  Reward-based crowdfunding platforms are becoming increasingly popular to
finance projects proposing innovative products, e.g., Kickstarter. One
important challenge of this form of financing is the uncertainty in the quality
of projects. To mitigate the negative effects of this uncertainty for backers,
platforms share information regarding the decisions of earlier backers visiting
the project campaign pages. This allows backers not only to rely on their
expertise to identify project qualities but also to learn from the decisions of
their fellow backers who might be more informed. Current studies on
observational learning (OL) in crowdfunding mainly focus on predicting the
success chances of projects, and there is a lack of understanding of how OL
affects crowdfunding dynamics for backers, project creators and platforms. This
paper aims to fill this gap by using a theoretical OL model involving two
projects competing for funding from backers who may have differentiated
expertness in identifying project quality. By introducing various performance
measures for backers, creators and platforms and comparing these measures under
OL to the case without learning, we provide a thorough analysis of how OL
impacts crowdfunding outcomes. We find that information sharing and OL always
benefit backers, especially when the early backers are experts. Regarding the
impact of OL on creators and platforms, our analysis reveals two understudied
but important aspects: the tightness of the competition for projects according
to the availability of funding, and the quality difference among the proposed
projects. Additionally, we investigate how OL affects the quality decisions of
creators and show that OL increases the incentive for high-quality products,
especially in situations where funding is scarce.
