1,403
Views
7
CrossRef citations to date
0
Altmetric
Articles

Factors influencing continued use of mobile easy payment service: an empirical investigation

, &

References

  • Amoroso, D. L., & Magnier-Watanabe, R. (2012). Building a research model for mobile wallet consumer adoption: The case of mobile Suica in Japan. Journal of Theoretical and Applied Electronic Commerce Research, 7(1), 94–110. doi: 10.4067/S0718-18762012000100008
  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. doi: 10.2307/3150783
  • Bilgihan, A., Kandampully, J., & Zhang, T. (2016). Towards a unified customer experience in online shoppling environments: Antecedents and outcomes. International Journal of Quality and Service Sciences, 8(1), 102–119. doi: 10.1108/IJQSS-07-2015-0054
  • Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109–126. doi: 10.1177/0092070302250897
  • Cespedes, F. V., & Smith, H. J. (1993). Database marketing: New rules for policy and practice. Sloan Management Review, 34(4), 7–23.
  • Chang, Y. F., Chen, C., & Zhou, H. (2009). Smart phone for mobile commerce. Computer Standards & Interfaces, 31(4), 740–747. doi: 10.1016/j.csi.2008.09.016
  • Chen, L.-D. (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1), 32–52. doi: 10.1504/IJMC.2008.015997
  • Chen, L.-D., Gillenson, M. L., & Sherrell, D. L. (2004). Consumer acceptance of virtual stores: A theoretical model and critical success factors for virtual stores. ACM Sigmis Database, 35(2), 8–31. doi: 10.1145/1007965.1007968
  • Chen, J. V., Yen, D. C., & Chen, K. (2009). The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics. Information & Management, 46(4), 241–248. doi: 10.1016/j.im.2009.03.001
  • Cho, I. (2015). Strategies for the duffusion of smartwatches (Doctoral dissertation). Retrieved from http://dcollection.yonsei.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000308767
  • Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165–181. doi: 10.1016/j.elerap.2007.02.001
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi: 10.2307/249008
  • Delone, W. H., & Mclean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. doi: 10.1287/isre.3.1.60
  • de Sena Abrahão, R., Moriguchi, S.N., & Andrade, D.F. (2016). Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221–230. doi: 10.1016/j.rai.2016.06.003
  • Dlačić, J., Arslanagić, M., Kadić-Maglajlić, S., Marković, S., & Raspor, S. (2014). Exploring perceived service quality, perceived value, and repurchase intention in higher education using structural equation modelling. Total Quality Management & Business Excellence, 25(1–2), 141–157. doi: 10.1080/14783363.2013.824713
  • Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron, OH: University of Akron Press.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. doi: 10.2307/3151312
  • Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 1–77.
  • Hsu, M.-H., Chang, C.-M., & Chuang, L.-W. (2015). Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35(1), 45–56. doi: 10.1016/j.ijinfomgt.2014.09.002
  • Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2002). Why customers stay: Measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55(6), 441–450. doi: 10.1016/S0148-2963(00)00168-5
  • Kim, H.-W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126. doi: 10.1016/j.dss.2005.05.009
  • Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582. doi: 10.2307/20650309
  • Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322. doi: 10.1016/j.chb.2009.10.013
  • Kim, S.-H., & Park, H.-S. (2011). The impact of service characteristics of smartphone application on perceived value, satisfaction and intention to recommend. Korean Business Education Review, 26(6), 121–142.
  • Kim, Y., Park, Y., & Choi, J. (2017). A study on the adoption of IoT smart home service: Using value-based adoption model. Total Quality Management & Business Excellence, 28(10), 1149–1165. doi: 10.1080/14783363.2017.1310708
  • Leavitt, C., & Walton, J. (1975). Development of a scale for innovativeness. NA-Advances in Consumer Research, 2, 545–554.
  • Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. doi: 10.1016/j.elerap.2008.11.006
