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<p>Food delivery platforms have thrived under the empowerment of algorithmic technology, but the dominance of technology has largely overshadowed our understanding of delivery workers. This study delves into the in-depth dynamics between food delivery workers and the algorithms. Focusing on four core systems—navigation, time estimation, order distribution, and customer rating—we reveal how these supposedly efficient systems often challenge the practice and autonomy of delivery workers and uncover the creative strategies workers employ to outmaneuver algorithmic constraints. Our findings spotlight a critical tension between algorithmic control and worker autonomy, highlighting the urgent need for platforms to adopt more transparent, flexible, and worker-centered designs. By reframing how these systems interact with human labor, this research offers a fresh perspective on the future of gig economy platforms, urging stakeholders to rethink algorithmic fairness in a way that empowers workers rather than constrains them.</p>

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From Routes to Ratings: Challenges and Strategies in Food Delivery Work

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Haosu Xu,
  • Yiming Luo,
  • Yihong Wang,
  • Ding Wang,
  • Yushan Pan,
  • Shaoyu Cai

摘要

Food delivery platforms have thrived under the empowerment of algorithmic technology, but the dominance of technology has largely overshadowed our understanding of delivery workers. This study delves into the in-depth dynamics between food delivery workers and the algorithms. Focusing on four core systems—navigation, time estimation, order distribution, and customer rating—we reveal how these supposedly efficient systems often challenge the practice and autonomy of delivery workers and uncover the creative strategies workers employ to outmaneuver algorithmic constraints. Our findings spotlight a critical tension between algorithmic control and worker autonomy, highlighting the urgent need for platforms to adopt more transparent, flexible, and worker-centered designs. By reframing how these systems interact with human labor, this research offers a fresh perspective on the future of gig economy platforms, urging stakeholders to rethink algorithmic fairness in a way that empowers workers rather than constrains them.