Reframing AI-Driven Recruitment as a Socio-Technical Governance System: Integrating Operational Efficiency, Candidate Experience, and Organizational Legitimacy

Artificial intelligence (AI) is increasingly transforming recruitment processes, yet existing research remains fragmented across technical, operational, and behavioral perspectives. While prior studies emphasize efficiency gains and predictive accuracy, they often overlook the socio-organizational dimensions that shape the effectiveness and acceptance of AI-driven hiring systems. This study addresses this gap by advancing a socio-technical governance framework that integrates operational efficiency, candidate perception, and organizational legitimacy into a unified analytical model. Drawing on legitimacy theory and interdisciplinary insights from human resource management, operations management, and marketing, the paper conceptualizes AI-driven recruitment as a dynamic and adaptive system. The proposed model introduces causal relationships between core dimensions, highlighting the mediating role of candidate perception and the moderating function of governance mechanisms. Furthermore, it extends existing approaches by incorporating feedback loops that capture the temporal evolution of AI systems within organizational and institutional environments. The study contributes to the literature by reframing AI recruitment from a purely technical tool to a governance-driven socio-technical system. It also contributes to interdisciplinary AI governance research by linking recruitment efficiency, candidate experience, and legitimacy within the emerging European regulatory context. While conceptual in nature, the framework provides a foundation for future empirical research and offers practical insights for organizations seeking to balance efficiency, fairness, and legitimacy in AI-enabled recruitment processes.