Adjustment of knowledge-connection structure affects the performance of knowledge transfer

The influences of the network properties on the transfer of knowledge within the network have been extensively studied. However, the “knowledge” properties of the network largely less-attended in literature. In this paper we investigate whether the performance of knowledge transfer in a network can be influenced by adjusting the “knowledge-connection” structure of that network, as a primitive attempt to study knowledge transfer from the aspect of the “knowledge” properties of the network. By the “knowledge-connection” structure we mean the network structure that describes the knowledge distribution within the network. Therefore, the agent-based modeling approach is adopted in this paper to compare the performance of knowledge transfer in a series of networks which differ from one another in their “knowledge-connection” structures. The results of computational simulations illustrate that the network adjustment to increase the knowledge diversity in the directly-connected agent-pairs is helpful for improving the overall performance of knowledge transfer in the entire network in the short term; but the improvement of the long-term performance is less significant. Especially, if the local knowledge-exchange follows the mutually-advantageous bidirectional-knowledge-diffusion (BKD) model, the proposed network adjustment would instead hamper the long-term effectiveness of knowledge transfer. Further investigations show that the limitations can be overcome by adopting a periodical re-adjustment mechanism, through which the knowledge diversity in the network is maintained and persistent knowledge flow becomes possible.