Project Report - Damith Final

FACTORS DETERMINING THE ADOPTION OF MOBILE ENTERPRISE

SOLUTIONS IN SME SECTOR

Special Reference to Etisalat Lanka Private Limited

By

A.B.G.D.C.Jayawardana

5266FM2013028

An Independent project Report

Submitted to the University of Sri Jayewardenepura

In partial fulfilment of the requirement or the degree of

Master of Business Administration

June, 2016

Department of Marketing

Faculty of Management Studies and Commerce

University of Sri Jayewardenepura

Nugegoda

This is to certify that the Project Report on

FACTORS DETERMINING THE ADOPTION OF MOBILE ENTERPRISE

SOLUTIONS IN SME SECTOR

Special Reference to Etisalat Lanka Private Limited

By


5266FM2013028

has been accepted by the University of Sri Jayewardenepura, in partial fulfilment of

requirements of the Master of Business Administration Degree

-------------------------------------

Supervisor

------------------------------------

Date

Declaration

I certify that this project report does not incorporate without acknowledgement, any

material previously submitted for a degree or diploma in any university, and to the best of

my knowledge and belief it does not contain any material previously published or written

by another person, except where due reference is made in the text.


February 5th , 2016

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TABLE OF CONTENTS

TABLE OF CONTENTS ...................................................................................................... i

LIST OF FIGURES ............................................................................................................ iv

LIST OF TABLES ............................................................................................................... v

LIST OF ABBREVIATIONS ............................................................................................. vi

Acknowledgement ............................................................................................................. vii

Executive Summary ......................................................................................................... viii

Chapter 1 .............................................................................................................................. 1

INTRODUCTION ............................................................................................................... 1

1.1 Industry Overview ................................................................................................. 1

1.1.1 Mobile Telecommunications industry ................................................................ 1

1.1.2 Small and Medium Enterprise ............................................................................ 3

1.1.3 Organisation ........................................................................................................ 4

1.2 Background of the study ....................................................................................... 6

1.3 Identification of the issues..................................................................................... 7

1.4 Objectives of the Study ......................................................................................... 8

1.5 Research Questions ............................................................................................... 9

1.6 Significance of the Study .................................................................................... 10

1.7 Limitations of the study....................................................................................... 11

1.8 Chapter Framework ............................................................................................. 11

Chapter 2 ............................................................................................................................ 13

LITRATURE REVIEW ..................................................................................................... 13

2.1 Introduction .............................................................................................................. 13

2.2 Adoption of Mobile Enterprise Solutions by SME sector ....................................... 13

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2.3 Diffusion of Innovation Theory ............................................................................... 15

2.4 Explaining rate of adoption ...................................................................................... 15

2.5 Perceived Attributes of Innovation .......................................................................... 16

2.5.1 Relative advantage ............................................................................................ 17

2.5.2 Compatibility .................................................................................................... 17

2.5.3 Complexity ........................................................................................................ 18

2.5.4 Trialability......................................................................................................... 18

2.5.5. Observerbility .................................................................................................. 19

2.5.6 Rate of Adoption ............................................................................................... 19

Chapter 3 ............................................................................................................................ 20

CASE FRAMEWORK AND METHODOLOGY............................................................. 20

3.1 Introduction .............................................................................................................. 20

3.2 Conceptual Framework ............................................................................................ 20

3.3 Questionnaire design and Data collection................................................................ 21

3.4 Validity and reliability ............................................................................................. 22

3.5 Operationalization of Variables ............................................................................... 24

3.5.1 Defining Variables and Development of questionnaire .................................... 24

Chapter 4 ............................................................................................................................ 29

ANALYSIS ........................................................................................................................ 29

4.1 Introduction .............................................................................................................. 29

4.2 Data analysis ............................................................................................................ 29

4.2.1 Case Screening & Variable Screening .............................................................. 29

4.3 Model Adequacy Checking...................................................................................... 30

4.4 Descriptive Analysis ................................................................................................ 31

4.4.1 Sample Composition ......................................................................................... 31

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4.5 Advanced Analysis .................................................................................................. 36

4.5.1 Correlation Analysis ......................................................................................... 36

4.5.2 Regression Analysis .......................................................................................... 38

4.5.3 Logistic Regression ........................................................................................... 41

Chapter 5 ............................................................................................................................ 43

DISCUSSION AND FINDINGS ....................................................................................... 43

5.1. Introduction ............................................................................................................. 43

5.2 Factor Analysis ........................................................................................................ 43

5.3 Descriptive Statistics ................................................................................................ 44

5.4 Correlation Analysis ................................................................................................ 44

5.5 Regression Analysis ................................................................................................. 44

Chapter 6 ............................................................................................................................ 46

CONCLUSSION................................................................................................................ 46

Chapter 7 ............................................................................................................................ 47

REFERENCE ..................................................................................................................... 47

Annex I - Questionnaire ................................................................................................. 49

Annexure II – SPSS Analysis ........................................................................................ 52

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LIST OF FIGURES

Figure 1: market share of Mobile Telecom Operators in Sri Lanka ............................................... 2

Figure 2: Technology acceptance model ...................................................................................... 14

Figure 3: Variables determining the rate of adoption of innovation ............................................. 16

Figure 4: Conceptual framework .................................................................................................. 21

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LIST OF TABLES

Table 1: Statistical Overview of the Telecommunication Sector as at end of Dec 2014................ 3

Table 2: Distribution of persons engaged and establishments across SME groups ........................ 4

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LIST OF ABBREVIATIONS

SME – Small and Medium Enterprise

MES – Mobile Enterprise Solutions

TRCSL – Telecommunications Regulatory Commission Sri Lanka

VPN – Virtual Private Network

MPABX – Mobile Private Automatic Branch Exchange

GSM- Global System for Mobile Communication

GDP- Gross Domestic production

3G – Third generation Mobile technology

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Acknowledgement

I am highly indebted to my supervisor Dr. Lalith Chandralal, for his guidance, advice and constant

supervision on this case study throughout its course. And Prof. Rohini Samarasinghe who always

guided me being a panel member during the project presentations.

Further I would like to convey my sincere gratitude to Dr Janak Kumarasinghe, Coordinator Msc,

MBA, MPM programme and all the lecturers whom have taught me during the MBA programme.

Without your guidance and support I wouldn’t be succeeded.

I would also like to forward my appreciation to the Mr. Eomal Munasinha (Head of HR - Etisalat

Lanka Private Limited) for giving me the permission to carry out this Project about the

organisation. Head of Enterprise, Mr. Ranjith Fernando who encourage me to select this topic for

my project and was extremely helpful in giving the necessary inputs and guidance for conducting

the survey.

Moreover, my sincere gratitude goes to all the participants who represent Small and Medium

Enterprises actively took part in the research through the questionnaires.

A special acknowledgement is extended to my parents, who brought me up to this stage and being

with me in the journey of life all throughout in the times of ups and downs and helping me always

with all the endeavours in my life. Also I would like to thank my wife, for all her sacrifices, inputs

and encouragement given.

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Executive Summary

This project study “Factors determining the adoption of mobile enterprise solutions in SME sector

– Special reference to Etisalat Lanka Pvt Ltd” is focus on the latest ‘Mobile solutions’ range

introduced by Enterprise division of Etisalat lanka Pvt Ltd, one of the five mobile

telecommunication service providers in Sri Lanka. Etisalat Lanka Private limited is third largest

telecommunications operator in Sri Lanka today, serving more than four million subscribers

(TRCSL, 2014).

Celltel Lanka Private Limited (Rebranded as Etisalat Lanka Private Limited, later) is the first

operator to start mobile telecommunication operations in the country, commenced its operations

in 1989. However with time Celltel Lanka Private Limited lost its market leadership mainly due

to the reactive strategic actions. Being in an industry, where the continuous innovation is the key

to survival and failing to invest on latest innovations in a timely manner can be seen as the main

reason for the loss of market leadership and the market share.

With the new brand changeover from Celltel to Tigo to Etisalat, company was able to introduce

few new innovations to Srilankan market. Especially 3.75G Mobile broadband, Mobile VPN,

Mobile MPABX etc. which differentiate Etisalat from other operators in terms of Enterprise

solution provider.

The objective of this study is to analyse what factors are determining the adoption of these Mobile

enterprise solutions in Small and Medium Enterprises. Based on the E.M Roger’s (1964)

Innovation adoption theory a conceptual framework was developed and information was collected

to validate that framework. The study analysed, whether there any correlation between Perceived

attributes of innovation (Relative advantage, Compatibility, Complexity, Trialability and

Observerbility) and the rate of adoption.

In the data collection, an online survey being carried out with the respective decision makers of

the Small & Medium Enterprises in Colombo & Suburbs. Questionnaire was content 30 questions

which tested 5 dimensions of “perceived attributes of innovations”

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Analysing the information it was found that recent innovative efforts that the company has made

has succeeded and has helped the company to outperform the competitors’. It also showed that the

company had been able to gain a position in the consumers mind above Mobitel, the second player

in the market, with its innovator platform.

The key findings are arrived at analysing the information collected and the literature. It is evident

that by branding the innovation of a company, it would be able to gain a competitive advantage

and improve the main brand’s image.

People, is a key deciding factor in driving the company in the innovator platform. The level of

empowerment, training had helped the organisation in achieving the state that it is in today. Also,

the strong culture that is present in the organisation and the steps that the company had taken to

improve the strength of the culture in favour of driving the organisation towards the goals has

succeeded for Etisalat.

