Factors Affecting E-commerceAdoption in Saudi Arabia From

The Web-User’s Perspective

Badr Alshuwai

A Dissertation submitted tothe School of Computing Sciences of the University of East Anglia

in partial fulfilment of the requirements for the degree of Master of Science

c©This copy of the Dissertation has been supplied on condition that anyone who consults

it is understood to recognise that its copyright rests with the author and that no quotation

from the Dissertation, nor any information derived therefrom, may be published without

the author or supervisor’s prior written consent.

Abstract

Based on an Extended Technology Acceptance Model (TAM) along with the Theoryof Planned Behaviour (TPB), an integrated model is developed that can be used toidentify factors affecting web users in adopting e-commerce in Saudi Arabia (KSA). Inorder to gain a better understanding of web-users behaviours toward online shopping,fourteen hypotheses are compiled into the proposed model, i.e. Demographic factors(gender, age, education level and income), Cultural influence, Awareness of service,Trust, Security, Facilitating conditions, Subjective norm, Perceived behavioural con-trol, Attitude towards using online shopping, Adoption intention and Actual use ofonline shopping. The data were collected through an online survey using random andsnowball sampling techniques, targeting web users in KSA. Both genders in the fivemain regions (Northern, Southern, Central, Eastern and Western) in Saudi Arabiawere targeted. Overall, 515 questionnaires were obtained and analysed. Quantitativemethods were employed to analyse data in this study.

The results show that the proposed hypotheses are accepted except for four, whichconsidered the relationship between age, education level, income and awareness ofservice with attitude toward using e-commerce; these are considered to have no sig-nificant relationship. The Saudi infrastructural conditions and suggestions for im-provement are also discussed. The findings suggest that the proposed integratedmodel serves as a very good predictor of consumers online purchasing behaviours.The linear regression model shows a respectable amount of variance, explaining theadoption and actual use of online shopping (R2 = 0.641). Suggestions are providedfor practitioners and ideas are presented for future research.

i

Acknowledgments

Praise belongs to Allah the Lord of the worlds; and may peace and blessings be uponthe seal of the Prophet Muhammad, and upon his family and all his companions tothe Day of Judgment.

First of all, my deep thanks to Almighty Allah for enabling me to complete thisdissertation. Without his blessings none of this work would have been possible.

Secondly, I would like to express my sincere gratitude and appreciation to my par-ents, especially to my brother Faisal for his continual support; without him, this workcould not have been completed.

I am grateful also to my loving wife Amal for her support and help, especially forher kindness and patience when I was busy with my Master’s programme; also to mylittle lovely sons Muhammad and Saleh, I am grateful also to the parents of my wife.

Thirdly, I would like also to express my deep gratitude to my supervisor, Mr. RobertVieira, for all his assistance, support and guidance, which I appreciate enormously;he made this project possible.

In addition, I am also greatly indebted to those who lent me a hand, one way oranother, in my work on this project, and whose help did not go unnoticed, but whomI did not name, and all my relatives, friends and colleagues, who offered their help,sympathy and/or prayers. Thanks to Mr. Mubarak Aldwsry for his guidance andassistance.

Finally, I would like to express my appreciation to all those who participated inevaluating, pre-testing and piloting the questionnaires; all persons who offered metheir time while I was collecting the data necessary for my research; and indeed allwho assisted and participated in my research in whatever capacity. The author wouldalso like to forward his gratitude to those people who were willing to take part in thesurveys and who provided important feedback, facilitating the progress of this work.

ii

Contents

Abstract i

Acknowledgments ii

1 Background 1

1.1 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 The Research Problem and Research Motivation . . . . . . . . . . . . 2

1.3 Research Aim and Objectives . . . . . . . . . . . . . . . . . . . . . . 2

1.3.1 Sub-objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Limitations of The Project . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Literature Review 4

2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 History of E-commerce . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 E-commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.4 The Benefits of Adopting E-commerce . . . . . . . . . . . . . . . . . 6

2.4.1 Benefits to Individuals . . . . . . . . . . . . . . . . . . . . . . 7

2.4.2 Benefits to Businesses . . . . . . . . . . . . . . . . . . . . . . 7

2.5 Limitations in the Adoption of E-commerce . . . . . . . . . . . . . . 8

2.6 E-Commerce in Developing Countries . . . . . . . . . . . . . . . . . . 8

2.7 The Lack of Sophisticated Infrastructure in KSA . . . . . . . . . . . . 8

2.7.1 Growth in Internet Usage Against Slower Adoption of E-commerce 9

2.7.2 Internet Filtration . . . . . . . . . . . . . . . . . . . . . . . . 11

2.7.3 Government E-readiness . . . . . . . . . . . . . . . . . . . . . 11

2.7.4 The Absence of an Adequate Addressing System for Homes andShops in KSA . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.7.5 Private Business E-commerce Penetration . . . . . . . . . . . 13

iii

3 Theoretical Framework 15

3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2 Technology Acceptance Model (TAM) . . . . . . . . . . . . . . . . . . 15

3.3 Theory of Planned Behaviour (TPB) . . . . . . . . . . . . . . . . . . 16

3.4 Extending the TAM Model . . . . . . . . . . . . . . . . . . . . . . . . 17

3.5 Research Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.6 Factors Affecting E-Commerce Adoption in KSA by Web-Users . . . 19

3.6.1 Demographic Factors . . . . . . . . . . . . . . . . . . . . . . . 19

3.6.2 The Gender Factor . . . . . . . . . . . . . . . . . . . . . . . . 19

3.6.3 The Age Factor . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.6.4 Education Level . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.6.5 Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.6.6 Cultural Influence . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.6.7 Awareness of Online Shopping . . . . . . . . . . . . . . . . . . 22

3.6.8 English Language . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.6.9 Religion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.6.10 Enjoyment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.6.11 Trust and Security Regarding E-commerce . . . . . . . . . . . 23

3.6.12 Attitudes Toward Using . . . . . . . . . . . . . . . . . . . . . 24

3.6.13 Behavioural Intention . . . . . . . . . . . . . . . . . . . . . . . 24

3.6.14 Perceived Behaviour Control . . . . . . . . . . . . . . . . . . . 24

3.6.15 Subjective Norm . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.6.16 Facilitating Conditions . . . . . . . . . . . . . . . . . . . . . . 25

3.6.17 Actual Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4 Research Methodology 27

4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2 Available Data Collection Methods . . . . . . . . . . . . . . . . . . . 27

4.3 Collecting Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.4 Secondary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.5 Primary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.6 Research Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

iv

4.7 Questionnaire Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.8 Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.9 Sampling Techniques and Size . . . . . . . . . . . . . . . . . . . . . . 32

4.10 Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.11 The Procedure of Distributing the Questionnaire . . . . . . . . . . . . 33

5 Results, Analysis and Discussion 34

5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.2 Instrument Reliability Results . . . . . . . . . . . . . . . . . . . . . . 34

5.3 Response Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.4 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.5 Analysis Results of hypotheses . . . . . . . . . . . . . . . . . . . . . . 40

5.5.1 Identifying Multicollinearity . . . . . . . . . . . . . . . . . . . 45

5.6 Multiple Regression Results . . . . . . . . . . . . . . . . . . . . . . . 46

5.6.1 Explaining Web-Users’ Attitude Toward Using Online Shopping 47

5.6.2 Explaining Web-Users’ Behavioural Intention to Adopt andContinue to Use Online Shopping . . . . . . . . . . . . . . . . 47

5.6.3 Explaining Web-Users’ Actual Use of Online Shopping . . . . 48

5.6.4 Summary of the Hypotheses and Results . . . . . . . . . . . . 49

5.7 Findings Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.7.1 Effect of behavioural intention on actual use of online shopping 50

5.7.2 Effect of perceived behavioural control on actual use and be-havioural intention . . . . . . . . . . . . . . . . . . . . . . . . 50

5.7.3 Effect of facilitating conditions on actual use of online shopping 50

5.7.4 Effect of trust and security on attitude toward using onlineshopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.7.5 Effect of subjective norm on behavioural intention . . . . . . . 51

5.7.6 Effect of attitude toward using online shopping on behaviouralintention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.7.7 Effect of cultural influence on attitude toward using onlineshopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.7.8 Effect of gender on attitude toward using online shopping . . . 52

5.7.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.8 Descriptive Analysis of Major Findings for Infrastructural Conditions 53

v

5.8.1 Government E-readiness . . . . . . . . . . . . . . . . . . . . . 54

5.8.2 Satisfaction with Internet Service . . . . . . . . . . . . . . . . 54

5.8.3 Satisfaction with Postal Service . . . . . . . . . . . . . . . . . 54

5.8.4 Private Business E-commerce Penetration . . . . . . . . . . . 54

6 Conclusion and Recommendations 55

6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.2 Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.4 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . 57

6.5 Professional and Ethical Issues . . . . . . . . . . . . . . . . . . . . . . 58

6.6 Personal Achievements of This Study . . . . . . . . . . . . . . . . . . 58

6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

bibliography 60

Appendix 68

A Questionnaires: Arabic and English 69

B Measurement of the Constructs 83

C Research Instrument and Reliability Test Results 85

D Multiple Regression Results 87

E Reliability Analysis of Cronbach’s Alpha 90

F Collinearity and Multicollinearity Results 94

G Some difficulties in publishing the questionnaire and plan B 95

H An example of the procedure of distributing the questionnaire 96

vi

List of Figures

2.1 A revised conceptual model of B2C e-commerce [Whiteley, 2000] . . . 5

2.2 The Number of Internet Users [CITC, 2012] . . . . . . . . . . . . . . 9

2.3 Total Fixed Broadband Subscriptions [CITC, 2012] . . . . . . . . . . 9

2.4 Total Mobile Broadband Subscriptions [CITC, 2012] . . . . . . . . . . 10

2.5 Saudi Usage of Internet Content [CITC, 2010] . . . . . . . . . . . . . 10

2.6 Postal address system is the missing link that affects e-commerce pro-cess in KSA [Aleid et al., 2009] . . . . . . . . . . . . . . . . . . . . . 13

2.7 Enterprises’ Use of E-Commerce [CITC, 2010] . . . . . . . . . . . . . 13

3.1 The original Technology Acceptance Model (TAM) [Davis, 1989] . . . 15

3.2 Theory of Planned Behavior (TPB) [Ajzen, 1991] . . . . . . . . . . . 16

3.3 Amoroso and Hunsinger Research Model [Amoroso et al., 2009] . . . 17

3.4 Research Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.1 Research Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.2 Likert scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.1 Response Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2 The number of individuals that support overseas sites . . . . . . . . . 38

5.3 Type of goods and services that individuals purchase . . . . . . . . . 38

5.4 The way that individuals pay for goods and services . . . . . . . . . . 39

5.5 The way that individuals receive goods and services . . . . . . . . . . 39

5.6 Correlations matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.7 An assessment of the proposed model . . . . . . . . . . . . . . . . . . 46

5.8 Satisfaction with Saudi infrastructural conditions in general . . . . . 53

vii

List of Tables

5.1 Demographic Statistics of Respondents . . . . . . . . . . . . . . . . . 36

5.2 Demographic Statistics of Respondents . . . . . . . . . . . . . . . . . 37

5.3 Regression results of web-users’ attitudes toward using online shopping 47

5.4 Regression results of web-users’ behavioural intention to adopt andcontinue using online shopping . . . . . . . . . . . . . . . . . . . . . . 48

5.5 Regression results of web-users’ actual use of online shopping . . . . . 49

5.6 Summary of results for the hypotheses . . . . . . . . . . . . . . . . . 49

viii

Chapter 1

Background

Since the Internet was made available to the public, the old fashioned way of con-ducting business, i.e. face to face, has changed forever. The Internet has becomethe main means for advertising and selling products and services, with hundreds ofonline stores. The developing countries represent 80% of the world’s population, andthis makes these countries a most suitable area for the growth of e-commerce. As faras the Saudi case is considered, opening the Internet to the public in 1999 has driventhe IT market to reach $3.8 billion (as of 2011) and it considered to be the biggestin the Gulf region [ystats.com, 2011].

Although it has been noticed that adopting e-commerce has a positive impact ona nation’s economy, the presence of e-commerce in developing countries is still verylow [Aleid et al., 2009]. Accordingly, the objective of this research is to focus onboth the current and potential uses of e-commerce in the Kingdom of Saudi Arabia(KSA). This study aims to answer the question: What are the main factors affectinge-commerce adoption in KSA from web-users’ perspectives. In addition, the factorsthat lead to the slow take-up of e-commerce are highlighted in order to overcomeissues that are related to the usage, acceptance and adoption rate of e-commerce viadesigning appropriate strategies. Moreover, online surveys are used to cover as muchas possible of the five main regions of KSA (the Northern, Southern, Central, Easternand Western Regions). In addition, the study mainly focuses on the web-users’ pointof view; 515 participants are involved in this research. As far as the term e-commerceis concerned, within the context of this study, e-commerce is generally equivalent toonline shopping.

1.1 Research Questions

What are the main factors (both enablers and barriers) that influence e-commerce(online shopping) adoption in KSA from web-users’ perspectives?

What aspects of society and infrastructure have the effect of slowing e-commercepractices?

1

How can the findings derived from this study be used to advance e-commerce in KSAand other Gulf and Middle East countries in similar contexts?

1.2 The Research Problem and Research Motiva-

tion

[Ha and Stoel, 2009] argue that the success of any new technology depends mainlyon its familiarity and acceptance among the upcoming users. This study seeks toanswer the following primary question:

What are the factors that affect web-users in KSA in making purchases online?

Although the importance of e-commerce is widely recognised, this field is still in itsinitial stages in KSA, with low adoption rates. Recently, a number of e-commerce-related activities, e.g. seminars, conferences and workshops have taken place in KSA,organized mainly by the Ministry of Commerce and the private sector [Altamimi,2009]. Taking into account that KSA is the only Arab member of the Group ofTwenty Finance Ministers and Central Bank Governors [G20, 2012], this makes thiscase study interesting and unique. Moreover, since Saudi Arabia joined the WorldTrade Organization (WTO) in 2005, competition in local markets should increase.Therefore, it is essential that KSA is not left behind in new era open markets and inthe Internet revolution. Finally, only a limited number of researchers have attemptedto investigate the major inhibitors and enablers adopting e-commerce in Saudi Arabiaand assessing existing practices.

1.3 Research Aim and Objectives

This research’s aim is to examine an extension of the Technology Acceptance Model(TAM) model of [Amoroso et al., 2009] and the Theory of Planned Behaviour (TPB)by [Ajzen, 1991], and (by reviewing the latest research findings on relevant topics)to provide an integrated model that can further identify web-users’ adoption factorswith respect to e-commerce in KSA, thereby gaining a better understanding of web-users’ behaviours towards e-commerce.

The main objective of this study is to investigate how each of several different factorsconstructed in the proposed research model influences web-users in shopping onlinein KSA.

2

1.3.1 Sub-objectives

1. To build a framework model that can be used to predict Internet-users’ be-haviours towards online shopping.

2. To identify how the key determinants influence Internet users in shopping on-line. Moreover, to identify the effect of attitude, subjective norm and perceivedbehaviour control on the intention to use e-commerce.

3. To define the difficulties that Internet users in KSA face during their experiencewith online shopping.

4. To generate suggestions that could assist in designing long-term plans for mar-keting and adapting e-commerce.

1.4 Limitations of The Project

There are some restrictions and limitations in this study that might affect the findings.Based on the time available for this study, online surveys will be used in order tocover as much as possible of the five main regions of KSA, as there are differencein culture, area, modernity and population. This study will cover the web-user’sperspective towards the adoption of e-commerce.

3

Chapter 2

Literature Review

2.1 Overview

The chapter will start by highlighting the history of e-commerce and will draw aclear picture of the benefits that can be obtained from adopting e-commerce for bothindividuals and businesses. Following this, there will be a brief description of thelimitations in terms of the adoption of e-commerce. Moreover, it will describe thecurrent state of e-commerce in Saudi Arabia. The main aim of this chapter is tohighlight the lack of sophisticated infrastructure in KSA.

2.2 History of E-commerce

E-commerce was developed as a result of the introduction of Electronic Data Inter-change (EDI) and Electronic Funds Transfer (EFT), both of which were introduced inthe late 1970s, and were confined to sending commercial documents such as purchaseorders or invoices electronically. Credit cards, telephone banking and automatedteller machines (ATM) were also forms of e-commerce that were widely accepted inthe 1980s [Johnston, 1998]. In the early 1980s, ATM was further developed for com-puter networks, as an extension to EFT to undertake financial transactions [Barnesand Hunt, 2000]. In 1991, when the World Wide Web was opened up for commercialuse, several firms established their own websites. Therefore, the growth of e-commercecan be attributed to Internet availability and the World Wide Web’s popularity since1994 [Turban et al., 2004]. The need to deal with security issues led to the estab-lishment of security protocols such as hyper text transfer protocol (HTTP). DigitalSubscriber Line (DSL) was first developed in the 1980s but was later adopted toprovide fast and consistent connection to the Internet. Moreover, by the year 2000,e-commerce was acknowledged as a means for doing business [eMarketer, 2002]; thisincludes the buying and selling of products and services via secured connection overthe Internet.

4

2.3 E-commerce

The definition of e-commerce can be interpreted in many ways due to the diverserange of research projects. E-commerce can be defined as an all electronically me-diated information exchanges between organizations and their external stakeholders[Chaffey, 2006]. Furthermore, e-commerce refers to the use of information and com-munication technology (ICT) in the same manner [Beynon, 2004]. E-commerce dealswith transactions and the selling of goods and services on the Internet [Goel, 2007, Li,2007]. A possible business-to-consumer (B2C) e-commerce process is shown below inFigure 2.1:

Figure 2.1: A revised conceptual model of B2C e-commerce [Whiteley, 2000]

Generally, there are four main models in e-commerce, namely: Business to Business(B2B), Business to Consumer (B2C), Consumer to Consumer (C2C) and Consumerto Business (C2B) [Kenneth and Carol, 2008]. This research will focus on one of themost important e-commerce models: Business to Consumer (B2C) [Goel, 2007].

The most commonly discussed e-commerce model is B2C, while B2B is the largestform of e-commerce [Laudon and Traver, 2003]. Amazon was the largest Americanonline vendor within the B2C sector in 2009, with a turnover of 24 billion USD. Therewas a rapid growth within the B2C sector and a turnover of more than $170 billionwere estimated in the USA alone in 2011 [ystats.com, 2011]. Nowadays, global onlinetransactions do an estimated $10 trillion annually. In the USA alone, according to theUS Census, an estimated $3.4 trillion of domestic online transactions were conductedin 2009.

Since the early 1990s, when online shopping was introduced as an e-commerce ap-plication, many consumers have directed their attention to using online shopping forpurchasing diversified products and services [Turban et al., 2004]. Given that thepopularity of the Internet is drawing more attention towards online stores, the ac-ceptance of online shopping needs to be universally viewed from the web-user’s pointof view.

5

Many firms are attempting to globalise their business. Some have created onlinestores besides their retail stores, and many run their businesses using online storesalone. Firms that run online shops work hard to offer the best possible services totheir consumers, and therefore, the Internet is considered as a primary means of ad-vertising and selling products and services globally, with hundreds of online stores.

Almost every transaction can now be done via the Internet. Customers pay their util-ity bills using smart phones; they purchase books and magazines, electronic equip-ment (e.g. computers, mobiles, games, accessories), fashion, jewellery and beautyitems, software packages, songs, films and games, as well as services (e.g. flightand hotel reservations, paying for distance learning courses), wherever and wheneverthe want. Many firms, from the smallest local store to the largest multinationalcorporation, order goods, pay merchants and sell to consumers over the Internet[commerce.gov, 2011].

