Antecedents of intention and usage toward customers’ mobile commerce: Evidence in Vietnam

References

• Agarwal, R. & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665.10.2307/3250951
   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, 204–215.10.1287/isre.9.2.204
   Web of Science ®Google Scholar
• Atkinson, M. & Kydd, C. (1997). Individual characteristics associated with world wide web use: an empirical study of playfulness and motivation. Data Base For Advances In Information Systems, 28, 53–62.10.1145/264701
   Web of Science ®Google Scholar
• Babin, B. J., Darden, W. R., Griffin, M., & Darden, R. (1994). Work and/or Fun : Shopping Measuring Value Hedonic and Utilitarian. Journal of Consumer Research, 20, 644–656.10.1086/jcr.1994.20.issue-4
   Web of Science ®Google Scholar
• Bhatti, T. (2007). Exploring factors influencing the adoption of mobile commerce. Journal of Internet Banking and Commerce, 12, 1–13.
   Google Scholar
• Bigne, E., Ruiz, C., & Sanz, S. (2005). The impact of internet user shopping patterns and demographics on consumer mobile buying behaviour. Journal of Electronic Commerce Research, 6, 193–209.
   Google Scholar
• Bruner, G. C. & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices. Journal of Business Research, 58, 553–558.10.1016/j.jbusres.2003.08.002
   Web of Science ®Google Scholar
• Chandon, P., Wansink, B., & Laurent, G. (2000). A benefit congruency framework of sales promotion effectiveness. Journal of Marketing.
   Google Scholar
• Chandon, P., Wansink, B., & Laurent, G. (2000). A benefit congruency framework of sales promotion effectiveness. Journal of Marketing, 64(4), 65–81.
   Web of Science ®Google Scholar
• Chang, T.-Z., & Wildt, A. R. (1994). Price, product information, and purchase intention: An empirical study. Journal of the Academy of Marketing Science, 22, 16–27.
   Google Scholar
• Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77, 511–535.10.1016/S0022-4359(01)00056-2
   Web of Science ®Google Scholar
• Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research Vol. 14, 189–217.
   Web of Science ®Google Scholar
• Cho, Y. C. (2008). Assessing user attitudes toward mobile commerce in the U.S. Vs. Korea: Implications For M-Commerce CRM. Journal of Business & Economics Research, 6, 91–102.
   Google Scholar
• Chong, A. Y.-L., Chan, F. T. S., & Ooi, K.-B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53, 34–43.10.1016/j.dss.2011.12.001
   Web of Science ®Google Scholar
• Chuan-Chuan Lin, J., & Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management 20, 197–208.
   Web of Science ®Google Scholar
• Compeau, D. & Higgins, C. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19, 189–211.10.2307/249688
   Web of Science ®Google Scholar
• Crabbe, M., Standing, C., Standing, S., & Karjaluoto, H. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications 7(5), 515–543.
   Web of Science ®Google Scholar
• Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Management.
   Google Scholar
• Davis, 1986. A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, (Doctoral Dissertation), Sloan School of Management, Massachusetts Institute of Technology, USA.
   Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology : A comparison of two theoritical models. Management Science, 35, 982.10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• DeLone, W. H. & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3, 60–95.10.1287/isre.3.1.60
   Web of Science ®Google Scholar
• Delone, W. H. & Mclean, E. R. (2004). Measuring e-Commerce success : Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce, 9, 31–47.
   Web of Science ®Google Scholar
• Deng, Z., Lu, Y., Deng, S., & Zhang, J. (2010). Exploring user adoption of mobile banking: an empirical study in China. International Journal of Information Technology Management, 9, 289–301.
   Google Scholar
• Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28, 307.10.2307/3172866
   Web of Science ®Google Scholar
• Dong, J. Q. & Zhang, X. (2011). Gender differences in adoption of information systems: New findings from China. Computers in Human Behavior, 27, 384–390.10.1016/j.chb.2010.08.017
   Web of Science ®Google Scholar
• Faqih, K. M. S. & Jaradat, M.-I. R. M. (2014). 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.
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (JMR), 18, 39–50.10.2307/3151312
   Web of Science ®Google Scholar
• Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27, 51–90.
   Web of Science ®Google Scholar
• Halawi, L. A., McCarthy, R. V., & Aronson, J. E. (2007). An empirical investigation of knowledge management systems’ success. Journal of Computer Information Systems, 48, 121–135.
