Identifying lead users in online user innovation communities based on supernetwork

Abstract

Lead users are the group of most valuable users in product innovation and new product development for a certain firm. Based on web mining methods on user innovation communities, this paper presents quantitative methodology to evaluate the contributions and values of online community users to identify lead users. Firstly, we analyze user behaviors and calculate the user interest index (UII) of posts, keywords and innovation fields to measure the popularity of innovations based on other users' focus. Secondly, the model of User Innovation Knowledge supernetwork (UIKSN) is proposed, in which UIIs and user contributions are considered as two types of node weights for integrating behavior data and content data. And then, rules and methods are suggested based on the UIKSN model for identifying lead users to meet various requirements including contributions and UIIs in posts, keywords, hot frontiers, core innovation fields, and even in certain fields or knowledge points. Furthermore, knowledge structures are analyzed through ego-network analysis. Case studies show that the proposed UIKSN model and methodology are more objective and credible and thus have good potentials for identifying and analyzing lead users from multiple levels, such as posts, keywords, hot front, core innovation fields, etc.