Conference proceeding
Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model
Proceedings of the 56th Annual Hawaii International Conference on System Sciences, pp.3527-3536
01/01/2023
DOI: 10.24251/HICSS.2023.433
Abstract
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.
Details
- Title: Subtitle
- Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model
- Creators
- Maryam Rahmani Moghaddam - Arizona State UniversityXiexin Liu - Dickinson CollegeWeiguo Fan - Univ Iowa, Dept Business Analyt, Iowa City, IA USA
- Contributors
- Tung X Bui (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 56th Annual Hawaii International Conference on System Sciences, pp.3527-3536
- DOI
- 10.24251/HICSS.2023.433
- Publisher
- HICSS
- Number of pages
- 10
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984799690902771