UCL School of Management is delighted to welcome Prasad Vana, LBS, to host a research seminar on ‘Modelling the role of uncertainty in cause-based crowdfunding: A multiple discrete continuous choice approach.’
Charitable giving from individuals hit a 60-year record high of $258.1 billion in 2014 in the US, with an increasing share of giving arising from online channels. One of the prominent ways of online fundraising is cause-based crowdfunding. Cause-based crowdfunding websites typically host projects, requests to raise a target amount of donations for a specific cause on their website for a limited amount of time. In our setting, which is termed an ‘All or Nothing’ (AoN) setting, if a project does not raise its target amount within its allocated time, all the funds raised are returned back to the donors as credit on the website (not cash) to be transferred to other projects. A major marketing challenge faced by crowdfunding projects is to raise their target amount within their allocated time. Likewise, donors who evaluate which crowdfunding projects to support are potentially concerned not only with their preference for the cause but also the uncertainty about whether or not a given project would eventually raise its target amount. In this research, we ask how the uncertainty of an individual donor over whether a particular crowdfunding project will eventually raise its funds contributes to their decision whether or not and how much to contribute to the project. We develop a structural model of donors’ contributions to AoN cause-based crowdfunding projects, accounting for their uncertainty about whether a project will reach its target and the effect of the donor’s uncertainty over project completion on the amount that they contribute. We estimate our model on data obtained from the US educational crowdfunding website Donors Choose. Our results demonstrate that donors’ evaluation of uncertainty of project completion is an important driver of whether and how much a donor contributes to a project. By comparing our results with a model without uncertainty, we demonstrate that not accounting for uncertainty leads to biased estimates of donors’ preferences for projects that have reached either a high or a low level of completion.