Staying or leaving? A person-centred view of algorithmic management and gig worker attrition

New working environments, such as online labour platforms (OLPs), increasingly rely on automated, intelligent algorithms for managing workforces. While existing research has explored benefits and concerns associated with algorithmic management, limited attention has been paid to its heterogeneous effects on different groups of gig workers. This study seeks to understand how algorithmic management in OLPs differentially affects turnover intention among various groups of gig workers. Adopting a person-centred approach, we examine the impact of gig workers’ perceptions of algorithmic management, combined with their psychological capital, on their intentions to leave the platform. Drawing on the Job Demands-Resources (JD-R) model and employing Fuzzy-Set Qualitative Comparative Analysis (fsQCA), this study reveals distinct configurations of algorithmic job demands and resources that, in interaction with psychological capital, shape turnover intention. The findings identify distinct profiles of gig workers with high and low intention to leave, suggesting five theoretical propositions that capture the combination of conditions influencing turnover across these groups. By adopting a configurational and contextualised approach, this research contributes to middle-range theorising in the digital work context, developing an empirically bounded yet theoretically meaningful explanation of how algorithmically mediated work systems influence workers’ intention to leave the platform. Our findings offer valuable implications for policymakers, platform designers, and researchers aiming to advance positive and sustainable work experiences in OLPs.