Two-stage Data Envelopment Analysis Research Articles

Performance evaluation of bank branches in the atmosphere of grey uncertainty

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Performance evaluation of bank branches in the atmosphere of grey uncertainty

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Application of a Performance Evaluation Model to the Paper and Paper Products Printing Sector: The DEA-AHP Hybrid Algorithm

The paper and paper products printing sector plays a crucial role in generating income, creating employment opportunities, and supporting exports and various industries. Measuring the efficiency of companies operating in this sector is important in identifying areas for improvement and enhancing overall performance. In this study, a two-stage DEA (data envelopment analysis)-AHP (analytic hierarchy process) approach is proposed to analyze the efficiency of twelve paper and paper products printing companies traded on Borsa Istanbul. The modified DEA method is employed to make pairwise comparisons of the companies. Total assets, total equity, and the number of employees are selected as inputs, while revenue and net profit are considered as outputs. The AHP method prioritizes the companies by considering the outputs of the mathematical models constructed via DEA. The proposed framework presents a different view because it contributes to identifying the most efficient company, benchmarking company performance, and determining areas for improvement.

Application of a Performance Evaluation Model to the Paper and Paper Products Printing Sector: The DEA-AHP Hybrid Algorithm

The paper and paper products printing sector plays a crucial role in generating income, creating employment opportunities, and supporting exports and various industries. Measuring the efficiency of companies operating in this sector is important in identifying areas for improvement and enhancing overall performance. In this study, a two-stage DEA (data envelopment analysis)-AHP (analytic hierarchy process) approach is proposed to analyze the efficiency of twelve paper and paper products printing companies traded on Borsa Istanbul. The modified DEA method is employed to make pairwise comparisons of the companies. Total assets, total equity, and the number of employees are selected as inputs, while revenue and net profit are considered as outputs. The AHP method prioritizes the companies by considering the outputs of the mathematical models constructed via DEA. The proposed framework presents a different view because it contributes to identifying the most efficient company, benchmarking company performance, and determining areas for improvement.

Towards greener hospitals: The effect of green organisational practices on climate change mitigation performance

Healthcare organisations have a significant carbon footprint due to their substantial energy consumption and waste generation. The existing literature has mostly focused on examining the isolated impact of specific green organisational practices on individual dimensions of environmental performance. Hence, policy makers and practitioners still lack a holistic view of how combinations of green organisational practices can mitigate the impact on climate change of healthcare organisations. Drawing on the practice-based view (PBV) of strategy, the study first examines the extent to which the adoption of imitable, individual green organisational practices impacts on the whole climate change mitigation performance of publicly owned hospitals. Second, it explores possible combinations of green organisational practices that can lead to high levels of climate change mitigation performance. Focusing on the illustrative case of publicly owned acute care hospitals in the English National Health Service and employing a two-stage Data Envelopment Analysis and a fuzzy set Qualitative Comparative Analysis (fsQCA), the analysis shows that the adoption of the practice of green energy procurement can, in isolation, positively lead to high climate change mitigation outcomes. Moreover, high performance levels can be achieved through multiple configurations of green organisational practices. Therefore, the findings suggest that the effectiveness of green organisational practices on climate change mitigation performance of hospitals depends on how practices are combined, rather than on their single effects, and indicate the presence of a complementary as well as a substitution effect between practices.

Effect of procurement devolution process of medical supplies on the technical efficiency of public health facilities in Nakuru and Baringo Counties of Kenya

This study analyzed the effect of procurement devolution process of medical supplies on the technical efficiency of public health facilities in Nakuru and Baringo Counties of Kenya. Although increased government expenditure was expected to improve efficiency and better health outcomes, Kenya’s public health sector remains inefficient. The study adopted a quantitative descriptive research design that targeted 3199 employees in 401 level 2-5 public health facilities (PHFs). Using Slovin’s formula, a sample size of 355 was proportionately sampled. While secondary data was obtained through desk review, primary data was collected through structured questionnaires that were administered to different cadres of employees. Primary data was coded and analyzed using STATA/SPSS. To compute technical efficiency of PHFs, the two-stage Data envelopment analysis (DEA) and multivariate logistic regression was adopted. Study findings show that devolution has improved demand for medical supplies, enhanced procurement efficiency, and promoted ethical procurement. However, complaints from patients have not reduced while training of procurement personnel has not increased. Regression analysis indicates a significant relationship between human capital and technical efficiency. The coefficient of human capital development was 0.199 (T-value= 9.514, p- value= 0.000< 0.05 significant level); implying a positive effect of procurement devolution process on technical efficiency. This study concludes that despite increased health expenditure in Kenya, public health healthcare services have deteriorated due to inefficiency of the public health sector. This study recommends increased financial and human resource support to Kenya’s public health care.

