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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 860216, 12 pages -

Research Article

School of Economics and Management, Beihang University, Beijing 100191, China

Library, Beihang University, Beijing 100191, China

College of Technology Management, University of Chinese Academy of Science, Beijing 100049, China

Received 9 April 2014; Accepted 13 May 2014; Published 2 June 2014

Academic Editor: L. W. Zhang

Copyright © 2014 Shuang Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper examines the differences of learning performance of 5 MNCs multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having dimensions, which denotes the heterogeneous knowledge about the reality. We further set innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer shorter distance between the knowledge of the individual and the reality denotes a lower higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

Author: Shuang Song, Xiangdong Chen, and Gupeng Zhang



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