Open Access
Issue
SHS Web of Conferences
Volume 18, 2015
ICoLASS 2014 – USM-POTO International Conference on Liberal Arts & Social Sciences
Article Number 01009
Number of page(s) 9
Section Economy and Sustainability
DOI https://doi.org/10.1051/shsconf/20151801009
Published online 10 July 2015
  1. Agarwal, R. and E. Karahanna (2000). Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage, MIS Quarterly. Vol (24)4, pp. 665–694. [CrossRef] [Google Scholar]
  2. Balasubramanian, S., Peterson, R. A. and Javenpaa, S. L. (2002). Exploring the of M-Commerce for markets and marketing, Journal of the Academy of Marketing Science, Vol. 30(4), pp. 348–361. [CrossRef] [Google Scholar]
  3. Biljon, J., and Kotze, P., (2007). Modelling the factors that influence mobile phone adoption. ACM International Conference Proceeding Series, 226, pp. 152–161. [Google Scholar]
  4. Blythe, M., Hassenzahl, M. and Wright, P. (2004). More funology. Interactions, Vol. 11 (5), p. 37. [Google Scholar]
  5. Burdett, D. (1999). The true cost of e-purse – A Mondex Perspective. European Business Review. Vol. 99 (4), pp. 265–270. [CrossRef] [Google Scholar]
  6. Byrne, B. M. (1998). Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming. New Jersey: Lawrence Erlbaum Associates. [Google Scholar]
  7. Cheah, C. M., Oon K. M.,Teo A. C., Tan B. I. and Sim J. J., (2011). Factors affecting malaysian mobile banking adoption: An empirical analysis. International Journal of Network and Mobile Technologies, Vol. 2(3), pp. 149–160. [Google Scholar]
  8. Cheema, U., Rizwan, M., Jalal, R., Durrani, F., Sohail, N., (2013). The trend of online shopping in 21st century: Impact of enjoyment in TAM Model. Asian Journal of Empirical Research. Vol. 3(2), pp. 131–141. [Google Scholar]
  9. Cooper, D. R., and Schindler, P. S., (2008). Business Research Methods (10th ed.). New York: McGraw-Hill. [Google Scholar]
  10. Davis, F.D., (1986). A Technology Acceptance Model For Empirically Testing New End-User Information Systems: Theory And Results. Massachusetts, United States: Sloan School of Management, Massachusetts Institute of Technology. [Google Scholar]
  11. Davis, F. D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. Vol. 13(3), pp. 319–340. [CrossRef] [Google Scholar]
  12. Davis F.D., Bogozzi, R. P., and Warshaw, P. R., (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science. Vol. 35, pp. 982–1003. [CrossRef] [Google Scholar]
  13. Guriting, P. and Ndubisi, N. O., (2006). Borneo online banking: Evaluating customer perceptions and behavioral intention. Management Research News, Vol. 29 (1/2), pp. 6–15. [CrossRef] [Google Scholar]
  14. Hair, J., Black, W., Babin, B. Y. A., Anderson, R., & Tatham, R. (2010). Multivariate Data Analysis (7th ed.). New Jersey: Pearson Prentice Hall. [Google Scholar]
  15. Henderson, R. and Divett., M. J., (2003). Perceived usefulness, ease of use and electronic supermarket use. International Journal of Human-Computer Studies. Vol. 59, pp. 383–395. [CrossRef] [Google Scholar]
  16. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. Vol. 6(1), pp. 1–55. [CrossRef] [Google Scholar]
  17. Hultman, J., (2005). The moment of truth: a key to successful services marketing. INTI Journal. Vol. 1(5), pp. 363–370. [Google Scholar]
  18. Kalakota R. and Whinston, A.B., (1996). Frontiers to Electronic Commerce. Reading, MA: Addison Wesley. [Google Scholar]
  19. Krejcie, R., and Morgan, D. W., (1970). Determining sample size for research activities. Educational and Psychological Measure. Vol. 30, pp. 607–610. [Google Scholar]
  20. Kline, R. B., (2011). Principles And Practice Of Structural Equation Modelling (3rd ed.). New York: Guilford Press. [Google Scholar]
  21. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior, Information Systems Research. Vol. 13(2), pp. 205–223. [CrossRef] [Google Scholar]
