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An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM

Environmental deterioration, the COVID-19 pandemic and the Russian-Ukrainian conflict had brought chronic and dramatic impacts on agricultural supply chain around the world, resulting in high inflation rates and unavoidable costs. In order to reduce the adverse impacts and achieve sustainability in...

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Detalles Bibliográficos
Autores principales: Wang, Muwen, Zhang, Yiwen, Tian, Yuan, Zhang, Kecheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482719/
https://www.ncbi.nlm.nih.gov/pubmed/36157790
http://dx.doi.org/10.1016/j.eswa.2022.118843
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author Wang, Muwen
Zhang, Yiwen
Tian, Yuan
Zhang, Kecheng
author_facet Wang, Muwen
Zhang, Yiwen
Tian, Yuan
Zhang, Kecheng
author_sort Wang, Muwen
collection PubMed
description Environmental deterioration, the COVID-19 pandemic and the Russian-Ukrainian conflict had brought chronic and dramatic impacts on agricultural supply chain around the world, resulting in high inflation rates and unavoidable costs. In order to reduce the adverse impacts and achieve sustainability in agricultural supply chain, it’s necessary to scientifically explore composite indicators interlinked with agricultural sustainable supply chain management (ASSCM). The current study developed an integrated rough-fuzzy WINGS-ISM method to reveal the hierarchal and causal structure of indicators. It is found that environmental legislation, regulation, licensing, and government subsidies are the main drivers of ASSCM. Specifically, the government can guide the sustainable development of ASSCM by regulating the business environment. The financial support needs to be enlarged to optimize the structure in science and technology of ASSCM. Moreover, corporates and organizations are highly motivated by the increasing awareness of social responsibility and sustainability consciousness to improve the economic performance and achieve the ASSCM goals. A comparative analysis is proposed to illustrate the practicality and reliability of the results obtained from the proposed method, which can be utilized as a reference in ASSCM.
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spelling pubmed-94827192022-09-19 An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM Wang, Muwen Zhang, Yiwen Tian, Yuan Zhang, Kecheng Expert Syst Appl Article Environmental deterioration, the COVID-19 pandemic and the Russian-Ukrainian conflict had brought chronic and dramatic impacts on agricultural supply chain around the world, resulting in high inflation rates and unavoidable costs. In order to reduce the adverse impacts and achieve sustainability in agricultural supply chain, it’s necessary to scientifically explore composite indicators interlinked with agricultural sustainable supply chain management (ASSCM). The current study developed an integrated rough-fuzzy WINGS-ISM method to reveal the hierarchal and causal structure of indicators. It is found that environmental legislation, regulation, licensing, and government subsidies are the main drivers of ASSCM. Specifically, the government can guide the sustainable development of ASSCM by regulating the business environment. The financial support needs to be enlarged to optimize the structure in science and technology of ASSCM. Moreover, corporates and organizations are highly motivated by the increasing awareness of social responsibility and sustainability consciousness to improve the economic performance and achieve the ASSCM goals. A comparative analysis is proposed to illustrate the practicality and reliability of the results obtained from the proposed method, which can be utilized as a reference in ASSCM. Elsevier Ltd. 2023-02 2022-09-18 /pmc/articles/PMC9482719/ /pubmed/36157790 http://dx.doi.org/10.1016/j.eswa.2022.118843 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wang, Muwen
Zhang, Yiwen
Tian, Yuan
Zhang, Kecheng
An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM
title An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM
title_full An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM
title_fullStr An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM
title_full_unstemmed An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM
title_short An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM
title_sort integrated rough-fuzzy wings-ism method with an application in asscm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9482719/
https://www.ncbi.nlm.nih.gov/pubmed/36157790
http://dx.doi.org/10.1016/j.eswa.2022.118843
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