Determinants of mobile commerce adoption among university students in Malaysia

Mobile commerce has an increasing importance and development in offering a new platform to sell products effectively and efficiently. Despite numerous studies in the area of technology adoption, little is known about mobile commerce adoption in Malaysia and appropriate models that could explain the...

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Main Author:	Nurul Labanihuda, Abdull Rahman
Format:	Thesis
Language:	eng eng
Published:	2018
Subjects:	HF5548.34 Mobile Commerce
Online Access:	https://etd.uum.edu.my/7834/1/Depositpermission_s900434.pdf https://etd.uum.edu.my/7834/2/s900434_01.pdf
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id	my-uum-etd.7834
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Hassan, Shahizan
topic	HF5548.34 Mobile Commerce
spellingShingle	HF5548.34 Mobile Commerce Nurul Labanihuda, Abdull Rahman Determinants of mobile commerce adoption among university students in Malaysia
description	Mobile commerce has an increasing importance and development in offering a new platform to sell products effectively and efficiently. Despite numerous studies in the area of technology adoption, little is known about mobile commerce adoption in Malaysia and appropriate models that could explain the behaviours of young generations on the use of mobile commerce. This study attempts to propose a conceptual model for mobile commerce adoption by adapting the integrated TAM3 model and using Individual-Collectivism at Individual Level (ICAIL) as the moderating variable in the context of mobile commerce in Malaysia. In addition, this research also identifies factors that affect the perceived usefulness (i.e. subjective norm, image, output quality, result demonstrability), and perceived ease of use (selfefficacy, anxiety, perception of external control, playfulness) in the context of mobile commerce adoption among university students in Malaysia. A sample of 550 students from four universities in Malaysia was surveyed through a self-administrated questionnaire. The findings of this study found eight direct significant relationships between the tested variables, while nine hypotheses were not accepted. Firstly, in terms of perceived usefulness variable, image showed a significant relationship, whereas subjective norm, output quality and result demonstrability showed vice versa. Secondly, for perceived ease of use variable, factors of self-efficacy, perception of external control and playfulness showed significant relationships, while anxiety was found to be insignificant. Thirdly, while subjective norm had significant relationship with image, perceived usefulness indicated insignificant relationship with behavioural intention. Fourthly, perceived ease of use had significant relationships with perceived usefulness and behavioural intention. Finally, the perceived ease of use, perceived usefulness, subjective norm and behavioural intention showed insignificant relationship with the moderating variable, ICAIL. As a conclusion, the results from this study are important to the advancement of knowledge to the mobile commerce companies, services provider, financial services and government.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Nurul Labanihuda, Abdull Rahman
author_facet	Nurul Labanihuda, Abdull Rahman
author_sort	Nurul Labanihuda, Abdull Rahman
title	Determinants of mobile commerce adoption among university students in Malaysia
title_short	Determinants of mobile commerce adoption among university students in Malaysia
title_full	Determinants of mobile commerce adoption among university students in Malaysia
title_fullStr	Determinants of mobile commerce adoption among university students in Malaysia
title_full_unstemmed	Determinants of mobile commerce adoption among university students in Malaysia
title_sort	determinants of mobile commerce adoption among university students in malaysia
granting_institution	Universiti Utara Malaysia
granting_department	Othman Yeop Abdullah Graduate School of Business
publishDate	2018
url	https://etd.uum.edu.my/7834/1/Depositpermission_s900434.pdf https://etd.uum.edu.my/7834/2/s900434_01.pdf
_version_	1747828273831739392
spelling	my-uum-etd.78342021-08-11T05:39:07Z Determinants of mobile commerce adoption among university students in Malaysia 2018 Nurul Labanihuda, Abdull Rahman Hassan, Shahizan Othman Yeop Abdullah Graduate School of Business Othman Yeop Abdullah Graduate School of Business HF5548.34 Mobile Commerce Mobile commerce has an increasing importance and development in offering a new platform to sell products effectively and efficiently. Despite numerous studies in the area of technology adoption, little is known about mobile commerce adoption in Malaysia and appropriate models that could explain the behaviours of young generations on the use of mobile commerce. This study attempts to propose a conceptual model for mobile commerce adoption by adapting the integrated TAM3 model and using Individual-Collectivism at Individual Level (ICAIL) as the moderating variable in the context of mobile commerce in Malaysia. In addition, this research also identifies factors that affect the perceived usefulness (i.e. subjective norm, image, output quality, result demonstrability), and perceived ease of use (selfefficacy, anxiety, perception of external control, playfulness) in the context of mobile commerce adoption among university students in Malaysia. A sample of 550 students from four universities in Malaysia was surveyed through a self-administrated questionnaire. The findings of this study found eight direct significant relationships between the tested variables, while nine hypotheses were not accepted. Firstly, in terms of perceived usefulness variable, image showed a significant relationship, whereas subjective norm, output quality and result demonstrability showed vice versa. Secondly, for perceived ease of use variable, factors of self-efficacy, perception of external control and playfulness showed significant relationships, while anxiety was found to be insignificant. Thirdly, while subjective norm had significant relationship with image, perceived usefulness indicated insignificant relationship with behavioural intention. Fourthly, perceived ease of use had significant relationships with perceived usefulness and behavioural intention. Finally, the perceived ease of use, perceived usefulness, subjective norm and behavioural intention showed insignificant relationship with the moderating variable, ICAIL. As a conclusion, the results from this study are important to the advancement of knowledge to the mobile commerce companies, services provider, financial services and government. 2018 Thesis https://etd.uum.edu.my/7834/ https://etd.uum.edu.my/7834/1/Depositpermission_s900434.pdf text eng public https://etd.uum.edu.my/7834/2/s900434_01.pdf text eng public https://sierra.uum.edu.my/record=b1698975~S1 Ph.D. doctoral Universiti Utara Malaysia Adams, D.A., Nelson, R.R., & Todd, P.A. (1992). Perceived usefulness, ease of use, and usage of information technology; A replication. MIS Quaterly, 16, 227-247. Andresen, E. M. (2000). Criteria for assessing the tools of disability outcomes research. Archives of physical medicine and rehabilitation, 81, S15-S20. Al-Louzi, B., & Iss, B. (2011). Factors influencing customer acceptance of mcommerce services in Jordan. Journal of Communication and Computer, 9(1), 1424-1436. Abdul Karim, N.S., Darus, S.H. & Hussin, R. (2006). Mobile phone applications in academic library services: a students' feedback survey. International Journal of Information and Learning Technology, 23(1), 35-51. AbuShanab, E., & Pearson, J. (2007). Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective. Journal of Systems and Information Technology, 9(1), 78-97. Adipat, B., Zhang,D., & Zhou,L.(2011). The effects of tree-view based presentation adaptation on model web browsing. Journal of the ACM, 35(1), 99-121. Ajzen, I. (1991). Theory of planned behavior. Organizational behavior and human decision processes. 50(2), 179-211. 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 & Management, vol.44, pp. 681–691. Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12(1), 27-50. Ali, S.,Choy. E. R.,Wan Amizah. W.M. & Roslina. A.L. (2013). Tracing the diffusion of internet in Malaysia: Then and now. Canadian Center of Science and Education, 9(6), 9-15 Al-Smadi, M.O., (2012). Factors affecting adoption of electronic banking: an analysis of the perspectives of bank’ customers. International Journal Business Social Sciences, 3 (17), 294–309. Amin, M.,Rezaei, S.& Abolghasemi, M.(2014). User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258 – 274 Anderson, J. E., & Schwager, P. H. (2004). SME adoption of wireless LAN technology: applying the UTAUT model. Proceedings of the 7th annual conference of the southern association for information systems.(pp.39-43).Georgia, USA. Andam, Z.R. (2003). E-Commerce and E-Business. (Web Document). Available: http://www.kau.edu.sa/Files/830/Files/61164_Ecommerce%20and%20E%20 Business.pdf (2014, 20 January). Agarwal, R., Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. Atkinson, M., & Kydd, C. (1997). Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation. ACM SIGMIS Database, 28(2), 53-62. Babbie, E. (2007). The Practice of Social Science Research (11th ed.) Belmont, CA: Wadsworth. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal personality and social psychology, 51(6), 1173. Baldi, S. & Thaung, P. H. (2002). The entertaining way to M-commerce: Japan’s approach to the mobile internet-a model for Europe?. International Journal on Networked Business, 12(1), 6-13. Baugh, P., Meckel, M., Walters, D. & Greenwood, A. (2004), A taxonomy of ebusiness adoption and strategies in small and medium sized enterprises. Journal of John Wiley & Sons, 13(5), 259–269. Bagozzi, P. R. (2007). The legacy of the Technology Acceptance Model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254. Barbeite, F. G., & Weiss, E. M. (2004). Computer self-efficacy and anxiety scales for an Internet sample: testing measurement equivalence of existing measures and development of new scales. Computers in Human Behavior, 20(1), 1-15. Benbasat, I., & Barki, H. (2007). Quo Vadis, TAM? Journal of the Association for Information System, 8(4), 211–218. Bentler,P.M.&Speckart,G. (1979). Models of attitude-behaviour relations. Psychological Review, 86(5), 425-464. Bigne-Alcaniz.E.,Ruiz-Mafe. C., Aldas-Manzano.J. & Sanz-Blas.S. (2008). Influence of online shopping information dependency and innovativeness on internet shopping adoption, Online Information Review,32(5),648-667. Bolton, R. N., Parasuraman, A., Hoefnagels, A., Migchels, N., Kabadayi, S., Gruber, T. & Solnet, D. (2013). Understanding generation Y and their use of social media: a review research agenda. Journal of Service Management, 24(3), 245-267. Bohrnstedt, G. W. (1970). Reliability and validity assessment in attitude measurement. In G. F. Summers (Ed.), Attitude measurement (pp. 80–99). London: Rand McNally. Black, S. A., & Porter, L. J. (1996). Identification of the critical factors of TQM. Decision Sciences, 27(1), 1-21. Bhalla, M. R., & Bhalla, A. V. (2010). Generations of mobile wireless technology: A survey. International Journal of Computer Applications, 5(4), 26-32. Burkhardt, M. E., & Brass, D. J. (1990). Changing patterns or patterns of change: The of a change in technology on social network structure and power. Administrative science quarterly, 104-127. Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York, NY: Taylor & Francis Group. BNM(2012).The 2013 Budget Speech, Retrieved from http://www.bnm.gov.my/files/2012/bs13.pdf Bough, P. (2004). Taxonomy of e-business adoption and strategies in small and medium enterprises. Journal of John Wiley & Sons. Brown, S. A., Fuller, R. M., & Vician, C. (2004). Who's afraid of the virtual world? Anxiety and computer-mediated communication. Journal of the Association for Information Systems, 5(2), 2. Cleveland, M., & Laroche, M. (2007). Acculturaton to the global consumer culture: Scale development and research paradigm. Journal of business research, 60(3), 249-259. Clarke III, I. (2001).Emerging value propositions for m-commerce. Journal Business Strategies, 18(2), 133-148. Chan, Y. H. (2003). Biostatistics 104: correlational analysis. Singapore Med J, 44(12), 614-9. Chitungo, S.K. & Munongo.S. (2013). Extending the technology acceptance mobile to mobile banking adoption in Rural Zimbabwe. Journal of Business Administration and Education. 3(1), 51-79. Charters, W. W., & Pellegrin, R. J. (1973). Barriers to the innovation process: Four case studies of differentiated staffing. Educational Administration Quarterly, 9(1), 3-14. Chaffey, D. (2009). E-Business and E-Commerce Management: Strategy, Implementation and Practice (4 ed.). Harlow, England: Pearson Education. Chan, S.C., Lu, M.T., (2004). Understanding internet banking adoption and use behaviour: A Hong Kong perspective, Journal Global Information Management, 12 (3), 12–43 Chan, F. T. S. & Chong, A. Y.-L. (2013). Analysis of the determinants of consumers’ m-commerce usage activities. Online Information Review Information, 37 (3), 443–461. Chee, S. S., Ismail, M. N., Ng, K. K., & Zawiah, H. (1997). Food intake assessment of adults in rural and urban areas from four selected regions in Malaysia Malaysia Journal of Nutrition, 3(2), 91-102. Chen, K., Chen, J. V., & Yen, D. C. (2011). Dimensions of self-efficacy in the study of phone acceptance. Computer Standards & Interfaces, 33(4), 422-431. Chismar, W. G., & Wiley-Patton, S. (2002). Test of the technology acceptance model for the internet in pediatrics. In Proceedings of the AMIA Symposium (p. 155). Medical Informatics Association. Compeau,D. R. & Higgins, C. A. (1995a). Application of social cognitive theory to training for computer skills. Information System Research, 6, 118-143. Compeau,D. R. & Higgins, C. A. (1995b). Computer self-efficacy: Development of a measure and initial test, MIS Quaterly, 19(2), 189-211. Chong, A. Y. L., Chan, F. T., & Ooi, K. B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34-43. Chong, A. Y. L., & Chan, F. T. (2012). Understanding the acceptance of RFID in the industry: extending the TAM Model. In Decision-Making for Supply Chain Integration (pp. 105-122). Springer London. Chen, M. C., Chen, S. S., Yeh, H. M., & Tsaur, W. G. (2016). The key factors influencing internet finances services satisfaction: An empirical study in Taiwan. American Journal of Industrial and Business Management, 6(06),748. Chitungo, S. K., & Munongo, S. (2013). Extending the technology acceptance model to mobile banking adoption in rural Zimbabwe. Journal of Business Administration and Education, 3(1). Churchill Jr, G. A. (1979). A paradigm for developing better measures of marketing constructs Journal of Marketing Research, 64-73. Chung, K. C., & Holdsworth, D. K. (2012). Culture and behavioural intent to adopt mobile commerce among the Y Generation: comparative analyses between Kazakhstan, Morocco and Singapore. Young Consumers, 13(3), 224-241. Damanpour, F. (1996). Organizational complexity and innovation: developing and testing multiple contingency models. Management science, 42(5), 693-716. Davidshofer, K. R., & Murphy, C. O. (2005). Psychological testing: principles and applications. Pearson/Prentice-Upper Saddle River, NJ. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51(1), 31–46 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), 982-1002. Dholakia, R.R. & Dholakia. N. (2004). Mobility and markets: emerging outlines of m-commerce. Journal of Business Research, 57(12), 1391-1396. Dillon, A. & Morris, M. (1996). User acceptance of new information technology: theories and models. Annual Review of Information Science and Technology, 31, 3-32. Downs Jr, G. W., & Mohr, L. B. (1976). Conceptual issues in the study of innovation. Administrative Science Quarterly, 700-714. Elliott, G., & Phillips, N. (2004). Mobile commerce and wireless computing systems. Pearson/Addison Wesley. Eze, U., Ten, M., & Poong, Y. (2011). Mobile commerce usage in malaysia assessing key determinants. Proceedings of the International Conference on Social Science and Humanity. (pp. 265–269).Singapore. Fang, T. (2012). Yin Yang: A new perspective on culture. Management and organization Review, 8(1), 25-50. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 3950. Faqih, K. M., & Jaradat, M. I. R. M. (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology:TAM3 perspective. Journal of Retailing and Consumer Services, 22, 37-52. Featherman, M. S., Miyazaki,A. D. & Sprott, D. E.(2010). Reducing online privacy risk to facilitate e-service adoption: The influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219-229 Field, A. P. (2009). Discovering statistics using SPSS (3 ed.). Thousand Oaks, CA: SAGE Publications Inc. Felton, G. W. (1995). Oxidative stress of vertebrates and invertebrates, Ahmad, Chapman and Hall, New York, 356-434 Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: and introduction to theory and research. Reading, MA: Addison-Wesley. Feng, H., Hoegler, T. and Stucky, W. (2006). Exploring the critical success factors for mobile commerce. Proceedings of the International Conference on Mobile Business, (pp. 40-46). Copenhagen, Denmark. Fong, K.K.K. & Wong, S.K.S (2015). Factor influencing the behaviour intention of mobile commerce service. Computer and Information Sciences, 8(1), 39-47. Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation. Information Systems Frontiers, 19(3), 525-548. Goi, C. L. (2016). M-Commerce: Perception of Consumers in Malaysia. The Journal of Internet Banking and Commerce. Gitau, L., Nzuki, D. (2014). Analysis of determinants of m-commerce adoption by online consumers. International Journal of Business, Humanities and Technology, 4(3),88-94 Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of e-banking services in Iran: The moderating role of age and gender. Middle-East Journal of Scientific Research, 12(6), 801-807. Goi, C. L., & Ng, P. Y. (2011). Perception of young consumers on mobile phone applications in Malaysia. World Applied Sciences Journal, 15(1), 47-55. Gopalakrishna, S.& Damanpour.F. (1997). A review of innovation research in economics, sociology and technology management. International Journal of Management Science, 25(1), 15-28. Green, E. G., Deschamps, J. C., & Paez, D. (2005). Variation of individualism and collectivism within and between 20 countries: A typological analysis. Journal of cross-cultural psychology, 36(3), 321-339. Grant,I. & O’Donohoe, S. (2007) Why young consumers are not open mobile marketing. International Journal of Advertising, 26(2), 223-246. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250-279. Hamilton, M. B. (2009). Online survey response rates and times: background and guidance for industry. Tercent, Inc. Retrieved Jun, 12, 2017 from www.supersurvey.com Hackbarth, G.; Grover, V.; and Yi, M.Y.(2003) Computer playfulness and anxiety: positive and negative mediatorsof the system experience effect on perceived ease of use. Information & Management, 40(3), 221-232. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. New Jersey: Pearson University Press. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Saddle River, NJ: Prentice-Hall International. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage. Hanson, M. J. (2005). An examination of ethnic differences in cigarette smoking intention among female teenagers. Journal of the American Academy of Nurse Practitioners, 17(4), 149-155. 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. Hashim, R., & Yusof, A. (1999). Internet in Malaysia. Retrieved January 10, 2012, from http://www.interasia.org/malaysia/hashim-yusof.html Haque, A. (2004). Mobile Commerce: customer perception and it's prospect on business operations in Malaysia. Journal of Financial Services Marketing, 8(3), 206-217. Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139. Hsu, M.-H. & Chiu, C.-M. (2004). Predicting electronic service continuance with a decomposed theory of planned behaviour. Behaviour & Information Technology,23(5), 359-373. Hsu, M. H., Yen, C. H., Chiu, C. M., & Chang, C. M. (2006). A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior. International Journal of Human-Computer Studies, 64(9), 889-904. Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators. Information Systems and e-Business Management, 10(4), 549-570. Hsieh,C.T.(2007).Mobile commerce: Assessing new business opportunities. Communications of the IIMA, 7(1), 87-100 Ho, C. T. B., Hsu, S. F., & Oh, K. B. (2009). Knowledge sharing: game and reasoned action perspectives. Industrial management & data systems, 109(9), 1211-1230. Hox,J.J & Bechger,T.M. (2007).An introduction to structural equation modelling. The British Journal of Psychiatry, 191 (1), 5-13. Hofstede, G. (1980). Culture and organizations. International Studies of Management & Organization, 10(4), 15-41. Hofstede, G., & Bond, M. H. (1988). The Confucius connection: From cultural roots to economic growth. Organizational dynamics, 16(4), 5-21. Hofstede, G., Hofstede, G.J. and Minkov, M. (2010). Cultures and organizations: Software of the mind. 3rd Edition. McGraw-Hill, New York. Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online readings in psychology and culture, 2(1), 8. Hung, C. L., Chou, J. C. L., Chung, R. Y., & Dong, T. P. (2010). A cross-cultural study on the mobile commerce acceptance model. In Management of Innovation and Technology (ICMIT), 2010 IEEE International Conference on (pp. 462-467). IEEE. Huang, T. C. K., Liu, C. C., & Chang, D. C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257-270. Islam, M. A., Ahmad, T. S. B., Khan, M. A., & Ali, M. H. (2010). Adoption of Mcommerce services: The case of Bangladesh. World Journal of Management, 2(1), 37-54. Jane,O. (2003). Some problems with social cognition models: A pragmatic and conceptual analysis. Health Psychology, 22(4), 424-428. Jaradat., M. R. M. & Al- Rababaa. M. S. (2013). Assessing key factor that influence on the acceptance of mobile commerce based on modified UTAUT. International Journal of Business and Management, 8(23), 102-112. Jahangir N. & Begum N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African Journal of Business Management, 2 (1), 032-040. Jehangir, M., Dominic, P. D. D., Naseebullah, & Khan, A. (2011). Towards digital economy: The development of ICT and E-commerce in Malaysia. Modern Applied Science, 5(2), 171-178. Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & management, 35(4), 237-250. Karahanna, E., Evaristo, J. R., & Srite, M. (2006). Levels of culture and individual behavior: An integrative perspective. Advanced Topics in Global Information Management, 5(1), 30-50. Karsten, R., & Roth, R. M. (1998). Computer self-efficacy: A practical indicator of student computer competency in introductory IS courses. Informing Science, 1(3), 61-68. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610. Kiesler, C. A. & Kiesler, S. B. (1969). Conformity. Addison,Wesley, Reading, MA. Conformity. Reading, MA: Addison-Wesley. Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: Guilford Press. Kieras, D. E. & Polson, P. G. (1985). An approach to the formal analysis of user complexity. International Journal of Man-Machine Studies, 22, 365-394. Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision support systems, 43(1), 111-126. Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323-333. Kim, K., Kim, G. M., & Kil, E. S. (2009). Measuring the compatibility factors in mobile entertainment service adoption. Journal of Computer Information systems, 50(1), 141-148. 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. Koufaris, M. (2002).Applying the technology acceptance model and flow theory to online consumer behaviour. Information Systems Research,13(2), 205-223 Khalifa, M. & Shen, K.N. (2008). Drivers for transactional B2C m-commerce adoption: Extended theory of planned behavior. Journal of Computer Information System,48(3),111-117. Kotler, P. (2003) Marketing Management: Analysis, Planning, Implementation and Control 11th edition. Englewood Cliffs, NJ: Prentice Hall Lenhart, A., & Madden, M. (2007). Teens, Privacy and Online Social Networks: How Teens Manage their Online Identities and Personal Information in the Age of MySpace. Pew Internet and American Life Project. Washington DC. Lin, L. C. (2005). Internet as a distribution channel of travel information: A case study. Consortium Journal of Hospitality & Tourism, 9(2). Lin, H.-F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433-442 Liska,A.E.(1984). A critical examination of the causal structure of the Fishbein- Ajzen model. Social Psychology Quarterly, 47(1), 61-74. Leung,C.H., Chan,Y.Y.& Chan,C.S.C (2003). Analysis of mobile commerce market in Hong Kong. Proceedings of the 5th international conference on Electronic commerce,(pp.408-412).Pittsburgh, Pennsylvania. Loher, B. T., Noe, R. A., Moeller, N. L., & Fitzgerald, M. P. (1985). A meta-analysis of the relation of job characteristics to job satisfaction. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in human behavior, 21(6), 873-891. Lu, X., & Viehland, D. (2008). Factors influencing the adoption of mobile learning. ACIS 2008 Proceedings, 56. Muthaiyah, S. (2004). Key success factors of 3rd generation mobile network services for m-commerce in Malaysia. American Journal of Applied Sciences, 1(4), 261-265. Marcoux, B. C., & Shope, J. T. (1997). Application of the theory of planned behavior to adolescent use and misuse of alcohol. Health Education Research, 12(3), 323-331 Maamar, Z. (2003). Commerce, E-commerce, and M-commerce: What comes next? Communications of the ACM, 46(12), 251-257. MOE (2013).National Education Statistic: Higher Education Sector. Ministry of Education Malaysia, (1ed.) Perpustakaan Negara Malaysia. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. SIGMIS Database, 32(3), 86-112. Mani, Y. (2015). Communications & multimedia pocket book of statistics (Web Document). Available:http://www.skmm.gov.my/skmmgovmy/media/General/pdf/CMQ2-2015-BI-%28pdf%29.pdf (2015, 7 August) Mohammed, M.R. (2013).Barriers to M-commerce adoption in developing countries- A qualitative study among the stakeholders of Bangladesh. International Journal of Technology Management, 3(2), 80-91. 