Network Centrality and Performance: Effects in the Automotive Industry

Ferreira, Augusto Squarsado; Neto, Mário Sacomano; Cândido, Silvio Eduardo Alvarez; Ferratti, Gustavo Mendonça

doi:10.7819/rbgn.v23i4.4132

Cited by 3 publications

(2 citation statements)

References 45 publications

(78 reference statements)

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“…In the case of China, the strong local demand has helped the country to create value through their national firms, such as SAIC Motors, Dongfeng, and Changan Automobile, among others. Performance of different firms was conducted by Ferreira et al ( 2021 ), highlighting the role of Japanese firms in the industry to have greater dominance with alliances such as the Nissan–Renault (Japan–Italy) and Ford–Mazda (Japan–United States). On the side of the aircraft, American companies dominate the industry with some relevant competition from Russia, France, Netherlands, Italy and Japan.…”

Section: Resultsmentioning

confidence: 99%

Transport equipment network analysis: the value-added contribution

GARCÍA

2022

Economic Structures

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Emerging in the twenty-first century, Network Science provides practical measures to interpret a system’s interactions between the components and their links. Literature has focused on countries’ interconnections on the final goods, but its application on the value-added from a network perspective in trade is still imitated. This paper applies network science properties and a multi-regional input–output analysis by using the UNCTAD-Eora Global Value Chain Database on the Transport Equipment value added on 2017 to unwrap the specific structural characteristics of the industry. Results show that the industry is highly centralized. The center of the network is dominated by developed countries, mainly from Europe, the United States, and Japan. Emerging countries such as China, Mexico, Thailand, and Poland also have an important position. In addition, the structure reveals two sub-hubs located in East Europe and North America. By extending to community detection, the network consists of three different communities led by Germany, the United States, and the United Kingdom, associated with more significant value-added flows. The study concludes that flows are not always consistent with the economy’s geographical location as usually final goods analysis suggests, and highlight the need to continue using the complex network to reveal the world trade structure.

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Section: Resultsmentioning

confidence: 99%

Transport equipment network analysis: the value-added contribution

GARCÍA

2022

Economic Structures

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“…A high closeness centrality company, on the other hand, is a company with important neighbours, and a high betweenness centrality company bridges different production lines. The automotive manufacturers are assemblers, which means that they have high in-degree centrality, and a correlation has been noted between high in-degree and performance of automotive companies (Ferreira et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Network Algorithm to Model Automotive Supply Chain Structure

Barros,

Turner

2024

Preprint

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In this study, we present a network algorithm that models the structure of automotive supply chains. We compiled a comprehensive dataset from a proprietary database, detailing automotive manufacturers and their suppliers across various tiers. An initial structural analysis was conducted using key performance indicators - including average path length, clustering coefficient, and degree distribution - to assess network configurations. The networks were then partitioned, with an emphasis on reflecting the operational dynamics of supply chain activities. Regression analysis was applied to each network segment, using the number of vertices as the independent variable, to develop an algorithm for generating synthetic networks. These synthetic constructs serve as benchmarks for the automotive sector and have shown a strong average correlation (0.94) with the structure of actual supply networks. The prevalence of tree-like structures within these synthetic networks challenges conventional beliefs regarding the complexity of automotive supply chains and prompts further investigation into the determinants of their resilience.

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Vicarious learning from peer innovation failures: The moderating effects of venture capital syndication networks

Tao,

Huang,

Guan

et al. 2024

Manage Decis Econ

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Firms can derive insights not only from their own failures but also from the innovation failures of counterparts in the same industry. This study investigates how 109 Chinese A‐share listed high‐tech firms (2013–2022) learn from counterparts' innovation failures to improve their success rate of innovation and emphasizes the crucial role of venture capital (VC) syndication networks in facilitating knowledge transfer. Central VCs within the network notably enhance vicarious learning from others' failures. Further empirical analysis shows a significant increase in the success rate of exploitative innovations, while the impact on exploratory innovations is minimal.

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Network Centrality and Performance: Effects in the Automotive Industry

Cited by 3 publications

References 45 publications

Transport equipment network analysis: the value-added contribution

Transport equipment network analysis: the value-added contribution

Network Algorithm to Model Automotive Supply Chain Structure

Vicarious learning from peer innovation failures: The moderating effects of venture capital syndication networks

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