Protecting workers from Algorithmic Management: a theoretical framework for four key protection factors

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.