Determinants of consumer adoption of Islamic mobile banking services in Indonesia
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DOIhttp://dx.doi.org/10.21511/bbs.18(4).2023.04
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Article InfoVolume 18 2023, Issue #4, pp. 30-43
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Islamic banking must concentrate on customer service and loyalty to be competitive because the financial sector delivers almost identical goods and services. Mobile banking is one of the most recent advances in the financial sector and can be advantageous to bank customers and banking institutions. This study aims to explore the elements that affect Islamic bank customers’ propensity to adopt Islamic mobile banking services. Internet connection quality, bank reputation, and awareness are included as new factors to the Technology Acceptance Model (TAM) theoretical framework used in this study to evaluate the relevant issue. The online survey was administered through a questionnaire, yielding 265 responses obtained from Islamic Mobile Banking users in Indonesia. The PLS-SEM method was used to analyze the data. Results indicated that perceived utility, internet connection quality, consumer awareness, and bank reputation had a substantial beneficial effect on customer inclinations to utilize Islamic vehicle banking services. However, perceived usability does not have a significant favorable effect. Understanding these characteristics would enable participants in the Islamic finance industry to design and plan relevant strategies to promote financial services to present and prospective users.
Acknowledgment
The author would like to acknowledge the Research and Innovation Institute (LRI), Universitas Muhammadiyah Surakarta, for providing significant financial assistance in writing this research through the HIT funding scheme with number 01/A.6-II/FAI/1/2022.
- Keywords
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JEL Classification (Paper profile tab)L86, G21, M21, O14
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References67
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Tables4
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Figures2
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- Figure 1. Conceptual framework
- Figure 2. Structural model
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- Table 1. Research constructs and their relative questions
- Table 2. Characteristics of respondents
- Table 3. Measurement model indicators
- Table 4. Partial least squares – structural equation modelling result
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