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ASSESSING MOBILE WEBSITES IN MALAYSIA: AN EXTENDED TAM MODEL
THANESHAN LETCHUMANAN
DOCTOR OF PHILOSOPHY UNIVERSITI UTARA MALAYSIA
OCTOBER 2021
i
ASSESSING MOBILE WEBSITES IN MALAYSIA: AN EXTENDED TAM MODEL
By
THANESHAN LETCHUMANAN
Thesis Submitted to College of Business University Utara Malaysia,
in Fulfillment of the Requirement for the Degree of Doctor of Philosophy
iv
PERMISSION TO USE
In presenting this thesis in fulfillment of the requirements for a Post Graduate degree from the Universiti Utara Malaysia (UUM), I agree that the Library of this university may make it freely available for inspection. I further agree that permission for copying this thesis in any manner, in whole or in part, for scholarly purposes may be granted by my supervisors or in their absence, by the Dean of School of Technology Management and Logistics College of Business where I did my thesis. It is understood that any copying or publication or use of this thesis or parts of it for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the Universiti Utara Malaysia (UUM) in any scholarly use which may be made of any material in my thesis.
Request for permission to copy or to make other use of materials in this thesis in whole or in part should be addressed to:
Director of Postgraduate Studies Unit, College of Business Universiti Utara Malaysia
06010 UUM Sintok Kedah Darul Aman
v ABSTRACT
Mobile devices are changing the way people access websites. Current user trends indicate a shift from desktop computers to mobile devices. Although mobile devices are highly flexible, accessible, and convenient, users still confront unresolved issues when rendering and navigating web contents on mobile devices. Present websites are mostly developed for desktop users and tend to be less mobile friendly. They are poorly suited for mobile devices, making the web content look visually unpleasant and hard to navigate. The purpose of this study was to identify and examine the factors that influence intention towards accessing mobile websites. Nine research questions were addressed in this research. Data were gathered from major mobile device users, namely students of four public universities in the northern region of Peninsular Malaysia, in which 441 usable questionnaires were collected. The result of this study showed that web designers must focus on the following factors when designing a web page:
perceived ease of use and perceived usefulness of a web page when viewed through mobile devices, convenience of using the mobile website, compatibility with users’
lifestyle, and compatibility of web pages when viewed through different types of devices and media richness of the page. This research advances the boundaries of knowledge by producing a mobile web acceptance model based on the factors confirmed in this study. The results have contributed towards the adoption and progressive advancement of mobile websites and to guide the management of organisations related to website development and management. It provides web service providers and policy makers with a checklist for determining potential factors that affect mobile web adoption.
Keywords: mobile web, convenient, compatibility, media richness
vi ABSTRAK
Peranti mudah alih telah mengubah cara masyarakat mengakses laman sesawang.
Lebih ramai pengguna telah berubah daripada menggunakan komputer meja ke peranti mudah alih. Walaupun peranti mudah alih sangat fleksibel, mudah diakses dan mudah untuk digunakan, pengguna masih menghadapi pelbagai masalah dalam menterjemah dan menavigasi kandungan laman sesawang menggunakan peranti mudah alih ini.
Kebanyakan laman sesawang pada masa sekarang dibangunkan untuk pengguna komputer meja sahaja dan tidak mesra peranti mudah alih. Laman sesawang ini tidak sesuai untuk diakses menggunakan peranti mudah alih, menjadikan kandungan laman sesawang kelihatan tidak menyenangkan dan sukar untuk dinavigasi. Tujuan kajian ini dijalankan adalah untuk mengenal pasti dan mengkaji faktor-faktor yang mempengaruhi niat untuk mengakses laman sesawang menggunakan peranti mudah alih. Terdapat sembilan persoalan kajian yang dibincangkan dalam pernyataan masalah penyelidikan ini. Data untuk kajian ini telah dikumpulkan daripada pengguna peranti mudah alih utama iaitu pelajar dari empat Universiti Awam di wilayah utara Semenanjung Malaysia, di mana 441 borang soal selidik yang telah lengkap diisi dikumpulkan. Hasil kajian ini menunjukkan bahawa pereka laman sesawang mesti memberi fokus pada faktor-faktor berikut ketika membangunkan laman sesawang:
persepsi mudah guna dan persepsi kebergunaan ketika melayari laman sesawang menggunakan peranti mudah alih, betapa senangnya menggunakan peranti mudah alih untuk melayari laman sesawang, kesesuaian dengan gaya hidup pengguna dan keserasian laman sesawang apabila dilayari menggunakan pelbagai jenis peranti serta kekayaan media laman sesawang tersebut. Kajian ini telah meluaskan sempadan ilmu dengan menghasilkan model penerimaan laman sesawang mudah alih berdasarkan faktor-faktor yang telah disahkan dalam kajian ini. Hasil kajian ini telah menyumbang kepada penerapan dan kemajuan laman sesawang mudah alih dan akan membimbing pihak pengurusan organisasi yang berkaitan dengan pembangunan dan pengurusan laman sesawang. Ia menyediakan senarai semak faktor yang mempengaruhi penggunaan laman sesawang mudah alih kepada pembekal perkhidmatan laman sesawang dan pembuat dasar.
Kata kunci: laman sesawang mudah alih, kesesuaian, mudah digunakan, kekayaan media
vii
ACKNOWLEDGEMENT
The completion of this thesis would not have been possible without the support and encouragement of various parties. Hence, I would like to take this opportunity to show my gratitude to those who have assisted me in a myriad of ways.
I would like to express my heartfelt thanks to my supervisors Dr. Fadhilah Mat Yamin and Assoc. Prof. Dr. Siti Norezam Othman for their guidance, patience, and faith in me. Thank you for your helpful advice and support. Thank you for your guidance and relaxed, thoughtful insight. Both of you always foster my academic growth by challenging and inspiring me to reach deeper, or to learn more, to expand my viewpoint, and to think critically. You allowed me to express my views openly and to disagree even when I was wrong.
I would like to thank my beloved wife Rajeswari, the one person who has been a constant source of support and encouragement and has made an untold number of sacrifices, especially for me to continue my studies. She is a great inspiration to me.
Hence, a great appreciation and enormous thanks to her, for without her understanding, I am sure this thesis would never have been completed. I have become a better man because of your support. Thank you and I love you.
I would like to thank my parents. They gave me my name, they gave me my life, and everything else in between. Thank you for your encouragement.
Finally, I would like to thank my brothers, sisters, brother in law, sisters in law, relatives and friends. Without them, I do not believe that I could sail through these challenges and made this a reality.
