Understanding the Relationships between Tourists’ Emotional Experiences, Perceived Overall Image, Satisfaction, and Intention to Recommend

The purpose of this study is to empirically test an integrative model linking tourists' emotional experiences, perceived overall image, satisfaction, and intention to recommend. The model was tested using data collected from domestic tourists visiting Sardinia, Italy. Results show that tourists' emotional experiences act as antecedents of perceived overall image and satisfaction evaluations. In addition, overall image has a positive influence on tourist satisfaction and intention to recommend. The study expands current theorizations by examining the merits of emotions in tourist behavior models. From a practical perspective, the study offers important implications for destination marketers

Mediating Effects of Place Attachment and Satisfaction on the Relationship between Tourists’ Emotions and Intention to Recommend

This study develops a model based on the developmental theory of place attachment. The model considers the influence of tourists’ emotions on place attachment and the mediating effects of satisfaction and place attachment on the relationship between tourists’ emotions and intention to recommend. The model was tested using data collected from 464 international tourists at the end of their trip to Thailand. Results show that positive emotions, negative emotions and satisfaction are significant determinants of place attachment. In particular, negative emotions display a positive relationship with place attachment. In addition, only satisfaction mediates the relationship between tourists’ emotions and intention to recommend. Findings highlight the need for researchers to incorporate emotions in modeling place attachment and offer implications for marketers promoting Thailand as a tourist destination

Early Fusion of Low Level Features for Emotion Mining

We study the discrimination of emotions annotated in free texts at the sentence level: a sentence can either be associated with no emotion (neutral) or multiple labels of emotion. The proposed system relies on three characteristics. We implement an early fusion of grams of increasing orders transposing an approach successfully employed in the related task of opinion mining. We apply a filtering process that consists in extracting frequent n-grams and making use of the Shannon’s entropy measure to respectively maintain dictionaries at balanced sizes and keep emotion specific features. Finally the overall system is implemented as a 2-step decision process: a first classifier discriminates between neutral and emotion bearing sentences, then one classifier per emotion is applied on emotion bearing sentences. The final decision is given by the classifier holding the maximum confidence. Results obtained on the testing set are promising

Quality Improvement of Mobile Apps: Tool-Supported Lightweight Feedback Analyses

Mobile apps have penetrated the market and are used everywhere. The success of apps also depends on user feedback as this enables users to influence other potential customers and provides new opportunities for identifying features. An efficient development process including quality assurance is obligatory for app-developing companies. However, developers also face challenges, such as short time to market, many release cycles, or low budgets for quality assurance. Therefore, we present a lightweight approach that considers textual feedback from users and a corresponding tool chain. With this, quality can be monitored and development and quality assurance decisions for upcoming sprints can be made fast and easily. Furthermore, examples of such textual analyses show how the approach can provide information to improve apps