Covert Resistance Against Algorithmic Control on Online Labor Platforms - A Systematic Literature Review
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Online labor platforms (OLPs) such as Uber or Upwork heavily rely on algorithms instead of human managers to control workers’ behavior. While algorithmic control (AC) allows platform providers to control their workers efficiently, it is often perceived by workers as a tighter control (compared to human-based control) which increases their motivation to resist. Especially covert resistance (i.e., workers’ hard-to-observe oppositional actions) provides essential insights into how workers deal with AC that affect platforms’ longevity. In this study, we conducted a systematic literature review to develop a theoretical framework showing how and why workers perform covert resistance against AC. Further, our analysis reveals the enabling role of sensemaking for diverse forms of covert resistance. Overall, our study expands the literature on AC by shedding light on the formation of workers’ covert resistance. Therefore, we offer platform providers and policymakers crucial insights to create fairer working environments for workers under AC.
Details
Original language | English |
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Title of host publication | Proceedings of the 31st European Conference on Information Systems (ECIS) |
Place of Publication | Kristiansand |
Publication status | Published - 2023 |
Peer-reviewed | Yes |