viii
TABLE OF CONTENTS
PERMISSION TO USE ... iv
ABSTRACT ... v
ABSTRAK ... vi
ACKNOWLEDGEMENT ... vii
TABLE OF CONTENTS ... viii
LIST OF TABLES ... xii
LIST OF FIGURES ... xiv
CHAPTER ONE INTRODUCTION ... 1
1.1 Background of Study ... 1
1.2 Problem Statement ... 7
1.3 Research Questions ... 9
1.4 Research Objectives ... 10
1.5 Significance of the Study ... 11
1.5.1 Practical Significance ... 11
1.5.2 Theoretical Significance... 12
1.6 Scope of the Study ... 13
1.7 Definition of Key Terms ... 14
1.8 Outline of the Thesis ... 16
CHAPTER TWO LITERATURE REVIEW ... 17
2.1 Introduction ... 17
2.2 Internet and ICT ... 17
2.3 Mobile Technology and Devices ... 20
2.4 Mobile Web ... 25
2.5 Information Technology/Information Systems Acceptance Research Background ... 26
2.6 Theories and Models of Technology Acceptance ... 28
2.6.1 Innovation Diffusion Theory (IDT) ... 29
2.6.2 Theory of Reasoned Action (TRA) ... 31
Theory of Reasoned Action (TRA ... 31
2.6.3 Theory of Planned Behaviour (TPB) ... 33
2.6.4 Technology Acceptance Model (TAM) ... 34
2.6.5 Unified Theory of Acceptance and Use of Technology (UTAUT) ... 35
2.6.6 Technology Acceptance Model 2 (TAM2) ... 37
ix
2.6.7 Technology Acceptance Model 3 (TAM3) ... 38
2.6.8 Media Richness Theory ... 40
2.7 Model Discussion and Justification ... 41
Theory of Reasoned Action (TRA ... 42
2.7.1 Overview of TAM ... 43
2.8 Intention to Use Mobile Web ... 45
2.9 Determinants of Mobile Web ... 47
2.9.1 Determinants of Mobile Web Adoption ... 47
2.9.2 Determinants of Behavioural Intention ... 49
2.9.3 Determinants of Attitude ... 50
2.10 Theoretical Model Creation ... 52
2.11 Research Hypothesis ... 56
2.11.1 The Influence of Perceived Usefulness ... 56
2.11.2 The Influence of Perceived Ease of Use ... 59
2.11.3 The Influence of Convenience ... 62
2.11.4 The Influence of Compatibility ... 63
2.11.5 The Influence of Media Richness ... 65
2.11.6 The Influence of Attitude ... 68
2.12 Summary of Hypothesis ... 69
2.13 Chapter Summary... 70
CHAPTER THREE RESEARCH METHOD ... 71
3.1 Introduction ... 71
3.2 Research Design and Approach ... 71
3.3 Research Instrument ... 76
3.3.1 Instrument Development and Structure ... 76
3.3.2 General Demographics ... 78
3.3.3 Perceived Usefulness ... 78
3.3.4 Perceived Ease of Use ... 79
3.3.5 Convenience ... 80
3.3.6 Compatibility ... 81
3.3.7 Media Richness ... 81
3.3.8 Attitude towards Mobile Web ... 82
3.3.9 Intention to Use Mobile Web ... 83
3.4 Instrument Scale ... 84
3.5 Instrument Translation ... 84
3.6 Content Validity ... 85
x
3.7 Population and Sample Frame ... 85
3.8 Sampling Design ... 88
3.8.1 Cluster Sampling ... 88
3.8.2 Simple Random Sampling... 90
3.8.3 Sample Size ... 91
3.9 Data Analysis Method ... 93
3.9.1 Data Entry and Screening ... 94
3.9.2 Descriptive Analysis ... 95
3.9.3 Non-Response Bias Test ... 95
3.9.4 Assessment of Linearity and Normality ... 96
3.9.5 Factor Analysis ... 96
3.9.6 Reliability Analysis ... 97
3.9.7 Validity Testing ... 98
3.9.8 Correlation Analysis ... 98
3.9.9 Multiple Regression ... 99
3.9.10 Assessment of Multicollinearity ... 100
3.10 Chapter Summary... 100
CHAPTER FOUR RESULTS AND FINDINGS ... 101
4.1 Introduction ... 101
4.2 Sample and Profiles... 101
4.2.1 Response Rate ... 101
4.2.2 Profile of the Respondents ... 102
4.3 Goodness of Measure ... 105
4.3.1 Factor Analysis ... 105
4.3.2 Reliability Test ... 113
4.3.3 Normality Test ... 115
4.3.4 Descriptive Analysis ... 116
4.4 Hypothesis Testing ... 125
4.4.1 Correlation Analysis ... 125
4.4.2 Regression Analysis ... 127
4.5 Chapter Summary... 131
CHAPTER FIVE DISCUSSIONS AND CONCLUSION ... 133
5.1 Recapitulation of the Study Findings ... 133
5.2 Discussion ... 135
5.2.1 Hypothesis ... 135
5.2.2 Research Questions and Objectives ... 136
xi
5.3 Research Contributions ... 144
5.4 Research Implications ... 146
5.5 Limitations of the Study ... 148
5.6 Future Research Direction... 150
5.7 Chapter Summary... 151
REFERENCES ... 153
APPENDIX A: RESEARCH QUESTIONNAIRE ... 168
APPENDIX B: FACTOR ANALYSIS ... 177
APPENDIX C: RELIABILITY TEST ... 191
APPENDIX D: DESCRIPTIVE ANALYSIS ... 198
APPENDIX E: REGRESSIOIN ANALYSIS ... 199
xii
LIST OF TABLES
Table 1.1 Definition of key terms………...…………15
Table 2.1 Percentage distribution of smartphone Internet activities by users in 2018……….25
Table 2.2 Determinants used in technology acceptance theories/models……..…….42
Table 2.3 Summary of hypotheses………..…69
Table 3.1 Items representing perceived usefulness………...…..79
Table 3.2 Items representing perceived ease of use………...…….80
Table 3.3 Items representing mobile web convenience……….….……80
Table 3.4 Items representing compatibility of mobile web………….………..….….81
Table 3.5 Items representing media richness of mobile web……….……… 82
Table 3.6 Items representing attitude………. 83
Table 3.7 Items representing behavioural intention……….…….. 83
Table 3.8 Number of Public Universities and Enrolment (2018)………...87
Table 3.9 Selected universities for this study……….…89
Table 3.10 Number of undergraduate students in selected universities, proportion of sampling, number of sample and number of questionnaire distributed ……..………...….90
Table 3.11 Determining sample size with 95% level of confidence……….….92
Table 3.12 Sample size for a given population size……….……….…….92
Table 3.13 Test of reliability………..98
Table 4.1 Total completed questionnaire and response rate……….…102
Table 4.2 Profile of the respondents…..………..….103
Table 4.3 Factor analysis of behavioral intention……….…..…..107
Table 4.4 Factor analysis of attitude………..………..……….108
Table 4.5 Factor analysis of perceived usefulness……….….…………..109
Table 4.6 Factor analysis of perceived ease of use………....….…..110
Table 4.7 Factor analysis of convenience………..…...…111
Table 4.8 Factor analysis of compatibility…………...…112
xiii
Table 4.9 Factor analysis of media richness………..……...…113
Table 4.10 Test of reliability……….114
Table 4.11 Normality test for each factor………...……..115
Table 4.12 Descriptive analysis……….…...116
Table 4.13 Frequencies and percentages of perceived usefulness………....118
Table 4.14 Frequencies and percentages of perceived ease of use………...…119
Table 4.15 Frequencies and percentages of convenience……….…....120
Table 4.16 Frequencies and percentages of compatibility………...….121
Table 4.17 Frequencies and percentages of media richness……….…123
Table 4.18 Frequencies and percentages of attitude……….…124
Table 4.19 Frequencies and percentages of behavioural intention……….…..125
Table 4.20 Correlation coefficients between variables……….…127
Table 4.21 Summary of multiple regression analysis for factors influencing behavioral intention……….………...128
Table 4.22 Summary of multiple regression analysis for factors influencing attitude………..….129
Table 4.23 Summary of multiple regression analysis for factors influencing Perceived usefulness……….…………..…….….130
Table 4.24 Summary of findings………...……...132
Table 5.1: A summary of hypotheses testing results………138
xiv
LIST OF FIGURES
Figure 1.1 Wireless data transmission average download speed in 2019….…………2
Figure 1.2 Devices used to access the Internet in Malaysia in 2018………3
Figure 1.3 Global Internet penetration in 2020……….…………4
Figure 1.4 Mobile device’s share of web traffic in 2020……….….5
Figure 1.5 Web page of Universiti Utara Malaysia accessed through desktop computer……….…..6
Figure 1.6 Web page of Universiti Utara Malaysia accessed through smartphone…..6
Figure 2.1 Percentage distribution for duration of daily use of Internet in Malaysia (2018)……….…….19
Figure 2.2 Internet users by age category in Malaysia (2018)……….…….…..19
Figure 2.3 Percentage of smartphone users by age category in Malaysia (2017)…...23
Figure 2.4 Basic concept underlying user acceptance models……….………...28
Figure 2.5 Theory of Reasoned Action (TRA)……….………..32
Figure 2.6 Theory of Planned Behaviour (TPB)……….…………33
Figure 2.7 Technology Acceptance Model (TAM)………35
Figure 2.8 Unified Theory of Acceptance and Use of Technology (UTAUT)……..37
Figure 2.9 Technology Acceptance Model 2 (TAM2)………...…38
Figure 2.10 Technology Acceptance Model 3 (TAM3)……….…………39
Figure 2.11 Research model………...55
Figure 3.1 Research process of the study……….……..75
1
CHAPTER ONE INTRODUCTION
1.1 Background of Study
The use of the World Wide Web (WWW) has seen a tremendous rise since its commercial application in 1994. The number of people and the amount of time spent on the Internet have been increasing parallel with the growth of information and communication technology (ICT). In a short period, the Internet has provided substantial market potential to the electronic market that allows a person to obtain information and communicate in an easier and efficient way (Bahrini & Qaffas, 2019).
