Chatbots are an emerging technology that is disrupting the tourism industry. Despite their implementation in companies and at destinations, there is little research that evaluates chatbots’ smart tourism technologies (STTs) attributes and their influence on tourist satisfaction. This study seeks to examine the relationship between informativeness, empathy, accessibility, interactivity and chatbot user satisfaction. The research was based on an experiment and a survey conducted on a sample of 468 potential tourists who used a chatbot during their trip. Statistical tools such as exploratory factor analysis and the hierarchical regression method were used in the data analysis. The results suggest that informativeness, empathy and interactivity of destination chatbots are the attributes that influence and predict tourist satisfaction while accessibility does not. The main contribution of this study is the analysis of the attributes of STTs applied to destination chatbots, which also provides valuable information for both tourism chatbot developers and smart destination managers who wish to adopt this technology.

Expectations about a destination influence the tourist experience during the travel process stages. In the post-COVID-19 normalcy, people are adjusting their priorities and social values. Therefore, it becomes crucial to identify tourists’ expectations before traveling. The objectives of this research were: (a) identify the preferences of tourists; (b) establish the attitudes of tourists; and (c) determine the expectations of tourists for post-COVID-19 destination selection. The study analyzed a sample of 491 people during pandemic lockdowns in Guayaquil, Ecuador. Statistical techniques such as exploratory and confirmatory factor analysis were used in data analysis. The results show that after the pandemic, tourists prefer urban tourism, followed by cultural tourism and traveling with relatives. It also shows a more responsible and supportive attitude when traveling. Likewise, the results support the dimensional structure that explains a set of post-pandemic tourist expectations. Five factors were identified: Smart Care, pricing strategy, safety, comfort, and social distancing. Finally, the theoretical and managerial implications of the results that will guide for tourism destination managers were discussed.

Smart cities and smart tourism destinations integrate technological infrastructures and end-user devices with the aim of providing more satisfying experiences. They generate expectations of superior innovation, greater interactivity, participation and a better image. Therefore, they need to communicate their smartness and include it in their branding. The study analyses the smart content present in the tourist and non-tourist official Twitter accounts of a selection of smart cities and smart destinations, through semantic analysis, in order to find out which smart concepts are strategically branded to create a smart tourism destination image. The results show that the best cities in the ranking for each smart topic are not the ones with highest percentages of tweets on that topic. The study also shows that the number and percentage of tweets that communicate smart aspects on the platforms analyzed are low, showing the smartness is not included in their branding. It has also been observed that, almost all cities communicate or do not communicate the same concepts, evincing an undifferentiated branding strategy. The results of this study are interesting for urban policy-makers and tourism destination marketers to improve their branding.