Journal
JOURNAL OF CLEANER PRODUCTION
Volume 227, Issue -, Pages 760-771Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.04.201
Keywords
Data-driven sustainable supply chain management performance; Fuzzy synthetic method; Decision making trial and evaluation laboratory; Sustainable supply chain management; Triple bottom line
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Funding
- MOST [107-2410-H-468 -026]
- National Natural Science Foundation of China [71701029]
- Liaoning Academy of Social Sciences Fund [L17BGL019]
- Fundamental Research Funds for the Central Universities [DUT18RC(4)002]
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This study contributes to the literature by assessing data-driven sustainable supply chain management performance in a hierarchical structure under uncertainties. Sustainable supply chain management has played a significant role in the general discussion of business management. While many attributes have been addressed in prior studies, there remains no convincing evidence that big data analytics improve the decision-making process regarding sustainable supply chain management performance. This study proposes applying exploratory factor analysis to scrutinize the validity and reliability of the proposed measures and uses qualitative information, quantitative data and social media applied fuzzy synthetic method-decision making trial and evaluation laboratory methods to identify the driving and dependence factors of data-driven sustainable supply chain management performance. The results show that social development has the most significant effect. The results also indicate that long-term relationships, a lack of sustainable knowledge or technology, reverse logistic, product recovery techniques, logistical integration, and joint development are the most effective criteria for enhancing sustainable supply chain management performance. The theoretical and managerial implications are discussed. (C) 2019 Elsevier Ltd. All rights reserved.
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