An integrated Fuzzy DEMATEL and Fuzzy TOPSIS method for analyzing students' mobile banking provider preferences
DOI:
https://doi.org/10.55573/JISED.118759Keywords:
Fuzzy DEMATEL, Fuzzy TOPSIS, Mobile Banking, Student Preferences, Multi-Criteria Decision-MakingAbstract
The increasing use of mobile banking among university students has created a need to understand the factors that shape their preferences for banking applications. This study evaluates students’ preferences for mobile banking providers by integrating the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) and Fuzzy Technique for Order Preference by Similarity to the Ideal Solution (Fuzzy TOPSIS) methods. Fuzzy DEMATEL is first applied to determine the relative weights and causal relationships among five evaluation criteria: accessibility, features and functionality, security, transaction speed, and user-friendly. The resulting criterion weights are subsequently incorporated into Fuzzy TOPSIS to rank three mobile banking providers: Bank Islam Malaysia Berhad, Malayan Banking Berhad (Maybank), and RHB Bank Berhad. Data were collected via an online questionnaire from five purposively sampled UiTM Cawangan Kelantan students with experience using all three selected providers. The Fuzzy DEMATEL results classify features and functionality, security, and transaction speed as cause criteria, while accessibility and user-friendliness are classified as effect criteria. Accessibility obtained the highest relative weight (0.2092), followed by user-friendly (0.2030), features and functionality (0.2025), security (0.1949), and transaction speed (0.1904). The Fuzzy TOPSIS results show that Maybank achieved the highest closeness coefficient (0.8909), followed by RHB Bank (0.4511) and Bank Islam (0.1060). The findings indicate that integrating causal analysis with preference ranking provides a structured approach for examining interrelated mobile banking evaluation criteria and can assist banking institutions in identifying areas for service improvement.
Downloads
References
Abdullah, F. M., Al-Ahmari, A. M., & Anwar, S. (2023). Analyzing interdependencies among influencing factors in smart manufacturing. Sustainability, 15(4), 3864.
Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9.
Esmaeili, A., Haghgoo, I., Davidaviciene, V., & Meidute-Kavaliauskiene, I. (2021). Customer loyalty in mobile banking: Evaluation of perceived risk, relative advantages, and usability factors. Engineering Economics, 32(1), 70–81.
Ho, J. C., Wu, C.-G., Lee, C.-S., & Pham, T.-T. T. (2020). Factors affecting the behavioral intention to adopt mobile banking: An international comparison. Technology in Society, 63, 101110.
Jebarajakirthy, C., & Shankar, A. (2021). Impact of online convenience on mobile banking adoption intention: A moderated mediation approach. Journal of Retailing and Consumer Services, 58, 102323.
Mostafa, R. B. (2020). Mobile banking service quality: A new avenue for customer value co-creation. International Journal of Bank Marketing, 38(5), 1107–1132.
Nadaban, S., Dzitac, S., & Dzitac, I. (2016). Fuzzy TOPSIS: A general view. Procedia Computer Science, 91, 823–831.
Noor, M. M. (2011). Determining critical success factors of mobile banking adoption in Malaysia. Australian Journal of Basic and Applied Sciences, 5(9), 252–265.
Owusu Kwateng, K., Osei Atiemo, K. A., & Appiah, C. (2019). Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151.
Roy, P., & Shaw, K. (2023). A fuzzy MCDM decision-making model for m-banking evaluations: Comparing several m-banking applications. Journal of Ambient Intelligence and Humanized Computing, 14(9), 11873–11895.
Roy, P. K., & Shaw, K. (2021). An integrated fuzzy model for evaluation and selection of mobile banking applications using new fuzzy-BWM and fuzzy-TOPSIS. Complex & Intelligent Systems, 1–22.
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142.
Tam, C., & Oliveira, T. (2017). Literature review of mobile banking and individual performance. International Journal of Bank Marketing, 35(7), 1044–1067.
Tzeng, G.-H., and Huang, J.-J. (2011). Multiple attribute decision making: methods and applications. CRC press.
Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507.
Zhu, X., Yan, R., & Xu, T. X. (2020). An integrated DEMATEL-ANP approach for mobile banking adoptions in China market. Discrete Dynamics in Nature and Society, 2020, 1–11.
