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

被引:0
|
作者
Woods C. [1 ]
Yu H. [1 ]
Huang H. [2 ]
机构
[1] Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, 80639, CO
[2] School of Information, University of South Florida, Tampa, 33620, FL
关键词
Crowdfunding; Entrepreneurial financing; Geographic component; INLA; Spatio-temporal modeling;
D O I
10.1186/s13731-020-00122-8
中图分类号
学科分类号
摘要
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. © 2020, The Author(s).
引用
收藏
相关论文
共 50 条
  • [31] INTERPRETATION OF IMAGE FLOW - A SPATIO-TEMPORAL APPROACH
    SUBBARAO, M
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1989, 11 (03) : 266 - 278
  • [32] A new approach for spatio-temporal data mining
    Cassat, Sabine
    Irani, Pourang
    Serrano, Marcos
    Dubois, Emmanuel
    ACTES DE LA 30 CONFERENCE FRANCOPHONE SUR L'INTERACTION HOMME-MACHINE - (IHM 2018), 2018, : 163 - 169
  • [33] A visual approach for spatio-temporal data mining
    Kechadi, M-Tahar
    Bertolotto, Michela
    IRI 2006: PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, 2006, : 504 - +
  • [34] A multiscale approach for spatio-temporal outlier detection
    Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
    Trans. GIS, 2006, 2 (253-263):
  • [35] An approach to characterise spatio-temporal drought dynamics
    Diaz, Vitali
    Perez, Gerald A. Corzo
    Van Lanen, Henny A. J.
    Solomatine, Dimitri
    Varouchakis, Emmanouil A.
    ADVANCES IN WATER RESOURCES, 2020, 137
  • [36] An image warping approach to spatio-temporal modelling
    Aberg, S
    Lindgren, F
    Malmberg, A
    Holst, J
    Holst, U
    ENVIRONMETRICS, 2005, 16 (08) : 833 - 848
  • [37] Predicting the Success Rate of Reward-Based Crowdfunding Campaigns: Evidence from Machine Learning
    Chan C.-L.
    Lee Y.-S.
    International Journal of Information and Management Sciences, 2023, 34 (03): : 179 - 191
  • [38] SPATIO-TEMPORAL PATTERNS OF FORAGING SUCCESS FOR FISHES IN AN ILLINOIS STREAM
    ANGERMEIER, PL
    AMERICAN MIDLAND NATURALIST, 1985, 114 (02): : 342 - 359
  • [39] Spatio-Temporal Prediction of Suspect Location by Spatio-Temporal Semantics
    Duan L.
    Hu T.
    Zhu X.
    Ye X.
    Wang S.
    Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2019, 44 (05): : 765 - 770
  • [40] Mining Rainfall Spatio-Temporal Patterns in Twitter: A Temporal Approach
    de Andrade, Sidgley Camargo
    Restrepo-Estrada, Camilo
    Delbem, Alexandre C. B.
    Mendiondo, Eduardo Mario
    de Albuquerque, Joao Porto
    SOCIETAL GEO-INNOVATION, 2017, : 19 - 37