Proposed Data Envelopment Analysis Research Articles

Target setting for airlines incorporating CO2 emissions: The DEA bargaining approach

Long-term sustainability in airline transportation entails mitigating the detrimental impact of CO2 emissions on the environment. However, it is also necessary that it does not affect the airlines' performance. In this regard, it is important to achieve optimal and efficient targets by inputs, good outputs, and undesirable outputs. The purpose of this study is to compute the input and output targets using the bargaining approach for a sample of major global airlines for the period 2018. This has been undertaken utilising the proposed data envelopment analysis (DEA)-based Nash bargaining model wherein its novelty lies in incorporating weakly disposable undesirable outputs. This model achieves reasonable and optimal targets for not just inputs and outputs but also for undesirable outputs by carrying out an unbiased, rational negotiation between the inputs and outputs. The results show that weakly efficient airlines attain the optimal targets by improving their environmental performance and inefficient airlines can achieve their input/output targets by carrying out an unbiased, rational negotiation between the inputs, good outputs, and bad output. The policy implications emanating from the findings have also been highlighted.

Performance evaluation and enrollment quota allocation for higher education institutions in China

Higher education has always played an important role in promoting economic and social progress. With the rapid development of economy, China's higher education has entered a new stage and made a series of extraordinary achievements. However, the uneven development of higher education has become a critical factor restricting China’s overall progress. In this paper, we propose Data Envelopment Analysis (DEA) approach to evaluate the performance of higher education resource utilization from the perspective of provincial level. In addition, we assess productivity changes of each province with the help of Malmquist Productivity Index (MPI). Further, by considering the enrollment quota as a special kind of resource, we construct a more reasonable allocation scheme, while keeping the total number of newly added enrollment quota unchanged in the next year. Finally, according to the results, some suggestions are put forward to help the Chinese government better solve the unbalanced development of higher education among provinces.

Labor inspectorates’ efficiency and effectiveness assessment as a learning path to improve work-related accident prevention

Labor inspectorates, by monitoring and enforcing labor legislation, giving information and technical advice to employers and employees, promoting awareness-raising campaigns and implementing occupational risk prevention policies, play a fundamental role in promoting safer and healthier working conditions and, ultimately, in reducing the number of work-related accidents. Even so, every year, hundreds of thousands of people die from accidents at work and around 2 million die from occupational diseases, highlighting that considerable room for improvement remains. The identification and sharing of best practices in labor inspection can be a useful first step in this direction. The main purpose of this research is to propose Data Envelopment Analysis as a suitable technique for assessing the performance of labor inspectorates and identifying best practices. The potential of the proposed methodology is tested by using it to assess the efficiency and the effectiveness of the 32 local branches of the Portuguese Authority for Working Conditions, a key player of the Portuguese system of occupational risk prevention, and by exploring its value in identifying best practices and possible paths for performance improvement.

Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China.

The performances of different types of banks may vary due to heterogeneous technology, which can be examined by metafrontier analysis. However, the metafrontier constructed in most existing literature is concave, resulting in a biased estimation of efficiency. Based on 93 Chinese commercial banks over the period of 2005–2016, we first evaluate the banking efficiency by using the proposed data envelopment analysis (DEA) model, NCMeta-US-NSBM, which simultaneously incorporates a non-concave metafrontier technique, undesirable outputs, and super efficiency into a network slacks-based measure (NSBM) model. Subsequently, the evolution of banking efficiency during the study period is investigated on the basis of the Dagum Gini index and kernel density estimation methods. The main empirical results show the following. 1) There exists significant disparity/heterogeneity in banking efficiency for overall efficiency, productivity efficiency, and profitability efficiency. 2) The results of the technology gap ratio (TGR) and the evaluation of stated-owned banks (SOB), joint-stock banks (JSB), and city commercial banks (CCB) in the productivity stage are higher than those in the profitability stage, indicating that most of the banks have a large space for improvement, especially for SOB and JSB in the profitability stage. 3) The major contribution of the overall difference of banking efficiency in China is the intensity of the transvariation. 4) Although the kernel density estimations for different efficiency scores have similar distributions in corresponding years, the multilevel differentiation phenomenon of banking efficiency may appear after 2008.

