The Role of AI in Shaping Future Tourism and Hospitality Trends

The Role of AI in Shaping Future Tourism and Hospitality Trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of AI in Shaping Future Tourism and Hospitality Trends Quoc-Loc Nguyen, Phi-Phung Tran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5280180/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Integrating artificial intelligence (AI) and other advanced technologies is transforming the Tourism and Hospitality industry, reshaping operations and elevating service standards. This study offers a comprehensive bibliometric analysis of AI’s impact on the sector, providing valuable insights into key research trends, influential authors, and significant contributions from academia and industry. Using data from the Web of Science, the study examines the evolution of AI-related research from 1990 to 2023, highlighting its role in enhancing customer satisfaction, operational efficiency, and innovation. The findings reveal the industry's growing reliance on AI to address challenges such as those posed by the COVID-19 pandemic and advance smart tourism and sustainable development. This research not only maps the current landscape of AI in hospitality and tourism but also identifies future directions for technology-driven growth and innovation. By bridging the gap between academic research and industry practice, this study offers a valuable resource for scholars and practitioners aiming to harness the potential of AI to drive competitive advantage and sustainable progress in the field. Bibliometric Analysis Smart Tourism Sustainable Development Technology Innovation Digital Transformation Trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The Tourism and Hospitality industry is undergoing rapid transformation, driven by technological advancements such as Artificial Intelligence (AI), robotics, and big data (Reis et al., 2020 ). The integration of these technologies into core operational systems like property management, revenue management, and customer relationship management has been a game-changer, enhancing performance metrics and personalizing services (Mariani et al., 2018; Chi et al., 2020). AI's role in redefining the industry is profound, offering tools that improve service quality, customer satisfaction, employee engagement, and overall productivity (Prentice et al., 2020 ). By disrupting traditional systems, AI allows customers to customize their travel experiences, from reservations to purchasing products and services, directly through digital platforms, bypassing traditional intermediaries like hotels and travel agencies (Chen, Y., & Prentice, C., 2024 ). Given the increasing reliance on AI, many hospitality businesses have escalated their technological investments to boost revenue and stay competitive in a fiercely evolving market (Kilichan and Yilmaz, 2020; Loureiro, 2022 ). Despite AI's transformative potential, there remains a gap in academic research that thoroughly examines AI's impact within the tourism context, particularly through the lens of bibliometrics (Gajdošík and Marciš, 2019 ; Shahrizoda, S., & Nargiza, A., 2024 ). While studies have explored AI tools in smart tourism and hospitality, including predictive models for room occupancy and resource management (Kirilenko et al., 2018 ), there is a notable absence of comprehensive bibliometric studies that map the evolution, impact, and future trends of AI in these sectors. This study aims to contribute significantly to the academic discourse on AI applications in the Tourism and Hospitality industries by addressing key research questions that have both theoretical and practical implications. The primary objectives include (1) The research seeks to identify the most influential journals, authors, and organizations that have shaped the discourse on AI in Tourism and Hospitality. Understanding the sources of significant contributions can guide future research and provide a comprehensive view of the knowledge landscape in this field; (2) By analyzing publication trends and keyword co-occurrence, the study aims to map the evolution of AI research in the Tourism and Hospitality sectors. This includes identifying emerging topics, shifts in research focus, and the development of new technological paradigms that are influencing industry practices; (3) The research also examines the geographical distribution of contributions to AI in Tourism and Hospitality, highlighting the countries that are leading in this domain. This global perspective is crucial for understanding the diversity of research approaches and the varying impacts of AI adoption across different regions; (4) The study delves into the specific applications of AI within the industry, from operational efficiencies to enhancing customer experiences. By exploring these applications, the research provides insights into how AI is being utilized to solve real-world problems in the Tourism and Hospitality sectors; (5) Finally, this research identifies gaps in the existing literature and suggests future research directions. By highlighting areas that require further exploration, the study aims to inspire more in-depth research that can drive innovation and improve industry practices. The bibliometric approach employed in this study is particularly valuable as it allows for a comprehensive analysis of the existing literature, providing a macro-level view of research developments over time. By combining co-authorship, co-citation, and co-occurrence analyses, this study offers a multidimensional perspective on AI's role in the Tourism and Hospitality industries, making it a critical resource for both researchers and practitioners. As the industry continues to evolve, the findings of this study will be essential for understanding how AI can be leveraged to enhance competitiveness, improve service quality, and meet the changing demands of consumers. The insights provided will not only contribute to the academic literature but also offer practical guidelines for industry stakeholders looking to integrate AI into their business strategies effectively. 2. Literature Review 2.1. Artificial Intelligence (AI)- A Technological Revolution Artificial intelligence (AI) is a term defined by Bulchand-Gidumal ( 2022 ) to describe machines or computers capable of replicating cognitive functions traditionally associated with human intelligence, such as learning and problem-solving. In the context of the Tourism and Hospitality industry, AI represents a significant leap forward, driven by advancements in Information and Communication Technology (ICT). The concept of e-tourism, enabled by ICT, has ushered in a new era where intelligent systems not only enhance the efficiency of evaluating tourist behavior but also enable the processing of vast datasets from tourists and destinations alike. ICT has fundamentally altered tourist behavior, influencing how they consume, purchase, and share their experiences (Buhalis, 2000 ). Tourists and service providers now benefit from greater mobility, improved decision-making capabilities, and access to more precise and relevant information, all of which contribute to a more satisfying travel experience (Gretzel, 2011 ). As the Tourism and Hospitality industry continues to evolve, AI emerges as the next pivotal phase, building upon the foundations laid by ICT. Studies by Bowen and Whalen ( 2017 ), Gajdošík and Marciš ( 2019 ), and Kazak et al. ( 2020 ) highlight AI's role in this transformation, underscoring its potential to reshape the industry. AI's advanced computational capabilities allow it to navigate complex relationships and problems across various concepts, particularly when working with large datasets—a crucial factor in the increasingly data-driven tourism sector (Inanc-Demir and Kozak, 2019). 