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Gig to the left, algorithms to the right: A case study of the dark sides in the gig economy

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  • Zhu, Guowei
  • Huang, Jing
  • Lu, Jinfeng
  • Luo, Yingyu
  • Zhu, Tingyu

Abstract

In the current wave of digital technology that continues to innovate platform business models, an increasing number of gig economy platforms are deploying algorithms to optimize and reshape legacy transaction processes and create new value for multi-stakeholders. Nevertheless, algorithmic management also leads to many unforeseen dark sides for multiple participants in the practice, compromising their rights and interests (e.g., price discrimination, labor process control, and privacy concerns). Accordingly, this study aims to examine the negative implications of the introduction of digital technology in platform innovation within gig economy platforms, specifically focusing on the dark sides of algorithmic management, from a multi-sided platform perspective. Through a series of interviews with multi-stakeholders of Meituan Takeaway, the largest food-delivery platform in China, and secondary data analysis based on rooting theory, we develop a theoretical framework to deepen the understanding of the dark sides of algorithmic management and provide valuable insights for platforms seeking to optimize their operations management.

Suggested Citation

  • Zhu, Guowei & Huang, Jing & Lu, Jinfeng & Luo, Yingyu & Zhu, Tingyu, 2024. "Gig to the left, algorithms to the right: A case study of the dark sides in the gig economy," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:tefoso:v:199:y:2024:i:c:s0040162523007035
    DOI: 10.1016/j.techfore.2023.123018
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