Data Envelopment Analysis Research Research Articles

Efficiency Measurement Using Data Envelopment Analysis (DEA) in Public Healthcare: Research Trends from 2017 to 2022

With the shifting healthcare environment, the importance of public healthcare systems is being emphasized, and the efficiency of public healthcare systems has become a critical research agenda. We reviewed recent research on the efficiency of public healthcare systems using DEA, which is one of the leading methods for efficiency analysis. Through a systematic review, we investigated research trends in terms of research purposes, specific DEA techniques, input/output factors used for models, etc. Based on the review results, future research directions are suggested. The results of this paper provide valuable information and guidelines for future DEA research on public healthcare systems.

Two-stage network structure with undesirable outputs approach

Articles in which nonparametric data envelopment analysis (DEA) is applied to estimate the efficiency based on a network structure usually consider the desirable intermediary parts that are the first stage’s outputs to be used as the second stage’s inputs. In many real-life situations, the intermediary parameters include desirable and undesirable outputs. This issue has drawn much attention from DEA researchers in recent years. The main motivation for this application is weak disposability for modeling the networks with undesirable intermediary parts. Undesirable outputs have been investigated in this study in two forms, i.e., the final outputs or intermediary parameters. In both cases, the non-cooperative game theory is offered for evaluating the relative efficiency of the executive units. In 2008, a real case was explicated for 39 airports in Spain in applied analysis of the proposed approaches.

Social network analysis for cross‐evaluation in data envelopment analysis

AbstractAs one of the important extensions of data envelopment analysis (DEA) methodology, cross‐evaluation combines self‐evaluation and peer‐evaluation processes for each decision making unit (DMU) to effectively eliminate unrealistic weight schemes and differentiate all the units for ranking. Differing from previous research, which mainly pays attention to either secondary goals for generating weights for peer evaluation or aggregation of self and peer‐evaluation efficiencies, our study focuses on evaluating the relationships among all DMUs. If we view each unit as a node to be evaluated, then peer‐evaluation among pairs of DMUs forms a bidirectional network among all the DMUs. Based on this idea, we introduce a data‐driven tool combining social network analysis and cross‐evaluation to examine self‐evaluated and peer‐evaluated scores in the network. Using the hyperlink‐induced topic search algorithm, we develop the concepts of self‐evaluation and peer‐evaluation objectivity coefficients based on how well DMUs accept the analysis results. The rankings based on our self‐evaluation and peer‐evaluation scores adjusted with objectivity coefficients are shown to be consistent. Our study provides a novel perspective for cross‐evaluation and DEA research. An application case study illustrates our new method.

Achieving economic and social sustainability through hyperconnectivity

PurposeThe purpose of this paper is to identify cross-country differences in socio-economic sustainability, which are operationalized as relative efficiency of economic and social impacts of hyperconnectivity (usage intensity of information and communication technologies (ICT) devices). The authors have a particular interest in the emerging economies because they enjoy outstanding growth rates and prospects for market expansion, and have undertaken significant economic reforms and, thus, should be expected to lead other two groups in the efficiency of transforming hyperconnectivity into sustainable growth.Design/methodology/approachThe authors use canonical correlation analysis (CCA) to confirm the existence of a strong and significant relationship between hyperconnectivity drivers and socio-economic outcomes on a country level. The authors test the difference in efficiency of transforming hyperconnectivity into socio-economic sustainability among three groups of countries: advanced, emerging and developing nations using data envelopment analysis (DEA).FindingsThe findings indicate that indeed emerging economies were the most effective ones to use infrastructure and digital content followed by developing and advanced countries, respectively. However, relatively better affordability of technologies in the emerging countries did not produce as much socio-economic impact as compared with developing nations. Favorable legislative conditions and high individual ICT usage in advanced economies did not contribute much to socio-economic sustainability either.Research limitations/implicationsOne of the limitations of this study stems from the classification of the countries. World Economy Forum and International Monetary Fund resources are utilized for the economy categories, but their basis for classification of counties is rather subjective. Lack of existing comparative efficiency studies on a country level prevents effective benchmarking of the results.Practical implicationsSince the key vehicles of transforming technology into socio-economic impact are organizations, they should design and implement an appropriate organizational architecture which would facilitate this transformation in the emerging markets more effectively.Social implicationsIn a climate of increasing public accountability, governments have been increasingly urged to introduce good administrative practices and performance standards to enable efficient utilization of their resources and enhance social implications within and across countries.Originality/valueAlthough the impact of ICT on macro-economic development has been previously studied, the efficiency of this impact was not. Using CCA as a complementary tool for DEA approach in this study constitutes a methodological contribution to existing DEA research, mostly done in the area of operations management. Using DEA on a country level is a novel approach which contributes to the realm of application of this methodology.

