Perceptions and experiences of consumers regarding the integration of artificial intelligence in travel agency services: a qualitative study

Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17812551 192 ISRG PUBLISHERS Abbreviated Key Title: Isrg J Econ Bus Manag ISSN: 2584-0916 (Online) Journal homepage: https://isrgpublishers.com/isrgjebm/ Volume – III Issue - VI (November-December) 2025 Frequency: Bimonthly Perceptions and experiences of consumers regarding the integration of artificial intelligence in travel agency services: a qualitative study Molka TRIKI PhD in Marketing Marketing Research Laboratory (LRM), University of Sfax Tunisia | Received: 28.11.2025 | Accepted: 02.12.2025 | Published: 04.12.2025 *Corresponding author: Molka TRIKI PhD in Marketing Marketing Research Laboratory (LRM), University of Sfax Tunisia Abstract This study examines how consumers perceive and experience the integration of artificial intelligence (AI) into travel agency services. In view of the limitations of technology acceptance models (TAM, UTAUT), which remain focused on perceived usefulness and behavioral intention, a qualitative approach based on semi structured interviews is adopted to capture the AI experience in a holistic way. Thematic analysis first reveals a strong attraction to the functional benefits of AI tools (time savings, comparison, personalization, 24/7 availability). However, AI is perceived as a complement to, rather than a substitute for, human advisors, especially in complex or emotionally charged situations. The findings then highlight several conditions for a positive experience: ergonomic and easy to use interfaces, reliable information, a sense of control over recommendations, non intrusiveness, and transparency about the role of AI. Generational and social differences also emerge, linked to age, digital literacy and relational expectations, which shape the acceptable scope of automation. Finally, the use of well designed AI tools strengthens trust, perceived innovativeness and intentions to revisit and recommend, underlining the strategic role of human–AI complementarity in the tourism customer–brand relationship. By proposing an integrated framework of AI experience in travel agencies, this research offers theoretical and managerial contributions for the responsible deployment of AI in tourism services. Keywords: artificial intelligence, travel agencies, customer experience, technology acceptance, trust. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17812551 193 Introduction Artificial intelligence (AI) is expanding rapidly and profoundly transforming how consumers communicate, work, shop and make decisions (Zarantonello et al., 2024). Virtual assistants, recommender algorithms, chatbots, mobile applications and connected devices are increasingly embedded in everyday life, shaping not only consumption practices but also well-being and the organisation of personal life (Panetta, 2018; Borges et al., 2021; Triki et al., 2025; Shalu et al., 2025). In marketing, AI has become a key strategic lever: it automates repetitive tasks, enables the analysis of massive data sets and powers advanced personalisation systems, thereby improving the relevance of communications and the effectiveness of commercial actions (Gao et al., 2023; Hicham et al., 2023; Sahebi et al., 2022). Firms view AI as a major opportunity to create smoother, more responsive experiences tailored to individual expectations. The tourism sector strongly illustrates this evolution. Faced with increasingly complex demand and intense competition, travel agencies and hospitality providers are progressively adopting intelligent technologies to optimise their services and enrich the traveller experience. These solutions rely on textual, visual or audio data to deliver real-time assistance, automate stages of the customer journey and offer personalised interactions (Cai et al., 2022; Côté & Su, 2021; Doborjeh et al., 2022). However, this transformation also makes consumers’ choices more difficult: confronted with an abundance of information and a proliferation of online offers, they struggle to quickly identify the most relevant alternative (Ewerhard et al., 2019). In this context, AI appears as a key tool to reduce search effort, simplify option comparison and support decision-making (Gao & Liu, 2023). Despite growing interest in AI integration within tourism services, academic knowledge remains fragmented. Existing studies have examined, for instance, chatbots in tourism (Rafiq et al., 2022), smart innovation in hospitality (Zarrad, 2024), voice assistants in hotels (Cai et al., 2022) and initial trust in conversational agents (Sboui et al., 2024). Yet few works have provided a comprehensive view of how consumers perceive, evaluate and accept these technologies in the specific context of travel agencies. Prior research underscores the role of factors such as interaction quality (Yao Zhu et al., 2023), perceived intelligence (Ling et al., 2025) and psychological or cultural dimensions influencing adoption (Goel et al., 2022), while also highlighting several limitations (Conti et al., 2023). First, the literature lacks integrated theoretical frameworks that systematically explain perceptual mechanisms related to AI in tourism. Existing measures are still embryonic and capture only part of the cognitive