A Two-Stage Data Envelopment Analysis Approach Incorporating the Global Bounded Adjustment Measure to Evaluate the Efficiency of Medical Waste Recycling Systems with Undesirable Inputs and Outputs

A Two-Stage Data Envelopment Analysis Approach Incorporating the Global Bounded Adjustment Measure to Evaluate the Efficiency of Medical Waste Recycling Systems with Undesirable Inputs and Outputs

Efficiency assessment of two-stage systems with fixed-sum outputs: A noncooperative DEA model with uncertain stage priority

Exogenous constraints such as fixed-sum output can affect the efficiency assessment of traditional two-stage data envelopment analysis (DEA) models. To address this issue, several studies have evaluated the DEA efficiencies for two-stage systems with fixed-sum output. They have proposed centralized models based on cooperative game theory and models under global mindset. However, in many cases, the first and second stages play a noncooperative game, and information on stage priority is often unobservable. In this study, we develop a noncooperative two-stage fixed-sum output DEA model with uncertain stage priority for calculating the overall system and stage efficiencies. Instead of stage priority as an endogenous variable, with aid of acceptaiblty analysis, we analyze the dominance relationship among decision-making units (DMUs) and provide a robust ranking by considering all possible stage priorities. We also conduct an empirical analysis with data from commercial banks to demonstrate the effectiveness of the developed model. After 10,000 groups of stage priority iterations, we report a robust ranking of commercial banks in which Hefei branch ranks first with 100 % probability.

A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks

In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.

Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared resources

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

Individualized second stage corrections in data envelopment analysis

In the context of two-stage data envelopment analysis (DEA) for efficiency correction, we shift the focus from the common central tendency orientation in its second stage to an individually oriented procedure. We propose to evaluate the influence of contextual variables on each unit's performance relative to the other operating units. This results in an alternative approach in which the second stage inherits the principal property of DEA in the first stage of putting each individual unit in its best possible light. We demonstrate the applicability of our approach using data from the energy sector in the domain of incentive regulation, where operators are natural monopolies. In such systems of incentives, ensuring fair performance evaluations is crucial, given the influence of contextual variables beyond management control. Our approach contributes to efficiency correction procedures under these circumstances. The results not only encourage operators to economize costs and improve service quality but also motivate them to, for example, minimize environmental impact of operations, adopt eco-friendly technologies, and invest in renewable energy sources.

Effect of human capital devolution process on the technical efficiency of public health facilities in Nakuru and Baringo Counties of Kenya

This study investigates how the process of human capital devolution affects the technical efficiency of public health facilities in Nakuru and Baringo Counties in Kenya. Despite the expectation that increased government expenditure would enhance efficiency, the public health sector in Kenya continues to need more efficiency. The study was guided by the Human Capital Theory. The human capital devolution process was measured by academic and professional qualification, training and conferences, research and publications, and expenditure on salaries and allowances. This study drew from the Cobb-Douglas production function theory. Employing a quantitative descriptive research design, the study targeted 3199 employees across 401 level 2-5 public health facilities, with a focused sample of 355 proportionately selected employees. Primary data was gathered using structured questionnaires coded and analysed using STATA/SPSS software. Technical efficiency was assessed using two-stage Data Envelopment Analysis (DEA) and multivariate logistic regression. The regression analysis results revealed a significant relationship between human capital and technical efficiency, with a coefficient of human capital development at 0.331 (T-value= 4.384, p-value= 0.000< 0.05 significant level). This indicates a positive impact of human capital devolution on technical efficiency, where a unit increase in human capital development leads to a 0.331 increase in technical efficiency. The study recommends that financial challenges, the unequal distribution of national resources, and poor funding of devolution services can be solved by transferring all functions earmarked to County Governments as per Schedule IV in the Constitution of Kenya, 2010.

Effect of procurement devolution process of medical supplies on the technical efficiency of public health facilities in Nakuru and Baringo Counties of Kenya

This study analysed the effect of the procurement devolution process of medical supplies on the technical efficiency of public health facilities in Nakuru and Baringo Counties of Kenya. Although increased government expenditure was expected to improve efficiency and better health outcomes, Kenya's public health sector remains inefficient. The study used a quantitative descriptive research design that targeted 3199 employees in 401 level 2-5 public health facilities (PHFs). Using Slovin’s formula, a sample size of 355 was proportionately sampled. While secondary data was obtained through desk review, primary data was collected through structured questionnaires that were administered to different cadres of employees. Primary data was coded and analysed using STATA/SPSS. To compute the technical efficiency of PHFs, the two-stage Data envelopment analysis (DEA) and multivariate logistic regression were adopted. Study findings show that devolution has improved demand for medical supplies, enhanced procurement efficiency, and promoted ethical procurement. However, complaints from patients have not been reduced, and the training of procurement personnel has not increased. Regression analysis indicates a significant relationship between human capital and technical efficiency. The coefficient of human capital development was 0.199 (T-value= 9.514, p- value= 0.000< 0.05 significant level), implying a positive effect of the procurement devolution process on technical efficiency. This study concludes that despite increased health expenditure in Kenya, public health healthcare services have deteriorated due to the inefficiency of the public health sector. This study recommends increased financial and human resource support for Kenya's public health care.

