AI-driven HRM digitization in effect: Mechanisms, conditions, and employee outcomes

Abstract

Artificial intelligence (AI) is increasingly used in contemporary organizations as a tool for digitizing systems and reconfiguring key functions such as human resource management (HRM). However, not all employees exposed to AI-driven HRM digitization benefit equally in their capacity to generate innovative outcomes. Drawing on self-determination theory and integrating perspectives from organizational learning, this study advances a moderated mediation model that explains how and when AI-enabled HRM digitization fosters innovation. We propose that, while AI-driven HRM practices can stimulate the metacognitive strategies of employees, the extent to which these strategies translate into enhanced innovation depends critically on their tacit knowledge awareness. In this proposal, we highlight the paradoxical nature of algorithmic systems: they may serve as autonomy-supportive tools that encourage exploration and self-directed learning or as autonomy-controlling mechanisms that intensify monitoring and reduce intrinsic motivation. Using a multi-wave, multi-source field study of Chinese employees and supervisors (N = 347), we found that AI-driven HRM positively predicts innovation through metacognitive strategies, but only when employees demonstrate high levels of tacit knowledge awareness. When awareness is low, AI-HRM digitization fails to enhance innovation, which reveals the limitations of digital HRM effectiveness. These findings underscore that tacit knowledge awareness fundamentally conditions how AI-driven HRM digitization supports or undermines innovation, which advances theory by extending self-determination perspectives to technology-mediated HRM. Thus, this study discusses the theoretical and practical implications for designing AI-enabled HRM systems that balance algorithmic control with employee autonomy.