Nonparametric approach to evaluation of economic and social development in the EU28 member states by DEA efficiency

Data envelopment analysis (DEA) methodology is used in this study for a comparison of the dynamic efficiency of European countries over the last decade. Moreover, efficiency analysis is used to determine where resources are distributed efficiently and/or were used efficiently/inefficiently under factors of competitiveness extracted from factor analysis. DEA measures numerical grades of the efficiency of economic processes within evaluated countries and, therefore, it becomes a suitable tool for setting an efficient/inefficient position of each country. Most importantly, the DEA technique is applied to all (28) European Union (EU) countries to evaluate their technical and technological efficiency within the selected factors of competitiveness based on country competitiveness index in the 2000-2017 reference period. The main aim of the paper is to measure efficiency changes over the reference period and to analyze the level of productivity in individual countries based on the Malmquist productivity index (MPI). Empirical results confirm significant disparities among European countries and selected periods 2000-2007, 2008-2011, and 2012-2017. Finally, the study offers a comprehensive comparison and discussion of results obtained by MPI that indicate the EU countries in which policy-making authorities should aim to stimulate national development and provide more quality of life to the EU citizens.Web of Science122art. no. 7

Evaluation of Corporate Sustainability

As a consequence of an increasing demand in sustainable development for business organizations, the evaluation of corporate sustainability has become a topic intensively focused by academic researchers and business practitioners. Several techniques in the context of multiple criteria decision analysis (MCDA) have been suggested to facilitate the evaluation and the analysis of sustainability performance. However, due to the complexity of evaluation, such as a compilation of quantitative and qualitative measures, interrelationships among various sustainability criteria, the assessor’s hesitation in scoring, or incomplete information, simple techniques may not be able to generate reliable results which can reflect the overall sustainability performance of a company. This paper proposes a series of mathematical formulations based upon the evidential reasoning (ER) approach which can be used to aggregate results from qualitative judgments with quantitative measurements under various types of complex and uncertain situations. The evaluation of corporate sustainability through the ER model is demonstrated using actual data generated from three sugar manufacturing companies in Thailand. The proposed model facilitates managers in analysing the performance and identifying improvement plans and goals. It also simplifies decision making related to sustainable development initiatives. The model can be generalized to a wider area of performance assessment, as well as to any cases of multiple criteria analysis

Efficiency of Research Performance of Australian Universities: A Reappraisal using a Bootstrap Truncated Regression Approach

The motivation of the study stems from the results reported in the Excellence in Research for Australia (ERA) 2010 report. The report showed that only 12 universities performed research at or above international standards, of which, the Group of Eight (G8) universities filled the top eight spots. While performance of universities was based on number of research outputs, total amount of research income and other quantitative indicators, the measure of efficiency or productivity was not considered. The objectives of paper are twofold. First, to provide a review of the research performance of 37 Australian universities using the data envelopment analysis (DEA) bootstrap approach of Simar and Wilson (J Econ, 136:31–64, 2007). Second, to determine sources of productivity drivers by regressing the efficiency scores against a set of environmental variables.Data envelopment analysis, efficiency, universities, bootstrap truncated regression, environmental variables.

Supplier Selection by the Pair of Nondiscretionary Factors-Imprecise Data Envelopment Analysis Models

Discretionary models for evaluating the efficiency of suppliers assume that all criteria are discretionary, that is, controlled by the management of each supplier and varied at its discretion. These models do not assume supplier selection in the conditions that some factors are nondiscretionary. The objective of this paper is to propose a new pair of nondiscretionary factors-imprecise data envelopment analysis (NF-IDEA) models for selecting the best suppliers in the presence of nondiscretionary factors and imprecise data. A numerical example demonstrates the application of the proposed method.Full Tex

Performance Evaluation of Airport Construction Energy-saving based on DEA Network Design and Performance Analysis

AbstractFuzzy Data Envelopment Analysis (DEA) evaluation model in Airport Construction Energy-saving have improved incomplete weight deficiency of information processing. Firstly, indices values were converted to trapezoid fuzzy numbers, then with incomplete information on indices weights as constraints, a fuzzy DEA model with outputs only and preference was established, and then by applying the α-cut approach, the model was transformed to a family of crisp DEA models and was solved. Experiments demonstrated the feasibility and applicability of the method

The data envelopment analysis method in benchmarking of technological incubators

This paper presents an original concept for the application of Data Envelopment Analysis (DEA) in benchmarking processes within innovation and entrepreneurship centers based on the example of technological incubators. Applying the DEA method, it is possible to order analyzed objects, on the basis of explicitly defined relative efficiency, by compiling a rating list and rating classes. Establishing standards and indicating “clearances” allows the studied objects - innovation and entrepreneurship centers - to select a way of developing effectively, as well as preserving their individuality and a unique way of acting with the account of local needs.benchmarking, the Data Envelopment Analysis method, assemblage ordering, rating classes.