Abstract
The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centred analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. The network properties disclose the internal corporate positioning of the three largest automotive manufacturers, Toyota, Volkswagen and Hyundai with respect to innovative trends and their international outlook. We tag web pages concerned with topics like e-mobility & environment or autonomous driving, and investigate their relevance in the network. Sentiment analysis on individual web pages uncovers a relationship between page linking and use of positive language, particularly with respect to innovative trends. Web pages of the same country domain form clusters of different size in the network that reveal strong correlations with sales market orientation. Our approach maintains the web content’s hierarchical structure imposed by the web page networks. It, thus, presents a method to reveal hierarchical structures of unstructured text content obtained from web scraping. It is highly transparent, reproducible and data driven, and could be used to gain complementary insights into innovative strategies of firms and competitive landscapes, which would not be detectable by the analysis of web content alone.
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- 1.
These are the three biggest automotive manufacturers by production numbers in 2016: Toyota (10.2 mio. vehicles), the Volkswagen Group (10.1 mio cars), and Hyundai with 7.9 mio. units [13].
- 2.
For this part of the analysis, which hinges on the identification of specific keywords, we analyse the US domains of the company websites (www.toyota.com, www.vw.com, www.hyundaiusa.com), as the United States is the second largest global car market behind China, and the largest English-language car market.
- 3.
For the international comparison, we use the manufacturers’ international web pages (www.toyota-global.com, www.volkswagen.com, www.hyundai.com/worldwide), as a starting point for the data collection.
- 4.
The textual components are all HTML tags (predominantly “title” and “body”) of a web page. Specifically excluded are the tags “script”, “style”, “head”, “[document]”. This way, we only include textual components visible to the user.
- 5.
The list of keywords has been created prior to looking at any company website, based only on the qualitative definitions in the literature. The respective keywords are:
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E-mobility & environment: e-mobility, battery, environment, biological, eco, ecological, electric, hybrid, environment, environmental-friendly;
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Connectivity & shared mobility: connectivity, shared, mobility, sharing, interconnectedness, cloud, cloud computing, wifi, 5G;
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Autonomous driving & artificial intelligence: autonomous, self-driving, ai, machine learning, artificial intelligence, intelligent, neural network, algorithm.
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- 6.
In the network visualisations, the nodes are coloured according to the keyword-category that appears most often in a web page. If none of the keywords occurs, the node is coloured in grey.
- 7.
Alternatively, the web page structures of those countries could be compared, in which the manufacturers apply different marketing strategies (there might be markets where the adoption rates of digital technologies in the automotive sector are higher).
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Stoehr, N., Braesemann, F., Frommelt, M., Zhou, S. (2020). Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends. In: Barbosa, H., Gomez-Gardenes, J., Gonçalves, B., Mangioni, G., Menezes, R., Oliveira, M. (eds) Complex Networks XI. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-40943-2_25
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