Data-driven sustainable supply chain management performance: A hierarchical structure assessment under uncertainties

Abstract

Abstract 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.