Nguyen, Truong Van, Pham, Hiep Cong, Nguyen, Minh Nhat, Zhou, Li ORCID: https://orcid.org/0000-0001-7132-5935 and Akbari, Mohammadreza (2023) Data-driven review of blockchain applications in supply chain management: key research themes and future directions. International Journal of Production Research. ISSN 0020-7543 (Print), 1366-588X (Online) (doi:10.1080/00207543.2023.2165190)

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Abstract

Blockchain (BC) applications in supply chain management (SCM) have recently received extensive attention. It is important to synthesise the extant literature on the field to identify key research themes and navigate potential future directions. This study thus develops an efficient, scalable data-driven review approach that uses text mining and Latent Dirichlet Allocation (LDA)-based topic modelling for automatic content analysis of full-text documents. Our method overcomes the drawbacks of traditional systematic literature reviews using either manual coding or bibliographic analysis for article classifications, which are highly time-consuming and biased when dealing with large amounts of text. 108 papers published between 2017 and 2022 were analysed which identified 10 key research themes, including revenue management, sustainability, traceability, manufacturing system, scheduling in cloud manufacturing, healthcare SCM, anti-counterfeit system, logistics and transportation, system architecture development, and food & agriculture SC. Five future directions are then suggested, including (1) integration of BC and other emerging technologies for global and scalable SCM, (2) crypto-X applications in SCM, (3) BC-enabled closed-loop SCM, (4) the environmental and social impacts of BC-based SCM and (5) decentralised autonomous organisations in SCM.

Item Type:	Article
Additional Information:	This journal applies the Taylor & Francis Basic Data Sharing Policy. Authors are encouraged to share or make open the data supporting the results or analyses presented in their paper where this does not violate the protection of human subjects or other valid privacy or security concerns. Authors are encouraged to deposit the dataset(s) in a recognized data repository that can mint a persistent digital identifier, preferably a digital object identifier (DOI) and recognizes a long-term preservation plan. If you are uncertain about where to deposit your data, please see this information regarding repositories. 2023 Informa UK Limited, an Informa Group Company.
Uncontrolled Keywords:	supply chain; blockchain; text mining; data driven; review; topic modelling
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of Systems Management & Strategy Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group Greenwich Business School > Networks and Urban Systems Centre (NUSC) Greenwich Business School > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group (CCRG)
Last Modified:	02 Dec 2024 15:59
URI:	http://gala.gre.ac.uk/id/eprint/38435

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