The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges
Open accessAbstract
Abstract Traceability plays a vital role in food quality and safety management. Traditional Internet of Things (IoT) traceability systems provide the feasible solutions for the quality monitoring and traceability of food supply chains. However, most of the IoT solutions rely on the centralized server-client paradigm that makes it difficult for consumers to acquire all transaction information and to track the origins of products. Blockchain is a cutting-edge technology that has great potential for improving traceability performance by providing security and full transparency. However, the benefits, challenges and development methods of blockchain-based food traceability systems are not yet fully explored in the current literature. Therefore, the main aim of this paper is to review the blockchain technology characteristics and functionalities, identify blockchain-based solutions for addressing food traceability concerns, highlight the benefits and challenges of blockchain-based traceability systems implementation, and help researchers and practitioners to apply blockchain technology based food traceability systems by proposing an architecture design framework and suitability application analysis flowchart of blockchain based food traceability systems. The results of this study contribute to better understanding and knowledge on how to improve the food traceability by developing and implementing blockchain-based traceability systems. The paper provides valuable information for researchers and practitioners on the use of blockchain-based food traceability management and has a positive effect on the improvement of food sustainability.
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Cites
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Cited by
Tsolakis, N., Niedenzu, D., Simonetto, M., Dora, M., & Kumar, M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.08.003 · Cited by 269
Metadata
- Authors
- Feng, H., Wang, X., Duan, Y., Zhang, J., & Zhang, X.
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 260
- Pages
- 121031
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W3011748039
- Cited by
- 788
- References
- 79
- FWCI
- 62.18
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.