Using the UTAUT Model to Determine Factors Affecting

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

https://research-repository.griffith.edu.au

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

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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

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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






9.4 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

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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

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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

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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

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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


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


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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


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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


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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


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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;


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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.


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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


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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.


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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.


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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.


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.


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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


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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

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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


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.


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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


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


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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).


Page 19

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


Page 20

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;


Page 21

• 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


Page 24

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


Page 25

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,


Page 26

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


Page 27

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).


Page 28

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


Page 29

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


Page 30

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


Page 31

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

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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


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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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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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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

http://thesaurus.reference.com/browse/accuracy


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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)


Effort Expectancy

(EE)


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


H3c: EE-BI to use e-government services is stronger for experienced users


H6c: FC-USE to use e-government services is stronger for experienced 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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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.

Chapter 6: 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


Page 99

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


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


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


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.

Chapter 7: Measurement Scale Analysis

Page 103


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,


Page 109

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


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



Bartlett’s Test of Sphericity

Approx. Chi-Square 733.503

df 6

Sig. 0.000


Page 110

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



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





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



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


loadings of the items are higher than the cut-off level. ). It is concluded that the seven

item scale measures of trust are unidimensional.



Approx. Chi-Square 305.687 df 3 Sig. 0.000


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Table27 7.18

Coding of Trust Variables


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





df 21

Sig. 0.000


Page 115

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


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


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





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


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


Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.728



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.

Chapter 8: Model Assessment

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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


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


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


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

Chapter 9: Qualitative Data Analysis

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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





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




10 Lack of security and privacy of information for government websites 381 46.6



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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



39 65.0



36 60.0








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










10 Lack of security and privacy of information on government websites 21 35.0


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.


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.


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).


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.


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.


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


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


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



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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


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


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.

Chapter 10: Discussion and Conclusion

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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).


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).


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.


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


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


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


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.


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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


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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.


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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


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.


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.


Page 197

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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.

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Appendix A: Survey Questionnaire (English Version)

Page 221

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]


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


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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


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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


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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


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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?

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

6. Are there any suggestions you would like to add here?

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

.....................................................................................................................................

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Page 227

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


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


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.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

[email protected]

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)

[email protected]


Page 230

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


Page 231


يزيد فرص العدل والمساواة بين

.المواطنين


.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


Page 232

.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


Page 233

.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


Page 234

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


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.


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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

[email protected]


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.


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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


FC2 I have the knowledge necessary to use


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


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.


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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?

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Thank you for your time and participation.

Appendix D: List of Abbreviations

Page 243


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


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


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]

Documents

Using the UTAUT Model to Determine Factors Affecting