Dynamic analysis of identifying user roles and evolutionary paths in collective intelligence design community

Collective intelligence design has become a new paradigm for product design. This design pattern breaks the situation of information closure, resource tension, and insufficient innovation caused by traditional product design. Individuals act and contribute in diverse ways in collective intelligence design communities, gradually developing distinct user roles. Identifying the dynamic evolution path of user roles is crucial to properly allocating community traffic and increasing user stickiness. However, the majority of previous research solely discusses the roles that users play and their contributions, omitting the reality that user roles alter dynamically as the community develops. Therefore, to refine existing research, this paper introduces a user role identification model and summarizes the user role evolution paths through time series analysis. The method's effectiveness is demonstrated by its use on Thingiverse, the world's largest online design platform. This paper contributes three points to the study of user role identification and evolution. First, this paper identifies six typical user roles. Secondly, this paper portrays four stages of the design community's evolution. Finally, this paper presents the dynamic evolution paths of different user roles and proposes evolutionary optimization strategies for distinct roles. These conclusions enrich user role research from both theoretical and practical perspectives.