Dissertation

Factors Affecting Information Communication

Technology Acceptance and Usage of Public

Organizations in Saudi Arabia

BY

Wael Shahhat M. Basri

International Islamic University

Malaysia

2012

i

Factors Affecting Information Communication

Technology Acceptance and Usage of Public

Organizations in Saudi Arabia


A Dissertation Submitted in Partial Fulfilment of

The Requirements for the Degree of Doctor of

Philosophy in Business Administration

Kulliyyah of Economic and Management Sciences

International Islamic University

Malaysia

September, 2012

ii

ABSTRACT

Recent developments in information communication technology (ICT) have

heightened the need of more study in this topic. There is a real risk of the acceptance

of ICT by some and not others contributing to the rejection. The study approaches the

technology acceptance from the perspective of administration by examining the use of

ICT and e-services in the public environment. The theoretical framework variables of

the technology acceptance model (TAM) are examined. The study also investigated

the effect of the model of organization readiness to change (MORC) Individual

Differences “recipients' beliefs” as external variables, in addition subjective norm, and

volunteer motivation as the moderating. The study tested the current usage as

mediating variable between ICT believes and attitude to change.

Most studies in ICT have been carried out in private sectors in Saudi Arabia.

The survey instrument uses to collect the data is a self administrated questioner

developed based on the technology acceptance questioner as used by Davis and

Venkatesh in (1989). The research population is Saudi workers in public organization.

The research tool is structure equation modelling (SEM), which required a minimum

sample of 200 respondents.

The study contributes to knowledge in the field of technology acceptance research.

Mean while Technology Acceptance Model (TAM) found to be applicable in the

Saudi public environment, the study found that leadership support and lack of training

are the factors obstacles of the e-government uses. When introduced as mediators, the

results verify that current usage has no effect on technology believes. Finally the

findings provide invaluable implication to theory and practice.

Key words: Technology Acceptance model; Information Communication Technology;

ICT Usage, Public organization; structural equation modelling; Saudi Arabia and

developing countries.

iii

البحث ملخص

زاد التطور األخير في تقنيه المعلومات واالتصاالت من حاجة إلى مزيد هناك خطر حقيقي من قبول تقنيه . المجال هذا من الدراسات في

المعلومات واالتصاالت من قبل البعض وليس الكل مما يسهم في رفض نيهمعوقات قبول تق معرفه الى هذه الدراسة تهدف.التقنيه هذه أستخدام

خالل دراسة استخدام تقنيه المعلومات واالتصاالت من المعوماتخالل فحص من واالتصاالت والخدمات اإللكترونية في البيئة الحكوميه

لقد أستخدمت (. TAM)متغيرات اإلطار النظري لنموذج قبول التقنيه (MORC)استعداد المنظمة للتغيير نموذج من الفرديه الدراسة الفروق

الى المعيار شخصي، والدافع التطوع ت خارجية، باإلضافةكمتغيرامتغيرات تقنيه كوسيط بين للتقنيه الحالي اختبار االستخدام تم و. كوسائط

.التغيير من المعلومات واالتصاالت المعتقد

السابقه في تقنيه المعلومات واالتصاالت في أجريت معظم الدراساتاألستبيان لجمع إستخدام تم. لسعوديةالقطاع الخاص في المملكة العربية ا

بواسطه ديفيز المستخدم التقنيه قبول أعتمد على إستبيان البيانات والذيهم الموظفون السعوديين في عينه البحث(. 9191)وفينكاتيش في ، (SEM) النموذجية الهيكلة المعادلةأداة البحث هو . المؤسسات العامة

.022قدره الستطالع ووالذي يتطلب حد االدنى من عينة ا

ووجدت . مجال بحوث القبول التقنيه الدراسة أسهمت في توسيع هذهقابل للتطبيق في البيئة العامة ( TAM)التقنيه قبول نموذج الدراسة انفي تطبيق و أن دعم القيادة والنقص في التدريب هي العقبات السعودية،

من النموذجية الهيكلة ةالمعادل تحليل نتائج كشفت .الحكومة اإللكترونية تقنيه متغيرات بين النتائج االستخدام الحالي ليس له أي تأثيرعلى العالقه

مقتراحات ضمنت النتائجهذه أخيرا إن .التغير من المعتقد و المعلومات .والممارسة للنظرية مهمه

iv

APPROVAL PAGE

v

The thesis of Wael Shahhat M. Basri has been approved by the following:

________________________

Mohamed Sulaiman

Supervisor

_______________________

Suhaimi Mhd Sarif

Internal Examiner

_______________________

Zainal Abidin Mohamed

External Examiner

_______________________

El-Fatih A. Abdel Salam

Chairman

DECLARATION

vi

I hereby declare that this dissertation is the result of my own investigations, except

where otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.


Signature Date ……14/09/2012………..

ACKNOWLEDGEMENT

vii

First and foremost I would like to thank God. In the process of putting

this dissertation together I realized how true this gift of writing is for me.

You given me the power to believe in my passion and pursue my dreams.

I could never have done this without the faith I have in you, the Almighty.

My deepest gratitude is to my Supervisor Emeritus Prof. Dr. Mohamed

Sulaiman. I have been amazingly fortunate to have a supervisor who gave

me the freedom to explore on my own and at the same time the guidance

to recover when my steps faltered. Do teach me how to question thoughts

and express ideas. His patience and support helped me overcome many

crisis situations and finish this dissertation. I hope that one day I would

become as good an advisor to my students as Don has been to me. I

consider it an honour to work under the supervision of Emeritus Prof. Dr.

Mohamed Sulaiman.

Most importantly, none of this would have been possible without the love

and patience of my family. I share the credit of my work with my wife

and kids, my immoderate family to whom this dissertation is dedicated to,

has been a constant source of love, concern, support, and strength all

these years. I would like to thank my wife for her understanding and love

during the past few years. Her support and encouragement was in the end

what made this dissertation possible. My kids, Bayan, Abdulrhman and

Mohammed, receive my deepest gratitude and love for their dedication

and the many years of support during my study. I would like to express

my heart-felt gratitude to my family. My extended family has aided and

encouraged me throughout this endeavour.

I am also thankful to the system staff who maintained all the needed help

in my paper work so efficiently that I never had to worry about following

the secretarial work. I do not envy their job. I feel that they are the

greatest system administrators in the world.

Finally, many friends have helped me stay sane through these difficult

years. Their support and care helped me overcome setbacks and stay

viii

focused on my graduate study. I greatly value their friendship and I

deeply appreciate their belief in me. I am also grateful to the Malaysian

people whom helped me adjust to a new country.

ix

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION

OF FAIR USE OF UNPUBLISHED RESEARCH

Copyright © 2012 by Wael Shahhat M. Basri All rights reserved.

FACTORS AFFECTING INFORMATION COMMUNICATION TECHNOLOGY

ACCEPTANCE AND USAGE OF PUBLIC ORGANIZATIONS IN SAUDI

ARABIA

No part of this unpublished research may be reproduced, stored in a retrieval system,

or transmitted, in any form or by any means, electronic, mechanical, photocopying,

recording, or otherwise without prior written permission of the copyright holder

except as provided below.

1. Any material contained in or derived from this unpublished

research may only be used by others in their writing with due

acknowledgement.

2. IIUM or its library will have the right to make and transmit

copies (print or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieval

system and supply copies of this unpublished research if requested

by other universities and research libraries.

Affirmed by Wael Shahhat M. Basri.

. ..........14/09/2012..............

Signature Date

x

TABLES OF CONTENTS

Abstract..........................................................................................................................ii

Abstract Arabic.............................................................................................................iii

Approval Page................................................................................................................v

Declaration....................................................................................................................vi

Acknowledgment.........................................................................................................vii

Copy Right Page...........................................................................................................ix

CHAPTER ONE INTRODUCTION ......................................................................... 1 1.1 E-Government is ICT Application.............................................................. 5

1.2 The Internet in Saudi Arabia........................................................................ 5

1.2.1 E-government Program in Saudi Arabia [YESSER] ....................... 6

1.3 Advantages of E-Government .................................................................... 7

1.3.1 Government Agencies Benefit .......................................................... 7

1.3.2 Individual Benefits ............................................................................ 7

1.3.3 International Trade Benefits .............................................................. 8

1.4 Challenges Facing E-Services in Saudi Arabia ........................................... 8

1.4.1 Infrastructure ..................................................................................... 9

1.4.2 Qualified Staff ................................................................................... 9

1.4.3 Internet Usage ................................................................................. 10

1.4.4 Resistance to Change ...................................................................... 10

1.4.5 Leadership Support ......................................................................... 11

1.4.6 Culture ............................................................................................. 12

1.5 Problem Statement ..................................................................................... 12

1.6 Research Justification And Research Question ......................................... 14

1.6.1 Research Significance ..................................................................... 15

1.6.2 Research Question(s) ...................................................................... 18

1.7 Research Objectives................................................................................... 19

1.8 Definition of Terms ................................................................................... 20

1.9 Chapter Summary ...................................................................................... 23

CHAPTER TWO LITERATURE REVIEW .......................................................... 24 2.1 Organizational Behaviour Management [Obm] ........................................ 24

2.2 Organization Change ................................................................................. 25

2.3 Management Change ................................................................................. 26

2.4 Meaning Of Change ................................................................................... 27

2.5 Forces Of Change ...................................................................................... 28

2.5.1 External Forces................................................................................ 28

2.5.2 Internal Forces ................................................................................. 30

2.6 Resistance Of Change ................................................................................ 30

2.7 Information Communication Technology And Organizations .................. 33

2.8 Technology Acceptance And Usage .......................................................... 43

2.9 Technology Acceptance............................................................................. 44

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2.9.1Technology Acceptance Theories ..................................................... 45

2.9.1.1 The Theory of Reasoned Action [TRA] ............................... 45

2.9.1.2 Theory of Planned Behaviour [TPB] .................................... 46

2.9.1.3 Task Technology Fit [TTF] .................................................. 46

2.9.1.4 Diffusion of Innovations [DI] ............................................... 47

2.9.1.5 Technology Acceptance Model [TAM] ............................... 47

2.9.1.6 Unified Theory of Acceptance and Use [UTAUT] .............. 48

2.10 Chapter Summary .................................................................................... 49

CHAPTER THREE DEVELOPING THE THEORETICAL FRAMEWORK .. 50 3.1 The Model Of Readiness For Organizational Change ............................... 51

3.2 Technology Acceptance Model (Tam) ...................................................... 55

3.3 Complaints Concerning Further Development of The Technology

Acceptance Model ............................................................................................ 60

3.4 Proposed Theoretical Framework .............................................................. 61

3.4.1 Intention to Use and Continue to Use ............................................. 63

3.4.2 Attitude to Change .......................................................................... 71

3.4.2.1 Resistance to Change ............................................................ 77

3.4.2.2 Readiness for Change ........................................................... 80

3.4.3 Beliefs Concerning Technology Acceptance .................................. 85

3.4.3.1 Perceived Ease of Use “has everyone bought into making the

change happen” ................................................................................. 86

3.4.3.2 Perceived Usefulness “Is this the right change" ................... 87

3.4.4 Organizational Change Recipients' Beliefs Technological Change-

Related Beliefs .......................................................................................... 88

3.4.4.1 Appreciation ......................................................................... 90

3.4.4.2 Principal Support .................................................................. 92

3.4.4.3 Motivation Valence .............................................................. 99

3.4.5 Moderators .................................................................................... 102

3.4.5.1 Subjective Norm ................................................................. 102

3.4.5.2 Perceived Voluntariness ..................................................... 104

3.4.6 Other Factors That Affect Intention to Use ICT ........................... 106

3.4.6.1 Nature of Work ................................................................... 107

3.4.6.2 Training .............................................................................. 108

3.4.6.3 Current Usage ..................................................................... 109

3.5 Summary of The Chapter......................................................................... 111

CHAPTER FOUR RESEARCH METHODOLOGY .......................................... 112 4.1 The Research Model ................................................................................ 114

4.2 The Variables ........................................................................................... 115

4.2.1 Dependent Variable ....................................................................... 115

4.2.1.1 Intention to use ................................................................... 115

4.2.2 Independent Variables ................................................................... 115

4.2.2.1 Perceived Ease of Use ........................................................ 115

4.2.2.2 Perceived Usefulness .......................................................... 116

4.2.2.3 Principal Support ................................................................ 116

4.2.2.4 Motivation Valance ............................................................ 117

4.2.2.5 Commitment to Change ...................................................... 117

4.2.2.6 Appreciation ....................................................................... 118

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4.2.2.7 Current Usage ..................................................................... 118

4.2.3 Moderator variables ...................................................................... 118

4.2.3.1 Subjective Norm ................................................................. 118


4.2.3.3 Training .............................................................................. 119

4.2.3.4 Nature of Work ................................................................... 119

4.3 The Hypotheses ....................................................................................... 120

4.4 Why Positivistic Paradigm? ..................................................................... 125

4.5 Research Design ...................................................................................... 125

4.6 Justification Of A Descriptive Research.................................................. 127

4.7 Why Quantitative Research ..................................................................... 128

4.8 Data Gathering And Data Analysis ......................................................... 129

4.9 Questionnaire Development .................................................................... 130

4.10 The Population And The Sample ........................................................... 131

4.11 Statistical Analysis................................................................................. 132

4.11.1 Structural Equation Modelling .................................................... 134

4.11.1.1 Why Structural Equation Modelling ................................ 135

4.11.1.2 Sample Size for SEM ....................................................... 139

4.12 Regression And Path Models Vs. Structural Equation Modelling ........ 140

4.13 Pilot Study ............................................................................................. 141

4.14 Questionnaire Testing ............................................................................ 145

4.14.1 Testing Question Sequencing ...................................................... 146

4.14.2 Testing Questionnaire Layout ..................................................... 147

4.14.3 Validity and Reliability of the Instrument .................................. 147

4.14.3.1 Content Validity ............................................................... 148

4.14.3.2 Face Validity .................................................................... 149

4.14.3.3 Construct Validity ............................................................ 150

4.14.3.4 Internal Consistency (Reliability) ..................................... 151

4.15 Pilot Study Cronbach's Alpha ................................................................ 152

4.16 Factors Analysis..................................................................................... 153

4.17 Kaiser-Meyer-Olkin (KMO) and Bartlett's Test .................................... 154

4.18 Summary of The Chapter....................................................................... 156

CHAPTER FIVE DATA COLLECTION AND ANALYSIS .............................. 157 5.1 Response Rate .......................................................................................... 159

5.2 Justification Stratified Random Sampling Technique ............................. 160

5.3 Data Analysis ........................................................................................... 160

5.3.1 Missing Data and Cleaning the Data ............................................. 161

5.3.2 AMOS ........................................................................................... 161

5.3.3 Correlation and Simple Regression ............................................... 162

5.3.4 Path Analysis ................................................................................. 162

5.3.5 Reliability Test for the Main Data ................................................ 162

5.3.6 Descriptive Analysis ..................................................................... 164

5.3.6.1 Participants Characteristics and Their Technology Beliefs 164

5.3.6.2 Principal Support ................................................................ 167

5.3.6.3 Motivation Valance ............................................................ 168

5.3.6.4 Appreciation ....................................................................... 169



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5.3.6.7 Current Usage ..................................................................... 172

5.3.6.8 Attitude to Change .............................................................. 173

5.3.6.9 Subjective Norm ................................................................. 174

5.3.6.10 Perceived Voluntariness ................................................... 175

5.3.6.11 Intention to use ................................................................. 176

5.4 The Model Summary ............................................................................... 177

5.4.1 Model Variables and Parameters .................................................. 178

5.4.2 Modification Indexes .................................................................... 179

5.4.2.1 Tests of Normality and Outliers ......................................... 180

5.4.2.2 Normality ............................................................................ 180

5.4.2.3 Outliers ............................................................................... 181

5.4.2.4 Collinearity (Multicollinearity) .......................................... 183

5.4.3Model Fit Indices ............................................................................ 185

5.4.3.1 Chi Square-Based Measures of Discrepancy Fit ................ 185

5.4.3.1.1 CMIN: the Minimum Discrepancy CMIN/DF ........ 186

5.4.3.2 Baseline Model Comparisons ............................................. 186

5.4.3.2.1 NFI Bentler-Bonett normed fit ................................. 186

5.4.3.2.2 CFI Comparative Fit Index ...................................... 186

5.4.3.2.3 GFI Goodness of Fit Index ...................................... 187

5.4.3.3 Parsimony Adjusted Fit Measures ...................................... 188

5.4.3.3.1 RMSEA Measures and PCLOSE ............................. 188

5.4.3.4 Measurement Adequacy and Considering Modification .... 189

5.4.4 Evaluating the Goodness of Fit ..................................................... 190

5.4.5 Exploratory Factor Analysis and Conformity Factor Analysis ..... 194

5.4.5.1 Exploratory Factor Analysis (EFA) .................................... 195

5.4.5.2 Kaiser-Meyer-Olkin Test .................................................... 196

5.4.6 Conformity Factor Analysis (CFA ) ............................................. 196

5.4.6.1 Principal Support Test ........................................................ 197

5.4.6.2 Motivation Valance Test .................................................... 199

5.4.6.3 Appreciation Test ............................................................... 201



5.4.6.6 Attitude to Change .............................................................. 208

5.4.6.7 Intention to Use .................................................................. 210

5.4.6.8 Subjective Norm ................................................................. 214


5.4.6.10 Current Usage ................................................................... 218

5.5 Assessment Of The Measurement Model ................................................ 220

5.6 Hypothesis Testing .................................................................................. 226

5.6.1 Hypothesis 1: Attitude to change negatively and directly influences

Intention to use. ....................................................................................... 228

5.6.2 Hypothesis 1a: Subjective Norms moderates the relationship

between attitude to change and Intention to use. .................................... 229

5.6.3 Hypothesis 1b: Perceived voluntariness moderates the relationship

between attitude to change and Intention to use. .................................... 230

5.6.4 Hypothesis 2: Current use positively and directly mediates the

attitude to change. ................................................................................... 231

5.6.5 Hypothesis 2a: The nature of work moderates the relationship

between current usage and attitude to change. ........................................ 233

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5.6.6 Hypothesis 2b: Training moderates the relationship between current

usage and attitude to change. .................................................................. 233

5.6.7 Hypothesis 3: Perceived Usefulness positively and directly

influences Current usage of technology. ................................................. 235

5.6.8 Hypothesis 4: Perceived ease of use positively and directly

influences Perceived usefulness. ............................................................. 236

5.6.9 Hypothesis 4a: Perceived ease of use positively and directly

influences Current usage of technology. ................................................. 237

5.6.10 Hypothesis H5: Principal Support positively and directly

influences perceived Usefulness. ............................................................ 238

5.6.11 Hypothesis H5a: Principal Support positively and directly

influences perceived ease of use. ............................................................ 239

5.6.12 Hypothesis 6: Motivation Valence negatively and directly

influences perceived usefulness. ............................................................. 240

5.6.13 Hypothesis 6a: Motivation Valence negatively and directly

influences perceived ease of use. ............................................................ 242

5.6.14 Hypothesis 7: Appreciation positively and directly influences

perceived ease of use. .............................................................................. 243

5.6.15 Hypothesis 7a: Appreciation positively and directly influences

perceived Usefulness. .............................................................................. 245

5.6.16 How Age, Income, and Education Affect the Relationship ........ 246

5.7 Summary Of Results Of The Hypothesis Testing ................................... 250

5.8 Conclusion ............................................................................................... 252

CHAPTER SIX DISCUSSION AND CONCLUSION ......................................... 255 6.1 Research Question Addressed ................................................................. 257

6.1.1What factors affect employee intention to accept and use information

communication technology in the Saudi public sectors? ........................ 258

6.2 Significant for The Model and Organization ........................................... 261

6.2.1Implications for Knowledge ............................................................ 261

6.2.2Implication for the Organization ..................................................... 264

6.3 Limitations of The Research and Future Studies ..................................... 267

6.3.1 Limitations .................................................................................... 268

6.3.2 Future study ................................................................................... 269

6.4 Conclusion ............................................................................................... 270

INDEX ....................................................................................................................... 274 7.1 Determining Sample Size for Research Activities .................................. 274

7.2 Questionnaire ........................................................................................... 276

7.2.1 Part One ......................................................................................... 277

7.2.2 Part Two ........................................................................................ 279

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LIST OF TABLES

Table No. Page No.

1.1 Internet Usage 2009-2010 Statistics for Selected Countries

in the Middle East Region 10

1.2 Summarized key Obstacles of Internet Usage 11

4.1 Participants’ Characteristics Pilot Study 143

4.2 Cronbach's Alpha for the Variables (Pilot Data Analysis) 152

4.3 KMO and Bartlett's Tests for the Variables (for Pilot Study) 155

5.1 Participants’ Characteristics Main Study 158

5.2 Reliability Statistics 163

5.3 Main Study Sample Participants Characteristics 164

5.4 Descriptive Statistic Principal Support Cronbach's Alpha 0.71 168

5.5 Descriptive Statistic Motivation Valance Cronbach's Alpha 0.77

169

5.6 Descriptive Statistic Appreciation Cronbach's Alpha 0.72 170

5.7 Descriptive Statistics Perceived Ease of Use with Cronbach's Alpha 0.75

171

5.8 Descriptive Statistics Perceived Usefulness with Cronbach's Alpha 0.72

172

5.9 Descriptive Statistics of Current Usage with Cronbach's Alpha 0.75

172

5.10 Descriptive Statistics of Attitude to Change with Cronbach's Alpha 0.77

173

5.11 Descriptive Statistics of Subjective Norm with Cronbach's Alpha 0.67

174

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5.12 Descriptive Statistics of Perceived Voluntariness with Cronbach's Alpha

0.8 175

5.13 Descriptive Statistics of Intention to Use with Cronbach's Alpha 0.74

176

5.14 Computation of Degrees of Freedom 177

5.15 The Research Model Summary 178

5.16 Parameter Summary 179

5.17 Assessment of Normality 181

5.18 Observations Farthest from the Centroid (Mahalanobis distance) 182

5.19 Coefficients Collinearity Test 184

5.20 Evaluating Results: Which Fit Indices & What Values? 188

5.21 Baseline Comparisons Whole Model 192

5.22 Parsimony-Adjusted Measures 192

5.23 RMSEA AND PCLOSE 193

5.24 RMR, GFI 194

5.25 Principal Support 197

5.26 EFA, KMO, Bartlett's Test Principal Support 198

5.27 Motivation Valance 200

5.28 EFA, KMO, and Burlett’s Tests Motivation Valance 200

5.29 Appreciation 202

5.30 EFA, KMO, and Burlett’s Tests Appreciation 202

5.31 Perceived Ease of Use 204

5.32 EFA, KMO, and Burlett’s Tests Perceived Ease of Use 205

5.33 Perceived Usefulness 206

5.34 EFA, KMO, and Burlett’s Tests Perceived Usefulness 206

5.35 Attitude to Change 209

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5.36 EFA, KMO, and Burlett’s Tests Attitude to Change 209

5.37 Intention to Use 211

5.38 EFA, KMO, and Burlett’s Tests Intention to Use 211

5.39 Summary Result for TAM Hypothesis 214

5.40 Subjective Norm 214

5.41 EFA, KMO, and Burlett’s Tests Subjective Norm 215

5.42 EFA, KMO, and Burlett’s Tests Perceived Voluntariness 217

5.43 Current Usage 218

5.44 EFA, KMO and Bartlett's Test Current Usage 219

5.45 Exogenous Variables: Measurement and Legends 222

5.46 Standardized Regression Weights and the Legend of Each Construct

226

5.47 Summarize Result of H1 229

5.48 Summarize Result of H1a 230

5.49 Summarize Result of H1b 231











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5.62 Standard Regression Weight for Models 247

5.63 Summary of the Result of the Hypothesis Testing 250

xix

LIST OF FIGURS

Figure No. Page No.

Figure ‎3.1The Model of Readiness for Change MROC (Holt et al., 2007a) 52

Figure ‎3.2 Variation on the Model of Readiness for Change (Holt et al., 2007b) 53

Figure ‎3.3 Theoretical Framework for the Technology Acceptance Model 57

Figure ‎4.1 The Reasearch Model 113

Figure ‎5.1CFA Measurement Principal Support 199

Figure ‎5.2 CFA Measurement Motivation Valance 201

Figure ‎5.3 CFA Measurement for Appreciation 203

Figure ‎5.4CFA Measurement Perceived Ease of Use 204

Figure ‎5.5 CFA Measurement Perceived Usefulness 207

Figure ‎5.6 CFA Measurement Attitude Change 210

Figure ‎5.7 CFA Measurement Itention to Use 212

Figure ‎5.8 CFA Measurement TAM 213

Figure ‎5.9 CFA Measurement Subjective Norm 215

Figure ‎5.10CFA Measurement Perceived Voluntariness 217

Figure ‎5.11 CFA Measurement of Current Usage 219

Figure ‎5.12 CFA The Research Model and Model Fit 225

Figure ‎5.13 Age 249

Figure ‎5.14 Income 249

Figure ‎5.15 Educaion 250

1

1 CHAPTER ONE

INTRODUCTION

“Many leading organizations tumble from the peak of

success to the bottom of failure when the surroundings

changes; because they cannot follow the stream. To the

contrary, they engage in too much action; action of the

incorrect kind. Suffering from active inertia, they trapped

in their attempt and true activities, even in the face of

dramatic shifts in the environment. Instead of digging

themselves out of the hole, they dig themselves in deeper.

Such companies are victims of their own success: they

have been so successful; they assume they have found the

winning formulas. But these same formulas become rigid

and no longer work when the market changes

significantly”.

Harvard Business Review (Sull, 1999, 1)

The world is electrified! Besides the pressure dealing with the normal

operational problems, organizations have to navigate change after change in a shifting

global economy. Leaders have to create the time to explore all the options available to

them so that they can advance their organizations electronically. It is now a

hyperactive world, and the most successful leaders will be those who tap into the

wires and maximize the present for the benefits of their organizations.

According to Downing, Fasano, Friedl, McCullough, Mizrahi, and Shapiro

(1991) Information Communication Technology [ICT] has introduced several social

changes in the world that cannot and should not be overlooked by leaders whose job is

2

preparing people to function successfully within this new rapidly changing

unpredicted economy. Such ICT is changing every day; therefore, the cost of keeping

up with this change is very high (Krigsman, 2009). In addition, it is extremely hard to

keep up with this change. The developing countries aim to take advantage of ICT in

their strategic planning (AlSheha, 2007; Al-Soma, 2009).

Today, anything may have an ‘e’ letter, e-business, e-literacy to e-government

and e-transaction. The prefix ‘e’ means manipulating data in digitized electronics form

followed by the phrase of action. For example, e-government means electronic

manipulating for control, and governing purposes (Tabatabaie and Monadi, 2006). By

definition, e-business is using any type of network connection to remain in touch with

clients, partners, and services provider (Morris, 2003). To engage in e-commerce

means: “adopting new web-enabled business models auctioning off surplus goods,

selling products directly to consumers, or joining in online purchasing cooperatives

with their competitors.

Andersen (2006) defines e-government as “utilize of computer technology

applications and web-based connection to provide services in the public sector”. The

World Bank [WB], (2010, Ol) spots the definition of e-government as “the use by

government agencies of information technologies (such as Wide Area Networks, the

Internet, and mobile computing) that have the ability to transform relations with

citizens, businesses, and other arms of government. These technologies can serve a

variety of different ends: better delivery of government services to citizens, improved

interactions with business and industry, citizen empowerment through access to

information, or more efficient government management. The resulting benefits can be

less corruption, increased transparency, greater convenience, revenue growth, and/or

3

cost reductions”. The technical definition of e-government is the use of technology to

boost the access to, and delivery of, public service to benefit the citizen (Deloitte,

2000).

Dawes (2008) asserts the main e-government’s objectives are electronic

information exchange, electronic verification, electronic identification of citizens

(chip cards), and electronic business’s registration. In addition, e-government

makes public bureau’s more efficient, transparent, convenient, cost efficient, and/or

increase income (Al-Soma, 2009; Brown, 2007; Morris, 2003). The e-government

links the citizen, businesses, not for profit organization with the government bureaus

(Rocheleau, 2007).

Recent developments in the field of public services have led to a renewed

interest in the use of e-government. E-government plays a significant role in the public

services industry. The developing countries sought to decrease government

expenditure and improve government efficiency, by improving public service delivery

through the use of e-government (DeBenedictis, Howell, Figueroa, and Boggs, 2002).

The Saudi government launched the strategic management initiative that outlined the

plan of delivering better government services to the public in 2004 (Al-Sabti, 2005).

This is clearly underpinned by a sustained commitment to modernization throughout

the public agencies. Today, the concept of modernization and change within the public

service is identical with a wide range of managerial, organizational, technological, and

legislative innovations, which have unfolded during the last decade (Kieran and

McDonagh, 2006).

Massive advantages of e-government and information technology drive the

developing countries, the gulf countries, and Saudi Arabia toward adoption of e-

4

government (Faisal, 2010). The e-government project requires contribution and total

involvement of administrators, resources, and commitment among public, private, and

non-profit sectors with the government (Faisal, 2010; Mofleh, 2008). Technical

requirements and technology infrastructure are essential for potentially efficient e-

government system (Oregon state e-government, 2006). Yet, total or partial failure

confronts e-government and Information technology projects due to other un-technical

factors (Heeks, 2003; Mofleh, 2008).

The unsuccessful story of information communication technology transfer in

some developing countries had led to abandon this essential strategic factor. Several

factors are behind the low technological adoption in the developing countries. These

factors are (1) the human capital (Arrow, Chenery, Minhas and Solow, 1961; Scacco,

2009; Al Khalid, 2010; Al-Faisal, 2010), (2) resources and wealth (Press Release,

2008; Scacco, 2009; Pavela, 2010), (3) employee resistance and valance

(Lanzendörfer, 1985; Haymes, 2008; Maru, 2009), (4) the country-specific culture,

norms and society (Ruttan, 2008; Amin, Khushman and Todman, 2009; Owyang,

2009; UNESCO, 2009; Pavela, 2010) and (5) leadership and management support

(DeBenedictis et al., 2002; Scacco, 2009).

Information technology is dependent upon technology, in fact, without

technology; there is no “e” in organization. The factors that impact information

technology are both internal and external, just as they are with a traditional business.

However, there are certain risk factors associated with information technology that

may be different (Lunenburg, 2010). Organizations adapt to the external forces, or

they try to find a way to change those forces (Lunenburg, 2010).

5

1.1 E-GOVERNMENT IS ICT APPLICATION

E-Government is one of ICT applications; it is a standalone system for delivering

services and provides information exchange between the three stakeholders mentioned

by DeBenedictis:

i. Government with Citizens [G2G] there is a possibility that the majority

of e-government applications as well as the services fall under the G2C

category, which concentrates on offering society with comprehensive and

wide-ranging electronic services in order to meet the individuals’ routine

concerns (Australian government information management office, [AGIMO]

2007; WB, 2010).

ii. Government with Business [G2B] the production, industrial, and

commerce organizations have transactions with the government; the

second application of e-government, for example being: renewing

registrations, updating information, and many others (AGIMO, 2007; WB,

2010).

iii. Government with Government [G2G] many government operations and

transactions require association between different departments, for example;

business registration forms require approval from several state agencies

(AGIMO, 2007; WB, 2010).

1.2 THE INTERNET IN SAUDI ARABIA

In January 1999, the Saudi public was granted access to the World Wide Web

[WWW] through local internet service providers. It did so while filtering and blocking

the flow of "unwanted" data online. The local governments, academic institutes, and

6

medical centres granted access to the internet, whoever residents of Saudi Arabia

could connect through foreigner internet services provider [ISP] (Communications

and Information Technology Commission [CIT], 2007).

In November 1999, the government approved applications from some

companies allowing local private internet service providers. However, King Abdul-

Aziz City for Science and Technology [KACST] is Saudi Arabia only gateway to the

World Wide Web [WWW]; this allows the government to control and limit the data

flow and internet surf (CIT, 2007).

1.2.1 E-government Program in Saudi Arabia [YESSER]

The Arabic meaning for “YESSER” is simplified “facilitate”, YESSER is the Arabic

name of Saudi Arabia's e-government scheme (YESSER, 2006). Kostopoulos (2007)

said an initial e-services attempt was between Ministry of Hajj and other Umra and

Hajj expedition operators. Since the early 2001, the government in Saudi Arabia has

taken a number of key initiatives. The objectives of Saudi development plans are to

ensure that government agencies’ efficiency meet the financial and every day needs of

Saudi citizens.

The framework of the action plan, based on a detailed strategic vision of e-

government that includes policies for establishing e-government projects have been

approved at the end of 2001. In March 2003, the Ministry of Finance and Monetary

under the royal directive of the Saudi King allocated the entire necessary fund for the

launching of the e-government (Bawazir, 2006). The Saudi Arabian government spent

two years building a centralized control system before it was introduced to public

service in February 2004.

7

1.3 ADVANTAGES OF E-GOVERNMENT

AGIMO illustrates the best e-services are to build structures that are intended to meet

people needs and life situations rather than construct the governments’ agencies

online. Government agencies must be free from the agencies’ boundaries, and follow

citizens’ current events, so they can maximize their production (AGIMO, 2007). In

the convenience of e-government, the relations between a citizen and business with

public agencies took place in service’s centres closer to the public, kiosks in the

agency, service’s kiosk near the public, or a computer in home or office (Bolívar,

Pérez and Hernández, 2007; Al Khalid, 2010).

1.3.1 Government Agencies Benefit

The amount of data exchange going on between government organizations is massive,

and the operating cost linked with that is very high. However, e-government can cut

the expenditure dramatically. A study conducted by the AGIMO in 2006, affirms that

cost-effective solutions achieved with e-government (Australian National Audit

Office, 2008; Al-Soma, 2009). A study done by AGIMO reveals the overall estimated

reductions in costs from the use of e-government were about one hundred million

Australian dollars from the investigated programs. Using e-government tools will

definitely have a significant impact on expenditure efficiency. In addition, the e-

government will provide efficient services to individuals, businesses and government

organizations (DeBenedictis et al., 2002; AGIMO, 2007).

1.3.2 Individual Benefits

Time is very important and significant to people these days, “how long” concept is

more critical to people than quality or “how good” particularly. The time dimension is

a decisive element of e-government adoption. Citizens can use government

8

services through agency websites twenty-four hours a day for seven days a week, not

just when a particular government agency is operational (O'Neill, 2000; Al Khalid,

2010). Moreover, services can be provided as self-serve, a final point, people in

the rural communities can also access government services, which expand the

government service’s coverage (DeBenedictis et al., 2002; Oregon State e-

government, 2006; Al-Soma, 2009).

1.3.3 International Trade Benefits

World trade organization [WTO] sets certain rules for its members which must be

fulfilled to join the organization. One of them is the e-government readiness matter.

Saudi Arabia ranked fifty-seventh of out one hundred ninety one of the United

Nations’ member as stated in the United Nations’ Global E-government readiness

report 2010 (DeBenedictis et al., 2002; United Nations [UN], 2010).

1.4 CHALLENGES FACING E-SERVICES IN SAUDI ARABIA

Aljifri, Collins and Pons (2003) stated several factors associated with the

failure of ICT acceptance and adoption in developing countries: (1) Information

Security (Abd.Mukti, 2000; DeBenedictis et al., 2002; Bwalya, 2009); (2) Technical

and industrial infrastructure (DeBenedictis et al; Vosloo and Van Belle, 2005); (3)

Educational (Vosloo and Van Belle, 2005); (4) Governmental regulation (Karcher,

Kuperminc, Portwood, Sipe and Taylor, 2006); (5) Social (Kang, 2007; Al-Somali,

Gholami and Clegg, 2009; Ahmad, Basha, Marzuki, Hisham, and Sahari, 2010); (6)

Qualified personnel (Al-Qahtani, 2010; Al-Faisal, 2010), (7) Age (Kennedy,

Dalgarno, Bennett, Judd, Gray, and Chang, 2008; Sam, Othman and Nordin, 2005;

Ahmad et al.,); (8) Lack of change of management (Bwalya, 2009); (9) Language

(Bwalya, 2009); (10) Empowerment (DeBenedictis et al).

9

AGIMO outlines the e-government challenges as follows:

1.4.1 Infrastructure

This factor is significant to developing countries; most of them suffer from

poor infrastructure that limits e-government development (Gartner, 2007; UN, 2010).

In addition, developing countries need to allocate the needed fund and offer the

supporting training programs (Al-Khalid, 2010). A report by the Ministry of

communication and information technology in Saudi Arabia (2010) forecasted that

infrastructure of the nation's e-government and telecommunications sector will need

more time to achieve the minimum requirement of e-government (Al-Gahtani, 2010).

1.4.2 Qualified Staff

In order to run effective e-government program, the organizations need to have

qualified individuals to the new task, or train the existing employee to perform

effectively (O'Neill, 2000; Ndubisi and Jantan, 2003; Gartner, 2007). As recently as

1995, The Ministry of Planning reported the foreign workers in Saudi Arabia on a

temporary basis accounted for about one-fifth of the country's total population,

meanwhile; most of Saudi government workers are unskilled and lack the appropriate

training to the new task (Al-Ghamdi, 2010). Moreover, Al-Ghamdi (2010) stated an

investigation in 2009 revealed that over ninety-three percent of employees from

twenty eight ministries and governmental entities in Saudi Arabia are not efficient and

not trained or had insufficient training (Al-Faisal, 2010; Al-Ghamdi, 2010). In June

2010, the ministry of communication and information technology reported that the

lack of qualified staff is the major obstacle of e-government implementation and

diffusion in Saudi Arabia. In addition, the report says that one hundred sixty five

organizations need more time to achieve this objective.

10

1.4.3 Internet Usage

According to Al Hoymany minister’s advisor for information technology and head of

the e-government infrastructure department, “in this year 2007 just thirteen to fifteen

percent of the Saudi Arabia population actually uses the internet” (AlSheha, 2007, 7).

In addition, the Internet World Stats [IWS] (2010) showed the current proportion of

actual internet users in Saudi Arabia is less than fifty percent of the total population.

However, the major problem with internet usage is the high cost of the internet usages

in Saudi Arabia (Aragaam Digital, 2010).

Table 1.1 shows Saudi Arabia internet usage growth is the slowest of the gulf

countries. Moreover, it shows that less than 5% of Saudi Arabia population is using

internet, this percentage is the lowest in the Arab countries.

Table 1.1

Internet Usage 2009-2010 Statistics for Selected Countries in the Middle East Region

Country

Population

2010Est.

thousand

Internet

Users,2000

thousand

Internet

Users 2009

Thousand

Use

Growth

(00-10)

Population

(Penetration)

Kuwait 2,789 150 1,100 39.0 % 633.3 %

Saudi Arabia 25,731 200 9,800 38.0 % 4,80 %

UAE 4,975 735 3,778 76.0 % 414.0 %

Qatar 841 30 436 52.0% 1,353.3 %

Source :Internetworldstats.com

1.4.4 Resistance to Change

E-government like any information technology project faces resistance from the users

and the operators, especially when it is presented quickly without investigation, causes

11

project failure (Adams, Berner and Wyatt 2004). People are likely to resist when they

are asked to do different task or extra effort. Therefore, e-government success

and failure depend on solving this important issue (Singh and Waddell, 2003; Joseph,

and Kitlan, 2008). Table 1.2 shows the major obstacles of technology implementation.

Table 1.2

Summarized key Obstacles of Internet Usage

No Obstacles Percentage

1 Resistance to change 52%

2 Integrating with existing technologies 41%

3 Security concerns 32%

4 Budget 25%

5 Product knowledge 23%

6 Tools not enterprise ready 22%

Source: Data Monitor, 2009

A study carried on 1,500 change management decision-making in the success/failure

rates of “change” ICT projects; finds less than 40% of ICT projects met the project

goal target (budget and time). Also, the main obstruction to success was people

factors, changing the idea and attitudes 58%, organization culture 49%, and lack of

top management support 32% (IBM, 2008).

1.4.5 Leadership Support

Leadership support is the common barrier in e-government implementation in

developing countries. A study by Bjorn and Fathul (2008) showed that the lack of

leaders or high officials support contributes to sixty per cent of e-government

initiative failure. Heeks (2003) noticed that the leader’s personal interests cause many

12

e-government projects’ failures in some developing countries (Scacco, 2009; Pavela,

2010).

1.4.6 Culture

Commonwealth Telecommunications Organization [CTO] (2006) stresses that e-

government project needs vigorous strategy. Furthermore, Gokhool (2007) addresses

technology projects should match the country culture, Principals, and desires. There is

a relationship between technology adoption and a country’s unique culture

characteristics, this relationship determines the acceptance and adoption time

(Sundqvist, Franka and Puumalainen, 2005; Mazman, Usluel, and Cevik, 2009).

Davison (2002) affirms the existence of complications in ICT implementation in

different culture.

UNESCO’s Director-General (2009, Ol) quoted “Culture is the great

forgotten issue among the Millennium Development Goals”. In addition, Owyang

(2009, Ol) asked, How do cultural and norms impact technology adoption? The

fact is that French has excellent internet infrastructure, knowledge of how to use social

tools, and the government is not resisting the social web (Miller and Khera, 2010).

However, Owyang (2009, Ol) affirms “Yet the adoption rates, according to the

data, are much lower!” than other developed countries.

1.5 PROBLEM STATEMENT

The changes experienced by public organizations in Saudi Arabia over the past years

remain unprecedented. YESSER program vision statement is "By the end of 2010,

everyone in the kingdom will be able to enjoy – from anywhere and at anytime –

world class government services offered in a perfect, friendly to use and secure way

13

by utilizing a variety of electronic means (YESSER, 2010)”. This is a commanding

declaration but can Saudi Arabia translate it into valid action? Al-hoymany states, “It

seems that the implementation of the e-government program is noticeably late”

(AlSheha, 2007). In addition, The Ministry of Labour and Ministry of civil workers

confirmed that e-government programs have not achieved their objectives (Al Khalid,

2010). Furthermore, a lack of desire and the old mentalities and the absence of a

binding system highlighted the constraints (Ateef, 2010).

Al-Senaidi, Lin and Poirot (2009) stated key factors of obstacles which are

identified to be tackled on technology acceptance in Arab Gulf Countries: (1)

confidence of public employees (Larner and Timberlake, 1995; Bradley and Russell,

1997; Bosley, Krechowiecka and Moon, 2005); (2) negative attitude toward change

(Veen, 1993; Ertmer, Addison, Lane, Ross and Woods, 1999; Mumtaz, 2000;

Snoeyink and Ertmer, 2001; Cuban, Kirkpatrick, and Peck, 2001); (3) lack of

reimbursement (Cox, Preston and Cox, 1999; Mumtaz, 2000; Snoeyink and Ertmer,

2001; Yuen and Ma, 2002); (4) lack of time (Fabry and Higgs, 1997; Cuban, 1999;

Jacobson, 2000; Cox et al; Snoeyink and Ertmer, 2001; Cuban et al; Ebersole and

Vorndam, 2002); (5) efficient training and ICT skill ( Veen, 1993; Wild, 1996; Van

Der Kuyl, Parton, and Grant, 2000); (6) lack of trialability of technology resources

(Bosley et al,. 2005; Fabry and Higgs, 1997; Mumtaz, 2000; Pelgrum, 2001; Preston

et al); and (7) lack of scientific research (Cuban, 1999; Snoeyink and Ertmer, 2001).

The wide investments in technology infrastructure in Saudi e-government

program cost thirteen trillion American dollars (Al-Arabia, 2012). Brady (2010)

assumed the failure rate of ‘IT project is up to 70%, and Krigsman (2009) assumed the

rate is rising, call for the need to investigate on this issue is even more critical

14

(Worldwide cost of information and communication technology failure: over six

trillion American dollars, Krigsman, 2009) and Saudi Arabia cost of information

communication technology failure is over 0.5 trillion American dollars (Aleqt, 2008).

Recent developments in public technology have heightened the need for study of the

technology adoption factors. It is becoming increasingly difficult to ignore the factors

that cause the adoption failures in the public organization and the low production of

the civil workers (Yacoub, 2010). In Saudi Arabia, considering organizations are

investing in technology projects at an alarming rate and the failure rate associated with

this investment is high (Al-Arabia, 2012).

The purpose of this dissertation is to evaluate and analyse the factors that

influence the implementation and adoption of information communication technology

(e- Government) in public organizations in Saudi Arabia.

1.6 RESEARCH JUSTIFICATION AND RESEARCH QUESTION

The past twenty years have seen increasingly rapid advanced use in the field of

technology and ICT. One of the most significant current factors of ICT is the internet.

It is becoming increasingly difficult to ignore the benefit of the internet; cost-

efficiency is an important component of the internet advantage (Ashley, 2006). ICT is

a significant factor in public organization, and plays a key capacity in the organization

operation; as well, it gives the organization the ability to attract customers to their

services, and information (Tan, Peng, Pakarinen, Pessa, Petryakov, Verevkin, Zhang,

Wang, Olaizola, Berthou and Tisserand, 2009); (Tan, Chong, Lin and Cyril Eze,

2009).

ICT is a critical means for achievement in the private and public sectors

together, but ensuring ICT acceptance is a very difficult assignment for the

15

organization given the barriers it will face. However, this rapid change is having a

serious effect on ICT project success rate and created problems that threaten the

organization’s existence (Cameron and Quinn, 1986). The benefits and the problems

associated with ICT implementation and adoption need more exploring.

There is increasing concern that most organizations in general, irrespective of

scale, have not been able to take the full potential advantages and the values brought

by Information communication technologies (Salwani, Marthandan, Norzaidi and

Chong, 2009). In order to realize the full advantages of ICT solutions, organizations

need to identify the factors affecting its adoption (Ngai, Moon, Riggins and Yi, 2008;

Ngai, Law and Wat, 2008; Ahmad et al., 2010). In addition, the failure rate in the

implementation of technology calls for an enhanced understanding of the scheme

(Xue, Liang, Boulton and Snyder, 2005; Levinson, 2009; Brady, 2010).

Levinson (2009) stated only around fifteen to twenty percent of the projects

was classified as successful. Krigsman (2009) claimed this rate will not change until

researches find explanation. The implementation of technology is a complex exercise,

and many adopters have encountered problems in different phases (Xue et al., 2005).

