Modeling Switching Intention of Mobile Payment Service in the Moderation of Usage Inertia and IT Self-Efficacy: Implications for User Education

References

• Ashfaq, M., Yun, J., & Yu, S. (2020). My smart speaker is cool! Perceived coolness, perceived values, and users’ attitude toward smart speakers. International Journal of Human–Computer Interaction, 7(6), 560–573. https://doi.org/10.1080/10447318.2020.1841404
   Google Scholar
• Bandura, A. (1995). Self-efficacy in changing societies. Cambridge University. http://tecfaetu.unige.ch/etu-maltt/wall-e/gosetto0/bases/mooc_motivation/ressources_motivations/auto_efficacite_bandura2.pdf#page=18
   Google Scholar
• Barnes, W., Gartland, M., & Stack, M. (2004). Old habits die hard: Path dependency and behavioral lock-in. Journal of Economic Issues, 38(2), 371–e377. https://doi.org/10.1080/00213624.2004.11506696
   Web of Science ®Google Scholar
• Bölen, M. C. (2020). From traditional wristwatch to smartwatch: Understanding the relationship between innovation attributes, switching costs and consumers' switching intention. Technology in Society, 63, 101439. https://doi.org/10.1016/j.techsoc.2020.101439
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1177/0092070302250897
   Web of Science ®Google Scholar
• Calvo-Porral, C., Faíña-Medín, A., & Nieto-Mengotti, M. (2017). Satisfaction and switching intention in mobile services: Comparing lock-in and free contracts in the Spanish market. Telematics and Informatics, 34(5), 717–729. https://doi.org/10.1016/j.tele.2016.08.022
   Web of Science ®Google Scholar
• Cao, J., Liu, F., Shang, M., & Zhou, X. (2021). Toward street vending in post COVID-19 China: Social networking services information overload and switching intention. Technology in Society, 66, 101669. https://doi.org/10.1016/j.techsoc.2021.101669
   Web of Science ®Google Scholar
• Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
   Web of Science ®Google Scholar
• Chen, X., Su, L., & Carpenter, D. (2020). Impacts of situational factors on consumers’ adoption of mobile payment services: A decision-biases perspective. International Journal of Human–Computer Interaction, 36(11), 1085–1093. https://doi.org/10.1080/10447318.2020.1722400
   Web of Science ®Google Scholar
• Cheng, S., Lee, S. J., & Choi, B. (2019). An empirical investigation of users’ voluntary switching intention for mobile personal cloud storage services based on the push-pull-mooring framework. Computers in Human Behavior, 92, 198–215. https://doi.org/10.1016/j.chb.2018.10.035
   Web of Science ®Google Scholar
• Chien, T. C. (2012). Computer self‐efficacy and factors influencing e‐learning effectiveness. European Journal of Training and Development, 36(7), 670–686. https://doi.org/10.1108/03090591211255539
   Google Scholar
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
   Web of Science ®Google Scholar
• Côté, S. (2007). Group emotional intelligence and group performance. In M. A. Neale, E. Mannix, & C. Anderson (Eds.), Research on managing groups and teams: Affect and groups (Vol. 10, pp. 309–336). JAI Press.
   Google Scholar
• de Morais Watanabe, E. A., Alfinito, S., Curvelo, I. C. G., & Hamza, K. M. (2020). Perceived value, trust and purchase intention of organic food: A study with Brazilian consumers. British Food Journal, 122(4), 1070–1184. https://doi.org/10.1108/BFJ-05-2019-0363
   Web of Science ®Google Scholar
• Ding, C. G., & Jane, T. D. (2015). Reexamining the effectiveness of the ULMC technique in CMV detection and correction. In Academy of management proceedings (Vol. 2015, No. 1, p. 13078). Academy of Management.
   Google Scholar
• Dogra, N., Bakshi, S., & Gupta, A. (2022). Exploring the switching intention of patients to e-health consultations platforms: Blending inertia with push–pull–mooring framework. Journal of Asia Business Studies. https://doi.org/10.1108/JABS-02-2021-0066
   Google Scholar
• Fields, D. (2021). An illustration of the actual steps in development and validation of a multi-item scale for quantitative research: From theory to practice. In M. Bocarnea, B. Winston, & D. Dean (Eds.), Handbook of research on advancements in organizational data collection and measurements: Strategies for addressing attitudes, beliefs, and behaviors (pp. 51–69). IGI Global.
