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Öğe A bootstrap data envelopment analysis model with stochastic reducible outputs and expandable inputs: an application to power plants(EDP Sciences, 2024) Amirteimoori, Alireza; Allahviranloo, Tofigh; Cezar, AsunurClean production of electricity is not only cost-effective but also effective in reducing pollutants. Toward this end, the use of clean fuels is strongly recommended by environmentalists. Benchmarking techniques, especially data envelopment analysis, are an appropriate tool for measuring the relative efficiency of firms with environmental pollutants. In classic data envelopment analysis models, decision-makers are faced with production processes in which reducible inputs are used to produce expandable outputs. In this contribution, we consider production processes when the input and output data are given in stochastic form and some throughputs are reducible and some others are expandable. A stochastic directional distance function model is proposed to calculate the relative technical efficiency of firms. In order to evaluate firm-specific technical efficiency, we apply bootstrap DEA. We first calculate the technical efficiency scores of firms using the classic DEA model. Then, the double bootstrap DEA model is applied to determine the impact of explanatory variables on firm efficiency. To demonstrate the applicability of the procedure, we present an empirical application wherein we employ power plants. © 2024 The authors. Published by EDP Sciences, ROADEF, SMAI 2024.Öğe 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, LeilaPurpose: 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.Öğe An efficiency score unification method in data envelopment analysis using slack-based models with application in banking(Elsevier inc, 2025) Hadi, Ali; Amirteimoori, Alireza; Kordrostami, Sohrab; Mehrabian, SaeidProviding a unique estimation model of the efficiency score for a bank branch has long been a primary concern for bank managers, who frequently reject efficiency studies because they contend that a single perspective evaluation cannot adequately display the multifunctional nature of decision-making units (DMUs). This paper presents a unification model for efficiency scores in the banking industry. The proposed model evaluates the efficiency of decision-making units through a two-stage method from different perspectives. This study calculates efficiency scores from several aspects and various inputs/outputs in the first stage, so an expanded space is used in the second stage. All viewpoints are transferred into a new space, creating a new efficient frontier in the expanded space. This model presents the unified efficiency score from DMUs by applying the slack-based model (SBM), which proposes enhancement guidelines. A unified efficiency score is created by considering three perspectives: production, profitability, and intermediary. Unlike the average score, the results show that the unified efficiency score can reflect the performance difference between the three scores achieved from the three perspectives. Additionally, this method demonstrates that DMUs cannot achieve overall efficiency if they are inefficient in at least one of the three aspects.Öğe An inverse stochastic two-stage DEA model to deal with resource planning(AMER INST MATHEMATICAL SCIENCES-AIMS, 2025) Vishghaei, Yasaman Zibaei; Kordrostami, Sohrab; Amirteimoori, Alireza; Shokri, SoheilResource planning is a significant aspect in organizations with particularly complex structures and uncertain data. Accordingly, an inverse stochastic network data envelopment analysis (ISNDEA) approach is proposed in this research to estimate the changes of stochastic inputs of two-stage processes for the perturbations of stochastic desirable outputs of stages one and two while the stochastic overall and stage efficiencies are preserved. The introduced ISNDEA model is a stochastic multi-objective optimization problem and it is transformed into a deterministic linear framework. Stochastic undesirable outputs are also included in the investigation due to their presence in many real cases. A real-world examination of the banking sector is presented to show the applicability of the planned technique. The results denote the proposed approach is advantageous to plan the resources of two-stage processes with stochastic data and undesirable outputs.Öğe Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking(Springer Science and Business Media Deutschland GmbH, 2023) Amirteimoori, Alireza; Sahoo, Biresh K.; Mehdizadeh, SaberIn the nonparametric data envelopment analysis literature, scale elasticity is evaluated in two alternative ways: using either the technical efficiency model or the cost efficiency model. This evaluation becomes problematic in several situations, for example (a) when input proportions change in the long run, (b) when inputs are heterogeneous, and (c) when firms face ex-ante price uncertainty in making their production decisions. To address these situations, a scale elasticity evaluation was performed using a value-based cost efficiency model. However, this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data. Therefore, in this study, we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty. An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years (1998–2005) was made to compare inferences about their efficiency and scale properties. The key findings are as follows: First, both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints. However, both models yield the same results at a tolerance level of 0.5, implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks. Second, the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart. Third, public banks exhibit higher efficiency than private and foreign banks. Finally, public and old private banks mostly exhibit either decreasing or constant returns to scale, whereas foreign and new private banks experience either increasing or decreasing returns to scale. Although the application of our proposed stochastic model is illustrative, it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs, which have ample potential for reaping scale and scope benefits. © 2023, The Author(s).