THE DETERMINANTS OF ORGANIZATIONAL INNOVATION …

THE DETERMINANTS OF ORGANIZATIONAL INNOVATION

MANAGEMENT EFFECTIVENESS IN THE THAI

BANKING INDUSTRY

Phanu Limmanont

A Dissertation Submitted in Partial

Fulfillment of the Requirements for the Degree of

Doctor of Philosophy (Development Administration)

School of Public Administration

National Institute of Development Administration

2010

iii

ABSTRACT

Title of Dissertation The Determinants of Organizational Innovation

Management Effectiveness in Thai Banking Industry

Author Mr.Phanu Limmanont

Degree Doctor of Philosophy (Development Administration)

Year 2010

The purpose of this study was to develop and to test a theoretical framework

for explaining the determinants of organizational innovation management effectiveness.

The main objective was to explain the determinants of seven contingency prediction

factors behind innovation management effectiveness in an organization, namely

Innovation Strategy, Organizational Structure, Organization Culture, Project Management,

Team Cohesiveness, Strategic Leadership and Coordination and Communication.

In addition, the dissertation attempted to employ the concept of resource-based

view theory organizational contingency theory to explain the determinants of the

seven contingency prediction factors, which examines whether the outputs of all these

positive alignments impact the innovative capacity and to test the model of the seven

contingency prediction factors of innovation management effectiveness.

In research methodology, the researcher provided a literature review of

documents from several sources and used the review to derive at the conceptual

framework and hypotheses model. Furthermore, the questionnaire was developed by

operationalizing the constructs or variables from the literature review to test the

hypotheses. The researcher also conducted a pre-and-test to test the reliability of the

questionnaires as a result of high reliability efficiency. The primary data will be

collected through questionnaires from the non-probability sampling population of the

staff of both commercial banks (SCB and KBank) and state-owned banks (KTB and

GHB). The research methodology is also considered critical to the success of this

work. The research employed both quantitative and qualitative methods and the

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quantitative data was analyzed using SPSS and Structural Equation Model-AMOS

program.

A theoretical, managerial and policy models were developed and the

constructs of innovation strategy and strategic leadership were found to be the main

influence on conceptual innovation management effectiveness while organization culture,

team cohesiveness and project management had a significant positive influence on

innovation management effectiveness. Furthermore, there were no significant differences

between commercial banks and state owned banks. Also, business policies which

formulated the business strategy and process were the key drivers behind improving

innovation management effectiveness. Finally, this dissertation provided the limitations of

the study as well as recommendations regarding policy implications and the future

directions of research study.

ACKNOWLEDGEMENTS

I owe my deepest gratitude to Associate Professor Dr. Chindalak Vadhanasindhu,

a major advisor and a member of the examining committee, who truly guided me

throughout this work, providing me with attentive and well-considered comments.

Thank also to Associate Professor Dr. Fredric William Swierczek, a chairperson of

the examining committee, who shared his valuable time to give me constructive

criticism regarding the study. I’d like also to express my gratitude to Associate

Professor Dr. Montree Socatiyanurak, a member of the examining committee, who

provided insightful comments and drew on his experiences in banking to aid the

development of this study. I would like to express again my sincere gratitude and

deep appreciation for all their kindness, support, guidance and generous assistance.

I want to thank all involved persons, all the lecturers and staff of the Graduate

School of Public Administration for their support and valuable advice during the past

period and also to those employees of the banks who assisted during data field

collection.

I also want to thank my lovely wife and my charming daughter for their

inspiration and understanding as well as continuous encouragement during my

accomplishment of my doctorate degree.

Lastly, I would like to express my appreciation to everybody else who has

been very supportive during the completion of the dissertation.

Phanu Limmanont

January 2011

TABLE OF CONTENTS

Page

ABSTRACT iii

ACKNOWLEDGEMENTS v

TABLE OF CONTENTS vi

LIST OF TABLES ix

LIST OF FIGURES xi

ABBREVIATION xii

CHAPTER 1 INTRODUCTION 1

1.1 The Purpose of the Study 1

1.2 Statement of the Problem 2

1.3 Approach of the Study 10

1.4 Objectives of the Study 12

1.5 Significant of the Study 13

CHAPTER 2 LITERATURE REVIEW 25

2.1 Innovation Management Effectiveness 25

2.2 Innovation Strategy 35

2.3 Organization Structure 39

2.4 Organization Culture 45

2.5 Project Management 50

2.6 Team Cohesiveness 54

2.7 Strategic Leadership 59

2.8 Coordination and Communication 64

CHAPTER 3 CONCEPTUAL THEORY 74

3.1 Resource-Based View 74

3.2 Organizational Theory 79

3.3 Contingency Theory 83

vii

3.4 HRM and Innovation 88

3.5 Definition of Effectiveness and Efficiency 89

CHAPTER 4 CONCEPTUAL FRAMEWORK AND HYPOTHESES 95

4.1 Conceptual Overview 95

4.2 Research Hypotheses 105

4.3 Operationalization of Variables 107

CHAPTER 5 RESEARCH METHODOLOGY 112

5.1 Research Design 112

5.2 Approaches of the Study 113

5.3 Units of Analysis 113

5.4 Target Population 113

5.5 Sampling 114

5.6 Method of Analysis 116

5.7 Measurement of Reliability and Validity 122

5.8 Research Process 128

CHAPTER 6 DATA ANALYSIS 130

6.1 Characteristics of the Respondents 130

6.2 Result of Descriptive Statistics 132

6.3 Comparison of Means 139

6.4 Reliability Analysis 142

6.5 Validity 143

6.6 Model Assessment 150

CHAPTER 7 DISCUSSION CONCLUSIONS AND IMPLICATIONS 167

7.1 Discussion 167

7.2 Conclusion of the Study 168

7.3 Contributions of the Study 175

7.4 Managerial Contribution and Implication 177

7.5 Recommendations 178

7.6 Limitations of the Study 179

7.7 Future Directions of Research Study 180

viii

BIBLIOGRAPHY 182

APPENDICES 222

Appendix A Cover Letter 223

Appendix B Survey Questionnaire 225

BIOGRAPHY 231

ix

LIST OF TABLES

Tables Page

1.1 Number of Commercial Bank Branches in Thailand 10

1.2 Summary of Key Characteristic Issues 21

2.1 Summary of Key Characteristic Factors 68

2.2 Summary of Key Factors That Influence On Another 72

3.1 Summary of the Conceptual Theories 90

4.1 Operationalization of Variables 107

5.1 The Number of Bank Branches in the Study 114

5.2 The Number of Staff of the four Banks in the Study 114

5.3 Sample Size for ±1%, ±5% and ±10% Precision Levels Where the 115

Confident Level is 95%

5.4 The Population and Sample of the four Banks in the Study 116

5.5 Measurement of the Structural Model Fit 122

5.6 Questions from the Pretesting Questionnaire 123

5.7 The Reliability Analysis of the Questionnaire from Pretesting 125

5.8 Pretesting of Descriptive Statistics from the Three Banks 126

6.1 Gender of the Respondents 131

6.2 Education Level of the Respondents 131

6.3 Workplace of the Respondents 131

6.4 Workplace of the Respondents 131

6.5 Work Experience of the Respondents 132

6.6 Work Position of the Respondents 132

6.7 Descriptive Statistics of Observed Variables 133

6.8 The Summary of all Constructs in Descriptive Statistics 136

6.9 Descriptive Statistics of all Constructs Spit by Bank 137

x

6.10 Descriptive Statistics of all Constructs Spit by Type of Bank 140

6.11 The Results of the T-Test between Commercial and State-owned 141

Banks

6.12 The Reliability Analysis of the Constructs 142

6.13 Construct Measurement 145

6.14 The Goodness of Fit for Convergent Validity 146

6.15 Pairwise Analysis of Discriminant Validity 148

6.16 Measurement of the Structural Model Fit 151

6.17 The Goodness of Fit for Proposed Model 1 154

6.18 The Relation of Parameters and Parameter Estimates of Proposed 154

Model 1

6.19 Summary of the Results of Hypotheses Testing on Theoretical Aspec 158



Model 2



Model 3

6.24 The Comparison of Three Proposed Models 165

LIST OF FIGURES

Figures Page

2.1 Provides a General Framework for Understanding Organizational 30

Innovation

2.2 Organizational Characteristics 42

2.3 Dimensions of Organizational Structure 44

2.4 Organizational Factors 45

2.5 Influence of Organizational Culture on Creativity and Innovation 49

(Preliminary Model Based on Literature Study)

3.1 Conceptual Model: Tacom Model 86

3.2 Model of Stages in the Innovation-Decision Process 88

4.1 The Conceptual Framework 96

4.2 Overall Conceptual Framework and Model 104

4.3 The Hypotheses Model 106

5.1 The Research Process of This Study 128

6.1 Construct Measurement Model 144

6.2 Fixed and Free Models for Testing Discriminant Validity 147

6.3 Proposed Model 1 153



7.1 The Summary of Theoretical Model 170

7.2 The Summary of Managerial Model 172

7.3 The Summary of Policy Model 173

ABBREVIATIONS

Abbreviations Equivalence

SCB Siam Commercial Bank

KBank Kasikorn Bank

KTB Krung Thai Bank

GHB Government Housing Bank

RBV Resource-Based View

SPSS Statistical Package for the Social Sciences

AMOS Analysis of Moment Structures

CHAPTER 1

INTRODUCTION

In this chapter, the researcher introduces the context of the research study, that

of the banking industry, in which the purpose of the study and the statement of

problems are identified. The approach of the study also employs the essential themes

of both resources-based view and organizational contingency theories to explain the

determinants of the contingency prediction factors in the field of innovation

management effectiveness. The objectives of the study are to study the determinant

factors with explanations of the results of the outputs and to test the model of the

hypotheses.

1.1 Purpose of the Study

The purpose of this study is to develop and to test a theoretical framework for

explaining the determinants of organizational innovation management effectiveness.

The main objective is to explain the determinants of the seven contingency prediction

factors behind innovation management effectiveness in an organization, namely

Innovation Strategy, Organizational Structure, Organization Culture, Project Management,

Team Cohesiveness, Strategic Leadership and Coordination and Communication.

The research also attempts to employ the concepts of the resource-based view

theory and organizational contingency theory to explain the determinants of the seven

contingency prediction factors, thereby examining whether the outputs of all these

positively impact innovative capacity by enhancing bank operation management

effectiveness in sustaining competitiveness. Bank operation management effectiveness is

an area of banking concerned with the production of financial goods and services, and

involves the responsibility of ensuring that bank operations are efficient in terms of

using as few resources as needed, and being effective in terms of meeting customer

requirements. It is concerned with managing the process that converts inputs (in the

2

forms of equipments, technology, knowledge and creativity) into outputs (in the forms

of goods and services).

Sample unit analysis is also carried out four banks representative of a non-

probability sampling population of the bank industry These comprise Commercial

Banks (Siam Commercial Bank (SCB) and Kasikorn Bank (KBank)) and State-

Owned Banks (Krung Thai Bank (KTB) and Government Housing Bank (GHB))-with

the number of respondents given below.

Siam Commercial Bank 137 Respondents

Kasikorn Bank 112 Respondents

Krung Thai Bank 127 Respondents

Government Housing Bank 21 Respondents

Total Sampling units 397 Respondents

1.2 Statement of the Problem

1.2.1 Forces of Change

In Thailand, the 1997 financial crisis was not only brought about by factors

related to the economic cycle but also exacerbated by structural weaknesses in the

system. A significant cause of the crisis was the massive capital inflows into the

economy without effective management mechanisms. Some examples of the weak

initial conditions were ineffective corporate governance, inadequate supervision and

regulation, and insufficient or, in some cases, inaccurate disclosure which resulted in

lax credit policies in banks and other financial institutions and misuse of funds in the

corporate sector.

Thailand initiated its financial liberalization efforts in the early 1990s. The

first step was the acceptance of Article VIII of the International Monetary Fund’s

Articles of Agreement, followed by a series of deregulatory measures in the financial

system. Market opening of the financial system ensued with the establishment of the

Bangkok International Banking Facilities (BIBFs) in 1993, which coincided with the

global trend of surges in capital flows to the emerging market economies. In July

1997, Thailand faced the worst economic and financial crisis in its recent history. In

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retrospect, the crisis was closely linked to the premature financial liberalization and

market opening.

Owing to the liberalization policy and the swiftness of capital mobility in the

integrated financial system, business has access to overseas funds at relatively low

cost and allocated such funds for rapid expansion and other purposes; a process which

was in general not subject to adequate monitoring and control. It cannot be denied that

liberalization at a time when the underlying economic strength and infrastructure were

not yet well instituted posed threats to both the host country and the global market

place because of the potential for contagion and systemic impacts. The following

subsections list some major infrastructure components that were absent at the

outbreak of the recent crisis and what has been done in order to prevent future crises.

1.2.2 Inadequate Information System

While information on foreign borrowing through the banking system was

monitored, inadequate information on non-bank private corporate foreign debt

resulted in an incomplete aggregate picture of the country’s foreign liabilities. Data on

sector allocation of credits did not show any signs of overextension to the property or

real estate sectors, and in fact funds seemed to be properly allocated to the productive

sectors. Direct lending to the property sector or real estate businesses was consistently

below 5% of total lending.

However, it became apparent after the crisis that these so-called productive

sectors, such as exporters, had used their BIBF proceeds to invest heavily in the

property and real estate sectors, thus turning the entire business group into NPLs

overnight as the baht depreciated.

Another area of information deficiency was demand and supply in the

property market. During the bubble economy period, the real estate industry grew

rapidly. However, developers were not fully aware until much later that supply was

considerably outgrowing demand.

Systems for collecting necessary data are now in place. Thailand became the

21st country to meet the specifications of the Special Data Dissemination Standard,

established in May 1996 by the International Monetary Fund to enhance the quality,

integrity, availability and timeliness of comprehensive economic and financial statistics.

4

Moreover, Thailand is in the process of enacting the Credit Bureau Act so that banks

can disclose both personal and corporate loan information about borrowers without

seeking the borrower’s consent.

1.2.3 Inability to Utilize Policy Instruments, Particularly the Exchange

Rate

Rigidity in the exchange rate system, and the lack of political will to tighten

fiscal policy in the midst of an overheating economy, sent a warning signal worldwide

of the unsustainability of the country’s economic situation. Pressure was also put on

monetary policy, which was further constrained by its inability to act autonomously.

The high interest rate policy, in turn, encouraged further foreign capital inflows,

exacerbating the already overheated economy.

Under a basket-peg exchange rate regime and volatile global capital flows,

little room was available for independent monetary policy. Defending or abandoning

the exchange rate peg was a difficult policy dilemma. In fact, before the float, the real

effective exchange rate had appreciated in line with the US dollar. However, whether

and to what extent it was overvalued was controversial, as it depends on the

equilibrium. real effective exchange rate. If one takes the period when the current

account was in equilibrium, say in 1990, as the benchmark, there was an overvaluation of

8% at most of the time of the float. However, recognizing the loss in competitiveness

and structural changes since 1990, such a benchmark would have been too simplistic.

Nevertheless, it was felt that tampering with the exchange rate system (e.g. band

widening) under the circumstances of intense speculative pressure and ebbing

domestic confidence would have resulted in a wholesale run on the baht and prompted

an immediate currency crisis. The Bank of Thailand, even with substantial foreign

reserves, would not have been able to stabilize the exchange rate, given the much

larger unhedged foreign currency debts of Thai corporations, which would have

rushed to close their exposure on the first signs of any weakening commitment to a

stable exchange rate. The decision was made to protect the exchange rate system as

long as possible in order to buy time for the authorities to tackle fundamental

problems in the economy and the financial sector without having to face a simultaneous

currency crisis.

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Such a policy decision was premised on the rationale that a devaluation of the

baht would have done more harm than good for the following reasons:

1.2.4 Outdated Legal Framework

Another factor contributing to the difficult pre-crisis environment was the

outdated legal and regulatory framework. The foreclosure law involved lengthy

procedures that did not allow financial institutions to sell, foreclose or dispose of bad

debts to stop losses. This law, as well as the bankruptcy law, has now been amended

to expedite legal procedures. The new laws, especially the bankruptcy law, also

facilitate the settling of cases in court as well as provide opportunities for both

creditors and debtors to rehabilitate the debts for mutual benefits. At the same time,

the new Financial Institutions Act has been drafted and is currently before parliament.

It will pave the way for Thai supervisors to improve their approaches in response to

the changing financial environment. The draft law broadens the scope of commercial

banks’ financial activities, by allowing them to form financial conglomerates,

empowering the Bank of Thailand to apply consolidated supervision and encourage

the practice of good governance. Not only can market discipline impose strong

incentives on banks to conduct their business in a safe, sound and efficient manner,

but it can also encourage them to efficiently allocate resources and maintain a strong

cushion against future losses. Since reliable and timely information enables all

stakeholders to make effective assessments, the Bank of Thailand has required

financial institutions to disclose specified information following the accounting

standards, which are in line with international standards.

1.2.5 Inexperienced Risk Management and Poor Governance of Financial

Institutions and Corporations

Banks’ lending practices were largely collateral-based. Less attention was paid

to cash flow or analyses of project feasibility. With the property and stock price

boom, financial institutions did not expend resources on valuing the underlying

collateral. Banks were also under pressure from shareholders to take on risky

investments in return for potential profits that would allow attractive dividend

payments. This, in turn, made them less vigilant in monitoring and taking appropriate

actions against borrowers once they showed signs of financial deterioration.

6

Unfortunately, the real estate and stock market booms overshadowed the growing

risks inherent in the system.

Before 1997, a number of Thai corporations had taken advantage of cheap

foreign funds and the pegged exchange rate to borrow heavily from abroad. Many of

them neither had foreign currency income, nor adequately hedged positions. When the

exchange rate regime changed to the managed float system, many found their foreign

liabilities had approximately doubled. Risk management has become an essential

component of the financial sector as financial institutions compete in an environment

of increased risk and larger and more liquid financial markets. The Bank of Thailand

organized a risk management symposium to promote a broader understanding of the

risks involved in the finance and banking sector as well as stimulate concerted action

to develop and strengthen prudential standards towards risk management for the

stability of the Thai financial system.

1.2.6 Lax Supervision

Prudential regulations, especially in the area of loan classification and

provisioning, were inadequate. The skills required for on-site examination, and off-

site supervision using a risk- based approach, were lacking. This, coupled with the

absence of proper credit risk analysis, led to financial structures that were inherently

fragile. To make matters worse, the weak standard of transparency and disclosure in

private financial institutions also brought on a sense of mistrust. Banks did not have

good internal control systems in place, while their managers had not been made more

accountable.

As such, the component of market discipline can indirectly assist in reinforcing

supervisory efforts in promoting risk management in banks and the financial system,

since it adds pressure for financial institutions to manage themselves in a safe and

sound manner. Effective market discipline requires reliable and timely information

that enables all stakeholders to make effective assessments.

1.2.7 State Banks: Privatization

Current Developments

The economic crisis in Asia in 1997 was the starting point for many

countries in this region to lay down measures to solve financial sector problems.

7

Giving foreign financial institutions an opportunity to invest in this region by, for

example, forming business alliances with domestic financial institutions or buying

financial institutions intervened by the government allows major international

financial institutions to play an essential role in Asia.

During the crisis, seven Thai banks were intervened and taken into state

ownership. In 1998, one bank was closed, and bad assets were transferred to an asset

management company (AMC), due to the large losses. One was fully acquired by a

state-owned bank. One was merged with a bank while another was merged with

another financial institution. The main purpose of intervention is to strengthen

financial institutions by merging them or selling them to the private sector through

open competitive bidding processes to ensure fair competition and transparency.

Consequently, two intervened banks were privatized in 1999 while the resolution of

the other two banks was expected to be completed by the end of 2000. In addition, the

authority gradually decreased gradually its stake in the remaining two banks in the

medium term.

1.2.8 Deposit Guarantee

While a deposit insurance scheme has not been implemented in Thailand, the

Financial Institution Development Fund (FIDF) has provided blanket insurance to

depositors and creditors of the closed banks, finance companies and credit fanciers in

full amount.

For purchasers of state-owned banks, the FIDF guaranteed to compensate

losses from NPL through yield maintenance and gain-/loss-sharing schemes.

1.2.9 Transaction Structure

In most cases, the FIDF is offering two distinct support packages to potential

buyers:

Loss Sharing

Under this structure, the FIDF will enter into a Loss Sharing Agreement

with the bank. The FIDF will thereby agree to reimburse the bank for a specified

percentage of losses on covered NPLs and for the cost of holding covered NPLs on its

balance sheet. Loss sharing will cover only loans above a certain size -those NPLs as

8

of the date of closure with no more than 15% of remaining loans which become NPLs

within six months of the date of closure. The loss-sharing agreement will establish

two loss-sharing percentages, two loss-sharing thresholds and the initial reserve

(representing the proportion of the specified losses to be borne by the FIDF). Under

the loss-sharing agreement, the FIDF will only share the losses above the first

threshold. Bidders must specify the first and second loss-sharing percentages as part

of the bids.

AMC with Loss Sharing

This option is similar to pure loss sharing as described above. The key

difference is that specified NPLs will be transferred to an AMC, which is owned by

the FIDF. There will then be a loss sharing agreement between the Bank and the

AMC.

1.2.10 Domestic Mergers

In a number of countries, mergers take place in order to create synergy, reduce

costs and increase the competitive edge of the merged institutions. In the case of

Thailand, recent mergers were the consequences of problem bank resolution and the

policy to reduce the number of smaller financial institutions. Any five or more finance

companies, finance and securities companies and credit fancier companies can merge

to form a restricted-license bank, which will be further upgraded to a fully fledged

bank at a later stage.

1.2.11 Entry of Foreign Banks

From 1993 the Bank of Thailand authorized the establishment of the Bangkok

International Banking Facilities (BIBFs) which allowed greater diversification of

service providers through new international entrants. In addition, it enabled domestic

banks to diversify their business towards international banking intermediation by

obtaining offshore funds to lend to either the domestic market (out-in) or to the

international market (out-out).

Since BIBFs are not allowed to take domestic deposits, they are subject to

fewer regulations than commercial banks. For instance, BIBFs are not required to

9

observe a minimum capital adequacy ratio. BIBFs also carry a lower corporate tax

rate of 10%, as opposed to 30% in the case of banks. However, current policy does

not allow the new entry of foreign banks through BIBFs or full-licensed branches.

Rather, foreign investors that have sound financial standing and high potential to help

strengthen local financial institutions are allowed to hold more than 49% of the

shares. With the relaxation of the regulation on foreign participation, five local banks

out of a total of 13 banks are now majority-owned by foreign institutions.

Before the 1997 financial crisis, the domestic banks dominated the retail

market through their extensive branch networks and their close relationship with local

customers. Foreign banks, on the other hand, focused their businesses on wholesale

customers. Nevertheless, the role of foreign banks will change significantly now that

they have acquired majority shares in domestic banks. Access to retail customers is

now possible. These foreign-owned banks are offering diversified financial services to

their retail customers.

For the details of bank branches in Thailand, the table below offers details on

the number of commercial bank branches in the country.

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Table 1.1 Number of Commercial Bank Branches in Thailand

Branches

Banks Bangkok Central Northeast North South Total

Bangkok Bank

Krung Thai Bank

Kasikorn Bank

Siam Commercial Bank

Bank of Ayudhya

TMB Bank

Siam City Bank

United Overseas Bank (Thai)

Standard Chartered Bank (Thai)

CIMB Thai Bank

Thanachart Bank

Tisco Bank

Kiatnakin Bank

ICBC Bank (Thai)

Land and Houses Retail Bank

The Thai Credit Retail Bank

MEGA International Commercial

Bank

Oversea Branches

247

234

295

331

192

157

145

85

23

75

117

13

9

8

18

5

2

15

285

275

266

348

200

137

147

35

4

42

63

13

19

3

6

5

2

0

135

152

85

106

67

50

36

9

0

8

24

6

10

4

0

0

0

0

142

141

91

98

52

56

43

8

1

9

23

3

9

1

2

0

0

0

118

129

81

122

69

55

52

8

0

13

30

5

7

3

2

0

0

0

927

931

818

1,005

580

455

423

145

28

147

257

40

54

19

28

10

4

15

Total 1,971 1,850 692 679 694 5,886

Source: Bank of Thailand, 2010.

1.3 Approach of the Study

The research study employs the essential themes of both organizational

contingency theory and the resource-based view in measuring organizational performance

improvement, which is primarily a consequence of the seven contingency prediction

factors of Innovation Strategy, Organization Structure, Organization Culture, Project

Management, Team Cohesiveness, Strategic Leadership and Coordination and

Communication. Structural contingency theorists have tended to focus on coherence,

11

consistency and fit harmony as critical factors of organization design, which also

focuses on interaction relationships and is selected according to internally consistent

groupings and which should be consistent with the situation of the organization as

concerns its age and size, industry conditions, and technology. The contingency

approach has also generated value creation in the relationships between innovation

approach and organizational contexts that explain the capability and effectiveness of

innovation with regard to supporting operation management efficiency.

Furthermore, the relationship between Human Resource Management (HRM)

and innovation in recent years has been explored from various angles. One direction

this research has taken assumes that HRM systems in general or HRM systems

comprised of specific practices influence innovation capacity indirectly. For instance,

empirical studies lend support to the contention that HRM influences mechanisms

such as the development and exploitation of intellectual capital (Wright, Dunford and

Snell, 2001: 701-721), knowledge creation and new product development (Collins and

Smith, 2006: 544-560), and organizational learning (Snell, Youndt and Wright, 1996:

61-90) that in turn facilitate innovation.

On the basis of a mixed sample of industrial firms in Spain, Jiminez-Jiminez

and Sanz-Valle (2005) demonstrated a link between performance appraisal systems,

incentive-based compensation, and internal career opportunities with innovation,

speculating that it is the impact of the HRM practices on employee participation that

provides opportunities for innovation. In a similar vein, Shipton et al. (2005) provided

evidence that combining training, appraisal and induction influences different stages

of the organizational learning cycle (i.e. creation, sharing and implementation of

knowledge). Moreover, a study by Shipton et al. (2006) showed that not only do

training, appraisal, and induction impact innovation, but that the influence of these

practices may differ according to the types of innovation activities (i.e. exploitative

vs. explorative). The contention that certain HRM practices impact different aspects

of innovation has been conceptualized by de Leede and Looise (2005) and Jørgensen,

Hyland and Kofoed Lise, 2008: 127-142.

These findings contribute substantially to our understanding of the relationship

between HRM and innovation, but they are also limited by having been conducted

exclusively in manufacturing firms. According to contingency theory models developed

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by Miles and Snow (1984) and Schuler and Jackson, 1987: 207-220, characteristics of

the organization (e.g. size, external market, industry) are critical factors in determining the

appropriate HRM practices for an innovation strategy; thus, research aimed at

explaining and describing the relationship between HRM in non-manufacturing

environments is clearly warranted.

Secondly, the resource-based view theory (Barney, 1991: 99-120; Wernerfelt,

1984: 171-180) asserts that the internal characteristics and performance of an

organization have idiosyncrasies that are not identical to strategic resources and not

perfectly mobile and therefore heterogeneous. On the other hand, the RBV of

knowledge management for competitive advantage (Halawi, Aronson and McCarthy,

2005: 75-86) defines a strategic asset as one that is rare, valuable, imperfectly

imitable and non-substitutable. Knowledge base is seen as a strategic asset of

innovation with the potential to be a source of competitive advantage for an

organization. This study also uses the contingency perspective toward establishing

boundaries for the RBV by hypothesizing contexts within which the determinants of

the seven contingency prediction factors are assessed as being more or less valuable.

This research also attempts to integrate the RBV model and Organizational

Contingency Model by identifying the determinants of the seven contingency

prediction factors of innovation management effectiveness through the characteristics

of organizations.

1.4 Objectives of the Study

1) To study the determinants of organizational innovation management

effectiveness as organizational improvement.

2) To explain the outputs of the factors of the determinants of organizational

innovation effectiveness (Innovation Strategy, Organization Structure, Organization

Culture, Project Management, Team Cohesiveness, Strategic Leadership and Coordination

and Communication).

3) To test a model of the seven contingency prediction factors of innovation

management effectiveness.

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1.5 Significance of the Study

1.5.1 The Role between the Determinants and Innovation Management

Effectiveness in Organization

In general, organizations are not successful at attending to the non-technical

aspects of changing technology. It is widely felt that there is little consideration of

these issues, that they are not well understood, that their importance is underestimated

and that action in this area is under-resourced. In particular, not enough attention is

paid to the impact of organizational structures and processes on the implementation

and use of strategic innovation management in the empowerment of individuals in an

organization for higher productivity while enhancing operational efficiency and sustaining

competitive advantage.

This study will again employ the integrated approach of well-know organizational

theories (contingency approach) and the resource-based view to gain further

understanding of the determinants of the contingency prediction factors of innovation

management effectiveness and its context. This integrated approach will offer both a

new perspective into the learning and innovation of management and how it can be

employed in manpower as well as a new way to conceptualize the impact of the

contingency prediction factors of innovation management effectiveness. Furthermore,

the study will provide a conceptual framework that can be used in future studies in

other non-academic environments.

1.5.2 Impact of Innovation Management Effectiveness

Innovation is defined as “the adoption of ideas that are new to the adopting

organization” (Rogers, 1983; Downs and Mohr, 1979: 307-408). It is about profiting

from innovation. Generating good ideas or adopting new ones, in and of itself, is

only a start. To be an innovation management, an idea must be converted into a

product or service that customers want. Coming up with an idea or prototype

invention is one thing. Championing it, shepherding it, and nurturing it into a product

or service that customers want is another. Innovation entails both invention and

commercialization. Innovation management is generally considered to be one of the

key drivers of organizational success (Schillewaert, Ahearne, Frambach and Moenaert,

14

2005: 323-336). There is empirical evidence indicating that implementing innovations

improves organizational performance. For example, a study of manufacturing companies

in Australia and New Zealand showed that implementing total quality management

practices in organizations increased organizational performance (as measured by

percentage growth in sales, Terziovski and Samson, 1999: 226). Yet, how can

organizations be assured that implemented innovations are improving their

performance? Organizations, particularly small to medium-sized enterprises, measure

innovation management effectiveness (using organizational performance as a distal

measure of perceived benefits from innovation). The consequences of innovation

management effectiveness are based on attitudes towards future innovation adoption.

Research in innovation and human resources sets out to develop hypotheses regarding

such measurement and its effects, and test these hypotheses in organizations.

Innovation can be defined as “a technology or practice that an organization is using

for the first time, regardless of whether other organizations have previously used the

technology or practice” (Klein, Conn and Sorra, 2001: 811). In this research,

innovation management effectiveness is defined as the overall organizational outcome

which results from implementing innovations. That is, how an organization perceives

the overall organizational improvement (including factors such as productivity,

finance, and employee morale) which arises specifically from implementing

innovations (Klein, Conn and Sorra, 2001: 811-824).

In short, the future of breakthrough ideas and technology is dependent on

building and sustaining innovative and creative organization structure and culture.

Interdisciplinary task orientation, cross-functional collaboration, teamwork cohesiveness

and convergence of project management are at the core of this organization

effectiveness. Creating cost-effective manufacturing, attractive design, superior

function and value are primary challenges for innovation management. A successful

idea requires many minds in many disciplines. Effective management is a key driver

of this success.

1.5.3 A Holistic Approach to Innovation Strategy

Innovation is recognized as a strategy to achieve competitive businesses and

products. Managing innovation at all levels requires the integration of knowledge and

15

interdisciplinary cooperation. Different understandings and approaches to innovation

between professions often result in communication problems. To overcome this

barrier a common ground is needed which requires a holistic approach to innovation

with a simple tool for facilitating cooperation on a diversity of innovation matters. It

also demonstrates its capacity to support interdisciplinary innovation in diverse

groups. The results are built on a sustained and emergent research process.

Innovation is also recognized as an important strategic means in achieving a

competitive business´ and products in a global market. As requirements and knowledge

increase, innovation becomes a complex matter (Burnett, 1997: 369-377) which needs

the involvement of more professions. Cultural diversities among professions as well

as different interpretations of innovation create an unstable basis for cooperation

resulting in coordination and communication problems (Stokholm, 2005: 54-57), the

losse of valuable information and a waste of time.

1.5.4 The Relationship of Forming Organization Structure and Culture

to Innovation Management Effectiveness

Organizational culture and structure concern the way staff are grouped and the

organizational culture within which they work. There has been considerable work on

the situational and psychological factors supportive of innovation in organizations.

Indeed, it has been widely demonstrated that the perceived work environment

(comprising both structural and cultural elements) does make a difference to the level

of innovation in organizations (Amabile, Conti, Coon, Lanzenby and Herron, 1996:

105-123; Ekvall, 1996: 105-123). Creative and innovative behaviors appear to be

promoted by work environment factors (Mathisen and Einarsen, 2004). Indeed, it is

clear that organizations can create environments in which innovation can be

encouraged or hampered (Dougherty and Cohen, 1995; Tidd et al., 1997). A common

theme is that of the polychromic organization – one with the capacity to be in two

states at once (Becker and Whisler, 1973). Shepard (1967) describes this as a two-state

organization maneuvering between being loose and being tight, while Mitroff (1987)

perceives it as business-as-usual versus business-not-as-usual. More prosaically, this

means organizations need to be able, for example, to provide sufficient freedom to

allow for the exploration of creative possibilities, but sufficient control to manage

innovation in an effective and efficient fashion.

16

1.5.5 The Relationship of Project Management to Innovation Management

Effectiveness

Löh and Katzy (2008) addressed the question of what makes innovation

projects more successful. The project management literature as well as the new

product development literature often takes a rather normative stance towards the best

way of managing innovation projects. Much research (Clark and Wheelwright, 1992;

Cooper 2001; Pahl, Beitz and Feldhusen, 2004; Project Management Institute, 2004;

Ulrich and Eppinger, 2003; Wheelwright and Clark, 1992) has made extensive

recommendations towards organizational setup, planning and execution processes, or

development methods in innovation projects. Heavy-weight project management

organization, detailed planning and work break down combined with a stage gate

process, and the use of some methods like Quality Function Deployment (QFD) seem

to guarantee project success.

