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

Muhammad Fadel Diva, Atik Aprianingsih


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.


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

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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.

Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information & Management, 44(4), 384–396.

Chen, L., & Aklikokou, A. K. (2020). Determinants of E-government adoption: Testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(10), 850–865.

Galib, M. H., Hammou, K. A., & Steiger, J. (2018). Predicting consumer behavior: An extension of technology acceptance model. International Journal of Marketing Studies, 10(3), 73-90.

Giovanis, A., Athanasopoulou, P., Assimakopoulos, C., & C., S. (2019). Adoption of mobile banking services: A comparative analysis of four competing theoretical models. International Journal of Bank Marketing, 37 (5), 1165-1189.

Grover, P., & Kar, A. K. (2020). User engagement for mobile payment service providers – introducing the social media engagement model. Journal of Retailing and Consumer Services, 53, 101718.

Grover, P., Kar, A. K., Janssen, M., & Ilavarasan, P. V. (2019). Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions–insights from user-generated content on Twitter. Enterprise Information Systems, 13(6), 771-800

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340.

Kaushik, A. K., Mohan, G., & Kumar, V. (2020). Examining the antecedents and consequences of customers’ trust toward mobile retail apps in India. Journal of Internet Commerce, 19(1), 1–31.

Li, Y., Qi, J., & Shu, H. (2008). Review of relationships among variables in TAM. Tsinghua Science and Technology, 13(3), 273–278.

Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14–24.

Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). The moderating effect of experience in the adoption of mobile payment tools in Virtual Social Networks: The m-Payment Acceptance Model in Virtual Social Networks (MPAM-VSN). International Journal of Information Management, 34(2), 151–166.

Malhotra, N. K. (2015). Essentials of Marketing Research: A hands-on orientation. Pearson.

Mubarok, M. M. (2022). The Mapping of Electronic Commerce Issues and Consumer Protection Policy in Indonesia. Journal of Economics, Business, & Accountancy Ventura, 24(3), 431-439.

Ninglasari, S. Y. (2021). Determinants of Online Zakat Intention amongst Muslim Millennials: An Integration of Technology Acceptance Model and Theory of Planned Behavior. Shirkah: Journal of Economics and Business, 6(2), 227.

Otoritas Jasa Keuangan. (2017). Microfinance Institutions.

Otoritas Jasa Keuangan. (2021a). Indonesia Banking Booklet 2021.

Otoritas Jasa Keuangan. (2021b). Strategi Nasional Literasi Keuangan Indonesia 2021 - 2025.

Pundir, V., Devi, E. B., & Nath, V. (2021). Arresting fake news sharing on social media: a theory of planned behavior approach. Management Research Review, 44(8), 1108–1138.

Purnawirawan, N., de Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244–255.

Ryu, H.-S. (2018). What makes users willing or hesitant to use Fintech?: the moderating effect of user type. Industrial Management & Data Systems, 118(3), 541–569.

Sánchez‐Prieto, J. C., Huang, F., Olmos‐Migueláñez, S., García‐Peñalvo, F. J., & Teo, T. (2019). Exploring the unknown: The effect of resistance to change and attachment on mobile adoption among secondary pre‐service teachers. British Journal of Educational Technology, 50(5), 2433-2449.

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035-1064.

Siringoringo, H. (2013a). Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia - Social and Behavioral Sciences, 81, 212–216.

Suki, N. M., Ramayah, T., & Ly, K. K. (2012). Empirical investigation on factors influencing the behavioral intention to use Facebook. Universal Access in the Information Society, 11(2), 223–231.

Sulong, Z., & Bakar, H. O. (2018). The role of financial inclusion on economic growth: theoretical and empirical literature review analysis. Journal of Business & Financial Affairs, 7(4), 2167-0234.

Suzianti, A., Haqqi, F. R., & Fathia, S. N. (2022). Strategic recommendations for financial technology service development: A comprehensive risk-benefit IPA-Kano analysis. Journal of Modelling in Management, 17(4), 1481-1503.

Tarhini, A., Alalwan, A. A., Shammout, A. B., & Al-Badi, A. (2019). An analysis of the factors affecting mobile commerce adoption in developing countries: Towards an integrated model. Review of International Business and Strategy, 29(3), 157-179.

The World Bank. (2016). Non-banking Financial Insitution.

Troise, C., O’Driscoll, A., Tani, M., & Prisco, A. (2020). Online food delivery services and behavioural intention – a test of an integrated TAM and TPB framework. British Food Journal, 123(2), 664-683.

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157.

Wang, P., & Li, H. (2019). Understanding the antecedents and consequences of the perceived usefulness of travel review websites. International Journal of Contemporary Hospitality Management, 31(3), 1086–1103.

Yan, C., Siddik, A. B., Akter, N., & Dong, Q. (2021). Factors influencing the adoption intention of using mobile financial service during the COVID-19 pandemic: The role of FinTech. Environmental Science and Pollution Research, 1-19

Zhang, X., Liu, S., Wang, L., Zhang, Y., & Wang, J. (2020). Mobile health service adoption in China: integration of theory of planned behavior, protection motivation theory and personal health differences. Online Information Review, 44(1), 1-23.



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