Using the UTAUT Model to Determine Factors AffectingAcceptance and Use of E-government Services in theKingdom of Saudi Arabia
Author
Alshehri, Mohammed
Published
2013
Thesis Type
Thesis (PhD Doctorate)
School
School of Information and Communication Technology
DOI
https://doi.org/10.25904/1912/1770
Copyright Statement
The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from
http://hdl.handle.net/10072/368130
Griffith Research Online
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Using the UTAUT Model to Determine Factors Affecting
Acceptance and Use of E-government Services in the
Kingdom of Saudi Arabia
Mohammed Abdulrahaman Alshehri
Bachelor of Computer Engineering, Master of Computer and
Communication Engineering
School of Information and Communication Technology Science, Environment, Engineering and Technology Group
Griffith University
Submitted in fulfilment of the requirements of the degree of
Doctor of Philosophy
Dec 2012
Declaration
Page i
DECLARATION This work has not previously been submitted for a degree or diploma in any
university. To the best of my knowledge and belief, the dissertation contains no
material previously published or written by another person except where due
reference is made in the thesis itself.
__________________________
Mohammed Alshehri
Acknowledgment
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ACKNOWLEDGEMENTS With much appreciation, I would like to thank all those who supported me during my
journey work on this dissertation.
First, thanks to Allah for giving me the ability, strength, and guidance for the
successful completion of this thesis.
Then, I would like to express my sincere gratitude and appreciation to my two
supervisors, Dr Steve Drew and Dr Ann Nguyen. Thank you, for your guidance,
continuous support, outstanding assistance, patience and your understanding in
difficult times that I faced during my treatment at Mater Hospital.
A special thank you goes to Professor Viswanath Venkatesh (University of Arkansas,
USA) for his response to my emails and useful hints about how to improve the
research model (UTAUT).
I am also very grateful to my oncologist, Dr. Kerry Taylor, his assistant, Dr. James
Daily, and all staff members in the oncology ward at the Mater Hospital for their great
support, kind words, and generous assistance during my chemotherapy treatment at
the Mater Hospital. To all of them, I owe a lot of respect.
I am greatly indebted to my colleagues, Rayed Alghamdi, Osama Alfarraj, and Saleh
Alshehri for their help, advice, suggestions, and strong encouragement throughout the
thesis process.
My sincere thanks go my parents who supported me and showed me their love and
their great empathy. Finally, my love and warmest appreciation go to my wife and my
children whose continued patience, constant support, and understanding enabled me
to complete this work.
List of Publications
Page iii
LIST OF PUBLICATIONS The following academic publications emerged from this PhD dissertation:
Journal Publications
Alshehri, M., & Drew, S. (2011). E-government principles: Implementation, advantages and challenges. International Journal of Electronic Business, 9(3), 255-270.
Alshehri, M., Drew, S., & Alfarraj, O. (2012). A comprehensive analysis of e-government services adoption in Saudi Arabia: Obstacles and challenges. International Journal of Advanced Computer Science and Applications, 3(2), 1-6.
Alshehri, M., Drew, S., Alhussain, T., & Alghamdi, R. (2012). The impact of trust on e-government services acceptance: A study of users’ perceptions by applying UTAUT model. International Journal of Technology Diffusion 3(2), 1-5.
Conference Papers
Alshehri, M., & Drew, S. (2010a). E-government fundamentals. Proceedings of the IADIS International Conference ICT, Society and Human Beings 2010. IADIS International Association for Development of the Information Society, Freiburg, Germany.
Alshehri, M., & Drew, S. (2010b). Challenges of e-government services adoption in Saudi Arabia from an e-ready citizen perspective. In World Academy of Science, Engineering and Technology, 66, 2010, World Academy of Science, Engineering and Technology, New Mexico, USA.
Alshehri, M., & Drew, S. (2010c). Implementation of e-government: Advantages and challenges. Proceedings of the IASK International Conference E-Activity and Leading Technologies & InterTIC 2010. International Association for Scientific Knowledge, Oviedo, Spain.
Alshehri, M., Drew, S., & Alghamdi, R. (2012). Analysis of citizens’ acceptance for e-government services: Applying the UTAUT model. Proceedings of the IADIS International Conference Internet Applications and Research 2012. IADIS Multi Conference on Computer Science and Information Systems, Lisbon, Portugal.
Alshehri, M., Drew, S., Alhussain, T., & Alghamdi, R. (2012). The effects of website quality on adoption of e-government service: An empirical study applying UTAUT model using SEM. Proceedings of the 23rd Australasian Conference on Information Systems. Geelong, Australia.
Abstract
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ABSTRACT E-government has become a popular focus of government efforts in many countries
around the world. More and more governments around the world are introducing
e-government as a means of reducing costs, improving services, saving time and
increasing effectiveness and efficiency in the public sector. E-government and the
Internet has made an essential change to the whole of Saudi societal structure, values,
and culture, as well as the ways of conducting business by utilizing the potential of
ICT as a tool of daily work. Therefore, e-government has been identified as one of the
top priorities for Saudi government and all its agencies. However, the adoption of
e-government faces many challenges and barriers, including political, cultural,
organizational, technological, and social issues which must be considered and treated
carefully by any government contemplating e-government adoption. Findings of
several studies indicate that despite the high cost of e-government projects, both
tangible and intangible, many e-government efforts are failing or are slowly diffusing.
This thesis presents a comprehensive study and investigation of the influential factors
on the acceptance of using e-government services (G2C) in the Kingdom of Saudi
Arabia (KSA) by adopting the Unified Theory of Acceptance and Use of Technology
(UTAUT) model. This study uses an amended version of the UTAUT model as its
theoretical foundation. UTAUT is an empirically validated model that combines eight
major models of technology acceptance and their extensions. The study investigates
the effect of proposed UTAUT constructs and moderating variables on e-government
services use and acceptance. Therefore, this study critically assesses key factors that
influence e-government service acceptance in the public sector in Saudi Arabia,
discusses the importance of citizen perspective about e-services, and provides a
comprehensive assessment of e-service providers and citizens’ perceptions about the
obstacles facing e-government services acceptance and use in Saudi Arabia. This
thesis provide a comprehensive view and deep understanding of e-government
services adoption based on the perceptions of e-services providers and Saudi citizens
through the utilisation of the UTAUT model.
Several past studies have provided significant knowledge and results regarding the
implementation and adoption of e-government sectors such as G2G, G2B, and G2E.
Moreover, some of these studies discussed the adoption of e-government from various
Abstract
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perspectives, including cultural aspect, the social aspect, the technical aspect, the
organizational aspect, and many others. However, there is still a demand for more
empirical and theoretically based studies that focus on the actual factors that affect the
acceptance and use of e-government services (G2C) from the perspective of citizens
and services providers. Moreover, despite the fact that implementation is an important
phase of e-government project structure, the acceptance and use of such services in an
inclusive and modelled manner within this particular context of the KSA has not been
comprehensively studied. Therefore, this research aims to address the gap in the
literature empirically by utilizing and developing the validated UTAUT model to
determine the factors that influence the actual usage of e-government services in the
KSA.
To achieve the research aims, a triangulation approach for data gathering was
employed. In the first step, a quantitative questionnaire survey method was used to
evaluate and refine the developed UTAUT model. A total of 686 questionnaires
were collected as primary data for this phase. In this stage, several multivariate
statistical techniques, including Exploratory Factor Analysis (EFA), Confirmatory
Factor Analysis (CFA), and Structural Equation Modelling (SEM) were utilized to
analysis and validate the developed research model. EFA and CFA were used to
discover and prove robust model structures. The SEM technique and Analysis of
Moment Structures (AMOS) Version 19.0 were then used to examine and refine the
model relationships. The proposed UTAUT model was examined with six
independent scales: Trust (TR), Performance Expectancy (PE), Effort Expectancy
(EE), Social Influence (SI), Website Quality (WQ), and Facilitating Conditions (FC).
It also used two dependent scales, Behaviour Intention (BI) and Use Behaviour
(USE), as well as three moderators of key relationships: Age, Gender, and Internet
Experiences. The proposed UTAUT model was then tested and modified, and the
final model result was evidenced by goodness of fit of the model to confirm its
validity and reliability.
The second phase of the research consisted of the employment of a qualitative focus
group method to support and validate the questionnaire findings. The focus groups
were conducted with two groups of five participants each. The first group consist of
five Saudi citizens from diverse levels of educational and age, while the second group
was comprised of five IT staff from several government sectors.
Abstract
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As a result of this empirical study, the new work and understanding that is reported in
this thesis, as validated by literature review, includes a number of interesting findings.
For instance, it was found that the five independent constructs of the UTAUT model,
that is, Trust (TR), Performance Expectancy (PE), Effort Expectancy (EE), Website
Quality (WQ) and Facilitating Conditions (FC), significantly affect the Behaviour
Intention (BI) to accept and use e-government services. In contrast, Social Influence
(SI) had an insignificant effect on the Behaviour Intention (BI) to accept and use
e-government services. Additionally, Use Behaviour of e-government services (USE)
was significantly influenced by Behaviour Intention (BI) to accept and use
e-government services. In addition, three moderators—age, gender and Internet
experience—impacted the influence of key determinants towards usage behaviour for
e-government services. Importantly, the results also indicate the importance of
government website support systems and citizen awareness about e-government
systems as significant determinants of the adoption of e-government services by
citizens.
Furthermore, this study provides a set of implications for innovation and key
conditions which could potentially help all Saudi government sectors and the Saudi
e-government program (Yesser) towards successful adoption and diffusion of
e-government services (G2C) in the KSA.
Moreover, the findings of this research provide an empirical result for other
developing counties that have a similar context to the KSA and face similar
difficulties for the adoption of e-government services (G2C) in their own country. All
e-government stakeholders, researchers in e-government fields, policy-makers and
academicians can also benefit from the findings of this research.
In summary, this research study significantly expands and improves upon the existing
knowledge of e-government services adoption within the KSA context. A validated
and practical model (UTAUT) was developed and used a variety of sophisticated
processing and analysis techniques to determine the key factors that affect the
acceptance and use of e-government services (G2C) in the KSA. This dissertation
concludes with a discussion of the contributions and limitations of this work, and
provides directions for future research.
Table of Contents
Page vii
TABLE OF CONTENTS DECLARATION .......................................................................................................... i
ACKNOWLEDGEMENTS ......................................................................................... ii
LIST OF PUBLICATIONS ........................................................................................ iii
ABSTRACT ................................................................................................................ iv
TABLE OF CONTENTS ........................................................................................... vii
LIST OF FIGURES .................................................................................................. xiv
LIST OF TABLES ......................................................................................................xv
Chapter 1: Introduction .................................................................................................1
1.1 Introduction .........................................................................................................1
1.2 Research Problem ...............................................................................................2
1.3 Research Aims and Objectives ...........................................................................5
1.4 Research Questions .............................................................................................6
1.5 Research Significance and Outcome ..................................................................7
1.6 Research Design and Process .............................................................................9
1.7 Thesis Structure ................................................................................................10
Chapter 2: E-Government Fundamentals: Literature Review ....................................14
2.1 Introduction .......................................................................................................14
2.2 Definitions .........................................................................................................14
2.2.1 E-government. ............................................................................................14
2.2.2 E-readiness. ................................................................................................16
2.2.3 E-services. ..................................................................................................16
2.3 Types of E-government .....................................................................................17
2.3.1 Government-to-Citizen (G2C). ..................................................................17
2.3.2 Government-to-Business (G2B). ...............................................................18
2.3.3 Government-to-Government (G2G). .........................................................19
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2.3.4 Government-to-Employee (G2E). ..............................................................19
2.4 Benefits of E-government .................................................................................20
2.5 Barriers to E-government Implementation .......................................................22
2.5.1 Technical barriers. ......................................................................................22
2.5.2 Organizational barriers. ..............................................................................26
2.5.3 Social barriers. ...........................................................................................28
2.5.4 Leaders and management support. .............................................................30
2.5.5 Financial barriers. ......................................................................................31
2.6 E-government and E-Commerce Relationship .................................................32
2.6.1 Definition of e-commerce. .........................................................................32
2.6.2 Common factors between e-government and e-commerce. .......................32
2.7 Chapter Summary .............................................................................................33
Chapter 3: Research Background ................................................................................34
3.1 Introduction .......................................................................................................34
3.2 Kingdom of Saudi Arabia: Location, Population, Economy, and Culture .......34
3.3 Information and Communication Technology (ICT) in Saudi Arabia ..............35
3.3.1 ICT infrastructure. ......................................................................................36
3.3.2 The Internet in the KSA. ............................................................................37
3.4 E-readiness of the KSA e-government .............................................................38
3.5 National ICT Plan .............................................................................................39
3.6 E-government Initiative ....................................................................................39
3.6.1 E-Readiness ................................................................................................40
3.6.2 E-Society ....................................................................................................40
3.6.3 IT Training .................................................................................................40
3.7 Saudi E-government Program (Yesser) ............................................................42
3.7.1 Overview. ...................................................................................................42
3.7.2 Program objectives. ....................................................................................43
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3.7.3 Program achievements. ..............................................................................43
3.8 Information Technology Regulation in Saudi Arabia .......................................44
3.9 Chapter Summary .............................................................................................45
Chapter 4: Theories and Models of Technology Acceptance .....................................46
4.1 Introduction .......................................................................................................46
4.2 Theory of Reasoned Action (TRA) ...................................................................46
4.2.1 Limitations of the TRA. .............................................................................47
4.3 Theory of Planned Behaviour (TPB) ................................................................48
4.3.1 Limitations of the TPB. ..............................................................................49
4.4 Technology Acceptance Model (TAM) ............................................................49
4.4.1 Limitations of the TAM. ............................................................................50
4.5 Extension of the Technology Acceptance Model (TAM2) ...............................51
4.6 Diffusion of Innovation Theory (DOI) .............................................................52
4.6.1 Limitations of DOI theory. ........................................................................53
4.7 Unified Theory of Acceptance and Use of Technology (UTAUT) ..................54
4.8 Literature Review of E-government Studies Using Technology Acceptance
Models .....................................................................................................................57
4.9 Selection and Justification of the Research Model ...........................................60
4.10 Chapter Summary ...........................................................................................62
Chapter 5: Research Methodology ..............................................................................63
5.1 Introduction .......................................................................................................63
5.2 Research Paradigms ..........................................................................................63
5.2.1 The positivist paradigm. .............................................................................64
5.2.2 The interpretive paradigm. .........................................................................64
5.2.3 The critical paradigm. ................................................................................64
5.3 Research Categories ..........................................................................................65
5.3.1 Quantitative research. ................................................................................65
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5.3.2 Qualitative research. ..................................................................................67
5.4 Selection and Justification of Research Method ...............................................70
5.4.1 Justification of using positivist paradigm. .................................................70
5.4.2 Justification of using quantitative and qualitative mixed approach. ..........71
5.5 Research Model ................................................................................................72
5.5.1 The significance of trust in the proposed research model. .........................75
5.5.2 Significance of Website Quality in the proposed research model. ............76
5.6 Research Hypotheses ........................................................................................77
5.6.1 Key constructs hypotheses. ........................................................................78
5.6.2 Moderating hypotheses. .............................................................................79
5.7 Data Collection Strategies ............................................................................81
5.7.1 Questionnaires. ...........................................................................................81
5.7.2 Focus group. ...............................................................................................85
5.7.3 Literature review. .......................................................................................86
5.8 Population and Sample .....................................................................................87
5.9 Data Analysis ....................................................................................................88
5.9.1 Quantitative analysis. .................................................................................88
5.9.2 Qualitative analysis ....................................................................................90
5.10 Reliability and Validity Analysis of the Instrument .......................................90
5.12 Ethical Considerations ....................................................................................91
5.13 Chapter Summary ...........................................................................................93
Chapter 6: Descriptive Data Analysis .........................................................................94
6.1 Introduction .......................................................................................................94
6.2 Overview of Research Questionnaire ...............................................................94
6.3 Data Screening and Management .................................................................95
6.3.1 Missing data management. .........................................................................95
6.3.2 Investigating univariate normality. ............................................................96
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6.3.3 Outliers screening. .....................................................................................97
6.4 Descriptive Statistics .........................................................................................97
6.4.1 Demographic analysis of Saudi citizens. ...................................................97
6.4.2 Demographic analysis of IT employees. ..................................................100
6.5 Chapter Summary ...........................................................................................101
Chapter 7: Measurement Scale Analysis ..................................................................103
7.1 Introduction .....................................................................................................103
7.2 Reliability ........................................................................................................103
7.2.1 Internal consistency. ................................................................................103
7.2.2 Item-total correlations. .............................................................................105
7.3 Validity ...........................................................................................................105
7.3.1 Exploratory Factor Analysis (EFA). ........................................................106
7.3.2 Confirmatory Factor Analysis (CFA) ......................................................120
7.4 Chapter Summary ...........................................................................................124
Chapter 8: Model Assessment ..................................................................................125
8.1 Introduction .....................................................................................................125
8.2 SEM overview ................................................................................................125
8.3 Measurement Model Assessment ...................................................................127
8.3.1 Procedure and assessment criteria. ..........................................................127
8.3.2 Measurement model results. ....................................................................128
8.4 Structural Model Assessment .........................................................................132
8.4.1 Procedure and assessment criteria. ..........................................................132
8.4.2 Structural model results. ..........................................................................133
8.4.3 Model refinement. ....................................................................................135
8.5 The Effect of Moderators ................................................................................139
8.5.1 Gender impact. .........................................................................................141
8.5.2 Age impact. ..............................................................................................143
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8.5.3 Internet experience impact. ......................................................................146
8.6 Chapter Summary ...........................................................................................149
Chapter 9: Qualitative Data Analysis .......................................................................151
9.1 Introduction .....................................................................................................151
9.2 Part Four of the Study Questionnaire: Obstacles of E-government Services .151
9.2.1 Perception of citizens towards obstacles of e-government services. .......153
9.2.2 Perception of IT employees towards obstacles of e-government services.
...........................................................................................................................156
9.2.3 Comparison of obstacles. .........................................................................159
9.3 Analysis of Open-ended Questions .................................................................160
9.3.1 Interpretation of Question 1. ....................................................................161
9.3.2 Interpretation of Question 2. ....................................................................161
9.3.3 Interpretation of Question 3. ....................................................................162
9.3.4 Interpretation of Question 4. ....................................................................162
9.3.5 Interpretation of Question 5. ....................................................................163
9.3.6 Interpretation of Question 6. ....................................................................163
9.4 Focus Groups Analysis ...................................................................................164
9.4.1 Analysis of Group A’s responses. ............................................................165
9.4.2 Analysis of Group B’s responses. ............................................................171
9.4.3 Summary of the Focus Group analysis. ...................................................177
9.5 Chapter Summary .......................................................................................178
Chapter 10: Discussion and Conclusion ...................................................................179
10.1 Introduction ...................................................................................................179
10.2 Discussion and Answering the Research Questions .....................................179
10.2.1 Questions related to the research’s UTAUT model. ..............................179
10.2.3 Discussion of general research questions. .............................................186
10.3 Summary of the Study: Findings and Implications .......................................188
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10.3.1 The UTAUT model findings. .................................................................189
10.3.2 The general question findings. ...............................................................189
10.3.3 Implications of this research. .................................................................191
10.4 Research Contributions .................................................................................193
10.4.1 Theoretical contributions. ......................................................................193
10.4.2 Methodological contributions. ...............................................................194
10.4.3 Practical contributions. .......................................................................194
10.5 Limitations and Directions for Future Research ...........................................194
10.6 Chapter Summary .........................................................................................196
References .................................................................................................................198
Appendix A: Survey Questionnaire (English Version) ............................................221
Appendix B: Survey Questionnaire (Arabic Version) ..............................................228
Appendix C: Focus Groups Guide ............................................................................237
Appendix D: List of Abbreviations ...........................................................................243
Appendix F: Ethical Clearance Certificate ...............................................................246
List of Figures
Page xiv
LIST OF FIGURES Figure11.1. The research process flowchart ................................................................ 10
Figure2 3.1. Internet growth in the KSA (MCIT, 2011) .............................................. 38
Figure3 4.1. Theory of Reasoned Action (Ajzen & Fishbein, 1980) ........................... 47
Figure4 4.2. Theory of Planned Behaviour (Ajzen, 2002) .......................................... 48
Figure5 4.3. Technology Acceptance Model (Davis, 1989) ........................................ 50
Figure6 4.4. Extended Technology Acceptance Model (TAM2) ................................ 52
Figure7 4.5. Roger’s Model in the Innovation-Decision Process (Rogers, 2003) ....... 53
Figure8 4.6. UTAUT model (Venkatesh et al., 2003).................................................. 56
Figure9 5.1. The proposed research model (based on UTAUT) .................................. 74
Figure10 5.2. An example of the literature review questions (adapted from Hart, 1998)
...................................................................................................................................... 86
Figure11 8.1. The measurement model. ..................................................................... 131
Figure12 8.4. Structural model .................................................................................. 133
Figure13 8.5. Initial structural model with standardized path coefficients ................ 134
Figure14 8.6. Hierarchical model options .................................................................. 138
Figure15 8.7. Final model with standardized path coefficients ................................. 139
Figure16 8.8. Standardized coefficients for the male sample .................................... 142
Figure17 8.9. Standardized coefficients for the female sample ................................. 143
Figure18 8.10. Standardized coefficients for younger respondents. .......................... 145
Figure19 8.11. Standardized coefficients for older respondents. ............................... 146
Figure20 8.12. Standardized coefficients for the experienced respondents. .............. 148
Figure21 8.13 Standardized coefficients for the inexperienced respondents. ........... 149
List of Tables
Page xv
LIST OF TABLES Table2 1.1 Structure of the Thesis .............................................................................. 12
Table3 2.1 E-government Types ................................................................................. 17
Table4 2.2 E-government Barriers ............................................................................. 22
Table5 3.1 E-government leaders in Asia (United Nations, 2012) ............................. 38
Table 63.2 E-government activities in Saudi Arabia .................................................. 41
Table7 6.1 Skewness and Kurtosis Statistics for the Study Variables (N = 878) ....... 96
Table8 6.2 Demographic information of Saudi citizens ............................................. 98
Table9 6.4 Demographic information of IT Staff ...................................................... 100
Table 106.5 Internet experience information of IT Staff ........................................... 101
Table11 7.1 Cronbach’s Alpha Reliability Results ................................................... 104
Table12 7.2 Coding of Performance Expectancy Variables ..................................... 107
Table13 7.3 Correlation Matrix for Performance Expectancy Scale ....................... 107
Table14 7.4 KMO and Bartlett’s Test for Performance Expectancy Scale .............. 108
Table15 7.5 Factor Loading for Performance Expectancy ...................................... 108
Table16 7.6 Coding of Effort Expectancy Variables ................................................ 109
Table17 7.7 Correlation Matrix for Effort Expectancy Scale ................................... 109
Table187.8 KMO and Bartlett’s Test for Effort Expectancy Scale ........................... 109
Table19 7.9 Factor Loading for Effort Expectancy .................................................. 110
Table20 7.10 Coding of Social Influence Variables ................................................. 110
Table21 7.11 Correlation Matrix for Social Influence Scale ................................... 111
Table22 7.12 KMO and Bartlett’s Test for Social Influence Scale ........................... 111
Table23 7.13 Factor Loading for Social Influence ................................................... 111
Table24 7.14 Coding of Facilitating Conditions Variables ...................................... 112
Table25 7.15 Correlation Matrix for Facilitating Condition Scale ......................... 112
Table26 7.16 KMO and Bartlett’s Test for Facilitating Condition Scale ................. 113
List of Tables
Page xvi
Table27 7.17 Factor Loading for Facilitating Condition ......................................... 113
Table28 7.18 Coding of Trust Variables ................................................................... 114
Table29 7.19 Correlation Matrix for Trust Scale ..................................................... 114
Table30 7.20 KMO and Bartlett’s Test for Trust Scale ............................................ 114
Table31 7.21 Factor Loading for Trust .................................................................... 115
Table 327.22 Coding of Website Quality Variables ................................................. 115
Table33 7.23 Correlation Matrix for Website Quality Scale .................................... 116
Table34 7.24 KMO and Bartlett’s Test for Website Quality Scale ........................... 116
Table35 7.25 Factor Loading for Website Quality ................................................... 116
Table36 7.26 Coding of Behavioural Intention Variables ........................................ 117
Table37 7.27 Correlation Matrix for Behavioural Intention Scale .......................... 117
Table38 7.28 KMO and Bartlett’s test for Behavioural Intention Scale .................. 118
Table39 7.29 Factor Loading for Behavioural Intention ......................................... 118
Table40 7.30 Coding of Use Behaviour Variables ................................................... 119
Table41 7.31 Correlation Matrix for Use Behaviour Scale ..................................... 119
Table42 7.32 KMO and Bartlett’s Test for Use Behaviour Scale ............................. 119
Table43 7.33 Factor Loading for Use Behaviour ..................................................... 120
Table44 7.34 Convergent Validity for the Constructs .............................................. 122
Table45 7.35 Discriminant Validity Results for the Measurement Model ................ 123
Table46 8.1 Measurement Model Assessment Criteria ............................................ 128
Table47 8.2 The Measurement Model Results .......................................................... 129
Table48 8.3. Structural Model Results ..................................................................... 134
Table49 8.4 Comparison between Hierarchical Models Fit Indices ........................ 137
Table50 8.5 Standardized Path Coefficients and t-values of the Final Model ......... 139
Table51 8.7. Simultaneous Analysis for Age ............................................................ 145
Table52 8.8. Simultaneous Analysis for Internet Experience ................................... 148
List of Tables
Page xvii
Table53 8.8. Summary of the Hypotheses Analysis .................................................. 150
Table54 9.1. Barriers to E-government Services Adoption ...................................... 152
Table55 9.2. Analysis of E-government Services Barriers from Citizens’ Perspectives
.................................................................................................................................... 153
Table56 9.3. Important Barriers from Citizens’ Perspective ................................... 154
Table57 9.4 ‘Very Important’ Barriers from Citizens’ Perspectives ........................ 155
Table58 9.5. Analysis of E-government Services Barriers from IT Employees’
Perspectives ............................................................................................................... 156
Table59 9.6. Important Barriers from IT Employees’ Perspective .......................... 157
Table60 9.7. ‘Very Important’ Barriers from IT employees’ perspective ................ 159
Table61 9.8. Common and Distinct Barriers between the Two Groups ................... 160
Table62 9.9 Yes/No Questions Analysis Result ......................................................... 160
Table63 9.10. Demographic Information for Group A ............................................. 164
Table64 9.11 Demographic information of Group B ................................................ 165
Table65 10.1 Summary of the Common Barriers between the Two Groups ............. 187
Chapter 1: Introduction
Page 1
Chapter 1: Introduction
1.1 Introduction
Information and Communication Technology (ICT) is one of the most important
characteristics of our age and, like every new development, it has changed our lives to
some extent. In particular, its evolution has dramatically changed how citizens interact
with their government, creating an important development in their expectations
(Dodd, 2000). Many countries around the world have realized the benefits of
e-government and they aspire to provide the full range of government services online.
They are introducing e-government services as a means of reducing costs, improving
services for citizens, and increasing effectiveness and efficiency in the public sector.
Moreover, e-government initiatives have been undertaken worldwide. However, the
success of such initiatives is dependent not only on government support, but also on
citizens’ willingness to accept, use and adopt e-government services (DeLone &
McLean 2003, Gil-Garcia & Pardo, 2005). In addition, the adoption of e-government
services raises important political, cultural, organisational, technological and social
issues which must be considered and treated carefully by any government
contemplating its adoption.
Recently, governments in the Middle East have started using e-government as a
means to achieve a high level of performance while providing cost effective
outcomes. However, many of these governments are still at the beginning stage of that
process. The Kingdom of Saudi Arabia (KSA), the largest country in the Middle East
geographically, is in the process of transitioning to e-government. The KSA has
recognized the essential role of e-government and IT and began implementing
national e-government projects in 1998 (Abanumy, Al-Badi, & Mayhew, 2005).
Today, most Saudi government agencies have their own websites; however, most of
these websites are inefficient, provide only basic and general information about the
organizations, and often the data are not updated. Therefore, it is hard to find a
government website where you can apply for a job, arrange an appointment, renew a
license, or get any online based services. The objective of this study, therefore, is to
determine and explore the factors that affect the acceptance and use of e-government
services in the public sector from the perspective of both government officials and
Chapter 1: Introduction
Page 2
users. The term ‘acceptance’ in this study refers to the “initial decision made by the
individual to interact with the technology” (Venkatesh, Morris, Davis, & Davis, 2003,
p.446). This research adapted the Unified Theory of Acceptance and Use of
Technology (UTAUT) model to determine factors affecting the acceptance and use of
e-government services in the KSA where e-government services are still being
developed. This model will help government decision makers to understand the
factors that influence citizens’ adoption of e-government services in the public sector.
1.2 Research Problem
Across the world, many governments are now using the Internet to provide their
citizens with more convenient access to government information and services.
Citizens do not need to go to government agencies to request services or follow-up
with another service. They can stay at home or in the office and use e-government
services online to obtain the government public services or information they need.
They can also use email to request information about any public services or ask for
help. E-government services, therefore, are a necessary requirement of modern
citizens and, as they are designed to meet the needs of citizens, they must be citizen-
centric (Scott, Golden, & Hughes, 2004). Many researchers have studied the adoption
and success of e-government services worldwide and concluded that many
governments are still suffering from low-level citizen adoption of e-government
services (Belanger & Carter, 2008; Carter & Belanger, 2005; Gupta et al., 2008;
Kumar et al., 2007; Reddick, 2005; Thomas & Streib, 2003). Additionally, Schuppan
(2009) and Alam and Hassan (2011) studied the implementation of e-government in
developing countries and reported that the low level of adoption of e-government
services is still facing most developing countries. According to Mofleh, Wanous, and
Strachan (2008b), the need to offer better services for citizens and respond to their
increasing demand for online services have been major drivers for the implementation
of e-government in developed countries.
Thus, the majority of services are focused on providing citizens with comprehensive
electronic resources to respond to individuals’ routine concerns and government
transactions. With government-to-citizen (G2C) applications, the organizations
publish information and contact details, and they offer regular services online. The
ultimate aim of these applications is to give users different options and
Chapter 1: Introduction
Page 3
communication channels for government transactions and to increase transparency so
that all citizens have equal and easy access to government services (West, 2004). A
number of studies found that, in many countries, e-services are still in the preliminary
stages of implementation and have not yet reached full efficiency and effectiveness
(Jaeger, 2003; Reddick, 2005). Worldwide, more than 60 percent of all e-government
projects either partially or totally fail to satisfy their main goals (Gardener, 2007).
Therefore, the success of such initiatives is dependent not only on government
support, but also on citizens’ willingness to accept and adopt those e-government
services (Carter & Belanger, 2004). Moreover, Carter and Belanger (2005)
emphasized that the success of any e-government project depends seriously on
customers’ acceptance of the services and the level of ease of accessing those
services. Government decision makers, consequently, need an understanding of the
factors that would encourage use of electronic service delivery channels rather than
more traditional service delivery methods.
In the Saudi context, the Yesser program summarized its vision statement as follows:
“By the end of 2010, everyone in the Kingdom will be able to enjoy from anywhere at
any time—world class government services offered in a seamless, user friendly and
secure way by utilizing a variety of electronic means”; their goal was to provide 150
electronic services to customers by the end of 2010 (Yesser, 2010, p.5). Moreover,
they aimed to increase the acceptance and use rate of e-government services to 75%
with respect to the number of users and ensure 80% user satisfaction rating for all
those provided with e-government services (Yesser, 2010). Despite these goals, a pilot
research study of 123 Saudi citizens indicated that only 21.5% of participants use
e-government services, leaving 78.5% who do not use it. With regard to the
availability of online services, more that 60% claimed that only a few services were
available. More than 55% of participants were dissatisfied with the current
e-government services offered by government agencies (Alshehri & Drew, 2010).
However, with the recognition of the small sample used in this study, but it clearly
gives the status and availability of electronic services in the KSA. In addition,
Al-Nuaim (2011) assessed the current state of the Saudi e-government by evaluating
its ministries’ websites using a citizen-centred e-government approach. It was found
that eight of 21 ministries (41%) did not implement the main features of an
e-government website. In addition, 10 ministries (45.4%) were completely or partially
Chapter 1: Introduction
Page 4
in the first stage of having only a web presence; that is, the government has a
website that provides users with information about the government, such as its
policies, laws and regulations, newsletters and reports, but does not provide any
online services. Al-Nuaim’s (2011) results showed there were only three ministries
(13.6%) in the second stage (one-way interaction), and six ministries had no online
service at all. These findings clearly confirmed that the evaluated ministries were not
citizen-centred e-government websites and lacked transactional services, resulting in
citizen dissatisfaction and frustration. Al-Nuaim (2011) concludes that Saudi ministry
websites are still in the early stages of e-government, primarily stage one, with a low
rate of progress. Therefore, the Lack of e-government services availability on
government websites greatly and directly affects cause a low uptake of e-services.
According to Al-Shehry (2008), the lack of citizen adoption of e-government services
is a major challenge for successful implementation of e-government systems in the
KSA. Moreover, the available studies about citizens’ usage of e-government systems
in the KSA found that the level of e-government usage is still low, even though e-
government has been implemented, albeit not completely, in the majority of advanced
countries (United Nations, 2012). In addition to the above findings, there were no
online forms available on any websites, and most ministries had problems with regard
to search, site map, information services, and the provision of online services.
However, the Saudi government’s efforts to facilitate the use of e-government
services are challenged by the problems they face in this regard. Given the huge
amount of funding budgeted for e-government development, approximately AUD $1
billion, this has been a disappointingly poor outcome (Alriyadh, 2011). For the most
part, the problems originate with the limited number of available e-government
services combined with a lack of communication between citizens and government
agencies. As a result, this research will concentrate on the perspectives of both
government agencies and citizens to investigate the research problem.
The Kingdom of Saudi Arabia (KSA) has been rising through the ranks of the United
Nations E-government Survey with regard to the e-government development index,
moving from 58th in 2010 to 41st in 2012 (United Nations, 2012). These findings
indicate that the KSA had made significant progress in implementing e-government
systems, but more effort needs to be made to develop e-government services (G2C)
and to improve web instruments to encourage potential users to use government
Chapter 1: Introduction
Page 5
online services. E-government adoption has been broadly studied in the KSA context
in terms of implementation, development, challenges, success factors, and technical
perspectives (Al-Shehry, 2008; Al-Solbi & Al-Harbi, 2008; Altameem, 2007; Abu
Nadi et al., 2008). However, there is relatively little empirical research that focuses on
citizen adoption of e-government services and considers intention and behavioural
issues based on a validated model (Al-Shehry et al., 2006). Consequently, an
empirical study which focused on citizens’ and services providers’ perspectives is
required to study e-government services (G2C) adoption to help governments and
decision makers understand the factors that affect citizen adoption of e-government
services so the level of e-government services adoption can be increased. Therefore,
this study aims to fill this gap in the literature by conducting an empirical research on
e-government services adoption in the KSA.
This research is grounded in an extended UTAUT model to determine and explain the
impacts of factors that influencing the adoption of e-government services. The
UTAUT model was chosen as the base theoretical model for this study because its
comprehensiveness and high explanatory power in comparison to other technology
acceptance and use models. The results of this study will help decision makers to gain
a better understanding of the factors that determine citizens’ acceptance and use of
e-government services.
1.3 Research Aims and Objectives
This research has the following principal aims and objectives:
1. To continuously review literature in the area of e-government in general and
concentrate on the KSA’s e-government initiatives and achievements;
2. To understand and measure the current level of awareness that exists among
citizens in the KSA about its e-government program and e-services;
3. To evaluate and measure the e-readiness of the public sector in the KSA while
the UN report measures the e-readiness of the KSA in general (i.e. all public
and private sectors);
4. To identify and study the challenges and factors that affect the acceptance and
use of e-government services in the public sector in the KSA from both
citizens’ and government agencies’ perspectives in a comprehensive view;
Chapter 1: Introduction
Page 6
5. To examine and evaluate the viability of the UTAUT model as a proposed
model for e-government services acceptance and use in the public sector in the
KSA; and
6. To offer a number of recommendations for decision makers to attain
successful e-government services systems in the Saudi public sector.
1.4 Research Questions
The aim of this research is to identify, understand, and study the factors that influence
the acceptance and use of e-government services in the public sector from the
perspectives of e-services providers (that is, the public sector) and citizens (the
customers and users for these services). In other words, it aims to find out the key
factors affecting e-government services acceptance and to determine how this
knowledge can be used to create a more effective dissemination and acceptance
process for the KSA. The UTAUT model will be utilized and developed to achieve
this goal. Consequently, the following questions have been identified to help to
achieve this aim:
1. How can the factors that influence the acceptance and use of e-government
services in the Saudi public sector be most effectively captured by using the
proposed UTAUT model?
2. How does stakeholder trust impact on the acceptance and use of e-government
service systems?
3. How does e-government website quality impact on acceptance and use of e-
government services in the KSA?
4. How do UTAUT moderators (i.e. age gender and Internet experiences)
influence the individual’s perceptions to use e-government services in the
KSA?
5. How are the acceptance and use of e-government services hindered or
facilitated by the perspectives of Saudi citizens and government service
providers?
However, in this study, Website quality (WQ) and Trust (TR) have been added as
independent constructs to the UTAUT model. Both constructs are affecting in
Behavioural intention (BI) directly and no relationship was assumed between them.
Chapter 1: Introduction
Page 7
1.5 Research Significance and Outcome
Moores (2003) noted that Middle East countries spend the same or more on
e-government programs than the other developing countries, but are getting less
response and have a lower ranking with regard to citizens’ response as compared to
other developing countries. Moreover, Heeks (2003) claimed that most e-government
projects in developing countries fail, with 35% being classified as total failures and
50% as partial failures. Moreover, the literature indicates that academic research on
e-government acceptance has been limited in general. In particular, there is no
academic research focusing on the acceptance and use of e-government services in the
public sector in Saudi Arabia.
On the other hand, this study focuses on Saudi Arabia, which is different from the
Western world and has its own characteristics. These differences have a direct impact
on the implementation and adoption process of electronic systems that have been
established and implemented successfully in the western world, of those differences,
for example,
1. There is a huge culture, social and political differences between Saudi Arabia and
western world. All life style in Saudi Arabia are heavily influenced by Islamic law.
Accordingly, the western e-systems, theories and models are not suitable and cannot
be applied without some modification to be applicable in Saudi Arabia.
2. The public sectors in Saudi Arabia have a range of differences compared to
western organizations. For instance, bureaucratic in the government systems ,
complexity of the public sector systems, lack of coordination and information
sharing between the public sectors. All of these factors affect directly the
implementation and adoption of e-government systems. Therefore, it is
important to develop an adoption model that fits its unique context.
3. As result of the above differences, several new laws and regulations need to be
issued under the Islamic law to accelerate and increase the adoption level of e-
government systems.
4. This study aim to utilize and amend the UTAUT model which was established
in western environment and apply successfully it in Saudi Arabia context.
In addition, Al-Shehry (2008) recommended studying the factors affecting the usage
of e-government services (Government to Citizens) in Saudi Arabia. It is considered
that studying the factors and challenges are significant in the successful acceptance
and use of e-government services. Furthermore, this study is significant because it
Chapter 1: Introduction
Page 8
provides a comprehensive view of the two pillars of the acceptance and use operation,
comprised of the government sectors on one side (services providers) and Saudi
citizens (customers of these services) on the other side. This research highlights the
importance of understanding the e-government fundamentals among the public to
facilitate the acceptance and use of e-government services. It expands the knowledge
on e-government services from citizen’s perspective in the KSA. This will help in
gaining a better understanding of the challenging factors for the acceptance and use
and diffusion of e-government services in the KSA. In doing so, the KSA government
stands to benefit from this evaluation and the findings provided may assist
government agencies to transform the ways in which they carry out and promote the
acceptance and use of their e-services. The principal outcomes that result from this
research are summarized as follows:
1. This study will provide new effective assessment measures of e-government
services acceptance and use in the public sector in Saudi Arabia.
The results of the research will help decision makers in Saudi Arabia to
consider the factors relevant to e-government services acceptance and use and
increase the possibility of future success within existing e-government
initiatives.
2. The results of the research will help developers to understand and eliminate
the barriers and challenges facing the process of e-government services
acceptance and use in the public sector.
3. This study provides new strategic approaches to facilitate acceptance and use
of e-government services as well as a new conceptual model of facilitators and
barriers to e-government services acceptance and use in the KSA that
combines the perceptions of both citizens and government service providers.
4. This study provides a new validated adaptation of the UTAUT model to
include aspects of system trust and website quality reflecting Saudi needs.
5. The new UTAUT model is examined and refined to identify the factors that
influence e-government services acceptance and use in the public sector in
Saudi Arabia.
6. The research result provides a base for future research to build on with respect
to the proposed acceptance and use model and its application to other contexts.
The research result will answer explicitly the research questions previously
outlined in Section 1.4.
Chapter 1: Introduction
Page 9
1.6 Research Design and Process
The research design or process is an overall procedure which consists of a series of
steps and techniques to carry out the research leading to successful completion of the
final step (Creswell, 2003). On other words, it is the operational description of how
the research will be conducted from the beginning to the conclusion of the study
(Leedy, 1993). The flow chart in Figure 1.1 presents t h e key steps within the
research design to carry out the research process. The process includes: research
problem definition and questions (Chapter 1); literature review (Chapter 2);
background of e-government in the KSA (Chapter 3); and review of the IS adoption
models and theories (Chapter 4). After achieving a completed view based on the
previous chapters, the second step is to present all methodology issues, procedures
and techniques (Chapter 5). This is followed by the research analysis stage, which
includes the analysis of the quantitative and qualitative data which are illustrated in
detail (Chapters 6, 7, 8, and 9). Finally, Chapter 10 discusses the research findings
and addresses the conclusions and recommendations of the study.
Chapter 1: Introduction
Page 10
Define Research Problem and Questions
Research Model
Selection and Modification(UTAUT)
Research Hypotheses
Research Method Quantitative &
Qualitative approach
Data CollectionMethods :
1. Survey Questionnaire 2. Focus Groups
Data Analysis
Literature Review
Selection testing method & technique
(SEM& AMOS)
Interpretation and Discussion of the Results
Finding of previous studies / Define research
scope and context
IS Adoption Theories and Models
Final Results and Conclusions
Figure11.1 The research process flowchart
1.7 Thesis Structure
This research is organized into ten chapters, as shown in Table 1.1. In this
introductory chapter, the research problem was presented and discussed.
Subsequently, the study research questions were presented, followed by an overview
of the research aims and objectives. The chapter concluded by summarizing the study
significance and outcome. The remainder of this thesis is structured as follows.
Chapter 1: Introduction
Page 11
Chapter 2 is dedicated to a review of the literature pertaining to e-government
fundamentals. Several main principles of e-government are presented, such as the
definition of e-government, stages, types, benefits, and challenges.
Chapter 3 focuses on the research background and context. It addresses key issues
relating to the KSA and ICT, such as: an overview of the KSA, ICT in the KSA, ICT
plans, and the Yesser program.
Chapter 4 presents the best known and most important technology acceptance models
that have been utilized globally. Some models incorporating the integrated UTAUT
model and the work covering these models are discussed in detail. Also, the selection
and justification of the research model is discussed.
Chapter 5 introduces the research methodology by addressing various research
paradigms and approaches, leading to the modification of the research’s proposed
model and hypotheses. The chapter also describes the data collection methods,
analysis procedure, as well as reliability and validity tests.
Chapter 6 presents a descriptive data analysis, which includes an overview of the
research questionnaire, data screening, and results of the participants’ demographic
analysis.
Chapter 7 presents the procedure and results of the measurement scale analysis. The
chapter presents the results of scale reliability and validity. Next, the exploratory
(EFA) and confirmatory (CFA) techniques are utilized and the results are presented.
Chapter 8 discusses the model assessment based on the results of the measurement
scale analysis. The chapter begins with an introduction of the SEM technique used in
the assessment procedure. This is followed by assessments of the measurement model
assessment and the structural model. Finally, the effects of moderators on the
relationships among the UTAUT models’ constructs are presented.
Chapter 9 discusses and analyses the qualitative data which was obtained using open-
ended questions with focus groups. Its aim is to validate the findings of the
quantitative analysis. The focus groups explored some unclear issues regarding
e-government services adoption and also provided some recommendations to treat and
accelerate the adoption process of e-government services.
Chapter 1: Introduction
Page 12
Chapter 10 revisits the research questions to confirm what has been accomplished in
this research. The results of the survey and focus groups analyses are summarized and
the research findings are highlighted. These findings are supported by findings from
previous studies. The chapter also provides implications for adoption of e-government
services in the KSA. Furthermore, the chapter outlines the implications of the findings
and identifies the contribution of this study to e-government literature. Finally, the
chapter addresses the limitations of the study and recommends future research
directions.
Table1 1.1
Structure of the Thesis Research Stages Chapter Structure
Def
ine
Res
earc
h C
onte
xt Chapter 1- Introduction
• Research problem • Research questions • Research aims and objectives • Research significance and outcome
Chapter 2- E-government Fundamentals
• Definitions • Types of e-government • Stages of e-government • Benefits and barriers
Chapter 3- Research Background
• KSA overview • ICT in the KSA • E-readiness of the KSA • National ICT Plan • Saudi e-government program (Yesser)
Res
earc
h m
etho
ds a
nd m
odel
Chapter 4- Theories and Models of Technology Acceptance
• Theory of Reasoned Action (TRA) • Theory of Planned Behaviour (TPB) • Technology Acceptance Model (TAM) • Extension of the Technology Acceptance Model
(TAM2) • Diffusion of Innovation Theory (DOI) • Unified Theory of Acceptance and Use of
Technology (UTAUT) • Selection and justification of the research model
Chapter 5- Research Methodology
• Research paradigms • Research categories • Selection and justification of research method • Research model and hypotheses • Data collection strategies • Data analysis • Reliability and validity • Ethical considerations
Dat
a C
olle
ctio
n an
d A
naly
sis
Chapter 6- Descriptive Data Analysis
• Overview of research questionnaire • Pre-analysis data screening • Descriptive statistics
Chapter 1: Introduction
Page 13
Research Stages Chapter Structure
Chapter 7- Measurement Scale Analysis
• Reliability • Validity • EFA & CFA
Chapter 8- Model Assessment
• SEM overview • Measurement model assessment • Structural model assessment • Effect of moderators
Chapter 9- Qualitative Data Analysis
• Obstacles of e-government services • Analysis of open-ended questions • Focus groups analysis
Res
ults
and
O
utco
mes
Chapter 10- Discussion and Conclusion
• Discussion and answering the research questions • Summary of the study: Findings and
recommendations • Research contributions • Limitations and directions for future research
Supp
lem
enta
ry
Info
rmat
ion Appendix A
Appendix B Appendix C Appendix D
• Survey questionnaire (English version) • Survey questionnaire (Arabic version) • Focus groups guide • Ethical clearance certificate
Chapter 2: E-government Fundamentals Literature Review
Page 14
Chapter 2: E-Government Fundamentals: Literature
Review
2.1 Introduction
This chapter reviews and discusses literature related to the research area. The
literature review is an essential stage in the research progression as it explores many
important issues related to the research and clarifies the ambiguity and difficulty of
the research (Gray, 2009). Many governments around the world are adopting e-
government hoping to reduce costs, improve services delivery for citizens, and to
increase effectiveness and efficiency in the public sector. E-government represents an
essential change in the whole public sector structure, values, culture, and the ways of
conducting business. The aim of this chapter is to review the previous work about
e-government and provide essential background knowledge on the research subject.
As presented in Table 1.1, Section 2.2 provides definitions of e-government, e-
readiness and e-services. Section 2.3 provides an overview of e-government types
which include: G2C, G2B, G2E, and G2G. Then, Section 2.4 presents several benefits
of e-government implementation. Section 2.5 discusses the main barriers to
e-government implementation from different aspects. The relationship between
e-government and e-commerce is presented in Section 2.6. Finally, Section 2.7
summarises this chapter.
2.2 Definitions
In this section, the definitions of e-government, e-readiness and e-services will be
illustrated in some detail.
2.2.1 E-government.
The term e-government, the preferred terminology of this research, is also known by
different synonyms, including electronic government, electronic governance, digital
government, online government, and e-gov (Grönlund, 2005). In fact, there are many
definitions for the term e-government and the differences reflect the priorities in the
government strategies. Moon and Norris (2005, p. 43) provide a simple definition of
Chapter 2: E-government Fundamentals Literature Review
Page 15
e-government: "a means of delivering government information and service". Isaac
(2007) defined electronic government as the government's use of technology,
particularly web-based Internet applications, to enhance the access to and delivery of
government information and service to citizens, business partners, employees, other
agencies, and government entities. Dada (2006) viewed e-government as the use of
information technology to improve relationships between the government and citizens
in different areas. Coursey and Norris (2008) defined e-government as the use of
electronic systems to deliver government information and services to all segments of
society, 24 hours per day, seven days per week. Fang (2002) defined e-government as
a way for governments to use the most innovative information and communication
technologies, particularly web-based Internet applications, to provide citizens and
businesses with more convenient access to government information and services, to
improve the quality of the services, and to provide greater opportunities to participate
in democratic institutions and processes. Moreover, the term ‘e-government’, as used
by the OECD E-government Project (2008), applies to the use of ICT as a tool to
achieve better government. Therefore, e-government is not about business as usual,
but should instead focus on using ICT to transform the structures, operations and,
most importantly, the culture of government. The report highlights that e-government
is an important component in terms of overall reform agendas because it: serves as a
tool for reform; renews interest in public management reform; highlights internal
consistencies; and underscores commitment to good governance objectives (OECD,
2003). Furthermore, the World Bank (2009) defined e-government as the use of ICT
applications to enhance and improve the communications between governments and
citizens, businesses, employees, and other governments sectors. Recently, Grönlund
(2010) and Srivastava (2011) defined e-government as the use of ICTs, the Internet,
and web-based applications to achieve improvement, as well as better access and
delivery, of all government services to stakeholders. From these definitions, it can be
concluded that e-government is a system that factually engages and covers every
entity in its area of authority (that is, citizens, businesses, and public organizations). In
other words, depending on the services offered, its scope includes everyone in its
influence.
Chapter 2: E-government Fundamentals Literature Review
Page 16
2.2.2 E-readiness.
It is important to introduce the e-readiness term in this research as it is considered one
of the most important parts in creating a successful e-government environment.
E-readiness is the ability to use information and communication technologies (ICT) to
develop one's economy and to foster one's welfare (United Nations, 2004). Moreover,
e-readiness, as defined by the Economist Intelligence Unit (EIU), is a measure of the
quality of a country’s information and communications technology (ICT), as well as
the infrastructure and the ability of its consumers, businesses, and governments to use
ICT to their benefit (EIU, 2008). E-readiness can also be defined as a measure by
which a country or government is prepared or ready to utilize, use, and benefit from
the digital economy (Lou & Goulding, 2010). Additionally, e-readiness is seen as a
measure of the government e-business environment based on Internet connectivity,
ICT infrastructure, and Internet-based facilities (Berthon et al., 2008).