Several studies have made efforts to measure project management efficiency,

mostly in the form of comparisons between budget and actual (project costs, project

duration, revenue forecasting). Another measure of project management success is

speed (time management). Innovation speed has been positively correlated with

product quality or the degree to which it satisfies customer requirements; measures

include speed (Hauser and Zettelmeyer, 1997), performance against schedule (Chiesa

and Masella, 1994), and duration of the process (Cebon and Newton, 1999).

To achieve efficiency, it is widely recommended that organizations seeking to

innovate should establish formal operation processes for innovating and make use of

tools and techniques that may facilitate innovative endeavors. The stage-gate process

(Cooper, 1990) is possibly the most familiar of these, but other methodologies for

innovation project management exist, including Phased Development, Product and

Cycle-time Excellence and Total Design (Jenkins, Forbes, Durrani and Banerjee

(1997: 359-378) for a discussion). These methodologies have in common the separation of

the product development process into structured and discrete stages, with each having

milestones in the form of quality control checkpoints at which stop/go decisions are

made with regard to the progress of the project. These highly structured approaches to

the management of the innovation process began to emerge in the 1990s (Veryzer,

1998).

17

1.5.6 The Relationship of Team Cohesiveness to Innovation Management

Effectiveness

Langfred (1998) defined team cohesion as the degree to which team members

identify positively with the team. It can also be defined as the extent to which

members of the team like each other and want to remain members, or to which team

members feel a part of the team as well as the strength of member ties to other

members. Mullen and Copper (1994) reported that it is chiefly the commitment to the

task (as an indicator of cohesion) that shows a significant impact on team innovation.

Keller (1986) found that team cohesiveness was the best predictor of the variables that

significantly correlated with innovation of team.

Team cohesion is a proxy to team functioning and defined as the resultant of

all the focuses acting on the members to remain in the team (Vianen and DeDreu,

2001: 97-120). Team cohesion is considered as being either interpersonal, that is

social cohesion, or task cohesion. Social cohesion concerns an individual’s attraction

to the group because of positive relationships with other members of the team. Group

cohesion usually has effects on group behavior and functioning. Those effects include

reduction of, or even elimination of or, social loafing, drop-out rate and absenteeism,

improvement in communication among group members, greater conformity to group

norms among sports team members, enhanced problem solving, and increased work

output.

A highly innovative team can hardly be achieved without an adequate level of

cohesion. If team members lack a sense of togetherness and belonging and, there is

little desire to keep the team going, then intensive collaboration seems unlikely. An

adequate level of cohesion is necessary to maintain a team, to engage in collaboration,

and thus to build the basis for high innovation effectiveness.

The one specifically group level factor which is commonly mentioned as an

antecedent to innovation is cohesiveness. However, on the basis of current knowledge

of the effects of cohesiveness on group performance, contradictory influences are

evident. On the one hand, it is argued that cohesiveness facilitates innovation because

it increases feelings of self-actualization and psychological safety (Nystrom, 1979).

On the other hand, an important factor in producing high cohesiveness is group

homogeneity which is likely to be an inhibitor of innovation because it leads to

18

unwillingness to question group decisions, and a focus on relationships rather than

tasks in the extreme leading to the 'Group Think' phenomenon (Janis, 1982).

Nystrom (1979) had earlier attempted to resolve the contradiction by stating

the need to alter group characteristics according to the current stage of the innovation

process. Early on loosely joined, heterogeneous groups are required to facilitate the

production of innovative ideas, while later groups should be cohesive and

homogeneous to facilitate implementation. The problem, of course, is how such a

structural transition could be achieved in practice, especially as any given group may

be involved in the introduction of several innovations at the same time, all at different

phases in the process.

1.5.7 Strategic Leadership: A Cross Disciplinary Function and

Collaboration

Managing innovation is about decision making (Mintzberg, 2004) based on

negotiation and uses, among others, the principle of interaction among diversities.

Innovating is different from traditional ways of planning, being a dynamic creative

process on a somewhat unsafe ground. Traditional organizations do not encourage

knowledge sharing and cross-disciplinary function and cooperation. Managing

cooperation on all levels and across functions of innovation requires a firm ground

with the ability of recognizing the value of new, external information, assimilating it

and finally applying it to the commercial end as well as confidence with the principles

of innovation and practical tools.

Many writers have concluded that a democratic-collaborative style encourages

group innovation (Nystrom, 1979). Farris (1973) showed that in research laboratories,

the more innovative groups had more collaboration with their supervisors and with

each other than the less innovative groups. Similarly, West and Wallace (1988) found

that peer leadership discriminated significantly between highly innovative and less

innovative teams in primary health care practices, as reliably rated by independent

experts. The highly innovative teams exhibited a higher degree of leadership support,

goal emphasis, team building and work facilitation.

Although at all levels of analysis, innovation is held to be encouraged by high

levels of discretion (Amabile, 1983; Nicholson and West, 1988), there is evidence

from work on scientific research teams that the highest levels of innovation are

19

elicited by leaders who exert moderate control over the group (Farris, 1973; Pelz and

Andrews, 1976). However, as King and Anderson (1990) point out, a major problem

with such research into leadership is that group factors have been neglected and

research has generally not gone beyond applying individual level concepts directly to

groups. They consider that until more is known about the kind of group environment

that encourages innovation, it is premature to make recommendations about how

leaders may influence groups to be innovative.

Manz et al. (1989) advocate more of a contingency approach, arguing that

multiple leadership approaches seem to be ideal in different innovation contexts and

at various stages of the innovation process. As Anderson and King (1993) argue, a

model of leadership moving from nurturing behaviors at the early stages of group

innovation to stimulate and support ideas, through consensus-seeking behaviors

during the middle stages of the proposal, to delegation and checking behaviors during

and after innovation implementation, is ideal for workgroup innovation. However,

they do caution that additional research is needed to examine in greater detail the

relationship between leadership style and the development of innovations over time.

1.5.8 The Relationship of Coordination and Communication to Innovation

Management Effectiveness

(Mintzberg, 1979) categorizes previous research into three main coordination

strategies: mutual adjustment is based on close collaboration suitable especially in

simple and very complex situations; direct supervision assumes that managers guide

and coordinate the activities of their subordinates, while standardization of processes,

output, or training employs experts that develop rules and policies, but who do not

directly coordinate operative work.

Communication is the vehicle through which personnel from multiple

functional areas share information critical to the successful implementation of a

project (Pinto and Pinto, 1990). The purpose of this research is to study the internal

communication involved in intra-project communication among team members

measured in terms of the reason for communication. The reasons for intra-project

communication among team members were classified into three categories: problem-

solving issues, administrative issues and performance feedback.

20

Pinto and Pinto (1990: 201) established that highly co-operative project teams made significantly more use of informal communication methods (particularly phone calls) than less effective teams. Also, their reasons for communication were more likely to be for brainstorming, obtaining project-related information, reviewing progress and receiving feedback, rather than resolving interpersonal differences.

Kratzer (2004: 63) studied creative performance and communication in 44 innovation teams and gathered data in eleven Dutch companies conducting innovation activities. The subsequent research found that both interaction frequency and subgroup-formation of communication have a negative relationship to team creativity. According to Amason (1996: 125), open communication facilitates creativity and synergistic learning.

Shalley et al. (2004: 951) found that for new product development teams a moderate frequency of communication was most conducive for creativity. This allowed team members to share their ideas and have a constructive dialogue, while a) not becoming distracted by the amount of information exchanged and b) still having the cognitive ability to focus on the value of that information. Furthermore, they found that a low level of communication centralization to be best for team creativity because ideas were not being filtered through just one or two of the members. According to Keller (1986: 720), a high level of communication in a project group in R&D teams may be expected to have high innovation.

Annukka (2008) asserted that internal communication reflects the extent of communication among an organization’s units or groups. It is measured for instance by the number of committees within the organization and the frequency of their meetings (Hage and Aiken, 1967: 631-652), the number of face-to-face contacts (Aiken, Bacharach et al. 1980) and the degree to which different units make common decisions (Damanpour, 1991). External communication refers to an organization’s ability to communicate and interact with its external environment. This is measured by the involvement of the organization’s members in extra-organizational professional events (Damanpour, 1991). Both internal and external communications are regarded as being positively interconnected organizational innovativeness (Huff and Munro, 1985; Nilakanta and Scamell, 1990; Damanpour, 1991; Prescott and Conger, 1995; Rogers, 1995).

21

In conclusion, the below table explains the summary of statement of problem

and the significance of the study

Table 1.2 Summary of Key Characteristic Issues

Key Issues Characteristics

1. Statement of the

Problem

- Forces of Change

- Inadequate Information System

- Inability to Utilize Policy Instruments, Particularly the

Exchange Rate

- Outdated Legal Framework

- Inexperienced Risk Management and Poor Governance of

Financial Institutions and Corporations

- Lax Supervision

- State Banks: Privatization

- Current Development

- Deposit Guarantee

- Transaction Structure

- Loss Sharing

- AMC with Loss Sharing

- Domestic Mergers

- Entry of Foreign Banks

2. Significance of the

Study

2.1 The Relationship

between the

Determinants and

Innovation Management

Effectiveness in

Organization

- Employ the integrated approach of well-known

organizational theories (contingency approach) and

resource-based view and to gain more understanding of

the determinants of the contingency prediction factors of

innovation management effectiveness and its context

22

Table 1.2 (Continued)


2.2 Impact of


Effectiveness

2.3 A Holistic Approach

to Innovation Strategy

2.4 The Relationship of

Forming Organization

Structure and Culture to


Effectiveness


Project Management to


Effectiveness

- Innovation management effectiveness is defined as the

overall organizational outcome which results from

implementing innovations

- That is, how an organization perceives the overall

organizational improvement (including factors such as

productivity, finance, employee morale) which arises

specifically from implementing innovations (Klein et al.,

2001)

- A holistic approach to innovation with a simple tool for

facilitating cooperation on a diversity of innovation matters

- It also demonstrates its capacity to support interdisciplinary

innovation in diverse groups

- The results are built on a sustained and emergent research

process (Burnett, 1997)

- Organizational culture and structure concerning the way

staff are grouped and the organizational culture within

which they work

- There has been considerable work on the situational and

psychological factors supportive of innovation in

organizations (Amabile et al., 1996; Ekvall, 1996)

- Löh and Katzy (2008) surmised the project management

literature as well as the new product development literature

as often taking a rather normative stance towards the best

way of managing innovation projects

23




Project Management to


Effectiveness


Team Cohesiveness to


Effectiveness

2.7 Strategic Leadership:

A Cross Disciplinary

Function and

Collaboration

- Much research (Clark and Wheelwright, 1992; Cooper,

2001; Pahl, Beitz and Feldhusen, 2004; Project

Management Institute, 2004; Ulrich and Eppinger, 2003;

Wheelwright and Clark, 1992) makes extensive

recommendations towards organizational setup, planning

and execution processes, or development methods in

innovation projects

- Langfred (1998) defined team cohesion as the degree to

which team members identify positively with the team,

which is the extent to which members of the team like

each other and want to remain members or team members

feel a part of the team and the strength of member ties to

other members (Mullen and Copper, 1994)

- Team cohesion is a proxy to team functioning and defined

as the resultant of all the focuses acting on the members to

remain in the team (Vianen and DeDreu, 2001)

- A democratic-collaborative style encourages group

innovation (Nystrom, 1979)

- Farris (1973) showed that in research laboratories, the more

innovative groups had greater collaboration with their

supervisors and with each other than the less innovative

ones.

- Similarly, West and Wallace (1988) found that peer

leadership discriminated significantly between highly

innovative and less innovative teams in primary health care

practices, as reliably rated by independent experts

24



2.7 Strategic Leadership:

A Cross Disciplinary

Function and

Collaboration


Coordination and

Communication to


Effectiveness

- The highly innovative teams exhibited a higher degree of

leadership support, goal emphasis, team building and work

facilitation

- (Mintzberg, 1979) categorizes previous research into three

main coordination strategies: mutual adjustment is based

on close collaboration and particularly suitable in simple

and very complex situations; direct supervision assumes

that managers guide and coordinate the activities of their

subordinates, while standardization of processes, output, or

training employs experts that develop rules and policies,

but who do not directly coordinate operative work

- Annukka (2008) asserted that internal communication

reflects the extent of communication among organization’s

units or groups

- External communication refers to an organization’s ability

to communicate and interact with its external environment

(Damanpour, 1991)

- Both internal and external communications are regarded

as being positively interconnected (Huff and Munro, 1985;

Nilakanta and Scamell, 1990; Damanpour, 1991; Prescott

and Conger, 1995; Rogers, 1995)

In conclusion, this chapter outlined the statement of the problems at banks

that affect the ability to innovate and covered both the objectives of the study and the

significance of the study within the context of innovation management effectiveness

in the Thai banking industry.

CHAPTER 2

LITERATURE REVIEW

This chapter provides the research findings about innovation management

effectiveness and also the determinants of the seven contingency prediction factors, of

innovation strategy, organization structure, organization culture, project management,

team cohesiveness, strategic leadership and coordination and communication, which

form the constructs for the conceptual framework

2.1 Innovation Management Effectiveness

2.1.1 The Interdisciplinary Nature of Innovation Management

2.1.1.1 What is Innovation Management?

Innovation management (Nermien Al-Ali, 2003: 1-288) emerged as a

discipline in the 1890s with Edison’s innovation factory. Edison changed the image of

the sole inventor by converting innovation into a process with recognized steps

practiced by a team of inventors working together – laying the foundations for the

basic design of the R&D department. These steps are streamlined to a major extent in

all industries and include idea generation, concept development, feasibility studies,

product development, market testing and launch. Innovation management thus

corresponds to the development of new products, processes and services. In cases

where the organization does not make or offer products (goods or services),

innovation lies in improving the way jobs are done to meet the organization’s mission

(i.e. process innovation).

2.1.1.2 Why Innovation Management?

The high demand for innovation in the knowledge economy – brought

about by shorter technology and product life cycles as well as the sophistication of

customers – increased the organizational demand for new ideas. This has meant two

things: first, innovation has to be pushed to the frontline where the knowledge of the

26

customer is and where the number of ideas generated is greater; second, top

management has had to adopt appropriate innovation strategies to lead the surge of

the innovative activity. As a result, innovation needed to be systemized as a business

process into the way that the organization does business – and hence the need for

innovation management. Only organizations that liberate the innovative spirit of their

employees, tap into the knowledge of their customers and partners, and manage

innovation projects as a portfolio are able to reduce time to market with successful

products. Examples of such companies are HP, 3M and Procter and Gamble (Nermien

Al-Ali, 2003: 1-288).

2.1.1.3 Main Goals of Innovation Management Effectiveness

Effective innovation management requires the implementation of a

number of processes and the employ of a number of tools. At the outset it is important

that the culture of the organization empowers employees and encourages them to

submit their ideas. Most importantly management should adopt an appropriate

innovation strategy to lead the innovation process and manage the innovation

portfolio. The following summarizes the various actions that management should aim

for under the innovation management stage (Nermien Al-Ali, 2003: 1-288)

1) Effect a shift in the way the organization sees itself where

innovation is recognized as the way of doing business.

2) Decide upon an innovation strategy that best fits the

organization’s situation, and enable it attain its vision.

3) Create a portfolio of innovation projects to translate

competitive strategies and to manage risk across the whole organization.

4) Define a criteria for the selection and prioritization of

projects within the portfolio to weed out less probable projects as soon as possible.

5) Effect the necessary structural changes to arrange skills

throughout the organization in competence centers, to enable the formation of the

right team for the purposes of the innovation project.

6) Arrange current and potential future alliances in a portfolio

that can be tapped when needed, and define when and how such alliances are to be

made (governing conditions).

27

7) Foster an organizational culture that promotes innovation by

allowing employees time to innovate and the implementation of their own ideas for

improving job performance.

8) Develop and implement methods that enable tapping into the

organization’s intellectual capital.

In addition, there are several factors that can explain innovation


Number of Innovations

In terms of the banking industry (Lane, 2009: 1-38), the number of innovations

is the number of activities (frequency) that banks undertake in order to improve their

way of business, such as innovations concerning ATMs, credit cards, grants, loans,

payment services, cross-sales between credit and non credit, etc. within a period of

time.

We focused on innovation activity, since technological progress depends

on purposeful effort to develop new technologies or , especially in developing

countries, to move closer to the frontier by adopting existing technologies develop

elsewhere. Even in the latter case, the adoption of existing technologies is costly,

requiring local R&D activity. In addition, the attainment of technological progress

typically involves resource reallocation across firms, with higher-productivity firms

expanding and laggards driven out of business. For this reason, economic environments

that facilitate such firm-level dynamism may be more conducive to higher rates of

effective innovation activity.

Speed of Innovation

Companies increasingly need to make the stark choice of accepting a

rapid decrease in market share or of becoming better informed so that they can

respond to customers’ increasing appetites for new and better solutions (Knowledge

Partners, 2008).

As a result of increased global competition, products and markets that

remain without change are very rapidly becoming commoditized. It is the intense

competition that drives commoditization and downward pressure on the profit

margins.

In many companies the only way to make a healthy profit margin is to

continually innovate and bring customers new experiences and benefits. For this they

28

can enjoy an “early mover” premium. As business cycles continue to speed up and

therefore the life-cycles of products get shorter, organizations have to be more agile

and able to continuously innovate to respond to new changes in customers’ needs and

perceptions.

Return on Investment of Innovation

A performance measure used to evaluate the effectiveness of a

company's investment in new products or services. The return on innovation

investment is calculated by comparing the profits of new product or service sales to

the research, development and other direct expenditures generated in creating these

new products or services (Investopedia, 2010).

Innovation Value-Added Chain

Afuah (1998) claimed that the innovation value-added chain model can

explain both why an incumbent can outperform new entrants at radical innovation,

and why it may also fail at incremental innovation (Porter, 1985). It differs from

previous models in that while these other models focus on the impact of innovation on

a firm’s capabilities and competitiveness, it focuses on what the innovation does to the

competiveness and capabilities of a firm’s suppliers, customers, and complementary

innovators.

The innovation may have a different impact at each of the stages of the

innovation value-added chain, suggesting that an innovation that is incremental to the

manufacturer may not be to suppliers, customers, or complementary innovators. Thus

incumbents for whom an innovation is competence destroying may still do well if the

innovation is competence enhancing to their value chain, and relations with the chain

are important and difficult to establish. The implications are that a firm’s success in

exploiting an innovation may depend as much on what the innovation does to the

capabilities of the firm as on what it does to the capabilities of its innovation value-

added chain of suppliers, customers and complementary innovators.

2.1.1.4 Organizational Performance Improvement

Innovation effectiveness is defined as organizational performance

improvement which results from implementing innovations. That is, how an

organization perceives the overall organizational improvement (including factors such

as productivity, finance, and employee morale) which arises specifically from

implementing innovations (Klein et al., 2001).

29

2.1.2 Organizational Innovation

If defined broadly, innovation (Baker, 2002) can be seen as the business of

science organizations. However, like most of the organizational literature, the

innovation literature has largely focused on innovation in private sector business

organizations. This literature may, nonetheless, have insights that can be used by

science organizations, both private and public.

Science organizations need to innovate - they have not necessarily taken the

lead in systematically studying how organizational and environmental factors can best

promote innovation. Also science organizations in both the private and public sector

are under greater pressure not only to generate innovative science but also to function

as a business. For example, there is greater emphasis on commercializing scientific

discoveries, having a solid and well-designed portfolio of science programs and

projects that help the organization adapt to external changes in funding priorities, and

demonstrating results and favorable cost/benefit ratios.

This innovation literature may provide insights into balancing innovation with

business realities. While the literature on innovation in private sector organizations

may be a source of useful insights, it may also be the case that studying science

organizations could provide critical insights into how to promote and sustain

innovation in private sector business organizations. Public science organizations

should consider playing a lead role in promoting strategies for encouraging and

sustaining innovation and developing a true innovation competency.

30

2.1.2.1 A Framework for Understanding Organizational Innovation

Figure 2.1 Provides a General Framework for Understanding Organizational Innovation

Source: Baker, 2002: 1-16.

2.1.2.2 What is Innovation?

Baker (2002) categorized three types of innovation (process, product/

service, and strategy) each of which can vary from incremental to radical and from

sustaining to discontinuous. There are also important relations between these types of

innovation. For example, a strategy innovation may necessitate process, and/or

product innovations.

2.1.2.3 Levels of Innovation

As the term broadened, innovation was seen as ranging from incremental

to radical. This distinction primarily focused on the extent of newness. An innovation

can be new within a particular context or new in terms of the overall marketplace of

ideas. Similarly, it can be a new twist on an old theme or a radically novel idea. This

distinction did not, however, clearly differentiate between newness and impact. In

terms of impact, the effect of an innovation can range from: (1) contributing to fairly

small improvements to products or to the way things are done, (2) causing a

fundamental transformation in the resulting products or services and/or the process

31

technology of an entire industry, or (3) transforming the market place and/or the

economy as a whole.

Christensen (1997) advanced the concept of innovation by disentangling

the attributes of newness and impact. Because radically new innovations do not

always have a significant impact, he differentiates between sustaining versus

discontinuous innovations. Sustaining innovations improve the performance of

established products or services. Discontinuous innovations bring to market very

different products or services that typically undermine established products and

services in the particular market sector. An example of a discontinuous innovation is

steel minimills (while the product was not significantly changed, a change in the

production process led to a drastic change in prices, firms, and markets). A

discontinuous innovation does not always have greater utility; it may, in fact, result in

a product that under-performs established products. The reason for this is that the

momentum of on-going sustaining innovations can push product and service

functionality beyond what many customers may actually require (in other words, the

established products and services eventually overshoot a large segment of their

market).

Christensen advises companies in all industries to be continually attuned

to a potentially discontinuous innovation that could cause their demise if they do not

quickly adapt and adjust to the fundamentally changing situation.

2.1.2.4 Types of Innovation

There are three main types of innovation (process, product/service, and

strategy), each of which can vary in the degree of newness (incremental to radical)

and impact (sustaining versus discontinuous).

2.1.2.5 Process Innovation

Process innovation became an important topic with the rise of quality

and continuous improvement movements and, then again, with the more recent

attention directed at change management, organizational learning and knowledge

management. Corporations today, at least in the developed world, are reaching the

limits of incremental process improvement. Some have argued that what is needed

today is radical process innovation. Hammer and Champy (1994) introduced the

concept of radical reengineering based on their assertion that for companies to achieve

32

maximum efficiency and effectiveness requires radical process reengineering of the

organization and its processes. Because processes lag far behind what is possible

given technological advancement, it is not possible to achieve the necessary

transformation through incrementalism.

2.1.2.6 Product / Service Innovation

Incremental product/service innovation is oriented toward improving the

features and functionality of existing products and services. Radical product/service

innovation is oriented toward creating wholly new products and/or services. Product

life cycles, in particular, have become shorter and shorter, causing business survival

to depend on new product development and, increasingly, on the speed of innovation

in order to develop and bring new products to market faster than the competition

(Jonach and Sommerlatte, 1999). Organizations must direct greater attention to new

product development, while maintaining and improving their existing products.

Discontinuous products and services are increasingly likely with ever-faster new

product/service development. Organizations must be constantly on the lookout for

discontinuous new products and/or services.

Although product/service innovation and process innovation are not the

same thing, they are often interconnected. For example, process innovation may be

required to support product or service.

2.1.2.7 Drivers of Innovation

The primary drivers of innovation include:

1) Financial pressures to decrease costs, increase efficiency, do

more with less

2) Increased competition

3) Shorter product life cycles

4) Value migration

5) Stricter regulations

6) Industry and community needs for sustainable development

7) Increased demand for accountability

8) Community and social expectations and pressures (giving

back to the community, doing good, etc.)

9) Demographic, social, and market changes

33

10) Rising customer expectations regarding service and quality

11) Greater availability of potentially useful new technologies

coupled with the need to keep up or exceed the competition in applying these new

technologies

12) The changing economy.

2.1.2.8 Innovation Capacity

However, Baker (2002) also explained two levels of innovation capacity.

The Individual Level: Factors to look for at the individual level include:

employee empowerment and engagement, trust, training, job rotation, and the extent

and range of individual networks.

The Organizational Level: Organizations must have effective, efficient,

and speedy systems and processes for the following:

1) Environmental scanning, identifying discontinuities, surveying

customer needs, encouraging new ideas to be advanced by staff members, and

innovation activist and other forms of training.

2) Other means of promoting knowledge absorption and

sharing, such as the ability to communicate across organizational boundaries, communities

of practice, enterprise level knowledge systems, and problem identification and

problem solving processes.

3) Deconstructing the dominant mental models regarding

business mission, market scope, relevant products and services, target customers and

questioning existing biases regarding the kinds of profit boosters that can be

exploited, the core competencies that are most important, pricing strategies, bundling

options, and partnering opportunities.

4) Sustained, innovative strategizing and strategy implementation.

5) On-going classification, screening, and prioritization of new

ideas.

6) Managing the innovation stream—the number of ideas being

pursued at a given time and their developmental stages.

7) Effective innovation project management.

8) Effective innovation utilization, transfer, diffusion—the

culmination of innovation is to transfer the innovation to those who will exploit it

34

through successful commercialization and, as needed, promoting its adoption into

organizational practice and/or individual life styles.

9) Effective change management.

10) Promoting a broad definition of business boundaries, fluid

organizational boundaries, and a wide and open market for ideas/talent.

11) Motivating, rewarding, and recognizing innovation.

Hamel (2000) suggests that an innovation competency requires both an

internal and external organizational perspective. To develop an innovation competency,

the organization must:

1) Have a fluid notion of organizational boundaries and an open

market for talent. It is not necessary to create all innovations internally. Partnerships

can be a useful strategy to promote innovation. Also, in addition to development,

acquisition can be an effective innovation strategy.

2) Transform organizational strategy. Typical strategic planning is

often antithetical to promoting radically innovative business models and strategies.

Innovation cannot be held to a scheduled strategic planning timeline; it should be on-

going. Also, strategy should not be restricted to the same set of top level decision-

makers. Innovative strategy does not necessarily come from the top but too often not a

word about contributing strategically appears in the performance criteria for anyone

below the level of senior executive.

3) Create an open market for capital investment and rewards.

Strategic thinking must not only be encouraged but also sponsored and rewarded.

When innovative ideas do not succeed, staff members and sponsors should not be

sanctioned in any way. On the other hand, it is very important to allow staff to share

in the rewards when an idea does pay-off.

4) Manage the risk. Strategy should not be monolithic; it should

be sufficiently varied to allow for organizational agility and flexibility. Although most

new ventures will fail, important learning can be acquired from each. Project risk

must be distinguished from portfolio risk—the risk of any new project will be high

but if there are enough innovation projects, the portfolio risk will be manageable.

5) Create a culture and a structure that promotes innovation.

Having an elastic business definition helps to ward against protectionist instincts.

35

Open up innovation opportunities to all staff and engage customers, suppliers,

competitors, and complementary organizations to develop new approaches to generating

new wealth.

2.2 Innovation Strategy

There is not a lot of literature dedicated completely to innovation strategies.

The majority of research is related to organizational factors that promote (or inhibit)

innovation, new product development or research and development (Mphahlele,

2006). Examples are articles by Martins and Terblanche (2003), Vlok (2005) and

Dewar and Dutton (1986). For those that discuss innovation strategies, very few have

attempted to define the term.

2.2.1 Definition

Grant and King (1984), as referenced by Ramanujam and Mensch (1985),

define a strategy as a timed sequence of internally consistent and conditional resource

allocation decisions that are designed to fulfill an organization’s objectives.

Ramanujam and Mensch (1985) use this definition to describe an innovation

strategy in terms of innovation goals, innovation resource allocation, innovation risk

(overall philosophy), timing (first to market or happy to wait) and a long term

perspective. Formulating an innovation strategy, therefore, is a matter of policy for

those organizations that would like to pursue a sustainable competitive advantage

through innovation.

Activities must be consistent with an overarching organizational strategy that

implies that management must take conscious decisions regarding innovation goals

(Sundbo, 1997: 432-455). Innovation strategy is generally understood to describe an

organization’s innovation posture with regard to its competitive environment in terms

of its new product and market development plans (Dyer and Song, 1998: 505-519).

This techno-centric view predominates in the literature and overlooks those

innovative initiatives that are internally focused (for example, the adoption of new

management techniques and practices).

36

In the conceptualization of innovation strategy as an articulation of the

organization’s commitment to the development of products that are new to itself

and/or to its markets, and because strategy does not operate in a vacuum, but requires

a structural context, two complementary approaches to its measurement, which have

been described as objective and subjective (Li and Atuahene-Gima, 2001: 1123-1134),

can be identified.

In the literature, scholars principally have adapted measures from strategic

management research to explore the existence, nature and extent of innovation

strategy. Richard, McMellan, Chadwick and Dwyer, 2003: 107-126. use three scale

items drawn from the strategic posture scale devised by Covin and Slevin (1989),

who, in turn, draw on Miles and Snow’s (1978) ‘prospectors’ and Mintzberg’s (1978)

‘entrepreneurial’ organizations, for their assessment of bank innovativeness. These

studies classify organizations based on their approach to innovation using classifications

that are ontologically grounded in the assumption that innovation orientation can be

deciphered from quantitative interpretations of new product and market activity.

Other objective evidence of an organization’s innovation strategic posture may

include many of the input measures (such as level of R&D expenditure) that we have

already discussed. It is argued that it is not their absolute magnitude but their

magnitude relative to industry rivals that is significant. As has been noted, although

these may be useful indicators of commitment or intent, they could mask process

inefficiencies. However, O’Brien’s (2003) observation that an interaction between

intended strategy and slack will influence performance suggests that process

inefficiencies are less likely to occur where an innovation strategy is not merely

nominally adopted, but is embedded in the culture, behaviors and actions of the

organization.

In Li and Atuahene-Gima’s (2001) terms, the evidence for an embedded

innovation strategy is subjective and may include evaluations of an organization’s

emphasis on New Product Development (NPD), such as resource allocation. Saleh

and Wang (1993) describe this as consisting of three main components: risk-taking,

pro-activeness and persistent commitment to innovation. These include top management

responsibility for innovation within the organization, including specifying and

communicating a direction for innovation. Cooper (1984) demonstrated that new

37

product performance is largely decided by the strategy that top management adopts.

The key components are the link between innovation strategy and overall business

goals (strategic orientation) and the provision of leadership to make innovation

happen via a strong vision (Pinto and Prescott, 1988) for innovation, a long-term

commitment to innovation and a clear allocation of resources (Cooper, Edgett and

Kleinschmidt, 2004: 50-59). This distinction between strategic planning or orientation

and strategic vision or leadership is frequently replicated in the literature.

Two distinct types of strategic orientation measures can be identified in the

literature. First, those that measure whether the organization has an innovation

strategy; this can be evaluated in several ways, such as commitment to differentiated

funding (White, 2002), explicit expression (does the organization have an innovation

strategy?) (Miller and Friesen, 1982) and identifiable roles for new products and

services (Cooper and Kleinschmidt, 1990; Geisler, 1995; Hauser and Zettelmeyer,

1997; Tipping and Zeffren, 1995). The second type of measure regards strategy as a

dynamic instrument that shapes and guides innovation in the organization. These

measures assume that strategy exists and asks questions about how effective it is in

shaping and guiding: ‘are structures and systems aligned?’ (Bessant, 2003), ‘do

innovation goals match strategic objectives?’ (Tipping and Zeffren, 1995) and further

similar measures of strategic fit (Bessant, 2003).

From a strategic leadership perspective, Dougherty and Cohen (1995) found

the behavior of senior managers to be influential. Those chief executives most likely

to make innovation happen are those with a clear vision of the future operation and

direction of organizational change and creativity (Shin and McClomb, 1998). Senior

management are responsible for developing and communicating a vision for

innovation, being supportive and adopting an attitude tolerant to change and championing

the notion of innovation within the organization. Managerial tolerance to change

creates the right climate for the implementation stage of innovation, where conflict

resolution might be necessary (Damanpour, 1991).

Managerial attitude is also reflected in norms or support for innovation. These

are the expectation, approval and practical support of attempts to introduce new and

improved ways of doing things in the work environment. Measures are mostly

qualitative in nature and explore perceptions, mostly about the extent to which

respondents recognize factors to be present (or absent).

38

Relatively few measures of championing and leadership have been uncovered

in this review other than those that simply ask the question whether or not one or the

other exists, perhaps through evidence of signs of commitment in annual reports

(Chiesa, Coughlan and Voss, 1996: 105-136) or levels of concern of top management

(Coughlan et al., 1994) or whether or not an individual has been physically assigned

to a particular role (Souitaris, 2002). Shane et al. (1995) provide a series of reflective

questions designed to allow organizations to determine for themselves their expectations

and understanding of the role of champion. For example, ‘to what extent should the

organization make it possible for the people working on an innovation to bend the

rules of the organization to develop the innovation, or be allowed to bypass certain

personnel procedures to get people committed to an innovation?’

It must be emphasized that the dominant perspective on strategy in the

literature is one that examines the relationship between strategy and performance.

However, a small number of studies have examined the nature of the transitions of

these states as organizations adopt and embed an innovation strategy. This sub-section

of the literature is underpinned by the assumption that firms that follow innovation

strategies differ from other firms in several respects. It is apparent that more

innovative firms adopt different operational strategies to accommodate flexibility and

quality capabilities (Alegre-Vidal, Lapiedra-Alcami and Chiva-Gomez, 2004: 829-

839), have different capital management practices to facilitate slack resources

(O’Brien, 2003), are more tolerant of internal conflict in support of creativity (Dyer

and Song, 1998), and maintain organizational structures that are in the ‘intermediate

zone between order and disorder’ (Brown and Eisenhardt, 1997: 22). What is clear

from this literature is that any transition towards an innovation strategy will

necessarily take several years because of the resources and energy necessary before

such a transformation can even be triggered (Hailey, 2001).

2.2.2 Key Considerations

One of the articles that looks directly at innovation strategy formulation is by

Kuratko, Ireland and Hornsby (2001), in which the authors describe in detail a

company’s financial success that was put in place. Kuratko et al., (2001) found that

39

for an innovation strategy to serve its purpose of improving the level of innovation in

an organization, it should include a vision, organizational design (e.g. creating venture

teams), management systems (authority, control) as well as a compensation system.

