ABSTRACT. In this article, I cumulate previous research findings indicating that, by leveraging natural language processing, sentiment analytics, business intelligence tools, data-driven decision making, and real-time Internet of Things data, online and virtual marketplaces configure customer loyalty and demand, shopping habits, and changing customer order patterns, while customizing user experiences. I contribute to the literature on predictive algorithms, data visualization tools, and artificial neural networks in the retail metaverse by showing that natural language processing and data sharing technologies, together with data visualization capabilities and sentiment analysis, can enhance business performance in livestreaming e-commerce, configuring consumer purchasing habits and improving customer expectations, confidence, loyalty, and engagement. Throughout March 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “retail metaverse” + “predictive algorithms,” “data visualization tools,” and “artificial neural networks.” As I inspected research published in 2022, only 84 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 17, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR.

Keywords: retail; metaverse; visualization; algorithm; neural network; artificial

How to cite: Rydell, L. (2022). “Predictive Algorithms, Data Visualization Tools, and Artificial Neural Networks in the Retail Metaverse,” Linguistic and Philosophical Investigations 21: 25–40. doi: 10.22381/lpi2120222.

Received 26 March 2022 • Received in revised form 23 May 2022
Accepted 27 May 2022 • Available online 30 May 2022

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