Now showing 1 - 3 of 3
  • Publication
    Metadata only
    Market definition of platform markets
    (Universitätsbibliothek der HSU / UniBwH, 2017) ;
    Heimeshoff, Ulrich
    ;
    Löw, Franziska
    Platform markets are characterized by the existence of indirect network effects that connect two or more market sides through a platform that internalizes these feedback effects. Conventional instruments of market definitions which consider price levels cannot easily applied in case of two-sided platform competition, as price structure of those markets are non-neutral. Instead of using prices, we use time series of quantities and simple correlation analysis to evaluate the substitutional relationship within two-sided media markets. As a benchmark model, we simulate a Cournot duopoly on order to calculate correlation coefficients for varying degrees of product differentiation and indirect network effects.
  • Publication
    Metadata only
    Media coverage and car manufacturers' sales
    (düsseldorf university press, 2016) ;
    Heimeshoff, Ulrich
    ;
    Thomas, Tobias
    A wide range of media provide information on many products based on reviews or expert opinions. The effects of such information on product sales is analyzed in a small but growing literature in economics and marketing science. However, there is much more coverage on companies and products in the media than product reviews and expert opinions. Based on a unique dataset, we test whether coverage of car manufacturers in opinion leading media have significant impact on registrations of new cars in Germany. We find that positive (or at least neutral) media coverage has statistically significant effect on the number of new cars sold by several leading manufacturers on the German car market.
  • Publication
    Metadata only
    Predicting advertising volumes: A structural time series approach
    (düsseldorf university press, 2016) ;
    Heimeshoff, Ulrich
    Media platforms typically operate in a two-sided market, where advertising space serves as a major source of revenues. However, advertising volumes are highly volatile over time and characterized by cyclical behavior. Firms' marketing expenditures in general are far from stable. Due to planning of future issues as well as financial planning, platforms have to forecast the demand for advertising space in their future issues. We use structural time series analysis to predict advertising volumes and compare the results with simple autoregressive models.