A Person-Centred View of Algorithmic Management and Gig Workers’ Turnover

New working environments, such as online labor platforms (OLPs), increasingly rely on automated, intelligent algorithms for managing workforces. While existing research has explored the benefits and concerns associated with algorithmic management, limited attention has been given to its diverse impacts on different groups of gig workers. This study seeks to understand how algorithmic management in OLPs differentially influences turnover among various groups of gig workers. Adopting a person-centered approach, the research examines how gig workers' perceptions of algorithmic management, in combination with their psychological capital, affect their turnover intentions. Drawing on the Job Demands-Resources (JD-R) model and utilizing Fuzzy-Set Qualitative Comparative Analysis (fsQCA), the study uncovers configurations of algorithmic job demands and workers’ psychological capital that explain gig workers’ turnover decisions. The findings reveal four distinct profiles of gig workers with a high intention to leave the platform, each influenced by a unique combination of factors, and three profiles representing workers inclined to remain with the platform. By identifying these distinct worker profiles, the research provides a nuanced understanding of how gig workers navigate algorithmic management. It contributes to the development of middle-range theories and offers actionable insights to policymakers, platform designers, and researchers aiming to foster positive and sustainable work experiences in OLPs.