1.6.1 Research Significance

Technology Acceptance Model [TAM] created by Davis (1989); is the most admirable

tool to measure ICT acceptance and usage. Lee, Kozar and Larsen (2003); Yousafzai,

Foxall and Pallister (2007) and Wu, Zhao, Zhu, Tan and Zheng (2011) conducted a

quantitative statistical analysis; “meta-analysis” of TAM and found that one of the

major problems with the research was scholars were performing replication studies,

which provide very little incremental advancement to the literature. Researchers were

not really expanding TAM. Lee et al., (2003) noted that many scholars felt that the

16

concept of a "cumulative tradition" was carried too far in all the repetitious studies of

TAM, because the model had become an inhibitor of more advanced theories of

information technology [IT] use. A study done by Venkatesh, Morris, Davis and

Davis (2003) proved that TAM is inadequate to explain variancess and that the

success rate of explanation ranged between twenty and fifty percent. In addition, they

stated that these researches were “individual oriented” rather than “organizational”.

Moreover, Turner, Kitchenham, Brereton, Charters, and Budgen (2010) emphasised

care must be taken using TAM outside the context in which it has been validated.

Turner et al., (2010) said TAM was proposed in 1989 by Davis as a tool to

predict the use of technology. However, it is used as a valid measurement of intention

to use as an alternative of actual usage. Ngai et al., (2008) argued that these studies

were based on different samples and research settings, yet; the researchers may have

placed more emphasis on successful factors but less on others. Different researchers

from different parts of the world discussed attitudes towards e-services. However,

some scholars and government officers found that there is a lack of research in this

area in Saudi Arabia (Al-Somali et al., 2009). The current study responds to the call

from previous scholars suggesting that more studies are needed on the factors

affecting technology adoption (Al-Somali et al; Amin, Khushman, and Todman, 2009;

Al-Ghaith, Sanzogni and Sandhu, 2010; Ceccucci, Peslak and Sendall, 2010; Bryson,

Berry and Yang, 2010; Ahamad et al., 2010), with specific reference to developing

economies (Austin, 1990; UN, 2010), of behavioural factors (Richardson, 2009;

Straub, 2009) in public organization ( Poister, Pitts and Edwards, 2010; Al-Ghaith et

al., 2010) in the Middle East countries (Al-Otaibi and Al-Zahrani, 2009) in Saudi

Arabia (Al-Somali et al).

17

Many researchers (Soto-Acosta and Merono-Cerdan, 2008; Al-Somali et al.,

2009; Amin, et al., 2009 and Al-Ghaith et al., 2010) argued on how organizations

manage problems associated with technology and e-services adoption, and maintain

the needs to be undertaken before the association between the factors affecting the

technology use and the e-government adoption. A commonly observed phenomenon

in e-services adoption in Saudi Arabia is that Saudis seems apprehensive to accept

technology (Al-Gahtani, Hubona and Wang, 2007). Some studies emphasis the need

of direct measure of the effect of the social norm and culture on the adoption and the

acceptance of e-services in Saudi Arabia (Al-Somali et al; Alsajjan and Dennis,

2010). Richardson (2009) stated one of the main streams of research is the explanation

and prediction of information technology adoption in the developing countries. In

addition, the study will investigate Loch, Straub, and Kamel (2003) assumption that

norms, beliefs, and values in Arab culture might affect people’s behaviour and

attitudes towards using the technology.

The most striking result emerges from a limited number of cross-validation

studies on technology acceptance, did show that culture moderated the fundamental

relationships (Straub, Loch, Evaristo, Karahanna, and Srite, 2002). Also, Yousafzai, et

al., (2007) argue that the differences in topic type, scheme type, technology type, and

usage are likely to moderate TAM hypothesized relationships. Unfortunately, further

analysis showed the effects of a third variable is ignored.

Finally, Saga and Zmud, (1994) stressed that TAM is one of the dominant

models used to illustrate technology acceptance. A considerable amount of literature

has been published on TAM; these studies showed mixed signals. Dasgupta, Granger

and McGarry (2002) and Teo (2009) drew the attention of conflicting results often

18

observed among the constructs of TAM in both the quantity and direction; Shih

(2004); Ahmad et al., (2010) pointed out unreliable associations. One unanticipated

reason for these discrepancies is the existence of different numbers of moderating

variables affecting technology acceptance unpredictably across the levels of the

independent variables. Teo (2009) argued that using predicted use of ICT instead of

actual use is weakening TAM studies.

1.6.2 Research Question(s)

Loch et al., (2003) felt there is a difference between Arab culture, in general, and the

western culture subsamples believe that specific components of Arab culture and

society have an influence on technology transfer across Arab culture resulting in

failure of technology adoption (Naqvi and Al-Shihi, 2009).

Based on the argument in the research problems, the research objective is to

answer the general research question which is “what factors affect employee

behaviour to accept and adopt information communication technology in the Saudi

public sectors?

The main research question attempts to discover the barrier of technology

adoption and development. There are also other minor questions besides the main

question which are what factors stand as obstacles to the acceptance and diffusion of

e-services among Saudi organization, to answer the question “To what extent the

current usage affects the acceptance process?“

Although ICT acceptance is rarely the motivation for public workers in Saudi

Arabia, it is an essential activity for many workers. However, little is known about

public worker’s behaviours and their preferences to use information communication

19

technology. This study will investigate acceptance factors of a public worker and will

profile his/her preferences. Organizations that implement ICT can benefit from the

understanding of employee’s behaviour, and can gain advantages over those

organizations that are less knowledgeable about their user.

The study will approach technology adoption from the perspective of public

employees and the management by examining the use of ICT and e-services in a real

environment, by examining the variables in a theoretical framework refer to (Figure

3.4).

1.7 RESEARCH OBJECTIVES

Levinson (2009) said at least 24 % of IT project was considered failure and 44% was

cancelled before completion. Brady (2010) reported that e-government and ICT

projects failure in developing and transitional countries; one-third was entirely

failures, one half was somewhat failures, and less than one quarter was fully

successful. Moreover, Levinson (2009) assured that this rate of project failures is

rising, and project success is declining. A report by the General Auditing Bureau

(2010) revealed that government agencies have not benefited from over eighteen

billion riyals allocated for ICT projects (Al-Faisal, 2010).

There is a real risk of acceptance by some and not others, contributing to the

rejection. Thus Information technology is often quoted as examples of costly failures,

with reported levels of investment between twenty and seventy percent of the total

budget (Singh and Waddell, 2003; Waddell, 2008). As previously noted, not only the

main public offices require efficient adoption of the e- government model, in addition

the support offices as well.

20

This study is an investigation of Saudi public worker willingness to use e-

services. Besides, it will study the effect of individual antecedents on technology

beliefs. Then, the study will examine the effects of training, work type, volunteer

motivation, and subjective norm as moderators. Finally, the study will try to find the

effect of current usage as mediating factors. Moreover, it will try to remove the shade

of social factors that affect the acceptance of e-government.

This study is to determine and to describe the factors that affect the acceptance

and usage of ICT in Saudi public organizations, and to increase our understanding of

public organization’s acceptance of the ICT. In addition, it is to determine the effect of

current usage and other factors that influence the usage and acceptance of technology

implementation strategy. Finally, it is to light the employee characteristics that will

improve the usage of ICT in public organization.

Finally, based on the research problems, the research objectives of this study

further advance knowledge in determining the factors that cause e-government

acceptance usage failure in public organization.

1.8 DEFINITION OF TERMS

In order to clearly comprehend this study, the definitions of some important terms are

considered essential and are presented next.

i. Intention to use:

The degree to which a person has formulated conscious plans to perform or not

perform some specified Task (Davis, 1989, 214).

21

ii. Attitude to change:

Whenever employees are confronted with organizational change, they are

likely to ask themselves why the proposed change is the right one (Linden,

1997). Herscovitch and Meyer (2002, 475) defined commitment to change as a

"force (mind-set) that binds an individual to a course of action deemed

necessary for the successful implementation of a change initiative. Within

this dissertation, affective commitment to organizational change is included

as an outcome variable. Related hypotheses are offered later in the

literature review.

iii. Subjective Norm:

Person's perception that most people who are important to him think he should

or should not perform the behaviour in question (Ajzen and Fishbein, 1975).

iv. Perceived Voluntariness:

Voluntariness Motivation of use is the degree to which use of the innovation is

perceived as being voluntary, or free will (Hebert and Benbasat, 1994).

v. Current Usage:

“Performance is referred to as being about doing the work, as well as being

about the results achieved. It can be defined as the outcomes of work because

they provide the strongest linkage to the strategic goals of an organization,

customer satisfaction, and economic contributions” (Salem, 2003, 1).

vi. Training:

It refers to an interrelated set of variables that organizations should consider as

part of their overall technology program (Vesset and McDonough, 2009, 6).

22

vii. Perceived ease of use:

Perceived ease of use has been defined as "the degree to which a person

believes that using a particular system would be free of effort" (Davis 1989,

320).

viii. Perceived usefulness:

Perceived usefulness has been defined as “the degree to which a person

believes that using a particular system would enhance his/her task” (Davis

1989, 320).

ix. Principal Support:

Principal support reflects the support provided by change agents and opinion

leaders, Armenakis et al., (1999, 103) defined principal support as a means by

which to "provide information and convince organizational members that the

formal and informal leaders are committed to successful implementation . . . of

the change”.

x. Motivation Valance:

Motivation valence corresponds to the cost-benefit appraisal process through

which a change recipient evaluates a proposed change effort in terms of

potential personal gains and losses of organizational benefits (Deci, Eghrari,

Patrick and Leone, 1994).

xi. Appreciation:

Whenever employees are confronted with organizational change, they are

likely to ask themselves why the proposed change is the right one (Linden,

1997).

23

1.9 CHAPTER SUMMARY

Following this chapter the introduction, the dissertation is organized as follows.

Chapter 2 -Literature review – the chapter documents critical review of the factors

affecting the technology and Information communication technology usage adoption

and acceptance. In addition, it provides a review to the technology acceptance models.

This review is the basic of the study framework.

Chapter 3- Developing the Framework- this part illustrates how the theoretical

framework has been developed. Also it provides proposed association between the

framework variables.

Chapter 4 –Research Methodology- this chapter discusses the research design and

methodology used. In addition, it gives a detailed description of the data analysis.

Chapter 5 – Data analysis- this chapter describes the steps taken in evaluating the

validity and reliability of the research instrument. In addition, it provides a detailed

desecration of the structure equation modelling.

Chapter 6 – Conclusion- this chapter describes the discussion of the result,

recommendation based on the finding. Also it states the research limitation and future

studies.

24

2 CHAPTER TWO

LITERATURE REVIEW

The slow respond! As well, failure to predict change is the two primary reasons for

organization’s dilemma. In addition, they are the main reasons for unsuccessful

organizational changes as discussed in literature (Barney and Griffin, 1992(.

2.1 ORGANIZATIONAL BEHAVIOUR MANAGEMENT [OBM]

“Basically, an organization is a group of people intentionally

organized to accomplish an overall, common goal or set of goals.

Business organizations can range in size from two people to tens

of thousands”.

(Nguen, 2009)

Organizational behaviour management as defined by Patrick and Riggar (1985) is the

development and assessment organization overall performance procedures using the

principles of behaviour adaptation. The inclusive efficiency of organization

performance is the main task of organizational behaviour management through direct

observation of the individual and the group at the same time (Rahim, 2007). Patrick

and Riggar (1985) reported successful use of organization behaviour management in

the areas of; employee training, organization improvement, problem recognition, work

deficiency, individual appraisal, project evaluation, and responsibility.

25

Technology is one of the four external factors behind organizational change.

The information technology has a massive impact on the organization effusion

(Christensen, 1997). Organizations manipulate technology with different resources

jointly to accomplish improvement in organization operations, cut of operation costs,

and increase in productivity.

Since the 1990s, organizations have increasingly depended on ICT and

employee willingness to use technology, making information technology use a key

workplace decision. Technology changes led researchers to identify factors that

influence the employee to use and accept information technology (Kaplan and Norton,

2008).

2.2 ORGANIZATION CHANGE

Palamer (2008) stated that we are living in a time where change has become the only

constant besides death and taxes, and with change comes anxiety about change and

what it might do to disrupt our lives and futures. Leaders and managers need to

develop a mental picture for their organizations, to recognize the uncertainty of

change and to generate an appreciative supportive environment (Nixon, 1994). For the

organization to survive in the new era; the technology era, organizations must be

adoptive, creative, flexible, wisdom and self-renewing.

French and Bell (1990, 17) define organizational development “a top-

management-supported, long-range effort to improve an organization’s problem-

solving and renewal process, particularly through a more effective and collaborative

diagnosis and management of an organization culture with special emphasis on formal

work team, temporary team, and inter-group culture with the assistance of a

26

consultant-facilitator and the use of the theory and technology of applied behavioural

science, including action research”.

Change management is currently sweeping organizations in both government

public and business private sector; it has been evolving as structured approach

discipline over time, and it is addressing the resistance changes facing the

organization, up until now. Change management, in the context of public e-

organization, is about how members of the public service make the transition from the

traditional approaches to the new means of administering in evolving environments.

2.3 MANAGEMENT CHANGE

Change management is a topic that all corporations have to deal with, for the simple

fact that what used to be true in the past is no longer an indication of how things will

work in the future (Boyett, and Boyett, 2000).

According to McLagan (2003) organizations deal with change in an

inappropriate way even though, the changes are increasingly becoming more complex.

Change is every-day mission and a way of organization life, not by chance

assignment. It is time to take the initiative of how to make organizations work

continually. McLagan emphasizes that change is not something to manage when

strategies shift or crises occur. It is a challenge and situation organization has to deal

with every day. Bagranoff, Eighme, Ellen, and Harvey (2002) argue that change

management is a very broad area, as it covers more than personal training, internal

communication, and secretarial work. Change management aims to study individual

and group behaviour, with the different organization aspects of change.

27

A famous quotation recognized a fundamental principle of success in today’s

environment: “Those who stand still get flattened”. One specific survey in the middle

of the 1990s concluded that sixty-six percent of all organization restructuring efforts

failed to accomplish their objective (Horney and Koonce, 1996). According to

Richard (2002) directors and GMs spend an average of thirty-five percent of their time

dealing with change. Unlike other disciplines, change management is not easily

measurable. It is not a key result area that is easy to be documented, reviewed, and

measured in specific terms. Change management is simply the art of managing change

rather than letting it manage you. Feldman (2002) explains the fact that no changes

should be attempted without first articulating the organization goals and benefits, in

the words of Franklin: “To fail to plan is to plan to fail”. Ainsworth (2009)

emphasizes changing business processes, and people habit is art of diplomacy and

persuasion than bullying. He states it is in a manager’s interest to motivate employees

into accepting the changes.

Successful change management depends on the attitude and intention of the

company’s entire workforce. It is not productive for only a portion of the employees

to embrace the proposed changes. The biggest motivator is to observe all people

embrace change and witness the successful implementation of the vision. Michael

Hammer describes the mysterious resistance “the most perplexing, annoying,

distressing, and confusing part of change” (Trout and Rivkin, 1995, 27).

2.4 MEANING OF CHANGE

In very simple words, change means making things different. Robbins and Delenzo

(2007, 236) give the definition of change: “Change is an alteration of an

organization’s environment structure, technology, or people.” Carlopio (1998, 2)

28

described change as “the adoption of an innovation, where the ultimate goal is to

improve outcomes through an alteration of practices”.

Bell and Ritchie (1999) stated that change is the way people improve, it is not

going to go away, nor should it. Fullan (1992, 22) defined “change is a process of

learning new ideas and things. It is learning to do and learning to understand

something new”.

From the definitions, change has the following characteristics: change is an

overall effect as a result from a set of factors that disturb existing status of

organization (Palmer, Dunford and Akin, 2008).

2.5 FORCES OF CHANGE

O’Toole (1996) acknowledged numbers of factors, which affect organizational

performance. The factors affecting e-services are both internal and external, just as

they are with the old fashioned traditional business. However, there are certain risk

factors associated with e-business that may be different (Lunenburg, 2010).

2.5.1 External Forces

Every organization exists in some context; no organization is an island in itself. It is

essential that each organization continues to cooperate and work together with

different organizations, clients, suppliers, investors, shareholders, regulations, etc.

Each organization has goals and responsibilities related to each other in the

environment (Lunenburg, 2010).

The active environment will concentrate on creating and bringing dynamic

modifications and enhancements in fiscal, social, legal and knowledge domains; these

changes allow the organizations to modify and change their structure. Consequently,

29

these changes would give rise to the changes in the production process, economic

variables, existing and future competition, relations between the management and the

labour, organizational procedures, etc. In order to survive in the changing

environment; organizations must change (Kotter and Schlesinger, 2008).

The various environmental factors that necessitate the change in the

organization are in following context: ICT is needed for a company's operation as well

as customers, other businesses (Sleurink, 2002). When there is ICT change, the

organization’s operations become cost-effective and its position becomes stronger.

Therefore, organization work structure is affected, and stability in work environment

established (Robey and Boudreau, 1999; Green, 2007).

Any organization has to face competition in market conditions; this factor may

require changes in the organization police and operation. Also, the change in customer

needs, cause the organization to change (Farley, Preston and Hayward 1998; Gupta,

2008). Because of the self-awareness of the world around us, and the rising economy

there is a need to visit business websites. Communication technology is becoming a

major support in organizations achievements. Kozma (2008) stated that in

organizations where there is well adoption of information communication technology,

employees are equipped for successful participation in the knowledge economy and

learning society.

People’s behaviours echoed their objective, desires, and their culture. Culture

is essential to the achievement of ICT acceptance and adoption. Understanding culture

is important for height management because it impacts strategic progress,

productivity, and learning at all stages of organization (Schneider, 2000). Social

30

changes like education, urbanization, self-sufficiency, and believe to impact the

behaviour of people in the organization (Farley et al., 1998).

Political and legal factors are the factors, which concentrate on describing the

activities which the company or firm assumes as well as the methods, which the

organization has to follow in broad terms. Any changes in political and legal factors

affect organization operation (Farley et al., 1998; Cliff- Notes, 2009).

2.5.2 Internal Forces

Internal factors also force changes. Such changes happen in respond to the two

important consequences: changes which take place in administrative and management

domain and the inadequacies found in the present practices of the organization (Farley

et al., 1998; Lunenburg, 2010).

Changes in the managerial personnel, the change of managerial staff brings on

new mentality in the organization. The attitude of worker changes even though there

are no changes in the worker themselves. The organization has to change accordingly

to the result of the attitude change (O’Toole, 1996). Change is an essential step

because of existing deficiency in the organizational operation, services and procedure

(Bharijoo, 2005).

2.6 RESISTANCE OF CHANGE

According to Diamond (1986) interventionists and organizational development

consultants need to acknowledge the peoples trend to employ in paradoxical actions:

human behaviours striving for security first then learning. Organizational development

consultants and interventionists are more efficient when the patrons’ level of

confidence and character strength is supported and the resistance to change is

31

acknowledged as an important part of the change process (Barger and Kirby, 1995;

Dalziel and Schoonover, 1988; Diamond, 1986). As Diamond (1986) stated “one must

acknowledge his or her reasons for resistance to change”.

Change has become a daily routine of organizational practices and any

resistance from staffs due to dissatisfaction of change can obstruct the organization.

The major cause of why people cannot accept change is that they have the

apprehension that their cohesiveness or existence is at stake by it. This specifically

applicable in groups, where people are cohesive and have a strong sense of

belongingness and where each member believes that the group is stronger and superior

than the other (De Jage, 2001).

According to Ackerman (1997) there are three types of change that occur

across the lifespan of the organization; continuous or developmental change,

situational change, and discontinuous change. People perceive of change as

frightening, exciting, overwhelming, or growth producing. Individuals approach

change with fear and trepidation or retreat to comfort of the known, while others

create to change and thrive on exhilarated feelings that take place with change.

Change by itself is unsettling; its unpredictability making it an unwelcome business

partner (Thomas and Hubbel, 1997).

In order to discover organizational resistance, it is important to come across its

definition first. Zander (1950) defined resistance to change as “Behaviour, which is

intended to protect an individual from the effects of real or imagined change” (cited in

Dent and Goldberg, 1999, 34). Skarlicki and Folger (1999, 25) defined resistance as

“Employee behaviour that seeks to challenge, disrupt, or invert prevailing

assumptions, discourses, and power relations”.

32

According to Dent and Goldberg (1999) employees are not really resisting the

change, but rather they may be resisting the loss of the status, loss of pay, or loss of

comfort or individual’s preference for receiving a reward called “Valance

motivation”. In Frederick Herzberg’s Motivation and Hygiene Theory (1959:1968) the

reason of change resistance among the employees is the fear of losing their job

positions. For example, an organization may decide to introduce the use of computers

to improve communication within the organization. Not all organization members will

take this positively. Some organization members may not be computer literate and

will think the introduction of computers will threaten their job since they cannot use

the system, (Management Hub.com, 2010)

Zafar, Zbib, Arokiasamy, Ramayah and Chiun (2006) declared six primary

reasons for resistance offered by Zander, (1950), (1) If the nature of the change is not

clear to the employees who are facing the change, (2) If the change has a wide variety

of interpretations and the mission is not clear, (3) The resistance force stronger than

the change force, which discourage the employees to change, (4) If the people

influenced by the change have pressure put on them to make it instead of having a say

in the nature or direction of the change, (5) If the change is made on individual or

personal grounds and (6) If change had ignored the already established society in the

group “Culture”.

The theories of organizational change and resistance are Principal-Agent

Theory, Goal Theory, and Stakeholders’ Theory. Theories help us understand the

concept of change and resistance. Many theories described the difference between

owners and employees’ targets and interests (e.g. Principal agent theory, neoclassical,

neo-Keynesian, and managerial) (Rees, 1985; Selden, Brewer and Jeffrey, 1999).

33

Stakeholder theory and principal-agent theory offer resolutions/measures and propose

recommendations for dropping and congruent objective conflict, to overcoming

resistance to change (Khan and Rehman, 2008).

2.7 INFORMATION COMMUNICATION TECHNOLOGY AND

ORGANIZATIONS

ICT is expected to grow at aggressive rates and create fundamental changes in the way

organizations conduct services. It has provided new business opportunities, reduced

costs, and facilitated exchanges with business partners and customers (Roy, Dewit and

Aubert, 2001). ICT can expedite the ordering, delivery, and payment for services,

while dropping operating and inventory costs, by dropping secretarial works and

reducing paper handling. In addition to technology, e-services are a combination of

strategic procedures and technology application preparation that are necessary to

conduct services automatically (McIvor, McHugh, and Cadden, 2002).

The implementation of ICT is considered to be a major intervention in the

organization. At present, it is considered to be one of the most controversial issues for

academics and practitioners in the information technology domain (Pozzebon, 2000).

Gichoya (2005) and Kozma (2008) found that information communication

technologies have intangible links and connections with nearly all the domains of

technology research. Thus, the differing descriptions and outlooks related to E-

administration linkages are dependent on how the ICT researchers conceptualize and

treat the connection between the ICT and organizations. From the literature of ICT,

the outlook and perspective of technology and their integration in organizations

recognize not only the technological aspect but also the human aspect.

34

The technical aspect is considered to be the hard aspect in which information

technologies are considered to be as "engineered artefacts’" expected to perform their

tasks accurately and precisely (Orlikowski, 1992). Similarly, organizations are

considered to be information processing systems in which there is exchange and

managing of information takes place on the basis of a set of rules (Katzenstein and

Lerch, 2000). There are firm faith and trust in "instrumental rationality" and formality.

That is, all the procedures and perspectives of the working structure and network can

be recreated and restructured to proper and exact mathematical models. Thus, from

this outlook, organization is supposed to have clear and accurate requirements.

On the contrary, many researchers and academics, including Mumford (1979)

suggested a visionary prototype for conseptualizing how the human and technology

components of organization can best be framed. Orlikowski (1992) supported the idea

of duality of technology: rethinking the concept of technology in organisations.

Probert (1997) argues that information technologies have the tangible and physical

object like processors and storage; they also include the subjective or particular

construct. This outlook is similar to that of Orlikowski's (1992, 257) view where she

argued, “Technology is not an external object but a product of ongoing action, design,

and appropriation”.

Even though organizations have the necessary and essential information

technology to assemble the detailed data, most of them do not have the right attitude

to employ it efficiently. Kull, Boyer, and Calantone (2007) suggested that web sites

can be used as an effective and efficient tool in order to develop and nurture the ability

of the organization to hear and understand the needs and requirements of different

audiences. Although technology is accessible and is easily found, there is a need for a

35

premeditated and reliable tactic and plan action in order to manage and collect data,

which would not only use the web site but the internal procedures of the organizations

as well. These conditions are necessary in order to effectively implement the use of

information technology (Ind and Riondino, 2001). Organizations must learn to

effectively use this information through knowledge management.

Technology adoption may be influenced by organizational level, which may be

influenced by many factors. Like the personal factors, these may also be seen as

interdependent. Age has been seen as an individual influence; however, this has been

looked at by Bertschek and Meyer (2009) and Meyer (2011), who looked at the age

structure of a workforce to assess how it would impact on the capture of technology.

There was an interesting finding, where firms had homogenous workforces with an

intensive concentration of the specific age groups, old or young, this would have a

high correlation with the adoption rate that would be expected for that age group as a

whole, irrespective of other mediating factors, indicating that their potential influences

from a concentrate workforce age structure.

When looking at the adoption of technology and the organizational factors the

idea of change and the concept of Senge may also be considered as indicative of the

addition as part of a change process. Senge outlines a number of factors, which will

impact on the acceptance of change at organizational and societal level, and the way

that the organization can adapt by transforming into learning organization. He argues

that we have been conditioned into resisting change by institutions such as schools

(Senge, 2002: 2006).

This is due to an organizational culture which persists. The world can be

viewed in our minds as a mechanical place where change is driven by the leaders

36

opposed to an organic world where things can evolve and change naturally (Senge,

2002: 2006). To accept change of any sort the mental attitude has to be adapted,

recognizing and seeing this for ourselves. This requires an open mind and the ability

to learn not only new techniques or systems, but also the individuals within the

organizations need to learn how to adapt and make use of new information, as well as

unlearn social conditioning responses, such as the resistance to change due to fear.

Senge puts outs forward ten challenges to organizations, the way in which each

of these is dealt with by the organization may be an impact on the adoption of

technology, many of these barriers to change can impact on technology adoptions. The

ten challenges he sees which businesses face are the idea that there is not enough time

to undertake the task required (Senge, 2002: 2006). Therefore, one of the influences

may be the time and resources that are dedicated to engorging and supporting

adoption. The next factor is the lack of help; this is seen by many as a stumbling block

as many managers may be shy to ask for help believing it to be a signal of their own

incompetence (Senge, 2002: 2006).

The support system in which may be seen in terms of paper support,

professional help desk support as well as document help may be an influence.

Organizational attitudes and management attitudes are also an influence; viewing the

change as not relevant or needed, can be a problem, especially for pilot groups where

the commitment has not been gained from the participants (Senge, 2002: 2006). From

this we can argue that the attitudes of management and the way that these are

documented will have an influence. This is linked to the concept of ‘Walking the

Talk', often what is said by management is not reflected in their actions (Senge, 2002:

2006). If managements are seen as insincere or uncommitted, this will also impact on

37

the change and as such adoption rates. Fear is also a limiting factor as when people are

afraid of the consequences, they will not commit to the project (Senge, 2002: 2006).

The change management models and issues such as communication and the ability to

alienate fears are the organizational factors that will impact the adoption rates.

Another issue may be the way that success is measured. For many pilot

schemes, the way in which success is traditionally measured will not be appropriate to

the new changes (Senge, 2002: 2006). Therefore, the way that any pilot schemes take

place and are reported will have an influence. In any organization there will be those

who believe in the change and those who do not (Senge, 2002: 2006). Conflicts of

power can occur, where the pilot is a success, it may then be seen to interfere and

conflict with the priorities and goals of others within the organization so other goals of

an organization and the way that internal politics and the organization pursues goals

can impact on the adoption rates and levels. The personal attitudes and the values may

also be spread, whether they were positive or negative, therefore, issues such as the

level of social commitment, and collectivism in the culture of the organization will

have an impact. Where there is a high level of collectivism, there is likely to be

increased interpersonal support and where there is a high level of social integration,

the ideas will be discussed, and the group values may impact on individual values and

influences them with the dominant personalities having an influence, so views of the

dominant personalities at the workplace are also an influence.

The activities of the firm are also a potential influence. Ball, Dambolena and

Hennessey (1987) looked at the activities of the firm and found that there was a

correlation, with positive reactions and faster adoption likely to take place where a

firm has high levels of research and development activity, or where there is a high

38

portion of engineers or other that work with technology, this can have a positive

impact on the organization as a whole.

There are a number of influences from technology itself. The adoption of ICT

does not necessarily mean moving from a position of no technology use to that of

technological use; the adoption of technology can also refer to the adoption of new

technology, with new systems put into place. The issues here are also broad ranging.

The first issue in terms of ICT influences includes the availability and ease of

use of alternative technology within the workplace. For example, it has been noted

that where a new system is implemented it may be put into place utilizing a parallel

strategy, in order to test and allow the employees to get used to the new system, but

reduce the risk in case there is some type of difficulty with that new system (Rob and

Coronel, 2004). However, where there is the ongoing availability of the alternative,

older, system as part of the resistance changes likely that employees will choose to use

the systems with which they are most familiar. Therefore, by turning the old system

users no longer have the potential to make use of the most familiar system, preventing

them adopting a new system, effectively forcing a change (Rob and Coronel, 2004).

Another approach can be looked at in terms of the ease of adoption, which will

be facilitated by the way in which the software is designed in terms of features such as

graphical interface and help files. Where software or technologies facilitate a high

level of intuitive use, can guide the user, and provide support it is more likely to be

taken up rapidly.

A model put forward by Rogers (2003) had the five attributes of innovation,

which seeks to recognize the factors that distress the take-up of innovation. These look

39

at the technology itself, and argued as accounting for eighty-seven percent of the

variability in adoption rates (Al-Gahtani, 2003). The free factors identified in this

model are the relative advantage, where the new technology is seen to have

advantages over the older technology, such as increased facilities and being easy to

use, it is more likely to be adopted. Likewise; compatibility is also an issue, with

adoption being directly impacted by the compatibility that technology has with

existing systems; we can extend this to incorporate human systems as well as

technological systems.

The third of the factors is that of complexity, the greater the complexity level

of technology the greater the degree of understanding required before the technology

can be adopted, which can have a negative impact on the scope rate of technological

adoption, it may also impact on the potential resistance levels (Johnson, 2005).

Moreover, Rogers (2003) argues that Trialability is important, where users can try the

technology before they have adopted fully. This may be also tie-in with aspects as

organization level, such as the way in which training takes place and employees are

allowed to get hands-on experience before the systems and technology need to be

used. The last of the five factors is that of observability, where the technology can be

observed already in use, either inside the organization or outside of the organization.

This may increase the level of trust and as such help to promote the adoption process.

Overall, it may be seen that there is a wide number of different influences on

the way that technology may be adopted, regardless of whether this is for a null

starting point, where there is a move from no technology to technology, or there is a

move from existing technology to a new form of technology, individual influences

may impact on organization influences, and the way in which technology itself is

40

perceived. Likewise, organization influences the impact on personal influences, all of

which are interdependent depending on the dominant forces in each situation.

There are a number of potential influences including age, experience in

childhood, previous workplace experience, general attitudes towards ICT, personal

ties to the organization, commitment level and type and a personal cost benefit

assessment.

In all the categories, many of the influences may be complex, often there are

likely to be interdependences. One individual influences that has been assessed is that

of age. Research has indicated that there is likely to be a higher level of resistance in

older workers (Umrani and Ghadially, 2008; Morris and Venkatesh, 2007). In research

undertaken in a single workplace with 118 workers studied over a five-month period

and measurements taken at two points it was found that the adoption rates differed

with a higher level of adaptability and adoption in the younger workers, but a more

considered and careful approach, which may increase the level of resistance in the

older employees. Furthermore, this was found in research by Bertschek and Meyer

(2009).

This is a factor that is interdependent or influenced by other factors, which may

be seen as independent as well as having this potential interdependency with age. One

key factor is that of previous experience with technology. Immature workers have

grown up with a higher level of technology in the home and at school, they have been

exposed to the use of ICT and as such, it is an easier adaptation to the change by the

younger employees as they have a different set of lifestyle standards and norms

(Bilton, Bonnett, Jones, Lawson, Skinner, Stanworth and Webster, 2004). In the

younger age groups that are now in the workplace there has been exposure to

41

computers in the school and in the homes. This can impact heavily on values as is the

impact on the norms of that individual.

The similar approaches have been seen to the use of other technologies, but

with a lower adoption rate. Those who were already matured in their attitude and

personality development when the technology came onto the market for the

microwave oven were able to adopt it (Kotler and Keller, 2009). A similar, but

separate related issue may be previous experience of IT, where it has been used before

and has helped there is likely to be more support, but negative experience may not

only include difficult usage, but events such as IT being used to create efficiencies

resulting in redundancies; increased resistance (Bilton et al., 2004).

The general attitude toward ICT may also be an influence, for example, those

who have a mistrust of information technology, possible because of bad experiences

or misunderstandings, or as a result of fears, which may be real or imaginary. Those

with a high level of fear that is highly resistance to adoption are technophobe, where

there is an attitude that computers are unlikely to bring any good, this is not only due

to their ability to work twenty-four hours. In addition, there are also fears over the

way information can be used and held, the control that technology may exert over

individuals and the loss of personal contact and the disruption of communities. These

are broader attitudes that come in from outside the workplace, and may be linked to

age and other influences, but this is not always the case. Technophobes will be highly

resistant to adopting technology (Haralambos and Holborn, 2004).

The personal relationship to the change is also seen as an influence, where the

workers are linked through family ties to management, or the owners, there is a

42

greater potential that there will be a faster adoption process compared to where there

is no family or personal link (Bruque and Moyano, 2007).

As well as, where there is a high level of commitment, there is likely to be

increase time, and effort placed into achieving the organization goals. This includes

persevering with technology as well as the way in which the commitment may affect

the attitude of the employee towards the use of technology, positive attitudes likely to

be linked to high commitment. This may be seen as one of the most important aspects,

but there are different types of commitment each of which may have a different

correlation with an adoption pattern. There are many concepts of commitment and the

way it is observed in the workplace.

An interesting model developed by Meyer and Allen (1991) can be very useful

when looking at different categories of commitment. They found several

commonalities, including the belief that commitment combines employee to

organization, which is likely to boost up the level of motivation. However, there are

significant differences in the potential mindsets in company level of commitment;

three different approaches were distinguished (Meyer and Allen, 1991). The first

mindset is that of an affective attachment to the organization, the second is the

obligation to remain in the organization the third is the perceived cost of leaving

These are expected to be referred to as affective commitment, normative commitment,

and continuance commitment (Meyer and Allen, 1991).

Of these, the greatest affinity with a high level of positive attitude and easy

adoption is the affective commitment, which is the traditional commitment that is

earned by an employer by managing the employment relationship and helping to

satisfy employee needs. Where the commitment is the obligation to the adoption

43

process may be less enthusiastic, and where it is out of cost then there may also be a

lower level of enthusiasm and take up rate compared to affective commitment, but

these are still supportive of take up.

The last individual influence that may affect the adoption and acceptance of

technology is the perceived individual cost; this is influenced by the individual

culture. Before change takes place individuals use their own values and perception in

an internal cost-benefit analysis, with acceptance of the change taking place where

there is the need for an acceptance of the need for change a benefit to the change

taking place (Ankem, 2004).

2.8 TECHNOLOGY ACCEPTANCE AND USAGE

Information technology is essential for today's organizations (Lunenburg, 2010). If a

company's Information technology fails, the company will lose value, and will fail too

(Lunenburg, 2010). This is true whether it is a Web-based industry or a bricks-and-

mortar industry (Sleurink, 2002). Information technology is needed for a company's

operation as well as customers, clients, other organizations and so on (Sleurink, 2002).

The concerns connected and linked to the resistance of users to new innovative

and information technology are not old. According to Lin and Ashcraft (1990)

professionals, academics, and researchers knew about the problems and issues related

to the user’s resistance to new and innovative information technology dates back since

early sixties. In order to deal with such issues, there have been different and

distinctive models and outlooks, which have been developed by professionals and

researchers in order to assist the organizations to conquer the resistance of users or to

assist them in accepting the new information technology (Claver, Llopis, González,

and Gascó, 2011).

44

Practitioners and researchers have found several problems related to

technological resistance. Timmons (2003) demonstrated that the resistance to the

implementation of the information technology takes place, and it may lead to

absenteeism, turnover of staff, low morale and complains. As suggested by Adams at

al., (2004), the study demonstrates that several individuals, assigned to implement the

new information system, must have the ability to identify and recognize the resistance

of users which leads to the failure of the system. However, Lucas (1975: 1975b) gives

the verdict that when the implementation process has been completed, the assessment

and the evaluation of information systems are hard. This takes place because the

setting is unrestrained and is commonly the one in which majority of the systems

works and designers are relieved that such a system has been employed.

There is no surprise that organizations are not heavily concentrating on the

resistance of users to changes associated with the technology. However, Dewan,

Lorenzi and Zheng (2004) argue that it is essential to recognize that resistance is of

two categories: resistance, because of specific change and resistance to the supposed

changers(s). When the resistance has been intended for a particular change in the

system, then the resistance is to a specific change; but when resistance takes place as

the product of negative emotions, directed at the organization, in general, specific

managers or units, then it is considered to be the resistance to the supposed changer(s).

2.9 TECHNOLOGY ACCEPTANCE

Acceptance of technology innovations for communication needs and factors that

influence acceptance and adoption, have been studied for decades. The theoretical

frameworks that were used to inform the studies include the diffusion of innovation

45

theory, the expectancy-value model, and TAM. The next section will review adoption

theories.

2.9.1 Technology Acceptance Theories

The word ‘acceptance’ has been used by different authors in diverse meanings and

context. As a matter of fact, the expression does not have any unique or specific

description in literature. TAM (Davis, 1989) has defined acceptance as users’ decision

about how and when they employ technology. Martinez-Torres, Marin, Garcia,

Vazquez, Oliva and Torres (2008) cited that perceived initial utilization (usage) or

acceptance is the primary essential and significant step towards the adoption of

technology, while maintainable usage and employment are dependent on its

continuance usage.

There is a wide-range of studies conducted on information communication

technology acceptance (Igbaria, Iivari and Maragahh 1995; Abdul-Gader, 2000; Al-

Gahtani et al., 2007; Baker, Al-Gahtani and Hubona, 2007; Yasin and Yavas, 2007;

Al-Thawwad, 2008). The plethora of different models had been introduced and

developed in order to describe and explain the acceptance of technology in

information and communication technology context as well as in general conditions.

The next part of the study will describe the models, which are being used in order to

explain this topic.

2.9.1.1 The Theory of Reasoned Action [TRA]

This theory was put forward by Ajzen and Fishbein (1975) in an attempt to give clear

clarification and reason for individual behaviour in a particular incident or situation.

According to this theory, the actual behaviour of an individual is the product of

46

individual or personal intensity to perform that behaviour. The attitude of the

individual towards behaviour and the subjective norms are considered to be the

loading factors with respect to the intention to use. Attitude is considered to be the

negative or positive perception and a tendency towards a thought or behaviour.

Subjective norm is considered to be the personal perception of whether important

individuals believe that the behaviour should be performed or not.

2.9.1.2 Theory of Planned Behaviour [TPB]

Theory of planned behaviour is considered to be a well-known and popular theory,

which is based on sociology. It has been extensively used to describe the relation

between social behaviour and the use of information technology (Ajzen, 1991; Conner

and Armitage, 1998; Sutton, 1998; Kwon and Onwuegbuzie, 2005). Moreover, as

noted by Fishbein and Ajzen (1984, 1991), intention is considered to be the direct and

instant forecaster or interpreter of a particular action or behaviour. Subjective Norm –

[SN]- (perceived social pressure) inserts the intention, the individual attitude one has

towards a particular behaviour and the Perceived Behavioural Control [PBC] (the

attitude or values related to the capability to manage and command the behaviour).

Furthermore, the behavioural belief (a particular kind of behaviour that leads to a

particular outcome or consequence), is influenced by the calculated appeal or

attractiveness of this outcome, forms an attitude (Kwon and Onwuegbuzie, 2005).

Ajzen (1991: 181) defines PBC as “the perceived ease or difficulty of performing the

behaviour”.

2.9.1.3 Task Technology Fit [TTF]

Klloppiing and McKiinney (2004) states that if the available information technology

meets the requirements and needs of the end user only, then it will be used. Actually,

47

the Task technology fit is equal to the needs and the requirements of the duties and the

capabilities of the technology, which has been selected. Since the behaviour has not

been considered, the early or the original version does not contain the ‘Actual Tool

Use’ as a product variable. As noted by Goodhue (1995) the individual capabilities or

abilities, including the computer knowledge along with experience emerged and

became an important aspect of TTF. Strong, Dishaw and Bandy (2002) present a

different version of Task technology fit which includes the factor of computer self-

efficacy.

2.9.1.4 Diffusion of Innovations [DI]

Innovation diffusion theory [IDT] by Rogers (2003) is believed to be a model, which

is also laid on the foundations of social psychology. The expressions diffusion and

diffusion theory was introduced by the social researchers and academics in the forties

(Rogers, 2003). This concept concentrates on giving out a structure or outline that can

be used to predict predictions for a particular time period in which the technology is

accepted. Constructs are considered to be the features or attributes of the innovative

and up-to-date technology, the features, or characteristics of the adopters and the

structure of the communication. This theory has four core elements: time, innovation,

communication and the social structure. It should be noted that the concept of new

idea is transported and conveyed in the entire social structure from one member to

another.

2.9.1.5 Technology Acceptance Model [TAM]

TAM has been derived from the Theory of Reasoned Action –TRA- and it considered

one of the most popular and commonly accepted models. This model was proposed by

Davis (1989) in order to give an explanation on the usage of computer and the

48

acceptance and approval of information technology. It should be noted that the

Institute for Scientific Information Social Science Citation indexed more than three

hundred different journal citations of the primary and original TAM manuscript,

which was published by Davis, Bagozzi and Warshaw (1989) and this has been

observed by Money and Turner (2004).

Klloppiing and McKiinney (2004) pointed out a weak point of TAM about task

focus. According to them, TAM differs from Theory of Reasoned Action in two keys.

Perceived ease of use [PEOU] and perceived usefulness [PU] defined as external

variables that determine the intention to use not the actual use. The second key is that

TAM does not include subjective norms. (Yi, Jackson, Park and Probst (2005)

claimed that TAM and Innovation diffusion theory have similarities. More specific

perceived ease of use and perceived usefulness are theoretically alike to relative

advantage and complexity (the opposite of ease of use).

Venkatesh and Davis (2000) have put forward TAM II, which is considered to

be the extended version of TAM. TAM II includes the process of social effect,

significance of job, the quality of output, and the demonstrability of the result or

outcome and the subjective norm.

2.9.1.6 Unified Theory of Acceptance and Use [UTAUT]

The Unified Theory of Acceptance and Use UTAUT proposed by Venkatesh et al.,

(2003) is an assembly of eight major models (Motivational Model, TPB, collective of

TAM-TPB, PC Utilization, IDT, Social Cognitive Theory and TRA). UTAUT is an

explanation of user intention to use ICT and continuous to usage behaviour.

According to (UTAUT) usage intention and intention to use depend on four

significant constructs (Venkatesh et al., 2003). They are performance expectancy,

49

effort expectancy, social influence, and facilitating conditions. The relationship

between usage intention and behaviour is moderated by the gender, age, experience,

and perceived voluntariness (Venkatesh et al., 2003).

2.10 CHAPTER SUMMARY

The purpose of this chapter is to present and review the technology and information

communication technology models; in addition it shows the variables that affect the

usage, adoption, and acceptance of the ICT. Part of the chapter shows the individual,

group and organization factors that affect the adoption and acceptance of the

technology and ICT. Another part in this chapter reviews some literature from

Malaysia and Arab scholars.

50

3 CHAPTER THREE

DEVELOPING THE THEORETICAL FRAMEWORK

“Technology adoption can differ based upon the

perceptions of others and how they interact within the

organization”.

(Segrest, Domke-Damonte, Miles and Anthony, 1998, 430)

The previous chapter documented the development of the technology acceptance

model. In this chapter, the research will discuss the theoretical framework of this

research and the test of the hypothesis.

An analysis as per the works of Toh, Muhamad and Ramayah (2004) revealed

the knowledge management in Malaysia managed to build hypotheses that assimilate

the advancement with the wide ranging theoretical foundations which are capable of

generalizing the situations in the global perspective. These theories are helpful in the

construction of the model of the technology acceptance and adoption. The important

decisive factor for an ultimate and idyllic analysis on the innovation is to study more

than one advancement trend because what is needed are the general principles of

acceptance and adoption.

According to Guriting and Ndubisi (2006) there are patterns when it comes to

the acceptance and adoption of any kind of innovation in Malaysia that is marked by

anything more – more effective management, more efficient auditing, more dynamic

51

labour quality, etc. All were dedicated to deal with the various behaviour of the

public.

Similar to the situations in Malaysia, scholars, analysts, and other researchers

have found the progress in organizational structures of businesses, enterprises, and

different ventures of the Arab communities. The impact of ICT is relative and more

than interesting. To give an overview, the ICT policy objectives in the Arab World

can be gauged by the “ICT- understanding” of the government in Arab States. This is

the basis of the logistics and strategies that are to be applied in the planning and

operations of the economic sectors.

3.1 THE MODEL OF READINESS FOR ORGANIZATIONAL CHANGE

Piderit (2000) suggested that the investigation on organizational attitude toward

change could benefit by distinguishing between affect, cognitions, and behaviours. In

addition, Chawla and Kelloway (2004) identified two components of resistance to

change; (a) behavioural, and (b) attitudinal. The attitudes supporting the psychological

negative response of an anticipated change precede obstructive behaviours. The theory

of planned behaviour also serves as the theoretical model for enhanced understanding

of the antecedents of workers’ intentions to continue with the change. This model was

developed to better understand change recipients' responses to change.

The Model of Readiness for Organizational (MROC) was developed by Holt,

Armenakis, Bernerth, Pitts, and Walker (2007) from the findings regarding change

recipients' readiness for change and typology of change antecedents reported in the

Armenakis and Bedeian (1999) literature review. The model of readiness for

organizational change was produced after the content analysis of thirty-three change

readiness instruments; Holt et al., (2007b) revealed that items contained within the

52

instruments fit within the domains of each of the four types of antecedents. The four

types of antecedents include:(1) content, representing what is being changed; (2)

process, representing how it is being changed; (3) context, representing the

circumstances within which the change is taking place, and (4) individual differences,

representing whom it is that is being changed.

