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The growing popularity of online fundraising (aka "crowdfunding") hasattracted significant research on the subject. In contrast to previous studiesthat attempt to predict the success of crowdfunded projects based on specificcharacteristics of the projects and their creators, we present a more generalapproach that focuses on crowd dynamics and is robust to the particularities ofdifferent crowdfunding platforms. We rely on a multi-method analysis toinvestigate the correlates, predictive importance, and quasi-causal effects offeatures that describe crowd dynamics in determining the success of crowdfundedprojects. By applying a multi-method analysis to a study of fundraising inthree different online markets, we uncover general crowd dynamics thatultimately decide which projects will succeed. In all analyses and across thethree different platforms, we consistently find that funders' behaviouralsignals (1) are significantly correlated with fundraising success; (2)approximate fundraising outcomes better than the characteristics of projectsand their creators such as credit grade, company valuation, and subject domain;and (3) have significant quasi-causal effects on fundraising outcomes whilecontrolling for potentially confounding project variables. By showing thatuniversal features deduced from crowd behaviour are predictive of fundraisingsuccess on different crowdfunding platforms, our work provides design-relevantinsights about novel types of collective decision-making online. This researchinspires thus potential ways to leverage cues from the crowd and catalysesresearch into crowd-aware system design.

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