The rules of business have changed since the Internet was introduced; similarly, cus-tomers’ banking experiences changed when ATMs were introduced [Schwartz, 1997].This may be due to the fact that, with the convenience, speed and ease that Internettechnology provides, consumers are more comfortable conducting their purchases on-line, rather than going to a physical store [Hui and Wan, 2007]. For example, ratherthan having the hassle of going to a physical store, purchasing software and thengoing back home to do the program installation on a computer, an online store sendsthe purchased software directly to the buyer’s e-mail.

2.4 The Benefits of Adopting E-commerce

Although early e-commerce (in the 1970s) was mainly used to support electronictransactions, advances in technology have introduced a new way of conducting busi-ness. Companies are investing heavily in Internet-based business in order to establishand maintain their share of the e-business market. E-business costumers gain manybenefits from online shopping, e.g. convenient shopping time, views and ideas throughreviews, a variety of choices in terms of stores and products, and search and com-parison tools. Therefore, the importance of online shopping can be seen by bothconsumers and suppliers. Adopting e-commerce has a positive impact on economicgrowth, providing several benefits for both customers and organisations, as shown onthe following page:

6

2.4.1 Benefits to Individuals

1. Shopping 24/7 with delivery service; shop at any time and receive purchases atany address, e.g. home or office [Anckar, 2003, Turban et al., 2008].

2. Global reach; customers can reach any product at any place. Hence, consumerswill save time and money as well as obtaining obvious environmental benefitsby avoiding travel and pollution [Turban et al., 2008, Whiteley, 2000].

3. Wider choices; consumers can compare stores and prices, and choose betweenwide selections of products and services [Anckar, 2003].

4. Saving time; shopping online saves time and allows consumers to access exten-sive information on products and services, such as an images, videos, detailsand specifications [Alnemer, 2009].

2.4.2 Benefits to Businesses

1. Reduced purchase prices; companies can reduce operational and employee costsand offer more competitive prices [Alnemer, 2009].

2. Offering fast services; companies can seek customer satisfaction via rapid ser-vices [AbouSaber, 2007].

3. Creating new markets; businesses can expand and increase their profits [AbouS-aber, 2007, Alnemer, 2009].

4. Building a close relationship with consumers; companies can promote and im-prove their products and services globally in respect of consumers needs [Kaynaket al., 2005].

5. Decreasing cost operations and employees; customers can shop online, whichleads to a significant cost reduction on operations and employees [Altamimi,2009].

6. Presenting and updating information; e-businesses present and update theirinformation quickly and it becomes available much faster than in traditionalshopping [Choi and Geistfeld, 2004].

7

2.5 Limitations in the Adoption of E-commerce

There are several limitations in the adoption of e-commerce; these can broadly begrouped into technical and non-technical limitations, as follows:

• Technical limitations: these include the cost of developing and maintaining web-sites, up-dating and changing software, limited telecommunications bandwidth,transaction error-correction systems, and the requirement of integrated digitaland non-digital sales [Humphrey et al., 2003, Alrayes, 2009].

• Non-technical limitations: these include privacy and security issues, absence ofobvious regulations, and lack of appropriate knowledge and skills to deal withnew generation markets [Draut and Silva, 2003, Alrayes, 2009].

2.6 E-Commerce in Developing Countries

The developing countries represent 80% of the population of the world, which makesthese countries suitable for economic growth and possibilities of success in e-commerce.According to a recent study, 35.6% of the population in the Middle East now use theInternet [Internetworldstats, 2012]. Theoretically, it has been stated that e-commercehas many benefits to consumers and enterprises in developing nations. Neverthe-less, tangible results of implementing e-commerce in these enterprises have not beenachieved to date [Alwahaishi et al., 2009]. Customer issues need to be considered inorder to gain a better understanding of the adoption and diffusion of e-commerce indeveloping countries. In this research, web user attitudes toward e-commerce adop-tion in KSA are presented and discussed. In addition, the absence of adequate basicinfrastructure has created a major barrier in the adoption and growth of e-commercein developing countries [Molla and Licker, 2005]. The following section discussesthese conditions in the context of Saudi Arabia, which is the targeted country in thisresearch.

2.7 The Lack of Sophisticated Infrastructure in

KSA

In 2001, the Saudi Ministry of Commerce established an e-commerce committee inorder to set up a general framework to prepare for e-commerce systems in KSA.This included implementing IT infrastructures, payment methods, addressing securityissues, legislation and regulations, and delivery systems [SMC, 2001]. The lack of therequired infrastructure will be explained in the following sections.

8

2.7.1 Growth in Internet Usage Against Slower Adoption ofE-commerce

The first use of the Internet in KSA was in King Faisal Hospital in 1994 and waslimited to certain organizations until 1999 when the Internet was opened for publicsubscription [Mirza, 1998, Alnemer, 2009]. Moreover, the number of Internet usersincreased dramatically from approximately one million in 2001 to 13.6 million bythe end of 2011 as seen in Figure 2.2 [CITC, 2012]. The spread of Internet serviceswas associated with the rapid growth in broadband services, (DSL) subscriptions,(Wimax) subscriptions and other fixed lines. In addition, the total subscriptions tomobile broadband have grown dramatically, as shown in Figure 2.3 and Figure 2.4on the following pages [CITC, 2012].

Figure 2.2: The Number of Internet Users [CITC, 2012]

Figure 2.3: Total Fixed Broadband Subscriptions [CITC, 2012]

9

Figure 2.4: Total Mobile Broadband Subscriptions [CITC, 2012]

Figure 2.5: Saudi Usage of Internet Content [CITC, 2010]

However, Saudi usage of Internet content varies, as shown in Figure 2.5 and thepractice of e-commerce activities are rather low. When considering the ICT infras-tructure in KSA, one of the most common obstacles to e-commerce adoption is thepoor quality of Internet connections, the costs and weak technical support. Clearly,utilizing the Internet for e-commerce is impossible if the quality of the connection ispoor [Al-Somali et al., 2009]. A recent study stated that most Saudi customers arenot happy with the current Internet services, especially those who live in small citiesand villages [AlGhamdi et al., 2011]. On the other hand, however, in the businessenvironment, 67% of organisations are satisfied with the Internet services they re-ceive, which implies that a better quality of service is provided to those organizations[CITC, 2009].

Applying e-commerce in KSA involves five elements working together to achieve themain aim; these include government, Internet providers, the postal service, enter-prises and customers. KSA is currently considered the largest country in the MiddleEast in terms of computer use. The IT market in KSA is considered to be the largest

10

in the Gulf region, valued at $3.8 billion in 2011, and is predicted to increase to $5.7billion by 2016. This includes sales of PCs, notebooks, accessories and local software[ystats, 2012].

Therefore, it is essential to identify the barriers to the adoption of e-commerce byweb-users in order to overcome them, and thus bring widespread advantages to bothindividuals and businesses [Sait et al., 2007].

It is also essential to make a number of improvements and investments in the existingtelecommunication infrastructure, which are needed in order to provide a high qualityof Internet services to the general public. KSA budget reports demonstrate that thespending on telecommunication infrastructure in 2008 was $3.227 billion, comparedto $2.613 billion in 2006. By 2012, a massive investment of nearly $10 billion hadbeen allocated to improve the infrastructure of the telecommunication sector. As aresult of this investment, such barriers are expected to be reduced or removed withinthe next few years [MinistryofFinance, 2012].

2.7.2 Internet Filtration

The Internet content in KSA is monitored by the King Abdulaziz City for Science andTechnology (KACST). KACST has been responsible for blocking websites that mightdisagree with Islamic laws and Saudi cultural values. This topic is hotly debatedamong Saudi citizens; some see it as an essential tool to guard against the spread ofmaterial that conflicts with Islamic laws and Saudi cultural values, such as sexuallyexplicit content. Others see it as a serious barrier hampering the development ofthe Internet and its applications in KSA, including e-commerce. However, thereare a number of drawbacks of Internet filtration; these are as follows [Al-Furaih,2002, Zittrain and Edelman, 2002, AbouSaber, 2007]:

• Over the Internet, individuals may use techniques and applications to circum-vent the filtration process.

• Further proxies have been created by KACST, which have resulted in moredelays in Internet access.

• As a result of the imposed regulations, a number of Internet service providershave been removed from the KSA market, and hence, online shopping has beenaffected by the considerable cost of Internet access, as a result of the lack ofcompetition in the KSA market.

2.7.3 Government E-readiness

In order to encourage robust establishment of e-commerce, it is necessary that reg-ulations are implemented. Such a step would support Internet usage and maintaingrowth of e-commerce activities. Setting up an appropriate flexible modern legal sys-tem is highly important for both developed and developing countries. Therefore, the

11

local legal environment should not consider its local IT related laws, but rather, takeinto account international legal systems [Blythe, 2007]. Moreover, individuals shouldbe aware of where and how to raise complaints in the case of issues while shoppingover the Internet. In a recent research, it was identified that 81.6% of the Saudi par-ticipants were of the opinion that Saudi Arabia should establish clear laws regardingonline services, which would protect all the involved parties’ rights in e-commercetransactions [Alhomaid, 2010]. Currently, there are no laws governing e-commerce inKSA [AlGhamdi et al., 2011, AlGhamdi et al., 2012].

Governments have a significant role to play in building reliable technological infras-tructure. The existence of e-commerce and IT application are driven by governmentaid [Goh, 1995]. Moreover, when government takes the lead in supporting businesses,individuals are likely to be encouraged and move closer towards adapting to onlineshopping [O’Cass and Fenech, 2003].

2.7.4 The Absence of an Adequate Addressing System forHomes and Shops in KSA

One of the most desired benefits of e-commerce is that consumers can purchase prod-ucts online and receive them at home easily and quickly [Dubelaar et al., 2005].Therefore, the lack of a postal addressing system makes the delivery process morecomplicated, as people are unable to receive items directly to their homes; rather,they must go to a post office to collect them [Aleid et al., 2009].The KSA has beenslow in implementing a postal addressing system. However, recently, the Saudi postalagency has started a project to develop the postal services it offers to include deliveryof items direct to homes and offices [AbouSaber, 2007]. For instance, ’Wasel’ is is anew mail service that allows residents to receive mail at their homes. Unfortunately,these services are still relatively new and do not cover all Saudi cities, and only 2%of the inhabitants own a mailbox [Post, 2010, Alriyadh, 2010]. This low percentagesuggests that there are certain factors that restrain citizens from applying to owna mailbox; these include the lack of awareness of the availability of mailboxes anddistrust in the security of receiving mail via this new service [AlGhamdi et al., 2012].Inaddition, in May 2011, the Saudi Post launched another new service called ’WaselAalami’, which enables its subscribers to buy from global e-markets such as Amazonand e-Bay by accessing four postal addresses free of charge in the UK, the USA, China and Australia. ’Wasel Aalami’, which is used in these four countries, allowsubscribers to ship their purchases from the global retail shops to their homes in KSAat competitive shipping prices [Post, 2012]. However, these services are relatively newto Saudi citizens and the existing postal addressing system in KSA is not conduciveto the development of e-commerce. Greater efforts are essential in order to improveand modernize the current addressing system, as the lack of postal addresses is clearlyconsidered to be a major barrier to the development of e-commerce in the country,and is the missing link shown in Figure 2.6 on the following page:

12

Figure 2.6: Postal address system is the missing link that affects e-commerce process inKSA [Aleid et al., 2009]

2.7.5 Private Business E-commerce Penetration

Businesses are now taking advantage of e-commerce, which is proving a significanttool in increasing an organization’s profits and customer numbers. However, althoughmost Saudi companies own websites, implementation of e-commerce is still poor.According to a recent study conducted in KSA, only 8% of businesses currentlysell their products or services online (see Figure 2.7 below); estimations indicatethat B2B was at the top of the list representing 54%, B2C at 28%, and B2G at18% [CITC, 2010]. For instance, the largest telecom company, the Saudi TelecomCompany (STC), still does not allow online payment for e-commerce purposes forits products (e.g. smartphones) or services (e.g. Broadband and Internet services).Moreover, only 10% of Saudi companies implement e-commerce activities on theirwebsites, and only 2.5% of this proportion offer an online payment system; othersstill use the cash on delivery (COD) method. In addition, with regard to e-commerce,it is noteworthy that only 3% of organizations implement e-government initiativeswhere users are able to carry out their activities online. This implies that KSA ismore concerned with developing e-government rather than e-commerce [CITC, 2009].

Figure 2.7: Enterprises’ Use of E-Commerce [CITC, 2010]

13

A further barrier to the development of e-commerce in local markets in KSA is thelack of IT companies and technology experts. This obstacle prevents businesses fromchoosing to implement or to expand in the area of online shopping. Moreover, in thelocal market, the larger companies often lack competition; this can be attributed tothe fact that there are only a few online shops in KSA [Aleid et al., 2009, AlGhamdiet al., 2011]. A recent study identified that everyday essentials were not available inlocal online stores. Furthermore, product features were not usually fully explainedand sometimes not known [AlGhamdi et al., 2011]. Therefore, building a good repu-tation, together with offering competitive prices and a wide choice of online sales areimportant factors that encourage people to shop online [Anckar, 2003, Kaynak et al.,2005, Alnemer, 2009].

14

Chapter 3

Theoretical Framework

3.1 Overview

The chapter will review some related theoretical frameworks in order to developan integrated model that can further explain the factors that affect web users inshopping online. In addition, this chapter describes those factors and proposes therelated hypotheses.

3.2 Technology Acceptance Model (TAM)

TAM was developed by [Davis, 1989]. It is considered as one of the most employedmodels for studying user behaviour toward technology, as described in [Hung et al.,2008]. It is constructed to measure two dimensions with respect to users’ intentionto use technology.

• Attitude toward using technology.

• Perceived usefulness (PU) and perceived ease of use (PEOU).

PU is the perception of a user accepting a specific technology, while PEOU is theuser’s perception about how easy it is to use a particular technology [Hung et al.,2008].

Figure 3.1: The original Technology Acceptance Model (TAM) [Davis, 1989]

15

3.3 Theory of Planned Behaviour (TPB)

TPB was introduced by [Ajzen, 1991]; it is appropriate for examining a user’s be-havioural control, as a third antecedent intention to address the limitation of theprevious model [Hung et al., 2008]. TTPB has three dimensions, as follows:

• Attitude (ATT)

• Subjective norms (SN)

• Perceived behaviour control (PBC)

Figure 3.2: Theory of Planned Behavior (TPB) [Ajzen, 1991]

16

3.4 Extending the TAM Model

[Amoroso et al., 2009] revised and extended the TAM model in order to study var-ious factors that may, to different extents, affect consumers’ acceptance of onlineshopping. These factors are shown in Figure 3.3; they include perceived usefulness,perceived ease of use, experience using the Internet, external variables (e.g. privacy,risk, trust, e-loyalty, perceived value), behavioural intention, perceived behaviouralcontrol, attitude toward using, social influence, facilitating conditions and voluntari-ness.

Figure 3.3: Amoroso and Hunsinger Research Model [Amoroso et al., 2009]

3.5 Research Model

An integrated model is here developed from the Extended Technology AcceptanceModel (TAM) of [Amoroso et al., 2009], the Theory of Planned Behaviour (TPB)[Ajzen, 1991], and form reviewing the latest research findings on relevant topics.This has been done to provide an expanded model that can further identify the fac-tors affecting web-users’ adoption of online shopping in KSA and to gain a betterunderstanding of web-user behaviours towards online shopping. However, because ofthe common perception of the usefulness and ease of use of the Internet in KSA, somefactors from previous models have been ignored, for instance, perceived usefulness,perceived ease of use, and experience using the Internet. Also, individuals from dif-ferent age groups and education levels have different experience in using the Internet,

17

and so these are considered. Figure 3.4 shows the factors used in the research model;these factors affect web-users in their actual usage of online shopping. Each factorwas hypothesised in order to study the relationships between the factors and theiraffect on the actual use of online shopping.

Figure 3.4: Research Model

18

3.6 Factors Affecting E-Commerce Adoption in KSA

by Web-Users

There are a number of factors affecting web-users’ adoption of e-commerce. Thesefactors will be broadly discussed and examined by integrating the extended TAM[Amoroso et al., 2009], the TPB [Ajzen, 1991], and the latest research findings onrelated topics. Therefore, this section describes those factors and proposes the relatedhypotheses.

3.6.1 Demographic Factors

The effect of demographic factors on the usage of technology has been investigatedsince the 1990s [Bellman et al., 1999, Donthu and Garcia, 1999, Li et al., 1999, Liaoand Cheung, 2001, Susskind, 2004, Mahmood et al., 2004, Zhou, 2007]. Demographicfactors such as gender, age, educational status and income have been considered inmany recent researches. In this section, demographic factors will be studied withregard to their influence on adopting e-commerce.

3.6.2 The Gender Factor

In terms of gender, many researchers claim that there is a difference between malesand females in utilising different types of technology [Burke, 2002]. This is supportedby a recent study conducted by [Zhou, 2007], who suggested that females are still notconvinced or are doubtful about online shopping benefits and advantages. Similarly,[Garbarino and Strahilevitz, 2004] indicate that females are still concerned and wor-ried in terms of the risks and threats associated with online shopping.

In addition, [Dittmar et al., 2004] highlighted that women have a more positive atti-tude toward traditional shopping than online shopping, whereas men’s attitudes arealmost the same. This can be attributed to the fact that from a cultural point ofview in some developing countries, men are more likely to have access to the Internetthan women, and that possibly this is the reason behind men doing online shoppingmore than women [Li et al., 1999, Susskind, 2004, Masrek et al., 2007].

Furthermore, a recent study was conducted in order to explore gender differencesin online shopping attitude in terms of three attitudinal components: (cognition,effect and behaviour). The study demonstrates that men have more positive onlineshopping attitudes than women in all three attitudinal components [Hasan, 2010].According to the literature, women seem to prefer traditional shopping and considerit more practical and convenient. Moreover, a study suggested that when measuringthe possibility of shopping online among people, 2.5 men are willing to shop onlineagainst every woman [Rodgers and Harris, 2003].

19

As far as gender differences are considered, researches have pointed out that malecomputer users seem to be less anxious and more tolerant when using computers,compared with female users [Durndell and Haag, 2002]. As a result, the hypothesisis:

H1: There is a positive match between gender and usage of online shopping. Thisleads to men coming first when it comes to adopting online shopping.

3.6.3 The Age Factor

Many studies agree that the acceptance of new technologies has a strong relationshipwith age. It has been stated that younger people are more interested in utilizing newtechnologies [Czaja et al., 2001, Lu et al., 2003]. For instance, Turkish people agedunder 35 are more likely to purchase online than are those aged 35 and above [Haqueet al., 2009]. Moreover, [Jones and Tollous, 2007] found that respondents in the USAassessed online shopping in a positive way. The study demonstrated that youngerpeople aged under 25 were more likely to use online shopping than people aged 35and over.

On the other hand, other researchers such as [Li et al., 1999, Joines et al., 2003, Rohmand Swaminathan, 2004] have found that there is either a negative, positive or norelationship. As a result, the different outcomes of the various researches may bedue to defining the age group being categorized differently. Moreover, it is noticeablethat younger people are more interested and capable of using computers and othertechnical devices. In addition, age seems to be associated with familiarity of usingcomputers and people needs and interests as they grow up. Therefore, older peopleseem to be less likely to do online shopping compared to younger people. In KSA,more than 70% of the population is under 30 years old [Alriyadh, 2012].

Therefore, it is important to distinguish whether young citizens in KSA are morelikely to adopt and be interested in using online shopping or not. Thus, the followinghypothesis is proposed:

H2: There is a positive match between age and attitude towards using online shop-ping in KSA. Young customers are more likely to adopt online shopping.