In general, there are several benchmarking indices at the global level, including those
calculated by the United Nations, the Economist Intelligence Unit (EIU), the World
Bank, and many others. The United Nations Global E-government Readiness Survey
2012 demonstrates an assessment of the use of ICT to provide services for all citizens
in different countries (United Nations, 2012). The survey identified the countries who
play a leadership role in e-government and promote e-government readiness as well as
those countries with problems in the development and the use of ICT for
e-government development. E-government readiness means the availability and full
functionality of technological and telecommunication infrastructure and the level of
human resource development. This survey revealed the strengths and weaknesses in
the e-government readiness of many countries worldwide.
2.2.3 E-services.
Rust and Kannan (2002) defined e-service as the provision of service using electronic
and communication facilities such as the Internet. E-service provides services through
Internet technologies and applications to all customers; it aims to have low cost and
high quality services and provide online transactions and quick communication
between customers and service providers (Hongxiu & Reima, 2009). In addition,
Prins and Verhoef (2007) state that e-services are a means of providing traditional
services to customers electronically via the Internet. Carter and Belanger (2005)
defined e-government services as the use of ICT to enable and improve the efficiency
Chapter 2: E-government Fundamentals Literature Review
Page 17
of government services provided to citizens, employees, businesses, and agencies.
Furthermore, Löfstedt (2005) defines e-services as a means of electronic service
delivery to customers, one which has been receiving growing attention as one of the
subgroups of e-government categories. According to Bertot, Jaeger, and McClure,
(2008), e-government services increase the quality and accessibility of government
services delivery. Nowadays, government agencies around the world are working hard
to increase their services and make them available online 24 hours per day and 7 days
per week. E-government services have become essential and in demand for citizens to
reduce costs, increase efficiency, and improve government services through the
utilisation of information and communication technologies (Fu et al., 2006).
2.3 Types of E-government
E-government offers services electronically to all beneficiaries, such as government
agencies, employees, citizens and businesses sectors. These services differ according
to users’ needs, and this diversity has given rise to the development of different types
of e-government. According to the World Bank (2007), the relationship of
government with recipients of its electronic services is characterized as: government
to citizen (G2C); government to business (G2B); government to employees (G2E);
and government to government (G2G). Therefore, e-government functions can be
classified into four main categories, as listed in Table 2.1. These types are defined in
the following subsections (Yilidiz, 2007).
Table2 2.1
E-government Types Type Abbreviation
Government to Citizen G2C
Government to Government G2G
Government to Business G2B
Government to Employee G2E
2.3.1 Government-to-Citizen (G2C).
The majority of government services come under this heading, with the aim of
providing citizens and others with comprehensive electronic resources to respond to
individuals’ routine concerns and government transactions. Government and citizens
will communicate continuously through the implementation of e-government, thus
Chapter 2: E-government Fundamentals Literature Review
Page 18
supporting accountability, democracy, and improvements to public services (Ndou,
2004). The primary goal of G2C e-government services is to serve the citizen and
facilitate citizen interaction with the government by making public information more
accessible through the use of websites, as well as reducing the time and cost of
conducting transactions (Pina, Torres, & Royo, 2010). In applying the concept of
G2C, citizens have instant and convenient access to government information and
services from everywhere at any time, through improved efficiency and more reliable
online interaction (Monga, 2008). In addition to making certain transactions, such as
certifications, paying government fees, and applying for benefits, the ability of G2C
initiatives to overcome possible time and geographic barriers may connect citizens
who may not otherwise come into contact with one another and may in turn facilitate
and increase citizen participation in government (Seifert & Bonham, 2003; Gil-Garcia
; Pardo, 2005 ; Yilidiz, 2007 & Rowley, 2011).
2.3.2 Government-to-Business (G2B).
Government to business, or G2B, is the second major type of e-government category
and one of the fastest growing e-government sectors. G2B can bring significant
efficiencies to both governments and businesses. G2B include various services
exchanged between government and the business sectors, including distribution of
policies, memos, rules and regulations (Tan et al., 2005). Government-to-Business
(G2B) e-services involve obtaining current business information, new regulations,
downloading application forms, lodging taxes, renewing licenses, registering
businesses, obtaining permits, and many others (Lu et al., 2010). The services offered
through G2B transactions also play a significant role in business development,
specifically the development of small and medium enterprises (Jovarauskiene &
Pilinkiene, 2009). Fang (2002) argued that G2B applications actively drive
e-transactions initiatives such as e-procurement and the development of an electronic
marketplace for government purchases; they also carry out government procurement
tenders through electronic means for the exchange of information and goods. This
system benefits government from businesses’ online experiences in areas such as
e-marketing strategies. The government-to-business G2B is as useful as the G2C
system, enhancing the efficiency and quality of communication and transactions with
business; it also increases the equality and transparency of government contracting
and projects (Hanshaw & Carter, 2008).
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2.3.3 Government-to-Government (G2G).
Government-to-Government (G2G) refers to the online communications between
government’s organizations and departments and has been seen as the base stone of
e-government implementation (Chen, Chen, Huang, & Ching, 2006). Moreover, it
refers to the relationship between the government and its employees; the purpose of
this relationship is to serve employees and offer some online services, such as
applying online for annual leave, checking the balance of remaining vacation time,
and reviewing salary payment records, among other things (Seifert, 2003). According
to Hamza, Sehl, Egide, & Diane (2011), the efficiency and efficacy between
government agencies are enhanced by the use of online communication and
cooperation which allows for the sharing of databases and resources, as well as the
fusion of skills and capabilities. G2G renders information regarding compensation and
benefit policies, training and learning opportunities, and civil rights laws in a readily
accessible manner. The vital aim of the G2G sector is to enhance and improve inter-
government organisations’ processes by streamlining cooperation and coordination
(Heeks, 2006).
On another G2G front, the use of information technologies by different government
agencies to share or centralize information, or to automate and streamline
intergovernment business processes, such as regulatory compliance, has produced
numerous instances of time and cost savings and service enhancements (Curtin,
2007). It is clear that the G2G sector represents the backbone of e-government and it
is both vital and fundamental for each level of government—federal, state, and
local—to enhance and update their own internal systems and procedures before
electronic transactions with citizens and businesses can be successful.
2.3.4 Government-to-Employee (G2E).
Government to employee is the least considered sector of e-government in most
e-government research. G2E refer to the relationship between the government and its
employees only. The purpose of this relationship is to serve employees and offer some
online services, such as applying online for an annual leave, checking the balance of
leave, and reviewing salary payment records, among other things (Ha & Coghill,
2006). It is a combination of information and services offered by government
institutions to their employees to interact with each other and their management. G2E
is a successful way to provide e-learning, bring employees together and to encourage
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knowledge sharing among them (Siau & Long, 2009). It gives employees the
possibility of accessing relevant information regarding: compensation and benefit
policies; training and learning opportunities; civil information; and it allows them
access to manage their benefits online in an easy and fast communication model
(Baležentis, & Paražinskaitė, 2012). G2E also includes strategic and tactical
mechanisms to encourage the implementation of government goals and programs as
well as human resource management, budgeting, and dealing with citizens (Ndou,
2004 & Yilidiz, 2007).
2.4 Benefits of E-government
The acceptance and use of the e-government strategy has significant benefits for
government in the delivery of more effective and efficient information and services to
all e-government sectors (Thompson, Rust, & Rhoda, 2005). E-government provides
many opportunities to improve the quality of services to citizens to meet the
expectations of citizens and business for interaction with the government. It will
enable agencies to align their efforts as needed to improve services and reduce
operating costs (Carter & Belanger, 2005; Dada, 2006). The concept of e-government
emerged as a result of the recognition that there are significant benefits to be gained
through the implementation of ICTs, particularly the Internet, to improve government
delivery of its services. These benefits of the use and application of e-government are
the same for both developing and developed countries (Ndou, 2004,). Almarabeh and
AbuAli (2010) discussed and summarized some of the advantages of e-government,
which are as follows:
• Improves efficiency, accuracy, and reliability in processing large quantities of
data;
• Improves services through a better understanding of users’ requirements, thus
aiming for seamless online services;
• Provides government services to citizens 24 hours per day, 7 days per week;
Helps achieve specific policy outcomes by enabling stakeholders to share
information and ideas;
• Assists a government’s economic policy objectives by promoting productivity
gains inherent in ICT and e-commerce;
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• Contributes to governments’ reform by improving transparency, facilitating
information sharing, and highlighting internal inconsistencies;
• Delivers all government services through online systems so all citizens can
access it easily from any place;
• Helps to build trust between governments and their citizens, an essential factor
in good governance, by using Internet-based strategies to involve citizens in
the policy process, illustrating government transparency and accountability;
• Decreases corruption and increases equity between all citizens.
The research study by Deloitte (2003) states that the strategic application of IT and
mainly e-government has the potential to radically reduce the amount of time, money,
and effort that businesses and citizens must spend to comply with rules and
regulations. A number of researchers (Reddick & Turner, 2012; Awamleh, 2011;
Colesca & Dobrica, 2008; Fenwick, John, & Stimac, 2009; Irani, Love, & Jones,
2008; Scholl, & Klischewski, 2007) acknowledged some of benefits of e-government
systems as follows:
• Provision of information in one easy-to access location;
• Simplified delivery of services to citizens;
• Improved interactions among government units and with business, industry,
and citizens;
• Improved productivity (and efficiency) of government agencies;
• Simplified and streamlined reporting requirements;
• Reduced number of paper forms;
• Capability of citizens, businesses, other levels of government, and
government employees to easily find information and obtain services from the
government and government agencies;
• Facilitation of transactions (paying fees, obtaining permits); and
• Effective, cheaper, and more convenient delivery of information, knowledge
and services.
Furthermore, the implementation of e-government not only saves resources, but it can
also significantly increase service levels by reducing time spent on bureaucracy. The
desire to provide new and improved services results in greater efforts to improve the
citizen’s experience interacting with the government when seeking out information or
trying to obtain various services. The evolution of e-government and technology
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creates the potential for new services to emerge, which contributes to improved
service quality (Ndou, 2004; Dada, 2006).
2.5 Barriers to E-government Implementation
There are several challenges that can delay progress towards realizing the promises of
e-government. The variety and complexity of e-government initiatives implies the
existence of a wide range of challenges and barriers to its implementation and
management for governments and citizens (Weerakkody, El-Haddadeh, & Al-Shafi,
2011; Aman & Kasimin, 2011; Jain & Kesar, 2011; Bhuiyan, 2010). This section, will
briefly introduce the most important and common challenges and barriers as shown in
Table 2.2
Table3 2.2
E-government Barriers
2.5.1 Technical barriers.
The implementation of e-government initiatives face some technological difficulties
such as lack of shared standards and compatible infrastructure among departments and
agencies. Also, privacy and security are critical obstacles in implementation of e -
government in citizen concern (Choudrie, Weerakkody & Jones, 2005). The guarantee
by the government will not suffice unless accompanied by technical solutions,
transparency of procedures and possibly independent auditing (OECD, 2003 ; Aman
& Kasimin, 2011).
Category Barriers
Technical • ICT Infrastructure
Privacy
• Security
Organizational • Policy and Regulation Issues
• Lack of Qualified Personnel and Training
• Lack Partnership and Collaboration
Social • Digital Divide
• Culture
Economical • High Cost
• Lack of Budget
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2.5.1.1 ICT infrastructure.
ICT infrastructure is recognized to be one of the main challenges for e-government.
Internetworking is required to enable appropriate sharing of information and open up
new channels for communication and delivery of new services (Ndou, 2004;
Almarabeh & AbuAli, 2010). For a transition to electronic government, an
architecture, that is, a guiding set of principles, models and standards, is needed.
Many developing countries suffer from the digital divide, and they are not able to
deploy the appropriate ICT infrastructure for e-government deployment (Rose &
Grant, 2010). The digital divide has been identified as one of the most important
challenges in implementation of e-governance systems (Yang & Rho, 2007; Helbig,
Gil-Garcia & Ferro, 2009). According to Brown and Thompson (2011), the
implementation of the whole e-government framework requires a strong technology
infrastructure. In order to deliver e-government services, government must therefore
develop an effective telecommunication infrastructure. In addition, they stated that
successful e-government implementation would depend upon how the capacities of
various infrastructures are structured and how they are capitalized with an integrated
focus. However, an ICT infrastructure does not consist simply of telecommunications
and computer equipment. E-readiness and ICT literacy are also necessary in order for
people to be able to use and benefit from e-government applications. According to
Katz et al., (2009) ICT literacy can be defended as: the ability to use information
technology tools, communications tools, ICT applications to access, use, integrate,
assesses, and create information in order to participate in an Information technology
society .Having the education, freedom and desire to access information is critical to
e-government efficacy. Presumably, the higher the level of human development, the
more likely citizens will be inclined to accept and use e-government services (Ndou,
2004). Therefore, governments should work closely with the private sector to
establish a modern infrastructure that will provide access opportunities to
disconnected groups and individuals. This lack of infrastructure is cited as one of the
primary barriers to e-government implementation (Nagi & Hamdan, 2009; Qaisar &
Khan, 2010; Jain & Kesar, 2011).
2.5.1.2 Privacy.
Privacy is a major issue in the implementation of e-government in both mature and
developing democracies. Concerns about website tracking, information sharing, and
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the disclosure or mishandling of private information are universally frequent
(Belanger & Hiller, 2006). There is also the concern that e-government will monitor
citizens and invade their privacy. Alan Westin in 1976 defined privacy as the claim of
individuals to decide what information about themselves can be known by others
(Westin, 1976). Moreover, it refers to the guarantee of an appropriate level of
protection regarding information attributed to an individual (Lean, Zailani, Ramayah,
& Fernando, 2009). Both technical and policy responses may be required when
addressing the privacy issue in an e-government context. The difficulty of protecting
individual privacy can be an important barrier to e-government implementation. In
addition, there is a need to deal effectively with privacy issues in e-networks in order
to increase citizen confidence in the use of e-government services. Citizen confidence
in the privacy and careful handling of any personal information shared with
government organizations is essential to e-government applications (Weerakkody et
al., 2011). Lean et al. (2009) notes that, in developing countries, many people are so
concerned with privacy and confidentiality issues they decide to forego e-government
opportunities.
However, the increased focus on security may lead to less interest in the protection of
citizens’ privacy. Government has an obligation to ensure citizens’ rights regarding
privacy, processing, and collecting personal data for legitimate purposes only (Sharma
& Gupta, 2003). Moreover, Belanger and Hiller (2006) consider privacy and
confidentiality as critical obstacles to the realization of e-government. Citizens are
deeply concerned with the privacy of their lives and the confidentiality of the personal
data they provide in order to obtain government services. Thus, privacy and
confidentiality must remain priorities when establishing and maintaining websites to
ensure the secure collection of data (Almarabeh & AbuAli, 2010).
Since privacy protections are difficult to interject once an e-system has been built, the
planning and design of e-government systems must include privacy considerations. A
comprehensive privacy policy should specify citizens’ rights to privacy and mandate
that personal data be collected and processed only for legitimate purposes (Shareef,
Kumar, Kumar, & Dwivedi, 2009). At the centre of most e-government projects is the
collection and management of large quantities of citizen data such as names,
addresses, phone numbers, employment histories, medical records and property
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records. It is important to note that different countries have different legal and cultural
understandings of what constitutes privacy (Belanger & Hiller, 2006).
2.5.1.3 Security.
Security is considered as one of the critical factor for the implementation of
e-government systems (Carter & Weerakkody, 2008; Al-Sebie &Irani, 2005). Many
studies have also resulted the security issue for both citizens and governments is one
of the challenges of e-government systems around the world (Al-Fakhri, Cropf,
Higgs, & Kelly, 2008; Al-Shehry, 2008; Colesca, 2009; Almarabeh & AbuAli, 2010).
Security means protection of information and systems against accidental or intentional
disclosure to unauthorized access, or unauthorized modifications or destruction
(Layton, 2007). Thus, it refers to protection of the information systems, assets, and the
control of access to the information itself (Lean et al. 2009). It is a vital component in
the trust relationship between citizens and government. Thus, security policies and
standards that meet citizen expectations are an important step towards addressing
these concerns (Colesca, 2009). In fact, information security is a costly but necessary
part of e-government, and involves the protection of data, as well as the integrity of
the software and hardware, the training and oversight of personnel, service continuity,
the latter being essential to the availability and delivery of services, and the
establishment of citizen confidence and trust.
Security commonly consists of several elements including: computer security,
network security, documents security and confidentiality of personal data (Smith &
Jamieson, 2006). It also includes maintenance and e-infrastructure protection in the
form of firewalls and limits to those who have access to the data. Furthermore, the use
of security technology, including digital signatures, encryption, user IDs, passwords,
credit card numbers, bank account numbers, and other such data being transmitted
over the Internet and stored electronically can aid in the fulfilment of security goals in
e-government applications (Stibbe, 2005; Weerakkody et al., 2011). Furthermore,
Seifert and Bonham (2003) point out that information security, referred to as cyber
security or computer security, is an important e-government challenge. In addition,
security involves continuous vigilance and protection against the increasing danger of
worms and viruses.
Users need to be educated on the importance of security measures, such as private
passwords, to ensure their own protection. Reddick and Frank (2007) point out that,
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while security will remain an obstacle to e-government, it will not extensively affect
its progress as the public learns to work with and accept its occasional lapses. Also,
they mention three keys that affect the success of security. The first involves
continuous improvement and upgrades in an attempt to stay ahead of criminals. The
second is that security must be visible and foreboding to deter would-be criminals.
Finally, it must be accepted that no security system is perfect and that all systems can
eventually be overcome. However, government organizations, being responsible for
the collection, maintenance, and distribution of sensitive or confidential information,
should consider methods of providing security for collected information as well as for
their websites. A national level security mechanism instituted to combat cybercrime
and fraud may help to win the trust in the public and businesses in their transactions
with the government. Thus, a body of security professionals could be setup to respond
to threats and breaches. The need for authority and an infrastructure encryption
system should be given top priority (Colesca, 2009).
2.5.2 Organizational barriers.
Feng (2003) points out that e-government is not a technical issue, but rather an
organizational issue. Also, he found that another key issue raised by the stakeholders
regarding e-government implementation is the need to view e-government as a change
management issue rather than an IT implementation issue. Organizational challenges
include: policy and regulation issues; lack of qualified personnel and training; and
lack of partnership and collaboration.
2.5.2.1 Policy and regulation issues.
The implementation of e-government principles and functions requires a range of new
rules, policies, laws, and government changes to address electronic activities
including electronic archiving, electronic signatures, transmission of information, data
protection, computer crime, intellectual property rights, and copyright issues
(Almarabeh & AbuAli, 2010; Tolbert & Mossberger, 2006). Dealing with
e-government means signing a contract or a digital agreement, protected and
recognized by a formalized law, which protects and secures these kinds of activities or
processes. In many countries, e-business and e-government laws are not yet available
(Dawes, 2008; Ndou, 2004). Establishing protections and legal reforms are needed to
ensure, among other things, the privacy, security, and legal recognition of electronic
interactions and electronic signatures. Policymakers implementing e-government must
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consider the impact of law and public policy. Otherwise, any initiative will encounter
significant problems (Ho, 2002; OECD, 2008; Bhuiyan, 2010).The effort must
incorporate a holistic view, one that is not just focused on technology. Archaic laws,
old regulatory regimes, and overlapping and conflicting authorities can all greatly
complicate or altogether halt a project. Legal reforms and new policy directives may
have to be adopted before the online world can function smoothly. Hence,
governments all over the world need to tackle the design and development of key
public infrastructure, which will guarantee secure transactions between organizations
and individuals (Al-Fakhri et al., 2008; Ndou, 2004). In fact, there is an international
commission, known as UNCITRAL, which issues and enacts international trade laws.
The United Nations Commission on International Trade Law (UNCITRAL) was
established by the United Nations General Assembly in 1966, with the aim of
reducing obstacles to international trade. However, UNCITRAL concentrates on
international trade law, which involves e-commerce law only. So, there are many
other laws still under the responsibility of local governments which need to be issued
and applied (UNCITRAL, 2009).
2.5.2.2 Lack of qualified personnel and training.
Another major challenge to e-government initiatives is the lack of ICT skills in the
public sector. This is a particular problem in developing countries, where the constant
lack of qualified staff and inadequate human resources training has been a problem for
years (UNPA & ASPA, 2009). The availability of appropriate skills is essential for
successful e-government implementation. E-government requires human capacities:
technological, commercial, and management. Technical skills for implementation,
maintenance, design, and installation of ICT infrastructure, as well as skills for using
and managing online processes, functions, and customers, are compulsory. To address
human capital development issues, knowledge management initiatives are required
focusing on staff training, seminars, workshops in order to create and develop the
basic skills for e-government usage (OECD, 2003). In general, it is vital to focus on
training and education programs to enhance the progress of e-government projects.
However, training is a fundamental prerequisite as the rate of change increases and
new technologies, practices, and competitive models appear. The full economic
benefits of ICT depend on a process of training and learning skills, which is still at an
important stage for all governments (Altameem, Zairi, & Alshawi, 2006).
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2.5.2.3 Lack of partnership and collaboration.
Collaboration and cooperation at local, regional and national levels, as well as
between public and private organizations, are important elements in the e-government
development process (Altameem et al., 2006). Nevertheless, collaboration and
cooperation are not simple to realize. Governments often exhibit considerable
resistance to open and transparent systems as they try to preserve their authority,
power, and hierarchical status (Cohen & William, 2002). Citizens distrust their
governments, especially where there has been a history of dictatorship, political
instability, or large-scale corruption. To ensure that the public and stakeholders will
be partners in the e-government effort, it is important to try to build trust in
government (Ebrahim & Irani, 2005).
Collaboration between the private and public sectors is needed too, in order to provide
the resources, skills and capabilities that the government lacks. For example, the ICT
private sector is able to support government with technical skills and infrastructure;
meanwhile, universities will provide the required staff, learning, and training courses
for government staff and citizens, and other government departments and agencies
can contribute in data and information flow and knowledge sharing for problem
solving of similar tasks or processes and so on. Almarabeh and AbuAli (2010) assert
that the lack of cooperation and collaboration between organizations is one of the
main factors in e-government project failures. Therefore, a ‘new’ development model
is emerging that focuses on partnership among stakeholders in the knowledge-based
development program. Government should play the role of facilitator and encourage
the private sector to participate in e-government development and implementation
(Ndou, 2004).
2.5.3 Social barriers.
Social issues are mainly concerned with the usability by a large variety of people.
This means that the interface must be usable by all kinds of people within the
government. Social Obstacles includes many factors, such as digital divide, culture,
education and income. In this area, the first two factors will be illustrated.
2.5.3.1 Digital divide.
The ability to use computers and the Internet has become a crucial success factor in
e-government implementation, and the lack of such skills may lead to marginalization
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or even social exclusion (UNPA & ASPA, 2009; Bhuiyan, 2010). The digital divide
refers to the gap in opportunity between those who have access to the Internet and
those who do not. Therefore, those who do not have access to the Internet will be
unable to benefit from online services (OECD, 2003). In the case of the digital divide,
not all citizens currently have equal access to computers and Internet, whether due to
a lack of financial resources, necessary skills, or other reasons. In fact, computer
literacy is required for people to be able to take advantage of e-government
applications. Government should train its employees and citizens in basic computer
and Internet skills so that they are able to participate in e-government development
applications. In addition, Gomez (2009) points out that making Internet services
available in public locations, such as grocery stores, post offices, libraries, and
shopping malls, may help to address the gap between those households that have
access to the Internet and data services and those who do not.
According to UNPAN (2004) the large majority of the population around the world is
not connected physically to a network; in many cases, connectivity in the traditional
sense is not even being planned for the foreseeable future, and the key access
elements are all at critically low levels. Thus, usage is limited to the top income
groups due to the high cost of access and a lack of education and skills; lack of local
language or local interest are additional problems, as are barriers imposed by the
government. Furthermore, Nam and Sayogo (2011) points out that the lack of Internet
access among certain sections of the population is considered one of the important
barriers to e-government development. Indeed, the lack of access among these
vulnerable or low-income citizens prevents them from being able to make use of those
services provided specifically for them. United Nations (2008) survey found that an
increasing digital divide increases the cost of technical barriers in launching and
sustaining e-government services. Sometimes, language is considered one of the
barriers that prevents participation in e-government applications, whether for citizens
or non-citizens.
2.5.3.2 Culture.
The main barriers to the implementation of e-government are not technical, but the
cultural implications of new technologies (Feng, 2003). Culture includes several
principles, such as the beliefs, religion, language, education, values, characteristics
and behaviour of a society (Burn & Robins, 2003). Personal characteristics and
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subjective conditions are more likely to be influenced by cultural factors than are the
objective conditions surrounding the development and diffusion of new technology.
Therefore, cultural and individual behaviour patterns play a significant role in how
citizens and policy makers use new technologies and online systems (Choudrie,
Umeoji, & Forson, 2010). Culture plays a significant role in an individual’s outlook;
many people resist change and adopt new technologies slowly and with great
deliberation (Chen et al., 2006; Scholl, Klischewski, 2007). Furthermore, Hackney
and Jones (2002) identified lack of relationships between internal departments and
external agencies and adopting a corporate approach as major barriers to successful
e-government. To achieve this, it was felt that major cultural changes are necessary.
In order to accommodate the internal cultural changes necessary, organizational
development must be included in the application process so that internal cultural
changes are accommodated. In summary, cultural changes, though less tangible, must
receive at least as much planning so that implemented e-government projects can be
successful (Altameem, 2007).
2.5.4 Leaders and management support.
The literature shows that without support from the top management, any innovation is
less likely to be adopted. Thus, e-government implementation needs support from the
highest level of government for successful implementation. Top management support
refers to the commitment from top management to provide a positive environment
that encourages participation in e-government applications (Hussein, Karim,
Mohamed, & Ahlan, 2007). Therefore, it plays a significant role in the adoption and
implementation of e-government (Akbulut, 2003). As mentioned previous, leadership
is one of the main driving factors in every new and innovative project or initiative, so
it is necessary for the implementation of e-government. Leadership involvement and
clear lines of accountability for making management improvements are required in
order to overcome the natural resistance to organizational change, to gather the
resources necessary for improving management, and to build and maintain the
organization-wide commitment to new methods of conducting e-government systems
(Almarabeh & AbuAli, 2010). The involvement of high-level leadership, as well as an
integrated vision of IT, is vital to vertical e-government planning, the acquisition of
necessary resources, the motivation of officials, the support of dealings with external
partners and stakeholders, and to interagency and ministry co-ordination. As can be
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observed in transitional democracies and developing countries, the development of e-
government is driven by political leadership and an integrated vision of IT. Leaders
who perceive a potential gain from the promotion of e-government are more likely to
support such initiatives, even in the face of obstacles, while those who believe that
they stand to lose from the implementation of e-government cannot be counted on for
sustained support (Seifert & Bonham, 2003). Therefore, government needs to educate
the upcoming ranks of government leaders, managers, and administrators in planning
and managing ICTs across all public sectors, focusing on access opportunity,
economic development, and effective delivery of public information and services
(OECD, 2003).
2.5.5 Financial barriers.
The most significant barrier to the implementation of e-government is a lack of money
since e-government implementations are usually very expensive (Stoltzfus, 2005;
UNPAN, 2004). It is necessary to ensure the availability of the existing and expected
budgetary resources in order to achieve the goals. Since every government budget is
already overburdened with every possible expense budget makers can fit into it, the
suggestion to expend the considerable sums that an excellent e-government will cost
is a non-starter, in budgetary terms, and in budgetary politics (OECD, 2003). Carvin,
Hill, and Smothers (2004) stated that because of the high cost of implementation and
maintenance the computer systems, many countries find themselves with the dilemma
of funding e-government programs, even when a government entity has a plan for
effective and accessible e-government. Brown and Thompson (2011) stated that a
major obstacle to e-government in developing countries is the lack of financial
support for capital investment in new ICT systems. Ndou (2004) noted that the
abilities of government offices to place services online and to use technology for
democratic outreach are hampered by budget considerations. Finally, the total cost,
including the high cost of systems hardware and maintenance, software, training, and
education, are always seen as major barriers inhibiting agencies and governments
from using the technologies.
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2.6 E-government and E-Commerce Relationship
2.6.1 Definition of e-commerce.
Electronic commerce (e-commerce) has become a priority for business, companies,
and academic research since the early 1990s because of its commercial importance
and rapid growth. It has changed the traditional ways of conducting business and
activities (Chan & Swatman, 1999; Srivastava & Teo, 2011). Like e-government,
there is no common accepted definition of e-commerce and it has many different
definitions from many different perspectives. For instance, e-commerce is a
commercial means used by businesses sectors and companies to conduct business and
deliver their products to customers by utilizing the power of ICT (Ngai & Wat, 2005).
Zwass (2003, p. 2) defines e-commerce as “the sharing of business information,
maintaining business relationships, and conducting business transactions by means of
telecommunications networks.” According to Laudon and Laudon (2003), there are
three main categories of e-commerce: Business-to-Consumer (B2C); Business-to-
Business (B2B); and Customer-to-Customer (C2C).
2.6.2 Common factors between e-government and e-commerce.
The literature indicates that e-commerce and e-government have many principles in
common and, indeed, there are some differences as well. According to Carter and
Belanger (2004), e-commerce and e-government both use ICT and the Internet as a
medium to deliver their information, goods, and services. Also, both aim to reduce
costs and time spent for their customers, while increasing service quality and gaining
customer satisfaction. However, there are many differences between e-commerce and
e-government. For instance, e-commerce can choose its customers while
e-government cannot; e-government services should be provided to all its customers.
Also, they differ in term of structure in the sense that the structure in the private sector
is flexible and can be modified easily compared to government agencies. The third
difference is accountability. All governments are required to deliver their services and
facilities in the best interests of their citizens, while companies present their services
and goods in commercial ways to all customers (Carter & Belanger, 2004).
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2.7 Chapter Summary
This chapter examined the literature to define and illustrate the types, stages,
advantages and barriers to e-government. In addition, the relationship between
e-government and e-commerce has been briefly discussed. It is clear that
e-government has many advantages to offer to all sectors of government. However,
many critical issues face the adoption and diffusion of e-government, some of which
are non-technical in nature yet have a wide impact and require comprehensive
planning. In Chapter 3, E-government and the Kingdom of Saudi Arabia, the main
characteristics of the Kingdom of Saudi Arabia (KSA) and its initiatives regarding IT
and e-government will be discussed in further detail.
Chapter 3: Research Background
Page 34
Chapter 3: Research Background
3.1 Introduction
E-government is a new wave in the information revolution. Many governments around
the world pursue this phenomenon hoping to reduce costs, improve services delivery for
citizens, and increase effectiveness and efficiency in the public sector. The government
of Saudi Arabia seeks to transform itself into an information society through a number
of major initiatives in a variety of fields. One such important initiative is e-government.
The importance of e-government stems from the great benefits it contributes to the
national economy and the welfare of citizens. The Kingdom of Saudi Arabia (KSA) has
been rising through the ranks of the United Nations E-government Survey 2012 from
58th in the 2010 to 41st in the 2012 world e-government rankings. While this is still
modest in comparison to more advanced countries, there are clear signs that the KSA is
working hard to build a strong e-government infrastructure that will assist in the gradual
transition to becoming a society of the information age (United Nations, 2012). This
chapter offers some brief information about the KSA in Section 3.2. Section 3.3
explores the main characteristics of ICT sectors in the KSA. E-readiness for
e-government is discussed in Section 3.4. Then, Section 3.5 presents an overview of the
national ICT plan. Section 3.6 discusses e-government initiatives. Saudi E-government
program (Yesser) is described in Section 3.7. The information technology regulatory
system in Saudi Arabia is discussed in Section 3.8. Finally, Section 3.9 summarizes the
chapter.
3.2 Kingdom of Saudi Arabia: Location, Population, Economy, and
Culture
The official name of the country is the Kingdom of Saudi Arabia (KSA). However,
internationally, it is widely known as Saudi Arabia and so both names will be used in
this research, with preference in this dissertation given to the acronym, KSA. The
official language of the KSA is Arabic. However, the English language is common in
the business and educational communities. The KSA is located in the Middle East and,
according to the KSA Central Department of Statistics, had a total population of 28
million in 2011, with an annual growth rate of 2.9 percent (MOEP, 2012). Al Riyadh is
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the capital city of Saudi Arabia with a population of 4 million. The economy of Saudi
Arabia is an oil-based economy due to the KSA having the largest reserves of oil in the
world; it also ranks as the largest exporter of petroleum (OPEC, 2012). Accordingly, the
Saudi government, through the public sector, plays a major role in the Kingdom's
economic activity (Al-Saggaf, 2004). There are many aspects that characterize Saudi
Arabian culture, such as religion, the tribal system, its rule, and modernisation. The
modernisation here means the transition from the traditional life style of society to the modern
style by utilizing the power of Information and Communication Technology. For example e-
government systems is a new way of communication between government sectors and Saudi
citizen, therefore, Saudi government highly support e-government systems and dedicate a huge
budget for implementation and adoption of e-government systems. However, the Saudi
government supports modernization in all aspects of life in Saudi society and so, for this
reason, the government has imported expertise from all over the world to support the
transformation of Saudi Arabia to a modern country. Saudi Arabia has conserved, albeit
in a new form, many values of Arab and Islamic civilization and the traditional system
of power and government while, at the same time, adopting Western technology, a
market economy, a modern state education system, healthcare, and other public sector
services (MCIT, 2011). The effect of culture can be illustrated in the adoption of the
Internet, which was introduced in Saudi Arabia in late January 1999 after a long period
of discussion and consultation among Saudi authorities. Finally, a huge filter system
was set up in Riyadh in conjunction with an American company. The reason for having
such a filter system was that the Saudi authorities had serious concerns about receiving
unwanted material on personal computer screens; there were other cultural, religious
and political reasons as well (Al-Saggaf, 2004).
3.3 Information and Communication Technology (ICT) in Saudi
Arabia
Information and Communication Technology (ICT) plays an essential role in the
economies of many countries and, as a result, the government of the KSA has given it
top priority. During the past fifty years, the IT sector has witnessed radical changes. For
instance, IT applications have spread rapidly to cover many sectors to improve
productivity and advance performance in the fields of finance, industry, commerce,
education, government, and health care (Al-Tawil, Sait & Hussain, 2003). However,
information technology in Saudi Arabia is still a relatively young technology when
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compared with other developed countries, such as the USA, the UK, Australia, and
Canada. IT systems have been rapidly diffused within private and public sectors and
organisations. So, many organisations have introduced information systems in some
form or another to support and improve the efficiency and effectiveness of their
functions; this is more apparent in the government sector and in large sized private
organisations than in small organisations (Al-Shehry, 2008).
Moreover, Saudi Arabia has the largest and fastest growing ICT marketplace in the
Arab region (Alghamdi, Drew & Alkhalaf, 2011). The Saudi government actively
encourages and promotes the utilisation of information technology systems in the
economy through its own consumption, as well as its import, trade and industrial
activities. These policies encourage public and private organisations to adopt and
implement modern and advanced IT systems (Al-Tawil et al., 2003). However, ICT
diffusion in a country like Saudi Arabia is a very complex process and is often
associated with many problems. These problems are not only scientific and technical,
but also, and possibly more importantly, cultural, educational, economic, political, and
social (Abanumy et al., 2005). According to Al-Soma (2008), the major problems faced
by many organisations in Saudi Arabia concerning the use of IT include: lack of
management support; lack of IT planning; lack of qualified human resources; and
insufficient training.
The next subsection highlights the ICT infrastructure and Internet diffusion. It offers
important indications regarding the real situation in Saudi Arabia in these areas.
3.3.1 ICT infrastructure.
National infrastructure refers to the availability of the basic structures and facilities in
different aspects of life; these include the economic, educational, scientific and
technological, social, telecommunication, and health facilities in a country. However, an
adequate and modern telecommunication infrastructure is the backbone of the economic
and social development of any country (Ndou, 2004). It is clear that, if any country
lacks adequate telecommunication infrastructure, its economic, IT, and social
development will, subsequently, be either weak or progress slowly. This is because, for
any country, communications represent the backbone of information technology
(Al-Smmary, 2005). As a consequence, attempts to build an ICT infrastructure in the
KSA took place early, in parallel with the rapid development of the country. Recently,
the Saudi government has concentrated on improving the information technology (IT)
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infrastructure by opening the telecommunication sectors to privatization in 2007 (Al-
Suwaiyel, 2007). Additionally, in 2003, the government created the Ministry of
Communication and Information Technology to control IT services by formulating IT
regulations and developing future plans. Moreover, it established the Communication
and Information Technology Authority (CITA) to control and monitor ICT services for
the whole country (Al-Smmary, 2005).
3.3.2 The Internet in the KSA.
In April 1997, the Saudi government officially allowed public access to the Internet.
However, many Saudi organisations and individuals already had some access to the
Internet prior to that date. For example, Saudi ARAMCO (The Saudi Oil Company),
KACST (King Abdul-Aziz City for Science and Technology), and King Faisal
Specialist Hospital had Internet access, either through satellites or through special
access facilities from outside the KSA (Al-Shehry, 2008). Recently, as of 2011, there
were more than 13.6 million Internet users in the country, which is more than 47% of
the total population, as shown in Figure 3.1 (MCIT, 2011). That means every one
included in the 47% has access to at least one computer and can be online any time and
communicate with everyone everywhere. They use e-mail to communicate instead of
traditional ways such as telephone or postal mail. Also, they do not buy newspapers
every morning; instead, they read the news online from many different websites. A
study by Al-Tawil et al. (2003) concerning the effect of the Internet in Saudi Arabia
indicated that the Internet has brought a new dynamic global platform to Saudi society,
education, and the economy. It has created virtually unlimited potential for
advancement in terms of economic development and capital growth. It has also
addressed Saudis’ issues and concerns for achieving ubiquitous education at all levels. It
is clear that the computers and Internet are becoming a basic part of modern Saudi
society, and a significant part of this includes access to e-government services and other
online services.
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Figure2 3.1. Internet growth in the KSA (MCIT, 2011)
3.4 E-readiness of the KSA e-government
E-readiness for e-government is the degree to which a government is prepared to
provide its information and services through multiple channels, including the Internet,
towards customer centricity. Further, society e-readiness is the degree to which a society
is prepared to contribute in the e-world. Thus, people should be ready to follow the new
path and use the techniques to communicate and obtain services (Al-Smmary, 2005).
Based on the UN reports of 2010 and 2012, Saudi Arabia made significant progress in
e-government readiness at both the international and regional levels with massive
investments in ICT infrastructure and a more prominent web presence as major
government projects went online. The United Nations Global E-government Survey
2012 ranked Saudi Arabia as number 41 worldwide, an improvement from its position
at number 58 in 2010. With an index of 0.6658 (the best being 1.00), Saudi Arabia
ranked 9th in 2012 in the Asia region, as shown in Table 3.1 (United Nations, 2012).
Table4 3.1
E-government leaders in Asia (United Nations, 2012)
No. Country 2012
Index
2010
Index
2012
Ranking
2010
Ranking
1 Republic of Korea 0.9283 0.8785 1 1
2 Singapore 0.8474 0.7476 10 11
3 Israel 0.8100 0.6552 16 26
4 Japan 0.8019 0.7152 18 17
5 United Arab Emirates 0.7344 0.5349 28 49
Chapter 3: Research Background
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6 Bahrain 0.6946 0.7363 36 13
7 Kazakhstan 0.6844 0.5578 38 46
8 Malaysia 0.6703 0.6101 40 32
9 Saudi Arabia 0.6658 0.5142 41 58
3.5 National ICT Plan
The National ICT plan (NICTP) includes a long-term vision and a first five-year plan
for ICT in the Kingdom Of Saudi Arabia. The long-term vision is “to transform the
country to an information society, so as to increase effectiveness and efficiency, and
provide e-services for all sectors of the society, and build a solid ICT industry to
become a major source of income for the nation” (CITC, 2005, p. 2). The objectives
seek to bridge the digital divide by enabling all societal sectors to reach and access ICT
services easily and utilize them effectively. Other objectives include creating job
opportunities, raising the efficiency of education and training through ICT, plus the
preparation of qualified workforces. The five-year plan includes projects that cover the
main aspects of ICT usage such as e-government, e-commerce, tele-medicine,
e-learning, digital Arabic and Islamic cultural content. Further, they cover the regulatory
activities such as issuing licenses for new voice and data operators, and regulating the
ICT market. The scope also includes ICT industry elements, such as identifying
investment opportunities, research, development, innovation, international cooperation,
and technology transfer. A set of indicators called the Information Society Indicators are
identified and measured against specific targets by the end of the plan (MCIT, 2011).
3.6 E-government Initiative
The IT National Plan in the KSA reflects the key interest of the Saudi government in
supporting the transformation towards e-government. The user-centric vision for Saudi
Arabia’s e-government initiative is summarized by the following vision statement: By
the end of 2010, everyone in the Kingdom will be able to enjoy from anywhere and at
any time world class government services offered in a seamless, user friendly and
secure way by utilizing a variety of electronic means. For this, e-government initiatives
were launched as part of the country’s overall information technology plans in 2003 and
this focused on ICT as a tool for reforming public organisations (MCIT, 2011). The
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main objectives focused on three areas, namely e-readiness, e-society, and IT training.
These are described in more detail below.
3.6.1 E-Readiness
E-readiness is addressed by improving IT infrastructure to: support the country’s
economy; initiate e-learning, e-government and e-health; improve productivity at low
additional cost; set up standards and guidelines for national networks; and develop a
security framework to preserve the characteristics of Saudi society in a digital age. In
light of this, in 2005, the Saudi government created ‘Yesser’, an e-government program
designed to achieve continuous growth and development within the government
(Al-Smmary, 2005). The main objectives of this program and other concepts will be
illustrated in Section 3.7.
3.6.2 E-Society
The government in the KSA has utilized the importance of reducing computer illiteracy
among citizens. To this end, in May 2005, the government introduced a new scheme to
provide a computer for each house at a low cost and this initiative is available for the
entire public in Saudi Arabia. Moreover, the government encourages the education
system to help the public embrace this initiative by teaching IT and by creating high
level computer labs for all classes and ages (MCIT, 2011).
3.6.3 IT Training
The Ministry of Education in the KSA introduced new courses to educate students in
public and private schools about IT and its applications. It has also devised a plan to
build computer labs for all public schools. In addition, most universities and colleges in
the KSA have technical colleges which teach ICT courses and graduate IT specialists.
Regarding e-government initiatives among organisations in the public sector, there are
many projects in Saudi Arabia that have implemented e-government activities in a
variety of ways, as shown in Table 3.2.
From Table 3.2, it can be seen that many significant efforts have been made in terms of
the implementation of e-government in the public sector in the KSA. In general, the aim
of the e-government initiative in the KSA is to ease and speed up transactions in
government organizations (G2G), between government organizations and citizens
(G2C), and between government organizations and business organizations (G2B). The
United Nations Survey Report 2012 confirmed that Saudi Arabia has devoted a high
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level of consideration and a large budget to expanding and consolidating e-government
services and to offer online services on par with those of global leaders in e-government
systems (United Nations, 2012).
Table 53.2
E-government activities in Saudi Arabia
Project Objectives
Home PCs
This is a national initiative led by the CITC and MCIT and executed by the private sector. It allows: • The citizen to buy a PC paying by monthly instalments (100 SR) for two years • Free Internet access for a limited time • Discounted training
E-Payment Gateway ‘Sadad’
Building the e-payment gateway to: • Facilitate G2B and B2B electronic payments • Include G2C in future
Madinah E-government Project
The Municipality of Almadinah portal offers: • G2B services • G2C services
MOI (Ministry of Interior ) Portal
This citizen portal: • Provides 20+ services electronically, including passports, birth certificates,
drivers’ licenses, etc. • Offers 100 kiosks
E-Umrah
Supports international import/export process: • Covers complete workflow (Customs, General Organization of Ports, cargo,
customs clearance agents, etc.) • It can speed up the process by a factor of seven • It can cut down the cost by half
Smart Cards Issuing national ID cards using smart card technology. This system: • Has computer chips for storing personal identification information and
thumbprints, as well as medical and driving records
IT Regulations (e-laws)
Regulatory Frameworks laws : • IT Criminal Act • E-Transactions Act • Telecommunications Act • Directive to shift from conventional to electronic methods • e-Gov implementation rules
The National Center for Digital Certification (www.pki.gov.sa )
• Manage the related PKI policies and procedures • Integrated security system used in: secure information, user IDs,
Certification Protecting data • Digital signatures
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Project Objectives
Government Staff Training (2010)
• Basic skills of computer applications and e-transactions (10,000 trainees) • Chief Information Officer Program (66 workshops)
E-government Awareness in 2010- Events:
• 16 workshops for Government Agencies • 4 training workshops • 3 introductory lectures at Universities • 9 national and international conferences/ participations
New e-government services: Examples of the most important online services
• Ministry of Labor – “Hafiz” Program: This online services is for unemployed Saudi jobseekers to apply for unemployment benefits using SMS systems or the Hafiz online website : www.hafiz.gov.sa
• Ministry of Civil Service – “Gadara” Program: an e-recruitment online services: recording all Saudi citizens (male and female) who want and are willing to be recruited in the public sector. (www.mcs.gov.sa/Pages/Gadarah.aspx )
• Ministry of Foreign Affairs: Extend Return Visa Application: This online service helps individuals and corporations to apply electronically to extend a re-entry visa
• Ministry of Higher Education – A comprehensive online e-services system providing many services such as:
o Student e-services o University e-services o Staff e-service o Cultural mission e-services o Academic services o General e-services
Source: Al-Sabti, 2005; Al-Smmary, 2005; Al Ghoson, 2010; Al-Soma, 2011
3.7 Saudi E-government Program (Yesser)
The Kingdom of Saudi Arabia (KSA) is on an ambitious program aimed at fast tracking
the country into an information society and one providing advanced and effective
e-government services. In early 2005, the Saudi Arabian government created an
e-government project called ‘Yesser’, meaning ‘simplify’ in English; Yesser has
developed a plan for e-government to facilitate its implementation among government
agencies in the KSA. The following subsection will explore this program in more detail.
3.7.1 Overview.
The government of Saudi Arabia attaches high significance to the e-government concept
and the transformation process that leads to its realization. It strongly believes in the
huge benefits the concept of e-government entails for the national economy. The Yesser
e-government program is responsible to implement the Saudi Government’s keen
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interest in the e-government concept. It is part of many initiatives and projects adopted
by the government to achieve sustained growth and development in all aspects of life
(Al-Soma, 2008; Yesser, 2010; Arab New, 2012).
3.7.2 Program objectives.
The Yesser program’s role in the KSA’s e-government initiative is therefore that of an
enabler and facilitator. On the one hand, it enables the implementation of individual
e-government services by ministries and other government agencies by building the
national infrastructure and defining common standards which these agencies can use; on
the other hand, it provides best practice examples and the accompanying
implementation of pilot services. Moreover, it will ensure an appropriate level of
coordination and collaboration between the implementing agencies (Al-Soma, 2008;
Yesser, 2010). The Yesser program was launched with the following main objectives:
• To raise the public sector’s productivity and efficiency;
• To provide better and more easy-to-use G2C services for individual citizens and
G2B for business customers;
• To increase return on IT investments; and
• To provide the required information in a timely and highly accurate fashion.
Within this framework, SR 3 billion (US $800 million) has been allocated to set up
infrastructure facilities required to provide the 150 e-government services by the end of
2010 (Al-Soma 2008; Yesser 2010).
3.7.3 Program achievements.
A number of e-government projects has been implemented and published, by different
government organizations, under the supervision and consultation of Yesser program.
Some examples of these projects include (Al-Soma, 2008):
3.7.3.1 E-government portal (http://www.saudi.gov.sa).
The Government Services Portal Project (G2C).The objective of this project is to build
a national portal for government services. The portal provides information on such
services, defines them, states their requirements, and includes their electronic forms, if
available. This represents the first phase of the government services portal. Later phases
of the portal’s development will take place within the second track of the program’s
work plan (Saudi E-government National Portal, 2011).
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3.7.3.2 National smart ID cards (G2C).
The Saudi Ministry of the Interior has given considerable attention to this technology
since its introduction. Several studies have been undertaken, and a number of its staff
have been trained on their use, development, and programming. Steps are being taken
by the Ministry of the Interior, at present, to replace personal identity cards with smart
cards. At a later stage, this project would also include the integration of other official
cards, such as driving licenses and family status cards into the smart card. Efforts are
being made to introduce electronic passports, which represent one of the latest
technological innovations in the world. One of the objectives of the Ministry of the
Interior in this regard is to establish the infrastructure for the Public Key Infrastructure
PKI, which would open the door wide for several smart card applications (Al Ghoson,
2010).
3.7.3.3 E-Payment gateway: SADAD: http://www.sadad.com
The e-payment gateway SADAD consists of G2B and G2C channels. SADAD was
established by the Saudi Arabian Monetary Agency (SAMA) to be the national
Electronic Bill Presentment and Payment (EBPP) service provider for the KSA. It aims
to facilitate and streamline bill payment transactions for end consumers through all
channels of Saudi Arabia’s banks. SADAD plans to link all the banks and bill payees in
Saudi Arabia in the near future. SADAD electronic bill payment service went live in
2004. Currently, over 85% of Saudi Arabia’s eight million bank account holders
routinely use some 5,000 countrywide Automatic Teller Machines (ATMs) located in
banks, shopping centres, and other public places. SADAD is the most powerful
e-government form that is currently in use in Saudi Arabia. It is a major requirement for
the wider implementation of e-government and e-trade plans; this explains why it
received tremendous support from the Saudi government (SADAD, 2008). As a result,
SADAD was awarded the best service enhancement in e-government projects in West
Asia by the United Nations Public Service Award (Al Ghoson, 2010; Al-Soma, 2008).
Moreover, a number of government services are currently available online, such as
investment licenses, visa applications, traffic ticket enquiry and payment, paying
passport fees, and paying utilities bills (Al-Soma, 2008, 2011).
3.8 Information Technology Regulation in Saudi Arabia
E-government is a relatively new phenomenon for Saudi society while e-government
projects and e-services systems appeared less than a decade ago. Moreover, most of
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Saudi’s government laws are old and not suitable for e-government applications and
services. The OECD (2003) study identified four main obstacles to the implementation
of e-government, including legislation and regulation, budget, technical barriers, and
digital barriers. Inadequate laws and legislation are considered one of e-government’s
implementation obstacles. Moreover, Almarabeh and AbuAli (2010) and Reffat (2006)
mention that e-government implementation faces several legal and policy obstacles.
Additionally, Al-Fakhri et al. (2008) studied the obstacles of e-government
implementation in Saudi Arabia and confirmed that the lack of e-laws is one of critical
challenges of adopting e-government systems in the KSA. So, new regulations and laws
must be enacted to ensure the success of e-government projects. However, for an
effective and complete implementation of the IT national plan the continuous support
from top leadership in Saudi government at all levels are very important and effective.
However, laws and regulation play an important role in promoting effective
communication between citizens, business, and government. In general, the Saudi
government has issued the necessary government regulations and laws such as the E-
transaction law, the Information Criminal Law, and the Shift to Electronic Methods
decision (Al-Soma, 2011).
3.9 Chapter Summary
As the KSA government moves towards the implementation of e-government, it is
useful to realize that the process requires a sustained commitment of political will,
resources, and engagement among the government, private and public sectors. It can be
said that there are many strengths that could help and facilitate the acceptance and use
of e-government services in the KSA. This chapter provided an overview of the KSA
context and ICT issues of relevance to the e-government from many aspects. The next
chapter will presents different theories and models of technology acceptance which
developed in different disciplines and is used to predict and understand individuals’
acceptance and adoption of new technologies.
