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  4. Attuning Signals in Scientific Crowdfunding: What Works and What Doesn’t?
 

Attuning Signals in Scientific Crowdfunding: What Works and What Doesn’t?

URI
https://arbor.bfh.ch/handle/arbor/45995
Version
Published
Identifiers
10.1109/TEM.2025.3589722
Date Issued
2025-08-01
Author(s)
Hopp, Christian  
Ooms, Ward
Kaminski, Jermain
Prats-Lopez, Montserrat
Type
Article
Language
English
Subjects

Crowdfunding

dynamic signals

persuasion

scientific research

static signals

Abstract
Research on crowdfunding has extensively explored the characteristics that predict campaign success, predominantly focusing on crowdfunding for business and for charity. Other crowdfunding models have received less attention, and this oversight is problematic, because the effects of predictors are known to vary across contexts, and setting up a campaign with the right signals can be costly. This study addresses this gap by examining the impact of static and dynamic signals on crowdfunding success in 1815 scientific crowdfunding campaigns using a Bayesian estimation framework and extensive robustness checks. Visual cues and gender signals emerge as powerful predictors of success, while visible support from experts can also enhance a campaign’s success probability. While the study reaffirms some established findings, it also uncovers unique aspects of scientific crowdfunding, such as the varying importance of previous campaign experience and the influence of specific signals across different scientific disciplines.
DOI
https://doi.org/10.24451/arbor.12444
Publisher DOI
10.1109/TEM.2025.3589722
Journal or Serie
IEEE Transactions on Engineering Management
ISSN
0018-9391
Publisher URL
https://ieeexplore.ieee.org/document/11085042
Organization
Wirtschaft  
Institut Applied Data Science & Finance  
Volume
72
Issue
1
Publisher
IEEE
Submitter
Hopp, Christian
Citation apa
Hopp, C., Ooms, W., Kaminski, J., & Prats-Lopez, M. (2025). Attuning Signals in Scientific Crowdfunding: What Works and What Doesn’t? In IEEE Transactions on Engineering Management (Vol. 72, Issue 1, pp. 3123–3137). IEEE. https://doi.org/10.24451/arbor.12444
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