Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review

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However, the field lacks a comprehensive synthesis of its intellectual landscape and thematic evolution, limiting understanding of research trajectories and emerging directions. Methods A systematic literature review following the SPAR-4-SLR procedure was conducted on 320 peer-reviewed papers published between 2003 and 2025, sourced from the Scopus database. Publication trends, leading journals, prolific authors, trending areas, and bibliographic coupling of documents and countries were visualized using bibliometric analysis tools (VOSviewer and Biblioshiny). Thematic analysis employed keyword co-occurrence networks to identify emerging research themes. Results Academic publications on AI in tourism and hospitality demonstrated a significant surge during 2017–2020, reflecting the industry’s growing emphasis on smart marketing applications. Thematic analysis identified four major research clusters: (i) Digital Influence and Tourist Behaviour Analytics; (ii) AI-Enabled Smart Tourism and Commerce Ecosystems; (iii) Technology-Driven Hospitality and Experience Innovation; and (iv) Data-Driven Decision Making in Predictive Tourism Modelling. Conclusions This bibliometric and thematic assessment reveals the evolving intellectual landscape of AI applications in tourism and hospitality marketing, documenting substantive research growth and the emergence of distinct thematic clusters that shape current and future research agendas in this dynamic field. 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F1000Research 2026, 15 :192 ( https://doi.org/10.12688/f1000research.177254.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] Pankaj Kumar Tyagi https://orcid.org/0000-0001-9504-541X 1 , Priyanka Aggarwal https://orcid.org/0000-0002-9707-5687 2 , Priyanka Tyagi 1 , Asokan Vasudevan https://orcid.org/0000-0002-9866-4045 3,4 , Premendra Kumar Singh https://orcid.org/0000-0002-6627-4560 5 Pankaj Kumar Tyagi https://orcid.org/0000-0001-9504-541X 1 , Priyanka Aggarwal https://orcid.org/0000-0002-9707-5687 2 , [...] Priyanka Tyagi 1 , Asokan Vasudevan https://orcid.org/0000-0002-9866-4045 3,4 , Premendra Kumar Singh https://orcid.org/0000-0002-6627-4560 5 PUBLISHED 05 Feb 2026 Author details Author details 1 University Institute of Tourism and Hospitality Management, Chandigarh University, Mohali, Punjab, India 2 Department of Law, Maharaja Agrasen Institute of Management Studies, Delhi, Delhi, India 3 Faculty of Business and Communications, INTI International University, Negeri Sembilan, Malaysia 4 Research Fellow, Wekerle Business School, Budapest, Hungary 5 Centre for Distance & Online Education, Sharda University, Greater Noida, Uttar Pradesh, India Pankaj Kumar Tyagi Roles: Conceptualization, Formal Analysis, Investigation, Software, Supervision, Writing – Original Draft Preparation Priyanka Aggarwal Roles: Data Curation, Investigation, Resources, Software, Validation, Writing – Review & Editing Priyanka Tyagi Roles: Formal Analysis, Resources, Validation, Visualization Asokan Vasudevan Roles: Funding Acquisition, Supervision, Writing – Review & Editing Premendra Kumar Singh Roles: Conceptualization, Methodology, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Background Artificial intelligence (AI) has fundamentally transformed tourism and hospitality marketing through enhanced data-driven decision-making, personalized customer experiences, and intelligent destination management. However, the field lacks a comprehensive synthesis of its intellectual landscape and thematic evolution, limiting understanding of research trajectories and emerging directions. Methods A systematic literature review following the SPAR-4-SLR procedure was conducted on 320 peer-reviewed papers published between 2003 and 2025, sourced from the Scopus database. Publication trends, leading journals, prolific authors, trending areas, and bibliographic coupling of documents and countries were visualized using bibliometric analysis tools (VOSviewer and Biblioshiny). Thematic analysis employed keyword co-occurrence networks to identify emerging research themes. Results Academic publications on AI in tourism and hospitality demonstrated a significant surge during 2017–2020, reflecting the industry’s growing emphasis on smart marketing applications. Thematic analysis identified four major research clusters: (i) Digital Influence and Tourist Behaviour Analytics; (ii) AI-Enabled Smart Tourism and Commerce Ecosystems; (iii) Technology-Driven Hospitality and Experience Innovation; and (iv) Data-Driven Decision Making in Predictive Tourism Modelling. Conclusions This bibliometric and thematic assessment reveals the evolving intellectual landscape of AI applications in tourism and hospitality marketing, documenting substantive research growth and the emergence of distinct thematic clusters that shape current and future research agendas in this dynamic field. READ ALL READ LESS Keywords Tourism, Hospitality, Marketing, Artificial Intelligence, Systematic literature review, Bibliometric Analysis, Thematic Analysis, Process Innovation, SPAR-4-SLR Corresponding Author(s) Premendra Kumar Singh ( [email protected] ) Close Corresponding author: Premendra Kumar Singh Competing interests: No competing interests were disclosed. Grant information: The authors offer special gratitude to INTI International University for the opportunity to conduct research and publish the research work. In particular, the authors would like to thank INTI International University for funding the publication of this research work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Tyagi PK et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Tyagi PK, Aggarwal P, Tyagi P et al. Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.12688/f1000research.177254.1 ) First published: 05 Feb 2026, 15 :192 ( https://doi.org/10.12688/f1000research.177254.1 ) Latest published: 20 Apr 2026, 15 :192 ( https://doi.org/10.12688/f1000research.177254.2 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction The tourism and hospitality sector faces a profound digital transformation, with Artificial Intelligence (AI) leading the effort to generate competitive edges in fields such as marketing or customer relationship management. With the advent of machine learning, big data analytics, natural language processing, and algorithmic decision-making technology, the way in which technology is employed for customer acquisition, conversion and retention for tourism and hospitality firms online has been transformed. AI-powered technologies are also embedded in marketing activities such as customer scoring, dynamic pricing, recommendation engines, demand planner forecast, sentiment analysis, and personalized campaign instead of typical strategic and tactical marketing. Prior research has demonstrated that AI may assist firms to enhance marketing effectiveness by handling and analyzing copious amounts of structured and unstructured data (e.g., past purchases, browsing behaviors, social media communications, web reviews, or real-time behavioral cues). Such insights derived from data can help companies in developing more personalized products and personalized promotional messages and also rationalising their pricing strategies for context-based user experiences, leading to an increase in conversion rate, brand loyalty and CLV ( Samala et al., 2022 ). Negligible AI-based marketing tools in tourism and hospitality sectors where the level of service differentiation is high, are yet even more potent as the characteristics of intangibility, perishability, and information asymmetry is existential through the destination image formation (as a precursor to pleasure), travel intention or motivation (driver towards psychological commitment), as well as perceptions toward services provided by the industry. Despite these opportunities, the growing reliance on AI in tourism and hospitality marketing activities has raised significant questions about ethics, data privacy, transparency of use of algorithms, trust from consumers as well as levels of “human touch” in service interactions ( Kandampully et al., 2023 ). However, despite the enabling aspects of automation and AI-driven systems in making things more efficient and scalable, an over-dependence on AI might lead to depersonalisation, biased decisions and reduced emotional engagement (critical for providing memorable tourist experiences). Consequently, current literature tries to overcome this kind of trade-off by emphasizing responsible, explainable and trust-based AI use as opposed to technology intelligence versus human-centric marketing. Although there is an abundance of research on AI, tourism and marketing, this literature is fragmented across domains, methodologies and application fields. The retrieved work covers a variety of theoretical underpinnings, methodological approaches and context of studies including social media analytics and sentiment analysis, smart tourism ecosystems, immersive technologies to predictive modeling. While narrative reviews and conceptual papers exist in the literature, a comprehensive and systematically built synthesis on the intellectual roots, publication patterns and emerging thematic clusters of AI tourism and hospitality marketing research is lacking. To fill this gap, the current article systematically investigates AI-based tourism and hospitality marketing research via a bibliometric and thematic review approach within the SPAR-4-SLR protocol framework with advanced bibliometric operations as well as keyword co-occurrence analysis. Through the lens of 320 peer-reviewed articles published from 2003 to 2025, this review paper attempts to offer a comprehensive picture on field development in terms of dominating and emerging research themes and suggestions for future work. Based on these theoretical and empirical gaps, the aims of this article are to answer three research questions (RQs) by reviewing existing literature: RQ1: What is the current state of research in the field of AI-Based Tourism and Hospitality Marketing? RQ2: What are the emerging themes associated with AI-Based Tourism and Hospitality Marketing? 