Chapter 4: Theories and Models of Technology Acceptance
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Chapter 4: Theories and Models of Technology Acceptance
4.1 Introduction
Information technology acceptance research has developed several competing models,
each with a different set of acceptance determinants. These models have evolved over
the years and came as a result of persistent efforts to validate and extend the models
during the period each was presented. This chapter presents the most distinguished
models in IS researches. Thus, the Theory of Reasoned Action (TRA) (Ajzen &
Fishbein, 1980) with its limitations is presented in Section 4.2. The Theory of Planned
Behaviour (TPB) (Ajzen, 1985) with its limitations is presented in Section 4.3. Then,
Section 4.4 presents the Technology Acceptance Model (TAM) (Davis, 1989) with its
limitations. The Extension of the Technology Acceptance Model (TAM2) (Venkatesh
& Davis, 2000) is presented in Section 4.5. Section 4.6 presents Diffusion of
Innovation Model (DOI) (Rogers, 2003) with its limitations. Unified Theory of
Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) is presented in
Section 4.7. Section 4.8 discusses the literature review of e-government acceptance
studies and models. A selection and justification of the research model is discussed in
Section 4.9. Finally, a summary is presented in Section 4.10.
4.2 Theory of Reasoned Action (TRA)
The earliest model used to explain technology acceptance was developed in the social
psychology field. The Theory of Reasoned Action (TRA) was developed by Ajzen
and Fishbein (1980, p. 21) to “organize and integrate research in the attitude area
within the framework of a systematic theoretical orientation”. They aimed to develop
a theory that could predict, explain, and influence human behaviour. The framework
provides a distinction between beliefs, attitudes, subjective norms, intention, and
behaviours; the major concern is the relationships between these variables. These
concepts form a model for the prediction of specific intentions and behaviours. Ajzen
and Fishbein (1980) insist that the TRA is an appropriate model for the study of the
determinants of user behaviour as a theoretical foundation, since it predicts and
explains behaviour across a wide variety of domains.
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According to the TRA, the primary determinant of behaviour is not the person’s
attitude towards the behaviour, but his or her intention to perform the behaviour.
Behavioural intention is, in turn, determined by two factors. The first factor is the
person’s attitude towards the behaviour, which is the extent to which the person has a
favourable or unfavourable evaluation of the behaviour. The second factor is the
subjective norm, or perceived social pressure to perform or not perform the behaviour.
These two factors are underpinned by sets of beliefs. For the attitude component, the
beliefs are behavioural beliefs concerned with the perceived likelihood that
performing the behaviour will lead to certain outcomes and the extent to which these
outcomes are valued. For the subjective norm component, the beliefs are normative
beliefs focusing on the perceived social pressure from certain referents and the
person’s motivation to comply with these referents. The theory looks at behavioural
intention (BI), rather than attitude, as the main predictor of behaviour (Ajzen &
Fishbein, 1980). The theory can be explained by the model in Figure 4.1.
Figure3 4.1. Theory of Reasoned Action (Ajzen & Fishbein, 1980)
4.2.1 Limitations of the TRA.
Ajzen (1985) noted that the theory was limited by what is called correspondence. In
order for the theory to predict specific behaviour, attitude and intention must agree on
action, target, context, time frame, and specificity (Sheppard, Hartwick & Warshaw,
1988). The greatest limitation of the theory stems from the assumption that behaviour
is under volitional control. That is, the theory only applies to behaviour that is
consciously thought out beforehand. Irrational decisions, habitual actions, or any
behaviour that is not consciously considered cannot be explained by this theory.
Attitude Toward
Behaviour
Subjective Norm
Behavioural Intention
Behaviour
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4.3 Theory of Planned Behaviour (TPB)
Ajzen (1985) proposed an extension to the TRA to address the problem of incomplete
volitional control. This extended TRA became known as the theory of planned
behaviour (TPB). TPB is the model widely used to predict and explain human
behaviour while also considering the roles of individual organizational members and
social systems in this process (Ajzen, 1991). TPB (Ajzen, 1985, 1991) was designed
to predict behaviours not entirely under volitional control by including measures of
perceived behavioural control. In fact, the TPB differs from the TRA in its addition of
the perceived behavioural control (PBC) component that accounts for situations where
an individual has less than complete control over the behaviour. This can vary across
situations and actions (Ajzen, 1991). The TPB places the construct of PBC within a
more general framework of relationships among beliefs, attitude, intentions, and
behaviour. PBC is held to influence both intention and behaviour as shown in Figure
4.2. The effect of PBC on behaviour can be direct or interactive (through behavioural
intention). As specified in the TRA, when the situation or behaviour affords a person
complete control over behavioural performance, intentions alone should be sufficient
to predict behaviour. Ajzen (1991) argues that, under conditions where behavioural
intention (BI) alone would account for only a small amount of variance in behaviour,
PBC should be independently predictive of behaviour. Both intentions and PBC are
important to predict behaviour, but one may be more important than the other given
the prevalence of certain conditions. In order to explain and predict behaviour, TPB
deals with the antecedents of attitude, subjective norms, and perceived behavioural
control. The TPB postulates that behaviour is a function of salient beliefs relevant to
that behaviour. These salient beliefs are considered as the prevailing determinants of a
person’s intentions and actions.
Figure4 4.2. Theory of Planned Behaviour (Ajzen, 2002)
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4.3.1 Limitations of the TPB.
A criticism of the TPB is that the model does not investigate the relation of intention
and behaviour, where there are often large amounts of unexplained variance. As a
psychological model focuses on internal processes, TPB does not include
demographic variables and assumes that everyone would experience the model’s
processes similarly. It also does not account well for change in behaviour (Armitage
& Conner, 2001). Taylor and Todd (1995a) criticized TPB for its use of one variable
(PBC) as a preventative to all non-controllable elements of the behaviour. Beliefs
behind the PBC were aggregated to create a measure for it. This aggregation has been
criticized for not identifying specific factors that might predict behaviour, as well as
for the biases it may create.
4.4 Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) developed by Davis (1989) is one of the
most well-known and influential theories relating to IT/IS acceptance and use
behaviour (USE). TAM is an adaptation of Ajzen and Fishbein’s (1980) theory of
reasoned action (TRA), and was designed to help explain why users accept and use
technology, and what influence factors are involved in these processes. As shown in
Figure 4.3, TAM uses two perceptions ‘perceived usefulness’ and ‘perceived-ease-of-
use’. The first one is ‘perceived usefulness’ (PU), which is defined as “the degree to
which a person believes using a particular system would enhance his or her job
performance” (Davis, 1989, p. 30). The second one is ‘perceived ease of use’
(PEOU), which is defined as “the degree of to which a person believes that using a
particular system would be free of effort” (Davis, 1989, p. 30). The TAM has
emerged as a powerful way to represent the antecedent of system usage through
beliefs about these two factors (Davis, Bagozzi, & Warshaw, 1992). Computer usage
is determined by intention, which is viewed as being jointly determined by the
person’s attitude towards using the system and its perceived usefulness. Figure 4.2
demonstrates the original TAM, which proposes that attitude (a positive response) and
usefulness may have the potential to influence the intention to actually use the system.
Particularly, the relationship between usefulness and intention implies that the person
believes that his or her job performance is enhanced, regardless of positive or negative
feelings (Davis et al., 1992). The external variables in the model refer to a set of
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External Variables
Perceived Usefulness
Perceived Ease of Use
Attitude Intention
Actual System Use
variables such as objective system design characteristics, training, computer self-
efficacy, user involvement in design, and the nature of the implementation process
(Davis & Venkatesh, 1996). However, as the TAM continued to evolve, new variables
were introduced as external variables affecting PU, PEOU, and actual use or
behaviour. Among the most frequently referenced are: system quality, compatibility,
computer anxiety, enjoyment, computing support, and experience (Lee, Kozar &
Larsen, 2003). According to Davis (1989), the goal of the TAM is to provide an
explanation of the determinants of computer acceptance that are generally capable of
explaining user behaviour across a broad range of end-user technology and user
populations. However, TAM (actually TAM2) has proven to be a successful
framework in predicting and explaining usage across a variety of systems (Venkatesh
& Davis, 2000).
Figure5 4.3. Technology Acceptance Model (Davis, 1989)
4.4.1 Limitations of the TAM.
The most commonly reported limitation of TAM is that of relying on respondents’
self-reporting and assuming that self-reported usage reflects actual usage (Legris et
al., 2003). The second limitation is related to the type of respondents, examined
systems, or the sample choice. In some studies, it was student samples or samples
from professional users, which made generalization of the findings difficult (Legris et
al., 2003). Moreover, Venkatesh (2000) cited that one of the limitations of the TAM is
that it only provides limited guidance about how to influence usage through design
and implementation, which does not help understand or explain acceptance in ways
that guide development beyond the suggestion that system characteristics impact ease
of use. Sun and Zhang (2006) stated two major shortcomings of studies on TAM: the
explanatory power of the model; and the inconsistencies between prior studies.
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Consequently, TAM2 was developed to overcome the limitations of the original TAM
model.
4.5 Extension of the Technology Acceptance Model (TAM2)
Venkatesh and Davis (2000) extended the TAM to include additional key
determinants of the TAM’s perceived usefulness and user intention in terms of social
influence and cognitive instrumental processes. The modified model, referred to as
TAM2, adds additional concepts covering social influence processes (subjective
norm, voluntariness, and image) and cognitive instrumental processes (job relevance,
output quality, result demonstrability, and perceived ease of use) into the original
TAM model, as presented in Figure 4.3.
Another important factor in the TAM2 model is experience. Venkatesh and Davis
(2000) do not categorize experience as a social influence process, but relate it to this
group of processes. The model assumes that, in an organization with mandatory
system use, the subjective norm will directly influence the intention to use in the early
stages of the implementation and, thus, usage of the system. Over time, however, the
influence of the subjective norm on intention to use will decrease and be replaced by
experience in using the system (Venkatesh & Davis, 2000).TAM2 theorizes that, in a
computer usage context, the direct compliance-based effect of subjective norm on
intention over and above perceived usefulness and perceived ease of use will occur in
mandatory, but not voluntary, system usage settings (Venkatesh & Davis, 2000).
Moreover, the subjective norms in the TAM2 will have a direct effect on intention
over PU and PEOU will occur in mandatory system usage settings. The model posits
voluntariness as a moderating variable to distinguish between mandatory versus
voluntary compliance with organizational settings. Nevertheless, subjective norms can
influence intention through PU or what is called internalization. In addition, TAM2
theorizes that internalization, rather than compliance, will occur no matter whether the
usage context is voluntary or mandatory. Finally, the findings reported that all the
social influences and cognitive instrumental processes have significantly strong affect
and influence users on the acceptance of technology (Venkatesh & Davis, 2000).
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Figure6 4.4. Extended Technology Acceptance Model (TAM2) (Venkatesh & Davis, 2000)
4.6 Diffusion of Innovation Theory (DOI)
The Diffusion of Innovation Model (DOI) (Rogers, 2003) explains how innovations
diffuse through society and how organizations and individuals accept new
innovations. Rogers differentiates the adoption process from the diffusion process in
that the diffusion process occurs within society, as a group process, whereas the
adoption process is related to an individual. According to Rogers (2003, p. 474),
diffusion is “the process by which an innovation is communicated through certain
channels over time among the members of a social system”, while adoption is “a
decision to make full use of an innovation as the best course of action available”
(Rogers, 2003, p. 473). Rogers’s diffusion of innovation theory contains an
innovation-decision process, innovation characteristics, adopter characteristics, and
opinion leadership (Rogers, 2003). Figure 4.5 below illustrates Rogers’ (2003) model
of five stages in the innovation-decision process, which describe the different stages
an individual or other decision-making unit must go through in the process of
adopting or rejecting an innovation.
1. The first stage, Knowledge, occurs when an individual or other decision-
making unit discovers the existence of an innovation and then learns to
understand how it functions.
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2. In the Persuasion phase, the perceived characteristics of the innovation give
rise to a favourable or an unfavourable attitude on the part of the potential
adopter.
3. In the Decision phase, the individual (or unit) interact in activities that lead to
a choice to adopt or reject the innovation. This may include confronting forces
of support or opposition that influence the process.
4. In the Implementation phase, the individual (or unit) decides to use an
innovation. Implementation contains an overt behaviour change as the new
idea is actually put into practice.
5. Confirmation is the last stage of the model; here the decision of adoption or
rejection of an innovation is reflected, and might even be changed if doubts or
problems with the innovation occur (Rogers, 2003).
Figure7 4.5. Roger’s Model in the Innovation-Decision Process (Rogers, 2003)
4.6.1 Limitations of DOI theory.
The limitations of the DOI theory have been highlighted by a number of researchers.
For instance, Clarke (1999, p.17) states that classical DOI theory, in the context of the
IS discipline, is “at its best a descriptive tool, less strong in its explanatory power, and
less useful still in predicting outcomes and providing guidance as to how to accelerate
the rate of adoption”. There is also some doubt about the extent to which DOI theory
can give rise to readily refutable hypotheses. On top of this, diffusion of innovation
theory has been criticized for the fact that “many of its elements may be specific to
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the culture in which it was derived (such as North America in the 60s)” and that it is
“less relevant in, for example, East Asian and African countries” (Clarke, 1999, p.19).
DOI theory has also been criticised for focusing on innovation demand, rather than on
innovation supply (Attewell, 1992). The underlying assumption of the demand view is
that adoption will occur at a rate governed by the spread of knowledge about the
innovation and by the time it takes for adopters to hear about the benefits of adoption.
Attewell (1992) argues that innovation suppliers can influence diffusion because they
often focus their marketing and educational initiatives on particular types of
businesses (so not all firms have equal chances to adopt). Further, he argues that with
complex innovations, knowledge of the innovation and its benefits can be widespread
but adoption still does not occur.
4.7 Unified Theory of Acceptance and Use of Technology (UTAUT)
The Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et
al., 2003) is one of the most popular frameworks in the field of general technology
acceptance models. Like earlier acceptance models, it aims to explain user intentions
to use an IS and further the usage behaviour. Venkatesh et al. (2003) created this
synthesized model to present a more complete picture of the acceptance process than
was possible with any previous individual models. Eight models previously used in
the IS field were merged in an integrated model, all of which had their origins in
psychology, sociology, and communications. These models are the TRA, TPB, TAM,
TAM2, the Motivational Model (MM), the Model of PC Utilization (MPCU), DOI,
and Social Cognitive Theory (SCT). Each model attempts to predict and explain user
behaviour using a variety of independent variables. A unified model was created
based on the conceptual and empirical similarities across these eight models. The
theory holds that four key constructs (performance expectancy, effort expectancy,
social influence, and facilitating conditions) are direct determinants of usage intention
and behaviour (Venkatesh et al., 2003). Gender, age, experience, and voluntariness of
use are posited to mediate the impact of the four key constructs on usage intention and
behaviour as indicated in Figure 4.6. Moreover, the UTAUT model attempts to
explain how individual differences influence technology use. More specifically, the
relationship between perceived usefulness, ease of use, and intention to use can be
moderated by age, gender, and experience. For example, the strength between
perceived usefulness and intention to use varies with age and gender such that it is
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more significant for male and younger workers. The effect of perceived ease of use on
intention is also moderated by gender and age, such that it is more significant for
female and older workers, and those effects decrease with experience (Venkatesh et
al., 2003). The UTAUT has four predictors of behavioural intention or usage:
performance expectancy, effort expectancy, social influence and facilitating
conditions. The predictors are defined as follows (Venkatesh et al., 2003, pp.
447-453):
1. Performance expectancy (PE): “is the degree to which an individual believes
that using the system will help him or her to attain gains in job performance.”
2. Effort expectancy (EE): “is the degree of ease associated with use of the
system.”
3. Social influence (SI): “is the degree to which an individual perceives that [it
is] important others believe he or she should use the new system.”
4. Facilitating conditions (FC): “is the degree to which an individual believes that
an organizational and technical infrastructure exists to support use of the
system.”
Performance expectancy (PE) in the UTAUT model is derived from a combination of
five similar constructs, including perceived usefulness, extrinsic motivation, job-fit,
relative advantage, and outcome expectations. Performance expectancy is the
strongest predictor of intention within each of the individual models reviewed and
was found significant at all points for both voluntary and mandatory settings in
Venkatesh et al.’s (2003) model-validation. In the UTAUT model, effort expectancy
(EE) captures the notions of perceived ease of use and complexity. Ease of use is the
second component in the classic study by Davis (1989) and is generally believed to
have a significant influence on technology acceptance as well as perceptions of
usefulness. In validation of the UTAUT, EE was significant in both voluntary and
mandatory usage contexts, although only for the first period of usage. Since practice
increases one’s comfort with software, effort-oriented constructs would become,
logically, less salient after learning hurdles are overcome. Social influence includes
consideration of the person’s perception of the opinion of others, his or her reference
group’s subjective culture, and specific interpersonal agreements with others, as well
as the degree to which use of an innovation is perceived to enhance one’s image or
status in one’s social system (Venkatesh et al., 2003). This encompasses constructs
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from previous models such as subjective norm, social factors and image. This
construct suggests that an auditor would be sensitive to the opinions of others,
resulting in decisions consistent with the social norms around them. In their validation
tests, Venkatesh et al. (2003) found that social influence was not significant in
voluntary contexts, but becomes important when its use is mandated. Facilitating
conditions (FC) represents organizational support, and includes the constructs of
perceived behavioural control, facilitating conditions, and compatibility from prior
models. Results from the UTAUT validation suggest that FC was significant in both
voluntary and mandatory settings in the initial usage period, but its influence on usage
intentions disappeared after this. Additionally, FC appears to be fully moderated by
effort expectancy, such that, when both PE and EE are present, FC becomes non-
significant in predicting intention. Finally, the UTAUT model was able to account for
70 percent of the variance in usage intention, which is considered a measured
improvement over any of the original models where the maximum was around 40
percent. The authors acknowledge a limitation of content validity due to measurement
procedures and recommend that future research should be targeted at more fully
developing and validating appropriate scales for each of the constructs with emphasis
on content validity and revalidating or extending UTAUT with the new measures
(Venkatesh et al., 2003).
Performance Expectancy
(PE)
Social Influence (SI)
Effort Expectancy
(EE)
Facilitating Conditions (FC)
Behavioural Intention(BI)
Gender Age Experiences
Use Behaviour
(USE)
Voluntariness of use
Figure8 4.6. UTAUT model (Venkatesh et al., 2003)
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4.8 Literature Review of E-government Studies Using Technology
Acceptance Models
The acceptance of using IT is considered as a first step in the successful adoption of
any e-system. Acceptance refers to the “initial decision made by the individual to
interact with the technology”; therefore, the adoption comes after “direct experience
with the technology and after an individual has decided to accept the technology”
(Venkatesh et al., 2004, p. 446). A number of studies based on technology acceptance
models have investigated the adoption of e-government services in developed
countries with respect to citizens’ perception (Carter & Belanger, 2003, 2004;
Dimitrova & Chen, 2006; Gilbert, Balestrini, & Littleboy, 2004; Hung, Chang, & Yu,
2006; Phang, Li, Sutanto, & Kankanhalli, 2005; Schaupp, Carter, & McBride, 2010;
Tung & Rieck, 2005). Similar studies have taken place in developing countries
(Al-Gahtani, 2003; Alhujran & Chatfield, 2008; Al-Shihi, 2005; Kanat & Ozkan,
2009; Mofleh & Wanous, 2008a). For instance, Carter and Belanger (2003) surveyed
140 students in the US to examine factors that influence citizens’ adoption of
e-government services. They adopted the DOI model to discover the most relevant
constructs: namely, relative advantage, compatibility, ease of use, and image, all of
which affect the intention of citizens to use e-government services. Their findings
showed that higher levels of relative advantage, compatibility, and image are
significantly associated with an increased intention to adopt e-government services.
In another study, Carter and Belanger (2004) studied citizens’ adoption of
e-government services based on an integrated model of the TAM and DOI theories,
and the web trust model. In a field survey, a questionnaire was distributed to 140
undergraduate students in the US. The findings revealed that perceived usefulness,
relative advantage, and compatibility were significant in increasing citizens’ intention
to use e-government services. However, in the main study, in which another group of
adults aged 14 to 83 years was examined, Carter and Belanger (2005) found that
perceived ease of use, compatibility, and trustworthiness were significant indicators of
citizens’ intentions to use e-government services. A comparison of the findings of
those studies showed that there were differences in the determinants of intention to
use e-government services. Citizens’ demographic attributes had a strong impact on
the factors indicating intention.
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Gilbert et al. (2004) utilized a combination of Diffusion of Innovation (DOI), TAM,
and service quality theories to study the reasons behind the use and adoption of
e-government services. They reported e-government adoption barriers to be end users’
attitudes towards online trust relationship establishment, security of financial data,
and quality of information provided, and time and money as adoption benefits factors
in predicting potential use of e-government. Tung and Rieck (2005) used TAM and
DOI to study e-government services adoption by business (such as G2B) in
Singapore. The findings showed that increased awareness of e-government services,
security, and quality of services may lead to a higher adoption of e-government
services rate. So, it showed that the more effective and secure online transactions will
encourage more customers to use e-government systems to accomplish their business
with government. Thereby, understanding the motivation and the benefits for citizens
and business organizations in using e-government service is important for the success
of any e-government initiative and will increase the usage of online government
services. The results of this study can be used as a guideline for government to
develop available services and to improve the potential of their e-government
systems. Hung et al. (2006) investigated the public’s acceptance of an online tax filing
and payment system (OTFPS), an e-government service in Taiwan. Based on TPB
model they study found that perceived usefulness, ease of use, perceived risk, trust,
compatibility, external influence, interpersonal influence, self-efficacy, and
facilitating conditions were critical factors in the adoption of OTFPS.
Dimitrova and Chen (2006) examined the effects of socio-psychological factors on the
adoption of e-government in the US by adopting TAM and DOI. The findings showed
that perceived usefulness, perceived uncertainty, and prior interest in government
were associated with the adoption of e-government in the US. Phang et al. (2005)
studied the adoption of e-government by Chinese senior citizens. They surveyed a
small sample of randomly selected senior citizens. Based on the TAM model, the
researchers modelled compatibility, image, and Internet safety perception as
determinants of perceived usefulness and ease of use. The study revealed that
perceived ease of use and Internet safety influenced the senior citizens’ perception of
the usefulness of e-government; however, cultural considerations, image, and
compatibility had less influence on the usefulness of IT as perceived by users. In
another study, Schaupp et al. (2010) studied US taxpayers' intentions to adopt E-file
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system and employed an amended UTAUT model with trust factor. The findings
confirmed the significant role of trust which affected taxpayers’ intentions to use the
E-file system.
On the other hand, some studies have been conducted in developing countries.
Al-Gahtani (2003) investigated Rogers’s five attributes of innovation: namely,
relative advantage, compatibility, complexity, trial-ability, and observability as
potential contributing factors to computer adoption in Saudi Arabia. The sample
consisted of 56 public and private medium and large organizations of different
managerial levels across a wide spectrum of industries across the KSA. Findings
showed that the innovation attributes descended in the following order according to
their strength in explaining computer use in the Saudi sample: observability,
compatibility, complexity, relative advantage, and trial-ability. The results
emphasized that diffusion of innovation research is supported in developing nations
although the relative impacts of these attributes on computer adoption may differ
among societies (Al-Gahtani, 2003). Al-Shihi (2005) for instance, investigated the
development and adoption of e-government services in Oman, another Arab country.
He interviewed employees in both the private and the public sector and surveyed
different segments of Omani society. He found a number of barriers to the uptake of
e-government in Oman which were related to: users’ lack of IT knowledge; awareness
and motivation; the under-marketing of e-government plans and initiatives; a lack of
proper legislation and laws; and a lack of trust and confidence by users. However, the
findings showed that culture had little effect on the adoption of e-government.
In another study in a developing country, Mofleh and Wanous (2008a) examined the
factors that influence citizens’ adoption of e-government services in Jordan based on
their own adoption model. A sample of 660 people was tested through an online
survey. As a result of their study, they identified compatibility with e-government,
trust in the Internet, and trust in the government as significant variables that will
increase citizens’ demand on e-government services. Similarly, Alhujran and
Chatfield (2008) studied the factors influencing the adoption of e-government in
Jordan grounded in the TAM model. The study examined the impact of cultural,
trustworthiness, and perceived public value on citizen adoption of e-government
services in Jordan. The study sample was small and limited to 65 students from
University students in Jordan. The result emphasized the role of cultural, trust, and
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nation value as main predictors for the successful adoption of e-government in Jordan.
Finally, Kanat and Ozkan (2009) developed a conceptual model based on the TPB and
trust factors to study the acceptance of e-government services by 48 citizens in
Turkey. The study explored the influences on citizens’ adoption of government online
services and it was found adoption is impeded by the lack of infrastructure, lack of
understanding of citizen needs, and a lack of trust and privacy.
Drawing on the previous discussion, a number of interesting points and research gaps
must be considered. First, there is strong evidence that trust is a significant factor
which should be involved in e-government adoption models and studies in order to
deeply understand citizens’ intentions and behaviour regarding acceptance and use of
e-government services. Therefore, this study will take this point strongly and integrate
trust as a dependent construct in the proposed UTAUT research model. The second
point is that most of the reviewed studies about e-government adoption in developing
countries utilized ATM, DOI, TPB, or developed their own model. So, there is strong
demand to apply the UTAUT model to developing countries to study and understand
the citizens’ behavioural intention to adopt e-government services since citizens’
behavioural intention to adopt new technology is very complex issue (Shareef,
Kumar, & Kumar, 2011). Third, most of previous studies used a quantitative approach
based only on a survey with a small sample of respondents. Small sample size will
affect the reliability of scale and, so, the result of such studies cannot be generalized.
4.9 Selection and Justification of the Research Model
The previous section has explored a number of study and theories of the adoption of
e-government services in developed countries and illustrated the gap of such studies
in developing countries. From the preceding discussion, it is clear that TAM, TRA,
and TPB have been widely used to examine technology acceptance in e-government
in many countries of the world. However, these models are criticized for their
relatively low explanatory power in terms of behavioural intention (BI), which range
between 30 and 40 per cent only. The integrated acceptance model (UTAUT) reports
a powerful explanation, amounting to 70 percent (Venkatesh et al., 2003).
It is also significant that the UTAUT is an empirically validated model that combines
eight major models of technology acceptance and their extensions. Despite the fact
that the UTAUT model is quite a new model since its inception in 2003, researchers
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are increasingly testing its suitability, validity, and reliability to explain technology
adoption in different contexts (Anderson et al., 2006; Carlsson et al., 2006; Li &
Kishore, 2006; Oshlyansky, Cairns, & Thimbleby, 2007; Venkatesh et al., 2003;
Wang & Yang, 2005). For instance, Anderson et al. (2006) used UTAUT to find the
drivers and modifiers of user acceptance of tablet PCs among business faculty in
higher education. Their results validated UTAUT constructs with performance
expectancy (PE) as the most important driver for PC tablet adoption. Carlsson et al.
(2006) used UTAUT to explain acceptance of m-devices/services in Finland, and
found that PE and effort expectancy (EE) were significant, but social influence
(UTSI) was not. Li and Kishore (2006) validated UTAUT construct scales in the
context of acceptance of an online community web log system, and found that PE and
EE scales are comparable among different groups; in contrast, Social Influence (SI)
scores may not be comparable among users with high or low frequency of using a web
log. They recommend caution when interpreting results from studies conducted using
UTAUT scales. Wang and Yang (2005) examined the roles that personality traits play
in the UTAUT model in the context of online stock investments and found support for
it.
Oshlyansky et al. (2007) attempted to validate the UTAUT model across nine
culturally diverse countries. Data on general website use were collected from
undergraduate and postgraduate students from numerous countries, including the US,
the UK, South Africa, Saudi Arabia, New Zealand, Malaysia, India, Greece, and the
Czech Republic. The UTAUT instrument was translated into six languages: Arabic,
Czech, Dutch, French, Greek, and Malay. Only native participants were used in the
analysis to ensure a truly representative homogeneous country sample. The samples
were matched for age, education, and access to technology, and were equally balanced
by gender. A country-by-country analysis of the UTAUT provided evidence that the
questionnaire was working as intended in each of the sample countries and that the
translation did not hinder the performance of the UTAUT. Principal Component
Analysis (PCA) was used to determine factors in the data set. All factors loaded
together across the sampled countries, although some constructs had different
amounts of influence in some samples. For example, the social influence variable only
emerged for the Saudi Arabia sample, indicating that this variable has greater weight
on website acceptance in that country than in the other countries sampled. However,
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the results showed that the UTAUT model is robust enough to withstand translation
and to be used cross-culturally, outside its original country and language of origin,
indicating that the UTAUT model can be useful in providing insight into cross-
cultural technology acceptance differences. Lastly, the questionnaire instrument (item
scales) in the UTAUT model has been validated in the UTAUT through a formula
with “0.97 reliability obtained for perceived usefulness indicates that a six-item scales
composed of items having comparable reliability would yield a scale reliability of
0.94” (Venkatesh et al., 2003, p.328).
Based on the arguments presented in this section and from the critically reviewed
existing literature, the researcher believes that the UTAUT model will be the best
model to be adopted for this study in order to explore and investigate the factors
affecting the acceptance of e-government services in Saudi Arabia, a developing
country, through empirical data collection and analysis from both pillars of
e-government systems: the government and its citizens.
4.10 Chapter Summary
This chapter examined the literature to explore and illustrate the most important
models of technology acceptance, such as TRA, TPB, TAM, TAM2, DOI, and
UTAUT. However, from the multitude of models and theories, the researcher must
select the best model for his research with the fewest limitations. Based on the
limitations and applicability of each of the models revealed in the literature, this
research will utilize the UTAUT model to study and explore the adoption of
e-government services in Saudi Arabia. The next chapter will present the research
methodology, including the research methods, selection, and justification of the
proposed methods and discussion on the research model.
Chapter 5: Research Methodology
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Chapter 5: Research Methodology
5.1 Introduction
The research methodology is a set of processes used to collect and analyse data
(Walliman, 2000). Methodology deals with the methods and principles that are used
in the research; it explains how the research is done, the methods of data collection,
the materials used, the subjects interviewed, the theories used, and the data analysis
technique. It also gives reasons on why a particular method is used, rather than other
methods. This chapter describes the research methods and procedures used to obtain
and analyse data in this research. In information systems research, there are many
research methodologies and strategies available. This chapter introduces an important
research issue, so the research paradigms are reviewed in Section 5.2. Then, Section
5.3 describes the research categories. Section 5.4 discusses and justifies the selection
of the research method. The proposed research model (UTAUT) is presented in
Section 5.5. Section 5.6 explains the research hypotheses. Section 5.7 describes the
data collection strategies for the research. Section 5.8 describes the population and
sample, while Section 5.9 describes the data analysis techniques. The reliability and
validity of the research are presented in Section 5.10. Section 5.11 presents the ethical
issues for study. Finally, Section 5.12 summarises the chapter.
5.2 Research Paradigms
A paradigm is a set of shared assumptions or ways of thinking about some aspects of
the world (Oates, 2006). Most researches in a social or natural science discipline is
dependent upon one of the philosophical paradigms: positivist, interpretive and
critical (Myers, 1997; Oates, 2006). This three classification system is widely
accepted today in IS research as each approach typifies a number of ways of
perceiving the world so as to observe, measure, and understand social reality (Myers,
1997). Although, in theory, these three paradigms are distinct, in practice the
distinctions are not so clear, with the result that elements from each paradigm tend to
get mixed up by researchers (Neuman, 2006). The following subsections will briefly
introduce these three philosophies and discuss out their importance and usage.
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5.2.1 The positivist paradigm.
The positivist paradigm assumes that there is an objective reality and that it can be
described, observed, and measured (Neuman, 2006). The main goal of positivist
research is the discovery of universal laws and causal relationships in natural and
social phenomena (Myers, 1997). The key aspect of positivist research is that it uses
variables with quantifiable measures, and extrapolates results from a sample to draw
inferences for a phenomenon to a stated population. A characteristic of positivist
studies is that it attempts to test theory in order to increase the understanding of
phenomena. Hypotheses and questions are put forward in advance as assumptions
which may undergo empirical testing with carefully controlled conditions (Neuman,
2006).
5.2.2 The interpretive paradigm.
The interpretive paradigm is founded on a social science approach, screening reality
as socially constructed; that is, reality is based on shared meanings created by
experiences (Neuman, 2006). Interpretive researchers assume that reality is subjective
and, generally, attempt in their studies to understand phenomena through the
meanings people assign to them (Myers, 1997). Their goal is to be able to use theory
as a sensitizing device to perceive the world, rather than as a means to substantiate
theories. Interpretive studies are appropriate where variables, both dependent and
independent, have not previously been defined, and where the complexity of human
sense making as the situation emerges is the point of interest. In other words, the
interpretive approach is appropriate for developing a rich understanding, and
exploring the context and the social and community interactions of the research
subjects (Klein & Myers, 1999). Interpretive methods of research in IS are aimed at
producing and understanding the context of the information system, and the process
whereby the information influences and is influenced by that context (Myers, 1997).
5.2.3 The critical paradigm.
The critical paradigm focuses on understanding the historical structure of situations
and contexts and how people can or cannot impact the situation. Critical theory
researchers believe that social reality is historically constituted and produced and
reproduced by people. Critical research is concerned with oppositions, contradictions,
and conflicts in modern society and sees itself as emancipator. Critical researchers
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believe that people can deliberately change their social and economic situation, but
that their actions are limited by social, cultural, and political control (Myers, 1997).
This approach use analytical methods to position users at the centre of attention and to
investigate the shared beliefs of members of social units. It is appropriate when the
researchers aim to intervene in the research environment and make comparisons with
the original or historic situation under study. It does not have agreed criteria for
accuracy and validity and it is neither generalizable nor repeatable. The principal
characteristics of the critical paradigm are the ambition to transform knowledge into
action and the belief that no research is value-free (Neuman, 2006).
5.3 Research Categories
According to the literature review on the field of research, there are two major
research categories: quantitative and qualitative research approaches. The choice of
either qualitative or quantitative research depends on the research’s nature, questions,
assumptions, and aims. The following sections highlight some issues regarding
quantitative and qualitative research and their respective associated research methods.
5.3.1 Quantitative research.
Quantitative research methods were developed to study naturally occurring
phenomena within the natural sciences in order to identify causal relationships. The
quantitative research is focused on collecting numeric data and then analysing that
information through techniques that involve counting or statistics. The focus of
quantitative research is objective measures. Data is collected in an objective and
replicable manner; empirical data are collected through experiments and/or sample
surveys which are outcome oriented and assume natural laws and mechanisms.
Normally, the sample size collected for a quantitative research approach is larger than
that used for a qualitative research and is based on maintaining statistical relevance
(Myers, 1997; Neuman, 2006). The following subsection will explore the methods
which are often used to conduct quantitative research.
5.3.1.1 Quantitative research methods.
A research method is a strategy of inquiry which moves from the underlying
philosophical assumptions to research design and data collection. The choice of
research method influences the way in which the researcher collects data. Specific
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research methods also involve different skills, assumptions and research practices.
Quantitative methods are research techniques that are used to gather quantitative data
or information dealing with numbers and anything that is measurable. Quantitative
research relies on gathering quantitative data using various methods (Easterby-Smith,
Thorpe, & Lowe, 2002), such as:
5.3.1.1.1 Participant Observation.
The observation method is the most commonly used in quantitative research.
Observers must consider the period of time that they should spend in observation;
also, the day of observation should be a representative day. Observation can be used
to prove or enhance information gathered through other techniques (Easterby-Smith et
al., 2002).
5.3.1.1.2 Survey.
The survey method is used to collect the same data from large groups of people. The
data may include demographic information, opinions, or satisfaction levels. The
survey can be managed in person, by mail, over the phone, or via email or the
Internet. In the survey, the researcher asks same questions to all participants
(Easterby-Smith et al., 2002).
5.3.1.1.3 Structured interviews.
Structured interviews are used where questions take the form of ‘when’ or ‘how
many’. In its simplest form, a structured interview involves the researcher asking
another person a list of predetermined questions about the research topic. It enables
the researcher to examine the level of understanding a respondent has about the
research topic, usually in slightly more depth than with a postal questionnaire. These
interviews can be used, for example, in opinion polls or market research to gather
quantitative data (Easterby-Smith et al., 2002).
5.3.1.1.4 Tests and measures.
Tests and measures methods are assessment tools, such as questionnaires, inventories,
and scales, designed to measure or gauge some quality, knowledge, need, behaviour,
or trend. They are used for diagnosis, research, and assessment in psychology,
education, and other social science and health disciplines. It can be applied to find out
what or how people think. They take a form of written questions with yes or no
answers (Easterby-Smith et al., 2002).
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Finally, an analysis of the data collected in quantitative research is typically
performed using statistical techniques and methods such as the Statistical Packages
for the Social Sciences (SPSS) to produce results which can then be used to prove or
disprove the hypothesis underpinning the research.
5.3.2 Qualitative research.
In the twentieth century, researchers in the field of social sciences realized the
limitations of quantitative research for understanding situations which involved the
complex interaction of human behaviour, interpersonal relationships, cultural
traditions, economics, and politics. Consequently, in recent decades, qualitative
research has become increasingly favoured, especially in the social sciences (Denzin
& Lincoln, 2002). Creswell (2007) defined qualitative study as an inquiry process of
understanding a social or human problem, based on a complex, holistic picture,
formed with words, and reporting in a natural setting. The following is a summary of
the qualitative research characteristics which have been gleaned from several research
sources:
• In qualitative research, the sample is non-random in nature and small, whereas
in quantitative research a sample is random and larger in nature (Merriam,
1998).
• Qualitative research is descriptive. The researcher is interested in the process,
meaning, and understanding gained through the words, interviews,
transactions, and field notes of observation (Yin, 2009; Myers, 1997).
• Qualitative research is interested in words rather than numbers. It is also
concerned in the participant’s perception, how people make sense of their
lives, future, thinking, and experiences (Lee, 1999).
• The process of qualitative research is inductive (that is, the conclusions are
derived from a set of observations), in which the researcher builds abstractions
and concepts, and generates theories from the details (Merriam, 1998).
• Qualitative research is the best option when studying and analysing a complex
phenomenon (Yin, 2009). Questions that start with ‘what’ or ‘how’ could be
categories under qualitative research. So if a study requires the answers to
these types of questions, then qualitative research is more suitable method
(Merriam, 1998).
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5.3.2.1 Qualitative research methods.
There are various qualitative research methods being used by researchers. The four
common research methods that will be discussed here are action research, case study
research, ethnography and grounded theory.
5.3.2.1.1 Action research.
In action research the researcher’s aim is to contribute to the real situation of the
people to gain feedback from their understanding in an immediate problematic
situation .Action research is applied research to develop a solution that is of practical
value to the people with whom the researchers are working and, at the same time, to
develop theoretical knowledge of value to a research community. Most of the action
research definitions focus on the collaboration between researchers and participants
involved in the study of the situation under investigation (Creswell, 2007).
5.3.2.1.2 Case study research.
Case study research is the most common qualitative method used in information
systems (Creswell, 2007). Yin (2009) defines the scope of a case study as an
empirical inquiry that investigates a modern phenomenon within its real-life context,
especially when the boundaries between phenomenon and context are not clear. Yin
(2009) further suggested the following steps techniques to organise and conduct the
case study research. The steps are: to determine and define the research questions; to
select the cases and determine data gathering and analysis techniques; to prepare to
collect data; to collect data in the field; to evaluate and analyse the data; and lastly, to
prepare the report.
5.3.2.1.3 Ethnography research.
The origin of this type of qualitative research comes from the discipline of social and
cultural anthropology (Myers, 1997). It is undertaken by observation, interviews, and
examination of documents. In the research, the researchers observe their collaborators
without prejudice or prior assumptions. Ethnography is widely used in the study of
information systems in organizations, from the study of the development of
information systems (Creswell, 2007). Ethnography, according to Myers (1997), is
suited to providing information systems researchers with rich insights into the human,
social, and organizational aspects of information systems development and
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application. The goal of ethnographic research is to improve the understanding of
human thought and action through interpretation of human actions in context.
5.3.2.1.4 Grounded theory.
Grounded theory is a research method that seeks to develop theory that is grounded in
gathered and analysed data. Grounded theory was developed by two sociologists,
Barney Glaser and Anselm Strauss in 1967, and was based on a need to conduct
qualitative research on the care of dying patients in American health institutions. It
aims to generate a theory based on data collected from interviews or from
observations to uncover the experiences and perspectives of participants; “generating
a theory from data means that most hypotheses and concepts not only come from the
data, but are systematically worked out in relation to the data during the course of
research” (Glaser & Strauss, 1967, p. 6). According to Corbin and Strauss (1990),
grounded theory is a theory discovery methodology that allows the researcher to
develop a theoretical account based on concepts, categories, and propositions.
5.3.2.1.5 Focus group.
A focus group is a qualitative research method which can be seen as a group
interview. It designed to gain group interactions, opinions, outcomes, and perceptions
(Morgan, 1997). The focus group provides the researcher with various perspectives on
a specific issue or topic at the time. Furthermore, the focus group allows the
researcher to examine both the behaviours of individuals and the interactions between
group members regarding the discussion topic (Neuman, 2006). Moreover, Morgan
(2001) believes that focus groups could produce opinions, suggestion, or solutions
that would not be obtained from individual interviews or any other method.
5.3.2.1.6 Documentary Research
Documentary methods refer to the analysis of documents that contain information about a
specific phenomenon which need to be studied. Documentary analysis covers a wide
variety of sources, involved written texts, reports, official statistics, historical
documents, newspaper, photographs, presentations and record. It allows the researcher
to understand the research question and to join it with the society social situation (Yin,
2009).
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5.4 Selection and Justification of Research Method
The aim of this research is to study, understand and identify the factors that affect the
acceptance and use of e-government services in the KSA by utilizing the power of
UTAUT model. It aims also, to investigate and analyse the fundamental relationships
among the proposed research model constructs .To achieve these aims, the researcher
needs to gain the possibly differing views and perceptions of citizens who are using
e-government services in the KSA and the e-government services provider, that is, the
public sector in the KSA. This research will utilize mixed qualitative and quantitative
research taking a positivist paradigm. The following subsections provide justification
for this combined selection.
5.4.1 Justification of using positivist paradigm.
The aim of this research will be achieved by utilising the UTAUT to collect the
research data. Positivism focuses on testing hypotheses from an existing theory and
understanding the individual behaviour to confirm the hypotheses (Neuman, 2006).
The result of this approach will display the citizens’ and services providers’
viewpoints about the factors that affect the acceptance and use of e-government
services in the KSA. In this study, the researcher seeks to gather large amounts of data
and employ statistics and content analysis to detect underlying regularities. Thus, a
positivist approach is the most appropriate one for this research. Also, a major drive
of this research is to test hypotheses related to the proposed model extension, as well
as a number of hypothesized relationships that were previously established in the
UTAUT model with the research context in order to increase the understanding of
e-government services status in the KSA. Moreover, from the enormous body of
research on information technology acceptance, it seems that technology acceptance
research has a main theoretical drive and force which is positivist in nature. However,
this research will test hypothesis and employ existing theory (UTAUT) with
previously defined variables, both dependent and independent. With regard to critical
research, the researcher is neither going to assess the current situation nor try to
change the current status, which is what critical researchers aim to do. Instead, the
researcher is trying to identify the factors that affect e-government services
acceptance and use in order to provide actual recommendations based on the result of
the research and the finding of solutions.
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5.4.2 Justification of using quantitative and qualitative mixed approach.
Quantitative and qualitative researches have their own strengths and weaknesses. It is
for this reason that combining them in a mixed methods approach has become a
favoured approach in a variety of research fields (Creswell, 2003). Depending upon
the definition of the problem and the nature of the information sought, researchers
choose one of these two approaches, or a combination of them (Punch, 2003).
Furthermore, Kaplan and Maxwell (2005), confirm that the mix of qualitative and
quantitative research will help to gain in-depth understanding of the research problem
and allow for generalization of study results. In this study, the mixed method was
selected as the best approach to fulfil the research aims and to answer the research
questions. The quantitative and qualitative phases were conducted at the same time
and this approach was adopted from Wolff, Knodel and Sittitrai’s (1993) four
approaches. However, the quantitative data was analysed first; then, the qualitative
data was used to prove and investigate the quantitative results and finding. This study
utilized the UTAUT model as the base theoretical model. This model was evaluated
using a series of quantitative data and analysis steps to produce a final model that best
explains the predominant phenomena of the collected data. This study aims also to
test a set of hypotheses to understand and study the affect between the models’
constructs. Therefore, a quantitative approach was chosen to be the primary approach
for this study to examine and study the proposed research model. It should be noted
that there is a gap in the literature in identifying ‘what’ the factors are that influence
and affect the acceptance and use of e-government services in the public sector in the
KSA from the perspective of government and citizens; therefore, the current research
attempts to understand and identify the factors that hinder or prompt citizens in the
KSA to use and deal with e-government program and services. The research is an
engaging in-depth analysis of ‘what’ these factors are and ‘how’ they impact, from the
viewpoints of citizens and e-services providers through the conduction of several
focus groups. Moreover, the e-government area in the KSA is still a relatively new
phenomenon and becoming an interesting area of research. Qualitative research is the
appropriate choice for this research since little is known about the phenomenon under
study (Creswell, 2007). For the above reasons, quantitative and qualitative mixed
method research with a positivist underlying position was chosen as most suitable for
achieving the aims of this research. In summary, the current research is quantitative in
principal with a follow-up qualitative study using a focus group and open-ended
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questions to obtain greater understanding and to fill the gaps of the quantitative study;
the employment of the amended UTAUT model will help determine the influencing
factors of technology acceptance in the KSA.
5.5 Research Model
The research model employed in the research was based on the Unified Theory of
Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). The research
uses the UTAUT model as a theoretical driver for this study; the research will follow
the original model, measurements, and analyses of Venkatesh et al. (2003) as closely
as possible in terms of reliability, validity, correlations, factor analysis, and structural
equation modelling. However, an amended version of the UTAUT will be used to suit
the context of the study and to achieve its aim. The original UTAUT model contains
four direct independents of behavioural intention (BI) and use behaviour (USE). In
this research, two new constructs, trust and website quality, have been added, so there
are six independent variables and two dependent variables as follows.
The independent variables in the proposed research model are presented below:
1. Performance Expectancy (PE)- the degree to which individuals believe that
using a system will help them improve their job performance. PE will be
measured by the perceptions of using e-government services in terms of
benefits, such as saving time, money and effort, facilitating communication
with government, improving the quality of government services, and by
providing citizens with an equal basis on which to carry out their business with
government.
2. Effort expectancy (EE)- the degree of ease associated with the use of the
system. EE will be measured by the perceptions of the ease of use of
e-government services, as well as the ease of learning how to use these
services.
3. Social influence (SI)- the degree to which peers influence the use of the
system, whether positive or negative. SI is a main factor in many aspects of
the lives of young people and is likely to be powerful (Venkatesh et al., 2003).
This variable will be measured by the perception of how peers affect citizens’
use of e-government services.
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4. Facilitating conditions (FC)- the degree to which an individual believes that
an organisational and technical infrastructure exists to support the system
(Venkatesh et al., 2003). This variable will be measured by the perception of
being able to access required resources, as well as to obtain knowledge and the
necessary support to use e-government services.
5. Trust (TR)- Rotter (1967) defined trust as an expectancy held by anyone that
the promise of an individual or group can be relied upon. Rotter’s research is
referenced in numerous studies of online trust, including that of McKnight,
Choudhury, and Kacmar (2002). Trust in e-government has two components
which need to be measured : trust in a specific entity (trust in the government),
and trust in the enabling technology (trust in the Internet) (Carter & Belanger,
2005; Pavlou, 2003).
6. Website Quality (WQ)- Zhong and Ying (2008) stated that WQ is the quality
of the website itself or the services provided by that web system. Therefore,
WQ is based on two pillars: website quality and information quality. WQ
includes many features, such as website design, website functions, security,
and information quality; these are measured by reliability, responsiveness,
empathy, clarity, and accuracy in the information and procedures (Ahn, Ryu,
& Han, 2007).
The dependent variables in the proposed research model are presented as follows:
1. Behavioural intention (BI) - is defined as the person’s subjective possibility
that he or she will perform the behaviour in question (Venkatesh et al., 2003,).
BI will be measured by the intention, prediction, and planned use of
e-government services. In the UTAUT model, behavioural intention (BI) has a
positive and strong influence on use behaviour (Venkatesh et al., 2003).
2. Use Behaviour of e-government services (USE)- defined as the actual use
behaviour (USE) of a specific system (Ong, Day, Chen, & Hsu, 2008).
According to Ajzen and Fishbein (1980) the actual use behaviour (USE) is
dominated by behavioural intention (BI). In the UTAUT model, the direct
influence of behaviour intention on use behaviour (USE) has been tested and
validated during the development of the UTAUT model (Venkatesh et al.,
2003).
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Trust (TR)
Performance Expectancy
(PE)
Social Influence (SI)
Effort Expectancy
(EE)
Facilitating Conditions (FC)
Website Quality (WQ)
Behavioural Intention(BI)
H3
Gender
H2
H1
Age Internet Experiences
H5
H6
Use Behaviour of e-
government services.
H7
H4
Independent Variables
Dependent Variables
Moderators
Figure9 5.1. The proposed research model (based on UTAUT)
The proposed model for this study is presented in Figure 5.1. It is mostly derived from
UTAUT with some modifiers, which are as follows. First, experience, in Venkatesh et
al.’s (2003), model, was changed to Internet experience. Several studies have shown
that Internet experience influences perceived usefulness and perceived ease of use
which, in consequence, affects people’s actual use or intention to use specific systems
(Agarwal & Prasad, 1999; Jiang, Hsu, Klein, & Lin, 2000). E-government services are
more likely to be used by experienced Internet users. Thus, Internet experience
needed to be considered in order to explain users’ effort and performance
expectancies (Lu et al., 2003).
A second modifier to the UTAUT model is that voluntariness of use was deleted
because e-government services are highly voluntary (AlAwadhi & Morris, 2008).
Also, the number of Internet users for 2011 in the KSA was 13 million, which is
around 47% of the total population (MCIT, 2011), which means that more than half of
Saudi citizens are not connected to the Internet or the ICT world. Thus, it is suitable to
consider the e-government services at this period of time as highly voluntary.
The third amendment to the UTAUT model was adding website quality (WQ) as an
independent variable to the original UTAUT model. The fourth amendment was trust
as an independent variable to the original UTAUT model. The following subsections
will explain in brief why website quality and trust were added to the original model.
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5.5.1 The significance of trust in the proposed research model.
