ABSTRACT. The objective of this paper is to systematically review metaverse customer engagement and virtual retail experiences. The findings and analyses highlight that augmented reality shopping tools articulate immersive and engaging content and multisensory customer experiences across interconnected virtual worlds. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “metaverse commerce” + “digital twin simulation and modeling tools,” “deep learning object detection technology,” and “visual perception and sensor fusion algorithms.” As research published in 2022 was inspected, only 178 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 38 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.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: digital twin; simulation; modeling; deep learning; object detection; visual perception; sensor fusion; metaverse commerce

How to cite: Kovacova, M., Oláh, J., and Popescu, G. H. (2022). “Digital Twin Simulation and Modeling Tools, Deep Learning Object Detection Technology, and Visual Perception and Sensor Fusion Algorithms in the Metaverse Commerce,” Economics, Management, and Financial Markets 17(3): 9–24. doi: 10.22381/emfm17320221.

Received 13 May 2022 • Received in revised form 17 September 2022
Accepted 22 September 2022 • Available online 27 September 2022