Stoehr, N;
Braesemann, F;
Frommelt, M;
Zhou, S;
(2020)
Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends.
In:
Complex Networks XI: Proceedings of the 11th Conference on Complex Networks CompleNet 2020.
(pp. pp. 297-308).
Springer Nature
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends |
| Event: | The 11th Conference on Complex Networks CompleNet 2020 |
| Location: | Exeter, England |
| Dates: | 31st March - 4th April 2020 |
| ISBN-13: | 978-3-030-40942-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-40943-2_25 |
| Publisher version: | https://doi.org/10.1007/978-3-030-40943-2_25 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Automotive industry, Network analysis, Complex networks, Digitisation, Web page mining, Competition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10097579 |
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