Purpose The integration of artificial intelligence (AI) into tourism offers transformative potential for advancing sustainable tourism development (STD). Despite growing interest in AI applications in tourism, existing literature lacks a theoretically grounded and empirically validated framework that links AI with sustainable empowerment strategies and tourist engagement (TE) as pathways to STD. To address this gap, the study aims to draw on the Value-Attitude-Behaviour (VAB) and Empowerment theories to conceptualise and validate a structural model to explain the relational dynamics between AI and STD. Design/methodology/approach Primary data were collected from 303 domestic and international tourists in the Hail region of the Kingdom of Saudi Arabia using a two-wave time-lagged survey. Quantitative methodology was used to analyse the hypothesised relationships. Findings Findings reveal that AI significantly enhances sustainable psychological, social and political empowerment, which in turn positively influences TE. Furthermore, TE serves as a strong predictor of STD, highlighting the crucial role of empowerment and engagement in translating AI capabilities into sustainable outcomes. Originality/value The study contributes to tourism literature by offering a novel theory-driven framework that explicates the mechanism through which AI drives STD.

Purpose The rapid growth of artificial intelligence has raised an emerging trend of adopting virtual influencers (VIs) for hospitality promotions. This paper aims to investigate how VIs’ attributes affect consumers’ attitudes and purchase intention in hospitality marketing, and to further examine the mediating role of attitude toward VIs. Design/methodology/approach To achieve the proposed research objective, this study used PLS-SEM to analyze data from 302 participants collected from the online survey platform WJX. Findings All the identified VIs’ attributes (VI–brand congruence, authenticity, personalization, novelty and social presence) positively predict consumers’ attitudes and purchase intention. Attitude toward VI directly affects purchase intention and presents a significant mediation role between all the identified attributes and purchase intention. The findings will enable hospitality marketers to adopt VI marketing with effective strategies. Originality/value This paper extends the attitude-toward-the-ad model and the affect transfer hypothesis in predicting consumers’ attitudes and purchase intention in VI hospitality marketing. Moreover, this paper clarifies the effect of the identified factors and highlights the independent persuasive role of the VI.

Purpose This study aims to examine how travel influencers’ congruence with endorsed travel brands and alignment with their followers affect travel purchase behavior, addressing a critical gap in tourism marketing literature. Design/methodology/approach A novel conceptual framework grounded in the source credibility model and the persuasion knowledge model is proposed. A quantitative cross-sectional survey was conducted among 374 Sri Lankan travelers who actively engage with travel-related content on Instagram. The hypotheses were tested using partial least squares structural equation modeling. Findings Travel influencers who are perceived as trustworthy, attractive and congruent with both the endorsed travel brand and their audience enhance perceived authenticity. This perceived authenticity positively influences attitudes toward the travel brand, which in turn drives travel purchase behavior. Originality/value This study frames perceived influencer authenticity as a process-based mediator critical to the recognition of native advertising. Leveraging Sri Lanka’s emerging tourism context as a boundary condition, it demonstrates how authenticity mediates influencer–brand and influencer–follower congruence and provides actionable insights for designing credible, trust-enhancing and effective tourism promotion strategies.

Purpose This study aims to present a framework for applying natural language processing techniques to analyze and classify hotel customer reviews. Design/methodology/approach Using a data set of over 500,000 hotel reviews, a supervised machine learning model is developed to predict whether a review is good or bad based on its textual content. The approach involved a comprehensive data preprocessing pipeline, including tokenization, stop-word removal and lemmatization. For feature engineering, a combination of sentiment analysis scores (using valence-aware dictionary and sentiment reasoner), basic text metrics and advanced text vectorization techniques is integrated such as Doc2Vec and TF-IDF. Findings Given the significant class imbalance in the data set, with a very low percentage of negative reviews, the model performance is rigorously evaluated using the precision–recall curve and the average precision (AP) metric, which are better suited for such scenarios than the traditional receiver operating characteristic curve. The final model, a random forest classifier, achieved an AP of 0.37, demonstrating its effectiveness in identifying the minority class of negative reviews. The results indicate that sentiment analysis features are the most influential in predicting reviewer satisfaction. Practical implications The framework created allows recognizing negative reviews in time to take action to facilitate immediate service recovery and proactive reputation management. The model can be applicable on a strategic level to uncover recurring operational issues, track customer satisfaction trends and derive marketing insights used in the reviews. Originality/value This paper provides a foundation for developing automated systems that enable hotels to better understand and respond to customer feedback in real time.

