The Theory Adaptation Approach to Trust in Algorithmic Management

• Freya Hughes

  Author

Algorithmic management, e.g., the use of software and machine-learning systems to delegate, evaluate, and discipline work—has expanded rapidly across platform-mediated gig economies and, increasingly, conventional organizations. Despite a growing body of empirical work on worker perceptions of algorithmic control, the field lacks a coherent theoretical account of why workers do or do not place trust in the algorithmic systems that govern their livelihoods. This paper adopts the Theory Adaptation Approach, an established methodology in information systems research, to systematically adapt integrative model of organizational trust for the algorithmic management context. Following the reflexive guidelines for theory adaptation and the integrated adaptation guidelines, we examine the fit between the source trust model and the phenomenon of trust in algorithmic management reconstruct the historical and disciplinary context from which the source theory emerged trace the methodological implications of relocating trust antecedents from human to non-human trustees, and articulate the cumulative theoretical contribution of the adaptation. We propose an Adapted Trust Model in Algorithmic Management that retains the three trustworthiness constructs of ability, benevolence, and integrity while introducing three algorithmic-context constructs—operationalizability, algorithmic transparency, and contestability—that reflect the sociotechnical character of algorithmic trustees. The paper contributes a theoretically grounded, construct-validated framework that future empirical work can use to investigate the antecedents and consequences of trust in algorithmic management across diverse organizational and labor settings.

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