Paper Number
ICIS2025-2040
Paper Type
Complete
Abstract
Cultural crowdfunding (CCF) enables creators to showcase artistic and creative work while seeking public support, but existing success prediction models often neglect the cultural attributes inherent in such projects. To address this gap, we propose the Cultural Value-based Prediction Model (CV-PM), grounded in Throsby’s cultural value framework. CV-PM systematically extracts and integrates six cultural values from multimodal project content. Beyond feature modeling, our model also uncovers how cultural values interact and contribute to outcomes, thereby operationalizing the cultural mechanisms. In evaluations on 14,539 Kickstarter projects, CV-PM outperforms traditional and multimodal baselines. Additional analyses validate the contribution of each cultural value and reveal domain-specific patterns. By bridging cultural theory with deep learning, this study offers a theoretically grounded and interpretable framework for CCF success prediction. It also provides practical insights for creators and platforms, enabling cultural design and strategy optimization in creative industries.
Recommended Citation
Xu, Yihao; Zhao, Wenxiang; and Yao, Xiaoyu, "Subtly There, Deeply Felt: Modeling Cultural Values in Crowdfunding Success Prediction" (2025). ICIS 2025 Proceedings. 10.
https://aisel.aisnet.org/icis2025/sharing_econ/sharing_econ/10
Subtly There, Deeply Felt: Modeling Cultural Values in Crowdfunding Success Prediction
Cultural crowdfunding (CCF) enables creators to showcase artistic and creative work while seeking public support, but existing success prediction models often neglect the cultural attributes inherent in such projects. To address this gap, we propose the Cultural Value-based Prediction Model (CV-PM), grounded in Throsby’s cultural value framework. CV-PM systematically extracts and integrates six cultural values from multimodal project content. Beyond feature modeling, our model also uncovers how cultural values interact and contribute to outcomes, thereby operationalizing the cultural mechanisms. In evaluations on 14,539 Kickstarter projects, CV-PM outperforms traditional and multimodal baselines. Additional analyses validate the contribution of each cultural value and reveal domain-specific patterns. By bridging cultural theory with deep learning, this study offers a theoretically grounded and interpretable framework for CCF success prediction. It also provides practical insights for creators and platforms, enabling cultural design and strategy optimization in creative industries.
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19-SharingEconomy