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Multi-attribute comprehensive evaluation of individual research output based on published research papers
Journal article   Peer reviewed

Multi-attribute comprehensive evaluation of individual research output based on published research papers

Jiuping Xu, Zongmin Li, Wenjing Shen and Benjamin Lev
Knowledge-based systems, v 43
May 2013
DOI: https://doi.org/10.1016/j.knosys.2013.01.029

Abstract

Bibliometric indicators Evaluation index system Multiple attribute evaluation Research output TOPSIS
This paper proposes a multi-attribute comprehensive evaluation method of individual research output (IRO). It highlights the fact that a single index can never give more than a rough approximation to IRO, and the evaluation of IRO is a multi-attribute complex problem. Firstly, an evaluation index system is established by determining evaluation attributes and choosing the appropriate bibliometric indicators. To address the multiple authorship problem, this paper develops an improved number-of-papers-published indicator. Following this, TOPSIS method is used to conduct a comprehensive IRO evaluation. Then this paper uses a case study to test the feasibility of the methodology. Finally, this paper discusses the effectiveness of the proposed method. Compared with traditional single-indicator evaluation approaches, the proposed multi-attribute evaluation takes more aspects into consideration, therefore it is able to effectively overcome the one-sidedness of a single indicator. The proposed method also has significant advantages compared with other comprehensive IRO evaluation methods.

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15 citations in Web of Science
20 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
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