Estimating and Comparing Cross-side Network Effects for Three Types of Platform Markets

The rise of the platform economy has completely changed the growth dynamics for businesses, such as Apple iOS and Uber, as well as for nonprofits, such as Wikipedia. The exponential growth of these platforms stem mainly from the cross-side network effect, i.e., a feedback loop, between the platform sides. In this paper, we set out to develop a generic differential-equation-based model for cross-side network effects. We evaluate our model with historical observed data from three platforms—Apple iOS, Uber, and Wikipedia—which represent three distinct types of platform markets—complementary innovation, transaction, and information markets—respectively. We estimate not only the overall strength of the cross-side network effect in both directions but also their dynamic behavior including: saturation level, exponential boost/decay, and network effect cutoff value. We compare and discuss these results for the two sides and between the platforms. Our contribution is threefold: (1) we distinguish between the growth from the feedback loop and the Metcale’s law style exponential factor; (2) we model two types of declining dynamic behavior in both directions; and (3) based on our generic model we offer a first comparison of cross-side network effects between three distinct types of platform markets.