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Digital Twin Simulation and Modeling Tools, Deep Learning Object Detection Technology, and Visual Perception and Sensor Fusion Algorithms in the Metaverse Commerce

Digital Twin Simulation and Modeling Tools, Deep Learning Object Detection Technology, and Visual... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

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 , Volume 17 (3): 16 – Jan 1, 2022

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
Publisher site
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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

Journal

Economics, Management, and Financial MarketsAddleton Academic Publishers

Published: Jan 1, 2022

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