Ireland, Kuratko and Covin (2003) went on to refine this finding into three key

elements of an innovation strategy: entrepreneurial strategic vision, pro-entrepreneurship

organizational architecture and the entrepreneurial strategic vision necessary to create

an awareness of the general direction that the entrepreneurial activities should take.

The importance of innovation goals as a component of an innovation strategy

is echoed by a few more authors – Deschamp (2005), Foster and Pryor (1986) and

Anthony et al.(2006) amongst others. Without clear goals, it will be difficult for any

organization to give meaningful direction to its employees.

Developing the overall vision should logically come before the identification

of goals and objectives because the vision tends to be more long term and more

compelling.

2.3 Organizational Structure

2.3.1 Organizational Structure

Burns and Stalker (1961) described a contingency approach to innovation

management, later developed to include concepts of functional differentiation,

specialization and integration (Lawrence and Lorsch, 1967), which suggests that

environmental change prompts a realignment of the fit between strategy and structure.

That is, to perform effectively, an organization must be appropriately differentiated,

specialized and integrated. Pugh et al. (1969) argued that the structure of an

organization is strongly related to the context within which it functions. Our closing

discussion in the previous section, that the characteristics of organizations following

an innovation-focused strategy will differ from those that do not, is consistent with

this contingency approach and, in the following section, we focus on those

dimensions of culture and structure that have been identified as differentiating

between innovative and non-innovative organizations.

Ernst (2002) specifies a range of generic characteristics for the dedicated

project group assigned the innovation task: multidisciplinarity, dedicated project

40

leader, inter-functional communication and co-operation, qualifications and know-

how of the project leader, team autonomy and responsibility for the process. And

these factors are echoed throughout the literature. Rothwell (1992) refers to them as

‘corporate conditions’, Chiesa et al. (1996) ‘enabling processes’ and O’Reilly and

Tushman (1997) ‘norms for innovation and change’. However, despite their perceived

importance, there is little guidance on how to measure them.

Volberda (1996) developed a conceptual model of alternative flexible

organizational forms that are argued to initiate or respond better to different types of

competition. Yet, in spite of its importance, there is relatively little evidence of the

extant measures of flexibility in the literature. Rothwell (1992) and Ekvall (1996)

propose a range of foci for measures of organizational and production flexibility, such

as ‘corporate flexibility and responsiveness to change’; Coughlan et al. (1994)

considers the flexibility of resource allocation. Several measures of personnel

flexibility are evident, such as ‘the adaptiveness of R&D personnel to technology

changes’ (Lee et al., 1996) and ‘the willingness to try new procedures and to

experiment with change, so as to improve a situation or a process’ (Abbey and

Dickson, 1983). At the level of the firm, Liao et al. (2003) introduce a similar

construct – ‘organizational responsiveness’.

Organizational complexity, the amount of specialization and task differentiation

reportedly have a positive relationship (Damanpour, 1996), though Wolfe (1994)

argues that it may favor initiation at the expense of implementation. Administrative

intensity, that is the ratio of managers to total employees, favors administrative

innovation (Damanpour, 1991, 1996), but possibly at the cost of other product or

technological innovations (Dougherty and Hardy, 1996). Centralization, the concentration

of decision-making authority at the top of the organizational hierarchy, and formalization,

the degree of emphasis on following rules and procedures in role performance, have

both been shown to have a negative impact on organizational innovation (Burns and

Stalker, 1961; Damanpour, 1991). Indeed, rigidity in rules and procedures may

prohibit organizational decision-makers from seeking new sources of information

(Vyakarnam and Adams, 2001).

41

2.3.2 Structuring for Innovation

From the perspective of Mphahlele (2006), the organization structure

represents the development of human resources in different parts of the organization

for doing specific work. Some organizational structures are more advanced in

supporting innovation than others. For example, a steeply hierarchical organization

that is overloaded with strict rules tends to stifle innovation because decision making

tends to be slow.

Organizational structures are broadly divided into two categories: Organic and

Mechanistic (Russel, 1999; Afuah, 2003; Russel and Russel, 1992).

Organic structures are those that facilitate lateral communication across

functions and where the decision-making is given to individuals who have the most

technical knowledge. Companies that have organic structures are characterized by a

higher degree of decentralization, formalization and complexity. This ensures that

decisions are taken by managers with the best knowledge of the business and that

employees at all levels can generate and champion new ideas.

2.3.3 Types of Organizational Structure

Another viewpoint offered by Savvakis C. Savvides is that Burns and Stalker

(1961) were the first to indicate that different types of organizational structures might

be effective in different situations. They identify two extreme types of organizational

structure. The mechanistic structure which is found in organizations operating under

stable conditions, and the organic structure which is found, or rather is best suited, to

organizations operating under unstable conditions. They also suggest nine characteristics

or factors of organizational form that vary between these two extreme forms of

organizational structure.

In a somewhat modified form I present these nine factors in what I believe to

be a more easily identifiable and workable manner than their original presentation

(Burns and Stalker, 1961: 120), in figure 2.2 below. The figure also attempts to relate

the innovation process to the two polar forms of organizational structure put forward

by Burns and Stalker, by showing which organizational structure better facilitates

which particular stage of the innovation process. It is observed that as the innovation

process moves on from the generation of ideas to implementation, the organizational

structure should become less organic and more mechanistic.

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Mechanistic Organic

Organization Organization

Organizational Characteristics

Low ----------------------- Task Complexity ------ High

Rigid ---------------------- Task Definition -------- Flexible

Specific ------------------- Responsibility --------- General

Strict ---------------------- Control ----------------- Loose

Presumed ----------------- Expertness ------------- Challenged

Instructive ---------------- Communication ------- Informative

To Superiors ------------- Loyalty ----------------- To Organization

Dependent on Independent of

Organizational ----------- Prestige ----------------- Organizational

Status Status

Stable Unstable

Environment Environment

Implementation Adoption Proposal Generation of Ideas

The Innovation Process

Figure 2.2 Organizational Characteristics

Source: Burns and Stalker, 1961: 103-108.

In explaining why organizations do not change structure from mechanistic to

organic when situations warrant it, Burns and Stalker emphasize the role of resistance

to change. They indicate that the political and status structures in the organization

might be threatened as the organization changes and becomes more organic to deal

with innovations. In changing from one form of organization to the other, it can be

seen from the characteristics identified, that each one can become a potential source

of conflict. For example, as the organization becomes more organic there is more

diffusion in decision making. The people that have to give room for this to happen

(through giving up some of their exclusive prerogatives) are likely to put up resistance

to defend their positions, thereby creating conflict.

43

Lawrence and Lorsch (1967) have indicated that different parts of the

organization may be different types of structures. Zaltman, Duncan and Holbek (1973)

goes one step further by showing that the decision unit in the organization may

implement different types of organizational structures at different points in time.

Zaltman et al. conceptualizes and measures structure in terms of five dimensions:

hierarchy of authority, degree of impersonality in decision making, degree of

participation in decision making, degree of specific rules and procedures, and degree

of division of labor. He then links the generalized concept of organization to that of

information and communication. In his view, the organization as a communication

network held together by the flow of information. When these dimensions are highly

structured, channels of communication and amount of information available are

restricted. By linking the amount of information needed at each point in time with the

level of uncertainty in the environment, Zaltman et al. shows that when information

demands are low, a decision unit within an organization can respond more quickly to

its environment by relying on pre-established rules and procedures, a well specified

division of labor and so on. But when new information inputs are required to adapt to

the uncertainty of the environment, strict emphasis on rules and procedures and

division of labor etc. may prohibit the unit from seeking new sources of information,

which may not have been foreseen when the rules and procedures were initially

developed.

When uncertainty is high the decision unit differentiates, or should be

encouraged to differentiate, its decision making procedures to deal with the

information gathering and processing requirements that accompany high uncertainty.

In figure 2.3, Zaltman et al.’s model is related to the innovation process in a

similar fashion as in figure 2.2 (Burns and Stalker). The two models are in many

ways similar although there are several distinct differences in approach. Zaltman et

al. concentrates on the individual (the decision unit) in the organization and shows

how different structures affect the individual’s impact towards organizational

effectiveness, while Burns and Stalker look at the notion of differentiation in

organizational structure by taking an overall view of the organization. Furthermore,

Zaltman et al., conducts his analysis focusing on the uncertainty-certainty environmental

44

situation, while Burns and Stalker examine the same problem in terms of stable and

unstable environmental situations.

Dimensions of Organizational Structure

Hierarchy of Authority

Degree of Impersonality in Decision making

HIGH - “ “ Participation “ “ “ - LOW

“ “ Specific Rules and Procedures

“ “ Division of Labor

Certain Uncertain

Environment

Environment



Figure 2.3 Dimensions of Organizational Structure

Source: Zaltman, Duncan and Holbek, 1973: 1-12.

Hage and Aiken (1970) specify several characteristics of organizations as they

affect “program change” in organizations. Program change is defined as “… the

addition of new services or products.” They identify seven organizational characteristics

that affect the rate of innovation in organizations.

In figure 2.4, an attempt is made to provide for this deficiency by trying to

relate the innovation process to the organizational factors of Hage and Aiken in a

similar fashion as for Burns and Stalker and the Zaltman et al. models. The figure

tries to show how their static analysis would appear in the form of a model when

related to the dynamic process of innovation.

Taking into consideration the findings of Wilson (1963), Burns and Stalker

and Zaltman et al. and this research’s definition of the different stages of the

innovation process, It is shown that, in terms of which organizational characteristics

are relevant to which stages of the innovation process, the organization must swing its

structure from right to left as the innovation process progresses. The Hage and Aiken

“program change” relationship fails as regards the final stages of the process,

especially the implementation stage.

45

Low Complexity High

High Centralization Low

High Formalization Low

High Stratification Low

High Production Low

High Efficiency Low

Low Job Satisfaction High

(Low Program change High)



Figure 2.4 Organizational Factors

Source: Hage and Aiken, 1970: 154-163.

The state of the art has reached a point where research must be undertaken to

generate information concerning how managers can actively facilitate the innovation

process by implementing different changes in the organization’s structure.

The position as it is at the moment is that different forms of organizational

structure facilitate the innovation process in its different stages. Specifically in

stimulating the initiation of innovations (generation of ideas and proposals) a higher

degree of complexity, lower formalization and lower centralization facilitate the

gathering and processing of information which is crucial at this stage. At the adoption

and implementation stage a higher level of formalization and centralization and a

lower level of complexity are likely to reduce role conflict and ambiguity which could

impair implementation. This conclusion therefore implies that the organization must

shift its structure as it moves through the various stages of innovation.

2.4 Organizational Culture

2.4.1 History and Evolution of the Literature on Organizational Culture

and Creativity and Innovation

McLean (2005) explained that, somewhat surprisingly given the importance of

creativity and innovation in organizations, there has been relatively little empirical

46

work done in the area of organizational culture and creativity and innovation (Oldham

and Cummings, 1996). The author conducted a search of the electronic catalogs of

several major university libraries, a number of journal indexes, and google.com.

Much of what has been written on the topic has appeared in the popular press and in

books written for practitioners, with little apparent empirical evidence to back up the

content of those books. The first scholarly article of some notoriety on the topic was

written by Burns and Stalker (1961), who compared electronics firms with more

established industrial enterprises and made the distinction between mechanistic and

organic forms of organizing. Mechanistic organizations were characterized as

hierarchical, highly structured organizations with well-defined, formal roles and

positions relative to others in the organization, with communication flowing primarily

vertically. Organic organizations, by contrast, were typified by their fluid organizational

design, with departments and teams forming and reforming to address new problems

and opportunities, with communication flowing primarily laterally. Burns and

Stalker’s environmental determinism view of organizations led to the conclusion that

organic organizations form to deal with unpredictability and volatility in an

organization’s environment. Compared with a mechanistic organization, an organic

one facilitated greater creativity and innovation. This conclusion was later challenged

when Kimberly (1981) found that centralized decision making may enhance an

organization’s ability to implement innovations, particularly in a more stable

environment. Also, whereas Burns and Stalker began a body of knowledge on

creativity and innovation in organizations over the next several decades, relatively

little of that research focused specifically on organizational culture or climate.

Nonetheless, a few key scholars have done work in this area and their work is

reviewed below.

2.4.2 Organizational Culture Framework

Another point made by Martins and Martins (2002) about organization culture

is that for most organizations change is inevitable. Organizational cultural issues are

becoming increasingly important and a source of a strategic competitive advantage.

Organizational changes usually promote and intensify competitiveness, as they

require dramatic changes in strategy, technology, working systems and management

47

style, among others. These changes require in-depth analysis of values, beliefs and

behavior patterns that guide day-to-day organizational performance. Creativity and

innovation have a role to play in this change process. The topic of organizational

culture often presents two contradictory images. The first is of culture as “the glue

that holds the organization together”, and the second regards it as a central part of the

change process (Denison, 2001: 347). According to Read (1996: 223), post-industrial

organizations today are knowledge-based organizations and their success depends on

creativity, innovation, discovery and inventiveness. The importance of creativity and

innovation is emphasized in the words Zaltman et al. (West and Farr, 1990: 3-4):

“The importance of new ideas cannot be overstated. Ideas and their manifestations as

practices and products are the core of social change.”

In the midst of change, organizations and leaders try to create an institutional

framework in which creativity and innovation are accepted as basic cultural norms. It

has become clear that “the unwritten rules of the game” (the norms of behavior) and

shared values influence morale, performance and the application of creativity and

innovation in many different ways. Deal and Kennedy (1982: 164) stated that

openness and trust in the change process influence whether and how change occurs.

Senge, Kleiner, Roberts, Ross, Roth and Smith (1999: 44) support this statement by

pointing out that openness (developing a genuine spirit of inquiry and trust) often

plays a critical role in profound change processes. Furthermore, compelling new ideas

help people think and act in new ways. In this respect there is a definite need to

understand how organizational culture should be dealt with in order to promote

creativity and innovation as part of constant change. This need has already been

identified by Schuster (1986).

Authors like Johnson (1996: 9-11), Judge, Fryxell and Dooley (1997: 73), Pienaar

(1994: 3-35), Shaughnessy (1988: 5), Tesluk, Faar and Klein (1997: 27) and Tushman and

O’Reilly (1997: 27) all agree that organizational culture is a contributing factor to the

degree to which creative and innovative behavior is found among employees in an

organization. Michela and Burke (2000) argue that quality and innovation in

organizations are inextricably intertwined with organizational culture.

There seems to be little agreement in the literature as to what type of

organizational culture promotes creativity and innovation (Judge, Fryxell and Dooley,

1997: 73). There also seems to be a paradox: Organizational culture can promote the

48

creativity and innovation necessary to be competitive and successful, but, on the other

hand, it can also be an obstacle to creative and innovative behavior (Glor, 1997: 44;

Tushman and O’Reilly, 1997: 31,35). The question is: What type of organizational

culture supports creativity and innovation in an organization?

Several researchers (Ahmed, 1998; Filipczak, 1997; Judge, Fryxell and

Dooley, 1997; Nÿstrom, 1990; O’Reilly 1989; Pinchot and Pinchot, 1996; Tesluk,

Faar and Klein, 1997) have worked on identifying the values, norms and assumptions

involved in promoting and implementing creativity and innovation. Very few

empirical studies, especially quantitative research, appear to have been carried out to

support the research findings, but several values, norms and beliefs have been

identified by researchers like (Judge, Fryxell and Dooley, 1997; Nÿstrom, 1990;

O’Reilly, 1989) in their empirical research. The purpose of the research presented here

is to identify, operationalize, measure and describe the determinants of organizational

culture that might influence creativity and innovation.

In order to synthesize the cultural values and norms that influence creativity

and innovation, as found in the literature, the integrated, interactive model shown in

figure 2.5 was created (Martins, 2000). This model is based on the work of Martins

(1989, 1997) which describes organizational culture and the importance of leadership

in creating an ideal organizational culture that influences organizational behavior.

The model developed by Martins (1989) to describe organizational culture was

based on the work of Edgar Schein and draws on open systems theory [originally

developed by Ludwig van Bertalanfy in 1950 and adapted by several authors like Kast

and Rosenzweig, 1985 and Kreitner and Kinicki, 1995. The systems approach offers a

holistic approach, but also emphasizes the interdependence between the different

subsystems and elements in an organization (French and Bell, 1995). The organizational

systems model explains the interaction between organizational subsystems (goals,

structure, management, technology and psycho-sociological). The complex interaction

which takes place on different levels between individuals and groups, and also with

other organizations and the external environment, can be seen as the primary determinants

of behavior in the work place. Based on the dimensions that describe organizational

culture (figure 2.5), Martins (2000) identified and synthesized the determinants of

organizational culture that influence creativity and innovation as found in the

literature.

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Dimensions measured to describe organization culture

Strategic vision and mission

Customer focus (External environment)

Means to achieve objectives

Management processes

Employee needs and objectives

Interpersonal relationships

Leadership

Determinants of organizational culture that influence creativity and innovation

Strategy Structure Support mechanisms Behavior that encourages

innovation

Communication

- Vision and mission

- Purposefulness

- Flexibility

- Freedom:

• Autonomy

• Empowerment

• Decision making

- Cooperative teams

and

group interaction

- Reward and

recognition

- Availability of

resources:

• Time

• Information

technology

• Creative people

- Mistake handling

- Idea generating

- Continuous learning

culture

- Risk taking

- Competitiveness

- Support for change

- Conflict handling

- Open communication

Figure 2.5 Influence of Organizational Culture on Creativity and Innovation

(Preliminary Model Based on Literature Study)

Source: Martins, 2000 : 171.

The purpose of Martins’ (2000) research was to determine empirically through

quantitative research, the determinants that influence creativity and innovation from a

cultural perspective, and to compare the findings with the model based on the

literature study (figure 2.5).

2.4.3 Organizational Culture in the Function of Vision

Adams, Bessant and Phelps (2006: 21-47) posited the notion that organizational

culture may include a shared vision, and it has been argued that the clearer the vision,

Creativity and

innovation

50

the more effective it is as a facilitator of innovation as it enables the focused

development of new ideas that can be assessed more precisely (West, 1990). Pinto

and Prescott (1988) found that the only factor to have predictive power in terms of

potential for success at all stages of the innovation process (conception, planning,

execution and termination) was having a clearly stated mission/vision, while West

(1990) contended that the quality of innovation is partly a function of vision. Keller

(1986) adopted Kirton’s (1976) 32-item adopter–innovator inventory to determine

individual and team orientation to innovation. Vision is operationalized in the team

climate inventory (Agrell and Gustafson, 1994; Anderson and West, 1996, 1998;

West and Anderson, 1996) and is deconstructed into a series of sub-dimensions such

as clarity, sharedness, attainability and value with regard to team objectives.

2.5 Project Management

2.5.1 Project Management Overview

Project management by Adams, Bessant and Phelps (2006) is concerned with

the processes that turn the inputs into a marketable innovation. The innovation

process is complex, comprising a myriad of events and activities- some of which can

be identified as a sequence and some of which occur concurrently- and it is clearly

possible that innovation processes will differ to some degree, across organizations and

even within organizations on a project-by-project basis. Having an efficient process

that is able to manage the ambiguity of the innovation is universally agreed to be

critical to innovation (Globe et al, 1973: 8-15).

Several studies have made efforts to measure project management efficiency,

mostly in the form of comparisons between budget and actual (project costs, project

duration, revenue forecasting). Another measure of project management success is

speed (time management). Innovation speed has been positively correlated with

product quality or the degree to which it satisfies customer requirements; measures

include speed (Hauser and Zettelmeyer, 1997: 32-48), performance against schedule

(Chiesa and Masella, 1994), and duration of the process (Cebon and Newton, 1999).

To achieve efficiency, it is widely recommended that organizations seeking to

innovate should establish formal operation processes for innovating and make use of

51

tools and techniques that may facilitate innovative endeavors. The stage-gate process

(Cooper, 1990) is possibly the most familiar of these, but other methodologies for

innovation project management exist, including Phased Development, Product and

Cycle-time Excellence and Total Design (Jenkins et al. (1997) for a discussion).

These methodologies have in common the separation of the product development

process into structured and discrete stages, which each have milestones in the form of

quality control checkpoints at which stop/go decisions are made with regard to the

progress of the project. These highly structured approaches to the management of the

innovation process began to emerge in the 1990s (Veryzer, 1998).

2.5.2 Innovation and Project Management

Filippov and Mooi (2009) described the relationship between two distinctive

disciplines – project management and innovation management (innovation studies).

Despite the seemingly interrelated nature of both subjects, these two research domains

have been developing relatively isolated from one another.

2.5.2.1 Innovation Studies

Innovation studies are rooted in the seminal writings of Joseph

Schumpeter in the 1920s-30s (Schumpeter, 1934), whose ideas started to gain

popularity in the 1960s, as the general interest among policymakers and scholars in

technological change, R&D and innovation increased. The field formed as a distinctive

academic discipline from the 1980s onwards. Scholars like Richard Nelson, Chris

Freeman, Bengt-Åke Lundvall, Keith Pavitt, Luc Soete, Giovanni Dosi, Jan Fagerberg,

Bart Verspagen, Eric von Hippel and others have shaped and formed this discipline.

The seminal publications in the area include, inter alia, Freeman (1982), Freeman and

Soete (1997), Lundvall (1992), Nelson and Winter (1977, 1982), von Hippel (1988).

Regarding the definition of innovation – a general consensus has been achieved among

innovation scholars who broadly understand this phenomenon as a transformation of

knowledge into new products, processes and services.

An in-depth review of the innovation literature is beyond the scope of

this paper (Fagerberg (2004) for such analysis). Our intention is to outline main

directions of research. In a recent paper, Fagerberg and Verspagen (2009: 218-233)

provided a comprehensive analysis of the cognitive and organizational characteristics

52

of the emerging field of innovation studies and consider its prospects and challenges.

The authors trace the evolution and dynamics of the field. Reflecting the complex

nature of innovation, the field of innovation studies unites various academic

disciplines. For example, Fagerberg and Verspagen (2009) defined four main clusters

of innovation scholars, namely “Management” (cluster 1), “Schumpeter Crowd”

(cluster 2), “Geography and Policy” (cluster 3.1), “Periphery” (cluster 3.2) and “Industrial

Economics” (cluster 4).

For the purposes of our analysis we shall have a closer look at the

“Management” cluster, since it is here where the connection between innovation and

Project Management can be found. In fact “Management” is the smallest cluster

within the entire network of innovation scholars, consisting of only 22 scholars,

mainly and Policy” (298 scholars) or “Schumpeter Crowd” (309). In terms of

publication preferences, apart from Research Policy, the favorite journal for

innovation scholars, members of the “Management” cluster see management journals

as the most relevant publishing outlets, particularly the Journal of Product Innovation

Management, Management Science and Strategic Management Journal. Fagerberg

and Verspagen (2009: 229) see a strong link between innovation and management and

provide the following description:

“Management is to some extent a cross-disciplinary field by default and

firm-level innovation falls naturally within its portfolio... So between innovation

studies and management there clearly is some common ground”.

2.5.2.2 Project Management

Project management as a human activity has a long history –the

construction of the Egyptian pyramids in 2000 BC may itself be regarded as a project

activity. However, the start off the modern Project Management era, as a distinctive

research area, was in the 1950s.

A project is an endeavor in which human, financial and material

resources are organized in a novel way to undertake a unique scope of work, of given

specification, with constraints of cost and time, so as to achieve the beneficial change

defined by quantitative and qualitative objectives.

There have been several attempts to provide an overview of the state-of-

the-art research in PM and outline its trends and future directions (PMI, 2004; Betts

53

and Lansley, 1995; Themistocleous and Wearne, 2000; Crawford et al., 2006;

Kloppenberg and Opfer, 2002). In a recent article, Kwak and Anbari (2009) reviewed

relevant academic journals and identified eight allied disciplines, in which PM is

being applied and developed. These disciplines include the following areas: Operation

Management, Organizational Behavior, Information Technology, Engineering and

Construction, Strategy/Integration, Project Finance and Accounting, and Quality and

Management. Notably, the other of these eight allied disciplines is Technology

Application / Innovation / New Product Development / Research and Development.

The authors found that only 11% of journal publications on the subject of project

management fell under the Innovation heading. Yet, importantly, this area has shown

sustained upward interest, and hence the number of publications, since the 1960s.

Overall, Kwak and Anbari (2009) conclude that the mainstream PM research proceeds

largely in the Strategy / Integration / Portfolio Management / Value of PM /

Marketing direction (30% of all publications examined by the authors).

2.5.2.3 Innovation Projects

As this brief literature review reveals, the interfaces between innovation

studies and (project) management do exist. Yet, it can be seen that both research

streams have developed in relative isolation from one another, and the connection

between the two domains is quite often implicit. As Keegan and Turner (2002: 368)

state,

With some notable exceptions, however, the traditional innovation

literature largely ignores project management and the intricacies of managing

innovation in project-based firms. In addition, the project management literature,

considerably expanded in recent decades, largely ignores innovations.

There are several important links between projects and innovations. This

can be seen in the origins of the two terms. Today we use the word project in a

number of different settings – to signify a group or an organization, to demarcate a

particularly complex transaction, to refer to a visionary plan or idea. Originally,

however, the term draws on the Latin word projicere of which the meaning might be

derived to throw something forward. Innovation is often used to signify something

new, either a new product, service or other output, and/or a new process and method.

The word is also traceable to Latin and the word innovo which could be translated as

54

to renew. In many ways the two fields of research have been kept apart leading to

neglect in the project management area to acknowledge and embrace the unique

processes of projects – to cope with uncertainty instead of eliminating it by the use of

advanced planning techniques. In the innovation arena, project management has often

been looked upon as a simple implementation endeavor with little problems.

However, research has time and time again pointed out the difficulties of moving

from invention to innovation, of moving from ideas to value creating products – a

process where project management potentially would have a very important role.

Keegan and Turner (2002: 367-388) analyzed the management of

innovation in project-based firms along three dimensions – context supportive for

innovation, slack resources and perception of innovation as being useful or not. The

authors observe that the interplay between innovation and projects is dominated by

the ideas on how to correctly manage projects, rather than how to effectively manage

innovation. In other words, the attitude towards managing innovation projects remains

mechanical in nature as traditional project management approaches are applied to

innovation projects. Keegan and Turner (2002) argue in favor of the evolution of the

traditional project management towards a more informal, organic management of

innovation, with a higher tolerance for slack resources and greater levels of

redundancy in order to create time, space and creativity for innovation.

2.6 Team Cohesiveness

2.6.1 Cohesion

It has long been recognized that the relationship between cohesion and team

innovation is inconsistent, in that cohesion can be associated with both high and low

team innovation. Mullen and Copper (1994: 210-227) asserted that it is chiefly the

commitment to the task (as an indicator of cohesion) that shows a significant impact

on team innovation. They included 51 effects of 46 empirical investigations in their

meta-analysis and concluded that cohesion influences performance, particular if the

team task requires coordination and communication (e.g., innovative tasks). These

results indicate that an adequate level of cohesion impacts the performance of

innovation teams through its positive influence on communication and coordination.

55

Forsyth (1999 quoted in Treadwell et al., 2001: 3-11) regarded cohesion as

analogous to the ‘glue’ that holds a group together or as the strength of the bonds

linking group members to the group. He observed that cohesive groups share some

common characteristics: a) enjoyment and satisfaction, b) a cooperative and friendly

atmosphere, c) exchange a praise for accomplishments, d) higher self-esteem and less

anxiety among group members, and e) greater member retention. Additionally, Secord

and Backham (1994 quoted in Treadwell et al., 2001) stated that members of highly

cohesive groups mutually accept each other’s ideas, contribute equally to problem

solving, and are not likely to be adversely affected by the power and status structures

within the group.

Mostafa (2005: 7-33) studied the effects of reward schemes, individualism-

collectivism, and intrinsic motivation on teams’ creative performance and found that

cohesiveness partially mediated some of the effects on creativity. The result indicated

that innovation can result in strong group cohesiveness.

Keller (1986: 715-726) found that the relationship between team cohesion

could generate intellectual competitiveness. Team cohesiveness was the best predictor

of the variables that significantly correlated with the innovation of teams. Rubenstein

(1975 quoted in Keller, 1986) found a cohesive scientific workgroup to be more likely

to build innovations than non-cohesive teams.

West and Wallace (1991: 303-315) found that there was significant positive

relationship between innovativeness and the team cohesiveness and participation in

decision making.

Griffith and Mullen (1972 quoted in Keller, 1986) examined teams of

researchers that had made major changes in their particular fields of science and

found that they had the high levels of cohesive bonds needed to make advance

revolutionary changes in their work. Dailey (1978) found that team cohesion is the

most significant predictor explaining 30 percent of the variance of team innovation.

In this study used the cohesion concept of Langfred (1998: 124-143) to

measure the cohesion of R&D projects. This concept addresses the degree to which

team members identify positively with the team and use. six items to measure the

extent to which team members feel a part of the team, the strength of member ties to

other members, and team members’ commitment to the team. Moreover, the previous

56

studies mentioned above have shown that cohesion has influence on team innovation.

Thus, this study hypothesizes that cohesion has a positive effect on team innovation.

2.6.2 Team Cohesiveness and Team Innovation

From the perspective of McDowell and Zhang (2009), innovativeness is a

psychological trait that adopts new ideas or technologies as indicated by Leavitt and

Walton (1975). This view of innovativeness as a trait can encompass the adoption of

products or processes from other sources (Roehrich, 2002). While innovation is

highly connected to creativity, the purpose of this study is to look at the broader scope

of innovation which includes the implementation of creative ideas and thought

processes within the team (Woodman, Sawyer and Griffin, 1993: 231-293). Calatone,

Cavusgil and Zhao (2002: 515-524) reinforce this idea with the understanding that

innovation and organizational learning are closely related. Thus, the organization

tends to evolve into a more innovative organization as better ideas and processes are

either developed or adopted.

The study of determining factors of overall team innovativeness can certainly

benefit the understanding of teams in the context of organizations. The various

dynamics of team interaction, such as the cultural composition (Hopkins and Hopkins,

2002; Balkundi et al., 2007) the existence of creativity (Kurtzberg and Amabile,

2001), trust (Leung, 2008) and shared leadership (Carson, Tesluk and Marrone, 2007:

1217-1234) are just some of the current variables being examined in regards to teams.

In addition, however, the examination of these factors in light of the current literature

concerning team cohesiveness and the effects that cohesiveness can have on team

innovation will help to understand how these factors work in relation to one another.

The current literature shows mixed findings concerning the role cohesiveness

plays in team performance (Turner et al., 1984; Lokshin et al., 2009). However, by

focusing solely on team innovativeness, this paper seeks to discover if a relationship

does exist between cohesiveness and this aspect of performance. While some research

would suggest that cohesiveness can eventually lead to a myopic view taken by the

group through groupthink (Janis, 1972), it is still believed that the closeness brought

about through cohesiveness can facilitate the members of a team working together

more freely with more communication to develop more innovative ideas.

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Therefore, the purpose of this study centers on studying the relationship

between team innovativeness and cohesiveness and potency. This adjustment, from

focusing on team performance to team innovativeness in light of cohesiveness will

hopefully develop a better understanding of what does indeed enhance the team

innovative process.

2.6.2.1 Determinants of Team Innovativeness

There are many determinants of team performance (Guzzo and Dickson,

1996; Hsu et al., 2005). These constructs commonly include cohesiveness, heterogeneity,

familiarity, motivation, goals, feedback and communication. Other variables such as

individual creativity (Taggar, 2002), self-efficacy, (Tierney and Farmer, 2002; Earley,

1994) potency, (Tesluk and Mathieu, 1999: 200-217), team training (Rothrock et al.,

2009) and member expertise (Bonner et al., 2002) are also aspects which determine

team performance.

The crossover from performance to innovation has relatively less

research devoted to it, but as Sethi, Smith and Park (2001: 73-85) indicate, many of

the same constructs are related to both. Similarly, Woodman et al. (1993) also listed

issues such as cohesiveness, longevity, and composition as antecedents of team

creativity which is an aspect of team innovation.

2.6.2.2 Team Cohesiveness

Team cohesiveness is greatly influenced by other factors such as

heterogeneity, composition, and longevity. Cohesiveness within teams is determined

by the individuals within the team having a desire to remain a part of that team

because their individual needs are being met Turner, Hogg, Turner and Smith (1984:

97-111) (Turner et al., 1984.) As Hackman (1992) indicates, there are two types of

cohesiveness in teams- task and interpersonal- and more cohesive teams have

members who are more likely to conform to team norms. However, there are differing

views as to whether cohesiveness within a team is beneficial to the overall outcome or

not. Both Hackman (1992) and (Turner et al., 1984) point out that there are various

studies that report both positive and negative outcomes. Guzzo and Dickson (1996:

307-338) found that high cohesiveness is good for team performance, especially when

the team is under pressure. Facilitating this may be that they found that high

interpersonal cohesiveness is highly correlated with communication within the team.

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However, they also found that low cohesiveness, or those teams that had low levels of

familiarity, actually had lower levels of performance. To counter that, Janis’ (1972)

introduction of groupthink would imply that a negative relationship could exist due to

high cohesiveness, but to think that only a negative relationship exists would be a

very narrow perspective. Tesluk and Mathieu (1999: 121-154) in their study seemed

to indicate that highly cohesive teams were more adaptable and prepared for more

critical problem solving. However, for this study, a positive relationship is expected

between cohesiveness and innovativeness because of increased communication and

the ability to coordinate with team members in a cross functional team.

2.6.2.3 Composition

Wallmark (1973) studied the importance of size in research groups and

found a positive correlation between group size and group productivity. This was

interpreted as resulting from the greater number of possible contacts and consequent

intellectual synergy. Stankiewicsz (1979) examined the innovativeness and productivity of

172 Swedish academic groups. The groups were randomly selected from the fields of

natural science and technology and ranged from 2 - 8 in size. The typical group

contained 4-5 scientists. A positive correlation was confirmed between group size and

productivity in groups high in cohesiveness and with very experienced leaders, but in

groups with low cohesiveness, Stankiewicsz found an optimum size of seven

members. Thus, there appears to be an interaction effect between size and cohesiveness,

since cohesiveness mediates between size and productivity.

