The role of user adaptation and trust in understanding continuance intention towards mobile shopping: An extended expectation-confirmation model

References

• Afshan, S., & Sharif, A. (2016). Acceptance of mobile banking framework in Pakistan. Telematics and Informatics, 33(2), 370–19. https://doi.org/https://doi.org/10.1016/j.tele.2015.09.005
   Google Scholar
• Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582. https://doi.org/https://doi.org/10.1111/j.1540-5915.1997.tb01322.x.
   Google Scholar
• Ambalov, I. A. (2018). A meta-analysis of IT continuance: An evaluation of the expectation-confirmation model. Telematics and Informatics, 35(6), 1561–1571. https://doi.org/https://doi.org/10.1016/j.tele.2018.03.016
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https://doi.org/https://doi.org/10.1037/0033-2909.103.3.411
   Google Scholar
• Ashraf, M., Ahmad, J., Hamyon, A. A., Sheikh, M. R., & Sharif, W. (2020). Effects of post-adoption beliefs on customers’ online product recommendation continuous usage: An extended expectation-confirmation model. Cogent Business & Management, 7(1), 1735693. https://doi.org/https://doi.org/10.1080/23311975.2020.1735693
   Google Scholar
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/https://doi.org/10.1007/BF02723327
   Google Scholar
• Barki, H., Titah, R., & Boffo, C. (2007). Information system use–related activity: An expanded behavioral conceptualization of individual-level information system use. Information Systems Research, 18(2), 173–192. https://doi.org/https://doi.org/10.1287/isre.1070.0122
   Google Scholar
• Beaudry, A., & Pinsonneault, A. (2005). Understanding user responses to information technology: A coping model of user adaptation. MIS Quarterly, 29(3), 493–524. https://doi.org/https://doi.org/10.2307/25148693
   Google Scholar
• Bhattacherjee, A. (2001a). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/https://doi.org/10.2307/3250921
   Google Scholar
• Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/https://doi.org/10.2307/3250921
   Google Scholar
• Bhattacherjee, A., & Barfar, A. (2011). Information technology continuance research: Current state and future directions. Asia Pacific Journal of Information Systems, 21(2), 1–18.
   Google Scholar
• Bhattacherjee, A., & Harris, M. (2009). Individual adaptation of information technology. Journal of Computer Information Systems, 50(1), 37–45. https://doi.org/https://doi.org/10.1080/08874417.2009.11645360
   Google Scholar
• Bhattacherjee, A., & Lin, C.-P. (2015). A unified model of IT continuance: Three complementary perspectives and crossover effects. European Journal of Information Systems, 24(4), 364–373. https://doi.org/https://doi.org/10.1057/ejis.2013.36
   Google Scholar
• Boakye, K. G., McGinnis, T., Prybutok, V. R., & Paswan, A. K. (2014). Development of a service continance model with IT service antecedents. Journal of Retailing and Consumer Services, 21(5), 717–724. https://doi.org/https://doi.org/10.1016/j.jretconser.2014.05.004
   Google Scholar
• Bölen, M. C., & Özen, Ü. (2020). Understanding the factors affecting consumers’ continuance intention in mobile shopping: The case of private shopping clubs. International Journal of Mobile Communications, 18(1), 101–129. https://doi.org/https://doi.org/10.1504/IJMC.2020.104423
   Google Scholar
• Brislin, R. W. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/https://doi.org/10.1177/135910457000100301
   Google Scholar
• Byrne, B. M. (2010). Multivariate applications series. 2nd ed., Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge/Taylor: https://doi.org/https://doi.org/10.4324/9780203805534
   Google Scholar
• Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: A trust transfer perspective. Internet Research, 28(2), 456–476. https://doi.org/https://doi.org/10.1108/IntR-11-2016-0359
   Google Scholar
• Cenfetelli, R. T., Benbasat, I., & Al-Natour, S. (2008). Addressing the what and how of online services: Positioning supporting-services functionality and service quality for business-to-consumer success. Information Systems Research, 19(2), 161–181. https://doi.org/https://doi.org/10.1287/isre.1070.0163
   Google Scholar
• Chen, S. C., & Dhillon, G. S. (2003). Interpreting Dimensions of Consumer Trust in E-Commerce. Information Technology and Management, 4(2), 303–318. https://doi.org/https://doi.org/10.1023/A1022962631249