Öğe Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking(Springer, 2023) Amirteimoori, Alireza; Sahoo, Biresh K.; Mehdizadeh, SaberIn the nonparametric data envelopment analysis literature, scale elasticity is evaluated in two alternative ways: using either the technical efficiency model or the cost efficiency model. This evaluation becomes problematic in several situations, for example (a) when input proportions change in the long run, (b) when inputs are heterogeneous, and (c) when firms face ex-ante price uncertainty in making their production decisions. To address these situations, a scale elasticity evaluation was performed using a value-based cost efficiency model. However, this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data. Therefore, in this study, we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty. An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years (1998–2005) was made to compare inferences about their efficiency and scale properties. The key findings are as follows: First, both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints. However, both models yield the same results at a tolerance level of 0.5, implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks. Second, the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart. Third, public banks exhibit higher efficiency than private and foreign banks. Finally, public and old private banks mostly exhibit either decreasing or constant returns to scale, whereas foreign and new private banks experience either increasing or decreasing returns to scale. Although the application of our proposed stochastic model is illustrative, it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs, which have ample potential for reaping scale and scope benefits.Öğe Efficiency analysis and CO2 emission reduction strategies in the US forest sector: a data envelopment analysis approach(Springer, 2024) Amirteimoori, Alireza; Zadmirzaei, Majid; Susaeta, Andres; Amirteimoori, ArashIndustrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO2 emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5-10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels.Öğe Efficiency analysis in bi-level on fuzzy input and output(Elsevier inc., 2025) Ghaziyani, Kh; Lotfi, F. Hosseinzadeh; Kordrostami, Sohrab; Amirteimoori, AlirezaTo enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA's 'black box' approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell's method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model's parameters.Öğe Efficiency analysis in two-stage data envelopment analysis with shared resources and undesirable outputs: an application in the banking sector(Wiley, 2024) Amirteimoori, AlirezaIn classic and traditional data envelopment analysis (DEA) models, the production process is considered as single stage process and the internal structures have been ignored. In many real-world occasions, however, the processes have two- or multi-stage structures with or without shared resources. Two-stage DEA models deal with the calculation of the technical efficiency of a system, taking into consideration its internal structures. In this contribution, we consider a two-stage production process in which both stages are fed by shared resources, and there are undesirable products from the second stage. In the model we will propose, an optimal split on shared resources is given. To demonstrate the applicability of the proposed approach, data on 40 bank branches in seven years 2014-2020 is presented. In our real application in the banking sector, we find out that the most important sources of inefficiency of bank branches are related to interest revenue and overdue debt (undesirable output). Moreover, we saw that almost all branches were inefficient in the second stage (sale and service section) in all the seven years of evaluation.Öğe Environmental performance evaluation in the forest sector: An extended stochastic data envelopment analysis approach(Elsevier science INC, 2024) Amirteimoori, Alireza; Cezar, Asunur; Zadmirzaei, Majid; Susaeta, AndresThis study addresses the global concern about undesirable outputs in the Forest Sector. We propose two innovative models, namely a directional weak disposable DEA model and an extended stochastic DEA model, to measure environmental efficiency. These models make a significant contribution to the field by specifically assessing the uncertain environmental efficiency of the forest sector. We validated our proposed models by conducting an empirical application using the United Nations Economic Commission for Europe (UNECE) forest sector dataset. The study examines important outputs such as above ground biomass stock, export unit prices of industrial roundwood, wood removals (desirable outputs), and CO2 emissions from wildfires (undesirable output). The results demonstrate that our novel stochastic weak disposability DEA model outperforms traditional approaches when the second scenario is applied. Specifically, the average technical efficiency (TE) score decreases to 0.92, and the number of efficient units reduces to 27, representing an approximate improvement of 55 %. Furthermore, the reduction rate of CO2 emissions is 4.09 % lower than the benchmark. Hence, our extended novel stochastic weak disposability DEA approach enhances the assessment of efficiency and inefficiency in decision-making units, contributing to the mitigation of risk and uncertainty. It also improves overall environmental performance in forest management.