Industrial eco-efficiency, regional disparity, and spatial convergence of China's regions

In this study, the industrial eco-efficiency measures of 30 Chinese provinces (municipalities and autonomous regions) from 2001 to 2015 were estimated based on a proposed data envelopment analysis (DEA) model and the non-convex metafrontier-undesirable outputs-super efficiency-slacks-based measure (NCMeta-US-SBM) which simultaneously incorporates non-convex metafrontier technology, undesirable outputs, and super efficiency into slacks-based measure. Subsequently, dynamic spatial econometric models were employed to test the spatial convergence of industrial eco-efficiency. The empirical results show that: 1) the average value of industrial eco-efficiency was the highest in the eastern region (0.67), followed by the western region (0.30) and central region (0.27), indicating the existence of a significant regional disparity. 2) Strong evidence for global and club spatial convergence of industrial eco-efficiency was found. With regard to regional heterogeneity, provinces in the eastern region converged faster than those in the central and western regions; while the convergence speed of industrial eco-efficiency increased after 2008 with respect to temporal heterogeneity. 3) The ownership structure appeared to have a higher impact on industrial eco-efficiency than other controls in the presence of spatial conditional β-convergence. These findings are robust with regard to both spatial econometric model and spatial weighting matrix specifications. Based on the empirical analysis, the following policies promote industrial eco-efficiency in China have been recommended. First, relevant authorities must take effective measures to encourage the central/western regions to adopt clean energy technologies. The energy subsidy policies should strive to break through the technological lock-in and turn to regional green development. Second, full advantage of the spatial effects must be taken and technological exchanges and cooperation among neighboring areas must be strengthened to ensure that the industrial eco-efficiency of the central and western regions converge with those of the eastern coastal areas. Policy implications of multidimensional heterogeneity and adjustment of industrial structure should be highlighted while promoting industrial eco-efficiency. Finally, it is crucial to emphasize factors, such as foreign direct investment (FDI), ownership and industrial structures to attract FDI and optimize the current industrial structure.

Measurement of overall performances of decision-making units in the presence of interval data

In this paper, for performance measurement of decision making units (DMUs) with interval data, it is suggested to integrate the efficiencies obtained from optimistic and pessimistic views in the form of an interval. The upper bound of efficiency interval from the optimistic view is obtained based on the most favourable position of each DMU using a set of the most favourable weights. The lower bound of efficiency interval from the pessimistic view is obtained based on the most unfavourable position of each DMU using a set of the most unfavourable weights. The obtained efficiency interval shows all possible evaluations from different perspectives. Thus, the efficiency interval provides the decision-maker with all possible efficiency values, reflecting different views. Two numerical examples will show the proposed data envelopment analysis approach.

Measurement of overall performances of decision-making units in the presence of interval data

In this paper, for performance measurement of decision making units (DMUs) with interval data, it is suggested to integrate the efficiencies obtained from optimistic and pessimistic views in the form of an interval. The upper bound of efficiency interval from the optimistic view is obtained based on the most favourable position of each DMU using a set of the most favourable weights. The lower bound of efficiency interval from the pessimistic view is obtained based on the most unfavourable position of each DMU using a set of the most unfavourable weights. The obtained efficiency interval shows all possible evaluations from different perspectives. Thus, the efficiency interval provides the decision-maker with all possible efficiency values, reflecting different views. Two numerical examples will show the proposed data envelopment analysis approach.

An improvement in DEA cross‐efficiency aggregation based on the Shannon entropy

AbstractThis paper improves a recently proposed Data Envelopment Analysis (DEA) cross‐efficiency aggregation method based on the Shannon entropy. The weights for determining cross‐efficiency are derived from minimizing the square distance of weighted cross‐efficiency and weighted CCR efficiency. Our calculation example indicates that this method may produce inappropriate weights, which is significantly inconsistent with a widely accepted viewpoint. A variance coefficient method based on the Shannon entropy is presented to overcome the drawbacks of the DEA cross‐efficiency aggregation method. In this study, comparisons of weights and cross‐efficiency scores are provided.

Efficiency assessment and output maximization possibilities of European small and medium sized airports

The aim of this research is to investigate the operational efficiency and potential output increase scenarios of 21 small and medium sized airports (SMA) located in 10 different European countries. A key feature of the proposed Data Envelopment Analysis (DEA) models is the consideration of two novel inputs related to the number of airlines and scheduled routes operating at each airport, in addition to the more common capital non-discretionary inputs (runways, boarding gates and apron). These novel inputs are a way of considering the effects of the air transport market on airports' performance and growth prospects. In particular, a model to estimate the growth in passenger numbers, aircraft movements and cargo that can be achieved by an airport through increasing its number of airlines and routes, and assuming an efficient operation, is presented. Also, a multi-objective DEA model is proposed to explore the possible trade-offs in the output Pareto efficient frontier of a given airport. In addition, a second stage fractional regression model specific for explaining DEA efficiency scores is conducted and the results suggest that ownership structure and hub airport status are significant factors for explaining variations in the operational efficiency of European SMA.

