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dc.contributor.authorDubey, R
dc.contributor.authorGunasekaran, A
dc.contributor.authorChilde, Stephen J
dc.contributor.authorBryde, DJ
dc.contributor.authorGiannakis, M
dc.contributor.authorForopon, C
dc.contributor.authorRoubaud, D
dc.contributor.authorHazen, BT
dc.date.accessioned2020-07-14T20:16:15Z
dc.date.issued2020-08
dc.identifier.issn0925-5273
dc.identifier.issn1873-7579
dc.identifier.other107599
dc.identifier.urihttp://hdl.handle.net/10026.1/16028
dc.description18 months embargo.
dc.description.abstract

The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms.

dc.format.extent107599-107599
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectBig data analytics
dc.subjectArtificial intelligence
dc.subjectEntrepreneurial orientation
dc.subjectOperational performance
dc.subjectSupply chain management
dc.subjectPLS SEM
dc.titleBig data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000572093100010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume226
plymouth.publication-statusPublished
plymouth.journalInternational Journal of Production Economics
dc.identifier.doi10.1016/j.ijpe.2019.107599
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.dateAccepted2019-12-19
dc.rights.embargodate2021-6-24
dc.identifier.eissn1873-7579
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.ijpe.2019.107599
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-08
rioxxterms.typeJournal Article/Review


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