3.6.4 Education Level

Education level has a significant impact on attitudes toward technology. Therefore,educated people are more likely to be engaged with technology, e.g. e-commerceactivities. According to [Burke, 2002], an individual’s level of Internet literacy andInternet use are positively associated with education.

20

As pointed out in [Moorman, 1990], the capacity to process information among peo-ple depends on their educational level. However, using the Internet for shopping doesnot indicate that a user is well educated. Some studies suggest that there is a posi-tive relationship between the educational level and the use of online shopping, whileother studies have refuted this [Bellman et al., 1999, Donthu and Garcia, 1999, Liet al., 1999, Liao and Cheung, 2001, Susskind, 2004, Mahmood et al., 2004]. Thisdisagreement may be due to the fact that online shopping is considered, to someextent, an easy task, which does not need a good education. Given the above points,the hypothesis will be proposed as follows:

H3: There is a positive match between educational level and usage of online shopping.This leads to university graduates and above coming first when it comes to adoptingonline shopping.

3.6.5 Income

The use of technology for e-commerce purposes seems to be influenced by an individ-uals’ income. Researches have shown there is a match between an individuals’ incomeand online shopping. In addition, other studies have pointed out that online shoppingmay help in saving money, compared with traditional shopping [Donthu and Garcia,1999, Korgaonkar and Wolin, 1999, Li et al., 1999, Mahmood et al., 2004, Susskind,2004]. Furthermore, [Venkatesh and Morris, 2000] found that people aged between26 and 45 are likely to be in better jobs with higher incomes. Given these points, thehypothesis will be proposed as follows:

H4: There is a positive match between individuals’ income and their attitudes to-wards online shopping. Therefore, individuals with high incomes are more likely totake the lead when it comes to adopting online shopping.

The findings of previous researches show that factors such as gender, age, educationalstatus and income have an effect on an individual’s attitude towards online shopping.Therefore, in this study these factors will be examined with reference to Saudi societyin order to identify their affects on the attitudes of individuals towards online shoppingin KSA.

3.6.6 Cultural Influence

It is important to take into account cultural differences when evaluating the adoptionof e-commerce services. It is mostly true for developing countries in the Middle Eastthat culture has an influence [Al Sukkar and Hasan, 2005]. Moreover, the conceptof culture represents a complex structure that is constantly changing and develop-ing, as individuals from different cultures interact with each other in different ways[Al Sukkar and Hasan, 2005]. This is supported by several studies that demonstratethat culture has an impact on the adoption of online shopping [Barbonis and Laspita,

21

2005]. Given the above points, the hypothesis will be proposed as follows:

H5: There is a positive match between the cultural influence and the usage of onlineshopping

3.6.7 Awareness of Online Shopping

Having people informed about the possible benefits and risks of e-commerce activitiesshould help in raising awareness about such activities, and should support accept-ing and adapting to it. [Minges and Union, 2002, Jennex et al., 2004, Molla andHeeks, 2007] pointed out that people in some developing countries are less awareof e-commerce activities, and that therefore, enterprises have not fully utilised e-commerce activities in their business. Furthermore, awareness of product featuresis essential; Saudi customers may prefer to hold and check the product in person.According to a recent study, 73.7% of the Saudis involved agreed that it is necessaryto hold and check the product before purchasing it [Alhomaid, 2010]. This can beattributed to the fact that what people receive in the post may not necessarily bewhat they want [Groucutt and Griseri, 2004]. For instance, when it comes to buying alaptop, many Saudis prefer to go to a real computer shop, meet the buyer personally,and check and try out the laptop before buying it, rather than buying a laptop froma store over the Internet. Given this, the hypothesis will be proposed as follows:

H6: There is a positive match between individuals’ awareness and their attitudes to-wards online shopping. Therefore, people who are less aware of e-commerce activitieswould be slower when it comes to adopting online shopping.

3.6.8 English Language

Arabic is the official language in KSA. Although most Saudi websites support Arabic,its implementation in e-commerce is still poor. The problem is particularly appar-ent when considering the fact that most foreign e-commerce websites do not supportArabic. Moreover, most Saudi’s cannot communicate effectively with others who can-not speak Arabic [Kurdi, 2008]. According to [Sharma and Mokthar, 2008], around80% of web content is in English, so any lack in English language skills can ham-per the adoption of e-commerce in KSA. In addition, the number of Internet usersfor commercial purposes within the Islamic world is still low and limited to moreeducated individuals [Kshetri, 2008]. Language problems can clearly prevent Saudisfrom engaging in e-commerce with websites that do not support Arabic. Therefore,this factor will be examined implicitly under cultural influence.

3.6.9 Religion

Religion might be considered as a barrier to online shopping adoption, which mayprevent some users from using new technology. Nowadays, most Saudi banks have

22

solved the problem of having interest charges and late fees for delayed payments byintroducing Islamic credit cards, which have no interest on payment processes. Arecent research demonstrated that religion is no longer a barrier [Aleid et al., 2009].

3.6.10 Enjoyment

Enjoyment has a positive impact on customers’ intentions to use new technology [Al-maghrabi et al., 2010]. For instance, the use of zoom-in or simulation can improvecustomer acceptance of online shopping [Joines et al., 2003]. Furthermore, well-designed websites positively influence customer responses, which leads to increasedonline purchases and the desire to browse [Kim and Srivastava, 2007]. In addition, animage that has two dimensions presents a low level of interactivity; the ability to makeproduct features more clearly presented in a website is essential. Therefore, providing3D technology can assist consumers in making decisions and can lead to increasedonline purchases through making online shopping much easier [Kim and Srivastava,2007]. Therefore, this factor will be examined implicitly in Attitude toward onlineshopping acceptance.

3.6.11 Trust and Security Regarding E-commerce

Each time a new trend is introduced, e.g. e-commerce, nefarious individuals maytry to disrupt and exploit the new pathway. Many threats are associated with e-commerce, e.g. hackers, spammers, cybercriminals and cheats. For instance, eachday, there are 67,000 new threats, including viruses, worms, malicious spyware andother threats [commerce.gov, 2011]. However, the battle goes on and many defencemethods are being introduced on a daily basis e.g. antivirus programs, encryptionmethods and laws.

Although online shopping is growing, security and trust combined together seem torepresent a significant issue that is influencing the development of online shopping.A study by [Fenech and OCass, 2001] indicated that Internet users who see the webas an insecure environment are less likely to adopt online shopping. Moreover, inanother research by [Rodgers and Harris, 2003], women have been found to trust theweb less than men. In addition, it has been reported that public networks in KSAseem to be less secure and require more protection. Therefore, many people thinktwice before sending their personal information over the Internet in KSA [Al-Gahtani,2003]. Given this, the hypotheses will be proposed as follows:

H7: There is a positive match between trust and the attitudes towards online shop-ping.

H8: There is a positive match between security and attitudes toward online shopping.

23

3.6.12 Attitudes Toward Using

Attitude broadly refers to the state of the individual when performing the activityand weather he/she is positive or negative about it. Attitude toward use of onlineshopping is one of the factors that affect the behaviour of online customers [Taylor andTodd, 1995, Lim and Dubinsky, 2004]. Furthermore, individuals’ attitudes towardusage of online shopping have been studied in recent years [Lim and Dubinsky, 2004].Most of the studies have focused on defining the factors that influence the usage ofonline shopping. However, here there is a special focus on studying behaviour andattitudes toward usage of online shopping. Attitudes toward online shopping can bedescribed as a personal feature that may influence the perception of a certain onlineshopping site [Lim and Dubinsky, 2004]. In addition, attitudes toward online shop-ping is heavily influenced by customers’ experience with the web, no matter how oldthey are [Lee and Johnson, 2002]. Moreover, Goodwin suggested that understandingthe attitudes of customers toward online shopping assists in establishing and fulfillinga successful online marketing plan [Goodwin, 1999]. Given this, the hypothesis willbe proposed and tested as follows:

H9: There is a positive match between attitudes toward using online shopping andbehavioural intentions to do online purchasing. Therefore, having positive attitudestoward using online shopping result in higher intentions to do online purchasing.

3.6.13 Behavioural Intention

A recent study was carried out by [Elgarah and Falaleeva, 2005] in order to examinethe effect of the adoption of biometric technology on behavioural intention. The re-searchers predicted that information privacy has no impact on behavioural intentionwhen the usage of a system is compulsory, but it has a negative impact when theusage is optional. Moreover, many researchers have examined the correlation betweenexperience in using the Internet and the individual’s behavioural intention to use theInternet [Shim et al., 2001, George, 2002, Amoroso and Hunsinger, 2009]. The studiessuggest that there is strong direct correlation between these two variables. In addi-tion, [Amoroso and Hunsinger, 2009] investigated whether behavioural intention hasan impact on consumers’ online shopping, and their study suggest that behaviouralintention does have a positive impact on actual use. Given this, the hypothesis willbe proposed and tested as follows:

H10: There is a significant match between behavioural intention and the actual useof online shopping.

3.6.14 Perceived Behaviour Control

Perceived behaviour control is a term that generally describes people’s expectation oftheir performance, and whether it is easy or difficult to adapt to a certain behaviour,e.g. adoption of online shopping. Many empirical studies have shown the effect of

24

perceived behavioural control on key dependent variables (e.g. behaviour intention)[Ajzen, 1991, Taylor and Todd, 1995, Teo and Pok, 2003]. Many studies have tar-geted individual professionals in order to study attitudes toward implementing andusing information technology. These studies have shown the impact of perceived be-havioural control on behavioural intention to apply and use information technology[Chau and Hu, 2001, Shim et al., 2001]. Given this, the hypotheses will be proposedand tested as follows:

H11: Individuals who have positive perceived behaviour control will reveal morebehavioural intention to use online shopping.

H12: There is a significant positive correlation between perceived behavioural controland actual use of online shopping.

3.6.15 Subjective Norm

Subjective norm refers to what people would expect from you when it comes to a cer-tain behaviour and whether you would perform it or not [Fishbein and Ajzen, 1975].Many researchers have reported that there is a significant match between subjectivenorm and behaviour intention, e.g. individuals’ acceptance of technology [Taylor andTodd, 1995, Venkatesh and Davis, 2000]. Given this, the hypothesis will be proposedand tested as follows:

H13: There is a positive match between subjective norm and behaviour intention topurchase online. Therefore, people who come under less pressure to use e-commerceactivities would be slower late at adapting to online shopping.

3.6.16 Facilitating Conditions

Facilitating conditions is a term referring to the positive factors that surround theactivity in question and assist in making it easy to perform. With regard to tech-nology, a study by [Venkatesh et al., 2003] showed that facilitating conditions have asignificant impact on the actual use of technology. Given this, the hypothesis will beproposed and tested as follows:

H14: There is a positive match between facilitating conditions and actual usageof online shopping. Therefore, having more and better facilitating conditions wouldallow for reasonable actual usage of online purchasing.

25

3.6.17 Actual Usage

Actual usage is the time consumed during the usage of technology. It is an importantfactor that is influenced by behavioural intention [Amoroso et al., 2009]. Moreover,[Venkatesh et al., 2003] reported a match between positive behavioural intentiontoward personal computers and the actual usage of personal computers. In addition,Amoroso and Hunsinger found that there is a positive match between behaviouralintention toward using the Internet and the actual usage of the Internet [Amorosoet al., 2009].

26

Chapter 4

Research Methodology

4.1 Overview

This chapter aims at providing a description of the methodology used in this projectincluding the data collection methods, questionnaire design and measurements, pi-lot study, sampling technique and required data. Furthermore, the overall researchstructure will be explained. In addition, available resources for this project and thedata analysis technique will be described in detail.

4.2 Available Data Collection Methods

As far as enablers and barriers to adopting online shopping in KSA are concerned,an evaluative study was carried out in order to meet the aims and objectives of thisproject. However, some critical data not available in recent literature needed to beobtained directly from Saudi citizens. Regarding collection methods, there are twomain types: qualitative and quantitative methods. However, there is much debate asto whether a combination of these two methods increases or decreases the quality ofthe results [Bryman, 1988, Hughes, 1997].

Quantitative methods are usually used to examine relationships and differences be-tween variables (e.g. questionnaires) [Bryman and Bell, 2004]. This allows lessresearcher interference in the participants’ responses, especially when using closedquestions. The main characteristics of quantitative methods according to [Hughes,2006] are summarized below:

1. Control: Causes of observations are identified and the research is conductedupon that.

2. Replication: Collected data is reliable and the same findings are likely to beacquired if the research is repeated.

3. Hypothesis testing: Creation of hypotheses and testing them empirically.

27

In contrast, qualitative methods require interpretation of the results by the researcherand this may lead to bias (e.g. case studies and observations of targeted subjects)[Bryman and Bell, 2004, Boeree, 2005]. As a result, the researcher may interfere inthe responses, thereby leading to bias in discussing the findings. The most importantcharacteristics of qualitative methods are summarized below:

1. The possibility to understand events within a context.

2. The study of regular situations that are not previously set up.

3. An interactive process.

4.3 Collecting Data

As the quantitative method has been chosen for this study, three forms of datacollection were used in the research. Firstly, questionnaires were used for gatheringprimary data. Secondly, examination of relevant research materials used for collectingsecondary data [Saunders et al., 2009]. Thirdly, fourteen proposed hypotheses weretested.

4.4 Secondary Data

Secondary data was mainly obtained from existing literature, such as books, disser-tations, academic publications and online resources; all of which are clearly presentedin Chapter 2 (Literature Review).

4.5 Primary Data

In order to achieve the main aim of this research, a questionnaire was carried out byusing an online survey to collect primary data. Choosing the questionnaire is basedon its flexibility and accuracy in gathering the required data from remote subjects.In addition, it would be time-consuming to gather large amount of data by interview.Moreover, the questionnaire method provides access to a wide range of views allowsthe researcher to shift among the users’ opinions, identifying trends and meeting theresearcher’s requirements [Veal, 2006, Birmingham, 2011]. Furthermore, the ques-tionnaire method offers standardized questions to all participants [Milne, 1999]. Forexample, closed questions were used, as they are quicker and easier for respondentsto answer and are also easier to analyse [Bradley, 2007, Birmingham, 2011].

Although using questionnaires has a number of disadvantages, these will be minimisedin order to obtain the maximum benefits of the survey. For instance, misunderstand-ing of questions was partially avoided by firstly piloting the questionnaire on a group

28

of Saudi students in the UK. With regard to possible superficial answers to the ques-tions, this was addressed by asking short and simple questions. In addition, thebenefits of the survey were clarified in advance in order to encourage participants toanswer the question honestly [Milne, 1999, Creswell, 2009]. Furthermore, a Likertscale i.e. Yes/No questions, box ticking options and Agree/Disagree responses wasemployed to gain specific answers relating to online shopping [Malhotra and Birks,2007]. Using a Likert scale is more likely to generate a highly reliable scale by findingthe weighted means of participants’ positions and overall trends towards an object[Hilary, 2003, Brown, 2011]. The research structure is shown in Figure 4.1.

Figure 4.1: Research Structure

29

4.6 Research Resources

Almost all the resources necessary for conducting the research are readily available.For example, the university library website allows access to e-books, academic topicsand journals. Moreover, several websites enable creation of e-questionnaires. How-ever, the (SPSS) software is needed for data analysis purposes.

4.7 Questionnaire Design

After the researcher had completed the literature review, he started to design thequestionnaire. Designing the questionnaire was based on the objectives of the disser-tation, the literature review and focus groups. The questionnaire was updated manytimes. There are three sections of questions in the questionnaire, namely: personalprofile, personal use of online shopping and possible motivators/enablers and barrierssurrounding online shopping in KSA using a Likert scale format e.g. Yes/No ques-tions, box ticking options and Agree/Disagree responses. Finally, there is a free spacefor additional comments from participants that might help during data analysis. Thequestionnaire is available in Arabic and English; Table 1 (refer to Appendix A).

The respondents completed a questionnaire, which included 60 items, shown in Ap-pendix B. Scales were developed by reviewing research findings on relevant topics inorder to derive various constructs to examine their effects on web-users in KSA ononline shopping. The content of the survey was divided into fourteen sections. Theseare: subjective norm, attitude towards online shopping acceptance, intention to adoptand continue using online shopping, perceived behavioural control, facilitating con-ditions, cultural influence, security, trust, awareness of online shopping, governmente-readiness, satisfaction with Internet services, satisfaction with the postal service,private business e-commerce penetration, the adoption and actual use of online shop-ping. Generally speaking, the questionnaire scales were adapted and adjusted fromtime to time through prior studies. Moreover, appropriate word adjustments wereconducted in order to fit the particular needs of this study. A five-point Likert scaleis used to rate each item, based on the responses in which the respondent must takeone of five positions, ranging from Strongly disagree to Strongly agree. The middlesection represents a neutral opinion, neither in agreement nor disagreement. TheLikert scale is shown in Figure 4.2.

Figure 4.2: Likert scale

30

4.8 Measurements

As mentioned above, scales were developed by reviewing research findings on relevanttopics, as reliable and valid measures in order to measure each of the variables. Table2 (see Appendix B) summarizes all the constructs with their respective sources. Thefollowing discussion explains the constructs used to measure each of the variables.

1. Subjective Norm: Five items were used to measure subjective norm, adaptedfrom [Chau and Hu, 2001, Chen et al., 2004, Amoroso et al., 2009] and selfconstructed.

2. Attitudes Towards Online Shopping Acceptance: Four items were used tomeasure attitudes towards online shopping, adapted from [Chaudhry, 2012,Amoroso et al., 2009, Al-Somali et al., 2009].

3. Intention to Adopt and Continue Using Online Shopping: Three items wereused to measure intention to adopt and continue using online shopping, adaptedfrom [Davis, 1989, Amoroso et al., 2009, Al-Somali et al., 2009].

4. Perceived Behavioural Control: Three items were used to measure perceivedbehavioural control, adapted from [Ajzen, 1991, Shim et al., 2001, Venkateshet al., 2003, Amoroso et al., 2009].

5. Facilitating Conditions: Two items were used to measure facilitating conditions,adapted from [Venkatesh et al., 2003, Amoroso et al., 2009].

6. Cultural Influence: Five items were used to measure cultural influence, adaptedfrom [Al-Somali et al., 2009, Abdulaziz Aleid et al., 2010] and self constructed.

7. Security: Three items were used to measure security, adapted from [Miyazakiand Fernandez, 2001, Chen et al., 2004, Aditya, 2011].

8. Trust: Four items were used to measure trust, adapted from [Holland andMenzel Baker, 2001, Chen et al., 2004, Aditya, 2011].

9. Awareness of Online Shopping: Three items were used to measure awarenessof online shopping, adapted from [Jennex et al., 2004, Molla and Heeks, 2007,Abdulaziz Aleid et al., 2010].

10. Government E-readiness: Four items were used to measure government e-readiness, adapted from [Goh, 1995, Tan and Teo, 2000, Abdulaziz Aleid et al.,2010].

11. Internet Services: Four items were used to measure Internet services, adaptedfrom [Abdulaziz Aleid et al., 2010] and self constructed.

12. Postal Services: Five items were used to measure postal services, adapted from[Abdulaziz Aleid et al., 2010] and self constructed.

13. Private Business E-commerce Penetration: Eight items were used to measureprivate business e-commerce penetration, adapted from [Abdulaziz Aleid et al.,2010] and self constructed.

31

14. Actual Usage: Six items were used to measure actual usage, adapted from[Amoroso et al., 2009].

Furthermore, both the Arabic and English versions of the questionnaire were sent forassessment to PhD and Masters students in both the UK and Saudi Arabia. Althoughno major modification has been made, their comments were taken into account inthe questionnaire.

