The influence of work overload on cybersecurity behavior: A moderated mediation model of psychological contract breach, burnout, and self-efficacy in AI learning such as ChatGPT

Abstract

In today's rapidly evolving digital landscape, organizations face the critical challenge of safeguarding their sensitive information and systems from an ever-increasing array of cybersecurity threats. As employees play a crucial role in maintaining organizational cybersecurity, it is essential to understand the factors that influence their cybersecurity behavior. This study investigates the impact of work overload on employee cybersecurity behavior, exploring the sequential mediating effects of psychological contract breach and burnout, as well as the moderating role of self-efficacy in AI learning. Drawing upon the Job Demands-Resources (JD-R) model, Conservation of Resources (COR) theory, and Social Cognitive Theory, we propose a moderated mediation model to elucidate the complex relationships among these variables. To test our hypotheses, we conducted a three-wave survey study involving 363 employees from various sectors in South Korea. Data was collected using an internet-based survey platform, and the study employed stratified random sampling to reduce sampling bias. Results from structural equation modeling (SEM) analyses revealed that work overload indirectly impacts cybersecurity behavior through the sequential mediation of psychological contract breach and burnout. Furthermore, self-efficacy in AI learning such as ChatGPT was found to moderate the relationship between work overload and psychological contract breach, acting as a buffer to mitigate the negative effects of work overload. This study contributes to the existing literature by addressing several research gaps. First, it provides a comprehensive examination of the impact of work overload on employee cybersecurity behavior. Second, it investigates the underlying psychological processes (i.e., psychological contract breach and burnout) that explain the relationship between work overload and cybersecurity behavior. Third, it explores the moderating role of self-efficacy in AI learning such as ChatGPT, an understudied factor in the context of work overload and cybersecurity behavior.