How and when AI-driven HRM promotes employee resilience and adaptive performance: A self-determination theory

<p dir="ltr">Despite growing research on AI in HRM, gaps remain, particularly in understanding the mechanisms through<br>which AI-driven HRM influences employee outcomes. This study addresses this gap by developing a conceptual<br>model to examine how AI-driven HRM impacts employee resilience and adaptive performance. Based on self-<br>determination theory, the model proposes that employee exploration mediates the relationships between AI-<br>driven HRM and employee outcomes. Additionally, trust in AI moderates these relationships. Two studies<br>were conducted to test the hypotheses: Study 1 developed and validated a 12-item AI-driven HRM scale across<br>three samples: 50 managers, 150 employees for exploratory factor analysis (EFA), and 150 employees for<br>confirmatory factor analysis (CFA). Study 2, with data from 274 US employees through a three-wave survey,<br>explored the effects of AI-driven HRM on resilience and performance. Results from Study 2 supported all pro-<br>posed relationships, thereby offering important implications for both theory and practice in the AI-driven HRM<br>field.