   Google Scholar
• Ghazal, S., Al-Samarraie, H., & Aldowah, H. (2018). “I am still learning”: Modeling LMS critical success factors for promoting students’ experience and satisfaction in a blended learning environment. IEEE Access, 6, 77179–77201. https://doi.org/10.1109/ACCESS.2018.2879677
   Web of Science ®Google Scholar
• Handarkho, Y. D., Harjoseputro, Y., Samodra, J. E., & Irianto, A. B. P. (2021). Understanding proximity mobile payment continuance usage in Indonesia from a habit perspective. Journal of Asia Business Studies, 15(3), 420–440. https://doi.org/10.1108/JABS-02-2020-0046
   Web of Science ®Google Scholar
• Hyun, M. Y., Kim, H. C., & O’Keefe, R. M. (2014). Inter-satisfaction between website and automated call distribution (ACD) systems. Journal of Travel & Tourism Marketing, 31(8), 1039–1056. https://doi.org/10.1080/10548408.2014.892467
   Web of Science ®Google Scholar
• Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system. Education and Information Technologies, 24(5), 2653–2675. https://doi.org/10.1007/s10639-019-09925-z
   Web of Science ®Google Scholar
• Igbaria, M., & Parasuraman, S. (1989). A path analytic study of individual characteristics, computer anxiety and attitudes toward microcomputers. Journal of Management, 15(3), 373–388. https://doi.org/10.1177/014920638901500302
   Web of Science ®Google Scholar
• Igbaria, M., Zinatelli, N., & Cavaye, A. L. M. (1998). Analysis of information technology success in small firms in New Zealand. International Journal of Information Management, 18(2), 103–119. https://doi.org/10.1016/S0268-4012(97)00053-4
   Web of Science ®Google Scholar
• Kang, N., Ding, D., Van Riemsdijk, M. B., Morina, N., Neerincx, M. A., & Brinkman, W. P. (2021). Self-identification with a virtual experience and its moderating effect on self-efficacy and presence. International Journal of Human–Computer Interaction, 37(2), 181–196. https://doi.org/10.1080/10447318.2020.1812909
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.chb.2009.10.013
   Web of Science ®Google Scholar
• Kurtovic, A., Vrdoljak, G., & Idzanovic, A. (2019). Predicting procrastination: The role of academic achievement, self-efficacy and perfectionism. International Journal of Educational Psychology, 8(1), 1–26. https://doi.org/10.17583/ijep.2019.2993
   Web of Science ®Google Scholar
• Lam, J. C., & Lee, M. K. (2006). Digital inclusiveness–longitudinal study of Internet adoption by older adults. Journal of Management Information Systems, 22(4), 177–206. https://doi.org/10.2753/MIS0742-1222220407
   Web of Science ®Google Scholar
• Lehmer, F., & Möller, J. (2010). Interrelations between the urban wage premium and firm-size wage differentials: A microdata cohort analysis for Germany. The Annals of Regional Science, 45(1), 31–53. https://doi.org/10.1007/s00168-009-0290-y
   Web of Science ®Google Scholar
• Li, J., Liu, M., & Liu, X. (2016). Why do employees resist knowledge management systems? An empirical study from the status quo bias and inertia perspectives. Computers in Human Behavior, 65, 189–200. https://doi.org/10.1016/j.chb.2016.08.028
   Web of Science ®Google Scholar
• Lim, S. H., Kim, D. J., Hur, Y., & Park, K. (2019). An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile fintech payment services. International Journal of Human–Computer Interaction, 35(10), 886–898. https://doi.org/10.1080/10447318.2018.1507132
   Web of Science ®Google Scholar
• Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134–159. https://doi.org/10.1108/IntR-05-2012-0100
   Web of Science ®Google Scholar
• Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003
   Web of Science ®Google Scholar
• Nel, J., & Boshoff, C. (2020). Status quo bias and shoppers’ mobile website purchasing resistance. European Journal of Marketing, 54(6), 1433–1466. https://doi.org/10.1108/EJM-02-2018-0144
   Web of Science ®Google Scholar
