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Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain

Author

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  • Dianhui Mao

    (Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Fan Wang

    (Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Zhihao Hao

    (Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

  • Haisheng Li

    (Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China)

Abstract

The food supply chain is a complex system that involves a multitude of “stakeholders” such as farmers, production factories, distributors, retailers and consumers. “Information asymmetry” between stakeholders is one of the major factors that lead to food fraud. Some current researches have shown that applying blockchain can help ensure food safety. However, they tend to study the traceability of food but not its supervision. This paper provides a blockchain-based credit evaluation system to strengthen the effectiveness of supervision and management in the food supply chain. The system gathers credit evaluation text from traders by smart contracts on the blockchain. Then the gathered text is analyzed directly by a deep learning network named Long Short Term Memory (LSTM). Finally traders’ credit results are used as a reference for the supervision and management of regulators. By applying blockchain, traders can be held accountable for their actions in the process of transaction and credit evaluation. Regulators can gather more reliable, authentic and sufficient information about traders. The results of experiments show that adopting LSTM results in better performance than traditional machine learning methods such as Support Vector Machine (SVM) and Navie Bayes (NB) to analyze the credit evaluation text. The system provides a friendly interface for the convenience of users.

Suggested Citation

  • Dianhui Mao & Fan Wang & Zhihao Hao & Haisheng Li, 2018. "Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain," IJERPH, MDPI, vol. 15(8), pages 1-21, August.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:8:p:1627-:d:161404
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    References listed on IDEAS

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    1. Peng, Yala & Li, Jiajie & Xia, Hui & Qi, Siyuan & Li, Jianhong, 2015. "The effects of food safety issues released by we media on consumers’ awareness and purchasing behavior: A case study in China," Food Policy, Elsevier, vol. 51(C), pages 44-52.
    2. Henry M. Kim & Marek Laskowski, 2018. "Toward an ontology‐driven blockchain design for supply‐chain provenance," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(1), pages 18-27, January.
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    Cited by:

    1. Boyu Liu & Xiameng Si & Haiyan Kang, 2022. "A Literature Review of Blockchain-Based Applications in Supply Chain," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
    2. Vincent Charles & Ali Emrouznejad & Tatiana Gherman, 2023. "A critical analysis of the integration of blockchain and artificial intelligence for supply chain," Annals of Operations Research, Springer, vol. 327(1), pages 7-47, August.
    3. Jiang Duan & Chen Zhang & Yu Gong & Steve Brown & Zhi Li, 2020. "A Content-Analysis Based Literature Review in Blockchain Adoption within Food Supply Chain," IJERPH, MDPI, vol. 17(5), pages 1-17, March.
    4. Liukun Wang & Chunjie Qi & Peng Jiang & Si Xiang, 2022. "The Impact of Blockchain Application on the Qualification Rate and Circulation Efficiency of Agricultural Products: A Simulation Analysis with Agent-Based Modelling," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    5. Feng Xue & Fangju Li, 2023. "A Quality Traceability System for Fruit and Vegetable Supply Chain Based on Multi-Chain Blockchain," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 16(1), pages 1-18, January.
    6. Mona Haji & Laoucine Kerbache & Mahaboob Muhammad & Tareq Al-Ansari, 2020. "Roles of Technology in Improving Perishable Food Supply Chains," Logistics, MDPI, vol. 4(4), pages 1-24, December.
    7. Anandika Sharma & Anupam Sharma & Tarunpreet Bhatia & Rohit Kumar Singh, 2023. "Blockchain enabled food supply chain management: A systematic literature review and bibliometric analysis," Operations Management Research, Springer, vol. 16(3), pages 1594-1618, September.
    8. Venkataiah Chittipaka & Satish Kumar & Uthayasankar Sivarajah & Jana Lay-Hwa Bowden & Manish Mohan Baral, 2023. "Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework," Annals of Operations Research, Springer, vol. 327(1), pages 465-492, August.
    9. Anulipt Chandan & Michele John & Vidyasagar Potdar, 2023. "Achieving UN SDGs in Food Supply Chain Using Blockchain Technology," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    10. Pandey, Vivekanand & Pant, Millie & Snasel, Vaclav, 2022. "Blockchain technology in food supply chains: Review and bibliometric analysis," Technology in Society, Elsevier, vol. 69(C).
    11. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Uncovering dimensions of the impact of blockchain technology in supply chain management," Operations Management Research, Springer, vol. 16(1), pages 99-125, March.
    12. Satish Kumar & Weng Marc Lim & Uthayasankar Sivarajah & Jaspreet Kaur, 2023. "Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis," Information Systems Frontiers, Springer, vol. 25(2), pages 871-896, April.
    13. Abderahman Rejeb & John G. Keogh & Suhaiza Zailani & Horst Treiblmaier & Karim Rejeb, 2020. "Blockchain Technology in the Food Industry: A Review of Potentials, Challenges and Future Research Directions," Logistics, MDPI, vol. 4(4), pages 1-26, October.
    14. Yu Gong & Yun Zhang & Mohammed Alharithi, 2022. "Supply Chain Finance and Blockchain in Operations Management: A Literature Review," Sustainability, MDPI, vol. 14(20), pages 1-21, October.
    15. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.
    16. Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Food Supply Chain Transformation through Technology and Future Research Directions—A Systematic Review," Logistics, MDPI, vol. 5(4), pages 1-24, November.
    17. Bikramaditya Ghosh & Dimitrios Paparas, 2023. "Is There Any Pattern Regarding the Vulnerability of Smart Contracts in the Food Supply Chain to a Stressed Event? A Quantile Connectedness Investigation," JRFM, MDPI, vol. 16(2), pages 1-12, January.

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