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      Grey Regulatory Focus Theory Weighting Method for the Multi-Criteria Decision-Making Problem in Evaluating University Reputation

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      Symmetry
      MDPI AG

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          Abstract

          Evaluating the reputation of higher institutions is important as a benchmark for the continuous development of universities. In multi-criteria decision-making (MCDM), the estimation of the criteria weight is significant in improving the accuracy of the evaluation results. However, criteria weights assigned by decision makers (DMs) can be inaccurate when not measured from the orientations from which the DMs pursue their decision goals. This paper combines the grey system theory (GST) and regulatory focus theory (RFT) in estimating the weights of criteria by measuring the promotional and prevention focus orientations of the DMs and representing it as grey numbers. In this paper, we showed not all weight measurements from the DMs are symmetrical. In essence, to improve the accuracy of the MCDM weights, the grey regulatory focus theory (GRFT) weighting method is proposed to estimate the weights of an MCDM problem as well as aggregating the weights of the group DMs, thereby overcoming the limitation of a single orientation measurement. Furthermore, the GRFT weighting method is applied in ranking four universities in Xi’an city of China from a sample data from 1,200 students. The most reputable university is consistent with the other ranking indexes in the literature.

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          Best-worst multi-criteria decision-making method

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            Beyond pleasure and pain.

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              Regulatory Focus and Strategic Inclinations: Promotion and Prevention in Decision-Making

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                SYMMAM
                Symmetry
                Symmetry
                MDPI AG
                2073-8994
                February 2019
                February 14 2019
                : 11
                : 2
                : 230
                Article
                10.3390/sym11020230
                0d3ffad9-b16c-4c38-827d-670532438e97
                © 2019

                https://creativecommons.org/licenses/by/4.0/

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