Tapping into User Effort? Improving Referral Outcomes in Crowdfunding Markets

Referral programs have received increasing attention as a way to enhance project fundraising in crowdfunding. To improve operational efficiency and performance, many crowdfunding platforms allow users to exert more effort (referral effort) when making referrals. In this paper, we study the impact of users’ referral efforts on referral outcomes, using a unique data set from a reward-based crowdfunding platform. We analyze three types of referral effort beyond merely sharing the referral link, namely, writing the referral message, matching the referral recipients’ pledges, and supporting their own referrals. Our results suggest that users’ referral efforts can help improve the outcome of referrals, as these efforts may be perceived as positive signals of quality, thus reducing referral recipients’ uncertainty about the crowdfunding project. We further explore the heterogeneity in this observed impact, and find that matching recipients’ pledges have a greater impact than the other two types of referral effort. Furthermore, the impact of referral effort is attenuated under conditions of low uncertainty, wherein project quality can be inferred from observable features, such as projects launched by large creators, selected by editors, or referred by experienced backers. Our analyses also indicate that referring users’ efforts can collectively improve the fundraising performance of projects that receive referrals. We discuss the implications of our findings for platform managers, policy makers, and users in crowdfunding marketplaces.

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Publication Details

Journal
Production and Operations Management
Published
2024-11-28
DOI
https://doi.org/10.1177/10591478241305340
Citations
2
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
1.06
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article

Tapping into User Effort? Improving Referral Outcomes in Crowdfunding Markets

Yong Tan, Jiang Yang, Xi Chen
2 citations
Production and Operations Management
FinTech, Crowdfunding, Digital Finance
1.06
article

Tapping into User Effort? Improving Referral Outcomes in Crowdfunding Markets

Yong Tan, Jiang Yang, Xi Chen
article en
2 citations

Abstract

Referral programs have received increasing attention as a way to enhance project fundraising in crowdfunding. To improve operational efficiency and performance, many crowdfunding platforms allow users to exert more effort (referral effort) when making referrals. In this paper, we study the impact of users’ referral efforts on referral outcomes, using a unique data set from a reward-based crowdfunding platform. We analyze three types of referral effort beyond merely sharing the referral link, namely, writing the referral message, matching the referral recipients’ pledges, and supporting their own referrals. Our results suggest that users’ referral efforts can help improve the outcome of referrals, as these efforts may be perceived as positive signals of quality, thus reducing referral recipients’ uncertainty about the crowdfunding project. We further explore the heterogeneity in this observed impact, and find that matching recipients’ pledges have a greater impact than the other two types of referral effort. Furthermore, the impact of referral effort is attenuated under conditions of low uncertainty, wherein project quality can be inferred from observable features, such as projects launched by large creators, selected by editors, or referred by experienced backers. Our analyses also indicate that referring users’ efforts can collectively improve the fundraising performance of projects that receive referrals. We discuss the implications of our findings for platform managers, policy makers, and users in crowdfunding marketplaces.

Production and Operations ManagementVol. 34(7)
University of Washington (US), Nanjing University (CN)
Openalex Percentile: Top 16%
FinTech, Crowdfunding, Digital Finance
1.06
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Tapping into User Effort? Improving Referral Outcomes in Crowdfunding Markets — Yong Tan, Jiang Yang, et al. · Production and Operations Management (2024) | TGRS Research Map | TGRS