Risk management of green supply chains for agricultural products based on social network evaluation framework
Wang, Wenke; Cao, Qilin; Liu, Yang; Zhou, Cheng; Jiao, Qinghui; Mangla, Sachin Kumar (2024-03-07)
Wang, Wenke
Cao, Qilin
Liu, Yang
Zhou, Cheng
Jiao, Qinghui
Mangla, Sachin Kumar
John Wiley & Sons
07.03.2024
Wang, W., Cao, Q., Liu, Y., Zhou, C., Jiao, Q., & Mangla, S. K. (2024). Risk management of green supply chains for agricultural products based on social network evaluation framework. Business Strategy and the Environment, 33(5), 4913–4934. https://doi.org/10.1002/bse.3731
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202406194722
https://urn.fi/URN:NBN:fi:oulu-202406194722
Tiivistelmä
Abstract
The green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five-star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities.
The green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five-star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities.
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