Paper Type

Complete

Abstract

As the constraints of algorithm-dominant control systems in gig platforms become more apparent globally, platforms are increasingly introducing human oversight to form human–algorithm control, yet prior research has understudied how these two forms of control interact in practice. This study examines how algorithmic and human control complement each other in shaping gig workers’ experiences and identifies the conditions under which this complementarity is strengthened or undermined through a case study of Meituan, China’s leading food delivery platform. We find that human managers complement algorithms by repairing their opacity and inflexibility, and algorithms complement human control by mitigating bias and enabling real-time efficiency. However, these complementarities are contingent on power imbalances and algorithmic rigidity, which can distribute their benefits unevenly across workers and undermine complementarity overall. Our findings advance human-algorithmic control interactions as contingent complementarity and offer insights into balancing these controls to promote worker well-being.

Paper Number

1579

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Aug 15th, 12:00 AM

Balancing the Bosses: Contingent Complementarity Between Human and Algorithmic Control in Gig Platforms

As the constraints of algorithm-dominant control systems in gig platforms become more apparent globally, platforms are increasingly introducing human oversight to form human–algorithm control, yet prior research has understudied how these two forms of control interact in practice. This study examines how algorithmic and human control complement each other in shaping gig workers’ experiences and identifies the conditions under which this complementarity is strengthened or undermined through a case study of Meituan, China’s leading food delivery platform. We find that human managers complement algorithms by repairing their opacity and inflexibility, and algorithms complement human control by mitigating bias and enabling real-time efficiency. However, these complementarities are contingent on power imbalances and algorithmic rigidity, which can distribute their benefits unevenly across workers and undermine complementarity overall. Our findings advance human-algorithmic control interactions as contingent complementarity and offer insights into balancing these controls to promote worker well-being.

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