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As some data to evaluate the efficiency of bank branches is qualitative or uncertain, only grey numbers should be used to calculate the efficiency interval. The combination of multi-stage models and grey data can lead to a more accurate and realistic evaluation to assess the performance of bank branches. This study aims to compute the efficiency of each branch of the bank as a grey number and to group all branches into four grey efficiency areas.

The key performance indicators are identified based on the balanced scorecard and previous research studies. They are included in the two-stage grey data envelopment analysis (DEA) model. The model is run using the GAMS program. The grey efficiencies are calculated and bank branches have been grouped based on efficiency kernel number and efficiency greyness degree.

As policies and management approaches for branches with less uncertainty in efficiency are different from branches with more uncertainty, considering the uncertainty of efficiency values of branches may be helpful for the policy-making of managers. The grey efficiency of branches of one bank is examined in this study using the two-stage grey DEA throughout one year. The branches are grouped based on kernel and greyness value of efficiency, and the findings show that considering the uncertainty of data makes the results more consistent with the real situation.

The performance of bank branches is modeled as a two-stage grey DEA, in which the efficiency value of each branch is obtained as a grey number. The main originality of this paper is to group the bank branches based on two grey indexes named “kernel number” and “greyness degree” of grey efficiency value.

The purpose of this paper is to evaluate the efficiency of a series network system with undesirable and unreturnable simultaneously.

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.

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.

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.

The purpose of this paper is to determine the most efficient hotels in the Indian hotel industry, the competitive positioning of these hotels, and the factors that affect their efficiency change.

This study conducts a two-stage analysis and uses data envelopment analysis (DEA) and Global Malmquist productivity index (MPI) approach in the first stage to calculate the managerial performance of a panel of 63 Indian hotels in 2019–2020 and their efficiency change from 2009–2010 to 2019–2020. Bootstrapped generalized least square (GLS) approach is applied in the second stage to evaluate the impact of contextual variables on efficiency change.

Using the results of the first stage analysis, the authors categorized the 63 Indian hotels into 7 distinct clusters. These clusters represent different levels of competitiveness and pace of growth. The GLS regression reveals a U-shaped relationship between hotel size and efficiency change and a negative relationship between pro social investments and efficiency.

This is the first study in the hotel industry that has used global MPI as a measure of efficiency change in the first stage and GLS in the second stage. In the Indian context, to the best of authors’ knowledge, no such study exists.

This article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.

The approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.

The outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.

The research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.

Decisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.

By integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.

The scope of this research is to investigate performing loan efficiency for fifty European banks during the period 2008–2017.

The study is structured as a two-stage analysis of performing loan efficiency and its driving factors. In the first stage of the proposed methodology “Data Envelopment Analysis” is used to estimate performing loan efficiency for each bank included in the sample. A bootstrap statistical procedure enhances the findings. In the second stage, the impact of other factors on the efficiency scores of loan performance using tobit regression is investigated.

The results are consistent with the findings of the individual banks' financial analyses. According to the findings of DEA implementation, the evaluated banks may enhance their cost efficiency by 39% on average. In addition, the results indicate that loan efficiency performance improves after 2015, coinciding with the business cycle's upward trend. The tobit regression is employed in the second stage to examine the influence of bank-related and macroeconomic factors on banks' loan management efficiency. According to the findings of the tobit regression, three factors, namely the capital adequacy ratio, GDP per capita and managerial inefficiency, have a substantial influence on performing loan efficiency.

This research investigates the effectiveness of European economic policy in protecting the European banking system from the consequences of the sovereign debt crisis in several euro area members. The results highlight the distance of the Eurozone from the level of the ‘optimal currency area’.

This study aims to explain the market-oriented transformation dilemma of Chinese urban investment and development companies (UIDCs; also known as local government investment and financing companies) and objectively evaluate their transformation efficiency from both static and dynamic perspectives. The results of the research provide methodological bases for improving the transformation efficiency of UIDCs, thus pointing out the direction for the rational planning of their transformation path.

This study takes Chinese UIDCs in market transformation during 2015–2019 as the research object and uses principal component analysis to screen the index system for measuring the efficiency of market transformation. It then uses a three-stage data envelopment analysis model and the Malmquist productivity index to evaluate the market transformation efficiency of these companies during 2015–2019 and comprehensively analyzes the influence of external environmental factors on the market transformation of Chinese UIDCs.

