Against acceptance as legitimacy: Ethics and algorithmic management

Algorithmic management systems are increasingly assessed through indicators of user acceptance, such as continued use, compliance, or adaptation. While acceptance-based models such as the Technology Acceptance Model remain useful for explaining adoption and usability, they are increasingly mobilized as proxies for ethical legitimacy in organizational contexts. This essay argues that acceptance is an inadequate basis for the ethical evaluation of algorithmic management, especially in settings marked by power asymmetries, dependency, and constrained choice. Drawing on research on algorithmic management and organizational ethics, the article shows how acceptance may reflect necessity, habituation, or lack of alternatives rather than moral endorsement. It then develops three questions for ethical evaluation beyond acceptance: who bears responsibility for a system’s consequences, how algorithmic systems reshape moral agency at work, and whose interests they ultimately serve. The essay concludes by calling for forms of evaluation and governance that foreground accountability, power, and justice rather than behavioral compliance.

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Published
2026-04-17
DOI
https://doi.org/10.1177/13505084261443023
Primary Topic
Ethics and Social Impacts of AI
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article
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article

Against acceptance as legitimacy: Ethics and algorithmic management

Piotr Zientara
Organization
Ethics and Social Impacts of AI
article

Against acceptance as legitimacy: Ethics and algorithmic management

Piotr Zientara
article en

Abstract

Algorithmic management systems are increasingly assessed through indicators of user acceptance, such as continued use, compliance, or adaptation. While acceptance-based models such as the Technology Acceptance Model remain useful for explaining adoption and usability, they are increasingly mobilized as proxies for ethical legitimacy in organizational contexts. This essay argues that acceptance is an inadequate basis for the ethical evaluation of algorithmic management, especially in settings marked by power asymmetries, dependency, and constrained choice. Drawing on research on algorithmic management and organizational ethics, the article shows how acceptance may reflect necessity, habituation, or lack of alternatives rather than moral endorsement. It then develops three questions for ethical evaluation beyond acceptance: who bears responsibility for a system’s consequences, how algorithmic systems reshape moral agency at work, and whose interests they ultimately serve. The essay concludes by calling for forms of evaluation and governance that foreground accountability, power, and justice rather than behavioral compliance.

Organization
University of Gdańsk (PL)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Ethics and Social Impacts of AI
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