Document Type : Articles

Authors

1 Student Research Committee, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran

2 Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

3 Associate Professor, Medical Library and Information Sciences Department, School of Management and Information Science, Kerman University of Medical Sciences, Kerman, Iran

Abstract

University rankings are often based on measurable outputs. Research institutes and universities are expected to be evaluated based on their capabilities, inputs, and outputs and it is important to do balanced and comprehensive evaluations. Criticisms towards global ranking systems have led them to reform their methodologies, consider the differences in the mission of universities, normalize data based on subjects, and consider the size and age of universities. The data envelopment analysis (DEA) method is a complementary tool to increase the ranking transparency. This study aimed to evaluate the research efficiency of Iranian universities ranked in the Times Higher Education World University Rankings by using the DEA method and to discover the relationship between universities' performance, their rank, research scores, and their citation score in the Times Higher Education Ranking System. The research population included 47 universities. Three inputs and 15 outputs were used in different models. The Microsoft Excel and DEAP software were used to extract the data, define the scenarios and analyze the data. The results showed the relatively good performance of the universities. There was no relationship between the universities’ efficiency score and their research score in the Times Ranking System, but a significant positive relationship was observed between the efficiency score and the universities’ rank and the citation score in the Times Ranking System (0.719 and 0.613, respectively). It seems that the DEA method can be used as a complementary tool to evaluate the technical performance and allocate funds to universities and research institutes.https://dorl.net/dor/20.1001.1.20088302.2022.20.3.6.3    

