Research Article

Efficiency Measurement With A Three-Stage Hybrid Method

Volume: 5 Number: 2 May 19, 2018
TR EN

Efficiency Measurement With A Three-Stage Hybrid Method

Abstract

Data Envelopment Analysis (DEA) is one of the most widely used efficiency measurement techniques in the literature. In the method developed by Charnes, Cooper, and Rhodes, the relation between input(s) and output(s) is examined and relative efficiency values are obtained for many decision-making units. In order to be able to accurately measure the efficiency with Data Envelopment Analysis, the selection of input and output variables needs to be done carefully otherwise, the results may be misleading. For this purpose, it is aimed to make an objective selection process by using Grey Relational Analysis (GRA) in the identification of variables in the study. Via this method 17 financial ratios of 20 firms in the BIST Food Index for the period of 2013-2015 categorized into 4 groups, then each category clustered and the ratios which have the highest correlation within each cluster selected as representative indicator. Thus, 3 inputs and 2 output variables were selected so that the number of variables was reduced from 17 to 5.  An input-oriented BCC model was established with selected variables to determine the efficiencies of firms in each period. The Malmquist Total Factor Productivity Index was used to analyze the productivity changes between periods. It was concluded that 7 firms were efficient in each year and the productivity of the sector increased between the periods as a result of the analysis.

Keywords

References

  1. Ahn, T., Charnes, A., & Cooper, W. W. (1988). Efficiency characterizations in different DEA models. Socio-Economic Planning Sciences, 22(6), 253-257.
  2. Atrill, P. (2012). Financial Management for Decision Makers (6th ed.). Essex: Pearson Education.
  3. Banker, R. D., & Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research, 62(1), 74-84.
  4. 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), 1078-1092.
  5. Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980-89. The Scandinavian Journal of Economics, 94, Supplement. Proceedings of a Symposium on Productivity Concepts and Measurement Problems: Welfare, Quality and Productivity in the Service Industries, S211-S228.
  6. Bloomberg. Financial Analysis Reports. Retrieved from Bloomberg Terminal.
  7. Bowlin, W. F. (1998). Measuring performance: An introduction to data envelopment analysis (DEA). The Journal of Cost Analysis, 15(2), 3-27. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
  8. Chavas, J. P., & Aliber, M. (1993). An analysis of economic efficiency in agriculture: A nonparametric approach. Journal of Agricultural and Resource Economics, 18(1), 1-16.

Details

Primary Language

English

Subjects

Studies on Education

Journal Section

Research Article

Publication Date

May 19, 2018

Submission Date

February 10, 2018

Acceptance Date

April 30, 2018

Published in Issue

Year 2018 Volume: 5 Number: 2

APA
Ertuğrul, İ., & Öztaş, T. (2018). Efficiency Measurement With A Three-Stage Hybrid Method. International Journal of Assessment Tools in Education, 5(2), 370-388. https://doi.org/10.21449/ijate.423602

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