Introducing and modeling inefficiency contributions

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Introducing and modeling inefficiency contributions. / Asmild, Mette; Kronborg, Dorte; Matthews, Kent.

In: European Journal of Operational Research, Vol. 248, No. 2, 2016, p. 725-730.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Asmild, M, Kronborg, D & Matthews, K 2016, 'Introducing and modeling inefficiency contributions', European Journal of Operational Research, vol. 248, no. 2, pp. 725-730. https://doi.org/10.1016/j.ejor.2015.07.060

APA

Asmild, M., Kronborg, D., & Matthews, K. (2016). Introducing and modeling inefficiency contributions. European Journal of Operational Research, 248(2), 725-730. https://doi.org/10.1016/j.ejor.2015.07.060

Vancouver

Asmild M, Kronborg D, Matthews K. Introducing and modeling inefficiency contributions. European Journal of Operational Research. 2016;248(2):725-730. https://doi.org/10.1016/j.ejor.2015.07.060

Author

Asmild, Mette ; Kronborg, Dorte ; Matthews, Kent. / Introducing and modeling inefficiency contributions. In: European Journal of Operational Research. 2016 ; Vol. 248, No. 2. pp. 725-730.

Bibtex

@article{5116e1daa90545108b193e7a4a0eb689,
title = "Introducing and modeling inefficiency contributions",
abstract = "Whilst Data Envelopment Analysis (DEA) is the most commonly used non-parametric benchmarking approach, the interpretation and application of DEA results can be limited by the fact that radial improvement potentials are identified across variables. In contrast, Multi-directional Efficiency Analysis (MEA) facilitates analysis of the nature and structure of the inefficiencies estimated relative to variable-specific improvement potentials.This paper introduces a novel method for utilizing the additional information available in MEA. The distinguishing feature of our proposed method is that it enables analysis of differences in inefficiency patterns between subgroups. Identifying differences, in terms of which variables the inefficiency is mainly located on, can provide management or regulators with important insights. The patterns within the inefficiencies are represented by so-called inefficiency contributions, which are defined as the relative contributions from specific variables to the overall levels of inefficiencies. A statistical model for distinguishing the inefficiency contributions between subgroups is proposed and the method is illustrated on a data set on Chinese banks.",
author = "Mette Asmild and Dorte Kronborg and Kent Matthews",
year = "2016",
doi = "10.1016/j.ejor.2015.07.060",
language = "English",
volume = "248",
pages = "725--730",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Introducing and modeling inefficiency contributions

AU - Asmild, Mette

AU - Kronborg, Dorte

AU - Matthews, Kent

PY - 2016

Y1 - 2016

N2 - Whilst Data Envelopment Analysis (DEA) is the most commonly used non-parametric benchmarking approach, the interpretation and application of DEA results can be limited by the fact that radial improvement potentials are identified across variables. In contrast, Multi-directional Efficiency Analysis (MEA) facilitates analysis of the nature and structure of the inefficiencies estimated relative to variable-specific improvement potentials.This paper introduces a novel method for utilizing the additional information available in MEA. The distinguishing feature of our proposed method is that it enables analysis of differences in inefficiency patterns between subgroups. Identifying differences, in terms of which variables the inefficiency is mainly located on, can provide management or regulators with important insights. The patterns within the inefficiencies are represented by so-called inefficiency contributions, which are defined as the relative contributions from specific variables to the overall levels of inefficiencies. A statistical model for distinguishing the inefficiency contributions between subgroups is proposed and the method is illustrated on a data set on Chinese banks.

AB - Whilst Data Envelopment Analysis (DEA) is the most commonly used non-parametric benchmarking approach, the interpretation and application of DEA results can be limited by the fact that radial improvement potentials are identified across variables. In contrast, Multi-directional Efficiency Analysis (MEA) facilitates analysis of the nature and structure of the inefficiencies estimated relative to variable-specific improvement potentials.This paper introduces a novel method for utilizing the additional information available in MEA. The distinguishing feature of our proposed method is that it enables analysis of differences in inefficiency patterns between subgroups. Identifying differences, in terms of which variables the inefficiency is mainly located on, can provide management or regulators with important insights. The patterns within the inefficiencies are represented by so-called inefficiency contributions, which are defined as the relative contributions from specific variables to the overall levels of inefficiencies. A statistical model for distinguishing the inefficiency contributions between subgroups is proposed and the method is illustrated on a data set on Chinese banks.

U2 - 10.1016/j.ejor.2015.07.060

DO - 10.1016/j.ejor.2015.07.060

M3 - Journal article

VL - 248

SP - 725

EP - 730

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

ER -

ID: 166280273