People’s lifestyle has changed and keeps changing with the emerging physical and non-physical technologies. In the same way, mobile devices are changing the way people access websites. More users have changed from using desktop computers to mobile devices. Smartphones and tablets are among the most widely used mobile devices in accessing websites all over the world (Hootsuite & We Are Social, 2020;
Youngblood et al., 2019).
The mobile technology industry has seen a drastic growth in terms of the number of adopters (Magsamen-Conrad & Dillon, 2020). This increase is in parallel with the increased bandwidth of data transmission speeds and large availability of wireless network (Youngblood et al., 2019). Figure 1.1 illustrates the rank of service providers from highest to lowest in terms of average download speeds in Malaysia in 2019. As a result of this incredible development, accessing the Internet via mobile devices has become common where mobile devices are expected to replace the functions of desktop computers because of their capability in handling most of the major tasks at the workplace and home (Yu & Kong, 2015).
153 REFERENCES
Adegbija, M. V., & Bola, O. O. (2015). Perception of undergraduates on the adoption of mobile technologies for learning in selected universities in Kwara state, Nigeria. Procedia - Social and Behavioral Sciences, 176, 352–356.
https://doi.org/10.1016/j.sbspro.2015.01.482
Adipat, B., Zhang, D., & Zhou, L. (2011). The Effects of Tree-View Based Presentation Adaptation on Mobile Web Browsing. MIS Quarterly, 35(1), 99–
122.
Agarwal, R., & Karahanna, E. (2000). Time Flies When You’re Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage. MIS Quarterly, 24(4), 665. https://doi.org/10.2307/3250951
Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23.
https://doi.org/10.1016/j.jretconser.2014.09.003
Ajzen, I. (1991a). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211., 50(2).
Ajzen, I. (1991b). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
Akour, H. (2009). Determinants of mobile learning acceptance: An empirical investigation in higher education. Oklahoma State University.
Al-Debei, M. M., & Al-Lozi, E. (2014). Explaining and predicting the adoption intention of mobile data services: A value-based approach. Computers in Human Behavior, 35, 326–338. https://doi.org/10.1016/j.chb.2014.03.011
Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. https://doi.org/10.1016/j.chb.2015.11.033
Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information and Management, 44(8), 681–691. https://doi.org/10.1016/j.im.2007.09.002
Al-Jabri. (2015). The intention to use mobile banking: Further evidence from Saudi Arabia. Business Management, 46(1), 23–34.
Al-Khalifa, H. S. (2014). A framework for evaluating university mobile websites.
Online Information Review, 38(2), 166–185. https://doi.org/10.1108/OIR-12- 2012-0231
Al-najjar, G. M. (2012). Mobile Information Systems: An Empirical Analysis of the Determinants of Mobile Commerce Acceptance in Jordan. Universiti Utara Malaysia.
Allen, I. E., & Seaman, C. a. (2007). Likert scales and data analyses. Quality Progress, 40(7), 64–65. https://doi.org/10.1111/j.1365-2929.2004.02012.x
Alnasser, M. S. A. (2014). The Impact of E-Service Quality on Attitude toward Online Shopping. Universiti Utara Malaysia.
154
Alzaza, N. S. (2012). Mobile Learning Services Acceptance Model among Malaysian Higher Education Students. Universiti Utara Malaysia.
Amichai-Hamburger, Y., & Hayat, Z. (2011). The impact of the Internet on the social lives of users: A representative sample from 13 countries. Computers in Human Behavior, 27(1), 585–589. https://doi.org/10.1016/j.chb.2010.10.009
Amin, H. (2008). Factors affecting the intentions of customers in Malaysia to use mobile phone credit cards. Management Research News, 31(7), 493–503.
https://doi.org/10.1108/01409170810876062
Araújo, A., & Giné, E. (1980). The central limit theorem for real and Banach valued random variables. John Wiley & Sons.
Badger, J. M., Kaminsky, S. E., & Behrend, T. S. (2014). Media richness and information acquisition in internet recruitment. Journal of Managerial Psychology, 29(7), 866–883. https://doi.org/10.1108/JMP-05-2012-0155
Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and Test of a Theory of Technological Learning and Usage. Human Relations, 45(7), 659–686.
Bahrini, R., & Qaffas, A. (2019). Impact of Information and Communication Technology on Economic Growth: Evidence from Developing Countries.
Economies, 7(1), 21. https://doi.org/10.3390/economies7010021
Baldwin, S. J., & Ching, Y.-H. (2020). Guidelines for Designing Online Courses for Mobile Devices. TechTrends, 64(3), 413–422. https://doi.org/10.1007/s11528- 019-00463-6
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change.
Phychological Review, 84(2), 191–215.
Bandura, A. (1982). Self-efficacy mechanism in human agency. In American Psychologist (Vol. 37, Issue 2, pp. 122–147). https://doi.org/10.1037/0003- 066X.37.2.122
Baturay, M. H., & Birtane, M. (2013). Responsive Web Design: A New Type of Design for Web-based Instructional Content. Procedia - Social and Behavioral Sciences, 106, 2275–2279. https://doi.org/10.1016/j.sbspro.2013.12.259
Belleau, B. D., Summers, T. a., Yingjiao Xu, & Pinel, R. (2007). Theory of Reasoned Action: Purchase Intention of Young Consumers. Clothing and Textiles Research Journal, 25(3), 244–257. https://doi.org/10.1177/0887302X07302768
Bentler, P. M., & Speckart, G. (1979). Models of attitude-behavior relations.
Psychological Review, 86(5), 452–464. https://doi.org/10.1037/0033- 295X.86.5.452
Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience.
Journal of Marketing, 66(3), 1–17.
Bohyun Kim. (2013). The Present and Future of the Library Mobile Experience.
