Developing a framework for mobile payments integration

This paper derives a theoretical framework for consideration of both the technologically driven dimensions of mobile payment solutions, and the associated value proposition for customers. Banks promote traditional payment instruments whose value proposition is the management of risk for both consumers and merchants. These instruments are centralised, costly and lack decision support functionality. The ubiquity of the mobile phone has provided a decentralised platform for managing payment processes in a new way, but the value proposition for customers has yet to be elaborated clearly. This inertia has stalled the design of sustainable revenue models for a mobile payments ecosystem. Merchants and consumers in the meantime are being seduced by the convenience of on-line and mobile payment solutions. Adopting the purchase and payment process as the unit of analysis, the current mobile payment landscape is reviewed with respect to the creation and consumption of customer value. From this analysis, a framework is derived juxtaposing customer value, related to what is being paid for, with payment integration, related to how payments are being made. The framework provides a theoretical and practical basis for considering the contribution of mobile technologies to the payment industry. The framework is then used to describe the components of a mobile payments pilot project being run on a trial population of 250 students on a campus in Ireland. In this manner, weaknesses in the value proposition for consumers and merchants were highlighted. Limitations of the framework as a research tool are also discussed.

Caring and anxiety among registered nurses in an acute hospital setting

Background: Even though caring remains the essence of nursing it is still an ambiguous concept as the lens through which each nurse perceives caring differs. The differences are due to multiple factors including the setting in which the nurse works. Nurses experience high levels of anxiety when caring for patients in acute settings. Despite an abundance of published studies on caring there is a dearth of research available that focuses on the relationship between caring and anxiety. Aim: The aim of this research study was to investigate caring and anxiety in a sample of registered nurses working in an acute hospital and to determine the relationship between these and other variables. Method: A quantitative descriptive study using a correlational design was employed, with a sample of 280 registered nurses. The Caring Behaviours Inventory-24 was used to measure caring and the State Trait Anxiety Inventory to measure Anxiety. The study was guided by the Theory of Human Caring (Watson 2008). Findings: Nurses reported high levels of caring and low levels of anxiety. A statistical significant relationship was found between caring and anxiety and between caring and supportive work environment and job satisfaction. A statistical significant relationship was found between anxiety and work environment, job satisfaction gender, age, relationship status and education. Conclusion: This is the first study to investigate the relationship between caring and anxiety in an acute hospital setting. This research contributes to advancing nursing knowledge by providing evidence of the relationship between caring and anxiety among nurses in an acute hospital setting. Despite nurses reporting high levels of caring and low levels of anxiety, it is important to further enhance caring and reduce anxiety levels among all nurses. Thus, educators and managers need to explore strategies for the alleviation of anxiety among nurses, practising in acute care settings.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.