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Exploring consumer adoption of proximity mobile payments

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Pages 209-223 | Received 01 Apr 2014, Accepted 08 Apr 2014, Published online: 29 Apr 2014

References

  • Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies. Decision Sciences, 30, 361–391.
  • Aroean, L. (2014). A taxonomy of mobile phone consumers: Insights for marketing managers. Journal of Strategic Marketing, 22, 73–89.
  • Au, Y., & Kauffman, R. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research & Applications, 7, 141–164.
  • BBC News. (2012). Android and Nokia MeeGo phones hijacked via wallet tech. Retrieved from http://www.bbc.co.uk/news/technology-19010945.
  • Brown, I., Cajee, Z., Davies, D., & Stroebel, S. (2003). Cell phone banking: Predictors of adoption in South Africa – An exploratory study. International Journal of Information Management, 23, 381–394.
  • Chandra, S., Srivastava, S., & Theng, Y.-L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information Systems, 27, 561–588.
  • Chang, M.-L., & Wu, W.-Y. (2012). Revisiting perceived risk in the context of online shopping: An alternative perspective of decision-making styles. Psychology and Marketing, 29, 378–400.
  • Chen, L.-D. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6, 32–52.
  • Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13, 319–339.
  • Deutskens, E., Ruyter, K., Wetzels, M., & Oosterveld, P. (2004). Response rate and response quality of internet-based surveys: An experimental study. Marketing Letters, 15, 21–36.
  • Durkin, M., O'Donnell, A., Mullholland, G., & Crowe, J. (2007). On e-banking adoption: From banker perception to customer reality. Journal of Strategic Marketing, 15, 237–252.
  • Farmer, A. (2013). NFC payments: Consumers lack awareness and trust, YouGov. Retrieved from http://yougov.co.uk/news/2013/12/04/nfc-payments-consumers-lack-awareness-and-trust/.
  • Featherman, M., & Pavlou, P. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59, 451–474.
  • Gartner. (2013). Gartner says worldwide mobile payment transaction value to surpass $235 billion in 2013 in Gartner Newsroom. Retrieved from http://www.gartner.com/newsroom/id/2504915.
  • Hair, J., Black, W., Anderson, R., & Tatham, R. (1998). Multivariate data analysis (5th ed.). London: Prentice-Hall.
  • Hinton, P., Brownlow, C., McMurray, L., & Cozens, B. (2004). SPSS explained. East Sussex: Routledge Inc.
  • Hongxia, P., Xianhao, X., & Weidan, L. (2011). Drivers and barriers in the acceptance of mobile payment in China. In International conference on E-business and E-government. May 6–8, Shanghai.
  • Hu, P., Chau, P., Sheng, O., & Tam, K. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16, 91–112.
  • Irani, Z., Dwivedi, Y., & Williams, M. (2009). Understanding consumer adoption of broadband: An extension of the technology acceptance model. Journal of the Operational Research Society, 60, 1322–1334.
  • Kapoor, K., Dwivedi, Y., & Williams, M. (2014). Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service. Information Systems Frontiers, doi:10.1007/s10796-014-9484-7.
  • Ko, E., Kim, E., & Lee, E. (2009). Modeling consumer adoption of mobile shopping for fashion products in Korea. Psychology & Marketing, 26, 669–687.
  • Leong, L.-Y., Hew, T.-S., Tan, G., & Ooi, K.-B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 30, 5604–5620.
  • Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). The moderating effect of experience in the adoption of mobile payments tool in virtual social networks: The m-payment acceptance model in virtual social networks. International Journal of Information Management, 34, 151–166.
  • Lu, Y., Yang, S., Chau, P., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48, 393–403.
  • Mallat, N. (2007). Exploring consumer adoption of mobile payments: A qualitative study. The Journal of Strategic Information Systems, 16, 413–432.
  • Mandrik, C., & Bao, Y. (2005). Exploring the concept and measurement of general risk aversion. Advances in Consumer Research, 32, 531–539.
  • McMaster, T., & Wastell, D. (2005). Diffusion or delusion? Challenging an IS research tradition. Information Technology & People, 18, 383–404.
  • McKnight, D., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13, 334–359.
  • Myers, R. (1990). Classical and modern regression with applications. Boston: PWS-KENT.
  • Ondrus, J., Lyytinen, K., & Pigneur, Y. (2009). Why mobile payments fail? Towards a dynamic and multi-perspective explanation. In 42nd Hawaii international conference on systems sciences, January 5–8, Waikoloa.
  • Ondrus, J., & Pigneur, Y. (2009). Near field communication: An assessment for future payment systems. Information Systems E-Business Management, 7, 347–361.
  • Peter, J., & Tarpey, L. (1975). A comparative analysis of three consumer decision strategies. Journal of Consumer Research, 2, 29–37.
  • Sauermann, H., & Roach, M. (2013). Increasing web survey response rates in innovation research: An experimental study of static and dynamic contact design features. Research Policy, 42, 273–286.
  • Schierz, O., Schilke, O., & Wirtz, B. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research & Applications, 9, 209–216.
  • Sichtmann, C. (2007). An analysis of antecedents and consequences of trust in a corporate brand. European Journal of Marketing, 41, 999–1015.
  • Shen, Y.-C., Huang, C.-Y., Chu, C.-H., & Hsu, C.-T. (2010). A benefit-cost perspective of the consumer adoption of the mobile banking system. Behaviour & Information Technology, 29, 497–511.
  • Shin, D.-H. (2010). Modelling the interaction of users and mobile payment system: Conceptual framework. International Journal of Human-Computer Interaction, 26, 917–940.
  • Slade, E., Williams, M., & Dwivedi, Y. (2013). Mobile payment adoption: Classification and review of the extant literature. The Marketing Review, 13, 167–190.
  • Straub, D., Boudreau, M., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13, 380–427.
  • Tan, G., Ooi, K.-B., Chong, S.-C., & Hew, S.-C. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31, 292–307.
  • Thakur, R. (2013). Customer adoption of mobile payment services by professionals across two cities in India: An empirical study using modified technology acceptance model. Business Perspectives and Research, 1, pp. 17.
  • The UK Cards Association. (2012). The UK cards association annual report 2012. Retrieved from http://www.theukcardsassociation.org.uk/UK-Cards-Annual-Report-2012/html/index.html#/1/zoomed.
  • Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
  • Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157–178.
  • Wampold, B., & Freund, R. (1987). Use of multiple regression in counselling psychology research: A flexible data analytic strategy. Journal of Counseling Psychology, 34, 372–572.
  • Wang, L., & Yi, Y. (2012). The impact of use context on mobile payment acceptance: An empirical study in China. In A. Xie & X. Huang (Eds.), Advances in computer science and education (pp. 293–300). Berlin: Springer.
  • Williams, M., Rana, N., Dwivedi, Y., & Lal, B. (2011). Is UTAUT really used or just cited for the sake of it? A systematic review of citations of UTAUT's originating articles. In Proceedings of the European conference on information systems, June 9–11, Helsinki, paper 231.
  • Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioural beliefs, social influences, and personal traits. Computers in Human Behaviour, 28, 129–142.
  • Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behaviour, 28, 1902–1911.
  • Zhou, T. (2014). An empirical examination of initial trust in mobile payment. Wireless Personal Communications, doi:10.1007/s11277-013-1596-8.
  • Zhou, T. (2013). An empirical examination of continuance intention of MP services. Decision Support Systems, 54, 1085–1091.

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