Beyond Words: An Experimental Study of Signaling in Crowdfunding

TL;DR

This paper addresses the question of how crowd signals—specifically variation in contribution amounts and inter-contribution times—influence funding decisions in crowdfunding. Using two large randomized experiments ($N=500$ and $N=750$) with controlled single- and dual-description layouts, the authors establish a causal link showing that high variation in crowd signals increases the likelihood of being funded, and that this effect is strongest among participants with higher susceptibility to social influence. The findings persist across project categories and fundraising goals, and qualitative analyses reveal that participants often misattribute their choices to project descriptions rather than to crowd signals. The work has practical implications for platform designers and campaigners, highlighting the need to manage the visibility and composition of crowd signals while also contributing to theories of social signaling and social translucence in online collective decision-making.

Abstract

Increasingly, crowdfunding is transforming financing for many people worldwide. Yet we know relatively little about how, why, and when funding outcomes are impacted by signaling between funders. We conduct two studies of N=500 and N=750 participants involved in crowdfunding to investigate the effect of certain characteristics of ``crowd signals'' on the decision to fund. We find that, under a variety of conditions, contributions of heterogeneous amounts arriving at varying time intervals are significantly more likely to be selected than homogeneous contribution amounts and times. The impact of signaling is strongest among participants who are susceptible to social influence. The effect is remarkably general across different project types, fundraising goals, participant interest in the projects, and participants' altruistic attitudes. Critically, the role of crowd signals in decision-making is typically unrecognized by participants. Our results underscore the fundamental nature of social signaling in crowdfunding, informing strategies for platforms, funders, and project creators.

Figures (10)

• Figure 1: An illustration of crowd signals on an example project that receives six contributions. For example, the first contribution of $50 arrives seven hours after the start of the campaign. In this example, the coefficient of variation in inter-contribution times is 0.44. The coefficient of variation in contribution amounts is 1.10. Our studies vary the contribution amounts and contribution timings, examining the effect of their variation on participants' decisions to contribute funds to mock crowdfunding projects.
• Figure 2: Outline of Study I: Single-Description Layout. The study begins with a screening and demographics survey to ensure that participants are familiar with crowdfunding and are demographically representative of crowdfunders. Participants are then randomly assigned to two of four project categories and asked to select a contribution list from each. Participants conclude by completing a questionnaire on susceptibility to social influence, altruistic tendencies, and interest in the project category.
• Figure 3: Participation in crowdfunding in Study I. Frequency of use of crowdfunding platforms (left). Type of use (right). Forty-six crowd workers (9.2%) who indicate that they had never participated in crowdfunding and who did not use such platforms as a contributor or project creator were excluded from participating in the study.
• Figure 4: Example task presented to participants in Study I. Each participant sees a project description with two different contribution lists with high variation, or heterogeneity, in contribution amounts and time and with low variation, or homogeneity, in contribution amounts and time. Project category in this example: Supporting the manufacture of Personal Protective Equipment (PPE) with 3D-printing.
• Figure 5: Percentage of participants (x-axis) that made their selections based on the reasons indicated on the y-axis. The research team coded the reasons using thematic analysis. Most participants attribute their decision to variation in crowd signals, and systematically prefer the condition with high variation in contribution amounts and timing.