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Factors Affecting ICT adoption
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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
Wael Shahhat M. Basri
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.
Wael Shahhat M. Basri
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
xi
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
xii
4.2.2.7 Current Usage ..................................................................... 118
4.2.3 Moderator variables ...................................................................... 118
4.2.3.1 Subjective Norm ................................................................. 118
4.2.3.2 Perceived Voluntariness ..................................................... 119
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
5.3.6.5 Perceived Ease of Use ........................................................ 170
5.3.6.6 Perceived Usefulness .......................................................... 171
xiii
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
5.4.6.4 Perceived Ease of Use ........................................................ 204
5.4.6.5 Perceived Usefulness .......................................................... 206
5.4.6.6 Attitude to Change .............................................................. 208
5.4.6.7 Intention to Use .................................................................. 210
5.4.6.8 Subjective Norm ................................................................. 214
5.4.6.9 Perceived Voluntariness ..................................................... 216
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
xiv
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
xv
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
xvi
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
xvii
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
5.50 Summarize Result of H2 232
5.51 Summarize Result of H2a 233
5.52 Summarize Result of H2b 234
5.53 Summarize Result of H3 235
5.54 Summarize Result of H4 236
5.55 Summarize Result of H4a 238
5.56 Summarize Result of H5 239
5.57 Summarize Result of H5a 240
5.58 Summarize Result of H6 242
5.59 Summarize Result of H6a 243
xviii
5.60 Summarize Result of H7 244
5.61 Summarize Result of H7b 245
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
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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.
4.2.2.3 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”.
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.
4.2.2.6 Appreciation
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).
4.2.2.7 Current Usage
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
4.2.3.1 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). 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).
4.2.3.2 Perceived Voluntariness
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
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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
135
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
145
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.
146
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
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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
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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
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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
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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.
5.3.6.2 Principal Support
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
Average Score 4.02 1.03
1= Extremely-disagree 5 = Extremely-agree
5.3.6.4 Appreciation
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.
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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
Average Score 4.24 0.99
1= Extremely-disagree 5 = Extremely-agree
5.3.6.5 Perceived Ease of Use
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.
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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
Average Score 4.22 1.09
1= Extremely-disagree 5 = Extremely-agree
5.3.6.6 Perceived Usefulness
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.
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Table 5.8
Descriptive Statistics Perceived Usefulness with Cronbach's Alpha 0.72
Statements Mean Std.Dv
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
Average Score 4.05 0.82
1= Extremely-disagree 5 = Extremely-agree
5.3.6.7 Current Usage
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
Statements Mean Std.Dv
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
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Table 5.9 Continue
Statements Mean Std.Dv
I estimate the current client usage is 3.29 1.27
Average Score 3.31 1.24
1= Extremely-disagree 5 = Extremely-agree
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
Statements Mean Std.Dv
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
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Table 5.10 Continue
Statements Mean Std.Dv
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
Average Score 3.70 0.80
1= Extremely-disagree 5 = Extremely-agree
5.3.6.9 Subjective Norm
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
Statements Mean Std.Dv
People who influence my behaviour think I should use the
computer.
4.44 0.66
175
Table 5.11Continue
Statements Mean Std.Dv
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
1= Extremely-disagree 5 = Extremely-agree
5.3.6.10 Perceived Voluntariness
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
Statements Mean Std.Dv
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
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Table 5.12 Continue
Statements Mean Std.Dv
Although it might be helpful, using computer is certainly not
compulsory in my business.
3.85 1.25
Average score 3.81 1.00
1= Extremely-disagree 5 = Extremely-agree
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
Statements Mean Std.Dv
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
Average Score 3.90 1.08
1= Extremely-disagree 5 = Extremely-agree
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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
Intention to use4 -1.61 -9.23 2.79
Intention to use1 -1.90 -10.84 3.45
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
182
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
186
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
187
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)”.
190
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
191
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
195
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.
196
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.
197
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
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Cu
mu
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Bart
lett
's
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t
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
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Cu
mu
lati
v
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ari
an
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Cro
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Alp
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t
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
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Cu
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lati
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Cro
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t
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
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Cu
mu
lati
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Cro
nb
ach
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Alp
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Bart
lett
's
Tes
t
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
5.4.6.4 Perceived Ease of Use
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
My objective for using the computers is clear and
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
ce
Cu
mu
lati
v
e vari
an
ce
Cro
nb
ach
's
Alp
ha
KM
O
Bart
lett
's
Tes
t
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
5.4.6.5 Perceived Usefulness
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
ce
Cu
mu
lati
v
e vari
an
ce
Cro
nb
ach
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Alp
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KM
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Bart
lett
's
Tes
t
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
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Per
cen
t of
vari
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Cu
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Bart
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Tes
t
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
ce
Cu
mu
lati
v
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an
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Cro
nb
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Alp
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Bart
lett
's
Tes
t
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
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Cu
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lati
v
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rian
ce
Cro
nb
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Alp
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KM
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Bart
lett
's
Tes
t
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
5.4.6.8 Subjective Norm
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
ce
Cu
mu
lati
v
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an
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nb
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Alp
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Bart
lett
's
Tes
t
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.
5.4.6.9 Perceived Voluntariness
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
Fact
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
ce
Cu
mu
lati
v
e vari
an
ce
Cro
nb
ach
's
Alp
ha
KM
O
Bart
lett
's
Tes
t
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
5.4.6.10 Current Usage
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
ors
Fact
ors
load
ing
Elg
envalu
e
Per
cen
t of
vari
an
ce
Cu
mu
lati
v
e vari
an
ce
Cro
nb
ach
's
Alp
ha
KM
O
Bart
lett
's
Tes
t
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:
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
being its first user.
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
My objective for using the computers is clear and
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
Summarize Result of H2
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
Summarize Result of H2a
Structural Path Structural
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
Summarize Result of H2b
Structural Path Structural
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
Summarize Result of H3
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
Summarize Result of H4
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
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
Summarize Result of H4a
Structural Path Std Regr.
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
Summarize Result of H5
Structural Path
Std Regr.
weight S.E. C.R. P.lv
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
Summarize Result of H5a
Structural Path Std Regr.
weight S.E. C.R. P.lv
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
Summarize Result of H6
Structural Path Std Regr.
weight S.E. C.R. P.lv
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
Summarize Result of H6a
Structural Path Std Regr.
weight S.E. C.R. P.lv
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
Summarize Result of H7
Structural Path Std Regr.
Weight S.E C.R. P.lv
Hypo.
Tested
Perceived Ease
of Use
Appreciation -0.29 4.59 -0.24 0.80
Not
Supported
245
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
Summarize Result of H7b
Structural Path Std Regr.
weight S.E. C.R. P.lv
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
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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
Table 5-53 Continued
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:
Perceived ease of use positively and directly
influences Current usage of technology. Not supported
252
Table 5-63 Continued
Hypothesis Hypothesis Hypothesis
Hypothesis 4b
Perceived ease of use positively and directly
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.
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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
being its first user.
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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