Project description and crowdfunding success: an exploratory study

Mi Zhou,Baozhou Lu,Weiguo Fan,G Alan Wang

doi:10.1007/s10796-016-9723-1

Mi Zhou, Baozhou Lu + Show 2 more

Open Access

https://doi.org/10.1007/s10796-016-9723-1

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Abstract

Existing research on antecedent of funding success mainly focuses on basic project properties such as funding goal, duration, and project category. In this study, we view the process by which project owners raise funds from backers as a persuasion process through project descriptions. Guided by the unimodel theory of persuasion, this study identifies three exemplary antecedents (length, readability, and tone) from the content of project descriptions and two antecedents (past experience and past expertise) from the trustworthy cue of project descriptions. We then investigate their impacts on funding success. Using data collected from Kickstarter, a popular crowdfunding platform, we find that these antecedents are significantly associated with funding success. Empirical results show that the proposed model that incorporated these antecedents can achieve an accuracy of 73 % (70 % in F-measure). The result represents an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties (or 44 % improvement of mainstream’s performance over informed guessing). The proposed model also has superior true positive and true negative rates. We also investigate the timeliness of project data and find that old project data is gradually becoming less relevant and losing predictive power to newly created projects. Overall, this study provides evidence that antecedents identified from project descriptions have incremental predictive power and can help project owners evaluate and improve the likelihood of funding success.

Highlights

In recent years, crowdfunding has emerged as a revolutionary financing model that allows small entrepreneurs to raise funding in the early stages of their projects, those that may otherwise struggle to obtain capital (Kuppuswamy and Bayus 2013; Belleflamme et al 2014)
The average accuracy rate (F-measure) of the mainstream model is around 69 % (66 %), and baseline model around 59 % (57 %). This indicates that the proposed model has an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties
We investigate the timeliness of project data and provide evidence that old project data is gradually becoming less relevant and losing predictive power to newly created projects

Summary

Introduction

In recent years, crowdfunding has emerged as a revolutionary financing model that allows small entrepreneurs to raise funding in the early stages of their projects, those that may otherwise struggle to obtain capital (Kuppuswamy and Bayus 2013; Belleflamme et al 2014). There are approximately 1250 active crowdfunding platforms across the world, which together channeled $16.2 billion in 2014, representing a 167 % increase from $6.1 billion in 2013 (Massolution 2015) Having their project successfully funded is crucial to project creators as it provides initial funds for project development and access to valuable future resources, and eventually turn their projects into successful entrepreneurial organizations (Mollick 2014). Previous research shows that only 45 % of the projects on these platforms are successfully funded (Greenberg et al 2013; Mollick 2014). The last one is equity based, where the supporters are treated as investors and are given certain shares of future profit of the project (Mollick 2014)

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