The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Supplier dependence and R&D intensity: The moderating role of network centrality and interconnectedness
Abstract
This study examines whether financial dependence upon a few customers is negatively related to the allocation of innovation resources of supplier firms. Furthermore, this study investigates whether these negative effects of supplier dependence on research and development (R&D) intensity are reduced when the supplier leverages social capital conceptualized in terms of eigenvector centrality and interconnectedness. Using panel data, we find that a supplier firm's dependence upon major customers has a negative relationship with the supplier firm's R&D intensity. Our results, however, reveal that a dependent supplier having high eigenvector centrality or working with other companies that are densely connected to each other mitigates the negative effects of supplier dependence on R&D intensity. These findings highlight the importance of external information or resources being available in supply networks when suppliers that are dependent upon major customers explore and exploit opportunities for new product development.
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Cites
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Metadata
- Authors
- Kim, D., & Zhu, P.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2018-11
- Volume
- 64
- Issue
- 1
- Pages
- 7-18
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W2912785107
- Cited by
- 253
- References
- 83
- FWCI
- 12.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kim, D., & Zhu, P. (2018). Supplier dependence and R&D intensity: The moderating role of network centrality and interconnectedness. Journal of Operations Management. https://doi.org/10.1016/j.jom.2018.11.002
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.