Mitigating the Risk of Qushing Threats (QR Phishing) using the Security Behavior Intentions Scale (SeBIS) in supporting digital economic security - Singkeruang, A. We Tenri Fatimah; Susanto, Setya Ega; Saeni, Nuraeni (Fakultas Ekonomi, Universitas Muslim Indonesia, 2025)
The object of research is the risk mitigation of Quishing (QR Phishing) in financial transactions using the Security Behavior Intentions Scale (SeBIS). The study focuses on how user behavior, financial security awareness, and technological adoption influence the ability to detect and mitigate Quishing threats. One of the most problematic areas is the growing vulnerability of digital payment users to fraudulent QR codes, which cybercriminals exploit to redirect users to malicious websites and steal sensitive financial information. Despite the rapid adoption of QR-based payments, primarily through Quick Response Indonesia Standard (QRIS) and e-wallets, there is a lack of comprehensive risk mitigation models that integrate user awareness, behavioral factors, and security technologies. The study used a quantitative approach with Structural Equation Modeling (SEM) to analyze the relationships between security behavior, user awareness, and Quishing risk mitigation. Data was collected from 100 respondents in Makassar, Indonesia, to evaluate their digital security practices and susceptibility to Quishing attacks. The results indicate that password management and user awareness significantly influence Quishing risk mitigation, whereas device security alone does not guarantee protection. The study confirms that digital financial resilience can be enhanced through targeted education, stronger authentication mechanisms, and AI-driven fraud detection. This is because the proposed integration of SeBIS-based behavioral assessment and security interventions addresses multiple vulnerabilities in digital transactions. This ensures that it is possible to improve the overall security of digital payments by enhancing user behavior and implementing proactive security measures. Compared to similar known models, this approach combines behavioral insights with technological solutions, leading to more effective mitigation strategies for financial cybersecurity risks.