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With the consideration of the different personalities of the drivers in a peer-to peer ride-sharing platform, this research studies their pricing behaviors. We first analyze the benchmark model by the auction model with the recommended price, then describe the pricing decision processes of drivers with different personalities through the one-shot decision theory, and build behavioral models by which a driver determines an optimal bidding price based on some appropriate events for him/her. Different from traditional approaches which are based on the maximum expected return, the event-based pricing procedure for a driver consists of two steps: in the first step, for each pricing action, the driver examines every potential lowest price offered by other drivers competed on the same platform and chooses some specific events as the focus points; in the second step, based on the focus points of all feasible actions, the driver determines a most preferred price. Theoretical analysis gains managerial insights into the pricing behaviors of drivers with different personalities in peer-to-peer ride-sharing platforms.
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