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dc.contributor.authorEngelseth, Per
dc.contributor.authorWang, Hao
dc.date.accessioned2019-05-03T08:41:06Z
dc.date.available2019-05-03T08:41:06Z
dc.date.created2018-11-15T09:24:36Z
dc.date.issued2018
dc.identifier.citationThe journal of business & industrial marketing. 2018, 33 (8), 1201-1208.nb_NO
dc.identifier.issn0885-8624
dc.identifier.urihttp://hdl.handle.net/11250/2596416
dc.description.abstractPurpose This study aims to consider the developing of strategic use of big data in association with long-linked physical goods supply focusing on risk management. Design/methodology/approach Analysis is grounded on a case study of organizing the import of machine parts from Shanghai, China, to Norway. An analytical framework is developed through a literature review on long linked supply chains, big data and risk management. Findings Analysis reveals that big data use in this scenario encompasses mainly around handling risks associated with transformations in the supply chain, a data-driven approach. Complexity is founded in transformation – the flows of goods and information. Supply chain dynamics represent an important source for data acquisition for big data analytics. Research limitations/implications The qualitative nature of the study limits the aim of generalization. An alternative view of big data as process is discussed and proposed, adapted to supply chain management and industrial marketing functionality. Originality/value This is the first part in an ongoing research project aimed at developing a research approach to study information technology use in the inherently complex setting and scope of a long linked supply network. This scope of investigation enhances big data associated with operations dynamics providing foundation for future research on how to use big data to mitigate risk in long linked supply chains.nb_NO
dc.language.isoengnb_NO
dc.publisherEmeraldnb_NO
dc.titleBig data and connectivity in long-linked supply chainsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1201-1208nb_NO
dc.source.volume33nb_NO
dc.source.journalThe journal of business & industrial marketingnb_NO
dc.source.issue8nb_NO
dc.identifier.doi10.1108/JBIM-07-2017-0168
dc.identifier.cristin1630765
dc.description.localcode© 2018. This is the authors' accepted and refereed manuscript to the article. The final authenticated version is available online at: https://doi.org/10.1108/JBIM-07-2017-0168nb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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