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dc.contributor.authorBag, Surajit
dc.contributor.authorWood, Lincoln C.
dc.contributor.authorXu, Lei
dc.contributor.authorDhamija, Pavitra
dc.contributor.authorKayıkcı, Yaşanur
dc.date.accessioned2021-01-08T21:51:21Z
dc.date.available2021-01-08T21:51:21Z
dc.date.issued2020
dc.identifier.issn0921-3449
dc.identifier.issn1879-0658
dc.identifier.urihttp://doi.org/10.1016/j.resconrec.2019.104559
dc.identifier.urihttps://hdl.handle.net/20.500.12846/162
dc.descriptionKayikci, Yasanur/0000-0003-2406-3164; /0000-0003-3385-6561; BAG, SURAJIT/0000-0002-2344-9551en_US
dc.descriptionWOS:000501403200012en_US
dc.description.abstractOperations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.en_US
dc.description.sponsorshipNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71672125, 91646117]en_US
dc.description.sponsorshipThe work of Lei XU was supported in part by National Natural Science Foundation of China (Grant No. 71672125), major research project of National Natural Science Foundation of China (Grant No. 91646117).en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBig Data Analyticsen_US
dc.subjectOperational Excellenceen_US
dc.subjectDynamic Capability Viewen_US
dc.subjectSupply Chain Sustainabilityen_US
dc.subjectLearning Performanceen_US
dc.titleBig data analytics as an operational excellence approach to enhance sustainable supply chain performanceen_US
dc.typearticleen_US
dc.relation.journalResources Conservation And Recyclingen_US
dc.identifier.volume153en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.contributor.institutionauthorKayıkcı, Yaşanur
dc.identifier.doi10.1016/j.resconrec.2019.104559
dc.identifier.wosqualityQ1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.wosWOS:000501403200012en_US


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