ABSTRACT. The purpose of this study is to examine machine and deep learning-based human resource management processes and practices. The contribution to the literature on algorithmic workforce management processes is by showing that big data-based organizational performance can integrate human resource algorithms and algorithmic management and predictive analytics tools. Throughout June 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “machine and deep learning-based human resource management processes and practices” + “job satisfaction,” “organizational knowledge sharing,” and “staff performance and productivity.” As research published in 2023 was inspected, only 129 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: AXIS, Dedoose, MMAT, and SRDR.
JEL codes: E24; J21; J54; J64

Keywords: machine and deep learning; human resource management process and practice; job satisfaction; organizational knowledge sharing; staff performance and productivity

How to cite: Puime-Guillén, F., Bonciu, C. I., Dumitrescu, C.-O., and Cug, J. (2023). “Machine and Deep Learning-based Human Resource Management Processes and Practices for Job Satisfaction, Organizational Knowledge Sharing, and Staff Performance and Productivity,” Psychosociological Issues in Human Resource Management 11(2): 65–78. doi: 10.22381/pihrm11220235.

Received 14 July 2023 • Received in revised form 23 November 2023
Accepted 29 November 2023 • Available online 30 November 2023