  • Lee, D., Moon, J., Kim, Y. J., & Yi, M. Y. (2015b). Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty. Information & Management, 52(3), 295–304. doi: 10.1016/j.im.2014.12.001
  • Lee, C., Yun, H., Lee, C., & Lee, C. C. (2015a). Factors affecting continuous intention to use mobile wallet: Based on value-based adoption model. The Journal of Society for e-Business Studies, 20(1), 117–135. doi: 10.7838/jsebs.2015.20.1.117
  • Leong, L.-Y., Hew, T.-S., Tan, G. W.-H., & Ooi, K.-B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620. doi: 10.1016/j.eswa.2013.04.018
  • Lin, T.-C., Wu, S., Hsu, J. S.-C., & Chou, Y.-C. (2012). The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention. Decision Support Systems, 54(1), 63–75. doi: 10.1016/j.dss.2012.04.004
  • Liu, F., Zhao, X., Chau, P. Y. K., & Tang, Q. (2015). Roles of perceived value and individual differences in the acceptance of mobile coupon applications. Internet Research, 25(3), 471–495. doi: 10.1108/IntR-02-2014-0053
  • Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134–159. doi: 10.1108/IntR-05-2012-0100
  • Lu, Y., Yang, S., Chau, P. Y., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393–403. doi: 10.1016/j.im.2011.09.006
  • Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. doi: 10.1016/j.chb.2004.03.003
  • Ma, G., Yi, J., & Choi, D. (2011). Security technology trend of smart channel for a mobile wallet. Journal of The Korea Institute of Information Security and Cryptology, 21(4), 7–13.
  • Mimouni-Chaabane, A., & Volle, P. (2010). Perceived benefits of loyalty programs: Scale development and implications for relational strategies. Journal of Business Research, 63(1), 32–37. doi: 10.1016/j.jbusres.2009.01.008
  • Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. doi: 10.1287/isre.2.3.192
  • Natarajan, T., Balasubramanian, S. A., & Manickavasagam, S. (2010). Customer’s choice amongst self service technology (SST) channels in retail banking: A study using analytical hierarchy process (AHP). Journal of Internet Banking and Commerce, 15(2), 1–16.
  • Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. doi: 10.1016/j.chb.2016.03.030
  • Podsakoff, P. M., Mackenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. doi: 10.1037/0021-9010.88.5.879
  • Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York, NY: Free pree.
  • Rogers, E. M. (2010). Diffusion of innovations (4th ed.). New York, NY: Free pree.
  • Shy, O. (2002). A quick-and-easy method for estimating switching costs. International Journal of Industrial Organization, 20(1), 71–87. doi: 10.1016/S0167-7187(00)00076-X
  • Slade, E., Williams, M., Dwivedi, Y., & Piercy, N. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23(3), 209–223. doi: 10.1080/0965254X.2014.914075
  • Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1999). The role of perceived risk in the quality-value relationship: A study in a retail environment. Journal of Retailing, 75(1), 77–105. doi: 10.1016/S0022-4359(99)80005-0
  • Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2), 292–307. doi: 10.1016/j.tele.2013.06.002
  • Thaichon, P., Lobo, A., & Mitsis, A. (2014). Achieving customer loyalty through service excellence in internet industry. International Journal of Quality and Service Sciences, 6(4), 274–289. doi: 10.1108/IJQSS-03-2014-0024
  • Venkatesh, V., & Davis, F. D. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. doi: 10.1287/isre.11.4.342.11872
  • Wang, Y.-S., Wu, S.-C., Lin, H.-H., Wang, Y.-M., & He, T.-R. (2012). Determinants of user adoption of web ‘automatic teller machines’: An integrated model of ‘transaction cost theory’ and ‘innovation diffusion theory’. The Service Industries Journal, 32(9), 1505–1525. doi: 10.1080/02642069.2010.531271
  • Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253–269. doi: 10.1108/IMDS-08-2014-0243
  • Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. doi: 10.2307/1251446
  • Zhang, C.-B., Li, Y.-N., Wu, B., & Li, D.-J. (2017). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior, 69, 284–293. doi: 10.1016/j.chb.2016.11.069

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.