Estimating the effect of organizational structure on knowledge transfer: A neural network approach

Artificial neural network has been put into abundant applications in social science research recently. In this study, we investigate the topological structures of organization network, which can possibly account for the different performances of intra-organizational knowledge transfer. We construct two types of networks including hierarchy and scale-free networks, and single-layer perceptron model (SLPM) was used to simulate the knowledge transfer from a remarkable member to the others. The statistical results indicate that although the performance of knowledge transfer is related to the aspiration of the remarkable member to transfer knowledge, but the scale-free structure is more effective in knowledge transfer than that in hierarchy structure.