Hip fracture surgery efficiency in Israeli hospitals via a network data envelopment analysis

Data envelopment analysis (DEA) has been used previously for examining hospital efficiency, based on administrative data. Yet, previous DEA research devoted to quality assurance rarely considered medical processes or outcomes in efficiency studies. The goal of this study is to examine the relative efficiency of hip fracture surgery, based on clinical data reflecting medical process indicators and outcomes. To accomplish our goal, recent developments in DEA research were harnessed to model an output-oriented two-stage DEA network. The proposed DEA model has: two input variables reflecting the condition of the patient, fracture type and Charlson index; two intermediate variables reflecting clinical decisions, surgery within 48 h and usage of a drain for 1 day (rate); and two output variables reflecting the success of the surgery, survival rate after surgery and the rate of no infection. Using data from orthopedic wards in most of the acute Israeli hospitals (20 out of 22), no statistically significant correlation was found, either between the socio-economic index of patients who had hip fracture surgery and the relative efficiency scores produced by the two-stage network DEA model, or between efficiency and the geographical periphery status of the hospital. In addition to this, which points to a degree of social equality regarding hip fracture surgeries, we also compared the two-stage network model and related DEA models, providing several lemmas that represent the relationships between the various models mathematically.

Total factor productivity measurement with carbon reduction

PurposeThe purpose of this paper is to present an approach for productivity measurement that considers both construction growth and carbon reduction.Design/methodology/approachThe approach applied is a sequential Malmquist-Luenberger productivity analysis based on a directional distance function and sequential benchmark technology using the data envelopment analysis (DEA) technique. The sequential Malmquist-Luenberger productivity change index is decomposed into pure technical efficiency, scale efficiency, and technological change indices, in order to investigate the driving forces for productivity change.FindingsThe construction industries of the Australian states and territories were selected implement the new approach. The results indicate that construction growth and carbon reduction can be achieved simultaneously through the learning of techniques from benchmarks.Practical implicationsCurrent research on total factor productivity (TFP) in construction generally neglects carbon emissions. This does not accurately depict the nature of construction and therefore yields biased estimation results. TFP measurement should consider carbon reduction, which is beneficial for policymakers to promote sustainable productivity development in the construction industry.Originality/valueThe approach developed here is generic and enhances productivity and DEA research levels in construction. This research can be used to formulate policies for evaluating performance in worldwide construction projects, organizations and industries by considering undesirable outputs and desirable outputs simultaneously, and for promoting sustainable development in construction by identifying competitiveness factors.

Traffic fatality indicators in Brazil: State diagnosis based on data envelopment analysis research

The intense economic growth experienced by Brazil in recent decades and its consequent explosive motorization process have evidenced an undesirable impact: the increasing and unbroken trend in traffic fatality numbers. In order to contribute to road safety diagnosis on a national level, this study presents a research into two main indicators available in Brazil: mortality rate (represented by fatalities per capita) and fatality rate (represented by two sub-indicators, i.e., fatalities per vehicle and fatalities per vehicle kilometer traveled). These indicators were aggregated into a composite indicator or index through a multiple layer data envelopment analysis (DEA) composite indicator model, which looks for the optimum combination of indicators’ weights for each decision-making unit, in this case 27 Brazilian states. The index score represents the road safety performance, based on which a ranking of states can be made. Since such a model has never been applied for road safety evaluation in Brazil, its parameters were calibrated based on the experience of more consolidated European Union research in ranking its member countries using DEA techniques. Secondly, cluster analysis was conducted aiming to provide more realistic performance comparisons and, finally, the sensitivity of the results was measured through a bootstrapping method application. It can be concluded that by combining fatality indicators, defining clusters and applying bootstrapping procedures a trustworthy ranking can be created, which is valuable for nationwide road safety planning.

