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This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

This paper aims to assess two distinct aspects of performance in terms of technical (sales) efficiency and efficiency in market value generation of a sample of Greek metallurgical firms listed on the Athens Exchange by using data envelopment analysis (DEA).

Both aspects of performance are measured by employing the DEA BCC model, combined with bootstrap and generalized proportional distance function (GPDF). Statistical analysis is performed to investigate whether there is a positive link between the two examined performance dimensions.

Inefficiency is uncovered in both performance dimensions, but there is a lower level of performance in market value generation than in technical efficiency. Correlation analysis results do not point out positive links between performance measures for the sample firms.

The derived performance measures allow firm managers to set their own priorities and to seek out improvements along the two dimensions of performance; moreover, they may contribute to the reduction of information asymmetry among investors.

This paper is one of a few that investigate the link between DEA-based sales performance and performance in market value generation. It contributes methodologically through the adoption of fundamental analysis principles in estimating efficiency in the two performance dimensions and the development of a DEA efficiency model in the presence of negative data.

This paper seeks to rediscover the most suitable efficiency evaluation variables (input and output variables) for digital libraries and to employ the data envelopment analysis (DEA) model to measure the resource utilization efficiency of university libraries.

In order to analyze and evaluate university library efficiency, the paper introduces the DEA‐CCR Model and the DEA‐BCC Model. Based on these research tools, the Technical Efficiency (CCR*BCC) was determined. First, a reference group was created with a 100 percent efficiency rate, then the factors contributing to inefficient DMUs were analyzed, and the difference in the efficiency rate compared according to the different governing bodies of the libraries. Finally, the difference of efficiency according to the introduction and rejection of electronic resources was analyzed. It was possible to measure the technical efficiency, pure‐technical efficiency, and scale efficiency.

The results showed that the efficiency of university libraries varied significantly according to whether or not electronic resources were included in the evaluation. In addition, the findings confirmed decision making units (DMUs) have a 100 percent efficiency rate and a low efficiency rate as well as proposed benchmarking DMUs for inefficient DMUs and a direction for future improvements.

The paper identifies that there was a significant difference in efficiency, according to the presence of electronic resources in university libraries.

The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area.

This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression.

DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India.

The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression.

This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector.

A cost-oriented data envelopment analysis (DEA) is a non-parametric tool for discriminating the best performers from a number of homogenous decision-making units (DMU) using allocative efficiency, technical efficiency and a cost-based efficiency score. Cost of each resource has been an important input in such cases. However, the purpose of this paper is to propose a method, which, in absence of it, helps to define the targeted output for all DMUs. Eigenvector derived from the first principal component of specific covariance matrix from each allocated outputs is used here for computing such targets. An orthogonal projection of resources to such radial directions is another indicator of a relative economic use of resources. Unlike regular cost-oriented DEA model, the current model proposes a multiplier model of BCC DEA. With the provision of the targeted output set for a DMU, the modified multiplier model measures the orientation of a DMU towards cost. A case study of six schools is incorporated here to identify the superior cost efficient school.

The problem referred here is concerned about six private pre-primary schools situated in a locality. The financial condition of the population is heterogeneous. The school management has the option to select the group of students according to the richness of the family. Thus, an average richness is taken into account here for each school to understand the motive of providing service to the targeted section of the society. Cost borne by each school per student per month is incorporated here to notice the intention of the school to offer education. The selection of input variables is inspired from the valuable findings of Hillman and Jenkner (2002). According to them in many developing countries, the governments lack either the financial resources or the political will to meet their citizens’ educational needs. Moreover, “Children are entitled to a free, quality basic education. Many children who do attend school receive an inadequate education because of poorly trained, underpaid teachers, overcrowded classrooms, and a lack of basic teaching tools such as textbooks, blackboards, and pens and paper […].” The inclusion of the first input is due to the measurement of willingness of a primary school to impart education. Commenting on the ill-effects, they mentioned “In an ideal world, primary education would be universal and publicly financed, and all children would be able to attend school regardless of their parents’ ability or willingness to pay. The reason is simple: when any child fails to acquire the basic skills needed to function as a productive, responsible member of society, […]. The cost of educating children is far outweighed by the cost of not educating them. Adults who lack basic skills have greater difficulty in finding well-paying jobs and escaping poverty […].” Thus, the second input plays a key role to measure the intention of a primary school to stand them in a good stead serve for the sake of ensuring social benefit. In this regard, two scores refer the outcome of the endeavor of whole system to create better students and to help society to progress.

