Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2013

Abstract

Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality, quantity, and citations of published journal articles) of researchers is largely ignored. To overcome these limitations, this paper proposes a social network-empowered research analytics framework (RAF) for research project selections. Scholarmate.com, a professional research social network with easy access to research relevant information, serves as a platform to build researcher profiles from three dimensions, i.e., relevance, productivity and connectivity. Building upon profiles of both proposals and researchers, we develop a unique matching algorithm to assist decision makers (e.g. panel chairs or division managers) in optimizing the assignment of reviewers to research project proposals. The proposed framework is implemented and tested by the largest government funding agency in China to aid the grant proposal evaluation process. The new system generated significant economic benefits including great cost savings and quality improvement in the proposal evaluation process.

Keywords

Research project selection, Research social networks, Research analytics

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Information Systems and Management

Publication

Decision Support Systems

Volume

55

Issue

4

First Page

957

Last Page

968

ISSN

0167-9236

Identifier

10.1016/j.dss.2013.01.005

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.dss.2013.01.005

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