RANKING AND BENCHMARKING OF THE ASIAN GAMES ACHIEVEMENTS BASED ON DEA: THE CASE OF GUANGZHOU 2010

Precedent research based on data envelopment analysis (DEA) has been conducted to rank and benchmark the achievements of participating nations/regions in the Olympics, while this paper contributes to the Asian Games, with further concerns on two main issues, namely the common comparison basis for ranking decision making units (DMUs) and the reference feasibility between the inefficient DMUs and their benchmark targets. This paper extends previous DEA research by introducing an improved context-dependent DEA model, in which empirical results establish a unique and fair ranking system for all participating nations/regions, implying two corresponding suggestions for rank improvement. A series of stepwise learning targets are further identified, alternatively providing a gradual performance improvement path for the inefficient participants. The above results will be helpful for strategic decision making in sport management of the Asian nations/regions.

Two-stage network structures with undesirable outputs: A DEA based approach

The non-parametric Data Envelopment Analysis (DEA) literature on network-structured performance analysis normally considers desirable intermediate measures. These measures are the outputs from the first stage and are used as inputs to the second stage. In many real situations, the intermediate measures consist of desirable and undesirable outputs. This subject has recently attracted considerable attention among DEA researchers. The motivation of this study is the application of the weak disposability to modeling network DEA with undesirable intermediate measures. Undesirable products in this paper are studied in two different cases: either as final outputs or as intermediate measures. In both cases, cooperative and non-cooperative game theories are proposed to assess the relative performance of the operational units. A real case on 39 Spanish Airports in 2008 has been illustrated to verify the applicability of the proposed approaches.

Data envelopment analysis: Prior to choosing a model

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.

The rat race between world cities: In search of Exceptional Places by means of super-efficient data development analysis

This paper aims to provide a new methodological and empirical contribution to the rising literature on the relative performance and benchmarking of large cities in a competitive world. On the basis of a recent detailed database on many achievement criteria of 35 major cities in the world, it seeks to arrive at a relative performance ranking of these cities by using Data Envelopment Analysis (DEA). A novel element is the use of a new type of ‘Super-Efficiency DEA’ to identify unambiguously the high performers (‘Exceptional Places’) in the group of world cities investigated. This new productivity-based approach is complemented with two new directions in DEA research, viz. a Distance Friction Method and a Context-Dependent method.

The Rat Race between World Cities: In Search of Exceptional Places by Means of Super-Efficient Data Development Analysis

This paper aims to provide a new methodological and empirical contribution to the rising literature on the relative performance and benchmarking of large cities in a competitive world. On the basis of a recent detailed database on many achievement criteria of 35 major cities in the world, it seeks to arrive at a relative performance ranking of these cities by using Data Envelopment Analysis (DEA). A novel element is the use of a new type of ‘Super-Efficiency DEA’ to identify unambiguously the high performers (‘exceptional places’) in the group of world cities investigated. This new productivity-based approach is complemented with two new directions in DEA research, viz. a Distance Friction Method and a Context-Dependent method.

Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis

In the last decade, ranking units in data envelopment analysis (DEA) has become the interests of many DEA researchers and a variety of models were developed to rank units with multiple inputs and multiple outputs. These performance factors (inputs and outputs) are classified into two groups: desirable and undesirable. Obviously, undesirable factors in production process should be reduced to improve the performance. Also, some of these data may be known only in terms of ordinal relations. While the models developed in the past are interesting and meaningful, they didn't consider both undesirable and ordinal factors at the same time. In this research, we develop an evaluating model and a ranking model to overcome some deficiencies in the earlier models. This paper incorporates undesirable and ordinal data in DEA and discusses the efficiency evaluation and ranking of decision making units (DMUs) with undesirable and ordinal data. For this purpose, we transform the ordinal data into definite data, and then we consider each undesirable input and output as desirable output and input, respectively. Finally, an application that shows the capability of the proposed method is illustrated.

Development pattern of the DEA research field: a social network analysis approach

This paper examines the development pattern of the DEA (Data Envelopment Analysis) research field using Social Network Analysis. Nine stylized facts are verified: it is shown that the distribution of research performance is highly skewed, satisfying a power law, and that collaborative research activity is concentrated achieving higher performance. Moreover, economics and OR (operations research)/MS (management science) groups developed without collaboration until the 1980s; however, the merger of the two groups began in 1994 with the collaboration of frontier researchers. Now the two groups have merged significantly, but they are still identifiable. Finally, research hubs and the emergence of new groups are examined. Nine stylized facts show that DEA has developed with unique attributes. Although it shares common characteristics with other academic fields, including a highly skewed distribution of publications and expansion of the network over time, the DEA field has had a pattern of development that is different from those of other academic fields. Independent development and interrelated evolution between economics and OR/MS contributed to the exchange of knowledge of these two separate fields.

