Context-dependent Data Envelopment Analysis Research Articles

Evaluating air route performance with context-dependent data envelopment analysis: A case study in Taiwan

The aviation industry has a key role in international trade activities. Most studies measure aviation performance based on airlines or airports. However, the frontline operation is delivered via air routes and more operating characteristics can be revealed with route-based performance evaluation. Further, the traditional data envelopment analysis (DEA) approach cannot rank performance in a particular context which represents an efficiency frontier built by decision making units (DMUs). Therefore, this study uses a context-dependent DEA approach to measure and rank the performance of air routes with a case study of 112 international routes from two airlines in Taiwan. Model results identify eight performance levels with most air routes at efficiency performance level 2 and level 3 and most route destinations in East Asia (e.g., Japan). The results of this study can inform operators about a clear relationship between performance and potential markets as well as improvement strategies.

Pathway towards carbon peaking cities in the Chinese transport sector

China has steadfastly pledged its dedication to accomplishing carbon peaking by 2030 and achieving carbon neutrality before 2060, a commitment widely recognized as the Dual Carbon Goal (DCG). The proposition of China's DCG presents formidable challenges for the advancement of the transport sector due to its significant contribution to carbon emissions. Consequently, new carbon abatement constraints have been enforced on carbon emissions within the Chinese transport sector. In pursuit of the carbon peaking goal, this research introduces the environmental context-dependent data envelopment analysis approach. The approach is employed to identify sustainable performance targets for the Chinese transport sector at the city level, while considering carbon abatement constraints. These sustainable performance targets not only provide insights on enhancing the environmental performance of these cities with minimum effort, but also offer a comprehensive pathway towards carbon peaking cities in the Chinese transport sector.

Pure output model-based context-dependent DEA: Tool for analyzing index published by assessing bodies

The institutions of world repute, like the United Nations, the World Bank, etc., assess and rank countries or firms based on various criteria pertaining to key areas to evaluate their performance and publish them as Indexes. These ranking tools aid decision-makers in formulating policies and procedures44PAP-Policies and procedures. (PAP) to achieve performance efficiency. In the real world, identifying the closest, most efficient country or firm whose PAP can be emulated with minimal effort is a management dilemma. These Indexes contain only output data, which compounds the problem. This paper uses Data envelopment analysis55DEA- Data envelopment analysis. (DEA) to resolve these issues. Each country or firm under evaluation is referred to as Decision-making unit66DMU-Decision-making unit. (DMU). The novelty of this study emanates from 1) segregating countries or firms into several frontiers using Pure Output Model-based Context-Dependent Data envelopment analysis77POMCDD-Pure output model-based context-dependent data envelopment analysis. (POMCDD), 2) proposing a technique to identify the most attractive nearest DMU that is performing better and is easy to emulate, 3) identifying the “Region of introspection” by using the translation technique of Pillar-Index scores concerning DMU under evaluation to demarcate reference DMU for the concerned Pillar, and 4) suggesting techniques such as “Rapid gain,” “Best yield”, and “Least effort” which are proposed to identify reference DMUs for the concerned Pillar. A case study analysis of the Logistics Performance Index88LPI-Logistics Performance Index. (LPI) is carried out to substantiate the contributions. The countries ranked in the Logistics Performance Index are segregated into several efficient frontiers using the POMCDD technique. The attractiveness of each country is evaluated relative to the frontiers of poorly performing countries. The translation of Pillar-Index scores concerning the country under evaluation is used to identify reference countries for that Pillar. The developed framework is helpful to the policy-makers in analyzing published Indexes.

Hierarchy performance assessment of industrial solid waste utilization - tracking resource recycling and utilization centers in China.

The massive production and accumulation of industrial solid waste (ISW) have led to environmental pollution and natural resource underutilization. China's efforts to build trial industrial waste resource utilization centers provide strong support for sustainable development. However, these centers and the factors driving ISW utilization have yet to be evaluated. This paper utilizes context-dependent data envelopment analysis models without explicit inputs (DEA-WEI) to evaluate the overall utilization performance of 48 industrial waste resource utilization centers in China from 2018 to 2020. It also builds a Tobit model to assess which indicators and waste types affect overall ISW utilization. The results show overall ISW utilization performance of centers in the sample has improved, with the average value falling from 1.7193 in 2018 to 1.5624 in 2020. However, there are clear regional performance gaps, with East China having the highest utilization performance (1.3113) while the Southwest had the lowest (2.2958). Finally, this paper proposes measures to improve the overall utilization of industrial waste resources based on an analysis of the factors driving solid waste utilization.