The model of readiness for organizational change suggests that (intended and

unintended) behavioural outcomes are due to intentions (and reactions) concerning

those behaviours. Researchers have previously argued that a positive and favourable

view toward organizational change, based on the level to which workers consider that

a change is likely to include positive and beneficial implications for themselves, and

the wider organization will lead to better reactions to change (Armenakis, Harris, and

Mossholder, 1993). In turn, these intentions and reactions are linked with the attitude

Change

Content ‘Attributes to the

change initiative

being

implemented’

Individual

Differences

‘Personal attributes of

the change recipient’

Change Context

‘Attributes to the

change initiative take

place’

Change Process ‘Steps taken to

implement change ‘

Change

Beliefs

‘Change

recipient

believe that

are related

to change initiative’

Intention

‘Change

recipient

Degree of

readiness for

the change’

Behaviours

‘Change

recipient Action and reaction

related to the

change initiative such as

Commitment to

the change resistance and

acceptance’

Figure 3.1The Model of Readiness for Change MROC (Holt et al., 2007a)

53

called readiness for change, which has been defined in numerous ways (Holt et al.,

2007b). This attitude is, in turn, believed to be due to various change-related beliefs.

Several attempts have been made to define change recipients' beliefs

(Armenakis et al., 2007; Holt et al., 2007a, 2007b). In addition, these change

recipients' beliefs are related to various antecedents that fit within the aforementioned

typology. Subjective norms play a crucial role. The proposition that subjective norms

help predict intentions relating to supporting organizational change comes from the

thought that social pressure will generate pressure among workers who direct them to

support change. Researchers have suggested that practitioners should take advantage

of the group culture “social networks” in organizations as an instrument for generating

influence bases and alliances that can influence and inform one another about a

Change

Content ‘Attributes to

the change

initiative being

implemented’

Individual

Differences ‘Personal attributes

of the change

recipient’

Change

Context ‘Attributes to

the change

initiative take

place’

Change

Process

‘Steps taken

to implement

change ‘

Change

Beliefs ‘Change

recipient

believe that

are related to

change

initiative’

Intention ‘Change

recipient

Degree of

readiness for

the change’

Behaviours

‘Change

recipient

Action and

reaction

related to the

change

initiative such

as

Commitment

to the change

resistance and

acceptance’

Figure 3.2 Variation on the Model of Readiness for Change (Holt et al., 2007b)

54

change in order to generate support and create a collective sense during times of

change (Tenkasi and Chesmore, 2003).

Because of the importance placed on content, process, context, and individual

difference constructs within this research, each of the four types of antecedents will be

discussed in greater detail in separate sections to follow. It is worth noting, however,

that perceived behavioural control is included within the MROC as change recipients'

appraisals of those antecedents. The degree to which workers consider that different

resources and demands can either assist or hinder their skill to act in support of a

change represents perceived behavioural control. In many respects, this relates to the

sense-making (Lüscher and Lewis, 2008). The researches’ results suggested

perceptions (or appraisals) of behavioural control (particularly the assessment of

resources), are influential in helping employees to cope and make adjustments during

times of organizational change (Terry and Jimmieson, 2003).

As with TAM, readiness for change as an attitude is not included within the

MROC, and the beliefs that are considered to be the most salient to the attitude are

directly linked in the model to intentions and reactions. These intentions and reactions

take the form of various cognitive assessments of one's own willingness to act or not

act in carrying out a particular behaviour. Commitment to the change, engagement,

and stress represent different types of intentions that are, in turn, postulated to be

determinants of behaviours.

This theoretical framework, or a similar framework based on the TPB, has been

used to various extents within several recent studies (Brown, Massey, Montoya-Weiss

and Burkman, 2002; Holt et al., 2007a 2007b). Within these studies, general support

was found for the validity of the application of the TPB to the study of change

55

constructs. Overall, the TPB offers a broad yet relatively parsimonious framework for

the MROC (please refer to Figure 3-1).

Later more complex variation on the integrated (MROC) is presented by Holt

et al., (2007b) as in Figure 3.2. In this model they points out that not only do

relationships exist between antecedents and change recipients' beliefs, but also among

the various antecedents. One example might be how participation as a change-related

strategy and organizational networks as an attribute of the internal context could

influence change-related training. Training might be easier if participation by change

recipients was used as a strategy in choosing and developing the specifics of the

change initiative.

3.2 TECHNOLOGY ACCEPTANCE MODEL (TAM)

The study of people's reactions to ICT has been an important issue in technology

research since the 1980s. The theoretical foundation for the study of whether a person

is willing to use a technology comes from research on adoption and diffusion (Rogers,

2003). Research in this area has continued to develop over the decades producing

other theories, including the technology acceptance model Davis et al., (1989);

Venkatesh and Davis (1996) the theory of planned behaviour Mathieson (1991),

Taylor and Todd (1995) and social cognitive theory Compeau and Higgins (1995).

In an effort to better understand how persons construct decisions concerning

new technology, studies based on these theories have examined variables related to

individuals' beliefs and intentions regarding the acceptance and continued use of new

ICT (Bhattacherjee, 2001). Researchers have investigated many aspects of the incident

and have formed insights into the cognitive, affective, and behavioural reactions of

individuals to information communication technology and into the factors which

56

impact these actions. No theoretical framework has been more successful at this than

TAM (Davis et al., 1989).

The stated purpose of TAM is to "provide an explanation of the determinants

of computer acceptance that is general, capable of explaining user behaviour across a

broad range of end-user computing technologies and user populations, while at the

same time being both parsimonious and theoretically justified" (Davis et al., 1989,

985). It assumes rationality within the decision-making process. Studies have provided

empirical support for TAM (Venkatesh et al., 2003). TAM also compares favourably

with other technology acceptance theories Mathieson (1991); Taylor and Todd (1995)

and consistently explains about forty percent of the discrepancy in individuals'

intentions to utilize ICT and actual usage. As such, TAM is said to be the most

influential technology acceptance theory and model (Saga and Zmud, 1994).

The Technology Acceptance Model proposes that the use of technology is

motivated by an individual's attitude toward using the technology, which is a function

of their beliefs about using the technology and an evaluation of the value of actually

using it. This is based on ''the cost-benefit paradigm from behavioural decision theory''

(Davis, 1989, 321), which postulates that individual deed is based on a person's

cognitive trade-off between the necessary effort to perform task and the consequences

of the attempt. Therefore, TAM declares that an individual will use a technology if the

benefits of doing so outweigh the effort required to use it (Davis, 1989).

Among the behaviours commonly measured are: system usage (Venkatesh,

1999), and user satisfaction (Bhattacherjee, 2001). Some researchers have studied

both dimensions as a composite (Gelderman, 1998). User satisfaction actually

represents a cognitive and affective outcome that is less tangible in terms of

57

classification as behaviour. System usage has been better measurement of ICT

acceptance (Al-Gahtani and King, 1999).

Actual use of technology behaviour is resulted of direct intention to use it,

because people, normally, act as they intend to, on condition that they have control

over their actions. The attitudes toward using the system, one after another, depend on

intention to use technology. Following the logic of TRA framework, users' belief

depends on attitudes toward the ICT system and about continuous use.

The Technology Acceptance Model adopts TRA's concept of beliefs in the

form of two variables: perceived ease of use and perceived usefulness (Igbaria et al.,

1995). These two beliefs are considered major determinants of ICT usage. The

System characters

Perceiv

ed

Usefu

lness

Beh

avio

ura

l

Inten

tion

Perceiv

ed

Ease o

f Use

Use

Behaviour

Person

actual

usage of the

technology

Individual

Differences

Social Influence

Facilitating

condition

Antecedents of Belief

Technology

User Believes

Figure 3.3 Theoretical Framework for the Technology Acceptance Model

58

definition of perceived ease of use by Davis (1989, 320) is "the degree of which a

person believes that using a particular system would be free of effort”, and perceived

usefulness is "the degree of which a person believes that using a particular system

would enhance his/her job performance”. Davis (1989) suggested that PEOU and

PERUSE predict the behaviour of actual system usage through the mediating variables

of attitude and intention, (which are sometimes not directly measured when

operationalizing TAM). A common operationalization of TAM is presented in Figure

3.3 (Igbaria et al., 1995).

TAM was directly compared with the TPB by Mathieson (1991). He pointed

out that both models were concrete in clearing up technology user intentions, though

TAM gave slightly more details variance in person intentions within a systematic

setting. Mathieson (1991) argued that the TPB could be more useful in developing a

better understanding of why users were more or less inclined to use a technology.

Despite the many advantages of TAM, Yayla and Hu (2007) noted that, when

compared to the TPB, TAM "only supplies general information on users’ opinions

about a system" (Mathieson, 1991, 173). Some studies on ICT implementation found

that certain controllable factors that were part of the implementation process or the

environment influenced technology acceptance yet they are completely ignored as

determinants of either technology user beliefs or as direct influences on intentions

(Szajna, 1996).

In furtherance of this deficit within TAM, a detailed version of the TPB, called

the decomposed TPB “DTPB” was tested (Taylor and Todd, 1995). This new

application of the TPB detailed specific predictors of attitude, subjective norms, and

perceived behavioural control. It suggested that attitude and subjective norms impact

59

the relationships of intentions with perceived usefulness and ease of use (Taylor and

Todd, 1995). This model increased the variance explained and also provided a greater

explanation of how managers might play a role in influencing organizational members

through subjective norms relating to the adoption of the new technology.

Few systematic efforts traced the development of TAM or evaluated the whole

body of findings, limitations, and future research opportunities, though there were

calls for it in the research (Legris, Ingham and Collerette, 2003). As such, an

evaluation was needed in order to integrate TAM's past research findings, identify

possible research topics, and conduct future studies. This led to the development of

the present incarnation of the Technology Acceptance Models, TAM III (Venkatesh

and Bala, 2008). They provided an integrated model that represented a complete

nomological group of determinants of individual level ICT adoption and use. They

tested their model empirically and produced a number of significant findings. They

advocated that researchers in the future should focus on implementation, particularly

examining potential constructs and relationships in both pre-implementation and post-

implementation phases in order for management to make better decisions concerning

its implementation strategies.

It was noted that few research studies have investigated the role that

managerial interventions (or change process) play in influencing ICT adoption and

use. The purpose was to understand users' beliefs better in order to design more

effective organizational interventions that could increase user acceptance and use of

new IT systems. They also noted that, for TAM to continue to evolve, more research

effort should be focused on examining similar research in other fields (Venkatesh and

Bala, 2008).

60

Interestingly, Venkatesh and Bala (2008) developed four categories of

antecedents of technology user beliefs. They labelled these categories as system

characteristics, individual differences, social influence, and facilitating conditions.

Notably, these four categories largely correspond to the typology of the antecedents of

change recipient beliefs within MROC (Holt et al., 2007a). Individual differences, as a

category, are identical within both models. System characteristics represent a category

very similar to the MROC category of content, though content might also include

other factors (the type of change - radical or incremental, the degree of volition in

choosing to participate). Social influence and facilitating conditions are categories that

tie-in with the change model categories of process and context. Change process can

include elements of social influence (managerial pressure directed toward the change,

encouragement, feedback sessions, etc.) as well as facilitating conditions (technical

support, help desks, training, etc...). Likewise, change context can have social

influence (LMX, co-worker support) as well as facilitating conditions (a learning

organizational culture, transparency, helpful management practices).

3.3 COMPLAINTS CONCERNING FURTHER DEVELOPMENT OF THE

TECHNOLOGY ACCEPTANCE MODEL

Despite the limitations of TAM, however, Lee et al., (2003, 766) asked, "Are there

areas of TAM that need more exploration?” The responses focused on several areas of

expansion, (including some that remain unexplored within the change literature).

Suggestions for future research included developing a greater understanding of factors

contributing to perceived usefulness and perceived ease of use was needed. Further

examination was also suggested in the area of developing an understanding of content

and context variables as determinants of beliefs, particularly; an examination of

61

different IS and work environments. Multi-user systems and team-level IS research

were called for as well. More research on emotion, habit, dispositional difference, and

societal acceptance technology change was noted as being underdeveloped, along with

a lack of examination regarding the differences between mandatory and voluntary

change settings.

More detailed examination of social factors was especially crucial, particularly

since many social factors that might impact IT acceptance were positioned outside

TAM's boundaries (Agarwal, 2000). With the exception of suggestions by Lee et al.,

(2003); Aubert, Barki, Michel and Roy (2008) noted more research was also needed

on managerial interventions (change process activities), such as user training

(Bostrom, Olfman, and Sein, 1990; Hashim, 2008), participation, and end-user

involvement (Barki and Hartwick, 2001).

3.4 PROPOSED THEORETICAL FRAMEWORK

The theoretical model proposed within this dissertation addresses the attitude to

change and intention to use stages of a change initiative, as it is defined by Armenakis

et al., (1999). These two stages align with Lewin's (1951) theory, being change

readiness (unfreezing) and adoption (moving). Maintaining change readiness

throughout these two phases, as well as the third phase, institutionalization (freezing),

is crucial (Armenakis et al., 1999).

Similarly, the proposed theoretical framework also addresses the pre-

implementation phase and early post-implementation phase as they are conceptualized

within TAM III model. The implementation of new IT is categorized as pre-

implementation and post-implementation interventions (Venkatesh and Bala, 2008),

based on the stage models presented by Cooper and Zmud (1990) and Saga and Zmud

62

(1994). The pre-implementation phase is characterized by everything leading to make

the system a day-to-day routine. This phase includes initiation (getting change

recipients accustomed to the idea), organizational adoption (making the change), and

adaptation (working out any issues in getting the IT system to function). The post-

implementation phase entails everything that follows the actual deployment of the

system, including user acceptance (getting change recipients to use the IT system),

routinization (getting change recipients to turn use into a habit), and infusion (the

evaluation of the ICT system as no longer new; Cooper and Zmud, 1990). The

proposed theoretical framework addresses the change readiness and adoption phases,

as well as the pre-implementation phase and user acceptance stage of the post-

implementation phase.

The theory of planned behaviour (TPB; Ajzen, 1991), which developed out of

the theory of reasoned action (TRA; Ajzen and Fishbein, 1980), the model of

readiness for organizational change (MROC) and the technology acceptance model

(TAM) provide the foundation of the model which is integrated in this dissertation

into the proposed theoretical model. This part reviews the literatures relevant to the

development of a proposed model of technological change and provides details to

support the study hypotheses.

For this dissertation, the MROC presented by Holt et al., (2007a) was

combined with components of TAM III (Venkatesh and Bala, 2008), the third iteration

of TAM (Davis et al., 1989). The theoretical framework proposed specifies potential

relationships among variables from both TAM and the model of organizational change

with other factors from the literature. For the theoretical model, the MROC serves as

the template and technology acceptance variables are included into the model.

63

The change-related beliefs chosen for the research framework consist of next

twelve interrelated variables. The three beliefs referred to as the organizational change

recipients' beliefs (OCRBs) include: appreciation, “is this the right change"; principal

support, "has everyone bought into making the change happen"; motivation valence”,

what is in it for me" and attitude to change (Armenakis, Harris and Feild, 1999);

(Reid, Riemenschneider, Allen and Armstrong, 2008). In addition to the three change-

related beliefs, the four primary beliefs of TAM, perceived usefulness, perceived ease

of use, Attitude behaviour to change and intention to use with current usage of the

system” are also included. In addition to these four factors which are voluntariness

motivation, subjective norm, training and nature of work as moderators. These seven

variables are not explained in this section since each one is focused on in greater detail

in the sections that follow within this literature review. It is proposed that these beliefs

are the result of sense making as it concerns any number of antecedents that could be

related to the organizational change involving technology.

3.4.1 Intention to Use and Continue to Use

Organizational change often results in modified work roles and altered performance

goals that go beyond existing duties and responsibilities (Hornung and Rousseau,

2007). Within such situations, change agents depend on employees to rise and meet

such challenges. When people are faced with organizational change, they respond not

just on a behavioural level, but also on cognitive and affective levels (Smollan, 2006).

Behavioural responses are often the results of cognitive and emotional actions and

reactions. These cognitive and emotional actions and reactions are framed as

intentions within the TPB, TAM, MROC, and proposed theoretical framework.

64

Numerous behavioural outcomes related to changing readiness have been

examined within the change literature, including absenteeism and turnover (Mack,

Nelson and Quick, 1998). Many studies have also examined affective outcomes

related to change of readiness, such as organizational commitment, job satisfaction

and psychological well-being (Eby, Adams, Russell, and Gaby, 2000). The focus

within the technology acceptance literature has been primarily on actual use of the

technology (Davis et al., 1989) and job satisfaction related to the technological change

(Bhattacherjee, 2001).

Other performance-related outcomes have also been examined within IS

studies that have focused on efficiency and effectiveness of ICT usage (Compeau,

Higgins and Huff, 1999). Despite the examination of specific outcomes, the

relationships between, and processes that link, initial sense-making of a change event

to many of these change-related outcomes remain underdeveloped in the

organizational change literature (Martin, Jones and Callan, 2005).

This research specifically examines technology continuance use and affective

commitment to organizational change, as outcome variables. A vast array of other

potential outcomes could have been chosen. Continuance was chosen because it

represents the core attitudinal outcome of TAM. Affective commitment to the change

was chosen because it is an emotion-related attitude.

Technology acceptance (trial usage), adoption (used to accomplish tasks,

testing the qualities of the technology over time), and continuance (adoption until

better technology is available) have been the major focus of Information systems (IS)

research for more than two decades (Premkumar and Bhattacherjee, 2008) because

65

they have been demonstrated to be key drivers in organizational performance (Devaraj

and Kohli, 2003).

The distinction between acceptance and continuance is made because it is

significant to recognize that the two ideas represent different outcomes and constructs

even though often discussed as a single outcome (Karahanna, Straub and Chervany,

1999). Technology acceptance is a critical, immoderate outcome. Technology

acceptance is necessary but not sufficient for an organizational change involving

technology to succeed. However, technology acceptance represents only the first

phase of the actual change process. Technology adoption and continuance are truly the

outcomes sought by the change process.

Research concerning technology acceptance and adoption has been informed

primarily by TAM (Davis et al., 1989). However, TAM, while proposed as a model of

technology acceptance and adoption, has been used to examine continued usage

(Karahanna et al., 1999; Venkatesh and Brown, 2001; Venkatesh and Davis, 2000).

Technology continuance has been informed by the expectation-disconfirmation theory

(EDT), which proposes that users of technology constantly make judgments as to

whether to continue to use it based on their own experiences and the opinions of

others (Oliver, 1980). Continuance has been explained with concepts such as

implementation (Zmud 1982), incorporation (Kwon and Zmud, 1987), and

routinization (Cooper and Zmud, 1990). These studies focus on technology usage

reaching a point that transcends conscious behaviour, becoming part of a person's

routine activities (Bhattacherjee, 2001).

Innovation diffusion theory, in its five-stages adoption decision process

(consisting of knowledge, persuasion, decision, implementation, and confirmation

66

phases), proposes that individuals re-evaluate their acceptance decisions during the

confirmation phase and decide whether to continue to use the technology (Rogers,

2003). All of these theoretical perspectives, nonetheless, view continuance as an

extension of acceptance behaviours, which would mean the same influencing factors

apply on both acceptance and continuance (Bhattacherjee, 2001). However, this

position makes it difficult to explain why some users obstruct the use of technology

even though they initially accepted it; this has been called the "acceptance-

discontinuance anomaly" (Bhattacherjee, 2001).

The concepts of acceptance, adoption, and continuance can add great value to

organizational change research. Very little research has been done within the change

literature to address this technology acceptance as an aspect of the change process. For

example, the application of new work practices and procedures that are part of change

content might also be viewed as a process spanning initial experimentation, adaptation

of one's regular work routine, and continued performance. IS-based theories might be

able to provide fresh insight into understanding how the content of change initiatives

is received and acted upon by change recipients over the course of time.

The theory of planned behaviour (TPB; Ajzen, 1991), which developed out of

the theory of reasoned action (TRA; Ajzen and Fishbein, 1980), the model of

readiness for organizational change (MROC) and the technology acceptance model

(TAM) provide the foundation of the model which is integrated in this dissertation

into the proposed theoretical model. This part reviews the literatures relevant to the

development of a proposed model of technological change and provides details to

support the study hypotheses.

67

The theory of planned behaviour (TPB; Ajzen, 1991) added the concept of

perceived behavioural control as a new antecedent to intentions and behaviour to help

explain behaviours that are not entirely volitional. Perceived behavioural control is

defined as “the person's belief as to how easy or difficult performance of the

behaviour is likely to be" (Ajzen and Madden, 1986, 457). In many ways, it is an

equivalent comparable to Bandura's (1982) concept of self-efficacy, which

individuals' confidence in their capacity to execute a particular behaviour. In fact,

Ajzen's (1991) conceptualization is based on research concerning self-efficacy

(Bandura, Adams, Hardy and Howells, 1980). In addition, the definition of action

includes actions that are subject to interference by internal and external forces.

The performance of behaviour is the combined effort of intentions and

perceived behavioural control. Whenever the opportunity to perform behaviour exists

with total volition, intentions alone should be enough and adequate to forecast the

behaviour, as explained in TRA theory. However, perceived behavioural control is

believed to become more and more significant as an interpreter as volitional control

over the behaviour declines. Intentions behavioural and perceived behavioural control

can contribute extensively to the forecast of behaviour when full volition is

impossible. In such cases, both are not necessarily equal, and either intentions or

perceived behavioural control may be more important than the other (Ajzen, 1991).

In addition, there are three distinguish salient beliefs in the literature having an

influence on attitudes (Ajzen, 1991). These beliefs are: (a) behavioural beliefs that

influence attitudes toward behaviour, (b) normative beliefs that constitute the

underlying determinants of subjective norms, and (c) control beliefs that provide the

basis for perceived behavioural control.

68

The theory of planned behaviour has been used successfully for prediction

purposes in a broad scope of study areas, including the use of structured interview

techniques for selection purposes, the prediction of managers' personal motivation to

develop better skills after receiving feedback, readiness for organizational change

(Jimmieson, Peach and White, 2008) technology adoption, intent toward participating

in an employee involvement program (Dawkins and Frass, 2005).

Organizational change often results in modified work roles and altered

performance goals that go beyond existing duties and responsibilities (Hornung and

Rousseau, 2007). Within such situations, change agents depend on employees to rise

and meet such challenges. When people are faced with organizational change, they

respond not just on a behavioural level, but also on affective levels (Smollan, 2006).

Behavioural responses are often the results of emotional actions and reactions. This

emotional actions and reactions are framed as intentions within the TPB, TAM,

MROC, and proposed theoretical framework.

Numerous intentional outcomes related to changing readiness have been

examined within the change literature, including absenteeism and turnover (Mack,

Nelson et al., 1998). Many studies have also examined affective outcomes related to

change of readiness, such as organizational commitment, job satisfaction and

psychological well-being (Eby et al., 2000). The focus within the technology

acceptance literature has been primarily on actual use of the technology (Davis et al.,

1989) and job satisfaction related to the technological change (Bhattacherjee, 2001).

Other performance-related outcomes have also been examined within IS studies that

have focused on efficiency and effectiveness of ICT usage (Compeau, Higgins and

Huff, 1999). Despite the examination of specific outcomes, the relationships between,

69

and processes that link, initial sense-making of a change event to many of these

change-related outcomes remain underdeveloped in the organizational change

literature (Martin et al., 2005).

This research specifically examines technology continuance use and affective

commitment to organizational change, as outcome variables. A vast array of other

potential outcomes could have been chosen. Continuance was chosen because it

represents the core attitudinal outcome of TAM. Affective commitment to the change

was chosen because it is an emotion-related attitude.

Technology acceptance (trial usage), adoption (used to accomplish tasks,

testing the qualities of the technology over time), and continuance (adoption until

better technology is available) have been the major focus of Information systems (IS)

research for more than two decades (Premkumar and Bhattacherjee, 2008) because

they have been demonstrated to be key drivers in organizational performance (Devaraj

and Kohli, 2003).

The distinction between acceptance and continuance is made because it is

significant to recognize that the two ideas represent different outcomes and constructs

even though often discussed as a single outcome (Karahanna et al., 1999). Technology

acceptance is a critical, immoderate outcome. Technology acceptance is necessary but

not sufficient for an organizational change involving technology to succeed. However,

technology acceptance represents only the first phase of the actual change process.

Technology adoption and continuance are truly the outcomes sought by the change

process.

70

Research concerning technology acceptance and adoption has been informed

primarily by TAM (Davis et al., 1989). However, TAM, while proposed as a model of

technology acceptance and adoption, has been used to examine continued usage

(Karahanna et al., 1999; Venkatesh and Brown, 2001; Venkatesh and Davis, 2000).

Technology continuance has been informed by the expectation-disconfirmation theory

(EDT), which proposes that users of technology constantly make judgments as to

whether to continue to use it based on their own experiences and the opinions of

others (Oliver, 1980). Continuance has been explained with concepts such as

implementation (Zmud, 1982), incorporation (Kwon and Zmud, 1987), and

routinization (Cooper and Zmud, 1990). These studies focus on technology usage

reaching a point that transcends conscious behaviour, becoming part of a person's

routine activities (Bhattacherjee, 2001).

Innovation diffusion theory, in its five-stages adoption decision process

(consisting of knowledge, persuasion, decision, implementation, and confirmation

phases), proposes that individuals re-evaluate their acceptance decisions during the

confirmation phase and decide whether to continue to use the technology (Rogers,

2003). All of these theoretical perspectives, nonetheless, view continuance as an

extension of acceptance behaviours, which would mean the same influencing factors

apply on both acceptance and continuance (Bhattacherjee, 2001). However, this

position makes it difficult to explain why some users obstruct the use of technology

even though they initially accepted it; this has been called the "acceptance-

discontinuance anomaly" (Bhattacherjee, 2001).

71

3.4.2 Attitude to Change

Organizational change originated from the organizational commitment literature.

Herscovitch and Meyer (2002, 475) defined commitment to organizational change as a

"force (mind-set) that binds an individual to a course of action deemed necessary for

the successful implementation of a change initiative”. In alignment with their previous

model of workplace commitment, they conceptualized commitment to organizational

change as multidimensional. It consists of: (a) affective commitment to organizational

change, reflecting support for a change initiative based on feelings and beliefs

concerning the value of the change; (b) continuance commitment to organizational

change, reflecting perceptions of the costs associated with failure to support the

change, such as loss of position, authority, pay, or job; and (b) normative commitment

to change, reflecting a sense of duty to support the change (Herscovitch and Meyer,

2002).

Examination of attitude to change as a construct has revealed it to be

theoretically and scientifically diverse from organizational commitment (Fedor

Caldwell and Herold, 2006) and to be a better forecaster of support for change

(Herscovitch and Meyer, 2002); (Reid et al., 2008). Similarly, Ford, Weissbein and

Plamondon (2003) found commitment to a significant change (labelled "strategy

change" in their research) to be conceptually and empirically distinct from

organizational commitment.

Organizational change and development literatures note that employee

commitment to a change plays a vital role in the success of a change initiative (Fedor

et al., 2006). Highly committed change recipients are more likely to comply with a

change initiative and usually put forth the necessary effort to achieve success (Porras

72

and Robertson, 1992). Thus, change agents must focus on building and sustaining

commitment to the change through implementation strategies (Conner and Patterson,

1982).

The examination of reactions to organizational change initiatives has revealed

that dedication to change reflects not only constructive attitudes toward the change but

also alignment with the change, willingness to support it, and intention to work toward

making it a success. Conner (1992, 147) described commitment to change as "the glue

that provides the vital bond between people and change goals”. It implies an

internalization of the organizational change goal as a personal goal, with change

recipients the need to put forth effort in order for the organization to succeed in

achieving the potential benefits from the change. It captures some aspects of the

absence of negative attitudes, such as resistance to the change (Piderit, 2000), the

presence of positive dispositions toward a change, such as readiness for change

(Armenakis et al., 1999) and an openness to change (Wanberg and Banas, 2000).

Any of the three sorts of commitment to change will likely lead to a change

recipient enacting "focal" or required behaviour mandatory for minimal success.

However, discretionary behaviour that goes beyond focal behaviour should differ

based on the type of commitment (Herscovitch and Meyer, 2002). Change recipients

with continuance commitment to organizational change are aware of the costs

associated with not complying with the change and support it simply because they

must. Change recipients with a sense of normative commitment may not wish to

participate in the change, but they still believe that they should support the change

initiative because of a sense of duty, not because they believe in the value of the

change.

73

Change recipients who are normatively committed to organizational change

engage in a manner similar to the way they would if they were continuance committed

to the change. This means they would likely demonstrate any mandatory support for

the change, but they might also engage, to some extent, in extra-role behaviours in

their support. Change recipients who are affectively and typically committed to

possess strong beliefs concerning the value of the change, are intrinsically motivated

to achieve the change initiative's goals, and verbally support the change.

Change recipients who are affectively committed to not only comply with the

change, but also tend to demonstrate an extra role behavioural support. Affective

commitment to change was found to be linked with "championing" behaviour

involving the positive promotion of the value of the change (Herscovitch and Meyer,

2002). Affectively committed change recipients may even make some level of

personal sacrifice in order to achieve the goals of the change initiative (Herscovitch

and Meyer, 2002). Notably, fostering affective commitment in the context of change

is a difficult task (Meyer, Allen, and Smith, 1993).

Researchers have proposed a wide variety of antecedents that are believed to

influence the development of commitment to change. Meyer and Allen (1997, 2002)

noted that the same processes by which each form of organizational commitment is

fostered most likely apply to other commitment domains, including organizational

change. Possible antecedents include a number of process antecedents, including

participation, justice, and communication (Zorn, Page, and Cheney, 2000). Individual

differences, specifically personal attributes, have also been linked to commitment to

organizational change (Herscovitch and Meyer, 2002).

74

There are often many reasons why organizational change initiatives fail, but

few are as critical as change recipients' attitudes toward the change event. The earliest

illustration of the importance of readiness for change comes from Lewin's (2008)

concept of unfreezing, which represents efforts taken to break up the complacency of

employees. He stated that it is necessary to provide evidence that certain old habits,

attitudes, and behaviours are no longer acceptable or appropriate in the organization.

Schein (2004) argued that failure is often traceable to an organization's inability to

effectively unfreeze and foster readiness for change before attempting to implement

(i.e., the moving phase) the change. All too often organizations begin implementation

before unfreezing, leaving change recipients psychologically unready for the change.

As research provides a greater understanding of the extent to which readiness for

change leads to successful implementation, more and more attention is given to

preparing employees for making the change (Jones, Jimmieson and Griffiths, 2005).

Building positive employee beliefs, perceptions, and attitudes is critical for

successful change interventions (Armenakis et al., 1999; Eby et al., 2000; Elias,

2009). Fostering readiness for change is an organizational development (OD) process

through which global and local change agents prepare change recipients for future

changes so that they can more proactively act and effectively react to the change. The

foundations for creating readiness can be found in several theoretical models, which

Van de Ven and Poole (1995) integrated from several disciplines in order for

researchers, management, and OD professionals to assess a theoretical means in

understanding the change phenomenon. Organizational leaders, acting as change

agents, introduce purposeful, system-wide change initiatives to achieve some

specified goals. This is called teleological change (Van de Ven and Poole, 1995).

75

When these purposeful changes are introduced, differences and conflicts arise

from constituencies of change recipients with competing goals and interests. For the

conflict to be resolved, the beliefs and cognitions of all of the change recipients must

fall into alignment with those of the change agents. Change when there is alignment of

this sort is called dialectical change (Van de Ven and Poole, 1995). In essence, change

recipient support and enthusiasm for the change initiative must be first created in

order to prevent conflict and failure (Piderit, 2000), and failure to create this readiness

produces resistance to change (Armenakis et al., 1999).

The models found in the organizational change literature follow Lewin's (1947-

2008) work and propose that building momentum, excitement, and buy-in to the

change initiative are all critical components of success (Armenakis et al., 1993; Eby et

al., 2000). This often involves increasing the decisional latitude, participation, and

empowerment of change recipients, thus mandating a participative managerial

approach rather than an authoritative one (Antonacopoulou, 2006).

Armenakis et al., (1999) developed the three-step conceptualization of

conducting a change initiative based on the work of Lewin (1947-2008). It described

the change process as readiness (unfreezing), preparing for the change; adoption

(moving), shifting from the old, no longer appropriate behaviours to the desired new

behaviours, often through changes in organizational structures and processes, and

institutionalization (freezing), reaching a new state of equilibrium, with lasting

changes in norms, policies, structures, and possibly even organizational culture.

Rather than viewing the process as moving cleanly through each of the three steps one

at a time, the model recognized that the change process can be somewhat more

convoluted (Isabella, 1990), with overlaps in the three steps making it more of a series

76

of phases. Because of this, the initial creation of readiness does not stop when

adoption begins, and the change agent must continue to manage readiness throughout

the entire change initiative (Armenakis et al., 1999). This is done through a change

message (Armenakis et al., 1993).

Research on readiness for change has linked it to several other constructs.

Perceived need for change, self-efficacy, and commitment to organizational change,

perceived behavioural control, active participation in the change process, as well as

productivity, and turnover intentions are all correlates of readiness for change (Ajzen,

1991; Armenakis et al., 1993). A positive relationship exists between employees’

commitment with change and readiness to change (Madsen, Miller, and John, 2005).

Readiness for change is most often used within both conceptual and empirical

research as a dependent variable (Armenakis et al., 1993; Eby et al., 2000). Readiness

for change is seldom used as a mediating variable between change management

strategies and change implementation success (Jones et al., 2005). Two studies, one

conducted by Wanberg and Banas (2000), and another by Oreg (2006) are exceptions;

both of these studies involved testing a mediating model of readiness for change,

proposing that several variables (self-efficacy, positive attitudes about the change,

information provision, and active participation) would encourage readiness for

change. In turn, readiness for change would be predictive of employee adjustment (job

satisfaction, organizational commitment, work irritation, intention to quit, and actual

turnover). The results revealed that several of the pre-implementation measures

predicted readiness for change perceptions, and readiness for change predicted

organizational commitment, work irritation, job satisfaction, and turnover intentions.

77

Within the present study, readiness for change is not directly addressed as a variable.

Instead, beliefs that contribute to readiness are tested in relation to the outcome

variables. In order to do so, variables must be chosen that represent the beliefs that

foster readiness for change.

3.4.2.1 Resistance to Change

Organizational change is linked to change recipients’ beliefs, interpretive schemata,

paradigms, and behaviours (Elias, 2009). Often change agents simply expect change

recipients to comply with change initiatives, or even enthusiastically support them, no

questions asked, and without any regard to those change recipients’ attitudes and

beliefs (Piderit, 2000). In truth, change agents must win hearts and minds for a change

initiative to be successful (Duck, 1993). Since the failure of many major change

initiatives can be attributed to employee change resistance (Clegg and Walsh, 2004), it

is very important to understand the role of affective, cognitive, and behavioural

processes among change recipients. If an organization does not take into account

psychological processes, the change initiative is likely to generate stress and cynicism

that will reduce organizational commitment, job satisfaction, trust in the organization,

and motivation (Reichers, Wanous, and Austin, 1997).

While remaining fairly distinct from organizational change literature, the body

of IS research focuses on understanding and managing employee reactions to changes

in IT (Agarwal and Karahanna, 2000). Speaking about computer systems, Davis et al.,

(1989, 587) proclaimed: “understanding why people accept or reject computers has

proven to be one of the most challenging issues in IS research”. Despite decades of

continued research and increased familiarity with innovation diffusion and the speed

of technological advancement, change recipients seemingly accept and reject ICT

78

systems unsystematically (Hasan, 2003). Just as organizational change cannot occur

without change recipients accepting the content of the change event, so too IT cannot

produce any positive outcomes unless the technology is adopted and utilized. The

research has come to the conclusion that ICT acceptance and usage are ultimately

determined by change recipients’ beliefs and attitudes (Venkatesh and Davis, 2000;

Venkatesh et al., 2003).

Within the literature, many conceptualizations of change recipients’ affective,

cognitive, and behavioural responses to change have been offered, including readiness

to change (Armenakis et al., 2007), change reluctance and inertia (Piderit, 2000),

openness and commitment to organizational change (Wanberg and Banas, 2000),

positive coping with organizational change (Avey, Wernsing, and Luthans, 2008), and

change-related cynicism and resistance (Bommer, Rich, and Rubin, 2005).

The bottom line is that change recipients’ acceptance of, and support for,

organizational changes are considered to be crucial for the success of planned

organizational changes (Armenakis et al., 1999). Change recipients with a strong,

positive attitude toward a change are likely to behave in a variety of helpful and

effortful ways that support and facilitate the change initiative. However, change

recipients with a strong, negative attitude toward a change are more likely to manifest

lower trust, disloyalty, and intention to quit, actively speaking out against the change,

deception, sabotage, aggression, and refusal to work or complete certain tasks (Fox,

2002).

Resistance to change can make things more difficult even within organizations

that have well-established structures and processes (Oreg, 2006). Resistance is often

the biggest obstacle change agents must face, and it can appear whenever any work

79

activity or ingrained pattern of activity has become the taken-for-granted way of doing

things (Avey et al., 2008). Resistance can be expressed both actively and passively,

hindering change efforts, lowering morale and productivity, increasing turnover, and,

as a result, increasing the likelihood of organizational failures (Dervitsiotis, 1998; Eby

et al., 2000).

Heath, Knez and Camerer (1993) posited that the psychological process of

experiencing change leads to negative reactions because: (a) humans prefer a known

situation over an unknown future; (b) while change involves both gain and loss,

people tend to experience the pain of loss with greater intensity than they experience

the pleasure of gain; and (c) people tend to see existing entitlements as greater than

they actually are. Change recipients find organizational change disconcerting because

of the ambiguity involved (Heath et al., 1993), and this ambiguity leads to uneasiness,

stress, and general mistrust of the decision-makers (Strebel, 1996). Similarly, Prospect

theory, described by Kahneman and Tversky (1979) suggests that how people

interpret their choices, as either gains or as losses, influences how much risk they are

willing to take. When a potential outcome is framed as a loss, more attention is given

to avoiding that loss as an outcome. In general, people prefer avoiding loss over

acquiring gain.

Change initiatives trigger the sense-making (Weick, 1995) processes of change

recipients causing them to first evaluate the personal significance of a change

initiative and then extend their appraisal to cover the impact of the change initiative on

other change recipients and the organization itself (Lazarus, 1999). Their secondary

appraisal includes examination of the causes of the change, the change agents, and

potential coping strategies (Jordan, Ashkanasy and Hartel, 2002).

80

Dent and Goldberg (1999) suggested that change recipients resist negative

consequences (e.g., losing one’s job) rather than change for its own sake. Change

often involves increased workload, pressure, and stress, which require sustained

cognitive efforts, thereby soliciting resistance to change (Ng, Ang and Chan, 2008).

Feelings related to loss of support, power, status, and job-related efficacy, as well as a

feeling of disconnection from the organizational culture has been suggested as factors

related to change resistance (Callan, 1993). Nord and Jermier (1994) argue that the

term “resistance to change” is often used to cover over and dismiss a whole multitude

of legitimate reasons for objecting to a change rather than trying to understand and

resolve real organizational problems.

3.4.2.2 Readiness for Change

Readiness for change as a concept dates back to the earliest investigations on

organizational change, being introduced by Lewin (1958) and remaining a central

element of the research ever since. The study of people's willingness to change has

become the countervailing perspective against the assumption that people

automatically resist change. Many scholars have challenged the axiom of resistance

(Jansen, 2000), and even argue that resistance is rare (Kotter, 2007). Viewing attitude

as readiness rather than resistance falls in line with the emerging body of literature

classified as "positive organizational behaviour” (Luthans and Youssef, 2007).

Positive psychology focuses on human strengths and optimal functioning rather than

on weaknesses and fallibilities. Luthans (2002b, 59) stated that positive organizational

behaviour is "the study and application of positively oriented human resource

strengths and psychological capacities that can be measured, developed, and

effectively managed for performance improvement”.

81

Readiness for change has been defined in a wide variety of ways. Wanberg and

Banas (2000) conceptualized it as openness to organizational change, consisting of a

willingness to support the change and positive effect about the potential consequences

of it. Armenakis et al., (1999) defined readiness as the beliefs, attitudes, and intentions

that people hold in regard to whether or not there should be a change and whether or

not the organization can change. Very similarly, Killing and Fry (1990) defined

readiness for change in terms of the extent to which organizational members are aware

of the need for change and whether or not they possess the necessary skills or

education to carry out the change (Brown, 2009) .

Hanpachern (1997, 11) defined readiness for change as "The extent to which

individuals are mentally, psychologically, or physically ready, prepared, or primed to

participate in organizational development activities". Kouzes and Posner (2002)

posited that successful change requires change recipients to be intrinsically motivated,

to see the change as a learning opportunity, and to feel as if in control over the change

process. Beckhard and Harris (1987, 61) stated that readiness was all about

"willingness, motives, and aims . . “. of change recipients. Other definitions focus on

change recipients' awareness of the need to change, their acceptance of the change,

and perceptions of the positive implications for themselves and the wider organization

(Jones et al., 2005).

One aspect of readiness for change is cognitive in the sense of developing

schemata and attitudes toward change (Bandura, 1982; Ajzen and Fishbein, 1975),

which are described as precursors of actual resistance or support (Armenakis et al.,

1999). Another aspect of readiness for change consists of the emotional reactions to

the change process (Jones et al., 2005). Related to this, the interpretive and interactive

82

aspects of coping with change were expressed in Lazarus and Folkman's (1987)

transactional model of stress and coping. In related research, scholars have examined

psychological capacity (PsyCap) as a person's cognitive and emotional resources, as

they relate to dealing with organizational change (Avey et al., 2008). They suggest

that if change recipients are optimistic and efficacious, they usually possess positive

expectations for goal achievement, cope well with the change, and experience positive

feelings of confidence. Setbacks and challenges are also better overcome through this

hopeful confidence, reducing cynicism and producing engagement (Avey et al., 2008).

Research on organizational climate suggests that the sum of a change

recipient's experiences shape his/her overall perceptions of the organization (James L.

A. and James, L. R. 2002), and this includes the extent to which the change recipient

thinks that the organization is ready to make a change initiative successful (Jones et

al., 2005). As such, readiness at the organizational level means that there is an

alignment of collective cognitions throughout the organization, possessing collective

efficacy, positively directed toward the change, which serves as the precursor to

successful action taken by the organization (Armenakis et al., 1999).

It is difficult to directly tie the concept of readiness for change to the ICT

adoption literature. At more of a macro level, the definition of readiness for change

offered by Beer and Walton (1987) that readiness for change represents the social,

technological, and systematic capability of an organization to change; fits with the IS

research conducted by Clark, Cavanaugh, Brown and Sambamurthy (1997) which

stated that technological change readiness is the ability of IS-based organizations to

deliver strategies.

83

ICT applications within short development cycle times utilize a highly skilled

internal IS workforce. It is implied that employees are already ready for the

implementation, with the human element remaining largely unaddressed. As discussed

within the previous section, however, change recipients play a major role in the

success of change events.

Some research within the ICT adoption literature recognizes Lewin's (1947-

2008) concept of unfreezing. This research has focused more on the individual level

psychological process involved in readiness for ICT-related change. One such

conceptualization is called technology readiness, which represents a person's trait-like

propensity to embrace and use new technologies for accomplishing goals in home life

and at work (Parasuraman, 2000). It was conceptualized by Parasuraman as more of a

trait than a state.

Technology readiness can also be viewed in a manner similar to readiness for

change in that it represents a person's willingness to use technology at least on a trial

basis (Lin, Shih, and Sher, 2007). Lin et al., (2007) described technology readiness as

consisting of four sub-dimensions, namely, optimism, innovativeness, discomfort, and

insecurity. Optimism reflects a positive view of technology in general and the belief

that it offers people increased control, flexibility, and efficiency. Innovativeness

represents a tendency to be a technology pioneer and opinion leader (Rogers, 2003).

Discomfort represents a sentiment of lack of control over technology and a feeling of

being overwhelmed by the new technology. Insecurity reflects mistrust of new

technology and scepticism about its ability to work properly.

Technology readiness is believed to influence the attitude toward use of a

specific technology, much like perceived ease of use and perceived usefulness, except

84

that technology readiness is a trait, and the other two are states. However, past

research findings Lin et al., (2007) suggested that perceived usefulness and perceived

ease of use together moderate the relationship between technology readiness and

someone's intention to use a technology, making it an even more distal variable,

putting it in line with the individual differences classification of variables.

TAM had a general "attitude towards technology acceptance" construct to

reflect change readiness, but it was removed in later research because it did not appear

to fully moderate the effects of perceived ease of use and perceived usefulness on

intention (Venkatesh and Davis, 1996). Research has consistently found, however,

that perceived ease of use and perceived usefulness directly predicted intentions to use

a variety of technologies (Marler, Fisher, and Ke, 2009). Some examinations of TAM

also excluded a general attitude construct (Venkatesh and Davis, 2000), though other

studies have included it (Chau and Hu, 2001). The inclination to leave out general

attitude may simply indicate that attitude toward technology acceptance is complex

and composed of multiple beliefs, two of which are perceived ease of use and

perceived usefulness, which are discussed in a later section of this literature review.

The widespread use of intention measures to predict adoption behaviour hinges

on the belief that intentions are accurate indicators of individuals’ behaviour (Young,

De Sarbo and Martin, 1998). Research in social psychology suggests that intention

measure should be among the best predictors of behaviour, because they allow each

individual to incorporate and appropriately balance all relevant factors that may

influence his or her actual behaviour (Ajzen, 2002: 2005).

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3.4.3 Beliefs Concerning Technology Acceptance

Just as the previous section on change recipient beliefs illustrated the importance of

beliefs in shaping attitudes toward organizational change, so too ICT acceptance

research supports the idea that beliefs shape an attitude toward ICT innovation (Davis,

1989; Davis et al., 1989; Venkatesh and Davis, 2000; Venkatesh and Bala, 2008). As

it applies to new technology, Rogers (2003, 223) pointed out that "the individuals'

perceptions of the attributes of an innovation, not the attributes as classified

objectively by experts or change agents, affect its rate of adoption”. The beliefs held

by individuals about the new technology strongly contribute to whether or not the

technology will be adopted.

ICT, IS and IT researchers (Venkatesh and Davis, 2000; Venkatesh and Bala,

2008) emphasise that far more scholarly effort is needed in identifying the

organizational and psychological mechanisms that influence ICT user beliefs and

attitudes. Technology Acceptance Model has been effective in predicting attitude

toward technology use through two beliefs across a wide variety of domains

(Thompson, Compeau and Higgins, 2006).

