Research on the characteristics of evolution in knowledge flow networks of strategic alliance under different resource allocation

Abstract This paper takes the four types of resource allocation (randomly oriented, relationship-oriented, cooperation oriented, and knowledge-embedded) as its premise and investigates the complex characteristics of knowledge flow network evolution in strategic alliances, taking into account the mutual variance effects of the evolution mechanism. Existing research has neglected the differences in resource allocation types, by and large employed statistical analysis methods, and identified only the linear relationships among experimental variances of cross-sectional data. The present study differs from existing research in the following ways: First, we thoroughly consider the multi-faceted nature of resource allocation. Second, we use the method of multi-agent imitation according to perspective of dynamic system evolution and the principle of phase theory, allowing the explicitly analysis of nonlinear functional logic, forms and patterns in the variance. Finally, we analyze the appropriateness of different resource allocation models. Our paper features several significant findings: (1) The evolution of the knowledge flow network of a strategic alliance can produce a bifurcation phenomenon composed of saddle-node bifurcation and transcritical bifurcation. (2) The number of nodes exhibits a logarithmic growth distribution, the connection intensity and the network gain exhibit exponential growth distributions, and the connectivity and knowledge flow frequency are mutually influential in the form of a power function. (3) Knowledge-embedded resource allocation is most effective for improving the knowledge flow rate of networks and can further supply ample impetus for evolution. (4) Cooperation-oriented resource allocation is most beneficial for quickly propelling the network into the evolution realm. (5) Relationship-oriented resource allocation can aid the network in capturing more profit. Furthermore, this research is beneficial for understanding the key problems of each resource allocation model and the evolution of strategic alliance in knowledge flow networks. Our proposed methods and framework can be more widely applied to the fields of complex networks, knowledge management, and strategic innovation.

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