Research On Innovation Diffusion Based On Social Network

The diffusion of innovation in social systems by time is a complex and dynamic process. Recently, researchers in this area have been focusing on the micro-level, analyzing the characteristics of individuals and the interactions among them. This paper takes the micro perspective, and uses computer simulations to establish models and characterize consumers which are placed in complex networks, so as to enrich the current researches.First of all, we make a review of literature from both the macro and micro perspectives in the area of innovation diffusion, so as to find out the main points and the flaws. Besides, we also take a look at the role of complex networks in the research of innovation diffusion. Based on the two work, we propose the hypotheses and models of this dissertation.Secondly, we characterize the individuals and social networks quantitatively. We analyze the cognation process of consumers and use linear functions to describe the decision process of them. On the other hand, we establish network models in two ways. The small-world network used in the dissertation is created by MATLAB coding, while the other two networks come from the real practice which we quote from other researchers.Thirdly, simulations are carried out on the basis of individual decision models and social networks. By discussing the way how individual heterogeneity and network structure act on the pattern of innovation diffusion, we have come to some conclusions as follows:The more people rely on others to feel about innovations, the fuller that an innovation could diffuse in this social group. People’s desire of individuality impacts the innovation diffusion degree negatively. The more people are different from each other, the faster that an innovation could diffuse in the social group, but the less consumers that the innovation could win. Opinion leaders have the potential to make innovations diffuse faster. The community structure in social networks may hinder the diffusion of innovation, and may also cause less people to adopt the innovation.Finally, we discuss the simulation conclusions and point out the limitations and the future prospect of this research.