User Potential Creativity

In the digital intelligence era, user participation in innovation is increasingly prevalent, underscoring the importance of user creativity in driving organizational change. Despite its relevance, user potential creativity—a critical factor in digital product innovation—remains insufficiently defined and measured. To address this gap, this study refines the concept of user potential creativity and introduces a preference revelation approach using machine learning. Drawing on data from Thingiverse and proxy variables such as intrinsic motivation and lead userness, the research combines machine learning with ordinary least squares regression to examine the impact of user potential creativity on digital product innovation. The findings reveal that intrinsic motivation significantly enhances innovation performance. The study argues that managing user creativity requires attention to both user capabilities and motivations, and it proposes user management adaptive change as a new framework for organizational adaptation.