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dc.contributor.authorDubey, R
dc.contributor.authorGunasekaran, A
dc.contributor.authorChilde, Stephen J
dc.contributor.authorPapadopoulos, T
dc.contributor.authorLuo, Z
dc.contributor.authorWamba, SF
dc.contributor.authorRoubaud, D
dc.date.accessioned2018-01-28T18:45:09Z
dc.date.issued2017-07-15
dc.identifier.issn0040-1625
dc.identifier.issn1873-5509
dc.identifier.urihttp://hdl.handle.net/10026.1/10674
dc.description.abstract

Although literature indicates that big data and predictive analytics (BDPA)convey a distinct organisational capability, little is known about their performance effects in particular contextual conditions (inter alia, national context and culture, and firm size). Grounding our investigation in the dynamic capability views and organisational culture and based on a sample of 205 Indian manufacturing organisations, we empirically investigate the effects of BDPA on social performance (SP)and environmental performance (EP)using variance based structural equation modelling (i.e. PLS). We find that BDPA has significant impact on SP/EP. However, we did not find evidence for moderating role of flexible orientation and control orientation in the links between BDPA and SP/EP. Our findings offer a more nuanced understanding of the performance implications of BDPA, thereby addressing the crucial questions of how and when BDPA can enhance social/environmental sustainability in supply chains.

dc.format.extent534-545
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectBig data
dc.subjectPredictive analytics
dc.subjectDynamic capability view
dc.subjectSupply chains
dc.subjectSocial sustainability
dc.subjectEnvironmental sustainability
dc.titleCan big data and predictive analytics improve social and environmental sustainability?
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000471735700048&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume144
plymouth.publication-statusPublished
plymouth.journalTechnological Forecasting and Social Change
dc.identifier.doi10.1016/j.techfore.2017.06.020
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.dateAccepted2017-06-18
dc.rights.embargodate2019-1-15
dc.identifier.eissn1873-5509
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.techfore.2017.06.020
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-07-15
rioxxterms.typeJournal Article/Review


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