and emotional dimensions at stake (Ling et al., 2025). Second, available knowledge remains fragmented, leaving practitioners with limited guidance on how to deploy these technologies coherently and in line with consumer expectations (Conti et al., 2023; García-Madurga et al., 2023). Moreover, the influence of cultural, social and psychological factors on AI acceptance in travel is still largely under-explored, despite their decisive role in usage intentions (Goel et al., 2022). Recent work on AI and customer experience also stresses the need to move beyond purely functional determinants of acceptance and to incorporate emotional, relational and contextual dimensions (Kacprzak & Hensel, 2023; Chen & Prentice, 2025). Finally, there is a shortage of qualitative research that captures consumers’ lived experience in a holistic manner, beyond behavioural intentions and technology-acceptance models (Garcia et al., 2021; Goel et al., 2022). In light of these gaps, a qualitative exploration is needed to grasp the complexity of consumers’ representations, expectations and reluctance towards AI in travel agencies. Such an approach makes it possible to uncover the subtle tensions between automation and human contact, the trade-offs made by consumers and the conditions under which these technologies are perceived as useful, reliable and acceptable (Chen & Prentice, 2025; Goel et al., 2022). By examining how AI shapes cognitive effort, perceived control, trust and the perception of the relationship with the brand, this type of study provides a complementary perspective to predominantly quantitative work. Against this backdrop, the central research question guiding this study is: How do consumers perceive the integration of artificial intelligence into travel agency services, and how do these perceptions influence their acceptance, trust and customer experience throughout the travel journey? Section 1 develops the theoretical framework on the use of AI in tourism services, with particular attention to the theoretical foundations of the TAM and UTAUT models. Section 2 details the qualitative methodology adopted. Section 3 presents the findings and discussion. Finally, the conclusion outlines the theoretical, methodological and managerial implications of this research and suggests directions for future work. Literature review 1. Use of artificial intelligence in tourism services: perceptions, experiences and behavioural determinants Artificial intelligence (AI) is radically transforming the tourism sector by reshaping interactions between consumers and service providers. Travel agencies, faced with increasingly demanding customers and an abundance of offers, are adopting intelligent technologies such as chatbots, virtual assistants and recommendation systems to enhance the customer experience (Cai, Wang & Li, 2022; Côté & Su, 2021). These tools not only automate routine tasks but also process diverse data in real time, thereby facilitating service personalisation. AI thus acts as a mediator between consumers and the complexity of tourism offers, reducing information overload and simplifying option comparison (Gao & Liu, 2023). This reduction in perceived complexity lowers the cognitive effort required from users and is viewed as a major benefit. However, research shows that AI acceptance largely depends on how these functional benefits are combined with more subjective expectations related to comfort, trust and relational quality (Erin Chao Ling et al., 2023). Perceptions of AI in tourism are ambivalent: while it is valued for its efficiency and constant availability, it also raises concerns about service dehumanisation and the loss of human interactions that are considered essential in emotionally intense or high-stakes situations (Ivanova et al., 2019; Nsenge et al., 2024). Consumers therefore tend to favour a balanced complementarity in which AI supports but does not fully replace human advisors (Garcia et al., 2021). This dynamic complicates behavioural determinants beyond the purely technical dimensions of usefulness and ease of use. Customer experience with AI in tourism is multidimensional, encompassing cognitive (perceived performance and ease of use), affective (trust, reassurance) and social aspects (norms and cultural expectations) (Samala, Raghu & Jeong, 2020). This plurality Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17812551 194 underscores the need for a holistic perspective that incorporates rational, subjective and contextual factors when studying AI adoption in this sector. Despite the large number of quantitative studies on usage intentions, AI devices in tourism still suffer from a lack of in-depth qualitative investigations, which limits understanding of users’ representations, expectations and tensions (Goel et al., 2022; Conti et al., 2023). This gap calls for exploratory studies capable of capturing the richness and contradictions of the customer experience with these emerging technologies. 