assistance program we recommend that Government assistance should concentrate on subsidising training in the use of the Internet and providing Internet adoption consulting services Such training workshops should highlight the potential costs and benefits of the Internet as lack of awareness is a main inhibitor

bull We also recommend that while providing Government support the focus should be on smaller firms those that are between 3-7 years old and firms that are high users of PCs

bull To promote the benefits of adopting the Internet we also recommend that the focus be on the speedy and cheap communication and ease of information availability as these were rated by far the most useful benefits

Related to the use of a Website

9


bull Beside one exception (ImportExport (Consumer durable goods)) we did not find any connection between Web adoption and revenue indicating that by and large there doesnrsquot seem to be any missed opportunities with regard to Web adoption However if the Government decides to embark on any assistance we recommend that the Government should promote the benefits of creating a Website by emphasising its use in marketingsales development

bull We also recommend that while providing assistance the focus should be on larger firm those firms less than 3 years or over 7 years old and firms that are high users of PCs and the Internet

bull As in the case of promoting PC and Internet usage we recommend that the Government assistance should focus on subsidising ICT training and providing ICT adoption consulting services and consider providing tax incentives for ICT investment

10


1 INTRODUCTION

In its 2008 Digital 21 Strategy the blueprint for Hong Kong ICT development the Government of the Hong Kong Special Administrative Region (the ldquoGovernmentrdquo) acknowledged the contribution of ICT to local economic development Nonetheless according to the annual survey on IT Usage and Penetration conducted by the Census and Statistics Department (ldquoCampSDrdquo) despite the increasing use of ICT in the business sector in Hong Kong there is a gap of ICT use between large companies and SMEs

To address this concern the Office of Government Chief Information Officer (ldquoOGCIOrdquo) issued a Consultancy Brief for a project titled ldquoStudy on Information and Communications Technology Adoption in Hong Kong Small and Medium Enterprisesrdquo The study aims at providing a better understanding of the degree of ICT adoption in Hong Kong SMEs including the barriers for SMEsrsquo ICT adoption and its economic impact It will help formulate strategies and measures to encourage SMEs to use and benefit from ICT More specifically the study will shed light on all issues related to adoptionnon-adoption of ICT among Hong Kong SMEs taking into account differences in industry sectors The study will also try to provide a deeper understanding of the perceived benefits of and barriers to ICT adoption so as to ascertain whether small firms make well-informed business decision towards ICT adoption

On 13 January 2009 the project was assigned to the University of Hong Kong An Inception Report was prepared which listed out a number of hypotheses However subsequent to the suggestions arising from the meeting with the OGCIO and the members of the Digital Inclusion Task Force on March 13 2009 the scope of the study was refined The conceptual frameworks were revisited to incorporate a SME stage growth model which would reflect the differences in strategic focus problems and potential values at different levels of ICT use and sophistication Further a more detailed repository of the factors that lead to ICT adoption by SMEs was incorporated

An Interim Report providing a status update of the progress of the Study since the Inception Report was presented to the OGCIO on August 20 2009

This Final Report is the conclusive document which details the research methodologies that have been defined for the Study along with details of the quality control mechanism response rate sampling design and pilot survey It demonstrates the flow in which the conceptual framework and literature review have been used to define the research methodology It provides details of the work done which includes the study of the existing statistical data received from the CampSD the on-site data analysis in the CampSD office and the data collection logistics and methods used for analysis for the surveys

11


conducted by the HKU It finally proposes a set of recommendations to address the above issues

2 BACKGROUND

Across the world it has been recognised that small and medium-sized enterprises (SMEs) are todayrsquos main engine of economic growth It is estimated that SMEs contribute to 815 of global economic growth 3 For example in Hong Kong alone more than 260000 Hong Kong SMEs accounting for 98 of business establishments provide about 12 million jobs or half of the total non-government employment4

The creation of SMEs may bring about the reinforcing dynamics of economic growth That is an increase in the total number of SMEs in an economy would increase job creation and job per capita When people become wealthier they would increase their consumption which in turn would open up new market opportunities to entice the creation of more businesses

SMEs are also entrepreneurial and flexible in nature thereby improving a nationrsquos innovative capacities5 They also contribute to the reinforcing loop of innovation that drives economic growth As the number of SMEs in a particular sector increases and they become more knowledgeable of their product and industry they can become more innovative and thus more profitable This profitability would then encourage more people to establish their own businesses6 From the governmentrsquos perspective SMEs are not only an important source of tax revenues but are also critical to societyrsquos stability by providing job opportunities thus contributing to the creation of wealth

Besides their importance in the domestic market SMEs can also be considered an engine for international development and the growth of exports This is particularly evident in

3 Jutla D Bodorik P and J Dhaliqal (2002) Supporting the e-business readiness of small and medium sized enterprises approaches and metrics Internet Research Electronic networking Applications and Policy 12(2) pp139-164

4 Trade and Industry Department ndash Support and Consultation Centre for SMEs (29 Dec 2008) ldquoSMEs in HKrdquo httpwwwsuccesstidgovhkenglishlin_sup_orggov_depservice_detail_6863html

5 Anderson AR J-H Li RT Harrison and PJA Robson (2003) The increasing role of small business in the Chinese economy Journal of Small Business Management 41(3) pp310-316

6 Kotelnikov V (2007) Small and Medium Enterprises and ICT UNDP-APDIP and APCICT

12


the East and Southeast Asia economies such as South Korea Indonesia Taiwan Hong Kong and Singapore7

In light of the above it is hence not surprising that governments around the world are keen to find ways to facilitate the development of SMEs ndash and among other initiatives through the effective adoption and utilisation of ICT Even though there is a lack of consensus as to how ICT investment and firm performance is linked there seems to be three general categories of the potential benefits of ICT adoption by SMEs8

First ICT can be of strategic importance to a SME Todayrsquos globalised economy has created an increasing need for SMEs to access and use ICT and their ability to do so will determine their success in regional and global business networks9 Second the use of ICT can improve the SMEsrsquo operational efficiencies Third ICT can help increase the organisational transparency of SMEs to internal and external stakeholders by facilitating communication within and between firms10

7 Tambunan T (2008) SME development economic growth and government intervention in a developing country The Indonesian story Journal of International Entrepreneurship 6 pp147-167

8 Farhoomand A and Huang M ldquoDoes IT pay off The tale of two banking giants strategiesrdquo Proceedings International Conference on Information Systems Montreal Canada Dec 2007

9 Chacko JG and G Harris (2005) ICT and small medium and micro enterprises in Asia Pacific ndash size does matter Journal of IT for Development March

10 Street and Meister (2004) Small Business Growth and Internal Transparency MIS Quarterly 28(3) define transparency as follows ldquoFrom the accounting and finance perspective transparency refers to the observability of transactions for both investment and regulatory purposes From the supply chain management perspective transparency refers to information exchange between supply chain partners From the marketing perspective information flow from the customer is seen to be valuablerdquo

13


3 ASSESSING ICT ADOPTION IN HONG KONG SMES

31 The annual survey conducted by the CampSD in 2008

According to the categorisation done by the Government of Hong Kong11 manufacturing enterprises with fewer than 100 employees and non-manufacturing enterprises with fewer than 50 employees in Hong Kong are defined as SMEs

The annual survey conducted by the Hong Kong CampSD in 2008 brought to light the following observations related to the ICT adoption among Hong Kong SMEs12

First compared to larger enterprises fewer SMEs have exploited the ICTs Fewer SMEs have personal computers local area network (LAN) connection intranet and extranet than large enterprises (Table 1)

Size of Establishments

Penetration rate

Large Medium Small

Personal computers

991 917 584

LAN Connection 842 572 224

Intranet 643 292 73

Extranet 286 104 32

Table 1 ICT Penetration Rates by Firm Size

Moreover SMEs are less likely to use the Internet to collect and disseminate information than large enterprises (Table 2)

11 httpwwwsuccesstidgovhkenglishlin_sup_orggov_depservice_detail_6863html 12 Report on 2008 Annual Survey on Information Technology Usage and Penetration in the Business Sector

Census and statistics Department HKSAR

14


15



Internet use

Large Medium Small

WebPageswebsites 753 450 145

Use industry portal sites 220 160 108

Table 2 Use of the Internet Resources by Firm Size

Second ICT adoption and usage varies among SMEs in different sectors (see Table 3) Compared to other industry sectors the financing insurance real estate and business services sector has the highest adoption rates in all four categories and the transport storage and communications sector has the lowest

of establishment having

Industry Sector

PCs Internet Connection

Webpage websites

Electronic Business

Financing insurance real estate and business services 835 792 243 804

Wholesale retail and import and export trades restaurants and

hotels 653 618 209 622

Community social and personal services

602 505 160 523

Construction 548 464 62 458

Manufacturing electricity and gas 522 510 176 502

16


Transport storage and communications

320 307 64 321

Table 3 Usage and Penetration of ICTs in SMEs by Industry Sector

The above survey proved to be a valuable document to base our research methodology

Existing literature also suggests that the role of government in facilitating ICT adoption in SMEs seems to be indirect13 In spite of government initiatives to encourage Internet adoption by SMEs around the world and the SMEsrsquo desire for government influence and regulation14 there is little evident that these policies are particularly successful in directly changing attitudes among SMEs15 At most governments could influence SMEsrsquo ICT adoption and diffusion by improving telecommunication infrastructure and making policies with regard to subsidies and mobilisation16 17

32 Additional literature review of ICT adoption by Hong Kong SMEs

In addition to the annual survey conducted by the Hong Kong Census and Statistics Department there are several other studies that have assessed the ICT adoption among Hong Kong SMEs

bull The Hong Kong Productivity Council (HKPC) carries out a series of surveys18 19 In a study of IT purchasing behaviours in Hong Kong SMEs through telephone interviews with 500 local SMEs from 10 industry sectors with in-house server systems the two most cited barriers for adopting an in-house server system were lack of internal technical expertise and budget constraint20 This study urges SMEs to look beyond the systemrsquos initial acquisition costs and consider other cost factors (such as

13 Scupola A (2009) SMEsrsquo e-commerce adoption perspectives from Denmark and Australia Journal of Enterprise Information Management 22(12) pp 152-166

14 Scupola A (2003) Government intervention in SMEs e-commerce adoption an institutional approach Proceedings of the 7th Pacific Asia Conference on Information Systems 10-13 July Adelaide Australia pp184-195

15 Beckinsale M and M Levy (2004) SMEs and Internet adoption strategy who do SMEs listen to Proceedings of European Conference on Information Systems

16 Lau ASM and VWS Kwok (2007) How e-government strategies influence e-commerce adoption by SMEs Electronic Government 4(1) pp20-42


18 Chan W (1999) Barriers and Solutions to Increase IT Application for SMEs HKPC 19 Tang TKY (2003) Hong Kong productivity council ndash the I-hub of productivity in Hong Kong Journal

of Materials Processing Technology 139(1-3) pp596-603 20 httpwwwhkpcorgtextengindustry_surveyall_industriesit_purchasingjsp

17


running costs) risk and business flexibility In another study of mould management SMEs HKPC found that most respondents expected to benefit from computerisation through saving time engaging less staff and enhancing work efficiency and

21accuracy

bull The Information Security Survey jointly conducted by the OGCIO the HKPC and the Commercial Crime Bureau of the Hong Kong Policy Force found that compared to large companies SMEs are less proactive in implementing a comprehensive security management strategy Also SMEs prefer to use measures that are free of charge and easy to implement and do not want to invest too much in security management22

bull A study of the adoption of electronic trading systems (ETS) in Hong Kong SMEs in 2004 found that the perceived desirability and feasibility of the technology under consideration have significant impacts on SMEsrsquo adoption decisions Cost is the main obstacle and transaction speed transaction volume transaction accuracy and order management are among the perceived benefits from ETS adoption23

bull In a study of the electronic data interchange (EDI) adoption in Hong Kong SMEs using a survey of 644 SMEs it was found that perceived direct benefits perceived cost IT knowledge government incentives and enforcement and trading partnersrsquo influence are the critical determining factors in EDI adoption24 The key determinants of small business EDI adoption was further explained by studying ValuNet an EDI software installed in personal computers for trading companies to lodge import and export trade declarations electronically SMEs were found to focus more on those immediate and direct benefits rather than long-term indirect benefits Perceived costs were seen as major impediments to the EDI adoption while prior EDI experience and perceived level of support from the vendor also determined EDI adoption by SMEs25

bull A similar study of EDI adoption among SMEs in Hong Kong It was found that EDI adopters perceive higher direct benefits (eg improvement of data accuracy data

21 httpwwwhkpcorghtmlengindustry_surveyall_industriesdocsurvey_mouldpdf 22 httpwwwhkpcorghtmlengindustry_surveyall_industriesdocInfoSecSurpdf 23 Khalifa M and RM Davison (2006) SME Adoption of IT The Case of Electronic Trading Systems

IEEE Transactions on Engineering Management 53(2) pp275-284 24 Chau PYK and V Liu (2004) Adoption of electronic data interchange in small- and medium-sized

enterprises in Hunter G and F Tan (eds) Advanced Topics in Global Information Management Vol 3 pp225-252 IGI Publishing

25 Chau PYK and KL Hui (2001) Determinants of Small Business EDI Adoption An Empirical Investigation Journal of Organizational Computing and Electronic Commerce 11(4) pp 229-252

18


security and operation efficiency) lower financial costs higher technical competence higher government pressure and lower industry pressure than non-adopters26

bull In a study of e-procurement adoption in SMEs in Hong Kong The following factors were found to affect SMErsquos e-procurement adoption value of e-procurement adoption trust on supplier trust on IT power of e-procurement and power of suppliers 27

bull In a study of the barriers to the adoption of global electronic commerce in ten companies in both Hong Kong and Finland most of which were SMEs it was found that resistance to change lack of education about the potentials of e-commerce and lack of flexible software were identified as the key inhibitors to adoption28

bull A study of e-commerce adoption in Hong Kong SMEs in Hong Kong found that SMEs often wait to follow major corporations to adopt e-commerce Data confidentiality quality assurance issues and human and capital investment were the main concerns of SMEs in their adoption of e-commerce 29

bull A study of Hong Kong SMEs in the clothing industry suggests that SMEs can take the full advantage of IT and the web by forming and expanding a network organisation and by integrating physical and virtual value chains30

bull A study of the computerisation strategy of SMEs in the manufacturing industry identified obsolete organisational structure and policies and employeesrsquo reluctance to change as the major impediments of computerisation 31

26 Kuan KKY and PYK Chau (2001) A perception-based model for EDI adoption in small business using a technology-organisation-environment framework Information and Management 38(8) pp507-521

27 Chan JKY and MKO Lee (2003) SME e-procurement adoption in Hong Kong ndash the role of power trust and value Proceedings of the 36th Hawaii International Conference on System Sciences

28 Farhoomand AF VK Tuunainen and LW Lee (2000) Barriers to Global Electronic Commerce A Cross-Country Study of Hong Kong and Finland Journal of Orgnizational Computing and Electronic Commerce 10(1) pp 23-48

29 Yeung JHY JP Shim and AYK Lai (2003) Current progress of e-commerce adoption small and medium enterprises in Hong Kong Communications of the ACM 46(9) pp226-232

30 Au KF and DCK Ho (2002) Electronic commerce a business model for Hong Kong clothing SMEs International Journal of Services Technology and Management 3(1) pp5-21

31 Chung WWC and SKO Chik (2001) Computerisation strategy for small manufacturing enterprises in Hong Kong International Journal of Computer Integrated Manufacturing 14(2) pp141-153

19


4 RESULTS LITERATURE REVIEW OF ICT ADOPTION BY SMEs IN OTHER COUNTRIES

As part of our conceptual framework we have carried out a detailed research of literature available for the adoption of ICT by SMEs across the world to understand if there are similar experiences in other parts of the globe Some of the interesting and relevant findings from this review have been detailed below

41 ldquoThe Role of Intermediating Institutions in the Diffusion of Electronic Data Interchange (EDI) How Industry Associations Intervened in Denmark Finland and Hong Kongrdquo Damsgaard J and Lyytinen K (2001) The Information Society 173 195 mdash210

This article studied the impacts of industry associations on facilitating the EDI diffusion process in the grocery sectors of Hong Kong Denmark and Finland The research found that

Trade and industry associations play an important role in launching industry-wide uses of complex and networked technologies However their strategies and policies need to be tailored to the local environment The success depends on the power dependencies competitive structure and traditions of the industry Additionally trade and industry associations are important not only during the introduction period but also the EDI diffusion phase to proceed beyond local dyadic relationships

Institutional players in Hong Kong Denmark and Finland all first considered common EDI solutions and avoided following the dominating ldquostrategicrdquo and short-term approach to EDI

The EDI diffusion process and the different deployment strategies of trade and industry associations are influenced by local contingencies past history and cultural tradition For example Hong Kongrsquos high rentals were considered as a factor triggering the need for a technological solution

42 ldquoEDI Information and Communication Technology (ICT) Adoption in Horticulture A Comparison to the EFITA Baselinerdquo Taragola N and Gelb B

In the symposium held in 2004 on the constraints of ICT adoption in agricultural production and rural development the respondents from ldquodevelopedrdquo countries suggested that the main constraints included no perceived economic benefits not understanding the value of ICT and not enough time to spend on technology The respondents from

20


developing countries were more concerned with the cost of technology and lack of technological infrastructure It appears that in developed countries (and this could be taken in the context of Hong Kong) understanding how to benefit from the use of ICT was one of the most limiting factors for ICT adoption

43 ldquoSkills technology and organisational innovation in Spanish firmsrdquo A Bayo-Moriones M Billo and F Lera-Loacutepez International Journal of Manpower (2008)

The article provides empirical evidence on the relationship between new technologies innovative work practices and up-skilling using a Spanish case study Using detailed plant-level data from a survey of Spanish manufacturing firms the research has found that

bull ICT and innovative work practices are positively related to skills Even without using organisational innovations ICT can enhance up-skilling

bull Firms using ICT in their production process tend to hire workers with a university degree or at least who have finished their secondary education

bull ICT positively impacts the presence of workers with secondary education or a university degree who have the sort of general knowledge that the educational system provides

bull At a firm level results showed that the greater the degree of implementation of new technologies and innovative work practices the higher the need for new skills which means training becomes most important

This may have implications to Hong Kong as under the framework of Spanish economy the majority of firms are SMEs and there is a lack of a long-term investment in innovation and technology

44 ldquoAssessment of e-business adoption in SMEs A study of manufacturing industry in the UK North East regionrdquo R Marasini K Ions and M Ahmad Journal of Manufacturing Technology Management (2008) Vol 19 No 5

This paper tried to understand the barriers that prevent most manufacturing SMEs in the UK from adopting and benefiting from Internet technologies It analysed 32 companies assisted in the Internet technology adoption initiative under government funding and recommended and implemented tailored ICT solutions The findings are summarised as follows

21


bull SMEs tend to favour the improvisational model of technology adoption over the classic change model which may be explained by the alignment of technology the organisational context and the change model used

bull Development of low-cost reliable solutions which are tailored to the needs of SMEs and offer demonstrable business benefits should be given a priority

bull Informal individual and informal group learning plays a vital role in the successful implementation of new technology in SMEs Such learning can be a more effective way of implementing ICT solutions than formal training because it allows employees to systemise concepts into their own frames of reference

45 United Nations Conference on Trade and Development 2008 ldquoMeasuring the impact of ICT use in business The case of manufacturing in Thailandrdquo Prepared jointly by the UNCTAD secretariat and the Thailand National Statistical Office

This study analysed the economic impact of ICT use in the Thai business sector

bull The study evaluated the relationship between computer Internet and web presence and the value of sales per employee Results suggest that firms with a combined use of computers the Internet and the web had on average 21 per cent higher sales than firms without any of the ICTs considered after controlling for a series of firm-specific economic characteristics as well as industry and regional aspects of demand and supply Among the three ICTs considered computers contributed with 14 per cent Internet access with 3 per cent and web presence with 4 per cent

bull In developing countries computer presence in firms is more closely related to economic performance than in developed countries Compared to 95 per cent computer penetration rates in the business sector in many developed countries in developing countries the percentage of firms that use at least one computer for business purposes has remained lower (60 in manufacturing Thailand in 2002)

bull General trends on ICT use in Thai business show that from 2004 to 2006 the proportion of businesses using computers has increased steadily on a general level with faster growth among small businesses with 26 to 50 employees

bull One fifth of the urban firms with more than 10 employees did not use computers on their premises which is a larger share than in a number of other Asian economies such as Hong Kong (China) the Republic of Korea and Singapore

22


bull The sectors with the highest Internet penetration rate in business belong to the services industry computer and related activities and hospitals in Thailand In addition only 15 per cent of the small businesses (11 to 25 employees) are present on the web Web presence is used most frequently in computer and relates services in hospitals and in real estate businesses mainly for marketing products and as an inquirycontact facility Firms receiving orders online make up for 7 per cent of the business sector

bull Results of the productivity analysis find that computer use Internet access and Web presence are associated with significantly higher sales per employee Moreover variation in the intensity of computer use in Thailand was also reflected in higher productivity gains In the theoretical setting considered computers bring value to businesses both through their intrinsic characteristics such as processing and storage capacity and as necessary means for acquiring a superior complexity of ICT use such as Internet access and web presence

bull Its estimated results also show that Internet use matters more in small firms located in Bangkok and the neighbouring region while web presence was correlated with higher labour productivity gains among large businesses located in the central region

bull The groups which seem to benefit from a stronger ICT -labour productivity relationship are the larger and medium-sized more experienced firms located in the Central region and also in Bangkok and its vicinity Younger firms however tended to use computers more effectively while in smaller firms Internet access makes a big difference

bull The estimates derived in this study indicate the need to support the small and newly founded businesses particularly by enhancing access to Internet and facilitating their presence on the web On a regional scale small and young businesses dominate the business sector in the north and northeast of the country and therefore these regions should be a target of public programmes to facilitate ICT use Furthermore data show that in developing countries exporting firms use the Internet and the web more frequently than domestic producers largely motivated by the need to access information and consumers in foreign markets This could be explained by the fact that developed countries have the highest population of potential buyers and suppliers using the Internet and which are present on the web (Clarke and Wallsten 2004) Policies aiming to facilitate the creation of local content available on the Internet could help to bridge this gap

23


bull A large proportion of the firms that do not use ICTs on their premises identify as most important barriers the lack of perceived benefits and ICTs being inappropriate or unnecessary for their business This finding calls for policy action to raise awareness about the benefits businesses can achieve by using ICTs especially among small and medium-sized businesses where the proportion of firms refraining from ICT use is higher

46 ICT for Manufacturing Report of Meeting with Group of Representatives of Five Expert Panels 15 March 2005 Brussels

At five workshops more than hundred experts from industry and academia proposed and discussed research issues with respect to the increasing relevance of ldquoICT for Manufacturingrdquo The findings from these workshops are

bull Modern high-tech products were intelligent and networked This opens up new possibilities for adding value to customers through new Internet-enabled services such as customisation upgrade or maintenance

bull Wireless technologies could have a tremendous impact on shop floor agility thereby enhancing significantly manufacturing competitiveness For example Europersquos automation industry which has worldwide dominance is an example of showing the possibilities of the ldquowireless manufacturing plantrdquo

bull In terms of the design issues of manufacturing a new generation of ICT environment is needed that will support creativity in the design process and facilitate more tightly integrated user experimentation and knowledge networking

bull As to IPR protection it highlights the relevance of ICT in facilitating better protection of knowledge and intellectual property in general however also the need for better management tools and practices in the handling of IPR across the whole product lifecycle

47 ldquoSmall-Firm Computing Motivators and Inhibitorsrdquo P Cragg MIS QuarterlyMarch 1993

This study studied small-firm computing in the manufacturing industry The findings include

bull While many firms had experienced growth in the number and type of IT applications there had been little change with respect to the management of IT in small firms Although some forces like the wish for better information were

24


identified as motivating forces in the firms application growth tended to take place where the owner was enthusiastic toward the technology

bull Many inhibitors to growth were identified some strong enough to halt IT growth in a firm including inadequate resources particularly in the form of finance managerial time and internal expertise discouraged growth

48 ldquoProductivity in Australiarsquos Wholesale and Retail Traderdquo by A Johnston D Porter T Cobbold R Dolamore published by Productivity Commission in 2000

This study investigates the productivity trends in Australiarsquos wholesale and retail sector

The major findings include

bull Developments within the wholesale sector consistent with the productivity surge include the widespread adoption of productivity-enhancing technologies (eg barcoding paperless pick systems and automatic re-ordering processes) which moved the sector from a storage-based system to a fast flow distribution network and greater competition acting as a catalyst for rationalisation (through mergers acquisitions and firm exits) and outsourcing of non-core functions

bull Key developments within retailing consistent with this improvement include intensified competition (eg Korean entry-level cars) leading to rationalisation in tandem with the widespread adoption of labour saving technologies retailersrsquo responses to social and demographic changes (such as the growth in lsquoincome-rich and time-poorrsquo consumers) and changes in legislation governing trading hours and reforms to industrial relations legislation which have increased the focus on enterprise-based work conditions

bull The impact of many factors such as technology social changes institutional reforms and increased competition means that it is appropriate to view wholesale and retail trade as part of an integrated lsquovalue networkrsquo rather than simply as elements of a sequential distribution function linking producers and consumers

49 ldquoDoes Experience Matter Innovations and the Productivity of ICT in German Servicesrdquo by T Hempell Discussion paper of ZEW Centre for European Economic Research in 2002

This research is based on a representative data set for German service firms covering the period between 1994 and 1999 The results suggest that the experience gained from past innovations is a specific complement that makes ICT investment more productive

25


Administrations of firms that have introduced innovations in the past are expected to be better prepared to assess the potentials and limits of introducing major changes they may be more successful in training and motivating their employees to take part actively in the subsequent innovations and they may have acquired some degree of innovative reputation in new business areas which facilitates the sale of new products and services

26


5 CONCEPTUAL FRAMEWORK

Our review of existing research showed that most studies on the adoption and use of ICT by SMEs were piecemeal and examined only parts of the complex tapestry that composes the SMErsquos ecosystem

By adapting and refining a holistic integrated framework of key issues facing SMEs we have tried to ensure that all the key issues that confront SMEs during different stages of their development at enterprise industry and national levels along with the various socio-economic dimensions (Figure 1) were addressed in a systematic and comprehensive manner

The conceptual framework proposed for this study is an outgrowth of our previous work on SMEs in Hong Kong and other work related to the adoption of ICT in Hong Kong32

Figure 1 An Integrated Framework of Key Issues Facing SMEs

As shown later each of the above concepts has been addressed while creating a list of the key indicatorsfactors for ICT adoption by SMEs

SMEs Stage Model of ICT Development

32 Small Business Management and Entrepreneurship in Hong Kong Farhoomand A (Editor) Hong Kong University Press 2005

27


As elaborated in Appendix 1 SMEs go through different stages of growth and invest in ICT for different sets of economic or strategic reasons As such we posit that their ICT needs usage and investment also go through a stage model We employ the SMEs Stage Model of ICT Development (SSMID) to gain a better understanding of how ICT is deployed and exploited by SMEs This model is based on a framework that charts ICT-enabled business transformation 33 refined to embed the inherent idiosyncratic characteristics of SMEs34 and modified to the incorporate the emerging roles of the Internet and e-commerce35

As shown in Figure 2 the SSMID consists of six stages starting with low-impact low-benefit investments in internal systems and culminating in high-impact high-benefit investments in integrated strategic and transformative systems

1 Localised Exploitation

2 Internal Integration

3 Web Presence

4 Transaction

5 External Integration

6 Transformation

Low

High B u s i n e s s

T r a n s f o r m a t i o n

Low HighRange of Potential Benefits

33 Venkatraman N (1994) IT-induced business transformation from automation to business scope redefinition Sloan Management Review Winter pp73-87 34 Levy M and Powell P (2008) Small Firm Transformation Through IS International Journal of Technology Management Vol 43 35 Subba Rao S Metts G and Mora Monge C (2003) Electronic commerce development in small and medium sized enterprises A stage model and its implications Business Process Management Journal Vol 9 Page11 ndash 32

28


Figure 2 SMEs Stage Model of ICT Development

As mentioned earlier initial investments in ICT by SMEs are primarily motivated by cost considerations In the first stage of ICT development localised exploitation stage SMEs use a computer and various software applications to improve their operational efficiency in one or more functional areas usually starting with accounting Package systems are ideal for SMEs as they require low IS expertise and are more affordable Table 4 shows the different applications applied to different functional areas

The second stage is internal integration stage In this stage ICT applications in different areas are integrated sharing the same database and different applications such as intranet and Enterprise Resource Planning (ERP) SMEs realise integration benefits if they can use the information to improve customer service or efficiency or to acquire more customers As integration efforts at this stage usually do not take into account the industry value chain the benefits to SMEs may be limited

Functional Area Applications

Production

bull Stock control bull Jobwork scheduling bull Capacity planning bull Computer controlled

equipment

Design bull Engineer analysis bull Computer-aided design

(CAD)

Costing bull Job estimatingquoting bull Job costingcost analysis

Office Administration

bull Invoicing debtors creditors bull Purchase order processing bull Word processing bull Payroll

Marketing bull Mail shots to customers

bull General ledger

29


Financial bull Budgeting

Table 4 ICT Applications by Functional Areas36

At web presence stage SMEs establish online presence They provide static information related to their products or services history and contact details But there is little interaction with customers and suppliers At this stage SMEs have the potential to expand their geographic customer base

During transaction stage SMEs have a clear e-business strategy to buy and sell on-line and allow their customers to make and track their orders online They also engage in e-procurement with their suppliers using such applications as Internet EDI e-marketplaces and e-bidding At this stage SMEs have the potential to improve productivity through service integration Few SMEs however seem to progress further than this stage37

At external integration stage some SMEs manage to exchange information across the value chain manage relationships with collaborators achieve full supply chain or value chain integration and focus on knowledge management At this stage SMEs can redesign their business network to focus on their core competencies streamline their business scopes for flexibility 38

Stage Applications Value


bull PCLaptop bull One or more applications

(see Table 2 for the list of applications)

bull Improve task efficiency bull Reduce costs


bull Applications allow technical connectivity and application interdependence (eg intranet ERP)

bull Improve customers service

bull Improve efficiency and effectiveness

3 Web Presence bull Webpage bull Geographical expansion

of potential customer base

36 Cragg PB and M King (1993) Small-firm computing motivators and inhibitors MIS Quarterly March pp47-60 37 Levy M and P Powell (2003) ldquoExploring SME internet adoption issues towards a contingent model Electronic Markets 13 pp173-18138 Rao SS G Metts and CAM Monge (2003) Electronic commerce development in small and medium sized enterprises a stage model and its implications Business Process Management Journal 9 pp11-32

30


bull Email bull Online marketing

4 Transaction bull EDI bull E-marketplaces bull E-procurement

bull Improve productivity through service integration


bull Full supply chainvalue chain integration

bull E-enterprise with a focus on knowledge management

bull Redesign business network to focus on core competence

bull Streamline business scope for flexibility

bull Exploit network opportunities

6 Transformation

bull Applications to allow the use of knowledge with a focus on customer needs as a strategic resource to create business value

bull Penetrate into new markets

bull Create new business models

bull Prompt response to market changes

Table 5 ICT Applications and Benefits at Different Stage

At transformation stage SMEs exploit ICT to change their business within an extended business network and to develop greater cooperative relationships with existing and new markets provide customer-focused products and services manage knowledge as a strategic resource and develop new business models At this stage business value is created through the use of knowledge with customer focus at the core The Internet offers new ways for SMEs to use new channels to build relationship with business partners to manage knowledge within and between the firm and the market to conduct electronic order processing and tracking and to deliver goods and products online Table 5 shows ICT applications and benefits at different stages

We have combined the above to arrive at three Stages of ICT Usage (refer to Appendix 2 for further details)


31


As shown in the next section we have used the above conceptual framework as the basis for our research methodology

32


6 OVERVIEW OF THE RESEARCH METHODOLOGY AND HYPOTHESES

We have conducted a multi-pronged approach to undertake this study After carrying out a comprehensive review of literature and study we identified the factors facilitating or hindering the adoption and use of ICT among SMEs We subsequently raised a number of hypotheses in the Inception Report

However on 13 March 2009 a meeting was held with the OGCIO and the members of the Digital Inclusion Task Force where it was suggested that the scope of the study was refined It was also suggested that the hypothesis should be refined to become more specific and actually reflect the differences in strategic focus problems and potential values at different levels of ICT use and sophistication for SMEs along with the possible factors that lead to ICT adoption

Consequently the following hypotheses as stated in the Inception report were culled out and revised to reflect the above thought process and formed an important foundation for our research If we find evidence in support of these hypotheses we can conclude that the factors affecting the Hong Kong SMEsrsquo ICT decision adoption process are similar to those in other countries If however the hypotheses are not accepted we need to further analyse the reasons such differences

H1 Organisational readiness measured by factors such as its ICT infrastructure managerial and language skills number of ICT qualified employees and absorptive capacity affects the ICT adoption decision of Hong Kong SMEs

H2 The stage of development affects the ICT adoption decisions of Hong Kong SMEs

H3 Business cyclical factors affect the ICT adoption decisions of Hong Kong SMEs

H4 Market competition measured by number of existing and new competitors affects the ICT adoption decisions of Hong Kong SMEs

H5 Pressure from business partners measured by the adoption of digital transactions along the supply chain affects the ICT adoption decisions of Hong Kong SMEs

H6 Perceived operational benefits measured by improved operation efficiency cost savings inventory control and accurate and timely management of information affect the ICT adoption decisions of Hong Kong SMEs

33


H7 Perceived strategic benefits measured by market expansion new business models new productsservices improved company image and developed innovative capacity affect the ICT adoption decisions of Hong Kong SMEs

H8 ICT affordability affects the ICT adoption decisions of Hong Kong SMEs

H9 Lack of information measured by the availability of information on the ICT its functions costs and benefits affects the ICT adoption decisions of Hong Kong SMEs

H10 Lack of confidence in the legal framework regulating electronic transactions affect the ICT adoption decisions of Hong Kong SMEs

The verification of these hypotheses has been addressed later in the Report

Subsequent to the above a synthesis of the literature combined with the relevant statistical data allowed us to modify our conceptual framework to develop a structural model of factors leading to the adoption or inhibiting the adoption of ICT

To begin with the project took two clear routes

bull Statistical analysis based on survey data (including IT usage data from business IT survey and business operating data from annual economic surveys) provided by the ldquoCensus amp Statistics Department Hong Kong SARrdquo (ldquoCampSDrdquo) for analysis of existing data using different statistical techniques

bull Conducting our own surveys through mail survey large-scale telephone survey and email survey



34


7 ANALYSIS amp RESULTS BASED ON THE CampSD DATA

In broad terms we conducted two sets of analyses with the assistance of the CampSD one based on ASIT data another based on analysis of the ASIT data and the Value Added data in CampSD

71 Analysis based on the ldquoAnnual Survey on IT Usage amp Penetration in the Business Sectorrdquo (ASIT) report

The base document used here was the ldquoAnnual Survey on IT Usage amp Penetration in the Business Sectorrdquo (ldquoASITrdquo) report In our analyses we used only SMEs and not the large enterprises We included in our analyses enterprises with less than 50 employees in all the sectors

Analysis was conducted on the ASIT questions to select those questions which could be mapped into the three Stages of ICT Usage (See Appendix 3 for details) Then we calculated the extent of ICT penetration in an enterprise by calculating a composite index for those selected questions (See Appendix 4 for details) The selected questions included in the calculations were then assigned weights so that each stage would fall within a certain range of points This index called ICT Usage (ICTU) designates the three levels of usage at the different stages of ICT Usage

Stage 1 ndash ICTU1 Localised Exploitation (PCs) amp Internal Integration Stage 2 ndash ICTU2 Web presence (Internet Connection) amp Online Transaction Stage 3 ndash ICTU3 External Integration (Electronic value chain) amp Transformation

It was found that information on the types of the Internet applications and the types of online transactions was not available from CampSD therefore the final analysis was carried out based on the factors listed in Appendix 5

As shown in the conceptual framework we first wished to analyse ICT Usage based on the sector the company belongs to the size of the company and its age

a Sector the company belongs to (A1 ndash ldquoNature of business of your firmrdquo)

As discussed in the meeting with the OGCIO on 1 April 2009 we focused on the 8 sectors They are

1) Manufacturing 2) Construction 3) WholesaleRetail 4) Import and Export Trade

35


5) Restaurants and Hotels 6) Transport and Storage 7) Financing and Insurance 8) Real Estate and Business Services

During our study this list was expanded into 12 sectors (refer to Appendix 6) where Import and Export Trade were further sub-divided into 3 parts Real Estate and Business Services were treated separately and the Communications sector was included because its data often appear with Transport and Storage in the CampSDrsquos reports (such as ASIT) for being under the same category in HSIC 11 The new list thus was as follows

1 Manufacturing 2 Construction 3 WholesaleRetail 4 Import and Export Trade (Consumer non-durable goods) 5 Import and Export Trade (Consumer durable goods) 6 Import and Export Trade (Miscellaneous goods) 7 Restaurants hotels and boarding houses 8 Transport and Storage 9 Communications 10 Financing and Insurance 11 Real Estate 12 Business Services

Analyses done

i For each sector we calculated the frequency breakdowns and corresponding histograms of the three levels of ICTU

ii We checked to see if there are significant differences between the three levels of ICTU among the 12 sectors

The results show that there is significant difference between the levels of ICT usage among the 12 sectors (refer to Appendix 7)

b Number of employees (A2 ndash ldquoNumber of persons engaged in your firmrdquo)

We checked to see if the ICT Usage is related to the number of employees engaged by the firm We included in our analyses enterprises with less than 50 employees in all the sectors

Analyses done

36


iii For each individual sector conducted simple correlation analysis between ICTU and number of employees to see if ICTU is dependent on number of employees and sector This allowed us to analyse whether companies employing more employees have greater ICT Usage or not

We find that the level of ICT usage is significantly correlated to the number of employees but only in the following sectors

Manufacturing WholesaleRetail Import and Export Trade (Consumer non-durable goods) Import and Export Trade (Consumer durable goods) Import and Export Trade (Miscellaneous goods) Business Services

This indicates that as SMEs in these sectors get larger their level of ICT usage increases

c Age of the company

Based on the literature review a study shows that the hazard rates of companies fluctuate over the years which can be categorised into three stages age 3 age 7 and age 16 The hazard rate declines from age 3 to age 7 and then remains relatively flat until about age 16

Based on the above for the purpose of our research the three stages have been categorised as birth (less than 3 years) growth (3-7 years) and maturity (7+ years)

Stage Average Age (years) Birth lt3 years Growth 3 ndash 7 years Maturity gt7 years

Unfortunately because the CampSD survey did not contain the year of a companyrsquos registration thus we were unable to perform the above analysis We will address this issue through data analyses of our own surveys

d Interactions between Companyrsquos Age and Sector

We wanted to see if ICT Usage is different across companies of different age and across different industry sectors

37



72 Analysis based on the ASIT report and Value Added obtained from the CampSD

We needed to match the SME cluster of the ASIT sample points with other databasessurvey that contained value added data related to individual companies This would enable us to gain a better understanding of the connection between ICT usage and value added39 of a company

As the data for deriving value added for business undertakings in different economic sectors are separately collected through annual economic surveys CampSD matched the common samples between ASIT and the programme of annual economic surveys [including the Annual Survey of Industrial Production (ASIP) Annual Survey of Building Construction and Real Estate Sectors (ASBCRE) Annual Survey of Wholesale Retail and Import and Export Trades Restaurants and Hotels (ASW) Annual Survey of Transport and Related Services (AST) and Annual Survey of Storage Communication Financing Insurance and Business Services (ASS)] (the latest refer to year for these surveys is 2007)

Analyses done

iv For each sector we performed regression analysis between a companyrsquos value added and ICTU to see if there is any correlation between the two variables

We found only 214 matched cases Because there were 12 sectors under consideration only some of the sectors had enough sample points necessary for meaningful statistical analyses Of these we found significant correlation between a companyrsquos Value Added and the ICT Usage in WholesaleRetail sector As detailed in Appendix 8 WholesaleRetail sector contributes only approximately 5 of the total Value Added of SMEs We selected this sector for more in-depth analysis

73 Conclusions

39 According to the CampSD website ldquoValue added = Gross output (net of stock appreciation for work-in-progress finished products and goods for resale) ndash Consumption of materials supplies and industrial workservices (net of stock appreciation for materialssupplies) ndash Rent rates and government rent for land and buildings ndash Rentals for machinery equipment and vehicles ndash Other operating expensesrdquo (httpwwwcenstatdgovhkhong_kong_statisticsstatistics_by_subjectglossaryindustrial_productioninde xjsp)

38


Based on our analysis at CampSD we find a correlation between ICT Usage and the number of employees at a firm

More important we find in the WholesaleRetail sector there is a significant correlation between the Value Added and the ICT Usage

In addition to this sector it was also decided that we would focus on the ImportExport Trade sector too as it contributes 48 of the Value Added of SMEs

39


8 OVERVIEW OF THE SURVEYS CONDUCTED BY HKU

Based on our multi-pronged approach to the study data was collected and analysed through various means and techniques These included mail survey questionnaires telephonic case study interviews and literature review of similar experiences in other parts of the world

81 Survey questionnaires

Sample Design

We acquired a sample dataset from the Census and Statistics Department containing 56588 companies The dataset included company name type of business company size (in terms of the number of persons engaged) and address The list included some companies that cannot be categorised as small and medium enterprises for example the Hong Kong branch of some multinational companies or a regional branch of a large well-known Hong Kong local company After an initial screening of these large companies the dataset was scaled down to 54087 companies

We categorised the companies using their industry codes This gave us the proportion of companies in each sector in the dataset We used this information to determine the proportional clustered sampling mechanism whereby the number of companies selected for the sample in each sector is in proportion to the whole dataset This principle applied to all the surveys namely the mail survey telephone survey as well as the email survey The samples were selected randomly (one in every ten entries)

We selected companies in such a way that a particular company would be contacted only once either by email or by phone or by post Once selected we first searched for email addresses on the web If not found we then looked for telephone numbers If no other contact detail was found we used the option of mailing by post

Questionnaires

We used the software ldquoSurveymonkeyrdquo to design a survey questionnaire Three separate sets of questionnaires were designed ndash one for print and mailing another for telephonic interviews and the last for e-mailing The questions selected for these are once again based on our conceptual framework following the three Stages of ICT Usage

A pilot survey on the indicators was conducted on June 12 2009 with ten companies to ensure the key indicators are clear and unambiguous The final indicators were then used in the final survey questionnaire

40


bull Mail questionnaire The mail questionnaire was prepared in both Chinese and English (refer to Appendices 9 and 10) A total of 10000 questionnaires were sent out

bull Telephone interviews We recognised the importance of recruiting the right personnel We recruited those people with previous experience in conducting telephone interviews to help us conduct the large-scale telephone interviews Detailed telephone guidelines were also issued to the interviewers (refer to Appendix 11) A total of 1000 telephone interviews were made

bull Email surveys We also sent out 3900 e-mail surveys to the SMEs with email address in both English and Chinese (refer to Appendices 12 and 13)

Response Rate Treatment

Non-response bias is defined by the OECD as ldquothe bias resulting from limiting the survey analysis to the available datardquo40 We have studied the non-response bias to find out whether differences exist between those people who respond to the survey and answer questions and those who do not respond More specifically we called up 30 random respondents to check whether they have replied to the survey If they had not we tried to go through the survey questionnaire with them on the phone so that we could compare their answers with our existing findings to ascertain there are no significant differences

Based on our research of survey responses in Hong Kong we expected a response rate of around 5-7 Our estimation of the low response rate is based on existing studies Historically Hong Kong has a very low response rate as evident in published studies41

For example in a response rate study among 22 countries Hong Kong has the lowest response rate which was 7142 This is further complicated by the fact that our targets are small and medium business owners ndash ldquoMail surveys of small business owners have notoriously low response ratesrdquo43 SMEs in HK have a tendency to move offices if the

40 httpstatsoecdorgglossarydetailaspID=5145 41 JB Cullen JL Johnson and T Sakano Japanese and Local Partner Commitment to IJVs Psychological Consequences of Outcomes and Investments in the IJV Relationship Journal of International Business Studies 26 (1995) pp 91ndash116 GL Harrison GL McKinnan S Panchapakesan and M Leung The Influence of Culture on Organization Design and Planning and Control in Australia and the United States Compared with Singapore and Hong Kong Journal of International Financial Management and Accounting Oct (1994) pp 242ndash261 CF Keown Foreign Mail Surveys Response Rates Using Monetary Incentives Journal of International Business Studies 16 (1985) pp 151ndash153 42 Harzing Anne-Will (2000) Cross-National Industrial Mail Surveys Why Do Response Rates Differ between Countries Industrial Marketing Management 29 pp243-253 43 Deninis Jr WJ (2003) ldquoRaising response rates in mail surveys of small business owners results of an experimentrdquo Journal of Small Business Management 41(3) pp278-295

41


rent can be lowered Moreover SMEs have a low rate of business survival and the financial crisis has made it harder for SMEs

Recent studies have suggested that a low response rate does not mean lower survey accuracy 44 and a low response rate only indicates a risk of lower accuracy45 Since we have no control over the response rate we tried to collect more information to reduce the risk of lower accuracy First we used other survey means to collect more data We sent out 10000 mailing surveys complemented by the surveys sent by email (3900) and conducted on the phone (1000) We collected 692 replies by mail 102 replies on the phone and 102 replies by email Hence the above response has given us sufficient data points for the analyses needed in the study Second we used treated potential non-response bias to check whether there is any significant difference in the survey answers between those who responded to our postal survey and those who did not respond Based on our analysis the difference of means in the two groups of respondents and non-respondents appears small and thus we do not perceive a non-response bias

82 Telephonic case study interviews

We also conducted in-depth interviews with a group of SMEs In total we called 1000 companies 102 of which agreed to participate in the survey Among these 102 companies 53 companies agreed to have a second interview However some of them subsequently withdrew Hence we then used yellow pages and personal contacts to arrange some more interviews We have finally conducted 12 interviews in selected industries Among them seven interviewees agreed to have telephone interviews and the remaining five interviewees agreed to be interviewed face-to-face

83 Literature review of similar experiences across the world

A detailed research of literature available for the adoption of ICT by SMEs across the world was carried out to see if there are similar experiences in other parts of the globe The findings therein have been detailed in Section 4

84 Selection of sectors

As stated in the previous section we identified the WholesaleRetail sector as showing a significant correlation between the Value Added and the ICT Usage

44 Holbrook Allyson Jon Krosnick and Alison Pfent 2007 ldquoThe Causes and Consequences of Response Rates in Surveys by the News Media and Government Contractor Survey Research Firmsrdquo In Advances in telephone survey methodology ed James M Lepkowski N Clyde Tucker J Michael Brick Edith D De Leeuw Lilli Japec Paul J Lavrakas Michael W Link and Roberta L Sangster New York Wiley ldquoResponse Rates ndash An Overviewrdquo American Association for Public Opinion Research (AAPOR) 29 Sept 2008 45 httpenwikipediaorgwikiResponse_rate

42


In addition we had also decided to focus on the ImportExport Trade sector as this contributes almost half of the Value Added of SMEs to the Hong Kong economy This ImportExport sector has been further sub-divided in 3 areas

bull Import and Export Trade (Consumer non-durable goods) bull Import and Export Trade (Consumer durable goods) bull Import and Export Trade (Miscellaneous goods)

Further preliminary analysis based on our analysis of the survey responses showed a significant correlation between revenue and extent of ICT use for Business Services sector Hence this was the sector was also selected for further investigation

The above three sectors were presented to the OGCIO in the Interim Report prepared by HKU It was decided that an in-depth interview would be conducted for two SMEs in each of the above sectors as well as the manufacturing sector

Hence in all there were twelve interviews conducted and the findings have been reported as qualitative case studies to complement our quantitative findings This has been detailed in Section 10

43


9 RESULTS ANALYSIS OF THE SURVEY QUESTIONNAIRES

We analysed the data received in response to our surveys - mail and email as well as the telephonic interviews While the mail and email surveys were largely analysed as quantitative data the telephonic interviews which had more in-depth questioning were used in both quantitative and qualitative analyses

Response Rate

The final response rate for each survey is as given below

bull Mail questionnaire The mail questionnaire was prepared in both Chinese and English and a total of 10000 questionnaires were sent out In all 693 responses were received

bull Telephone interviews A total of 1000 telephone interviews were made 102 respondents replied to the calls

bull Email surveys We sent out 3900 e-mail surveys to the SMEs with email address in both English and Chinese 39 English responses and 63 Chinese responses were received

In all 897 responses were received The first step in our analysis was to exclude all those responses which were not from SMEs even though we had made provisional attempt to exclude such companies in our initial sample Hence responses received from manufacturing firms with over 100 employees and from firms in non-manufacturing sectors with over 50 employees were excluded from this analysis There were thus 789 responses in all to work with (for further details on the merging and transformation of the data refer to Appendix 14)

The breakdown of the sample according to sector size and age are as follows

44


By Sector

Frequency Percent Valid

Percent Cumulative

Percent

Manufacturing - 301 81 103 104 104

Construction - 501 68 86 87 191

WholesaleRetail ndash 601

ImportExport ndash

176 223 225 416

Consumer non-durable goods - 602

ImportExport ndash

57 72 73 489

Consumer durable goods - 603

ImportExport ndash

60 76 77 566

Miscellaneous goods - 604

94 119 120 686

RestaurantsHotels amp Boarding Houses - 605

13 16 17 703

Transport amp Storage - 701

32 41 41 744

Communications - 702 8 10 10 754

Finance amp Insurance - 801

35 44 45 799

Real Estate - 802 18 23 23 822

Business Services - 803 139 176 178 1000

Total 781 990 1000 Missing 8 10

Total 789 1000

Hence the Communications sector (702) was excluded from most analysis because of its small sample size (8 respondents)

45


By Number of employees


Percent Cumulative

Percent

Less than 10 people 10-49 people 50-100 people

604 178

7 789

766 226

9 1000

766 226

9 1000

766 991

1000

We have excluded from most quantitative analysis those companies with more than 50 employees (Manufacturing) because of the small sample size

By Number of Years in Business


Percent Cumulative

Percent

Less than 3 years 3-7 years More than 7 years Total Missing from the system Total

109 193 477 779

10

789

138 245 605 987

13

1000

140 248 612

1000

140 388

1000

By total revenuesales in the last fiscal year

N Valid 390

Mean 16838817

46


Median 1535280 Std Deviation 89245778 Minimum 5 Maximum (exponential format)

1E9

(Further break down of revenue by industry age and number of employees is provided in Appendix 15)

91 Usage of Personal Computers (PCs) ndash Stage 1

We find that 87 of the sample use PCs (refer to Appendix 16) Hence the first question raised was about the remaining 13 (100 responses) who do not use PCs This was a particularly interesting finding as it allows us to gain some insights into the inhibitors of PC adoption

911 Not Using PCs

What could be the reasons for not using PCs Analysis of the data showed that the most popular response is that it is not needed In order of importance the following are rated as the reasons the firm did not use PCs (refer to Appendix 17)

It is not really needed

It is difficult and expensive to maintain

It is costly

There may be security issues

There is no one to train the personnel

There is difficulty finding trained personnel to use PCs

It appears that need and cost are the two critical issues We further tried to analyse the above data to understand if there are any differences in responses based on the sector the firm belonged to the age of the firm or the size of the firm

47


Where the sample size was not large enough (less than 10) these sectors were excluded from this analysis or were clustered in the case of Import amp Export firms We find that there are no significant differences about the reasons for not using PCs across different sectors or based on the companyrsquos size or number of years in existence (refer to Appendix 18) Hence we can see that the reasons for not using PCs are fairly similar across the demographic characteristics

Our next step was to try and understand whether the respondents realised that the use of PCs could have many benefits If so it would be easier to promote the use of PCs despite the perceived barriers Accordingly our next analysis was on the response to the question ldquoDo you think that using PCs would help improve the operations of your firm If so howrdquo

We received a total of 54 responses to this open-ended question (refer to Appendix 19) Of these replies approximately half (25 respondents) said that using PCs would not help the operations of their business The remaining 29 respondents thought that the use of use of PCs could be beneficial The main advantages as represented by the following responses revolved around PCsrsquo speed accuracy and efficiency

ldquoYes it will improve the companys management and increase the operational speedrdquo

ldquoOf course it will bring more convenience to the companys operations We do not have to record the financial accounts manually The products serial numbers will be clearer so it will be faster and more convenient when we check the goodsrdquo

ldquoMore convenient in terms of record-keeping paperwork and record-chasingrdquo

Respondents also thought that the PC could be useful in communications electronic banking and marketing efforts This suggests that this group once initiated to PCs would also be open to moving on to Stage 2 and use the Internet

We then asked the respondents ldquoWhat would really motivate or push you to use PCsrdquo We received 54 responses to this open-ended question (refer to Appendix 20) As would be logical further to the principal deterrent being the lack of business need for a PC the primary motivators that would encourage the respondents to use PCs are clustered around the expanding business requirements

ldquoWhen the business further develops and when the high technology is useful for businessrdquo

48


ldquoWhen there are too many products that needed to be recordedrdquo

ldquoWe will consider it when there are enough human resources and time for back-office trainingrdquo

The other common replies concerned cost (being provided with financial support and a cheap PCpackage) as well as training of personnel to use the PCs

In sum the respondents cited the following factors as the prime motivator for use of PCs

When business needs grow

Financial support is provided

Technical support is provided

These motivators would also give us some direction to answer the question of ldquoHow useful would the different types of Government support be for the firm to install and use computersrdquo

The first motivator is a function of the macro-level state of the economy and the business environment Governments cannot usually play an effective role in directly addressing this factor

However in terms of addressing the provision of financial and technical support our analysis shows that in order of importance the following are rated as most useful types of support that the Government could provide to promote the use of PCs (refer to Appendix 21)

Subsidising training in using PCs

Providing PC adoption consulting services

Providing tax incentives

Providing more information on the costs and benefits of using PCs

49


Holding training workshopsseminars

Subsidising training in the use of PCs and providing PC adoption consulting services is the most popular suggestions with 57 of the respondents who replied to this question perceiving this to be veryhighly useful

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm and its number of employees (refer to Appendix 22) We find that there are no significant differences in perceiving the use of different types of Government support across different sectors or the number of employees

However there are some significant differences based on the age of the company In particular the older firms (those who had been in existence for a period over 7 years) find subsidising training in using PCs providing PC adoption consulting services and providing tax incentives to be far less useful than the younger firms This is an important conclusion as it suggests that while the Government is trying to promote the use of PCs through these measures its focus should be on the younger firms who appear to be far more open to Government support in using PCs

912 Using PCs

As stated earlier in the section 87 of the respondents (685 responses) used PCs Our first level of enquiry was to understand what the PC was being used for This was answered by the question ldquoPlease mark the extent to which your firm uses software for the following tasksrdquo

Analysis of the data shows that the most popular software packages are Word processing Computer security and spreadsheet The least popular are HRMTraining and Presentation (refer to Appendix 23) This insight is useful as it shows where the focus should be when the use of PCs is being promulgated

We further tried to analyse the above data to understand if there are any differences in responses based on the sector the firm belonged to the age of the firm and its number of employees Where the sample size was not large enough (n lt 10) these sectors were excluded from this analysis We find significant differences in the usage of PC software across these factors (refer to Appendix 24)

bull Sector We first focused on the top-3 most popular software - namely Word spreadsheet and Computer security Word while being used actively across all sectors is much less used in the Real Estate sector Spreadsheet too is used less in the

50


Real Estate sector as well as in the Construction and WholesaleRetail sector Computer security software is not popular in the Real Estate and the Transport amp Storage sector

While Multimedia and Finance software are used more or less equally across sectors all the other software applications show significant differences across sectors

We then analysed the software used at the selected sectors On the whole the WholesaleRetail sector uses all these software packages less than the other sectors Business Services and the ImportExport sectors are higher users of all types of software as compared to the other sectors

bull Number of Employees The use of database finance sales and HRM software is significantly different across companies of different size and increases linearly with an increase in the number of employees (We have excluded those firms with gt50 people from our analysis as the sample size lt10)

bull Number of Years in Business Younger firms (less than 3 years old) use databases much less than older firms There are some differences in the use of Presentation and Multimedia software but these packages are not used as frequently

As a supplement to this question moving higher on the technology curve our next question was to ldquomark the extent to which the firm uses tailor-made computer systems for FinanceAccounting PurchasingSale and Order Processing and HRMTrainingrdquo

Analysis of the data show that HRMtraining package is seldom used while the Finance and Purchasing packages are also used very little or sometimes (refer to Appendix 25) We further tried to analyse the above data to understand if there are any differences in responses based on the sector the firm belongs to the age of the firm and its number of employees Where the sample size was not large enough (n lt 10) these sectors were excluded from analysis

While the number of years in business has no impact on the usage of these custom-made packages there are significant differences in their usage across the number of employees in the firm (refer to Appendix 26) Interestingly as the number of employees increases so does the usage of these custom-made packages This would suggest that these packages would be boughtused when a certain critical mass is achieved in terms of the firmrsquos size (We have excluded those firms with gt50 people from our analysis as the sample size lt10)

51


In terms of sector the use of the finance and purchasing packages differs across sectors Finance is used significantly more in the Business Services and Transport amp Storage sectors Similarly as compared to the other sectors custom-made purchasing packages are used more in Import amp Export ndash Consumer Durable Goods Import amp Export ndash Miscellaneous Goods and WholesaleRetail

913 Value Added from PC Usage

To understand the link between ICT Usage and the Value Added we asked the question ldquoHow much value do you think PCs have added to the following aspects of your businessrdquo becomes necessary

In order of importance the following are perceived as the most important value additions made by the usage of PCs to the business (refer to Appendix 27)

Improving operational efficiencyreducing costs

Improving customer services

Providing competitive advantage

Exploring new opportunities for growing business

Increasing sales

Improving operational efficiency and customer service are the two top value additions made by the use of PCs This also ties in with the earlier analysis of where the respondents who do not yet have PCs perceive the maximum value addition to be

We next analysed the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm and its number of employees (refer to Appendix 28) We find significant differences in the perception of value added across sectors and according to the age of the firm (not its number of employees)

bull Sector Other than the ldquoefficiencyrdquo factor there are significant differences in the perception of value added across sectors

Improving customer service While there is generally high agreement among all the sectors that the usage of PCs improves customer service this was more pronounced in

52


the Finance amp Insurance Business Services and Import amp Export ndash Consumer Non-durable Goods sectors On the lower end of the spectrum the Transport amp Storage Construction and WholesaleRetail sectors do not see such a high correlation between PC usage and customer service

Increasing sales Firms belonging to the Finance amp Insurance and Import amp Export ndash Consumer Non-durable Goods sectors find high support for increasing sales by using PCs However those belonging to the Transport amp Storage and Construction sectors do not perceive much value addition in terms of sales increasing when PCs are used

Exploring new opportunities for growing business ndash The Import amp Export along with the Manufacturing sector sees much value in exploring new opportunities for business growth ensuing through the use of PCs However those firms belonging to the Transport amp Storage and Construction sectors do not perceive such high value addition

Providing competitive advantage ndash Business Services Finance amp Insurance and the Import amp Export sector perceived significant value added in this dimension However this was comparatively lower for the Transport amp Storage and Real Estate sectors

bull Number of Years in Business Two of the dimensions are affected by the age of the firm (Increasing sales and providing competitive advantage) The perception of value addition along these two dimensions increases from those firms less than 3 years old to those firms in the 3-7 years It then drops in for the oldest subset

914 Relationship between Perceived Value and PC Usage

Because one of the primary objectives of the study is to find whether the perceived value of ICT and its usage are correlated we calculated a composite index of PCsrsquo value and usage by adding up the scores of the corresponding indicators We then performed correlation analysis between value and usage We conducted our analyses according to industry firmrsquos size and age to check for possible differences along these three dimensions (refer to Appendix 29) We had to exclude from the analysis Restaurants and Communications sectors because of small sample size We should point out that these relationships are correlational and not causal

bull Sector We found significant relationships between perceived value of PCs and usage in all but Import and Export Trade (Consumer non-durable goods) sectors

bull Number of Employees We had to exclude from analysis companies with more than 50 employees because of small sample size We found significant

53


relationship between perceived value of PCs and usage in both very small (less than 10 employees) and small (10-49 employees) firms

bull Number of Years in Business We found significant relationship between perceived value of PCs and usage irrespective of the age of the business

Overall we find a correlation between perceived value of PCs to the business and the extent to which firms use PCs

915 Summary of findings for Section 91 ndash Stage 1

ICT Stage - Not Using PC Reasons for not using PCs 1 It is not really needed

2 It is difficult and expensive to maintain 3 It is costly 4 There may be security issues 5 There is no one to train the personnel 6 There is difficulty finding trained personnel to use PCs No significant differences in responses across industry age and size

Perceptions whether it Approximately half the sample felt the use of PCs would would help if PCs are used not help The remaining feel it would help in improving

speed accuracy and efficiency PC could also be useful in communications e-banking and marketing efforts

Possible motivators to use PCs

1 When business needs grow 2 Financial support is provided 3 Technical support is provided

Perceptions of most 1 Subsidising training in using PCs favoured types of 2 Providing PC adoption consulting services Government support 3 Providing tax incentives

4 Providing more information on the costs and benefits of using PCs 5 Holding training workshopsseminars Older firms find subsidising training in using PCs providing PC adoption consulting services and providing tax incentives to be far less useful than the younger firms

ICT Stage - Using PCs What are the most used Most popular software packages are Word processing software packages Computer security and spreadsheet (The least popular are

HRMTraining and Presentation)

On the whole the use of software packages is different

54


across industries Are custom-made software packages popular

Overall these packages are used very little However when the number of employees increases so does the usage of these custom-made packages The usage also differs across industries

Where have PCs added value

1 Improving operational efficiencyreducing costs 2 Improving customer services 3 Providing competitive advantage 4 Exploring new opportunities for growing business 5 Increasing sales

Differences in perceptions of value added across industry and age

Is there a relationship between Perceived Value and PC Usage

Yes we find a correlation between perceived value of PCs to the business and the extent to which firms use PCs

92 Usage of the Internet ndash Stage 2

This section focuses on those respondents who have PCs and may or may not use the Internet As stated earlier 87 (789 responses) of the survey respondents use PCs We found that 90 of the PC users also use the Internet only 10 do not (refer to Appendix 30)

921 Not Using the Internet

What are the reasons for the respondents to use PCs and not the Internet In order of importance the following are the top 3 reasons the firm does not use the Internet despite having a PC (refer to Appendix 31)


It is costly

Business partners and customers are not using the Internet

However there was not any clear support for any of the reasons as being the overwhelming cause for not using the Internet

55


We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees Where the sample size for a sector was not large enough (less than10) these sectors were excluded from the analysis or in case of ImportExport sub-sectors they were clustered together We found no significant differences about the reasons for not using the Internet across different sectors the companyrsquos size or age (refer to Appendix 32)

Our next step was to understand whether these respondents who did not use the Internet were actually aware of the possible benefits of the Internet If they did not think the Internet was useful it would suggest that there is a larger issue of improving their awareness On the other hand if they perceived the Internet to have clear benefits but still did not use it it would imply that the barriers mentioned are the real cause of the lack of Internet usage

Accordingly our next analysis was on the response to the question ldquoDo you think that using the Internet would help improve your business If so howrdquo

There were six responses to this open-ended question (refer to Appendix 33) Five of them said that the Internet would not help improve their business while one stated that it could be more convenient This may suggest that a lack of business need combined with a lack of awareness of the benefits of the Internet is the reason these respondents who have not opted for the Internet

Our next question was to ask these respondents ldquoWhat would really motivate or push you to use the Internetrdquo

Nine replies were received to this question (refer to Appendix 34) five of which said that nothing would motivate them to use the Internet The other replies stated that the Internet would be considered when the business required it Only one said that a reasonable price would be a good motivator to use the Internet

We then asked the respondents ldquoHow useful would the different types of Government support be for the firm to install and use computersrdquo

We find the following as most useful types of support that the Government could provide to promote the use of the Internet (refer to Appendix 35)

Providing Internet adoption consulting services

Subsidising Internet training

56


Providing more information on the costs and benefits of using the Internet


Building the right legal and regulatory framework

Providing tax incentives for using the Internet

Investing in ICT infrastructure

As in the case of PCs providing adoption consulting services and subsidising training are the most popular suggestions with approximately 40 of the respondents who replied to this question perceiving it to be veryhighly useful

These findings show that the respondents are looking for knowledge about the Internet and hence training (particularly subsidised training) becomes their favoured demands from the Government This conclusion also ties in to our previous suggestion that it is perhaps a lack of awareness of the benefits of the Internet that acts as the greatest barrier to its use

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees We found no significant differences in the perception of the use of different types of Government support sectors the companyrsquos size or age (refer to Appendix 36)

922 Using the Internet

As stated earlier 90 of those using PCS (665 responses) also use the Internet Our first question was to understand what the Internet was being used for This was answered by the question ldquoPlease mark the extent to which your firm uses the Internet for the following purposesrdquo

By far the most popular uses of the Internet are e-mail on-line sourcing of general information and e-banking The Internet was not used much for any other tasks (refer to Appendix 37)

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees Where the sample size for a sector was not large enough (less than10) these sectors were excluded from the analysis or in case of ImportExport sub-sectors they

57


were clustered together We found significant differences in the use of the Internet across several dimensions however have focused on the three most frequently used applications of e-mail on-line sourcing of general information and e-banking for the purpose of our analysis (refer to Appendix 38)

bull Sector Email is used across all sectors Only the Real Estate sector uses it less in comparison with other sectors

bull Number of Employees There are no significant differences noted in the use of the Internet based on the number of employees

bull Number of Years in Business The use of e-banking differs significantly based on the age of the company The younger firms use e-banking far more than the older firms

The next stage was to gauge the popularity of the Internet among its users which was answered by the open-ended question ldquoDo you think that using the Internet more would help improve the business of your firm If so howrdquo

We received 123 replies to this question from the mail email and telephone surveys of which 53 respondents felt that more usage would not help improve the business (refer to Appendix 39)

The main areas where the remaining respondents suggested that operations could further improve with the further use of Internet were

bull MarketingSales development

ldquoNeed it for searching for new customers opening up markets etcrdquo

ldquoWe can sell more products and get new products information We can compare the price of products in the same industry We can also give pictures to clients more easilyrdquo

ldquoTo set up the companys website so the clients can make online orders for our productsrdquo

bull Speedy communication

ldquoIt would increase the internal communication between each branchrdquo

58


ldquoWe mainly use the Internet for sending and receiving emails in our business operationsrdquo

bull Ease of searching for information

ldquoSearch enginesrdquo

ldquoYes it will help us to search for new products and improve our knowledge about clients and manufacturersrdquo

bull Efficiency and cost reduction

ldquoIt will fasten the communication with clients and will save more timerdquo

923 Value Added by Internet Usage

The next stage was to understand the link between the Internet usage and the perceived value added Towards this end to understand in which areas of the business was the value addition perceived we first analysed the responses to the question ldquoHow much value do you think the Internet has added to the following aspects of your businessrdquo

The following are seen in order of importance as the most important areas were the use of the Internet brings value to the business by (refer to Appendix 40)




Prompt response to market changes

Exploitation of network opportunities

Increasing sales

Creation of new productsservices

Penetration into new markets

59


Similar to the usage of PCs improving operational efficiency and improving customer service are also perceived to be the two top value-adding dimensions of Internet use

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees (refer to Appendix 41) We find significant differences in the perception of value added across sectors and according to the age of the firm across each and every dimension (not however based on the companyrsquos size)

bull Sector We find significant differences in all the dimensions across sectors Below we highlight the top two value-adding dimensions

Increasing operational efficiency ndash Firms belonging to the Business Services Import amp Export ndash Consumer durable Goods and Import amp Export ndash Consumer Non-durable Goods ndash see high potential for improving efficiency using the Internet However those belonging to the Transport amp Storage Real Estate and Construction sectors do not in comparison perceive as much value

Improving customer service ndash The usage of the Internet being perceived to improve customer service is far more pronounced in the Import amp Export ndash Consumer durable Goods Business Services and Import amp Export ndash Consumer Non-durable Goods sectors On the other end of the spectrum similar to the PC usage such potential is not seen by the respondents in the Transport amp Storage Construction and WholesaleRetail sectors

bull Number of Years in Business The value added by improving operational efficiency due to Internet usage is perceived to be significantly higher by the younger companies than the older ones and hence shows a linear relationship

For all the other dimensions such as improving customer service the value-adding perception of the Internet first increases from those firms less than 3 years old to those firms in the 3-7 years group and then again drops in the more than 7 years old set Hence it shows an inverted U relationship

924 Relationship between Perceived Value and Internet Usage

We performed correlation analysis between value and Internet usage We conducted our analyses according to industry firmrsquos size and age to check for possible differences along these three dimensions (refer to Appendix 42) We had to exclude from analysis Restaurants Communications and Real Estates sectors because of small sample size

60


bull Sector We found significant relationships between perceived value of Internet and usage for all sectors

bull Number of Employees We had to exclude from analysis companies with more than 50 employees because of small sample size We found significant relationship between perceived value of Internet and usage in both very small (less than 10 employees) and small (10-49 employees) firms

bull Number of Years in Business We found significant relationship between perceived value of Internet and usage irrespective of the age of the business

Overall we find a correlation between perceived value of the Internet to the business and the extent to which firms use the Internet


ICT Stage - Not Using the Internet

Reasons for not using the Internet

1 It is not really needed 2 It is costly 3 Business partners and customers are not using the Internet No significant differences in responses across industry age and size

Perceptions whether it would help if the Internet is used

Only six responses of which five said that the Internet would not help improve their business

Possible motivators to use the Internet

Among the nine replies received to this question five said that nothing would motivate them to use the Internet Others stated business needs and a reasonable price as possible motivators to use the Internet

Perceptions of most favoured types of Government support to use the Internet

1 Providing Internet adoption consulting services 2 Subsidising Internet training 3 Providing more information on the costs and benefits of using the Internet 4 Holding training workshopsseminars 5 Building the right legal and regulatory framework 6 Providing tax incentives for using the Internet 7 Investing in ICT infrastructure

ICT Stage - Using the Internet

61


What is the Internet most used for

The most popular uses of the Internet are e-mail on-line sourcing of general information and e-banking

Areas where using the Internet more would help improve the business

1 Marketing and sales development 2 Speedy communication 3 Ease of searching for information 4 Efficiency and cost reduction

Where has the Internet added value

1 Improving operational efficiencyreducing costs 2 Improving customer services 3 Providing competitive advantage 4 Prompt response to market changes 5 Exploitation of network opportunities Significant differences in the various dimensions across industries and age

Is there a relationship between Perceived Value and Internet Usage

Yes there is a correlation between perceived value of the Internet to the business and the extent to which firms use it

93 Usage of a Website ndash Stage 3

This section focuses on those respondents who use the Internet but may or may not have a Website Of the total sample size of 897 responses in the survey 322 respondents have a Website (refer to Appendix 43)

931 Not Using a Website

We first analysed the data to try and understand the reasons for those respondents who use the Internet but do not have a Website

There were no real strong views expressed as to why the firm do not have a Website In order of importance the following are the top five reasons (refer to Appendix 44)


Maintenance is difficult and expensive

It is costly

62


Online marketing is not common in the industry

Online transaction is not common in the industry

Hence it seems that the main reasons for not creating a Website has to do with the fact that the businessmarket does not see the need for it or perceive the expense associated with creating and maintaining a Website to be high

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees Where the sample size for a sector was not large enough (less than10) these sectors were excluded from the analysis We found no significant differences about the reasons for not having a Website across different sectors the companyrsquos size or age (refer to Appendix 45)

Our next step was to understand whether these respondents who did not have a Website were actually aware of the possible benefits of one Depending on the answer it would be a case of either increasing their awareness (assuming that there were some real benefits for them having a Website which they were not aware of) or attempting to alleviate their perceived barriers to creating one (if despite of not having a Website they perceive that having one would be beneficial)

Accordingly our next analysis was based on the responses received to the question ldquoDo you think that creating a Website would help improve your business If so howrdquo

This question was addressed to those respondents who use computers and the Internet but do not have a Website In all 134 responses were received to this question (refer to Appendix 46) Of these responses 75 felt that a Website would help These reasons can be clustered around the following three themes

bull Improving the firmrsquos presence and promote marketing efforts by attracting new customers thereby increasing revenue

ldquoIt would allow the clients to learn about our companys products and their characteristics even when they stay at homerdquo

ldquoOverseas clients can search for our companys information and increase business opportunitiesrdquo

ldquoYes it will allow the customers to get access to the companys information such as the branches addresses and telephone numbersrdquo

63


bull Enhancing the firmsrsquo credibility reputation and image

ldquoIt will provide presence credibility PRrdquo

ldquoIt will give people the feel that the firm is of larger scalerdquo

bull Providing additional information on the company and its products

ldquoYes it will be more convenient for clients to understand the company and increase business opportunitiesrdquo ldquoYes increase the companys reputation and increase the availability of the product informationrdquo

Having understood the reasons a Website would be considered useful for business the next stage was to understand ldquoWhat would really motivate or push you to create a Websiterdquo

As above this question was addressed to those respondents who used the Internet but did not have a Website There were 135 replies to this question (refer to Appendix 47) The principal factors given by the respondents that would really motivate or push them to create a Website can be clustered as follows

bull When it is perceived that the business requires it

ldquoWhen there are business needsrdquo

ldquoThe company is at dormancy period we will consider about it after the business recoversrdquo

ldquoTo search for networking opportunitiesrdquo

ldquoWhen we have a certain number of customers and business volumerdquo

bull When suitable training and technical support is available

ldquoWhen there is an expert to prepare the Websites contentrdquo

ldquoWhen it is easy to create the Website When the web hosting is cheap and easy to updaterdquo

bull When some financial support is provided

64


ldquoCheap pricerdquo

ldquoWhen we do not have to pay a huge feerdquo

ldquoWhen the costs is proportional to the returnsrdquo

ldquoFree of charge low costs with subsidy providedrdquo

bull When the firm has adequate resources

ldquoHave spare time and moneyrdquo

ldquoWhen there are enough human material and capital resourcesrdquo

Once again as in the case of PC and Internet usage it appears that business needs is the main driver for creating a Website Our next step was to understand the areas where the respondents perceived that the Government could specifically intervene to help them develop a Website This was done through the question ldquoHow useful would the different types of Government support be for the firm for creating a Websiterdquo