Data envelopment analysis 1978–2010: A citation-based literature survey

This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.

Identifying the efficiency status in network DEA

Data envelopment analysis (DEA) is commonly employed to evaluate the efficiency performance of a decision making unit (DMU) that transforms exogenous inputs into final outputs. In such a black-box DEA approach, details of an internal production process of the DMU are typically ignored and hence the locations of inefficiency are not adequately provided. In view of this, DEA researchers have recently developed various network approaches by looking into the black box, where the inputs that enter the box and the outputs that come out of it are only considered. However, most of these network approaches evaluate divisional efficiency by using an optimal solution of their respective optimization problem. If such an optimal solution is used in the case when there are multiple optima, then managerial guidance based on this solution alone may be inappropriate because more appropriate targets from the viewpoint of management may be ignored. Taking this fact into account, therefore, we propose a network approach for identifying the efficiency status of each DMU and its divisions. This approach provides a practical computational procedure.

The data envelopment analysis model with intersection form production possibility set

The data envelopment analysis (DEA) model is used to evaluate the relative economic efficiency of a given set of decision making units (DMUs). In this paper, the DEA production possibility set is transferred from the conventional sum form into the intersection form which is represented by a linear inequality system. Although it is time consuming to obtain the intersection form of the production possibility set, it suggests a new angle to investigate the properties of DMUs and to extend the DEA research further beyond the efficiency measurement. Following the intersection form, the analytical formula of the efficiency indicator and projection is given. Various aspects of technical efficiency, returns to scale and evidence of congestion of the DMUs are studied. The relationship between the weak DEA efficiency and the weak Pareto solution is discussed. Finally, a procedure for DMU grouping is proposed to help the decision makers for better resource reallocation and strategy adjustment.

Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment

Abstract This study compares DEA (data envelopment analysis) with DEA–DA (discriminant analysis) in terms of bankruptcy assessment. Recently, many DEA researchers propose a use of DEA as a quick-and-easy tool to assess corporate bankruptcy. Meanwhile, other DEA researchers discuss a use of DEA–DA for bankruptcy-based financial analysis. The two groups are very different from the conventional use of DEA because we have long applied DEA to the measurement of operational performance, or productivity analysis. The two research groups open up a new application area (bankruptcy-based financial assessment) for DEA. This study discusses methodological strengths and weaknesses of DEA and DEA–DA from the perspective of corporate failure. The proposed comparative analysis has the three main criteria: (a) how to handle negative data in financial variables, (b) how to handle data imbalance between default and non-default firms, and (c) how to identify a failure process over time. This study finds that DEA is a managerial tool for the initial assessment of corporate failure and DEA is useful for busy corporate leaders and financial managers. In contrast, DEA–DA is useful for researchers and individuals who are interested in the detailed assessment of bankruptcy and its failure process in a time horizon.

Pushing the DEA research envelope

This brief article first investigates key dimensions underlying the progress realized by data envelopment analysis (DEA) methodologies. The resulting perspective is then used to encourage reflection on future paths for the field. Borrowing from the social sciences literature, we distinguish between problematization and gap identification in suggesting strategies to push the DEA research envelope. Emerging evidence of a declining number of influential methodological (theory)-based publications, and a flattening diffusion of applications imply an unfolding maturity of the field. Such findings suggest that focusing on known limitations of DEA, and/or of its applications, while searching for synergistic partnerships with other methodologies, can create new and fertile grounds for research. Possible future directions might thus include ‘DEA in practice’, ‘opening the black-box of production,’ ‘rationalizing inefficiency,’ and ‘the productivity dilemma.’ What we are therefore proposing is a strengthening of the methodology's contribution to fields of endeavor both including, and beyond, those considered in the past.

Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA

Since the original Data Envelopment Analysis (DEA) study by Charnes et al. [Measuring the efficiency of decision-making units. European Journal of Operational Research 1978;2(6):429–44], there has been rapid and continuous growth in the field. As a result, a considerable amount of published research has appeared, with a significant portion focused on DEA applications of efficiency and productivity in both public and private sector activities. While several bibliographic collections have been reported, a comprehensive listing and analysis of DEA research covering its first 30 years of history is not available. This paper thus presents an extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as “real-world” applications from inception to the year 2007. A listing of the most utilized/relevant journals, a keyword analysis, and selected statistics are presented.