Exploring the Agency of Algorithmic Technology in Talent Management Decision‐Making: An Actor‐Network Theory Perspective

Guided by the Actor-Network Theory (ANT) perspective, this study explores user experiences with the perceived agency of algorithmic technology in the context of talent management (TM) decisions. Through a qualitative analysis of 11 client companies utilizing algorithmic TM decision-making technology provided by an HR tech service firm, we identified divergent perceptions of algorithmic technology's agency. While some participants viewed the technology as enhancing decision quality and explainability by promoting objectivity and standardization, others perceived it as constraining managerial judgment and overlooking context-specific, non-quantifiable factors. These contrasting perceptions gave rise to three distinct behavioral responses—endorsing, conforming to, or undermining the technology's agency. In some cases, employees strategically adjusted their behaviors to align with algorithmic outputs, thereby reshaping the actor-network and influencing TM decision-making dynamics. This study contributes to the ANT perspective by showing how perceptions of agency and behavioral responses reconfigure human–technology networks, offering a relational understanding of algorithmic agency in organizational settings. It also contributes to the HRM and technology literature by examining how algorithmic technologies, as relational actors, influence managerial and employee responses, particularly in the management of high-potential employees. Finally, it advances TM research by demonstrating how different organizational actors interpret and respond to algorithmic TM decisions, revealing how employees may strategically leverage such systems.