LAUSR

Purpose: This article aims to provide an integrated model and methodology for discovering valuable innovation knowledge and creative users in online innovative communities. Design/methodology/approach: The super-network integration approach is applied in constructing the user's innovation knowledge super-network (UIKSN) model based on knowledge fragments discovered from user-generated content (UGC) data using text mining methods. The social network analysis (SNA) methodology is then used to analyze the KSN model. An empirical research is conducted with China's Xiaomi online community to illustrate the UIKSN model and the SNA methodology. Findings: Compared with current methods, the proposed KSN model and SNA methodology are demonstrated to be more effective and valid in discovering valuable innovation knowledge including valuable innovations, innovation trends, creative users and their knowledge background, etc. Research limitations/implications: The results of KSN modeling and analysis are inevitably influenced by the performance of text mining methods. Practical implications: More and more companies start to adopt online community user's innovations in their new product developments. However, it is difficult to discover users’ opinions and innovations from the UGC due to its tremendous volume. Therefore, the KSN model and SNA methodology presented in this article are helpful for companies to manage online communities and to utilize users’ innovations effectively and efficiently. Originality/value: The article provides two main contributions in the study of online communities: 1) a well-justified model (users' innovation KSN, UIKSN) to explore online users’ contributions; 2) a new and more effective methodology to discover and analyze valuable innovations and innovative users.