A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016

In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided.     

The impact of the economic crisis on the (in)efficiency of public Higher Education institutions in Southern Europe: The case of Spanish universities

The evaluation of university efficiency in Europe began timidly when the European Higher Education Area (EHEA) was created. However, this issue is currently becoming increasingly important in Southern European countries, where the limitation of public funding following the economic crisis in 2008 has put greater pressure on their public universities to achieve excellence and improve competitiveness. In this context, the goals of this paper are: first, to measure the relative technical (in)efficiency of Spanish public Higher Education Institutions in the period 2002–03 to 2012–13, comparing the situation before and during the last economic crisis; and, second, to analyze the determinants of university (in)efficiency and, especially, the direct impact of the crisis. After applying the two-stage double bootstrap DEA methodology, the results show that Spanish public universities have become more efficient during the crisis than before it. In fact, the regression analysis confirms that the “crisis” variable has had a statistically significant positive impact on university efficiency. We also find that age has favorably influenced how these institutions utilize their resources to produce teaching and research outputs, but technical specialization has had a negative effect. Moreover, the regional location of public universities has been also a crucial determinant of their efficiency level. Our findings are therefore relevant for political and academic decision-makers in order to know if public universities have been adequately managed in the crisis period and to identify factors that could improve their efficiency, and hence to help them to enhance their international competitiveness in the future.     

Efficiency analysis of emergency departments in metropolitan areas

The demand in the healthcare industry is increasing exponentially due to aging population of the world and this is leading to a rapid increase in the cost of healthcare. The emergency departments of the hospitals are the frontline of health care systems and play an additional critical role in providing an efficient and high-quality response for patients. The overcrowding at the emergency departments due to growing demand results in a situation where the demand for ED services exceeds the ability to provide care in a reasonable amount of time. This has led countries to reconsider their health policies in a way to increase their efficiency in their healthcare systems in general and in emergency departments, in particular. As in many countries, there has been a steady and significant increase in the number of patients that seek health services at the emergency departments of state hospitals of Turkey, due to the significant structural reforms in health services since 2003. While meeting this increasing demand, it is ever more important to provide these critical health services efficiently. Therefore, the efficiency of the emergency departments of seven general hospitals run by Istanbul's Beyoglu State Hospitals Association have been analyzed using categorical Data Envelopment Analysis (DEA) models. The analysis of DEA results is supported by a set of statistical methods to make it easier for the hospital administrators to interpret the analysis and draw conclusions. The analysis shows that less-equipped EDs are supported by better equipped, larger EDs, resulting in a hub-and-spoke type of structure among the EDs where “satellite” EDs serve an important referral function and thus evaluating their efficiency without taking the interoperability among these units into account would not be an accurate assessment of their performance.     

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Data envelopment analysis (DEA) is in fact more than just being an instrument for measuring the relative efficiencies of a group of decision making units (DMU). DEA models are also means of expressing appreciative democratic voices of DMUs. This paper proposes a methodology for allocating premium points to a group of professors using three models sequentially: (1) a DEA model for appreciative academic self-evaluation, (2) a DEA model for appreciative academic cross-evaluation, and (3) a Non-DEA model for academic rating of professors for the purpose of premium allocations. The premium results, called DEA results, are then compared with the premium points “nurtured” by the Dean, called N bonus points. After comparing DEA results and N bonus points, the Dean reassessed his initial bonus points and provided new ones – called DEA-N decisions. The experience indicates that judgmental decisions (Dean's evaluations) can be enhanced by making use of formal models (DEA and Non-DEA models). Moreover, the appreciative and democratic voices of professors are virtually embedded in the DEA models.     

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.     

An empirical approach to the organisational determinants of spin-off creation in European universities

Given the importance in recent years of the phenomenon of academic entrepreneurship in European universities, knowledge about determinants and performance of this technology transfer mechanism may have important managerial and policy implications. Applying the resource-based view at universities, we have tested the influence of five bundles of resources and capabilities on spin-off activity rates as well as on the performance of new spin-offs in a sample of seven European universities. These five bundles are: technology transfer policies and strategies; human capital; stock of technology; resources and capabilities of technology transfer offices; and support measures for academic entrepreneurship. Our results show that excellence of human capital and the presence of university-based financial support measures are strongly associated with both spin-off activity and performance. Another interesting finding is related to the availability of university-based non-economic support measures (training, counselling, etc.), which have no influence on spin-off activity but are significantly associated with performance of new spin-offs. We also discuss some interesting relationships between explanatory variables.     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).     