Öğe Exploring technical efficiency in the European forest sector: A two-stage chance-constrained data envelopment analysis(Elsevier B.V., 2024) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, MajidThis study analyses the technical efficiency of the forestry sector in Europe which comprises 40 countries. The novelty of this study is the stochasticity of the data and the existence of contextual variables in the two-stage production process of the forest sector. We first developed a two-stage chance-constrained data envelopment analysis model in which the forestry and exploitation stages occur at country-specific levels within the European forest production sector. It was found that the forest management stage is generally more efficient than the exploitation stage and total production at the country-specific level. Contextual variables have a significant impact on efficiency scores, which means that efficiency calculations in the subsequent stage need to be adjusted to take these influences into account. By mitigating these contextual effects, the study improved technical efficiency scores, highlighting top performers like the Russian Federation (DMU31 in North zone), Switzerland (DMU37 in Central-West zone), and Iceland (DMU16 in North zone) with TE scores of 1.0322, 1.0209, and 1.0198 respectively, while also identifying areas for enhancement in countries such as Turkey (DMU38 in South-East zone), Slovakia (DMU33 in Central-East zone), and Romania (DMU30 in Central-East zone) which fall into the lowest three ranks based on their performance with TE scores of 0.5583, 0.5058, and 0.4482 respectively. An important conclusion is that these findings are crucial for policymakers and stakeholders in Europe when developing strategies to improve efficiency and sustainability in the forest sector. © 2024 Elsevier B.V.Öğe Exploring technical efficiency in the European forest sector: a two-stage chance-constrained data envelopment analysis(Elsevier b.v., 2025) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, MajidThis study analyses the technical efficiency of the forestry sector in Europe which comprises 40 countries. The novelty of this study is the stochasticity of the data and the existence of contextual variables in the two-stage production process of the forest sector. We first developed a two-stage chance-constrained data envelopment analysis model in which the forestry and exploitation stages occur at country-specific levels within the European forest production sector. It was found that the forest management stage is generally more efficient than the exploitation stage and total production at the country-specific level. Contextual variables have a significant impact on efficiency scores, which means that efficiency calculations in the subsequent stage need to be adjusted to take these influences into account. By mitigating these contextual effects, the study improved technical efficiency scores, highlighting top performers like the Russian Federation (DMU31 in North zone), Switzerland (DMU37 in Central-West zone), and Iceland (DMU16 in North zone) with TE scores of 1.0322, 1.0209, and 1.0198 respectively, while also identifying areas for enhancement in countries such as Turkey (DMU38 in South-East zone), Slovakia (DMU33 in Central-East zone), and Romania (DMU30 in Central-East zone) which fall into the lowest three ranks based on their performance with TE scores of 0.5583, 0.5058, and 0.4482 respectively. An important conclusion is that these findings are crucial for policymakers and stakeholders in Europe when developing strategies to improve efficiency and sustainability in the forest sector.Öğe A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks(Springer, 2024) Amirteimoori, Alireza; Allahviranloo, Tofigh; Nematizadeh, MaryamIn the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.Öğe Improving decision-making units in performance analysis methods: a data envelopment analysis approach(Springer Heidelberg, 2023) Amirteimoori, Alireza; Allahviranloo, Tofigh; Kordrostami, Sohrab; Bagheri, Seyed FatemehClassifying decision-making units into efficient and inefficient classes is a common procedure in nonparametric data envelopment analysis approach. The inefficient units can be projected onto the production frontier by decreasing their inputs or increasing their outputs. However, if the closer projection point is clarified, the units will achieve their best situation. Previous methods have only focused on one objective, and other features have been ignored. This paper presents an alternative definition for the best projection by considering three main aspects: cost, revenue and closest projection points. The proposed procedure provides the closest possible distance, the lowest cost and the highest revenue, simultaneously. The goal programming has been used in this model. The results of implementing this approach on China's textile industry have shown the model applicability.Öğe Improving technical efficiency in data envelopment analysis for efficient firms: A case on Chinese banks(Elsevier Inc., 2024) Amirteimoori, Alireza; Allahviranloo, TofighData Envelopment Analysis (DEA) as a data-oriented benchmarking tool is considered a powerful and promising instrument for performance evaluation in various application areas. In DEA, the set of all decision-making units (DMUs) is divided into efficient and inefficient subsets. Inefficient DMUs are improved by reducing the input and/or increasing the output, and as far as we know, efficient DMUs are abandoned with the conclusion that they are all technically and relatively efficient, and no further analysis has been suggested in the literature. In this article, we first show that there is a gap between the actual efficiency and the efficiency estimated using benchmarking tools such as DEA. This means that there is no guarantee that the efficient DMUs characterized by DEA are really efficient. Thus, there is a gap in improving the technical efficiency of efficient DMUs. In this paper, we attempt to close this gap by introducing a method to improve efficient DMUs. First, we introduce a random variable as a corrector of efficiency evaluation, and then an inverse DEA model (IDEA) is proposed to improve efficient DMUs. To demonstrate the actual applicability of the proposed approach, we present an illustrative empirical application using 106 Chinese bank data from 2021. © 2024 Elsevier Inc.