However there are shortcomings identified in the last chapter of the study and suitable

recommendations to overcome those are suggested by the author. Key recommendations were,

company to invest in new technologies more proactively and with a long-term plan, improve the

synergies as a Etisalat group, provide cross geographical training are some of the key

recommendations.

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Chapter 1

INTRODUCTION

1.1 Industry Overview

1.1.1 Mobile Telecommunications industry

In its initial stages the Sri Lankan telecommunication industry was dominated by the fixed line

networks which purely provided voice call facility, later on they have introduces other solutions

specially to Enterprise segment , such as Internet, Virtual private networks (VPN), PABX

services, Data centres etc.

The inception of the Sri Lankan mobile telecommunications industry is marked in the year of 1989,

when Millicom International Cellular S.A. started the first mobile network in Sri Lanka which is

Celltel Lanka Private Limited. Since then the mobile telecommunications industry is one of the

industries in the country, which has shown a tremendous growth in numerous aspects such as

technology, utilisation, usage and financials.

Later, with the ease of access and purchase, increased availability of coverage, development of the

technology, decrease in the rates the mobile telecommunications overtook the fixed line

technology. Due to these reasons mobile services was seen as the primary connectivity provider,

whereas the fixed line was seen as a secondary substitute, which is the opposite of early days.

As per the statistics published by the Telecommunications Regulations Commission of Sri Lanka

(TRCSL) the penetration of mobile subscriptions is over 100 per cent, leaving a fully saturated

market. Making the situation more difficult to the players, at present there are five mobile telecom

operators in the country. According to industry experts for a country with 21.7 million populations,

the market is overcrowded.

Very high subscriber acquisition and retention costs exert pressure on the operational costs. Due

to the capital intensive nature of the industry the capital costs are substantial. On the other hand,

due to the severe competition the pressure on prices and revenues are also high. This rigorously

affects the profitability of the industry, threatening the long-term sustainability of some of the

operators which has failed to achieve even the profitable subscriber mass.

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Compared to the global technological trends, telecom industry in Sri Lanka is in the forefront of

technology with the rapid adoption of technology to stay competitive in the highly competitive

market.

Growth in internet penetration of the country is another factor that would affect the growth in the

telecommunication industry of Sri Lanka. Mobile voice services are becoming a commodity and

the profits from this service for the organisation are very low. Therefore the focus of the industry

is moving towards the Mobile Enterprise Solutions (MES). In addition, though the mobile

subscription penetration is well above 100 per cent, the penetration of the internet is less than 10%

and penetration of mobile broadband internet and MES are further less. Therefore there is an

opportunity for the operators in this segment. Growth in smart phones in Sri Lanka following the

same global trend has further increase the focus of the Sri Lankan telecom operators on mobile

internet & Enterprise solutions.

As per the latest published records in December 2014, the mobile market is dominated by Dialog

with a subscriber market share of 38 per cent, followed by Mobitel with 23 per cent, Etisalat at 21

per cent, Airtel at 11 per cent and Hutch with 07 per cent.

Figure 1: market share of Mobile Telecom Operators in Sri Lanka

Source: Telecommunication Regulatory Commission Sri Lanka (TRCSL)

Dialog, 38%

Mobitel, 23%

Etisalat, 21%

Airtel, 11%

Hutch, 7%

MARKET SHARE

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Table 1: Statistical Overview of the Telecommunication Sector as at end of Dec 2014

Category of Service

Licensed under Section 17 of the Act.

2014 Dec

Fixed Access Telephone service 3

Cellular Mobile phones 5

Data Communications (Facility based) 5

Data Communications (Non-facility based) & ISP’s 10

Trunk Mobile Radio 1

Leased Circuit Providers 1

Licensed Payphone Service Providers 1

1.1 External Gateway Operators 06

1.2 Direct-to-Home Satellite Broadcasting Service 03

1.3 Cable TV Distribution Network 03

1.4 Satellite Services 01

Sub Total 39

Source: Telecommunication Regulatory Commission Sri Lanka (TRCSL)

This project will analyse the factors which determining the adoption of Mobile Enterprise

Solutions (MES). MES are new technological innovations and direct substitutes for fix solutions

offered by operators such as SLT, Lanka Bell and Suntel.

Based on E.M.Roger’s Innovation adoption model, author will discuss the Perceived attributes of

innovations namely, Relative advantage, Compatibility, Complexity, Trialability and

Observerbility and how they relate to the rate of adoption. Also how the company achieved,

facilitated and sustained the technological innovation and how it capitalise them to create a

competitive advantage over the other competition.

1.1.2 Small and Medium Enterprise

The small and medium enterprise (SME) sector is well recognised for its contribution to

employment, innovation and economic dynamism and is considered as an engine of growth and is

considered as an engine of growth and an essential part of a healthy economy (Wickramasinghe,

2011). Currently, Sri Lanka doesn’t have a generally accepted criteria for SMEs, instead different

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agencies use deferent criteria based on their objectives and there is no consistency between them.

Identifying SMEs on a commonly acceptable criteria was a long felt need of the country, and

number of forums were organized and different surveys were conducted by different agencies in

view of achieving that goal (Department of census & Statistics, 2014). According to the

department of census and statistics there are 81,531 organizations under SME sector and out of

which 71,126 represent small and 10,405 organizations represent medium enterprises. 529,751

employments provided by small enterprises and 386,756 people have engaged with medium

enterprises respectively. SMEs account for nearly 70% of employment in the industry sector, while

contributing to about 26% of GDP (Wickramasinghe, 2011)

Table 2: Distribution of persons engaged and establishments across SME groups

No of Establishments Persons Engaged

Number % Number %

Total 1,019,681 100 3,003,119 100

SOHO 935,736 91.8 1,338,675 44.6

Small 71,126 7.0 529,751 17.6

Medium 10,405 1.0 386,756 12.9

Large 2,414 0.2 747,937 24.9

Source: Department of census & statistics (2014)

The concept and scope of a SME are different in different countries and the distinction between

‘large’ and ‘small’ is often arbitrary. The abbreviation SME is commonly used in the European

Union and international organizations, while in the United States, it is designated as small business

encompassing, manufacturing, services and also trading in some limited areas (Wikipedia)

The small and medium enterprises (SME) in Sri Lanka encompass establishments operating in

agriculture, mining, manufacturing, construction, and the service sector (Wickramasinghe, 2011)

1.1.3 Organisation

Etisalat Lanka Private limited is third largest telecommunications operator in Sri Lanka today,

serving more than four million subscribers.

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Celltel Lanka Private Limited (Rebranded as Etisalat Lanka Private Limited, later) is the first

operator to start mobile telecommunication operations in the country, commenced its operations

in 1989. Millicom International Cellular S.A. was the mother company. At start, its name was

“Celltel Lanka (Pvt) Ltd”, and it was able to enjoy the monopoly for 4 years until Mobitel Private

Limited (Mobitel) entered in to the market. The brand name “Celltel” was extremely popular

among the population, as people called the mobile phone as “Celltel”.

At the inception, the company earned very high profits due to its monopoly that it had and charged

very high rates. During this time, the company’s target market was elite market segment of users,

who were among the very few who could afford to buy a handset. Only the post-paid (the user has

to pay for the service, after usage) technology was present by that time and mobile was a luxurious

item. During the time the 1st generation mobile technology (1G) was used by the organisation.

However later on, as the competition grew in the market, the company lost its hold and started

losing its market share to the competitors. Dialog and Mobitel was able to get most of its market

share. With the new technology, the competitors adapt quickly, whereas Celltel failed to adapt

with the change. For instance both the other operator adopted the GSM technology before then

Celltel leading to a loss significant market share.

Millicom was having 16 operations in Asia, Latin America and Africa with 30 million subscribers

in all operations. Mid 2009, Millicom decided to sell its Asian operations as a strategic move to

concentrate their operations in Africa and Latin America as they were strong in those markets. As

a result Tigo (Pvt) Ltd was sold to Etisalat on 16th October 2009 to Dubai based telecommunication

giant, Etisalat. Therefore, again in February 2010, the company was taken over by the “Etisalat

Telecommunication Corporation” in the UAE, and rebranded to “Etisalat”, which is the current

name. With this rebranding Etisalat again totally changed its positioning in the market from more

youth oriented prepaid position to a higher layer of the market cutting across all the segments with

different products.

At present the company operates throughout the island and the coverage of the network covers the

full island. There are approximately 650 employees working for the company.

The vision and the mission of the company are as follows.

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Vision of the company is,

“A world where people’s reach is not limited by matter or distance.

People will effortlessly move around the world, staying in touch with family, making new

Friends as they go, as well as developing new interests.

Businesses of all sizes, no longer limited by distance, will be able to reach new markets.

Innovative technologies will open up fresh opportunities across the globe, allowing the supply of

new goods and services to everyone who wants them.”

Mission of the company is,

To extend people's reach. At Etisalat, we are actively developing advanced networks that will

enable people to develop, to learn and to grow.

1.2 Background of the study

As the topic of the project shows, this study is mainly centred on factors determining the adopting

mobile enterprise solutions in SME sector, Mobile telecommunication industry in Sri Lanka is one

of the rapidly growing industry and handful of major telecommunications service providers

striving to enhance their market share. Sri Lanka’s telecommunications industry contributes

significantly towards the country’s development and plays an integral part in the lives of many. It

is also a key component of the commercial world. Notably, the domestic telecommunications

sector has been charting exponential growth, and continues to enjoy promising prospects.