In order of importance the following are the three most useful types of support that the Government could provide to promote the use of Websites (refer to Appendix 48)

Subsidising ICT training

Providing ICT adoption consulting services

Providing tax incentives for ICT investments

Subsidising training in the use of ICT was the most popular suggestions with 60 of the respondents who replied to this question perceiving it to be veryhighly useful

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the size or the age of the firm (refer to Appendix 49) We found no significant differences across different sectors the companyrsquos size or age

65


932 Respondents Having a Website

Our first level of analysis from this group was to understand what the Website was being used for through the question ldquoPlease mark the extent that your firm uses its Website for the following purposesrdquo

The most popular uses of the Website are for ldquoProviding information on the firms products and servicesrdquo followed by ldquoHandling of business enquiryrdquo None of the other purposes were at all popular in comparison (refer to Appendix 50) This would suggest that these should be the two key focus areas while spreading awareness of the benefits of creating a Website

We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees Where the sample size for a sector was not large enough (less than10) these sectors were excluded from the analysis We found some differences in the use of the Internet across these dimensions across different sectors the companyrsquos size or age (refer to Appendix 51)

bull Sector Usage of the Website to receive online orders varies significantly with Construction and Finance amp Insurance sectors using this facility far less than the other sectors

bull Number of Employees Access to Government Information is more popular in the firms that employed 10-49 people than the smaller firms (We have excluded the firms with 50-100 people from the analysis as their sample size was less than 10)

bull Number of Years in Business Receiving of online payments is more popular for the younger firms than the older ones

933 Value Added from Having a Website

The next stage was to gauge how much value the respondents perceived a Website provides This was answered by analysing the responses to the open-ended question ldquoHow much value do you think your Website has added to the following aspects of your businessrdquo

On the whole the respondents do not see any factor as having added significant or great value The maximum support was for ldquoImproving operational efficiencyreducing costsrdquo

In order of importance the following are the top five areas where some value is perceived to ensue by having a Website (refer to Appendix 52)

66



Improving customer services through integrated processes


Strengthening the relationships with business partners

Improved productivity through service integration


We further tried to analyse the above data to understand if there were any differences in responses based on the sector the firm belonged to the age of the firm or its number of employees Where the sample size for a sector was not large enough (less than10) these sectors were excluded from the analysis or in case of ImportExport sub-sectors they were clustered together (refer to Appendix 53) We found significant differences in the perception of value across different sectors and the companyrsquos size but not the companyrsquos age

bull Sector There is a significant difference in the perception of value for network opportunities across sectors but this dimension is not perceived to be overall an influential factor

bull Number of Employees There are significant differences in the perception of value for ldquoStreamlining of the business promoting flexibilityrdquo with the value added for this factor increasing with the number of employees This seems logical as it can be expected that the larger the firm the greater would be its demand for streamlining the business (We have excluded the firms with 50-100 people from the analysis as their sample size was less than 10)

934 Relationship between Perceived Value and Website Usage

We performed correlation analysis between perceived value and Website usage We conducted our analyses according to industry firmrsquos size and age to check for possible differences along these three dimensions (refer to Appendix 54) We had to exclude from analysis Manufacturing Construction Restaurants Transport and Storage

67


Communications Financing and Insurance and Real Estates sectors because of small sample size We also clustered the three sub-sectors of Import and Export because of small sample size

bull Sector We found no significant relationships between perceived value of a Website and its usage for any of the three sectors remaining in the analysis

bull Number of Employees We found no significant relationship between perceived value of a Website and its usage irrespective of the firmrsquos size

bull Number of Years in Business We found no significant relationship between perceived value of a Website and its usage irrespective of the age of the business

In sum we find no evidence of any correlation between perceived value of a Website to the business and the extent to which firms use a Website


ICT Stage - Not having a Website Reasons for not having a Website

1 It is not really needed 2 Maintenance is difficult and expensive 3 It is costly 4 Online marketing is not common in the industry 5 Online transaction is not common in the industry No significant differences in responses across industry age and size

Perceptions whether it would help having a Website

56 felt that it would help by 1 Improving the firmrsquos presence and promote marketing

efforts by attracting new customers thereby increasing revenue

2 Enhancing the firmsrsquo credibility reputation and image 3 Providing additional information on the company and

its products Possible motivators to 1 Business needs create a Website 2 When suitable training and technical support is

available 3 When some financial support is provided 4 When the firm has adequate resources

Perceptions of most 1 Subsidising ICT training favoured types of 2 Providing ICT adoption consulting services Government support to 3 Providing tax incentives for ICT investments create a Website

68


ICT Stage ndash Having a Website What is the Website most used for

1 Providing information on the firmrsquos products and services

2 Handling of business enquiry

Where has using the Website added value

1 Improving operational efficiencyreducing costs 2 Improving customer services through integrated processes 3 Providing competitive advantage 4 Strengthening the relationships with business partners 5 Improved productivity through service integration

A few differences in the various dimensions across industries and size

Is there a relationship between Perceived Value and Website Usage

No evidence of any correlation between perceived value of a Website to the business and the extent to which firms use a Website

94 Integration of the Website

In accordance with our conceptual framework the final stage of ICT usage appears when the firmrsquos Website is integrated with the firmrsquos and the business partnerrsquos database

Of the 322 respondents who had a Website we found that approximately 20 (67 firms) had it integrated with the firmrsquos database (refer to Appendix 55) Approximately 15 (47 firms) had their Website integrated with their business partnersrsquo databases (refer to Appendix 56)

95 Perceptions of Overall ICT Usage and Value Added

As part of the overall analysis we sought the perception of all respondents as to the extent that they use ICT and the value they think such use brings to the firm

There were in all 584 valid responses received to this question taking into account the PC Users and after excluding RestaurantsHotel (605) and Communications (702) on account of a low sample size (refer to Appendix 40)

69


The overall ICT usage was quite high with about 61 using it oftenvery often Of the remaining 39 25 use it sometimes and 14 use it not at allvery little (refer to Appendix 57)

A similar break-up was seen in the perception of using more ICT contributing to the value added of the firm with about 61 of the respondents seeing significantgreat value added with more ICT Usage 29 see some value added and only 10 see novery little value added (refer to Appendix 58)

Further analysis showed that these perceptions varied across sectors and companyrsquos size and age (refer to Appendix 59)

bull Sector There are significant differences in overall ICT usage in different sectors Firms belonging to the Finance amp Insurance Import amp Export and Business Services sector are high users of ICT On the other hand the firms belonging to the Restaurants Real Estate and WholesaleRetail are low users

In terms of the perceptions vis-agrave-vis the value-adding effect of ICT we found significant differences across sectors with firms belonging to the Finance amp Insurance Import amp Export Business Services and Manufacturing having a much higher perception of increased value added with ICT usage than those in the Restaurants Real Estate and WholesaleRetail sectors

bull Number of Employees Overall ICT usage is higher in firms employing 10-49 people than those employing less than 10 people (We have excluded the firms with 50-100 people from the analysis as their sample size was less than 10) This also held true for the perception of using more ICT contributing to the value added of the firm being the highest for this group Once again it suggests that the size of the firm is critical for it being perceived that there is value addition to be had with ICT usage

bull Number of Years in Business Overall ICT usage is perceived to be lowest in the group of firms over 7 years in business This suggests that the newer and younger firms perceive that they use ICT far more than the older ones There are no significant differences in the perceptions vis-agrave-vis the value-adding effect of ICT based on the number of years in business

96 Relationships between Overall Value and Extent of ICT Use

We performed regression analysis of the two questions soliciting perceptions about the overall extent of ICT use and the extent to which using more ICT would add value to the firm More precisely we wanted to know if the overall perceived value of ICT is

70


dependent on the extent of ICT use We conducted our analyses according to sector firmrsquos size and age to check for possible differences along these three dimensions (Appendix 60) We had to exclude from analysis Restaurants and Communications sectors because of small sample size

bull Sector We found direct relationship between the overall ICT value and the extent of ICT use for all the ten sectors remaining in the analysis In other words SMEs see an increase in value to the firm with the use of ICT increases irrespective of the sector

bull Number of Employees We found direct relationship between the overall ICT value and the extent of ICT use irrespective of the firmrsquos size

bull Number of Years in Business We found direct relationship between the overall ICT value and the extent of ICT use irrespective of the firmrsquos age

In sum the respondents see a very clear connection between the perceived overall value of ICT and the overall extent of ICT use

97 Stages of ICT Development

We checked to see if there are differences in responses across the three stages of ICT development with regard to issues related to reasons for not using different types of ICT (PCs the Internet Website) and government support that would facilitate and encourage ICT adoption at the three stages In the process we took into consideration the firmrsquos age size and sector We also examined the effect of the stage of ICT development on perceived value of ICT taking into consideration the firmrsquos age size and sector46

971 Government Support

We asked non-PC users non-Internet users and non-Website users to rate the usefulness of different types of Government support as a means to help companies adopt these technologies The results of these analyses are shown in Appendix 62

The following table shows the results of the hypotheses that there are no differences between responses regarding various types of Government support at different stages of ICT development

46 Two-Way Analysis of Variance was used to test the hypothesis that there are no differences in a variable (eg degree of Government support) across the three stages of ICT development simultaneously taking into account the effect of other factors such as the firmrsquos size age or sector For ease of presentation we do not include the effect of interaction between stage of development and the firmrsquos size age or sector

71


Type of Government Support Hypothesis

Holding training Workshop Supported

Providing tax incentive Not Supported

Providing more information on costsbenefits Supported

Providing consulting Supported

Subsidising training Supported

Investing in ICT Infrastructure Supported

Building legal framework Supported

More specifically we found that the importance of providing tax incentive is significantly different at different stages of development non-PC users perceive it to be more important than non-Website users than non-Internet users

972 Reasons for Not Using ICT

We also asked non-PC users non-Internet users and non-Website users to rate the importance of different set of reasons for not using ICT at different stages of development The results of these analyses are shown in Appendix 63

The following table shows the results of the hypotheses that there are no differences between responses regarding reasons for not using ICT at different stages of development

Applicable to only Stages 2 and 3

72


Reasons for Not Using ICT Hypothesis

It is costly Supported

It is not really needed Supported

Difficulty in finding trained personnel Not Supported

No one to train the personnel Supported

Difficulty and expensive to maintain Supported

Security issues Supported

Partners not use Internet Supported

Competitor not use Internet Supported

We found significant difference only in terms of difficulty in finding trained personnel at different stages of development non-Website users perceive this factor more important than non-PC users than non-Internet users

973 ICT Value

We asked PC users Internet users and Website users to rate the extent to which ICT results in business value at different stages of development The results of these analyses are shown in Appendix 64

The following table shows the results of the hypotheses that there are no differences between responses regarding the value-adding effect of ICT at different stages of development


73


ICT Value Hypothesis Comment

Improving operational efficiency Not Supported PC users gt Internet users gt Website users (This implies that Internet users perceive greater value added for this factor than Website users while PC users perceive even more value added than the Internet users)

Improving customer services Not Supported PC users gt Internet users gt Website users

Increasing sales Not Supported PC users gt Website users gt Internet users

Penetration into new markets Not Supported PC users gt Website users gt Internet users

Providing competitive advantage Not Supported PC users gt Website users gt Internet users

Prompt response to market changes Supported No difference

Exploitation of network opportunity Supported No difference

Creation of new productsservices Not Supported Website users gt Internet users

We found significant differences among all but two factors The value of ICT in terms of improving operational efficiency and customer services is highest among PC users flowed by Internet and website users The value of ICT in terms of increasing sales penetration into new markets and providing competitive advantage is highest among PC users followed by Website users and then Internet users Website users see higher value in ICT than Internet users in terms of creation of new productsservices

In sum the above analyses show that by and large SMEs see different value in ICT at different stages of ICT development


74


98 Factors Influencing ICT Adoption

981 Value Added as a Function of ICT Usage Size Age Stage and Sector

We performed regression analysis between value added47 and ICT usage size age stage and sector We excluded from our analysis three sectors (Restaurants Communications and Real Estate) because of their small sample size (refer to Appendix 65)

We found an overall significant model with a high R square of 63648 More specifically we found

bull Significant and direct relationship between value added and ICT usage bull No significant relationship between value added and size bull Significant relationship between value added and age with newer firms seeing

greater value added than the older ones bull Significant relationship between value added and stage of ICT usage with highest

value added perceived in Stage 3

982 Revenue as a Function of ICT Usage Size Age Stage and Sector

We repeated the above analysis but this time used revenue as the dependent variable We found an overall significant model with a modest R square of 083 (refer to Appendix 66) More specifically we found

bull No significant relationship between revenue and ICT usage bull Significant relationship between revenue and size Not surprisingly we found a

direct correlation between revenue and size with the negative sign of slope showing that the revenue is directly related to firmrsquos size with the larger the firm the higher the revenue

bull No significant relationship between revenue and age bull Significant relationship between revenue and stage of ICT usage There is a direct

relationship here with a higher stage showing higher revenue

47 As stated earlier for the purpose of this report ldquoValue addedrdquo refers to the responses received to the survey question of the type ldquoHow much value do you think PCs have added to the following aspects of your businessrdquo This question was repeated for each stage that is ldquoHow much value do you think the Internet has added to the following aspects of your businessrdquo and ldquoHow much value do you think the Website has added to the following aspects of your businessrdquo

48 In regression a significant prediction means that a significant proportion of the variability in the predicted variable can be explained by the predictor variable R square ranges from 0 to 1 with 0 showing no correlation and 1 showing a perfect correlation

75


bull No significant relationship between revenue and sector except for Import amp Export ndash Consumer durable goods

99 Predictors of ICT Adoption

We performed discriminant analysis49 to find the predictors of ICT adoption at various stages (refer to Appendix 67)

Discriminant models can be used to predict the likelihood of adoption of PC or other types of ICT in a firm possessing different characteristics in terms of revenue size age and sector By plugging in the corresponding values of these variables in the model for example we can calculate the value of the discriminant function for a very small manufacturing firm (say with 4 employees) that has been in business less than say three years and that has certain amount of revenue We can then compare this value with that of another manufacturing firm with different size age or revenue to see which one is more likely to adopt PC The higher the value the discriminant function the higher likelihood of the adoption would be

991 PC Adoption

We used revenue size and age as predictors of PC adoption We found the model not to be significant In other words the three independent variables used in the model did not have predicting power to discriminate between PC adoptersnon adopters

We repeated the above analysis by introducing sector as an additional predictor This time we found a significant model where

bull PC adoption is inversely related to revenue (smaller firms are more likely to adopt PCs)

bull PC adoption is inversely related to size (smaller firms are more likely to adopt to adopt PCs)

bull PC adoption is more likely among firms that are between 3-7 years old

992 Internet Adoption

49 Discriminant analysis uses one or more independent variables to predict the likelihood of a categorical variable (eg adoptnot adopt)

76


We repeated the analysis for Internet adoption but we also included the level of PC usage as an additional predictor We found a significant model where Internet adoption is directly related to PC usage More specifically we found that

bull Internet adoption is inversely related to revenue bull Internet adoption is inversely related to size (smaller firms are more likely to

adopt) bull Internet adoption is more likely among firms that are between 3-7 years old This

makes intuitive sense as the latter firms have had time to experiment with PCs and thus would be more likely to adopt the Internet

bull Internet adoption is directly related to PC usage

993 Website Adoption

We repeated the analysis for Website adoption but this time included the level of PC and Internet usage as an additional predictor We found a significant model where

bull Website adoption is directly related to revenue (larger firms are more likely to adopt)

bull Website adoption is directly related to size (larger firms are more likely to adopt) bull Website adoption is more likely among firms less than 3 years and those over 7

years old than those 3-7 years old bull Website adoption is directly related to PC and Internet usage

In summary the above results show that



bull Both Internet adoption and Website adoption are influenced by prior usage of ICT

910 Performance Differences between ICT Adopters and Non-Adopters

We also tried to see if there are any performance differences between ICT adopters and non-adopters This would help answering the question of whether or not some SMEs missed opportunities by not adopting ICT In other words it would be useful to find out if the adopters performed better than their non-adopting counterparts

77


For each of the three stages of the adoption we used revenue as a proxy for the firmrsquos performance We tried to see if revenue is dependent of adoptionnon-adoption accounting for the sector (refer to Appendix 68)

The models related to PC and Internet adoption were found to be not significant indicating that there doesnrsquot seem to be any ldquomissed opportunityrdquo among the non-adopters The model for Web adoption had a very small R-square and was good only at 10 level of significance with only one sector having significant coefficient (ImportExport ndash Consumer durable goods) This indicates that there might be missed opportunities only among this sector when it comes to the decision to adopt Website

911 Hypotheses


We have found support for this hypothesis at all stages of ICT development It was expected that qualified and highly skilled workers could make ICT adoption easier as higher education would make them more innovative and increases the SMErsquos overall readiness for innovation One of the principal perceived inhibitors to ICT Usage has been consistently mentioned as a lack of technical skills with ldquothere is no one capable of using itrdquo being oft-mentioned in the responses


This hypothesis was related to the age of the firm and it was suggested that there is a gradual development of ICT Usage which is in line with the firmrsquos style of business where strategy is evolved through a process of learning and experimentation50

This hypothesis was supported to some extent where analysis of our data particularly the interviews and case studies confirmed that there are two distinct types of SMEs - the traditional ones who have been in business for some years and tend to stick to their old style of doing business and then the modern SMEs who are quicker in adopting new technologies

In general it was also found that overall ICT Usage was perceived to be lowest in the group of firms over 7 years in business This suggests that the newer and younger firms

50 Simmons G Armstrong G A and Durkin M G (2008) A conceptualization of the determinants of small business Website adoption Setting the research agenda International Small Business Journal 26(3) pp351-389

78


perceive that they use ICT far more than the older ones Even among those that had no ICT adoption as in no PCs it was observed that the younger firms which had been in existence for a period less than 7 years found subsidised training in using PCs providing PC adoption consulting services and providing tax incentives to be far more useful as Government support than the older firms This would suggest that these younger firms also appear to be far more open to the use of PCs


It was hypothesised that in a period of economic downturn SMEs are often the worst affected and would not normally invest in ICTs We found significant support for this hypothesis with a large number of SMEs stating that they would invest in ICT only when their business improves given the current economic downturn



We did not see much support for these hypotheses in the first Stages of PC adoption However by Stage 2 where the Internet was being used there was some mention of competition and the market conditions for Internet Usage It was only in Stage 3 though where the respondents had a Website that there was clear support for this hypothesis with providing competitive advantage listed as the third most popular area where the Website added value to the business This was followed by strengthening the relationships with business partners and improved productivity through service integration


This hypothesis was strongly supported by our analysis of data At each stage of ICT adoption improving operational efficiency was rated as the top value addition made on account of the use of the PCInternetWebsite For those who had not yet made this investment this factor was perceived to be the greatest attraction


79


Support for this hypothesis was largely seen in Stage 3 only where the creationuse of a Website was perceived to add strategic benefits in terms of improved company image market expansion and new productsservices In Stage 1 and 2 this was not as visible


This hypothesis was supported by the analysis of data whereby it was observed that limited financial resources particularly given the current financial downturn in the economy was frequently cited as a barrier to ICT investment Cost of buying and maintaining the ICT equipment was thus an important constraint for ICT adoption decisions


We would suggest that this hypothesis has been supported by the conclusion we have drawn that a lack of knowledgeawareness about the benefits of the ICT adoption has recurred as a barrier to its investment Our conclusion is further supported by the fact that consistently across all stages of ICT adoption the top two Government support types requested for are providing (subsidised) ICT training and ICT adoption consulting services


We did not find any support for this hypothesis as the SME respondents did not perceive the legal environment in Hong Kong to be a significant deterrent for ICT adoption

Conclusion

Based on literature review we formulated the above set of hypotheses We found general support for Hypotheses 1 to 9 thus providing evidence that in terms of making ICT adoption decisions Hong Kong SMEs are not different than those in other countries

We believe the reason we did not find support for the last hypothesis has to do with the misconception of Hong Kong SMEs about the legal framework regulating electronic transactions given that Hong Kong has an excellent legal and regulatory framework

80


10 SELECTED INDUSTRIES CASE STUDIES AND COMPARISONS WITH OTHER COUNTRIES

As stated earlier we have identified the following industries for further analysis

WholesaleRetail sector which shows a significant correlation between the Value Added and the ICT Usage

ImportExport Trade sector as this contributes almost half of the Value Added of SMEs to the Hong Kong economy This ImportExport sector has been further sub-divided in 3 areas


Business Services which shows a significant correlation between revenue and the extent of ICT use

Manufacturing sector based on the correlation observed in the analysis done at CampSD

For each of the above 6 sectors there were two detailed interviews conducted the findings of which have been reported as qualitative case studies to complement our quantitative findings

101 WholesaleRetail Sector

The WholesaleRetail sector has been identified as one of the sectors with high potential to benefit from further ICT investments This is based on the analysis of our survey where we found a significant correlation between the Value Added and the ICT Usage in the WholesaleRetail sector

This is echoed by a productivity study in Australia51

bull In the Wholesale sector it has been found that technology has led to an increase of productivity by changing inter- and intra-organisational relationships New technologies used in product numbering and scanning have improved accuracy and reduced the labour content of distribution by allowing real-time transmission of information and inventory control For larger firms the use of warehouse

51 Johnston A Porter D Cobbold T and Dolamore R 2000 Productivity in Australiarsquos Wholesale and Retail Trade Productivity Commission Staff Research Paper AusInfo Canberra

81


management system a component of Enterprise Resource Planning has also helped to create an integrated base for business management Transportation efficiency can also be improved by using new technologies that can take account of vehicle capacity traffic flows and congestion patterns

bull In the Retail sector it shows that ICTs particularly scanning technologies has reduced labour input and changed management systems hence improving productivity

Our study also suggests that the Governmentrsquos provision of IT consulting services and training support to the WholesaleRetail sector is necessary

[

This part of the document is not disclosed Please refer to endnotes (1) and (2)

]

The understanding of online customers has also been emphasized in other studies53

Therefore it seems to suggest that the training and consulting services provided by the Government to develop online business should not only focus on technical aspects also business management aspects

[


]

53 Shern S (2001) Online Retailing Ernst amp Young Lee MY and Johnson KP (2001) Exploring differences between internet apparel purchasers browsers and non-purchasers Journal of Fashion Marketing and Management 6(2) pp 146-157

82


[


]

83


[


]

84


[


]

85


[


]

86


102 Import and Export Trade

Our study has suggested that the Government should focus on the Import and Export SME sector for promoting ICT investments - not only because it contributes a significant 48 of the value added of SMEs in Hong Kong but also the nature of this industry suggests that ICT usage such as web presence will become a necessity in the future

However unlike what we found in the WholesaleRetail and Business Services sector our analysis using the data from the CampSD and our own survey data did not show a significant co-relation between ICT usage and value added We only found a direct relationship between revenue and the extent of ICT use for the Import amp Export ndash Miscellaneous Goods sector Indeed it appears that the use of the Internet or other Internet-based applications may not increase the revenue for all importers and exporters in the long term even though some trading companies can enjoy some short-term benefits The rationale behind our above conjecture is that the level of consumer spending which determines industrial demand in international trade will not be affected by the penetration of the Internet of importers and exporters54

However a lack of a positive co-relation between ICT investment and revenue does not imply that there is no need to adopt ICTs (such as Internet and Website adoption) Our survey suggests that Import and Export sector generally perceives that the use of PCs and the Internet can help generate new business opportunities and provide competitive advantages

1021 Import and Export Trade - Consumer non-durable goods

The Import and Export ndash Consumer Non-durable sector has some special needs for business and generally perceives the use of PCs and the Internet to be beneficial

Our survey results suggest that the Import and Export ndash Consumer Non-durable sector generally perceive the use of ICTs can bring value added to the business as follows

bull The use of PCs is perceived to improve customer services and increase sales

bull The use of the Internet is perceived to improve efficiency and customer services

However this sector is also the only sector in our study which showed no significant relationship between the perceived value of PCs and its usage Therefore the Government can offer more help to this sector to use PCs and educating the people of the various benefits associated with its use

54 Peterson RA S Balasubramanian and BJ Bronnenberg (1997) Exploring the implications of the Internet for consumer marketingrdquo Journal of the Academy of Marketing Science 25 pp329-346

87


[


]

While encouraging businesses to use the Internet and Website the Government also needs to make it clear that access to the Internet or developing a website alone cannot guarantee sustainable competitive advantages as some studies show 55 Existing literature56 suggests that besides a website exporters also need to develop an export-specific infrastructure and export marketing strategies taking into account local product standards competition legal and regulatory considerations and many other macro and micro management issues

[


]

55 Samiee Saeed (1998) Exporting and the Internet a conceptual perspective International Marketing Review 15(5) pp 413-426 56 Samiee Saeed (1998) Exporting and the Internet a conceptual perspective International Marketing Review 15(5) pp 413-426

88


[


]

89


[


]

90


[


]

91


[


]

1022 Import and Export Trade - Consumer durable goods

Our analysis of the two in-depth case studies conducted in this sector suggest that the Government needs to address the potential benefits of using the Internet as a marketing and promotion tool to this sector The two case studies show an interesting contrast one is a one-year old firm using the Internet Website and other online marketing channel to promote business and the other is an 80-years old firm relying on well-established business relationships with suppliers and customers and rejecting the ideas of online marketing and promotion In our literature review we find a study in the UK57 which shows that the Internet has the potential of eroding some existing advantages of better established firms and creating a level playing field by allowing almost any interested exporter to obtain a presence on the Internet and to list its address on various directories and Internet search engines

This would also suggest that the Government needs to provide different advice to firms of different sizes and history For example with web presence younger firms may overcome some barriers faced by new businesses while older firms may use the website to present itself as a well-established experienced trader

57 Hamill J and K Gregory (1997) Internet marketing in the internationalisation of UK SMEs Journal of Marketing Management 13 pp9-28

92


[


]

93


[


]

94


[


]

95


[


]

96


[


]

97


[


]

1023 Import and Export Trade ndash Miscellaneous

The two case studies presented in this sector show that businesses have special needs which must be considered before deciding on the type of Government support to be offered

[


]

98


[


]

99


[


]

100


[


]

101


[


]

102


[


]

103 Business Services

Business Services is a promising sector in terms of expected benefits from further ICT investments Our analysis from the data available at the CampSD showed a significant correlation between revenue and the extent of ICT use for this sector

Similarly a study on US trade data has suggested that the Internet use is related to growth in business services trade 59 The Internet and other new technologies have provided a medium of exchange that can overcome historical trading hurdles for many types of business services such as the face-to-face interaction between service providers and customers therefore effectively reducing transport costs The same study has also found a correlation between Internet adoption and international trades in business services although the causality is unclear It might be both ways the increasing number of trades in business services leads firms to adopt the Internet to facilitate the trade or greater Internet penetration leads firms to use the Internet more often

[


]

59 Freund C and D Weinhold (2002) The Internet and International Trade in Services The Economics of Technology and Innovation May 92(2) pp138-140

103


[


]

Nevertheless the Government can promote ICT usage to those well-established SMEs with a long-term business relationship by suggesting that it would be beneficial in further increasing their presence We have found that a series of qualitative case studies in the UK60 have shown that marketing focus can bring benefits to older established SMEs if they can make their web presence in innovative ways for example using this as an opportunity to change their corporate image

[


]

60 Martin LM and H Matlay (2003) Innovative use of the Internet in established small firms the impact of knowledge management and organisational learning in accessing new opportunities Qualitative Market Research An International Journal 6(1) pp18-26

104


[


]

105


[


]

106


[


]

104 Manufacturing Sector

107


Further to the findings obtained from our Study it appears that if the Government wants to promote the benefits of using PCs to the manufacturing sector it needs to take into account the specific needs of this industry

[


]

The need to address business benefits is also addressed by similar studies in other countries A study of the ICT adoption in manufacturing SMEs in the UK61 suggests that SMEs need to be advised of low-cost reliable solutions which address the needs of SMEs and show demonstrable business benefits

[


]

61 R Marasini K Ions and M Ahmad (2008) ldquoAssessment of e-business adoption in SMEs A study of manufacturing industry in the UK North East regionrdquo Journal of Manufacturing Technology Management Vol 19 No 5

108


[


]

109


[


]

110


[


]

111


[


]

112


[


]

113


11 FINDINGS amp RECOMMENDATIONS


We recommended that the proposed Government assistance first focus on the WholesaleRetail Business Services and ImportExport Trade industries

111 Prior to Stage 1 PC Non-Adopters

Barriers to ICT adoption

For these firms the most important reason for not adopting a PC appears to be SMEsrsquo perception that they do not really need it They feel that they have managed well without a PC so far and see no need to make such an investment that would be difficult and expensive to maintain These perceptions are similar across all sectors size and age of the firm

When asked if they would consider buying a PC approximately 50 of these respondents said that they would not as they could not perceive how the use of PCs would help the operations of their business Lack of awareness of the benefits of PCs is in reality a great barrier to these firms


Facilitators to ICT adoption

With the principal perceived inhibitor to ICT Usage being the lack of business need for a PC the primary motivators relate to ICT being adopted when there is a business needrequirement for it The other popular facilitator mentioned is addressing the expense of the ICT investment (providing financial support and an inexpensive PCpackage) Technical support is the third facilitator

Government assistance

114


Our analysis shows the following (in order of importance) as the most useful types of support that the Government could provide to promote the use of PCs

bull Subsidising training in using PCs bull Providing PC adoption consulting services bull Providing tax incentives bull Providing more information on the costs and benefits of using PCs bull Holding training workshopsseminars

The above suggestions are particularly notable as other than the third support of providing tax incentives (which is addressing the financial deterrent) all the others stem from the respondents attempting to address their lack of knowledgeawareness in ICT adoption

Subsidising training in the use of PCs (which addresses both the knowledge and cost barriers) and providing PC adoption consulting services are the most popular suggestions with 57 of the respondents perceive this to be veryhighly useful We found that the younger firms find subsidising training in using PCs providing PC adoption consulting services and tax incentives to be far more useful than the older firms

We recommend that the Government be aware that while providing subsidised training in the use of PCs and PC adoption consulting services the younger firms would be far more receptive than the older firms

112 Stage 1 PCs Adopters Who Have Not Yet Adopted the Internet


This group largely restricts their use to word processing computer security and spreadsheet software The comparatively more technically advanced packages or custom-made software such as HRMtraining and presentation are the least popular Finance and purchasing packages are also used very little This would perhaps be an expected finding as these respondents are at the lowest stage of ICT adoption and hence the use of PCs would be essentially for the most basic tasks

We noted that the use of software packages and custom-based packages differs across industries For instance finance packages are used significantly more in the Business Services than other sectors This leads us to believe that some barriers to ICT adoption are specific to the requirementsdemands of specific sectors

115


The size of the firm also appears to be an inhibitor to using advanced PC software such as finance sales and HRM packages We noted that the usage of these packages increased with an increase in the number of employees Similarly for the custom-based packages too as the number of employeesrsquo increases so does the usage of these custom-made packages This would suggest that these packages would be bought and used only when a certain critical mass is achieved in terms of the firmrsquos size

Overall we also found a correlation between perceived value of PCs to the business and the extent to which firms use PCs

We next arrived at our conclusions on why these users of the PC had not invested in the Internet The analysis of the data shows that the most popular responses are that the Internet is not needed that it is costly and that the firmrsquos business partners and customers are not using the Internet We found no significant differences in the reasons given by firms across different sectors the firmrsquos size and age


Improving operational efficiency and customer service are the two top value additions made by the use of PCs

While the ldquoefficiencyrdquo factor is common across all sectors we found significant differences in the perception of value of PCs across industries For instance the use of PCs is seen to add far more value in terms of ldquoImproving customer servicerdquo in the Finance amp Insurance Business Services and Import amp Export ndash Consumer Non-durable Goods as compared to the Transport amp Storage Construction and WholesaleRetail sectors Similarly firms belonging to the Finance amp Insurance and Import amp Export ndash Consumer Non-durable Goods industries find high support for increasing sales by using PCs However those belonging to the Transport amp Storage and Construction sectors do not in comparison perceive much value addition in terms of sales increasing because of PCs use

Government Assistance

While promulgating the benefits of further using PCs we note that each industry has some specific requirementsdemands and different perceptions of where and how much value PCs bring to business

In terms of motivating these firms to adopt the Internet the following are the top four most popular types of support that Government can provide

bull Providing Internet adoption consulting services

116


bull Subsidising Internet training bull Providing more information on the costs and benefits of using the Internet bull Holding training workshopsseminars

We found that there are no significant differences in the perception of the use of different types of Government support across different industries size of the firm or its number of employees

Providing consulting services and subsidising training are the most popular suggestions with approximately 40 of the respondents who replied to this question perceiving these to be veryhighly useful

The above findings show clearly that the respondents are looking for knowledge about the Internet and hence training and consulting become their most desired types of support expected from the Government This conclusion also ties in to our previous suggestion that at least for some segment of the population the greatest barrier to using the Internet is a lack of awareness of its benefits

Our analysis shows that on the whole Business Services and the ImportExport sectors are high users of different software packages as compared to the other industries such as WholesaleRetail sector

We recommend that while the Government promotes the benefits of using PCs it must consider the specific needsbarriers and facilitators for that sector

In terms of adopting the Internet the focus should be on the top-rated benefits of speedy and cheap communication and ease of information availability We recommended that Government assistance should concentrate on subsidising training in the use of Internet and providing Internet adoption consulting services Such training workshops should highlight the potential costs and benefits of the Internet

113 Stage 2 Internet Adopters who have not yet adopted the Web


We found that the most popular uses of the Internet are e-mail and on-line sourcing of general information applications that do not require a Website We also found that some companies especially the young ones use the Internet for e-banking

The respondents were also asked if they perceived additional use of the Internet would further improve their business The responses were mixed with almost half stating that

117


more Internet usage would not help improve their business while the other half felt that it would

Those who saw the potentials of the Internet identified speedy communication and ease of searching for information and improvement in marketingsales development as areas with greatest potential This is notable because marketingsales development is also the top reason for having a Website Hence it would suggest that this group would be more receptive to moving on to the next stage of creating a Website

The following reasons are mentioned as to why the firm do not create a Website lack of business need difficulty of maintenance costs lack of industry critical mass Through our interviews we further collaborate these findings as we observed that non-adoption of a Website had to do with the fact that the businessmarket had not yet evolved sufficiently for a great need of a Website and the expense associated with creating and maintaining a Website perceived to be high



The greatest facilitator for further Internet use would perhaps be the firmrsquos perception that ICT usage is beneficial As in the case of PCs improving operational efficiency and enhancing customer service are perceived to be the two top values ensued by the use of the Internet Providing competitive advantage and prompt response to market changes were the next most popular perception of where Internet use adds value to the business


Having addressed what would possibly facilitate the use of additional Internet use the next stage was to understand the principal factors that would motivate the firms to create a Website

First it was necessary to understand whether these respondents who did not have a Website were actually aware of the possible benefits of having one While 44 stated that creating a Website would not help the remaining perceive it to be useful as it would

118


bull Improve the firmsrsquo presence and promote marketing efforts by attracting new customers thereby increasing revenue

bull Enhance the firmrsquos credibility reputation and image bull Provide additional information on the company and its products



It would be useful to note that each sector has some specific requirementsdemands and different perceptions of where and how much value can be added by using Internet

In terms of motivating these firms to create a Website the following are the top three most popular types of support suggested

bull Subsidising ICT training bull Providing ICT adoption consulting services bull Providing tax incentives for ICT investments

Subsidising training in the use of ICT is the most popular suggestion with 60 of the respondents perceiving this to be very useful

We noted that there were no significant differences in perceiving the use of different types of Government support across different sectors and firmrsquos size or age

We recommend that the Government when promoting the benefits of creating a Website emphasise its use in marketingsales development We also recommend that Government assistance should focus on subsidising ICT training and providing ICT adoption consulting services

114 Stage 3 Adopters of the Web

The most popular uses of the Website are for ldquoProviding information on the firms products and servicesrdquo followed by ldquoHandling of business enquiryrdquo None of the other purposes were at all popular in comparison


119


However unlike the PC and the Internet we find no evidence of any correlation between perceived value of a Website to the business and the extent to which firms use a Website

115 Overall ICT Usage and Value Added

We performed regression analysis of the two questions soliciting perceptions about the overall extent of ICT use and the extent to which using more ICT would add value to the firm More precisely we wanted to know if the overall perceived value of ICT is dependent on the extent of ICT use We found that the respondents see a very clear connection between the perceived overall value of ICT and the overall extent of ICT use

116 Differences in ICT Adoption across Stages of ICT Development

We checked to see if there are differences in responses across the three stages of ICT development with regard to issues related to Government support that would facilitate and encourage ICT adoption reasons for not using different types of ICT (PCs the Internet Website) and value of ICT We also took into account the firmrsquos age size and sector and further examined the effect of the stage of ICT development on perceived value of ICT taking into consideration these variables

In terms of rating the usefulness of different types of Government support as a means to help companies adopt these technologies we found that by and large there are no differences between responses regarding various types of Government support at different stages of ICT development The exception relates to the importance of providing tax incentive which is significantly different at the three different stages of development with non-PC users perceiving it to be more important than non-Website users than non-Internet users

We also asked non-PC users non-Internet users and non-Website users to rate the importance of different set of reasons for not using ICT at different stages of development We found that by and large there are no differences between responses regarding reasons for not using ICT at different stages of development The exception relates to difficulty in finding trained personnel at different stages of development with non-Website users perceiving this factor more important than non-PC users than non-Internet users

We then asked PC users Internet users and Website users to rate the extent to which ICT results in business value at different stages of development In general we found significant differences between responses regarding the value-adding effect of ICT at different stages of development More specifically we found improving operational efficiency and customer services are highest among PC users followed by Internet and Website users Further the value of ICT in terms of increasing sales penetration into

120


new markets and providing competitive advantage is highest among PC users followed by Website users and then Internet users Finally Website users see higher value in ICT than Internet users in terms of creation of new productsservices In sum the above analyses show that by and large SMEs see different value in ICT at different stages of ICT development

117 Value added as a function of ICT usage size age stage and sector

We checked to find the relationship between value added and ICT Usage size age stage and sector

We found that there was a significant and direct relationship between value added and ICT usage This was also true for the value added and stage of ICT usage with highest value added perceived in Stage 3 There was also a significant relationship between value added and age with newer firms seeing greater value added than the older ones There was however no significant relationship between value added and size

118 Revenue and ICT usage size age stage and sector

We checked to find the relationship between Revenue and ICT Usage size age stage and sector

We found that there was as expected a significant relationship between revenue and size with the larger the firm the higher the revenue There was a significant relationship between revenue and stage of ICT usage with a higher stage showing higher revenue

There was however no significant relationship between revenue and ICT usageage There was also no significant relationship between revenue and sector with the only exception being the Import amp Export ndash Consumer durable goods

119 Predictors differentiating ICT adopters from non-adopters

We tried to ascertain the influential predictors of ICT adoption at various stages that is PC Internet and Website

We used revenue size and age as predictors of PC adoption We found that these three independent variables used in model did not have the predicting power to discriminate between PC adoptersnon adopters However when we repeated the above analysis by including sector as an additional predictor we found a significant model where PC adoption is inversely related to revenue and size (smaller firms are more likely to adopt)

121


Further PC adoption is more likely among firms less than 3 years and those over 7 years old than those 3-7 years old

We repeated the analysis for Internet adoption but we also included the level of PC usage as an additional predictor We found a significant model where Internet adoption is directly related to PC Usage

We repeated the analysis for Website adoption but we also included the level of PC and Internet usage in addition to revenue size age and sector We found a significant model where Website adoption is directly related to PC and Internet Usage

122

Appendices


Appendix 1 SMEs Stages of Growth

The evolution of SMEs follows an S-curve growth model consisting of four phases65 66

Extensive ICT Use

G r o w t h

M a t u r I t y

Stage I Conception amp Development

Stage III Growth

Stage IV Stability

Developing ICT use

Maturing ICT Use

No or little ICT Use

Stage II Commercialisation

65 Churchill NC and VL Lewis (1983) The Five Stages of Small Business Growth Harvard Business Review May-June 66 Kazanjian R (1988) Relation of dominant problems to stages of growth in technology-based new ventures Academy of Management Journal 31 pp257-279

123


Figure 1 SME Stages of Growth

SMEs within the same industry need to develop different ICT capabilities at different stage of their growth67 In conception and development stage the firm is hungry for financial support market recognition and necessary resources At this stage SMEs want to develop their capabilities fast and at a low cost In commercialisation stage the firm needs to commercialise its new products or services The challenge in this stage is the coordination of various activities to make a quick response to market changes Therefore ICT capabilities require a higher level of resource commitment in terms of finances training and time to implement and master In growth stage SMEs look to increase market share through sales and marketing thus needing to develop ICT capabilities not only to market their products but also to manage relationships with the increasing number of customers In stability stage SMEs reach a level of maturity in terms of sales revenues and try to secure their profitable positions while looking for the next sources of revenues At this stage they need ICT capabilities to improve efficiency back-office support and collaboration

67 Lester Donald L and TT Tran (2008) Information Technology Capabilities Suggestions for SME Growth Journal of Behavioural and Applied Management pp72-88

124


Appendix 2 Calculation of ICT Usage

The idea was to source the information from the census data available in ASIT and assign points to various factors that could determine the level of use of ICT by a firm These would then be summed up and we could get an overall picture of where different firms are on the scale



3 Web Presence

4 Transaction


6 Transformation

Low

High

B u s i n e s s

T r a n s f o r m a t i Low Higho Range of Potential Benefits n

For our purpose of ICT Usage we have combined the above as 3 stages


In the next table we would identify and give marks to the relevant questions from ASIT to test the ICT Usage in terms of the stages defined above

125


Stage Applications

Stage 1 Localised Exploitation amp Internal Integration

bull PCLaptop bull One or more applications bull Applications allow technical connectivity and

application interdependence (eg intranet ERP)

Stage 2 Web presence (Internet Connection) amp Online Transaction

bull Webpage bull Email bull EDI bull E-marketplaces bull E-procurement

Stage 3 External Integration (Electronic value chain) amp Transformation

bull Full supply chainvalue chain integration bull E-enterprise with a focus on knowledge management bull Applications to allow the use of knowledge with a

focus on customer needs as a strategic resource to create business value

The details of the questions selected for analysis have been shown in Appendix 3

126


Appendix 3 Analysis of the questions selected for calculation of ICT Usage from the ASIT Report

Each question from the ASIT survey was analysed to ascertain if it should be included in the calculation If to be included it was then decided which stage of ICT Usage the firm belonged to

Index Required (Y) Not If required in Form of required (N) in the Stage of ICT

Question ASIT Answer analysis If not required why Usage Does the firm use PCs B1 YesNo N Covered in B6

Not relevant to the ICT Does it plan to use PCs If not why B2 YesNo N Usage calculation

Number of PCs in use by different categories - Number of Not relevant to the ICT DesktopLaptop etc B3 sets N Usage calculation

Whether the firms PCs have installedaccess to the following application software B4 i Word processing YesNo Y Stage 1 ii Spreadsheet YesNo Y Stage 1 iii Database Management YesNo Y Stage 1 iv Communication YesNo Y Stage 1 v Presentation YesNo Y Stage 1 vi Computer security YesNo Y Stage 1

127


vii viii ix x xi xii xiii xiv

Multi-media Other utility CADCAM End-user software for DEDS Internet browser Banking amp FinanceAccounting PurchaseSales amp Order processing HRMTraining

YesNo YesNo YesNo YesNo YesNo YesNo YesNo YesNo

Y Y Y Y Y Y Y Y

Stage 1 Stage 1 Stage 1 Stage 2 Stage 2 Stage 1 Stage 1 Stage 1

xv Others YesNo Y Stage 1

If the firms PCs have access to the following tailor made computer systems

i CADCAM ii Banking amp FinanceAccounting iii PurchasingSale and order processing iv Human Resource ManagementTraining

B5

YesNo YesNo YesNo YesNo

N Y Y Y

Not commonsectors to

all

Stage 1 Stage 1 Stage 1

The proportion of the firmrsquos employees using PCs at work B6 Y Stage 1

Does the firm have internet Does it plan to use internet If not why

C1 C2

YesNo YesNo

N N

Covered in C5

What is the internet usually used for C3

128


i Email Y Stage 2 ii On-line sourcing of general information Y Stage 2 iii On-line purchaseordering Y Stage 2 iv Receipt Y Stage 2 v Sales Y Stage 2 vi Delivery Y Stage 2 vii Payments Y Stage 2 viii Government information Y Stage 2 ix Enquiry to suppliers Y Stage 2 x Provision of informationfeedback Y Stage 2 xi Software downloads Y Stage 2 xii E-banking services Y Stage 2 xiii Financial transaction Y Stage 2 xiv Internal use applications such as ERP Y Stage 2 xv Video conference Y Stage 2 xvi Others Y Stage 2

Not relevant to the ICT How is it connected to the internet Dial-up etc C4 N Usage calculation

The proportion of employees using the Internet at work C5 Y Stage 2

Indicate the effects of using the internet on the business operations and degree of impact such as Not relevant to the ICT increased revenue etchellip C6 N Usage calculation

129


Not relevant to the ICT Does it have a BCP C7 N Usage calculation

What happens if there is an IT disaster C8 N Not relevant to the ICT Usage calculation

What would motivate the firm to adopt IT more C9 N Not relevant to the ICT Usage calculation

Has your firm LANIntranetExtranet C10 N Not relevant to the ICT Usage calculation

Website ndash YesNo D1 YesNo Y Stage 2

Does it plan to set up a website D2 YesNo N Not relevant to the ICT Usage calculation

Own Web server ndash YesNo D3 YesNo Y Stage 2

Websitewebpage integrated with the firmrsquos database D4 YesNo Y Stage 3

Websitewebpage integrated with the business partnersrsquo database D5 YesNo Y Stage 3

Type of transactions offered by the D6

130


websitewebpage i Providing information on the firm products

and services ii Ordering of the firmrsquos products iii Delivery of products iv After sales services v Collection of feedback vi Handling of business enquiry vii Others

YesNo YesNo YesNo YesNo YesNo YesNo YesNo

Y Y Y Y Y Y Y

Stage 2 Stage 2 Stage 2 Stage 2 Stage 2 Stage 2 Stage 2

Have you used any portal sites for your industry in the past 12 months D7 YesNo N

Not relevant to the ICT Usage calculation

Has your firm purchased goods through electronic means E1 YesNo Y Stage 2

Reasons for ordering convenience etc

goods electronically E2 N


Types of goods ordered electronically - inventory travel etc E3 N


Percentage of value of goods services or information purchased through electronic means to the total value of purchase by the firm i lt1

E4(a) N Not universal sectors

to all

131


ii 1-5 iii 6-10 iv 11-15 v gt15

Percentage purchased electronically through Not relevant to the ICT different means E4 (b) N Usage calculation i Internet via PCs ii Internet via mobile iii Designated private network iv Interactive Voice Response System through

telecom v Other Interactive Response System (such as

SMS) through mobile telecom vi Others

Percentage of payment made through Internet to total payments E5 Y Stage 2 i 0 ii lt1 iii 1-20 iv 21-40 v 41-60 vi 61-80 vii 81-100

132


Firm obtained government goodsservices through electronic means E6 YesNo Y Stage 2

Type of government goods received i Receipt of e-products ii Receiptbrowsing of other information iii Submission of trade related documents iv Submission of other forms or documents v Submission of enquiry vi E-payment services vii Others

E7 N Not relevant to the ICT Usage calculation

Firm obtained goodsservices other than government through electronic means E8 YesNo Y Stage 2

Type of goodsservices other than government through electronic means received i Electronic financial services ii Receipt of e-products iii Receiptbrowsing of other information iv Receipt of on-line customer services v Others


Has your firmmeans

sold goods through electronic

E10

YesNo Y Stage 2

133


Reasons for ordering convenience rice etc



Value of goods services or information sold through electronic means E12(a) N Covered in E12(d)

Percentage of value of goods services or information sold through electronic means by type of electronic means i Internet via WAP version of website ii Internet via website other than WAP iii Designated private network iv Interactive Voice Response System through

telecom

E12b N Not relevant to the ICT Usage calculation

v

vi

Other Interactive Response System (such as SMS) through mobile telecom Others

Breakdown by customer groups E12 c N Not relevant to the ICT Usage calculation

Percentage of payment received through Internet to total receipts of goods sold through the internet i 0 ii lt1 iii 1-5

E12(d) Y Stage 2

134


iv v vi

6-10 11-15 gt15

Type of payments accepted through electronic means i Electronic banking services ii Credit cards iii Third-party payment services iv Debit from pre-paid accounts with the

company v Non-electronic payment methods vi Others

E12e Y Stage 2

Total business receipts of the firm E13 N Not relevant to the ICT Usage calculation

Has the firm delivered goodsservices through electronic means E14 YesNo Y Stage 2

Reasons for delivering through electronic means - convenience etc E15 N


Type of goods delivered through electronic means E16 N Not relevant to the ICT Usage calculation

Media for delivering your goods services or E17 N Not relevant to the ICT

135


information sold through electronic means isare Usage calculation i ii iii iv

Internet via WAP version of website Internet via website other than WAP Designated private network Interactive Voice Response System through

telecom v

vi


Does the firm have a budget for IT for the current year F1 YesNo N


Has the budget for IT increasedchanged over the previous year i Increased ii Decreased iii Same iv Uncertain v Not applicable

F2 N Not relevant to the ICT Usage calculation

Does the firm have a budget for IT for the next year F3 YesNo N


Has the budget for the next year increasedchanged over the previous year F4 N


136


i Increased ii Decreased iii Same

iv Uncertain v Not applicable

Has it suffered security incidents G1 N Not relevant to the ICT Usage calculation

Type of incidents suffered G2 N Not relevant to the ICT Usage calculation

What actions does it take G3 N Not relevant to the ICT Usage calculation

What security technology has it adopted to protect itself G4 N


Has it implemented any security measure G5 N Not relevant to the ICT Usage calculation

What are the obstacles to addressing security incidents G6 N


How is authentication provided to clients G7 N Not relevant to the ICT Usage calculation

137


Does the firm have a digital certificate G8 YesNo Y Stage 2

Has it used the digital certificate in the past 12 months G9 N


Reasons for not wanting a digital certificate G10 N Not relevant to the ICT Usage calculation

Does the firm use any of the following wireless and mobile devices i Mobile phone connected to a wireless data

communication network ii PDA connected to a wireless data

communication network iii Laptopnotebook connected to a wireless data

communication network iv Desktop computer connected to a wireless data

communication network v WLAN access point vi Radio Frequency Identification device vii Contactless smart card reader viii Others

H1 Y Stage 2

Does the firm use any of the following wireless and mobile transmission technology i GPRS ii EDGE

H2 Y Stage 2

138


iii 3G iv WAP v WLAN vi Bluetooth vii RFID viii Mobile security solution ix Others

Does the firm use any of the following wireless and mobile services H3 Y Stage 2 i SMS ii EMS iii MMS iv LBS v GPS vi Wireless e-mail vii Wireless web-surfing viii Infotainment service ix Video call x Others

Does the firm use any of the following wireless and mobile applications H4 Y Stage 2 i GIS ii SMSMMS

139


iii Office automation iv WAP v Job Management vi Fleet management vii Outdoor site survey and documentation viii Inventory control ix Mobile financial services x Mobile POS xi Transaction with business partners

(including Government) xii Video streaming xiii Accesssecurity control xiv Others

What are the obstacles of using these Not relevant to the ICT technologies H5 N Usage calculation

140


Appendix 4 Weights assigned to the questions selected for each stage of ICT Usage calculation from the ASIT Report

Index in Form of Questions of Stage 1 ASIT Answer Points Weight Max Min

Whether the firms PCs have installedaccess to the following application software (Note not x and xi) B4 i Word processing YesNo Y=1 N=0 1 1 0 ii Spreadsheet YesNo Y=1 N=0 1 1 0 iii Database Management YesNo Y=1 N=0 1 1 0 iv Communication YesNo Y=1 N=0 1 1 0 v Presentation YesNo Y=1 N=0 1 1 0 vi Computer security YesNo Y=1 N=0 1 1 0 vii Multi-media YesNo Y=1 N=0 1 1 0 viii Other utility YesNo Y=1 N=0 1 1 0 ix CADCAM YesNo Y=1 N=0 1 1 0 xii Banking amp FinanceAccounting YesNo Y=1 N=0 1 1 0 xiii PurchaseSales amp Order processing YesNo Y=1 N=0 1 1 0 xiv HRMTraining YesNo Y=1 N=0 1 1 0 xv Others YesNo Y=1 N=0 1 1 0

If the firms PCs have access to the following tailor made computer systems B5 i Banking amp FinanceAccounting YesNo Y=1 N=0 1 1 0 ii PurchasingSale and order processing YesNo Y=1 N=0 1 1 0 iii Human Resource ManagementTraining YesNo Y=1 N=0 1 1 0

141


Put the exact figure The proportion of the firmrsquos employees using PCs at work B6 provided in decimals 005 5 0

Questions of Stage 2 Index in ASIT

Form of Answer Points

Total

Weight

21

Max

0

Min

Whether the firms PCs have installedaccess to the following application software i End-user software for DEDS ii Internet browser

B4 YesNo YesNo

Y=1 N=0 Y=1 N=0

1 1

1 1

0 0

What is the internet usually used for i Email ii On-line sourcing of general information iii On-line purchaseordering iv Receipt v Sales

C3 If not used give 0 If lt4 uses give 1

If 4 to 8 uses give 2 If 8 to 16 uses give 3

1 1 1 1 1

1 1 1 1 1

0

vi viiviii

Delivery Payments Government information

1 1 1

1 1 1

ix x xi

Enquiry to suppliers Provision of informationfeedback Software downloads

1 1 1

1 1 1

xii E-banking services 1 1

142


xiii Financial transaction xiv Internal use applications such as ERP xv Video conference xvi Others

The proportion of employees using the Internet at work C5 Put the exact figure

provided in decimals

1 1 1 1

005

1 1 1 1

5 0

Website ndash YesNo D1 YesNo Y=1 N=0 1 0

Own Web server ndash YesNo D3 YesNo Y=1 N=0 1 0

Type of transactions offered by the websitewebpage i Providing information on the firm products and services ii Ordering of the firmrsquos products iii Delivery of products iv After sales services v Collection of feedback vi Handling of business enquiry vii Others

D6 YesNo YesNo YesNo YesNo YesNo YesNo YesNo

For each usage give one point

Y Y Y Y Y Y Y

1 1 1 1 1 1 1

7

1 1 1 1 1 1 1

0

Has your firm purchased goods through electronic means E1 YesNo Y=1 N=0 1 0

Percentage of payment made through Internet to total payments i 0

E5 0 3 0

143


ii iiiiv v vi vii

lt1 1-20 21-40 41-60 61-80 81-100

1 1 2 2 3 3

Firm obtained government goodsservices through electronic means Firm obtained goodsservices other than government through electronic means

E6 E8

YesNo YesNo

Y=1 N=0 Y=1 N=0

1 1

1 1

0 0

Has your firm sold goods through electronic means E10 YesNo Y=1 N=0 1 1 0

Percentage of payment received through Internet to total receipts of goods sold through the internet i 0 ii

lt1 iii 1-5 iv 6-10 v 11-15 vi gt15

E12(d) 0 1 1 2 2 3

3 0

Type of payments accepted through electronic means i Electronic banking services ii Credit cards iii Third-party payment services

E12e If not used = 0 If using upto 2 = 1 If using 2 to 4 = 2 If using 4 to 6 = 3

3 0

144


iv v vi

Debit from pre-paid accounts with the company Non-electronic payment methods Others

Has the firm delivered goodsservices through electronic means E14 YesNo Y=1 N=0 1 0

Does the firm have a digital certificate G8 YesNo Y=1 N=0 1 0

Does the firm use any of the following wireless and mobile devices i Mobile phone connected to a wireless data communication network ii PDA connected to a wireless data communication network iii Laptopnotebook connected to a wireless data communication network iv Desktop computer connected to a wireless data communication network v WLAN access point vi Radio Frequency Identification device vii Contactless smart card reader viii Others

H1 If any are used give 1

point else 0 1 0

Does the firm use any of the following wireless and mobile transmission technology i GPRS ii

EDGE iii 3G iv WAP


point else 0 1 0

145


v WLAN vi Bluetooth vii RFID viii Mobile security solution ix Others

If any are used give 1 Does the firm use any of the following wireless and mobile services H3 point else 0 1 0 i SMS ii EMS iii MMS iv LBS v GPS vi Wireless e-mail vii Wireless web-surfing viii Infotainment service ix Video call x Others

If any are used give 1 Does the firm use any of the following wireless and mobile applications H4 point else 0 1 0 i GIS ii SMSMMS iii Office automation iv WAP

146


v vi vii viii ix x xi xii xiii xiv

Job Management Fleet management Outdoor site survey and documentation

Inventory control Mobile financial services Mobile POS Transaction with business partners (including Government) Video streaming

Accesssecurity control Others

Total 53 0

Questions of Stage 3 Websitewebpage integrated with the firmrsquos database

Index in ASIT D4

Form of AnswerYesNo

Points Y=1 N=0

Weight 5

Max 5

Min 0

Websitewebpage integrated with the business partnersrsquo database D5 YesNo Y=1 N=0 5 5 0

Total 10 0

147


Appendix 5 Details of the questions selected for calculation of ICT Usage from the ASIT Report based on the Codes available at CampSD

(The items which are appearing in grey are those which were excluded from analysis)

Form Index in of

Part 1 ASIT Answer Points Weight Max Min Whether the firms PCs have installedaccess to the following application software (Note not x and xi) B4

i Word processing YesNo Y=1 N=0 1 1 0

ii Spreadsheet YesNo Y=1 N=0 1 1 0

iii Database Management YesNo Y=1 N=0 1 1 0

iv Communication YesNo Y=1 N=0 1 1 0

v Presentation YesNo Y=1 N=0 1 1 0

vi Computer security YesNo Y=1 N=0 1 1 0

vii Multi-media YesNo Y=1 N=0 1 1 0

viii Other utility YesNo Y=1 N=0 1 1 0

ix CADCAM YesNo Y=1 N=0 1 1 0

x Banking amp FinanceAccounting YesNo Y=1 N=0 1 1 0

xi PurchaseSales amp Order processing YesNo Y=1 N=0 1 1 0

xii HRMTraining YesNo Y=1 N=0 1 1 0

xiii Others YesNo Y=1 N=0 1 1 0

If the firms PCs have access to the following tailor made computer systems B5

i Banking amp FinanceAccounting YesNo Y=1 N=0 1 1 0

ii PurchasingSale and order processing YesNo Y=1 N=0 1 1 0

iii Human Resource ManagementTraining YesNo Y=1 N=0 1 1 0

148


Use figure given as The proportion of the firmrsquos employees using PCs at work B6 percentage 005 5 0

ICTU1 = 21 0

Part 2 Index in

ASIT

Form of

Answer Points Weight Max Min

Whether the firms PCs have installedaccess to the following application software

i End-user software for DEDS

ii Internet browser

B4

YesNo

YesNo

Y=1 N=0

Y=1 N=0

1

1

1

1

0

0

What is the internet usually used for

i Email

ii On-line sourcing of general information

iii On-line purchaseordering

iv Receipt

v Sales

vi Delivery

vii Payments

viii Government information

ix Enquiry to suppliers

x Provision of informationfeedback

xi Software downloads

xii E-banking services

xiii Financial transaction

xiv Internal use applications such as ERP

xv Video conference

C3

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

149


xvi Others 16 1 1 0

The proportion of employees using the Internet at work C5 Use figure given as

percentage 005 5 0



Type of transactions offered by the websitewebpage

i Providing information on the firm products and services

ii Ordering of the firmrsquos products

iii Delivery of products

iv After sales services

v Collection of feedback

vi Handling of business enquiry

vii Others

D6

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo


Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

Has your firm purchased goods through electronic means E1 YesNo Y=1 N=0 1 1 0

Percentage of payment made through Internet to total payments

i 0

ii lt1

iii 1-20

iv 21-40

v 41-60

vi 61-80

vii 81-100

E5

1

2

3

4

5

6

7

0

1

1

2

2

3

3

3 0

150


Firm obtained government goodsservices through electronic means E6 YesNo Y=1 N=0 1 1 0

Firm obtained goodsservices other than government through electronic means E8 YesNo Y=1 N=0 1 1 0


Percentage of payment received through Internet to total receipts of goods sold through the internet

i 0 ii lt1

iii 1-5

iv 6-10

v 11-15

vi gt15

E12(d)

1

2

3

4

5

6

0

1

1

2

2

3

3 0

Type of payments accepted through electronic means

i Electronic banking services

ii Credit cards

iii Third-party payment services

iv Debit from pre-paid accounts with the company

v Non-electronic payment methods

vi Others

E12e 1

2

3

4

5

6

If not used = 0 If using 1 or 2

assign 1 If using 3 or 4


assign 3

3 0



Does the firm use any of the following wireless and mobile devices H1 If any are used give

1 point else 0 1 0

151


i Mobile phone connected to a wireless data communication network YesNo

ii PDA connected to a wireless data communication network YesNo

iii Laptopnotebook connected to a wireless data communication network YesNo

iv Desktop computer connected to a wireless data communication network YesNo

v WLAN access point YesNo

vi Radio Frequency Identification device YesNo

vii Contactless smart card reader YesNo

viii Others YesNo

Does the firm use any of the following wireless and mobile transmission technology H2

i GPRS YesNo

ii EDGE YesNo

iii 3G YesNo

iv WAP YesNo

v WLAN YesNo

vi Bluetooth YesNo

vii RFID YesNo

viii Mobile security solution YesNo

ix Others YesNo

Does the firm use any of the following wireless and mobile services H3

i SMS YesNo

ii EMS YesNo

iii MMS YesNo

iv LBS YesNo

v GPS YesNo

vi Wireless e-mail YesNo

vii Wireless web-surfing YesNo

If any are used give 1 point else 0 1 0


152


viii

ix

x

Infotainment service

Video call

Others

YesNo

YesNo

YesNo

Does the firm use any of the following wireless and mobile applications

i GIS

ii SMSMMS

iii Office automation

iv WAP

v Job Management

vi Fleet management

vii Outdoor site survey and documentation

viii Inventory control

ix Mobile financial services

x Mobile POS

xi Transaction with business partners (including Government)

xii Video streaming

xiii Accesssecurity control

xiv Others

H4

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo


ICTU2 = 28 0

Part 3

Websitewebpage integrated with the firmrsquos database

Index in ASIT

D4

Form of

Answer

YesNo

Points

Y=1 N=0

Weight

5

Max

5

Min

0


153


ICTU3 = 10 0

ICTU = ICTU1 +ICTU2 + ICTU3

154


Appendix 6 Number of sampled establishments [1] in 2008 ASIT with PC and Internet connection

Internal reference only

Personal Computer and Internet Penetration Usage in Business Sectors

Industry Sector All establishments of SMEs [1] establishments

of SMEs [1] having used personal

computer of SMEs [1] having Internet connection

Manufacturing 16 539 988 522 510 Electricity and gas 20 800 1000 450 Construction 20 355 984 548 464 Wholesale and retail trades 60 073 988 343 290 Import and export trades 87 896 991 912 899

Consumer non-durable goods [2] 47 633 992 914 911 Consumer durable goods [3] 12 488 986 876 869 Miscellaneous goods [4] 27 775 994 924 891

Restaurants hotels and boarding houses 12 748 934 308 203 Transport and storage 31 777 983 309 296 Communications 859 914 770 752 Financing and insurance 8 711 951 699 616 Real estate 5 246 940 646 498 Business Services 28 946 976 908 896 Community social and personal services 34 730 959 602 505 Overall 307 900 980 623 581

155


Source 2008 Annual Survey of Information Technology Usage and Penetration in the Business Sector (ASIT)

[1] Establishments are categorised into small and medium enterprises (SMEs) according to their number of persons engaged as follows (i) with less than 100 persons for manufacturing sector (ii) with less than 50 persons for non-manufacturing sector

[2] Consumer non-durable goods include foodstuffs alcoholic drinks and tobacco clothing footwear and allied products toys watches and clocks and other consumer non-durable goods

[3] Consumer durable goods include telecommunications equipment electrical goods computers computer peripherals and software packages and other consumer durable goods

[4] Miscellaneous goods include machinery equipment and parts transport equipment raw materials and semi-manufactures and fuels

156


Appendix 7 Analysis of CampSD data ndash ICT Usage across Industries

Anova68 Single Factor

SUMMARY

Groups Count Sum Average Variance Sector 1 235 24229 1031021277 106561132 Sector 2 167 173365 1038113772 9453241469 Sector 3 467 5119 109614561 9692695729 Sector 4 175 206535 11802 9390268966 Sector 5 142 18078 1273098592 7329777744 Sector 6 73 89405 1224726027 776249239 Sector 7 21 1585 7547619048 1839386905 Sector 8 217 24887 1146866359 1099308756 Sector 9 62 7754 1250645161 102406954 Sector 10 65 8432 1297230769 7473752404 Sector 11 14 16565 1183214286 1863100275 Sector 12 335 43343 1293820896 5423415944

ANOVA Source of Variation SS df MS F P-value F crit Between Groups 216832708 11 1971206437 2183316525 173123E-42 1793521277

68 ANOVA stands for ldquoAnalysis Of VAriance between groupsrdquo ANOVA analyses the variation present in an experiment by giving a statistical test of whether the means of several groups are all equal

157


Within Groups 1770488052 1961 9028495933

Total 1987320761 1972

158


Anova Single Factor

SUMMARY


ANOVA Source of Variation Between Groups Within Groups

SS 7143541864 4157821969

df 11 1961

MS 6494128968 2120255976

F 3062898556

P-value 311374E-60

F crit 1793521277

Total 4872176155 1972

159


Anova Single Factor

SUMMARY



SS 4986186738 3223293368

df 11 1857

MS 4532897035 1735753025

F 261148733

P-value 533615E-51

F crit 1793794387

Total 3721912042 1868

160


Anova Single Factor

SUMMARY



SS 3891069269 8605317807

df 11 1961

MS 3537335699 4388229376

F 8060963537

P-value 66661E-14

F crit 1793521277

Total 8994424734 1972

161


Anova Single Factor

SUMMARY

Groups Count Sum Average Variance Sector 1 13 85 6538461538 5769230769 Sector 3 34 200 5882352941 3743315508 Sector 4 20 120 6 4210526316 Sector 5 17 105 6176470588 4779411765 Sector 8 25 165 66 5666666667 Sector 9 13 85 6538461538 5769230769 Sector 10 11 65 5909090909 4090909091 Sector 12 68 485 7132352941 6206101844

ANOVA Source of Variation SS df MS F P-value F crit Between Groups 5100960183 7 7287085975 1390858555 0211121109 2057279878 Within Groups 1011179453 193 5239271777

Total 1062189055 200

162


Anova Single Factor

SUMMARY



SS 2156286631 1184873131

df 11 1961

MS 1960260573 6042188326

F 3244289101

P-value 806607E-64

F crit 1793521277

Total 1400501794 1972

163


Appendix 8 Value Added of Small and Medium Enterprises (SMEs) by select industry sector 2007

Industry VA of SMEs

[1] (HK$ million)

VA of all establishments (HK$ million)

Manufacturing 146509 438127

Building construction and real estate NA 1280826 Wholesale retail and import and export trades restaurants and hotels 2781226 4482108

Wholesale 101923 133002 Retail 134890 451844 Import and export 2354056 3396335

Restaurants 183952 329224 Hotels and boarding houses

6405 171703

Transport and related services 342724 1128399

Storage communication financing insurance and business services (exclude banking) 719849 2205227

Source Programme of Annual Economic Surveys (2007)


164


Appendix 9 Mail survey in English

1 GENERAL INFORMATION

Please mark the business sector your firm belongs to ＯManufacturing ＯRestaurants Hotels and Boarding Houses ＯConstruction ＯTransport and Storage ＯWholesaleRetail ＯCommunications ＯImport amp Export-Consumer non-durable goodsＯFinancing and Insurance ＯImport amp Export ndash Consumer durable goods ＯReal EstateＯImport amp Export ndash Miscellaneous goods ＯBusiness Services

Number of persons engaged in your firm (including overseas operations)ＯLess than 10 people Ｏ10-49 people Ｏ50-100 people Ｏ Over 100 people

Number of years your firm has been in businessＯLess than 3 years Ｏ3-7 years ＯMore than 7 years

Total revenuesales in the last fiscal year

165


2 ICT ndash PERSONAL COMPUTERS

Does your firm use personal computers (PCs)

ＯYes (Please skip the next Section and move to Section 4)

ＯNo

166


3 NOT USING PCs

How important are the following reasons for your firm not to use PCs Not Little Important Very Highly

important important important important It is costly ＯＯＯＯＯ

It is not really needed ＯＯＯＯＯ

There is difficulty finding trained personnel to use PCs ＯＯＯＯＯ

There is no one to train the personnel ＯＯＯＯＯ

It is difficult and expensive to maintain ＯＯＯＯＯ

There may be security issues ＯＯＯＯＯ

Other (please specify)

Do you think that using PCs would help improve the operations of your firm If so how

What would really motivate or push you to use PCs

How useful would the following types of Government support be to your firm for installing and using computers

Not Little Not sure Very useful Highly useful useful useful

Holding training workshopsseminars ＯＯＯＯＯ

Providing tax incentives ＯＯＯＯＯ

Providing more information on the costs and benefits of using PCs ＯＯＯＯＯ

Providing PC adoption consulting services ＯＯＯＯＯ

Subsidising training in using PCs ＯＯＯＯＯ


167


Please move to Section 11

168


4 USING PCs

Please mark the extent to which your firm uses software for the following tasks Not at Very Sometimes Often Very NA

all little often Word processing ＯＯＯＯＯＯ

Spreadsheet ＯＯＯＯＯＯ

Database Management ＯＯＯＯＯＯ

Communication ＯＯＯＯＯＯ

Presentation ＯＯＯＯＯＯ

Computer security ＯＯＯＯＯＯ

Multi-media ＯＯＯＯＯＯ

FinanceAccounting ＯＯＯＯＯＯ

PurchaseSales amp Order processing ＯＯＯＯＯＯ

HRMTraining ＯＯＯＯＯＯ


How much value do you think PCs have added to the following aspects of your business No Very little Some Significant Great value

value value value value added added added added added

Improving operational efficiencyreducing costs ＯＯＯＯＯ

Improving customer services ＯＯＯＯＯ

Increasing sales ＯＯＯＯＯ

Exploring new opportunities for growing business ＯＯＯＯＯ

Providing competitive advantage ＯＯＯＯＯ


Please mark the extent to which your firm uses the following tailor-made computer systems

Not at all Very little Sometimes Often Very often FinanceAccounting ＯＯＯＯＯ

PurchasingSale and Order Processing ＯＯＯＯＯ

Human Resource ManagementTraining ＯＯＯＯＯ


169


5 ICT ndash INTERNET USAGE

Does your firm use the Internet


ＯNo

170


6 NOT USING THE INTERNET

How important are the following reasons for your firm not to use the Internet Not Little Important Very Highly

important important important importan t

Employees will waste their time using it ＯＯＯＯＯ

There are concerns about Internet security ＯＯＯＯＯ

Maintenance is difficult and expensive ＯＯＯＯＯ

Business partners and customers are not using the ＯＯＯＯＯInternetCompetitors are not using the Internet ＯＯＯＯＯ

It is costly ＯＯＯＯＯ


There is difficulty finding trained personnel to use it ＯＯＯＯＯ



Do you think that using the Internet would help improve your business If so how

What would really motivate or push you to use the Internet

How useful would the following types of Government support be to your firm for using the Internet Not Little Not Very Highly

useful useful sure useful useful Investing in ICT infrastructure ＯＯＯＯＯ

Building the right legal and regulatory framework to ensure security trust privacy and consumer ＯＯＯＯＯprotectionProviding tax incentives for using the Internet ＯＯＯＯＯ

Providing more information on the costs and benefits of using the Internet ＯＯＯＯＯ

Providing Internet adoption consulting services ＯＯＯＯＯ

171


Subsidising Internet training ＯＯＯＯＯ




172


7 USING THE INTERNET

Please mark the extent that your firm uses the Internet for the following purposes Not Very Sometimes Often Very NA

at all little often Email ＯＯＯＯＯＯ

Online sourcing of general information ＯＯＯＯＯＯ

Online ordering from suppliers ＯＯＯＯＯＯ

Online payments to suppliers ＯＯＯＯＯＯ

Online sales to customers ＯＯＯＯＯＯ

Online receipt of payments from customers ＯＯＯＯＯＯ

Government information ＯＯＯＯＯＯ

Provision of informationfeedback to customers ＯＯＯＯＯＯ

Software downloads ＯＯＯＯＯＯ

E-banking services ＯＯＯＯＯＯ

Video conferencing ＯＯＯＯＯＯ


How much value do you think the Internet has added to the following aspects of your business No Very little Some Significant Great

value value value value added value added added added added

Penetration into new markets ＯＯＯＯＯ

Prompt response to market changes ＯＯＯＯＯ

Exploitation of network opportunities ＯＯＯＯＯ


Improving customer services through integrated processes ＯＯＯＯＯ


Creation of new productsservices ＯＯＯＯＯ

Providing competitive advantages ＯＯＯＯＯ


Does your firm have a digital certificateＯYes ＯNo

173


Do you think that using the Internet more would help improve the business of your firm If so how

174


8 ICT ndash WEBSITE USAGE

Does your firm have a Website


ＯNo

175


9 NOT USING A WEBSITE

How important are the following reasons for your firm not having a Website Not Little Important Very Highly

important important important important Business partners do not have a Website ＯＯＯＯＯ

Online transaction is not common in the industry ＯＯＯＯＯ

Online marketing is not common in the industry ＯＯＯＯＯ









Do you think that creating a Website would help improve your business If so how

What would really motivate or push you to create a Website

How useful would the following types of Government support be to your firm for creating a Website Not Little Not Very Highly

useful useful sure useful useful Holding training workshopsseminars ＯＯＯＯＯ

Investing in ICT infrastructure ＯＯＯＯＯ

Building the right legal and regulatory framework to ensure security trust privacy and consumer protection ＯＯＯＯＯ

Providing tax incentives for ICT investments ＯＯＯＯＯ

Providing more information on the costs and benefits of ＯＯＯＯＯ

176


using ICT Providing ICT adoption consulting services ＯＯＯＯＯ

Subsidising ICT training ＯＯＯＯＯ



177


10 USING A WEBSITE

Please mark the extent that your firm uses its Website for the following purposes Not Very Sometimes Often Very NA

at all little often Providing information on the firmrsquos products and services ＯＯＯＯＯＯ

Handling of business enquiry ＯＯＯＯＯＯ

Receiving online orders ＯＯＯＯＯＯ

Receiving online payments ＯＯＯＯＯＯ

Access to government information ＯＯＯＯＯＯ

Delivery of products ＯＯＯＯＯＯ

After sales services ＯＯＯＯＯＯ

Collection of feedbacks ＯＯＯＯＯＯ

Online marketing ＯＯＯＯＯＯ

Knowledge management ＯＯＯＯＯＯ


How much value do you think your Website has added to the following aspects of your business No Very little Some Significant Great

value value value value value added added added added added

Availability of online transaction ＯＯＯＯＯ

Strengthening the relationships with business partners ＯＯＯＯＯ


Improve productivity through service integration ＯＯＯＯＯ

Creation of new business models ＯＯＯＯＯ

Streamlining of the business promoting flexibility ＯＯＯＯＯ









178


Is your firmrsquos Websitewebpage integrated with the firmrsquos databaseＯYes ＯNo

Is your firmrsquos Websitewebpage integrated with your business partnersrsquo database ＯYes ＯNo

179


11 OVERALL ICT USAGE

Overall to what extent do you feel that your firm currently uses ICTＯ Not at all Ｏ Very littleＯ SometimesＯ Often Ｏ Very often

Overall to what extent do you feel that using more ICT would increase the value added to your firm Ｏ No value addedＯ Very little value added Ｏ Some value addedＯ Significant value addedＯ Great value added

Thank you for answering this survey

180


Appendix 10 Mail survey in Chinese 1 貴公司基本情况

貴公司所屬的行業是

Ｏ製造業Ｏ飲食及酒店業

Ｏ建造業Ｏ運輸倉庫業

Ｏ批發業零售業Ｏ通訊業

Ｏ進出口貿易業（非消費耐用品）Ｏ金融保險業

Ｏ進出口貿易業（消費耐用品）Ｏ地產業

Ｏ進出口貿易業（其他）Ｏ商用服務業

貴公司總共的員工人數（包括海外機構）是

Ｏ少於 10 人Ｏ10-49 人Ｏ50-100 人Ｏ100 人以上

貴公司從商的時間有

Ｏ不足 3 年Ｏ3-7 年Ｏ7 年以上

貴公司去年的總收入是

181


2 資訊及通訊科技 - 個人電腦

貴公司是否使用個人電腦

Ｏ是（請跳過下一部份直接進入第 4 部份）

Ｏ否

182


3 沒有使用個人電腦

貴公司不使用個人電腦的決定是基於以下什麼原因完全沒有沒有關系有關系重要原因很重要

關系原因

成本太昂貴ＯＯＯＯＯ

無此需求ＯＯＯＯＯ

難尋會用電腦的員工ＯＯＯＯＯ

無人去培訓員工ＯＯＯＯＯ

保養困難且費用昂貴ＯＯＯＯＯ

擔心保安問題ＯＯＯＯＯ

其他（請注明）

您認為如果使用電腦會對貴公司帶來好處嗎如果會是什麼好處呢

在什麼樣的情況下您才會用個人電腦呢

如果政府可以提供以下各項支援您認為對貴公司安裝和使用電腦有用嗎

完全没用有少少用不知道有用很有用舉辦培訓課程ＯＯＯＯＯ

提供稅務優惠ＯＯＯＯＯ

提供更多使用電腦的好壞相關ＯＯＯＯＯ

的資訊

提供技術支援ＯＯＯＯＯ

資助技術培訓費用ＯＯＯＯＯ


183


請直接進入第 11 部份

184


4 使用個人電腦

請問您用過以下哪幾種軟件有幾經常

完全没用很少用有時用經常用很經常用不適用

文字編輯ＯＯＯＯＯＯ

試算表電子表格ＯＯＯＯＯＯ

數據庫管理ＯＯＯＯＯＯ

通訊交流ＯＯＯＯＯＯ

示範演示ＯＯＯＯＯＯ

電腦安全軟件ＯＯＯＯＯＯ

多媒體ＯＯＯＯＯＯ

財政會計ＯＯＯＯＯＯ

買賣交易訂單處理ＯＯＯＯＯＯ

人事管理培訓ＯＯＯＯＯＯ


使用個人電腦在以下幾個方面對貴公司有没有用

完全没有少少有時有頗有用極之有

用用用用

提高效率降低成本ＯＯＯＯＯ

提高客戶服務質素ＯＯＯＯＯ

增加銷售額ＯＯＯＯＯ

尋求新的商機ＯＯＯＯＯ

增強競爭力ＯＯＯＯＯ


以下哪些電腦系統是貴公司專門訂製的經常用嗎完全没用很少用有時用經常用很經常用

財政會計ＯＯＯＯＯ

買賣交易訂單處理ＯＯＯＯＯ

人事管理培訓ＯＯＯＯＯ


185


5 資訊及通訊科技 ndash互聯網

貴公司是否使用互聯網


Ｏ否

186


6 沒有使用互聯網

貴公司不使用互聯網是基於以下什麼原因完全沒有

關系

沒有

關系有關系重要原因

很重要

原因

員工會浪費時間ＯＯＯＯＯ

擔心網絡安全問題ＯＯＯＯＯ


商業夥伴及顧客沒用互聯網ＯＯＯＯＯ

競爭對手沒用互聯網ＯＯＯＯＯ



缺乏經過培訓的員工ＯＯＯＯＯ



您認為如果使用互聯網會對貴公司帶來好處嗎如果會是什麼好處呢

您認為在什麼樣的情況下您才會用互聯網呢

如果政府可以提供以下各項支援您認為對貴公司安裝和使用互聯網有用嗎

完全有少少用不知有用很有用

没用道

投資資訊及通訊科技基礎設施ＯＯＯＯＯ

立法保證安全私隱權和消費者權益ＯＯＯＯＯ


提供更多使用互聯網的好壞的相關資訊ＯＯＯＯＯ



187


舉辦培訓課程ＯＯＯＯＯ



188

189


7 使用互聯網

請問您用過以下哪幾種互聯網功能有幾經常

沒用過極少使用有時用經常用用得非常多不適用

收發電子郵件ＯＯＯＯＯＯ

網上搜尋資料ＯＯＯＯＯＯ

向供應商訂貨ＯＯＯＯＯＯ

付款給供應商ＯＯＯＯＯＯ

接受顧客訂單ＯＯＯＯＯＯ

接受顧客付款ＯＯＯＯＯＯ

獲取政府資訊ＯＯＯＯＯＯ

反饋信息給顧客ＯＯＯＯＯＯ

下載軟件ＯＯＯＯＯＯ

電子銀行ＯＯＯＯＯＯ

視像會議ＯＯＯＯＯＯ


使用互聯網在以下幾個方面對貴公司有没有用

完全没用有少少用有時有用頗有用極之有用

打入新的市場ＯＯＯＯＯ

對市場變化作出即時反應ＯＯＯＯＯ

尋找網絡機會ＯＯＯＯＯ




開拓新的產品和服務ＯＯＯＯＯ



貴公司是否有電子証書

Ｏ是Ｏ否

貴公司如果更多地使用互聯網會令生意更上一層樓嗎會的話在哪方面呢


8 資訊及通訊科技 ndash網站

貴公司是否有網站


Ｏ否

190


9 沒有使用網站

貴公司不建立網站是基於以下什麼原因

完全沒有沒有關系有關系重要很重要

關系原因原因

商業夥伴沒有網站ＯＯＯＯＯ

網上交易在本行業不普遍ＯＯＯＯＯ

網上營銷在本行業不普遍ＯＯＯＯＯ










您認為如果建立網站會為貴公司帶來好處嗎如果會是什麼好處呢

您認為在什麼樣的情況下您才會建立網站呢

如果政府可以提供以下各項支援您認為對貴公司建立網站有用嗎

完全

没用

有少

少用

不知

道有用

很有

用





提供更多使用資訊科技的好壞的相關資訊ＯＯＯＯＯ


191





192


10 使用網站

請問您用過以下哪幾種網站功能有幾經常

沒用過極少有時用經常用用得非不適用

使用常多

提供公司產品及服務資訊ＯＯＯＯＯＯ

處理客戶查詢ＯＯＯＯＯＯ




交付産品和服務ＯＯＯＯＯＯ

提供售後服務ＯＯＯＯＯＯ


網上營銷ＯＯＯＯＯＯ

知識管理ＯＯＯＯＯＯ


使用網站在以下幾個方面對貴公司有没有用


提供電子商務ＯＯＯＯＯ

增強與商業夥伴聯系ＯＯＯＯＯ


整合服務提高效率ＯＯＯＯＯ

創造新的商業模式ＯＯＯＯＯ

改善商業流程提高靈活性ＯＯＯＯＯ






增加銷售量ＯＯＯＯＯ



193


貴公司的網頁是否與公司的數據庫整合

Ｏ是Ｏ否

貴公司的網頁是否與商業夥伴的數據庫整合

Ｏ是Ｏ否

194


11 縱觀資訊及通訊科技的運用

總的來說您覺得貴公司運用資訊及通訊科技的頻率是Ｏ完全没用

Ｏ用得很少

Ｏ有用一些

Ｏ常用

Ｏ很常用

總的來說您覺得資訊及通訊科技對貴公司的用處有多大

Ｏ完全没用

Ｏ有少少用

Ｏ有一些用

Ｏ有不少用

Ｏ非常有用

謝謝您完成我們的問卷調查

195


Appendix 11 Telephone interview guidelines

Background information The interview follows a semi-structured format The following questions as well as the survey provided in both Chinese and English are used as guidelines The purposes of this interview are three folded

bull To find out the motivations behind ICT adoptions bull To find out the issues hindering the companyrsquos decision to adopt ICT bull To find out the main benefits perceived by the company by adopting ICT bull To look for good practices of adopting ICT

Procedures The interview should be conducted in either Chinese or English depending on the language the interviewee prefers The interviewee needs to have the authority to make IT investment decisions Try to ask open-ended questions The survey in hand can be used as a guide when the interviewee is unable to answer The structure of the survey needs to be learned by heart After the interview interviewers need to fill in the telephone survey response online

Interviewee information bull The name of interviewer bull The name of the firm interviewed bull The name of the interviewee bull The contact number of the interviewee bull Whether the company would like to conduct a second more in-depth interview

(Please ask this question before closing the interview)

Company background information bull The industry (the industry code is already provided please confirm by asking the

products and services they provided) bull The number of people working for the company including their overseas

operations bull The number of years the firm has been in the business bull The total revenue of the firm in the last financial year (Please ask this question at

the end of the interview and the answer is optional)

Questions The purpose is to find out whether the firm has used PC (personal computer) Internet or Company Website or not

196


If yes then ask which functionssoftware they use most often and what benefits they have gained from using them If not then ask why not under what circumstances they will consider the adoption and what the Government can do to help them

197


Appendix 12 Online survey in English69






69 An email invitation providing a link to the online survey in English and a link to the online survey in Chinese is sent to company directors

198


2 USING PCs


























199




























200



201




ＯYes

ＯNo

202























203





204


6 USING A WEBSITE





























205




206


7 OVERALL ICT USAGE




207


Appendix 13 Online survey in Chinese70

1 貴公司基本情况









Ｏ少於 10 人Ｏ10-49 人Ｏ50-100 人Ｏ100 人以上





208


2 使用个人電腦



























209

210


3 使用互聯網



























Ｏ是Ｏ否





Ｏ是

Ｏ否

211





關系原因原因
















完全

没用

有少

少用

不知

道有用

很有

用







212




213


6 使用網站



使用常多




























214



Ｏ是Ｏ否


Ｏ是Ｏ否

215




Ｏ用得很少

Ｏ有用一些

Ｏ常用

Ｏ很常用


Ｏ完全没用

Ｏ有少少用

Ｏ有一些用

Ｏ有不少用

Ｏ非常有用


216


Appendix 14 Data Merge and Transformation for further analysis

1 Data merge

Merge the data into one table (a) Survey ndash by mail (b) Survey ndash by phone (c) Survey ndash by email (English) (d) Survey ndash by email (Chinese)

Source Total Survey Survey ndash by mail 693 Survey ndash by phone 102 Survey ndash by email

bull English -39 bull Chinese - 63

Total 897 (Total Survey)

2 Data transformation

(a) Number of persons engaged in your firm Raw data Transformed data

Less than 10 people 5 10 - 49 people 25 50 - 100 people 75 Over 100 people 101

For manufacture if number of employee gt 100 then NOT SME ignore it For other industries if number of employee gt 50 then NOT SME ignore it

(b) Number of years your firm has been in business

Raw data Transformed data

Less than 3 years 2 3-7 years 5 More than 7 years 8

(c) Industrial Sectors

217


bull Manufacturing -gt 301 bull Construction -gt 501 bull WholesaleRetail -gt 601 bull Import and Export - Consumer non-durable goods -gt 602 bull Import amp Export - Consumer durable goods -gt 603 bull Import amp Export - Miscellaneous goods -gt 604 bull Restaurants Hotels and Boarding Houses -gt 605 bull Transport and Storage -gt 701 bull Communications -gt 702 bull Financing and Insurance -gt 801 bull Real Estate -gt 802 bull Business Services -gt 803

(d) Values assigned to the responses

bull Not important --gt 0 Little important---gt 1 Important--gt2 Very important- --gt 3 High important--gt 4 bull Not useful --gt 0 Little useful--- 1gt Not sure --gt2 Very useful- --gt 3 High useful --gt 4 bull Not at all --gt 0 Very little--- 1gt Sometimes --gt2 Often- --gt 3 Very often --gt 4 NA--gt 0 bull No value added --gt 0 Very little value added --gt 1 Some value added --gt 2 Significant value added --gt 3 Great value added --gt 4 bull NO --gt 0 YES--- 1gt

3 Data selection

Select SME only

For manufacture if number of employee lt 100 then is SME For other industries if number of employee lt 50 then is SME

218

N Mean Std Deviation Std Error


Appendix 15 RevenueSales in the last fiscal year ndash by industry size and age

151 Revenue Breakdown By Sector

95 Confidence Interval for Mean

Minimum Maximum Lower Bound Upper Bound

301 41 2923982739 1219E8 19034310422 -922994898 6770960376 59000 8E8

501 33 285743403 3741336262 651283047 153081388 418405418 150000 15000000

601 90 1664371319 1055E8 11125197493 -546181726 3874924363 10000 1E9

602 24 1773216667 27323788509 5577444974 619434267 2926999066 219000 1E8

603 26 5962015385 1972E8 38669139962 -2002043071 139E8 50000 1E9

604 36 1440023083 25635847654 4272641276 572630791 2307415376 125000 1E8

605 9 369611111 4038952353 1346317451 59149750 680072472 90000 12000000

701 18 703168733 14677261222 3459463647 -26714296 1433051763 5 63327855

702 5 913940440 13299818631 5947859709 -737450158 2565331038 110610 30000000

801 21 2902182857 1281E8 27950740415 -2928239426 8732605140 100000 6E8

802 7 73750100 1052509304 397811124 -23590775 171090975 70000 3000000

803 76 600408357 34360255601 3941391627 -184757118 1385573832 824 3E8

Total 386 1699904247 89693952915 4565302001 802299762 2597508733 5 1E9

219




152 Revenue Breakdown By Number of Employee

95 Confidence Interval for

Mean


5 303 473571424 19977266559 1147663977 247728342 699414506 5 3E8

25 82 6154411376 1848E8 20412653467 2092933633 102E8 100000 1E9

75 5 1712000000 22095180470 9881265101 -1031479012 4455479012 100000 50000000

Total 390 1683881729 89245778704 4519135672 795383022 2572380436 5 1E9

153 Revenue Breakdown By Age Business year


Mean


2 51 1302927202 82159196233 11504588621 -1007837429 3613691832 10000 6E8

5 95 1438550640 81082800535 8318919808 -213190389 3090291669 11500 8E8

8 240 1854151498 94606234393 6106806171 651147685 3057155310 5 1E9

Total 386 1679036159 89684007888 4564795812 781531198 2576541121 5 1E9

220


Appendix 16 Use of Personal Computers (PCs) - Does your firm use Personal Computers (PCs)

Does your firm use Personal Computers (PCs)

Code

0 NO

1 YES

Statistics Have_PC

N Valid 785

Missing 4

Have_PC


Percent Cumulative

Percent

Valid 0

1

Total Missing System Total

100

685

785 4

789

127

868

995 5

1000

127

873

1000

127

1000

221


Appendix 17 Use of Personal Computers (PCs) - Not using PCs - How important are the following reasons for your firm not to use PCs

Not using PCs - How important are the following reasons for your firm not to use PCs

Sector No_of_employee Business_year

N Valid

Missing Mean Median Std Deviation Minimum Maximum

98

2

100

0

100

0

Statistics

No_trained_ No_one_train Difficult_mai Security_issue It_costly It_not_need person _ person ntain s

N Valid 63 75 59 59 59 56

Missing 37 25 41 41 41 44 Mean 144 177 100 122 154 123 Median 100 200 100 100 100 100 Std Deviation 1175 1420 1083 1301 1264 1175 Minimum 0 0 0 0 0 0 Maximum 4 4 4 4 4 4

222


Appendix 18 Use of Personal Computers (PCs) - Not using PCs - ANOVA

Not using PCs - ANOVA

In the frequency table of sectors cluster Sectors 602 603 and 604 as one industry (Import amp Export) and exclude 605 701 801 and 802 as these industries had a sample size lt10

181 ANOVA - By Sector (industry)

Sum of Squares df Mean Square F Sig

It_costly Between Groups 5155 4 1289 905 469

Within Groups 65472 46 1423

Total 70627 50

It_not_need Between Groups 12901 4 3225 1626 180


Total 127937 62

No_trained_person Between Groups 776 4 194 166 955


Total 49915 46

No_one_train person

Between Groups

Within Groups

Total

4627

75852

80479

4

43

47

1157

1764

656 626

Difficult_maintain Between Groups 2742 4 685 392 813


Total 77917 47

Security_issues Between Groups 443 4 111 079 988


Total 56578 44

223


182 ANOVA - By Number_of_employee




Total 70627 50



Total 127937 62



Total 49915 46

No_one_train_perso n

Between Groups

Within Groups

Total

768

79712

80479

1

46

47

768

1733

443 509



Total 77917 47



Total 56578 44

224


183 ANOVA - By Business_year




Total 70627 50



Total 127937 62



Total 49915 46

No_one_train_ person

Between Groups

Within Groups

Total

2743

77736

80479

2

45

47

1371

1727

794 458



Total 77917 47



Total 56578 44

225


Appendix 19 Use of Personal Computers (PCs) - Do you think that using PCs would help improve the operations of your firm If so how


Of the 693 responses received to the mail survey 82 respondents stated that they do not use PCs 35 of these respondents replied to this question

1 容易找尋產品 Easy to search for products

2 會有幫助 (PCs will be helpful) 3 有好處會使公司管理更好運作速度加快

Yes it will improve the companys management and increase the operational speed

4 無好處因目前不順用 No as it is not suitable now

5 方便有效率處理工作 Convenient get the work done more efficiently

6 會查詢客人資料比較方便 Yes it will be more convenient to ask for clients information

7 較直接 More direct

8 Nil 9 當然會令公司運作帶來方便不用再手寫入數貨號會清楚

查貨時更快更方便 Of course it will bring more convenience to the companys operations We do not have to records the financial accounts manually The products serial numbers will be clearer so it will be faster and more convenient when we check the goods

10 More effective and easy for communications to USA and China colleagues and worldwide customers

11 不會 No

12 無需要 It is not needed

13 不需要 It is not needed

226


14 Not many advantages 15 沒有

No 16 會方便解悶

Yes They are convenient and can help you feel less bored 17 有找資料快

Yes it is very fast for searching information 18 有好處記錄會很清楚

Yes it will make the records very clear 19 會提高效率

Yes it will increase the efficiency 20 會因在鋪內資訊會多 d同可做更多工作

Yes It will bring more information to the shop and it will increase our outputs 21 不會

no 22 認為較方便

Yes It is more convenient 23 不知字沒有使用電腦

I am illiterate I do not use PCs 24 會可以使用電子銀行

Yes as we can use electronic banking 25 No 26 管理存貨及帳目上有一定好處

PCs will have certain advantages for the management of inventories and financial accounts

27 方便 Convenience

28 方便查料 It is convenient to search for information

29 增加客源 To increase the client base

30 快節省人力 Fast Saving man power

31 Stock control 32 方便快捷

Convenient fast 33 方便查找客人過往的購貨紀錄

IT is convenient to chase a customers previous purchase record 34 有好處可將業務擴張到世界各地

227


Yes it can help extend the business across the globe 35 儲存和文書上會較方便且翻查紀錄會容易些

More convenient in terms of record-saving paper work and record-chasing

Hence of the 35 replies 10 respondents said that using PCs would not help the operations of their business Of the 25 respondents who said that the use of PCs would be beneficial the main reasons given were

bull Improving efficiency by saving time and reducing manpower bull Added convenience bull Helps marketing efforts and increasing the client base (which has to do with the

Internet or Stage 2) bull Easy to search for products (which has to do with the Internet or Stage 2) bull Communications (which has to do with the Internet or Stage 2)

Of the 98 ldquoSMErdquo responses received in the telephone survey 19 respondents said that they did not use a PC and replied to this question

1 yes fast and accurate 2 yes can communicate instantly with others 3 No 4 may be yes because storage booking date more accurate and fast 5 No 6 no because dont know how to use computer 7 No 8 no idea since she is nearly retired 9 no because his company nearly closed 10 no not business related 11 no not business related 12 cant improve 13 she dont know since she is working without computer nowadays 14 no this is a traditional business and no need computer 15 no computer not related to her business 16 no because they are working in a traditional business and seldom use computer 17 yes can help to retrieve data easily 18 No 19 No

228


Of these 19 responses 15 respondents said that using PCs would not help the operations of their business The remaining 4 said that the use of PCs would be beneficial the main reasons given were

bull Fast and accurate bull Communications (which has to do with the Internet or Stage 2)

229




Of the 82 respondents who stated that they do not use PCs in the mail survey 35 answered this question

1 識操作（I know how to use) 2 員工人數增加

Increase in number of staff 3 如果我學會使用電腦 (因我還不識使用電腦)

If I learn how to use computers (As I do not know how to use the computer) 4 業務再發展高科技用到時合用

When the business further develops and when the high technology is useful for business

5 編輯合同(工作 ) Editing contracts (work)

6 當生意額有所增長 When the business amount increases

7 有專人教授 When there is an expert teaching it

8 聯絡及接收客人訊息 Communicate can receive clients messages

9 整理帳目 To manage the financial accounts

10 開單查貨查公司資料時設計圖樣 When issuing invoices checking goods checking company information and designing drawings

11 Cheap package 12 貨品太多需要紀錄

When there are too many products that needed to be recorded 13 如果公司有好的盈利可考慮的

If the company has good profits we can consider about it 14 沒有研究

We have not considered about it yet 15 增加收入

If it can increase the revenue 16 不需要

230


It is not needed 17 Not necessary 18 Business Expansion 19 有需要的時候

When it is needed 20 當我退休後

After I retire 21 有大量資料要記錄時

When there is huge amount of information to be recorded 22 影印文具

Photocopying stationery (not sure what does the interviewee mean) 23 一回家就用多用途例查看什麼都可以

I use the PCs once I am back home We can search for everything 24 有需要

when needed 25 不曾使用電腦

I have never used PCs before 26 半退休個人 78歳

I am semi-retired and is 78 years old now 27 查資訊及電子銀行

To search for information and use electronic banking 28 有需要有正面效益

When they are needed and provide positive benefits 29 有足夠人手及時間做好後勤培訓才考慮

We will consider it when there are enough human resources and time for back-office training

30 不再有電腦病毒 When there is no more computer virus

31 網上銷售 On-line sales

32 熟悉電腦 When (i am) familiar with computer

33 有足夠空間 Provided we have enough space

34 在家 At home

35 暫無需要 Temporarily unnecessary

231


Hence of the 35 replies some of the primary motivators to encourage the use of PCs were given as

bull Increase in number of staff bull Increase in space available bull Business needs grow bull If the respondent learns how to use the computer bull Overcome fear of viruses bull If cheap packages are available

Of the 98 responses received in the telephone survey 19 respondents said that they did not use a PC and replied to this question

1 if he can employ people that know how to use he will 2 move to a larger place then will 3 no business need 4 if the company become larger it may be consider 5 no business need so will not use 6 no idea 7 no need 8 no need since she is nearly retired 9 no because his company nearly closed 10 No 11 no need 12 no need since not business related 13 yes if some one buy her a computer 14 no need computer at all 15 no need 16 financial support and technical support 17 financial help from government 18 No 19 No

Hence of the 19 replies 13 replied that there was no need for a computer The remaining 6 causes for motivating a respondent to use a computer

bull If staff are aware of how to use the computer bull Increase in space available bull Business needs grow

232


bull If a computer was presented bull Financial and technical support bull Financial help from the government

233


Appendix 21 Use of Personal Computers (PCs) - How useful would the following types of Government support be to your firm for installing and using computers

How useful would the following types of Government support be to your firm for

installing and using computers

Statistics

Holding workshops Tax incentive Provide info

Provide consulting

Subside training

N Valid


70

30 164 100

1465 0 4

64

36 191 300

1611 0 4

62

38 184 200

1428 0 4

64

36 219 300

1521 0 4

65

35 222 300

1546 0 4

234


Appendix 22 Use of Personal Computers (PCs) - How useful would the following types of Government support be to your firm for installing and using computers -

ANOVA

Not using PCs - How useful would the following types of Government support be to your firm for installing and using computers

221 ANOVA - BY Sector (industry)


Holding_workshop Between Groups

Within Groups

Total

22666

124986

147652

9

59

68

2518

2118

1189 319

Tax_incentive Between Groups

Within Groups

Total

32070

130152

162222

10

52

62

3207

2503

1281 265

Provide_info Between Groups

Within Groups

Total

27797

95875

123672

9

51

60

3089

1880

1643 128

Provide_consulting Between Groups

Within Groups

Total

19273

123140

142413

9

53

62

2141

2323

922 514

Subside_training Between Groups

Within Groups

Total

16969

135390

152359

9

54

63

1885

2507

752 660

235


222 ANOVA - BY (No_of_Employee)



Within Groups

Total

687

147385

148071

1

68

69

687

2167

317 575


Within Groups

Total

468

162969

163438

1

62

63

468

2629

178 675


Within Groups

Total

5026

119361

124387

1

60

61

5026

1989

2526 117


Within Groups

Total

3581

142169

145750

1

62

63

3581

2293

1561 216


Within Groups

Total

7601

145383

152985

1

63

64

7601

2308

3294 074

236


223 ANOVA - BY (Business_year)



Within Groups

Total

4417

143654

148071

2

67

69

2209

2144

1030 363


Within Groups

Total

12803

150635

163437

2

61

63

6401

2469

2592 083


Within Groups

Total

3809

120578

124387

2

59

61

1904

2044

932 400


Within Groups

Total

10989

134761

145750

2

61

63

5494

2209

2487 092


Within Groups

Total

11643

141342

152985

2

62

64

5821

2280

2554 086

Further analysis of the significant differences by Business Year

N Mean Std Deviation

Holding_workshops 2

5

8

Total

5

9

44

58

220

211

145

162

837

1616

1517

1497

237


Tax_incentive 2 5 220 1095

5 9 289 1453

8 39 156 1619

Total 53 185 1610

Provide_info 2 5 240 894

5 9 222 1394

8 37 157 1501

Total 51 176 1450

provide_consulting 2 5 320 837

5 10 290 1370

8 39 185 1548

Total 54 217 1539

Subside_training 2 5 340 894

5 10 300 1247

8 39 192 1628

Total 54 226 1592

238


Appendix 23 Use of Personal Computers (PCs) - Using PCs - Please mark the extent to which your firm uses software for the following tasks

Using PCs - Please mark the extent to which your firm uses software for the following tasks


N Valid

Missing

679

6

685

0

675

10

Statistics

Word Spreadsheet Database Communication Presentation

N Valid


604

81 319 300

1003 0 4

581

104 295 300

1221 0 4

559

126 215 200

1484 0 4

574

111 313 400

1187 0 4

529

156 140 100

1283 0 4

Statistics

Security MultiMedia Finance Sales HRM

N Valid

Missing Mean Median Std Deviation

570

115 304 300

1133

549

136 215 200

1328

572

113 259 300

1418

564

121 229 300

1552

534

151 109 100

1208

239


Minimum Maximum

0 4

0 4

0 4

0 4

0 4

240


Appendix 24 Use of Personal Computers (PCs) - Using PCs - Please mark the extent to which your firm uses software for the following tasks ndash ANOVA across Industry

Number of Employees Age of the firm

Using PCs - Please mark the extent to which your firm uses software for the following tasks ndash ANOVA across Industry Number of Employees Age of the firm

(Sectors 605 and 702 have been excluded from the ANOVA analysis as their sample size lt10)



Word Between Groups

39095 9 4344 4541 000


Total 591077 586

Spreadsheet Between Groups

Within Groups

Total

40662

813739

854401

9

553

562

4518

1471

3070 001

Database Between Groups

36738 9 4082 1862 055


Total 1200957 540

Communicatn Between Groups

34571 9 3841 2760 004


Total 794437 555

241


Presentation Between Groups

50035 9 5559 3587 000


Total 829579 512

Security Between Groups

Within Groups

Total

27353

671020

698373

9

543

552

3039

1236

2459 009

Multi_Media Between Groups

13114 9 1457 819 599


Total 939797 530

Finance Between Groups

26405 9 2934 1445 165


Total 1130681 553

Sales Between Groups

137742 9 15305 6963 000


Total 1315855 545

HRM Between Groups

27264 9 3029 2114 027


Total 752347 515

Differences noted by Industry


242


Word 301 64 316 946

501 53 317 1069

601 114 289 1132

602 43 333 808

603 52 352 727

604 77 345 867

701 27 278 1086

801 32 322 870

802 10 220 1619

803 115 335 946

Total 587 320 1004

Spreadsheet 301 61 282 1310

501 45 253 1471

601 111 265 1319

602 40 335 921

603 49 308 1288

604 78 322 1040

701 25 288 1301

801 31 319 946

802 9 222 1481

803 114 309 1125

Total 563 295 1233

Database 301 59 185 1460

501 40 205 1431

601 105 186 1553

602 39 195 1468

603 51 241 1525

604 72 232 1422

701 24 200 1474

243


801 31 245 1312

802 10 190 1729

803 110 246 1482

Total 541 216 1491

Communication 301 59 303 1245

501 44 314 1173

601 107 286 1270

602 41 339 1070

603 49 347 1043

604 78 323 1139

701 25 252 1418

801 30 360 498

802 10 280 1398

803 113 313 1228

Total 556 312 1196

Presentation 301 54 137 1233

501 41 141 1303

601 100 111 1163

602 37 135 1207

603 43 160 1312

604 68 138 1372

701 23 43 728

801 29 207 998

802 9 111 1364

803 109 161 1333

Total 513 139 1273

Security 301

501

601

57

43

108

289

300

285

1191

1272

1252

244


602 40 325 954

603 49 322 1066

604 79 313 1079

701 23 252 1163

801 32 338 1008

802 9 233 1414

803 113 323 935

Total 553 305 1125

Multi_Media 301 58 219 1290

501 42 205 1413

601 103 207 1278

602 37 222 1272

603 46 239 1341

604 72 212 1383

701 22 168 1393

801 30 197 1326

802 9 156 1590

803 112 221 1330

Total 531 212 1332

Finance 301 60 278 1303

501 42 243 1309

601 110 245 1469

602 38 261 1443

603 50 274 1468

604 75 275 1376

701 24 233 1373

801 31 210 1491

802 10 180 1932

803 114 274 1433

245


Total 554 258 1430

Sales 301 58 257 1512

501 43 195 1430

601 106 243 1441

602 38 268 1435

603 49 280 1554

604 78 295 1318

701 25 184 1599

801 29 159 1570

802 9 100 1500

803 111 169 1565

Total 546 229 1554

HRM 301 54 122 1208

501 40 90 982

601 102 89 1218

602 36 106 1194

603 46 141 1408

604 68 124 1259

701 23 39 656

801 29 124 1123

802 9 78 1093

803 109 122 1220

Total 516 109 1209



246


Word Between Groups

Within Groups

Total

884

590192

591077

2

584

586

442

1011

438 646


Within Groups

Total

4388

850013

854401

2

560

562

2194

1518

1445 237


Within Groups

Total

26601

1174357

1200957

2

538

540

13300

2183

6093 002


Within Groups

Total

3781

790656

794437

2

553

555

1891

1430

1322 267


Within Groups

Total

2678

826901

829579

2

510

512

1339

1621

826 438


Within Groups

Total

3554

694818

698373

2

550

552

1777

1263

1407 246


Within Groups

Total

2642

937155

939797

2

528

530

1321

1775

744 476


Within Groups

37797

1092884

2

551

18898

1983

9528 000

247


Total 1130681 553


Within Groups

Total

22362

1293493

1315855

2

543

545

11181

2382

4694 010

HRM Between Groups

Within Groups

Total

33078

719269

752347

2

513

515

16539

1402

11796 000

Differences due to the number of employees


Word 5

25

75

Total

446

134

7

587

318

327

314

320

1002

1034

378

1004

Spreadsheet 5

25

75

Total

431

125

7

563

290

310

314

295

1250

1177

1069

1233

Database 5

25

75

Total

412

122

7

541

203

257

214

216

1488

1443

1464

1491

Communication 5

25

75

427

122

7

308

328

300

1220

1093

1414

248


Total 556 312 1196


25 115 137 1328

75 7 200 1291

Total 513 139 1273

Security 5 424 301 1143

25 122 320 1060

75 7 300 1000

Total 553 305 1125

Multi_Media 5 407 209 1341

25 117 226 1314

75 7 214 1069

Total 531 212 1332

Finance 5 421 244 1454

25 126 303 1277

75 7 329 488

Total 554 258 1430

Sales 5 417 218 1566

25 122 265 1471

75 7 271 1380

Total 546 229 1554

HRM 5 396 96 1116

25 113 153 1396

75 7 186 1215

Total 516 109 1209


249



Word Between Groups

Within Groups

Total

2046

575508

577554

2

576

578

1023

999

1024 360


Within Groups

Total

2360

848689

851048

2

555

557

1180

1529

772 463


Within Groups

Total

14548

1175391

1189938

2

533

535

7274

2205

3298 038


Within Groups

Total

4941

776652

781593

2

547

549

2470

1420

1740 176


Within Groups

Total

11828

801109

812937

2

505

507

5914

1586

3728 025


Within Groups

Total

4623

686842

691465

2

545

547

2312

1260

1834 161


Within Groups

Total

9832

920874

930706

2

524

526

4916

1757

2797 062

250



Within Groups

Total

8300

1107481

1115781

2

545

547

4150

2032

2042 131


Within Groups

Total

096

1304148

1304244

2

539

541

048

2420

020 980

HRM Between Groups

Within Groups

Total

4481

727230

731710

2

508

510

2240

1432

1565 210

Differences by Business Year


Word 2

5

8

Total

85

154

340

579

327

327

315

320

968

950

1029

1000

Spreadsheet 2

5

8

Total

85

148

325

558

285

304

292

294

1435

1160

1215

1236

Database 2

5

8

Total

80

142

314

536

181

235

214

214

1527

1429

1499

1491

Communication 2

5

84

147

304

328

1357

1090

251


8

Total

319

550

308

312

1190

1193


5 136 160 1189

8 299 125 1261

Total 508 137 1266

Security 2 80 311 1201

5 146 318 968

8 322 298 1167

Total 548 305 1124

Multi_Media 2 78 208 1448

5 140 234 1285

8 309 203 1312

Total 527 212 1330

Finance 2 82 239 1569

5 143 245 1438

8 323 268 1381

Total 548 258 1428

Sales 2 81 228 1637

5 144 231 1552

8 317 227 1536

Total 542 228 1553

HRM 2 76 99 1238

5 138 123 1198

8 297 103 1185

Total 511 108 1198

252


Appendix 25 Use of Personal Computers (PCs) - Using PCs - The extent to which the firm uses the following tailor-made computer systems of FinanceAccounting

PurchasingSale and order processing HRMTraining

Using PCs - The extent to which the firm uses the following tailor-made computer systems of FinanceAccounting PurchasingSale and order processing HRMTraining

Statistics

Sector No_of

employee Business_year Have PC

N Valid

Missing

664

0

664

0

656

8

664

0

Statistics

Tailor_Finance Tailor_

Purchasing Tailor_HRM

N Valid


618

46 182 200

1579 0 4

609

55 174 200

1574 0 4

592

72 75 00

1078 0 4

253



PurchasingSale and order processing HRMTraining ndash ANOVA

Using PCs - The extent to which the firm uses the following tailor-made computer systems of FinanceAccounting PurchasingSale and order processing HRMTraining ndash ANOVA

(Sectors 605 amp 702 have been excluded as the sample size was lt10)

261 ANOVA By Sector (industry)


Tailor_Finance Between Groups 56586 9 6287 2581 006


Total 1537702 617

Tailor_ Purchasing

Between Groups

Within Groups

Total

66918

1439607

1506525

9

599

608

7435

2403

3094 001

Tailor_HRM Between Groups 16262 9 1807 1568 121


Total 687000 591

Analysis of differences


Tailor_Finance 301

501

601

602

65

52

117

48

180

117

193

171

1573

1294

1507

1570

254


603 56 189 1659

604 88 168 1550

701 28 204 1598

801 30 123 1591

802 12 167 1826

803 122 221 1622

Total 618 182 1579

Tailor_Purchasing 301 64 172 1527

501 52 131 1502

601 115 201 1513

602 50 184 1621

603 56 207 1683

604 88 206 1541

701 28 196 1551

801 29 100 1414

802 12 100 1651

803 115 145 1552

Total 609 174 1574

Tailor_HRM 301 61 89 1082

501 50 56 884

601 113 56 935

602 48 60 917

603 56 86 1119

604 84 79 1162

701 26 50 1030

801 29 107 1307

802 12 100 1595

803 113 88 1124

Total 592 75 1078

255


256


262 ANOVA By Number of employee




Total 1537702 617

Tailor_ Purchasing

Between Groups

Within Groups

Total

20097

1486429

1506525

2

606

608

10048

2453

4097 017



Total 687000 591



Tailor_Finance 5

25

75

Total

466

145

7

618

170

213

300

182

1547

1626

1528

1579

Tailor_Purchasing 5

25

75

Total

453

149

7

609

164

205

214

174

1532

1658

1773

1574

Tailor_HRM 5

25

444

141

66

101

1005

1233

257


75 7 129 1380

Total 592 75 1078

263 ANOVA By Business Year




Total 1512802 609

Tailor_Purchasin g

Between Groups

Within Groups

Total

531

1487154

1487685

2

600

602

266

2479

107 898



Total 681625 585

258


Appendix 27 Use of Personal Computers (PCs) - Using PCs - How much value do you think PCs have added to the following aspects of your business

Using PCs - How much value do you think PCs have added to the following aspects of your business

Statistics

Efficiency C_service Inc_sales New

opportunity Competitive

N Valid


593

92 299 300

1025 0 4

576

109 268 300

1176 0 4

575

110 197 200

1379 0 4

578

107 209 200

1347 0 4

570

115 236 300

1289 0 4

259


Appendix 28 Use of Personal Computers (PCs) - Using PCs - How much value do you think PCs have added to the following aspects of your business - ANOVA




Efficiency Between Groups

20091 11 1826 1757 058


Total 618957 587

C_service Between Groups

46210 11 4201 3146 000


Total 792708 570

Inc_sales Between Groups

60685 11 5517 3004 001


Total 1085551 569

New opportunity

Between Groups

63606 11 5782 3322 000


Total 1040098 572

Competitive Between Groups

Within Groups

Total

53472

883148

936619

11

553

564

4861

1597

3044 001

260


Differences based on Industry


Efficiency 301 61 293 1031

501 47 300 978

601 113 283 1117

602 41 324 734

603 53 296 1126

604 80 286 1052

701 25 276 1200

801 31 306 772

802 10 270 1252

803 114 324 944

Total 575 299 1027

C_service 301 57 270 1101

501 45 236 1334

601 110 245 1224

602 39 292 870

603 51 273 1313

604 79 261 1275

701 24 221 1215

801 31 313 619

802 9 222 1302

261


803

Total

113

558

301

268

1031

1175

Inc_sales 301 58 217 1340

501 45 153 1325

601 111 205 1334

602 40 230 1181

603 52 206 1420

604 77 194 1463

701 26 96 1216

801 30 233 1093

802 9 178 1787

803 110 200 1414

Total 558 197 1378

New_opportunity 301 59 231 1221

501 44 157 1388

601 110 209 1296

602 40 225 1127

603 52 237 1415

604 77 230 1288

701 24 92 1176

801 31 203 1048

802 10 170 1636

803 113 218 1434

Total 560 209 1343

Competitive 301 59 241 1219

501 45 211 1283

601 108 228 1281

602 36 247 1000

603 51 251 1362

262


604 77 227 1344

701 25 156 1261

801 31 248 1151

802 9 156 1509

803 111 274 1241

Total 552 237 1284

263





Within Groups

Total

2589

619384

621973

2

590

592

1295

1050

1233 292


2695 2 1348 974 378


Total 795222 575


1966 2 983 516 597


Total 1091437 574

New_opport Between Groups

Within Groups

Total

1488

1044652

1046140

2

575

577

744

1817

410 664


Within Groups

Total

2124

943974

946098

2

567

569

1062

1665

638 529



264



Within Groups

Total

1145

614828

615973

2

583

585

573

1055

543 581


5167 2 2583 1864 156


Total 790956 569


13673 2 6836 3607 028


Total 1088226 569

New opportunity

Between Groups

7164 2 3582 1979 139


Total 1037138 571


Within Groups

Total

13123

928530

941652

2

561

563

6561

1655

3964 020

Differences due to the Business Year


Efficiency 2

5

87

153

293

307

1087

985

8 329 297 1034

Total 569 299 1029

C_service 2 85 273 1179

5 150 285 1145

265


8 318 259 1187

Total 553 268 1178

Inc_sales 2 84 208 1382

5

8

Total

149

320

553

219

184

197

1358

1382

1382

266


New_opportunity 2

5

8

Total

85

149

321

555

218

226

199

209

1424

1321

1330

1345

Competitive 2

5

8

Total

84

146

317

547

232

264

225

237

1355

1231

1280

1287

267


Appendix 29 Correlations of VA1 and ICTU1

(We have excluded Sectors 605 and 702 due to the small sample size)

Sector = 301

Correlations

ICTU_1 VA_1

ICTU_1 Pearson Correlation

Sig (2-tailed)

N

1

64

642

000

64

VA_1 Pearson Correlation

Sig (2-tailed)

N

642

000

64

1

64 Correlation is significant at the 001 level (2-tailed)

Sector = 501

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

52

656

000

52


Sig (2-tailed)

N

656

000

52

1

52

268


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

52

656

000

52


Sig (2-tailed)

N

656

000

52

1


Sector = 601

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

114

583

000

114


Sig (2-tailed)

N

583

000

114

1


Sector = 602

269


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

43

241

119

43


Sig (2-tailed)

N

241

119

43

1

43

270


Sector = 603

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

54

777

000

54


Sig (2-tailed)

N

777

000

54

1


Sector = 604

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

80

555

000

80


Sig (2-tailed)

N

555

000

80

1


271


Sector = 701

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

26

478

014

26


Sig (2-tailed)

N

478

014

26

1


Sector = 801

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

32

690

000

32


Sig (2-tailed)

N

690

000

32

1


272


273


Sector = 802

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

10

707

022

10


Sig (2-tailed)

N

707

022

10

1


Sector = 803

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

116

461

000

116


Sig (2-tailed)

N

461

000

116

1


274


Number of Employee Less than 10 people

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

450

578

000

450


Sig (2-tailed)

N

578

000

450

1


Number of Employee 10-49 people

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

134

645

000

134


Sig (2-tailed)

N

645

000

134

1


275



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

7

590

163

7


Sig (2-tailed)

N

590

163

7

1

7

Business Year (age) Less than 3 years

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

85

567

000

85


Sig (2-tailed)

N

567

000

85

1


276


Business Year (age) 3-7 years

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

154

551

000

154


Sig (2-tailed)

N

551

000

154

1


Business Year (age) More than 7 years

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

345

601

000

345


Sig (2-tailed)

N

601

000

345

1


277


278


Appendix 30 Use of the Internet - Internet Usage - Does your firm use the Internet

Internet Usage - Does your firm use the Internet

Have_Internet

N Valid 739

Missing 50

Have_Internet


Percent Cumulative

Percent

Valid 0

1


74

665

739 50

789

94

843

937 63

1000

100

900

1000

100

1000

279


Appendix 31 Use of the Internet - Internet Usage - Not using Internet How important are the following reasons for your firm not to use the Internet

Internet Usage - Not using Internet How important are the following reasons for your firm not to use the Internet


N Valid

Missing

74

0

74

0

73

1

Statistics

Waste time Concern security

Maintenance_ difficult

Partner_not use

Competition_ not_use

N Valid


37

37 62 00

1010 0 4

38

36 71 00

898 0 3

38

36 113 100

1359 0 4

35

39 114 100

1264 0 4

36

38 100 100

1287 0 4

Statistics

It_costly It_not_need No_trained_

person No_one Train

N Valid

Missing Mean Median Std Deviation Minimum

39

35 126 100

1390 0

45

29 140 100

1529 0

38

36 84 00

1151 0

39

35 92 00

1265 0

280


Statistics


person No_one Train

N Valid


39

35 126 100

1390 0 4

45

29 140 100

1529 0 4

38

36 84 00

1151 0 4

39

35 92 00

1265 0 4

281


Appendix 32 Use of the Internet - Internet Usage - Not using Internet How important are the following reasons for your firm not to use the Internet - ANOVA


(We have clustered the Import amp Export Sectors 602 603 and 604 and excluded sectors 301 605 701 801 802 and 803 as the sample size lt10)



Waste_time Between Groups 1458 2 729 486 622


Total 32958 23

Concern_security Between Groups 1312 2 656 650 532


Total 22500 23

Maintenance difficult

Between Groups

Within Groups

Total

1875

32750

34625

2

21

23

938

1560

601 557

Partner_not_use Between Groups 026 2 013 007 993


Total 32955 21


Between Groups

Within Groups

Total

3743

40083

43826

2

20

22

1871

2004

934 410



282


Total 47840 24

It_not_need Between Groups

Within Groups

Total

6417

55690

62107

2

25

27

3208

2228

1440 256

No_trained_person Between Groups

Within Groups

Total

2029

29971

32000

2

22

24

1015

1362

745 486

No_one_Train Between Groups

Within Groups

Total

1835

37550

39385

2

23

25

917

1633

562 578

283




Waste_time Between Groups

Within Groups

Total

2458

30500

32958

2

21

23

1229

1452

846 443

Concern_security Between Groups

Within Groups

Total

1812

20687

22500

2

21

23

906

985

920 414


Between Groups

Within Groups

Total

188

34438

34625

2

21

23

094

1640

057 945

Partner_not_use Between Groups

Within Groups

Total

1597

31357

32955

2

19

21

799

1650

484 624


Between Groups

Within Groups

Total

4726

39100

43826

2

20

22

2363

1955

1209 319

It_costly Between Groups

Within Groups

Total

090

47750

47840

2

22

24

045

2170

021 979


Within Groups

Total

679

59618

60296

2

24

26

339

2484

137 873


Within Groups

Total

2250

29750

32000

2

22

24

1125

1352

832 448

No_one_Train Between Groups 2690 2 1345 843 443

284


Within Groups

Total

36694

39385

23

25

1595

285



There are 3 records with Number_of_employee=25 who say they donrsquot have the Internet but do not give any reasons For other records Number_of_employee=5 Hence ANOVA has not been calculated

286


Appendix 33 Use of the Internet - Internet Usage - Not using Internet Do you think that using the Internet would help improve your business If so how

Internet Usage - Not using Internet Do you think that using the Internet would help improve your business If so how

In the mail survey there were in total 20 respondents who used a PC but did not use the Internet Of these 20 2 replied to this question One said that it would not help while the other stated that it may be more convenient

The analysis was subsequently enlarged to include those additional 11 replies that had been received to this question from respondents who did not have a PC but had answered this question While 5 replies stated that there would be no additional benefit to the business in using the Internet the others gave ease of searching for information speedy communication and marketing to new clients as some of the main areas where the Internet could help in improving the business

1 資訊 Information

2 找物件快 It is quick to find an object

3 未曾有過電腦在公司不懂怎樣填寫 I have never had computers in the company I do not know how to answer these questions

4 一般 Average


6 不知道應沒什麼好處 Not sure there should be no advantages

7 方便 Convenient

8 會睇好多知訊網上商店及最新知識 Yes we can get to know more information updated knowledge and get access to online shops

9 沒有什麼好處根本對生意額毫無用處 No It has no use for increasing the business figures at all

10 有加快聯絡速度 Yes it increases the speed of contacts

287


11 沒有什麼好處 No

12 擴寬客路 To broaden client base

13 可以擴大業務范圍 It can help us to extend to other business areas

In the telephone survey there were in total 4 respondents who used a PC but did not use the Internet They all answered that it would not help in improving the business operations

The analysis was subsequently enlarged to include those additional 17 replies that had been received to this question from respondents who did not have a PC but had answered this question

1 No idea 2 Yes can communicate with others and save cost 3 Not business related 4 No 5 No 6 No 7 No because dont know how to use computer 8 No 9 No 10 May be yes because he can contact with client from email and message and more

efficiency 11 No 12 No need 13 May be no since problem may be arise like virus 14 No 15 No use Internet and computer will make them more trouble 16 No idea 17 No his business no need computer 18 No not business related 19 No since as previous experience 20 No 21 No

Again all but 2 replies said that there was no gain to be had from the use of the Internet The 2 had stated quicker communication savings and improved efficiency as possible benefits

288


Appendix 34 Use of the Internet - Internet Usage - Not using Internet What would really motivate or push you to use the Internet

Internet Usage - Not using Internet What would really motivate or push you to use the Internet

Of the 20 respondents who do not use the Internet but have PCs 5 replied to this question

1 業務轉型 When we transform the business

2 合理價錢之網絡供應商 When we can find an Internet Service Provider (ISP) which offers reasonable prices

3 在公司內沒有考慮用互聯網 We have never considered about using the Internet in the company

4 有需要時 When it is necessary

5 業務擴展至 3個員工以上 When the business has expanded to have over 3 employees

The view was that Internet would be considered when the business had grown to require it A reasonable price was also stated as one motivator to use the Internet

In the telephone survey there were in total 4 respondents who used a PC but did not use the Internet They all answered that nothing would motivate them to use the Internet

289


Appendix 35 Use of the Internet - Internet Usage - Not using Internet How useful would the following types of Government support be to your firm for using the

Internet

Internet Usage - Not using Internet How useful would the following types of Government support be to your firm for using the Internet

ICT_ infrastructure

Legal regulatory

Tax incentive

Provide info

N Valid


40

34 142 100

1299 0 4

37

37 157 200

1425 0 4

37

37 143 100

1444 0 4

39

35 172 200

1432 0 4

Provide_ Subside Hold consulting training workshop

N Valid 39 39 39

Missing 35 35 35 Mean 182 172 169 Median 200 200 200 Std Deviation 1449 1486 1507 Minimum 0 0 0 Maximum 4 4 4

290



Internet - ANOVA




ICT_infrastructure Between Groups

Within Groups

Total

10740

55035

65775

10

29

39

1074

1898

566 828

Legal_regulatory Between Groups

Within Groups

Total

12531

60550

73081

10

26

36

1253

2329

538 847


Within Groups

Total

16181

58900

75081

10

26

36

1618

2265

714 704


Within Groups

Total

14956

62941

77897

10

28

38

1496

2248

665 746

Provide consulting

Between Groups

Within Groups

Total

11302

68441

79744

10

28

38

1130

2444

462 900


Within Groups

Total

14701

69196

83897

10

28

38

1470

2471

595 804

Hold_workshop Between Groups

Within Groups

15190

71118

10

28

1519

2540

598 802

291





Within Groups

Total

10740

55035

65775

10

29

39

1074

1898

566 828


Within Groups

Total

12531

60550

73081

10

26

36

1253

2329

538 847


Within Groups

Total

16181

58900

75081

10

26

36

1618

2265

714 704


Within Groups

Total

14956

62941

77897

10

28

38

1496

2248

665 746

Provide consulting

Between Groups

Within Groups

Total

11302

68441

79744

10

28

38

1130

2444

462 900


Within Groups

Total

14701

69196

83897

10

28

38

1470

2471

595 804


Within Groups

Total

15190

71118

86308

10

28

38

1519

2540

598 802

292





Within Groups

Total

2433

63342

65775

1

38

39

2433

1667

1460 234


Within Groups

Total

1838

71243

73081

1

35

36

1838

2036

903 348


Within Groups

Total

2410

72671

75081

1

35

36

2410

2076

1161 289


Within Groups

Total

1289

76608

77897

1

37

38

1289

2070

623 435

Provide consulting

Between Groups

Within Groups

Total

973

78770

79744

1

37

38

973

2129

457 503


Within Groups

Total

1289

82608

83897

1

37

38

1289

2233

577 452


Within Groups

Total

3605

82703

86308

1

37

38

3605

2235

1613 212

293





Within Groups

Total

096

65679

65775

2

37

39

048

1775

027 973


Within Groups

Total

2914

70167

73081

2

34

36

1457

2064

706 501


Within Groups

Total

662

71893

72556

2

33

35

331

2179

152 860


Within Groups

Total

379

77519

77897

2

36

38

189

2153

088 916

Provide consulting

Between Groups

Within Groups

Total

410

79333

79744

2

36

38

205

2204

093 911


Within Groups

Total

2564

81333

83897

2

36

38

1282

2259

567 572


Within Groups

Total

974

85333

86308

2

36

38

487

2370

206 815

294


Appendix 37 Use of the Internet - Internet Usage - Using Internet Please mark the extent that your firm uses the Internet for the following purposes

Internet Usage - Using Internet Please mark the extent that your firm uses the Internet for the following purposes

No_of Business Sector employee year

N Valid 659 665 656

Missing 6 0 9

Statistics

Ordering_ Payment_ Sales Receipt_ Email General_info supplier supplier Customer Customer

N Valid 580 572 556 552 558 550


Statistics Continued

Gov_info Feedback_ customer

Software_ download E_banking

Video_ conference

N Valid

Missing Mean Median

552

113 206 200

553

112 214 200

550

115 188 200

554

111 234 300

544

121 76 00

295


Std Deviation Minimum Maximum

1115 0 4

1363 0 4

1176 0 4

1394 0 4

1031 0 4

Appendix 38 Use of the Internet - Internet Usage - Using Internet Please mark the extent that your firm uses the Internet for the following purposes - ANOVA


(We excluded Sectors 605 and 702 from the ANOVA analysis as their sample size lt10)



Email Between Groups 6949 9 772 1968 041


Total 223530 561

General_info Between Groups 8433 9 937 1475 154


Total 353951 553

Ordering_supplier Between Groups 166025 9 18447 11518 000


Total 1011645 537

Payment_supplier Between Groups 67339 9 7482 4294 000


Total 980301 533

Sales_Customer Between Groups 203502 9 22611 12188 000


Total 1186748 539

296


Receipt_Customer Between Groups 74574 9 8286 4991 000


Total 941233 531

Gov_info Between Groups 24154 9 2684 2195 021


Total 664734 533

Feedback_customer Between Groups 46880 9 5209 2912 002


Total 985918 534

Software_download Between Groups 18585 9 2065 1525 136


Total 725308 531

E_banking Between Groups 24164 9 2685 1385 192


Total 1043879 535

Video_conference Between Groups 21673 9 2408 2278 016


Total 567165 525


Email 301 60 363 637

501

601

602

603

604

46

105

42

52

77

363

354

371

385

365

645

707

596

364

580

701 23 335 832

297


801 31 368 653

802 9 322 833

803 117 362 598

Total 562 363 631

General_info 301 60 298 854

501 46 320 719

601 102 322 779

602 40 320 853

603 49 335 855

604 77 322 788

701 23 300 798

801 31 303 836

802 9 300 866

803 117 334 756

Total 554 320 800

Ordering_supplier 301 58 222 1298

501 43 140 1178

601 99 220 1204

602 39 259 1229

603 49 269 1262

604 76 236 1402

701 21 105 1322

801 31 74 999

802 8 112 1553

803 114 154 1284

Total 538 195 1373

Payment_supplier 301

501

601

58

43

100

164

88

144

1266

1074

1343

298


602 37 176 1480

603 48 175 1605

604 76 171 1403

701 20 60 1095

801 31 61 844

802 8 112 1553

803 113 127 1289

Total 534 138 1356

Sales_Customer 301 58 252 1405

501 43 135 1289

601 102 193 1373

602 40 272 1320

603 49 288 1333

604 75 239 1384

701 22 150 1535

801 31 71 1006

802 8 100 1604

803 112 144 1400

Total 540 195 1484

Receipt_Customer 301 57 140 1425

501 43 51 856

601 102 102 1350

602 37 119 1309

603 47 147 1412

604 75 159 1453

701 20 50 827

801 31 26 514

802 7 100 1528

803 113 114 1315

299


Total 532 111 1331

Gov_info 301 58 209 996

501 43 230 1036

601 100 179 1113

602 38 189 981

603 48 204 1202

604 75 211 1226

701 20 170 1218

801 31 174 1032

802 7 243 1134

803 114 230 1088

Total 534 205 1117

Feedback_customer 301 58 260 1242

501 43 179 1473

601 100 199 1299

602 38 216 1197

603 48 262 1409

604 76 226 1408

701 21 138 1499

801 30 187 1279

802 7 214 1215

803 114 211 1319

Total 535 214 1359

Software_download 301 56 196 1159

501 43 186 1302

601 100 178 1106

602 38 179 811

603 48 204 1352

604 75 165 1168

300


701 20 145 1276

801 31 177 1055

802 8 162 1598

803 113 213 1146

Total 532 187 1169

E_banking 301 57 223 1337

501 42 210 1265

601 100 240 1414

602 37 238 1421

603 49 280 1399

604 76 243 1417

701 20 170 1455

801 31 239 1383

802 8 200 1195

803 116 228 1418

Total 536 234 1397

Video_conference 301 55 82 884

501 42 60 989

601 98 54 852

602 37 78 976

603 48 106 1405

604 74 91 1196

701 20 30 571

801 31 55 995

802 8 62 744

803 113 95 1042

Total 526 77 1039

301




Email Between Groups

Within Groups

Total

2032

221498

223530

2

559

561

1016

396

2564 078

General_info Between Groups

Within Groups

Total

1980

351972

353951

2

551

553

990

639

1550 213

Ordering_supplier Between Groups

Within Groups

Total

873

1010772

1011645

2

535

537

437

1889

231 794

Payment_supplier Between Groups

Within Groups

Total

270

980031

980301

2

531

533

135

1846

073 929

Sales_Customer Between Groups

Within Groups

Total

3263

1183485

1186748

2

537

539

1632

2204

740 477

Receipt_Customer Between Groups

Within Groups

Total

034

941199

941233

2

529

531

017

1779

010 991

Gov_info Between Groups

Within Groups

Total

5277

659457

664734

2

531

533

2639

1242

2125 120

Feedback_customer Between Groups

Within Groups

Total

721

985196

985918

2

532

534

361

1852

195 823

Software_download Between Groups

Within Groups

6536

718772

2

529

3268

1359

2405 091

302


Total 725308 531

E_banking Between Groups

Within Groups

Total

2322

1041557

1043879

2

533

535

1161

1954

594 552

Video_conference Between Groups

Within Groups

Total

5444

561721

567165

2

523

525

2722

1074

2534 080


Email 5 429 359 647

25 126 374 568

75 7 357 535

Total 562 363 631


25 123 329 807

75 7 286 378

Total 554 320 800


25 121 197 1341

75 7 229 1113

Total 538 195 1373

Payment_supplier 5 408 138 1372

25 119 137 1314

75 7 157 1272

Total 534 138 1356

Sales_Customer 5

25

75

411

122

7

196

189

257

1478

1517

1272

303


Total 540 195 1484


25 121 110 1319

75 7 114 1345

Total 532 111 1331

Gov_info 5 407 200 1143

25 120 223 1027

75 7 200 816

Total 534 205 1117


25 119 211 1268

75 7 243 976

Total 535 214 1359


25 119 168 1119

75 7 229 756

Total 532 187 1169

E_banking 5 410 236 1410

25 119 228 1352

75 7 186 1464

Total 536 234 1397


25 116 84 1119

75 7 157 976

Total 526 77 1039


304




Within Groups

Total

128

215548

215676

2

553

555

064

390

164 848


Within Groups

Total

1320

351831

353151

2

546

548

660

644

1024 360


Within Groups

Total

7740

990572

998311

2

530

532

3870

1869

2071 127


Within Groups

Total

32312

942553

974866

2

526

528

16156

1792

9016 000


Within Groups

Total

3570

1171355

1174925

2

533

535

1785

2198

812 444


Within Groups

Total

27508

907674

935182

2

525

527

13754

1729

7955 000


Within Groups

Total

2395

660518

662913

2

527

529

1198

1253

956 385


Within Groups

Total

13689

962530

976219

2

527

529

6845

1826

3747 024


Within Groups

Total

5287

715938

721226

2

524

526

2644

1366

1935 145

305



Within Groups

Total

16191

1016684

1032875

2

527

529

8096

1929

4196 016


Within Groups

Total

12012

550005

562017

2

519

521

6006

1060

5667 004

By Business Year


Email 2 80 360 668

5 151 365 580

8 325 363 633

Total 556 363 623


5 148 328 747

8 323 317 815

Total 549 320 803


5 144 212 1356

8 311 185 1350

Total 533 194 1370


5 144 177 1437

8 309 120 1247

Total 529 138 1359


306


5

8

Total

144

313

536

203

189

196

1467

1467

1482


5 143 143 1392

8 308 93 1244

Total 528 111 1332

Gov_info 2 76 189 1150

5 144 203 1064

8 310 209 1137

Total 530 205 1119


5 145 238 1291

8 309 201 1362

Total 530 214 1358


5 143 203 1171

8 309 181 1124

Total 527 187 1171

E_banking 2 77 268 1261

5 145 243 1413

8 308 219 1408

Total 530 233 1397


5 143 97 1119

8 304 64 965

Total 522 76 1039

307


Appendix 39 Use of the Internet - Internet Usage - Using the Internet Do you think that using the Internet more would help improve the business of your firm If so

how

Internet Usage - Using the Internet Do you think that using the Internet more would help improve the business of your firm If so how

This question was addressed to those respondents who had computers and used the Internet There were 521 such respondents in the mail survey of which 54 replied to this question The responses are detailed below

1 Improved delivery of information at much lower cost 2 提升效率（ improve efficiency) 3 找尋

Searching 4 會在尋找客源開拓市場方面等等

Yes searching for new customers opening up markets etc 5 B to C 方面 ex E-bay

B to C area and ex E-bay 6 否

No 7 公司以互聯網為主要收入來源即幫公司製作網頁故此必成正比

The company relies on the Internet to earn the major source of income ie to help firms creating websites So it must be directly proportional

8 不知道 Not sure

9 在宣傳方面 For promotion

10 不會 No

11 會因有助開拓海外市場 Yes as it will help open up overseas markets

12 提供信息給顧客 13 Main business now is a mobile portal So the more usage means the better business 14 NA 15 無意見

No comment

308


16 網頁的提升能吸引更多顧客 improvement on the web page can attract more customers

17 會增強各分行之內部溝通 Yes to increase the internal communication between each branches

18 Sourcing arrange sample research 19 增加銷售機會

Increase the chance of sales 20 不一定

Not necessarily 21 可以售買更多產品及取得新產品資料比較行業間的產品售價容易取得圖片

給客戶 We can sell more products and get new products information We can compare the price of products in the same industry We can also give pictures to clients more easily

22 更有效率 Increase the efficiency

23 Promote new products in my company website 24 會在外銷售帶來幫助

It will help the sales 25 不太可能

May not be possible 26 在互聯網上做產品推廣

To promote the products via the Internet 27 Sourcing and attract new customers and new markets 28 宣傳

Advertise the company 29 不會

No 30 Yes Only if your staff use it for business searching Not for facebook 31 設立公司網頁令客戶在網頁上落訂單

To set up the companys website so the clients can make online orders for our products

32 會在銷售方面 Yes In the sales area

33 No 34 Yes

It really helped a lot when looking for Manufacturers and Suppliers even goods categories

35 不會 No

309


36 I think I use it often as it is If I have more staff I could set up to use it more and more effectively

37 產戶會找尋新品及增加對客及廠商的認識 Yes it will help us to search for new products and improve our knowledge about clients and manufacturers

38 與客戶之間的溝通更加快捷省時

It will fasten the communication with clients and will save more time 39 不一定可提高生意額

It is not certain if the Internet can help increase the business volume 40 打入新的市場尋找商機

To penetrate into new markets and search for new business opportunities 41 No 42 No 43 現在應用互聯網在生意上主要是收發電郵方面這方面是很重要但其他方

面則很少用主要是尋找資訊下載軟件等 We mainly use the Internet for sending and receiving emails in our business operations But we seldom use the Internet for other uses We just use it for searching information downloading software etc

44 會因為應該可以增加更多商機 Yes as it may increase more business opportunities

45 有機會 Maybe

46 會在銷售方面提高效率 Yes in the sales area - to increase the efficiency

47 No 48 能更清楚業界情況市場發展

To be able to get to know the business environment and market development better 49 效率

Efficiency 50 廣告推廣

Advertising and promotion 51 Yes communication with internal staff and customers improves business efficiency 52 設立銷售網站

To build a website for sales 53 新市場開拓找新客

Exploitation of new market and new customers 54 Online sales development

Of the 54 replies 14 did not feel it would help any more The main areas as suggested by the remaining respondents where operations could improve were

310


bull Ease of searching for information bull Efficiency and cost reduction bull Speedy communication bull MarketingSales development

In the telephone survey of the 98 respondents there were 75 that fit these criteria of which 55 replied to this question

1 No 2 No 3 No 4 Yes Internet is a huge client source of them 5 No 6 yes Internet can give them more market news and will increase sales 7 No 8 No 9 No 10 No 11 No 12 No 13 Yes more contact through web with client can increase sale 14 No 15 No 16 No 17 No 18 No 19 No 20 No 21 No 22 No 23 Yes communicate more will have business return 24 No 25 No 26 No 27 Yes more famous of his company and thus increase sales 28 No 29 Yes more info will be get from Internet and will generate more business 30 No 31 No idea 32 No

311


33 Yes more known about the recent market trend and thus increase sales 34 Yes their product will be more update and thus increase sales 35 No 36 No 37 yes more efficiency and thus business 38 No 39 No 40 Yes update more market trend can increase sales 41 No 42 Yes may be more supplier will be contacted thus the business 43 No comment 44 Yes can enhance her company brand and thus business 45 No 46 No 47 Yes because part of business are find through Internet 48 No because we are really small so we dont think we really need information

technology support 49 No 50 Yes Basically it makes things easier 51 Yes because they are trading company and using Internet can meet many new

opportunities all around 52 Dont know because right now we only have less than 5 staff 53 Yes But dont know how to help as we only use it for email and Internet surfing 54 Promote company brand and increase sales 55 Yes new business may be explore

Thus of the 55 replies received to this question in the telephone survey only 18 respondents stated that the use of more Internet would be beneficial The primary reasons given were

bull Ease of searching for information bull Efficiency bull Speedy communication bull MarketingSales development

7 respondents answered this question by English email

1 Yes it helps to explore the potential clients all over the world

Well make more online marketing

312


2 Yes it further enhances global reach 3 Yes improve the efficiency of the whole operation 4 No 5 Name of our firm says it all Internet Web Technology Limited 6 Yes 7 Yes communicate to the potential customer over the Internet Like the IO or any

kind of landlords

Thus additional use of the Internet would be beneficial as it would assist in bull Efficiency bull Speedy communication bull MarketingSales development

7 respondents answered this question by Chinese email

1 Finding new potential customers 2 Online booking payment gateway 3 Search engines 4 Better website and ecommerce capabilities 5 Web conference EDI management 6 Yes can expand the customer base and improve market share 7 No considering our size and business for the time being

313


Appendix 40 Use of the Internet - Internet Usage - Using the Internet How much value do you think the Internet has added to the following aspects of your business

Internet Usage - Using the Internet How much value do you think the Internet has added to the following aspects of your business

Statistics

To_new market

Prompt_ response

Network_ opportunity Efficiency

N Valid


559

106 146 100

1186 0 4

553

112 176 200

1205 0 4

556

109 167 200

1239 0 4

560

105 226 200

1135 0 4

Statistics

Inc_Customer _ service Inc_sales New_product

Competitive_ advantage

N Valid


553

112 212 200

1139 0 4

557

108 162 200

1228 0 4

552

113 162 200

1254 0 4

552

113 191 200

1210 0 4

314



- ANOVA

Internet Usage - Using the Internet How much value do you think the Internet has added to the following aspects of your business - ANOVA

(Sectors 605 and 702 have been excluded as their sample size lt10)



To_new_market Between Groups

Within Groups

Total

36627

743221

779848

11

542

553

3330

1371

2428 006

Prompt_response Between Groups

Within Groups

Total

38840

760902

799743

11

536

547

3531

1420

2487 005

Network_opportunity Between Groups

Within Groups

Total

50750

792136

842886

11

539

550

4614

1470

3139 000


Within Groups

Total

45955

671224

717178

11

543

554

4178

1236

3380 000

Inc_Customer_service Between Groups

Within Groups

Total

45002

668755

713757

11

536

547

4091

1248

3279 000


Within Groups

Total

48150

782196

830346

11

540

551

4377

1449

3022 001

New_product Between Groups 53133 11 4830 3204 000

315


Within Groups

Total

806530

859664

535

546

1508

Competitive advantage

Between Groups

Within Groups

Total

33461

768671

802132

11

535

546

3042

1437

2117 018

Differences


To_new_market 301 59 163 1128

501 44 116 1200

601 100 146 1150

602 39 141 1044

603 48 171 1320

604 77 162 1148

701 23 70 1063

801 30 113 1074

802 9 144 1740

803 113 148 1196

Total 542 145 1185

Prompt_response 301 59 183 1315

501 42 143 1151

601 99 181 1226

602 38 171 898

603 48 198 1211

604 77 173 1210

701 21 86 910

801 30 213 1137

316


802

803

Total

8

113

535

112

176

174

1458

1205

1205

Network_opportunity 301 59 180 1186

501 42 124 1226

601 101 174 1246

602 39 167 1084

603 48 206 1327

604 76 180 1233

701 21 52 1030

801 31 139 1086

802 9 167 1323

803 112 167 1233

Total 538 166 1241

Efficiency 301 59 231 1193

501 44 189 1185

601 101 213 1128

602 39 244 1021

603 48 265 1158

604 77 212 1076

701 21 157 1165

801 30 207 1015

802 9 189 1167

803 114 255 1048

Total 542 225 1132

Inc_Customer_service 301 59 219 1137

501 43 163 1215

601 99 191 1089

602 38 232 1016

317


603 48 254 1184

604 76 195 1153

701 21 157 978

801 30 223 898

802 8 200 926

803 113 237 1151

Total 535 211 1141

Inc_sales 301 60 187 1186

501 44 105 987

601 99 167 1152

602 39 177 1158

603 48 194 1375

604 76 154 1216

701 23 83 984

801 30 173 1081

802 9 144 1667

803 112 161 1283

Total 540 160 1222

New_product 301 59 190 1241

501 44 118 1147

601 98 170 1203

602 39 174 1117

603 48 190 1403

604 76 172 1282

701 22 59 854

801 30 160 1133

802 8 88 1458

803 111 159 1260

Total 535 162 1253

318


Competitive_advantage 301 59 212 1190

501 43 147 1202

601 100 190 1159

602 38 208 1171

603 48 212 1362

604 75 180 1185

701 21 124 1044

801 30 200 1017

802 8 125 1581

803 113 206 1212

Total 535 191 1211




Within Groups

Total

3902

781021

784923

2

556

558

1951

1405

1389 250


Within Groups

Total

4108

797935

802043

2

550

552

2054

1451

1416 244


Within Groups

Total

2155

849622

851777

2

553

555

1078

1536

701 496


Within Groups

Total

1500

718984

720484

2

557

559

750

1291

581 560


Within Groups

986

715137

2

550

493

1300

379 685

319


Total 716123 552

Inc_sales Between Groups 3614 2 1807 1200 302


Total 837781 556



Total 865868 551


Between Groups

Within Groups

Total

4623

802027

806650

2

549

551

2312

1461

1582 206

320


413 ANOVA - - BY (Business_year)



Within Groups

Total

14571

766974

781545

2

551

553

7286

1392

5234 006


Within Groups

Total

15890

779295

795185

2

544

546

7945

1433

5546 004


Within Groups

Total

20329

824035

844364

2

547

549

10165

1506

6747 001


Within Groups

Total

6611

711513

718125

2

551

553

3306

1291

2560 078


Within Groups

Total

8022

703735

711757

2

545

547

4011

1291

3106 046


Within Groups

Total

11916

819270

831186

2

550

552

5958

1490

4000 019

New_product Between Groups

Within Groups

Total

12345

845181

857526

2

545

547

6173

1551

3980 019


Between Groups

Within Groups

Total

10923

788515

799438

2

545

547

5462

1447

3775 024

321



To_new_market 2 76 154 1380

5 147 167 1155

8 314 132 1139

Total 537 145 1188


5 145 194 1212

8 309 159 1166

Total 530 173 1206


5 145 188 1239

8 311 148 1207

Total 533 165 1243

Efficiency 2 77 245 1070

5 146 234 1146

8 314 216 1140

Total 537 225 1135


5 146 226 1151

8 309 200 1152

Total 531 211 1142

Inc_sales 2 75 172 1258

5 146 179 1231

8 315 148 1198

Total 536 160 1222

New_product 2

5

76

146

166

184

1292

1279

322


8

Total

309

531

150

161

1216

1251

Competitive_advantage 2

5

8

Total

77

145

309

531

204

209

179

191

1251

1178

1204

1210

323


Appendix 42 Correlation between VA2 and ICTU2

(We have excluded Sectors 605 702 and 802 due to their small sample size)

Sector = 301

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

58

736

000

58


Sig (2-tailed)

N

736

000

58

1


Sector = 501

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

44

564

000

44


Sig (2-tailed)

N

564

000

44

1

44

324


Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

44

564

000

44


Sig (2-tailed)

N

564

000

44

1


325


Sector = 601

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

103

713

000

103


Sig (2-tailed)

N

713

000

103

1


Sector = 602

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

42

619

000

42


Sig (2-tailed)

N

619

000

42

1


326


Sector = 603

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

51

684

000

51


Sig (2-tailed)

N

684

000

51

1


Sector = 604

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

77

630

000

77


Sig (2-tailed)

N

630

000

77

1


327


Sector = 701

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

21

768

000

21


Sig (2-tailed)

N

768

000

21

1


Sector = 801

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

31

464

009

31


Sig (2-tailed)

N

464

009

31

1


328


Sector = 803

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

113

680

000

113


Sig (2-tailed)

N

680

000

113

1



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

415

654

000

415


Sig (2-tailed)

N

654

000

415

1


329



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

118

752

000

118


Sig (2-tailed)

N

752

000

118

1



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

7

535

216

7


Sig (2-tailed)

N

535

216

7

1

7

330



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

75

719

000

75


Sig (2-tailed)

N

719

000

75

1


Business Year (age) 3-7 years Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

147

641

000

147


Sig (2-tailed)

N

641

000

147

1


331



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

312

671

000

312


Sig (2-tailed)

N

671

000

312

1


332


Appendix 43 Website Usage - Website Usage - Does your firm have website

Website Usage - Does your firm have website

Statistics Have_Website

N Valid 697

Missing 92

Have_Website


Percent Cumulative

Percent

Valid 0

1


375

322

697 92

789

475

408

883 117

1000

538

462

1000

538

1000

665 respondents had replied that they had Internet usage However 697 responses were received to this section probably on account of the following

(1) The respondents fill in the error choiceskip the [Internet] part

(2) They have out-sourced the website and do not have Internet in the company

In all there 322 respondents said that they had a website Of these there were 2 respondents who said they did not have Internet and 1 respondent who skipped the Internet question Further 2 of these 3 special sample also answered the section on website usage Because the percentage is very low it has been included as it will not affect the statistics analysis result

333


Appendix 44 Website Usage - Not using Website How important are the following reasons for your firm not having a website

Website Usage - Not using Website How important are the following reasons for your firm not having a website

Statistics


N Valid

Missing

374

1

375

0

370

5

Statistics

Partner_not e_Transaction e_Marketing_ Maintenance_ Partner_not have_web not_common not_common Security difficult use_Internet

N Valid 278 284 286 280 280 279


Statistics

Competitior_ not_use Internet It_costly It_not_need

No_trained_ person

No_one_to_ train_person

N Valid

Missing Mean Median

277

98 101 100

283

92 185 200

287

88 193 200

282

93 148 100

277

98 145 100

334



885 0 4

1209 0 4

1265 0 4

1160 0 4

1171 0 4

335


Appendix 45 Website Usage - Not using Website How important are the following reasons for your firm not having a website ndash ANOVA

Website Usage - Not using Website How important are the following reasons for your firm not having a website ndash ANOVA

(We have excluded IndustrySectors 605 and 702 from further ANOVA calculations as sample size lt10)



Partner_not_have_web Between Groups

Within Groups

Total

7618

259749

267367

9

260

269

846

999

847 573

e_Transaction_not_ common

Between Groups

Within Groups

Total

18801

455587

474388

9

266

275

2089

1713

1220 283

e_Marketing_not_ common

Between Groups

Within Groups

Total

19769

451903

471673

9

268

277

2197

1686

1303 235


Within Groups

Total

21660

353811

375471

9

262

271

2407

1350

1782 072

Maintenance_difficult Between Groups

Within Groups

Total

15800

424167

439967

9

262

271

1756

1619

1084 375

Partner_not_use_ Internet

Between Groups

Within Groups

Total

7068

275028

282096

9

261

270

785

1054

745 667

Competitior_not_use_ Between Groups 10629 9 1181 1519 141

336


Internet Within Groups

Total

201371

212000

259

268

777


Within Groups

Total

17947

385615

403562

9

264

273

1994

1461

1365 204

337



Within Groups

Total

17539

423565

441104

9

269

278

1949

1575

1238 272


Within Groups

Total

17452

352989

370442

9

264

273

1939

1337

1450 167

No_one_to_train_ person

Between Groups

Within Groups

Total

17725

355033

372758

9

259

268

1969

1371

1437 172



Partner_not_have_web Between Groups 110 1 110 111 740


Total 267367 269


Between Groups

Within Groups

Total

1144

473243

474388

1

274

275

1144

1727

662 416


Between Groups

Within Groups

Total

610

471062

471673

1

276

277

610

1707

358 550

Security Between Groups 001 1 001 000 984


Total 375471 271

Maintenance_difficult Between Groups 2515 1 2515 1552 214


Total 439967 271

338


Partner_not_use Internet

Between Groups

Within Groups

Total

067

282029

282096

1

269

270

067

1048

064 801

339


Competitior_not_use_ Internet

Between Groups

Within Groups

Total

282

211718

212000

1

267

268

282

793

356 551



Total 403562 273



Total 441104 278



Total 370442 273


Between Groups

Within Groups

Total

1098

371661

372758

1

267

268

1098

1392

789 375





Total 266507 267


Between Groups

Within Groups

Total

1688

469232

470920

2

271

273

844

1731

487 615


Between Groups

Within Groups

Total

4542

466078

470620

2

273

275

2271

1707

1330 266

340



Within Groups

Total

1172

374090

375262

2

268

270

586

1396

420 658

341




Total 434652 269


Between Groups

Within Groups

Total

166

281908

282074

2

267

269

083

1056

079 924


Between Groups

Within Groups

Total

201

211799

212000

2

264

266

101

802

126 882



Total 398202 271



Total 440259 277



Total 363868 271


Between Groups

Within Groups

Total

2126

364142

366269

2

265

267

1063

1374

774 462

342


Appendix 46 Website Usage - Do you think that creating a website would help improve your business If so how

Website Usage - Do you think that creating a website would help improve your business If so how

This question was addressed to those respondents who used computers and the Internet but did not have a website There were 265 such respondents in the mail survey of which 86 replied to this question

1 Yes presence credibility PR 2 增強公司形象 (improve companys image) 3 不

No 4 增加聲譽但要顧及成本

To increase the reputation but we need to consider about the costs 5 無

No 6 有更廣客路

Yes greater sources of customers 7 方便競爭力

Convenient competitive advantage 8 有廣告效應

There is advertising effect 9 不會

No 10 增加公司知名度方便客戶找尋資料

Increase the companys reputation and it will be more convenient for clients to search for information

11 否 No

12 將不同代表的產品一次性展出 To display different products all at one time


14 讓客人認識我們 To allow customers to know about us

15 帶來新商機

343


It brings new business opportunities 16 不會

No 17 沒有

No 18 公司予人感覺更有規模

It will give people the feel that the firm is of larger scale 19 令顧客足不出戶也可了解自己公司的產品和特點

To allow the clients to learn about our companys products and their characteristics even when they stay at home

20 不會 No

21 可推廣商品並節省紙張減少浪費 We can promote products and save papers to reduce wastage

22 暫時沒此需要 We do not have this need at the moment

23 多些人認識 To allow more people know the company

24 不會帶來什麼好處 It will not bring any advantages

25 會因顧客可方便快捷地了解本公司的產品 Yes as clients will understand our companys products more easily and quickly

26 會增加客人數量 Yes to increase the number of customers

27 會方便新客戶了解本公司及增加商機 Yes it will be more convenient for clients to understand the company and increase business opportunities

28 暫時無用因公司規模細 We dont use it at the moment as the companys scale is small

29 推銷產品提升形象宣傳 To promote the products enhance the image and publicity

30 不會 No

31 Networking 32 不會

No 33 未能估計

It cannot be estimated

344


34 不會 No

35 Increase companys reputation 36 未能估計

It cannot be estimated 37 多人認識

More people will know the company 38 沒有好處

No 39 客人可能從網站聯絡本公司 (但比率不高 )

Clients can contact our company via the website (but the percentage is not high) 40 會因為可快捷聯絡

Yes because it can fasten the contacts 41 不會

No 42 不一定會

May not 43 方便客戶查詢資料

It will help clients inquiries 44 增加海外客戶搜尋本公司的資料和增加商機

Allow overseas clients to search for our companys information and increase business opportunities

45 不會 No

46 開拓多些資訊給客戶

It will open up more information for clients 47 有好處可以讓客戶更容易查閱我公司資料

Yes it can allow the clients to check for the companys information 48 I think so 49 不太可能

May not be possible 50 不會

No 51 提供宣傳效果令更多人認識你

To provide advertising effect to allow more people to get to know you 52 有好處可尋找新客戶

Yes we can search for new clients 53 建立知名度

To build up reputation of the firm

345


54 不會 No

55 會令客戶更加了解本公司 Yes it will allow to clients to understand more about the company

56 內戶會在網多一些客能找到自己公司 Yesmore clients can get to know our company through the Internet

57 No 58 Yes It will let more companies know us and increase the business opportunities 59 會有增加接觸潛在客戶的機會

Yes it will increase the chance of getting in touch with potential clients 60 Solicit more clients more out-reach to untouched sector 61 Service promotion 62 增加客戶

Yes it will increase the number of customers 63 會使客戶知道我公司資料如各鋪的地址電話

Yes it will allow the customers to get access to the companys information such as the branches addresses and telephone numbers

64 增加客方了解公司 Allow the clients to know more about the company

65 不會垃圾電郵太多 No there are too many junk mails

66 運作模式不需要 The companys operation style does not need a website

67 無好處 No advantage

68 不單只做本地生意其他地方也可知道產品 We can not only carry out local businesses but people in other places will also be able to learn about our products

69 會提升公司形象方便客戶 Yes it will enhance the companys image and will be more convenient for the clients

70 會有更多人認識公司 More people will get to know the company

71 No 72 不會

No 73 暫時不會有太大好處

There is be not much use at the moment 74 會可以使更多公司認識我們

346


Yes this can allow more companies to know us 75 會增加公司聲名提高公司產品信息

Yes increase the companys reputation and increase the availability of the product information

76 會可能會有 d 生意 Yes It may bring a little bit more business opportunities

77 吸引新客源 To attract new sources of customers

78 會增加客戶及服務質素 Yes it will increase customers and (improve) quality of service

79 不會 No

80 不會 No

81 行業不普遍 It is not popular within the business sector

82 Customer over the world can visit the web site for more product information at any time

83 增加營業額 It would increase the sales revenue

84 没有 No

85 暫時看不到會帶來好處 For the moment I could not see any benefits it may bring

86 會 YES

Of the 86 responses 33 did not see the benefits for creating a website The remaining respondents gave the following reasons why they felt that creating a website could be useful for their firm

bull Improving the firmsrsquo presence and promote marketing efforts by attracting new customers thereby increasing revenue

bull Enhance the firmsrsquo credibility reputation and image bull Provide additional information on the company and its products bull Improve service

This question was addressed to those respondents who used computers and the Internet but did not have a website There were 42 such respondents in the telephone survey of which 42 replied to this question

347


1 yes can let more people know their company 2 No 3 yes the name will be more known 4 no idea 5 may be yes since the company may be more famous but she also think it is

costing may be she like using leaflet on the street is also ok 6 may be yes convenience to be known by people 7 No 8 yes may be can meet more new clients and clients can see our product easily 9 no because this is no business need 10 maybe can increase sales since the company become famous 11 no help 12 no existing client mainly 13 yes may be more people know his company and more sales 14 No 15 yes more famous brand name of company 16 yes her company will more famous 17 yes more people will know more about his company and may have referral client 18 No 19 no because business partner usually no use computer 20 yes more people know more about company product 21 yes more people will know about her company 22 yes may be more people know more about her company and increase sale 23 yes new clients may be found 24 yes it should be more ppl know about out company and can contact us 25 no idea 26 Not really as it is not useful for them to sell commodity 27 yes company will be more well known 28 no cost of maintainance technical and law is also a problem of setup of website 29 no because may need many resources to setup and follow up 30 yes more known by others about his company 31 no need 32 yes may be more people will know about her company thus increase sales 33 no probably since existing client is main target 34 no film size is small and exist client is enough 35 No 36 No it wont make big difference to us small company 37 no since he dont think the business can enhance through net 38 Not Right now as my company is really small 39 she think no because most are existing client 40 no because the company in hk just a office but no actual business involve in it

348


41 no idea 42 no since the company works with existing client mostly

Of the 42 responses there were 18 respondents who agreed that creating a website would be useful and gave the following reasons


bull Enhance the firmsrsquo credibility reputation and image bull Provide additional information on the company and its products

3 answered by English email 1 Not at all 2 Yes That is our trade when starting up Still is if given the incentive to do more in

this arena No more a profitable undertaking though 3 We had our website many years ago and the content is very informative to our users

3 answered by Chinese email 1 No 2 Yes Promote company image develop new business opportunity 3 Improve work efficiency and flexibility

349


Appendix 47 Website Usage - What would really motivate or push you to create a website

What would really motivate or push you to create a website

This question was addressed to those respondents who used computers and the Internet but did not have a website There were 274 such respondents of which 91 replied to this question

1 生意上有此需要 (when there are business needs) 2 Business expansion additional funding requirements 3 正在打算成立網站隻因太忙未暇跟進（Im planning to develop a website but

Im too busy so havent got to take action) 4 有客戶需要時

When we need it for customers 5 公司現正處於休眠期待生意復甦才考慮

The company is at dormancy period we will consider about it after the business recovers

6 有即時需要平 When there is an urgent need cheap

7 方便便宜 Convenient cheap

8 公司進一步發展 The company further develops

9 尋找網絡機會 To search for networking opportunities

10 商業上推廣 To promote the business

11 暫時不考慮 We do not consider about it at the moment

12 成熟的環境 When the environment gets mature

13 不會 No

14 稍後待落實代理權 Later we are waiting to get the franchise confirmed

15 不知道

350


Not sure 16 盡快

As soon as possible 17 價錢平宜

Cheap price 18 不知道

Not sure 19 空閒得要命時

When we are too free 20 有一定顧客的數量和生意額

When we have a certain number of customers and business volume 21 公司規模增大建立網站成本合宜

When the companys scale expand The costs of creating a website is reasonable 22 再有新產品推出時才會

We will need it only when we have to promote new products 23 考慮人才及成本

Considering about talents and costs 24 事務絕不需要網站

Our business does not need a website 25 無需要付太昂貴費用的情況下

When we do not have to pay a huge fee 26 遲些就會建立網站現在正在做網站當中

We are creating the website now 27 有專門人才編寫網站內容時

When there is an expert to prepare the websites content 28 拓展業務但財力物俱無機會甚微

When we expand our business But since we lack human and material resources the chance is slim

29 成本低管理方便 Low costs easy for management

30 有良好宣傳效果的話才會考慮 We will only consider about it if there will be good propaganda effect

31 有自己的品牌 When I have my own brand

32 Have spare time and money 33 沒有此需要

There is no such need 34 經濟改善後

351


After the economy recovers 35 有需要時

When it is needed 36 完全不考慮

I will not consider about it completely 37 Can increase our sales 38 有需要時

When it is needed 39 網頁製作容易網頁寄存便宜及更新容易

When it is easy to create the website When the web hosting is cheap and easy to update

40 生意很好 When the business doing good

41 公司總收入增加後 After the companys total revenue increases

42 價錢便宜及保養容易和便宜 The price is low The maintenance is easy and at low price

43 有龐大客戶 When we have a huge number of customers

44 目前的經營環境應該不會 We will not create one under the current business environment

45 有必要性 When it is needed

46 沒有人能抄襲商品設計有強力市場推廣配合下 When people will not copy the product designs illegally When it is coordinated with strong marketing

47 本行業很少因有網站能找到新客戶因此不需 Our industry can seldom attract new clients by setting up a website So it is not necessary for the us

48 價錢合理便宜 The price is reasonable and cheap

49 成本低有人 backup有需要時 Low costs when there is someone who can back-up and when it is needed

50 資金充裕下 When we have sufficient capital

51 要先解決的是資金及培訓問題 We need to solve the problems of capitals and training

52 More knowledge 53 業務需要而成本比對收入有利

352


When it is needed for the business and the benefits outweigh the costs 54 不會建立網站

We will not set up a website 55 建設性暫時不大故未有即時建立網站

Since the usefulness of a website is temporarily not very large So we have not set up the website immediately

56 免費提供網站設立及培訓員工 When it is free to set up a website and train the staff

57 有相關人材 When there are related talents

58 若網站帶來更多生意 If the website can bring more businesses

59 降低成本 Decrease the costs

60 普及化後 After the use of Internet become more popular

61 When we need to exploit and promote our business 62 成本跟回報成正比例

When the costs is proportional to the returns 63 Less costly to set up and maintain easy to be searched by potential clients 64 Web maintenance service charge reduce to an affordable rate 65 人力物力資產有充足盈餘

When there are enough human material and capital resources 66 開設至五間或以上的分店

When the company possesses five branches or more 67 成本低更新方便

Low costs and when it is convenient to update the computer 68 有員工處理網站資料時

When there are staff who can administer the website information 69 目前不需要

It is not necessary at the moment 70 無需要

No such need 71 公司有足夠人力財力

When the company has enough human and financial resources 72 價錢問題

A matter of the costs prices 73 暫時不需要

It is not needed at the moment

353


74 公司在擴展後才會建立網站 The company will only set up a website after its expansion

75 成立大公司及上市公司 When setting up a large firm or a listed company

76 如對工作有幫助 If it is useful for the work

77 確實需要考慮 I really need to consider about it

78 暫時看不到有迫切需要 There is no pressing need at the moment

79 有適當培訓及成本下降安全問題改善 When there are suitable training the costs decrease and safety problems are improved

80 免費低收費津貼 Free of charge low costs with subsidy provided

81 主要商業伙伴有需要 The major business partner(s) has have the need

82 如有資金擴大公司規模 If there is a sum of capital for expanding the companys scale

83 費用便宜 When the expenses are low

84 銷售 For sales

85 有產品銷售 When carrying out product sales

86 有足夠資源 With sufficient resources

87 同上 Same reason as above

88 Lower running cost Charges from HKDNR and E-Cert is not reasonable 89 有盈利的情况

If there is a profit 90 没此必要

It would be unnecessary 91 可以帶來額外的收入

Possibly extra earnings income

Of the 91 responses received the reasons given by the respondents that would really motivate or push them to create a website

354


bull When it is perceived that the business requires it bull Convenient bull Cheap to create and maintain bull Economy recovers bull Suitable training is available bull Adequate resources

Similarly in the telephone survey this question was addressed to those respondents who used computers and the Internet but did not have a website There were 42 such respondents in the telephone survey of which 40 replied to this question

1 no idea 2 no idea 3 no idea 4 financial support 5 no idea 6 if they want to change their business trend from only receive projects to selling

construction product they will and they are doing it also 7 if the business they do is not work for tailor made but only can see a sample of

product then they will setup 8 setting a website already 9 no need 10 no idea 11 no need 12 no idea 13 no idea 14 no need 15 no idea 16 no idea 17 if no business 18 No 19 No 20 No 21 no idea 22 financial support 23 financial subsidize and training course provided 24 no unless company use 25 no idea since this is company decision 26 financial and technical support 27 financial support technical support and law enquires is provide

355


28 technical support and guildlines how to setup and follow the website 29 financial and technical support 30 no need 31 financial and technical support is favour 32 no need 33 no idea 34 technical support and money support 35 Not really any 36 financial support to set up 37 Maybe later expansion 38 no comment 39 Nothing 40 no idea

Of the 40 responses received 16 respondents gave suggestions of what would motivate or push them to create a website These were

bull When it is perceived that the business requires it bull Suitable training and technical support is available bull Financial support

The only English email response was ldquomarketing needsrdquo

The following were the three responses received by the Chinese email 1 I donrsquot see the need 2 We are seriously considering develop a web site 3 We should have developed a website when the business started

356


Appendix 48 Website Usage - Not using Website How useful would the following types of Government support be to your firm for creating a website

Website Usage - Not using Website How useful would the following types of Government support be to your firm for creating a website

Statistics

Hold_ workshop

ICT_ infrastructure

Legal_ regulatory

Tax_ incentive

N Valid


287

88 171 100

1272 0 4

280

95 188 200

1252 0 4

282

93 204 200

1364 0 4

285

90 235 300

1375 0 4

Statistics

Provide_more Provide_ Subside_ _ info consulting training

N Valid 283 284 283


357


Appendix 49 Website Usage - Not using Website How useful would the following types of Government support be to your firm for creating a website - ANOVA

Website Usage - Not using Website How useful would the following types of Government support be to your firm for creating a website - ANOVA




Within Groups

Total

17370

443543

460913

11

274

285

1579

1619

975 469


Within Groups

Total

9222

426634

435857

11

267

278

838

1598

525 886


Within Groups

Total

27644

494000

521644

11

269

280

2513

1836

1368 188


Within Groups

Total

28489

508001

536489

11

272

283

2590

1868

1387 179

Provide_more_ info

Between Groups

Within Groups

Total

26027

484256

510284

11

270

281

2366

1794

1319 213

Provide_ consulting

Between Groups

Within Groups

Total

21574

511380

532954

11

271

282

1961

1887

1039 412


Within Groups

26389

537256

11

270

2399

1990

1206 283

358





Within Groups

Total

17370

443543

460913

11

274

285

1579

1619

975 469


Within Groups

Total

9222

426634

435857

11

267

278

838

1598

525 886


Within Groups

Total

27644

494000

521644

11

269

280

2513

1836

1368 188


Within Groups

Total

28489

508001

536489

11

272

283

2590

1868

1387 179

Provide_more_ info

Between Groups

Within Groups

Total

26027

484256

510284

11

270

281

2366

1794

1319 213

Provide_ consulting

Between Groups

Within Groups

Total

21574

511380

532954

11

271

282

1961

1887

1039 412


Within Groups

Total

26389

537256

563645

11

270

281

2399

1990

1206 283

359





Within Groups

Total

4591

457981

462571

1

285

286

4591

1607

2857 092


Within Groups

Total

815

436296

437111

1

278

279

815

1569

519 472


Within Groups

Total

1254

521317

522571

1

280

281

1254

1862

673 413


Within Groups

Total

548

536364

536912

1

283

284

548

1895

289 591

Provide_more_ info

Between Groups

Within Groups

Total

1138

509993

511131

1

281

282

1138

1815

627 429

Provide_ consulting

Between Groups

Within Groups

Total

1579

531787

533366

1

282

283

1579

1886

837 361


Within Groups

Total

430

563613

564042

1

281

282

430

2006

214 644

360





Within Groups

Total

918

456410

457329

2

283

285

459

1613

285 752


Within Groups

Total

1147

430695

431842

2

275

277

573

1566

366 694


Within Groups

Total

3508

514135

517643

2

277

279

1754

1856

945 390


Within Groups

Total

1368

532815

534183

2

281

283

684

1896

361 697

Provide_more_ info

Between Groups

Within Groups

Total

4891

502545

507436

2

279

281

2445

1801

1358 259

Provide_ consulting

Between Groups

Within Groups

Total

4868

525796

530664

2

280

282

2434

1878

1296 275


Within Groups

Total

1420

558089

559509

2

278

280

710

2008

354 702

361


Appendix 50 Website Usage - Using Website Please mark the extent that your firm uses its website for the following purposes

Website Usage - Using Website Please mark the extent that your firm uses its website for the following purposes

Statistics


N Valid

Missing

318

4

322

0

319

3

Statistics

Provide_firm _ info Hand_enquiry

Receive_ order

Receive_ payment Gov_info

N Valid


288

34 295 300 970

0 4

278

44 225 200

1244 0 4

274

48 155 100

1383 0 4

275

47 96

100 1222

0 4

269

53 142 100

1251 0 4

Statistics

Delivery_ product

After_sale_ service

Collection_ feedback

Online_ marketing

Knowledge_ mgt

N Valid

Missing Mean Median

272

50 129 100

279

43 159 100

274

48 179 200

274

48 145 100

266

56 139 100

362



1305 0 4

1364 0 4

1333 0 4

1396 0 4

1302 0 4

363


Appendix 51 Website Usage - Using Website Please mark the extent that your firm uses its website for the following purposes ndash ANOVA

Website Usage - Using Website Please mark the extent that your firm uses its website for the following purposes ndash ANOVA

(IndustrySector 702 and 802 were excluded from the ANOVA calculations as they had a sample size lt 10)



Provide_firm_info Between Groups

Within Groups

Total

9662

254743

264405

10

273

283

966

933

1035 414

Hand_enquiry Between Groups

Within Groups

Total

18697

400927

419624

10

263

273

1870

1524

1226 274

Receive_order Between Groups

Within Groups

Total

38935

473617

512552

10

259

269

3894

1829

2129 023

Receive_payment Between Groups

Within Groups

Total

19078

387623

406701

10

260

270

1908

1491

1280 242


Within Groups

Total

13590

399633

413223

10

254

264

1359

1573

864 568

Delivery_product Between Groups

Within Groups

Total

23626

432494

456119

10

257

267

2363

1683

1404 179

After_sale_service Between Groups

Within Groups

20221

489576

10

264

2022

1854

1090 370

364


Total 509796 274


Between Groups

Within Groups

Total

8921

469064

477985

10

259

269

892

1811

493 894

365


Online_marketing Between Groups

Within Groups

Total

16601

506365

522967

10

259

269

1660

1955

849 582

Knowledge_mgt Between Groups

Within Groups

Total

23867

421248

445115

10

251

261

2387

1678

1422 171


Provide_firm_info 301 33 285 795

501 12 292 996

601 55 302 1080

602 20 310 852

603 29 272 841

604 37 308 894

701 10 230 1160

801 12 283 937

803 67 301 1052

Total 275 294 972

Hand_enquiry 301 32 216 1139

501 10 200 1414

601 53 258 1167

602 19 237 1257

603 28 200 1186

604 36 219 1142

701 9 167 1732

801 12 175 1215

803 66 229 1298

366


Total 265 224 1240

Receive_order 301 32 172 1301

501 9 56 726

601 53 191 1445

602 18 161 1650

603 27 130 1203

604 36 172 1344

701 9 144 1740

801 11 36 674

803 66 150 1316

Total 261 154 1371

Receive_payment 301 32 112 1238

501 9 56 527

601 53 94 1322

602 18 89 1323

603 28 79 1134

604 36 125 1296

701 9 22 441

801 12 33 651

803 66 114 1264

Total 263 97 1223

Gov_info 301 32 162 976

501 8 112 1126

601 50 132 1220

602 18 111 1278

603 27 126 1289

604 36 147 1276

701 9 111 1537

801 12 117 1030

367


803

Total

65

257

174

144

1326

1243

Delivery_product 301 32 153 1344

501 9 56 726

601 52 142 1319

602 19 105 1433

603 28 100 1217

604 35 151 1269

701 9 44 726

801 11 82 874

803 65 145 1370

Total 260 129 1297

After_sale_service 301 33 164 1365

501 10 90 1101

601 53 164 1360

602 20 155 1701

603 28 114 1145

604 36 200 1287

701 9 122 1302

801 12 158 1379

803 66 167 1351

Total 267 159 1352

Collection_feedback 301 33 200 1199

501 9 122 1394

601 52 177 1381

602 18 156 1338

603 28 182 1307

604 36 194 1308

701 9 167 1500

368


801

803

Total

12

65

262

175

186

181

1422

1298

1313

Online_marketing 301 33 155 1371

501 9 78 1093

601 53 158 1447

602 17 112 1495

603 28 132 1278

604 35 166 1413

701 9 78 972

801 12 125 1485

803 66 155 1405

Total 262 145 1385

Knowledge_mgt 301 31 158 1285

501 9 89 1269

601 52 115 1274

602 17 135 1169

603 27 100 920

604 34 150 1261

701 9 89 1537

801 12 183 1528

803 63 170 1375

Total 254 140 1296



Provide_firm_info Between Groups 3765 2 1882 2013 135

369


Within Groups

Total

266454

270219

285

287

935


Within Groups

Total

5040

423334

428374

2

275

277

2520

1539

1637 196


Within Groups

Total

944

520939

521883

2

271

273

472

1922

246 782


Within Groups

Total

2288

407188

409476

2

272

274

1144

1497

764 467


Within Groups

Total

20950

398581

419532

2

266

268

10475

1498

6991 001


Within Groups

Total

351

461281

461632

2

269

271

176

1715

102 903


Within Groups

Total

1824

515409

517233

2

276

278

912

1867

488 614


Between Groups

Within Groups

Total

4494

480648

485142

2

271

273

2247

1774

1267 283


Within Groups

Total

1524

530154

531679

2

271

273

762

1956

390 678


Within Groups

Total

1245

448094

449338

2

263

265

622

1704

365 694

370




25 82 301 853

75 7 229 951

Total 275 294 972

Hand_enquiry 5 177 221 1273

25 82 235 1159

75 6 150 1225

Total 265 224 1240


25 80 145 1349

75 7 129 1254

Total 261 154 1371


25 81 81 1130

75 7 114 1215

Total 263 97 1223

Gov_info 5 175 126 1198

25 76 184 1265

75 6 167 1211

Total 257 144 1243


25 80 121 1260

75 7 114 1464

Total 260 129 1297

After_sale_service 5

25

179

81

164

152

1351

1361

371


75

Total

7

267

114

159

1345

1352

Collection_feedback 5

25

75

Total

175

80

7

262

177

195

129

181

1328

1282

1254

1313

Online_marketing 5

25

75

Total

175

80

7

262

151

135

114

145

1422

1303

1464

1385

Knowledge_mgt 5

25

75

Total

168

79

7

254

136

144

171

140

1315

1278

1113

1296

372





Within Groups

Total

1342

267760

269102

2

282

284

671

950

707 494


Within Groups

Total

1700

426050

427750

2

273

275

850

1561

545 581


Within Groups

Total

209

517169

517379

2

269

271

105

1923

054 947


Within Groups

Total

9361

398111

407473

2

270

272

4681

1474

3174 043


Within Groups

Total

3656

413026

416682

2

264

266

1828

1564

1168 312


Within Groups

Total

1856

456310

458167

2

267

269

928

1709

543 582


Within Groups

Total

6881

508203

515083

2

274

276

3440

1855

1855 158


Between Groups

Within Groups

Total

2647

479556

482202

2

269

271

1323

1783

742 477

Online_marketing Between Groups 1936 2 968 496 610

373


Within Groups

Total

525237

527173

269

271

1953

374



Within Groups

Total

3384

442976

446360

2

261

263

1692

1697

997 370



5 83 299 848

8 156 288 1035

Total 272 294 976


5 79 241 1138

8 150 215 1241

Total 263 224 1244


5 77 156 1313

8 149 152 1373

Total 259 154 1370


5 79 113 1295

8 149 81 1117

Total 261 97 1224

Gov_info 2 33 109 1208

5 78 146 1203

8 144 150 1268

Total 255 144 1243


375


5

8

Total

79

147

258

142

120

128

1392

1220

1297


5 79 186 1394

8 152 145 1291

Total 265 158 1354


5 79 200 1301

8 148 172 1278

Total 260 180 1314


5 78 151 1336

8 149 139 1339

Total 260 145 1384


5 74 153 1241

8 145 130 1292

Total 252 139 1296

376


Appendix 52 Website Usage - Using Website How much value do you think your website has added to the following aspects of your business

Website Usage - Using Website How much value do you think your website has added to the following aspects of your business

Statistics

Online_ transaction

Relation_ partner New_product

Imp_ productivity

New_bus_ model

N Valid


278

44 170 200

1212 0 4

280

42 204 200

1159 0 4

277

45 193 200

1173 0 4

278

44 203 200

1144 0 4

274

48 180 200

1191 0 4

Stream_ business New_market

Prompt_ response


N Valid


276

46 191 200

1187 0 4

278

44 182 200

1173 0 4

273

49 178 200

1168 0 4

275

47 180 200

1128 0 4

278

44 211 200

1127 0 4

377


Imp_customer service Inc_sales


N Valid


278

44 209 200

1130 0 4

282

40 187 200

1161 0 4

274

48 204 200

1149 0 4

378


Appendix 53 Website Usage - Using Website How much value do you think your website has added to the following aspects of your business ndash ANOVA

Website Usage - Using Website How much value do you think your website has added to the following aspects of your business ndash ANOVA

(We have excluded Sectors 702 and 802 as these had a sample size lt10)



Online_transaction Between Groups

Within Groups

Total

16346

385902

402248

10

263

273

1635

1467

1114 352

Relation_partner Between Groups

Within Groups

Total

10744

357963

368707

10

265

275

1074

1351

795 633


Within Groups

Total

15345

358040

373385

10

262

272

1535

1367

1123 345

Imp_productivity Between Groups

Within Groups

Total

9570

348299

357869

10

264

274

957

1319

725 700

New_business_model Between Groups

Within Groups

Total

13436

365804

379240

10

260

270

1344

1407

955 483

Stream_business Between Groups

Within Groups

Total

12490

369025

381515

10

261

271

1249

1414

883 549

New_market Between Groups

Within Groups

17469

358122

10

263

1747

1362

1283 240

379


Total 375591 273


Within Groups

Total

18549

347558

366107

10

259

269

1855

1342

1382 188

380


Network_opportunity Between Groups 24091 10 2409 1963 038


Total 343240 270

Efficiency Between Groups 5791 10 579 448 921


Total 345493 273

Imp_customer_service Between Groups 8429 10 843 655 766


Total 347069 273



Total 373338 277


Between Groups

Within Groups

Total

11452

342014

353467

10

259

269

1145

1321

867 565


Online_transaction 301

501

34

9

191

211

1055

1269

601

602

603

604

701

801

51

19

27

38

9

13

151

179

152

166

111

185

1302

1134

1282

1214

1167

1214

803

Total

66

266

167

167

1232

1215

381


Relation_partner 301 34 232 1065

501 9 189 1364

601 54 194 1054

602 18 217 1200

603 27 204 1091

604 38 189 1290

701 9 133 1414

801 13 200 1291

803 66 206 1162

Total 268 202 1163

New_product 301 33 212 1166

501 10 160 1265

601 50 188 1100

602 17 188 1166

603 27 185 1099

604 38 216 1151

701 10 110 1449

801 13 215 1144

803 67 184 1201

Total 265 191 1171

Imp_productivity 301 33 227 911

501 9 211 1054

601 53 189 1068

602 18 200 1138

603 27 181 1331

604 38 195 1229

701 9 156 1509

801 13 223 1092

803 67 212 1148

382


Total 267 201 1140

New_business_model 301 33 209 980

501 9 178 1302

601 52 179 1210

602 17 171 1160

603 27 159 1248

604 37 184 1280

701 9 100 1323

801 13 200 1080

803 66 180 1126

Total 263 179 1174

Stream_business 301 33 233 957

501 9 178 1302

601 53 179 1116

602 17 171 1160

603 27 170 1325

604 37 203 1213

701 9 167 1581

801 13 192 1115

803 66 188 1183

Total 264 190 1176

New_market 301 33 218 1158

501 9 178 1302

601 53 198 1118

602 18 172 1227

603 27 181 1210

604 37 173 1194

701 9 89 1269

801 13 177 1013

383


803

Total

67

266

173

182

1162

1174


501 9 156 1236

601 52 185 1127

602 17 182 1074

603 27 163 1149

604 37 173 1217

701 9 89 1269

801 12 192 1084

803 66 168 1205

Total 262 178 1170


501 9 156 1236

601 52 190 1125

602 17 176 1091

603 27 170 1137

604 37 170 1151

701 9 67 1323

801 13 215 987

803 65 169 1131

Total 263 180 1136


501 9 222 972

601 54 209 1014

602 18 222 1060

603 27 200 1301

604 37 208 1187

701 9 156 1509

384


801

803

Total

13

65

266

200

214

212

1080

1197

1125

Imp_customer_service 301 33 227 977

501 9 178 1093

601 53 213 1001

602 18 206 1056

603 27 185 1262

604 37 205 1177

701 9 167 1500

801 13 215 1068

803 67 216 1201

Total 266 209 1124

Inc_sales 301 33 209 1042

501 9 156 1509

601 54 202 1037

602 19 184 1068

603 28 157 1136

604 37 197 1166

701 10 110 1370

801 13 185 1214

803 66 180 1193

Total 269 185 1149


501 9 144 1333

601 51 210 1005

602 18 200 1085

603 27 185 1134

604 36 192 1228

385


701 9 189 1364

801 13 238 961

803 67 203 1231

Total 262 204 1141




Within Groups

Total

785

405834

406619

2

275

277

392

1476

266 767


Within Groups

Total

5541

369026

374568

2

277

279

2771

1332

2080 127


Within Groups

Total

344

379353

379697

2

274

276

172

1384

124 883


Within Groups

Total

5741

357083

362824

2

275

277

2870

1298

2211 112


Within Groups

Total

1776

385582

387358

2

271

273

888

1423

624 537


Within Groups

Total

9991

377560

387551

2

273

275

4995

1383

3612 028

New_market Between Groups 1705 2 853 618 540

386



Total 381007 277

Prompt_response Between Groups 4505 2 2253 1660 192


Total 370813 272



Total 348596 274



Total 351543 277



Total 353928 277



Total 378879 281


Between Groups

Within Groups

Total

6348

354210

360558

2

271

273

3174

1307

2429 090



25

75

Total

180

79

7

266

165

167

200

167

1253

1140

1155

1215


387


25

75

Total

80

7

268

222

229

202

1091

951

1163

New_product 5 179 191 1184

25 79 190 1172

75 7 214 900

Total 265 191 1171


25 80 221 1002

75 7 229 951

Total 267 201 1140


25 79 180 1018

75 7 229 951

Total 263 179 1174


25 80 216 1084

75 7 229 951

Total 264 190 1176

New_market 5 180 180 1179

25 79 182 1185

75 7 229 951

Total 266 182 1174


25 79 190 1150

75 7 229 951

Total 262 178 1170

Network_opportunity 5

25

177

79

173

191

1151

1112

388


75

Total

7

263

229

180

951

1136


25 80 231 1063

75 7 229 951

Total 266 212 1125


25 80 230 1036

75 7 229 951

Total 266 209 1124

Inc_sales 5 182 183 1175

25 80 188 1107

75 7 214 1069

Total 269 185 1149


25 77 225 1041

75 7 229 951

Total 262 204 1141

389





Within Groups

Total

1201

401905

403105

2

272

274

600

1478

406 667


Within Groups

Total

344

371425

371769

2

274

276

172

1356

127 881


Within Groups

Total

793

375441

376234

2

271

273

396

1385

286 751


Within Groups

Total

058

359884

359942

2

272

274

029

1323

022 978


Within Groups

Total

736

383735

384471

2

269

271

368

1427

258 773


Within Groups

Total

257

384882

385139

2

271

273

128

1420

090 914


Within Groups

Total

2530

375673

378203

2

273

275

1265

1376

919 400


Within Groups

Total

1771

366045

367815

2

268

270

885

1366

648 524


390


Within Groups

Total

344439

345678

270

272

1276

391




Total 349953 275



Total 352246 275



Total 376286 279


Between Groups

Within Groups

Total

4955

353747

358702

2

269

271

2478

1315

1884 154

392


Appendix 54 Correlation of VA3 and ICTU 3

(We have excluded Sectors 301 501 701 702 801 802 and clustered sectors 602 603 and 604 due to the small sample size)

Sector = 601

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

16

061

824

16


Sig (2-tailed)

N

061

824

16

1

16

Sector = 803

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

23

224

303

23


Sig (2-tailed)

N

224

303

23

1

23

393


Sector = Cluster sectors 602 603 and 604

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

17

-033

901

17


Sig (2-tailed)

N

-033

901

17

1

17


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

39

096

563

39


Sig (2-tailed)

N

096

563

39

1

39

394



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

17

-055

833

17


Sig (2-tailed)

N

-055

833

17

1

17


No cases in data set


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

7

316

490

7


Sig (2-tailed)

N

316

490

7

1

7

395


396



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

22

-276

214

22


Sig (2-tailed)

N

-276

214

22

1

22


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

27

176

380

27


Sig (2-tailed)

N

176

380

27

1

27

397


Appendix 55 Integration of the Website - Is your firms websitewebpage integrated with the firmrsquos database

Website Integration Is your firms websitewebpage integrated with the firmrsquos database

Web_inte_firm_DB

N Valid 304

Missing 18

Code 0-gt Not integrated 1-gt Integrated

Frequency Table

Web_inte_firm_DB

Frequency Percent Valid Percent Cumulative

Percent

Valid

Missing Total

0

1

Total System

237

67

304 18

322

736

208

944 56

1000

780

220

1000

780

1000

398


Appendix 56 Integration of the Website - Is your firms websitewebpage integrated with your business partnersrsquo database

Website Integration Is your firms websitewebpage integrated with your business partnersrsquo database

Web_inte_Partner_DB

N Valid 303

Missing 19


Frequency Table

Web_inte_Partner_DB


Percent

Valid

Missing Total

0

1

Total System

256

47

303 19

322

795

146

941 59

1000

845

155

1000

845

1000

399



Overall ICT Usage Overall to what extent do you feel that your firm currently uses ICT

(For records of PC user and ICTU_1 gt 0 excluding Sectors 605 and 702)

Statistics

Sector No_of_employee Business_

year

N Valid

Missing

591

0

591

0

584

7

OverAll_ICT

N Valid 573

Missing 18 Mean 268 Median 300 Std Deviation 1003 Minimum 0 Maximum 4

400


OverAll_ICT


Percent Cumulative

Percent

Valid 0

1

2

3

4


10

68

143

224

128

573 18

591

17

115

242

379

217

970 30

1000

17

119

250

391

223

1000

17

136

386

777

1000

401


Appendix 58 Overall Value Added ICT Usage - Overall to what extent do you feel that using more ICT would increase the value added to your firm

Overall Value AddedICT Usage Overall to what extent do you feel that using more ICT would increase the value added to your firm


Statistics


N Valid

Missing

591

0

591

0

584

7

OverAll_VA

N Valid 573


402


OverAll_VA


Percent Cumulative

Percent

Valid 0

1

2

3

4


10

49

165

148

201

573 18

591

17

83

279

250

340

970 30

1000

17

86

288

258

351

1000

17

103

391

649

1000

403


Appendix 59 Overall ICT UsageValue Added - Overall to what extent do you feel that your firm currently uses ICT Overall to what extent do you feel that using

more ICT would increase the value added to your firm ndash ANOVA

(Sector 702 has been excluded from this analysis as it had a sample size lt10)

591 ANOVA ndash by industry


Overall_ICT_usage Between Groups

Within Groups

Total

77972

933307

1011279

10

716

726

7797

1304

5982 000

Over_ICT_increase_ Value_added

Between Groups

Within Groups

Total

81024

1015674

1096697

10

719

729

8102

1413

5736 000


Overall_ICT_usage 301

501

601

72

63

162

261

230

209

1095

1328

1282

602

603

604

605

701

801

53

60

94

11

30

33

249

280

266

136

223

294

1103

1102

1063

809

1073

827

802

803

17

132

188

278

1453

1029

404


Total 727 247 1180

405



301

501

72

63

283

251

1245

1306

601 164 227 1259

602 53 279 1183

603 60 293 1163

604 94 283 1104

605 12 167 1231

701 30 237 1098

801 33 303 1015

802 17 182 1468

803 132 298 1095

Total 730 266 1227

592 ANOVA ndash number of employees



Within Groups

Total

40199

982198

1022398

2

729

731

20100

1347

14918 000


Between Groups

Within Groups

Total

46467

1055756

1102223

2

732

734

23234

1442

16109 000


Overall_ICT_usage 5

25

567

158

235

292

1224

896

406


75 7 243 1134

Total 732 247 1183

407


Over_ICT_increase_ 5 570 252 1255 Value_added 25 158 313 982

75 7 300 1155

Total 735 265 1225

408



593 ANOVA ndash by Business_year



Within Groups

Total

7818

1000033

1007851

2

720

722

3909

1389

2814 061


Between Groups

Within Groups

Total

6042

1083392

1089434

2

723

725

3021

1498

2016 134

Overall_ICT_usage 2

5

8

Total

100

181

442

723

251

263

239

246

1219

1081

1207

1181


2

5

8

Total

100

181

445

726

258

281

260

265

1216

1141

1258

1226

409


Appendix 60 Overall ICT UsageValue Added ndash Regression Analysis

(For PC Users excluding sectors 605 and 702 which have a sample size lt10)

Sector = 301 Overall_VA = f (Overall_ICT_Usage)

Model Summary

Model R R Square Adjusted R

Square Std Error of the Estimate

1 767a 589 582 707 a Predictors (Constant) OverAll_ICT_Usage

ANOVAb

Model Sum of Squares df Mean Square F Sig

1 Regression

Residual

Total

42233

29504

71738

1

59

60

42233

500

84455 000a

a Predictors (Constant) OverAll_ICT_Usage b Dependent Variable OverAll_VA

Coefficientsa

Model

Unstandardised Coefficients

Standardised Coefficients

t SigB Std Error Beta

1 (Constant) 480 296 1623 110

410


OverAll_ICT_Usa ge

922 100 767 9190 000

a Dependent Variable OverAll_VA

411


Sector = 301 Revenue = f (Overall_ICT_Usage)

Model Summary



1 073a 005 -024 1315E8 a Predictors (Constant) OverAll_ICT_Usage

ANOVAb


1 Regression

Residual

Total

3171E15

5881E17

5912E17

1

34

35

3171E15

1730E16

183 671a

a Predictors (Constant) OverAll_ICT_Usage b Dependent Variable Revenue

Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

5598E6

9770E6

6587E7

2282E7 073

085

428

933

671

a Dependent Variable Revenue

412


413



Model Summary




ANOVAb


1 Regression

Residual

Total

35148

18403

53551

1

47

48

35148

392

89765 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

829

741

220

078 810

3767

9474

000

000


414



Model Summary




ANOVAb


1 Regression

Residual

Total

3472E13

3988E14

4336E14

1

23

24

3472E13

1734E13

2002 170a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

373696884

1109E6

2143E6

783606583 283

174

1415

863

170


415



Model Summary




ANOVAb


1 Regression

Residual

Total

81668

40369

122037

1

107

108

81668

377

216463 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

534

821

153

056 818

3503

14713

001

000


416



Model Summary




ANOVAb


1 Regression

Residual

Total

2947E15

9801E17

9831E17

1

65

66

2947E15

1508E16

195 660a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

4712E6

5991E6

3826E7

1355E7 055

123

442

902

660


417



Model Summary




ANOVAb


1 Regression

Residual

Total

27336

11069

38405

1

40

41

27336

277

98786 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

566

876

247

088 844

2297

9939

027

000


418



Model Summary




ANOVAb


1 Regression

Residual

Total

5802E14

1663E16

1721E16

1

21

22

5802E14

7917E14

733 402a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

3463E6

5273E6

1761E7

6159E6 184

197

856

846

402


419


420



Model Summary




ANOVAb


1 Regression

Residual

Total

38692

25234

63926

1

52

53

38692

485

79732 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

650

811

276

091 778

2356

8929

022

000


421



Model Summary



1 226a 051 011 1961E8 a Predictors (Constant) OverAll_ICT_Usage

ANOVAb


1 Regression

Residual

Total

4956E16

9225E17

9720E17

1

24

25

4956E16

3844E16

1289 267a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-5639E7

4021E7

1091E8

3541E7 226

-517

1136

610

267


422


423



Model Summary




ANOVAb


1 Regression

Residual

Total

36967

44421

81387

1

78

79

36967

569

64911 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

904

707

249

088 674

3639

8057

000

000


424



Model Summary




ANOVAb


1 Regression

Residual

Total

2849E15

1954E16

2239E16

1

31

32

2849E15

6303E14

4520 042a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1051E7

1004E7

1266E7

4724E6 357

-830

2126

413

042


425



Model Summary




ANOVAb


1 Regression

Residual

Total

6746

15254

22000

1

23

24

6746

663

10172 004a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1188

572

413

179 554

2873

3189

009

004


426



Model Summary




ANOVAb


1 Regression

Residual

Total

1876E14

3349E15

3536E15

1

13

14

1876E14

2576E14

728 409a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1563E7

-4142E6

1026E7

4854E6 -230

1524

-853

152

409


427


428



Model Summary




ANOVAb


1 Regression

Residual

Total

17162

10709

27871

1

29

30

17162

369

46474 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

080

1006

451

148 785

177

6817

861

000


429



Model Summary



1 232a 054 -002 1529228761 a Predictors (Constant) OverAll_ICT_Usage

ANOVAb


1 Regression

Residual

Total

2256E12

3976E13

4201E13

1

17

18

2256E12

2339E12

965 340a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-142204397

437459701

1336E6

445374398 232

-106

982

916

340


430


431



Model Summary




ANOVAb


1 Regression

Residual

Total

17210

890

18100

1

8

9

17210

111

154652 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-159

1024

224

082 975

-707

12436

499

000


432



Model Summary




ANOVAb


1 Regression

Residual

Total

1073E11

3180E11

4253E11

1

1

2

1073E11

3180E11

338 665a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-201253500

401253500

1196E6

690649197 502

-168

581

894

665


433



Model Summary




ANOVAb


1 Regression

Residual

Total

49230

64628

113857

1

110

111

49230

588

83792 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1021

707

232

077 658

4408

9154

000

000


434



Model Summary




ANOVAb


1 Regression

Residual

Total

2729E15

8560E16

8833E16

1

66

67

2729E15

1297E15

2104 152a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1306E7

6948E6

1415E7

4791E6 176

-923

1450

359

152


435


436


Number of Employee Less than 10 people Overall_VA = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

288679

209092

497771

1

435

436

288679

481

600574 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

689

791

091

032 762

7584

24507

000

000


437


Number of Employee Less than 10 people Revenue = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

1903E15

1171E17

1190E17

1

241

242

1903E15

4858E14

3917 049a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1660E6

2747E6

3814E6

1388E6 126

-435

1979

664

049


438


Number of Employee 10-49 people Overall_VA = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

64204

55486

119690

1

127

128

64204

437

146955 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

726

813

204

067 732

3552

12122

001

000


439


Number of Employee 10-49 people Revenue = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

1875E16

2439E18

2457E18

1

65

66

1875E16

3752E16

500 482a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

5313E6

1918E7

8514E7

2713E7 087

062

707

950

482


440



Model Summary




ANOVAb


1 Regression

Residual

Total

4667

3333

8000

1

5

6

4667

667

7000 046a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1111

778

778

294 764

1429

2646

212

046


441



Model Summary




ANOVAb


1 Regression

Residual

Total

5762E14

1377E15

1953E15

1

3

4

5762E14

4588E14

1256 344a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1025E7

1053E7

2623E7

9394E6 543

-391

1121

722

344


442


443


Business Year (age) Less than 3 years Overall_VA = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

57734

24754

82487

1

78

79

57734

317

181922 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

480

845

181

063 837

2660

13488

009

000


444


Business Year (age) Less than 3 years Revenue = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

2182E11

1793E14

1795E14

1

39

40

2182E11

4598E12

047 829a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1716E6

-75827787

960185400

348072513 -035

1787

-218

082

829


445


Business Year (age) 3-7 years Overall_VA = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

65414

85419

150833

1

148

149

65414

577

113338 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

865

742

207

070 659

4177

10646

000

000


446


Business Year (age) 3-7 years Revenue = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

2427E13

6154E17

6154E17

1

79

80

2427E13

7790E15

003 956a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1466E7

632330104

3470E7

1133E7 006

422

056

674

956


447


448


Business Year (age) More than 7 years Overall_VA = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

235966

156031

391997

1

334

335

235966

467

505110 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

684

809

101

036 776

6791

22475

000

000


449


Business Year (age) More than 7 years Revenue = f (Overall_ICT_Usage)

Model Summary




ANOVAb


1 Regression

Residual

Total

4788E16

2075E18

2123E18

1

188

189

4788E16

1104E16

4338 039a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1675E7

1527E7

1999E7

7330E6 150

-838

2083

403

039


450


Appendix 61 Overall ICT UsageValue Added ndash Revenue Regression Analysis ndash Composite Indices

Revenue Regression Analysis ndash Composite Indices

ICTU1 Revenue Regression

Excluding Sectors 605 amp 702

Sector = 301 Revenue = f (ICTU_1)

Model Summary



1 103a 011 -017 1277E8 a Predictors (Constant) ICTU_1

ANOVAb


1 Regression

Residual

Total

6244E15

5866E17

5929E17

1

36

37

6244E15

1629E16

383 540a

a Predictors (Constant) ICTU_1 b Dependent Variable Revenue

Coefficientsa

Model




451


1 (Constant)

ICTU_1

-5083E6

1619E6

6134E7

2615E6 103

-083

619

934

540 a Dependent Variable Revenue


Model Summary



1 082a 007 -035 4242328265 a Predictors (Constant) ICTU_1

ANOVAb


1 Regression

Residual

Total

2926E12

4319E14

4349E14

1

24

25

2926E12

1800E13

163 690a


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E6

39812145

2195E6

98729675 082

1049

403

304

690

452


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E6

39812145

2195E6

98729675 082

1049

403

304


453



Model Summary



1 320a 103 090 1132E8 a Predictors (Constant) ICTU_1

ANOVAb


1 Regression

Residual

Total

1011E17

8848E17

9858E17

1

69

70

1011E17

1282E16

7882 006a


Coefficientsa

Model




1 (Constant)

ICTU_1

-7495E7

4629E6

3660E7

1649E6 320

-2048

2808

044


454



Model Summary




ANOVAb


1 Regression

Residual

Total

1496E15

1598E16

1747E16

1

22

23

1496E15

7262E14

2061 165a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1402E7

1355E6

2229E7

944159291 293

-629

1435

536


455



Model Summary




ANOVAb


1 Regression

Residual

Total

1080E16

9612E17

9720E17

1

24

25

1080E16

4005E16

270 608a


Coefficientsa

Model




1 (Constant)

ICTU_1

-5220E6

2500E6

1308E8

4815E6 105

-040

519

969


456



Model Summary




ANOVAb


1 Regression

Residual

Total

3448E15

1894E16

2239E16

1

31

32

3448E15

6109E14

5643 024a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2625E7

1646E6

1779E7

692834279 392

-1476

2376

150


457



Model Summary




ANOVAb


1 Regression

Residual

Total

4180E14

3136E15

3554E15

1

14

15

4180E14

2240E14

1866 193a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1260E7

1055E6

1546E7

771942283 343

-815

1366

429


458



Model Summary




ANOVAb


1 Regression

Residual

Total

1021E14

3271E17

3272E17

1

18

19

1021E14

1817E16

006 941a


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E7

316607483

1035E8

4223E6 018

223

075

826


459



Model Summary




ANOVAb


1 Regression

Residual

Total

1770E11

2483E11

4253E11

1

1

2

1770E11

2483E11

713 554a


Coefficientsa

Model




1 (Constant)

ICTU_1

796579705

-26681949

484411840

31599544 -645

1644

-844

348


460



Model Summary




ANOVAb


1 Regression

Residual

Total

3856E14

8801E16

8840E16

1

68

69

3856E14

1294E15

298 587a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1422E6

313224467

1478E7

573875768 066

-096

546

924


461


Number of Employee Less than 10 people Revenue = f (ICTU_1)

Model Summary




ANOVAb


1 Regression

Residual

Total

1842E15

1173E17

1191E17

1

249

250

1842E15

4709E14

3912 049a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2593E6

361167362

4213E6

182595787 124

-615

1978

539


462


Number of Employee 10-49 people Revenue = f (ICTU_1)

Model Summary




ANOVAb


1 Regression

Residual

Total

7807E16

2657E18

2735E18

1

69

70

7807E16

3850E16

2028 159a


Coefficientsa

Model




1 (Constant)

ICTU_1

-3433E7

4040E6

7641E7

2837E6 169

-449

1424

655


463



Revenue = f (ICTU_1)

Model Summary




ANOVAb


1 Regression

Residual

Total

5320E14

1421E15

1953E15

1

3

4

5320E14

4736E14

1123 367a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2509E7

1636E6

4099E7

1543E6 522

-612

1060

584


464


Business Year (age) Less than 3 years Revenue = f (ICTU_1)

Model Summary




ANOVAb


1 Regression

Residual

Total

7515E14

3354E17

3362E17

1

42

43

7515E14

7987E15

094 761a


Coefficientsa

Model




1 (Constant)

ICTU_1

2917E6

560292822

4179E7

1827E6 047

070

307

945


465


Business Year (age) 3-7 years Revenue = f (ICTU_1)

Model Summary




ANOVAb


1 Regression

Residual

Total

1036E15

6147E17

6157E17

1

81

82

1036E15

7589E15

137 713a


Coefficientsa

Model




1 (Constant)

ICTU_1

3437E6

518019598

3587E7

1402E6 041

096

370

924


466


Business Year (age) More than 7 years Revenue = f (ICTU_1)

Model Summary




ANOVAb


1 Regression

Residual

Total

9511E16

2032E18

2127E18

1

195

196

9511E16

1042E16

9129 003a


Coefficientsa

Model




1 (Constant)

ICTU_1

-3801E7

2698E6

2100E7

892974189 211

-1810

3021

072


467



Excluding Sectors 605 702 amp 802


Model Summary




ANOVAb


1 Regression

Residual

Total

6980E14

5912E17

5919E17

1

35

36

6980E14

1689E16

041 840a


Coefficientsa

Model




1 (Constant)

ICTU_2

4012E7

-264324127

4753E7

1300E6 -034

844

-203

404


468


Sector = 501


Model Summary




ANOVAb


1 Regression

Residual

Total

4411E12

4200E14

4244E14

1

22

23

4411E12

1909E13

231 635a


Coefficientsa

Model




1 (Constant)

ICTU_2

4148E6

-38835311

1991E6

80784609 -102

2083

-481

049


469


Sector = 601


Model Summary




ANOVAb


1 Regression

Residual

Total

5449E15

9752E17

9807E17

1

61

62

5449E15

1599E16

341 562a


Coefficientsa

Model




1 (Constant)

ICTU_2

3539E6

595025787

3537E7

1019E6 075

100

584

921


470


Sector = 602


Model Summary




ANOVAb


1 Regression

Residual

Total

4215E12

1720E16

1721E16

1

21

22

4215E12

8191E14

005 943a


Coefficientsa

Model




1 (Constant)

ICTU_2

1687E7

25515710

1273E7

355718740 016

1325

072

199


471


Sector = 603


Model Summary




ANOVAb


1 Regression

Residual

Total

6422E15

9623E17

9687E17

1

23

24

6422E15

4184E16

154 699a


Coefficientsa

Model




1 (Constant)

ICTU_2

2005E7

1125E6

1143E8

2872E6 081

175

392

862


472


Sector = 604


Model Summary




ANOVAb


1 Regression

Residual

Total

1469E14

2220E16

2235E16

1

30

31

1469E14

7400E14

198 659a


Coefficientsa

Model




1 (Constant)

ICTU_2

1012E7

145732042

1184E7

327118372 081

855

446

399


473


Sector = 701


Model Summary




ANOVAb


1 Regression

Residual

Total

1085E13

3456E15

3467E15

1

11

12

1085E13

3142E14

035 856a


Coefficientsa

Model




1 (Constant)

ICTU_2

9965E6

-89253997

9595E6

480355550 -056

1038

-186

321


474


Sector = 801


Model Summary



1 420a 176 128 1426980191 a Predictors (Constant) ICTU_2

ANOVAb


1 Regression

Residual

Total

7395E12

3462E13

4201E13

1

17

18

7395E12

2036E12

3631 074a


Coefficientsa

Model




1 (Constant)

ICTU_2

-358579784

57492686

844124906

30169605 420

-425

1906

676


475


Sector = 803


Model Summary




ANOVAb


1 Regression

Residual

Total

1206E15

8709E16

8830E16

1

65

66

1206E15

1340E15

900 346a


Coefficientsa

Model




1 (Constant)

ICTU_2

-1385E6

240581087

9483E6

253638748 117

-146

949

884


476




Model Summary




ANOVAb


1 Regression

Residual

Total

1417E15

1173E17

1187E17

1

231

232

1417E15

5078E14

2790 096a


Coefficientsa

Model




1 (Constant)

ICTU_2

978212467

157736521

3142E6

94438934 109

311

1670

756


477




Model Summary




ANOVAb


1 Regression

Residual

Total

5012E14

2448E18

2449E18

1

63

64

5012E14

3886E16

013 910a


Coefficientsa

Model




1 (Constant)

ICTU_2

5912E7

170835543

5868E7

1504E6 014

1007

114

318


478




Model Summary




ANOVAb


1 Regression

Residual

Total

1191E14

1834E15

1953E15

1

3

4

1191E14

6112E14

195 689a


Coefficientsa

Model




1 (Constant)

ICTU_2

6928E6

286292320

2560E7

648542329 247

271

441

804


479




Model Summary




ANOVAb


1 Regression

Residual

Total

4080E12

1714E14

1754E14

1

37

38

4080E12

4631E12

881 354a


Coefficientsa

Model




1 (Constant)

ICTU_2

950276580

19302217

695194361

20565148 152

1367

939

180


480




Model Summary




ANOVAb


1 Regression

Residual

Total

8055E14

6145E17

6153E17

1

79

80

8055E14

7778E15

104 748a


Coefficientsa

Model




1 (Constant)

ICTU_2

2268E7

-175766626

2131E7

546168623 -036

1064

-322

290


481




Model Summary




ANOVAb


1 Regression

Residual

Total

3542E16

2082E18

2118E18

1

178

179

3542E16

1170E16

3028 084a


Coefficientsa

Model




1 (Constant)

ICTU_2

-5476E6

975119286

1824E7

560375624 129

-300

1740

764


482



(Due to the sample size we have excluded Sectors 301 501 701 702 801 802 and clustered sectors 602 603 and 604)


Model Summary




ANOVAb


1 Regression

Residual

Total

3128E16

8480E17

8793E17

1

7

8

3128E16

1211E17

258 627a


Coefficientsa

Model




1 (Constant)

ICTU_3

2894E8

-2836E7

3603E8

5581E7 -189

803

-508

448


483


484



Model Summary




ANOVAb


1 Regression

Residual

Total

1126E13

5714E14

5827E14

1

13

14

1126E13

4395E13

256 621a


Coefficientsa

Model




1 (Constant)

ICTU_3

6593E6

-391767855

5193E6

774196262 -139

1269

-506

227


485


Sector = Cluster sectors 602 603 and 604 Revenue = f (ICTU_3)

Model Summary




ANOVAb


1 Regression

Residual

Total

5671E16

7964E17

8531E17

1

5

6

5671E16

1593E17

356 577a


Coefficientsa

Model




1 (Constant)

ICTU_3

4010E8

-3985E7

4551E8

6678E7 -258

881

-597

418


486



Model Summary




ANOVAb


1 Regression

Residual

Total

2133E11

7875E13

7896E13

1

18

19

2133E11

4375E12

049 828a


Coefficientsa

Model




1 (Constant)

ICTU_3

2242E6

-47696427

1429E6

216023611 -052

1569

-221

134


487



Model Summary




ANOVAb


1 Regression

Residual

Total

1382E17

1469E18

1607E18

1

9

10

1382E17

1632E17

847 381a


Coefficientsa

Model




1 (Constant)

ICTU_3

5096E8

-5034E7

3688E8

5471E7 -293

1382

-920

200


488



NO such samples


Not enough samples In this group only 1 sample has revenue data


Model Summary




ANOVAb


1 Regression

Residual

Total

8977E12

8105E13

9003E13

1

10

11

8977E12

8105E12

1108 317a


Coefficientsa

Model Unstandardised

Coefficients Standardised Coefficients t Sig

489


B Std Error Beta

1 (Constant)

ICTU_3

4770E6

-399494044

2511E6

379586332 -316

1900

-1052

087



Model Summary




ANOVAb


1 Regression

Residual

Total

8555E16

1673E18

1758E18

1

16

17

8555E16

1045E17

818 379a


Coefficientsa

Model




1 (Constant)

ICTU_3

3129E8

-3078E7

2304E8

3403E7 -221

1358

-905

193

379

490


Coefficientsa

Model




1 (Constant)

ICTU_3

3129E8

-3078E7

2304E8

3403E7 -221

1358

-905

193


491


Appendix 62 Stages of ICT Development ndash Differences in Government Support

(We have excluded Sector 702 and firms with more than 50 employees as the sample size lt10)

621 ANOVA Difference related to Government support Holding training workshops By Age (Business year)

Between-Subjects Factors

N

Stage 01_No-PC 70

02_No-Internet 39

Business_year

03_No-Web

2

5

8

285

60

85

249

Tests of Between-Subjects Effects

Dependent VariableHolding_workshop

Source

Type III Sum of

Squares df Mean Square F Sig

Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

6608a

373834

1085

947

4650

683615

1816000

8

1

2

2

4

385

394

826

373834

543

473

1162

1776

465

210537

306

267

655

880

000

737

766

624

492




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

6608a

373834

1085

947

4650

683615

1816000

690223

8

1

2

2

4

385

394

393

826

373834

543

473

1162

1776

465

210537

306

267

655

880

000

737

766

624

a R Squared = 010 (Adjusted R Squared = -011)

622 ANOVA Difference related to Government support Holding training workshops By Number of Employee


N

Stage 01_No-PC 70

02_No-Internet 39

No_of_employee

03_No-Web

5

25

286

348

47



493


Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee

Stage No_of_employee

Error

Total

Corrected Total

9007a

168372

4432

2551

7195

686537

1832000

695544

5

1

2

1

2

389

395

394

1801

168372

2216

2551

3598

1765

1021

95402

1256

1445

2039

405

000

286

230

132

a R Squared = 013 (Adjusted R Squared = 000)

494


623 ANOVA Difference related to Government support Holding training workshops By Industry (Sector)


N

Stage 01_No-PC 69

02_No-Internet 39

03_No-Web 285

Sector 301 32

501 45

601 110

602 21

603 24

604 39

605 12

701 18

801 20

802 12

803 60



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

53712a

299521

8298

26837

32474

31

1

2

10

19

1733

299521

4149

2684

1709

978

169042

2342

1515

965

504

000

098

132

503

495


Error

Total

Corrected Total

639646

1822000

693359

361

393

392

1772

a R Squared = 077 (Adjusted R Squared = -002) 624 ANOVA Difference related to Government support

Tax Incentive By Age (Business year)


N

Stage 01_No-PC 64

02_No-Internet 36

Business_year

03_No-Web

2

5

8

283

60

84

239


Dependent VariableTax_incentives

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

49471a

475983

18892

5790

9619

752456

2618000

801927

8

1

2

2

4

374

383

382

6184

475983

9446

2895

2405

2012

3074

236582

4695

1439

1195

002

000

010

239

312


496


497


625 ANOVA Difference related to Government support Tax Incentive By Number of Employee


N

Stage 01_No-PC 64

02_No-Internet 37

No_of_employee

03_No-Web

5

25

284

337

48



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

36573a

203771

1941

2282

3361

769334

2643000

805906

5

1

2

1

2

379

385

384

7315

203771

971

2282

1680

2030

3603

100385

478

1124

828

003

000

620

290

438


498


499


626 ANOVA Difference related to Government support Tax Incentive By Industry (Sector)


N

Stage 01_No-PC 63

02_No-Internet 37

03_No-Web 283

Sector 301 31

501 45

601 103

602 21

603 24

604 40

605 12

701 18

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

107517a

252882

25296

34163

45713

697052

32

1

2

10

20

350

3360

252882

12648

3416

2286

1992

1687

126976

6351

1715

1148

013

000

002

076

299

500


Total

Corrected Total

2625000

804569

383

382


627 ANOVA Difference related to Government support Provide more information By Age (Business year)


N

Stage 01_No-PC 62

02_No-Internet 39

Business_year

03_No-Web

2

5

8

281

59

83

240


Dependent VariableProvide_more_info

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

15418a

475509

4627

2460

3020

696571

2232000

711990

8

1

2

2

4

373

382

381

1927

475509

2313

1230

755

1867

1032

254625

1239

659

404

411

000

291

518

806

501




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

15418a

475509

4627

2460

3020

696571

2232000

8

1

2

2

4

373

382

1927

475509

2313

1230

755

1867

1032

254625

1239

659

404

411

000

291

518

806


502


628 ANOVA Difference related to Government support Provide more information By Number of Employee


N

Stage 01_No-PC 62

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

336

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

13636a

210249

1154

3717

7353

702364

2248000

716000

5

1

2

1

2

377

383

382

2727

210249

577

3717

3676

1863

1464

112853

310

1995

1973

201

000

734

159

140


503


504


629 ANOVA Difference related to Government support Provide more information By Industry (Sector)


N

Stage 01_No-PC 61

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 44

601 102

602 21

603 24

604 39

605 12

701 18

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

70928a

318217

7431

29448

41008

31

1

2

10

19

2288

318217

3716

2945

2158

1242

172699

2017

1598

1171

180

000

135

105

280

505


Error

Total

Corrected Total

643072

2238000

714000

349

381

380

1843

a R Squared = 099 (Adjusted R Squared = 019) 6210 ANOVA Difference related to Government support

Provide consulting By Age (Business year)


N

Stage 01_No-PC 64

02_No-Internet 39

Business_year

03_No-Web

2

5

8

282

59

85

241


Dependent VariableProvide_consulting

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

26547a

622144

10971

4166

8061

736897

2743000

763444

8

1

2

2

4

376

385

384

3318

622144

5485

2083

2015

1960

1693

317448

2799

1063

1028

098

000

062

347

392

506




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

26547a

622144

10971

4166

8061

736897

2743000

8

1

2

2

4

376

385

3318

622144

5485

2083

2015

1960

1693

317448

2799

1063

1028

098

000

062

347

392


507


6211 ANOVA Difference related to Government support Provide consulting By Number of Employee


N

Stage 01_No-PC 64

02_No-Internet 39

No_of_employee

03_No-Web

5

25

283

339

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

16316a

252086

1488

2499

5964

750122

2759000

766438

5

1

2

1

2

380

386

385

3263

252086

744

2499

2982

1974

1653

127703

377

1266

1511

145

000

686

261

222


508


6212 ANOVA Difference related to Government support Provide consulting By Industry (Sector)


N

Stage 01_No-PC 63

02_No-Internet 39

03_No-Web 282

Sector 301 32

501 43

601 107

602 21

603 24

604 39

605 12

701 17

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

59945a

377304

8302

23104

24452

31

1

2

10

19

1934

377304

4151

2310

1287

968

188931

2079

1157

644

519

000

127

319

871

509


Error

Total

Corrected Total

702962

2734000

762906

352

384

383

1997


Subsidising Training By Age (Business year)


N

Stage 01_No-PC 65

02_No-Internet 39

Business_year

03_No-Web

2

5

8

280

60

86

238


Dependent VariableSubsidise_training

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

30394a

585613

18945

1291

11990

777895

2793000

808289

8

1

2

2

4

375

384

383

3799

585613

9473

645

2998

2074

1831

282306

4567

311

1445

070

000

011

733

218

510




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

30394a

585613

18945

1291

11990

777895

2793000

8

1

2

2

4

375

384

3799

585613

9473

645

2998

2074

1831

282306

4567

311

1445

070

000

011

733

218


511


6214 ANOVA Difference related to Government support Subsidising Training By Number of Employee


N

Stage 01_No-PC 65

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

339

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

23900a

266361

3631

5189

8902

788991

2810000

812891

5

1

2

1

2

380

386

385

4780

266361

1816

5189

4451

2076

2302

128287

875

2499

2144

044

000

418

115

119


512


6215 ANOVA Difference related to Government support Subsidising Training By Industry (Sector)


N

Stage 01_No-PC 64

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 43

601 105

602 20

603 24

604 40

605 12

701 18

801 20

802 11

803 59



Source

Type III Sum of


Corrected Model 69991a 31 2258 1071 368

513


Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

382922

7967

23959

22688

741843

2792000

811833

1

2

10

19

352

384

383

382922

3983

2396

1194

2108

181694

1890

1137

567

000

153

333

929


514


6216 ANOVA Difference related to Government support Invest ICT Infrastructure By Age (Business year)


N

Stage 02_No-Internet 40

Business_year

03_No-Web

2

277

50

5 73

8 194


Dependent VariableInvest_ICT_infrastructure

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

8330a

243398

3708

264

128

495058

1550000

503388

5

1

1

2

2

311

317

316

1666

243398

3708

132

064

1592

1047

152905

2329

083

040

390

000

128

920

961


515


6217 ANOVA Difference related to Government support Invest ICT Infrastructure By Number of Employee


N


03_No-Web 279

No_of_employee 5 277

25 42



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

10383a

101747

086

1736

2955

498432

1567000

508815

3

1

1

1

1

315

319

318

3461

101747

086

1736

2955

1582

2187

64302

054

1097

1867

089

000

816

296

173


516


6218 ANOVA Difference related to Government support Invest ICT Infrastructure By Industry (Sector)


N


03_No-Web 278

Sector 301 27

501 37

601 71

602 19

603 23

604 40

605 8

701 16

801 18

802 9

803 50



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

25752a

186308

4354

11546

8719

481670

1558000

21

1

1

10

10

296

318

1226

186308

4354

1155

872

1627

754

114492

2676

710

536

775

000

103

715

864

517




Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

25752a

186308

4354

11546

8719

481670

1558000

507421

21

1

1

10

10

296

318

317

1226

186308

4354

1155

872

1627

754

114492

2676

710

536

775

000

103

715

864


6219 ANOVA Difference related to Government support Building legal framwork By Age (Business year)


N


Business_year

03_No-Web

2

279

51

5 72

8 193


Dependent VariableBuilding_legal_framwork

518


Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

13616a

317497

2571

3740

2431

580182

1826000

593797

5

1

1

2

2

310

316

315

2723

317497

2571

1870

1216

1872

1455

169644

1374

999

649

204

000

242

369

523


519


6220 ANOVA Difference related to Government support Building legal framwork By Number of Employee


N


03_No-Web 281


25 42



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

10041a

113248

012

1155

2449

588805

1843000

598846

3

1

1

1

1

314

318

317

3347

113248

012

1155

2449

1875

1785

60393

006

616

1306

150

000

937

433

254


520


6221 ANOVA Difference related to Government support Building legal framwork By Industry (Sector)


N


03_No-Web 280

Sector 301 27

501 38

601 69

602 19

603 23

604 40

605 8

701 16

801 19

802 8

803 50



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

43248a

209171

3253

16954

9813

554550

1834000

21

1

1

10

10

295

317

2059

209171

3253

1695

981

1880

1096

111271

1731

902

522

352

000

189

532

874

521




Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

43248a

209171

3253

16954

9813

554550

1834000

597798

21

1

1

10

10

295

317

316

2059

209171

3253

1695

981

1880

1096

111271

1731

902

522

352

000

189

532

874


Appendix 63 Stages of ICT Development ndash Reasons for Not Using ICT

(We have excluded Sector 702 as the sample size lt10)

631 ANOVA Difference related to Reasons for not use PC Web Internet Itrsquos costly By Age (Business year)


N

Stage 01_No-PC 63

02_No-Internet 39

03_No-Web 280

522


Business_year 2 61

5 86

8 235


Dependent VariableIt_costly

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

19037a

283541

13727

941

788

564429

1710000

583466

8

1

2

2

4

373

382

381

2380

283541

6863

471

197

1513

1573

187377

4536

311

130

131

000

011

733

971


632 ANOVA Difference related to Reasons for not use PC Web Internet Itrsquos costly By Number of Employee


Stage 01_No-PC

02_No-Internet

03_No-Web

No_of_employee 5

N

63

39

282

336

523



N

Stage 01_No-PC 63

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

336

48



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

21592a

95175

5790

888

1862

567593

1727000

589185

5

1

2

1

2

378

384

383

4318

95175

2895

888

931

1502

2876

63384

1928

592

620

015

000

147

442

538


524


633 ANOVA Difference related to Reasons for not use PC Web Internet Itrsquos costly By Industry (Sector)


N

Stage 01_No-PC 62

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 43

601 104

602 21

603 24

604 38

605 13

701 20

801 19

802 10

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

55936a

194583

10622

18598

17313

31

1

2

10

19

1804

194583

5311

1860

911

1186

127859

3490

1222

599

233

000

032

275

907

525


Error

Total

Corrected Total

532650

1722000

588586

350

382

381

1522


634 ANOVA Difference related to Reasons for not use PC Web Internet Itrsquos not really needed By Age (Business year)


N

Stage 01_No-PC 75

02_No-Internet 44

Business_year

03_No-Web

2

285

66

5 86

8 252


Dependent VariableIt_not_really_needed

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

12919a

443448

4968

1053

1681

699566

2090000

8

1

2

2

4

395

404

1615

443448

2484

527

420

1771

912

250387

1402

297

237

506

000

247

743

917

526




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

12919a

443448

4968

1053

1681

699566

2090000

712485

8

1

2

2

4

395

404

403

1615

443448

2484

527

420

1771

912

250387

1402

297

237

506

000

247

743

917


527


635 ANOVA Difference related to Reasons for not use PC Web Internet Itrsquos not really needed By Number of Employee


N

Stage 01_No-PC 75

02_No-Internet 45

No_of_employee

03_No-Web

5

25

286

360

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

13855a

159281

215

585

2131

702739

2091000

716594

5

1

2

1

2

400

406

405

2771

159281

107

585

1065

1757

1577

90663

061

333

606

165

000

941

564

546


528


636 Difference related to Reasons for not use PC Web Internet Itrsquos not really needed By Industry (Sector)


N

Stage 01_No-PC 74

02_No-Internet 45

03_No-Web 285

Sector 301 32

501 47

601 109

602 25

603 28

604 37

605 13

701 20

801 19

802 12

803 62



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

88358a

340340

6846

41453

47630

32

1

2

10

20

2761

340340

3423

4145

2381

1636

201644

2028

2456

1411

018

000

133

008

113

529


Error

Total

Corrected Total

626184

2081000

714542

371

404

403

1688


637 ANOVA Difference related to Reasons for not use PC Web Internet Difficulty to find trained person By Age (Business year)


N

Stage 01_No-PC 59

02_No-Internet 38

Business_year

03_No-Web

2

5

8

279

60

83

233


Dependent VariableDifficulty_find_trained_person

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

28837a

112654

23317

6498

3853

480940

1180000

8

1

2

2

4

367

376

3605

112654

11659

3249

963

1310

2751

85965

8897

2479

735

006

000

000

085

568

530




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

28837a

112654

23317

6498

3853

480940

1180000

509777

8

1

2

2

4

367

376

375

3605

112654

11659

3249

963

1310

2751

85965

8897

2479

735

006

000

000

085

568


531


638 ANOVA Difference related to Reasons for not use PC Web Internet Difficulty to find trained person By Number of Employee


N

Stage 01_No-PC 59

02_No-Internet 38

No_of_employee

03_No-Web

5

25

281

332

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

24254a

55114

4367

020

1739

492723

1197000

516976

5

1

2

1

2

372

378

377

4851

55114

2184

020

869

1325

3662

41610

1649

015

656

003

000

194

902

519


532


639 ANOVA Difference related to Reasons for not use PC Web Internet Difficulty to find trained person By Industry (Sector)


N

Stage 01_No-PC 58

02_No-Internet 38

03_No-Web 280

Sector 301 29

501 43

601 102

602 20

603 24

604 38

605 12

701 20

801 19

802 10

803 59



Source

Type III Sum of



533


Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

93164

11541

14138

13639

460232

1195000

516742

1

2

10

19

344

376

375

93164

5770

1414

718

1338

69636

4313

1057

537

000

014

395

945


6310 ANOVA Difference related to Reasons for not use PC Web Internet No one to train the person By Age (Business year)


N

Stage 01_No-PC 59

02_No-Internet 39

Business_year

03_No-Web

2

5

8

275

60

83

230


Dependent VariableNo_one_to_train

Source

Type III Sum of


Corrected Model

Intercept

Stage

21009a

129700

14700

8

1

2

2626

129700

7350

1838

90793

5145

069

000

006

534


Business_year

Stage Business_year

Error

Total

Corrected Total

9541

4555

519983

1222000

540992

2

4

364

373

372

4770

1139

1429

3339

797

037

528


535


6311 ANOVA Difference related to Reasons for not use PC Web Internet No one to train the person By Number of Employee


N

Stage 01_No-PC 59

02_No-Internet 39

No_of_employee

03_No-Web

5

25

276

328

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

12870a

63399

3221

050

1575

535119

1238000

547989

5

1

2

1

2

368

374

373

2574

63399

1610

050

788

1454

1770

43599

1107

034

542

118

000

331

854

582


536


6312 ANOVA Difference related to Reasons for not use PC Web Internet No one to train the person By Industry (Sector)


N

Stage 01_No-PC 58

02_No-Internet 39

03_No-Web 275

Sector 301 30

501 43

601 100

602 20

603 24

604 36

605 12

701 19

801 19

802 10

803 59



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

49246a

101309

7071

12409

19980

31

1

2

10

19

1589

101309

3536

1241

1052

1084

69100

2411

846

717

352

000

091

584

801

537


Error

Total

Corrected Total

498485

1236000

547731

340

372

371

1466

a R Squared = 090 (Adjusted R Squared = 007) 6313 ANOVA Difference related to Reasons for not use PC Web Internet

Difficult to maintain By Age (Business year)


N

Stage 01_No-PC 59

02_No-Internet 38

Business_year

03_No-Web

2

5

8

277

61

83

230


Dependent VariableDifficulty_to_maintain

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

27175a

244044

20555

4792

4056

590087

1740000

617262

8

1

2

2

4

365

374

373

3397

244044

10277

2396

1014

1617

2101

150954

6357

1482

627

035

000

002

229

643

538




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

27175a

244044

20555

4792

4056

590087

1740000

8

1

2

2

4

365

374

3397

244044

10277

2396

1014

1617

2101

150954

6357

1482

627

035

000

002

229

643


539


6314 ANOVA Difference related to Reasons for not use PC Web Internet Difficult to maintain By Number of Employee


N

Stage 01_No-PC 59

02_No-Internet 38

No_of_employee

03_No-Web

5

25

279

330

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

27437a

105915

7009

000

6276

595496

1757000

622934

5

1

2

1

2

370

376

375

5487

105915

3504

000

3138

1609

3410

65808

2177

000

1950

005

000

115

989

144


540


6315 ANOVA Difference related to Reasons for not use PC Web Internet Difficult to maintain By Industry (Sector)


N

Stage 01_No-PC 58

02_No-Internet 38

03_No-Web 278

Sector 301 32

501 44

601 100

602 20

603 23

604 38

605 12

701 19

801 18

802 10

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

61029a

186500

9892

17130

18195

31

1

2

10

19

1969

186500

4946

1713

958

1200

113636

3014

1044

583

219

000

050

406

918

541


Error

Total

Corrected Total

561292

1752000

622321

342

374

373

1641


Security issues By Age (Business year)


N

Stage 01_No-PC 56

02_No-Internet 38

Business_year

03_No-Web

2

5

8

278

61

83

228


Dependent VariableSecurity_issues

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

24353a

135556

14614

4588

2353

482644

1171000

506997

8

1

2

2

4

363

372

371

3044

135556

7307

2294

588

1330

2289

101952

5496

1725

442

021

000

004

180

778


542


543


6317 ANOVA Difference related to Reasons for not use PC Web Internet Security issues By Number of Employee


N

Stage 01_No-PC 56

02_No-Internet 38

No_of_employee

03_No-Web

5

25

279

327

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

20403a

64429

5056

201

1741

486707

1172000

507110

5

1

2

1

2

367

373

372

4081

64429

2528

201

871

1326

3077

48582

1906

152

657

010

000

150

697

519


544


6318 ANOVA Difference related to Reasons for not use PC Web Internet Security issues By Industry (Sector)


N

Stage 01_No-PC 55

02_No-Internet 38

03_No-Web 278

Sector 301 30

501 43

601 99

602 20

603 23

604 37

605 12

701 19

801 19

802 10

803 59



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

56882a

103804

8608

11998

14098

31

1

2

10

19

1835

103804

4304

1200

742

1404

79435

3293

918

568

079

000

038

517

928

545


Error

Total

Corrected Total

442999

1155000

499881

339

371

370

1307


Partner not use internet By Age (Business year)


N


Business_year

03_No-Web

2

277

51

5 70

8 191


Dependent VariablePartner_no_Internet

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

2229a

100623

083

1791

1474

338281

749000

340510

5

1

1

2

2

306

312

311

446

100623

083

895

737

1105

403

91021

075

810

667

846

000

784

446

514


546


547


6320 ANOVA Difference related to Reasons for not use PC Web Internet Partner not use internet By Number of Employee


N


03_No-Web 278


25 41



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

888a

27947

627

875

786

339643

750000

340530

3

1

1

1

1

309

313

312

296

27947

627

875

786

1099

269

25425

571

796

715

848

000

451

373

398


548


6321 ANOVA Difference related to Reasons for not use PC Web Internet Partner not use internet By Industry (Sector)


N


03_No-Web 277

Sector 301 25

501 37

601 70

602 20

603 22

604 37

605 8

701 17

801 18

802 7

803 51



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

21253a

77564

037

14169

10559

319256

21

1

1

10

10

290

1012

77564

037

1417

1056

1101

919

70456

033

1287

959

566

000

855

237

479

549


Total

Corrected Total

749000

340510

312

311


550


6322 ANOVA Difference related to Reasons for not use PC Web Internet Competitior not use internet By Age (Business year)


N


Business_year

03_No-Web

2

274

52

5 70

8 188


Dependent VariableCompetitor_no_Internet

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

6105a

61255

1555

4837

5451

266892

585000

272997

5

1

1

2

2

304

310

309

1221

61255

1555

2419

2725

878

1391

69772

1771

2755

3104

227

000

184

065

046


551


6323 ANOVA Difference related to Reasons for not use PC Web Internet Competitior not use internet By Number of Employee


N


03_No-Web 276


25 41



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

1235a

23700

605

264

813

271762

587000

272997

3

1

1

1

1

308

312

311

412

23700

605

264

813

882

466

26861

686

299

922

706

000

408

585

338


552


6324 ANOVA Difference related to Reasons for not use PC Web Internet Competitior not use internet By Industry (Sector)


N


03_No-Web 275

Sector 301 25

501 36

601 70

602 19

603 22

604 38

605 8

701 17

801 18

802 7

803 51



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

22259a

45274

1217

16075

6493

250738

21

1

1

10

10

289

1060

45274

1217

1608

649

868

1222

52182

1402

1853

748

231

000

237

052

679

553


Total

Corrected Total

586000

272997

311

310


Appendix 64 Stages of ICT Development ndash Differences in Value added from ICT

(We have excluded firms with more than 50 employees as the sample size lt10)

641 ANOVA PC Internet Web added value to your company Improve efficiency By Age (Business year)


N

Stage 01-Have-PC 580

02-Have-Internet 548

Business_year

03-Have-Web

2

5

8

270

200

388

810


Dependent VariableImprove_Efficiency

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

223337a

5375558

142123

3351

4425

1659075

8

1

2

2

4

1389

27917

5375558

71062

1675

1106

1194

23373

4500E3

59494

1403

926

000

000

000

246

448

554


Total

Corrected Total

10816000

1882412

1398

1397


555


642 ANOVA PC Internet Web added value to your company Improve efficiency By Number of Employee


N



No_of_employee

03-Have-Web

5

25

271

1062

348



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

223664a

6030763

158757

7832

1853

1667525

10920000

1891189

5

1

2

1

2

1404

1410

1409

44733

6030763

79378

7832

927

1188

37664

5078E3

66834

6594

780

000

000

000

010

459


556


643 ANOVA PC Internet Web added value to your company Improve efficiency By Industry (Sector)


N



03-Have-Web 267

Sector 301 133

501 100

601 268

602 98

603 128

604 194

605 12

701 55

702 20

801 74

802 21

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

284095a

2646718

105246

37420

22575

34

1

2

11

21

8356

2646718

52623

3402

1075

7145

2263E3

44998

2909

919

000

000

000

001

566

557


Error

Total

Corrected Total

1591641

10812000

1875736

1361

1396

1395

1169

a R Squared = 151 (Adjusted R Squared = 130) 644 ANOVA PC Internet Web added value to your company

Improve customer service By Age (Business year)


N



Business_year

03-Have-Web

2

5

8

270

197

384

795


Dependent VariableImprove_Customer_service

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

121263a

4682660

81653

8084

2701

1824626

9439000

1945889

8

1

2

2

4

1367

1376

1375

15158

4682660

40827

4042

675

1335

11356

3508E3

30587

3028

506

000

000

000

049

731


558


559


645 ANOVA PC Internet Web added value to your company Improve customer service By Number of Employee


N



No_of_employee

03-Have-Web

5

25

271

1045

341



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

114941a

5133309

68699

3057

4473

1841033

9530000

1955974

5

1

2

1

2

1380

1386

1385

22988

5133309

34349

3057

2236

1334

17231

3848E3

25747

2291

1676

000

000

000

130

187


560


646 ANOVA PC Internet Web added value to your company Improve customer service By Industry (Sector)


N



03-Have-Web 267

Sector 301 128

501 97

601 262

602 95

603 126

604 192

605 12

701 54

702 20

801 74

802 19

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

207310a

2288851

36719

49472

26612

34

1

2

11

21

6097

2288851

18359

4497

1267

4696

1763E3

14139

3464

976

000

000

000

000

491

561


Error

Total

Corrected Total

1736132

9421000

1943442

1337

1372

1371

1299


Increase sales By Age (Business year)


N



Business_year

03-Have-Web

2

5

8

274

196

385

804


Dependent VariableIncrease_sales

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

62067a

3042411

25575

19920

1509

2244132

6790000

2306199

8

1

2

2

4

1376

1385

1384

7758

3042411

12787

9960

377

1631

4757

1865E3

7841

6107

231

000

000

000

002

921


562


563


648 ANOVA PC Internet Web added value to your company Increase sales By Number of Employee


N



No_of_employee

03-Have-Web

5

25

275

1050

343



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

37976a

3234964

26028

1520

058

2277339

6838000

2315315

5

1

2

1

2

1387

1393

1392

7595

3234964

13014

1520

029

1642

4626

1970E3

7926

926

018

000

000

000

336

982


564


649 ANOVA PC Internet Web added value to your company Increase sales By Industry (Sector)


N



03-Have-Web 271

Sector 301 130

501 98

601 264

602 98

603 128

604 190

605 10

701 59

702 20

801 73

802 21

803 288



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

161417a

1545256

6799

72503

27371

34

1

2

11

21

4748

1545256

3400

6591

1303

2988

972701

2140

4149

820

000

000

118

000

696

565


Error

Total

Corrected Total

2135110

6789000

2296526

1344

1379

1378

1589


6410 ANOVA PC Internet Web added value to your company New opportunity or new market By Age (Business year)


N



Business_year

03-Have-Web

2

5

8

270

197

385

802


Dependent VariableNew_opportunity_or_Market

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

132003a

2947658

76657

17497

1365

2152898

6661000

8

1

2

2

4

1375

1384

16500

2947658

38329

8748

341

1566

10538

1883E3

24479

5587

218

000

000

000

004

928

566




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

132003a

2947658

76657

17497

1365

2152898

6661000

2284901

8

1

2

2

4

1375

1384

1383

16500

2947658

38329

8748

341

1566

10538

1883E3

24479

5587

218

000

000

000

004

928


567


6411 ANOVA PC Internet Web added value to your company New opportunity or new market By Number of Employee


N



No_of_employee

03-Have-Web

5

25

271

1052

342



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

115599a

3148952

73346

2438

625

2184975

6734000

2300574

5

1

2

1

2

1388

1394

1393

23120

3148952

36673

2438

313

1574

14687

2000E3

23296

1549

199

000

000

000

214

820


568


6412 ANOVA PC Internet Web added value to your company New opportunity or new market By Industry (Sector)


N



03-Have-Web 267

Sector 301 130

501 97

601 263

602 97

603 127

604 191

605 11

701 56

702 20

801 74

802 21

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

224741a

1419361

24903

64991

25831

34

1

2

11

21

6610

1419361

12451

5908

1230

4322

927981

8141

3863

804

000

000

000

000

717

569


Error

Total

Corrected Total

2057197

6685000

2281938

1345

1380

1379

1530


Competitive advantage By Age (Business year)


N



Business_year

03-Have-Web

2

5

8

266

196

378

792


Dependent VariableCompetitive_advantage

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

84969a

3963568

38906

22011

1534

2055215

8246000

2140184

8

1

2

2

4

1357

1366

1365

10621

3963568

19453

11006

384

1515

7013

2617E3

12844

7267

253

000

000

000

001

908


570


571


6414 ANOVA PC Internet Web added value to your company Competitive advantage By Number of Employee


N



No_of_employee

03-Have-Web

5

25

267

1038

337



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

71274a

4379616

39281

11189

1297

2081640

8320000

2152913

5

1

2

1

2

1369

1375

1374

14255

4379616

19641

11189

649

1521

9375

2880E3

12917

7359

427

000

000

000

007

653


572


6415 ANOVA PC Internet Web added value to your company Competitive advantage By Industry (Sector)


N



03-Have-Web 263

Sector 301 129

501 97

601 259

602 92

603 126

604 188

605 11

701 55

702 20

801 74

802 19

803 291



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

154684a

1841623

13787

47261

25314

34

1

2

11

21

4550

1841623

6893

4296

1205

3055

1237E3

4629

2885

809

000

000

010

001

711

573


Error

Total

Corrected Total

1974794

8245000

2129478

1326

1361

1360

1489


Prompt response By Age (Business year)


N

Stage 02-Have-Internet 541

Business_year

03-Have-Web

2

265

111

5 228

8 467


Dependent VariablePrompt_response

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

16054a

1594171

849

7000

4555

1132821

3619000

1148875

5

1

1

2

2

800

806

805

3211

1594171

849

3500

2278

1416

2267

1126E3

600

2472

1609

046

000

439

085

201


574


575


6417 ANOVA PC Internet Web added value to your company Prompt response By Number of Employee


N


03-Have-Web 266


25 208



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

5762a

1865755

010

5566

053

1149958

3660000

1155719

3

1

1

1

1

808

812

811

1921

1865755

010

5566

053

1423

1349

1311E3

007

3911

037

257

000

932

048

847


576


6418 ANOVA PC Internet Web added value to your company Prompt response By Industry (Sector)


N


03-Have-Web 263

Sector 301 78

501 51

601 151

602 55

603 75

604 114

605 6

701 30

702 13

801 42

802 10

803 179



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

55332a

824294

138

33284

13678

22

1

1

11

10

2515

824294

138

3026

1368

1796

588782

098

2161

977

014

000

754

015

462

577


Error

Total

Corrected Total

1093398

3625000

1148730

781

804

803

1400


578


6419 ANOVA PC Internet Web added value to your company Network opportunity By Age (Business year)


N


Business_year

03-Have-Web

2

267

112

5 229

8 470


Dependent VariableNetwork_opportunity

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

23089a

1607292

680

10716

3315

1155461

3503000

1178550

5

1

1

2

2

805

811

810

4618

1607292

680

5358

1657

1435

3217

1120E3

474

3733

1155

007

000

492

024

316


579


6420 ANOVA PC Internet Web added value to your company Network opportunity By Number of Employee


N


03-Have-Web 268


25 210



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

5415a

1768471

2856

2549

494

1180115

3547000

1185530

3

1

1

1

1

813

817

816

1805

1768471

2856

2549

494

1452

1244

1218E3

1968

1756

340

293

000

161

186

560


580


6421 ANOVA PC Internet Web added value to your company Network opportunity By Industry (Sector)


N


03-Have-Web 264

Sector 301 79

501 51

601 153

602 56

603 75

604 113

605 6

701 30

702 13

801 44

802 11

803 177



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

75477a

770714

1948

46005

14968

1096043

22

1

1

11

10

785

3431

770714

1948

4182

1497

1396

2457

551995

1395

2995

1072

000

000

238

001

381

581


Total

Corrected Total

3508000

1171520

808

807


582


6422 ANOVA PC Internet Web added value to your company New product or new service By Age (Business year)


N


Business_year

03-Have-Web

2

268

109

5 231

8 470


Dependent VariableNew_Product_or_Service

Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

29365a

1601291

9692

6798

1957

1209035

3610000

1238400

5

1

1

2

2

804

810

809

5873

1601291

9692

3399

978

1504

3906

1065E3

6445

2260

651

002

000

011

105

522


583


6423 ANOVA PC Internet Web added value to your company New product or new service By Number of Employee


N


03-Have-Web 270


25 207



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

18819a

1828429

12294

487

210

1229139

3646000

1247958

3

1

1

1

1

811

815

814

6273

1828429

12294

487

210

1516

4139

1206E3

8112

321

138

006

000

005

571

710


584


6424 ANOVA PC Internet Web added value to your company New product or new service By Industry (Sector)


N


03-Have-Web 266

Sector 301 78

501 54

601 148

602 56

603 75

604 114

605 5

701 32

702 13

801 43

802 10

803 178



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

81743a

805165

12248

40582

11201

1151761

22

1

1

11

10

783

3716

805165

12248

3689

1120

1471

2526

547374

8327

2508

761

000

000

004

004

666

585


Total

Corrected Total

3610000

1233504

806

805


586


Appendix 65 Regression between Value added and ICTU Size Age Stage and Sector

Stage 1 PC Only

[If a sample has (PC and NOT have Internet) then stage label = 1] (1) Have PC (Yes- have PC) (2) ICTU_1 gt 0 (3) Not have Internet (4) Sector (industry) is not null

VA = VA_1

ICTU = ICTU_1

Stage 2 PC + Internet

[If a sample has (PC + Internet and NOT Integrated) then stage label = 2 ] (1) Have PC (Yes- have PC) (2) ICTU_1 gt 0 (3) Have Internet (Yes- have Internet) (4) ICTU_2 gt 0 (5) Not Integrated (6) Sector (industry) is not null

VA = VA_2

ICTU = ICTU_1 + ICTU_2

Stage 3 PC + Internet + Web Integration

[If a sample has (web and Integrated) then stage label = 3 ] (1) Have Web (Yes- have Web) (2) ICTU_3 gt 0 （Website integrated with database Partnerrsquos database）(3) Sector (industry) is not null

587


VA = VA_3

ICTU = ICTU_1 + ICTU_2 + ICTU_3

VA = f (ICTU)

VA = f (ICTU Size Age Stage Sector)

Size Age Stage Sector are dummy variables

Model Summary



1 797a 636 626 626490 a Predictors (Constant) S_801 Business_year_5 S_701 Emp_5 S_501 Stage_1 S_603 S_602 S_301 Business_year_2 S_604 ICTU Stage_2 S_601

ANOVAb

Model Sum of Squares Df Mean Square F Sig

1 Regression

Residual

Total

36837412

21115941

57953353

14

538

552

2631244

39249

67040 000a

a Predictors (Constant) S_801 Business_year_5 S_701 Emp_5 S_501 Stage_1 S_603 S_602 S_301 Business_year_2 S_604 ICTU Stage_2 S_601 b Dependent Variable VA

Coefficientsa

Model



t Sig B Std Error Beta

1 (Constant) 4226 1518 2783 006

588


ICTU 294 014 669 20906 000

Emp_5 817 676 033 1209 227

Business_year_2 1421 810 049 1754 080

Business_year_5 1197 635 052 1886 060

Stage_1 -3405 1827 -064 -1864 063

Stage_2 -7323 873 -272 -8392 000

S_301 1734 1063 050 1632 103

S_501 -848 1107 -023 -766 444

S_601 009 844 000 011 992

S_602 722 1141 019 633 527

S_603 177 1050 005 169 866

S_604 -1047 924 -036 -1133 258

S_701 -2131 1463 -042 -1457 146

S_801 1686 1279 038 1318 188 a Dependent Variable VA

589


Appendix 66 Regression between Revenue and ICT Usage Size Age Stage and Sector

Revenue = f (ICTU Size Age Stage Sector)


Model Summary



1 353a 125 083 9049E7 a Predictors (Constant) S_801 Business_year_5 S_701 S_602 Emp_5 S_501 Stage_1 S_603 S_604 Business_year_2 S_301 ICTU S_601 Stage_2

ANOVAb


1 Regression

Residual

Total

3433E17

2407E18

2751E18

14

294

308

2452E16

8188E15

2995 000a

a Predictors (Constant) S_801 Business_year_5 S_701 S_602 Emp_5 S_501 Stage_1 S_603 S_604 Business_year_2 S_301 ICTU S_601 Stage_2 b Dependent Variable Revenue

Coefficientsa

Model




1 (Constant) 1063E8 3060E7 3475 001

590


ICTU -161748033 289644814 -039 -558 577

Emp_5 -4826E7 1357E7 -211 -3556 000

Business_year_2 641462352 1637E7 002 039 969

Business_year_5 -2583E6 1258E7 -012 -205 838

Stage_1 -7558E7 3547E7 -155 -2131 034

Stage_2 -6147E7 1733E7 -249 -3547 000

S_301 1788E7 2028E7 058 882 379

S_501 2019E6 2160E7 006 093 926

S_601 1563E7 1564E7 069 999 318

S_602 1513E7 2214E7 042 683 495

S_603 5664E7 2113E7 167 2681 008

S_604 1298E7 1966E7 042 660 509

S_701 2118E6 2773E7 005 076 939

S_801 -1174E7 2383E7 -030 -493 623 a Dependent Variable Revenue

591


Appendix 67 Discriminant Analysis 671 PCs

Dependent Have_PC Independent Revenue Size and Age (Size and Age are dummy variables)

Analysis Case Processing Summary

Unweighted Cases N Percent

Valid 369 502 Excluded Missing or out-of-range

2 3group codes At least one missing

363 494discriminating variable Both missing or out-of-range group codes and

1 1at least one missing discriminating variable Total 366 498

Total 735 1000

Group Statistics

Have_PC

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

40

40

40

40

40000

40000

40000

40000

1 Revenue

Emp_5

329

329

329000

329000

592


Year_2 329 329000

Year_5 329 329000

Total Revenue 369 369000

Emp_5

Year_2

Year_5

369

369

369

369000

369000

369000

Analysis

Summary of Canonical Discriminant Functions

Eigenvalues

Functi on Eigenvalue

of Variance

Cumulative Canonical Correlation

1 021a 1000 1000 142 a First 1 canonical discriminant functions were used in the analysis

Wilks Lambda

Test of Functi on(s)

Wilks Lambda Chi-square df Sig

1 980 7472 4 113

Standardised Canonical Discriminant Function Coefficients

Function

1

Revenue 238 Emp_5 -767

593


Year_2 024 Year_5 575

Structure Matrix

Function

1

Emp_5 -790 Year_5 518 Revenue 427 Year_2 -238 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function

Functions at Group Centroids

Have_PC

Function

1

0 -411 1 050 Unstandardised canonical discriminant functions evaluated at group means

Dependent Have_PC Independent Revenue Size Age and Sector (Size Age and Sector are dummy variables)




group codes 2 3

At least one missing discriminating variable

363 494

594


Both missing or out-of-range group codes and at least one missing discriminating variable Total

Total

Group Statistics

1

366 735

1

498 1000

Have_PC

Valid N (listwise)

Unweighted Weighted

0 Revenue 40 40000

Emp_5 40 40000

Year_2 40 40000

Year_5 40 40000

S_301 40 40000

S_501 40 40000

S_601 40 40000

S_602 40 40000

S_603 40 40000

S_604 40 40000

S_701 40 40000

S_801 40 40000

1 Revenue 329 329000

Emp_5 329 329000

Year_2 329 329000

Year_5 329 329000

S_301 329 329000

S_501 329 329000

595


S_601 329 329000

S_602 329 329000

S_603 329 329000

S_604 329 329000

S_701 329 329000

S_801 329 329000


Emp_5 369 369000

Year_2 369 369000

Year_5 369 369000

S_301 369 369000

S_501 369 369000

S_601 369 369000

S_602 369 369000

S_603 369 369000

S_604 369 369000

S_701 369 369000

S_801 369 369000

Analysis


Eigenvalues


of Variance

Cumulative

Canonical Correlation


596


Wilks Lambda



1 934 24456 12 018


Function

1

Revenue -102 Emp_5 446 Year_2 048 Year_5 -271 S_301 134 S_501 508 S_601 664 S_602 028 S_603 061 S_604 -157 S_701 290 S_801 -221

597


Structure Matrix

Function

1

S_601 562 Emp_5 429 S_501 379 S_801 -325 S_604 -323 Year_5 -282 Revenue -232 S_701 140 Year_2 129 S_603 -129 S_602 -117 S_301 -111 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function

598



Have_PC

Function

1

0 757 1 -092 Unstandardised canonical discriminant functions evaluated at group means

672 Internet

(1) Discriminant Analysis

Dependent Have_Internet Independent Revenue Size and Age (Size and Age are dummy variables)







Total 735 1000

Group Statistics

Have_Internet

Valid N (listwise)

Unweighted Weighted

599


0 Revenue

Emp_5

30

30

30000

30000

Year_2

Year_5

30

30

30000

30000

1 Revenue

Emp_5

Year_2

Year_5

319

319

319

319

319000

319000

319000

319000

Total Revenue

Emp_5

349

349

349000

349000

Year_2

Year_5

349

349

349000

349000

Analysis

Summary of Canonical Discriminant Functions Eigenvalues


of Variance



Wilks Lambda



1 969 10917 4 028

600



Function

1

Revenue 159 Emp_5 -648 Year_2 146 Year_5 800

601


Structure Matrix

Function

1

Year_5 719 Emp_5 -612 Revenue 308 Year_2 -145 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function


Have_Internet

Function

1



Dependent Have_ Internet Independent Revenue Size Age and Sector (Size Age and Sector are dummy variables)




group codes 22 30


344 468

602



Total

Group Statistics

20

386 735

27

525 1000

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue 30 30000

Emp_5 30 30000

Year_2 30 30000

Year_5 30 30000

S_301 30 30000

S_501 30 30000

S_601 30 30000

S_602 30 30000

S_603 30 30000

S_604 30 30000

S_701 30 30000

S_801 30 30000

1 Revenue 319 319000

Emp_5 319 319000

Year_2 319 319000

Year_5 319 319000

S_301 319 319000

S_501 319 319000

603


S_601 319 319000

S_602 319 319000

S_603 319 319000

S_604 319 319000

S_701 319 319000

S_801 319 319000


Emp_5 349 349000

Year_2 349 349000

Year_5 349 349000

S_301 349 349000

S_501 349 349000

S_601 349 349000

S_602 349 349000

S_603 349 349000

S_604 349 349000

S_701 349 349000

S_801 349 349000

Analysis


Eigenvalues


of Variance



604


Wilks Lambda



1 911 31917 12 001


Function

1

Revenue -087 Emp_5 368 Year_2 -052 Year_5 -478 S_301 -187 S_501 517 S_601 496 S_602 041 S_603 213 S_604 -245 S_701 169 S_801 -182

605


Structure Matrix

Function

1

S_501 469 S_601 418 Year_5 -412 Emp_5 350 S_301 -334 S_604 -323 S_801 -242 Revenue -176 Year_2 083 S_701 082 S_603 071 S_602 -039 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function

606



Have_Internet

Function

1



Dependent Have_ Internet Independent Revenue Size and Age ICTU 1 (Size and Age are dummy variables)







Total 735 1000

Group Statistics

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

30

30

30000

30000

607


Year_2 30 30000

Year_5 30 30000

ICTU_1 30 30000

1 Revenue

Emp_5

Year_2

Year_5

ICTU_1

319

319

319

319

319

319000

319000

319000

319000

319000

Total Revenue

Emp_5

Year_2

Year_5

349

349

349

349

349000

349000

349000

349000

ICTU_1 349 349000

Analysis


Eigenvalues


of Variance



Wilks Lambda



608


Wilks Lambda



1 733 106830 5 000


Function

1

Revenue -046 Emp_5 -016 Year_2 -004 Year_5 132 ICTU_1 982

609


Structure Matrix

Function

1

ICTU_1 990 Year_5 214 Emp_5 -182 Revenue 092 Year_2 -043 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function


Have_ Function Intern et 1





group codes 22 30


344 468

610



Total

Group Statistics

20 27

386 525 735 1000

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue 30 30000

Emp_5 30 30000

Year_2 30 30000

Year_5 30 30000

S_301 30 30000

S_501 30 30000

S_601 30 30000

S_602 30 30000

S_603 30 30000

S_604 30 30000

S_701 30 30000

S_801 30 30000

ICTU_1 30 30000

1 Revenue 319 319000

Emp_5 319 319000

Year_2 319 319000

Year_5 319 319000

S_301 319 319000

611


S_501 319 319000

S_601 319 319000

S_602 319 319000

S_603 319 319000

S_604 319 319000

S_701 319 319000

S_801 319 319000

ICTU_1 319 319000


Emp_5 349 349000

Year_2 349 349000

Year_5 349 349000

S_301 349 349000

S_501 349 349000

S_601 349 349000

S_602 349 349000

S_603 349 349000

S_604 349 349000

S_701 349 349000

S_801 349 349000

ICTU_1 349 349000

Analysis


Eigenvalues

612



of Variance

Cumulative



Wilks Lambda



1 698 122303 13 000


Function

1

Revenue -035 Emp_5 021 Year_2 010 Year_5 159 S_301 271 S_501 -133 S_601 -106 S_602 064 S_603 -130 S_604 133 S_701 030 S_801 100 ICTU_1 931

613


Structure Matrix

Function

1

ICTU_1 908 S_501 -224 S_601 -199 Year_5 196 Emp_5 -167 S_301 159 S_604 154 S_801 115 Revenue 084 Year_2 -039 S_701 -039 S_603 -034 S_602 019 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function

614



Have_Internet

Function

1


673 Website

1 Discriminant Analysis

Dependent Have_Web Independent Revenue Size and Age (Size and Age are dummy variables)







Total 735 1000

615


Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

179

179

179

179

179000

179000

179000

179000

1 Revenue

Emp_5

Year_2

Year_5

154

154

154

154

154000

154000

154000

154000

Total Revenue

Emp_5

Year_2

Year_5

333

333

333

333

333000

333000

333000

333000

Analysis


Eigenvalues


of Variance

Cumulative



616


Wilks Lambda

Test of Function(s)

Wilks Lambda

Chi- square df Sig

1 912 30480 4 000


Function

1

Revenue -291 Emp_5 884 Year_2 028 Year_5 -166

617


Structure Matrix

Function

1

Emp_5 943 Revenue -483 Year_2 249 Year_5 -117 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function


Have_ Function

Web 1



Dependent Have_Web

Independent Revenue Size Age and Sector (Size Age and Sector are dummy

variables)



Valid 333 453

618


Excluded Missing or out-of-range group codes At least one missing discriminating variable Both missing or out-of-range group codes and at least one missing discriminating variable Total

Total

Group Statistics

38 52

324 441

40 54

402 547 735 1000

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue 179 179000

Emp_5 179 179000

Year_2 179 179000

Year_5 179 179000

S_301 179 179000

S_501 179 179000

S_601 179 179000

S_602 179 179000

S_603 179 179000

S_604 179 179000

S_701 179 179000

S_801 179 179000

1 Revenue

Emp_5

Year_2

154

154

154

154000

154000

154000

619


Year_5 154 154000

S_301 154 154000

S_501 154 154000

S_601 154 154000

S_602 154 154000

S_603 154 154000

S_604 154 154000

S_701 154 154000

S_801 154 154000


Emp_5 333 333000

Year_2 333 333000

Year_5 333 333000

S_301 333 333000

S_501 333 333000

S_601 333 333000

S_602 333 333000

S_603 333 333000

S_604 333 333000

S_701 333 333000

S_801 333 333000

Analysis


Eigenvalues


of Variance

Cumulative


620



Wilks Lambda



1 878 42303 12 000


Function

1

Revenue -214 Emp_5 780 Year_2 119 Year_5 -062 S_301 181 S_501 541 S_601 170 S_602 176 S_603 -029 S_604 151 S_701 319 S_801 011

621


Structure Matrix

Function

1

Emp_5 788 S_501 433 Revenue -404 S_603 -209 Year_2 208 S_701 151 S_301 -122 Year_5 -098 S_602 034 S_601 -030 S_801 -015 S_604 010 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function

622



Have_ Function

Web 1



Dependent Have_Web Independent Revenue Size and Age ICTU 1 + ICTU 2 (Size and Age are dummy variables)







Total 735 1000

Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue 179 179000

623


Emp_5 179 179000

Year_2 179 179000

Year_5 179 179000

ICTU_1_2 179 179000

1 Revenue

Emp_5

Year_2

Year_5

ICTU_1_2

154

154

154

154

154

154000

154000

154000

154000

154000


Emp_5

Year_2

Year_5

ICTU_1_2

333

333

333

333

333000

333000

333000

333000

Analysis


Eigenvalues


of Variance

Cumulative



Wilks Lambda

624




1 653 140080 5 000


Function

1

Revenue 094 Emp_5 -305 Year_2 -090 Year_5 -153 ICTU_1_2 932

Structure Matrix

Function

1

ICTU_1_2 918 Emp_5 -403 Revenue 207 Year_2 -106 Year_5 050 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function


625


Have_ Function

Web 1


626



Dependent Have_Web Independent Revenue Size Age and Sector ICTU 1 + ICTU 2 (Size Age and Sector are dummy variables)







Total 735 1000

Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

S_301

S_501

S_601

S_602

179

179

179

179

179

179

179

179

179000

179000

179000

179000

179000

179000

179000

179000

627


S_603 179 179000

S_604 179 179000

S_701 179 179000

S_801 179 179000

ICTU_1_2 179 179000

1 Revenue 154 154000

Emp_5 154 154000

Year_2 154 154000

Year_5 154 154000

S_301 154 154000

S_501 154 154000

S_601 154 154000

S_602 154 154000

S_603 154 154000

S_604 154 154000

S_701 154 154000

S_801 154 154000

ICTU_1_2 154 154000


Emp_5 333 333000

Year_2 333 333000

Year_5 333 333000

S_301 333 333000

S_501 333 333000

S_601 333 333000

S_602 333 333000

S_603 333 333000

S_604 333 333000

628


S_701 333 333000

S_801 333 333000

ICTU_1_2 333 333000

Analysis


Eigenvalues


of Variance

Cumulative



Wilks Lambda



1 646 141824 13 000


Function

1

Revenue 088 Emp_5 -306 Year_2 -107

629


Year_5 -168 S_301 -020 S_501 -102 S_601 068 S_602 -047 S_603 007 S_604 -088 S_701 006 S_801 048 ICTU_1_2 922

Structure Matrix

Function

1

ICTU_1_2 904 Emp_5 -397 S_501 -218 Revenue 203 S_603 105 Year_2 -105 S_701 -076 S_301 061 Year_5 049 S_602 -017 S_601 015 S_801 007 S_604 -005

630


Structure Matrix

Function

1

ICTU_1_2 904 Emp_5 -397 S_501 -218 Revenue 203 S_603 105 Year_2 -105 S_701 -076 S_301 061 Year_5 049 S_602 -017 S_601 015 S_801 007 S_604 -005 Pooled within-groups correlations between discriminating variables and standardised canonical discriminant functions Variables ordered by absolute size of correlation within function


Have_ Function

Web 1


631


Appendix 68 Performance Differences between ICT Adopters and Non-Adopters

(We have excluded Sectors 605 702 and 802 and firms with more than 50 employees)

681 Revenue = f (AdoptionNon adoption of PC sector)

Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 159a 025 001 9165E7

a Predictors (Constant) S_801 S_701 Have_PC S_602 S_603

S_301 S_501 S_604 S_601

ANOVAb


1 Regression

Residual

Total

7804E16

3016E18

3094E18

9

359

368

8671E15

8400E15

1032 413a

a Predictors (Constant) S_801 S_701 Have_PC S_602 S_603 S_301 S_501 S_604

S_601

b Dependent Variable Revenue

Coefficientsa

Model


Standardised

Coefficients


1 (Constant)

Have_PC

S_301

-9156E6

1613E7

2442E7

1803E7

1572E7

1834E7

055

080

-508

1026

1332

612

306

184

632


S_501

S_601

S_602

S_603

S_604

S_701

S_801

-879281590

1254E7

9983E6

4953E7

7875E6

2231E6

2205E7

1900E7

1436E7

2050E7

2023E7

1851E7

2352E7

2259E7

-003

059

028

143

026

005

056

-046

873

487

2449

425

095

976

963

383

627

015

671

925

330


682 Revenue = f (AdoptionNon adoption of Internet sector)

Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 158a 025 -001 9425E7

a Predictors (Constant) S_801 S_701 Have_Internet S_602

S_603 S_604 S_501 S_301 S_601

ANOVAb


1 Regression

Residual

Total

7666E16

3011E18

3088E18

9

339

348

8518E15

8883E15

959 474a

a Predictors (Constant) S_801 S_701 Have_Internet S_602 S_603 S_604 S_501 S_301

S_601


Coefficientsa

633


Model


Standardised

Coefficients


1 (Constant)

Have_Internet

S_301

S_501

S_601

S_602

S_603

S_604

S_701

S_801

-1038E7

1730E7

2376E7

-455567026

1485E7

1013E7

5029E7

8050E6

2560E6

2355E7

2065E7

1857E7

1909E7

1987E7

1531E7

2112E7

2086E7

1947E7

2531E7

2371E7

052

077

-001

066

029

145

025

006

058

-503

931

1244

-023

970

480

2411

413

101

994

615

352

214

982

333

632

016

680

919

321


683 Revenue = f (AdoptionNon adoption of Web sector)

Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 211a 044 018 9031E7

a Predictors (Constant) S_801 Have_Web S_602 S_701 S_603

S_604 S_301 S_501 S_601

ANOVAb


1 Regression

Residual

1224E17

2634E18

9

323

1360E16

8156E15

1667 096a

634


Total 2757E18 332

a Predictors (Constant) S_801 Have_Web S_602 S_701 S_603 S_604 S_301 S_501

S_601


Coefficientsa

Model


Standardised

Coefficients


1 (Constant)

Have_Web

S_301

S_501

S_601

S_602

S_603

S_604

S_701

S_801

-6584E6

2489E7

2518E7

3954E6

1386E7

1190E7

4924E7

9991E6

5872E6

-4081E6

1184E7

1011E7

1879E7

1942E7

1496E7

2098E7

2040E7

1862E7

2570E7

2330E7

136

084

013

063

034

148

034

013

-010

-556

2462

1340

204

927

567

2414

537

228

-175

579

014

181

839

355

571

016

592

819

861


635


Endnotes

Certain parts of this document are not disclosed since they contain commercially sensitive information and are related to one or more of the following reasons

(1) Information held for or provided by a third party under an explicit or implicit understanding that it would not be further disclosed

(2) Information including commercial financial scientific or technical confidences trade secrets or intellectual property whose disclosure would harm the competitive or financial position of any person

636


Table of Content

EXECUTIVE SUMMARY 1

1 INTRODUCTION 11

2 BACKGROUND 12

3 ASSESSING ICT ADOPTION IN HONG KONG SMES 14

31 The annual survey conducted by the CampSD in 2008 14 32 Additional literature review of ICT adoption by Hong Kong SMEs 17

4 RESULTS LITERATURE REVIEW OF ICT ADOPTION BY SMES IN OTHER COUNTRIES 20

5 CONCEPTUAL FRAMEWORK 27

6 OVERVIEW OF THE RESEARCH METHODOLOGY AND HYPOTHESES 33

7 ANALYSIS amp RESULTS BASED ON THE CampSD DATA 35

71 Analysis based on the ldquoAnnual Survey on IT Usage amp Penetration in the Business Sectorrdquo (ASIT) report 35 72 Analysis based on the ASIT report and Value Added obtained from the CampSD 38 73 Conclusions 38

8 OVERVIEW OF THE SURVEYS CONDUCTED BY HKU 40

81 Survey questionnaires 40 82 Telephonic case study interviews 42 83 Literature review of similar experiences across the world 42 84 Selection of sectors 42

9 RESULTS ANALYSIS OF THE SURVEY QUESTIONNAIRES 44

91 Usage of Personal Computers (PCs) ndash Stage 1 47 911 Not Using PCs 47 912 Using PCs 50 913 Value Added from PC Usage 52 914 Relationship between Perceived Value and PC Usage 53 915 Summary of findings for Section 91 ndash Stage 1 54

92 Usage of the Internet ndash Stage 2 55 921 Not Using the Internet 55 922 Using the Internet 57 923 Value Added by Internet Usage 59 924 Relationship between Perceived Value and Internet Usage 60 925 Summary of findings for Section 92 ndash Stage 2 61

93 Usage of a Website ndash Stage 3 62 931 Not Using a Website 62 932 Respondents Having a Website 66 933 Value Added from Having a Website 66














APPENDICES 123





ASIT Report 141































































database 398





EXECUTIVE SUMMARY

I Objective









1






IV Data Collection









2












3










4













5











6










7












VI Recommendations


8











9





10


1 INTRODUCTION






11



2 BACKGROUND









12









13








Penetration rate

Large Medium Small

Personal computers

991 917 584


Intranet 643 292 73

Extranet 286 104 32





14


15



Internet use

Large Medium Small






Industry Sector


Webpage websites

Electronic Business



hotels 653 618 209 622


602 505 160 523



16



320 307 64 321














17



21accuracy









18














19











20












21











22







23












24












25



26










27






3 Web Presence

4 Transaction


6 Transformation

Low





28






Production


equipment


(CAD)





bull General ledger

29



















30











6 Transformation









31



32












33













34













35





Analyses done






Analyses done

36













37






Analyses done



73 Conclusions


38





39





Sample Design




Questionnaires



40










41











42







43




Response Rate







44


By Sector


Percent Cumulative

Percent

Manufacturing - 301 81 103 104 104

Construction - 501 68 86 87 191


ImportExport ndash

176 223 225 416


ImportExport ndash

57 72 73 489


ImportExport ndash

60 76 77 566


94 119 120 686


13 16 17 703


32 41 41 744



35 44 45 799

Real Estate - 802 18 23 23 822


Total 781 990 1000 Missing 8 10

Total 789 1000


45




Percent Cumulative

Percent


604 178

7 789

766 226

9 1000

766 226

9 1000

766 991

1000




Percent Cumulative

Percent


109 193 477 779

10

789

138 245 605 987

13

1000

140 248 612

1000

140 388

1000


N Valid 390

Mean 16838817

46



1E9




911 Not Using PCs




It is costly





47











48
















49






912 Using PCs





50










51










Increasing sales





52











53

















54














It is costly



55












56














57















58
















Increasing sales



59











60

















61

















It is costly

62













63


















64
















65













66













67

















68
















69











70













71















72












973 ICT Value




73














74

















75






991 PC Adoption








76


















77




911 Hypotheses








78











79









Conclusion



80















81





[


]



[


]


82


[


]

83


[


]

84


[


]

85


[


]

86













87


[


]


[


]


88


[


]

89


[


]

90


[


]

91


[


]





92


[


]

93


[


]

94


[


]

95


[


]

96


[


]

97


[


]



[


]

98


[


]

99


[


]

100


[


]

101


[


]

102


[


]




[


]


103


[


]


[


]


104


[


]

105


[


]

106


[


]


107



[


]


[


]


108


[


]

109


[


]

110


[


]

111


[


]

112


[


]

113













114











115












116













117











118















119










120













121





122

Appendices




Extensive ICT Use

G r o w t h

M a t u r I t y


Stage III Growth

Stage IV Stability

Developing ICT use

Maturing ICT Use




123





124






3 Web Presence

4 Transaction


6 Transformation

Low

High

B u s i n e s s





125


Stage Applications










126









127





Y Y Y Y Y Y Y Y





B5


N Y Y Y


all




C1 C2

YesNo YesNo

N N

Covered in C5


128






129












130





Y Y Y Y Y Y Y












to all

131


ii 1-5 iii 6-10 iv 11-15 v gt15





132








Has your firmmeans


E10

YesNo Y Stage 2

133







telecom


v

vi




E12(d) Y Stage 2

134


iv v vi

6-10 11-15 gt15



E12e Y Stage 2







135




telecom v

vi










136













137











H1 Y Stage 2


H2 Y Stage 2

138





139





140






141





Total

Weight

21

Max

0

Min


B4 YesNo YesNo

Y=1 N=0 Y=1 N=0

1 1

1 1

0 0




1 1 1 1 1

1 1 1 1 1

0

vi viiviii


1 1 1

1 1 1

ix x xi


1 1 1

1 1 1


142





1 1 1 1

005

1 1 1 1

5 0






Y Y Y Y Y Y Y

1 1 1 1 1 1 1

7

1 1 1 1 1 1 1

0



E5 0 3 0

143


ii iiiiv v vi vii

lt1 1-20 21-40 41-60 61-80 81-100

1 1 2 2 3 3


E6 E8

YesNo YesNo

Y=1 N=0 Y=1 N=0

1 1

1 1

0 0



lt1 iii 1-5 iv 6-10 v 11-15 vi gt15

E12(d) 0 1 1 2 2 3

3 0



3 0

144


iv v vi






point else 0 1 0


EDGE iii 3G iv WAP


point else 0 1 0

145





146






Total 53 0


Index in ASIT D4

Form of AnswerYesNo

Points Y=1 N=0

Weight 5

Max 5

Min 0


Total 10 0

147




Form Index in of



















148



ICTU1 = 21 0

Part 2 Index in

ASIT

Form of




ii Internet browser

B4

YesNo

YesNo

Y=1 N=0

Y=1 N=0

1

1

1

1

0

0


i Email



iv Receipt

v Sales

vi Delivery

vii Payments








xv Video conference

C3

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

149


xvi Others 16 1 1 0


percentage 005 5 0










vii Others

D6

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo


Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0



i 0

ii lt1

iii 1-20

iv 21-40

v 41-60

vi 61-80

vii 81-100

E5

1

2

3

4

5

6

7

0

1

1

2

2

3

3

3 0

150






i 0 ii lt1

iii 1-5

iv 6-10

v 11-15

vi gt15

E12(d)

1

2

3

4

5

6

0

1

1

2

2

3

3 0



ii Credit cards




vi Others

E12e 1

2

3

4

5

6




assign 3

3 0




1 point else 0 1 0

151









viii Others YesNo


i GPRS YesNo

ii EDGE YesNo

iii 3G YesNo

iv WAP YesNo

v WLAN YesNo

vi Bluetooth YesNo

vii RFID YesNo


ix Others YesNo


i SMS YesNo

ii EMS YesNo

iii MMS YesNo

iv LBS YesNo

v GPS YesNo





152


viii

ix

x


Video call

Others

YesNo

YesNo

YesNo


i GIS

ii SMSMMS


iv WAP

v Job Management

vi Fleet management




x Mobile POS


xii Video streaming


xiv Others

H4

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo


ICTU2 = 28 0

Part 3


Index in ASIT

D4

Form of

Answer

YesNo

Points

Y=1 N=0

Weight

5

Max

5

Min

0


153


ICTU3 = 10 0


154











155







156




SUMMARY




157


Within Groups 1770488052 1961 9028495933

Total 1987320761 1972

158


Anova Single Factor

SUMMARY



SS 7143541864 4157821969

df 11 1961

MS 6494128968 2120255976

F 3062898556

P-value 311374E-60

F crit 1793521277

Total 4872176155 1972

159


Anova Single Factor

SUMMARY



SS 4986186738 3223293368

df 11 1857

MS 4532897035 1735753025

F 261148733

P-value 533615E-51

F crit 1793794387

Total 3721912042 1868

160


Anova Single Factor

SUMMARY



SS 3891069269 8605317807

df 11 1961

MS 3537335699 4388229376

F 8060963537

P-value 66661E-14

F crit 1793521277

Total 8994424734 1972

161


Anova Single Factor

SUMMARY



Total 1062189055 200

162


Anova Single Factor

SUMMARY



SS 2156286631 1184873131

df 11 1961

MS 1960260573 6042188326

F 3244289101

P-value 806607E-64

F crit 1793521277

Total 1400501794 1972

163



Industry VA of SMEs

[1] (HK$ million)






6405 171703





164








165





ＯNo

166


3 NOT USING PCs



















167



168


4 USING PCs


























169





ＯNo

170





















171






172




























173



174





ＯNo

175























176






177


10 USING A WEBSITE





























178




179






180











Ｏ少於 10 人Ｏ10-49 人Ｏ50-100 人Ｏ100 人以上




181





Ｏ否

182




關系原因














的資訊




183



184


















用用用用












185





Ｏ否

186




關系

沒有


很重要

原因















没用道







187





188

189


7 使用互聯網



























Ｏ是Ｏ否






Ｏ否

190





關系原因原因
















完全

没用

有少

少用

不知

道有用

很有

用







191





192


10 使用網站



使用常多




























193



Ｏ是Ｏ否


Ｏ是Ｏ否

194




Ｏ用得很少

Ｏ有用一些

Ｏ常用

Ｏ很常用


Ｏ完全没用

Ｏ有少少用

Ｏ有一些用

Ｏ有不少用

Ｏ非常有用


195













196



197









198


2 USING PCs


























199




























200



201




ＯYes

ＯNo

202























203





204


6 USING A WEBSITE





























205




206


7 OVERALL ICT USAGE




207












Ｏ少於 10 人Ｏ10-49 人Ｏ50-100 人Ｏ100 人以上





208





























209

210


3 使用互聯網



























Ｏ是Ｏ否





Ｏ是

Ｏ否

211





關系原因原因
















完全

没用

有少

少用

不知

道有用

很有

用







212




213


6 使用網站



使用常多




























214



Ｏ是Ｏ否


Ｏ是Ｏ否

215




Ｏ用得很少

Ｏ有用一些

Ｏ常用

Ｏ很常用


Ｏ完全没用

Ｏ有少少用

Ｏ有一些用

Ｏ有不少用

Ｏ非常有用


216



1 Data merge













217





3 Data selection

Select SME only


218







301 41 2923982739 1219E8 19034310422 -922994898 6770960376 59000 8E8

501 33 285743403 3741336262 651283047 153081388 418405418 150000 15000000

601 90 1664371319 1055E8 11125197493 -546181726 3874924363 10000 1E9

602 24 1773216667 27323788509 5577444974 619434267 2926999066 219000 1E8

603 26 5962015385 1972E8 38669139962 -2002043071 139E8 50000 1E9

604 36 1440023083 25635847654 4272641276 572630791 2307415376 125000 1E8

605 9 369611111 4038952353 1346317451 59149750 680072472 90000 12000000

701 18 703168733 14677261222 3459463647 -26714296 1433051763 5 63327855

702 5 913940440 13299818631 5947859709 -737450158 2565331038 110610 30000000

801 21 2902182857 1281E8 27950740415 -2928239426 8732605140 100000 6E8

802 7 73750100 1052509304 397811124 -23590775 171090975 70000 3000000

803 76 600408357 34360255601 3941391627 -184757118 1385573832 824 3E8

Total 386 1699904247 89693952915 4565302001 802299762 2597508733 5 1E9

219






Mean


5 303 473571424 19977266559 1147663977 247728342 699414506 5 3E8

25 82 6154411376 1848E8 20412653467 2092933633 102E8 100000 1E9

75 5 1712000000 22095180470 9881265101 -1031479012 4455479012 100000 50000000

Total 390 1683881729 89245778704 4519135672 795383022 2572380436 5 1E9



Mean


2 51 1302927202 82159196233 11504588621 -1007837429 3613691832 10000 6E8

5 95 1438550640 81082800535 8318919808 -213190389 3090291669 11500 8E8

8 240 1854151498 94606234393 6106806171 651147685 3057155310 5 1E9

Total 386 1679036159 89684007888 4564795812 781531198 2576541121 5 1E9

220




Code

0 NO

1 YES

Statistics Have_PC

N Valid 785

Missing 4

Have_PC


Percent Cumulative

Percent

Valid 0

1


100

685

785 4

789

127

868

995 5

1000

127

873

1000

127

1000

221





N Valid


98

2

100

0

100

0

Statistics


N Valid 63 75 59 59 59 56


222









Total 70627 50



Total 127937 62



Total 49915 46

No_one_train person

Between Groups

Within Groups

Total

4627

75852

80479

4

43

47

1157

1764

656 626



Total 77917 47



Total 56578 44

223






Total 70627 50



Total 127937 62



Total 49915 46


Between Groups

Within Groups

Total

768

79712

80479

1

46

47

768

1733

443 509



Total 77917 47



Total 56578 44

224






Total 70627 50



Total 127937 62



Total 49915 46


Between Groups

Within Groups

Total

2743

77736

80479

2

45

47

1371

1727

794 458



Total 77917 47



Total 56578 44

225















11 不會 No



226





















227









228




229




















230


















34 在家 At home


231








232



233





Statistics


Provide consulting

Subside training

N Valid


70

30 164 100

1465 0 4

64

36 191 300

1611 0 4

62

38 184 200

1428 0 4

64

36 219 300

1521 0 4

65

35 222 300

1546 0 4

234



ANOVA





Within Groups

Total

22666

124986

147652

9

59

68

2518

2118

1189 319


Within Groups

Total

32070

130152

162222

10

52

62

3207

2503

1281 265


Within Groups

Total

27797

95875

123672

9

51

60

3089

1880

1643 128


Within Groups

Total

19273

123140

142413

9

53

62

2141

2323

922 514


Within Groups

Total

16969

135390

152359

9

54

63

1885

2507

752 660

235





Within Groups

Total

687

147385

148071

1

68

69

687

2167

317 575


Within Groups

Total

468

162969

163438

1

62

63

468

2629

178 675


Within Groups

Total

5026

119361

124387

1

60

61

5026

1989

2526 117


Within Groups

Total

3581

142169

145750

1

62

63

3581

2293

1561 216


Within Groups

Total

7601

145383

152985

1

63

64

7601

2308

3294 074

236





Within Groups

Total

4417

143654

148071

2

67

69

2209

2144

1030 363


Within Groups

Total

12803

150635

163437

2

61

63

6401

2469

2592 083


Within Groups

Total

3809

120578

124387

2

59

61

1904

2044

932 400


Within Groups

Total

10989

134761

145750

2

61

63

5494

2209

2487 092


Within Groups

Total

11643

141342

152985

2

62

64

5821

2280

2554 086



Holding_workshops 2

5

8

Total

5

9

44

58

220

211

145

162

837

1616

1517

1497

237



5 9 289 1453

8 39 156 1619

Total 53 185 1610


5 9 222 1394

8 37 157 1501

Total 51 176 1450


5 10 290 1370

8 39 185 1548

Total 54 217 1539


5 10 300 1247

8 39 192 1628

Total 54 226 1592

238





N Valid

Missing

679

6

685

0

675

10

Statistics


N Valid


604

81 319 300

1003 0 4

581

104 295 300

1221 0 4

559

126 215 200

1484 0 4

574

111 313 400

1187 0 4

529

156 140 100

1283 0 4

Statistics


N Valid


570

115 304 300

1133

549

136 215 200

1328

572

113 259 300

1418

564

121 229 300

1552

534

151 109 100

1208

239


Minimum Maximum

0 4

0 4

0 4

0 4

0 4

240








Word Between Groups

39095 9 4344 4541 000


Total 591077 586


Within Groups

Total

40662

813739

854401

9

553

562

4518

1471

3070 001


36738 9 4082 1862 055


Total 1200957 540


34571 9 3841 2760 004


Total 794437 555

241



50035 9 5559 3587 000


Total 829579 512


Within Groups

Total

27353

671020

698373

9

543

552

3039

1236

2459 009


13114 9 1457 819 599


Total 939797 530


26405 9 2934 1445 165


Total 1130681 553


137742 9 15305 6963 000


Total 1315855 545

HRM Between Groups

27264 9 3029 2114 027


Total 752347 515



242


Word 301 64 316 946

501 53 317 1069

601 114 289 1132

602 43 333 808

603 52 352 727

604 77 345 867

701 27 278 1086

801 32 322 870

802 10 220 1619

803 115 335 946

Total 587 320 1004


501 45 253 1471

601 111 265 1319

602 40 335 921

603 49 308 1288

604 78 322 1040

701 25 288 1301

801 31 319 946

802 9 222 1481

803 114 309 1125

Total 563 295 1233

Database 301 59 185 1460

501 40 205 1431

601 105 186 1553

602 39 195 1468

603 51 241 1525

604 72 232 1422

701 24 200 1474

243


801 31 245 1312

802 10 190 1729

803 110 246 1482

Total 541 216 1491


501 44 314 1173

601 107 286 1270

602 41 339 1070

603 49 347 1043

604 78 323 1139

701 25 252 1418

801 30 360 498

802 10 280 1398

803 113 313 1228

Total 556 312 1196


501 41 141 1303

601 100 111 1163

602 37 135 1207

603 43 160 1312

604 68 138 1372

701 23 43 728

801 29 207 998

802 9 111 1364

803 109 161 1333

Total 513 139 1273

Security 301

501

601

57

43

108

289

300

285

1191

1272

1252

244


602 40 325 954

603 49 322 1066

604 79 313 1079

701 23 252 1163

801 32 338 1008

802 9 233 1414

803 113 323 935

Total 553 305 1125

Multi_Media 301 58 219 1290

501 42 205 1413

601 103 207 1278

602 37 222 1272

603 46 239 1341

604 72 212 1383

701 22 168 1393

801 30 197 1326

802 9 156 1590

803 112 221 1330

Total 531 212 1332

Finance 301 60 278 1303

501 42 243 1309

601 110 245 1469

602 38 261 1443

603 50 274 1468

604 75 275 1376

701 24 233 1373

801 31 210 1491

802 10 180 1932

803 114 274 1433

245


Total 554 258 1430

Sales 301 58 257 1512

501 43 195 1430

601 106 243 1441

602 38 268 1435

603 49 280 1554

604 78 295 1318

701 25 184 1599

801 29 159 1570

802 9 100 1500

803 111 169 1565

Total 546 229 1554

HRM 301 54 122 1208

501 40 90 982

601 102 89 1218

602 36 106 1194

603 46 141 1408

604 68 124 1259

701 23 39 656

801 29 124 1123

802 9 78 1093

803 109 122 1220

Total 516 109 1209



246


Word Between Groups

Within Groups

Total

884

590192

591077

2

584

586

442

1011

438 646


Within Groups

Total

4388

850013

854401

2

560

562

2194

1518

1445 237


Within Groups

Total

26601

1174357

1200957

2

538

540

13300

2183

6093 002


Within Groups

Total

3781

790656

794437

2

553

555

1891

1430

1322 267


Within Groups

Total

2678

826901

829579

2

510

512

1339

1621

826 438


Within Groups

Total

3554

694818

698373

2

550

552

1777

1263

1407 246


Within Groups

Total

2642

937155

939797

2

528

530

1321

1775

744 476


Within Groups

37797

1092884

2

551

18898

1983

9528 000

247


Total 1130681 553


Within Groups

Total

22362

1293493

1315855

2

543

545

11181

2382

4694 010

HRM Between Groups

Within Groups

Total

33078

719269

752347

2

513

515

16539

1402

11796 000



Word 5

25

75

Total

446

134

7

587

318

327

314

320

1002

1034

378

1004

Spreadsheet 5

25

75

Total

431

125

7

563

290

310

314

295

1250

1177

1069

1233

Database 5

25

75

Total

412

122

7

541

203

257

214

216

1488

1443

1464

1491

Communication 5

25

75

427

122

7

308

328

300

1220

1093

1414

248


Total 556 312 1196


25 115 137 1328

75 7 200 1291

Total 513 139 1273

Security 5 424 301 1143

25 122 320 1060

75 7 300 1000

Total 553 305 1125

Multi_Media 5 407 209 1341

25 117 226 1314

75 7 214 1069

Total 531 212 1332

Finance 5 421 244 1454

25 126 303 1277

75 7 329 488

Total 554 258 1430

Sales 5 417 218 1566

25 122 265 1471

75 7 271 1380

Total 546 229 1554

HRM 5 396 96 1116

25 113 153 1396

75 7 186 1215

Total 516 109 1209


249



Word Between Groups

Within Groups

Total

2046

575508

577554

2

576

578

1023

999

1024 360


Within Groups

Total

2360

848689

851048

2

555

557

1180

1529

772 463


Within Groups

Total

14548

1175391

1189938

2

533

535

7274

2205

3298 038


Within Groups

Total

4941

776652

781593

2

547

549

2470

1420

1740 176


Within Groups

Total

11828

801109

812937

2

505

507

5914

1586

3728 025


Within Groups

Total

4623

686842

691465

2

545

547

2312

1260

1834 161


Within Groups

Total

9832

920874

930706

2

524

526

4916

1757

2797 062

250



Within Groups

Total

8300

1107481

1115781

2

545

547

4150

2032

2042 131


Within Groups

Total

096

1304148

1304244

2

539

541

048

2420

020 980

HRM Between Groups

Within Groups

Total

4481

727230

731710

2

508

510

2240

1432

1565 210



Word 2

5

8

Total

85

154

340

579

327

327

315

320

968

950

1029

1000

Spreadsheet 2

5

8

Total

85

148

325

558

285

304

292

294

1435

1160

1215

1236

Database 2

5

8

Total

80

142

314

536

181

235

214

214

1527

1429

1499

1491

Communication 2

5

84

147

304

328

1357

1090

251


8

Total

319

550

308

312

1190

1193


5 136 160 1189

8 299 125 1261

Total 508 137 1266

Security 2 80 311 1201

5 146 318 968

8 322 298 1167

Total 548 305 1124


5 140 234 1285

8 309 203 1312

Total 527 212 1330

Finance 2 82 239 1569

5 143 245 1438

8 323 268 1381

Total 548 258 1428

Sales 2 81 228 1637

5 144 231 1552

8 317 227 1536

Total 542 228 1553

HRM 2 76 99 1238

5 138 123 1198

8 297 103 1185

Total 511 108 1198

252





Statistics

Sector No_of


N Valid

Missing

664

0

664

0

656

8

664

0

Statistics



N Valid


618

46 182 200

1579 0 4

609

55 174 200

1574 0 4

592

72 75 00

1078 0 4

253










Total 1537702 617

Tailor_ Purchasing

Between Groups

Within Groups

Total

66918

1439607

1506525

9

599

608

7435

2403

3094 001



Total 687000 591



Tailor_Finance 301

501

601

602

65

52

117

48

180

117

193

171

1573

1294

1507

1570

254


603 56 189 1659

604 88 168 1550

701 28 204 1598

801 30 123 1591

802 12 167 1826

803 122 221 1622

Total 618 182 1579


501 52 131 1502

601 115 201 1513

602 50 184 1621

603 56 207 1683

604 88 206 1541

701 28 196 1551

801 29 100 1414

802 12 100 1651

803 115 145 1552

Total 609 174 1574

Tailor_HRM 301 61 89 1082

501 50 56 884

601 113 56 935

602 48 60 917

603 56 86 1119

604 84 79 1162

701 26 50 1030

801 29 107 1307

802 12 100 1595

803 113 88 1124

Total 592 75 1078

255


256






Total 1537702 617

Tailor_ Purchasing

Between Groups

Within Groups

Total

20097

1486429

1506525

2

606

608

10048

2453

4097 017



Total 687000 591



Tailor_Finance 5

25

75

Total

466

145

7

618

170

213

300

182

1547

1626

1528

1579

Tailor_Purchasing 5

25

75

Total

453

149

7

609

164

205

214

174

1532

1658

1773

1574

Tailor_HRM 5

25

444

141

66

101

1005

1233

257


75 7 129 1380

Total 592 75 1078





Total 1512802 609

Tailor_Purchasin g

Between Groups

Within Groups

Total

531

1487154

1487685

2

600

602

266

2479

107 898



Total 681625 585

258




Statistics



N Valid


593

92 299 300

1025 0 4

576

109 268 300

1176 0 4

575

110 197 200

1379 0 4

578

107 209 200

1347 0 4

570

115 236 300

1289 0 4

259







20091 11 1826 1757 058


Total 618957 587


46210 11 4201 3146 000


Total 792708 570


60685 11 5517 3004 001


Total 1085551 569

New opportunity

Between Groups

63606 11 5782 3322 000


Total 1040098 572


Within Groups

Total

53472

883148

936619

11

553

564

4861

1597

3044 001

260




Efficiency 301 61 293 1031

501 47 300 978

601 113 283 1117

602 41 324 734

603 53 296 1126

604 80 286 1052

701 25 276 1200

801 31 306 772

802 10 270 1252

803 114 324 944

Total 575 299 1027

C_service 301 57 270 1101

501 45 236 1334

601 110 245 1224

602 39 292 870

603 51 273 1313

604 79 261 1275

701 24 221 1215

801 31 313 619

802 9 222 1302

261


803

Total

113

558

301

268

1031

1175

Inc_sales 301 58 217 1340

501 45 153 1325

601 111 205 1334

602 40 230 1181

603 52 206 1420

604 77 194 1463

701 26 96 1216

801 30 233 1093

802 9 178 1787

803 110 200 1414

Total 558 197 1378


501 44 157 1388

601 110 209 1296

602 40 225 1127

603 52 237 1415

604 77 230 1288

701 24 92 1176

801 31 203 1048

802 10 170 1636

803 113 218 1434

Total 560 209 1343


501 45 211 1283

601 108 228 1281

602 36 247 1000

603 51 251 1362

262


604 77 227 1344

701 25 156 1261

801 31 248 1151

802 9 156 1509

803 111 274 1241

Total 552 237 1284

263





Within Groups

Total

2589

619384

621973

2

590

592

1295

1050

1233 292


2695 2 1348 974 378


Total 795222 575


1966 2 983 516 597


Total 1091437 574


Within Groups

Total

1488

1044652

1046140

2

575

577

744

1817

410 664


Within Groups

Total

2124

943974

946098

2

567

569

1062

1665

638 529



264



Within Groups

Total

1145

614828

615973

2

583

585

573

1055

543 581


5167 2 2583 1864 156


Total 790956 569


13673 2 6836 3607 028


Total 1088226 569

New opportunity

Between Groups

7164 2 3582 1979 139


Total 1037138 571


Within Groups

Total

13123

928530

941652

2

561

563

6561

1655

3964 020



Efficiency 2

5

87

153

293

307

1087

985

8 329 297 1034

Total 569 299 1029

C_service 2 85 273 1179

5 150 285 1145

265


8 318 259 1187

Total 553 268 1178

Inc_sales 2 84 208 1382

5

8

Total

149

320

553

219

184

197

1358

1382

1382

266


New_opportunity 2

5

8

Total

85

149

321

555

218

226

199

209

1424

1321

1330

1345

Competitive 2

5

8

Total

84

146

317

547

232

264

225

237

1355

1231

1280

1287

267




Sector = 301

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

64

642

000

64


Sig (2-tailed)

N

642

000

64

1


Sector = 501

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

52

656

000

52


Sig (2-tailed)

N

656

000

52

1

52

268


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

52

656

000

52


Sig (2-tailed)

N

656

000

52

1


Sector = 601

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

114

583

000

114


Sig (2-tailed)

N

583

000

114

1


Sector = 602

269


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

43

241

119

43


Sig (2-tailed)

N

241

119

43

1

43

270


Sector = 603

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

54

777

000

54


Sig (2-tailed)

N

777

000

54

1


Sector = 604

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

80

555

000

80


Sig (2-tailed)

N

555

000

80

1


271


Sector = 701

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

26

478

014

26


Sig (2-tailed)

N

478

014

26

1


Sector = 801

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

32

690

000

32


Sig (2-tailed)

N

690

000

32

1


272


273


Sector = 802

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

10

707

022

10


Sig (2-tailed)

N

707

022

10

1


Sector = 803

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

116

461

000

116


Sig (2-tailed)

N

461

000

116

1


274



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

450

578

000

450


Sig (2-tailed)

N

578

000

450

1



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

134

645

000

134


Sig (2-tailed)

N

645

000

134

1


275



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

7

590

163

7


Sig (2-tailed)

N

590

163

7

1

7


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

85

567

000

85


Sig (2-tailed)

N

567

000

85

1


276



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

154

551

000

154


Sig (2-tailed)

N

551

000

154

1



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

345

601

000

345


Sig (2-tailed)

N

601

000

345

1


277


278




Have_Internet

N Valid 739

Missing 50

Have_Internet


Percent Cumulative

Percent

Valid 0

1


74

665

739 50

789

94

843

937 63

1000

100

900

1000

100

1000

279





N Valid

Missing

74

0

74

0

73

1

Statistics



Partner_not use


N Valid


37

37 62 00

1010 0 4

38

36 71 00

898 0 3

38

36 113 100

1359 0 4

35

39 114 100

1264 0 4

36

38 100 100

1287 0 4

Statistics


person No_one Train

N Valid


39

35 126 100

1390 0

45

29 140 100

1529 0

38

36 84 00

1151 0

39

35 92 00

1265 0

280


Statistics


person No_one Train

N Valid


39

35 126 100

1390 0 4

45

29 140 100

1529 0 4

38

36 84 00

1151 0 4

39

35 92 00

1265 0 4

281









Total 32958 23



Total 22500 23


Between Groups

Within Groups

Total

1875

32750

34625

2

21

23

938

1560

601 557



Total 32955 21


Between Groups

Within Groups

Total

3743

40083

43826

2

20

22

1871

2004

934 410



282


Total 47840 24


Within Groups

Total

6417

55690

62107

2

25

27

3208

2228

1440 256


Within Groups

Total

2029

29971

32000

2

22

24

1015

1362

745 486


Within Groups

Total

1835

37550

39385

2

23

25

917

1633

562 578

283





Within Groups

Total

2458

30500

32958

2

21

23

1229

1452

846 443


Within Groups

Total

1812

20687

22500

2

21

23

906

985

920 414


Between Groups

Within Groups

Total

188

34438

34625

2

21

23

094

1640

057 945


Within Groups

Total

1597

31357

32955

2

19

21

799

1650

484 624


Between Groups

Within Groups

Total

4726

39100

43826

2

20

22

2363

1955

1209 319


Within Groups

Total

090

47750

47840

2

22

24

045

2170

021 979


Within Groups

Total

679

59618

60296

2

24

26

339

2484

137 873


Within Groups

Total

2250

29750

32000

2

22

24

1125

1352

832 448


284


Within Groups

Total

36694

39385

23

25

1595

285




286









4 一般 Average



7 方便 Convenient




287










288












289



Internet


ICT_ infrastructure

Legal regulatory

Tax incentive

Provide info

N Valid


40

34 142 100

1299 0 4

37

37 157 200

1425 0 4

37

37 143 100

1444 0 4

39

35 172 200

1432 0 4


N Valid 39 39 39


290



Internet - ANOVA





Within Groups

Total

10740

55035

65775

10

29

39

1074

1898

566 828


Within Groups

Total

12531

60550

73081

10

26

36

1253

2329

538 847


Within Groups

Total

16181

58900

75081

10

26

36

1618

2265

714 704


Within Groups

Total

14956

62941

77897

10

28

38

1496

2248

665 746

Provide consulting

Between Groups

Within Groups

Total

11302

68441

79744

10

28

38

1130

2444

462 900


Within Groups

Total

14701

69196

83897

10

28

38

1470

2471

595 804


Within Groups

15190

71118

10

28

1519

2540

598 802

291





Within Groups

Total

10740

55035

65775

10

29

39

1074

1898

566 828


Within Groups

Total

12531

60550

73081

10

26

36

1253

2329

538 847


Within Groups

Total

16181

58900

75081

10

26

36

1618

2265

714 704


Within Groups

Total

14956

62941

77897

10

28

38

1496

2248

665 746

Provide consulting

Between Groups

Within Groups

Total

11302

68441

79744

10

28

38

1130

2444

462 900


Within Groups

Total

14701

69196

83897

10

28

38

1470

2471

595 804


Within Groups

Total

15190

71118

86308

10

28

38

1519

2540

598 802

292





Within Groups

Total

2433

63342

65775

1

38

39

2433

1667

1460 234


Within Groups

Total

1838

71243

73081

1

35

36

1838

2036

903 348


Within Groups

Total

2410

72671

75081

1

35

36

2410

2076

1161 289


Within Groups

Total

1289

76608

77897

1

37

38

1289

2070

623 435

Provide consulting

Between Groups

Within Groups

Total

973

78770

79744

1

37

38

973

2129

457 503


Within Groups

Total

1289

82608

83897

1

37

38

1289

2233

577 452


Within Groups

Total

3605

82703

86308

1

37

38

3605

2235

1613 212

293





Within Groups

Total

096

65679

65775

2

37

39

048

1775

027 973


Within Groups

Total

2914

70167

73081

2

34

36

1457

2064

706 501


Within Groups

Total

662

71893

72556

2

33

35

331

2179

152 860


Within Groups

Total

379

77519

77897

2

36

38

189

2153

088 916

Provide consulting

Between Groups

Within Groups

Total

410

79333

79744

2

36

38

205

2204

093 911


Within Groups

Total

2564

81333

83897

2

36

38

1282

2259

567 572


Within Groups

Total

974

85333

86308

2

36

38

487

2370

206 815

294





N Valid 659 665 656

Missing 6 0 9

Statistics


N Valid 580 572 556 552 558 550





Video_ conference

N Valid

Missing Mean Median

552

113 206 200

553

112 214 200

550

115 188 200

554

111 234 300

544

121 76 00

295



1115 0 4

1363 0 4

1176 0 4

1394 0 4

1031 0 4








Total 223530 561



Total 353951 553



Total 1011645 537



Total 980301 533



Total 1186748 539

296




Total 941233 531



Total 664734 533



Total 985918 534



Total 725308 531



Total 1043879 535



Total 567165 525


Email 301 60 363 637

501

601

602

603

604

46

105

42

52

77

363

354

371

385

365

645

707

596

364

580

701 23 335 832

297


801 31 368 653

802 9 322 833

803 117 362 598

Total 562 363 631


501 46 320 719

601 102 322 779

602 40 320 853

603 49 335 855

604 77 322 788

701 23 300 798

801 31 303 836

802 9 300 866

803 117 334 756

Total 554 320 800


501 43 140 1178

601 99 220 1204

602 39 259 1229

603 49 269 1262

604 76 236 1402

701 21 105 1322

801 31 74 999

802 8 112 1553

803 114 154 1284

Total 538 195 1373


501

601

58

43

100

164

88

144

1266

1074

1343

298


602 37 176 1480

603 48 175 1605

604 76 171 1403

701 20 60 1095

801 31 61 844

802 8 112 1553

803 113 127 1289

Total 534 138 1356


501 43 135 1289

601 102 193 1373

602 40 272 1320

603 49 288 1333

604 75 239 1384

701 22 150 1535

801 31 71 1006

802 8 100 1604

803 112 144 1400

Total 540 195 1484


501 43 51 856

601 102 102 1350

602 37 119 1309

603 47 147 1412

604 75 159 1453

701 20 50 827

801 31 26 514

802 7 100 1528

803 113 114 1315

299


Total 532 111 1331

Gov_info 301 58 209 996

501 43 230 1036

601 100 179 1113

602 38 189 981

603 48 204 1202

604 75 211 1226

701 20 170 1218

801 31 174 1032

802 7 243 1134

803 114 230 1088

Total 534 205 1117


501 43 179 1473

601 100 199 1299

602 38 216 1197

603 48 262 1409

604 76 226 1408

701 21 138 1499

801 30 187 1279

802 7 214 1215

803 114 211 1319

Total 535 214 1359


501 43 186 1302

601 100 178 1106

602 38 179 811

603 48 204 1352

604 75 165 1168

300


701 20 145 1276

801 31 177 1055

802 8 162 1598

803 113 213 1146

Total 532 187 1169

E_banking 301 57 223 1337

501 42 210 1265

601 100 240 1414

602 37 238 1421

603 49 280 1399

604 76 243 1417

701 20 170 1455

801 31 239 1383

802 8 200 1195

803 116 228 1418

Total 536 234 1397


501 42 60 989

601 98 54 852

602 37 78 976

603 48 106 1405

604 74 91 1196

701 20 30 571

801 31 55 995

802 8 62 744

803 113 95 1042

Total 526 77 1039

301





Within Groups

Total

2032

221498

223530

2

559

561

1016

396

2564 078


Within Groups

Total

1980

351972

353951

2

551

553

990

639

1550 213


Within Groups

Total

873

1010772

1011645

2

535

537

437

1889

231 794


Within Groups

Total

270

980031

980301

2

531

533

135

1846

073 929


Within Groups

Total

3263

1183485

1186748

2

537

539

1632

2204

740 477


Within Groups

Total

034

941199

941233

2

529

531

017

1779

010 991


Within Groups

Total

5277

659457

664734

2

531

533

2639

1242

2125 120


Within Groups

Total

721

985196

985918

2

532

534

361

1852

195 823


Within Groups

6536

718772

2

529

3268

1359

2405 091

302


Total 725308 531


Within Groups

Total

2322

1041557

1043879

2

533

535

1161

1954

594 552


Within Groups

Total

5444

561721

567165

2

523

525

2722

1074

2534 080


Email 5 429 359 647

25 126 374 568

75 7 357 535

Total 562 363 631


25 123 329 807

75 7 286 378

Total 554 320 800


25 121 197 1341

75 7 229 1113

Total 538 195 1373


25 119 137 1314

75 7 157 1272

Total 534 138 1356

Sales_Customer 5

25

75

411

122

7

196

189

257

1478

1517

1272

303


Total 540 195 1484


25 121 110 1319

75 7 114 1345

Total 532 111 1331

Gov_info 5 407 200 1143

25 120 223 1027

75 7 200 816

Total 534 205 1117


25 119 211 1268

75 7 243 976

Total 535 214 1359


25 119 168 1119

75 7 229 756

Total 532 187 1169

E_banking 5 410 236 1410

25 119 228 1352

75 7 186 1464

Total 536 234 1397


25 116 84 1119

75 7 157 976

Total 526 77 1039


304




Within Groups

Total

128

215548

215676

2

553

555

064

390

164 848


Within Groups

Total

1320

351831

353151

2

546

548

660

644

1024 360


Within Groups

Total

7740

990572

998311

2

530

532

3870

1869

2071 127


Within Groups

Total

32312

942553

974866

2

526

528

16156

1792

9016 000


Within Groups

Total

3570

1171355

1174925

2

533

535

1785

2198

812 444


Within Groups

Total

27508

907674

935182

2

525

527

13754

1729

7955 000


Within Groups

Total

2395

660518

662913

2

527

529

1198

1253

956 385


Within Groups

Total

13689

962530

976219

2

527

529

6845

1826

3747 024


Within Groups

Total

5287

715938

721226

2

524

526

2644

1366

1935 145

305



Within Groups

Total

16191

1016684

1032875

2

527

529

8096

1929

4196 016


Within Groups

Total

12012

550005

562017

2

519

521

6006

1060

5667 004

By Business Year


Email 2 80 360 668

5 151 365 580

8 325 363 633

Total 556 363 623


5 148 328 747

8 323 317 815

Total 549 320 803


5 144 212 1356

8 311 185 1350

Total 533 194 1370


5 144 177 1437

8 309 120 1247

Total 529 138 1359


306


5

8

Total

144

313

536

203

189

196

1467

1467

1482


5 143 143 1392

8 308 93 1244

Total 528 111 1332

Gov_info 2 76 189 1150

5 144 203 1064

8 310 209 1137

Total 530 205 1119


5 145 238 1291

8 309 201 1362

Total 530 214 1358


5 143 203 1171

8 309 181 1124

Total 527 187 1171

E_banking 2 77 268 1261

5 145 243 1413

8 308 219 1408

Total 530 233 1397


5 143 97 1119

8 304 64 965

Total 522 76 1039

307



how











10 不會 No



No comment

308

















33 No 34 Yes


35 不會 No

309










45 有機會 Maybe









310





311










312



kind of landlords




313




Statistics

To_new market

Prompt_ response


N Valid


559

106 146 100

1186 0 4

553

112 176 200

1205 0 4

556

109 167 200

1239 0 4

560

105 226 200

1135 0 4

Statistics



N Valid


553

112 212 200

1139 0 4

557

108 162 200

1228 0 4

552

113 162 200

1254 0 4

552

113 191 200

1210 0 4

314



- ANOVA






Within Groups

Total

36627

743221

779848

11

542

553

3330

1371

2428 006


Within Groups

Total

38840

760902

799743

11

536

547

3531

1420

2487 005


Within Groups

Total

50750

792136

842886

11

539

550

4614

1470

3139 000


Within Groups

Total

45955

671224

717178

11

543

554

4178

1236

3380 000


Within Groups

Total

45002

668755

713757

11

536

547

4091

1248

3279 000


Within Groups

Total

48150

782196

830346

11

540

551

4377

1449

3022 001


315


Within Groups

Total

806530

859664

535

546

1508


Between Groups

Within Groups

Total

33461

768671

802132

11

535

546

3042

1437

2117 018

Differences


To_new_market 301 59 163 1128

501 44 116 1200

601 100 146 1150

602 39 141 1044

603 48 171 1320

604 77 162 1148

701 23 70 1063

801 30 113 1074

802 9 144 1740

803 113 148 1196

Total 542 145 1185


501 42 143 1151

601 99 181 1226

602 38 171 898

603 48 198 1211

604 77 173 1210

701 21 86 910

801 30 213 1137

316


802

803

Total

8

113

535

112

176

174

1458

1205

1205


501 42 124 1226

601 101 174 1246

602 39 167 1084

603 48 206 1327

604 76 180 1233

701 21 52 1030

801 31 139 1086

802 9 167 1323

803 112 167 1233

Total 538 166 1241

Efficiency 301 59 231 1193

501 44 189 1185

601 101 213 1128

602 39 244 1021

603 48 265 1158

604 77 212 1076

701 21 157 1165

801 30 207 1015

802 9 189 1167

803 114 255 1048

Total 542 225 1132


501 43 163 1215

601 99 191 1089

602 38 232 1016

317


603 48 254 1184

604 76 195 1153

701 21 157 978

801 30 223 898

802 8 200 926

803 113 237 1151

Total 535 211 1141

Inc_sales 301 60 187 1186

501 44 105 987

601 99 167 1152

602 39 177 1158

603 48 194 1375

604 76 154 1216

701 23 83 984

801 30 173 1081

802 9 144 1667

803 112 161 1283

Total 540 160 1222

New_product 301 59 190 1241

501 44 118 1147

601 98 170 1203

602 39 174 1117

603 48 190 1403

604 76 172 1282

701 22 59 854

801 30 160 1133

802 8 88 1458

803 111 159 1260

Total 535 162 1253

318



501 43 147 1202

601 100 190 1159

602 38 208 1171

603 48 212 1362

604 75 180 1185

701 21 124 1044

801 30 200 1017

802 8 125 1581

803 113 206 1212

Total 535 191 1211




Within Groups

Total

3902

781021

784923

2

556

558

1951

1405

1389 250


Within Groups

Total

4108

797935

802043

2

550

552

2054

1451

1416 244


Within Groups

Total

2155

849622

851777

2

553

555

1078

1536

701 496


Within Groups

Total

1500

718984

720484

2

557

559

750

1291

581 560


Within Groups

986

715137

2

550

493

1300

379 685

319


Total 716123 552



Total 837781 556



Total 865868 551


Between Groups

Within Groups

Total

4623

802027

806650

2

549

551

2312

1461

1582 206

320





Within Groups

Total

14571

766974

781545

2

551

553

7286

1392

5234 006


Within Groups

Total

15890

779295

795185

2

544

546

7945

1433

5546 004


Within Groups

Total

20329

824035

844364

2

547

549

10165

1506

6747 001


Within Groups

Total

6611

711513

718125

2

551

553

3306

1291

2560 078


Within Groups

Total

8022

703735

711757

2

545

547

4011

1291

3106 046


Within Groups

Total

11916

819270

831186

2

550

552

5958

1490

4000 019


Within Groups

Total

12345

845181

857526

2

545

547

6173

1551

3980 019


Between Groups

Within Groups

Total

10923

788515

799438

2

545

547

5462

1447

3775 024

321




5 147 167 1155

8 314 132 1139

Total 537 145 1188


5 145 194 1212

8 309 159 1166

Total 530 173 1206


5 145 188 1239

8 311 148 1207

Total 533 165 1243


5 146 234 1146

8 314 216 1140

Total 537 225 1135


5 146 226 1151

8 309 200 1152

Total 531 211 1142

Inc_sales 2 75 172 1258

5 146 179 1231

8 315 148 1198

Total 536 160 1222

New_product 2

5

76

146

166

184

1292

1279

322


8

Total

309

531

150

161

1216

1251


5

8

Total

77

145

309

531

204

209

179

191

1251

1178

1204

1210

323




Sector = 301

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

58

736

000

58


Sig (2-tailed)

N

736

000

58

1


Sector = 501

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

44

564

000

44


Sig (2-tailed)

N

564

000

44

1

44

324


Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

44

564

000

44


Sig (2-tailed)

N

564

000

44

1


325


Sector = 601

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

103

713

000

103


Sig (2-tailed)

N

713

000

103

1


Sector = 602

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

42

619

000

42


Sig (2-tailed)

N

619

000

42

1


326


Sector = 603

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

51

684

000

51


Sig (2-tailed)

N

684

000

51

1


Sector = 604

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

77

630

000

77


Sig (2-tailed)

N

630

000

77

1


327


Sector = 701

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

21

768

000

21


Sig (2-tailed)

N

768

000

21

1


Sector = 801

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

31

464

009

31


Sig (2-tailed)

N

464

009

31

1


328


Sector = 803

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

113

680

000

113


Sig (2-tailed)

N

680

000

113

1



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

415

654

000

415


Sig (2-tailed)

N

654

000

415

1


329



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

118

752

000

118


Sig (2-tailed)

N

752

000

118

1



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

7

535

216

7


Sig (2-tailed)

N

535

216

7

1

7

330



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

75

719

000

75


Sig (2-tailed)

N

719

000

75

1



ICTU_2 VA_2


Sig (2-tailed)

N

1

147

641

000

147


Sig (2-tailed)

N

641

000

147

1


331



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

312

671

000

312


Sig (2-tailed)

N

671

000

312

1


332





N Valid 697

Missing 92

Have_Website


Percent Cumulative

Percent

Valid 0

1


375

322

697 92

789

475

408

883 117

1000

538

462

1000

538

1000





333




Statistics


N Valid

Missing

374

1

375

0

370

5

Statistics


N Valid 278 284 286 280 280 279


Statistics


No_trained_ person


N Valid

Missing Mean Median

277

98 101 100

283

92 185 200

287

88 193 200

282

93 148 100

277

98 145 100

334



885 0 4

1209 0 4

1265 0 4

1160 0 4

1171 0 4

335








Within Groups

Total

7618

259749

267367

9

260

269

846

999

847 573


Between Groups

Within Groups

Total

18801

455587

474388

9

266

275

2089

1713

1220 283


Between Groups

Within Groups

Total

19769

451903

471673

9

268

277

2197

1686

1303 235


Within Groups

Total

21660

353811

375471

9

262

271

2407

1350

1782 072


Within Groups

Total

15800

424167

439967

9

262

271

1756

1619

1084 375


Between Groups

Within Groups

Total

7068

275028

282096

9

261

270

785

1054

745 667


336



Total

201371

212000

259

268

777


Within Groups

Total

17947

385615

403562

9

264

273

1994

1461

1365 204

337



Within Groups

Total

17539

423565

441104

9

269

278

1949

1575

1238 272


Within Groups

Total

17452

352989

370442

9

264

273

1939

1337

1450 167


Between Groups

Within Groups

Total

17725

355033

372758

9

259

268

1969

1371

1437 172





Total 267367 269


Between Groups

Within Groups

Total

1144

473243

474388

1

274

275

1144

1727

662 416


Between Groups

Within Groups

Total

610

471062

471673

1

276

277

610

1707

358 550



Total 375471 271



Total 439967 271

338



Between Groups

Within Groups

Total

067

282029

282096

1

269

270

067

1048

064 801

339



Between Groups

Within Groups

Total

282

211718

212000

1

267

268

282

793

356 551



Total 403562 273



Total 441104 278



Total 370442 273


Between Groups

Within Groups

Total

1098

371661

372758

1

267

268

1098

1392

789 375





Total 266507 267


Between Groups

Within Groups

Total

1688

469232

470920

2

271

273

844

1731

487 615


Between Groups

Within Groups

Total

4542

466078

470620

2

273

275

2271

1707

1330 266

340



Within Groups

Total

1172

374090

375262

2

268

270

586

1396

420 658

341




Total 434652 269


Between Groups

Within Groups

Total

166

281908

282074

2

267

269

083

1056

079 924


Between Groups

Within Groups

Total

201

211799

212000

2

264

266

101

802

126 882



Total 398202 271



Total 440259 277



Total 363868 271


Between Groups

Within Groups

Total

2126

364142

366269

2

265

267

1063

1374

774 462

342














11 否 No




15 帶來新商機

343



No 17 沒有




20 不會 No










30 不會 No


No 33 未能估計


344


34 不會 No







No 42 不一定會




45 不會 No









345


54 不會 No














71 No 72 不會



346







79 不會 No

80 不會 No




84 没有 No


86 會 YES





347




348









349
















13 不會 No


15 不知道

350


Not sure 16 盡快



















351




















352





















353












84 銷售 For sales









354







355







356




Statistics

Hold_ workshop

ICT_ infrastructure

Legal_ regulatory

Tax_ incentive

N Valid


287

88 171 100

1272 0 4

280

95 188 200

1252 0 4

282

93 204 200

1364 0 4

285

90 235 300

1375 0 4

Statistics


N Valid 283 284 283


357







Within Groups

Total

17370

443543

460913

11

274

285

1579

1619

975 469


Within Groups

Total

9222

426634

435857

11

267

278

838

1598

525 886


Within Groups

Total

27644

494000

521644

11

269

280

2513

1836

1368 188


Within Groups

Total

28489

508001

536489

11

272

283

2590

1868

1387 179

Provide_more_ info

Between Groups

Within Groups

Total

26027

484256

510284

11

270

281

2366

1794

1319 213

Provide_ consulting

Between Groups

Within Groups

Total

21574

511380

532954

11

271

282

1961

1887

1039 412


Within Groups

26389

537256

11

270

2399

1990

1206 283

358





Within Groups

Total

17370

443543

460913

11

274

285

1579

1619

975 469


Within Groups

Total

9222

426634

435857

11

267

278

838

1598

525 886


Within Groups

Total

27644

494000

521644

11

269

280

2513

1836

1368 188


Within Groups

Total

28489

508001

536489

11

272

283

2590

1868

1387 179

Provide_more_ info

Between Groups

Within Groups

Total

26027

484256

510284

11

270

281

2366

1794

1319 213

Provide_ consulting

Between Groups

Within Groups

Total

21574

511380

532954

11

271

282

1961

1887

1039 412


Within Groups

Total

26389

537256

563645

11

270

281

2399

1990

1206 283

359





Within Groups

Total

4591

457981

462571

1

285

286

4591

1607

2857 092


Within Groups

Total

815

436296

437111

1

278

279

815

1569

519 472


Within Groups

Total

1254

521317

522571

1

280

281

1254

1862

673 413


Within Groups

Total

548

536364

536912

1

283

284

548

1895

289 591

Provide_more_ info

Between Groups

Within Groups

Total

1138

509993

511131

1

281

282

1138

1815

627 429

Provide_ consulting

Between Groups

Within Groups

Total

1579

531787

533366

1

282

283

1579

1886

837 361


Within Groups

Total

430

563613

564042

1

281

282

430

2006

214 644

360





Within Groups

Total

918

456410

457329

2

283

285

459

1613

285 752


Within Groups

Total

1147

430695

431842

2

275

277

573

1566

366 694


Within Groups

Total

3508

514135

517643

2

277

279

1754

1856

945 390


Within Groups

Total

1368

532815

534183

2

281

283

684

1896

361 697

Provide_more_ info

Between Groups

Within Groups

Total

4891

502545

507436

2

279

281

2445

1801

1358 259

Provide_ consulting

Between Groups

Within Groups

Total

4868

525796

530664

2

280

282

2434

1878

1296 275


Within Groups

Total

1420

558089

559509

2

278

280

710

2008

354 702

361




Statistics


N Valid

Missing

318

4

322

0

319

3

Statistics


Receive_ order


N Valid


288

34 295 300 970

0 4

278

44 225 200

1244 0 4

274

48 155 100

1383 0 4

275

47 96

100 1222

0 4

269

53 142 100

1251 0 4

Statistics

Delivery_ product

After_sale_ service


Online_ marketing

Knowledge_ mgt

N Valid

Missing Mean Median

272

50 129 100

279

43 159 100

274

48 179 200

274

48 145 100

266

56 139 100

362



1305 0 4

1364 0 4

1333 0 4

1396 0 4

1302 0 4

363








Within Groups

Total

9662

254743

264405

10

273

283

966

933

1035 414


Within Groups

Total

18697

400927

419624

10

263

273

1870

1524

1226 274


Within Groups

Total

38935

473617

512552

10

259

269

3894

1829

2129 023


Within Groups

Total

19078

387623

406701

10

260

270

1908

1491

1280 242


Within Groups

Total

13590

399633

413223

10

254

264

1359

1573

864 568


Within Groups

Total

23626

432494

456119

10

257

267

2363

1683

1404 179


Within Groups

20221

489576

10

264

2022

1854

1090 370

364


Total 509796 274


Between Groups

Within Groups

Total

8921

469064

477985

10

259

269

892

1811

493 894

365



Within Groups

Total

16601

506365

522967

10

259

269

1660

1955

849 582


Within Groups

Total

23867

421248

445115

10

251

261

2387

1678

1422 171



501 12 292 996

601 55 302 1080

602 20 310 852

603 29 272 841

604 37 308 894

701 10 230 1160

801 12 283 937

803 67 301 1052

Total 275 294 972

Hand_enquiry 301 32 216 1139

501 10 200 1414

601 53 258 1167

602 19 237 1257

603 28 200 1186

604 36 219 1142

701 9 167 1732

801 12 175 1215

803 66 229 1298

366


Total 265 224 1240


501 9 56 726

601 53 191 1445

602 18 161 1650

603 27 130 1203

604 36 172 1344

701 9 144 1740

801 11 36 674

803 66 150 1316

Total 261 154 1371


501 9 56 527

601 53 94 1322

602 18 89 1323

603 28 79 1134

604 36 125 1296

701 9 22 441

801 12 33 651

803 66 114 1264

Total 263 97 1223

Gov_info 301 32 162 976

501 8 112 1126

601 50 132 1220

602 18 111 1278

603 27 126 1289

604 36 147 1276

701 9 111 1537

801 12 117 1030

367


803

Total

65

257

174

144

1326

1243


501 9 56 726

601 52 142 1319

602 19 105 1433

603 28 100 1217

604 35 151 1269

701 9 44 726

801 11 82 874

803 65 145 1370

Total 260 129 1297


501 10 90 1101

601 53 164 1360

602 20 155 1701

603 28 114 1145

604 36 200 1287

701 9 122 1302

801 12 158 1379

803 66 167 1351

Total 267 159 1352


501 9 122 1394

601 52 177 1381

602 18 156 1338

603 28 182 1307

604 36 194 1308

701 9 167 1500

368


801

803

Total

12

65

262

175

186

181

1422

1298

1313


501 9 78 1093

601 53 158 1447

602 17 112 1495

603 28 132 1278

604 35 166 1413

701 9 78 972

801 12 125 1485

803 66 155 1405

Total 262 145 1385


501 9 89 1269

601 52 115 1274

602 17 135 1169

603 27 100 920

604 34 150 1261

701 9 89 1537

801 12 183 1528

803 63 170 1375

Total 254 140 1296




369


Within Groups

Total

266454

270219

285

287

935


Within Groups

Total

5040

423334

428374

2

275

277

2520

1539

1637 196


Within Groups

Total

944

520939

521883

2

271

273

472

1922

246 782


Within Groups

Total

2288

407188

409476

2

272

274

1144

1497

764 467


Within Groups

Total

20950

398581

419532

2

266

268

10475

1498

6991 001


Within Groups

Total

351

461281

461632

2

269

271

176

1715

102 903


Within Groups

Total

1824

515409

517233

2

276

278

912

1867

488 614


Between Groups

Within Groups

Total

4494

480648

485142

2

271

273

2247

1774

1267 283


Within Groups

Total

1524

530154

531679

2

271

273

762

1956

390 678


Within Groups

Total

1245

448094

449338

2

263

265

622

1704

365 694

370




25 82 301 853

75 7 229 951

Total 275 294 972


25 82 235 1159

75 6 150 1225

Total 265 224 1240


25 80 145 1349

75 7 129 1254

Total 261 154 1371


25 81 81 1130

75 7 114 1215

Total 263 97 1223

Gov_info 5 175 126 1198

25 76 184 1265

75 6 167 1211

Total 257 144 1243


25 80 121 1260

75 7 114 1464

Total 260 129 1297


25

179

81

164

152

1351

1361

371


75

Total

7

267

114

159

1345

1352


25

75

Total

175

80

7

262

177

195

129

181

1328

1282

1254

1313

Online_marketing 5

25

75

Total

175

80

7

262

151

135

114

145

1422

1303

1464

1385

Knowledge_mgt 5

25

75

Total

168

79

7

254

136

144

171

140

1315

1278

1113

1296

372





Within Groups

Total

1342

267760

269102

2

282

284

671

950

707 494


Within Groups

Total

1700

426050

427750

2

273

275

850

1561

545 581


Within Groups

Total

209

517169

517379

2

269

271

105

1923

054 947


Within Groups

Total

9361

398111

407473

2

270

272

4681

1474

3174 043


Within Groups

Total

3656

413026

416682

2

264

266

1828

1564

1168 312


Within Groups

Total

1856

456310

458167

2

267

269

928

1709

543 582


Within Groups

Total

6881

508203

515083

2

274

276

3440

1855

1855 158


Between Groups

Within Groups

Total

2647

479556

482202

2

269

271

1323

1783

742 477


373


Within Groups

Total

525237

527173

269

271

1953

374



Within Groups

Total

3384

442976

446360

2

261

263

1692

1697

997 370



5 83 299 848

8 156 288 1035

Total 272 294 976


5 79 241 1138

8 150 215 1241

Total 263 224 1244


5 77 156 1313

8 149 152 1373

Total 259 154 1370


5 79 113 1295

8 149 81 1117

Total 261 97 1224

Gov_info 2 33 109 1208

5 78 146 1203

8 144 150 1268

Total 255 144 1243


375


5

8

Total

79

147

258

142

120

128

1392

1220

1297


5 79 186 1394

8 152 145 1291

Total 265 158 1354


5 79 200 1301

8 148 172 1278

Total 260 180 1314


5 78 151 1336

8 149 139 1339

Total 260 145 1384


5 74 153 1241

8 145 130 1292

Total 252 139 1296

376




Statistics

Online_ transaction


Imp_ productivity

New_bus_ model

N Valid


278

44 170 200

1212 0 4

280

42 204 200

1159 0 4

277

45 193 200

1173 0 4

278

44 203 200

1144 0 4

274

48 180 200

1191 0 4


Prompt_ response


N Valid


276

46 191 200

1187 0 4

278

44 182 200

1173 0 4

273

49 178 200

1168 0 4

275

47 180 200

1128 0 4

278

44 211 200

1127 0 4

377




N Valid


278

44 209 200

1130 0 4

282

40 187 200

1161 0 4

274

48 204 200

1149 0 4

378








Within Groups

Total

16346

385902

402248

10

263

273

1635

1467

1114 352


Within Groups

Total

10744

357963

368707

10

265

275

1074

1351

795 633


Within Groups

Total

15345

358040

373385

10

262

272

1535

1367

1123 345


Within Groups

Total

9570

348299

357869

10

264

274

957

1319

725 700


Within Groups

Total

13436

365804

379240

10

260

270

1344

1407

955 483


Within Groups

Total

12490

369025

381515

10

261

271

1249

1414

883 549


Within Groups

17469

358122

10

263

1747

1362

1283 240

379


Total 375591 273


Within Groups

Total

18549

347558

366107

10

259

269

1855

1342

1382 188

380




Total 343240 270



Total 345493 273



Total 347069 273



Total 373338 277


Between Groups

Within Groups

Total

11452

342014

353467

10

259

269

1145

1321

867 565



501

34

9

191

211

1055

1269

601

602

603

604

701

801

51

19

27

38

9

13

151

179

152

166

111

185

1302

1134

1282

1214

1167

1214

803

Total

66

266

167

167

1232

1215

381



501 9 189 1364

601 54 194 1054

602 18 217 1200

603 27 204 1091

604 38 189 1290

701 9 133 1414

801 13 200 1291

803 66 206 1162

Total 268 202 1163

New_product 301 33 212 1166

501 10 160 1265

601 50 188 1100

602 17 188 1166

603 27 185 1099

604 38 216 1151

701 10 110 1449

801 13 215 1144

803 67 184 1201

Total 265 191 1171


501 9 211 1054

601 53 189 1068

602 18 200 1138

603 27 181 1331

604 38 195 1229

701 9 156 1509

801 13 223 1092

803 67 212 1148

382


Total 267 201 1140


501 9 178 1302

601 52 179 1210

602 17 171 1160

603 27 159 1248

604 37 184 1280

701 9 100 1323

801 13 200 1080

803 66 180 1126

Total 263 179 1174


501 9 178 1302

601 53 179 1116

602 17 171 1160

603 27 170 1325

604 37 203 1213

701 9 167 1581

801 13 192 1115

803 66 188 1183

Total 264 190 1176

New_market 301 33 218 1158

501 9 178 1302

601 53 198 1118

602 18 172 1227

603 27 181 1210

604 37 173 1194

701 9 89 1269

801 13 177 1013

383


803

Total

67

266

173

182

1162

1174


501 9 156 1236

601 52 185 1127

602 17 182 1074

603 27 163 1149

604 37 173 1217

701 9 89 1269

801 12 192 1084

803 66 168 1205

Total 262 178 1170


501 9 156 1236

601 52 190 1125

602 17 176 1091

603 27 170 1137

604 37 170 1151

701 9 67 1323

801 13 215 987

803 65 169 1131

Total 263 180 1136


501 9 222 972

601 54 209 1014

602 18 222 1060

603 27 200 1301

604 37 208 1187

701 9 156 1509

384


801

803

Total

13

65

266

200

214

212

1080

1197

1125


501 9 178 1093

601 53 213 1001

602 18 206 1056

603 27 185 1262

604 37 205 1177

701 9 167 1500

801 13 215 1068

803 67 216 1201

Total 266 209 1124

Inc_sales 301 33 209 1042

501 9 156 1509

601 54 202 1037

602 19 184 1068

603 28 157 1136

604 37 197 1166

701 10 110 1370

801 13 185 1214

803 66 180 1193

Total 269 185 1149


501 9 144 1333

601 51 210 1005

602 18 200 1085

603 27 185 1134

604 36 192 1228

385


701 9 189 1364

801 13 238 961

803 67 203 1231

Total 262 204 1141




Within Groups

Total

785

405834

406619

2

275

277

392

1476

266 767


Within Groups

Total

5541

369026

374568

2

277

279

2771

1332

2080 127


Within Groups

Total

344

379353

379697

2

274

276

172

1384

124 883


Within Groups

Total

5741

357083

362824

2

275

277

2870

1298

2211 112


Within Groups

Total

1776

385582

387358

2

271

273

888

1423

624 537


Within Groups

Total

9991

377560

387551

2

273

275

4995

1383

3612 028


386



Total 381007 277



Total 370813 272



Total 348596 274



Total 351543 277



Total 353928 277



Total 378879 281


Between Groups

Within Groups

Total

6348

354210

360558

2

271

273

3174

1307

2429 090



25

75

Total

180

79

7

266

165

167

200

167

1253

1140

1155

1215


387


25

75

Total

80

7

268

222

229

202

1091

951

1163

New_product 5 179 191 1184

25 79 190 1172

75 7 214 900

Total 265 191 1171


25 80 221 1002

75 7 229 951

Total 267 201 1140


25 79 180 1018

75 7 229 951

Total 263 179 1174


25 80 216 1084

75 7 229 951

Total 264 190 1176

New_market 5 180 180 1179

25 79 182 1185

75 7 229 951

Total 266 182 1174


25 79 190 1150

75 7 229 951

Total 262 178 1170


25

177

79

173

191

1151

1112

388


75

Total

7

263

229

180

951

1136


25 80 231 1063

75 7 229 951

Total 266 212 1125


25 80 230 1036

75 7 229 951

Total 266 209 1124

Inc_sales 5 182 183 1175

25 80 188 1107

75 7 214 1069

Total 269 185 1149


25 77 225 1041

75 7 229 951

Total 262 204 1141

389





Within Groups

Total

1201

401905

403105

2

272

274

600

1478

406 667


Within Groups

Total

344

371425

371769

2

274

276

172

1356

127 881


Within Groups

Total

793

375441

376234

2

271

273

396

1385

286 751


Within Groups

Total

058

359884

359942

2

272

274

029

1323

022 978


Within Groups

Total

736

383735

384471

2

269

271

368

1427

258 773


Within Groups

Total

257

384882

385139

2

271

273

128

1420

090 914


Within Groups

Total

2530

375673

378203

2

273

275

1265

1376

919 400


Within Groups

Total

1771

366045

367815

2

268

270

885

1366

648 524


390


Within Groups

Total

344439

345678

270

272

1276

391




Total 349953 275



Total 352246 275



Total 376286 279


Between Groups

Within Groups

Total

4955

353747

358702

2

269

271

2478

1315

1884 154

392




Sector = 601

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

16

061

824

16


Sig (2-tailed)

N

061

824

16

1

16

Sector = 803

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

23

224

303

23


Sig (2-tailed)

N

224

303

23

1

23

393



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

17

-033

901

17


Sig (2-tailed)

N

-033

901

17

1

17


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

39

096

563

39


Sig (2-tailed)

N

096

563

39

1

39

394



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

17

-055

833

17


Sig (2-tailed)

N

-055

833

17

1

17




Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

7

316

490

7


Sig (2-tailed)

N

316

490

7

1

7

395


396



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

22

-276

214

22


Sig (2-tailed)

N

-276

214

22

1

22


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

27

176

380

27


Sig (2-tailed)

N

176

380

27

1

27

397




Web_inte_firm_DB

N Valid 304

Missing 18


Frequency Table

Web_inte_firm_DB


Percent

Valid

Missing Total

0

1

Total System

237

67

304 18

322

736

208

944 56

1000

780

220

1000

780

1000

398




Web_inte_Partner_DB

N Valid 303

Missing 19


Frequency Table

Web_inte_Partner_DB


Percent

Valid

Missing Total

0

1

Total System

256

47

303 19

322

795

146

941 59

1000

845

155

1000

845

1000

399





Statistics


year

N Valid

Missing

591

0

591

0

584

7

OverAll_ICT

N Valid 573


400


OverAll_ICT


Percent Cumulative

Percent

Valid 0

1

2

3

4


10

68

143

224

128

573 18

591

17

115

242

379

217

970 30

1000

17

119

250

391

223

1000

17

136

386

777

1000

401





Statistics


N Valid

Missing

591

0

591

0

584

7

OverAll_VA

N Valid 573


402


OverAll_VA


Percent Cumulative

Percent

Valid 0

1

2

3

4


10

49

165

148

201

573 18

591

17

83

279

250

340

970 30

1000

17

86

288

258

351

1000

17

103

391

649

1000

403








Within Groups

Total

77972

933307

1011279

10

716

726

7797

1304

5982 000


Between Groups

Within Groups

Total

81024

1015674

1096697

10

719

729

8102

1413

5736 000



501

601

72

63

162

261

230

209

1095

1328

1282

602

603

604

605

701

801

53

60

94

11

30

33

249

280

266

136

223

294

1103

1102

1063

809

1073

827

802

803

17

132

188

278

1453

1029

404


Total 727 247 1180

405



301

501

72

63

283

251

1245

1306

601 164 227 1259

602 53 279 1183

603 60 293 1163

604 94 283 1104

605 12 167 1231

701 30 237 1098

801 33 303 1015

802 17 182 1468

803 132 298 1095

Total 730 266 1227




Within Groups

Total

40199

982198

1022398

2

729

731

20100

1347

14918 000


Between Groups

Within Groups

Total

46467

1055756

1102223

2

732

734

23234

1442

16109 000


Overall_ICT_usage 5

25

567

158

235

292

1224

896

406


75 7 243 1134

Total 732 247 1183

407



75 7 300 1155

Total 735 265 1225

408






Within Groups

Total

7818

1000033

1007851

2

720

722

3909

1389

2814 061


Between Groups

Within Groups

Total

6042

1083392

1089434

2

723

725

3021

1498

2016 134

Overall_ICT_usage 2

5

8

Total

100

181

442

723

251

263

239

246

1219

1081

1207

1181


2

5

8

Total

100

181

445

726

258

281

260

265

1216

1141

1258

1226

409





Model Summary




ANOVAb


1 Regression

Residual

Total

42233

29504

71738

1

59

60

42233

500

84455 000a


Coefficientsa

Model




1 (Constant) 480 296 1623 110

410


OverAll_ICT_Usa ge

922 100 767 9190 000


411



Model Summary




ANOVAb


1 Regression

Residual

Total

3171E15

5881E17

5912E17

1

34

35

3171E15

1730E16

183 671a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

5598E6

9770E6

6587E7

2282E7 073

085

428

933

671


412


413



Model Summary




ANOVAb


1 Regression

Residual

Total

35148

18403

53551

1

47

48

35148

392

89765 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

829

741

220

078 810

3767

9474

000

000


414



Model Summary




ANOVAb


1 Regression

Residual

Total

3472E13

3988E14

4336E14

1

23

24

3472E13

1734E13

2002 170a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

373696884

1109E6

2143E6

783606583 283

174

1415

863

170


415



Model Summary




ANOVAb


1 Regression

Residual

Total

81668

40369

122037

1

107

108

81668

377

216463 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

534

821

153

056 818

3503

14713

001

000


416



Model Summary




ANOVAb


1 Regression

Residual

Total

2947E15

9801E17

9831E17

1

65

66

2947E15

1508E16

195 660a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

4712E6

5991E6

3826E7

1355E7 055

123

442

902

660


417



Model Summary




ANOVAb


1 Regression

Residual

Total

27336

11069

38405

1

40

41

27336

277

98786 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

566

876

247

088 844

2297

9939

027

000


418



Model Summary




ANOVAb


1 Regression

Residual

Total

5802E14

1663E16

1721E16

1

21

22

5802E14

7917E14

733 402a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

3463E6

5273E6

1761E7

6159E6 184

197

856

846

402


419


420



Model Summary




ANOVAb


1 Regression

Residual

Total

38692

25234

63926

1

52

53

38692

485

79732 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

650

811

276

091 778

2356

8929

022

000


421



Model Summary




ANOVAb


1 Regression

Residual

Total

4956E16

9225E17

9720E17

1

24

25

4956E16

3844E16

1289 267a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-5639E7

4021E7

1091E8

3541E7 226

-517

1136

610

267


422


423



Model Summary




ANOVAb


1 Regression

Residual

Total

36967

44421

81387

1

78

79

36967

569

64911 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

904

707

249

088 674

3639

8057

000

000


424



Model Summary




ANOVAb


1 Regression

Residual

Total

2849E15

1954E16

2239E16

1

31

32

2849E15

6303E14

4520 042a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1051E7

1004E7

1266E7

4724E6 357

-830

2126

413

042


425



Model Summary




ANOVAb


1 Regression

Residual

Total

6746

15254

22000

1

23

24

6746

663

10172 004a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1188

572

413

179 554

2873

3189

009

004


426



Model Summary




ANOVAb


1 Regression

Residual

Total

1876E14

3349E15

3536E15

1

13

14

1876E14

2576E14

728 409a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1563E7

-4142E6

1026E7

4854E6 -230

1524

-853

152

409


427


428



Model Summary




ANOVAb


1 Regression

Residual

Total

17162

10709

27871

1

29

30

17162

369

46474 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

080

1006

451

148 785

177

6817

861

000


429



Model Summary




ANOVAb


1 Regression

Residual

Total

2256E12

3976E13

4201E13

1

17

18

2256E12

2339E12

965 340a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-142204397

437459701

1336E6

445374398 232

-106

982

916

340


430


431



Model Summary




ANOVAb


1 Regression

Residual

Total

17210

890

18100

1

8

9

17210

111

154652 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-159

1024

224

082 975

-707

12436

499

000


432



Model Summary




ANOVAb


1 Regression

Residual

Total

1073E11

3180E11

4253E11

1

1

2

1073E11

3180E11

338 665a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-201253500

401253500

1196E6

690649197 502

-168

581

894

665


433



Model Summary




ANOVAb


1 Regression

Residual

Total

49230

64628

113857

1

110

111

49230

588

83792 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1021

707

232

077 658

4408

9154

000

000


434



Model Summary




ANOVAb


1 Regression

Residual

Total

2729E15

8560E16

8833E16

1

66

67

2729E15

1297E15

2104 152a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1306E7

6948E6

1415E7

4791E6 176

-923

1450

359

152


435


436



Model Summary




ANOVAb


1 Regression

Residual

Total

288679

209092

497771

1

435

436

288679

481

600574 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

689

791

091

032 762

7584

24507

000

000


437



Model Summary




ANOVAb


1 Regression

Residual

Total

1903E15

1171E17

1190E17

1

241

242

1903E15

4858E14

3917 049a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1660E6

2747E6

3814E6

1388E6 126

-435

1979

664

049


438



Model Summary




ANOVAb


1 Regression

Residual

Total

64204

55486

119690

1

127

128

64204

437

146955 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

726

813

204

067 732

3552

12122

001

000


439



Model Summary




ANOVAb


1 Regression

Residual

Total

1875E16

2439E18

2457E18

1

65

66

1875E16

3752E16

500 482a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

5313E6

1918E7

8514E7

2713E7 087

062

707

950

482


440



Model Summary




ANOVAb


1 Regression

Residual

Total

4667

3333

8000

1

5

6

4667

667

7000 046a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1111

778

778

294 764

1429

2646

212

046


441



Model Summary




ANOVAb


1 Regression

Residual

Total

5762E14

1377E15

1953E15

1

3

4

5762E14

4588E14

1256 344a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1025E7

1053E7

2623E7

9394E6 543

-391

1121

722

344


442


443



Model Summary




ANOVAb


1 Regression

Residual

Total

57734

24754

82487

1

78

79

57734

317

181922 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

480

845

181

063 837

2660

13488

009

000


444



Model Summary




ANOVAb


1 Regression

Residual

Total

2182E11

1793E14

1795E14

1

39

40

2182E11

4598E12

047 829a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1716E6

-75827787

960185400

348072513 -035

1787

-218

082

829


445



Model Summary




ANOVAb


1 Regression

Residual

Total

65414

85419

150833

1

148

149

65414

577

113338 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

865

742

207

070 659

4177

10646

000

000


446



Model Summary




ANOVAb


1 Regression

Residual

Total

2427E13

6154E17

6154E17

1

79

80

2427E13

7790E15

003 956a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1466E7

632330104

3470E7

1133E7 006

422

056

674

956


447


448



Model Summary




ANOVAb


1 Regression

Residual

Total

235966

156031

391997

1

334

335

235966

467

505110 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

684

809

101

036 776

6791

22475

000

000


449



Model Summary




ANOVAb


1 Regression

Residual

Total

4788E16

2075E18

2123E18

1

188

189

4788E16

1104E16

4338 039a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1675E7

1527E7

1999E7

7330E6 150

-838

2083

403

039


450







Model Summary




ANOVAb


1 Regression

Residual

Total

6244E15

5866E17

5929E17

1

36

37

6244E15

1629E16

383 540a


Coefficientsa

Model




451


1 (Constant)

ICTU_1

-5083E6

1619E6

6134E7

2615E6 103

-083

619

934



Model Summary




ANOVAb


1 Regression

Residual

Total

2926E12

4319E14

4349E14

1

24

25

2926E12

1800E13

163 690a


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E6

39812145

2195E6

98729675 082

1049

403

304

690

452


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E6

39812145

2195E6

98729675 082

1049

403

304


453



Model Summary




ANOVAb


1 Regression

Residual

Total

1011E17

8848E17

9858E17

1

69

70

1011E17

1282E16

7882 006a


Coefficientsa

Model




1 (Constant)

ICTU_1

-7495E7

4629E6

3660E7

1649E6 320

-2048

2808

044


454



Model Summary




ANOVAb


1 Regression

Residual

Total

1496E15

1598E16

1747E16

1

22

23

1496E15

7262E14

2061 165a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1402E7

1355E6

2229E7

944159291 293

-629

1435

536


455



Model Summary




ANOVAb


1 Regression

Residual

Total

1080E16

9612E17

9720E17

1

24

25

1080E16

4005E16

270 608a


Coefficientsa

Model




1 (Constant)

ICTU_1

-5220E6

2500E6

1308E8

4815E6 105

-040

519

969


456



Model Summary




ANOVAb


1 Regression

Residual

Total

3448E15

1894E16

2239E16

1

31

32

3448E15

6109E14

5643 024a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2625E7

1646E6

1779E7

692834279 392

-1476

2376

150


457



Model Summary




ANOVAb


1 Regression

Residual

Total

4180E14

3136E15

3554E15

1

14

15

4180E14

2240E14

1866 193a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1260E7

1055E6

1546E7

771942283 343

-815

1366

429


458



Model Summary




ANOVAb


1 Regression

Residual

Total

1021E14

3271E17

3272E17

1

18

19

1021E14

1817E16

006 941a


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E7

316607483

1035E8

4223E6 018

223

075

826


459



Model Summary




ANOVAb


1 Regression

Residual

Total

1770E11

2483E11

4253E11

1

1

2

1770E11

2483E11

713 554a


Coefficientsa

Model




1 (Constant)

ICTU_1

796579705

-26681949

484411840

31599544 -645

1644

-844

348


460



Model Summary




ANOVAb


1 Regression

Residual

Total

3856E14

8801E16

8840E16

1

68

69

3856E14

1294E15

298 587a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1422E6

313224467

1478E7

573875768 066

-096

546

924


461



Model Summary




ANOVAb


1 Regression

Residual

Total

1842E15

1173E17

1191E17

1

249

250

1842E15

4709E14

3912 049a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2593E6

361167362

4213E6

182595787 124

-615

1978

539


462



Model Summary




ANOVAb


1 Regression

Residual

Total

7807E16

2657E18

2735E18

1

69

70

7807E16

3850E16

2028 159a


Coefficientsa

Model




1 (Constant)

ICTU_1

-3433E7

4040E6

7641E7

2837E6 169

-449

1424

655


463




Model Summary




ANOVAb


1 Regression

Residual

Total

5320E14

1421E15

1953E15

1

3

4

5320E14

4736E14

1123 367a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2509E7

1636E6

4099E7

1543E6 522

-612

1060

584


464



Model Summary




ANOVAb


1 Regression

Residual

Total

7515E14

3354E17

3362E17

1

42

43

7515E14

7987E15

094 761a


Coefficientsa

Model




1 (Constant)

ICTU_1

2917E6

560292822

4179E7

1827E6 047

070

307

945


465



Model Summary




ANOVAb


1 Regression

Residual

Total

1036E15

6147E17

6157E17

1

81

82

1036E15

7589E15

137 713a


Coefficientsa

Model




1 (Constant)

ICTU_1

3437E6

518019598

3587E7

1402E6 041

096

370

924


466



Model Summary




ANOVAb


1 Regression

Residual

Total

9511E16

2032E18

2127E18

1

195

196

9511E16

1042E16

9129 003a


Coefficientsa

Model




1 (Constant)

ICTU_1

-3801E7

2698E6

2100E7

892974189 211

-1810

3021

072


467





Model Summary




ANOVAb


1 Regression

Residual

Total

6980E14

5912E17

5919E17

1

35

36

6980E14

1689E16

041 840a


Coefficientsa

Model




1 (Constant)

ICTU_2

4012E7

-264324127

4753E7

1300E6 -034

844

-203

404


468


Sector = 501


Model Summary




ANOVAb


1 Regression

Residual

Total

4411E12

4200E14

4244E14

1

22

23

4411E12

1909E13

231 635a


Coefficientsa

Model




1 (Constant)

ICTU_2

4148E6

-38835311

1991E6

80784609 -102

2083

-481

049


469


Sector = 601


Model Summary




ANOVAb


1 Regression

Residual

Total

5449E15

9752E17

9807E17

1

61

62

5449E15

1599E16

341 562a


Coefficientsa

Model




1 (Constant)

ICTU_2

3539E6

595025787

3537E7

1019E6 075

100

584

921


470


Sector = 602


Model Summary




ANOVAb


1 Regression

Residual

Total

4215E12

1720E16

1721E16

1

21

22

4215E12

8191E14

005 943a


Coefficientsa

Model




1 (Constant)

ICTU_2

1687E7

25515710

1273E7

355718740 016

1325

072

199


471


Sector = 603


Model Summary




ANOVAb


1 Regression

Residual

Total

6422E15

9623E17

9687E17

1

23

24

6422E15

4184E16

154 699a


Coefficientsa

Model




1 (Constant)

ICTU_2

2005E7

1125E6

1143E8

2872E6 081

175

392

862


472


Sector = 604


Model Summary




ANOVAb


1 Regression

Residual

Total

1469E14

2220E16

2235E16

1

30

31

1469E14

7400E14

198 659a


Coefficientsa

Model




1 (Constant)

ICTU_2

1012E7

145732042

1184E7

327118372 081

855

446

399


473


Sector = 701


Model Summary




ANOVAb


1 Regression

Residual

Total

1085E13

3456E15

3467E15

1

11

12

1085E13

3142E14

035 856a


Coefficientsa

Model




1 (Constant)

ICTU_2

9965E6

-89253997

9595E6

480355550 -056

1038

-186

321


474


Sector = 801


Model Summary




ANOVAb


1 Regression

Residual

Total

7395E12

3462E13

4201E13

1

17

18

7395E12

2036E12

3631 074a


Coefficientsa

Model




1 (Constant)

ICTU_2

-358579784

57492686

844124906

30169605 420

-425

1906

676


475


Sector = 803


Model Summary




ANOVAb


1 Regression

Residual

Total

1206E15

8709E16

8830E16

1

65

66

1206E15

1340E15

900 346a


Coefficientsa

Model




1 (Constant)

ICTU_2

-1385E6

240581087

9483E6

253638748 117

-146

949

884


476




Model Summary




ANOVAb


1 Regression

Residual

Total

1417E15

1173E17

1187E17

1

231

232

1417E15

5078E14

2790 096a


Coefficientsa

Model




1 (Constant)

ICTU_2

978212467

157736521

3142E6

94438934 109

311

1670

756


477




Model Summary




ANOVAb


1 Regression

Residual

Total

5012E14

2448E18

2449E18

1

63

64

5012E14

3886E16

013 910a


Coefficientsa

Model




1 (Constant)

ICTU_2

5912E7

170835543

5868E7

1504E6 014

1007

114

318


478




Model Summary




ANOVAb


1 Regression

Residual

Total

1191E14

1834E15

1953E15

1

3

4

1191E14

6112E14

195 689a


Coefficientsa

Model




1 (Constant)

ICTU_2

6928E6

286292320

2560E7

648542329 247

271

441

804


479




Model Summary




ANOVAb


1 Regression

Residual

Total

4080E12

1714E14

1754E14

1

37

38

4080E12

4631E12

881 354a


Coefficientsa

Model




1 (Constant)

ICTU_2

950276580

19302217

695194361

20565148 152

1367

939

180


480




Model Summary




ANOVAb


1 Regression

Residual

Total

8055E14

6145E17

6153E17

1

79

80

8055E14

7778E15

104 748a


Coefficientsa

Model




1 (Constant)

ICTU_2

2268E7

-175766626

2131E7

546168623 -036

1064

-322

290


481




Model Summary




ANOVAb


1 Regression

Residual

Total

3542E16

2082E18

2118E18

1

178

179

3542E16

1170E16

3028 084a


Coefficientsa

Model




1 (Constant)

ICTU_2

-5476E6

975119286

1824E7

560375624 129

-300

1740

764


482





Model Summary




ANOVAb


1 Regression

Residual

Total

3128E16

8480E17

8793E17

1

7

8

3128E16

1211E17

258 627a


Coefficientsa

Model




1 (Constant)

ICTU_3

2894E8

-2836E7

3603E8

5581E7 -189

803

-508

448


483


484



Model Summary




ANOVAb


1 Regression

Residual

Total

1126E13

5714E14

5827E14

1

13

14

1126E13

4395E13

256 621a


Coefficientsa

Model




1 (Constant)

ICTU_3

6593E6

-391767855

5193E6

774196262 -139

1269

-506

227


485



Model Summary




ANOVAb


1 Regression

Residual

Total

5671E16

7964E17

8531E17

1

5

6

5671E16

1593E17

356 577a


Coefficientsa

Model




1 (Constant)

ICTU_3

4010E8

-3985E7

4551E8

6678E7 -258

881

-597

418


486



Model Summary




ANOVAb


1 Regression

Residual

Total

2133E11

7875E13

7896E13

1

18

19

2133E11

4375E12

049 828a


Coefficientsa

Model




1 (Constant)

ICTU_3

2242E6

-47696427

1429E6

216023611 -052

1569

-221

134


487



Model Summary




ANOVAb


1 Regression

Residual

Total

1382E17

1469E18

1607E18

1

9

10

1382E17

1632E17

847 381a


Coefficientsa

Model




1 (Constant)

ICTU_3

5096E8

-5034E7

3688E8

5471E7 -293

1382

-920

200


488



NO such samples




Model Summary




ANOVAb


1 Regression

Residual

Total

8977E12

8105E13

9003E13

1

10

11

8977E12

8105E12

1108 317a


Coefficientsa



489


B Std Error Beta

1 (Constant)

ICTU_3

4770E6

-399494044

2511E6

379586332 -316

1900

-1052

087



Model Summary




ANOVAb


1 Regression

Residual

Total

8555E16

1673E18

1758E18

1

16

17

8555E16

1045E17

818 379a


Coefficientsa

Model




1 (Constant)

ICTU_3

3129E8

-3078E7

2304E8

3403E7 -221

1358

-905

193

379

490


Coefficientsa

Model




1 (Constant)

ICTU_3

3129E8

-3078E7

2304E8

3403E7 -221

1358

-905

193


491






N

Stage 01_No-PC 70

02_No-Internet 39

Business_year

03_No-Web

2

5

8

285

60

85

249



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

6608a

373834

1085

947

4650

683615

1816000

8

1

2

2

4

385

394

826

373834

543

473

1162

1776

465

210537

306

267

655

880

000

737

766

624

492




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

6608a

373834

1085

947

4650

683615

1816000

690223

8

1

2

2

4

385

394

393

826

373834

543

473

1162

1776

465

210537

306

267

655

880

000

737

766

624




N

Stage 01_No-PC 70

02_No-Internet 39

No_of_employee

03_No-Web

5

25

286

348

47



493


Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

9007a

168372

4432

2551

7195

686537

1832000

695544

5

1

2

1

2

389

395

394

1801

168372

2216

2551

3598

1765

1021

95402

1256

1445

2039

405

000

286

230

132


494




N

Stage 01_No-PC 69

02_No-Internet 39

03_No-Web 285

Sector 301 32

501 45

601 110

602 21

603 24

604 39

605 12

701 18

801 20

802 12

803 60



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

53712a

299521

8298

26837

32474

31

1

2

10

19

1733

299521

4149

2684

1709

978

169042

2342

1515

965

504

000

098

132

503

495


Error

Total

Corrected Total

639646

1822000

693359

361

393

392

1772




N

Stage 01_No-PC 64

02_No-Internet 36

Business_year

03_No-Web

2

5

8

283

60

84

239



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

49471a

475983

18892

5790

9619

752456

2618000

801927

8

1

2

2

4

374

383

382

6184

475983

9446

2895

2405

2012

3074

236582

4695

1439

1195

002

000

010

239

312


496


497




N

Stage 01_No-PC 64

02_No-Internet 37

No_of_employee

03_No-Web

5

25

284

337

48



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

36573a

203771

1941

2282

3361

769334

2643000

805906

5

1

2

1

2

379

385

384

7315

203771

971

2282

1680

2030

3603

100385

478

1124

828

003

000

620

290

438


498


499




N

Stage 01_No-PC 63

02_No-Internet 37

03_No-Web 283

Sector 301 31

501 45

601 103

602 21

603 24

604 40

605 12

701 18

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

107517a

252882

25296

34163

45713

697052

32

1

2

10

20

350

3360

252882

12648

3416

2286

1992

1687

126976

6351

1715

1148

013

000

002

076

299

500


Total

Corrected Total

2625000

804569

383

382




N

Stage 01_No-PC 62

02_No-Internet 39

Business_year

03_No-Web

2

5

8

281

59

83

240



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

15418a

475509

4627

2460

3020

696571

2232000

711990

8

1

2

2

4

373

382

381

1927

475509

2313

1230

755

1867

1032

254625

1239

659

404

411

000

291

518

806

501




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

15418a

475509

4627

2460

3020

696571

2232000

8

1

2

2

4

373

382

1927

475509

2313

1230

755

1867

1032

254625

1239

659

404

411

000

291

518

806


502




N

Stage 01_No-PC 62

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

336

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

13636a

210249

1154

3717

7353

702364

2248000

716000

5

1

2

1

2

377

383

382

2727

210249

577

3717

3676

1863

1464

112853

310

1995

1973

201

000

734

159

140


503


504




N

Stage 01_No-PC 61

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 44

601 102

602 21

603 24

604 39

605 12

701 18

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

70928a

318217

7431

29448

41008

31

1

2

10

19

2288

318217

3716

2945

2158

1242

172699

2017

1598

1171

180

000

135

105

280

505


Error

Total

Corrected Total

643072

2238000

714000

349

381

380

1843




N

Stage 01_No-PC 64

02_No-Internet 39

Business_year

03_No-Web

2

5

8

282

59

85

241



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

26547a

622144

10971

4166

8061

736897

2743000

763444

8

1

2

2

4

376

385

384

3318

622144

5485

2083

2015

1960

1693

317448

2799

1063

1028

098

000

062

347

392

506




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

26547a

622144

10971

4166

8061

736897

2743000

8

1

2

2

4

376

385

3318

622144

5485

2083

2015

1960

1693

317448

2799

1063

1028

098

000

062

347

392


507




N

Stage 01_No-PC 64

02_No-Internet 39

No_of_employee

03_No-Web

5

25

283

339

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

16316a

252086

1488

2499

5964

750122

2759000

766438

5

1

2

1

2

380

386

385

3263

252086

744

2499

2982

1974

1653

127703

377

1266

1511

145

000

686

261

222


508




N

Stage 01_No-PC 63

02_No-Internet 39

03_No-Web 282

Sector 301 32

501 43

601 107

602 21

603 24

604 39

605 12

701 17

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

59945a

377304

8302

23104

24452

31

1

2

10

19

1934

377304

4151

2310

1287

968

188931

2079

1157

644

519

000

127

319

871

509


Error

Total

Corrected Total

702962

2734000

762906

352

384

383

1997




N

Stage 01_No-PC 65

02_No-Internet 39

Business_year

03_No-Web

2

5

8

280

60

86

238



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

30394a

585613

18945

1291

11990

777895

2793000

808289

8

1

2

2

4

375

384

383

3799

585613

9473

645

2998

2074

1831

282306

4567

311

1445

070

000

011

733

218

510




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

30394a

585613

18945

1291

11990

777895

2793000

8

1

2

2

4

375

384

3799

585613

9473

645

2998

2074

1831

282306

4567

311

1445

070

000

011

733

218


511




N

Stage 01_No-PC 65

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

339

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

23900a

266361

3631

5189

8902

788991

2810000

812891

5

1

2

1

2

380

386

385

4780

266361

1816

5189

4451

2076

2302

128287

875

2499

2144

044

000

418

115

119


512




N

Stage 01_No-PC 64

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 43

601 105

602 20

603 24

604 40

605 12

701 18

801 20

802 11

803 59



Source

Type III Sum of



513


Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

382922

7967

23959

22688

741843

2792000

811833

1

2

10

19

352

384

383

382922

3983

2396

1194

2108

181694

1890

1137

567

000

153

333

929


514




N


Business_year

03_No-Web

2

277

50

5 73

8 194



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

8330a

243398

3708

264

128

495058

1550000

503388

5

1

1

2

2

311

317

316

1666

243398

3708

132

064

1592

1047

152905

2329

083

040

390

000

128

920

961


515




N


03_No-Web 279


25 42



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

10383a

101747

086

1736

2955

498432

1567000

508815

3

1

1

1

1

315

319

318

3461

101747

086

1736

2955

1582

2187

64302

054

1097

1867

089

000

816

296

173


516




N


03_No-Web 278

Sector 301 27

501 37

601 71

602 19

603 23

604 40

605 8

701 16

801 18

802 9

803 50



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

25752a

186308

4354

11546

8719

481670

1558000

21

1

1

10

10

296

318

1226

186308

4354

1155

872

1627

754

114492

2676

710

536

775

000

103

715

864

517




Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

25752a

186308

4354

11546

8719

481670

1558000

507421

21

1

1

10

10

296

318

317

1226

186308

4354

1155

872

1627

754

114492

2676

710

536

775

000

103

715

864




N


Business_year

03_No-Web

2

279

51

5 72

8 193



518


Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

13616a

317497

2571

3740

2431

580182

1826000

593797

5

1

1

2

2

310

316

315

2723

317497

2571

1870

1216

1872

1455

169644

1374

999

649

204

000

242

369

523


519




N


03_No-Web 281


25 42



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

10041a

113248

012

1155

2449

588805

1843000

598846

3

1

1

1

1

314

318

317

3347

113248

012

1155

2449

1875

1785

60393

006

616

1306

150

000

937

433

254


520




N


03_No-Web 280

Sector 301 27

501 38

601 69

602 19

603 23

604 40

605 8

701 16

801 19

802 8

803 50



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

43248a

209171

3253

16954

9813

554550

1834000

21

1

1

10

10

295

317

2059

209171

3253

1695

981

1880

1096

111271

1731

902

522

352

000

189

532

874

521




Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

43248a

209171

3253

16954

9813

554550

1834000

597798

21

1

1

10

10

295

317

316

2059

209171

3253

1695

981

1880

1096

111271

1731

902

522

352

000

189

532

874






N

Stage 01_No-PC 63

02_No-Internet 39

03_No-Web 280

522


Business_year 2 61

5 86

8 235



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

19037a

283541

13727

941

788

564429

1710000

583466

8

1

2

2

4

373

382

381

2380

283541

6863

471

197

1513

1573

187377

4536

311

130

131

000

011

733

971




Stage 01_No-PC

02_No-Internet

03_No-Web

No_of_employee 5

N

63

39

282

336

523



N

Stage 01_No-PC 63

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

336

48



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

21592a

95175

5790

888

1862

567593

1727000

589185

5

1

2

1

2

378

384

383

4318

95175

2895

888

931

1502

2876

63384

1928

592

620

015

000

147

442

538


524




N

Stage 01_No-PC 62

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 43

601 104

602 21

603 24

604 38

605 13

701 20

801 19

802 10

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

55936a

194583

10622

18598

17313

31

1

2

10

19

1804

194583

5311

1860

911

1186

127859

3490

1222

599

233

000

032

275

907

525


Error

Total

Corrected Total

532650

1722000

588586

350

382

381

1522




N

Stage 01_No-PC 75

02_No-Internet 44

Business_year

03_No-Web

2

285

66

5 86

8 252



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

12919a

443448

4968

1053

1681

699566

2090000

8

1

2

2

4

395

404

1615

443448

2484

527

420

1771

912

250387

1402

297

237

506

000

247

743

917

526




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

12919a

443448

4968

1053

1681

699566

2090000

712485

8

1

2

2

4

395

404

403

1615

443448

2484

527

420

1771

912

250387

1402

297

237

506

000

247

743

917


527




N

Stage 01_No-PC 75

02_No-Internet 45

No_of_employee

03_No-Web

5

25

286

360

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

13855a

159281

215

585

2131

702739

2091000

716594

5

1

2

1

2

400

406

405

2771

159281

107

585

1065

1757

1577

90663

061

333

606

165

000

941

564

546


528




N

Stage 01_No-PC 74

02_No-Internet 45

03_No-Web 285

Sector 301 32

501 47

601 109

602 25

603 28

604 37

605 13

701 20

801 19

802 12

803 62



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

88358a

340340

6846

41453

47630

32

1

2

10

20

2761

340340

3423

4145

2381

1636

201644

2028

2456

1411

018

000

133

008

113

529


Error

Total

Corrected Total

626184

2081000

714542

371

404

403

1688




N

Stage 01_No-PC 59

02_No-Internet 38

Business_year

03_No-Web

2

5

8

279

60

83

233



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

28837a

112654

23317

6498

3853

480940

1180000

8

1

2

2

4

367

376

3605

112654

11659

3249

963

1310

2751

85965

8897

2479

735

006

000

000

085

568

530




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

28837a

112654

23317

6498

3853

480940

1180000

509777

8

1

2

2

4

367

376

375

3605

112654

11659

3249

963

1310

2751

85965

8897

2479

735

006

000

000

085

568


531




N

Stage 01_No-PC 59

02_No-Internet 38

No_of_employee

03_No-Web

5

25

281

332

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

24254a

55114

4367

020

1739

492723

1197000

516976

5

1

2

1

2

372

378

377

4851

55114

2184

020

869

1325

3662

41610

1649

015

656

003

000

194

902

519


532




N

Stage 01_No-PC 58

02_No-Internet 38

03_No-Web 280

Sector 301 29

501 43

601 102

602 20

603 24

604 38

605 12

701 20

801 19

802 10

803 59



Source

Type III Sum of



533


Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

93164

11541

14138

13639

460232

1195000

516742

1

2

10

19

344

376

375

93164

5770

1414

718

1338

69636

4313

1057

537

000

014

395

945




N

Stage 01_No-PC 59

02_No-Internet 39

Business_year

03_No-Web

2

5

8

275

60

83

230



Source

Type III Sum of


Corrected Model

Intercept

Stage

21009a

129700

14700

8

1

2

2626

129700

7350

1838

90793

5145

069

000

006

534


Business_year

Stage Business_year

Error

Total

Corrected Total

9541

4555

519983

1222000

540992

2

4

364

373

372

4770

1139

1429

3339

797

037

528


535




N

Stage 01_No-PC 59

02_No-Internet 39

No_of_employee

03_No-Web

5

25

276

328

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

12870a

63399

3221

050

1575

535119

1238000

547989

5

1

2

1

2

368

374

373

2574

63399

1610

050

788

1454

1770

43599

1107

034

542

118

000

331

854

582


536




N

Stage 01_No-PC 58

02_No-Internet 39

03_No-Web 275

Sector 301 30

501 43

601 100

602 20

603 24

604 36

605 12

701 19

801 19

802 10

803 59



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

49246a

101309

7071

12409

19980

31

1

2

10

19

1589

101309

3536

1241

1052

1084

69100

2411

846

717

352

000

091

584

801

537


Error

Total

Corrected Total

498485

1236000

547731

340

372

371

1466




N

Stage 01_No-PC 59

02_No-Internet 38

Business_year

03_No-Web

2

5

8

277

61

83

230



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

27175a

244044

20555

4792

4056

590087

1740000

617262

8

1

2

2

4

365

374

373

3397

244044

10277

2396

1014

1617

2101

150954

6357

1482

627

035

000

002

229

643

538




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

27175a

244044

20555

4792

4056

590087

1740000

8

1

2

2

4

365

374

3397

244044

10277

2396

1014

1617

2101

150954

6357

1482

627

035

000

002

229

643


539




N

Stage 01_No-PC 59

02_No-Internet 38

No_of_employee

03_No-Web

5

25

279

330

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

27437a

105915

7009

000

6276

595496

1757000

622934

5

1

2

1

2

370

376

375

5487

105915

3504

000

3138

1609

3410

65808

2177

000

1950

005

000

115

989

144


540




N

Stage 01_No-PC 58

02_No-Internet 38

03_No-Web 278

Sector 301 32

501 44

601 100

602 20

603 23

604 38

605 12

701 19

801 18

802 10

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

61029a

186500

9892

17130

18195

31

1

2

10

19

1969

186500

4946

1713

958

1200

113636

3014

1044

583

219

000

050

406

918

541


Error

Total

Corrected Total

561292

1752000

622321

342

374

373

1641




N

Stage 01_No-PC 56

02_No-Internet 38

Business_year

03_No-Web

2

5

8

278

61

83

228



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

24353a

135556

14614

4588

2353

482644

1171000

506997

8

1

2

2

4

363

372

371

3044

135556

7307

2294

588

1330

2289

101952

5496

1725

442

021

000

004

180

778


542


543




N

Stage 01_No-PC 56

02_No-Internet 38

No_of_employee

03_No-Web

5

25

279

327

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

20403a

64429

5056

201

1741

486707

1172000

507110

5

1

2

1

2

367

373

372

4081

64429

2528

201

871

1326

3077

48582

1906

152

657

010

000

150

697

519


544




N

Stage 01_No-PC 55

02_No-Internet 38

03_No-Web 278

Sector 301 30

501 43

601 99

602 20

603 23

604 37

605 12

701 19

801 19

802 10

803 59



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

56882a

103804

8608

11998

14098

31

1

2

10

19

1835

103804

4304

1200

742

1404

79435

3293

918

568

079

000

038

517

928

545


Error

Total

Corrected Total

442999

1155000

499881

339

371

370

1307




N


Business_year

03_No-Web

2

277

51

5 70

8 191



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

2229a

100623

083

1791

1474

338281

749000

340510

5

1

1

2

2

306

312

311

446

100623

083

895

737

1105

403

91021

075

810

667

846

000

784

446

514


546


547




N


03_No-Web 278


25 41



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

888a

27947

627

875

786

339643

750000

340530

3

1

1

1

1

309

313

312

296

27947

627

875

786

1099

269

25425

571

796

715

848

000

451

373

398


548




N


03_No-Web 277

Sector 301 25

501 37

601 70

602 20

603 22

604 37

605 8

701 17

801 18

802 7

803 51



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

21253a

77564

037

14169

10559

319256

21

1

1

10

10

290

1012

77564

037

1417

1056

1101

919

70456

033

1287

959

566

000

855

237

479

549


Total

Corrected Total

749000

340510

312

311


550




N


Business_year

03_No-Web

2

274

52

5 70

8 188



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

6105a

61255

1555

4837

5451

266892

585000

272997

5

1

1

2

2

304

310

309

1221

61255

1555

2419

2725

878

1391

69772

1771

2755

3104

227

000

184

065

046


551




N


03_No-Web 276


25 41



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

1235a

23700

605

264

813

271762

587000

272997

3

1

1

1

1

308

312

311

412

23700

605

264

813

882

466

26861

686

299

922

706

000

408

585

338


552




N


03_No-Web 275

Sector 301 25

501 36

601 70

602 19

603 22

604 38

605 8

701 17

801 18

802 7

803 51



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

22259a

45274

1217

16075

6493

250738

21

1

1

10

10

289

1060

45274

1217

1608

649

868

1222

52182

1402

1853

748

231

000

237

052

679

553


Total

Corrected Total

586000

272997

311

310






N



Business_year

03-Have-Web

2

5

8

270

200

388

810



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

223337a

5375558

142123

3351

4425

1659075

8

1

2

2

4

1389

27917

5375558

71062

1675

1106

1194

23373

4500E3

59494

1403

926

000

000

000

246

448

554


Total

Corrected Total

10816000

1882412

1398

1397


555




N



No_of_employee

03-Have-Web

5

25

271

1062

348



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

223664a

6030763

158757

7832

1853

1667525

10920000

1891189

5

1

2

1

2

1404

1410

1409

44733

6030763

79378

7832

927

1188

37664

5078E3

66834

6594

780

000

000

000

010

459


556




N



03-Have-Web 267

Sector 301 133

501 100

601 268

602 98

603 128

604 194

605 12

701 55

702 20

801 74

802 21

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

284095a

2646718

105246

37420

22575

34

1

2

11

21

8356

2646718

52623

3402

1075

7145

2263E3

44998

2909

919

000

000

000

001

566

557


Error

Total

Corrected Total

1591641

10812000

1875736

1361

1396

1395

1169




N



Business_year

03-Have-Web

2

5

8

270

197

384

795



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

121263a

4682660

81653

8084

2701

1824626

9439000

1945889

8

1

2

2

4

1367

1376

1375

15158

4682660

40827

4042

675

1335

11356

3508E3

30587

3028

506

000

000

000

049

731


558


559




N



No_of_employee

03-Have-Web

5

25

271

1045

341



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

114941a

5133309

68699

3057

4473

1841033

9530000

1955974

5

1

2

1

2

1380

1386

1385

22988

5133309

34349

3057

2236

1334

17231

3848E3

25747

2291

1676

000

000

000

130

187


560




N



03-Have-Web 267

Sector 301 128

501 97

601 262

602 95

603 126

604 192

605 12

701 54

702 20

801 74

802 19

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

207310a

2288851

36719

49472

26612

34

1

2

11

21

6097

2288851

18359

4497

1267

4696

1763E3

14139

3464

976

000

000

000

000

491

561


Error

Total

Corrected Total

1736132

9421000

1943442

1337

1372

1371

1299




N



Business_year

03-Have-Web

2

5

8

274

196

385

804



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

62067a

3042411

25575

19920

1509

2244132

6790000

2306199

8

1

2

2

4

1376

1385

1384

7758

3042411

12787

9960

377

1631

4757

1865E3

7841

6107

231

000

000

000

002

921


562


563




N



No_of_employee

03-Have-Web

5

25

275

1050

343



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

37976a

3234964

26028

1520

058

2277339

6838000

2315315

5

1

2

1

2

1387

1393

1392

7595

3234964

13014

1520

029

1642

4626

1970E3

7926

926

018

000

000

000

336

982


564




N



03-Have-Web 271

Sector 301 130

501 98

601 264

602 98

603 128

604 190

605 10

701 59

702 20

801 73

802 21

803 288



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

161417a

1545256

6799

72503

27371

34

1

2

11

21

4748

1545256

3400

6591

1303

2988

972701

2140

4149

820

000

000

118

000

696

565


Error

Total

Corrected Total

2135110

6789000

2296526

1344

1379

1378

1589




N



Business_year

03-Have-Web

2

5

8

270

197

385

802



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

132003a

2947658

76657

17497

1365

2152898

6661000

8

1

2

2

4

1375

1384

16500

2947658

38329

8748

341

1566

10538

1883E3

24479

5587

218

000

000

000

004

928

566




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

132003a

2947658

76657

17497

1365

2152898

6661000

2284901

8

1

2

2

4

1375

1384

1383

16500

2947658

38329

8748

341

1566

10538

1883E3

24479

5587

218

000

000

000

004

928


567




N



No_of_employee

03-Have-Web

5

25

271

1052

342



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

115599a

3148952

73346

2438

625

2184975

6734000

2300574

5

1

2

1

2

1388

1394

1393

23120

3148952

36673

2438

313

1574

14687

2000E3

23296

1549

199

000

000

000

214

820


568




N



03-Have-Web 267

Sector 301 130

501 97

601 263

602 97

603 127

604 191

605 11

701 56

702 20

801 74

802 21

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

224741a

1419361

24903

64991

25831

34

1

2

11

21

6610

1419361

12451

5908

1230

4322

927981

8141

3863

804

000

000

000

000

717

569


Error

Total

Corrected Total

2057197

6685000

2281938

1345

1380

1379

1530




N



Business_year

03-Have-Web

2

5

8

266

196

378

792



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

84969a

3963568

38906

22011

1534

2055215

8246000

2140184

8

1

2

2

4

1357

1366

1365

10621

3963568

19453

11006

384

1515

7013

2617E3

12844

7267

253

000

000

000

001

908


570


571




N



No_of_employee

03-Have-Web

5

25

267

1038

337



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

71274a

4379616

39281

11189

1297

2081640

8320000

2152913

5

1

2

1

2

1369

1375

1374

14255

4379616

19641

11189

649

1521

9375

2880E3

12917

7359

427

000

000

000

007

653


572




N



03-Have-Web 263

Sector 301 129

501 97

601 259

602 92

603 126

604 188

605 11

701 55

702 20

801 74

802 19

803 291



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

154684a

1841623

13787

47261

25314

34

1

2

11

21

4550

1841623

6893

4296

1205

3055

1237E3

4629

2885

809

000

000

010

001

711

573


Error

Total

Corrected Total

1974794

8245000

2129478

1326

1361

1360

1489




N


Business_year

03-Have-Web

2

265

111

5 228

8 467



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

16054a

1594171

849

7000

4555

1132821

3619000

1148875

5

1

1

2

2

800

806

805

3211

1594171

849

3500

2278

1416

2267

1126E3

600

2472

1609

046

000

439

085

201


574


575




N


03-Have-Web 266


25 208



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

5762a

1865755

010

5566

053

1149958

3660000

1155719

3

1

1

1

1

808

812

811

1921

1865755

010

5566

053

1423

1349

1311E3

007

3911

037

257

000

932

048

847


576




N


03-Have-Web 263

Sector 301 78

501 51

601 151

602 55

603 75

604 114

605 6

701 30

702 13

801 42

802 10

803 179



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

55332a

824294

138

33284

13678

22

1

1

11

10

2515

824294

138

3026

1368

1796

588782

098

2161

977

014

000

754

015

462

577


Error

Total

Corrected Total

1093398

3625000

1148730

781

804

803

1400


578




N


Business_year

03-Have-Web

2

267

112

5 229

8 470



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

23089a

1607292

680

10716

3315

1155461

3503000

1178550

5

1

1

2

2

805

811

810

4618

1607292

680

5358

1657

1435

3217

1120E3

474

3733

1155

007

000

492

024

316


579




N


03-Have-Web 268


25 210



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

5415a

1768471

2856

2549

494

1180115

3547000

1185530

3

1

1

1

1

813

817

816

1805

1768471

2856

2549

494

1452

1244

1218E3

1968

1756

340

293

000

161

186

560


580




N


03-Have-Web 264

Sector 301 79

501 51

601 153

602 56

603 75

604 113

605 6

701 30

702 13

801 44

802 11

803 177



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

75477a

770714

1948

46005

14968

1096043

22

1

1

11

10

785

3431

770714

1948

4182

1497

1396

2457

551995

1395

2995

1072

000

000

238

001

381

581


Total

Corrected Total

3508000

1171520

808

807


582




N


Business_year

03-Have-Web

2

268

109

5 231

8 470



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

29365a

1601291

9692

6798

1957

1209035

3610000

1238400

5

1

1

2

2

804

810

809

5873

1601291

9692

3399

978

1504

3906

1065E3

6445

2260

651

002

000

011

105

522


583




N


03-Have-Web 270


25 207



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

18819a

1828429

12294

487

210

1229139

3646000

1247958

3

1

1

1

1

811

815

814

6273

1828429

12294

487

210

1516

4139

1206E3

8112

321

138

006

000

005

571

710


584




N


03-Have-Web 266

Sector 301 78

501 54

601 148

602 56

603 75

604 114

605 5

701 32

702 13

801 43

802 10

803 178



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

81743a

805165

12248

40582

11201

1151761

22

1

1

11

10

783

3716

805165

12248

3689

1120

1471

2526

547374

8327

2508

761

000

000

004

004

666

585


Total

Corrected Total

3610000

1233504

806

805


586



Stage 1 PC Only


VA = VA_1

ICTU = ICTU_1



VA = VA_2




587


VA = VA_3


VA = f (ICTU)



Model Summary




ANOVAb


1 Regression

Residual

Total

36837412

21115941

57953353

14

538

552

2631244

39249

67040 000a


Coefficientsa

Model




1 (Constant) 4226 1518 2783 006

588


ICTU 294 014 669 20906 000

Emp_5 817 676 033 1209 227

Business_year_2 1421 810 049 1754 080

Business_year_5 1197 635 052 1886 060

Stage_1 -3405 1827 -064 -1864 063

Stage_2 -7323 873 -272 -8392 000

S_301 1734 1063 050 1632 103

S_501 -848 1107 -023 -766 444

S_601 009 844 000 011 992

S_602 722 1141 019 633 527

S_603 177 1050 005 169 866

S_604 -1047 924 -036 -1133 258

S_701 -2131 1463 -042 -1457 146


589





Model Summary




ANOVAb


1 Regression

Residual

Total

3433E17

2407E18

2751E18

14

294

308

2452E16

8188E15

2995 000a


Coefficientsa

Model




1 (Constant) 1063E8 3060E7 3475 001

590


ICTU -161748033 289644814 -039 -558 577

Emp_5 -4826E7 1357E7 -211 -3556 000

Business_year_2 641462352 1637E7 002 039 969

Business_year_5 -2583E6 1258E7 -012 -205 838

Stage_1 -7558E7 3547E7 -155 -2131 034

Stage_2 -6147E7 1733E7 -249 -3547 000

S_301 1788E7 2028E7 058 882 379

S_501 2019E6 2160E7 006 093 926

S_601 1563E7 1564E7 069 999 318

S_602 1513E7 2214E7 042 683 495

S_603 5664E7 2113E7 167 2681 008

S_604 1298E7 1966E7 042 660 509

S_701 2118E6 2773E7 005 076 939


591










Total 735 1000

Group Statistics

Have_PC

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

40

40

40

40

40000

40000

40000

40000

1 Revenue

Emp_5

329

329

329000

329000

592


Year_2 329 329000

Year_5 329 329000


Emp_5

Year_2

Year_5

369

369

369

369000

369000

369000

Analysis


Eigenvalues


of Variance



Wilks Lambda



1 980 7472 4 113


Function

1


593


Year_2 024 Year_5 575

Structure Matrix

Function

1



Have_PC

Function

1






group codes 2 3


363 494

594



Total

Group Statistics

1

366 735

1

498 1000

Have_PC

Valid N (listwise)

Unweighted Weighted

0 Revenue 40 40000

Emp_5 40 40000

Year_2 40 40000

Year_5 40 40000

S_301 40 40000

S_501 40 40000

S_601 40 40000

S_602 40 40000

S_603 40 40000

S_604 40 40000

S_701 40 40000

S_801 40 40000

1 Revenue 329 329000

Emp_5 329 329000

Year_2 329 329000

Year_5 329 329000

S_301 329 329000

S_501 329 329000

595


S_601 329 329000

S_602 329 329000

S_603 329 329000

S_604 329 329000

S_701 329 329000

S_801 329 329000


Emp_5 369 369000

Year_2 369 369000

Year_5 369 369000

S_301 369 369000

S_501 369 369000

S_601 369 369000

S_602 369 369000

S_603 369 369000

S_604 369 369000

S_701 369 369000

S_801 369 369000

Analysis


Eigenvalues


of Variance

Cumulative



596


Wilks Lambda



1 934 24456 12 018


Function

1


597


Structure Matrix

Function

1


598



Have_PC

Function

1


672 Internet









Total 735 1000

Group Statistics

Have_Internet

Valid N (listwise)

Unweighted Weighted

599


0 Revenue

Emp_5

30

30

30000

30000

Year_2

Year_5

30

30

30000

30000

1 Revenue

Emp_5

Year_2

Year_5

319

319

319

319

319000

319000

319000

319000

Total Revenue

Emp_5

349

349

349000

349000

Year_2

Year_5

349

349

349000

349000

Analysis



of Variance



Wilks Lambda



1 969 10917 4 028

600



Function

1


601


Structure Matrix

Function

1



Have_Internet

Function

1







group codes 22 30


344 468

602



Total

Group Statistics

20

386 735

27

525 1000

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue 30 30000

Emp_5 30 30000

Year_2 30 30000

Year_5 30 30000

S_301 30 30000

S_501 30 30000

S_601 30 30000

S_602 30 30000

S_603 30 30000

S_604 30 30000

S_701 30 30000

S_801 30 30000

1 Revenue 319 319000

Emp_5 319 319000

Year_2 319 319000

Year_5 319 319000

S_301 319 319000

S_501 319 319000

603


S_601 319 319000

S_602 319 319000

S_603 319 319000

S_604 319 319000

S_701 319 319000

S_801 319 319000


Emp_5 349 349000

Year_2 349 349000

Year_5 349 349000

S_301 349 349000

S_501 349 349000

S_601 349 349000

S_602 349 349000

S_603 349 349000

S_604 349 349000

S_701 349 349000

S_801 349 349000

Analysis


Eigenvalues


of Variance



604


Wilks Lambda



1 911 31917 12 001


Function

1


605


Structure Matrix

Function

1


606



Have_Internet

Function

1










Total 735 1000

Group Statistics

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

30

30

30000

30000

607


Year_2 30 30000

Year_5 30 30000

ICTU_1 30 30000

1 Revenue

Emp_5

Year_2

Year_5

ICTU_1

319

319

319

319

319

319000

319000

319000

319000

319000

Total Revenue

Emp_5

Year_2

Year_5

349

349

349

349

349000

349000

349000

349000

ICTU_1 349 349000

Analysis


Eigenvalues


of Variance



Wilks Lambda



608


Wilks Lambda



1 733 106830 5 000


Function

1


609


Structure Matrix

Function

1








group codes 22 30


344 468

610



Total

Group Statistics

20 27

386 525 735 1000

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue 30 30000

Emp_5 30 30000

Year_2 30 30000

Year_5 30 30000

S_301 30 30000

S_501 30 30000

S_601 30 30000

S_602 30 30000

S_603 30 30000

S_604 30 30000

S_701 30 30000

S_801 30 30000

ICTU_1 30 30000

1 Revenue 319 319000

Emp_5 319 319000

Year_2 319 319000

Year_5 319 319000

S_301 319 319000

611


S_501 319 319000

S_601 319 319000

S_602 319 319000

S_603 319 319000

S_604 319 319000

S_701 319 319000

S_801 319 319000

ICTU_1 319 319000


Emp_5 349 349000

Year_2 349 349000

Year_5 349 349000

S_301 349 349000

S_501 349 349000

S_601 349 349000

S_602 349 349000

S_603 349 349000

S_604 349 349000

S_701 349 349000

S_801 349 349000

ICTU_1 349 349000

Analysis


Eigenvalues

612



of Variance

Cumulative



Wilks Lambda



1 698 122303 13 000


Function

1


613


Structure Matrix

Function

1


614



Have_Internet

Function

1


673 Website









Total 735 1000

615


Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

179

179

179

179

179000

179000

179000

179000

1 Revenue

Emp_5

Year_2

Year_5

154

154

154

154

154000

154000

154000

154000

Total Revenue

Emp_5

Year_2

Year_5

333

333

333

333

333000

333000

333000

333000

Analysis


Eigenvalues


of Variance

Cumulative



616


Wilks Lambda

Test of Function(s)

Wilks Lambda

Chi- square df Sig

1 912 30480 4 000


Function

1


617


Structure Matrix

Function

1



Have_ Function

Web 1



Dependent Have_Web


variables)



Valid 333 453

618



Total

Group Statistics

38 52

324 441

40 54

402 547 735 1000

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue 179 179000

Emp_5 179 179000

Year_2 179 179000

Year_5 179 179000

S_301 179 179000

S_501 179 179000

S_601 179 179000

S_602 179 179000

S_603 179 179000

S_604 179 179000

S_701 179 179000

S_801 179 179000

1 Revenue

Emp_5

Year_2

154

154

154

154000

154000

154000

619


Year_5 154 154000

S_301 154 154000

S_501 154 154000

S_601 154 154000

S_602 154 154000

S_603 154 154000

S_604 154 154000

S_701 154 154000

S_801 154 154000


Emp_5 333 333000

Year_2 333 333000

Year_5 333 333000

S_301 333 333000

S_501 333 333000

S_601 333 333000

S_602 333 333000

S_603 333 333000

S_604 333 333000

S_701 333 333000

S_801 333 333000

Analysis


Eigenvalues


of Variance

Cumulative


620



Wilks Lambda



1 878 42303 12 000


Function

1


621


Structure Matrix

Function

1


622



Have_ Function

Web 1










Total 735 1000

Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue 179 179000

623


Emp_5 179 179000

Year_2 179 179000

Year_5 179 179000

ICTU_1_2 179 179000

1 Revenue

Emp_5

Year_2

Year_5

ICTU_1_2

154

154

154

154

154

154000

154000

154000

154000

154000


Emp_5

Year_2

Year_5

ICTU_1_2

333

333

333

333

333000

333000

333000

333000

Analysis


Eigenvalues


of Variance

Cumulative



Wilks Lambda

624




1 653 140080 5 000


Function

1


Structure Matrix

Function

1



625


Have_ Function

Web 1


626










Total 735 1000

Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

S_301

S_501

S_601

S_602

179

179

179

179

179

179

179

179

179000

179000

179000

179000

179000

179000

179000

179000

627


S_603 179 179000

S_604 179 179000

S_701 179 179000

S_801 179 179000

ICTU_1_2 179 179000

1 Revenue 154 154000

Emp_5 154 154000

Year_2 154 154000

Year_5 154 154000

S_301 154 154000

S_501 154 154000

S_601 154 154000

S_602 154 154000

S_603 154 154000

S_604 154 154000

S_701 154 154000

S_801 154 154000

ICTU_1_2 154 154000


Emp_5 333 333000

Year_2 333 333000

Year_5 333 333000

S_301 333 333000

S_501 333 333000

S_601 333 333000

S_602 333 333000

S_603 333 333000

S_604 333 333000

628


S_701 333 333000

S_801 333 333000

ICTU_1_2 333 333000

Analysis


Eigenvalues


of Variance

Cumulative



Wilks Lambda



1 646 141824 13 000


Function

1


629



Structure Matrix

Function

1


630


Structure Matrix

Function

1



Have_ Function

Web 1


631





Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 159a 025 001 9165E7


S_301 S_501 S_604 S_601

ANOVAb


1 Regression

Residual

Total

7804E16

3016E18

3094E18

9

359

368

8671E15

8400E15

1032 413a


S_601


Coefficientsa

Model


Standardised

Coefficients


1 (Constant)

Have_PC

S_301

-9156E6

1613E7

2442E7

1803E7

1572E7

1834E7

055

080

-508

1026

1332

612

306

184

632


S_501

S_601

S_602

S_603

S_604

S_701

S_801

-879281590

1254E7

9983E6

4953E7

7875E6

2231E6

2205E7

1900E7

1436E7

2050E7

2023E7

1851E7

2352E7

2259E7

-003

059

028

143

026

005

056

-046

873

487

2449

425

095

976

963

383

627

015

671

925

330



Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 158a 025 -001 9425E7


S_603 S_604 S_501 S_301 S_601

ANOVAb


1 Regression

Residual

Total

7666E16

3011E18

3088E18

9

339

348

8518E15

8883E15

959 474a


S_601


Coefficientsa

633


Model


Standardised

Coefficients


1 (Constant)

Have_Internet

S_301

S_501

S_601

S_602

S_603

S_604

S_701

S_801

-1038E7

1730E7

2376E7

-455567026

1485E7

1013E7

5029E7

8050E6

2560E6

2355E7

2065E7

1857E7

1909E7

1987E7

1531E7

2112E7

2086E7

1947E7

2531E7

2371E7

052

077

-001

066

029

145

025

006

058

-503

931

1244

-023

970

480

2411

413

101

994

615

352

214

982

333

632

016

680

919

321



Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 211a 044 018 9031E7


S_604 S_301 S_501 S_601

ANOVAb


1 Regression

Residual

1224E17

2634E18

9

323

1360E16

8156E15

1667 096a

634


Total 2757E18 332


S_601


Coefficientsa

Model


Standardised

Coefficients


1 (Constant)

Have_Web

S_301

S_501

S_601

S_602

S_603

S_604

S_701

S_801

-6584E6

2489E7

2518E7

3954E6

1386E7

1190E7

4924E7

9991E6

5872E6

-4081E6

1184E7

1011E7

1879E7

1942E7

1496E7

2098E7

2040E7

1862E7

2570E7

2330E7

136

084

013

063

034

148

034

013

-010

-556

2462

1340

204

927

567

2414

537

228

-175

579

014

181

839

355

571

016

592

819

861


635


Endnotes




636














APPENDICES 123





ASIT Report 141































































database 398





EXECUTIVE SUMMARY

I Objective









1






IV Data Collection









2












3










4













5











6










7












VI Recommendations


8











9





10


1 INTRODUCTION






11



2 BACKGROUND









12









13








Penetration rate

Large Medium Small

Personal computers

991 917 584


Intranet 643 292 73

Extranet 286 104 32





14


15



Internet use

Large Medium Small






Industry Sector


Webpage websites

Electronic Business



hotels 653 618 209 622


602 505 160 523



16



320 307 64 321














17



21accuracy









18














19











20












21











22







23












24












25



26










27






3 Web Presence

4 Transaction


6 Transformation

Low





28






Production


equipment


(CAD)





bull General ledger

29



















30











6 Transformation









31



32












33













34













35





Analyses done






Analyses done

36













37






Analyses done



73 Conclusions


38





39





Sample Design




Questionnaires



40










41











42







43




Response Rate







44


By Sector


Percent Cumulative

Percent

Manufacturing - 301 81 103 104 104

Construction - 501 68 86 87 191


ImportExport ndash

176 223 225 416


ImportExport ndash

57 72 73 489


ImportExport ndash

60 76 77 566


94 119 120 686


13 16 17 703


32 41 41 744



35 44 45 799

Real Estate - 802 18 23 23 822


Total 781 990 1000 Missing 8 10

Total 789 1000


45




Percent Cumulative

Percent


604 178

7 789

766 226

9 1000

766 226

9 1000

766 991

1000




Percent Cumulative

Percent


109 193 477 779

10

789

138 245 605 987

13

1000

140 248 612

1000

140 388

1000


N Valid 390

Mean 16838817

46



1E9




911 Not Using PCs




It is costly





47











48
















49






912 Using PCs





50










51










Increasing sales





52











53

















54














It is costly



55












56














57















58
















Increasing sales



59











60

















61

















It is costly

62













63


















64
















65













66













67

















68
















69











70













71















72












973 ICT Value




73














74

















75






991 PC Adoption








76


















77




911 Hypotheses








78











79









Conclusion



80















81





[


]



[


]


82


[


]

83


[


]

84


[


]

85


[


]

86













87


[


]


[


]


88


[


]

89


[


]

90


[


]

91


[


]





92


[


]

93


[


]

94


[


]

95


[


]

96


[


]

97


[


]



[


]

98


[


]

99


[


]

100


[


]

101


[


]

102


[


]




[


]


103


[


]


[


]


104


[


]

105


[


]

106


[


]


107



[


]


[


]


108


[


]

109


[


]

110


[


]

111


[


]

112


[


]

113













114











115












116













117











118















119










120













121





122

Appendices




Extensive ICT Use

G r o w t h

M a t u r I t y


Stage III Growth

Stage IV Stability

Developing ICT use

Maturing ICT Use




123





124






3 Web Presence

4 Transaction


6 Transformation

Low

High

B u s i n e s s





125


Stage Applications










126









127





Y Y Y Y Y Y Y Y





B5


N Y Y Y


all




C1 C2

YesNo YesNo

N N

Covered in C5


128






129












130





Y Y Y Y Y Y Y












to all

131


ii 1-5 iii 6-10 iv 11-15 v gt15





132








Has your firmmeans


E10

YesNo Y Stage 2

133







telecom


v

vi




E12(d) Y Stage 2

134


iv v vi

6-10 11-15 gt15



E12e Y Stage 2







135




telecom v

vi










136













137











H1 Y Stage 2


H2 Y Stage 2

138





139





140






141





Total

Weight

21

Max

0

Min


B4 YesNo YesNo

Y=1 N=0 Y=1 N=0

1 1

1 1

0 0




1 1 1 1 1

1 1 1 1 1

0

vi viiviii


1 1 1

1 1 1

ix x xi


1 1 1

1 1 1


142





1 1 1 1

005

1 1 1 1

5 0






Y Y Y Y Y Y Y

1 1 1 1 1 1 1

7

1 1 1 1 1 1 1

0



E5 0 3 0

143


ii iiiiv v vi vii

lt1 1-20 21-40 41-60 61-80 81-100

1 1 2 2 3 3


E6 E8

YesNo YesNo

Y=1 N=0 Y=1 N=0

1 1

1 1

0 0



lt1 iii 1-5 iv 6-10 v 11-15 vi gt15

E12(d) 0 1 1 2 2 3

3 0



3 0

144


iv v vi






point else 0 1 0


EDGE iii 3G iv WAP


point else 0 1 0

145





146






Total 53 0


Index in ASIT D4

Form of AnswerYesNo

Points Y=1 N=0

Weight 5

Max 5

Min 0


Total 10 0

147




Form Index in of



















148



ICTU1 = 21 0

Part 2 Index in

ASIT

Form of




ii Internet browser

B4

YesNo

YesNo

Y=1 N=0

Y=1 N=0

1

1

1

1

0

0


i Email



iv Receipt

v Sales

vi Delivery

vii Payments








xv Video conference

C3

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

149


xvi Others 16 1 1 0


percentage 005 5 0










vii Others

D6

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo


Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

Y=1 N=0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0

0

0

0

0

0

0



i 0

ii lt1

iii 1-20

iv 21-40

v 41-60

vi 61-80

vii 81-100

E5

1

2

3

4

5

6

7

0

1

1

2

2

3

3

3 0

150






i 0 ii lt1

iii 1-5

iv 6-10

v 11-15

vi gt15

E12(d)

1

2

3

4

5

6

0

1

1

2

2

3

3 0



ii Credit cards




vi Others

E12e 1

2

3

4

5

6




assign 3

3 0




1 point else 0 1 0

151









viii Others YesNo


i GPRS YesNo

ii EDGE YesNo

iii 3G YesNo

iv WAP YesNo

v WLAN YesNo

vi Bluetooth YesNo

vii RFID YesNo


ix Others YesNo


i SMS YesNo

ii EMS YesNo

iii MMS YesNo

iv LBS YesNo

v GPS YesNo





152


viii

ix

x


Video call

Others

YesNo

YesNo

YesNo


i GIS

ii SMSMMS


iv WAP

v Job Management

vi Fleet management




x Mobile POS


xii Video streaming


xiv Others

H4

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo

YesNo


ICTU2 = 28 0

Part 3


Index in ASIT

D4

Form of

Answer

YesNo

Points

Y=1 N=0

Weight

5

Max

5

Min

0


153


ICTU3 = 10 0


154











155







156




SUMMARY




157


Within Groups 1770488052 1961 9028495933

Total 1987320761 1972

158


Anova Single Factor

SUMMARY



SS 7143541864 4157821969

df 11 1961

MS 6494128968 2120255976

F 3062898556

P-value 311374E-60

F crit 1793521277

Total 4872176155 1972

159


Anova Single Factor

SUMMARY



SS 4986186738 3223293368

df 11 1857

MS 4532897035 1735753025

F 261148733

P-value 533615E-51

F crit 1793794387

Total 3721912042 1868

160


Anova Single Factor

SUMMARY



SS 3891069269 8605317807

df 11 1961

MS 3537335699 4388229376

F 8060963537

P-value 66661E-14

F crit 1793521277

Total 8994424734 1972

161


Anova Single Factor

SUMMARY



Total 1062189055 200

162


Anova Single Factor

SUMMARY



SS 2156286631 1184873131

df 11 1961

MS 1960260573 6042188326

F 3244289101

P-value 806607E-64

F crit 1793521277

Total 1400501794 1972

163



Industry VA of SMEs

[1] (HK$ million)






6405 171703





164








165





ＯNo

166


3 NOT USING PCs



















167



168


4 USING PCs


























169





ＯNo

170





















171






172




























173



174





ＯNo

175























176






177


10 USING A WEBSITE





























178




179






180











Ｏ少於 10 人Ｏ10-49 人Ｏ50-100 人Ｏ100 人以上




181





Ｏ否

182




關系原因














的資訊




183



184


















用用用用












185





Ｏ否

186




關系

沒有


很重要

原因















没用道







187





188

189


7 使用互聯網



























Ｏ是Ｏ否






Ｏ否

190





關系原因原因
















完全

没用

有少

少用

不知

道有用

很有

用







191





192


10 使用網站



使用常多




























193



Ｏ是Ｏ否


Ｏ是Ｏ否

194




Ｏ用得很少

Ｏ有用一些

Ｏ常用

Ｏ很常用


Ｏ完全没用

Ｏ有少少用

Ｏ有一些用

Ｏ有不少用

Ｏ非常有用


195













196



197









198


2 USING PCs


























199




























200



201




ＯYes

ＯNo

202























203





204


6 USING A WEBSITE





























205




206


7 OVERALL ICT USAGE




207












Ｏ少於 10 人Ｏ10-49 人Ｏ50-100 人Ｏ100 人以上





208





























209

210


3 使用互聯網



























Ｏ是Ｏ否





Ｏ是

Ｏ否

211





關系原因原因
















完全

没用

有少

少用

不知

道有用

很有

用







212




213


6 使用網站



使用常多




























214



Ｏ是Ｏ否


Ｏ是Ｏ否

215




Ｏ用得很少

Ｏ有用一些

Ｏ常用

Ｏ很常用


Ｏ完全没用

Ｏ有少少用

Ｏ有一些用

Ｏ有不少用

Ｏ非常有用


216



1 Data merge













217





3 Data selection

Select SME only


218







301 41 2923982739 1219E8 19034310422 -922994898 6770960376 59000 8E8

501 33 285743403 3741336262 651283047 153081388 418405418 150000 15000000

601 90 1664371319 1055E8 11125197493 -546181726 3874924363 10000 1E9

602 24 1773216667 27323788509 5577444974 619434267 2926999066 219000 1E8

603 26 5962015385 1972E8 38669139962 -2002043071 139E8 50000 1E9

604 36 1440023083 25635847654 4272641276 572630791 2307415376 125000 1E8

605 9 369611111 4038952353 1346317451 59149750 680072472 90000 12000000

701 18 703168733 14677261222 3459463647 -26714296 1433051763 5 63327855

702 5 913940440 13299818631 5947859709 -737450158 2565331038 110610 30000000

801 21 2902182857 1281E8 27950740415 -2928239426 8732605140 100000 6E8

802 7 73750100 1052509304 397811124 -23590775 171090975 70000 3000000

803 76 600408357 34360255601 3941391627 -184757118 1385573832 824 3E8

Total 386 1699904247 89693952915 4565302001 802299762 2597508733 5 1E9

219






Mean


5 303 473571424 19977266559 1147663977 247728342 699414506 5 3E8

25 82 6154411376 1848E8 20412653467 2092933633 102E8 100000 1E9

75 5 1712000000 22095180470 9881265101 -1031479012 4455479012 100000 50000000

Total 390 1683881729 89245778704 4519135672 795383022 2572380436 5 1E9



Mean


2 51 1302927202 82159196233 11504588621 -1007837429 3613691832 10000 6E8

5 95 1438550640 81082800535 8318919808 -213190389 3090291669 11500 8E8

8 240 1854151498 94606234393 6106806171 651147685 3057155310 5 1E9

Total 386 1679036159 89684007888 4564795812 781531198 2576541121 5 1E9

220




Code

0 NO

1 YES

Statistics Have_PC

N Valid 785

Missing 4

Have_PC


Percent Cumulative

Percent

Valid 0

1


100

685

785 4

789

127

868

995 5

1000

127

873

1000

127

1000

221





N Valid


98

2

100

0

100

0

Statistics


N Valid 63 75 59 59 59 56


222









Total 70627 50



Total 127937 62



Total 49915 46

No_one_train person

Between Groups

Within Groups

Total

4627

75852

80479

4

43

47

1157

1764

656 626



Total 77917 47



Total 56578 44

223






Total 70627 50



Total 127937 62



Total 49915 46


Between Groups

Within Groups

Total

768

79712

80479

1

46

47

768

1733

443 509



Total 77917 47



Total 56578 44

224






Total 70627 50



Total 127937 62



Total 49915 46


Between Groups

Within Groups

Total

2743

77736

80479

2

45

47

1371

1727

794 458



Total 77917 47



Total 56578 44

225















11 不會 No



226





















227









228




229




















230


















34 在家 At home


231








232



233





Statistics


Provide consulting

Subside training

N Valid


70

30 164 100

1465 0 4

64

36 191 300

1611 0 4

62

38 184 200

1428 0 4

64

36 219 300

1521 0 4

65

35 222 300

1546 0 4

234



ANOVA





Within Groups

Total

22666

124986

147652

9

59

68

2518

2118

1189 319


Within Groups

Total

32070

130152

162222

10

52

62

3207

2503

1281 265


Within Groups

Total

27797

95875

123672

9

51

60

3089

1880

1643 128


Within Groups

Total

19273

123140

142413

9

53

62

2141

2323

922 514


Within Groups

Total

16969

135390

152359

9

54

63

1885

2507

752 660

235





Within Groups

Total

687

147385

148071

1

68

69

687

2167

317 575


Within Groups

Total

468

162969

163438

1

62

63

468

2629

178 675


Within Groups

Total

5026

119361

124387

1

60

61

5026

1989

2526 117


Within Groups

Total

3581

142169

145750

1

62

63

3581

2293

1561 216


Within Groups

Total

7601

145383

152985

1

63

64

7601

2308

3294 074

236





Within Groups

Total

4417

143654

148071

2

67

69

2209

2144

1030 363


Within Groups

Total

12803

150635

163437

2

61

63

6401

2469

2592 083


Within Groups

Total

3809

120578

124387

2

59

61

1904

2044

932 400


Within Groups

Total

10989

134761

145750

2

61

63

5494

2209

2487 092


Within Groups

Total

11643

141342

152985

2

62

64

5821

2280

2554 086



Holding_workshops 2

5

8

Total

5

9

44

58

220

211

145

162

837

1616

1517

1497

237



5 9 289 1453

8 39 156 1619

Total 53 185 1610


5 9 222 1394

8 37 157 1501

Total 51 176 1450


5 10 290 1370

8 39 185 1548

Total 54 217 1539


5 10 300 1247

8 39 192 1628

Total 54 226 1592

238





N Valid

Missing

679

6

685

0

675

10

Statistics


N Valid


604

81 319 300

1003 0 4

581

104 295 300

1221 0 4

559

126 215 200

1484 0 4

574

111 313 400

1187 0 4

529

156 140 100

1283 0 4

Statistics


N Valid


570

115 304 300

1133

549

136 215 200

1328

572

113 259 300

1418

564

121 229 300

1552

534

151 109 100

1208

239


Minimum Maximum

0 4

0 4

0 4

0 4

0 4

240








Word Between Groups

39095 9 4344 4541 000


Total 591077 586


Within Groups

Total

40662

813739

854401

9

553

562

4518

1471

3070 001


36738 9 4082 1862 055


Total 1200957 540


34571 9 3841 2760 004


Total 794437 555

241



50035 9 5559 3587 000


Total 829579 512


Within Groups

Total

27353

671020

698373

9

543

552

3039

1236

2459 009


13114 9 1457 819 599


Total 939797 530


26405 9 2934 1445 165


Total 1130681 553


137742 9 15305 6963 000


Total 1315855 545

HRM Between Groups

27264 9 3029 2114 027


Total 752347 515



242


Word 301 64 316 946

501 53 317 1069

601 114 289 1132

602 43 333 808

603 52 352 727

604 77 345 867

701 27 278 1086

801 32 322 870

802 10 220 1619

803 115 335 946

Total 587 320 1004


501 45 253 1471

601 111 265 1319

602 40 335 921

603 49 308 1288

604 78 322 1040

701 25 288 1301

801 31 319 946

802 9 222 1481

803 114 309 1125

Total 563 295 1233

Database 301 59 185 1460

501 40 205 1431

601 105 186 1553

602 39 195 1468

603 51 241 1525

604 72 232 1422

701 24 200 1474

243


801 31 245 1312

802 10 190 1729

803 110 246 1482

Total 541 216 1491


501 44 314 1173

601 107 286 1270

602 41 339 1070

603 49 347 1043

604 78 323 1139

701 25 252 1418

801 30 360 498

802 10 280 1398

803 113 313 1228

Total 556 312 1196


501 41 141 1303

601 100 111 1163

602 37 135 1207

603 43 160 1312

604 68 138 1372

701 23 43 728

801 29 207 998

802 9 111 1364

803 109 161 1333

Total 513 139 1273

Security 301

501

601

57

43

108

289

300

285

1191

1272

1252

244


602 40 325 954

603 49 322 1066

604 79 313 1079

701 23 252 1163

801 32 338 1008

802 9 233 1414

803 113 323 935

Total 553 305 1125

Multi_Media 301 58 219 1290

501 42 205 1413

601 103 207 1278

602 37 222 1272

603 46 239 1341

604 72 212 1383

701 22 168 1393

801 30 197 1326

802 9 156 1590

803 112 221 1330

Total 531 212 1332

Finance 301 60 278 1303

501 42 243 1309

601 110 245 1469

602 38 261 1443

603 50 274 1468

604 75 275 1376

701 24 233 1373

801 31 210 1491

802 10 180 1932

803 114 274 1433

245


Total 554 258 1430

Sales 301 58 257 1512

501 43 195 1430

601 106 243 1441

602 38 268 1435

603 49 280 1554

604 78 295 1318

701 25 184 1599

801 29 159 1570

802 9 100 1500

803 111 169 1565

Total 546 229 1554

HRM 301 54 122 1208

501 40 90 982

601 102 89 1218

602 36 106 1194

603 46 141 1408

604 68 124 1259

701 23 39 656

801 29 124 1123

802 9 78 1093

803 109 122 1220

Total 516 109 1209



246


Word Between Groups

Within Groups

Total

884

590192

591077

2

584

586

442

1011

438 646


Within Groups

Total

4388

850013

854401

2

560

562

2194

1518

1445 237


Within Groups

Total

26601

1174357

1200957

2

538

540

13300

2183

6093 002


Within Groups

Total

3781

790656

794437

2

553

555

1891

1430

1322 267


Within Groups

Total

2678

826901

829579

2

510

512

1339

1621

826 438


Within Groups

Total

3554

694818

698373

2

550

552

1777

1263

1407 246


Within Groups

Total

2642

937155

939797

2

528

530

1321

1775

744 476


Within Groups

37797

1092884

2

551

18898

1983

9528 000

247


Total 1130681 553


Within Groups

Total

22362

1293493

1315855

2

543

545

11181

2382

4694 010

HRM Between Groups

Within Groups

Total

33078

719269

752347

2

513

515

16539

1402

11796 000



Word 5

25

75

Total

446

134

7

587

318

327

314

320

1002

1034

378

1004

Spreadsheet 5

25

75

Total

431

125

7

563

290

310

314

295

1250

1177

1069

1233

Database 5

25

75

Total

412

122

7

541

203

257

214

216

1488

1443

1464

1491

Communication 5

25

75

427

122

7

308

328

300

1220

1093

1414

248


Total 556 312 1196


25 115 137 1328

75 7 200 1291

Total 513 139 1273

Security 5 424 301 1143

25 122 320 1060

75 7 300 1000

Total 553 305 1125

Multi_Media 5 407 209 1341

25 117 226 1314

75 7 214 1069

Total 531 212 1332

Finance 5 421 244 1454

25 126 303 1277

75 7 329 488

Total 554 258 1430

Sales 5 417 218 1566

25 122 265 1471

75 7 271 1380

Total 546 229 1554

HRM 5 396 96 1116

25 113 153 1396

75 7 186 1215

Total 516 109 1209


249



Word Between Groups

Within Groups

Total

2046

575508

577554

2

576

578

1023

999

1024 360


Within Groups

Total

2360

848689

851048

2

555

557

1180

1529

772 463


Within Groups

Total

14548

1175391

1189938

2

533

535

7274

2205

3298 038


Within Groups

Total

4941

776652

781593

2

547

549

2470

1420

1740 176


Within Groups

Total

11828

801109

812937

2

505

507

5914

1586

3728 025


Within Groups

Total

4623

686842

691465

2

545

547

2312

1260

1834 161


Within Groups

Total

9832

920874

930706

2

524

526

4916

1757

2797 062

250



Within Groups

Total

8300

1107481

1115781

2

545

547

4150

2032

2042 131


Within Groups

Total

096

1304148

1304244

2

539

541

048

2420

020 980

HRM Between Groups

Within Groups

Total

4481

727230

731710

2

508

510

2240

1432

1565 210



Word 2

5

8

Total

85

154

340

579

327

327

315

320

968

950

1029

1000

Spreadsheet 2

5

8

Total

85

148

325

558

285

304

292

294

1435

1160

1215

1236

Database 2

5

8

Total

80

142

314

536

181

235

214

214

1527

1429

1499

1491

Communication 2

5

84

147

304

328

1357

1090

251


8

Total

319

550

308

312

1190

1193


5 136 160 1189

8 299 125 1261

Total 508 137 1266

Security 2 80 311 1201

5 146 318 968

8 322 298 1167

Total 548 305 1124


5 140 234 1285

8 309 203 1312

Total 527 212 1330

Finance 2 82 239 1569

5 143 245 1438

8 323 268 1381

Total 548 258 1428

Sales 2 81 228 1637

5 144 231 1552

8 317 227 1536

Total 542 228 1553

HRM 2 76 99 1238

5 138 123 1198

8 297 103 1185

Total 511 108 1198

252





Statistics

Sector No_of


N Valid

Missing

664

0

664

0

656

8

664

0

Statistics



N Valid


618

46 182 200

1579 0 4

609

55 174 200

1574 0 4

592

72 75 00

1078 0 4

253










Total 1537702 617

Tailor_ Purchasing

Between Groups

Within Groups

Total

66918

1439607

1506525

9

599

608

7435

2403

3094 001



Total 687000 591



Tailor_Finance 301

501

601

602

65

52

117

48

180

117

193

171

1573

1294

1507

1570

254


603 56 189 1659

604 88 168 1550

701 28 204 1598

801 30 123 1591

802 12 167 1826

803 122 221 1622

Total 618 182 1579


501 52 131 1502

601 115 201 1513

602 50 184 1621

603 56 207 1683

604 88 206 1541

701 28 196 1551

801 29 100 1414

802 12 100 1651

803 115 145 1552

Total 609 174 1574

Tailor_HRM 301 61 89 1082

501 50 56 884

601 113 56 935

602 48 60 917

603 56 86 1119

604 84 79 1162

701 26 50 1030

801 29 107 1307

802 12 100 1595

803 113 88 1124

Total 592 75 1078

255


256






Total 1537702 617

Tailor_ Purchasing

Between Groups

Within Groups

Total

20097

1486429

1506525

2

606

608

10048

2453

4097 017



Total 687000 591



Tailor_Finance 5

25

75

Total

466

145

7

618

170

213

300

182

1547

1626

1528

1579

Tailor_Purchasing 5

25

75

Total

453

149

7

609

164

205

214

174

1532

1658

1773

1574

Tailor_HRM 5

25

444

141

66

101

1005

1233

257


75 7 129 1380

Total 592 75 1078





Total 1512802 609

Tailor_Purchasin g

Between Groups

Within Groups

Total

531

1487154

1487685

2

600

602

266

2479

107 898



Total 681625 585

258




Statistics



N Valid


593

92 299 300

1025 0 4

576

109 268 300

1176 0 4

575

110 197 200

1379 0 4

578

107 209 200

1347 0 4

570

115 236 300

1289 0 4

259







20091 11 1826 1757 058


Total 618957 587


46210 11 4201 3146 000


Total 792708 570


60685 11 5517 3004 001


Total 1085551 569

New opportunity

Between Groups

63606 11 5782 3322 000


Total 1040098 572


Within Groups

Total

53472

883148

936619

11

553

564

4861

1597

3044 001

260




Efficiency 301 61 293 1031

501 47 300 978

601 113 283 1117

602 41 324 734

603 53 296 1126

604 80 286 1052

701 25 276 1200

801 31 306 772

802 10 270 1252

803 114 324 944

Total 575 299 1027

C_service 301 57 270 1101

501 45 236 1334

601 110 245 1224

602 39 292 870

603 51 273 1313

604 79 261 1275

701 24 221 1215

801 31 313 619

802 9 222 1302

261


803

Total

113

558

301

268

1031

1175

Inc_sales 301 58 217 1340

501 45 153 1325

601 111 205 1334

602 40 230 1181

603 52 206 1420

604 77 194 1463

701 26 96 1216

801 30 233 1093

802 9 178 1787

803 110 200 1414

Total 558 197 1378


501 44 157 1388

601 110 209 1296

602 40 225 1127

603 52 237 1415

604 77 230 1288

701 24 92 1176

801 31 203 1048

802 10 170 1636

803 113 218 1434

Total 560 209 1343


501 45 211 1283

601 108 228 1281

602 36 247 1000

603 51 251 1362

262


604 77 227 1344

701 25 156 1261

801 31 248 1151

802 9 156 1509

803 111 274 1241

Total 552 237 1284

263





Within Groups

Total

2589

619384

621973

2

590

592

1295

1050

1233 292


2695 2 1348 974 378


Total 795222 575


1966 2 983 516 597


Total 1091437 574


Within Groups

Total

1488

1044652

1046140

2

575

577

744

1817

410 664


Within Groups

Total

2124

943974

946098

2

567

569

1062

1665

638 529



264



Within Groups

Total

1145

614828

615973

2

583

585

573

1055

543 581


5167 2 2583 1864 156


Total 790956 569


13673 2 6836 3607 028


Total 1088226 569

New opportunity

Between Groups

7164 2 3582 1979 139


Total 1037138 571


Within Groups

Total

13123

928530

941652

2

561

563

6561

1655

3964 020



Efficiency 2

5

87

153

293

307

1087

985

8 329 297 1034

Total 569 299 1029

C_service 2 85 273 1179

5 150 285 1145

265


8 318 259 1187

Total 553 268 1178

Inc_sales 2 84 208 1382

5

8

Total

149

320

553

219

184

197

1358

1382

1382

266


New_opportunity 2

5

8

Total

85

149

321

555

218

226

199

209

1424

1321

1330

1345

Competitive 2

5

8

Total

84

146

317

547

232

264

225

237

1355

1231

1280

1287

267




Sector = 301

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

64

642

000

64


Sig (2-tailed)

N

642

000

64

1


Sector = 501

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

52

656

000

52


Sig (2-tailed)

N

656

000

52

1

52

268


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

52

656

000

52


Sig (2-tailed)

N

656

000

52

1


Sector = 601

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

114

583

000

114


Sig (2-tailed)

N

583

000

114

1


Sector = 602

269


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

43

241

119

43


Sig (2-tailed)

N

241

119

43

1

43

270


Sector = 603

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

54

777

000

54


Sig (2-tailed)

N

777

000

54

1


Sector = 604

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

80

555

000

80


Sig (2-tailed)

N

555

000

80

1


271


Sector = 701

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

26

478

014

26


Sig (2-tailed)

N

478

014

26

1


Sector = 801

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

32

690

000

32


Sig (2-tailed)

N

690

000

32

1


272


273


Sector = 802

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

10

707

022

10


Sig (2-tailed)

N

707

022

10

1


Sector = 803

Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

116

461

000

116


Sig (2-tailed)

N

461

000

116

1


274



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

450

578

000

450


Sig (2-tailed)

N

578

000

450

1



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

134

645

000

134


Sig (2-tailed)

N

645

000

134

1


275



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

7

590

163

7


Sig (2-tailed)

N

590

163

7

1

7


Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

85

567

000

85


Sig (2-tailed)

N

567

000

85

1


276



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

154

551

000

154


Sig (2-tailed)

N

551

000

154

1



Correlations

ICTU_1 VA_1


Sig (2-tailed)

N

1

345

601

000

345


Sig (2-tailed)

N

601

000

345

1


277


278




Have_Internet

N Valid 739

Missing 50

Have_Internet


Percent Cumulative

Percent

Valid 0

1


74

665

739 50

789

94

843

937 63

1000

100

900

1000

100

1000

279





N Valid

Missing

74

0

74

0

73

1

Statistics



Partner_not use


N Valid


37

37 62 00

1010 0 4

38

36 71 00

898 0 3

38

36 113 100

1359 0 4

35

39 114 100

1264 0 4

36

38 100 100

1287 0 4

Statistics


person No_one Train

N Valid


39

35 126 100

1390 0

45

29 140 100

1529 0

38

36 84 00

1151 0

39

35 92 00

1265 0

280


Statistics


person No_one Train

N Valid


39

35 126 100

1390 0 4

45

29 140 100

1529 0 4

38

36 84 00

1151 0 4

39

35 92 00

1265 0 4

281









Total 32958 23



Total 22500 23


Between Groups

Within Groups

Total

1875

32750

34625

2

21

23

938

1560

601 557



Total 32955 21


Between Groups

Within Groups

Total

3743

40083

43826

2

20

22

1871

2004

934 410



282


Total 47840 24


Within Groups

Total

6417

55690

62107

2

25

27

3208

2228

1440 256


Within Groups

Total

2029

29971

32000

2

22

24

1015

1362

745 486


Within Groups

Total

1835

37550

39385

2

23

25

917

1633

562 578

283





Within Groups

Total

2458

30500

32958

2

21

23

1229

1452

846 443


Within Groups

Total

1812

20687

22500

2

21

23

906

985

920 414


Between Groups

Within Groups

Total

188

34438

34625

2

21

23

094

1640

057 945


Within Groups

Total

1597

31357

32955

2

19

21

799

1650

484 624


Between Groups

Within Groups

Total

4726

39100

43826

2

20

22

2363

1955

1209 319


Within Groups

Total

090

47750

47840

2

22

24

045

2170

021 979


Within Groups

Total

679

59618

60296

2

24

26

339

2484

137 873


Within Groups

Total

2250

29750

32000

2

22

24

1125

1352

832 448


284


Within Groups

Total

36694

39385

23

25

1595

285




286









4 一般 Average



7 方便 Convenient




287










288












289



Internet


ICT_ infrastructure

Legal regulatory

Tax incentive

Provide info

N Valid


40

34 142 100

1299 0 4

37

37 157 200

1425 0 4

37

37 143 100

1444 0 4

39

35 172 200

1432 0 4


N Valid 39 39 39


290



Internet - ANOVA





Within Groups

Total

10740

55035

65775

10

29

39

1074

1898

566 828


Within Groups

Total

12531

60550

73081

10

26

36

1253

2329

538 847


Within Groups

Total

16181

58900

75081

10

26

36

1618

2265

714 704


Within Groups

Total

14956

62941

77897

10

28

38

1496

2248

665 746

Provide consulting

Between Groups

Within Groups

Total

11302

68441

79744

10

28

38

1130

2444

462 900


Within Groups

Total

14701

69196

83897

10

28

38

1470

2471

595 804


Within Groups

15190

71118

10

28

1519

2540

598 802

291





Within Groups

Total

10740

55035

65775

10

29

39

1074

1898

566 828


Within Groups

Total

12531

60550

73081

10

26

36

1253

2329

538 847


Within Groups

Total

16181

58900

75081

10

26

36

1618

2265

714 704


Within Groups

Total

14956

62941

77897

10

28

38

1496

2248

665 746

Provide consulting

Between Groups

Within Groups

Total

11302

68441

79744

10

28

38

1130

2444

462 900


Within Groups

Total

14701

69196

83897

10

28

38

1470

2471

595 804


Within Groups

Total

15190

71118

86308

10

28

38

1519

2540

598 802

292





Within Groups

Total

2433

63342

65775

1

38

39

2433

1667

1460 234


Within Groups

Total

1838

71243

73081

1

35

36

1838

2036

903 348


Within Groups

Total

2410

72671

75081

1

35

36

2410

2076

1161 289


Within Groups

Total

1289

76608

77897

1

37

38

1289

2070

623 435

Provide consulting

Between Groups

Within Groups

Total

973

78770

79744

1

37

38

973

2129

457 503


Within Groups

Total

1289

82608

83897

1

37

38

1289

2233

577 452


Within Groups

Total

3605

82703

86308

1

37

38

3605

2235

1613 212

293





Within Groups

Total

096

65679

65775

2

37

39

048

1775

027 973


Within Groups

Total

2914

70167

73081

2

34

36

1457

2064

706 501


Within Groups

Total

662

71893

72556

2

33

35

331

2179

152 860


Within Groups

Total

379

77519

77897

2

36

38

189

2153

088 916

Provide consulting

Between Groups

Within Groups

Total

410

79333

79744

2

36

38

205

2204

093 911


Within Groups

Total

2564

81333

83897

2

36

38

1282

2259

567 572


Within Groups

Total

974

85333

86308

2

36

38

487

2370

206 815

294





N Valid 659 665 656

Missing 6 0 9

Statistics


N Valid 580 572 556 552 558 550





Video_ conference

N Valid

Missing Mean Median

552

113 206 200

553

112 214 200

550

115 188 200

554

111 234 300

544

121 76 00

295



1115 0 4

1363 0 4

1176 0 4

1394 0 4

1031 0 4








Total 223530 561



Total 353951 553



Total 1011645 537



Total 980301 533



Total 1186748 539

296




Total 941233 531



Total 664734 533



Total 985918 534



Total 725308 531



Total 1043879 535



Total 567165 525


Email 301 60 363 637

501

601

602

603

604

46

105

42

52

77

363

354

371

385

365

645

707

596

364

580

701 23 335 832

297


801 31 368 653

802 9 322 833

803 117 362 598

Total 562 363 631


501 46 320 719

601 102 322 779

602 40 320 853

603 49 335 855

604 77 322 788

701 23 300 798

801 31 303 836

802 9 300 866

803 117 334 756

Total 554 320 800


501 43 140 1178

601 99 220 1204

602 39 259 1229

603 49 269 1262

604 76 236 1402

701 21 105 1322

801 31 74 999

802 8 112 1553

803 114 154 1284

Total 538 195 1373


501

601

58

43

100

164

88

144

1266

1074

1343

298


602 37 176 1480

603 48 175 1605

604 76 171 1403

701 20 60 1095

801 31 61 844

802 8 112 1553

803 113 127 1289

Total 534 138 1356


501 43 135 1289

601 102 193 1373

602 40 272 1320

603 49 288 1333

604 75 239 1384

701 22 150 1535

801 31 71 1006

802 8 100 1604

803 112 144 1400

Total 540 195 1484


501 43 51 856

601 102 102 1350

602 37 119 1309

603 47 147 1412

604 75 159 1453

701 20 50 827

801 31 26 514

802 7 100 1528

803 113 114 1315

299


Total 532 111 1331

Gov_info 301 58 209 996

501 43 230 1036

601 100 179 1113

602 38 189 981

603 48 204 1202

604 75 211 1226

701 20 170 1218

801 31 174 1032

802 7 243 1134

803 114 230 1088

Total 534 205 1117


501 43 179 1473

601 100 199 1299

602 38 216 1197

603 48 262 1409

604 76 226 1408

701 21 138 1499

801 30 187 1279

802 7 214 1215

803 114 211 1319

Total 535 214 1359


501 43 186 1302

601 100 178 1106

602 38 179 811

603 48 204 1352

604 75 165 1168

300


701 20 145 1276

801 31 177 1055

802 8 162 1598

803 113 213 1146

Total 532 187 1169

E_banking 301 57 223 1337

501 42 210 1265

601 100 240 1414

602 37 238 1421

603 49 280 1399

604 76 243 1417

701 20 170 1455

801 31 239 1383

802 8 200 1195

803 116 228 1418

Total 536 234 1397


501 42 60 989

601 98 54 852

602 37 78 976

603 48 106 1405

604 74 91 1196

701 20 30 571

801 31 55 995

802 8 62 744

803 113 95 1042

Total 526 77 1039

301





Within Groups

Total

2032

221498

223530

2

559

561

1016

396

2564 078


Within Groups

Total

1980

351972

353951

2

551

553

990

639

1550 213


Within Groups

Total

873

1010772

1011645

2

535

537

437

1889

231 794


Within Groups

Total

270

980031

980301

2

531

533

135

1846

073 929


Within Groups

Total

3263

1183485

1186748

2

537

539

1632

2204

740 477


Within Groups

Total

034

941199

941233

2

529

531

017

1779

010 991


Within Groups

Total

5277

659457

664734

2

531

533

2639

1242

2125 120


Within Groups

Total

721

985196

985918

2

532

534

361

1852

195 823


Within Groups

6536

718772

2

529

3268

1359

2405 091

302


Total 725308 531


Within Groups

Total

2322

1041557

1043879

2

533

535

1161

1954

594 552


Within Groups

Total

5444

561721

567165

2

523

525

2722

1074

2534 080


Email 5 429 359 647

25 126 374 568

75 7 357 535

Total 562 363 631


25 123 329 807

75 7 286 378

Total 554 320 800


25 121 197 1341

75 7 229 1113

Total 538 195 1373


25 119 137 1314

75 7 157 1272

Total 534 138 1356

Sales_Customer 5

25

75

411

122

7

196

189

257

1478

1517

1272

303


Total 540 195 1484


25 121 110 1319

75 7 114 1345

Total 532 111 1331

Gov_info 5 407 200 1143

25 120 223 1027

75 7 200 816

Total 534 205 1117


25 119 211 1268

75 7 243 976

Total 535 214 1359


25 119 168 1119

75 7 229 756

Total 532 187 1169

E_banking 5 410 236 1410

25 119 228 1352

75 7 186 1464

Total 536 234 1397


25 116 84 1119

75 7 157 976

Total 526 77 1039


304




Within Groups

Total

128

215548

215676

2

553

555

064

390

164 848


Within Groups

Total

1320

351831

353151

2

546

548

660

644

1024 360


Within Groups

Total

7740

990572

998311

2

530

532

3870

1869

2071 127


Within Groups

Total

32312

942553

974866

2

526

528

16156

1792

9016 000


Within Groups

Total

3570

1171355

1174925

2

533

535

1785

2198

812 444


Within Groups

Total

27508

907674

935182

2

525

527

13754

1729

7955 000


Within Groups

Total

2395

660518

662913

2

527

529

1198

1253

956 385


Within Groups

Total

13689

962530

976219

2

527

529

6845

1826

3747 024


Within Groups

Total

5287

715938

721226

2

524

526

2644

1366

1935 145

305



Within Groups

Total

16191

1016684

1032875

2

527

529

8096

1929

4196 016


Within Groups

Total

12012

550005

562017

2

519

521

6006

1060

5667 004

By Business Year


Email 2 80 360 668

5 151 365 580

8 325 363 633

Total 556 363 623


5 148 328 747

8 323 317 815

Total 549 320 803


5 144 212 1356

8 311 185 1350

Total 533 194 1370


5 144 177 1437

8 309 120 1247

Total 529 138 1359


306


5

8

Total

144

313

536

203

189

196

1467

1467

1482


5 143 143 1392

8 308 93 1244

Total 528 111 1332

Gov_info 2 76 189 1150

5 144 203 1064

8 310 209 1137

Total 530 205 1119


5 145 238 1291

8 309 201 1362

Total 530 214 1358


5 143 203 1171

8 309 181 1124

Total 527 187 1171

E_banking 2 77 268 1261

5 145 243 1413

8 308 219 1408

Total 530 233 1397


5 143 97 1119

8 304 64 965

Total 522 76 1039

307



how











10 不會 No



No comment

308

















33 No 34 Yes


35 不會 No

309










45 有機會 Maybe









310





311










312



kind of landlords




313




Statistics

To_new market

Prompt_ response


N Valid


559

106 146 100

1186 0 4

553

112 176 200

1205 0 4

556

109 167 200

1239 0 4

560

105 226 200

1135 0 4

Statistics



N Valid


553

112 212 200

1139 0 4

557

108 162 200

1228 0 4

552

113 162 200

1254 0 4

552

113 191 200

1210 0 4

314



- ANOVA






Within Groups

Total

36627

743221

779848

11

542

553

3330

1371

2428 006


Within Groups

Total

38840

760902

799743

11

536

547

3531

1420

2487 005


Within Groups

Total

50750

792136

842886

11

539

550

4614

1470

3139 000


Within Groups

Total

45955

671224

717178

11

543

554

4178

1236

3380 000


Within Groups

Total

45002

668755

713757

11

536

547

4091

1248

3279 000


Within Groups

Total

48150

782196

830346

11

540

551

4377

1449

3022 001


315


Within Groups

Total

806530

859664

535

546

1508


Between Groups

Within Groups

Total

33461

768671

802132

11

535

546

3042

1437

2117 018

Differences


To_new_market 301 59 163 1128

501 44 116 1200

601 100 146 1150

602 39 141 1044

603 48 171 1320

604 77 162 1148

701 23 70 1063

801 30 113 1074

802 9 144 1740

803 113 148 1196

Total 542 145 1185


501 42 143 1151

601 99 181 1226

602 38 171 898

603 48 198 1211

604 77 173 1210

701 21 86 910

801 30 213 1137

316


802

803

Total

8

113

535

112

176

174

1458

1205

1205


501 42 124 1226

601 101 174 1246

602 39 167 1084

603 48 206 1327

604 76 180 1233

701 21 52 1030

801 31 139 1086

802 9 167 1323

803 112 167 1233

Total 538 166 1241

Efficiency 301 59 231 1193

501 44 189 1185

601 101 213 1128

602 39 244 1021

603 48 265 1158

604 77 212 1076

701 21 157 1165

801 30 207 1015

802 9 189 1167

803 114 255 1048

Total 542 225 1132


501 43 163 1215

601 99 191 1089

602 38 232 1016

317


603 48 254 1184

604 76 195 1153

701 21 157 978

801 30 223 898

802 8 200 926

803 113 237 1151

Total 535 211 1141

Inc_sales 301 60 187 1186

501 44 105 987

601 99 167 1152

602 39 177 1158

603 48 194 1375

604 76 154 1216

701 23 83 984

801 30 173 1081

802 9 144 1667

803 112 161 1283

Total 540 160 1222

New_product 301 59 190 1241

501 44 118 1147

601 98 170 1203

602 39 174 1117

603 48 190 1403

604 76 172 1282

701 22 59 854

801 30 160 1133

802 8 88 1458

803 111 159 1260

Total 535 162 1253

318



501 43 147 1202

601 100 190 1159

602 38 208 1171

603 48 212 1362

604 75 180 1185

701 21 124 1044

801 30 200 1017

802 8 125 1581

803 113 206 1212

Total 535 191 1211




Within Groups

Total

3902

781021

784923

2

556

558

1951

1405

1389 250


Within Groups

Total

4108

797935

802043

2

550

552

2054

1451

1416 244


Within Groups

Total

2155

849622

851777

2

553

555

1078

1536

701 496


Within Groups

Total

1500

718984

720484

2

557

559

750

1291

581 560


Within Groups

986

715137

2

550

493

1300

379 685

319


Total 716123 552



Total 837781 556



Total 865868 551


Between Groups

Within Groups

Total

4623

802027

806650

2

549

551

2312

1461

1582 206

320





Within Groups

Total

14571

766974

781545

2

551

553

7286

1392

5234 006


Within Groups

Total

15890

779295

795185

2

544

546

7945

1433

5546 004


Within Groups

Total

20329

824035

844364

2

547

549

10165

1506

6747 001


Within Groups

Total

6611

711513

718125

2

551

553

3306

1291

2560 078


Within Groups

Total

8022

703735

711757

2

545

547

4011

1291

3106 046


Within Groups

Total

11916

819270

831186

2

550

552

5958

1490

4000 019


Within Groups

Total

12345

845181

857526

2

545

547

6173

1551

3980 019


Between Groups

Within Groups

Total

10923

788515

799438

2

545

547

5462

1447

3775 024

321




5 147 167 1155

8 314 132 1139

Total 537 145 1188


5 145 194 1212

8 309 159 1166

Total 530 173 1206


5 145 188 1239

8 311 148 1207

Total 533 165 1243


5 146 234 1146

8 314 216 1140

Total 537 225 1135


5 146 226 1151

8 309 200 1152

Total 531 211 1142

Inc_sales 2 75 172 1258

5 146 179 1231

8 315 148 1198

Total 536 160 1222

New_product 2

5

76

146

166

184

1292

1279

322


8

Total

309

531

150

161

1216

1251


5

8

Total

77

145

309

531

204

209

179

191

1251

1178

1204

1210

323




Sector = 301

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

58

736

000

58


Sig (2-tailed)

N

736

000

58

1


Sector = 501

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

44

564

000

44


Sig (2-tailed)

N

564

000

44

1

44

324


Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

44

564

000

44


Sig (2-tailed)

N

564

000

44

1


325


Sector = 601

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

103

713

000

103


Sig (2-tailed)

N

713

000

103

1


Sector = 602

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

42

619

000

42


Sig (2-tailed)

N

619

000

42

1


326


Sector = 603

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

51

684

000

51


Sig (2-tailed)

N

684

000

51

1


Sector = 604

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

77

630

000

77


Sig (2-tailed)

N

630

000

77

1


327


Sector = 701

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

21

768

000

21


Sig (2-tailed)

N

768

000

21

1


Sector = 801

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

31

464

009

31


Sig (2-tailed)

N

464

009

31

1


328


Sector = 803

Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

113

680

000

113


Sig (2-tailed)

N

680

000

113

1



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

415

654

000

415


Sig (2-tailed)

N

654

000

415

1


329



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

118

752

000

118


Sig (2-tailed)

N

752

000

118

1



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

7

535

216

7


Sig (2-tailed)

N

535

216

7

1

7

330



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

75

719

000

75


Sig (2-tailed)

N

719

000

75

1



ICTU_2 VA_2


Sig (2-tailed)

N

1

147

641

000

147


Sig (2-tailed)

N

641

000

147

1


331



Correlations

ICTU_2 VA_2


Sig (2-tailed)

N

1

312

671

000

312


Sig (2-tailed)

N

671

000

312

1


332





N Valid 697

Missing 92

Have_Website


Percent Cumulative

Percent

Valid 0

1


375

322

697 92

789

475

408

883 117

1000

538

462

1000

538

1000





333




Statistics


N Valid

Missing

374

1

375

0

370

5

Statistics


N Valid 278 284 286 280 280 279


Statistics


No_trained_ person


N Valid

Missing Mean Median

277

98 101 100

283

92 185 200

287

88 193 200

282

93 148 100

277

98 145 100

334



885 0 4

1209 0 4

1265 0 4

1160 0 4

1171 0 4

335








Within Groups

Total

7618

259749

267367

9

260

269

846

999

847 573


Between Groups

Within Groups

Total

18801

455587

474388

9

266

275

2089

1713

1220 283


Between Groups

Within Groups

Total

19769

451903

471673

9

268

277

2197

1686

1303 235


Within Groups

Total

21660

353811

375471

9

262

271

2407

1350

1782 072


Within Groups

Total

15800

424167

439967

9

262

271

1756

1619

1084 375


Between Groups

Within Groups

Total

7068

275028

282096

9

261

270

785

1054

745 667


336



Total

201371

212000

259

268

777


Within Groups

Total

17947

385615

403562

9

264

273

1994

1461

1365 204

337



Within Groups

Total

17539

423565

441104

9

269

278

1949

1575

1238 272


Within Groups

Total

17452

352989

370442

9

264

273

1939

1337

1450 167


Between Groups

Within Groups

Total

17725

355033

372758

9

259

268

1969

1371

1437 172





Total 267367 269


Between Groups

Within Groups

Total

1144

473243

474388

1

274

275

1144

1727

662 416


Between Groups

Within Groups

Total

610

471062

471673

1

276

277

610

1707

358 550



Total 375471 271



Total 439967 271

338



Between Groups

Within Groups

Total

067

282029

282096

1

269

270

067

1048

064 801

339



Between Groups

Within Groups

Total

282

211718

212000

1

267

268

282

793

356 551



Total 403562 273



Total 441104 278



Total 370442 273


Between Groups

Within Groups

Total

1098

371661

372758

1

267

268

1098

1392

789 375





Total 266507 267


Between Groups

Within Groups

Total

1688

469232

470920

2

271

273

844

1731

487 615


Between Groups

Within Groups

Total

4542

466078

470620

2

273

275

2271

1707

1330 266

340



Within Groups

Total

1172

374090

375262

2

268

270

586

1396

420 658

341




Total 434652 269


Between Groups

Within Groups

Total

166

281908

282074

2

267

269

083

1056

079 924


Between Groups

Within Groups

Total

201

211799

212000

2

264

266

101

802

126 882



Total 398202 271



Total 440259 277



Total 363868 271


Between Groups

Within Groups

Total

2126

364142

366269

2

265

267

1063

1374

774 462

342














11 否 No




15 帶來新商機

343



No 17 沒有




20 不會 No










30 不會 No


No 33 未能估計


344


34 不會 No







No 42 不一定會




45 不會 No









345


54 不會 No














71 No 72 不會



346







79 不會 No

80 不會 No




84 没有 No


86 會 YES





347




348









349
















13 不會 No


15 不知道

350


Not sure 16 盡快



















351




















352





















353












84 銷售 For sales









354







355







356




Statistics

Hold_ workshop

ICT_ infrastructure

Legal_ regulatory

Tax_ incentive

N Valid


287

88 171 100

1272 0 4

280

95 188 200

1252 0 4

282

93 204 200

1364 0 4

285

90 235 300

1375 0 4

Statistics


N Valid 283 284 283


357







Within Groups

Total

17370

443543

460913

11

274

285

1579

1619

975 469


Within Groups

Total

9222

426634

435857

11

267

278

838

1598

525 886


Within Groups

Total

27644

494000

521644

11

269

280

2513

1836

1368 188


Within Groups

Total

28489

508001

536489

11

272

283

2590

1868

1387 179

Provide_more_ info

Between Groups

Within Groups

Total

26027

484256

510284

11

270

281

2366

1794

1319 213

Provide_ consulting

Between Groups

Within Groups

Total

21574

511380

532954

11

271

282

1961

1887

1039 412


Within Groups

26389

537256

11

270

2399

1990

1206 283

358





Within Groups

Total

17370

443543

460913

11

274

285

1579

1619

975 469


Within Groups

Total

9222

426634

435857

11

267

278

838

1598

525 886


Within Groups

Total

27644

494000

521644

11

269

280

2513

1836

1368 188


Within Groups

Total

28489

508001

536489

11

272

283

2590

1868

1387 179

Provide_more_ info

Between Groups

Within Groups

Total

26027

484256

510284

11

270

281

2366

1794

1319 213

Provide_ consulting

Between Groups

Within Groups

Total

21574

511380

532954

11

271

282

1961

1887

1039 412


Within Groups

Total

26389

537256

563645

11

270

281

2399

1990

1206 283

359





Within Groups

Total

4591

457981

462571

1

285

286

4591

1607

2857 092


Within Groups

Total

815

436296

437111

1

278

279

815

1569

519 472


Within Groups

Total

1254

521317

522571

1

280

281

1254

1862

673 413


Within Groups

Total

548

536364

536912

1

283

284

548

1895

289 591

Provide_more_ info

Between Groups

Within Groups

Total

1138

509993

511131

1

281

282

1138

1815

627 429

Provide_ consulting

Between Groups

Within Groups

Total

1579

531787

533366

1

282

283

1579

1886

837 361


Within Groups

Total

430

563613

564042

1

281

282

430

2006

214 644

360





Within Groups

Total

918

456410

457329

2

283

285

459

1613

285 752


Within Groups

Total

1147

430695

431842

2

275

277

573

1566

366 694


Within Groups

Total

3508

514135

517643

2

277

279

1754

1856

945 390


Within Groups

Total

1368

532815

534183

2

281

283

684

1896

361 697

Provide_more_ info

Between Groups

Within Groups

Total

4891

502545

507436

2

279

281

2445

1801

1358 259

Provide_ consulting

Between Groups

Within Groups

Total

4868

525796

530664

2

280

282

2434

1878

1296 275


Within Groups

Total

1420

558089

559509

2

278

280

710

2008

354 702

361




Statistics


N Valid

Missing

318

4

322

0

319

3

Statistics


Receive_ order


N Valid


288

34 295 300 970

0 4

278

44 225 200

1244 0 4

274

48 155 100

1383 0 4

275

47 96

100 1222

0 4

269

53 142 100

1251 0 4

Statistics

Delivery_ product

After_sale_ service


Online_ marketing

Knowledge_ mgt

N Valid

Missing Mean Median

272

50 129 100

279

43 159 100

274

48 179 200

274

48 145 100

266

56 139 100

362



1305 0 4

1364 0 4

1333 0 4

1396 0 4

1302 0 4

363








Within Groups

Total

9662

254743

264405

10

273

283

966

933

1035 414


Within Groups

Total

18697

400927

419624

10

263

273

1870

1524

1226 274


Within Groups

Total

38935

473617

512552

10

259

269

3894

1829

2129 023


Within Groups

Total

19078

387623

406701

10

260

270

1908

1491

1280 242


Within Groups

Total

13590

399633

413223

10

254

264

1359

1573

864 568


Within Groups

Total

23626

432494

456119

10

257

267

2363

1683

1404 179


Within Groups

20221

489576

10

264

2022

1854

1090 370

364


Total 509796 274


Between Groups

Within Groups

Total

8921

469064

477985

10

259

269

892

1811

493 894

365



Within Groups

Total

16601

506365

522967

10

259

269

1660

1955

849 582


Within Groups

Total

23867

421248

445115

10

251

261

2387

1678

1422 171



501 12 292 996

601 55 302 1080

602 20 310 852

603 29 272 841

604 37 308 894

701 10 230 1160

801 12 283 937

803 67 301 1052

Total 275 294 972

Hand_enquiry 301 32 216 1139

501 10 200 1414

601 53 258 1167

602 19 237 1257

603 28 200 1186

604 36 219 1142

701 9 167 1732

801 12 175 1215

803 66 229 1298

366


Total 265 224 1240


501 9 56 726

601 53 191 1445

602 18 161 1650

603 27 130 1203

604 36 172 1344

701 9 144 1740

801 11 36 674

803 66 150 1316

Total 261 154 1371


501 9 56 527

601 53 94 1322

602 18 89 1323

603 28 79 1134

604 36 125 1296

701 9 22 441

801 12 33 651

803 66 114 1264

Total 263 97 1223

Gov_info 301 32 162 976

501 8 112 1126

601 50 132 1220

602 18 111 1278

603 27 126 1289

604 36 147 1276

701 9 111 1537

801 12 117 1030

367


803

Total

65

257

174

144

1326

1243


501 9 56 726

601 52 142 1319

602 19 105 1433

603 28 100 1217

604 35 151 1269

701 9 44 726

801 11 82 874

803 65 145 1370

Total 260 129 1297


501 10 90 1101

601 53 164 1360

602 20 155 1701

603 28 114 1145

604 36 200 1287

701 9 122 1302

801 12 158 1379

803 66 167 1351

Total 267 159 1352


501 9 122 1394

601 52 177 1381

602 18 156 1338

603 28 182 1307

604 36 194 1308

701 9 167 1500

368


801

803

Total

12

65

262

175

186

181

1422

1298

1313


501 9 78 1093

601 53 158 1447

602 17 112 1495

603 28 132 1278

604 35 166 1413

701 9 78 972

801 12 125 1485

803 66 155 1405

Total 262 145 1385


501 9 89 1269

601 52 115 1274

602 17 135 1169

603 27 100 920

604 34 150 1261

701 9 89 1537

801 12 183 1528

803 63 170 1375

Total 254 140 1296




369


Within Groups

Total

266454

270219

285

287

935


Within Groups

Total

5040

423334

428374

2

275

277

2520

1539

1637 196


Within Groups

Total

944

520939

521883

2

271

273

472

1922

246 782


Within Groups

Total

2288

407188

409476

2

272

274

1144

1497

764 467


Within Groups

Total

20950

398581

419532

2

266

268

10475

1498

6991 001


Within Groups

Total

351

461281

461632

2

269

271

176

1715

102 903


Within Groups

Total

1824

515409

517233

2

276

278

912

1867

488 614


Between Groups

Within Groups

Total

4494

480648

485142

2

271

273

2247

1774

1267 283


Within Groups

Total

1524

530154

531679

2

271

273

762

1956

390 678


Within Groups

Total

1245

448094

449338

2

263

265

622

1704

365 694

370




25 82 301 853

75 7 229 951

Total 275 294 972


25 82 235 1159

75 6 150 1225

Total 265 224 1240


25 80 145 1349

75 7 129 1254

Total 261 154 1371


25 81 81 1130

75 7 114 1215

Total 263 97 1223

Gov_info 5 175 126 1198

25 76 184 1265

75 6 167 1211

Total 257 144 1243


25 80 121 1260

75 7 114 1464

Total 260 129 1297


25

179

81

164

152

1351

1361

371


75

Total

7

267

114

159

1345

1352


25

75

Total

175

80

7

262

177

195

129

181

1328

1282

1254

1313

Online_marketing 5

25

75

Total

175

80

7

262

151

135

114

145

1422

1303

1464

1385

Knowledge_mgt 5

25

75

Total

168

79

7

254

136

144

171

140

1315

1278

1113

1296

372





Within Groups

Total

1342

267760

269102

2

282

284

671

950

707 494


Within Groups

Total

1700

426050

427750

2

273

275

850

1561

545 581


Within Groups

Total

209

517169

517379

2

269

271

105

1923

054 947


Within Groups

Total

9361

398111

407473

2

270

272

4681

1474

3174 043


Within Groups

Total

3656

413026

416682

2

264

266

1828

1564

1168 312


Within Groups

Total

1856

456310

458167

2

267

269

928

1709

543 582


Within Groups

Total

6881

508203

515083

2

274

276

3440

1855

1855 158


Between Groups

Within Groups

Total

2647

479556

482202

2

269

271

1323

1783

742 477


373


Within Groups

Total

525237

527173

269

271

1953

374



Within Groups

Total

3384

442976

446360

2

261

263

1692

1697

997 370



5 83 299 848

8 156 288 1035

Total 272 294 976


5 79 241 1138

8 150 215 1241

Total 263 224 1244


5 77 156 1313

8 149 152 1373

Total 259 154 1370


5 79 113 1295

8 149 81 1117

Total 261 97 1224

Gov_info 2 33 109 1208

5 78 146 1203

8 144 150 1268

Total 255 144 1243


375


5

8

Total

79

147

258

142

120

128

1392

1220

1297


5 79 186 1394

8 152 145 1291

Total 265 158 1354


5 79 200 1301

8 148 172 1278

Total 260 180 1314


5 78 151 1336

8 149 139 1339

Total 260 145 1384


5 74 153 1241

8 145 130 1292

Total 252 139 1296

376




Statistics

Online_ transaction


Imp_ productivity

New_bus_ model

N Valid


278

44 170 200

1212 0 4

280

42 204 200

1159 0 4

277

45 193 200

1173 0 4

278

44 203 200

1144 0 4

274

48 180 200

1191 0 4


Prompt_ response


N Valid


276

46 191 200

1187 0 4

278

44 182 200

1173 0 4

273

49 178 200

1168 0 4

275

47 180 200

1128 0 4

278

44 211 200

1127 0 4

377




N Valid


278

44 209 200

1130 0 4

282

40 187 200

1161 0 4

274

48 204 200

1149 0 4

378








Within Groups

Total

16346

385902

402248

10

263

273

1635

1467

1114 352


Within Groups

Total

10744

357963

368707

10

265

275

1074

1351

795 633


Within Groups

Total

15345

358040

373385

10

262

272

1535

1367

1123 345


Within Groups

Total

9570

348299

357869

10

264

274

957

1319

725 700


Within Groups

Total

13436

365804

379240

10

260

270

1344

1407

955 483


Within Groups

Total

12490

369025

381515

10

261

271

1249

1414

883 549


Within Groups

17469

358122

10

263

1747

1362

1283 240

379


Total 375591 273


Within Groups

Total

18549

347558

366107

10

259

269

1855

1342

1382 188

380




Total 343240 270



Total 345493 273



Total 347069 273



Total 373338 277


Between Groups

Within Groups

Total

11452

342014

353467

10

259

269

1145

1321

867 565



501

34

9

191

211

1055

1269

601

602

603

604

701

801

51

19

27

38

9

13

151

179

152

166

111

185

1302

1134

1282

1214

1167

1214

803

Total

66

266

167

167

1232

1215

381



501 9 189 1364

601 54 194 1054

602 18 217 1200

603 27 204 1091

604 38 189 1290

701 9 133 1414

801 13 200 1291

803 66 206 1162

Total 268 202 1163

New_product 301 33 212 1166

501 10 160 1265

601 50 188 1100

602 17 188 1166

603 27 185 1099

604 38 216 1151

701 10 110 1449

801 13 215 1144

803 67 184 1201

Total 265 191 1171


501 9 211 1054

601 53 189 1068

602 18 200 1138

603 27 181 1331

604 38 195 1229

701 9 156 1509

801 13 223 1092

803 67 212 1148

382


Total 267 201 1140


501 9 178 1302

601 52 179 1210

602 17 171 1160

603 27 159 1248

604 37 184 1280

701 9 100 1323

801 13 200 1080

803 66 180 1126

Total 263 179 1174


501 9 178 1302

601 53 179 1116

602 17 171 1160

603 27 170 1325

604 37 203 1213

701 9 167 1581

801 13 192 1115

803 66 188 1183

Total 264 190 1176

New_market 301 33 218 1158

501 9 178 1302

601 53 198 1118

602 18 172 1227

603 27 181 1210

604 37 173 1194

701 9 89 1269

801 13 177 1013

383


803

Total

67

266

173

182

1162

1174


501 9 156 1236

601 52 185 1127

602 17 182 1074

603 27 163 1149

604 37 173 1217

701 9 89 1269

801 12 192 1084

803 66 168 1205

Total 262 178 1170


501 9 156 1236

601 52 190 1125

602 17 176 1091

603 27 170 1137

604 37 170 1151

701 9 67 1323

801 13 215 987

803 65 169 1131

Total 263 180 1136


501 9 222 972

601 54 209 1014

602 18 222 1060

603 27 200 1301

604 37 208 1187

701 9 156 1509

384


801

803

Total

13

65

266

200

214

212

1080

1197

1125


501 9 178 1093

601 53 213 1001

602 18 206 1056

603 27 185 1262

604 37 205 1177

701 9 167 1500

801 13 215 1068

803 67 216 1201

Total 266 209 1124

Inc_sales 301 33 209 1042

501 9 156 1509

601 54 202 1037

602 19 184 1068

603 28 157 1136

604 37 197 1166

701 10 110 1370

801 13 185 1214

803 66 180 1193

Total 269 185 1149


501 9 144 1333

601 51 210 1005

602 18 200 1085

603 27 185 1134

604 36 192 1228

385


701 9 189 1364

801 13 238 961

803 67 203 1231

Total 262 204 1141




Within Groups

Total

785

405834

406619

2

275

277

392

1476

266 767


Within Groups

Total

5541

369026

374568

2

277

279

2771

1332

2080 127


Within Groups

Total

344

379353

379697

2

274

276

172

1384

124 883


Within Groups

Total

5741

357083

362824

2

275

277

2870

1298

2211 112


Within Groups

Total

1776

385582

387358

2

271

273

888

1423

624 537


Within Groups

Total

9991

377560

387551

2

273

275

4995

1383

3612 028


386



Total 381007 277



Total 370813 272



Total 348596 274



Total 351543 277



Total 353928 277



Total 378879 281


Between Groups

Within Groups

Total

6348

354210

360558

2

271

273

3174

1307

2429 090



25

75

Total

180

79

7

266

165

167

200

167

1253

1140

1155

1215


387


25

75

Total

80

7

268

222

229

202

1091

951

1163

New_product 5 179 191 1184

25 79 190 1172

75 7 214 900

Total 265 191 1171


25 80 221 1002

75 7 229 951

Total 267 201 1140


25 79 180 1018

75 7 229 951

Total 263 179 1174


25 80 216 1084

75 7 229 951

Total 264 190 1176

New_market 5 180 180 1179

25 79 182 1185

75 7 229 951

Total 266 182 1174


25 79 190 1150

75 7 229 951

Total 262 178 1170


25

177

79

173

191

1151

1112

388


75

Total

7

263

229

180

951

1136


25 80 231 1063

75 7 229 951

Total 266 212 1125


25 80 230 1036

75 7 229 951

Total 266 209 1124

Inc_sales 5 182 183 1175

25 80 188 1107

75 7 214 1069

Total 269 185 1149


25 77 225 1041

75 7 229 951

Total 262 204 1141

389





Within Groups

Total

1201

401905

403105

2

272

274

600

1478

406 667


Within Groups

Total

344

371425

371769

2

274

276

172

1356

127 881


Within Groups

Total

793

375441

376234

2

271

273

396

1385

286 751


Within Groups

Total

058

359884

359942

2

272

274

029

1323

022 978


Within Groups

Total

736

383735

384471

2

269

271

368

1427

258 773


Within Groups

Total

257

384882

385139

2

271

273

128

1420

090 914


Within Groups

Total

2530

375673

378203

2

273

275

1265

1376

919 400


Within Groups

Total

1771

366045

367815

2

268

270

885

1366

648 524


390


Within Groups

Total

344439

345678

270

272

1276

391




Total 349953 275



Total 352246 275



Total 376286 279


Between Groups

Within Groups

Total

4955

353747

358702

2

269

271

2478

1315

1884 154

392




Sector = 601

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

16

061

824

16


Sig (2-tailed)

N

061

824

16

1

16

Sector = 803

Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

23

224

303

23


Sig (2-tailed)

N

224

303

23

1

23

393



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

17

-033

901

17


Sig (2-tailed)

N

-033

901

17

1

17


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

39

096

563

39


Sig (2-tailed)

N

096

563

39

1

39

394



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

17

-055

833

17


Sig (2-tailed)

N

-055

833

17

1

17




Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

7

316

490

7


Sig (2-tailed)

N

316

490

7

1

7

395


396



Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

22

-276

214

22


Sig (2-tailed)

N

-276

214

22

1

22


Correlations

ICTU_3 VA_3


Sig (2-tailed)

N

1

27

176

380

27


Sig (2-tailed)

N

176

380

27

1

27

397




Web_inte_firm_DB

N Valid 304

Missing 18


Frequency Table

Web_inte_firm_DB


Percent

Valid

Missing Total

0

1

Total System

237

67

304 18

322

736

208

944 56

1000

780

220

1000

780

1000

398




Web_inte_Partner_DB

N Valid 303

Missing 19


Frequency Table

Web_inte_Partner_DB


Percent

Valid

Missing Total

0

1

Total System

256

47

303 19

322

795

146

941 59

1000

845

155

1000

845

1000

399





Statistics


year

N Valid

Missing

591

0

591

0

584

7

OverAll_ICT

N Valid 573


400


OverAll_ICT


Percent Cumulative

Percent

Valid 0

1

2

3

4


10

68

143

224

128

573 18

591

17

115

242

379

217

970 30

1000

17

119

250

391

223

1000

17

136

386

777

1000

401





Statistics


N Valid

Missing

591

0

591

0

584

7

OverAll_VA

N Valid 573


402


OverAll_VA


Percent Cumulative

Percent

Valid 0

1

2

3

4


10

49

165

148

201

573 18

591

17

83

279

250

340

970 30

1000

17

86

288

258

351

1000

17

103

391

649

1000

403








Within Groups

Total

77972

933307

1011279

10

716

726

7797

1304

5982 000


Between Groups

Within Groups

Total

81024

1015674

1096697

10

719

729

8102

1413

5736 000



501

601

72

63

162

261

230

209

1095

1328

1282

602

603

604

605

701

801

53

60

94

11

30

33

249

280

266

136

223

294

1103

1102

1063

809

1073

827

802

803

17

132

188

278

1453

1029

404


Total 727 247 1180

405



301

501

72

63

283

251

1245

1306

601 164 227 1259

602 53 279 1183

603 60 293 1163

604 94 283 1104

605 12 167 1231

701 30 237 1098

801 33 303 1015

802 17 182 1468

803 132 298 1095

Total 730 266 1227




Within Groups

Total

40199

982198

1022398

2

729

731

20100

1347

14918 000


Between Groups

Within Groups

Total

46467

1055756

1102223

2

732

734

23234

1442

16109 000


Overall_ICT_usage 5

25

567

158

235

292

1224

896

406


75 7 243 1134

Total 732 247 1183

407



75 7 300 1155

Total 735 265 1225

408






Within Groups

Total

7818

1000033

1007851

2

720

722

3909

1389

2814 061


Between Groups

Within Groups

Total

6042

1083392

1089434

2

723

725

3021

1498

2016 134

Overall_ICT_usage 2

5

8

Total

100

181

442

723

251

263

239

246

1219

1081

1207

1181


2

5

8

Total

100

181

445

726

258

281

260

265

1216

1141

1258

1226

409





Model Summary




ANOVAb


1 Regression

Residual

Total

42233

29504

71738

1

59

60

42233

500

84455 000a


Coefficientsa

Model




1 (Constant) 480 296 1623 110

410


OverAll_ICT_Usa ge

922 100 767 9190 000


411



Model Summary




ANOVAb


1 Regression

Residual

Total

3171E15

5881E17

5912E17

1

34

35

3171E15

1730E16

183 671a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

5598E6

9770E6

6587E7

2282E7 073

085

428

933

671


412


413



Model Summary




ANOVAb


1 Regression

Residual

Total

35148

18403

53551

1

47

48

35148

392

89765 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

829

741

220

078 810

3767

9474

000

000


414



Model Summary




ANOVAb


1 Regression

Residual

Total

3472E13

3988E14

4336E14

1

23

24

3472E13

1734E13

2002 170a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

373696884

1109E6

2143E6

783606583 283

174

1415

863

170


415



Model Summary




ANOVAb


1 Regression

Residual

Total

81668

40369

122037

1

107

108

81668

377

216463 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

534

821

153

056 818

3503

14713

001

000


416



Model Summary




ANOVAb


1 Regression

Residual

Total

2947E15

9801E17

9831E17

1

65

66

2947E15

1508E16

195 660a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

4712E6

5991E6

3826E7

1355E7 055

123

442

902

660


417



Model Summary




ANOVAb


1 Regression

Residual

Total

27336

11069

38405

1

40

41

27336

277

98786 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

566

876

247

088 844

2297

9939

027

000


418



Model Summary




ANOVAb


1 Regression

Residual

Total

5802E14

1663E16

1721E16

1

21

22

5802E14

7917E14

733 402a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

3463E6

5273E6

1761E7

6159E6 184

197

856

846

402


419


420



Model Summary




ANOVAb


1 Regression

Residual

Total

38692

25234

63926

1

52

53

38692

485

79732 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

650

811

276

091 778

2356

8929

022

000


421



Model Summary




ANOVAb


1 Regression

Residual

Total

4956E16

9225E17

9720E17

1

24

25

4956E16

3844E16

1289 267a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-5639E7

4021E7

1091E8

3541E7 226

-517

1136

610

267


422


423



Model Summary




ANOVAb


1 Regression

Residual

Total

36967

44421

81387

1

78

79

36967

569

64911 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

904

707

249

088 674

3639

8057

000

000


424



Model Summary




ANOVAb


1 Regression

Residual

Total

2849E15

1954E16

2239E16

1

31

32

2849E15

6303E14

4520 042a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1051E7

1004E7

1266E7

4724E6 357

-830

2126

413

042


425



Model Summary




ANOVAb


1 Regression

Residual

Total

6746

15254

22000

1

23

24

6746

663

10172 004a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1188

572

413

179 554

2873

3189

009

004


426



Model Summary




ANOVAb


1 Regression

Residual

Total

1876E14

3349E15

3536E15

1

13

14

1876E14

2576E14

728 409a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1563E7

-4142E6

1026E7

4854E6 -230

1524

-853

152

409


427


428



Model Summary




ANOVAb


1 Regression

Residual

Total

17162

10709

27871

1

29

30

17162

369

46474 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

080

1006

451

148 785

177

6817

861

000


429



Model Summary




ANOVAb


1 Regression

Residual

Total

2256E12

3976E13

4201E13

1

17

18

2256E12

2339E12

965 340a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-142204397

437459701

1336E6

445374398 232

-106

982

916

340


430


431



Model Summary




ANOVAb


1 Regression

Residual

Total

17210

890

18100

1

8

9

17210

111

154652 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-159

1024

224

082 975

-707

12436

499

000


432



Model Summary




ANOVAb


1 Regression

Residual

Total

1073E11

3180E11

4253E11

1

1

2

1073E11

3180E11

338 665a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-201253500

401253500

1196E6

690649197 502

-168

581

894

665


433



Model Summary




ANOVAb


1 Regression

Residual

Total

49230

64628

113857

1

110

111

49230

588

83792 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1021

707

232

077 658

4408

9154

000

000


434



Model Summary




ANOVAb


1 Regression

Residual

Total

2729E15

8560E16

8833E16

1

66

67

2729E15

1297E15

2104 152a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1306E7

6948E6

1415E7

4791E6 176

-923

1450

359

152


435


436



Model Summary




ANOVAb


1 Regression

Residual

Total

288679

209092

497771

1

435

436

288679

481

600574 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

689

791

091

032 762

7584

24507

000

000


437



Model Summary




ANOVAb


1 Regression

Residual

Total

1903E15

1171E17

1190E17

1

241

242

1903E15

4858E14

3917 049a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1660E6

2747E6

3814E6

1388E6 126

-435

1979

664

049


438



Model Summary




ANOVAb


1 Regression

Residual

Total

64204

55486

119690

1

127

128

64204

437

146955 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

726

813

204

067 732

3552

12122

001

000


439



Model Summary




ANOVAb


1 Regression

Residual

Total

1875E16

2439E18

2457E18

1

65

66

1875E16

3752E16

500 482a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

5313E6

1918E7

8514E7

2713E7 087

062

707

950

482


440



Model Summary




ANOVAb


1 Regression

Residual

Total

4667

3333

8000

1

5

6

4667

667

7000 046a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1111

778

778

294 764

1429

2646

212

046


441



Model Summary




ANOVAb


1 Regression

Residual

Total

5762E14

1377E15

1953E15

1

3

4

5762E14

4588E14

1256 344a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1025E7

1053E7

2623E7

9394E6 543

-391

1121

722

344


442


443



Model Summary




ANOVAb


1 Regression

Residual

Total

57734

24754

82487

1

78

79

57734

317

181922 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

480

845

181

063 837

2660

13488

009

000


444



Model Summary




ANOVAb


1 Regression

Residual

Total

2182E11

1793E14

1795E14

1

39

40

2182E11

4598E12

047 829a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1716E6

-75827787

960185400

348072513 -035

1787

-218

082

829


445



Model Summary




ANOVAb


1 Regression

Residual

Total

65414

85419

150833

1

148

149

65414

577

113338 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

865

742

207

070 659

4177

10646

000

000


446



Model Summary




ANOVAb


1 Regression

Residual

Total

2427E13

6154E17

6154E17

1

79

80

2427E13

7790E15

003 956a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

1466E7

632330104

3470E7

1133E7 006

422

056

674

956


447


448



Model Summary




ANOVAb


1 Regression

Residual

Total

235966

156031

391997

1

334

335

235966

467

505110 000a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

684

809

101

036 776

6791

22475

000

000


449



Model Summary




ANOVAb


1 Regression

Residual

Total

4788E16

2075E18

2123E18

1

188

189

4788E16

1104E16

4338 039a


Coefficientsa

Model




1 (Constant)

OverAll_ICT_Usa ge

-1675E7

1527E7

1999E7

7330E6 150

-838

2083

403

039


450







Model Summary




ANOVAb


1 Regression

Residual

Total

6244E15

5866E17

5929E17

1

36

37

6244E15

1629E16

383 540a


Coefficientsa

Model




451


1 (Constant)

ICTU_1

-5083E6

1619E6

6134E7

2615E6 103

-083

619

934



Model Summary




ANOVAb


1 Regression

Residual

Total

2926E12

4319E14

4349E14

1

24

25

2926E12

1800E13

163 690a


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E6

39812145

2195E6

98729675 082

1049

403

304

690

452


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E6

39812145

2195E6

98729675 082

1049

403

304


453



Model Summary




ANOVAb


1 Regression

Residual

Total

1011E17

8848E17

9858E17

1

69

70

1011E17

1282E16

7882 006a


Coefficientsa

Model




1 (Constant)

ICTU_1

-7495E7

4629E6

3660E7

1649E6 320

-2048

2808

044


454



Model Summary




ANOVAb


1 Regression

Residual

Total

1496E15

1598E16

1747E16

1

22

23

1496E15

7262E14

2061 165a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1402E7

1355E6

2229E7

944159291 293

-629

1435

536


455



Model Summary




ANOVAb


1 Regression

Residual

Total

1080E16

9612E17

9720E17

1

24

25

1080E16

4005E16

270 608a


Coefficientsa

Model




1 (Constant)

ICTU_1

-5220E6

2500E6

1308E8

4815E6 105

-040

519

969


456



Model Summary




ANOVAb


1 Regression

Residual

Total

3448E15

1894E16

2239E16

1

31

32

3448E15

6109E14

5643 024a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2625E7

1646E6

1779E7

692834279 392

-1476

2376

150


457



Model Summary




ANOVAb


1 Regression

Residual

Total

4180E14

3136E15

3554E15

1

14

15

4180E14

2240E14

1866 193a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1260E7

1055E6

1546E7

771942283 343

-815

1366

429


458



Model Summary




ANOVAb


1 Regression

Residual

Total

1021E14

3271E17

3272E17

1

18

19

1021E14

1817E16

006 941a


Coefficientsa

Model




1 (Constant)

ICTU_1

2304E7

316607483

1035E8

4223E6 018

223

075

826


459



Model Summary




ANOVAb


1 Regression

Residual

Total

1770E11

2483E11

4253E11

1

1

2

1770E11

2483E11

713 554a


Coefficientsa

Model




1 (Constant)

ICTU_1

796579705

-26681949

484411840

31599544 -645

1644

-844

348


460



Model Summary




ANOVAb


1 Regression

Residual

Total

3856E14

8801E16

8840E16

1

68

69

3856E14

1294E15

298 587a


Coefficientsa

Model




1 (Constant)

ICTU_1

-1422E6

313224467

1478E7

573875768 066

-096

546

924


461



Model Summary




ANOVAb


1 Regression

Residual

Total

1842E15

1173E17

1191E17

1

249

250

1842E15

4709E14

3912 049a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2593E6

361167362

4213E6

182595787 124

-615

1978

539


462



Model Summary




ANOVAb


1 Regression

Residual

Total

7807E16

2657E18

2735E18

1

69

70

7807E16

3850E16

2028 159a


Coefficientsa

Model




1 (Constant)

ICTU_1

-3433E7

4040E6

7641E7

2837E6 169

-449

1424

655


463




Model Summary




ANOVAb


1 Regression

Residual

Total

5320E14

1421E15

1953E15

1

3

4

5320E14

4736E14

1123 367a


Coefficientsa

Model




1 (Constant)

ICTU_1

-2509E7

1636E6

4099E7

1543E6 522

-612

1060

584


464



Model Summary




ANOVAb


1 Regression

Residual

Total

7515E14

3354E17

3362E17

1

42

43

7515E14

7987E15

094 761a


Coefficientsa

Model




1 (Constant)

ICTU_1

2917E6

560292822

4179E7

1827E6 047

070

307

945


465



Model Summary




ANOVAb


1 Regression

Residual

Total

1036E15

6147E17

6157E17

1

81

82

1036E15

7589E15

137 713a


Coefficientsa

Model




1 (Constant)

ICTU_1

3437E6

518019598

3587E7

1402E6 041

096

370

924


466



Model Summary




ANOVAb


1 Regression

Residual

Total

9511E16

2032E18

2127E18

1

195

196

9511E16

1042E16

9129 003a


Coefficientsa

Model




1 (Constant)

ICTU_1

-3801E7

2698E6

2100E7

892974189 211

-1810

3021

072


467





Model Summary




ANOVAb


1 Regression

Residual

Total

6980E14

5912E17

5919E17

1

35

36

6980E14

1689E16

041 840a


Coefficientsa

Model




1 (Constant)

ICTU_2

4012E7

-264324127

4753E7

1300E6 -034

844

-203

404


468


Sector = 501


Model Summary




ANOVAb


1 Regression

Residual

Total

4411E12

4200E14

4244E14

1

22

23

4411E12

1909E13

231 635a


Coefficientsa

Model




1 (Constant)

ICTU_2

4148E6

-38835311

1991E6

80784609 -102

2083

-481

049


469


Sector = 601


Model Summary




ANOVAb


1 Regression

Residual

Total

5449E15

9752E17

9807E17

1

61

62

5449E15

1599E16

341 562a


Coefficientsa

Model




1 (Constant)

ICTU_2

3539E6

595025787

3537E7

1019E6 075

100

584

921


470


Sector = 602


Model Summary




ANOVAb


1 Regression

Residual

Total

4215E12

1720E16

1721E16

1

21

22

4215E12

8191E14

005 943a


Coefficientsa

Model




1 (Constant)

ICTU_2

1687E7

25515710

1273E7

355718740 016

1325

072

199


471


Sector = 603


Model Summary




ANOVAb


1 Regression

Residual

Total

6422E15

9623E17

9687E17

1

23

24

6422E15

4184E16

154 699a


Coefficientsa

Model




1 (Constant)

ICTU_2

2005E7

1125E6

1143E8

2872E6 081

175

392

862


472


Sector = 604


Model Summary




ANOVAb


1 Regression

Residual

Total

1469E14

2220E16

2235E16

1

30

31

1469E14

7400E14

198 659a


Coefficientsa

Model




1 (Constant)

ICTU_2

1012E7

145732042

1184E7

327118372 081

855

446

399


473


Sector = 701


Model Summary




ANOVAb


1 Regression

Residual

Total

1085E13

3456E15

3467E15

1

11

12

1085E13

3142E14

035 856a


Coefficientsa

Model




1 (Constant)

ICTU_2

9965E6

-89253997

9595E6

480355550 -056

1038

-186

321


474


Sector = 801


Model Summary




ANOVAb


1 Regression

Residual

Total

7395E12

3462E13

4201E13

1

17

18

7395E12

2036E12

3631 074a


Coefficientsa

Model




1 (Constant)

ICTU_2

-358579784

57492686

844124906

30169605 420

-425

1906

676


475


Sector = 803


Model Summary




ANOVAb


1 Regression

Residual

Total

1206E15

8709E16

8830E16

1

65

66

1206E15

1340E15

900 346a


Coefficientsa

Model




1 (Constant)

ICTU_2

-1385E6

240581087

9483E6

253638748 117

-146

949

884


476




Model Summary




ANOVAb


1 Regression

Residual

Total

1417E15

1173E17

1187E17

1

231

232

1417E15

5078E14

2790 096a


Coefficientsa

Model




1 (Constant)

ICTU_2

978212467

157736521

3142E6

94438934 109

311

1670

756


477




Model Summary




ANOVAb


1 Regression

Residual

Total

5012E14

2448E18

2449E18

1

63

64

5012E14

3886E16

013 910a


Coefficientsa

Model




1 (Constant)

ICTU_2

5912E7

170835543

5868E7

1504E6 014

1007

114

318


478




Model Summary




ANOVAb


1 Regression

Residual

Total

1191E14

1834E15

1953E15

1

3

4

1191E14

6112E14

195 689a


Coefficientsa

Model




1 (Constant)

ICTU_2

6928E6

286292320

2560E7

648542329 247

271

441

804


479




Model Summary




ANOVAb


1 Regression

Residual

Total

4080E12

1714E14

1754E14

1

37

38

4080E12

4631E12

881 354a


Coefficientsa

Model




1 (Constant)

ICTU_2

950276580

19302217

695194361

20565148 152

1367

939

180


480




Model Summary




ANOVAb


1 Regression

Residual

Total

8055E14

6145E17

6153E17

1

79

80

8055E14

7778E15

104 748a


Coefficientsa

Model




1 (Constant)

ICTU_2

2268E7

-175766626

2131E7

546168623 -036

1064

-322

290


481




Model Summary




ANOVAb


1 Regression

Residual

Total

3542E16

2082E18

2118E18

1

178

179

3542E16

1170E16

3028 084a


Coefficientsa

Model




1 (Constant)

ICTU_2

-5476E6

975119286

1824E7

560375624 129

-300

1740

764


482





Model Summary




ANOVAb


1 Regression

Residual

Total

3128E16

8480E17

8793E17

1

7

8

3128E16

1211E17

258 627a


Coefficientsa

Model




1 (Constant)

ICTU_3

2894E8

-2836E7

3603E8

5581E7 -189

803

-508

448


483


484



Model Summary




ANOVAb


1 Regression

Residual

Total

1126E13

5714E14

5827E14

1

13

14

1126E13

4395E13

256 621a


Coefficientsa

Model




1 (Constant)

ICTU_3

6593E6

-391767855

5193E6

774196262 -139

1269

-506

227


485



Model Summary




ANOVAb


1 Regression

Residual

Total

5671E16

7964E17

8531E17

1

5

6

5671E16

1593E17

356 577a


Coefficientsa

Model




1 (Constant)

ICTU_3

4010E8

-3985E7

4551E8

6678E7 -258

881

-597

418


486



Model Summary




ANOVAb


1 Regression

Residual

Total

2133E11

7875E13

7896E13

1

18

19

2133E11

4375E12

049 828a


Coefficientsa

Model




1 (Constant)

ICTU_3

2242E6

-47696427

1429E6

216023611 -052

1569

-221

134


487



Model Summary




ANOVAb


1 Regression

Residual

Total

1382E17

1469E18

1607E18

1

9

10

1382E17

1632E17

847 381a


Coefficientsa

Model




1 (Constant)

ICTU_3

5096E8

-5034E7

3688E8

5471E7 -293

1382

-920

200


488



NO such samples




Model Summary




ANOVAb


1 Regression

Residual

Total

8977E12

8105E13

9003E13

1

10

11

8977E12

8105E12

1108 317a


Coefficientsa



489


B Std Error Beta

1 (Constant)

ICTU_3

4770E6

-399494044

2511E6

379586332 -316

1900

-1052

087



Model Summary




ANOVAb


1 Regression

Residual

Total

8555E16

1673E18

1758E18

1

16

17

8555E16

1045E17

818 379a


Coefficientsa

Model




1 (Constant)

ICTU_3

3129E8

-3078E7

2304E8

3403E7 -221

1358

-905

193

379

490


Coefficientsa

Model




1 (Constant)

ICTU_3

3129E8

-3078E7

2304E8

3403E7 -221

1358

-905

193


491






N

Stage 01_No-PC 70

02_No-Internet 39

Business_year

03_No-Web

2

5

8

285

60

85

249



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

6608a

373834

1085

947

4650

683615

1816000

8

1

2

2

4

385

394

826

373834

543

473

1162

1776

465

210537

306

267

655

880

000

737

766

624

492




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

6608a

373834

1085

947

4650

683615

1816000

690223

8

1

2

2

4

385

394

393

826

373834

543

473

1162

1776

465

210537

306

267

655

880

000

737

766

624




N

Stage 01_No-PC 70

02_No-Internet 39

No_of_employee

03_No-Web

5

25

286

348

47



493


Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

9007a

168372

4432

2551

7195

686537

1832000

695544

5

1

2

1

2

389

395

394

1801

168372

2216

2551

3598

1765

1021

95402

1256

1445

2039

405

000

286

230

132


494




N

Stage 01_No-PC 69

02_No-Internet 39

03_No-Web 285

Sector 301 32

501 45

601 110

602 21

603 24

604 39

605 12

701 18

801 20

802 12

803 60



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

53712a

299521

8298

26837

32474

31

1

2

10

19

1733

299521

4149

2684

1709

978

169042

2342

1515

965

504

000

098

132

503

495


Error

Total

Corrected Total

639646

1822000

693359

361

393

392

1772




N

Stage 01_No-PC 64

02_No-Internet 36

Business_year

03_No-Web

2

5

8

283

60

84

239



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

49471a

475983

18892

5790

9619

752456

2618000

801927

8

1

2

2

4

374

383

382

6184

475983

9446

2895

2405

2012

3074

236582

4695

1439

1195

002

000

010

239

312


496


497




N

Stage 01_No-PC 64

02_No-Internet 37

No_of_employee

03_No-Web

5

25

284

337

48



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

36573a

203771

1941

2282

3361

769334

2643000

805906

5

1

2

1

2

379

385

384

7315

203771

971

2282

1680

2030

3603

100385

478

1124

828

003

000

620

290

438


498


499




N

Stage 01_No-PC 63

02_No-Internet 37

03_No-Web 283

Sector 301 31

501 45

601 103

602 21

603 24

604 40

605 12

701 18

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

107517a

252882

25296

34163

45713

697052

32

1

2

10

20

350

3360

252882

12648

3416

2286

1992

1687

126976

6351

1715

1148

013

000

002

076

299

500


Total

Corrected Total

2625000

804569

383

382




N

Stage 01_No-PC 62

02_No-Internet 39

Business_year

03_No-Web

2

5

8

281

59

83

240



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

15418a

475509

4627

2460

3020

696571

2232000

711990

8

1

2

2

4

373

382

381

1927

475509

2313

1230

755

1867

1032

254625

1239

659

404

411

000

291

518

806

501




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

15418a

475509

4627

2460

3020

696571

2232000

8

1

2

2

4

373

382

1927

475509

2313

1230

755

1867

1032

254625

1239

659

404

411

000

291

518

806


502




N

Stage 01_No-PC 62

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

336

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

13636a

210249

1154

3717

7353

702364

2248000

716000

5

1

2

1

2

377

383

382

2727

210249

577

3717

3676

1863

1464

112853

310

1995

1973

201

000

734

159

140


503


504




N

Stage 01_No-PC 61

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 44

601 102

602 21

603 24

604 39

605 12

701 18

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

70928a

318217

7431

29448

41008

31

1

2

10

19

2288

318217

3716

2945

2158

1242

172699

2017

1598

1171

180

000

135

105

280

505


Error

Total

Corrected Total

643072

2238000

714000

349

381

380

1843




N

Stage 01_No-PC 64

02_No-Internet 39

Business_year

03_No-Web

2

5

8

282

59

85

241



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

26547a

622144

10971

4166

8061

736897

2743000

763444

8

1

2

2

4

376

385

384

3318

622144

5485

2083

2015

1960

1693

317448

2799

1063

1028

098

000

062

347

392

506




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

26547a

622144

10971

4166

8061

736897

2743000

8

1

2

2

4

376

385

3318

622144

5485

2083

2015

1960

1693

317448

2799

1063

1028

098

000

062

347

392


507




N

Stage 01_No-PC 64

02_No-Internet 39

No_of_employee

03_No-Web

5

25

283

339

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

16316a

252086

1488

2499

5964

750122

2759000

766438

5

1

2

1

2

380

386

385

3263

252086

744

2499

2982

1974

1653

127703

377

1266

1511

145

000

686

261

222


508




N

Stage 01_No-PC 63

02_No-Internet 39

03_No-Web 282

Sector 301 32

501 43

601 107

602 21

603 24

604 39

605 12

701 17

801 20

802 11

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

59945a

377304

8302

23104

24452

31

1

2

10

19

1934

377304

4151

2310

1287

968

188931

2079

1157

644

519

000

127

319

871

509


Error

Total

Corrected Total

702962

2734000

762906

352

384

383

1997




N

Stage 01_No-PC 65

02_No-Internet 39

Business_year

03_No-Web

2

5

8

280

60

86

238



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

30394a

585613

18945

1291

11990

777895

2793000

808289

8

1

2

2

4

375

384

383

3799

585613

9473

645

2998

2074

1831

282306

4567

311

1445

070

000

011

733

218

510




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

30394a

585613

18945

1291

11990

777895

2793000

8

1

2

2

4

375

384

3799

585613

9473

645

2998

2074

1831

282306

4567

311

1445

070

000

011

733

218


511




N

Stage 01_No-PC 65

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

339

47



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

23900a

266361

3631

5189

8902

788991

2810000

812891

5

1

2

1

2

380

386

385

4780

266361

1816

5189

4451

2076

2302

128287

875

2499

2144

044

000

418

115

119


512




N

Stage 01_No-PC 64

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 43

601 105

602 20

603 24

604 40

605 12

701 18

801 20

802 11

803 59



Source

Type III Sum of



513


Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

382922

7967

23959

22688

741843

2792000

811833

1

2

10

19

352

384

383

382922

3983

2396

1194

2108

181694

1890

1137

567

000

153

333

929


514




N


Business_year

03_No-Web

2

277

50

5 73

8 194



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

8330a

243398

3708

264

128

495058

1550000

503388

5

1

1

2

2

311

317

316

1666

243398

3708

132

064

1592

1047

152905

2329

083

040

390

000

128

920

961


515




N


03_No-Web 279


25 42



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

10383a

101747

086

1736

2955

498432

1567000

508815

3

1

1

1

1

315

319

318

3461

101747

086

1736

2955

1582

2187

64302

054

1097

1867

089

000

816

296

173


516




N


03_No-Web 278

Sector 301 27

501 37

601 71

602 19

603 23

604 40

605 8

701 16

801 18

802 9

803 50



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

25752a

186308

4354

11546

8719

481670

1558000

21

1

1

10

10

296

318

1226

186308

4354

1155

872

1627

754

114492

2676

710

536

775

000

103

715

864

517




Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

25752a

186308

4354

11546

8719

481670

1558000

507421

21

1

1

10

10

296

318

317

1226

186308

4354

1155

872

1627

754

114492

2676

710

536

775

000

103

715

864




N


Business_year

03_No-Web

2

279

51

5 72

8 193



518


Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

13616a

317497

2571

3740

2431

580182

1826000

593797

5

1

1

2

2

310

316

315

2723

317497

2571

1870

1216

1872

1455

169644

1374

999

649

204

000

242

369

523


519




N


03_No-Web 281


25 42



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

10041a

113248

012

1155

2449

588805

1843000

598846

3

1

1

1

1

314

318

317

3347

113248

012

1155

2449

1875

1785

60393

006

616

1306

150

000

937

433

254


520




N


03_No-Web 280

Sector 301 27

501 38

601 69

602 19

603 23

604 40

605 8

701 16

801 19

802 8

803 50



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

43248a

209171

3253

16954

9813

554550

1834000

21

1

1

10

10

295

317

2059

209171

3253

1695

981

1880

1096

111271

1731

902

522

352

000

189

532

874

521




Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

43248a

209171

3253

16954

9813

554550

1834000

597798

21

1

1

10

10

295

317

316

2059

209171

3253

1695

981

1880

1096

111271

1731

902

522

352

000

189

532

874






N

Stage 01_No-PC 63

02_No-Internet 39

03_No-Web 280

522


Business_year 2 61

5 86

8 235



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

19037a

283541

13727

941

788

564429

1710000

583466

8

1

2

2

4

373

382

381

2380

283541

6863

471

197

1513

1573

187377

4536

311

130

131

000

011

733

971




Stage 01_No-PC

02_No-Internet

03_No-Web

No_of_employee 5

N

63

39

282

336

523



N

Stage 01_No-PC 63

02_No-Internet 39

No_of_employee

03_No-Web

5

25

282

336

48



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

21592a

95175

5790

888

1862

567593

1727000

589185

5

1

2

1

2

378

384

383

4318

95175

2895

888

931

1502

2876

63384

1928

592

620

015

000

147

442

538


524




N

Stage 01_No-PC 62

02_No-Internet 39

03_No-Web 281

Sector 301 32

501 43

601 104

602 21

603 24

604 38

605 13

701 20

801 19

802 10

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

55936a

194583

10622

18598

17313

31

1

2

10

19

1804

194583

5311

1860

911

1186

127859

3490

1222

599

233

000

032

275

907

525


Error

Total

Corrected Total

532650

1722000

588586

350

382

381

1522




N

Stage 01_No-PC 75

02_No-Internet 44

Business_year

03_No-Web

2

285

66

5 86

8 252



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

12919a

443448

4968

1053

1681

699566

2090000

8

1

2

2

4

395

404

1615

443448

2484

527

420

1771

912

250387

1402

297

237

506

000

247

743

917

526




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

12919a

443448

4968

1053

1681

699566

2090000

712485

8

1

2

2

4

395

404

403

1615

443448

2484

527

420

1771

912

250387

1402

297

237

506

000

247

743

917


527




N

Stage 01_No-PC 75

02_No-Internet 45

No_of_employee

03_No-Web

5

25

286

360

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

13855a

159281

215

585

2131

702739

2091000

716594

5

1

2

1

2

400

406

405

2771

159281

107

585

1065

1757

1577

90663

061

333

606

165

000

941

564

546


528




N

Stage 01_No-PC 74

02_No-Internet 45

03_No-Web 285

Sector 301 32

501 47

601 109

602 25

603 28

604 37

605 13

701 20

801 19

802 12

803 62



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

88358a

340340

6846

41453

47630

32

1

2

10

20

2761

340340

3423

4145

2381

1636

201644

2028

2456

1411

018

000

133

008

113

529


Error

Total

Corrected Total

626184

2081000

714542

371

404

403

1688




N

Stage 01_No-PC 59

02_No-Internet 38

Business_year

03_No-Web

2

5

8

279

60

83

233



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

28837a

112654

23317

6498

3853

480940

1180000

8

1

2

2

4

367

376

3605

112654

11659

3249

963

1310

2751

85965

8897

2479

735

006

000

000

085

568

530




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

28837a

112654

23317

6498

3853

480940

1180000

509777

8

1

2

2

4

367

376

375

3605

112654

11659

3249

963

1310

2751

85965

8897

2479

735

006

000

000

085

568


531




N

Stage 01_No-PC 59

02_No-Internet 38

No_of_employee

03_No-Web

5

25

281

332

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

24254a

55114

4367

020

1739

492723

1197000

516976

5

1

2

1

2

372

378

377

4851

55114

2184

020

869

1325

3662

41610

1649

015

656

003

000

194

902

519


532




N

Stage 01_No-PC 58

02_No-Internet 38

03_No-Web 280

Sector 301 29

501 43

601 102

602 20

603 24

604 38

605 12

701 20

801 19

802 10

803 59



Source

Type III Sum of



533


Intercept

Stage

Sector

Stage Sector

Error

Total

Corrected Total

93164

11541

14138

13639

460232

1195000

516742

1

2

10

19

344

376

375

93164

5770

1414

718

1338

69636

4313

1057

537

000

014

395

945




N

Stage 01_No-PC 59

02_No-Internet 39

Business_year

03_No-Web

2

5

8

275

60

83

230



Source

Type III Sum of


Corrected Model

Intercept

Stage

21009a

129700

14700

8

1

2

2626

129700

7350

1838

90793

5145

069

000

006

534


Business_year

Stage Business_year

Error

Total

Corrected Total

9541

4555

519983

1222000

540992

2

4

364

373

372

4770

1139

1429

3339

797

037

528


535




N

Stage 01_No-PC 59

02_No-Internet 39

No_of_employee

03_No-Web

5

25

276

328

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

12870a

63399

3221

050

1575

535119

1238000

547989

5

1

2

1

2

368

374

373

2574

63399

1610

050

788

1454

1770

43599

1107

034

542

118

000

331

854

582


536




N

Stage 01_No-PC 58

02_No-Internet 39

03_No-Web 275

Sector 301 30

501 43

601 100

602 20

603 24

604 36

605 12

701 19

801 19

802 10

803 59



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

49246a

101309

7071

12409

19980

31

1

2

10

19

1589

101309

3536

1241

1052

1084

69100

2411

846

717

352

000

091

584

801

537


Error

Total

Corrected Total

498485

1236000

547731

340

372

371

1466




N

Stage 01_No-PC 59

02_No-Internet 38

Business_year

03_No-Web

2

5

8

277

61

83

230



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

27175a

244044

20555

4792

4056

590087

1740000

617262

8

1

2

2

4

365

374

373

3397

244044

10277

2396

1014

1617

2101

150954

6357

1482

627

035

000

002

229

643

538




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

27175a

244044

20555

4792

4056

590087

1740000

8

1

2

2

4

365

374

3397

244044

10277

2396

1014

1617

2101

150954

6357

1482

627

035

000

002

229

643


539




N

Stage 01_No-PC 59

02_No-Internet 38

No_of_employee

03_No-Web

5

25

279

330

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

27437a

105915

7009

000

6276

595496

1757000

622934

5

1

2

1

2

370

376

375

5487

105915

3504

000

3138

1609

3410

65808

2177

000

1950

005

000

115

989

144


540




N

Stage 01_No-PC 58

02_No-Internet 38

03_No-Web 278

Sector 301 32

501 44

601 100

602 20

603 23

604 38

605 12

701 19

801 18

802 10

803 58



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

61029a

186500

9892

17130

18195

31

1

2

10

19

1969

186500

4946

1713

958

1200

113636

3014

1044

583

219

000

050

406

918

541


Error

Total

Corrected Total

561292

1752000

622321

342

374

373

1641




N

Stage 01_No-PC 56

02_No-Internet 38

Business_year

03_No-Web

2

5

8

278

61

83

228



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

24353a

135556

14614

4588

2353

482644

1171000

506997

8

1

2

2

4

363

372

371

3044

135556

7307

2294

588

1330

2289

101952

5496

1725

442

021

000

004

180

778


542


543




N

Stage 01_No-PC 56

02_No-Internet 38

No_of_employee

03_No-Web

5

25

279

327

46



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

20403a

64429

5056

201

1741

486707

1172000

507110

5

1

2

1

2

367

373

372

4081

64429

2528

201

871

1326

3077

48582

1906

152

657

010

000

150

697

519


544




N

Stage 01_No-PC 55

02_No-Internet 38

03_No-Web 278

Sector 301 30

501 43

601 99

602 20

603 23

604 37

605 12

701 19

801 19

802 10

803 59



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

56882a

103804

8608

11998

14098

31

1

2

10

19

1835

103804

4304

1200

742

1404

79435

3293

918

568

079

000

038

517

928

545


Error

Total

Corrected Total

442999

1155000

499881

339

371

370

1307




N


Business_year

03_No-Web

2

277

51

5 70

8 191



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

2229a

100623

083

1791

1474

338281

749000

340510

5

1

1

2

2

306

312

311

446

100623

083

895

737

1105

403

91021

075

810

667

846

000

784

446

514


546


547




N


03_No-Web 278


25 41



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

888a

27947

627

875

786

339643

750000

340530

3

1

1

1

1

309

313

312

296

27947

627

875

786

1099

269

25425

571

796

715

848

000

451

373

398


548




N


03_No-Web 277

Sector 301 25

501 37

601 70

602 20

603 22

604 37

605 8

701 17

801 18

802 7

803 51



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

21253a

77564

037

14169

10559

319256

21

1

1

10

10

290

1012

77564

037

1417

1056

1101

919

70456

033

1287

959

566

000

855

237

479

549


Total

Corrected Total

749000

340510

312

311


550




N


Business_year

03_No-Web

2

274

52

5 70

8 188



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

6105a

61255

1555

4837

5451

266892

585000

272997

5

1

1

2

2

304

310

309

1221

61255

1555

2419

2725

878

1391

69772

1771

2755

3104

227

000

184

065

046


551




N


03_No-Web 276


25 41



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

1235a

23700

605

264

813

271762

587000

272997

3

1

1

1

1

308

312

311

412

23700

605

264

813

882

466

26861

686

299

922

706

000

408

585

338


552




N


03_No-Web 275

Sector 301 25

501 36

601 70

602 19

603 22

604 38

605 8

701 17

801 18

802 7

803 51



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

22259a

45274

1217

16075

6493

250738

21

1

1

10

10

289

1060

45274

1217

1608

649

868

1222

52182

1402

1853

748

231

000

237

052

679

553


Total

Corrected Total

586000

272997

311

310






N



Business_year

03-Have-Web

2

5

8

270

200

388

810



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

223337a

5375558

142123

3351

4425

1659075

8

1

2

2

4

1389

27917

5375558

71062

1675

1106

1194

23373

4500E3

59494

1403

926

000

000

000

246

448

554


Total

Corrected Total

10816000

1882412

1398

1397


555




N



No_of_employee

03-Have-Web

5

25

271

1062

348



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

223664a

6030763

158757

7832

1853

1667525

10920000

1891189

5

1

2

1

2

1404

1410

1409

44733

6030763

79378

7832

927

1188

37664

5078E3

66834

6594

780

000

000

000

010

459


556




N



03-Have-Web 267

Sector 301 133

501 100

601 268

602 98

603 128

604 194

605 12

701 55

702 20

801 74

802 21

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

284095a

2646718

105246

37420

22575

34

1

2

11

21

8356

2646718

52623

3402

1075

7145

2263E3

44998

2909

919

000

000

000

001

566

557


Error

Total

Corrected Total

1591641

10812000

1875736

1361

1396

1395

1169




N



Business_year

03-Have-Web

2

5

8

270

197

384

795



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

121263a

4682660

81653

8084

2701

1824626

9439000

1945889

8

1

2

2

4

1367

1376

1375

15158

4682660

40827

4042

675

1335

11356

3508E3

30587

3028

506

000

000

000

049

731


558


559




N



No_of_employee

03-Have-Web

5

25

271

1045

341



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

114941a

5133309

68699

3057

4473

1841033

9530000

1955974

5

1

2

1

2

1380

1386

1385

22988

5133309

34349

3057

2236

1334

17231

3848E3

25747

2291

1676

000

000

000

130

187


560




N



03-Have-Web 267

Sector 301 128

501 97

601 262

602 95

603 126

604 192

605 12

701 54

702 20

801 74

802 19

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

207310a

2288851

36719

49472

26612

34

1

2

11

21

6097

2288851

18359

4497

1267

4696

1763E3

14139

3464

976

000

000

000

000

491

561


Error

Total

Corrected Total

1736132

9421000

1943442

1337

1372

1371

1299




N



Business_year

03-Have-Web

2

5

8

274

196

385

804



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

62067a

3042411

25575

19920

1509

2244132

6790000

2306199

8

1

2

2

4

1376

1385

1384

7758

3042411

12787

9960

377

1631

4757

1865E3

7841

6107

231

000

000

000

002

921


562


563




N



No_of_employee

03-Have-Web

5

25

275

1050

343



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

37976a

3234964

26028

1520

058

2277339

6838000

2315315

5

1

2

1

2

1387

1393

1392

7595

3234964

13014

1520

029

1642

4626

1970E3

7926

926

018

000

000

000

336

982


564




N



03-Have-Web 271

Sector 301 130

501 98

601 264

602 98

603 128

604 190

605 10

701 59

702 20

801 73

802 21

803 288



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

161417a

1545256

6799

72503

27371

34

1

2

11

21

4748

1545256

3400

6591

1303

2988

972701

2140

4149

820

000

000

118

000

696

565


Error

Total

Corrected Total

2135110

6789000

2296526

1344

1379

1378

1589




N



Business_year

03-Have-Web

2

5

8

270

197

385

802



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

132003a

2947658

76657

17497

1365

2152898

6661000

8

1

2

2

4

1375

1384

16500

2947658

38329

8748

341

1566

10538

1883E3

24479

5587

218

000

000

000

004

928

566




Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

132003a

2947658

76657

17497

1365

2152898

6661000

2284901

8

1

2

2

4

1375

1384

1383

16500

2947658

38329

8748

341

1566

10538

1883E3

24479

5587

218

000

000

000

004

928


567




N



No_of_employee

03-Have-Web

5

25

271

1052

342



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

115599a

3148952

73346

2438

625

2184975

6734000

2300574

5

1

2

1

2

1388

1394

1393

23120

3148952

36673

2438

313

1574

14687

2000E3

23296

1549

199

000

000

000

214

820


568




N



03-Have-Web 267

Sector 301 130

501 97

601 263

602 97

603 127

604 191

605 11

701 56

702 20

801 74

802 21

803 293



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

224741a

1419361

24903

64991

25831

34

1

2

11

21

6610

1419361

12451

5908

1230

4322

927981

8141

3863

804

000

000

000

000

717

569


Error

Total

Corrected Total

2057197

6685000

2281938

1345

1380

1379

1530




N



Business_year

03-Have-Web

2

5

8

266

196

378

792



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

84969a

3963568

38906

22011

1534

2055215

8246000

2140184

8

1

2

2

4

1357

1366

1365

10621

3963568

19453

11006

384

1515

7013

2617E3

12844

7267

253

000

000

000

001

908


570


571




N



No_of_employee

03-Have-Web

5

25

267

1038

337



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

71274a

4379616

39281

11189

1297

2081640

8320000

2152913

5

1

2

1

2

1369

1375

1374

14255

4379616

19641

11189

649

1521

9375

2880E3

12917

7359

427

000

000

000

007

653


572




N



03-Have-Web 263

Sector 301 129

501 97

601 259

602 92

603 126

604 188

605 11

701 55

702 20

801 74

802 19

803 291



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

154684a

1841623

13787

47261

25314

34

1

2

11

21

4550

1841623

6893

4296

1205

3055

1237E3

4629

2885

809

000

000

010

001

711

573


Error

Total

Corrected Total

1974794

8245000

2129478

1326

1361

1360

1489




N


Business_year

03-Have-Web

2

265

111

5 228

8 467



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

16054a

1594171

849

7000

4555

1132821

3619000

1148875

5

1

1

2

2

800

806

805

3211

1594171

849

3500

2278

1416

2267

1126E3

600

2472

1609

046

000

439

085

201


574


575




N


03-Have-Web 266


25 208



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

5762a

1865755

010

5566

053

1149958

3660000

1155719

3

1

1

1

1

808

812

811

1921

1865755

010

5566

053

1423

1349

1311E3

007

3911

037

257

000

932

048

847


576




N


03-Have-Web 263

Sector 301 78

501 51

601 151

602 55

603 75

604 114

605 6

701 30

702 13

801 42

802 10

803 179



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

55332a

824294

138

33284

13678

22

1

1

11

10

2515

824294

138

3026

1368

1796

588782

098

2161

977

014

000

754

015

462

577


Error

Total

Corrected Total

1093398

3625000

1148730

781

804

803

1400


578




N


Business_year

03-Have-Web

2

267

112

5 229

8 470



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

23089a

1607292

680

10716

3315

1155461

3503000

1178550

5

1

1

2

2

805

811

810

4618

1607292

680

5358

1657

1435

3217

1120E3

474

3733

1155

007

000

492

024

316


579




N


03-Have-Web 268


25 210



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

5415a

1768471

2856

2549

494

1180115

3547000

1185530

3

1

1

1

1

813

817

816

1805

1768471

2856

2549

494

1452

1244

1218E3

1968

1756

340

293

000

161

186

560


580




N


03-Have-Web 264

Sector 301 79

501 51

601 153

602 56

603 75

604 113

605 6

701 30

702 13

801 44

802 11

803 177



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

75477a

770714

1948

46005

14968

1096043

22

1

1

11

10

785

3431

770714

1948

4182

1497

1396

2457

551995

1395

2995

1072

000

000

238

001

381

581


Total

Corrected Total

3508000

1171520

808

807


582




N


Business_year

03-Have-Web

2

268

109

5 231

8 470



Source

Type III Sum of


Corrected Model

Intercept

Stage

Business_year

Stage Business_year

Error

Total

Corrected Total

29365a

1601291

9692

6798

1957

1209035

3610000

1238400

5

1

1

2

2

804

810

809

5873

1601291

9692

3399

978

1504

3906

1065E3

6445

2260

651

002

000

011

105

522


583




N


03-Have-Web 270


25 207



Source

Type III Sum of


Corrected Model

Intercept

Stage

No_of_employee


Error

Total

Corrected Total

18819a

1828429

12294

487

210

1229139

3646000

1247958

3

1

1

1

1

811

815

814

6273

1828429

12294

487

210

1516

4139

1206E3

8112

321

138

006

000

005

571

710


584




N


03-Have-Web 266

Sector 301 78

501 54

601 148

602 56

603 75

604 114

605 5

701 32

702 13

801 43

802 10

803 178



Source

Type III Sum of


Corrected Model

Intercept

Stage

Sector

Stage Sector

Error

81743a

805165

12248

40582

11201

1151761

22

1

1

11

10

783

3716

805165

12248

3689

1120

1471

2526

547374

8327

2508

761

000

000

004

004

666

585


Total

Corrected Total

3610000

1233504

806

805


586



Stage 1 PC Only


VA = VA_1

ICTU = ICTU_1



VA = VA_2




587


VA = VA_3


VA = f (ICTU)



Model Summary




ANOVAb


1 Regression

Residual

Total

36837412

21115941

57953353

14

538

552

2631244

39249

67040 000a


Coefficientsa

Model




1 (Constant) 4226 1518 2783 006

588


ICTU 294 014 669 20906 000

Emp_5 817 676 033 1209 227

Business_year_2 1421 810 049 1754 080

Business_year_5 1197 635 052 1886 060

Stage_1 -3405 1827 -064 -1864 063

Stage_2 -7323 873 -272 -8392 000

S_301 1734 1063 050 1632 103

S_501 -848 1107 -023 -766 444

S_601 009 844 000 011 992

S_602 722 1141 019 633 527

S_603 177 1050 005 169 866

S_604 -1047 924 -036 -1133 258

S_701 -2131 1463 -042 -1457 146


589





Model Summary




ANOVAb


1 Regression

Residual

Total

3433E17

2407E18

2751E18

14

294

308

2452E16

8188E15

2995 000a


Coefficientsa

Model




1 (Constant) 1063E8 3060E7 3475 001

590


ICTU -161748033 289644814 -039 -558 577

Emp_5 -4826E7 1357E7 -211 -3556 000

Business_year_2 641462352 1637E7 002 039 969

Business_year_5 -2583E6 1258E7 -012 -205 838

Stage_1 -7558E7 3547E7 -155 -2131 034

Stage_2 -6147E7 1733E7 -249 -3547 000

S_301 1788E7 2028E7 058 882 379

S_501 2019E6 2160E7 006 093 926

S_601 1563E7 1564E7 069 999 318

S_602 1513E7 2214E7 042 683 495

S_603 5664E7 2113E7 167 2681 008

S_604 1298E7 1966E7 042 660 509

S_701 2118E6 2773E7 005 076 939


591










Total 735 1000

Group Statistics

Have_PC

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

40

40

40

40

40000

40000

40000

40000

1 Revenue

Emp_5

329

329

329000

329000

592


Year_2 329 329000

Year_5 329 329000


Emp_5

Year_2

Year_5

369

369

369

369000

369000

369000

Analysis


Eigenvalues


of Variance



Wilks Lambda



1 980 7472 4 113


Function

1


593


Year_2 024 Year_5 575

Structure Matrix

Function

1



Have_PC

Function

1






group codes 2 3


363 494

594



Total

Group Statistics

1

366 735

1

498 1000

Have_PC

Valid N (listwise)

Unweighted Weighted

0 Revenue 40 40000

Emp_5 40 40000

Year_2 40 40000

Year_5 40 40000

S_301 40 40000

S_501 40 40000

S_601 40 40000

S_602 40 40000

S_603 40 40000

S_604 40 40000

S_701 40 40000

S_801 40 40000

1 Revenue 329 329000

Emp_5 329 329000

Year_2 329 329000

Year_5 329 329000

S_301 329 329000

S_501 329 329000

595


S_601 329 329000

S_602 329 329000

S_603 329 329000

S_604 329 329000

S_701 329 329000

S_801 329 329000


Emp_5 369 369000

Year_2 369 369000

Year_5 369 369000

S_301 369 369000

S_501 369 369000

S_601 369 369000

S_602 369 369000

S_603 369 369000

S_604 369 369000

S_701 369 369000

S_801 369 369000

Analysis


Eigenvalues


of Variance

Cumulative



596


Wilks Lambda



1 934 24456 12 018


Function

1


597


Structure Matrix

Function

1


598



Have_PC

Function

1


672 Internet









Total 735 1000

Group Statistics

Have_Internet

Valid N (listwise)

Unweighted Weighted

599


0 Revenue

Emp_5

30

30

30000

30000

Year_2

Year_5

30

30

30000

30000

1 Revenue

Emp_5

Year_2

Year_5

319

319

319

319

319000

319000

319000

319000

Total Revenue

Emp_5

349

349

349000

349000

Year_2

Year_5

349

349

349000

349000

Analysis



of Variance



Wilks Lambda



1 969 10917 4 028

600



Function

1


601


Structure Matrix

Function

1



Have_Internet

Function

1







group codes 22 30


344 468

602



Total

Group Statistics

20

386 735

27

525 1000

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue 30 30000

Emp_5 30 30000

Year_2 30 30000

Year_5 30 30000

S_301 30 30000

S_501 30 30000

S_601 30 30000

S_602 30 30000

S_603 30 30000

S_604 30 30000

S_701 30 30000

S_801 30 30000

1 Revenue 319 319000

Emp_5 319 319000

Year_2 319 319000

Year_5 319 319000

S_301 319 319000

S_501 319 319000

603


S_601 319 319000

S_602 319 319000

S_603 319 319000

S_604 319 319000

S_701 319 319000

S_801 319 319000


Emp_5 349 349000

Year_2 349 349000

Year_5 349 349000

S_301 349 349000

S_501 349 349000

S_601 349 349000

S_602 349 349000

S_603 349 349000

S_604 349 349000

S_701 349 349000

S_801 349 349000

Analysis


Eigenvalues


of Variance



604


Wilks Lambda



1 911 31917 12 001


Function

1


605


Structure Matrix

Function

1


606



Have_Internet

Function

1










Total 735 1000

Group Statistics

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

30

30

30000

30000

607


Year_2 30 30000

Year_5 30 30000

ICTU_1 30 30000

1 Revenue

Emp_5

Year_2

Year_5

ICTU_1

319

319

319

319

319

319000

319000

319000

319000

319000

Total Revenue

Emp_5

Year_2

Year_5

349

349

349

349

349000

349000

349000

349000

ICTU_1 349 349000

Analysis


Eigenvalues


of Variance



Wilks Lambda



608


Wilks Lambda



1 733 106830 5 000


Function

1


609


Structure Matrix

Function

1








group codes 22 30


344 468

610



Total

Group Statistics

20 27

386 525 735 1000

Have_Internet

Valid N (listwise)

Unweighted Weighted

0 Revenue 30 30000

Emp_5 30 30000

Year_2 30 30000

Year_5 30 30000

S_301 30 30000

S_501 30 30000

S_601 30 30000

S_602 30 30000

S_603 30 30000

S_604 30 30000

S_701 30 30000

S_801 30 30000

ICTU_1 30 30000

1 Revenue 319 319000

Emp_5 319 319000

Year_2 319 319000

Year_5 319 319000

S_301 319 319000

611


S_501 319 319000

S_601 319 319000

S_602 319 319000

S_603 319 319000

S_604 319 319000

S_701 319 319000

S_801 319 319000

ICTU_1 319 319000


Emp_5 349 349000

Year_2 349 349000

Year_5 349 349000

S_301 349 349000

S_501 349 349000

S_601 349 349000

S_602 349 349000

S_603 349 349000

S_604 349 349000

S_701 349 349000

S_801 349 349000

ICTU_1 349 349000

Analysis


Eigenvalues

612



of Variance

Cumulative



Wilks Lambda



1 698 122303 13 000


Function

1


613


Structure Matrix

Function

1


614



Have_Internet

Function

1


673 Website









Total 735 1000

615


Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

179

179

179

179

179000

179000

179000

179000

1 Revenue

Emp_5

Year_2

Year_5

154

154

154

154

154000

154000

154000

154000

Total Revenue

Emp_5

Year_2

Year_5

333

333

333

333

333000

333000

333000

333000

Analysis


Eigenvalues


of Variance

Cumulative



616


Wilks Lambda

Test of Function(s)

Wilks Lambda

Chi- square df Sig

1 912 30480 4 000


Function

1


617


Structure Matrix

Function

1



Have_ Function

Web 1



Dependent Have_Web


variables)



Valid 333 453

618



Total

Group Statistics

38 52

324 441

40 54

402 547 735 1000

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue 179 179000

Emp_5 179 179000

Year_2 179 179000

Year_5 179 179000

S_301 179 179000

S_501 179 179000

S_601 179 179000

S_602 179 179000

S_603 179 179000

S_604 179 179000

S_701 179 179000

S_801 179 179000

1 Revenue

Emp_5

Year_2

154

154

154

154000

154000

154000

619


Year_5 154 154000

S_301 154 154000

S_501 154 154000

S_601 154 154000

S_602 154 154000

S_603 154 154000

S_604 154 154000

S_701 154 154000

S_801 154 154000


Emp_5 333 333000

Year_2 333 333000

Year_5 333 333000

S_301 333 333000

S_501 333 333000

S_601 333 333000

S_602 333 333000

S_603 333 333000

S_604 333 333000

S_701 333 333000

S_801 333 333000

Analysis


Eigenvalues


of Variance

Cumulative


620



Wilks Lambda



1 878 42303 12 000


Function

1


621


Structure Matrix

Function

1


622



Have_ Function

Web 1










Total 735 1000

Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue 179 179000

623


Emp_5 179 179000

Year_2 179 179000

Year_5 179 179000

ICTU_1_2 179 179000

1 Revenue

Emp_5

Year_2

Year_5

ICTU_1_2

154

154

154

154

154

154000

154000

154000

154000

154000


Emp_5

Year_2

Year_5

ICTU_1_2

333

333

333

333

333000

333000

333000

333000

Analysis


Eigenvalues


of Variance

Cumulative



Wilks Lambda

624




1 653 140080 5 000


Function

1


Structure Matrix

Function

1



625


Have_ Function

Web 1


626










Total 735 1000

Group Statistics

Have_Web

Valid N (listwise)

Unweighted Weighted

0 Revenue

Emp_5

Year_2

Year_5

S_301

S_501

S_601

S_602

179

179

179

179

179

179

179

179

179000

179000

179000

179000

179000

179000

179000

179000

627


S_603 179 179000

S_604 179 179000

S_701 179 179000

S_801 179 179000

ICTU_1_2 179 179000

1 Revenue 154 154000

Emp_5 154 154000

Year_2 154 154000

Year_5 154 154000

S_301 154 154000

S_501 154 154000

S_601 154 154000

S_602 154 154000

S_603 154 154000

S_604 154 154000

S_701 154 154000

S_801 154 154000

ICTU_1_2 154 154000


Emp_5 333 333000

Year_2 333 333000

Year_5 333 333000

S_301 333 333000

S_501 333 333000

S_601 333 333000

S_602 333 333000

S_603 333 333000

S_604 333 333000

628


S_701 333 333000

S_801 333 333000

ICTU_1_2 333 333000

Analysis


Eigenvalues


of Variance

Cumulative



Wilks Lambda



1 646 141824 13 000


Function

1


629



Structure Matrix

Function

1


630


Structure Matrix

Function

1



Have_ Function

Web 1


631





Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 159a 025 001 9165E7


S_301 S_501 S_604 S_601

ANOVAb


1 Regression

Residual

Total

7804E16

3016E18

3094E18

9

359

368

8671E15

8400E15

1032 413a


S_601


Coefficientsa

Model


Standardised

Coefficients


1 (Constant)

Have_PC

S_301

-9156E6

1613E7

2442E7

1803E7

1572E7

1834E7

055

080

-508

1026

1332

612

306

184

632


S_501

S_601

S_602

S_603

S_604

S_701

S_801

-879281590

1254E7

9983E6

4953E7

7875E6

2231E6

2205E7

1900E7

1436E7

2050E7

2023E7

1851E7

2352E7

2259E7

-003

059

028

143

026

005

056

-046

873

487

2449

425

095

976

963

383

627

015

671

925

330



Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 158a 025 -001 9425E7


S_603 S_604 S_501 S_301 S_601

ANOVAb


1 Regression

Residual

Total

7666E16

3011E18

3088E18

9

339

348

8518E15

8883E15

959 474a


S_601


Coefficientsa

633


Model


Standardised

Coefficients


1 (Constant)

Have_Internet

S_301

S_501

S_601

S_602

S_603

S_604

S_701

S_801

-1038E7

1730E7

2376E7

-455567026

1485E7

1013E7

5029E7

8050E6

2560E6

2355E7

2065E7

1857E7

1909E7

1987E7

1531E7

2112E7

2086E7

1947E7

2531E7

2371E7

052

077

-001

066

029

145

025

006

058

-503

931

1244

-023

970

480

2411

413

101

994

615

352

214

982

333

632

016

680

919

321



Model Summary

Model R R Square

Adjusted R

Square

Std Error of the

Estimate

1 211a 044 018 9031E7


S_604 S_301 S_501 S_601

ANOVAb


1 Regression

Residual

1224E17

2634E18

9

323

1360E16

8156E15

1667 096a

634


Total 2757E18 332


S_601


Coefficientsa

Model


Standardised

Coefficients


1 (Constant)

Have_Web

S_301

S_501

S_601

S_602

S_603

S_604

S_701

S_801

-6584E6

2489E7

2518E7

3954E6

1386E7

1190E7

4924E7

9991E6

5872E6

-4081E6

1184E7

1011E7

1879E7

1942E7

1496E7

2098E7

2040E7

1862E7

2570E7

2330E7

136

084

013

063

034

148

034

013

-010

-556

2462

1340

204

927

567

2414

537

228

-175

579

014

181

839

355

571

016

592

819

861


635


Endnotes




636

Deliverable - ICT Adoption Study

Documents

Transcript of Deliverable - ICT Adoption Study