Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
The performance of a crowdfunding project is highly situational-dependent. In this study, we quantify the interactions between crowdfunding projects in order to understand how these interactions can help predict the performance of crowdfunding campaigns. Specifically, we utilize Natural Language Processing (NLP) techniques to create a semi-automated system to label the associated product for each crowdfunding campaign. We also propose three sets of metrics to measure how crowdfunding projects learn from and compete with each other. Finally, we propose a machine learning model and demonstrate that the proposed metrics and the proposed model outperform other combinations when predicting the performance of crowdfunding projects.
Recommended Citation
Liu, Xiexin; Rahmani Moghaddam, Maryam; and Fan, Weiguo (Patrick), "Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 6.
https://aisel.aisnet.org/hicss-56/in/crowd-based_platforms/6
Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model
Online
The performance of a crowdfunding project is highly situational-dependent. In this study, we quantify the interactions between crowdfunding projects in order to understand how these interactions can help predict the performance of crowdfunding campaigns. Specifically, we utilize Natural Language Processing (NLP) techniques to create a semi-automated system to label the associated product for each crowdfunding campaign. We also propose three sets of metrics to measure how crowdfunding projects learn from and compete with each other. Finally, we propose a machine learning model and demonstrate that the proposed metrics and the proposed model outperform other combinations when predicting the performance of crowdfunding projects.
https://aisel.aisnet.org/hicss-56/in/crowd-based_platforms/6