Can prescriptive analytics empower metaverse for sustainable operations and supply chains? A text mining and introspective analysis
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.
Authors
- Ashutosh Samadhiya (ORCID: https://orcid.org/0000-0002-7613-7252)
- Jose Arturo Garza‐Reyes (ORCID: https://orcid.org/0000-0002-5493-877X)
- Sanjeev Yadav (ORCID: https://orcid.org/0000-0001-6226-4587)
- Anil Kumar (ORCID: https://orcid.org/0000-0002-1691-0098)
- Rohit Agrawal (ORCID: https://orcid.org/0000-0002-2712-5941)
Institutions
- London Metropolitan University (GB)
- University of Derby (GB)
- Jaipuria Institute of Management (IN)
- Quantitative BioSciences (US)
- O. P. Jindal Global University (IN)
- Global University (LB)
Publication Details
- Journal
- The International Journal of Logistics Management
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1108/ijlm-07-2024-0463
- Citations
- 4
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 9.14