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

TL;DR

This paper presents a network analysis of automotive company web pages to uncover corporate positioning, technological trends, and market strategies using publicly available web data and sentiment analysis.

Contribution

It introduces a transparent, reproducible method to analyze web networks and content hierarchies, revealing corporate strategies and industry trends beyond traditional content analysis.

Findings

Network properties reveal corporate positioning and innovation focus.

Sentiment analysis links positive language to technological trends.

Web page clustering correlates with market orientation.

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.