ABSTRACT. The aim of this systematic review is to synthesize and analyze metaverse applications, technologies, and infrastructure in relation to predictive algorithms, real-time customer data analytics, and virtual navigation tools. With increasing evidence of metaverse live-video shopping events, there is an essential demand for comprehending whether consumer digital engagement as regards virtual assets improves immersive shopping journeys and experiences. In this research, prior findings were cumulated indicating that consumer journey analytics and data-driven decision making can determine purchase intentions across immersive 3D worlds and in virtual stores. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including “metaverse” + “predictive algorithms,” “real-time customer data analytics,” and “virtual navigation tools.” As we analyzed research published between 2021 and 2022, only 86 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 20, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.

Keywords: metaverse; customer; virtual; predictive algorithm; data analytics

How to cite: Balica, R.-Ș., Majerová, J., and Cuțitoi, A.-C. (2022). “Metaverse Applications, Technologies, and Infrastructure: Predictive Algorithms, Real-Time Customer Data Analytics, and Virtual Navigation Tools,” Linguistic and Philosophical Investigations 21: 219–235. doi: 10.22381/lpi21202214.

Received 24 March 2022 • Received in revised form 21 May 2022
Accepted 25 May 2022 • Available online 30 May 2022