Geschka (1983: 169-183), in a theoretical article, proposed that specially

trained innovation planning teams in organizations should contain 6-8 members from

different functional areas and also include opinion leaders who can help in the

implementation phase. The combination of a small workgroup and the presence of an

opinion leader, who pays attention to dissenting minorities, should especially favor

innovation (Geschka, 1983). Avoiding excessive homogeneity of experience and

training of individual members has been advocated as a necessary condition for

securing a diversity of views, and hence innovation (Geschka, 1983; Kanter, 1983).

2.6.2.4 Longevity

Another variable, which has been discussed as a possible influence on

innovation, is group longevity. Lovelace (1986: 161-174) suggests that research

59

scientists will be more creative if not assigned to permanent groups, and Nystrom

(1979) too argues for the advantages of relatively short-lived groups, at least as far as

the early stages of the innovation process are concerned. A study by Katz (1982: 81-

104) investigated the performance of fifty R&D teams over several years. An inverse

relationship between longevity and innovativeness was reported: the longer groups

had been in existence, the less innovative they became. Other authors have, on the

basis of these findings, argued for restricting the active lifespan of groups, as a means

for maximizing innovativeness (Nystrom 1979; Payne, 1990). Work by applied

psychologists into group development and task performance sheds more light upon

the relationship between group innovation and development over time. In initial

experimental studies, Gersick (1989: 274-309) presented groups of subjects with a

simulated project task of one hour's duration, informing subjects in advance of this

time limitation. Groups were videotaped and their interpersonal behavior subsequently

analyzed. The results show an unequivocal change in subjects' task orientation at

roughly the midpoint of the lifespan of the group, whereby individuals reappraised

their approach to the task, developed new ideas, and initiated modified work

practices. Gersick concludes that groups adhered to a ‘punctuated equilibrium model

of group development’, instituting a quantum leap in their work styles at the known

midpoint of the group's lifespan. Subsequent field studies of this effect, e.g. Gersick,

1989, have confirmed the salience of the punctuated equilibrium model in naturally

occurring workgroups, and thus hint at the applicability of this model to innovation

processes in real-life task groups of fixed-term nature.

2.7 Strategic Leadership

2.7.1 The Role of Leadership

The role of leadership in enabling innovation in organizations is acknowledged by

a lot of researchers (Foster and Pryor, 1986; Rothwell, 1994; Kanter, 1985).

Generally, the consensus is that the most significant role that the leadership should

play is that of developing the culture that supports innovation. Some researchers have

suggested other roles not specifically related to cultivating the right culture:

60

1. Facilitate and champion innovation activity, provide the necessary

resources, expertise or protection (Hornsby et al., 2002)

2. Developing and maintaining measurement systems, assessing strategic

performance and reviewing performance indicators (Soosay, 2005)

3. Focusing on networks for collaboration with internal and external

partners, balancing incremental and disruptive innovations, developing metrics for

feedback (Shelton and Davila, 2005)

4. Commitment, walking the talk, declaring the vision (Ahmed, 1998)

Leadership in innovation should, however, not be limited to the top but should

rather be present at all levels of the organization (Roach and Sager, 2000). The role

that innovation leadership at other levels plays includes picking the right teams for

innovation activities, using the right facilitators and distributing ideas throughout the

organization for future use.

Deschamps (2005: 31-38) makes reference to innovation leaders as executives

who create and lead the innovation process, develop and coach the innovators and

promote the innovation culture. Deschamps sees these kinds of leaders as falling

under two categories: front-end innovation leaders who innovate from the market side

by sensing new market needs and back-end innovation leaders who concentrate on

launching or deploying the products, services or processes fast. According to

Deschamps, CEOs must ensure that their organization has both kinds in order to

ensure that their innovation effort is effective.

2.7.2 Strategic Leadership in Exploratory and Exploitative Innovation

With reference to Jansen, Vera and Crossan’s work (2009: 5-18) in strategic

leadership, The researchers built on March’s (1991) logic to argue that both exploratory and

exploitative innovation involve organizational learning. Exploratory innovations

require new knowledge or departure from existing knowledge sources and involve the

“experimentation with new alternatives [that produce] returns [that] are uncertain,

distant, and often negative” (March, 1991: 85). They offer new designs, create new

markets, and develop new channels of distribution (Abernathy and Clark, 1985;

Jansen et al., 2006). Exploitative innovations broaden existing knowledge and skills

and are associated with the “refinement and extension of existing competences,

61

technologies, and paradigms [that produce] returns [that] are positive, proximate, and

predictable” (March, 1991: 85). They improve established designs, expand existing

products and services, and increase the efficiency of existing distribution channels

(Abernathy and Clark, 1985; Jansen et al., 2006). Consistent with prior discussion

about the multi-level processes of organizational learning, leadership behavior and

organizational learning has been linked to innovation by proposing that feed-forward

and feedback flows of learning that challenge institutionalized learning lead to

exploratory innovation, whereas feed-forward and feedback flows of learning that

reinforce institutionalized learning lead to exploitative innovation. In order to support

exploration and exploitation innovation, strategic leaders need to manage a rich

combination of multi-level learning processes.

2.7.3 Strategic Leadership: Leadership Behavior and Style

While the importance of innovation and adoption of new technologies to

improve the performance of the banking/financial services sector is widely recognized

(Fung, 2008; Barrutia, 2005; Alexander and Hixon, 2008; Slonaker, 2005; Conrad,

2007), success implementing the required changes is far from assured (Bielski, 2008a,

2008b; Fisher, 2009; Trombly, 2007; Watts and Garret, 2006). According to the large

body of literature, regardless of the technologies and/or change methodologies being

employed, in general, the factors important to innovation success or failure are many

but can be grouped into a few major areas. Most authors would agree that the change

process used for innovation has to bear certain characteristics. Many researchers have

looked to improvements in strategic leadership as critical to developing an organization

environment conducive to innovation (Waldman, Ramirez, House and Puranam,

2001; Williams, 2004). To help define and prioritize important problems and

opportunities to the organization, many have proposed Competitive Intelligence (CI)

programs as important to company success (Tarraf and Molz, 2006; duToit, 2003;

Vedder and Guynes, 2002; Guimaraes and Armstrong, 1998). Further, effective

Management of Technology (MOT) is thought to be a critical requirement for

successfully implementing most modern business changes (Beattie and Fleck, 2005).

While these propositions are exceedingly important, the existing literature contains

little empirical evidence supporting them. As called for in the study by Guimaraes and

62

Armstrong (1998), while the constructs being studied are well established, much can

be done for empirically testing these propositions. Particularly useful might be testing

an expanded integrated model of the factors potentially important to the effective

implementation of business change within the banking sector. This field test was

undertaken to accomplish these objectives.

In the area of strategic leadership, several implications can be derived from

this study. Charismatic leadership (showing determination while accomplishing goals,

inspiring confidence, making people feel good around you, communicating expectations

for high performance, generating respect, transmitting a sense of mission, and

providing a vision of what lies ahead) seems to be on average relatively scarce in the

banking industry today, and judging by its nature it should be difficult to develop.

Nevertheless, given its importance, managers must try. Also apparently important for

successful business innovation but less scarce than charismatic leadership,

transactional leadership-- taking action if mistakes are made, pointing out what people

will receive if they do what needs to be done, reinforcing the link between achieving

goals and obtaining rewards, focusing attention on deviations from what is expected,

and rewarding good work- by its nature should be easier to develop. Pawar and

Eastman (1997) proposed that transactional leadership is more relevant within an

existing organization environment instead of one attempting to implement changes.

Katz and Kahn (1978) argued that charismatic leadership may be more relevant where

organization change is important, but that both types of strategic leadership are

potentially important. Our results indicate that for successful business innovation both

types of leadership are important.

2.7.4 Strategic Leadership: Action Implementation

Louis Kasekende (2010) concluded in Citation for Outstanding Leadership

that Prof. Emmanuel Tumusiime-Mutebile has led the Bank of Uganda for almost 10

years. It has been a decade of remarkable achievements; achievements which were

made possible both by the Governor’s strategic leadership of the Bank and by his

ability to inspire and motivate its staff. His stewardship of the Bank has been notable,

not just because it has embodied the highest standards of professional expertise, but

also because he has never shied away from taking difficult decisions or of speaking

63

the truth about economic realities, however unpopular this may have been in some

quarters.

The Bank of Uganda’s core mandates are to deliver macroeconomic stability

and to regulate the banking system. In terms of both objectives, the Bank has been

very successful. The Bank’s monetary policy has faced many challenges during the

course of the last 10 years: from the difficulties of sterilising large aid inflows in the

first half of the decade; from the supply side shocks to international fuel prices and

food prices which pushed up inflation temporarily in 2008 and 2009; and then from

the consequences of the global financial crisis for the balance of payments and

aggregate demand. Under Prof. Tumusiime-Mutebile’s leadership, the Bank’s monetary

policy was able to steer the economy through the turbulence created by the external

shocks and keep it on an even keel. In particular, the Bank has delivered low inflation;

the primary policy objective of its monetary policy. Headline inflation over the course

of the last decade has averaged only 6.5 percent per annum. In turn, price stability has

enabled the economy to attract increasing amounts of private investment and grow

rapidly at an average of 7.4 percent per annum in real terms over the course of the

decade. Sound macroeconomic management ensured that our economy was able to

withstand the external shocks from the global economic crisis and avoid recession.

Prof. Tumusiime-Mutebile’s leadership of the Bank of Uganda in its role as

banking regulator has been equally successful. He assumed the stewardship of the

Bank at a time when parts of the banking system in Uganda were experiencing

distress. Several banks had failed towards the end of the 1990s, and the largest bank,

Uganda Commercial Bank, was under the statutory management of the Bank of

Uganda as a result of its insolvency. The Governor’s decision to resolve Uganda

Commercial Bank by selling it to a reputable international bank, in the teeth of

widespread scepticism and in some cases outright opposition, proved to be one of the

most prescient ever taken in the history of banking in the country. UCB was sold to

Stanbic Bank, a sale which preserved the nationwide branch network of UCB, and

protected its deposits. Since acquiring UCB, Stanbic has been in the forefront of the

dynamic expansion of the Ugandan banking system which occurred in the 2000s,

which included the very rapid growth of bank lending to the private sector.

64

The Governor also spearheaded major legislative reforms to financial

regulation which were essential to strengthen the legal framework governing the

financial sector in Uganda. The 2003 Micro-finance Deposit Taking Act brought, for

the first time in Uganda, deposit taking microfinance institutions under the regulatory

aegis of the Bank of Uganda. In the following year, the Financial Institutions Act was

enacted, which upgraded banking legislation in Uganda and incorporated important

elements of international best practice, such as prompt corrective action provisions for

resolving distressed banks. The success of financial regulation in Uganda over the last

decade is indisputable. Despite the shocks emanating from the global financial crisis,

Uganda’s banking system has remained in a markedly robust financial condition, with

average capital adequacy levels comfortably in excess of statutory minimum levels,

low levels of non performing loans and impressive profitability.

Prof. Tumusiime-Mutebile was recently appointed to a third five year term as

Governor. The Bank will face new challenges in the years ahead. The emergence of

Uganda as a “frontier market” will make effective financial regulation, including

macro-prudential regulation, even more critical to the stability of its economy.

2.8 Coordination and Communication

While studying routines alone should already be beneficial for better

understanding of coordination and communication in innovation projects, there is a

danger of not seeing the forest for the woods. One routine (e.g. planning and

assigning tasks) is only a lego piece in the overall project activities and might have

little effect in isolation. However, studies of, for example, the Toyota production

system (Liker, 2003) demonstrate that all routines in the organization follow similar

principles, which one might even call coordinative strategies. With the notion of

coordinative principles or strategies, a new way of integrating from the detailed level

to the overall project level is now possible as well as the formulation of related

research questions. However, the idea of coordinative patterns or principles is well

established in the organizational literature, but not linked to the more detailed analysis

of work activities and routines.

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2.8.1 Coordination

Mintzberg, (1979) categorizes previous research into three main coordination

strategies: mutual adjustment is based on close collaboration suitable especially in

simple and very complex situations; direct supervision assumes that managers guide

and coordinate the activities of their subordinates, while the standardization of

processes, output, or training employs experts that develop rules and policies, but who

do not directly coordinate operative work.

Grant, (1996: 109-122) builds on Mintzberg and others but proposes a

somewhat different categorization: Rules and directives (similar to Mintzberg’s

standardization of process) capture the knowledge of specialists and convert it to

impersonal coordination. Sequencing of activities allows relatively independent

contributions with minimal coordination, while the mechanism of group problem

solving and decision making is close to Mintzberg’s mutual adjustment. In Grant’s

categorization, routines are themselves coordination mechanisms, emergent from the

mode of mutual adjustment towards an implicit standardization of activities.

While these categorizations have received wide recognition, they seem

somewhat simplistic and abstract especially for the domain of managing innovation

projects. The suggestions of the project management institute (PMI, PMBook) are the

first level of application of the aforementioned principles: Mutual adjustment in teams

is replaced by supervision and directives of project managers based on a standardized

set of project coordination and communication processes.

2.8.2 Communication

Communication is the vehicle through which personnel from multiple

functional areas share information that is critical to the successful implementation of a

project (Pinto and Pinto, 1990: 202-212). The purpose of this research is to study the

internal communication involved in intra-project communication among team

members which was measured in terms of the reason for communication. The reasons

for intra-project communication among team members were classified into three

categories: problem-solving issues, administrative issues and performance feedback.

According to Amason (1996), open communication facilities creativity and synergistic

learning. Shalley et al. (2004) found that for new product development teams, a

moderate frequency of communication was the best for creativity.

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Johnson et al. (2001: 24-42) asserted that the communication and participation

of individuals are critical factors in innovation. In particular, enhanced communication

quality results in a broader awareness of the implications of an innovation, and as a

result, facilitates further involvement by employees. In a meta-analysis of 782 studies,

Damanpour (1991: 555-590) found a positive association between internal communication

(the extent of cross-functional communication and coordination) and organizational

innovativeness. The mechanisms by which ideas are shared and integrated are

frequent meetings, face-to-face contact in both horizontal and vertical relationships

and units or departments sharing decisions (Han, Kim and Srivastana, 1998: 30-45).

Madhavan and Grover, (1998: 1-12) refer to these mechanisms as the “richness of

personal interaction.”

However, organizational praxis exhibits partly completely different coordination

and communication principles. For example, Weick and Roberts (1993) and

Dougherty and Takacs (2004: 569-590) observe principles which they call “heedful

interrelating” in high reliability, but also in highly innovative organizations. In these

organizations, most routines incorporate these principles, but also specific routines

occur: people develop a shared understanding of objectives, approaches, and personal

roles and competencies, then perform their own contributions as part of the whole and

make them visible, while observing others and adapting their own behavior and

contributions towards the activities of others and the common goal.

Kellogg, Orlikowski and Yates (2006) observed some elements of Weick and

Roberts (1993: 357-381) and Dougherty and Takacs (2004: 569-590), but with a

further reduction of interrelation and communication. In their case of a high-speed

service firm, coordination happened through little direct interaction or even common

goal setting through shared artifacts. Some team members established a structure of,

for example, a deliverable to which other team members contributed. By seeing and

reviewing other peoples’ contributions, they adapted their own contributions in

iterative cycles.

2.8.2.1 Communication in Project Management

Communications are important in project management. Damanpour

(1991) demonstrated the existence of a positive relationship between internal

communication and innovation. Internal communication facilitates the dispersion of

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ideas within an organization, increases the diversity and also contributes to the team

‘climate’. Communication can be measured by various integration mechanisms, e.g.

committees, number of meetings and contacts (Damanpour, 1991). There are also

measures of external communications, which tend to focus on whether communication is

taking place, the level at which it occurs and with whom (Cebon and Newton, 1999;

Lee et al., 1996; Rothwell, 1992; Souitaris, 2002). These measures of internal and

external communication in the literature are based on both subjective evaluations and

objective frequency counts. Subjective measures include: ‘we always consult

suppliers/customers on new product ideas’ (Parthasarthy and Hammond, 2002) and

‘degree of organization members involved and participating in extra-organizational

professional activities’ (Damanpour, 1991). Objective counts include: ‘extent of

communication amongst organizational units or groups measured by various

integrating mechanisms such as numbers of committees and frequency of meetings’

(Damper, 1991), ‘frequency of formal meetings concerning new ideas’ (Anderson and

West, 1998), and ‘how well do the technical and finance people communicate with

one another’ (Souitaris, 2002; Szakonyi, 1994).

Various approaches have been taken to modeling innovation processes

as: a series of events (Zaltman et al., 1973), a social interaction (Voss et al., 1999), a

series of transactions (Nelson and Winter, 1982) and a process of communication

(Farrukh et al., 2000). The history of project management research is partly

characterized by a debate regarding the extent to which events and activities within

the process occur in linearly sequential, discrete, identifiable stages (Zaltman et al.,

1973), or whether events are more disorganized (King, 1992) or even chaotic (Koput,

1997). However, despite these different viewpoints, there are a number of common

elements that can be summarized as the major components of innovation project

management. These are project efficiency, tools, communications and collaboration.

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Table 2.1 Summary of Key Characteristic Factors

Key Factors Characteristics


Effectiveness

- No.of innovations (Van Ark, Broersma and De Jong, 1999)

- Speed of Innovation (Knowledge Partners, 2008)

- Return on Investment of Innovation (Investopedia, 2010)

- Innovation Value Added Chain (Afuah, 1998)

Organizational

Innovation

- Innovation (Baker, 2002) can be seen as the business of

science organizations

- Science organizations in both the private and public sectors

are under greater pressure not only to generate innovative

science but also to function as a businesses

- Science organizations could provide critical insights into

how to promote and sustain innovation in private sector

business organizations

- Public science organizations should consider playing a lead

role in promoting strategies for encouraging and sustaining

innovation and developing a true innovation competency

Innovation Strategy - Grant and King (1984) as referenced by Ramanujam and

Mensch (1985) define a strategy as a timed sequence of

internally consistent and conditional resource allocation

decisions that are designed to fulfill an organization’s

objectives

- Ramanujam and Mensch (1985) use this definition to

describe an innovation strategy in terms of innovation

goals, innovation resource allocation, innovation risk

(overall philosophy), timing (first to market or happy to

wait) and a long term perspective. Formulating an

innovation strategy therefore is a matter of policy for those

organizations that would like to pursue a sustainable

competitive advantage through innovation

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Organizational Structure - Burns and Stalker (1961) described a contingency approach

to innovation management, later developed to include

concepts of functional differentiation, specialization and

integration (Lawrence and Lorsch, 1967), which suggests

that environmental change prompts a realignment of the fit

between strategy and structure

- Organizational structures are broadly divided into two

categories: Organic and Mechanistic (Russel, 1999; Afuah

2003; Russel and Russel, 1992)

Organizational Structure - The two polar forms of organizational structure put forward

by Burns and Stalker, show what organizational structure

facilitates better which particular stage of the innovation

process. It will be observed that as the innovation process

moves on from the generation of ideas to implementation,

the organizational structure should become less organic and

more mechanistic

Organizational Culture - McLean (2005) asserted the importance of creativity and

innovation in organizations

- Burns and Stalker’s environmental determinism view of

organizations led to the conclusion that organic

organizations form to deal with unpredictability and

volatility in an organization’s environment. Compared with

a mechanistic organization, an organic one facilitates

greater creativity and innovation

- Martins and Martins (2002) argue that in organizational

culture, for most organizations change is inevitable

- Organizational changes usually promote and intensify

competitiveness, as they require dramatic changes in

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strategy, technology, working systems and management style, among others. These changes require an in-depth analysis of values, beliefs and behavior patterns that guide day-to-day organizational performance. Creativity and innovation have a role to play in this change process

Project Management

- Project management by Adams, Bessant and Phelps (2006) is concerned with the processes that turn the inputs into a marketable innovation. The innovation process is complex, comprising a myriad of events and activities- some of which can be identified as a sequence and some of which occur concurrently- and it is clearly possible that innovation processes will differ to some degree, across organizations and even within organizations on a project-by-project basis

Team Cohesiveness - Innovativeness is a psychological trait that adopts new ideas or technologies as indicated by Leavitt and Walton (1975)

- Team innovativeness can certainly benefit the understanding of teams in the context of organizations. There are various dynamics of team interaction, such as the cultural composition (Hopkins and Hopkins, 2002; Balkundi et al., 2007), the existence of creativity (Kurtzberg and Amabile, 2001), trust (Leung, 2008) and shared leadership (Carson, Tesluk and Marrone, 2007)

Strategic Leadership - Facilitate and champion innovation activity, provide the necessary resources, expertise or protection (Hornsby et al., 2002)

- Develop and maintain measurement systems, assesss trategic performance and review performance indicators (Soosay, 2005)

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Strategic Leadership - Focus on networks for collaboration with internal and

external partners, balance incremental and disruptive

innovations, develop metrics for feedback (Shelton and

Davila, 2005)

- Commitment, walking the talk, declaring the vision (Ahmed,

1998)

Coordination and

Communication

- Liker (2003) with the notion of coordinative principles or

strategies, a new way to integrate from the detailed level to

the overall project level has been made possible as well as

the formulation of related research questions

- Mintzberg (1979) categorizes previous research into three

main coordination strategies: mutual adjustment is based on

close collaboration suitable especially in simple and very

complex situations; direct supervision assumes that

managers guide and coordinate the activities of their

subordinates, while standardization of processes, output, or

training employs experts that develop rules and policies

- Johnson et al. (2001) found the communication and

participation of individuals to be critical factors in

innovation. In particular, they found that enhanced

communication quality results in a broader awareness of the

implications of an innovation

- Different coordination and communication principles. Weick

and Roberts (1993) and Dougherty and Takacs (2004),

observed principles which they call “heedful interrelating” in

high reliability, but also highly innovative organizations

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In addition, the researcher developed the table below to summarize the key

factors which have relationships to one another together with the citations of their

authors’ works

Table 2.2 Summary of Key Factors That Influence One Another

Key Factor → Key Factor Scholar’s Name

Innovation Strategy →


Effectiveness

Mphahlele (2006), Martins and Terblanche (2003), Vlok

(2005) and Dewar and Dutton (1986)

Organization Structure →


Effectiveness

Mphahlele (2006), Burns and Stalker (1961), Zaltman,

Duncan and Holbek (1973) and Hage and Aiken (1970)

Organization Culture →


Effectiveness

Martins and Martins (2002), Martins (2000) and Adams,

Bessant and Phelps (2006)

Project Management →


Effectiveness

Adams, Bessant and Phelps (2006), Globe et al. (1973)

and Keegan and Turner (2002)

Team Cohesiveness →


Effectiveness

Mullen and Cooper (1994), Langfred (1998) and Guzzo

and Dickson (1996)

Strategic Leadership →


Effectiveness

Foster and Pryor (1986), Rothwell (1994) and Kanter

(1985)

Strategic Leadership →

Innovation Strategy

Pinto and Prescott (1988) and Dougherty and Cohen

(1995)

Coordination and

Communication →

Organization Structure

Burns and Stalker (1961) and Zaltman, Duncan and

Holbek (1973)

Coordination and Martins (2000)

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Key Factor → Key Factor Scholar’s Name

Communication →

Organization Culture

Coordination and

Communication → Project

Management

Pinto and Pinto (1990) and Damanpour (1991)

Coordination and

Communication → Team

Cohesiveness

Mullen and Cooper (1994) and Visaetsilapanonta (2006)

In summary, this chapter detailed the literature review used to construct the

conceptual framework and describes the relationships among the variables or

constructs and the research findings of the key literature related to the research. These

areas consist of innovation management effectiveness, innovation strategy, organization

structure, organization culture, project management, team cohesiveness, strategic

leadership, and coordination and communication.

CHAPTER 3

CONCEPTUAL THEORY

In this chapter, the researcher describes the significant conceptual theories-

theoretical resources management and structure and performance – and their relationship

to innovation management effectiveness. The core assumptions and statements are

also explained with regard to the contingency approach to the management and

adoption of innovation to the organization.

3.1 Resource-Based View

3.1.1 Resource-Based View

Fortuin (2006) regarded the resource-based view of the firm (RBV) as an

influential theoretical framework for understanding how competitive advantage within

firms is achieved and how that advantage can be sustained over time (Schumpeter,

1934; Penrose, 1959; Wernerfelt, 1984; Prahalad and Hamel, 1990; Barney, 1991;

Nelson, 1991; Peteraf, 1993; Teece et al., 1997; Scholten, 2006). This perspective

focuses on a firm’s internal resources and how these are acquired from factor markets,

e.g. the labor and financial markets. In contrast to the industrial perspective that views

resources as immediately accessible, the RBV stresses the inherent immobility or

stickiness of valuable factors of production and the time and cost required to

accumulate those resources (Peteraf, 1993). This causes firms to be idiosyncratic

because throughout their history they accumulate different physical assets and often,

more importantly, acquire different intangible organizational assets of tacit learning

and dynamic routines (Dosi, 1988). Competitive imitation of these assets is only

possible through the same time consuming process of irreversible investment or

learning that the firm itself underwent (Dierickx and Cool, 1989). The irreversible

investments made in these assets function as commitments that deter the duplication

of valuable product-market positions and secure the distinctive value of the firm

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(Ghemawat, 1991). Such assets also produce ‘path dependency’ (Dosi et al., 1988).

So a firm's history, strategy and organization combine to yield the unique bundle of

resources it possesses. Had it made different decisions in the past, its path of asset

accumulation and hence the firm today would be different. Moreover, the future

strategy of the firm is determined by its history with its strategy constrained by, and

dependent on, the current level of resources (Collis, 1991). As a consequence, in RBV

thought, Chandler’s (1962) famous adage ‘Structure follows strategy’ is merely

reversed to ‘Strategy follows structure’.

In short, in the resource-based view, competitive advantage rests within the

firm's idiosyncratic and difficult-to-imitate resources. A resource refers to an asset or

input to production (tangible or intangible) that an organization owns, controls or has

access to on a semi-permanent basis (Helfat and Peteraf, 2003). It follows that a firm’s

resources include all those attributes that enable it to conceive of and implement

strategies. They can be divided into four types: financial resources, physical resources,

human resources and organizational resources (trust, teamwork, friendship and

reputation). In RBV the firm’s resources must be unique in four ways. First, they must

add value. The resources must enable the firm to exploit external opportunities or

neutralize external threats. Second, they must be rare. Ideally, no competing firms

possess the same resources. Third, they must be inimitable. Competitors should not be

able to imitate the resource either by duplicating it or by developing a substitute

resource. For example, fast food discount coupons are a very poor competitive

resource because competitors can quickly and easily print their own (duplication) or

simply offer a temporary lower price (substitution). However, another set of barriers

impedes imitation in advanced industrial countries. This is the system of intellectual

property rights, such as patents, trade secrets, and trademarks. Intellectual property

protection is not uniform across products, processes, and technologies, and is best

thought of as an island in a sea of open competition. It presents an imitation barrier in

certain contexts, although one should not overestimate its importance (Omta and

Folstar, 2005). Finally, the firm must have the ability to exploit. the resources. The

firm should have the systems, policies, procedures, and processes in place to take full

competitive advantage of the resources.

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RBV builds on two basic assumptions about the firm’s resources: (1) that they

can vary significantly across firms (assumption of resource heterogeneity) and (2) that

their differences can be stable (assumption of resource immobility). Furthermore,

RBV considers firms to be rent-seekers rather than profit maximizers (Rumelt, 1987).

Rent can be defined as the excess return to a resource over its opportunity costs. In

other words, the payment received above and beyond that amount necessary to retain

or call the resource into use. Rent-seeking behavior therefore emphasizes the

important role of entrepreneurship and innovation. Firms continuously seek new

opportunities to generate rents rather than contenting themselves with the normal

avenues for profit. If control over scarce resources is the source of economic profits,

then it follows that such issues as skill acquisition, the management of knowledge and

know-how (Shuen, 1994) and learning become fundamental strategic issues. The

more tacit the firm's knowledge, the harder it is for its competitors to replicate it.

When the tacit component is high, imitation may well be impossible.

3.1.2 Resource-Based View in Economics and Management

Within the Economics and Management literature, Lemaire (2003) suggested

that the resource-based view has been gaining broad intellectual support among

scholars interested in issues related to sustainable competitive advantage. The

resource-based view perspective is useful for studying innovation for it adopts a

dynamic approach that emphasizes the importance of resources accumulation.

The resource-based view has its roots in the work of Edith Penrose’s “Theory

of the Growth of the Firm” (1959). The resource-based view of organizations focuses

on resources that can be sources of competitive advantage within the industry. The

basic types of resources providing this competitive advantage are: physical capital

resources, organizational capital resources, and human capital resources (Barney,

1991). The concept of human capital is that employees have the skills, experience,

and knowledge that provide economic value to the firm. Barney and Wright (1998)

noted that in order for human capital to contribute to sustainable competitive

advantage, it must create value, remain hard to imitate, and appear rare. Verona

(1999) shows the strength of the resource-based view in dealing with new product

development. A resource-based view of product development identifies the knowledge

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required for successful new product development, in the different dimensions of

functional and integrative capabilities.

Within the resource- based view, Miller and Shamsie (1996) distinguished

property- based resources controlled by property rights and knowledge-based

resources protected from imitation by knowledge barriers. The property rights include

contracts, deeds of ownership and patents. The benefits of property-based resources

are quite specific and fixed and thus the resources are adapted for the environment

they have been developed for. Knowledge-based resources are of greater utility in an

uncertain changing environment they give firms the skills to adapt their products to

market need and to deal with competitive challenges.

3.1.3 A Resource-Based View of the Firm’s Capacity to Innovate

According to Kostopoulos, Spanos and Prastacos (2001), one of the most

important research questions in management literature, traditionally, has been the

relationships among innovation, firm structural characteristics (e.g., formalization,

centralization, specialization) and industrial environment. Hereafter, when we use the

term ‘innovation’ we are referring to organizational (or firm-level) innovation.

Organizational innovation is generally defined as an internally generated or externally

purchased device, system, policy, process, product or service that is new to the

adopting organization (Damanpour, 1991). From this traditional perspective,

innovation represents a means of transforming an organization, whether as a response

to changes in its internal or external environment or as a proactive action taken to

influence its environment. It is supposed that differences in the firm’s innovative

activities are basically explained by industry and organizational structure characteristics

(Kimberly and Evanisko, 1981; Damanpour, 1991; Wolfe, 1994; Duncan, 1976; Daft,

1992). In contrast, more behaviorally oriented research streams, and especially

evolutionary economics (Nelson and Winter, 1982) have studied innovation activities

and performance not only in terms of organizational structure or industry characteristics

but also in terms of resources and capabilities (Dosi, 1988).

With the same line of reasoning, a growing body of literature that embraces

the resource-based view of the firm (Brown and Eisenhardt, 1997; Henderson and

Cockburn, 1994; Iansiti and Clark, 1994; Leonard-Barton, 1995) offers new insights

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into innovation management. According to this influential perspective, the presence of

different organizational resources and capabilities positively affects the outcome of

the innovation process and, thus, can be used to extend the findings -gained by past

research- on the firm’s capacity to innovate.

3.1.4 The Resource-Based View, the Knowledge-Based View and

Organizational Learning Theory

Vanhaverbeke, Cloodt and Van de Vrande (2008) considered the resources,

competencies and knowledge dimension of the open innovation model as being

clearly embedded in the resource-based view, the knowledge-based view and the

organizational learning theory of the firm. The resource-based view of the firm finds

its origins in the work of Wernerfelt (1984) and Penrose (1959). A core feature of this

management theory is that value is created when the firm consists of a unique

collection of difficult-to-imitate resources, competencies and capabilities (Barney,

1986, 1991; Grant, 1996; Wernerfelt, 1984). To create a competitive advantage and

capture an above-normal rate of returns (e.g. rents) these resources must, by

definition, be scarce, valuable and reasonably durable (Barney, 1991). One work that

is closely related to the resource-based view is a study by Grant (1996) -one of the

founders of the knowledge-based view. The knowledge-based view is an outgrowth of

the resource-based view to the extent that it focuses upon knowledge as the

strategically most important resource of the firm (Conner and Prahalad, 1996; Grant,

1996; Nonaka, 1991; Spender, 1989). Knowledge is also a central issue in several

other research traditions that stress the importance of both organizational learning and

the transfer and diffusion of innovative capabilities within the firm (Boisot, 1995;

Grant, 1996; Huber, 1991; Levitt and March, 1988).

The resource-based view and the knowledge-based view argue that firms can

create and capture value according to the unique bundle of resources they possess and

the differences between these resources are held responsible for the differences in

performances between firms (Bierly and Chakrabarti, 1996). In other words,

proponents of the resource-based view or the knowledge-based view emphasize the

fact that a sustainable competitive advantage is based on those resources (knowledge)

and capabilities that are owned and controlled within the boundaries of a single firm

(Dyer and Singh, 1998).

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An important question that we have to ask ourselves in that respect is: "What

is the value of this introspective viewpoint centered on the firm itself, in relation to

open innovation?" Although the resource- based view emphasizes that a firm’s

competitive advantage results from difficult-to-imitate bundles of resources within the

boundaries of the firm, we argue that from the perspective of open innovation these

resources should not be closed off within one single firm. Rather scarce, valuable and

reasonably durable resources of different (previously independent) companies should

be brought together in order to offer value for the targeted customers. Consequently, a

firm’s critical resources should extend beyond its boundaries and enable resource

flows (knowledge flows) with external firms.

Subsequently, whereas the resource-based view and the knowledge-based

view stresses such issues as independence and the crucial role of competition between

autonomous companies based on the unique set of resources and capabilities they

possess, open innovation emphasizes the interdependence of complementary

resources of firms to introduce an innovative product in the market. This implies that

we are in need of an extension of the resource-based view and the knowledge-based

view with respect to the main theoretical premise underlying open innovation. Part of

this task has been carried out in the area of technology alliances (Kale and Singh,

1999; Kale et al., 2002; Grant and Baden-Fuller, 2004; Gulati and Sytch, 2007) but

more work is required to understand open innovation practices in terms of the

resource or knowledge access and acquisition.

