Strategic driver’s acceptance-or-rejection behavior and cognitive hierarchy in on-demand platforms

It is critical for an on-demand platform to understand its users’ strategic-decision behaviors and thereby design a good policy to match the supply with the demand. Through behavioral game-theoretical modeling, we investigate the effects of drivers’ strategic acceptance-or-rejection behavior on their incomes and the overall platform users’ welfare, in an on-demand service platform with two service locations (i.e., a hot area and a cold area), wherein the competition between drivers arises due to overall supply exceeding overall demand and the location uncertainty of drivers. We first construct a full-rationality model to investigate drivers’ decision strategy in a competitive environment and attain the Nash Equilibrium outcomes in the stationary state of the system in the long run. We then employ the Cognitive Hierarchy theory to capture the heterogeneity effect in drivers’ strategic acceptance-or-rejection behaviors, which is more realistic than the full-rationality model. We find that the predictions of the Cognitive Hierarchy model can approach the Nash Equilibrium outcomes under an extreme condition. We can conclude that (i) the optimal acceptance probability of the drivers does not decrease with the rejection penalty or with the relative popularity of the cold area under our research settings. (ii) From the profit/welfare perspective, increasing the popularity of the cold area is beneficial for the platform users; however, a higher rejection penalty may not be always better for the platform users as a whole. Managerial implications are discussed.