2. Conceptual frameworks of technology adoption: contributions of TAM and UTAUT The Technology Acceptance Model (TAM) proposed by Davis (1989) is a major theoretical framework for analysing users’ adoption of technologies. It relies on two key variables—perceived ease of use and perceived usefulness—which shape attitudes toward the technology and, in turn, intention to use. In tourism, TAM helps explain how the perceived simplicity and functional value of AI tools influence consumer adoption (Gajdošík & Marciš, 2019; Samala et al., 2020). For example, personalised recommendation systems enhance perceived usefulness by offering relevant suggestions tailored to travellers’ needs, thereby fostering acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. (2003) extends TAM by incorporating four additional determinants: performance expectancy, effort expectancy, social influence and facilitating conditions. This robust model is particularly relevant for studying the adoption of digital technologies in tourism, where contextual and social factors play a major role (Vorobeva et al., 2023; Christou et al., 2023). UTAUT thus makes it possible to move beyond individual factors and account for the influence of the social and organisational environment on AI use. However, TAM and UTAUT show limitations in the specific context of AI applied to tourism, as they struggle to fully integrate several key dimensions. First, the emotional and affective dimension remains insufficiently addressed, even though it is crucial for capturing feelings of comfort, trust or anxiety triggered by human–machine interaction. Second, the cultural dimension is often neglected, although it is essential for explaining differences in attitudes toward AI across users’ social and cultural backgrounds (Goel et al., 2022). Finally, these models capture poorly the complexity of technological autonomy combined with the preservation of human control, which requires deeper conceptual reflection on the complementarity, or even symbiosis, between human and artificial intelligence (Nsenge et al., 2024). Integrating these dimensions is necessary to move beyond the simple measurement of usage intentions and to better understand the underlying mechanisms that shape the reception and experience of AI technologies in tourism services. Methodology Data collection To address the objectives of this study, an exploratory qualitative approach based on individual semi-structured interviews was adopted. This design is particularly suitable when the phenomenon under investigation, here, the perception and use of artificial intelligence (AI) tools in the travel booking process, requires an in-depth, contextualised understanding of lived experiences. In line with Marshall’s (1996) recommendations, purposive judgement sampling was used to recruit participants who had actually used AI-based tools during the previous six months in their booking activities: hotel reservations, airline ticket purchases, destination choices, and the planning of itineraries or activities through AI assistants. The tools considered included chatbots, booking platforms with predictive algorithms, conversational assistants, smart mobile applications and automatic recommendation systems embedded in tourism websites or apps. Data collection continued until semantic saturation was reached (Evrard et al., 2000), i.e. when additional interviews no longer yielded new information. The final corpus comprises 29 Tunisian consumers, exceeding the minimal thresholds commonly recommended in qualitative research (Glaser & Strauss, 1967; Thompson & Haytko, 1997). The sample reflects a diversity of sociodemographic profiles (age, occupation, socio-economic status), and includes 18 women and 11 men, as presented in Table 1. Interviews lasted on average 20 to 35 minutes, which is consistent with qualitative marketing standards (Miles & Huberman, 2003). They were conducted face to face, audio-recorded with participants’ consent and fully transcribed, resulting in a rich and exploitable corpus. Table 1. Profile of participants in individual interviews Participant code Gender Age Occupation P1 Female 23 Student P2 Female 29 Teacher P3 Male 34 Engineer P4 Female 31 Architect P5 Male 40 Administrative manager P6 Female 27 Student P7 Female 36 Physician P8 Male 45 Entrepreneur P9 Female 33 Administrative manager P10 Male 52 Civil servant P11 Female 28 Consultant P12 Female 54 Retired P13 Male 26 Student P14 Female 39 Teacher P15 Female 47 Pharmacist P16 Male 30 Entrepreneur P17 Female 25 Designer P18 Female 41 Shop owner P19 Male 37 Engineer P20 Female 32 HR manager P21 Female 50 Teacher P22 Male 28 Student Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17812551 195 P23 Female 34 Communication officer P24 Female 56 Physician P25 Male 43 Marketing manager P26 Female 29 Consultant P27 Female 61 Retired P28 Male 38 Entrepreneur P29 Female 30 Teacher A semi-structured interview guide organised around five themes, presented in Table 2, was developed beforehand, based on the literature on AI use in services and on the TAM and UTAUT models: Table 2: Interview guide Main theme Description Examples of issues explored 1. Interactions with AI tools Understand how consumers concretely use AI when booking a hotel, flight or trip. Types of tools used (chatbots, intelligent comparison sites, airline/hotel apps), frequency of use, nature of tasks (search, comparison, recommendations). 2. Perceived usefulness Explore the added value provided by AI in the booking process. Speed, convenience, relevance of suggestions, improvement of the final choice, optimisation of time and effort. 