Toward a sustainable supply chain for social credit: learning by experience using single-valued neutrosophic sets and fuzzy cognitive maps
Social credit’s goal of fighting poverty and social inequality has meant that this concept has attracted increasing interest, particularly after Muhammad Yunus was awarded the 2006 Nobel Peace Prize. However, studies that have analyzed the supply chain and socio-economic impacts of this type of micro-credit are still extremely rare. Social credit is an issue that needs to be taken seriously because its objectives differ from those of other types of credit, that is, its main goals go beyond profit to embrace additional social concerns. Adopting a process-oriented stance that used single-valued neutrosophic sets and fuzzy cognitive maps, this study sought to develop a cognitive structure that facilitates a deeper understanding of social credit’s supply chain. Group meetings were held with a panel of professional credit analysts. The resulting framework shows that the socio-technical approach applied provides value for those analyzing the cause-and-effect relationships between the supply chain components of social credit. The results thus contribute to fulfilling social credit’s goals of promoting sustainability and improving human lives. The advantages, managerial implications, and limitations of this research are also discussed.
KeywordsSocial credit Sustainable supply chain (SSC) Fuzzy cognitive mapping Single-valued neutrosophic sets (SVNSs)
This study is an outcome of a larger research project on social credit, which was partially funded by the Portuguese Foundation for Science and Technology (Grant UID/GES/00315/2013). Records of the expert panel meetings, including pictures, software output and non-confidential information of the study, can be obtained from the corresponding author upon request. The authors gratefully acknowledge the superb contribution and knowledge sharing of the expert panel members: Amílcar Lourenço, Carlos Morais, Cláudia Rato, Humberto Alves, Rita Fortunato, and Rui Leal. We are also heartily thankful to Maria Xavier and Ricardo Barroso for their assistance in earlier stages of this project.
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