Distant and Local Knowledge: Investigating the Effect of Changing Interest in Knowledge Generation

This study examines how changes between drawing inspiration from distant knowledge and focusing on local knowledge affect contributions in online communities. The research compares two theoretical frameworks for understanding knowledge generation: The tension-based view highlights the tensional perspective of initially engaging with distant knowledge before narrowing the focus to specific domains to foster creative behavior. Conversely, the foundational view posits that creative behavior requires local expertise before it is combined with insights from distant knowledge domains. We collected data from 15 Q&A forums hosted by Stack Exchange and used natural language processing to analyze users’ contributions and changes in interest. Our findings suggest that both theories explain knowledge generation. Individuals need to engage with more distant knowledge over time but also streamline their interests between local and distant knowledge domains to generate more valuable and novel contributions. The study enriches understanding of knowledge generatio

Identifying User Innovations through AI in Online Communities– A Transfer Learning Approach

Identifying innovative users and their ideas is crucial, for example, in crowdsourcing. But, analyzing large amounts of unstructured textual data from such online communities poses a challenge for organizations. Therefore, researchers started developing automated approaches to identify innovative users. Our study introduces an advanced machine-learning approach that minimizes manual work by combining transfer learning with a transformer-based design. We train the model on separate datasets, including an online maker community and various internet texts. The maker community posts represent need-solution pairs, which express needs and describe fitting prototypes. Then, we transfer the model and identify potential user innovations in a kitesurfing community. We validate the identified posts by manually checking a subsample and analyzing how words affect the model\u27s classification decision. This study contributes to the growing portfolio of user innovation identification by combining state-of-the-art natural language processing and transfer learning to improve automated identification

Inspiration before focus - Time-dependent Interest allocation and Idea innovativeness in online communities.

Online communities such as crowdsourcing platforms or user innovation communities are valuable sources for innovation. Community members interact with each other to exchange their ideas. These social interactions express community members’ interests which may change over time. Building on prior research, we investigate a leading open hardware online community to analyse how community members’ time-dependent interest allocation influences their idea generation. Utilizing the topic modelling technique LDA to extract hidden knowledge elements from the idea descriptions, our findings suggest that it is favourable for community members to focus on specific domains after receiving inspiration to generate innovative ideas. This effect is further amplified for an increasing difference between broad and focused interest. With these findings, we contribute to the literature on IS, innovation, and social networks