Algorithmic Management in the Logistics and Delivery Industry: An Empirical Study of Riders’ Job Burnout and Turnover Intention
DOI:
https://doi.org/10.64229/qr3w2w04Keywords:
Algorithmic management, Platform labor, Delivery riders, Job burnout, Job satisfaction, Turnover intentionAbstract
The rapid expansion of platform-based delivery services has made algorithmic management a central feature of riders’ everyday work. Although such systems improve coordination efficiency, they may also be associated with heightened psychological strain and workforce instability. This study examines how different dimensions of perceived algorithmic management are associated with riders’ job burnout and turnover intention, and whether job satisfaction helps explain the relationship between burnout and turnover intention. Drawing on Maslach’s multidimensional burnout framework and Conservation of Resources theory, the study uses survey-based primary data collected from 458 delivery riders in China. Statistical analyses were conducted using SPSS 26.0, including reliability and validity assessment, descriptive statistics, correlation analysis, multiple regression, and bootstrap mediation testing. The results show that algorithmic monitoring intensity, the stringency of reward and punishment rules, and order assignment volatility were all positively associated with emotional exhaustion, cynicism, and reduced personal accomplishment. Among these dimensions, the stringency of reward and punishment rules showed the strongest association with burnout across the regression models. The findings further indicate that emotional exhaustion, cynicism, and reduced personal accomplishment were all positively associated with turnover intention, with emotional exhaustion emerging as the strongest predictor. In addition, job satisfaction partially mediated the relationships between burnout and turnover intention. By differentiating among multiple forms of perceived algorithmic management and linking them to multidimensional burnout and turnover intention, this study extends current research on platform labor and algorithmic governance. The findings also offer practical implications for platform firms seeking to improve rider well-being, reduce turnover intention, and develop more sustainable management practices.
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Copyright (c) 2026 Qian Li, Peilin Li, KeongSai Chan (Author)
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