The Application of Data Envelopment Analysis in Evaluating the Performance of Universities and Higher Education Institutions: A Systematic Review of the Literature

Document Type : Original Article

Authors

1 MSc. Student, University of Mazandaran.

2 Assistant Professor, University of Mazandaran.

3 Professor, University of Mazandaran.

Abstract

The efficiency of universities and higher education institutions is considered by many researchers because of their strategic role in the development and economy of each country, because the evaluation of the efficiency of universities helps to implement effective programs for the development of higher education. The literature on evaluating the efficiency of universities and higher education institutions has evolved over the past decades. However, the divergence of approaches, process areas, differences in output and input variables of previous studies unveils the importance of conducting a systematic review on the use of data envelopment analysis technique in evaluating the performance of universities and higher education institutions. The purpose of this study is conducting such a review and identifying future trends in this field of research using a combination of systematic literature review and citation network analysis. After determining the search protocol and article selection criteria, 165 articles were finally selected and analyzed. The results show that, in recent years, in addition to educational and research activities, the performance of universities has been evaluated in terms of the performance of entrepreneurship and university-industry relations, which can be considered in development and improvement programs.

Keywords

References

  1. Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis. Economics of Education review, 22(1), 89-97.
  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.
  3. Agasisti, T., Catalano, G., Landoni, P., & Verganti, R. (2012). Evaluating the performance of academic departments: An analysis of research-related output efficiency. Research Evaluation, 21(1), 2-14.
  4. Azar, A., Gholamrezaei, D., Danaei Fard, H., Khodadad Hosseini, H. (2013). Analysis of University-Industry Relation in Higher Education Policies of the Fifth Development Plan using System Dynamics. Journal of Industrial Management Perspective, 3(Issue 1), Spring 2013, 79-115.(in Persian)
  5. Banker, R. D. (1984). Estimating most productive scale size using data envelopment analysis. European journal of operational research, 17(1), 35-44.
  6. Banker, R. D. (1989). An introduction to DEA with some of its models and their uses. Research in Governmental and Nonprofit accounting, 5, 125-163.
  7. Batagelj, V. (2003). Efficient algorithms for citation network analysis. arXiv preprint cs/0309023.
  8. Berbegal-Mirabent, J., García, J. L. S., & Ribeiro-Soriano, D. E. (2015). University–industry partnerships for the provision of R&D services. Journal of Business Research, 68(7), 1407-1413.
  9. Berbegal-Mirabent, J., Lafuente, E., & Solé, F. (2013). The pursuit of knowledge transfer activities: An efficiency analysis of Spanish universities. Journal of Business Research, 66(10), 2051-2059.
  10. Calero-Medina, C., & Noyons, E. C. (2008). Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field. Journal of Informetrics, 2(4), 272-279.
  11. Chang, T. Y., Chung, P. H., & Hsu, S. S. (2012). Two-stage performance model for evaluating the managerial efficiency of higher education: Application by the Taiwanese tourism and leisure department. Journal of Hospitality, Leisure, Sport & Tourism Education, 11(2), 168-177.
  12. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
  13. Chen, Y., Chen, Y., & Oztekin, A. (2017). A hybrid data envelopment analysis approach to analyse college graduation rate at higher education institutions. INFOR: Information Systems and Operational Research, 55(3), 188-210.
  14. Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)–Thirty years on. European journal of operational research, 192(1), 1-17.
  15. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software: Springer Science & Business Media. New York, USA.
  16. De França, J. M. F., de Figueiredo, J. N., & dos Santos Lapa, J. (2010). A DEA methodology to evaluate the impact of information asymmetry on the efficiency of not-for-profit organizations with an application to higher education in Brazil. Annals of Operations Research, 173(1), 39-56.
  17. De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek (Vol. 27) Cambridge University Press.
  18. Dufrechou, P. A. (2016). The efficiency of public education spending in Latin America: A comparison to high-income countries. International Journal of Educational Development, 49, 188-203.
  19. Edirisinghe, N. C. P., & Zhang, X. (2010). Input/output selection in DEA under expert information, with application to financial markets. European journal of operational research, 207(3), 1669-1678.
  20. Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8.
  21. Eva, M., Sagarra, M., & Agasisti, T. (2016). Assessing organizations’ efficiency adopting complementary perspectives: An empirical analysis through data envelopment analysis and multidimensional scaling, with an application to higher education. In Handbook of operations analytics using data envelopment analysis (pp. 145-166). Springer, Boston, MA.
  22. Fan, D., Lo, C. K., Ching, V., & Kan, C. W. (2014). Occupational health and safety issues in operations management: A systematic and citation network analysis review. International Journal of Production Economics, 158, 334-344.
  23. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
  24. Foladi, S., Solimanpur, M., & Rezaee, M. J. (2020). Inverse Dynamic Data Envelopment Analysis for Evaluating Faculties of University with Quasi-Fixed Inputs. Social Indicators Research, 148(1), 323-347.
  25. Gharib, A., Azar, A., Moghbel Ba Erz, A., Dehghan Nayeri, M. (2019). Designing Organizational Innovation Measuring Model with Dynamic Network DEA (Case Study: Iranian First Level Universities). Journal of Industrial Management Perspective, 9(Issue 1), Spring 2019, 9-29.(In Persian)
