Paper Type
ERF
Abstract
Generative AI tools are increasingly used in entrepreneurial pitch videos, yet their behavioral and economic consequences remain unclear. This study introduces AI Co-Creation Intensity (AICI), a continuous multimodal measure capturing the degree of AI involvement in visual, audio, and editing components of crowdfunding videos. We examine its relationship with funding performance. Drawing on signaling theory and trust in automation research, we theorize an inverted-U relationship: moderate AI augmentation enhances perceived authenticity and trust, while excessive AI reliance reduces them. We test this framework using large-scale Kickstarter data (3,000–5,000 campaigns) and a controlled experiment manipulating AI intensity levels. This research advances behavioral AI analytics by linking machine-detected AI involvement to economic outcomes and provides insights into optimal calibration of human–AI co-creation in digital platforms.
Paper Number
1652
Recommended Citation
Syed, Amir Alisha and Ganji, Gajanan L., "Human–AI Co-Creation Intensity and Entrepreneurial Authenticity: An Emergent Research Framework" (2026). AMCIS 2026 Proceedings. 20.
https://aisel.aisnet.org/amcis2026/sig_dsa/sig_dsa/20
Human–AI Co-Creation Intensity and Entrepreneurial Authenticity: An Emergent Research Framework
Generative AI tools are increasingly used in entrepreneurial pitch videos, yet their behavioral and economic consequences remain unclear. This study introduces AI Co-Creation Intensity (AICI), a continuous multimodal measure capturing the degree of AI involvement in visual, audio, and editing components of crowdfunding videos. We examine its relationship with funding performance. Drawing on signaling theory and trust in automation research, we theorize an inverted-U relationship: moderate AI augmentation enhances perceived authenticity and trust, while excessive AI reliance reduces them. We test this framework using large-scale Kickstarter data (3,000–5,000 campaigns) and a controlled experiment manipulating AI intensity levels. This research advances behavioral AI analytics by linking machine-detected AI involvement to economic outcomes and provides insights into optimal calibration of human–AI co-creation in digital platforms.
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