ArticleFrontiers in public health2025
Life against algorithmic management: a study on burnout and its influencing factors among food delivery riders.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithmic management in the global gig economy: an interdisciplinary systematic literature review and critical discourse analysis.Frontiers in sociology · 2026Pooled it
- From algorithmic efficiency to cascading health burdens: a text-mining study of online food delivery riders in the platform economy.Frontiers in public health · 2026Article
- Enterprise characteristics and occupational health literacy among essential service workers in Guangdong Province, China: a cross-sectional study.Frontiers in public health · 2025Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: With the rapid development of global digital economy, burnout among food delivery riders has become an important public health issue. Although burnout has been widely studied, research on burnout among food delivery riders, particularly the impact of algorithmic management on riders' burnout remains limited. This study adopts a novel perspective on the intersection of algorithmic management and burnout, offering an in-depth examination of the burnout levels of food delivery riders under the strict control of algorithmic management and identifying its influencing factors. Methods: A survey of 953 food delivery riders was conducted using the Maslach Burnout Inventory-General Survey (MBI-GS). SPSS was used to conduct independent sample t-tests, one-way ANOVA, Pearson correlation, and multiple linear regression to investigate burnout status and identify factors affecting riders' burnout. Results: The findings indicate that food delivery riders are experiencing moderate level of burnout, with Emotional Exhaustion, Depersonalization, and Reduced Personal Accomplishment as the primary dimensions. In the context of algorithmic management, key factors affecting riders' burnout include gender, age, working years, ranking system, Punishment system, work rules, Work monitoring mechanism, workflow design, customer feedback, and restaurant preparation time. Conclusion: Under algorithmic management, burnout is prevalent among China's food delivery riders and influenced by multiple factors. Individualized support, humane organizational systems, satisfied work mechanism, and supportive social environment can help lessen algorithmic management's negative effects on food delivery riders and reduce their burnout. This study provides theoretical recommendations to protect occupational health of gig workers in platform economy, and offers valuable guidance for practical application.
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