Tingkat Kepercayaan Mahasiswa Akuntansi Terhadap Sistem Keamanan QRIS dalam Mencegah Kecurangan Transaksi
DOI:
https://doi.org/10.55587/jla.v6i2.319Keywords:
Perceived Security, Trust, QRIS, Fraud Prevention, Digital PaymentAbstract
Purpose: This study aims to examine the effect of perceived security on the trust of accounting students in using the Quick Response Code Indonesian Standard (QRIS) as a digital payment system, particularly in the context of fraud prevention.
Method:This study employs a quantitative approach using a survey method. Data were collected through questionnaires distributed to 100 accounting students selected using purposive sampling, with the criterion of having used QRIS at least once. The data were analyzed using the Statistical Package for the Social Sciences (SPSS), including validity and reliability tests, classical assumption tests, and simple linear regression analysis.
Findings:The results indicate that perceived security has a positive and significant effect on user trust. This finding suggests that higher perceived security leads to greater trust in using QRIS. Furthermore, security plays an essential role in reducing fraud risks and enhancing user confidence in digital transactions.
Novelty:The novelty of this study lies in the integration of perceived security, trust, and fraud prevention within a single research framework, focusing on accounting students who possess a deeper understanding of internal control and fraud risk. This study also extends technology acceptance theories by incorporating a security perspective in QRIS usage.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alalwan, A. A., et al. (2025). Digital payment security and human vulnerability in fintech adoption.
Akram, V., et al. (2025). AI-assisted fraud in digital payment systems: Emerging threats and mitigation strategies.
Almeida, R., et al. (2025). Security awareness and fraud reduction in digital financial services.
Association of Certified Fraud Examiners (ACFE). (2022). Report to the nations on occupational fraud and abuse. ACFE.
Bank Indonesia. (n.d.). Quick Response Code Indonesian Standard (QRIS). https://www.bi.go.id
Bianchi, S., et al. (2024). QR code phishing (quishing) attacks in digital payment systems.
Chawla, D., & Joshi, H. (2024). Integrating TAM with security factors in fintech adoption
Dahlberg, T., et al. (2024). Mobile payment systems and QR adoption in developing economies.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
Ghozali, I. (2018). Aplikasi analisis multivariate dengan program IBM SPSS 25. Badan Penerbit Universitas Diponegoro.
Gujarati, D. N., & Porter, D. C. (2012). Basic econometrics (5th ed.). McGraw-Hill.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson.
Hassan, M., et al. (2025). Factors influencing perceived security in digital payment systems.
Kumar, P., et al. (2025). QR code security threats and quishing attacks: A systematic review.
Kurniasari, D., et al. (2025). QRIS manipulation and fraud patterns in digital transactions.
Liébana-Cabanillas, F., et al. (2024). Factors influencing QR payment adoption in mobile environments.
Marinković, V., et al. (2024). Extending TAM in digital financial services with security constructs.
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce. Information Systems Research, 13(3), 334–359.
Ngai, E. W. T., et al. (2024). Machine learning approaches for fraud detection in digital payments.
Nguyen, T., et al. (2025). Digital literacy and fintech adoption: A moderating role.
Nabila, S., et al. (2025). Security and trust influence on QRIS usage intention.
Oliveira, T., et al. (2025). Trust and security in digital payment adoption.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
Pratama, A., & Supriyadi, B. (2022). Factors influencing QRIS usage intention among users.
Putri, R., & Nugroho, A. (2023). Digital literacy and fraud prevention in fintech usage.
Rahmawati, D. (2024). Perceived risk and trust in fintech adoption.
Romney, M. B., & Steinbart, P. J. (2018). Accounting information systems (14th ed.). Pearson.
Safa, N. S., et al. (2024). Information security and user trust in digital payment systems.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.
Setiawan, R., & Tambun, S. (2025). Digital literacy, financial literacy, and QRIS usage intention.
Singh, P., et al. (2025). Trust as a mediator in digital financial adoption models.
Sugiyono. (2019). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.
Troise, C., et al. (2024). Perceived security and behavioral intention in fintech services.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Zhang, Y., et al. (2025). Fraud detection and risk analysis in digital payment systems.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jurnal Literasi Akuntansi

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










