Unveiling the Dynamics of Online Shopping Intentions: A Unified Theory of Acceptance and Use of Technology (UTAUT) Perspective
Abstract:
Online shopping has become increasingly prevalent, necessitating a deeper understanding of consumer behavior. This study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore key factors influencing online shopping intentions and behaviors. To investigate the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on behavioral intention and actual use behavior in online shopping. A structured online survey resulted in 335 valid responses. Statistical analyses, including reliability testing, validity checks, and regression analysis, were performed using SPSS. The findings reveal that performance expectancy (β = 0.373, p < 0.001), effort expectancy (β = 0.091, p < 0.001), and social influence (β = 0.097, p < 0.001) significantly positively impact behavioral intention. Facilitating conditions (β = 0.405, p < 0.001) and behavioral intention (β = 0.409, p < 0.001) significantly influence actual use behavior. The model explained 16.4% of the variance in behavioral intention and 16.8% in use behavior. The study underscores the importance of technological and social factors in shaping online shopping behaviors. Insights from this research can guide e-commerce platform developers in improving user experience and engagement.
KeyWords:
UTAUT, Online Shopping, Behavioral Intention, Use Behavior, E-Commerce
References:
- Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177-1190. https://doi.org/10.1016/j.tele.2017.05.008
- Banay, J. G., Ong, J.-L. S., Ong, D. U., Malubag, R. K. T., Olivar, J. A. H., & Balaria, F. E. (2021). Factors influencing consumers’ participation in E-Commerce in the New Normal. International Journal of Advanced Engineering Management and Science, 7(7), 6. https://doi.org/10.22161/ijaems.77.2
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
- Cheung, C. M. K., & Lee, M. K. O. (2010). A theoretical model of intentional social action in online social networks. Decision Support Systems, 49(1), 24-30. https://doi.org/10.1016/j.dss.2009.12.006
- Dhanapal, S., Vashu, D., & Subramaniam, T. (2015). Perceptions on the challenges of online purchasing: A study from baby boomers, generation X and generation Y’s point of views. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2550117
- Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
- 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
- Guthrie, C., Wamba, S. F., & Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570. https://doi.org/10.1016/j.jretconser.2021.102570
- Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 35(2), 183-191. https://doi.org/10.1016/j.ijinfomgt.2014.12.005
- Hamzah, A. (2018). Mapping the determining factors of mobile learning adoption in high school. Proceedings of the 19th International Symposium on Management, 1–10. https://doi.org/10.1145/3291078.3291082
- Jadil, Y., Rana, N. P., & Dwivedi, Y. K. (2021). A meta-analysis of the UTAUT model in the mobile banking literature: The moderating role of sample size and culture. Journal of Business Research, 132, 354–365. https://doi.org/10.1016/j.jbusres.2021.04.052
- Kim, D. J., & Peterson, R. A. (2017). A meta-analysis of online trust relationships in e-commerce. Journal of Business Research, 70, 247-257. https://doi.org/10.1016/j.jbusres.2016.08.016
- Lee, D., Moon, J., Kim, Y. J., & Yi, M. Y. (2015). Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty. Information & Management, 52(3), 295-304. https://doi.org/10.1016/j.im.2014.12.001
- Liang, T. P., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5-14. https://doi.org/10.2753/JEC1086-4415160201
- Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143. https://doi.org/10.2307/25148720
- Trisnawati, J. D. (2020). Effect of use of mobile banking on the studentu2019s satisfaction and loyalty. Proceedings of the 19th International Symposium on Management (INSYMA 2022), 1–15. https://doi.org/10.2991/aebmr.k.200127.021
- 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
- Zhang, M. (2023). Sustainability transitions in e-commerce research—Academic achievements and impediments [Review of Sustainability transitions in e-commerce research—Academic achievements and impediments]. Circular Economy and Sustainability, 3(4), 1725. https://doi.org/10.1007/s43615-023-00252-7
- Zhou, T. (2013). Understanding continuance usage of mobile services: The role of perceived value. Electronic Commerce Research and Applications, 12(2), 123-130. https://doi.org/10.1016/j.elerap.2012.11.003