Invisible algorithms, visible outcomes: Roles of emotional labor and work gamification in gigs

As algorithm governance becomes increasingly crucial in gig economy ecosystems, understanding the behavioral impact of algorithmic transparency remains a key research gap. Using the stimulus-organism-response framework and uncertainty management theory, this study examines how platform algorithmic transparency affects gig workers' in-role and extra-role service behaviors, with emotional labor acting as a mediator and work gamification as a moderator. Analyzing survey data from 325 ride-hailing drivers using partial least squares structural equation modeling (PLS-SEM), the findings reveal that greater algorithmic transparency enhances both in-role and extra-role behaviors. Emotional labor mediates this relationship: deep acting strengthens both behaviors, while surface acting primarily supports in-role behavior. Meanwhile, work gamification diminishes the positive effect of algorithmic transparency on extra-role service behavior. These insights clarify the mechanisms and boundary conditions of algorithmic transparency in gig work, offering practical guidance for designing platform algorithms that optimize worker performance and satisfaction.