  26. Huang, M. H., & Chen, D. Z. (2017). How can academic innovation performance in university–industry collaboration be improved?. Technological Forecasting and Social Change, 123, 210-215.
  27. Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social networks, 11(1), 39-63.
  28. Johnes, G., & Johnes, J. (1993). Measuring the research performance of UK economics departments: an application of data envelopment analysis. Oxford economic papers, 332-347.
  29. Johnes, J. (2004). 16 Efficiency measurement. International handbook on the economics of education, 613.
  30. Johnes, J. (2006). Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of education review, 25(3), 273-288.
  31. Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering–a tertiary study. Information and software technology, 52(8), 792-805.
  32. Köksal, G., & Nalçaci, B. (2006). The relative efficiency of departments at a Turkish engineering college: A data envelopment analysis. Higher Education, 51(2), 173-189.
  33. Kuan, C. H., Huang, M. H., & Chen, D. Z. (2018). Missing links: Timing characteristics and their implications for capturing contemporaneous technological developments. Journal of Informetrics, 12(1), 259-270.
  34. Liang, N., Chen, Y., Zha, Y., & Hu, H. (2015). Performance evaluation of Individuals in workgroups with shared outcomes using DEA. INFOR: Information Systems and Operational Research, 53(2), 78-89.
  35. Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). A survey of DEA applications. Omega, 41(5), 893-902.
  36. Lu, W. M. (2012). Intellectual capital and university performance in Taiwan. Economic Modelling, 29(4), 1081-1089.
  37. Lucio‐Arias, D., & Leydesdorff, L. (2008). Main‐path analysis and path‐dependent transitions in HistCite™‐based historiograms. Journal of the American Society for Information Science and Technology, 59(12), 1948-1962.
  38. Mariano, E. B., Sobreiro, V. A., & do Nascimento Rebelatto, D. A. (2015). Human development and data envelopment analysis: A structured literature review. Omega, 54, 33-49.
  39. Navas, L. P., Montes, F., Abolghasem, S., Salas, R. J., Toloo, M., & Zarama, R. (2020). Colombian higher education institutions evaluation. Socio-Economic Planning Sciences, 100801.
  40. Nazarko, J., & Šaparauskas, J. (2014). Application of DEA method in efficiency evaluation of public higher education institutions. Technological and Economic development of Economy, 20(1), 25-44.
  41. Park, H., & Magee, C. L. (2017). Tracing technological development trajectories: A genetic knowledge persistence-based main path approach. PloS one, 12(1), e0170895.
  42. Pelone, F., Kringos, D. S., Romaniello, A., Archibugi, M., Salsiri, C., & Ricciardi, W. (2015). Primary care efficiency measurement using data envelopment analysis: a systematic review. Journal of medical systems, 39(1), 156.
  43. Podinovski, V. V., & Husain, W. R. W. (2017). The hybrid returns-to-scale model and its extension by production trade-offs: an application to the efficiency assessment of public universities in Malaysia. Annals of Operations Research, 250(1), 65-84.
  44. Qin, C., Zhang, W., & Zhu, Y. (2018). Study on the Contribution Rate Variation of Teaching and Research of University Teachers Based on the Joint Benefit Assessment Method. Educational Sciences: Theory & Practice, 18(5), 1887-1906.
  45. Rhaiem, M. (2017). Measurement and determinants of academic research efficiency: a systematic review of the evidence. Scientometrics, 110(2), 581-615.
  46. Sahoo, B. K., Singh, R., Mishra, B., & Sankaran, K. (2017). Research productivity in management schools of India during 1968-2015: A directional benefit-of-doubt model analysis. Omega, 66, 118-139.
  47. Salo, A., & Punkka, A. (2011). Ranking intervals and dominance relations for ratio-based efficiency analysis. Management Science, 57(1), 200-214.
  48. Shaout, A., & Yousif, M. K. (2014). Performance evaluation–Methods and techniques survey. International Journal of Computer and Information Technology, 3(5), 966-979.
  49. Small, H. G. (1978). Cited documents as concept symbols. Social studies of science, 8(3), 327-340.
  50. Talebi, D., Arashpour, A. (2013). Educational Performance Evaluation with the Comparative Approach of ANP and DEMATEL. Journal of Industrial Management Perspective, 3(Issue 2), Summer 2013, 85-100.(in Persian)
  51. Thursby, J. G., & Kemp, S. (2002). Growth and productive efficiency of university intellectual property licensing. Research policy, 31(1), 109-124.
  52. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.
  53. Visbal-Cadavid, D., Martínez-Gómez, M., & Guijarro, F. (2017). Assessing the efficiency of public universities through DEA. A case study. Sustainability, 9(8), 1416.
  54. Wang, X., & Hu, H. (2017). Sustainable evaluation of social science research in higher education institutions based on data envelopment analysis. Sustainability, 9(4), 644.
  55. Wilding, R., Wagner, B., Colicchia, C., & Strozzi, F. (2012). Supply chain risk management: a new methodology for a systematic literature review. Supply Chain Management: An International Journal, 17(4), 403-418.
  56. Witte, K. D., & López-Torres, L. (2017). Efficiency in education: a review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339-363.
  57. Wolszczak-Derlacz, J. (2017). An evaluation and explanation of (in) efficiency in higher education institutions in Europe and the US with the application of two-stage semi-parametric DEA. Research Policy, 46(9), 1595-1605.
  58. Wolszczak-Derlacz, J., & Parteka, A. (2011). Efficiency of European public higher education institutions: a two-stage multicountry approach. Scientometrics, 89(3), 887.
  59. Zhou, H., Yang, Y., Chen, Y., & Zhu, J. (2018). Data envelopment analysis application in sustainability: The origins, development and future directions. European Journal of Operational Research, 264(1), 1-16.
  60. Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European journal of operational research, 189(1), 1-18.
  61. Zhu, W., Wan, M., Zhou, Y., & Pan, W. (2018). Fuzzy computation of teaching performance based on data envelopment analysis method. Cognitive Systems Research, 52, 351-358.

Files

Share

How to cite

Statistics