An Assessment of the Operational Effect of Non-Financial Information Exchange on Equity Crowdfunding Project Success

Equity crowdfunding has supported numerous entrepreneurial projects and businesses lacking internal funding to commercialize their innovative products and grow them into businesses. Despite the above benefit, existing research has established challenges confronting the crowdfunding models. From the equity crowdfunding market perspective, information asymmetry leads to high product quality uncertainty and weakening potential investors’ willingness to invest. To solve the above challenges, many platforms have improved their operational designs and policies to enhance information exchange and accessibility by investors. Despite the improvements made by various platforms, limited knowledge exists on the effect of investor and sponsor participation, subsequent project updates, and the informative content of the updates on projects’ outcomes. In contributing to the above research gap, this paper uses data from Seedr’s equity crowdfunding platform to test hypotheses. The empirical research section used two sets of models: the first set involved cross-sectional data instrumental variables, and the second set utilized panel data to explore the longitudinal nature of the sample, providing additional support for the results of the cross-sectional data models. In the analysis of the topics of project updates, natural language processing techniques from machine learning were employed to extract content from unstructured data. The LDA (Latent Dirichlet Allocation) model was used as a topic modeling approach to identify the themes in the updated contents. The quantified results of the extracted topics were then incorporated into the empirical models. The results show that higher comments and update frequency will attract more subsequent potential investors to invest. If the project sponsor makes project modifications according to the wishes of the comments, the success rate of the projects will increase. Different update themes also have different impacts on project success. The findings are significant for entrepreneurs and investors involved in equity crowdfunding, providing valuable insights and empowering them to make informed decisions.

Authors

Publication Details

Journal
Journal of Data Analysis and Information Processing
Published
2025-01-01
DOI
https://doi.org/10.4236/jdaip.2025.132010
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
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article

An Assessment of the Operational Effect of Non-Financial Information Exchange on Equity Crowdfunding Project Success

Gladys Tuo, Yi Feng, Xin Liu
Journal of Data Analysis and Information Processing
FinTech, Crowdfunding, Digital Finance
article

An Assessment of the Operational Effect of Non-Financial Information Exchange on Equity Crowdfunding Project Success

Gladys Tuo, Yi Feng, Xin Liu
article en

Abstract

Equity crowdfunding has supported numerous entrepreneurial projects and businesses lacking internal funding to commercialize their innovative products and grow them into businesses. Despite the above benefit, existing research has established challenges confronting the crowdfunding models. From the equity crowdfunding market perspective, information asymmetry leads to high product quality uncertainty and weakening potential investors’ willingness to invest. To solve the above challenges, many platforms have improved their operational designs and policies to enhance information exchange and accessibility by investors. Despite the improvements made by various platforms, limited knowledge exists on the effect of investor and sponsor participation, subsequent project updates, and the informative content of the updates on projects’ outcomes. In contributing to the above research gap, this paper uses data from Seedr’s equity crowdfunding platform to test hypotheses. The empirical research section used two sets of models: the first set involved cross-sectional data instrumental variables, and the second set utilized panel data to explore the longitudinal nature of the sample, providing additional support for the results of the cross-sectional data models. In the analysis of the topics of project updates, natural language processing techniques from machine learning were employed to extract content from unstructured data. The LDA (Latent Dirichlet Allocation) model was used as a topic modeling approach to identify the themes in the updated contents. The quantified results of the extracted topics were then incorporated into the empirical models. The results show that higher comments and update frequency will attract more subsequent potential investors to invest. If the project sponsor makes project modifications according to the wishes of the comments, the success rate of the projects will increase. Different update themes also have different impacts on project success. The findings are significant for entrepreneurs and investors involved in equity crowdfunding, providing valuable insights and empowering them to make informed decisions.

Journal of Data Analysis and Information ProcessingVol. 13(02)
Partnerships for the goals
Openalex Percentile: Top 84%
FinTech, Crowdfunding, Digital Finance
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