Generative AI as a tourism actor: Reconceptualising experience co-creation, destination governance and responsible innovation in the synthetic experience economy

Christou, Evangelos; Fotiadis, Anestis; Giannopoulos, Antonios Article — Published Version Generative AI as a tourism actor: Reconceptualising experience cocreation, destination governance and responsible innovation in the synthetic experience economy Journal of Tourism, Heritage & Services Marketing Suggested Citation: Christou, Evangelos; Fotiadis, Anestis; Giannopoulos, Antonios (2025) : Generative AI as a tourism actor: Reconceptualising experience co-creation, destination governance and responsible innovation in the synthetic experience economy, Journal of Tourism, Heritage & Services Marketing, ISSN 2529-1947, International Hellenic University, Thessaloniki, Vol. 11, Iss. 2, pp. 16-41, https://doi.org/10.5281/zenodo.16562068 , https://zenodo.org/records/16562068 This Version is available at: https://hdl.handle.net/10419/322477 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Except otherwise noted, this work is licensed under https://creativecommons.org/licenses/by-nc-nd/4.0 Generative AI as a tourism actor: Reconceptualising experience co-creation, destination governance and responsible innovation in the synthetic experience economy Evangelos Christou International Hellenic University, Greece Anestis Fotiadis Zayed University, United Arab Emirates Antonios Giannopoulos International Hellenic University, Greece Abstract: Purpose: This conceptual study examines how Generative Artificial Intelligence (GenAI) reshapes value co creation, destination governance, and responsible innovation in tourism. It seeks to reposition GenAI from a backstage tool to a tourism actor and to present the Synthetic Experience System, a triadic framework connecting Tourist, GenAI, and Place/Community through data, content, and emotion layers. Methods: The paper follows an integrative theory building approach. It abductively synthesises tourism literature, information systems, marketing, psychology and ethics to surface recurring constructs, situates them within the service dominant logic and the actor–network theory, and iteratively refines a model through comparison of GenAI applications focusing on responsible research and innovation. Results: Analysis reveals three continuous co creation loops that circulate agency among actors and four boundary conditions—authenticity, bias, sustainability, privacy—that determine system viability. The Synthetic Experience System clarifies where value emerges, identifies points of potential value co destruction, and yields fifteen research propositions spanning tourist cognition, firm capabilities, destination policy, and planetary carbon limits. Implications: The framework provides a roadmap for destination management organisations, platform designers, and regulators to audit algorithms, design participatory prompts, and adopt carbon aware deployment. By naming actors, layers, and boundaries, the study offers a shared vocabulary that can anchor empirical investigations and stimulate cross disciplinary citations in tourism, information systems, and sustainability research. Keywords: generative artificial intelligence, synthetic tourism, experience co-creation, service-dominant logic, actor–network theory, algorithmic governance, responsible research, responsible innovation JEL Classification: L83, O33, L86, M31 Citation: Christou, E., Fotiadis, A., & Giannopoulos, A. (2025). Generative AI as a tourism actor: Reconceptualising experience co-creation, destination governance and responsible innovation in the synthetic experience economy. Journal of Tourism, Heritage & Services Marketing, 11(2), 16-41. http://doi.org/10.5281/zenodo.16562068 Biographical note: Evangelos Christou is a Professor of Tourism Marketing at International Hellenic University, Thessaloniki, Greece. Anestis Fotiadis is a Professor at Zayed University, Abu Dhabi, UAE (Anestis.Foti[email protected].ae). Antonios Giannopoulos is an Assistant Professor of Services Marketing & Management in Hospitality & Tourism at International Hellenic University, Thessaloniki, Greece ([email protected]). Corresponding author: Evangelos Christou ([email protected]) 1 INTRODUCTION: FROM DIGITAL TO SYNTHETIC TOURISM Over the past twenty years, tourism has experienced successive waves of digital disruption, each promising richer, more seamless experiences yet often delivering only incremental efficiency gains. In the pre Internet era, travelers relied on paper brochures, telephone based travel agents, and static guidebooks. The rise of online booking platforms in the early 2000s centralised inventory and payments, while “smart tourism” initiatives introduced sensor-based wayfinding and context-aware mobile guides (Buhalis & Amaranggana, 2015; Neuhofer, Buhalis & Ladkin, 2015). By the mid 2010s, rulebased chatbots appeared across hotel websites and destination GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 17 portals, automating routine tasks—room availability checks, weather forecasts, basic destination FAQs—via decision trees and scripted templates (Ivanov & Webster 2017). Although these systems delivered 24/7 responsiveness and reduced staffing costs, they left the core visitor experience untouched: exchanges remained transactional, constrained by fixed response matrices that could neither learn from interaction nor spark genuine dialogue. Tourism theory long mirrored this instrumental stance. Service-dominant logic, which underpins much tourism research, positioned technology as a passive operand resource within human-led value co-creation systems (Vargo & Lusch, 2004). Actor–network research likewise treated digital artefacts as inert intermediaries rather than agents with their own intentionality (Xiang et al., 2017). Creativity, anticipation, and adaptive behaviour were reserved for human hosts, while machines simply executed predefined instructions. A review of the most recent literature show the extent to which this perspective still persists. Meta-analyses have rigorously catalogued early GenAI experiments—user-acceptance surveys of ChatGPT (Zheng et al., 2024), studies on hallucination control in AI-generated destination content (Chen & Lee, 2024), and optimisation tactics for AI-driven recommender systems (García-Sánchez et al., 2025). Valuable as these assessments are, they remain descriptive and siloed. The field still lacks a cohesive, theory-driven roadmap that simultaneously folds GenAI into its dominant conceptual traditions, recognises technology as a genuine co-creative actor, and confronts the attendant ethical, regulatory, and environmental stakes. That gap has widened since the advent of foundation models— massively pre-trained neural networks such as GPT-3 (Brown et al., 2020) and BERT (Devlin et al., 2019). These models, along with multimodal successors like CLIP and DALL·E (Ramesh et al., 2021), are not shackled to rigid decision trees; they generate original outputs by sampling across vast probability spaces learned from text, images, and code. In tourism, foundation models introduce three intertwined dynamics. First, they confer creative autonomy, allowing, for example, AI to weave a traveller’s sustainability values and gastronomic preferences into a bespoke destination itinerary (e.g. the case of Barcelona as found in the literature) that feels curated rather than computed (Sigala, 2019; Tussyadiah & Miller, 2021). Second, their seamless multi-modality means a single prompt can return, for example, an English-Indonesian myth about a Balinese temple, overlay hidden carvings in augmented reality, and stream an evocative soundscape that conjures the site’s ritual ambience (Gretzel et al., 2022; Neuhofer et al., 2015). Third, the models enable adaptive real-time personalisation: live weather feeds, traffic data, and even biometric signals can trigger on-the-fly itinerary adjustments—rerouting a hiker to a shaded lookout when cortisol levels spike or shifting a museum visit indoors when rain approaches (Rantala, 2023; Xiang & Fuchs, 2017). Collectively, these capabilities inaugurate what we call synthetic tourism, an ecosystem in which GenAI migrates from backstage tool to distributed, co-creating actor. Acknowledging this migration forces a triple conceptual pivot. Service-dominant logic must evolve to grant non-human intelligences partial agency in the ‘choreography’ of value-creation, echoing the collaborative ethos championed by Vargo & Lusch (2017) and foreshadowed by Prahalad & Ramaswamy’s (2004) dialogues on co-creation. Actor–network maps must be redrawn to capture the fluid constellations of tourists, AI agents, local communities, and regulators, acknowledging—as Latour (2005) argued—that agency is a property of networks rather than individual nodes. Finally, Responsible Research & Innovation (European Commission, 2021) must anchor the conversation, bringing algorithmic bias, data-governance risk, and the carbon intensity of model training (Floridi et al., 2018) into the same frame as visitor delight and destination competitiveness. Crucially, the arguments advanced here are not confined to tourism studies. They take their cues from information-systems research on socio-technical assemblages (Orlikowski & Iacono, 2001), marketing science on customer-journey orchestration and experiential value (Lemon & Verhoef, 2016), psychological work on need satisfaction and emotion regulation (Deci & Ryan, 2000), responsible-AI literature on fairness and transparency (Floridi & Cowls, 2019), and innovation-management insights into open ecosystems and diffusion (Chesbrough, 2003). By weaving these strands together, the discussion positions generative tourism as a node in a much wider scientific conversation—one that invites citation and debate across multiple domains. Against this backdrop, the rest of the paper pursues five interlocking objectives. It first revisits the experience economy, service-dominant logic, actor–network theory, and Responsible Research and Innovation (RRI) to build a conceptual scaffold robust enough for synthetic tourism. It then surveys the rapidly growing body of GenAI applications across pre-trip planning, on-site augmentation, and back-office optimisation, distilling patterns and exposing blind spots. Building on these insights, it introduces the Synthetic Experience System, a triadic model that threads Tourist, GenAI, and Place/Community through data, content, and emotion layers while flagging boundary conditions such as authenticity, bias, energy use, and intellectual-property rights. A forward-looking research agenda follows, posing fifteen high-impact questions that range from micro-level issues of traveller wellbeing through meso-level firm capabilities and workforce futures to macro-level governance and the planetary sustainability of AI infrastructure. Finally, the paper translates theory into action, outlining AI-literacy toolkits for destination-management organisations, risk-audit protocols for platform operators, participatory-design guidelines for communities, and regulatory roadmaps consonant with the EU AI Act (European Commission, 2021) and UNWTO AI-Ethics Guidance (UNWTO, n.d.). By tracing the arc from static automation to dynamic co-creation—and by fusing tourism literature with insights from IS, marketing, psychology, ethics, and innovation studies—this study offers both an analytic lens and a practical roadmap for scholars and practitioners navigating the new era of Gen AI in tourism. 2 CONCEPTUAL FOUNDATIONS The argument that GenAΙ is becoming tourism actor rests on five strands of research that have shaped, and continue to reshape, tourism thought: the experience economy, value co creation, actor–network theory (ANT), algorithmic governance, and Responsible Research & Innovation (RRI). Rather than offering five parallel summaries, this section shows how each tradition moves the debate forward, where its 18 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos explanatory power now strains under GenAI’s weight, and what conceptual extensions—often drawn from adjacent disciplines—are needed for a next generation research agenda. 2.1. From staged experiences to programmable “synthetic moments” The experience economy thesis of Pine and Gilmore (1999) repositioned tourism as a theatre in which economic value stems from choreographed memories rather than from the exchange of physical goods. Subsequent literature deepened the metaphor: Carù and Cova (2003) unpacked multisensory immersion; Neuhofer, Buhalis and Ladkin (2014) traced the fusion of on site and digital touch points; Punpeng and Yodnane (2024) highlighted how guests step “on stage” as co producers; Coudounaris et al. (2025) investigated the influence of the ‘Big Five’ personality traits on memorable tourism experiences. Yet the stage remained squarely human directed—the guide, designer or host was presumed to hold the script, while technology merely extended the scenery. GenAI destabilises that hierarchy; for instance, when a visitor co-authors an origin myth for a Balinese temple with a large language model fine tuned on Javanese epics, the dramaturgy is no longer flesh and blood but a probabilistic architecture whose “creative well” is a trillion token corpus. Authenticity work—long analysed as the host–guest dialectic of existential meaning making (Wang, 1999)—is transformed into a triadic negotiation among host, guest and algorithmic weights. The stage becomes a cloud endpoint, and the “props” are real-time data feeds. This shift from staging singular events to programming synthetic moments has at least four conceptual implications. First, temporality compresses. Classic experience design assumed phases of anticipation, on site enactment and recollection (Tung & Ritchie, 2011); foundation models collapse those phases by iterating narratives in milliseconds. Second, authorship blurs. Interaction design research shows that users tend to over attribute agency to conversational agents (Nass & Moon 2000), complicating long standing tourism debates about authorship, interpretation and power. Third, value metrics diversify. Whereas satisfaction and memorability dominated the experience economy toolkit, Human-Computer Interaction (HCI) research now calls for tracking emotional granularity, arousal curves and even eudaimonic outcomes such as meaning and self-growth (Hassenzahl, 2010; McCarthy & Wright, 2004). Fourth, the politics of curation intensify. Algorithm studies literature warns that generative models may reproduce hegemonic narratives or hallucinate culturally insensitive content (Bender et al. 2021), raising new stakes for destinations seeking to safeguard intangible heritage. Critical tourism researchers have begun to sense these tremors. Guttentag (2022) argues that VR and AI are moving experiences from place based to code based, decoupling value from physical proximity. Gavalas et al. (2024) and Halder et al. (2024) show how algorithmic itinerary builders already shape expectations before travellers arrive. Yet we still lack a framework clarifying how human intentionality and machine creativity intersect, and who is accountable when an autogenerated storyline misrepresents living culture. A way forward lies in bridging tourism with design oriented disciplines. Interaction design scholars propose “co experience prototyping” in which humans and AI iteratively adjust each other’s outputs in situ (Rezwana & Ford, 2025). HCI work on explainable AI (Abdul et al., 2018) offers methods for exposing a model’s narrative pathways, letting guides audit or override problematic arcs. Meanwhile, marketing science is experimenting with “algorithmic dramaturgy”—dynamic storytelling that adapts to biometric feedback (Privitera et al., 2025). Importing these insights would let tourism move from post-hoc evaluation to frontstage design of synthetic moments. Future research, then, should treat experience design as an unfolding conversation rather than a pre-written script. Longitudinal field experiments in living labs—where tourists, guides and GenAI agents co-construct storylines under controlled but naturalistic conditions—could reveal how agency, authorship and accountability shift over time (Wu et al., 2019). Agent-based simulations, grounded in empirical interaction data, can potentially assess the impact of subtle changes in prompts on a range of measures including satisfaction, learning, and cultural influence (Dwivedi et al., 2021). Finally, qualitative studies must critically analyse whose voices are amplified and whose are suppressed when models synthesise local knowledge at scale. Essentially, Pine and Gilmore's ‘theatre’ is still in progress; however, its backstage has been augmented with generative technologies that generate new dialogue during the play. Tourism scholars now face the dual challenge of theorising this programmable dramaturgy and of equipping practitioners with design principles that harness machine creativity without forfeiting cultural integrity. 