Robert Hunt, Concordia University; Nathan Beard, University of Maryland;

The pandemic-fuelled turn to remote work spurred the implementation of a variety of technologies-primarily software-to manage office workers who could no longer be monitored directly. This "bossware" (as it's popularly known) was hardly new; its conceptual and technical roots can be traced far into the industrial past. But Covid's seismic impact highlighted the extent to which these technologies of surveillance, analysis, and control have become a fact of life for workers in numerous industries and across sectors, from deeply precarious clickwork and algorithmically managed gig work to purportedly more autonomous forms of knowledge work. Data analytic and automated processes (often marketed as "AI") are now used to accomplish a range of management and human resources tasks and goals, including recruiting, hiring, and training; personality profiling; optimizing schedules and workflows; measuring productivity; evaluating 'talent'; promoting wellness; preventing burnout; meeting diversity, equity, and inclusion targets; legal compliance; detecting security threats; and gauging worker sentiment. The proliferation of bossware raises a host of questions. How are these technologies changing the world of work and the politics of labour? How have workers responded and resisted? What role does scientific research play in legitimating these technologies? How can we situate these developments in the history of management? We invite papers on topics such as: • Automating managerial practice • Emerging techniques of monitoring and management • Psychometric and biometric employee assessment • Management technologies and the built environments of work • Worker resistance and sabotage • Histories of technological management • Use of psychological and sociological research in bossware • Legal, regulatory, and governance issues • Comparative studies (geographically or cross-sector)


Keywords: Information, Computing and Media Technology, Big Data, AI, and Machine Learning, Data and Quantification, Labour and Technology

Published: 04/07/2023