New model of utility analysis and performance prediction in crowdfunding: A perspective of behavior-related decision

Abstract

A new model of utility analysis and performance prediction in crowdfunding is proposed in this paper with considering backer’s behavior-related decision. To establish a connection between the backer’s behavior-related decision and crowdfunding performance, the proposed model includes a modeling analysis and an empirical analysis. The modeling analysis focuses on analyzing backer’s backing decision with establishing the utility-driven model, while the empirical analysis verifies the association between the utility-related features and crowdfunding performance, as well as the prediction improvement of the proposed model. Specifically, prospect theory (PT) is applied first in the modeling analysis part to analyze backer’s prospect utilities, and then evidential theory (ET) is applied to aggregate the multi-source dynamic feature’s utility. To validate the proposed model, and based on the results of modeling analysis, we conduct the significant test with regression model and the prediction test with several machine-learning prediction model to verify whether the proposed utility features can improve the prediction results in terms of crowdfunding performance. The empirical results show that the correlation between behavior-related factors and crowdfunding performance is significant. And the area under the curve (AUC) index of the proposed model is, on average, 7.39% higher than the baseline model on several machine-learning prediction models. In addition to providing new insights for predicting crowdfunding results, this research provides a powerful tool to assist crowdfunding platform and fundraisers in operational management with analyzing backers’ backing behavior.