Library Technology Reports, 49, 15–28.
http://ezproxy.library.dal.ca/login?url=http://search.ebscohost.com/login.aspx?d irect=true&db=aph&AN=90405355&site=ehost-
live%5Cnhttp://content.ebscohost.com.ezproxy.library.dal.ca/ContentServer.asp
?T=P&P=AN&K=90405355&S=R&D=aph&EbscoContent=dGJyMNLe80Seq K8
155
Bonnardel, N., Piolat, A., & Le Bigot, L. (2011). The impact of colour on Website appeal and users’ cognitive processes. Displays, 32(2), 69–80.
https://doi.org/10.1016/j.displa.2010.12.002
Borca, G., Bina, M., Keller, P. S., Gilbert, L. R., & Begotti, T. (2015). Internet use and developmental tasks: Adolescents’ point of view. Computers in Human Behavior, 52, 49–58. https://doi.org/10.1016/j.chb.2015.05.029
Boruff, J. T., & Storie, D. (2014). Mobile devices in medicine: a survey of how medical students, residents, and faculty use smartphones and other mobile devices to find information. Journal of the Medical Library Association, 102(1), 22–30.
https://doi.org/10.3163/1536-5050.102.1.006
Bowling, A. (2005). Mode of questionnaire administration can have serious effects on data quality. Journal of Public Health, 27(3), 281–291.
https://doi.org/10.1093/pubmed/fdi031
Brislin, R. W. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), 185–216.
https://doi.org/10.1177/135910457000100301
Carvalho, J., Francisco, R., & Relvas, A. P. (2015). Computers in Human Behavior Family functioning and information and communication technologies : How do they relate ? A literature review. Computers in Human Behavior, 45, 99–108.
https://doi.org/10.1016/j.chb.2014.11.037
Checkland, P. (1981). Systems thinking, systems practice. John Wiley & Sons.
Chen, J. V., Lin, C., Yen, D. C., & Linn, K.-P. (2011). The interaction effects of familiarity, breadth and media usage on web browsing experience. Computers in Human Behavior, 27(6), 2141–2152. https://doi.org/10.1016/j.chb.2011.06.008 Chen, Q., & Wells, W. D. (1999). Attitude toward the site. Journal of Advertising
Research, September/October, 27–37.
Chen, Y.-F., & Lan, Y.-C. (2014). An Empirical Study of the Factors Affecting Mobile Shopping in Taiwan. International Journal of Technology and Human Interaction, 10(1), 19–30. https://doi.org/10.4018/ijthi.2014010102
Chiu, Y.-B., Lin, C.-P., & Tang, L.-L. (2005). Gender differs: assessing a model of online purchase intentions in e-tail service. In International Journal of Service Industry Management (Vol. 16, Issue 5, pp. 416–435).
https://doi.org/10.1108/09564230510625741
Cho, C. H., Phillips, J. R., Hageman, A. M., & Patten, D. M. (2009). Media
Richness, User Trust, and Perceptions of Corporate Social Responsibility: An Experimental Investigation of Visual Website Disclosures. Accounting, Auditing
& Accountability Journal, 22(6), 933–952.
https://doi.org/10.1108/09513570910980481
Churchill, D., Fox, B., & King, M. (2012). Study of Affordances of iPads and Teachers’ Private Theories. International Journal of Information and Education Technology, 2(3), 251–254. https://doi.org/10.7763/IJIET.2012.V2.122
156
Clarke, I. (2008). Emerging Value Propositions for M-commerce. Journal of Business Strategies, 25(2), 41–57.
http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site
&authtype=crawler&jrnl=08872058&AN=36541869&h=xljdYwMfeHAfOQjo unUGtYhG22YVlhWxP2xW6zINTw/eC0xmI9n2Gria/wi/xyN2vecvs2vsEboJd k9iKT5Sfg==&crl=c
Compeau, D. R., & Higgins, C. a. (1995). Application of Social Cognitive Theory to Training for Computer Skills. Information Systems Research, 6(2), 118–143.
https://doi.org/10.1287/isre.6.2.118
Coyle, J. R., & Thorson, E. (2001). The Effects of Progressive Levels of Interactivity and Vividness in Web Marketing Sites. Journal of Advertising, 30(3), 65–77.
https://doi.org/10.1080/00913367.2001.10673646
Daft, R. L., & Lengel, R. H. (1984). Information Richness: A New Approach to Managerial Behaviour and Organizational Design. Research in Organizational Behaviour, 6, 191–233.
Daft, R. L., Lengel, R. H., & Trevino, L. K. (1987a). Message equivocality, media selection, and manager performance: Implications for information systems. MIS Quarterly, 11(3), 355–366.
Daft, R. L., Lengel, R. H., & Trevino, L. K. (1987b). Message equivocality , media selection , and manager performance : Implications for information systems. MIS Quarterly, 11(3), 355–366. https://doi.org/10.2307/248682
Davis, F. D. (1985). A technology acceptance model for empirically testing new end- user information systems: Theory and results. In Massachusetts Institute of Technology. https://doi.org/oclc/56932490
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quaterly, 13(3), 319–340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Davis, M. M., & Vollmann, T. E. (1990). A Framework for Relating Waiting Time and CustomerSatisfaction in a Service Operation. Journal of Services Marketing, 4(1), 61–69. https://doi.org/10.1108/EUM0000000002506
Dennis, A. R., & Kinney, S. T. (2014). Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality. Information Systems Research, 9(3), 256–274.
Department of Statistics Malaysia. (2020). ICT Use and Access by Individuals and Households Survey Report. In 10 April 2020.
https://www.statistics.gov.my/index.php?r=column/cthemeByCat&cat=395&bu l_id=Q3l3WXJFbG1PNjRwcHZQTVlSR1UrQT09&menu_id=amVoWU54UTl 0a21NWmdhMjFMMWcyZz09
Dillon, A., & Morris, M. G. (1996). User acceptance of new information technology:
theories and models. Annual Review of Information Science and Technology, Vol.
31, 3–32.
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. College Publishers.
157
Ellahi, A., & Bokhari, R. H. (2013). Key quality factors affecting users’ perception of social networking websites. Journal of Retailing and Consumer Services, 20(1), 120–129. https://doi.org/10.1016/j.jretconser.2012.10.013
Faziharudean, T. M., & Li-Ly, T. (2011). Consumers’ behavioral intentions to use mobile data services in Malaysia. African Journal of Business Management, 5(5), 1811–1821. https://doi.org/10.5897/AJBM10.794
Fazio, R. H., & Zanna, M. P. (1981). Direct Experience And Attitude-Behavior Consistency. Advances in Experimental Social Psychology, 14(C), 161–202.
https://doi.org/10.1016/S0065-2601(08)60372-X
Feldman, L. P., & Hornik, J. (1981). The Use of Time: An Integrated Conceptual Model. Journal of Consumer Research, 7(4), 407–419.
https://doi.org/10.1086/208831
Feng, Y., & Zhu, Y. (2019). PES: Proactive Event Scheduling for Responsive and Energy-Efficient Mobile Web Computing. Proceedings of the 46th
International Symposium on Computer Architecture, 66–78.
https://doi.org/10.1145/3307650.3322248
Ferdousi, B., & Bari, J. (2015). Infusing Mobile Technology into Undergraduate Courses for Effective Learning. Procedia - Social and Behavioral Sciences, 176, 307–311. https://doi.org/10.1016/j.sbspro.2015.01.476
Fernandes, N., Rodrigues, A., Duarte, C., Hijon-Neira, R., & Carrico, L. (2014). Web Accessibility of Mobile and Desktop Representations. Proceedings of BCS HCI, 1–8.
