QRIS ADOPTION IN RELIGIOUS ORGANIZATIONS: EXTENDED UTAUT2 WITH GEOGRAPHIC MODERATION

Mei Hotma Mariati Munthe; Jadongan Sijabat; Johan Putra Octavian Siahaan

doi:10.22437/jiituj.v10i2.53581

Authors

Mei Hotma Mariati Munthe Universitas HKBP Nommensen https://orcid.org/0009-0003-4487-106X
Jadongan Sijabat Universitas HKBP Nommensen Pematangsiantar https://orcid.org/0009-0001-1257-9908
Johan Putra Octavian Siahaan Universitas HKBP Nommensen Pematangsiantar https://orcid.org/0000-0003-0018-5001

DOI:

https://doi.org/10.22437/jiituj.v10i2.53581

Keywords:

Digital Divide, Geographic Moderation, Mobile Payment, QRIS Adoption, UTAUT2

Abstract

This study examines the determinants of Quick Response Code Indonesian Standard (QRIS) mobile payment usage within religious organizations by applying an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The model integrates perceived risk, perceived security, and exposure to innovation, while testing geographic location as a moderating variable. Using a quantitative approach, a survey was conducted among 4,516 congregants of the Huria Kristen Batak Protestan (HKBP) Church across 32 districts in Indonesia, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The results indicate that performance expectancy, facilitating conditions, habit, perceived security, and exposure to innovation significantly influence QRIS usage, with habit emerging as the most dominant predictor. In contrast, effort expectancy and perceived risk do not show significant effects, suggesting that ease of use has become standardized and institutional trust mitigates perceived risk in this context. Geographic location significantly moderates the effects of effort expectancy, facilitating conditions, and habit, highlighting the role of regional digital infrastructure disparities. The model explains 74.7% of the variance in QRIS usage. This study contributes to the literature by extending UTAUT2 into a trust-based non-profit institutional context and introducing geographic moderation in digital payment adoption research, while providing practical insights for policymakers and religious institutions to design more inclusive and infrastructure-sensitive digital financial strategies.

Downloads

Download data is not yet available.

References

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2. Information Systems Frontiers, 19(3), 791–807. https://doi.org/10.1007/s10796-015-9592-9

Becker, J. M., Ringle, C. M., & Sarstedt, M. (2018). Estimating moderating effects in PLS-SEM. Long Range Planning, 51(1), 9–25. https://doi.org/10.1016/j.lrp.2017.01.001

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2020). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT). International Journal of Information Management, 55, 102112. https://doi.org/10.1016/j.ijinfomgt.2020.102112.

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00033-3

Hayes, D., & Macleod, N. (2006). Packaging places: Designing heritage trails using an experience economy perspective to maximize visitor engagement. Journal Of Vacation Marketing, 13(1), 45. https://doi.org/10.1177/1356766706071205

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-10-2021-0666.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8.

Hilbert, M. (2016). The digital divide conceptualization. Telecommunications Policy, 40(6), 567–581. https://doi.org/10.1016/j.telpol.2016.05.005

Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2018). M-payment adoption for bottom of pyramid segment: an empirical investigation. International Journal of Bank Marketing, 37(1), 362. https://doi.org/10.1108/ijbm-01-2018-0013

Johnson, V., Kiser, A. I. T., Washington, R., & Torres, R. (2017). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111. https://doi.org/10.1016/j.chb.2017.10.035

Kapoor, K. K., Dwivedi, Y. K., & others. (2022). Advances in digital transformation research. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2021.102414

Koenig‐Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. Service Industries Journal, 35(10), 537. https://doi.org/10.1080/02642069.2015.1043278

Liébana‐Cabanillas, F., Sánchez‐Fernández, J., & Muñoz‐Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464. https://doi.org/10.1016/j.chb.2014.03.022

Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting m-commerce acceptance. International Journal of Information Management, 37(5), 470–478. https://doi.org/10.1016/j.ijinfomgt.2017.05.003.

Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of mobile banking adoption. International Journal of Information Management, 46, 224–236. https://doi.org/10.1016/j.ijinfomgt.2019.01.013.

Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment adoption. Information Systems Frontiers, 18(2), 315–332. https://doi.org/10.1007/s10796-015-9593-8

Patil, P. P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144

Pick, J., Sarkar, A., & Johnson, J. (2020). Digital divide and regional adoption. Government Information Quarterly, 37(1). https://doi.org/10.1016/j.giq.2019.101417.

Raza, S. A., Shah, N., & Ali, M. (2019). Determinants of mobile wallet adoption. International Journal of Bank Marketing, 37(7), 1471–1490. https://doi.org/10.1108/IJBM-10-2018-0256.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Shankar, A., & Datta, B. (2018). Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective. Global Business Review, 19. https://doi.org/10.1177/0972150918757870

Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2015). Extending UTAUT2 in mobile payment adoption. Psychology & Marketing, 32(3), 305–324. https://doi.org/10.1002/mar.20764.

Sun, Y., Wang, N., & Guo, X. (2021). Understanding digital divide. Information & Management, 58(1). https://doi.org/10.1016/j.im.2020.103414.

Tam, C., Santos, D., & Oliveira, T. (2020). Exploring mobile payment continuance intention. Information Systems Frontiers, 22, 1213–1233. https://doi.org/10.1007/s10796-019-09902-9.

van Deursen, A., & van Dijk, J. (2019). The digital inequality shift. New Media & Society, 21(2), 354–375. https://doi.org/10.1177/1461444818797134.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of IT: Extending UTAUT2. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412

Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2011). 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. https://doi.org/10.1016/j.chb.2011.08.019