Unpacking Algorithmic Management: Pathways to Team Resilience Through Reduced Objectification and Informal Leadership

As algorithmic management (AM) becomes an increasingly prominent feature of the digital workplace, understanding its implications for human resource management (HRM) is both timely and critical. This study investigates how AM, as an emerging HRM practice, contributes to team resilience (TR)—a key strategic goal for HR professionals seeking to foster adaptability and sustainability in uncertain environments. Drawing on social cognitive theory, we develop and test a model in which AM reduces managerial objectification (MO) and facilitates informal leadership emergence, ultimately enhancing TR. Based on a multi-wave field survey of enterprise managers in China, our findings reveal a dual-path mechanism: AM improves TR both cognitively, by reshaping managerial perceptions of employees, and behaviorally, by triggering bottom-up leadership processes. A sequential mediation pathway—AM → MO → informal leadership → TR—is also supported. This study contributes to HRM research by demonstrating how algorithmic systems can be designed and leveraged to promote resilient, self-organizing teams. Practical implications are offered for HR practitioners aiming to implement AM in ways that empower both managers and employees.