HR analytics adoption and managerial decision-making style: The mediating role of human–AI interaction and the moderating role of attitude toward AI

Managerial decision-making style is critical to organizational success, especially in the era of artificial intelligence (AI), which has introduced a wide range of AI models with advanced decision-making capabilities. In this context, the adoption of HR analytics presents significant challenges in leveraging AI to enhance human-AI interaction and incorporate human judgment. However, limited empirical evidence exists on how HR analytics is associated with managerial decision-making style through human-AI interaction and how AI-supported HR systems reshape managerial judgment in organizational decision processes. This study empirically tests the proposed model using 388 valid responses from respondents with exposure to HR analytics and AI-assisted systems. The results of partial least squares structural equation modeling (PLS-SEM) confirm that HR analytics adoption is positively and significantly associated with managerial decision-making style through the mediating role of human-AI interaction. The findings also reveal a significant negative moderating effect of attitude toward AI, suggesting potential risks of overdependence, reduced critical verification, and weaker user attention to AI-supported outputs. This study contributes to the literature by showing that AI-enabled HR analytics should not be viewed solely as a technical decision-support tool, but also as part of an analytics-supported context in which human-AI interaction, managerial judgment, and attitude toward AI jointly shape decision-making style.