This study examines factors influencing the intention of tourists with mobility disabilities to adopt technological implants, introducing the concept of the impaired cyborg tourist. Grounded in Innovation Diffusion Theory and the Unified Theory of Acceptance and Use of Technology (UTAUT), the research tests an integrated model incorporating subjective well-being. Data were collected from 221 mobility-impaired tourists via a scenario-based online survey and analyzed using partial least squares structural equation modeling. Results reveal that innovation attributes, autonomy, perceived convenience, and social inclusion, positively impact subjective well-being, which significantly predicts implant adoption intention. UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) also significantly influence adoption intention. The study contributes theoretically by extending technology adoption frameworks into the emerging domain of implantable travel technologies and by integrating affective outcomes into user behavior models. Practically, it offers insights for developers, policymakers, and tourism providers aiming to enhance inclusive, tech-enabled tourism experiences.

This study examines the impact of robotic gastronomic experiences (RGE) on customer emotional and functional value, delight, and loyalty in service robot restaurants. Using validated scales from prior research, a 5-point Likert scale survey was conducted with 302 participants via Amazon Mechanical Turk, targeting individuals with experience in such establishments. The analysis, performed with SmartPLS 4.0, utilized partial least squares-path modelling (PLS-PM) to test the hypothesized relationships. Results show that RGE, characterized by competence and coolness, positively affects emotional and functional values, which significantly influence delight and loyalty. Methodological measures were implemented to address common method variance (CMV), confirming the robustness of the findings. This research underscores the potential of service robots to enhance customer satisfaction and loyalty through efficient and engaging service delivery, providing valuable insights for integrating advanced robotics in the hospitality industry.

This study consolidates the existing literature and develops a conceptual framework for a holistic understanding of the tourist experience with AI-enabled technologies. It entails performance analysis of scientific actors, thematic analysis, and content analysis of the intellectual structure of the research. The key technologies shaping the tourist experience include service robots, personalized recommender systems, IoT, chatbots, AR/VR, big data analytics, text mining, text analytics, and NLP. The study contributes by offering performance and science mapping in the field of AI for the tourist experience. Accordingly, several future research directions and a consolidated conceptual framework are proposed.

Drawing on action identification and complexity theories, this study explores lifestyle congruency and chatbot identification as drivers of engagement, leading to chatbot advocacy. Data collected from 304 individuals were assessed symmetrically through PLS-SEM. Moreover, configuration causal paths were assessed through fsQCA. Findings reveal the role of chatbot identification in the relationship between lifestyle congruency and customer chatbot engagement. Lifestyle congruency and chatbot identification significantly influence all the dimensions of chatbot engagement. Nevertheless, only customer referrals and customer influence lead to chatbot advocacy. Findings from fsQCA reveal six and seven different paths, leading to high and low levels of chatbot advocacy, respectively. This is one of the first studies to apply both symmetrical and asymmetrical analysis to examine different casual paths to chatbot advocacy.

Through multiple studies, this research examines Gen Zers’ perceptions and acceptance of working with service robots in the hotel industry. Study 1 used interviews with 30 respondents and extensive literature to generate a list of items to measure determinants of Gen Zers’ acceptance of working with service robots. Study 2 purified and refined the item pool, generated in Study 1 using the data collected from 200 Gen Zers via an online survey to conduct an exploratory factor analysis. Study 3 used a second dataset (N = 552) to conduct confirmatory factor analysis on the 19-item scale by employing partial least squares-based structural equation modelling. Results indicate that hedonic motivations, utilitarian motivations, and social skills towards service robots have a positive whereas insecurity, and technical and interactional barriers have a negative impact on Gen Zers’ intentions to work with service robots. Theoretical implications for technology adoption in the hotel industry and relevant practical implications are also discussed.