Fernández-López, Á., Rodríguez-Fórtiz, M. J., Rodríguez-Almendros, M. L., &
Martínez-Segura, M. J. (2013). Mobile learning technology based on iOS devices to support students with special education needs. Computers & Education, 61, 77–90. https://doi.org/10.1016/j.compedu.2012.09.014
Field, A. (2009). Discovering statistics using SPSS. SAGE Publications Ltd.
Fink, A. (2003). The survey handbook (2nd Editio). SAGE Publications.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.
Gay, L. R., Mills, G. E., & Airasian, P. W. (2011). Educational research:
Competencies for analysis and applications. Pearson Higher Ed.
Goggin, G. (2012). Cell phone culture: Mobile technology in everyday life. Routledge.
Grabowsky, A., & Wright, M. (2013). Connecting with health science students and faculty to facilitate the design of a mobile library website. Medical Reference Services Quarterly, 32(October 2014), 151–162.
https://doi.org/10.1080/02763869.2013.776882
Green, I. F. R. (2005). The Emancipatory Potential of a New Information System and Its Effect on Technology Acceptance. University of Pretoria.
Hair, F., J., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006).
Multivariate data analysis (Vol. 6). Pearson Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Prentice Hall.
158
Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobile- banking adoption by Iranian bank clients. Telematics and Informatics, 31(1), 62–
78. https://doi.org/10.1016/j.tele.2012.11.001
Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior. International Journal of Information Management, 24(6), 539–550.
https://doi.org/10.1016/j.ijinfomgt.2004.08.004
Hartley, J. (2014). Some thoughts on Likert-type scales. International Journal of Clinical and Health Psychology, 14(1), 83–86. https://doi.org/10.1016/S1697- 2600(14)70040-7
Hashim, K. F., Tan, F. B., & Rashid, A. (2015). Adult learners’ intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology, 46(2), 381–390. https://doi.org/10.1111/bjet.12148
Hayes, B. E. (1998). Measuring customer satisfaction: Survey design, use, and statistical analysis methods (2nd ed.). ASQ Quality Press.
Henry, A. (2013). Optimize Your Website for the Mobile User. Rural Telecom, 32(3), 18–22.
Heo, J., Ham, D.-H., Park, S., Song, C., & Yoon, W. C. (2009). A framework for evaluating the usability of mobile phones based on multi-level, hierarchical model of usability factors. Interacting with Computers, 21(4), 263–275.
https://doi.org/10.1016/j.intcom.2009.05.006
Hogg, R. V, & Craig, A. T. (1995). introduction to mathematical statistics (5th ed.).
Prentice Hall.
http://lrc.tnu.edu.vn/upload/collection/brief/39941_221020131015121.pdf Hootsuite & We Are Social. (2020). Digital 2020 Global Digital Overview. In Data
Reportal. https://datareportal.com/reports/digital-2020-global-digital-overview Horrigan, J. (2009). The Mobile Difference: Wireless connectivity has drawn many
users more deeply into digital life. In Pew Internet and American Life Project.
http://pewinternet.org/Reports/2009/5-The-Mobile-Difference-Typology.aspx Hsieh, J.-K., Hsieh, Y.-C., Chiu, H.-C., & Yang, Y.-R. (2014). Customer Response
to Web Site Atmospherics: Task-relevant Cues, Situational Involvement and PAD. Journal of Interactive Marketing, 28(3), 225–236.
https://doi.org/10.1016/j.intmar.2014.03.001
Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853–868. https://doi.org/10.1016/j.im.2003.08.014
Hu, P. J., Chau, P. Y. K., Liu Sheng, O. R., & Tam, K. Y. (1999). Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. Journal of Management Information Systems, 16(2), 91–112.
https://doi.org/10.2307/40398433
Huff, K. C. (2015). The comparison of mobile devices to computers for web-based assessments. Computers in Human Behavior, 49, 208–212.
https://doi.org/10.1016/j.chb.2015.03.008
159
Ilie, V., Slyke, craig Van, Green, G., & Lou, H. (2005). Gender Differences in Perceptions and Use of Communication Technologies. Information Resources Management Journal, 18(3), 13–31. https://doi.org/10.4018/irmj.2005070102 Ioannidis, K., Hook, R., Goudriaan, A. E., Vlies, S., Fineberg, N. A., Grant, J. E., &
Chamberlain, S. R. (2019). Cognitive deficits in problematic internet use: meta- analysis of 40 studies. British Journal of Psychiatry, 215(5), 639–646.
https://doi.org/10.1192/bjp.2019.3
Isaac, O., Abdullah, Z., Aldholay, A. H., & Abdulbaqi Ameen, A. (2019).
Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology
(UTAUT) model. Asia Pacific Management Review, 24(4), 335–354.
https://doi.org/10.1016/j.apmrv.2018.12.003
Jackson, C. M., Chow, S., & Leitch, R. a. (1997). Toward an Understanding of the Behavioral Intention to Use an Information System. Decision Sciences, 28(2), 357–389. https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
Jayasingh, S., & Eze, U. (2010). The Role of Moderating Factors in Mobile Coupon Adoption: An Extended TAM Perspective. Communications of the IBIMA, 2010, 1–13. https://doi.org/10.5171/2010.596470
Johnson, T., & Seeling, P. (2013). Desktop and Mobile Web Page Comparison:
Characteristics, Trends, and Implications. Communications Magazine, IEEE, 1–
14. http://arxiv.org/abs/1309.1792
Justiti, N. D. N., & Adhi Prasetyo, S. T. (2018). The Analysis about Behavior Intention of Customers on Using Smartphone. International Journal of Scientific and Research Publications (IJSRP), 8(3), 354–360.
https://doi.org/10.29322/IJSRP.8.3.2018.p7551
Kang, Y.-S., & Kim, Y. J. (2006). Do visitors’ interest level and perceived quantity of web page content matter in shaping the attitude toward a web site? Decision Support Systems, 42(2), 1187–1202. https://doi.org/10.1016/j.dss.2005.10.004 Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Adoption Across Technology
Information Time : a Cross-Sectional Comparison of. MIS Quarterly, 23(2), 183–
213.
Kemp, S. (2019). Digital in 2018: Essential insights into Internet, social media, mobile and ecommerce use around the world. https://datareportal.com/reports/digital- 2019-somalia?rq=Somalia
Kende, M. (2015). Global Internet Report 2015. Mobile evolution and development of the Internet.
Keong, E. Y. (2014). The Adoption of Web 2.0 Technology in Malaysian Retail-Chain Businesses [Universiti Utara Malaysia]. http://etd.uum.edu.my/4320/
Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322. https://doi.org/10.1016/j.chb.2009.10.013
Kleijnen, M., de Ruyter, K., & Wetzels, M. (2007). An assessment of value creation in mobile service delivery and the moderating role of time consciousness. Journal of Retailing, 83, 33–46. https://doi.org/10.1016/j.jretai.2006.10.004
160
Kobus, M. B. W., Rietveld, P., & van Ommeren, J. N. (2013). Ownership versus on- campus use of mobile IT devices by university students. Computers & Education, 68, 29–41. https://doi.org/10.1016/j.compedu.2013.04.003
Kotler, P. (2000). Marketing Management, Millenium Edition. In Prentice Hall (10th ed.). https://doi.org/10.1016/0024-6301(90)90145-T
Krejcie, R. V, & Morgan, D. W. (1970). Determining Sample Size for Research Activities Robert. Educational and Psychological Measurement, 38(1), 607–610.
https://doi.org/10.1177/001316447003000308
Kroski, E. (2008). On the Move with the Mobile Web: Libraries and Mobile Technologies. In Library technology reports (Vol. 44, Issue 5).
http://eprints.rclis.org/15024/1/mobile_web_ltr.pdf
Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103–110. https://doi.org/10.1016/j.chb.2008.07.007
Lai, J.-Y., & Chang, C.-Y. (2011). User attitudes toward dedicated e-book readers for reading: The effects of convenience, compatibility and media richness. In Online Information Review (Vol. 35, Issue 4, pp. 558–580).
https://doi.org/10.1108/14684521111161936
Lee, S. (2013). An integrated adoption model for e-books in a mobile environment:
Evidence from South Korea. Telematics and Informatics, 30(2), 165–176.
https://doi.org/10.1016/j.tele.2012.01.006
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model:
Past, Present, and Future. Communications of the Association for Information Systems, 12(50), 752–780.
