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

  • Authors:

  • Jiuping Xu;Zongmin Li;Wenjing Shen;Benjamin Lev

  • Affiliations:

  • Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, PR China;Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, PR China and Decision Sciences Department, LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA;Decision Sciences Department, LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA;Decision Sciences Department, LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2013

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Abstract

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.