2. Methodology The study employed a three-step methodology consisting of the SPAR-4-SLR protocol, bibliometric analysis, and thematic analysis to systematically review research publications. Systematic literature reviews (SLRs) are designed to minimize bias and maximize transparency and reproducibility ( Brignardello-Petersen et al., 2025 ; Shehata & D’Souza, 2025 ). SLRs start from an explicit question, eligibility criteria, and a registered or predefined protocol, reducing selective decision-making and reporting bias ( Boell & Cecez-Kecmanovic, 2015 ; Tranfield et al., 2003 ). A systematic literature review provides a comprehensive overview of the existing research, addresses gaps, and presents avenues for future studies ( Paul & Criado, 2020 ; Tranfield et al., 2003 ). In the first phase, the SPAR-4-SLR protocol is utilised to systematically gather the research papers from the Scopus database. The second step consisted of bibliometric analysis using VOSviewer (version 1.6.8) and Biblioshiny software. The bibliometric review was particularly fit for purpose in this study because it focuses on statistics and trends of a review domain ( Castillo-Vergara et al., 2018 ). Last, a thematic analysis was conducted through keyword co-occurrence by VOSviewer. 2.1 SPAR-4-SLR protocol The SPAR-4-SLR protocol, proposed by Paul et al., (2021) , delineates the methodology’s framework, comprising three stages and six substages: 2.1.1 Assembling 2.1.1.1 Identification This study delineates AI-based tourism and hospitality marketing as the focal domain of the review. The research is guided by Research Questions 1 and 2 (presented at the end of the Introduction), which direct the examination of bibliometric patterns, research characteristics, intellectual relationships, thematic structures, and future research agendas within the domain. To achieve these objectives, scholarly publications related to AI-based tourism and hospitality marketing were systematically retrieved from the Scopus database. 2.1.1.2 Acquisition Scopus was selected as the primary database for literature retrieval due to its extensive coverage and reliability, despite the possibility of minor publication time lags ( Paul et al., 2021 ). Relevant publications within the management and allied disciplines were collected in December 2025, covering the period from 2003 to 2025. A comprehensive search strategy employing Boolean operators was adopted. Consistent with prior review methodologies, the operator “OR” was predominantly used to avoid excessive restriction in retrieval Paul et al., (2021) . The search string included the following keywords: “artificial intelligence” OR “AI” OR “machine learning” OR “deep learning” OR “big data” OR “chatbot*” OR “algorithm*” AND “tourism” OR “travel” OR “hospitality” OR “hotel*” OR “destination*” OR “tourism industry” AND “marketing” OR “digital marketing” OR “destination marketing” OR “branding” OR “promotion” OR “advertising” OR “customer engagement”. No temporal restrictions were imposed at the initial stage, resulting in a dataset spanning twenty-three years (2003–2025). The search was conducted across titles, abstracts, and keywords to ensure alignment with the predefined search terms, yielding an initial pool of 2,228 documents ( Paul et al., 2021 ). 2.1.2 Arranging 2.1.2.1 Organization Following the approach recommended by Paul et al., (2021) , a structured codebook was developed to systematically record and classify the retrieved studies. The coded variables included citation details, references, journal title, document type, year of publication, citation count, author keywords, and country of origin. 2.1.2.2 Purification To enhance the relevance and quality of the dataset, rigorous inclusion and exclusion criteria were applied. A manual screening process confirmed the absence of duplicate records. The inclusion criteria encompassed: (i) document types such as journal articles, conference papers, reviews, and editorials; (ii) subject areas including Business, Management and Accounting, Social Sciences, Environmental Science, Economics, Econometrics and Finance; and (iii) publications written in English. Documents unrelated to AI-based tourism and hospitality marketing were excluded through a detailed assessment of titles, keywords, abstracts, and, where necessary, introductions. This purification process resulted in the exclusion of 1,908 records. Consequently, a final sample of 320 relevant documents was retained for in-depth analysis, as summarized in Table 1 . Table 1. Purification process. Search criteria Entry Results Search Field Article title, Abstract, Keywords Articles identified through Keywords and Boolean term searches (Scopus Database) “artificial intelligence” OR “AI” OR “machine learning” OR “deep learning” OR “big data” OR “chatbot*” OR “algorithm*” AND “tourism” OR “travel” OR “hospitality” OR “hotel*” OR “destination*” OR “tourism industry” AND “marketing” OR “digital marketing” OR “destination marketing” OR “branding” OR “promotion” OR “advertising” OR “customer engagement” 2,228 Filter 1 Document type: Articles, Conference Paper, Review, Editorial 1,741 Filter 2 Scopus Categories: Business, Management, and Accounting, Social Sciences, Environmental Science, Economics, Econometrics and Finance 910 Filter 3 Language: English 867 Filter 4 Documents not pertinent to AI and Tourism (irrelevant papers) – evaluated based on the title, keywords, abstract, and, when applicable, the introduction 547 Full-text Articles included in qualitative analysis Articles were selected after reading the complete research papers 320 2.1.3 Assessing 2.1.3.1 Evaluation In line with the framework proposed by Paul et al., (2021) , the analysis of the selected studies was primarily conducted using bibliometric techniques. To comprehensively map the intellectual structure and thematic evolution of the literature, multiple analytical methods were employed, including bibliographic coupling, cluster analysis, and keyword co-occurrence analysis. In addition to these techniques, sentiment analysis was performed using R software to capture the prevailing and emerging sentiments within the AI-based tourism and hospitality marketing literature and to infer potential future research directions. 2.1.3.2 Reporting The findings of the systematic literature review are presented through a combination of narrative discussion and visual representations. These outputs emphasize the classification, structure, and thematic organization of the existing body of knowledge. The study concludes with a critical discussion of its limitations, thereby providing transparency and context for the interpretation of the results. 2.2 Bibliometric and thematic analysis This study adopts a bibliometric and thematic approach to systematically review the literature on AI-based tourism and hospitality marketing, utilizing the VOSviewer and Biblioshiny software packages. Bibliometric analysis has indeed become highly prominent and methodologically sophisticated in business and management, largely because of powerful tools and broader access to Scopus and Web of Science ( Caputo & Kargina, 2022 ; Donthu et al., 2021 ). In the present study, VOSviewer and Biblioshiny were employed to generate comprehensive descriptive and relational insights, including publication trends, three-field plots, identification of influential publications, country-level and document-level bibliographic coupling, keyword co-occurrence networks, thematic evolution, trend analysis, and WordCloud visualizations. Together, these analyses provide a structured and multidimensional understanding of the intellectual foundations, thematic clusters, and emerging trajectories in AI-based tourism and hospitality marketing research. 3. Findings 3.1 Annual scientific production The search of the Scopus database identified 320 publications concerning AI-driven tourism and hospitality marketing, with the earliest publication dating back to 2003. Based on the analysis of the publication structures, it is evident that the volume of literature has significantly increased over the past four years ( Figure 1 ). The expanding corpus of literature establishing the legitimacy of AI-driven tourism and hospitality marketing as a scholarly discipline. Figure 1. Annual scientific production (Source: Biblioshiny). 3.2 Three-fields plot The three field diagram has shown the conceptual framework of research themes on AI, tourism and marketing with links among sources (journal names), descriptors (author keywords), and authors ( Figure 2 ). This chart is based on the most popular Sankey-diagrams. The three components are visualized with the grey links representing their connection, from the keywords to countries and journal titles. Sizes of the boxes reflect the frequencies ( Riehmann et al., 2005 ). As a result, the size of every rectangle in every list shows the number of documents to that element. The reference dimension reveals that the International Journal of Hospitality Management, International Journal of Contemporary Hospitality Management, Tourism Management, Tourism Tourism Management Perspectives, Journal of Travel & Tourism Marketing and Information Technology & Tourism are identified as key publications outlets by showing that hospitality- tourism marketing and technologyfocused research provides the foundation to this social universe. From the descriptor field, it can be seen that technology-oriented topics such as artificial intelligence (AI), big data, machine learning, deep learning and generative AI are dominating the research scene and marketing-based ideas such as tourism marketing, destination marketing, destination image communication with customers and customer satisfaction complete this picture. It also shows that for the tourism industry, social media sentiment analysis and big data analytics should be increasingly focused on the move towards more personal marketing strategies. In conclusion, the solid nodes linking sources to keywords reveal a fast growing interdisciplinary field of research to reshape marketing strategy, destination management and customer experience in tourism and hospitality industry by AI-driven technologies. Figure 2. Three-fields plot (Sources, keywords and authors) (Source: Biblioshiny). 