Nexus of User and Firm Factors to Promote Cybersecurity Practices and Proactive Behavior Through a Novel SEM-Machine Learning Approach

In Industry 5.0 (I5.0), cybersecurity is a strategic priority for manufacturing firms. In the I5.0 context, human–machine collaboration requires proactive employee behavior to anticipate and mitigate cyber threats. However, existing literature largely exam-ines user or technical factors in isolation and overlooks the joint optimization of social and technical factors. To address these,this study investigates the user-level social and firm-level technical factors to explain the cybersecurity awareness, compliance,and employee proactive behavior in the I5.0 context. Based on the socio-technical systems (STS) theory, a concept framework has been developed. A survey-based quantitative method was applied to collect data from 198 employees working in Malaysian manufacturing enterprises. A dual-stage analytical technique was used, combining partial least squares structural equation modeling (PLS-SEM) with machine learning (ML). The originality of this technique stems from the combination of PLS-SEMexplanatory power with ML prediction capabilities. The PLS-SEM findings affirmed that both user-level social factors and firm-level technical factors positively drive cybersecurity compliance. Surprisingly, the relationship between cybersecurity awareness and compliance was positive but insignificant. ML outcomes highlight that user and firm factors predict (85.79%) employee pro-active behavior. It reinforces the robustness and predictive capability of the integrated framework. This study provides a novel explanation of social and technical factors to employee proactive behavior through cybersecurity awareness and compliance inI5.0. The study findings provide both theoretical contributions, particularly in the advancement of STS theory, as well as practical implications for managers and policymakers seeking to improve cybersecurity frameworks.