Research on the Nonlinear Mechanism of Gig Workers’ Perception of Algorithmic Control and Their Counterproductive Work Behaviors

Against the backdrop of the rapid development of the platform economy, gig workers’ mental health and behavior impact both individual well-being and the long-term sustainability of platform operations. Based on the cognitive appraisal theory of emotion, this study reveals the nonlinear psychological mechanism through which perceived algorithmic management influences gig workers’ behavior. Using hierarchical regression and Bootstrap analysis on data from 385 Chinese gig workers, we examine mediating and moderating effects. The findings indicate that a U-shaped relationship between them: both excessively low and high algorithmic control intensify counterproductive behaviors, while moderate control suppresses them. Negative emotions mediate this effect, uncovering the mechanism by which algorithmic control influences behavior through emotional pathways. Locus of control moderates this relationship: externally controlled workers are more sensitive to algorithmic changes, amplifying the U-shaped effect, while internally controlled workers buffer negative emotions, reducing counterproductive behaviors. This study extends the cognitive appraisal theory of emotion to the context of algorithmic management, revealing the threshold effect of perceived control and the moderating role of individual attribution tendencies. It provides theoretical guidance for platform enterprises to optimize algorithmic design and guide gig workers’ behavior, thereby facilitating the coordinated development of dual sustainability for both gig workers and platform operations.