3.3 Most influential publications Figure 3 reveals top 25 influential articles according to the number of global citations. Global citation count is an index tracking the overall number of citations from databases, including the whole of Scopus ( Garfield, 2003 ). The analysis also identifies emerging leading in works to be located by Buhalis, as the articles of which he was author/co-author dominated highly positioned ranks, demonstrating his trailblazing role in shaping technology and AI-based tourism academic research. The most influential study is particularly Buhalis’s ( Buhalis et al., 2019 ) paper from the Journal of Travel & Tourism Marketing—in terms of global citations—followed closely by Jiang & Wen, (2020) in the International Journal of Contemporary Hospitality Management. Figure 3. Most influential publications (Source: Biblioshiny). 3.4 Word cloud The word cloud indicates the central academic research topics on artificial intelligence for tourism marketing—artificial intelligence, marketing, machine learning, big data and tourist destination ( Figure 4 ). The abundant use of social media and the tourism management perspective emphasized the significance of digital ecosystems and data-driven action in tourist domains. Instead, terms such as “tourist behaviour,” “destination image” or “customer engagement” show a clear concern about understanding and enhancing the experience of consumers. Overall, the visualization suggests a growing interest in the use of AI-enhanced and analytics-enabled marketing strategies to support destination competitive advantage development and tourism market performance. Figure 4. Word cloud (Source: Biblioshiny). 3.5 Trend topics In this subsection, trending topics are examined in terms of authors’ keywords which are explicitly assigned by authors and categorize into the main topical focus for each article ( Song et al., 2019 ). In Figure 5 , a temporal analysis was used to show the development and hierarchical structure of research themes in AI-driven tourism and marketing. Early works (approximately 2010–2014) mainly focused on general topics like tourism and management and online reviews, possibly due to an initial concern for digital sources. Keywords of big data, data mining and social media become increasingly prevalent from 2016 onward, which implies that a transition to the data-intensive paradigm is taking place. Artificial intelligence, machine learning, and algorithms became the keyword also following 2019 with topics associated to technology and advertisement strongly correlated with marketing, destination and customer relations. Furthermore, the increasing emphasis on environmental sustainability and tourism development in recent years coupled with a heightened awareness of customer satisfaction indicates that responsible though experience-focused as well as intelligent forms of tourism marketing are emerging priority areas. Taken all together, the evolution analysis reveals an obvious transformation from descriptive digital tourism to AI-driven analytics-based and strategic-oriented marketing research in the context of tourism. Figure 5. Trend topics (Source: Biblioshiny). 3.6 The thematic map Thematic analysis is critical in informing researchers and other stakeholders of potential paths for the future development of research within thematic areas ( Agbo et al., 2021 ). The thematic analysis is based on the collation and connecting of authors’ abstracts. There are two characteristics that characterize these themes: (i) centrality and (ii) density. Centrality (on the horizontal axis) quantifies the extent of correlation among various topics, while density (on the vertical axis) assesses the cohesion among nodes. These are the two elements that end up deciding the overall development and relevance of a particular theme. The more edges a node has, the more central and important it becomes with respect to a thematic network. The cohesiveness of a node, which indicates the denseness of a research area, is an indicator of potential to grow and impacts. For the detailed topical summary of AI-enabled tourism and hospitality marketing sector (Q1–Q4), see Figure 6 . The upper right quadrant (Quad 1) contains the propelling themes, the lower right quadrant (Quad 4) contains fundamental themes, the upper left quadrant (Quad 2) is concerned with highly specialized themes and finally, the lower left quadrant (Quad 3) is related to emergent or dying off themes: 1. Motor Themes (Quad 1): The motor themes quadrant (high centrality and high density) is dominated by marketing, tourist destination, and social media, indicating that these topics are both well-developed and central to the field, and thus act as the main driving forces of current research. Closely related, artificial intelligence, customer engagement, and consumption behavior appear as highly relevant basic themes, suggesting that while these topics are foundational and widely connected to other themes, they continue to evolve and expand in terms of theoretical and methodological depth. 2. Niche Themes (Quad 2): The niche themes quadrant includes market segmentation, willingness to pay, and hospitality industry–focused studies, often linked with big data analytics and sentiment analysis. These themes are well developed but remain relatively specialized, addressing specific applications rather than the core of the field. 3. Emerging/Declining Themes (Quad 3): The emerging or declining themes quadrant features big data, tourism market, and tourism development, indicating either emerging research directions that have yet to gain strong conceptual integration or mature topics experiencing a gradual decline in scholarly attention. 4. Basic Themes (Quad 4): The basic themes quadrant highlights machine learning, sustainability, and ecotourism, reflecting their growing importance and strong connections with the broader research domain, although these themes are still in the process of consolidation. Figure 6. Thematic map (Source: Biblioshiny). 3.7 Bibliographic coupling of countries Figure 7 shows the bibliographic coupling of countries, which illustrates the interlink between national research communities through their common references in works on AI in tourism marketing. Countries like China, the U.S., the U.K., Spain, Italy, and Australia are displayed as bigger nodes in the centre of this network suggesting higher research productivity and stronger intellectual links with other countries. These countries cluster closely, indicating a common theoretical and methodological basis to AI, big data marketing in tourism and customer analytics. Conversely, peripheral countries of the network exhibit weaker coupling strengths representing incipient, or more domain-specific research contributions. Taken together, the map reveals a globally networked but clustered research system, with a group of country actors at its core that are pushing the intellectual boundaries -and potential- for more cross-country collaboration. Figure 7. Bibliographic coupling of countries (Source: VOSviewer). 3.8 Bibliographic coupling of documents Figure 8 is a citation network map of the most cited documents on AI-based tourism and marketing research created with VOSviewer. The network is clearly led by Dwivedi et al., (2024) , who emerges as the tallest and most central node, indicating a high level of impact and the integrative character of his recent AF-ETM publications in AI tourism marketing literature. Seminal contributions of Buhalis et al., (2019) , Buhalis & Foerste, (2015) and Marine-Roig & Anton Clavé, (2015) , in particular, occupy core locations, emphasizing their informing roles on digital tourism, smart tourism, and technology-enabled destination marketing. In addition, research by Giglio et al., (2019) , Yang & Fik, (2014) , Kirilenko & Stepchenkova, (2018) and ( Moreno-Luna et al., 2021 ) composes closely related clusters around these central works to depict thematic consistency around hot topics including big data, social media analytics, tourist behavior, and sustainability. Recent (2021–2024) papers tend to appear in the vicinity of these influential nodes, indicating that current studies can be closely linked to previous theories and methodologies. In sum, the network unveils a coherent and cumulative structure of knowledge development in which a set of a few strongly cited seminal papers stakes out territory after which AI applications in tourism and hospitality marketing are extended or refined by younger works. Figure 8. Bibliographic coupling of documents (Source: VOSviewer). 