Machine and Deep Learning-based Human Resource Management Processes and Practices for Job Satisfaction, Organizational Knowledge Sharing, and Staff Performance and Productivity
Machine and Deep Learning-based Human Resource Management Processes and Practices for Job Satisfaction, Organizational Knowledge Sharing, and Staff Performance and Productivity

Summary/Abstract: The purpose of this study is to examine machine and deep learningbased 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 learningbased 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.

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