Confidential Areas In Innovation Communities: An Agent-Based Model Using Fuzzy Logic and Qualitative Empirical Data

This paper examines the relationship between social and utilitarian incentives in innovation communities. We develop an agent-based model using insights from a qualitative empirical study. This method combination is motivated by shortcomings of empirical research to capture the dynamics and the interactions between social and utilitarian incentives. Thus, we use the synergy between complex systems thinking and its strengths in modeling in combination with qualitative empirical research, which is powerful to recognize phenomena. The problem of incorporating decisions driven by multiple motivations is approached by including fuzzy interfaces in the model. With the method combination of qualitative empirical research, agent-based modeling, and fuzzy logic we can answer questions of social and utilitarian motives and their relation to innovation performance in communities. We find that community members do not only want to innovate but have social motives in addition. Further, some innovation communities have areas which we call confidential areas. These are areas for high performers. In the simulation we find that such confidential areas improve innovation performance. However, this effect depends on the access restriction. The contribution of this paper is twofold. First, it improves the understanding of innovation communities and the incentives for contributing. The extant literature on innovation management profits from a dynamic perspective on community-based innovation. Second, we provide a novel method combination that is useful for capturing complex phenomena from the empirical perspective.