The two belief constructs that have been found to be extremely beneficial in

predicting attitudes toward using technology are ease of use (PEOU) and perceived

usefulness (PERUSE; Venkatesh, 2000). Venkatesh and Bala (2008) have suggested

that these two beliefs align to some degree to the two main classes of motivation,

intrinsic and extrinsic (Vallerand, 1997). Extrinsic motivation relates to the desire to

perform a behaviour in order to gain specific goals and rewards (Deci and Ryan,

1987), while intrinsic motivation relates to the perceptions of pleasure, and

satisfaction experienced from actually performing the behaviour (Vallerand, 1997).

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PEOU somewhat relates to intrinsic motivation, at least within the current

conceptualization of TAMII (Venkatesh and Davis, 2000), though the two are not

analogous. PERUSE more closely relates to extrinsic motivation and associated

instrumentality (Venkatesh and Davis, 2000). The research has largely been restricted

to features of the technology itself such as ease of use and perceived usefulness,

creating the need to examine other beliefs that contribute to technology acceptance

(Marler et al., 2009).

The basic premise for the inclusion of these two beliefs in the model of

technological change is that, if the new technology is easy to use and helps job

performance, individuals are more likely to have a positive attitude toward using the

technology (Davis et al., 1989). These two beliefs have been found to be important

determinants of technology use (Venkatesh and Bala, 2008). Because of the

prevalence of these two variables within the ICT acceptance literature, they are

examined within this dissertation.

3.4.3.1 Perceived Ease of Use “has everyone bought into making the change

happen”

Perceived ease of use (PEOU) has been defined as "the degree to which a person

believes that using a particular system would be free of effort" (Davis 1989, 320). The

construct reflects the amount of effort that would be required, relative to the people

perceived capabilities, in terms of being able to use the technology to accomplish the

intended functions.

A theoretical model put forth by Venkatesh (2000) found a number of control-

intrinsic motivation-related and emotion-related determinants for PEOU. Control was

divided into perceptions of internal control (computer self-efficacy) and perceptions of

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external control (facilitating conditions). Intrinsic motivation was conceptualized as

computer playfulness, while emotion was conceptualized as computer anxiety. Thus,

computer self-efficacy, facilitating conditions, computer playfulness, and computer

anxiety were system independent variables.

They were examined, and all of them were found to play a critical role in

shaping perceived ease of use beliefs related to the new system. The influence of these

determinants was reduced over time due to increasing experience with the system.

Venkatesh put forth that objective usability, perceptions of external control

(facilitating conditions) over system use, and perceived enjoyment would have a

stronger influence on perceived ease of use during continuance.

Findings concerning the relationships between PEOU and attitude toward

adoption and intention to use have proven inconsistent (Lee et al., 2003). PEOU has

shown a significant effect on perceived usefulness in many studies (Gyampah, 2004).

In fact, in a review by Venkatesh and Bala (2008), forty three out of fifty studies

revealed a significant relationship between PEOU and PERUSE. In one exception,

where PEOU had no effect on PERUSE, the users were physicians who differed from

many technology users in education, intellectual capacity, and independence,

suggesting that individual differences among technology users are important to

consider (Chau and Hu, 2002).

3.4.3.2 Perceived Usefulness “Is this the right change"

Perceived usefulness (PERUSE) has been defined as “the degree to which a person

believes that using a particular system would enhance his/her job performance” (Davis

1989, 320). The construct reflects an employee’s level of conviction that a particular

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system will increase their work performance (Davis et al., 1989). The relationship

between PEOU and PERUSE may be reduced over time (Szajna, 1996).

The quality of the output, particularly the more precise and up-to-date the

information provided, the greater the PERUSE. In addition, the greater the ease of

information accessibility, comprehension, and analysis, the greater the PERUSE

(Kraemer, Danziger, Dunkle, and King, 1993). Goodwin (1987) opined that perceived

usefulness depends on the usability and the counting use of the technology,

represented by PEOU. Mathieson (1991) and Szajna (1996) reported that PEOU

accounts for a significant portion of the variance in PERUSE. In TAM II, PERUSE’s

significant antecedents have included subjective norm, image, job relevance, output

quality, and result demonstrability (Venkatesh and Davis, 2000).

Similar constructs have been offered as outcome expectations in the Computer

Self-Efficacy model. Also, PERUSE matches the description of extrinsic motivation

in the Motivational Model. These models have produced similar findings, further

indicating that PERUSE plays an important role in forming a technology user’s

attitude and intentions regarding IT acceptance and continuance of use (Gyampah,

2004). Unlike PEOU, which has produced inconsistent findings, PERUSE has

consistently served as the best predictor of a user’s attitude toward IT usage,

especially during later stages of usage and the user become familiar with the system

(Venkatesh et al., 2003).

3.4.4 Organizational Change Recipients' Beliefs Technological Change-Related

Beliefs

Antoni (2004, 198) stated that "one has to change the beliefs of the organizational

members, which shape their behaviour, in order to support sustainable organizational

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change". In order to successfully execute a change initiative, change agents must

prepare change recipients for the challenges involved. In order to assess whether or

not change recipients are ready, specific beliefs that they have about the change can be

assessed as a reflection of their overall attitude of readiness for change (Piderit, 2000).

A belief is an opinion or a conviction about the truth of something. A belief may not

be readily obvious or subject to any form of systematic verification.

As it applies to organizational studies, any description of an organizational

outcome, event, or action that occurs is subject to being interpreted by organizational

members who will likely form one or more beliefs around what they perceived as a

result of sense-making. Several means of assessing readiness have been created which

measure specific beliefs. In a review of these instruments, Holt et al., (2007b)

confirmed through an examination of thirty-two different quantitative instruments that

more work was needed to improve the measurement of readiness for change.

Through their research on readiness for change, Gregory, Armenakis et al.,

(2007) developed a higher order construct that takes into account five interrelated

beliefs, or components, that capture the thoughts of change recipients. The five beliefs

are: efficacy, principal support, discrepancy, appreciation, and motivation valence.

They examined the literature and found 41 publications dating between 1948 and 2006

that included one or more of those beliefs. A similar instrument was produced by Holt

et al., (2007b), composed of four of the five beliefs, excluding discrepancy.

Armenakis et al., (1999) stated that change recipients' beliefs could be

measured at any time during a change initiative to gauge the level of readiness for

change. The information obtained from the assessment of these beliefs is stated to

serve as useful in revealing the degree of buy-in among change recipients and areas in

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which deficiencies in supportive beliefs exist that could negatively impact the change.

By assessing these beliefs, change agents can better plan and execute the activities that

follow during the change implementation process (Armenakis et al., 2007). In this

research, the researcher will address three of those five change beliefs: appreciation,

principal support and motivation valence.

3.4.4.1 Appreciation

Whenever employees are confronted with organizational change, they are likely to ask

themselves why the proposed change is the right one (Linden, 1997). Recognition of

the problem (discrepancy) does not mean that whatever solution is offered will be

accepted; the solution must be perceived as appropriate (Armenakis et at., 1999). The

concept of change agents presenting a vision, a sense of what will be accomplished

through the change, has been considered to be the communication of the appreciation

of the proposed change (Kotter, 2007). Brown, Massey, Montoya-Weiss and Burkman

(2002), in a study concerning the introduction of new technology within a financial

institution, found communication to be especially important in situations in which

change is mandatory. They found that several communication tools, including

testimonials, formation of user groups, and other means were necessary to gain

employee acceptance of the usefulness of the new technology.

Kissler (1991) posited that even if change recipients perceive a need for

change, they could disagree with the proposed change initiative. Kissler described an

organization in which supervisors, as change recipients, were told to use persuasion

rather than positional power to create a more participative environment that would

increase organizational effectiveness. Many supervisors did not agree with the

participative approach and did not support the change. This study illustrates that, if

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change recipients hold a different perception of the appreciation of the change, they

may oppose it (Rousseau and Tijoriwala, 1999).

Even if a change entails uncertainty and hardships, if there is legitimacy to the

change proposed and procedural justice in the decision-making, change recipients are

more likely to support the change, regardless of the favourableness of decision

outcomes (Korsgaard and Roberson, 1995). Also, if change recipients trust the change

agents, they are typically more supportive (Hultman, 1998). If the process by which a

change initiative is implemented is inconsistent with the reasons given for the change,

the change recipients are likely to view the change agents as untrustworthy (Kernan

and Hanges, 2002). This will negatively impact the outcome of the change and will

likely also affect future attempts to change as well.

When change initiatives in the past have failed or have been mishandled, it is

usually more difficult to implement change initiatives in the future. This becomes a

major issue when new change initiatives are attempted on a regular basis without any

strong commitment or follow-through (Beer, Eisenstadt and Spector, 1990). These

types of change initiatives become viewed as gimmicky “program of the month"

wastes of time, thus leading to cynicism and resistance (Armenakis et al., 1999).

As it relates more to the appreciation of technology, TAM II focused on

perceived usefulness. Inherently, the more useful a new technology appears to be to a

potential user, the more appropriate it is perceived as a likely solution. An

examination of potential correlates with perceived usefulness found job relevance,

result demonstrability, and output quality to be strongly related. Therefore, change

recipients found technology to be more useful when it served job-related duties,

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performed efficiently as a piece of technology, and improved information quality

(Venkatesh and Davis, 2000).

Appreciation has implications for ICT vendors as well. Given that many ICT

systems are predesigned, especially ERP systems, vendors must be very sensitive to

the needs and perceptions of ICT managers. As Benamati and Lederer (2008) noted,

ICT vendors must constantly reassess their strategies and efforts to solve the problems

involving ICT managers' perceptions of poor quality of the system, incompatibility

with other technologies, management confusion about what their products can deliver,

and the training demands. Because of vendor competitiveness, many ICT managers

are wary of vendor marketing claims and pressures to adopt their products. Often the

claims made by the vendors about their technology do not prove to be true. Some

vendors push products, before they are fully functional and error-free. In addition, the

level of support that vendors claim they will provide is not always met (Benamati and

Lederer, 2008).

In addition, management may not fully grasp the actual level of expertise

required for organizational members’ use of the technology effectively. As such, they

often underestimate the training required and the time that it will take in implementing

the new ICT (Venkatesh and Davis, 2000). Management must stay informed on the

products of many vendors, but even doing so, it still remains difficult to choose from

among them. Benamati and Lederer (2008) advised that management should focus on

demanding fewer errors and more truthfulness in information concerning new ICT.

3.4.4.2 Principal Support

Principal support reflects the support provided by change agents and opinion leaders.

There are two categories of change agents: global change agents, operating at the

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highest level in the organization, such as the CEO and top management team, and

local change agents, the immoderate supervisors and the opinion leaders (horizontal

change agents) who are enlisted. Principal support is especially important when past

change efforts have failed to achieve their intended goals. The past failures of top

managers in trying to conduct change initiatives can lead to scepticism on the part of

lower level employees about whether the current change will succeed. Armenakis et

al., (1999, 103) described principal support as a means by which to "provide

information and convince organizational members that the formal and informal

leaders are committed to successful implementation . . . of the change”.

Organizational members prefer dependable and consistent job functions, as

well as predictable relationships with top leaders, supervisors, and co-workers

(Bernerth, 2004). When they find themselves in the midst of change events, they often

engage in sense-making (Weick, 1995) by gathering information from sources that

they believe are credible, and by comparing their own past experiences with their

observations of present events. They sense nonverbal cues and explicit information in

formulating beliefs about the change. Co-workers and others within the organizational

community are looked to for meaning, and to provide guidance in how to respond to

the change event (Mossholder, Settoon, Armenakis, and Harris, 2000). According to

social learning theory (Bandura, 1986), employees sense the support that is available

throughout the organization through their interpersonal networks.

Useful sense-making information often takes the form of perceptions of

whether or not change agents demonstrate behavioural integrity through the alignment

of their words and deeds (Simons, 2002); in other words, whether or not they "walk

the talk" when it comes to supporting the change themselves. If there is a disparity

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between what change agents say and what they do, and change recipients perceive that

principal support is not sufficiently demonstrated, the change recipients may not

support the change initiative either.

Top leaders, change initiatives are typically initiated from the top leader.

According to Shaw (1995, 70), during a change initiative involving a radical

transformation, a CEO must hold". . . A deep conviction that the change must occur

“in order for it to succeed, and the senior-management team should ". . . Collectively

assume responsibility for the change initiative's success". The importance of buy-in,

support, and commitment by top management has been noted in several studies that

pointed out that failure to bring key partners onboard in implementing a change

initiative can doom it to failure before it begins (Kotter, 2007).

Relevant to this point is an example from a study conducted by Kotter (2007)

involving a large domestic bank. Top management failed to put together a powerful

guiding coalition to support a proposed change initiative and because several

managers were not directly involved in the process, the change initiative failed. Going

even further, Kotter offered an example of a high-ranking executive in one

organization who actively prevented a proposed change from succeeding simply

because the executive did not believe that the change was necessary.

The behaviours of leaders serve as powerful communicators of how other

organizational members should behave. The responses of senior management to the

change help shape lower level employees' beliefs about the change. In addition, trust

in leaders can often compensate for a lack of information, and can reduce the

speculation and reservations related to uncertainty (Weber, P. and Weber, J. 2001).

Covin and Kilmann (1990) noted in one study that visible top-management support

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and commitment led to positive perceptions of a change initiative. Conversely, a lack

of visible management support and commitment foster negative perceptions.

Supervisors, researchers have noted that when there is a high quality leader-

member exchange (Van den Bos, Wilke and Lind, 1998) or more trust in a supervisor

(Naumann, Bennett, Bies, and Martin, 1998), employees are likely to view

organizational efforts in a more optimistic way. Most employees view supervisors as

important referents because of their power to reward behaviours or punish non-

behaviour (Warshaw, 1980). For instance, pressure put on employees by supervisors

has been found to be positively related to the adoption of new technology (Marler et

al., 2009).

In addition, change recipients who receive supervisory support and

encouragement are more likely to voluntarily support a change initiative. Larkin and

Larkin (1994, 85) opined that frontline supervisors are the most important change

agents when it comes to getting change recipients to embrace a change initiative,

noting, "Programs don't change workers supervisors do". They found that, when a

change initiative is introduced, all too often top managements assume that simply

delivering the change message and publicizing it throughout the organization is

enough for the change to succeed. Top management assumes that the change

recipients will understand and fall into line by accepting the change. Larkin and

Larkin (1994) noted, however, that the supervisors were the ones who change

recipients turned to in seeking advice and information to understand the change. They

also noted that the supervisors could be as unaware of the reasons for the change as

the subordinate.

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Co-workers, employees place a lot of importance on the how their peers

perceive organizational events, and the sense of community among co-workers has

been found to buffer negative emotions related to feelings of inequity in the workplace

(Truchot and Deregard, 2001). Similar findings have been reported by Rousseau and

Tijoriwala (1999) in their study of a change initiative at a hospital concerning a shared

governance initiative; they found that, while many of the staffs did not trust top

management, they were responsive to the opinions of their peers. Nurse leaders made

speeches, prepared and distributed memos, and made informal contacts to demonstrate

their support for the change.

Perceived support the concept of principal support is not the same as perceived

support, but the two are somewhat similar in domain, and an examination of perceived

support provides some theoretical basis for the types of relationships that could be

related to principal support. Several studies suggest that organizational change is often

more successful when employees believe that they are being supported by the

organization (Schalk, Campbell and Freese 1998). When employees do not feel that

they are receiving organizational support, and when they think that the decision

making process concerning whether to engage in the change and how the change

would be conducted was unfair, they may believe that their loyalty to the organization

has been misplaced and withdraw emotionally as a result (Mossholder et al., 2000).

Perceived organizational support has been defined as an employee’s "global

beliefs concerning the extent to which the organization values their contributions and

cares about their well being" (Rhoades and Eisenberger, 2002, 698). When employees

believe that they are being treated well by their organization, according to the norm of

reciprocity (Gouldner, 1960), they are more likely to adopt a positive attitude and

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reciprocate the support of the organization by engaging in behaviours that benefit the

organization. As such, a high level of perceived organizational support leads an

employee to favor obligations and opportunities that help the organization achieve its

goals (Eisenberger, Armeli, Rexwinkel, Lynch, and Rhoades, 2001). An employee's

perceived support -related motivation to comply with their organization may also be

associated with a belief that, in exchange for current efforts by employees, the

organization will reward them in the future (Marler et al., 2009).

Principal support and technology implementation, as it relates to IS research,

Karahanna et al., (1999) found that top management, supervisors, and friends play the

greatest role as social influences on employees adopting a new technology. Top

management, co-workers, and local ICT specialists were found to be the strongest

source of encouragement on employees who were currently using the technology. The

MIS department, in particular was also found to socially influence organizational

members as it relates to both adopting and continuing to use a new change.

Managers at all organizational levels (i.e., direct supervisors, middle

management, and top management) are considered vital sources of interventions

(Jasperson, Carter and Zmud, 2005). Management can intervene by providing

resources, sponsoring or championing the ICT change, and issuing directives and

mandates. It can also intervene more directly by using features of ICT, by directing

modification or enhancement of ICT applications, through incentive structures, and

through work tasks/processes in the implementation process of an ICT (Jasperson et

al., 2005).

In addition to inclusion of principal support as a change recipient belief within

the MROC, a similar conceptualization of management support has been included

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within TAM III: "Management support refers to the degree to which an individual

believes that management has committed to the successful implementation and use of

a system" (Venkatesh and Bala, 2008, 296). Top management support within the ICT

literature has been the focus of many studies as an antecedent to implementation

success (Liang, Saraf, Hu and Xue, 2007; Somers and Nelson, 2001). However, this

support was not conceptualized as an intervention strategy that could influence user

acceptance (Venkatesh and Bala, 2008).

In ICT implementations, sense-making often takes place due to the fact that

such change initiatives require substantial changes to organizational structure, change

recipients' roles and duties, reward systems, control and coordination mechanisms,

and work processes. Commitment provided by top management, and supportive

communication related to system implementation, are crucial in making the change

legitimate and maintaining employee morale throughout the institutionalization phase

of the change (Venkatesh and Bala, 2008).

Researchers believed that top management support influences organizational

members' perceptions of subjective norm and image, which are considered two

important determinants of perceived usefulness (Jasperson et al., 2005). ICT literature

also suggests that direct involvement in the development and implementation process

helps employees form judgments concerning the job relevance, output quality, and

result demonstrability of the ICT system. Direct involvement by management in

modifying the system features, work processes, and incentive structures is believed to

help reduce anxiety related to the use of the new ICT system, and is believed to

influence perceived ease of use (Marler et al., 2009).

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One finding from the ICT acceptance literature that could have some

implications within the domain of organizational change is that, over time, the impact

of social influences wanes because employees who adopted a new technology develop

their own opinions through their own use of the new technology (Karahanna et al.,

1999). In a study involving the use of the Windows operating system, social pressure

was found to be an effective mechanism in overcoming initial resistance to adopting

new ICT (Agarwal and Prasad, 1999). However, during post-implementation, it did

not have a significant relationship with intention to continue using Windows

(Karahanna et al., 1999).

Venkatesh and Bala (2008, 297) state there has been a call within the ICT

adoption literature for a better understanding of the role of principal support as it

relates to technology acceptance and continuance.

While top management support has been conceptualized and operationalized as

organizational mandate and compliance, particularly in the individual-level ICT

adoption literature, we suggest that there is a need to develop a richer

conceptualization of management support to enhance our understanding of its role in

ICT adoption contexts. We suggest that social network theory and analysis, and

leader-member exchange theory can be used to understand the influence of

management support in ICT adoption and use. Social network analysis can help

pinpoint the mechanisms through which management support can influence the

determinants of perceived usefulness and perceived ease of use.

3.4.4.3 Motivation Valence

Motivation valence corresponds to the cost-benefit appraisal process through which a

change recipient evaluates a proposed change effort in terms of potential personal

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gains and losses of organizational benefits. Even if top management convinces

employees of the necessity of supporting a change initiative for the organization to

remain successful, they still may wonder, "What is in it for me?" If they can see no

gain for their efforts, they are likely to resist the change (Boardman and Sundquist,

2009).

Motivation valence represents the extent to which the change is perceived as

beneficial versus detrimental to him/her (Voorm, 1964). The attractiveness (from the

change recipient's perspective) associated with the perceived outcome of the change

constitutes the ''rational'' component of resistance. Scholars have pointed to motivation

valence as perhaps the most valid reason to resist change (Dent and Goldberg, 1999).

Research has found that an employee's perceptions of the value of possible outcomes

can strongly impact his/her overall evaluation of whether or not to support the

decision (Duncan and Zaltman, 1977). According to Vroom's (1964) motivation

valence is one of the primary determinants of whether a change recipient will accept

or resist change.

Motivation valence-related perceptions can be segmented into two categories:

extrinsic and intrinsic. Extrinsic motivation valence refers to gain through financial

and other equally tangible rewards and benefits that will be derived from adopting a

new behaviour. Incentive systems, such as gain sharing, pay for performance, and so

forth can contribute through extrinsic benefits to motivation valence perceptions, thus

influencing change outcomes (Bullock and Tubbs, 1990). Intrinsic motivation valence

refers to self-actualization gains derived in the form of cognitive and affective

satisfaction with the process or outcomes of participating in the activity, and other,

fewer tangible rewards. Bandura (1986) stressed the value of intrinsic motivation

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valence to organizational change efforts. Also, Morse and Reimer (1956) noted that

organizational change can provide an intrinsic reward in the form of decision-making

control.

Change recipients are more likely to "buy in" to a change initiative when the

consequences of the proposed change are more easily identified as personally

beneficial, and unless benefits are seen early, change recipients are likely to anticipate

that personal losses will result from the change rather than gain (Rousseau and

Tijoriwala, 1999). In turn, research has found that when employees believe that they

will suffer losses of organizational benefits in a change situation, they will also

question the legitimacy of the change and the intentions of management, thereby

jeopardizing the entire employment relationship (Korsgaard, Sapienza and Schweiger,

2002).

Asking a change recipient to embrace a change that may cost his/her job or

cause a loss in status will result in a less than the enthusiastic response. The

importance of the relationship between motivation valence and distributive justice

should not be overlooked. Change events will likely result in the redistribution of

organizational resources, power, prestige, responsibilities, and rewards (Cobb, Helliar

and Innes, 2002). Change recipients will be concerned about this redistribution and

negative motivation valence, and many who are impacted negatively may view the

change initiative as unfair. This negative perception can lead to feelings of anger,

outrage, and resentment (Skarlicki and Folger, 1997).

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3.4.5 Moderators

Ajzen and Fishbein (1975) confirmed the influence of the subjective norm on the

individual attitude behaviour. Furthermore, Agarwal and Prasad (1997) confirmed the

effect of volunteer motivation on the attitude behaviour.

3.4.5.1 Subjective Norm

According to Ajzen and Fishbein (1975) the antecedent most closely related to social

pressure is the subjective norm. Subjective norm reflects the individual’s perception of

social support or opposition to his performance of the behaviour (Ajzen and Fishbein,

1975). Subjective norms have two components: normative beliefs and motivation.

Normative beliefs are the individual’s perceptions that certain people want them to

perform the behaviour. The individual’s compliance represents the relative importance

of the referent person to the individual. This element of intention to use is determined

by the extent to which the individual believes others who are considered significant

the individual’s desire to comply with the wishes and desires of those significant

others who desire the behaviour.

Lin and Lee (2004) conducted a study that evaluated the social pressure

produced by top managers to give confidence or discourage knowledge sharing

conducts. Higher-ranking managers were more likely to be influenced in deciding

whether to encourage knowledge sharing behaviour by peer opinions or suggestions.

The study’s results demonstrated that the main determinants of company-wide

knowledge sharing behaviour were through the encouragement of senior managers.

Their attitudes, subject norms, and perceived behavioural control positively influenced

intentions to encourage knowledge sharing.

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Individual demographics appear to be significant when the sample is

homogenous and insignificant when the sample is heterogeneous. Organizations that

define themselves as public service providers are more likely to behave in accordance

with the norms established by the GOs. Therefore, self-image is added to the model as

an individual characteristic that directly influences the local organization attitude.

Social norms, as it relates to e-government, means local organization can feel

pressure from the head office to adopt technology that supports a particular technical

initiative. Similarly, employees of the companies with the adopted technology would

be expected to use the technology in order to improve efficiencies in the local agency.

Quaddus, Xu and Hoque, (2005) in their study on the factors of adoption of online

auction by consumers in China, confirmed the significant impact of the subjective

norm on using a Web site.

According to Karahanna et al., (1999) the key constructs for the decision

process to adopt technology are the technology’s perceived attributes, the individual’s

attitude and beliefs, and communications received by the individual from his

peer/social environment about the technology. The peer/social environment, or

subjective norm, refers to the individual’s perception of social pressure to adopt the

technology. Part of their study examined the behaviour of potential adopters and users

of technology and determined that the normative component is the dominant

characteristic that determines intention to adopt e-business technology. The finding

suggests that social pressures from an organizational environment may be an effective

mechanism to overcome adopter initial inertia in adopting e-government. Social

norms only encourage initial technology adoption while continuous usage decisions

are based solely on attitudinal considerations.

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3.4.5.2 Perceived Voluntariness

Li (2004) discusses ICT adoption from the effects of the group. Herding occurs when

an organization adopts an ICT technology based on a “me too” attitude. In many

cases, the adoption of technology is in response to not being left behind, the “herding

effect”. The herding effect results when the first bureau adopts a technology and

subsequent users adopt the technology in order to minimize the risk of choosing an

alternative technology. In situations of incompatible information about technologies,

committing to a technology is more advantageous to the agency earlier rather than

later, due to the commitment power when the choice is irreversible (Choi, 1997).

This herding behaviour may appear because of information flow, which occurs

when logical persons pay no attention to their confidential information and instead

follow the behaviour of preceding decision makers (Li, 2004). In addition to

informational cascading, Li (2004) also notes that positive network feedback can

cause leading technology to grow more dominant. They usually result in positive

network effect that creates an ICT adopter’s return positively linked with the number

of adopters who have already committed themselves to the same technology.

Therefore, herding is rewarded by increasing the payoffs of those ICT adopters who

associated themselves with the majority.

Coltman, Devinney, Latukefu and Midgley (2002) note that social

requirements still govern technology and current efforts to virtualized commerce and

business exchanges are noteworthy but have not been sufficiently pervasive or process

oriented to consider e-business pervasive. E-business is part of a broad, more

historically pervasive movement. Clearer distinctions must be made between the

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internet’s influence on the cost of conducting specific transactions and where it has

transformed the fundamentals of transactions themselves.

Elgarah, Falaleeva, Saunders, Ilie, Shim, and Courtney (2005) reviewed several

studies on the factors that lead to better data exchange partnerships. Their review

concentrated on issues directly related to inter-organizational relationships in a wide

range of countries. There are several reasons for organizations to adopt and implement

using data exchange through ICT. Efficiency was mentioned as one motive for the

adoption and use of data exchanges in a majority of the studies. Operational cost

saving was the underlying factor in almost all decisions to adopt and continued use of

Electronic data interchange [EDI].

Perceived voluntariness has been recognized as an instrumental factor in

innovation diffusion literature. Agarwal and Prasad (1997) defined perceived

voluntariness as “the extent to which potential adopters perceive the adoption decision

to be non-mandated”. The existence of external pressure is recognized in TRA (Ajzen

and Fishbein, 1980). They recognized that subjective norm is included as a

determinant of intention to use an innovation. However, in TAM, the relationship

between social pressure and intention to use is not explicitly included. In TRA, it is

not clear that empirical results are related to the influence of the subjective norm. The

Moore and Benbasat (1991) study demonstrated the influence of perceived

voluntariness on acceptance behaviour (Agarwal and Prasad, 1999).

The internet, intranet, and other groupware facilitate information storage and

sharing. Data warehousing and data mining techniques are known to be helpful in

structuring data (Rowley, 2002). The development of cost effective communication

and coordination capabilities has played a significant role in making inter-firm

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collaboration for the production of complex systems composed of components and

sub-assemblies.

Information and communication technology infrastructures are among the

many tools of knowledge management (Moffat and Archer, 2004). Moffat and Archer

also note, “The commitment of management resources for inter-organizational

information and communication systems (IOICS) and knowledge management in the

venture then operates as a mediating factor between the venture and performance”.

3.4.6 Other Factors That Affect Intention to Use ICT

Change has always been considered a necessary aspect that defines the development

of organizations. In fact, change management is a role requirement of management in

organizations. However, transition to change is not always as easy as the

organizational development necessitates. Resistance from employees or part of the

management is always experienced considering that change brings with it the

acquisition of newer tasks, and roles or a varied organizational structure (Chonko,

2004). Furthermore, there is the aspect of new technology that seems to challenge the

skills knowledge, expertise, and roles of the existing employees. Nevertheless, change

in organizations has been mandated by the pressures in the current organization

environment. There are external pressures of competition, political and economic

factors. Additionally, the nature of work in the organization, the current organizational

performance, the organizational structure, and kind of leadership influence the

readiness of the organization to change (Burke and Litwin, 1992).

Spitzer (1995) said that work is much more than just a single task or even a

series to tasks. It is made up of a large number of other elements, including co-

workers, managers, customers, the physical environment, rules, work nature, training,

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and evaluation which he calls contextual factors. Organizational readiness to change is

determined by the current performance, and other factors that influence the urge to

change for the sake of increased performance. The current organization environment

faces hyper-competition, advances in technology, changing nature of work and

leadership styles among other factors. Therefore, change is necessary, and factors that

affect the organizational readiness for change need to be investigated.

3.4.6.1 Nature of Work

As technology advances, organizations adopt newer tasks. This can result to the

change of the nature of work in the organization. For instance, tasks that were done

manually are now done automatically with the aid of machines. Most organizations

increase the readiness to change because the nature of work is changing (Lee, Rainey,

and Chun, 2009; Reid et al., 2008; Kim, 2005).

Madsen et al., (2005) define change as a transition from a stage to another, and

that existing structures are broken down to create new ones. According to Armenakis,

et al., (1993), there are certain workplace and individual features which, may lead to

the development of positive behaviours and attitudes for the organizational readiness

for change. The authors assert that organizational readiness for change is a

multifaceted as well as a multilevel construct. At the organizational multilevel

construct, organizational readiness for change results from organizational members

who share the same belief on resolution for change, and the capability to do so

(Desplaces, 2005). Organizational readiness for change depends on the level at which

the organizational members perceive the value of the change, and how they appraise

the implementation capability, such as availability of resources, task demands, and

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situational factors. Therefore, the commitment to change and the change efficacy

determines the ease at which the readiness occurs.

One of the most significant current discussions in commitment to change is

work-related attitudes and behaviours. Perceived risk and habit is an important

component in the resistance to user resistance to a new technology (AL-Aadwani,

2001). In addition, Mowday, Steers, and Porter (1979) argue that a relationship exists

between job nature and affective organization commitment, defined as an employee’s

desire to remain attached to an organization and work to help accomplish its goal

(Mowday et al., 1979, 225).

3.4.6.2 Training

As already discussed, technological advancements lead to the need for change in the

organization. Technology also influences the nature of work, for instance, from

manual to automatic tasks. Adopting new technologies is also mandatory for improved

performance and retaining a competitive advantage for the organization (Lan and

Cayer, 1994). However, the organizational readiness for change will depend on first,

the availability of resources to adapt new technologies; and second the employees’

ability to coexist with the introduced technology (Chonko, 2004). New technologies

necessitate the need for knowledge, skills, and expertise on how to use them. When

employees are unfamiliar with the new technology, intimidation may occur, and hence

resistance towards change.

The organizational readiness to change can therefore be achieved depending on

how the organizational people are introduced to the new technology. This requires

training, which not only acts as an instrument towards empowering the people with

the knowledge and skills for the technology, but also motivates employees in their

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work. Training is one way in which the needs of personal development for the

employees are met, and in turn benefits the organization with improved performance.

Training therefore increase the organizational readiness for change, as the employees

are empowered towards the acquisition of newer tasks (Davis and Bostrom, 1993). On

the other hand, lack of training reduces the organizational readiness for change and

instead increases resistance to change. Resistance to change in organizations at the

moment is associated with reduced business development. It has been suggested that

commitment to change is dependent of the job redesign and empowerment (Mishra

and Spreitzer, 1998).

3.4.6.3 Current Usage

Organizational readiness to change is determined by the current performance, and

other factors that influence the urge to change for the sake of increased performance.

The current organization environment faces competition, advances in technology,

changing nature of work and leadership styles among other factors. Therefore, change

is necessary, and factors that improve the organizational readiness for change need to

be enhanced. The most important indicator of achievement is organizational

performance (Cameron, 1986; and Cameron and Whetten, 1996).

The discrepancy between the desired and the current performance levels can

trigger the call for change in the organizations. Specifically, if the current performance

is faced with perceived dissatisfaction, the organizational construct suggests that

changes occur in the organization (Chonko, 2004). Alternatively, the leaders can

create an appealing visionary of the organization state of affairs in the future can

increase the perception of how urgently the change is needed and thus the readiness

for change. Despite the level of current performance, there is always the urge to

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improve the performance further, and thus keep the organization at a competitive

advantage (O'Toole and Meier, 2008).

For performance improvement, a lot of key factors come into play, for instance

the leadership style, the motivation that employees get, and how the goals and values

of the organizational culture are implemented. This means that dissatisfaction in

performance is greatly contributed by the perception that the employees have towards

the organizational structure, and the management models. Supervisory models

influence the effort that the employees are willing to put in the work for the benefit of

the organization in terms of performance. Dissatisfaction in performance also results

from external factors such as competition. The biggest problem often facing public

organization when it comes to evaluation knows what to evaluate. It is much more

important to measure outcomes rather than inputs or outputs (Salem, 2003). Winslow

and Bramer (1994) state a model for human performance is shown where optimum

performance lies in the middle of three intersecting circles of ability, context, and

motivation. A considerable study done by Burke and Litwin (1992) showed a strong

relationship between performance and organization change, moreover, have argued

that numerous studies (Hackman and Oldham, 1980; Guzzo, Sette, and Katzell, 1985)

have attempted to explain the impact of reword, nature of work individual needs and

values on motivation and job satisfaction on the work performance and organization

change.

Organization can make contact with a client electronically right the way

through the ICT applications (Straub, 2009) which reveal effectiveness. In addition,

Straub (2009) believes a skilled worker can assess the value of ICT. It can moderate

the work setting, which, brings out positive change in the employee intention to use

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and ICT acceptance. More importantly, worker’s ICT efficiency is determined by past

knowledge, trialability, social influence, and affective encouragement. Therefore,

employee’s ICT self-efficacy, is unpredictable, could be enhanced through

involvement and throughout effective training (Kimmel and Kilbridge, 1991).

The satisfaction on the new tool depends on the performance of this new

instrument (Ptricio, Fisk, and Cunha, 2003). A study done by Floh and Treiblmaier

(2006) identified that satisfaction, represented by the management performance is a

very important attribute of technology adoption.

3.5 SUMMARY OF THE CHAPTER

This chapter reviewed literatures on previous technology acceptance researches

conducted in different countries. This chapter also embodied the importance of

Technology acceptance, definitions of the variables. The conceptual framework is

developed on the technology acceptance model. The next chapter describes how the

research is conducted by discussing the development of hypothesises and the

methodology employed to fulfil the objectives of the study.

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4 CHAPTER FOUR

RESEARCH METHODOLOGY

The descriptive design is undertaken to better understand workers behaviours and to

accept and adopt “continue to use” ICT. This chapter includes the research design,

sampling, data collection, the instrument and the statistical methods used for the data

analysis.

The research concentrated primarily on the design of the research, which is

based on the positivistic model with quantitative methodology in collection and

analysis of the data. These comprise survey questionnaires, soundness and strength

and the reliability of measures. Then, data analysis will be discussed.

The research is designed to test the twelve variables. Four attributes of Holt et

al., (2007a) Model of Readiness for Organizational Change, Appreciation, Principal

support, Motivation valance, and Attitude to change in addition to the current Usage

(actual). The study model combined variables from Holt et al., (2007a) Model of

Readiness for Organizational Change (MROC), Venkatesh, and Bala’s (2008)

technology acceptance model III. In addition to the four attributes of Holt s’ model the

instrument considered the factors developed by Venkatesh and Bala (2008)

technology acceptance model III, Perceived usefulness, Perceived ease of use and the

four moderators Training and Subjective norms in addition to Nature of work. And

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Antecedents of Belief

Tec

hn

olo

gy

user

believ

es

Moderating Factors

Appreciation

Motivation

Valence

Principal Support

Perceived

Usefulness

Perceived

Ease of Use

Current Usage

Attitude to Change

Intention to Use

Subjective

Norm

Perceived

Voluntariness

H3

H5a

H1

H2

H7a

H7

H6 H6

a

H5

H1b

H4a

H4

H1a

Training

Work

H2b

H2a

Moore and Benbasat (1991) Voluntariness motivation of use has been added, which is

defined as “the degree to which use of the innovation is perceived as being voluntary

or free will”.

Figure 4.1 The Reasearch Model

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The research model variables construct based on the relationships among the

independent variables and the single dependent variable intention to use according to

Ajzen and Fishbein (1975, 12); intention to use is the degree to which a person has

formulated conscious plans to perform or not perform some specified future

behaviour.

4.1 THE RESEARCH MODEL

Sekaran and Bougie (2010) said that the research model is important description of the

linkage of association of information needed be verified and it develops the

components and represents the relationship between the research components. In

addition, Musa (2004) asserted that the research must become experts in exploring and

recognising the gab in the existing literature, and consequently formulating the

theoretical framework, which explains the relationships between the variables that are

predicted toward the analysis of the research context.

Figure 4.1 shows the theoretical model, the relationships between the variables.

The intention of this study is devoted to forecast the workers’ behaviour in carrying

out the execution of technology in government organizations in Saudi Arabia. This is

done through the model by introducing informal channels among the variables of

study. In this regard, the assumption behind the ontology has been established to be

objective, observable, and measurable in nature. It was shown by Hussey and Hussey

(1997) that the method focuses on the literature review, which falls in areas of

positivistic paradigm, because it helps in arriving at the right formulation of theory

and hypotheses. Therefore, it follows that the theories or models, and hypotheses put

forward in this current study stem from the literatures which are examinable and

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assessable through the application of appropriate statistical analysis as shown in

(Figure 4-2).

4.2 THE VARIABLES

The widespread use of intention measures to predict adoption behaviour hinges on the

belief that intentions are accurate indicators of individuals’ behaviour (De Sarbo,

Morwitz and Martin, 1998). Research in social psychology suggests that intention

measure should be among the best predictors of behaviour, because they allow each

individual to incorporate and appropriately balance all relevant factors that may

influence his or her actual behaviour (Ajzen, 2002).

4.2.1 Dependent Variable

4.2.1.1 Intention to use

Intention to use is the degree to which a person has formulated conscious plans to

perform or not perform some specified future behaviour (Davis, 1989: 214). Intention

concerning the use of technology is composed of beliefs about the following twelve

factors. Theory of planned behaviour is significant for the understanding of these

variables. Theory of planned behaviour specifies the natures of relation between belief

and attitude. Individuals’ evaluations of attitudes towards behaviour usually are

determined by accessible beliefs about behaviour, Mischel, (1968).

4.2.2 Independent Variables

4.2.2.1 Perceived Ease of Use

Perceived ease of use has been defined as "the degree to which a person believes that

using a particular system would be free of effort" (Davis 1989: 320). Perceive ease of

use is the tendency to which people believe that the use of a certain type of system

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would enhance the ease of their job performance, (Davis, 1989). With this belief, there

is a favourable characteristic towards the use of this certain criteria that affects one job

performance positively.

4.2.2.2 Perceived Usefulness

Perceived usefulness has been defined as “the degree to which a person believes that

using a particular system would enhance his/her job performance” (Davis 1989: 320).

Perceive usefulness helps to determine the reason why people in an organization

accepts or rejects information technology. Davis (1989) described perceive usefulness

as the way individuals trust or rather believe that certain information system or

innovation will make them achieve their goals with ease or without effort.


Principal support reflects the support provided by change agents and opinion leaders.

Armenakis et al., (1999, 103) defined principal support as a means by which to

"provide information and convince organizational members that the formal and

informal leaders are committed to successful implementation . . . of the change”.

Principal support was consistently identified as the most important and crucial success

factor in ICT implementation projects (Bancroft, Seip and Sprengel, 1996). Principal

support takes into consideration the value at which organization members support the

new information technology. Looking at the modern world, there is a great influence

of ICT.

Welti (1999) suggested that active top management is important to provide

enough resources, fast decisions, and support the acceptance of the project throughout

the company. Jarrar, Al-Mudimigh and Zairi (2000) pointed out that the top

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management support and commitment does not end with initiation and facilitation, but

must extend to the full implementation of an ICT. They should continually monitor

the progress of the project and provide direction to the implementation teams (Bingi,

Sharma and Godla 1999).

4.2.2.4 Motivation Valance

Motivation valence corresponds to the cost-benefit appraisal process through which a

change recipient evaluates a proposed change effort in terms of potential personal

gains and losses of organizational benefits (Deci et al., 1994). Valance motivation

emphasizes on the importance of individual’s perception and assessment of

organizational behaviour. This may require that what the employees perceive to be the

best motivator to their performance will as well be the manager’s perception. This also

becomes tricky because the management expectation will sometimes differ from the

employees’ expectations. In order for the management to make the employees have a

positive valance, there should be a point of agreement between the organization

manager and the employee (Poter and Lawler, 1968).

4.2.2.5 Commitment to Change

Whenever employees are confronted with organizational change, they are likely to ask

themselves why the proposed change is the right one (Linden, 1997). Herscovitch and

Meyer (2002, 475) defined commitment to change as a "force (mind-set) that binds an

individual to a course of action deemed necessary for the successful implementation

of a change initiative. Within this dissertation, affective commitment to organizational

change is included as an outcome variable. Related hypotheses are offered later in the

literature review.

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Cooke and Peterson (1998) identified commitment to management, in terms of

adopting and continue to use of ICT, as activities, processes, and methodologies that

support employee understanding and organisational shifts during the implementation of

ICT and reengineering initiatives.


Appreciation is the increasing value of the use of information communication

technology. Whenever employees are confronted with organizational change, they are

likely to ask themselves why the proposed change is the right one (Linden, 1997).

With the principal appreciation in ICT especially in business world, it has a positive

influence. Many business organizations have tried their best to incorporate ICT in

their daily business activities in order to increase productivity (Maio-Taddeo, 2006).


The principal of current usage suggests that ICT is currently in use. This shows the

appreciating value of the information communication technology within organizations

and the employees. “Performance is referred to as being about doing the work, as well

as being about the results achieved. It can be defined as the outcomes of work because

they provide the strongest linkage to the strategic goals of an organization, customer

satisfaction, and economic contributions” (Salem, 2003: 1).

4.2.3 Moderator variables


Person's perception that most people who are important to him think he should or

should not perform the behaviour in question (Ajzen and Fishbein, 1975). Normative

belief is an individual’s belief about the extent to which other people that are

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important to him/her think they should or not perform particular behaviour. With this,

people become conscious of what they are doing in the organization in connection to

what others will say (Ajzen and Fishbein, 1975).


Perceived Voluntariness of use is the degree to which use of the innovation is

perceived as being voluntary, or free will (Hebert and Benbasat, 1994). Volunteer is a

term showing an activity. A volunteer is a person that takes up a certain responsibility

on behalf of others with no intention to receive anything in return. This means

assuming someone’s responsibility without any pay (Cherry, 2011).

4.2.3.3 Training

It refers to an interrelated set of variables that organizations should consider as part of

their overall technology program (Vesset and McDonough, 2009: 6). In order for the

organization to continue with the effective use of ICT, there is a need for serious

training of employees on ICT. The training of the employees on ICT makes them take

it positively and apply it in all aspects of the business.

4.2.3.4 Nature of Work

Burke and Litwin (1992) have argued that there is a strong relationship between

nature of work individual needs and values on motivation and job satisfaction on the

work performance and organization change. Moreover, a UN (2005) reported the

relationship between the type of work a person does and ICT usage, the relationship

outcome depends on the benefit of the usage.

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4.3 THE HYPOTHESES

Researchers have investigated various aspects of the ICT usage phenomenon and have

come into factors which impact individuals’ behavioural responses to use of it. On the

other hand, defence mechanisms are generally used instinctively by a person in

reaction to divine hazard (Andrews, Singh, and Bond, 1993; Oldham and Kleiner,

1990), therefore, attitude to change in relation to intention to use was hypothesized

that:

Hypothesis 1: Attitude to change negatively and directly influences intention to

use.

Subjective Norm in relation to an innovation was hypothesized to influence

significantly the user’s behavioural intent to adopt the innovation. Therefore, it was

hypothesized that:

Hypothesis 1a: Subjective Norms moderate the relationship between attitude to

change and intention to use.

Perceived voluntariness towards an innovation was hypothesized to influence

significantly the user’s intention to accept and use that innovation. Therefore, it was

hypothesized that:

Hypothesis 1b: Perceived voluntariness moderates the relationship between

attitude to change and intention to use.

The satisfaction on the new tool depends on the usage of this new instrument (Ptricio

et al., 2003). In a study done by Floh and Treiblmaier (2006) satisfaction was

identified which represented the management performance as a very important

121

attribute of technology adoption. Chia-Ling, David, Kai-Chun, and Wu (2010)

claimed the influence of current performance on the change process. Therefore, it was

hypothesized that:

Hypothesis 2: Current usage positively and directly mediates the attitude to

change.

Madsen et al., (2005) define change as a transition from a stage to another, and that

existing structures are broken down to create new ones. According to Armenakis, et

al., (1993) there are certain workplace and individual features, which may lead to the

development of positive behaviours and attitudes for the organizational readiness for

change. The authors assert that organizational readiness for change is a multifaceted

as well as a multilevel construct. At the organizational multilevel construct,

organizational readiness for change results from organizational members who share

the same belief on resolution for change, and the capability to do so (Desplaces,

2005).

Hypothesis 2a: The nature of work moderates the relationship between current

usage and attitude to change.

Management may not fully grasp the actual level of expertise required for

organizational members’ use of the technology effectively. As such, they often

underestimate the training required and the time that it will take in implementing the

new ICT (Venkatesh and Davis, 2000).

Hypothesis 2b: Training moderates the relationship between current usage and

attitude to change.

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Perceived usefulness is defined as the degree to which a person believes that using a

particular technology will ease his or her job performance (Davis et al., 1989).

Hypothesis 3: Perceived Usefulness positively and directly influences Current

usage of technology.

