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Dual networks: how does knowledge network embeddedness affect firms' supply chain learning?

Yan, Ji; Yu, Zihao; Fernandes, Kiran; Xiong, Yu

Dual networks: how does knowledge network embeddedness affect firms' supply chain learning? Thumbnail


Authors

Zihao Yu zihao.yu@durham.ac.uk
PGR Student Doctor of Philosophy

Zihao Yu zihao.yu@durham.ac.uk
PGR Student Doctor of Philosophy



Abstract

Purpose: To explore the mechanism that shapes firms' supply chain learning (SCL) practices, this study examines the relationship between firms' knowledge network embeddedness and their SCL practice in a supply chain network, as well as the moderating role of supply chain network cohesion in this relationship. Design/methodology/approach: Using patent application data and supply chain partner information from 869 listed firms between 2011 and 2020 in China, this study uses fixed-effect regression models to reduce endogeneity problems by controlling for individual heterogeneity effects that cannot be observed over time. Findings: Firms' knowledge network embeddedness has an inverted U-shaped effect on their SCL, and this non-linear relationship is conditional on supply chain network cohesion, which strengthens (weakens) the positive (negative) effect of knowledge network embeddedness on SCL. Practical implications: The findings show that managers can reconcile the downsides of knowledge network embeddedness on SCL by fostering greater supply chain network cohesion. Originality/value: Drawing from the network pluralism perspective, this study contributes to supply chain literature by extending the research context of the antecedents of SCL from a single-network setting to a dual-network setting. It extends the network pluralism perspective by showing that not only positive effects but also negative effects of network embeddedness can transfer from one network to another.

Citation

Yan, J., Yu, Z., Fernandes, K., & Xiong, Y. (2023). Dual networks: how does knowledge network embeddedness affect firms' supply chain learning?. International Journal of Operations & Production Management, 43(8), 1277-1303. https://doi.org/10.1108/ijopm-08-2022-0507

Journal Article Type Article
Acceptance Date Jan 1, 2023
Online Publication Date Mar 14, 2023
Publication Date Aug 8, 2023
Deposit Date Jun 26, 2023
Publicly Available Date Jun 26, 2023
Journal International Journal of Operations & Production Management
Print ISSN 0144-3577
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 43
Issue 8
Pages 1277-1303
DOI https://doi.org/10.1108/ijopm-08-2022-0507
Public URL https://durham-repository.worktribe.com/output/1170883

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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact permissions@emerald.com





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