Improvement Pathways for Urban Land Use Efficiency in the Beijing-Tianjin-Hebei Urban Agglomeration at the County Level: A Context-Dependent DEA Based on the Closest Target.

One of the most effective ways to achieve sustainable land use and the regional coordinated development of urban agglomerations lies in improving the urban land use efficiency (ULUE) of both large, medium, and small cities and small towns. However, in previous studies, less attention has been paid to pathways for potential improvement, especially at the county level. The main purpose of this paper is to examine potential improvement paths for the ULUE at the county level in urban agglomerations, while attempting to provide more practical targets for improvement and formulate more reasonable improvement steps for inefficient counties. Therefore, a total of 197 counties in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in 2018 were taken as examples to build a context-dependent data envelopment analysis (DEA) model based on the closest target. In addition, by utilizing methods such as the significant difference test and system clustering analysis, the shortest path and steps to achieve efficiency were identified for inefficient counties, and the characteristics of improvement paths at different levels were summarized. Furthermore, improvement pathways were compared for two dimensions: administrative type and region. The results showed that the causes of polarization for ULUE at different levels were mainly reflected in more complex targets to be improved in the middle- and low-level counties than at high levels. Improving environmental and social benefits was essential to achieving efficiency in most inefficient counties, especially at the middle and low levels. The improvement paths for inefficient counties between different administrative types, as well as the prefecture-level cities, were heterogeneous. The results of this study can provide a policy and planning basis for improving urban land use. This study is of practical significance in accelerating the development of urbanization and the promotion of regional coordination and sustainable development.

A recommendation system for effective learning strategies: An integrated approach using context-dependent DEA

Universities have been focusing on increasing individualized training and providing appropriate education for students. The individual differences and learning needs of college students should be given enough attention. From the perspective of learning efficiency, we establish a clustering hierarchical progressive improvement model (CHPI), which is based on cluster analysis and context-dependent data envelopment analysis (DEA) methods. The CHPI clusters students' ontological features, employs the context-dependent DEA method to stratify students of different classes, and calculates measures, such as obstacles, to determine the reference path for individuals with inefficient learning processes. The learning strategies are determined according to the gap between the inefficient individual to be improved and the individuals on the reference path. By the study of college English courses as an example, it is found that the CHPI can accurately recommend targeted learning strategies to satisfy the individual needs of college students so that the learning of individuals with inefficient learning processes in a certain stage can be effectively improved. In addition, CHPI can provide specific, efficient suggestions to improve learning efficiency comparing to existing recommendation systems, and has great potential in promoting the integration of education-related researches and expert systems.

Nested frontier-based best practice regulation under asymmetric information in a principal–agent framework

This study focuses on public or private organisations with a large set of operation units. Central managers in these organisations face asymmetric information on the operating costs of sub-units and wish to incentivise them to reveal their minimal costs. In this paper, best practice regulation is implemented to deal with the asymmetric information via a regulation game. In the regulation game, the operating costs of sub-units are divided into several cost levels. To describe the unknown cost levels, a nested frontier-based approach, called context-dependent data envelopment analysis (CD-DEA), is introduced. By using CD-DEA, the efficient feasible incentive contract to the regulation game is proposed, and the condition that guarantees optimality of the proposed contract is also presented. Moreover, the efficient feasible incentive contract shows that the best responses of sub-units in the regulation game are to announce their actual cost levels, which constitute the Nash equilibrium. Several discussions are further provided to ensure the applicability of the proposed approach in realistic environments. Lastly, the proposed approach is illustrated by data from electricity generation and distribution sectors.

Sequential benchmark selection on Pareto-efficient frontiers with endogenous directions

Directional distance function (DDF) has been a popular technique in performance evaluation and benchmark selection. However, one drawback of DDF in benchmark selection lies in its inability to ensure Pareto-efficient benchmarks because of the selection of directional vectors. In the current article, we develop an approach to identify endogenous directions to guarantee selected benchmarks on Pareto-efficient frontiers in sequential benchmark selection. The approach synthesizes DDF and context-dependent data envelopment analysis (CD-DEA). The endogenous directions are determined by a “trade-off” between potential reductions and expansions of inputs and outputs, respectively. We prove that the selected benchmarks are DEA-based strongly efficient (Pareto efficient) and affine invariant. Moreover, we conceptualize the effort of realizing the selected benchmarks as a benchmark index. We demonstrate that the benchmark index can be described by DDF and Euclidean distance between the evaluated decision-making unit and its benchmark. Several properties of the benchmark index, namely positive, weak monotonic, translation invariant, and reference-set dependent, are proven. Finally, a detailed analysis is conducted to select sequential benchmarks for China’s transportation system.

