Study Of Tacit Knowledge Transfer Based On Individual Differences In Complex Social Networks

The knowledge has been treated as the most important recourse for theorganization’s survival, development and competition, when knowledge economiccame. How the tacit knowledge is transferred in an organization will affectorganization’s performance. As known, it’s very difficult to transfer the tacitknowledge, so it’s such important for us to know its rule which tacit knowledge beentransferred. It can help to people’s management of knowledge.As exploratory application of theory regarding complex social networks, studyingknowledge transfer from the perspective of social network has become a trend in theresearch field of knowledge management.Compared with traditional research, knowledge transfer study based on theperspective of social network does not put bodies involved in the knowledge transferout of social environment any more, and begins to pay attention to a third party thatmay have impact on knowledge transfer. Therefore, it can more truly reflectknowledge transfer between individuals.This paper aims to analyze the dynamic characteristics of tacit knowledgediffusion, by mean of a simulation model. Throughout this paper, the main works areas follow:1. The simulation model is established based on Jackson’s algorithm, whichcapture five social networks’properties.2. Different local-level tacit knowledge diffusion abilities, including transferability, absorb capability and the level of trust between individuals, are considered inthis model.3. The steady-state knowledge level and the process of tacit knowledge diffusionunder different network topology and tacit knowledge exchange patterns aresimulated and analyzed.The main contribution of this study is having modeled the tacit knowledge transferin organization. Both the properties of local-level tacit knowledge diffusion patternand the system-level network structure were reflected in the simulation model. Thesimulation results demonstrate that the efficiency of tacit knowledge diffusion increase as the social network heterogeneity decrease. And different tacit knowledgediffusion abilities can significantly affect the efficiency of tacit knowledge diffusion.