The effect of algorithmic management on gig workers’ safety behavior and life satisfaction: the roles of emotions and social support

Purpose The topic of algorithmic management has garnered wide scholarly attention, but its impact on the life satisfaction and safety behavior of gig workers remains insufficiently explored. This study aims to draw from affective events theory and propose a model in which the relationships between algorithmic management and gig workers’ life satisfaction and safety behavior are mediated by both positive and negative emotions and moderated by social support. Design/methodology/approach A multiwave survey data from 405 Chinese food delivery workers was used. Findings Results reveal that algorithmic management enhances life satisfaction and safety behavior by influencing positive and negative emotions. Moderated mediation analyses showed that social support enhances the indirect effects of algorithmic management on gig workers’ life satisfaction and safety behavior via negative emotions, but not through positive emotions. Research limitations/implications The limited focus on Chinese food delivery workers necessitates future research across diverse organizational and cultural settings to broaden the generalizability of these findings on emotional responses to algorithmic management. Practical implications This study offers crucial practical implications for platform companies in designing algorithmic management systems to better consider emotional well-being and social support. Originality/value This study provides a novel affective perspective on algorithmic management’s impact, showing how it influences gig workers’ life satisfaction and safety behavior through positive and negative emotions, and how social support critically moderates these emotional pathways.