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OPERATIONS, INFORMATION & TECHNOLOGY

DETERMINANTS AFFECT MOBILE WALLET CONTINUOUS USAGE IN COVID 19 PANDEMIC: EVIDENCE FROM VIETNAM

, ORCID Icon & ORCID Icon
Article: 2041792 | Received 23 Dec 2021, Accepted 07 Feb 2022, Published online: 02 Mar 2022

References

  • Aji, H. M., Berakon, I., Md Husin, M., & Tan, A. W. K. (2020). COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia. Cogent Business & Management, 7(1), 1804181. https://doi.org/10.1080/23311975.2020.1804181
  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior Action control. Springer.
  • Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–20. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
  • Arning, K., & Ziefle, M. (2009 November). Different perspectives on technology acceptance: The role of technology type and age. n Symposium of the Austrian HCI and usability engineering group (pp. 20-41). Springer, Berlin, Heidelberg.
  • Azjen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs.
  • Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024
  • Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Planning, 45(5–6), 359–394. https://doi.org/10.1016/j.lrp.2012.10.001
  • Brown, S. A., & Venkatesh, V. (2005). A model of adoption of technology in the household: A baseline model test and extension incorporating household life cycle. Management Information Systems Quarterly, 29(3), 11. https://doi.org/10.2307/25148690
  • Chan, K. Y., Gong, M., Xu, Y., & Thong, J. (2008). Examining user acceptance of SMS: An empirical study in China and Hong Kong. PACIS 2008 Proceedings, 294 https://citeseerx.ist.psu.edu/viewdoc/download?doi=10 .1.1.930.6274&rep=rep1&type=pdf.
  • Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. https://doi.org/10.1016/S0022-4359(01)00056-2
  • Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
  • Cimperman, M., Brenčič, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22–31 doi:10.1016/j.ijmedinf.2016.03.002. https://www.sciencedirect.com/science/article/abs/pii/S1386505616300338?via%3Dihub
  • 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. https://www.koreascience.or.kr/article/JAKO202031064817089.view?orgId=kodisa
  • DO, N. B., & DO, H. N. T. (2020). An investigation of generation Z’s intention to use electronic wallet in Vietnam. The Journal of Distribution Science, 18(10), 89–99.
  • Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70–88 doi:10.1016/j.tourman.2014.01.017. https://www.sciencedirect.com/science/article/abs/pii/S0261517714000181?via%3Dihub
  • Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2 130–132).
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  • Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725–737
  • Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725–737.
  • Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400. https://doi.org/10.2307/249720
  • Gelb, A., & Mukherjee, A. (2020). Digital technology in social assistance transfers for COVID-19 relief: Lessons from selected cases. CGD Policy Paper, 181 https://www.ictworks.org/wp-content/uploads/2020/09/lessons-learned-digital-technology-social-assistance-programs.pdf.
  • Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. The Journal of Strategic Information Systems, 17(2), 140–154. https://doi.org/10.1016/j.jsis.2007.12.004
  • Hair, J. F., Jr, Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review 26(2) 106–121 doi:10.1108/EBR-10-2013-0128 .
  • Hair, J. F., Jr, Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling: SaGe publications https://us.sagepub.com/en-us/nam/advanced-issues-in-partial-least-squares-structural-equation-modeling/book243803#preview.
  • Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. https://doi.org/10.2307/25148660
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
  • Hong, S., Thong, J. Y., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834. https://doi.org/10.1016/j.dss.2006.03.009
  • Hongxia, P., Xianhao, X., & Weidan, L. (2011). Drivers and barriers in the acceptance of mobile payment in China. Paper presented at the 2011 International Conference on E-business and E-government (ICEE) (Shanghai, China: ICEE).
  • Jesuthasan, S., & Umakanth, N. (2021). Impact of behavioural intention on E-wallet usage during Covid-19 period: A study from Sri Lanka. Sri Lanka Journal of Marketing, 7(2), 24. https://doi.org/10.4038/sljmuok.v7i2.63
  • Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior, 70, 460–474. https://doi.org/10.1016/j.chb.2017.01.001
  • Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416. https://doi.org/10.1016/j.ijmedinf.2008.12.005
  • Kim, H.-W., & Kwahk, K.-Y. (2007). Comparing the usage behavior and the continuance intention of mobile Internet services. Paper presented at the Eighth World Congress on the Management of eBusiness (WCMeB 2007) (Toronto, Canada).
  • Kim, J., & Park, H.-A. (2012). Development of a health information technology acceptance model using consumers’ health behavior intention. Journal of Medical Internet Research, 14(5), e133. https://doi.org/10.2196/jmir.2143
  • Lee, T. (2005). The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. Journal of Electronic Commerce Research, 6(3), 165 http://www.jecr.org/sites/default/files/06_3_p01.pdf.
  • Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology.
  • Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