3.2 Organizational Theory

3.2.1 Theories of Structure and Performance

Sine, Mitsuhashi and Kirsch (2006) elaborated upon the theory of structure

and performance, by stating that research on the formal structure of an organization is

typically defined as “the sum total of the ways in which it divides its labor into

distinct tasks and then achieves coordination among them” (Mintzberg, 1979: 2). The

notion that the formal structure is “the documented, official relationships among

members of the organization,” and informal structure is the “unofficial relationships

within the workgroup” (Mintzberg, 1979: 9–10) in organizations hearkens back to the

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very origins of organizational theory. Weber’s classic text on bureaucracy proclaimed

that the bureaucratic organization, with its clear-cut division of activities, assignment

of roles, and hierarchically arranged authority, is “technically superior to all other

forms of organization” (1947: 196). According to Weber, the formal structures that

made up the modern organization enabled greater precision, speed, task knowledge,

and continuity, while reducing friction and ambiguity. Building on Merton (1949) and

Durkheim (1997), Burns and Stalker (1961) proposed a contingent relationship

between formal structure and organizational performance, arguing that organizations

with organic structures, or loosely coupled networks of workers, are better adapted to

dynamic environments. Organizations with the Weberian mechanistic structure

(bureaucracy), where work is “distributed among specialist roles within a clearly

defined hierarchy” (Burns and Stalker, 1961: 6), were viewed as being more suitable

for static environments.

During the past four decades, a host of studies have examined Burns and

Stalker’s propositions and have generally confirmed that organizations in dynamic

environments do better if their structures are more organic (Aiken and Bacharach,

1994; French, 1980; Covin and Slevin, 1989; however, note Wally and Baum’s work

(1994) as an exception). The majority of the empirical tests of this theory have used

samples of mature organizations.

Despite this strong research tradition, little is known about whether or not this

theory applies to newly created ventures in emerging industries. Stinchcombe (1965)

suggested that the relative lack of structure that characterizes new ventures is a

liability, not a benefit. He argued that this liability of newness is particularly difficult

to overcome in emerging economic sectors that lack industry norms about work

processes and organizational design. In this study, we focused on three attributes of

new venture structures: role formalization, specialization, and administrative intensity

(Pugh, Hickson, Hinings, MacDonald, Turner and Lupton, 1963; Stinchcombe, 1965)

and their association with new venture performance.

Since the seminal work of Burns and Stalker (1961), researchers have

considered the organic organizational form, characterized by a lack of formally

defined tasks and an emphasis on horizontal as opposed to vertical coordination to be

the exemplar structure for firms operating in turbulent environments. Although past

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research supports this proposition for large, established firms, is it also true for small,

new organizations in turbulent, emerging economic sectors? In this study, we explore

the limits of this conventional wisdom by examining how structural features influence

performance during the earliest phase of organizational existence in a turbulent setting

assumed to be inhospitable to mechanistic structure.

In dynamic contexts, new ventures and large, mature organizations face

fundamentally different structural challenges (Cameron and Quinn, 1983; Gilbert,

2005, 2006; Kimberly, 1979; Shane, 2003). These differences are particularly evident

in emergent economic sectors, which are typically characterized by turbulence and

uncertainty (Aldrich, 1999; Sine and David, 2003). As a result of embedded formalized

roles and routines, functional silos, and administration by managers insulated by

multiple bureaucratic layers from the changing realities of the marketplace, large,

mature organizations often have difficulty responding to environmental turbulence

(Mintzberg, 1978). In contrast, new ventures in emerging sectors initially lack

formalized roles and routines and are small, flexible, and innovative; their employees

and founding teams have frequent interactions with customers. These firms are, in

essence, founded as a reaction to opportunities in a changing environment. Instead of

needing more flexibility, these organizations suffer from a structural “liability of

newness” (Stinchcombe, 1965).

Herein lies the puzzle. On the one hand, both theorists and practitioners

suggest that in turbulent environments, organizations should become more organic

(Burns and Stalker, 1961). On the other hand, in his classic essay, Stinchcombe

(1965) argued that one of the key reasons that new organizations in new economic

sectors are at a disadvantage visa`- vis older, established firms is their lack of

structure, which results in role ambiguity and uncertainty. High levels of uncertainty

impede individual and organizational action (David and Han, 2003; O’Toole and

Meier, 2003). Formalized organizational roles reduce work ambiguity, enable

individual focus, learning, and decision making, decrease the cost of coordination,

and increase efficiency (Perrow, 1986)- all outcomes of vital importance for new

ventures with meager resources. Moreover, Stinchcombe (1965) suggested that new

ventures in emerging sectors not only need formalization and specialization, but also

require greater managerial resources than mature firms. Whereas mature firms are often

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impeded by intensive administration and the structural inertia of legacy bureaucracies,

new ventures need extensive managerial resources and a structural framework to

reduce uncertainty and increase organizational efficiency and responsiveness.

3.2.2 Organization Structure: Lessons from Other Innovative Organizations

In all organizations (International Fund for Agricultural Development-IFAD,

2007), new ideas that improve effectiveness and efficiency emerge naturally from

people’s desires for better solutions. However, high-performing organizations

enhance this process by carefully managing the following:

1) People’s competence and motivations. Research on innovative

organizations (Amabile and Conti, 1999) shows that primary requirements are staff

who are knowledgeable in their disciplines, who have good creative problem-solving

skills and who are intrinsically motivated. Moreover, creative thinking is an essential

element of leadership, especially when bringing about change (Puccio, Murdoch and

Mance, 2007).

2) How challenges are understood and goals set. Innovative organizations

analyze their challenges from different perspectives (Christensen, 1997) and empower

staff with a clear sense of ownership of the challenges (Van Gundy, 2005). Direct

responsibility for meeting new and complex challenges greatly motivates staff.

3) Diversity and networks among staff and with the outside world. New

solutions emerge at the intersection of disciplines, industries and approaches, because

intersections invite looking at challenges from different perspectives and applying

unobvious solutions (Johansson, 2004). Innovative organizations create teams and

networks that emphasize the diversity and cross-pollination of ideas, and they connect

staff with people outside the organization through innovation networks that link

people from different worlds and that broker new ideas. A similar brokering process

has recently led innovative businesses to begin looking at poor people as a vast,

relatively untapped market, and to develop ways of building them into their value

chains (Prahalad, 2004).

4) Rapid prototyping of new ideas. Research indicates that 90 per cent

of successful innovations fail the first time they are tried (Christensen, 1997). Rather

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than extensively designing ideas ex ante, innovative organizations have processes that

rapidly implement, test, evaluate, revise and re-implement ideas in order to refine

them.

5) A system for retaining ideas with potential. One of the keys to

developing new ideas is to maintain a variety of half-baked and failed ideas that might

be of use in a different application (Hardington, 2003).

6) Core business and innovation processes. For innovative organizations,

innovation is not an occasional activity. They have specific processes to help them

define and redefine challenges, collect ideas from unusual sources, find unobvious

solutions, encourage creative thinking, and test and revise ideas before discarding

them. All these elements are built into core operations (Leonard-Barton and Swap,

2005).

7) The organizational environment. In innovative organizations, managers

actively model innovative thinking, recognize innovators and encourage staff to

network and to take time to think (Hardington, 2003). Moreover, systems, processes

and rewards are consistent with the focus on innovation.

3.3 Contingency Theory

3.3.1 Core Assumptions and Statements

Wiio (1979) and Goldhaber (1993) concluded that differences in innovation

effectiveness are a function both of type of organization and composition of work

force (age, sex, education, tenure). The innovation process is influenced by many

internal and external constraints from the organization and its subsystems. The

constraints determine the status of the organization of the environmental supra system

and the state of each subsystem. The innovation process is thus contingent upon

external and internal stimuli and upon the degree of freedom of states within the

system allowed by the organizational constraints. Some internal contingencies are:

structural contingencies, output, and demographic, spatiotemporal and traditional

contingencies. External contingencies are: economic, technological, legal, sociopolitical

cultural and environmental contingencies. Persons interested in organizational

innovation should consider such questions as the following. What are the contingencies

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under which organizations’ innovative ability is best when confronting their environment?

Specifically, do different types of organizations have different absorptive capacity

needs? Do organizational internal contingencies (demographics such as age, sex,

education, seniority, management level, and amount of communication training)

affect innovation effectiveness? Are functional diversity and corporate culture better

predictors of innovation effectiveness?

3.3.2 Contingency Approach to Management

During the past decades there has been evident a new trend in the study of

organizational phenomena. Underlying this approach is the idea that the internal

functioning of organizations must be consistent with the demands of organizational

task, technology, or external environment, and the needs of its members if the

organization is to be effective. Rather than searching for the one best way to organize

under all conditions, investigators have increasingly tended to examine the

functioning of organizations in relation to the needs of their particular members and

external pressures facing them.

A contingency approach to management occurs when the behavior of one

department, individual, work group, or entire organization is dependent on its

relationship to others. Basically, this approach seems to be leading to the development

of a “contingency” theory of organization with the appropriate internal states and

processes of the organization contingent upon external requirements and member

needs.

It has never been and never will be the task of theory and science to prescribe

what should be done. Theory and science are intended as a search for fundamental

relationships, for basic techniques, and for organization of available knowledge – all,

it is hoped, based on clear concepts. How these are applied in practice depends on the

situation. Effective management is always contingency, or situational, management.

The essence of the contingency is that management practices should be

generally consistent with tasks, the external environment, the relative needs of the

members, and other contingency variables. The contingency variables that need to be

considered and their relative emphasis will depend on the general type of managerial

issues or problems being considered. In brief, one of management’s goals in using the

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contingency approach would be to create an appropriate match-up among the

contingency variables.

The contingency approach has been used in many managerial areas by several

researchers. These include the contingency approach to organizational design

(Woodward, 1965; Thomson, 1967; Lawrence and Lorch, 1967; Galbraith, 1971), the

contingency approach to motivation (Vroom, 1964; Litwin and Stringer, 1968;

Lawler, 1974), and the contingency approach to leadership (Argyris, 1964; Fiedler,

1967). Some researchers have used a contingency approach to study the relationship

between technology and strategy (Newman, 1972; King, 1978), between information

system strategy and business strategy (Chan et al., 1997), between organizational

complexity and innovation (Damanpour, 1996), among executive judgement,

organizational congruence, and firm performance (Rackoff et al., 1985), between task

and work-unit design (Van de Ven and Delbecq,1974; Kim 1988; Umanath and Kim,

1992), among others.

This research employs a contingency approach, which seeks to understand the

interrelationships between innovation effectiveness and the various components of an

organization. When an information system acts in response to various service requests

by other organizational components, the information system’s behavior is contingent

on the demands placed upon it.

3.3.3 Contingency Rules Theory

Smith’s contingency rules theory (1984) is an example of a rules approach to

persuasion. Smith utilizes the idea of cognitive schemas, expectations about the

attributes that a given person or policy will have or expectancies about the

consequences of behaving in a particular manner. These schemata function as

contingency rules that both shape the way something is viewed and structure

behavior. Smith suggests that rules and schemata explain persuasion better than the

traditional concept of attitude. According to Smith’s contingency rules theory, rules

are used to create responses to persuasive messages. Self-evaluative rules are

associated with our self-concept and our image. Adaptive rules are those that will

apply effectively in a particular situation – the rules most likely to generate a positive

outcome. Behavioral contingency rules are contextual. In some situations, certain

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consequences are considered and certain rules are activated which guide behavior. In

other situations, other rules are activated. External threats and rewards are meaningful

only if they apply to one’s personal goals.

Figure 3.1 Conceptual Model: Tacom Model

Source: Woudstra and Gemert 1994: C5.2.3-C5.2.28.

3.3.4 Adoption of Innovations in Organizations

New product and service innovations take time to diffuse into markets. The

speed of the diffusion of an innovation depends on different factors that facilitate the

diffusion process. According to Rogers (1995), diffusion is a process where an

innovation is communicated via certain channels, through time, among the individuals

of a social system. Van de Ven (1986) supports the idea that the innovation adoption

is a networking process among people, who become committed to the innovation

through transactions. In other words, the diffusion occurs on the macro level but it

would not be possible without the adoption of the innovation. The adoption is viewed

as a process in which an organization analyses the positive and negative aspects of an

innovation on the basis of gathered knowledge. The actual adoption of an innovation

takes place when the end result of these analyses is a positive one.

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3.3.5 Organizational Adoption Process of Innovations

All types of organizations adopt innovations to respond to changes in their

external and internal environments. Innovations come into organizations in two ways.

They can be generated in an organization or they can be adopted from another

organization (Damanpour and Gopalakrishnan, 1998). According to Damanpour and

Gopalakrishnan (1998), the adoption of an innovation means improved effectiveness

or performance of the adopting organization, and the environment has a great

influence on the decision-making process concerning the adoption of an innovation.

Ozanne and Churchill (1971) argue that the organizational adoption process is a

decision process that eventually leads through the purchase to the implementation of

an innovation. The adoption process is always based on the decision of an individual

or on the consensus of a decision making unit (Van de Ven, 1986; Khalfan et al.,

2001). For the adopting organization, the innovation adoption process (figure 1)

includes awareness of the innovation, attitude formation, evaluation, decision to

adopt, trial implementation and sustained implementation (Rogers, 1995; Frambach et

al., 1998).

Thus the initiation stage in the adoption process begins with awareness of the

innovation’s existence. After this follows the formation of attitudes toward the

innovation and the evaluation of it with the help of the information received from the

markets. On the basis of the received information, the potential adopter forms

favorable and/or unfavorable attitudes toward the innovation and assesses it through

the perceived innovation characteristics (Rogers, 1995). After the initiation, the

organization either adopts or rejects the innovation. The decision to adopt is a

decision to make full use of the innovation as the best course of action available

(Rogers, 1995). Thereafter, the innovation is first implemented on a trial basis, and if

this is successful, the implementation of the innovation will continue and the

innovation becomes systematically used (Zaltman et al., 1973).

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Figure 3.2 Model of Stages in the Innovation-Decision Process

Source: Rogers, 1995.

3.4 HRM and Innovation

Jørgensen, Becker and Matthews (2009) explained Human Resource Management

(HRM) may be defined broadly in terms of all management activities impacting

relationships between organization and employee (Beer et al., 1984) or more specifically

as a system of operational functions such as staffing, selection, job design, training

and (career) development, performance appraisal and compensation (Pfeffer, 1998).

Further, there is an increasing tendency to also consider more strategic level functions

such as human resource planning and forecasting (Koch and McGrath, 1996).

Although there is considerable discussion regarding the relative importance of

specific HRM practices and how they should be configured, there is general

agreement concerning the importance of the alignment between HRM practices and

organizational strategy (Lengnick-Hall and Lengnick-Hall, 1988).

Kesting, Müller, Jørgensen, and Ulhøi (2009) cited Armstrong (2006: 3) as

defining Human Resource Management (HRM) as a strategic and coherent approach

to the management of an organization’s most valued assets – the people working there

who individually and collectively contribute to the achievement of its objectives.

Generally, HRM is associated with rather broadly-defined functions such as staffing,

training and development, remuneration, job design and performance appraisal and

how these can help support improved performance. Although the initial focus in the

field of HRM was often on concrete measures of performance such as quality and

productivity (Cascio, 1991), there is currently much interest on how HRM can support

learning, change and innovation behaviors (Scarbrough and Carter, 2000).

1. Know-ledge

2. Persuasion 3. Decision

4. Imple-mentation

5. Confir-mation

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3.5 Definition of Effectiveness and Efficiency

Effectiveness is a broad concept. It implicitly takes into consideration a range

of variables. Etzioni (1964) defines organizational effectiveness (OE) as the degree to

which an organization realizes its goals while efficiency is a more limited concept that

pertains to the internal workings of the organization. Efficiency is the amount of

resources used to produce a unit of output. Pfeffer and Salancik (1978) defines

effectiveness is an external standard applied to the output or activities of an

organization whereas efficiency is an internal standard of organizational performance.

Cameron (1986) argues that organizational effectiveness necessarily deals with values

and preferences that cannot be determined objectively.

Although there is general agreement that effectiveness is something all

organizations should seek, there is no consensus on what the concept means; and the

criteria for assessment remains unclear. This is because organizations typically pursue

multiple and often conflicting goals and also tend to differ according to the nature of

the organization and its external environment. Moreover, managers conceptualize

organization effectiveness differently; consequently, there is also disagreement over

the most appropriate strategy for attaining effectiveness. Another reason for the lack

of agreement about the nature of effectiveness is the ambiguity of the concept.

Organizational analysts often incorrectly assume that it is relatively easy to identify

the various assessment criteria, but the criteria are somewhat intangible. They depend

largely on who is doing the evaluating and the specific frame of reference. The

problems are pointed out in attempts to identify the relevant aspects of effectiveness

that can serve as useful evaluating criteria.

There is no best criterion for evaluating an organization’s effectiveness. There

is neither a single goal that everyone can agree upon nor a consensus on which goals

take precedence over others. Therefore, the concept of OE itself is subjective. It

depends on who the evaluator is and the interest they represent.

3.5.1 The Relationship between Innovation Effectiveness and Attitude

toward Future Adoption

A positive perception of the benefits of implemented innovations will then also

have knock- on effects to future innovations. Many innovation researchers ( Damanpour,

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1991; Frambach and Schillewaert, 2002; Lehman, Greener and Simpson, 2002) have

identified positive beliefs and motivational readiness as facilitators of implementing

new innovations; much of this positive attitude will come from past experiences with

innovation. Because implementing an innovation can be accompanied by risk,

uncertainty, and expenditures of money, time and effort, organizations must perceive

the direct benefits from innovations to make them worthwhile (West, 2001). This

suggestion is supported by a study of the implementation of organizational websites

by 288 Chamber of Commerce members in the United States (Flanagin, 2000). This

study suggested that the perceived benefit from a technology was one of the best

predictors of its future adoption. Likewise, a survey of 298 companies in Hong Kong

indicated that perceived the benefit is positively related to attitude towards adoption

(Au and Enderwick, 2000).

In conclusion, the table below provides a summary of the conceptual theories.

Table 3.1 Summary of the Conceptual Theories

Conceptual Theories Characteristics

1. Resource-Based View 1.1 Resource-Based View

- Fortuin (2006) asserted that the resource-based view of the

firm (RBV) is an influential theoretical framework for understanding how competitive advantage within firms is achieved and how that advantage can be sustained over time (Schumpeter, 1934; Penrose, 1959; Wernerfelt, 1984; Prahalad and Hamel, 1990; Barney, 1991; Nelson, 1991; Peteraf, 1993; Teece et al., 1997; Scholten, 2006)

- This perspective focuses on a firm’s internal resources and how these are acquired from factor markets, e.g. the labor and financial markets

- RBV builds on two basic assumptions about the firm’s resources: (1) that they can vary significantly across firms (assumption of resource heterogeneity) and (2) that their differences can be stable (assumption of resource immobility)

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1.2 Resource-Based

View in Economics and

Management

1.3 A Resource-Based

View of the Firm’s

Capacity to Innovate

1.4 The Resource-Based

View, the Knowledge-

Based View and

Organizational Learning

Theory

- Within the Economics and Management literature, Lemaire

(2003) suggested that the resource-based view has been

gaining broad intellectual support among scholars

interested in issues related to sustainable competitive

advantage

- The resource-based view perspective is useful for studying

innovation for it adopts a dynamic approach that

emphasizes the importance of resources accumulation

- According to Kostopoulos, Spanos and Prastacos (2001),

one of the most important research questions traditionally

in the management literature has been the relationships

among innovation, firm structural characteristics (e.g.,

formalization, centralization, specialization) and industrial

environment

- From this view, innovation represents a means of

transforming an organization, whether as a response to

changes in its internal or external environment or as a

proactive action taken to influence its environment

(Damanpour, 1991)

- Vanhaverbeke, Cloodt and Van de Vrande (2008)

described the resources, competencies and knowledge

dimension of the open innovation model as,- clearly being

embedded in the resource-based view, the knowledge-

based view and the organizational learning theory of the

firm

2. Organizational Theory

2.1 Theories of Structure

and Performance

- Sine, Mitsuhashi and Kirsch (2006) elaborated upon the

theory of structure and performance, by stating that

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2.2 Organization

Structure: Lessons from

other Innovative

Organizations

- research on the formal structure, which is the structure of

an organization, is typically defined as “the sum total of the

ways in which it divides its labor into distinct tasks and

then achieves coordination among them” (Mintzberg, 1979: 2)

- Stinchcombe (1965) suggested that new ventures in

emerging sectors not only need formalization and

specialization, but also require greater managerial resources

than mature firms

- Whereas mature firms are often impeded by intensive

administration and the structural inertia of legacy

bureaucracies, new ventures need extensive managerial

resources and a structural framework to reduce uncertainty

and increase organizational efficiency and responsiveness

- In all organizations (International Fund for Agricultural

Development-IFAD, 2007), new ideas that improve

effectiveness and efficiency emerge naturally from

people’s desire for better solutions

- High-performing organizations enhance this process by

carefully managing:

(1) People’s competence and motivations

(2) How challenges are understood and goals set.

(3) Diversity and networks among staff and with the

outside world.

(4) Rapid prototyping of new ideas.

(5) A system for retaining ideas with potential.

(6) Core business and innovation processes.

(7) The organizational environment.

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3. Contingency Theory

3.1 Core Assumptions

and Statements

- Core: Wiio (1979) and Goldhaber (1993) concluded that

differences in innovation effectiveness are a function both

of type of organization and composition of work force (age,

sex, education, tenure). The innovation process is

influenced by many internal and external constraints from

the organization and its subsystems

- The innovation process is thus contingent upon external

and internal stimuli and upon the degree of freedom of

states within the system allowed by the organizational

constraints

3.2 Contingency

Approach to

Management

3.3 Adoption of

Innovations in

Organizations

3.4 Organizational

Adoption Process of

Innovations

- Over recent decades, there has been evident a new trend in

the study of organizational phenomena. Underlying this

approach is the idea that the internal functioning of

organizations must be consistent with the demands of

organizational task, technology, or external environment,

and the needs of its members if the organization is to be

effective

- Rogers (1995) argued that adoption is viewed as a process

in which an organization analyses the positive and negative

aspects of an innovation on the basis of gathered

knowledge

- The actual adoption of an innovation takes place when the

end result of these analyses is a positive one

- According to Damanpour and Gopalakrishnan (1998), the

adoption of an innovation means improved effectiveness or

performance of the adopting organization, and the

environment has a great influence on the decision-making

process of the adoption of an innovation

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In summary, this chapter employs the conceptual theory of the resource-based

view theory and organizational contingency theory to explain the determinants of

seven contingency prediction factors, which form the constructs of the conceptual

framework and hypotheses model.

CHAPTER 4

CONCEPTUAL FRAMEWORK AND HYPOTHESES

This chapter describes the overall conceptual framework and model which are

interconnected and explained by the influential theories of both the resources-based

view and the contingency approach to the investigation, with a hypotheses model of

the determinant factors of innovation management effectiveness and the assumption

of the seven contingency prediction factors.

4.1 Conceptual Overview

The variables in this framework are drawn from an integration of the

contingency approach and the resource-based view approach of the determinants of

the organizational innovation management effectiveness as proposed by prominent

scholars (discussed in Chapters 2 and 3). The variables are conceptualized according

to the organizational environmental perspective. Innovation management is implemented

in organizations and always operates as an open system. Changes in one component

of an organization frequently have an effect on the other variables; that is to say, all

variables are interconnected. To be effective, the determinant factors must align with

the other organizational variables-innovation strategy, organization structure,

organization culture, project management, team cohesiveness, strategic leadership and

coordination and communication.

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Figure 4.1 The Conceptual Framework

4.1.1 Innovation Management Effectiveness

Innovation is defined as “the adoption of ideas that are new to the adopting

organization” (Rogers, 1983; Downs and Mohr, 1976). For the organization, it is

about how the organization can gain profits from innovation. Generating a good idea

or adopting a new one, in and of itself, is only a start. Innovation management

Strategic Leadership

Innovation Strategy

Coordination and

Communication

Innovation Management Effectiveness



Project Management

Team Cohesiveness

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involves the management of the processes involved in innovation, converting the idea

into a product or service that customers want. Coming up with the idea or prototype

invention is one thing. Championing it, shepherding it, and nurturing it into a product

or service that customers want is another. Innovation entails both invention and

commercialization. Indeed innovation is generally considered to be one of the key

drivers of organizational success (Schillewaert, Ahearne, Frambach and Moenaert,

2005). There is empirical evidence indicating that implementing innovations improves

organizational performance. For example, a study of manufacturing companies in

Australia and New Zealand showed that implementing total quality management

practices in organizations increased organizational performance (as measured by

percentage growth in sales (Terziovski and Samson, 1999). On the other hand,

innovation can be defined as “a technology or practice that an organization is using

for the first time, regardless of whether other organizations have previously used the

technology or practice” (Klein, Conn and Sorra, 2001: 811). This can therefore be

measured by the overall organizational improvement including productivity, finance

and employee morale.

There are also several factors that can help explain innovation


1) Number of Innovations

In terms of the banking industry, the number of innovations is the

number of activities (frequency) that banks undertake in order to improve their way of

business, such as innovations about ATMs, credit cards, grants, loans, payment

services, cross sales between credit and non credit, etc. within a period of time (Van

Ark, Broersma and De Jong, 1999).

The World Bank (2008) measures the inputs into knowledge

management, such as how much is being spent and the number of programs. The

World Bank also measures the outputs, which involve the number of best practices,

new tools, knowledge nuggets, resources added, or processes put in place. It then

measures the utilization of knowledge products and services such as the number of

unique visitors to a web site, the number of queries on an information and statistics

system, or the number of requests for advisory services. The World Bank also

conducts internal client surveys. Each of the World Bank’s sector boards surveys the

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community concerning what knowledge products are most effective, how often are

they visited, and the respondents’ contributions over the last year.

2) Speed of Innovation

Companies increasingly need to make the stark choice of accepting a

rapid decrease in market share or becoming better informed in order to respond to

customers’ increasing appetite for new and better solutions (Knowledge Partners,

2008).

As a result of increased global competition, products and markets that

remain without change are very rapidly becoming commoditized. It is the intense

competition that drives commoditization and downward pressure on the profit

margins.

In many companies the only way to make a healthy profit margin is to

continually innovate and bring customers new experiences and benefits. For this they

can enjoy an “early mover” premium. As business cycles continue to speed up and

therefore the life-cycles of products get shorter, organizations have to be more agile

and able to continuously innovate to respond to new changes in customers’ needs and

perceptions.

3) Return on Investment of Innovation

A performance measure is used to evaluate the effectiveness of a

company's investment in new products or services. The return on innovation

investment is calculated by comparing the profits of new products or service sales to

the research, development and other direct expenditures generated in creating these

new products or services (Investopedia, 2010).

4) Innovation Value-Added Chain

Afuah (1998) professed that the innovation value-added chain model

can explain both why an incumbent can outperform new entrants at radical

innovation, and why it may also fail at incremental innovation (Porter, 1985). It

differs from previous models in that while these other models focus on the impact of

innovation on a firm’s capabilities and competitiveness, it focuses on what the

innovation does to the competiveness and capabilities of a firm’s suppliers,

customers, and complementary innovators.

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The innovation may have a different impact at each of the stages of the

innovation value-added chain, suggesting that an innovation that is incremental to the

manufacturer may not be to suppliers, customers, or complementary innovators. Thus

incumbents for whom an innovation is competence destroying may still do well if the

innovation is competence enhancing to their value chain, and relations with the chain

are important and difficult to establish. The implications are that a firm’s success in

exploiting an innovation may depend as much on what the innovation does to the

capabilities of the firm as on what it does to the capabilities of its innovation value-

added chain of suppliers, customers and complementary innovators.

Organizational Performance Improvement

Innovation effectiveness is defined as organizational performance

improvement which results from implementing innovations. That is, how an organization

perceives the overall organizational improvement (including factors such as productivity,

finance, and employee morale) which arises specifically from implementing innovations

(Klein et al., 2001).

4.1.2 Innovation Strategy

Ramanujam and Mensch (1985) defined innovation strategy as a timed

sequence of internally consistent and conditional resource allocation decisions that are

designed to fulfill an organization’s objectives in terms of innovation goals,

innovation resource allocation, innovation risk (overall philosophy), timing (first to

market or happy to wait) and a long term perspective. Formulating an innovation

strategy therefore is a matter of policy for those organizations that would like to

pursue a sustainable competitive advantage through innovation.

4.1.2.1 Innovation Focus

Innovation focus involves guidelines for developing innovation and

promoting innovation and goals which are effected to managerial attitude in norm

support for innovation effectiveness. These are the expectations, approval and

practical support of attempts to introduce new concept and improve the way of doing

things in the work environment. Measures are mostly qualitative in nature and explore

perceptions about the extent to which respondents recognize factors to be present or

absent (Damanpour, 1991). Innovation focus will therefore widen from incremental

improvements to system innovations.

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4.1.3 Organization Structure

Mphahlele (2006) stated that the organization structure represents the

development of human resources in different parts of the organization for doing

specific work. Some organizational structures are more advanced in supporting

innovation than others. For example, a steeply hierarchical organization that is

overloaded with strict rules tends to stifle innovation because decision making tends

to be slow. These can be measured in terms of the degree of specific rules and

procedures and the degree of division of function.

4.1.3.1 Rules and Procedures

Centralization, the concentration of decision-making authority at the top

of the organizational hierarchy, and formalization, the degree of emphasis on

following rules and procedures in role performance, have both been shown to have a

negative impact on organizational innovation (Burns and Stalker, 1961; Damanpour,

1991). Indeed, rigidity in rules and procedures may prohibit organizational decision-

makers from seeking new sources of information (Vyakarnam and Adams, 2001).

4.1.3.2 Division of Function

Burns and Stalker (1961) put forward a contingency approach to

innovation management, later developed to include concepts of functional differentiation,

specialization and integration (Lawrence and Lorsch, 1967), which suggests that

environmental change prompts a realignment of the fit between strategy and structure.

That is, to perform effectively, an organization must be appropriately differentiated,

specialized and integrated.

4.1.4 Organization Culture

The topic of organizational culture often presents two contradictory images.

The first is of culture as “the glue that holds the organization together”, and the

second regards it as a central part of the change process (Denison, 2001: 347).

According to Read (1996: 223), post-industrial organizations of today are knowledge-

based organizations and their success depends on creativity, innovation, discovery and

inventiveness. The importance of creativity and innovation is emphasized as follows

by Zaltman et al. (West and Farr, 1990: 3-4) “The importance of new ideas cannot be

overstated. Ideas and their manifestations as practices and products are the core of

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social change.” Measuring organization culture concerns basic cultural norms,

continuous learning support and reward and recognition that it influences creativity

and innovation.

4.1.4.1 Cultural Norms

In the midst of change, organizations and leaders are trying to create an

institutional framework in which creativity and innovation are accepted as basic

cultural norms. It has become clear that “the unwritten rules of the game” (the norms

of behavior) and shared values influence morale, performance and the application of

creativity and innovation in many different ways (Martins and Martins, 2002).

4.1.4.2 Reward and Recognition

In terms of support mechanisms, reward and recognition is a determinant

of organizational culture that influences creativity and innovation (Martins, 2000:

171). While in terms of behavior that encourages innovation, continuous learning

support is also a determinant of organizational culture that influences creativity and

innovation (Martins, 2000: 171).

4.1.5 Project Management

Project management refers to project activity in regard to the management of

innovation studies within organizations on a project-by-project basis. It can be seen

that the research streams for both project management and innovation have developed

in relative isolation from one another, and the connection between the two domains

(project and innovation) is quite often implicit (Keegan and Turner, 2002).

4.1.5.1 Innovation Support

There are several important links between projects and innovation

through three instruments as innovation support, namely context supportive for

innovation, slack resources and greater level of redundancy. In such a situation, the

risk exists that priorities may shift away from projects that are likely to create returns

in the long run, like innovation support, toward measures that promise to address

urgent and short-term challenges. In order to maintain long-term support for

innovation, there is therefore a need for better demonstrating the actual and/or

potential benefits of innovation support instruments.

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4.1.6 Team Cohesiveness Cohesiveness within teams is determined by the desire of its individuals to

remain a part of that team because their individual needs are being met (Turner et al., 1984.). While some research would suggest that cohesiveness can eventually lead to a myopic view taken by the group through groupthink (Janis, 1972), it is still believed that the closeness brought about through cohesiveness can facilitate the members of a team working together more freely with more communication to develop more innovative ideas.

4.1.6.1 Cohesion Mullen and Copper (1994) indicated that an adequate level of cohesion

impacts the performance of innovation teams through its positive influence on communication and coordination. Langfred (1998) also used the degree to which team members identify positively with the team to measure the cohesion shown to influence team innovation.

4.1.6.2 Composition and Longevity For composition, Wallmark (1973) studied the importance of size in

research groups and found a positive correlation between group size and group productivity. This was interpreted as resulting from the greater number of possible contacts and consequent intellectual synergy. While in terms of longevity, Lovelace (1986) suggests that research scientists will be more creative if not assigned to permanent groups, and Nystrom (1979) too argues for the advantages of relatively short-lived groups, at least as far as the early stages of the innovation process are concerned.

4.1.7 Strategic Leadership The strategic leadership view argues that the strategic incentive to invest in an

innovation or the failure to exploit it as a result of destroyed competence comes only after a firm’s top management has recognized the potential of the innovation (Afuah, 1996). Top management makes the decisions to invest in an innovation, or if such decisions are made by lower level managers, they still reflect the beliefs and values of top management (Hambrick and Mason, 1984). However, it is incentive for strategic leadership to invest in an innovation or its ability to embrace and exploit the innovation as a function of the extent to which the firm’s top management recognizes the potential of an innovation is a function of its managerial logic, or view of the

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world (Finkelstein and Hambrick, 1990; Hamel and Prahalad, 1994). This in turn, depends on management experiences, organizational logic, and industry logic. Thus, whether a firm is a new entrant or an incumbent may not matter much. What matters is the strategic leadership’s dominant logic. Strategic leadership is creating and leading the innovation process, developing and coaching the innovators and promoting the innovation (Deschamps, 2005) as measured by the degree of cross-functional collaboration as well as a leader’s behavior and style.

4.1.7.1 Functional Collaboration A role that influences innovation strategy is focusing on networks for

collaboration with internal and external partners, balancing incremental and disruptive innovations as well as developing metrics for feedback (Shelton and Davila, 2005).

