Abstract
This paper develops a method based on data envelopment analysis (DEA) for efficiency assessments taking into account the effect of non-discretionary factors. A typology that classifies the non-discretionary factors into two groups is proposed: the factors that characterize the external conditions where the decision making units (DMUs) operate (external factors), and the factors that are internal to the production process but cannot be controlled by the decision makers (internal factors). This paper proposes an enhanced DEA model that accommodates non-discretionary inputs and outputs and treats them differently depending on their classification as internal or external to the production process. This generalized model integrates the previous approaches for dealing with non-discretionary variables described in the DEA literature. The model defines the efficient frontier based exclusively on the discretionary variables and internal non-discretionary factors, but the potential peers of each DMU are restricted to other units facing comparable external conditions (represented by the external non-discretionary factors). The peer selection criteria implemented in the DEA model is informed by decision makers' opinion, The applicability of the model developed is illustrated with a real-world assessment of retailing stores.

Abstract
This paper describes a method for the assessment of retail store performance based on Data Envelopment Analysis (DEA). The assessment considers the stores as complex organizations that aggregate several subunits, corresponding to sections with management autonomy. This structure motivated an analysis at two different levels: the section level and the store level. The performance assessment of the sections envolves a comparison among similar sections located in different stores, and evaluates efficiency spread. This is followed by an analysis at the store level to define targets for the sections. This analysis takes into account the interdependencies of the sections composing a store, as they share limited resources such as the floor area. This is achieved using a Network DEA model, which determines the maximum store sales allowing for reallocations of area among the sections within a store. The method developed is illustrated using a case study consisting of a Portuguese chain of supermarkets.

Abstract
Purpose: The purpose of this paper is to illustrate the implementation of a web-based platform integrating benchmarking and data envelopment analysis (DEA) for the Portuguese secondary schools. Design/methodology/approach: The benchmarking platform is designed around a set of key performance indicators that are displayed using benchmarking graphs. These indicators are also aggregated through the methodology of DEA to provide a summary measure of performance. Findings: The benchmarking platform developed enables schools to perform internal and external evaluation through a standard model that is based on indicators of school activities. It encourages schools' efforts of continuous improvement and increases society awareness regarding schools' context and results obtained. Practical implications: The benchmarking platform can be useful for schools and general public. For the general public, there is a tool that allows the construction of user-defined rankings online and benchmarking tools that allow the comparison of performance of a specific school with others. In addition to these features, schools have in the BESP platform a repository of historical data, and the possibility to see a set of graphs that show for some indicators their evolution over time. Originality/value: This paper describes a breakthrough in the Portuguese education context. The BESP platform is the first in this context to combine DEA and benchmarking tools in a web-based environment, designed to enable real-time performance assessments. © Emerald Group Publishing Limited.

Abstract
The web benchmarking systems broadly used in the construction industry (CI) are designed to provide results based on key performance indicators (KPIs). No insights concerning organization overall performance and improvements targets are available. This research aims to fulfill this gap using data envelopment analysis (DEA) as a method to complement the information provided by a set of KPIs. The methodology proposed is useful to all organizations involved in benchmarking routines. To enable a more realistic assessment of CI companies, two types of DEA models were used, one allows factor weights to vary freely and the other includes weight restrictions. These models assign an efficiency score to each organization, identifying efficient organizations and providing performance improvements targets for the others. To enable suggesting targets for all organizations, expert opinion was used to specify virtual units which were included in the efficiency assessment to define a practical frontier located beyond the productivity levels of the original DEA frontier. Based on a sample of 20 Portuguese leading contractors, the Portuguese web benchmarking system for CI, icBench, was used to demonstrate the advantages of integrating the DEA method with KPIs benchmark scores.

Abstract
This paper develops a framework for performance appraisal in the context of a bank branch network. A data envelopment analysis model is advocated that can identify both input and output inefficiencies from a cost minimization perspective. The model is then applied to the analysis of a bank branch network and the production and value-added approaches are deployed jointly to give a comprehensive assessment of bank branch efficiency. The sources of underperformance are identified and their managerial implications are explored.

Abstract
This paper describes a performance assessment of Portuguese secondary schools using data envelopment analysis (DEA). The assessment adopts a perspective where schools are viewed as promoting students achievement given their characteristics in terms of academic abilities and socio-economic background. Our sample comprised all secondary schools in Portugal with both basic and secondary education levels. Two types of DEA analysis are performed: one using an output-oriented model that restricts output (exam scores) weights to be linked to the number of students that have done that exam in the school, and the other using a model that restricts factor weights to be equal for all schools. In this model the weight restrictions are linked to the total number of exams done nationally. The first model is well suited for identifying worst performing schools and to assess schools that may specialize in certain subjects, whereas the latter is best suited for improving discrimination between best performing schools when pursuing the identification of benchmarks, as well as to construct performance rankings. Journal of the Operational Research Society (2012) 63, 1098-1115. doi: 10.1057/jors.2011.114 Published online 16 November 2011