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Assessing the drivers of machine learning business value

2020-09Artigo científico Acesso aberto
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dc.contributor.authorReis, Carolina
dc.contributor.authorRuivo, Pedro
dc.contributor.authorOliveira, Tiago
dc.contributor.authorFaroleiro, Paulo
dc.contributor.institutionNOVA School of Business and Economics (NOVA SBE)
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier
dc.date.accessioned2020-06-29T22:15:47Z
dc.date.available2025-01-03T01:31:37Z
dc.date.embargoedUntil2023-06-09
dc.date.issued2020-09
dc.descriptionReis, C., Ruivo, P., Oliveira, T., & Faroleiro, P. (2020). Assessing the drivers of machine learning business value. Journal of Business Research, 117, 232-243. https://doi.org/10.1016/j.jbusres.2020.05.053 ---%ABS3%
dc.description.abstractMachine learning (ML) is expected to transform the business landscape in the near future completely. Hitherto, some successful ML case-stories have emerged. However, how organizations can derive business value (BV) from ML has not yet been substantiated. We assemble a conceptual model, grounded on the dynamic capabilities theory, to uncover key drivers of ML BV, in terms of financial and strategic performance. The proposed model was assessed by surveying 319 corporations. Our findings are that ML use, big data analytics maturity, platform maturity, top management support, and process complexity are, to some extent, drivers of ML BV. We also find that platform maturity has, to some degree, a moderator influence between ML use and ML BV, and between big data analytics maturity and ML BV. To the best of our knowledge, this is the first research to deliver such findings in the ML field.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent765302
dc.identifier.doi10.1016/j.jbusres.2020.05.053
dc.identifier.issn0148-2963
dc.identifier.otherPURE: 18642163
dc.identifier.otherPURE UUID: acb7fdb7-e722-4f7c-b1f4-be99660b431e
dc.identifier.otherScopus: 85086088649
dc.identifier.otherORCID: /0000-0001-6523-0809/work/76258579
dc.identifier.otherWOS: 000556889900021
dc.identifier.urihttp://hdl.handle.net/10362/100122
dc.identifier.urlhttps://www.scopus.com/pages/publications/85086088649
dc.language.isoeng
dc.peerreviewedyes
dc.subjectBusiness value
dc.subjectCompetitive advantage
dc.subjectDynamic capabilities theory
dc.subjectMachine learning
dc.subjectMarketing
dc.subjectSDG 8 - Decent Work and Economic Growth
dc.titleAssessing the drivers of machine learning business valueen
dc.typejournal article
degois.publication.firstPage232
degois.publication.lastPage243
degois.publication.titleJournal of Business Research
degois.publication.volume117
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)