Can prescriptive analytics empower metaverse for sustainable operations and supply chains? A text mining and introspective analysis
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Date
2025-04-07
Journal Title
Journal ISSN
Volume Title
Publisher
The International Journal of Logistics Management
Abstract
Purpose
The arrival of the Metaverse is expected to revolutionize organizational practices, which substantially impact sustainability in logistics and supply chain. In addition, prescriptive analytics-based methodological improvements might make Metaverse self-sustaining. This study assesses the current reflective discussion about the function of prescriptive analytics in Metaverse. It proposes alternative streams for additional research in this area so that we can understand the relationship between Metaverse, prescriptive analytics, sustainable operations and supply chain.
Design/methodology/approach
We use structural topic modeling (STM), a text-mining approach, to critically assess the literature and analyze 161 articles.
Findings
Primary and secondary topics were developed using STM findings for comparison. Also, a research framework is created by sketching out the study following the findings of the review. Finally, we conclude with a list of unanswered research issues that might serve as a starting point for future investigations into the role of prescriptive analytics in empowering Metaverse for sustainable operations.
Originality/value
This study provides original insights into how prescriptive analytics can drive sustainable operations through Metaverse, offering a roadmap for future empirical research in this emerging area.
Description
Keywords
Metaverse, Logistics and supply chain, Prescriptive analytics, Sustainable operations, Structural topic modeling
Citation
Samadhiya A, Agrawal R, Kumar A, Yadav S, Garza-Reyes JA (2025;), "Can prescriptive analytics empower metaverse for sustainable operations and supply chains? A text mining and introspective analysis". The International Journal of Logistics Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJLM-07-2024-0463