  22. Liao, C., Tsou, C., and Shu, Y., (2008). The roles of perceived enjoyment and price perception in determining acceptance of multimedia-on-demand. International Journal of Business and Information. Vol. 3(1), pp. 27–52. [Google Scholar]
  23. Malaysia Census Report (2010). The 2010 Population and Housing Census of Malaysia. Retrieved 30 June 2013, from http://www.statistics.gov.my/portal/index.php?option=com_content&view=article&id=1215%3Apopulation-distribution-and-basic-demographic-characteristic-report-population-and-housing-census-malaysia-2010-updated-2972011&catid=130%3Apopulation-distribution-and-basic-demographic-characteristic-report-population-and-housing-census-malaysia-2010&lang=en. [Google Scholar]
  24. Moon, J. W., and Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management. Vol. 38, pp. 217–230. [CrossRef] [Google Scholar]
  25. Oppenheim, A.N., (1992). Questionnaire Design, Interviewing And Attitude Measurement (2nd ed.). London and New York: Continuum International Publishing. [Google Scholar]
  26. Pagani, M., (2004). Determinants of adoption of third generation mobile multimedia services. Journal of Interactive Marketing. Vol. 18(3), pp. 46–59. [CrossRef] [Google Scholar]
  27. Phan, K., and Diam, T., (2011). Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management. Vol. 4(2), pp. 339–360. [CrossRef] [Google Scholar]
  28. Schwartz, E.I., (2001). Digital cash payoff. Technology Review. Vol. 104(10), pp. 62–68. [Google Scholar]
  29. Sekaran, U. (2003), Research Methods for Business: A Skill-Building Approach. 4th ed. USA: John Wiley and Sons, Inc. [Google Scholar]
  30. Shen, J., (2012). Social comparison, social presence, and enjoyment in the acceptance of social shopping websites. Journal of Electronic Commerce Research, Vol. 13 (3), pp. 198–212. [Google Scholar]
  31. Shih, H.P. (2004). Extended technology acceptance model of Internet utilization behaviour. Information and Management. Vol. 41(6), pp. 719–729. [CrossRef] [Google Scholar]
  32. Sumanjeet, S., (2009). Emergence of payment systems in the age of electronic commerce: The state of art. Global Journal of Business Research. Vol. 2(2), pp. 17 – 36. [Google Scholar]
  33. Sun, H., and Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: an alternative approach. Journal of the Association for Information Systems. Vol. 7(9), pp. 618–645. [Google Scholar]
  34. Wheaton, B., Muthen, B., Alwin, D. F., and Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology. Vol. 8(1), pp. 84–136. [CrossRef] [Google Scholar]
  35. Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics. Boston: Pearson Education Inc. [Google Scholar]
  36. Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in the Netherlands, Information & Management. Vol. 40(6), pp. 541–549. [CrossRef] [Google Scholar]
  37. Venkatesh, V. (1999) Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly. Vol. 23(2), pp. 239. [CrossRef] [Google Scholar]
  38. Venkatesh, V. and Davis, F. D., (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science. Vol. 46 (2), pp. 186–204. [CrossRef] [Google Scholar]
  39. Wong, S.L., & Teo, T. (2009). Investigating the technology acceptance among student teachers in Malaysia: An application of the Technology Acceptance Model (TAM). The Asia-Pacific Education Researcher. Vol. 18(2), pp. 261–272. [CrossRef] [Google Scholar]
  40. Wu, J.H. and Wang S.C., (2005). What drives mobile commerce? An empirical evaluation of the revised Technology Acceptance Model. Information & Management. Vol. 42, pp. 719–729. [CrossRef] [Google Scholar]
  41. Yi, M., Y., Y. Hwang, (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the Technology Acceptance Model. International Journal of Human-Computer Studies. Vol. 59, pp. 431–449. [CrossRef] [Google Scholar]
  42. Yu, J., Ha, I., Choi, M. and Rho, J., (2005). Extending the TAM for a T-Commerce. Information & Management. Vol. 42(7), pp. 965–976. [CrossRef] [Google Scholar]

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