Research Article
Open Access Peer-reviewed

Evaluation of the Operational Efficiency of Selected Senior and Vocational High Schools in Taiwan with DEA Meta-Frontier Approach: A Managerial Perspective

Hsiang-Hsi Liu1,, Fu-Hsiang Kuo2

1Graduate Institute of International Business, National Taipei University, Taiwan

22Department of Information Management, Chaoyang University of Technology, Taiwan

Journal of Business and Management Sciences. 2020, 8(2), 67-76. DOI: 10.12691/jbms-8-2-5
Received April 25, 2020; Revised May 27, 2020; Accepted June 03, 2020

Abstract

This study aims to evaluate and compare the operational efficiency and technology gap of selected senior and vocational high schools in Taiwan under different teaching and learning levels based on the DEA meta-frontier approach. The empirical results show that these two types of high schools have different technology gap ratios (TGRs) or meta-technology ratios (MTRs). We also perform a statistical test to examine the evidence that there is a significant difference in the operating performance of these two types of schools and result shows evidence that the different operational performance of senior and vocational high schools is due to different characteristics/attributes. Regarding TGRs or MTRs, senior high schools outperform vocational high schools. A relatively low average TGRs or MTRs of vocational high schools mean that the existing technology in vocational high schools are not near the frontier of meta-technology and that there is more room for improvement in management skills or operational processes. These findings can also provide a reference for educational agencies or high schools when formulating policies and strategies on the efficiency of school operations.

Keywords:

operational efficiency, technology gap ratio, Data Envelopment Analysis (DEA), DEA meta-frontier model, senior and vocational high school
[1]  Banker, R. D., Charnes, A. and Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.View Article
 
[2]  Battese, G. E., Rao, D. P. and O'donnell, C. J. (2004). A meta-frontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of productivity analysis, 21(1), 91-103.View Article
 
[3]  Chiang, W. M. (2009). Impact of low birth rate on high school education and recommendations. Secondary Education Monthly, 60(1), 26-34.
 
[4]  Charnes, A., Cooper, W. W. and Rhodes, E. (1978). Measuring the efficiency of decision-making units. European journal of operational research, 2(6), 429-444.View Article
 
[5]  Cooper, W. W., Huang, Z., Li, S. X., Parker, B. R., and Pastor, J. T. (2007). Efficiency aggregation with enhanced Russell measures in data envelopment analysis. Socio-Economic Planning Sciences, 41(1), 1-21.View Article
 
[6]  Cordero, J. M., Prior, D. and Simancas, R. (2016). A comparison of public and private schools in Spain using robust nonparametric frontier methods. Central European Journal of Operations Research, 24(3), 659-680.View Article
 
[7]  Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.View Article
 
[8]  Fried, H. O., Schmidt, S. S., and Yaisawarng, S. (1999). Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of productivity Analysis, 12(3), 249-267.View Article
 
[9]  Gao, J. L., and Cai, M. G. (2018). On the obstacles and strategies for the implementation of information technology in the field of science and technology in the national primary and secondary schools in the 12th year of the national education. Taiwan Educational Review Monthly, 7(2), 80-84.
 
[10]  Kritikos, M. N. (2018). A meta-frontier analysis for performance evaluation of public schools in Athens city-center. Journal of Statistics and Management Systems, 21(7), 1251-1272.View Article
 
[11]  Kruskal, W. H. and Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, 47(260), 583-621.View Article
 
[12]  O’Donnell, C. J., Rao, D. P. and Battese, G. E. (2008). Meta-frontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical economics, 34(2), 231-255.View Article
 
[13]  Ostertagová, E., Ostertag, O. and Kováč, J. (2014). Methodology and application of the Kruskal-Wallis test. Applied Mechanics and Materials, 611, 115-120.View Article
 
[14]  Portela, M. C. A. S. and Thanassoulis, E. (2001). Decomposing school and school-type efficiency. European journal of operational Research, 132(2), 357-373.View Article
 
[15]  Rao, D. S., O'donnell, C. J. and Battese, G. E. (2003). Meta-frontier functions for the study of inter-regional productivity differences. The Journal of Productivity Analysis, 3, 153-169.
 
[16]  Setirek, A. C. and Tanrikulu, Z. (2015). Significant developmental factors that can affect the sustainability of mobile learning. Procedia-Social and Behavioral Sciences, 191, 2089-2096.View Article
 
[17]  Thieme, C., Prior, D. and Tortosa-Ausina, E. (2013). A multilevel decomposition of school performance using robust nonparametric frontier techniques. Economics of Education Review, 32, 104-121.View Article
 
[18]  Wongchai, A., Liu, W. B. and Peng, K. C. (2012). DEA meta-frontier analysis on technical efficiency differences of national universities in Thailand. International Journal on New Trends in Education and their implications, 3(4), 30-42.