Chris Zhu

Generative artificial intelligence (GenAI) is rapidly transforming tourism and hospitality, yet reviews have largely focused on bibliometric mapping, conceptual discussions, or specific applications, leaving limited integration of research methods, theories, benefits, risks, and tourism experience personalization. This study systematically reviews GenAI research in tourism and hospitality to identify its methodological and theoretical foundations, major applications and constraints, personalization mechanisms, and future research directions. Following PRISMA 2020 guidelines, the Web of Science and Scopus were searched for studies published from 2022 to June 2026, yielding 125 eligible peer-reviewed journal articles. Through qualitative content analysis and systematic coding, the studies were classified by methodology, theoretical foundation, application context, benefits, constraints, and personalization mechanisms. The findings show that quantitative approaches, particularly SEM/PLS-SEM and scenario-based experiments, dominate the field, while S-O-R, TAM, trust-related theories, social exchange theory, and UTAUT/UTAUT2 are the most frequently adopted theoretical foundations. Four major benefit categories and four constraint categories are identified, while personalization occurs mainly through situational adaptation, role-based interaction, and cognitive activation. The study contributes an integrated analysis of the field and proposes a Technology–Psychology–Organization framework linking GenAI characteristics, tourist responses, and organizational implementation. Practically, it highlights the need for transparent, risk-sensitive, and human-centered GenAI deployment.

The development of Artificial intelligence (AI) technology has brought new ideas and opportunities to destination marketing. However, existing studies lack sufficient empirical research to explore the impact of AI anchors on tourists’ travel intentions. To fill this research gap, this study explores the influence of perceived anthropomorphism and perceived playfulness on tourists’ telepresence, inspiration, and travel intention in AI virtual anchor-based travel livestreaming. Through the analysis of 291 valid data sets, it was found that in AI virtual anchor-based travel livestreaming, perceived anthropomorphism positively affects telepresence but does not affect tourists’ inspiration. Playfulness positively affects tourists’ telepresence and inspiration in AI virtual anchor-based travel livestreaming. This study also found that neither perceived anthropomorphism nor perceived playfulness directly affects tourists’ travel intention, but both can be achieved through the mediating effect of telepresence. The findings provide empirical evidence of the value for tourism researchers and destinations in adopting AI technology for livestreaming.

Although the rise of Virtual Reality (VR) technology has brought new opportunities to tourism experiences and marketing, limited research has explored how VR technology affects tourists’ pro-sustainable behaviors in heritage tourism research. To address this research gap, this study constructs a theoretical model by integrating the technology acceptance model (TAM) framework to explore the relationship among perceived ease of use (PEU), perceived usefulness (PUS), awe, attitude, and pro-sustainable behavior of tourists. Through the analysis of 304 valid questionnaires, this study found that in the heritage tourism experience presented by VR, PEU and PUS positively influence awe and attitude. Furthermore, awe positively affects attitude and pro-sustainable behaviors. However, PUS has no effect on tourists’ pro-sustainable behaviors. This study expands the theoretical framework of TAM and the related research on awe in heritage tourism presented by VR, providing valuable practical insights for heritage managers in the adoption of immersive technology.

Purpose: This study selected the Zhou Enlai Memorial Hall, a national Museum of China, as the research context to explore relationships among memorable tourism experience (MTE), nostalgia, celebrity attachment, cultural identity and word-of-mouth (WOM) in museum tourism. Design/methodology/approach: Structural equation modeling was used to analyze a total of 345 survey-based data to test relationships between MTE, nostalgia, celebrity attachment, cultural identity and word-of-mouth (WOM) in museum tourism. Findings: This study found significant positive relationships among the research variables. MTE positively affects nostalgia and celebrity attachment. Celebrity attachment positively influences consumers’ cultural identity and WOM. Originality/value: This research helps museum scholars and managers understand the tourist experience in Chinese museum tourism.

Purpose: Drawing upon attachment theory, this work examines the antecedents (cognitive image, affective image and conative image) and consequences (festival identity) of place attachment (place dependence, place identity, place affect and place social bonding) in short video presenting Macau festival marketing. Design/methodology/approach: SmartPLS software is a suitable data analysis tool for handling complex models and predicting outcomes in structural equation modeling (SEM) research. Therefore, this study utilized SmartPLS version 3 to conduct data analysis. Findings: Through online survey collection, PLS analysis found that all hypotheses were accepted except for the impact of cognitive image on place dependence, place identity, place social bonding and the effect of place dependence on festival identity. Originality/value: This research applied attachment theory to short-video festival marketing. The research results assist the promotion of festivals using short videos.

ChatGPT has gained increased attention as an artificial intelligence (AI)‐based tool in the field of tourism and hospitality education in recent years. This study investigates the impact of perceived ease of use, perceived usefulness and authenticity on student attitude and intention to use ChatGPT by incorporating the concept of authenticity into the technology acceptance model (TAM) theoretical framework in tourism and hospitality education. This study found that authenticity had the strongest indirect effect on student intention to use ChatGPT compared with perceived ease of use and perceived usefulness. This study broadens the applicability of TAM theory in ChatGPT and provides practical insights into its application.