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Optimizing Two Sided Promotion for IS Enabled Transportation Network: A Conditional Bayesian Learning Model

This paper investigates a typical two-sided micro-level business model of IS enabled transportation network. Specifically, we focus on how two-sided sales promotion interacts with users’ learning about attribute, and measure the effectiveness of sales promotion for their platform introductions. Our paper applies Bayesian learning model with an extension to account for multiple serial unobserved correlation. We find the measurable evidence of taxi driver’s learning about order commitment and payment methods of passenger. We furtherly identify that the attribute value of transportation network is undervalued in prior, which indicates that intensive promotion would not only attract user instantly, but also enhance user learning in long run. We name the effect on adoption rate driven by user learning as indirect effect of sales promotion. By running simulations, we quantify the indirect effect of sales promotion explicitly, and furtherly propose more efficient sales promotion strategy as managerial implication for industry.