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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.



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