ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore artificial intelligence-based workplace decisions. Throughout May 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “machine and deep learning-based collaborative working environments” + “artificial intelligence workplace and sensor network-based employee tracking technologies,” “organizational decision-making augmentation and labor productivity measurement tools,” and “algorithmic workforce management and employment relation automation processes.” As research published in 2022 and 2023 was inspected, only 172 articles satisfied the eligibility criteria, and 38 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: Abstrackr, Citationchaser, PICO Portal, ROBIS, SRDR+, and SWIFT-Active Screener.
JEL codes: E24; J21; J54; J64

Keywords: artificial intelligence; sensor network-based employee tracking; organizational decision-making augmentation; labor productivity measurement; algorithmic workforce management; employment relation automation process

How to cite: Nica, E., Poliak, M., Zvarikova, K., and Pârvu, I. A. (2024). “Artificial Intelligence Workplace and Sensor Network-based Employee Tracking Technologies, Organizational Decision-Making Augmentation and Labor Productivity Measurement Tools, and Algorithmic Workforce Management and Employment Relation Automation Processes in Machine and Deep Learning-based Collaborative Working Environments,” Psychosociological Issues in Human Resource Management 12(2): 50–66. doi: 10.22381/pihrm12220243.

Received 28 June 2024 • Received in revised form 24 November 2024
Accepted 27 November 2024 • Available online 29 November 2024