文献类型：期刊文献

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

作者：Chen, Wansi[1];Li, Anya[1,3];Pan, Chengkai[1];Yu, Ting[2];Ali, Aamir[1];Sun, Yuanyuan[1]

机构：[1]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Sichuan Univ, Sch Business, 24 South Sect 1,Yihuan Rd, Chengdu, Sichuan, Peoples R China;[3]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2026

卷号：86

外文期刊名：INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT

收录：;EI(收录号：20254519477022);WOS:【SSCI(收录号:WOS:001616519500001)】；

基金：This study was partially supported by Ministry of Education in China Liberal arts and Social Sciences Foundation (grant number 21YJA630129) .

语种：英文

外文关键词：Topic modeling; Workplace algorithmic management; Duality of employee-algorithm autonomy; Socio-technical systems theory

摘要：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 sociotechnical 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.