Racial Inequity in Donation-Based Crowdfunding Platforms: The Role of Facial Emotional Expressiveness

Donation-based crowdfunding platforms often claim to pursue equitable outcomes for all beneficiaries, yet many face criticism for failing to do so across different demographic profiles. In response, platform managers are eager to understand how these inequities emerge and explore solutions to address them. In this research, the authors show that the degree of facial emotional expressiveness of beneficiaries in uploaded images can differentially impact donation amounts for White versus Black beneficiaries. Drawing on social vision theory, the authors propose that facial emotional expressiveness in images, combined with the beneficiary’s race, activates racial stereotypes of emotion expression that result in differential donation amounts to Black and White individuals. Analyzing a sample of 4,153 campaigns from GoFundMe between June 2021 and September 2022, along with a follow-up experiment, the authors find that higher facial emotional expressiveness is associated with significantly lower donation amounts for Black compared with White beneficiaries. Further exploring our moderating constructs reveals that the use of call-to-action cues, affective messaging, and race-gender homophily cues can attenuate the activation of stereotypes and therefore reduce differences in donation amounts between racial groups. Based on these findings, the authors offer targeted recommendations for platform managers to reduce racial inequities in crowdfunding outcomes.

Authors

Institutions

Publication Details

Journal
Journal of Marketing
Published
2024-11-05
DOI
https://doi.org/10.1177/00222429241300320
Citations
7
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
3.70
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Racial Inequity in Donation-Based Crowdfunding Platforms: The Role of Facial Emotional Expressiveness

J. Jeffrey Inman, Elham Yazdani, Anindita Chakravarty
7 citations
Journal of Marketing
FinTech, Crowdfunding, Digital Finance
3.70
article

Racial Inequity in Donation-Based Crowdfunding Platforms: The Role of Facial Emotional Expressiveness

J. Jeffrey Inman, Elham Yazdani, Anindita Chakravarty
article en
7 citations

Abstract

Donation-based crowdfunding platforms often claim to pursue equitable outcomes for all beneficiaries, yet many face criticism for failing to do so across different demographic profiles. In response, platform managers are eager to understand how these inequities emerge and explore solutions to address them. In this research, the authors show that the degree of facial emotional expressiveness of beneficiaries in uploaded images can differentially impact donation amounts for White versus Black beneficiaries. Drawing on social vision theory, the authors propose that facial emotional expressiveness in images, combined with the beneficiary’s race, activates racial stereotypes of emotion expression that result in differential donation amounts to Black and White individuals. Analyzing a sample of 4,153 campaigns from GoFundMe between June 2021 and September 2022, along with a follow-up experiment, the authors find that higher facial emotional expressiveness is associated with significantly lower donation amounts for Black compared with White beneficiaries. Further exploring our moderating constructs reveals that the use of call-to-action cues, affective messaging, and race-gender homophily cues can attenuate the activation of stereotypes and therefore reduce differences in donation amounts between racial groups. Based on these findings, the authors offer targeted recommendations for platform managers to reduce racial inequities in crowdfunding outcomes.

Journal of MarketingVol. 89(4)
University of Georgia (US), Science Council (GB), Wesley College (US)
Gender equality
Openalex Percentile: Top 6%
FinTech, Crowdfunding, Digital Finance
3.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.