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Angelia Angelia
Erwin Setiawan Panjaitan
Roni Yunis


This research aims to evaluate the effect of attitude on mobile banking acceptance using the extended UTAUT Model. Specifically, surveying the Medan city area covered 392 mobile banking users from several banks. By using Structural Equation Modeling (SEM) and SmartPLS software. This research’s man contribution is introducing attitude variables in the combination of UTAUT models, task technology fit, and trust, which have a significant effect on behavior intention. Based on this study results, it shows that social influence and attitude are a significant effect on behavior intention,facilitating condition, and behavior intention is a significant effect use behavior of mobile banking users from several banks. While performance expectancy, effort expectancy, task technology fit, and trust didn’t significantly effect on the behavior intention of mobile banking users from several banks. This Research implies two important policymakers’ findings. First, Banking Management needs to ensure that it always makes necessary improvements in simplifying technology to understand it easily. Second, need to ensure that we regularly evaluate the performance of mobile banking users from several banks to mae benefits of using M-Banking technology to support financial transactions needs.


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Angelia, A., Panjaitan, E. S. and Yunis, R. (2021) “Effect of Attitude on Mobile Banking Acceptance Using Extended UTAUT Model”, Jurnal Mantik, 5(2), pp. 1006-1013. doi: 10.35335/jurnalmantik.Vol5.2021.1440.pp1006-1013.
[1] A. M. Baabdullah, A. A. Alalwan, N. P. Rana, H. Kizgin, and P. Patil, “Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model,” Int. J. Inf. Manage., vol. 44, no. July 2018, pp. 38–52, 2019.
[2] A. M. Baabdullah, A. A. Alalwan, N. P. Rana, P. Patil, and Y. K. Dwivedi, “An integrated model for m-banking adoption in Saudi Arabia,” Int. J. Bank Mark., vol. 37, no. 2, pp. 452–478, 2019.
[3] A. Shankar, C. Jebarajakirthy, and M. Ashaduzzaman, “How do electronic word of mouth practices contribute to mobile banking adoption?,” J. Retail. Consum. Serv., vol. 52, no. August 2018, p. 101920, 2020.
[4] Y. K. Dwivedi, N. P. Rana, A. Jeyaraj, M. Clement, and M. D. Williams, “Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical
[5] A. A. Alalwan, Y. K. Dwivedi, and N. P. Rana, “Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust,” Int. J. Inf. Manage., vol. 37, no. 3, pp. 99–110, 2017.Model,” Inf. Syst. Front., vol. 21, no. 3, pp. 719–734, 2019.
[6] M. Islam, “Data Analysis: Types, Process, Methods, Techniques and Tools,” Int. J. Data Sci. Technol., vol. 6, no. 1, pp. 10–15, 2020.
[7] M. Michaelides and A. Spanos, “On modeling heterogeneity in linear models using trend polynomials,” Econ. Model., vol. 85, no. April, pp. 74–86, 2020.
[8] J. Hair, C. L. Hollingsworth, A. B. Randolph, and A. Y. L. Chong, “An updated and expanded assessment of PLS-SEM in information systems research,” Ind. Manag. Data Syst., vol. 117, no. 3, pp. 442–458, 2017.
[9] B. M. Izzati, “Analysis of Customer Behavior in Mobile Food Ordering Application Using UTAUT Model (Case Study: GoFood Application),” Int. J. Innov. Enterp. Syst., vol. 4, no. 01, pp. 23–34, 2020.
[10] M. Sciarelli, M. H. Gheith, and M. Tani, “The relationship between soft and hard quality management practices, innovation and organizational performance in higher education,” TQM J., vol. 32, no. 6, pp. 1349–1372, 2020.
[11] L. Lishomwa and J. Phiri, “Adoption of Internet Banking Services by Corporate Customers for Forex Transactions Based on the TRA Model,” Open J. Bus. Manag., vol. 08, no. 01, pp. 329–345, 2020.