   Web of Science ®Google Scholar
• Ho Cheong, J. & Park, M. (2005). Mobile internet acceptance in Korea. Internet Research, 15, 125–140.10.1108/10662240510590324
   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, 42–60.10.1016/S1567-4223(03)00008-5
   Google Scholar
• Igbaria, M., & Tan, M. (1997). The consequences of information technology acceptance on subsequent individual performance. Information & Management 32, 113–121.
   Web of Science ®Google Scholar
• Ikart, E. M. (2005). Executive information systems and the top-officers’ roles: An exploratory study of user-behaviour model and lessons learnt. Australasian Journal of Information Systems, 13, 78–100.
   Google Scholar
• Jayawardhena, C., Kuckertz, A., Karjaluoto, H., & Kautonen, T. (2009). Antecedents to permission based mobile marketing: an initial examination. European Journal of Marketing 43, 473–499.
   Web of Science ®Google Scholar
• Junglas, I.a. & Watson, R. T. (2008). Location-based services. Communications of the ACM, 51, 65–69.10.1145/1325555
   Web of Science ®Google Scholar
• Jurisic, B. & Azevedo, A. (2011). Building customer–brand relationships in the mobile communications market: The role of brand tribalism and brand reputation. Journal of Brand Management, 18, 349–366.10.1057/bm.2010.37
   Google Scholar
• Kannan, P. K., Chang, A.-M., & Whinston, A. B. (2001). Wireless commerce: marketing issues and possibilities. Proceedings of the 34th Annual Hawaii International Conference on System Sciences.
   Google Scholar
• Khayun, V., Ractham, P., & Firpo, D. (2012). Assessing e-excise success with Delone and McLean’s model. Journal of Computer Information Systems, 52, 31–40.
   Web of Science ®Google Scholar
• Kim, D., & Hwang, Y. (2006). A study of mobile internet usage from utilitarian and hedonic user tendency perspectives. In Americas Conference on Information Systems (AMCIS) (pp. 2015–2023).
   Google Scholar
• Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43 111–126.10.1016/j.dss.2005.05.009
   Web of Science ®Google Scholar
• Lee, H.-M. & Chen, T. (2014). Perceived quality as a key antecedent in continuance intention on mobile commerce. International Journal of Electronic Commerce Studies, 5, 123–142.10.7903/ijecs
   Google Scholar
• Lee, W. J., Kim, T. U., & Chung, J. (2007). User acceptance of the mobile internet. Interacting with Computers, 19, 472–483.
   Web of Science ®Google Scholar
• Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27, 657–678.
   Web of Science ®Google Scholar
• Limayem, M., Khalifa, M., & Frini, A. (2000). What makes consumers buy from Internet? A longitudinal study of online shopping. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans., 30, 421–432.10.1109/3468.852436
   Web of Science ®Google Scholar
• Zhou, Lina & Linwei Dai, D. Z. (2007). online shopping acceptance model-A CRITICAL SURVEY OF CONSUMER FACTORS IN ONLINE SHOPPING. Journal of Electronic Commerce Research, 8, 41–63.
   Google Scholar
• Lip-Sam, T. & Hock-Eam, L. (2011). Estimating the determinants of B2B E-commerce adoption among small & medium enterprises. International Journal of Business and Society, 12, 15–30.
   Google Scholar
• Lockheed, M. E. (1985). Women, girls, and computers: A first look at the evidence. Sex Roles, 13, 115–122.10.1007/BF00287904
   Web of Science ®Google Scholar
• Lu, J., Yu, C.-S., Liu, C., & Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research: Electronic Networking Applications and Policy, 13, 206–222.10.1108/10662240310478222
   Web of Science ®Google Scholar
• M.Khalifa, Cheng, S., & Shen, K. (2012). Adoption of mobile commerce : A confidence model. Computer Information System 14–22.
   Google Scholar
• Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2, 173–191.10.1287/isre.2.3.173
   Web of Science ®Google Scholar
• Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: The influence of perceived user resources. Database for Advances in Information Systems, 32, 86–112.10.1145/506724
   Google Scholar
• Midgley, D. F. & Dowling, G. R. (1978). The innovativeness: The concept and its concept measurement. Journal of Consumer Research, 4, 229–242.10.1086/jcr.1978.4.issue-4
   Web of Science ®Google Scholar
• Moon, J. W. & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information and Management, 38, 217–230.10.1016/S0378-7206(00)00061-6
   Web of Science ®Google Scholar
• Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005). Explaining intention to use mobile chat services: moderating effects of gender. Journal of Consumer Marketing 22, 247–256.
