Skip to main content
Springer Nature Link
Log in

Measuring the efficiency of university departments: an empirical study using data envelopment analysis and cluster analysis

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Universities continue to play a key role in the development of a country. As university income streams shift away from government and as the income from admissions decline under the sub-replacement fertility phenomenon, the efficiency of resource utilization has become an important issue for university administrators. This paper applies data envelopment analysis (DEA) and the concept of an assurance region to evaluate the relative efficiency (including aggregate, technical, and scale efficiencies) of academic departments at National Chung Cheng University in Taiwan. The input factors considered are personnel (expressed in the number of faculty-equivalent persons), operating expenses, and floor area, and the output factors are teaching (expressed in total credit hours), publications (expressed in the number of papers), and external grants. Notably, teaching quality is included by considering the classroom capacity in calculating credit hours, and publication quality is included by considering the author contribution to calculate the single-author-equivalent numbers of papers. In addition, a cluster analysis based on the efficiency decomposition to the contributions of the three outputs is applied to classify the departments into three groups. The results of this paper not only provide the department head with the relative efficiency and improvement directions for the department but also serve as a reference for resource allocation and future strategy development for the university administration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • AACSB. (2020). Educational members. https://www.aacsb.edu/membership/listings/all-educational-members?F_Accreditation=Business&F_Country=Taiwan. Accessed June 21, 2020.

  • Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: A data envelopment analysis. Economics of Education Review, 22, 89–97.

    Article  Google Scholar 

  • Academic Affairs Office of the National Chung Cheng University. (2020). Annual Report 2018, National Chung Cheng University. http://oaa.ccu.edu.tw/services_03.php Accessed April 30, 2020.

  • Agasisti, T., & Wolszczak-Derlacz, J. (2015). Exploring efficiency differentials between Italian and Polish universities, 2001–11. Science and Public Policy, 43, 128–142.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.

    Article  Google Scholar 

  • Barra, C., & Zotti, R. (2016). Measuring efficiency in higher education: an empirical study using a bootstrapped data envelopment analysis. International Advances in Economic Research, 22, 11–33.

    Article  Google Scholar 

  • Bowlin, W. F. (1998). Measuring performance: an introduction to data envelopment analysis (DEA). Journal of Cost Analysis, 7, 3–27.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  MathSciNet  Google Scholar 

  • Department of Statistics of the Ministry of Education, Taiwan. (2018). Highlights of education statistics. http://stats.moe.gov.tw/files/brief/%E5%A4%A7%E5%B0%88%E6%A0%A1%E9%99%A2%E5%AD%B8%E7%94%9F%E4%BA%BA%E6%95%B8%E5%88%86%E6%9E%90.pdf. Accessed July 8, 2020.

  • Department of Statistics of the Ministry of Education, Taiwan. (2020). Main statistical tables: Overview of schools at all levels over the years. https://depart.moe.edu.tw/ed4500/cp.aspx?n=1B58E0B736635285&s=D04C74553DB60CAD. Accessed June 21, 2020.

  • Ding, T., Yang, J., Wu, H., Wen, Y., Tan, C., & Liang, L. (2020). Research performance evaluation of Chinese university: A non-homogeneous network DEA approach. Journal of Management Science and Engineering. https://doi.org/10.1016/j.jmse.2020.10.003.

    Article  Google Scholar 

  • Dumitrescu, D., Costică, I., Simionescu, L. N., & Gherghina, ŞC. (2020). A DEA approach towards exploring the sustainability of funding in higher education: Empirical evidence from Romanian public universities. Amfiteatru Economic, 22, 593–607.

    Google Scholar 

  • Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and Protocols in DEA. European Journal of Operational Research, 132, 245–259.

    Article  Google Scholar 

  • Francis, J. (1995). Statistica for Windows. Blood Coagulation & Fibrinolysis, 6, 145.

    Article  Google Scholar 

  • Gander, J. P. (1995). Academic research and teaching productivities: A case study. Technological Forecasting and Social Change, 49, 311–319.

    Article  Google Scholar 

  • Ghasemi, N., Najafi, E., Lotfi, F. H., & Sobhani, F. M. (2020). Assessing the performance of organizations with the hierarchical structure using data envelopment analysis: An efficiency analysis of Farhangian University. Measurement, 156, 107609.

    Article  Google Scholar 

  • Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17, 237–250.

    Article  Google Scholar 

  • Guironnet, J. P., & Peypoch, N. (2018). The geographical efficiency of education and research: The ranking of US universities. Socio-Economic Planning Sciences, 62, 44–55.

    Article  Google Scholar 

  • Higgins, J. C. (1989). Performance measurement in universities. European Journal of Operational Research, 38, 358–368.

    Article  Google Scholar 

  • Hou, B., Hong, J., & Shi, X. (2019). Efficiency of university–industry collaboration and its determinants: evidence from Chinese leading universities. Industry and Innovation. https://doi.org/10.1080/13662716.2019.1706455.

    Article  Google Scholar 

  • Johnson, R. A., & Wichern, D. W. (2002). Applied multivariate statistical analysis. (5th ed.). Upper Saddle River: Prentice Hall.

    MATH  Google Scholar 

  • Kao, C. (2017). Network data envelopment analysis: Foundations and extensions. Springer.

    Book  Google Scholar 

  • Kao, C., & Hung, H.-T. (2008). Efficiency analysis of university departments: An empirical study. Omega, 36, 653–664.

    Article  Google Scholar 

  • Kaycheng, S. (2015). Multicolinearity and indicator redundancy problem in world university rankings: An example using times higher education world university ranking 2013–2014 data. Higher Education Quarterly, 69, 158–174.

    Article  Google Scholar 

  • Klumpp, M. (2018). The index number problem with DEA: Insights from European University efficiency data. Education Sciences, 8, 79. https://doi.org/10.3390/educsci8020079.

