Perceived algorithmic control and anti-algorithm behaviors: the catalytic role of perceived overqualification

The rapid expansion of algorithmic management in the gig economy has sparked widespread concerns over workers' behavioral resistance to algorithmic constraints, yet the underlying psychological mechanism between perceived algorithmic control and workers' anti-algorithm behavior remains underexplored. This study examines the mechanism linking perceived algorithmic control to workers' anti-algorithm behavior in the gig economy, with particular attention to the mediating role of perceived overqualification.

A three-wave longitudinal design was adopted to collect valid data from 483 food delivery riders across three cities in eastern China, and partial least squares structural equation modeling (PLS-SEM) was employed for empirical analysis.

The findings indicate that perceived algorithmic control - operationalized through stringent normative guidance, real-time surveillance, and behavioral constraints - is positively associated with anti-algorithm behavior. Moreover, perceived algorithmic control is positively related to workers' sense of overqualification by triggering the perceived mismatch between their capabilities and task demands, which in turn correlates with anti-algorithm behavior. Perceived overqualification significantly mediates the control-resistance link, revealing a clear path that algorithmic management practices reshape workers' overqualification perceptions, and further induce their coping behaviors against algorithmic constraints.

These findings illuminate the psychological experience and adaptive process of gig workers when facing algorithmic governance, filling the research gap in the internal transmission mechanism between digital labor management and individual resistance. It also offers targeted theoretical and practical implications for optimizing digital labor regulation and building more harmonious labor relations in the platform economy.