3.9 Thematic analysis using keywords co-occurrence network This section examines the thematic structure of research on artificial intelligence–enabled tourism and marketing through a keywords co-occurrence network analysis conducted using VOSviewer. Author-defined keywords were analysed to identify frequently co-occurring terms, enabling the detection of dominant research themes and their interrelationships. As illustrated in Figure 9 , the network visualization reveals distinct clusters based on the strength of keyword associations, reflecting the intellectual organization and evolving focus areas of the field. To facilitate interpretation, the identified clusters were grouped into four major thematic domains, each representing a coherent stream of research. Table 2 summarizes these clusters by highlighting their corresponding colours, theme names, core research focus, and representative keywords, thereby providing a structured overview of the key thematic directions shaping AI-driven tourism and hospitality marketing literature. Figure 9. Keywords co-occurrence network (Source: VOSviewer). Table 2. Keywords co-occurrence network summary table. Cluster colour Theme name Core focus Representative keywords Red Digital Influence and Tourist Behaviour Analytics Examines the role of social media in shaping tourist perceptions and travel intentions using data analytics and sentiment analysis. Social media, tourist destination, tourist behavior, sentiment analysis, online reviews, customer engagement, Instagram, Twitter, tourism development Green AI-Enabled Smart Tourism and Commerce Ecosystems Focuses on the integration of artificial intelligence with tourism operations, commerce, sustainability, and emerging digital technologies to enhance value creation and efficiency. Artificial intelligence, tourism, commerce, sales, leisure industry, sustainable practices, blockchain, pricing, emerging technologies Blue Technology-Driven Hospitality and Experience Innovation Explores the use of immersive and digital technologies to transform hospitality services, customer experience, and tourism business models. Hospitality, virtual reality, augmented reality, immersive tourism, business models, digital transformation, operational efficiencies Yellow Data-Driven Decision Making and Predictive Tourism Modelling Addresses the application of algorithms, machine learning, and predictive analytics for forecasting, optimization, and strategic decision-making in tourism management. Algorithms, prediction, forecasting, optimization, decision making, numerical models, spatial analysis, airline industry Theme 1: Digital Influence and Tourist Behaviour Analytics (Red Cluster) Digital platforms, social media, and data analytics now shape how tourists choose destinations, move, spend, and share experiences. Research combines psychological models with big data and AI to understand and predict these behaviours for smarter tourism management. Destination online content (TDOC) quality and user-friendly accessibility strongly shape intentions to visit and eWOM; satisfaction mediates these effects but may affect sharing more than revisits ( Armutcu et al., 2023 ). Social media attributes such as source credibility, homophily, and content quality build destination image and trust, driving impulsive travel intentions ( Wei et al., 2025 ). Technology acceptance factors (usefulness, ease of use, interactivity) foster positive attitudes toward social media and influence travel behaviour (W. Chen et al., 2024 ). Gen X values personal recommendations and reliability; Gen Y/Z rely more on online reviews, social media, visual and real-time content, with mobile-first decision-making ( Mikhailov & Kashevnik, 2020 ). Geo-tagged photos, taxi GPS, and social media check-ins are mined to model movement patterns, popular zones, and flows for destination management ( Mikhailov & Kashevnik, 2020 ). Clustering, fuzzy clustering, and ontological “digital pattern of life” models segment tourist types and predict preferences over time ( Mikhailov & Kashevnik, 2020 ). Neural networks and Bayesian networks improve prediction of spending behaviour, demand, and resource allocation, outperforming traditional models ( Mikhailov & Kashevnik, 2020 ; Tian & Tang, 2025 ). Digital influence on tourist behaviour is substantial: high-quality, usable content; credible influencers; and engaging, mobile-friendly social media shape where people go, how they spend, and what they share. At the same time, big data and AI techniques enable fine-grained prediction of movement and spending, supporting more targeted marketing, smarter infrastructure, and improved visitor experience. Theme 2: AI-Enabled Smart Tourism and Commerce Ecosystems (Green Cluster) AI is reshaping tourism into connected “smart” ecosystems that blend destinations, services, and commerce into seamless, personalized journeys. Across recent work, the focus is on integrating AI with IoT, cloud, and platform models to optimize experience, sustainability, and monetization. Machine learning, NLP, and computer vision process IoT sensor data to enable real-time monitoring, demand forecasting, and context-aware services in Smart Tourism Destinations (STDs) ( Aliyah et al., 2023 ). Integrated GenAI–NLP–IoT systems support trip planning, predictive analytics, multilingual dialogue, and accessibility (audio/voice for disabled tourists) in real-world pilots ( Suanpang & Pothipassa, 2024 ). Foundation-model–based “Parallel Tourism Systems” link virtual simulations with real services, governed by DAOs, to offer agentic, autonomous, end-to-end travel support (). AI-driven marketing in smart destinations supports hyper-personalized offers, SoCoMo campaigns, and revenue optimization while attracting investment and improving residents’ quality of life ( Huang et al., 2025 ). Across tourism and e-commerce, recommendation engines, conversational agents, dynamic pricing, and fraud detection turn journeys into continuous, data-driven engagement loops ( Erdős et al., 2025 ). Systematic reviews highlight AI-enabled customer experience (AICX) as a distinct construct, stressing customer-facing touchpoints and human–AI balance (Y. Chen & Prentice, 2025 ). AI-enabled smart tourism and commerce ecosystems integrate AI, IoT, cloud, and platform governance to deliver personalized, real-time, and sustainable services while optimizing revenue and operations. The opportunity is large, but success depends on ethical data practices, transparency, inclusivity, and preserving meaningful human experiences in increasingly automated environments. Theme 3: Technology-Driven Hospitality and Experience Innovation (Blue Cluster) Digital technologies are reshaping how hospitality firms design, deliver, and manage guest experiences, from mobile apps and AI to VR, robotics, and IoT. Research shows that the most successful innovations blend high-tech with high-touch, using technology to personalize, de-risk, and streamline experiences while preserving the human “soul” of hospitality. Smart environments (IoT, AI, VR/AR) enable extra-sensory, hyper-personalized, and beyond-automation experiences in tourism and hospitality ( Buhalis et al., 2019 ). Digital hospitality includes VR, contactless tech, AI, mobile apps, and cloud systems that reconfigure service encounters and operations ( Buhalis et al., 2019 ). Human-related service innovation has a stronger impact on satisfaction and delight than purely technology-related innovation; technology best works as a moderator/amplifier of human service ( Buhalis et al., 2019 ). Frameworks emphasize a human-centered, experience-oriented approach where technology frees staff for emotional engagement and relationship-building ( Kandampully et al., 2023 ). Luxury and upscale hotels use “techno-business strategies” to balance state-of-the-art tech with personalized, unscripted service tailored to micro-segments ( Kandampully et al., 2023 ). Research gaps around cocreative technologies, HR and change management, strategy, and digital transformation remain. Barriers include implementation cost, resistance to change, and maintaining authenticity and privacy ( Neuhofer et al., 2015 ). Future agendas call for context-specific studies, sustainable digital hospitality, and deeper integration of people-centric design with emerging tech ecosystems ( Park et al., 2023 ; Tai et al., 2021 ). Technology-driven hospitality innovation is most effective when it enhances personalization, safety, and efficiency without displacing the human connection that defines memorable stays. Evidence supports a “high-tech, high-touch” model where smart technologies enable richer co-created experiences, while strategic, human-centered innovation safeguards trust, emotion, and the enduring “soul” of hospitality ( Roy & Pagaldiviti, 2023 ). Theme 4: Data-Driven Decision Making and Predictive Tourism Modelling (Yellow Cluster) Data-driven decision making and predictive modelling now underpin how destinations, hotels, and policymakers plan capacity, manage risk, and design tourism strategies. Research spans big data analytics, AI/machine learning forecasting models, and organizational adoption of data-centric cultures. AI and deep learning models (e.g., deep networks, decomposed deep learning, BiLSTM–Transformer hybrids) significantly outperform traditional econometric/time-series models in forecasting tourist arrivals and demand, while also revealing key drivers of demand ( Dimitriadou et al., 2025 ; Hu et al., 2025 ). Machine learning algorithms such as SVM, Random Forest, Gradient Boosting, and mixed-frequency BiLSTM-MIDAS improve accuracy for arrivals, hotel occupancy, keyword trends, and cross-country demand under uncertainty ( Dimitriadou et al., 2025 ; Hu et al., 2025 ). Big data sources (search queries, social media, geotagged photos, UGC) support fine-grained prediction of flows, preferences, and behaviors at destinations ( Li et al., 2021 ). Recent work pushes hybrid AI models, mixed-frequency data, and uncertainty measures; however, integration of analytics capabilities, governance, and sustainability indicators into routine strategic decisions remains underdeveloped. Data-driven tourism now relies heavily on AI, machine learning, and big data to predict demand, behavior, and financial trends, consistently beating traditional models in accuracy. The main frontier is less technical than organizational: building data cultures, governance, and skills so destinations and firms can embed these predictive tools into every day, ethical, and sustainability-oriented decision making ( Mu et al., 2025 ; Zhang et al., 2021 ). 