    Article  Google Scholar 

  • Koçak, D., Türe, H., & Atan, M. (2019). Efficiency measurement with network DEA: An application to sustainable development goals 4. International Journal of Assessment Tools in Education, 6, 415–435.

    Google Scholar 

  • Kong, W.-H., & Fu, T.-T. (2012). Assessing the performance of business colleges in Taiwan using data envelopment analysis and student based value-added performance indicators. Omega, 40, 541–549.

    Article  Google Scholar 

  • Łącka, I., & Brzezicki, Ł. (2020). Efficiency of the research and development activities of technical universities in Poland. Social Inequalities and Economic Growth, 63, 258–274.

    Google Scholar 

  • Laws and Regulations Database of Taiwan. (2015). National University Endowment Fund Establishment Act. https://law.moj.gov.tw/LawClass/LawAll.aspx?PCode=H0030026. Accessed June 21, 2020.

  • Lee, B. L., & Worthington, A. C. (2016). A network DEA quantity and quality-orientated production model: An application to Australian university research services. Omega, 60, 26–33.

    Article  Google Scholar 

  • Lu, W.-M. (2012). Intellectual capital and university performance in Taiwan. Economic Modelling, 29, 1081–1089.

    Article  Google Scholar 

  • Moncayo-Martínez, L. A., Ramírez-Nafarrate, A., & Hernández-Balderrama, M. G. (2020). Evaluation of public HEI on teaching, research, and knowledge dissemination by data envelopment analysis. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2019.06.003.

    Article  Google Scholar 

  • Moreno-Gómez, J., Calleja-Blanco, J., & Moreno-Gómez, G. (2019). Measuring the efficiency of the Colombian higher education system: A two-stage approach. International Journal of Educational Management, 34, 794–804. https://doi.org/10.1108/IJEM-07-2019-0236.

    Article  Google Scholar 

  • Olcay, G. A., & Bulu, M. (2017). Is measuring the knowledge creation of universities possible? A review of university rankings. Technological Forecasting and Social Change, 123, 153–160.

    Article  Google Scholar 

  • Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20, 134–148.

    Article  Google Scholar 

  • Quiroga-Martinez, F., Fernandez-Vazquez, E., & Alberto, C. L. (2018). Efficiency in public higher education on Argentina 2004–2013: Institutional decisions and university-specific effects. Latin American Economic Review. https://doi.org/10.1186/s40503-018-0062-0.

    Article  Google Scholar 

  • Roll, Y., & Golany, B. (1993). Alternate methods of treating factor weights in DEA. Omega, 21, 99–109.

    Article  Google Scholar 

  • Sagarra, M., Mar-Molinero, C., & Agasisti, T. (2017). Exploring the efficiency of Mexican universities: Integrating data envelopment analysis and multidimensional scaling. Omega, 67, 123–133.

    Article  Google Scholar 

  • Salas-Velasco, M. (2020). Measuring and explaining the production efficiency of Spanish universities using a non-parametric approach and a bootstrapped-truncated regression. Scientometrics, 122, 825–846.

    Article  Google Scholar 

  • Sellers-Rubio, R., Mas-Ruiz, F. J., & Casado-Díaz, A. B. (2010). University efficiency: Complementariness versus trade-off between teaching, research and administrative activities. Higher Education Quarterly, 64, 373–391.

    Article  Google Scholar 

  • Shamohammadi, M., & Oh, D.-H. (2019). Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2019.119730.

    Article  Google Scholar 

  • Sinuany-Stern, Z., Mehrez, A., & Barboy, A. (1994). Academic departments efficiency via DEA. Computers and Operations Research, 21, 543–556.

    Article  Google Scholar 

  • Thompson, R. G., Singleton, F. D., Jr., Thrall, R. M., & Smith, B. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces, 16, 35–49.

    Article  Google Scholar 

  • Tomkins, C., & Green, R. (1988). An experiment in the use of data envelopment for evaluating the efficiency of UK university departments of accounting. Financial Accountability and Management, 44, 147–164.

    Article  Google Scholar 

  • Tran, P. P., Kuo, K.-C., Lu, W.-M., & Kweh, Q. L. (2020). Benchmarking in Vietnam universities: Teaching and research and revenue efficiencies. Asia Pacific Education Review, 21, 197–209.

    Article  Google Scholar 

  • Visbal-Cadavid, D., Martínez-Gómez, M., & Guijarro, F. (2017). Assessing the efficiency of public universities through DEA. A Case Study. Sustainability, 9, 1416. https://doi.org/10.3390/su9081416.

    Article  Google Scholar 

  • Ward, J. H., Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.

    Article  MathSciNet  Google Scholar 

  • 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, 10–30.

    Article  Google Scholar 

  • Zhang, G., Wu, J., & Zhu, Q. (2020). Performance evaluation and enrollment quota allocation for Higher Education Institutions in China. Evaluation and Program Planning. https://doi.org/10.1016/j.evalprogplan.2020.101821.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Editor-in-Chief Prof. Glänzel and the two anonymous reviewers for their valuable comments, which led to significant improvements of this paper. The authors also thank to the Administration at CCU for providing the partial data regarding operating expenses and external grants.

Author information

Authors and Affiliations

  1. Department of Business Administration, National Chung Cheng University, Chia-Yi, 621, Taiwan

    Shih-Pin Chen & Chung-Wei Chang

Authors
  1. Shih-Pin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung-Wei Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shih-Pin Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, SP., Chang, CW. Measuring the efficiency of university departments: an empirical study using data envelopment analysis and cluster analysis. Scientometrics 126, 5263–5284 (2021). https://doi.org/10.1007/s11192-021-03982-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-021-03982-3

Keywords

Search

Navigation