Predicting the success of entrepreneurial campaigns in crowdfunding: a spatio-temporal approach

Clinton Woods, Han Yu () and Hong Huang
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Clinton Woods: University of Northern Colorado
Han Yu: University of Northern Colorado
Hong Huang: University of South Florida

Journal of Innovation and Entrepreneurship, 2020, vol. 9, issue 1, 1-23

Abstract: Abstract As an alternative to traditional venture capital investment, crowdfunding has emerged as a novel method and potentially disruptive innovation for financing a variety of new entrepreneurial ventures without standard financial intermediaries. It is still unknown to scholars and people who use crowdfunding services whether the crowdfunding efforts reinforce or contradict existing theories about the dynamics of successful entrepreneurial financing as well as the general distribution and use of crowdfunding mechanisms. This paper presents new results obtained from investigating the Kickstarter campaign data of over ninety-nine thousand projects totaling about 1 billion USD in pledges from 2009 until the most recent 2017 through dynamical spatio-temporal modeling. The funding level, the percentage of a project’s goal actually raised from online communities, is used as the outcome of interest in the modeling to associate with dollar pledged and backer count that reflect the signals of underlying project quality. Evidence from the results was found to support the dynamic impact of the geographic location of a Kickstarter on its success and the associations between the observed project traits and the success of the entrepreneurial effort in the presence of the unmeasured spatio-temporal confounding. These results offer further insight into the empirical dynamics of the emerging phenomenon of online entrepreneurial financing about the role the spatio-temporal component plays in both the type of projects proposed and the association of sociocultural traits of successful fundraising with the underlying quality.

Keywords: Entrepreneurial financing; Crowdfunding; Geographic component; Spatio-temporal modeling; INLA (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1186/s13731-020-00122-8

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