Evaluation of bank efficiency by considering the uncertainty of nonperforming loans

Research of banking operations has recently attracted increasing interest among bank managers and scholars, especially research on the evaluation of bank efficiency. As a prominent factor, nonperforming loans are always considered when measuring bank efficiency. Actually, the complex structures and uncertainty of non-performing loans have been neglected in previous studies. In our study, a new robust two-stage DEA (data envelopment analysis) model is proposed to analyze bank efficiency while considering the structure and uncertainty of nonperforming loans. The proposed model was utilized to assess Chinese commercial banks’ performance. Results indicated traditional model may overestimate banks’ performances, which will mislead bank decision-makers seeking to implement improvement strategies. Moreover, the majority of banks performed better in producing deposits compared with earning profits. Lastly, gaps between small medium banks and state-owned banks are still larger when considering the structure and uncertainty of nonperforming loans. Specific recommendations are proposed for each type of bank.

Retraction Note: efficiency evaluation of the high-tech industry chain with a two-stage data envelopment analysis approach

Retraction Note: efficiency evaluation of the high-tech industry chain with a two-stage data envelopment analysis approach

Two-stage network data envelopment analysis production games

<abstract> <p>DEA (Data Envelopment Analysis) production games combine DEA theory with cooperation games and assess the benefits to production organizations with single-stage structure. However, in practical production problems, the production organizations are always with network structures. The structure of the production organization not only affects its own benefits, but also relates to the cooperation among organizations. Therefore, it is necessary to study DEA production games with network structures. In this paper, we consider the production organizations with two-stage processes, wherein the organizations are assumed to possess available resources and own technologies. The technology level of each organization is reflected by the observed units based on the network DEA (NDEA) production possibility set. Suppose that the organizations can cooperate through the ways of pooling the initial resources and (or) sharing the technology in each production process. According to the different cooperation styles of each stage in the alliance, seven types of cooperation among organizations are considered. The models of maximizing the revenues of coalitions, namely the NDEA production games, are established corresponding to the seven types, by which the maximal revenue for each coalition can be calculated. We prove that two-stage DEA production games have the super-additive property, and can be expressed as linear programming games. Hence, they are equivalent to the linear production games, and they are totally balanced. Therefore, the proposed cooperative games have a non-empty core, and hence have nucleolus, and the Owen set belongs to the core. In addition, based on the basic conceptions of the nucleolus and the Owen set, the revenue can be allocated among organizations in the alliance. Finally, a numerical example and an empirical application to 17 bank branches of the China Construction Bank in the Anhui Province are presented to illustrate the applicability of the proposed approach, and the relationship between the cooperative manners and the revenue allocation is reflected in analytical results.</p> </abstract>

The Measurement of Efficiency of Sharia Banks in Indonesia: Two-Stage Data Envelopment Analysis (DEA)

The Measurement of Efficiency of Sharia Banks in Indonesia: Two-Stage Data Envelopment Analysis (DEA)

Efficiency of Micro and Small Wood-Processing Enterprises in the EU—Evidence from DEA and Fractional Regression Analysis

Micro and small wood-processing enterprises represent the heart of European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the provision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is used in the current research, which is a nonparametric, linear-programming-based approach that is commonly used to analyse the efficiency of organisational units. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterprises in EU countries and reveal the hidden inputs that facilitate efficiency generation. The economic efficiency evaluation was carried out on the basis of the official statistical data for the micro and small wood-processing companies in EU member states for the period 2015–2020 by performing a two-stage DEA analysis. The data used were standardised by value per employee. In addition to the first stage of DEA, a fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically efficient entities. The only contextual variable affecting the economic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase in the investments.

Sustainability performance efficiency in the banking sector

Traditional sustainability reporting is based on an internally developed methodology and does not fully cope with the sustainability performance evaluation, especially from an external perspective. This study aims to fill this gap by offering a tool that may be used to assess the company’s performance based on the results presented to external users. For this purpose, a two-stage data envelopment analysis (DEA) is proposed, that enables a comparison of the sustainability performance disclosed in non-financial reports in the banking industry in Poland, Croatia, and Romania. The findings of our study identify differences between the efficiency of sustainability performance in the banking sectors of all three countries. The proposed approach to compare hard-to-compare sustainability performance may significantly contribute to the decision-making process for stakeholders and, therefore, to the advancement of sustainability performance measurement research.

Inverse two-stage data envelopment analysis with interval measures for resource planning

PurposeAssessing 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.Design/methodology/approachActually, 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.FindingsAn 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.Originality/valueTo 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.