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The purpose of this research paper is to prove the superiority of a two‐stage data envelopment analysis compared to a one‐stage approach in measuring a football club's efficiency. Moreover it provides best practice benchmarks for the research sample which supports football officials to orient themselves to the right clubs.

A non‐parametric two‐stage data envelopment analysis for the seasons 2006/07 to 2008/09 is introduced to measure the efficiency of English Premier League football clubs from an off‐field and an on‐field perspective. The results are compared with those of the traditional one‐stage data envelopment analysis approach to identify insufficiencies of the latter.

The results show evidence that different conclusions derive from either the one‐ or the two‐stage approach with the threat of potential misinterpretations in the case of the former. Furthermore, this study provides football clubs with information to focus on specific efficiency‐enhancing strategies at the individual stages of the production process and therefore acts as a supportive tool for the football club officials for setting corrective actions if inefficiencies are identified.

The present article provides a foundation for future studies in other football leagues as well as for an intertemporal analysis which evaluates the efficiency of a club on a yearly basis.

This is the first paper that introduces a two‐stage data envelopment analysis approach in football research. It has proven that it can identify sources of inefficiencies more accurately than a one‐stage data envelopment analysis and provides football officials with valuable information about their club.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

The main objective of this paper is to present a holistic approach for measuring overall bank efficiency and its decomposition in intermediation and profitability efficiencies.

Two-stage network data envelopment analysis (NDEA) model has been used for obtaining intermediation and profitability efficiencies along with overall bank efficiency. Additionally, bootstrap truncated regression has also been adopted to explore the influential predictors of two stages.

A comparative analysis between Indian private-sector and public-sector banks showed that the former is efficient than the latter in profitability efficiency stage. Another interesting finding is that none of the banks is efficient in overall study tenure. Finally, outcomes of bootstrap truncated regression show that differences in intermediation efficiency are explained by firm size, return on asset, market share and ownership while profitability stage is determined by diverse, gross domestic product and ownership.

This study will guide the Indian banking sector to act on which they are lagging, for the betterment of their overall performances. Finally, parameters like loan waives and disposal income of non-performing assets (NPAs) are not considered because of the unavailability of information in the output measures of NDEA model.

This paper not only provides a detailed performance assessment of Indian banks but also examines banks’ internal efficiency by deposits as an intermediary measure.

The purpose of this paper is to apply data envelopment analysis (DEA) techniques to the collective decision-making environment to appraise two-stage production process under different decision preferences.

The authors propose a novel multi-criteria group decision-making approach that uses consensus-strategic data envelopment analysis (CSDEA) to appraise two-stage production process under two different decision strategies, which are efficiency- and fairness-based group decision preferences.

The authors find that the proposed CSDEA model evaluates the performance of the decision-making units (DMUs) not by diminishing other competitors but rather based on group interests of the entire decision set.

The authors extend Li’s two-stage model to cases that consider both intermediate inputs and outputs. The authors address the issue of incorporating collective managerial strategy into multi-criteria group decision-making and propose a novel CSDEA model that considers not only the individual-level performance of a DMU but also the group-level or collective decision strategies.

In China, more than 80,000 people have been diagnosed with COVID-19, and more than 3,000 people have lost their lives. It seems that there will be more deaths since the epidemic is not over. All the Chinese provinces have reported the COVID-19 cases. This chapter aims to explore the trend of COVID-19 treatment efficiency in Chinese provinces using the data released daily by China Center for Disease Control and Prevention. Since China Center for Disease Control and Prevention began to release data daily from January 24 to March 12, we have more than 40 groups of daily data for 31 provinces in China mainland. In the calculation, we take the daily data of each province as a sample and then we have more than 1,200 samples in this study.

We use additive two-stage data envelopment analysis as an efficiency evaluation tool to calculate the COVID-19 treatment efficiency. In our framework, the first stage is to understand the infection rate and the second stage is to evaluate the treatment efficiency. In the first stage for the tth day, we use total population (p) and number of people infected in the previous day (inf _t−1) as the inputs and cumulative number of people infected in the current day (inf _t ) as the output. In the second stage for the tth day, we use cumulative number of people infected in the current day (inf _t ) as the input and cumulative death in the current day (death _t ) and cumulative recovery in the current day (recov _t ) as the outputs. Some techniques on how to deal with undesirable outputs such as inf _t and death _t are employed in this study.

