ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore real-time event and network visual analytics, employee engagement data and performance metrics, and realistic movement simulations. We contribute to the literature by indicating that Web3 technology-based immersive remote work experiences can be attained by use of generative artificial intelligence and extensive vision data processing and emotional state prediction tools. Throughout June 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “3D immersive spaces” + “geolocation data mining and tracking,” “generative artificial intelligence and haptic and biometric sensor technologies,” and “network visual and employee engagement analytics.” As research published in 2023 was inspected, only 177 articles satisfied the eligibility criteria, and 51 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.

Keywords: geolocation data mining and tracking; generative artificial intelligence; haptic and biometric sensor technologies; network visual and employee engagement analytics; 3D immersive spaces

How to cite: Rashid, M., Strakova, J., and Valaskova, K. (2023). “Geolocation Data Mining and Tracking, Generative Artificial Intelligence and Haptic and Biometric Sensor Technologies, and Network Visual and Employee Engagement Analytics in 3D Immersive Spaces,” Contemporary Readings in Law and Social Justice 15(2): 122–140. doi: 10.22381/CRLSJ 15220237.

Received 26 July 2023 • Received in revised form 21 November 2023
Accepted 24 November 2023 • Available online 30 November 2023