2.2. Value co-creation and its darker twin Service-dominant logic (S-D Logic) reshaped how tourism scholars perceive value—from a static entity exchanged at market points to a dynamic phenomenon realised during consumption, where tourists actively combine their skills, knowledge, and situational contexts to create memorable experiences (Vargo & Lusch, 2008, 2016). This paradigm has notably illuminated diverse tourism practices, including crowdsourced route planning, peer-to-peer accommodation, and participatory cultural interpretations (Buhalis & Foerste, 2015; Prebensen, 2014; Shaw et al., 2011). However, the rise of GenAI, specifically through sophisticated foundation models, challenges and extends the theoretical foundations of S-D Logic beyond its original human-centric assumptions. These advanced models emerge as active operant resources rather than passive tools (Maglio & Spohrer, 2013), capable of autonomously integrating diverse data streams— such as real-time traffic conditions, user preferences, and cultural insights—to dynamically co-create highly personalised tourism experiences (Breidbach & Brodie, 2017). For instance, a large language model crafting a bespoke gastronomic tour in Lisbon is effectively performing resource integration independently, functioning as a proactive agent rather than a reactive interface. Despite the importance highlighted by Breidbach and Maglio (2016), tourism research has only just begun to empirically investigate the transformative potential of AI as an active co-creator within SD Logic frameworks, representing a critical research gap in the current academic research. Concurrent with this is a growing need to examine further the lesser-studied but highly pertinent opposite of servicedominant logic—value co-destruction. Inconsistencies between stakeholder goals can strongly inhibit processes of value generation (Järvi et al., 2018). For GenAI, sources of goal inconsistencies can include entrenched algorithms containing harmful stereotypes or discriminatory conduct, favoring certain individuals or populations (Bolukbasi et al., 2016; Caliskan et al., 2017), as well as substantial environmental consequences related to power-hungry model GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 19 training at variance with sustainability goals of many destinations (Luccioni et al., 2023; Strubell et al., 2019). Additionally, Paluch and Wünderlich (2016) highlight how technology-enabled service failures fueled by social media can generate widespread dissatisfaction and lead to substantial reputation and operational damage. In tackling the intricacies of value co-creation and codestruction, transformative service research calls for inclusive and integrative metrics of well-being that consider all-around individual, societal, and ecological effects (Anderson et al., 2011). Empirical research using longitudinal "living labs," including the Helsinki Smart Tourism Lab (City of Helsinki, n.d.), offers a pragmatic research framework to enable realtime examination of dynamic interdependencies between tourists, residents, and GenAI technology. Embedding innovative agent-based simulations into the empirical research design allows for the creation of sophisticated modeling tools to identify key systemic tipping points, where algorithmic bias or sustainability compromises break equilibrium and cause value degradation, threatening the resilience of the entire tourism system (Gajdošík, 2022; Jorzik et al., 2024). In conclusion, integration of GenAI into Sustainable Developmental Learning requires more than superficial adjustments but a comprehensive and intrinsic conceptual shift that takes explicit note of algorithmic agency, ethical implications, inclusivity, and planetary boundaries (Yang & Lee, 2024). These issues must be addressed for tourism to promote genuinely transformative and equitable experiences and not perpetuate exploitative tendencies. 2.3. Actor–network theory: Reconfiguring the sociomaterial assemblage It is argued by actor–network theory (ANT) that agency arises from relational interaction among varied entities, including both non-human and human actors, which constantly reconfigure social realities (Latour, 2005). In the tourism literature, ANT has been used extensively to show how tourism experiences and governance are constructed together through material-discursive networks. For example, Franklin (2004) illustrated how Maasai guides, safari vehicles, and photographic equipment construct Kenya’s famous “looked-at landscape,” undermining simplistic opposition between passive environments and active observers. Similarly, Van der Duim (2007) convincingly argued that seemingly unromantic spreadsheet calculations and not charismatic talk or great speeches actually support the practice of Dutch destination management. Such detailed analyses however largely represent technologies as stable intermediaries—entities that enable relationships rather than being active agents constantly remapping them (Ren et al., 2012). On the contrary, Generative Artificial Intelligence (GenAI) severely undermines such stability. GPT-4 and similar foundation models are fluid "obligatory passage points" with parameter settings continuously being in transition, reacting to new prompts, software updates, and the iterative processes involved with reinforcement learning (Schneider et al., 2024). GenAI is not static like its entities but dynamically evolves and changes real-time, thus continuously reconfiguring tourism practice (Huang et al., 2025). For instance, a seemingly negligible adjustment in a developer's command for a prompt in San Francisco can quickly spread through cloud-based app programming interfaces and affect tourists' interactions with varied cultures in Bali, and quickly alter local traditions and interpretations. The speed with which such transformations occur requires new approaches (Thees et al., 2021). Digital multi-sited ethnography offers methodological approaches through which researchers can track changes between algorithms and streams of data, tracing interactions that stretch from code hubs located in North America, through servers located in East Asia, and down to tangible effects felt by smartphone users located in Indonesia (Pink et al., 2022). Trace ethnography offers another perspective through critical examination of log files and online traces and how minor changes within algorithms—such as changes to neural network weights—can have profound effects on ranking and visibility of culturally prominent sites (Geiger, 2017). Walkthrough methods also shed light on how interface configurations guide people through particular prompts or stories and thereby reveal underlying dynamics of cultural orientation and governance (Light et al., 2018). It is critical that there is an thorough elaboration of current literature on training data aimed at demystifying how such sources inform the perpetuation of impactful discourses. Such training sets as LAION-5B and Common Crawl inevitably represent Eurocentrism and sexism (Birhane & Prabhu, 2021) that can preserve inequalities upon their deployment within tourism recommendation platforms. It is also argued by Crawford and Paglen (2021) that such representation politics are embedded within the outputs of algorithms and thus perpetuate ongoing hierarchies. At the same time, Kitchin (2017) finds that such data infrastructures themselves expose geographical inequalities that lead to latency differentials and environmental externalities affecting non-Western digital landscapes. Future ANT-based investigations ought to carefully trace out the shifting sociotechnical arrangements of GenAI, which include not just conventional actors like tourists and tour guides but also allegedly minor actors such as GitHub issues and version updates (Andres et al., 2024; Li & Zhu, 2024). Such rich mappings clarify how meanings, values, and power dynamics within the tourism industry constantly crystallise and disintegrate within an increasingly synthetic reality. 2.4. Algorithmic governance: From platforms to foundation models Early explorations for algorithmic governance have described online platforms as authoritative actors that govern market processes and set up private regulatory models through complex software methods (Gillespie, 2014; Yeung, 2018). Studies on tourism have identified platforms such as TripAdvisor and Airbnb as performing subtle and considerable forms of governance. For instance, Smart Pricing on Airbnb subtly motivates hosts toward adopting normative behaviors that maximise platforms' revenues and shift their behaviors in practically unapparent ways (Bouchon & Rauscher, 2019). Similarly, TripAdvisor rankings from user-generated ratings computed through non-transparent algorithms constantly affect tourism mobility and guide local economic processes (Gretzel, 2011; Scott & Orlikowski, 2021). These initial understandings based on deterministic views on algorithms have inferred fixed regulatory models and pre-established outcomes, but these are hidden through a lack of transparency (Singh & Sibi, 2023). But the advent of foundation models undermines the earlier premise of deterministic stability. Unlike traditional platform algorithms, foundation models like GPT-4 generate outputs probabilistically and demonstrate dynamic responsiveness to fluctuations in data inputs, nuanced changes in prompts, and regular updates (Bender et al., 2021; Bommasani et al., 2021). Governance thus shifts from clearly 20 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos defined platform interfaces to complex "cloud stacks" that include data curation practices, holistic training methodologies, fine-tuning processes, and advanced prompt engineering cultures (Amoore, 2019). Algorithmic opacity levels in this setup are unprecedented: even within teams responsible for developing the models, it becomes harder to link specific generated outputs to specific rules or inputs (Burrell, 2016; Pasquale, 2015). This shift generates three significant implications. First, levels of opacity and ambiguity rise, calling for rigorous documentation practices like "model cards" (Mitchell et al., 2019) and "datasheets for datasets" (Gebru et al., 2021). Although they may be beneficial, stakeholders in the tourism industry rarely give these transparency tools priority, leaving significant knowledge gaps unresolved. Second, accountability is broadly dispersed across a range of stakeholders—including data providers, algorithm developers, platform intermediaries, destination managers, and tourists themselves—thus closely resonating with Ananny and Crawford's (2018) seminal framework of distributed moral responsibility. Third, geopolitical asymmetries are exacerbated, as artificial intelligence models trained on predominantly Anglo-American datasets reproduce and reinforce biased Global North perspectives, exacerbating "data colonialism" concerns and entrenching exploitative tourism dynamics in the Global South (Couldry & Mejias, 2019; Milan & Treré, 2019). To deal with such complexities on a comprehensive level, tourism research must prioritise a detailed exploration of model stacks and not just platforms and user interfaces. Advanced explainable AI methods—such as SHAP value assessment (Lundberg & Lee, 2017) and counterfactual fairness testing (Kusner et al., 2017)—can empirically identify which factors reliably affect algorithmic-generated itineraries and content. Furthermore, deployment of rigorous algorithmic audits—a growing trend among AI ethics research (Raji et al., 2020)—can systematically assess AI-generated tourism content on the basis of set international norms such as UNESCO heritage guidelines or environmental sustainability. Finally, scholarly research on regulation must carefully examine how the developing transnational governance environment—specifically risk-based requirements set out by the EU AI Act—meets with or contradicts international digital trade liberalisation guidelines managed by the World Trade Organisation (WTO) and thus impacts on the prospective governance regime for synthetic tourism. 