Empirical Investigation of Data Analytics Capability and Organizational Flexibility as Complements to Supply Chain Resilience
dc.contributor.author | Dubey, R | |
dc.contributor.author | Gunasekaran, A | |
dc.contributor.author | Childe, Stephen J | |
dc.contributor.author | Fosso Wamba, S | |
dc.contributor.author | Roubaud, D | |
dc.contributor.author | Forupon, C | |
dc.date.accessioned | 2019-01-30T16:40:48Z | |
dc.date.issued | 2019-02-27 | |
dc.identifier.issn | 0020-7543 | |
dc.identifier.issn | 1366-588X | |
dc.identifier.other | 1 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/13253 | |
dc.description.abstract |
Supply chain resilience and data analytics capability have generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organisational information processing theory (OIPT). Four research hypotheses are tested using responses from 213 Indian manufacturing organisations collected via a pre-tested survey-based instrument. We further test our model using variance-based structural equation modelling, popularly known as PLS-SEM. All of the hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions. | |
dc.format.extent | 110-128 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Taylor & Francis | |
dc.subject | Data analytics | |
dc.subject | ripple effect | |
dc.subject | disruption | |
dc.subject | supply chain resilience | |
dc.subject | competitive advantage | |
dc.subject | structural equation modelling | |
dc.subject | organisational information processing theory | |
dc.title | Empirical Investigation of Data Analytics Capability and Organizational Flexibility as Complements to Supply Chain Resilience | |
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:000608358100007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 1 | |
plymouth.volume | 59 | |
plymouth.publication-status | Published | |
plymouth.journal | International Journal of Production Research | |
dc.identifier.doi | 10.1080/00207543.2019.1582820 | |
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 | 2019-01-30 | |
dc.rights.embargodate | 2020-2-27 | |
dc.identifier.eissn | 1366-588X | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1080/00207543.2019.1582820 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2019-02-27 | |
rioxxterms.type | Journal Article/Review |