EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 18 (2015) SHS Web of Conferences, 18 (2015) 01009 References
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. [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. [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. [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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.