Does work overload of odd-job platform workers lead to turnover intention? An empirical study on platform workers

Purpose Against the background of the digital economy, odd-job platforms rely on artificial intelligence algorithms to efficiently allocate tasks and monitor platform workers’ performance, putting these workers under enormous pressure. This paper explores the relationship between work overload and turnover intention of platform workers on odd-job platforms and the factors that lead to platform workers’ turnover. Design/methodology/approach Based on the job demands–resources model (JD-R), we construct a theoretical model to explain the relationship between work overload and turnover intention of platform workers. We test job burnout as a mediator variable and perceived algorithmic fairness and job autonomy as moderating variables. We conducted a study at food delivery platforms and ride-hailing platforms in China. Findings The empirical results show that: (1) work overload increases the turnover intention of platform workers by increasing job burnout and (2) perceived algorithmic fairness and job autonomy moderate the positive relationship between work overload and job burnout. Originality/value We provide a theoretical basis to explain the influence of work overload on turnover intention of odd-job platform workers and provide practical recommendations for management of platform workers.