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dc.contributor.authorMikalef, Patrick
dc.contributor.authorKrogstie, John
dc.date.accessioned2019-03-28T11:19:49Z
dc.date.available2019-03-28T11:19:49Z
dc.date.created2018-11-15T10:29:29Z
dc.date.issued2018
dc.identifier.citationLecture Notes in Computer Science. 2018, LNCS 11080 426-441.nb_NO
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11250/2592171
dc.description.abstractA central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. Anecdotal claims suggest that big data can enhance a firm’s incremental and radical process innovation capabilities; yet, there is a lack of theoretically grounded empirical research to support such assertions. To address this question, this study builds on the Resource-Based View and examines the fit between big data analytics resources and organizational contextual factors in driving a firm’s process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms is analyzed by means of fuzzy set qualitative comparative analysis (fsQCA). Results demonstrate that under different patterns of contextual factors the significance of big data analytics resources varies, with specific combinations leading to high levels of incremental and radical process innovation capabilities. These findings suggest that IS researchers and practitioners should look beyond direct effects, and rather, identify key combinations of factors that lead to enhanced process innovation capabilities.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleBig Data Analytics as an Enabler of Process Innovation Capabilities: A Configurational Approachnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber426-441nb_NO
dc.source.volumeLNCS 11080nb_NO
dc.source.journalLecture Notes in Computer Sciencenb_NO
dc.identifier.doi10.1007/978-3-319-98648-7_25
dc.identifier.cristin1630811
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article published in [Lecture Notes in Computer Science] Locked until 11.8.2019 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-98648-7_25nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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