TECHNOLOGY ACCEPTANCE MODEL FOR IMPROVING PERCEIVED EASE OF USE AND PERCEIVED USEFULNESS IN THE ISLAMIC BANKING INDUSTRY
DOI:
https://doi.org/10.22437/jmk.v15i03.59279Abstract
Abstract
Digital transformation is driving Islamic banks to maximize their corporate websites as tools for marketing, education, and communication with customers. However, the product information presented on these websites remains incomplete, so the effectiveness of digital services has not yet been fully realized. This study aims to explore the application of the Technology Acceptance Model in the development of corporate websites, with a focus on Perceived Ease of Use and Perceived Usefulness. This study employs a descriptive qualitative approach through observation, in-depth interviews, and documentation involving supervisors, marketing staff, customer service representatives, and clients. Data analysis was conducted using the interactive model by Miles, Huberman, and Saldaña through source triangulation to ensure the validity of the research findings. The findings indicate that a user-friendly website enhances the perception of benefits, broadens access to information, supports Islamic financial literacy, and strengthens the effectiveness of digital marketing communication. This study concludes that developing a website focused on the user experience can support digital transformation and sustainably improve the quality of Islamic banking services.
Keywords: Technology Acceptance Model (TAM); Perceived Ease of Use; Perceived Usefulness; Corporate Website Implementation; Islamic Banking Industry.
References
Abdenebi, H. B. (2023). M-banking adoption from the developing countries’ perspective: A mediated model. Digital Business, 3, 100065. https://doi.org/10.1016/j.digbus.2023.100065
Abuhassna, H., Yahaya, N., Megat Zakaria, M. A. Z., Mohd Zaid, N., Abu Samah, N., Awae, F., Nee, C. K., & Alsharif, A. H. (2023). Trends in the use of the Technology Acceptance Model (TAM) for online learning: A bibliometric and content analysis. International Journal of Information and Education Technology, 13(1). https://doi.org/10.18178/ijiet.2023.13.1.1788
Abu-Taieh, E. M., AlHadid, I., Abu-Tayeh, S., Masa'deh, R., Alkhawaldeh, R. S., Khwaldeh, S., & Alrowwad, A. (2022). Continued Intention to Use M-Banking in Jordan by Integrating UTAUT, TPB, TAM, and Service Quality with ML. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 120. https://doi.org/10.3390/joitmc8030120
Acikgoz, F., Perez-Vega, R., Okumus, F., & Stylos, N. (2023). Consumer engagement with AI-powered voice assistants: A behavioral reasoning perspective. Psychology & Marketing, 40(11), 2226–2243. https://doi.org/10.1002/mar.21873
Aiolfi, S. (2023). How shopping habits change with artificial intelligence: Smart speakers' usage intention. International Journal of Retail & Distribution Management, 51(9/10), 1288–1312. https://doi.org/10.1108/IJRDM-11-2022-0441
Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the Technology Acceptance Model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28, 15381–15413. https://doi.org/10.1007/s10639-023-11816-3
Ali, M., Hossain, M. S., & Rahman, M. (2025). Measuring the influence of FinTech innovation on consumers’ attitudes: The moderating role of perceived usefulness. Sustainable Futures, 10, 100885. https://doi.org/10.1016/j.sftr.2025.100885
Almajali, D. A., Masa'Deh, R., & Dahalin, Z. M. (2022). Factors influencing the adoption of cryptocurrency in Jordan: An application of the extended TRA model. Cogent Social Sciences, 8(1), 2103901. https://doi.org/10.1080/23311886.2022.2103901
Alshebami, A. S. (2022). Crowdfunding platforms as a substitute financing source for young Saudi entrepreneurs: Empirical evidence. SAGE Open, 12(3). https://doi.org/10.1177/21582440221126511
Alshurideh, M., et al. (2025). Beyond compliance: Exploring the synergy of Islamic fintech and CSR in fostering inclusive financial adoption. Future Business Journal, 11, Article 7. https://doi.org/10.1186/s43093-025-00430-z
Alturki, U., & Aldraiweesh, A. (2023). Integrated TTF and self-determination theories in higher education: The role of actual use of massive open online courses. Frontiers in Psychology, 14, 1108325. https://doi.org/10.3389/fpsyg.2023.1108325
Alyoussef, I. Y. (2023). Acceptance of e-learning in higher education: The role of task-technology fit within the information systems success model. Heliyon, 9, e13751. https://doi.org/10.1016/j.heliyon.2023.e13751
Alzoubi, H., Alshurideh, M., Al Kurdi, B., Akour, I., & Aziz, R. (2022). Does BLE technology contribute toward improving marketing strategies, customer satisfaction, and loyalty? The role of open innovation. International Journal of Data and Network Science, 6, 449–460. https://doi.org/10.5267/j.ijdns.2021.12.009
Amoako, G. K., Ampong, G. O., Gabrah, A. Y. B., de Heer, F., & Antwi-Adjei, A. (2023). Service quality affecting student satisfaction in higher education institutions in Ghana. Cogent Education, 10(2), 2238468. https://doi.org/10.1080/2331186X.2023.2238468
Aremu, A. Y., & Arfan, S. (2023). Factors influencing the use of e-business to improve SME performance. International Journal of E-Business Research, 19(1). https://doi.org/10.4018/IJEBR.324065
Atasoy, F., & Eren, D. (2023). Serial mediation: Destination image and perceived value in the relationship between perceived authenticity and behavioral intentions. European Journal of Tourism Research, 33, 3309.
