Multiple Objective Approach as an Alternative to Radial Projection in DEA

Radial projection is a standard technique applied in data envelopment analysis (DEA) to calculate efficiency scores for input and/or output variables. In this paper, we have studied the appropriateness of radial projection for target setting. We have created a situation where the decision making units (DMUs) are free to choose their own target values on the efficient frontier and then compared the results to those of radial projection. In practice, target values are primarily used for future goal attainment; hence, not only preferences but also, and on the whole, change in time frame, affect the choice of target values. Based on that, we conducted an empirical experiment with an aim to study how the DMUs choose their most preferred target values on the efficient frontier. The subjects, who all were students of the Helsinki School of Economics, were given the freedom to explore their personalized efficient frontiers by using a multiple objective linear programming (MOLP) approach. To study various and relevant scenarios, the personalized efficient frontiers for all students were constructed in such a way that the current position of each student in relation to the frontier made him/her inefficient, efficient, or super-efficient. The results show that the use of radial projection for target setting is too restrictive.     

Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis

In this paper we propose a new technique for incorporating environmental effects and statistical noise into a producer performance evaluation based on data envelopment analysis (DEA). The technique involves a three-stage analysis. In the first stage, DEA is applied to outputs and inputs only, to obtain initial measures of producer performance. In the second stage, stochastic frontier analysis (SFA) is used to regress first stage performance measures against a set of environmental variables. This provides, for each input or output (depending on the orientation of the first stage DEA model), a three-way decomposition of the variation in performance into a part attributable to environmental effects, a part attributable to managerial inefficiency, and a part attributable to statistical noise. In the third stage, either inputs or outputs (again depending on the orientation of the first stage DEA model) are adjusted to account for the impact of the environmental effects and the statistical noise uncovered in the second stage, and DEA is used to re-evaluate producer performance. Throughout the analysis emphasis is placed on slacks, rather than on radial efficiency scores, as appropriate measures of producer performance. An application to nursing homes is provided to illustrate the power of the three-stage methodology.     

On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.     

An insight into the specification of the input-output set for DEA-based bank efficiency measurement

Data envelopment analysis (DEA) has become one of the most widely used instruments for measuring bank efficiency. However, its application encounters many problems, which is evidenced by continuous evolvements in the DEA method so far. Our paper addresses the pitfalls of DEA in the context of measuring bank efficiency, with focus on the specification of performance factors. We aim at examining whether the input-output specification for banks in DEA applications is in consistence with the criteria upon which banks make decisions. Four bank behaviour models which are most popularly employed to determine input and output factors in DEA studies—the intermediation approach, production approach, user cost approach and value added approach—are comprehensively discussed and reviewed. The comparative reflection on the bank behaviour models and the standard DEA models shows that the input-output related pitfalls of a DEA application are associated with its implicitly fixed preference structure, flexible weight determination and limited explanatory power. Due to the pitfalls, the conventional DEA models may fail to capture bank behaviours. In such cases, DEA results can hardly reflect the performance in its true sense, i.e. how banks perform against the goals that they decide to pursue. The findings suggest focusing on (DEA-based) performance measurement from a goal-oriented perspective, i.e. from the point of view of multi criteria decision making.     

Colombian higher education institutions evaluation

Over the last twenty years, access to higher education has grown extraordinarily in Latin America. Higher education systems have been challenged to improve their efficiency while strengthening quality assurance processes. In Colombia, the government and the researchers developed models to assess the performance of Higher Education Institutions (HEIs). Nevertheless, the current scholarship does not have a model that allows the system to measure multiple efficiencies in a diverse environment. In this study, we address the challenge of evaluating the efficiency of HEIs taking into account different goals of the Colombian education system. To this aim, we extend a cross-efficiency data envelopment analysis (DEA) approach to evaluate the efficiency of Colombian HEIs in the presence of flexible measures. While some HEIs are efficient in terms of teaching or employment, others are efficient in terms of research. Therefore, the model suggests broader policies to achieve the efficiency of the institutions under multiple goals.     

中小企业导入平衡计分卡实务研讨——以台湾家具业为例

平衡计分卡(BSC)通过设计出一套能使高级管理者快速且全面了解企业经营状况的指标体系,帮助企业改善经营管理决策,最终建立长远的战略竞争优势。然而目前占企业比重超过90%的中小企业却很少实行,这对于中小企业乃至全国企业的发展都非常不利。本研究针对家具业采用实务案例加以研讨,希望能对有意提升竞争力的中小企业作出些许贡献。     

Implications of variant efficiency measures for policy evaluations in UK higher education

Policy goals in UK higher education encourage the publicly funded universities to become more-specialised and larger in size without compromising output quality. Efficiency gains are expected to flow from this increased specialisation in accordance with universities’ comparative research and teaching strengths. Mergers to reap further gains from economies of scale are also being actively encouraged. Given this scenario, the paper investigates whether best-practice efficiency measurement based on data envelopment analysis (DEA) provides empirical support for the current policy goals. It also assesses whether such support is dependent on the specific type of efficiency measure used in the DEA modelling. This assessment finds that a selection of (nine) commonly used, variant efficiency measures generally support the current policy goals. The paper also uses the principal-agent framework to explore the issues involved in using computed DEA-based efficiency scores for policy evaluations and possible funding guidance in UK higher education. This highlights empirically how policy-makers and universities can have very different preferences about which efficiency measure is to be used for policy evaluations and possible funding guidance.     

