Overtourism spoils the good economic and social results produced by the tourism sector, causing reductions in the quality of service of the tourist destination and rejection by the local population. Previous literature has suggested that social networks and new electronic channels could be accelerators of the process of overcrowding destinations; however, this link has not been established. For this reason, in this exploratory study, the influence of social networks on overtourism is analysed using Barcelona as a base, as Barcelona is a massively popular destination in the country that is second in the world in reception of tourists to Spain. This study is also focused on Chinese tourism, which brings large numbers of tourists and presents great economic potential. Two types of study have been used: big data techniques applied to social media with sentimental analysis, and analysis of travel packages offered in China to travel to Spain. Relevant results are obtained to understand the influence of social networks on the travel behaviour of tourists, possible contributions to overtourism, and recommendations for the management of tourism.

The recent global increase in the competitiveness of tourism has made the implementation of eco-innovations a differentiating element among both the destinations and companies in the sector, with quality management and contribution to sustainable development being increasingly valued. However, the eco-innovations that have been developed and implemented in tourist industries have rarely been studied. In this study, the eco-innovations that have been developed and implemented by 57 tourism businesses worldwide are analysed. The identified eco-innovations are classified by using different qualitative methodologies. The obtained results shed light on the limited development of eco-innovations in the tourism industry and the industry focuses mainly on product eco-innovations. Several examples by the tourist sub-industry and types of eco-innovation are analysed. Furthermore, this study provides practical information about measures that both businesses and governmental organisations can adopt to promote eco-innovation in the sector.

The models used for analyzing and measuring quality in tourist destinations are changing with the incorporation of new techniques derived from data science and artificial intelligence. Recent studies show how social media and e-word of mouth (e-WoM) are playing key roles in the perception and image diffusion of tourist destinations. Thus, it is necessary to look for new methods for analyzing the tourist management and attractiveness of tourist spots. This includes conducting a sentiment analysis of tourists that modifies former research methods based on previously proposed model, supported by a survey, which obtained predefined and incomplete results. This study analyzed the quality of tourism in Spain, a major tourist destination that is considered to be the country with the greatest tourist competitiveness according to the World Economic Forum, and in China, the country with the greatest level of development and potential. A sentiment analysis was carried out to measure the quality of tourist destinations in Spain, and this involved three challenges: (1) the analysis of the sentiments of Chinese tourists obtained from e-WoM; (2) the use of new models to measure the quality of a destination based on information from Chinese social networks, and (3) the use of the latest artificial intelligence analytical technologies. Our findings demonstrate how sentiment analysis can be a determining factor in measuring WoM and identifying areas of development in tourist destinations in order to build a more sustainable destination. The results includes the following aspects: (1) the use of real images with more empirical evidence, (2) the use of impressive and disappointing sentiments, (3) a “no comment status”, (4) elimination of stereotypes, and (5) the identification of new opportunities and segments.

Most research on AI readiness assumes that preparedness is shaped by demographic characteristics such as age, education and gender. The evidence presented here challenges that assumption. Drawing on a sample of 930 workers from Spain and China, this study examines whether AI Literacy and Data Interpretation predict Perceived AI Business Value after controlling for AI Knowledge, Human Skills and organizational characteristics. Multivariate regression is employed to distinguish capability effects from demographic influences.,Capability-related variables explain substantially more variation in Perceived AI Business Value than demographic characteristics. Human Skills emerge as the strongest predictor, followed by AI Literacy, AI Knowledge and Data Interpretation, while gender and educational attainment show no significant effects once capabilities are considered. The most pronounced differences are observed across national contexts: respondents from Spain exhibit lower average capability scores and perceived AI business value than respondents from China, whereas demographic characteristics explain relatively little variation within either country.,The findings suggest that AI readiness depends less on demographic attributes than on the capabilities required to interpret, evaluate and apply AI-generated outputs effectively. If this pattern extends beyond the present sample, debates about AI readiness may need to shift from who is exposed to AI toward how organizations cultivate the capabilities that make AI economically useful.