Search results

The purpose of this paper is to improve the accuracy of evaluation efficiency by constructing parallel structures considering the main components of industrial pollutants, and then to consider some external influence factors to eliminate random errors.

In this paper, data transformation has been used to deal with undesirable output, and a model with a parallel structure based on the three-stage data envelopment analysis model to calculate the efficiency scores of different division in pollution treatment has been composed.

The analysis shows that the external environmental factors and random factors of the economy and society greatly affect the efficiency of industrial pollutant treatment; moreover, there is an imbalance between regions in China in the treatment of industrial pollutants.

Optimal improvement requires each province to take targeted measures to improve its efficiency of pollutant treatment measures, which are tailored to specific situations and determined by efficiency analysis in this paper.

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.

This study investigates the performance of Indian states based on infrastructural investment in social and economic sectors using data envelopment analysis (DEA). Most of the studies in the literature are based on how different elements of infrastructure such as transport, energy, education, healthcare system affect the economy of different countries/regions. In this study, we consider these elements under two different sub-systems, namely, social and economic infrastructure and measure the cooperative efficiency for competitive growth.

A four-stage DEA approach is proposed for the analysis of a sample of 28 Indian states for the years 2011, 2013 and 2015 under consideration. First stage calculates the per capita GDP contribution, while stage-2 evaluates the efficiency of investments in social infrastructure followed by the efficiency analysis in economic infrastructure in stage-3. Finally, fourth stage evaluates the co-operative efficiency for the overall performance.

The findings of three different cases based on population sizes, viz., highly populated, moderately populated and less populated regions suggest that the government can identify the top and poor performers. It also studies the variations in efficiency tally of states using Malmquist indices.

This kind of study will vigilant government and local authorities on the investments made in all the states for social and economic infrastructure and establish a competitive environment among state governments to compete for improved infrastructural growth.

This study is the first of its kind in developing countries like India, which focuses on efficiency analysis using DEA based on two sub-sectors of social–economic infrastructural investments.

The existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.

A three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.

The efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.

A three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

This paper aims to present a hybrid approach to measure the efficiency of virtual contact centers (VCCs) started during the pandemic and benchmark them for service performance. The results are used to plot the VCC's efficiency score (performance) and customer perception (Importance) to propose appropriate strategies.

Using the survey method, 854 responses were collected from customers who used VCC services during the pandemic. This data was then employed to assess the performance of VCCs using SERVPERF and DEA methods, followed by the development of the model for performance analysis.

Results reveal the ranking of different VCCs started during the pandemic for the telecom company using SERVPERF and DEA methods. Further, the performance analysis model highlighted the strategies appropriate for each VCCs.

The findings add to the body of knowledge on how multiple service units of a large organization can assess service efficiency utilizing a combination of SERVPERF-DEA. The present work also contributes to the performance analysis field by proposing a model to assess the service centers and provide improvement guidelines.

The work is one of the first to assess the service efficiency of the VCCs started during the pandemic by using a unique hybrid approach of SERVPERF and DEA. This approach provides a direction to whom to benchmark and to what degree service quality should be improved. Further, the study proposes a unique performance analysis model based on performance scores and customer perception.

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.

Since airlines that employ their resources effectively will achieve operating profitability, air route resource allocation is significant for airlines. This study aims to investigate an appropriate model to reallocate resources into each air route of an airline company.

This study proposes a network centralized data envelopment analysis (DEA) models with slack-based measure (SBM). The proposed model not only takes into account the two interconnected stages but also considers the nonradial approach with transfer-in and transfer-out slacks for resource reallocating. Furthermore, the authors modify the objective function to an input-oriented function with SBM, and divide the model into passenger and freight parts, which makes the model more realistic for the characteristic of air routes.

The empirical analysis using an airline company's internal data provides airline operators with information on how they increase or decrease input resources, which can serve as a practical guideline of resource reallocation. Specifically, the results indicate that the airline company should increase their input resources into long-haul air routes such as KOR-OCN while decreasing their input resources into short-haul air routes such as Korean-Oceania (KOR-OCN), Korean-Chinese (KOR-CHN), Korean-Southeast Asian (KOR-SEA), Korean-Japanese (KOR-JPN).

Although some papers evaluate air route efficiencies based on the DEA approach, a few existing papers have addressed resource allocation for air routes. This paper is the first to study the resource reallocation for air routes based on the DEA approach, contributing to the literature in expanding the scope of research on resource reallocation.

The objective of this paper is to propose a new data envelopment analysis (DEA) model for assessing sustainability of suppliers with partial impacts between inputs, desirable outputs and undesirable outputs.

This paper examines partial impacts of inputs on desirable and undesirable outputs and applies weak disposability assumption to propose a novel DEA model to determine the sustainability of suppliers.

This paper shows the type of resource sharing in DEA models and takes into account sustainable development and sustainability assessment concepts for sustainable supplier selection problem and develops a DEA model for selecting the most sustainable suppliers with partial sharing of resources. To select the most sustainable suppliers, this model helps managers to consider aggregate efficiency, overall efficiency and bundle efficiency. The paper introduces the supplier which is efficient at all levels as the most sustainable supplier.

For the first time, this paper suggests a new DEA model by partial impact between inputs and good outputs/bad outputs for selecting sustainable supplier and deals with the situations in which each supplier has several subunits. The new model calculates aggregate efficiency, overall efficiency and subunit efficiency of supplier. paper introduces the supplier which is efficient in all levels including aggregate efficiency, overall efficiency and subunit efficiency as the best supplier.

The purpose of this paper is to develop an inverse network data envelopment analysis (INDEA) model to solve resource allocation problems.

The authors estimate inputs’ variations based on outputs so that the efficiencies of decision-making unit under evaluation (DMU_o) and other decision-making units (DMUs) are constant.

The new INDEA model is developed to allocate resources such that inputs are not increased while efficiency scores of all DMUs remain constant. Furthermore, the authors obtain new combinations of inputs and outputs, together with a growth in efficiency score of DMU_o such that efficiency scores of other DMUs are not changed. A case study is provided.

This paper proposes INDEA model to estimate inputs (outputs) without changing efficiency scores of DMUs.

The purpose of this paper is to benchmark the operational and financial performances of the major Indian seaports to help derive useful insights to improve their performance.

A two-stage data envelopment analysis (DEA) methodology has been used with the help of data collected on the 13 major seaports of India. The first stage of the DEA captured the operational efficiencies, while the second stage the financial performance.

A window analysis over a period of three years revealed that no port was able to score an overall average efficiency of 100 per cent. The study identified the better performing units among their peers in both the stages. The contrasting results of the study with the traditional operational and financial performance measures used by the ports helped to derive useful insights.

The data used in the study were majorly limited to the available sources in the public domain. Also, the study was limited to the major seaports which are under the Government of India and no comparisons were carried out with other local or international ports.

There is a need to prioritize investments and improvement efforts where they are most needed, instead of following a generalized approach. Once the benchmark ports are identified, the port authorities and other relevant stakeholders should work in detail on the factors causing inefficiencies, for possible improvements in performance.

This paper carried out a two-stage DEA that helped to derive useful insights on operational efficiency and financial performance of the India seaports. A combination of the financial and operational parameters, along with a comparison of the DEA results with the traditional measures, provided a different perspective on the Indian seaport performance. Considering the scarcity of research papers reported in the literature on DEA-based benchmarking studies of seaports in the Indian context, it has the potential to attract future research in this field.