   Google Scholar
• Chen, S.-C., Yen, D. C., & Hwang, M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28(3), 933–941. https://doi.org/https://doi.org/10.1016/j.chb.2011.12.014
   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(1), 34–43. https://doi.org/https://doi.org/10.1016/j.dss.2011.12.001
   Google Scholar
• Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123–139. https://doi.org/https://doi.org/10.1287/mnsc.36.2.123
   Google Scholar
• Coppola, D. (2020). Mobile commerce in the United States - Statistics & Facts. Retrieved 15 May 2021 from https://www.statista.com/statistics/249855/mobile-retail-commerce-revenue-in-the-united-states/
   Google Scholar
• Corbitt, B. J., Thanasankit, T., & Yi, H. (2003). Trust and e-commerce: A study of consumer perceptions. Electronic Commerce Research and Applications, 2(3), 203–215. https://doi.org/https://doi.org/10.1016/S1567-4223(03)00024-3
   Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/https://doi.org/10.1287/mnsc.35.8.982
   Google Scholar
• Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51(1), 31–46. https://doi.org/https://doi.org/10.1109/TEM.2003.822468
   Google Scholar
• DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/https://doi.org/10.1080/07421222.2003.11045748
   Google Scholar
• DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Foundations and Trends in Information Systems, 2(1), 1–116. https://doi.org/https://doi.org/10.1561/2900000005
   Google Scholar
• DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–147. https://doi.org/https://doi.org/10.1287/orsc.5.2.121
   Google Scholar
• Duc, V. N. (2020). Retail in Vietnam - an accelerated shift towards omnichannel retailing. Retrieved 20 December 2020 from https://www2.deloitte.com/vn/en/pages/consumer-business/articles/vietnam-consumer-retail-2020.html
   Google Scholar
• Faber, R., & de Reuver, M. (2019). Consumer studies on digital platforms adoption and continuance: A structured literature review. https://aisel.aisnet.org/ecis2019_rp/121
   Google Scholar
• 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/https://doi.org/10.1177/002224378101800104
   Google Scholar
• Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–A case of China. Computers in Human Behavior, 53, 249–262. https://doi.org/https://doi.org/10.1016/j.chb.2015.07.014
   Google Scholar
• Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51–90. https://doi.org/https://doi.org/10.2307/30036519
   Google Scholar
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/https://doi.org/10.2307/249689
   Google Scholar
• Grayson, K., & Ambler, T. (1999). The Dark Side of Long-Term Relationships in Marketing Services. Journal of Marketing Research, 36(1), 132–141. https://doi.org/https://doi.org/10.1177/002224379903600111
   Google Scholar
• Groß, M. (2016). Impediments to mobile shopping continued usage intention: A trust-risk-relationship. Journal of Retailing and Consumer Services, 33, 109–119. https://doi.org/https://doi.org/10.1016/j.jretconser.2016.08.013
   Google Scholar
• Hair, J., Anderson, R., Babin, B., & Black, W. (2010). Multivariate data analysis: A global perspective. Pearson Upper Saddle River.
   Google Scholar
• Holmes, A., Byrne, A., & Rowley, J. (2014). Mobile shopping behaviour: Insights into attitudes, shopping process involvement and location. International Journal of Retail & Distribution Management, 42(1), 25–39. https://doi.org/https://doi.org/10.1108/IJRDM-10-2012-0096
   Google Scholar
• 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/https://doi.org/10.1016/j.dss.2006.03.009
   Google Scholar
• Hoppe, F., & Baijal, A. (2019). e-Conomy SEA 2019. Google & Temasek/Bain & Company. Retrieved 20 Setember 2020 from https://www.bain.com/insights/e-conomy-sea-2019
   Google Scholar
• Hsiao, C.-H., Chang, -J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342–355. https://doi.org/https://doi.org/10.1016/j.tele.2015.08.014
   Google Scholar
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/https://doi.org/10.1080/10705519909540118
   Google Scholar
• Hung, M.-C., Yang, S., & Hsieh, T.-C. (2012). An examination of the determinants of mobile shopping continuance. Int. J. Electron. Bus. Manag, 10(1), 29–37.