4.9 Sampling Techniques and Size

A combination of random and snowball sampling techniques were used in this study.Web-users in KSA are the targeted population for meeting the aims and objectives ofthis project. Sampling techniques facilitate the author in acquiring an approximationof the truth at a low cost and within a short period of time [StatPac, 2012]. Basedon the time allotted for selecting the participants, a combination of random andsnowball sampling techniques were employed in order to attract as many subjects aspossible who met the conditions for participating in the questionnaire. The Snowballsampling method is a recruitment method in which participants actively encouragemembers of their own social networks to take part in the study [Browne, 2005]. Thismethod is widely used when the targeted population is hard to recruit, due to a lackof time and other circumstances (as shown in Appendix G).

Therefore, Snowball sampling, together with random techniques, was employed todetermine potential participants. Firstly, the researcher randomly approached web-users of popular social networks, such as Twitter and Facebook, and posted thequestionnaire on academic website forums, such as university and technical collegeforums, requesting them to kindly participate in this study and to encourage othersto do so. They assisted in recruiting other web-users randomly from their socialnetworks, e-mail groups and forums. They might be the general public or friendsand relatives. Thus, individuals who are familiar with the Internet were recruited.However, the Snowball sampling technique has a number of limitations. For instance,there is no guarantee that whoever receives an invitation will take part in the study.Additionally, it is time-consuming because of the effort needed to communicate withall the contacts in order to achieve the desired population. Furthermore, the collectedresponses from the sample do not imperatively represent the entire population. There-fore, great efforts were made to avoid these drawbacks when designing the samplingfor this research.

4.10 Pilot Study

Prior to the distribution of the questionnaires, a pilot study was conducted to evaluateits effectiveness, and to test the completion time and the question wording. Thefindings of the pilot study helped in detecting any inappropriate questions allowingfor correction before distribution for the main study [Burgess, 2001, Saunders et al.,2009]. According to [Saunders et al., 2009], ten participants must be involved in order

32

for a questionnaire to be meaningful. Therefore, with the assistance of my supervisor,six PhD students, two MSc students and one BSc student were involved who providedhelpful notes to ensure that the questionnaire was clear and met the requirements ofthe study. The results were then taken into consideration in the main stage.

4.11 The Procedure of Distributing the Question-

naire

The procedure of distributing the questionnaire lasted from 1st June, 2012 to 25thJuly, 2012. Firstly, an ethical approval was obtained from Dr. Dan Smith in orderto distribute the survey. The subjects were randomly chosen using social networks,Twitter, Facebook and so on. They were then kindly requested to complete the onlinequestionnaire. To describe the procedure, the researcher went to Twitter and ran-domly searched for participants by typing ’Saudi Arabia’ in the search engine. Afterreceiving responses from literally hundreds of people, the researcher then checked theprofile of the respondents to ensure that they were Saudi citizens. If they provedto be Saudis, the researcher then sent them a tweet, requesting to re-tweet again totheir own personal Saudi contacts and so on.

The information about the research was provided on the questionnaire cover page.Participants were told that they had the right to withdraw from the survey if theyfelt uncomfortable at any time. In addition, at the beginning of the questionnairethe individuals were asked whether they would be happy to have their data publishedas a case study or not. If they did not agree to this, then it was made clear thattheir data would remain confidential and would not be used in any circumstancesexcept for statistics manipulation. In addition, they were clearly informed of thepurpose of the collected data; that it was collected solely to assist the researcher ingathering information for the study, and therefore there would be no direct benefitto them as well as no risk to them. Also, they were clearly informed that all thedata gathered would be confidentially stored and no user other than the researcherhimself could access it. Furthermore, no personal information would be requested inany event. All information was gathered through the online questionnaire. Outputsof the analysis were used to suggest possible solutions to effectively applying onlineshopping. The survey was administered using an online survey website, dotSurvey,on the following link:(http://dotsurvey.me/681dig08-c16ma08). Some difficulties inpublishing the questionnaire and ’Plan B’ have been explained (see Appendix G).

33

Chapter 5

Results, Analysis and Discussion

5.1 Overview

To analyse the primary data collected, it has been pointed out that the usage of asoftware package is recommended; this is due to large amount of quantitative datainvolved [Robson, 2002]. Consequently, SPSS software was utilized in order to analysethe quantitative data. SPSS and Excel were employed in this research in order toexamine the various factors and their impact on web-users in shopping online. Firstly,the construct reliability test is performed. Secondly, these factors are analysed usingdifferent tests to identify the extent to which these measures are correlated, andto reveal how the independent variables affect the dependent variable. In addition,threats and barriers will be identified, as well as the mechanisms to overcome them.Finally, some recommendations will be proposed for the future.

5.2 Instrument Reliability Results

The construct reliability test was performed. Cronbach’s Alpha was employed to testinternal construct consistency. Apart from the cultural construct, which has a valueof 0.68 (just under 0.7), all the constructs have a Cronbach’s Alpha value above 0.7,as in Table 3. However, a Cronbach’s Alpha value above 0.6 is considered to be asatisfactory level [Van de Ven and Ferry, 1980, Malhotra and Hall, 1996, Hair et al.,2000, Dinev and Hart, 2002].

Table 3 (refer to Appendix C) Reliability Analysis of Cronbach’s Alpha. More detailson the reliability analysis results are shown in Appendix E.

34

5.3 Response Summary

The response summary is broken down into the percentages of people that completedand did not complete (or left without starting) the survey. In all, 3,123 surveys weredistributed. There were 531 responses. The collected data was filtered by excludingquestionnaires that were not complete e.g. missing values in the questionnaires thatwere downloaded from the dotsurvey website. Hence, 16 surveys were excluded. Theresponse summary is shown in the figure below:

Figure 5.1: Response Summary

5.4 Participants

This study focuses on the factors affecting web users in KSA in shopping online, andon how any combination of these factors controls the intention to shop online. Theanalysis in this study is based on 515 subjects. As far as the subjects’ specificationsare concerned, adult Saudis from both genders were targeted, who have or have notever visited and purchased items from online stores. Therefore, if the respondentsare already users of online shopping, they were asked to answer the questions basedon their experience of online shopping usage. If not, they were asked to answer thequestions based on their expectations of online shopping usage in the future. Theonline survey was distributed to web users in KSA using Twitter, Facebook, e-mailgroups, university forums, technical college forums, etc. This research gained a totalof 515 respondents. 386 have experience in online shopping, while 129 do not. Thedemographic characteristics of the respondents and personal use of online shoppingare summarised in the Tables and Figures, as shown on the following pages:

35

Table 5.1: Demographic Statistics of Respondents

36

Table 5.2: Demographic Statistics of Respondents

37

Figure 5.2: The number of individuals that support overseas sites

Figure 5.3: Type of goods and services that individuals purchase

38

Figure 5.4: The way that individuals pay for goods and services

Figure 5.5: The way that individuals receive goods and services

39

As shown in the Tables and Figures, 55.3% of the respondents were male and 44.7%were female. Over 72.2% of the respondents were between 18 and 35 years old. Thesurvey shows that 75.4% of the respondents attended university or above. The ma-jority of them were government employees and students; the government employeesrepresented around 43% whereas the students represented about 28%. 58.6% of therespondents had a monthly income of more than SR 7,000 (approximately £1000).Surprisingly, 29.5% of the respondents had experience of overseas online purchasingwhile 19.8% of the respondents had experience of local online purchasing. 25% of therespondents had never experienced online purchasing. The majority of the respon-dents, about 73%, live in the Central and Western Regions, 74% of the respondentslive in cities, 20% in towns and 6% in villages. Approximately only 8% of the respon-dents purchase products or services on a regular basis. About 53% think that overseaswebsites are better than local ones for conducting online shopping whereas only 6%think disagree; around 41% do not know. Half of the respondents are concernedabout purchasing goods such as fashion items, electronic equipment (e.g. computers,mobiles, games, accessories), or services (e.g. flight and hotel reservations, and payingfor distance learning courses). Around 53% of the respondents use Visa credit/debitcards or Master cards to purchase their goods or services, and about 25% use thecash on delivery’ method; the others use other methods. Around 59% of the respon-dents receive their purchases (either goods or services) through agency mailboxes,while 35% receive them directly at their home mailbox; 21% must go to a post officeto collect them. Others receive their purchases through different methods such asrelative’s or friend’s mailbox, work mailbox, and e-mail.

5.5 Analysis Results of hypotheses

The researcher used One-Way-ANOVA test to identify any differences between theindividuals in terms of age, gender, education level and income. Moreover, The re-searcher used Pearson correlation, as it is one of the most frequently used statisticstools and fundamental to theory-building. It is also used to build the foundations formore sophisticated methods such as Multiple Regression [Anglim, 2007]. It is used toexamine the relationships between the constructs because the sample size was quitelarge (n = 515). This can be performed as an initial test to examine how well thehypotheses were supported. Note: **correlation is significant at P<0.05(%5), ***correlation is significant

at P<0.01(%1).

Moreover, the Summated scales method was used in this research. The purpose ofthis method is to combine several variables that measure the same concept into asingle variable in order to raise the reliability of the measurement; the average scoreof these variables is then used in the analysis [Obrien, 2007]. According to [Obrien,2007], there are two benefits to Summated scale, as follows:

• It overcomes and reduces the measurement-error potential in all measured vari-ables by using multiple indicators in order to minimise the dependence on asingle response. By using the average, the measurement error that might arisein a single question will be diminished.

40

• Representing multiple aspects of a concept into a single measure is the secondbenefit of the method, when the summated scale is properly constructed. Theemployment of more variables in multivariate models in order to represent themany facets of a concept make the interpretation of the results more complicatedbecause of redundancy in the constructed items associated with the concept.

H1: There is a positive match between gender and usage of online shopping. Thisleads to men coming first when it comes to adopting online shopping.

To find any difference between males and females, the One-Way-ANOVA test wasperformed. The test indicates that there is a significant difference of attitude amongpeople of different genders (male = 3.84, female = 3.51, p = 0.000 < 0.01). Themean value shows that males have a more positive attitude toward online shopping,compared with female. Accepted.

H2: There is a positive match between age and attitude toward using online shop-ping in KSA. Young customers are more likely to adopt to online shopping.

To identify any differences between different age groups, the One-Way-ANOVA testwas performed. The test indicates that there is no significant difference of attitudeamong people from different age groups (18-25 = 3.61, 26-35 = 3.77, 36-45 = 3.72,46-60 = 3.45, over 60 = 4.08, p = 0.151 > 0.05). The mean value shows that all havesimilar attitudes toward online shopping, i.e. the different ages have same attitude.Rejected.

H3: There is a positive match between educational level and usage of online shop-ping. This leads to university graduates and above coming first when it comes toadopting online shopping.

To identify any differences between the different education levels, the One-Way-ANOVA test was performed. The test indicates that there is no significant differenceof attitude among people with different education levels (less than high school = 3.60,high school = 3.57, diploma = 3.60, BA/BSc = 3.80, PhD = 3.85, p = 0.500 > 0.05).The mean value shows that all have similar attitude toward online shopping, i.e. thedifferent education levels have the same attitude. Rejected.

H4: There is a positive match between individuals’ income and their attitudes to-ward online shopping. Therefore, individuals with high incomes are more likely totake the lead when it comes to adopting online shopping.

To identify any differences between the different income groups, the One-Way-ANOVAtest was performed. The test indicates that there are some significant relationshipsbetween attitude and income (less than SR 3,000 = 3.60, between SR 3,000 and SR7,000 = 3.56, more than SR 7,000 and less than SR 12,000 = 3.69, between SR 12,000

41

and SR 20,000 = 3.88, more than SR 20,000 = 3.77; p = 0.05 = 0.05). The meanvalue shows that not all have similar attitudes toward online shopping. Partly Ac-cepted. According to Pot Hoc Tests, the main significant difference is between thevery high income group and the other groups; people with higher incomes have amore positive attitude toward e-commerce online shopping.

H5: There is a positive match between cultural influence and usage of online shop-ping.

To determine any relationship between cultural influence and attitude towards us-ing online shopping, Pearson correlation was performed. The correlation coefficientindicates that there is significant correlation between cultural influence and attitudetowards using online shopping (r = 0.581, p = 0.000 < 0.01). There is a moderate butsignificant relationship between cultural influence and attitude towards using onlineshopping. Accepted.

H6: There is a positive match between individuals’ awareness and their attitudes to-ward online shopping. Therefore, people who are less aware of e-commerce activitieswould be slower when it comes to adopting online shopping.

To determine any relationships between the awareness of online shopping and attitudetoward using online shopping, Pearson correlation was performed, and the correlationcoefficient indicates that there is a significant correlation between one’s awareness ofe-commerce online shopping and attitude toward using online shopping (r = 0.322,p = 0.000 < 0.01). There is small but significant relationship between awareness ofonline shopping and attitude toward using online shopping. Accepted.

H7: There is a positive match between trust and attitudes toward online shopping.

To determine any relationship between trust and attitudes toward using online shop-ping, Pearson correlation was performed. The correlation coefficient indicates thatthere is a significant correlation between trust and attitude toward using online shop-ping (r = 0.367, p = 0.000 < 0.01). There is a small but significant relationshipbetween trust and attitude toward using online shopping. Accepted.

H8: There is a positive match between security and attitudes toward online shop-ping.

To determine any relationship between security and attitude toward using onlineshopping, Pearson correlation was performed. The correlation coefficient indicatesthat there is a significant correlation between security and attitude toward usingonline shopping (r = 0.302, p = 0.000 < 0.01). There is a small but significant rela-tionship between security and attitude toward using online shopping. Accepted.

42

H9: There is a positive match between attitude toward using online shopping andbehavioural intention to do online purchasing. Therefore, having a positive attitudetoward using online shopping results in a higher intention to do online purchasing.

To determine any relationship between attitude toward online shopping and be-havioural intention to use online shopping, Pearson correlation was performed. Thecorrelation coefficient indicates that there is a significant correlation between attitudetoward online shopping and behavioural intention to use online shopping (r = 0.739,p = 0.000 < 0.01). There is a strong significant relationship between attitude towardonline shopping and behavioural intention to use online shopping. Accepted.

H10: There is a significant match between behavioural intention and the actual useof online shopping.

To determine any relationship between behavioural intention and actual usage ofonline shopping, Pearson correlation was performed. The correlation coefficient indi-cates that there is a significant correlation between behavioural intention and actualusage of online shopping (r = 0.782, p = 0.000 < 0.01). There is a very strong signifi-cant relationship between behavioural intention and actual usage of online shopping.Accepted.

H11: Individuals who have positive perceived behaviour control will reveal morebehavioural intention to use online shopping.

To determine any relationship between perceived behavioural control and behaviouralintention to use online shopping, Pearson correlation was performed. The correla-tion coefficient indicates that there is a significant correlation between perceivedbehavioural control and behavioural intention to use online shopping (r = 0.448, p= 0.000 < 0.01). There is a moderate but significant relationship between perceivedbehavioural control and behavioural intention to use online shopping. Accepted.

H12: There is a significant positive correlation between perceived behavioural con-trol and actual use of online shopping.

To determine any relationship between perceived behavioural control and actual useof online shopping, Pearson correlation was performed. The correlation coefficientindicates that there is a significant correlation between perceived behavioural controland actual use of online shopping (r = 0.487, p = 0.000 < 0.01). There is a moderatebut significant relationship between perceived behavioural control and actual use ofonline shopping. Accepted.

43

H13: There is a positive match between subjective norm and behavioural intention topurchase online. Therefore, people who come under less pressure to use e-commerceactivities would be slower at adapting to online shopping.

To determine any relationship between subjective norm and behavioural intentionto use online shopping, Pearson correlation was performed. The correlation coef-ficient indicates that there is a significant correlation between subjective norm andbehavioural intention to use online shopping (r = 0.383, p = 0.000 < 0.01). There is asmall but significant relationship between subjective norm and behavioural intentionto use online shopping. Accepted.

H14: There is a positive match between facilitating conditions and actual usageof online shopping. Therefore, having more and better facilitating conditions wouldallow for reasonable actual usage of online purchasing.

To determine any relationship between facilitating conditions and actual use of onlineshopping, Pearson correlation was performed. The correlation coefficient indicatesthat there is a significant correlation between facilitating conditions and actual use ofonline shopping (r = 0.236, p = 0.000 < 0.01). There is a small but significant rela-tionship between facilitating conditions and actual use of online shopping. Accepted.Correlations matrix are summarised on the following page:

44

Figure 5.6: Correlations matrix

5.5.1 Identifying Multicollinearity

Collinearity refers to the evaluation of the correlation matrix for the independent vari-ables, and multicollinearity refers to the evaluation of the correlation among threeor more independent variables [Hair et al., 2010]. The purpose of multicollinear-ity according to [Hair et al., 2010], is to diminish any single independent variable’spredictive power by the degree to which the independent variable is associated withthe other independent variables. For each independent variable, as collinearity in-creases, the unique variance decreases and the shared prediction percentage increases.The presence of high correlations (generally 0.90 and above) indicates problems withcollinearity. Sometimes collinearity can be shown to be the result of the combinedeffect of two or more other independent variables, which is called multicollinearity.It occurs mainly at relatively high levels of multicollinearity (above 0.80) [Hair et al.,2010]. Moreover, Variance Inflation Factor (VIF) provides an indicator that evalu-

45

ates how much the variance of an estimated regression coefficient is raised becauseof collinearity, whereas Tolerance refers to the percentage of variance in the indepen-dent variable that is not accounted for by the other independent variable(s) [Obrien,2007]. A Tolerance value of less than 0.20 or 0.10 and/or a VIF of 5 or 10 and aboveare considered multicollinearity problems [Obrien, 2007].

However, all the correlations in the above correlation matrix are under 0.8, indicatingno problems with collinearity and multicollinearity. In addition, all Tolerance andVIF values indicate no problems with collinearity and multicollinearity. More detailsof the multicollinearity results are shown in Appendix F.

5.6 Multiple Regression Results

Multiple regression was used to test the hypotheses. It examines the correlation be-tween several independent variables (predictor variables) and the dependent variable.The independent variables indicate relationship and affect the dependent variableswhen the p-value of the regression coefficient is significant (less than 0.05). Regres-sion equations are used in order to predict a metric-scaled dependent variable by oneor more independent variables [Richardson, 2012]. The researcher developed modelsusing a multiple regression equation between dependent variable and independentvariables. Only those variables were considered for regression for which the correla-tion coefficient were significant, as shown in the figure below :

Figure 5.7: An assessment of the proposed model

**correlation is significant at P<0.05(%5)***correlation is significant at P<0.01(%1)

46

5.6.1 Explaining Web-Users’ Attitude Toward Using OnlineShopping

The first regression was between attitude toward using online shopping and the in-dependent variables, gender, security, trust, awareness and cultural influence. Themultiple regression coefficient between the dependent variable and independent vari-ables was found to be significant (ANOVA F = 56.2, p = 0.000 < 0.01). Coefficientof determination R = 632 was moderate, indicating that there is a moderate rela-tionship between the observed values of attitude and the predicted values of attitude.Adjusted R2 (0.392) shows that this multiple regression model can predict attitudewith 39% accuracy. The coefficient of regression table indicates that all coefficients ofindependent variables are significant (p < 0.01) except for awareness. The regressionequation can be written as:

Attitude= a+β1*Dummygender+β2*SC+β3*TR+β4*AW+β5*CIAttitude= -0.152+0.085*Dummygender+0.098*SC+0.160*TR+0.034*AW+0.487*CI

This model shows that cultural influence has the greatest effect on attitude followedby trust, security and gender. Awareness has no significant effect on the attitude ofweb-users. Table 5.3 shows the regression results of the web-users’ attitudes towardusing online shopping.