2.5. Ethics in responsible research and innovation: A global perspective Responsible Research and Innovation (RRI) first emerged in tourism research to address major controversies surrounding overtourism, community acceptance, and social license to operate in the industry (Berselli et al.. 2022). However, the rapid development of GenAI significantly expands the ethical scope, raising key moral challenges across cultural, environmental, and systemic dimensions. First and foremost, GenAI poses serious issues concerning epistemic justice. The intrinsic discriminatory prejudice found with conventional recommender algorithms is further augmented to an unprecedented extent by the generative nature of GenAI. Large language models, for instance, have demonstrated a propensity toward generation of culturally insensitive or offensive content known as "hallucinations," which are directly and unrepresentatively generated from biased or poorly represented training materials (Bender et al., 2021; Birhane & Prabhu, 2021). These incidents risk perpetuating epistemic harm through spreading biased and largely harmful depictions of marginalised cultures, thereby reflecting the wider social prejudices embedded within algorithmic programming (Hagendorff, 2020; Noble, 2018). Second, the environmental externalities of GenAI present significant challenges to sustainable tourism. The training of complex transformer models, including different versions of GPT, can lead to carbon dioxide emissions equivalent to the amount emitted by multiple transatlantic flights (Strubell et al., 2019). Recent comprehensive lifecycle assessments also report that ongoing inference—the continual use and operational deployment of these models across diverse applications— constitutes an even larger contributor to environmental degradation than the training process itself (Luccioni et al., 2023; Patterson et al., 2021). Given the already sizeable carbon footprint of tourism, the unregulated use of GenAI can threaten the climate pledges of many destinations, thus creating tensions with set international sustainability norms (Gössling & Higham, 2020). The double use potential of GenAI requires stringent ethical oversight. While it has the potential to perpetuate harmful stereotypes, such generative technologies also show great potential for pushing forward on regenerative tourism's goals—designing tour programs aimed at minimizing environmental impacts, promoting off-season tourism, and enabling local ecosystem and site restoration and revival (Gallego & Font, 2020; Higgins-Desbiolles, 2018). Conventional Responsible Research and Innovation (RRI) frameworks—anticipate, reflect, engage, act—provide essential starting points for addressing these challenges, yet they require specific adjustments that are expressly tailored to the tourism industry (Stilgoe et al., 2013). Audits of algorithmic bias, for instance, must go beyond simple demographic parity to closely examine the cultural authenticity of AI-generated content against established international heritage norms, as outlined by UNESCO’s conventions on intangible heritage, for instance (UNESCO, 2024). Federated learning techniques may also facilitate ethical practices through enabling tourism destinations to internally develop and refine localised language models, thus safeguarding sensitive community knowledge and advancing Indigenous data sovereignty (Boscarino et al., 2022; Kairouz et al., 2021; Kukutai & Taylor, 2016). Lastly, while life-cycle carbon accounting has been advocated for in computer science literature (Schwartz et al., 2020), it seems critical that this practice becomes an integral aspect of procurement policies within destination management organisations. Successful execution of these frameworks requires proactive and interdisciplinary coordination. Computer science-derived tools, including exhaustive audits for explainability and biasmitigating strategies (Raji et al., 2020), can complement insights from environmental psychology on the eco-feedback mechanisms influencing behavioral changes among tourists (Schmuck & Vlek, 2003). Legal expertise will also be needed to maneuver through complex regulatory structures, including the risk-tiered mandates developed by the European Union’s AI Act, that intersect with digital services regulations upheld by the World Trade Organisation (Veale et al., 2021). Tourism literature will also be important, as it entails testing multiple interventions in varied real-world settings and assessing their impact in advancing distributive equity, safeguarding cultural identities and their integrity, and enhancing planetary sustainability. It is through such comprehensive and GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 21 collaborative approaches that GenAI can indeed be an ethical enabler and not a harmful force in pursuing sustainable tourism futures. 2.6. Synthesis: Taking stock and looking forward The contemporary conceptual themes that are being examined—staged experiences, value (co-)dynamics, actornetwork assemblages, governance by algorithms, and Responsible Research and Innovation (RRI)—offer a rich intellectual agenda that can build upon and advance synthetic tourism research. Each offers a distinctive analytical lexicon stressing distinctive yet complementary processes: performance (Pine & Gilmore, 1999), resource integration (Vargo & Lusch, 2008), network building (Latour, 2005), rule building (Gillespie, 2014), and ethical responsibilisation (Stilgoe et al., 2013). However, these theoretical models are presently receiving challenging empirical evidence from the developing capabilities of GenAI. Orlikowski’s (2007) early support for sociomaterial analysis has become increasingly pertinent: GenAI increasingly entangles code, capital, culture, and cognition such that any distinctions recede into arbitrary and politically freighted analytical outcomes (Kitchin, 2014; Leonardi, 2011). Hence, researchers must develop—rather than discard—their current theoretical models. A major development of these theoretical frameworks requires a revision of experiences as performances that are not only conducted by human planners but also by probabilistic, algorithmically-controlled agents. This requires reconsidering value as being both co-created, co-eroded, and co-managed by learning models (Paluch & Wünderlich, 2021). Additionally, it demands reimagining networks as dynamic, reactive configurations constantly reformed by real-time API interactions (Kitchin, 2017). Additionally, the process involves an exploration of algorithmic governance through the lens of stochastic, context-dependent models whose outputs challenge conventional deterministic governance strategies (Burrell, 2016). This study also widens ethical consideration from only localised tourism impacts to include the global data ecologies and systemic inequalities of digital infrastructures (Crawford, 2021). Navigating these complex dimensions calls for innovative methods through the combination of mixed-method research toolkits. AI-extended ethnography that combines standard participant observation with real-time algorithmic logging offers rich insights about how tourists and GenAI jointly generate meaning (Pink et al., 2022). Trace ethnography (Geiger, 2017) when combined with big-data ethnographic approaches (Varis & Hou, 2020) can trace subtle algorithmic adjustments from development environments through global data networks and highlight how small changes can have large consequences within local tourism environments. Agent-based simulation, a standard methodological strategy within the social sciences (Epstein, 1999; Gilbert & Troitzsch, 2005), can draw upon empirical prompt-response data in simulating critical points at which personalised pleasure suddenly shifts to confusion or dissonance. In addition, mixed-method explainability audits combining algorithmic interpretability methods like SHAP or LIME (Ribeiro et al., 2016) with phenomenological research (Weick, 1995) have the potential to deliver deeper insights not just on what GenAI suggests but on how travelers process these suggestions cognitively and affectively. In reality, answering these questions requires a joint effort combining several disciplines. Information systems provide tested and proven models related to socio-technical governance (Leonardi & Barley, 2008); marketing provides deep insights into customer journey complexity (Lemon & Verhoef, 2016; Machado et al., 2025); psychology-based frameworks provide further interpretations through emotion regulation theories (Gross, 2015); innovation research offers rich knowledge on technology adoption and participation within ecosystems (Chesbrough, 2003); and computer science ethics provides detailed methods for evaluating algorithmic bias (Jobin et al., 2019). Tourism scholars who draw upon these multi-discipline insights place their work on par with what George et al. (2016) define as “grand challenge” research—an inquiry combining academic rigor and societal relevance and transformative effect. In summary, by systematically expanding its theoretical worldview and adopting a pluralistic stance, tourism research can act as a vibrant laboratory for experiments seeking to answer our most burning technological questions: Who actually brings tourism experiences to life in an age distinguished by generative intelligence? How are cultural values continuously negotiated and contested through algorithms? And crucially, how can global sustainability be embedded within the very digital infrastructures now orchestrating global tourist mobility? 3 STATE OF THE ART MAPPING (2018 – 2025) Although research on generative or large-scale AI remains at a nascent stage compared to traditional recommender systems research, recent developments clearly demonstrate an emerging three-stream architecture reflecting GenAI's increasingly active role in tourism (illustrated in Figure 1). The first stream focuses on pre-trip decision-making, documenting the evolution from simple scripted FAQ bots towards sophisticated, large language model-driven itinerary "coauthors." These AI-driven agents actively engage with travelers, shaping trip decisions through collaborative, conversational interactions. A second stream emphasises GenAI's real-time, in situ augmentation capabilities, highlighting multimodal models that power digital concierges, instantaneous translation services, and emotionally attuned augmented reality experiences. Here, GenAI directly impacts tourists' experiences by influencing their perceptions and engagement levels in an active way. The third stream, which is imperceptible to tourists, examines enhancements of backoffice processes, where generative technologies like GPTpowered demand forecasting and reinforcement learningbased staff scheduling actually improve tourism services in a stealthy way. This critical examination goes beyond a cursory chronicle, pointing out particular presumptions in the extant literature. Empirical research on a regular basis targets pilot projects based in data-rich, English-speaking urban areas, thus overlooking relevant rural and Global South settings. Moreover, study designs currently emphasise positivist measures of performance, which ultimately degrades the prominence of ethnographic and longitudinal approaches that can shed light on critical issues surrounding power dynamics, authenticity and culture, and environmental outcomes. Conceptually, much research still represents GenAI as a passive tool, with little consideration for deeper notions of AI agency, possible co-destructive outcomes, and overarching planetary ethical questions. These largely neglected considerations highlight the importance of recognizing GenAI 22 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos as an active agent of tourism—a position that is core to the following conceptual debate. Figure 1. Emerging three-stream architecture reflecting GenAI's increasingly active role in tourism 3.1. Consumer decision making: From scripted chat bots to co creative itinerary engines Early empirical studies on AI in tourism decision-making often relegated conversational interfaces to the role of glorified FAQ repositories. Pillai and Sivathanu (2020) demonstrated that traveler acceptance of hotel chatbots was primarily motivated by perceived usefulness and ease of use, echoing Davis’s (1989) Technology Acceptance Model and subsequent refinements such as UTAUT (Venkatesh et al., 2003). These initial chatbots, limited by rigid decision trees, effectively conveyed information but failed to actively shape tourist preferences or foster deeper engagement (Ivanov & Webster, 2019a). A paradigm shift emerged as natural language processing (NLP) systems powered by deep learning gained traction. Lu, Cai and Gursoy (2019) expanded the acceptance framework by validating a "service robot integration willingness" scale among American travelers, revealing trust and hedonic enjoyment—not merely utility—as critical determinants of AI acceptance. This shift toward more emotionally and experientially anchored acceptance criteria aligns with findings by Ivanov and Webster (2017) within European hospitality contexts, indicating a transition from utilitarian interactions to richer, experiential co-design possibilities. The availability of publicly accessible foundation models has greatly accelerated this development. Ivasciuc, Candrea, and Ispas (2025) together with Ghesh, Alexander, and Davis (2024) compared tour itineraries generated by GPT with those composed manually. Their study reveals a general tendency towards AI recommendations despite instances of factual errors and logistical errors, the latter corresponding to wider critiques expressed by Bender et al. (2021) that view large language models as "stochastic parrots" that favor plausible reasoning over factuality and, by extension, highlight an inherent tension between creativity and truthfulness in content generated artificially. Adding to these intricacies, Florido-Benítez and del Alcázar Martínez (2024) used eye-tracking methods to show that recommendations made by generative language models (LLMs) significantly influence users' future online search behavior, thus subtly but conclusively limiting the range of options considered. Such algorithmic guidance finds echoes in the behavioral "nudge" theory of Thaler and Sunstein (2008), enabled by agents that function in a non-transparent and undisclosed environment. Despite these developments, current literature is generally focused on individual user engagement with limited examination of broader market impact. Fouad, Salem, and Fathy (2024) are a notable exception, utilizing simulation studies to illustrate how GPT-based platforms significantly increase visibility for urban destinations with rich data. At the same time, equity-oriented scholars like Benjamin (2019) and Noble (2018) caution that datasets may result in racialised or neo-colonial bias in AI-generated narratives in the tourism context; yet, full audits tailored to the tourism sector are extremely uncommon. Therefore, future studies need to go beyond single behavioral experiments to include tests at the market level as well as indepth investigations of training datasets (Yu & Meng, 2025). This will enable detailed analysis to ascertain if generative itinerary systems actually enhance the democratisation of tourism or only perpetuate prevailing structural inequalities in the name of personalisation. 