   Google Scholar
• IDEA. (2020). Vietnam E-commerce and Digital Economy Whitebook 2020. Retrieved 15 May 2021 from http://idea.gov.vn/
   Google Scholar
• Jasperson, J. S., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557. https://doi.org/https://doi.org/10.2307/25148694
   Google Scholar
• Jocevski, M., Ghezzi, A., & Arvidsson, N. (2020). Exploring the growth challenge of mobile payment platforms: A business model perspective. Electronic Commerce Research and Applications, 40, 100908. https://doi.org/https://doi.org/10.1016/j.elerap.2019.100908
   Google Scholar
• Joia, L. A., & Altieri, D. (2018). Antecedents of continued use intention of e-hailing apps from the passengers’ perspective. The Journal of High Technology Management Research, 29(2), 204–215. https://doi.org/https://doi.org/10.1016/j.hitech.2018.09.006
   Google Scholar
• JPMorgan. (2020). E-commerce Payments Trends Report: Vietnam. Retrieved 30 April 2021 from https://www.jpmorgan.com/merchant-services/insights/reports/vietnam-2020
   Google Scholar
• Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs. MIS Quarterly, 23(2), 183–213. https://doi.org/https://doi.org/10.2307/249751
   Google Scholar
• Kim, Y., & Peterson, R. A. (2017). A Meta-analysis of Online Trust Relationships in E-commerce. Journal of Interactive Marketing, 38, 44–54. https://doi.org/https://doi.org/10.1016/j.intmar.2017.01.001
   Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
   Google Scholar
• Ko, E., Kim, E. Y., & Lee, E. K. (2009). Modeling consumer adoption of mobile shopping for fashion products in Korea. Psychology & Marketing, 26(7), 669–687. https://doi.org/https://doi.org/10.1002/mar.20294
   Google Scholar
• Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In R. J. Boland Jr., & R. A. Hirschheim (Eds.), Critical issues in information systems research (pp. 227–251). John Wiley and Sons.
   Google Scholar
• Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Applications, 10(3), 342–357. https://doi.org/https://doi.org/10.1016/j.elerap.2010.11.005
   Google Scholar
• Leonard-Barton, D. A. (1988). Implementation as mutual adaptation of technology and organization. Research Policy, 17(5), 251–267. https://doi.org/https://doi.org/10.1142/9789814295505_0019
   Google Scholar
• Li, H., & Liu, Y. (2014). Understanding post-adoption behaviors of e-service users in the context of online travel services. Information & Management, 51(8), 1043–1052. https://doi.org/https://doi.org/10.1016/j.im.2014.07.004
   Google Scholar
• Liang, T.-P., Ling, Y.-L., Yeh, Y.-H., & Lin, B. (2013). Contextual factors and continuance intention of mobile services. International Journal of Mobile Communications, 11(4), 313–329. https://doi.org/https://doi.org/10.1504/IJMC.2013.055746
   Google Scholar
• Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14–24. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2016.10.008
   Google Scholar
• Lin, H.-F. (2013). Examining the factors influencing knowledge management system adoption and continuance intention. Knowledge Management Research & Practice, 11(4), 389–404. https://doi.org/https://doi.org/10.1057/kmrp.2012.24
   Google Scholar
• Lin, -H.-H., & Wang, Y.-S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 43(3), 271–282. https://doi.org/https://doi.org/10.1016/j.im.2005.08.001
   Google Scholar
• Lu, H. P., & Yu‐Jen, S. P. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442–458. https://doi.org/https://doi.org/10.1108/10662240910981399
   Google Scholar
• Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 2. https://doi.org/https://doi.org/10.1108/IntR-05-2012-0100
   Google Scholar
• Marinković, V., Đorđević, A., & Kalinić, Z. (2020). The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: A UTAUT-based perspective. Technology Analysis & Strategic Management, 32(3), 306–318. https://doi.org/https://doi.org/10.1080/09537325.2019.1655537
   Google Scholar
• Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/https://doi.org/10.5465/amr.1995.9508080335
   Google Scholar
• Morgan, R. M., & Hunt, S. D. (1994). The Commitment-Trust Theory of Relationship Marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/https://doi.org/10.1177/002224299405800302
   Google Scholar
• Mou, J., Shin, D.-H., & Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: A longitudinal investigation. Behaviour & Information Technology, 36(2), 125–139. https://doi.org/https://doi.org/10.1080/0144929X.2016.1203024
   Google Scholar
• Muhammad, S. S., Dey, B., Alwi, S., & Babu, M. M. (2020). Examining the underlying attitudinal components driving technology adoption, adaptation behaviour and outcome in entirety. In N. P. Rana, E. L. Slade, G. P. Sahu, H. Kizgin, N. Singh, B. Dey, A. Gutierrez, & Y. K. Dwivedi (Eds.), Digital and social media marketing (pp. 197–226). Springer.
