Abstract for:Are On-Demand Platforms Winner-Take-All Markets?
On-demand platforms such as Uber, Caviar and TaskRabbit are a key engine of growth in the digital economy, bringing together time-sensitive customers and independent service providers. The existence of strong network externalities in these two-sided platforms is suggestive of on-demand platforms being winner-take-all markets. We argue, however, that the behavioral learning of customers about platforms provides a potentially sufficient mechanism for multi-platform equilibria, in the presence of systemic market-wide shocks. If customers attribute a negative experience (such as a slow pickup by a ride-hailing service at a busy airport) to the specific platform they used, they are more likely to switch to a competitor in future. This learning is more detrimental for larger players, creating a balancing feedback that can neutralize the network effects. We test the robustness of this result with respect to factors including: the size of the market, the rate of consumer learning, existence of a minimum quality threshold, and the number of competing platforms.