Emotion influences our actions, and this means that emotion has subjective decision value. Emotions, properly interpreted and understood, of those affected by decisions provide feedback to actions and, as such, serve as a basis for decisions. Accordingly, "affective computing" represents a wide range of technological opportunities toward the implementation of emotions to improve human-computer interaction, which also includes insights across a range of contexts of computational sciences into how we can design computer systems to communicate and recognize the emotional states provided by humans. Today, emotional systems such as software-only agents and embodied robots seem to improve every day at managing large volumes of information, and they remain emotionally incapable to read our feelings and react according to them. From a computational viewpoint, technology has made significant steps in determining how an emotional behavior model could be built; such a model is intended to be used for the purpose of intelligent assistance and support to humans. Human emotions are engines that allow people to generate useful responses to the current situation, taking into account the emotional states of others. Recovering the emotional cues emanating from the natural behavior of humans such as facial expressions and bodily kinetics could help to develop systems that allow recognition, interpretation, processing, simulation, and basing decisions on human emotions. Currently, there is a need to create emotional systems able to develop an emotional bond with users, reacting emotionally to encountered situations with the ability to help, assisting users to make their daily life easier. Handling emotions and their influence on decisions can improve the human-machine communication with a wider vision. The present thesis strives to provide an emotional architecture applicable for an agent, based on a group of decision-making models influenced by external emotional information provided by humans, acquired through a group of classification techniques from machine learning algorithms. The system can form positive bonds with the people it encounters when proceeding according to their emotional behavior. The agent embodied in the emotional architecture will interact with a user, facilitating their adoption in application areas such as caregiving to provide emotional support to the elderly. The agent's architecture uses an adversarial structure based on an Adversarial Risk Analysis framework with a decision analytic flavor that includes models forecasting a human's behavior and their impact on the surrounding environment. The agent perceives its environment and the actions performed by an individual, which constitute the resources needed to execute the agent's decision during the interaction. The agent's decision that is carried out from the adversarial structure is also affected by the information of emotional states provided by a classifiers-ensemble system, giving rise to a "decision with emotional connotation" included in the group of affective decisions. The performance of different well-known classifiers was compared in order to select the best result and build the ensemble system, based on feature selection methods that were introduced to predict the emotion. These methods are based on facial expression, bodily gestures, and speech, with satisfactory accuracy long before the final system. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: Accepted.</p>

There has been considerable research interest in the nature of service failure and recovery over the past few decades. In this context, the role of frontline service employees has emerged as a crucial factor in successful service recovery. Interestingly, while management and organizational behaviour literatures have looked at the favorable influence positive psychological capacities (optimism, hope, self-efficacy, and resilience) have on employee performance, this literature has not yet been used to shed light on how such capacities in frontline service employees might impact service recovery. By bringing this literature into the service recovery context, this research aims to examine how, and to what extent, these internal positive psychological capacities in frontline employees affect service recovery and complaint handling. Using emotion contagion theory, the broaden-and-build theory of positive emotions, the theory of cognitive appraisal, and justice theory, the study develops a number of hypothesized relationships, centered on the proposition that employee positive psychological capacities influence service recovery and complaint handling through both an emotional and a behavioral path. Specifically, it is posited that frontline employee positive emotions influence customer perceived interactional justice through the emotional path, while the behavioral path influences frontline employee problem solving, thus influencing customer perceived distributive and procedural justice. Data to examine these questions was collected using two studies. The first, based on a survey of service providers, investigates the influence of positive psychological capacities on positive emotions and problem solving competencies of frontline employees. The second uses an experimental design with service customers as subjects, investigating the influence of employee problem solving levels and positive emotions on customer perceptions of justice. Data analysis supports both paths with a stronger influence for the behavioural paths. The study brings new insight to service managers and service recovery.

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