Algorithmic HRM and Labour Supply in Platform Work: Scheduling Autonomy as a Mechanism and Transparency as a Boundary Condition

This study examines how algorithmic human resource management (HRM) functions as manpower governance in platform work by shaping workers’ scheduling autonomy and, in turn, their labour supply. Using a three-wave time-lagged panel design with platform workers, algorithmic control intensity and transparency/explainability were measured at Time 1, scheduling autonomy at Time 2, and labour supply was captured as hours worked over the prior period at Time 3. Structural equation modelling with bootstrapping showed that higher algorithmic control intensity is associated with lower scheduling autonomy, while higher scheduling autonomy is associated with greater labour supply. Scheduling autonomy significantly mediates the relationship between algorithmic control intensity and labour supply, supporting an autonomy-based mechanism through which algorithmic HRM affects workforce utilisation in platform work. However, transparency/explainability does not significantly weaken the autonomy-reducing effect of algorithmic control intensity, suggesting that informational clarity alone may be insufficient to preserve workable discretion under intensive control. The findings clarify how algorithmic HRM shapes labour supply and refine platform-governance arguments by showing the limits of transparency as a protective mechanism.