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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Published on: October 24, 2019

Structural efficiency to manipulate public research institution networks.

Hyeonchae Yang¹, Woo-Sung Jung²

¹Graduate Program for Technology Innovation & Management, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.

Technological Forecasting and Social Change

|April 15, 2020

Summary

This summary is machine-generated.

Identifying key change initiators in public research networks is crucial for driving network-wide outcomes. Our study reveals that less than 30% of nodes can effectively influence inter-organizational networks, regardless of their connections or academic history.

Keywords:

Network controllability Network effectiveness Network structure Public research institute Research portfolio Thematic network

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Area of Science:

Network Science
Public Administration
Organizational Studies

Background:

Network analysis is increasingly applied in the public sector for managing complex, dynamic entities.
Guiding large inter-organizational networks presents significant challenges due to their intricate structures.

Purpose of the Study:

To quantify structural efficiency for controlling inter-organizational networks within public research institutions.
To identify key nodes (change initiators) capable of influencing network-wide research profiles.

Main Methods:

Network analysis based on research similarity in portfolios.
Examination of structural efficiency and topological properties.
Comparative study across Government-funded Research Institutes (Korea), Max-Planck-Gesellschaft (Germany), and National Laboratories (USA).

Main Results:

Less than 30% of network nodes are sufficient to initiate a cascade of changes across institutions.
Effective change initiators do not necessarily have numerous connections or long academic histories.
Structural efficiency varies across different public research institution types.

Conclusions:

A novel indicator for structural efficiency can assess and guide development plans for multiunit public research institution networks.
Targeting specific, less-connected nodes can be a strategy for efficient network management.
Understanding network topology is key to optimizing research collaborations and outcomes.