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

Author

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  • Clinton Woods

    (University of Northern Colorado)

  • Han Yu

    (University of Northern Colorado)

  • Hong Huang

    (University of South Florida)

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.

Suggested Citation

  • Clinton Woods & Han Yu & Hong Huang, 2020. "Predicting the success of entrepreneurial campaigns in crowdfunding: a spatio-temporal approach," Journal of Innovation and Entrepreneurship, Springer, vol. 9(1), pages 1-23, December.
    • Handle: RePEc:spr:joiaen:v:9:y:2020:i:1:d:10.1186_s13731-020-00122-8
    DOI: 10.1186/s13731-020-00122-8
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Frank, Mark W., 1998. "Schumpeter on Entrepreneurs and Innovation: A Reappraisal," Journal of the History of Economic Thought, Cambridge University Press, vol. 20(4), pages 505-516, December.
    3. Mollick, Ethan, 2014. "The dynamics of crowdfunding: An exploratory study," Journal of Business Venturing, Elsevier, vol. 29(1), pages 1-16.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. Quan-Hoang Vuong, 2016. "Impacts of geographical locations and sociocultural traits on the Vietnamese entrepreneurship," Working Papers CEB 16-012, ULB -- Universite Libre de Bruxelles.
    6. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
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    Cited by:

    1. Manuel Chaves-Maza & Eugenio M. Fedriani, 2022. "Defining entrepreneurial success to improve guidance services: a study with a comprehensive database from Andalusia," Journal of Innovation and Entrepreneurship, Springer, vol. 11(1), pages 1-26, December.
    2. Dohyeon Kim & Su Yong Lee, 2022. "When venture capitalists are attracted by the experienced," Journal of Innovation and Entrepreneurship, Springer, vol. 11(1), pages 1-18, December.
    3. Joseph Ochieng Onginjo & Zhou Dong Mei, 2023. "A study on the social and economic sustainability of rewards-based crowdfunding in Africa," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9619-9646, September.

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