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Abstract
This study investigates the influence of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Condition on Behavioral Intention to use the OVO digital payment system in Indonesia. Utilizing a quantitative approach with regression and T-test analysis, data were collected from 150 respondents representing various age groups and genders. The analysis reveals that all four independent variables significantly and positively affect users’ behavioral intention to adopt and continue using OVO. Performance Expectancy has the strongest influence, followed by Facilitating Condition, Social Influence, and Effort Expectancy. These findings confirm that users are more likely to adopt OVO when they perceive it as useful, easy to use, socially supported, and adequately facilitated by infrastructure. The study provides important implications for fintech developers, policymakers, and marketers to enhance user adoption through targeted strategies tailored to these key determinants.
Keywords: Behavioral Intention, Digital Payment System, Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Condition
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References
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References
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
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
Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003
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
Park, Y., Lee, H., & Kim, Y. (2007). Understanding the behavioral intention to play online games: An extension of the theory of planned behavior. Computers in Human Behavior, 23(6), 2220–2239. https://doi.org/10.1016/j.chb.2006.03.001
Tan, G. W. H., Ooi, K. B., Chong, S. C., & Lin, B. (2010). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-neural networks approach. Computers & Education, 55(3), 1036–1044. https://doi.org/10.1016/j.compedu.2010.04.014
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
Zhou, T. (2012). Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Computers in Human Behavior, 28(4), 1518–1525. https://doi.org/10.1016/j.chb.2012.03.021