Keywords

  1. Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review, 22(1), 89-97. https://doi.org/https://doi.org/10.1016/S0272-7757(01)00068-1
  2. Abramo, G., Cicero, T., & D’Angelo, C. A. (2011). A field-standardized application of DEA to national-scale research assessment of universities. Journal of Informetrics, 5(4), 618-628. https://doi.org/https://doi.org/10.1016/j.joi.2011.06.001
  3. Aguillo, I. F., Bar-Ilan, J., Levene, M., & Ortega, J. L. (2010). Comparing university rankings. Scientometrics, 85(1), 243-256. https://doi.org/10.1007/s11192-010-0190-z
  4. Ahmad, A. B., & Shah, M. (2018). International students’ choice to study in China: an exploratory study. Tertiary Education and Management, 24(4), 325-337. https://doi.org/10.1080/13583883.2018.1458247
  5. Altbach, P. G., & Hazelkorn, E. (2017). Pursuing rankings in the age of massification: For most—forget about it. International Higher Education(89), 8-10.
  6. Chakoli, A. N., & Ghazavi, R. (2016). Normalization and Valuation of Research Evaluation Indicators in Different Scientific Fields. Journal of Information Science Theory and Practice, 4(1).
  7. Comprehensive Scientific Map of Iran. (2010). Supreme Council of the Cultural Revolution Retrieved from https://www.msrt.ir/file/download/page/1488284345-m01.pdf
  8. Dumitrescu, D., Costică, I., Simionescu, L. N., & Gherghina, Ş. C. (2020). A DEA Approach Towards Exploring the Sustainability of Funding in Higher Education. Empirical Evidence from Romanian Public Universities. Amfiteatru Economic, 22(54), 593-607.
  9. Frey, B. S., & Rost, K. (2010). Do Rankings Reflect Research Quality? Journal of Applied Economics, 13(1), 1-38. https://doi.org/10.1016/S1514-0326(10)60002-5
  10. Gong, X., & Huybers, T. (2015). Chinese students and higher education destinations: Findings from a choice experiment. Australian Journal of Education, 59(2), 196-218. https://doi.org/10.1177/0004944115584482
  11. Gonzalez-Garay, A., Pozo, C., Galan-Martin, Á., Brechtelsbauer, C., Chachuat, B., Chadha, D., Hale, C., Hellgardt, K., Kogelbauer, A., Matar, O. K., McDowell, N., Shah, N., & Guillen-Gosalbez, G. (2019). Assessing the performance of UK universities in the field of chemical engineering using data envelopment analysis. Education for Chemical Engineers, 29, 29-41. https://doi.org/https://doi.org/10.1016/j.ece.2019.06.003
  12. Ioannidis, J. P. A., Patsopoulos, N. A., Kavvoura, F. K., Tatsioni, A., Evangelou, E., Kouri, I., Contopoulos-Ioannidis, D. G., & Liberopoulos, G. (2007). International ranking systems for universities and institutions: a critical appraisal. BMC Medicine, 5(1), 30. https://doi.org/10.1186/1741-7015-5-30
  13. Ji, Y.-b., & Lee, C. (2010). Data envelopment analysis. The Stata Journal, 10(2), 267-280.
  14. Lin, T. T., Lee, C.-C., & Chiu, T.-F. (2009). Application of DEA in analyzing a bank’s operating performance. Expert Systems with Applications, 36(5), 8883-8891. https://doi.org/https://doi.org/10.1016/j.eswa.2008.11.018
  15. Martin, E. (2006). Efficiency and Quality in the Current Higher Education Context in Europe: an application of the data envelopment analysis methodology to performance assessment of departments within the University of Zaragoza. Quality in Higher Education, 12(1), 57-79. https://doi.org/10.1080/13538320600685172
  16. Mehrolhassani, M. H., Goudarzi, R., Yazdi Feyzabadi, V., Pourhosseini, S. S., & Darvishi, A. (2019). Efficiency and Productivity Measurement in Research Sector of Iranian Medical Sciences Universities Using Data Envelopment Analysis and Malmquist Index [سنجش کارایی و بهره‌وری پژوهشی دانشگاه‌های علوم پزشکی در ایران با استفاده از روش تحلیل پوششی داده‌ها و شاخص مالم کوئیست]. irje, 14(0), 1-11. http://irje.tums.ac.ir/article-1-6139-en.html
  17. Moed, H. F. (2017). A critical comparative analysis of five world university rankings. Scientometrics, 110(2), 967-990. https://doi.org/10.1007/s11192-016-2212-y
  18. Montoneri, B., Lin, T. T., Lee, C.-C., & Huang, S.-L. (2012). Application of data envelopment analysis on the indicators contributing to learning and teaching performance. Teaching and Teacher Education, 28(3), 382-395. https://doi.org/https://doi.org/10.1016/j.tate.2011.11.006
  19. Muñoz-Suarez, M., Guadalajara, N., & Osca, J. M. (2020). A Comparative Analysis between Global University Rankings and Environmental Sustainability of Universities. Sustainability, 12(14). https://doi.org/10.3390/su12145759
  20. Perez-Esparrells, C., & Orduna-Malea, E. (2018). Do the technical universities exhibit distinct behaviour in global university rankings? A Times Higher Education (THE) case study. Journal of Engineering and Technology Management, 48, 97-108. https://doi.org/https://doi.org/10.1016/j.jengtecman.2018.04.007
  21. Rabar, D. (2017). An overview of data envelopment analysis application in studies on the socio-economic performance of OECD countries. Economic Research-Ekonomska Istraživanja, 30(1), 1770-1784. https://doi.org/10.1080/1331677X.2017.1383178
  22. Saisana, M., d’Hombres, B., & Saltelli, A. (2011). Rickety numbers: Volatility of university rankings and policy implications. Research Policy, 40(1), 165-177. https://doi.org/https://doi.org/10.1016/j.respol.2010.09.003
  23. Salas-Velasco, M. (2020). The technical efficiency performance of the higher education systems based on data envelopment analysis with an illustration for the Spanish case. Educational Research for Policy and Practice, 19(2), 159-180. https://doi.org/10.1007/s10671-019-09254-5
  24. Samoilenko, S., & Osei-Bryson, K.-M. (2008). Increasing the discriminatory power of DEA in the presence of the sample heterogeneity with cluster analysis and decision trees. Expert Systems with Applications, 34(2), 1568-1581. https://doi.org/https://doi.org/10.1016/j.eswa.2007.01.039
  25. Samoilenko, S., & Osei-Bryson, K.-M. (2013). Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system. Omega, 41(1), 131-142. https://doi.org/https://doi.org/10.1016/j.omega.2011.02.010
  26. Taylor, P., & Braddock, R. (2007). International University Ranking Systems and the Idea of University Excellence. Journal of Higher Education Policy and Management, 29(3), 245-260. https://doi.org/10.1080/13600800701457855
  27. Torres-Samuel, M., Vasquez, C. L., Luna, M., Bucci, N., Viloria, A., Crissien, T., & Manosalva, J. (2020). Performance of Education and Research in Latin American Countries through Data Envelopment Analysis (DEA). Procedia Computer Science, 170, 1023-1028. https://doi.org/https://doi.org/10.1016/j.procs.2020.03.079
  28. Vernon, M. M., Balas, E. A., & Momani, S. (2018). Are university rankings useful to improve research? A systematic review. PLoS One, 13(3), e0193762. https://doi.org/10.1371/journal.pone.0193762
  29. Wu, J., Zhang, G., Zhu, Q., & Zhou, Z. (2020). An efficiency analysis of higher education institutions in China from a regional perspective considering the external environmental impact. Scientometrics, 122(1), 57-70. https://doi.org/10.1007/s11192-019-03296-5
  30. Yang, G.-l., Fukuyama, H., & Song, Y.-y. (2018). Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model. Journal of Informetrics, 12(1), 10-30. https://doi.org/https://doi.org/10.1016/j.joi.2017.11.002