A Multi-Platform Study of Crowd Signals Associated with Successful Online Fundraising

TL;DR

This study demonstrates that crowd behavioral signals are strong, platform-independent predictors of online fundraising success, surpassing traditional project and creator characteristics, and have quasi-causal effects across multiple crowdfunding platforms.

Contribution

It introduces a multi-method analysis revealing universal crowd dynamics that predict crowdfunding success across different online platforms, emphasizing crowd signals over project features.

Findings

Crowd behavioral signals correlate with success across platforms.

Crowd signals outperform project characteristics in prediction.

Crowd signals have quasi-causal effects on fundraising outcomes.

Abstract

The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover general crowd dynamics that ultimately decide which projects will succeed. In all analyses and across the three different platforms, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.