Research On Knowledge Flow Of Regional Innovation System (RIS):Complexity Science Management Persepective

The time of a new economic era has come that knowledge is gradually replacing traditional resources and become the core resource of gaining competitive edge. However, knowledge creating value does not solely rely on its stationary capacity but knowledge flow and integration, leading to continuous emergence of new resources. As the foundation and a critical component of national innovation system, regional innovation system has a capacity that is not equal to the total amount of various tangible and intangible resources. In many regions, high technology resources have not been able to translate into growth advantage.With such background, this thesis is built on research on knowledge and knowledge flow, as well as complex scientific management theory and classic mechanics theory. It focuses on regional knowledge innovation system as an open and complex system and adopts a research strategy from part to whole, i.e., using a combined methodology including quantitive analysis to study part of knowledge system (between two nodes) and novel complexity science management (CSM) theory to study the whole system. The following aspects are explored in details.First, in terms of basic loop of systematic knowledge flow, I clearly define knowledge field and its relevant concepts, and use qualitative research methodology to explore knowledge self-movement (flow) and its rule and mechanism and reach the conclusion that, within knowledge field, knowledge moves from high potential to low potential with energy coming from knowledge difference, and this movement can be described using a group of non-linear differential equations, including dynamic equations of steady knowledge flow and non-steady knowledge flow within the basic loop. In addition, we point out the critical role that knowledge source plays during the process of knowledge flow, and put its innovation mechanism as an important issue to be studied in Chapter5using CSM integration theory. As the solution of non-steady knowledge movement equation is easily affected by the non-linear structure within the system and go into wrong direction with uncertainty, research on knowledge flow system will be guided by CSM network theory and fall into the scope of non-linear and uncertain issues.Second, speaking from the whole point, regional innovation system (RIS) consists of knowledge subject, knowledge object, organization and environment and also has social nature. The most appropriate metaphor is a complex network between orderly network and random network. It displays typical characteristics being small world and scale-free. Therefore, scale-free complex network model is the optimal choice for studying such system. I establish a network model of RIS knowledge system that is both small world and scale-free (S-S) and its characteristics are studied using simulation analysis.Third, based on the simulation results of the S-S network model and also under the guidance of complexity science management (CSM) theory, we perform in-depth analysis how systematic interaction can be adjusted by properly tuning interaction frequency and thus reducing the minimal length among nodes. Through interaction among individuals and organizations, a system with excessive order and rigidity will turn more active, efficient and creative. On the other hand, by omitting reconnection or increasing length of characteristic path, interaction among some members will be cut off or regulated, thus transforming system from randomness into order. I also discuss that the structure of scale-free network is both robust and vulnerable.Fourth, based on CSM theory, I propose a knowledge integration process (KIP) model that is both stable and flexible. With knowledge flow serving as link, I examine how knowledge is effectively passed along and integrated, and thus drive everything available within RIS including material resource, human resource, and economic resource. Various types of resource will interact and integrate, leading to mutual connection, penetration and affecting one another. What is more, under the guidance of order-disorder theory, I build a RIS knowledge system knowledge flow-integration-innovation (F-I-I) spiral model, i.e., using knowledge flow as link, resource integration as tool and system innovation as goal, to bring large-scale RIS to the edge of chaos area through interaction and therefore new materials continuously emerge during the course of changing between order and disorder. As this process continues, RIS will step in a healthy cycle of innovation. Based on the F-I-I spiral model and further combined with its network structure, I quantitatively describe resource integration through relationship matrix and establish evaluation criterion. Focusing on the robustness issue caused by the scale-free nature of network in Chapter3,1also study pinning control using flexible CSM model integration.Fifth, with the help of knowledge flow dynamic equation in Chapter2, RIS knowledge system R-S network model in Chapter3and the F-I-I spiral model in Chapter5, I establish an evaluation index system to perform an in-depth analysis of current knowledge flow situation of Wuhan, and raise fundamental thoughts and strategies to promote knowledge flow within this area and improves regional innovation capacity.