Perceived ease of use has shown a significant effect on perceived usefulness in many

studies (Gyampah, 2004). People tend to use or not to use an application, to the extent

that they believe it will enhance their job usage and performance (Davis et al., 1989).

Hypothesis 4: Perceived ease of use positively and directly influences Perceived

usefulness.

Researchers have previously argued that a positive and favourable view toward

organizational change, based on the extent to which employees believe that a change

is likely to contain positive and beneficial implications for them, and the wider

organization will lead to better reactions to change (Armenakis et al., 1993). Change

is necessary, and factors that improve the organizational readiness for change need to

be enhanced. The most important indicator of achievement is organizational

performance (Cameron, 1986; and Cameron and Whetten, 1996).

Hypothesis 4a: Perceived ease of use positively and directly influences Current

usage of technology.

In the ICT implementation, principal support is the measure of result achievement

after the implementation of ICT in an institution or organization. When observed from

all perspectives, one will realize that ICT implementation is a big challenge to the

institution or organization and its members during its initial use or the implementation

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period. The aim of principal support is achieving proper implementation of ICT in

order to make it have a positive challenge.

Hypothesis H5: Principal Support positively and directly influences perceived

Usefulness.

Principal support brings into consideration the adoption of information

communication technology and its implementation in an organization or institution.

This means that information communication technology (ICT) implementation is

becoming easier because of the awareness and availability of simple machines, Davis,

(1989).

Hypothesis H5a: Principal Support positively and directly influences perceived

ease of use.

Scholars suggest that the influence of negative motivation valence can be mitigated

through conscientious efforts on the part of management in terms of how they treat

their employees (Armenakis et al., 1993, 1999).

Hypothesis 6: Motivation Valence negatively and directly influences perceived

usefulness.

There are some conditions influencing the relative valance outcomes of individuals.

Some of these conditions could be age, kind of education achieved and kind of jobs.

Individuals may have a positive or negative valance to a job depending on their

positive or negative goal preference. If an individual is indifferent with an outcome,

he/she has a zero valance.

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Hypothesis 6a: Motivation Valence negatively and directly influences perceived

ease of use.

The more people appreciate the use of ICT, the more they apply it in their daily

activities making them improve and have expectancy for more ICT models. With this,

I have no doubt accepting that Appreciation has a positive effect on precise Ease of

use.

Hypothesis 7: Appreciation positively and directly influences perceived

Usefulness.

Appreciation has been shown to be a useful predictor of change readiness in the

research. Based on the previous research concerning appreciation, affective

commitment to the change and technology acceptance, the following hypotheses are

offered:

Hypothesis 7a: Appreciation positively and directly influences perceived ease of

use.

In order to find the real and accurate answers of the research questions, the concept of

paradigm in the social science domain was used. According to Hart (2003), paradigm

is considered to be the development or growth of scientific practice in order to define

and explain how scientists or researchers work within accepted ways of describing,

classifying, hypothesizing, conceiving, and formulating methods within the different

disciplines. Different research paradigms need different research methods and

methodology for data collection and find a solution to problems and giving an

explanation for different events. Conceptually, several paradigms are found in the

field of social science, which have been subjected to severe critical analysis.

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4.4 WHY POSITIVISTIC PARADIGM?

In carrying out research, the necessary choice of paradigm, which depends on the

assumption of the branch of philosophy that deals with the nature of existence

ontological assumption, has become a complex task. Reasonably, positivistic

paradigm could be used based on the study and the method of qualitative be employed

for data collection and analysis. The justification for making this choice is as follows:

In the first instance, the idea behind the positivistic paradigm on the basis of

the principle is that the research on how human beings behave and the study in areas

of natural sciences should be carried out in a similar way. There has been a lack of

optimism in viewing the social reality as being unbiased or not dependent in nature of

its existence whether or not we know. The belief or feeling of epistemology relies on

the basis that happenings are measured and observable (May, 1998). For this reason,

there is devotion to providing quantities and bigger size of sample information (May,

1998, Hussey and Hussey, 1997).

4.5 RESEARCH DESIGN

Research design offers a framework with which a researcher can obtain data and

analyse them because it was believed that individual decision regarding the order of

importance attached to different dimensions of the research process is shown by

design. Besides, research methods were viewed as the means of gathering data

through the use of particular tools like questionnaires or interviews (Bryman and Bell,

2007; Cooper and Schindler, 2011). Research design performs the role of making sure

that evidence gotten provides the clue in responding to the earlier question clearly

without any doubt (De Vaus, 2001).

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According to Sekaran and Bougie (2010) research design has to do with

reasonable decisions in making choices among different methods of analysis such as

exploratory, descriptive, hypothesis testing, etc... The design is to realize the study’s

objective. In addition such decision takes into consideration the setting of the study or

its location, what examination to be carried out, at what space of time, the degree of

author’s involvement and influence, and the unit of analysis, which determine how

data will be analyzed. The authors were of the opinion that methods (ways) referred to

are component of design and this opinion was in line with Bryman and Bell (2007)

who also argued that methods (ways) serve the purpose of explaining the collection of

such data.

Phenomena

Prediction

Observation

Hypothesis (Testable)

Refuse

Revise/ reject

hypothesis

Systemic observation / data collection

Data analysis

Hypothesis test

Confirm hypothesis

Theory made up of confirmed hypothesis

Figure 4.2 Hypothetic - McNeill and Townley (1986)

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In this study, quantitative, cross-sectional, and descriptive methods will be

employed. It involves the use of primary data that are sourced from the design self-

administrated questionnaire distributed to the organization (Al-Subaihi, 2008). The

scale of questionnaire for data collection follows a five-point Likert type scale. This

takes the following format: Option A stands for “strongly” disagree; Option B stands

for disagree; Option C stands for uncertain; While option D stands for agree, option E

stands for “strongly” agree. Option A and B are the two opposite extreme points

showing the opinions of the respondents.

4.6 JUSTIFICATION OF A DESCRIPTIVE RESEARCH

The descriptive method has often been used in most of the disciplines of sciences such

as social science and psychology to have a general description or an outline of the

subject. Due to the difficulties involved in the observation of some subjects, studying

a social cause of a particular subject is descriptive in nature since it gives room for an

observation, and the normal behaviour is not tampered with. In addition, it serves as

essential means of solving the problem faced in testing and measuring large sample

size required for quantitative forms of experimentation (Biscoe, 2003; Adanza, 1995;

Gay 1996).

Anthropologists, psychologists, and social scientists always make use of these

forms of experiments in order to study the natural behaviours such that it will have no

impact on them somehow. The market researchers and companies also employed it in

order to decide customers’ habits, and decide the staff morale respectively. However,

descriptive research’s results may not be applicable to a definitive answer or counter

hypothesis, but given the insights into the limitations; it can serve as essential

instrument in several scientific research fields (Sekaran and Bougie, 2010).

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4.7 WHY QUANTITATIVE RESEARCH

The quantitative experiments are considered to be real science, particularly in their use

of orthodox mathematical and statistical methods of measurement. In addition to the

use of quantitative research by physical scientists, it has also frequently been used and

popular among the researchers in the field of social sciences, education, and

economics (Weinreich, 2006).

Quantitative test utilizes a generally acceptable design in formulating

hypothesises (which may or may not be supported) which might slightly be different

from other inter-disciplinary. The formulation of hypothesises should be based on

mathematical and statistical means, which from time to time must not be subjected to

question. Furthermore, the use of randomization of groups in the study by quantitative

research and the inclusion of a control group where necessary are very important. The

way of forming study should be such that it could be done in the same way with same

results (Weinreich, 2006).

Furthermore, information relating to the respondents’ age, gender, occupation,

level of education, and monthly income are also gathered using the questionnaire. To

get in touch with the respondents for the information required, an online survey via

the e-mail is adopted. Besides, the onsite survey ware carried out (Cano, 2001). Many

scales of measurement have been developed in the past for measuring the attitudes of

respondents but the most accepted and popularly used is the Likert Scale. According

to Likert (1932), the attitudes of the respondents are often measured appropriately

using five points Likert scale. The idea of measuring attitudes was developed by

Likert (1932) in which a series of statements relating to specific variables are expected

to be responded to, by the concerned people, indicating how much they have really

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agreed with the statement, thus tapping into the cognitive and affective constituents of

attitudes.

As a forerunner to quantitative research designs, descriptive research, provide a

general description or an outline which guides in making choice of the variable

necessary for quantitative testing. The expensive and time-consuming natures of

quantitative experiments justify the rationality behind having a prior knowledge of

necessary hypotheses required to be tested (Biscoe, 2003).

4.8 DATA GATHERING AND DATA ANALYSIS

The development of tools used in gathering data was done by taking into

consideration review of literature on technology acceptance model, and the

organization readiness of the change model.

The nature and purpose of this study required the application of quantitative

research techniques. As qualitative and quantitative research methods each had their

inherent inadequacies in addressing a complex and often interrelated social scenario,

this research takes the quantitative approach.

The survey mechanism is an essential tool in testing the hypotheses and

generating suitable data for numerical analyses (Cook, Dickinson and Eccles, 2009).

The self administrated questionnaire was the instrument used to collect the data;

TAMQ questionnaire is used and the other part was developed according to TAM I,

TAM III and MROC. The questionnaire was validated by a group of English speaker

Ph. D students and Arabic experts in Saudi universities and ministry of higher

education, the pilot-tested was done among several members at public organization in

Medinah and Jeddah. After that, the survey was verified and validated; it was

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distributed to some members of the targeted organizations including Ministry of

Information and Technology, Ministry of Education, Ministry Interior, Ministry of

Hajj, The Civil aviation commission, and Ministry Higher Education. The participants

were asked to specify the extent they approve or disapprove with each of the statement

on a five-point Likert rating scale the five options are 1 = strongly disagree, 2 =

disagree, 3 = neutral, 4 = agree, and 5 = strongly agree.

4.9 QUESTIONNAIRE DEVELOPMENT

The development of a questionnaire could take different forms, which could be in two

parts. The first part of a questionnaire can be developed for the respondents with the

intention of creating a friendly relation with them and to establish their features. At

the beginning of the questionnaire designed are questions relating to the respondents’

gender, monthly income, the respondent’s age, organizational position, and education

attained. The second aspect of the questionnaire design has to do with questions

concerning the particular variables used in the research work.

In this study, the designed questionnaire contains questions relating to one of

the variables used, which is the Technology Acceptance Model [TAQ] while the

literatures provide the source for other questions in the questionnaire. Many authors

have developed the antecedent’s attitude. For example, Deci et al., (1994) and Wee

(2000) develop management Support; Porter and Lawler (1968) develop motivation

valance, while Linden (1997) comes up with the appreciation. The development of

technology user believes was the work of Davis (1989), while Venkatesh, et al.,

(2003); and Venkatesh and Davis (2000) came up with moderators.

The scale of response for the questionnaire ranges from ‘‘strongly disagree’’ to

‘‘strongly agree’’ in which the respondents are to show their level of agreement

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regarding the questions posed to them. This scale has been popularly used among the

scholars with the belief that it is reliable, simple to construct and provide sufficient

relevant data relating to the opinions of the respondents. In this study, Saudi public

workers in western Saudi Arabia are the targeted population. Such targeted population

covers all genders (males and females), groups, many educational levels, several

categories of income level, and the nature or kind of job. Since difficulties could not

be avoided in reaching out to the population of employees in the Saudi Kingdom, a

representative sample was thus drawn using non-probability convenience sampling

from the entire population.

4.10 THE POPULATION AND THE SAMPLE

This study focuses on all public organizations in west Saudi Arabia as target

population. This involves 4 cities in the western part of Saudi Arabia which are

densely populated. These cities consist of Makkah, Medinah Jeddah, and Yanbu

(Ministry of Health, 2010). These western cities have the largest population with

many big organizations due to convenience sake, budget sake and the limitation of

time (Villavicencio, 2006).

The employees in the public organizations in these cities represent the

population required for the purpose of this study. In particular, the targeted public

organizations from these cities are the Ministry of Hajj, Ministry of Transportation,

Airport Authority, Police Department, and Ministry of Labour. A non-probability

convenience sampling design will be employed to draw a sample. The choice of this

method is guided by consideration of time constraint and practical alternative.

Moreover, authors often make use of convenience samples for economic sake and

time saving, in gathering a large number of completed questionnaires appropriately

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(Zikmund, 2003). For the right and appropriate to the application of structural

equation modelling (SEM), the sample size must be at least two hundred (Garson,

2009).

4.11 STATISTICAL ANALYSIS

The relationships between the variables in this study are taken care of by the model,

and worked in and out in an across sectional manner. The cross-sectional analysis

gives room for the extrapolation of results in line with the population.

In this study, Statistical Package for the Social Sciences AMOS and (PASW

18.0) were employed for the analysis of data collected. Below are the types of analysis

to be carried out:

Structure equation modelling is a major expansion of regression that allows

scholars to forecast dependent variable DV (path analysis) and/or multiple DVs and/or

look at the factor structure of a set of data (confirmatory factor analysis –

measurement models).

AMOS (Analysis of Mooment Structures) was used as the data analysis tool.

AMOS is the more recent analysis package which is easy to use graphical analysis

application, and has grown to be accepted as an uncomplicated tool of stipulating

SEM. AMOS also has an ease encoding interface as another option (Kline, 2005;

Kline and Little, 2011).

The sample was a convenience sample of at least 200 (Hair, Black, Babin and

Anderson, 2010) of civic employees who work in the western area of Saudi Arabia. A

self-administered questionnaire was used to collect data at Medinah, Jeddah, and

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Yanbu. The study was conducted at different organizations and ministries offices to

reduce bias.

The instrument for data collection was developed based on the review of the

literature on technology acceptance studies change behaviour and TAM I, TAM II and

TAM III, Model of organization readiness of change.

The study started by investigating the applicability of Antecedents of Belief

variables on TAM technology user beliefs. Then the study examined the effect of

current usage on TAM. Finally, the study examined the influence of training and work

type on the relationship between current usage and attitude behaviour. Moreover, the

study explored the effect of norm and volunteer motivation on the association between

attitude behaviour and behaviour intention to use.

Derived from research tool analysis out-comes regarding the paths linking

between antecedent beliefs, there is low covariances between these dimensions.

Significant positive structures were introduced successfully between motivation

valance and appreciation with perceive usefulness, as another point of view, the

association with perceive ease of use were significantly negative. However, the

Principal support has opposite models’ structures; it had positive significant

relationship with perceive ease of use and negative significant with perceive

usefulness.

Overall, this study is still far from being able to explain the acceptance factors

of technology adoption in public organizations in Saudi Arabia. Nevertheless, this

study provides some insight relating to organization acceptance of top public

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organization in Saudi Arabia. With weak support on the overall proposition and

limitations of this study, the opportunities for future research are still extensive.

The significant role played by SEM in the knowledge development for

the social work profession has been on an increase in recent times (Guo, Perron and

Gillespie, 2009). Besides that, an important instrument of analysis is the confirmatory

factor analysis [CFA] for the validation of construct in the social and behavioural

sciences. It is a form of SEM which is used particularly for models’ measurement. It

deals with the determination of the associations between latent variables (factors) and

observed measures such as test items, test scores, and observed behavioural ratings

(Brown, 2006). They tend to pursue the following objective: one, to confirm the

psychometric properties of the hypothesized measurement model (Schmidt, Wang and

McKnight, 2005); and two, to make a reasonable measure of a construct; an index

/composite of subsets of items.

4.11.1 Structural Equation Modelling

In this study, structural equation modelling was used to express the associations

between variables. SEM has more advantages and is considered being better and more

efficient than multiple regression and factor analysis in the sense that it offers an extra

advantage in dealing with the problem of multicollinearity, and takes into

consideration the unreliability of response data (Fox, 1997). As dated back to the 80s,

structural equation modelling has been dependent upon to investigate the hypotheses

concerning how latent and observed variables are associated and also examine their

dimensions (Muthen, 2002).

In the first instance, a model is formulated on the basis of relevant theory used.

This was followed by the determination of the way to measure the constructs and

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gather data. Next the data were keyed into the SEM software package (PASW 18.0).

The data were fitted by the software package to the model already formulated, and the

results produce. This consists of the overall fitness of the model and the estimates of

parameters (Byrne, 2009).

4.11.1.1 Why Structural Equation Modelling

Structural equation modelling is “an equation representing the strength and nature of

the hypothesized relations among (the ‘structure’ of) sets of variables in a theory”

(Vogt, 2005, 313). SEM never chose or named a particular statistical technique for use

but different procedures, which are related, could be used (Kline and Little, 2011).

Moreover, Chin and Todd (1995) stated that SEM is a valid tool to measure model in

social science after both reliable and valid tests have been met.

The structural equation modelling consists of several structural equations; models

which express the associations among latent variables with the inclusion of

coefficients for endogenous variables (Vogt, 2005). SEM is otherwise referred to as

Data

Interpretation

Model

Theory

Measureme

nt

Results

Figure 4.3 SEM - Analysis Follows

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covariance structure modelling because of its implication of structure for the

covariance between variables. In addition, SEM is as well called LISREL (Linear

structural relations) models. LISREL is the name for SEM computer program, which

is the first and popularly cited program. SEM serves as a substitute to other

multivariate techniques, which are a constraint by considering only a single

association of the dependent variable with that of the independent variable. It also

merges the logic of factor analysis and multiple regressions since the objective of

SEM is to formulate a model, which described the reason behind the association of

observed variables. In this case, the variance-covariance matrix () is described given

the simultaneous solution of equations, which stand for the research model (Hair,

Black, Babin, Anderson, and Tatham, 2006).

The implementation of structural equation modelling was carried out to

investigate the fit between the model variables Figure 4-1, as well as; the data

collected. In precedent studies of management information system, SEM has been

largely used to evaluate and decides the simultaneous models. This will be applied to

(cross-sectional data) the panel data (Hair et al., 2006). The choice of this method is

justified by the fact that it is capable of examining a combination of dependence

associations at the same time, and determining the effects (direct and indirect) among

the constructs within the model (Hair et al., 2006; Blunch, 2008; Byrne, 2009). At this

point, it is pertinent to discuss the likely effect of size.

Important objectives of the structural equation modelling process could be

viewed in two ways. Firstly, it involves the validation of the measurement model, and

secondly, fitting the structural model. The first one was achieved with the use of

confirmatory factor analysis, and the second one was with the use of path analysis

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with latent variables. Given that the application of SEM is efficient with samples size

more than 100 variables, factor analysis (common factor analysis or principal axis

factoring), was employed and not principle component’s analysis to show that

indicators likely measure the equivalent of latent variables (or factors). After the

measure model has been validated the analysis follows. A comparison of the

alternative models is made with respect to "model fit” that measures the degree to

which the observed covariance in the data is equivalent to the covariance predicted by

the model. In order to enhance the fit of some variables in the model, "Modification

indexes” and other coefficients can be employed by the researchers to change some

variables (Blunch, 2008; Byrne, 2009; AMOS, 2010).

Blunch (2008) and Byrne (2009) have emphasised that SEM has many

significant virtues that attract the use of it. Firstly, it has clear and testable

assumptions behind the statistical analyses which enable the researcher to have

complete control and get a better insight into the analyses. Secondly, creativity is

enhanced by the graphical interface software which encourages the debugging of a

rapid model. Thirdly, SEM programs offer the opportunity of to simultaneously

investigate the total model fit and the separate estimate of the parameter. Fourthly,

simultaneously a distinction of regression coefficients, means, and variances can be

carried out, across multiple between-subjects groups. Fifthly, errors can be removed

with the use of measurement and confirmatory factor analysis models thereby making

estimated relationships among latent variables to be free of error of measurement.

Sixthly, it has the power to fit non-standard models, with soft treatment of

longitudinal databases, which have errors structure that are auto correlated in the case

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of time-series analysis, and databases whose variables are not normally distributed,

and whose data are not complete.

In structural equation modelling, the degree the sample data proposed model fit

or the extent to which it could properly explain the model is the most important. In the

case where the result proves not to be of a good fit, the right thing to do is to find out

where the problem lies within the models. It is proper that assessment of model fit

should come from different views and depend on many requirements that evaluate the

model fit from different views. Specifically, the assessment requirement pays attention

to the sufficient estimates of the parameter and the complete model.

There are three requirements to be taken into consideration when reviewing the

parameter estimates of the model:

Firstly, the feasibility of the parameter estimates. It is important for the initial

stage of evaluation the fit of parameter estimates of each variable in a model to

identify the viability of the value estimated. Specifically, estimates of parameters must

show the right sign and size and be consistent with the theory guiding the study. If an

estimate lies beyond the acceptance region, it is a sign that either the model is not well

specified or there is not enough information in the input matrix. For instance, if the

correlation is more than 1.0, then the estimate of the parameters is not appropriate.

Similarly, it is also the same case if there is negative variance, and covariance or

correlation matrices are not positively definite (Hair et al., 2006).

Secondly, the appreciation of the standard errors: whether or not the estimates

of the parameters have been accurate is shown by the standard errors. For the accurate

estimates, the values of standard error should be small. Furthermore, when the

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standard errors are too large or too small, it shows a poor model fit. For instance, a

standard error tending to zero implies that the statistical test for its associated

parameter cannot be determined (Bentler, 2005). Standard errors that are too-large

show that the parameters cannot be found (Joreskog and Sorbom, 1993). Since the

measurement units in observed and latent variables, and the size of the estimate

parameter influence standard error, it becomes difficult to have a well-defined

requirement of “small” and “large” standard error (Joreskog and Sorbom, 1993; Kline

and Little, 2011).

Thirdly, sample size for SEM, the rule of thumb in the application of

multivariate statistics for the social sciences by Stevens (2001) is that there should be

15 cases per independent variable in a multiple regression analysis. Multiple

regression and SEM are somehow related. Therefore, in SEM, 15 cases per measured

variable can be logically acceptable. Bentler and Chou, (1987) argue that authors may

reduce it to five cases per parameter estimate while analysing SEM on the condition

that the data is distributed normally, there is no data lost and there is no cases of

outliers. It should be noted that Bentler and Chou (1987) advocated for five cases per

parameter estimate rather than per measured variable. Measured variables usually

entail a minimum of one path coefficient related to another variable in addition to

error term or estimate of variance. Therefore, it is essential to realize that the

minimum suggested by Bentler and Chou (1987) and Stevens (2001) boil down to

fifteen cases per measured variable approximately.

4.11.1.2 Sample Size for SEM

Researchers have suggested appropriate sample size in factor analysis. These

recommendations take the form of least amount sample size (N) for a specific analysis

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or minimum ratio of N to the variable number, p. This variable number represents the

number of items surveyed for factor analysis (MacCallum, Widaman, Zhang and

Hong, 1999). Author like Gorsuch (1983) suggested five subjects per item, with at

least one hundred subjects without taking into consideration the items numbers.

Guilford (1954) also suggested that two hundred (200) should be the minimum for N

and Cattell (1978) argue for three to six subjects per item, with at least 250 subjects.

Comrey and Lee (1992) suggested different categories of sample size and gave

remarks in respect of each one chosen. The following guidance is given in deciding

the required sample size. According to Chin and Todd (1995), 100 is regarded as

being poor, 200 is fair, 300 is considered good, 500 is considered to be very good, and

1,000 or above is believed to be excellent. While Everitt (1975) recommends sample

size with at least ten subjects per item, Cureton and D’Agostino (1983) suggest an

ideal large sample size of say several hundred but not less than 200 (see Table 8.1).

4.12 REGRESSION AND PATH MODELS VS. STRUCTURAL EQUATION

MODELLING

In spite of the fact that SEM packages very commonly used for to execute models

with latent variables, it can be applied to run regression or path models (Blunch, 2008;

Byrne, 2009).

Under the regression models, the researchers modelled the observed variables

only and the error term can only be found in the endogenous variables. The exogenous

variables considered to be free of error while modelling them. There are also the

graphical models where the arrow points from the exogenous variable to the

endogenous variable. Regardless of having actual causal effects, the partial coefficient

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for an exogenous variable controls for the rest of exogenous (Horton and Kleinman,

2007; Blunch, 2008; Kline and Little, 2011).

Under the path models, there is only observed variables and no latent variables.

Path models resemble regression models in this regard. Path model is distinct from

regression models but similar to structural equation modelling in the sense that the

exogenous variable can be responsible for the causes and effects of other variables.

This means that path models are similar to SEM models with the attribute of circle-

and-arrow causal diagrams, but has only the "star" design of regression models. The

error terms could also be found in the dependent variables only in path models.

Dependent variables in path models are supposed to be measured error free. In the

calculation of partial coefficients, only the exogenous are used in a direct path to the

dependent variable (Lance, 1988; Maasen and Bakker, 2001; Kline and Little, 2011).

4.13 PILOT STUDY

The main goal of this research is to identify the potential variables that might hinder

the development or usage of ICT represented by the e-government in Public

organizations in Saudi Arabia. To realize this goal, the right methodology was adopted

by the researcher. In this section, the researcher tries as much as possible to describe

the analysis of the pilot study. The researcher also attempted to explain the population

targeted procedure for sampling, instruments employed for the study, data collection

approach, and finally, the techniques of data analysis.

Cooper and Schindler (2011) suggest that the size of the pilot group could be

from 25 to 100 subjects. This pilot survey according to Ticehurst and Veal (2000) may

be employed to investigate all aspects of the survey as well as question wording.

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In order to ensure a successful monitored research, sampling data should be

subject to analysis, interpretation and presented. It is important to have a precise data

set. Data, which are hastily collected and stored, hastily analyzed, and reported, makes

the credibility of the research result to be challenged. In order to make sure that data

are accurately collected the laid down procedure of collection should be followed. The

storage of data should also follow the procedures set. New data must be examined and

time must be spent to prepare the report for precise analysis (Kumar, 2005).

While attempting to execute the pilot exercise, the researcher organized

instrument for the research. Having completed the organization of instrument, the

organization was told about the pilot exercise to be carried out. Thereafter, the

exercise of the pilot study began with the involvement of some of the employees who

constitute the respondents in the main study. When the main study began those

participants in the pilot exercise were excluded from taking part to avoid biasness

because they were already aware of the questions in the main questionnaire (Sekaran

and Bougie, 2010).

In this study, the pilot study conducted included twenty five employees from

nine organizations. Table 4-1 below indicates that the majority of participants were

males in the middle age group category and their average income range from 6000-

8000. They have university first degree and perform a supervisory role in the

organization.

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Table 4.1

Participants’ Characteristics Pilot Study

Variables Frequency Percent

Ed

uca

tion

High school 3 12.0

Diploma 7 28.0

Graduate 14 56.0

Post graduate 1 4.0

Total 25 100.0

Age

29 1 4.0

34 6 24.0

39 8 32.0

44 7 28.0

Above 45 3 12.0

Total 25 100.0

Inco

me

Lev

el 4,000 – 5,999 7 28.0

6,000 – 7,999 13 52.0

8,000 – 9,999 5 20.0

Total 25 100.0

Posi

tion

Vice. G.M 1 4.0

Supervisor 10 40.0

Assistance M 6 24.0

Clerical 8 32.0

Total 25 100.0

The pilot study is a trial test or prior test carried out with the respondent to

discover the likely problems, which may occur in the questionnaire instructions or

design; to discover if the respondents did not properly understand the questionnaire, or

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to discover if there is uncertainty in any of the design questions or if the questions are

bias (Sekaran and Bougie, 2010).

The pre-testing must be conducted with the sample which is hopeful for

respond in the same way to the samples where the scale will finally be applied. The

goal of pre-testing is to assess the items used in the design questionnaire (Hair et al.,

2006). Sekaran and Bougie (2010) noted that it is essential to carried out pre-test on

the used questionnaire in the survey so that the respondents are cleared about the

questions asked; the questionnaires are freed from any ambiguity; and free from any

problems in the wording or measurement. According to Cooper and Schindler (2011),

pre-testing is done in order to refine the instrument of measuring and might depend on

colleagues, respondent representatives, or actual respondents. Zikmund (2003)

suggested that the size of the pre-testing group could be twenty or fifty subjects.

The pilot study was carried out to confirm the validity and reliability of the

main study by getting rid of the likely biases and to enhance the validity of the

questionnaire (Edwin, Teijlingen and Hundley, 2001). Biases which could occur have

been provided in a list by Cook and Campbell (1979). The main objective of the pilot

study is to help providing a reliable and consistent surrogate of the instrument and

finally make the study valid and reliable. To decide the reliability of the model,

structural equation modelling is employed.

The goals of pilot surveys as stated by Ticehurst and Veal (2000) include the

following: Testing questionnaire wording; Testing question sequencing; Testing

questionnaire layout; Gaining familiarity with respondents; Testing field work

arrangements (if required); Training and testing fieldworkers (if required); Estimating

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response rate; Estimating interview or questionnaire completion time; Testing analysis

procedures.

4.14 QUESTIONNAIRE TESTING

Researchers use questionnaires because it was considered efficient in gathering data

from the respondents for research purpose. Therefore, it is essential to design

questionnaires in order to improve its efficiency in gathering the dependable data.

Designed good questionnaire for the survey should start with a brief and

understandable statement followed by asking simple questions. It is expected that the

general questions asked ought to create a kind of rapport with the respondents. With

simple questions, respondents found it easy and non time wasting to give responses.

The researcher carried out pre-test but divided it into two parts. Part one of the

pre-test has to do with the focus group in which case employees working in Medina

with Doctorate degree or master degree were gathered for briefing. These employees

were from various organizations such as Ministry of Education, Ministry of Hajj,

Ministry of Higher Education, and Ministry of Interior; and also from where there

were non-users of ICT. The instrument was carefully explained to make sure that the

statements and construct measurement were well understood.

The focus group discussion shows that the statements measuring the model

constructs were sufficient and self explanatory excluding an identified uncertainty

with respect to one volunteer motivation and subjective norm concept. This was taken

into consideration, and the statement was later made clearer or such was born in mind

at the analysis stage. Some omission of statement was also made as a result of

resemblance of the statements in the confirmatory factor analysis, and at the time of

measuring the model fit and validity.

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Part two of the pre-test was carried out in Jeddah using a convenient sample of

fourteen respondents working at a civil aviation organization. The respondents

individually completed the first aspect of the questionnaire, and a feedback was

obtained from them regarding the process such as the time available to complete the

questionnaire, how clear was the direction and how the wording of measures was.

Generally speaking, it was reported by the respondents that the questionnaire was not

ambiguous and simple to fill. The researcher made some minor adjustment of the

instrument after the pre-test, though not the statement repetition.

4.14.1 Testing Question Sequencing

It is good to test the sequencing of the questions in a questionnaire because of their

ability to influence the responses of the respondents. In most cases, neutral questions

are put at the start of the questionnaire in order to create friendly relation with the

respondents. For this reason, the forefront question should be soft and not harsh

anybody. The questions should be arranged in line with factors to measure such that

they can achieve their objectives since sequencing of questions has influence on the

responses. The effect of sequencing on questionnaire findings can be looked at from

two perspectives. In the first instance, issues mentioned at the start inform respondents

more and thus prompt them to answer subsequent questions. Failure to mention this

issue at the start may not let the respondents realize the issue in the course of

answering questions. In the second instance, there is a problem of habituation, which

is a consequence of unorganized sequence of questions. In this case, the same answer

can be provided by respondents to a closely related or follow-up question if not

explained properly.

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4.14.2 Testing Questionnaire Layout

The questionnaire layout shows how prepared questions are offered to the respondents

to answer whether in paper form or on the internet. As pointed out by Cook et al

(2009) questionnaire layout may decide the respondents thoughts, will he or she

answer it or retain it without answer. Therefore, it is essential that questionnaire layout

is given proper attention by the researchers. The layout of the questionnaire must be

simply presented according to the objective set to be easy to answer. When a

questionnaire is well-presented it pays both the respondents and the researcher in the

sense that the respondent will be clear about it and gives appropriate answer, and the

researcher will find it easy to enter the data. The provision of answers option could be

in a vertical or horizontal form. With a one answer on each line, the presentation could

be considered neat. Levine and Gordon (1958) affirmed that the right position for

questionnaire answers should be to the right hand side while the questions are

arranged downward. This makes it convenient for the respondents to go through

accordingly (Walonick, 1993). The questions and answers grid is the famous

questionnaire layouts because they are fascinating, conserve paper space, and prevent

the long series of questions, which are replicable.

4.14.3 Validity and Reliability of the Instrument

In a Nomological network of knowledge, composite reliability as well as discriminate

and construct validity have become an essential issue in determining durable

constructs (Bollen, 1989 and Raykov, 1997). In the use of a particular instrument there

should be clarity as to whether the indicators are single items, total scores, or other

scale of measurement. It is necessary to be familiar with the impact of the indicator’s

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number of per factor and the extent of item deciding the estimate of the model (Marsh,

Hau, Balla, and Grayson, 1998).

Validity according to Leedy (1980) describes the extent to which a test

measures what is supposed to measure. It has to do with how effective and sound the

instrument of measurement is. There are three types of validity and one reliability test:

4.14.3.1 Content Validity

Content ValidityAccording to Pilot and Hunger (1999) and DeVon, Block, Moyle-

Wright, Ernst, Hayden, Lazzara, Savoy and Kostas-Polston (2007) a content validity

method should be undertaken to confirm whether the content of the questionnaire is

suitable and closely connected to the objective of the study. The researcher sought

advice from the expert and as well as the reviewed relevant literatures in stating the

objectives of the research and conceptual framework concisely in order to calculate

the content validity of the questionnaire.

Content validity of constructs shows the extent to which items measured are a

well measure of all variables. Through judgment, it is assumed to be basic (Kerlinger,

1986). Previous researchers have used research measures in order to estimate and

decide similar constructs.

Four scholars and lecturers in the areas of questionnaire design and information

technology were selected by the researcher to assess the 58 items of the questionnaire

drafted. This is done to cross check the consistency with the conceptual framework.

With the use of a 4 point Likert Scale designed as follows: (1 = not relevant 2 =

somewhat relevant; 3 = relevant; 4 = very relevant), the rating by the individual

assessor confirms that the items used on the questionnaire are closely connected to the

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conceptual framework as well as to the estimate of the validity of the items. In this

case the researcher followed the content validity Index (CVI) used by (Wynd, Schmidt

and Schaefer, 2003). The researcher estimates CVI index for each item under each

construct and it was found that the CVI index was high for all questions except for

only one question where it is lower.

DeVon et al., (2007) suggested that the level of items must be up to 7/8 or

(0.87), if not it is advisable to be dropped. The item in quote: “Using computers will

improve my work” which was believed to be same with new items in quote: “Using

computers will increase my productivity” and “Using computers enables to

accomplish tasks quickly” indicated lower CVI (CVI = 6/8 = 0.75). Interestingly, the

rest of the items complied with CVIs as they vary from 0.88 (7/8) to 1.00 (8/8). For

this reason, these items were maintained. Similarly, the statements in quote: “Using

technology compatible with all aspects of our work” and “Using computer fits well

with the way I like to do work” have a problem. Furthermore, in the rating of clarity

and easy to answer, a scale ranging from 1 to 5 was used, and it was found that 90%

of the whole respondents rated 3 and 4 for “clarity and easy to answer the questions”.

Also, 97% of the whole respondents reported that the questionnaire layout and

appearance have the intention of targeting the population.

4.14.3.2 Face Validity

Face validity was suggested to be carried out for the purpose of assessing the

appearance of the questionnaire with respect to the clarity of the language used;

consistency of style and formatting, readability and feasibility (Haladyna, 1999;

DeVon et al., 2007; Trochim, 2006). In addition, face validity of the questionnaire

was considered proper for the content area and, for the purpose of study since it is

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taken to be a kind of usability and not reliability. The current study carried out face

validity of the questionnaire. An assessment form was designed to evaluate the style

and layout, wordings clarity and questionnaire sequencing.

The researcher conducted face validity among nine respondents employed from

each organization with a Likert Scale ranging from 1-5. In the first extreme is

“strongly disagree” = 1; disagree = 2; neutral = 3 agree = 4; and the second extreme is

“strongly agree” = 5.

4.14.3.3 Construct Validity

The use of construct validity was appropriate in an attempt to relate the instrument

closely connected with the theoretical construct, (DeVon et al., 2007; Kane 2001).

Reference is made by construct validity to quantitative nature but not a qualitative

difference which distinguishes ‘invalid’ from ‘valid’. Hunter and Schmidt (1990)

noted that it has to do with the extent to which the intended exogenous variable

(construct) associates itself to the surrogates of exogenous variable (indicator)”. The

current study made the use of factor analysis to decide the construct validity.

Three steps should be followed in order to get insight into whether research has

construct validity or not. One is the specification of the theoretical associations. Two,

is to investigate the empirical associations existing between the measures of the

concepts. Three, is to make interpretation of the empirical evidence with respect to the

way the construct validity of a specific measure being examined are made clear

(Carmines and Zeller, 1991).

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4.14.3.4 Internal Consistency (Reliability)

The internal consistency should be investigated to evaluate how well the items fit

together conceptually and to investigate the inter-item relationships within instrument

(Nunnally and Bernestein, 1994; DeVon et al., 2007). Also, in order to calculate the

consistency of the entire questionnaire, there is a need to compute the total score of all

the items. The use of Cronbach alpha correlation coefficient and Split-Half reliability

(Torchim and Donnelly, 2001) give the measurement of internal consistency. If the

items measure the same construct, they are split into two parts, and correlation was

computed between the two parts in Split-Half reliability. The commonly employed

reliability statistics to determine internal consistency reliability is the Cronbach’s

alpha. It corresponds to the average of all the possible Split-half estimated (Torchim

and Donnelly, 200; DeVon et al). Therefore, the current study also employs the

computation of Cronbach’s alpha for each subscale.

The Cronbach's alpha is not a statistical test but a coefficient of reliability (or

consistency). Yet, Cronbach's alpha used as a measure of internal consistency

determines how closely related is a set of items as a group is. An estimated result

showing alpha with value (0.7 to 1.0) is always taken to be that the items actually

measure an underlying (or latent) construct (DeVon et al., 2007).

Another aspect to be undertaken following the investigation of the instrument

validity is the reliability. Haladyna (1999) and DeVon et al., (2007) describe the

capability of a questionnaire to measure an attribute and to find out whether the items

fit well together. The following issues are taken into consideration while deciding

reliability as suggested by Cronbach and Shavelson (2004). This is an instrument

standard error, sampling independency, content heterogeneity, and instrument usage.

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Reliability is the measurement constructed by Cronbach in 1951(Mohsen

Tavakol, Reg Dennick), which determines the extent or the degree to which this

measure can be repeated. The most frequently used in the determination of reliability

is the Cronbach alpha coefficient (Revelle and Zinbarg, 2009).

The current study used internal consistency reliability as a result of the

particular nature of the study, and the employees are from the public and government

organizations.

4.15 PILOT STUDY CRONBACH'S ALPHA

The sizes of Cronbach Alpha for all the variables for the pilot study data are shown in

Table 4.2. It was indicated that the magnitude of all the variables ranges from “good

to very good”, with the exception of the perceived voluntariness which has poor value

of reliability. The Cronbach's Alpha for the pilot study data reported was 0.96.

Table 4.2

Cronbach's Alpha for the Variables (Pilot Data Analysis)

Variables

Cronbach's Alpha (items)

Principal Support 0.88 (6)

Valance Motivation 0.71 (5)

Appreciation 0.92 (4)

Perceived Usefulness 0.97 (8)

Perceived Ease of Use 0.88(4)

Current Usage 0.84 (3)

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4.16 FACTORS ANALYSIS

At the time to develop an instrument where there is clustering of factors, factor

analysis is often employed Bryman and Cramer (2011). Individual factor clusters

together in line with high loadings that measure the relationship of an item with a

factor. In this case, unrelated factors are removed while the constructs are grouped

with one another (Munro, 2005).

Exploratory factor analysis is employed in the investigation of the association

among various variables where a specific hypothetical model is not determined. The

definition of the construct on the basis of theoretical framework and the trend of

measurement are shown by exploratory factor analysis (De Von et al., 2007). It also

shows large scores variation with fewer numbers of factors.

Large sample is necessarily required for the running of the factor analysis

(Bryman and Cramer 2011). Munro (2005) pointed out that the recommended

participants should be at one to five with respect to the variable even though

discussion is still on as a regard to the acceptable number of participants required to

run the factor analysis. The current study put into consideration two requirements to

Table 4.2 Continue

Variables

Cronbach's Alpha (items)

Commitment to Change 0.70 (4)

Subjective Norms 0.75(4)

Perceived Voluntariness 0.78(4)

Intention to Use 0.93 (6)

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make sure that the right sample size to run a factor analysis is employed. The first one

is a sampling adequacy analysis Kaiser-Meyer-Olkin [KMO] and the second one is a

factor, factor loadings and the variable correlations.

Various kinds of extraction methods were employed in order to carry out factor

analysis. The most frequent used approaches are Principal Axis Factoring [PAF] and

Principal Component Analysis [PCA] (Bryman and Cramer 2011). Common variance

and all variable variance are respectively analyzed in PAF and in PCA (Bryman and

Cramer, 2011). The total variance consists of both specific and common variances as

well as the variance shared by the subjects’ scores with the other variables while

specific variance often called common variance explains the specific changes in a

variable (Bryman and Cramer 2011). Therefore, PCA is supposed to be perfectly

reliable and free of error (Bryman and Cramer 2011).

Two basic approaches are employed to decide the amount of factors not to

remove (Bryman and Cramer, 2011). Eigenvalue greater or equal to 1 are chosen in

line with the Kaiser Criterion. But sometimes, eigenvalue greater or equal to 1

regarded as general criterion may not properly represent the amount of factors

necessary (Heppner, Heppner, Lee, Wang and Park, 2006; Gorsuch, 1983).

4.17 KAISER-MEYER-OLKIN (KMO) AND BARTLETT'S TEST

From the output, the next item is the examination by test of Kaiser-Meyer-Olkin

(KMO) and Bartlett Table 4.3. The sufficiency of sampling considered to be more

than 0.5 for efficient factor analysis is measured by KMO. The Table below shows

that the KMO measure for all variables is more than 0.5. Also, lowest value of 0.5 and

highest value of 0.84 are for the usage and the intention to use respectively.

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The uncertainties associated with the research questions discovered through

pilot study were removed before the main study analysis was executed. Also, the

items not closely connected with the questionnaires were removed. The interview was

properly reworded to achieve the needed answer from the responded. In general, all

irregularities discovered during the pilot study were corrected and brought forward

into the actual study.

Table 4.3

KMO and Bartlett's Tests for the Variables (for Pilot Study)

Variables KMO

Principal Support 0.64

Valance Motivation 0.62

Appreciation 0.82

Perceived Usefulness 0.82

Perceived Ease of Use 0.82

Attitude to Change 0.50

Intention to Use 0.84

Subjective Norms 0.52

Perceived Voluntariness 0.70

Usage 0.50

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4.18 SUMMARY OF THE CHAPTER

In this chapter, the research methodology, the study’s design, sampling procedures,

and number of samples required were discussed. In order to realize the objectives of

the study, 15 hypotheses were formulated. The development of these hypotheses on

the basis of different factors considered to have a relationship with TAM and MROC.

The methods of analysis employed in this study are descriptive analysis and statistical

analysis. These will be discussed in the next chapter.

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5 CHAPTER FIVE

DATA COLLECTION AND ANALYSIS

This chapter presents the results of the study on the basis of data gathered from

government organizations in two cities in the kingdom of Saudi Arabia. For the

confirmation of factors that stand for the variables, factor analyses were employed.

For the analysis of the gathered data, statistical package for social sciences (PASW

18.0), version 16.0 was employed. This chapter presents the findings of the study

based on the data collected between Augustus 2010 and March 2011 from eight public

organizations of two cities Medinah and Jeddah in Saudi Arabia.

Kerlinger (1986) pointed out that proportion of the targeted population can be

used as a representative sample. Stratified random sampling technique was employed.

The distribution of the questionnaires was made during the visit or by snail

mail. The researcher got the letter of consent and met with the representatives of ten

organizations for the distribution of questionnaires. Prior to the distribution of the

questionnaire, the researcher met the employees to introduce and explain the aim of

the study and gave verbal instruction regarding the completion of the questionnaire.

For proper understanding and because of language barrier, the researcher had the

questionnaire translated and printed in Arabic and later translated back to English

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following Brislin method (Brislin, 1980). It was completed by the respondents, and

collected back that same day.

At random, ten organizations from three western Saudi cities were chosen, and

the population chosen for the purpose of the study was 4,580 as presented in (Table

5.1).

Table 5.1

Participants’ Characteristics Main Study

Organization Name Total Number of

Employee

Number of Employees

Selected for the Study Return

MEDINAH Total

Ministry of Education 650 65 (0.1) 47 (0.7)

Ministry of Interior 950 95(0.1) 57 (0.6)

Ministry of Labour 150 75 (0.5) 36 (0.5)

Ministry of Information

and communication

120 53 (0.45) 23 (0.4)

Ministry of Hajj 130 75(0.57) 64 (0.8)

Ministry of Higher

Education

133 75 (0.56) 57 (0.7)

Civil Aviation 160 80 (0.5) 53 (0.7)

Total 2293 518 (0.23) 337

(0.7)

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Table 5.1 Continue

Organization Name Total Number of

Employee

Number of Employees

Selected for the Study Return

MEDINAH Total

JEDDAH Total Return

Airport Authority 450 54(0.1) 34(0.6)

Ministry of Interior 1500 150 (0.1) 63 (0.4)

Sabic1 345 35 (0.1) 13 (0.4)

Total 2295 239 (0.1) 110

(0.5)

Total A and B 4588 757 (0.2) 4472

(0.59)

5.1 RESPONSE RATE

Out of the 4580 employees, 757 employees from two cities got the questionnaire.

However, the rate of response was 59% representing 457 employees who returned the

questionnaires. From these, 419 actually completed the questionnaires. The adjusted

responded rate was 55% out of which 427 was processed in the study. The

respondents are entirely males.

1 One of the ARAMCO company

2 Of the total 427 male was acceptable and used in the analysis

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5.2 JUSTIFICATION STRATIFIED RANDOM SAMPLING TECHNIQUE

The adoption of stratified sampling instead of simple random sampling depends on the

advantages derived from both. Therefore, stratified sampling was preferred to simple

random sampling for the following reason. It guarantees the representation of main

subgroups of population, a particularly few minority groups instead of only overall

population (Cochran 1977; Schreuder, Ernst, and Ramirez-Maldanado, 2004). One

will be able to discuss effectively about the subgroups through this way. Various

fractions of sampling within various strata to randomly oversample the small group

may be used given that the subgroup is very small (Trochim and Donnelly, 2006).

