ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore human resource management algorithms and artificial intelligence and automation technologies. In this research, previous findings were cumulated showing that generative artificial intelligence technologies can optimize algorithmic employee recruitment, selection, engagement, and performance management and staff training and empowerment, and the contribution to the literature is by indicating that performance measurement tools can further employee monitoring and performance appraisals and job efficiency and productivity assessment. Throughout February 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “automated employee data mining and talent management tools +” + “job efficiency,” “performance appraisal,” and “algorithmic decision-making processes.” As research published in 2023 was inspected, only 160 articles satisfied the eligibility criteria, and 15 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: automated employee data mining; talent management; job efficiency; performance appraisal; algorithmic decision-making process

How to cite: Poliakova, A., Adilbekova, K., Condeianu, O., and Niță, M. I. (2023). “Automated Employee Data Mining and Talent Management Tools for Job Efficiency, Performance Appraisal, and Algorithmic Decision-making Processes,” Psychosociological Issues in Human Resource Management 11(1): 80–94. doi: 10.22381/pihrm11120235.

Received 18 March 2023 • Received in revised form 23 May 2023
Accepted 28 May 2023 • Available online 30 May 2023