Paper Type
Complete
Paper Number
PACIS2026-1726
Description
Governments and platforms worldwide increasingly mandate disclosure of AI-generated content (AIGC), yet how such transparency shapes market outcomes remains underexplored. Situated in reward-based crowdfunding, this study examines the association between AI-use disclosure and funding performance, and whether this association differs across disclosed AIGC modalities (visual vs. textual) and application stages (final product vs. marketing material). Grounded in signaling theory and legitimacy theory, we analyze 41,073 Kickstarter projects using an LLM-assisted text classification approach and entropy balancing. The results indicate that AI-use disclosure is associated with a significant decline in funding performance. However, this association exhibits systematic heterogeneity: disclosing AI involvement in visual content creation is associated with a weaker penalty than disclosing AI involvement in textual content creation, and this modality gap widens for final products yet narrows for marketing materials. These findings illuminate the modality-level heterogeneity and application-stage boundary conditions of consumer responses to AI-use disclosure in crowdfunding.
Recommended Citation
Bai, Wenhao; Yao, Zhong; and Xu, Wuhuan, "The Transparency Penalty Unraveled: How AIGC Modality Moderates Consumer Responses to AI Disclosure in Crowdfunding" (2026). PACIS 2026 Proceedings. 11.
https://aisel.aisnet.org/pacis2026/ai_ethic/ai_ethic/11
The Transparency Penalty Unraveled: How AIGC Modality Moderates Consumer Responses to AI Disclosure in Crowdfunding
Governments and platforms worldwide increasingly mandate disclosure of AI-generated content (AIGC), yet how such transparency shapes market outcomes remains underexplored. Situated in reward-based crowdfunding, this study examines the association between AI-use disclosure and funding performance, and whether this association differs across disclosed AIGC modalities (visual vs. textual) and application stages (final product vs. marketing material). Grounded in signaling theory and legitimacy theory, we analyze 41,073 Kickstarter projects using an LLM-assisted text classification approach and entropy balancing. The results indicate that AI-use disclosure is associated with a significant decline in funding performance. However, this association exhibits systematic heterogeneity: disclosing AI involvement in visual content creation is associated with a weaker penalty than disclosing AI involvement in textual content creation, and this modality gap widens for final products yet narrows for marketing materials. These findings illuminate the modality-level heterogeneity and application-stage boundary conditions of consumer responses to AI-use disclosure in crowdfunding.
Comments
03-EthicsSocietalImpact