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Overhead aversion and facial expressions in crowdfunding

Authors
Yoo, Jenny JeongeunSong, SangyoungJhang, Jihoon
Issue Date
Nov-2022
Publisher
Elsevier Ltd
Keywords
Crowdfunding; Overhead aversion; Big data; Deep learning; Facial expression detection
Citation
Journal of Retailing and Consumer Services, v.69, pp.1 - 12
Indexed
SSCI
SCOPUS
Journal Title
Journal of Retailing and Consumer Services
Volume
69
Start Page
1
End Page
12
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/186166
DOI
10.1016/j.jretconser.2022.103101
ISSN
0969-6989
Abstract
Overhead aversion—donors’ negative attitudes toward their donated money being used for overhead costs—has been explored in many contexts. In this study, we first confirm findings from prior research that high overhead costs imposed on project initiators have a negative effect on supporters' funding decisions and that higher overhead costs make project funding less successful in crowdfunding. Thereafter, we investigate how the negative effect of overhead costs on funding performance is moderated by the project initiator's facial expressions in the image on the project webpage. A smiling face mitigates overhead aversion, whereas a sad face amplifies it. We collected a large sample of data from Kiva, a loan-based crowdfunding platform, and used a deep learning algorithm to categorize the facial expressions of project initiators. Subsequently, a propensity score weighting approach was applied to minimize confounding effects. This empirical study indicates that the project initiator's smiling face, rather than a sad face, can alleviate supporters' overhead aversion.
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