Optimizing Two-Sided Promotion for Transportation Network Companies: A Structural Model with Conditional Bayesian Learning

The mobile app of a transportation network company (TNC) has reshaped the taxi business model by providing new features and allowing the TNC platform to run a diverse two-sided sales promotion to help introduce those new features. We investigate the economic value of this app and how drivers build an initial preference for passenger matching, the cancellation feature, and online pay as well as how a two-sided sales promotion affects drivers’ willingness to use the TNC app. We estimate a structural model of drivers’ decisions to accept orders and to cancel generated orders and their perception of passengers’ willingness to utilize a sales promotion. Bayesian learning processes are introduced to account for drivers’ learning new features. We find evidence of the economic value of new features on a TNC app and drivers’ learning about the value of those features. Our results show that a platform subsidy and bids from passengers might signal low quality of service, and that platform cashback to passengers has a positive effect on drivers by increasing drivers’ chances of being rewarded. Our results further indicate that the substantial value of early promotion not only encourages current usage but also fosters learning that sustains drivers’ continued use of the app, and show how cashback for passengers affects the decisions of drivers. Finally, our policy simulations show improved performance with regard to drivers’ willingness to use the app as well as its cost effectiveness.