Evaluating the Actual Usage of UPI Payment Services in North-East India through the Lens of UTAUT 2 Framework
Abstract
Abstract
Business in India witnessed many significant changes after the COVID-19 pandemic, one of them being the increasing use of digital money instead of hard cash. This use of digital money is facilitated by UPI (Unified Payment Interface) services, whereby payments can be easily made via easily accessible device like mobile phones. Though many players have created their own UPI services, only a few have the major market share and are able to provide requisite customer satisfaction. This research is done to understand the factors influencing the behavior intention and actual usage of UPI payment services. The identified factors may help the providers with sufficient knowledge to address the customer needs.
The relationship between latent variables with behavioral intention and actual usage of UPI Payment services is tested with structural model UTAUT2 proposed by Venkatesh et al. (2012) along with defining relation between behavioral intention and actual usage. The UTAUT2 questionnaire (adapted version) was utilized to collect data from 456 UPI users to understand the proposed model using variable based SmartPLS v4.0. The proposed structural model was moderate in predicting actual usage and behavioral intention to use UPI payment services through Q-Square predictive relevance calculations. In the study, it was proved that behavioral intention played a mediating role between the exogenous variables and actual usage. It was also verified that age and use experience plays a moderator role in the relationship between behavioral intention and actual usage. The study concludes that the proposed model is fit, reliable, and valid using various statistical analysis and can be considered as an addition to the existing literature on UTAUT2 and UPI usage.
Keywords: UPI payment services, Extended UTAUT2, PLS-SEM, SmartPLS, Predictive relevance, R-square, mediating variable, moderating variable.
Full Text:
PDFReferences
References
Ajzen, I. (1991). “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, 50(2), 179-211, doi:10.1016/0749-5978(91)90020-T
Anderson, J.C., and Gerbing, D.W. (1988), “Structural Equation Modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, vol. 103(3), pp. 411-423
Alalwan, A.A. (2020), “Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse”, International Journal of Information Management, vol. 50(1), 28-44
Baron R.M. & Kenny, D.A. (1986). “The moderator-mediator variable distinction in social psychological research: Conceptual, Strategic, and Statistical considerations”, Journal of Personality and Social Psychology, 51(6), 1173-1182, doi:10.1037//0022-3514.51.6.1173
Benitez, J., Henseler, J., Castilo, A., & Schuberth, F. (2020). “How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research”. Information & Management, 57(2), 103168, doi:10.1016/j.im.2019.05.003
Bentler, P.M. and Bonett, D.G. (1980). “Significance tests and goodness-of-fit in the analysis of covariance structures”, Psychological Bulletin, 88, 588-600
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), 399-426.
Chua, P.Y., Rezaei, S., Gu, M.-L., Oh, Y., and Jambulingam, M. (2018). “Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence”, Nankai Business Review International, 9(2), 118-142, doi:10.1108/NBRI-01-2017-0003
Cohen, J. (1992), “A Power Primer”, Quantitative Methods in Psychology, 155-159.
Dhiman, N., Arora, N., Dogra, N., & Gupta, A. (2019). “Consumer adoption of smartphone fitness apps: an extended UTAUT2 perspective”, Journal of Indian Business Research, 12(3), doi: 10.1108/JIBR-05-2018-0158
Duarte, P., and Pinho, J.C. (2019), “A mixed methods UTAUT2 – based approach to assess mobile health adoption”, Journal of Business Research, Vol. 102, 140-150
Du Plessis, K. & De Witte, H. (2020). “Job insecurity and employee well-being: The moderating role of emotion regulation”, Journal of Happiness Studies, 21(7), 2569 – 2589.
Farooq, M.S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., and Sajid, A. (2017). “Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2”, Interactive Technology and Smart Education, 14(4), 329-348, doi:10.1108/ITSE-06-2016-0015
Febrianto, G., Hidayatullah, S., and Ardianto, Y.T. (2018). “The effect of intention to usage to actual usage e-purchasing application”, International Journal of Scientific and Engineering Research, 9(12), 363-370
Foon, Y.S. & Fah, B.C. Yin (2011). “Internet Banking Adoption in Kuala Lumpur: An application of UTAUT model”, International Journal of Business and Management, 6(4), 161-167
Fornell, C., Larcker, D.F. (1981). “Evaluating structural equation models with unobservable variables and measurement error”. J. Mark. Res., 18, 39-50
Gupta, A., Dogra, N., and George, B. (2018). “What determines tourist adoption of smartphone apps?, An analysis based on the UTAUT2 framework”, Journal of Hospitality and Tourism Technology, 9(4), doi: 10.1108/JHTT-02-2017-0013
Hair, J.F., Ringle, C.M., & Sarstedt, M. (2011). “PLS-SEM: Indeed a silver bullet”. Journal of Marketing Theory and Practice, 19(2), 139-152, doi: 10.2753/MTP1069-6679190202
Hair, J.F., Risher, J.J., Sarstedt, M., and Ringle, C.M. (2019). “When to use and how to report the results of PLS-SEM”, European Business Review, Vol.31(1), pp. 2-24
Henseler, J., Ringle, C.M., and Sarstedt, M. (2015). “A new criterion for assessing Dicriminant validity in variance based structural equation modeling”, Journal of the Academy of Marketing Science, 43(1), 115-135.
