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

Authors:
Fangcheng Tang;Youmin Xi;Jun Ma
Affiliations:
School of Economics and Management, Tsinghua University, Beijing 100084, People' Republic of China;School of Management, Xián Jiaotong University, Xián 710049, People's Republic of China;School of Management, Xián Jiaotong University, Xián 710049, People's Republic of China
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

Citing 3
Cited 3

Survival-Enhancing Learning in the Manhattan Hotel Industry, 1898-1980

Management Science
Introduction to the Special Issue on Managing Knowledge in Organizations: Creating, Retaining, and Transferring Knowledge

Management Science
Multiplex Multi-Core Pattern of Network Organizations: An Exploratory Study

Computational & Mathematical Organization Theory

Absorptive and disseminative capacity: Knowledge transfer in intra-organization networks

Expert Systems with Applications: An International Journal
Knowledge stickiness in the buyer-supplier knowledge transfer process: The moderating effects of learning capability and social embeddedness

Expert Systems with Applications: An International Journal
Adjustment of knowledge-connection structure affects the performance of knowledge transfer

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

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.