Immersive Engagement and Geospatial Mapping Technologies, Employee Behavioral Data, and Workplace Tracking Systems in the Virtual Economy of the Metaverse
Zuzana Rowland1 and Mark Newell2ABSTRACT. The present study systematically reviews the existing research on algorithmic monitoring systems, remote sensing tools, and metaverse engagement metrics. Our findings clarify that virtual immersive workspaces integrate image recognition tools, employee monitoring software, and spatial data mining algorithms in extended reality environments, and we contribute to the literature by indicating that digital twin technologies, 3D immersive content, and blockchain-enabled decentralized applications shape the virtual reality visualization environment in relation to remote workforce. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “virtual economy of the metaverse” + “immersive engagement and geospatial mapping technologies,” “employee behavioral data,” and “workplace tracking systems.” As research published in 2022 was inspected, only 163 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 31 mainly empirical sources. 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.
JEL codes: E24; J21; J54; J64
Keywords: immersive engagement and geospatial mapping technologies; employee behavioral data; workplace tracking systems; metaverse
How to cite: Rowland, Z., and Newell, M. (2022). “Immersive Engagement and Geospatial Mapping Technologies, Employee Behavioral Data, and Workplace Tracking Systems in the Virtual Economy of the Metaverse,” Psychosociological Issues in Human Resource Management 10(2): 87–102. doi: 10.22381/pihrm10220226.
Received 27 June 2022 • Received in revised form 22 October 2022
Accepted 26 October 2022 • Available online 30 October 2022