ABSTRACT. The present study systematically reviews the existing research on customer behavior analytics and engagement tools building customer engagement across immersive virtual environments. My findings indicate that decision and behavior visualization can optimize data mining, processing, and modeling, enhancing shopping experiences by capturing consumer data, and I contribute to the literature by indicating that retail brands can deploy analytical artificial intelligence in experiential shopping, thus improving online buying experiences across interactive digital worlds. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “metaverse” + “multi-sensor fusion technology,” “visual imagery and predictive modeling tools,” and “big geospatial data analytics.” As research published in 2022 was inspected, only 173 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, I selected 29 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: D53; E22; E32; E44; G01; G41

Keywords: multi-sensor fusion; visual imagery; predictive modeling; big geospatial data analytics; virtual economy; metaverse

How to cite: Duncan, G. (2022). “Multi-Sensor Fusion Technology, Visual Imagery and Predictive Modeling Tools, and Big Geospatial Data Analytics in the Virtual Economy of the Metaverse,” Economics, Management, and Financial Markets 17(3): 42–57. doi: 10.22381/emfm17320223.

Received 18 May 2022 • Received in revised form 20 September 2022
Accepted 24 September 2022 • Available online 27 September 2022

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