{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T17:43:00Z","timestamp":1793468580773,"version":"4.5.0"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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\u2019 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2019 burnout.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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\u2019 burnout include gender, age, working years, ranking system, Punishment system, work rules, Work monitoring mechanism, workflow design, customer feedback, and restaurant preparation time.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Under algorithmic management, burnout is prevalent among China\u2019s 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\u2019s 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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1531541","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T05:21:19Z","timestamp":1744694479000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Life against algorithmic management: a study on burnout and its influencing factors among food delivery 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