Hu, L. –t., and Bentler, P.M. (1998). “Fit Indices in Covariance Structure Modeling : Sensitivity to underparameterized model missspecification”, Psychological Methods, 3(4), 424-453
Huang, C.Y., and Kao, Y.S. (2015). “UTAUT2 based predictions of factors influencing the technology acceptance of Phablets by DNP”, Mathematical Problems in Engineering, 2015(1), 1-23
Jambulingam, M. (2013). “Behavioral intention to adopt mobile technology among tertiary students”. World Applied Sciences Journal, 22(9), 1262-1271
Kwateng, K.O., Atiemo, K.A.O., and Appiah, C. (2018). “Acceptance and use of mobile banking: an application of UTAUT2”. Journal of Enterprise Information Management, 32(11), doi:10.1108/JEIM-03-2018-0055
Latif, M.I., & Zakaria, Z. (2020), “Factors determine the behavioral intention in adopting the Blockchain technology by Malaysian Public sector offers”, Journal of Advanced Research in Business and Management Studies, 20(1), 34-43
Limayem, M., Hirt, S.G., and Cheung, C.M. (2007). “How habit limits the predictive power of intention: the case of information systems continuance”, MIS Quarterly, 31(4), 705-737
Moradi, S., Karimi, L., & Khodadad Hosseini, S.H. (2021). “The moderating role of gender on the relationship between perceived organizational support and employee job satisfaction”. Journal of Human Resources Management Research, 10(1), 87-103
Nair, P.K., Ali, F., and Leong, L.C. (2015). “Factors affecting acceptance & use of ReWIND: validating the extended unified theory of acceptance and use of technology”. Interactive Technology and Smart Education, 12(3), 183-201, doi:10.1108/ITSE-02-2015-0001
Oliveira, T., Thomas, M., Baptista, G., and Campos, F. (2016). “Mobile payment: understanding the determinants of customer adoption and intention to recommend the technology”, Computers in Human Behavior, 61, 404-414
Preacher, K.J. & Hayes, A.F. (2008). “Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models”. Behavior Research, 40, 879-891
Razi-ur-Rahim, M., Rabbani, M.R., Udding, F., & Shaikh, Z.H. (2024). Adoption of UPI among Indian users: Using extended meta-UTAUT model. Digital Business, 4(2): 100093. Doi: 10.1016/j.digbus.2024.100093
Ringle, C.M., Sarstedt, M., Mitchell, R., & Gudergan, S.P. (2020). “Partial least squares structural equation modeling in HRM research”. The International Journal of Human Resource Management, 31(12), 1617-1643
Sair, S.A., & Danish, R.Q. (2018). “Effect of Performance Expectancy and Effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers”. Pakistan Journal of Commerce and Social Sciences, 12(2), 501-520
Sarstedt, M., Ringle, C.M. and Hair, J. (2017). “Partial Least Squares Structural Equation Modeling”, In book: Handook of Market research: Chapter 15: doi:10.1007/978-3-319-05542-8-15-1, downloaded @https://www.researchgate.net/publication/319669432
Shrout, P.E. & Bolger, N. (2002). “Mediation in Experimental and Nonexperimental Studies: New Procedures and Recommendation”, Psychological Methods, 7(4), 422-445, doi:10.1037/1082-989X.7.4.422
Tak, P., and Panwar, S. (2017). “Using UTAUT 2 model to predict mobile app based shopping : evidence from India”, Journal of Indian Business Research, 9(4), doi: 10.1108/JIBR-11-2016-0132
Tamilmani, K., Rana, N., Dwivedi, Y., Sahu, G.P. and Sian, R., (2018). “Exploring the role of Price Value for understanding consumer adoption of technology: A review and meta-analysis of UTAUT2 based empirical studies”, PACIS 2018 Proceedings, 64, https://aisel.aisnet.org/pacis2018/64
Tan, G.W.H, Ooi, K.B., Sim, J.J. & Phusavat, K. (2011). “Determinants of Mobile Learning Adoption: An Empirical Analysis”, Journal of Computer Information Systems, 52(3), 82-91, doi:10.1080/08874417.2012.11645561
Tao, D.(2009). “Intention to use and Actual use of Electronic Information Resources: Further exploring technology acceptance model (TAM)”, AMIA Annual Symposium Proceedings Archive, 629-633
Teo, T., & Noyes, J. (2012). “Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the UTAUT”, Interactive Learning Environments, 22(1), 1-16.
Venkatesh, V., Morris, M.G., Davis, G.B. & Davis, F.D. (2003). “Use acceptance of information technology: towards a unified view”, MIS Quarterly, vol. 27(3), 425-478
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”. Management Information Systems Quarterly, 36(1), 157-178, doi: 10.2307/41410412
Wu, Y.L., Tao, Y.H., and YangJ, P.C. (2008). “Using UTAUT to explore the behavior of 3G mobile communication users”, Conference: Industrial Engineering and Engineering management, Source: IEEE Xplore, doi: 10.1109/IEEM.2007/4419179
Yuan, S., Ma, W., Kanthawala, S. and Peng, W. (2015). “Keep using MyHealth apps: Discover user’s perception of Health and Fitness apps with the UTAUT2 model”, Telemedicine and e-Health, 21(9), doi:10.1089/tmi.2014.0148
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Journal of Management in Practice (Online Only)