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Based on an in-depth survey of the literature, the purpose of the paper is to explore augmented analytics tools, interconnected decision-making processes, and computer vision algorithms in relation to blockchain-based metaverse platforms. In this research, previous findings were cumulated showing that changing consumer demands during purchase journeys can be optimized through visual analytics, messaging tools, natural language processing technologies, and real-time interoperable networks, and I contribute to the literature by indicating that purchase intentions, customer behavior, immersive virtual experiences, and online retail spending on livestreaming shopping platforms can be assessed by data visualizations, voice biometrics, augmented analytics, and search engine algorithms. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “metaverse” + “augmented analytics tools,” “interconnected decision-making processes,” and “computer vision algorithms.” As research published in 2022 was inspected, only 86 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 18 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. Keywords: metaverse; computer vision; augmented analytics; algorithm; retail
Linguistic and Philosophical Investigations – Addleton Academic Publishers
Published: Jan 1, 2022
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