Deterministic and Probabilistic Service Hailing Modes Choices for On-Demand Service Platform

On-demand service platform that offers diverse service classes has allowed consumers to hail all the available services simultaneously and wait for an assignment. Such a probabilistic service-hailing mode has been widely adopted in practice, however, whether it always performs better than the traditional deterministic one in the context of on-demand service remained unexplored. Considering a platform which offers on-demand service of two classes: standard versus premium, we study the interaction between consumers and independent agents to endogenize the supply and demand, and invesitigate the platform's optimal price and wage decisions, and drive the associated profit to offer the insights on the platform's service hailing modes choices. Building on the consumer behavior relating to service quality, price, and congestion level, we find that the payout ratio for premium service should be adjusted dynamically and different service-hailing modes imply the opposite directions. We further show that by switching to the probabilistic manner, the platform can benefit from efficiency improvement, especially for a market with higher delay sensitivity, however, consumers' uncertainty in service quality can erode its dominance