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Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

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.

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

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.

The purpose of this paper is to investigate whether banks’ environmental, social and governance (ESG) initiatives increase or decrease banks’ efficiency.

The sample used includes all listed banks in Saudi Arabia over the years 2016–2021. The authors performed different methods, including data envelopment analysis (DEA), ordinary least squares (OLS) and quantile regressions.

The OLS regression results show a negative linkage between ESG and banks’ efficiency. Further, the quantile regression analysis indicates that the ESG effect on banks' efficiency is negative across different quantiles. However, the DEA method shows that the DEA-generated scores for Banks’ efficiency are higher for ESG-adjusted scores in comparison to efficiency scores without incorporating ESG. Further, the comparison of the DEA-generated efficiency scores, over the sample period, of adjusted ESG banks still suffers from decreasing in their efficiency over the years. Concerning existing theory, the results are consistent with the stakeholders and the resource-based theories postulating that banks' ESG practices are ethical commitments and enable firms to gain competitive advantage and increase their reputation among stakeholders.

The findings of this study offer important implications for regulators and bankers. Policymakers and bank regulators should make collective efforts to encourage financial institutions to adopt green finance initiatives to create an efficient financial system capable of counteracting risks from the external environment and stimulating economic growth. Banks’ managers should be aware that ESG initiatives serve society and the environment and offer a positive influence on banks’ efficiency.

To the best of the authors’ knowledge, this is the first study to explore the influence of ESG activities on banks' efficiency using DEA for banks in Saudi Arabia.

This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study.

An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them.

The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints.

The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.