A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation.

Justice, emotions and satisfaction in complaint behavior in services

This study proposes a marketing approach to service recovery (SR) models in order to help to explain what factors affect cumulative satisfaction, loyalty and word-of-mouth following complaint behavior. The model has its base on the definition of perceived justice and its influence on satisfaction with service recovery (SSR) and on emotions (positive and negative). Trust acts as a central construct in the model, receiving influence from the affective and cognitive aspect and mediating the relationship between SSR and cumulative satisfaction and between positive/negative emotions and loyalty. The sample for this study consists of 303 Spanish B2C-EC users who made a complaint after an electronic transaction. Results from the analysis show the influence of perceived justice ?mainly interactional justice and procedural justice? on SSR, and the relevance of positive emotions as a key factor in SSR processes, in contrast to the major role which negative emotions have traditionally played in these models. Furthermore, trust mediates the relation between SSR and cumulative satisfaction, and is the factor which has a higher influence on loyalty, whilst cumulative satisfaction becomes the more relevant factor affecting WOM.

Are team personality and climate related to satisfaction and software quality? Aggregating results from a twice replicated experiment

Research into software engineering teams focuses on human and social team factors. Social psychology deals with the study of team formation and has found that personality factors and group processes such as team climate are related to team effectiveness. However, there are only a handful of empirical studies dealing with personality and team climate and their relationship to software development team effectiveness. Objective We present aggregate results of a twice replicated quasi-experiment that evaluates the relationships between personality, team climate, product quality and satisfaction in software development teams. Method Our experimental study measures the personalities of team members based on the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and team climate factors (participative safety, support for innovation, team vision and task orientation) preferences and perceptions. We aggregate the results of the three studies through a meta-analysis of correlations. The study was conducted with students. Results The aggregation of results from the baseline experiment and two replications corroborates the following findings. There is a positive relationship between all four climate factors and satisfaction in software development teams. Teams whose members score highest for the agreeableness personality factor have the highest satisfaction levels. The results unveil a significant positive correlation between the extraversion personality factor and software product quality. High participative safety and task orientation climate perceptions are significantly related to quality. Conclusions First, more efficient software development teams can be formed heeding personality factors like agreeableness and extraversion. Second, the team climate generated in software development teams should be monitored for team member satisfaction. Finally, aspects like people feeling safe giving their opinions or encouraging team members to work hard at their job can have an impact on software quality. Software project managers can take advantage of these factors to promote developer satisfaction and improve the resulting product.