Abstract:

In recent years， the growing demand for user travel has driven the rapid development of ride-hailing platforms. However， despite the continuous expansion of the user base and the increasing number of drivers， the phenomenon of “drivers without orders and passengers unable to hail a ride” remains prevalent in the ride-hailing market. To enhance ride-hailing efficiency and improve driver order acceptance rates， platforms are continuously optimizing their matching algorithms. These platforms aim to facilitate better transactions between drivers and passengers through dynamic pricing， thereby achieving more efficient supply-demand matching. The enhancement of supply-demand matching capabilities in ride-hailing platforms directly strengthens the cross-side network effects between the driver and passenger ends， increasing the utility for both passengers and drivers who join the platform. This， in turn， influences the platform's pricing decisions for its bilateral user base. However， investments in matching capabilities also result in higher operational costs for the platform. Consequently， it is becoming increasingly crucial for ride-hailing platforms to optimize their bilateral pricing strategies while simultaneously enhancing their matching capabilities to improve overall revenue.In addition， passengers often have different preferences for switching cost attributes and price attributes in different scenarios. For example， on ride-hailing platforms， passengers are more sensitive to switching cost attributes such as learning costs and account information transfer involved in switching to a new platform， while drivers are more sensitive to price attributes. Currently， there are relatively few pricing decisions for ride-hailing platforms that take into account the different preferences of passengers and drivers. However， as the ability of platforms to match supply and demand improves， the impact of user preference differences on platform pricing decisions becomes more important. Therefore， studying the pricing decisions of ride-hailing platforms in this situation is of great importance.Given the differences in perceived prices and switching costs between passengers and drivers， a game-theoretic model is constructed to explore the bilateral pricing of ride-hailing platforms in the context of matching ability can be improved and network effects can be observed. It is found that： （1） When both passengers and drivers are switching cost-sensitive or both passengers and drivers are price-sensitive， the platform should lower prices for passengers and drivers as the cost coefficient for the service level improvement increases. （2） When passengers are price-sensitive and drivers are switching-cost-sensitive， or when passengers are switching-cost-sensitive and drivers are price-sensitive， if the network effect on the passenger （driver） side is greater than a certain threshold， the platform's optimal pricing for the passenger （driver） increases with the increase of cost coefficient for the service level improvement and the service improvement cost. When the network externality on the passenger （driver） side is less than a certain threshold， the platform should decrease its pricing for the passenger （driver） with the increase of cost coefficient for the service level improvement and the service improvement cost. （3） As the cost of platform matching services increases， the scale of users on both sides of the platform increases. （4） When the basic utility that passengers/drivers can obtain by joining the platform exceeds a certain threshold， the platform’s profit increases with the increase of matching ability and cost. In contrast， the platform’s profit decreases with the increase of matching ability and cost.

Key words: ride-hailing platforms, matching ability, network effects, pricing