dc.contributor.author | Mikalef, Patrick | |
dc.contributor.author | van de wetering, Rogier | |
dc.contributor.author | Krogstie, John | |
dc.date.accessioned | 2019-03-08T09:42:49Z | |
dc.date.available | 2019-03-08T09:42:49Z | |
dc.date.created | 2018-11-15T10:35:27Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Lecture Notes in Business Information Processing. 2018, 320 135-147. | nb_NO |
dc.identifier.issn | 1865-1348 | |
dc.identifier.uri | http://hdl.handle.net/11250/2589310 | |
dc.description.abstract | Big data and analytics have been credited with being a revolution that will radically transform the way firms operate and conduct business. Nevertheless, the process of adopting and diffusing big data analytics, as well as actions taken in response to generated insight, necessitate organizational transformation. Nevertheless, as with any form of organizational transformation, there are multiple inhibiting factors that threaten successful change. The purpose of this study is to examine the inertial forces that can hamper the value of big data analytics throughout this process. We draw on a multiple case study approach of 27 firms to examine this question. Our findings suggest that inertia is present in different forms, including economic, political, socio-cognitive, negative psychology, and socio-technical. The ways in which firms attempt to mitigate these forces of inertia is elaborated on, and best practices are presented. We conclude the paper by discussing the implications that these findings have for both research and practice. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer | nb_NO |
dc.title | Big Data Enabled Organizational Transformation: The Effect of Inertia in Adoption and Diffusion | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 135-147 | nb_NO |
dc.source.volume | 320 | nb_NO |
dc.source.journal | Lecture Notes in Business Information Processing | nb_NO |
dc.identifier.doi | 10.1007/978-3-319-93931-5_10 | |
dc.identifier.cristin | 1630819 | |
dc.description.localcode | © Springer International Publishing AG, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Business Information Processing. Locked until 16 June 2019 due to copyright restrictions. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-93931-5_10 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |