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Using the neo-classical theory of production economics as the analytical framework, this book, first published in 2004, provides a unified and easily comprehensible, yet fairly rigorous, exposition of the core literature on data envelopment analysis (DEA) for readers based in different disciplines. The various DEA models are developed as nonparametric alternatives to the econometric models. Apart from the standard fare consisting of the basic input- and output-oriented DEA models formulated by Charnes, Cooper, and Rhodes, and Banker, Charnes, and Cooper, the book covers developments such as the directional distance function, free disposal hull (FDH) analysis, non-radial measures of efficiency, multiplier bounds, mergers and break-up of firms, and measurement of productivity change through the Malmquist total factor productivity index. The chapter on efficiency measurement using market prices provides the critical link between DEA and the neo-classical theory of a competitive firm. The book also covers several forms of stochastic DEA in detail.

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

This article discusses how to enhance corporate sustainability by simultaneously measuring operational and environment achievements. In past decades, most companies have made steady efforts to enhance their sustainability levels. However, they still have strategic space for improving sustainability. This research proposes a new use of environmental measurement by data envelopment analysis. We apply the approach to Japanese industrial sectors and obtain five implications. First, they maintain a high level of unified efficiency on resource allocation and energy usage under natural disposability (priority: operation). Second, the efficiency under managerial one (priority: environment) is generally lower than that of natural disposability. Third, among the industries with high operational achievement, only the pharmaceutical product industry presents high attainment on environmental protection. Fourth, the pulp and paper industry as well as the textile product industry have a potential for efficiency improvement by investing in green technology. Finally, desirable congestion indicates a potential of performance improvement by investing in green technology. Those results imply that the current business situation is different from the previous image on Japanese industries, often referred to as “Japan Inc.”, where all firms used to operate like a single entity under the governmental regulation.

The aim of this study is to analyze the operational efficiency of tourism firms using weights-restricted data envelopment analysis (DEA) in a mathematical optimization framework. The use of weight restrictions has been applied in several studies on efficiency and performance evaluation, in order to account for the decision maker's information. In this study, a nonstandard approach to operational efficiency evaluation is applied in an attempt to overcome certain limitations in tourism analysis- namely, the fact that the analyst may choose to consider only a few rather than all of the input variables. One of the main findings of this study is that total weight flexibility can lead to nonrational weights due to the fact that certain inputs are effectively ignored. Furthermore, total weight flexibility can result in too many units being assessed as efficient, reducing the discriminatory power of the model. This problem can be solved by applying weight restrictions. A first practical implication is that the weighted DEA model yields better efficiency estimations. Moreover, since a high number of efficient units could be considered an unrealistic result, the findings of this study demonstrate that the choice of weighted or restricted DEA model produces more accurate efficiency results.

In many data envelopment analysis (DEA) applications, the analyst always confronts the difficulty that the selected data set is not suitable to apply traditional DEA models for their poor discrimination. This paper presents an approach using Shannon’s entropy to improve the discrimination of traditional DEA models. In this approach, DEA efficiencies are first calculated for all possible variable subsets and analyzed using Shannon’s entropy theory to calculate the degree of the importance of each subset in the performance measurement, then we combine the obtained efficiencies and the degrees of importance to generate a comprehensive efficiency score (CES), which can observably improve the discrimination of traditional DEA models. Finally, the proposed approach has been applied to some data sets from the prior DEA literature.

Many rural areas face difficulty in how to motivate farmers to embark on diversified activities, such as tourism, while raising managerial efficiency. Thus, this paper conceptually and empirically evaluated how a farmer’s identity correlates with managerial efficiency since the connection between the two has not been explored fully. We have addressed this issue through examining farmers’ efforts in providing an emerging new educational tourism service by focusing on the Educational Dairy Farms in Japan. Conceptually, this paper classified farmers’ identity into two types: traditional identity as a simple farm producer offering an educational service as a volunteer, and, enlarged identity, which is oriented toward viability of a new service activity. Empirically, data envelopment analysis revealed that those with the enlarged identity realized a higher managerial efficiency although there was much room for improvement in overall managerial efficiency. Consequently, support measures with a wider perspective that include identity issues should be designed for capacity building of farmers who are conducting tourism.

In this study, performance and its basic concepts, efficiency, and productivity, are explained and performance measurement methods are discussed.The Analytical Hierarchy Process (AHP) was used to measure efficiency in multi-criteria problems and to determine the weights of criteria for efficiency measurement.With the help of the Data Envelopment Analysis (DEA), which is one of the best methods of measurement of efficiency, efficiency levels of the banks in Turkey, whose data can be fully reached, was evaluated. The criteria for bank efficiency were weighted with the AHP and then the efficiency scores of the banks were determined using the Weighted DEA method. Potential improvements have been proposed for inefficient banks.

This paper proposes that data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics in performance evaluation and benchmarking. While computational algorithms have been developed to deal with large volumes of data (decision making units, inputs, and outputs) under the conventional DEA, valuable information hidden in big data that are represented by network structures should be extracted by DEA. These network structures, e.g., transportation and logistics systems, encompass a broader range of inter-linked metrics that cannot be modelled by conventional DEA. It is proposed that network DEA is related to the value dimension of big data. It is shown that network DEA is different from standard DEA, although it bears the name of DEA and some similarity with conventional DEA. Network DEA is big data enabled analytics (big DEA) when multiple (performance) metrics or attributes are linked through network structures. These network structures are too large or complex to be dealt with by conventional DEA. Unlike conventional DEA that are solved via linear programming, general network DEA corresponds to nonconvex optimization problems. This represents opportunities for developing techniques for solving non-linear network DEA models. Areas such as transportation and logistics system as well as supply chains have a great potential to use network DEA in big data modeling.

Current studies that use traditional data envelopment analysis (DEA) neglect the 100% market share restriction. This study adopts zero-sum gains data envelopment analysis to measure the efficiency scores of securities firms (SFs) and indicates that the traditional DEA model underestimates the efficiency scores of inefficient SFs. This research analyses 266 integrated securities firms in Taiwan from 2001 to 2005 and employs three inputs (fixed assets, financial capital, and general expenses) and a single output (market share). The foreign-affiliated ownership of SFs positively affects the efficiency scores. The two-stage least squares procedure confirms that the market share and efficiency score simultaneously reinforce each other.

The purpose of this research is to determine the efficiency of energy usage and its role in carbon dioxide emissions (CI) and economic-environmental efficiency (EEE) for some countries Organization for Economic Co-operation and Development (OECD) economies. For environment quality assessment, data envelopment analysis (DEA) is used to assess the data cover the period from 2013 to 2017. In this study, primary energy consumption (PEC) and population are two basic inputs along with gross domestic product (GDP) and carbon dioxide emissions that are desirable and undesirable  outputs, respectively. The practical outcomes illustrate that Brunei, Australia, Singapore, and Hong Kong are the most effective and efficient states for the 5 years periods (2013–2017) in terms of energy efficiency and to reduce emission of carbon dioxide. In addition, other states in the OECD region shows greater economic proficiency than environmental proficiency. Furthermore, the results shows that energy efficiency has strong bonding with carbon emissions; however there is a weaker association between economic-environmental efficiency. Thus, the attainment of optimal level of energy efficiency could be more pivotal than economic efficiency to improve environmental efficiency in countries from the OECD region.