Examining the factors influencing cybersecurity behaviors in m-payment contactless technologies using SEM–ANN approach

The widespread adoption of mobile payment (m-payment) contactless technologies has revolutionized financial transactions by enhancing convenience and efficiency. However, these advancements have introduced new cybersecurity risks, making it critical to understand the behavioral drivers behind secure user practices. Therefore, this study develops a theoretical model integrating protection motivation theory (PMT), theory of planned behavior (TPB), and technology threat avoidance theory (TTAT), with the addition of cybersecurity awareness as a key construct to examine cybersecurity behaviors. A cross-sectional survey was conducted among 820 m-payment users. Data were analyzed using a hybrid approach that combines structural equation modeling (SEM) and artificial neural networks (ANN). The SEM analysis supported most of the proposed hypotheses, indicating strong linear relationships among the constructs. ANN analysis confirmed the robustness of the findings, revealing that cybersecurity awareness is the most influential determinant of cybersecurity behavior, with a normalized importance of 100%. The integration of cognitive, motivational, and social-behavioral theories provides a nuanced understanding of cybersecurity behavior in digital financial ecosystems. The findings offer theoretical contributions to the cybersecurity and financial services marketing literature and deliver actionable insights for policymakers and technology developers aiming to enhance secure user behavior in m-payment environments.