   Google Scholar
• Nabavi, A., Taghavi-Fard, M. T., Hanafizadeh, P., & Taghva, M. R. (2016). Information technology continuance intention: A systematic literature review. International Journal of E-Business Research (IJEBR), 12(1), 58–95. https://doi.org/https://doi.org/10.4018/IJEBR.2016010104
   Google Scholar
• Nafea, I., & Younas, M. (2014). Improving the performance and reliability of mobile commerce in developing countries. In I. Awan, M. Younas, X. Franch, & C. Quer (Eds.), Mobile web information systems (pp. 114–125). Cham.
   Google Scholar
• Nan, D., Kim, Y., Park, M. H., & Kim, J. H. (2020). What Motivates Users to Keep Using Social Mobile Payments? Sustainability, 12(17), 6878. https://doi.org/https://doi.org/10.3390/su12176878
   Google Scholar
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw-Hill.
   Google Scholar
• Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/https://doi.org/10.1177/002224378001700405
   Google Scholar
• Ozok, A. A., & Wei, J. (2010). An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: Results from a college student population. Electronic Commerce Research, 10(2), 111–137. https://doi.org/https://doi.org/10.1007/s10660-010-9048-y
   Google Scholar
• Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/https://doi.org/10.1080/10864415.2003.11044275
   Google Scholar
• Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Hong Thuy, T. T., & Merani, S. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Business & Management, 8(1), 1869363. https://doi.org/https://doi.org/10.1080/23311975.2020.1869363
   Google Scholar
• Qureshi, I., Fang, Y., Ramsey, E., McCole, P., Ibbotson, P., & Compeau, D. (2009). Understanding online customer repurchasing intention and the mediating role of trust – An empirical investigation in two developed countries. European Journal of Information Systems, 18(3), 205–222. https://doi.org/https://doi.org/10.1057/ejis.2009.15
   Google Scholar
• Rice, R. E., & Rogers, E. M. (1980). Reinvention in the Innovation Process. Knowledge, 1(4), 499–514. https://doi.org/https://doi.org/10.1177/107554708000100402
   Google Scholar
• Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604. https://doi.org/https://doi.org/10.1016/j.chb.2007.06.001
   Google Scholar
• Rogers, E. M.(1983). ( 3 ed). Diffutions of innovations. The Free Pass A Division of Macmillan Publishing Co.