  • Liu, G.-S., & Tai, P. T. (2016). A study of factors affecting the intention to use mobile payment services in Vietnam. Economics World, 4(6), 249–273 http://www.davidpublisher.com/Public/uploads/Contribute/5795c20c3bdc3.pdf.
  • Neufeld, D. J., Dong, L., & Higgins, C. (2007). Charismatic leadership and user acceptance of information technology. European Journal of Information Systems, 16(4), 494–510. https://doi.org/10.1057/palgrave.ejis.3000682
  • Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). Mcgraw hill book company.
  • Or, C. K., Karsh, B.-T., Severtson, D. J., Burke, L. J., Brown, R. L., & Brennan, P. F. (2011). Factors affecting home care patients’ acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, 18(1), 51–59. https://doi.org/10.1136/jamia.2010.007336
  • Putri, D. A. (2018). Analyzing factors influencing continuance intention of e-payment adoption using modified UTAUT 2 model. Paper presented at the 2018 6th International Conference on Information and Communication Technology (ICoICT) (Bandung, Indonesia https://www.icoict.org/wp-content/uploads/sites/5/2017/10/Call-For-Papers-ICoICT-2018.pdf).
  • Qasim, H., & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18(5), 1021–1034. https://doi.org/10.1007/s10796-015-9598-6
  • Revathy, C., & Balaji, P. (2020). Determinants of behavioural intention on E-Wallet usage: An empirical examination in amid of Covid-19 lockdown period. International Journal of Management (IJM), 11(6), 92–104 10.34218/IJM.11.6.2020.008.
  • Rogers, E. M. (1976). New product adoption and diffusion. Journal of Consumer Research, 2(4), 290–301. https://doi.org/10.1086/208642
  • Saraswati, D. A., Desvi, P. S., Putra, N. S., & Hendriana, E. (2021). Examination of the extended UTAUT model in mobile wallet continuous usage intention during the COVID-19 outbreak Turkish Online Journal of Qualitative Inquiry (TOJQI) 12(6) https://www.researchgate.net/profile/Praditta-Desvi/publication/354754021_Examination_of_the_Extended_UTAUT_Model_in_Mobile_Wallet_Continuous_Usage_Intention_during_the_COVID-19_Outbreak/links/614b2035a595d06017e18523/Examination-of-the-Extended-UTAUT-Model-in-Mobile-Wallet-Continuous-Usage-Intention-during-the-COVID-19-Outbreak.pdf .
  • Shilpi Saraswat, M. M. (2017). Cashless transaction: Challenges faced by the consumers. International Journal of Research Culture Society, 10(1) 228–236 http://ijrcs.org/wp-content/uploads/201712042.pdf .
  • Shin, D.-H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343–1354. https://doi.org/10.1016/j.chb.2009.06.001
  • Shou, Y., & Smithson, M. (2015). Evaluating predictors of dispersion: A comparison of dominance analysis and Bayesian model averaging. Psychometrika, 80(1), 236–256. https://doi.org/10.1007/s11336-013-9375-8
  • Slade, E., Williams, M., Dwivedi, Y., & Piercy, N. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23(3), 209–223. https://doi.org/10.1080/0965254X.2014.914075
  • Sun, Y., Bhattacherjee, A., & Ma, Q. (2009). Extending technology usage to work settings: The role of perceived work compatibility in ERP implementation. Information & Management, 46(6), 351–356. https://doi.org/10.1016/j.im.2009.06.003
  • Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2020). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers 23(4) , 1–19 doi:10.1007/s10796-020-10007-6.
  • Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155. https://doi.org/10.1016/0167-8116(94)00019-K
  • 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(2), 17–30. https://doi.org/10.1177/2278533720130203
  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. https://doi.org/10.2307/249443
  • Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60. https://doi.org/10.1006/obhd.2000.2896
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157–178 MIS quarterly.
  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  • Verkijika, S. F. (2018). Factors influencing the adoption of mobile commerce applications in Cameroon. Telematics and Informatics, 35(6), 1665–1674. https://doi.org/10.1016/j.tele.2018.04.012
  • Williams, M. N., Grajales, C. A. G., & Kurkiewicz, D. (2013). Assumptions of multiple regression: Correcting two misconceptions. Practical Assessment, Research, and Evaluation, 18(1), 11 doi:10.7275/55hn-wk47.
  • Wong, C. Y., & Mohamed, M. I. P. (2021). Understanding the factors that influence consumer continuous intention to use E-wallet In Malaysia. Research in Management of Technology and Business, 2(1), 561–576 doi:10.30880/rmtb.2021.02.01.042.
  • Wu, J. J., & Chang, Y. S. (2005). Towards understanding members’ interactivity, trust, and flow in online travel community. Industrial Management & Data Systems, 105(7), 937–954. https://doi.org/10.1108/02635570510616120
  • Zhanyou, W., Dongmei, H., & Yaopei, Z. (2020). How to improve users’ intentions to continued usage of shared bicycles: A mixed method approach. PLoS one, 15(2), e0229458.
  • Zhanyou, W., Dongmei, H., Yaopei, Z., & Kato, H. (2020). How to improve users’ intentions to continued usage of shared bicycles: A mixed method approach. PLoS One, 15(2), e0229458. https://doi.org/10.1371/journal.pone.0229458
  • Zmijewska, A., Lawrence, E., & Steele, R. (2004). Towards understanding of factors influencing user acceptance of mobile payment systems. Icwi, 2004, 270–277 https://www.researchgate.net/publication/220969444_Towards_Understanding_of_Factors_Influencing_User_Acceptance_of_Mobile_Payment_Systems.