Fundraising and public relations: a comparison of programme concepts and characteristics

This study compares characteristics of successful academic unit fundraising programmes and successful public relations programmes. Using interviews and surveys it explores whether academic unit fundraisers value the roles and responsibilities that align with identified strategic‐managerial and historical‐technical characteristics in public relations, whether demographic differences affect the role perception of the fundraisers, and whether the universities provide support for programme excellence. The results indicate that the fundraising programmes exhibit most of the characteristics of successful public relations programmes and that the academic units in which these fundraisers work provide support for them to do excellent fundraising. Demographics do not affect the results significantly. The data lead to questions for further study, including: How should fundraisers balance the benefit of having donors involved in programmes they support, with the university's need to retain autonomy and set its own priorities? Do excellent fundraising programmes strengthen relationships between alumni and universities? Do alumni with stronger relationships with the university contribute more, or more often, than those with less strong relationships? Copyright © 2002 Henry Stewart Publications     

Is human and financial investment in Chinese universities effective?

To obtain high-quality human resources and to gain advantages in the context of global competition, countries are constantly promoting the development of higher education. Enormous human and financial resources were invested in this “battle”. However, the “inputs” consumed by universities are largely ignored. A major issue, evaluating how well a country's universities make use of input resources in their output path, is of economic and social significance. The focus of this study is to embed the concept of capacity utilization in education and to assess the university's performance. We construct an attainable output-oriented capacity utilization measurement model by taking into account the heterogeneity of the region. According to the usage of the input, we divide the decision making units into four areas and identify the causes of inefficiency. Thus, the methodology proposed in this study provides policymakers an applicable tool on differences in educational inputs across regions. To illustrate the method, we conduct an empirical analysis using data from 54 universities directly under the Ministry of Education in China.     

北京新常态经济增长的金融支持问题研究

从金融资源空间布局、金融与其他产业的关联性以及金融对经济增长的DEA效率等方面,该文分析了北京金融对经济的整体支持情况与各区县支持情况,发现北京金融业发展的波动性、金融业与其他产业的较低关联性及各区县金融资源空间布局的不平衡性不利于新常态经济增长。建议顶层做好总体规划,同时,各区县应根据整体发展需要与自身优势,提高金融对经济增长的支持效率与质量。     

政策支持、企业投资与旅游业效率

本文对30家旅游上市公司2009—2018年的数据包络分析效率值进行纠偏修正,并构建政策支持与企业投资对于旅游企业运营效率的影响因素模型,运用Bootstrap截断回归法进行实证分析。研究结果表明,旅游上市公司整体上处于非有效状态;政府补贴与金融支持会对企业运营效率产生抑制作用;企业投资对效率的提升作用是决定性的,远远大于政府补贴和金融支持的负面作用。     

The impact of public pension board of trustee composition on state bond ratings

The declining financial health of public pension systems is increasingly becoming a budgetary concern for many state and local governments. While the academic literature has identified several factors behind the growth in unfunded state and local public pension liabilities, there is mixed evidence on how the composition of a pension system’s board of trustees affects a pension’s financial health. This article contributes to this literature by measuring how public pension board composition affects fund financial health as measured by state bond ratings. With a panel dataset of state pensions between 2001 and 2014 our results indicate that elected board members are consistently associated with lower bond ratings (and thus higher borrowing costs) while appointed and ex-officio board members are associated with higher bond ratings. These results are robust to a number of specifications.     

