Algorithmic management practices, trust, and gig-worker continuance: A mixed-methods study of platform work

The gig economy's reliance on algorithmic management systems necessitates understanding how distinct practices influence worker retention. This study examines how five algorithmic practices (compensation, goal setting, monitoring, performance rating, and scheduling) shape gig workers' intention to continue as gig workers, mediated by trust in the platform. Grounded in social exchange theory and algorithmic management theory, the research employs an explanatory-sequential design, drawing on survey data from 790 gig workers across China, Pakistan, Turkey, and Australia, and conducting in‐depth interviews with 40 participants (10 from each country). Quantitative results reveal that performance rating and scheduling significantly enhance trust, while compensation has no direct effect. Trust mediates the effects of goal setting, monitoring and control, performance rating, and scheduling on platform-anchored continuance intention, while the additional mediation assessment indicates complementary/partial mediation rather than a trust-only pathway. Qualitative insights corroborate these findings, highlighting transparency in goal setting and flexible scheduling as key trust drivers, while exposing compensation opacity as a trust-undermining factor that does not directly influence retention. Theoretically, the findings show that algorithmic management operates as a multidimensional set of practices with differentiated trust-formation and continuance implications; practically, results indicate that platforms should prioritize explainable performance metrics, transparent goal-setting systems, flexibility-preserving scheduling architectures, and accountable compensation procedures to support fair and sustainable platform work.