Overhead aversion and facial expressions in crowdfunding
Jenny Jeongeun Yoo,
Sangyoung Song and
Journal of Retailing and Consumer Services, 2022, vol. 69, issue C
Overhead aversionâ€”donorsâ€™ negative attitudes toward their donated money being used for overhead costsâ€”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.
Keywords: Crowdfunding; Overhead aversion; Big data; Deep learning; Facial expression detection (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:69:y:2022:i:c:s0969698922001941
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