How Knowledge Enables Innovative Behavior: A Temporal and Network Perspective

In times of digitalization and the democratization of information, individuals face information overload, misinformation, and missing orientation. Considering the corporate word, the question occurs, how individuals can create value through creative behavior considering the information flood. Therefore, this dissertation investigates the processes behind knowledge generation and the role of social interactions in fostering individual creativity, with a specific focus on innovation within organizations. Drawing on a multidisciplinary approach, the research explores three critical perspectives: network structures, the dynamic process of knowledge exchange, and the application of natural language processing (NLP) for identifying creative contributions. The first research question focuses on how knowledge and social network structures jointly enable future learning and innovation. The findings highlight that knowledge network saturation plays a significant role in creative behavior, particularly in determining the balance between explorative and exploitative search activities. This interaction between knowledge and social networks, where both can compensate for each other, offers a nuanced understanding of how organizations can leverage social dynamics and knowledge structures to stimulate creativity. The second research question examines the impact of knowledge exchange on innovative behavior throughout the idea journey. Through an analysis of online communities, this research demonstrates that changes in individual interests over time are critical to fostering creativity. The dissertation identifies key temporal patterns that enhance the likelihood of creative outcomes, emphasizing the importance of managing both knowledge diversity and depth during the ideation process. The third research question explores the potential of advanced NLP techniques to automatically identify creative behavior from textual data. The research proposes a transfer learning methodology that demonstrates superior accuracy compared to traditional methods, offering a scalable solution for organizations seeking to evaluate large volumes of idea descriptions. This novel approach opens new avenues for utilizing artificial intelligence in innovation management. Overall, the dissertation contributes to innovation literature by providing theoretical and practical insights into knowledge generation processes, social networks, and AI-driven creativity assessment. These findings offer actionable strategies for organizations to cultivate environments that support creative individuals, enabling them to navigate the complexities of knowledge recombination and social interaction for successful innovation in times of information overload.