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ABSTRACT. The aim of this systematic review is to synthesize and analyze generative artificial intelligence and virtual communication and collaboration tools deploying image processing computational algorithms, geolocation data mining and tracking, and employee engagement data. With increasing evidence of generative artificial intelligence algorithms pivotal in decentralized autonomous organizations with regard to automated workplace tasks, cognitive analytics and meaningful performance management, immersive job training, and talent attraction and retention, there is an essential demand for comprehending whether generative artificial intelligence and virtual recruitment tools can optimize employee autonomy and creativity, virtual teamworks, and tailored job upskilling. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout July 2023, with search terms including “generative artificial intelligence and spatial analytics tools” + “geolocation data mining and tracking,” “cognitive and behavioral algorithms,” and “immersive extended reality environments.” As research published in 2023 was inspected, only 154 articles satisfied the eligibility criteria, and 50 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.

Keywords: geolocation data mining and tracking; generative artificial intelligence; spatial analytics tool; cognitive and behavioral algorithms; immersive; extended reality

How to cite: Stan, C. I., Potcovaru Bâgu, A.-M., Dumitrescu, C.-O., and Hayward, R. (2023). “Geolocation Data Mining and Tracking, Generative Artificial Intelligence and Spatial Analytics Tools, and Cognitive and Behavioral Algorithms in Immersive Extended Reality Environments,” Analysis and Metaphysics 22: 294–312. doi: 10.22381/am22202316.

Received 17 August 2023 • Received in revised form 19 December 2023
Accepted 27 December 2023 • Available online 30 December 2023

1The Bucharest University of Economic Studies, Bucharest, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it..
1The Bucharest University of Economic Studies, Bucharest, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it..
1The Bucharest University of Economic Studies, Bucharest, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it..
2The Center for Big Geospatial Data Analytics at ISBDA, Seattle, WA, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.. (corresponding author)

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