Increasingly organizations are start using mobile enterprise solutions (MES) to boost their

competitive posture by maintaining constant contact with employees in an attempt to meet

evolving demands, firms in the mobile services industry are operating under intense competitive

pressures rapidly deploying new services and features. MES is becoming increasingly more

commonplace among workers and consumers alike. Initially, the primary use of MES was to

facilitate voice communication and the technology was analogue. Later on when GSM technology

introduce, there was a revolutionary change in the industry. Services like mobile broadband

internet, WiFi, Virtual private networks, Mobile PABX, Virtual data centre, Cloud solutions,

Desktop virtualization and so many mobile enterprise solutions were introduced to the market. On

the consumer’s side, individuals are using MES as a vehicle for web surfing, text messaging, and

various m-commerce activities while organizations on the other hand are capitalizing and building

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upon the ease of use, efficiency and cost effectiveness, MES provides employees with greater

mobility, flexibility and communication options in their day to day operations (Kim and

Garrison,2009)

The dynamic nature of the telecommunications sector allows little respite for industry operators.

The government’s liberalisation of this industry can be seen as the main driving force behind the

rapid development of the country’s telecommunication infrastructure and services.

Small & Medium Enterprises in Sri Lanka account for 70% of employment, contribute around

26% to the GDP, and the 90% of the industry sector consist with SME’s. (Wickramasinghe, 2011)

as published in the department of census & statistics in 2014 there are 81,531 registered SME’s in

Sri Lanka. Therefore the mobile telecommunication industry has a huge potential to penetrate in

to Small & medium sector. In the current context connectivity / communication is one of the

essential part of the business. Therefore an organizations which are operating with a business will

be using some kind of a connectivity media to communicate within and out of the organization,

transfer data / Information etc; In this backdrop mobile telecommunication companies could play

a major role.

Also this study will further emphasis how the management of Etisalat should develop strategies

to acquire new SMEs. And also study will try to find out the relationship between the Perceived

attributes of innovations (Relative advantage, Compatibility, Complexity, Trialability, and

Observerbility) and the rate of adoption.

1.3 Identification of the issues

Mobile telecommunication industry is a highly volatile and competitive industry in Sri Lankan

economy. Exsisting 5 players trying their level best to retain their market shares and subscriber

base without losing. Since the market is highly saturated and there is no growth, only way of

increasing the base and revenue by acquiring customers from competitors. Since the regulator

(TRCSL) has impose certain restrictions on tariffs, operators unable to competing on price factor.

Only way of acquire new customers or increase revenue could be done though new innovations,

differentiation, Excellent customer care, and better coverage. Voice service being a conventional

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and basic feature all the service providers trying to differentiate themselves from others by

introducing new innovative technologies like mobile enterprise solutions.

Small & medium enterprise sector is predominantly contribute towards the development of the

national economy, which accounts for providing nearly 70% of employments, represent 90% in

industrial sector, and contributing 26% of GDP. Hence the SME sector would be very much vital

and lucrative prospect market for any business.

Therefore, it is quite essential for the mobile operators to find out the relationship between various

factors that are affecting for the Rate of adoption of a new innovations. Based on the past literature

available, in the servicing sector, suggested that perceived attributes of innovations ( Relative

advantage, Compatibility, Complexity, Trialability and Observerbility) has a relationship between

rate of adoption.

Considering the above, mobile operators needs to focus on the areas which has the direct impact

of customer’s adoption rate and manage and align the resources accordingly.

Etisalat has introduced its Mobile enterprise solutions portfolio to SME sector few years back and

still it’s generating a constant monthly revenue without any positive growth rate, that’s indicate

that there are no new acquisition on solutions front and it’s just a matter of continuing with

exsisting customers and current revenue. Therefore management of Etisalat need to analyse what

factors affecting the adoption of new innovations and work out strategies and execute action plans

accordingly.

1.4 Objectives of the Study

It is quite important for any telecommunication company to significantly capture the market

share of Small & medium enterprise segments. Therefore telecommunication companies should

focus on appropriate strategies to acquire SME organizations. In order to do that they have to have

a solid understanding on the factors determining the adoption rate of an innovation. Therefore the

study focus on the following objectives.

1. To understand the relationship between Relative advantage and the rate of adoption of a

Mobile Enterprise Solutions.

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2. To understand the relationship between Compatibility and the rate of adoption of a


3. To understand the relationship between Complexity and the rate of adoption of a Mobile

Enterprise Solutions.

4. To understand the relationship between Trialability and the rate of adoption of a Mobile

Enterprise Solutions.

5. To understand the relationship between Observerbility and the rate of adoption of a


1.5 Research Questions

In order to achieve the above objectives of the research, it is required find the answers to the below

questions with regard to the relationship between the factors identified based on the past literature

and subsequently, establish the hypothesis to test.

1. Is there a relationship between Relative advantage and Rate of adoption?

Based on the above question following null and alternative hypothesis could be derived.

H10: Relative Advantage does not significantly effect on Rate of Adoption of MES

H11: Relative Advantage significantly effects in Rate of Adoption of MES

2. Is there a relationship between Compatibility and Rate of adoption?


H20: Compatibility does not significantly effect on Rate of Adoption of MES

H21: Compatibility significantly effects in Rate of Adoption of MES

3. Is there a relationship between Complexity and Rate of adoption?


H30: Complexity does not significantly effect on Rate of Adoption of MES

H31: Complexity significantly effects in Rate of Adoption of MES

4. Is there a relationship between Trialability and Rate of adoption?


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H40: Trialability does not significantly effect on Rate of Adoption of MES

H41: Trialability significantly effects in Rate of Adoption of MES

5. Is there a relationship between Observerbility and Rate of adoption?


H50: Observerbility does not significantly effect on Rate of Adoption of MES

H51: Observerbility significantly effects in Rate of Adoption of MES

1.6 Significance of the Study

Mobile Enterprise Solutions are the emerging trend in contemporary mobile telecommunication

industry. Since the market is saturated and overcrowded there is no any significant growth in voice

component. Which further supressed by the competition and lower tariff rates imposed by the

regulator. But the Mobile Enterprise Solutions market is rapidly growing and there are potential

markets available in different industries.

Due to the advantages such as Flexibility, Mobility and Affordability many organizations are

switching from fixed solutions to Mobile solutions. Which has created virtual office and business

environment generating more profits while reducing wastage, improving efficiency and increasing

productivity. Many organizations encourage their employees to work from where they located

rather than coming to office. By using a Virtual private network this has become a popular practice

in other countries. Mobile PABX, Virtual Data centres, Desktop virtualization, Cloud solutions,

Wi-Fi hot spots and Mobile VPN are few of popular mobile solutions available.

Small & Medium Enterprises are the highest contributor to GDP and national economy which

contribute approximately 80% to the Sri Lanka’s economy. SME’s has spread across the country

and there are 81,581 registered SME organisations island wide. SME’s have provided 26% of the

total employments in various industries.

All five mobile Telco operators are trying to grab the market share from the large enterprises but

doesn’t really focus on the SME sector. It is very important fact that there will be a huge potential

for the Telco companies in SME sector with adopting Mobile Enterprise Solutions.

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1.7 Limitations of the study

However in the process of doing the study, number of limitations can be foreseen with regard to

the case study.

Due to the very nature of the corporate information, the confidentiality of some information

becomes a limitation in drawing a clear picture to determine the success of the organisation.

The definitions of some information are different from an operator to operator. For instance, the

way that a particular operator defines its active base might be different from another. This becomes

a barrier when analysing the results against the competition, to measure the success of the

organisation.

This study involves information collected through questionnaire, Due to the specialisation of their

job roles, the individual understanding about other areas will be limited.

Some of the investments that the company had made to achieve the continuous innovation are long

term investments. To generate the results of them, it will take a longer period of time, beyond the

duration of doing the case study. For instance Etisalat recently invested in the 3.75G dual carrier

technology. However to establishment of the positioning of the market will take more time and the

generation of results will take further more time. Therefore there is a possibility that some

information with regard to the results might not be captured in this study.

1.8 Chapter Framework

The objective of this chapter was to provide the user a prologue to the study to be followed. This

provided a background to the project and an overview of the company, Etisalat Lanka Private

Limited. Moreover, the chapter conveyed the Issue, objectives, scope and the limitation of the

study to the reader.

Etisalat Lanka Private limited is third largest telecommunications operator in Sri Lanka today,

serving more than four million subscribers. Small & medium enterprise are significant sector in

national economy which is very lucrative to any telco operator in terms of increasing revenue as

well as market share. Even few years after introducing Mobile enterprise solutions to SME sector,

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company hasn’t shown any significant growth in terms of revenue, and market share. The objective

of the study is to analyse some of the factors which could have helped this exercise.

13

Chapter 2

LITRATURE REVIEW 2.1 Introduction

The focus on the study mainly targeted to evaluate the impact of the Perceived attributes of

innovation, namely – Relative advantage, Compatibility, Complexity, Trialability and

Observerbility on the Rate of adoption of the mobile connection purchased by the decision makers

of the SME sector. There are several researches found in the literature analyse the impact on

several factors on perceived attributes of innovations.

2.2 Adoption of Mobile Enterprise Solutions by SME sector

Technology has been recognized as competitive resources and strategies to maintain organizational

effectiveness. Organizational ability to adopt technology would render its competitiveness and

sustainability in today’s dynamic business environment. This is particularly relevant to small &

medium enterprises (SME’s) as the use of technology would enable them to compete with their

larger counterparts ( Abdullah, Wahab & Shamsuddin, 2013). Tidd and Bessant (2009) further

claimed that successful SME’s are those who innovate by adopting technologies that give them a

market competitive edge.

