Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study
Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study
Supply chain risk management (SCRM) literature is heterogeneous. While much attention has been given to the economic and environmental dimensions, the social dimension has so far received less focus. Thus, this study analyzes the social risks involved in the agri-food supply chains (AFSCs) of Argentina and China by employing an integrated approach. Semi-structured interviews were used to collect data, followed by using a combination of three complementary data analysis methods: thematic analysis to identify social risks, total interpretive structural modeling (TISM) to build interrelationships among the identified social risks, and fuzzy MICMAC (cross-impact matrix multiplication applied to classification analysis) to cluster social risks into four categories. Next, we conducted a comparative analysis between the two countries. Theoretical contributions are mainly threefold. First, we identified various social risks involved in the AFSCs of Argentina and China, including those just touched on by scholars, such as cultural issues, government’s weak monitoring system, the power differential between managers and subordinates, inappropriate disposal of agrichemical containers, and the lack of basic literacy skills. Second, we believe that our study is the first to establish connections among the identified AFSC social risks, which represents the originality of this work. Third, we discover that cultural issues is the key risk that has the highest capability to elicit other social risks involved in the AFSCs. Our work extends scholarship’s knowledge to understand AFSC social risks from the cultural perspective. This study also generates contributions to policymakers, migrant associations, and the government tax departments of Argentina and China.
Agri-food supply chain, Cross-country study, Fuzzy MICMAC analysis, Social risks, Thematic analysis, Total interpretive structural modeling
1241-1282
Zhao, Guoqing
99c6432e-eb8e-46d4-8798-f5e4046c7cfa
Liu, Shaofeng
fd329729-5beb-4217-a0cb-5bcdf443736f
Lopez, Carmen
f11f88d5-36c4-4beb-a4c5-ceb16a6df19c
Wang, Yi
aa6a67f8-e22e-484d-8077-638d6c9b2f1a
Lu, Haiyan
bca844c5-6521-4a1e-af78-ac374a5e34e4
Zhang, Jinhua
fc9b1f90-33b2-4381-ac93-57e7c79fc10c
9 May 2024
Zhao, Guoqing
99c6432e-eb8e-46d4-8798-f5e4046c7cfa
Liu, Shaofeng
fd329729-5beb-4217-a0cb-5bcdf443736f
Lopez, Carmen
f11f88d5-36c4-4beb-a4c5-ceb16a6df19c
Wang, Yi
aa6a67f8-e22e-484d-8077-638d6c9b2f1a
Lu, Haiyan
bca844c5-6521-4a1e-af78-ac374a5e34e4
Zhang, Jinhua
fc9b1f90-33b2-4381-ac93-57e7c79fc10c
Zhao, Guoqing, Liu, Shaofeng, Lopez, Carmen, Wang, Yi, Lu, Haiyan and Zhang, Jinhua
(2024)
Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study.
Annals of Operations Research, 338 (2-3), .
(doi:10.1007/s10479-024-06040-2).
Abstract
Supply chain risk management (SCRM) literature is heterogeneous. While much attention has been given to the economic and environmental dimensions, the social dimension has so far received less focus. Thus, this study analyzes the social risks involved in the agri-food supply chains (AFSCs) of Argentina and China by employing an integrated approach. Semi-structured interviews were used to collect data, followed by using a combination of three complementary data analysis methods: thematic analysis to identify social risks, total interpretive structural modeling (TISM) to build interrelationships among the identified social risks, and fuzzy MICMAC (cross-impact matrix multiplication applied to classification analysis) to cluster social risks into four categories. Next, we conducted a comparative analysis between the two countries. Theoretical contributions are mainly threefold. First, we identified various social risks involved in the AFSCs of Argentina and China, including those just touched on by scholars, such as cultural issues, government’s weak monitoring system, the power differential between managers and subordinates, inappropriate disposal of agrichemical containers, and the lack of basic literacy skills. Second, we believe that our study is the first to establish connections among the identified AFSC social risks, which represents the originality of this work. Third, we discover that cultural issues is the key risk that has the highest capability to elicit other social risks involved in the AFSCs. Our work extends scholarship’s knowledge to understand AFSC social risks from the cultural perspective. This study also generates contributions to policymakers, migrant associations, and the government tax departments of Argentina and China.
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Accepted/In Press date: 26 April 2024
e-pub ahead of print date: 9 May 2024
Published date: 9 May 2024
Additional Information:
Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Keywords:
Agri-food supply chain, Cross-country study, Fuzzy MICMAC analysis, Social risks, Thematic analysis, Total interpretive structural modeling
Identifiers
Local EPrints ID: 490085
URI: http://eprints.soton.ac.uk/id/eprint/490085
ISSN: 0254-5330
PURE UUID: bbe46539-cee1-4916-b18c-49b9bfdf3b0e
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Date deposited: 14 May 2024 16:43
Last modified: 03 Sep 2024 02:01
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Contributors
Author:
Guoqing Zhao
Author:
Shaofeng Liu
Author:
Carmen Lopez
Author:
Yi Wang
Author:
Haiyan Lu
Author:
Jinhua Zhang
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