Table 5.3: Regression results of web-users’ attitudes toward using online shopping

5.6.2 Explaining Web-Users’ Behavioural Intention to Adoptand Continue to Use Online Shopping

The second regression was between behavioural intention to adopt and continuing touse online shopping and the independent variables, attitude toward using, subjectivenorm, and perceived behavioural control. The multiple regression coefficient betweenthe dependent variable and the independent variables was found to be significant(ANOVA F = 236.9, p = 0.000 < 0.01). Coefficient of determination R = 763 was

47

strong, indicating that there is strong relationship between the observed values ofbehavioural intention and predicted values of behavioural intention. Adjusted R2

(0.579) shows that this multiple regression model can predict behavioural intentionwith 58% accuracy. The coefficient of regression table indicates that all coefficients ofindependent variables are significant (p < 0.01). The regression model can be writtenas:

Behavioural intention = a+β1*ATT+β2*SN+β3*PBCBehavioural intention =0.954+0.624*ATT+0.113*SN+0.173*PBC

This model shows that attitude has the highest effect on behavioural intention, fol-lowed by perceived behavioural control and subjective norm. Table 5.4. shows theregression results of the web-users’ behavioural intention to adopt and continue usingonline shopping.

Table 5.4: Regression results of web-users’ behavioural intention to adopt and continueusing online shopping

5.6.3 Explaining Web-Users’ Actual Use of Online Shopping

The third regression was between actual use and the independent variables, perceivedbehavioural control, facilitating conditions, and behavioural intention. The multipleregression coefficient between the dependent variable and the independent variableswas found to be significant (ANOVA F = 306.2, p = 0.000 < 0.01). Coefficient ofdetermination R = 802 was very strong, indicating that there is a very strong rela-tionship between the observed value of actual use and the predicted values of actualuse. Adjusted R2 (0.641) shows that this multiple regression model can predict ac-tual use with 64.1% accuracy. The coefficient of regression table indicates that allcoefficients of independent variables are significant (p < 0.01). The regression modelcan be written as:

Actual use= a+β1*PBC+β2*FC+β3*BIActual use= 0.305+0.160*PBC+0.091*FC+0.695*BI

This model shows that behavioural intention has the highest effect on actual use, fol-

48

lowed by perceived behavioural control and facilitating conditions. Table 5.5. showsthe regression results of the web-users’ actual use of online shopping.

Table 5.5: Regression results of web-users’ actual use of online shopping

5.6.4 Summary of the Hypotheses and Results

Table 5.6: Summary of results for the hypotheses

From the research model, it can be seen that cultural influence (β = 0.487) is astrong determining factor for attitude toward using, followed by trust (β = 0.160),security (β = 0.098) and gender (β = 0.085). In addition, attitude toward using (β= 0.624) is a very strong determining factor for behavioural intention, followed byperceived behavioural control (β = 0.173) and subjective norm (β = 0.113). Finally,behavioural intention (β = 0.695) is a very strong determining factor for actual use ofonline shopping, followed by perceived behavioural control (β = 0.160) and facilitatingconditions (β = 0.091). The regression analysis results for all hypotheses are presentedin Appendix D.

49

5.7 Findings Discussion

5.7.1 Effect of behavioural intention on actual use of onlineshopping

The findings affirm that behavioural intention has the most significant relationshipwith actual use. The path coefficient is 0.695. This finding is congruent with previousstudies [Amoroso et al., 2009]. Behavioural intention (p< 0.001) is a highly significantconstruct. The results show that the model is rather strong in predicting the actualusage of online shopping. The mean value of 4.11 indicates that web users alwaystry to use online shopping whenever it meets their shopping needs. They will tendto shop and continue shopping if it meets their shopping needs. Therefore, Saudie-commerce websites should offer a vast selection and competitive prices comparedto traditional stores in order to attract web users into browsing their websites andmaking purchases.

5.7.2 Effect of perceived behavioural control on actual useand behavioural intention

The findings affirm that perceived behavioural control has a significant relationshipwith actual use and behavioural attention. The path coefficient is 0.160. This findingis congruent with previous studies [Shim et al., 2001, Amoroso et al., 2009]. Perceivedbehavioural control (p < 0.001) is a highly significant construct. Furthermore, thefindings also affirm that perceived behavioural control has a significant relationshipwith behavioural attention. The path coefficient is 0.173. This finding is congru-ent with previous studies [Ajzen, 1991]. The results show that the model is ratherstrong in predicting the actual usage and behavioural intention. The mean value of3.75 indicates that if web users have control over how to shop online along with theresources needed to shop, opportunities and knowledge, they will tend to shop, posi-tively influencing their behavioural attention and their actual use. For instance, theuse of zoom-in or simulation can improve customer acceptance of e-comerece [Joineset al., 2003]. Furthermore, well-designed websites positively influence customer re-sponses, which leads to increased online purchases and the desire to browse [Kim andSrivastava, 2007]. The ability to make product features more clearly presented in awebsite is essential; providing 3D technology can assist consumers in making deci-sions, which can lead to increased online purchases through making online shoppingmuch easier [Kim and Srivastava, 2007]. Moreover, developing a convenient paymentmethod as well as enhancing alternative payment methods would positively influencebehavioural attention and the actual use of e-commerce.

5.7.3 Effect of facilitating conditions on actual use of onlineshopping

The findings affirm that facilitating conditions has a significant relationship withactual use. The path coefficient is 0.091. This finding is congruent with previous

50

studies [Venkatesh et al., 2003]. Facilitating conditions (p = 0.001) is a highly signif-icant construct. The results show that the model is rather strong in predicting theactual usage. The mean value of 3.10 indicates that enterprises should make theire-commerce interaction clearer and more understandable; they should also offer guid-ance and assist users if ever difficulties occur. Furthermore, they should maintainand develop the facilitating conditions of their websites to enhance actual use.

5.7.4 Effect of trust and security on attitude toward usingonline shopping

The findings affirm that trust and security have significant relationships with actualuse. The path coefficient for trust is 0.166, while the path coefficient for securityis 0.104. This finding is congruent with previous studies [Miyazaki and Fernandez,2001, Chen et al., 2004, Aditya, 2011]. Trust (p < 0.001) and security (p < 0.05) aresignificant constructs. The results show that the model is rather strong in predictingattitude toward using online shopping. The results indicate that web users are worriedabout issues such as the misuse of information and secure payment methods. There-fore, Saudi e-commerce website should have very strict security measures. Thesecould include secure browsing mechanisms (HTTPS ), encrypting all exchanged in-formation, login time-based session expiry (to avoid unauthorised access to consumeraccounts), and digital certificates (to provide secure Internet communications). Theseshould increase trust in technology in order to positively influence web users attitudetoward using online shopping.

5.7.5 Effect of subjective norm on behavioural intention

The findings affirm that subjective norm has a significant relationship with be-havioural attention. The path coefficient is 0.113. This finding is congruent withprevious studies [Hu et al., 2003, Venkatesh et al., 2003, Kleijnen et al., 2004, Amorosoet al., 2009]. Subjective norm (p < 0.001) is a highly significant construct. The resultsshow that the model is rather strong in predicting behavioural attention. The meanvalue of 3.45 indicates that web users tend to agree with the statement that theyare positively affected by people who are important to them. Thus, subjective normplays a role in the acceptance of a technology system. Subjective norm affects userbehaviour as each individual wants to know that his/her opinion coincides with thoseof his/her peers. Therefore, Saudi e-commerce websites should promote themselvesmore through social networking websites such as Twitter, Facebook, forums and soforth. Social networking websites have a big impact on individuals’ behavioural in-tention; also, it was the only guaranteed way of collecting a large enough sample sizefor this study, obviating any difficulties in distributing the questionnaire.

51

5.7.6 Effect of attitude toward using online shopping on be-havioural intention

The findings affirm that attitude toward using online shopping has a highly significantrelationship with behavioural intention. The path coefficient is 0.624. This findingis congruent with previous studies [George, 2002, Amoroso et al., 2009]. Attitudetoward using online shopping (p < 0.001) is a highly significant construct. The re-sults show that the model is rather strong in predicting behavioural intention. Themean value of 3.93 indicates that web users tend to agree with the statement thatthey are positively affected by appealing websites. For instance, the use of zoom-inor simulation can improve customer acceptance of e-commerce [Joines et al., 2003].Furthermore, well-designed websites positively influence customer responses, whichleads to increased online purchases and the desire to browse [Kim and Srivastava,2007]. The ability to make product features more clearly presented in a website isessential, and therefore, providing 3D technology should be included to assist con-sumers in making decisions; this would lead to increased online purchases and makeonline shopping much easier [Kim and Srivastava, 2007]. Moreover, designing user-friendly interfaces would positively influence individuals’ behavioural intention to usee-commerce websites.

5.7.7 Effect of cultural influence on attitude toward usingonline shopping

The findings affirm that cultural influence has a highly significant relationship withattitude toward using online shopping. The path coefficient for cultural influence is0.488. This finding is congruent with previous studies [Straub et al., 2003, Barbonisand Laspita, 2005, Al Sukkar and Hasan, 2005]. Cultural influence (p < 0.001) isstrongly significant construct. The results show that the model is rather strong inpredicting attitude toward using online shopping. The results indicate that culturalinfluence is a key factor in predicting the success or failure of technology acceptance[Straub et al., 2003].

5.7.8 Effect of gender on attitude toward using online shop-ping

The findings affirm that gender has a significant relationship with attitude towardusing online shopping. The path coefficient for gender is 0.082. This finding iscongruent with many previous studies [Li et al., 1999, Durndell and Haag, 2002,Rodgers and Harris, 2003, Dittmar et al., 2004, Garbarino and Strahilevitz, 2004,Susskind, 2004, Masrek et al., 2007, Zhou, 2007, Hasan, 2010]. Gender (p < 0.001) isa significant construct. The results show that the model is rather strong in predictingattitude toward using online shopping. The results indicates that males are morelikely to purchase online.

52

5.7.9 Summary

The researcher has demonstrated strong support for all the proposed hypotheses, ex-cluding those hypotheses related to age, education level, income and awareness, forwhich the p-value meant that they are not significant predictors of attitude towardsusing online shopping. The results for these indicate insignificant relationships (p> 0.05); income, age, awareness and education scored 0.056, 0.151, 0.348 and 0.500,respectively.

Moreover, hypotheses H2, H3, H4 and H6 (predicting a positive relationship betweenage, education level, income and awareness with attitude toward using online shop-ping) were not supported and found not to be significant at 5% (p <0.05) and 1%(p <0.01). This finding is congruent with previous studies reporting that age, educa-tion and income are not found to have a direct effect on the adoption of technology[Bellman et al., 1999, Donthu and Garcia, 1999, Li et al., 1999, Liao and Cheung,2001, Susskind, 2004, Mahmood et al., 2004, Rohm and Swaminathan, 2004]. Inaddition, although awareness about online shopping was found not to be significant,there is a very positive perspective related to awareness about online shopping. Thismay be due to the fact that the majority of respondents are already aware aboutonline shopping; about 9 individuals were aware of online shopping against each onewho was not. However, further studies are suggested using a larger sample size anddifferent sample techniques in order to be sure of the results.

5.8 Descriptive Analysis of Major Findings for In-

frastructural Conditions

Figure 5.8: Satisfaction with Saudi infrastructural conditions in general

53

5.8.1 Government E-readiness

With regard to infrastructural conditions, about 46% of the participants think thatthere is a lack of effective laws for protecting consumer privacy and combating cy-ber crime. However, only 14% of the participants agreed that the Saudi governmentprovides facilitating conditions to support businesses to expand via online shopping.Moreover, only 13% of the participants think that there is a suitable legal envi-ronment. Overall, about a quarter of the participants seem to be happy with thegovernment’s support for online shopping.

5.8.2 Satisfaction with Internet Service

As far as satisfaction with the Saudi Internet services is concerned, about 50% of theparticipants think that the Internet service in KSA is expensive. Furthermore, 53% ofthe participants reported that the Internet service in KSA is unreliable and does notprovide sufficient (24/7) access to online stores. Moreover, 20% of the participantsthink that Internet filtration is a key barrier to online shopping. Positively, 25%of the participants are satisfied with the Internet service and its support for onlineshopping.

5.8.3 Satisfaction with Postal Service

When it comes to awareness of Saudi postal services, only 17% are aware of the postservice conditions. As a matter of fact, more than half the participants agreed thatthe Saudi post has no clear addressing system for delivery purposes. On the otherhand, 21% agreed that the Saudi post is reliable (no shipping losses or damage, andno delays in delivery). Moreover, 55% of people are forced to ship via third partyagencies, even though they are expensive. In general, 26% of the participants aresatisfied with post service and the support provided for online shopping.

5.8.4 Private Business E-commerce Penetration

When it comes to private business e-commerce penetration, only 26% of the partic-ipants think that private businesses are keen to implement online shopping, while35% think that there are enough IT companies and professionals available to meetdemand (e.g. designing, managing and maintenance of online shops). With regard tocompetition, 30% of the participants think that Saudi online stores offer diversity anda vast selection of products and services. The most interesting findings are that only12% of the participants are satisfied with the customer support and after-sales serviceprovided by Saudi online stores. Moreover, only 16% of the participants think thatSaudi online stores offer competitive prices, compared with traditional stores. Thesemight be considered key factors in private business e-commerce penetration. How-ever, 24% of the participants showed satisfaction with private business e-commercepenetration in terms of supporting online shopping.

54

Chapter 6

Conclusion and Recommendations

6.1 Overview

After analysing the questionnaires’ findings in the previous chapter, this chaptersummarises all research findings and analyses all the gathered information carried outby this study. This chapter consists of six sections, key findings, recommendations,limitations and future research, professional and ethical issues, researcher’s personalachievements and conclusion.

6.2 Key Findings

Firstly, it is clear that people with positive perceptions towards trust, security andculture reveal a better attitude toward using online shopping.

Secondly, the findings have demonstrated that behavioural intention to adopt and ac-tual use of online shopping can be predicted by attitude, subjective norm, perceivedbehaviour control and facilitating conditions.

Thirdly, this study has suggested that gender may be an influencing factor in the ac-ceptance of online shopping in KSA. In contrast, it has confirmed that demographicfactors, such as age, educational level and income have no particular impact on theadoption of online shopping.

Fourthly, awareness about online shopping is not considered a significant factor inpredicting the effect of attitude toward using online shopping. This may be due tothe fact that the majority of respondents were already aware of online shopping.

Fifthly, the absence of e-commerce laws in KSA is one of the major barriers to theadoption of online shopping.

55

Sixthly, the rarity of online stores among Saudi enterprises is one of the main barriersto its adoption. Finally, the limitations and weakness of the existing Internet andpostal service in KSA are considerable barriers to the adoption of online shopping.

6.3 Recommendations

To summarise, it would appear that Saudi infrastructural conditions are not yetadequate to adopt online shopping. Nearly half of the responses gathered from theparticipants demonstrate that they are dissatisfied with the current infrastructuralconditions in KSA. It is essential that KSA is not left behind in the open marketscreated by this technical revolution. Therefore, this study suggests the followingrecommendations as possible means to overcome them:

1. Effective and clear laws should be implemented by the Saudi government tocontrol the process of online shopping, as in order to encourage consumer trustand confidence in this form of commercial activity, which would benefit societyas a whole.

2. The Saudi government must address the deficiencies in the current postal codesystem in order to facilitate the delivery of goods purchased online direct toconsumers’ homes.

3. Internet providers should offer easier access to the wider public and higher speedInternet connection at lower costs.

4. The Saudi postal agency should introduce a clear addressing system for deliverypurposes, plus clear terms and conditions.

5. Saudi enterprises should establish online stores in order to expand their busi-nesses and bring greater profits for themselves and benefits for their customers.

6. Saudi firms that have already established online stores should promote theirproducts via TV, Internet, newspapers, street posters and screens, and shouldattract more customers by providing exclusive offers that can only be accessedonline.

7. Saudi firms should present their online stores in Arabic but should also usemore than one language. Furthermore, Saudi consumers should learn at leastbasic English to be able to access more global facilities and products.

8. Saudi firms should develop, maintain and update their security systems forelectronic transactions regularly, including security of payment methods andsafeguards for the privacy of personal information, as it is clear that customersmust be sufficiently assured of the safety and security of electronic transactions.This will increase their confidence in online shopping and encourage more peopleto adopt to it.

9. Online stores should develop a convenient payment online method, as well asoffering a wider range of alternative payment methods.

56

10. Online stores should have professional and easy-to-understand websites. Theability to make product features more clearly presented in a website is essential,and therefore, 3D technology should be provided.

11. Online stores should offer a superior selection of products and services at morecompetitive prices, and provide accessible customer support and aftersales ser-vice when compared to traditional stores in order to attract web users to visittheir websites and make purchases.

12. Online shops in Saudi Arabia should have clear returns, exchange and refundpolicies.

13. Feedback and customer reviews should be available for consumers to exchangetheir experiences of buying online to encourage others to join the online shop-ping community.

6.4 Limitations and Future Research

There were a number of limitations in this research that might affect the results.Firstly, regarding the timing and distribution of the paper-based questionnaires, be-fore and during the month of Ramadan, some difficulties were experienced, as mostpeople were taking a holiday in order to prepare for the traditional rituals of prayerand fasting during this period. Therefore, the researcher chose to transfer fromrandom paper-based questionnaires to random online questionnaires using snowballsampling technique.

Moreover, the sample size was not large enough to identify relationships between de-pendent and independent variables precisely. In addition, because of the considerablesize of KSA, different cultures vary from region to region; this might also affect theresults of this study. For instance, people who live in the western region of KSA areconsidered more open-minded and progressive than in other regions of the country.Therefore, it is important to consider all the differences in education, geography, cul-tural background and lifestyle; this requires careful attention. In addition, in futureresearch, expanding and diversifying the target sample size of population is recom-mended in order to produce stronger evidence when testing the hypotheses.

Furthermore, due to the lack of time to select the participants, as well as other cir-cumstances, random Snowball sampling technique was chosen; this method does notprecisely represent the entire population. In addition, the majority of the respondentswere young people.

Future studies should target a larger sample size using different sample techniquesin order to increase their reliability and findings accuracy. People from differentareas of the country may hold different behaviours and beliefs; it is necessary to takeinto account that about 75% of the respondents of this study were from large cities inKSA. Due to these limitations, the results should be interpreted with utmost caution.

57

6.5 Professional and Ethical Issues

Learning to use the SPSS programme was a task that required a great deal of effortin order to present the hard statistics in a suitable manner. In addition, it was timeconsuming for the project plan to re-input data in the software and transfer it fromArabic to English, as the online survey was initially translated into Arabic. More-over, some data within the survey responses were received in Arabic and needed tobe translated. Also, for the purpose of processing the responses, some data needed tobe transformed from words into digital numbers, also, all missing values and emptylines had to be deleted.

Due to the lack of reliable Arabic language online survey websites, there were someproblems with alignment while using the English language survey website. Further-more, some subjects’ responses needed to be excluded because of uncompleted orcontradictory answers in the surveys.

With regard to the privacy and confidentiality of the gathered data, no data havebeen revealed; they are dealt with on the basis that they are strictly for researchpurposes only. No personal information relating to the participants was recorded orwritten down.

6.6 Personal Achievements of This Study

Several topics and skills were learnt throughout the project, which will assist mewhen conducting further work in the future. For instance, different methods (quali-tative and quantitative methods), besides the types of collection data (primary andsecondary data), were reviewed to decide which would benefit this study accordingto the time given and other circumstances.

