Algorithmic temporal governance: a sociotechnical theory of labor control, worker adaptation and resistance

Algorithmically mediated work systems are reshaping how organizations govern workers’ time. This paper develops the Temporal Sociotechnical Algorithmic Model (TSAM), a sociological theory explaining how computationally mediated information asymmetries alter workers’ capacity to anticipate, allocate, withhold, and collectively contest their labor time. Drawing on a critical interpretive synthesis of 56 peer-reviewed publications identified through a structured search covering 2000–2024, TSAM begins from a generative condition in which labor time becomes an optimizable organizational resource while workers retain only partial visibility into the processes governing allocation, availability, and evaluation. From this condition, the model specifies three system-level mechanisms: unpredictability, latency compression, and temporal opacity. It distinguishes these mechanisms from temporal anticipatory compliance, an emergent behavioral adaptation through which workers reorganize their availability, responsiveness, and temporal boundaries in anticipation of uncertain consequences. By integrating labor process theory, the sociology of time, and sociotechnical systems theory, TSAM explains how computational mediation reconfigures the informational and temporal conditions through which control is exercised, experienced, and contested. The model further theorizes why resource-protective behavior may become less effective when reduced availability carries uncertain consequences, conceptualizes temporal opacity as epistemic asymmetry rather than simple information deficit, and positions resistance as mechanism-specific modification of algorithmic temporal governance. Five propositions translate these theoretical claims into empirically testable and refutable expectations.