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Quality assessment of scientific outputs using the BWM

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  • Negin Salimi

    (Leiden University)

Abstract

Assessing the quality of scientific outputs (i.e. research papers, books and reports) is a challenging issue. Although in practice, the basic quality of scientific outputs is evaluated by committees/peers (peer review) who have general knowledge and competencies. However, their assessment might not comprehensively consider different dimensions of the quality of the scientific outputs. Hence, there is a requirement to evaluate scientific outputs based on some other metrics which cover more aspects of quality after publishing, which is the aim of this study. To reach this aim, first different quality metrics are identified through an extensive literature review. Then a recently developed multi-criteria methodology (best worst method) is used to find the importance of each quality metric. Finally, based on the importance of each quality metric and the data which are collected from Scopus, the quality of research papers published by the members of a university faculty is measured. The proposed model in this paper provides the opportunity to measure quality of research papers not only by considering different aspects of quality, but also by considering the importance of each quality metric. The proposed model can be used for assessing other scientific outputs as well.

Suggested Citation

  • Negin Salimi, 2017. "Quality assessment of scientific outputs using the BWM," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 195-213, July.
  • Handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2284-3
    DOI: 10.1007/s11192-017-2284-3
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    References listed on IDEAS

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    2. Geerten Van de Kaa & Daniel Scholten & Jafar Rezaei & Christine Milchram, 2017. "The Battle between Battery and Fuel Cell Powered Electric Vehicles: A BWM Approach," Energies, MDPI, vol. 10(11), pages 1-13, October.
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    4. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    5. Željko Stević & Irena Đalić & Dragan Pamučar & Zdravko Nunić & Slavko Vesković & Marko Vasiljević & Ilija Tanackov, 2019. "A new hybrid model for quality assessment of scientific conferences based on Rough BWM and SERVQUAL," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 1-30, April.
    6. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    7. Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
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    13. Mi, Xiaomei & Tang, Ming & Liao, Huchang & Shen, Wenjing & Lev, Benjamin, 2019. "The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?," Omega, Elsevier, vol. 87(C), pages 205-225.
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