In addition, I learned to be patient and not surrender to any problem easily. Forexample, I spent a long time on designing two online surveys in two different web-sites but both websites crashed. As a result, I designed a third survey on a newsurvey website that offered the same features, including unlimited number of ques-tions and responses, which encourage participants to complete it fully. Moreover, Ilearned how to conduct a pilot study on the questionnaire in order to evaluate it andtest the completion time and the question wording. The findings of the pilot studygreatly assisted in detecting questions flaws, affording me the opportunity to makecorrections before the main study.

Furthermore, learning SPSS passed through several steps. Firstly, I was advised toobtain help in this matter through the university; after visiting the Dean of Studentsand the library, I was informed that the weekly drop-in advice sessions for SPSS wasstopped. Then, I tried to self-study the basics over the Internet, e.g. YouTube andother websites, but that did not work for me. Finally, I attended a private SPSS

58

lessons in London from which I learned many skills, e.g. how to find correlationsbetween variables, how to calculate frequencies and percentages, and how to evaluatethe reliability of variables by critically reviewing different literature resources onrelated studies and by conducting a reliability test. I also learned how to conductseveral tests such as the Pearson correlation test, Pot Hoc Tests, the One-Way-ANOVA test and multiple regression.

6.7 Conclusion

Due to the lack of time, this project has focused only on the web-users’ point of viewregarding online shopping. It would possible to assess the customers’ point of viewin general or the firms’ point of view if there adequate time were allocated for thisproject to examine the issues more deeply. Further study is recommended in order toexamine the adoption of online shopping from the customers’ point of view in generaland from the firms’ point of view.

The researcher designed an integrated model, combining the Extending TechnologyAcceptance Model (TAM) model, the Theory of Planned Behaviour (TPB), and re-search findings on relevant topics. This was done to provide an expanded model thatcan further identify web-users’ adoption of online shopping in KSA and to gain betterunderstanding of web-users’ behaviours towards online shopping.

The purpose of this study goes beyond providing an insight of web-users’ behaviourwith respect to online shopping; it can also supply web marketing recommendationsfor e-commerce in order to identify the best direction for local marketing strategies.Moreover, it can help local markets to understand how to meet their customers’ needsand to gain the greatest benefit by expanding their businesses.

Trust in the Saudi government, the Saudi Post Office and Saudi Internet providers isnot an issue with the e-retailers. Rather, the issue is with the online environment ofSaudi Arabia. This contributes to distrust in the Saudi online environment in termsof conducting a purchase. Therefore, the Saudi government, the Saudi Post Officeand Saudi Internet providers should work hard to satisfy Saudi consumers.

However, further studies are also suggested, using a larger sample size and differentsample techniques in order to obtain more reliable results. Finally, by conductingthis study, a better understanding of the factors affecting online shopping has beenobtained.

59

Bibliography

[Abdulaziz Aleid et al., 2010] Abdulaziz Aleid, F., Rogerson, S., and Fairweather, B.(2010). A consumers’ perspective on e-commerce: practical solutions to encourageconsumers’ adoption of e-commerce in developing countries-a saudi arabian empir-ical study. In Advanced Management Science (ICAMS), 2010 IEEE InternationalConference on, volume 2, pages 373–377. IEEE.

[AbouSaber, 2007] AbouSaber, I. (2007). Assessing e-commerce adoption in the king-dom of saudi arabia. MSc Dissertation.

[Aditya, 2011] Aditya, M. (2011). Factors determining acceptance level of internetbanking implementation. Journal of Economics, Business, and Accountancy (ven-tura), 14(2).

[Ajzen, 1991] Ajzen, I. (1991). The theory of planned behavior. Organizational be-havior and human decision processes, 50(2):179–211.

[Al-Furaih, 2002] Al-Furaih, I. (2002). Internet regulations: The saudi arabian expe-rience. In INET 2002 Conference, Washington, DC Available at: http://inet2002.org/CD-ROM/lu65rw2n/papers/u05-a. pdf.

[Al-Gahtani, 2003] Al-Gahtani, S. (2003). Computer technology adoption in saudiarabia: Correlates of perceived innovation attributes. Information Technology forDevelopment, 10(1):57–69.

[Al-maghrabi et al., 2010] Al-maghrabi, T., Dennis, C., and Halliday, S. (2010).Adapting tam and ect: Continuance intention of e-shopping in saudi arabia. InEuropean and Mediterranean Conference on Information Systems 2010, pages 1–20.

[Al-Somali et al., 2009] Al-Somali, S., Gholami, R., and Clegg, B. (2009). An in-vestigation into the acceptance of online banking in saudi arabia. Technovation,29(2):130–141.

[Al Sukkar and Hasan, 2005] Al Sukkar, A. and Hasan, H. (2005). Toward a modelfor the acceptance of internet banking in developing countries. Information Tech-nology for Development, 11(4):381–398.

[Aleid et al., 2009] Aleid, F., Rogerson, S., and Fairweather, B. (2009). Factors af-fecting consumers adoption of ecommerce in saudi arabia from a consumersper-spective. e-COMMERCE 2009, page 11.

[AlGhamdi et al., 2011] AlGhamdi, R., Drew, S., and Alfaraj, O. (2011). Issuesinfluencing saudi customers decisions to purchase from online retailers in the ksa:a qualitative analysis. European Journal of Scientific Research, 55(4):22–35.

[AlGhamdi et al., 2012] AlGhamdi, R., Drew, S., and Alhussain, T. (2012). A con-ceptual framework for the promotion of trusted online retailing environment insaudi arabia. International Journal of Business and Management, 7(5):p140.

60

[Alhomaid, 2010] Alhomaid, M. (2010). The role of culture in the adoption anddiffusion of e-commerce in saudi arabia. MSc Dissertation.

[Alnemer, 2009] Alnemer, W. M. (2009). E-commerce in saudi arabia: Between in-adequacy and necessity. MSc Dissertation.

[Alrayes, 2009] Alrayes, M. (2009). The challenges of e-commerce in saudi arabiafocusing on the telecom industry online shopping service. MSc Dissertation.

[Alriyadh, 2010] Alriyadh (2010). No e-government without the application of mail-ing addressing system saudi post (arabic source. http://www.sp.com.sa/arabic/news/pages/newsdetails.aspx?ItemID=419. [Online; accessed 08-March-2012].

[Alriyadh, 2012] Alriyadh (2012). Young. http://www.alriyadh.com/2011/11/07/article681478.html. [Online; accessed 19-June-2012].

[Altamimi, 2009] Altamimi, A. (2009). Impact of societys response and behaviouron b2c e-commerce adoption in saudi arabia. MSc Dissertation.

[Alwahaishi et al., 2009] Alwahaishi, S., Nehari-Talet, A., and Snasel, V. (2009).Electronic commerce growth in developing countries: Barriers and challenges. InNetworked Digital Technologies, 2009. NDT’09. First International Conference on,pages 225–232. IEEE.

[Amoroso et al., 2009] Amoroso, D., Hunsinger, D., and Jaynes, J. (2009). Under-standing consumers acceptance of online purchasing. Journal of Information Tech-nology Management, 10(1):15–41.

[Amoroso and Hunsinger, 2009] Amoroso, D. and Hunsinger, S. (2009). Measuringthe acceptance of internet technology by consumers. International Journal of E-Adoption (IJEA), 1(3):48–81.

[Anckar, 2003] Anckar, B. (2003). Drivers and inhibitors to e-commerce adoption:Exploring the rationality of consumer behaviour in the electronic marketplace.Proceedings of the 11th ECIS on New Paradigms in Organizations, Markets andSociety (Ciborra, C. et al. Ed.), Napoli, Italy.

[Anglim, 2007] Anglim, J. (2007). Correlation, multiple regression andlogistic regression. http://web.psych.unimelb.edu.au/jkanglim/correlationandreggression.pdf. [Online; accessed 10-August-2012].

[Barbonis and Laspita, 2005] Barbonis, P. and Laspita, S. (2005). Some factors influ-encing adoption of e-commerce in greece. In Engineering Management Conference,2005. Proceedings. 2005 IEEE International, volume 1, pages 31–35. IEEE.

[Barnes and Hunt, 2000] Barnes, S. and Hunt, B. (2000). E-Commerce and V-Business: Business Models for Fglobal Success. Butterworth-Heinemann.

[Bellman et al., 1999] Bellman, S., Lohse, G., and Johnson, E. (1999). Predictors ofonline buying behavior. Communications of the ACM, 42(12):32–38.

[Beynon, 2004] Beynon, P. (2004). e-Business. Palgrave Macmillan.

[Birmingham, 2011] Birmingham, U. (2011). evalued: An evaluation toolkit for e-library developments. http://www.evalued.bcu.ac.uk. [Online; accessed 12-March-2012].

[Blythe, 2007] Blythe, S. (2007). The dubai electronic transactions statute: A proto-type for e-commerce law in the united arab emirates and the gcc countries. Journalof Economic and Administrative Sciences, 23(1):1–23.

61

[Boeree, 2005] Boeree, C. (2005). Qualitative methods. Shippensburg University.Shippensburg, PA. Retrieved May, 16:2006.

[Bradley, 2007] Bradley, N. (2007). Marketing research: tools & techniques. OxfordUniversity Press, USA.

[Brown, 2011] Brown, J. (2011). Likert items and scales of measurement? Statistics,15(1).

[Browne, 2005] Browne, K. (2005). Snowball sampling: using social networks to re-search non-heterosexual women. International Journal of Social Research Method-ology, 8(1):47–60.

[Bryman, 1988] Bryman, A. (1988). Quantity and quality in social research, vol-ume 18. Psychology Press.

[Bryman and Bell, 2004] Bryman, A. and Bell, E. (2004). combining quantitativeand qualitative research in social research methods. Oxford.

[Burgess, 2001] Burgess, T. (2001). A general introduction to the design of question-naires for survey research.

[Burke, 2002] Burke, R. (2002). Technology and the customer interface: what con-sumers want in the physical and virtual store. Journal of the Academy of MarketingScience, 30(4):411–432.

[Chaffey, 2006] Chaffey, D. (2006). E-business and E-commerce Management: Strat-egy, Implementation and Practice. Financial Times/Prentice Hall.

[Chau and Hu, 2001] Chau, P. and Hu, P. (2001). Information technology acceptanceby individual professionals: A model comparison approach*. Decision Sciences,32(4):699–719.

[Chaudhry, 2012] Chaudhry, M. (2012). Country-specific trust as a barrier to e-shopping adoption a developing country perspective.

[Chen et al., 2004] Chen, L., Gillenson, M., and Sherrell, D. (2004). Consumer accep-tance of virtual stores: a theoretical model and critical success factors for virtualstores. ACM SIGMIS Database, 35(2):8–31.

[Choi and Geistfeld, 2004] Choi, J. and Geistfeld, L. (2004). A cross-cultural in-vestigation of consumer e-shopping adoption. Journal of Economic Psychology,25(6):821–838.

[CITC, 2009] CITC (2009). Internet usage in the kingdomof saudi arabia(government sector). http://www.citc.gov.sa/NR/rdonlyres/3347D395-BCDE-45A8-82E4-A2473DA43F21/0/CITCBusinessEstablishmentReport2008English.pdf. [Online; accessed29-February-2012].

[CITC, 2010] CITC (2010). It report 2010 on the internet ecosystem insaudi arabia. http://www.citc.gov.sa/English/Reportsandstudies/Reports/Documents/IT%20006%20E%20-%20IT%20Report%202010.pdf. [Online; accessed29-February-2012].

[CITC, 2012] CITC (2012). Ict indicators 2011. http://www.citc.gov.sa/English/Reportsandstudies/Indicators/Indicators%20of%20Communications%20and%20Information%20Techn/2011_ICT%20Indicators%20_English.pdf. [Online; accessed 17-May-2012].

62

[commerce.gov, 2011] commerce.gov (2011). Fact sheet: Digital lit-eracy. http://www.commerce.gov/news/fact-sheets/2011/05/13/fact-sheet-digital-literacy#_edn1. [Online; accessed 29-February-2012].

[Creswell, 2009] Creswell, J. (2009). Research design: Qualitative, quantitative, andmixed methods approaches. Sage Publications, Inc.

[Czaja et al., 2001] Czaja, S., Sharit, J., Ownby, R., Roth, D., and Nair, S. (2001).Examining age differences in performance of a complex information search andretrieval task. Psychology and Aging, 16(4):564.

[Davis, 1989] Davis, F. (1989). Perceived usefulness, perceived ease of use, and useracceptance of information technology. MIS quarterly, pages 319–340.

[Dinev and Hart, 2002] Dinev, T. and Hart, P. (2002). Internet privacy concerns andtrade-off factors–empirical study and business implications. In Proceedings of theInternational Conference On Advances In Infrastructure For E-Business, L’Aquila,Italy.

[Dittmar et al., 2004] Dittmar, H., Long, K., and Meek, R. (2004). Buying on theinternet: Gender differences in on-line and conventional buying motivations. SexRoles, 50(5):423–444.

[Donthu and Garcia, 1999] Donthu, N. and Garcia, A. (1999). The internet shopper.Journal of advertising research, 39(3):52–58.

[Draut and Silva, 2003] Draut, T. and Silva, J. (2003). Borrowing to make ends meet:The growth of credit card debt in the’90s. Demos.

[Dubelaar et al., 2005] Dubelaar, C., Sohal, A., and Savic, V. (2005). Benefits, im-pediments and critical success factors in b2c e-business adoption. Technovation,25(11):1251–1262.

[Durndell and Haag, 2002] Durndell, A. and Haag, Z. (2002). Computer self efficacy,computer anxiety, attitudes towards the internet and reported experience with theinternet, by gender, in an east european sample. Computers in Human Behavior,18(5):521–535.

[Elgarah and Falaleeva, 2005] Elgarah, W. and Falaleeva, N. (2005). Adoption ofbiometric technology: Information privacy and tam. In Proceedings of the EleventhAmericas Conference on Information Systems, pages 1209–1212.

[eMarketer, 2002] eMarketer (2002). Europe ecommerce: B2b b2c: Executive sum-mary. http://www.infoedge.com/samples/EM-2067sam.pdf. [Online; accessed29-February-2012].

[Fenech and OCass, 2001] Fenech, T. and OCass, A. (2001). Internet users adop-tion of web retailing: user and product dimensions. Journal of Product & BrandManagement, 10(6):361–381.

[Fishbein and Ajzen, 1975] Fishbein, M. and Ajzen, I. (1975). Belief, attitude, in-tention and behaviour: An introduction to theory and research. Addison-Wesley.

[G20, 2012] G20, M. (2012). Members. http://www.g20.org/en/g20/members.[Online; accessed 19-February-2012].

[Garbarino and Strahilevitz, 2004] Garbarino, E. and Strahilevitz, M. (2004). Gen-der differences in the perceived risk of buying online and the effects of receiving asite recommendation. Journal of Business Research, 57(7):768–775.

63

[George, 2002] George, J. (2002). Influences on the intent to make internet purchases.Internet Research, 12(2):165–180.

[Goel, 2007] Goel, R. (2007). E-commerce. New Age International.

[Goh, 1995] Goh, H. (1995). The diffusion of internet in singapore, academic exercise,faculty of business administration. National University of Singapore.

[Goodwin, 1999] Goodwin, T. (1999). Measuring the effectiveness of online market-ing. JOURNAL-MARKET RESEARCH SOCIETY, 41:403–406.

[Groucutt and Griseri, 2004] Groucutt, J. and Griseri, P. (2004). Mastering e-business. Palgrave Macmillan.

[Ha and Stoel, 2009] Ha, S. and Stoel, L. (2009). Consumer e-shopping acceptance:Antecedents in a technology acceptance model. Journal of Business Research,62(5):565–571.

[Hair et al., 2010] Hair, J., Black, B., and R., A. (2010). Multivariate Data Analysis:a Global Perspective. 7th Edition. Pearson Prentice Hall, New Jersey.

[Hair et al., 2000] Hair, J., Bush, R., and Ortinau, D. (2000). Marketing research: Apractical approach for the new millennium. Irwin Professional Publishing.

[Haque et al., 2009] Haque, A., Khatibi, A., and Al Mahmud, S. (2009). Factorsdeterminate customer shopping behaviour through internet: the malaysian case.Australian Journal of Basic and Applied Sciences, 3(4):3452–3463.

[Hasan, 2010] Hasan, B. (2010). Exploring gender differences in online shoppingattitude. Computers in Human Behavior, 26(4):597–601.

[Hilary, 2003] Hilary, P. (2003). The value of likert scales in measuring attitudesof online learners. http://www.hkadesigns.co.uk/websites/msc/reme/likert.htm. [Online; accessed 14-March-2012].

[Holland and Menzel Baker, 2001] Holland, J. and Menzel Baker, S. (2001). Cus-tomer participation in creating site brand loyalty. Journal of Interactive Marketing,15(4):34–45.

[Hu et al., 2003] Hu, P., Clark, T., and Ma, W. (2003). Examining technology ac-ceptance by school teachers: a longitudinal study. Information & Management,41(2):227–241.

[Hughes, 1997] Hughes, C. (1997). Mystifying through coalescence: The underlyingpolitics of methodological choices. K Watson, C Modgil and S Modgil (Eds) Edu-cational Dilemmas: Debate and Diversity, Quality in Education, pages 413–420.

[Hughes, 2006] Hughes, C. (2006). Qualitative and quantitative approaches. http://www2.warwick.ac.uk/fac/soc/sociology/staff/academicstaff/chughes/hughesc_index/teachingresearchprocess/quantitativequalitative/quantitativequalitative. [Online; accessed 11-March-2012].

[Hui and Wan, 2007] Hui, T. and Wan, D. (2007). Factors affecting internet shoppingbehaviour in singapore: gender and educational issues. International Journal ofConsumer Studies, 31(3):310–316.

[Humphrey et al., 2003] Humphrey, J., Mansell, R., Pare, D., and Schmitz, H.(2003). Reality of e-commerce with developing countries.

64

[Hung et al., 2008] Hung, S., Ku, Y., and Chien, J. (2008). Re-examining factorsinfluencing information technology acceptance by individual professionals. Pro-ceedings of PACIS.

[Internetworldstats, 2012] Internetworldstats (2012). Internet usage in the mid-dle east. middle east internet usage population statistics. http://www.internetworldstats.com/stats5.htm. [Online; accessed 28-May-2012].

[Jennex et al., 2004] Jennex, M., Amoroso, D., and Adelakun, O. (2004). E-commerce infrastructure success factors for small companies in developingeconomies. Electronic Commerce Research, 4(3):263–286.

[Johnston, 1998] Johnston, R. (1998). Trading systems and electronic commerce.Eruditions Pub.

[Joines et al., 2003] Joines, J., Scherer, C., and Scheufele, D. (2003). Exploring mo-tivations for consumer web use and their implications for e-commerce. Journal ofconsumer marketing, 20(2):90–108.

[Jones and Tollous, 2007] Jones, K. and Tollous, R. (2007). E-commerce: Attitudesin the us and mexico. Working paper.

[Kaynak et al., 2005] Kaynak, E., Tatoglu, E., and Kula, V. (2005). An analysis ofthe factors affecting the adoption of electronic commerce by smes: Evidence froman emerging market. International Marketing Review, 22(6):623–640.

[Kenneth and Carol, 2008] Kenneth, L. and Carol, T. (2008). E-commerce businesstechnology society. Pearson Prentice Hall.

[Kim and Srivastava, 2007] Kim, Y. and Srivastava, J. (2007). Impact of social in-fluence in e-commerce decision making. In Proceedings of the ninth internationalconference on Electronic commerce, pages 293–302. ACM.

[Kleijnen et al., 2004] Kleijnen, M., Wetzels, M., and De Ruyter, K. (2004). Con-sumer acceptance of wireless finance. Journal of Financial Services Marketing,8(3):206–217.

[Korgaonkar and Wolin, 1999] Korgaonkar, P. and Wolin, L. (1999). A multivariateanalysis of web usage. Journal of advertising research, 39(1):53–68.

[Kshetri, 2008] Kshetri, N. (2008). Barriers to e-commerce and competitive businessmodels in developing countries: A case study. Electronic Commerce Research andApplications, 6(4):443–452.

[Kurdi, 2008] Kurdi, R. (2008). Study of factors influencing people’s attitudes to-wards online shopping in saudi arabia. MSc Dissertation.

[Laudon and Traver, 2003] Laudon, K. and Traver, C. (2003). E-commerce. AddisonWesley.

[Lee and Johnson, 2002] Lee, M. and Johnson, K. (2002). Exploring differences be-tween internet apparel purchasers, browsers and non-purchasers. Journal of Fash-ion Marketing and Management, 6(2):146–157.

[Li, 2007] Li, F. (2007). What is e-business?: how the Internet transforms organiza-tions. Wiley-Blackwell.

[Li et al., 1999] Li, H., Kuo, C., and Rusell, M. (1999). The impact of perceivedchannel utilities, shopping orientations, and demographics on the consumer’s onlinebuying behavior. Journal of Computer-Mediated Communication, 5(2):0–0.

65

[Liao and Cheung, 2001] Liao, Z. and Cheung, M. (2001). Internet-based e-shoppingand consumer attitudes: an empirical study. Information & Management,38(5):299–306.

[Lim and Dubinsky, 2004] Lim, H. and Dubinsky, A. (2004). Consumers’ perceptionsof e-shopping characteristics: an expectancy-value approach. Journal of ServicesMarketing, 18(7):500–513.

[Lu et al., 2003] Lu, J., Yu, C., Liu, C., and Yao, J. (2003). Technology acceptancemodel for wireless internet. Internet Research, 13(3):206–222.

[Mahmood et al., 2004] Mahmood, M., Bagchi, K., and Ford, T. (2004). On-lineshopping behavior: Cross-country empirical research. International Journal ofElectronic Commerce, 9(1):9–30.

[Malhotra and Birks, 2007] Malhotra, N. and Birks, D. (2007). Marketing research:an applied approach: 3rd european edition. Harlow Prentice HallFinancial Times,pages 161–167.

[Malhotra and Hall, 1996] Malhotra, N. and Hall, J. (1996). Marketing research. anapplied orientation. Sydney: Prentice Hall.

[Masrek et al., 2007] Masrek, M., Karim, N., and Hussein, R. (2007). Investigatingcorporate intranet effectiveness: a conceptual framework. Information management& computer security, 15(3):168–183.

[Milne, 1999] Milne, J. (1999). Questionnaires: Some advantages and disadvantages.Centre for CBL in Land Use and Environmental Sciences.

[Minges and Union, 2002] Minges, M. and Union, I. T. (2002). Kretek Internet: In-donesia Case Study. ITU.

[MinistryofFinance, 2012] MinistryofFinance (2012). Saudi arabias 2004-2012 budgetreports. http://www.mof.gov.sa/English/DownloadsCenter/Pages/Budget.aspx. [Online; accessed 07-March-2012].

[Mirza, 1998] Mirza, A. (1998). Saudi arabia and the internet technology. World.

[Miyazaki and Fernandez, 2001] Miyazaki, A. and Fernandez, A. (2001). Consumerperceptions of privacy and security risks for online shopping. Journal of ConsumerAffairs, 35(1):27–44.

[Molla and Heeks, 2007] Molla, A. and Heeks, R. (2007). Exploring e-commerce ben-efits for businesses in a developing country. The Information Society, 23(2):95–108.

[Molla and Licker, 2005] Molla, A. and Licker, P. (2005). ecommerce adoption indeveloping countries: a model and instrument. Information & Management,42(6):877–899.

[Moorman, 1990] Moorman, C. (1990). The effects of stimulus and consumer charac-teristics on the utilization of nutrition information. Journal of Consumer Research,pages 362–374.

[Obrien, 2007] Obrien, R. (2007). A caution regarding rules of thumb for varianceinflation factors. Quality & Quantity, 41(5):673–690.

[O’Cass and Fenech, 2003] O’Cass, A. and Fenech, T. (2003). Web retailing adop-tion: exploring the nature of internet users web retailing behaviour. Journal ofRetailing and Consumer services, 10(2):81–94.

66

[Post, 2010] Post, S. (2010). Production and services, saudi post. http://www.sp.com.sa/English/SaudiPost/ProductsServices/Pages/Wasel1.aspx. [On-line; accessed 08-March-2012].

[Post, 2012] Post, S. (2012). Wasel aalami. http://www.sp.com.sa/English/SaudiPost/ProductsServices/Pages/myaddressinus.aspx. [Online; accessed30-May-2012].

[Richardson, 2012] Richardson, R. (2012). Business Applications of Multiple Regres-sion. Business Expert Press.

[Robson, 2002] Robson, C. (2002). Real world research: A resource for social scien-tists and practitioner-researchers. Wiley-Blackwell.

[Rodgers and Harris, 2003] Rodgers, S. and Harris, M. (2003). Gender and e-commerce: an exploratory study. Journal of Advertising Research, 43(03):322–329.

[Rohm and Swaminathan, 2004] Rohm, A. and Swaminathan, V. (2004). A typologyof online shoppers based on shopping motivations. Journal of business research,57(7):748–757.

[Sait et al., 2007] Sait, S., Al-Tawil, K., and Hussain, S. (2007). E-commerce in saudiarabia: Adoption and perspectives. Australasian Journal of Information Systems,12(1).

[Saunders et al., 2009] Saunders, M., Lewis, P., and Thornhill, A. (2009). Researchmethods. Prentice Hall.

[Schwartz, 1997] Schwartz, E. (1997). Webonomics: Nine essential principles forgrowing your business on the world wide web.

[Sharma and Mokthar, 2008] Sharma, R. and Mokthar, I. (2008). Bridging the digi-tal divide in asia-challenges and solutions.

[Shim et al., 2001] Shim, S., Eastlick, M., Lotz, S., and Warrington, P. (2001). Anonline prepurchase intentions model: The role of intention to search: Best over-all paper awardthe sixth triennial ams/acra retailing conference, 2000 11 decisionmade by a panel of journal of retailing editorial board members. Journal of retail-ing, 77(3):397–416.

[SMC, 2001] SMC (2001). E-commerce in the kingdom: Breakthrough for the future.Riyadh.

[StatPac, 2012] StatPac (2012). Survey sampling methods. http://www.statpac.com/surveys/sampling.htm. [Online; accessed 14-March-2012].

[Straub et al., 2003] Straub, D., Loch, K., and Hill, C. (2003). Transfer of informa-tion technology to the arab world: a test of cultural influence modeling. AdvancedTopics in Global information Management, Idea Group Publishing, Hershey, PA,pages 141–172.

[Susskind, 2004] Susskind, A. (2004). Electronic commerce and world wide web ap-prehensiveness: An examination of consumers’ perceptions of the world wide web.Journal of Computer-Mediated Communication, 9(3):00–00.

[Tan and Teo, 2000] Tan, M. and Teo, T. (2000). Factors influencing the adoptionof internet banking. Journal of the AIS, 1(1es):5.

67

[Taylor and Todd, 1995] Taylor, S. and Todd, P. (1995). Understanding informationtechnology usage: A test of competing models. Information systems research,6(2):144–176.

[Teo and Pok, 2003] Teo, T. and Pok, S. (2003). Adoption of wap-enabled mobilephones among internet users. Omega, 31(6):483–498.

[Turban et al., 2004] Turban, E., King, D., Lee, J., and Viehland, D. (2004). Elec-tronic Commerce: a managerial perspective 2004, volume 2. Prentice Hall.

[Turban et al., 2008] Turban, E., McKay, D., Marshall, J., Lee, P., and Viehland, J.(2008). Electronic commerce a managerial perspective.

[Van de Ven and Ferry, 1980] Van de Ven, A. and Ferry, D. (1980). Measuring andassessing organizations. Wiley New York.

[Veal, 2006] Veal, A. (2006). Research methods for leisure and tourism: A practicalguide. Pearson Education.

[Venkatesh and Davis, 2000] Venkatesh, V. and Davis, F. (2000). A theoretical ex-tension of the technology acceptance model: Four longitudinal field studies. Man-agement science, pages 186–204.

[Venkatesh and Morris, 2000] Venkatesh, V. and Morris, M. (2000). Why don’t menever stop to ask for directions? gender, social influence, and their role in technologyacceptance and usage behavior. Mis Quarterly, pages 115–139.

[Venkatesh et al., 2003] Venkatesh, V., Morris, M., Davis, G., and Davis, F. (2003).User acceptance of information technology: Toward a unified view. MIS quarterly,pages 425–478.

[Whiteley, 2000] Whiteley, D. (2000). E-commerce: Strategy, technologies and appli-cations. McGraw-Hill Publishing Company.

[ystats, 2012] ystats (2012). Pc demand in saudi ara-bia to rise 30 http://saudieconomicsurvey.com/2012/02/pc-demand-in-saudi-arabia-to-rise-30/. [Online; accessed 29-February-2012].

[ystats.com, 2011] ystats.com (2011). Usa b2c e-commerce report 2011. http://www.ystats.com/uploads/report_abstracts/805.pdf. [Online; accessed 29-February-2012].

[Zhou, 2007] Zhou, L. (2007). A critical survey of consumer factors in online shop-ping. Marketing Journal, 8(1):41–62.

[Zittrain and Edelman, 2002] Zittrain, J. and Edelman, B. (2002). Documentationof internet filtering in saudi arabia. Berkman Center for Internet and Society,Harvard Law School.¡ http://cyber. law. harvard. edu/filtering/saudiarabia.

68

69

Appendix A

Questionnaires: Arabic and English

70

71

72

73

74

75

76

77

English Questionnaire

Dear participant,

Thank you for your time; I am a Master’s student of Information Systems at UEA University

in Norwich, UK. In order to complete my final project (dissertation), I am conducting a survey

to answer the question ‘What are the factors that affect e-commerce (online shopping)

adoption in Saudi Arabia from web-users’ perspective’.

Your participation in this research is valuable and highly appreciated. The information

gathered for this project will only be used for research purposes. However, you have the

right to withdraw from the survey if you feel uncomfortable at any time.

If you have any questions, please do not hesitate to contact me at B.Al-Shuwai@uea.ac.uk.

I greatly appreciate your time and interest.

I agree to the information that I provided to be published as a case study.

Yes □ No □

Important Note: If you are already a customer of online shopping, please answer the

questions based on your experience of online shopping usage. If no, please answer the

questions based on your expectations of online shopping usage in the future.

(This questionnaire is available in Arabic and English)

Thank you for your participation.

Badr Alshuwai

78

Section A: Personal information:

1- Gender

Male Female

2- Age

18– 25 26 – 35 36 – 45 46 – 60 Over 60

3- Education level

Less than High school High school Diploma BA/BSc MA/MSc

PhD Other, Please specify:.................................

4- Occupation

Unemployed Government employee Private sector employee

Self employed Housewife Retired Student

5- In which region of Saudi Arabia do you live?

Northern Region Southern Region Central Region Eastern Region

Western Region

6- The area you live in is considered as?

City Town Village

7- Your monthly income. (SR)

Less than 3,000 Between 3,000 and 7,000 More than 7,000 and less than 12,000

Between 12,000 and 20,000 More than 20,000

79

Section B: Personal use of online shopping:

1- How often do you practice e-commerce activities(buying products or services)?

Regularly Sometimes Rarely Never

2- Could you specify the location of the e-commerce sites(e.g. saudi.souq.com, eBay,

Amazon, Alrajhi bank, etc...) that you usually prefer to use either for goods or services?

(Please check all that apply)

Local sites Overseas sites Both I have never practice e-commerce activities

3- In general, do you think that overseas sites are better than local sites for conducting online shopping?

Yes No Don't know

4- What type of goods and services do/would you obtain from the internet? (please

check all that apply)

Nothing Fashion Books/Magazines Electronic equipment (e.g. computers,

mobiles, games, accessories) Software, songs Jewelers &Beauty

Services (flight and hotel reservation, distance courses, group SMS, etc ...) Other ,

Please specify:………………….........................................

5- How do/would you pay for your goods or services over the internet? (please check

all that apply)

Not applicable Direct Debit Visa Credit/Debit or Master Card Cash

upon delivery Third party (e.g. PayPal) Special Internet credit card

banking transfer Other, Please specify:…………….........................................

6- How do/would you receive your purchases either goods or services? (please check

all that apply)

Not applicable Home mailbox Work mailbox Relative or friend mailbox

Agencies mailboxes E-mail Other, Please specify:...........................................

80

Section C: Factors affecting online purchase

The following statements are to give your opinion about online shopping:

Strongly disagree=1, Disagree=2 , Neutral=3, Agree=4 ,Strongly agree=5

Item Questions 1-strongly disagree to 5-strongly agree

Attitude towards using online shopping (ATT)

ATT1 Online shopping is appealing.

1 2 3 4 5

ATT2 I like online shopping.

1 2 3 4 5

ATT3 It is a good idea to buy online rather than from a physical store.

1 2 3 4 5

ATT4 I have fun when using online shopping.

1 2 3 4 5

Subjective Norm (SN)

SN1 I would be affected by multi-media to shop online.

1 2 3 4 5

SN2

People whose opinions I value would prefer that I use e-commerce websites to buy things.

1 2 3 4 5

SN3 People who influence my behaviour would think I should use e-commerce websites to buy things. 1 2 3 4 5

SN4 People who are important to me would recommend that I use e-commerce websites to buy things. 1 2 3 4 5

SN5 My colleagues and friends encourage me to shop online.

1 2 3 4 5

Intention to adopt and continue to use online shopping (BI)

BI1 I always try to use online shopping whenever it meets my shopping needs.

1 2 3 4 5

BI2 I plan to use online shopping in the future.

1 2 3 4 5

BI3 I expect my use of online shopping to continue in the future.

1 2 3 4 5

Perceived behavioural control (PBC)

PBC1 I have control over how to shop online.

1 2 3 4 5

PBC2 I have the resources needed to shop online (e.g. computer, credit card).

1 2 3 4 5

PBC3 In general, resources, opportunities and knowledge make it easy for me to shop online. 1 2 3 4 5

Facilitating conditions (FC)

FC1 I can find guidance in the online shopping website.

1 2 3 4 5

FC2 I can find a specific person or group to help when I have difficulties in an online shopping website. 1 2 3 4 5

FC3 My interaction with Saudi e-commerce websites will be or has been clear and understandable. 1 2 3 4 5

Cultural influence (CI)

CI1 I am interested to hear about new technological developments.

1 2 3 4 5

CI2 Technological developments have enhanced our lives.

1 2 3 4 5

CI3 I feel comfortable in changing and using e-commerce services activities.

1 2 3 4 5

CI4 I think having adequate English language skills would enhance e-commerce adoption in KSA. 1 2 3 4 5

CI5 I think people who have been to a developed country for study or work would be more interested in online shopping. 1 2 3 4

5

Security (SC)

SC1 Online shops provide security guarantee on payment over the Internet.

1 2 3 4 5

SC2 I would feel secure when providing personal information over the Internet.

1 2 3 4 5

SC3 Security policies are clearly visible on e-commerce websites.

1 2 3 4 5

81

Trust (TR)

TR1 The lack of product trial / inspect by hand does not affect my decision me to use online shopping. 1 2 3 4 5

TR2 Local online shopping websites that I buy from have good reputations.

1 2 3 4 5

TR3 Local online shopping websites tend to fulfil their promises.

1 2 3 4 5

TR4 I trust local online shops.

1 2 3 4 5

Awareness of e-commerce service (AW)

AW1 I am aware of the benefits and risks of using online shopping.

1 2 3 4 5

AW2 I am aware that the Internet allows online shopping.

1 2 3 4 5

AW3 I am aware of using online shopping.

1 2 3 4 5

Beliefs about government e-readiness (GOV)

GOV1 There are effective laws to protect consumer privacy.

1 2 3 4 5

GOV2 There are effective laws to combat cyber crime. 1 2 3 4 5

GOV3 The Saudi government provides facilitating conditions to private businesses and small companies to expand their business via e-commerce. 1 2 3 4 5

GOV4 The legal environment is conducive to conducting business on the Internet (e.g. clear standard policy for returns, exchange and refund). 1 2 3 4 5

Beliefs about Internet service (IS)

IS1 Internet service in KSA is affordable.

1 2 3 4 5

IS2 Internet service in KSA saves my time for online shopping.

1 2 3 4 5

IS3 Internet service in KSA is reliable with 24/7 access to e-commerce websites.

1 2 3 4 5

IS4 Overall, there is good Internet service and support in KSA (e.g. high speed, no disconnecting from time to time, very good support). 1 2 3 4 5

Beliefs about postal service (PS)

PS1 Post services in KSA are trustworthy.

1 2 3 4 5

PS2 Post services in KSA via private companies are affordable.

1 2 3 4 5

PS3 Post services in KSA have clear terms and conditions.

1 2 3 4 5

PS4 Post services in KSA have a clear a dressing system for delivery purposes.

1 2 3 4 5

PS5 Overall, there are reliable postal services in KSA (e.g. clear home addressing, no shipping loss or damage, no delay of delivery). 1 2 3 4 5

Beliefs about private business e-commerce penetration (PBEP)

PBEP1 Private businesses are keen on implementing e-commerce.

1 2 3 4 5

PBEP2 There are enough IT companies and professionals available to meet demand (e.g. designing, managing and maintenance for online shops). 1 2 3 4 5

PBEP3 There is diversity and competition among online shops in KSA.

1 2 3 4 5

PBEP4 Saudi e-commerce websites have a vast selection of products and services.

1 2 3 4 5

PBEP5 Saudi e-commerce websites offer competitive prices compared to traditional stores. 1 2 3 4 5

PBEP6 I am satisfied with the customer support provided by Saudi e-commerce websites.

1 2 3 4 5

PBEP7 I am satisfied with the after-sales service provided by Saudi e-commerce websites.

1 2 3 4 5

PBEP8 Overall, I like the quality of the products and services that are offered by Saudi ecommerce websites. 1 2 3 4 5

Actual use of e-commerce online shopping (AU)

AU1 I will continue to shop online.

1 2 3 4 5

AU2 I would consider shopping online in the short term.

1 2 3 4 5

AU3 I would consider shopping online in the long term.

1 2 3 4 5

AU4 I will continue to use the online shopping websites that I have purchased from.

1 2 3 4 5

AU5 I plan to purchase products or services from new online shopping web sites.

1 2 3 4 5

AU6 In general, I would buy online rather than going to a physical store.

1 2 3 4 5

82

Section D: More information

Would you like to add information or comment?

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

…………………………………………………………………………………………………………………………………………………………………

The end of the questionnaire

Thank you very much for completing this questionnaire and sharing your opinions.

I wish you all the best.

Badr Alshuwai

83

Appendix B

Measurement of the Constructs

Item No. Questions Source Attitude towards using online shopping (ATT)

ATT1 Online shopping is appealing. - Lau and Woodman (1995)

- Alsomali et al. (2008)

- Amoroso and Hunsinger (2009)

ATT2 I like online shopping.

ATT3 It is a good idea to buy online rather than from a physical store.

ATT4 I have fun when using online shopping.

Subjective Norm (SN)

SN1 I would be affected by multi-media to shop online. - Chau and Hu (2003)

- Chan and Lu (2004)

- Amoroso and Hunsinger (2009)

SN2

People whose opinions I value would prefer that I use e-commerce websites to buy things.