3.2. On-site augmentation: Digital representations in real time, linguistic translation and emotional recognition In the field of travel-planning apps, little research has focused on how the application of GenAI (GenAI) affects the actual experience of traveling; however, the technologies do have great transformative potential (Zhu et al., 2024). On-site augmentation, encompassing digital avatars, real-time language translation, and affective sensing, radically redefines travelers' interactions with destinations, deepening immersion and recalibrating cultural encounters (Liu and & Hao, 2024). The debut of "digital humans"—photorealistic, voiceresponsive avatars—during the 2018 PyeongChang Winter Olympics offered a glimpse into this new experiential landscape (Sylaiou & Fidas, 2022). Korea’s MBC’s sister channel MBN AI launched an AI anchor, synthesizing highlights instantaneously, exemplified the immediacy and realism now achievable (Korea JoongAng Daily, 2020). Extending these capabilities into heritage contexts, Yovcheva, Buhalis, Gatzidis and van Elzakker (2014) observed a significant increase in visitor recall when historical sites were enhanced with mobile augmented reality (AR), confirming Reeves and Nass’s (1996) early theorisation on multisensory memory augmentation. Subsequent research has been divided into two prominent strands. The first involves interactive avatar concierges. For example, Velasco, Vargas and Petit (2024) demonstrated noteworthy rise in retail conversions through deploying a highly realistic AI sommelier in wineries. However, critical qualitative follow-up revealed visitors mistakenly attributing authority to avatars rather than human experts, resonating with the "media equation" effect—individuals responding socially and emotionally to media agents as if they were human (Nass & Moon, 2000). Moreover, the unsettling realism inherent in these avatars risks falling into Mori’s (1970) "uncanny valley," a phenomenon supported by recent empirical evidence, noting how hyperrealistic virtual guides evoked discomfort and skepticism among consumers (Thaler et al., 2021). This effect, extensively substantiated in psychological research, describes a phenomenon whereby agents that closely resemble humans—but possess subtle imperfections—trigger feelings of unease, distrust, or even revulsion (Mathur & Reichling, 2016). In the tourism context, recent empirical studies have identified this same pattern: tourists interacting with highly realistic virtual hosts or guides often report discomfort and On-site Augmentation (Digital Avatars, Real-time Translation) • Emotional sensing and biometrics • Privacy and ethical issues Back-Office Optimization (Staffing, Revenue, Forecasting) • GPT-enhanced forecasting • Reinforcement learning • Efficiency vs interpretability • Labor and surveillance issues Customer Decision-making (Pre-trip Planning) • Scripted FAQ bots as soft co-authors • Trust and hedonism asymmetry • Novelty vs accuracy • Algorithmic steering Generative AI as Active Tourism Actor Cross-cutting Themes: • Longitudinal blind spots • Methodological gaps • AI as co-creative actor • Ethical imperatives GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 23 suspicion, particularly when the agents’ movements or expressions fall just short of natural human behavior (Alipour et al., 2025). However, comprehensive large-N ethnographic studies remain scarce, leaving the nuanced interactive dynamics between visitors and avatars underexplored (Padricelli et al., 2021). In tourism, where visitor immersion and trust in the guide are critical (Sihombing et al., 2024), these uncanny valley effects are particularly problematic. It is therefore critical that destination managers and technologists find a balanced harmony between realism and subtle abstraction such that AI generative guides promote engagement without creating uneasy overhumanisation that undermines authenticity and detracts from tourists' experiences. The second refers to real-time linguistic mediation. Developments like OpenAI’s Whisper mark the dawn of an age of seamless translation with the potential to overcome conventional communication barriers. Graham and Roll’s (2023) research has already proven that Whisper’s performance can compare favorably with that of human interpreters in transactional conversations; there are still significant weaknesses, however, in its rendering of idiomatic expressions and culturally rich metaphors. In accordance with Cronin’s (2013) timely warning, machine translation is prone to trade-off core "contextual thickness," thus threatening insidious cultural nuances that are vital for authentic intercultural understanding. Moreover, as O’Hagan (2016) contends, real-time machine translation risks creating a "false fluency," which can mask underlying power imbalances in cultural exchange—a moral hazard that is largely unaddressed across tourism literature. A nascent domain, affective sensing, leverages advances in vision-language-action systems to adapt stories in real-time based on affective indicators. Expanding on Picard's (1997) initial work on affective computing, recent practical applications show great promise. Liu and Shin (2025, in press) introduce an augmented reality prototype that adapts story tempo to fit with tourists' face expressions and thus effectively encourages affective engagement. However, widespread biometric monitoring caused by this technology poses real privacy concerns. Drawing on Solove's (2006) taxonomy of privacy harms, we can see these technologies pose serious risks around "aggregation" (capturing large-scale personal emotional information) and "exclusion" (changing access on basis of affective state); however, strong debate on regulatory control is noticeably absent within tourism research (Yeung et al., 2019). Within these different fields of augmentation two main knowledge gaps arise. Firstly, there is a significant gap in longitudinal research on the long-term cultural implications of narratives constructed through artificial intelligence. Do repeated exposures to algorithmically curated content promote cultural literacy, or does it constrain interpretative skills by creating echo chambers through algorithms (Zuboff, 2019; Milan & Treré, 2019)? Secondly, environmental implications are too commonly ignored; as edge inference technologies lower latency, they migrate computational complexity to consumer devices and thus consume more power and conflict with sustainability goals related to sustainable tourism (Luccioni et al., 2023; Morley, Widdicks, & Hazas, 2021). Explaining these complexities involves using multi-method research designs that include ethnographic inquiry, assessments of energy footprints, and translation critical analyses, thus exhaustively studying how GenAI redefines the concept of "being there" as digitally co-crafted cultural interactions. 3.3. Back-office operations optimisation: Personnel management, revenue optimisation, and demand forecasting While avatars and chatbots mesmerise popular imagination in the context of tourism business, it is arguable that more profound operational changes wrought by GenAI merely occur at more subtle levels. The earliest approaches toward automating work initially concentrated primarily on physical and tangible service robots. Ivanov and Webster (2017) had solid evidence demonstrating economic viability through the use of relay robots as replacements for hotel night-shift receptionists, especially for geographies that continue to face ongoing understaffing. Ivanov (2020) also drew similar conclusions on cost savings while also discussing concerns about a possible long-term dependence on robotic solutions. However, as attention moved from physical to virtual systems in the metaverse era (Assiouras et al., 2024a, 2024b; Sousa et al., 2024), algorithmic management became the prime driving force for improved productivity. Early algorithmic methods, including dynamic pricing models, achieved quantifiable but limited quality improvement. Significant room yield gains were experienced due to fairly simple AI-based pricing processes by Guo et al. (2023). Nevertheless, the use of complex reinforcement learning methods based on fundamental revenue management paradigms conceived by Talluri and van Ryzin (2004) has significantly supplemented these benefits. New evidence by Tuncay et al. (2023) suggests that reinforcement learning models can currently improve on complex pricing schemes on their own above and beyond static or conventional heuristic models developed with this aim by a wide margin but at a cost of diminished transparency and interpretability (Molnar, 2022). Demand forecasting is an ever more dynamic domain in the context of AI-driven innovation. Conventional ARIMA methods had been dominant until the revolutionary introduction of attention-based neural networks directly inspired by the landmark Transformer architecture introduced by Vaswani et al. (2017). In their research, Law et al. (2019) employed such neural networks to predict tourist arrivals in Macau, and they reported considerable improvements in terms of accuracy by decreasing mean absolute percentage errors by about 12%. Meanwhile, Menzel et al. (2022) demonstrated the considerable predictive power involved in leveraging Google Trends coupled with gradient boosting algorithms, uncovering COVID-19-related demand shocks weeks in advance of official public health announcements. The Long Short Term Memory deep learning artificial neural network by Polyzos Samitas and Spyridou (2020) stressed the need for adaptive forecasting models proficient in identifying and responding to "black swan" events, a persistent weakness identified in conventional methodologies (Taleb, 2007). Despite such advances, there exist serious operational and ethical issues. The use of energy, hitherto ignored, has quickly grown as a top priority. Strubell, Ganesh, and McCallum (2019) estimated that training a single Transformer model generates carbon footprint equaling several transatlantic flights. Additionally, Patterson et al. (2021) highlighted the unexpected spike in energy demands at inference phases of operational deployment and thus posed challenges for tourism organisations regarding their sustainability and net-zero carbon initiatives (Gössling & Higham, 2020). 30 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos deployments might reshape employees' skill trajectories, emphasizing models of human-AI complementarity rather than substitution (Davenport & Ronanki, 2018). RP6. What organisational capabilities and AI literacies do tourism enterprises need to manage generative systems ethically and effectively? While technological adoption frameworks emphasise readiness and capability building, managing GenAI ethically introduces additional complexity, requiring nuanced understanding beyond traditional digital competencies (Leonardi & Neeley, 2022; Christou et al., 2025). Recent literature underscores the urgency of developing organisational literacies that encompass not only technical proficiency but also ethical foresight, interpretive capabilities, and robust governance practices (Westerman et al., 2014). Employing mixed-method organisational studies combining qualitative interviews and quantitative surveys can systematically uncover the critical capabilities needed to ethically operationalise GenAI, thereby enabling proactive, responsible innovation within tourism firms (Baum, 2015; Morley et al., 2021). RP7. In what ways does GenAI-driven automation influence workforce perceptions of job satisfaction, precarity, and professional identity? This question directly follows from the growing discussion on the psychosocial implications of workplace automation, including job insecurity, diminished autonomy, and changing professional identities (Brynjolfsson & Mitchell, 2017; Frey & Osborne, 2017). Employees in the tourism industry are particularly vulnerable to these changes because of the precarious work arrangements and the high emotional labor demands typical for the sector (Hochschild, 2012). Ethnographic and mixed-method investigations of the workforce have the potential to clearly explain how workers perceive and cope with artificial intelligence-driven automation, thus offering important insights into sustaining healthy work environments and retaining dignity and meaningful work amidst technological progress. RP8. How do tourists perceive firm credibility and trust when experiences are co-created by GenAI versus human staff? This field of research study examines the implications of incorporating GenAI on perceived authenticity and trust of consumers—factors pivotal in service-intensive tourist settings (Lu et al., 2019; Mayer et al., 1995; Nechoud et al., 2021). Since there is extensive literature portraying the high level of consumer receptivity to service authenticity and trustbased relationships (Grayson & Martinec, 2004), experimental scenario approaches along with systematic survey research are necessary to carefully test tourist consumers' reactions to situations where they are working alongside AI. 5.3. Macro-level: Destination governance and policy framework More broadly, the research agenda directly addresses governance and policy issues stemming from the integration of GenAI into destination-level strategies. These research propositions arise from perceived shortcomings in understanding the complex interplay between technological innovation, cultural representation, visitor management, and ethics across different scales of destinations. RP9. How can destination management organisations (DMOs) establish effective governance mechanisms for AIgenerated content and cultural representation? This question arises from the critical understanding that DMOs traditionally serve as custodians of cultural authenticity and image management (Bui et al., 2024) yet currently lack adequate governance models for AI-generated outputs. Prior research underscores that ungoverned AI-generated content may risk trivializing or misrepresenting local cultures, causing long-term reputational damage (Zhu et al., 2024; Luong, 2024; Lan et al., 2025). Employing comparative case studies and detailed policy document analysis, research here aims to identify best practices and critical governance frameworks that DMOs can adopt to manage AI-mediated cultural representations responsibly and ethically, thereby preserving destination authenticity and integrity (Dangi & Jamal, 2016). RP10. What policy frameworks are required to mitigate algorithmic bias and discrimination embedded in GenAI applications at a destination level? The necessity for this inquiry originates from established concerns surrounding systemic biases embedded in algorithmic decision-making processes, notably influencing destination branding (Giannopoulos et al., 2021; Csapó & Kusumaningrum, 2025) and visitor experiences through discriminatory practices (Noble, 2018; O'Neil, 2016). Given documented cases of algorithmic bias influencing tourism marketing and destination portrayal, critical policy analysis and rigorous algorithmic audits become essential tools for proactively identifying, mitigating, and preventing biases that perpetuate racial, cultural, or socio-economic inequities. Research outcomes would ideally inform concrete policy frameworks to foster equitable and inclusive AI governance practices at the destination scale (Eubanks, 2018). RP11. How can destinations responsibly use GenAI to balance visitor flow and successfully mitigate the impacts of badlymanaged tourism? This question draws on extensive literature across academics on overtourism and the need for sustainable approaches to managing tourist numbers (McKercher & Prideaux, 2014). The predictive potential of GenAI can enable dynamic and realtime management of tourist movement, thus mitigating overcrowding and environmental degradation concerns (Bollenbach et al., 2024; Viñals et al., 2024). However, the ethical use of the tools requires advanced understanding of visitor behavior patterns (Misirlis et al., 2021), related tradeoffs, and potential unintended effects (OECD, 2024). Agentbased simulation and scenario modeling offer solid tools for evaluating and projecting the efficacy of GenAI in real-life visitor management situations (Bollenbach et al., 2022), enabling DMOs to create forward-thinking, data-led strategies balancing the benefits of tourism and the demands of sustainable management (Koens et al., 2018). RP12. What regulatory steps should be taken to protect the privacy and autonomy of tourists in artificial intelligenceenhanced environments? The relevance of the study arises from mounting concerns of privacy erosion and violations of personal liberties due to large-scale data gathering and artificial intelligence monitoring in the tourism industry (Nissenbaum, 2010; Zuboff, 2019). It is necessary to use privacy impact assessments in conjunction with participatory governance models to maintain tourist control of personal data and informed consent. An in-depth GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 31 analysis of such regulatory measures can shed light on the way in which tourist areas can efficiently balance privacy protection, visitor agency, and technology, thus enhancing ethical regulation of tourism in the modern technology-driven environment (Wachter et al., 2017). 