Research Article
Analysis of the Supply Network of Global Automakers in China: Comparison of Transaction Network and Tie Strength Network
Sookmyung Women's University
Sookmyung Women's University
Sookmyung Women's University
Hoseo University
Published: January 2019 · Vol. 48, No. 1 · pp. 105-131
DOI: https://doi.org/http://dx.doi.org/10.17287/kmr.2019.48.1.105
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Abstract
This study aims to examine how the centrality of a specific node (firm) changes when tie strength is reflected in a link (buyer - supplier relationship). By using actual transaction data, we researched eight global automakers (SAIC Volkswagen, FAW Volkswagen, GAC Toyota, Guangqi Honda, SAIC GM, Changan Ford, Beijing Hyundai, and Dongfeng Yueda Kia) and various suppliers who have entered China and supplied them with several items. All of eight automakers are joint ventures with global carmakers and Chinese state-owned enterprises We have examined how the degree centrality and eigenvector centrality change in transaction network and tie strength network of the airbag, interior, and seat-related product categories. As a result, adding the tie strength to the transaction relationship would increase the value of degree centrality or eigenvector centrality in all product categories in common. This is quite different from existing research that only considered transaction networks. It also confirmed that Korean carmakers have strong ties with their suppliers in all tie strength networks. The reason for this is that Korean suppliers have been in business for a long time with customers in Korea. This study has significant meaning in terms of understanding the dynamics and network structure of the actual supply network of automobile industry considering the tie strength, but there are limitations due to the data. First, we measured the tie strength with the number of product items because we were unable to figure out the sales between buyer and supplier. Second, we could not grasp the tier of the supplier and the transaction relation between suppliers in the data. Therefore, improving these limitations in future studies will allow us to analyze supply networks closer to reality.
