Data Envelopment Analysis for the Efficiency of Higher Education Departments at Sepuluh Nopember Institute of Technology, Indonesia
Zakiatul Wildani
,
Wahyu Wibowo
,
Sri Pingit Wulandari
,
Lucia Ari Dinanti
The quality of higher education is vital for a country’s future, not only in terms of transferring knowledge to younger generation but also for .
- Pub. date: April 15, 2023
- Pages: 1153-1169
- 287 Downloads
- 366 Views
- 2 Citations
The quality of higher education is vital for a country’s future, not only in terms of transferring knowledge to younger generation but also for supporting economic development. This paper applies data envelopment analysis (DEA) to evaluate the relative efficiency of 38 academic departments at Sepuluh Nopember Institute of Technology, Surabaya, Indonesia. The input factors are the number of lecturers, the number of staff and budget allocations, whereas the output is the performance achievement level. The empirical analysis incorporates two traditional DEA models: the Charnes, Cooper and Rhodes (CCR) and the Banker, Charnes and Cooper (BCC) models with input orientation. The results indicate that the CCR model considers five departments efficient while the BCC model considers ten departments efficient, five of which are those considered efficient by the CCR model. It may seem counterintuitive that a department with an output performance achievement below 100% is deemed efficient, and vice versa. However, the underlying principle of efficiency under input-oriented DEA model is resource utilization. Finally, we provide recommendations for the departments with low efficiency scores to improve their performance.
Keywords: Data envelopment analysis, efficiency measurement, higher education.
References
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/10.1016/S0272-7757(01)00068-1
Abdullah, D., Suwilo, S., Tulus, Mawengkang, H., & Efendi, S. (2017). Data envelopment analysis with upper bound on output to measure efficiency performance of departments in Malaikulsaleh University. Journal of Physics: Conference Series, 890, Article 012102. https://doi.org/10.1088/1742-6596/890/1/012102
Avkiran, N. K. (2001). Investigating technical and scale efficiencies of Australian universities through data envelopment analysis. Socio-Economic Planning Sciences, 35(1), 57–80. https://doi.org/10.1016/S0038-0121(00)00010-0
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1031-1142. https://doi.org/10.1287/mnsc.30.9.1078
Barros, C. P., & Mascarenhas, M. J. (2005). Technical and allocative efficiency in a chain of small hotels. International Journal of Hospitality Management, 24(3), 415–436. https://doi.org/10.1016/j.ijhm.2004.08.007
Casu, B., & Thanassoulis, E. (2006). Evaluating cost efficiency in central administrative services in UK universities. Omega, 34(5), 417–426. https://doi.org/10.1016/j.omega.2004.07.020
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through. Management Science, 27(6), 607-730. https://doi.org/10.1287/mnsc.27.6.668
Chen, S.-P., & Chang, C.-W. (2021). Measuring the efficiency of university departments: An empirical study using data envelopment analysis and cluster analysis. Scientometrics, 126, 5263–5284. https://doi.org/10.1007/s11192-021-03982-3
Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1–4. https://doi.org/10.1016/j.omega.2013.09.004
Cossani, G., Codoceo, L., Cáceres, H., & Tabilo, J. (2022). Technical efficiency in Chile’s higher education system: A comparison of rankings and accreditation. Evaluation and Program Planning, 92, Article 102058. https://doi.org/10.1016/j.evalprogplan.2022.102058
De Witte, K., & 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. https://doi.org/10.1057/jors.2015.92
Duan, S. X. (2019). Measuring university efficiency: An application of data envelopment analysis and strategic group analysis to Australian universities. Benchmarking: An international journal, 26(4), 1161-1173. https://doi.org/10.1108/BIJ-10-2017-0274
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(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1
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. https://doi.org/10.1016/j.seps.2017.01.008
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253–281. https://doi.org/10.2307/2343100
Fatimah, S., & Mahmudah, U. (2017). Two-stage Data Envelopment Analysis (DEA) for measuring the efficiency of elementary schools in Indonesia. International Journal of Environmental and Science Education, 12(8), 1971–1987. http://www.ijese.net/makale/1955.html
Jiang, J., Lee, S. K., & Rah, M.-J. (2020). Assessing the research efficiency of Chinese higher education institutions by data envelopment analysis. Asia Pacific Education Review, 21, 423–440. https://doi.org/10.1007/s12564-020-09634-0
Johnes, J. (2006). Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993. European Journal of Operational Research, 174(1), 443–456. https://doi.org/10.1016/j.ejor.2005.02.044
Johnes, J., & Johnes, G. (1995). Research funding and performance in U.K. University Departments of Economics: A frontier analysis. Economics of Education Review, 14(3), 301–314. https://doi.org/10.1016/0272-7757(95)00008-8
Johnes, J., Portela, M., & Thanassoulis, E. (2017). Efficiency in education. Journal of the Operational Research Society, 68(4), 331-338. https://doi.org/10.1057/s41274-016-0109-z
Johnes, J., & Yu, L. (2008). Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Economic Review, 19(4), 679–696. https://doi.org/10.1016/j.chieco.2008.08.004
Kao, C., & Hung, H.-T. (2008). Efficiency analysis of university departments: An empirical study. Omega, 36(4), 653–664. https://doi.org/10.1016/j.omega.2006.02.003
Kim, S., & Lee, J.-H. (2006). Changing facets of Korean higher education: Market competition and the role of the state. Higher education, 52, 557-587. https://doi.org/10.1007/s10734-005-1044-0
Kumar, S., & Gulati, R. (2008). An examination of technical, pure technical, and scale efficiencies in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33–69. https://bit.ly/421OzvU
Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega, 41(5), 893–902. https://doi.org/10.1016/J.OMEGA.2012.11.004
Mahmudah, U., & Lola, M. S. (2016). The efficiency measurement of Indonesian universities based on a fuzzy data envelopment analysis. Open Journal of Statistics, 6, 1050-1066. https://doi.org/10.4236/ojs.2016.66085
Ozcan, Y. A. (2008). Health care benchmarking and performance evaluation. Springer. https://doi.org/10.1007/978-0-387-75448-2
Panwar, A., Olfati, M., Pant, M., & Snasel, V. (2022). A review on the 40 years of existence of data envelopment analysis models: Historic development and current trends. Archives of Computational Methods in Engineering, 29, 5397-5426. https://doi.org/10.1007/s11831-022-09770-3
Seiford, L. M. (1996). Data envelopment analysis: The evolution of the state of the art (1978–1995). Journal of Productivity Analysis, 7, 99–137. https://doi.org/10.1007/BF00157037
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, 148, Article 119730. https://doi.org/10.1016/j.techfore.2019.119730
Yang, G., 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/10.1016/j.joi.2017.11.002
Youtie, J., & Shapira, P. (2008). Building an innovation hub: A case study of the transformation of university roles in regional technological and economic development. Research Policy, 37(8), 1188–1204. https://doi.org/10.1016/j.respol.2008.04.012