Measurement equivalence of executives' performance ratings for same‐ and opposite‐gender dyads

The measurement equivalence of performance ratings from different raters is important to establish if researchers and practitioners want to have confidence that raters are using the performance measurement system in the same way. The purpose of the present study was to examine the measurement equivalence of performance ratings from male and female direct reports. Women and men executives from various organizations participated in a multisource assessment process for feedback and development purposes and were rated by both women and men direct reports. The results indicated gender equivalence for direct reports' ratings of men and women executives, meaning that regardless of gender, raters provided ratings using the same psychological metric. The implications and limitations of the study are discussed.     

Measuring the efficiency of European education systems by combining Data Envelopment Analysis and Multiple-Criteria Evaluation

Education is considered an important factor of economic growth, employment and social inclusion. However, the economic crisis has put the need to achieve educational goals in the most efficient way ever more to the fore. The main objective of this paper is to assess the spending efficiency of European compulsory educational systems, creating a ranking of countries based on the efficiency scores of their systems using a number of standard variables from the literature. To this end, we also present a methodological innovation that combines Data Envelopment Analysis (DEA) with discrete Multiple Criteria Evaluation (MCE), two methods that we consider complementary if used for providing a performance analysis. Moreover, both methods identify a set of common variables which are associated with higher levels of efficiency in educational systems (e.g. some characteristics of teachers, the stock of adults’ human capital and lower expenditures per student). The results show that findings using DEA are largely confirmed by MCE.

     

A PCA–DEA approach to measure the quality of life in Estonian counties

This paper investigates the application of a PCA–DEA model to assess the quality of life (QOL) scores in Estonian counties and analyses the model's results. The dataset is a balanced panel of 15 Estonian counties covering the period from 2000 to 2011. We consider a PCA–DEA model as an alternative method to estimate and predict QOL scores and rankings of Estonian counties. The method consists of a two-stage analysis that begins with a principal component analysis. In the second stage, the standard DEA is used. The results from the conventional DEA model and the PCA–DEA model are compared and discussed. A comparison of the methodologies demonstrates that a PCA–DEA model provides a powerful tool for performance ranking. The rankings of Estonian counties using QOL scores for different model specifications are presented. Finally, the QOL ranking of Estonian counties is revised using PCA–DEA.     

Productivity drivers and market dynamics in the Spanish first division football league

This paper analyses efficiency drivers of a representative sample of Spanish football clubs by means of the two-stage data envelopment analysis (DEA) procedure proposed by Simar and Wilson (J Econ, 136:31–64, 2007). In the first stage, the technical efficiency of football clubs is estimated using a bootstrapped DEA model in order to establish which of them are the most efficient; the ranking is based on total productivity in the period 1996–2004. In the second stage, the Simar and Wilson (J Econ, 136:31–64, 2007) procedure is used to bootstrap the DEA scores with a truncated bootstrapped regression. Policy implications of the main findings are also considered.     

Research efficiency analysis of China's university faculty members: A modified meta-frontier DEA approach

Traditionally, data envelopment analysis (DEA) requires all decision-making units (DMUs) to have similar characteristics and experiences within the same external conditions. In many cases, this assumption fails to hold, and thus, difficulties will be encountered to some extent when measuring efficiency with a standard DEA model. Ideally, the performance of DMUs with different characteristics could be examined using the DEA meta-frontier framework. However, some of these DMUs are mixed-type DMUs that may affiliate with more than one group. Furthermore, the total number of observations of these mixed-type DMUs is limited. This is one of the common problems when studies focus on faculty research performance in higher education institutions. In general, a faculty member is affiliated with a certain department, and if the departmental assessment policy is not suitable for faculty members who are involved in interdisciplinary research, their performance could be underestimated. Therefore, the proposed model is an extension of the DEA meta-frontier framework that can assess the performance of mixed-type DMUs by constructing the reference set without the same type of DMUs. In this paper, the scientific research efficiency of faculty members at the Inner Mongolia University is used as an example to provide a better understanding of the proposed model. The proposed model is intended to provide a fair and balanced performance assessment method that reflects actual performance, especially for mixed-type DMUs.     

