A Mixed-Methods Analysis of the Algorithm-Mediated Labor of Online Food Deliverers in China

TL;DR

This study investigates how algorithms mediate the labor of online food deliverers in China, combining large-scale data analysis and interviews to understand their work, perceptions, and stakeholder relationships.

Contribution

It offers a comprehensive mixed-methods analysis revealing the influence of algorithms on deliverers' work and perceptions, a novel approach in gig economy research.

Findings

Algorithms significantly shape delivery procedures.

Deliverers perceive their relationships with stakeholders differently due to algorithm mediation.

Insights suggest ways to improve labor experiences in gig economies.

Abstract

In recent years, China has witnessed the proliferation and success of the online food delivery industry, an emerging type of the gig economy. Online food deliverers who deliver the food from restaurants to customers play a critical role in enabling this industry. Mediated by algorithms and coupled with interactions with multiple stakeholders, this emerging kind of labor has been taken by millions of people. In this paper, we present a mixed-methods analysis to investigate this labor of online food deliverers and uncover how the mediation of algorithms shapes it. Combining large-scale quantitative data-driven investigations of 100,000 deliverers' behavioral data with in-depth qualitative interviews with 15 online food deliverers, we demonstrate their working activities, identify how algorithms mediate their delivery procedures, and reveal how they perceive their relationships with different stakeholders as a result of their algorithm-mediated labor. Our findings provide important implications for enabling better experiences and more humanized labor of deliverers as well as workers in gig economies of similar kinds.