A Nonradial Super Efficiency DEA Framework Using a MCDM to Measure the Research Efficiency of Disciplines at Chinese Universities

Su, Weihua; Ding-xin, Wang; Xu, Lili; Zeng, Shouzhen; Zhang, Chonghui

doi:10.1109/access.2020.2993108

Cited by 15 publications

(20 citation statements)

References 51 publications

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“…With more and more investment in world-class universities, the government and society pay more and more attention to the efficiency and benefit of funds. It is of great significance to formulate a scientific performance evaluation system and corresponding incentive-restraint mechanisms [11]. Compared with teaching activities, the performance evaluation of S&T innovation has more observable indicators, and it is also an In recent decades, researchers have fully discussed on the definition, characteristics, evaluation criteria, and international ranking of world-class universities [15], [16].…”

Section: Introductionmentioning

confidence: 99%

The Scientific and Technological Innovation Performance of Chinese World-Class Universities and its Influencing Factors

Chen

Shu

2021

IEEE Access

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This paper explores the scientific & technological innovation performance of world-class universities in China from 2014 to 2019, based on the super-efficiency DEA model and Malmquist index. Then we provide a novel fivedimensional analysis framework to research influencing factors of their S&T innovation efficiency with the Tobit-DEA model. The results show that 36.6% of the sample universities are DEA efficient, and their efficiency varies according to their type and location. Besides, the technical efficiency index of universities is relatively high and the total factor productivity is volatile, which are reflected in the ability of transforming scientific research achievements into practical productivity. From the perspective of dynamic analysis, the sample universities expand slowly in the frontier of S&T innovation efficiency. In addition, the S&T innovation performance is highly related to the input quality and matching structure of scientific research elements, government relevance, and industry-academia-research collaboration level. Among these main factors, the high proportion of full-time teachers with senior titles, reasonable resource allocation structure and government support have significant positive impact, whereas the number of participants in international academic has negative impact. INDEX TERMSInfluencing factor, Malmquist index, scientific & technological innovation, superefficient DEA model, world-class university.

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Section: Introductionmentioning

confidence: 99%

The Scientific and Technological Innovation Performance of Chinese World-Class Universities and its Influencing Factors

Chen

Shu

2021

IEEE Access

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“…Recently, parallel to the increase in competition among universities, studies related to their evaluation have gained increasing attention from different perspectives such as overall performance (Aliyev et al, 2020 ; Nazari-Shirkouhi et al, 2020 ; Wu et al, 2012 ; Zolfani & Ghadikolaei, 2013 ), research capacity (Duc et al, 2020 ; Su et al, 2020 ), innovation (Chen & Chen, 2010 ), a department of a university (Muhammad et al, 2021 ), sustainability (Arora et al, 2020 ; Puente et al, 2020 ), lean philosophy (Kazancoglu & Ozkan-Ozen, 2019 ; Klein et al, 2021 ), and student viewpoint (Castro-Lopez et al, 2021 ; Goyal et al, 2021 ; Kabak & Dağdeviren, 2014 ; Nanath et al, 2021 ; Nojavan et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…They applied this model to evaluate research capacity at Vietnam University. Su et al ( 2020 ) proposed a model that uses MCDM and output-oriented nonradial super efficiency Data Envelopment Analysis (DEA) to evaluate the research efficiency of Chinese universities. There are few applications in the literature on university ranking problems based on the perspective of innovation.…”

Section: Literature Reviewmentioning

confidence: 99%

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A novel hybrid MCDM approach to evaluate universities based on student perspective

et al. 2022

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University rankings are an essential source of comparisons between universities according to specific combinations of criteria. International or national rankings have an increasing impact on higher education institutions, stakeholders, and their environments. Thereby, on behalf of effective decision-making, university-ranking efforts should be a process involving some conflicting criteria and uncertainties in a more sensitive manner. This study presents a detailed university evaluation procedure under certain service criteria via multi-criteria decision-making (MCDM) methodologies and provides an appropriate clustering of universities according to teaching and research factors. A hierarchical cluster-based Interval Valued Neutrosophic Analytic Hierarchy Process (IVN-AHP) integrated VIKOR methodology that includes two stages, clustering and ranking, is proposed for the university evaluation problem. The hierarchical clustering method is performed using teaching and research factors in the first stage. The second stage addresses the determination weights of service criteria through IVN-AHP and the ranking of universities by using VIKOR according to service criteria under determined clusters. This study, in which the proposed methodology is applied to Turkish universities, is the most comprehensive in terms of the number of universities evaluated and participating students. Furthermore, the integration of IVN-AHP and VIKOR to solve MCDM problems is presented for the first time. This study differs from other studies in terms of novelties both methodological-based and application based. Moreover, categorizing universities with similar characteristics into groups using cluster analysis and ranking them with the MCDM methodology provide a more realistic and effective interpretation of the results.

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“…When the literature is examined, it is seen that research efficiency is measured for different units and with different methods. Research programs (Cherchye and Abeele, 2005), researchers (Amara et al, 2020), higher education institutions (Dimyati and Hermanu, 2022;Jiang et al, 2020;Munoz, 2016), universities (Canal et al, 2015;Li et al, 2023;Shi et al, 2022;Wang and Wang, 2021), R&D programs (Jimenez-Saez et al, 2011), academic disciplines (Su et al, 2020), a specific field (Zhang et al, 2022), institute (Amara et al, 2020;Liao et al, 2016) IJEM 37,6/7 1162…”

Section: Introductionmentioning

confidence: 99%

Examination of research efficiency of research universities in Türkiye by data envelopment analysis

Maral

2023

IJEM

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PurposeThe purpose of this study is to examine the research efficiency of the research universities in Türkiye and to identify potential areas for improvement and to examine the factors affecting efficiency.Design/methodology/approachFor this purpose, data envelopment analysis was conducted with the output-oriented Banker, Charnes and Cooper (BCC) model with five-year article and citation counts data of 23 universities which were obtained from the Web of Science Core Collection.FindingsThe findings of the study show that only eight research universities are efficient. There are areas of development for universities to be effective. In addition, the findings of the regression analysis conducted to reveal the determinants of efficiency revealed significant results.Research limitations/implicationsResearch universities in Türkiye should allocate their resources in a way to increase research performance. Policies should be developed to increase the number of publications and, more importantly, the quality of publications.Originality/valuePotential areas for improvement were identified for the universities to become efficient. The results revealed that both publication quality and productivity need to be improved, but there is more room for improvement in publication quality. Regression analysis with the determinants of efficiency scores shows that the development level of the region where the universities are located has a positive effect on the research efficiency of universities. In addition, it is concluded that smaller universities have better efficiency scores.

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A Nonradial Super Efficiency DEA Framework Using a MCDM to Measure the Research Efficiency of Disciplines at Chinese Universities

Cited by 15 publications

References 51 publications

The Scientific and Technological Innovation Performance of Chinese World-Class Universities and its Influencing Factors

The Scientific and Technological Innovation Performance of Chinese World-Class Universities and its Influencing Factors

A novel hybrid MCDM approach to evaluate universities based on student perspective

Examination of research efficiency of research universities in Türkiye by data envelopment analysis

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