Abstract
The use of mobile phones to make payments is already a wide-spreading reality. While some mobile payment solutions achieved a considerable success and are already in use, others failed in the pilot phase. Nevertheless, there is an area where mobile payments have been quite successful: mobile ticketing in public transport. In fact, there are several advantages of mobile ticketing over traditional ticketing systems, such as queue avoidance, ubiquitous and remote access to payment, and the lack of need to carry coins and cash. This paper intends to propose a mobile payment system to be implemented in the Public Transport of Metropolitan Area of Oporto. After defining the payment ticketing model, a prototype was developed and tested by a sample of users. These tests allowed gathering some feedback about the feasibility of the system as well as useful insights about the concept, new in public transport in Portugal. The findings attained so far suggest that users considered the system extremely useful, since it is more convenient than traditional systems, improving the travelling process and experience. It was also clear that users valued the integration of additional and complementary services with mobile payments, such as real-time traffic information, maps and schedules. There are also several barriers to the adoption of such a system elicited by users, such as premium price, complex interfaces and perceived risks, such as security and privacy concerns.

Abstract
The profit resulting from customer relationship is essential to ensure companies viability, so an improvement in customer retention is crucial for competitiveness. As such, companies have recognized the importance of customer centered strategies and consequently customer relationship management (CRM) is often at the core of their strategic plans. In this context, a priori knowledge about the risk of a given customer to mitigate or even end the relationship with the provider is valuable information that allows companies to take preventive measures to avoid defection. This paper proposes a model to predict partial defection, using two classification techniques: Logistic regression and Multivariate Adaptive Regression Splines (MARS). The main objective is to compare the performance of MARS with Logistic regression in modeling customer attrition. This paper considers the general form of Logistic regression and Logistic regression combined with a wrapper feature selection approach, such as stepwise approach. The empirical results showed that MARS performs better than Logistic regression when variable selection procedures are not used. However, MARS loses its superiority when Logistic regression is conducted with stepwise feature selection.

Abstract
This paper aims to provide an assessment of urban quality of life (QoL) of European cities from the perspective of qualified human resources. The competitiveness of cities relies increasingly in their capacity to attract highly educated workers, as they are important assets for firms when choosing a location. Qualified human resources, on the other hand, tend to value QoL over other urban features. This is why policymakers and urban planners need to evaluate QoL of cities and be provided with tools that can guide action to improvements in this area. We assess urban QoL by means of a composite indicator constructed using data envelopment analysis, based on Urban Audit data and Mercer's framework of analysis, to give account of 246 European cities. Besides presenting a ranking of the best and the worst scores of QoL, this methodology allows benchmarking strategies.

Abstract
Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.

Abstract

Abstract
This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.