Algorithmic Control and Negative Emotions in the Platform Economy: The Moderating Role of Work Gamification

Abstract

In the gig economy context, algorithmic control has become an important tool for platform governance, but its impact on the negative emotions of frontline workers still lacks systematic empirical testing. Grounded in stress appraisal theory, this study investigates a moderated mediation model, examining how algorithmic control influences riders’ negative emotions via challenge-hindrance appraisals and the role of work gamification in this process. Data from a two-wave survey of 401 food delivery riders were analyzed using structural equation modeling and the Bootstrap method. The results indicate that algorithmic control not only directly exacerbates riders’ negative emotions but also exerts an indirect effect through heightened hindrance stress; conversely, the mediating role of challenge stress is non-significant. Notably, rather than mitigating the negative impact of algorithmic control, work gamification showed a positive but non-significant moderating trend ( p  = 0.122), descriptively strengthening the relationship between algorithmic control and hindrance stress. This study reveals the dual-path mechanism by which algorithmic control affects riders’ emotions through empirical data, pointing out that work gamification may have the double-edged sword characteristic of “both incentives and constraints,” and providing data support and practical insights for optimizing algorithmic governance and promoting human-centered system design on gig platforms.