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Two-stage common weight DEA-Based approach for performance evaluation with imprecise data

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  • Goker, Nazli
  • Karsak, E.Ertugrul

Abstract

A multi-criteria decision making approach based on data envelopment analysis (DEA) is presented to identify the best performing decision making unit (DMU) accounting for multiple inputs and multiple outputs with the presence of imprecise data. The developed α-cut based two-stage mathematical programming approach, which yields feasible solutions for all α-cut levels, generates common set of weights for inputs and outputs, and thus, provides more practical and realistic performance assessment of DMUs. A single rank-order is obtained through OWA operator that is employed for aggregating the efficiency scores regarding α-levels for each DMU. The robustness of the developed methodology is illustrated by examples taken from earlier research studies along with a case study that is conducted to aid an expatriate to identify the most desirable country in terms of quality of living. The proposed approach provides a ranking with improved discriminating power and enhanced weight dispersion with regard to inputs and outputs while also guaranteeing to determine a single best performing DMU.

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  • Goker, Nazli & Karsak, E.Ertugrul, 2021. "Two-stage common weight DEA-Based approach for performance evaluation with imprecise data," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:soceps:v:74:y:2021:i:c:s0038012120307801
    DOI: 10.1016/j.seps.2020.100943
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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    2. Aparicio, Juan & Cordero, Jose M. & Ortiz, Lidia, 2019. "Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    3. Tiantan Yang & Pingchun Wang & Feng Li, 2018. "Centralized Resource Allocation and Target Setting Based on Data Envelopment Analysis Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, February.
    4. Li, Shenxue & Scullion, Hugh, 2010. "Developing the local competence of expatriate managers for emerging markets: A knowledge-based approach," Journal of World Business, Elsevier, vol. 45(2), pages 190-196, April.
    5. HATAMI-MARBINI, Adel & ROSTAMY-MALKHALIFEH, Mohsen & AGRELL, Per J. & TAVANA , Madjid & MOHAMMADI, Fatemeh, 2015. "Extended Symmetric and Asymmetric Weight Assignment Methods in Data Envelopment Analysis," LIDAM Reprints CORE 2693, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Wagner, Janet M. & Shimshak, Daniel G., 2007. "Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives," European Journal of Operational Research, Elsevier, vol. 180(1), pages 57-67, July.
    8. Zahra Mohmmad Nejad & Alireza Ghaffari-Hadigheh, 2018. "A novel DEA model based on uncertainty theory," Annals of Operations Research, Springer, vol. 264(1), pages 367-389, May.
    9. GHASEMI, M.R. & IGNATIUS, J. & LOZANO, S. & EMROUZNEJAD, A. & HATAMI-MARBINI, Adel, 2015. "A fuzzy expected value approach under generalized data envelopment analysis," LIDAM Reprints CORE 2716, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Ali, Imran & Ali, Murad & Leal-Rodríguez, Antonio L. & Albort-Morant, Gema, 2019. "The role of knowledge spillovers and cultural intelligence in enhancing expatriate employees' individual and team creativity," Journal of Business Research, Elsevier, vol. 101(C), pages 561-573.
    11. Omrani, Hashem & Valipour, Mahsa & Jafari Mamakani, Saeid, 2019. "Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    12. Jenkins, Larry & Anderson, Murray, 2003. "A multivariate statistical approach to reducing the number of variables in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 51-61, May.
    13. Mahajan, Ashish & Toh, Soo Min, 2014. "Facilitating expatriate adjustment: The role of advice-seeking from host country nationals," Journal of World Business, Elsevier, vol. 49(4), pages 476-487.
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