The Boston consulting group (2000) reported that there were more than 15 million m-commerce

users worldwide. It was projected that the number of mobile wireless handheld users will be around

2 billion before the end of year 2007 (Varshney 2003). The technological environment in which

contemporary small and medium size enterprises (SME) operate can only be described as dynamic.

The exponential rate of technological change, characterised by perceived increases in the benefits

associated with various technologies, shortening product life cycle ands and changing standards,

provides for the SME a complex and challenging operational context. (Akkeren and Harker,2003)

Since adoption of technology is complex processes which are affected by multiple factors,

identification of the factors that significantly affect technology adoption would provide insight on

how to increase technology adoption among SME’s. This is a particularly significant issue since

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SMEs have been recognized as the economic impetus in both developed and developing countries

(La Rovere 1998, Normah 2006)

Adoption refers to the stage in which technology is selected for use by an individual or

organization. Consequently, theories or models on technology adoption tend to cluster around

individual and organizational levels. However at the individual level, technology adoption is

commonly referred to as technology acceptance. (Abdullah, Wahab & Shamsuddin, 2013)

Technology Acceptance Model (TAM) introduced by Davis (1986) is one of the more widely used

and accepted models researchers use to explain information technology (IT) and Information

systems (IS) acceptance and usage. (Kim & Garrison, 2009). TAM rooted in the Theory of

Reasoned Action (Ajzen and Fishbein, 1980 ; Fishbein and Ajzen , 1975). TAM addresses IT

adoption, Implementation and diffusion in terms of perceived ease of use and perceived usefulness

based on behavioural intentions (Akkeren & Harker, 2003). According to the Davis (1989), the

perceived usefulness of a system is defined as the extent to which individuals believe that using

the new technology will enhance their task performance. There is extensive research in the

information systems and M-Commerce that provides evidence of the significant effect of perceived

usefulness on usage or adoption intention (Davis et al, 1989, Kim & Garrison, 2009, Khalifa &

Shen, 2008). According to the Davis (1989), the perceived ease of use for a system is defined as

the degree to which an individual believes that using a particular technology will be free of effort.

The perceived ease of use has been incorporated as an important factor in adopting mobile

commerce (Davis, 1989, Li et al ., 2007, Wei et al., 2009, Bhatti, 2007) Attitude towards using the

system is defined as ‘the degree of evaluative affect that an individual associate with using the

target system in his job’. (Davis et al ., 1989)

Figure 2: Technology acceptance model

Source: (Davis 1989, Davis et al. 1989)

15

2.3 Diffusion of Innovation Theory

Diffusion of innovation theory, developed by Rogers (1995), has brought a deep understand with

regards to the characteristics of adopters, innovation decision process and adopter behaviour over

time. (Baran, 2009). Rogers noted that creating a general classification system to characterize the

attributes of an innovation, is an eventual objective within innovation adoption and diffusion

research. Such a unifying framework does not yet exist, but there are however attributes that have

been widely accepted throughout the innovation adoption literature as a general approach when

measuring perceptions of innovation attributes. These attributes derive from the past research on

innovation diffusion and adoption and include (1) relative advantage, (2) compatibility, (3)

complexity, (4) Trialability, and (5) Observerbility (Rogers, 2003).

Rogers (1995) points out that diffusion is not a single, all-encompassing theory. It is several

theoretical perspectives that relate to the overall concept of diffusion; it is a meta-theory (Yates,

2001). There are four factors that influence adoption of an innovation (Rogers, 1995), including:

The innovation itself

The communication channels used to spread information about the innovation

Time

The nature of the society to whom it is introduced.

Rogers (1995) explains that there are four major theories that deal with the diffusion of

innovations. These are the innovation-decision process theory, the individual innovativeness

theory, the rate of adoption theory, and the theory of perceived attributes.

2.4 Explaining rate of adoption

Rate of adoption is the relative speed with which an innovation is adopted by members of a social

system. It is generally measured as the number of individuals who adopt a new idea in a specified

period, such as each year. So the rate of adoption is a numerical indicator of the steepness of the

adoption curve for an innovation .The perceived attributes of an innovation are one important

explanation of the rate of adoption of an innovation. From 49 to 87 percent of the variance in rate

16

of adoption is explained by five attributes: Relative advantage, compatibility, complexity,

Trialability, and Observerbility (Rogers, 1983).

Figure 3: Variables determining the rate of adoption of innovation

Source: Rogers (1995)

2.5 Perceived Attributes of Innovation

The attributes of an innovation refers to the characteristics of the innovation that affects the rate at

which it is adopted. Rogers defined rate of adoption as “the relative speed with which an innovation

is adopted by members of a social system” (Worum, 2014).

“The perceived attributes of an innovation are one important explanation of the rate of adoption of

an innovation” (Rogers, 1995, p. 206).The theory of perceived attributes holds that individuals or

a social unit will adopt an innovation if they perceive it to have particular attributes. Although

some researchers have identified as many as 25 perceived attributes (Kearns, 1992); it is obvious

that these attributes can be subsumed in Rogers’ five perceived attributes. Rogers (2003) identifies

relative advantage, payoffs associated with the innovation; compatibility, the ease with which it

17

fits current ways of doing things; complexity, the ease or difficulty associated with learning the

innovation; Trialability, the ease or difficulty associated with trying it out; and Observerbility, the

extent to which results of adopting the innovation are visible to others, as the characteristics which

when judged by potential adopters, will differentiate easily adopted innovations from those that

fail to be adopted.

2.5.1 Relative advantage

Rogers (1995) has stated that the Relative advantage is the degree to which an innovation is

perceived as being better than the idea it supersedes. The degree of relative advantage is often

expressed as economic profitability, social prestige, or other benefits. The nature of the innovation

determines what specific type of relative advantage (such as economic, social, and the like) is

important to adopters, although the characteristics of the potential adopters also affect which sub

dimensions of relative advantage are most important.

“Relative advantage is the degree to which an innovation is perceived as being better than the idea

it supersedes” (Rogers, 2003, p.229). Relative advantage is often expressed in terms of economic

gains, social prestige, and other benefits. While the type of the innovation influences the particular

relative advantage that is important to the potential adopters, the characteristics of the potential

adopters also determines what particular elements of relative advantage are important (Rogers,

1995).

2.5.2 Compatibility

Compatibility is the degree to which an innovation is perceived as consistent with the existing

values, past experiences, and needs of potential adopters. An idea that is more compatible is less

uncertain to the potential adopter, and fits more closely with the individual's life situation. Such

compatibility helps the individual give meaning.to the new idea so that it is regarded as familiar.

An innovation can be compatible or incompatible (1) with sociocultural values and beliefs, (2)

with previously introduced ideas, or (3) with client needs for the innovation. (Rogers, 1995).

There is evidence that suggests that compatibility, just like relative advantage, correlates positively

to a potential adopter’s adoption rate (Liao& Lu, 2008; Rogers, 2003). Innovations that are attuned

to potential adopters’ values, norms and perceived needs have higher chances of being adopted

(Greenhalgh et al., 2005). The more compatible an innovation is, the less uncertainty it brings to

the potential adopter, the more consistent it is with the individual’s present situation. At

18

organizational level, the more compatible an innovation is with the organizations norms and

values, the more easily it will be assimilated. Hence, customs, beliefs, religion, personal and

political factors are likely to influence an individual or a social system’s likelihood of adopting an

innovation irrespective of it being a needed innovation (Rogers, 2003).

2.5.3 Complexity

Complexity is the degree to which an innovation is perceived as relatively difficult to understand

and use. Any new idea may be classified on the complexity-simplicity continuum. Some

innovations are clear in their meaning to potential adopters whereas others are not. Although the

research evidence is not conclusive, we suggest the complexity of an innovation, as perceived by

members of a social system, is negatively related to its rate of adoption. (Rogers, 1995)

Complexity is negatively correlated to an innovation’s rate of adoption (Tornatzky & Klein, 1982).

Innovations that are perceived as simple by potential adopters will be more easily adopted

(Greenhalgh, et al., 2005). It is suggested that demonstrations, breaking the innovation into

manageable parts and adopting it bit by bit will facilitate its adoption (Rogers, 2003).

2.5.4 Trialability

Trialability is the degree to which an innovation may be experimented with on a limited basis.

New ideas that can be tried on the instalment plan are generally adopted more rapidly than

innovations that are not divisible. Some innovations are more difficult to divide for trial than are

others. The personal trying-out of an innovation is a way to give meaning to an innovation, to find

out how it works under one's own conditions. This trial is a means to dispel uncertainty about the

new idea. The Trialability of an innovation, as perceived by members of a social system, is

positively related to its rate of adoption. (Rogers, 1995).

Innovations that intended users can experiment with on a trial basis are more easily adopted and

assimilated because an innovation that is trialable presents less uncertainty to the potential adopter

than does the innovation that is not divisible (Rogers & Scott, 1997) by affording the individual

an opportunity to learn by doing. This has been noted to be particularly true for early adopters who

may lack models to imitate and hence require hands-on experience with the innovation before

adopting it. For late adopters, trialling may take the form of observing and monitoring experiences

of the early adopters (Rogers, 2003).