Benchmarking Sports Sponsorship Performance: Efficiency Assessment With Data Envelopment Analysis

Over the last decades, sports sponsorship has become a popular and expensive marketing instrument. However, in business practice, projects are often not evaluated properly and academic research considering both costs and benefits of sponsorship is limited. In response to the concern that investments in sports sponsorship should be made more accountable, we propose data envelopment analysis (DEA) as a method for benchmarking sponsorship efficiency, and illustrate its usefulness by applying it on a sample of 72 major Dutch sports sponsorship projects. We find an average efficiency level of almost 0.3, which implies that the average project would have attained the same results with 30% of its fee if it had been performing as well as its benchmark. The results reveal that 12.5% of the investigated sponsorships are fully efficient. Moreover, we find a high degree of variety in efficiency scores; 37.5% of the projects with an efficiency below 0.1. In addition, we show how DEA scores may be used by sponsor managers to identify peers, which are those projects that attain roughly the same sponsorship outcomes, but at lowest budgets. After estimating the efficiency scores, a second step in the analyses involves investigating which sponsorship characteristics affect sponsorship efficiency. For this purpose, we use the DEA scores as a dependent variable in a Tobit regression model. The findings suggest that sponsorship clutter negatively affects sponsorship efficiency, whereas sponsorship duration has a positive effect.

Learning-Based Data Envelopment Analysis for External Cloud Resource Allocation

A mature cloud system needs a complete resource allocation policy which includes internal and external allocation. They not only enable users to have better experiences, but also allows the cloud provider to cut costs. In the other words, internal and external allocation are indispensable since a combination of them is only a total solution for whole cloud system. In this paper, we clearly explain the difference between internal allocation (IA) and external allocation (EA) as well as defining the explicit IA and EA problem for the follow up research. Although many researchers have proposed resource allocation methods, they are just based on subjective observations which lead to an imbalance of the overall cloud architecture, and cloud computing resources to operate se-quentially. In order to avoid an imbalanced situation, in previous work, we proposed Data Envelopment Analysis (DEA) to solve this problem; it considers all of a user's demands to evaluate the overall cloud parameters. However, although DEA can provide a higher quality solution, it requires more time. So we use the Q-learning and Data Envelopment Analysis (DEA) to solve the imbalance problem and reduce computing time. As our simulation results show, the proposed DEA+Qlearning will provide almost best quality but too much calculating time.

PORTFOLIO PERFORMANCE BENCHMARKING WITH DATA ENVELOPMENT ANALYSIS

This paper proposes data envelopment analysis (DEA) as a suitable data analysis tool to overcome facility management (FM) benchmarking difficulties: FM performance benchmarking analysis is often unsophisticated, relying heavily on simple statistical representation, linking hard cost data with soft customer satisfaction data is often problematic. A case study is presented to show that DEA can provide FM personnel with an objective view on performance improvements. An objective of the case study is to investigate the relative efficiency of nine facilities with the same goals and to determine the most efficient facility. The case is limited to nine buildings in FM on four inputs and nine output criteria. The paper concludes by demonstrating that DEA-generated improvement targets can be applied when formulating FM outsourcing policies, strategies and improvements. Facility manager can apply DEA-generated improvement targets in formulating FM outsourcing policies, specifications development, FM strategy and planning. FM benchmarking with DEA can enhance continuous improvement in service efficiency and cost saving. This will help reduce utility cost as well as pollution. This paper fills the gap in the research of FM benchmarking by applying DEA which studies both soft and hard data simultaneously. It also contributes to a future research of a trade-off sensitivity test between FM cost, services performance and reliability.