Trust has been identified as one of the bases for human interactions. Its importance is
mentioned in many fields, such as communication, leadership, management by
objectives, negotiation, performance appraisal, and implementation of self-managed
work teams (Mayer, Davis, & Schoorman, 1995). Trust is critical in many economic
and social interactions, especially in the online world where the visual aspect and the
clarity of the tangible are both absent (Reichheld & Schefter, 2000). According to the
literature review of online transactions, several studies (including Gefen, Karahanna
and Straub, 2003b; Holsapple & Sasidharan, 2005; Pavlou, 2003; and Pavlou &
Fygenson, 2006) emphasized the significance of trust in different acceptance and use
models to gain a more comprehensive understanding of user acceptance of electronic
services. Moreover, Armida (2008) used UTAUT as a theoretical framework to study
VOIP systems in USA. The research model was modified by relating Trust to
performance expectancy, effort expectancy, facilitating conditions and behavioral
intention. The main objective was to test the original UTAUT model and the model
adding trust in order to measure what factors have the highest influence on
consumers’ intention to adopt VIOP technology. The result of this research concluded
that adding trust to UTAUT model components showed a better performance in its
relationship with the other variables in the UTAUT model. Furthermore, Cody-Allen
and Kishore (2006) extended UTAUT model by adding new constructs including,
trust, e-quality and satisfaction to develop an e-Business systems. A new link between
trust and p e r fo rman ce expectancy was established to study an individual's
intention to use an e-business system. The result of this study confirms the proposed
causal relationships of trust on intent to use e-business model.
In the e-government context, trust has two elements: trust in a specific entity, which is
the government public sector or organization, and trust in the providing technology,
which is the Internet as the vehicle by which the services get to the customers (Carter
& Belanger, 2005; Pavlou, 2003). Carter and Belanger (2005) integrated constructs
from the TAM, DOI and web trust models to study the adoption of e-government
initiatives in Ireland. In their study, 105 questionnaires were completed and used in
the analyses. As result, they indicated that perceived ease of use, compatibility and
trustworthiness are significant predictors of citizens' intentions to use an e-
government service.
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However, e-government acceptance and use is dependent upon citizens’ belief that
the Internet is a reliable and safe technology. So, trust in the Internet is the most
important predictor of e-service acceptance and use (Carter & Belanger, 2005; Pavlou,
2003; Welch, Hinnant & Moon, 2005). Moreover, trust has proven to be an essential
part of e-government acceptance and adoption models and studies (Belanger & Carter,
2008; Carter & Belanger, 2005; Warkentin, Gefen, Pavlou, & Rose, 2002; Welch et
al., 2005). Oxendine, Borgida, Sullivan, & Jackson (2003) compared citizen
acceptance and the use of electronic networks in different regions of the US; they
found that system acceptance and use was more prominent in localities where citizens
are more trusting in general. Due to the impersonal nature of the Internet, citizens
must believe the agency providing the service is reliable. Wang and Emurian (2005)
emphasized that lack of trust is one of the most formidable barriers to e-service
acceptance and use, especially when financial or personal information is required.
Connolly and Bannister (2008) investigated the factors influencing trust in Internet
shopping in Ireland and emphasised that trust is an essential factor for consumers to
make purchase from the Internet. Therefore, e-service provider need to implement
security measures such as authentication, encryption, and high protection systems to
ensure that customers’ transaction are secure and trustworthy. To conclude, the
literature identifies that trust is an essential element in the acceptance and use of any
e-usage and it is highly recommended to add it to the proposed acceptance and use
model for this research.
5.5.2 Significance of Website Quality in the proposed research model.
Aladwani and Palvia (2002) defined web quality as a user’s evaluation of a website’s
features that meets the user’s needs and reflects the overall excellence of the website.
Three dimensions of web quality were identified: technical adequacy, web content,
and web appearance (Aladwani & Palvia, 2002). Moreover, Zhong and Ying (2008)
stated that website quality includes the features of a website system which presents
website quality measures, such as system, information, and service quality. In the
website quality literature, several researchers (Aladwani & Palvia, 2002; DeLone &
McLean, 2003; Hoffman & Novak, 2009; Urban, Cinda, & Antonio, 2009;
Papadomichelaki & Mentzas (2009) declared that website quality should include
multiple dimensions, such as information quality, system quality, security, ease of
use, user satisfaction, and service quality. For instance, Papadomichelaki and Mentzas
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(2009) developed an e-government service quality model (e-GovQual) that consists of
25 quality attributes classified into six quality factors: ease of use reliability,
efficiency, user support, content & appearance, and trust. Furthermore, Floh and
Treiblmaire (2006) emphasized that website quality, which includes web design,
structure and content, is an important factor for achieving customer satisfaction.
Schaupp, Fan, and Belanger (2006) conducted a survey to investigate the impact of
information quality and system quality on website satisfaction. The results showed
that information quality and system quality were significant predictors of website
satisfaction and, therefore, were also significant predictors of intention to use the
website. Moreover, Lin and Lu (2000) emphasized through an empirical study that
website quality, with its dimensions of design and content, is an important factor in
achieving customer satisfaction and user acceptance. In addition, Zhong and Ying
(2008) confirmed that there is a significant relationship between website quality and
user satisfaction and that the relationship affects the actual use of the online services.
Connolly, Bannister and Kearney (2010) designed an instrument called E-PS-QUAL
to evaluate the e-service quality of the Irish revenue online service. The finding
showed that the dimensions of efficiency, ease of completion, system availability,
privacy and contact are the most important factors influencing users’ perceptions of
service quality and are good predictors of continued use. On the other hand, website
information quality, which includes trust, reliability, and responsiveness, are
significant factors in predicting overall service quality and customer satisfaction,
which affect the behavioural intention (BI) to adopt e-services (Lee & Lin, 2005). In
addition, website quality perceptions have been reported to affect behavioural
intention (BI) and usage decisions in many studies (Ahn et al., 2007; Barnes &
Vidgen, 2002; Collier & Bienstock, 2009; Nelson, Todd, and Wixom, 2005;
Parasuraman, Zeithaml, & Malhotra, 2005; Van der Heijden, 2003; Wixom & Todd,
2005). To summarize, it is clear that the quality of a government’s websites providing
e-services is an essential factor and needs to be investigated and included in the
proposed model. If e-government website design is professional and high quality, then
it will gain both user satisfaction and adoption.
5.6 Research Hypotheses
A set of hypotheses which connect the research model constructs was proposed based
on the review of the original UTAUT model. The current developed model consists of
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eight constructs linking seven fundamental hypothesized relationships. Based on the
proposed research model, the hypothesis will be divided into two categories to
facilitate testing. The first category is the hypothesis for direct paths among the key
constructs in the research model. The second category is the moderating hypothesis
(that is, the effect of moderators).
5.6.1 Key constructs hypotheses.
The key constructs hypotheses are the direct relationships between the eight
constructs in the proposed research model as presented in Figure 5.1. This set of
hypotheses addresses the relationship between independent variables in the proposed
research model: TR, PE, EE, SI, FC, WQ, and the dependent variables, BI and USE.
The researcher hypothesized relationships between the constructs as follows:
H1: Trust (TR) will have a positive influence on behavioural intention (BI)
to use e-government services. This hypothesis is related to RQ2: How
does stakeholder trust impact on the acceptance and use of
e-government service systems?
H2: Performance expectancy (PE) will have a positive influence on
behavioural intention (BI) to use e-government services. Hypotheses
H2, H3, H4, H5 and H7 are related to RQ1: How can the factors that
influence the acceptance and use of e-government services in the Saudi
public sector be most effectively captured by using the proposed
UTAUT model?
H3. Effort expectancy (EE) will have a positive influence on behavioural
intention (BI) to use e-government services.
H4. Social influence (SI) will have a positive influence on behavioural
intention (BI) to use e-government services.
H5. Facilitating conditions (FC) will have a positive influence on
behavioural intention (BI) to use e-government services.
H6. Website quality (WQ) will have a positive influence on behavioural
intention (BI) to use e-government services. This hypothesis is related
to RQ3: How does e-government website quality impact on acceptance
and use of e-government services in the KSA?
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H7. Behavioural intention (BI) will have a positive influence on use
behaviour (USE) to use e-government services.
As hypothesized in many research studies, the behavioural intention to use
e-government services will have a positive and direct influence on e-government
usage behaviour as well (Venkatesh and Brown, 2001; Venkatesh et al., 2003). Also,
Irani, Gabel, Hughes, Swartz, and Palasik (2009) state that the majority of technology
adoption researches have utilized behaviour intention to predict technology adoption.
Furthermore, Ajzen (1991) noted that behavioural intention has a direct influence on
technology adoption. The measurement of behavioural intention includes the intention
to use and predicted use of e-government services (Benbasat & Barki, 2007; Rogers
2003; Venkatesh et al., 2003; Davis 1989). In addition, the relationship between the
behavioural intention to use a technology and actual usage is well-established (Ajzen,
1991; Mathieson, 1991; Sheppard et al., 1988; Taylor & Todd, 1995b; Venkatesh &
Morris, 2000) and both variables could be used to measure technology acceptance.
5.6.2 Moderating hypotheses.
The moderating hypotheses are the set of hypotheses that will be tested for
moderators. The amended research model considers the influence of the three
moderators which are: gender, age, and Internet experience. Accordingly, the current
study is investigating the impact of these moderators on behavioural intention (BI)
and use behaviour (USE) to use e-government services. These hypotheses are related
to RQ4: How do UTAUT moderators (i.e. age gender and Internet experiences)
influence the individual’s perceptions to use e-government services in the KSA?
5.6.2.1 Gender.
Venkatesh et al. (2003) presented gender as a moderator to the relationships between
PE-BI (stronger for men), EE-BI (stronger for women), and SI-BI (stronger for
women under mandatory use conditions only). Moreover, as a result of adding trust
(TR) as an independent construct, the research hypothesised the relationship between
TR-BI (stronger for men). Consequently, the gender moderator hypotheses are as
follows:
H1a: TR- BI to use e-government services is stronger for females than males.
H2a: PE- BI to use e-government services is stronger for males than females.
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H3a: EE-BI to use e-government services is stronger for females than males.
H4a: SI-BI to use e-government services is stronger for females than males.
5.6.2.2 Age.
Venkatesh et al. (2003) reported age as a moderator for the relationships within the
UTAUT model. The path PE-BI was stronger for younger workers; SI-BI was valid
for older workers under mandatory use conditions. FC-UB was stronger for older
workers with increased experience. Based on that finding and on the current research,
the age moderator was hypothesised as follows:
H1b: TR-BI to use e-government services is stronger for younger users than
older users.
H2b: PE- BI to use e-government services is stronger for younger users than
older users.
H3b: EE-BI to use e-government services is stronger for younger users than
older users.
H6b: FC-USE to use e-government services is stronger for younger users than
older users.
5.2.2.3 Internet experience.
In this study, the experience moderator was renamed ‘Internet experience’ and treated
as an added moderator to the original model. Based on previous researches, Internet
experience has a strong influence on the intention to use new systems such as
e-government systems (Jiang et al., 2000). Therefore, users with Internet experience
are more likely to accept and use electronic services. Accordingly, the following
hypotheses are proposed to explain these effects:
H1c: TR-BI to use e-government services is stronger for experienced users
than inexperienced users.
H2c: PE- BI to use e-government services is stronger for experienced users
than inexperienced users.
H3c: EE-BI to use e-government services is stronger for experienced users
than inexperienced users.
H6c: FC-USE to use e-government services is stronger for experienced users
than inexperienced users.
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Later on, the assessment of these hypotheses will be verified by empirical data and the
analysis tolls will be used to test the hypothesis among the variables in the UTAUT
model.
5.7 Data Collection Strategies
Denzin and Lincoln (2002) emphasized that the use of multiple methods for data
collection will guarantee an in-depth understanding of the phenomenon under study.
Therefore, this study will use multiple sources of ‘evidence triangulation’ from
different sources and that will increase the construct validity (Yin, 2009). Moreover,
Kripanont (2006) confirmed that using more than one technique for data collection is
beneficial; where one technique might be weak, the other may be strong, and so the
two will complement each other. Consequently, three data collection techniques will
be used throughout this research which are: survey questionnaire, focus group, and
archival or content analysis. For the archival or content analysis strategy, a literature
review was used for data gathering. The following sections will explain these
techniques in detail.
5.7.1 Questionnaires.
Questionnaires are self-report data collection tools which are answered at a distance
from the researcher. A high quality of a questions and questionnaire design give the
researcher high validity and reliable measures that also will help the participants to
understand the questions and answer them with the appropriate response (Neuman,
2006). Gray (2009) stated that the questionnaire is one the most widely used data
collection tools and considered the best choice for targeting the administration of a
large numbers of participants in a short period of time. A questionnaire contains a set
of well-designed questions used to obtain the information and answers from the
respondents of the research questions by following the provided advice (Sekaran,
2003). In this study, the questionnaire was used to find out the factors that affect the
acceptance and use of e-government services in the public sector in the KSA by
utilizing the proposed UTAUT model. Several other researchers employed this
technique to study the adoption of e-government services (Akman, Yazici, Mishra, &
Arifoglu, 2005; Carter & Belanger, 2003, 2004, 2005; Reddick, 2005; West, 2005).
According to Neuman (2006), a successful questionnaire should avoid ambiguity by
adhering to two main principles: clarity, and keeping the participants’ perspective in
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mind. With regard to implementing the questionnaire in this study, the researcher
followed Leedy’s (1993) four practical guidelines to develop the questionnaire draft
as follows: using clear language; meeting research aims; planning the development,
sample, distribution, and collection of the questionnaire; and creating a solid cover
letter.
The purpose of the questionnaires is to gather information from IT employees and
Saudi citizens about the factors facing the acceptance and use of e-government
services. The aim is to understand their e-service needs and how they can use it and
interact with the public sector through the e-government gate. This information will
be helpful in answering the research questions and identifying potential factors to
examine and develop the proposed UTAUT model for the acceptance and use of
e-government services in the public sector. Based on information gathered from the
literature review in UTAUT studies, and keeping in mind the research questions, the
researcher was able to design and develop a questionnaire instrument to answer the
research questions and concerns. The questionnaire was pre-tested and modified
before distribution for data collection. In summary, the procedure of the questionnaire
data collection includes these steps: designing and development the questionnaire;
pre-testing and modifying; and producing the Arabic version of the questionnaire for
collecting the research data.
The following subsections will describe these steps in more details.
5.7.1.1 Questionnaire design and development.
The questionnaire method was the main method used to collect the primary data in
this study. Therefore, the questionnaire was developed, based on various UTAUT
studies to choose the best questions to determine the actual usage and intention to use
e-government services. The questionnaire was divided into different sections for easy
reading and completion. The researcher used a Likert scale with five levels of possible
answer with respect to the UTAUT model (from Strongly Disagree to Strongly Agree)
according to the measurement scales adapted from Davis (1989). A Likert scale is
appropriate when the research needs to measure the respondent’s attitude towards
constructs (McDaniel & Gates, 2006) (see Appendix A and B). The design of the
research questionnaire consists of three pages and a cover letter, which explained the
aims of the study and contact details for the researcher and the supervisors’ team. At
the beginning of the questionnaire, the researcher explained the purpose of the survey
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and directions for filling out the questionnaire through the cover page which was
created to inform the participants of the aims and importance of the current research.
The questionnaire was written carefully using clear and simple language to encourage
participants to partake and express their viewpoint freely, and emphasized the privacy
and confidentiality measures that were put in place. The questionnaire consists of five
parts. Part one collected demographic information about the respondents. Part two of
the survey included mutable choices questions that were designed to collect additional
information about the respondent’s computer and Internet experience. Part three
contained UTAUT model statements which measure the attitude towards
e-government services and describe participants’ perceptions about e-government
services in the KSA. All the UTAUT constructs were measured according to a five
point Likert-type scale. Possible responses included: 1 = strongly disagree; 2 =
disagree; 3 = neutral; 4 = agree; 5 = strongly agree. Table 4.5 presents a summary of
items that were adapted in this study for the UTAUT model. Part four contained
eleven barriers to be identified by respondents as: not a barrier (0); important barrier
(1); or very important barrier (2). This part was included to gain a better
understanding of the challenges and obstacles that prevent or influence e-government
services acceptance and their use in the KSA. Finally, part five of the questionnaire
included the open-ended questions which were employed in this study. These gave the
participants the opportunity to express their opinions and make suggestions in an open
forum without any restrictions from the researcher (Collis & Hussey, 2003).
Moreover, the open-ended questions are a way of asking in-depth questions, and the
answers provided further explanations and a clearer understanding of the findings
from the model questions (Collis & Hussey, 2003).
5.7.1.2 Questionnaire pre-testing and modifying.
Pre-testing of the research questionnaire is highly recommended to ensure that the
questionnaire items are clear and understood by any normal respondent (Sekaran,
2003). Pre-testing was conducted to minimize the causes of measurement errors and
to attain content reliability and validity (Hair, Black, Babin, Anderson, & Tatham,
2006). In the current study, the research questionnaire was pre-tested using the expert
review technique (Sekaran, 2003). The validity of the instrument was checked in
different ways. First, the questions used in the measurement of the research model
were based on validated items from previous studies (Aladwani, 2006; Carter &
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Belanger, 2005; Cheong & Park, 2005; Hess, Wigang, Mann, & Walter, 2007;
Kripanont, 2007; Taylor & Todd, 1995a, 1995b; Venkatesh & Davis, 2000;
Venkatesh et al., 2003). However, the survey questions were paraphrased to suit the
research object (that is, some wording was modified to fit the current research object
and aims).
The second part of the instrument validity testing consisted of the researcher sending
the questionnaire to five researchers (PhD students) who have extensive knowledge of
e-government, e-applications, and have a sound knowledge of Arabic, which is also
their mother tongue. They were requested to review and answer both versions
(English and Arabic) of the instrument and provide feedback on the sufficiency,
simplicity, and clarity of the instrument. The feedback from the PhD students
recommended small changes of the wording; splitting up and changing the order of
some questions were also recommended changes. The draft questionnaire was revised
as per their comments and the final survey questionnaire was presented and approved.
In the last step, the questionnaire was tested through an online survey website as a
pilot study. Finally, questionnaires were distributed to a variety of Saudi citizens in
public places such as: shopping centres, parks, hospitals, and Internet cafés.
5.7.1.3 Arabic translation for the research questionnaire.
Arabic is the official language of Saudi Arabia and the original version of the research
questionnaire was in English, so the questionnaire had to be translated into Arabic.
Sekaran (2003) emphasized the importance of choosing a clear and easily understood
questionnaire language that is on a level participants will be able to understand. In this
case, the researcher followed the back translation procedure. Back translation has
become an in-demand methodology in academic translation and among professional
studies. It is a useful method to translate questionnaires, surveys, and research
instruments. Back translation is a technique used when a translated document is
translated back into the original language, in this case, English. It provides extra
quality checks and verifies whether the translation covers all aspects of the original
(Ozolins, 2008).
Consequently, the translation process used in this study includes the following steps.
The original version of the questionnaire in English was translated by the researcher
into Arabic. The translated Arabic version and the original English version were sent
to a Saudi PhD student who is a linguist and specialized in teaching English as a
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second language at the University of Queensland (UQ). He reviewed both versions
and provided feedback on the adequacy, sufficiency, clarity, and simplicity of the
instrument. The researcher updated the Arabic version from the previous step and sent
it to another Saudi PhD student, also a linguist, to translate it back to the original
language, English. Finally, the researcher compared the two English versions of the
questionnaire to check for any inconsistencies, mistranslations, or problems with
meaning. As result of this final step, the two versions were highly identical, which
confirmed the efficiency of the translation process and the quality of the Arabic
version.
5.7.2 Focus group.
A focus group, also known as a group interview, is a very common qualitative
research method (Morgan, 2001). However, in this study, the principal data collection
method is quantitative and the use of a qualitative method by applying focus groups
aims to improve and enhance the effectiveness of the quantitative results. It also helps
the researcher to gain an in-depth understanding and generalization of research
findings and it provides recommendations from participants to understand and explore
the research topic further (Kaplan & Maxwell, 2005). Moreover, the focus group
provides a wider understanding of participants’ opinions, beliefs, problems,
suggestions, and perceptions about the research topic (Creswell, 2003). According to
Neuman (2006), focus groups seek to exchange ideas, opinions, and experiences
between participants and the researcher that will lead to an enrichment of the research
topics. Also, it allows the researcher to attain various opinions and views on the
research topic from different participants at the same time (Neuman, 2006).
5.7.2.1 Sample size and time frame for the focus groups.
Considering the time limitation and the work responsibilities of participants in these
focus groups, the researcher believes that two groups should be sufficient to fulfil the
purpose of conducting the focus groups. Therefore, two focus groups were conducted
in this study and each group contained six participants from different levels of
knowledge and experience. According to Krueger (2000), a group of between four
and six participants is suitable to generate sufficient content from a focus group.
Consequently, the two groups are:
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• Group One: IT Staff group: Contained five members from IT sectors in
different government organizations.
• Group Two: Citizens group: Contained five Saudi citizens from different
education and age levels.
The focus groups were carried out on two days: on February 2, 2012 with the IT staff
group, and on February 9, 2012 with the citizens group. A digital audio recorder was
used to record the conversations which took about two hours. The focus group activity
was done in strict accordance with the research ethics approved process and protocol.
All participants were informed about the recording device and signed the consent
before starting. After finishing the focus groups, the researcher thanked all
participants for their contributions.
5.7.3 Literature review.
A literature review is an examination and evaluation of the available documents and
studies in a particular field or topic (Hart, 1998). The main purpose of the literature
review is to explore and investigate what is already known on a particular subject.
Also, it aims to identify the gap in the existing research in order to study and address
that gap. Consequently, the research is able to identify the research approach and
justify the study (Punch, 2000). The literature review should help the researcher to
address and answer various important questions related to the research topic. For
instance, some of these questions are presented in Figure 5.2.
Figure10 5.2. An example of the literature review questions (adapted from Hart, 1998)
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The current study, based on a thorough investigation via the literature review enables
the researcher to address several important research objectives, such as:
• Reviewing numerous researches and studies related to e-government
principles and fundamentals from a different perspective;
• Focusing on KSA initiatives for the e-government system and investigating
the current status of e-government systems in order to address current, relevant
gaps; and
• Finding out factors that facilitate or impede the adoption of e-government
systems based on global experiences.
The literature review in Chapter 2 provided the researcher with a basic knowledge and
fundamental information about e-government and several important related issues
such as: e-government definition, categories, implementation stages, benefits and
challenges. It draws a comprehensive picture about e-government phenomena form
different aspects. The review of literature in Chapter 3 focused on the Kingdom of
Saudi Arabia (KSA) and its e-government program. That chapter provides important
information on the KSA and discovered the main characteristics of its ICT sectors, IT
initiatives, and e-government program (Yesser). Moreover, Chapter 4 was an essential
foundation to study the various adoption theories and models. It provided sufficient
information and discussion about several models, including the selected UTAUT
model. In addition, the researcher continued with the literature review until the end of
the study to maintain and update developments in the research subject area.
5.8 Population and Sample
Gray (2009) defines a population as the entire number of possible groups or elements
that the researcher wishes to include in the study. The population of this study consists
of two individual groups: Saudi citizens; and IT staff (such as programmers, engineers
and web designers) in the public sector.
With regard to the targeted population of this study, this researcher is targeting the
two main pillars of e-government services. These are the service providers (the
government sectors staffed by IT staff who work in IT departments), and the customer
of these services, who are the Saudi citizens. Without involving the two groups in this
study, the result of this research would be limited to one viewpoint and would not
draw a complete and inclusive picture about e-government services in the KSA.
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The survey questionnaires were distributed among Saudi citizens in three big cities
which represent the biggest three regions in the KSA. The aim is to obtain their views
and comments about the acceptance and use process of e-government in the KSA,
starting with the acceptance and use process from a low level with the implantation of
the services, up to the publishing phase. Their opinions are useful and valuable as they
may narrow the possible factors that affect the acceptance and use of e-government
services in the public sector. It would be neither practical, economical, or time-
efficient to conduct face-to-face or telephone interviews with a large number of
citizens. To gain as much as possible from Saudi citizens’ opinions about the research
topic, the researcher distributed the study questionnaire among a relatively large
sample; a minimum of 500 Saudi citizens is the target set for this study, taking into
consideration the study’s complexity and the importance of the views gleaned from
this sample.
Finally, from these two samples, IT staff and Saudi citizens, the researcher will be
able to obtain the perspective of citizens who use e-services and the government who
provides them. The combined data helps to answer the research questions and
provides practical recommendations for decision makers.
5.9 Data Analysis
Data analysis is the operation of examining, categorizing, grouping, or otherwise
recombining the collected raw data with the aim of finding answers to the research
questions (Walliman, 2000). As this research has both qualitative and quantitative
data, the following subsections describe in detail the specific analysis strategies
undertaken during the analysis phase for both of these data.
5.9.1 Quantitative analysis.
Punch (2003) identified three main guidelines for quantitative data analysis as
follows: creating variables; distributing variables across the sample; and creating
relationships. The Statistical Package for the Social Sciences (SPSS) software and its
supplement AMOS (Version 19) were found to be the appropriate and the most
suitable tools for analysing the quantitative data for this study because of its ability to
model latent variables for data screening and data analysis. The current study used
two exploratory procedures, namely, Exploratory Factor Analysis (EFA) and
Confirmatory Factor Analysis (CFA) to identify the underlying data structure for each
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construct. Structural Equation Modeling (SEM) was used to specify the relationships
between these constructs. EFA has been used to examine only a single relationship at
a time and to explore the construct validity of the test scales (Hair et al., 2006). CFA
has been used to assess the multidimensionality and the factorial validity of the
constructs of the theoretical model (Byrne, 2001). Moreover, this study is applying the
SEM technique to evaluate the relationships in the UTAUT model and to test the
hypothesis among the variables in the model. SEM is a statistical methodology that
takes a confirmatory (hypothesis testing) approach to the structural analysis of data
representing some phenomena (Kline, 2005). According to Hair et al. (2006), SEM is
used to test theoretical models, so this technique is considered adequate the
investigation carried out by this study. SEM is a fairly new technique for testing
models that have already been validated, or for testing models which have a strong
theoretical basis. Thus, SEM is helpful as a confirmatory technique, with strong
mathematical and statistical grounds (MacCallum & Austin, 2000).
It is worth mentioning here that Venkatesh et al. (2003) used the PLS technique to
analyse and test their original UTAUT model, while the current study uses the SEM
approach. There are some differences between the two techniques. PLS aims to
maximum variance explained (achieving high R²). It produces parameter estimates
that maximize explained variance and so focuses more on prediction. SEM, on the
other hand, tries to produce the observed covariances among measures, which enables
an assessment of fit based on how well they are produced (Gefen et al., 2003b; Hair et
al., 2006). Also, PLS does not have an inherent ability to test models using a statistical
test, but can only fit given models to data (Lohmoller, 1989). According to Hair et al.
(2006), the structural equation model (SEM) analysis process consists of a two-step
analysis approach; these are assessment of the measurement model, and assessment of
the structural model. However, SEM can be conducted with various software,
including LISREL, EQS, and AMOS. In this study, AMOS (Version 19) was used to
conduct the SEM analysis. The results of the SEM analysis are presented in Chapter
8. Furthermore, a descriptive data analysis was conducted using the SPSS program
(Version 19.0) to describe the characteristics of the research data. The descriptive
analysis included a presentation of the participants’ profiles, and data screening. It
also included an analysis of the factors that facilitate or impede the adoption of
e-government services from the perspectives of citizens and IT staff.
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5.9.2 Qualitative analysis
Denscombe (2007) provided four significant principles that guide a successful
analysis of qualitative data. The analysis of the data and the conclusion drawn from
the research should be firmly rooted in the data. That is, all analyses and conclusions
are grounded directly in the evidence that has been collected.
1. The researcher’s explanation of the data should emerge from a careful and
meticulous reading of the whole data. This involves a process of interpretation
in which the researcher produces meaning out of the raw data.
2. The researcher should avoid introducing unwarranted assumptions into the
data analysis.
3. It is important that each piece of raw data material should be identified with a
unique serial number for reference purposes. The format of this does not
matter particularly, as long as it enables each separate item to be identified
exactly in terms of where it should be located.
4. The analysis of the data should involve an iterative process.
These main principles were followed in the analysis of the data collected. In this study
the qualitative data analysis depended on the data analysis of the focus groups.
Therefore, the qualitative data was analysed to support the quantitative findings using
the procedures described below:
1. All focus groups discussions were initially transcribed.
2. The transcripts then were read and investigated carefully.
3. The key constructs question regarding to the UTAUT model that obtained
from the focus groups were analysed.
4. The focus groups results were used to support and confirm the research
quantitative data.
5.10 Reliability and Validity Analysis of the Instrument
According to Walliman (2000), there are two common measurements that need to be
considered when determining if a study has been successful or not: reliability and
validity. Reliability is the degree of accuracy of the collected data; for instance, if the
study is repeated, the identical results emerge. However, reliability in technology
acceptance models refers to the degree to which the variables, or indicators, are stable
and consistent with what they are assumed to be measuring (Singleton & Straits,
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2004). Venkatesh et al. (2003) measured the reliability of the UTAUT instrument
several times during the development of the instrument and all of the reliability
coefficients were approximately 0.70. In this research, reliability analysis was
conducted using SPSS Version 19 for all eight constructs of the UTAUT model. In
SPSS, the most popular test of reliability is Cronbach’s coefficient alpha (Sekaran,
2003). According to Sekaran (2003) and Hair et al. (2006), Cronbach’s alpha value
should be in the 0.7 range to be acceptable and to indicate adequate internal
consistency. The results of the reliability analysis are discussed in detail in Chapter 7.
However, the analysis results showed that all of the constructs had a high reliability of
more than 0.7.
Validity is concerned with if the researchers have studied what they intended to do
and nothing else (Neuman, 2006). Moreover, it refers to the extent to which the data
collected truly measures what it is meant to measure (Field, 2005). According to
Kripanont (2007), validity tests for the instruments include content validity and
construct validity. First, content validity was achieved by employing the pre-testing
technique to achieve content reliability and validity (Hair et al., 2006) as explained in
Section 5.7. Second, the construct validity was examined and assessed through a
series of processes by applying the exploratory (EFA) and confirmatory (CFA)
techniques. The results of validity analysis are discussed and summarised in Chapter
7.
5.12 Ethical Considerations
Ethical considerations are an important aspect of any research design (Neuman,
2006). In the context of this study a number of steps were implemented to ensure that
standards of ethical research practice were met. First, the research was approved by
the University’s Human Research Ethics Centre with reference number
ICT/10/09/HREC. Second, all participants were informed about the researcher’s topic
(Using UTAUT Model to Determine Factors Affecting Acceptance and Use of
E-government Services in the Kingdom of Saudi Arabia) and how this study will help
citizens and decision makers to provide more efficient and effective services through
online means. Also, participants were free to withdraw at any time and the contact
details of the researcher and supervisor were given in the cover letter if respondents
had any ethical concerns. Furthermore, participation in the survey was voluntary and
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anonymous. Finally, depending on the data collection technique and analysis,
additional measures were taken to ensure that participants remained informed of the
research result; an oral seminar was conducted at the end of the study period with the
cooperation of Yesser program management.
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5.13 Chapter Summary
Chapter 5 presented and discussed many aspects including: research paradigms,
categories, the research model, and it presented the research methodology selected for
this research. The chapter explained the reasons for the selection of the research
methods, model, and participants. The chapter also explained how the questionnaire
and focus groups were selected and designed. Finally, issues of data analysis,
reliability and validity, and ethical issues were illustrated in detail. The next chapter
presents the results of the descriptive data analysis.
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Chapter 6: Descriptive Data Analysis
6.1 Introduction
This chapter presents the analysis and findings of the quantitative data collected from
the survey questionnaires. Descriptive data analysis was chosen as an appropriate way
to analyse the descriptive questionnaire data. Frequency and percentage were
calculated for each variable. This chapter presents an overview of research
questionnaire in Section 6.2. Section 6.3 discusses and presents the results of the data
screening. Section 6.4 follows, which presents the descriptive statistics. Finally, the
chapter is summarized in Section 6.5.
6.2 Overview of Research Questionnaire
A questionnaire survey was conducted in Saudi Arabia and distributed among Saudi
citizens in three large population centres. The questionnaire began with a cover letter
explaining the purpose of study, the nature of questions, the ethical considerations of
the research, and contact information for the research team. As explained in Chapter
4, the questionnaire consists of five parts. Part one collected demographic information
about the respondents. Part two of the survey includes multiple choice questions
designed to collect additional information about participants’ computer and Internet
experience, as well as participants’ knowledge of e-government and their desire to use
it. Part three contains UTAUT model statements which measured participants’
attitudes towards e-government services and describes participants’ perceptions about
e-government services in the KSA. All UTAUT constructs were measured by five
scales on the Likert-type scale. Responses were ordered as follows: 1 = strongly
disagree; 2 = disagree; 3 = neutral; 4 = agree; and 5 = strongly agree. Part four
contains eleven barriers which were identified by respondents as: not a barrier (0),
important barrier (1), or very important barrier (2). This was included to gain a better
understanding of the challenges and obstacles that prevent or influence e-government
services acceptance and use in the KSA. Finally, part five contained yes/no questions
in order to explore, in a simple manner, the participant’s intention to use e-services.
As mentioned before, the sample of this study consist of two groups of people: Saudi
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citizens, and IT employees in the public sector. The main reason behind the selection
of IT employees is to explore their views and answers to part four of the research
questions. It is worthwhile to gain their opinions regarding the obstacles to e-
government services as they are the implementers and providers of those services. The
research questionnaires were distributed to more than 1500 participants randomly
chosen from three big cities from three different geographic regions: Riyadh, Jeddah,
and Abha. This was done to cover a wide area and obtain greater cultural diversity in
participant respondents.
6.3 Data Screening and Management
Pre-analysis data screening was conducted on the raw data before starting the analyses
processing. Data screening is a fundamental step before starting the data analysis to
avoid incorrect findings and results (Field, 2005). According to Levy (2006),
screening is an essential step in the analysis process for four reasons: first, to
investigate the accuracy of the collected data; second, to study extreme cases, or
outliers and fix them; third, to treat missing data values; and fourth, to manage the
response set issues (Levy, 2006). In the following subsections, in accordance with
Hair et al. (2006), the main issues of the data screening procedure such as missing
data, univariate normality, and outliers, which are related to the UTAUT model
variables, will be discussed in detail.
6.3.1 Missing data management.
Missing data is one of the common barriers in data analysis within social research
(Kline, 2005; Tabachnick & Fidell, 2007). Therefore, an essential step before starting
the analysis procedure is to define and treat any kind of missing data, such as
incomplete answers or missing sections (Hair et al., 2006). In this study, any
questionnaire with any missing answers related to the UTAUT model especially was
discarded. Any missing data in the UTAUT model (constructs or variables) will cause
several problems in computing the fit measures such as Goodness-of-Fit-Index (GFI)
in Structural Equation Modelling using AMOS (Arbuckle, 2006). As mentioned, more
than 1500 questionnaires were distributed randomly among Saudi citizens in different
places during a three month period. As a result, a total of 1045 (69.6%) of
questionnaires were returned. Of the 1045 questionnaires collected, 167
questionnaires were considered unusable because they had missing response items,
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which made them unusable according to the researcher’s rule. The remaining 878
(58.5%) questionnaires were completed and used in the analysis. This response rate is
considered sufficient considering that, according to Sekaran (2003), a response rate of
30% is acceptable for surveys.
6.3.2 Investigating univariate normality.
It is important and instructive to test whether the data could have been generated by a
common theoretical distribution before empirically fitting the distributions to data.
Normality refers to the shape of the data distribution for an individual variable and its
correspondence to the normal distribution (Hair et al., 2006). According to Hair et al.
(2006), univariate normality can be tested graphically or statistically. The statistical
techniques for testing univariate normality are Pearson’s skewness parameter, while
the graphical analysis is a visual check of the histogram that compares the experiential
data values with a distribution approximating the normal distribution. In this study,
visual examination of the histogram of the data was mainly used to test the univariate
normality. According to Field (2005), the statistical techniques of testing normality
are sensitive to the size of research data; as a result, it is recommended to check the
histogram with the values of skewness and kurtosis to evaluate univariate normality.
In this study, visual assessment of the histogram of the data distribution of all
constructs demonstrated that the shapes of all the univariate distributions were
reasonably usual and acceptable. Additionally, the findings in Table 6.1 indicate that
all values of the variables were within the accepted range of skewness and kurtosis
(i.e. -2.58 +2.58, Hair et al., 2006, p. 82).
Table6 6.1
Skewness and Kurtosis Statistics for the Study Variables (N = 878)
Scale Skewness Kurtosis
Performance expectancy -0.97 0.23
Effort expectancy 0.37 -0.70
Social influence -0.54 0.28
Facilitating conditions -1.03 1.86
Trust -0.23 0.66
Web quality 0.10 -0.53
Behavioural intent 0.29 -0.81 Note. SE for skewness statistic = 0.08. SE for kurtosis statistic = 0.17.
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6.3.3 Outliers screening.
In a research study environment, it is fundamental to screen the data to identify
outliers because they can change the results of the data analysis. An outlier is
considered a data point for which the values are very different from the data values for
the majority of cases in the data set (Hair et al., 2006). Moore and McCabe (2006)
described outliers as observations that are statistically distant from the rest of the
research data. According to Kline (2005), outliers can be classified into two types: a
univariate outlier (the case of an unusual value on a single variable), and a
multivariate outlier (the case that there is an unusual combination of values for a
number of variables). In this study, univariate and multivariate outliers were examined
using the residual analysis (Tabachnick & Fidell, 2007). In order to discover outliers
in this study; the following steps were applied transparently. Mean composites were
created for each of the variables. Then, to detect univariate outliers, the composites
were standardized; cases whose standardized values exceeded the absolute value of
3.29 were considered outliers (Tabachnick & Fidell, 2007). Therefore, the result of
this analysis showed that there were no univariate outlier cases with residuals above
3.29. To detect multivariate outliers, Cook’s Distance value was used to test the
influence of the outliers on the research data (Hair et al., 2006). Outliers on the x- and
y-spaces were detected via Cook’s Distance. Cases whose Cook’s D values were
above 0.0069 (i.e., the Cook’s D mean + two SDs, as per Norusis, 1991) were
considered to be outliers. As a result, no multivariate outliers were detected and the
data were normally distributed.
6.4 Descriptive Statistics
The survey was completed by 878 respondents. Of these, 60 respondents were IT staff
in the public sector and 818 respondents were Saudi citizens from a variety of
backgrounds. The following sections will describe each group and provide the
findings of the analysis.
6.4.1 Demographic analysis of Saudi citizens.
The following Table 6.2 provides a general overview of the Saudi citizens group in
terms of demographic information, such as gender, age, education level, computer
knowledge, and Internet knowledge.
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Table7 6.2
Demographic information of Saudi citizens
Variable Frequency Percent
Gender Male 513 62.7
Female 305 37.3
Age
20 or under 5 0.6
21-30 395 48.3
31-40 380 46.5
41-50 29 3.5
More than 50 9 1.1
Education
High School 4 0.5
Diploma 180 22.0
Bachelor 567 69.3
Higher education 67 8.2
Computer knowledge
Poor 29 3.5
Moderate 456 55.7
Good 325 39.7
Very good 8 1.0
Internet knowledge
Poor 29 3.5
Moderate 408 49.9
Good 369 45.1
Very good 12 1.5
6.4.1.1 Gender and age.
As shown in Table 6.2, 513 (62.7%) from Saudi citizens group were male and 305
(37.3%) were female. Also, the age distribution shows that about half of respondents
(48.3%) were aged 21 to 30 and the second group were aged 31 to 40 (46.5%). The
percentage of the 41 to 50 year old age group was 3.5% and the percentage of those
who were older than 50 years was 1.1%. Finally, only 0.6% of the first age group was
20 or younger than 20 years old.
6.4.1.2 Education level.
Respondents were asked to specify their education level. As shown in Table 6.2,
about two thirds (69.3%) have a bachelor degree, while 22.0% have a diploma degree.
About 8.2% have attained what is termed higher education, including masters or
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doctorate degrees. Finally, a small percentage, about 0.5%, have only a high school
diploma.
6.4.1.3 Computer knowledge.
As Table 6.2 reveals, more than half of the respondents (55.7%) were from the
moderated group. 39.7% of the participants were good in computer knowledge, while
a small percentage of about 3.5% did not have basic computer skills or experience. As
normal in a random sample, only 1.0% reported they had very good knowledge and
information about computers.
6.4.1.4 Internet knowledge.
The following Table 6.3 provides a general overview of Internet experience in terms
of Internet use history and Internet use frequency per day. First, respondents were
asked to specify the length of time they had been using the Internet. The majority of
participants (71.8%) had more than three years of computer experience. More than a
quarter of the sample (28%) of the respondents had one to three years of computer
experience, while a small percentage (0.2%) had less than one year of computer
experience. Second, respondents were also asked to indicate the length of time they
had been browsing the Internet on a daily basis. As shown in Table 6.3, more than
half (62.7%) of respondents browsed Internet for more than three hours daily, while
more than a quarter (26.7%) used the Internet between two and three hours per day.
Approximately 9.3 % accessed the Internet one or two hours daily, while a small
group of 1.3% accessed it less than one hour daily.
This finding indicates that there is a high usage of internet and web applications
amongst the sample. Over than 70% of the sample have a high level of Internet
knowledge and experiences. Consequently, this result has a significant affects on
users’ intention to adopt e-government services.
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Table 6.3.
Internet experience information of Saudi citizens
Variable Frequency Percent
Internet Use: history
less than 1 yr. 2 0.2
1- 3 yrs. 229 28.0
>3 yrs. 587 71.8
Internet use: frequency /day
>1 hr. 11 1.3
1-2 hrs. 76 9.3
2-3 hrs. 218 26.7
>3 hrs. 513 62.7
6.4.2 Demographic analysis of IT employees.
The following Table 6.4 provides a general overview of the IT staff group in terms of
the demographic information, such as gender, age, education level, computer
knowledge and Internet knowledge.
Table8 6.4
Demographic information of IT Staff
Variable Frequency Percent
Gender Male 60 100
Age 21-30 25 41.7
31-40 35 58.3
Education Diploma 20 33.3
Bachelor 40 66.7
Computer knowledge Good 13 21.7
Very good 47 78.3
Internet knowledge Good 9 15.0
Very good 51 85.0
6.4.2.1 Gender and age.
As shown in Table 6.4, all participants were male because the majority of IT
employees in the public sector in the KSA are male, while females work in female
sections which offer services for females only. Also, the majority of employed
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females work in education sectors, such as female schools, universities, colleges, and
institutes. For the age distribution, Table 6.4 shows that more than half of the
respondents (58.3%) were aged 31 to 40 years while the rest (41.7%) were 21 to 30
years of age.
6.4.2.2 Education level.
Respondents were asked to specify their education level, as shown in Table 6.4.
About two thirds (66.7%) have a bachelor degree, while 33.3% have a diploma
degree.
6.4.2.3 Computer knowledge.
As Table 6.4 shows, the majority of respondents (78.3%) had a very good level of
computer knowledge, while 21.7% had a good level of computer knowledge.
6.4.2.4 Internet knowledge.
The following Table 6.5 provides a general overview of the Internet experience in
terms of Internet use history and Internet use frequency per day. Respondents were
asked to specify the length of time they had been using the Internet. All participants
(100%) had more than 3 years of computer experience. Also, respondents were asked
to indicate how often they browsed the Internet on a daily basis. As shown in Table
6.5, all participants browsed the Internet for more than three hours daily.
Table 96.5
Internet experience information of IT Staff
Variable Frequency Percent
Internet Use: history >3 yrs. 60 100
Internet use: frequency/day >3 hrs. 60 100
6.5 Chapter Summary
This chapter presented the descriptive data analysis of research quantitative data in
order to explore the characteristics of the data collected from the questionnaire survey
of Saudi citizens and IT staff using e-government services system in the KSA. It gave
an overview of the research survey, explained the data screening procedure, and
presented the demographic analysis of the respondents. The overall response rate for
the survey was (69.6%), and this is considered fairly high. Additionally, it showed
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that the collected data were free from univariate and multivariate outliers. This made
the data eligible for the next step in the analyses, including EFA, CFA, and SEM in
the following chapters. It also presented the demographic information of Saudi
citizens and IT employees according to gender, age, education level, computer
knowledge, and Internet knowledge.
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Chapter 7: Measurement Scale Analysis
7.1 Introduction
This chapter presents the details and results of the analysis of the measurement scales
utilized in the questionnaire to test the constructs proposed in the conceptual model.
Each of the seven measurement scales, representing each of the model constructs, was
assessed to determine its overall reliability. Additionally, Factor Analysis (FA) was
conducted on each scale to study, and confirm, the validity of the factor structures that
represent each individual model construct. Section 7.2 presents the results of the
analysis of scale reliability through the assessment of internal consistency. Section 7.3
details the procedures and presents the results of the Exploratory Factor Analysis
(EFA) and the Confirmatory Factor Analysis (CFA), both of which are employed to
confirm and refine the identified structure of each model construct to ensure its
validity and unidimensionality. Finally, Section 7.4 summarizes the chapter.
7.2 Reliability
The reliability of a measure refers to the degree to which the instrument is free of
random error. It is concerned with the consistency and stability of the measurement.
In the current study, there were six independent scales and two dependent scales used
in part four of the survey questionnaire to measure the constructs of the proposed
UTAUT model (Figure 5.1). The independent scales are: trust (TR); performance
expectancy (PE); effort expectancy (EE); social influence (SI); website quality (WQ);
and facilitating conditions (FC). The dependent scales are: behavioural intention (BI);
and use behaviour (USE) to use e-government services. To prove that the set of scales
captures the meaning of the model constructs consistently and accurately, a scale
reliability analysis was performed to assess the internal consistency and item-total
correlations. The following sections present the assessment procedures for the
reliability of the scales.
7.2.1 Internal consistency.
Internal consistency reliability is a frequently used type of reliability in the IS domain
(Sekaran, 2003). It refers to the degree to which responses are consistent across the
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items (variables) within a single measurement scale (Kline, 2005). In this study,
Cronbach’s coefficient alphas, which are calculated based on average inter-item
correlations, were used to measure internal consistency. As stated by Straub (1989,
p. 151), “high correlations between alternative measures or large Cronbach’s alphas
are usually signs that the measures are reliable” . Cronbach’s coefficient alpha value
was assessed to examine the internal research consistency of measuring (Field, 2005;
Hinton, Brownlow, McMurray, & Cozens, 2004; Straub, Boudreau, & Gefen, 2004).
Hinton et al. (2004) propose four degrees of reliability scale: excellent (0.90 and
above); high (0.70 to 0.90); high moderate (0.50 to 0.70); and low (0.50 and below).
The reliability values reported in Straub et al.’s (2004) study should be equal to or
above 0.70 for a confirmatory study. Pallant (2005) states that Cronbach’s coefficient
alphas of 0.70 and above are deemed acceptable. Moreover, Hair et al. (2006)
mentioned that construct reliability should be 0.7 or higher to indicate adequate
convergence or internal consistency (Hair et al., 2006). According to the current
realest model as well as Venkatesh et al. (2003), the construct constituting the
UTAUT should have a good internal consistency with a reported Cronbach’s alpha (α)
value greater than 0.70. In this study, there were eight scales used in the survey
questionnaire to measure the constructs proposed in the model (Figure 4.3), namely
performance expectancy (PE), effort expectancy (EE), social influence (SI),
facilitating condition (FC), trust (TR), website quality (WQ), behavioural intention
(BI), and use behaviour (USE). To prove that those scales satisfied the model
constructs consistently and accurately, a scale reliability analysis was performed to
assess the internal consistency. A reliability coefficient was run on SPSS for each set
of constructs and the results are presented in Table 7.1, which shows the Cronbach’s
alpha (α) value for each variable. The results of the analysis show that all of the
constructs got a high reliability of more than 0.7. Cronbach’s α value result varied
between 0.73 for use behaviour and 0.95 for the facilitating condition. Overall, the
result shows that all alpha values of the study instrument are reliable and exhibit
appropriate construct reliability.
Table10 7.1
Cronbach’s Alpha Reliability Results
Constructs No. of Items
Cronbach’s Alpha (α) Comments
Performance expectancy (PE) 4 0.74 High Reliability
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Effort expectancy (EE) 4 0.75 High Reliability
Social influence (SI) 4 0.89 High Reliability
Facilitating conditions (FC) 3 0.95 Excellent Reliability
Trust (TR) 4 0.81 High Reliability
Website quality (WQ) 5 0.76 High Reliability
Behavioural intent (BI) 3 0.93 Excellent Reliability
Use behaviour (USE) 4 0.73 High Reliability
7.2.2 Item-total correlations.
Item-total correlation or corrected item-total correlation refers to the correlation of a
variable, with the composite score of all variables forming the measure of the
construct (Lu, Lai, & Cheng, 2007). The study of correlations demonstrates the
relationships between the variables of the research model. It also provides
comparisons with the existing sample data. These relationships provide a check for
how well the proposed model captures important properties of the research sample
(Koufteros, 1999). In this study, the corrected item–total correlation analyses were
performed for all constructs of the proposed model. According to Pallant (2005) and
Hair et al. (2006), a value of the corrected item-total correlation of less than 0.30
indicates that the variable is measuring something different from the construct as a
whole. The results of item-total correlations are presented in Tables 7.2 to 7.9.
7.3 Validity
Construct validity is defined as the degree to which an operational measure correlates
with the theoretical concept being investigated. It provides the researcher with
assurance that the research’s instrument truly measures what it is intended to be
measured (Gable, 1993; Netemeyer, Bearden & Sharma, 2003; Turocy, 2002).
According to Turocy (2002), factor analysis is most often associated with construct
validity and considered one of the analytic tools to assess construct validity. Factor
analysis can be used to “examine empirically the interrelationships among the items
and to identify clusters of items that share sufficient variation to justify their existence
as a factor or construct to be measured by the instrument” (Gable, 1993, p. 108). In
this study, the validity and unidimensionality of the scales was assessed by using
exploratory factor analysis (EFA) and an examination of the correlation coefficients
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for all of instrument scales. In addition, convergent and discriminant validity of the
measurement scales was also assessed using confirmatory factor analysis (CFA).
7.3.1 Exploratory Factor Analysis (EFA).
Exploratory factor analysis (EFA) can be defined as an orderly simplification of
interrelated measures. EFA has been used to explore the possible underlying factor
structure of a set of observed variables without imposing a preconceived structure on
the outcome (Child, 1990). EFA is used to explore data to determine the number or
the nature of factors that account for the covariation between variables when the
researcher does not have, a priori, sufficient evidence to form a hypothesis about the
number of factors underlying the data. Therefore, EFA is generally thought of as more
of a theory-generating procedure, as opposed to a theory-testing procedure (Stevens,
2002). EFA is useful in assessing the relationships among variables and in exploring
the construct validity of test scales. In reality, the majority of factor analysis studies
have, historically, been exploratory (Gorsuch, 1983; Kim & Mueller, 1978).
Moreover, EFA is “data driven rather than theory or hypothesis driven” (Brown,
2006, p. 14). The statistical package SPSS 19.0 was used to conduct the exploratory
factor analysis. All scales of research model were analysed one by one, and details of
the validation process and results are discussed in the following subsections.
