This study applies Structural Equation Modeling (SEM) to examine the impact of AI system performance dimensions: AI accuracy, AI-driven automation, AI computing power and capacity, AI real-time capability, and AI personalization on employee turnover among quoted companies in Nigeria, with particular emphasis on the mediating role of AI data quality (AIDQ). The results indicate that AI system performance significantly enhances data quality, improving the accuracy, timeliness, and reliability of organizational information. At the same time, AI system performance exerts a significant direct effect on turnover intention (TIN), suggesting that increased automation, monitoring, and technological intensity may elevate job-related pressure. Furthermore, AI data quality significantly influences turnover intention and is found to partially mediate the relationship between AI system performance dimensions and employee outcomes. This implies that AI affects turnover both directly and indirectly through improvements in data quality, highlighting the dual role of AI in enhancing organizational efficiency while shaping employee perceptions and behavior.
Keywords: AI-driven automation, SEM, data accuracy, data quality, mediating