19

2.5.5. Observerbility

Observerbility is the degree to which the results of an innovation are visible to others. The results

of some ideas are easily observed and communicated to others, whereas some innovations are

difficult to observe or to describe to others. The Observerbility of an innovation, as perceived by

members of a social system, is positively related to its rate of adoption. (Rogers, 1995)

If potential adopters can see the benefits of an innovation, they will easily adopt it. Sometimes,

Observerbility refers to the ease with which the innovation is communicated to potential adopters

(Rogers, 2003; Tornatzky & Klein, 1982). Hence Observerbility might be dependent on the other

attributes like relative advantage and compatibility (Tornatzky & Klein,1982). For instance, if an

individual observes others using an innovation and perceive it as being compatible with their

values and norms, they are likely to adopt it.

2.5.6 Rate of Adoption

Another important idea that Rogers (1995) describes is the rate of adoption, According to Rogers

(1995) Rate of adoption is the relative speed with which an innovation is adopted by members of

a social system it is generally measured as the number of individuals who adopt a new idea in a

specified period such as a year, so the rate of adoption is numerical indicator of the steepness of

the adoption curve for an innovation.

In this theory, the adoption process of an innovation is viewed as taking an S-curve on a graph.

The theory holds that at the beginning, the adoption of an innovation will be slow and gradual.

After a certain time period, it will grow rapidly and become stable and eventually decline

(Rogers, 1995). According to Rogers (2003), each innovation has characteristics which when

judged by the individual or social unit, determines the possibility of adoption taking place

(Rogers, 2003). The following section outlines the theory of perceived attributes.

20

Chapter 3

CASE FRAMEWORK AND METHODOLOGY

3.1 Introduction

Developing the conceptual framework is the most important and the most fundamental step in any

research or project. Objective of this chapter is to discuss and elaborate the conceptual framework

based upon which this project is developed.

Analysing the literature review carried out in the previous chapter in this chapter will arrive at the

framework which will be discussed in the project as mentioned below. (Figure 4)

3.2 Conceptual Framework

Rogers (1995) has proposed a model ‘Attributes of Innovations and their rate of adoption’ there

he discussed 5 variables which determined the rate of adoption of an innovations.

1. Perceived attributes of Innovation

2. Type of innovation decision

3. Communication Channel

4. Nature of the Social System

5. Extent of change agent’s promotion efforts.

Out of above 5 variables Perceived attributes of innovation having 5 items that Rogers has explain

in details.

1. Relative advantage

2. Compatibility

3. Complexity

4. Trialability

5. Observerbility

21

Based on above 5 attributes I have developed a frame work to check the relationship between

Relative advantage, Compatibility, complexity, Trialability and Observerbility with the Rate

of adoption of an innovation. There I have taken Perceived attributes as Independent variable

and Rate of adoption as Dependent variable.

Figure 4: Conceptual framework

Independent Variable Dependent Variable

P

H1

H2

H3

H4

H5

3.3 Questionnaire design and Data collection

Data were collected using an online survey to test and validate the “Perceived attributes of an

innovation and their rate of adoption with regards to Mobile Enterprise solutions”. Items used to

assess Relative advantage, Compatibility, Complexity, Trialability and Observerbility (Rogers,

1995) were adopted from Atkinson (2007) but were modified to reflect a Mobile Enterprise

Perceived Attributes of

innovation Rate of Adoption

Relative Advantage

Compatibility

Complexity

Trialability

Observerbility

Rate of Adoption

22

Solutions context. All questionnaire items were assessed using a 5-point Likert-type scale

ranging from (1) Strongly Disagree to (5) strongly Agree.

Online questionnaire was sent to a 250 base which was randomly pick, which include

professionals from finance, service, IT, manufacturing, construction, Apparel, Hospitality and

other sectors. There were 63 participants has responded and out of them 54 despondence

qualified for analysis.

Data Collection method – Online Survey

Type of Questions – Likert scale question

Scale of measurement - 1 to 5 with the responses of strongly disagree, disagree, neither

agree nor disagree, agree and strongly agree.

No. of questionnaires emailed – 250

Total No. of respondents – 63

Sample method – Simple Random sampling

3.4 Validity and reliability

Factor analysis was performed to check the convergent validity and discriminant validity.

Principle component method has been used as the extraction method and Promax method used as

the rotation method in this factor analysis. Factor loadings on factor components in the pattern

matrix were examined for this validity analysis. First the factor analysis was performed for all

the items and the pattern matrix is shown in the appendix. From this found that some items had

cross loadings and negative loadings, and some items from deferent constructs loaded into same

component. These issues deviates the validity. But, by removing some items (T3 and T4), we

could improve the pattern matrix as shown below.

23

Pattern Matrixa

Component

1 2 3 4 5 6

Co5 1.136 .301

Co2 .924

Co7 .839

Co4 .827

Co3 .792

Co6 .742 .342

Co1 .738 .328

RoA3 .755

RoA2 .716

RoA1 .652 .346

RA2 .967

RA1 .918

RA6 .750

RA3 .670

RA4 .655

RA5 .511

C3 .814

C1 .798

C2 .313 .669

C6 .648

C5 .311 .619

C4 .539 .391

T5 .984

T1 .744

T2 .583

O2 .923

O3 .850

O4 .789

O1 .317 .667

O5 .574

O6 .339 .567

Extraction Method: Principal Component Analysis.

Rotation Method: Promax with Kaiser Normalization.

a. Rotation converged in 15 iterations.

24

In this table, higher (>0.5) factor loadings of items are nicely organized in the diagonal of the

table. These diagonal loadings are high enough to conclude that these items have good

correlation inside their own construct. And this satisfies the convergent validity. And there are

some cross loadings in this table. Since these cross loadings are fairly small (<0.5), we can

assume that these items do not correlate with other constructs. So that proves the discriminant

validity.

And then proceeded to reliability analysis with the remaining items from factor analysis.

Cronbach’s Alpha has been used to examine internal consistency or the reliability of questions.

Following table shows the results from reliability analysis. See the appendix for SPSS outputs.

Construct Cronbach’s Alpha

Relative Advantage .920

Compatibility .865

Simplicity .932

Trial Ability .836

Observerbility .903

Rate of Adoption .861

Since the Cronbach’s Alpha values of all the above constructs are greater than 0.7, so it can be

said that there is a good internal consistency in these constructs and reliable. Finally we can say

that the items are valid and reliable.

3.5 Operationalization of Variables

3.5.1 Defining Variables and Development of questionnaire

The questionnaire developed to test the above mentioned hypothesis. The questions developed

considering the literature developed by different authors and all the questions are Likert scale

questions. The scale of measurement is from 1 to 5 with the responses of strongly disagree,

disagree, neither agree nor disagree, agree and strongly agree respectively.

25

Variable Variable

type

Dimensions Questions Reference

Relative

Advantage

Independent Economic

Social Status

1. Mobile solutions are better than

using fixed solutions when it’s

comes to organizational

communication process.

2. Using of mobile solutions are

more interesting than using fixed

solutions

3. Use of mobile solutions make

communication a better

experience than I would have

otherwise

4. Use of mobile solutions provide

faster and easier communication

process within and outside the

organization

5. I’m enjoying my day to day

communication activities

because of using mobile

solutions

6. Mobile solutions offered me real

advantages over the way I

usually doing my

communications through fixed

solutions

Atkinson, 2007

Variable Variable

type


Compatibility Independent Economic

Socio Cultural

Philosophical

value system

1. Mobile solutions are fitting right

into the way which I prefer to

use

2. I think other SME organizations

also should use Mobile solutions

for their communication

activities.

3. Concept “Mobility” and

“Mobile Solutions” made me

want to practically use the

solution

4. Using of mobile solutions make

what I’m doing as

Atkinson, 2007

26

communication activities in day

to day, more relevant to me.

5. Mobile solutions help to learn

more about my business area

while using it or communication

activities

6. Mobile solutions help me to

learn more about Technology

while using it for

communication activities.

Variable Variable

type


Complexity Independent Simplicity 1. There is no difficulty in

handling Mobile solutions.

2. There is no difficulty in

understanding how to get

around in Mobile solutions.


understanding how mobile

solutions work.

4. There are no difficulties in

getting the mobile solutions

working on a computers and

mobile phones.

5. There are no difficulties in using

available options under

particular mobile solutions.


controlling attributes of voice

and Data segments.


understanding the information

in Mobile solutions.

Atkinson, 2007

27

Variable Variable

type


Trialability Independent Experience of

Peers

Vicarious trials

1. Being able to testing

mobile solutions most

important in deciding

whether or not to purchase

it.

2. Being able to try out

Mobile solutions is

important in deciding to

use it.

3. Im more likely to want to

use Mobile solutions

because of being part of

this survey.

4. There is not much of lose

by trying out mobile

solutions even if I don’t

like it.

5. I like being able to try out

Mobile Solutions before

deciding whether I like it

or not.

Atkinson, 2007

Variable Variable type Dimensions Questions Reference

Observerbili

ty

Independent Model 1. Other SME organizations seemed

interested in mobile solutions

when they see us using it.

2. Employees of other SME”s can

tell that we do our

communication efficient and

effectively since we are using

mobile solution.

3. Other organizations which are

currently using Mobile solutions

like using it.

4. There is no difficulty in telling

others what Mobile solutions are

like.

5. I would have no difficulty in

promoting Mobile solutions with

the other organizations how it

Atkinson,

2007

28

improve the communication

within my organization.

6. Regulators and other relevant

stakeholders whom control the

organizational activities are

seemed to like using Mobile

Solutions.

