Dynamic pricing and funding policy in reward‐based crowdfunding with social learning and strategic waiting behavior

Abstract Motivated by the practice of reward‐based crowdfunding (RBC), in this paper, we explore the optimal dynamic pricing and funding policy in RBC, taking into account social learning and strategic waiting behavior. To this end, a two‐period model is developed in which the creator first seeks funding via a crowdfunding platform and, if successful, enters the retail market. Based on this framework, we find that under the preannounced pricing, the optimal price path is determined by the success rate under the high funding goal (HFG) policy, whereas it is driven by the social learning outcome under the low funding goal (LFG) policy. In contrast, under the responsive pricing, the optimal price path is increasing with the LFG policy but decreasing with the HFG policy only if the success rate is low. Accordingly, the LFG policy is preferred under the preannounced pricing strategy only when the success rate is low, whereas theHFG policy is preferred under the responsive pricing strategy only when both the success rate and altruistic motivation are high. We further find that the preannounced pricing strategy is optimal for the creator with the LFG, while the responsive pricing strategy is recommended for that with the HFG, but only if the success rate and social learning outcome are high. These findings offer practical guidance for the creator in RBC.