• Park, S. C., & Ryoo, S. Y. (2013). An empirical investigation of end-users’ switching toward cloud computing: A two factor theory perspective. Computers in Human Behavior, 29(1), 160–170. https://doi.org/10.1016/j.chb.2012.07.032
   Web of Science ®Google Scholar
• Patterson, P. G., & Smith, T. (2003). A cross-cultural study of switching barriers and propensity to stay with service providers. Journal of Retailing, 79(2), 107–e120. https://doi.org/10.1016/S0022-4359(03)00009-5
   Web of Science ®Google Scholar
• Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36(1), 21–e42. https://doi.org/10.2307/41410404
   Web of Science ®Google Scholar
• Rieder, A., Eseryel, U. Y., Lehrer, C., & Jung, R. (2021). Why users comply with wearables: The role of contextual self-efficacy in behavioral change. International Journal of Human–Computer Interaction, 37(3), 281–294. https://doi.org/10.1080/10447318.2020.1819669
   Web of Science ®Google Scholar
• Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research, 8(1), 177–191. https://www.learntechlib.org/p/111397/ https://doi.org/10.28945/166
   Google Scholar
• Sharma, S., Aragón-Correa, J. A., Rueda, A. (2006). The contingent influence of organizational capabilities on environmental strategy in North American and European ski resorts. In Proceedings of the International Association for Business and Society (Vol. 17, pp. 201–206). https://www.pdcnet.org/iabsproc/content/iabsproc_2006_0017_0201_0206.
   Google Scholar
• Siddiqui, M. I. A., & Siddiqui, D. A. (2020). Impact of green supply chain management on economic and organizational performance of food industry in Sindh and Punjab. CenRaPS Journal of Social Sciences, 2(3), 439–455. https://doi.org/10.46291/cenraps.v2i3.42
   Google Scholar
• Sun, Y., Liu, D., Chen, S., Wu, X., Shen, X. L., & Zhang, X. (2017). Understanding users' switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework. Computers in Human Behavior, 75, 727–738. https://doi.org/10.1016/j.chb.2017.06.014
   Web of Science ®Google Scholar
• Wang, L., Luo, X. R., 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/10.1016/j.im.2019.02.005
   Web of Science ®Google Scholar
• Wang, J., Zheng, B., Liu, H., & Yu, L. (2021). A two-factor theoretical model of social media discontinuance: role of regret, inertia, and their antecedents. Information Technology & People, 34(1), 1–24. https://doi.org/10.1108/ITP-10-2018-0483
   Web of Science ®Google Scholar
• Williams, L. J., Cote, J. A., & Buckley, M. R. (1989). Lack of method variance in self-reported affect and perceptions at work: reality or artifact? Journal of Applied Psychology, 74(3), 462–468. https://doi.org/10.1037/0021-9010.74.3.462
   Web of Science ®Google Scholar
• Xu, H., & Gupta, S. (2009). The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services. Electronic Markets, 19(2–3), 137–149. https://doi.org/10.1007/s12525-009-0012-4
   Web of Science ®Google Scholar
• Xu, X. Y., Wang, L. Y., Zhao, K., & Chang, F. K. (2021). The migration of viewers in gaming streaming: The perspective of a push-pull-mooring model. International Journal of Human–Computer Interaction, 37(14), 1330–1346. https://doi.org/10.1080/10447318.2021.1886480
   Web of Science ®Google Scholar
• Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129–142. https://doi.org/10.1016/j.chb.2011.08.019
   Web of Science ®Google Scholar
• Zhang, Q., Onita, C. G., Shane Banks, M., & Zhang, X. (2021). Understanding switching behavior of mobile payment enabled transportation apps: A push-pull-mooring perspective. Issues in Information Systems, 22(1), 124–135. https://doi.org/10.48009/1_iis_2021_124-135
   Google Scholar
• Zhou, W., Gu, X., & Yang, X. (2021). How does employees’ Zhong-Yong thinking improve their innovative behaviours? The moderating role of person–organisation fit. Technology Analysis & Strategic Management, 34(7), 803–814. https://doi.org/10.1080/09537325.2021.1925103
   Web of Science ®Google Scholar