Balakrishnan, J., & Dwivedi, Y. K. (2024). Conversational commerce: Entering the next stage of AI-powered digital assistants. Annals of Operations Research, 333, 653–687. https://doi.org/10.1007/s10479-021-04049-5
Balakrishnan, J., Dwivedi, Y. K., Hughes, L., & Boy, F. (2024). Enablers and inhibitors of AI-powered voice assistants: A dual-factor approach integrating status quo bias and the Technology Acceptance Model. Information Systems Frontiers, 26, 921–942. https://doi.org/10.1007/s10796-021-10203-y
De Cicco, R., Francioni, B., Curina, I., & Cioppi, M. (2025). AI, human, or a blend? How the educational content creator influences consumer engagement and brand-related outcomes. Journal of Services Marketing, 39(10), 54–72. https://doi.org/10.1108/JSM-10-2024-0539
Denovan, R. F., & Marsasi, E. G. (2025). Perceived ease of use, perceived usefulness, and satisfaction to maximize behavioral intention using the Technology Acceptance Model among Generation Y and Z consumers. Pamator Journal, 18(1), 1–36. https://doi.org/10.21107/pamator.v18i1.29461
Kim, L., Wichianrat, K., Yeo, S. F., & Limna, P. (2025). How ease of use, usefulness, value, and innovation in electronic banking influence customer satisfaction. International Journal of Asian Business and Information Management, 16(1). https://doi.org/10.4018/IJABIM.370562
Marsasi, E. G. (2025). Implementing marketing programs to improve product knowledge based on the Stimulus–Organism–Response (S-O-R) model in the telecommunications industry. Jurnal Ecogen, 8(4), 614–631. https://doi.org/10.24036/ecogen.v8.i4.29
Marsasi, E. G., Albari, A., & Muthohar, M. (2023). How utilitarian motivation and trust can increase intention to use based on functional attitude theory. International Journal of Professional Business Review, 8(12), e4086. https://doi.org/10.26668/businessreview/2023.v8i12.4086
Marsasi, E. G., Barqiah, S., & Gusti, Y. K. (2024a). Investigation of the effects of social capital on information/knowledge-sharing behavior that drives Gen Z purchase intentions through social commerce. Media Ekonomi dan Manajemen, 39(1), 42–60. https://doi.org/10.24856/mem.v39i1.3812
Marsasi, E. G., & Wiganda, S. (2023). How can gratitude and self-image congruency affect satisfaction, trust, and affective commitment? MIX: Scientific Journal of Management, 13(2), 288–304. https://doi.org/10.22441/mix.2023.v13i2.008
Marsasi, E. G., Rizan, M., Barqiah, S., & Gusti, Y. K. (2024b). Customer self-congruity and brand image on purchase decision: The role of gender and age as control variables. Media Ekonomi dan Manajemen, 39(2), 199–214. https://doi.org/10.24856/mem.v39i2.4105
Nagy, S., & Hajdu, N. (2023). Consumer acceptance of the use of artificial intelligence in online shopping: Evidence from Hungary. Journal of Retailing and Consumer Services.
Rahman, M., Hossain, M. A., & Islam, M. T. (2025). Adoption of blockchain technology in the banking sector: Extending the Technology Acceptance Model with trust and security in the context of Bangladesh. Software Impacts, 102299. https://doi.org/10.1016/j.simpa.2025.102299
Shin, N., & Son, J. (2026). Impacts of AI-integrated services and corporate reputation on digital banking recommendations: A view from goal-framing theory. International Journal of Bank Marketing, 44(2), 173–194. https://doi.org/10.1108/IJBM-09-2024-0560
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
Wang, Y., Shi, J., Ow, T. T., Yun, J., & Yang, Y. (2025). The impact of technological innovations on consumer behavior in e-commerce: A systematic literature review. Journal of Organizational and End User Computing, 37(1). https://doi.org/10.4018/JOEUC.372896
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