Exploring job embeddedness in the era of artificial intelligence (AI) and machine learning: a bibliometric analysis and future trends

Purpose As artificial intelligence (AI) and machine learning (ML) technologies continue to revolutionize various industries, understanding their impact on job embeddedness becomes crucial. This study examine the role of AI and ML technologies on job embeddedness, identifying key trends and proposing future research directions. It seeks to understand how these technologies influence employee attachment within organizations. Design/methodology/approach This study uses bibliometric analysis to assess 890 articles published from 2001 to 2023 on job embeddedness and its relationship with AI and ML. The Scopus database is examined utilizing VOSviewer and Biblioshiny applications to determine themes and research deficiencies. This study visualizes the intellectual landscape of this area. Findings This study highlights the growing interest in AI and ML job embeddedness, highlighting the complex relationship between AI adoption and employee attachment, links and fit. It also highlights emerging themes like AI-enabled talent management, remote work implications and ethical considerations in AI-driven workplaces. Research limitations/implications The extent of the Scopus database, the time period and the metadata correctness all provide restrictions on this investigation of how AI and ML affect job embeddedness. Nonetheless, the results underscore the need for empirical study on the effects of AI and ML and provide researchers with useful insights. This study also highlights how technological improvements influenced employee attitudes and actions. Originality/value This study presents a comprehensive overview of job embeddedness and AI/ML technologies, utilizing bibliometric techniques to evaluate research publications. It reveals key trends, identifies gaps and suggests future directions in this field.