3. Ease of use Analyse the simplicity and accessibility of AI tools. Ease of learning, smooth navigation, intuitive interface, feeling of control over the tool, ease of use. 4. Enhanced user experience Examine how AI enriches the overall tourism experience. Satisfaction, sense of control, trust in the tool, comfort in decision-making, perception of a more personalised experience. 5. Future intentions and relationship with brands Study how positive AI use influences future behaviours. Intention to reuse, recommendation, trust in airlines/hotels/platforms, potential loyalty. Data analysis method The interview transcripts were examined using categorical thematic content analysis (Giannelloni & Vernette, 2001). The analysis followed an inductive and comparative approach in line with grounded theory principles (Glaser & Strauss, 1967). Coding was carried out with the QDA Miner software, in accordance with the methodological recommendations of Masmoudi and El Aoud (2021). To ensure the reliability and robustness of the coding process, part of the corpus (four randomly selected interviews) was coded independently by two researchers. Inter-coder agreement was assessed using the percentage agreement measure (Ronan & Latham, 1974) and Scott’s Pi (Scott, 1955). The resulting coefficients, 86.7% agreement and Scott’s Pi = 0.76, indicate excellent consistency between coders and support a confident interpretation of the findings. Results Thematic analysis of the interviews reveals five overarching themes that characterise consumers’ perceptions of artificial intelligence (AI) integration in tourism services. Attraction to the efficiency and practicality of AI tools The findings show that participants express a strong attraction to AI tools because of the functional benefits they provide. This supports the assumptions of the Technology Acceptance Model (TAM), where perceived usefulness is a central determinant of adoption (Davis, 1989), and of UTAUT, which highlights the role of performance expectancy in usage intention (Venkatesh et al., 2003). Participants first stress speed and time savings: they appreciate the ability of chatbots, comparison sites and intelligent platforms to instantly provide information on flights, hotels and destinations, in line with Gajdošík and Marciš (2019), who show that AI improves the fluidity of the tourist journey. As one participant notes, ―with the app, I can compare hotels in a few minutes and find exactly what suits me, without waiting for an agent‖ (P12, 54). Comparison and personalisation then emerge as key benefits: recommendation systems help travellers filter, rank and adjust their options, echoing Gao and Liu’s (2023) conclusions on algorithmic accuracy and Samala et al.’s (2020) work on uncertainty reduction in planning. Another participant confirms this dimension: ―the suggestions really match what I’m looking for, I no longer need to spend hours checking everything‖ (P7, 36). Finally, the permanent availability of AI tools is perceived as a major advantage: 24/7 access reinforces perceived usefulness and smooths the travel experience, consistent with Gayathri (2025), who underlines the importance of continuous digital service in tourist satisfaction. AI perceived as a complement, not a substitute Participants largely view AI as a complementary tool that facilitates the booking process without replacing human intervention, supporting Garcia et al.’s (2021) notion of ―human-AI symbiosis‖ and aligning with the social and trust factors identified by UTAUT. AI is mainly mobilised for punctual, repetitive or informational tasks, checking flight availability, comparing offers, making a pre-booking, thus acting as a functional assistant rather than an autonomous decision-maker. This perception echoes Bachimont (2020) and Chapuis (2018), who argue that AI should mobilise humans’ social and creative intelligence within a collaborative rather than substitutive logic, as well as Naulet’s (2019) view that AI can contribute to decisions but does not replace human judgement. In complex situations or those involving emotional or financial stakes, participants express a clear need for human contact, confirming Sampaio et al. (2020) and Samala et al. (2020), who state that AI ―cannot surpass human contact‖ and remains a complementary layer in tourism services. This nuanced view is illustrated by statements such as: ―I prefer to check with the agent for some important bookings, but AI really makes my preliminary searches easier‖ (P26, 29); and ―AI is useful to sort options, but when there is a problem, I want to talk to a real person‖ (P11, 28). Overall, AI is clearly valued as effective Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17812551 196 support, but never as a total substitute, confirming the need for theoretical models that integrate human–AI complementarity (Nsenge et al., 2024). Conditions for a positive experience Participants identify several essential conditions that make the use of AI in tourism services pleasant and satisfying, in line with the ease-of-use construct in TAM (Davis, 1989) and effort expectancy in UTAUT (Venkatesh et al., 2003). A first dimension concerns simplicity of use, characterised by intuitive interfaces, smooth navigation and a user journey that limits cognitive effort. This requirement echoes the recommendations of Pillai and Sivathanu (2020), who argue that the adoption of tourism technologies depends