7.3.1.1 Analysis of Performance Expectancy scale (PE).
By using the SPSS package, the correlation coefficients matrix was calculated for the
four items used in the measure of the performance expectancy scale, as shown in
Table 7.2. The results revealed, as shown in Table 7.3, that the correlation coefficients
between items are generally greater than 0.3, which indicates that they are suitable for
factor analysis (Coakes, 2005). According to Pallant (2005), a value of the corrected
item-total correlation of less than 0.30 indicates that the variable is measuring
something different from the construct as a whole. Moreover, the researcher assessed
sampling adequacy by examining the Kaiser-Meyer-Olkin (KMO) output provided in
the factor analysis. According to Coakes (2005) and Pallant (2005), the KMO and
Bartlett’s test of sphericity are generally applied to determine the factorability of the
output matrix. A KMO correlation above 0.60 to 0.70 is considered adequate for
analysing the EFA output (Netemeyer et al, 2003). Generally, a KMO measure should
be greater than 0.5 (De Vaus, 2002; Field, 2005). As Table 7.4 shows, the KMO
statistic is 0.654, which is above the minimum acceptable level of 0.60 (Coakes,
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Steed, & Dzidic, 2006), indicating sampling adequacy. Additionally, Bartlett’s test of
sphericity was (chi-square = 818.389), which was highly significant at (p<0.001)
indicating that there were adequate relationships between the variables included in the
analysis (Field, 2005). Therefore, it can be concluded that the data is appropriate for
factor analysis.
Table11 7.2
Coding of Performance Expectancy Variables
Construct Variable Code Questionnaire Statement
Performance Expectancy (PE)
PE1 Using e-government services enables me to accomplish my needs from the public sector more quickly and more efficiently.
PE2 Using e-government services increases the equity between all citizens.
PE3 Using e-government services would save citizen’s time.
PE4 Using e-government services increases the quality of services.
Table12 7.3
Correlation Matrix for Performance Expectancy Scale
PE1 PE2 PE3 PE4
Correlation
PE1 1.000 0.602 0.440 0.387
PE2 0.602 1.000 0.374 0.430
PE3 0.440 0.374 1.000 0.563
PE4 0.387 0.430 0.563 1.000
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Table13 7.4
KMO and Bartlett’s Test for Performance Expectancy Scale
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.654
Bartlett’s Test of Sphericity Approx. Chi-Square 818.389
df 10
Sig. 0.000
Finally, factor loading of scale items was examined. Generally, factor loadings below
0.4 are considered low, and low-loading items should be suppressed (Field, 2005;
Hair et al., 2006). In this study, the recommended cut-off factor loading of 0.50 was
used to ensure that all variables had practical significance (Hair et al., 2006). As
shown in Table 7.5, the loading values of all four items exceed the cut-off level of
0.50.
Table14 7.5
Factor Loading for Performance Expectancy
Component Matrixa
Component
1
PE1 0.661
PE2 0.542
PE3 0.770
PE4 0.778 Extraction Method: Principal Component Analysis. a.1 component extracted.
7.3.1.2 Analysis of Effort Expectancy scale.
As can been seen from Table 7.6, the Effort Expectancy scale (EE) has four
questionnaire statements to measure the degree of ease of use of e-government
services. The correlation matrix for the four scale items, EE1 to EE4, indicate that the
correlation coefficients are generally greater than 0.3, as shown in Table 7.7. Also,
both the KMO analysis (0.685, a highly significant result) and the Bartlett’s test (chi-
square = 733.503) are highly significant (p<0.001), as presented in Table 7.8. Finally,
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as shown in Table 7.9, the factor loadings of the items are higher than the cut-off
level. It is concluded that the measures for the four items scale for effort expectancy is
unidimensional.
Table15 7.6
Coding of Effort Expectancy Variables
Construct Variable Code Questionnaire Statement
Effort Expectancy (EE)
EE1 Learning e-government services system is easy.
EE2 Using e-government services system is easy.
EE3 It is easy for me to become skilful at using e-government services system.
EE4 By using the e-government system I am able to get government services easily.
Table16 7.7
Correlation Matrix for Effort Expectancy Scale
Correlation Matrix A EE1 EE2 EE3 EE4
Correlation
EE1 1.000 0.370 0.482 0.547
EE2 0.370 1.000 0.620 0.355
EE3 0.482 0.620 1.000 0.461
EE4 0.547 0.355 0.461 1.000 Determinant = 0.406
Table177.8
KMO and Bartlett’s Test for Effort Expectancy Scale
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.685
Bartlett’s Test of Sphericity
Approx. Chi-Square 733.503
df 6
Sig. 0.000
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Table18 7.9
Factor Loading for Effort Expectancy
Component Matrixa
Component
1
EE1 0.693
EE2 0.821
EE3 0.833
EE4 0.689 Extraction Method: Principal Component Analysis. a. 1 component extracted.
7.3.1.3 Analysis of Social Influence scale.
Table 7.10 present the four questionnaire statements to study how an individual
perceives that others believe it is important that he or she use e-government services.
The correlation matrix for the four scale items (SI1 to SI4) indicate that the
correlation coefficients are generally greater than 0.3 as shown in Table 7.11. Also,
both the KMO analysis (0.703, a highly significant result) and the Bartlett’s test (chi-
square = 321.146) is highly significant (p<0.001) as presented in Table 7.12. Finally,
as shown in Table 7.13, the factor loadings of the items are higher than the cut-off
level of (0.50). It is concluded that the four item scale measures of social influence are
unidimensional.
Table19 7.10
Coding of Social Influence Variables
Construct Variable Code Questionnaire Statement
Social Influence (SI)
SI1 People who are important to me think that I should use e-government services.
SI2 People who influence my behaviour think I should use e-government services.
SI3 I would use e-government services if my friends and colleagues used them.
SI4 The government sectors encourage citizen to use e-government services system.
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Table20 7.11
Correlation Matrix for Social Influence Scale
Correlation Matrix
SI1 SI2 SI3 SI4 SI5
Correlation
SI1 1.000 0.742 0.572 0.512 0.632
SI2 0.742 1.000 0.774 0.669 0.590
SI3 0.572 0.774 1.000 0.681 0.573
SI4 0.512 0.669 0.681 1.000 0.739 Determinant = 0.032
Table21 7.12
KMO and Bartlett’s Test for Social Influence Scale
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.703
Bartlett’s Test of Sphericity
Approx. Chi-Square 321.146
df 10
Sig. 0.000
Table22 7.13
Factor Loading for Social Influence
Component Matrixa
Component
1
SI1 0.813
SI2 0.894
SI3 0.852
SI4 0.850 Extraction Method: Principal Component Analysis a. 1 component extracted.
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7.3.1.4 Analysis of Facilitating Condition scale.
Table 7.14 shows the three questionnaire statements which were used to measure how
an individual believes that technical infrastructure, resources, and support exists to
facilitate the use of e-government services. The correlation matrix for the three scale
items (FC1 to FC3) indicated that the correlation coefficients are generally greater
than 0.3 as shown in Table 7.15. Also, both the KMO analysis (0.510, a highly
significant result) and the Bartlett’s test (chi-square = 1305.687) is highly significant
(p<0.001) as presented in Table 7.16. Finally, as shown in Table 7.17, the factor
loadings of the items are higher than the cut-off level. It is concluded that the three
item scale measures of the facilitating condition are unidimensional.
Table23 7.14
Coding of Facilitating Conditions Variables
Construct Variable Code Questionnaire Statement
Facilitating Conditions (FC)
FC1 I have the resources necessary to use e-government services.
FC2 I have the knowledge necessary to use e-government services.
FC3 There is a specific person or group available for assistance with any technical problem I may encounter.
Table24 7.15
Correlation Matrix for Facilitating Condition Scale
Correlation Matrixa
FC1 FC2 FC3 Correlation FC1 1.000 .890 .470
FC2 .890 1.000 .529
FC3 .470 .529 1.000 Determinant = 0.202
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Table25 7.16
KMO and Bartlett’s Test for Facilitating Condition Scale
Table26 7.17
Factor Loading for Facilitating Condition
Component Matrixa
Component
1
FC1 0.964
FC2 0.957
FC3 0.907 Extraction Method: Principal Component Analysis 1 component extracted
7.3.1.5 Analysis of trust scale.
The same process has been repeated for the trust scale. Table 7.18 present the
questionnaire statements which used to study the effect of trust on citizens’ acceptance
and use of e-government services in the KSA. The correlation matrix for the four scale
items of trust (TR1 to TR4) indicated that the correlation coefficients are generally
greater than 0.3 as shown in Table 7.19. Also, both the KMO analysis (0.748, a highly
significant result) and the Bartlett’s test (chi-square = 4163.501) is highly significant
(p<0.001) as presented in Table 7.20. Finally, as shown in Table 7.21, the factor
loadings of the items are higher than the cut-off level. ). It is concluded that the seven
item scale measures of trust are unidimensional.
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.510
Bartlett’s Test of Sphericity
Approx. Chi-Square 305.687 df 3 Sig. 0.000
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Table27 7.18
Coding of Trust Variables
Construct Variable Code Questionnaire Statement
Trust (TR)
TR1 The Internet is trustworthy
TR2 I have confidence in the technology used by government agencies to operate e-government services
TR3 Government agencies can be trusted to carry out online transactions faithfully
TR4 I believe that e-government services are trustworthy.
Table28 7.19
Correlation Matrix for Trust Scale
T1 T2 T3 T4
Correlation
TR1 1.000 0.719 0.477 0.477
TR2 0.719 1.000 0.569 0.691
TR3 0.477 0.569 1.000 0.606
TR4 0.577 0.691 0.606 1.000
Determinant = 0.245
Table29 7.20
KMO and Bartlett’s Test for Trust Scale
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.748
Bartlett’s Test of Sphericity
Approx. Chi-Square 563.501
df 21
Sig. 0.000
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Table30 7.21
Factor Loading for Trust
Component Matrixa
Component
1
TR1 0.765
TR2 0.782
TR3 0.886
TR4 0.845 Extraction Method: Principal Component Analysis a.1 component extracted.
7.3.1.6 Analysis of Website Quality scale.
Table 7.22 provides the five questionnaire statements which used to study the effect
of website quality on actual usage of e-government services. The correlation matrix
for the five scale items of website quality indicated that the correlation coefficients are
generally greater than 0.3 as shown in Table 7.23. Also, both the KMO analysis
(0.696, a highly significant result) and the Bartlett’s test is highly significant
(p<0.001) as presented in Table 7.24. Finally, as shown in Table 7.25, the factor
loadings of the items are higher than the cut-off level. It is concluded that the three
items scale measures the facilitating condition is unidimensional.
Table 317.22
Coding of Website Quality Variables
Construct Variable Code Questionnaire Statement
Website Quality (WQ)
WQ1 Government websites looks secured and safe for carrying out transactions.
WQ2 Government websites looks attractive and uses fonts and colour properly.
WQ3 Government websites looks organized.
WQ4 Government websites are always up and available 24/7.
WQ5 Content of Government websites are useful and updated.
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Table32 7.23
Correlation Matrix for Website Quality Scale
WQ1 WQ2 WQ3 WQ4 WQ5
Correlation
WQ1 1.000 0.406 0.337 0.732 0.662
WQ2 0.406 1.000 0.833 0.537 0.477
WQ3 0.437 0.833 1.000 0.619 0.545
WQ4 0.732 0.537 0.619 1.000 0.803
WQ5 0.662 0.477 0.545 0.803 1.000
Determinant = 0.102
Table33 7.24
KMO and Bartlett’s Test for Website Quality Scale
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.696
Bartlett’s Test of Sphericity
Approx. Chi-Square 291.374
df 11
Sig. 0.000
Table34 7.25
Factor Loading for Website Quality
Component Matrixa
Component
1
WQ1 0.627
WQ2 0.758
WQ3 0.780
WQ4 0.788
WQ5 0.896 Extraction Method: Principal Component Analysis a. 1 component extracted.
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7.3.1.7 Analysis of Behavioural Intention scale.
Table 7.26 provides the three questionnaire statements which used to study the
influence of behavioural intention (BI) on the use of e-government services. The
correlation matrix for the three scale items (BI1 to BI3) indicated that the correlation
coefficients are generally greater than 0.3 as shown in Table 7.27. Also, both the
KMO analysis (0.728, a highly significant result) and the Bartlett’s test (chi-square =
598.483) is highly significant (p<0.001) as presented in Table 7.28. Finally, as shown
in Table 7.29, the factor loadings of the items are higher than the cut-off level. It is
concluded that the three items scale measures the facilitating condition is
unidimensional.
Table35 7.26
Coding of Behavioural Intention Variables
Construct Variable Code Questionnaire Statement
Behavioural Intention (BI)
BI1 I intend to use e-government services in the next 12 months.
BI2 I predict I will use e-government services in the next 12 months.
BI3 I plan to use e-government services in the next 12 months.
Table36 7.27
Correlation Matrix for Behavioural Intention Scale
Correlation Matrixa
BI1 BI2 BI3
Correlation
BI1 1.000 0.777 0.659
BI2 0.777 1.000 0.722
BI3 0.659 0.722 1.000 a Determinant = 0.180
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Table37 7.28
KMO and Bartlett’s test for Behavioural Intention Scale
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.728
Bartlett’s Test of Sphericity
Approx. Chi-Square 598.483
df 3
Sig. 0.000
Table38 7.29
Factor Loading for Behavioural Intention
Component Matrixa
Component
1
BI1 0.901
BI2 0.927
BI3 0.877 Extraction Method: Principal Component Analysis. a. 1 component extracted.
7.3.1.7 Analysis of Use Behaviour of e-government services scale.
Table 7.30 provides the three questionnaire statements which used to study the actual
use of e-government services. The correlation matrix for the three scale items of use
behaviour (USE) of e-government services indicated that the correlation coefficients
are generally greater than 0.3 as shown in Table 7.31. Also, both the KMO analysis
(0.788, a highly significant result) and the Bartlett’s test (chi-square = 582.483) is
highly significant (p<0.001) as presented in Table 7.32. Finally, as shown in Table
7.33, the factor loadings of the items are higher than the cut-off level. It is concluded
that the three items scale measures the use behaviour (USE) of e-government services
is unidimensional.
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Table39 7.30
Coding of Use Behaviour Variables
Construct Variable Code
Questionnaire Statement
Use Behaviour USE
USE1 I really want to use e-government services to perform my government requests.
USE2 I frequently use e-government services.
USE3 I use e-government services on a regular basis.
USE4 Most of my government requests done through e-government services.
Table40 7.31
Correlation Matrix for Use Behaviour Scale
Correlation Matrixa
USE 1 USE 2 USE3
Correlation
USE1 1.000 0.545 0.511
USE 2 0.545 1.000 0.489
USE 3 0.511 0.489 1.000 a Determinant = 0.172
Table41 7.32
KMO and Bartlett’s Test for Use Behaviour Scale
KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.788 Bartlett’s Test of Sphericity Approx. Chi-Square 582.483
df 10 Sig. 0.000
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Table42 7.33
Factor Loading for Use Behaviour
Component Matrixa
Component
1
USE1 0.776
USE2 0.827
USE3 0.807 Extraction Method: Principal Component Analysis. a. 1 component extracted.
7.3.2 Confirmatory Factor Analysis (CFA)
The EFA conducted in the previous section was useful as a preliminary technique, but
it does not provide a complete assessment of construct validity and unidimensionality,
which are important elements in the measurement theory (Hair et al., 2006).
Furthermore, construct validity was also evaluated by using confirmatory factor
analysis (CFA) to assess the multidimensionality and the factorial validity of the
constructs of the theoretical model (Byrne, 2001). CFA is considered an appropriate
approach in studies with pre-validated measurement scales (Bhattacherjee &
Premkumar, 2004), as in this research. Confirmatory factor analysis (CFA) is the
extent to which the hypothesized model ‘ fits’ or adequately describes the data (Byrne,
2001). It is used to study the relationships between a set of observed variables and a
set of continuous latent variables (Baker, 2004). Moreover, CFA is used to determine
the goodness of fit between a model already obtained by another researcher and the
research collected data (Weitzner, Meyers, Steinbruecker, Saleeba, & Sandifer, 1997).
In other words, CFA is a technique commonly used for the analysis of latent
variables, and has been applied to analyse complex IS constructs (Chin & Todd,
1995). In this study, confirmatory factor analysis was conducted to assess and
examine the convergent and discriminant validity. The evaluation of convergent
validity and discriminant validity is a common part of confirmatory factor analysis.
The measurement model was drawn using AMOS 19.0 (Analysis of Moment
Structures). AMOS is the structural equation modelling software (Byrne, 2001) which is
used for CFA.
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7.3.2.1 Assessment of construct validity and unidimensionality.
The main objective of CFA is to assess the construct validity of the proposed
measurement (Hair et al., 2006). Assessing construct validity using the CFA involved
an assessment of the convergent validity and the discriminant validity.
7.3.2.1.1 Convergent validity.
Convergent validity is a function of the association between two different
measurement scales which are supposed to measure the same concept, and is achieved
when multiple indicators operate in a consistent manner (Straub et al., 2004).
Convergent validity is considered a subtype of construct validity, in which an
instrument correlates highly with other scales and constructs that are theoretically
related (Anastasi & Urbina, 1997). Convergent validity is the extent to which items
are thought to reflect one particular construct (Straub et al., 2004). In the confirmatory
factor analysis, convergent validity relies on the average variance extracted (AVE) as
a base. AVE was mainly used to calculate the explanatory power of all variables of
the dimension to the average variations. The higher the AVE, the higher the reliability
and convergent validity of the dimension was. According to Bagozzi and Yi (1988),
AVE should be above at least 0.5. Moreover, an AVE in excess of 0.5 generally
signifies appropriate convergent validity (Fornell & Larcker, 1981). The composite
reliability and the average variance extracted were used to measure the convergent
validity of the constructs. The constructs have convergent validity when the
composite reliability exceeds the criterion of 0.70 (Hair et al., 2006) and the average
variance extracted is above 0.50 (Bagozzi, 1994). It is worthwhile mentioning that
when the CFA is run, the AMOS output does not produce the values for both
measures. It was calculated according to the formula (Hair et al., 2006):
Average variance extracted (AVE) =
Where: n = total number of items; and = standardized factor loadings.
In addition Composite reliability was calculated according to the formula (Hair et al.,
2006):
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Composite reliability =
Where: n = total number of items; = standardized factor loadings; and = error variance term.
Table 7.34 shows that all composite reliabilities exceeded the criterion of 0.70. There
was no overlap between the study measures. In addition, it shows also, that the
average variance extracted (AVE) for each construct exceeds the recommended limit
of 0.50 recommended by Fornell and Larcker (1981). Since the factor loadings and
the reliability of this construct are at acceptable levels, this construct is considered
satisfactory and is thus retained. In summary, all the results support the instrument’s
adequate convergent validity.
Table43 7.34
Convergent Validity for the Constructs
Construct Composite Reliability
Average Variance Extracted
Trust 0.74 0.85
Performance expectancy 0.71 0.69
Effort expectancy 0.79 0.78
Facilitating conditions 0.91 0.91
Social influence 0.84 0.84
Web quality 0.91 0.91
Behavioural intent 0.91 0.90
Use behaviour 0.78 0.76
7.3.2.1.2 Discriminant validity.
Discriminant validity is the extent to which the scales reflect their suggested construct
differently from the relationship with all the other scales in the research model (Straub
et al., 2004). Hersen (2004) defined discriminant validity as an instrument’s ability to
differentiate among groups between which it should theoretically be able to
differentiate. Discriminant validity was tested through inter-factor correlations
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(Anderson & Gerbing, 1988). Discriminant validity is assessed by comparing the
square roots of average variance extracted (AVE) to the inter-factor correlations
between constructs. According to Fornell and Larcker (1981), to test discriminant
validity, the square roots of the AVEs should be higher than the correlations in order
to satisfy discriminant validity requirement. Moreover, Hair et al. (2006) asserted that
if the AVE is higher than the squared inter-scale correlations of the construct, then
discriminant validity is supported. In this study, discriminant validity was assessed by
comparing the absolute value of the correlations between the constructs and the
square root of the average variance extracted by a construct. When the correlations are
lower than the square root of the average variance extracted by a construct, constructs
are said to have discriminant validity (Fornell & Larcker, 1981). As shown in Table
7.35, all square roots of the AVEs (diagonal cells) are higher than the correlations
between constructs and that definitely confirms adequate discriminant validity.
Table44 7.35
Discriminant Validity Results for the Measurement Model
Construct 1 2 3 4 5 6 7 8
1 Trust 0.86
2 Performance expectancy 0.18 0.84
3 Effort expectancy 0.16 0.22 0.89
4 Facilitating conditions 0.14 0.27 0.50 0.95
5 Social influence 0.28 0.31 0.30 0.25 0.92
6 Website quality 0.27 0.33 0.05 0.44 0.06 0.95
7 Behavioural intent 0.04 0.33 0.05 0.30 0.05 0.39 0.95
8 Use behaviour 0.23 0.32 0.01 0.36 0.27 0.43 0.37 0.88 Note. The values of the square root of the average variance extracted are on the diagonal; all other entries are the correlations.
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7.4 Chapter Summary
This chapter presented the process and results of the measurement scale analysis, with
regards to the assessment of scale reliability and validity by employed EFA, and CFA
techniques. First, the assessment of the scale reliability showed that the measurement
scales, which were used to capture the meaning of the model constructs, were reliable,
as indicated by the high values of Cronbach’s alpha for each individual construct.
Following this, the EFA was conducted for all individual constructs to explore the
validity of the whole model. Finally, the CFA technique was used to uncover and
confirm the convergent and discriminant validity.
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Chapter 8: Model Assessment
8.1 Introduction
This chapter presents the process of assessment of the conceptual modified UTAUT
model in Figure 5.1 in Chapter 5. The assessment includes testing of the structural and
measurement models. One of the main aims of the current research is to test
hypotheses related to the proposed UTAUT model, as well as a number of
hypothesized relationships that were previously established in the UTAUT model. In
this study, and in order to reach a baseline model that fits both samples, the two data
sets (citizens and IT staff) were combined in one file and used as the working file.
Also, the two data sets were merged to achieve the minimum acceptable sample size
of 100 which is a basic requirement of Factor Analysis (FA) techniques (Tabachnick
& Fidell, 2007). Section 8.2 provides an overview of Structural Equation Modelling
(SEM), the technique that has been employed in this research to evaluate the
relationships between the model’s constructs. Section 8.3 details the assessment of the
measurement model and the analysis result. Section 8.4 reports the results of the
structural model assessment. Section 8.5 discusses the effect of the model moderators.
Finally, Section 8.6 provides a summary of the chapter.
8.2 SEM overview
The previous chapter presented the statistical analysis and results which indicated that
the research model has demonstrated satisfactory reliability and validity. The next
step is testing the structural model, which includes testing the theoretical hypothesis
and the relationships between latent constructs. The testing of the amended UTAUT
model was done using structural equation modelling (SEM). SEM is a statistical
methodology based on latent variable theory. SEM is not a single technique, but a
family of related procedures, with a number of important characteristics in common
(Kline, 2005). SEM provides a basis for hypothesis testing by estimating path
coefficients of the fundamental links of the linear relationships among observed and
unobserved variables (Byrne, 2001). Gefen, Straub, and Boudreau (2000) highly
recommended the use of SEM in behavioural sciences research and mainly in IT/IS
research. As argued by Kline (2005), SEM is a better choice for explanatory analysis
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of non-experimental data and provides a clear description of the relationships between
variables through a graphic diagram. Blunch (2008) defined SEM as a statistical
technique for testing causal relationships based on non-experimental data. Further,
Bollen (1989) described SEM as a multivariate technique used to test models
proposing causal relationships between their variables; it consists of two primary
components: a measurement model and a structural model. According to Hair et al.
(2006), SEM is used to test theoretical models. A structural equation model normally
consists of two types of models:
1. The measurement model that represents the theory and which specifies how
measured variables come together to represent latent factors. That is, the
model implies that variables represent the factors; and
2. The structural model which represents the theory specifying how constructs
are related to other constructs in the model.
The structural model differs from the measurement model in that the emphasis moves
from the relationships between latent constructs and measured variables to the nature
and magnitude of the relationships between constructs (Hair et al., 2006). In general,
SEM allows researchers to explore the overall structural model at once.
SEM is thus designed to maximize, then test, the degree of consistency between the
theoretical model, and the actual data (Kline, 2005). Byrne (2001) claimed that SEM
has four significant benefits over other multivariate techniques:
SEM takes a confirmatory approach, rather than an exploratory approach, to the data
analysis, although SEM can also address the latter approach. SEM lends itself well to
the analysis of data for the purposes of inferential statistics. On the contrary, most
other multivariate techniques are essentially descriptive in nature (e.g., exploratory
factor analysis), so that hypothesis testing is possible but it is rather difficult to do so.
1. SEM can provide explicit estimates of error variance parameters, but
traditional multivariate techniques are not capable of either assessing or
correcting for measurement error.
2. Data analysis using SEM procedures can incorporate both unobserved (i.e.
latent) and observed variables, but the former data analysis methods are based
on observed measurements only.
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3. SEM methodology has many important features available including modelling
multivariate relationships, or for estimating point and/or interval indirect
effects, whilst there are no widely and easily applied alternative methods for
these kinds of features. 4. SEM takes a confirmatory approach rather than an exploratory approach to the
data analysis.
Furthermore, the basic statistic in SEMs is covariance. Within information systems
research, partial least squares models (PLS) are sometimes also described as SEMs,
but this use of the term is an exception within the wider SEM community (Rouse &
Corbitt, 2008). SEM software includes LISREL, AMOS, EQS, and SEPATH. In this
research, a two-step approach has been followed. First step, the whole measurement
model was assessed to assess its validity and unidimensionality; then the structural
model was assessed to test the relationships between the constructs (Anderson &
Gerbing, 1988). In both steps SEM was employed using the AMOS 19.0 package.
8.3 Measurement Model Assessment
8.3.1 Procedure and assessment criteria.
The measurement model (a CFA model) specifies the relationships that suggest how
measured variables represent a construct that is not measured directly (Hair et al.,
2006). The measurement model was assessed using the goodness-of-fit (GOF) tests.
The basic index of this test is Chi-square (χ2) statistics, degree of freedom (df), and
significance level (p-value). Moreover, Comparative Fit Index (CFI), the Root Mean
Square Error of Approximation (RMSEA), goodness-of-fit index (GFI), Tucker-
Lewis index (TLI), incremental-fit index (IFI), and the relative Chi-square (χ2/df) test
were used to evaluate the measurement model. According to Hair et al. (2006), the
following GOF tests are sufficient to assess the measurement model: Chi-square (χ2),
degree of freedom df , χ2/df, CFI, TLI, IFI, and RMSEA. AMOS presents more than
20 different goodness-of-fit measures and the choice of which to report is a matter of
argument between methodologists. Hair at al. (2006) recommend reporting Chi
squared statistics in addition to another absolute index, such as RMSEA, and an
incremental index, such as CFI. Model fit was assessed by interpreting several fit
indices including the Comparative Fit Index (CFI), the Root Mean Square Error of
Approximation (RMSEA), and the likelihood ratio χ2 test. A model is deemed to fit
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the data well when the CFI value is above 0.95 (Hu & Bentler, 1999). Brown and
Cudeck (1993) suggest that a model with an RMSEA value less than 0.05 has good
fit, one with a value than 0.08 has reasonable fit, and a model with an RMSEA less
than 0.10 has poor fit. A small χ2 value relative to the degrees of freedom (i.e., values
lower than 3) indicates a good model fit (Hu & Bentler, 1998). Moreover, the factor
loading of the measurement items is used to assess the measurement model. The
larger the factor loadings with the corresponding significant t-values, the stronger the
evidence that the measured variables represent the underlying constructs (Bollen,
1989). Hair et al. (2006) recommend that factor loadings should be greater than 0.50.
In addition to evaluating the model as a whole, the significance of the individual
parameters was also assessed (Byrne, 2001). Parameters were evaluated at the 0.05
level. The direction of the standardized path coefficients was checked to see if it was
consistent with expectations. The assessment criteria of the model fit were
summarized in Table 8.1.
Table45 8.1
Measurement Model Assessment Criteria
GOF Test Requirement References
χ2 χ2 < df
Hair et al. (2006) Byrne (2001) Kline (2005) Hu and Bentler (1998)
df > 0
χ2/df < 3
GFI > 0.90
TLI > 0.90
IFI > 0.90
CFI > 0.90
RMSEA < 0.08
Factor loadings > 0.50
8.3.2 Measurement model results.
The results for the measurement model are depicted in Figure 8.1, while the fit indices
are summarized in Table 8.2. The measurement model was drawn using AMOS
Version 19 graphics. In this model, distinguishing between dependent and
independent variables is not necessary at this stage. So, latent variables are shown in
the oval shapes. Two-headed arrows indicate covariance between constructs while
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one-headed connectors indicate a causal path from a construct to an indicator. As
presented in Table 8.2, the model showed an acceptable level of fit (χ2= 579.74, df =
437, χ2/df = 1.33, GFI = 0.91, TLI = 0.92, CFI = 0.94, IFI = 0.93, RMSEA = 0.07).
All the factors had significant loadings greater than 0.50 (p < 0.001) on their
respective constructs. Finally, all of the correlation coefficients between each pair of
the constructs were less than 0.850 (Kline, 2005).
Table46 8.2
The Measurement Model Results
Construct/ Factor Loading Composite Reliability AVE Correlation between
constructs Trust (RT) 0. 74 0.85 TR PE:0.711
TREE:0.751 TRSI:0.707 TRFC:0.625 TRWQ:0.601 TRBI:0.593 TRUSE:0.585 PEEE:0.722 PESI:0.746 PEFC:0.642 PEQW:0.563 PEBI:0.774 PEUSE:0.641 EESI:0.821 EEFC:0.611 EEWQ:0.603 EEBI:0.842 EEUSE:0.658 SIFC:0.745 SIWQ:0.655 SIBI:0.726 SIUSE:0.791 FCWQ:0.788 FCBI:0.823 FCUSE:0.822 WQBI:0.788 WQUSE:0.829 BIUSE:0.808
TR1 0.66
TR2 0.81
TR3 0.77
TR4 0.72 Performance Expectancy (PE) 0.71 0.69
PE1 0.75
PE2 0.73
PE3 0.79
PE4 0.69 Effort Expectancy (EE) 0.79 0.78
EE1 0.88
EE2 0.81
EE3 0.74
EE4 0.68 Social Influence (SI) 0.84 0.84
SI1 0.76
SI2 0.75
SI3 0.68
SI4 0.71 Facilitating Conditions (FC) 0.91 0.91
FC1 0.83
FC2 0.77
FC3 0.78
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Construct/ Factor Loading Composite Reliability AVE Correlation between
constructs Website Quality (WQ) 0.91 0.91
WQ1 0.87
WQ2 0.77
WQ3 0.89
WQ4 0.86
WQ5 0.78 Behavioural Intention (BI) 0.91 0.90
BI1 0.66
BI2 0.76
BI3 0.75 Use Behaviour (USE) 0.78 0.76
USE1 0.76
USE2 0.83
USE3 0.71
USE4 0.82 χ2= 579.74, df = 437, χ2/df = 1.33, GFI =0.91, TLI = 0.92, CFI = 0.94, IFI = 0.93, RMSEA =
0.07
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Figure11 8.1. The measurement model.
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8.4 Structural Model Assessment
8.4.1 Procedure and assessment criteria.
After the assessment of the measurement model was completed successfully, the next
step was to assess the structural model in order to test the hypothesized theoretical
model or the relationships between its constructs. The structural model differs from
the measurement model in that the emphasis moves from the relationships between
constructs and measured variables to the importance and significance of the
relationships between constructs (Hair et al., 2006). The structural model was
designed by replacing all double-headed arrows, representing the correlations between
the constructs, with single-headed (causal) arrows. These causal arrows signified the
hypothesized relationships between the constructs, as presented in the UTAUT
conceptual model. Figure 8.3 shows the full proposed structural model, incorporating
the factor structures and the hypothesized relationships. In general, testing the
hypotheses aims to determine which predictors (independent variables) provide a
meaningful contribution to the explanation of the dependent variables (Hair et al.,
2006). Generally, the model specified trust (TR), performance expectancy (PE), effort
expectancy (EE), social influence (SI), facilitating condition (FC), and web quality as
exogenous (independent) constructs, whereas behavioural intention (BI) and use
behaviour (USE) were specified as endogenous (dependent) constructs, as revealed in
Figure 8.4. The procedure of the assessment of the structure model included an
inspection of model fit indices and the standardized path coefficients, to explore
which hypothesized relationships are supported or not. The criteria for the model fit
indices adopted in this section were similar to those used in the measurement model
assessment in the previous section (see Section 8.3.1). For the hypothesized
relationships to be supported, the standardized path coefficients are required to be
significant at the p < 0.05 level and greater than 0.30 to be considered meaningful
(Byrne, 2001). The results of the structure model assessment are presented in the next
section.
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Figure12 8.4. Structural model
8.4.2 Structural model results.
The fit indices are summarized in Table 8.3 while the proposed structural model is
depicted in Figure 8.4. Overall, the model showed a good level of fit: (χ2 = 615.79, df
= 351, χ2/df = 1.75, GFI = 0.91, TLI = 0.92, CFI = 0.94, IFI = 0.93, RMSEA = 0.07).
According to the findings in Table 8.3, six out of the seven path coefficients
(hypotheses) were statistically significant and were considered meaningful (ranging
from 0.34 to 0.72). The findings reveal that the trust (TR) construct in the
e-government website positively predicted the behavioural intention (BI) construct
(0.51, p < 0.001), thus supporting H1. Second, performance expectancy (PE)
positively predicted behavioural intention (BI) (0.34, p < 0.001); therefore, H2 was
supported. Third, effort expectancy (EE) significantly predicted behavioural intent
(0.39, p < 0.001); therefore, H3 was supported. Fourth, social influence (SI) did not
significantly predict behavioural intent (-0.03, n.s.); therefore, H4 was not supported.
Fifth, website quality (WQ) positively predicted behavioural intention (0.72, < 0.001)
therefore, H5 was supported. Sixth, facilitating conditions (FC) positively predicted
behavioural intent (0 .48, p < 0.001), thus providing support for H6. Lastly,
behavioural intention (BI) positively predicted use behaviour (USE) (0.62, < 0.001).
As a result of the assessment of the proposed structure model, the developed
conceptual model was mostly supported by the data; six out of the seven relationships
were supported. However, for greater improvement and enhancement, the model was
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refined in order to identify the final model that best fitted and explicated the research
data. The assessment and results of the model’s refinement are illustrated in the next
section.
Table47 8.3.
Structural Model Results
Path (Hypothesis) Standardised path coefficient (Beta) t-value Hypothesis testing
result TR BI(H1) 0.51 4.51*** Supported
PE BI (H2) 0.34 4.22*** Supported
EE BI(H3) 0.39 4.57 *** Supported
SIBI (H4) -0.03 0.81n.s. Not supported
WQBI(H5) 0.72 7.03*** Supported
FCUSE(H6) 0.48 3.20*** Supported
BIUSE(H7) 0.62 4.92*** Supported
Note. Model fit indices: χ2 = 615.79, df = 351, χ2/df = 1.75, GFI = 0.91, TLI = 0.92, CFI = 0.94, IFI = 0.93, RMSEA = 0.07 *** p < 0.001; n.s. Not significant.
Figure13 8.5. Initial structural model with standardized path coefficients
***p < 0.001, n.s. = not significant
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8.4.3 Model refinement.
Model refinement technique is commonly technique known as model correction or
model updating. The goal of this method is to improve the initial structural model to
fit the study data and result by adding or deleting subsystem (Minas & Inman, 1990).
According to Hatcher (2002), model refinement involves the assessment of the initial
model and new models. The procedures to construct a new model include adding a
new path to the initial model, removing an existing path, or reversing the direction of
an existing path. This operation is known as hierarchical analysis and it will produce
new models, known as nested models. A nested model is a copy of an initial structural
model, a new path between the model constructs having been added or removed
(Garson, 2006). Based on the previous discussion, two nested models, namely Model
B and Model C, were developed to compare it with the original structural model in
order to find the best and more appropriate final model.
• Model B was the initial structural model, with added direct relationships
from TR, PE, EE, SI, and WQ to USE; also, a direct link form FC to BI
was added as shown in Figure 8.6.
• Model C was the initial structural model, with a non-significant path and
its construct (i.e. SIBI) removed, as shown in Figure 8.6.
8.4.3.1 Model refinement procedure.
In the model refinement procedure, the following steps were applied to achieve the
final research model (Kline, 2005). The Chi-square values (χ2) of the original
structural model (Model A) are compared with the two nested models (Models B &
C). If the Chi-square difference (Δχ2) between the two models is significant, then the
model with the better fit indices becomes the favoured model. If the Chi-square
difference is not significant and both models have a similar fit, then the principle of
parsimony advises that the less complicated model (that is, the model with higher
degree of freedom (df) value) is the preferred model (Kline, 2005).
As shown in Table 8.4, all the Chi-square differences were not significant at p < 0.05,
and that leads one to conclude that none of the models differed extensively. Moreover,
the results show that Model B was not acceptable; it contained negative variances and
many non-significant values of the standardized path coefficient between the models
constructs (Kline, 2005). Accordingly, only Models A and C were compared. The fit
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indices of both models were equivalent, indicating that they had equal explanatory
power. As mentioned above, the principle of parsimony suggests that, when there are
two different models with similar explanatory powers, the simpler one is preferred.
Thus, Model C with a degree of freedom (df) value = 357 became the best option and
was chosen as the final research model.
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Table48 8.4
Comparison between Hierarchical Models Fit Indices
n.s Not significant at p < 0.05 level.
Original structural model: Model A
Fit Indices Model A Model B Model C
χ2 615.79 606.72 612.87
df 351 348 357
Δχ2 ---- 9.07n.s 2.92 n.s
χ2/ df 1.75 1.74 1.72
GFI 0.91 0.91 0.91
TLI 0.92 0.92 0.92
CFI 0.94 0.94 0.94
IFI 0.93 0.93 0.93
RMSEA 0.07 0.07 0.07
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Nested model 1: Model B
Nested model 2: Model C
Figure14 8.6. Hierarchical model options
8.4.3.2 Assessment of the final model.
The final model consists of seven constructs in addition to the three moderators. The
analysis of the final research model (model C) showed an excellent and significant
result in general. The assessment results for the final model are shown in Table 8.5
and Figure 8.7. According to the results, all the standardized path coefficients were
extremely significant, ranging from 0.34 to 0.72. The results show that the WQ
construct had a high, strong, and positive influence on the BI construct (0.72, p <
0.001). Furthermore, the TR (0.51, p < 0.001), PE (0.34, p < 0.001), and EE (0.39, p <
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0.001) constructs were found to positively influence the BI construct. Finally, the BI
construct and FC (0.48, p < 0.001) were found to positively influence the USE
construct (0.62, p > 0.001).
Table49 8.5
Standardized Path Coefficients and t-values of the Final Model
Path (Hypothesis) Standardised path coefficient (Beta) t-value
TR BI(H1) 0.51 4.51***
PE BI (H2) 0.34 4.22***
EE BI(H3) 0.39 4.57 ***
WQBI(H5) 0.72 7.03***
FCUSE(H6) 0.48 3.20***
BIUSE(H7) 0.62 4.92*** ***p < 0.001
Figure15 8.7. Final model with standardized path coefficients
8.5 The Effect of Moderators
This section presents the effect of moderators on the research model. Moderators are
variables that affect the strength or weakness of relationships between independent
and dependent constructs in the model (Serenko, Turel, & Yol, 2006). According to
Hair et al. (2006), the moderating effect is interpreted as an explanation of a
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moderated relationship. The effect of the independent variable on the dependent
variable is assumed to vary as a function of the moderator variable. This means that
the relationship between the independent and dependent variables could be stronger or
weaker because of the effect of the moderators. In this study, the moderators that have
been investigated are gender, age, and Internet experience. These tests were
performed at the end of the modelling process because modelling moderation in
AMOS is not possible. Further, no relationships were expected to completely reverse
due to moderation and it was expected that any robust relationship between the latent
variables would show, even when moderated. As Chin, Marcolin, and Newsted (1996,
p. 30) indicate, testing of moderators with covariance-based techniques such as SEM
is “tedious and technically demanding”. In practice, it is hard to find the moderator
effects even when sophisticated methods are used (McClelland & Judd, 1993; Jaccard,
Wan, & Turrisi, 1990) and when they are found, interpretation is difficult as even the
sign of the regression coefficient of the moderator may not indicate anything
(Mossholder, Kemery & Bedeian, 1990). Therefore, in this study simultaneous group
analyses were conducted to test the moderating effects of gender, age, and Internet
experience. Although these procedures are used to test the invariance of models across
samples, they can also be used to test moderation effects. The following procedure
was applied to examine the effect of moderators in the final model:
1. Prior to conducting the simultaneous group procedures, the fit of the model
was assessed by checking the CFI value within each subgroup (Byrne, 2001).
If the CFI values were close to 0.90, the model was deemed a good fit.
2. Simultaneous group procedures were then conducted to determine whether
gender, age, and experience moderated the model relationships. Then the
change in chi-square between the baseline and subsequent models was
evaluated at 0.05.
In the first procedure, all paths were free to vary. This model served as the baseline
model. In the second procedure, all paths were constrained to be equal. If the change
in chi-square was not statistically significant, the simultaneous group analysis was
stopped. At this point, it was concluded that the demographic variable in question did
not significantly moderate that relationship. If the change in chi-square was
statistically significant, further tests were conducted to determine which paths were
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not invariant across groups and, thus, which paths were moderated by the
demographic variable in question.
8.5.1 Gender impact.
The analysis of whether the influence of trust (TR), performance expectancy (PE),
and effort expectancy (EE) on behaviour intention (BI) is moderated by gender is
performed by testing three moderating hypotheses which are: H1a, H2a, and H3a. The
results for the simultaneous group analysis for males and females are summarized in
Table 8.6, as well as in Figures 8.8 and 8.9. As shown in Table 8.6, the change in chi-
square from the baseline model to the constrained model was statistically significant
(Δχ2 (5) = 134.41, p = 0.001). Therefore, not all paths were invariant across gender
groups.
In this respect, the applicable moderating hypotheses are illustrated as follows:
• H1a: TR-BI to use e-government services is stronger for females than
males.
Further tests revealed that the relationship between trust and behaviour intent varied
significantly across males and females (Δχ2 (1) = 79.50, p = 0.001). In the male
sample, the relationship between trust and behaviour intent was stronger (β = 0.41, p
= 0.001) than it was in the female sample (β = 0.17, p = 0.001). As result of this
finding, the hypothesis of gender effect (H1a) is not supported.
• H2a: PE-BI to use e-government services is stronger for males than
females.
The relationship between performance expectancy (PE) and behaviour intent (BI) also
differed across males and females (Δχ2 (1) = 21.03, p = 0.001). The relationship
between performance expectancy and behaviour intent was stronger in the male
sample (β = 0.30, p = 0.001) than it was in the female sample (β = 0.13, p = 0.001).
Accordingly, it is obvious that the hypothesis of gender effect (H2a) is supported.
• H3a: EE-BI to use e-government services is stronger for females than
males.
The relationship between effort expectancy (EE) and behaviour intent (BI) varied
across males and females (Δχ2 (1) = 15.92, p = 0.01). In the sample of females, the
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relationship was stronger (β = 0.33, p = 0.001) than the sample of males (β = 0.28, p =
0.001). In light of this result, the hypothesis of gender effect (H3a) is supported.
Table 8.6.
Simultaneous Analysis for Gender
Model χ2 df CFI Δχ2 Δdf
Males only 396.44 65 0.96
Females only 544.63 65 0.91 Unconstrained model (baseline) 941.41 130 0.94
Fully constrained model 1075.82 135 0.94 134.41*** 5
TR to BI constrained 1020.91 131 0.90 79.50*** 1
PE to BI constrained 962.44 131 0.90 21.03*** 1
EE to BI constrained 957.33 131 0.91 15.92** 1 ** p < 0.01 *** p < 0.001
Figure16 8.8. Standardized coefficients for the male sample
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Figure17 8.9. Standardized coefficients for the female sample
8.5.2 Age impact.
The sample descriptive for the age variable was divided into two groups: a younger
sample and an older sample. Respondents who were 30 years and younger were
categorized into the younger sample of respondents. Respondents who were 31 years
and older were categorized into the older sample of respondents. The analysis of
whether the influence of trust (TR), performance expectancy (PE), and effort
expectancy (EE) on behaviour intention (BI) and facilitating condition (FC) on use
behaviour (USE) moderated by age is performed by testing four moderating
hypotheses which are: H1b, H2b , H3b and H6b. The results for the simultaneous
group analysis for males and females are summarized in Table 8.6 and Figures 8.10
and 8.11. The results for the simultaneous group analysis for younger and older
respondents are summarized in Table 8.7 and Figures 8.10 and 8.11. As shown in
Table 8.7, the model fit both groups adequately. Further, the change in chi-square
from the baseline model to the constrained model was statistically significant (Δχ2 (5)
= 52.73, p = 0.001). Therefore, not all paths were invariant across age groups.
Accordingly, the relevant moderating hypotheses are illustrated as follows:
• H1b: TR-BI to use e-government services is stronger for younger users
than older users.
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The relationship between trust and behaviour intent varied significantly across age
groups (Δχ2 (1) = 44.27, p = .001). The relationship between trust and behaviour
intent was statistically significant in the younger sample of respondents (β = 0.31, p =
0.001) and was stronger than the older sample of respondents (β = 0.11, p = 0.152). In
summary, this results show that the hypothesis of trust effect (H1b) is supported.
• H2b: PE- BI to use e-government services is stronger for younger users
than older users.
The relationship between performance expectancy and behaviour intent also differed
across age groups (Δχ2 (1) = 46.37, p = 0.01). Although the path between performance
expectancy and behaviour intent was statistically significant in both groups, the
relationship was stronger in the sample of younger respondents (β = 0.54, p = 0.001)
than it was in the sample of older respondents (β = 0.29, p = 0.016). To sum up, the
hypothesized moderating effect of trust (H2b) is supported.
• H3b: EE-BI to use e-government services is stronger for younger users
than older users.
The relationship between effort expectancy and behaviour intent also was statistically
significant and differed across age groups (Δχ2 (1) = 63.25, p = 0.01). The relationship
was stronger in the sample of younger respondents (β = 0.33, p = 0.001) than it was in
the sample of older respondents (β = 0.13, p = 0.001). As result of this finding, the
hypothesis of trust effect (H3b) is supported.
• H6b: FC-USE to use e-government services is stronger for younger users
than older users.
The relationship between facilitating conditions and use behaviour differed across age
groups (Δχ2 (1) = 55.29, p = 0.01). The relationship between facilitating conditions
and use behaviour was stronger in the sample of younger respondents (β = 0.34, p =
0.001) than it was in the older sample of respondents (β = 0.22, p = 0.001). Therefore,
these results show that the moderating hypothesis of trust effect (H6b) is supported.
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Table50 8.7.
Simultaneous Analysis for Age
Model χ2 df CFI Δχ2 Δdf 52.73 623.34 64 0.92 52.73 557.55 63 0.94 52.73 1180.05 128 0.94 52.73 1232.78 133 0.94 52.73*** 5 52.73 1135.78 129 0.93 44.27*** 1 52.73 1133.68 129 0.93 46.37** 1 52.73 1116.80 130 0.96 63.25** 1 52.73 1124.76 131 0.93 55.29** 1 ** p < .01. *** p < .001.
Figure18 8.10. Standardized coefficients for younger respondents.
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Figure19 8.11. Standardized coefficients for older respondents.
8.5.3 Internet experience impact.
An experience composite was created by summing up the responses to the four
Internet and computer use and knowledge items. Thereafter, respondents were
categorized into two groups: inexperienced and experienced groups via the experience
composite median. Therefore, respondents who scored 14 and below were categorized
into the inexperienced group. Respondents who scored 15 and above were categorized
into the experienced group. The results for the simultaneous group analysis for
inexperienced and experienced respondents are summarized in Table 8.8 and depicted
in Figures 8.12 and 8.13. Initial test findings, as shown in Table 8.8, reveal that the
final structural model fit both samples effectively. Further, the change in chi-square
from the baseline model to the constrained model was statistically significant (Δχ2 (5)
= 120.97, p = 0.001). Therefore, not all paths were invariant across experience groups.
Accordingly, the relevant moderating hypotheses are presented as follows:
• H1c: TR-BI to use e-government services is stronger for experienced users
than inexperienced users.
The relationship between trust and behaviour intent varied significantly across
experience groups (Δχ2 (1) = 76.24, p = 0.001). The relationship between trust and
behaviour intent was statistically significant and stronger in the sample of experienced
respondents (β = 0.36, p = 0.001) than it was in the inexperienced sample of
respondents (β = 0.17, p = 0.001). As result of this finding, the moderating hypothesis
of the experience effect (H1c) is supported.
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• H2c: PE- BI to use e-government services is stronger for experienced users
than inexperienced users.
The relationship between performance expectancy and behaviour intent also differed
across experience groups (Δχ2 (1) = 89.26, p = 0.001). The relationship between
performance expectancy and behaviour intent was statistically significant and stronger
in the sample of experienced respondents (β = 0.30, p = 0.001) than it was in the
inexperienced sample of respondents (β = 0.12, p = 0.489). In summary, these results
show that the moderating hypothesis of experience effect (H2c) is supported
• H3c: EE-BI to use e-government services is stronger for experienced users
than inexperienced users.
The relationship between effort expectancy and behaviour intent also differed across
experience groups (Δχ2 (1) = 55.94, p = 0.01). In the sample of experienced
respondents, the relationship was statistically significant (β = 0.56, p = 0.001) and
stronger than it was in the sample of inexperienced respondents (β = 0.27, p = 0.001).
As a result, this finding supports the moderating hypothesis of experience effect
(H3c).
• H6c: FC-USE to use e-government services is stronger for experienced
users than inexperienced users.
The relationship between facilitating conditions and use behaviour differed across
experienced groups (Δχ2 (1) = 65.68, p = 0.01). The relationship between facilitating
conditions and use behaviour was statistically significant in the sample of experienced
respondents (β = 0.46, p = 0.001) and stronger than it was in the inexperienced sample
of respondents (β = 0.30, p = 0.001). Therefore, these results supported the
moderating hypothesis of experience effect (H6c).
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Table51 8.8.
Simultaneous Analysis for Internet Experience
Model χ2 df CFI Δχ2 Δdf
Inexperienced only 564.37 65 0.94
Experienced only 557.96 65 0.93
Unconstrained model (baseline) 1142.45 130 0.94
Fully constrained model 1263.42 135 0.92 120.97*** 5
TR to BI constrained 1187.18 131 0.93 76.24*** 1
PE to BI constrained 1174.71 131 0.94 89.26*** 1
EE to BI constrained 1207.48 131 0.92 55.94** 1
FC-USE constrained 1197.74 131 0.94 65.68** 1 ** p < 0.01. *** p < 0.001
Figure20 8.12. Standardized coefficients for the experienced respondents.
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Figure21 8.13 Standardized coefficients for the inexperienced respondents.
