Research On Knowledge Diffusion On Networks Under Diverse Individual Strategies

With the rise of knowledge-based economy, knowledge has become the critical resources of organization. How to effectively exchange and share knowledge is the key of knowledge accumulation, so knowledge diffusion of knowledge management field is an important research direction.The increase of organizational knowledge is based on the increase of individual knowledge; and new ideas and new knowledge of people increase in the process of communication and learning. Therefore, the behavior of individuals can't be neglected when we study the problems of knowledge diffusion. At present, the networking is a common phenomenon of individuals and organizations, and knowledge diffusion is one of the symptoms of network behavior. The complex network theory provides a new direction for the study of knowledge diffusion:using the complex network theory to simulate.In this thesis, two knowledge diffusion models are proposed and analyzed with the individual based simulation method, to cope with the above problems.(1) Static network:a small-world network is established. Two modes or strategies of interpersonal knowledge diffusion are taken into account, i.e. unconditional diffusion and exchange knowledge with conditions. In this thesis we analyze how the individuals' knowledge diffusion strategies and the network structure influence knowledge diffusion in the overall network. We find that the individuals who diffuse knowledge voluntarily play an important role for knowledge diffusion; and the small world network is suitable for knowledge diffusion. The time interval of changing strategies has a great impact on knowledge diffusion when the network scale is small.(2) Dynamic network:In this thesis, knowledge diffusion on a dynamic collaboration network is further studied. Two modes or strategies of interpersonal knowledge diffusion are taken into account, i.e. unconditional diffusion and exchange knowledge with conditions. The whole diffusion process of individuals based on this network is simulated, in which the average knowledge level and the knowledge deviation of the individuals are recorded. The effect of individual strategies and innovative environment are also taken into account and studied.