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

• We conceptualize and develop a new measure of AI-driven HRM through the HRM behavioral perspective. • We examine the underlying mechanisms between AI-driven HRM and employee resilience/adaptive performance through the theoretical underpinnings of self-determination theory. • Employee exploration mediates the relationships between AI-driven HRM and employee resilience/adaptive performance. • High levels of trust in AI strengthens the mediated relationships between AI-driven HRM and employee resilience/adaptive performance. Despite growing research on AI in HRM, gaps remain, particularly in understanding the mechanisms through which AI-driven HRM influences employee outcomes. This study addresses this gap by developing a conceptual model to examine how AI-driven HRM impacts employee resilience and adaptive performance. Based on self-determination theory, the model proposes that employee exploration mediates the relationships between AI-driven HRM and employee outcomes. Additionally, trust in AI moderates these relationships. Two studies were conducted to test the hypotheses: Study 1 developed and validated a 12-item AI-driven HRM scale across three samples: 50 managers, 150 employees for exploratory factor analysis (EFA), and 150 employees for confirmatory factor analysis (CFA). Study 2, with data from 274 US employees through a three-wave survey, explored the effects of AI-driven HRM on resilience and performance. Results from Study 2 supported all proposed relationships, thereby offering important implications for both theory and practice in the AI-driven HRM field.