Research results show that the transformation efficiency of Chinese UIDCs is low and slow overall and that large spatial and temporal differences exist. The transformation efficiency of UIDCs located in eastern China is higher than that of UIDCs in central and western China. The higher the external environmental factors of regional GDP, local debt service pressure and credit rating, the more likely they are to cause input redundancy in the transformation process of Chinese UIDCs, which is not conducive to their market-oriented transformation. In addition, the higher the urbanization rate, the more effective it is to improve the efficiency of market-oriented transformation of UIDCs. If the influence of environmental factors is stripped away, both the overall efficiency value and pure technical efficiency value of market-oriented transformation of Chinese UIDCs will increase while the scale efficiency value becomes smaller.

This research measures the transformation efficiency of Chinese UIDCs and comprehensively analyzes the influence of external environmental factors on their market-oriented transformation. The goal is to enrich the study of the market-oriented transformation efficiency evaluation index system of Chinese UIDCs at the theoretical level and provide important reference values for improving the efficiency of market-oriented transformation of Chinese UIDCs at the practical level.

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 examine how environmental, social and governance (ESG) initiatives and ISO 14001, which is an internationally agreed standard to set out the requirements for an environmental management system, affect firm performance in the context of the Industry 4.0 supply chain.

The authors develop a new chance-constrained network data envelopment analysis (DEA) in the presence of non-positive data to estimate innovation, operational and profitability performances for three main relation groups (suppliers, partners and customers) in Microsoft's supply chain.

Results of this study show the following: (1) the application of ISO 14001 will reduce profitability but increase overall performance (OP); (2) ESG implementation has a convex U-shaped influence on profitability and OP, which means that firms will benefit when ESG investment goes beyond a particular level; (3) the nonlinear U-shape is presented in the E and G components, but not in the S of the individual ESG initiatives, and (4) only specific subcomponents of S and G in the subcomponent of individual ESG initiatives are nonlinearly connected to OP. Research's results reveal that the customer group has a higher performance value than the other two groups, which suggests that this group will create competitive advantages for Microsoft.

Overall, the authors provide an insightful viewpoint into supply chain management by examining the ESG initiatives, ISO 14001 and performances of Microsoft's supply chain.

The purpose of this study is to quantify and evaluate the efficiency of knowledge services public libraries (PLKS) in China and to analyze the factors affecting the efficiency of PLKS. The focus of this study is to explore the characteristics and changing trends of PLKS and how to improve the efficiency of PLKS. The conclusions drawn in this paper are expected to provide important references for academic research and practical exploration in related fields.

This study constructs a three-stage process of PLKS. On this basis, the efficiency is analyzed by using the Super-NSBM model, and the influencing factors of PLKS are analyzed by using OLS. Finally, through the comprehensive interpretation of the measured data and the existing social development conditions, the authors put forward relevant recommendations.

In the first stage, the PLKS in China meets the basic requirements, but there is an obvious waste of resources in the second stage. In addition, China’s public libraries show obvious regional differences in knowledge service efficiency. Finally, it was found that economic and technological factors can increase the efficiency of PLKS, but the increase in the number of university students will reduce the efficiency of PLKS.

In this study, the authors provide an exhaustive deconstruction and explanation of PLKS, which leads to the proposal of a three-stage PLKS efficiency conversion process. Furthermore, the authors identify a diverse set of quantifiable metrics that are readily accessible for open collection. Notably, the authors correlated the efficiency of PLKS with the influencing factors, exploring PLKS from a new perspective and enriching the research related to PLKS.

This paper aims to investigate the efficiency and performance of the Islamic banking industry amid the COVID-19 pandemic.

The authors used a two-stage data envelopment analysis to first estimate the efficiency of 78 Islamic banks (IBs) across 15 countries for the 2005–2020 period (a total of 782 bank-year observations) and then to examine their determinants, including the COVID-19 pandemic.

The authors found that the Islamic banking industry performed at a moderate level during the 2005–2020 period, providing evidence that IBs are resilient to the financial shocks created by COVID-19. The authors also found that bank-level characteristics (such as bank size) and country-level characteristics (such as inflation) can contribute to the bank’s operational efficiency.

The results of this study suggested that banking management and government macroeconomic policy, especially in terms of precautions and continuous support, are important for IBs to improve their performance.

To the best of the authors’ knowledge, this is the first study to examine the efficiency and performance of IBs amid COVID-19.