Abstract There has been an increasing effort to measure the research performance of universities in recent years. Previous studies have addressed performance with a holistic approach, ignoring universities' research expertise and university differences and resources. The aim of this study is to present a research performance evaluation model based on a clustering approach and multi-criteria decision making (MCDM) methods that take into account the research expertise of universities. In this study, research performance criteria are weighted by BWM and MEREC methods. The BWM method uses data collected from experts, while the MEREC method uses data from 1500 universities collected from six research areas of the OECD. A clustering approach that takes into account research expertise and hybrid MCDM methods are used to rank research performance of universities with similar research tendencies. The proposed model was applied on 199 universities in Türkiye. Universities were clustered in six research areas and universities in each area were ranked according to their performance. According to the findings of the study, clustering approach is an effective and necessary approach in performance evaluation. Although the weight values of the criteria differ among the areas in performance evaluation, the most important criteria are the criteria focusing on research excellence. The findings also revealed that MCDM methods in performance evaluation are effective in addressing the complex nature of research performance. Validation and sensitivity analyses revealed that the proposed model is robust and consistent. This study makes innovative contributions to the performance evaluation literature and has theoretical and practical implications.
Muammer Maral (Thu,) studied this question.