FACILITY MANAGEMENT BENCHMARKING: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS IN HONG KONG

This paper proposes Data Envelopment Analysis (DEA) as a suitable data analysis tool to overcome facility management (FM) benchmarking difficulties: FM performance benchmarking analysis is often unsophisticated, relying heavily on simple statistical representation, linking hard cost data with soft customer satisfaction data is often problematic. A case study is presented to show that DEA can provide FM personnel with an objective view on performance improvements. An objective of the case study is to investigate the relative efficiency of nine facilities with the same goals and to determine the most efficient facility. The case is limited to nine buildings in FM on four inputs and nine output criteria. The paper concludes by demonstrating that DEA-generated improvement targets can be applied when formulating FM outsourcing policies, strategies and improvements. Facility manager can apply DEA-generated improvement targets in formulating FM outsourcing policies, specifications development, FM strategy and planning. FM benchmarking with DEA can enhance continuous improvement in services' efficiency and cost saving. This will help reduce utility cost as well as pollution. This paper fills the gap in the research of FM benchmarking by applying DEA which studies both soft and hard data, simultaneously. It also contributes to a future research of a tradeoff sensitivity test between FM cost, services performance and reliability.

Heifer Retention Program in the Pantanal: a study with data envelopment analysis (DEA) and Malmquist index

The objective of this study was to propose Data Envelopment Analysis (DEA) models and non-parametric Malmquist indexes to evaluate the efficiency of 11 livestock farms in the Pantanal region. These farms took out a loan from 'Fundo Constitucional de Financiamento do Centro-Oeste' - FCO (Constitutional Financial Fund for the Midwest), a line of credit for financing heifer retention. The beneficiaries were the farmers whose properties met the following conditions: farms located in the plain of Pantanal, with at least 50% of native pasture; farms that were part of managerial capacity building projects intended to ensure better management and animal performance indexes; and farms with pasture areas where the activity could potentially evolve. The management of the loan on 11 farms were studied in relation to the technology introduced in the period from 2004 to 2008. Five properties presented technical efficiency below 1, that is, the technical management efficiency with the objective to increase the cattle through heifer retention and the purchase of higher genetic quality heifers was not achieved. However, the index of technological progress was above 1 on all tested farms, which means that technology was in fact incorporated in all properties, which resulted in total factor productivity growth on all farms. The main objective was fully met, even on the technically inefficient farms.

Research and development project evaluation based on an integrated common set of weights in data envelopment analysis and balanced scorecard approach

The objective of this article is to propose a new approach for evaluating research and development (R and D) projects in different stages of their life cycle. More recently, some researchers have proposed data envelopment analysis (DEA) as a tool for evaluating R&D projects. In order to eliminate the inconsistency caused by using different frontier facets to calculate efficiency, common set of weights DEA models have been developed, under which a group of decision making units (DMUs) can be ranked for a specific period. Our approach integrates the balanced scorecard (BSC) and common set of weights (CSW) in DEA and develops a new model for project evaluation. Key words: Data envelopment analysis, common set of weights, balanced scorecard, project evaluation.

In the Eye of the Beholder: Alternative Approaches for Ranking Hospital Performance

Assessing and reporting hospital performance has become a worldwide phenomenon. How performance is measured and ranked may determine results. This study evaluates the implications of alternative methods of aggregating hospital performance measures. Data are from patient abstracts and financial Medicare reports from U.S. acute hospitals in 2009, as cleaned and prepared by the Thomson-Reuters 100 Top Hospitals initiative. We evaluate the stability of individual hospital rankings under rank averaging and Data Envelopment Analysis (DEA). Rankings of individual hospitals can shift across four quartiles with inclusion or exclusion of different performance dimensions or when using rank averaging versus DEA. Comparing DEA models with rank averaging, only one third of cases remain in the same quartile, and one third move two or more quartiles. Two proposed DEA models are more similar. Selection among commonly used variables substantially affects the results in ranking hospitals and the method of ranking (DEA vs. r...

Data Envelopment Analysis (DEA) Integrated Risk Assessment Technique on Hedge Funds Investment: Theory and Practical Application

This paper discusses Hedge funds' risks, in particular the leverage risk, as it has the greater effect when applied both to derivatives or other asset classes alike. Evaluation of the effect of leverage could become complicated as this effect could be escalated by the liquidity and credit risks underlying the invested assets. Unlike market risk, these are the risks that are not easy to hedge and need constant closed monitoring. Moreover, market risks could also evolve over time. This reinforced the importance of correlation in estimating the eventual effect, especially for complex portfolios and structured products. Under most cases, by segregating risks geographically and at a granular positional level, Hedge fund managers can also have more transparency to estimate direct or indirect relationships between strategies and correlations can still be calculated. The proposed Data Envelopment Analysis (DEA) method in this paper, shows how the use of correlations of returns, and the consideration of the distributional characteristics of the Hedge funds, by suitably using appropriate risk and returns measures, is able to rank Hedge funds' performance. By further assessing the statistical properties of the performance and the ranked positions of Hedge funds, the proposed method aims to provide a reliable tool to assess risk of Hedge funds appropriately, which should support the Hedge funds selections process for the Funds of Hedge funds managers.

