Entrepreneurial Learning and Disincentives in Crowdfunding Markets

Yan Xu, Jian Ni
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引用次数: 7

Abstract

Reward-based crowdfunding has enabled entrepreneurs to interact with consumers even before product launches. However, this market persistently suffers from a high failure rate; that is, entrepreneurs fail to launch and deliver their products as promised. We investigate the extent to which this high failure rate is because of information distortion—entrepreneurs have uncertainty about consumers’ evaluation of the new products. We model the product launch decisions of different types of entrepreneurs who raise funds through preselling on reward-based crowdfunding platforms and subsequently decide whether to continue with the product launch. To do so, we collect structured and unstructured data from Kickstarter’s digital video game category and classify attributes using supervised learning methods. We develop and estimate an integrated model of crowdfunding demand and entrepreneurs’ product launch decisions. We find that the information entrepreneurs gather from crowdfunding sales has sizable impacts on the product launch decisions of entrepreneurs with low managerial capital or new to the crowdfunding platform. Our counterfactual simulations suggest that platform policy regulating overfunded projects can reduce the product launch failure rate by about 13%. This paper was accepted by Matthew Shum, marketing.
众筹市场中的创业学习和抑制因素
基于奖励的众筹使企业家甚至在产品发布之前就能与消费者互动。然而,这个市场的失败率一直很高;也就是说,企业家没有像承诺的那样推出和交付产品。我们调查了这种高失败率在多大程度上是由于信息扭曲——企业家对消费者对新产品的评价不确定。我们对不同类型的企业家的产品发布决策进行建模,这些企业家通过在基于奖励的众筹平台上预售融资,然后决定是否继续产品发布。为此,我们从Kickstarter的数字电子游戏类别中收集结构化和非结构化数据,并使用监督学习方法对属性进行分类。我们开发并估计了众筹需求和企业家产品发布决策的集成模型。我们发现,企业家从众筹销售中收集到的信息对管理资金较低或新进入众筹平台的企业家的产品发布决策有相当大的影响。我们的反事实模拟表明,规范资金过剩项目的平台政策可以将产品发布失败率降低约13%。这篇论文被市场营销学的Matthew Shum接受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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