Long term evolution mobile services and intention to adopt: a Malaysian perspective

References

• Aarts, H., Verplanken, B., & Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit?Journal of Applied Social Psychology, 28, 1355–1374.
   Web of Science ®Google Scholar
• Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204–215.
   Web of Science ®Google Scholar
• Aladwani, A. M., & Palvia, P. C. (2002). Developing and validating an instrument for measuring user-perceived web quality. Information & Management, 39, 467–476.
   Web of Science ®Google Scholar
• Amin, H. (2009). An analysis of online banking usage intentions: An extension of the Technology Acceptance Model. International Journal Business & Society, 10, 27–40.
   Google Scholar
• Bell, G. (2006). The age of the thumb: A cultural reading of mobile technologies from Asia. Knowledge, Technology & Policy, 19, 41–57.
   Google Scholar
• Birke, D., & Swann, G. P. (2006). Network effects and the choice of mobile phone operator. Journal of Evolutionary Economics, 16, 65–84.
   Web of Science ®Google Scholar
• Buss, D. M. (1987). Selection, evocation, and manipulation. Journal of Personality and Social Psychology, 53, 1214–1218.
   PubMed Web of Science ®Google Scholar
• Chan, J. (2011, January 21). Smartphones take Lion's share of RM4.5b sector. New Straits Times. Retrieved from http://www.nib.com.my/cross-search/_1390357373/.
   Google Scholar
• Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39, 705–719.
   Web of Science ®Google Scholar
• Cheong, J. H., & Park, M. C. (2005). Mobile internet acceptance in Korea. Internet Research, 15, 125–140.
   Web of Science ®Google Scholar
• Chong, A. Y. L. (2013). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40, 523–530.
   Web of Science ®Google Scholar
• Chong, A. Y. L., Darmawan, N., Ooi, K. B., & Lee, V. H. (2011). Determinants of 3G adoption in Malaysia: A structural analysis. Journal of Computer Information Systems, 51, 71–80.
   Web of Science ®Google Scholar
• Chong, A. Y. L., Darmawan, N., Ooi, K. B., & Lin, B. (2010). Adoption of 3G services among Malaysian consumers: An empirical analysis. International Journal of Mobile Communications, 8, 129–149.
   Web of Science ®Google Scholar
• Constantiou, I. D. (2009). Consumer behavior in the mobile telecommunications' market: The individual's adoption decision in innovative service. Telematics and Informatics, 26, 270–281.
   Google Scholar
• Dahlman, E., Parkvall, S., Skold, J., & Beming, P. (2010). 3G evolution: HSPA and LTE for mobile broadband(2nd ed.). Amsterdam: Elsevier.
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
   Web of Science ®Google Scholar
• Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17, 4–11.
   Web of Science ®Google Scholar
• El-Gayar, O., Moran, M., & Hawkes, M. (2011). Students' acceptance of tablet PCs and implications for educational institutions. Journal of Educational Technology & Society, 14, 58–70.
   Web of Science ®Google Scholar
• Field, A. (2005). Discovering statistics using SPSS. London: Sage.
   Google Scholar
• Govil, J. (2007, November). 4G mobile communication systems: Turns, trends and transition. In Convergence Information Technology, 2007. International Conference on Convergence Information Technology (pp. 13–18). Gyongju: IEEE.
   Google Scholar
• Hill, R., & Troshani, I. (2007). A conceptual framework and propositions for the acceptance of mobile services. Journal of Theoretical and Applied Electronic Commerce Research, 2, 61–73.
   Google Scholar
• Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41, 853–868.
   Web of Science ®Google Scholar
• Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended Technology Acceptance Model. The Electronic Library, 25, 585–598.
   Web of Science ®Google Scholar
• Hung, S. Y., Ku, C. Y., & Chang, C. M. (2003). Critical factors of WAP services adoption: An empirical study. Electronic Commerce Research and Applications, 2, 42–60.
   Google Scholar
• Ifenthaler, D., & Schweinbenz, V. (2013). The acceptance of Tablet-PCs in classroom instruction: The teachers' perspectives. Computers in Human Behavior, 29, 525–534.
   Web of Science ®Google Scholar
• 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, 103–110.
   Web of Science ®Google Scholar
• Lapin, L. L. (1993). Statistics for modern business decisions (6th ed.). San Diego, CA: Harcourt Brace Jovanovich.
   Google Scholar
• Li, H., & Lai, M. M. (2011). Demographic differences and internet banking acceptance. MIS Review, 16, 55–92.
   Google Scholar
• Liao, C. H., Tsou, C. W., & Huang, M. F. (2007). Factors influencing the usage of 3G mobile services in Taiwan. Online Information Review, 31, 759–774.
   Web of Science ®Google Scholar
• Liao, Z., & Cheung, M. T. (2011). Internet-based e-shopping and consumer attitudes: an empirical study. Information and Management, 38, 299–306.
