TY - GEN
T1 - Emerging dynamics in crowdfunding campaigns
AU - Rao, Huaming
AU - Xu, Anbang
AU - Yang, Xiao
AU - Fu, Wai Tat
PY - 2014
Y1 - 2014
N2 - Crowdfunding platforms are becoming more and more popular for fund-raising of entrepreneurial ventures, but the success rate of crowdfunding campaigns is found to be less than 50%. Recent research has shown that, in addition to the quality and representations of project ideas, dynamics of investment during a crowdfunding campaign also play an important role in determining its success. To further understand the role of investment dynamics, we did an exploratory analysis of the time series of money pledges to campaigns in Kickstarter to investigate the extent to which simple inflows and first-order derivatives can predict the eventual success of campaigns. Using decision tree models, we found that there were discrete stages in money pledges that predicted the success of crowdfunding campaigns. Specifically, we found that, for the majority of projects that had the default campaign duration of one month in Kickstarter, money pledges inflow occurring in the initial 10% and 40-60%, and the first order derivative of inflow at 95-100% of the duration of the campaigns had the strongest impact on the success of campaigns. In addition, merely utilizing the initial 15% money inflows, which could be regarded as "seed money", to build a predictor can correctly predict 84% of the success of campaigns. Implication of current results to crowdfunding campaigns is also discussed.
AB - Crowdfunding platforms are becoming more and more popular for fund-raising of entrepreneurial ventures, but the success rate of crowdfunding campaigns is found to be less than 50%. Recent research has shown that, in addition to the quality and representations of project ideas, dynamics of investment during a crowdfunding campaign also play an important role in determining its success. To further understand the role of investment dynamics, we did an exploratory analysis of the time series of money pledges to campaigns in Kickstarter to investigate the extent to which simple inflows and first-order derivatives can predict the eventual success of campaigns. Using decision tree models, we found that there were discrete stages in money pledges that predicted the success of crowdfunding campaigns. Specifically, we found that, for the majority of projects that had the default campaign duration of one month in Kickstarter, money pledges inflow occurring in the initial 10% and 40-60%, and the first order derivative of inflow at 95-100% of the duration of the campaigns had the strongest impact on the success of campaigns. In addition, merely utilizing the initial 15% money inflows, which could be regarded as "seed money", to build a predictor can correctly predict 84% of the success of campaigns. Implication of current results to crowdfunding campaigns is also discussed.
KW - Crowdfunding
KW - decision tree
KW - dynamic
KW - predictor
UR - http://www.scopus.com/inward/record.url?scp=84958520529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958520529&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-05579-4_41
DO - 10.1007/978-3-319-05579-4_41
M3 - Conference contribution
AN - SCOPUS:84958520529
SN - 9783319055787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 333
EP - 340
BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
PB - Springer
T2 - 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
Y2 - 1 April 2014 through 4 April 2014
ER -