ABSTRACT. The aim of this systematic review is to synthesize and analyze immersive virtual shopping and metaverse brand experiences. With increasing evidence of metaverse technologies integrating cognitive computing systems and virtual retail algorithms, there is an essential demand for comprehending whether customer predictive analytics leverages machine learning-based product recognition tools, picture-making neural networks, and cognitive artificial intelligence algorithms across interconnected digital realms and immersive virtual spaces. In this research, prior findings were cumulated indicating that immersive shopping experiences can be achieved through sensory data mining techniques and machine vision algorithms in the virtual retail market. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including “metaverse economy” + “customer engagement and data visualization tools,” “ambient sound recognition software,” and “deep learning-based sensing technologies.” As I analyzed research published between 2021 and 2022, only 214 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, I decided on 51, 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.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: customer engagement; data visualization; ambient sound recognition; deep learning; sensing technologies; metaverse economy

How to cite: Newell, M. (2022). “Customer Engagement and Data Visualization Tools, Ambient Sound Recognition Software, and Deep Learning-based Sensing Technologies in the Metaverse Economy,” Economics, Management, and Financial Markets 17(3): 25–41. doi: 10.22381/emfm17320222.

Received 16 June 2022 • Received in revised form 18 September 2022
Accepted 22 September 2022 • Available online 27 September 2022

Home | About Us | Events | Our Team | Contributors | Peer Reviewers | Editing Services | Books | Contact | Online Access

© 2009 Addleton Academic Publishers. All Rights Reserved.

Joomla templates by Joomlashine