FEATURE INNOVATION AND PERCEIVED EASE OF USE AS PREDICTORS OF E-WALLET USAGE: EVIDENCE FROM A LOCAL RETAIL BUSINESS IN PALEMBANG, INDONESIA
DOI:
https://doi.org/10.61397/mfc.v4i2.591Keywords:
e-wallet usage; feature innovation; perceived ease of use; digital payments; technology acceptance; IndonesiaAbstract
This study examines the associations of feature innovation and perceived ease of use with e-wallet usage among consumers of Global Cell, a digital retail business in Palembang, Indonesia. A quantitative cross-sectional survey was conducted with 99 purposively selected consumers who used non-cash payment services. Data were collected through a five-point Likert-scale questionnaire and analysed using multiple linear regression in IBM SPSS Statistics 26. The reported instrument results indicated item–total correlations above the stated critical value and Cronbach’s alpha coefficients of .937 for feature innovation, .888 for perceived ease of use, and .899 for e-wallet usage. The regression results showed that feature innovation was positively associated with e-wallet usage (B = 0.499, β = .587, t = 10.243, p < .001), as was perceived ease of use (B = 0.458, β = .403, t = 7.036, p < .001). The overall model was statistically significant, F(2, 96) = 284.432, p < .001, and explained 85.6% of the observed variance in e-wallet usage (R² = .856; adjusted R² = .853). Feature innovation had the larger standardized coefficient in this model. The findings suggest that users’ evaluations of functional feature development and operational simplicity are relevant correlates of e-wallet usage in a local retail setting. Because the study is cross-sectional, purposively sampled, and based on self-reported measures, the results should be interpreted as associations rather than causal effects or population-wide estimates. The paper contributes context-specific evidence to technology-acceptance research and offers practical considerations for retailers and digital-payment providers.
References
Abdullah, S., & Khan, M. N. (2021). Determining mobile payment adoption: A systematic literature search and bibliometric analysis. Cogent Business & Management, 8(1), Article 1893245. https://doi.org/10.1080/23311975.2021.1893245
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Bank Indonesia. (2017). Peraturan Bank Indonesia Nomor 19/12/PBI/2017 tentang penyelenggaraan teknologi finansial. Bank Indonesia.
Bank Indonesia. (2019). Peraturan Anggota Dewan Gubernur Nomor 21/18/PADG/2019 tentang implementasi standar nasional Quick Response Code untuk pembayaran. Bank Indonesia.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India—An empirical study. International Journal of Bank Marketing, 37(7), 1590–1618. https://doi.org/10.1108/IJBM-09-2018-0256
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
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165–181. https://doi.org/10.1016/j.elerap.2007.02.001
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
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/10.1080/07421222.2003.11045748
Estiyanti, N. M., Agustia, D., Mulia, R. A., Alfarisyi, R., Frandha, R., Hidayanto, A. N., & Kurnia, S. (2021). The impact of perceived usability on mobile wallet acceptance: A case of GoPay Indonesia. International Journal of Innovation and Learning, 30(2), 154–174. https://doi.org/10.1504/IJIL.2021.117220
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
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., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jain, V., & Jain, N. (2025). From cash to clicks: A systematic review of digital payment adoption using the ADO framework. Asia-Pacific Journal of Management Research and Innovation, 32(4). https://doi.org/10.1177/09711023241312523
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
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83
Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. https://doi.org/10.1016/j.elerap.2008.11.006
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, 464–478. https://doi.org/10.1016/j.chb.2014.03.022
Mallat, N. (2007). Exploring consumer adoption of mobile payments—A qualitative study. The Journal of Strategic Information Systems, 16(4), 413–432. https://doi.org/10.1016/j.jsis.2007.08.001
Mombeuil, C. (2020). An exploratory investigation of factors affecting and best predicting the renewed adoption of mobile wallets. Journal of Retailing and Consumer Services, 55, Article 102127. https://doi.org/10.1016/j.jretconser.2020.102127
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497–510. https://doi.org/10.1016/j.im.2014.03.006
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/10.1080/10864415.2003.11044275
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216. https://doi.org/10.1016/j.elerap.2009.07.005
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
Singh, N., & Sinha, N. (2020). How perceived trust mediates merchant’s intention to use a mobile wallet technology. Journal of Retailing and Consumer Services, 52, Article 101894. https://doi.org/10.1016/j.jretconser.2019.101894
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Talwar, S., Dhir, A., Khalil, A., Mohan, G., & Islam, A. K. M. N. (2020). Point of adoption and beyond. Initial trust and mobile-payment continuation intention. Journal of Retailing and Consumer Services, 55, Article 102086. https://doi.org/10.1016/j.jretconser.2020.102086
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144
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., & 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/10.1287/mnsc.46.2.186.11926
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. L., & 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
World Bank. (2022). The Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. World Bank. https://doi.org/10.1596/978-1-4648-1897-4
Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, 21(5), 527–540. https://doi.org/10.1108/10662241111176353
Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034
Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems, 114(6), 936–948. https://doi.org/10.1108/IMDS-02-2014-0068
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Tania Faradilla, Erdiansyah Erdiansyah, Mohammad Nur Arriyanto

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.





