ABSTRACT. This article reviews and advances existing literature concerning immersive retail experiences as regards metaverse assets. In this research, previous findings were cumulated showing that consumer analytics harnesses retail data to attain frictionless user engagement processes in immersive virtual environments, and we contribute to the literature by indicating that deep learning-based ambient sound processing and real-time sensor data further immersive retail experiences in extended reality environments. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “metaverse” + “biometric sensor technologies,” “virtual marketplace dynamics data,” and “computer vision and deep learning algorithms.” As research published in 2022 was inspected, only 184 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 37 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.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: biometric sensor technologies; virtual marketplace; computer vision; deep learning; metaverse

How to cite: Oláh, J., and Nica, E. (2022). “Biometric Sensor Technologies, Virtual Marketplace Dynamics Data, and Computer Vision and Deep Learning Algorithms in the Metaverse Interactive Environment,” Journal of Self-Governance and Management Economics 10(3): 7–22. doi: 10.22381/jsme10320221.

Received 20 May 2022 • Received in revised form 23 September 2022
Accepted 25 September 2022 • Available online 30 September 2022