Abstract
The purpose of the paper is to examine trends in the performance of the Portuguese construction industry, and identify the factors that promote excellence and innovation in the sector. From a methodological perspective, this study enhances the construction of composite indicators using the principles of the "benefit of the doubt" weighting. This involves the use of Data Envelopment Analysis to estimate weights for aggregating the key performance indicators of the construction companies. The paper also proposes a new DEA-based method to assess innovation within an industry, identifying the innovative companies and the extent of innovation. The determinants of good performance and innovation are examined using regression techniques and the statistical significance of the results is ensured by the use of bootstrapping. The study reveals that the Portuguese construction industry experienced a remarkable performance improvement during the 1990s, but this growth trend in performance slowed down in recent years. It is concluded that company performance is strongly affected by the national economic context, and that small specialized companies and large contractors tend to achieve the best performance levels.

Abstract
The most popular weight restrictions are assurance regions (ARs), which impose ratios between weights to be within certain ranges. ARs can be categorized into two types: ARs type I (ARI) and ARs type II (ARII). ARI specify bounds on ratios between input weights or between output weights, whilst ARII specify bounds on ratios that link input to output weights. DEA models with ARI successfully maximize relative efficiency, but in the presence of ARII the DEA models may under-estimate relative efficiency or may become infeasible. In this paper we discuss the problems that can occur in the presence of ARII and propose a new nonlinear model that overcomes the limitations discussed. Also, the dual model is described, which enables the assessment of relative efficiency when trade-offs between inputs and outputs are specified. The application of the model developed is illustrated in the efficiency assessment of Portuguese. secondary schools.

Abstract
This paper enhances cost efficiency measurement methods to account for different scenarios relating to input price information. These consist of situations where prices are known exactly at each decision making unit (DMU) and situations with incomplete price information. The main contribution of this paper consists of the development of a method for the estimation of upper and lower bounds for the cost efficiency (CE) measure in situations of price uncertainty, where only the maximal and minimal bounds of input prices can be estimated for each DMU. The bounds of the CE measure are obtained from assessments in the light of the most favourable price scenario (optimistic perspective) and the least favourable price scenario (pessimistic perspective). The assessments under price uncertainty are based on extensions to the Data Envelopment Analysis (DEA) model that incorporate weight restrictions of the form of input cone assurance regions. The applicability of the models developed is illustrated in the context of the analysis of bank branch performance. The results obtained in the case study showed that the DEA models can provide robust estimates of cost efficiency even in situations of price uncertainty.

Abstract
This paper develops measures, based on the Malmquist index, that enable the decision making units' internal inefficiencies to be distinguished from those associated with their group (or program) characteristics. The applicability of these measures is illustrated with the assessment of bank branches' performance. The analysis involves the construction of an index reflecting the relative performance of branches in four different regions, which can be decomposed into an index for the comparison of within-group efficiency spread, evaluating internal managerial efficiencies, and an index for the comparison of frontier productivity, reflecting the impact of environmental factors and regional managerial policies on branches' productivity.

Abstract
A good relationship between companies and customers is a crucial factor of competitiveness. The improvement of service levels has become a key issue to develop and maintain a loyal relationship with customers. This paper proposes a method for promotions design for retailing companies, based on knowledge extraction from transactions records of customer loyalty cards, aiming to improve service levels and increase sales. At first, customers are segmented using k-means and then the segments' profile is characterized according to the rules extracted from a decision tree. This is followed by the identification of product associations within segments, which can base the identification of the products most suitable for customized promotions. The research reported is done in collaboration with an European retailing company.

Abstract
Retaining customers has been considered one of the most critical challenges among those included in Customer Relationship Management (CRM), particularly in the grocery retail sector. In this context, an accurate prediction whether or not a customer will leave the company, i.e. churn prediction, is crucial for companies to conduct effective retention campaigns. This paper proposes to include in partial churn detection models the succession of first products' categories purchased as a proxy of the state of trust and demand maturity of a customer towards a company in grocery retailing. Motivated by the importance of the first impressions and risks experienced recently on the current state of the relationship, we model the first purchase succession in chronological order as well as in reverse order, respectively. Due to the variable relevance of the first customer-company interactions and of the most recent interactions, these two variables are modeled by considering a variable length of the sequence. In this study we use logistic regression as the classification technique. A real sample of approximately 75,000 new customers taken from the data warehouse of a European retail company is used to test the proposed models. The area under the receiver operating characteristic curve and 1%, 5% and 10% percentiles lift are used to assess the performance of the partial-churn prediction models. The empirical results reveal that both proposed models outperform the standard RFM model.