With the rapid development of digital technology, algorithmic control has emerged as a core management instrument in online labor platforms. Through mechanisms such as automated decision-making, task allocation, and performance monitoring, it has fundamentally reshaped work patterns within the gig economy. Investigating the impact of perceived algorithmic control on platform workers' thriving at work is consequently crucial for optimizing human resource management while enhancing productivity and competitiveness. Grounded in self-determination theory, this study examines the mechanism through which perceived algorithmic control influences thriving at work, specifically investigating the mediating role of self-objectification and moderating function of perceived platform support. Through a two-wave follow-up survey of platform workers, we conducted an empirical analysis of the data from 264 questionnaires. The statistical results indicate that: (1) Perceived algorithmic control exerts significant negative effects on thriving at work; (2) Perceived algorithmic control substantially diminishes thriving at work by intensifying self-objectification; (3) Perceived platform support effectively buffers the detrimental impact of algorithmic control on self-objectification. This research constructs a comprehensive theoretical model delineating the impact of perceived algorithmic control on thriving at work. It pioneers the conceptualization of technologically-reinforced manifestations of self-objectification, while elucidating the precise mechanism whereby perceived algorithmic control reinforces self-objectification to ultimately suppress thriving at work. Crucially, we empirically verify perceived platform support's moderating effect within this pathological pathway. These insights provide robust theoretical foundations and actionable references for managing gig economy platforms.

Perceived Algorithmic Control, Thriving at Work, Self-Objectification, Perceived Platform Support