4. Conclusion This holistic bibliometric and thematic review exhaustively mapped the intellectual domain of AI-enabled tourism and hospitality marketing research by analyzing 320 peer-reviewed publications published between 2003 to 2025. The paper uncovers exponential growth in articles since 2017. The bibliometric study reveals a mature but evolving knowledge domain formed on the depth of work from early scholars like Buhalis, whose research has been central in developing concepts for technology and tourism. The study also reveals intellectual leadership exerting from China, followed by the United States, UK, Spain, Italy and Australia. The thematic analysis through keyword co-occurrence networks uncovered four distinct yet interconnected research clusters: (1) Digital Influence and Tourist Behaviour Analytics, (2) AI-Enabled Smart Tourism and Commerce Ecosystems, (3) Technology-Driven Hospitality and Experience Innovation, and (4) Data-Driven Decision Making and Predictive Tourism Modelling. These themes collectively define the current state and future trajectory of the field. The temporal analysis reveals a clear evolutionary trajectory from early descriptive studies of digital tourism and online reviews toward sophisticated AI-powered analytics, predictive modeling, and sustainability-oriented marketing approaches. Recent literature increasingly addresses critical ethical considerations including data privacy, algorithmic transparency, consumer trust, and the delicate balance between automation efficiency and human-centric service delivery. 5. Future research directions The future research directions are illustrated in Table 3 . Table 3. Future research directions. S.No Cluster Future scope 1 Cluster 1 - Red: Digital Influence and Tourist Behaviour Analytics Examine how tourists consume information and content from social media platforms (e.g., Instagram, TikTok, YouTube, WeChat) and the role of platform-specific features on destination image formation, travel intentions’ generation and booking decision making. Conduct longitudinal studies examining multigenerational reactions to AI-generated personalized marketing communications, influencer content and user-generated reviews giving consideration to the influence of digital provides on cultural and technological literacy. Create state-of-the-art deep neural network structures for real-time multimodal (geo-locale, social media sentiment, weather, local events) tourist-behaviour prediction to enable dynamic destination management and crisis response. 2 Cluster 2 - Green: AI-Enabled Smart Tourism and Commerce Ecosystems Design the overall governance frameworks for AI-driven smart tourism ecosystems, accommodating the conflicting interests of different stakeholders (tourists, companies, citizens and governments) between ROI (Return on Investment) of using AI technology; data sovereignty as well as fair value distribution. Examine the relationship between blockchain technology and AI system for transparent & secure decentralized tourism commerce system that demonstrate loyalty behavior, authenticity certification of experiential tour program or peer-to-peer service exchange in this pioneering path. Analyse how AI-IoT-powered integrated systems can efficiently optimise resource levels, such as energy and water utilisation, carbon footprints and overtourism; influence sustainable tourist behaviours through real-time monitoring/predictive analytics/nudging. 3 Cluster 3 - Blue: Technology-Driven Hospitality and Experience Innovation Discover the emergent phenomenon of metaverse tourism —virtual travel experiences, digital destinations and hybrid physical-virtual tourism—from business model, experience design principles & impact on traditional travel industries. Analyze for comparison the dynamics of interaction between humans and service robots chatbot a AI concierge at hospitality: impact on tourists’ acceptance, satisfaction and emotional attachment respectively to service robot, chatbot and AI concierge. Investigate how technology-mediated experiences affect genuine relationship building, cultural understanding and meaningful engagement with a particular description on the discussion of the tension between emergent technology experiences and place-based authenticity. 4 Cluster 4 - Yellow: Data-Driven Decision Making and Predictive Tourism Modelling Create XAI methodologies for tourism forecasting models, recommendation algorithms and price decisions to become transparent and interpretable by managers, ensuring trustworthiness, regulatory compliance and actionable insights. Build specialist algorithms to forecast low-frequency, high-impact events in travel (political upheavals, pandemics, natural catastrophes, viral social media moments) that significantly impact demand but which are hard to predict with traditional methods. 6. Implications This systematic review makes several important theoretical contributions by providing a comprehensive intellectual structure of the field, identifying knowledge gaps and underexplored areas, and establishing a baseline for understanding the maturation of AI applications in tourism. From a practical standpoint, the findings offer actionable insights for tourism practitioners, hospitality managers, destination marketing organizations, and technology vendors. In conclusion, AI-based tourism and hospitality marketing has matured into a vibrant, multidisciplinary field characterized by robust theoretical foundations, diverse methodological approaches, and significant practical implications. As the field continues to evolve, maintaining a balanced focus on technological innovation, ethical responsibility, sustainability, and human-centric design is required for preserving the authentic, transformative essence of travel. 7. Limitations This study, like others, possesses limitations that suggest new directions for research. The data was first acquired from the Scopus database, predominantly from journals and scholarly papers. Given the constraints of the current research, subsequent studies may explore alternate conceptualizations by utilizing data from additional pertinent sources, like Web of Science and EBSCOhost. Secondly, the dataset comprised exclusively papers published in English, which were identified using specific keywords in the Scopus database. Future investigations may yield more comprehensive results by employing different keywords and examining research published in various languages. Ethics and consent Ethics and consent not required for this study. Data availability statement Zenodo: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review, https://doi.org/10.5281/zenodo.18296037 ( Tyagi & Singh, 2026 ). 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Publisher Full Text Tyagi PK, Singh PK: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review. [Dataset]. Zenodo. 2026. Publisher Full Text Wei Y, Liu H, Zhuo W, et al. : The Influence of Social Media Attributes on Impulsive Travel Intentions: Integrating the Stimulus–Organism–Response Theory and Information Adoption Model. Sustainability. 2025; 17 (10): 4404. Publisher Full Text Yang Y, Fik T: Spatial effects in regional tourism growth. Ann. Tour. Res. 2014; 46 : 144–162. Publisher Full Text Zhang Y, Li G, Muskat B, et al. : Tourism Demand Forecasting: A Decomposed Deep Learning Approach. J. Travel Res. 2021; 60 (5): 981–997. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 05 Feb 2026 ADD YOUR COMMENT Comment Author details Author details 1 University Institute of Tourism and Hospitality Management, Chandigarh University, Mohali, Punjab, India 2 Department of Law, Maharaja Agrasen Institute of Management Studies, Delhi, Delhi, India 3 Faculty of Business and Communications, INTI International University, Negeri Sembilan, Malaysia 4 Research Fellow, Wekerle Business School, Budapest, Hungary 5 Centre for Distance & Online Education, Sharda University, Greater Noida, Uttar Pradesh, India Pankaj Kumar Tyagi Roles: Conceptualization, Formal Analysis, Investigation, Software, Supervision, Writing – Original Draft Preparation Priyanka Aggarwal Roles: Data Curation, Investigation, Resources, Software, Validation, Writing – Review & Editing Priyanka Tyagi Roles: Formal Analysis, Resources, Validation, Visualization Asokan Vasudevan Roles: Funding Acquisition, Supervision, Writing – Review & Editing Premendra Kumar Singh Roles: Conceptualization, Methodology, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The authors offer special gratitude to INTI International University for the opportunity to conduct research and publish the research work. In particular, the authors would like to thank INTI International University for funding the publication of this research work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 20 Apr 2026, 15:192 https://doi.org/10.12688/f1000research.177254.2 version 1 Published: 05 Feb 2026, 15:192 https://doi.org/10.12688/f1000research.177254.1 Copyright © 2026 Tyagi PK et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Tyagi PK, Aggarwal P, Tyagi P et al. Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.12688/f1000research.177254.