ABSTRACT. In this article, previous research findings were cumulated, indicating that artificial intelligence technologies can optimize employee motivation, satisfaction, efficiency, and productivity, organizational commitment, performance, and knowledge sharing, and talent attraction and retention. The contribution to the literature on organizational human resource management practices, roles, and functions in machine and deep learning-based organizational environments is by showing that artificial intelligence-based professional knowledge sharing mechanisms can assess employee productivity augmentation, increase organizational performance and effectiveness, and impact job satisfaction and turnover intention. Throughout July 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence-assisted human resource management algorithms” + “employee motivation,” “employee efficiency,” and “employee productivity.” As research published in 2023 was inspected, only 136 articles satisfied the eligibility criteria, and 12 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, ROBIS, and SRDR.
JEL codes: E24; J21; J54; J64

Keywords: artificial intelligence; human resource management algorithm; employee motivation, efficiency, and productivity

How to cite: Popescu Ljungholm, D. (2023). “Artificial Intelligence-assisted Human Resource Management Algorithms for Employee Motivation, Efficiency, and Productivity,” Psychosociological Issues in Human Resource Management 11(2): 51–64. doi: 10.22381/pihrm11220234.

Received 28 August 2023 • Received in revised form 22 November 2023
Accepted 25 November 2023 • Available online 30 November 2023