Lin, H.-F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260.
https://doi.org/10.1016/j.ijinfomgt.2010.07.006
Lin, J. C.-C., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197–
208. https://doi.org/10.1016/S0268-4012(00)00005-0
Lin, K., & Lu, H.-P. (2015). Predicting mobile social network acceptance based on mobile value and social influence. Internet Research, 25(1), 107–130.
https://doi.org/10.1108/IntR-01-2014-0018
Liu, D., & Chen, W. (2009). An Empirical Research on the Determinants of User M- Commerce Acceptance. In Studies in Computational Intelligence (Vol. 209, pp.
93–104). https://doi.org/10.1007/978-3-642-01203-7_8
Liu, Y., & Li, H. (2011a). Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior, 27(2), 890–898. https://doi.org/10.1016/j.chb.2010.11.014 Liu, Y., & Li, H. (2011b). Exploring the impact of use context on mobile hedonic
services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior, 27(2), 890–898. https://doi.org/10.1016/j.chb.2010.11.014
161
Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268.
https://doi.org/10.1016/j.jsis.2005.07.003
Lu, Ying, & Rastrick, K. (2014). Impacts of Website Design on the Adoption Intention of Mobile Commerce: Gender as a Moderator. New Zealand Journal of Applied Business Research, 12(2), 51–69.
Lu, Yu, Kim, Y., Douc, X. (Yuki), & Kumar, S. (2014). Promote Physical Activity among College Students: Using Media Richness and Interactivity in Website Design. Computers in Human Behavior, 41, 40–50.
https://doi.org/10.1016/j.chb.2014.08.012
Mafe, C. R., Blas, S. S., & Tavera-Mesı´as, J. F. (2010). A comparative study of mobile messaging services acceptance to participate in television programmes.
Journal of Service Management, 21(1), 69–102.
https://doi.org/10.1108/09564231011025128
Magsamen-Conrad, K., & Dillon, J. M. (2020). Mobile technology adoption across the lifespan: A mixed methods investigation to clarify adoption stages, and the influence of diffusion attributes. Computers in Human Behavior, 112(May), 106456. https://doi.org/10.1016/j.chb.2020.106456
Malaysian Communication and Multimedia Commision. (2014). Malaysian Communication and Multimedia Commision Annual Report 2014.
https://doi.org/10.1007/s13398-014-0173-7.2
Malaysian Communications and Multimedia Commission. (2014). Communications and Multimedia Pocket Book of Statistics.
Malaysian Communications and Multimedia Commission. (2017). HAND PHONE USERS SURVEY 2017.
Malaysian Communications and Multimedia Commission (MCMC). (2018). Internet Users Survey 2018.
https://www.mcmc.gov.my/skmmgovmy/media/General/pdf/Internet-Users- Survey-2018.pdf
Male, G., & Pattinson, C. (2011). Enhancing the quality of e-learning through mobile technology: A socio-cultural and technology perspective towards quality e- learning applications. Campus-Wide Information Systems, 28(5), 331–344.
https://doi.org/10.1108/10650741111181607
Malik, A., Suresh, S., & Sharma, S. (2017). Factors influencing consumers’ attitude towards adoption and continuous use of mobile applications: a conceptual model. Procedia Computer Science, 122, 106–113.
https://doi.org/10.1016/j.procs.2017.11.348
Marcano Belisario, J. S., Huckvale, K., Saje, A., Porcnik, A., Morrison, C. P., & Car, J. (2014). Comparison of self administered survey questionnaire responses collected using mobile apps versus other methods. In J. S. Marcano Belisario (Ed.), Cochrane Database of Systematic Reviews. John Wiley & Sons, Ltd.
https://doi.org/10.1002/14651858.MR000042
Martin, F., Pastore, R., & Snider, J. (2012). Developing Mobile Based Instruction.
TechTrends, 56(5), 46–51. https://doi.org/10.1007/s11528-012-0598-9
162
Masters, K., & Herrmann-Werner, A. (2020). Medical student internet usage: is the literature correct to call it addiction? An opinion piece. GMS Journal for Medical Education, 37(6), 1–12. https://doi.org/10.3205/zma001351
Matell, M. S., & Jacoby, J. (1971). Is There an Optimal Number of Alternatives for Likert Scale Items? Study I: Reliability and Validity. Educational and
Psychological Measurement, 31(3), 657–674.
https://doi.org/10.1177/001316447103100307
Mathieson, K. (1991). Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing Among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self- Service Technologies. Journal of Marketing, 69(2), 61–83.
https://doi.org/10.1509/jmkg.69.2.61.60759
Mininistry of Education Malaysia. (2018). Quick Facts 2018: Malaysia Educational Statistics. In Mininistry of Education Malaysia.
Ministry of Education Malaysia. (2014). National Education statistic: Higher Education Sector 2013.
Moon, Ji-won, & Kim, Y. (2001). Extending the TAM for a World-Wide-Web context.
Information & Management, 38, 217–230.
Moon, JW, & Kim, Y. (2001). Extending the TAM for a World-Wide-Web context.
Information & Management, 38, 217–230.
http://www.sciencedirect.com/science/article/pii/S0378720600000616
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
Mordkoff, J. T. (2016). The Assumption(s) of Normality.
Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017).
Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25–38.
https://doi.org/10.1016/j.sjme.2016.12.001
Myers, J. L., Well, A., & Lorch, R. F. (2010). Research Design and Statistical Analysis (3rd ed.). Routledge.
Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37(July 2016), 8–22.
https://doi.org/10.1016/j.jretconser.2017.02.010
Neuman, W. L. (2006). Social research methods: Quantitative and qualitative approaches (6th ed.). Pearson Education.
Newberry, B. (2001). Raising Student Social Presence in Online Classes. World Conference on the WWW and Internet Proceedings, Orlando, F, 7.
http://eric.ed.gov/ERICWebPortal/recordDetail?accno=ED466611
163
Nikkheslat, M., & Zohoori, M. (2012). The important theories in term of applying green technologies and green processes in organizations : A study of Malaysian Universities. Interdisciplinary Journal of Contemporary Research in Business, 4(7), 88–103.
