Examining the impact of work overload on cybersecurity behavior: highlighting self-efficacy in the realm of artificial intelligence.

This study investigates the relationship between work overload and employee cybersecurity behavior, with a particular focus on the moderating role of self-efficacy in artificial intelligence (AI) use. Grounded in a multidisciplinary theoretical framework, the research integrates concepts from the Job Demands-Resources (JD-R) Model, Cognitive Load Theory, Social Identity Theory, and Protection Motivation Theory. Data were collected from 410 employees in South Korea through a three-wave time-lagged survey, analyzing the impact of work overload on cybersecurity behavior mediated by job stress and organizational identification, and moderated by self-efficacy in AI. The current study reveals a negative impact of work overload on employee cybersecurity behavior, mediated by job stress and organizational identification. Also, self-efficacy in AI use significantly moderates the relationship between work overload and job stress, mitigating the adverse effects of workload on cybersecurity behavior. The findings extend existing literature by demonstrating how organizational psychology theories can be applied to understand cybersecurity behavior in the workplace. This study highlights the importance of managing work overload and enhancing AI self-efficacy to improve cybersecurity practices in organizations. The study underscores the need for organizations to address work overload as a part of their cybersecurity strategy and to invest in AI self-efficacy training. The findings advocate for an interdisciplinary approach to cybersecurity, integrating insights from organizational psychology and AI technology. Future research should explore these dynamics in different cultural and organizational contexts and consider longitudinal designs to further validate the findings. [ABSTRACT FROM AUTHOR]