Proportionate stratified random sampling is carried out if within strata, the

same sampling fraction is used, and a disproportionate stratified random sampling was

carried out if different sampling fractions in the strata are used. Furthermore, the

statistic of stratified random sampling is in general more precise as compared to

simple random sampling. This is realized on condition that the strata or groups are

homogeneous, and it is anticipated that the change within-groups is smaller than the

change for the entire population if the strata or groups are actually homogeneous.

Based on this fact and depending on the choice of estimator, stratified sampling

prevents bias in estimation (Castillo, 2009).

5.3 DATA ANALYSIS

The gathered data from the respondents was inputted into a Microsoft® Excel

workbook for clean up. After that it was transferred to PASW 18.0 for Windows. The

employment of data descriptive statistics were done for rechecking of any inbuilt

errors in the data entered (Newton and Rudestam, 1999). While doing this, missing

data was detected within the data entered. However, it complies with the presumption

161

that the missing data is not avoidable (Newton and Rudestam, 1999). Consequently,

statistical techniques for managing missing data were applied.

The (Analysis of Moments Structures) AMOS 18.0 software was employed to

analyze the data. AMOS is a statistical package design and efficient for structural

equation modelling which builds measure, complete structural models and examines,

makes modification and re-examines models. AMOS also investigates the competing

models, correspondence across groups or samples and makes proposition regarding

means and intercepts. Maximum Likelihood (ML) estimation was used for treating the

missing data and offers boots trapping guidelines (Arbuckle, 2009).

5.3.1 Missing Data and Cleaning the Data

It is essential and required to clean the data. Therefore, when data was cleaned, it

reduced the adverse effects of these errors. Despite that, AMOS gives perfect

analytical instrument for dealing with missing data, error of omission of one

endogenous measure may cause an error in the analysis. The researchers removed any

observation not answered by the respondent from the analysis. In addition, the data

was cleaned to be sure that all values lie within the anticipated range.

5.3.2 AMOS

To examine the association among the observed and latent variables with those

models used to investigate the hypotheses, AMOS is a simple and efficient SEM

program (Blunch, 2008; Byrne, 2009; AMOS, 2010; Kline and Little, 2011). It has the

following qualities: it has graphical language such that it is not necessary to write or

type equations or commands; it is simple and easy to operate, which makes it to be

user-friendly since it has characteristics, which include drag and drop capabilities,

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drawing tools, and configurable toolbars; lastly, fast- models that once took days to

create are now completed in minutes. Significantly, AMOS can represent the

association between regression analysis, path analysis, and factor analysis (Blunch,

2008; Byrne, 2009 ( .

5.3.3 Correlation and Simple Regression

While the determination of strength and trends of the association between two or more

variables are measured by correlation. Simple Regression analysis measures the

degree of prediction made by the independent variable about the (criterion measure)

dependent variable, and in multiple regression, many independent variables were used

to predict one dependent variable (Blunch, 2008; Byrne, 2009; AMOS, 2010).

5.3.4 Path Analysis

Cochran, (1977) supposed in path analysis that author is investigating the capability of

many independent variables to explain or predict many endogenous variables. For the

summary of a large amount of variables and for decreasing it to a regulated level such

as demographic variables, the method used in this study was factor analysis (Hair et

al., 2006). It means that it is a way of investigating and representing sets of factors

that are connected to a smaller one (Hair et al). Factor analysis is either confirmatory

or exploratory; both are employed in this study, confirmatory factor analysis and

exploratory factor analysis was used as an instrument of consolidating items.

5.3.5 Reliability Test for the Main Data

The most widely used measure to know how many multiple indicators for a latent

variable are closely related is Cronbach's alpha. The range is from 0.0 to 1.0, and the

acceptable level or value of the indicators is Cronbach's alpha of 0.70 to decide the

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reliability. It should be noted that it is likely to have Cronbach's alpha of some items

under 0.7, and different fit indices in confirmatory factor analysis may be greater than

the required 0.9 levels. Having low Alpha could be due to failure of the items to have

the same variance or when the items in the scale/factor are very few. The estimated

Cronbach's alpha for this study’s instrument is 0.83 showing that the model is well

fitted, and the internal consistency is very good. The model consists of 52 numbers of

items in Table 5.2.

Table 5.2

Reliability Statistics

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items

No of

Items

0.83 0.81 52

The study also employed multiple regression analysis to find the association existing

among the dependent variable, behavioural intent to adopt, and the other independent

exogenous variables. The analysis used to find out the relationship between the

endogenous and exogenous variables was employed to examine the hypotheses. The

association between the endogenous variable behavioural intent to adopt (continue to

use) ICT and the conceptual exogenous variables was decided with the use of linear

regression analysis method for the PASW 18.0 and AMOS program so as to

investigate each hypothesis in accordance with the research questions. “Multiple

regression analysis is used for analyzing data when the researcher is interested in

164

exploring the relationship between multiple continuously distributed independent

variables and a single dependent variable" (Newton and Rudestam, 1999, 248).

Furthermore, the Pearson chi-square (χ2) test was also employed to evaluate

whether there was significant association between the variables or not. The chi-square

test is often referred to as a test of independence since more than one variable are

independent of the population under consideration as stated by the hypothesis (Aliaga

and Gunderson, 2003).

5.3.6 Descriptive Analysis

This section is to provide summaries about the sample and the measures. In addition,

it is to present insight of the respondents’ behaviours.

5.3.6.1 Participants Characteristics and Their Technology Beliefs

In the actual study, most of the respondents were in the middle aged group, having an

income of 6000-8000 on average; they were mostly supervisors at their place of work,

and they were graduates. These features were similar to the information obtained

during the pilot study as shown below in Table 5.3.

Table 5.3

Main Study Sample Participants Characteristics

Variable Frequency Percent

Gender

All Male

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Table 5. 3Continued

Variable Variable Variable

Age

25 – 29 26 6.1

30 – 34 49 11.5

35 – 39 271 63.5

40 – 44 73 17.1

Above 45 8 1.9

Income Level per month (SR)

2,000 – 3,999 21 4.9

4,000 – 5, 999 36 8.4

6,000 – 7, 999 205 48.0

8,000 – 9,999 11 2.6

10,000 > 154 36.1

Position

Vice. General Manager 7 1.6

Head of Dep. Manger 81 19.0

Supervisor 236 55.3

Clerical 94 22.0

166

Table 5. 3Continued

Variable Variable Variable

Education Level

Middle School and below 3 0.7

High school 62 14.5

Diploma 34 8.0

Graduate ( Bachelor) 195 45.7

Postgraduate 133 31.1

Training Type

None 334 78.2

Department 84 19.7

Operator 5 1.2

Both 4 0.9

None 334 78.2

Training Time

Less than one Week 82 19.2

Week – one Month 9 2.1

More than one Month 2 0.5

167

From the above table, male constitutes 100 % of the participants, and 46 % of them

are graduates. The ages of the respondents fall between 35-39 groups. Interestingly,

the supervisors with an average income level between SR 6000-7999 would have been

a significant influence on the survey. It should be noted that most of the respondents

as almost 20 % of them received their training in the department within a week which

is negligible.

The opinions of the 427 respondents are made known regarding their

awareness about acceptance and usage of technology. Descriptive analysis is carried

out on the antecedents’ attribute of belief, technology user beliefs, subjective norm,

volunteer motivation, and attitude to change. These items are measured with the use of

five-point Likert scale design as: A equals Extremely Disagree, B equals Disagree, C

equals Uncertain, D equals Agree, and finally, E equals Extremely Agree.


From the Table 5.4, mean score of the respondents is reasonably high with the value

of 2.68 (Std. = 0.77). This implies that the opinion of employees is that Principal

support has effect on the technology acceptance since the factor like “It is easy for me

to observe others using e-government in my organization” received highest rate while,

on the other hand, the factor “I get management support” received the lowest score

rate, and the measure of reliability was very low. The significant of the reliability is

welcomed, and can be made better on condition that number four factor which is “It is

easy for me to observe others using e-government in my organization” is dropped.

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Table 5.4

Descriptive Statistic Principal Support Cronbach's Alpha 0.71

Statements Mean Std. Dev

Sr. Mgt. Thinks I Should Use computer. 2.63 0.85

Management supports computer in my organization. 2.33 0.58

I get management support. 2.26 0.54

It is easy for me to observe others using e-government in my

ORG.

3.52 1.10

Average Score 2.68 0.77

1= Extremely-disagree 5 = Extremely-agree

5.3.6.3 Motivation Valance

Table 5.5 shows the mean score of the respondents is high with the value of 4.02 (Std.

= 1.03). It implies that there was agreement by the employee who tends to motivation

valance or personal gain has influence on the acceptance of technology, meaning that

both factors affect technology acceptance in almost the same way. “I do not wish to

expose myself or my organization to the high risks and learning costs associated with

a new technology by being its first user” was reported to have the highest score. While

the factor like “I intend to use computer if it helps the organization performance”

recorded lower score. The acceptable reliability of these factors is 0.77 measures.

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Table 5 5.5

Descriptive Statistic Motivation Valance Cronbach's Alpha 0.77

Statements Mean Std. Dv

I do not wish to expose myself or my organization to the high risks

and learning costs associated with a new technology by being its

first user.

4.12 0.96

I intend to use computer if it help the organization performance. 3.88 1.07

I intend to use computer if it does not help me. 4.04 1.04

I am satisfied with my performance at this task 4.04 1.06




Table 5.6 shows the mean score of the respondents is also high having the value 4.24

(Std. = 0.99) and the factor reliability value is 0.72 making it to be acceptance. It

implies that majority of the employees consent with the appreciation to the use of

technology and also that ICT has an effect on the progress of acceptance indicated by

the factor like “Working with computers is fun” whose score was highest and

“Computer makes work more interesting” which is the least factor.

170

Table 5.6

Descriptive Statistic Appreciation Cronbach's Alpha 0.72

Statements Mean Std. Dv.

Computers make work more interesting. 4.20 1.03

Working with computers is fun. 3.97 1.16

I like using computers. 4.43 0.84

I find computers a useful tool in my work. 4.49 0.88

I want to learn a lot about computers. 4.08 1.04




From Table 5.7 it is shown that the employees’ perceive ease of use is high. It

indicates that the technology use and adoption have the mean value of 4.22 (SD =

1.09), and the Cronbach's Alpha equals 0.75. It can be deduced from the data that

majority of the respondent consent with the ease of using computer but still have to

put more effort in using it. The factor “My objective for using the computers is clear

and understandable” has greatest effect. Conversely, the factor “I find computers

easy to use” has score that is lowest.

171

Table 5.7

Descriptive Statistics Perceived Ease of Use with Cronbach's Alpha 0.75

Statements Mean Std.Dv

My interaction with computers is clear and understandable 4.37 1.07

My objective for using the computers is clear and

understandable.

4.42 1.14

I find computers easy to use. 3.91 1.10

Easy to Get computer to Perform what I wish. 4.19 1.06




From Table 5.8, one can get insight into the opinion of the employees regarding the

perceived usefulness. The score was considerably high with the technology perceived

usefulness of a mean value 4.05 (SD = 0.82) while the Cronbach's Alpha gives 0.72.

The indication is that “Using computers will enhance my effectiveness” has the highest

score while “Using computers enables to accomplish tasks quickly” has little influence

on the perceived usefulness. The respondents agree that computer will improve their

work despite that they are natural of the effectiveness of the computer as a tool. The

reliability measure could be improved significantly if the factor “Using computers

makes job easier” number 5 is deleted giving the value (0.8) which is a good

reliability indicator.

172

Table 5.8

Descriptive Statistics Perceived Usefulness with Cronbach's Alpha 0.72


Using computers will enhance my effectiveness. 4.32 0.86

Using computers will increase my productivity. 4.22 0.81

Using technology compatible w/all aspects of our work. 4.32 0.75

Using computers enables to accomplish tasks quickly. 3.63 0.87

Using computers makes job easier. 3.74 0.83




Table 5.9 shows the scores value of employee’s use of ICT and that of the

organization client to be 61-70 % and 71-80 % respectively (survey). While the

employee score value is 61-70 %, its mean value is 3.31 (SD = 1.24) with Cronbach's

Alpha value of 0.75. The lowest average uses are for the patrons.

Table 5.9

Descriptive Statistics of Current Usage with Cronbach's Alpha 0.75


My Usage of the computer in my daily work is 3.36 1.21

I estimate the current usage of computer in my department very high 3.28 1.24

173

Table 5.9 Continue


I estimate the current client usage is 3.29 1.27



5.3.6.8 Attitude to Change

Table 5.10 shows that employees agreed with the factor “employee commitment to

change”. ICT commitment to change has the reliability score of 0.77 with the mean

equals 3.70 (SD = 0.80). The factors like “I would accept almost any type of job

assignment in order to keep working for this organization” recorded the highest mean.

It implies that majority of employees are not ready to change. Therefore, to have

better indicator, and increase the reliability score, factor like number one could be

dropped.

Table 5.10

Descriptive Statistics of Attitude to Change with Cronbach's Alpha 0.77


I feel very little loyalty to this change. 3.46 0.79

I would accept almost any type of job assignment in order to keep

working for this organization.

3.91 0.71

174

Table 5.10 Continue


I am willing to put in a great deal of effort beyond that normally

expected in order to help the organization be successful.

3.69 0.94

I find that my values and the organization’s values are very

similar.

3.73 0.75




Table 5.11 shows the effect of the subjective norm on the adoption process. The mean

value is recorded to be 4.11 (SD= 0.83). The result implies that subjective norms have

strong effect on the adoption progression. The score of reliability is 0.67, and this can

be enhanced by removing factors like ”People who influence my behaviour think I

should use the computer” and “People who are important to me think I should use the

computer”. Having done that, the new score for the reliability equals 0.9.

Table 5.11

Descriptive Statistics of Subjective Norm with Cronbach's Alpha 0.67


People who influence my behaviour think I should use the

computer.

4.44 0.66

175

Table 5.11Continue


People who are important to me think I should use the computer. 4.38 0.74

My immoderate supervisors think that I should use computer. 3.87 0.87

I want to do what the people who report to me think I should do. 3.75 1.05

Average score 4.11 0.83



Table 5.12 shows that the factor “I use the computer all the time” recorded the lowest

score, and the factor “although it might be helpful, using computer is certainly not

compulsory in my business recorded highest score. The effect of other attributes are

almost the same on the technology adoption, with average value as mean 3.81 (SD =

1.00). The result indicates the natural effect of the volunteer motivation factors on

technology acceptance, which shows that the reliability is very good.

Table 5.12

Descriptive Statistics of Perceived Voluntariness with Cronbach's Alpha 0.8


I never use the computer. 3.84 1.04

I examine unusual things. 3.81 1.01

I use the computer all the time. 3.74 0.69

176

Table 5.12 Continue


Although it might be helpful, using computer is certainly not

compulsory in my business.

3.85 1.25

Average score 3.81 1.00


5.3.6.11 Intention to use

From Table 5.13 above, the employee’s intend behaviour to use the ICT at the

workplace was observed to be high. Out of the all factors, two factors have high to

extremely high mean, while the others got natural to high objective of using the ICT at

work. The value of Cronbach's Alpha is 0.74, while the mean is 3.90 (SD = 1.08).

Table 5.13

Descriptive Statistics of Intention to Use with Cronbach's Alpha 0.74


I will use computers in my work in future. 3.89 1.02

I plan to use computers in my daily life often. 3.56 1.24

I will encourage my colleague to use computer. 3.69 1.07

I will encourage my organization costumers’ to use the system 4.24 0.92

Assuming I had access to the computer, I intend to use it 4.14 1.16



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5.4 THE MODEL SUMMARY

The foregoing section gave a brief overview of the model, and necessary information

that might be useful in the determination of the status of its identification.

Furthermore, 527 distinct sample moments can be observed in Table 5.14, which the

sample elements in a covariance matrix are 527. The total of 116 parameters is

estimated. Base on an over-identified model, the degree of freedom is 415. The

research may not realize any reasonable p value because of large sample size; the

value of chi-square is 1718.18 and the p value is 0.00. Table 5.14 shows, the way to

estimate the degree of freedom in PASW 18.0 and AMOS are shown.

Table 5.14

Computation of Degrees of Freedom

Number of distinct sample moments: 527

Number of distinct parameters to be estimated: 112

Degrees of freedom (527 - 112): 415

It is essential to note that only data for observe variables will be worked with in SEM,

which is 44 in this case. On the basis of the formula given as p (p + 1) / 2, the sample

covariance matrix for these data ought to produce five hundred and twenty seven (31

[32]/2) sample moments, and it actually produces it. “Model Variables and

Parameters” provides the breakdown of the parameter estimates as shown in the

following section. Also, a detail of ML chi-square statistic and further information

connected with model fit are provided in the “Model Evaluation”.

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5.4.1 Model Variables and Parameters

The earlier brief discussion about AMOS text output file has the benefit of guiding to

solve any problem that might be associated with the specification of a model. In Table

5.15, all the variables in the model are listed and grouped to be observed or

unobserved variable, and dependent or independent. In line with the path diagram

shown in Figure 4.1, the data of all the observed variables, (intention to use) which is

dependent variables are inputted in the model; Secondly, all unobserved variables

such as factors and error terms, and operate, which are independent variables are also

entered in the model. Thereafter, a summary of the whole variables in the model and

the number variable in each of the four groups are provided.

Table 5.15

The Research Model Summary

Number of variables in your model: 73

Number of observed variables: 31

Number of unobserved variables: 42

Number of exogenous variables: 38

Number of endogenous variables: 35

Next in the output file is the summary of the parameters in the model as shown in

Table 5.16. From the table, starting from the left-hand side to the opposite side, there

are 80 regression weights. Out of these 80 regression weights, 38 are calculated. The

rest 42 are the first of each set of three factor loadings and the error terms. All the 6

covariance and 38 variances there are calculated. Out of the total of 154 parameters,

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112 are calculated. Having obtained the summary, the right number of degrees of

freedom could be decided after which the model is identified.

Table 5.16

Parameter Summary

Weights Covariance Variances Means Intercepts Total

Fixed 42 0 0 0 0 42

Labelled 38 0 0 0 0 0

Unlabeled 0 6 38 0 30 112

Total 80 6 38 30 154

5.4.2 Modification Indexes

The researcher started by inputting the data into PASW 18.0 program after which the

diagram is drawn, and the model’s parameters are investigated. For the purpose of

confirmatory factor analysis, there are three fundamental choices available for

reducing discrepancies in estimating the model. These estimates are a variation of

ordinary least squares, generalized least squares, and maximum likelihood estimation

(Hair et al., 2010).

Hair et al., (2010) pointed out that the modification index serves as the guide

which calculates the impacts that will be on discrepancy if the parameters are not

subject to any constraint. For the report of these analyses in detail, a change in the

maximum likelihood ratio with the minimum of 4 is employed.

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5.4.2.1 Tests of Normality and Outliers

For the investigation of multivariate normality or univariate evaluation of skewness

and kurtosis and Mardia’s coefficient of multivariate kurtosis, normality and outlier

are the appropriate statistics used. Once it has been found that a coefficient of

multivariate kurtosis is statistically significant then; an examination of the presence of

outliers using Mahalanobis’ distance is done.

5.4.2.2 Normality

With application of AMOS, multivariate normality is decided with the use of

skewness and kurtosis. In the Table 5.17 shown below, skewness and kurtosis seem

never to constitute an important difficulty in the set. Given the benchmark as - 2.0 to

+2.0 majorities of the items prove to show significant skewness. In particular, the

following items provide evidence of kurtosis: Principal Support 3, Attitude to change

2, Perceived ease of use 2, and Perceived usefulness 3. Arbuckle (2009) supposed the

findings of the analysis may either be affected or not by the deviations from

multivariate normality. As a rule of thumb, normal distribution of data should have the

value of Skeweness and the Kurtosis to be greater than 3.0 but less than -3.0. The data

is highly significant if their value is zero or approaching zero. From the Table since

the values of the demographic factors for Skeweness-Kurtosis are almost zero or fall

between 1.0 and -1.0, there is normality distribution.

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Table 5.17

Assessment of Normality

Variable Skew

INDICATOR C.r.

Kurtosis

INDICATOR

Principal Support 3 2.13 12.18 5.49

Intention to use5 -1.73 -9.88 2.49



Attitude to change 2 -2.30 -13.15 7.48

Attitude to change 3 -1.32 -7.55 2.08

Perceived ease of use 2 -2.09 -11.96 3.87

Perceived usefulness 3 -2.20 -12.58 4.51

5.4.2.3 Outliers

Table 5.18 gives the findings of AMOS’s test of outliers by employing the

Mahalanobis distance statistic. Hair et al., (2010) declared that outliers are decided as

part of the analysis. This Mahalanobis distance statistic stands for the squared distance

at the centroid of a data set, and AMOS gives two other complementary statistics, p1,

and p2. The probability of any observation greater than the squared Mahalanobis

distance of that observation is indicated by the p1 column while the probability that

the largest squared distance of any observation will be greater than the Mahalanobis

distance calculated is indicated by the p2 column. To determine which of the

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observations could be an outlier Arbuckle (2009), believed that p1 column must give

small numbers. Therefore, if p2 column gives small numbers, this shows observations

are unexpectedly not close to the centroid given the normality hypothesis.

In order to verify which of the observation outliers were in the main data set if

there were any, all observations contained in Table 5.18 having p2 values below 0.1

were separately investigated. This analysis showed observations with two or more “0”

answers to the perceived ease of use questions and volunteer motivation or two or

more “1” answers on the outcome attitude tools and subjective norm. The score

recorded for “perceived ease of use” is low but the answer is still valid and need not

be dropped from the data set. Similarly, the low scores were also acceptable on the

“outcome attitude” because of the same reason. On the whole, nine observations were

in the first instance pointed out as possible outliers but were later accepted and

maintained in the set of data after thorough examination was made.

Table 5.18

Observations Farthest from the Centroid (Mahalanobis distance)

Outlier

Observation

Number

Mahalanobis

d-squared

p1 p2

Observation

Number

Mahalanobis

d-squared

p1 p2

1 62.51 0.01 0.00 101 50.23 0.10 0.01

14 54.68 0.04 0.00 121 66.59 0.00 0.00

17 48.29 0.14 0.08 124 53.52 0.06 0.00

26 52.25 0.07 0.00 135 42.88 0.30 0.12

44 54.60 0.05 0.00 152 51.37 0.08 0.00

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Table 5.18 Continue

Observation

Number

Mahalanobis

d-squared p1 p2

Observation

Number

Mahalanobis

d-squared p1 p2

54 59.62 0.01 0.01 161 54.88 0.04 0.00

56 38.81 0.47 0.70 162 54.68 0.04 0.00

5.4.2.4 Collinearity (Multicollinearity)

Collinearity (or multicollinearity) occurs when there are strong relationships among

the exogenous variables such that it raises the standard errors. There is an occurrence

of multicollinearity when two or more variables move in the same direction,

particularly if the interrelationship among particular variables are very high

(Tabachinck and Fidell, 2001).

When there is multicollinearity, the standard errors of the coefficients rise and

the rise in standard errors could cause the coefficients of some exogenous variables

not to be significantly different from zero, which would not have been if there had not

been multicollinearity (Hair et al., 2010).

Variance inflation factors (VIF) measure the amount of the variance in the

estimated coefficients is increased in a situation where there is no correlation among

the variables. If there is no correlation between two variables, then the whole VIFs

will equal 1 but if VIF for any of the variables is about or more than 5.0, and if the

tolerance value is less than 0.1, there is collinearity associated with that variable (Hair

et al., 2010). If there are two or more variables that have a VIF around or greater than

5.0, one of these variables must be removed from the regression model. The

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Collinearity test was conducted upon all the variables of the model. Table 5.19 shows

the factors with the largest VIF, which are clearly lesser than 5.0.

Table 5.19

Coefficients Collinearity Test

Un-standardized Coefficients

Standardized

Coefficients T

Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

(Constant) 0.3 0.2 1.4 0.2

NRM1 -0.5 0.1 -0.3 -7.7 0.0 0.3 3.3

NRM2 0.4 0.1 0.3 6.6 0.0 0.3 3.3

NRM3 1.0 0.0 0.9 35.6 0.0 1.0 1.0

(Constant) 2.3 0.3 7.2 0.0

PUF2 0.0 0.1 0.0 0.6 0.6 0.6 1.6

PUF3 0.5 0.1 0.4 5.6 0.0 0.5 2.1

PUF4 -0.1 0.1 -0.1 -1.5 0.1 0.7 1.4

PUF1 -0.1 0.1 -0.1 -1.1 0.3 0.6 1.6

(Constant) 2.1 0.2 12.4 0.0

ATT1 0.0 0.0 0.0 -0.5 0.6 0.8 1.2

ATT4 0.2 0.1 0.3 4.5 0.0 0.5 1.9

ATT3 0.3 0.0 0.4 6.4 0.0 0.5 2.1

(Constant) 2.1 0.2 12.4 0.0

PEU1 0.1 0.1 0.1 0.9 0.4 0.5 1.9

PEU2 0.3 0.1 0.2 3.6 0.0 0.5 2.0

PEU4 0.1 0.1 0.2 2.8 0.0 0.7 1.5

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Multicollinearity exists when tolerance is below 1.0; and VIF is greater than 10.0 or

an average much greater than 1.0. In this case, there is no multicollinearity. After that,

most often the following estimation results are of primary interest: estimates of the fit

of the model, estimates of model parameters, and estimates of the (asymptotic)

standard errors of parameter estimates.

5.4.3 Model Fit Indices

The next sections will discuss the data analysis out but from the AMOS. In the first

instance, it will discuss the test for goodness-of-fit. Thereafter, covariance, regression,

and correlation shall also be discussed. In the model summary, the model graphic and

factors analysis will be presented.

In order to know how the data fit well, the measurement model validity is

evaluated in which the comparison is made between the theoretical measurement

model and the reality model. In the assessment of measurement model validity for

instance, the factor loading latent variable should be more than 0.7. The test of chi-

square and other goodness of fit statistics like, RMR, GFI, NFI, RMSEA, SIC, BIC,

etc., are some of the main indicators, which assist in the measurement of the model

validity (Hair et al., 2010; Hair et al., 2006).

In most cases, the likelihood ratio chi square test is employed in evaluating the

results of confirmatory factor analysis but when large sample sizes of more than 200

are used, it somehow rejected acceptable models (Kenny, 2010).

5.4.3.1 Chi Square-Based Measures of Discrepancy Fit

In the investigation of measurement Model Fit Indexes, Kenny (2010) asserts that the

likelihood ratio chi square test is more often employed to evaluate confirmatory factor

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analysis results, even though it has the tendency of rejecting an acceptable model

having large sample size, which is more than two hundred.

5.4.3.1.1 CMIN: the Minimum Discrepancy CMIN/DF

An attempt is made to adjust for model difficulties by this step. The value of an

acceptable model should be near 1.0 but if a ratio is more than 2.0, then the fit is

inadequate, which means the model must be adjusted to be fit (Byrne, 2009).

5.4.3.2 Baseline Model Comparisons

Baseline model comparisons are measures, which deal with the distinction of some

baseline model (not always a null hypothesis model) with another measurement model

(Sivo, Fan, and Robinson, 2010).

5.4.3.2.1 NFI Bentler-Bonett normed fit

In this case, it was recommended that to have fitness of a model the value should be

more than 0.8 or 0.9 and there is perfect fit of a model to the data if the value is 1.0.

This may be bias when the sample size is small against models even though it is likely

paid for the upward biasness of the chi square when the sample size is large ( 2007;

Sivo et al., 2010). This statistic assesses the model by comparing the χ2 value of the

model to the χ2 of the null model. The null/independence model is the worst case

scenario as it specifies that all measured variables are uncorrelated.

5.4.3.2.2 CFI Comparative Fit Index

Bentler (1990) stated that comparative fit index is like that of NFI in which case it

should be greater than 0.9 (Sivo et al., 2010). CFI is a revised form of the NFI which

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takes into account sample size (Byrne, 2009) that performs well even when sample

size is small (Tabachnick and Fidell, 2007). CFI statistic assumes that all latent

variables are uncorrelated and compares the sample covariance matrix with this null

model.

5.4.3.2.3 GFI Goodness of Fit Index

For the goodness of fit index (GFI), it should be greater than 0.9 (Sivo et al., 2010).

The Goodness-of-Fit statistic (GFI) was created as an alternative to the Chi-Square

test and calculates the proportion of variance that is accounted for by the estimated

population covariance (Tabachnick and Fidell, 2007). By looking at the variances and

covariances accounted for by the model it shows how closely the model comes to

replicating the observed covariance matrix (Diamantopoulos and Siguaw, 2000).

Other fit index could be used in this measurement like, Bentler-Bonett normed

fit [NFI], this measure has fitness of a model the value should be more than 0.8 or 0.9

and there is perfect fit of a model to the data if the value is 1.0. This may be biased

when the sample size is small against models even though it is likely paid for the

upward biasness of the chi square when the sample size is large (Sivo et al., 2010).

Relative Fit Index [RFI] this fit allows the score range to be greater than 1.0,

but the approved fit is considered to be nearest to 1.0 and greater than 0.90 (Sivo et

al., 2010). Also , In the case of Incremental Fit Index [IFI], the range of value should

be greater than zero but less than 1.0, even the approval for judgment is the closeness

to 1.0 (Sivo et al., 2010). Also for the Tucker-Lewis Index [TLI ] the range of value

should be greater than zero but less than 1.0, even the approval for judgement is the

closeness to 1.0 (Sivo et al., 2010).

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5.4.3.3 Parsimony Adjusted Fit Measures

There are compensation attempts by this related group of measures for the models’

complexity (Bentler, 1990). In this case, the whole sizes of fit measurement are

decreased by a given constant referred to as “parsimony ratio”. There is no general

rule for the interpretation of the coefficients. However, the better it will be the model

fitness if the value is nearest to 1.0 (and more parsimonious) (Sivo et al., 2010).

5.4.3.3.1 RMSEA Measures and PCLOSE

As noted by Hu and Bentler (2007) an agreeable rule of thumb for model to be

acceptable is one, to have the RMSEA to be lesser than 0.05 or 0.06 (Sivo et al.,

2010); two, to have PCLOSE statistical test for RMSEA to be significant: with the

result of significance, the researcher is able to draw a conclusion that the theoretical

model is significantly different from the real association existing among variables

(Fan and Sivo, 2007; Sivo et al., 2010). The significant load for each measure is

summarized by Fan and Sivo (2007). Fan and Sivo (2007) summarized the significant

load for each measurement in their distinguished paper as shows in Table 5.20

Table 5.20

Evaluating Results: Which Fit Indices & What Values?

Decision

Goodness fit Badness Fit

p of

χ2/df

CFI

Gamma Hat GFI PCLOSE RMSEA SRMR

Good < 2.0 > 0.95 > 0.95 > 0.50 < 0.05 < 0.06

Acceptable < 3.0-2.0 > 0.90 > 0.90 > 0.4 < 0.08 < 0.08

Marginal < 3.0-5.0 0.85-0.89 0.85-0.89

> 0.10

Reject < 5.0 < 0.85 < 0.85 > 0.10 > 0.08

Source: Sivo, Fan, and Robinson (2010)

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Finally, Tanaka (1993) asserts that it is not needed to give all estimated results

as it was released by a structural equation estimation program, even if only default

options are being used.

5.4.3.4 Measurement Adequacy and Considering Modification

After a successful confirmatory factor analysis had been realized, a well used

reliability tool like Cronbach’s coefficient alpha was employed despite that a measure

model could display a good model fit by different tests used. In case of low reliability

when Cronbach's coefficient alpha is used, cross validation work is still continued

(Sivo et al., 2010).

In case of unsuccessful confirmatory factor analysis according to Fan and Sivo

(2007), review of the basic concept of the confirmatory factor analysis model is

required for modifications. There should be search for the models in which error terms

are related (any existence of extra variables causing non-random effects could be

shown by correlated error terms). On condition that the choice is based on theory,

correlations are at times allowed among common factors.

When there are two factors that are strongly correlated it could show that there

is only one underlying factor. In order to investigate that empirically, comparison

could be made between the fit of the structure model of the main factor hypothesized

with the fit of a measure model where the associations among the factors are restricted

to be equal to one. “If the constrained model is not significantly worse than the

unconstrained one, the researcher concludes that a one-factor model would fit the data

as well as a multi-factor one and, on the principle of parsimony, the one-factor model

is to be preferred (Garson, 2009, 16)”.

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A new model is constructed with a parameter path having the largest

modification index. Next is to verify whether the fit of this new model is well fitted to

the data using tests of fit like the chi-square distribution. The new model can be

retained if it makes better the fit (Fan and Sivo, 2007).

5.4.4 Evaluating the Goodness of Fit

The goodness of fit tests is used to decide whether to accept or reject the tested model.

Jaccard and Wan (1996) suggest the minimum use of fit tests to be three, and one

should come from each three groups stated under Kline (2005) below to reflect broad

criteria. Kline (2005) suggested the minimum of four tests like chi-square; normed fit

index (NFI) or Parsimony comparative fit index (PCFI) Root; Tucker-Lewis Index or

Non-Normed Fit Index (NNFI); and Standardized root mean square residual, (SRMR).

Furthermore, others suggested are the incremental fit index (IFI), Root mean square

residuals RMSR, and mean square error of approximation, RMSEA. To have good fit,

the value of chi-square (CMIN), is recommended to be more than 0.9. Others are the

RMSEA, and one of the baseline fit measures (NFI, The relative fit index, RFI, IFI,

TLI, and the comparative fit index, CFI).

To compare a model, one of these parsimony measures are used: PNFI; PCFI

and for the information theory measures, the most frequently used are the Bayesian

Information Criterion BIC, the Akaike Information Criterion AIC, the CAIC, the

expected cross-validation index ECVI, the modified expected cross-validation index,

MECVI, the Browne-Cudeck criterion BCC. There have been arguments as regards

the particular fit indexes to report. There has not been a preference for adjusted

goodness-of-fit index (AGFI) as it was before and consideration is now been given to

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goodness-of-fit index (GFI). However, agreements have been made that to make a

report of them the shot-gun method should be prevented.

As regard the Amos's baseline model, a mean of zero is needed from individual

observed variable, and the following are to have the value of 0.90 as requirements for

a well-fitted model: the comparative fit index (CFI); the relative fit index (RFI); the

Tucker-Lewis Index or Non-Normed Fit Index (TLI); the incremental fit index (IFI);

the parsimony ratio PRATIO; the parsimony Normed fit index (PNFI) and the

parsimony comparative fit index (PCFI). Furthermore, the p-value of close fit

PCLOSE is expected to be more than 0.5, and the root mean square error of

approximation (RMSEA) is expected to be below 0.05 (Kline and Little, 2011). These

are indicated in Tables 5.21, 5.22, 5.23, and 5.24.

The relative chi-square which is otherwise known as normal chi-square,

normed chi-square, or simply chi-square to DF ratio, is determined by dividing the

chi-square fit index by the degrees of freedom. The essence of norming is to make the

chi-square model independent on sample size to some extent. Carmines and McIver

(1983, 80) suggested that for the acceptance of a model, relative chi-square is

expected to range from 2:1 or 3:1. From the perspective of Ullman (2001) good fit

should have value of 3 or 4 while the author such as Kline (2005) believes that 3 or

less than 3 should be accepted. Schumacker and Lomax (2010) consent the allowance

of values of 5 for a well-fitted model but it has been stressed by others that the relative

chi-square is expected to have 2 or less while value below 1 signifies poor model fit.

The relative chi-square is listed by AMOS as CMIN/DF. This is shown above.

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Table 5.21

Baseline Comparisons Whole Model

Relative to progress in the field is the fit indexes despite the rules of thumb for an

approved model fit (CFI is expected to have a minimum of 0.90). As suggested, the

normal requirements could be just making comparison of two models fit where they

are prior models having similar phenomenon. For instance, where 0.85 from a CFI

might show improvement in a field, the best preceding model could have a fit of 0.70

(Kline and Little, 2011).

Table 5.22

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI

Default model 0.89 0.72 0.80

The parsimony normed fit index often referred to as PNFI is determined by the

multiplication of PRATIO and NFI. If the model is nearer to saturated model, the NFI

is penalized more. No unanimous value is considered to be the requirement value for

model acceptance. When compared, the parsimony-adjusted coefficients are below

their non-adjusted opposite ones, and there was no application of 0.95 as the required

RFI IFI TLI CFI

Default Model 0.79 0.90 0.89 0.90

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value. In the comparison of the nested models, a better model is the one which

possesses greater PNFI. By conference, the PNFI is anyhow decided to be more than

0.60 showing better parsimonious fit, while other authors suggested the use of value

more than 0.50 (Kline and Little, 2011; Shah and Goldstein, 2006).

Table 5.23

RMSEA AND PCLOSE

Also by conference, Schumacker and Lomax (2010) pointed out that there will be a

well model fit if RMSEA is below or equal to 0.05. If RMSEA is below or equal to

0.08, there will be sufficient fit. In recent times, Hu and Bentler (2007) have

recommended that RMSEA should be below or equal to 0.06 as a requirement for a

better model fit. There have been unanimous approvals of RMSEA of 0.10 or above to

be a poor fit. The p-value of close fit, PCLOSE, carried out tests on the null

hypothesis and asserts that RMSEA should not be above 0.05. For a lesser PCLOSE

below 0.05, the hypothesis is rejected and with RMSEA being above 0.05, there is no

closeness of fit.

Model RMSEA PCLOSE

Default model 0.04 0.81

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Table 5.24

RMR, GFI

Model RMR GFI

Default model 0.085 0.90

Joreskog and Sorbom (1984) devised the GFI (goodness of fit index) for the

calculation of Ml and Uls and make applicable to another requirement of calculation

by Tanaka and Huba (1985). For GFI having a value of 0.9, it is considered acceptable

but the value of GFI below or equal to 1.0, shows that there is a perfect fit.

Figure 5.13 and Figure 5.14 show that all the values have acceptable values,

which show a perfect fit of the research model. These values met the requirement of

the acceptable fit recommended by Hire (2010).

5.4.5 EXPLORATORY FACTOR ANALYSIS AND CONFORMITY FACTOR

ANALYSIS

According to Hair et al., (2010), Factor analysis (FA) provides information about

which group of variables moves together. The objective is to reduce the correlation

matrix into small number pieces in order to allow the variables existing in the pieces

to be greatly related with each other as compared to the variables in the other pieces.

In the reality, the factor analysis is a causal model. It is presumed by the research that

observed variables are related due to their sharing of one or more causes (Kline and

Little, 2011).

Factor analysis tried to point out the variables, or factors, which provide

information about the relationship’s pattern taking place among a set of observed

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variables. It always employs data reduction to point out a small number of factors,

which provide information about the variance noted in a much bigger number of

noticeable variables. Factor analysis is also useful in the formulation of hypotheses

concerning causal mechanisms or to assess variables for the following analysis to

point out the collinearity before performing a linear regression analysis. Factors are

regarded as the causes of the underlying (Darlington, 1999).

5.4.5.1 Exploratory Factor Analysis (EFA)

For the identification of the latent variables or factors within the observed variables,

EFA is employed. If data consist of many variables, factor analysis can be employed

to reduce variables to a smaller number. Variables having same features are studied

together by EFA. Given the factor analysis, a small number of factors are made out of

large variables, which have the ability to explain the observed variance within the

larger number of variables. Meanwhile, the factors may be reduced further in the

analysis (Fabrigar, Wegener, MacCallum, and Strahan, 1999).

Three stages of factor analysis are identified. These are as stated below:

One, for all variables, a correlation matrix is formulated. A correlation matrix is an

array, rectangular in shape, which shows the variables coefficients of correlation with

others.

Two, based on the correlation coefficients of the variables, factors are extracted from

the correlation matrix.

Three, in order to maximize the association between the variables and some of the

factors, the factors are rotated.

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5.4.5.2 Kaiser-Meyer-Olkin Test

The tests of sampling adequacy measured carried out by Kaiser-Meyer-Olkin are to

confirm if the partial correlations among variables are not large. The test of sphericity

done by Bartlett was also to confirm if an identity matrix is the correlation matrix, as

this will show that the factor model is not the right one. The measurement of sampling

adequacy by KMO is expected to be above 0.5 in order to have a continued

satisfactory factor analysis (Raftery, 2001)

5.4.6 Conformity Factor Analysis (CFA )

Confirmatory factor analysis is an analysis of factors carried out to test hypotheses or

verify theories concerning the factors to be examined by a researcher. CFA is a

“subtype of structural equation modelling” which search for the inter-relationships

existing among the variables to confirm if those variables can be categorized into a

smaller set of factors (Vogt, 2005. 56).

SEM is just like the factors in factor analysis in which indicators of the variable

also possess loadings on their concerned latent variables. These coefficients are

related with the arrows coming from latent variables to meet their concern indicators

of the variables. Based on the convention, the indicators are expected to possess 0.70

loadings or more on the latent variable (Schumacker and Lomax, 2010). Just as it is in

factor analysis, the loadings can serve to give labels to the latent variables. Logically,

SEM starts with theory which encompasses the labelling of the constructs, and

thereafter examines the model fit using confirmatory factor analysis. Loadings also

serve the purpose of evaluating the reliability of the latent variables, as illustrated

below.

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The existing factor structure is confirmed as well as a hypothesized factor

structure by CFA, and it meets the third application of FA. Therefore, the main roles

of CFA are to confirm a hypothesize factor structure, and to serve as a validity

procedure of measure in research work. Furthermore, CFA is employed with the

following aims: examining whether a set of measures still show similar factor

structure as hypothesized; to serve in a causal model building; to serve in finding the

differences and similarity in alternative factor solutions from the information (data); to

serve in finding the differences and similarity in alternative factor solutions from

various individuals.

Confirmatory factor analysis and structural equation modelling have many

measurements of fit. Among them, the RMSEA is anticipated to be there while the

Chi-square, the degrees of freedom, and the probability of the chi-square should often

be reported. In this study, KMO test, factors loading, GFI, CFI and PCLOSE will be

examined (McDonald and Ho 2002; Boomsma, 2000).

5.4.6.1 Principal Support Test

The Principal support is the first model measured, and it has four factors on the basis

of Armenakis et al., (1999). It is to be noted that the exploratory factor analysis comes

before confirmatory factor analysis is conducted.

Table 5.25

Principal Support

Statements Legend

Sr. Mgt. thinks I should use computer. PRS1

198

Table 5.25 Continue

Statements Legend

Management supports computer in my organization. PRS2

I get management support. PRS3

It is easy for me to observe others using e-government in my ORG. PRS4

The four factors were analyzed, and the findings show that the Kiser-Mayer-Olin

statistic of sampling adequacy is 0.65 as shown in Table 5.25. Only a factor was

extracted with more than 30% of the total variance was explained as given in Table

5.26. All items reveal factors loading above 0.5 (Hair et al., 2010) while the reliability

coefficient (Cronbach's Alpha) is 0.71 indicating an acceptable value (Bruin, 2011).

Table 5.26

EFA, KMO, Bartlett's Test Principal Support

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PS1 1.79 1.133 28.31 28.31 0.71 0.658 165.431

PS2 0.89

PS3 0.74

PS4 0.56

199

Next is the conduction of CFA. To carry out CFA, AMOS requires that one of the

factors must be assigned the value of 1.0. This serves as necessary condition for the

model identification. It was shown that the model was well fitted to the data and there

was no need for adjustment. The measures for the model fit were as follows in Figure

5.1 and they show that the model is of good fit: CMIN/DF = 1.82, GFI = 0.99, CFI =

0.99, RMSEA = 0.04 and PCLOSE = 0.45 (Hair et al., 2010).

5.4.6.2 Motivation Valance Test

Table 5.27 shows the measured model of motivation valance, which consists of four

indicators on the basis of Armenakis et al., (1999). The exploratory factor analysis and

confirmatory factor analysis were done one after the other.

Principal

Support

PS4 PS3

PS2

PS1

CMIN/DF = 1.82,

GFI = 0.99,

CFI = 0.99,

RMSEA = 0.04

AND

PCLOSE = 0.45

1.0

0.9 0.9 0.8

0.5

7

0.9 0.2

7

0.2

Figure 5.1CFA Measurement Principal Support

200

Table 5.27

Motivation Valance

Statements Legend

I do not wish to expose myself or my organization to the high

risks and learning costs associated with a new technology by

being its first user.

VL1

I intend to use computer if it help the organization performance. VL2

I intend to use computer if it does not help me. VL3

I am satisfied with my performance at this task VL4

The sampling adequacy statistic of Kiser-Mayer-Olin is yielded at 0.66 as indicated in

Table 5.28. Only a factor was extracted and about 50% of the total variance was

explained as shown in Table 5.28. The factors loading that lowest recorded 0.54 (Hair

et al., 2010) while the reliability coefficient (Cronbach's Alpha) is 0.73 representing

the required value (Bruin, 2011).

Table 5.28

EFA, KMO, and Burlett’s Tests Motivation Valance

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VL1 2.22 1.64 41.09 41.09 0.73 0.65 336.22

VL2 0.69

VL3 0.54

VL4 0.54

201

With the assignment of 1.0 value by the factor VL1, the measure for the model fit

yielded the following: CMIN/DF = 0.99, GFI = 0.99, CFI = 1.0, RMSEA = 0.00 and

PCLOSE = 0.67. All the measure met the requirement and so stand for a good fit

model (Hair et al., 2010). In these cases, there is no need for adjustment as Figure 5.2

shows.

5.4.6.3 Appreciation Test

The Table 5.29 shows the measured model of appreciation, with five factors. Just as

the two preceding ones, the exploratory factor analysis and confirmatory factor

analysis were conducted.

Motivation

Valance

VL4 VL1 VL2 VL3

CMIN/DF = 0.99,

GFI = 0.99,

CFI = 1.0,

RMSEA = 0.0

AND

PCLOSE = 0.67

1.0 1.0 0.8

1.0

0.6 0.7 0.8 0.7

Figure 5.2 CFA Measurement Motivation Valance

202

Table 5.29

Appreciation

Statements Legend

Computers make work more interesting. APR1

Working with computers is fun. APR2

I like using computers. APR3

I find computers a useful tool in my work. APR4

I want to learn a lot about computers. APR5

The outcome of Kiser-Mayer-Olin statistic test was shown in Tables 5.30. 0.70 is the

KMO sampling adequacy and only one factor was extracted with 21% of the total.

0.66 is the lowest factors loading (Hair et al., 2010) and the Cronbach's Alpha

reliability coefficient is 0.72, which is a required value for acceptance (Bruin, 2011).