Qualitative data envelopment analysis by affinity set: a survey of subjective opinions for NPOs

Measuring the performance of Non-Profit Organizations (NPOs) is a complicated issue: data envelopment analysis (DEA) is a popular quantitative tool in the past literature. However, the subjective opinions of NPOs could disturb their actual performance, and this problem is seldom considered. In this study, we use the qualitative DEA as a tool to find the emphasized inputs and outputs for these NPOs. Most DEA models are established by the basis of quantitative data, they are difficult to describe the qualitative performance of NPOs. This paper proposes a new perspective for computing the efficiency of a Decision Making Unit based on qualitative data by affinity Set. The DEA model for qualitative data could be traced back to the work of Cook et al. early in 1993. Our contribution prevents the identical efficiency scores from the model of Cook et al., and a combinatorial optimization technique is used to solve the new problem. Finally, we found most NPOs would like to get more resources from outside; but interestingly, they don’t like to be officially monitored. Therefore, we should use the quantitative DEA on NPOs very carefully.     

The relationship between bank performance and intellectual capital in East Asia

In this paper, we develop a performance efficiency value by using data envelopment analysis (DEA) to integrate five perspectives of CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity), which is used by the Federal Deposit Insurance Corporation to evaluate banking performance. In addition, we utilize a tiered DEA to categorize banks into four groups. One-way ANOVA is used to analyze differences in CAMEL and intellectual capital (IC) variables across the four groups. CAMEL variables are manifested more strongly in highly efficient groups when compared with inefficient groups. The findings also reveal the importance of IC in achieving high levels of bank efficiency.     

Efficiency in fundraising and distributions to cause-related social profit enterprises

Managerial efficiency is as important in social profit enterprises (SPEs) as it is for more traditional financial-profit organizations. In this regard, both donors and SPE executives use efficiency information in making decisions. Here, we suggest a linked, two-stage Data Envelopment Analysis (DEA) methodology for assessing efficiency in both charitable fundraising and cause delivery, while empirically investigating results for international aid organizations. The model allows efficiency assessment for both the fundraising and utilization of generated funds when directed for cause-related purposes. This, in particular, allows for measurement of the organization’s managerial efficiency relative to both multiple phased goals and peer organizations. Additionally, the approach provides benchmarks for identifying sources of improved performance in fundraising and program/cause service delivery. It can also project the results of changes in inputs on the amount of resources available for the charitable organization’s cause.The proposed model(s) allow the examiner to assess performance while, at the same time, identifying those instances wherein the simple ratio measures commonly used in non-profit assessment are (1) deficient, and/or (2) misleading because of the use of ‘incorrect’ variables, or the ‘hiding’ of inefficiency if/when tax form categories are filed by an SPE. Importantly, the suggested two-stage DEA methodology can be useful for any organization with multiple-linked goals.     

A note extending Grosskopf, Hayes, Taylor and Weber, “Anticipating the consequences of school reform: A new use of DEA”

The article cited in the current paper's title shows how to extend Data Envelopment Analysis (DEA) to evaluate future, as well as past and present, performance. This is accomplished by relaxing some of the constraints that reflect the presence of imposed regulations, and then re-computing the evaluations under these alternate conditions. We here extend this approach to account for “long-run” and “short-run” behaviors, and the related bodies of theory that play prominent roles in microeconomics.     

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.     

基于AHP-DEA的第三方物流企业绩效评价管理

适时、全面、有效地对物流企业绩效进行评估,是确保企业能够有效运行,实现企业自身价值和目标的基础和关键。针对第三方物流企业管理中的绩效评价问题,文章基于层次分析法(AHP)和数据包络分析法(DEA),建立第三方物流企业绩效评价管理体系,构建AHP-DEA组合评价模型。该组合模型首先针对第三方物流企业进行指标层次分析,再将分析结果作为数据包络分析的投入产出指标进行DEA评价,并基于供应链视角为企业提供新思路,实证分析表明该模型能够客观评价企业物流绩效,为企业经营管理提供依据。     

Performance Management: Perceiving Goals as Invariable and Implications for Perceived Job Autonomy and Work Performance

In this study, we investigated whether perceiving goals as invariable is negatively related to work performance and whether this relationship is mediated by perceived job autonomy. Perceiving goals as invariable refers to the extent to which employees believe that the goals in a performance management system represent absolute standards that they must meet without exception, even if they think other factors are more important (e.g., situational factors or factors that are not associated with goals). In support of our hypotheses, we found a negative relationship between perceiving goals as invariable and work performance and that perceived job autonomy mediated this relationship. Theoretical and practical implications and directions for future research are discussed. © 2014 Wiley Periodicals, Inc.