Abstract:

Knowledge about existing user preferences and requirements with regard to electric vehicles is crucial to the development of convenient e-mobility solutions. For a good market adoption of electric vehicles, they are decisive. In this article, the authors analyze German Twitter data as fast and unbiased feedback from customers on current products. It demonstrates how this new data source can be applied in the field of e-mobility. The authors compare their analysis to the traditional approach of a literature survey. Results show that for the German-speaking region the frequency distribution of needs derived from the literature survey differs significantly from the frequency distribution derived from the sample of tweets. Price-related needs and needs concerning Car characteristics are comparably overrepresented in the literature survey. On the other hand, Charging-related needs in particular as well as needs classified in the society cluster are overrepresented in the Twitter data set. They derive interpretations from these insights and illustrate possibilities for stakeholders and policymakers to increase acceptance.