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 05 Feb 2026 Views 0 Cite How to cite this report: Yadav N. Reviewer Report For: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.5256/f1000research.195448.r456307 ) The direct URL for this report is: https://f1000research.com/articles/15-192/v1#referee-response-456307 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Feb 2026 Nagendra Yadav , Manipal Academy of Higher Education, Manipal, Karnataka, India; Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education (Ringgold ID: 177107), Manipal, Karnataka, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.195448.r456307 SUMMARY OF THE ARTICLE This manuscript presents a comprehensive bibliometric and thematic analysis of artificial intelligence applications in tourism and hospitality marketing, analysing 320 peer-reviewed articles from Scopus database spanning 2003 to 2025. Using the SPAR-4-SLR protocol combined with ... Continue reading READ ALL SUMMARY OF THE ARTICLE This manuscript presents a comprehensive bibliometric and thematic analysis of artificial intelligence applications in tourism and hospitality marketing, analysing 320 peer-reviewed articles from Scopus database spanning 2003 to 2025. Using the SPAR-4-SLR protocol combined with VOSviewer and Biblioshiny software, the authors map the intellectual structure, publication trends, influential contributions, and thematic evolution of the field. The study identifies exponential publication growth post-2017, highlighting China, USA, UK, Spain, Italy, and Australia as leading research hubs, with Buhalis emerging as a seminal contributor. Through keyword co-occurrence analysis, four interconnected thematic clusters are revealed: Digital Influence and Tourist Behaviour Analytics; AI-Enabled Smart Tourism and Commerce Ecosystems; Technology-Driven Hospitality and Experience Innovation; and Data-Driven Decision Making and Predictive Tourism Modelling. REVIEW REPORT Question 1: Clarity, Accuracy, and Literature Review - Yes The manuscript demonstrates a strong command of bibliometric methodology literature and effectively contextualises AI's transformative impact on tourism marketing with recent citations (2023-2025) demonstrating currency. However, critical issues require attention. The introduction explicitly states "three research questions" but only presents RQ1 and RQ2, with RQ3 never articulated. This fundamental structural flaw must be corrected. A corrupted citation "[()] {mark}" appears in Theme 2, compromising scholarly integrity and requiring immediate correction. Question 2: Study Design and Technical Soundness - Yes The SPAR-4-SLR protocol provides a rigorous, systematic framework appropriate for bibliometric reviews, with a well-structured three-stage methodology following established best practices. The integration of bibliometric and thematic analysis offers complementary insights, Scopus selection is justified, and the 23-year timespan appropriately captures field evolution. While single-database limitation and grey literature exclusion are noted, these are acknowledged in limitations and acceptable given resource constraints. Question 3: Methods and Replicability - Yes The methodology provides excellent transparency, enabling independent replication. The search strategy is comprehensively documented with specific Boolean operators. Inclusion/exclusion criteria are explicit, covering document types, subject areas, and language restrictions. The purification process transparently explains the systematic exclusion of 1,908 documents. Software specifications are provided (VOSviewer 1.6.8, Biblioshiny). Most impressively, the complete dataset is deposited in Zenodo (DOI: 10.5281/zenodo.18296037) under Creative Commons Attribution 4.0 license in accessible CSV format, representing gold-standard open science practice enabling full independent verification. Question 4: Statistical Analysis - Not applicable The analytical approaches, frequency analysis, bibliographic coupling, keyword co-occurrence networks, temporal trends, and thematic mapping are entirely appropriate for exploratory bibliometric research objectives and represent established methodological practices. Question 5: Data Availability and Reproducibility - Yes Exemplary standard achieved. Full dataset openly deposited in Zenodo with permanent DOI under Creative Commons Attribution 4.0 license in universally accessible CSV format. This comprehensive data sharing enables verification, replication, and extension reproducibility. The combination of transparent methodology, detailed search documentation, and open data represents best practice in contemporary bibliometric research. Question 6: Validity of Conclusions - Yes This manuscript represents high-quality bibliometric scholarship with rigorous methodology, comprehensive analysis of 320 publications, and exemplary data transparency. The SPAR-4-SLR framework is appropriately applied, the four thematic clusters provide valuable intellectual structure, and the temporal analysis effectively documents field evolution. The Zenodo data deposit represents a gold-standard reproducibility practice. The work makes meaningful theoretical contributions by synthesising fragmented literature and identifying research trajectories. Minor typographical refinements during copyediting would enhance polish, but the manuscript is scientifically sound and suitable for publication. This review will serve as a valuable reference for researchers, practitioners, and policymakers navigating AI-tourism marketing intersections. Strengths of the Study Methodological Rigor: The study employs a well-structured SPAR-4-SLR protocol combined with bibliometric analysis using VOSviewer and Biblioshiny, which is appropriate for mapping research trends. The systematic approach with clear inclusion/exclusion criteria (filtering from 2,228 to 320 documents) enhances reproducibility and transparency. Comprehensive Coverage: The analysis spans 23 years (2003-2025) and identifies four distinct thematic clusters: (i) Digital Influence and Tourist Behaviour Analytics; (ii) AI-Enabled Smart Tourism and Commerce Ecosystems; (iii) Technology-Driven Hospitality and Experience Innovation; and (iv) Data-Driven Decision Making in Predictive Tourism Modelling. This provides a holistic view of the field's evolution. Clear Structure and Presentation: The manuscript follows a logical progression from introduction through methodology to findings, with well-organized sections and systematic presentation of bibliometric results including publication trends, influential authors, and country-level contributions. Major Improvement Fix the research-question logic: the Introduction says, “three research questions,” but only RQ1 and RQ2 are shown; either add the missing RQ3 explicitly (and ensure results address it) or revise the text to “two research questions.” Resolve the “sentiment analysis” gap: the Methods state that sentiment analysis in R was performed, but the Findings sections you present are overwhelmingly bibliometric/thematic; either (a) add a clear sentiment-analysis subsection (data inputs, method/lexicon/model, outputs, validity checks) and integrate it into results, or (b) remove/soften the claim if it wasn’t actually executed/reported. Minor Improvement Some references appear incomplete or inconsistently formatted. A thorough proofread against the journal citation guidelines is needed. Several sentences require revision for clarity and grammatical accuracy. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Hospitality & Tourism I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Yadav N. Reviewer Report For: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.5256/f1000research.195448.r456307 ) The direct URL for this report is: https://f1000research.com/articles/15-192/v1#referee-response-456307 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Ranjan A. Reviewer Report For: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.5256/f1000research.195448.r456302 ) The direct URL for this report is: https://f1000research.com/articles/15-192/v1#referee-response-456302 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Feb 2026 Aditya Ranjan , ITM University, Gwalior, Madhya Pradesh, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.195448.r456302 SUMMARY This manuscript presents a systematic bibliometric and thematic review examining the intellectual landscape of artificial intelligence (AI) applications in tourism and hospitality marketing. The authors analyzed 320 peer-reviewed publications from the Scopus database spanning 2003-2025, employing the SPAR-4-SLR ... Continue reading READ ALL SUMMARY This manuscript presents a systematic bibliometric and thematic review examining the intellectual landscape of artificial intelligence (AI) applications in tourism and hospitality marketing. The authors analyzed 320 peer-reviewed publications from the Scopus database spanning 2003-2025, employing the SPAR-4-SLR protocol alongside VOSviewer and Biblioshiny software for bibliometric analysis. The study addresses two primary research questions: (1) What is the current state of research in AI-based tourism and hospitality marketing? (2) What are the emerging themes in this domain?. The analysis reveals exponential growth in publications since 2017, with China, the United States, UK, Spain, Italy, and Australia emerging as intellectual leaders. Through keyword co-occurrence network analysis, four distinct thematic clusters were identified: Digital Influence and Tourist Behaviour Analytics, AI-Enabled Smart Tourism and Commerce Ecosystems, Technology-Driven Hospitality and Experience Innovation, and Data-Driven Decision Making and Predictive Tourism Modelling. The manuscript contributes to theoretical understanding by mapping the field's evolution from descriptive digital tourism studies toward sophisticated AI-powered analytics and sustainability-oriented marketing approaches. 1. Clarity, Accuracy, and Literature Review The manuscript is clearly and accurately presented. The authors have structured the paper logically, making it easy for the reader to follow the progression of the study. The language is precise, and the key concepts related to AI in tourism marketing are well explained. The literature review is comprehensive, citing a wide range of relevant studies from foundational research to more recent publications. The references are up-to-date and cover a broad spectrum of topics within the field of AI and tourism marketing. The review appropriately highlights the emerging trends, methodologies, and key areas of research. However, the manuscript could benefit from a more detailed discussion of the limitations or potential biases in the existing literature to provide a more balanced view. Suggestion : The authors may consider adding a brief section on the limitations of the studies reviewed, particularly with regard to study methodologies, sample sizes, and potential biases that could influence the findings. 