ABSTRACT. The objective of this paper is to systematically review generative artificial intelligence substituting both human labor and decisions. The findings and analyses highlight that people analytics and machine learning algorithms can be pivotal in generative artificial intelligence-driven human resource and sustainable organizational development and systems in terms of job and employee sentiment analysis and monitoring. Throughout May 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “generative artificial intelligence algorithms” + “talent and performance management,” “job displacement and creation,” and “employee productivity and well-being.” As research published in 2023 was inspected, only 172 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 30 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: generative artificial intelligence algorithms; talent and performance management; job displacement and creation; employee productivity and well-being

How to cite: Popescu Ljungholm, D., and Popescu, V. (2023). “Generative Artificial Intelligence Algorithms in Talent and Performance Management, Job Displacement and Creation, and Employee Productivity and Well-Being,” Contemporary Readings in Law and Social Justice 15(2): 9–25. doi: 10.22381/CRLSJ15220231.

Received 29 June 2023 • Received in revised form 22 September 2023
Accepted 28 September 2023 • Available online 30 September 2023