Augmented Reality Shopping Experiences, Retail Business Analytics, and Machine Vision Algorithms in the Virtual Economy of the Metaverse
Augmented Reality Shopping Experiences, Retail Business Analytics, and Machine Vision Algorithms in the Virtual Economy of the Metaverse

Summary/Abstract: The purpose of this study is to examine augmented reality shopping experiences, retail business analytics, and machine vision algorithms in the virtual economy of the metaverse. In this article, we cumulate previous research findings indicating that visualization tools, sentiment analytics, and ambient scene detection can optimize customer engagement and journeys on livestreaming shopping platforms across online marketplaces. We contribute to the literature on connected data governance in the metaverse economy by showing that visualization tools, sentiment analytics, and ambient scene detection can optimize customer engagement and journeys on livestreaming shopping platforms across online marketplaces. Throughout February 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “metaverse” + “augmented reality shopping experiences,” “retail business analytics,” “machine vision algorithms,” and “virtual economy.” As we inspected research published between 2021 and 2022, only 71 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 16, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR.

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