Carbon mitigation by quota allocation

To avoid the calamitous consequences of an even warmer world, the efforts are focused on overarching and immediate solutions to reduce the greenhouse gases. The emissions trading scheme is deemed worldwide as an ecological and robust management mechanism to curb carbon emissions. The challenge is how to design such a scheme to attain the basic purpose of installing a uniform system and equilibrium efficiency achievement. For the first time, existing idea of group method of data handling (GMDH) type neural network (NN) is developed to predict capital stock, labor force, CO2 emission, energy consumption, and gross domestic product (GDP) based on past information for the top-26 emitting countries. Then, this study deals with the allocation of emission quotas by proposing a two-step optimization mechanism that takes full advantages of the context-dependent data envelopment analysis (DEA) and equilibrium efficient frontier DEA (EEFDEA) models. In the first step, under the premise of constant total carbon emissions, a pragmatic efficiency-oriented carbon quota trading system is established to attain equilibrium state. In the second step, under the measured total emission mitigation target, the carbon quotas allocation mechanism is formulated to translate the top emitters' mitigation target into national purposes from two features of efficiency and fairness as well as to specify the comprehensive targets for the top emitters to maintain their equilibrium state. Two of the main findings are: 1) The top emitters should decrease the total CO2 emission by at least 37% by 2023. 2) In light of the CO2 emission mitigation allocation, the countries with larger potentials are China, Japan, and the US yet to receive the larger portions of 20%, 9% and 22%, respectively. Finally, the allocation method that takes regional heterogeneity into account is more logical since it alleviates pressure on the emitters to decrease carbon emissions and establishes a baseline for distributing CO2 emission quotas across the emitters to enhance adaptation to nations' present circumstances.

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

Individual health insurance reforms in the U.S.: Expanding interstate markets, Medicare for all, or Medicaid for all?

To help enhancing affordability and availability in the U.S. individual health insurance markets, we evaluate whether expanding interstate markets is associated with efficiency improvement, and the potentials of “Medicaid for All” and “Medicare for All”. This research aims to provide insights and evidence for data-driven decision making in reforming individual health insurance markets and optimizing individual health insurance operations. We employ traditional, non-oriented slack-based, order-α partial frontier, bootstrapped bias-corrected, and modified context-dependent data envelopment analysis (DEA) models, as well as generalized linear, Tobit, and residual inclusion regression models. We find that higher competition or expansion is not associated with higher consumer efficiency or societal efficiency. Our results also indicate that, in minimizing premiums or expenses given enrollment and utilization of medical services, individual health plans are less efficient than Medicaid managed care plans, but more efficient than Medicare Advantage plans. Our findings imply that, for individual plans, expanding interstate markets is not accompanied with lower premiums or expenses without the sacrifice of medical services. This research suggests that it should be advisable to structure individual health insurance markets following the Medicaid managed care model but not the Medicare Advantage model. To “Medicaid-ize” individual markets, we propose to structure the individual coverage in two layers: a conditionally subsidized Medicaid managed care program with mandatory essential benefits, and an unsubsidized “Medicaid Supplement” program for optional additional coverages.

Context-dependent DEASort: A multiple criteria sorting method for ecological risk assessment problems

• Context-Dependent DEASort is proposed based on BWM for multi-criteria sorting problem. • Categories can be simply obtained without predefined classification categorical rules. • A common set of weight model is introduced for evaluating attractiveness of each DMU. • Case study concerning risk assessment of Yangtze River Economic Zone is provided. • Sensitivity analysis is conducted to illustrate the stability of the proposed method. With the increasing urban economic development and awareness of environment protection, ecological risk assessment (ERA), as a management mode combined with ecological research and risk assessment, has become a highly relevant topic in environment sustainable development. In this paper, we propose a novel sorting model based on Data Envelopment Analysis (DEA) and Best Worst method (BWM) to solve multi-criteria sorting problems and apply it to ERA. The proposed Context-Dependent DEASort method based on the idea of Context-Dependent DEA to position decision-making units (DMUs) into diverse categories in uncertain circumstance. It also takes experts’ preference into full account as well, which makes the sorting solution more flexible and reasonable. Besides, a common set of weight-based model is introduced to deal with the situation when evaluating attractiveness and progress of each DMU. The common weight set model can ensure to provide an overall evaluation context compared to the original Context-Dependent DEA model, which only distinct the DMU from a single virtual DMU. Furthermore, a case study concerning ecological risk assessment of Yangtze River Economic Zone in China is provided to illustrate the applicability of the developed environment. Finally, comparative analysis and sensitivity analysis are carried out to demonstrate the practicality and effectiveness of the proposed method.