Variable Variable type Dimensions Questions Reference

Rate of

Adoption

Dependent 1. Assuming I have access to mobile

enterprise solutions, I intend to use

it.

2. Given that I had access to Mobile

enterprise solutions, I predict that

I would use it.

3. Assuming I have an opportunity

to recommend mobile enterprise

solutions to my friends, I would

do it

Kim and

Garrison,

2008

29

Chapter 4

ANALYSIS

4.1 Introduction

This chapter analysis the statistical data gathered in this study to find out the relationship between

Relative advantage, Compatibility, Complexity, Trialability and Observerbility with Rate of

adoption of an innovation.

4.2 Data analysis

4.2.1 Case Screening & Variable Screening

Before the analysis, data was screened for missing data and outliers, case and variable wise. First

we looked at the standard deviations of responses (cases) to check whether they are really engaged

with the questionnaire or not. Four cases which had lower standard deviations (<0.4) were removed

assuming that they are not really engaged with the questions in the questionnaire. Other cases had

good standard deviations which we can assume that those respondents have involved well with the

questions or have good variance in their responses. Another three cases also removed which are

useless since it had over 50% missing values.

Since the questionnaire data has been collected in Likert scale, median values were used to address

the missing values. The SPSS output of the missing value imputation is shown below.

Result Variables

Result Variable N of Replaced

Missing Values

Case Number of Non-Missing

Values

N of Valid Cases Creating

Function

First Last

1 RA3 1 1 54 54 MEDIAN(RA3,2)

2 RA5 1 1 54 54 MEDIAN(RA5,2)

3 RA6 1 1 54 54 MEDIAN(RA6,2)

4 C3 1 1 54 54 MEDIAN(C3,2)

30

5 C4 2 1 54 54 MEDIAN(C4,2)

6 C5 2 1 54 54 MEDIAN(C5,2)

7 C6 1 1 54 54 MEDIAN(C6,2)

8 Co2 1 1 54 54 MEDIAN(Co2,2)

9 Co4 1 1 53 53 MEDIAN(Co4,2)

10 Co5 1 1 54 54 MEDIAN(Co5,2)

11 Co6 1 1 54 54 MEDIAN(Co6,2)

12 Co7 2 1 54 54 MEDIAN(Co7,2)

13 T3 1 1 54 54 MEDIAN(T3,2)

14 O1 2 1 54 54 MEDIAN(O1,2)

15 O2 1 1 54 54 MEDIAN(O2,2)

16 O3 1 1 54 54 MEDIAN(O3,2)

17 O6 2 1 54 54 MEDIAN(O6,2)

18 RoA2 2 1 54 54 MEDIAN(RoA2,2

)

Since there were only 1 respondent in less than 25 years age group, it was included to 26-35

members group and renamed the group as less than 35 members. Also since there were only 3

cases in 1-10 number of employees group, those were included to 11-100 number of employees

group and renamed the group as less than 100 number of employees.

4.3 Model Adequacy Checking

In regression we build models under some assumption about the error term of the model. So, it is

a must to check whether the assumptions are not violated before we use regression model.

Assumptions,

1. Random error terms are normally distributed with mean zero and constant variance.

2. Random error terms are uncorrelated.

31

The dots in the P-P plot of residuals are aligned on the normal line, and dots do not deviate much

away from the normal line, so it can be assumed that normality assumption of error term is not

violated. And the scatter plot of residual vs predicted values doesn’t show any pattern, which

means the error terms are uncorrelated. And dots are evenly scattered around zero in a constant

band. So this plot evident that mean zero and constant variance of errors as well.

Since all the regression model assumptions are not violated it can be said that this multiple

regression model is adequate to be used.

4.4 Descriptive Analysis

4.4.1 Sample Composition

By Gender

Gender

Frequency Percent Valid Percent Cumulative

Percent

Valid

Male 35 64.8 64.8 64.8

Female 19 35.2 35.2 100.0

Total 54 100.0 100.0

32

By Age Group

Age Group


Percent

Valid

<= 35 years 44 81.5 81.5 81.5

36 - 45 years 10 18.5 18.5 100.0

Total 54 100.0 100.0

By Region

Region


Percent

Valid

Colombo & Suburbs 44 81.5 81.5 81.5

Other Regions 10 18.5 18.5 100.0

Total 54 100.0 100.0

33

By whether using MES or not

Using MES?


Percent

Valid

Yes 43 79.6 79.6 79.6

No 11 20.4 20.4 100.0

Total 54 100.0 100.0

By Industry

Industry

Frequency Percent Valid Percent Cumulative Percent

Valid

IT 12 22.2 22.2 22.2

Manufacturing 11 20.4 20.4 42.6

Construction 3 5.6 5.6 48.1

Apparel 3 5.6 5.6 53.7

Finance 4 7.4 7.4 61.1

Service 8 14.8 14.8 75.9

34

Hospitality 2 3.7 3.7 79.6

Other 11 20.4 20.4 100.0

Total 54 100.0 100.0

By Legal Definition

Legal Definition


Percent

Valid

Public Limited 15 27.8 27.8 27.8

Private Limited 25 46.3 46.3 74.1

Partnership 2 3.7 3.7 77.8

Sole Propriotor 4 7.4 7.4 85.2

Other 8 14.8 14.8 100.0

Total 54 100.0 100.0

By number of employees

35

No of Employees


Percent

Valid

<= 100 10 18.5 18.5 18.5

> 100 44 81.5 81.5 100.0

Total 54 100.0 100.0

By Annual Turnover

Annual Turnover


Percent

Valid

<= 30 Million 11 20.4 20.4 20.4

30 - 250 Million 14 25.9 25.9 46.3

> 250 Million 29 53.7 53.7 100.0

Total 54 100.0 100.0

36

Descriptive Statistics of the factors and Rate of Adoption

Descriptive Statistics

N Mean Std. Deviation Variance

Relative Advantage 54 3.9769 .86215 .743

Compatibility 54 3.8565 .77423 .599

Simplicity 54 3.7765 .81607 .666

Trial Ability 54 3.8130 .65590 .430

Observerbility 54 3.7176 .75347 .568

Rate of Adoption 54 3.9691 .76962 .592

Valid N (listwise) 54

In this descriptive statistics table we can see that mean values of all the variables are greater than

3.5. This implies that there might be positive answers for these factors since we’ve used 3 as

neutral.

4.5 Advanced Analysis

4.5.1 Correlation Analysis

This correlation analysis is performed to examine how the measured factors (Relative Advantage,

Compatibility, Simplicity, Trial Ability and Observerbility) relate to Rate of Adoption of MES.

First we looked at the scatter plots to check whether these variables linearly relate or not.

37

Observerbility, Compatibility and Simplicity factors have shown moderately linear positive

relationship with Rate of Adoption of MES. But, these relationships except Observerbility Vs

Rate of Adoption are not very strong according to above scatter plots. And it is not possible to

see linear relationships from Relative Advantage and Trial Ability with Rate of Adoption. Since

there is no any curvilinear relationship, Pearson’s correlation coefficient has been used to

quantify these linear relationships. Results as follows,

38

2 Person’s Correlation analysis with Rate of Adoption of MES

3 Variable 4 Sig. Value 5 Correlation Coefficient

6 Relative Advantage 7 .000 8 .643**

9 Compatibility 10 .000 11 .738**

12 Simplicity 13 .000 14 .741**

15 Trial Ability 16 .000 17 .691**

18 Observerbility 19 .000 20 .798**

Since the p-values of correlation analysis between all the factors and Rate of Adoption of MES are

less than 0.5, so it can be concluded with 95% confidence that all these correlations are significant.

But when we look at the correlation coefficients, it can be seen that Observerbility, Simplicity and

Compatibility factors have moderately strong positive linear relationships with Rate of Adoption

of MES. As well, it can be said that, Rate of Adoption of MES has good positive linear correlation

with Relative Advantage and Trial Ability.

4.5.2 Regression Analysis

Hypothesis:

H10: Relative Advantage does not significantly effect on Rate of Adoption of MES

H11: Relative Advantage significantly effects in Rate of Adoption of MES

H20: Compatibility does not significantly effect on Rate of Adoption of MES

H21: Compatibility significantly effects in Rate of Adoption of MES

H30: Simplicity does not significantly effect on Rate of Adoption of MES

H31: Simplicity significantly effects in Rate of Adoption of MES

H40: Trial Ability does not significantly effect on Rate of Adoption of MES

H41: Trial Ability significantly effects in Rate of Adoption of MES

H50: Observerbility does not significantly effect on Rate of Adoption of MES

H51: Observerbility significantly effects in Rate of Adoption of MES

There are five factors measured in this study. Therefore, multiple regression analysis has been

performed to find most significant factors to Rate of Adoption of MES. The results as follows,

39

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .405 .349 .284 .042

Relative Advantage .003 .119 .004 .027 .979

Compatibility .166 .152 .167 1.088 .282

Simplicity .285 .097 .303 2.952 .005

Trial Ability .217 .120 .185 1.805 .077

Observerbility .353 .129 .346 2.736 .009

a. Dependent Variable: Rate of Adoption

This coefficients table implies that Observerbility and Simplicity factors have significant effect on

Rate of Adoption of MES. This can be concluded because the p-values of these two factors are

less than 0.05 so we reject null hypothesises (H30 and H50) of these factors. Therefore, it can be

concluded with 95% confidence, that Observerbility and Simplicity have significant effect on Rate

of Adoption of MES.

