Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture
dc.contributor.author | Dubey, R | |
dc.contributor.author | Gunasekaran, A | |
dc.contributor.author | Childe, Stephen J | |
dc.contributor.author | Blome, C | |
dc.contributor.author | Papadopoulos, T | |
dc.date.accessioned | 2019-02-19T17:06:27Z | |
dc.date.issued | 2019-05-08 | |
dc.identifier.issn | 1045-3172 | |
dc.identifier.issn | 1467-8551 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/13321 | |
dc.description | Embargo 2 years from publication. "This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions." | |
dc.description.abstract |
The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. Literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource-based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills, and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre-tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under moderating effect of big data culture and their utilisation for capability building, and how this capability affects cost and operational performance. | |
dc.format.extent | 341-361 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Wiley | |
dc.subject | Big Data | |
dc.subject | Predictive Analytics | |
dc.subject | Institutional Theory | |
dc.subject | Resource Based View | |
dc.subject | Manufacturing Performance | |
dc.subject | PLS SEM | |
dc.title | Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture | |
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:000467305200008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 2 | |
plymouth.volume | 30 | |
plymouth.publication-status | Published | |
plymouth.journal | British Journal of Management | |
dc.identifier.doi | 10.1111/1467-8551.12355 | |
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 | 2019-02-17 | |
dc.rights.embargodate | 2021-5-7 | |
dc.identifier.eissn | 1467-8551 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.version | Accepted Manuscript | |
rioxxterms.versionofrecord | 10.1111/1467-8551.12355 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2019-05-08 | |
rioxxterms.type | Journal Article/Review |