Quantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model

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dc.contributor.authorLiu, Xiexin
dc.contributor.authorRahmani Moghaddam, Maryam
dc.contributor.authorFan, Weiguo (Patrick)
dc.date.accessioned2022-12-27T19:08:53Z
dc.date.available2022-12-27T19:08:53Z
dc.date.issued2023-01-03
dc.description.abstractThe 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.433
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other81e1ea67-cd65-4d38-b891-c20a1c8e8d49
dc.identifier.urihttps://hdl.handle.net/10125/103064
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCrowd-based Platforms
dc.subjectinterpretable machine learning
dc.subjectpredictive analysis
dc.subjectreward-based crowdfunding
dc.titleQuantifying Learning and Competition among Crowdfunding Projects: Metrics and a Predictive Model
dc.type.dcmitext
prism.startingpage3527

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