   Web of Science ®Google Scholar
• Lin, F. T., Wu, H. Y., & Nga, T. T. N. (2013, September). Adoption of Internet banking: An empirical study in Vietnam. In E-Business Engineering (ICEBE), 2013 IEEE 10th International Conference on (pp. 282–287). Coventry: IEEE.
   Google Scholar
• Lu, J., Liu, C., Yu, C. S., & Wang, K. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45, 52–64.
   Web of Science ®Google Scholar
• 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, 245–268.
   Web of Science ®Google Scholar
• Lu, J., Yu, C. S., Liu, C., & Yao, J. E. (2003). Technology Acceptance Model for wireless Internet. Internet Research, 13, 206–222.
   Web of Science ®Google Scholar
• Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38, 217–223.
   Web of Science ®Google Scholar
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
   Google Scholar
• Ong, J. W., Poong, Y. S., & Ng, T. H. (2008). 3G services adoption among university students: Diffusion of innovation theory. Communications of the IBIMA, 3, 114–121.
   Google Scholar
• Pagani, M. (2004). Determinants of adoption of third generation mobile multimedia services. Journal of Interactive Marketing, 18, 46–59.
   Google Scholar
• Pagani, M., & Fine, C. H. (2008). Value network dynamics in 3G–4G wireless communications: A systems thinking approach to strategic value assessment. Journal of Business Research, 61, 1102–1112.
   Web of Science ®Google Scholar
• Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31, 3–15.
   Web of Science ®Google Scholar
• Park, E., & Del Pobil, A. P. (2013). Modeling the user acceptance of long-term evolution (LTE) services. Annals of Telecommunications – annales des télécommunications, 68, 307–315.
   Web of Science ®Google Scholar
• Park, E., & Kim, K. J. (2013). User acceptance of long term evolution (LTE) services. An Application of Extended Technology Acceptance Model, 47, 188–205.
   Google Scholar
• Pedersen, P. E. (2005). Adoption of mobile Internet services: An exploratory study of mobile commerce early adopters. Journal of Organizational Computing and Electronic Commerce, 15, 203–222.
   Web of Science ®Google Scholar
• Peng, G., Fan, M., & Dey, D. (2011). Impact of network effects and diffusion channels on home computer adoption. Decision Support Systems, 51, 384–393.
   Web of Science ®Google Scholar
• Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59, 999–1007.
   Web of Science ®Google Scholar
• Rippin, H. (2005). The mobile phone in everyday life. Fast Capitalism, 1(1). Retrieved from http://www.fastcapitalism.com/1_1/rippin.html.
   Google Scholar
• Sekaran, U. (2006). Research methods for business: A skill building approach. New York, NY: Wiley.
   Google Scholar
• Shih, Y. Y., & Chen, C. Y. (2013). The study of behavioral intention for mobile commerce: Via integrated model of TAM and TTF. Quality & Quantity, 47, 1009–1020.
   Web of Science ®Google Scholar
• Shin, D., & Choo, H. (2011). Modeling the acceptance of socially interactive robotics: Social presence in human–robot interaction. Interaction Studies, 12, 430–460.
   Web of Science ®Google Scholar
• Smith, C. (2007). 3G wireless networks (2nd ed.). New York, NY: McGraw-Hill.
   Google Scholar
• Strader, T. J., Ramaswami, S. N., & Houle, P. A. (2007). Perceived network externalities and communication technology acceptance. European Journal of Information Systems, 16, 54–65.
   Web of Science ®Google Scholar
• Teng, W., Lu, H. P., & Yu, H. (2009). Exploring the mass adoption of third-generation (3G) mobile phones in Taiwan. Telecommunications Policy, 33, 628–641.
   Web of Science ®Google Scholar
• Teo, T. S., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among Internet users. Omega, 31, 483–498.
   Web of Science ®Google Scholar
• Tseng, F. M., & Lo, H. Y. (2011). Antecedents of consumers' intentions to upgrade their mobile phones. Telecommunications Policy, 35, 74–86.
   Web of Science ®Google Scholar
• Van Slyke, C., Ilie, V., Lou, H., & Stafford, T. (2007). Perceived critical mass and the adoption of a communication technology. European Journal of Information Systems, 16, 270–283.
   Web of Science ®Google Scholar
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342–365.
   Web of Science ®Google Scholar
• Wang, R., Pan, T., & Cao, Y. (2011, May). Users' acceptance behavior for m-commerce based on Customer Perceived Value and TAM. In E-Business and E-Government (ICEE), 2011 International Conference on (pp. 1–4). Coventry: IEEE.
   Google Scholar
• Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42, 719–729.
   Web of Science ®Google Scholar
• Yang, K. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22, 257–277.
   Google Scholar
• Yoo, B., & Donthu, N. (2001). Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL). Quarterly Journal of Electronic Commerce, 2, 31–45.
   Google Scholar