Institutional Knowledge Structure and Management based on Bibliometric Analysis

Abstract

Building an effective research portfolio is vital to successful knowledge management for public research organizations. Hence, research administrators and planners need to cultivate the capability to steer research portfolios according to their intention. However, the difficulty lies because the scientific development as an example of complex systems is the production of interacting elements that dynamically change their states. Therefore, this study addresses the need to holistically manage institutional research by reflecting the characteristics of scientific systems. To accomplish that, here, two types of research networks are considered by deducing relations based on scientific outputs: research similarities between sub-organizations and causal relations between research areas. Analytical methods primarily taken advantage of information theory, information retrieval, and network modelling are applied to the three cases of public research institutes: the Korean Government-funded Research Institutes (GRIs), the National Laboratories (NLs) of the United States Department of Energy, and the German Max-Planck-Gesellschaft (MPG). In particular, as a result of network modelling, major nodes to completely function a research network are extracted by means of the concept of structural controllability. According to research similarities between GRIs, little common ground was found because individual GRIs have distinct research portfolios. Furthermore, their research structure may be vulnerable to research failure in any of GRIs, which would split a network into pieces in that the network show a tendency to be centralized into GRIs with high betweenness. An institutional hierarchy of research in the GRIs is less matured than it is in other organizations. Therefore, most GRIs are at equivalent levels on the periphery. The findings also indicate that the information theoretic indicator, transfer entropy, well detects causal relations between emerging areas. The structural characteristics of emerging domains are revealed. For example, the causality network of the GRIs is sparsely connected, centralized into nodes with high betweenness. Moreover, the developmental balance between disciplines can be diagnosed, and the excessively biased development of the GRIs toward a discipline, Chemical, Mechanical, and Civil Engineering, is captured. Moreover, the selected nodes by the structural controllability correspond to positions requiring individual management from a structural perspective when the whole institutional research portfolio is renovated. At the same time, the selected nodes mean the possible hindrances for a collective action. Theoretically, an institution that has the capability to control the research system, can regulate the speed and depth of its development. The share of extracted nodes in a network can be interpreted into structural efficiency to steer, and the results indicate that research networks of the GRIs are operated at a low efficiency. The cause of inefficient structures would come from segregated research domains across GRIs without common fields. Therefore, this study suggests to the GRIs the necessity to build an integrated plan for institutional research development.