This paper proposes a data envelopment analysis (DEA) approach to measurement and benchmarking of service quality. Dealing with measurement of overall service quality of multiple units with SERVPERF as multiple-criteria decision-making (MCDM), the proposed approach utilizes DEA, in particular, the pure output model without inputs. The five dimensions of SERVPERF are considered as outputs of the...

Data Envelopment Analysis (DEA) has been widely studied in the literature since its inception with Charnes, Cooper and Rhodes work in 1978. The methodology behind the classical DEA method is to determine how much improvements in the outputs (inputs) dimensions is necessary in order to render them efficient. One of the underlying assumptions of this methodology is that the units consume and prod...

the need for monitoring the overall performance of countries in sustainable development (sd) is widely recognized, but scant attention has been devoted to methods for aggregating and analyzing vast amounts of empirical data. this paper describes the development and application of a data envelopment analysis (dea) methodology for addressing the challenges of benchmarking sustainable development....

Data envelopment analysis (DEA) is a data-driven non-parametric approach for measuring the efficiency of a set of decision making units (DMUs) using multiple inputs to generate multiple outputs. Conventionally, DEA is used in ex post evaluation of actual performance, estimating an empirical best-practice frontier using minimal assumptions about the shape of the production space. However, DEA ma...

An objective measure of building energy performance is crucial for performance assessment and rational decision making on energy retrofits and policies of existing buildings. One of the most popular measures of building energy performance benchmarking is Energy Use Intensity (EUI, kwh/m2). While EUI is simple to understand, it only represents the amount of consumed energy per unit floor area ra...

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with ratio scale inputs and outputs, but it cannot handle interval scale data. This study develop an approach to efficiency analysis to deal with both interval-scale data and ratio scale data. This approach introduces a measure of inefficiency and identifies effi...

The purpose of this study is to present a complementary modeling approach using data envelopment analysis (DEA) and artificial neural network (ANN) as an adaptive decision support tool in promoting best performance benchmarking and performance modeling. DEA and ANN are combined to take advantages of optimization and prediction capabilities inherent in each method. DEA is used as a preprocessor ...

At present, a first round of hospital benchmarking as required by German law on health care reform takes place. After extensive discussions between hospitals and insurance companies, which are jointly responsible to deliver benchmarking results, a method with some peculiar characteristics was chosen. In this paper it is argued that the deficiencies of said method could be overcome by using Data...

a r t i c l e i n f o The study offers a decision support tool for stepwise benchmarking. Within a benchmarking exercise, we propose a method for selecting a preferable development path that consists of a sequence of intermediate benchmarks. First, an outranking multi-criteria approach is used to establish the hierarchical position of benchmarking units. For each unit, we evaluate possible deve...