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Behavioral mechanisms influencing sustainable supply chain governance decision-making from a dyadic buyer-supplier perspective

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  • Belhadi, Amine
  • Kamble, Sachin S.
  • Mani, Venkatesh
  • Venkatesh, V.G.
  • Shi, Yangyan

Abstract

This paper explores the behavioral tendencies of buyer-supplier dyads during sustainable supply chain governance (SSCG) decision-making under the complexity and uncertainty of the sustainable performance of their partners. In doing so, this work builds on Behavioral Decision Theory (BDT), together with the literature on dyadic buyer-supplier relationships, to develop a theoretical framework to examine factors affecting the SSCG decisions of supply chain managers and firm representatives under uncertainty of the sustainable performance of their partners. An experimental vignette methodology (EVM) is used to collect and analyze data from 374 matched buyer-supplier dyads. The findings suggest that behavioral uncertainty could lead to both contractual and relational SSCG. However, the interaction of interpersonal trust and relational experience is critical to shaping the SSCG decision. The findings of the study suggest that behavioral uncertainty does not play significant role in shaping SSCG decisions. However, the interplay with negative relational experience may lead to more contractual SSCG. Furthermore, the degree of interpersonal trust between the buyer and supplier representatives significantly encourages them to opt for more relational SSCG. The study supports that positive relational experience helps to strengthen the trust. Using our findings, supply chain managers and executives should strive to build interpersonal trust between their employees and positive relational experience with their supply chain partners to develop relational SSCG throughout the supply chains.

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  • Belhadi, Amine & Kamble, Sachin S. & Mani, Venkatesh & Venkatesh, V.G. & Shi, Yangyan, 2021. "Behavioral mechanisms influencing sustainable supply chain governance decision-making from a dyadic buyer-supplier perspective," International Journal of Production Economics, Elsevier, vol. 236(C).
  • Handle: RePEc:eee:proeco:v:236:y:2021:i:c:s0925527321001122
    DOI: 10.1016/j.ijpe.2021.108136
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    6. Lin Chen & Ting Dong & Jin Peng & Dan Ralescu, 2023. "Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review," Mathematics, MDPI, vol. 11(11), pages 1-45, May.

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