ABSTRACT. The present study systematically reviews the existing research on big data algorithm-based human resource planning, training, and development, virtual team collaboration and workplace decision-making skill enhancement, and human resource planning processes. The findings indicate that machine learning-based organizational decision making and performance improvements can further artificial intelligence work environments and job markets, increasing labor productivity, smart operation employee empowerment, and organizational knowledge and experience sharing. The contribution to the literature is by clarifying that artificial intelligence-driven managerial and workplace adoption decisions can assist big data management and governance practices, subjective norms, organizational rules, systems, and structures, and job performance. Throughout July 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “human resource management algorithms” + “employee behavioral and performance data,” “artificial intelligence decision-making processes,” and “organizational values, routines, and workflows.” As research published in 2023 was inspected, only 130 articles satisfied the eligibility criteria, and 14 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: human resource management algorithm; employee behavioral; performance data; artificial intelligence; decision-making process; organizational value, routine, and workflow

How to cite: Bratu, S. (2023). “Human Resource Management Algorithms for Employee Behavioral and Performance Data, Artificial Intelligence Decision-making Processes, and Organizational Values, Routines, and Workflows,” Psychosociological Issues in Human Resource Management 11(2): 21–35. doi: 10.22381/pihrm11220232.

Received 21 August 2023 • Received in revised form 22 November 2023
Accepted 26 November 2023 • Available online 30 November 2023