Dergiler / International Journal of Assessment Tools in Education / 2018 / Cilt: 5 - Sayı: 2

Efficiency Measurement With A Three-Stage Hybrid Method

Sayfa
370–388
Yayın Tarihi
2022-10-19
DOI
—
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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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Data Envelopment Analysis (DEA) is one of the most widely used efficiency measurement techniques in the literature.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2013-2015 Verileri
⚙️Değişkenler:Efficiency measurement · Grey Relational Analysis · Data Envelopment Analysis

Özet

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.

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.

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Bu Makaleye Atıf Yap

ÖZTAŞ, T., ERTUĞRUL, İ. (2022). Efficiency Measurement With A Three-Stage Hybrid Method. International Journal of Assessment Tools in Education, Cilt: 5 - Sayı: 2, 370–388. https://ajindex.com/articles/540242

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Kaynakça (10)

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  3. 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.
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  7. Chiang-Ku, F., Shu-Wen, C., & Cheng-Ru, W. (2009). Using GRA and DEA to compare efficiency of bancassurance sales with an insurer’s own team. The Journal of Grey System, 21(4), 395-406.
  8. Chorafas, D. N. (2015). Business Efficiency and Ethics: Values and Strategic Decision Making. New York, NY: Palgrave Macmillan. Coelli, T. J., & Rao, D. S. P. (2005). Total factor productivity growth in agriculture: A Malmquist index analysis of 93 countries, 1980-2000. Agricultural Economics, 32(s1), 115-134.
  9. Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York, NY: Springer Science & Business Media. Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research, 192(1), 1-17.
  10. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). New York, NY