中文摘要
创新竞赛激励参与者付出努力,将其现有知识加以组合(重新组合)以创造解决方案。本文考察系列竞赛的情形,即参与者为早期竞赛创造解决方案的努力也能扩展其知识,而这些知识在未来竞赛中可能具有价值。我们构建了一个新颖的框架,显式纳入知识驱动型系列创新竞赛中参与者的知识生成与利用,并分析该框架对最优激励设计的含义。模型分析表明,参与者在竞赛中付出的努力可能取决于未来的奖励,尤其当学习作为执行努力的"副产品"出现时(即"边干边学")。事实上,即使早期竞赛的奖励为零,参与者也会付出努力。相反,当显性的知识生成努力可行时(即"先学后干"),竞赛设计者应提高早期竞赛的奖励,以防止参与者推迟其学习。模型表明,应将更高的奖励分配给早期竞赛还是后期竞赛,取决于学习的模式、参与者群体的事前知识,以及学习在竞赛之间的可转移性。
Abstract
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.