Efficiency assessment of Chinese logistics firms using DEA

This paper proposes data envelopment analysis (DEA) to evaluate the operational efficiency of three categories, and a total of 36 listed logistics firms in China. DEA-CCR, BCC, NIRS and SUP models, including two groups of financial and non-financial input and output measures, are used to discover the level and sources of efficiency and inefficiency of sample firms. The empirical results show that there are only one third of them with DEA efficiency value of 1, indicating that the overall efficiency of Chinese logistic industry is relatively low. And among three categories of service offerings, efficiency value of transporting firms is less than the average of logistic industry, with the lowest value for port firms, which can be attributed to scale inefficiency, mainly stemming from decreasing returns to scale (DRTS). Finally, the feasibility of DEA method of evaluating efficiency of logistic firms is demonstrated, and the related suggestions for efficiency improvement of Chinese logistic industry are also presented.

Logistics sector development potential of world's oil exporters

PurposeOil exporters have been identified as having problems in the friendliness and performance of their logistics sector, particularly in general cargo group. The purpose of this research is to identify, through proposed data envelopment analysis (DEA) models, those oil exporters with most potential to develop (who have resources for this through their economic prosperity). Benchmarks of improvement are not only identified among top oil exporters, but also from larger group of emerging countries.Design/methodology/approachThe research uses two different DEA models, and quantitative data available from economic prosperity and general cargo logistics performance. Models are input oriented, and use the most recent data from year 2009.FindingsMajor oil exporters are not homogeneous group in their performance turning logistics competence on prosperity. Actually, there could be one group of very low performance identified in general cargo logistics performance as compared to the DEA models, and other group with similar or slightly above performance with general cargo handling. Those performing at the lower end, and with most development potential, include such countries as Russia, Venezuela, Algeria, Qatar, Azerbaijan and Turkmenistan (possibly also including Iran, Kuwait and UAE). From the group of major oil exporters, these countries should learn exceptional logistics competence from Malaysia and Canada. Similarly, this research shows that the emerging economies, particularly China, but also India as well as Philippines, Thailand and South Africa are useful benchmarks to develop general cargo logistics performance further (to some extent Korea (South), Taiwan, Czech Republic, Poland, Turkey and Lebanon also could be included).Research limitations/implicationsThe research is not based on longitudinal data, and should be enlarged to take into account earlier logistics performance index ratings from the World Bank studies (year 2007); this not only to verify the results, but also to highlight possible progress in the development of the logistics sector. Also, other global logistics performance ratings (e.g. from infrastructure) should be taken into account, and DEA models be developed further (often infrastructure drives other layers of performance).Originality/valueResearch work is seminal to study in respect to identifying which countries “lower performing oil producers” include, and which have potential in better developing their logistics sector (general cargo). Among this, research also proposes benchmarks for these countries, not only among oil exporters, but also from other emerging economies. The research findings give a unique and fresh perspective on the logistics performance of oil exporters and emerging economies in general.

Comparative efficiencies of specialty coffee retailers from the perspectives of socially responsible global sourcing

Purpose – The purpose of this paper is to illuminate light on the assessment of operating efficiency of specialty coffee retailers from the perspective of socially responsible global sourcing. This paper evaluates the impact of socially responsible sourcing on the operating efficiencies of specialty coffee retailers before and after implementing fair‐trade practices and compares the operating efficiencies of fair‐trade coffee retailers to those of non‐fair‐trade coffee retailers.Design/methodology/approach – The paper proposes data envelopment analysis (DEA) to measure the comparative efficiency of seven specialty coffee retailers, relative to prior periods and their key competitors. It develops the Charnes‐Cooper‐Rhodes (CCR) model that is designed to derive weights without their being fixed in advance. The CCR version of DEA is adopted to assess the impact of the coffee retailers' policy initiatives (i.e. fair‐trade practices) on their efficiency and measure the change over time (i.e. before and after f...