<![CDATA[Purpose: of this research is to analyze both the direct effect of AI-driven HR analytics on employee performance and its indirect effect through decision-making quality as a mediating mechanism Research Design and Methodology: Using a quantitative explanatory research design, data were collected through a structured questionnaire from retail employees and supervisors in organizations that utilize AI-supported HR systems. A purposive sampling technique was employed, and the data were analyzed using Structural Equation Modeling (SEM). Findings and Discussion: AI-driven HR analytics has a significant positive effect on employee performance and decision-making quality. Furthermore, decision-making quality significantly influences employee performance and partially mediates the relationship between AI-driven HR analytics and employee performance. These results suggest that the performance benefits of AI-driven HR analytics are realized primarily when analytics insights are effectively integrated into managerial decision-making processes. The study contributes to the growing literature on people analytics by highlighting the importance of decision quality as a key explanatory mechanism, particularly in retail contexts. Practically, the findings provide insights for retail managers and policymakers to strengthen analytical capability, managerial literacy, and governance frameworks to maximize the value of AI-driven HR analytics. Implications: Future research is recommended to explore longitudinal effects and additional contextual moderators.]]>
