Protecting workers from Algorithmic Management: a theoretical framework for four key protection factors
4 affiliations
- 1Department of Public Health, Graduate School, Yonsei University
- 2Department of Preventive Medicine, Yonsei University College of Medicine
- 3Institute for Occupational Health, Yonsei University College of Medicine
- 4Institute for Innovation in Digital Healthcare, Yonsei University Health System
Abstract
Algorithmic Management (AM) is increasingly being linked to adverse occupational health outcomes. Although previous research has focused on the risks generated by AM, the protective factors that mitigate these risks remain theoretically underdeveloped. Drawing on the self-determination theory, social acceleration theory, surveillance capitalism theory, and the human oversight provisions of the EU AI Act, this study identifies and theoretically operationalises four protection factors in AM environments: Decision Autonomy, Pace Control, Feedback Transparency, and Loop Presence. By applying the Layers of Protection Analysis model, we argue that each factor functions as an independent protective barrier against AM-related health risks. Together, they form an integrated framework with direct implications for worker health measurements, algorithmic system designs, and occupational health policies. A conceptual six-step procedure for translating the framework into workplace assessment is also outlined.
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BibTeX
@article{lee2026protecting,
title = {{Protecting workers from Algorithmic Management: a theoretical framework for four key protection factors}},
author = {Jian Lee and Jin-Ha Yoon},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.2000600},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.2000600/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Protecting workers from Algorithmic Management: a theoretical framework for four key protection factors
AU - Jian Lee
AU - Jin-Ha Yoon
PY - 2026
DA - 2026/10/21
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.2000600
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.2000600/full
AB - Algorithmic Management (AM) is increasingly being linked to adverse occupational health outcomes. Although previous research has focused on the risks generated by AM, the protective factors that mitigate these risks remain theoretically underdeveloped. Drawing on the self-determination theory, social acceleration theory, surveillance capitalism theory, and the human oversight provisions of the EU AI Act, this study identifies and theoretically operationalises four protection factors in AM environments: Decision Autonomy, Pace Control, Feedback Transparency, and Loop Presence. By applying the Layers of Protection Analysis model, we argue that each factor functions as an independent protective barrier against AM-related health risks. Together, they form an integrated framework with direct implications for worker health measurements, algorithmic system designs, and occupational health policies. A conceptual six-step procedure for translating the framework into workplace assessment is also outlined.
SN - 2296-2565
ER -