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. Moorty, M. K., Sann, C. W., Ling, C. Y., Yin, T. P., Yan, W. K., & Ee, Y. Y. (2014). Adoption of Mobile Commerce in Malaysia-A Generation Y Perception. International Journal of Research, 1(8), 825-845. Min, Q., Ji, S., &Qu, G. (2008). Mobile commerce user acceptance study in China: a revised UTAUT model. Journal of Tsinghua Science & Technology, 13(3), 257–264 Md. Aminul. I, Tunku. S.A.,Mohammad. A. K. & Mohammad H. A.(2010). Adoption of M-commerce services: the case of Bangladesh. World Journal of Management, 2(1), 37-54. MCMC (2013).Reinforcing Basics for connected services: Industry performance report. Retrieved from http://www.skmm.gov.my/skmmgovmy/media/General/pdf/IPR2013_English.pdf MCMC (2015). Public consultation paper: review of rates rules. Retrieved from http://www.skmm.gov.my/skmmgovmy/media/General/pdf/PCPaperRatesRules.pdf MCMC (2016). Internet users survey 2016. Retrieved from https://www.mcmc.gov.my/resources/statistics/internet-users-survey Mohd Fuaad, S.,Khairul. A.A & Farzana. Q.(2013). Strategic posturing of Malaysian mobile phone service providers. Journal Social Sciences & Humanities, 21(S), 1-36 Moore, G. C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192-222. Monzavi, T., Zarei, B., & Ghapanchi, A. H. (2013). Investigating the Impact of External Factors on User Perceptions: A Case Study of Software Adoption in Middle East. The International Technology Management Review, 3(3), 160-174. Muhamad, N. (2008). Muslim consumers' motivation towards Islam and their cognitive processing of performing taboo behaviors (Doctoral dissertation, University of Western Australia). Namahoot, K. S., & Laohavichien, T. (2015). An Analysis of Behavioral Intention to use Thai Internet Banking with Quality Management and Trust. The Journal of Internet Banking and Commerce, 20(3). Nunnally, J. C., & Bernstein, I. H. (1994).Psychometric theory (3rd ed.).New York: McGraw-Hill Noordin, M. F., & Sadi, A. H. M. (2010). The adoption of mobile commerce in Malaysia: an exploratory study on the extension of theory of planned behavior. Journal of Busines Analyst, 31(1), 1-30. Ngai, E. W. T., and Gunasekaran, A. (2007). A review for mobile commerce research and applications. Decision Support System, 43(1), 3-15. Njuguna, P. K., Ritho, C., Olweny, T., & Wanderi, M. P. (2012). Internet banking adoption in Kenya: The case of Nairobi County. International Journal of Business and Social Science, 3(18). NITC Malaysia (1996). National IT Agenda – NITA. Retrieved from http://nitc.kkmm.gov.my/index.php/national-ict-policies/national-it-agenda- Nielson Global E-commerce and the New Retail (2015) Percent using/willing to use E-commerce Options. Retrieved from http://www.nielsen.com/content http://www.nielsen.com/content/dam/nielsenglobal/vn/docs/Reports/2015/Nielson Nemat, R. (2011). Taking a look at different types of e-commerce. World Applied Programming, 1(2), 100-104. Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729-733. Panis, S., Morphis, N., Felt, E., Reufenheuser, B., Bohm, A., Nitz, J., & Saarlo, P. (2002). Mobile commerce service scenarios and related business models. Eurescom project P, 1102. Pavlou, P. A. & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143 Puschel, J., Mazzon,J. A.,& Hernandez,J. M. C.(2010).Mobile banking: Proposition of an integrated adoption intention framework. International Journal of Bank Marketing, 28(5), 389-409 Polson, P. G. (1987). A Quantitative Theory of Human-Computer Interaction, Interfacing thought: Cognitive Aspects of Human-Computer Interaction. MIT Press, Cambridge, MA, 184-235. Plsek, P. E., & Greenhalgh, T. (2001). The challenge of complexity in health care. British Medical Journal, 323(7313), 625. Priyambodo,L., Tjiptono.F. & Suyoto (2012), M-commerce in indonesia: problems and prospects.International Journal of Computer Applications & Information Technology,1(2),71-76. 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. Rao. B.& Minakakis. L. (2003). Evolution of Mobile Location-based Services. Communication of the ACM, 46(12), 61-65. Rammile,N.&Nel,J.(2012).Understanding resistance to cell phone banking adoption through the application of the technology acceptance model (TAM). African Journal of Business Management, 6 (1), 86-97. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), 3-14 Robertson, M., Swan, J., & Newell, S. (1996). The Role of Networks in the Diffusion of Technological Innovation. Journal of Management Studies, 33(3), 333-359. Rogers, Everett M. (1983). Diffusion of Innovations. New York: Free Press. Rogers Everett, M. (1995). Diffusion of innovations. New York, 12. Rogers, E.M. (2003). Diffusion of innovations (3th ed.). New York: The Free Press, A division of Macmillan Publishing Co. Inc. Rouibah, K., Ramayah, T., & May, O. S. (2009). User acceptance of internet banking in Malaysia: Test of three acceptance models. International Journal for EAdoption, 1(1), 1-19. Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self- efficacy. Journal of Information Technology Education, 8. Sam, H. K., Othman, A. E. A., Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the Internet: A study among undergraduates in Unimas. Educational Technology & Society, 8(4), 205-219. Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5 ed.). Essex, England: Prentice Hall Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling (2 ed.). Mahwah, NJ: Lawrence Erlbaum. Sekaran, U. (2003). Research method for business: a skill building approach (4th ed.). Singapore: John Wiley & Sons. Sekaran, U., & Bougie, R. (2009). Research Methods for Business a skill Building Approach. West Sussex, England: John Wiley & Sons. Sekaran, U., & Bougie, R. (2010). Research methods for business: a skill building approach (5th ed.). Chichester: John Willey & Sons Ltd. Seong, L. C., Sik, S. H., & Seong, K. D. (2004). A classification of mobile business models and its applications. Industrial Management & Data Systems, 104(1), 78-87. Segar, A.H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quaterly, 17, 517-526. Shih, Y. Y., & Fang, K. (2004). The use of decomposed theory of planned behavior to study internet banking in Taiwan. Internet Research, 14(3), 213-223 Samuelsson, M., & Dholakia, N. (2003). Assessing the market potential of network enabled 3G m-business services. Wireless communications and mobile commerce, 23-48. Sadi, A. S., and Noordin, M. F. (2011). Factors influencing the adoption of mcommerce: An exploratory Analysis. Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, (pp.492-499). Sumita, U., and Yoshii, J. (2010). Enhancement of e-commerce via mobile accesses to the Internet. Electronic commerce research and applications, 9(3), 217–227. Silveira.G.J.C., (2003). Towards a framework for operations management in ecommerce International Journal of Operations & Production Management, 23(2), 200-212. Shahizan, H.(2012). Trends and perceived impact of social media for business in Malaysia. Malaysian Communications and Multimedia Commission, 7(1) Shin, D.-H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354. Saifullah Sadi AHM, Noordin MF (2011) Factors Influencing the Adoption of MCommerce: An Exploratory Analysis. 2011 International Conference on Industrial Engineering and Operations Management. Kuala Lumpur, Malaysia. Schepers,J. & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigation subjective norm and moderation effects. Information Management, 44(1), 90-103. Straub, D., Loch, K., Evaristo, R., Karahanna, E., & Srite, M. (2002). Toward a theory-based measurement of culture. Human factors in information systems, 10(1), 61-65. Stafford,T.F. & Gillenson.M.L (2003). Mobile commerce: what it is what it could be?. Communications of the ACM, 46 (12), 33-34. Sreenivasan J, Mohd Noor MN (2010) A Conceptual Framework on Mobile Commerce Acceptance and Usage among Malaysian Consumers: The Influence of Location, Privacy, Trust and Purchasing Power. WSEAS Transactions on Information Science and Applications 5: 661-670. Schwiderski-Grosche, S. & Knospe, H. (2002). Secure mobile commerce. Journal of Electronics and Communication Engineering, 12(5), 228-238. Shahizan, H.,Rashdan. R., & Feng L. (2015). Utilising modified utaut to understand students' online shopping behaviour: a case of e-retail co-operative website in Malaysia. Journal of Electronic Commerce in Organizations (JECO), 13(4), 74-90. Shahizan, H., Norshuhada, S., Norlaily, H., NL, Sobihatun, NAS, & Mohd Samsu, S. (2012). Persuasive as social media technology for business: trends and perceived impact in malaysia. Sintok, Kedah: Universiti Utara Malaysia. Somekh, B., & Lewin, C. (2005). Research methods in the social sciences. Thousand Oaks,CA: Sage Publications Ltd. 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. Srite, M., Karahanna, E., (2006). The influence of national culture on the acceptance of information technologies: an empirical study. MIS Q. 30 (3), 679–704. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 111-147 Sun, H., Zhang, P., (2006). The role of moderating factors in user technologyacceptance. International Journal Human Computing Study. 64 (2), 53–78. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5 ed.). Boston, MA: Pearson Education. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. Temme, D., Kreis, H., & Hildebrandt, L. (2006). PLS path modeling: A software review (No. 2006, 084). SFB 649 discussion paper. Taylor, S., Todd, P. (1995). Understanding information technology usage: a test of competing models. Information Systems Research,6(2), 144-176. Taylor, S. and Todd, P.A (1995b) .Understanding lnformation Technology Usage: A Test of Competing Models. lnformation Systems Research ,6(2), 144-1 76. Taylor, S. and Todd, P.A. (1995a).Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561-70. The Star (2013).Snap, list and Carou-sell.(Web Document). Retrieved from http://www.thestar.com.my/News/Nation/2013/12/04/Snap-list-and-Carousell The Star (2013).Budget 2013: Tax breaks, affordable housing and cash for the needy. Retrieved from http://www.thestar.com.my/News/Nation/2012/09/28/Budget- 2013 Tax-breaks-affordable-housing-and-cash-for-the-needy Teo, T.S.H., Pok, S.H., (2003). Adoption of WAP-enabled mobile phones among Internet users. Omega 31 (6), 483–498. Valkenburg, P. M., Peter, J., & Schouten, A. P. (2006). Friend networking sites and their relationship to adolescents' well-being and social selfesteem. CyberPsychology & Behavior, 9(5), 584-590. Van Slyke, C., Belanger, F., Sridhar, V., (2005). A comparison of American and Indian consumers’ perceptions of electronic commerce. Inf. Resour. Manage. J. 18 (2),24–40. Van Slyke, C., Lou, H., Belanger, F., Sridhar, V., (2010). The influence of culture on consumer-oriented electronic commerce adoption. J. Electron. Comm. Res. 11(1), 30–40. VanderStoep, S. W., & Johnson, D. D. (2009). Research Methods for Everyday Life. San Francisco, CA: Jossey-Bass Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(4), 342–365 Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478 Varshney, U. & Vetter, R. (2002). Mobile Commerce: Framework, applications and networking support. Mobile Networks and Applications, 7(3), 185-198. Venkatesh, V& Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Journal of the Decision Sciences Institute, 39(2), 273–315. Venkatesh, V., & Davis, F.D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences Journal, 27(3), 451-481. Venkatesh, V., Morris, M.G. & Ackerman, P. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision making processes. Organizational Behavior and Human Decision Processes, 83(1), 33-60. Venkatesh, V., Morris, M.G., Davis, G.B. & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157-179. Wang, J., & Lei, P. (2007). Mobile Commerce. Taniar, David, Encyclopedia of Mobile Computing and Commerce, Hershey, Ed. IGI Global, 455-460. Wang, Y.S., & Shih, Y.W. (2009). Why do people use information kiosks? a validation of the unified theory of acceptance and use of technology. Government Information Quarterly, vol.26, pp.158–165. 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). Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS quarterly, 201-226. Wei, T.T., Marthandan, G., Chong, A.Y.L., Ooi, K.B. & Arumugam, S. (2009).What drives Malaysian m-commerce adoption? An empirical analysis. Journal of Industrial Management &Data Systems, 109(3), 370-388. Wetzels, M., Odekerken-Schroder, G., & Van-Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. Management Information Systems Quarterly, 33(1), 11. Wolfe, R. A. (1994). Organizational innovation: Review, critique and suggested research directions. Journal of management studies, 31(3), 405-431. Wong, C. K. (2014). Understanding mobile users, m-commerce, m-payment in Malaysia. Retrieved Jun, 2017, from http://www.ecommercemilo.com/2014/03/mobileusersmcommercempaymentmalaysia.html#.U-y-qvmSyD8 ) Wu. A. D., Li, Z., & Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment, Research and Evaluation, 12(3), 1-26. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation the revised technology acceptance model. Information & management, 42(5), 719-729. Woodrow, J. E. (1991). A comparison of four computer attitude scales. Journal of Educational Computing Research, 7(2), 165-187. Wood, R., & Bandura, A. (1989). Social cognitive theory of organizational management. Academy of management Review, 14(3), 361-384. Zendehdel, M., & Paim, L. H. (2016). Predicting Intention of Mobile Internet Usage in Malaysia: Extending the Unified Theory of Acceptance and Use of Technology. Xanthidis. D. & Nicholas, D. (2004). Evaluating internet usage and ecommerce growth in Greece. Aslib Proceedings: New Information Perspectives. 56(6), 356-366 Xue, S. (2005). Internet policy and diffusion in China, Malaysia and Singapore. Journal of Information Science, 31(3), 238-250. Yap, C. S., & Hii, J. W. H. (2009). Factors Affecting the Adoption of Mobile Commerce in Malaysia. IUP Journal of Information Technology, 5(3). Yang, K., (2005). Exploring factors affecting the adoption of mobile commerce in Singapore, Telematics and Informatics, 22(3), 257-277. Yang, K. (2010). The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers. Journal of International Consumer Marketing, 22(2), 117-127. Yager, S. E., Kappleman, L. A., Maples, G. A., & Prybutok, V. R. (1997). Microcomputer playfulness: Stable or dynamic trait. Data Base, 28(2), 43-51. Yaseen. S.G & Zayed. S. (2010). Exploring critical determinants in deploying mobile commerce technology, American Journal of Applied Sciences. 7(1), 120-126 tYing-chen, L. M., & Kinzie, M. B. (2000). Computer technology training for prospective teachers: Computer attitudes and perceived self-efficacy. Journal of Technology and Teacher Education, 8(4), 373-396. Yoon, C. (2009). The effects of national culture values on consumer acceptance of e-commerce: online shoppers in China. Inf. Manage. 46 (5), 294–301. Zikmund, W., Babin, B.J.(2007).Exploring marketing research,(9th ed.),Thomson Learning,Ohio Zhang. J.J., Yuan, Y. & Archer,N.(2002). Driving forces for m-commerce success. Journal of Internet commerce, 1(3), 81-104. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in human behavior, 28(5), 1902-1911. Zendehdel, M., & Paim, L. (2015). predicting intention of mobile internet usage in malaysia: extending the unified theory of acceptance and use of technology. Taylor's Business Review, 5(1), 81-97.

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