Purpose This study aims to explore how cultural interest in the Korean Wave (Hallyu) influences the distribution and performance of Korean restaurants across US regions. It examines the intersection of cultural trends, consumer behavior and regional business outcomes, offering insights into the geographical dynamics of ethnic restaurant growth. Design/methodology/approach This study analyzes 1,228,596 Yelp reviews from 4,585 Korean restaurants across the 150 most populous US cities. A Hallyu Influence Index was developed using Python’s Pytrends library, based on 46 Hallyu-related keywords sourced from the Korea Culture and Information Service. Google Trends data (2004–2023) were used to capture regional cultural interest over time. A mixed-effects model was used to account for both fixed regional trends and random state-level variations. Interaction terms tested whether the cultural impact differed by region. The dependent variable, the number of Korean restaurants, was transformed using the Box-Cox method for model optimization. Findings The Hallyu Influence Index significantly predicted the number of Korean restaurants across states. While the West had the highest baseline presence, regions such as the Midwest and South showed stronger marginal effects of Hallyu. This suggests regional variation in how cultural trends influence restaurant growth, supporting the importance of cultural interest as a driver of business success. Originality/value This study introduces a novel Hallyu Influence Index to measure regional cultural interest and links it to real-world business data. It bridges big data analytics, acculturation theory and geospatial approaches, offering actionable insights for tourism marketers and hospitality professionals seeking to leverage cultural trends in market strategy.

Purpose With the growing prevalence of virtual reality and augmented reality technologies, immersive digital tourism offers college students a highly realistic destination experience. However, the influencing factors on travel decision-making remain unclear. This study aims to investigate the factors and pathways shaping university students’ behavioral intentions (BIs) within the context of immersive digital tourism. Design/methodology/approach Based on the Technology Acceptance Model, this study explores the pathways of college students’ BIs within the context of immersive digital tourism. The study analyzes 927 valid questionnaires using partial least squares structural equation modeling and constructs an extended TAM framework that incorporates technology perception (TP), immersive experience perception (IEP), perceived usefulness (PU), perceived ease of use (PEOU), perceived interactivity (PI) and BI. Findings The results indicate that the driving effect of perceived immersive experience on travel intention is significantly stronger than that of perceived technology. Both perceived usefulness and interactivity have a direct positive impact on BI, while PEOU does not pass the significance test. Originality/value The study reveals that college students prioritize functional value and interactive experiences in immersive environments, with technical ease of operation having a weaker explanatory power in decision-making. Therefore, it suggests that digital tourism products targeting college students should focus on enhancing interactivity and emotional resonance, while downplaying technical parameters.

Purpose As global tourism accelerates, ecotourism has emerged as a strategic response to the environmental and social challenges posed by mass tourism. The integration of Industry 4.0 technologies into ecotourism holds the potential to support sustainable development, yet also introduces significant implementation barriers. This study aims to examine the impacts, challenges and opportunities of applying Industry 4.0 technologies in ecotourism, while mapping thematic clusters and future research directions with attention to environmental, social and economic sustainability. Design/methodology/approach A hybrid systematic literature review was conducted by using the PRISMA framework and Natural Language Processing (NLP). A total of 163 peer-reviewed articles published between 2017 and 2024 were retrieved from Scopus and Web of Science. Bibliometric analysis using VOSviewer was used to examine author collaboration and keyword co-occurrence. At the same time, topic modeling via latent Dirichlet allocation and semantic clustering (Sentence-BERT and Uniform Manifold Approximation and Projection) identified five key research clusters. Findings These clusters relate to the application of Internet of Things, artificial intelligence, blockchain, augmented reality/virtual reality and big data in ecotourism. While these technologies offer benefits such as real-time monitoring, personalized experiences and transparent certification, adoption remains limited because of infrastructure, cost and governance challenges. A four-stage digital roadmap (2020–2050) is proposed to guide the ecotourism digital transition. Research limitations/implications This study is limited by its reliance on peer-reviewed sources indexed in Scopus, which may exclude recent innovations and practitioner-led initiatives in ecotourism. The focus on publications from 2017 to 2024 could also overlook earlier foundational work. While the hybrid PRISMA and NLP approach improves thematic depth, variations in terminology and regional context may result in the omission of relevant studies. Future research should broaden data sources and explore multilingual and practice-based perspectives to strengthen the applicability of findings across diverse ecotourism settings. Practical implications The findings provide actionable insights for multiple stakeholders aiming to implement Industry 4.0 technologies in tourism destinations. For policymakers, the quadrant mapping of research clusters and roadmap visualization serve as strategic tools to prioritize digital infrastructure development, capacity building and adaptive policy design based on technology readiness and contextual needs. Tourism operators can leverage the findings to identify scalable solutions, such as real-time monitoring or AI-driven planning, while being aware of adoption barriers, such as interoperability, training gaps and visitor acceptance. Technology providers may use the thematic clusters to develop localized, low-cost applications suited to underserved ecotourism regions. Furthermore, the framework equips funding bodies and NGOs with an evidence-based foundation to align innovation investments with measurable sustainability outcomes, particularly in regions with limited digital readiness but high ecotourism potential. Social implications This study offers several theoretical contributions to the field of sustainable tourism and digital innovation. First, it extends the literature on smart ecotourism by applying a hybrid NLP bibliometric review framework, demonstrating how NLP and semantic clustering can complement traditional PRISMA-based approaches in uncovering latent research patterns. This methodology enhances the systematic analysis of large, unstructured corpora and supports the development of evidence-based research agendas in emerging tourism technologies. Originality/value This study contributes a novel hybrid NLP bibliometric framework for tourism research and provides actionable insights for policymakers, tourism operators and technology providers aiming to foster inclusive, technology-enabled sustainable tourism.

