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Exploring Risks and Challenges in Crowdfunding Performance Using Text Analytics and Deep Learning
Abstract
The inherent delivery risks of crowdfunding campaigns force crowdfunding platforms to take risk disclosure measures to alleviate information asymmetry between creators and crowdfunders. However, creators might perceive such risk disclosures as a threat to crowdfunding success. Based on the language expectancy theory, this study aims to examine how the contextual characteristics of risk disclosure affect crowdfunding performance. By separating the project description and R&C sections of 21,287 projects on Kickstarter from 2009 to March 2022, we find that the two-sided persuasion topics extracted by LDA model from the R&C section have positive impacts on the funding success. This result indicates that providing two-sided persuasion content in the R&C section can increase the credibility of crowdfunding project narratives and thus the crowdfunding performance. Furthermore, by incorporating the R&C text into the prediction models, the prediction accuracy of funding support is improved by 0.9% from 83.9%, and the results have been proven to be robust through cross-validation experiments.
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