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Causality seafood processing circular supply chain capabilities in qualitative data analytics

Authors

Tseng,  Ming-Lang
External Organizations;

Tran,  Thi Phuong Thuy
External Organizations;

Wu,  Kuo-Jui
External Organizations;

/persons/resource/144

Xue,  Bing
IASS Institute for Advanced Sustainability Studies Potsdam;

Chen,  Xiaobo
External Organizations;

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Citation

Tseng, M.-L., Tran, T. P. T., Wu, K.-J., Xue, B., Chen, X. (2021 online): Causality seafood processing circular supply chain capabilities in qualitative data analytics. - Industrial management & data systems.
https://doi.org/10.1108/IMDS-06-2021-0357


Cite as: https://publications.iass-potsdam.de/pubman/item/item_6001204
Abstract
Purpose This study establishes a set of seafood processing circular supply chain capabilities (CSCCs) in Vietnam using qualitative data analytics. This study specifies the interrelationships and hierarchical structure comprising six aspects and 24 criteria for the seafood processing circular supply chain in Vietnam. Design/methodology/approach Fuzzy Delphi method is used to confirm the validity. Fuzzy set theory is used to deal with the complexity and uncertainties from the qualitative information. The decision-making trial and evaluation laboratory method is used to examine the interrelationships among attributes. The analytical network process segregates (or displays) the capabilities in a hierarchical structure. Findings The results show that management control and technological capability dominate in circular design, circular sourcing, circular production and resource recovery. In practices, the strategic planning, action planning, information technology and technological facilities are important to seafood processing industry. Originality/value The CSCCs are pivotal in establishing a concrete foundation for the execution of circular supply chain management, with the aim of optimizing resource utilization and eliminating waste; however, prior studies have lacked a focus on the capability associated interrelationships and hierarchical structure in qualitative data analytics.