Management Review

Management Review ›› 2025, Vol. 37 ›› Issue (6): 141-152.

• Organizational Behavior and Human Resource Management • Previous Articles    

Manufactured “Commitment”: The Impact of Algorithmic Gamification on the Career Commitment of Gig Workers

Liang Xiaojie1, Qu Jiaojiao2, Yang Mengxi3,4   

  1. 1. School of Labor Economics, Capital University of Economics and Business, Beijing 100070;
    2. School of Business Administration, Huaqiao University, Quanzhou 362021;
    3. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    4. MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation, University of Chinese Academy of Sciences, Beijing 100190
  • Received:2023-12-20 Published:2025-07-10

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Abstract: Against the backdrop of the burgeoning gig economy in China, platform companies are attempting to retain and motivate gig workers through gamified algorithmic management, yet the effectiveness of this mechanism lacks empirical support. Accordingly, this study, grounded in Manufactured Consent Theory, uses three-stage data from 338 platform-dependent gig workers to explore the mechanism and boundary conditions of how platform algorithmic gamification affects gig workers’ career commitment. The results show that: (1) algorithmic gamification has a significant positive impact on gig workers’ career commitment, with job control playing a mediating role in this relationship; (2) algorithmic transparency enhances the positive effect of algorithmic gamification on job control, but does not affect the mediating effect of job control; (3) algorithmic fairness has a substitutive effect on algorithmic gamification, namely, algorithmic fairness weakens the positive impact of algorithmic gamification on job control and further diminishes its mediating role. This study not only expands the research on the effects of algorithmic gamification but also provides insights for the optimization of platform algorithmic management.

Key words: manufactured consent, algorithmic gamification, career commitment, algorithmic transparency, algorithmic fairness