2. Study Design and Technical Soundness The study design is appropriate for the research objectives. The authors utilize a systematic approach, adhering to the SPAR-4-SLR protocol, which is a recognized methodology for conducting systematic literature reviews. The use of bibliometric analysis tools like VOSviewer and Biblioshiny is technically sound, and these tools are well-suited for identifying trends, publication patterns, and thematic clusters within the literature. The design allows the authors to answer their research questions comprehensively, offering both descriptive and relational insights into the field. The methodology is robust, ensuring the reliability of the results. 3. Methods and Replicability The authors provide sufficient details regarding the methodology used in the study, allowing for full replication by other researchers. The process for selecting the studies from the Scopus database is clearly explained, as are the inclusion and exclusion criteria. The use of VOSviewer and Biblioshiny for bibliometric analysis, as well as the thematic analysis through keyword co-occurrence networks, is well-described. The clear description of the search strategy and the tools used makes it possible for others to replicate the study. Suggestions : The manuscript could be strengthened by providing more information on the exact search terms and Boolean operators used in the query would help future researchers replicate the study more easily. 4. Statistical Analysis (if applicable) The study does not involve traditional statistical analysis, as the focus is on bibliometric and thematic analysis using specialized software tools like VOSviewer and Biblioshiny. These tools provide visual insights into the relationships and trends within the literature, but no formal statistical hypothesis testing is performed. Given the nature of the research, this absence of statistical analysis is appropriate. 5. Data Availability and Reproducibility The data underlying the study is fully available, ensuring reproducibility. The authors have made their dataset accessible via Zenodo, which includes the underlying research papers and data used in the bibliometric analysis. This enhances the transparency of the research and allows others to verify or build upon the findings. The detailed explanation of the data retrieval process from Scopus and the associated tools used ensures that the results can be independently reproduced. 6. Validity of Conclusions The conclusions drawn in the manuscript are adequately supported by the results. The authors provide a well-founded synthesis of the trends and thematic clusters within AI-based tourism and hospitality marketing, and their conclusions align with the findings presented in the study. The thematic clusters identified (such as digital influence, AI-enabled smart tourism, and predictive tourism modeling) are well-supported by the bibliometric and thematic analyses conducted. The study also highlights the growing importance of AI in the field, drawing attention to emerging trends and research areas. Suggestions : While the conclusions are well-supported, the authors could further discuss the limitations of their study, particularly regarding the exclusion of non-English articles and the potential biases introduced by the search strategy. This would add transparency and provide a more nuanced understanding of the study's findings. Major Suggestions 1: Missing Research Question The Introduction explicitly states "the aims of this article are to answer three research questions (RQs)" but subsequently presents only two questions (RQ1 and RQ2). RQ3 is never articulated. This creates ambiguity about the research scope and leaves readers questioning whether analysis is incomplete or if a typographical error occurred. 2. Incomplete Citation In Theme 2 (Green Cluster), the text references "Parallel Tourism Systems" followed by an incomplete citation: "[()]" without author or year. Minor Suggestion 1: Acronym Definitions Define "CLV" on first use Expand "SoCoMo" Clarify "TDOC": 2: Citation Format Consistency Review the entire manuscript to ensure uniform citation style. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Tourism, Spiritual Tourism I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ranjan A. Reviewer Report For: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.5256/f1000research.195448.r456302 ) The direct URL for this report is: https://f1000research.com/articles/15-192/v1#referee-response-456302 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 05 Feb 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 20 Apr 26 Version 1 05 Feb 26 read read Aditya Ranjan , ITM University, Gwalior, India Nagendra Yadav , Manipal Academy of Higher Education, Manipal, India; Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education (Ringgold ID: 177107), Manipal, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Yadav N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Feb 2026 | for Version 1 Nagendra Yadav , Manipal Academy of Higher Education, Manipal, Karnataka, India; Welcomgroup Graduate School of Hotel Administration, Manipal Academy of Higher Education (Ringgold ID: 177107), Manipal, Karnataka, India 0 Views copyright © 2026 Yadav N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions SUMMARY OF THE ARTICLE This manuscript presents a comprehensive bibliometric and thematic analysis of artificial intelligence applications in tourism and hospitality marketing, analysing 320 peer-reviewed articles from Scopus database spanning 2003 to 2025. Using the SPAR-4-SLR protocol combined with VOSviewer and Biblioshiny software, the authors map the intellectual structure, publication trends, influential contributions, and thematic evolution of the field. The study identifies exponential publication growth post-2017, highlighting China, USA, UK, Spain, Italy, and Australia as leading research hubs, with Buhalis emerging as a seminal contributor. Through keyword co-occurrence analysis, four interconnected thematic clusters are revealed: Digital Influence and Tourist Behaviour Analytics; AI-Enabled Smart Tourism and Commerce Ecosystems; Technology-Driven Hospitality and Experience Innovation; and Data-Driven Decision Making and Predictive Tourism Modelling. REVIEW REPORT Question 1: Clarity, Accuracy, and Literature Review - Yes The manuscript demonstrates a strong command of bibliometric methodology literature and effectively contextualises AI's transformative impact on tourism marketing with recent citations (2023-2025) demonstrating currency. However, critical issues require attention. The introduction explicitly states "three research questions" but only presents RQ1 and RQ2, with RQ3 never articulated. This fundamental structural flaw must be corrected. A corrupted citation "[()] {mark}" appears in Theme 2, compromising scholarly integrity and requiring immediate correction. Question 2: Study Design and Technical Soundness - Yes The SPAR-4-SLR protocol provides a rigorous, systematic framework appropriate for bibliometric reviews, with a well-structured three-stage methodology following established best practices. The integration of bibliometric and thematic analysis offers complementary insights, Scopus selection is justified, and the 23-year timespan appropriately captures field evolution. While single-database limitation and grey literature exclusion are noted, these are acknowledged in limitations and acceptable given resource constraints. Question 3: Methods and Replicability - Yes The methodology provides excellent transparency, enabling independent replication. The search strategy is comprehensively documented with specific Boolean operators. Inclusion/exclusion criteria are explicit, covering document types, subject areas, and language restrictions. The purification process transparently explains the systematic exclusion of 1,908 documents. Software specifications are provided (VOSviewer 1.6.8, Biblioshiny). Most impressively, the complete dataset is deposited in Zenodo (DOI: 10.5281/zenodo.18296037) under Creative Commons Attribution 4.0 license in accessible CSV format, representing gold-standard open science practice enabling full independent verification. Question 4: Statistical Analysis - Not applicable The analytical approaches, frequency analysis, bibliographic coupling, keyword co-occurrence networks, temporal trends, and thematic mapping are entirely appropriate for exploratory bibliometric research objectives and represent established methodological practices. Question 5: Data Availability and Reproducibility - Yes Exemplary standard achieved. Full dataset openly deposited in Zenodo with permanent DOI under Creative Commons Attribution 4.0 license in universally accessible CSV format. This comprehensive data sharing enables verification, replication, and extension reproducibility. The combination of transparent methodology, detailed search documentation, and open data represents best practice in contemporary bibliometric research. Question 6: Validity of Conclusions - Yes This manuscript represents high-quality bibliometric scholarship with rigorous methodology, comprehensive analysis of 320 publications, and exemplary data transparency. The SPAR-4-SLR framework is appropriately applied, the four thematic clusters provide valuable intellectual structure, and the temporal analysis effectively documents field evolution. The Zenodo data deposit represents a gold-standard reproducibility practice. The work makes meaningful theoretical contributions