Understanding experiences of food-delivery-platform workers under algorithmic management using topic modeling

Jongho Won, Daeho Lee and Junmin Lee

Technological Forecasting and Social Change, 2023, vol. 190, issue C

Abstract: With the emergence of COVID-19, the food-delivery market has grown and now employs large numbers of people as drivers. The independent contractors enjoy flexibility, but their schedules are at the mercy of algorithmic management. This study explores the experiences of food-delivery-platform workers with algorithmic management and their perceptions in response to algorithmic decisions. A collection of 1046 posts containing “AI” (for artificial intelligence) and “algorithm” made to online communities frequently used by platform workers is subjected to keyword analysis, semantic network analysis, and topic modeling using latent Dirichlet allocation. Fifteen latent topics are classified into AI matching, driver behavior, and platform system, and discussed using representative posts from online communities. The results highlight the advancement of AI technology and collaboration strategies used by platform operators to generate trust in algorithmic decisions among food-delivery-platform workers. In addition, this study uses using topic modeling to comprehensively and objectively explore how food-delivery-platform workers with experience working under AI algorithmic management perceive that management.

Keywords: Food-delivery-platform worker; Algorithmic management; Algorithmic decision; AI algorithm; Platform labor; Topic modeling (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:190:y:2023:i:c:s0040162523000549

DOI: 10.1016/j.techfore.2023.122369

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