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Constructing and Analysing Global Corporate Networks With Wikidata: The Case of Electric Vehicle Industry

Zsofia Baruwa et al.

Global Networks: A Journal of Transnational Affairs2025https://doi.org/10.1111/glob.70029article
AJG 3ABDC A
Weight
0.41

Abstract

Constructing comprehensive datasets for corporate network analysis remains a significant challenge for the business research community. This study introduces a novel Python tool, NetVizCorpy, which leverages Wikidata to generate such a dataset. We demonstrate its applications by constructing and analysing a global corporate network based on 44 seed electric vehicle (EV) companies and their three‐level ownership structures. This dataset includes 1354 unique companies and 1575 ownership relations spanning 58 countries. We provide network characteristics, metrics and statistical insights, along with three detailed analytical applications. First, betweenness centrality identifies key influential companies, highlighting the role of financial institutions in industry resilience. Second, community detection reveals strategic positioning by EV manufacturers within global markets. Third, we find a nonlinear inverse U‐shaped relationship between Global Network Connectivity (GNC) and Gross Competitive Intensity (GCI) at the country level. These findings offer new directions for understanding the resilience and competitiveness of the global EV industry.

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https://doi.org/https://doi.org/10.1111/glob.70029

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@article{zsofia2025,
  title        = {{Constructing and Analysing Global Corporate Networks With Wikidata: The Case of Electric Vehicle Industry}},
  author       = {Zsofia Baruwa et al.},
  journal      = {Global Networks: A Journal of Transnational Affairs},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1111/glob.70029},
}

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Constructing and Analysing Global Corporate Networks With Wikidata: The Case of Electric Vehicle Industry

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.