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

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

Technological Forecasting and Social Change, 2024, vol. 199, issue C

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.

Keywords: Gig economy; Multi-sided platform; Algorithmic management; The dark sides (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:199:y:2024:i:c:s0040162523007035

DOI: 10.1016/j.techfore.2023.123018

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