Estimating the effect of organizational structure on knowledge transfer: A neural network approach
Introduction
The volume of knowledge management research in organizations has increased in recent years, as it has in many disciplines, including organizational behavior and theory, information systems, psychology, sociology, economics and strategy. Research on knowledge management focuses on various aspects of the three fundamental questions: how organizations create, retain, and transfer knowledge (Argote, McEvily, & Reagans, 2003). Indeed, the growing empirical evidence indicates that organizations that are able to transfer knowledge effectively are more productive and more likely to survive than those are less adept at knowledge transfer (Baum & Ingram, 1998). Several academic literatures have developed that many factors affect knowledge transfer in organizations such as similarity between tasks (Darr & Kurtzberg, 2000), characteristics of the source of knowledge, the recipient, the context, and the knowledge itself (Szulanski, 2000), characteristics of individual member (Baldwin & Ford, 1988), characteristics of the social network (McEvily & Zaheer, 1999), the nature of the social ties (Hansen, 1999), network structure (Reagans & McEvily, 2003). Generally speaking, knowledge transfer is considered in two aspects, one is associative learning and absorptive capacity (Cohen and Levinthal, 1990, Simon, 1991), the other is tie strength (Hansen, 1999, Uzzi, 1997), social cohesion and network range (Reagans & McEvily, 2003). However, little concerning the effect of characteristics of network structure on knowledge transfer is taken into account. In particular, when the structure of organization network become more complex increasingly, how to estimate the performance of knowledge transfer in this type of organizational network structure have been little considered. Recently, many large networks were found that the vertex connectivities follow a scale-free power-law distribution (Barabási & Albert, 1999), especially in informal networks such as friendship networks, citation patterns networks and WWW networks. Here, we try to find comparatively, if this new form of organization networks will influence the convection of knowledge.
In this study, we examined the topology of organization network and its effect on the ability of individuals to access to knowledge from the remarkable member in the same unit or organization. We believed that the ability of individuals to possess new knowledge would be different by changing the topology of networks. Specifically, our study focused on the different performance of knowledge transfer between two organizational network topologies: hierarchical and scale-free networks. We addressed this idea by utilizing single-layer perceptron model (SLPM) of neural network (Haykin, 1994, Hertz et al., 1991, Lippman, 1987) to simulate the knowledge transfer process in an organization, that consists of 100 members which are linked by hierarchical or scale-free preferential attachment mechanism.
Section snippets
Organizational network models
Xi and Tang (2004) think that an organization can be considered as graph, each vertex represents a member in the organization and edges indicate the communication paths between them. Thus, without loss of generality, an organization network can be measured by graph theoretic approach in this form: G=(V, E), where V denotes the set of members and E is a token of the relations set in the networks (Xi & Tang, 2004).
Two organization models both of which have 100 members are constructed. One is a
Interpretation of simulation process
First, we construct a common four layers hierarchical model with 156 vertexes, and in order to compare with this model in the same scale, we create a scale-free model also including 156 vertexes following Eq. (1). Each member has an ID number to specify the sequence. The ingredients [S, D, A] were also engendered in the way that we present in Section 2.1.
Then we select a vertex to be a remarkable member. In hierarchical model, we appoint the vertex in top level as remarkable member and endow it
Results and discussion
The results of the computational experiments are summarized in Table 1 and Fig. 3. The values in Table 1 represent the performance of the knowledge transfer. We present the results in high aspiration and normal aspiration of the remarkable member, because we have found that if the remarkable member has a very high aspiration A=1, then the performance of knowledge transfer will be very different from the normal aspiration A randomized in 0 to 1.
The values in Table 1 indicate that the scale-free
Conclusion
The findings generated by this study demonstrate that the characteristics of organization structure affect performance of the intra-organizational knowledge transfer. As computational experiments accumulate, the scale-free structure contributes to better knowledge transfer, and the aspiration of remarkable member is also important to the results of knowledge transfer.
Acknowledgements
We would like to thank Wenqiang Dai for offering the helps on computer program. Also, we would like to thank, in Part National Science Excellent Innovation Research Group Fund of China under contract No. 7012001, National Science Fund of China through the project No. 70233001, and Postdoctoral Foundation of China under contract No. 2005038072 for their support.
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