How to improve firm performance using big data analytics capability and business strategy alignment?
dc.contributor.author | Akter, S | |
dc.contributor.author | Fosso Wamba S, | |
dc.contributor.author | Gunasekaran A, | |
dc.contributor.author | Dubey R, | |
dc.contributor.author | Childe, Stephen J | |
dc.date.accessioned | 2016-10-03T10:16:24Z | |
dc.date.issued | 2016-12-01 | |
dc.identifier.issn | 0925-5273 | |
dc.identifier.issn | 1873-7579 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/5545 | |
dc.description.abstract |
The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings from two Delphi studies and 152 online surveys of business analysts in the U.S. confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC–FPER relationship. | |
dc.format.extent | 113-131 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.subject | Capabilities | |
dc.subject | Entanglement view | |
dc.subject | Big data analytics | |
dc.subject | Hierarchical modeling | |
dc.title | How to improve firm performance using big data analytics capability and business strategy alignment? | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000389090400009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 0 | |
plymouth.volume | 182 | |
plymouth.publication-status | Published | |
plymouth.journal | International Journal of Production Economics | |
dc.identifier.doi | 10.1016/j.ijpe.2016.08.018 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Arts, Humanities and Business | |
plymouth.organisational-group | /Plymouth/Faculty of Arts, Humanities and Business/Plymouth Business School | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA17 Business and Management Studies | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2016-08-17 | |
dc.rights.embargodate | 2018-2-21 | |
dc.identifier.eissn | 1873-7579 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1016/j.ijpe.2016.08.018 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2016-12-01 | |
rioxxterms.type | Journal Article/Review |