scholarly journals Self-defined information indices: application to the case of university rankings

2020 ◽  
Vol 124 (3) ◽  
pp. 2443-2456
Author(s):  
A. Ferrer-Sapena ◽  
E. Erdogan ◽  
E Jiménez-Fernández ◽  
E. A. Sánchez-Pérez ◽  
F. Peset
Keyword(s):  
University Rankings ◽  
Information Indices

Multiscale Mapping of AIDS in U.S. Countries vs Anti-HIV Drugs Activity with Complex Networks and Information Indices

2015 ◽  
Vol 10 (5) ◽  
pp. 639-657 ◽  
Author(s):  
Diana Maria Herrera-Ibata ◽  
Ricardo Alfredo Orbegozo-Medina ◽  
Humberto Gonzalez-Diaz
Keyword(s):  
Complex Networks ◽  
Information Indices ◽  
Hiv Drugs ◽  
Anti Hiv

scholarly journals A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 459
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Central Tendency ◽  
Performance Variables ◽  
World University Rankings ◽  
Goal Programming Model ◽  
Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

Corporate Social Responsibility and Sustainability (CSRS) Initiatives among European and Asian Business Schools: A Web-based Content Analysis

2017 ◽  
Vol 20 (5) ◽  
pp. 1231-1247 ◽  
Author(s):  
Mohsin Abdur Rehman ◽  
Muhammad Kashif ◽  
Michela Mingione
Keyword(s):  
Corporate Social Responsibility ◽  
Content Analysis ◽  
Social Responsibility ◽  
Business School ◽  
University Rankings ◽  
Global Business ◽  
Web Based ◽  
Corporate Social ◽  
Business And Management ◽  
Practical Implications

The purpose of this study is to explore the extent to which MBA programmes offered by top European and Asian B-schools have a corporate social responsibility and sustainability (CSRS) orientation as per their websites. The websites of top-200 (based on the QS Global Business and Management University Rankings 2015) European and Asian B-schools were explored and content analysed to reach meaningful conclusions. The findings reveal European B-schools have much stronger CSRS orientation once compared with the Asian B-schools. Furthermore, only few B-schools promote CSRS centres on their websites which has some useful practical implications. This is the first study to explore the CSRS orientation among top-200 European and Asian B-schools based on an analysis of their respective websites. Additionally, a cross-continental comparison between European and Asian MBA programmes is unique to this study. The results have implications for global managers, in general, and business school policymakers, in specific, to embark the CSR initiatives to gain competitive advantage.

Local (University) Rankings and Quality of Education: Identification of Publication Activity Indicators

2021 ◽  
Author(s):  
Artem Artyukhov ◽  
Oleksandr Dluhopolskyi ◽  
Tetiana Vasylieva ◽  
Serhiy Lyeonov ◽  
Tetiana Dluhopolska ◽  
...  
Keyword(s):  
Publication Activity ◽  
Quality Of Education ◽  
University Rankings

How Rankings Produce Competition: The Case of Global University Rankings

2018 ◽  
Vol 47 (4) ◽  
pp. 270-288 ◽  
Author(s):  
Jelena Brankovic ◽  
Leopold Ringel ◽  
Tobias Werron
Keyword(s):  
University Rankings ◽  
Global University

ZusammenfassungDer Zusammenhang zwischen Rankings und Konkurrenz wird häufig unterstellt, aber selten genauer untersucht. Der vorliegende Aufsatz geht ihm am Beispiel globaler Universitätsrankings nach. Ausgehend von einem soziologischen Verständnis von Konkurrenz bestimmen wir „Ranken“ als eine soziale Operation, die vier Teiloperationen miteinander kombiniert: Vergleich von Leistungen, Quantifizierung, Visualisierung, und wiederholte Publikation. Visualisierung und Publikation stehen für die in der Literatur bisher kaum berücksichtigte performative Dimension von Rankings, die für die Analyse des Zusammenhangs zwischen Rankings und Konkurrenz von zentraler Bedeutung ist. Auf dieser Grundlage zeigen wir, wie globale Universitätsrankings zur Konstruktion von Konkurrenz beitragen: durch (a) Globalisierung eines spezifischen Exzellenzdiskurses; (b) Verknappung von Reputation; (c) Transformation einer stabilen in eine dynamische Statusordnung. Wir schließen mit einer Diskussion von Implikationen dieser Analyse für die soziologische Erforschung von Konkurrenz und ihrer gesellschaftlichen Effekte.

Is it possible to rank universities using fewer indicators? A study on five international university rankings

2019 ◽  
Vol 71 (1) ◽  
pp. 18-37 ◽  
Author(s):  
Güleda Doğan ◽  
Umut Al
Keyword(s):  
Similarity Measure ◽  
Decision Makers ◽  
Cosine Similarity ◽  
University Rankings ◽  
University Ranking ◽  
Data Set ◽  
Content Type ◽  
Research Questions ◽  
Cosine Similarity Measure ◽  
International University

Purpose The purpose of this paper is to analyze the similarity of intra-indicators used in research-focused international university rankings (Academic Ranking of World Universities (ARWU), NTU, University Ranking by Academic Performance (URAP), Quacquarelli Symonds (QS) and Round University Ranking (RUR)) over years, and show the effect of similar indicators on overall rankings for 2015. The research questions addressed in this study in accordance with these purposes are as follows: At what level are the intra-indicators used in international university rankings similar? Is it possible to group intra-indicators according to their similarities? What is the effect of similar intra-indicators on overall rankings? Design/methodology/approach Indicator-based scores of all universities in five research-focused international university rankings for all years they ranked form the data set of this study for the first and second research questions. The authors used a multidimensional scaling (MDS) and cosine similarity measure to analyze similarity of indicators and to answer these two research questions. Indicator-based scores and overall ranking scores for 2015 are used as data and Spearman correlation test is applied to answer the third research question. Findings Results of the analyses show that the intra-indicators used in ARWU, NTU and URAP are highly similar and that they can be grouped according to their similarities. The authors also examined the effect of similar indicators on 2015 overall ranking lists for these three rankings. NTU and URAP are affected least from the omitted similar indicators, which means it is possible for these two rankings to create very similar overall ranking lists to the existing overall ranking using fewer indicators. Research limitations/implications CWTS, Mapping Scientific Excellence, Nature Index, and SCImago Institutions Rankings (until 2015) are not included in the scope of this paper, since they do not create overall ranking lists. Likewise, Times Higher Education, CWUR and US are not included because of not presenting indicator-based scores. Required data were not accessible for QS for 2010 and 2011. Moreover, although QS ranks more than 700 universities, only first 400 universities in 2012–2015 rankings were able to be analyzed. Although QS’s and RUR’s data were analyzed in this study, it was statistically not possible to reach any conclusion for these two rankings. Practical implications The results of this study may be considered mainly by ranking bodies, policy- and decision-makers. The ranking bodies may use the results to review the indicators they use, to decide on which indicators to use in their rankings, and to question if it is necessary to continue overall rankings. Policy- and decision-makers may also benefit from the results of this study by thinking of giving up using overall ranking results as an important input in their decisions and policies. Originality/value This study is the first to use a MDS and cosine similarity measure for revealing the similarity of indicators. Ranking data is skewed that require conducting nonparametric statistical analysis; therefore, MDS is used. The study covers all ranking years and all universities in the ranking lists, and is different from the similar studies in the literature that analyze data for shorter time intervals and top-ranked universities in the ranking lists. It can be said that the similarity of intra-indicators for URAP, NTU and RUR is analyzed for the first time in this study, based on the literature review.

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