by synthesising fragmented literature and identifying research trajectories. Minor typographical refinements during copyediting would enhance polish, but the manuscript is scientifically sound and suitable for publication. This review will serve as a valuable reference for researchers, practitioners, and policymakers navigating AI-tourism marketing intersections. Strengths of the Study Methodological Rigor: The study employs a well-structured SPAR-4-SLR protocol combined with bibliometric analysis using VOSviewer and Biblioshiny, which is appropriate for mapping research trends. The systematic approach with clear inclusion/exclusion criteria (filtering from 2,228 to 320 documents) enhances reproducibility and transparency. Comprehensive Coverage: The analysis spans 23 years (2003-2025) and identifies four distinct thematic clusters: (i) Digital Influence and Tourist Behaviour Analytics; (ii) AI-Enabled Smart Tourism and Commerce Ecosystems; (iii) Technology-Driven Hospitality and Experience Innovation; and (iv) Data-Driven Decision Making in Predictive Tourism Modelling. This provides a holistic view of the field's evolution. Clear Structure and Presentation: The manuscript follows a logical progression from introduction through methodology to findings, with well-organized sections and systematic presentation of bibliometric results including publication trends, influential authors, and country-level contributions. Major Improvement Fix the research-question logic: the Introduction says, “three research questions,” but only RQ1 and RQ2 are shown; either add the missing RQ3 explicitly (and ensure results address it) or revise the text to “two research questions.” Resolve the “sentiment analysis” gap: the Methods state that sentiment analysis in R was performed, but the Findings sections you present are overwhelmingly bibliometric/thematic; either (a) add a clear sentiment-analysis subsection (data inputs, method/lexicon/model, outputs, validity checks) and integrate it into results, or (b) remove/soften the claim if it wasn’t actually executed/reported. Minor Improvement Some references appear incomplete or inconsistently formatted. A thorough proofread against the journal citation guidelines is needed. Several sentences require revision for clarity and grammatical accuracy. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Hospitality & Tourism I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Yadav N. Peer Review Report For: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.5256/f1000research.195448.r456307) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/15-192/v1#referee-response-456307 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Ranjan A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Feb 2026 | for Version 1 Aditya Ranjan , ITM University, Gwalior, Madhya Pradesh, India 0 Views copyright © 2026 Ranjan A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions SUMMARY This manuscript presents a systematic bibliometric and thematic review examining the intellectual landscape of artificial intelligence (AI) applications in tourism and hospitality marketing. The authors analyzed 320 peer-reviewed publications from the Scopus database spanning 2003-2025, employing the SPAR-4-SLR protocol alongside VOSviewer and Biblioshiny software for bibliometric analysis. The study addresses two primary research questions: (1) What is the current state of research in AI-based tourism and hospitality marketing? (2) What are the emerging themes in this domain?. The analysis reveals exponential growth in publications since 2017, with China, the United States, UK, Spain, Italy, and Australia emerging as intellectual leaders. Through keyword co-occurrence network analysis, four distinct thematic clusters were identified: Digital Influence and Tourist Behaviour Analytics, AI-Enabled Smart Tourism and Commerce Ecosystems, Technology-Driven Hospitality and Experience Innovation, and Data-Driven Decision Making and Predictive Tourism Modelling. The manuscript contributes to theoretical understanding by mapping the field's evolution from descriptive digital tourism studies toward sophisticated AI-powered analytics and sustainability-oriented marketing approaches. 1. Clarity, Accuracy, and Literature Review The manuscript is clearly and accurately presented. The authors have structured the paper logically, making it easy for the reader to follow the progression of the study. The language is precise, and the key concepts related to AI in tourism marketing are well explained. The literature review is comprehensive, citing a wide range of relevant studies from foundational research to more recent publications. The references are up-to-date and cover a broad spectrum of topics within the field of AI and tourism marketing. The review appropriately highlights the emerging trends, methodologies, and key areas of research. However, the manuscript could benefit from a more detailed discussion of the limitations or potential biases in the existing literature to provide a more balanced view. Suggestion : The authors may consider adding a brief section on the limitations of the studies reviewed, particularly with regard to study methodologies, sample sizes, and potential biases that could influence the findings. 2. Study Design and Technical Soundness The study design is appropriate for the research objectives. The authors utilize a systematic approach, adhering to the SPAR-4-SLR protocol, which is a recognized methodology for conducting systematic literature reviews. The use of bibliometric analysis tools like VOSviewer and Biblioshiny is technically sound, and these tools are well-suited for identifying trends, publication patterns, and thematic clusters within the literature. The design allows the authors to answer their research questions comprehensively, offering both descriptive and relational insights into the field. The methodology is robust, ensuring the reliability of the results. 3. Methods and Replicability The authors provide sufficient details regarding the methodology used in the study, allowing for full replication by other researchers. The process for selecting the studies from the Scopus database is clearly explained, as are the inclusion and exclusion criteria. The use of VOSviewer and Biblioshiny for bibliometric analysis, as well as the thematic analysis through keyword co-occurrence networks, is well-described. The clear description of the search strategy and the tools used makes it possible for others to replicate the study. Suggestions : The manuscript could be strengthened by providing more information on the exact search terms and Boolean operators used in the query would help future researchers replicate the study more easily. 4. Statistical Analysis (if applicable) The study does not involve traditional statistical analysis, as the focus is on bibliometric and thematic analysis using specialized software tools like VOSviewer and Biblioshiny. These tools provide visual insights into the relationships and trends within the literature, but no formal statistical hypothesis testing is performed. Given the nature of the research, this absence of statistical analysis is appropriate. 5. Data Availability and Reproducibility The data underlying the study is fully available, ensuring reproducibility. The authors have made their dataset accessible via Zenodo, which includes the underlying research papers and data used in the bibliometric analysis. This enhances the transparency of the research and allows others to verify or build upon the findings. The detailed explanation of the data retrieval process from Scopus and the associated tools used ensures that the results can be independently reproduced. 6. Validity of Conclusions The conclusions drawn in the manuscript are adequately supported by the results. The authors provide a well-founded synthesis of the trends and thematic clusters within AI-based tourism and hospitality marketing, and their conclusions align with the findings presented in the study. The thematic clusters identified (such as digital influence, AI-enabled smart tourism, and predictive tourism modeling) are well-supported by the bibliometric and thematic analyses conducted. The study also highlights the growing importance of AI in the field, drawing attention to emerging trends and research areas. Suggestions : While the conclusions are well-supported, the authors could further discuss the limitations of their study, particularly regarding the exclusion of non-English articles and the potential biases introduced by the search strategy. This would add transparency and provide a more nuanced understanding of the study's findings. Major Suggestions 1: Missing Research Question The Introduction explicitly states "the aims of this article are to answer three research questions (RQs)" but subsequently presents only two questions (RQ1 and RQ2). RQ3 is never articulated. This creates ambiguity about the research scope and leaves readers questioning whether analysis is incomplete or if a typographical error occurred. 2. Incomplete Citation In Theme 2 (Green Cluster), the text references "Parallel Tourism Systems" followed by an incomplete citation: "[()]" without author or year. Minor Suggestion 1: Acronym Definitions Define "CLV" on first use Expand "SoCoMo" Clarify "TDOC": 2: Citation Format Consistency Review the entire manuscript to ensure uniform citation style. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Tourism, Spiritual Tourism I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Ranjan A. Peer Review Report For: Mapping Research Trends in AI-Based Tourism and Hospitality Marketing: A Bibliometric and Thematic Review [version 1; peer review: 2 approved] . F1000Research 2026, 15 :192 ( https://doi.org/10.5256/f1000research.195448.r456302) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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