ABSTRACT. Despite the relevance of virtual marketplace dynamics data, spatial analytics, and customer engagement tools in a real-time interoperable decentralized metaverse, only limited research has been conducted on this topic. In this article, we cumulate previous research findings indicating that tailored product data enhancement and targeting can lead to customer engagement through integrated machine learning predictions by leveraging personalized content. We contribute to the literature on scalable and sustainable businesses in the metaverse by showing that tailored product data enhancement and targeting can lead to customer engagement through integrated machine learning predictions by leveraging personalized content. Throughout February 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “metaverse” + “virtual marketplace dynamics data,” “spatial analytics,” and “customer engagement tools.” As we inspected research published in 2022, only 83 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 17, 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, Distiller SR, and MMAT.

Keywords: virtual; spatial analytics; customer; metaverse; engagement; interoperable

How to cite: Valaskova, K., Machova, V., and Lewis, E. (2022). “Virtual Marketplace Dynamics Data, Spatial Analytics, and Customer Engagement Tools in a Real-Time Interoperable Decentralized Metaverse,” Linguistic and Philosophical Investigations 21: 105–120. doi: 10.22381/lpi2120227.

Received 27 February 2022 • Received in revised form 22 May 2022
Accepted 26 May 2022 • Available online 30 May 2022