8.6 Chapter Summary
This chapter presented the analysis procedures and results from the research concept
UTAUT model developed in Chapter 4. The chapter began with an overview of the
Structural Equation Modeling (SEM) technique which was utilized to assess and
refine the theoretically amended model. The analysis procedures comprised an
assessment of the two main SEM components, the measurement model and the
structural model. The SEM method was used to test the overall proposed model; it
was then refined to produce the final model. The hypotheses tests were accomplished
through a series analysis of the survey data. This chapter also investigated and
reported the effect of moderators on the UTAUT model. Table 8.8 summarizes the
hypotheses that were examined in the SEM analysis (as described in Section 8.4). The
analyses result presented stronger statistical evidence that citizens’ behavioural
intention (BI) and use behaviour (USE) of e-government services was positively
influenced by trust (TR), website quality (WQ), performance expectancy (PE), effort
expectancy (EE), and facilitating conditions (FC). Also, it discovered that social
influence (SI) did not significantly affected citizens’ behavioural intention (BI) and
use behaviour (USE) of e-government services.
In general, the results of the SEM analysis along with EFA and CFA analysis have
provided satisfactory answers to the UTAUT research questions. Moreover, the
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findings of the quantitative study were validated through the focus group analysis
presented in next chapter.
Table52 8.8.
Summary of the Hypotheses Analysis
Affecting Construct
Affected Construct Hypothesis
Hypothesis testing result
Trust (TR)
Behavioural Intention (BI)
TR BI (H1) Supported Performance Expectancy (PE) PE BI (H2) Supported
Effort Expectancy (EE) EE BI (H3) Supported
Social Influence (SI) SIBI (H4) Not supported
Website Quality (WQ) WQBI (H5) Supported
Facilitating Conditions (FC) Use Behaviour (USE)
FCUSE (H6) Supported
Behavioural Intention (BI) BIUSE (H7) Supported
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Chapter 9: Qualitative Data Analysis
9.1 Introduction
In addition to the quantitative data collection and statistical analysis, supplementary
qualitative data were obtained from two sources, namely, open-ended questions and
focus groups. The open-ended questions aim to identify and discover any other factors
affecting the acceptance and use of e-government services which have not been
covered by the UTAUT model. It allows respondents to express their views, opinions,
and make suggestions (Creswell, 2003). Focus groups were conducted as well to
confirm and validate the findings of the quantitative analysis of the UTAUT model
presented in Chapter 8. Moreover, this chapter discusses and explores the obstacles to
the adoption of e-government as derived from surveys conducted with citizens and IT
employees in government sectors. This step will discover the obstacle of
e-government services from the perspectives of citizens and government employees in
order to present a comprehensive view and discover the common obstacles which
need to be treated carefully. The chapter begins in Section 9.2 with the analysis of part
four of the study questionnaire. Section 9.3 discusses and analyses the open-ended
questions in the study questionnaire. Section 9.4 analyses and discusses the focus
groups. Finally, the chapter is summarized in Section 9.5.
9.2 Part Four of the Study Questionnaire: Obstacles of E-government
Services
Part four of the research’s questionnaire is concerned with the barriers to
e-government services adoption in the KSA. There are many organizational, technical,
social, and financial barriers facing e-government services adoption and diffusion in
the KSA. Berge, Muilenburg, & Haneghan (2002) emphasized that the diffusion of
technology into society and citizens is not without obstacles and barriers. However,
government sectors face many challenges derived from the higher expectations of
citizens, who require higher levels of service from the public sector than from the
private sector (Chavez, 2003). The researcher identified eleven barriers based on a
review of the literature of e-government researches such as: Al-Shihi (2005);
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Altameem, (2007); Al-Solbi, & Al-Harbi, (2008) and Al-Shehry (2008).
Consequently, participants were asked to identify the level of each barrier according
to the following selection: (0) not a barrier; (1) important barrier; (2) very important
barrier. To prioritize the degree of importance of the selected barriers a “very” as a
term has been used to differentiate between barriers while all of them are important
barriers. So, the e-government services providers have to address and deal with very
important barriers before important barriers. In other words, this style of priority order
will help government sectors to improve and accelerate e-government services system
adoption. These challenges and barriers are listed in Table 9.1 and explained in the
following sections, based on the survey questionnaire groups (citizens and IT
employees). The aim in selecting these two groups is to identify the common barriers
from the perspectives of citizens and services providers in order to create a guideline
of the most common important barriers between these stakeholders. This guideline
will help service providers to prioritize and address those barriers to speed up and
increase the adoption level of e-government services in the KSA. Moreover, this
guideline could be adopted by the private sector as well to improve provision of their
businesses and e-commerce services to citizens.
Table53 9.1.
Barriers to E-government Services Adoption
No. Barriers
1 IT Infrastructure weakness of the government public sector
2 Lack of knowledge and ability to use computers and technology efficiently
3 Lack of knowledge about e-government services
4 Lack of security and privacy of information on government websites
5 Lack of user trust and confidence to use e-government services
6 Lack of policy and regulation for e-usage in the KSA
7 Lack of partnership and collaboration between government sectors
8 Lack of technical support from government websites support teams
9 Government employees resistance to changing to e-ways
10 Shortage of financial resources in government sectors
11 Availability and reliability of Internet connection
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9.2.1 Perception of citizens towards obstacles of e-government services.
As shown in Table 9.2, all eleven barriers were selected as an ‘important’ or ‘very
important’ barrier; none of them was selected as ‘not a barrier’. This outcome
emphasises the importance and the veracity of the barriers that were identified in this
question.
Table54 9.2.
Analysis of E-government Services Barriers from Citizens’ Perspectives
No. Barriers Important barrier Very important
barrier Frequency Percent Frequency Percent
1 IT infrastructure weakness of the government public sector 438 53.5 380 46.5
2 Lack of knowledge and ability to use computers and technology efficiently 419 51.2 399 48.8
3 Lack of knowledge about e-government services 274 33.5 544 66.5
4 Lack of security and privacy of information on government websites 437 53.4 381 46.6
5 Lack of user trust and confidence to use e-government services 401 49.1 417 50.9
6 Lack of policy and regulation for e-usage in the KSA 405 49.5 413 50.5
7 Lack of partnership and collaboration between government sectors 338 41.3 480 58.7
8 Lack of technical support from government websites’ support teams 264 32.3 554 67.7
9 Government employees resistance to change to e-ways 346 42.3 472 57.7
10 Shortage of financial resources in government sectors 403 49.3 415 50.7
11 Availability and reliability of Internet connection 268 32.8 550 67.2
9.2.1.1 Barriers perceived as being ‘important’.
Table 9.3 shows that among the top three barriers those citizens perceived as being
‘important’, IT infrastructure weakness of the government public sector got the
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highest percentage at 53.5%. Moon (2002) confirmed that the lack of technical ability,
personnel, and financial capacities are seen as significant obstacles to the
development of e-government services in many countries. Lack of security and
privacy of information on government websites come in as the second barrier at
53.4%. Hwang and Syamsuddin (2008) emphasized that lack of security is responsible
for unsuccessful e-government services in many countries. Therefore, the security has
been acknowledged as one of key factors for achieving a high level of e-government
services adoption. A lack of knowledge and ability to use computers and technology
efficiently was the view of 51.2% of respondents. Lack of Internet and computer
experience is an important barrier that is relevant to the Saudi Arabia context
(AlAwadhi, & Morris, 2008).
Table55 9.3.
Important Barriers from Citizens’ Perspective
Rank Barriers Important barrier
Frequency Percent
1 IT infrastructure weakness of the government public sector 438 53.5
2 Lack of security and privacy of information on government websites 437 53.4
3 Lack of knowledge and ability to use computers and technology efficiently 419 51.2
4 Lack of policy and regulation for e-usage in the KSA 405 49.5
5 Shortage of financial resources in government sectors 403 49.3
6 Lack of users’ trust and confidence to use e-government services 401 49.1
7 Government employees resistance to change to e-ways 346 42.3
8 Lack of partnership and collaboration between government sectors 338 41.3
9 Lack of knowledge about e-government services 274 33.5
10 Availability and reliability of Internet connection 268 32.8
11 Lack of technical support from government websites support teams 264 32.3
9.2.1.2 Barriers perceived as being ‘very important’.
From the ‘very important’ angle, Table 9.4 illustrates that the ‘lack of technical
support from government websites support teams’ got the highest percentage with
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67.7%, followed by the ‘availability and reliability of Internet connection’ with
67.2%. In addition, Feng (2003) mentioned that the lack of Internet access among the
population was considered the most important barrier to e-government development.
In fact, the poor quality of Internet services must be solved; otherwise, citizens will be
unwilling to use any e-government services and it will be even more difficult to re-
establish their trust in e-services (Al-Shehry, 2008). Lack of knowledge about
e-government services received 66.5%. This indicates the need for marketing and
promotion as significant factors of successful of e-government systems. For any new
technology, there are many steps to convince and encourage people to use it and adapt
to it; promotion and marketing are main tools to accomplish this task.
Table56 9.4
‘Very Important’ Barriers from Citizens’ Perspectives
Rank Barriers Very important barrier
Frequency Percent
1 Lack of technical support from government websites support teams 554 67.7
2 Availability and reliability of Internet connection 550 67.2
3 Lack of knowledge about e-government services 544 66.5
4 Lack of partnership and collaboration between government sectors 480 58.7
5 Government employees’ resistance to change to e-ways 472 57.7
6 Lack of user trust and confidence to use e-government services 417 50.9
7 Shortage of financial resources in government sectors 415 50.7
8 Lack of policy and regulation for e-usage in the KSA 413 50.5
9 Lack of knowledge and ability to use computers and technology efficiently 399 48.8
10 Lack of security and privacy of information for government websites 381 46.6
11 IT infrastructure weakness of the government public sector 380 46.5
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9.2.2 Perception of IT employees towards obstacles of e-government
services.
Table 9.5 summarizes the barriers from the analysis of IT employees’ perspective. To
limit the length of the discussion, only three barriers from each view will be illustrated
in the following subsections.
Table57 9.5.
Analysis of E-government Services Barriers from IT Employees’ Perspectives
No. Barriers Important Barrier Very Important
Barrier Frequenc
y Percent Frequency Percent
1 IT infrastructure weakness of the government public sector 12 20.0 48 80.0
2 Lack of knowledge and ability to use computers and technology efficiently 41 68.3 19 31.7
3 Lack of knowledge about e-government services 11 18.3 49 81.7
4 Lack of security and privacy of information on government websites 39 65.0 21 35.0
5 Lack of user trust and confidence to use e-government services 26 43.3 34 56.7
6 Lack of policy and regulation for e-usage in the KSA 38 63.3 22 36.7
7 Lack of partnership and collaboration between government sectors 36 60.0 24 40.0
8 Lack of technical support from government websites support teams 4 6.7 56 93.3
9 Government employees’ resistance to change to e-ways 33 55.0 27 45.0
10 Shortage of financial resources in government sectors 18 30.0 42 70.0
11 Availability and reliability of Internet connection 15 25.0 45 75.0
9.2.2.1 Barriers perceived as ‘important’.
Of the barriers that IT employees perceived as ‘important’, it is clear from Table 9.6
that ‘lack of knowledge and ability to use computers and technology efficiently’
ranked as the first barrier in the important barriers list with (68.3%). The ability to use
computers and the Internet has become a critical success factor in e-government
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projects, and the lack of such skills may lead to marginalization or even social
exclusion (UNPA & ASPA, 2001). Lack of security and privacy of information on
government websites came in as the second barrier with 65.0%. Several researchers
such as Colesca and Dobrica (2008) and Norris (2007) confirmed that privacy and
confidentiality remain as critical obstacles towards the realization of e-government.
Citizens are deeply concerned with the privacy of their information and the
confidentiality of the personal data they are providing as part of obtaining government
services. Thus, they pointed out that privacy and confidentiality must remain priorities
when establishing and maintaining websites in order to ensure the secure collection of
data. In fact, security, privacy, and confidentiality are significant and essential issues
for all citizens and governments worldwide. Citizens want to ensure that their
information and all other data are safe when they are using e-services. Governments
should provide secure and appropriate access to their online services in order to gain
citizen trust and use of e-government services. Practically, more awareness seminars
and brochures about using the Internet and security principles for its use are would be
beneficial and an important issue in accepting the e-government system.
E-government systems are new innovations in many countries around the world and
to use this technology well requires new policies and a regulation framework to
protect both providers and users of e-services (Ndou, 2004). These laws and
regulation should cover all e-applications, such as e-payments, e-mail usage,
copyright rules, e-crimes, e-business, e-commerce, and many others (Ndou, 2004). In
the case of the KSA, the Saudi government has issued many new government
regulations and laws, such as e-transaction laws, information criminal laws, a shift to
electronic methods decision, and many other laws . These laws and regulations play
an important function in promoting effective communication between citizens,
business, and government to accelerate the adoption of e-government services on all
levels. The existence of these laws and regulations is a positive and welcome step in
the e-government adoption process, but information about these legal changes and
updates need to be promoted and used in the community.
Table58 9.6.
Important Barriers from IT Employees’ Perspective
Rank Barriers Important Barrier
Frequency Percent
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Rank Barriers Important Barrier
Frequency Percent
1 Lack of knowledge and ability to use computers and technology efficiently 41 68.3
2 Lack of security and privacy of information on government websites
39 65.0
3 Lack of policy and regulation for e-usage in the KSA 38 63.3
4 Lack of partnership and collaboration between government sectors
36 60.0
5 Government employees’ resistance to change to e-ways 33 55.0
6 Lack of user trust and confidence to use e-government services 26 43.3
7 Shortage of financial resources in government sectors 18 30.0
8 Availability and reliability of Internet connection 15 25.0
9 IT infrastructure weakness of the government public sector 12 20.0
10 Lack of knowledge about e-government services 11 18.3
11 Lack of technical support from government websites support teams 4 6.7
9.2.2.2 Barriers perceived as being ‘very important’.
Table 9.7 reveals the barriers perceived as ‘very important’ and the highest rated here
was the lack of technical support from government websites support teams which got
the highest percentage at 93.35. Fast and accurate technical support is an essential part
of e-government systems. Citizens are easily deterred by technical failures, so it is
important to have a professional team dedicated to responding to customers’ needs for
help as soon as possible. Citizens require high-quality technical support, and need to
learn how to use e-services and become familiar with them. Ralph (1991, p. 72)
defined technical support as “knowledge people assisting the users of computer
hardware and software products”, which can include help desks, information centre
support, online support, telephone response systems, e-mail response systems, and
other facilities. Technical support is one of the significant factors which directly affect
user acceptance, use, and satisfaction of technology (Hofmann, 2002; Mirani & King,
1994; Williams 2002). The second barrier was a lack of knowledge about
e-government services (at 81.7%). Indeed, promotion is one of the most significant
factors of successful e-government systems. For any new technology, promotion and
marketing are the main tools used to convince and encourage people to use it and to
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adapt to its use. The survey results indicate that one of the significant barriers to the
adoption of e-government in Saudi society is the lack of programs to promote the e-
government services benefits and advantages. The third barrier was IT infrastructure
weakness in the government public sector (at 80.0%). ICT infrastructure is an
essential part of e-government implementation and diffusion. It enables government
agencies to cooperate, interact, and share work in effective and professional manner.
ICT infrastructure, particularly in e-government adoption and diffusion processing, is
an important challenge that must be carefully handled at the government and private
level. The importance of this factor was noted by several other researchers who also
emphasised its importance.
Table59 9.7.
‘Very Important’ Barriers from IT employees’ perspective
Rank Barriers Very important
barrier Frequency Percent
1 Lack of technical support from government websites support teams 56 93.3
2 Lack of knowledge about e-government services 49 81.7
3 IT infrastructure weakness of the government public sector 48 80.0
4 Availability and reliability of Internet connection 45 75.0
5 Shortage of financial resources in government sectors 42 70.0
6 Government employees’ resistance to change to e-ways 27 45.0
7 Lack of user trust and confidence to use e-government services 34 56.7
8 Lack of partnership and collaboration between government sectors 24 40.0
9 Lack of policy and regulation for e-usage in the KSA 22 36.7
10 Lack of security and privacy of information on government websites 21 35.0
11 Lack of knowledge and ability to use computers and technology efficiently 19 31.7
9.2.3 Comparison of obstacles.
The aim of this section is to compare between the views point of Saudi citizens and IT
employees about e-government services barriers. It is clear from the previous sections
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that they are many common barriers between both groups. First, both side nominated
the lack of technical support from government websites support as very important
barrier and ranked that as number one in very important barriers list. This agreement
on both sides indicates that this barrier is critical and essential to success of
e-government services adoption in the KSA. Second, both groups agreed that the lack
of knowledge about the e-government services was considered barrier number two in
the very important barriers list. Finally, the availability and reliability of Internet
connection was selected from both teams as a very important barrier to e-government
services and ranked third in the common list of barriers. Table 9.8 provides a
summary of the common barriers between the two groups and rank them according to
their percentage.
Table60 9.8.
Common and Distinct Barriers between the Two Groups
Rank Barriers Percent
Citizens IT employees
1 Lack of technical support from government websites support teams 67.7 93.3
2 Lack of knowledge about the e-government services 66.5 81.7
3 Availability and reliability of Internet connection 67.2 75.0
To conclude, the objective of this question was to identify the common barriers that
affect the adoption of e-government services from the point of view of citizens and
services providers. So, the focused and concentrated study of these barriers could help
to increase the level of the adoption of e-government services.
9.3 Analysis of Open-ended Questions
Part five of the research survey includes several open-ended questions that were
designed to collect additional information about the intention to use e-services among
respondents and to explore their desire to adopt e-government services, as shown in
Table 9.9.
Table61 9.9 Yes/No Questions Analysis Result
No. Question Yes
Percent No
Percent
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1 Have you ever heard about e-government services or have you used it before?
55.4 44.6
2 Do you prefer to do your government transaction electronically via e-government services? Why?
98.6 1.4
3 Do you think that e-government services can increase the transparency of government procedures?
91.4 8.6
4 Do you think that e-government services are going to reduce corruption in government sectors?
95.7 4.3
5 What other services do you think it should be available online or any suggestion would you like to add it here?
6 Is there any suggestion you would like to add here?
9.3.1 Interpretation of Question 1.
First, both groups of participants (citizens and IT employees) were asked to mark
‘Yes’ or ‘No’ in response to the first question: ‘Have you ever heard about
e-government services or have you used it before?’ Table 9.9 reveals that more than
55% of participants had heard of or used e-government services, while about 44.6%
had not. The percentage of those not using e-government services is considered a high
percentage as it represents almost the half of the research sample. Some participants
cited the lack of programs promoting e-government benefits and advantages as one of
the most important reasons behind the delay of e-government services adoption and
diffusion; it is certainly one of the important barriers to the adoption of e-government
in Saudi society. Therefore, an effective action plan needs to be established to educate
citizens about the benefits of e-government, as suggested by Damodaran, Nicholls,
Henney, and Land (2005). This also suggests that the Yesser program and all
government agencies might benefit from the execution of a campaign to raise and
promote awareness of e-government and other new e-services, along with their
benefits and advantages. Cross-media advertisements might include newspapers,
brochures, TV, messages on public transport and in subway systems, banners in
public places, road shows, and seminars would also increase the number of
e-government users. This will also increase general awareness, acceptance, and usage
of e-government services among the public.
9.3.2 Interpretation of Question 2.
Second, both groups of participants (citizens and IT employees) were asked to mark
‘Yes’ or ‘No’ in response to the first question: ‘Do you prefer to do your government
transactions electronically? Why?’ As shown in Table 9.9, a very high percentage of
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respondents (98.6%) prefer to use e-government services, while a very small
percentage (1.4%) do not want to use e-government services. Cook, LaVigne, Pagano,
Dawes, and Pardo (2002) defined e-services as delivery of government information
electronically to all customers. This high percentage reflects the willingness of
participants to accept technology and use e-government services in particular instead
of traditional ways. Citizens gave many reasons for their selection, including saving
time, saving money, increased value of products and services, improving equity, and
providing higher valued and faster services. Chavez (2003) argued that staying at
home and conducting all your government transactions using e-government services
will be greatly valued by any citizen who normally has to wait in a long line or waste
time looking for a car park.
9.3.3 Interpretation of Question 3.
Subsequently, respondents were asked to mark ‘Yes’ or ‘No’ in response to the
second question: ‘Do you think that e-government services can increase the
transparency of government procedure? Why?’ Most participants (91.4%) agreed that
e-services can increase the transparency of government procedure, while only 8.6%
did not hold this viewpoint. From the perspective of transparency, the electronic
delivery of government services will increase transparency of the government itself by
offering citizens better access to government information and sources, and provide
equal opportunities to all citizens (IDABC eGovernment Observatory, 2005).
9.3.4 Interpretation of Question 4.
The fourth question was about corruption and participants were asked to answer to
this question: ‘Do you think that e-government services are going to reduce
corruption? How?’ Also, a high percentage of respondents (95.7%) believe that
e-government services are going to reduce corruption while 4.3% disagreed.
Bhatnagar (2002) believed that services should be delivered to citizens electronically
for the express purpose of reducing corruption, strengthening accountability, reducing
time and cost, and increasing transparency. It is worth noting that, in Arab countries,
there is a special form of corruption called ‘wasta’. According to Mohamed and
Hamdy (2008), wasta refers to using one’s connections and relationships with those in
positions of power to get things done without the normal protocol considerations or
regulations, and sometimes against the rules. Historically, wasta has played a critical
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role in recruitment, promotion, and obtaining services in Arab countries. The majority
of respondents expected and wished that e-government services would restrict the
power of connections and give all citizens an equal chance when ordering or
conducting any government services, business, or jobs. However, in order to
minimize, explore, and end corruption in the KSA, a new government organization,
known as the Anti-Corruption Commission, was formed in 2011, by order of the King
of Saudi Arabia. Its mission is to fight crime relating to the duties entailed in the
public sector, crimes that include bribery, embezzlement, getting personal benefit
from government jobs, and misusing authority and power.
9.3.5 Interpretation of Question 5.
The fifth question was about new services that have not been available online but
participants need to be available online. All participants were asked to list other
e-services they would like to see online and the following is a summary list of their
responses:
1. Online driving license renewal system;
2. Car registration renewal system;
3. Online passport renewal system;
4. Online birth certificate issued system;
5. Online system to register the incidence of marriage or divorce;
6. Applying online for all government jobs; and
7. Applying online for all military jobs.
9.3.6 Interpretation of Question 6.
A number of suggestions were made by participants to improve and enhance
e-government services systems. Furthermore, some of those suggestions aim to
develop the communication systems between government sectors and citizens. A
range of suggestions made in answer to this question are listed below:
• A strong and modern ICT infrastructure in all Saudi government organizations
and agencies should be implemented and ready to provide high quality
e-services;
• All government sectors should link together using high speed, secure
connections to help all citizens to use e-government systems from anywhere in
the KSA;
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• Awareness campaigns are required to raise and promote the e-government
benefits and advantages;
• Training needs to be provided for government employees to increase their
understanding of and skills with e-government systems;
• There should be professionally built and updated government websites that
provide a high level of e-services;
• Security and privacy of all governments’ systems and data should be enhanced
to protect citizen’s information and rights;
• Internet service should be provided in public places, at competitive prices;
• Government sectors that provide successful e-government services should be
publicly acknowledged and rewarded monthly.
• Internet services should be provided for free in all ministries, government
organizations, and airports to allow all citizens to access government websites
form their location readily and easily.
• A high level of collaboration between all governments sectors should be
established to aid with the success of the e-government service systems; and
• Effective and fast online support systems should be available with all
government websites to ensure the quality of services provided.
9.4 Focus Groups Analysis
As mentioned in Chapter 4, two focus groups were conducted to confirm the results of
the quantitative analysis presented in Chapter 8. The analysis of the focus groups is
driven by the purpose of the study and concentrates on the constructs of the UTAUT
models as the main questions. The first focus group (Group A) consisted of five IT
staff members representing the government side, labelled A1 to A5. Table 9.10 shows
the demographic information for each participant. The second focus group (Group B)
consisted of five Saudi citizens who had a good level of Internet experience; they are
labelled B1 to B5. Table 9.11 presents the demographic information about all the
members of this group.
Table62 9.10.
Demographic Information for Group A
Group A
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Participant Age Education Level Position Internet Experience
A1 48 Ph.D. General Manager 15 years
A2 36 MBA General Manager 12 years
A3 34 BSc Senior Engineer 10 years
A4 33 BSc Senior Programmer 10 years
A5 32 BSc Senior system Analysis 10 years
Table63 9.11
Demographic information of Group B
Group B Participan
t Age Education Level Position Internet Experience
B1 34 BSc Government employee 7 years
B2 30 BSc Government employee 5 years
B3 28 Diploma Private sector 4 years
B4 22 University Student 5 years
B5 18 High School Student 3 years
9.4.1 Analysis of Group A’s responses.
The discussion with the group members concentrated on the main purpose of the
research and the key constructs of the UTAUT model. The research starts with a brief
introduction about the research topic and aims. Also, the proposed UTAUT model is
presented and explained in detail. The analysis of the main points is discussed below.
9.4.1.1 Performance expectancy (PE).
In this study, the performance expectancy is used as the degree to which customers or
users believe that using the e-government system will help them achieve gains in job
performance (Venkatesh et al., 2003). The following arguments were the result of the
debate among the participants.
All participants agreed that using e-services will allow all customers to accomplish
their needs from the public sector faster and more efficiently than in the traditional
way. Participant A1 commented that, “E-government systems enable any user to
access government services and information 24 hours/day, 7 days/week without any
need to visit a physical location”.
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All participants agreed that e-government systems will give all citizens an equal
chance to assert their government claims and accomplish their business with
government sectors. Participants A1 and A2 both claimed the new services will end
any possibility of corruption or misuse of authority. Participant A2 said,
“E-government systems deal with all citizens at the same level and guarantees them
their full rights”.
All participants strongly agreed that using an e-government system would save both
citizens’ time and effort, which are normally wasted in visits to government sectors.
Participant A3 said, “Some citizens travelled from their city to another and paid a lot
of money just to apply for a job or follow up with a business, but by using e-services,
they will be able to do all of that from their homes, which will make communication
with the government easier”.
All participants strongly agreed that e-government systems will improve the quality of
government services and increase the productivity of the employees. Participants A4
and A5 emphasised that the use of e-systems will enable managers to track their
employees and their efficiency and that of course will reflect on the quality of services
and the completion speed of daily tasks.
Based on the previous discussion, the evidence indicates that performance expectancy
positively affects the behaviour intention and use of e-government services.
9.4.1.2 Effort expectancy (EE).
According to Venkatesh et al. (2003), the effort expectancy variable is defined as the
degree of ease that is related to the use of a specific system; in this research, it was
defined as the ease of use of e-government systems. The following viewpoints were
drawn from the respondents’ discussion. Three of the participants (A3, A4, and A5)
expected that e-government systems would be easy to learn; they asserted that anyone
with a basic understanding of the Internet and computers can easily use e-government
systems. In contrast, A1 and A2 felt this would not be the case for all citizens,
especially the elderly. All participants agreed that by having a good Internet
experience, users will find it easy to become skilful at using e-government services.
All participants agreed that users who are able to use e-government services
successfully will find it easy to take advantage of the available services.
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Based on the analysis of this focus group, the evidence indicates that effort
expectancy (EE) positively affects the behaviour intention (BI) and use of
e-government services.
9.4.1.3 Social influence (SI).
The participants investigated the importance of others’ opinions on whether one
should use e-government services. The following statements reflect their observations.
All participants disagreed with the argument that the decision to use of e-government
services will depend on the opinions of important friends or colleagues. Participant
A1 said, “That is a personal decision and I should not follow anyone to accept it or
ignore it.”
All participants agreed that the use of e-government services will depend on their own
beliefs and experience rather than on their friends’ opinions or views. Participant A3
stated, “I would not follow anyone in this age of knowledge and Internet; I have the
ability and resources to find out what is the right and wrong approach, and then I will
decide what to do and make a selection.”
Some participants agreed that the government sectors were not doing what needs to be
done to encourage citizens to use e-government systems. Participants A1 and A2
argued that it is very important to introduce the e-services’ concepts and benefits to
customers through an informational campaign before asking them to use it.
The analysis of the above responses indicates that social influence does not affect the
acceptance and use of e-government services.
9.4.1.4 Facilitating conditions (FC).
In relation to facilitating the conditions and the availability of resources to support the
use of the e-government system, the participants’ comments are presented as follows.
Regarding having the necessary resources to use the e-government systems, such as
computers and Internet access, all participants believed that these have become
available for at least 70% of Saudi citizens in their homes. However, this high
percentage of Internet access could be true to some extent in the big cites in Saudi
Arabia while it is smaller than that in small cites and villages .Participants A1 and A2
mentioned that Internet cafes are scattered throughout all the cities and villages in the
KSA, which will facilitate and encourage the use of e-government services from
anywhere in the KSA. Moreover, Participant A1 suggested that government services
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should be accessible through the new generation of smart phones; thus, government
sectors should ensure their services are adapted to that mode of delivery. Participant
A1 said, “It is a great chance to target more and more users by providing e-services
through smart phones which have become popular and widely used these days.”
All participants agreed that the use of e-government services does not require a great
deal of experience or a high level of education. They believed that the normal user
with a minimal knowledge about the Internet and computers will be able to use
e-services without difficulty.
With respect to the service providers’ online technical support for e-government
services, all participants agreed that there are some shortcomings and lack of support
from government sectors. Participants A1 and A2 claimed that while there is a team of
specialists in place to offer the needed support to all users, they are not able to meet
the overwhelming demand for assistance from the hundreds of thousands of users
which has caused problems. Participants A4 and A5 mentioned that sometimes the
user is the cause of the problem because s/he makes the same online request many
times until the system ‘hangs’ and does not respond at all, while the user complains
about the slowness of the system. Participant A3 said that there are many support
channels that users can try, such as email, telephone, or online chatting, and these
services are available in some government sectors.
From the above arguments, there is evidence showing that facilitating conditions (FC)
positively affect the actual usage of e-government services.
9.4.1.5 Trust (TR).
In this research, trust in e-government services relates to citizens’ perceptions of the
e-government systems and their degree of trust to use it safely. Trusting e-government
services is essential and is based on two important principles:
1. Trust in government entities; and
2. Trust in the Internet and the information technology channels that are used to
provide e-services.
Regarding the first point about trust in government organizations, the majority of
participants claimed that all government agencies are trustworthy and can be relied
upon to carry out online transactions and keep all information secure and safe.
Participant A1 said, “The trust in government sectors is a significant factor in the
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successful implementation of e-government services… I have no problems paying
government fees with my credit card or giving my personal information; I trust the
government websites.” Participant A2 said, “Citizens should trust e-government
services because it originates from the government itself and their government is
looking for their help and support and provides the best services for them.”
Participants A4 and A5 mentioned an important point, which was that government
employees should be educated about others’ privacy and rights and should follow
ethical guidelines when they deal with personal information. On the subject of trust in
the Internet and the technology used by government agencies to operate the
e-government systems, all participants confirmed their belief that all government
sectors use the newest and most secure technology to carry out their e-services.
Participants A1 and A2 stated that their organizations, for example, paid more than
ten million riyals in 2011 (about A$3.5 million) to update their data centres and
Internet services in order to provide more professional, secure, and faster e-services.
From another perspective, participants A3 and A4 emphasised that the new IT laws
that already regulated online transactions, aimed at protecting customers’ personal
data and online rights, will increase trust in the Internet in general. To conclude, it is
clear that the successful implementation of e-government services’ adoption highly
depends on citizens’ trust in e-services and in the providers of those services.
9.4.1.6 Website quality (WQ).
Website quality is defined as the quality of the website’s structural design, which was
based on various principles, including technical quality, content quality and
appearance quality (Aladwani, 2006). The participants’ descriptions of these qualities
are as follows.
All participants confirmed the importance of website quality and the significant effect
it has on the adoption of e-government services. Participants A1 and A2 emphasised
that they made a great effort to achieve the highest degree of quality for their
government website. Participant A1 said, “In our programming department we
followed a high level of web design quality and applied the appropriate international
standards for all web applications.”
Participant A4 was proud to report that the ministry where he works provides more
than 300 electronic services to a variety of users and audience. Moreover, he said that
his “Ministry website has ranked first in the Digital Excellence Award for government
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periodically organized by the Ministry of Communications and Information
Technology for 2011.”
Moreover, participants A3 and A4 mentioned that the Yesser program, also, has its
standard of website quality, which should be followed by all development sections in
government agencies. Participants A3 commended the Yesser program, saying it “did
a great job in cooperating between all government sectors and providing advice and
recommendations for successful adoption; there is no doubt about that.”
Based on the above discussion, it is confirmed that website quality (WQ) positively
affects the acceptance and use of e-government services.
9.4.1.7 Behavioural intention and use behaviour.
Based on the UTAUT model, the actual use of technology is subject to an individual’s
interest (behavioural intention, or BI) towards it. However, it is important to mention
here that all participants said that they do not notice any difference between their
intention to use e-government services and their actual usage of it. Therefore, the two
constructs of behavioural intention (BI) and use behaviour (USE) of e-government
services will be discussed as one construct for this focus group. The main
observations of the participants are as follows.
Initially, all the participants acknowledged the importance and usefulness of
e-government services and confirmed that they had used them since the launch of
e-government services. Furthermore, they described the advantages and benefits of
using e-services, such as saving time and effort, saving money, increasing the service
quality, easy tracking of requests, and increasing the transparency and equity between
all citizens.
Participant A3 stated “there is a strong relationship between the usage of
e-government services and the trust that the customer has in the government’s
websites.” Therefore, the government should provide a high level of security for their
websites and enhance trust through efficient awareness efforts.
Participant A1 agreed with the previous statement by A3 and emphasised the
importance of trust in the user’s intentions to use e-services; he also mentioned the
role of security as an important part of protecting users’ data.
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Participants A4 and A5 expressed their belief that Internet experience influences the
users’ behavioural intention (BI) and actual usage of e-services. In that respect, the
user with more Internet experience is more likely to adopt e-services.
Finally, all participants concluded by suggesting that all citizens should accept and
use e-government services, effective immediately, while it is still optional because it
will became mandatory for everyone who wishes to interact with government sectors
in the near future.
As seen in the above discussion, the intention of all participants to accept and make
use of e-government services is confirmed; moreover, there is no difference between
their view of their intention to use and their actual usage.
9.4.2 Analysis of Group B’s responses.
The conversation with the second group (Group B) covered the research issues in
e-government services from the perspective of Saudi citizens, which was one of the
main research aims. The issues that emerged from this focus group are discussed
below.
9.4.2.1 Performance expectancy.
Regarding the performance expectancy, the participants raised these points, as
discussed below.
All participants acknowledged the usefulness of e-government services in general and
mentioned international efforts to be part of the global information age. Also, they
showed appreciation to the Saudi government for its effort and its huge financial
support for e-government programs and e-services initiatives.
Participant B1 stated, “I used e-services from three ministries’ websites many times
and had found it very useful, enabling me to save time and effort.”
Participants B2, B3, and B4 agreed that e-government systems will endeavour to
preserve the rights of citizens and there is no way for a misuse of authority and that,
of course, will increase the level of equity among all citizens. Participant B2
recommended using e-government services: “I advise everyone to use e-government
services and save their time and effort.”
Participant B5 affirmed that “e-government systems will ensure all citizens’ rights and
provide all transactions with government agencies through a trusted system”.
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All participants agreed that e-government systems will definitely increase the level of
service quality and also improve the performance of employees through the use of
tracking systems.
Based on the above arguments, performance expectancy had a strong effect on the
behavioural intention (BI) to use e-government services.
9.4.2.2 Effort expectancy.
Effort expectancy (EE) in this study means the ease of learning and use of
e-government systems. Some of the participants’ perceptions are listed below.
All participants acknowledged that in this age of computer and information
technology, any web application must be easy to learn and use. Participants B4 and
B5 confirmed this and, as an example, B4 said, “I had experienced many websites and
I found that e-government systems and websites are very easy to use and benefit
from.”
Additionally, participants B1 and B2 claimed that even citizens with low levels of
education and Internet experience are expected to be able to explore government
websites and meet their needs in an easy way. Participants B2 described his
experience with e-government service: “I have used e-services since 2009 and I found
it easy to learn and use, [as it would be] by anyone.”
However, to become professional and expert users of e-government services,
participants B1, B2, and B3 confirmed an average level of Internet experience will be
needed and, as all government websites are totally in the Arabic language, the user’s
level of education or familiarity with the English language will not have much effect
on the user’s experience. Participants B3 confirmed this by saying, “My IT and
Internet background is medium, but I have used e-government services many times
successfully. It was so easy, even for inexperienced users.”
Finally, all participants confirmed that not all government agencies have a website
that provides e-services and mentioned that only a few of them, such as the Ministry
of Civil Affairs, Ministry of Labour, and Ministry of Higher Education have
professional e-services systems.
The results of this discussion showed that effort expectancy had a strong effect on
behavioural intention (BI) to use e-government services.
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9.4.2.3 Social influence.
This section will discuss the user’s intention to use e-government services and the
degree to which that is expected to be affected by their peers’ usage of that same
technology. The participants raised the following points.
Participants B1 and B2 mentioned that, generally speaking, they consult their
colleagues about new things, and then they decide to use it or not, but they have their
own beliefs and decisions and nobody can affect those decisions if they have already
reached a decision. Participants B2 claimed that social influence would not control his
own future decisions: “Nobody can affect or change my decision without reasonable
purpose.”
Participant B3 said, “I depend heavily on my own experience and use any new
services provided by government sectors without fear.”
B4 agreed with Participant B3, saying, “Others’ use of new technology does not play
an important role in influencing me; I rely on my own opinions and experiences.”
Participants B5 said, “I followed my friends on some issues like sport or TV
programs, but not for critical issues like to use or not use e-services when it relates to
my personal information and my privacy.” In that regard, he said he would “follow
the official instructions from the government sectors and apply them exactly.”
Finally, all participants requested that all government sectors increase their e-services
that serve citizens and encourage all users to use these by promoting and marketing
them through professional channels.
In conclusion, the opinions gathered regarding the perceptions of social influence
confirmed that social influence appears to be a non-significant factor on the
behavioural intention (BI) to use e-government services.
9.4.2.4 Facilitating conditions.
The effects of facilitating conditions (FC) on the usage behaviour of e-government
services are discussed through the following points.
All participants agreed to a large extent with the availability of the needed resources
to access and use e-government services. Participants B1, B2, and B4 acknowledged
the availability of the Internet services in almost all cities and counties around the
kingdom and they pointed out the wide variety of Internet services companies.
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Participant B1 confirmed this view: “ In my opinion, I think that a high percentage of
Saudi citizens have at least one computer and they can access Internet easily.” Also,
Participant B2 commented: “ The Internet services is available in all cities, small
towns, and even villages and computer devices have become cheap so a wide range of
Saudi citizens can use e-government services easily.” Participant B4 supported this
view, saying, “ Internet cafes are numerous and are available everywhere so those who
don’ t have Internet services or a computer at home can easily use Internet cafes to
access e-government services.” Participant B5 said, “I can access the Internet very
quickly within my university or from my home and I can access any government
website very easily.”
All participants confirmed that they have enough knowledge and the ability to use
e-government services, are able to finish up their requests easily, and that doing so
does not require any great effort or deep experience.
Participants B1 and B2 mentioned that technical support is a real barrier to the use of
and benefit from e-services, as e-services are lacking and weak in terms of that kind
of support.
To conclude, there is evidence that the influence of facilitating conditions (FC) on the
use behaviour (USE) of e-government services is significant.
9.4.2.5 Trust.
As mentioned earlier, there are two dimensions of citizens’ trust in e-government
services: trust in government sectors and their ability to provide professional
e-services; and trust in the Internet and information technology. The discussion with
the participants included the following points.
All participants said that they trust the government because the government already
has all their personal data and information, so it is reliable and trustworthy. Also, they
confirmed that the government is more trusted than the private sector or foreign
companies.
All participants confirmed that the government should provide them with protection
from all kinds of risks and that it should preserve and protect their data and
information in safe and secure ways. Participants B2 confirmed this by saying,
“Citizens need security and privacy guarantees to accept and use e-government
services with tranquillity and comfort.” Likewise, Participant B3 confirmed the
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importance of trust in the Internet and said, “The government should give a guarantee
of security to the citizens so they can use e-services securely and with peace of mind.”
Participants B1 stated, “I trust the government and its e-systems because I work with
the government and I know the high level of systems reliability and ethics of the staff
in government sectors.”
Participant B1 said, “I always shop online from many international stores and I have
not faced any problems”. So, he trusts the Internet in general and recommends that all
citizens trust and use e-government services. Similarly, Participant B5 stated, “I trust
my government’s systems, but my concern is about the Internet itself but whenever I
feel that my personal data are secure and safe, then I use e-government services.”
Participant B5 believed that trust is a compulsory issue, one which the government
should take care of in order to give citizens confidence and safety as they use
e-government services.
Participant B4 stated, “The government should protect its systems by using the newest
and highest standard technologies of security systems, which will enhance citizens’
trust in e-government systems.” Participant B2 agreed and said, “Government sites
should be secured and protected for any threat , and the government is responsible to
keep our data and information safe.”
Based on the above discussion, there is evidence that the trust for the use behaviour
(USE) of e-government services is significant.
9.4.2.6 Website quality.
Website quality (WQ) with its dimensions of technical quality, content quality, and
appearance quality, and its effect on the use behaviour (USE) of e-government
services, are summarised in the following points.
All participants acknowledged that the website quality is one of the most important
factors that affects their intention and actual usage of e-government websites.
All participants mentioned that the government’s websites appear to be secure and
safe and they are confident they can complete their transactions with the government
through those websites. Participant B1 said, “The information and service quality of
the government’s websites is still less than the expectation level of citizens, which
definitely affects the actual usage of e-government services.” Participant B2 stated,
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“the design and appearance of many of the government’s websites is excellent and
most are easy to navigate, while some are still below the acceptable level.”
Participants B3, B4, and B5 confirmed the importance of the content of the
government’s websites and reported that the content of many government websites is
out of date and poorly presented. For example Participant B3 stated, “I visited two
government websites many times during the past three months and I found the same
news and events, still the same, without any update or change.” Similarly, Participant
B4 suggested government website quality could be better; he said, “Up to today, a lot
of my friends and colleagues are not satisfied with e-government services and its level
[of quality] when we compare with other countries e-systems.”
Participants B3, B4, and B5 claimed that many important websites cannot be accessed
in peak times or in the season of applying for new jobs and that the interruption and
disconnection of services wastes their time and effort.
As a result of the above responses, it is clear that website quality positively affects the
behaviour intention and use of e-government services.
9.4.2.7 Behavioural intention and use behaviour of e-government services.
In the first group discussion regarding the behaviour intention and use behaviour
(USE), it was concluded that there is no difference between the two constructs and it
was decided they would both be treated as one construct. The same result was
confirmed by the members of the second group, who raised the following points.
All participants reported they are convinced of the usefulness of e-government
services and they trust and rely on the government and its ability to provide and
operate a successful e-services system. All participants confirmed that they already
use many government services electronically and had experienced excellent service
and successful experiments. All participants confirmed that, in the future, they will
use any new services from e-government systems for which the government is
responsible and official websites have been established. All participants look forward
to accomplishing all their transactions with government sectors from their homes
through e-government systems, which will certainly save their time, money and effort
and increase the quality of services and efficiency of the government employees.
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Based on the above argument, there is evidence confirming the intention of all
participants to accept and use e-government services and there is no difference in their
view between the intention to use and the actual usage.
9.4.3 Summary of the Focus Group analysis.
As previously mentioned, the focus groups were conducted to complement and
validate the survey findings, to eliminate the disadvantages of relying on a single
research method, and also to give the participants more space to express their opinions
about the research topic. Based on the interpretation analysis of the two focus groups,
the result is completely consistent with the quantitative findings for all seven
hypotheses. The survey data results supporting the six hypotheses also found support
from the focus groups analysis. Only one hypothesis, that of Social Influence (SI),
was not supported by the quantitative analysis or the focus groups analysis.
Some participants raised several important points and suggestions that were unrelated
to the UTAUT model but are worthwhile mentioning here so they can be addressed by
the government to help it succeed with establishing and improving e-government
services. Some of these suggestions are summarized as follows; the government needs
to:
• Address the leak of experienced IT and specialist staff from the government
sector to the private sector because of low salaries and poor incentives in the
government sector;
• Create more jobs with different levels and qualifications to support all IT
sectors in sufficient numbers;
• Update and standardize the ICT infrastructure for all government sectors and
agencies;
• Conduct an efficient and effective national awareness campaign to address the
lack of citizens’ information and knowledge about e-government applications
and services;
• Regulate and establish new e-laws, at a government level, which guarantees
user rights and privacy when they use e-government services, and update the
existing laws;
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• Provide all government services electronically to respond to national demand,
leading to a reduction in corruption and accountability and, at the same time,
contribute to improving transparency; and
• Increase the collaboration and cooperation between all government sectors and
agencies to provide a comprehensive e-services system.
9.5 Chapter Summary
This chapter presented and discussed the analysis of the qualitative data which were
collected from open-ended survey questions and focus groups. The qualitative data
analysis was undertaken to explore the factors that not have been covered by the
UTAUT model and to explain and validate the quantitative findings. Moreover, a
number of participants’ suggestions were presented to improve the quality of the
e-services and to increase the level of e-government services adoption. The findings
of the focus groups’ responses was consistent with the quantitative findings for all the
research hypotheses. In Chapter 10, a summary of the research findings, a review of
the research questions, as well as contributions and limitations of the study will be
discussed and summarized.
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Chapter 10: Discussion and Conclusion
10.1 Introduction
This final chapter presents and summarizes the research findings and results of the
empirical study. In addition, it addresses many key issues such as the study’s
contributions and limitations, as well as the recommended future research directions.
This chapter is organized as follows. Section 10.2 reviews and answers the research
questions from several perspectives, such as the UTAUT model question, the
moderators question, and other general questions. Section 10.3 provides a
comprehensive summary of the research finding. This is followed by Section 10.4,
which explores the contributions made by this study to the body of knowledge.
Section 10.5 discusses the limitations of the study and offers recommendations for
future research. Finally, Section 10.6 concludes the chapter.
10.2 Discussion and Answering the Research Questions
In this section, the findings of the research are presented in response to the research
questions. The results of the analysis are discussed under the heading of the related
question category.
10.2.1 Questions related to the research’s UTAUT model.
The proposed research model UTAUT was empirically tested through a series of
processes and steps for quantitative and qualitative data to effectively carry out the
research. This section will discuss the results and findings with respect to the
variables of the proposed UTAUT research model: trust (TR), effort expectancy (EE),
performance expectancy (PE), social influence (SI), website quality (WQ), and their
relationship with the dependent variables, behavioural intention (BI) and use
behaviour (USE). This section will provide the answers for the research questions as
follow:
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10.2.1.1 Answering RQ1.
The original UTAUT model constructs that affect the acceptance and use of
e-government services in the KSA are discussed below while trust and website quality
will be discussed in answer to research questions three and four.
10.2.1.1.1 Performance expectancy (PE).
In this study, the performance expectancy used is the degree to which the user
believes that using the e-government services will help him or her to facilitate
communication with government in terms of benefits: saving time and money,
improving the quality of government services, and increasing the equity between all
citizens. The research result supports the hypothesis H2 which states that performance
expectancy (PE) positively predicts behavioural intention (BI) to use e-government
services. The effect of performance expectancy (PE) on behavioural intention (BI)
was significant and strong and that definitely reflects the perceived benefits obtained
from using e-government services. This suggests that the public’s performance
expectancy for e-government services might be increased by focusing on the
usefulness of e-government services and the availability of such services through
modern technological channels. In other words, if the advantages and benefits of
e-government systems were demonstrated and promoted to the public in an interactive
manner, the acceptance and use of e-government systems would most likely increase.
This result is was consistent with previous researches findings (Al-Qeisi, 2009;
Garfield, 2005; Louho, Kallioja, & Oittinen, 2006; Rosen, 2005; Schaper & Pervan,
2004; Venkatesh et al., 2003; Zhou, Lu, & Wang, 2010).
10.2.1.1.2 Effort expectancy (EE).
The effort expectancy (EE) variable in this study was defined as the degree of ease
associated with the use of e-government services system in the KSA. It was measured
by the perception of ease of learning and using these systems, as well as how much
effort should be spent to use these systems. The link between effort expectancy (EE)
and behavioural intention (BI) was significant and supported by the research finding
RQ1: How can the factors that influence the acceptance and use of e-government
services in the Saudi public sector be most effectively captured using the
proposed UTAUT model?
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(H3). This result confirmed that users prefer to adopt an easy to use system which
demanded little effort and less time than traditional methods to accomplish their
online transactions. Moreover, this significant influence of effort expectancy (EE) can
be supported by providing simple e-government services, improving the quality of
services, using simple and easily understood words and phrases, providing web based
assistance tools, and declaring the procedures and instructions for all services.
Consequently, this finding is consistent with the results of other studies which also
confirmed that effort expectancy has a strong effect on use intention (Birth & Irvine,
2009; Helaiel, 2009; Louho et al., 2006; Rosen, 2005; Venkatesh et al., 2003.
10.2.1.1.3 Social influence.
The social influence (SI) construct in this study was defined as the extent to which an
individual perceives others’ opinions are important in one’s decision to use
e-government services. It was measured by the perception of how social
communications affect users’ intentions to use e-government services. The study
result revealed the insignificant impact of social influence on behavioural intention
(BI) to use e-government services. As a result, the relationship and hypothesis (H4)
between SI and BI was unsupported. This result confirms previous findings reported
in several studies (Davis, Bagozzi, & Warshaw, 1989; Karahanna & Straub, 1999;
Rosen, 2005; Taylor and Todd, 1995c; Venkatesh & Davis, 2000; Venkatesh et al.
2003). Social influence does not, in fact, affect people in the KSA to adopt
e-government services, although it has an indirect impact on other decision-making
processes unrelated to e-government services. This indicates that the adoption of
e-government services depend on the user’s confidence, ability and self-esteem to use
a technological system, rather than other beliefs and opinions. Moreover, this result
indicates that the use of e-government systems is a personal and individual issue, one
unaffected by social influence. Venkatesh et al. (2003) confirms that the usage of a
system depends on individual user’s beliefs, rather than on others’ opinions or
advices. In the present study, it can be determined that the acceptance and use of
e-government services is related directly to a person’s attitude towards e-government
services.
10.2.1.1.4 Facilitating conditions (FC).
In this study, facilitating conditions (FC) refers to the availability of technological and
organizational resources that are used to support the use of the e-government system
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(Venkatesh et al., 2003). It was measured by assessing the perception of accessing the
required resources, the necessary knowledge, and the technical support needed to use
e-government services systems. The study results confirmed that facilitating
conditions (FC) have a direct and significant effect on usage behaviour (USE) of
e-government services. That result supports the established direct link between
facilitating conditions (FC) and usage behaviour (USE). With respect to the KSA,
facilitating conditions include ICT infrastructure of government sectors, Internet
connectivity, the accessibility and reliability of government websites, technical
support services, and any other available services to assist individuals to adopt and
use e-government services. Therefore, it is necessary to improve facilitating
conditions in terms of both technological and human resources in order to improve
and increase the adoption of e-government services. This result was comparable with
other empirical studies (Helaiel, 2009; Hung et al., 2006; Taylor & Todd, 1995a;
Venkatesh et al., 2003; Zhou et al., 2010).