Table 5.30

EFA, KMO, and Burlett’s Tests Appreciation

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APR1 1.84 1.088 21.75 21.75 0.73 0.703 152.33

APR2 0.88

APR3 0.85

203

Table 5.30 Continue

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APR4 0.75

APR5 0.66

The CFA measurement output for the model fit was as follows: CMIN/DF = 0.63, GFI

= 0.99, CFI = 1.0, RMSEA = 0.00 and PCLOSE = 0.93, which represent a good fit

model (Hair et al., 2010). And no modification was done with reliability measurement

of 0.73 as shown in Figure 5.3.

Appreciation

APR1 APR5 APR4 APR3 APR2

1.0

1.1

1.2 0.7

0.9

0.8 0.8

0.5

0.6 1.2

CMIN/DF = 0.63,

GFI = 0.99,

CFI = 1.0,

RMSEA = 0.0

AND

PCLOSE = 0.93

Figure 5.3 CFA Measurement for Appreciation

204


There are four factors in the model of PEOU adopted from the questionnaire of

technology acceptance (Davis, 1989). The exploratory factor analysis and

confirmatory factor analysis were investigated after the coding of the model.

Table 5.31

Perceived Ease of Use

Statements Legend

My interaction with computers is clear and understandable PEU1


understandable.

PEU2

I find computers easy to use. PEU3

Easy to get computer to Perform what I wish. PEU4

Perceived

Ease of Use

PEOU4 PEOU1 PEOU2 PEOU3

CMIN/DF = 1.56,

GFI = 0.99,

CFI = 0.99,

RMSEA = 0.0

AND

PCLOSE = 0.51

1.0

0.9 0.5

0.8

0.5 0.3 0.9 0.8

Figure 5.4CFA Measurement Perceived Ease of Use

205

The Kiser-Mayer-Olin statistic of sampling adequacy is 0.73 as shown by the

Table 5.32. Only one factor was extracted and 47% of the total variance was explained

as indicated by Table 5.32. Furthermore, the lowest factors loading yielded 0.32 (Hair

et al., 2010). The Cronbach's Alpha coefficient of reliability yielded 0.75confirming

the acceptable value (Bruin, 2011).

Table 5.32

EFA, KMO, and Burlett’s Tests Perceived Ease of Use

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PEOU1 2.34 1.08 1.89 47.48 0.75 0.73 479.30

PEOU2 0.78

PEOU3 0.54

PEOU4 0.32

In similar step, the measures for the model fit are as follows: CMIN/DF = 1.56,

GFI = 0.99, CFI = 0.99, RMSEA = 0.37 and PCLOSE = 0.51 confirming a good fit

model (Hair et al., 2010). There was no adjustment or modification made. PEU3 has a

loading below 0.6 offering some doubt about the convergent validity (Anderson and

Gerbing, 1988). Notwithstanding these, the model indicates a good fit as shown in

Figure 5.4.

206


There are five factors in the model of PUF adopted from the questionnaire of the

technology acceptance (Davis, 1989). Also in this case, the exploratory factor analysis

and confirmatory factor analysis were conducted.

Table 5.33

Perceived Usefulness

Statements Legend

Using computers will enhance my effectiveness. PUF1

Using computers will increase my productivity. PUF2

Using technology compatible w/all aspects of our work. PUF3

Using computers enables to accomplish tasks quickly. PUF4

Using computers makes job easier. PUF5

Table 5.34

EFA, KMO, and Burlett’s Tests Perceived Usefulness

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PUFL1 2.54 2.073 41.455 41.455 0.72 0.76 547.74

PUFL2 0.94

PUFL3 0.62

207

Table 5.34Continue

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PUFL4 0.55

PUFL5 0.32

Following the analysis of the factors, the Kiser-Mayer-Olin statistic of

sampling adequacy was shown to be 0.73 in the Table 5.34. Only one factor was

extracted and 47% of the total variance was explained. As indicated in Table 5.34, the

value of 0.32 was recorded as the lowest factors loading (Hair et al., 2010). The

Cronbach's Alpha reliability coefficient yielded 0.75, which confirms the acceptability

of a good fit model (Bruin, 2011).

Perceived

Usefulness

PUF4 PUF1 PUF2 PUF3

CMIN/DF =0. 62,

GFI = 0.99,

CFI = 1.0,

RMSEA = 0.0

AND

PCLOSE = 0.78

1.0 1.1 1.4

0.6 0.8 0.3

0.9

0.8

Figure 5.5 CFA Measurement Perceived Usefulness

208

The steps of the model fit were pursued. The measures for the model fit were

recorded as follows: CMIN/DF = 2.6, GFI = 0.98, CFI = 0.98, RMSEA = 0.061 and

PCLOSE = 0.78. The results of fit indicators for perceived usefulness were not

acceptable for the CMIN/DF and PCLOSE (Anderson and Gerbing, 1988). For this

reason, there is a need for adjustment or modification. The model was reinvestigated,

and it was found necessary to drop factors number PUFL5 with the item like “Using

computers makes job easier”. After the modification was made, the new fit indicators

recorded are as follows: CMIN/DF = 0.62, GFI = 0.99, CFI = 1.00, RMSEA = 0.0 and

PCLOSE = 0.78.

Next was the analysis of the two factors model with the use of CFA for the

viability of the endogenous association existing between construct and the indictors

and the use of the covariance matrix of Perceived Usefulness and Perceived ease of

use indications. From Figure 5.5, there was a good model fit given the following

values: CMIN/DF = 1.23, GFI = 0.98, CFI = 0.99, RMSEA = 0.02 and PCLOSE =

0.94. Lastly, the reliability coefficients (Cronbach's Alpha) yielded 0.86.

5.4.6.6 Attitude to Change

There are four factors in the model of Attitude to change adopted from the

questionnaire of technology acceptance (Davis, 1989). The exploratory factor analysis

and confirmatory factor analysis were carried out.

209

Table 5.35

Attitude to Change

Statements Legend

I feel very little loyalty to this change. ATT1

I would accept almost any type of job assignment in order to keep

working for this organization. ATT2

I am willing to put in a great deal of effort beyond that normally expected

in order to help the organization be successful. ATT3

I find that my values and the organization’s values are very similar. ATT4

From the Table 5.36, the result of the Kiser-Mayer-Olin statistic of sampling adequacy

was 0.73. A four factors were extracted, and half (50%) of the total variance was

explained as indicated in Table 5.36. About 0.3 factors loading was recorded as the

lowest value (Hair et al., 2010). The Cronbach's Alpha reliability coefficient was 0.77

confirming the reliability (Bruin, 2011).

Table 5.36

EFA, KMO, and Burlett’s Tests Attitude to Change

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ATT1 2.41 1.99 49.93 49.93 0.77 0.73 538.08

ATT2 0.79

ATT3 0.50

ATT4 0.29

210

The following are the measures for the model fit: CMIN/DF = 3.6, GFI = 0.98, CFI =

0.98, RMSEA = 0.071 and PCLOSE = 0.17 which do not confirm a good fit model

(Hair et al., 2010). An adjustment is to be done in which case the error measurement

of ATT1 and ATT2 were to be correlated (Anderson and Gerbing, 1988). The new

value (fit indications) recorded after modifications are as follows: CMIN/DF = 2.6,

GFI = 0.98, CFI = 0.98, RMSEA = 0.06 and PCLOSE = 0.3 shown in Figure 5.6 in

the next page. It was ultimately referred to as the model of attitude.

5.4.6.7 Intention to Use

In the last factor of TAM, which is intention to use (IU), five factors are adopted from

the questionnaire of the technology acceptance, Davis (1989).

Attitude to

Change

ATT4

ATT1

ATT2

ATT3

CMIN/DF = 3.6,

GFI = 0.98,

CFI = 0.98,

RMSEA = 0.0 7

AND

PCLOSE = 0.78

1.0 1.1 1.4

0.6 0.8 0.3 0.9

0.8

Figure 5.6 CFA Measurement Attitude Change

211

Table 5.37

Intention to Use

Statements Legend

I will use computers in my work in future. IU1

I plan to use computers in my daily life often. IU2

I will encourage my colleague to use computer. IU3

I will encourage my organization costumers’ to use the system IU4

Assuming I had access to the computer, I intend to use it IU5

The Kiser-Mayer-Olin statistic of sampling adequacy was indicated to be 0.71

from Table 5.37. A factor was extracted and 40% of the whole variance was explained

as indicated by Table 5-45. In addition, 0.35 was recorded as the lowest factors

loading (Hair et al., 2010). The Cronbach's Alpha reliability coefficient yielded 0.75,

which confirm the reliability (Bruin, 2011).

Table 5.38

EFA, KMO, and Burlett’s Tests Intention to Use

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IU1 2.38 1.85 37.06 37.06 0.70 0.71 464.741

IU2 0.89

IU3 0.83

IU4 0.52

IU5 0.35

212

The researcher also carried out model fit steps and the measures for the model fit were

recorded as follows: CMIN/DF = 1.1, GFI = 0.99, CFI = 0.99, RMSEA = 0.123 and

PCLOSE = 0.001 showing that they were not a good fit (Hair et al., 2010). For this

reason, there was a need for modification in which case measures such as e1 with e2,

e3 with e4 and e3 with e5 were correlated. Consequently, it gave results, which

confirm in a goodness of fit of model with the measure of indicators as: CMIN/DF =

7.4, GFI = 0.96, CFI = 0.92, RMSEA = 0.01 and PCLOSE = 0.63 refer to Figure 5.7.

The analysis of TAM factors model was followed with the use of CFA for

the viability of the endogenous relationship between construct and the indictors. There

was no indication of good model fitness. It was suggested to go through the model

again, and it was ultimately found improved as shown in Figure 5.8.

Intention to Use

IU1

IU2 IU3 IU4 IU5

CMIN/DF = 1.1,

GFI = 0.99,

CFI = 0.99,

RMSEA = 0.016

AND

PCLOSE = 0.63

0.8 0.8

0.6 0.5 1.2

0.6 0.4

0.3

0.6 1.0

Figure 5.7 CFA Measurement Itention to Use

213

Figure 5.8 is very important since it indicates the relationship between

TAM variables caught up with and buttressed by the level of reliability with high

significant and measure of fit. Furthermore, Davis, (1989); Venkatesh and Davis,

(2000); Venkatesh et al., (2003); Venkatesh and Bala (2008) indicate that there are

positive direct relations between TAM variables as shown in Table 5.39.

PUFL

ATT

IU

PEU

BEU4

BEU2

BEU1

PUFL1

PUFL2

PUFL3

3

PUFL4

PUFL5

IU 1

IU 2

IU 4

IU 5

ATT1

ATT3

ATT4

0.92

0.30 0.70

1.02

0.7

0.6 0.4

0.7

0.7

0.6

0.7

0.3

0.5

0.8

0.6

0.6

0.7

0.8

0.6

CMIN/DF = 1.86,

GFI = 0.93,

CFI = 0.97,

RMSEA = 0.045

AND

PCLOSE = 0.75

Figure 5.8 CFA Measurement TAM

214

Table 5.39

Summary Result for TAM Hypothesis

Structural Path Regression

Wight

Hypotheses

Tested

Perceived ease of use Perceived usefulness 0.9 Supported

Perceived ease of use Attitude Behaviour 0.3 Supported

Perceived usefulness Attitude Behaviour 0.7 Supported

Attitude Behaviour Intention to use 1.0 Supported


Four factors are contained in the subjective norm model adopted from the

questionnaire of technology acceptance (Davis, 1989).

Table 5.40

Subjective Norm

Statements Legend

People who influence my behaviour think I should use the computer. Norm1

People who are important to me think I should use the computer. Norm2

My immoderate supervisors think that I should use computer. Norm3

I want to do what the people who report to me think I should do. Norm4

215

The Kiser-Mayer-Olin statistic of sampling adequacy was found to be 0.42 while 0.1

was found to be the lowest factors loading (Hair et al., 2010). The Cronbach's Alpha

reliability coefficient was 0.66 confirming the reliability (Bruin, 2011). As it could be

seen, the measures were not reliable as shown by the indicators. Modification required

the removal of the Norm3 and Norm4. Thereafter, the revision of the model yielded

improved measures with Cronbach's Alpha yielding 0.9, and the sampling adequacy

equals 0.5. Both show better measures as can be seen in the Table 5.40. In this, two

factors were extracted and 60 % was explained of the total variance as indicated in the

Figure 5.9.

Table 5.41

EFA, KMO, and Burlett’s Tests Subjective Norm

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Norm1 1.81 1.861 62.03 62.03 0.90 0.50 547.74

Norm2 0.97

Figure 5.9 CFA Measurement Subjective Norm

Subjective

Norm

NORM4 NORM 1 NORM 2 NORM 3

CMIN/DF = 3.5,

GFI = 0.99,

CFI = 0.99,

RMSEA = 0.07

AND

PCLOSE = 0.19

1.0

1.4

-0.4 -1.0

0.2 0.02 0.6 1.2

216

There was no fit indicator for CFA at all, and the following measures were recorded:

CMIN/DF = 237.5, GFI = 0.72, CFI = 0.50, RMSEA = 0.8 and PCLOSE = 0.199. The

sizes recorded indicated a poor model fit, and the adjustment required the omission of

two factors. Norm3 representing item “My immoderate supervisors think that I should

use computer”, and norm4 representing item “I want to do what the people who

report to me think I should do” otherwise an alternative could be to correlate e3 with

e4. The first alternative was done and the new fit indicators recorded were as follows:

CMIN/DF = 3.5, GFI = 0.99, CFI = 0.99, RMSEA = 0.07 and PCLOSE = 0.199. For

the second model adjusted, the new outcome model reveals a good fit model with high

consistent measures as shown in Figure 5.9.


There are four factors in the model of Perceived Voluntariness adopted from the

questionnaire of technology acceptance, Davis (1989) Figure 5.10. The exploratory

factor analysis and confirmatory factor analysis were carried out.

Having analyzed the Perceived Voluntariness factors, the Kiser-Mayer-Olin

statistic of sampling adequacy was shown to be 0.76 from the Table 5-42 below. Only

a factor was extracted and 61 % of the whole variance was explained as indicated by

Table 5-51. Also, 0.34 was recorded as the lowest factors loading (Hair et al., 2010).

The Cronbach's Alpha reliability coefficient was shown to be 0.78 confirming the

reliability of the scale (Bruin, 2011).

217

Table 5.42

EFA, KMO, and Burlett’s Tests Perceived Voluntariness

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VL1 2.45 2.453 61.316 61.316 0.78 0.76 501.50

VL2 0.62

VL3 0.57

VL4 0.34

Similarly, the steps of the model fit were pursued in the model. The following

are the measures for the model fit: CMIN/DF = 0.52, GFI = 0.99, CFI = 0.99, RMSEA

= 0.00 and PCLOSE = 0.82 as Figure 5-11 shows. It implies that the model is well

fitted (Hair et al., 2010). Therefore, there was no need for modification.

Perceived

Voluntariness

VLR4 VLR1 VLR2 VLR3

1.0

1.0 1.0

0.9

0.6 0.7 0.8 0.7

CMIN/DF = 0.52,

GFI = 0.99,

CFI = 0.99,

RMSEA = 0.0

AND

PCLOSE = 0.82

Figure 5.10CFA Measurement Perceived Voluntariness

218


For the current usage model, The EFA and CFA were carried out, and the model has

three factors.

Table 5.43

Current Usage

Statements Legend

My Usage of the computer in my daily work is high Usage1

I estimate the current usage of computer in my department very high Usage2

My current usage of the computer is high. Usage3

The analysis of the factors shows that the Kiser-Mayer-Olin statistic of

sampling adequacy was 0.68 as shown by Table 5.44. Only a factor was extracted, and

half (50%) was explained of the total variance as indicated by Table 5.43. The

measurement recorded was 0.53 as the lowest factors loading (Hair et al., 2010). The

Cronbach's Alpha coefficient was 0.75 confirming the reliability of the scale (Bruin,

2011).

219

Table 5.44

EFA, KMO and Bartlett's Test Current Usage

Fact

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Usage1 2.00 1.52 50.74 50.74 0.75 0.68 304.48

Usage2 0.56

Usage3 0.43

The steps for model fit were pursued in the model, and the following were the

measures for the model fit: CMIN/DF = 0.0, GFI = 0.98, CFI = 0.98, RMSEA = 0.061

and PCLOSE = 0.27. The value shows goodness of fit of the model (Hair et al., 2010).

Figure 5.11, shows the conclusion of the test. While, all other indicators show a

goodness of fit of measures, the measure of RMSEA has not, but recorded 0.75 for the

measure of reliability. According to Yatim (2011), if five of good measure were met,

then the model is considered acceptable.

USGOR

Current

Usage

USGEM

USGC

0.7

1.3 1.0

0.9

1.2

0.7

CMIN/DF = 0.0,

GFI = 0.98,

CFI = 0.98,

NNFI = 1.0

RMSEA = 0.06

AND

PCLOSE = 0.27

Figure 5.11 CFA Measurement of Current Usage

220

5.5 ASSESSMENT OF THE MEASUREMENT MODEL

In the previous section, attention was given to the establishment of a measure model

which fit uni-dimensionality, validity, and reliability criteria. After estimating the

model, next is to evaluate the association between the constructs in the proposition

made in the theoretical framework model. As pointed out earlier, AMOS package was

the instrument employed to examine the estimation of maximum likelihood. The

process was demonstrated by a series of structural association between variables as

shown in Figure 4.1. Following the steps of hair et al., 2010, the structural measure

model was investigated for offending estimate.

The findings of the analysis should be assessed, and the empirical cycle should

reflect the feedback to the hypothesised theory which forms the model in the first

instance. When the model is well fitted, next is to find out whether the estimates of

parameters comply with the expectations sign and size based on the theory. If the

theoretical expectations are right, then the postulated model could be made simple by

removing some structural relations in which case the parameters are fixed to zero.

Conversely, suppose the model was not a good fit, there may be an extension of the

model in some case, which hypothesized relations are added.

It was argued by Kaplan (1990) that while attempting to modify a model by the

simplification or the expansion of the model, the adjustment, or modification of

hypotheses should be primarily based on theory in order to be defended. Statistical

data from the model estimated may be useful as well: the values of t calculated could

give hints on simplifications. Also, the indexes of estimated modification with

expected parameter change statistics may hint on particular model expansions.

221

For example, Kaplan (1990, 1991), made suggestions on the combinational use

of both the modification index and the expected parameter change statistic for model

assessment and modification. It was also argued that the use of only data-driven

decisions to model modifications cannot be justified. There should be substantive

reasons to prove any addition or subtraction of relationship and there should be

appropriate interpretations of parameters that can rightly be connected to the subject-

matter (Cox and Wermuth, 1996). Most important difficulty or danger is the

permission of occurrence of error variances to correlate in order to enhance fit, which

is not reasonably based on empiric or theory. For this reason, the current study will be

validated showing a cause-effect association between the exogenous and endogenous

variables.

Figure 4.1 shows association between the variables based on postulations. The

diagrams consist of some aspects in addition to the representation of the linear

equation associations with arrows. Ovals stand for the variables in rectangular boxes

while manifest variables are contained in boxes within the path diagram. Latent

variables are also contained in an oval or circle. From Same Figure the Principal

support is the latent’s variables which consist of four factors with indicators

“Motivation valance”. There are four factors for “Valance” as well and the final

antecedent of belief is appreciation, which contains five indicators. The technology

belief is the group two variables and contains four variables namely the Perceived

ease of use “PEOU”; attitude behaviour “Attitude” all of which are indicators;

Perceived usefulness “PUSFL” and intention to use having five factors. There are two

moderators’ namely subjective norm “Norm” and volunteer motivation “VOLNTR”.

Both of them have four factors. The following are the last three observed variables:

http://www.statsoft.com/textbook/statistics-glossary/m.aspx?button=m#Manifest variable

http://www.statsoft.com/textbook/statistics-glossary/l.aspx?button=l#Latent variable

http://www.statsoft.com/textbook/statistics-glossary/l.aspx?button=l#Latent variable

222

work, training, and usage. The following Table shows the coding of the variables

factors of the model in Figure 5.12.

Table 5.45

Exogenous Variables: Measurement and Legends

Con

stru

ct

Code Variables item

Pri

nci

pal

sup

port

PS1 Sr. Mgt. Thinks I Should Use computer.

PS2 Management supports computer in my organization.

PS4

It is easy for me to observe others using e-government in my

ORG.

Moti

vati

on

Vala

nce

VL1

I do not wish to expose myself or my organization to the high

risks and learning costs associated with a new technology by


VL2

I intend to use computer if it help the organization

performance.

VL3 I intend to use computer if it does not help me.

VL4 I am satisfied with my performance at this task

223

Ap

pre

ciati

on

APR1 Computers make work more interesting.

APR2 Working with computers is fun.

APR3 I like using computers.

APR4 I find computers a useful tool in my work.

APR5 My interaction with computers is clear and understandable

Per

cei

ved

Ease

of

Use

PEOU1


understandable.

PEOU2 I find computers easy to use.

PEOU3 Easy to Get computer to Perform what I wish.

Per

cei

ved

Use

fuln

ess

PUSFL1 Using technology compatible w/all aspects of our work.

PUSFL2 Using computers enables to accomplish tasks quickly.

Att

itu

de

Beh

avio

ur

ATT1 I feel very little loyalty to this change.

ATT2

I would accept almost any type of job assignment in order to

keep working for this organization.

ATT3

I am willing to put in a great deal of effort beyond that

normally expected in order to help the organization be

successful.

ATT4 I find that my values and the organization’s values are very

similar.

224

Figure 5.12 indicates the postulated model association between the constructs

and also shows the model tested fit measurements. These measurements made a valid

model the fit parameters are: CMIN/DF = 1.6, GFI = 0.9, CFI = 0.9, RMSEA = 0.035

and PCLOSE = 1.0. These values show a representation of a good fit model and the

value of Cronbach's Alpha 0.79 shows reliability of the scale (Hair et al., 2010).

Su

bje

ctiv

e

Norm

NRM1

People who influence my behaviour think I should use the

computer.

NRM2

People who are important to me think I should use the

computer.

Per

cei

ved

Volu

nta

rin

ess

VLR1 I use the computer all the time.

VLR2 Although it might be helpful, using computer is certainly not

compulsory in my business.

Inte

nti

on

to u

se

IU1 I will use computers in my work in future.

IU2 I will encourage my colleague to use computer.

IU3 Assuming I had access to the computer, I intend to use it

IU4 My usage of the computer in my daily work is high

Cu

rren

t u

sage

Usage1 I estimate the current usage of computer in my department

very high

Usage2 My current usage of the computer is high.

225

Figure 5.12 CFA The Research Model and Model Fit

226

5.6 HYPOTHESIS TESTING

In the evaluation of the postulated path suggested in the structure model, it was

verified whether the path coefficients are significant and whether the same direction is

presumed in the model. In addition, the mediators are investigated and assessed on the

basis of the literature upon which the relationship has been constructed. The essence is

to check the influence of the new variables on the model. Generally, fifteen

postulations were made and recognized in the model of this study. The Table 5.46

shows the standardized regression weight of the model postulated.

Table 5.46

Standardized Regression Weights and the Legend of Each Construct

Structural Path

Std

Regr.

weight

S.E. C.R P.lv

Perceived Ease of Use Principal Support 1.46 5.18 0.53 0.59

Perceived Ease of Use Appreciation -0.29 4.59 -0.24 0.80

Perceived Ease of Use Motivation Valance -0.78 6.88 -0.40 0.68

Perceived Usefulness Principal Support -0.83 4.13 -0.35 0.72

Perceived Usefulness Motivation Valance 0.36 4.91 0.24 0.80

Perceived Usefulness Perceived Ease of Use 1.22 0.57 2.02 0.04

Current Usage Perceived Usefulness 0.81 0.49 1.70 0.08

Current Usage Perceived Ease of Use -1.01 0.46 -2.11 0.03

227

Table 5-46 Continued

Structural Path

Std

Regr.

weight

S.E. C.R P.lv

Struct

ural

Path

Std

Regr.

Weig

ht

Work Type Current Usage -0.08 0.06 -1.38 0.16

Training Time Current Usage 0.06 0.15 0.87 0.38

Attitude Behaviour Training Time 0.20 0.02 3.05 0.00

Attitude Behaviour Work Type -0.02 0.04 -0.33 0.73

Attitude Behaviour Current Usage -0.19 0.05 -2.93 0.00

Perceived Voluntariness Attitude Behaviour 0.03 0.06 0.25 0.79

Subjective Norm Attitude Behaviour -0.01 0.11 -0.14 0.88

Intention to use Attitude Behaviour 0.03 0.07 0.32 0.74

Intention to use

Perceived

Voluntariness 0.68 0.27 2.91 0.00

Intention to use Subjective Norm 0.31 0.10 1.85 0.06

Note: Std. Regr. Weight : Standard regression weight.

S.E. : Standard error of the regression weight.

C.R. : Critical ratio of regression weight.

P. lv. : Level of significant for the regression weight.

228

5.6.1 Hypothesis 1: Attitude to change negatively and directly influences

Intention to use.

It is to be noted that attitude to change is not all the time positive. However,

organization considers a change to be crucial but the workers may see such change as

a threat. Several times, it may be to the interest of the organizations to bring in

changes to their business activities, but the members of the organization would often

want to oppose it. For more insight into these variables, the theory of planned

behaviour is important. The theory of planned behaviour states the natures of

association of belief with attitude. The assessments of attitude by individuals

regarding the behaviour are decided by the accessible belief of the behaviour, Mischel

(1968).

The belief that certain behaviour will yield a particular outcome is subjective in

nature. The assessment of an outcome adds to the attitude in direct proportion to

individual’s subjective probability, which yields the result in question (Fishbein and

Ajzen, 1981). The positive association between attitude and intention to use was

confirmed by Davis (1989) which is contrary to the study finding. In this study, Table

5.47 shows, the results of the hypotheses that positive non significant weight

association existed and that the level of significant is 0.7.

The objective of TAM is to "provide an explanation of the determinants of

computer acceptance that is general, capable of explaining user behaviour across a

broad range of end-user computing technologies and user populations, while at the

same time being both parsimonious and theoretically justified" (Davis et al., 1989; p.

985). The decision-making process is rational. Empirical studies in support of TAM

have been given (Venkatesh et al., 2003).

229

Table 5.47

Summarize Result of H1

Structural Path

Std

Regr.

weight

S.E. C.R. P.lv Hypo.

Tested

Intention to

use

Attitude

to change 0.03(n.s) 0.07 0.32 0.74

Not

Supported

5.6.2 Hypothesis 1a: Subjective Norms moderates the relationship between

attitude to change and Intention to use.

There was postulation made about subjective norm in association with an innovation

to significantly affect the user’s behavioural intent to adopt the innovation. Norm

could be categorized under the normative beliefs and subjective norms. Normative

belief is the belief by a person about the degree to which other important individuals

to him/her feels they should or should not behave in a certain way. Given this,

individual is aware of what their actions in the organization as a regard to the thought

of others. Therefore, it is essential to note that norm affects individual attitude to

change and the intention to use.

It is important to know the meaning of attitude and intention to use before

discussing how a norm influences the attitude of employees to change intention to use.

Attitude concerns with the evaluation of related beliefs and behaviour towards

anything. It is not stable since communication and behaviour of other individuals

affect them. Social effect causes some people to change, and people’s motivation can

as well influence the attitudes of the people.

230

In this study Table 5.48 shows, subjective norm moderates the association of attitude

with intention to use, but the effect is not significant as p value is below 0.08 as

suggested by Hair (2006; 2010).

Table 5.48

Summarize Result of H1a

Structural Path Struc.

Path

Struc

Path

Direct

Path P.lv

Hypo.

Tested

Intention to use

Subjective

Norm

0.31 0.85 0.19 0.05

Su

pp

ort

ed

Intention to use

Attitude

To Change

-0.03 0.27 0.02 0.27

5.6.3 Hypothesis 1b: Perceived voluntariness moderates the relationship

between attitude to change and Intention to use.

There is a proposition about the perceived voluntariness with respect to an innovation

to significantly affect the behavioural intent of user to adopt that innovation.

Volunteer motivation shows an activity and is an individual who performs a certain

role in respect of others without intention of getting anything in return. How can such

a person decide the association between attitude to change and intention to use to be

negative remains a question to be answered in this study! Some organizations are

interested in using the services of volunteers to offer training to them. Any chance of

that form of organization decision, will affect the attitude to change.

231

Table 5.49 indicates that volunteer motivation moderates the association of

attitude with the behaviour. The direct path has weight 0.81, p value is 0.3. Mediating

the association is significant in accordance with Hair (2010).

Table 5.49

Summarize Result of H1b

Structural Path Structural

Path

Structural

Path

Direct

Path P.lv

Hypo.

Tested

Intention to

use

Perceived

Voluntariness 0.68 1.15 0.81 0.34

Su

pp

ort

ed

Intention to

use

Attitude to

Change 0.03 0.27 0.02 0.27

5.6.4 Hypothesis 2: Current use positively and directly mediates the attitude to

change.

Next is to analyse how current usage positively influences the attitude to change. The

use of ICT has considerably increased, and it has positively influenced the attitude to

change. To explain how this occurs, it is essential to know the meaning of change in

the context of an organization. Change is known to involve the bringing in of new

methods and systems in executing business activities in an organization.

Organizations carry out change and make its members comply to with it in order to

realize better results.

232

It was found by Floh and Treiblmaier (2006) that satisfaction which connotes

the performance of management is an essential feature of technology adoption. Lee,

David, Yen and Wu (2010) assert that current usage positively influences the change

process. The satisfaction on the new instrument relies upon the performance of this

new tool (Ptricio et al., 2003).

In this study, Table 5.50 indicates that current usage has a negative influence

on the attitude behaviour and the level of significant was very low. As a result of

change, management has come to be very important in the modern business of the

world as all of them are striving to manage change so as to realize their goals. Despite

that change is very essential to enhance the performance of the organization;

employees sometimes stand against change. The motive behind standing against

change by the employees is the anxiety of forfeiting their job positions and all the

organization members never take it positively. Also, not all the organization members

are knowledgeable in using computer and may think that such change to computer

usage may frustrate their job since they could not use the system, (Management Hub,

2010).

Table 5.50


Structural Path

Std

Regr.

weight

S.E. C.R. P.lv

Hypo.

Tested

Attitude to change Current Usage -0.19 (n.s) 0.05 -2.93 0.003

Not

Supported

233

5.6.5 Hypothesis 2a: The nature of work moderates the relationship between

current usage and attitude to change.

It has been argued that a positive thought about the organizational change depends on

the degree of worker’s belief that a change will benefit him and the organization at

large such if the degree is high, then it will result in better reactions to change

(Armenakis et al., 1993). It is of necessity for change to occur, so the factors that

promote the readiness of organization for change must be improved. Organizational

performance is one of the essential indicators of organization achievement (Cameron,

1986; and Cameron and Whetten, 1996).

From Table 5.51, it is concluded that the nature of work did not moderate the

association between usage and attitude. The significant level is reported to be 0.9.

Table 5.51



Path

Structural

Path

Direct

Path P.lv

Hypo.

Tested

Attitude

Behaviour

Nature of

Work -0.02 (n.s) 0.07 -0.01 0.96

Not

Supported Attitude

Behaviour

Current

Usage -0.19 0.02 -0.15 0.24

5.6.6 Hypothesis 2b: Training moderates the relationship between current usage

and attitude to change.

It is possible that management may not be able to get the expert needed for the

member of the organization to effectively use the ICT. This means that the training,

234

and the time needed for carrying out the new IT may not be met (Venkatesh and

Davis, 2000).

It was believed that if the perception of individuals is negative regarding a

particular information system arranged for them to use, such system will not perform.

On the other hand, if the perception is positive with respect to the system, then such

system is going to work. It should be noted that all individuals will not often accept

positively all information systems. There is a need for the organization to make

training available to its workers on the application of information systems.

Furthermore, it was required of the organization to generally provide training to the

members of the organization on ICT. By providing room for learning about

information systems (IS) and ICT in general, there is a likelihood of the members

having an interest (Ahmad et al., 2010).

Table 5.52 shows that training moderates the association of current usage and

attitude behaviour. The direct path is 0.14 for the association between training and

attitude behaviour, and there was negative relationship between usage and attitude

behaviour. (Hair et al., 2010) suggested that the value of the path should be above

0.08 to be significant (0.38).

Table 5.52



Path

Structural

Path

Direct

Path P.lv

Hypo.

Tested

Attitude

Behaviour Training 0.20 0.09 0.14 0.38

Su

pp

ort

ed

Attitude

Behaviour

Current

Usage -0.19 -0.13 -0.15 0.00

235

5.6.7 Hypothesis 3: Perceived Usefulness positively and directly influences

Current usage of technology.

Perceived usefulness is described by Davis (1989) as a subjective probability that the

use of a particular technology will enhance how a user would finish a given job.

According to Al-Gahtani et al., (2007) perceived usefulness is described as the degree

of person’s acceptance that the use of a particular method of technology will have no

cost. Perceived usefulness has various connotations based on the context of study.

Given an insight into perceived usefulness, the next question is of its working in the

business organization to enhance information technology. Most essentially is the

acceptance of the concept by individuals.

The use of ICT has been on the increase in organizations, and this raises the

value of employees thus, making customer perception to be positive. ICT mostly

enables the success of any organization or institution and adds value to the

performance of the organizations. For the organization to proceed with the effective

use of ICT, it is important to train the employees on ICT. This makes the employees to

positively accept and apply it in every aspect of the business. Table 5.53 shows

perceived usefulness has effects on the current usage.

Table 5.53


Structural Path

Std

Regr.

weight

S.E. C.R. P.lv Hypo.

Tested

Current

Usage

Perceived

Usefulness

0.81 0.5 1.70 0.08

Su

pp

ort

e

d

236

5.6.8 Hypothesis 4: Perceived ease of use positively and directly influences

Perceived usefulness.

In the analysis of the technology acceptance model, Perceived ease of use (PEOU)

describes the extent of people belief that using a specific system will lessen effort used

(Davis 1989). Thus, it implies that with Perceived ease of use people are capable of

applying any form of information systems to meet their aim of ICT.

Perceived ease of use decides people’s intention to use to make use of

information technology. Perceived ease of use is categorized under the Technology

acceptance model (TAM), a theory which indicates how users grant and use a

particular technology. The theory has it that several factors will affect individual

users’ decision on how and when to use a new technology when presented to them

Meuter, Bitner, Ostrom and Brown (2005). PEOU has indicated a direct positive

significant influence on perceived usefulness in numerous studies (Gyampah, 2004).

Finally, this research disembarks to the same output of the previous studies,

which is indicated in Table 5.54 with low level of significant and high regression

weight.

Table 5.54


Structural Path Std Regr.

Weight S.E. C.R. P.lv

Hypo.

Tested

Perceived

Usefulness

Perceived

Ease of Use 1.22 0.57 2.02 0.04

Su

pp

ort

ed

http://en.wikipedia.org/wiki/Technology_acceptance_model#CITEREFDavis1989

237

5.6.9 Hypothesis 4a: Perceived ease of use positively and directly influences

Current usage of technology.

Perceived usefulness described the extent of a person’s belief that job performance

will improve by the use of a particular technology (Davis et al., 1989). Perceived ease

of use is the main factor under the Technology Acceptance Model. Past studies have

noted that perceived ease of use is the degree of acceptance by the individual with the

belief that the use of a particular method of information systems allows work to be

done without incurring any cost (Mathieson, 1991; Gefen and Straub, 1997).

According to Rogers (2003) perceived ease of use is the extent the customer

perceives that a new product or service is better than their substitutes. Zeithaml,

Parasuraman and Malhotra (2002) noted that the extent of using and understanding an

innovation easily is perceived ease of use. Jahangir and Noorjahan (2008) described

Perceived ease of use when customers are able to try or test the innovation and assess

easily its benefits.

Rogers (2003) asserts that Perceived ease of use does not only serve as the

people belief of information system, but it is also the extent of perceiving an

innovation to be simple to learn, understand, and operate. On the contrary, the study

shows in Table 5.55, that there is a direct negative association between current usage

“performances” and Perceived ease of use. This result confirms the result of the most

current study by Nagli, Rahmat, Samsudin, Hamid, Ramli, Zaini and Jusoff (2011)

that perceived ease of use has no significance in the operation these days.

238

Table 5.55



weight S.E. C.R. P.lv

Hypo.

Tested

Current

Usage

Perceived Ease of

Use -1.01 0.46 -2.11 0.03

Not

Supported

5.6.10 Hypothesis H5: Principal Support positively and directly influences

perceived Usefulness.

It was claimed that people within the employees are anxious to learn the new ICT but

unfortunately, there was no seriousness from the top management (Bobbit, 2001). In

such case, the management has not received the significance of ICT, and thus the

organization members are not willing as well. Given the principal support, such an

organization needs to show its weakness in respect of granting the new information

system so as to start with better execution of ICT (Bobbit, 2001).

Principal of support assist the management to start the method making ICT

connected with mission and vision of the organization. With the organization mission

and vision in mind there will be a possibility of analyzing the organization strategic

plan or executing ICT to the organization.

Table 5.56 indicates that the principal support negatively influences the

perceived usefulness. The results confirm the result of Bjorn and Fathul (2008) which

indicates that fai.ure of leaders or high officials support leads to 60% of e-government

initiative’s failure. It also buttresses the result of Heeks (2003) that in some

239

developing countries, the leaders personal interests lead to several e-government

projects failures.

Principal of support assist the management to start a method of making ICT

connected with the mission and vision of the organization. With the organization

mission and vision in mind there will be a possibility of analyzing the organization

strategic plan or executing ICT to the organization.

Table 5.56


Structural Path

Std Regr.


Hypo.

Tested

Perceived

Usefulness

Principal

Support -0.83 4.13 -0.35 0.72

Not

Supported

5.6.11 Hypothesis H5a: Principal Support positively and directly influences

perceived ease of use.

Perceived ease of use is described as the belief by people over the use of a particular

kind of system that it would enhance the ease of their job performance (Davis, 1989).

Perceived ease of usage is enhancing the execution of the ICT since it generated from

the big offices to personal level. Given the perceived ease of use, people believe and

find it beneficiary to know more on the importance of information technology

systems. When the use of the technology system has been learnt by people, their idea

about ICT will increase, which implies that the implementation of the ICT has eased.

240

Davis, (1989) and Venkatesh (2000) noted that the principal support and

perceived ease of use are impact the ICT implementation. The enhancement of the

ICT systems is essential to provide an ease to the users. When there is ease, it implies

to the organization that the execution of ICT has improved (Venkatesh, 2000).

In the current study, the Principal support significantly and positively

influenced the perceived ease of use as shown in Tables 5.57. This association was

supported in this study.

Table 5.57




Hypo.

Tested

Perceived Ease

of Use

Principal

Support 1.46 5.18 0.53 0.59 Supported

5.6.12 Hypothesis 6: Motivation Valence negatively and directly influences

perceived usefulness.

Valance is considered the strength of performance of individual for a reward, and

expectance is the probabilities that a specific action will result in a desired reward.

Instrumentality shows the calculation of individuals that performance will lead to

reward. The implication is that if a person has a specific objective to realize, the

person should exhibit particular behaviour to comprehend this objective. In addition,

people have to weigh the assumed benefit of certain behaviours in realizing the

241

desired objectives. People will like a new specific behaviour if such is needed for an

anticipation of getting more success (Cherry, 2011).

At the time, a particular objective is realized; individuals have various

valances. The valance outcomes of people are affected by some conditions such as

age, type of education obtained and type of jobs. There could be positive or negative

valance to a job by people based on their positive or negative objective preference.

Any person who is indifferent with an outcome has zero valances. Table 5-58 reveals

that motivation valances and perceives usefulness have direct positive relationship.

For more insight into the issue of valance, there is a need to understand the

expectancy which is referred to be the performance reward. The expectancy theory

offers the probability of the performance which will result into a desired objective or

outcome (Vroom, 1964). In this case, motivation has come to be valance,

instrumentality and expectancy. Depending on the range of valance and the degree of

expectancy and instrumentality, it is possible for the three factors existing in the

expectancy model to be in an infinite number of contributions. The realization of high

positive valance is made possible when there is a combination that yields an elevated

motivation. In this case, as Table 5.60 shows valance becomes positive at the time the

three values are high and give high motivation value as Holdford, and Lovelace-

Elmore (2001) suggested.

242

Table 5.58




Hypo.

Tested

Perceived

Usefulness

Motivation

Valance 0.36 4.91 0.24 0.80

Not

Supported

5.6.13 Hypothesis 6a: Motivation Valence negatively and directly influences

perceived ease of use.

The significance of people’s opinion and evaluation of organizational behaviour are

stressed by valance motivation. It may mean that the manager’s opinion will be

similar to employees’ perception of a specific motivation as the best motivation to

their performance. However, at times, the expectation of management will be different

from that of employees. To make the employees have a positive valance, a point of

agreement must be reached between the management and the employee.

The idea behind the valance instrumentally expectation (VIE) theory is that

people are motivated to do their job only on condition that their expectation from the

job done will be realized. The theory is of the views that workers are rational in their

thought about rewards and how much the rewards worth to them before engaging in

doing any job. In addition to the thought of people the theory provides connection

with other aspects affecting job performance.

On the issue of valance as a way of motivating employees, it was found

following the assessment of valance that it never took in a particular means of

motivation. There are different ways of motivation valance (Poter and Lawler, 1968).

243

It implies that motivation is never sourced from only the activity, but from other

external factors (Kjerulf, 2010).

In the discussion of valance instrumentality expectance (VIE), Vroom (1964)

pointed out that, authors like Poter and Lawler (1968) offer that the three factors are

essential. Once the three factors: expectation, instrumentality, and valance operate at

higher level the motivation will be high. Furthermore, the influence of motivation

value in the organization will be null if none of these elements operates, or if they are

rated zero. People who believe in their efforts to yield higher performance of a

particular system, and also believe to have a better reward will not have motivation

value once the valance is zero. The current study buttresses the valance

instrumentality expectance assertion of zero or negative influence. Table 5.59 shows

that there was direct negative association between motivation valance and perceived

ease of use.

Table 5.59




Hypo.

Tested

Perceived Ease

of Use

Motivation

Valance -0.78 6.88 -0.40 0.68 Supported

5.6.14 Hypothesis 7: Appreciation positively and directly influences perceived

ease of use.

New types of ICT with its various models have been adopted by many organizations.

Appreciation is very important in the ICT as it serves as an increasing use of the

244

information communication system (Maio-Taddeo, 2006). When information system

increases there will be increase in the use of ICT (Davis, 1989; and Venkatesh, 2000).

Andam (2003) points out that there is positive influence of the appreciation in

ICT, particularly in government organization. Trials have been made by many

organizations to make ICT part of their daily business activities for the purpose of

raising productivity. The most fact important of appreciation indicates the tendency of

the people to appreciate the introduction of information technology.

Surprisingly, it was shown from Table 5.60 that there is a significant negative

association between perceived ease of use and appreciation. The reason for this was

traced to the motivation approach which defines how the goals of individuals affect

their efforts. Also, the approach indicates that the behaviour of individuals chosen is

based on their probability evaluation. Values such as job promotion, high job security

and condition of services with better income are not implemented with the objective

of enhancing the employee opinion regarding performance and positive attitude in the

government organization in Saudi Arabia (Vroom, 1964).

Table 5.60



Weight S.E C.R. P.lv

Hypo.

Tested

Perceived Ease

of Use

Appreciation -0.29 4.59 -0.24 0.80

Not

Supported

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5.6.15 Hypothesis 7a: Appreciation positively and directly influences perceived

Usefulness.

Davis (1989) and Venkatesh (2000) pointed out that there is appreciation when the use

value of ICT increases and ICT adoption is the way people appreciate ICT. The

demand for the value of information technology is greatly rising in the modern

business world because the significance of using ICT by public organization has been

realized. ICT gives more value to the organization performance as well as the

employees.

Having gone through the importance of appreciation, it is essential to review

the perceived usefulness. Perceived usefulness assist in deciding the motive behind the

acceptance or rejection of information technology by people in an organization. The

use of the system may be affected by how people tend to use or not use an application,

and the belief that it will assist in doing the job better. In fact, perceived usage reflects

the individuals who will assist in working well without much effort.

Interestingly, the result of this research offers similar findings as Davis (1989)

and Venkatesh (2000). Table 5.61 indicates that appreciation influences perceived

usefulness positively.

Table 5.61




Hypo.

Tested

Perceived

Usefulness Appreciation 0.20 2.93 0.24 0.80 Supported

246

5.6.16 How Age, Income, and Education Affect the Relationship

In the Figure 5.16, 5.17, and 5.18, the three aspects have a good response to people’s

attitude in the organization. There will be an analysis of how these aspects may

influence the attitude of the employee in the organization.

In any organization established compensation scheme is very sensitive. Vroom

(1964) pointed out that the members can positively or negatively be influenced in

return. There may be a problem of disagreement if the organization is not committed

on how it offers remuneration to its people. Employees expected their income level to

be equal to their colleagues in the same position. Members may have poor relations

with the management if the organization increases the income of one member while

other are not treated the same way. There should be clarity of intention in case the

income level of one member is to be increased by the organization in order to prevent

conflicts from other members (MCS, 2011).

In any organizational setting; education level may also influence the

employees’ relationship. For example, if an individual with a certain level of

education is given a job in an organization where salary is moderated without

considering the level of education, an employee with higher education may feel

unsatisfied and so deter his performance. The management can quickly pass

information regarding this to the employees so as to avoid the occurrence of such

issues.

Table 5.62 shows the various influences of the age group, income level and

education level on the model Figures 5.13, 5.14 and 5.15. The model indicates that the

groups are just the same as the real sample in the association of motivation valance

247

with perceived usefulness, of perceived ease of use with perceived usefulness, of

perceived usefulness with current usage, of perceived ease of use with usage, of work

with attitude behaviour, of current usage with attitude behaviour, of attitude behaviour

with intention to use, and of volunteer motivation with intention to use.

Furthermore, the associations among the age group are also similar with the

result of the main study. The reason may be as a result of the majority of the

respondents who belong to this same group. For the characteristics of the respondents,

Table 5-3 indicates that majority of the respondents belongs to the same age group.

There is also similarity to some degree in the group with the same education and those

with the same income. The reason may be the result of the fact that the level of

education and income level determine the position level which in turn decides the

level of income in Saudi public organizations (MCS, 2011).