Leveraging competitiveness to develop optimal strategies: evidence from the restaurant industry

PurposePerformance evaluation is a good way to improve competitiveness, and leveraging competitiveness is vital to developing optimal strategies and sustainable development for the growing restaurant industry in the USA and across the globe. This study can benefit from the methods to inform business strategies and decisions. Through the combination of two methods, this paper aims to provide novel perspectives to realize each restaurant company’s resource allocation and performance, to know their market position compared to with other competitors, and then to develop their own business strategies.Design/methodology/approachThis study proposes an application of slack-based measure (SBM) to determine input excesses and output shortfalls of the restaurant industry. Subsequently, context-dependent data envelopment analysis (DEA) is used to identify the level of each restaurant company and compute the relative attractiveness and progress scores for each restaurant at each level, positioning each restaurant company’s stance in their markets.FindingsThe empirical results reveal that management teams need to pay more attention to human resources, asset management and food cost control because the analysis of SBM model shows an opportunity for restaurant companies to adjust their hiring numbers to reach the optimal amount of employees. In addition, restaurant companies are categorized by their efficient frontiers into four levels using context-dependent DEA. Furthermore, the findings provide the four quadrants that each restaurant company falls into. Each of those four quadrants can guide the types of strategies a restaurant should adopt.Originality/valueThis study can help the publicly traded restaurant companies to identify their market position with target benchmarks, and then to develop optimal strategies and future plans for growth.

Improving Efficiency Assessment of Psychiatric Halfway Houses: A Context-Dependent Data Envelopment Analysis Approach.

This study proposes the approach of context-dependent data envelopment analysis (DEA) to measure operating performance in halfway houses to enable suitable adjustments at the current economic scale. The proposed approach can be used to discriminate the performance of efficient halfway houses and provide more accurate DEA results related to the performance of all halfway houses in a region or a country. The relative attractiveness and progress were also evaluated, and individual halfway houses’ competitive advantage and potential competitors could be determined. A case study of 38 halfway houses in Taiwan was investigated by our proposed approach. Findings suggest that fifteen halfway houses belong to the medium level, which can be classified into a quadrant by examining both their attractiveness score and progress score. The results can be used to allocate community resources to improve the operational directions and develop incentives for halfway houses with attractive and progressive values, which can reduce the institutionalization and waste of medical resources caused by the long-term hospitalization of patients with mental illnesses. Our proposed approach can also provide references for operators and policy makers to improve the management, accreditation, and resource allocation of institutions.

Bounded-change target-setting approach: Selection of a realistic benchmarking path

The closest target-setting models based on data envelopment analysis (DEA) provide substantial contributions to benchmarking for requiring minimal effort to improve the performance of decision-making units (DMUs). However, these target-setting models may become impractical when several real factors, such as technology level, environmental influence, and governmental policy, are considered. In this study, we develop a novel DEA approach to target setting and benchmarking path selection by considering such real factors. A new bounded-change target-setting approach based on context-dependent DEA, in which the distance of evaluated unit to the best-practice frontier is minimized, is proposed. This approach sets benchmarks for inefficient DMUs in the short term and selects a realistic benchmarking path in the long term. Our approach also suggests a cross-level benchmarking path that few DEA-based benchmarking studies concern. This approach is applied to set sequential benchmarks and identify realistic benchmarking paths for inefficient DMUs via energy efficiency evaluation in the transport sector of China.

Exploring the performance and competitiveness of Vietnam port industry using DEA

In the port industry, productivity is one of the vital factors for ports to develop competitiveness and market potential. The paper integrates context-dependent data envelopment analysis and forecasting models to conduct an efficient measurement of 53 Vietnamese ports and predict future performance in the port industry. By using the technique to get the measure of performance, this study builds the ports’ benchmark-learning stepping tools for the inefficient ports to enhance increasingly. The attractiveness and progress scores are resulted to assist the ports in situating themselves. Moreover, forecasting port performance results help decision-makers having more detailed information to make better decisions in their strategies and investments.