5.4. Global framework: Carbon emissions, sustainable and regenerative tourism The planetary agenda clearly outlines the global environmental impacts related to the integration of GenAI into tourism systems, in congruence with sustainability science and the exploration of planetary boundaries. Every research agenda arises from an increased awareness of the resource-demanding nature of GenAI and its potential ability to foster regenerative approaches in the tourism industry. RP13. What are the environmental implications of GenAI adoption in tourism and to what extent can carbon-conscious AI practices be applied appropriately? The high-energy requirements for training large-scale artificial intelligence models (Strubell et al., 2019) highlight an important gap in the technology implementation dimensions of the tourism industry's sustainability. Lifecycle analysis (LCA) approaches, as explained by Patterson et al. (2021), provide an organised method for the in-depth consideration of the environmental footprints across the whole lifecycle of AI— covering data processing, model training, inference, and ultimate disposal. Bridging this important gap will provide stakeholders in the travel industry with essential information regarding environmental trade-offs, thus enabling the use of carbon-minimised practices, such as the use of renewable power-based data centers, model architecture minimisation, and the use of localised data hosting mechanisms to limit the production of carbon emissions (Hilty & Aebischer, 2015; Jones, 2018). RP14. Can AI-generated scenarios effectively facilitate regenerative tourism practices, enhancing environmental rejuvenation at destinations? This question aligns with the growing promotion of regenerative tourism that aims not merely to reduce negative impacts but actively contribute to ecological restoration and community wellbeing (Gibbons, 2020; Reed, 2007). In light of tourism's past environmental impacts, it is imperative to examine how AI-driven predictive scenarios can contribute practically towards advancing ecological restoration strategies. The application of participatory action research approaches, combined with ecological scenario modeling, can empirically determine whether AI can effectively support destination stakeholders in realizing restorative results, such as biodiversity recovery, habitat conservation, and socioecological resilience enhancement, and consequently address essential knowledge gaps regarding the practical applications of regenerative concepts (Sharpley, 2020; Lariza CorralGonzalez et al., 2023). RP15. Which system changes are needed in tourism value chains to formally include planetary boundaries in innovation strategies led by generative artificial intelligence? The planetary boundaries approach, as conceptualised by Rockström et al. (2009), provides a cogent rationale for the deliberate integration of ecological limits into innovation planning. This component of critical research explicitly addresses the urgent need for reframing tourism development around sustainable ecological standards to ensure that innovations do not inadvertently exacerbate existing environmental issues. The use of systems thinking, coupled with Delphi analysis among sustainability experts, allows for the systematic specification of needed changes across different stages of tourism value chains, including supply chains, resource extraction, transportation, infrastructure construction, and waste disposal. Addressing these systemic changes makes it possible to ensure that the integration of GenAI supports global sustainability goals instead of undermining them (Steffen et al., 2015; Whiteman et al., 2013). 5.5. Expanding horizons: Developing an inclusive framework to explore GenAI in the tourism sector The systematic research approach incorporating micro, meso, macro, and planetary levels of analysis is the best way to answer the theoretical gap present in the latest scholarly debate surrounding the use of GenAI, expanding previous research on the tourism ecosystem including the micro, meso, and macrolevels (Giannopoulos et al., 2020, 2021). To the best of our knowledge, the literature is dominated by systematic reviews aggregating early empirical results or in-depth studies of particular generative tools. Academic circles are interested in the use of particular GenAI tools like ChatGPT (Shawal et al., 2017), dealing with the issue of content hallucination (Christensen et al., 2025), and improving recommender algorithms to gain superior accuracy (Kzaz et al., 2025). Although the existing literature is useful, it largely provides descriptive information that is limited to particular contexts and somewhat isolated from larger theoretical constructs. As a result, it fails to present a comprehensive examination of the far-reaching implications of GenAI across the complex sociotechnical systems of tourism (Dwivedi et al., 2023). In contrast, the newly introduced research agenda skillfully pushes these bounded horizons by theoretically assimilating GenAI into well-established academic traditions. By rigorously applying GenAI to exemplary tourism theories like S-D Logic (Vargo & Lusch, 2016), experience economy theory (Pine & Gilmore, 1999), value co-creation and codestruction frameworks (Echeverri & Skålén, 2011; Järvi et al., 2018), and theoretical investigations of algorithmic governance (Yeung et al., 2019), this agenda greatly expands its theoretical horizons. Significantly, in doing so, this approach reconceptualises GenAI as an active participant endowed with distributed agency in relational encounters, instead of reducing it to the level of being passive technology. Doing so undermines anthropocentric assumptions and enriches theoretical discussions concerning power arrangements in tourism studies (Leonardi & Barley, 2008; Orlikowski & Scott, 2008). Additionally, by representing GenAI as an interactive partner sometimes substituting for human agents and not only augmenting the operations of the latter, this approach encourages in-depth exploration of potential social-cultural and organisational changes. It also stimulates research debate into rethinking professional identities, reassessing connotations of authenticity and representation of cultures, and the ethical pitfalls of raised autonomy in AI-mediated encounters (Benjamin, 2019; Floridi & Cowls, 2019). This change calls for exhaustive empirical investigation into the power dynamics arising from artificial intelligence's inherent capabilities, which reshape the relationships between tourists, service industries, and host communities. This presages the pressing need to safeguard human agency and dignity in algorithmically intermediated tourist experiences (Andrejevic & Selwyn, 2020; Picard, 1997). Secondly, the framework presented herein pronounces a clear synthesis of 32 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos ethical, regulatory, and environmental concerns. It notably emphasises anticipatory governance regimes that entail rigorous privacy-by-design approaches (Acquisti et al., 2015), extensive audits for uncovering algorithmic biases (Buolamwini & Gebru, 2018), and the creation of technologies with regards to carbon emissions, aligned with global climate pledges (Patterson et al., 2021; Gössling et al., 2021). Placing tourism research within the larger context of planetary boundaries (Rockström et al., 2009; Steffen et al., 2015) highlights the need for sustainable innovation strategies that consider the sometimes neglected environmental consequences of GenAI. Addressing the social and environmental effects of artificial intelligence in tourism effectively requires ongoing interdisciplinary cooperation among computer science researchers, sustainability scholars, and policy researchers (Jobin et al., 2019). Finally, the holistic research framework not only fills a relevant conceptual lacuna but also provides a sound theoretical and ethical underpinning for ensuing scholarly investigation. It calls for an advanced, cross-disciplinary approach to research that critically explores the transformative capabilities of GenAI while concurrently dealing with related ethical, cultural, and environmental concerns. Participation in this ambitious framework allows tourism scholars to make meaningful contributions to the broader scholarly conversation, thus facilitating an inclusive, equitable, and truly sustainable advance in GenAI. 6 MANAGEMENT AND POLICY IMPLICATIONS To successfully govern the rapidly evolving environment of GenAI, Destination Management Organisations (DMOs), tourism platforms, and policymakers require holistic and practical toolkits and regimes of governance appropriate to address the various ethical, legislative, and operational dilemmas presented by GenAI. Lacking an underpinning for systemic readiness, stakeholders risk exacerbating alreadyexisting inequalities, violating privacy, and entrenching cultural stereotypes typical of AI deployments (Dwivedi et al., 2023). 6.1. Toolkit for DMOs and Platforms AI literacy Based on the values of AI literacy, Destination Management Organisations (DMOs) and their related platforms should actively enlighten stakeholders—executive decision-makers, front-line employees, and tourists—about the potentials, limitations, ethical aspects, and a range of effects of GenAI on cultural authenticity and the visitor experience (Buhalis & Sinarta, 2019; Long & Magerko, 2020). Based on Westerman, Bonnet, and McAfee (2014), digital literacy is more than just basic technical skills, but includes higher-level interpretive, ethical, and analytical skills. As such, formal AI literacy training must go beyond technical descriptions to include indepth understanding of algorithmic bias, possible privacy vulnerabilities, and the dangers of AI-driven cultural uniformity and standardisation (Benjamin, 2019; Noble, 2018; Buolamwini & Gebru, 2018). Prioritizing critical AI literacy enables stakeholders in the tourism industry to foresee and successfully resolve ethical challenges, increase transparency, and facilitate fair human-AI interactions. Risk auditing Given the well-documented issues of algorithmic opacity, inherent biases, and profound privacy vulnerabilities created by the use of artificial intelligence (Pasquale, 2015; O'Neil, 2016; Yeung et al., 2019), the implementation of robust risk auditing procedures is essential. Destination Management Organisations (DMOs) must integrate systematic algorithmic audits with transparency reporting systems (Raji et al., 2020), thereby enabling stakeholders to test algorithmic fairness, cultural sensitivity, and operational transparency stringently. Evidence-based research in information systems provides established frameworks to recognise algorithmic bias, quantify privacy risks, and assess ethical implications to enhance the successful application of AI regulation (Floridi & Cowls, 2019; Mittelstadt et al., 2016). Applying these frameworks facilitates the empowerment of stakeholders to preemptively address the harms caused by algorithms in accordance with ethical best practices. Participatory design Participatory design is therefore crucial for inclusive and culturally responsive deployment of GenAI (Sanders & Stappers, 2008). Destination management organisations and platforms need to actively engage a diverse range of stakeholder groups—including communities, cultural heritage representatives, and frontline service providers—in the codevelopment of AI prompts, culturally responsive narratives, and ethical governance structures. Such participatory approaches not only encourage stronger stakeholder buy-in but also reduce the risks of cultural erasure, digital colonialism, and exclusion of minority voices (Benjamin, 2019; Couldry & Mejias, 2019). Effective participatory design further adds to the legitimacy of the system and encourages sustainable cultural engagement. 