ABSTRACT. Despite the relevance of employee performance and productivity appraisals, organizational knowledge development, automatized management decision mechanisms, and task assignment, monitoring, and evaluation, only limited research has been conducted on this topic. The contribution to the literature on talent retention and professional expertise development, job satisfaction, performance, and productivity, and employee knowledge and experience sharing processes is by showing that artificial intelligence reskilling and upskilling can enhance workforce retention, job creation and destruction, skill development and displacement, and job design disruption, while improving employee engagement, creativity, and innovation. Throughout January 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence-driven human resource analytics” + “employee motivations,” “employee attitudes,” and “employee perceptions.” As research published in 2023 was inspected, only 131 articles satisfied the eligibility criteria, and 11 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: AXIS, Dedoose, Distiller SR, and MMAT.
JEL codes: E24; J21; J54; J64

Keywords: artificial intelligence; human resource analytics; employee motivations, attitudes, and perceptions

How to cite: Szpilko, D., Alpopi, C., Pârvu, I. A., and Zvarikova, K. (2023). “Artificial Intelligence-driven Human Resource Analytics for Employee Motivations, Attitudes, and Perceptions,” Psychosociological Issues in Human Resource Management 11(1): 66–79. doi: 10.22381/pihrm11120234.

Received 20 February 2023 • Received in revised form 24 May 2023
Accepted 26 May 2023 • Available online 30 May 2023