Table 5.62

Standard Regression Weight for Models

Structural Path

Std Regression weight

Main Age Edu Income

Perceived Ease of

Use Principal Support 1.46 1.08 -0.44 -0.20

Perceived Ease of

Use Appreciation -0.29 -0.37 -0.02 -1.47

Perceived Ease of

Use Motivation Valance -0.78 -0.48 0.50 1.60

Perceived

Usefulness Principal Support -0.83 -0.29 -0.01 -0.20

248

Table 5.62 continue

Structural Path

Std Regression weight

Main Age Edu Income

Perceived

Usefulness Appreciation 0.20 -0.05 -0.09 0.23

Perceived

Usefulness Motivation Valance 0.36 0.15 -0.02 -0.16

Perceived

Usefulness

Perceived Ease of

Use 1.22 1.07 1.00 0.94

Current Usage Perceived Usefulness 0.81 2.62 3.60 2.40

Current Usage Perceived Ease of

Use -1.01 -2.52 -3.61 -2.37

Work Type Current Usage -0.08 -0.08 -0.10 -0.11

Training Time Current Usage 0.06 -0.11 -0.07 0.05

Attitude Behaviour Training Time 0.20 0.00 -0.06 -0.12

Attitude Behaviour Work Type -0.02 -0.05 -0.03 -0.18

Attitude Behaviour Current Usage -0.19 -0.13 -0.07 -0.07

Perceived

Voluntariness Attitude Behaviour 0.03 -0.00 -0.04 -0.20

Subjective Norm Attitude Behaviour -0.01 -0.05 0.02 -0.02

Intention to Use Attitude Behaviour 0.03 0.01 0.04 0.02

Intention to Use Perceived

Voluntariness 0.68 0.74 0.99 0.42

Intention to Use Subjective Norm 0.31 0.26 -0.01 0.54

249

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Figure 5.13 Age

Figure 5.14 Income

250

B. In

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5.7 SUMMARY OF RESULTS OF THE HYPOTHESIS TESTING

The result of hypotheses testing the relationships among the variables and the

mediators involved are summarized in Table 5.63. The findings show that out of the

whole model relationship, eight were supported and six were not.

Table 5.63

Summary of the Result of the Hypothesis Testing

Hypothesis Hypothesis Path Hypothesis Testing

Hypothesis 1

Attitude to change negatively and directly

influences Intention to use. Not Supported

Figure 5.15 Educaion

251


Hypothesis Hypothesis Path Hypothesis Testing

Hypothesis 1a

Subjective Norms moderates the relationship

between attitude to change and Intention to

use.

Supported

Hypothesis 1b

Perceived voluntariness moderates the

relationship between attitude to change and

Intention to use.

Supported

Hypothesis 2

Current use positively and directly influences

the Attitude to change. Not supported

Hypothesis 2a

Training moderates the relationship between

current usage and attitude to change. Supported

Hypothesis 2b

The nature of work moderates the

relationship between current usage and

attitude to change moderate.

Not Supported

Hypothesis 3

Perceived Usefulness positively and directly

influences Current usage of technology. Supported

Hypothesis 4

Perceived ease of use positively and directly

influences Perceived usefulness. Supported

Hypothesis 4a:


influences Current usage of technology. Not supported

252


Hypothesis Hypothesis Hypothesis

Hypothesis 4b


influences Perceived usefulness. Supported

Hypothesis 5

Principal Support positively and directly

influences perceived ease of use. Supported

Hypothesis 5a

Principal Support positively and directly

influences perceived Usefulness. Not supported

Hypothesis 6

Motivation Valence negatively and directly

influences perceived usefulness Not supported

Hypothesis 6a

Motivation Valence negatively and directly

influences perceived ease of use Supported

Hypothesis 7 Appreciation positively and directly

influences perceived ease of use. Not supported

Hypothesis 7a Appreciation positively and directly

influences perceived Usefulness. Supported

5.8 CONCLUSION

Chapter five commences with the reassessment of the demographic characteristic of

the employees. Accordingly, the majority of the respondents were male (100 %)

253

graduates middle-aged. Additionally, they were supervisors with average incomes

between SR 6000 -7999.

The chapter also reviews the descriptive data of the respondents. The

explanatory statistics of the constructs are shown in chapter five, which shows all

means are above midpoint of 3.00 except for Principal support, which has a mean of

2.7. Consequently, the demographic characteristics and the fact leader interest, mostly

cause e-government failure in the developing countries (Scacco, 2009; Pavela, 2010).

On the other hand, appreciation has the highest mean. Thus, Maio-Taddeo (2006)

declared that awareness has been identified as the major contributing factors to accept

and use ICT.

The standard deviations range from 0.77 to 1.24, and this indicates a narrow

spread around the mean. The skew index ranges between - 0.6 and - 2.0, and kurtosis

index ranges between -0.1 and 4.6. Following steps of Kline and Littel (2011)

suggestion that the skew and kurtosis index should be between the value of 3 and 10,

for the sake of structural equation modelling the research data is considered normal.

Accordingly, to assess the model fit all the models are considered a good fit model

and met all the requirements suggested by Hair et al; 2010.

In general fifteen hypotheses were recognized in the model for this study.

Overall, eight hypotheses were supported by the data. Moreover, the researcher tested

whether the path coefficients are significant, and the matching path assumed in the

original model. Also the mediators were inspected and evaluated in the same way

based on the literature which the relationship has been constructed. Significantly, it is

to check the effect of the new variables on the model. The model indicates similarity

between the groups and the main sample with respect to the association: motivation

254

valance with perceived usefulness, perceived ease of use with perceived usefulness,

perceived usefulness with current usage, perceived ease of use with usage, work with

attitude behaviour, current usage with attitude behaviour, attitude behaviour with

intention to use, and volunteer Motivation with intention to use.

The findings of the study were reviewed for the purpose of answering the

research questions and realizing the research objectives. In the first instance, a

descriptive analysis was executed to find out the characteristics of the respondents and

to check if there was any missing data. Then the path analysis was carried out to

examine the association between the model variables. Other findings will be analyzed

in chapter six.

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6 CHAPTER SIX

DISCUSSION AND CONCLUSION

In Saudi Arabia, ICT acceptance is not a motivation for government employees;

however it is an important activity to improve organization productions. As mentioned

earlier many authors have pointed out that TAM is one of the best models which could

be useful in examining information technology. Furthermore, they have studied the

variables that hinder ICT acceptance (Abd Mukti, 2000; DeBenedictis et al., 2002;

Bwalya, 2009; Al-Senaidi et al., 2009; Al-Ghaith et al., 2010). However, not much is

known about the attitudes and preferences of public workers with regard to the

acceptance, continuation to use ICT in Saudi Arabia.

This study was carried out to spell out the cultural drivers’ factors and other

variables, which might stand against the implementation and acceptance of ICT in

developing countries, specifically in Saudi Arabia. Furthermore, it had the objectives

of investigating the Saudi public employees’ awareness, willingness, and readiness to

use ICT, and to deliver analysis on the obstacles or problems.

The study attempted to make suggestions for the problem identified by finding

the causes of low acceptance, and slow implementation and adoption of e-

government. Furthermore, the research aimed to answer the research questions “What

factors affect employee intention to use and accept information communication

256

technology in the Saudi public industries?” Finally, the study tried to fill in the gap in

literature by expanding TAM by exploring the impact of current usage of ICT on it.

Self administrated questionnaire was the instrument used to collect the research

data. Structural equation Modelling was the tool employed to predict the dependent

variable DV (path) and look at the factors’ structure of a research data (CFA –

measurement models). Using the structural equation modelling technique, the research

model showed a good model fit for both the measurement and structural models.

AMOS was used as the research data analysis tool.

The Technology Acceptance Model by (Davis, 1989) and Model of

Organization Readiness to Change (MROC) by Holt at al., (2007) were used as the

framework for this study. Motivation valance (Boardman and Sundquist, 2009),

principal support (Warshaw, 1980), and appreciation (Benamati and Lederer, 2008)

were used as external factors disturbing the acceptance of ICT. The subjective norm

(Lin and Lee, 2004) and volunteer motivation (Li, 2004) were used as controlled

variables on the behaviour. The analysis of the research data showed eight out of the

fifteen of the research hypotheses were supported. Unexpectedly, the research found

that there was negative association between perceived ease of use and ICT current

usage. Furthermore, it has no effect on the attitude behaviour. which has no effect on

the attitude to change.

The influences of the age group, income level, and level of education on the

model were also reported. The model indicated similarity between the groups (age,

education, income) and the research main sample with respect to the association: of

motivation valance with perceived usefulness; of perceived ease of use with perceived

usefulness; of perceived usefulness with current usage; of perceived ease of use with

257

usage; of work with attitude behaviour; of current usage with attitude behaviour; of

attitude behaviour with intention to use; and of volunteer motivation with intention to

use.

The recommendations of this study will give organizations advantages of

gaining more insight into the behaviour of the employees as well as having an edge

over the organization that had little knowledge of their user. These objectives and

findings of this research gives the opportunity of helping government organizations in

Saudi Arabia and other nations having similar features across the main variables in the

planning and starting off the e-services in government organizations.

6.1 RESEARCH QUESTION ADDRESSED

Earlier studies have contributed to the important understanding into why and how

organizations made a choice about the adoption and use of ICT (Saga and Zmud,

1994). However, the other main concern is how workers as ICT user make

knowledgeable decisions about better contributions that lead to improved effective

acceptance effectively and what is the best way to utilize ICT in the workplace

(Salwani et al., 2009). There are limited researches in the factors affecting ICT

adoption and acceptance in public organizations in Saudi Arabia (Al-Somali et al.,

2009). Indeed, it is very important for the organizations and administrations to deal

and understand such factors to improve the organization acceptance process.

The main research question in this study is “What are the factors which

influence the behaviour of the employee to accept and adopt ICT in the public

organizations of Saudi Arabia?” Furthermore, other factors such as volunteer

motivation and norms and culture might hinder the acceptance and adoption ICT. Also

258

the study tried to examine the extent to which the current usage moderates ICT

acceptance.

6.1.1 What factors affect employee intention to accept and use information

communication technology in the Saudi public sectors?

The study started examining the research questions and explored factors affecting the

acceptance and adoption progression. It started on the basis of the Antecedents of

Belief results; significant positive structures were brought in between both motivation

valance and appreciation with perceived usefulness. This opposes the argument of

Skarlicki and Folger, (1997) but confirms the conclusion of Brown et al., (2002)

accordingly. Conversely, both bear a significant negative relationship with perceived

ease of use. Nonetheless, the structure of the model of principal support is opposite

such that its association with perceived ease of use is significantly positive which,

confirms Covin and Kilmann (1990) result, but its association with perceived

usefulness is significantly negative which, support the argument of Scacco (2009).

Technology Acceptance Model structure in the current study was in the line

with what Davis (1989) confirmed; all TAM hypotheses established, and the model fit

met. From the current study however, user technology believes to have opposite

significant influence on current usage. The perceived ease of use negatively

influenced the usage while the perceived usefulness positively influenced usage.

There were measurements weights influence regarding the effect of moderators

(Karahanna et al., 1999; Venkatesh and Davis, 2000; Venkatesh and Brown, 2001).

The results of the subjective norm imply that they moderate the association

between attitude and intention to use (Ajzen and Fishbein, 1975). The current findings

also indicate that volunteer motivation acts as a moderator to the association between

259

attitude to change and intention to use (Agarwal and Prasad, 1997). The various

influences of the age group, income level, and level of education on the model were

also reported by the study.

This research finally arrived at a similar outcome of the previous studies but

the levels of significance were different. The current study found that the influence of

current usage on attitude to change was negative, and the significant level was very

low. Also, it was indicated that training did not moderate the association existing

between current usage and attitude to change. The analysis shows that nature of work

did not moderate the association between usage and attitude, and the significant level

was high. The relation between attitude and intention to use was thus not supported by

the results of the study.

Conversely, the current study reported that current usage and perceived ease of

use bear a direct negative association. Furthermore, the current results support most of

present studies which validate the claim that in today’s operation, perceived ease of

use is insignificant. Even though all TAM hypotheses were supported, overall; the

outcome presents some proof that TAM is an effective tool to explain ICT acceptance

in Saudi Arabia. The relationship between current usage and attitude was negative

while the effect of attitude on intention to use to change was insignificant.

Also, the question: what factors stand as obstacles to the acceptance and diffusion of

e-services among Saudi organizations and to what extent the current usage affects the

acceptance process?

Based on the empirical analysis factors that stand as obstacles to the acceptance

and adoption are different depend on the technology beliefs of “perceived ease of use

260

and perceived usefulness”. Motivation valance and appreciation act as obstruction on

perceived ease of use (motivation valance perceived ease of use) and (appreciation

perceived ease of use). On the other hand principle support affected perceived

usefulness negatively. This is true despite the fact that, most of the employees who

responded have the view that the advantages of using ICT are more than the related

costs on it.

Poter and Lawler (1968) introduced a model of inherent and external work

motivation. The intrinsic motivation was of the view that people execute a particular

activity as a result of the fact that they find it interesting such that they get satisfaction

from it. From the other angle, extrinsic motivation needs an instrumentality between

the activity and separable effects such as tangible or verbal rewards. The implication

is that encouragement never resulted from the activity alone, but also from other

external factors (Voorm, 1964).

Porter and Lawler (1968) offer the structuring of the job environment putting in

mind the objective of intrinsic and extrinsic rewards to create satisfaction work and

could be followed by the enlargement of the job as this will make the job more

interesting. It is more interesting and thus become more rewarding intrinsically. For

the extrinsic value, incentive (rewards) is provided to workers in the form of high pay,

which consequently raises the employees’ performance motivation.

On the other hand, based on the research analysis, principal support holds back

the perceived usefulness. This supports the finding of Heeks (2003) who said that an

ICT project failure is due to personal interests of some leaders.

261

The study also inspects the mediating role of current usage on attitude

behaviour. The study established that current usage has no or low effect on the attitude

behaviour, because skills are needed of the workers in order for the ICT to be applied

effectively. Eighty percent of all the respondents claimed not to have training, above

80 % of the trained employees used below one week for training or learning the

fundamental works on a computer. Most importantly the research comes to a

conclusion that current usage affects TAM association. Furthermore, the current result

supports the most current study which validates the claim that in today’s operation,

perceived ease of use bears no significance on usage (Nagli et al., 2011).

Finally, the subjective norm and volunteer motivation have moderate the

relationship between the attitude to change and intention to use. The study confirms

the argument of Lin and Lee (2004) and Quaddus et al., (2005) that subjective norm

impact ICT usage. Also, this proves the finding of Moore and Benbasat (1991) that

perceived voluntariness influence the ICT usage.

6.2 SIGNIFICANT FOR THE MODEL AND ORGANIZATION

The purpose of the study is to explore the factors affecting the ICT implementation

and adoption in Saudi public organizations. An important contribution is made to the

knowledge by throwing light on culture and other factors in the development of ICT

and spread of knowledge in Saudi Arabia public organization. One of the results from

this investigation are some suggestion to the model and the organizations.

6.2.1 Implications for Knowledge

In this research the technology user beliefs’ factors perceived ease of use and

perceived usefulness have a different influence on the usage of ICT, is that both have

262

significant opposite effects on ICT usage. The current study reports the association

between perceived ease of use and current usage has a direct effect which is negative

(1.1). And the total effect of perceived usefulness to currents usage is positive (0.8.)

Therefore, perceived usefulness is essential in deciding the information technology

acceptance. As a factor which causes ICT to be accepted, perceived usefulness is of

the notion that acceptance of new information technology enhances the productivity of

the organization. What is less clear is if the current usage has a motivation effect on

attitude behaviour due to a low level of usage and training.

Second, the research showed the association between TAM variables have

been supported, with a high significant level of reliability, with good fit model

measurements. Nevertheless, it could be observed from the analysis that current ICT

usage, which is the constant use of technology within the organization, has influenced

the relationship between TAM factors. This has led to call for further research to

confirm the conclusion of the research result.

Third, the study expands the understanding that TAM is very relevant to a non-

western nation. However, more studies are still required particularly when the

explanatory power of the model employed is not as high as TAM. The current study

tested the association between the variables of TAM; the results uncovered a pattern

similar to the western pattern that is applicable to the Saudi public sector.

Fourth, the study outlined that current usage has an insignificant negative effect

on the attitude behaviour, which confirms (Management Hub, 2005; 2010) the

argument that not all the organization members are knowledgeable in using computer

and may think that such change to computer usage may frustrate their job since they

could not use the system.

263

Fifth, as mentioned previously many authors have noted that subjective norm

and volunteer motivation influence the ICT usage (Lin and Lee, 2004; Quaddus et al.,

2005), and this study comes to a similar conclusion and expands the understanding.

Sixth, many researchers have come to the conclusion that training enhances the

readiness to change (Lan and Cayer, 1994) and (Davis and Bostrom, 1993); this study

found that training insignificantly increases the employee readiness for change. In the

defence of this insignificant outcome, the majority of the respondents (80 %) had no

training, while the rest had less than one-week training.

Seventh, from the data analysis the research noted that age, education, and

income never influence the association between the Perceived Ease of Use and

Perceived Usefulness, Perceive Usefulness and usage, Perceived Ease of Use and

usage, usage and Attitude Behaviour. The study comes up to the conclusion that

various factors have an impact on the ICT adoption in public organization in Saudi

Arabia. Age, income and level of education had an impact on the model and the data

analysis outcome.

Eighth, the research uses Hair et al., (2010) suggestions of the data analysis the

new construct measure is “god-consciousness” which offers methodological

contraption.

Finally, this study finds out that type of work has no effect on the attitude to

change, and did not moderate the relationship between current usage and attitude to

change; therefore, the study did not support the argument of Al-Aadwani (2001) and

Steers and Porter (1979) that a relationship exists between job nature and affective

attitude to change.

264

6.2.2 Implication for the Organization

The objectives of this investigation are to gain knowledge of the typologies of

employees and their preferences of ICT acceptance and adoption, and to identify the

factors behind ICT acceptance and adoption failure. This will give the public

organizations the upper hand over acceptance and usage failure in the future.

In this section some understanding and findings of obstacles to ICT adoption in

government organization in Saudi Arabia were provided. More is to uncover the key

obstacles from the survey tools to find out the association and effects of the main

factors affecting respondent’s perceptions and attitudes to those obstacles. Nine major

obstacles were identified in the survey answer given as follows:

There is a primary issue to deal with before bringing in ICT in government

organization in Saudi Arabia. This has to do with the questions of: are employees

capable of using it? Are they willing to use it?” and have the proper training to use

ICT application granted? The readiness of employees was observed to be low in

developing countries, and this is the factor that significantly affects the willingness of

organizations to adopt the latest technologies. The government sector is influenced

also by the society’s level of readiness.

There was a case as reported by an official of the Ministry of Civil Status,

when the public was called to tender an application for a job in the ministry following

the launching of new e-services with the ministry’s new website. Many job-seekers

could not do the work online because of lack of understanding of the operation. This

causes failure to meet the job requirements. It was shown by the survey that almost 80

% of the employees did not have the right training.

265

The first important issue is resistance to change, and it resulted from poor e-

readiness among users and employees. It was asserted that when the organization

brought in the ICT system such as a G2B solution facilitating the transfer of e-

documents over safe lines, most of the workers and employers stood against it at the

initial time (Baidhani, 2012). Accordingly, providing the necessity for an enhanced

solution with the use of an ICT-based application it has the tendency of lessening the

difficulties.

The second issue is language barrier associated with the new technologies. A

Ministry of Interior official expressed how this issue led to the inability to get one

another right when launching e-services and training the workers of the company on

how to use it. The result was not properly interpreted due to different levels of

translations. According to the secretary of the Ministry of Commerce, language is a

vital barrier to any e-systems where the majority of people speak Arabic. Majority of

the officials pointed out that cultural issues and how to make the rural people do the

online transactions is a big challenge because of lack of adequate infrastructure and

high level of illiteracy in computer.

The third issue is the required level of ICT Knowledge and Expertise. It has

been shown through the surveys carried out that employees with literacy in ICT are

very low in Saudi. Most of the officials showed that there were delays and complaints

in carrying out the day’s work caused by some ICT introduced in Saudi Arabia instead

of making the work fast. It was also reported by the officials that absence of adequate

know-how hinders governments to adopt and start ICT-based projects.

Fourth, Porter and Lawler (1968) offer the structuring of the job environment

putting in mind the objective of intrinsic and extrinsic rewards to create satisfaction

266

work and could be followed by the enlargement of the job as this will make the job

more interesting. It is more interesting and thus become more rewarding intrinsically.

For the extrinsic value, incentive (rewards) is provided to workers in the kind of high

pay, which consequently raises the employees’ performance motivation.

Fifth, when considered along with the ICT execution, motivation enhances the

acceptance of new information systems in the organization. With high motivation, it is

believed that employees will be ready for any change, as well to accept the ICT

process. This is because once they understand the organization expectation about

them; they tend to take steps according to the implementation of ICT (Boardman and

Sundquist, 2009).

With motivation, the workers will be ready to face the challenge and will not

wait for the question before deciding the necessary result. If it is necessary to execute

ICT, the workers who are already motivated will comply with the attempt to accept

ICT (Duncan and Zaltman, 1977). This will decide their effort in enhancing the

performance of organization, and making their job simple rather than to stand against

it. Thus, the workers will simply grant execution. It implies that valance motivation to

the workers has improved ICT.

Sixth, Culture in Saudi Arabia can influence gender, which may affect

technology acceptance. According to the senior official of the Ministry of Civil Status,

the users of the internet and e-government would mostly be females because of

cultural issues in which women are expected by tradition to stay home. Due to

spending long time at home, they will likely use the e-services at home frequently. In

addition, it was stated that this was reflected in the ministry’s website, where the

majority of women were reported as most users.

267

Seventh, the research findings suggest that the formation of positive attitude of

ICT should occur before the adoption of technology and as a result, the researcher

should investigate the training effectiveness.

Lastly, the issue of leadership support. It was reported by the head of the

information systems department at the Ministry of Finance that having leadership

support plays an essential role in the execution and spread of e-government.

According to the official, there must be high priority for ICT, and it should be

considered as the major contributor to economy otherwise any important development

initiatives such as IT education will not be paid attention to. Leadership support has

great influence on the allocation of resources for technology and e-government

adoption. Furthermore, the undersecretary of the Ministry of Commerce also argued

that leadership and top officials’ commitments and enthusiasm over ICT is crucial.

These have the capability of affecting the allocated budget for ICT adoption and

development in any organisation. However, the official stated that budgets cannot just

be raised to bring about the increase in the awareness of ICT but some other

institutions of government usually budgeted for ICT and have their top officials

willing to work hard with their time and energy devoted to ICT. There are other

organisations with low budget allocation to ICT while some such as Ministry of

Education have the commitment to ICT.

6.3 LIMITATIONS OF THE RESEARCH AND FUTURE STUDIES

Generally speaking, this study offers some understanding about acceptance and

adoption of ICT in government organizations in Saudi Arabia. However, they still

could not completely examine all the factors that obstruct the development. Also, the

268

support for the overall hypotheses was limited and there were limitations to the study.

Therefore, there are opportunities for future research to extent the analysis.

6.3.1 Limitations

Despite the care given to this study, there are limitations.

Firstly, the research employs self-reports to gather the research data which may

cause to the regular means variance, a condition where exact relationships between

variables are overstated.

Secondly, due to the cultural and budget constraints the research sample was

totally comprise Saudi male government workers. Gender differences associated with

the ICT adoption, and acceptance will extend the understanding of the ICT acceptance

issue. The sample needs to be extended to take account Saudi female workers and may

extend to involve the private sector. It was likely that no male worker may hold

diverse views about ICT adjustment from that of the female employee.

Thirdly, more research designs are likely to strengthen the insight into the

aggregated model. A qualitative and/or longitudinal data collection within the ICT in

government organization usage will give more in depth insight to the phenomena.

Fourthly, the findings of the current study considered the non-moderating

impacts of education, income, and age. Therefore, a suggestion is made for future

examination of the situations in which gender, language; different groups of age and

regions might bring on more in-depth understanding of acceptance behaviour.

Fifthly, apparently from the data collection a precise measurement means of

training and training effectiveness is needed.

269

Sixthly, the longitudinal studies which examine the hypothesised associations

as they were open for some time now. The inclusion of other sets of antecedents or

moderators such as system efficacy and utilitarian versus hedonic aspects of website

design quality will be of benefit.

Seventhly, a conceptual model for the problem is required, conceptual

modelling more effective and informative when investigating a phenomenon and gives

comprehensive assessment (Lee, 1995).

Finally, the use of current usage as a measure for attitude to use and change the

system for non ICT user may have weakened and contributed to the loss of

explanatory power of the model in this study.

6.3.2 Future study

There is still a need for more investigation of other possible variables that likely give

high powered analysis of ICT behaviour in other countries besides western. TAM

extended or UTAUT model may be employed to analyse other behaviours of ICT in

private sectors. The need for further examination of the role of experience in

technology acceptance modelling was shown by the findings. Therefore, there is room

for future research, particularly with respect to training and compensation.

Furthermore, there is a need for an intensive study of current usage as a mediating

variable. More research designs are likely to strengthen the insight into the aggregated

model. A cross-section of people within the ICT in government organization usage

context was investigated. Therefore, studies in the future might examine other

controlled sets of employees and contexts to point out the limitations and exceptions

regarding the usage behaviour, website quality and the integrated model.

270

Also recommended for future research is longitudinal study which examines

the hypothesised associations as they were open for some time now. The inclusion of

other sets of ICT experience or mediators such as ICT application skill; and effective

versus user-friendly aspects of ICT application design will be of benefit.

Again, future research is suggested to be carried out to examine the effect of

the following moderators: gender, ICT skill, organization magnitude, and region on

ICT usage. The findings of this research considered the non-moderating impacts of

education, income, and age. Therefore, suggestion is made for future examination of

the situations in which female equality might be dominant. Past studies on technology

acceptance behaviour have paid attention to gender differences in the place of work.

Nonetheless, the existence of ICT usage on the personal level and at the level of work

needs more study on female as a deciding factor of usage in the framework of

reasoning.

Furthermore, examination is required regarding the age group which might be

considered while investigating information communication technology acceptance and

adoption, particularly as the clients in this recent generation are confident, young and

educated. This research has paid attention on the group within the workplace (mostly

older); another one may be needed by paying attention to younger users. That type of

demographic profiles would give the officials the right target (specific segments)

regarding products and services based on technology.

6.4 CONCLUSION

From the findings, it was observed that the employees in general never stand against

the usage of ICT, and believed that the ICT used match their work. In addition, the

employees noted that they never had adequate training, especially with the ICT usage.

271

Therefore, employees should be trained on the way the system works as well on the

parts that are associated with their jobs. In a nutshell, it is imperative to build a proper

way of gaining more insights into how the whole systems work instead of part that

related to the need of users.

Leadership styles affect the performance of the organization and determine the

readiness of organizational change (Miller, Madsen and John, 2006; Reid et al., 2008).

Through the leader’s beliefs and values, the people can sense the urgency for the

change that needs to occur. Moreover, the leadership style influences the acceptability

for change in the organization.

According to Eby et al., (2000), a positive approach, and readiness of the

organization to change occur in organizations where there is clan culture. By a clan

culture, the author refers to a working environment that is friendly, people-oriented,

and colleagues share common beliefs and values. In such an organization, the

executives are perceived as mentors as opposed to control and they are involved in

management of employees at a personal level. Once the leader is able to address the

needs of the people and show a caring and supportive attitude, then the organizational

readiness for change can be easily achieved. Leaders who are supportive and assist in

task management are likely to create mutual trust with those working for them.

In turn the people are willing to walk the talk with their leaders and therefore,

accept change with little resistance, in case there will be any. This is because the

employees trust the leaders to lead them through the change process. On the other

hand, organizational change readiness behaviours reduced with autocratic leadership

models because the needs of the employees are rarely addressed. The change can take

272

place as a result of fear and obedience but this will carry with it negative attitudes, and

withdrawal from participation in the decisions of the management.

Dori (2006) implied that what the Arab world needs is to define exactly in

which way they are directed in the field of ICT. Here, we can quote the term

Technological Sovereignty in which the national intentions are protected. With this,

the fate of the state is dependent on all the initiatives being put towards it by the

government, private sector, and the public. This required the regulation to be receptive

to the current situations and the desired goal. Arab nations have a huge potential in

building the ICT industries internally with the considerations on the development of

its local features.

ICT industries will need coordination and connectivity system between

regional infrastructures. This will promote the security and the resilience of the local

industry. Along this, there must be an incorporation of the national plan for the

application and management of ICT. This plan needs to be receptive on the changes in

the business environment which this must ensure its maintenance. This kind of

venture ought to seek to influence a number of national hubs of superiority.

Moreover, Arif (2008) suggests that the firm involvement together with the

government, to ensure the more prolific utility of ICT. Some extra factors such as

economic, social, and cultural needs to be regarded in Arab states, ICT policies

initiated by the government did not reveal proof of qualitative development of the

nations but the quests remain there and promising.

As we enter the professed ‘information age’, every sector of the community is

going to employ Information and Communication Technology to exist, toil and take

273

part in. To be able to remain in touch with the people and the rest of market factors,

frameworks of the market-place will be altered and continuously evolve to develop

into a more practical model to make the correlation between various business

functions better served. Latest technologies will incessantly materialize throughout the

coming years thus only by acceptance and adoption that can we handle this phase of

evolution well.

Finally, As per the sector of business and public, organizations can have this

met by assuring the proponents will initiate the implementations of a congregating

atmosphere, which will guarantee a sustainable ICT- relying economy and society in

the present days and years to come. We cannot stop the trend for the acceptance and

usage of ICT in the global scene, whether it’s public, or private sector. ICT is here to

hang on thus it can make more sense to embrace receptively rather than let it slip.

274

7 INDEX

7.1 DETERMINING SAMPLE SIZE FOR RESEARCH ACTIVITIES

Population

size

Sample

Size

Population

size

Sample

Size

Population

size

Sample

Size

10 10 220 140 1200 291

15 14 230 144 1300 297

20 19 240 148 1400 302

25 24 250 152 1500 306

30 28 260 155 1600 310

35 32 270 159 1700 313

40 36 280 162 1800 317

45 40 290 165 1900 320

50 44 300 169 2000 322

55 48 320 175 2200 327

60 52 340 181 2400 331

65 56 360 186 2600 335

70 59 380 191 2800 338

75 63 400 196 3000 341

80 66 420 201 3500 346

85 70 440 205 4000 351

90 73 460 210 4500 354

95 76 480 214 5000 357

275

Population

size

Sample

Size

Population

size

Sample

Size

Population

size

Sample

Size

100 80 500 217 6000 361

110 86 550 226 7000 364

120 92 600 234 8000 367

130 97 650 242 9000 368

140 103 700 248 10000 370

150 108 750 254 15000 375

160 113 800 260 20000 377

170 118 850 265 30000 379

180 123 900 269 40000 380

190 27 950 274 50000 381

200 132 1000 278 75000 382

210 136 1100 285 100000 384

Note:- This Table to determining needed size S of a randomly chosen sample from given finite population of N

cases such that the sample proportion P will be within +/- 0.05 of the populations proportion P with a 95 level of

confidence (Krejcie and Morgan, 1970).

276

7.2 QUESTIONNAIRE

English Version

My name is Wael Sh. Basri I am a Ph. D candidate at the International Islamic

university in Malaysia, school of economics and management science. The researcher

is undertaking a comprehensive survey of public organization such yours for the

purpose of assessing and defining the factors affecting the adoption of e-government

in public organization in Saudi Arabia. This project aims to investigate the

behavioural factors on the adoption of E-government in public organization. It seeks

to define potential variables that might hinder the development or usage of E-

government in public organization in Saudi Arabia. The research will assist the Saudi

government in cutting red tape and enhancing the efficiency and effectiveness of their

public services.

In order to achieve the desired goals, the researcher is conducting a survey with public

worker and officials such as you, who are in a position to provide valuable

information on attitudes to E-government and other related data. The study considers

your cooperation in this undertaking to be very valuable. The researcher wish to

assure you that all information obtained in this study will be kept in strict confidence.

The identity of the institution will not be revealed in any way as the report will only

deal with aggregates.

The questionnaire has two parts; part A is demographic information. Part B is a five

point questions. A extremely disagree with the statement and E extremely agree with

the statement. As much as possible, please do not leave any item in the questionnaire

blank.

In general, no total in-depth study in public organization has so far been

completed. The reason for this is that questionnaire sent are not answered and

returned on time. The return of this questionnaire in between two to three weeks

is therefore earnestly requested.

Note: The questionnaire will be collected from the department public relation

office.

Thank you.

Wael Sh. Basri

Ph. D Student IIU

277

7.2.1 Part One

Department

Gender

Male

Female

Age Income per month (SR)

25 – 29 2,000 – 3,999

30 – 34 4,000 – 5, 999

35 – 39 6,000 – 7, 999

40 – 44 8,000 – 9,999

Above 45 9,999>

Position Education Level

Clerical Middle School

Supervisor High school

Head of Department

Manger Diploma

V. General Manager Graduate ( bachelor)

General Manager Postgraduate

The nature of my work…

Routine ( every day the same)

Non- routine (change about every day)

Training Giving

Yes

No

278

Training Length......

Training by.......

No training

01-07 Days

08-29 Days

30-120 Days

120 > Days

Department

Operator

Both

Education

279

7.2.2 Part Two

All measures have a 5 point Likert scale with end points:

A= Extremely Disagree, B = Disagree, C= Uncertain, D = Agree, E= Extremely

Agree.

Instructions: Select one level of agreement for each statement to indicate how you

feel.

Perceived Usefulness

1 Using computers will enhance my

effectiveness. A B C D E

2 Using computers will increase my

productivity. A B C D E

3 Using technology compatible w/all aspects of

our work. A B C D E

4 Using computers gives me greater control

over my work. A B C D E

5 I find computers a useful tool in my work. A B C D E

Perceived Ease of Use

1 My interaction with computers is clear and

understandable. A B C D E

2 I find it easy to get computers to do what I

want it to do. A B C D E

3 Using the computers does not require a lot of

mental effort. A B C D E

4 I find computers easy to use. A B C D E

Intention to Use

1 I will use computers in my work in future. A B C D E

2 I plan to use computers in my daily life often. A B C D E

3 I will encourage my collage to use computer. A B C D E

4 I will encourage my organization costumers’’

to use the system A B C D E

5 Given that I have access to the system, I

predict that I would use it. A B C D E

280

Subjective Norms

1 People who influence my behaviour think I

should use the computer. A B C D C

2 People who are important to me think I

should use the computer. A B C D E

3 I want to do what my immoderate supervisors

think I should do. A B C D E

4 I want to do what the people who report to me

think I should do. A B C D E

Principal support

1 Sr. Mgt. Thinks I Should Use computer. A B C D E

2 Management supports computer in my

organization. A B C D E

3 I get management support A B C D E

4 .It is easy for me to observe others using e-

government in my ORG A B C D E

Valance motivation

1

I do not wish to expose myself or my

organization to the high risks and learning

costs associated with a new technology by


A B C D E

2 I intend to use computer if it help the

organization performance. A B C D E

3 I intend to use computer if it does not help

me. A B C D E

4 I am satisfied with my performance at this

task A B C D E

Voluntariness

1 Although it might be helpful, using computer

is certainly not compulsory in my business. A B C D E

2 I examine unusual things. A B C D E

281

3 I use the computer all the time. A B C D E

4 I never use the computer. A B C D E

Attitude to change

1

I am willing to put in a great deal of effort

beyond that normally expected in order to

help the organization be successful.

A B C D E

2 I feel very little loyalty to this change. A B C D E

3

I would accept almost any type of job

assignment in order to keep working for this

organization.

A B C D E

4 I find that my values and the organization’s

values are very similar. A B C D E

Appreciation

1 Computers make work more interesting. A B C D E

2 Working with computers is fun. A B C D E

3 I like using computers. A B C D E

4 I find computers a useful tool in my work. A B C D E

5 I want to learn a lot about computers. A B C D E

Current Usage

1 My Usage of the computer in my daily work

is high High Med Low

2 I estimate the current usage of computer in

my department very high High Med Low

3 My current usage of the computer is high. A B C D E

282

بسم هللا الرحمن الرحيم

الحكوميه االدارات في االلكترونيه الحكومه استخدام في المؤثره العوامل لدراسه إستبانه

,,,,بركاته و هللا ورحمه عليكم السالم

تفيد التي االلكترونيه الحكومه االستخدامات هذه من. استخدامتها تعددت و االنترنت انتشرت

. المكتبيه المصروفات تخفيض و بسرعه المعامالت إنجاز في الحكوميه االدارات و المواطن

.االنترنت خدمه و الحاسب يتوفر ان يجب الخدمه هذه من المواطن استفاده تتم لكي

االلي الحاسب بتطبيقات ملم الموظف يكون ان يجب الخدمه هذه من الحكوميه االدارات لتستفيد و

. الخدمه هذه توفر من المرجوه ائدهبالف ملم يكون ان ويجب

بعمل بماليزيا االسالميه الجامعه في الدكتوراه طالب بصري محمد شحات وائل الباحث يقوم لذلك

العربيه المملكه في االلكترونيه الحكومه للتطبيقات الحكومي الموظف تقبل عنوانها دراسه

.السعوديه

من الحكومي العام القطاع موظف تمنع التي ثرهالمؤ العوامل و االسباب لمعرفه الدراسه تهدف

. اليوميه المعامالت إلنجاز االلكترونيه الحكومه في ممثلة التقنيه استخدام

المستهدفه الشريحه تمثل النك اختيارك تم لقد و انجازها في تعاونك على كثيرا تعتمد الدراسه هذه

بخصوصيه تعامل سوف االستبيان هذا في المتوفره المعلومات جميع ان الباحث يعد و, البحث في

الشخص او االداره اسم وان تحليل من الباحث به يقوم ما اال فيه معلومه اي نشر يتم لن و شديده

.نشره يتم لن

,,,التحيه و الشكر خالص لكم

بصري محمد شحات وائل/ الباحث

ماليزيا الدوليه االسالميه الجامعه

283

بالتدريب تتعلق أسئله عن يحتوي و.الوظيفه و الموظف عن عامه أسئله على يحتوي الجزء هذا -:االول الجزء

.الرجاء تعبئه الفراغ. تطبيقاته و الحاسب استخدام على الموظف تلقاه الذي

...(الي حاسب,استقبال, موظفين شؤون) االداره (بها تعمل التي االداره في) الموظفين عدد

051

الجنس

( √) ذكر

) ( انثى

العمر

01الى 02من

03الى 02من

01الى 02من √

33الى 32من

32اكبر من

الدخل

0222 - 0111

3222 -2111

0222 - 9111

9222- 1111

92222من اكثر √

الوظيفه

مشرف مديرعام

مساعد مدير نائب مدير عام

إداري √ مديرإداره

284

المستوى التعليمي

فوق جامعي متوسط و اقل

√ جامعي ثانوي

دبلوم

التدريب

√ وفرعن طرق االداره

√ المستخدم النظام موفر طريق وفرعن

√ معا

مده التدريب

م يتوفرل اقل من اسبوع

اسابيع 3 أشهر 3 √

اشهر 3اكثر من اخرى

نوعيه عملي

(يتغير ال يوميا العمل نفس روتيني

(تقريبا يوم كل يتغير) روتيني غير √

عملي عباره عن

مهني غير مكتبي

√ اداري

تخطيط

االول الجزء تم

يالثان الجزء يتبع

285

-: الثاني الجزء بين االختيار فيمكنك لإلجابه مطابقه الجمله كانت فإذا للجمله المطابقه مستوى بإختيار االجابه الرجاء الجزء هذا في

عدم الهح في اما بشده اوافق ال او اوافق ال بين االختيار فيمكنك الجمله و االجابه بين الموافقه عدم حاله في اما أوافق ال او بشده اوافق

خمسه الجمل من جمله لكل يوجد.متأكد غير اختيار فيمكننك الموافقه غير او الموالفقه بين تتردد المطابقه ان او االجابه من التأكد

الرقم ان بحيث المطابقه من درجات

هبشد أوافق ال. 5 أوافق ال. 4 متاكد غير. 3 اوافق. 2 بشده اوافق. 0

(√) عالمه بوضع اجابتك الى االقرب الرقم إختيار الرجاء

5 4 3 2 0 الجمله

√ استخدام الحاسب يطور عملي 0

عملي جوده من يفعل الحاسب استخدام 2 √

عملي في انتاجي من يزيد الحاسب استخدام 3 √

عملي متطلبات مع يتطابق الحاسب استخدام 4 √

عملي انجاز في يسرع الحاسب استخدام 5 √

√ استخدام الحاسب يسهل عملي 6

عملي من متمكن يجعلني حاسبال استخدام 7 √

العمل في مفيده اداه الحاسب 8 √

0 2 3 4 5

معقد وغير ولومقب واضح للحاسب استخدامي 9 √

باحترافيه الحاسب مع التعامل يمكنني 01 √

حاسبال مع التعامل في التفكير كثيرمن الى احتاج ال 00 √

√ استخدام الحاسوب سهل جدا 02

0 2 3 4 5

عملي في مستقبال الحاسب استخدم سوف 03 √

√ اليوميه اعمالي في الحاسب استخدم سوف 04

الحاسب استخدام على اصدقائي اشجع سوف 05 √

الحاسب استخدام على المراجعين اشجع سوف 06 √

استخدمه سوف الحاسب توفر حاله في 07 √

الحاسب استخدام عن الكثير تعلم في ارغب 08 √

0 2 3 4 5

الحاسب استخدم ان يفضلون عملي على المؤثرين االشخاص 09 √

286

الحاسب استخدم ان االفضل من انه يعتقدون حياتي في المهمين االشخاص 21 √

اعمل ان المباشر المدير يرغب ما سأفعل 20 √

√ اعمل ان عملي بتقيم يقوم من يرغب ما سأفعل 22

اعمل ان( االداره مدير و العام المدير) العليا االداره ترغب ما سافعل 23 √

0 2 3 4 5

الحاسب استخدام تفضل( االداره مدير و العام المدير) العليا االداره 24 √

√ االداره تطلب استخدام الحاسب 25

العمل في الحاسب استخدام مني يطلب ال المباشر مديري 26 √

لها طلبي حاله في االداره من المساعده على احصل 27 √

مستمر بشكل الحاسب استخدام يتم فيها اعمل التي االداره في 28 √

فيها اعمل التي االداره في الحاسب يسخدمون الموظفين مالحظه السهل من 29 √

√ الفعلي االستخدام قبل كافي لوقت الحاسب تجربه و بأستخدام لي سمح 31

0 2 3 4 5

الحاسب من متمكن و قادر انني اشعر 30 √

..(خطابات كتابه, الكتروني بريد) الحاسب تطبيقات استخدام ممن متمكن انني اشعر 32 √

√ الحاسب استخدام من اتمكن لكي الكافي التدريب توفير تم 33

0 2 3 4 5

تالمعامال اجراء في الحاسب الستخدام الكافيه المعرفه لديهم المراجعين معظم 34 √

المعامالت اجراء في الحاسب استخدام يفضلون المراجعين معظم 35 √

أإلداره نمراجعي لمعظم االستخدام سهل الحاسب 36 √

الحاسب ألستخدام للمراجعين المساعده توفر 37 √

0 2 3 4 5

الحاسب بأستخدام المخاطره الى دارهاال او عملي يتعرض ان أفضل ال 38 √

اليومي العمل اداء في منه االداره استفاده عدم حاله في حتى الحاسب استخدم سوف 39 √

لي مفيد غير كان ان حتى الحاسب استخدم سوف 41 √

√ الحاسب ممتع 40

الحاسب خداماست في ادائي من مقتنع 42 √

بكفاءه الحاسب استخدم ان المهم من 43 √

287

√ استخدام الحاسب ممل 44

0 2 3 4 5

ضروري استخدامه يكن لم وإن حتى عملي اداء في الحاسب استخدم سوف 45 √

√ اعتياديه الغير االشياء تجربه افضل 46

√ استخدم الحاسب دائما 47

√ لم استخدم الحاسب ابدا 48

الحاسب استخدام مني تطلب ال االداره 49 √

0 2 3 4 5

فيها اعمل التي االداره اساعد لكي اعتيادي فوق جهد اضع سوف 51 √

فيها اعمل التي االداره الى بأنتماء أشعر ال 50 √

عمله مني يطلب الذي العمل من نوع اي اقبل 52 √

فيها اعمل التي االداره اهداف مع تتطابق الشخصيه اهدافي ان اعتبر 53 √

0 2 3 4 5

√ الحاسب يجعل عملي ممتع 54

مرح فيه و شيق الحاسب على العمل 55 √

√ انا احب استخدام الحاسب 56

ستخدامي للحاسب مرتفع جداا 57 √

حاليا ككل ادارتي في الحاسب استخدام. 58 استخدامي للحاسب في العمل. 59

90- 011 % √ 90- 011 % √

80 – 91 % 80 – 91 %

70 – 81 % 70 – 81 %

60 – 71 % 60 – 71 %

50 – 61 % 50 – 61 %

% 51اقل من % 51اقل من

,,,العرفان خالص مع لكم شكرا............... الثاني الجزء تم

وائل شحات بصري

288

GLOSSARY OFACRONYMS

NAME Abbreviation

Adjusted Goodness-Of-Fit Statistic AGFI

Analysis of Moments Structures AMOS

Australian Government Information Management Office AGIMO

Chi Square-Based Measures of Discrepancy CMIN

Commonwealth Telecommunications Organization CTO

Comparative Fit Index CFI

Confirmatory Factor Analysis CFM

Diffusion of Innovations DI

Goodness-Of-Fit Statistic GFI

Government with Business G2B

Government With Citizens G2VC

Government With Government G2G

Incremental fit indices IFI

Information Communication Technology ICT

Information Systems IS

Innovation Diffusion Theory IDT

Internet Services Provider ISP

Inter-Organizational Information and Communication

Systems

IOICS

Kaiser-Meyer-Olkin KMO

King Abdul-Aziz City for Science and Technology KACST

Leader-Member Exchange LMX

Linear Structural Relations LISREL

Maximum Likelihood ML

289

Normed-fit index NFI

Organizational Behaviour Management OBM

Parsimony fit indices PFI

perceived ease of use PERUSE

perceived usefulness PEOU

Principal Axis Factoring PAF

Principal Component Analysis PCA

Relative Fit Index RFI

Root mean square error of approximation RMSEA

Root mean square residual RMR

standardised root mean square residual ARMR

Structural Equation Modelling SEM

Task Technology Fit TTF

Technology Acceptance Model TAM

The Model of Readiness for Organizational MROC

The Theory of Reasoned Action TRA

Theory of Planned Behaviour TPB

Tucker-Lewis Index TLI

Unified Theory of Acceptance and Use UTAUT

Variance inflation factors VIF

World trade organization WTO

World Wide Web WWW

290

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