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

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

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