Nikou, S., & Bouwman, H. (2014). Ubiquitous use of mobile social network services. Telematics and Informatics, 31(3), 422–433.
https://doi.org/10.1016/j.tele.2013.11.002
Nysveen, H., Pedersen, P. E., & Thorbjornsen, H. (2005). Intentions to Use Mobile Services: Antecedents and Cross-Service Comparisons. Journal of the Academy of Marketing Science, 33(3), 330–346.
https://doi.org/10.1177/0092070305276149
Okazaki, S., & Mendez, F. (2013). Exploring convenience in mobile commerce:
Moderating effects of gender. Computers in Human Behavior, 29(3), 1234–1242.
https://doi.org/10.1016/j.chb.2012.10.019019
Oppenheim, C. (1997). The correlation between citation counts and the 1992 research assessment exercise ratings for British research in genetics, anatomy and archaeology. Journal of Documentation, 53(5), 477–487.
https://doi.org/10.1108/EUM0000000007207
Otondo, R. F., Scotter, J. R. Van, Allen, D. G., & Palvia, P. (2008). The complexity of richness: Media, message, and communication outcomes. Information and Management, 45(1), 21–30. https://doi.org/10.1016/j.im.2007.09.003
Pallant, J. (2020). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS (7th ed.). Routledge. https://doi.org/10.4324/9781003117452
Park, E., & Ohm, J. (2014). Factors influencing users’ employment of mobile map services. Telematics and Informatics, 31(2), 253–265.
https://doi.org/10.1016/j.tele.2013.07.002
Pollara, P. (2011). Mobile Learning in Higher Education: A Glimpse and a Comparison of Student and Faculty Readiness, Attitudes and Perceptions (Issue December). Louisiana State University.
Raman, J. (2015). Mobile technology in nursing education: where do we go from here?
A review of the literature. Nurse Education Today.
https://doi.org/10.1016/j.nedt.2015.01.018
Ramayah, T., Rouibah, K., Gopi, M., & Rangel, G. J. (2009). A decomposed theory of reasoned action to explain intention to use Internet stock trading among Malaysian investors. Computers in Human Behavior, 25(6), 1222–1230.
https://doi.org/10.1016/j.chb.2009.06.007
Rao, R. S., Vaishnavi, T., & Pais, A. R. (2019). PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices. Pervasive and Mobile Computing, 60, 101084. https://doi.org/10.1016/j.pmcj.2019.101084 Reychav, I., Dunaway, M., & Kobayashi, M. (2015). Understanding mobile technology-fit behaviors outside the classroom. Computers & Education, 87, 142–150. https://doi.org/10.1016/j.compedu.2015.04.005
Roach, G. (2009). Consumer perceptions of mobile phone marketing: a direct marketing innovation. Direct Marketing: An International Journal, 3(2), 124–
138. https://doi.org/10.1108/17505930910964786
164
Robey, D. (1979). User Attitudes and Management Information System Use. Academy of Management Journal, 22(3), 527–538.
Rogers, E. M. (1983). Diffusion of Innovtions. In Diffusion of innovations (Third edit). The free press.
http://hollis.harvard.edu/?itemid=%7Clibrary/m/aleph%7C006256656
Roudaki, A., Kong, J., & Yu, N. (2015). A classification of web browsing on mobile devices. Journal of Visual Languages & Computing, 26, 82–98.
https://doi.org/10.1016/j.jvlc.2014.11.010
Russ, G. S., Daft, R. L., & Lengel, R. H. (1990). Media selection and managerial characteristics in organizational communications. Management Communication Quarterly, 4(3), 151–175.
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students. Pearson Education.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
Schimmel, K., Motley, D., Racic, S., Marco, G., & Eschenfelder, M. (2010). The importance of university web pages in selecting a higher education institution.
Research in Higher Education Journal, 9, 1–16.
http://m.www.aabri.com/manuscripts/10560.pdf
Seiders, K., & Berry, L. L. (1998). Service fairness: What it is and why it matters.
Academy of Management Executive, 12(2), 8–20.
https://doi.org/10.5465/AME.1998.650513
Sekaran, U., & Bougie, R. (2019). Research Methods for Business: A Skill Building Approach (8th ed.). John Wiley & Sons, Inc.
Shamsudin, M. F. Bin. (2012). Determinants of Customer Loyalty Towards Prepaid Mobile Cellular Services in Malaysia. Universiti Utara Malaysia.
Sharma, S., & Gutiérrez, J. A. (2010). An evaluation framework for viable business models for m-commerce in the information technology sector. Electronic Markets, 20, 33–52. https://doi.org/10.1007/s12525-010-0028-9
Sheppard, B. H., Hartwick, J., Warshaw, P. R., & Hartwick, J. O. N. (1988). The Theory of Reasoned Past Action : Meta-Analysis of with Modifications for Recommendations and. Journal of Consumer Research, 15(3), 325–343.
http://www.jstor.org/stable/2489467
Shin, D. H. (2011). The influence of perceived characteristics of innovating on 4G mobile adoption. International Journal of Mobile Communications, 9(3), 261.
https://doi.org/10.1504/IJMC.2011.040606
Simon, S. J., & Peppas, S. C. (2004). An examination of media richness theory in product Web site design: an empirical study. Info, 6(4), 270–281.
https://doi.org/10.1108/14636690410555672
Singh, J. (2011). Instruments of Social Research. Rawat Publications.
Slevin, D. P., & Schultz, R. L. (1975). Implementation and Organizational Validity:
An Empirical Investigation. In Implementing Operations Research/Management Science. American Elsevier, New York.
165
Sohn, S. Y., & Kim, Y. (2008). Searching customer patterns of mobile service using clustering and quantitative association rule. Expert Systems with Applications, 34(2), 1070–1077. https://doi.org/10.1016/j.eswa.2006.12.001
Sommer, R., & Sommer, B. (2002). A Practical Guide to Behavioral Research (5th ed.). Oxford University Press.
Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-banking in metropolitan bangkok and a comparison with other countries. Journal of Computer Information Systems, 51(3), 67–76.
Stafford, L., & Hillyer, J. D. (2012). Information and Communication Technologies in Personal Relationships. Review of Communication, 12(4), 290–312.
https://doi.org/10.1080/15358593.2012.685951
Suh, K. S. (1999). Impact of communication medium on task performance and satisfaction: an examination of media-richness theory. Information &
Management, 35(5), 295–312. https://doi.org/10.1016/S0378-7206(98)00097-4 Szajna, B. (1996). Empirical Evaluation of the Revised Technology Acceptance
Model. Management Science, 42(1), 85–92.
https://doi.org/10.1287/mnsc.42.1.85
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Allyn
& Bacon.
Tam, C., Santos, D., & Oliveira, T. (2018). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 2018, 1–27.
https://doi.org/10.1007/s10796-018-9864-5
Tan, G. W.-H., Ooi, K.-B., Leong, L.-Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36, 198–213.
https://doi.org/10.1016/j.chb.2014.03.052
Taylor, S. (1994). Waiting for service: The relationship between delays and evaluations of service. Journal of Marketing, 58(2), 56–69.
https://doi.org/10.2307/1252269
Taylor, S., & Todd, P. (1995a). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176.
Taylor, S., & Todd, P. (1995b). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155. https://doi.org/10.1016/0167- 8116(94)00019-K
Thompson, R. L., Higgins, C. a., & Howell, J. . (1991). Personal Computing : Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 124–143.
https://doi.org/10.2307/249443
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review.
Information and Software Technology, 52(5), 463–479.
https://doi.org/10.1016/j.infsof.2009.11.005
166
van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852.
https://doi.org/10.1016/j.compedu.2006.09.001
VanderStoep, S., & Johnston, D. (2009). Research methods for everyday life: Blending qualitative and quantitative approaches. Vol. 32. John Wiley & Sons, Inc.
Venkatesh, V, & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315.
https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, Viswanath. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation and emotion into the technology acceptance model.
Information Systems Research, 11(4), 342–365.
Venkatesh, Viswanath, & Davis, F. D. (2000). A Theoretical Acceptance Extension Model : Field Four Studies of the Technology Longitudinal. Management Science, 46(2), 186–204.
Venkatesh, Viswanath, & Morris, G. M. (2000). Why don’t men ever stop to ask for direction? Gender, social influence and their role in technology acceptance and usage behaviour. MIS Quarterly, 24(1), 115–137.
