Implementation Of Knowledge Transfer In Industrial Clusters

In the past 20 years, one of the most prominent features of economic activity is the central tendency of business location. By gathering together, enterprises can easily obtain what they need from resources and information to improve innovation, a global competitive advantage. Therefore, enterprises can promote collaboration and take advantage of the use of different resources and capabilities each enterprise has. The sum is far greater than the total value of each enterprise or institution. Industry cluster produce synergies. But many studies have focused on the possible promotion of industrial cluster development and growth of the external factors. The focus is always on such factors as resources, local conditions, labor and infrastructure, which support industry clusters as a whole. Rather, the success of industrial clusters, especially in the cluster of a specific enterprise may be influenced by many internal factors, such as partnerships with other companies or information, the transfer of knowledge. In other words, collective learning and knowledge transfer could strengthen industry clusters, business growth and development. Therefore, it is of double value both in theory and practice to systematically study the realization of knowledge transfer in this economic environment of industrial clusters.In this dissertation, focusing on the knowledge transfer in a cluster, basing on the classification, analysis and arrangement of the relevant literature, the author clarify the meaning of industrial clusters and structural characteristics and propose two types of knowledge in industrial clusters:the component knowledge and structure knowledge, of which the respective features and impact on competitive advantage are discussed from the enterprise level and the cluster level. The author then furthers its study into the interpretation of the definition of the transfer of knowledge itself from a philosophical point of view. On this basis, the author puts forward the knowledge transfer in accordance with industry cluster organization:three modes of knowledge of binary point knowledge transfer, knowledge chain transfer and network-chain transfer, among which knowledge chain transfer is analyzed in depth, by presenting two chain structures --- knowledge transfer based on the supply chain and knowledge transfer based on an innovative chain. In addition, in order to better understand the process in which knowledge transfer in industry clusters is achieved, the concept of industrial cluster model of knowledge transfer were analyzed. By analyzing the node model, process model and the node-process model, the author points out the defects in the linear model and construct the optimal linear model. Moreover, the characteristics and structure of communities of practice are analyzed. The author, setting forth from the power of innovation and knowledge creation and exchange point of start, taking into account the social dimension of knowledge capital in nature and spatial dimensions, the author studies the characteristics of four distinct communities of practice model set:task/process based, professional, cognitive/high creative and virtual. Through the analysis of these two types of conceptual models, the author points out that whatever forms of distance will have great impacts on the generation and exchange of knowledge. Based on this, the dissertation will study in depth the impact of the internal characteristics of cluster knowledge on the transfer efficiency; the impact of knowledge spillover on industry cluster knowledge transfer basing on Game theory; the impact of geographical proximity basing on physical distance and communication technology; the impact of organizational proximity basing on the organizational culture, organizational control and organizational forms; The impact of the interdependence of non-trade on knowledge transfer by comparing it to that of social capital on knowledge transfer:structural dimension, cognitive dimension and relationship dimension. By analyzing the factors mentioned above, on the basis of the system dynamics rate-variable in-tree method, the author established such the complex system models as the amount established including knowledge transfer volume, knowledge spillover volume, knowledge attributes, knowledge acceptance, the degrees of geographical proximity, the degree of organization proximity, the degree of recognition proximity, non-trade dependency, finally achieving the purpose of effectively describing the complex systems by the system of scientific methods. By the use of innovative X-0-1 determinant feedback archetype calculation, the author calculates the feedback archetype set of this model complex system. Through the analysis of lowest feedback archetypes schema structure, the author further analyses the promotion or restriction which influence the interaction between these factors.