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Sustainable supply-chain management (SSCM) is a hot topic among scholars and managers (Marshall et al., 2015; Neutzling et al., 2018). People are increasingly aware that companies must incorporate sustainability principles in their supply chains (Hong et al., 2018, Abidi et al., 2017). SSCM refers to the strategic and transparent integration and realization of multiple goals regarding the economy, environment, society, and culture. It is achieved by systematically coordinating cross-organizational core business processes, and aims to improve the long-term economic benefits of an enterprise’s supply chain. Supply chain managers incorporate environmental, economic, and social issues in their daily work to achieve sustainable performance (Mota et al., 2018).
Studies of fresh food supply chain management have rarely analyzed sustainability (Sgarbossa & Russo, 2017). SSCM faces various driving factors, such as economic, environmental, social, and institutional few of which may have a significant impact on its performance. Managers must identify key driving factors, especially when risk management resources are limited. SSCM driving factors are often interrelated. This interrelationship between SSCM driving factors may influence decisions about their priorities. The “implicit influence” of some driving factors may cause an erroneous decision, and the direct and indirect relationships between variables may affect the entire supply chain (Dubey et al., 2017).
Most scholars have used quantitative or qualitative empirical methods (Ahi & Searcy, 2013; Soltani et al., 2014, Mohseni et al., 2019). When analyzing the background of SSCM driving factors, few studies consider each factor’s importance, which may affect their priority. Existing methods are less concerned about the correlation of driving factors. Some scholars apply total interpretive structural modeling (TISM) and a cross-influence matrix (MICMAC) to classification analysis, and further test the framework to infer SSCM driving factors and their relationships (Dubey et al., 2017).
This study aims to find the key SSCM driving factors and their relationship with the new method of rough DEMATEL. The innovations of this study include: (1) simultaneously considering the strength and influence of driving factors in DEMATEL to fully reflect their positioning; (2) using a flexible rough number to deal with fuzziness and subjectivity while requiring little prior information (such as fuzzy membership functions and data distribution in fuzzy methods); and (3) understanding the relationship between SSCM driving factors, and generating useful insights and perspectives to help supply chain managers focus on key issues that may affect SSCM performance.