   Google Scholar
• O’Cass, A. & Fenech, T. (2003). Web retailing adoption: exploring the nature of internet users Web retailing behaviour. Journal of Retailing and Consumer Services, 10, 81–94.10.1016/S0969-6989(02)00004-8
   Google Scholar
• Pagani, M. (2004). Determinants of adoption of third generation mobile multimedia services. Journal of Interactive Marketing 18, 46–59.
   Google Scholar
• Pedersen, P. (2005). Adoption of Mobile Internet Services: An Exploratory Study of Mobile Commerce Early Adopters. Journal of Organizational Computing and Electronic Commerce, 15(2), 203–222.
   Web of Science ®Google Scholar
• Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service Quality: A Measure of Information Systems Effectiveness. MIS Quarterly, 19, 173–187.10.2307/249687
   Web of Science ®Google Scholar
• Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing 28, 328–341.
   Google Scholar
• Rodgers, S. & Harris, M. A. (2003). Gender and E-Commerce: An exploratory study. Journal of Advertising Research, 43, 322–329.10.1017/S0021849903030307
   Web of Science ®Google Scholar
• Rogers, E. M. (1995). Diffusion of Innovations, In elements of Diffusion, New York: Free Press.
   Google Scholar
• Saidi, E. (2010). Towards a faultless mobile commerce implementation in Malawi. Journal of Internet Banking and Commerce, 15 1–11.
   Google Scholar
• Scott, C. R. & Rockwell, S. C. (1997). The effect of communication, writing, and technology apprehension on likelihood to use new communication technologies. Communication Education, 46, 44–62.10.1080/03634529709379072
   Web of Science ®Google Scholar
• Serenko, A., Turel, O., & Yol, S. (2006). Moderating roles of user demographics in the American customer satisfaction model within the context of mobile services. Journal of Information Technology Management, XVII, 20–32.
   Google Scholar
• Teo, T. S., Lim, V. K., & Lai, R. Y. (1999). Intrinsic and extrinsic motivation in internet usage. Omega: The International Journal of Management Science, 27, 25–37.
   Web of Science ®Google Scholar
• Vaghjiani, K. (2012). Patterns of IT-enabled Innovation Adoption in Developed and Developing Countries: A Comparative Case Study of Australian and Indian Banking Industries (doctoral dissertation). Australia: The University of New South Wales.
   Google Scholar
• Varshney, U. & Vetter, R. (2002). Mobile commerce: Framework, applications and networking support. Mobile Networks and Applications, 7, 185–198.10.1023/A:1014570512129
   Web of Science ®Google Scholar
• Venkatesh, V. & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39, 273–315.10.1111/j.1540-5915.2008.00192.x
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46, 186–204.
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
   Web of Science ®Google Scholar
• Venkatesh, V. & Morris, M. G. (2000). Why don ‘ t men ever stop to ask for directions ? Gender, social influence, and their role in technology and usage behavior1. MIS Quarterly, 24, 115–139.10.2307/3250981
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and user of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157–178.
   Web of Science ®Google Scholar
• Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting Consumer Intention to Use Mobile Service. Information Systems Journal, 16(2), 157–179.
   Web of Science ®Google Scholar
• Wang, H. Y. & Wang, Y. S. (2008). Gender differences in the perception and acceptance of online games. British Journal of Educational Technology, 39, 787–806.
   Web of Science ®Google Scholar
• Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40, 92–118.10.1111/bjet.2009.40.issue-1
   Web of Science ®Google Scholar
• Wang, Y.-S., Wang, Y.-M., Lin, H.-H., & Tang, T.-I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International Journal of Service Industry Management 14, 501–519.
   Google Scholar
• Wei, et al. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109, 370–388.
   Web of Science ®Google Scholar
• DeLone, William H. & McLean, E. R. (2003). The DeLone and McLean model of information systems success : A ten-year update. Journal of Management Information System, 19, 9–31.
   Web of Science ®Google Scholar
• Wu, J.-H. H. & Wang, S.-C. 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., & Jolly, L. D. (2008). Age cohort analysis in adoption of mobile data services: gen Xers versus baby boomers. Journal of Consumer Marketing 25, 272–280.
   Google Scholar
• Yang, K. & Folly, L. D. (2008). Age Cohort Analysis in Adoption of Mobile Data Services: gen Xers versus baby boomers. Journal of Consumer Marketing, 25(2), 272–280.
   Google Scholar
• Yaseen, S. G. & Zayed, S. (2010). Exploring critical determinants in deploying mobile commerce technology. International Journal of Information Science and Management, 35–46.
   Google Scholar
• Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52, 2–22.10.2307/1251446
   Web of Science ®Google Scholar
• 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, 1902–1911.10.1016/j.chb.2012.05.008
   Web of Science ®Google Scholar