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Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

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  • Niklas Stoehr
  • Fabian Braesemann
  • Michael Frommelt
  • Shi Zhou

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 and 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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  • Niklas Stoehr & Fabian Braesemann & Michael Frommelt & Shi Zhou, 2019. "Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends," Papers 1912.10097, arXiv.org, revised Jan 2020.
    • Handle: RePEc:arx:papers:1912.10097
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    1. Adan Ortiz-Cordova & Bernard J. Jansen, 2012. "Classifying web search queries to identify high revenue generating customers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(7), pages 1426-1441, July.
    2. Stephany, Fabian & Braesemann, Fabian, 2017. "An Exploration of Wikipedia Data as a Measure of Regional Knowledge Distribution," SocArXiv c2gd8, Center for Open Science.
    3. Fabian Braesemann & Niklas Stoehr & Mark Graham, 2019. "Global networks in collaborative programming," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 6(1), pages 371-373, January.
    4. Adan Ortiz‐Cordova & Bernard J. Jansen, 2012. "Classifying web search queries to identify high revenue generating customers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(7), pages 1426-1441, July.
    5. Stephany, Fabian & Braesemann, Fabian & Graham, Mark, 2019. "Coding Together - Coding Alone: The Role of Trust in Collaborative Programming," SocArXiv 8rf2h, Center for Open Science.
    6. Swaminathan, Anand & Hoetker, Glenn & Mitchell, Will, 2002. "Network Structure and Business Survival: The Case of U.S. Automobile Component Suppliers," Working Papers 02-0105, University of Illinois at Urbana-Champaign, College of Business.
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