Algorithmic management in the workplace: A systematic review and topic modeling integration using BERTopic

Abstract

As algorithmic systems become increasingly embedded in organizational processes, algorithmic management in the workplace has emerged as a central mechanism for guiding, evaluating, and coordinating employee attitudes and behaviors. While existing research has extensively examined the application of algorithmic management in gig platforms, there remains a lack of systematic review and theoretical integration concerning its diverse configurations, deployment conditions, and employee response mechanisms within standardized workplace settings. To address this gap, we conducted a systematic review of 167 peer-reviewed articles on workplace algorithmic management using the BERTopic topic modeling method. Guided by socio-technical systems (STS) theory and a bi-dimensional framework of algorithmic and employee autonomy, we identify four archetypal configurations: surveillance, supervision, supplementary, and complementary. These archetypes reflect distinct employee-algorithm interaction logics across role allocation, task interdependence, and goal alignment. We further examine the technological and organizational conditions required for each configuration and synthesize employee responses across cognitive, emotional, and behavioral domains. By constructing a configuration-based taxonomy rooted in the (in)consistencies of employee-algorithm autonomy, this study explicates the socio-technical deployment mechanisms underlying each archetype and illustrates how employees adapt to algorithmic systems through complex and dynamic engagement trajectories. Our findings offer an integrative framework linking configuration logics, deployment demands, and response patterns, contributing to a more nuanced understanding of how intelligent systems reshape organizational structures and employee experiences.