ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore metaverse engagement and experiences in virtual retail environments. In this research, previous findings were cumulated showing that business intelligence tools, visual analytics, and digital machines can determine consumer behavior and engagement in retail livestreaming across virtual environments, and we contribute to the literature by indicating that predictive customer analytics, natural language processing algorithms, and simulation modeling tools are instrumental in virtual shopping sessions in extended reality environments. Throughout June 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “retail metaverse” + “visual and spatial analytics,” “immersive virtual simulation technologies,” and “motion planning and object recognition algorithms.” As research published in 2022 was inspected, only 223 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 53 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: visual and spatial analytics; immersive virtual simulation; motion planning; object recognition; retail metaverse

How to cite: Valaskova, K., Popp, J., and Balica, R.-Ș. (2022). “Visual and Spatial Analytics, Immersive Virtual Simulation Technologies, and Motion Planning and Object Recognition Algorithms in the Retail Metaverse,” Economics, Management, and Financial Markets 17(3): 58–74. doi: 10.22381/emfm17320224.

Received 16 July 2022 • Received in revised form 18 September 2022
Accepted 24 September 2022 • Available online 27 September 2022

1Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
2John von Neumann University, Hungarian National Bank–Research Center, Hungary; College of Business and Economics, University of Johannesburg, South Africa, This email address is being protected from spambots. You need JavaScript enabled to view it..
3University of Craiova, Craiova, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it.. (corresponding author)

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