on functional clarity and the fluidity of human–machine interaction. In this regard, one participant notes: ―When everything is simple, clear and reliable, it almost becomes a pleasure to use these tools‖ (P19, 37). The reliability of information and the relevance of recommendations also appear as central elements, strengthening the feeling of trust. This finding is consistent with Samala et al. (2020), who show that algorithmic personalisation and the accuracy of suggestions improve travellers’ decision experience. As another participant summarises: ―When AI suggests hotels that really match what I’m looking for, I save time and I feel confident‖ (P14, 39). Participants also attach particular importance to having control over the suggestions proposed by AI. The possibility of modifying, validating or ignoring recommendations contributes to a positive experience, in line with Christou et al. (2023), who stress the need to maintain an active human role in AI-assisted decision processes. Finally, non-intrusiveness is perceived as a key factor: notifications must be limited, relevant and non-invasive. This observation resonates with Triki and Makni Turki’s (2025) work on information overload and the balance between digital assistance and cognitive comfort. The idea of human-centred design, capable of supporting rather than interrupting the experience, is supported by Wüst et al. (2025), who highlight the importance of ―human-like‖ interaction for strengthening emotional engagement. Generational and social variations in AI perception The discourse analysis reveals generational and social nuances in how consumers perceive and use AI in tourism services. Younger participants, more accustomed to digital interfaces and self-service, are more willing to delegate search and comparison tasks to apps and recommendation systems: ―Honestly, with the apps, I prefer to do everything myself, I save time and I don’t depend on anyone’s schedule‖ (P9, 27). Conversely, older participants or those less comfortable with digital tools recognise the usefulness of AI for preparing trips but wish to retain human validation for important decisions: ―The app is good to get a first idea, but for booking a big trip, I still want someone to check with me‖ (P27, 61). These differences relate not only to age, but also to digital literacy levels and internalised norms of what constitutes ―good‖ service. Some participants, whatever their profile, appreciate the control offered by AI over comparison and personalisation ―I like being able to test several scenarios in the app before asking the agent for advice‖, P25, 43, while others associate service quality with the human advisor’s ability to ask implicit questions and provide reassurance: ―The agent asks me questions I wouldn’t have thought of, AI doesn’t see that‖ (P21, 50). AI acceptance thus appears to be shaped by a set of social factors (generation, digital capital, consumption style) that define the perceived legitimate scope of automation. These results converge with recent studies showing that AI adoption in tourism varies across generations, particularly in terms of trust, risk perception and the meaning attributed to algorithmic personalisation (Xue et al., 2024; Chen et al., 2024). They also confirm extensions of TAM and UTAUT that emphasise the role of sociodemographic and contextual factors, such as age, technological familiarity and usage type, in interpreting perceived usefulness, effort expectancy and social influence (Venkatesh et al., 2003; Chen et al., 2024). Finally, they respond to calls in the literature for better integration of social and cultural dimensions in the study of AI acceptance in tourism, by showing that the same technological devices are neither perceived nor used in the same way across traveller profiles (Goel et al., 2022; Thees et al., 2024). Influence on brand relationships and overall experience The use of AI directly shapes how travellers perceive tourism brands and evaluate their overall experience. Participants stress that well-designed tools enhance trust, satisfaction and the perceived professionalism of agencies, hotels and airlines, echoing Zhu et al. (2023), who highlight the decisive role of technological quality in brand perception. Several accounts underline this positive dynamic: ―If the hotel or agency offers an efficient app, I trust them more and I will come back‖ (P3, 34). This technological trust also fuels revisit and recommendation intentions, a mechanism already identified by Flores et al. (2007), who show that smooth, personalised experiences foster positive word-of-mouth and long-term loyalty. AI solutions also strengthen brands’ image of modernity: participants consistently associate these tools with innovative companies capable of delivering a connected, individualised experience. This finding is consistent with Guede and Filipe (2019), who argue that digital interactions, websites, apps, chatbots, are now a major lever for relationship quality and brand loyalty. One participant testifies: ―When the app anticipates my needs, I feel the agency is really cutting-edge‖ (P29, 30). Similarly, Ku (2023) shows that anthropomorphic chatbots enhance trust and the sensory perception of the brand, thereby improving the overall experience. In sectors such as airlines and hospitality, several studies (Pelet