10.2.1.2 Answering RQ2.
Carter and Belanger (2005) reported the significance of citizens’ trust in the
government and technology in influencing e-government adoption. Moreover, Wang
and Emurian (2005) emphasized that a lack of trust is one of the most formidable
barriers to e-service acceptance and use, especially when financial or personal
information is required. In this study, trust was measured based on two principles:
first, trust in government refers to an individual’s perceptions regarding the integrity
and ability of the government agency providing the online services (Carter and
Belanger, 2005); and, second, trust in the Internet (technology) is the extent to which
the Web site users trust in the reliability, proficiency and security of the Internet and
believing that desired task can be accomplished satisfactorily (Carter and Belanger,
2005).
The study result confirmed that trust (TR) had a positive and significant effect on
behaviour intention (BI) to use e-government services in the Saudi context. This study
found that Saudi citizens’ acceptance and use of e-government services is
RQ2: How does stakeholder trust impact on the acceptance and use of
e-government service systems?
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significantly influenced by their trust in both of trust elements: government sectors
and Internet services. This is consistent with the findings of Belanger and Carter
(2008), Carter and Belanger (2005), Chang, Cheung and Lai (2005), Colesca and
Dobrica (2008), Hung et al. (2006), Lee and Lei (2007), Mofleh and Wanous (2008a),
and Tan et al. (2008). The finding showed trust is an important factor affecting the
intention to use e-government services, particularly when users are required to
provide confidential personal information, such as identity card numbers, bank
account details, credit card information, or contact information. The result indicate
that in order to increase e-government services usage among the public, the level of
citizens’ trust in government entities and its e-systems should be increased and
developed. Moreover, e-government systems might benefit from being implemented
and developed through the utilisation of smart systems and new technology to
maintain and protect citizens’ data and information. Also, important technical
principles, including security, privacy, protection, and encryption solutions, could be
implemented with e-government systems to increase the citizens’ trust on
e-government systems, which positively impacts on citizens’ intention to adopt
e-government services. On the other hand, lack of trust in online transactions has been
identified as one of the major obstacles in the adoption of e-government services
(Carter & Belanger, 2005).
10.2.1.3 Answering RQ3.
Website quality (WQ) was reported in several studies as an important factor that
directly affects the intention to use e-applications in general (Ahn et al., 2007; Nelson
et al., 2005; Wixom & Todd, 2005). In addition, Abanumy et al. (2005) conducted a
study in Saudi Arabia and Oman and their finding emphasized the importance of web
quality in using of e-government websites. Moreover, Choudrie et al. (2010) reported
that government websites in developing countries are suffering from several
difficulties, including poor layout, weak search and navigation engines, and a lack of
clear procedures and use instructions. In this study, website quality (WQ) was
integrated into the UTAUT model as an independent variable to study its impact on
RQ3: How does e-government website quality impact on acceptance and use of
e-government service in KSA?
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Saudi’s citizen intention to adopt e-government systems. It was measured based on
several principles, including technical quality, content quality, appearance quality,
accessibility, and availability. The study result confirmed that website quality (WQ)
had a positive and significant effect on behaviour intention (BI) to use e-government
services. The relationship between website quality (WQ) and behaviour intention
(BI) was the highest powerful link with a standardized regression weight estimate of
0.72. This result affirmed that the website quality factor has a direct impact on Saudi
citizens’ behaviour intentions to use e-government services and that its total impact
is greater than any other construct in the UTAUT model. This finding confirms that
website quality significantly influences Saudi citizens to adopt e-government services
and that it affects their satisfaction and actual usage of e-government services.
To increase the adoption level of e-government websites, several issues regarding
website quality could be improved, including improving website layouts to be more
attractive, providing interactive services and two-way communication, optimizing the
response time, and improving information and system quality. As a result, high
quality, well-designed government websites will raise the adoption level of
e-government services as well as citizens’ overall satisfaction. This finding is
consistent with the findings of several studies which showed that website quality
affected behavioural intention (BI), use behaviour (USE), and users’ satisfaction to
adopt e-government systems (Ahn et al., 2007; DeLone & Mclean, 2003; Nelson et
al., 2005; Wixom & Todd, 2005). To conclude, this positive result demonstrates the
successful addition of website quality as an independent variable in the proposed
UTAUT model.
10.2.1.4 Answering RQ4.
This section will discuss the impact of moderators on the relationships between the
UTAUT constructs. These moderators are gender, age, and Internet experience. The
discussion is presented as follows.
10.2.1.4.1 Gender impact.
RQ4: How do factors of age, gender, and Internet experience influence acceptance
and use of e-government services in KSA?
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In this study, gender moderated the relationship between trust (TR), performance
expectancy (PE), and effort expectancy (EE) on behaviour intention (BI). The finding
shows that age plays a significant moderating role for trust (TR), performance
expectancy (PE), and effort expectancy (EE) towards behavioural intention (BI) to use
e-government services. In general, all hypothesized relationships were significant and
confirmed the importance of gender as a model moderator, regardless of whether it
was supported or not. This empirical evidence demonstrates that Saudi males and
females have strong intentions to adopt e-government systems, despite having
different perceptions about the effect of performance expectancy, effort expectancy,
and trust on e-government systems usage behaviour. This result encourages Saudi
sectors to continue to improve and deliver more online services to the whole of Saudi
society, and confirms that both genders strongly accept and use government online
services. This result corresponds to other study result, affirming the importance of the
gender effect on the adoption of technology usage (Akman et al., 2005; Louho et al.,
2006; Morris & Venkatesh, 2000; Venkatesh et al., 2003).
10.2.1.4.2 Age impact.
With respect to the moderating effect of age, in this study, age moderated the
relationship between trust (TR), performance expectancy (PE), and effort expectancy
(EE) on behaviour intention (BI). Also, it moderated the relationship between the
facilitating conditions (FC) and use behaviour of e-government services. According to
the findings of this study, it was concluded that all age moderating hypotheses were
supported, confirming that age is an important moderator in the Saudi context. More
specifically, the analysis result showed that younger citizens in the KSA are more
likely to adopt and use e-government services than older citizens. There are several
possible explanations for this result. For instance, older users, as late adopters of
computer technology, are less familiar with the Internet and technology compared
with the younger generation who have grown up in the Internet age and technological
revolution. Also, in the past, computer devices, communication facilities, and Internet
services were less common and expensive, and only traditional methods were
available. Moreover, with respect to the KSA context, Internet services have only
become popular in the last 15 years (since 1997, when public Internet access was first
granted in the KSA); thus, the younger generation of users have more experience in
using the Internet than older users. In fact, the moderating effect of age was reported
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in many studies (including Morris & Venkatesh, 2000; Morris, Wu, & Finnegan,
2005; Venkatesh et al., 2000; and Venkatesh et al., 2003).
10.2.1.4.3 Internet experience impact.
According to Venkatesh et al. (2003), Internet experience is considered one of the
important factors that affects behaviour intention (BI). In this study, internet
experience moderated the relationship between trust (TR), performance expectancy
(PE), and effort expectancy (EE) on behaviour intention (BI). Also, it moderated the
relationship between the facilitating conditions (FC) and use behaviour of
e-government services. It was measured based on Internet usage history (the duration
and frequency of Internet use). According to the findings of this study, it was
revealed that, in terms of Internet usage, experienced users were more likely to
accept and use e-government services than inexperienced users. These results are
in line with the popular belief that the experienced user’s adoption uptake is
always higher than those inexperienced users. Also, it confirmed that the effect of
effort expectancy (EE) i s stronger for inexperienced users, which was expected, due
to their lack of Internet experience. The results suggest the need for provision of easy,
simple, and uncomplicated e-services; this will decrease the effect of effort
expectancy (EE) by inexperienced users and increase the adoption level of
e-government services. Furthermore, these results reveal that all moderating
hypotheses were supported and, interestingly, it was confirmed that Internet
experience does play a potential role in the acceptance and use of e-government
services. The literature reported that, in an online context, experienced users are more
likely to adopt new information systems more than inexperienced users. The result of
this study is consistent with the results of several studies, including Lu et al. (2003),
Jaruwachirathanakul and Fink (2005), Jiang et al. (2000), Venkatesh et al. (2003),
Venkatesh and Bala (2008), and Venkatesh and Morris (2000).
10.2.3 Discussion of general research questions.
This section is dedicated to exploring other factors affecting the acceptance and use of
e-government services that have not been covered by the UTAUT model. Also, it will
present a summary of the results derived from the open-ended questions.
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10.2.3.1 Obstacles of e-government services.
In order to address this question, there must be an investigation of the citizens’ and
service providers’ perspectives about the factors affecting their intention to accept and
use e-government services in order to create a comprehensive picture of the research
issues. As explained in Section 9.2, eleven barriers were identified based on the
literature review. Consequently, participants consisting of citizens and IT staff were
asked to identify the level of each barrier as: not a barrier, an important barrier or a
very important barrier. The analysis of the result of this question generated a list of
three common barriers between the two targeted groups. The lack of technical support
from government websites support was ranked first in that list. It was followed by
lack of knowledge about e-government services in Saudi society. Consequently, this
technical support and awareness about e-government services are both necessary to
increase the level of acceptance and use about e-government services in the KSA. In
addition, the availability and reliability of Internet connection was ranked third in the
barrier list. However, the rank of the other barriers differed between the two groups
and it was difficult to merge them together. So, the research generated a common list
selected from the viewpoints of ‘important’ and ‘very important’ in order to come up
with a complete view of all eleven barriers. Table 10.1 summarizes the common
barriers derived from the viewpoints of Saudi citizens and IT employees about
e-government services.
Table64 10.1
Summary of the Common Barriers between the Two Groups
Rank Barrier
1 Lack of technical support from government websites support teams
2 Lack of knowledge about e-government services
3 Availability and reliability of Internet connection
4 Lack of partnership and collaboration between government sectors
5 Government employees’ resistance to change to e-ways
RQ5: How are the acceptance and use of e-government services hindered or
facilitated from the perspectives of Saudi citizens and government service?
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6 Lack of user trust and confidence to use e-government services
7 Shortage of financial resources in government sectors
8 Lack of policy and regulation for e-usage in the KSA
9 IT infrastructure weakness of the government public sector
10 Lack of security and privacy of information on government websites
11 Lack of knowledge and ability to use computers and technology efficiently
10.2.3.2 Result of open-ended questions.
The aim of the open-ended questions was to capture any new factors influencing the
adoption of e-government other than what has been mentioned in the survey
questionnaire. Moreover, it gives participants the opportunity to express their opinions
and views about the study topic in their own style, and in their own words. There were
six questions mixed between yes or no questions and open-ended questions. The
results of these questions were presented in Section 9.3. However, some participants
mentioned various obstacles preventing the adoption of e-government; most of these
obstacles had already been mentioned in the questionnaire or were closely related to
obstacles that were stated in the survey. Also, participants listed a number of
e-services they would like to see online, and they made several suggestions to develop
the communication systems between government sectors and citizens in order to
accelerate and enhance the adoption of e-government systems in the KSA.
10.3 Summary of the Study: Findings and Implications
The purpose of this study was to investigate factors affecting the acceptance and use
of e-government services in Saudi Arabia so as to provide a number of implications
that could enhance and increase the use of e-government services and encourage
citizens to accept and use those services. The study was motivated by the notable
problems associated with the lack of researches and studies which discuss the
adoption of e-government services (G2C) based on a validated model and which
identify the key factors that influence Saudis’ intention to accept and use
e-government systems. The UTAUT model was amended to be used as the basic
theoretical model for the study. Also, to cover all possible factors affecting the
adoption process, another set of questions including open-ended questions were
employed in this study. This was done in order to create a complete and
comprehensive picture of the research subject. Moreover, focus groups were
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employed to validate and confirm the survey findings. The findings of the study are
summarized below relation to the UTAUT model result and the results of the other
questions.
10.3.1 The UTAUT model findings.
The UTAUT model of this research was closely examined to identify the effect of its
constructs on the acceptance and use of e-government services in the KSA. The final
results of the effect of the UTAUT model are as follows.
With respect to the main constructs of the UTAUT model, the findings showed that
trust (TR), effort expectancy (EE), performance expectancy (PE), and website quality
(WQ) contribute significantly to citizen adoption of e-government services and
directly affect the behavioural intention to use e-government services in the KSA. Several
studies reported that the success of e-government services usage relies on users’
intentions to adopt these services (Carter & Belanger, 2003, 2005; Gefen & Straub,
2000; Pavlou, 2003).
The influence of the social influence (SI) variable on behavioural intention (BI) to use
e-government services was insignificant for Saudi citizens, so the social influence (SI)
variable was removed from the final model.
Moreover, the investigation of the moderating effect in the UTAUT model showed
that age, gender, and Internet experience had a moderating influence on all of the
UTAUT constructs which affect the behavioural intention to use e-government
services.
10.3.2 The general question findings.
Beside the UTAUT factors, the others research questions reported a number of
important factors, which are discussed in brief in the following subsections.
10.3.2.1 Lack of technical support for government websites.
The study identified that the lack of technical support for government’s websites by
support team is the first and strongest barrier against the adoption of e-government
services. Thus, a fast and accurate technical support service is an essential part of an
effective and efficient e-government system. Citizens may be, understandably, easily
deterred by technical failures, so it is very important to have a professional team to
detect and respond to technical issues and to help users as soon as possible. Citizens
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require high-quality technical support in order to become familiar with e-services and
learn how to use them effectively. Hofman (2002) defined technical support as
“knowledge people assisting the users of computer hardware and software products”;
technical support can include help desks, information centre support, online support,
telephone response systems, e-mail response systems, and other facilities. Related to
that, Williams (2002) and Geetika (2007) confirmed that technical support is one of
the significant factors in the acceptance and use of technology in general, and in the
adoption of e-applications such as e-government services.
10.3.2.2 Lack of awareness of e-government services.
The study result indicates that a lack of awareness and knowledge about
e-government services is considered the second most significant barrier from the
perspective of citizens and IT staff. Therefore, more information about e-government
systems and a better understanding of the benefits needs to be provided to Saudi
society in order to increase the adoption level of e-government services. A program of
marketing and promotion is would help promote and encourage people to use e-
government in the KSA. For any new technology, there are many steps to convince
and encourage people to accept it and then use it; other studies have shown that the
lack of awareness of a new system and its benefits (in this case, e-government
services) has previously been reported as a key barrier for the usage and adoption of
that new system (Baker & Bellordre, 2004; Jaruwachirathanakul & Fink, 2005; Jeager
& Thompson, 2003; Relyea, 2002).
10.3.2.3 Availability and reliability of Internet connection.
Slow and frequently disconnected Internet services w e r e a main concern raised by
the majority of the respondents in this study. T h e p r o v i s i o n of high speed
Internet services by the Ministry of Communication and Information Technology
(MCIT) and Communication and Information Technology Commission (CITC) at a
reasonable cost to the consumer would meet one of citizens’ basic needs to use of
e-government services. A high quality and stable Internet connection will encourage
all citizens to use e-government services because they will be able to observe the
difference between performing services in a few minutes online using e-government
services as compared to the traditional method, which often involved time-consuming
travel and waiting periods over hours and sometimes days by visiting government
organizations in person.
Chapter 10: Discussion and Conclusion
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10.3.3 Implications of this research.
Based on the study findings, a list of implications and guidelines is presented for the
government leaders and e-services providers to improve the usage, efficiency, and
effectiveness of e-government services in the KSA.
10.3.3.1 Awareness.
It is obvious that awareness is a foundation stone in the success of e-government
services projects. Increasing awareness of e-government services and benefits leads to
increase adoption and usage levels in e-government services. Therefore, it is
important to conduct comprehensive awareness campaigns that would address any
misunderstandings or issues about e-government systems and the benefits of
e-government adoption. These awareness campaigns will be more effective if all
citizens around the KSA can be reached using all possible channels, such as TV
advertisements, radio programs, newspapers, free seminars and workshops, and
distributed brochures. These efforts will enhance the chance of success of adoption
and usage of e-government services in the KSA.
10.3.3.2 Improving website quality and support systems.
Website quality was identified as one of the important factors that affect the
acceptance and use of e-government services in the KSA. The government is
responsible for designing high quality websites, following international standards, and
using the newest tools and technologies. It is also needs to increase the efficiency
and effectiveness of its websites by improving the loading and exploring speed of
webpages, updating information and procedures frequently, providing a clear and
logical structure, improving the essential web services (i.e. a search engine, help
features, website map, and contact information) , using fonts and colour properly,
concentrating on providing services, keeping websites available 24/7, and preventing
it from collapsing under consumer demand and use.
10.3.3.3 Trust enhancement.
The findings presented trust (TR) as an important factor affecting the intention to use
e-government services, particularly when users are required to provide confidential
personal information such as identity card numbers or contact information. The results
indicate that, in order to increase e-government services usage among the public, the
government should increase the public’s trust in the various government entities and
Chapter 10: Discussion and Conclusion
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in its e-systems as well. Consequently, factors that affect citizens’ trust in e-
government services should be addressed in the e-government strategies and future
projects by studying that factors and increasing their role in e-government solutions.
Also, to expand trust in e-government services, governments should implement a solid
and reliable communication channels between government entities and citizens. For
instance, engaging citizens to participate in any new e-services polices and decisions
can increase their trust and confidence in e-government services system. Moreover, it
is important that governments encourage citizens to trust e-government services by
enhancing confidence levels through the utilization of security technologies and
programs to protect their systems and data.
10.3.3.4 Education.
The expansion of ICT training and free workshops for the public to enhance the
computer and Internet skills will also increase trust and provide them with the
required knowledge and capabilities to adapt to using e-government services and
applications. Training government employees to increase their understanding of
e-government services, the technology, and an awareness of the benefits of e-
government services is also an important factor in accelerating the e-government
adoption process at the agency level.
10.3.3.5 Collaboration between government bodies.
A high level of collaboration and cooperation between all government agencies and
with the Yesser project is a fundamental factor in the adoption process of
e-government. This will assist with services integration and greater reliability in the
provision of services.
10.3.3.6 Strategic planning.
Creating a uniform strategic plan for e-government projects is an important step for
the successful adoption of e-government services. Each government organization’s
strategic plan could include development of processes and policies, purchase and
maintenance of hardware and software, development of operating environments and
services, management, outsourcing of consultancy, and ongoing training courses for
their staff.
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10.3.3.7 Mobile technology.
The popularity and ease of use of smartphones makes the delivery of e-government
services through mobile phones a logical next step. The high adoption of smartphones
and the increasing quality and speed of Internet access makes it worthwhile for all
government sectors to deliver their e-services through these devices. The majority of
Saudi citizens have mobile phones with Internet access, making it easier for them to
complete their e-services tasks and communications with government through their
mobiles. So, the government should consider this channel as a principal way of
delivering e-government services as part of its long term plan.
10.4 Research Contributions
This study is an important effort towards a deeper understanding of the factors
affecting the acceptance and use of e-government services in the KSA. It used a
modified UTAUT model as a theoretically valid approach incorporating a qualitative
and quantitative mixed method. This section explains the research contributions. It is
divided into several subsections: theoretical contributions; methodological
contributions; and practical contributions.
10.4.1 Theoretical contributions.
This study has a number of theoretical contributions to the body of knowledge in
information systems, IT adoption, and e-government studies in particular. First and
foremost, based on the available and updated literature review on e-government
studies in the KSA, this is the first study to utilize and apply the UTAUT model in the
context of the KSA to determine and study factors that influence an individual’s
intentions to accept and use e-government services. The study relies on a modified
UTAUT model as a basic theoretical model, which was amended by adding trust
(TR) and website quality (WQ) as independent variables and changing the experience
moderator in the original UTAUT model to Internet experience.
The study validated and confirmed the significant role of trust and website quality
as potential factors which affect the acceptance and use of e-government services in
the KSA. This study succeeded in validating the proposed research UTAUT model
and the supporting relationships among the key constructs within the Saudi context.
Chapter 10: Discussion and Conclusion
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10.4.2 Methodological contributions.
In this study, all UTAUT constructs displayed a sufficient and acceptable degree o f
convergent and discriminant validity, reliability, and fit indices through all the
research stages. These results support the use of UTAUT as a predictor of intention to
use e-government services in Saudi context. Therefore, this study contributes to the
literature b y e x a mi n in g the viability a n d v a l i d i t y of the UTAUT model,
which was established in a western culture, to explain a similar behaviour in a
non-western culture.
10.4.3 Practical contributions.
This study has contributed to the practice of e-government services adoption by
identifying and discovering th e c o r e factors that influence the adoption process in
the KSA based on the amended UTAUT model. Throughout the study, project
factors were identified, so solutions have been suggested to government sectors in
order to accelerate and increase the usage of e-government services in the KSA. The
empirical analysis of this research contributed to knowledge in the area of
e-government adoption research. The findings of this study are important to all
government sectors and the directors and IT departments of these sectors. It
provides a comprehensive analysis of the factors that influence the adoption of
e-government services from the perspectives of citizens and services providers.
This study expands knowledge in the area of IT adoption and e-government usage
within Saudi society, as an example of a developing nation, by utilizing the
UTAUT model and its proposed extension.
This study provided a deep understanding for the critical factors affecting the
acceptance and use of e-government services in the KSA based on the analysis of the
research survey and focus groups. This study is the most up to date analysis of the
factors that affecting the acceptance and use of e-government services from the
perspectives of citizens and the government. The result of this study produced a
practical guideline and a strategic document based on the findings of this research
which could help Saudi government sectors and the Yesser program to gain citizens’
satisfaction with e-government services.
10.5 Limitations and Directions for Future Research
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Like any research project, there were limitations to this study. The research results
were interesting findings in terms of the examination and validation of the amended
UTAUT model to understand the acceptance and use of e-government services in the
KSA. However, there were two limitations in this study: first, the fact that this study
was a single cross-sectional study; and s e c o n d , the limited number of focus
groups. However, these limitations could provide direction for some interesting
future research, as detailed below.
The first limitation of the study was the cross-sectional design which was required to
accommodate the amount of time allowed for the study. Several UTAUT studies
(Alawadhi & Morris, 2008; Schaper & Pervan, 2004, 2007; Venkatesh et al., 2003;
Venkatesh & Davis, 2000) were longitudinal studies, which means that the data were
collected at different points in time in order to measure behavioural intention (BI) and
use behaviour (USE) at different points in time to see the change in t h e dependent
variables (Sekran, 2000). However, this study was a cross-sectional study in which
the data was collected over a single time period. Therefore, in this study, both
behavioural intention (BI) and use behaviour (USE) of e-government services were
measured simultaneously (at the same time), which is consistent with other studies
(Agarwal & Prasad, 1999; Gefen et al., 2003b; Venkatesh & Morris, 2000). However,
for the adoption of newer IT systems such as e-government systems, there is a need
to examine the system over several points of time and investigate the differences
between behavioural intention (BI) and use behaviour (USE) (Venkatesh & Davis,
2000; Venkatesh et al., 2003). A longitudinal study as a future research suggestion
would provide a better interpretation of the fundamental factors of the UTAUT, as
well as the impact of interventions on behavioural intention. It would also provide a
better understanding of the relationship between behaviour intention (BI) and actual
usage (USE) of e-government services in the KSA.
The second limitation was that the focus groups were limited to only two groups. As
mentioned before, the aim of conducting focus groups is to gather qualitative data to
complement and support the main findings of the quantitative data. The focus groups
were held in Riyadh and it was prohibitively difficult to conduct more focus groups in
other cities as a high degree effort is required to communicate with the participants
and organise sessions that accommodate the availability and responsibilities of each
participant. However, the conducted focus groups were successful and in the desired
Chapter 10: Discussion and Conclusion
Page 196
goals were met. Therefore, future research could conduct more focus groups or
individual interviews with various stakeholders, including Saudi citizens, e-services
providers, decision makers, and the Yesser program team members, in order to
explore other new factors that could affect the adoption of e-government services.
Also, focus groups or individual interviews conducted in different cities within the
KSA might lead to generalization of the findings of this research or the discovery of
other factors that appear only in small cities or in the countryside.
According to the geographical scope of this study (Saudi Arabia) and the previous
limitations so, there are many opportunities for further research by broadening the
UTAUT model to look at different elements. One future research could include an
investigation of the impact of social influence (SI), as the current study result was
inconsistent with some existing researches in that regard. This study result, however,
shows that users’ intentions to accept and use e-government services is not influenced
by social relationships between Saudi citizens. This finding confirms that the adoption
decision is a dependent assessment and depends only on the user evaluation of the
acceptance or rejection of that system. Therefore, it would be valuable for future
research to address that issue and its lack of influence of on the e-government services
in the KSA. In addition, further research could examine the impact of other
independent factors on the UTAUT model, such as culture aspects, the economic level
of participants, and awareness. This should provide a better understanding of citizens’
intentions to use e-government services. Finally, another interesting area of research
could include more interviews and focus groups with several segments of society and
different government sectors around the Gulf Cooperation Council (GCC) countries,
which would widen the cultural scope of the study.
10.6 Chapter Summary
This chapter summarized the findings of the study according to the research questions.
Theoretical contributions, methodology contributions, and practical contributions
were provided for researchers. The limitations of the study and suggestions for further
research are also discussed. In conclusion, this study was conducted in a relatively
new and rapidly progressing domain, and the findings of this research should provide
valuable information about the adoption and use of e-government in the KSA to all
government organizations in Saudi Arabia and also to others Arab countries as well.
Chapter 10: Discussion and Conclusion
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References
Page 198
References Abanumy, A., & Mayhew, P. (2005). Information provision assessment and
difficulties @ ministries. E-government Workshop (eGOV05), Brunel University, UK.
Abanumy, A., Al-Badi, A. & Mayhew, P. (2005). E-government website accessibility: In-depth evaluation of Saudi Arabia and Oman. The Electronic Journal of eGovernment, 3(3), 99-106.
Abu Nadi, I., Sanzogni, L., Sandhu, K., & Woods, P. (2008). Success factors contributing to eGovernment adoption in Saudi Arabia. Proceedings of the 2008 Saudi International Innovation Conference, Riyadh.
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information and Management, 44(3), 263-275.
Ajzen, I. (1985). From intentions to actions: A theory of planned behaviour. In J. Kuhl & J. Beckman (Eds.). Action-control: From cognition to behaviour (pp. 11-39). New York: Springer Verlag.
Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179-211.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice-Hall, Inc.
Akbulut, A. (2003). An investigation of the factors that influence electronic information sharing between state and local agencies. (Unpublished doctoral dissertation, Louisiana State University, USA, 2003).
Akman, I., Yazici, A., Mishra, A., & Arifoglu, A. (2005). E-government: A global view and an empirical evaluation of some attributes of citizens. Government Information Quarterly, 22(2), 239-257.
Aladwani, A. (2006). An empirical test of the link between website quality and forward enterprise integration with web customers. Business Process Management Journal, 12(2), 178-190.
Aladwani, A., & Palvia, P. (2002). Developing and validating an instrument for measuring user-perceived web quality. Information and Management, 39(6) 467-476.
Alam, M., & Hassan, M. (2011). Problems when implementing e-governance systems in developing countries: A quantitative investigation of implementation problems in Bangladesh. (Unpublished master’s thesis, University of Borås, Sweden, 2011).
AlAwadhi, S., & Morris, A. (2008, January). The use of the UTAUT model in the adoption of e-government services in Kuwait. Proceedings of the 41st Hawaii International Conference on System Sciences (HICSS-41 2008), Waikoloa, Big Island, HI, USA.
References
Page 199
Al-Fakhri, M. O., Cropf, R. A., Higgs, G. & Kelly, P. (2008). E-government in Saudi Arabia: Between promise and reality. International Journal of Electronic Government Research, 4(2), 59-85.
Al-Gahtani, S. (2003). Computer technology adoption in Saudi Arabia: Correlates of perceived innovation attributes. Information Technology for Development, 10(1), 57-69.
Alghamdi, R., Drew, S., & Alkhalaf, S. (2011). Government initiatives: The missing key for e-commerce growth in KSA. International Conference on e-Commerce, e-Business and e-Service, 77, 772-775. Paris, France.
Al-Ghoson, A. M. (2010). IT strategic plan for e-government Program in Saudi Arabia (YESSER). IABR and ITLC Conference Proceedings, Virginia Commonwealth University, USA. Retrieved from http://www.gimi.us/clute_institute/orlando_2010/Article%20453.pdf
Alhujran, O., & Chatfield, A. (2008, July). Toward a model for e-government services adoption: The case of Jordan. Proceedings of the 8th European Conference on eGovernment, Ecole Polytechnique, Lausanne, Switzerland, 13-22.
Almarabeh T., & AbuAli A. (2010). A general framework for e-government: Definition maturity challenges, opportunities, and success. European Journal of Scientific Research, 39(1), 29-42.
Al-Nuaim, H. (2011). An evaluation framework for Saudi e-government. Journal of e-Government Studies and Best Practices 2011, 12 pp. doi: 10.5171/2011.820912
Al-Qeisi, K. I. (2009). Analyzing the use of UTAUT model in explaining an online behaviour: Internet banking adoption. (Unpublished doctoral dissertation, Brunel University, 2009). Retrieved from http://bura.brunel.ac.uk/bitstream/2438/1/kholoudThesis.pdf
Al Riyadh Newspaper. (2011, December 27). The budget of 2011. Al Riyadh Newspaper, Saudi Arabia. Retrieved from http://www.alriyadh.com/
Al-Saggaf, Y. (2004). The effect of online community on offline community in Saudi Arabia. Electronic Journal of Information Systems in Developing Countries EJISDC, 1(2), 1-16.
Al-Shehry, A. M. (2008). Transformation towards e-government in the Kingdom of Saudi Arabia: Technological and organizational perspectives. (Unpublished doctoral dissertation, De Montfort University, UK, 2008).
Al-Sebie, M. & Irani, Z. (2005). Technical and organisational challenges facing transactional e-government systems: an empirical study. Electronic Government: An International Journal, 2 (3), 247-76.
Al-Shehry, A., Rogerson, S., Fairweather, N. B., & Prior, M. (2006, September). The motivations for change towards e-government adoption: Case studies from Saudi Arabia. Proceedings of the eGovernment Workshop '06 (eGOV06), Brunel University, UK.
Al-Shihi, H. (2005). E-government development and adoption dilemma: Oman case study. 6th International We-B (Working for e-Business) Conference, Victoria University, Melbourne, Australia.
References
Page 200
Al-Smmary, S. (2005). E-readiness in Saudi Arabia: Work report. Damamm, KSA: King Fahd University of Petroleum and Minerals.
Al-Solbi, A., & Al-Harbi, S. (2008). An exploratory study of factors determining e-government success in Saudi Arabia. Communications of the IBIMA, 4(25), 188-192.
Al-Soma, A. (2008, November 18-20). Saudi e-government ‘Yesser’ plans and achievements. Electronic/Mobile Government in Arab States: Building Capacity in Knowledge Management through Partnership, Beirut, Lebanon.
Al-Soma, S. A. (2011). E-Government in KSA. Presented at QITCOM 2011. Retrieved from http://www.slideshare.net/QITCOM/2-ali-al-soma
Al-Suwaiyel, M. (2007, June). Telecom sector reforms in Saudi Arabia: Towards full market liberalization. Saudi Telecom Society.
Altameem, T. (2007). The critical factors of e-government adoption: An empirical study in the Saudi Arabia public sectors. (Unpublished doctoral dissertation, Brunel University, UK).
Altameem, T., Zairi, M., & Alshawi, S. (2006, November 19-21) Critical success factors of e-government: A proposed model for e-government implementation. Proceedings of the International Conference on innovations in Information Technology (IIT 2006), Dubai, UAE.
Al-Tawil, K., Sait, S., & Hussain, S. (2003). Use and effect of Internet in Saudi Arabia. Proceedings of the 6th World Multi-conference on Systemic, Cybernetics and Informatics, Orlando, Florida, USA.
Aman, A. & Kasimin, H. (2011). E-procurement implementation: a case of Malaysia government. Transforming Government: People, Process and Policy, 5(4), 330-344.
Anastasi, A., & Urbina, S. (1997). Psychological Testing (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Anderson, J., Schwager, P., & Kerns, R. (2006). The drivers for acceptance of tablet PCs by faculty in a college of business. Journal of Information Systems Education, 17(4), 429-440.
Arab news Newspaper. (2012, May 1). Kingdom's support for e-governance bolstering quality of public services. Retrieved from http:// www.arabnews.com/node/412225
Arbuckle, J. L. (2006). Amos 7.0 user’s guide. Chicago, IL: Smallwaters Corporation.
Armida, E. 2008. Adoption Process for VOIP: The Influence of Trust in the UTAUT Model, Unpublished Ph.D. Dissertation, Purdue University.
Armitage, C., & Conner, M. (2001). The theory of planned behaviour: Assessment of predictive validity and perceived control. British Journal of Social Psychology, 38(1), 35-54.
References
Page 201
Attewell, P. (1992). Technology diffusion and organisational learning: The case of business computing. Organisation Science, 3(1), 1-19.
Awamleh, N. (2011). E-governance: An endless ES to the same end: A field survey in the university community in Bahrain. Asian Journal of Business Management, 3(1), 40-49.
Bagozzi, R. P. (1994), Structural equation models in marketing research: Basic principles. In R. Bagozzi (Ed.), Principles of Marketing Research (pp. 317-385). Oxford: Blackwell.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Baležentis, A. & Paražinskaitė, A. (2012). The benchmarking of the government to employee (G2e) technology development: Theoretical aspects of the model construction. Social Technologies, 2(1), 53-66.
Baker, F. B. (2004). Item response theory: Parameter estimation techniques. New York: Marcel Dekker.
Baker, P. M. A., & Bellordre, C. (2004). Adoption of Information and Communication Technologies: Key policy issues, barriers and opportunities for people with disabilities. Proceedings of the 37th Hawaii International Conference on System Sciences (HICSS'04). IEEE Computer Society.
Barnes, S., & Vidgen, R. An integrative approach to the assessment of e-commerce quality. Journal of Electronic Commerce Research, 3(3), 27-114.
Belanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. Journal of Strategic Information Systems, 17(2), 165-176.
Belanger, F., & Hiller, J. (2006). A framework for e-government: Privacy implications. Business Process Management Journal, 12(1), 48–60.
Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal of the Association for Information Systems, 8, 211-218.
Berge, Z. L., Muilenburg, L. Y., & Haneghan, J. V. (2002). Barriers to distance education and training. The Quarterly Review of Distance Education, 3(4), 409-418.
Berthon, P., Pitt, L., Cyr, D., & Campbell, C. (2008). E-readiness and trust: Macro and micro dualities for e-commerce in a global environment. International Marketing Review, 25(6), 700-14.
Bertot, J. C., Jaeger, P. T., & McClure, C. R. (2008, May). Citizen-centered e-government services: Benefits, costs, and research needs. (pp. 137-142). Proceedings of the 9th Annual International Digital Government Research Conference, Montreal, Canada.
Bhatnagar, S. (2002). E-government: Lessons from implementation in developing countries. Regional Development Dialogue, UNCRD 24, 1-9.
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229-254.
References
Page 202
Bhuiyan, M.S. (2010). E-Government in Kazakhstan: Challenges and Its Role to Development. Public Organization Review, 10(2), 31-47.
Birth, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295-315.
Blunch, N. (2008). Introduction to structural equation modelling using SPSS and AMOS. Sage Publications.
Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley.
Boyer, K. K., Hallowell, R., & Roth, A. V. (2002). E-services: Operating strategy—A case study and a method for analyzing operational benefits. Journal of Operations Management, 20(2), 175-188.
Brown, H. D., & Thompson, S. (2011), Priorities, policies, and practice of e-government in a developing country context: ICT infrastructure and diffusion in Jamaica. European Journal of Information Systems, 20 (3), 329-342.
Brown, M. W., & Cudeck, S. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.). Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage Publications.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.
Burn, J., & Robins, G. (2003), Moving towards e-government: A case study of organizational change processes. Logistics Information Management, 16 (1), 25-35.
Byrne, B. M. (2001). Structural equation modelling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum.
Byrne, B. M. (2006). Structural equation utilization with EQS: Basic concepts, applications, and programming (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., & Walden, P. (2006). Adoption of mobile devices/services: Searching for answers with the UTAUT. Proceedings of the 39th Hawaii International Conference on Systems Sciences, Hawaii, 1-10.
Carter, L., & Belanger, F. (2003). The influence of perceived characteristics of innovating on e-government adoption. Electronic Journal of eGovernment, 2(1), 11-20.
Carter, L., & Belanger, F. (2004). Citizen adoption of electronic government initiatives. Proceedings of the 37th Hawaii International Conference on System Sciences, Hawaii, 5-8.
Carter, L., & Belanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5-25.
Carter, L. & Weerakkody, V. (2008). E-government adoption: a cultural comparison. Information Systems Frontiers, 10 (4), 473-82.
References
Page 203
Carvin, A., Hill, J., & Smothers, S. (2004). E-government for all: Ensuring equitable access to online government services. New York: The EDC Center for Media and Community and the NYS Forum.
Chan, E., & Swatman, P. M. C. (1999, November). Electronic commerce: A component model. 3rd Annual CollECTeR Conference on Electronic Commerce, Wellington, New Zealand.
Chang, M., Cheung, W., & Lai, V. (2005). Literature derived reference models for the adoption of online shopping. Information & Management, 42(4), 543-559.
Chavez, R. (2003). The utilization of the Mazmanian and Sabatier model as a tool for implementation of e-government for Fresno County, California. (Unpublished doctoral dissertation, California State University, 2003).
Chen, Y. N., Chen, H. M., Huang, W., & Ching, R. K. H. (2006). E-government strategies in developed and developing countries: An implementation framework and case study. Journal of Global Information Management, 14(1), 23-46.
Cheong, J. H., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet Research, 15(2), 125-140.
Child, D. (1990). The essentials of factor analysis (2nd ed.). London: Cassel Educational Limited.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (1996). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and voice mail emotion/adoption study. Proceedings of the 17th International Conference on Information Systems, (ICIS 1996), Paper 6, pp. 21-41. Available from http://aisel.aisnet.org/icis1996/6
Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MIS Quarterly, 19(2), 237-246.
Choudrie, J., Umeoji, E., & Forson, C. (2010). Diffusion of e-government in Nigeria: A qualitative study of culture and gender. Pre-ICIS SIG Global Development Workshop, Saint Louis, Missouri, USA.
Choudrie, J., Weerakkody, V., & Jones S. (2005). Realising e-government in the UK: Rural and urban challenges. The Journal of Enterprise Information Management, 18(5), 568-585.Clarke, R. (1999). A primer in diffusion of innovations theory. Department of Computer Science, Australian National University. Retrieved from http://www.rogerclarke.com/SOS/InnDiff.html
Coakes, S. J. (2005). SPSS: Analysis without anguish: Version 12.0 for Windows. Milton, Qld: John Wiley & Sons.
Coakes, S. J., Steed, L. & Dzidic, P. (2006). SPSS Version 13.0 for Windows: Analysis without anguish. India: Wiley-India.
Cody-Allen, E. and Kishore, R. (2006) . An Extension of the UTAUT Model with E- Eqality, Trust & Satisfaction Constructs. CPR: 183-189.
Cohen, S., & William, W. (2002). The future of e-government: A projection of potential trends and issues. New York: Columbia University.
References
Page 204
Colesca, S. (2009) Increasing e-trust: A solution to minimize risk in e-government adoption. Journal of Applied Quantitative Methods (JAQM), 4(1), 31-44.
Colesca, S. E., & Dobrica, L. (2008) Adoption and use of e-government services: The case of Romania. Journal of Applied Research and Technology, 6(3), 204-217.
Collier, J. E., & Bienstock, C. C. (2009). Model misspecification: Contrasting formative and reflective indicators for a model of e-service quality. Journal of Marketing Theory and Practice, 17(3), 283-293.
Collis, J., & Hussey, R. (2003). Business research: A practical guide for undergraduate and postgraduate students (2nd ed.). Houndmills: Palgrave Macmillan.
Communications and Information Technology Commission (CITC) (2005). Saudi Arabia: Towards the information society. Saudi Arabia: Report, ITU. Digital Reach. Available from http://www.yesser.gov.sa/documents/ksa-to-information-society.pdf
Cook, M. E. (2000). What citizens want from e-government. Center for Technology in Government, State University of New York at Albany, Occasional Paper.
Cook, M. E., LaVigne, M. F., Pagano, C. M., Dawes, S. S., & Pardo, T. A. (2002). Making a case for local e-government. Albany, New York: Center for Technology in Government.
Connolly, R., & Bannister, F. (2008). Factors influencing Irish consumers’ trust in internet shopping, Management Research News, 31(5), 339-358.
Connolly, R., Bannister, F., & Kearney, A. (2010). Government website service quality: A study of the Irish revenue online service. European Journal of Information Systems, 19(6), 649-667.
Corbin, J. M., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13(1), 3-21.
Coursey, D., & Norris, D. F. (2008). Models of e-government: Are they correct? An empirical assessment. Public Administration Review, 68(3), 523-536.
Creswell, J. W. (2003) Research design: Qualitative, quantitative, and mixed methods approach (2nd ed.). Thousand Oaks, CA: Sage Publications.
Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage.
Curtin, G. G. (2007, April). E-government. Encyclopedia of Political Communications. Los Angeles: Sage Publications.
Dada, D. (2006). The failure of e-government in developing countries: A literature review. Electronic Journal of Information Systems in Developing Countries, 26(7), 1-10.
Damodaran, L., Nicholls, J., Henney, A., & Land, F. (2005.). The contribution of sociotechnical systems thinking to the effective adoption of e-government and the enhancement of democracy. Electronic Journal of eGovernment, 3(1), 1-12.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
References
Page 205
Davis, F. D., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Davis, F. D., Bagozzi, R., & Warshaw, P. (1992) Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human Computer Studies, 45(1), 19-45.
Dawes, S. S. (2008). The evolution and continuing challenges of e-governance. Public Administration Review, 68(1), S86-S102.
De Vaus, D. A. (2002). Surveys in social research. St. Leonards, NSW: Allen & Unwin.
Deloitte Research. (2003). Citizen advantage: Enhancing economic competitiveness through e-government. New York: Deloitte & Touche.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten year update. Journal of Management Information Systems, 19(4), 9-30.
Denscombe, M. (2007). The good research guide: For small-scale social research projects. Maidenhead, New York: Open University Press.
Denzin, N., & Lincoln, Y. (2002). The qualitative inquiry reader. Thousand Oaks, CA: Sage Publications.
Dimitrova, D. V., & Chen, Y. C. (2006). Profiling the adopters of e-government information and services: The influence of psychological characteristics, civic mindedness, and information channels. Social Science Computer Review, 24(2), 172-188.
Dodd, J. (2000) Delivering on the e-government promise: A government technology industry profile: NIC. Available from http://bilisimsurasi.org.tr/cg/egitim/kutuphane/NIC.qxd.pdf
Easterby-Smith, M., Thorpe, R., & Lowe, A. (2002). Management research (2nd ed.). Thousand Oaks, CA: Sage Publications.
Ebrahim, Z., & Irani, Z. (2005). E-government adoption: Architecture and barriers, Business Process Management Journal, 11(5), 589-611.
Economist Intelligence Unit (EIU). (2008). E-readiness rankings 2008: Maintaining momentum. Retrieved from http://www.eiu.com on 20/10/2009.
Fang, Z. (2002). E-government in digital era: Concept, practice, and development. International Journal of the Computer, the Internet and Management, 10(2), 1-22.
Featherman, M., & Pavlou, P. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451- 474.
References
Page 206
Feng, L. (2003). Implementing e-government strategy in Scotland: Current situation and emerging issues. Journal of Electronic Commerce in Organizations, 1(2), 44-65.
Fenwick, W., John, E., & Stimac, J. (2009). The necessity of e-government. Santa Clara Computer and High-Technology Law Journal, 25(3), 427.
Field, A. (2005). Discovering statistics using SPSS (2nd ed). London: Sage Publications.
Floh, A., & Treiblmaier, H. (2006). What keeps the e-banking customers loyal? A multi-group analysis of the moderating role of customer characteristics on e-loyalty in the finance service industry. Journal of Electronic Commerce Research, 7(2), 97-110.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Fu, J. R., Farn, C. K., & Chao, W. P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information & Management 43(1), 109-126.
Gable, R. K. (1993). Instrument development in the affective domain: Measuring attitudes and values in corporate and school settings (2nd ed). Boston, MA: Sage Publications.
Garfield, M. J. (2005). Acceptance of ubiquitous computing. Information Systems Management, 22(4), 24-31.
Garson, G. D. (2006). Quantitative research in public administration: Structural equation modeling. Available from http://www2.chass.ncsu.edu/garson/pa765/structur.html
Gartner, A. (2002, May 1). Majority of e-government initiatives fail or fall short of expectations. Government Technology. San Diego: Inc’s Executive Programs. Retrieved from http://www.govtech.com/e-government/Gartner-Majority-of-E-Government-Initiatives-Fail.html
Geetika, P. (2007). Strategic marketing of e-government for technology adoption facilitation. Available from http://www.csisigegov.org/critical_pdf/6_51-60.pdf
Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(8), 1-28.
Gefen, D., Karahanna, E., & Straub, D. W. (2003a). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on Engineering Management, 50(3), 307-321.
Gefen, D., Karahanna, E., & Straub, D. W. (2003b). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modeling techniques and regression: Guidelines for research practice. Communications of the Association for Information Systems, 7(7), 1-78.
References
Page 207
Gilbert, D., Balestrini, P., & Littleboy, D.(2004). Barriers and benefits in the adoption of e-government. International Journal of Public Sector Management, 17(4), 286-301.
Gil-García, J. R., & Pardo, T. A. (2005). E-government success factors: Mapping practical tools to theoretical foundations. Government Information Quarterly, 22(2), 187-216.
Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Chicago: Aldine.
Gomez, R. (2009). Measuring global public access to ICT: Landscape summary reports from 25 countries around the world. CIS Working Paper No. 7, Available from http://www.cis.washington.edu/depository/landscape/documents/CIS-WorkingPaperNo7.pdf.
Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.
Gray, D. E. (2009). Doing research in the real world. Los Angeles: Sage Publications.
Grönlund, Å. (2010), Ten years of e-government: The end of history and a new beginning. In M. A. Wimmer, H. J. Scholl, & M. Janssen (Eds.). EGOV 2010, LNCS. Springer-Verlag Berlin Heidelberg.
Grönlund, Å., & Horan, T. (2005). Introducing e-gov: History, definitions and issues. Communications of the Association for Information Systems, 15(1), 713-729.
Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. Journal of Strategic Information Systems, 17(2), 140-154.
Ha, H., & Coghill, K. (2006). E-government in Singapore: A SWOT and PEST analysis. Asia-Pacific Social Science Review, 6(2), 103-130.
Hackney, R., & Jones, S. (2002). Towards e-government in the Welsh (UK) assembly: An information systems evaluation. Paper presented at the ISO in World Conference and Convention, Las Vegas, Nevada, USA.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson Education, Inc.
Hamza, H., Sehl, M., Egide, K., & Diane, P. (2011, August 28-September 2). A conceptual model for G2G relationships. Proceedings of the 10th IFIP WG 8.5 International Conference on Electronic Government, EGOV, Delft, The Netherlands, 285-295. Berlin, Heidelberg: Springer.
Hanshaw, S., & Carter, L. (2008). Using information technology for strategic growth from single-mission transportation company to multi-faceted global logistics corporation. Journal of Cases on Information Technology, 10(3), 10-20.
Hart, C. (1998). Doing a literature review: Releasing the social science research imagination. London: Sage Publications.
Hatcher, L. (2002). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling (5th ed.). Cary, NC: SAS Institute.
Heeks, R. (2003). Most e-government-for-development projects fail: How can risks be reduced?” iGovernment Working Paper Series, 14.
References
Page 208
Heeks, R. (2006, July). Understanding and measuring e-government: International benchmarking studies. Paper presented at the UDESA workshop, E-Participation and E-government: Understanding the Present and Creating the Future, Budapest, Hungary.
Helaiel, A. (2009). Validating the unified theory of acceptance and use of technology in Kuwait ministries: A structural equation modeling approach. International journal of information systems and change management, 3(3), 191-219.
Helbig, N., Gil-Garcia, J., & Ferro, E. (2009). Understanding the complexity of electronic government: Implications from the digital divide literature. Government Information Quarterly, 26(1), 89−97.
Hersen, M., (2004). Comprehensive handbook of psychological assessment. Hoboken, NJ: John Wiley & Sons.
Hess, T., Wigang, R. T., Mann, F., & Walter, B. V. (2007). Open access and science publishing: Results of a study on researchers’ acceptance and use of open access publishing. Management report. Available from http://openaccessstudy.com/Hess_Wigang_Mann_Walter_2007_Open_Access_management_report.pdf
Hinton, P. R., Brownlow, C., McMurray, I., & Cozens, B. (2004). SPSS explained. East Sussex, England: Routledge, Inc.
Ho, A. T. (2002). Reinventing local governments and the e-government initiative. Public Administration Review, 62(4), 434-44.
Hoffman, D. L., & Novak, T. P. (2009). Flow online: Lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23-34.
Hofmann, D. W. (2002). Internet-based distance learning in higher education. Tech Directions, 62(1), 28-32.
Holsapple, C., & Sasidharan, S. (2005). The dynamics of trust in B2C e-commerce: A research model and agenda. Information Systems and E-Business Management, 3(4), 377-403.
Hu, L., & Bentler, P. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Hung, S. Y., Chang, C. M., & Yu, T. J. (2006). Determinants of user acceptance of the e-government services: The case of online tax filing and payment system. Government Information Quarterly, 23(1), 97-122.
Hussein, R., Karim, N. S. A., Mohamed, N., & Ahlan, A. R. (2007). The influence of organizational factors on information systems success in e-government agencies in Malaysia. Electronic Journal on Information Systems in Developing Countries, 29(1), 1-17.
Hwang,J. & Syamsuddin,I. (2008). Failure of EGovernment Implementation: A Case Study of South Sulawesi., Proceeding of IEEE International Conference on Convergence and Hybrid Information Technology ICCIT2008, pp. 952-960.
IDABC eGovernment Observatory. (2005). The impact of eGovernment on competitiveness, growth and jobs. Available from http://europa.eu.int/idabc/egovo
References
Page 209
Irani, F., Gabel, R., Hughes, S., Swartz, E. R., & Palasik, S. T. (2009). Role entrapment of people who stutter reported by K-12 teachers. Contemporary Issues in Communication Science and Disorders, 36, 45-54.
Irani, Z., Love, P.E.D., & Jones, S. (2008). Learning lessons from evaluating e-government: Reflective case experiences that support transformational government. Journal of Strategic Information Systems, 17(2), 155-64.
Isaac, W. (2007). Performance measurement for the e-government initiatives: A comparative study. (Unpublished doctoral dissertation, Nova Southeastern University, 2007).
Jaccard, J., Wan, C., & Turrisi, R. (1990). The detection and interpretation of interaction effects between continuous-variables Multiple-Regression, Multivariate Behavioral Research, 25(4), 467-478.
Jaeger, P. T., & Thompson, K. M. (2003). E-government around the world: Lessons, challenges, and new directions. Government Information Quarterly, 20(4), 389-394.
Jain, V. & Kesar, S. (2011). E-government implementation challenges at local level: A comparative study of government and citizens' perspectives. Electronic Government, an International Journal, 8(2-3), 208-225
Jaruwachirathanakul, B., & Fink, D. (2005). Internet banking adoption strategies for development country: The case of Thailand. Internet Research, 15(3), 295-311.
Jiang, J. J., Hsu, M. K., Klein, G., & Lin, B. (2000). E-commerce user behaviour model: An empirical study. Human Systems Management, 11(4), 265-276.