   Google Scholar
• Rosenberg, L. J., & Czepiel, J. A. (1984). A marketing approach for customer retention. Journal of Consumer Marketing, 1(2), 45–51. https://doi.org/https://doi.org/10.1108/eb008094
   Google Scholar
• Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404. https://doi.org/https://doi.org/10.5465/amr.1998.926617
   Google Scholar
• Saeed, K. A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45(6), 376–386. https://doi.org/https://doi.org/10.1016/j.im.2008.06.002
   Google Scholar
• Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286–301. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2019.08.008
   Google Scholar
• Schmitz, K. W., Teng, J. T., & Webb, K. J. (2016). Capturing the Complexity of Malleable IT Use: Adaptive Structuration Theory for Individuals. MIS Quarterly, 40(3), 663–686. https://doi.org/https://doi.org/10.25300/MISQ/2016/40.3.07
   Google Scholar
• Shaikh, A. A., & Karjaluoto, H. (2015). Making the most of information technology & systems usage: A literature review, framework and future research agenda. Computers in Human Behavior, 49, 541–566. https://doi.org/https://doi.org/10.1016/j.chb.2015.03.059
   Google Scholar
• Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019). Antecedents of trust and continuance intention in mobile payment platforms: The moderating effect of gender. Electronic Commerce Research and Applications, 33, 100823. https://doi.org/https://doi.org/10.1016/j.elerap.2018.100823
   Google Scholar
• Sreelakshmi, C., & Prathap, S. K. (2020). Continuance adoption of mobile-based payments in Covid-19 context: An integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications, 16(4), 351–369. https://doi.org/https://doi.org/10.1108/IJPCC-06-2020-0069
   Google Scholar
• Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model. Industrial Management & Data Systems, 116(3), 508–525. https://doi.org/https://doi.org/10.1108/IMDS-05-2015-0195
   Google Scholar
• Tam, C., Santos, D., & Oliveira, T. (2018). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243–257. https://doi.org/https://doi.org/10.1007/s10796-018-9864-5
   Google Scholar
• Tyre, M. J., & Orlikowski, W. J. (1994). Windows of opportunity: Temporal patterns of technological adaptation in organizations. Organization Science, 5(1), 98–118. https://doi.org/https://doi.org/10.1287/orsc.5.1.98
   Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/https://doi.org/10.1287/mnsc.46.2.186.11926
   Google Scholar
• 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/https://doi.org/10.2307/30036540
   Google Scholar
• Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. https://doi.org/https://doi.org/10.1111/j.1365-2575.2011.00373.x
   Google Scholar
• Wang, L., Luo, X., Yang, X., & Qiao, Z. (2019). Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications. Information & Management, 56(7), 103150. https://doi.org/https://doi.org/10.1016/j.im.2019.02.005
   Google Scholar
• Wang, -M.-M., & Wang, -J.-J. (2019). Understanding Solvers’ Continuance Intention in Crowdsourcing Contest Platform: An Extension of Expectation-Confirmation Model. Journal of Theoretical and Applied Electronic Commerce Research, 14(3), 17–33. https://doi.org/https://doi.org/10.4067/S0718-18762019000300103
   Google Scholar
• Wang, R. J.-H., Malthouse, E. C., & Krishnamurthi, L. (2015). On the Go: How Mobile Shopping Affects Customer Purchase Behavior. Journal of Retailing, 91(2), 217–234. https://doi.org/https://doi.org/10.1016/j.jretai.2015.01.002
   Google Scholar
• Wang, Y., Min, Q., & Han, S. (2016). Understanding the effects of trust and risk on individual behavior toward social media platforms: A meta-analysis of the empirical evidence. Computers in Human Behavior, 56, 34–44. https://doi.org/https://doi.org/10.1016/j.chb.2015.11.011
   Google Scholar
• Wei, T. T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K. B., & Arumugam, S. (2009). What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388. https://doi.org/https://doi.org/10.1108/02635570910939399
   Google Scholar
• Weng, G. S., Zailani, S., Iranmanesh, M., & Hyun, S. S. (2017). Mobile taxi booking application service’s continuance usage intention by users. Transportation Research Part D: Transport and Environment, 57, 207–216. https://doi.org/https://doi.org/10.1016/j.trd.2017.07.023
   Google Scholar
• Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572–581. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2011.03.004
   Google Scholar
• Wu, Y., Choi, B., Guo, X., & Chang, K.-T.-T. (2017). Understanding user adaptation toward a new IT system in organizations: A social network perspective. Journal of the Association for Information Systems, 18(11), 787–813. https://doi.org/https://doi.org/10.17705/1jais.00473
   Google Scholar
• Yang, K. (2010). Determinants of US consumer mobile shopping services adoption: Implications for designing mobile shopping services. Journal of Consumer Marketing, 27(3), 262–270. https://doi.org/https://doi.org/10.1108/07363761011038338
   Google Scholar
• Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal of Hospitality Management, 91, 102683. https://doi.org/https://doi.org/10.1016/j.ijhm.2020.102683
   Google Scholar
• Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/https://doi.org/10.1016/j.dss.2012.10.034
   Google Scholar