Fundraiser engagement, third-party endorsement and crowdfunding performance: A configurational theory approach

Reward-based crowdfunding is a typical two-sided platform (fundraiser side and backer side) with high information asymmetry. While existing research indicates that signals from fundraisers and backers can impact crowdfunding performance, the interplay among these signals warrants further investigation. Drawing on signaling theory, this study adopts a configurational perspective and utilizes the fsQCA method and linear regression to investigate the combined effects of fundraiser engagement (update and fundraiser comment), third-party endorsement (backer comment and Facebook sharing), and project preparedness (video, image, and description) on crowdfunding performance. Drawing data from the reward-based crowdfunding platform Indiegogo, this research pointed out that these signals cannot generate better crowdfunding performance alone and examined substitution and complementary effects among different signals. Based on the linear regression and fsQCA results, configurations that lead to high crowdfunding performance are identified. We found that project preparedness must work with other signals to produce high crowdfunding performance. Besides, we summarized these configurations into two patterns that may lead to high crowdfunding performance: a fundraiser engagement-driven pattern and a third-party endorsement-driven pattern. This study contributes a configurational perspective and valuable insights into how signals can work together to mitigate information asymmetry in crowdfunding.

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Publication Details

Journal
PLoS ONE
Published
2024-08-15
DOI
https://doi.org/10.1371/journal.pone.0308717
Citations
1
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.53

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article

Fundraiser engagement, third-party endorsement and crowdfunding performance: A configurational theory approach

Q.C. Li, Nianxin Wang
1 citations
PLoS ONE
FinTech, Crowdfunding, Digital Finance
0.53
article

Fundraiser engagement, third-party endorsement and crowdfunding performance: A configurational theory approach

Q.C. Li, Nianxin Wang
article en
1 citations

Abstract

Reward-based crowdfunding is a typical two-sided platform (fundraiser side and backer side) with high information asymmetry. While existing research indicates that signals from fundraisers and backers can impact crowdfunding performance, the interplay among these signals warrants further investigation. Drawing on signaling theory, this study adopts a configurational perspective and utilizes the fsQCA method and linear regression to investigate the combined effects of fundraiser engagement (update and fundraiser comment), third-party endorsement (backer comment and Facebook sharing), and project preparedness (video, image, and description) on crowdfunding performance. Drawing data from the reward-based crowdfunding platform Indiegogo, this research pointed out that these signals cannot generate better crowdfunding performance alone and examined substitution and complementary effects among different signals. Based on the linear regression and fsQCA results, configurations that lead to high crowdfunding performance are identified. We found that project preparedness must work with other signals to produce high crowdfunding performance. Besides, we summarized these configurations into two patterns that may lead to high crowdfunding performance: a fundraiser engagement-driven pattern and a third-party endorsement-driven pattern. This study contributes a configurational perspective and valuable insights into how signals can work together to mitigate information asymmetry in crowdfunding.

PLoS ONEVol. 19(8)
Jiangsu University of Science and Technology (CN)
National Science Foundation, National Natural Science Foundation of China, Government of Jiangsu Province
Openalex Percentile: Top 27%
FinTech, Crowdfunding, Digital Finance
0.53
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Fundraiser engagement, third-party endorsement and crowdfunding performance: A configurational theory approach — Q.C. Li, Nianxin Wang · PLoS ONE (2024) | TGRS Research Map | TGRS