Designing Knowledge-Driven Innovation Contests
研究了知识驱动的系列创新竞赛中，参与者如何通过前期竞赛积累知识并影响后期表现，分析了不同学习模式下最优奖励分配策略，对竞赛设计者具有指导意义。
Innovation contests incentivize the participants’ to exert effort toward combining (recombining) their existing knowledge to create solutions. In the current work, we consider the case of serial contests, where effort to create solutions for earlier contests can also expand the participant’s knowledge, which can then be valuable in future contests. We develop a novel framework that explicitly includes the generation and utilization of knowledge by participants in knowledge-driven serial innovation contests, and we analyze the implications of this framework for optimal incentive design. Analysis of our model reveals that the efforts expended by participants in a contest can depend on future rewards, especially when learning emerges as a “side effect” of execution effort (i.e., learning while doing). In fact, participants will exert effort in an earlier contest even when its associated reward is zero. In contrast, when explicit knowledge generation effort is feasible (i.e., learning before doing), the contest designer should increase the reward for the earlier contest to prevent participants from postponing their learning. Our model demonstrates that whether one should assign higher reward to the earlier or later contest depends on the mode of learning, the participant pool’s ex ante knowledge, and the transferability of learning from one contest to the next. This paper was accepted by Ashish Arora, entrepreneurship and innovation. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03369 .