Logotipo do repositório
 

Publicação:
Management theory and big data literature: From a review to a research agenda

dc.contributor.authorFiorini, Paula de Camargo [UNESP]
dc.contributor.authorRoman Pais Seles, Bruno Michel [UNESP]
dc.contributor.authorJabbour, Charbel Jose Chiappetta
dc.contributor.authorMariano, Enzo Barberio [UNESP]
dc.contributor.authorJabbour, Ana Beatriz Lopes de Sousa
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionMontpellier Business Sch
dc.date.accessioned2019-10-04T12:31:33Z
dc.date.available2019-10-04T12:31:33Z
dc.date.issued2018-12-01
dc.description.abstractThe purpose of this study is to enrich the existing state-of-the-art literature on the impact of big data on business growth by examining how dozens of organizational theories can be applied to enhance the understanding of the effects of big data on organizational performance. While the majority of management disciplines have had research dedicated to the conceptual discussion of how to link a variety of organizational theories to empirically quantified research topics, the body of research into big data so far lacks an academic work capable of systematising the organizational theories supporting big data domain. The three main contributions of this work are: (a) it addresses the application of dozens of organizational theories to big data research; (b) it offers a research agenda on how to link organizational theories to empirical research in big data; and (c) it foresees promising linkages between organizational theories and the effects of big data on organizational performance, with the aim of contributing to further research in this field. This work concludes by presenting implications for researchers and managers, and by highlighting intrinsic limitations of the research.en
dc.description.affiliationSao Paulo State Univ, Prod Engn Dept, Av Engn Luiz Edmundo C Coube 14-01, BR-17033360 Bauru, SP, Brazil
dc.description.affiliationMontpellier Business Sch, Montpellier Res Management, 2300 Ave Moulins, F-34185 Montpellier 4, France
dc.description.affiliationUnespSao Paulo State Univ, Prod Engn Dept, Av Engn Luiz Edmundo C Coube 14-01, BR-17033360 Bauru, SP, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdCAPES: 88881.133599/2016-01
dc.format.extent112-129
dc.identifierhttp://dx.doi.org/10.1016/j.ijinfomgt.2018.07.005
dc.identifier.citationInternational Journal Of Information Management. Oxford: Elsevier Sci Ltd, v. 43, p. 112-129, 2018.
dc.identifier.doi10.1016/j.ijinfomgt.2018.07.005
dc.identifier.issn0268-4012
dc.identifier.lattes6639164567036709
dc.identifier.orcid0000-0002-9577-3297
dc.identifier.urihttp://hdl.handle.net/11449/184974
dc.identifier.wosWOS:000447963300010
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofInternational Journal Of Information Management
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectBig data
dc.subjectBig data analytics
dc.subjectOrganizational theory
dc.subjectFirms' performance
dc.subjectResearch agenda
dc.titleManagement theory and big data literature: From a review to a research agendaen
dc.typeResenha
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
dspace.entity.typePublication
unesp.author.lattes6639164567036709[4]
unesp.author.orcid0000-0002-6143-4924[3]
unesp.author.orcid0000-0001-6423-8868[5]
unesp.author.orcid0000-0002-9577-3297[4]
unesp.departmentEngenharia de Produção - FEBpt

Arquivos