ABSTRACT. This article reviews and advances existing literature concerning immersive virtual experiences and metaverse interoperability. In this research, previous findings were cumulated showing that digitally extended social interactions and consumer behavior data articulate virtual consumer engagement in immersive 3D environments, and I contribute to the literature by indicating that natural language processing algorithms, augmented reality shopping tools, and predictive and retail analytics assist 3D metaverse experiences across virtual environments. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the decentralized metaverse” + “virtual retail algorithms,” “behavioral predictive analytics,” and “geospatial mapping technologies.” As research published in 2022 was inspected, only 140 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 27 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR.

Keywords: image processing computational algorithms; customer behavior analytics; data mining and virtual navigation tools; spatial cognition and simulation optimization algorithms; remote sensing systems; blockchain-based metaverse

How to cite: Beckett, S. (2022). “Virtual Retail Algorithms, Behavioral Predictive Analytics, and Geospatial Mapping Technologies in the Decentralized Metaverse,” Review of Contemporary Philosophy 21: 154–170. doi: 10.22381/RCP21202210.

Received 20 April 2022 • Received in revised form 26 August 2022
Accepted 28 August 2022 • Available online 30 August 2022

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