4.1.7.2 Leadership Behavior and Style Leadership behavior and style is linked to innovation. Dougherty and

Cohen (1995) found the behavior of senior managers to be influential. Those chief executives most likely to make innovation happen are those with a clear vision of the future operation and direction of organizational change and creativity (Shin and McClomb, 1998).

4.1.8 Coordination and Communication Coordination and communication refer to all routines in the organization, both

internally and externally, namely (Cebon and Newton, 1999; Lee et al., 1996; Rothwell, 1992; Souitaris, 2002) frequency counts and subjective evaluations together with sharing information, closing collaboration and guiding activity which are crucial to the innovation management (Pinto and Pinto, 1990; Mintzberg, 1979).

4.1.8.1 Objective Frequency Counts Objective counts include (Damanpour, 1991) ‘extent of communication

amongst organizational units or groups measured by various integrating mechanisms such as ‘Number of committees and frequency of meetings’, (Anderson and West, 1998) ‘Frequency of formal meetings concerning new ideas’, (Souitaris, 2002; Szakonyi, 1994) and ‘How well do the technical and finance people communicate with one another?’

4.1.8.2 Subjective Evaluations

Subjective measures include: ‘We always consult suppliers/customers on

new product ideas’ (Parthasarthy and Hammond, 2002) and ‘Degree of organization

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members involved and participating in extra-organizational professional activities’

(Damanpour, 1991).

Innovation Strategy

• Innovation Focus


Organization Structure • Functional Collaboration • Leadership Behavior and • Rules and Procedures Style • Division of Function

Organization Culture Innovation

Management • Culture Norm Effectiveness • Reward and • Organizational Recognition Performance

Improvement Coordination and Communication

• Objective Frequency Counts • Subjective Evaluations

Project Management

• Innovation Support Team Cohesiveness

• Cohesion • Composition and Longevity

Figure 4.2 Overall Conceptual Framework and Model

Note: The overall conceptual framework and model is given which explains the link

between each factors, which are all interconnected to one another.

105

4.2 Research Hypotheses

The study is based on the investigation of the determinant factors of

innovation management effectiveness and the assumption that the seven contingency

prediction factors-innovation strategy, organization structure, organization culture,

project management, team cohesiveness, strategic leadership and coordination and

communication- determine innovation effectiveness as a result of sustaining

competitive advantage. The hypotheses are below:

H1: Innovation strategy has a positive effect on innovation management

effectiveness

H2: Organization structure has a positive effect on innovation management

effectiveness

H3: Organization culture has a positive effect on innovation management

effectiveness

H4: Project management has a positive effect on innovation management

effectiveness

H5: Team cohesiveness has a positive effect on innovation management

effectiveness

H6: Strategic leadership has a positive effect on innovation management

effectiveness

H6-1: Strategic leadership has a positive effect on innovation strategy

H7-1: Coordination and communication has a positive effect on organization

structure

H7-2: Coordination and communication has a positive effect on organization

culture

H7-3: Coordination and communication has a positive effect on project

management

H7-4: Coordination and communication has a positive effect on team

cohesiveness

106

Figure 4.3 The Hypotheses Model

The research attempts to test the hypothesis model through Structural Equation

Modeling using the SPSS AMOS program First it begins with innovation strategy

with innovation as the focus in order to improve organizational performance.

Secondly, organization structure with rules, procedures and division function has a

key impact on innovation management effectiveness. Thirdly, the organization culture

of both creativity and innovation are described as basic cultural norms and rewards




Project Management

Team Cohesiveness

Innovation Strategy

H1

H2

H3

H4

H5


Coordination and

Communication

H6

H6-1

H7-1

H7-2

H7-3

H7-4

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and recognition are based creativity and innovation in the organization. Fourth,

project management literature often takes a rather normative stance toward innovation

support being the best way of managing innovation project effectiveness. While, fifth,

team cohesiveness can certainly benefit the understanding of various factors such as

cohesion, composition and longevity in the context of organizations and greatly

influence team cohesiveness. Sixth, strategic leadership with the ability to engage in

transformational or transactional behaviors, depending on the specific innovation

needs with functional collaboration and leadership behavior and style. Lastly,

coordination and communication is very important to organizational improvement

with objective frequency counts and subjective evaluations and also crucial for

innovation management effectiveness.

4.3 Operationalization of Variables For describing how the researcher operationalized the construct or variables, the tables below demonstrate the operationalization for each variable. Table 4.1 Operationalization of Variables

Variables Definitions Operationalization References


- Innovation management effectiveness is defined as the adoption of ideas that are new to the adopting organization which indicates that the implementation of innovation improves organizational performance, productivity, finance and employee morale

- Overall organizational improvement

- Productivity, finance and employee morale

- Klein, Conn and Sorra (2001)

- Terziovski and Samson (1999)

- Schillewaert, Ahearne, Frambach and Moenaert (2005)

- Rogers (1983) - Downs and

Mohr (1976)

108



Innovation

Strategy

- Innovation strategy is

defined as a timed sequence

of internally consistent and

conditional resource

allocation decisions that are

designed to fulfill an

organization’s objectives in

terms of innovation

measured by innovation

focus such as guidelines for

innovation, organization

factors that promote

innovation and the

importance of innovation

goals

- Guidelines for

developing

innovation

- Factors that

promote innovation

- Innovation goals

- Ramanujam and

Mensch (1985)

- Mphahlele(2006)

- Bessant (2003)

- Martins and Ter-

blanche (2003)

- Vlok (2005)

- Dewar and

Dutton (1986)

- Deschamp(2005)

- Foster and Pryor

(1986)

- Anthony et al.

(2006)

Organization

Structure

- Organization structure

represents the development

of human resources in

different parts of the

organization for doing

specific work as measured

in terms of the degree of

specific rules and

procedures and of division

of function

- Degree of rules and

procedures

- Degree of division

of function

- Mphahlele

(2006)

- Zaltman, Duncan

and Holbek

(1973)

- Burns and

Stalker (1961)

- Damanpour

(1991)

Organization

Culture

- Organizational culture

concerns basic cultural

norms, continuous

- Creativity and

innovation as basic

cultural norms

- Martins and

Martins (2002)

- Martins (2000)

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learning support and

reward and recognition

that influence creativity

and innovation

- Continuous learning

support Reward

and recognition

- Judge et al.

(1997)

- Nÿstrom (1990)

O’Reilly (1989)

Project

Management

- Project management

refers to project

activity in regards to

the management of

innovation studies

within organizations

on a project-by-project

basis and the link

between project and

innovation is

supported by three

instruments as

innovation support,

namely context

supportive of

innovation, slack

resources and level of

redundancy

- Context supportive

of innovation

- Level of tolerance

for slack resources

- Greater level of

redundancy

- Adams, Bessant

and Phelps

(2006)

- Keegan and

Turner (2002)

Team

Cohesiveness

- Determined by the

individuals within the

team having the desire to

remain a part of that

team because their

individual needs are

- Degree of

cohesiveness

- Level of

composition and

longevity

- Turner, Hogg,

Turner and

Smith (1984)

- Janis (1972)

- McDowell and

Zhang (2009)

110



being met

- Through cohesiveness can

facilitate the members of

team working together

more freely with more

communication to develop

more innovative ideas

- Guzzo and

Dickson (1996)

Strategic

Leadership

- Strategic leadership is

creating and leading the

innovation process,

developing and coaching

the innovators and

promoting the innovation

focused on the

collaboration with

internal and external

partners, balancing

incremental and

disruptive innovations

and the leader’s behavior

and style

- Degree of cross-

functional

collaboration

- Degree of leader’s

behavior and style

- Deschamps

(2005)

- Shelton and

Davila (2005)

- Jansen, Vera

and Crossan

(2009)

Coordination

and

Communication

- Coordination and

Communication refer

to all routines in the

organization

information both

internal and external

by objective

- Objective

frequency counts

(Number of

Committees and

Frequency of

Meetings)

- Subjective

- Damanpour

(1991)

- Cebon and

Newton (1999)

- Lee, Son and

Lee (1996)

- Rothwell(1992)

111



frequency counts and

subjective evaluations

together with sharing

information, closing

collaboration and

guiding activity which

is crucial to innovation

management

consultation and

evaluations

- Souitaris(2002)

- Partgasarthy

and Hammond

(2002)

- Anderson and

West (1998)

- Szakonyi

(1994)

In conclusion, this chapter discusses the conceptual framework and hypothesis

model which are comprised of the determinants of seven contingency factors. In

addition, the researcher has proposed the number of research hypotheses to be tested

in order to understand their relationships of variables and constructs and how they can

be operationalization in terms of each variable.

CHAPTER 5

RESEARCH METHODOLOGY

This chapter describes the research methodology applied in analyzing the

relationships of the factors affecting innovation management effectiveness in the

banking industry. Prior to conducting the research, several key areas were addressed

such as the research design, units of analysis, target population, sampling and method

of analysis for the use in the data analysis. The measurement and sampling

procedures aim at improving the reliability of observations, facilitate imitation

studies, and allow generalization to a larger population.

5.1 Research Design

The primary objectives of the research are to study and test a theoretical

framework and model of seven contingency factors for explaining the output of the

factors of the determinants of organizational innovation management effectiveness in

sustaining competitive advantage.

The research study also focuses on the determinant factors, attempting to

employ the concept of alignment in contingency theory and resource-based view

theory and to explain the determinants of the seven contingency prediction factors of

innovation strategy, organization structure, organization culture, project management,

team cohesiveness, strategic leadership and coordination and communication. The

research examines whether the outputs of all these factors impact the increase in

innovation management effectiveness at both commercial and state-owned in the

selected banks.

The primary data was collected through questionnaires from the non

probability sampling population of the staff of both commercial banks (SCB and

KBank) and state-owned banks (KTB and GHB). The research methodology is also

considered critical to the success of the determinants of organizational innovation


113

5.2 Approaches of the Study

The research is based on surveys in which data were collected from a sample

of the target population through both quantitative and qualitative methods. This work

can be categorized as exploratory research- which tries to explain the relationships

between the determinants of the seven contingency prediction factors to the increasing

in innovation management effectiveness.

The qualitative research method was conducted using both secondary and

primary data of the four selected banks. Quantitative analysis was conducted through

the Statistical Package for the Social Sciences (SPSS) program adopting descriptive

analysis and independent samples t-test techniques and also the Analysis of Moment

Structures (AMOS) program adopting the structural equation modeling.

However, the research study focuses on the causal relationships among

quantitative constructs and an empirical study is conducted. Thus, the quantitative

method is the main method in this study. Other research involved the literature review

of documents and from the internet, wherever possible.

5.3 Units of Analysis

The units of analysis are the entities or objectives of the study which are

generally formed in a hierarchical order including the higher levels, such as the

organizational level, the lower level such as the individual level (Babbie, 1999). In

this research, the units of analysis are at the individual level which requires individual

to answer the questionnaires so as to find the determinants of organizational innovation


5.4 Target Population

The target population in this study is the selected employees of the four banks-

the two commercial banks Siam Commercial Bank (SCB) and Kasikorn Bank

(KBank) and the two state-owned banks Krung Thai Bank (KTB) and Government

Housing Bank (GHB), whose number of branches as shown in below.

114

Table 5.1 The Number of Bank Branches in the Study

Bank Number of Branches


Kasikorn Bank

Krung Thai Bank

Government Housing Bank

1,005

818

931

150

Total 2,904

Source: Bank of Thailand, 2010.

The approximate number of staff per branch is 19.66. Thus, the number of

staffs in these banks is approximately 57,092 as shown, in the table 5.2

Table 5.2 The Number of Staff of the Four Banks in the Study

Bank Number of Staff


Kasikorn Bank

Krung Thai Bank


19,758

16,082

18,303

2,949

Total 57,092

5.5 Sampling

The researcher formulated the sample size by employing proportional

stratified method, using Taro Yamane’s formula (1967) to give the minimum sample

size at a confidence level of 95% as shown below:-

Taro Yamane’s formula 2Ne1Nn

+=

n = Sample size

N = Number of population

e = Error rate of sample

115

With reference to Yamane (1967), the researcher used the sampling table

given below to calculate the sample size.

Table 5.3 Sample Size for ±1%, ±5% and ±10% Precision Levels Where the Confident

Level is 95%

Sample Size (n) for Precision (e) of: Size of Population

±1% ±5% ±10%

500

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

20,000

30,000

40,000

50,000

57,092

100,000

>100,000

*

*

*

*

*

*

*

*

*

*

5,000

6,667

7,500

8,000

8,333

8,510

9,091

10,000

222

286

333

353

364

370

375

378

381

383

385

392

395

396

397

397

398

400

83

91

95

97

98

98

98

99

99

99

99

100

100

100

100

100

100

100

Source: Yamane, 1967: 1-405.

Note: * is assumption of normal population that is poor, the entire population should be

sampled.

116

According to table 5.3, the table shows if the size of population is 57,092 for a

precision level of ±5% at confidential level of 95%, the sample size is 397. In

addition, the minimum size for structural equation modeling should be 150 (Anderson

and Gerbing, 1988), while Hair, Black, Babin, Anderson and Tatham (2006)

concluded that sample size of 200 is small but reasonable which the sample size in

this study passes the criteria. The sample size for the four banks that will be

contributed by simple random sampling will be shown as below.

Table 5.4 The Population and Sample of the four Banks in the Study

Number of Staff Bank

Population Sample


Kasikorn Bank

Krung Thai Bank


19,758

16,082

18,303

2,949

137

112

127

21

Total 57,092 397

5.6 Method of Analysis

The researcher used descriptive statistics and the t-test to analyze the data

using SPSS program as well as the AMOS program to analyze the data in terms of

structural equation modeling.

5.6.1 Descriptive Statistics

Descriptive statistics describe the main features of a collection of data

quantitatively (Mann, 1995). Descriptive statistics are distinguished from inferential

statistics (or inductive statistics), in that descriptive statistics aim to summarize a data

set quantitatively without employing a probabilistic formulation (Dodge, 2003), rather

than use the data to make inferences about the population that the data are thought to

represent. Even when a data analysis draws its main conclusions using inferential

statistics, descriptive statistics are generally also presented. For example in a paper

117

reporting on a study involving human subjects, there typically appears a table giving

the overall sample size, sample sizes in important subgroups (e.g., for each treatment

or exposure group), and demographic or clinical characteristics such as the average

age, the proportion of subjects of each gender, and the proportion of subjects with

related commodities.

5.6.2 Independent Samples T-Test

The t-test is the most commonly used method to evaluate the differences in

means between two groups. Theoretically, the t-test can be used even if the sample

sizes are very small (e.g., as small as 10; some researchers claim that even smaller

number are possible), as long as the variables are normally distributed within each

group and the variation of scores in the two groups is not reliably different (StatSoft,

Inc., 2010). As mentioned before, the normality assumption can be evaluated by

looking at the distribution of the data (via histograms) or by performing a normality

test. The equality of variances assumption can be verified with the F test, or the more

robust Levene's test. If these conditions are not met, then the differences in means

between two groups can be evaluated one of the nonparametric alternatives to the t-

test (StatSoft, Inc., 2010).

5.6.3 Structural Equation Modeling

Structural Equation Modeling (SEM) is a statistical technique for testing and

estimating causal relations using a combination of statistical data and qualitative

causal assumptions. This definition of SEM was articulated by the geneticist Sewall

Wright (1921), the economist Trygve Haavelmo (1943) and the cognitive scientist

Herbert Simon (1953), and formally defined by Judea Pearl (2000) using a calculus of

counterfactuals.

5.6.3.1 Fundamentals of Structural Equation Modeling

The use of SEM is predicated on a strong theoretical model by which

latent constructs are defined (measurement model) and these constructs are related to

each other through a series of dependence relationships (structural model). The

emphasis on strong theoretical support for any proposed model underlies the

confirmatory nature of most SEM applications.

118

However, many times overlooked is exactly how the proposed structural

model is translated into structural relationships and how their estimation is

interrelated. Path analysis is the process wherein the structural relationships are

expressed as direct and indirect effects in order to facilitate estimation. The importance of

understanding this process is not so that the research can understand the estimation

process, but instead to understand how model specification (and respecification)

impacts the entire set of structural relationships.

Structural Equation Models (SEM) allows both confirmatory and

exploratory modeling, which are both suited to theory testing and theory development.

Confirmatory modeling usually starts out with a hypothesis that gets represented in a

causal model. The concepts used in the model must then be operationalized to allow

testing of the relationships between the concepts in the model. The model is tested

against the obtained measurement data to determine how well the model fits the data.

The causal assumptions embedded in the model often have falsifiable implications

which can be tested against the data (Bollen and Long, 1993).

With an initial theory SEM can be used inductively by specifying a

corresponding model and using data to estimate the values of free parameters. Often

the initial hypothesis requires adjustment in light of model evidence. When SEM is

used purely for exploration, this is usually in the context of exploratory factor analysis

as in psychometric design.

Among the strengths of SEM is the ability to construct latent variables:

variables which are not measured directly, but are estimated in the model from several

measured variables each of which is predicted to 'tap into' the latent variables. This

allows the modeler to explicitly capture the unreliability of measurement in the

model, which in theory allows the structural relations between latent variables to be

accurately estimated. Factor analysis, path analysis and regression all represent

special cases of SEM.

In SEM, the qualitative causal assumptions are represented by the

missing variables in each equation, as well as vanishing covariance among some error

terms. These assumptions are testable in experimental studies and must be confirmed

judgmentally in observational studies.

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5.6.3.2 Model Specification

When SEM is used as a confirmatory technique, the model must be

specified correctly based on the type of analysis that the researcher is attempting to

confirm. When building the correct model, the researcher uses two different kinds of

variables, namely exogenous and endogenous variables. The distinction between these

two types of variables is whether the variable regresses on another variable or not. As

in regression the dependent variable (DV) regresses on the independent variable (IV),

meaning that the DV is being predicted by the IV. In SEM terminology, other

variables regress on exogenous variables. Exogenous variables can be recognized in a

graphical version of the model, as the variables sending out arrowheads, denoting

which variable it is predicting. A variable that regresses on a variable is always an

endogenous variable, even if this same variable is also used as a variable to be

regressed on. Endogenous variables are recognized as the receivers of an arrowhead

in the model.

It is important to note that SEM is more general than regression. In

particular a variable can act as both an independent and dependent variable.

The two main components of models are distinguished in SEM: the

structural model showing potential causal dependencies between endogenous and

exogenous variables, and the measurement model showing the relations between

latent variables and their indicators. Exploratory and Confirmatory factor analysis

models, for example, contain only the measurement part, while path diagrams can be

viewed as an SEM that only has the structural part.

In specifying the pathways in a model, the modeler can posit two types

of relationships: (1) free pathways, in which hypothesized causal (in fact counterfactual)

relationships between variables are tested, and therefore are left 'free' to vary, and (2)

relationships between variables that already have an estimated relationship, usually

based on previous studies, which are 'fixed' in the model.

5.6.3.3 Estimation of Free Parameters

Parameter estimation is done by comparing the actual covariance

matrices representing the relationships between variables and the estimated

covariance matrices of the best fitting model. This is obtained through the numerical

maximization of a fit criterion as provided by maximum likelihood estimation,

120

weighted least squares or asymptotically distribution-free methods. This is often

accomplished by using a specialized SEM analysis program of which several exist.

5.6.3.4 Assessment of Fit

Assessment of fit is a basic task in SEM modeling: forming the basis for

accepting or rejecting models and, more usually, accepting one competing model over

another. The output of SEM programs includes matrices of the estimated relationships

between variables in the model. Assessment of fit essentially calculates how similar

the predicted data are to matrices containing the relationships in the actual data.

Formal statistical tests and fit indices have been developed for these

purposes. Individual parameters of the model can also be examined within the

estimated model in order to see how well the proposed model fits the driving theory.

Most, though not all, estimation methods make such tests of the model possible.

Of course as in all statistical hypothesis tests, SEM model tests are based

on the assumption that the correct and complete relevant data have been modeled. In

the SEM literature, discussion of fit has led to a variety of different recommendations

on the precise application of the various fit indices and hypothesis tests.

There are several fit indices for model assessment. With the reference to

Hair et al. (2006), Hu and Bentler (1999), MacCallum and Austin (2000), main fit

indices are used for model assessment, including Comparative Fit Index (CFI),

Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), and Root Mean Square

Error of Approximation (RMSEA). Many researchers have studied fit indices to

identify if the model fits with the data and several fit indices are used, including CFI,

NFI, NNFI and RMSEA. Hair et al. (2006) compiled guidelines for setting up

acceptable fit indices. They recommended the use of three to four fit indices to show

evidence of model fit, but that to report to all of the fit indices seems to be

unnecessary. Only one absolute index should be reported. In conclusion, Hair et al.,

(2006) and Katsikea, Theodosiou, Morgan and Papavassiliou (2005) suggested a

model that provides the fit indices of CFI and RMSEA can assess model fit with the

appropriate information.

According to Schumacker (1992) and Kenny (2008), the fit indices are

shown inthe formula below:

121

1) Comparative Fit Index (CFI)

The Comparative Fit Index (CFI) measures model fit directly

based on the non-centrality measure.

CFI = Model) d(Null

Model) d(Proposed - Model) d(Null

Where:

d = χ2 - df where df are the degrees of freedom of the model.

For the value of CFI, if after calculating the index is greater than

one, it is set at one and if less than zero, it is set to zero. CFI is based on the average

size of the correlations in the data and if the average correlation between variables is

not high, then the CFI will not be very high. CFI is greater than 0.9, indicating that the

model is well-fitted.

2) Bentler Bonett Index or Normed Fit Index (NFI)

The main concept of the NFI is to define the null model as a

model in which all of the correlations or covariance are zero. The null model is also

referred to as the independence model.

NFI = Model) (Nullχ

Model) (Proposedχ - Model) (Nullχ2

22

The NFI of 0.90 to 0.95 is acceptable, and above 0.95 is good.

3) Tucker Lewis Index or Non-Normed Fit Index (NNFI)

A problem with the the NFI is that there is no penalty for

adding parameters. NNFI adds χ2/df and therefore,

NNFI= 1-Model) /df(Nullχ

Model) ed/df(Proposχ - Model) /df(Nullχ2

22

When NNFI is larger than one, it is set at one. An NNFI of

0.90 to 0.95 is acceptable, and above 0.95 is good.

Note: NNFI is labeled in AMOS as the Tucker Lewis Index

(TLI)

4) Root Mean Square Error of Approximation (RMSEA)

This measure is based on the non-centrality parameter.

RMSEA= 1)] - 1)/(N - /df[(χ 2

122

Where:

N = sample size and df = the degrees of freedom of the model.

If χ2 is less than df, then the RMSEA is set to zero. The RMSEA

is less than 0.08, this indicates an acceptable model fit.

According to MacCallum and Austin (2000: 201-226), RMSEA

is strongly recommended for use as one of the fit indices for structural equation

modeling because it is appropriately sensitive to the misspecification model, a

commonly used interpretative guideline for the conclusions about model quality and

helpful for building confidence intervals around the its values.

Therefore, the researcher of this study reports the four fit

indices, CFI, NFI, NNFI and RMSEA in order to indicate the model fit and in this

way the researcher can analyze other aspects of the model after identifying the model fit.

Table 5.5 Measurement of the Structural Model Fit

Items Criteria

Comparative Fit Index (CFI)

Normed Fit Index (NFI)

Non-Normed Fit Index (NNFI)

Root Mean Square Error of Approximation (RMSEA)

>0.90

>0.90

>0.90

<0.08

Source: Hu and Bentler, 1999: 1-55; Hair et al., 2006: 1-405.

5.7 Measurement of Reliability and Validity

In section, the researcher discusses the reliability and validity of the research.

5.7.1 Reliability

According to Hair et al. (2006), and Katsikea et al. (2005), reliability means

the degree to which measures are free from error and therefore yield consistent

results. Hair et al. (2006) have also suggested that Cronbach’s alpha can be used as a

measurement. To be reliable, the Cronbach’s alpha should exceed the threshold of

0.70, although a 0.60 level can be used in exploratory research.

123

5.7.2 Pretesting

The researcher conducted a pretest to test the reliability of the questionnaires

using three banks (Bangkok Bank, Siam Commercial Bank and United Overseas

Bank), which were excluded from the main study, and these data were used only for

the testing the reliability of pretesting.

Table 5.6 Questions from the Pretesting Questionnaire

Constructs Observed Variables

Innovation management

effectiveness

IME1. Compared to the last year, your bank has

improved its overall organizational performance.


improved productivity.


improved employee morale.


improved employee competency.

Innovation strategy IS1. Your bank has guidelines for developing

organizational innovation.

IS2. Your bank has promoted innovation.

IS3. Your bank plans to set up innovation goals.

Organization structure OS1. Your bank has specific rules for managing

innovation.

OS2. Your bank has specific procedures for managing

innovation.

OS3. Your bank has established a new division of

functional differentiation.


functional specialization.


functional integration.

124


Constructs Observed Variables

Organization culture OC1. Your bank has promoted cultural norms of

innovation behavior.

OC2. Your bank has supported continuous learning.

OC3. Your bank has supported reward.

OC4. Your bank has supported recognition.

Project management PM1. Your bank has slack resources to support


PM2. Your bank has greater levels of time management

for innovation.

PM3. Your bank has greater levels of operation

management for innovation.

PM4. Your bank has greater levels of creativity for

innovation management.

Team cohesiveness TC1. Your bank supports team cohesiveness.

TC2. Your bank has put together quality teamwork.

TC3. Your bank strengthens the longevity relationship.

TC4. Your bank continues to build team cohesiveness.

Strategic leadership SL1. Your bank encourages collaboration with regard to


SL2. Your bank has supported cross functional

collaboration with regard to innovation management

effectiveness.

SL3. Your bank has empowered its leader.

SL4. Your bank has supported innovative leadership.

Coordination and

communication

CC1. Your bank has set up a number of committees.

CC2. Your bank always has regular project meeting.

CC3. Your bank always supports consultation.

CC4. Your bank always supports evaluation.

125

The questionnaires employed a Likert 7 rating scale (1 = lowest degree of

agreement to 7 = highest degree of agreement).

Table 5.7 The Reliability Analysis of the Questionnaire from Pretesting

Constructs Conbrach’s Alpha

Innovation management effectiveness (IME1-IME4) (4

items)

Innovation strategy (IS1-IS3) (3 items)

Organization structure (OS1-OS5) (5 items)

Organization culture (OC1-OC4) (4 items)

Project management (PM1-PM4) (4 items)

Team cohesiveness (TC1-TC4) (4 items)

Strategic leadership (SL1-SL4) (4 items)

Coordination and communication (CC1-CC4) (4 items)

0.860

0.929

0.934

0.935

0.944

0.956

0.920

0.926

Note: The pre-testing was conducted on three banks which were then excluded from the

study

Regarding the reliability analysis of pretesting, the results of the analysis are

given from the highest Conbrach’s alpha score to the lowest: Team cohesiveness - 4

items (0.956), Project management - 4 items (0.944), Organization culture - 4 items

(0.935), Organization structure - 5 items (0.934), Innovation strategy - 3 items

(0.929), Coordination and communication - 4 items (0.926), Strategic leadership - 4

items (0.920), and Innovation management effectiveness - 4 items (0.860) respectively.

Nevertheless, all of the constructs provided high reliability with a Cronbach’s alpha of

greater than 0.8, which means that the questionnaire has high reliability.

126

Table 5.8 Pretesting of Descriptive Statistics from the Three Banks

Observed Items Number of Respondent Mean Std. Deviation

IME1

IME2

IME3

IME4

IS1

IS2

IS3

OS1

OS2

OS3

OS4

OS5

OC1

OC2

OC3

OC4

PM1

PM2

PM3

PM4

TC1

TC2

TC3

TC4

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

5.08

5.48

5.20

5.18

5.20

5.15

5.20

4.53

4.58

5.03

5.03

4.78

4.83

5.15

4.83

4.80

4.68

4.90

4.95

4.97

4.13

4.70

4.33

4.33

1.141

1.062

1.471

1.130

1.067

1.075

1.067

1.198

1.217

1.271

1.250

1.349

1.152

1.406

1.238

1.265

1.163

0.928

1.085

1.000

1.800

1.224

1.655

1.685

SL1

SL2

SL3

SL4

40

40

40

40

4.90

4.90

5.23

5.18

1.128

1.128

1.271

1.259

127


Observed Items Number of Respondent Mean Std. Deviation

CC1

CC2

CC3

CC4

40

40

40

40

5.38

5.28

5.10

5.15

1.213

1.485

1.317

1.350

In the pretesting, 40 observed variables were studied for ten constructs. The

range of the mean of these items ranged from 4.13 to 5.48.

5.7.3 Validity

Validity means the ability of the scale or measuring instrument to measure

what it is intended to measure (Hair et al., 2006). In addition, with reliability

established, two major types of validity should be assessed:

- Convergent validity – scale correlates with other like scales

- Discriminant validity – scale is sufficiently different from other related scales

Hair et al. (2006) have stated that construct validity is the ability of a measure

to confirm a network of related hypotheses generated from a theory based on the

concepts. It also implies that the empirical evidence generated by a measure is

consistent with the theoretical logic about the concepts. Convergent validity is the

extent to which the scale correlates positively with other measures of the same

construct. Furthermore, convergent validity is assessed by using the t-statistics for the

path coefficients for the latent construct to the corresponding items of the observed

data (Anderson and Gerbing, 1988; Sabherwal and Becerra-Fernandez, 2003). In

addition, Sin, Tse and Yim (2005) have stated that convergent validity represents the

degree of agreement in at least two measures or observed variables of the same

construct. Fornell and Larcker (1981) have suggested that convergent validity is

established when the variance extracted value is higher than 0.4 for one factor.

Discriminant validity means the ability of some measures to have a low

correlation with measures of dissimilar concepts (Hair et al., 2006). Discriminant

validity is the extent to which a measure does not correlate with other constructs from

which it is supposed to differ. For discriminant validity, Fornell and Larcker (1981)

128

and, Sin, Tse and Yim (2005) indicated that discriminant validity showed the degree

that measures of conceptually distinct construct are different from one another. The

pairwise correlations between constructs or factors received from the factor-correlated

model were compared with the variance extracted estimates for the dimensions

constituting each possible pair. To prove the existence of discriminant validity,

variance extracted estimates must be higher than the square of the correlation between

the factors constituting each pair. According to discriminant validity analysis, all

constructs indicated that they were distinct from one another, meeting the criteria of

the chi-square difference of each pair of constructs exceeding 3.841.

5.8 Research Process

Data Collection

Reliability Analysis

Convergent Validity

Discriminant Validity

Data Meet Criteria for Further

Analysis with Structural

Equation Modeling

Model Assessment with Fit

Indices

Research Findings

Conclusions and Implications

Figure 5.1 The Research Process of This Study

129

In summary, this chapter discussed the research process and provided the

sampling methods with appropriate sample size for structural equation modeling

(n>200). The results of the pretesting showed that the questions in the questionnaires

provided a Cronbach’s alpha score of greater than 0.80, thereby reflecting the high

reliability of the construct. Moreover, the researcher discussed the convergent and

discriminant validity to be tested in order to ensure that the data were suitable for

further analyses with structural equation modeling (SEM). Additionally, the fit

indices, such as CFI and RMSEA, were identified and the criteria for the use of each

fit index were elaborated.

CHAPTER 6

DATA ANALYSIS

This chapter provides the results of the descriptive statistics and t-test,

including reliability analysis and structural equation model using AMOS for

convergent validity, and discriminant validity by examining whether the constructs

are suitable for further analysis. So, the researcher has developed the models with the

seven main constructs- innovation strategy, organization structure, organization

culture, project management, team cohesiveness, strategic leadership and coordination

and communication- as factors affecting innovation management effectiveness. In this

process, the models were developed in through a step-by-step process to show how

each model met the criteria of model fit. Finally, the most appropriate model was

identified to help test the research hypotheses. In the previous chapter, the researcher

showed how the results could explain and support the research hypotheses proposed.

6.1 Characteristics of the Respondents

The characteristics of the respondents in the study are important to ensure that

the questionnaires are distributed to the right group of participants who fit the correct

criteria. This section describes the main characteristics of the respondents.

The population of this study comprised commercial banks (SCB and KBank)

and state-owned banks (KTB, GHB) as explained in the previous chapter. The

participants were invited to respond to the personal data section in the questionnaire.

This form was primarily designed to obtain the background information concerning

each respondent’s gender, education, workplace (which type of bank), work experience

and work position. These data are detailed in the following tables.

131

Table 6.1 Gender of the Respondents

Gender Frequency Percent

Male

Female

183

214

46.1

53.9

Total 397 100.0

Table 6.2 Education Level of the Respondents

Education Level Frequency Percent

Bachelor’s degree

Master’s degree

Doctor’s degree

237

159

1

59.7

40.1

0.3

Total 397 100.0

Table 6.3 Workplace of the Respondents

Bank Frequency Percent


Kasikorn Bank

Krung Thai Bank


137

112

127

21

34.5

28.2

32.0

5.3

Total 397 100.0

Table 6.4 Workplace of the Respondents

Type of Bank Frequency Percent

Commercial Bank

State-owned Bank

249

148

62.7

37.3

Total 397 100.0

132

Table 6.5 Work Experience of the Respondents

Work Experience Frequency Percent

1 year – 5 years

6 years – 10 years

11 years – 20 years

More than 20 years

131

27

155

84

33.0

6.8

39.0

21.2

Total 397 100.0

Table 6.6 Work Position of the Respondents

Work Position Frequency Percent

Manager

Assistant manager

Chief

Officer

148

26

47

176

37.3

6.5

11.8

44.3

Total 397 100.0

6.2 Result of the Descriptive Statistics

In this section, the researcher describes the statistically variable constructs.

The are many constructs comprise innovation management effectiveness, innovation

strategy, organization structure, organization culture, project management, team

cohesiveness, strategic leadership and coordination and communication. The observed

variables, they were measured on a scale from 1 to 7.

133

Table 6.7 Descriptive Statistics of Observed Variables

Constructs Observed Variables Mean S.D. Min Max

IME1. Compared to the last year, your

bank has improved the overall

organizational performance.

5.23 1.040 1 7


bank has improved productivity.

5.44 1.030 2 7


bank has improved employee morale.

5.33 1.080 2 7

Innovation

management

effectiveness


bank has improved employee

competency.

5.26 1.097 1 7

IS1. Your bank has guidelines for

developing organizational innovation.

