This paper introduces the concept of comparative algorithmic management preference (CAMP) to explain why workers may sometimes prefer algorithmic oversight to human managerial supervision. It reframes worker responses from binary acceptance or resistance toward comparative judgments grounded in perceived consistency, impartiality, and predictability.
Drawing on research in algorithmic management, human–computer interaction, organizational behavior, and labor relations, the paper develops CAMP as a conceptual framework. It specifies its evaluative dimensions, temporal comparison, reinforcing mechanisms, and contextual boundary conditions.
CAMP suggests that algorithmic oversight is not always resisted; workers may prefer it when it is perceived as comparatively more consistent, impartial, and predictable than prior experiences of human managerial supervision. Temporal comparison and reinforcing mechanisms help explain how such preferences form and persist.
As a conceptual framework, CAMP requires empirical examination through surveys, experiments, and qualitative studies across platform and cultural contexts.
For platforms, managers, and regulators, CAMP highlights that worker responses are shaped by comparative evaluations rather than algorithmic performance alone. Procedural transparency, impartiality, and consistency therefore emerge as important sources of managerial legitimacy.
CAMP reframes debates on platform governance by highlighting how comparative preferences for algorithmic oversight may shape worker consent and resistance in platform labor.
CAMP integrates algorithmic management, trust in automation, and labor relations scholarship to explain comparative evaluations of managerial oversight. In doing so, it offers a novel framework for understanding managerial legitimacy in digitally mediated work environments.
