ABSTRACT. The aim of this systematic review is to synthesize and analyze artificial intelligence-driven managerial and workplace adoption decisions. In this research, prior findings were cumulated indicating that artificial intelligence technologies can enhance knowledge support, work–life balance, work design, organization, and practices, employee trust, and business operations. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout June 2023, with search terms including “deep neural network-based people management tools and workplace automation technologies” + “organizational behavior,” “employee participation,” and “performance monitoring.” As research published in 2023 was inspected, only 134 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, Distiller SR, ROBIS, and SRDR.
JEL codes: E24; J21; J54; J64

Keywords: deep neural network; people management; workplace automation; organizational behavior; employee participation; performance monitoring

How to cite: Nica, E., Hysa, E., Șanta, A.-M. I., AlAkoum, A., and Novak Sedlackova, A. (2023). “Deep Neural Network-based People Management Tools and Workplace Automation Technologies for Organizational Behavior, Employee Participation, and Performance Monitoring,” Psychosociological Issues in Human Resource Management 11(2): 7–20. doi: 10.22381/pihrm11220231.

Received 21 July 2023 • Received in revised form 23 November 2023
Accepted 27 November 2023 • Available online 30 November 2023