TECHNOLOGICAL UNEMPLOYMENT IN THE AGE OF GENERATIVE AI: A BIBLIOMETRIC REVIEW OF LABOUR MARKET DISRUPTION, JOB EXPOSURE, AND RESKILLING RESEARCH

Abstract

This study maps the intellectual, conceptual, and social structure of research on technological unemployment in the age of generative artificial intelligence (GenAI), with particular attention to labour market disruption, job exposure, and reskilling. Unlike prior reviews that discuss artificial intelligence and employment in broad terms, this study specifically integrates these three dimensions within the context of GenAI, thereby offering a more focused understanding of how the future-of-work literature is evolving. Using a bibliometric review design, the study analyses 810 documents published between 2022 and 2026 across 538 sources. The findings show a sharp increase in publication output after 2023, indicating that the field has entered a rapid expansion phase following the widespread diffusion of large language models and related generative systems. The intellectual structure of the field is anchored in four major domains: automation and displacement economics, AI and digital transformation, organisational and human resource management, and sociotechnical perspectives on digital work. Conceptually, the field is shifting from broad concerns with automation and technological change toward future of work, decision making, generative AI, occupational exposure, and adaptive capability. These findings suggest that technological unemployment in the GenAI era is increasingly framed not merely as job loss, but as a broader process of task transformation, workforce vulnerability, and institutional adaptation, with important implications for research, policy, and organisational strategy.