Öğe An inverse multi-period FDH model with undesirable outputs(Croatian Operational Research Soc, 2023) Asadi, Farzaneh; Kordrostami, Sohrab; Amirteimoori, Alireza; Noveiri, Monireh Jahani SayyadIn some examinations, the changes in performance measures and the efficiency of entities should be addressed over a span of time despite the undesirable outputs and unsatisfactory convexity property. In this study, the performance of processes is analyzed over several periods of time by presenting a multi-period model based on the free disposal hull (FDH) approach, incorporating undesirable outputs. Using the product of availability, performance (efficiency) and quality, the overall equipment effectiveness (OEE) is calculated which shows the level of productivity. After estimating the performance and OEE, an inverse multi-period FDH model is introduced to measure the input changes for the changes made to outputs in different time periods. The proposed approaches are used in the automotive industry to analyze the performance and changes in input measures over multiple periods of time. The results show that the proposed technique is reasonable for assessing the multi-period efficiency, the period and overall OEE of the systems, and the input changes related to several periods while the convexity assumption is violated.Öğe Managerial ability and productivity growth in the European forest sector(Springer, 2023) Amirteimoori, Alireza; Banker, Rajiv D.; Zadmirzaei, Majid; Susaeta, AndresThis paper aims to examine how the data envelopment analysis (DEA) technique can be applied to evaluate managerial ability and productivity growth for 29 European forest sectors over the period 2011-2020. Toward this end, we first applied DEA to evaluate the technical efficiency (TE) from both periodical-frontier and met-frontier perspectives in which results showed that the average TE was 0.645 and the annual operating efficiency of the years studied was reduced by 35%. A modified regression test is secondly developed in order to determine the effect of contextual variables on the log of TE. The findings showed that the regional density, time series and gross domestic product had the highest positive influence on improving the TE results, respectively. In the following, by considering the explanatory variables, a modified DEA-based Malmquist productivity index is used to calculate the productivity growth over the period 2011-2020. The results indicated that there was a decline of 12% for total factor productivity in 2019-2020 compared to 2014-2015 and 7% compared to 2015-2016 which is due to the uniform growth of technological change (TC) and efficiency change recession compared to previous periods. Hence, productivity growth is mainly due to frontier shift (TC).Öğe 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, LeilaMarginal 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.Öğe On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms(Pergamon-Elsevier Science Ltd, 2023) Amirteimoori, Alireza; Allahviranloo, Tofigh; Zadmirzaei, Majid; Hasanzadeh, FahimehGreenhouse gases (GHG) remain in the atmosphere for a very long-time causing alarmingly fast warming worldwide (global warming); especially Carbon dioxide (CO2) emissions have become a worldwide concern because of their harmful effects on the climate, and they are considered as an undesirable product of a lot of production systems. Various models dealing with undesirable outputs for measuring environmental efficiency have been employed to control greenhouse gas emissions via forecasting and/or optimizing their emissions. In this regard, this study proposes a novel modified Fuzzy Undesirable Non-discretionary DEA (FUNDEA) model to Measure environmental efficiency, and combine it with some novel artificial intelligence algorithms (Artificial Neural Network (ANN), Gene Expression Programming (GEP) and Artificial Immune System (AIS)) in order to predict optimal values of inefficient Decision-Making Units (DMUs) for being more efficient and mitigating their Co2 emissions in the uncertain environment for the first time herein. The model is applied to a dataset of 24 Iranian forest management units. Although our findings show that 17 DMUs are inefficient with a weak efficiency dispersion, these inefficient DMUs could improve their efficiency border by following the combined approaches (FUNDEA-ANN, FUNDEA-GEP and FUNDEA-AIS). As a consequence, the applied FUNDEA- artificial intelligent approaches are performed very well in predicting the optimal values of CO2 emissions and, hence increasing the total environmental efficiency.Öğe Performance analysis and managerial ability in the general insurance market: a study of India and Iran(Springer, 2024) Banker, Rajiv. D.; Amirteimoori, Alireza; Allahviranloo, Tofigh; Sinha, Ram PratapManagerial ability and managerial efforts play key role in the performance of business enterprises. However, this indicator is not directly observable. The current paper focuses on estimating managerial ability in the context of the Indian and Iranian general insurance sectors. The study is based on 140 firm-year observations in India and 140 firm-year observations in Iran spread over 7 years (2012-13 to 2018-19). For measuring managerial ability, we have used a three-stage procedure that involves the estimation of insurer-wise efficiency using Data Envelopment analysis (DEA) in the first stage and then regression of the logarithm of technical efficiency on a set of explanatory variables. In the final stage, we have estimated managerial ability from regression residuals (the difference between the observed and fitted values of insurer efficiency). In order to test the validity of the relationship between return on equity and managerial ability we use the general additive model (GAM). Our results confirmed that there is a positive relationship between return on equity and managerial ability. Our findings also revealed that the mean technical and output allocative efficiencies and managerial ability of Iranian markets highly fluctuated with a high variance. In contrast, these indicators did not fluctuate much in India.
