The dark side of artificial intelligence adoption: linking artificial intelligence adoption to employee depression via psychological safety and ethical leadership

Abstract

Artificial intelligence (AI) is increasingly being integrated into business practices, fundamentally altering workplace dynamics and employee experiences. While the adoption of AI brings numerous benefits, it also introduces negative aspects that may adversely affect employee well-being, including psychological distress and depression. Drawing upon a range of theoretical perspectives, this study examines the association between organizational AI adoption and employee depression, investigating how psychological safety mediates this relationship and how ethical leadership serves as a moderating factor. Utilizing an online survey platform, we conducted a 3-wave time-lagged research study involving 381 employees from South Korean companies. Structural equation modeling analysis revealed that AI adoption has a significant negative impact on psychological safety, which in turn increases levels of depression. Data were analyzed using SPSS 28 for preliminary analyses and AMOS 28 for structural equation modeling with maximum likelihood estimation. Further analysis using bootstrapping methods confirmed that psychological safety mediates the relationship between AI adoption and employee depression. The study also discovered that ethical leadership can mitigate the adverse effects of AI adoption on psychological safety by moderating the relationship between these variables. These findings highlight the critical importance of fostering a psychologically safe work environment and promoting ethical leadership practices to protect employee well-being amid rapid technological advancements. Contributing to the growing body of literature on the psychological effects of AI adoption in the workplace, this research offers valuable insights for organizations seeking to address the human implications of AI integration. The section discusses the practical and theoretical implications of the results and suggests potential directions for future research.