Adjusting for cultural differences,a new DEA model applied to a merged bank

The usefulness and application of Data Envelopment Analysis (DEA) efficiency measurements is usually limited by the requirement of consistent operating circumstances. However, in many real world situations this is not the case, so to overcome this problem, this paper reports on a new strategy by inventing a Culturally Adjusted DEA model to benchmark business units that operate under different cultural (business) environments. This is especially useful when these environmental factors are partial causes of inefficiency and can not be simply incorporated into a DEA model as inputs or outputs. A simulation analysis is conducted to examine the effectiveness of the CA-DEA model for controlling these environmental effects. This model is applied to a real life efficiency study of two major financial firms in Canada in 2000, when the two entities started to consolidate and merge their branch networks. Two cultural indices are identified to represent a firm’s unique operating environment, one to capture the nature of a firm’s corporate strategies (Corporate Index), and the other to estimate the effectiveness of a firm’s operational systems (Service Index). The results show that a firm’s corporate culture has a significant influence on its branches’ efficiency and this, we found, is often neglected in such studies. This paper also makes a contribution to the bank merger literature by providing an internal view of the potential benefits that may result from sharing cultural advantages while identifying the true managerial inefficiencies.     

Some statistical and DEA evaluations of relative efficiencies of public and private institutions of higher learning

This paper uses the Charnes, Cooper and Rhodes (CCR) ratio form of Data Envelopment Analysis (DEA) (1) to examine how DEA can be utilized in analyzing different aspects of production behavior of institutions of higher learning (IHLs) as an alternative to more traditional approaches such as econometric-regression models, and (2) to compare “specifically” relative efficiencies of public and private doctoral-granting universities in the U.S. and to analyze technical and scale efficiencies of those universities. The separation of doctoral-granting universities into universities with and without medical colleges represents a departure from preceding studies. This division proved very important in uncovering substantial differences in behavior between the two groups even when using the “statistical averaging” approaches that were customary in previous studies. For both groups, public universities proved more efficient than private universities when managerial and program inefficiencies are present in the data. When managerial inefficiencies are disentangled from the data and medical schools are not present, private universities have more efficient programs. However, their managers are less efficient users of program opportunities than are managers in public universities. Another portion of the current study dealt with returns-to-scale possibilities. These differed markedly (even on average) between IHLs with and without medical schools. Moreover, analyses by DEA showed marked ranges of variation for returns-to-scale possibilities for individual IHLs within each group. These possibilities have generally been concealed by the statistical averaging utilized in previous econometric studies. Further, their identification is beyond the ability of the usual types of one-at-a-time ratio and trend analyses.     

Environmental performance evaluation: A state-level DEA analysis

In recent years, the continuous development of every country's economic activities has generated undesirable impacts on the environment. Common problems are high water and energy consumption rates, jointly with harmful pollution levels. This situation has gained the research community's interest in exploring and analyzing the extent to which initiatives to reduce such environmental problems have succeeded. Therefore, it is relevant to have measures that encompass information on the results obtained by such initiatives. Using the data envelopment analysis (DEA) methodology, it is possible to measure the efficiency of an entity under evaluation, such as an industry, state, or country. DEA also allows one to compare the performance measures of entities operating in similar circumstances and identify which entities are performing best, given the inputs they use and the outputs they produce. This study evaluates different states in Mexico in terms of their environmental performance and provides a perspective on how environmental initiatives can contribute to protecting and preserving the environment. By addressing this problem, best-performers and practices are identified, and valuable insights are gained regarding how each state carries out such initiatives.     

Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture

This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.     

Measuring productivity of research in economics: A cross-country study using DEA

We measure productivity in leading edge economic research by using data envelopment analysis (DEA) for a sample of 21 countries belonging to the Organization for Economic Cooperation and Development (OECD). Publications in ten top journals of economics from 1980 to 1998 are taken as the research output. Inputs are measured by R&D expenditure, the number of universities with economics departments and (as an uncontrollable variable) population. Under constant returns-to-scale, the US emerges as the only efficient country. Under variable returns-to-scale, the efficiency frontier is defined by the US, Ireland and New Zealand. With the exception of the US, all countries in our sample display increasing returns-to-scale, and thus have the potential to raise their efficiency by scaling up their research activities.     

Using data envelopment analysis to measure the efficiency of non-profit organizations: A critical evaluation

This study examines the potential effects of variable set expansion and data variations upon the efficiency scores generated using the Data Envelopment Analysis (DEA) model. It was found that variable set expansion (either through disaggregation of existing variables or addition of new factors) should produce an upward trend in efficiency scores. In addition, ample opportunity exists for ‘decision-making units’ to increase their efficiency scores through manipulation of reported data. In real-world applications of DEA, these problems must be resolved as much as possible (e.g. increased audit of data) in order to improve DEA's practical usefulness and reliability.