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dc.contributor.authorPapadopoulos, T
dc.contributor.authorGunasekaran, A
dc.contributor.authorDubey, R
dc.contributor.authorAltay, N
dc.contributor.authorChilde, Stephen J
dc.contributor.authorFosso-Wamba, S
dc.date.accessioned2018-01-28T20:04:56Z
dc.date.issued2016-04-12
dc.identifier.issn0959-6526
dc.identifier.issn1879-1786
dc.identifier.urihttp://hdl.handle.net/10026.1/10675
dc.description.abstract

The purpose of this paper is to propose and test a theoretical framework to explain resilience in supply chain networks for sustainability using unstructured Big Data, based upon 36,422 items gathered in the form of tweets, news, Facebook, WordPress, Instagram, Google+, and YouTube, and structured data, via responses from 205 managers involved in disaster relief activities in the aftermath of Nepal earthquake in 2015. The paper uses Big Data analysis, followed by a survey which was analyzed using content analysis and confirmatory factor analysis (CFA). The results of the analysis suggest that swift trust, information sharing and public-private partnership are critical enablers of resilience in supply chain networks. The current study used crosssectional data. However the hypotheses of the study can be tested using longitudinal data to attempt to establish causality. The article advances the literature on resilience in disaster supply chain networks for sustainability in that (i) it suggests the use of Big Data analysis to propose and test particular frameworks in the context of resilient supply chains that enable sustainability; (ii) it argues that swift trust, public private partnerships, and quality information sharing link to resilience in supply chain networks; and (iii) it uses the context of Nepal, at the moment of the disaster relief activities to provide contemporaneous perceptions of the phenomenon as it takes place.

dc.format.extent1108-1118
dc.languageen
dc.language.isoen
dc.publisherElsevier
dc.subjectResilience
dc.subjectBig Data
dc.subjectSustainability
dc.subjectDisaster
dc.subjectExploratory factor analysis
dc.subjectConfirmatory factor analysis
dc.titleThe role of Big Data in explaining disaster resilience in supply chains for sustainability
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000391897400058&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue2
plymouth.volume142
plymouth.publication-statusPublished
plymouth.journalJournal of Cleaner Production
dc.identifier.doi10.1016/j.jclepro.2016.03.059
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/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.dateAccepted2016-03-22
dc.rights.embargodate2018-4-12
dc.identifier.eissn1879-1786
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.jclepro.2016.03.059
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-04-12
rioxxterms.typeJournal Article/Review


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