Consumers’ Perspectives toward Car Financing Application: The Case of MIKA Application
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Keywords

Car financing application
Technology adoption model
Perceived trust
Perceived risk
Social influence

How to Cite

Diva, M. F., & Aprianingsih, A. (2023). Consumers’ Perspectives toward Car Financing Application: The Case of MIKA Application. Journal of Economics, Business, & Accountancy Ventura, 25(3), 301-311. https://doi.org/10.14414/jebav.v25i3.3105

Abstract

Considering that critical factors driving consumer intention to use car financing applications are still unclear, we empirically aim to investigate how substantial perceived usefulness, perceived ease of use, perceived trust, perceived risk, social influence, and attitude influence consumer intention to use car financing. We involved 101 active car financing application users in estimating our proposed research framework and analyzed it through a two-stage PLS-SEM approach using SmartPLS software. We revealed that perceived ease of use has the strongest correlation with attitude toward using car financing applications, and perceived trust has the most substantial influence on the intention to use car financing applications. However, perceived risk is the most determinant factor that should be improved because consumers felt a moderate level of risk when using a car financing application. Practically, this research recommends that car financing providers focus on increasing consumers’ trust and decreasing the risk consumers feel about their application services.

References

Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324.

Al Nawayseh, M. K. (2020). FinTech in COVID-19 and beyond: What factors are affecting customers’ choice of fintech applications? Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 2-15.

Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882–893.

Bosnjak, M., Ajzen, I., & Schmidt, P. (2020). The theory of planned behavior: Selected recent advances and applications. Europe’s Journal of Psychology, 16(3), 352-356.

Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Research, 28(2), 456–476.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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