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Visual and Spatial Analytics, Immersive Virtual Simulation Technologies, and Motion Planning and Object Recognition Algorithms in the Retail Metaverse

Visual and Spatial Analytics, Immersive Virtual Simulation Technologies, and Motion Planning and... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Visual and Spatial Analytics, Immersive Virtual Simulation Technologies, and Motion Planning and Object Recognition Algorithms in the Retail Metaverse

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
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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

Journal

Economics, Management, and Financial Markets – Addleton Academic Publishers

Published: Jan 1, 2022

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