An analytical method for investigating multi-tier sustainable supply chains: simplifying the complex

被引:2
|
作者
Bai, Chunguang [1 ,6 ]
Sarkis, Joseph [2 ,3 ]
Ibrahim, Sherwat [4 ,5 ]
机构
[1] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu, Peoples R China
[2] Worcester Polytech Inst, Business Sch, Worcester, MA USA
[3] Humlog Inst, Hanken Sch Econ, Helsinki, Finland
[4] Amer Univ Cairo, Sch Business, New Cairo, Egypt
[5] Princeton Univ, Sch Publ & Int Affairs, Princeton, NJ USA
[6] Univ Elect Sci & Technol China, Sch Management & Econ, 2006,Xiyuan Ave,West Hitech Zone, Chengdu 611731, Peoples R China
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
Multi-tier supply chains; supplier; sustainability; complexity; equifinality; rough set; ROUGH SET-THEORY; MANAGEMENT; NETWORKS; STRATEGY; IMPACT;
D O I
10.1080/00207543.2023.2182148
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Multi-tier sustainable supply chain relationships (MSSCR) are complex with involvement of multiple organisations, practices, objectives, and interconnectivities. These complexities make it difficult to effectively study MSSCR using traditional empirical statistical correlative research. In MSSCR the relationship between the focal firm and sub-supplier may be affected by the relationship between the focal firm and direct-supplier, and the relationship between the direct-supplier and the sub-supplier. This paper introduces a research methodology, based on K-Means clustering, rough set theory, and cluster membership to investigate complex relationships across multi-tier sustainable supply chain triads. Collaborative advantage variables including knowledge-sharing routines, relation-specific investments, complementary capabilities, effective governance mechanisms, and long-term commitment are conceptually presented to evaluate relationships among MSSCR practices and sustainability performance. Insights for practitioners and researchers are provided. Research insights can be gained by analytically providing alternative explanatory and equifinality relationships amongst variables and multi-pair organisations. The methodology can mitigate the endogeneity problem, such as omitted factors (variables), measurement error, even in small sample data situations. Directions for future research are discussed.
引用
收藏
页码:1318 / 1335
页数:18
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