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Factor analysis and prediction of crowdfunding project funding ratio: an empirical study based on Zhongchou Wang in China
Author(s) -
Yifei Guo,
Meihong Zhu,
Aihua Li
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.12.011
Subject(s) - univariate , china , computer science , construct (python library) , set (abstract data type) , lasso (programming language) , empirical research , data science , political science , world wide web , statistics , mathematics , multivariate statistics , machine learning , law , programming language
This study aims at revealing the factors that may influence the funding ratio of crowdfunding (CF) projects based on Zhongchou Wang platform. We construct a variable set which includes the sponsors factors and netizens factors. Based on the TF-IDF method, we determine the exact type of projects in each category. Based on univariate test and lasso regression model, our empirical study suggests that both netizens and sponsors can influence the funding ratio of the five categories of CF projects respectively. Furthermore, our analysis highlights that a positive netizen’s reaction toward the CF project shown on the platform increases the funding ratio of it. Besides, there are several types and regions of each category that receives higher funding ratio than others. These findings help the sponsors to make proper decisions, and to increase the funding ratio of each CF project.

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