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| Discovering the Innovation User Groups by Converging Knowledge Characteristics and Collaborative Attributes |
| Tang Hongting1, Li Zhihong2, Zhang Shaqing1,3 |
1.Guangdong University of Technology, Guangzhou 510520
2.South China University of Technology, Guangzhou 510641
3.Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co., Ltd., Huizhou 516025 |
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Abstract The open innovation community, as a typical application of innovation including user participation, can effectively gather user wisdom and provide new ideas for enterprise in product improvement and innovation. However, the openness of the community brings with it unorganized user-generated content, which in turn leads to a low efficiency of knowledge innovation in the community. At the same time, users, as independent individuals, are often limited by their cognitive constraints. To effectively gather and organize the users’ wisdom in an open innovation community, this research proposes to identify innovation user groups with great potential for product innovation under a specific knowledge context. To this end, we utilize a super-network model in analyzing complex systems to quantify the knowledge characteristics of users as well as their collaborative attributes. A genetic algorithm is used to achieve the optimal group solution. By conducting experimental research using real data from the MIUI Community, this research proves the feasibility and effectiveness of user identification. The findings theoretically contribute to the development of knowledge quantification and user identification. In addition, this research provides decision references for community management practices, especially for collaborative innovation.
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Received: 19 May 2020
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