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Cited by 700

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Big data analytics as an operational excellence approach to enhance sustainable supply chain performance

Bag, Surajit, Wood, Lincoln C., Xu, Lei, Dhamija, Pavitra, Kayikci, Yaşanur·2020·Resources, Conservation and Recycling
DOI 10.1016/j.resconrec.2019.104559
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Abstract

Abstract Operations 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.

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Metadata

Authors
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y.
Venue
Resources, Conservation and Recycling
Publisher
Elsevier BV
Published
2020-02
Volume
153
Pages
104559
ISSN
0921-3449 (ISSN-L)
DOI
10.1016/j.resconrec.2019.104559
OpenAlex
W2989240182
Cited by
700
References
10
FWCI
73.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. https://app.cochat.ai/papers/10.1016/j.resconrec.2019.104559
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Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling. https://doi.org/10.1016/j.resconrec.2019.104559

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.