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Incorporating performance measures with target levels in data envelopment analysis

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  • Lim, Sungmook
  • Zhu, Joe

Abstract

Data envelopment analysis (DEA) is a technique for evaluating relative efficiencies of peer decision making units (DMUs) which have multiple performance measures. These performance measures have to be classified as either inputs or outputs in DEA. DEA assumes that higher output levels and/or lower input levels indicate better performance. This study is motivated by the fact that there are performance measures (or factors) that cannot be classified as an input or output, because they have target levels with which all DMUs strive to achieve in order to attain the best practice, and any deviations from the target levels are not desirable and may indicate inefficiency. We show how such performance measures with target levels can be incorporated in DEA. We formulate a new production possibility set by extending the standard DEA production possibility set under variable returns-to-scale assumption based on a set of axiomatic properties postulated to suit the case of targeted factors. We develop three efficiency measures by extending the standard radial, slacks-based, and Nerlove–Luenberger measures. We illustrate the proposed model and efficiency measures by applying them to the efficiency evaluation of 36 US universities.

Suggested Citation

  • Lim, Sungmook & Zhu, Joe, 2013. "Incorporating performance measures with target levels in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 230(3), pages 634-642.
    • Handle: RePEc:eee:ejores:v:230:y:2013:i:3:p:634-642
    DOI: 10.1016/j.ejor.2013.04.051
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    References listed on IDEAS

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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, April.
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    Cited by:

    1. Chen, Ya & Pan, Yongbin & Liu, Haoxiang & Wu, Huaqing & Deng, Guangwei, 2023. "Efficiency analysis of Chinese universities with shared inputs: An aggregated two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    2. Fafaliou, Irene & Zervopoulos, Panagiotis, 2014. "Strategy in practice: a quantitative approach to target setting," MPRA Paper 54054, University Library of Munich, Germany.
    3. Wade D. Cook & Chuanyin Guo & Wanghong Li & Zhepeng Li & Liang Liang & Joe Zhu, 2017. "Efficiency Measurement of Multistage Processes: Context Dependent Numbers of Stages," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(06), pages 1-18, December.
    4. Ya Chen & Wade D. Cook & Juan Du & Hanhui Hu & Joe Zhu, 2017. "Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China," Annals of Operations Research, Springer, vol. 255(1), pages 347-366, August.
    5. Zervopoulos, Panagiotis D. & Brisimi, Theodora S. & Emrouznejad, Ali & Cheng, Gang, 2016. "Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US," European Journal of Operational Research, Elsevier, vol. 250(1), pages 262-272.
    6. Aparicio, Juan & Pastor, Jesus T., 2014. "Closest targets and strong monotonicity on the strongly efficient frontier in DEA," Omega, Elsevier, vol. 44(C), pages 51-57.
    7. Mustapha Daruwana Ibrahim & Sahand Daneshvar & Hüseyin Güden & Bela Vizvari, 2020. "Target setting in data envelopment analysis: efficiency improvement models with predefined inputs/outputs," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1319-1336, December.
    8. Park, Jaehun & Lee, Dongha & Zhu, Joe, 2014. "An integrated approach for ship block manufacturing process performance evaluation: Case from a Korean shipbuilding company," International Journal of Production Economics, Elsevier, vol. 156(C), pages 214-222.
    9. Li, Yongjun & Xie, Jianhui & Wang, Meiqiang & Liang, Liang, 2016. "Super efficiency evaluation using a common platform on a cooperative game," European Journal of Operational Research, Elsevier, vol. 255(3), pages 884-892.

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