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    A novel ranking method in data envelopment analysis: a real case on Chinese banking industry
    (Emerald Publishing, 2024) Nematizadeh, Maryeh; Amirteimoori, Alireza; Kordrostami, Sohrab; Khoshandam, Leila
    Purpose: This study aims to address the lack of discrimination between fully efficient decision-making units in nonparametric efficiency analysis models by introducing a new ranking technique that incorporates contextual variables. Design/methodology/approach: The proposed method combines Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS). First, DEA evaluates the partial efficiency of each unit, considering all inputs and only one output. Next, OLS removes the influence of contextual variables on the partial efficiencies. Finally, a ranking criterion based on modified partial efficiencies is formulated. The method is applied to data from 100 Chinese banks, including state-owned, commercial and industrial institutions, for the year 2020. Findings: The ranking results show that the top six positions are assigned to highly esteemed banks in China, demonstrating strong alignment with real-world performance. The method provides a comprehensive ranking of all units, including nonextreme efficient ones, without excluding any. It resolves infeasibility issues that arise during the ranking of efficient units and ensures uniqueness in efficiency scores, leading to a more reliable and robust ranking process. Contextual variables exerted a greater influence on the first partial efficiency compared to the second. Notably, Total Capital Adequacy (TCA) significantly impact bank efficiency. Originality/value: This study introduces a novel ranking method that effectively integrates contextual variables into DEA-based efficiency analysis, addressing limitations of existing methods. The practical application to Chinese banks demonstrates its utility and relevance. © 2024, Emerald Publishing Limited.
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    Marginal rates of technical changes and impact in stochastic data envelopment analysis: An application in power industry
    (Pergamon-Elsevier Science Ltd, 2024) Amirteimoori, Alireza; Allahviranloo, Tofigh; Khoshandam, Leila
    Marginal rates of technical changes and marginal impact are useful tools to calculate the trade-offs between production factors in a production process. In the data envelopment analysis (DEA) framework, the calculation of these trade-offs (marginal rates of technical substitution, marginal rates of transformation, marginal productivity and marginal costs) using the existing deterministic approaches may be sensitive to uncertainty and variability of the input and output data. Therefore, in this contribution, we introduce a stochastic DEA model based on chance constrained programming to develop a measure of the marginal rates of firms facing data uncertainty. In this contribution, chance-constrained programming is used to develop a procedure to calculate these trade-offs. Our proposed stochastic procedure is applied to sample data on 31 power plants. The empirical results on marginal rates obtained from our proposed stochastic programs revealed that the results are different at various tolerance levels of chance constraints.