FinTech Intelligent Recommendation Systems for Crowdfunding Success

In China’s evolving FinTech ecosystem, intelligent recommendation systems have become pivotal for enhancing crowdfunding outcomes. This study integrates the Information Systems Success Model, Dynamic Capabilities Theory, Signaling Theory, and Trust Theory to examine how such systems shape crowdfunding success. Adopting a positivist and quantitative approach, data were collected from 302 users of Chinese crowdfunding platforms employing intelligent recommendation engines. Structural Equation Modeling (SEM) results indicate that system quality, information quality, signaling, trust, and user engagement significantly boost project financing outcomes, while social influence further moderates these effects.In terms of optimizing the design and governance of intelligent recommendation systems for crowdfunding success, a deeper understanding among platform developers and policymakers was sought through the importance-performance matrix analysis (IPMA). The results of IPMA can help identify strategic focus areas for optimizing system features, strengthening trust, and matching projects with investors more effectively, thereby enhancing crowdfunding viability within China’s unique FinTech ecosystem.