ABSTRACT. The objective of this paper is to systematically review deep and machine learning-based task execution and performance optimization across human‒machine interaction workplace environments. The findings and analyses highlight that human resource management algorithms can improve employee performance and productivity and job role and structure redesign. Throughout January 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence-enabled human resource management processes and practices” + “employee engagement,” “employee performance,” and “employee productivity.” As research published in 2023 was inspected, only 142 articles satisfied the eligibility criteria, and 10 mainly empirical sources were selected. 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.
JEL codes: E24; J21; J54; J64

Keywords: artificial intelligence; human resource management process and practice; employee engagement, performance, and productivity

How to cite: Vasic, M., Ciurlău, C. F., Curteanu, A.-B., and Novak, A. (2023). “Artificial Intelligence-enabled Human Resource Management Processes and Practices for Employee Engagement, Performance, and Productivity,” Psychosociological Issues in Human Resource Management 11(1): 95–108. doi: 10.22381/pihrm11120236.

Received 27 February 2023 • Received in revised form 20 May 2023
Accepted 22 May 2023 • Available online 30 May 2023