三阶段数据包络分析中的效率分解与不可取因素和可返回因素

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-05-21 DOI:10.1108/jm2-05-2022-0129

Mohammad Malmir, Farhad Hosseinzadeh Lotfi, Reza Kazemi Matin, Mahnaz Ahadzadeh Namin

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引用次数: 0

摘要

本文的目的是评估同时存在不良和不可逆产出的系列网络系统的效率。研究采用数据包络分析法（DEA），分别测算第二和第三阶段存在不良产出的系统和子系统的效率得分。研究结果银行业是一个国家发展中最重要的问题之一。本研究将 51 家商业银行的分行视为一个三阶段系统，其中第二阶段的产出是不可取和不可回报的。原创性/价值评估了存在不良和不可回收产出的三级网络的效率。该模型采用了库斯马宁技术。

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Efficiency decomposition in three-stage data envelopment analysis with undesirable and returnable factors

Purpose The purpose of this paper is to evaluate the efficiency of a series network system with undesirable and unreturnable simultaneously. Design/methodology/approach The research was conducted by applying data envelopment analysis (DEA) approach to measure the efficiency score of a system and substages with an undesirable output of the second and third stages separately. For each case, new production technology was introduced, and based on them, novel DEA models were proposed. Findings One of the most important issues in the development of a country is the banking industry. In this study, 51 branches of commercial banks as a three-stage system with undesirable and unreturnable outputs in the second stage are considered. Then, the efficiency of each branch and substages is measured by using proposed models. Originality/value The efficiency of a three-stage network in the presence of undesirable and unreturnable outputs was assessed. In this model, Kousmanen’s technology was used.

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.