SN3 People who influence my behaviour would think I should use e-commerce websites to buy things.

SN4 People who are important to me would recommend that I use e-commerce websites to buy things.

SN5 My colleagues and friends encourage me to shop online.

Intention to adopt and continue to use online shopping (BI)

BI1 I always try to use online shopping whenever it meets my shopping needs. - Davis (1989)

- Alsomali et al. (2008)

- Amoroso and Hunsinger (2009)

BI2 I plan to use online shopping in the future.

BI3 I expect my use of online shopping to continue in the future.

Perceived behavioural control (PBC)

PBC1 I have control over how to shop online. - Ajzen (1991)

- Shim, et al. (2001)

- Venkatesh,et al. (2003)

PBC2 I have the resources needed to shop online (e.g. computer, credit card).

PBC3 In general, resources, opportunities and knowledge make it easy for me to shop online.

Facilitating conditions (FC)

FC1 I can find guidance in the online shopping website. - Venkatesh,et al. (2003)

- Amoroso and Hunsinger (2009)

FC2 I can find a specific person or group to help when I have difficulties in an online shopping website.

FC3 My interaction with Saudi e-commerce websites will be or has been clear and understandable.

Cultural influence (CI)

CI1 I am interested to hear about new technological developments. - Alsomali et al. (2008)

- Aleid et al. (2010)

- Self constructed

CI2 Technological developments have enhanced our lives.

CI3 I feel comfortable in changing and using e-commerce services activities.

CI4 I think having adequate English language skills would enhance e-commerce adoption in KSA.

CI5 I think people who have been to a developed country for study or work would be more interested in online shopping.

Security (SC)

SC1 Online shops provide security guarantee on payment over the Internet. - Miyazaki & Fernandez (2001)

- Chen et al. (2004)

-Widjana and Rachmat(2011)

SC2 I would feel secure when providing personal information over the Internet.

SC3 Security policies are clearly visible on e-commerce websites.

84

Trust (TR)

TR1 The lack of product trial / inspect by hand does not affect my decision me to use online shopping. - Holland & Baker (2001)

- Chen et al. (2004)

- Widjana and Rachmat(2011)

TR2 Local online shopping websites that I buy from have good reputations.

TR3 Local online shopping websites tend to fulfil their promises.

TR4 I trust local online shops.

Awareness of e-commerce service (AW)

AW1 I am aware of the benefits and risks of using online shopping. - Jennex et al. (2004)

- Molla (2005)

- Aleid et al. (2010)

AW2 I am aware that the Internet allows online shopping.

AW3 I am aware of using online shopping.

Beliefs about government e-readiness (GOV)

GOV1 There are effective laws to protect consumer privacy. - Goh (1995)

- Tan and Teo (2000)

- Aleid et al. (2010)

- Al Ghamdi et al. (2011)

GOV2 There are effective laws to combat cyber crime.

GOV3 The Saudi government provides facilitating conditions to private businesses and small companies to expand their business via e-commerce.

GOV4 The legal environment is conducive to conducting business on the Internet (e.g. clear standard policy for returns, exchange and refund).

Beliefs about Internet service (IS)

IS1 Internet service in KSA is affordable. - Aleid et al. (2010)

- Al Ghamdi et al. (2011)

IS2 Internet service in KSA saves my time for online shopping.

IS3 Internet service in KSA is reliable with 24/7 access to e-commerce websites.

IS4 Overall, there is good Internet service and support in KSA (e.g. high speed, no disconnecting from time to time, very good support).

Beliefs about postal service (PS)

PS1 Post services in KSA are trustworthy. - Aleid et al. (2010)

- Al Ghamdi et al. (2011)

PS2 Post services in KSA via private companies are affordable.

PS3 Post services in KSA have clear terms and conditions.

PS4 Post services in KSA have a clear a dressing system for delivery purposes.

PS5 Overall, there are reliable postal services in KSA (e.g. clear home addressing, no shipping loss or damage, no delay of delivery).

Beliefs about private business e-commerce penetration (PBEP)

PBEP1 Private businesses are keen on implementing e-commerce. - Aleid et al. (2010)

- Al Ghamdi et al. (2011)

- Self constructed

PBEP2 There are enough IT companies and professionals available to meet demand (e.g. designing, managing and maintenance for online shops).

PBEP3 There is diversity and competition among online shops in KSA.

PBEP4 Saudi e-commerce websites have a vast selection of products and services.

PBEP5 Saudi e-commerce websites offer competitive prices compared to traditional stores.

PBEP6 I am satisfied with the customer support provided by Saudi e-commerce websites.

PBEP7 I am satisfied with the after-sales service provided by Saudi e-commerce websites.

PBEP8 Overall, I like the quality of the products and services that are offered by Saudi ecommerce websites.

Actual use of e-commerce online shopping (AU)

AU1 I will continue to shop online. - Amoroso and Hunsinger (2009)

AU2 I would consider shopping online in the short term.

AU3 I would consider shopping online in the long term.

AU4 I will continue to use the online shopping websites that I have purchased from.

AU5 I plan to purchase products or services from new online shopping web sites.

AU6 In general, I would buy online rather than going to a physical store.

85

Appendix C

Research Instrument and Reliability Test Results Reliability Analysis of Cronbach's Alpha (Strong Level: Alpha >= 0.7).

Item No. Variables Mean

Standard

deviation

Cronbach’s

alpha Attitude towards using online shopping (ATT) 3.70 0.858

ATT1 Online shopping is appealing. 3.93

0.882

0.879 ATT2

I like online shopping. 3.59 1.025

ATT3 It is a good idea to buy online rather than from a physical store. 3.70

1.023

ATT4 I have fun when using online shopping. 3.57

1.066

Subjective Norm (SN) 2.94 0.736

SN1 I would be affected by multi-media to shop online. 2.91

1.072

0.745

SN2 People whose opinions I value would prefer that I use e-commerce websites to buy things.

2.74 1.053

SN3 People who influence my behaviour would think I should use e-commerce websites to buy things.

2.50 0.999

SN4 People who are important to me would recommend that I use e-commerce websites to buy things.

3.45 1.032

SN5 My colleagues and friends encourage me to shop online. 3.12

1.073

Intention to adopt and continue to use online shopping (BI) 4.05 0.829

BI1 I always try to use online shopping whenever it meets my shopping needs. 4.11

0.906

0.902 BI2 I plan to use online shopping in the future. 4.06

0.875

BI3 I expect my use of online shopping to continue in the future. 3.99

0.940

Perceived behavioural control (PBC) 3.60 1.051

PBC1 I have control over how to shop online. 3.41

1.190

0.862 PBC2 I have the resources needed to shop online (e.g. computer, credit card). 3.75

1.174

PBC3 In general, resources, opportunities and knowledge make it easy for me to shop online.

3.64 1.195

Facilitating conditions (FC) 3.00 0.767

FC1 I can find guidance in the online shopping website. 3.03

1.033

0.726 FC2 I can find a specific person or group to help when I have difficulties in an online shopping website.

2.88 0.937

FC3 My interaction with Saudi e-commerce websites will be or has been clear and understandable.

3.10 0.885

Cultural influence (CI) 4.03 0.618

CI1 I am interested to hear about new technological developments. 3.88

0.947

0.688

CI2 Technological developments have enhanced our lives. 4.41

0.692

CI3 I feel comfortable in changing and using e-commerce services activities. 3.70

1.005

CI4 I think having adequate English language skills would enhance e-commerce adoption in KSA.

4.12 0.936

CI5 I think people who have been to a developed country for study or work would be more interested in online shopping.

4.05 1.017

Security (SC) 2.77 0.838

SC1 Online shops provide security guarantee on payment over the Internet. 3.04

0.949

0.800 SC2 I would feel secure when providing personal information over the Internet. 2.61

1.024

SC3 Security policies are clearly visible on e-commerce websites. 2.67

1.002

86

Trust (TR) 3.03 0.681

TR1 The lack of product trial / inspect by hand does not affect my decision me to use online shopping.

2.88 1.121

0.680 TR2 Local online shopping websites that I buy from have good reputations. 3.33

0.869

TR3 Local online shopping websites tend to fulfil their promises. 2.99

0.867

TR4 I trust local online shops. 2.92

0.931

Awareness of e-commerce service (AW) 3.99 0.741

AW1 I am aware of the benefits and risks of using online shopping. 4.32

0.748

0.717 AW2 I am aware that the Internet allows online shopping. 4.08

0.880

AW3 I am aware of using online shopping. 3.58

1.117

Beliefs about government e-readiness (GOV) 2.53 0.865

GOV1 There are effective laws to protect consumer privacy. 2.53 1.072

0.846

GOV2 There are effective laws to combat cyber crime. 2.61 1.068

GOV3 The Saudi government provides facilitating conditions to private businesses and small companies to expand their business via e-commerce.

2.55 1.005

GOV4 The legal environment is conducive to conducting business on the Internet (e.g. clear standard policy for returns, exchange and refund).

2.45 1.038

Beliefs about Internet service (IS) 2.76 0.863

IS1 Internet service in KSA is affordable. 2.60

1.112

0.740 IS2

Internet service in KSA saves my time for online shopping. 2.49 1.134

IS3 Internet service in KSA is reliable with 24/7 access to e-commerce websites. 3.34

1.184

IS4 Overall, there is good Internet service and support in KSA (e.g. high speed, no disconnecting from time to time, very good support).

2.60 1.173

Beliefs about postal service (PS) 2.68 0.860

PS1 Post services in KSA are trustworthy. 2.84 1.146

0.806

PS2 Post services in KSA via private companies are affordable. 2.77

1.138

PS3 Post services in KSA have clear terms and conditions. 2.61

1.008

PS4 Post services in KSA have a clear a dressing system for delivery purposes. 2.55

1.329

PS5 Overall, there are reliable postal services in KSA (e.g. clear home addressing, no shipping loss or damage, no delay of delivery).

2.63 1.090

Beliefs about private business e-commerce penetration (PBEP) 2.83 0.684

PBEP1 Private businesses are keen on implementing e-commerce. 2.98 0.860

0.879

PBEP2 There are enough IT companies and professionals available to meet demand (e.g. designing, managing and maintenance for online shops).

3.08 0.942

PBEP3 There is diversity and competition among online shops in KSA. 2.94

0.991

PBEP4 Saudi e-commerce websites have a vast selection of products and services. 2.92 0.983

PBEP5 Saudi e-commerce websites offer competitive prices compared to traditional stores.

2.60 0.982

PBEP6 I am satisfied with the customer support provided by Saudi e-commerce websites.

2.71 0.846

PBEP7 I am satisfied with the after-sales service provided by Saudi e-commerce websites.

2.66 0.897

PBEP8 Overall, I like the quality of the products and services that are offered by Saudi ecommerce websites.

2.75 0.920

Actual use of e-commerce online shopping (AU) 3.74 0.798

AU1 I will continue to shop online. 3.78

0.948

0.893

AU2 I would consider shopping online in the short term. 3.83

0.983

AU3 I would consider shopping online in the long term. 3.93

0.960

AU4 I will continue to use the online shopping websites that I have purchased from. 3.83

0.943

AU5 I plan to purchase products or services from new online shopping web sites. 3.62

0.954

AU6 In general, I would buy online rather than going to a physical store. 3.42

1.136

87

Appendix D Multiple Regression Results

First Regression Results Variables Entered/Removed

b

Model Variables Entered

Variables

Removed Method

1 Culture influence,

Dummygender,

Security,

Awareness,

Trust

. Enter

a. All requested variables entered.

b. Dependent Variable: Attitude

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .631a .398 .392 .66851

a. Predictors: (Constant), Culture influence, Dummygender, Security, Awareness, Trust

b. Dependent Variable: Attitude

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 150.627 5 30.125 67.409 .000a

Residual 227.473 509 .447

Total 378.100 514

a. Predictors: (Constant), Culture influence, Dummygender, Security, Awareness, Trust

b. Dependent Variable: Attitude

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) -.152- .223 -.679- .497

Dummygender .146 .061 .085 2.409 .016

Security .100 .045 .098 2.213 .027

Trust .201 .056 .160 3.565 .000

Awareness .040 .045 .034 .879 .380

Culture influence .676 .054 .487 12.521 .000

a. Dependent Variable: Attitude

88

Second Regression Results

Variables Entered/Removedb

Model Variables Entered

Variables

Removed Method

1 Perceived Behavioural Control,

Subjective Norm, Attitude

. Enter

a. All requested variables entered.

b. Dependent Variable: Intention

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .763a .582 .579 .53795

a. Predictors: (Constant), Perceived Behavioural Control, Subjective Norm, Attitude

b. Dependent Variable: Intention

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 205.708 3 68.569 236.948 .000a

Residual 147.876 511 .289

Total 353.584 514

a. Predictors: (Constant), Perceived Behavioural Control, Subjective Norm, Attitude

b. Dependent Variable: Intention

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) .954 .128 7.437 .000

Attitude .603 .033 .624 18.539 .000

Subjective Norm .128 .035 .113 3.656 .000

Perceived Behavioural Control .136 .025 .173 5.498 .000

a. Dependent Variable: Intention

89

Third Regression Results

Variables Entered/Removedb

Model

Variables

Entered

Variables

Removed Method

1 Intention,

Facilitating

Conditions,

Perceived

Behavioural

Control

. Enter

a. All requested variables entered.

b. Dependent Variable: Actual Use of Online Shopping

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .802a .643 .641 .47873

a. Predictors: (Constant), Intention, Facilitating Conditions, Perceived

Behavioural Control

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 210.593 3 70.198 306.293 .000a

Residual 117.113 511 .229

Total 327.706 514

a. Predictors: (Constant), Intention, Facilitating Conditions, Perceived Behavioural Control

b. Dependent Variable: Actual Use of Online Shopping

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) .305 .126 2.412 .016

Perceived Behavioural

Control

.122 .023 .160 5.379 .000

Facilitating Conditions .094 .028 .091 3.358 .001

Intention .669 .029 .695 23.351 .000

a. Dependent Variable: Actual Use of Online Shopping

90

Appendix E Reliability Analysis of Cronbach's Alpha

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Item Statistics

Mean Std. Deviation N

ATT1 3.93 .882 515

ATT2 3.59 1.025 515

ATT3 3.70 1.023 515

ATT4 3.57 1.066 515

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.862 .862 3

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.879 .880 4

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.745 .747 5

Item Statistics

Mean Std. Deviation N

SN1 2.91 1.072 515

SN2 2.74 1.053 515

SN3 2.50 .999 515

SN4 3.45 1.032 515

SN5 3.12 1.073 515

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.902 .902 3

Item Statistics

Mean Std. Deviation N

INT1 4.11 .906 515

INT2 4.06 .875 515

INT3 3.99 .940 515

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

91

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.688 .702 5

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.726 .725 3

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Item Statistics

Mean Std. Deviation N

FC1 3.03 1.033 515

FC2 2.88 .937 515

FC3 3.10 .885 515

Item Statistics

Mean Std. Deviation N

RC1 3.88 .947 515

RC2 4.41 .692 515

RC3 3.70 1.005 515

RC4 4.12 .936 515

RC5 4.05 1.017 515

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.800 .799 3

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Item Statistics

Mean Std. Deviation N

SEQ1 3.04 .949 515

SEQ2 2.61 1.024 515

SEQ3 2.67 1.002 515

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized Items N of Items

.680 .706 4

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.717 .727 3

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

92

Item Statistics

Mean Std. Deviation N

AW1 4.32 .748 515

AW2 4.08 .880 515

AW3 3.58 1.117 515

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.846 .846 4

Item Statistics

Mean Std. Deviation N

IS1 2.60 1.112 515

IS2 2.49 1.134 515

IS3 3.34 1.184 515

IS4 2.60 1.173 515

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Item Statistics

Mean Std. Deviation N

GS1 2.53 1.072 515

GS2 2.61 1.068 515

GS3 2.55 1.005 515

GS4 2.45 1.038 515

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.740 .742 4

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Item Statistics

Mean Std. Deviation N

PS1 2.84 1.146 515

PS2 2.77 1.138 515

PS3 2.61 1.008 515

PS4 2.55 1.329 515

PS5 2.63 1.090 515

93

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.806 .814 5

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.893 .896 6

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.879 .880 8

Case Processing Summary

N %

Cases Valid 515 100.0

Excludeda 0 .0

Total 515 100.0

a. Listwise deletion based on all variables in the

procedure.

Item Statistics

Mean Std. Deviation N

PBS1 2.98 .860 515

PBS2 3.08 .942 515

PBS3 2.94 .991 515

PBS4 2.92 .983 515

PBS5 2.60 .982 515

PBS6 2.71 .846 515

PBS7 2.66 .897 515

PBS8 2.75 .920 515

Item Statistics

Mean Std. Deviation N

AU1 3.78 .948 515

AU2 3.83 .983 515

AU3 3.93 .960 515

AU4 3.83 .943 515

AU5 3.62 .954 515

AU6 3.42 1.136 515

94

Appendix F Collinearity and Multicollinearity Results

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Security .607 1.647

Trust .608 1.645

Awareness .883 1.133

Dummygender .971 1.030

a. Dependent Variable: Cultural influence

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Culture Influence .796 1.257

Security .607 1.647

Trust .591 1.692

Awareness .785 1.275

a. Dependent Variable: Dummygender

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Attitude .850 1.177

Subjective Norm .850 1.177

a. Dependent Variable: Perceived Behavioural Control

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Trust .891 1.122

Awareness .790 1.266

Dummygender .952 1.050

Culture Influence .781 1.281

a. Dependent Variable: Security

Coefficients

a

Model

Collinearity Statistics

Tolerance VIF

1 Trust .593 1.686

Dummygender .963 1.038

Culture Influence .888 1.126

Security .619 1.616

a. Dependent Variable: Awareness

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Subjective Norm .975 1.026

Perceived Behavioural Control .975 1.026

a. Dependent Variable: Attitude

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Facilitating Conditions .971 1.030

Perceived Behavioural Control .971 1.030

a. Dependent Variable: Intention

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Intention .971 1.030

Facilitating Conditions .971 1.030

a. Dependent Variable: Perceived Behavioural Control

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Dummygender .953 1.049

Culture Influence .803 1.245

Security .916 1.092

Awareness .779 1.284

a. Dependent Variable: Trust

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

1 Perceived Behavioural

Control

.829 1.206

Attitude .829 1.206

a. Dependent Variable: Subjective Norm

95

Appendix G Some difficulties in publishing the questionnaire and ‘Plan B’

The paper-based questionnaire was planned to be published by myself on Saturday 02/06/2012; however, that

was not possible because of some issues regarding the formal process of issuing tickets at the Saudi Embassy in

London. This situation greatly affected my plan to distribute the questionnaires in KSA. Therefore, I moved to

‘Plan B’, which involved taking a long time to design two surveys and making them available on two different

websites (http://www.kwiksurveys.com and http://www.surveysurvey.com) in order to meet the target sample

size. However, both websites crashed at the start of July and the data were destroyed; I was unable to find any

website offering the same features, including unlimited number of questions and responses, at that time. After a

while, a new website was used to post the survey (http://dotsurvey.me/681dig08-c16ma08).

Although turning to Plan B seemed to be at first unfortunate, the distribution step went very well with more

than 500 responses, and I reached the target sample size. Moving to Plan B consumed much time and required

very hard work, involving the design and distribution of the survey many times. Moreover, in order to meet

acceptable standards for the survey, people were encouraged to fill out the survey honestly using Twitter,

Facebook, email groups, university forums and technical college forums.

96

Appendix H An example of the procedure of distributing the questionnaire

Step1:

Step2:

97

Step3:

Step4:

Step5: