The impact of an unstable job on mental health: the critical role of self-efficacy in artificial intelligence use

Abstract

In an era where artificial intelligence (AI) is reshaping workplace dynamics, this study addresses the pressing need to understand the intricate relationships between job insecurity, psychological safety, and employee depression. Despite extensive research in this domain, significant gaps remain, particularly in comprehending how technological advancements influence these relationships. This research overcomes these challenges by introducing a novel approach that incorporates the mediating role of psychological safety and the moderating influence of employee self-efficacy in AI use. To empirically test our hypotheses, we employed a stratified random sampling method to collect data from 408 employees across various South Korean firms. Also, we utilized 3 wave time-lagged research design to enhance the robustness of the findings. The results which were based on the structural equation modeling analysis indicate that all study hypotheses are supported. The findings not only bridge a crucial gap in the existing literature but also offer a more comprehensive understanding of the psychological impacts of job insecurity in an AI-integrated work environment. This research marks a significant contribution by elucidating the nuanced mechanisms through which job insecurity affects employee well-being in the context of rapid technological change. © 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.