Purpose: The purpose of this study was to uncover representative emergent areas and to examine the research area of marketing, tourism and big data (BD) to assess how these thematic areas have developed over a 27-year time period from 1996 to 2022. This study analyzed 1,152 studies to identify the principal thematic areas and emergent topics, principal theories used, predominant forms of analysis and the most productive authors in terms of research. Design/methodology/approach: The articles for this research were all selected from the Web of Science database. A systematic and quantitative literature review was performed. This study used SciMAT software to extract indicators. Specifically, this study analyzed productivity and produced a science map. Findings: The findings suggest that interest in this area has increased gradually. The outputs also reveal the innovative effort of industry in new technologies for developing models for tourism marketing. Ten research areas were identified: “destination marketing,” “mobility patterns,” “co-creation,” “gastronomy,” “sustainability,” “tourist behavior,” “market segmentation,” “artificial neural networks,” “pricing” and “tourist satisfaction.” Originality/value: This work is unique in proposing an agenda for future research into tourism marketing research with new technologies such as BD and artificial intelligence techniques. In addition, the results presented here fill the current gap in the research since while there have been literature reviews covering tourism with BD or marketing, these areas have not been studied as a whole.

Purpose: This study aims to analyze communication from the perspective of social marketing, positive emotions, and the topics chosen by Spanish tourist destinations to show their destination image. Additionally, this research shows a message classification model, based on the aforementioned characteristics, that has generated a greater impact, offering clarity to tourism managers on the type of content they should publish to achieve greater visibility.Design/methodology/approach: The methodology used in this work combines content analysis and data mining techniques. The classification tree using the chi-square automatic interaction detector (CHAID) algorithm was selected to determine predictors of like behaviour.Findings: The results show that the predictor variables have been emotions, social marketing and topics. Also, the characteristics of the messages most likely to have a high impact are those related to emotions of joy or happiness, their purpose is behavioural, and they talk about rural, cultural issues, special dates, getaways, or highlights of a town or city for something specific.Originality/value: This study is the first to analyze the content of the tweets shared by destination tourism managers from a social marketing, positive emotions, and sustainability perspective, determining the possible predictors of likes on Twitter. The authors contribute to the literature by deepening the understanding of how social marketing and the positive emotions promoted drive a more significant impact in tourism communication campaigns on social media. The authors provide destination managers with a way better to understand the variables relevant to users in tourism content.

More and more tourists are sharing their experiences on their social media through a combination of photos, texts, and hashtags. But there is a scarcity of studies in literature on analyzing tourists’ visual content in relation to tourism destinations. To address this gap in literature, this study explores how and with whom users express the greatest happiness in holiday destinations, and how they share it with their community, through a mixed methods approach composed of analysis of images, text, and metadata. This approach allows us to address the objective of this research, which is the prediction of the happiness felt by tourists during their experience, using innovative techniques that allow the independent variables to be obtained. To predict tourist satisfaction, two sources of data, photos and texts, are analyzed: a novel approach and little explored in the literature, but necessary due to the interaction of both variables. This study applies various artificial intelligence analyses on visual content (deep learning), and textual and metadata content (machine learning) to 39,235 Instagram posts shared by tourists since the emergence of Instagram thirteen years ago, at a cultural and gastronomic tourist destination. The findings of the visual content analysis showed that socialization and company, that is, traveling and interacting with people, was a key aspect of a positive tourism experience. In addition, the gender of the people with whom they traveled, and the tourist’s narcissism were also key aspects in the generation and expression of positive emotions. Regarding the analysis of the textual content, the results showed that when tourists enjoyed a positive experience, they became more involved in the generation of content, that is, they showed their happiness through positive words.

This research analyses the critical factors influencing employee satisfaction and retention in the context of the Great Resignation by exploring the key organizational elements that significantly impact employee contentment and loyalty. The justification for this study lies in the need fororganizations to understand and address the issues leading to high turnover rates and employee dissatisfaction, which have been exacerbated byrecent global workforce changes. The main objectives are to identify the key factors that contribute to employee satisfaction and to provide insights into how organizations can enhance their work environments, career growth opportunities, and management practices to retain talent. Themethodology involves a semantic network analysis of 138,764 employee reviews from 136 companies, collected via Glassdoor between 2021 and 2023. These reviews were analyzed using the Semantic Networks Tool to identify recurring themes related to work environment, career growth, workload, and management practices. The main results indicate that both current and former employees highly value a supportive and positive work environment and career growth opportunities. However, significant disadvantages include overwork, long hours, and poor managementpractices. The study highlights that the critical factors influencing employee satisfaction and retention include a positive work environment,opportunities for career growth, and effective management practices