et al., 2019; Zaoui et al., 2024) highlight similar benefits: higher satisfaction, optimised journeys, stronger personalisation and the elicitation of positive emotions during AI interactions. These observations match participants’ comments that ―when AI really simplifies booking, you remember the brand more‖ (P21, 50). Together, the results confirm that, when used thoughtfully and in a user-centred way, AI is a powerful lever for enhancing the customer–brand relationship. They are fully consistent with UTAUT, in which performance expectancy, trust and usage experience strongly influence behavioural intentions (Venkatesh et al., 2003). Thus, AI is not limited to facilitating transactions: it helps create a positive, personalised and emotionally engaging experience capable of sustainably reinforcing loyalty and brand equity. Conclusion This study set out to explore how consumers perceive and accept artificial intelligence (AI) in tourism services, with a focus on travel agencies. The findings highlight five core dimensions: attraction to the efficiency and practicality of AI tools, the perception of AI as a complement rather than a substitute, conditions for a positive experience, generational and social variations in AI perceptions, and the influence of AI on brand relationships and overall experience. Theoretical contributions This research enriches the technology-acceptance literature in the context of AI-enabled tourism services. The results show that Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17812551 197 perceived usefulness, performance expectancy, ease of use and effort expectancy remain structuring determinants, but are not sufficient to capture the actual experience without integrating human–AI complementarity in high-contact services. The study proposes a holistic framework of AI experience that articulates functional (efficiency, time saving, personalisation), emotional (comfort, trust, anxiety), relational (need for human contact, perceived professionalism) and social dimensions (generational variations, digital literacy), thereby moving beyond a sole focus on usage intention. It also underlines the central role of trust and perceived control (ability to modify/ignore recommendations, escalate to a human) as mechanisms linking perceived AI quality to satisfaction, reassurance and post-experience intentions (revisit, recommendation), while contextualising these mechanisms in the still under-researched setting of travel agencies. Managerial contributions The findings invite tourism managers to conceive AI not as a mere automation tool but as a co-pilot of the customer relationship, responsible for streamlining pre-selection (search, filtering, comparison) and information, while leaving complex, high-stakes or emotionally charged situations to human advisors. In this perspective, the most effective systems combine: careful ergonomics (readable interfaces, short journeys, contextual assistance), high information quality (up-to-date data, relevant recommendations, transparency about criteria used) and a strong sense of customer control (preference settings, ability to correct or ignore suggestions, explicit ―talk to an agent‖ button). Transparency about AI’s exact role, fine-tuned notification management (frequency, relevance, opt-out options) and clear explanations of customer benefits help strengthen trust, perceived innovativeness and, ultimately, brand loyalty. Operationally, this implies orchestrating truly hybrid journeys (AI to inspire, filter and simulate scenarios; humans to advise, co-construct itineraries and secure decisions), training staff in AI co-production (taking over at the right time, explaining system suggestions, detecting irritants) and segmenting service offers according to digital literacy, relational expectations and trip type (e.g. ―assisted self-service‖ modes for autonomous customers vs. ―guided advice‖ modes for those seeking more support). Finally, managers should define specific performance indicators (self-service resolution rate, handling time, post-AI-interaction NPS, AI-to-human transfer rate, customer-lifetime-value evolution) and implement data governance (quality, protection, explainability) to maximise efficiency gains without degrading perceived relationship quality or long-term trust. Limitations and avenues for future research Grounded in interviews, this study offers an in-depth but not statistically generalisable understanding, based on a sample that is relatively digitally literate and in which cultural factors are only implicitly addressed. Future research could test extended models (TAM/UTAUT plus trust/control/complementarity) on larger and more diverse samples, compare socio-demographic and cultural segments (generations, countries, literacy levels), and track over time the impact of AI on trust, loyalty and channel migration through longitudinal designs. Mixed approaches could also be mobilised (usage diaries, in-situ observations, A/B testing of hybrid journeys, experiments on levels of autonomy or anthropomorphism) to explore dimensions still under-represented in tourism-acceptance models, such as perceptions of algorithmic ethics and fairness, privacy protection, information fatigue and the management of emotions in uncertain situations. References 1. 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