The open innovation platform provides opportunities for product users to exchange innovative ideas and experiences. Maintaining high-quality idea output is a prerequisite for the long-term development of the platform. We adopt the overall theoretical framework of signal theory and propose a model to explain the influence of idea language features on idea recognition. Through empirical research on 18777 ideas of Salesforce TrailBlazer Community, a language signal model that affects the idea recognition of open innovation platforms is constructed, and the influence of different language signals on idea recognition is analyzed. The results show that information redundancy, text legibility, and text valence have a positive impact on idea recognition, while subject diversity and emotional subjective have a negative impact on idea recognition. The research results can provide useful guidance and reference for text feature recognition of idea quality.
Wang, Yujie; Qi, Guijie; Wang, Kaiping; and Li, Ziqing, "What Kind of Ideas Are More Attractive? ——A Language Signal Model of Idea Recognition" (2021). PACIS 2021 Proceedings. 58.
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