Jovarauskiene, D., & Pilinkiene, V. (2009). E-business or e-technology? Engineering Economics 1(61), 83-89.
Kanat, I., & Ozkan, S. (2009). Exploring citizens' perception of government to citizen services: A model based on theory of planned behaviour (TBP). Transforming Government: People, Process and Policy, 3(4), 406-419.
Kaplan, B., & Maxwell, J. A. (2005). Qualitative research methods for evaluating computer information systems. In J. G. Anderson & C. E. Aydin (Eds.). Evaluating the organizational impact of healthcare information systems (2nd ed.) (pp. 30-55). New York: Springer. Karahanna, E., & Straub, D. (1999). The psychological origins of perceived usefulness and ease of use. Information and Management, 35(3), 237-250.
Katz, I., Elliot, N., Attali, Y., Scharf, D., Powers, D., Huey, H., Joshi, K., & Briller, V. (2009), Multiple methods of assessing information literacy: A case study. ETS Research Spotlight, 2, 21-27.
Kim, J. O., & Mueller, C. W. (1978). Introduction to factor analysis. Beverly Hills: Sage Publications.
Klein, H. K., & Myers, M. D. (1999). A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quarterly, 23(1), 67-93.
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guildwood.
References
Page 210
Koufteros, X. A. (1999). Testing a model of pull production: A paradigm for manufacturing research using structural equation modeling. Journal of Operations Management, 17(4), 467-488.
Kripanont, N. (2006). Using technology acceptance model to investigate academic acceptance of the Internet. Journal of Business Systems, Governance, and Ethics, 1(2),13-28.
Kripanont, N. (2007). Examining technology acceptance model of internet usage by academics within Thai business schools. (Unpublished doctoral dissertation, Victoria University).
Krueger, R. A. (2000). Focus groups: A practical guide for applied research (3rd ed.). Thousand Oaks, CA: Sage Publications.
Kumar, V., Mukerji, B., Butt, I., & Persaud, A. (2007). Factors for successful e-government adoption: A conceptual framework. Electronic Journal of eGovernment, 5(1), 63-76.
Hongxiu, L., & Reima, S. (2009). A proposed scale for measuring e-service quality. International Journal of e-Service, Science and Technology, 2(1), 1-10.
Laudon, K., & Laudon, J. (2003). Essentials of Management Information Systems (5th ed.). New Jersey: Prentice Hall.
Layne, K., & Lee, J. (2001). Developing fully functional e-government: A four stage model. Government Information Quarterly, 18(3), 122-136.
Layton, T. (2007). Information security: Design, implementation, measurement, and compliance. Boca Raton, FL: Auerbach Publications.
Lean, O. K., Zailani, S., Ramayah, T., & Fernando, Y. (2009). Factors influencing intention to use e-government services among citizens in Malaysia. International Journal of Information Management, 29(6), 458-475.
Lee, C. B., & Lei, U. L. (2007). Adoption of e-government services in Macao. Proceedings of the 1st International Conference on Theory and Practice of Electronic Governance, ACM International Conference Proceeding Series, 232, 217-220
Lee, G., & Lin, H. (2005). Customer perception of e-service quality in online shopping. International Journal of Retail and Distribution Management, 33(2), 161-176.
Lee, T. (1999) Using qualitative methods in organizational research. Thousand Oaks, CA: Sage Publications.
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) The technology acceptance model: Past, present and future. Communications of the Association for Information Systems, 12(50), 752-780.
Leedy, P. D. (1993). Practical research: Planning and design. New York: Maxwell Macmillan.
Levy, Y. (2006). Assessing the value of e-learning systems. Hershey, PA: Information Science Publishing.
Li, J., & Kishore, R. (2006). How robust is the UTAUT instrument? A multigroup invariance analysis in the context of acceptance and use of online community
References
Page 211
weblog systems. Proceedings of the 2006 ACM SIGMIS CPR Conference on Computer Personnel Research, Claremont, CA, 183-189.
Lin, J. C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a website. International Journal of Information Management, 20, 197-208.
Löfstedt, U. (2005) Assessment of current research and some proposals for future direction. International Journal of Public Information Systems, (1)1, 39-52. Mid Sweden University, Sweden.
Lohmoller, J. B. (1989). Latent variables path modeling with partial least squares. Heidelberg, Germany: Physica-Verlag.
Lou, E. C. W., & Goulding, J. S. (2010). The pervasiveness of e-readiness in the global built environment arena. International Journal of Systems and Information Technology, 12(3), 180-195.
Louho, R., Kallioja, M., & Oittinen, P. (2006). Factors affecting the use of hybrid media applications. Graphic Arts in Finland, 35(3), 11-21.
Lu, C. S., Lai, K. H., & Cheng, T. C. E. (2007). Application of structural equation modeling to evaluate the intention of shippers to use Internet services in liner shipping. European Journal of Operational Research, 180(2), 845-867.
Lu, J., Shambour, Q., Xu, Y., Lin, Q., & Zhang, G. (2010). Bizseeker: A hybrid semantic recommendation system for personalized government-to-business e-services. Internet Research, 20(3), 342-365.
Lu, J., Yu, C. S., Liu, C., & Yao, J. (2003). Technology acceptance model for wireless Internet. Journal of Internet Research, 13(2), 206-222.
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51(1), 201-226.
Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review 20(3), 709-734.
McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114(2), 376-390.
McDaniel, C. J., & Gates, R. (2006). Marketing research essentials (5th ed.). Hoboken: John Wiley & Sons, Inc.
MCIT. (2011, September 20). Ministry of Communication and Information Technology. Available from http://www.mcit.gov.sa
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359.
Merriam, S. B. (1998). Qualitative research and case study applications in education: Revised and expanded from case study research in education. San Francisco: Jossey-Bass Publishers.
References
Page 212
Minas, C., & Inman, D. (1990). Matching finite-element models to modal data. Journal of Vibration and Acoustics: Transactions of the ASME, 112(1), 84-92.
Mirani, R., & King, W. R. (1994). Impacts of end-user and information center characteristics on end-user computing support. Journal of Management Information Systems, 11(1), 141-166.
MOEP. (2012). Ministry of Economy and Planning. Available from www.moep.gov.sa
Mofleh, S., & Wanous, M. (2008a). Understanding factors influencing citizens’ adoption of e-government services in the developing world: Jordan as a case study. INFOCOMP: Journal of Computer Science, 7(2), 1-11.
Mofleh, S., Wanous, M., & Strachan, P. (2008b). The gap between citizens and e-government projects: The case for Jordan. Electronic Government: An International Journal, 5(3), 275-287.
Mohamed, A. & Hamdy, H. (2008). The stigma of wasta: The effect of wasta on perceived competence and morality. Working Paper Series 5. German University in Cairo, Egypt.
Monga, A. (2008). E-government in India: Opportunities and challenges. Journal of Administration and Governance, 3(2), 52-61.
Moon, M. (2002).The evolution of e-government among municipalities: Rhetoric or reality. Public Administration Review, 62(4), 424-33.
Moon, M. J., & Norris, D. (2005). Does managerial orientation matter? The adoption of reinventing government and e-government at the municipal level. Information Systems Journal, 15(1), 43-60.
Moore, D. S., & McCabe, G. P. (2006). Introduction to the practice of statistics. New York: W. H. Freeman and Co.
Moores, S. (2003). E-government in the Middle East. Keynote address, Zentelligence. Future IT 2003 Conference, Bahrain.
Morgan, D. (2001). Focus group interviewing. In J. Bugruium & J. Holstein (Eds.), Handbook of interview research: Context & method (pp. 141-159). Thousand Oaks, CA: Sage Publications.
Morgan, D.L (1997). Focus groups as qualitative research. Thousand Oaks, CA: Sage Publications.
Morris, L. V., Wu, S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. American Journal of Distance Education, 19(1), 23-36.
Morris, M., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375- 403.
Mossholder, K. W., Kemery, E. R., & Bedeian, A. G. (1990). On using regression coefficients to interpret moderator effects. Educational and Psychological Measurement, 50(2), 255-263
Myers, M. (1997) Qualitative research in information systems. MIS Quarterly, 21(2), 241-242.
References
Page 213
Nagi, E. A., & Hamdan, M. (2009). Computerization and e-government implementation in Jordan: Challenges, obstacles and successes. Government Information Quarterly, 26(4), 577-583.
Nam, T., & Sayogo , D. S. (2011). Who uses e-government? Examining the digital divide in e-government use. Proceedings of the 5th International Conference on Theory and Practice of Electronic Governance (ICEGOV ’11), Tallinn, Estonia.
Ndou, V. (2004). E-government for developing countries: Opportunities and challenges. Electronic Journal on Information Systems in Developing Countries, 18(1), 1-24.
Nelson, R., Todd, P., & Wixom, B. (2005) Antecedents of information and system quality: An empirical examination within the context of data warehousing. Journal of Management Information Systems, 21(4), 199-235.
Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Thousand Oaks, CA: Sage Publications.
Neuman, W. (2006). Social research methods: Qualitative and quantitative approaches (6th ed.). Boston, MA: Allyn & Bacon.
Ngai, E. W. T., & Wat, F. K. T. (2005). Fuzzy decision support system for risk analysis in e-commerce development. Decision Support Systems, 40(2), 235-255.
Norris, D. F. (2007). Current issues and trends in e-government research. Hershey, Pa: IGI Global
Norusis , M. (1991). The SPSS guide to data analysis for SPSS/PC+. New Jersey: Prentice Hall.
Oates, B. (2006). Researching information systems and computing. Thousand Oaks, CA: Sage Publications.
OECD. (2003). OECD E-government flagship report: The e-government imperative. Paris: Public Management Committee, OECD.
OECD, (2008). The e-government project. Retrieved from http://webdomin01.0ecd.org/COMNET/PUM/egovproweb.nsf
Ong, C., Day, M., Chen, K., & Hsu, W. (2008, July). User centered evaluation of question answering systems. Proceedings of the IEEE International Conference on Intelligence and Security Informatics (ISI 2008), 286-287. doi:10.1109/ISI.2008.4565087
OPEC. (2012). Organization of the Petroleum Exporting Countries: Annual Statistical Bulletin 2012. Retrieved September 13, 2012 from http://www.opec.org/opec_web/static_files_project/media/downloads/publications/ASB2012.pdf
Oshlyansky, L., Cairns, P., & Thimbleby, H. (2007).Validating the unified theory of acceptance and use of technology (UTAUT) tool cross-culturally. In D. Ramduny-Ellis & D. Rachivides (Eds.). Proceedings of the HCI 2007, 2, BCS, 83-86.
References
Page 214
Oxendine, A., Borgida, E., Sullivan, J. L., & Jackson, M. S. (2003). The importance of trust and community in developing and maintaining a community electronic network. International Journal of Human-Computer Studies, 58(6), 671-196.
Ozolins, U. (2008). Issues of back translation methodology in medical translations. Proceedings of FIT [International Federation of Translators], XVIII Congress, Shanghai.
Pallant, J. (2005). SPSS survival manual: A step by step guide to data analysis using SPSS (2nd ed.). Crows Nest, NSW: Allen & Unwin.
Papadomichelaki, X., & Mentzas, G. (2012). e-GovQual: A multiple-item scale for assessing e-government service quality. Government Information Quarterly, 29(1), 98-109.
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-Qual: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-33.
Pavlou, P. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 69-103.
Pavlou, P., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115.
Phang, C. W., Li, Y., Sutanto, J., & Kankanhalli, A. (2005). Senior citizens’ adoption of e-government: In quest of the antecedents of perceived usefulness. Proceedings of the 38th Hawaii International Conference on System Science, pp. 1-8.
Pina, V., Torres, L., & Royo, S. (2010). Is e-government leading to more accountable and transparent local governments? An overall view. Financial Accountability & Management, 26(1), 3-20.
Prins, R., & Verhoef, P. C. (2007). Marketing communication drivers of adoption timing of a new e-service among existing customers. Journal of Marketing, 71(April), 169-183.
Punch, K. F. (2000). Developing effective research proposals. London: Sage Publications.
Punch, K. F. (2003). Survey research: The basics. London: Sage Publications.
Qaisar, N., & Khan, H. G. A. (2010). E-government challenges in public sector: A case study of Pakistan. IJCSI International Journal of Computer Science Issues, 7(5), 310-317.
Ralph, W. (1991). Help! The art of computer technical support. California: Peachpit Press.
Reddick, C. G. (2005) Citizen interaction with e-government: From the street to servers? Government Information Quarterly, 21(1), 51-64.
Reddick, C. G., & Frank, H. A. (2007). The perceived impacts of e-government on U. S. cities: A survey of Florida and Texas city managers. Government Information Quarterly, 24(3), 576-594.
Reddick, C. G., & Turner, M. (2012). Channel choice and public service delivery in
References
Page 215
Canada: Comparing e-government to traditional service delivery. Government Information Quarterly, 29(1), 1-11.
Reffat, R. (2006). Developing a successful e-government. Working Paper. University of Sydney.
Reichheld, P., & Schefter, P. (2000). E-loyalty: Your secret weapon on the web. Harvard Business Review, 78(4), 105-113.
Relyea, H. (2002). E-gov: Introduction and overview. Government Information Quarterly, 19(1), 9-35.
Rogers, E. (2003). Diffusion of innovations. New York: Free Press.
Rose, W. R., & Grant, G. G. (2010). Critical issues pertaining to the planning and implementation of e-government initiatives. Government Information Quarterly, 27(1), 26-33.
Rosen, P. (2005). The effect of personal innovativeness on technology acceptance and use. (Unpublished doctoral dissertation, Oklahoma State University).
Rotter, J. B. (1967). A new scale for the measurement of interpersonal trust. Journal of Personality, 35(4), 651.
Rouse, A. C., & Corbitt, B. (2008). There’s SEM and ‘SEM’: A critique of the use of PLS regression in information systems research. Paper presented at the Australasian Conference in Information Systems (ACIS).
Rowley, J. (2011). e-Government stakeholders – Who are they and what do they
want?. International Journal of Information Management, 31(1), 53-62.
Rust, R. T., & Kannan, P. K. (2002). E-service: New direction in theory and practice. Armonk, NY: M. E. Sharpe.
SADAD. (2008). Payments newsletter. SADAD Payment System, 2(10). Retrieved from http://www.sadad.com/Arabic/News/e-Newsletters Saudi E-government National Portal. (2012). Retrieved from http://www.saudi.gov.sa
Schaper, L. K., & Pervan, G. P. (2004). A model of information and communication technology acceptance and utilization by occupational therapists. Proceedings of the IFIP International Conference on Decision Support Systems. Prato, Italy.
Schaper, L. K., & Pervan, G. P. (2007). ICT and OTs: A model of information and communication technology acceptance and utilization by occupational therapists. International Journal of Medical Informatics, 76S(1), 212-221.
Schaupp, L. C., Carter, L., & McBride, M. E. (2010). E-file adoption: A study of US taxpayers’ intentions. Computers in Human Behavior, 26(4), 636-644.
Schaupp, L. C., Fan, W., & Belanger, F. (2006). Determining success for different website goals. Proceedings of the 39th Hawaii International Conference on System Sciences (HICSS ’06). Kauai, HI.
Scholl,H. & Klischewski, R. (2007). E-government integration and interoperability: Framing the research agenda. International Journal of Public Administration, 30(8/9), 889-920.
References
Page 216
Schuppan, T. (2009). E-government in developing countries: Experiences from sub-Saharan Africa. Government Information Quarterly, 26(1), 118-127.
Seifert, W. & Bonham, G. (2003). The transformative potential of e-government in transitional democracies. Public Management, 2 (3), 22.
Seifert, W. (2003). A primer on e-government: Sectors, stages, opportunities, and challenges of online governance. Congressional Research Service: The Library of Congress. Retrieved from http://www.fas.org/sgp/crs/RL31057.pdf
Sekaran, U. (2003). Research methods for business: A skill-building approach (4th ed.). John Wiley & Sons, Inc.
Serenko, A., Turel, O., & Yol, S. (2006). Moderating roles of user demographics in the American customer satisfaction model within the context of mobile services. Journal of Information Technology Management, 17(4), 20-32.
Shareef, M. A., Kumar, U., & Kumar, V. (2011). E-government development: Performance evaluation parameters. In M. Shareef, V. Kumar, U. Kumar, & Y. Dwivedi (Eds.), Stakeholder Adoption of E-Government Services: Driving and Resisting Factors (pp. 197-213). Hershey, PA: Information Science Reference. doi:10.4018/978-1-60960-601-5.ch010.
Shareef, M. A., Kumar, U., Kumar, V., & Dwivedi, Y. K. (2009). Identifying critical factors for adoption of e-government. Electronic Government: An International Journal, 6(1), 70-96.
Sharma, S., & Gupta, J. (2003). Building blocks of an e-government: A framework. Journal of Electronic Commerce in Organizations, 1(4), 1-15.
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modification and future research. Journal of Consumer Research, 15(3), 325-343.
Siau, K., & Long, Y., (2009). Factors impacting e-government development. The Journal of Computer Information Systems, 50(1), 98.
Singleton, R. A., & Straits, B. C. (2004). Approaches to social research (4th ed.). Oxford, UK: Oxford University Press.
Smith, S. & Jamieson, R. (2006). Determining key factors in e-government information system security, Information Systems Management, 23(3), 23-32.
Srivastava, S. C. (2011). Is e-government providing the promised returns? A value framework for assessing e-government impact. Transforming Government: People, Process and Policy, 5(2), 107-13.
Srivastava, S. C., & Teo, T. S. H. (2011). Development and impact of e-government: The intertwined role of e-commerce from a cross-country stakeholder's perspective. Electronic Government, an International Journal, 8(2/3), 144.
Stevens, J. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates
Stibbe, M. (2005). E-government security. Infosecurity Today, 2(3), 8-10.
References
Page 217
Stoltzfus, K. (2005). Motivations for implementing e-government: An investigation of the global phenomenon. Proceedings of the 2005 National Conference on Digital Government Research, Atlanta, Georgia, 333-338.
Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(24), 380-427.
Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-166.
Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies (IJHCS), 64(2), 53-78.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson/Allyn & Bacon.
Tan, C. W., Benbasat, I., & Cenfetelli, R. T. (2008, January). Building citizen trust towards e-government services: Do high quality websites matter? Proceedings of the 41th Hawaii International Conference on System Sciences (HICSS’08). doi: 10.1109/HICSS.2008.80.
Tan, C. W., Pan, S. L., Lim, E. T. K. (2005). Managing stakeholder interests in e-government implementation: Lessons learned from a Singapore e-government project. Journal of Global Information Management 13(1), 31-53.
Taylor, S., & Todd, P. (1995a). Decomposition and crossover effects in the theory of planned behaviour: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155.
Taylor, S. & Todd, P. (1995b). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.
Taylor, S., & Todd, P. (1995c). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.
Teicher, J., & Dow, N. (2002) E-government in Australia: Promise and progress. Information Policy, 7(4), 231-246.
Thomas, J. C., & Streib, G. (2003). The new face of government: Citizen-initiated contacts in the era of e-government. Journal of Public Administration and Theory, 13(1), 83-102.
Thompson, D., Rust, R., & Rhoda, J. (2005). The business value of e-government for small firms. International Journal of Service Industry Management, 16(4), 385-407.
Tolbert, C. J., & Mossberger, K. (2006). The effects of e-government on trust and confidence in government. Public Administration Review, 66(3), 354-369.
Tung, L., & Rieck, O. (2005). Adoption of electronic government services among business organizations in Singapore. Journal of Strategic Information Systems 14(4), 417-440.
Turocy, P. S. (2002). Survey research in athletic training: The scientific method of development and implementation. Journal of Athletic Training, 37(4S), 174-179.
References
Page 218
UNCITRAL (2009). Practice guide on cross-border insolvency cooperation. United Nations. Retrieved May 22, 2011 from http://www.uncitral.org/uncitral/en/uncitral_texts/insolvency/2009PracticeGuide.html
United Nations. (2004). UN global e-government readiness report: Toward access for opportunity. New York: United Nations.
United Nations. (2008). UN e-government survey 2008: From e-government to connected governance. New York: United Nations. Available from http://unpan1.un.org/intradoc/groups/public/documents/UN/UNPAN028607.pdf
United Nations. (2012). United Nations e-government survey 2012: E-government for the people. New York: United Nations Publishing. Retrieved 05/08/2012 from: http://unpan1.un.org/intradoc/groups/public/documents/un/unpan048065.pdf
UNPA, & ASPA. (2001). Benchmarking e-government, a global perspective: Assessing the progress of the UN member states. Retrieved from http://unpan1.un.org/intradoc/groups/ public/documents/un/unpan003984.pdf Urban, G., Cinda, A., & Antonio, L. (2009).Online trust: State of the art, new frontiers, and research potential. Journal of Interactive Marketing, 23(2), 179-90.
Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in the Netherlands. Information and Management, 40 (6), 541-549.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information System Research, 11(4), 342-365.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39(2), 273-315.
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly 25(1), 71-102.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., & Morris, M. G. (2000) Why don’t men ever stop to ask for directions? Gender, social influence and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3), 425-478.
Venkatesh, V., Morris, M. G., Sykes, T., & Ackerman, P. (2004). Individual reactions to new technologies in the workplace: The role of gender as a psychological construct. Journal of Applied Social Psychology, 34(3), 445-467.
Walliman, N. (2000). Your research project. A step-by-step guide for the first-time researcher. London: Sage Publications.
References
Page 219
Wang, H., Yang, H. (2005). The role of personality traits in UTAUT model under online stocking. Contemporary Management Research, 1(1), 69-82.
Wang, Y. D., & Emurian, H. H. (2005). An overview of online trust: Concepts, elements, and implications. Computers in Human Behavior, 21(5), 105-125.
Warkentin, M., Gefen, D., Pavlou, P., & Rose, M. (2002). Encouraging citizen adoption of e-government by building trust. Electronic Markets, 12(3), 157-162.
Weerakkody, V., El-Haddadeh, R., & Al-Shafi, S. (2011). Exploring the complexities of e-government implementation and diffusion in a developing country: Some lessons from the State of Qatar. Journal of Enterprise Information Management, 24(2), 172-196.
Weitzner, M. A., Meyers, C. A., Steinbruecker, S., Saleeba, A. K., & Sandifer, S. D. (1997). Developing a care giver quality-of-life instrument: Preliminary steps. Cancer Practice, 5(1), 25-31.
Welch, E., Hinnant, C., & Moon, M. (2005). Linking citizen satisfaction with e-government and trust in government. Journal of Public Administration Research and Theory, 15(3), 371-391.
Wescott, C. G. (2001). E‐government in the Asia‐pacific region. Asian Journal of Political Science, 9(2), 1-24.
West, D. M. (2004). E-government and the transformation of service delivery and citizen attitudes. Public Administration Review, 64(1), 15-27.
West, D. M. (2005). Digital government: Technology and public sector performance. Princeton, NJ: Princeton University Press.
Westin, A.F. (1976). Privacy and Freedom. New York NY, Atheneum.
Williams, P. (2002). The learning web: The development, implementation, and evaluation of Internet-based undergraduate materials for the teaching of key skills. Active Learning in Higher Education, 3(1), 40-53.
Wixom, B., & Todd, P. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.
Wolff, B., Knodel, J., & Sittitrai, W. (1993). Focus groups and surveys as complementary research methods: A case example. In D. L. Morgan (Ed.), Successful focus groups: Advancing the state of the art (pp. 118-138). Newbury Park, CA: Sage Publications.
World Bank. (2007). National e-government strategies: Designing for success. Retrieved May 13, 2010 from http://extsearch.worldbank.org/servlet/SiteSearchServlet?q=e government.
World Bank. (2009). Definition of e-government. Retrieved September 22, 2012 from http://go.worldbank.org/M1JHE0Z280.
Yang, K., & Rho, S. (2007). E-government for better performance: Promises, realities, and challenges. International Journal of Public Administration, 30(11), 1197-1217.
References
Page 220
YESSER E-government Team. (2006). The national e-government strategy and action plan: Yesser report. Ministry of Information and Communication Technology, Riyadh.
Yesser. (2010). E government project: Website of Saudi Arabian e-government. Retrieved October 8, 2011 from http://www.yesser.gov.sa/english/default.asp
Yildiz, M. (2007). E-government research: Reviewing the literature, limitations, and ways forward. Government Information Quarterly, 24(3), 646-665.
Yin, R. (2009). Case study research, design, and methods (4th ed.). Thousand Oaks, CA: Sage Publications.
Zhong, L. W., & Ying, J. A. (2008, October). The impact of website and offline quality on relationship quality: An empirical study on e-retailing. Proceedings of the 4th International Conference on Wireless Communications, Networking, and Mobile Computing Conference (WiCOM ‘08) , 1-5. doi: 10.1109/WiCom.2008.2011.
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking adoption. Computers in Human Behaviour, 26(4), 760-767.
Zwass, V. (2003). Electronic commerce and organisational innovation: Aspects and opportunities. International Journal of Electronic Commerce, 7(3), 7-37.
Appendix A: Survey Questionnaire (English Version)
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Appendix A: Survey Questionnaire (English Version) Respondent /
This survey is part of PhD research about e-government services adoption in the public sector in the Kingdom of Saudi Arabia from citizens’ perspective. The purpose of this research is to explore the factors that might facilitate or hinder e-government services adoption in public sector by utilizing the UTAUT model as adoption theory. The outcome of this study will help policy makers and e-government developers to take into account factors which experts consider as important in order to maximize the benefits and avoid the problems of an e-government services system. It would be greatly appreciated if you would take just a few minutes of your time to complete the questionnaire. Please follow the instructions, and complete the survey.
Your participation in this survey is completely voluntary; you don't have to respond to every item, and you can discontinue participation at any time without reprisals. The information collected during the study will only be used to accomplish the research requirements, and all responses provided on this survey will remain confidential. By reading this information and completing the survey below, your consent to participate in this study will be implied.
This research is conducted under the supervision of:
Dr Steve Drew: [email protected]
Dr Ann Nguyen: [email protected]
at the Faculty of Engineering and Information Technology at Griffith University in Australia.
If you have any questions or concerns about this questionnaire or the study, please do not hesitate to contact me through my email address: [email protected]
Also, if you have any concerns about the ethical conduct of the research, please contact:
Dr. Gary Allen Manager, Research Ethics Office for Research G39 room 3.55 Gold Coast Campus Griffith University Ph: 3735 5585 Fax: 5552 9058 Email: [email protected]
Thank you very much in advance for you cooperation and support.
Sincerely,
Mohammed Alshehri
PhD candidate, ICT School Griffith University, Australia [email protected]
Appendix A: Survey Questionnaire (English Version)
Page 222
Part 1: Personal Information: (Please tick the appropriate answer)
1. Gender:
Male Female
2. Age:
a. 20 or under b. 21-30 c. 31-40 d. 41-50 e. 51+
3. Education Level:
a. High School or below b. Diploma c. Bachelor’s degree d. Post-graduate degree
Part 2: Computer Knowledge and Internet Experience: (Please circle the
appropriate answer)
4. How do you describe your general computer knowledge?
a. Very poor b. Poor c. Moderate d. Good e. Very good
5. How would you describe your Internet knowledge?
a. Very poor b. Poor c. Moderate d. Good e. Very good
6. How long have you been using the Internet?
a. Don’t use b. Less than 1 year c. 1-3 years d. More than 3 years
7. How often do you use the Internet per day?
a. Less than 1 hour b. 1-2 hours c. 2-3 hours d. More than 3 hours
Part 3: UTAUT model questions: (Using a rating scale of 1 to 5, please circle the
number that indicates your level of disagreement/agreement with the following
statements)
No. Statements
Performance expectancy (PE) Strongly disagree disagree Neutral agree Strongly
agree
PE1
Using e-government services enables me to accomplish my needs from the public sector more quickly and more efficiently
1 2 3 4 5
PE2 Using the e-government services increases the equity between all citizens
1 2 3 4 5
Appendix A: Survey Questionnaire (English Version)
Page 223
PE3 Using e-government services would save citizens’ time 1 2 3 4 5
PE4 Using the e-government services increases the quality of services 1 2 3 4 5
Effort expectancy (EE)
EE1 Learning to use the e-government services system is easy 1 2 3 4 5
EE2 Using the e-government services system is easy 1 2 3 4 5
EE3 It is easy for me to become skilful at using the e-government services system
1 2 3 4 5
EE4 By using the e-government system, I am able to get government services easily.
1 2 3 4 5
Social influence (SI)
SI1 People who are important to me think that I should use e-government services.
1 2 3 4 5
SI2 People who influence my behaviour think I should use the e-government services.
1 2 3 4 5
SI3 I would use e-government services if my friends and colleagues used them.
1 2 3 4 5
SI4 Government sectors encourage citizens to use the e-government services system
1 2 3 4 5
Facilitating conditions (FC)
FC1 I have the resources necessary to use e-government services 1 2 3 4 5
FC2 I have the knowledge necessary to use e-government services 1 2 3 4 5
FC3
There is a specific person or group available for assistance with any technical problem I may encounter
1 2 3 4 5
Trust (TR)(New questions)
TR1 The Internet is trustworthy 1 2 3 4 5
TR2
I have confidence in the technology used by government agencies to operate the e-government services
1 2 3 4 5
TR3 Government agencies can be trusted to carry out online transactions faithfully
1 2 3 4 5
Appendix A: Survey Questionnaire (English Version)
Page 224
TR4 I believe that e-government services are trustworthy 1 2 3 4 5
Behavioural intention (BI)
BI1 I intend to use the e-government services in the next 12 months 1 2 3 4 5
BI2 I predict I will use the e-government services in the next 12 months
1 2 3 4 5
BI3 I plan to use the e-government services in the next 12 months 1 2 3 4 5
Website quality (WQ) (New questions)
WQ1 Government websites look secure and safe for carrying out transactions
1 2 3 4 5
WQ2 Government websites look attractive and uses fonts and colour properly.
1 2 3 4 5
WQ3 Government websites looks organized 1 2 3 4 5
WQ4 Government websites are always up and available 24/7 1 2 3 4 5
WQ5 Content of government websites are useful and updated. 1 2 3 4 5
Use Behaviour of e-government service (USE)
USE1 I really want to use e-government services to perform my government requests
1 2 3 4 5
USE2 I frequently use e-government services 1 2 3 4 5
USE3 I use e-government services on a regular basis 1 2 3 4 5
USE4 Most of my government requests are done through e-government services
1 2 3 4 5
Appendix A: Survey Questionnaire (English Version)
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Part 4: Barriers to e-government services adoption in the public sector (New questions) In your opinion, what are the barriers to e-government services adoption in the public
sector? Indicate all that apply: 0: Not a barrier; 1: important barrier; or 2: Very
important barrier.
No. Barriers Not a barrier
Important barrier
Very important
barrier
1 IT infrastructure weakness of the government public sector 0 1 2
2 Lack of knowledge and ability to use computers and technology efficiently 0 1 2
3 Lack of knowledge about the e-government services 0 1 2
4 Lack of security and privacy of information in government’s websites 0 1 2
5 Lack of users’ trust and confidence to use e-government services 0 1 2
6 Lack of policy and regulation for e-usage in the KSA 0 1 2
7 Lack of partnership and collaboration between government sectors 0 1 2
8 Lack of technical support from government websites support teams 0 1 2
9 Government employees’ resistance to change to e-ways 0 1 2
10 Shortage of financial resources in government sectors 0 1 2
11 Availability and reliability of Internet connection 0 1 2
12 Others (Please specify)……..
Part 5: General Questions: (New questions)
1. Have you ever heard about e-government services or have you used them before?
Yes
No
2. Do you prefer to do your transactions electronically or face to face?
Yes. Why? ..........................................
No. Why? ............................................
3. Do you think that e-government services can increase the transparency of
government procedures?
Yes
No
Appendix A: Survey Questionnaire (English Version)
Page 226
4. Do you think that e-government services are going to reduce corruption in
government secretors? How?
Yes
No
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
5. What other services do you think should be available online?
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
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.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
6. Are there any suggestions you would like to add here?
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
.....................................................................................................................................
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.....................................................................................................................................
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Appendix A: Survey Questionnaire (English Version)
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I would be very happy to send you a report of this study’s findings. If this interests
you, please provide your mailing or e-mail address below. Should you require
additional information, please do not hesitate to contact me at +61 (0) 431525960
(Aus.) or +966 (0) 505152015 (KSA) or by email: [email protected]. You
are also welcome to contact my thesis supervisor, Dr Steve Drew
([email protected]) for further information. Your cooperation is highly
appreciated.
Thank you for your time and participation.
Appendix B: Survey Questionnaire (Arabic Version)
Page 228
Appendix B: Survey Questionnaire (Arabic Version)
أستراليا -جامعة قريفث
كلية تكنولوجيا المعلومات والاتصالات
محمد الشهري: الباحث : المشرفون
ستيف دروو. د نجوين آن.د
العوامل المؤثرة على قبول واستخدام الخدمات الحكومية أستخدام النظرية الموحدة لدراسة : عنوان البحث
الالكترونية في المملكة العربية السعودية
/عزيزي المشارك
السلام عليكم و رحمة االله و بركاته ،،،
آمل منك المشاركة في تعبئة . أنا طالب دكتوراه في كلية تقنية المعلومات والاتصالات بجامعة قريفث باستراليا
.المرفق الاستبيان
موضوع الدراسة:
السعودية دراسة وتحليل العوامل المؤثرة في قبول واستخدام الخدمات الحكومية الالكترونية في المملكة العربية
.(UTAUT) باستخدام النظرية الموحدة لقبول واستخدام التقنية
الفائدة المتوقعة لهذا البحث:
ان ستستخدم لغرض البخث العلمي فقط، وهذا الاستبيان هو جزء من جميع المعلومات التي ستجمع في هذا الاستبي
دراسة دكتوراه يقوم بها الباحث لدراسة استخدام أنظمة الخدمات الحكومية الالكترونية في المملكة العربية
.السعودية
المشاركة تطوعية:
عنا موضع تقدير وامتنان، مع دقيقة ومشاركتك م 10إن تعبئة الاستبيان لن تستغرق من وقتك الثمين أكثر من
.العلم أن جميع المعلومات ستعامل بسرية تامة
الحصول على نتائج هذه الدراسة:
يمكنك مراسلة الباحث للحصول على النتائج النهائية لهذه الدراسة عن طريق البريد الالكتروني المدون بالأسفل
وفي جميع الحالات النتائج الأولية . الدراسةأو كتابة بريدك الالكتروني وطلب الحصول على موجز لنتائج
.إن شاء االله 2013بحلول نهاية شهرأبريل ستكون متاحة
Appendix B: Survey Questionnaire (Arabic Version)
Page 229
.وختاما، أشكر لك مشاركتك ووقت الثمين في تعبئة هذا لاستبيان
وتقبل فائق الاحترام والتقدير،،،
:يرجى الاتصال بـأو الاستفسارات المتعلقة بهذا الاستبيان معلوماتللحصول على المزيد من ال
The Manager for Research Ethics,
Office for Research,
Bray Centre, Nathan Campus,
Griffith University
Ph: +61 7 3735 5585 or
المشرف الدراسيستيف درو.د
ICT School, GC campus Griffith University
Ph: +61 7 5552 7088 [email protected]
محمد الشهري: الباحث
تكنولوجيا المعلومات والاتصالات
أستراليا -جامعة قريفث
M:+61 431525960(AUS)
+ 966505152015(KSA)
Appendix B: Survey Questionnaire (Arabic Version)
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معلومات شخصية/ الجزء الأول
:الجنس
ذكر أنثى
: العمر -2
20 51+ 50-41 40-31 30-21 أو أقل
: المؤهل الدراسي -3
دراسات عليا بكالوريوس دبلوم أقل أو عامة ثانوية
معرفتك بالحاسب الآلي/ لجزء الثانيا
الآلي؟ الحاسب في مستواك تقيم كيف -1
جدا جيد جيد متوسط ضعيف جدا ضعيف
؟)الانترنت( العنكبوتية بالشبكة معرفتك تقييم كيف -2
جدا ضعيف ضعيف متوسط جدا جيد جيد
الانترنت؟ تستخدم وأنت متى منذ -3
سنة من أقل أستخدمه لا سنة 2 -1 سنتين من أكثر
يوميا؟ الانترنت لتصفح الوقت من تقضي كم -3
ساعة من أقل 3-2 ساعة 1-2 ساعة 3 من أكثر ساعات
الأداء المتوقعلا أوافق
بشدة موافق محايد لا أوافق
أوافق
بشدة
استخدام الخدمات الحكومية الالكترونية 1
يساعدني على أنجاز معاملاتي الحكومية
.بسرعة وسهولة
أسئلة النظرية الموحدة لاستخدام التقنية/ الجزء الثالث
مدى اتفاقك أو مخالفتك للنقاط التالية؟ وما ه
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استخدام الخدمات الحكومية الالكترونية 2
يزيد فرص العدل والمساواة بين
.المواطنين
استخدام الخدمات الحكومية الالكترونية 3
.يحفظ وقت المواطنين وجهدهم
استخدام الخدمات الالكترونية يزيد من 4
.جودة الخدمات المقدمة للمواطنين
الجهد المتوقع
.تعلم نظام الخدمات الالكترونية سهل 1
.نظام الخدمات الالكترونية سهل استخدام 2
الالكترونية تعلم نظام الخدمات الحكومية 3
.بكفاءة هو أمر سهل بالنسبة لي
استخدام نظام الحكومة الالكترونية يسهل 4
.على الحصول على الخدمات الحكومية
التأثير الاجتماعي
المهمون بالنسبة لي يعتقدون الأشخاص 1
أنه يجب علي استخدام الخدمات الحكومية
.الالكترونية
الأشخاص الذين لهم تأثير على قراراتي 2
يعتقدون أنه يجب استخدام الخدمات
.الحكومية الالكترونية
ة قد أستخدم الخدمات الحكومية الالكتروني 3
.إذا استخدمها زملائي و أصدقائي
القطاعات الحكومية تشجع المواطنين 4
.استخدام الخدمات الالكترونيةعلى
التسهيلات
لدي المتطلبات الضرورية لاستخدام 1
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.الخدمات الحكومية الالكترونية
لدي المعرفة الكافية لاستخدام الخدمات 2
.الحكومية الالكترونية
هناك شخص أو مجموعة أشخاص تقنيين 3
في الجهات الحكومية لتقديم الدعم و
.المساندة في حال الحاجة لهم
الموثوقية و الأمان
.اعتقد أن الانترنت موثوقة بشكل كاف 1
لدي الثقة في الأدوات والبرامج 2
المستخدمة في تقديم الخدمات الحكومية
.الالكترونية
القطاعات الحكومية موثوقة وقادرة على 3
.تقديم خدمات الكترونية موثوقة ومؤتمنة
من وجهه نظري أن الخدمات الحكومية 4
.الالكترونية موثوقة
)النية الداخلية(الدوافع الذاتية
لدي الرغبة في استخدم الخدمات 1
الحكومية الالكترونية خلال الاثنا عشر
.القادمةشهر
أعتقد أنني سأستخدم الخدمات الحكومية 2
الالكترونية خلال الاثنا عشر شهر
.القادمة
لدي خطة لاستخدام الخدمات الخدمات 3
الحكومية الالكترونية خلال الاثنا عشر
.شهر القادمة
جودة المواقع الحكومية الالكترونية
المواقع الحكومية تبدو أمنة وموثوقة 1
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.لتقديم الخدمات الحكومية الالكترونية
المواقع الحكومية تبدو جذابة وتستخدم 2
.الألوان والخطوط بشكل صحيح
المواقع الحكومية تبدو منظمة بشكل 3
.ممتاز
المواقع الحكومية دائما تعمل علي مدار 4
.أيام اسبوعياً 7ساعة و 24
محتوى المواقع الحكومية مفيد جداً وكامل 5
.ومحدث
)سلوك الاستخدام(الاستخدام الفعلي للخدمات الحكومية الالكترونية
استخدم الخدمات أرغب بكل تأكيد أن 1
طلباتي لتنفيذ الحكومة الالكترونية
.الحكومية ومعاملاتي
الحكومة الالكترونية الخدمات أستخدم 2
.كثيرا
الحكومة الالكترونية الخدمات أستخدم 3
.بشكل منتظم
معظم طلباتي الحكومية التي أحتاجها تتم من 4
.خلال الخدمات الحكومة الإلكترونية
عائق مهم جدا عائق مهم ليس بعائق العائق الرقم
ضعف البنية التحتية في القطاعات الحكومية 1
الحاسب الألي قلة المعرفة والقدرة على استخدام 2
وبرامجه
الحكومية للخدمات الالكترونيةعوائق تبني الجهات / الجزء الرابع
:من وجهة نظرك، قيم عوائق تبني الخدمات الالكترونية في الجهات الحكومية حسب التالي ليس بعائق: 0 عائق مهم: 1 عائق مهم جدا: 2
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قلة المعرفة والعلم بالخدمات الحكومية الالكترونية 3
ضعف الأمن والخصوصية في محتوى المواقع 4
الحكومية
المستخدم في المواقع الحكوميةضعف ثقه 5
قلة التشريعات والقوانين المتعلقة بالتعاملات 6
الالكترونية في المملكة
قلة التعاون بين الجهات الحكومية 7
الحكومية من قبل ضعف الدعم الفني للمواقع 8
موظفيها التقنيين
مقاومة الموظفين الحكومية للتغييرمن الطريقة 9
التقليدية إلى الطريقة الالكترونية
الخدمات قلة الموارد المالية المخصصة لدعم 10
الحكومية
توفر و استقرار خدمة الانترنت 11
سبق وسمعت عن الخدمات الحكومية الالكترونية أو استخدمت أيا منها ؟ هل
لا نعم .1
؟ لماذا و شخصيا الحكومية الجهة بزيارة أو الكترونيا الحكومية معاملاتك اداء تفضل هل
لا نعم .2
: لماذا
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أسئلة عامة/ الجزء الخامس
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؟ الحكومية الإجراءات في الوضوح و الشفافية من تزيد الالكترونية الحكومية الخدمات أن تعتقد هل
لا نعم .3
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؟ الحكومية الجهات ستقلل من الفساد الاداري في الالكترونية الحكومية الخدمات أن تعتقد هل .4
لا نعم .5
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؟ الكترونياً واستخدامها وجودها ترشح التي الحكومية الخدمات ماهي .6
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، يرجى كتابتها هنا الباحث تفيد قد أو اقتراحات اضافات أية لديك إذا كان .7 :
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انتهت اسئلة الاستبانة
على تفضلك بالمشاركة شكرا لك
Appendix C: Focus Groups Guide
Page 237
Appendix C: Focus Groups Guide
The Topic:
Using the UTAUT Model to Determine Factors Affecting Acceptance and Use of
E-government Services in the Kingdom of Saudi Arabia
The Aims:
1. To collect complementary data from Saudi citizens and IT staff about the
adoption of e-services in the KSA.
2. To identify the factors that influence the adoption of e-services from the
perspectives of services providers and citizens.
3. To test the UTAUT hypothesis and validate the quantitative analysis findings.
Instructions to conduct the interview:
1. Make an appointment in advance with all participants and agree on a specific
time and place to meet for the focus group interview.
2. Welcome all participants and give them an overview of the study and its aims.
3. Give an introduction about the research model UTAUT and explain its
constructs
4. Clarify some key concepts like e-government, e-services, adoption, and trust.
5. Ask for permission to record the focus group interview. Explain the nature of
confidentiality and privacy of the information.
6. Inform the participants that they can refuse to answer any question. Give the
participant an opportunity for comments at any time and at the end of the
session.
7. Value the time and cooperation of the participants.
Appendix C: Focus Groups Guide
Page 238
Respondent/
This focus group is part of PhD research which is about e-government services
adoption in the public sector in the Kingdom of Saudi Arabia. The purpose of this
research is to explore the factors that might facilitate or hinder e-government services
adoption in the public sector by utilizing the UTAUT model as the adopted theory.
The outcome of this study will help policy makers and e-government developers to
take into account factors which experts consider important in order to maximize the
benefits and avoid the problems of an e-government services system.
It would be greatly appreciated if you could take a few minutes of your time to
participate in this interview. Your participation in this focus group is completely
voluntary. You do not have to respond to every item, and you can discontinue
participation at any time without reprisals. The information collected during the study
will only be used to accomplish the research requirements, and all responses provided
in this survey will remain confidential. By reading this information and completing
the survey below, your consent to participate in this study will be implied.
This research is conducted under the supervision of:
Dr Steve Drew: [email protected]
Dr Ann Nguyen: [email protected]
at the Faculty of Engineering and Information Technology,
Griffith University, Australia
Thank you very much in advance for your cooperation and support.
Sincerely,
Mohammed Alshehri
PhD Candidate, ICT School
Griffith University, Australia
Appendix C: Focus Groups Guide
Page 239
Part 1: Participants demographics
Participant 1 Participant 2 Participant 3 Participant 4 Participant 5
Position
Education level
Age
Internet Experience
Date / /
Duration (hours)
Part 2: UTAUT Model Questions:
No. Statements
Performance Expectancy (PE) Response and Comments
PE1 Using e-government services enables me
to accomplish my public sector needs
more quickly and efficiently.
PE2 Using e-government services increases the
equity between all citizens.
PE3 Using e-government services would save
citizens’ time.
PE4 Using e-government services increases the
quality of services.
Effort Expectancy (EE) Response and Comments
EE1 Learning e-government services system is
easy.
EE2 Using e-government services system is
easy.
EE3 It is easy for me to become skilful at using
the e-government services system.
Appendix C: Focus Groups Guide
Page 240
EE4 By using the e-government system I able
to obtain government services easily.
Social Influence (SI) Response and Comments
SI1 People who are important to me think that
I should use e-government services.
SI2 People who influence my behaviour think
I should use e-government services.
SI3 I would use e-government services if my
friends and colleagues used them.
SI4 The government sectors encourage
citizens to use e-government services.
Facilitating Conditions (FC) Response and Comments
FC1 I have the resources necessary to use
e-government services.
FC2 I have the knowledge necessary to use
e-government services.
FC3
There is a specific person or group
available for assistance with any technical
problems I may encounter.
Trust (TR) Response and Comments
TR1 The Internet is trustworthy.
TR2 I have confidence in the technology used
by government agencies to operate
e-government services.
TR3 Government agencies can be trusted to
carry out online transactions faithfully.
TR4 I believe that e-government services are
trustworthy.
Behavioural Intention (BI) Response and Comments
BI1 I intend to use e-government services in
the next 12 months.
Appendix C: Focus Groups Guide
Page 241
BI2 I predict I will use e-government services
in the next 12 months.
BI3 I plan to use the e-government services in
the next 12 months.
Website Quality (WQ) Response and Comments
WQ1 Government websites looks secure and
safe for carrying out transactions.
WQ2 Government websites look attractive and
uses fonts and colour properly.
WQ3 Government websites look organized.
WQ4 Government websites are always up and
running and are available 24/7.
WQ5 Content of government websites are
useful and updated.
Use Behaviour of E-government Services (USE) Response and Comments
USE1 I really want to use e-government services
to perform my government requests.
USE2 I frequently use e-government services.
USE3 I use e-government services on a regular
basis.
USE4 Most of my government requests are done
through e-government services.
Part 3: Final Question
Are there any additional comments, which you feel would be helpful to this study? In
particular, are there any difficulties, barriers, important factors, or considerations
which have not been mentioned?
…………………………………………………………………………………………
………………………………………………………………………………………….
…………………………………………………………………………………………
…………………………………………………………………………………………
Appendix C: Focus Groups Guide
Page 242
…………………………………………………………………………………………
…………………………………………………………………………………………
…………………………………………………………………………………………
………………………………………………………………………………………….
Thank you for your time and participation.
Appendix D: List of Abbreviations
Page 243
Appendix D: List of Abbreviations
AMOS Analysis of Moment Structures
ARAMCO Saudi Oil Company
AUD Australia Dollar
AVE average variance extracted
BI Behavioural intention
CFA Confirmatory Factor Analysis
CFI Comparative Fit Index
CFI Goodness-of-Fit-Index
CITA Communication and Information Technology Authority
CITC Communications and Information Technology Commission
df degree of freedom
DOI Diffusion of Innovation Model
EBPP Electronic Bill Presentment and Payment
E-Commerce Electronic Commerce
EE Effort expectancy
EFA Exploratory Factor Analysis
EIU Economist Intelligence Unit
EoL end-of-life
E-waste Electronic Waste
FC Facilitating conditions
G2B Government to Business
G2C Government to Citizen
G2E Government to Employee
G2G Government to Government
Appendix D: List of Abbreviations
Page 244
GFI goodness-of-fit index
ICT Information and Communication Technology
IFI incremental-fit index
IS Information System
IT Information Technology
KACST King Abdul-Aziz City for Science and Technology
KMO Kaiser-Meyer-Olkin
KSA Kingdom of Saudi Arabia
MCIT Ministry of Communication and Information Technology
MM Motivational Model
MOEP Ministry of Economy and Planning
MPCU Model of PC Utilisation
NIC National Information Centre
NICTP National ICT plan
OECD Organisation for Economic Co-operation and Development
PBC perceived behavioural control
PE Performance Expectancy
PEOU perceived-ease-of-use
PKI Public Key Infrastructure
PLS Partial Least Squares
PU perceived usefulness
RMSEA Root Mean Square Error of Approximation
SADAD E- Payment gateway
SAMA Saudi Arabian Monetary Agency
SCT Social Cognitive Theory
SEM Structural Equation Modeling
Appendix D: List of Abbreviations
Page 245
SI Social influence
SPSS Statistical Packages for the Social Sciences
TAM Technology Acceptance Model
TAM2 Extension of the Technology Acceptance Model
TLI Tucker-Lewis index
TPB Theory of Planned Behaviour
TR Trust
TRA Theory of Reasoned Action
UN United Nations
UNCITRAL United Nations Commission on International Trade Law
UQ University of Queensland
USE USE behaviour
UTAUT Unified Theory of Acceptance and Use of Technology
WQ Website quality
χ2 Chi-square
Appendix F:Ethical Clearance Certificate
Page 246
Appendix F: Ethical Clearance Certificate
HUMAN RESEARCH ETHICS COMMITTEE
ETHICAL CLEARANCE CERTIFICATE
This certificate generated on 03-07-2012.
This certificate confirms that protocol 'NR: Identifying the Factors Influencing the Adoption of E-government Services in the Public Sector in Saudi Arabia by Using UTAUT Model' (GU Protocol Number ICT/02/10/HREC) has ethical clearance from the Griffith University Human Research Ethics Committee (HREC) and has been issued with authorisation to be commenced.
The ethical clearance for this protocol runs from 18-10-2011 to 31-07-2012.
The named members of the research team for this protocol are:
Dr Steve Drew
Mr Mohammed Alshehri
The research team has been sent correspondence that lists the standard conditions of ethical clearance that apply to Griffith University protocols.
The HREC is established in accordance with the National Statement on Ethical Conduct on Research Involving Humans. The operation of this Committee is outlined in the HREC Standard Operating Procedure, which is available from www.gu.edu.au/or/ethics
Please do not hesitate to contact me if you have any further queries about this matter.
Dr Gary Allen Manager, Research Ethics Office for Research N54 room 0.10 Nathan Campus Griffith University Phone: 07 3735 5585 Facsimile: 07 373 57994 Email: [email protected]