Why and when algorithmic management influences gig workers' work-family interface? Based on goal-setting theory

Purpose The purpose of this study is to explore the mechanism and boundary conditions underlying the effect of algorithmic management on work-family spillover among gig workers in the gig economy. Design/methodology/approach Based on goal-setting theory, this study constructed a moderated mediation model to examine the mediating role of concentration and the moderating effect of depletion sensitivity in the relationship between algorithmic management and work-family spillover. By collecting three-wave time-lagged survey data from 658 gig workers, this study tested the hypotheses. Findings This study demonstrates that algorithmic management can promote positive work-family spillover and inhibit negative work-family spillover by enhancing concentration. As gig workers' depletion sensitivity increases, the positive indirect effect of algorithmic management on positive work-family spillover and its negative indirect effect on negative work-family spillover, are both attenuated. Practical implications To help gig workers balance work and family life, gig platform managers should fully leverage the advantages of algorithmic management in goal setting and provide necessary support for gig workers with high depletion sensitivity. Originality/value These findings empirically reveal the positive effects of algorithmic management on gig workers' family life.