Abstract
In recent years, mass adoption of increasingly powerful mobile devices and ubiquitous communication networks have paved the way to smart environments. Such environments allow for the collection of user and environment data with the final goal to improve users' experience. In this context a number of opportunities and challenges are presented to Human Computer Interaction. This poster explores Quality of Experience, a subjective aspect of interaction, informally defined as the degree to which a system meets users' expectations. Furthermore, a mobile application was developed for the collection of user and environment data and delivery of personalised services in the context of Public Transport. This application will be used in a real-world environment, to further investigate the factors that have an influence on User eXperience, as well as the delivery of relevant services with the potential to enhance users' journeys while in transit.

Abstract
In recent years the mass adoption of mobile devices and increasingly ubiquitous connectivity have contributed to a radical change in the way people interact with computer systems. Moreover cloud computing infrastructures have paved the way for the development of smart systems in such settings, whose goal is to provide a service to enhance user experience based on environment and user sensed data. In this context, there is a clear disconnection between the two streams that flow continuously between user and cloud-based systems. On the one hand, user- and environment-generated data is being, for the most part, disregarded by service providers. On the other hand, services offered do not address users' specific needs and preferences. In addition, service discovery is a cognitive demanding process and it may have detrimental consequences in user experience. In this paper we propose a user-centric framework that addresses the disconnection between these two streams: Cloud2Bubble. The framework facilitates the design and development of smart systems. It aims at leveraging existing technology, such as environment sensors and personal devices, to aggregate localised user-related data - defined as a bubble - into the cloud. This aggregation later supports the delivery of personalised services, contextually relevant to users. The delivery of services with such characteristics has the potential to enhance quality of experience and influence user behaviour. A first iteration of the platform was developed and an evaluation in a simulated environment was performed with encouraging results. Thus, the platform will be further expanded for instantiation and evaluation in the context of urban public transports. We intend to investigate the effects of relevant service delivery in terms of enhancement of quality of experience and influencing user behaviour. The delivery of a service with these characteristics presents benefits for both users and service providers. © 2012 ACM.

Abstract
Recently, we are witnessing the effort of healthcare providers to move from paper-based records to electronic records, in order to reduce data access times and also to share clinical information. However, many of these electronic records projects have failed, as they are not well fitted to the healthcare professionals' practices. This paper presents a study on the impact of Information Technology in the daily routine of healthcare providers, to support the development of Electronic Health Records that are well adapted to these organizations' operational processes and are successfully adopted.

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.

Abstract
Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products' first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products' sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests.

Abstract
Regulators of electricity distribution networks have typically applied Data Envelopment Analysis (DEA) to cross-section data for benchmarking purposes. However, the use of panel data to analyse the impact of regulatory policies on productivity change over time is less frequent. The main purpose of this paper is to construct a Malmquist productivity index to examine the recent productivity change experienced by Norwegian distribution companies between 2004 and 2007. The Malmquist index is decomposed in order to explore the sources of productivity change, and to identify the innovator companies that pushed the frontier forward each year. The input and output variables considered are those used by the Norwegian regulator. In order to reflect appropriately the exogenous conditions where the companies operate, the efficiency model used in this paper incorporates geography variables as outputs of the DEA model. Unlike the model used by the regulator, we included virtual weight restrictions in the DEA formulation to correct the biases in the DEA results that may be associated to a judicious choice of weights by some of the companies. Journal of the Operational Research Society (2012) 63, 982-990. doi: 10.1057/jors.2011.82 Published online 26 October 2011