When play becomes pressure: how gamification and algorithms shape gig workers’ burnout

Abstract

Purpose Grounded in Affective Events Theory and Self-Determination Theory, this study examines how work gamification shapes gig workers’ emotional labor strategies and burnout under different algorithmic features. Design/methodology/approach Using a three-wave survey of 311 gig workers, we tested relationships among work gamification, emotional labor strategies and burnout, along with the moderating roles of algorithmic monitoring and algorithmic fairness. Findings Under low algorithmic monitoring, work gamification reduces surface emotional labor and thereby lowers burnout. Under high monitoring, this indirect effect weakens. Under high algorithmic fairness, gamification promotes deep emotional labor and thereby reduces burnout; under low fairness, the deep emotional labor pathway disappears. Practical implications Platforms should balance monitoring intensity and fairness while using gamification to improve gig workers’ experience and well-being. Originality/value By treating algorithmic features as contextual moderators, this study extends understanding of how gamification affects gig workers’ emotional labor and identifies differentiated pathways linking gamification to burnout.