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An integrate method for performance of women in science and technology based on entropy measure for objective weighting

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  • Ying-Chyi Chou
  • Hsin-Yi Yen
  • Chia-Chi Sun

Abstract

In the 21st century the role of women in the science and technology (WIST) field is very important. Many countries are actively promoting policies of women in higher education and R&D careers. A nation’s economic growth depends heavily upon its talent base, specifically, the quantity, quality, and utilization of HRST. Therefore, assessing WIST productivity variation is important. The problem of the complexity of performance evaluation makes the development and the application of standard models more difficult. This study uses the VIKOR and entropy method to evaluate the performance of WIST and rank the priorities for them accordingly. The relative weight of different evaluation criteria for the performance of WIST comes from the entropy results. We can see that, the first two important factors for the performance of WIST are a higher education sector and government sector women researchers, as a percentage of the total researchers. The results show that the Korean alternative has the best performance of WIST followed by the Netherlands and Japan. Copyright Springer Science+Business Media B.V. 2014

Suggested Citation

  • Ying-Chyi Chou & Hsin-Yi Yen & Chia-Chi Sun, 2014. "An integrate method for performance of women in science and technology based on entropy measure for objective weighting," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 157-172, January.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:1:p:157-172
    DOI: 10.1007/s11135-012-9756-6
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    References listed on IDEAS

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    1. Büyüközkan, Gülçin & Ruan, Da, 2008. "Evaluation of software development projects using a fuzzy multi-criteria decision approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(5), pages 464-475.
    2. Kathryn L. Shaw, 2005. "Women's contribution to productivity," Regional Review, Federal Reserve Bank of Boston, issue Q 1, pages 44-48.
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    Cited by:

    1. Hisham Alidrisi, 2021. "An Innovative Job Evaluation Approach Using the VIKOR Algorithm," JRFM, MDPI, vol. 14(6), pages 1-19, June.
    2. Sharma, Ashutosh & Chauhan, Ranchan & Singh, Tej & Kumar, Anil & Kumar, Raj & Kumar, Anil & Sethi, Muneesh, 2017. "Optimizing discrete V obstacle parameters using a novel Entropy-VIKOR approach in a solar air flow channel," Renewable Energy, Elsevier, vol. 106(C), pages 310-320.
    3. Jamal Ouenniche & Kais Bouslah & Blanca Perez-Gladish & Bing Xu, 2021. "A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction," Annals of Operations Research, Springer, vol. 296(1), pages 495-512, January.

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