Abstract:

With the widespread adoption of algorithmic management in the gig economy, gig workers’ labor processes have become increasingly embedded in digital work contexts jointly shaped by platform rules and customer evaluations. In this context, the impact of customer mistreatment has become increasingly prominent. Existing research has primarily focused on the effects of algorithmic control on gig workers’ work stress and behavioral outcomes, while paying insufficient attention to the mechanisms through which algorithms reshape gig work contexts by granting customers greater evaluative and disciplinary power. Drawing on cognitive appraisal theory, this study develops an integrative analytical framework to examine how gig workers cope with customer mistreatment in algorithmic contexts. First, the study analyzes how algorithmic management reshapes the manifestations of customer mistreatment and develops a measurement instrument tailored to the gig work context. Second, from the perspective of long-term stress accumulation, it examines the effects of customer mistreatment on gig workers’ burnout, employability, and turnover intention, reveals the mediating roles of deep acting and surface acting, and investigates the moderating effects of personal, platform, and social resources. Finally, the study proposes an AI-mediated responsive forgiveness intervention and explores pathways through which such an intervention may facilitate cognitive adjustment and emotional recovery among gig workers. This research deepens the application of customer mistreatment and stress-coping theories in emerging forms of work. It also provides important theoretical insights and empirical implications for human-centered platform governance and the technological empowerment of workers.

Key words: gig workers, customer mistreatment, algorithmic management, stress coping, AI intervention