Venkatesh, Viswanath, Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. Management Information Systems, 27(3), 425–478.
Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2010).
Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3), 242–255. https://doi.org/10.1016/j.tele.2009.11.001
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping:
The case for an augmented technology acceptance model. Information and Management, 41(6), 747–762. https://doi.org/10.1016/j.im.2003.08.011
Wai, C. K. (2015). Intention and Adoption of Mobile Coupon among Mobile Phone Users in Klang Valley. Universiti Utara Malaysia.
Wangpipatwong, S., Chutimaskul, W., & Papasratorn, B. (2008). Understanding citizen’s continuance intention to use e-government website: A composite view of technology acceptance model and computer self-efficacy. Electronic Journal of E-Government, 6(1), 55–64.
http://unpan1.un.org/intradoc/groups/public/documents/apcity/unpan045451.pdf Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intentions and behavioral expectations. Journal of Experimental Social Psychology, 21, 213–
228.
Weiss, S. (2002). Handheld Usability. John Wiley & Sons.
Wu, C.-S., Cheng, F.-F., Yen, D. C., & Huang, Y.-W. (2011). User acceptance of wireless technology in organizations: A comparison of alternative models.
Computer Standards & Interfaces, 33(1), 50–58.
https://doi.org/10.1016/j.csi.2010.03.002
167
Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms.
Computers in Human Behavior, 98, 166–173.
https://doi.org/10.1016/j.chb.2019.04.015
Youngblood, N. E., Tirumala, L. N., Hallaq, T., & Cozma, R. (2019). College TV news websites : Accessibility and mobile readiness. Electronic News, 13(3), 115–133.
https://doi.org/10.1177/1931243119883653
Yu, N., & Kong, J. (2015). User experience with web browsing on small screens:
Experimental investigations of mobile-page interface design and homepage design for news websites. Information Sciences.
https://doi.org/10.1016/j.ins.2015.06.004
Zeithaml, V. a, Berry, L. L., & Parasuraman, A. (1996). The Behavioral Consequences of Service Quality. Journal of Marketing, 60(2), 31.
https://doi.org/10.2307/1251929
Zhang, D., & Lai, J. (2011). Can Convenience and Effectiveness Converge in Mobile Web? A Critique of the State-of-the-Art Adaptation Techniques for Web
Navigation on Mobile Handheld Devices. In International Journal of Human- Computer Interaction (Vol. 27, Issue 12, pp. 1133–1160).
https://doi.org/10.1080/10447318.2011.559876
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2010). Business Research Methods (8th ed.). Cengage Learning.
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APPENDIX A: RESEARCH QUESTIONNAIRE
ASSESSING MOBILE WEBSITES IN MALAYSIA: AN EXTENDED TAM MODEL
Dear Sir/Madam,
I am currently undertaking a study on the above topic to fulfil the partial requirement of the academic program leading to Doctor of philosophy (PhD) at Universiti Utara Malaysia (UUM). I hereby would like to invite you to participate in this survey by completing the attached questionnaire.
The general purpose of this study is to understand Mobile Web adoption among undergraduate students in Malaysia. Mobile Web can be defined as World Wide Web (web page) which is accessed through mobile devices such as smartphone, tablet and netbook. It comprised of all the web pages available in the Internet and is not limited to web pages which have been specifically designed to be viewed through mobile devices. Mobile apps are not classified as a mobile web. Mobile devices concerned in this study consist of smartphone, tablet, netbook and convertible. Laptop computer is not included in the group of mobile device in this study.
This study is conducted only for academic purpose and not for other uses. All of the answers provided will be kept strictly confidential. The questionnaire is designed to take minimum of your valuable time. Your participation and contribution is highly appreciated.
Yours sincerely,
Thaneshan Letchumanan
School of Technology Management and Logistics, Universiti Utara Malaysia, Kedah
Tel: 014-9047724
E-mail: s95039@student.uum.edu.my
Website accessed through mobile device (Mobile web)
Website accessed through desktop computer
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Section 1
Demographic Profile
Please tick the appropriate box to answer the questions 1. Gender: Male Female
2. Age: 15-19 20-24 25-29 30-34 Above 34
3. Nationality: Malaysian Non-Malaysian
4. Years of Internet experience: Less than 1 year 1 to 5 years
6 to 10 years More than 10 years
5. Hours of daily Internet usage: Less than 1 hour 1 to 5 hours
6 to 10 hours More than 10 hours 6. Do you own any mobile device that has Internet accessibility? If “Yes” please complete the remaining parts of this questionnaire, if “No” you do not have to continue this survey.
Yes No
7. Years of mobile device ownership: Less than 1 year 1 to 3 years 4 to 6 years More than 6 years
8. Types of mobile device owned: Smartphone Tablet Netbook Convertible
9. Did you ever use mobile devices to access websites?
Yes No
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Section 2
Please indicate the extent of your opinion with the statements describing the statements by “circling” the corresponding numbers using the following scales
Strongly disagree
Disagree Partially disagree
Neutral Partially agree
Agree Strongly agree
1 2 3 4 5 6 7
Perceived Usefulness
1
Using mobile web can help me to accomplish tasks more quickly.
Menggunakan web mudah alih boleh membantu saya untuk melaksanakan tugas-tugas saya dengan lebih cepat.
1 2 3 4 5 6 7
2
Using mobile web would improve my performance in my daily life.
Menggunakan web mudah alih akan meningkatkan prestasi saya dalam kehidupan seharian saya.
1 2 3 4 5 6 7
3
Using mobile web would increase my productivity in my daily life.
Menggunakan web mudah alih akan meningkatkan produktiviti saya dalam kehidupan seharian saya.
1 2 3 4 5 6 7
4
Using mobile web would enhance my effectiveness in my daily life.
Menggunakan web mudah alih akan meningkatkan keberkesanan saya dalam kehidupan seharian saya.
1 2 3 4 5 6 7
5
Using mobile web would make my daily life tasks easier.
Menggunakan web mudah alih akan membuatkan tugas- tugas kehidupan seharian saya lebih mudah.
1 2 3 4 5 6 7
6
Overall, I believe mobile web will be useful.
Secara keseluruhan, saya percaya web mudah alih adalah berguna.
1 2 3 4 5 6 7
171
Please indicate the extent of your opinion with the statements describing the statements by “circling” the corresponding numbers using the following scales
Strongly disagree
Disagree Partially disagree
Neutral Partially agree
Agree Strongly agree
1 2 3 4 5 6 7
Perceived Ease of Use
1
Learning to use the mobile web is easy for me.
Belajar untuk menggunakan web mudah alih adalah mudah bagi saya.
1 2 3 4 5 6 7
2
I find it is easy to use the mobile web to get what I want.
Saya mendapati web mudah alih adalah mudah untuk digunakan bagi mendapatkan apa yang saya mahu.
1 2 3 4 5 6 7
3
My interaction with the mobile web is clear and understandable.
Interaksi saya dengan web mudah alih adalah jelas dan mudah difahami.
1 2 3 4 5 6 7
4
I find the mobile web is flexible to interact with.
Saya mendapati interaksi dengan web mudah alih adalah fleksibel.
1 2 3 4 5 6 7
5
It is easy for me to become skilful in using the mobile web.
Ia adalah mudah bagi saya untuk menjadi mahir dalam menggunakan web mudah alih.
1 2 3 4 5 6 7
6
Overall, I find the mobile web is easy to use.
Secara keseluruhan, saya mendapati web mudah alih adalah mudah untuk digunakan.
1 2 3 4 5 6 7