Regression analysis performed again only with the significant factors and the output is shown

below.

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .850a .722 .711 .41359

a. Predictors: (Constant), Observerbility, Simplicity

b. Dependent Variable: Rate of Adoption

This model has adjusted R2 at 0.72, which says that this model explains 72% of the total variance

of Team Performance. In this kind of social studies models with R2 at 72% is a very good model.

ANOVA table of the multiple regression model has shown below. Since the p value of the model

is less than 0.05, it can be said that this model is significant with 95% level of confidence.

40

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 22.669 2 11.335 66.264 .000b

Residual 8.724 51 .171

Total 31.393 53


b. Predictors: (Constant), Observerbility, Simplicity

Coefficients table

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig. Collinearity Statistics

B Std. Error Beta Tolerance VIF

1

(Constant) .319 .305 1.701 .045

Simplicity .362 .092 .384 3.953 .000 .577 1.732

Observerbility .560 .099 .548 5.646 .000 .577 1.732


From this multiple regression model we can conclude with 95% confidence that Observerbility

and Simplicity have a significant effect on Rate of Adoption of MES. Also there are no

multicolinearity issues between independent variables since the VIF values are very close to 1.

So the multiple regression model is,

Rate of Adoption of MES = 0.319 + 0.384 ∗ X1 + 0.548 ∗ X2

X1 = Simplicity

X2 = Observerbility

Since all these factors measured in same scale, we can order these factors effect on Rate of

Adoption of MES according to weight of the model coefficients. So, it can be said that

Observerbility has highest and Simplicity has lowest significant impact on Rate of Adoption of

MES.

41

Since the coefficients of Simplicity and Observerbility are positive, we can say that these factors

have positive effect on Rate of Adoption of MES.

4.5.3 Logistic Regression

Since whether using MES or not variable measured in dichotomous (yes or no) type, logistic

regression was performed to make a model to predict whether using MES or not according to other

independent variables. Following is the results of the analysis.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a

Relative Advantage -.009 .761 .000 1 .990 .991

Simplicity 1.292 .970 1.773 1 .023 .275

Compatibility -.476 .652 .534 1 .465 .621

Trial Ability .760 .791 .923 1 .337 2.139

Observerbility 1.021 .993 1.057 1 .034 2.777

Constant 1.418 2.083 .463 1 .006 .242

a. Variable(s) entered on step 1: Relative Advantage, Simplicity, Compatibility, Trial Ability,

Observerbility.

Above table implies that Simplicity and Observerbility factors have significant effect on whether

using MES or not. This can be concluded because the p-values of these two factors are less than

0.05. Therefore, it can be concluded with 95% confidence, that Observerbility and Simplicity have

significant effect on whether using MES or not.

Regression analysis performed again only with the significant factors and the output is shown

below.

Model Summary

Step -2 Log likelihood Cox & Snell R

Square

Nagelkerke R

Square

1 66.652a .287 .518

42

a. Estimation terminated at iteration number 5 because

parameter estimates changed by less than .001.

This model has a R2 value at 0.518 so, it can be concluded that this logistic regression model

explains 52% of the total variability of whether using MES or not.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a

Simplicity .534 .940 .920 1 .028 .586

Observerbility .422 .983 .441 1 .037 1.525

Constant .948 1.784 .283 1 .015 .387

a. Variable(s) entered on step 1: Simplicity, Observerbility.

From this logistic regression model we can conclude with 95% confidence that Observerbility and

Simplicity have a significant effect on whether using MES or not.

So the logistic regression model is,

Using MES or not = 0.948 + 0.534 ∗ X1 + 0.422 ∗ X2

X1 = Simplicity

X2 = Observerbility

Since all these factors measured in same scale, we can order these factors effect on whether using

MES or not according to weight of the model coefficients. So, it can be said that Simplicity has

highest and Observerbility has lowest significant impact on whether using MES or not.

Since the coefficients of Simplicity and Observerbility are positive, we can say that these factors

have positive effect on whether using MES or not.

43

Chapter 5

DISCUSSION AND FINDINGS

5.1. Introduction

The objective of this chapter is to discuss the findings of the exercise in details. This will be

structured according to the conceptual framework and will go in to detail of each aspect that was

highlighted in the framework. Further support will be obtained from the data collected through the

questionnaire will be referred wherever applicable.

5.2 Factor Analysis

Convergent validity and discriminant validity were checked by performing Factor analysis.

Principle component method has been used as the extraction method and Promax method used as

the rotation method in this factor analysis. It was revealed that, some items had cross loadings and

negative loadings, and some items from deferent constructs loaded into same component. These

issues deviates the validity. But, by removing some items (T3 and T4),. Factor loadings of items

are nicely organized in the diagonal of the table. These diagonal loadings are high enough to

conclude that these items have good correlation inside their own construct. And this satisfies the

convergent validity. And there are some cross loadings in this table. Since these cross loadings are

fairly small (<0.5), we can assume that these items do not correlate with other constructs. So that

proves the discriminant validity.

And then proceeded to reliability analysis with the remaining items from factor analysis.

Cronbach’s Alpha has been used to examine internal consistency or the reliability of questions.

Since the Cronbach’s Alpha values of all the above constructs are greater than 0.7, so it can be

said that there is a good internal consistency in these constructs and reliable. Finally we can say

that the items are valid and reliable.

44

5.3 Descriptive Statistics

In descriptive statistics analysis it revealed that the mean values of all the variables are greater than

3.5. This implies that there might be positive answers for these factors since we’ve used 3 as

neutral.

5.4 Correlation Analysis

This correlation analysis is performed to examine how the measured factors (Relative Advantage,

Compatibility, Simplicity, Trial Ability and Observerbility) relate to Rate of Adoption of MES.

First we looked at the scatter plots to check whether these variables linearly relate or not.

Observerbility, Compatibility and Simplicity factors have shown moderately linear positive

relationship with Rate of Adoption of MES. But, these relationships except Observerbility Vs Rate

of Adoption are not very strong according to above scatter plots. And it is not possible to see linear

relationships from Relative Advantage and Trial Ability with Rate of Adoption. Since there is no

any curvilinear relationship, Pearson’s correlation coefficient has been used to quantify these linear

relationships. Since the p-values of correlation analysis between all the factors and Rate of

Adoption of MES are less than 0.5, so it can be concluded with 95% confidence that all these

correlations are significant. But when we look at the correlation coefficients, it can be seen that

Observerbility, Simplicity and Compatibility factors have moderately strong positive linear

relationships with Rate of Adoption of MES. As well, it can be said that, Rate of Adoption of MES

has good positive linear correlation with Relative Advantage and Trial Ability.

5.5 Regression Analysis

There are five factors measured in this study. Therefore, multiple regression analysis has been

performed to find most significant factors to Rate of Adoption of MES. This coefficients table

implies that Observerbility and Simplicity factors have significant effect on Rate of Adoption of

MES. This can be concluded because the p-values of these two factors are less than 0.05 so we

reject null hypothesises (H30 and H50) of these factors. Therefore, it can be concluded with 95%

confidence, that Observerbility and Simplicity have significant effect on Rate of Adoption of MES.

Regression analysis performed again only with the significant factors and the model has adjusted

R2 at 0.72, which says that this model explains 72% of the total variance of Team Performance. In

45

this kind of social studies models with R2 at 72% is a very good model. ANOVA table of the

multiple regression model has shown below. Since the p value of the model is less than 0.05, it

can be said that this model is significant with 95% level of confidence. From this multiple

regression model we can conclude with 95% confidence that Observerbility and Simplicity have a

significant effect on Rate of Adoption of MES. Also there are no multicolinearity issues between

independent variables since the VIF values are very close to 1.

So the multiple regression model is,

Rate of Adoption of MES = 0.319 + 0.384 ∗ X1 + 0.548 ∗ X2

X1 = Simplicity

X2 = Observerbility

Since all these factors measured in same scale, we can order these factors effect on Rate of

Adoption of MES according to weight of the model coefficients. So, it can be said that

Observerbility has highest and Simplicity has lowest significant impact on Rate of Adoption of

MES. Since the coefficients of Simplicity and Observerbility are positive, we can say that these

factors have positive effect on Rate of Adoption of MES.

46

Chapter 6

CONCLUSSION

In the reliability and validity analysis we could conclude that the measured data are reliable and

valid. In the descriptive statistics analysis we can see that mean values of all the variables are

greater than 3.5. This implies that there might be positive answers for all five factors since we’ve

used 3 as neutral. In another words its proving that there is a positive relationship between the

‘Perceived attributes of innovation and Rate of Adoption.

It was found in the correlation analysis that Observerbility, Simplicity and Compatibility factors

have moderately strong positive linear significant correlations with Rate of Adoption of MES. As

well other factors also have significant positive correlations with Rate of Adoption of MES.

Regression analysis also has proved that only Observerbility and Simplicity factors have

significant effect on Rate of Adoption of MES. As well it could be said that Observerbility has

highest and Simplicity has lowest significant impact on Rate of Adoption of MES according to

regression coefficients. Since the coefficients of Observerbility and Simplicity are positive, we can

say that these factors have positive effect on Team Performance. This positive effect is also proved

by the correlation analysis.

It is evident that Relative advantage, Compatibility, Complexity (Simplicity), Trialability and

Observerbility directly effect on the rate of adoption of Mobile Enterprise Solutions. Hence it is

advisable to Etisalat that whenever they approach a SME client to use their MES, highlight the

advantages of Etisalat MES over to competitors, and highlight the fact that it doesn’t make any

additional burden to the operational level staff as it highly compatible with the exsisting fixed

solutions. Always explain clients that its simplicity of the usage. Arranging testing or practical

demonstrations for a stipulated period will enable client use the new innovations without any

charge or commitment. Giving the referrals or evidence of the current users and share their success

stories will inspire new customers to ignite their purchasing decision.

47

Chapter 7

REFERENCE

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Atkinson,N.L. (2007). Developing a questionnaire to measure Perceived Attributes of eHealth

Innovations, American medical Journal of health Behaviour,Vol.31, No 6,pp.612-621

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48

Naqvi,S.J,Shihi, H.L.(2014) Factors Affecting M-commerce Adoption in Oman using Technology

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the adoption intention, Master’s Thesis in Leadership, Innovation, and Marketing.

49

ANNEXURES

Annex I - Questionnaire

Factors Determining the Adoption of Mobile Enterprise Solutions in SME Sector

Questionnaire

I'm a MBA Student, conducting a research on the "Factors determining the adoption of mobile enterprise solutions in SME sector" for MBA programme at University of Sri Jayewardenepura. I would love to get your honest feedback for the questions in my survey. The survey would only take 10-15 minutes and your response are completely anonymous. Appreciate your valuable contribution and input for this task a meaning full.

Key for the selections

1 - Strongly Disagree (SD)

2- Disagree (DA)

3 - Neither agree Nor disagree (NN)

4 - Agree (A)

5 - Strongly Agree (SA)

Section 01 - Perceived Attributes of innovations

Relative Advantage. (RA)

SD D NN A SA

Mobile solutions are better than using fixed solutions when it’s comes to organizational communication process. 1 2 3 4 5

Using of mobile solutions are more interesting than using fixed solutions 1 2 3 4 5

Use of mobile solutions make communication a better experience than I would have otherwise 1 2 3 4 5

Use of mobile solutions provide faster and easier communication process within and outside the organization 1 2 3 4 5

I’m enjoying my day to day communication activities because of using mobile solutions 1 2 3 4 5

50

Mobile solutions offered me real advantages over the way I usually doing my communications through fixed solutions 1 2 3 4 5

Compatibility (C)

SD D NN A SA

Mobile solutions are fitting right into the way which I prefer to use 1 2 3 4 5

I think other SME organizations also should use Mobile solutions for their communication activities. 1 2 3 4 5

Concept “Mobility” and “Mobile Solutions” made me want to practically use the solution 1 2 3 4 5

Using of mobile solutions make what I’m doing as communication activities in day to day, more relevant to me. 1 2 3 4 5

Mobile solutions help to learn more about my business area while using it or communication activities 1 2 3 4 5

Mobile solutions help me to learn more about Technology while using it for communication activities. 1 2 3 4 5

Complexity (Co)

SD D NN A SA

There is no difficulty in handling Mobile solutions. 1 2 3 4 5

There is no difficulty in understanding how to get around in Mobile solutions. 1 2 3 4 5

There is no difficulty in understanding how mobile solutions work. 1 2 3 4 5

There are no difficulties in getting the mobile solutions working on a computers and mobile phones. 1 2 3 4 5

There are no difficulties in using available options under particular mobile solutions. 1 2 3 4 5

There is no difficulty in controlling attributes of voice and Data segments. 1 2 3 4 5

There is no difficulty in understanding the information in Mobile solutions. 1 2 3 4 5

Trial ability (T)

SD D NN A SA

Being able to testing mobile solutions most important in deciding whether or not to purchase it. 1 2 3 4 5

Being able to try out Mobile solutions is important in deciding to use it. 1 2 3 4 5

51

Im more likely to want to use Mobile solutions because of being part of this survey. 1 2 3 4 5

There is not much of lose by trying out mobile solutions even if I don’t like it. 1 2 3 4 5

I like being able to try out Mobile Solutions before deciding whether I like it or not. 1 2 3 4 5

Observerbility (O)

SD D NN A SA

Other SME organizations seemed interested in mobile solutions when they see us using it. 1 2 3 4 5

Employees of other SME”s can tell that we do our communication efficient and effectively since we are using mobile solution.

1 2 3 4 5

Other organizations which are currently using Mobile solutions like using it. 1 2 3 4 5

There is no difficulty in telling others what Mobile solutions are like. 1 2 3 4 5

I would have no difficulty in promoting Mobile solutions with the other organizations how it improve the communication within my organization. 1 2 3 4 5

Regulators and other relevant stakeholders whom control the organizational activities are seemed to like using Mobile Solutions. 1 2 3 4 5

Section 02 - Rate of adoption (RoA)

Assuming I have access to mobile enterprise solutions, I intend to use it. 1 2 3 4 5

Given that I had access to Mobile enterprise solutions, I predict that I would use it. 1 2 3 4 5

Assuming I have an opportunity to recommend mobile enterprise solutions to my friends, I would do it 1 2 3 4 5

Section 03 - Demographic Information

Designation :-

Age :-

Gender :-

Industry Sector :-

Area :-

Legal definition of the organization :-

52

No of Employees :-

Annual Turnover :-

Annexure II – SPSS Analysis

Pattern Matrix for all the items.

Pattern Matrixa

Component

1 2 3 4 5 6

Co5 1.202

Co2 .980

Co4 .930

Co7 .872

Co3 .817

Co1 .630 .307 .473

T4 .467 .321

RoA3 .430 .303

O6 .423 .313

RA2 1.078

RA1 1.072

RA6 .892

RA3 .837

C3 .623 .379

RA5 .611

RA4 .577 .312

C5 .545 -.311

C1 .504

C4 .422 .351

C6 .325 .316

O2 -.340 1.077

O1 .902

O4 .873

O3 .818

T5 .752 .440

RoA1 .356 .597

O5 .571

T2 .306 .485 .377

53

RoA2 .430 .447

T1 .917

C2 .378 .509

T3 1.067

Co6 .523 .730

Extraction Method: Principal Component Analysis.

Rotation Method: Promax with Kaiser Normalization.

a. Rotation converged in 9 iterations.

SPSS output from reliability analysis for Communication

Reliability Statistics

Cronbach's

Alpha

N of Items

.920 6

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

RA1 19.94 17.906 .842 .896

RA2 19.92 18.922 .769 .906

RA3 19.98 18.886 .796 .903

RA4 19.68 19.671 .694 .916

RA5 19.84 19.093 .745 .909

RA6 19.95 18.795 .788 .903

SPSS output from reliability analysis for Mutual Support


Cronbach's

Alpha

N of Items

.865 6

54


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

C1 19.21 16.326 .690 .840

C2 19.06 16.010 .581 .856

C3 19.37 15.492 .611 .851

C4 19.25 15.696 .724 .833

C5 19.44 14.066 .724 .831

C6 19.36 14.749 .671 .841

SPSS output from reliability analysis for Cohesion


Cronbach's

Alpha

N of Items

.932 7


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

Co1 22.69 24.012 .775 .923

Co2 22.56 23.429 .878 .912

Co3 22.63 23.819 .889 .912

Co4 22.62 24.268 .738 .927

Co5 22.54 25.450 .814 .920

Co6 22.73 27.073 .634 .935

Co7 22.67 24.682 .769 .923

55

SPSS output from reliability analysis for Value Diversity


Cronbach's

Alpha

N of Items

.836 3


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

T1 8.07 2.674 .625 .845

T2 7.94 2.733 .728 .748

T5 7.98 2.434 .749 .720

SPSS output from reliability analysis for Trust


Cronbach's

Alpha

N of Items

.903 6


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

O1 18.55 15.116 .678 .894

O2 18.62 14.216 .789 .878

O3 18.60 14.353 .747 .884

O4 18.56 13.812 .783 .878

O5 18.56 13.793 .808 .874

O6 18.63 15.521 .602 .904

SPSS output from reliability analysis for Coordination of Expertise


Cronbach's

Alpha

N of Items

.861 3

56


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

RoA1 7.94 2.544 .768 .777

RoA2 8.00 2.528 .800 .748

RoA3 7.87 2.530 .653 .889

Correlation Analysis

SPSS output of correlation test of Rate of Adoption and Factors

Correlations

Relative

Advantage

Compatibility Simplicity Trial

Ability

Observerbility Rate of

Adoption

Relative

Advantage

Pearson Correlation 1 .835** .616** .469** .659** .643**

Sig. (2-tailed) .000 .000 .000 .000 .000

N 54 54 54 54 54 54

Compatibility

Pearson Correlation .835** 1 .657** .594** .752** .738**

Sig. (2-tailed) .000 .000 .000 .000 .000

N 54 54 54 54 54 54

Simplicity

Pearson Correlation .616** .657** 1 .549** .650** .741**

Sig. (2-tailed) .000 .000 .000 .000 .000

N 54 54 54 54 54 54

Trial Ability

Pearson Correlation .469** .594** .549** 1 .692** .691**

Sig. (2-tailed) .000 .000 .000 .000 .000

N 54 54 54 54 54 54

Observerbility

Pearson Correlation .659** .752** .650** .692** 1 .798**

Sig. (2-tailed) .000 .000 .000 .000 .000

N 54 54 54 54 54 54

Rate of

Adoption

Pearson Correlation .643** .738** .741** .691** .798** 1

Sig. (2-tailed) .000 .000 .000 .000 .000

N 54 54 54 54 54 54

**. Correlation is significant at the 0.01 level (2-tailed).

Project Report - Damith Final

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