6.2. Roadmap for regulators Privacy-by-design The regulators should embed strong privacy-by-design principles within legal policies to enable the timely implementation of privacy protection mechanisms in artificial intelligence systems without downgrading these concerns to secondary priority (Cavoukian, 2009; Nissenbaum, 2010). Mandatory end-to-end comprehensive privacy impact assessments (PIAs) should be implemented, underscoring the value of proactive identification and mitigation of potential privacy violations, risks of commodification of data, and vulnerabilities to emotional manipulation in algorithmic tourism situations (Acquisti et al., 2015; Solove, 2006). Proactive regulation fosters the independence, dignity, and trust of the tourist through the assurance of the adoption of AI in accordance with the current ethical norms in the tourism industry. Algorithmic transparency Governance frameworks should require high levels of transparency and accountability from tourism platforms using GenAI technology. Regulations should involve clear provisions for the aim of the objectives pursued by the AI, detailed descriptions of the training datasets used, the decision algorithms adopted, and recognition of the possibility of inherent bias and limitations (Pasquale, 2015; Mittelstadt et al., 2016; Jobin et al., 2019). Oversight bodies must promote the obligatory use of model cards and datasheets, which are standardised documentation templates (Mitchell et al., 2019; Gebru et al., 2021). The use of these tools greatly increases the level of stakeholder awareness, promotes proper public oversight, and encourages accountability in the complex sociotechnical arrangements relevant to tourism. GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 33 Together, the above policy and managerial interventions are the critical measures for successfully guiding GenAI in the tourism industry. These strategies provide Destination Management Organisations, platforms, and policymakers with not just clear and actionable toolkits but also ethical, culturally responsive, and sustainable advances, hence safeguarding the integrity of tourism in a future with GenAI. 7 CONCLUSION: TOWARD A RESPONSIBLE “SYNTHETIC EXPERIENCE ECONOMY” This study has established an informed and comprehensive foundation for understanding and ethically embracing the substantial impact of GenAI in the tourism industry in enabling the shift towards an ethical "Synthetic Experience Economy." Describing the role of GenAI not only as an inert technological facilitator or single operator, but also as an active participant and driver, the Synthetic Experience System (SES) problematises and expands established tourism paradigms generally based on experiential co-creation by humans (Pine & Gilmore, 1999; Vargo & Lusch, 2016). This shift highlights the enhanced complexity and agency of GenAI, calling for consideration of its potential to profoundly reshape interaction, narrative creation, and value exchange in tourism systems. The research agenda, developed through diverse analytical lenses—micro (tourist cognition and well-being), meso (organisational capabilities and labor processes), macro (governance and regulatory environments), and planetary (environmental consequences and regenerative approaches)— discloses considerable knowledge gaps alongside equally remarkable opportunities for intellectual advancement. Significantly, however, this agenda challenges and expands the service-dominant logic conceptual framework by positioning the role of GenAI as part of the co-creative activity of experience creation, as opposed to confining it to the role of passive tool or resource (Leonardi, 2011; Orlikowski & Scott, 2008; Vargo & Lusch, 2016). GenAI's integration into theoretical frameworks in tourism research not only strengthens existing models but also encourages the development of novel theoretical constructs able to adequately capture issues around non-human agency and ethical concerns. In addition, the inclusion of ethical, regulative, and environmental aspects in the envisioned framework identifies critical and commonly neglected issues like algorithmic bias, surveillance capitalism, data commodification, cultural representation, and sustainability (Couldry and Mejias, 2019; Noble, 2018; Zuboff, 2019;). By taking these key determinants into explicit consideration, the use of GenAI becomes linked to the broader sustainability goals of international agendas, including the Glasgow Declaration on Climate Action in Tourism (Gössling et al., 2021). Notably, the multidisciplinary framework combines theoretical coherence with empirical correctness and, as a result, enables the development of tourism science to be directed towards responsible innovation practices. The SES framework offers Destination Management Organisations (DMOs), tourism platforms, and regulatory bodies a comprehensive and actionable toolkit for responsible governance of GenAI. The recommendations outlined in this paper—including measures to promote AI literacy, pursue rigorous risk assessments, institute inclusive design processes, enact regulations grounded in privacy-by-design fundamentals, and guarantee practices facilitating algorithmic transparency—represent concrete and actionable measures to promote the ethical, culturally responsive, and environmentally sustainable use of generative technologies (Floridi & Cowls, 2019; Mitchell et al., 2019; Pasquale, 2015; Westerman et al., 2014). These steps are intended to preemptively mitigate risks of the misuse of personal data, homogenisation of cultural expressions, and exacerbation of global environmental problems, while also allowing tourism stakeholders to fully leverage the transformative potential of GenAI. In short, the path of a responsible synthetic experience economy requires continuous, collaborative, and multidisciplinary interaction among tourism experts, practitioners, policymakers, ethicists, technologists, and scholars of sustainability (Buhalis & Sinarta, 2019; Dwivedi et al., 2023; Jobin et al., 2019). The importance of transdisciplinarity and integrative approaches cannot be overemphasised in approaching the innovative possibilities and ethical objectives of GenAI. The SES approach specifically advocates these integrative approaches, understanding the tourism industry as a critical case for wider societal dialogue on technology stewardship, digital ethics, and sustainable innovation. It is only through such all-encompassing, cross-disciplinary collaborations that the tourism sector can properly and responsibly harness the immense potential of GenAI, and hence proactively mitigate its societal, ethical, and environmental impacts, thereby ensuring truly sustainable, equitable, and meaningful future advances in tourism. REFERENCES Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. S. (2018). Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–18). ACM. https://doi.org/10.1145/3173574.3174156 Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514. https://doi.org/10.1126/science.aaa1465 Alipour, A., Hartmann, T., & Alimardani, M. (2025). Would you rely on an eerie agent? A systematic review of the impact of the uncanny valley effect on trust in human-agent interaction. arXiv. https://doi.org/10.48550/arXiv.2505.05543 Amoore, L. (2019). Cloud ethics: Algorithms and the attributes of ourselves and others. Duke University Press. https://doi.org/10.1215/9781478009276 Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973– 989. https://doi.org/10.1177/1461444816676645 Anderson, L., Ostrom, A. L., & Bitner, M. J. (2011). Surrounded by Services: A New Lens for Examining the Influence of Services as Social Structures on Well-Being. Working Study, WP Carey School of Business, Arizona State University. Andreassen, T. W., Lervik-Olsen, L., Snyder, H., Van Riel, A. C., Sweeney, J. C., & Van Vaerenbergh, Y. (2018). Business model innovation and value-creation: The triadic way. Journal of Service Management, 29(5), 883-906. https://doi.org/10.1108/JOSM-05-2018-0125 Andrejevic, M., & Selwyn, N. (2020). Facial recognition technology in schools: Critical questions and concerns. Learning, Media and Technology, 45(2), 115–128. https://doi.org/10.1080/17439884.2020.1686014 Andres, J., Danta, C., Bianchi, A., Hong, S., Li, Z., Sandoval, E. B., Martin, C., & Cooper, N. (2024). Understanding and shaping 34 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos human–technology assemblages in the age of generative AI [Workshop]. arXiv. https://doi.org/10.48550/arXiv.2404.18405 Assiouras, I., Giannopoulos, A., Mavragani, E., & Buhalis, D. (2024a). Virtual reality and mental imagery towards travel inspiration and visit intention. International Journal of Tourism Research, 26(2), e2646. https://doi.org/10.1002/jtr.2646 Assiouras, I., Giannopoulos, A., Mavragani, E., & Buhalis, D. (2024b). Virtual reality facilitated travel inspiration: the role of pleasure and arousal. Current Issues in Tourism, 1-14. https://doi.org/10.1080/13683500.2024.2406412 Assiouras, I., Skourtis, G., Giannopoulos, A., Buhalis, D., & Karaosmanoglu, E. (2023). Testing the relationship between value co-creation, perceived justice and guests’ enjoyment. Current Issues in Tourism, 26(4), 587-602. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3 Avlonitis, G. J., & Giannopoulos, A. A. (2012). Balanced market orientation: qualitative findings on a fragile equilibrium. Managing Service Quality: An International Journal, 22(6), 565-579. Baum, T. (2015). Human resources in tourism: Still waiting for change?—A 2015 reprise. Tourism Management, 50, 204–212. https://doi.org/10.1016/j.tourman.2015.02.001 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922 Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press. Berselli, C., Pereira, L. A., Pereira, T., & Limberger, P. F. (2022). Overtourism: Residents’ perceived impacts of tourism saturation. Tourism Analysis, 27(2), 161–172. https://doi.org/10.3727/108354221X16187814403074 Bigné, J. E., Mattila, A. S., & Andreu, L. (2008). The impact of experiential consumption cognitions and emotions on behavioral intentions. Journal of Services Marketing, 22(4), 303–315. https://doi.org/10.1108/08876040810881704 Birhane, A., & Prabhu, V. U. (2021). Large image datasets: A pyrrhic win for computer vision? Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 1536–1546. https://doi.org/10.1109/WACV48630.2021.00158 Bollenbach, J., Neubig, S., Hein, A., Keller, R., & Krcmar, H. (2024). Enabling active visitor management: Local, short-term occupancy prediction at a touristic point of interest. Information Technology & Tourism, 26, 521–552. https://doi.org/10.1007/s40558-024-00291-2 Bollenbach, J., Neubig, S., Hein, A., Keller, R., & Krcmar, H. (2022). Using machine learning to predict POI occupancy to reduce overcrowding. INFORMATIK 2022. https://doi.org/10.18420/INF2022_34 Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS 2016), 4356–4364. https://doi.org/10.48550/arXiv.1607.06520 Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint. https://doi.org/10.48550/arXiv.2108.07258 Boscarino, N., Cartwright, R. A., Fox, K., & Tsosie, K. S. (2022). Federated learning and Indigenous genomic data sovereignty. Nature Machine Intelligence, 4(11), 909–911. https://doi.org/10.1038/s42256-022-00551-y Bouchon, F., & Rauscher, M. (2019). Cities and tourism, a love and hate story: Towards a conceptual framework for urban overtourism management. International Journal of Tourism Cities, 5(4), 598–619. https://doi.org/10.1108/IJTC-06-20190080 Breidbach, C. F., & Brodie, R. J. (2017). Engagement platforms in the sharing economy: Conceptual foundations and research directions. Journal of Service Theory and Practice, 27(4), 761– 777. https://doi.org/10.1108/JSTP-04-2016-0071 Breidbach, C. F., & Maglio, P. P. (2016). Technology-enabled value co-creation: An empirical analysis of actors and resources. Industrial Marketing Management, 56, 73–85. https://doi.org/10.1016/j.indmarman.2016.03.011 Brouder, P. (2020). Reset redux: Possible evolutionary pathways towards the transformation of tourism in a COVID-19 world. Tourism Geographies, 22(3), 484-490. https://doi.org/10.1080/14616688.2020.1760928 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … Amodei, D. (2020). Language models are fewshot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165 Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534. https://doi.org/10.1126/science.aap8062 Buchmann, A., Moore, K., Månsson, M., & Fisher, D. (2021). Authenticity in tourism theory and experience: Practically indispensable and theoretically mischievous? Annals of Tourism Research, 89, 103208. https://doi.org/10.1016/j.annals.2021.103208 Buhalis, D., & Foerste, M. (2015). SoCoMo marketing for travel and tourism: Empowering co-creation of value. Journal of Destination Marketing & Management, 4(3), 151–161. https://doi.org/10.1016/j.jdmm.2015.04.001 Buhalis, D., & Sinarta, Y. (2019). Real-time co-creation and nowness service: Lessons from tourism and hospitality. Journal of Travel & Tourism Marketing, 36(5), 563-582. https://doi.org/10.1080/10548408.2019.1592059 Buhalis, D., Amaranggana, A. (2015). Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services. In: Tussyadiah, I., Inversini, A. (eds) Information and Communication Technologies in Tourism 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-14343-9_28 Bui, H. T., Filimonau, V., & Sezerel, H. (2024). AI-thenticity: Exploring the effect of perceived authenticity of AI-generated visual content on tourist patronage intentions. Journal of Destination Marketing & Management, 32, Article 100956. https://doi.org/10.1016/j.jdmm.2024.100956 Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91. http://proceedings.mlr.press/v81/buolamwini18a.html Burrell, J. (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512 Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain humanlike biases. Science, 356(6334), 183–186. https://doi.org/10.1126/science.aal4230 Carù, A., & Cova, B. (2003). Revisiting consumption experience: A more humble but complete view of consumption. Marketing Theory, 3(2), 267-286. https://doi.org/10.1177/14705931030032004 Cassani, A., Ruberl, M., Salis, A., Giannese, G., & Boanelli, G. (2024). zIA: A GenAI-powered local auntie assists tourists in Italy. arXiv. https://doi.org/10.48550/arXiv.2407.11830 Cavoukian, A. (2009). Privacy by design: The 7 foundational principles. Information and Privacy Commissioner of Ontario. https://www.ipc.on.ca/en/media/1826/download?attachment GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 35 Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press. Christensen, J., Hansen, J. M., & Wilson, P. (2024). Understanding the role and impact of Generative Artificial Intelligence (AI) hallucination within consumers’ tourism decision-making processes. Current Issues in Tourism, 28(4), 545–560. https://doi.org/10.1080/13683500.2023.2300032 Christou, E., Giannopoulos, A., & Simeli, I. (2025). The Evolution of Digital Tourism Marketing: From Hashtags to AI-Immersive Journeys in the Metaverse Era. Sustainability, 17(13), 6016. https://doi.org/10.3390/su17136016 City of Helsinki. (n.d.). Smart tourism in Helsinki. Retrieved June 28, 2025, from https://www.hel.fi/en/business-andwork/develop-tourism-and-events/smart-tourism-in-helsinki Cohen, S. A., & Cohen, E. (2019). New directions in the sociology of tourism. Current Issues in Tourism, 22(2), 153-172. https://doi.org/10.1080/13683500.2017.1347151 Coudounaris, D. N., Björk, P., Trifonova Marinova, S., Jafarguliyev, F., Kvasova, O., Sthapit, E., Varblane, U., & Talias, M. A. (2025). ‘Big-5’ personality traits and revisit intentions: The mediating effect of memorable tourism experiences. Journal of Tourism, Heritage & Services Marketing, 11(1), 46–60. https://doi.org/10.5281/zenodo.15655588 Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), 336-349. https://doi.org/10.1177/1527476418796632 Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press. https://doi.org/10.1515/9781503609754 Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. https://doi.org/10.2307/j.ctv1ghv45t Crawford, K., & Paglen, T. (2021). Excavating AI: the politics of images in machine learning training sets. AI & Society, 36(4), 1105–1116. https://doi.org/10.1007/s00146-021-01162-8 Cronin, M. (2013). Translation in the digital age. Routledge. https://doi.org/10.4324/9780203073599 Csapó, J., & Kusumaningrum, S. D. (2025). Uncovering trends in destination branding and destination brand equity research: Results of a topic modelling approach. Journal of Tourism, Heritage & Services Marketing, 11(1), 34–45. https://doi.org/10.5281/zenodo.15652600 Cummings, M. L. (2014). Man versus machine or man + machine? IEEE Intelligent Systems, 29(5), 62-69. https://doi.org/10.1109/MIS.2014.87 Dangi, T. B., & Jamal, T. (2016). An integrated approach to "sustainable community-based tourism". Sustainability, 8(5), 475. https://doi.org/10.3390/su8050475 Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01 Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT (pp. 4171– 4186). Doan Do, T. T. M., Silva, J. A. M., Del Chiappa, G., & Pereira, L. N. (2024). The moderating role of sense of power and psychological risk on the effect of eWOM and purchase intentions for Airbnb. Journal of Tourism, Heritage & Services Marketing, 10(2), 3–14. https://doi.org/10.5281/zenodo.14535723 Dredge, D., & Jamal, T. (2015). Progress in tourism planning and policy: A post-structural perspective on knowledge production. Tourism Management, 51, 285–297. https://doi.org/10.1016/j.tourman.2015.06.002 Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., & Al-Debei, M. M. (2023). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542. https://doi.org/10.1016/j.ijinfomgt.2022.102542 Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Echeverri, P., & Skålén, P. (2011). Co-creation and co-destruction: A practice-theory based study of interactive value formation. Marketing Theory, 11(3), 351–373. https://doi.org/10.1177/1470593111408181 Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60. https://doi.org/10.1002/(SICI)10990526(199905/06)4:5%3C41::AID-CPLX9%3E3.0.CO;2-F Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press. European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). COM(2021) 206 final. Retrieved May 17 2025 from https://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=CELEX%3A52021PC0206 Fleming, P. (2017). The human capital hoax: Work, debt and insecurity in the era of Uberization. Polity Press. Floridi, L. (2016). Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions. Philosophical Transactions of the Royal Society A 374:20160112. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1 Florido-Benítez, L., & del Alcázar Martínez, B. (2024). How Artificial Intelligence (AI) Is Powering New Tourism Marketing and the Future Agenda for Smart Tourist Destinations. Electronics, 13(21), 4151. https://doi.org/10.3390/electronics13214151 Fouad, A. M., Salem, I. E., & Fathy, E. A. (2024). Generative AI insights in tourism and hospitality: A comprehensive review and strategic research roadmap. Tourism and Hospitality Research, 0(0). https://doi.org/10.1177/14673584241293125 Franklin, A. (2004). Tourism as an ordering: Towards a new ontology of tourism. Tourist Studies, 4(3), 277-301. https://doi.org/10.1177/1468797604057328 Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019 Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big data analytics for knowledge generation in tourism destinations–A case from Sweden. Journal of Destination Marketing & Management, 3(4), 198-209. https://doi.org/10.1016/j.jdmm.2014.08.002 Gallego, I., & Font, X. (2020). Changes in air passenger demand as a result of the COVID-19 crisis: Using Big Data to inform tourism policy. Journal of Sustainable Tourism, 29(9), 1470– 1489. https://doi.org/10.1080/09669582.2020.1773476 Gavalas, D., Pantziou, G., Konstantopoulos, C., & Vansteenwegen, P. (2024). Tourist trip planning: Algorithmic foundations. 36 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos Applied Soft Computing, 166, Article 112280. https://doi.org/10.1016/j.asoc.2024.112280 Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723 Geiger, R. S. (2017). Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture. Big Data & Society, 4(2). https://doi.org/10.1177/2053951717730735 George, G., Howard-Grenville, J., Joshi, A., & Tihanyi, L. (2016). Understanding and tackling societal grand challenges through management research. Academy of Management Journal, 59(6), 1880–1895. https://doi.org/10.5465/amj.2016.4007 Gervais, Daniel J., The Machine As Author (March 25, 2019). Iowa Law Review, Vol. 105, 2019, 2053-2106, Vanderbilt Law Research Paper No. 19-35, Available at SSRN: https://ssrn.com/abstract=3359524 Ghesh, N., Alexander, M., & Davis, A. (2024). The artificial intelligence-enabled customer experience in tourism: A systematic literature review. Tourism Review, 79(5), 1017– 1037. https://doi.org/10.1108/TR-04-2023-0255 Giannopoulos, A., Livas, C., Simeli, I., & Achlada, C. (2022). Is destination image instagrammable? Visit intentions and value co-creation through social media content. International Journal of Technology Marketing, 16(4), 349-369. Giannopoulos, A., Piha, L., & Skourtis, G. (2021). Destination branding and co-creation: a service ecosystem perspective. Journal of Product & Brand Management, 30(1), 148-166. https://doi.org/10.1108/JPBM-08-2019-2504 Giannopoulos, A., Skourtis, G., Kalliga, A., Dontas-Chrysis, D. M., & Paschalidis, D. (2020). Co-creating high-value hospitality services in the tourism ecosystem: towards a paradigm shift?. Journal of Tourism, Heritage & Services Marketing, 6(1), 3-11. http://dx.doi.org/10.5281/zenodo.3822065 Gibbons, L. V. (2020). Regenerative—The new sustainable? Sustainability, 12(13), 5483. https://doi.org/10.3390/su12135483 Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62(1), 451–482. https://doi.org/10.1146/annurev-psych-120709-145346 Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist. McGraw-Hill Education. Gillespie, T. (2014). The relevance of algorithms. Media Technologies: Essays on Communication, Materiality, and Society, 167–194. https://doi.org/10.7551/mitpress/9780262525374.003.0009 Gössling, S., & Higham, J. (2020). The Low-Carbon Imperative: Destination Management under Urgent Climate Change. Journal of Travel Research, 60(6), 1167-1179. https://doi.org/10.1177/0047287520933679 Gössling, S., Scott, D., & Hall, C. M. (2021). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1-20. https://doi.org/10.1080/09669582.2020.1758708 Govers, R., & Go, F. M. (2016). Place branding: Glocal, virtual and physical identities, constructed, imagined and experienced. Palgrave Macmillan. Graham, C., & Roll, N. (2024). Evaluating OpenAI's Whisper ASR: Performance analysis across diverse accents and speaker traits. JASA Express Letters, 4, Article 025206. https://doi.org/10.1121/10.0024876 Grayson, K., & Martinec, R. (2004). Consumer perceptions of iconicity and indexicality and their influence on assessments of authentic market offerings. Journal of Consumer Research, 31(2), 296–312. https://doi.org/10.1086/422109 Gretzel, U. (2011). Intelligent systems in tourism: A social science perspective. Annals of Tourism Research, 38(3), 757–779. https://doi.org/10.1016/j.annals.2011.04.014 Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., ... & Xiang, Z. (2020). e-Tourism beyond COVID-19: A call for transformative research. Information Technology & Tourism, 22(2), 187-203. https://doi.org/10.1007/s40558-020-00181-3 Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781 Guo, Q., Zhu, D., Lin, M.-T. (B)., Li, F. (S)., Kim, P. B., Du, D., & Shu, Y. (2023). Hospitality employees’ technology adoption at the workplace: Evidence from a meta-analysis. International Journal of Contemporary Hospitality Management, 35(7), 2437–2464. https://doi.org/10.1108/IJCHM-06-2022-0701 Guttentag, D. (2022). Virtual Reality and the End of Tourism? A Substitution Acceptance Model," Springer Books, in: Zheng Xiang & Matthias Fuchs & Ulrike Gretzel & Wolfram Höpken (ed.), Handbook of e-Tourism, chapter 79, pages 1901-1919, Springer. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8 Halder, S., Lim, K. H., Chan, J., & Zhang, X. (2024). A survey on personalized itinerary recommendation: From optimisation to deep learning. Applied Soft Computing, 152, Article 111200. https://doi.org/10.1016/j.asoc.2023.111200 Hassenzahl, M. (2010). Experience design: Technology for all the right reasons. Synthesis Lectures on Human‐Centered Informatics, 3(1), 1–95. https://doi.org/10.2200/S00261ED1V01Y201003HCI008 Higgins-Desbiolles, F. (2018). Sustainable tourism: Sustaining tourism or something more? Tourism Management Perspectives, 25, 157–160. https://doi.org/10.1016/j.tmp.2017.11.017 Hilty, L. M., & Aebischer, B. (Eds.). (2015). ICT innovations for sustainability. Springer. https://doi.org/10.1007/978-3-31909228-7 Hochschild, A. R. (2012). The managed heart: Commercialization of human feeling (3rd ed.). University of California Press. Huang, J., Xu, Y., Wang, Q., Wang, Q. (C.), Liang, X., Wang, F., Zhang, Z., Wei, W., Zhang, B., Huang, L., Chang, J., Ma, L., Ma, T., Liang, Y., Zhang, J., Guo, J., Jiang, X., Fan, X., An, Z., Li, T., … Fei, A. (2025). Foundation models and intelligent decision-making: Progress, challenges, and perspectives. The Innovation, 6(6), Article 100948. https://doi.org/10.1016/j.xinn.2025.100948 International Labour Organization (ILO). (2021). World Employment and Social Outlook 2021: The role of digital labour platforms in transforming the world of work. Geneva: ILO. Ivanov, S. (2020). The impact of automation on tourism and hospitality jobs. Information Technology & Tourism, 22(2), 205–215. https://doi.org/10.1007/s40558-020-00175-1 Ivanov, S., & Webster, C. (2017). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies—a cost-benefit analysis. Prepared for the International Scientific Conference "Contemporary Tourism – Traditions and Innovations", Sofia University, 19-21 October 2017, Available at SSRN: https://ssrn.com/abstract=3007577 Ivanov, S., & Webster, C. (2019a). Perceived appropriateness and intention to use service robots in tourism. In Pesonen, J., & Neidhardt, J. (Eds.), Information and Communication Technologies in Tourism 2019 (pp. 237–248). Springer. https://doi.org/10.1007/978-3-030-05940-8_19 Ivanov, S., & Webster, C. (2019b). Economic fundamentals of the use of robots, artificial intelligence, and service automation in travel, tourism, and hospitality. In S. Ivanov & C. Webster (Eds.), Robots, artificial intelligence, and service automation in travel, tourism and hospitality (pp. 39–55). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78756-687-320191002 Ivasciuc, I.-S., Candrea, A. N., & Ispas, A. (2025). Exploring Tourism Experiences: The Vision of Generation Z Versus Artificial Intelligence. Administrative Sciences, 15(5), 186. https://doi.org/10.3390/admsci15050186 GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 37 Järvi, H., Kähkönen, A. K., & Torvinen, H. (2018). When value cocreation fails: Reasons that lead to value co-destruction. Scandinavian Journal of Management, 34(1), 63–77. https://doi.org/10.1016/j.scaman.2018.01.002 Jo, Y., & Shin, H. (2025). Can Gamification and Augmented Reality (AR) Revitalize Declining Destinations? Investigating its Impact on Tourist Attitudes and Behavior. Journal of Travel Research, 0(0). https://doi.org/10.1177/00472875251332961 Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389– 399. https://doi.org/10.1038/s42256-019-0088-2 Jones, N. (2018). How to stop data centres from gobbling up the world’s electricity. Nature, 561(7722), 163–166. https://doi.org/10.1038/d41586-018-06610-y Jorzik, P., Klein, S. P., Kanbach, D. K., & Kraus, S. (2024). AIdriven business model innovation: A systematic review and research agenda. Journal of Business Research, 182, Article 114764. https://doi.org/10.1016/j.jbusres.2024.114764 Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., … Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083 Kim, J., & Fesenmaier, D. R. (2015). Measuring emotions in real time: Implications for tourism experience design. Journal of Travel Research, 54(4), 419–429. https://doi.org/10.1177/0047287514550100 Kirchner, S., Meyer, S. & Tisch, A. (2023). “Digital Taylorism” for some, “digital self-determination” for others? Inequality in job autonomy across different task domains. Zeitschrift für Sozialreform, 69(1), 57-84. https://doi.org/10.1515/zsr-20220101 Kirillova, K., Lehto, X. Y., & Cai, L. (2017). Existential authenticity and anxiety as outcomes: The tourist in the experience economy. International Journal of Tourism Research, 19(1), 13–26. https://doi.org/10.1002/jtr.2080 Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage. https://doi.org/10.4135/9781473909472 Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14– 29. https://doi.org/10.1080/1369118X.2016.1154087 Koens, K., Postma, A., & Papp, B. (2018). Is overtourism overused? Understanding the impact of tourism in a city context. Sustainability, 10(12), 4384. https://doi.org/10.3390/su10124384 Kukutai, T., & Taylor, J. (2016). Indigenous data sovereignty: Toward an agenda. ANU Press. https://doi.org/10.22459/CAEPR38.11.2016 Kusner, M. J., Loftus, J. R., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems, 30, 4066–4076. https://doi.org/10.48550/arXiv.1703.06856 Kzaz, L., Dakhchoune, D., & Dahab, D. (2018). Tourism recommender systems: An overview of recommendation approaches. International Journal of Computer Applications, 180(20), 40–46. https://doi.org/10.5120/ijca2018916467 Lariza Corral-Gonzalez, L., Judith Cavazos-Arroyo, J., & GarcíaMestanza, J. (2023). Regenerative tourism: A bibliometric analysis. Journal of Tourism, Heritage & Services Marketing, 9(2), 41–54. https://doi.org/10.5281/zenodo.10539722 Latour, B. (2005). Reassembling the Social: An Introduction to Actor–Network–Theory. Oxford University Press. Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410–423. https://doi.org/10.1016/j.annals.2019.01.014 Lee, J. L.-M., Lau, C. Y.-L., & Wong, C. W.-G. (2023). Reexamining brand loyalty and brand awareness with social media marketing: A collectivist country perspective. Journal of Tourism, Heritage & Services Marketing, 9(2), 3–10. https://doi.org/10.5281/zenodo.10539175 Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420 Lenzen, M., Sun, Y.-Y., Faturay, F., Ting, Y.