After we have the infection rate and treatment efficiency for the samples more than 1,200, we analyze the COVID-19 treatment efficiency and its development trend from January 24 to March 12 in 34 regions of China from static and dynamic aspects. The results show that, on the whole, the overall efficiency and phased efficiency of COVID-19 treatment efficiency in all regions of China are relatively high, which reflects the key factor for the Chinese government to quickly control the epidemic in the short term. Relatively speaking, the average efficiency value in the infection stage (first stage) is lower than the average efficiency value in the healing stage (second stage), which shows that the focus of anti-epidemic in China should be early detection and prevention rather than treatment process. In terms of trend, the total efficiency of COVID-19 treatment in each region shows a trend of “increasing first and then decreasing.” Our analysis indicates that in the initial stage, the continuous increase of various resources leads to the rise of the total efficiency, while in the later stage, the rapid decline of the number of infected people leads to the decrease of the total efficiency. Based on the results of the efficiency analysis, this study provides corresponding management implications and policy suggestions, hoping to provide some enlightenment and suggestions for the anti-epidemic work of other countries in the severe environment where the epidemic is spreading rapidly.

Sustainable supplier selection is of vital importance in sustainability decision of supply chain under carbon neutrality. Multi-criteria decision-making approaches are widely used in sustainable supplier selection and generally classified the involved criteria into three sustainable development (SD) dimensions: Environmental, Social and Economic. During the assignment of appropriate weighted scores to the criteria, most of the methods considered mutually exclusive criteria. However, some criteria cover multidimensions since ambiguity vagueness makes them difficult to classify into one dimension exclusively. The purpose of this paper is to find proper approaches addressed to multidimensional overlapping criteria in the evaluation of suppliers’ sustainability performance.

This study proposes three approaches to resolve the multidimensional overlapping criteria issue by data envelopment analysis (DEA) methods. The first approach uses all dimensional criteria and “dimensional overlapping criteria” in a single DEA model. The second approach consists of two-stage DEA. The first stage is to find SD dimensional performances, which are used in the second stage. The third approach uses an aggregate weight-constrained DEA model with additional constraints. Such approaches are applied to an empirical case study with six dimensions.

Results indicate that the third approach is better than the first two approaches in balancing the development among all dimensions instead of focusing on the superiority dimension to obtain high performance.

Discussing overlapping criteria in the context of sustainable supplier evaluation and other multi-criteria evaluation have a noticeable impact on evaluation systems, but appropriate approaches for this issue are currently under-researched.

The purpose of this paper is to present a yardstick efficiency comparison of 269 Indonesian municipal water utilities (MWUs) and measures the impact of exogenous environmental variables on efficiency scores.

Two-stage Stackelberg leader-follower data envelopment analysis (DEA) and artificial neural networks (ANN) were employed.

Given that serviceability was treated as the leader and profitability as the follower, the first and second stage DEA scores were 55 and 32 percent (0 percent = totally inefficient, 100 percent = perfectly efficient), respectively. This indicates sizeable opportunities for improvement, with 39 percent of the total sample facing serious problems in both first- and second-stage efficiencies. When profitability instead leads serviceability, this results in more decreased efficiency. The size of the population served was the most important exogenous environmental variable affecting DEA efficiency scores in both the first and second stages.

The present study was limited by the overly restrictive assumption that all MWUs operate at a constant-return-to-scale.

These research findings will enable better management of the MWUs in question, allowing their current level of performance to be objectively compared with that of their peers, both in terms of scale and area of operation. These findings will also help the government prioritize assistance measures for MWUs that are suffering from acute performance gaps, and to devise a strategic national plan to revitalize Indonesia’s water sector.

This paper enriches the body of knowledge by filling in knowledge gaps relating to benchmarking in Indonesia’s water industry, as well as in the application of ensemble two-stage DEA and ANN, which are still rare in the literature.

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

The banking industry plays a key role in China's financial industry. In the past decade, the speed of the development of China's commercial banks has gradually declined. Commercial banks with different ownership structures also have certain differences in terms of operating efficiency, and their monetary policies are often different. Therefore, the authors study the impact of ownership structure on the efficiency of commercial banks under different monetary policies. This study also provides relevant reference opinions with regard to the healthy, sustainable and stable development of China's banking industry.

This paper mainly uses the two-stage data envelope analysis (DEA) model under meta-frontier and group frontier to study the deposit and loan efficiency changes of 16 banks from 2007 to 2014 under ownership structure heterogeneity. Furthermore, the model introduces the balance parameters between deposits and loans, in order to realize the mathematical abstraction description of macro-monetary policy.

First, based on bank efficiency analysis, the paper finds that most banks' loan efficiency is higher than their deposits. Second, the paper concludes that different monetary policies have little effect on bank deposit and loan efficiency, while ownership heterogeneity has a significant impact on bank performance. Finally, through the decomposition of the sources of inefficiency in bank performance, this paper finds that management and technology are two factors that affect the inefficiency of banks.

The authors work contributes to the existing literature in the following ways: First, to the best of the authors’ knowledge, this is the first attempt to use the DEA model to study the relationship between monetary policies and bank supply chain efficiency. The results may provide additional managerial implications for the banking industry from the perspective of monetary policies. The result is helpful in terms of explaining how and why banks should strengthen risk management, as well as how to deal with non-performing loans in management terms and finally, why banks should make financial technology innovations in technology terms.