Upgrade Mechanism Of Industrial Clusters Based On Knowledge-based Service Organizations Embedded Perspective

Nowadays, the globalization of economy and technology is actively and profoundly changing the territory of world economy, while boosting the growth of China's economy. As an export-oriented economy, Zhejiang Province, starting with family workshops and partnerships, has formed industrial clusters and specialized industrial regions with comparative advantages. Agglomerative economy has become a prominent characteristic of Zhejiang economy. However, after 20 years of development, most industrial clusters in Zhejiang province are still wandering at the bottom of global value chain. It becomes a constraint restricting the development of industrial clusters that the products are of low added value for lagging in technology and brand. The most exigent task confronted by the industrial clusters is to realize the upgrading from the role of "foundry" to that of "collaborator", and then to "competitor".After a review of related work, this dissertation categorizes the upgrading research of industrial clusters into two major viewpoints, i.e. "GVC" and "localization" viewpoints. Regarding that the external impetus of upgrading firstly influences and changes the internal state of clusters to activate internal momentum and ultimately results in cluster upgrading, we establish the "localized" viewpoint. Secondly, by referring to the theory of cluster evolution, theory of cluster competitive advantage and upgrading theory of external value chain, we discover the two major impetuses and one representative factor for cluster localized upgrading, i.e. network structure, knowledge behavior and knowledge structure. Incorporating the cluster practice and the related theoretical achievements on Knowledge-intensive Services(KIBS), we analyze and consider that KIBS plays an essential role during the process of cluster upgrading, therefore establish KIBS embedding viewpoint towards industrial cluster upgrading.By constructing the concept model of "Embedding of KIBS—Network Structure—Cluster Upgrading", this dissertation explores the two-phase model, i.e. one from Embedding of KIBS to Network Structure and the other from Network Structure to Cluster Upgrading respectively, and then reveals the impacting mechanism of KIBS embedding towards upgrading of industrial clusters. We investigated 797 enterprises within 19 different industrial clusters on the relationships between enterprise and enterprise, enterprise and KIBS during 2 years, 3 years and even 4 years. This dissertation combines the evolving analysis of overall network and the statistical analysis on the level of industrial clusters, verifies the impact of KIBS' embedding towards the network structure, then subsequently towards the knowledge behavior of clusters and finally towards the upgrading of industrial clusters, based on which we make policy proposals. This dissertation makes the following conclusions based on the theoretical analysis and empirical study above. (1) It is an intrinsic approach to analyzing the internal motives of industrial cluster upgrading from a viewpoint of localization. The external motives of cluster upgrading achieve results by activating the internal motives. (2) The nature of cluster upgrading is the intensification, updating and reconstruction of internal knowledge structure within the clusters. The degradation and renewal of knowledge structure represent the process and product upgrading of clusters, while the transition and stabilization of knowledge structure represent the functional upgrading of clusters. (3) One of the essential paths to cluster upgrading is effective embedding of KIBS into cluster networks. The embedding of KIBS influences the knowledge behaviors and knowledge structures by changing the networking structure of clusters and consequently promotes the upgrading. (4) The embedding of KIBS can be depicted by analysis methods of relation and structure, which can be modeled by social networking analysis (SNA). (5) The impact of KIBS embedding on network structure is mainly realized through the function of Knowledge Bridge. Different types of knowledge bridges have different points of focus towards impact on network structure. (6) The embedding degree of KIBS is affected by the gap of knowledge potential and technical upgrading velocity between KIBS and member firms. Therefore in the design of cluster upgrading mechanism, the internal characteristics of the industrial cluster need to be taken into account. (7) The KIBS serving industrial clusters has 16 classes, which can be divided into two major categories and further divided into four subdivisions.Compared with existing research results in the domain of cluster upgrading, the novelties of this dissertation are as follows.(1) Existing research on cluster upgrading was mostly stuck to the repetitive case studies by the analysis tools of GVC, but neglects the definition of connotation and motive factors of upgrading within the clusters. Especially, the existing studies are lack of exploration into internal upgrading mechanism within clusters. On a basis of defining the connotation and categorization of cluster upgrading, we reveal the internal upgrading mechanism of clusters and provide a novel viewpoint for understanding and studying cluster upgrading mechanisms, by illustrating three key upgrading factors and one impacting mechanism of upgrading types.(2) As a group of special nodes within cluster networks, KIBS has a significant impact on cluster upgrading. However, there is still a lack of such specialized and profound research on this topic. By constructing a two-phase model, we analyze the mechanism of cluster upgrading from a viewpoint of KIBS. The model of first phase puts an emphasis on the inspection how KIBS is embedded and the impact which the embedding of KIBS makes on the network structure. In the second phase our model mainly focuses on the analysis of the relationship between the decisive motive factors, i.e. network structure and knowledge behavior, and the upgrading of clusters. (3) The literature mainly focuses on theoretical analysis and case descriptions, while we depict the evolving process of network structure with the embedding of KIBS with three structural variables based on an empirical study of overall network evolution during a period of seven years. By inspecting the transformation of these structural variables, and integrating the real case study of the second phase, we describe the mechanism by which the embedding of KIBS influences the network structure and subsequently influences the upgrading of industrial clusters through knowledge behaviors.