-P., Geschke, A., & Malik, A. (2018). The carbon footprint of global tourism. Nature Climate Change, 8(6), 522–528. https://doi.org/10.1038/s41558-018-0141-x Leonardi, P. M. (2011). When flexible routines meet flexible technologies. MIS Quarterly, 35(1), 147–168. https://doi.org/10.2307/23043493 Leonardi, P. M., & Barley, S. R. (2008). Materiality and change: Challenges to building better theory about technology and organizing. Information and Organization, 18(3), 159–176. https://doi.org/10.1016/j.infoandorg.2008.03.001 Leonardi, P. M., & Neeley, T. (2022). The digital mindset: What it really takes to thrive in the age of data, algorithms, and AI. Harvard Business Review Press. Li, X., Law, R., Vu, H. Q., & Rong, J. (2013). Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral. Tourism Management, 36, 321–330. https://doi.org/10.1016/j.tourman.2012.10.017 Li, Y., & Zhu, J. (2024). An ethical study of generative AI from the Actor–Network Theory perspective. arXiv. https://doi.org/10.48550/arXiv.2406.11860 Light, B., Burgess, J., & Duguay, S. (2018). The walkthrough method: An approach to the study of apps. New Media & Society, 20(3), 881–900. https://doi.org/10.1177/1461444816675438 Liu, B., Ge, J., & Wang, J. (2025). Vaiage: A multi-agent solution to personalized travel planning. arXiv. https://doi.org/10.48550/arXiv.2505.10922 Liu, S., & Hao, F. (2024). Engaging with avatar in virtual regenerative tourism. Journal of Travel & Tourism Marketing, 41(6), 864–879. https://doi.org/10.1080/10548408.2024.2350053 Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference, 1-16. https://doi.org/10.1145/3313831.3376727 Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005 Luccioni, A. S., Viguier, S., & Ligozat, A.-L. (2023). Estimating the carbon footprint of BLOOM, a 176B parameter language model. Journal of Machine Learning Research, 24, 1–15. https://hal.science/hal-04288059v1 Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://doi.org/10.48550/arXiv.1705.07874 Luong, T.-B. (2024). The impact of uses and motivation gratifications on tourist behavioral intention: The mediating role of destination image and tourists’ attitudes. Journal of Tourism, Heritage & Services Marketing, 10(1), 3–13. https://doi.org/10.5281/zenodo.13771484 Machado, M., Dias, Á., Patuleia, M., & Pereira, L. (2025). A model of marketing-driven innovation in lifestyle tourism businesses. Journal of Tourism, Heritage & Services Marketing, 11(1), 21– 33. https://doi.org/10.5281/zenodo.15651896 Maglio, P. P., & Spohrer, J. (2013). A service science perspective on business model innovation. Industrial Marketing Management, 42(5), 665–670. https://doi.org/10.1016/j.indmarman.2013.05.007 38 Evangelos Christou, Anestis Fotiadis and Antonios Giannopoulos Mathur, M. B., & Reichling, D. B. (2016). Navigating a social world with robot partners: A quantitative cartography of the uncanny valley. Cognition, 146, 22–32. https://doi.org/10.1016/j.cognition.2015.09.008 Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335 McCarthy, J., & Wright, P. (2004). Technology as experience. MIT Press. McKercher, B., & Prideaux, B. (2014). Academic myths of tourism. Annals of Tourism Research, 46, 16-28. https://doi.org/10.1016/j.annals.2014.02.003 Menzel, S., Springer, S., Zieger, M., & Strzelecki, A. (2023). Google Trends confirms (COVID)-19 impact on tourist industry. Tourism Culture & Communication, 23(2–3), 97–102. https://doi.org/10.3727/109830422X16600594683418 Michael, N., Chunawala, M. A., & Fusté-Forné, F. (2025). Instagrammable destinations: the use of photographs in digital tourism marketing in the United Arab Emirates. Journal of Tourism, Heritage & Services Marketing, 11(1), 3–10. https://doi.org/10.5281/zenodo.15650944 Michael, N., & Fotiadis, A. (2022). Employee turnover: The hotel industry perspective. Journal of Tourism, Heritage & Services Marketing, 8(1), 38-47. https://zenodo.org/records/6583698 Milan, S., & Treré, E. (2019). Big Data from the South(s): Beyond data universalism. Television & New Media, 20(4), 319–335. https://doi.org/10.1177/1527476419837739 Misirlis, N., Elshof, M., & Vlachopoulou, M. (2021). Modeling Facebook users\’ behavior towards the use of pages related to healthy diet and sport activities. Journal of Tourism, Heritage & Services Marketing, 7(2), 49–57. https://doi.org/10.5281/zenodo.5549911 Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (FAT ’19)* (pp. 220–229). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287596 Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (pp. 220–229). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287596 Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679 Mkono, M. (2013). African and Western tourists: Object authenticity quest? Annals of Tourism Research, 41, 195–214. https://doi.org/10.1016/j.annals.2013.01.002 Molnar, C. (2022). Interpretable Machine Learning (2nd ed.). Leanpub. Mori, M. (1970). The uncanny valley. Energy, 7(4), 33–35. https://doi.org/10.1109/MRA.2012.2192811 (reprint DOI) Morley, J., Elhalal, A., & Garcia, F. (2021). Ethics as a service: A pragmatic operationalisation of AI ethics. Minds and Machines, 31(2), 239–256. https://doi.org/10.1007/s11023-021-09563-w Morley, J., Widdicks, K. V., & Hazas, M. D. (2018). Digitalisation, energy and data demand: The impact of Internet traffic on overall and peak electricity consumption. Energy Research & Social Science, 38, 128–137. https://doi.org/10.1016/j.erss.2018.01.018 Nass, C., & Moon, Y. (2000). Machines and mindless social attributions: Computers as social actors. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153 Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81– 103. https://doi.org/10.1111/0022-4537.00153 Nechoud, L., Ghidouche, F., & Seraphin, H. (2021). The influence of eWOM credibility on visit intention: An integrative moderated mediation model. Journal of Tourism, Heritage & Services Marketing, 7(1), 54–63. https://doi.org/10.5281/zenodo.4521314 Neuhofer, B., Buhalis, D., & Ladkin, A. (2015). Smart technologies for personalized experiences: A case study in the hospitality domain. Electronic Markets, 25(3), 243–254. https://doi.org/10.1007/s12525-015-0182-1 Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. https://doi.org/10.2307/j.ctt1pwt9w5 Nurski, L., & Hoffman, M. (2022). The impact of artificial intelligence on the nature and quality of jobs (Working Paper 14/2022). Bruegel. Retrieved from https://www.bruegel.org/system/files/202207/WP%2014%202022.pdf O’Hagan, M. (2016). Massively open translation. International Journal of Communication, 10, 929–946. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group. Organisation for Economic Co-operation and Development (OECD). (2024). Artificial Intelligence and Tourism: G7/OECD Policy Paper. OECD Publishing. Orlikowski, W. J. (2007). Sociomaterial practices: Exploring technology at work. Organization Studies, 28(9), 1435–1448. https://doi.org/10.1177/0170840607081138 Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the “IT” in IT research—a call to theorizing the IT artifact. Information Systems Research, 12(2), 121–134. https://doi.org/10.1287/isre.12.2.121.9700 Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work, and organization. Academy of Management Annals, 2(1), 433–474. https://doi.org/10.5465/19416520802211644 Padricelli, G. M., Punziano, G., & Saracino, B. (2021). Virtual vs. digital: Examples of netnography and digital ethnography in tourism studies for a comparison between methods. Athens Journal of Social Sciences, 8(4), 259–278. https://doi.org/10.30958/ajss.8-4-4 Paluch, S., & Wünderlich, N. V. (2016). Contrasting risk perceptions of technology-based service innovations in interorganizational settings. Journal of Business Research, 69(7), 2424–2431. https://doi.org/10.1016/j.jbusres.2016.01.012 Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. https://doi.org/10.4159/harvard.9780674736061 Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv. https://doi.org/10.48550/arXiv.2104.10350 Pérez-Ricardo, E. del C., & García-Mestanza, J. (2023). Booking intention research progress: Emerging trends and research agenda. Journal of Tourism, Heritage & Services Marketing, 9(2), 11–27. https://doi.org/10.5281/zenodo.10539221 Pernecky, T. (2023). Mobilizing relational ontology: meeting the pluriversal challenge in tourism studies. Current Issues in Tourism, 27(22), 3623–3635. https://doi.org/10.1080/13683500.2023.2281599 Picard, R. W. (1997). Affective computing. MIT Press. Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259 Pine, J. B., & Gilmore, J. H. (1999). The experience economy: Work is theatre & every business a stage. Harvard Business School Press. GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 39 Pink, S., Horst, H., Postill, J., Hjorth, L., & Lewis, T. (2022). Digital ethnography: Principles and practice (2nd ed.). SAGE Publications. Pitakaso, R., Golinska-Dawson, P., Luesak, P., Srichok, T., & Khonjun, S. (2025). Embracing open innovation in hospitality management: Leveraging AI-driven dynamic scheduling systems for complex resource optimization and enhanced guest satisfaction. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), Article 100487. https://doi.org/10.1016/j.joitmc.2025.100487 Polyzos, S., Samitas, A., & Spyridou, A. Ef. (2020). Tourism demand and the COVID-19 pandemic: an LSTM approach. Tourism Recreation Research, 46(2), 175–187. https://doi.org/10.1080/02508281.2020.1777053 Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of Interactive Marketing, 18(3), 5–14. https://doi.org/10.1002/dir.20015 Prebensen, N. K., Chen, J. S., & Uysal, M. (Eds.). (2014). Creating experience value in tourism. CABI Publishing. https://doi.org/10.1079/9781780643489.0000 Privitera, A.G., Fontana, F., & Geronazzo, M. (2025). The Role of Audio in Immersive Storytelling: a Systematic Review in Cultural Heritage. Multimedia Tools & Applications, 84, 16105–16143. https://doi.org/10.1007/s11042-024-19288-4 Punpeng, G., & Yodnane, P. (2024). The route to immersion: a conceptual framework for cross-disciplinary immersive theatre and experiences. Humanities & Social Sciencies Communications, 11, 1477. https://doi.org/10.1057/s41599024-03971-w Pym, A. (2014). Exploring translation theories (2nd ed.). Routledge. Raji, I. D., et al. (2020). Closing the AI accountability gap. Proceedings of FAT 2020*, 33–44. https://doi.org/10.1145/3351095.3372873 Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-toend framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20) (pp. 33–44). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873 Reed, M. S. (2007). Participatory technology development for agroforestry extension: An innovation-decision approach. African Journal of Agricultural Research, 2(8), 334–341. Reeves, B., & Nass, C. (1996). The media equation. Cambridge University Press. Ren, C., Jóhannesson, G. T., & van der Duim, R. (Eds.). (2012). Actor-network theory and tourism: Ordering, materiality and multiplicity (1st ed.). Routledge. https://doi.org/10.4324/9780203122976 Ren, C., Jóhannesson, G. T., & van der Duim, R. (Eds.). (2017). Cocreating tourism research: Towards collaborative ways of knowing (1st ed.). Routledge. https://doi.org/10.4324/9781315393223 Rezwana, J., & Ford, C. (2025). Human-centered AI communication in co-creativity: An initial framework and insights. In C&C '25: Proceedings of the 2025 Conference on Creativity and Cognition (pp. 651–665). https://doi.org/10.1145/3698061.3726932 Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778 Rickly, J. M., & Vidon, E. S. (Eds.). (2018). Authenticity & tourism: Materialities, perceptions, experiences. Emerald Publishing. Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin III, F. S., Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., de Wit, C. A., Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R., Svedin, U., Falkenmark, M., … Foley, J. A. (2009). A safe operating space for humanity. Nature, 461(7263), 472–475. https://doi.org/10.1038/461472a Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., III, Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., de Wit, C. A., Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R., Svedin, U., … Foley, J. A. (2009). A safe operating space for humanity. Nature, 461(7263), 472–475. https://doi.org/10.1038/461472a Samuelson, P. (2023). Generative AI meets copyright. Science, 381(6654), 158–161. https://doi.org/10.1126/science.adi0656 Sanders, E. B. N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign, 4(1), 5–18. https://doi.org/10.1080/15710880701875068 Schmuck, P., & Vlek, C. (2003). Psychologists can do much for environmental sustainability. European Psychologist, 8(2), 66– 76. https://doi.org/10.1027//1016-9040.8.2.66 Schneider, J., Meske, C., & Kuss, P. (2024). Foundation models. Business & Information Systems Engineering, 66(2), 221–231. https://doi.org/10.1007/s12599-024-00851-0 Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63. https://doi.org/10.1145/3381831 Scott, D. (2014). The tourist: a new theory of the leisure class. Annals of Leisure Research, 17(1), 113–114. https://doi.org/10.1080/11745398.2014.890513 Sharpley, R. (2020). Tourism, sustainable development and the theoretical divide: 20 years on. Journal of Sustainable Tourism, 28(11), 1932–1946. https://doi.org/10.1080/09669582.2020.1779732 Shaw, G., Bailey, A., & Williams, A. M. (2011). Service dominant logic and its implications for tourism management. Tourism Management, 32(2), 207–214. https://doi.org/10.1016/j.tourman.2010.05.020 Shawal, N. S. M., Bakhtiar, M. F. S., Nurzaman, M. A. A. K., Kedin, N. A., & Talib, A. H. (2023). Exploring User Acceptance, Experience and Satisfaction towards Chatbots in an Online Travel Agency (OTA). International Journal of Academic Research in Business and Social Sciences, 13(5), 196 – 206. http://dx.doi.org/10.6007/IJARBSS/v13-i5/17015 Sigala, M. (2018). Implementing social customer relationship management: A process framework and implications in tourism and hospitality. International Journal of Contemporary Hospitality Management, 30(7), 2698-2726. https://doi.org/10.1108/IJCHM-10-2015-0536 Singh, R., & Sibi, P.S. (2023). E-loyalty formation of Generation Z: Personal characteristics and social influences. Journal of Tourism, Heritage & Services Marketing, 9(1), 3–14. https://doi.org/10.5281/zenodo.8054004 Solove, D. J. (2006). A taxonomy of privacy. University of Pennsylvania Law Review, 154(3), 477–560. https://doi.org/10.2307/40041279 Sousa, N., Alén González, M. E., Losada, N., & Melo, M. (2024). The adoption of Virtual Reality technologies in the tourism sector: Influences and post-pandemic perspectives. Journal of Tourism, Heritage & Services Marketing, 10(2), 47-57. https://zenodo.org/records/14524968 Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I., Bennett, E. M., ... & Sörlin, S. (2015). Planetary boundaries: Guiding human development on a changing planet. Science, 347(6223), 1259855. https://doi.org/10.1126/science.1259855 Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008 Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th ACL, 3645–3650. https://doi.org/10.18653/v1/P19-1355 Sylaiou, S., & Fidas, C. (2022). Virtual Humans in Museums and Cultural Heritage Sites. Applied Sciences, 12(19), 9913. https://doi.org/10.3390/app12199913