Bounded and discrete data in data envelopment analysis with assurance regions

PurposeThe purpose of this paper is to evaluate design performance of 51 gear shaping machines by using data envelopment analysis (DEA).Design/methodology/approachExisting studies extend traditional DEA by handling bounded and discrete data based on envelopment models. However, value judgment is usually neglected and fail to be incorporated in these envelopment models. In many cases, there is a need for prior preferences. Using existing DEA approaches as a backdrop, the current paper presents a methodology for incorporating assurance region (AR) restrictions into DEA with bounded and discrete data, i.e. the assurance region bounded discrete (AR-BD) DEA model. Then, the AR-BD DEA model is combined with a context-dependent DEA to obtain an efficiency stratification.FindingsThe authors examine different AR restrictions and calculate efficiency scores of five scenarios of AR restrictions by using the proposed AR-BD DEA model. It shows that AR restrictions have a great impact on the efficiency scores. The authors also identify nine efficient frontiers in total. For each decision-making unit, it could set benchmarks and improve its performance based on each higher efficient frontier.Originality/valueThis paper first evaluates efficiency of gear shaping machines by considering different (bounded and discrete) variable types of data and including AR restrictions. The AR-BD DEA model and context-dependent AR-BD DEA model proposed in this paper further enrich the DEA theory. The findings in this paper certainly provide useful information for both producers and consumers to make smart decisions.

Ranking sustainable suppliers by context-dependent data envelopment analysis

Sustainable supplier selection is one of the most critical activities for establishing an effective sustainable supply chain and is typically a multi-criteria decision-making problem. One of the important ways for evaluating sustainable suppliers is to use data envelopment analysis (DEA) models. There are many DEA models for ranking decision making units (DMUs) that are applicable for selecting the best sustainable suppliers. This paper presents a new DEA model for ranking DMUs (suppliers) based on a modification of context-dependent DEA models. Using the concept of attractiveness and progress for each supplier, this paper suggests a new DEA ranking approach. The proposed method is able to rank all efficient and inefficient suppliers. A case study demonstrates the efficiency of the proposed method. Our proposed method is used to determine the most sustainable suppliers of Sazeh Gostare Saipa Company. There are fourteen active suppliers for Sazeh Gostare Saipa Co., and results show that Hydraulic Structures Engineering Services is the most sustainable supplier.

Road map for progress and attractiveness of Iranian hospitals by integrating self-organizing map and context-dependent DEA

Hospitals play an important role in healthcare systems and usually stay on the end node of the healthcare chain. Thus, determining their road map to get close to the desired efficiency frontier and developing short-term and long-term plans could help to manage costs and resources, efficiently. As the efficiency frontier depends on the size of the hospital and the complexity of its structure, the homogeneity in benchmarking must be considered. For tackling this problem, the self-organizing map (SOM) is used to create homogeneous groups. On the other hand, data envelopment analysis (DEA) is a well-known methodology for evaluating decision-making units. Each unit obtains the efficiency score based on the ratio of weighted outputs to weighted inputs, where each unit can take the desirable weights for inputs and outputs to provide the maximum value. One of the problems of DEA is the selection of the reference set and distinguishing between the efficient hospitals. To overcome these problems, the context-depended DEA has been applied and the progress and attractiveness of hospitals are obtained. To evaluate the capability of the proposed approach, data of 288 Iranian hospitals are utilized. By applying SOM the hospitals are clustered into appropriate homogeneous groups and by applying context-dependent DEA, the road map for progress and attractiveness of each hospital is determined. In other words, using the proposed approach the hospitals are able to determine the short and long-term goals according to their strategic plans.

Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators

We propose a methodology to employ composite indicators for performance analysis of units of interest using and extending the family of Stochastic Multiattribute Acceptability Analysis. We start evaluating each unit by means of weighted sums of their elementary indicators in the whole set of admissible weights. For each unit, we compute the mean, μ, and the standard deviation, σ, of its evaluations. Clearly, the former has to be maximized, while the latter has to be minimized as it denotes instability in the evaluations with respect to the variability of weights. We consider a unit to be Pareto–Koopmans efficient with respect to μ and σ if there is no convex combination of μ and σ of the rest of the units with a value of μ that is not smaller, and a value of σ that is not greater, with at least one strict inequality. The set of all Pareto–Koopmans efficient units constitutes the first Pareto–Koopmans frontier. In the spirit of context-dependent Data Envelopment Analysis, we assign each unit to one of the sequence of Pareto–Koopmans frontiers. We measure the local efficiency of each unit with respect to each frontier, but also its global efficiency taking into account all frontiers in the σ−μ plane, thus enhancing the explicative power of the proposed approach. To illustrate its potential, we present a case study of ‘world happiness’ based on the data of the homonymous report that is annually produced by the United Nations’ Sustainable Development Solutions Network.