Advancing Job Design through Artificial Intelligence: Bibliometric Data-based Insights and Suggestions for Future Research - Eftimov, Lјupcho; Kitanovikj, Bojan (My University, 2024)
As the digital transformation of businesses reshapes jobs to delegate tasks to technology, human resource professionals and managers find themselves at a crossroads when it comes to designing and redesigning jobs, especially under the influence of artificial intelligence (AI). Being an emerging topic, this article aims to synthesize the current state-of-the-art literature regarding the application of AI for job design purposes using a multi-technique bibliometric analysis followed by a literature review in compliance with the rigorous Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The research presents the findings grounded in data from 67 Scopus-indexed publications, which was analyzed with a combination of descriptive bibliometric analysis, co-authorship, bibliographic coupling, and co-occurrence analysis, helping us identify past scientific directions as well as draft a future research agenda. As one of the first bibliometric analyses in the field, it contributes to the scientific discourse by revealing the core themes of the literature, including job characteristics impacted by AI and data-driven human resource (HR) practices, group-level AI integration in job design, AI-related job skills of the future of the workforce, human-AI trust and labor relations and the role of algorithmic human resource management (HRM) in job design. Further, we stress seven distinct pathways for future research.
Keywords: Job design; Work design; Artificial intelligence; Bibliometric review