The impact of instrumental attribution in AI-enabled monitoring on counterproductive work behavior - ZHANG, Qiang (Institutional Knowledge at Singapore Management University, 2024)
AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception can foster feelings of job insecurity and moral disengagement, ultimately leading to counterproductive work behavior (CWB), which includes unethical employee behavior and turnover. These outcomes may undermine the intended effectiveness of AI-enabled monitoring tools. This study aims to explore the impact of Instrumental Attribution in AIenabled Monitoring (IAAIM) on CWB, specifically focusing on unethical employee behavior and turnover, through both theoretical and empirical lenses. Drawing on attribution theory, the study first reviews existing research on the psychological and behavioral consequences of AI-enabled monitoring, the drivers of unethical employee behavior and turnover, and the roles of job insecurity, moral disengagement, and ethical management control. A theoretical framework is then systematically constructed to explain how IAAIM influence CWB. Subsequently, an empirical analysis is conducted using survey data from 72 work teams comprising 558 sales representatives within the marketing department of a large real estate company in China. This analysis provides empirical evidence on the influence of IAAIM on unethical employee behavior and turnover, examining the mediating roles of job insecurity and moral disengagement, as well as the moderating effect of ethical management control. The findings reveal the following: First, IAAIM positively affects job insecurity, though the impact of job insecurity on unethical employee behavior and turnover is not significant. Second, IAAIM positively influences moral disengagement. However, the mediating effect of IAAIM on employee turnover through moral disengagement is not significant. Third, ethical management control mitigates the relationship between IAAIM and job insecurity, and negatively moderates the indirect effect of IAAIM on unethical employee behavior through job insecurity. This suggests that stronger ethical management control weakens this indirect effect. However, this moderating effect is not significant concerning the indirect effect on employee turnover. Fourth, ethical management control also reduces the relationship between IAAIM and moral disengagement, and negatively moderates the indirect effect of IAAIM on unethical employee behavior through moral disengagement, indicating that more effective ethical management control diminishes this indirect effect. Again, this moderating effect is not significant concerning the indirect effect on employee turnover. Theoretically, this study elucidates the internal mechanisms through which IAAIM influences CWB by increasing job insecurity and moral disengagement, and clarifies the moderating role of ethical management control. This research contributes to the literature on the consequences of AI-enabled monitoring and the driving factors of unethical employee behavior and turnover. Practically, it offers valuable insights for enterprises to effectively utilize AI in controlling and preventing unethical employee behavior and turnover, thereby continuously improving human resource management.