5.26 1.150 1 7

IS2. Your bank has promoted

innovation.

5.21 1.176 1 7

Innovation

strategy

IS3. Your bank plans to set up

innovation goals.

5.17 1.168 1 7

OS1. Your bank has specific rules for

managing innovation.

4.78 1.154 1 7

OS2. Your bank has specific

procedures for managing innovation.

4.83 1.195 2 7

OS3. Your bank has established a new

division of functional differentiation.

5.28 1.113 2 7


division of functional specialization.

5.16 1.124 1 7

Organization

structure


division of functional integration.

5.07 1.196 1 7

OC1. Your bank has promoted cultural

norms of innovation behavior.

4.89 1.195 1 7

OC2. Your bank has supported

continuous learning.

5.34 1.157 2 7

Organization

culture

OC3. Your bank has supported reward. 5.14 1.344 1 7

134



OC4. Your bank has supported

recognition.

5.11 1.310 1 7

PM1. Your bank has slack resources to

support innovation management

effectiveness.

4.91 1.230 1 7

PM2. Your bank has greater levels of

time management for innovation.

4.93 1.177 1 7


operation management for innovation.

4.96 1.144 1 7

Project

management


creativity for innovation management.

5.02 1.145 1 7

TC1. Your bank supports team

cohesiveness.

4.77 1.337 1 7

TC2. Your bank has put together of

quality teamwork.

4.87 1.243 1 7

TC3. Your bank strengthens the

longevity relationship.

4.75 1.402 1 7

Team

cohesiveness

TC4. Your bank continues to build

team cohesiveness.

4.74 1.352 1 7

SL1. Your bank encourages

collaboration with regard to innovation


4.98 1.180 1 7

SL2. Your bank has supported cross

functional collaboration with regard to


4.89 1.190 1 7

SL3. Your bank has empowered its

leader

5.13 1.101 1 7

Strategic

leadership

SL4. Your bank has supported

innovative leadership.

5.15 1.152 2 7

135



CC1. Your bank has set up a number of

committees.

5.28 1.141 1 7

CC2. Your bank always has regular

project meeting.

5.20 1.176 2 7

CC3. Your bank always supports

consultation.

5.00 1.163 1 7

Coordination

and

communication

CC4. Your bank always supports

evaluation.

5.07 1.192 1 7

Work experience (years) 13.52 9.138 1 34

The means of all the data are for the observed variables in the range of 4.74 to

5.44 (see table 6.7). The question with the highest mean is “Compared to the last year,

your bank has improved productivity” with a mean of 5.44, the second highest mean

is “Your bank has supported continuous learning” with a mean of 5.34 and the lowest

mean is “Your bank continues to build team cohesiveness” with a mean of 4.74.

For the innovation management effectiveness construct, the question with the

highest mean is “Compared to the last year, your bank has improved productivity”

with a mean of 5.44 while the question with the lowest mean is “Compared to the last

year, your bank has improved the overall organizational performance” with a mean of

5.23.

For the innovation strategy construct, the question with the highest mean is

“Your bank has guidelines for developing organizational innovation” with a mean of

5.26 while the question with the lowest mean is “Your bank plans to set up innovation

goals” with a mean of 5.17.

For the organization structure construct, the question with the highest mean is

“Your bank has established a new division of functional differentiation” with a mean

of 5.28 while the question with the lowest mean is “Your bank has specific rules for

managing innovation” with a mean of 4.78.

For the organization culture construct, the question with the highest mean is

“Your bank has supported continuous learning” with a mean of 5.34 while the

136

question with the lowest mean is “Your bank has promoted cultural norms of

innovation behavior” with a mean of 4.89.

For the project management construct, the question with the highest mean is

“Your bank has greater levels of creativity for innovation management” with a mean

of 5.02 while the question with the lowest mean is “Your bank has slack resources to

support innovation management effectiveness” with a mean of 4.91.

For the team cohesiveness construct, the question with the highest mean is

“Your bank has put together quality teamwork” with a mean of 4.87 while the

question with the lowest mean is “Your bank continues to build team cohesiveness”

with a mean of 4.74.

For the strategic leadership construct, the question with the highest mean is

“Your bank has supported innovative leadership” with a mean of 5.15 while the

question with the lowest mean is “Your bank has supported cross functional

collaboration with regard to innovation management effectiveness” with a mean of

4.89.

For the coordination and communication construct, the question with the

highest mean is “Your bank has set up a number of committees” with a mean of 5.28

while the question with the lowest mean is “Your bank always supports consultation”

with a mean of 5.00.

For work experience, the length of the respondent’s work experience ranges

between 1 and 34 years. The average years of experience is 13.52 years

In addition, the summary of each construct is shown in descriptive statistics as

the following table.

Table 6.8 The Summary of all Constructs in Descriptive Statistics

Constructs Number of

RespondentsMean

Standard

Deviation

Innovation Management Effectiveness (4 items)

Innovation Strategy (3 items)

Organization Structure (5 items)

397

397

397

5.32

5.21

5.03

0.932

1.094

1.010

137


Constructs Number of

Respondents

Mean Standard

Deviation

Organization Culture (4 items)

Project Management (4 items)

Team Cohesiveness (4 items)

Strategic Leadership (4 items)

Coordination and Communication (4 items)

397

397

397

397

397

5.12

4.96

4.78

5.04

5.14

1.123

1.100

1.249

1.054

1.072

From the 397 respondents from the four banks, the results of the summary of

all the constructs in the descriptive statistics are given from the highest to the lowest:

Innovation Management Effectiveness (5.32), Innovation Strategy (5.21), Coordination

and Communication (5.14), Organization Culture (5.12), Strategic Leadership (5.04),

Organization Structure (5.03), Project Management (4.96), and Team Cohesiveness

(4.78) respectively. This indicates that innovation strategy is one of the most important

factors for banks in terms of affecting innovation management effectiveness.

Furthermore, the researcher shows the descriptive statistics of all constructs

according to bank in the next table to compare the means of the constructs in each bank.

Table 6.9 Descriptive Statistics of all Constructs Spit by Bank

Constructs Bank N Mean Standard

Deviation


Effectiveness (4 items)

SCB

KBank

KTB

GHB

137

112

127

21

5.22

5.67

5.15

5.10

0.883

0.988

0.884

0.744

Innovation Strategy (3 items) SCB

KBank

KTB

GHB

137

112

127

21

5.00

5.74

5.07

4.62

1.047

0.981

1.101

0.933

138



Deviation

Organization Structure (5 items) SCB

KBank

KTB

GHB

137

112

127

21

4.98

5.49

4.73

4.67

0.925

1.007

1.001

0.744

Organization Culture (4 items) SCB

KBank

KTB

GHB

137

112

127

21

4.97

5.59

4.96

4.54

1.164

0.985

1.093

0.966

Project Management (4 items) SCB

KBank

KTB

GHB

137

112

127

21

4.83

5.43

4.81

4.20

1.012

1.061

1.101

1.005

Team Cohesiveness (4 items) SCB

KBank

KTB

GHB

137

112

127

21

4.55

5.24

4.70

4.40

1.251

1.165

1.258

1.014

Strategic Leadership (4 items) SCB

KBank

KTB

GHB

137

112

127

21

4.86

5.59

4.83

4.51

1.050

0.936

0.997

0.927

Coordination and Communication

(4 items)

SCB

KBank

KTB

GHB

137

112

127

21

4.97

5.54

5.02

4.81

1.016

1.028

1.105

0.955

As can seen in the table above, KBank has the highest score (5.67), SCB the

second highest (5.22), KTB has the third highest (5.15) and GHB has the lowest score

(5.10) for the innovation management effectiveness construct.

139

For the innovation strategy construct, KBank (5.74), KTB (5.07), SCB (5.00)

and GHB (4.62) have the highest to lowest scores respectively.

For organization structure construct, KBank (5.49), SCB (4.98), KTB (4.73)

and GHB (4.67) have the highest to the lowest score respectively.

For the organization culture construct, KBank (5.59), SCB (4.97), KTB (4.96)


For the project management construct, KBank (5.43), SCB (4.83), KTB (4.81)


For the team cohesiveness construct, KBank (5.24), KTB (4.70), SCB (4.55)


For the strategic leadership construct, KBank (5.59), SCB (4.86), KTB (4.83)


For the coordination and communication construct, KBank (5.54), KTB

(5.02), SCB (4.97) and GHB (4.81) have the highest to lowest scores respectively.

6.3 Comparison of Means

There are several techniques available to achieve this objective through the

comparison of the means of independent groups. In this section, the researcher makes

a comparison of means by studying two factors. A t-test was used to identify the

differences among the two types of bank.

6.3.1 Comparison of Means by Type of Bank

In this study, the selected banks comprised both types of bank, that is to say

commercial and state-owned banks. The commercial banks group consisted of Siam

Commercial Bank and Kasikorn Bank while the state-owned banks group consisted of

Krung Thai Bank and Government Housing Bank. The researcher compared all the

constructs between commercial and state-owned banks

140

Table 6.10 Descriptive Statistics of all Constructs according to Type of Bank


Deviation



Commercial Bank

State-owned Bank

249

148

5.42

5.14

0.957

0.864

Innovation Strategy (3 items) Commercial Bank

State-owned Bank

249

148

5.33

5.01

1.082

1.088

Organization Structure (5

items)

Commercial Bank

State-owned Bank

249

148

5.21

4.72

0.994

0.966

Organization Culture (4 items) Commercial Bank

State-owned Bank

249

148

5.25

4.90

1.128

1.083

Project Management (4 items) Commercial Bank

State-owned Bank

249

148

5.10

4.72

1.075

1.105

Team Cohesiveness (4 items) Commercial Bank

State-owned Bank

249

148

4.86

4.66

1.259

1.227

Strategic Leadership (4 items) Commercial Bank

State-owned Bank

249

148

5.19

4.78

1.063

0.991

Coordination and

Communication (4 items)

Commercial Bank

State-owned Bank

249

148

5.22

4.99

1.057

1.084

As can be seen from the above table, the mean scores for the commercial

banks are higher than those for state-owned banks for all constructs. However, in

order to test the difference between commercial and state-owned banks, the researcher

analyzed the data by using an independent samples t-test.

141

Table 6.11 The Results of the T-Test between Commercial and State-owned Banks

Levene’s Test for

Equality of Variances

T-Test for Equality of

Means Constructs

Test of Equality

Variances F Sig. T

Sig.

(2-tailed)



Equal variances

assumed

Equal variances not

assumed

1.272 0.260 2.881

2.957

0.004

0.003

Innovation Strategy

(3 items)

Equal variances

assumed

Equal variances not

assumed

0.777 0.378 2.915

2.911

0.004

0.004


(5 items)

Equal variances

assumed

Equal variances not

assumed

0.000 0.983 4.729

4.763

0.000

0.000


(4 items)

Equal variances

assumed

Equal variances not

assumed

0.837 0.361 3.022

3.054

0.003

0.002

Project Management

(4 items)

Equal variances

assumed

Equal variances not

assumed

1.489 0.223 3.354

3.331

0.001

0.001

Team Cohesiveness

(4 items)

Equal variances

assumed

Equal variances not

assumed

0.092 0.762 1.550

1.560

0.122

0.120


(4 items)

Equal variances

assumed

Equal variances not

assumed

0.365 0.546 3.770

3.838

0.000

0.000

Coordination and

Communication (4 items)

Equal variances

assumed

Equal variances not

assumed

0.313 0.576 2.082

2.068

0.038

0.039

From the table above it can be seen that the results of the t-test for all

constructs indicate that there are significant differences between commercial banks

142

and state-owned banks except team cohesiveness at p<0.05. Therefore, it can be

concluded that there are statistically significant differences between commercial and

state-owned banks for all constructs except team cohesiveness at a confidence level of

95%.

6.4 Reliability Analysis

Hair et al. (2006) stated that reliability is one of the indicators of convergent

validity. High reliability shows that internal consistency exists, indicating that

measures can represent the same latent construct. The reliability estimate of 0.70 or

higher shows good reliability. For this study, there are eight main constructs, as

shown in the table below.

Table 6.12 The Reliability Analysis of the Constructs

Construct Conbrach’s Alpha

Innovation Management Effectiveness (IME1-IME4) (4

items)

Innovation Strategy (IS1-IS3) (3 items)

Organization Structure (OS1-OS5) (5 items)

Organization Culture (OC1-OC4) (4 items)

Project Management (PM1-PM4) (4 items)

Team Cohesiveness (TC1-TC4) (4 items)

Strategic Leadership (SL1-SL4) (4 items)

Coordination and Communication (CC1-CC4) (4 items)

0.900

0.933

0.922

0.918

0.954

0.953

0.932

0.937

As shown in the table above, the constructs came in the following order in

terms of highest to lowest of Conbrach’s alpha to the lowest: Project Management

(0.954), Team Cohesiveness (0.953), Coordination and Communication (0.937),

Innovation Strategy (0.933), Strategic Leadership (0.932), Organization Structure

(0.922), Organization Culture (0.918) and Innovation Management Effectiveness

143

(0.900). These scores indicates that all constructs are highly reliable because the

Cronbach’s alpha scores of all eight constructs are higher than 0.8.

6.5 Validity

6.5.1 Convergent Validity

According to Hair et al. (2006), convergent validity means the ability of some

measures to have convergent validity when they are highly correlated with different

measures of similar constructs. In other words, convergent validity is the extent to

which the scale correlates positively with other measures of the same construct. The

results shown as convergent validity are tested by evaluating the magnitude of factor

loadings of observed variables on the proposed constructs or latent variables.

Anderson and Gerbing (1988) have suggested that good convergent validity exists

when the standardized factor loadings of each item exceeds 0.40 (Lin and Germain,

2003) and all t-values are higher than the significant level (i.e. t-value is higher than 2).

In this study, the construct measurement model was tested for convergent

validity as follows:-.

144

Figure 6.1 Construct Measurement Model

145

Table 6.13 Construct Measurement

Factors Standardized Loading a

Innovation Management effectiveness (F1)

IME1

IME3

IME4

0.755 b

0.883 (18.062)

0.898 (18.304)

Innovation Strategy (F2)

IS1

IS2

IS3

0.865 b

0.932 (27.154)

0.927 (26.865)

Organization Structure (F3)

OS1

OS2

0.955 b

0.963 (40.621)

Organization Culture (F4)

OC1

OC2

OC3

OC4

0.826 b

0.808 (19.124)

0.915 (23.184)

0.894 (22.366)

Project Management (F5)

PM1

PM2

PM3

PM4

0.847 b

0.951 (27.453)

0.947 (27.232)

0.924 (25.888)

Team Cohesiveness (F6)

TC1

TC2

TC3

TC4

0.896 b

0.903 (28.029)

0.933 (30.465)

0.928 (30.059)

Strategic Leadership (F7)

SL1

0.950 b

146


Factors Standardized Loading a

SL2

SL3

SL4

0.920 (35.000)

0.825 (25.300)

0.823 (25.201)

Coordination and Communication (F8)

CC2

CC3

CC4

0.886 b

0.940 (29.815)

0.942 (29.983)

Note: a t-values from the unstandardized solution are in parentheses. b fixed parameter

For the construct measurement model, there are 27 items, measuring eight

constructs. The fit criteria are as follows: comparative fit index (CFI) = 0.953; normed

fit index (NFI) = 0.932; non-normed fit index (NNFI) = 0.945 and the root mean

square error of approximation (RMSEA) = 0.071. All of these indices confirmed a

good model fit.

Table 6.14 The Goodness of Fit for Convergent Validity

Items Fit Indices Criteria





0.953

0.932

0.945

0.071

>0.90

>0.90

>0.90

<0.08

Eight constructs were tested for convergent validity, namely innovation

management effectiveness, innovation strategy, organization structure, organization

culture, project management, team cohesiveness, strategic leadership, and

coordination and communication. All standardized estimates (or loadings) exceed 0.4,

147

and all associated t-values are significant at a level of 0.05 (t-values >±1.96; Byrne,

2001). Therefore, we can conclude that convergent validity has been established.

6.5.2 Discriminant Validity

To ensure the one construct is truly distinct from another discriminant validity

is used as measurement. Discriminant validity shows that different instruments are

used to measure different constructs. According to Anderson and Gerbing (1988) and

Jiang, Klein and Crampton (2000), the result should exceed χ2 (1, 0.05) = 3.841 in

order to conclude that two constructs have discriminant validity.

In addition, another useful measure for assessing discriminant validity

between two constructs is related to computing a confidence interval of ± 2 standard

errors around the correlation of those two constructs, and if the interval does not

include 1.0, it can be concluded that discriminant validity exists between those

constructs.

Fixed Model Free Model

Figure 6.2 Fixed and Free Models for Testing Discriminant Validity

148

In order to identify whether two constructs are different, chi-square values of

the fixed model and free model are compared and a chi-square difference greater than

3.841 (p<0.05) indicates that two constructs are statistically different and that

discriminant validity exists.

Table 6.15 Pairwise Analysis of Discriminant Validity

Construct Construct χ2 fixed χ2 free

χ2

difference

[d.f. = 1]


Innovation Strategy 363.947 60.023 303.924



399.333 18.361 380.972



490.624 119.081 371.543


Project Management

450.913 33.021 417.892


Team Cohesiveness 543.789 106.224 437.565


Strategic Leadership 421.181 72.057 349.124



428.590 22.474 406.116

Innovation Strategy Organization Structure

305.466 18.165 287.301

Innovation Strategy Organization Culture

549.519 108.981 440.538

Innovation Strategy Project Management

644.445 32.045 612.400

149


Construct Construct χ2 fixed χ2 free

χ2

difference

[d.f. = 1]

Innovation Strategy Team Cohesiveness 824.487 93.417 731.070

Innovation Strategy Strategic Leadership 549.484 56.902 492.582

Innovation Strategy Coordination and Communication

659.853 10.093 649.760



597.276 117.611 479.665


Project Management

528.593 14.308 514.285







537.983 3.281 534.702


Project Management

775.424 123.108 652.316







795.142 85.026 710.116

Project Management

Team Cohesiveness 1,262.421 126.106 1,136.315

Project Management


Project Management


770.361 25.983 744.378

Team Cohesiveness Strategic Leadership 827.176 143.728 683.448

Team Cohesiveness Coordination and Communication

686.738 108.816 577.922



636.021 63.492 572.529

150

According to the discriminant validity analysis, all constructs indicate that

they are distinct from one another, meeting the criteria of the chi-square difference of

each pair of constructs exceeding 3.841. In addition, the highest χ2 difference is

1,136.315 for project management and team cohesiveness, and the lowest χ2

difference is 287.301 for innovation strategy and organization structure.

For these reasons, each construct is tested and there was sufficient evidence to

indicate that each construct was distinct from one another (Katsikea et al., 2005).

6.6 Model Assessment

One of the main goals for using confirmatory factor analysis is to identify how

well the observed data fit the proposed model of the researcher. However, there is no

one absolute statistical significance test for assessing the goodness-of-fit of the model

to the observed data of researchers. It is crucial to use multiple criteria and to assess

model fit on the basis of several measures or criteria simultaneously.

In this study, the data were analyzed by using structural equation modeling

(SEM) and the confirmatory factor analysis (CFA) technique. The researcher

analyzed the data with SPSS AMOS 18.0. With the research questions proposed,

structural equation modeling and confirmatory factor analysis were chosen as the

most appropriate methods because they offered the most appropriate and most

efficient estimation technique (Hair et al., 2006). In addition, ERLS (elliptical

reweighted least squares) was applied because this method minimizes the problems

occurring from data skewness and kurtosis, and this method has been shown to

provide unbiased parameter estimates for both normal and non-normal data (Sharma,

Durvasula and Dillon, 1989) because the data distribution was found to deviate from

the normal distribution. Sharma, Durvasula and Dillon (1989) further stated that

ERLS for non-normal data are equivalent in performance to the Maximum Likelihood

(ML) estimation. Moreover, compared with all other elliptical estimation techniques,

ERLS was superior in performance. This ERLS method has an advantage over

Maximum Likelihood (ML) in that it is less constrained by the normality assumptions

of the data (Browne, 1984). Moreover, with normal data conditions, ERLS performs

as well as ML, but it performs better than the ML procedure when data are non-

151

normal (Sharma, Durvasula and Dillon, 1989). Many scholars also recommend the

use of ERLS methods for CFA (Singh, 1993; Zou and Cavusgil, 2002; Townsend,

Yeniyurt, Deligonul and Cavusgil, 2004; Xu, Cavusgil and White, 2006). Sharma,

Durvasula and Dillon (1989) have recommended that ERLS be used for both normal

and non-normal data.

According to Hair et al. (2006), in order to demonstrate that a model exhibits

an acceptable fit, at least three to four fit indices must be used. In this study, four fit

indices were used to measure the model, including CFI (comparative fit index), NFI

(normed fit index), NNFI (nonnormed fit index) and RMSEA (root mean square error

of approximation). CFI, NFI and NNFI are not sensitive to sample size (Bentler,

1990). According to Byrne (2001), CFI, NFI, and NNFI with a value of 0.90 or higher

indicate a well-fitting model, while 0.80 or higher indicates a reasonably well-fitting

model. For RMSEA, also known as the badness of fit index, when the values are 0.05

or lower, this suggests that the model has a good fit, and values of more than 0.05 to

0.10 indicate that the model has an average fit. In addition, MacCallum, Browne and

Sugawara (1996) have suggested that the cut-off point for RMSEA is in the range of

0.80- 0.10, indicating mediocre fit and when the RMSEA is higher than 0.1, the

model has a poor fit. Furthermore, Jiménez-Jimenez, Valle and Hernandez-Espallardo

(2008) also suggested that a RMSEA below 0.08 should be considered acceptable.

In this study, the researcher has proposed criteria to measure model fit as

discussed in the previous chapter and shown in the table below.

Table 6.16 Measurement of the Structural Model Fit

Items Criteria





>0.90

>0.90

>0.90

<0.08

152

Tests of Proposed Models

The researcher has developed the models in steps, as follows:

1) In the first proposed model of innovation strategy, organization structure,

organization culture, project management, team cohesiveness and strategic leadership

(six factors) are in the path of innovation management effectiveness.

2) In the second proposed model of innovation strategy, organization culture,

project management and team cohesiveness (four factors) are in the path of innovation

management effectiveness, strategic leadership is in the path of innovation strategy

and the other coordination and communication is in the path of team cohesiveness.

3) In the third proposed model of innovation strategy, organization culture,

project management, team cohesiveness, organization type and manager position and

strategic leadership are in the path of innovation strategy and coordination and

communication. are also in the path of team cohesiveness.

6.6.1 Proposed Model 1

The first proposed model of innovation strategy, organization structure,

organization culture, project management, team cohesiveness and strategic leadership

(six factors) are in the path of innovation management effectiveness.

153

Figure 6.3 Proposed Model 1

154

Table 6.17 The Goodness of Fit for Proposed Model 1






0.953

0.933

0.943

0.076

>0.90

>0.90

>0.90

<0.08

According to the fit indices above, the model provided good fit, as CFI, NFI

and NNFI indicated, because their values are greater than 0.90. RMSEA was slightly

lower than the criteria. However, according to Hair et al. (2006), when the value of

RMSEA is higher than 1.0, the model indicates poor fit. Therefore, overall the model

is sufficient to indicate a model fit.

Table 6.18 The Relation of Parameters and Parameter Estimates of Proposed Model 1

The Relation of Parameters Standardized Estimates

Innovation Strategy → Innovation Management

Effectiveness

0.296*

(4.004)

Organization Structure → Innovation Management

Effectiveness

0.011

(0.183)

Organization Culture → Innovation Management

Effectiveness

0.142*

(2.332)

Project Management → Innovation Management

Effectiveness

0.073

(1.224)

Team Cohesiveness → Innovation Management

Effectiveness

0.109*

(2.805)

Strategic Leadership → Innovation Management

Effectiveness

0.024

(0.316)

Note: * indicates statistical significance at 0.05 (t-values >±1.96) and t-values are

shown in parentheses.

155

The results show that in respective order innovation strategy (path coefficient

= 0.296 and t-value = 4.004), organization culture (path coefficient = 0.142 and t-

value = 2.332) and team cohesiveness (path coefficient = 0.109 and t-value = 2.805)

have statistically significant positive effect on innovation management effectiveness,

while there were no statistically significant effects of organization structure (path

coefficient = 0.011 and t-value = 0.183), strategic leadership (path coefficient = 0.024

and t-value = 0.316) and project management (path coefficient = 0.073 and t-value =

1.224) on innovation management effectiveness, respectively.

6.6.1.1 Testing of Research Hypotheses

According to the framework in the previous chapter, the researcher

proposed research hypotheses and the results of the hypotheses testing are as follows:

Theoretical Aspect (Proposed Model 1)

1) Innovation Strategy

H1: Innovation strategy has a positive effect on innovation

management effectiveness

This hypothesis is supported in that innovation strategy has a

positive effect on innovation management effectiveness with a path coefficient of

0.296 and t-value of 4.004. This indicates that higher innovation strategy leads to a

higher degree of innovation management effectiveness. As suggested in the literature,

the relationship of innovation strategy is a positive one with innovation management

effectiveness. This result supported the work of Mphahlele (2006), Martins and

Terblanche (2003), Vlok (2005) and Dewar and Dutton (1986).

2) Organization Structure

H2: Organization structure has a positive effect on innovation


This hypothesis is not supported in that organization structure

has a positive effect on innovation management effectiveness with a path coefficient

of 0.011 and t-value of 0.183. This indicates that organization structure has no

statistical effect on innovation management effectiveness. However, the work of

Mphahlele (2006), Burns and Stalker (1961), Zaltman, Duncan and Holbek (1973)

and Hage and Aiken (1970) supported the assertion that relationship of organization

156

structure is positive with innovation management effectiveness as suggested in the

literature.

3) Organization Culture

H3: Organization culture has a positive effect on innovation


This hypothesis is supported in that organization culture has a

positive effect on innovation management effectiveness with a path coefficient of

0.142 and t-value of 2.332. This indicates that higher organization culture leads to a


the relationship of organization culture has a positive effect on with innovation

management effectiveness. This result supports the work of Martins and Martins

(2002), Martins (2000) and Adams, Bessant and Phelps (2006).

4) Project Management

H4: Project management has a positive effect on innovation


This hypothesis is not supported in that project management

has a positive effect on innovation management effectiveness with a path coefficient

of 0.073 and t-value of 1.224. This indicates that project management has no

statistical effect on innovation management effectiveness. However, the work of

Adams, Bessant and Phelps (2006), Globe et al. (1973) and Keegan and Turner (2002)

which supported the relationship of project management as being positive with

innovation management effectiveness as suggested in the literature.

5) Team Cohesiveness

H5: Team cohesiveness has a positive effect on innovation


This hypothesis is supported in that team cohesiveness has a

positive effect on innovation management effectiveness with a path coefficient =

0.109 and t-value = 2.805. This indicates that higher team cohesiveness leads to a


the relationship of team cohesiveness is positive with innovation management

effectiveness. This result supports the work of Mullen and Cooper (1994), Langfred

(1998) and Guzzo and Dickson (1996).

157

6) Strategic Leadership

H6: Strategic leadership has a positive effect on innovation


This hypothesis is not supported in that strategic leadership has

a positive effect on innovation management effectiveness with a path coefficient of

0.024 and t-value of 0.316. This indicates that strategic leadership has no statistical

effect on innovation management effectiveness. However, the work of Foster and

Pryor (1986), Rothwell (1994) and Kanter (1985) supported the relationship of

strategic leadership as being positive one with innovation management effectiveness

as suggested in the literature.

In conclusion, from the testing of the hypotheses on Proposed Model 1

above, the path coefficients of innovation strategy, organization culture, team

cohesiveness have a positive influence on innovation management effectiveness.

However, the level of impact on the relationship among the constructs

is different. The three constructs of innovation strategy, organization culture and team

cohesiveness and their relationships to innovation management effectiveness indicates

that innovation strategy with a path coefficient of 0.296 has the highest statistically

positive impact on innovation management effectiveness followed by organization

culture with a path coefficient of 0.142 and team cohesiveness with a path coefficient

of 0.109. On the other hand, the three constructs of organization structure, project

management and strategic leadership have a positive influence on innovation

management effectiveness but the lack of statistical no significance indicates that

organization structure with path of coefficient of 0.011, strategic leadership with path

of coefficient of 0.024 and project management with a path coefficient of 0.073 have

no statistically positive impact on innovation management effectiveness, respectively.

From the implication point of view, Proposed Model 1 can be

explained by the strategic innovation process rather than resources-based view and

organizational theory. From the theoretical aspect, innovation strategy is one of the

key strategic implementation while organization culture also plays a key role in terms

of the development of culture norm and the building of team cohesiveness under the

organization climate of creativity and innovation behavior.

158

The summary of the results of all research hypotheses from the data

collected from the banks to measure innovation management effectiveness is given in

the table below:-.

Table 6.19 Summary of the Results of Hypotheses Testing on the Theoretical Aspect

Hypotheses Relationship

Results with

Standardized

Estimates

H1 Innovation strategy has a positive effect on

innovation management effectiveness

Supported

(0.296)

H2 Organization structure has a positive effect on


Not Supported

(0.011)

H3 Organization culture has a positive effect on


Supported

(0.142)

H4 Project management has a positive effect on


Not Supported

(0.073)

H5 Team cohesiveness has a positive effect on


Supported

(0.109)

H6 Strategic leadership has a positive effect on


Not Supported

(0.024)


The second proposed model of innovation strategy, organization culture,

project management and team cohesiveness (four factors) are in the path of innovation


and the other, coordination and communication is in the path of team cohesiveness.

159








0.944

0.923

0.936

0.078

>0.90

>0.90

>0.90

<0.08

160

According to the fit indices above, the model provides good fit, as CFI, NFI

and NNFI indicate, because their values are greater than 0.90. RMSEA is lower than

0.08. Therefore, overall the model is sufficient to indicate a good model fit.

The results indicate that innovation strategy, organization culture, project

management and team cohesiveness (four constructs) have a positive effect on

innovation management effectiveness, strategic leadership has a positive effect on

innovation strategy and coordination and communication has a positive effect on team

cohesiveness.




Effectiveness

0.308*

(6.478)


Effectiveness

0.155*

(3.291)


Effectiveness

0.085**

(1.887)


Effectiveness

0.117*

(3.688)

Strategic Leadership → Innovation Strategy 0.704*

(18.261)

Coordination and Communication → Team

Cohesiveness

0.899*

(17.614)

Note: * indicates statistical significance at 0.05 (t-values >±1.96)

** indicates statistical significance at 0.10 (t-values >±1. 645)

and t-values are shown in parentheses.

All relationships are statistically significant for all parameters. Innovation

strategy has the highest positive effect on innovation management effectiveness (path

coefficient = 0.308 and t-value = 6.478) followed by organization culture (path

161

coefficient = 0.155 and t-value = 3.291) and team cohesiveness (path coefficient =

0.117 and t-value = 3.688) while project management has the lowest positive effect on

innovation management effectiveness (path coefficient = 0.085 and t-value = 1.887).

Strategic leadership has a positive effect on innovation strategy (path coefficient =

0.704 and t-value = 18.261) and coordination and communication has a positive effect

on team cohesiveness (path coefficient = 0.899 and t-value = 17.614).

6.6.2.1 Managerial Aspect ( Proposed Model 2)

From the implication point of view, Proposed Model 2 investigates the

determinants factors from the innovation process to innovation management

effectiveness, which also explains the positive relationship of each construct that

affects innovation management and goals. The innovation management theory also

asserts that innovation strategy and strategic leadership are the key enablers for

strategic implementation of innovation management effectiveness.


In the third proposed model of innovation strategy, organization culture,

project management and team cohesiveness (four factors) are related to innovation


and the other one, coordination and communication is in the path of team

cohesiveness with control variables.

In this model, the researcher explored the effects of the control variables-

Organizational type and manager position- in this study, which are organizational type

and manager position. The new hypotheses are given below:

H8: Organization type has a positive effect on innovation management

effectiveness

H9: Manager position has a positive effect on innovation management

effectiveness

The model was developed as shown in the following figure.

162


After analyzing the data using structural equation modeling, the factors

affecting innovation management effectiveness are shown as follows.







0.943

0.919

0.934

0.074

>0.90

>0.90

>0.90

<0.08

163

For testing the model, the results show that all fit indices of the model indicate

good fit.




Effectiveness

0.293*

(5.419)


Effectiveness

0.192*

(2.352)


Effectiveness

0.060

(0.661)


Effectiveness

0.091*

(2.120)

Strategic Leadership → Innovation Strategy 0.705*

(18.241)


Cohesiveness

0.898*

(17.594)

Organization Type → Innovation Management

Effectiveness

0.029

(0.024)

Manager Position → Innovation Management

Effectiveness

-0.778

(-0.954)


All relationships are statistically significant except project management and

control variables. Innovation strategy has the highest positive effect on innovation

management effectiveness (path coefficient = 0.293 and t-value = 5.419) followed by

organization culture (path coefficient = 0.192 and t-value = 2.352) and team

cohesiveness (path coefficient = 0.091 and t-value = 2.120) while project management

has no statistically significant effect on innovation management effectiveness (path

coefficient = 0.060 and t-value = 0.661). Strategic leadership has a positive effect on

164

innovation strategy (path coefficient = 0.705 and t-value = 18.241) and coordination

and communication has a positive effect on team cohesiveness (path coefficient =

0.898 and t-value = 17.594).

As this model was aimed at testing the effect of control variables on

innovation management effectiveness, the results indicate that there are no

statistically significant effects of organizational type (path coefficient = 0.029 with t-

value of 0.024) or manager position (path coefficient = -0.778 with t-value of -0.954)

6.6.3.1 Policy Aspect (Proposed Model 3)

The three constructs of innovation strategy, organization culture and

team cohesiveness directly affect innovation management effectiveness. While, the

two constructs of strategic leadership to innovation strategy and coordination and

communication have indirect impact on innovation management effectiveness. On

the other hand, organization structure, strategic leadership and organization type,

manager position give no significant differences as regards in innovation management

effectiveness.

From the implication point of view, although model assessment

display criteria fit (can be seen in table 6.11) in terms of descriptive analysis using

the t-test, there are significant differences for all the constructs between commercial

banks and state-owned banks. The t-test analysis explains partly the equality of the

means of each construct but does not represent the interrelationships between each

construct and innovation management effectiveness.