The cost-oriented multiplier BCC DEA model is presented here to cite a proof of an existence of an ideal cost frontier originating from an MPSS-based DEA (referred in Sarkar, 2014a). The former model has mentioned that it is not necessary for a CCR efficient DMU to remain cost competent. However, the major drawback of that model was its inability to show the impact of return to scale. In the present model, this problem has been tackled nicely. School A, in this example, under the variable return to scale, can become a cost efficient school. However, the proposed model, in this paper, under constant return to scale, has accepted the ranking, which was proposed before.

Only six schools, situated around Northwest Durgapur, were observed.

The prescribed model iterates how a smaller number of intermediate inputs can be used in DEA to identify benchmark. These variables, which emblem the control through lean approaches, can be representative of a large number of other actual inputs which have already been mentioned by many erstwhile researchers.

The selection of input variables is inspired from the valuable findings of Hillman and Jenkner (2002). According to them in many developing countries, the governments’ lack either the financial resources or the political will to meet their citizens’ educational needs. Moreover, “Children are entitled to a free, quality basic education. Many children who do attend school receive an inadequate education because of poorly trained, underpaid teachers, overcrowded classrooms, and a lack of basic teaching tools such as textbooks, blackboards, and pens and paper […].” The inclusion of the first input is due to the measurement of willingness of a primary school to impart education. Commenting on the ill-effects, they mentioned “In an ideal world, primary education would be universal and publicly financed, and all children would be able to attend school regardless of their parents’ ability or willingness to pay. The reason is simple: when any child fails to acquire the basic skills needed to function as a productive, responsible member of society, […]. The cost of educating children is far outweighed by the cost of not educating them. Adults who lack basic skills have greater difficulty in finding well-paying jobs and escaping poverty […].” Thus, the second input plays a key role to measure the intention of a primary school to stand them in a good stead serve for the sake of ensuring social benefit. In this regard, two scores refer the outcome of the endeavor of whole system to create better students and to help society to progress.

The application of the directional distance model (prescribed by Chambers et al., 1998) in the present problem is because the target for each DMU is settled using the non-central PCA. Such a radial direction not only explains a comprehensive variation of the corresponding specific covariance matrix but also provides a cost function, which is orthogonal to it. The targeted output for any DMU is predicted by minimizing the cost function bearing with the respective utilization of resources. The current model allows deriving the radial efficiency score in reference to such targeted goals. The outcome of this model is validated with the outputs of an MPSS-based constant return on scale frontier function described in Sarkar (2014a). Both models show a substantial association in this regard.

The purpose of this paper is to evaluate the functioning of the main Spanish and Portuguese containers ports to observe if they are operating below their production capabilities.

To achieve the above-mentioned objective, one possible method is to calculate the data envelopment analysis (DEA) efficiency, and the scale efficiency (SE) of targets, and in order to consider the variability across different samples, a bootstrap scheme has been applied.

The results showed that the DEA bootstrap-based approach can not only select a suitable unit which accords with a port’s actual input capabilities, but also provides a more accurate result. The bootstrapped results indicate that all ports do not need to develop future investments to expand port infrastructure.

The proposed DEA bootstrap-based approach provides useful implications in the robust measurement of port efficiency considering different samples. The study proves the usefulness of this approach as a decision-making tool in port efficiency.

This study is one of the first studies to apply bootstrap to measure port efficiency under the background of the Spain and Portugal case. In the first stage, two models of DEA have been used to obtain the pure technical, and the technical and SE, and both the input-oriented options: constant return scale and variable return scale. In the second stage, the bootstrap method has been applied in order to determine efficiency rankings of Iberian Peninsula container ports taking into consideration different samples. Confidence interval estimates of efficiency for each port are reported. This paper provides useful insights into the application of a DEA bootstrap-based approach as a modeling tool to aid decision making in measuring port efficiency.

