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dc.contributor.authorZhao, G
dc.contributor.authorLiu, Shaofeng
dc.contributor.authorLopez, Carmen
dc.contributor.authorChen, H
dc.contributor.authorLu, H
dc.contributor.authorKUMAR MANGLA, SACHIN
dc.contributor.authorElgueta, S
dc.date.accessioned2020-02-03T12:37:37Z
dc.date.issued2020-08-17
dc.identifier.issn0020-7543
dc.identifier.issn1366-588X
dc.identifier.urihttp://hdl.handle.net/10026.1/15353
dc.description.abstract

Agri-food supply chains (AFSCs) are becoming more complex in structure, and thus more susceptible to different vulnerabilities and risks. Therefore, to enhance performance, we need to manage the risks in AFSCs effectively and efficiently. This study analyses various AFSC risks using a multi-method approach, including thematic analysis, total interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis. Based on the empirical data collected from experienced AFSC practitioners and following thematic analysis, eight categories of risk and 16 risk factors were identified as important. Furthermore, the interrelationships among the identified risks were built using TISM. Finally, the identified risks were classified into various categories according to their dependence and driving power using fuzzy MICMAC analysis. The research results indicate that the weather-related and political risks have the highest driving power and are located at the lowest level in the TISM hierarchy. These risks have a high tendency to disturb the whole flow of AFSC and so  should be managed effectively. This study advances existing literature on identifying risk factors, defining interrelations between different AFSC risks, and determining the key risks. The risk analysis results can help AFSC practitioners in AFSC to identify, categorise and analyse the risks.

dc.format.extent4851-4876
dc.languageen
dc.language.isoen
dc.publisherTaylor & Francis
dc.subjectagri-food supply chain
dc.subjectrisk identification
dc.subjectthematic analysis
dc.subjecttotal interpretive structural modelling
dc.subjectfuzzy MICMAC
dc.titleRisk analysis of the agri-food supply chain: A multi-method approach
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000514421400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue16
plymouth.volume58
plymouth.publication-statusPublished
plymouth.journalInternational Journal of Production Research
dc.identifier.doi10.1080/00207543.2020.1725684
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Arts, Humanities and Business
plymouth.organisational-group/Plymouth/Faculty of Arts, Humanities and Business/Plymouth Business School
plymouth.organisational-group/Plymouth/PS - Academic Partnerships
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA17 Business and Management Studies
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-01-27
dc.rights.embargodate2021-2-15
dc.identifier.eissn1366-588X
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1080/00207543.2020.1725684
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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