In conducting model assessment, there are some constructs which have

both a positive relationship to and significant differences in innovation management

effectiveness and by adding the control variables such as organizational type and

manager position. As the result there are no significant differences in innovation

management effectiveness. Therefore, both the organizational type and manager

position do not affect innovation management effectiveness which also explains why

both commercial banks and state-owned banks have no differences in policy. The

commercial banks focus more on innovation strategy with strategic leadership as the

main part of the business strategy and state owned banks should employ business

policies besides supporting government policies with the emphasis on business

strategy and customer needs in order to be competitive and sustainable in the business

environment.

165

Table 6.24 The Comparison of Three Proposed Models

Standardized Estimates The Relation of Parameters

Model 1 Model 2 Model 3


Effectiveness

0.296*

(4.004)

0.308*

(6.478)

0.293*

(5.419)

Organization Structure → Innovation

Management Effectiveness

0.011

(0.183)

- -


Effectiveness

0.142*

(2.332)

0.155*

(3.291)

0.192*

(2.352)


Effectiveness

0.073

(1.224)

0.085**

(1.887)

0.060

(0.661)


Effectiveness

0.109*

(2.805)

0.117*

(3.688)

0.091*

(2.120)

Strategic Leadership → Innovation Management

Effectiveness

0.024

(0.316)

- -

Strategic Leadership → Innovation Strategy - 0.704*

(18.261)

0.705*

(18.241)


Cohesiveness

- 0.899*

(17.614)

0.898*

(17.594)

Organization Type → Innovation Management

Effectiveness

- - 0.029

(0.024)

Manager Position → Innovation Management

Effectiveness

- - -0.778

(-0.954)





0.953

0.933

0.943

0.076

0.944

0.923

0.936

0.078

0.943

0.919

0.934

0.074


** indicates statistical significance at 0.10 (t-values >±1. 645)

and t-values are shown in parentheses.

166

From the three models discussed above, there are only three constructs

in Proposed Model 1 in theoretical aspect, which are innovation strategy, organization

culture and team cohesiveness, that directly affect innovation management

effectiveness. As for the managerial aspect, the chosen model used to test the research

hypotheses is Proposed Model 2. However, Proposed Model 3 with the control

variables of organization type (commercial banks and state-owned banks) and

manager position (manager and non- manager) have no statistically significant effect

on innovation management effectiveness. Therefore, from the statistical standpoint,

the researcher will attempt to compare the best fits of Proposed Model 1,2,3 as the

comparison models in order to test the research hypotheses.

In conclusion, this chapter described the data characteristics and

characteristics of the respondents. A reliability analysis of the constructs in the study

and Cronbach’s alpha were provided to confirm the reliability of each construct. In

addition, the researcher tested convergent and discriminant validity before conducting

model assessment in order to prove that the constructs met the criteria for both

validities. Furthermore, the researcher conducted structural equation modeling for the

three Proposed Models. All models satisfactorily only met the criteria of fit indices,

including CFI, NFI, NNFI, and RMSEA. However, there were variation in the

significant differences in all three models, Finally, the results from the theoretical

model testing were used to respond to the research hypotheses which were clearly

supported by the findings of this empirical study while Proposed Model 2- the

Managerial model- explained innovation management theory which perceives both

innovation strategy innovation process and strategic leadership as affecting innovation


CHAPTER 7

DISCUSSION, CONCLUSIONS AND IMPLICATIONS

7.1 Discussion

In this chapter, the researcher will discuss all of, the important aspects covered

by the study, its objectives, conclusions as well as its contribution and policy

implications. The researcher also proposes recommendations together with the

limitations of the study and suggestions for further studies in the future.

As the result of the study, innovation management theory is used to explain

the conceptual framework and hypotheses model. It mainly emphasizes innovation

strategy, process, strategic leadership, coordination and communication and project

management. The main aim is to generate the value creation both innovation approach

and process management as well as support operation management efficiency.

As a result of the study, there are three types of conclusion to be drawn-

theoretical, managerial and policy conclusions. The conceptual theory on innovation

management theory mainly explains the constructs that affect innovation management

effectiveness while the resource-based view can partly explain the organization

culture based on cultural norms and empowerment. During the study, many

constructs were introduced to measure their reliability and validity regarding

innovation management effectiveness. The hypotheses are tested with empirical data

in order to understand how the data fit with the conceptual framework. It is also

important that the researcher decides to introduce additional theories of innovation

management theory in order to have both theoretical and managerial explanations of


168

7.2 Conclusions of the Study

7.2.1 Theoretical Conclusions

7.2.1.1 The Theoretical Model

The theoretical model was selected to develop and to support the

conceptual framework for investigating the determinants of organizational innovation

management effectiveness with the main objective to describe the determinants of six

prediction factors of innovation strategy, organization structure, organization culture,

project management, team cohesiveness and strategic leadership. However, the

results of the study show that there are only three constructs which have positive

influences on innovation management effectiveness. Through the literature review

also describes the significant of each contingency factor that has positive influence to

the innovation management effectiveness.

First, innovation strategy with a path coefficient of 0.296 is defined as a

timed sequence of internally consistent and conditional resource allocation decisions

that are designed to fulfill an organization’s objectives in terms of innovation goals,

innovation resource allocation, innovation risk (overall philosophy), timing (first to

market or happy to wait) and a long term perspective (Ramanujam and Mensch,

1985). Innovation strategy is related to organizational factors that promote innovation

management effectiveness (Mphahlele, 2006; Martins and Terblanche, 2003; Vlok,

2005; Dewar and Dutton, 1986). Then, organization structure represents the development

of human resources in different parts of the organization for doing specific work

(Mphahlele, 2006).

Third, organization culture with a path coefficient of 0.142 partly

explains how to give employees incentives through reward and recognition (Nystrom

(1990) and O’Reilly (1989) as part of the innovation process Organization culture

influences innovation management effectiveness (Martins and Martins, 2002; Martins,

2000; Adams, Bessant and Phelps, 2006).

Forth, team cohesiveness with a path coefficient of 0.109 is determined

by the individuals within the team having the desire to remain a part of that team

because their individual needs are being met (Turner et al., 1984.). It is still believed

that the closeness brought about through cohesiveness can facilitate the members of a

169

team working together more freely with more communication to develop more

innovative ideas. Team cohesiveness has influenced on innovation management

effectiveness (Mullen and Cooper, 1994; Langfred, 1998; Guzzo and Dickson, 1996).

Lastly, strategic leadership is creating and leading the innovation process, developing

and coaching the innovators and promote the innovation (Deschamps, 2005).

However, there remained three constructs- organization structure with a

path coefficient of 0.011, project management with a path coefficient of 0.073 and

strategic leadership with a path of coefficient of 0.024- that have no significant affect


From the theoretical point of view, the contingency factors can be

explained by innovation management theory (Criag, 1995; Dougherty and Hardy,

1996; Hatch and Mowery, 1988 ) which also addresses the relationships among the

three prediction factors that positively affect the innovation management effectiveness.

Through the previous literature review, only the resource-based view can

partly explain the relationship of firm characteristics to organization culture (Martins,

2002) while the innovation management theory will mainly support the evidence of

the study on innovation strategy (Ramanujam and Mensh, 1985), and strategic

leadership (Deschamps, 2005) and how innovation process is integrated in all the

constructs that affect innovation management effectiveness.

170

Figure 7.1 The Summary of the Theoretical Model

Note: * indicates statistical significance at 0.05

7.2.2 Managerial Conclusion

7.2.2.1 The Managerial Model

The managerial model was selected to interpret the result of the

research study.

The first objective was to study the determinants of organizational

innovation management effectiveness. Four factors, including innovation strategy,

organization culture, team cohesiveness and project management had a statistically

significant positive relationships to innovation management effectiveness. Furthermore,

171

strategic leadership has a positive influence on innovation management effectiveness

via innovation strategy and finally, coordination and communication has a positive

influence on innovation management effectiveness via team cohesiveness.

The second objective was to explain the outputs of the factors of the

determinants of organizational innovation effectiveness. The results indicated that

innovation strategy with a path coefficient of 0.308 has the highest statistically

significant positive impact on innovation management effectiveness followed by

organization culture with a path coefficient of 0.155, team cohesiveness with a path

coefficient of 0.117 and project management with a path coefficient of 0.085, while

strategic leadership has a positive effect on innovation management effectiveness via

innovation strategy with a path coefficient of 0.704 and coordination and

communication has a positive effect on innovation management effectiveness via

team cohesiveness with a path coefficient of 0.899.

The third objective was to test a model of seven contingency prediction

factors of innovation management effectiveness. The model was successfully tested in

that the proposed model was shown to have a good fit with the data collected from

banks. In order to test the model, the researcher used the structural equation modeling

technique, in which all relationships in the model were tested simultaneously. The fit

indices were the Comparative Fit Index (CFI), the Normed Fit Index (NFI), the Non-

Normed Fit Index (NNFI) and the Root Mean Square Error of Approximation

(RMSEA). To conclude, all the fit indices indicated that the model fitted well with the

data. Therefore, this objective was proven satisfactory by the results of the study.

172

The summary of the three objectives is given below.

Figure 7.2 The Summary of the Managerial Model

Note: * indicates statistical significance at the 0.05 level

** indicates statistical significance at the 0.10 level

This study considered the innovation strategy of the firm in terms of

innovation goals, innovation resource allocation, innovation risk, timing, and long

term perspective (Ramaujam and Mensch,1985). The relationships between several

factors and innovation management effectiveness, representing the integrated view of

organizational performance improvement, are considered the main focus of the study.

This research proved the importance of developing innovation management

effectiveness. It is crucial to consider innovation management effectiveness as the

integration of innovation strategy, organization culture, project management, team

cohesiveness, strategic leadership via innovation strategy and coordination and

communication via team cohesiveness. The significance of the components is clearly

173

identified in that an organization can achieve higher innovation management

effectiveness in many aspects, such as through the development of innovation strategy

and strategic leadership, improved coordination and communication, the overall

organizational culture and performance and by building employee morale,

competency and team cohesiveness as shown in this study.

7.2.3.1 The Policy Model

The policy model was selected to test the significant differences between

organizational type (commercial banks and state-owned banks) and manager

position(manager and non-manager) using dummy variables by running on AMOS

program. There are no significant differences on innovation management effectiveness by

organizational type with a path coefficient of 0.029 and manager position with path

coefficient of -0.778 respectively.

The result also shows that innovation strategy with a path coefficient of

0.293 has the highest positive influence on the innovation management effectiveness,

while second is organization culture with a path coefficient of 0.192 followed by

team cohesiveness with path coefficient of 0.091. The strategic leadership with a

path coefficient of 0.705 has a positive effect to innovation management effectiveness

via innovation strategy , while second is coordination and communication with a path

coefficient of 0.898 has a positive impact on innovation management via team

cohesiveness. However, project management with a path coefficient of 0.060 has no

significant differences as the result of statistically adding control variables.

From the policy view point, commercial banks and state-owned banks

has no significant impact to innovation management effectiveness in term of

organization type and manager position which also implies that state-owned banks

should adopt business policies in order to stay competitive with commercial banks. In

addition, with the different goals between state-owned enterprises and private firms,

business policy plays important roles in terms of decision making in organizations in

order to be more competitive as a firm. Although state owned enterprises are expected

to cooperate with the government to achieve certain political roles including adopting

government policies and adapting to the market environment at the same time they

actually serve many shareholders and pursue multiple goals.

174

For these reasons, innovation strategy and strategic leadership affect the performance

of both state enterprises and commercial firms. Ramanujam and Mensch(1985)

suggest that formulating an innovation strategy is a matter of policy for those

organizations that would like to pursue a sustainable competitive advantage through

innovation. Strategic leadership with coordination and communication in enabling

innovation plays an important roles in facilitating innovation activity, providing the

necessary resources, expertise or protection(Hornsby et al., 2002), developing and

maintaining measurement systems, assessing strategic performance indicators(Soosay,

2005) with commitment and declaring the vision(Ahmed, 1998).

Figure 7.3 The Summary of Policy Model

Note: * indicates statistical significance at the 0.05 level

** indicates statistical significance at the 0.10 level

175

7.3 Contributions of the Study

As the results of the study show that the theoretical contributions of this work

were greatly significant in developing the study in the areas of innovation

management theory though merely supportive of the resource-based view and

organizational contingency theory to some extent. However, one of the most

important aspects of the research is to contribution to the theory and the field of study.

In terms of theoretical contribution, this study has made the following a fundamental

strong contribution.

7.3.1 Theoretical Contribution

This study has expanded the determinants of organizational innovation

management effectiveness and provided more understanding about innovation in the

Thai banking industry. This research has supported the determinants of innovation

management effectiveness to better clarify and understand innovation. It has achieved

the following:

1) Provided empirical data regarding the measurement of innovation

management effectiveness in the social science aspect.

2) Document innovation management effectiveness within the context

of Thai culture; and limited the variables suitable for the characteristics of bank that

can define the variables affecting innovation management effectiveness.

3) Detailed the phenomenon of innovation management effectiveness in

the Thai banking industry, specifically in the areas of the determinants of organizational

innovation management effectiveness. The study has also contributed to the body of

research based on empirical innovation in organizations or banks by attempting to

identify the critical factors affecting innovation management effectiveness.

4) Provided a model of innovation management effectiveness implementation

that treats the phenomena as if they were simply dependent variables with all

constructs relatively interconnected in social science. This is important for enabling

the development of more appropriate systems for selecting and developing innovation


176

7.3.2 Theoretical Contribution on Innovation Management Theory

Within this line of reasoning, the study provides evidence that further explains

innovation management theory’s contribution to innovation management effectiveness

mainly in the area of improving organizations’ capacity to innovate. Tim Craig (1995)

provides innovation management theory by using bureaucratic method to foster

innovation, requiring a flexible process in order to free creativity and having the

discipline in transforming ideas into a market product which may be more important

than just a novelty. Nile W.Hatch and David C.Mowery(1998) offered the innovation

theory of the relationship between process innovation and learning by doing which

are two main factors affecting superior performance in the introduction of new

process and factors that influence the rate of improvement. Willaim Q. Judge, Gerald

E. Fryxell and Robert S. Dooley (1997) investigated what kind of workplace culture is

conducive to creativity and how managers can create and maintain an innovative

culture. Charlan Jeanne Nemeth(1997) also pointed out that managing innovation is to

encourage innovation and the creative process. Although firms tend to promote strong

cohesion, in terms of the theoretical approach firms attempt to overcome some of the

inhibitions that make employees reluctant to engage in creative dissent and research

shows the formidable pressure employees of the firm face toward conformity.

The theory also confirms the positive relationship between innovation strategy

and innovation management effectiveness. For example, Ramanujam and Mensch

(1985) defined innovation as a timed sequence of internally consistent and conditional

resource allocation decisions designed to fulfill the objectives of organizational

innovation and stated that innovation strategy has a positive effect on innovation

management effectiveness. Mphahlele (2006) found that innovation strategy was

related to organizational factors that promoted innovation. Mintzberg (1978)

confirmed that the strategic posture enhances bank innovativeness by entrepreneurial

assessment. Furthermore, there is an interrelationship between strategic leadership

and innovation strategy. The provision of leadership makes innovation happen via a

strong vision (Pinto and Prescott, 1988) for innovation, a long-term commitment to

innovation and a clear allocation of resources (Cooper et al., 2004). Dougherty and

Cohen (1995) found the behavior of senior managers to be influential in making

innovation happen with a clear vision of the future operation and direction of

organizational change and creativity (Shin and McClomb, 1998).

177

7.4 Managerial Contribution and Implications

7.4.1 Managerial Contribution

The results of study reveal that there are various factors that have statistical

significance in affecting innovation management effectiveness. The following is the

summary of the contributions:-

1) Practitioners of organizational innovation can utilize the research

results to refine their own contribution to the organization. Some of the ways they can

contribute is through the consideration of the innovation management effectiveness

building. This is important both to research and the information surrounding competitive

advantage.

2) The results of the study provided information to directors and

managers in the banking industry regarding decision making on the establishment of

innovation management effectiveness within the Thai banking industry.

3) Team leaders of the banking industry and human resources and

organizational development specialists who support bank structures benefit from the

information on how to facilitate innovation management effectiveness and provide the

best support innovation management efforts.

4) Banks should also aim at formulating innovation strategy, creating

organization culture norms and supporting continuous learning and empowerment,

building up team work cohesiveness and strengthening longevity while managing

the structure of the organization with rules, procedures and hierarchy of authority. At

the same time to ensure innovation management effectiveness, they need to manage

the link between innovation project and project management with enhanced

coordination and communication quality.

7.4.2 Implications

Internal Bank Policy

The Differences in Bank Policy between Commercial Banks and State

Owned Banks in innovation management effectiveness

The commercial banks(SCB and KBank) have placed emphasis on the

commercial financial services in every segments from the multi-corporate business,

178

retail business to medium and small business with policies aimed at achieving their

own visions, goals and objectives towards the shareholders and customers, providing

the best possible solutions and most comprehensive and diversified products and

services. From the result of the study on descriptive statistics, the determinants of the

contingency factors reveals that KBank has the highest score in every construct,

particularly innovation strategy and strategic leadership while SCB is ranked second.

The managerial model also reveals that the model fit and significance difference of

the innovation strategy, strategic leadership, organization culture, team cohesiveness,

communication and coordination to innovation management effectiveness. Therefore,

the commercial banks (SCB and KBank) will continue to formulate the business

policy and employ innovation strategy with strategic leadership and aim at increasing

profit through innovation projects and improving their competitive advantages in the

market environment.

While the state-owned banks (KTB and GHB) have the policies to

fulfill their own operation objectives and steer the economy as directed by the

government and continue to provide financial services to the commercial, low end

housing, government and state enterprise sectors. The research also found that state-

owned banks have also a policies to embrace their vision of being stable financial

institutions for saving and national economic and social development, especially as

regards the grassroots economy However, KTB is the state-owned bank with one of

the largest commercial customer bases in Thailand, that not only supports government

policy but also mainly does business in the commercial sector. While GHB has policy

mainly on government projects, it also supports low and middle class families who

would like to have low cost budget housing. Because the results of the study show no

significance in between organization types -commercial banks and state-owned

banks- they should therefore put emphasis on business policy and formulate business

strategy by implementing innovation strategy at the core of contributing more in

diversified innovative products and services and being competitive.

7.5 Recommendations

Overall, the main recommendation of the research in innovation strategy is to

put additional efforts based on the right understanding of customer needs in each

179

segment via the stage-gate process of new product development. In identifying

customer needs is a key to unleashing customer innovation (Seybold, 2006) are to:

1) Find lead users who are already closing the gap between how they

do things today and what they’d ideally like to be able to do and commercialize their

innovation.

2) Empower your lead customers with co-design tools and innovation

toolkits so they can design their own solutions, innovating as they goKaplan and

Norton( 2004) introduced a formal stage-gate process that specifically identifies a

series of development stages through which a new product must pass as it moves from

an initial concept to a fully defined product ready for release to large-scale production.

The critical importance of strategic leadership and high levels of employee

engagement, as posited by Kotter(1996), leads change with the eight stage process

establishing a sense of urgency, creating the guiding coalition, developing in vision

and strategy, communicating the change vision, empowering employees for broad-

based action, generating short-term wins, consolidating gains and producing more

change with anchoring new approaches in the culture. Those two priorities drive the

bank’s human sigma and culminate in managing innovation effectiveness as a result

of sustainable growth and profit, and competitive advantage.

Lastly, a bank must forge an innovation management effectiveness that is

aligned with its overall innovation strategy with strategic leadership by choosing the

project implementation with the best value propositions by improving the

organization culture, managing the operation system and process efficiently so it does

not waste time or resources and commercialize innovations well, with everyone

working together as a team (Jaruzelski et al., 2005) and improving coordination

(Mintzberg, 1979) with communication and participation as critical factors in

innovation (Johnson et al., 2001).

7.6 Limitations of the Study

The results and findings of this research are clearly valuable regarding the

theoretical aspects. However, several limitations exist and the details are as follows.

First, this research is a cross-sectional study, using data from questionnaires and

180

collected from bank representatives at one point in time; therefore the relationship of

constructs from this study should be considered and applied with caution, and future

studies may consider longitudinal study in order to confirm the results of this study.

Second, the area of study is the banking industry. The generalizability of the

results may be limited. Moreover, the research was conducted only within Thailand

and in the Bangkok area. Therefore, future studies may apply the same questionnaires

in other cultures or geographical areas in order to confirm the results. Additionally,

cross-industry research is also recommended for future research to help improve the

generalizability of the research findings. Third, there are uncertain situations with

time constraints and unpredictable factors associated with these risks during the

dissertation study.

7.7 Future Directions of Research Study

This research and its findings provide a number of directions for future

research as follows.

1) The research results clearly shows the effect of the causal variables

on innovation management effectiveness. It is the merit of the present study that it

has provided evidence that confirms these theses by combining a cross-sectional and a

longitudinal approach for the improvement of innovation management effectiveness.

2) The cross-sectional approach with the research should expand on the

number of local banks in Thailand under the conceptual framework and hypotheses

model, taking more on the qualitative approach with more interviews with high level

management which would be beneficial for a better understanding of the antecedents

of innovation management effectiveness.

3) A longitudinal approach could be taken to test the research model

with comparison between banks in Thailand and banks in other foreign countries for

causal explanations of innovation management effectiveness.

4) This research model should also be empirically tested in the different

environments in cluster industry sectors to determine its predictive validity in

alternate settings.

181

5) Taking into account the large contextual variation between industries,

the comparison has showed surprisingly consistent results. Consequently, the results

may be generalized for innovation management effectiveness at large.

In conclusion, this chapter has summarized the findings that directly answer

both the significance and the objectives of the study by providing the discussion,

conclusions, contribution and implications with recommendation as well as the

limitations of the study and suggested future directions of research study.

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APPENDICES

APPENDIX A

COVER LETTER

ที่ ศธ 0526.02/ คณะรัฐประศาสนศาสตร สถาบันบัณฑิตพัฒนบริหารศาสตร

คลองจั่น บางกะป กทม. 10240

วันที่ 30 ตุลาคม 2553

เรื่อง ขอความอนุเคราะหขอมูล เรียน

ดวย นายภาณุ ลิมมานนท นักศึกษาระดับปริญญาเอก หลักสูตรปรัชญาดุษฎีบัณฑิต (การบริหารการพัฒนา) หลักสูตรนานาชาติ คณะรัฐประศาสนศาสตร สถาบันบัณฑิตพัฒน บริหารศาสตร กําลังอยูระหวางการจัดทําวิทยานิพนธ เรื่อง ปจจัยที่มีผลกระทบตอประสิทธิภาพของการจัดการนวัตกรรมขององคกรในกลุมธนาคารภาครัฐและเอกชน (The Determinants of Organizational Innovation Management Effectiveness in Thai Banking Industry) โดยมี รองศาสตราจรย ดร. จินดาลักษณ วัฒนสินธุ เปนที่ปรึกษาวิทยานิพนธหลัก ในการทํางานวิจัยเฉพาะกรณีครั้งน้ี ผลการวิจัยจะกอใหเกิดประโยชนทางดานวิชาการเพ่ือใชประกอบการศึกษาเก่ียวกับการบริหารการพัฒนา นวัตกรรมขององคกรอยางมีประสิทธิภาพโดยพิจารณาวามีปจจัยที่สําคัญใดบางที่ชวยสงเสริมศักยภาพขององคกรและขีดสมรรถนะของบุคลากรในเชิงสรางสรรคอันมีผลตอการเพ่ิมประสิทธิภาพและประสิทธิผลในที่สุด หากหนวยงานของทานตองการติดตอกับนักศึกษา ขอความกรุณาติดตอโดยตรงที่หมายเลขโทรศัพท 081-614-9363 หรือผูประสานงานในการเก็บขอมูล นางสาว วราภรณ สีระคุณ หมายเลขโทรศัพท 086-344-2697 คณะรัฐประศาสนศาสตร หวังเปนอยางยิ่งวาจะไดรับความอนุเคราะหจากทานเปนอยางดี จึงขอขอบคุณมา ณ โอกาสน้ี ขอแสดงความนับถือ

รองศาสตราจารย ทิพวรรณ หลอสุวรรณรัตน

คณบดีคณะรัฐประศาสนศาสตร หลักสูตรปริญญาเอก โทรศัพท/โทรสาร 02 374 4977

APPENDIX B

SURVEY QUESTIONNAIRE

Questionnaire (แบบสอบถาม)

แบบสอบถามน้ีเป นส วนหน่ึงของการศึกษาวิจัยเพ่ือการศึกษา คําช้ีแจง 1. แบบสอบถามศึกษาเก่ียวกับปจจัยที่มีผลกระทบตอการจัดการนวัตกรรมที่มีประสิทธิภาพของ

ธนาคารพาณิชยและรัฐวิสาหกิจ และคําตอบทั้งหมด ถือเป นความลับ เพ่ือการศึกษาในทางวิชาการ

เท าน้ัน

2. โปรดทําเคร่ืองหมาย ลงใน ที่ตรงกับความเปนจริงตามระดับตางๆ จาก 1 ถึง 7 (โดยที ่1

หมายถึงน อยที่สุด และ 7 หมายถึงมากที่สุด) ส วนท่ี 1 โปรดระบุความเห็นของท านต อขอความดังต อไปน้ี 1. Innovation management effectiveness (การจัดการนวัตกรรมอยางมีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 IME1. Compared to the last year, your bank has improved the overall organizational performance. (เปรียบเทียบกับป ที่ผานมา ธนาคารของทานมีการพัฒนาความสามารถในองคกรไดอยางมีประสิทธิภาพมากขึ้น)

IME2. Compared to the last year, your bank has improved productivity. (เปรียบเทียบกับป ที่ผานมา ธนาคารของทานมีการปรับปรุงประสิทธิผลของงานบริการลูกคาเพ่ิมขึ้น)

IME3. Compared to the last year, your bank has improved employee morale. (เปรียบเทียบกับป ที่ผ านมา ธนาคารของทานไดมีการสงเสริมและผลักดันความกระตือรือรนของพนักงานมากขึ้น)

IME4. Compared to the last year, your bank has improved employee competency. (เปรียบเทียบกับป ที่ผ านมา ธนาคารของทานไดมีการสงเสริมและผลักดันขีดความสามารถของพนักงานมากขึ้น)

2. Innovation strategy (กลยุทธนวัตกรรม) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 IS1. Your bank has guidelines for developing organizational innovation. (ธนาคารของทานมีแนวทางการ

227

พัฒนาการจัดการนวัตกรรมในองคกร) Measurement 1 2 3 4 5 6 7

IS2. Your bank has promoted innovation. (ธนาคารของทานไดมีการสงเสริมการจัดการนวัตกรรม)

IS3. Your bank plans to set up innovation goals. (ธนาคารของทานไดมีการวางแผนที่จะกําหนดเปาหมายการจัดการนวัตกรรม)

3. Forming organization structure to innovation management effectiveness (มีการจัดการโครงสรางขององคกรท่ีมีผลตอการจัดการนวัตกรรมอยางมีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 OS1. Your bank has specific rules for managing innovation. (ธนาคารของทานไดมีการกําหนดกฎเกณฑในการจัดการนวัตกรรมโดยเฉพาะ)

OS2. Your bank has specific procedures for managing innovation. (ธนาคารของทานไดมีการกําหนดขั้นตอนในการจัดการนวัตกรรมโดยเฉพาะ)

OS3. Your bank has established a new division of functional differentiation. (ธนาคารของทานไดมีการจัดแบงหนวยงานใหมตามลักษณะความแตกตางของฟงกช่ัน)

OS4. Your bank has established a new division of functional specialization. (ธนาคารของทานไดมีการจัดแบงหนวยงานใหมตามลักษณะความเช่ียวชาญของฟงกช่ัน)

OS5. Your bank has established a new division of functional integration. (ธนาคารของทานไดมีการจัดแบงหนวยงานใหมตามลักษณะการบูรณาการของฟงกช่ัน)

4. Organization culture to innovation management effectiveness (วัฒนกรรมขององคกรท่ีมีผลตอการบริหารนวัตกรรมท่ีมีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 OC1. Your bank has promoted cultural norms of innovation behavior. (ธนาคารของทานมีการสนับสนุนสงเสริมพนักงานที่มีพฤติกรรมในเชิงนวัตกรรม)

Measurement 1 2 3 4 5 6 7 OC2. Your bank has supported continuous learning.

228

(ธนาคารของทานสนับสนุนใหมีการเรียนรูสิ่งใหมๆเสมอ) OC3. Your bank has supported reward. (ธนาคารของทานมีการสนับสนุนใหรางวัลแกพนักงานผูที่มีผลงานสรางสรรค)

OC4. Your bank has supported recognition. (ธนาคารของทานมีการสนับสนุนใหคําชมเชยแกพนักงานผูที่มีผลงานสรางสรรค)

5. Project management to innovation management effectiveness (การจัดการโครงการที่มีผลตอการบริหารนวัตกรรมที่มีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 PM1. Your bank has slack resources to support innovation management effectiveness. (ธนาคารของทานมีทรัพยากรท่ีเพียงพอในการสนับสนุนในการบริหารโครงการจัดการนวัตกรรมอยางมีประสิทธิภาพ)

PM2. Your bank has greater levels of time management for innovation. (ธนาคารของทานมีกําลังและความสามารถมากขึ้นในการจัดสรรระยะเวลาในโครงการจัดการนวัตกรรม)

PM3. Your bank has greater levels of operation management for innovation. (ธนาคารของทานมีกําลังและความสามารถมากขึ้นในการจัดสรรการดําเนินงานในโครงการจัดการนวัตกรรม)

PM4. Your bank has greater levels of creativity for innovation management. (ธนาคารของทานมีกําลังและความสามารถมากขึ้นในการจัดสรรความคิดสรางสรรคในโครงการจัดการนวัตกรรม)

6. Team cohesiveness to innovation management effectiveness (ความผูกพันท่ีเหนียวแนนในทีมท่ีมีผลตอการบริหารนวัตกรรมที่มีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 TC1. Your bank supports team cohesiveness. (ธนาคารของทานสนับสนุนใหพนักงานมีความผูกพันที่เหนียวแนนตอการทํางาน)

Measurement 1 2 3 4 5 6 7 TC2. Your bank has put together quality teamwork.

229

(ธนาคารของทานมีการวางองคประกอบของทีมงานที่มีคุณภาพ) TC3. Your bank strengthens the longevity relationship. (ธนาคารของทานมีการสรางความเขมแข็งในความสมานฉันทที่แนบแนนยาวนาน)

TC4. Your bank continues to build up team cohesiveness. (ธนาคารของทานมีสรางความผูกพันที่เหนียวแนนในทีมงานอยางตอเน่ือง)

7. Strategic leadership to innovation management effectiveness (ความเปนผูนําทางกลยุทธท่ีมีผลตอการบริหารนวัตกรรมที่มีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 SL1. Your bank has encourage collaboration with regard to innovation management effectiveness. (ธนาคารของทานมีความรวมมือในการสนับสนุนการบริหารจัดการนวัตกรรมอยางมีประสิทธิภาพ)

SL2. Your bank has supported cross functional collaboration with regard to innovation management effectiveness. (ธนาคารของทานมีการสนับสนุนความรวมมือตางฝายตางฟงกช่ันในการบริหารจัดการนวัตกรรมอยางมีประสิทธิภาพ)

SL3. Your bank has empowered its leader. (ธนาคารของทานใหการสนับสนุนพฤติกรรมความเปนผูนํา)

SL4. Your bank has supported innovative leadership. (ธนาคารของทานใหการสนับสนุนบทบาทความเปนผูนําในเชิงสรางสรรค)

8. Coordination and communication to innovation management effectiveness (การประสานงานและการติดตอสื่อสารท่ีมีผลตอการบริหารนวัตกรรมที่มีประสิทธิภาพ) ทานเห็นดวยกับขอความตอไปนี้มากนอยเพียงใด 1 (เห็นดวยนอยที่สุด) ถึง 7 (เห็นดวยมากที่สุด)

Measurement 1 2 3 4 5 6 7 CC1. Your bank has set up a number of committees. (ธนาคารของทานมีการจัดต้ังคณะทํางานโครงการ)

CC2. Your bank always has regular of project meeting. (ธนาคารของทานมีการนัดประชุมโครงการอยางสม่ําเสมอ)

Measurement 1 2 3 4 5 6 7 CC3. Your bank always supports consultation. (ธนาคารของทานสนับสนุนการรับคําปรึกษาในโครงการอยางสมํ่าเสมอ)

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CC4. Your bank always supports evaluation. (ธนาคารของทานสนับสนุนการประเมินผลโครงการอยางสมํ่าเสมอ)

ส วนท่ี 2 ข อมูลท่ัวไปของสาขาธนาคาร โปรดกรอกข อมูลในช องว างท่ีกําหนด หรือ เติมเคร่ืองหมาย ลงในช อง

ขอมูลสวนตัว 1. เพศ � ชาย � หญิง 2. ระดับการศึกษา � ปริญญาตรี � ปรญิญาโท � ปริญญาเอก

ข อมูลของธนาคาร 1. ช่ือธนาคาร ธนาคาร_____________________

2. ระยะเวลาที่ท านทํางานในธนาคารแห งน้ี ____________________ ป ี

3. ตําแหน งงานของท านในธนาคารแหงน้ี

� ผู จัดการ � รองผู จัดการ � หัวหนาฝายงาน � อื่นๆ(โปรดระบ)ุ________________________________

ขอขอบคุณเปนอยางสูงในความรวมมือของท าน

BIOGRAPHY

NAME Phanu Limmanont

ACADEMIC BACKGROUND M.B.A (Marketing), North Texas

State University, Denton, Texas,

U.S.A.

B.A.(Economics), Thammasat

University, Bangkok, Thailand.

PRESENT POSITION Managing Director

Pharinas Co.,Ltd, 50 Sukhumvit 62,

Banjak, Prakanong, Bangkok,

Thailand.

EXPERIENCE 27 years in Modern Management

and Strategic Plan Management

10 years in training and Consulting

5 years in research and development

THE DETERMINANTS OF ORGANIZATIONAL INNOVATION …

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