The purpose of this paper is to employ an efficiency analysis and compare the efficiency scores of publicly or privately operated airports in Turkey.

This study uses analytic hierarchy process, which is one of the widely known multi-criteria decision-making methods to calculate the relative weights of input and outputs. This study also uses data envelopment analysis and assurance region (AR) method to calculate the efficiency scores of airports at the empirical analysis stage.

The empirical results reveal that DEA-BCC and DEA-CCR methods produced almost the same efficiency scores, and 14 airports were found as efficient. Also, AR method was employed and under this method, it was only two airports operated by the private sector that were found as efficient. None of the publicly operated airports was found as efficient.

The main practical implication of this study is that publicly operated airports must improve their efficiency levels in Turkey. This situation indicates that the government policy for the aviation sector must be changed. It is not a coincidence that all publicly operated airports are inefficient. To cope with this situation, it may be a useful policy that establishes a regional airport system or applies the privatization process to all airports.

The most significant contribution of this study to literature is that the AR method, which was never used before in a single country’s airport performance evaluation study, was applied for the first time. Also, this technique was applied first time to Turkish airports for measuring their efficiency levels.

This study used Data Envelopment Analysis (DEA) to measure and evaluate the operational efficiency of 26 isolation hospitals in Egypt during the COVID-19 pandemic, as well as identifying the most important inputs affecting their efficiency.

To measure the operational efficiency of isolation hospitals, this paper combined three interrelated methodologies including DEA, sensitivity analysis and Tobit regression, as well as three inputs (number of physicians, number of nurses and number of beds) and three outputs (number of infections, number of recoveries and number of deaths). Available data were analyzed through R v.4.0.1 software to achieve the study purpose.

Based on DEA analysis, out of 26 isolation hospitals, only 4 were found efficient according to CCR model and 12 out of 26 hospitals achieved efficiency under the BCC model, Tobit regression results confirmed that the number of nurses and the number of beds are common factors impacted the operational efficiency of isolation hospitals, while the number of physicians had no significant effect on efficiency.

The limits of this study related to measuring the operational efficiency of isolation hospitals in Egypt considering the available data for the period from February to August 2020. DEA analysis can also be an important benchmarking tool for measuring the operational efficiency of isolation hospitals, for identifying their ability to utilize and allocate their resources in an optimal manner (Demand vs Capacity Dilemma), which in turn, encountering this pandemic and protect citizens' health.

Despite the intensity of studies that dealt with measuring hospital efficiency, this study to the best of our knowledge is one of the first attempts to measure the efficiency of hospitals in Egypt in times of health' crisis, especially, during the COVID-19 pandemic, to identify the best allocation of resources to achieve the highest level of efficiency during this pandemic.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

This study aims to develop and test a framework for integration between data envelopment analysis (DEA) and artificial neural networks (ANN) to predict the best financial performance concerning return on assets and return on equity for banks listed on the Egyptian Exchange, to help managers generate what-if scenarios? For performance improvement and benchmarking.

The study empirically tested the three-stage DEA-ANN framework. First, DEA was used as a preprocessor of the banks’ efficiency scores. Second, a back-propagation neural network as a multi-layer perceptron-ANN’s model was designed using expected data sets from DEA to learn optimal performance patterns. Third, the superior performance of banks was forecasted.

The results indicated that banks are not operating under their most productive operations, and there is room for potential improvements to reach outperformance. Moreover, the neural networks’ empirical test results showed high correlations between the actual and expected values, with low prediction errors in both the test and prediction phases.

Based on best performance prediction, banks can generate alternative scenarios for future performance improvement and enabling managers to develop effective strategies for performance control under uncertainty and limited data. Besides, supporting the decision-making process and proactive management of performance.

Despite the growing research stream supporting DEA-ANN integration applications, these are still limited and scarce, especially in the Middle East and North Africa region. Therefore, the study trying to fill this gap to help bank managers predict the best financial performance.