Abstract
The aim of the present study was to evaluate how the business-to-business (B2B) networks in the Japanese textile and apparel industry changed between 2005 and 2010 using data on 200 companies. Network analysis was used to study the properties of the B2B networks, and how their structures changed was characterized using the USD/JPY exchange rate. The network analysis revealed power-law properties of the B2B networks, and the core networks characterized by the largest degree centrality exhibited positive correlations with the USD/JPY exchange rate. By contrast, the peripheral networks characterized by the network path length exhibited the negative correlations with the exchange rate USD/JPY. Therefore, the changes that occurred in the B2B networks are explained as the complementarity of comparative advantages originating in the USD/JPY exchange rate. Moreover, the USD/JPY exchange rate affected the B2B networks through not only the complementarity of importing and exporting but also by changing the structures.
Highlights
Overview of the present study Generally, there have been trade frictions between Chinese-U.S industries involved in information communication technology (ICT) (CNN 2019), and those trade frictions seem to have influenced those various industries
The present study focuses on the Japanese textile and apparel industry the U.S.-Japan trade frictions of which occurred in the 1960s (Yachi 1989)
Second approach: changing structures of the B2B networks with the USD/JPY exchange rate In Table 2, the network path length showed that the changing structures of the entire network were correlated negatively with the USD/JPY exchange rate, which is represented as USD/JPY
Summary
Overview of the present study Generally, there have been trade frictions between Chinese-U.S industries involved in information communication technology (ICT) (CNN 2019), and those trade frictions seem to have influenced those various industries. The adaptations to these economic fluctuations are necessary for the industries and the comprised companies in various regions. It is important to understand the state and its changing of the industries influenced by macroeconomy for these necessaries of the companies These phenomena are difficult to analyze because the required datasets cannot be obtained synchronously and spatiotemporally.
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