Artificial Intelligence-enabled Human Resource Management and Organizational Decision-making Automation and Augmentation Tools for Employee Motivation, Performance Monitoring, and Job Satisfaction
Gheorghe H. Popescu1, Nikola Ćurčić2, Mehmet Emin Kalgi3, and Katarina Frajtova Michalikova4ABSTRACT. This paper draws on a substantial body of theoretical and empirical research on algorithmic employment relation automation processes and decisions. With increasing evidence of artificial intelligence-enabled human resource management and organizational decision-making automation and augmentation tools, there is an essential demand for comprehending whether human resource management algorithms can measure job and organizational commitment and performance, facilitate staff recruitment and retention, enhance employee training, empowerment, experience, and engagement, and intensify collaborative innovation. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout July 2023, with search terms including “artificial intelligence-enabled human resource management and organizational decision-making automation and augmentation tools” + “employee motivation,” “performance monitoring,” and “job satisfaction.” As research published in 2023 was inspected, only 139 articles satisfied the eligibility criteria, and 13 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, Distiller SR, ROBIS, and SRDR.
JEL codes: E24; J21; J54; J64
Keywords: artificial intelligence; human resource management; organizational decision-making automation and augmentation; employee motivation; performance monitoring; job satisfaction
How to cite: Popescu, G. H., Ćurčić, N., Kalgi, M. E., and Frajtova Michalikova, K. (2023). “Artificial Intelligence-enabled Human Resource Management and Organizational Decision-making Automation and Augmentation Tools for Employee Motivation, Performance Monitoring, and Job Satisfaction,” Psychosociological Issues in Human Resource Management 11(2): 79–93. doi: 10.22381/pihrm11220236.
Received 22 August 2023 • Received in revised form 24 November 2023
Accepted 26 November 2023 • Available online 30 November 2023