Purpose Rapid digital transformation following COVID-19 has increased the need for immersive tourism technologies. However, most prior studies focus on consumer adoption and overlook post-adoption and experiential factors among small and medium-sized enterprises (SMEs). Addressing this gap, the purpose of this study is to integrate the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), Flow Theory and Expectation-Confirmation Theory (ECT) into a unified Metaverse Adoption Continuance Framework to examine both adoption and continuance of metaverse tourism platforms among SMEs in Kuala Lumpur, Malaysia. Design/methodology/approach Data were collected from 388 SME owners and managers through structured surveys. Using partial least squares structural equation modelling, this study tested the mediating role of user experience and the moderating effects of technology-savviness and familiarity. Findings This study found that performance expectancy, effort expectancy, social influence, facilitating conditions, flow state, perceived enjoyment and presence positively influenced user experience, whereas privacy concerns had a negative effect. User experience significantly affects both behavioral intention and continued usage, which are strong predictors of adoption. Technology-savviness and familiarity moderate these relationships. Additionally, ethical and equity considerations, particularly privacy and digital inclusion, emerged as important contextual factors shaping SMEs’ adoption decisions. Research limitations/implications This study used a cross-sectional design, which limits the ability to see how perceptions and metaverse use change over time. Future research should adopt longitudinal methods to observe such changes. Because the data were self-reported, there is a possibility of bias, so future studies could include actual usage indicators. The focus on Kuala Lumpur tourism SMEs also limits generalization; comparing regions or cultures could improve validity. The Metaverse Adoption and Continuance Framework can be expanded by adding elements such as AI-based personalization and blockchain security. Qualitative studies can further explore emotional, social and ethical aspects of metaverse use. Practical implications This study provides useful insights for tourism SMEs, policymakers and technology developers. Tourism SMEs can enhance user experience by creating simple, engaging and affordable metaverse content such as guided virtual tours and browser-based virtual reality. Collaboration with universities and start-ups can reduce costs and promote innovation. Policymakers can support digital transformation through grants, training and improved broadband access. Ethical practices, including transparent data protection and inclusive design, are vital to build user trust and social equity. Balancing immersive engagement with privacy safeguards ensures sustainable adoption and wider community benefits. Social implications This study highlights the potential of metaverse technologies to enhance digital inclusion and innovation among tourism SMEs, particularly in developing economies. By promoting immersive virtual tourism, SMEs can reach broader and more diverse audiences, including individuals with mobility limitations. The findings of this study emphasize the need for digital literacy and privacy awareness to encourage socially responsible technology adoption. Moreover, supporting SMEs in this digital transition can generate employment opportunities in VR content development, digital training and related services, contributing to inclusive economic growth and stronger community resilience in the post-pandemic tourism landscape. Originality/value This study refines existing hybrid adoption continuance models by benchmarking against prior frameworks and contextualizing them for Malaysian tourism SMEs. This study contributes theoretically by integrating experiential and post-adoption constructs and practically by offering actionable insights into SME digital readiness, ethical data governance and sustainable metaverse implementation.

Purpose This study aims to examine the factors influencing WeChat users’ intention to share food photography at nostalgia-themed restaurants in China. Design/methodology/approach Data for this study were collected from 700 WeChat users in China and analyzed using SmartPLS 4.0 and artificial neural networks (ANNs). Findings Results from PLS and ANN showed that staff service and nostalgia emotion significantly influence users’ intention to share food photography. However, concerns about personal can inhibit sharing intentions, particularly when these intentions are motivated by status seeking or nostalgia emotions. Originality/value This study extends the application of uses and gratifications theory to the context of nostalgia-themed restaurants. It also enhances the understanding of Chinese WeChat users’ intention to share their gastronomy experiences by highlighting restaurant attributes. Additionally, this research enables stakeholders, such as restaurant operators and social media platform developers, to devise more effective strategies to encourage customers of nostalgic restaurants to share food photography.

Purpose This paper seeks to develop a new conceptual framework for extended reality (XR)-powered mobile apps in medical hospitality. Drawing on service-dominant logic (SDL) and immersive service design principles, it aims to fill the research gap regarding the sustainable, human-centered integration of XR technology in healthcare settings. Design/methodology/approach A systematic literature review was conducted following PRISMA guidelines to ensure rigor, transparency and replicability across four primary academic databases (i.e. Scopus, Web of Science, PubMed and IEEE). Thematic analysis resulted in four core pillars of our proposed framework: user-centered design, immersive content architecture, operational integration and stakeholder alignment. Findings This paper presents a comprehensive, coherent framework for co-creating and operationalizing XR-enabled mobile apps in medical hospitality settings. This framework explicates the requisites of situational adaptation, stakeholder synergy, emotional engagement and patient experiences. Practical implications Findings provide valuable, actionable alignment for XR developers, service designers and healthcare managers across immersive technology and clinical workflows. They encompass designed workflows to scope XR-enabled mobile apps’ prototyping, testing and scaling in healthcare and hospitality service ecosystems. Originality/value This paper integrates SDL with medical hospitality XR-enabled mobile app design to develop a multidimensional framework for immersive technologies as innovation components in digital healthcare.