2.2. The Role of Bibliometric Methods and Artificial Intelligence in Advancing Tourism and Hospitality Bibliometric methods have become instrumental in evaluating the impact of scientific research on various aspects of the Tourism and Hospitality industry. These methods have provided insights into fields such as smart tourism (Johnson and Samakovlis, 2019 ), food and beverage services (Okumus et al., 2018 ), accommodation services (Köseoglu et al., 2018 ; Okumus et al., 2019 ), sustainable tourism (Ruhanen et al., 2015 ), rural tourism (Ruiz-Real et al., 2020 ), wine tourism (Sánchez et al., 2017) and the economic impact of tourism (Comerio and Strozzi, 2019 ). Since the late 1990s, Artificial Intelligence (AI) has been increasingly applied in the Tourism and Hospitality sectors to enhance predictive capabilities, such as forecasting hotel occupancy and tourism demand. Over time, AI's role has expanded, encompassing tasks like analyzing social media data and online reviews (Kirilenko et al., 2018 ), assessing tourist satisfaction through facial expression recognition (González-Rodríguez et al., 2020 ), forecasting traffic and tourist arrivals (Zhang et al., 2020 ), and generating intelligent recommendations (Zheng et al., 2020 ). According to a McKinsey Global report (Chui et al., 2018 ), AI can significantly outperform traditional analytical methods in the tourism industry, potentially increasing performance and driving revenue growth from 7–11.6%. This positions the tourism industry as one of the primary beneficiaries of AI advancements. The findings from the most cited and co-cited articles highlight that AI is predominantly utilized for forecasting, demand analysis, and recommendation systems within the Tourism and Hospitality sectors. These applications underscore the transformative impact of AI and bibliometric analyses in driving innovation and enhancing service quality across the industry 3. Methodology 3.1 Research data The research data for this study were sourced from the Web of Science (WoS) database, a leading resource that offers comprehensive information on journals, articles, and cited references (Norris and Oppenheim, 2007 ). The WoS database was selected due to its extensive coverage and reliable indexing of scholarly literature, making it an ideal foundation for a thorough bibliometric analysis. To gather the relevant literature, a targeted search strategy was employed using specific topic words and keywords within the WoS database. The keywords "AI" or "artificial intelligence" and "hospitality" or "tourism" were searched across all fields and content. The search syntax utilized was: “AI” OR “artificial intelligence” (All fields) AND “hospitality” OR “tourism” (All fields). This approach ensured the inclusion of a diverse array of documents, including articles, book chapters, conference papers, reviews, records, notes, and letters, thereby providing a comprehensive overview of the existing scientific literature on the subject. The search was conducted without restrictions on document type or language, allowing for a broad and inclusive dataset. The final dataset comprised 5,474 documents published between January 1, 1990, and January 13, 2023, which were downloaded from WoS on January 13, 2023. These documents will be thoroughly analyzed in Chap. 4 - Research Results. The bibliographic data, including articles, authors, titles, keywords, and references, were downloaded in text format to facilitate subsequent analysis. The systematic analysis of AI-related articles in tourism and hospitality was conducted using VOSviewer software (version 1.6.20), which enabled the analysis and direct visualization of data in the form of networks (van Eck and Waltman, 2009 ). 3.2 Research methods To conduct this study, the articles were analyzed by keywords, bibliographic method was selected to evaluate and analyze the topic of artificial intelligence in the Tourism and Hospitality industry, through content analysis including co-authorship, co-citation and co-occurrence analysis. In general, this study investigates cited articles, author collaboration, co-citation, analysis of topic evolution through co-occurrence of keywords over time and popular keywords of AI in tourism research, respectively. Additionally, the results of this analysis contribute to answering the research question raised at the beginning of this study. Co-authorship analysis is performed at the author and organization level. Collaboration networks describe clusters of papers comprising authors and organizations. These networks are a prominent feature of contemporary research because scholars tend to act as members of a group rather than as individual researchers (Glänzel and Schubert, 2005 ). Evaluating co-authorship networks sheds light on how scientific knowledge is analyzed among authors and reveals prominent scholars, thus providing important insights into the future of the field. According to Perianes et al. (2016), papers with only one author will not be able to provide any co-authorship links. Therefore, the paper assumes that each publication analyzed has at least two authors. This means t j > 1 for each publication j. We have the formula as below: $$\:{t}_{j}=\:{\sum\:}_{x=1}^{M}{a}_{xj}$$ Formula for analyzing co-authorship networks 1 In which: M: Number of researchers N: Number of publications analyzed a xj = M × N (a xj equals 1 if researcher x is the author of publication j and equals 0 otherwise) t j : Number of authors of publication k Add to that: q xy : full co-authorship matrix (elements in the matrix equal all the number of co-authorship links between researchers x and y) $$\:{q}_{xy}={\sum\:}_{k=1}^{B}{a}_{xk}{a}_{xk}$$ Formula for analyzing co-authorship networks 2 Co-citation analysis provides valuable insights into the interconnections and relationships among articles based on how frequently they are cited together. This method reveals how knowledge is accumulated and shared within a research field. When two articles are often cited together, it suggests a strong intellectual connection between them, often indicating that they share common methodologies, theoretical frameworks, or research questions. In bibliometric research, co-citation analysis is a crucial tool for mapping the intellectual structure of a field. By identifying clusters of co-cited papers, researchers can uncover the core topics and influential studies that shape a particular area of research. These clusters often represent different schools of thought or subfields within a broader discipline. Let N and M be the number of researchers and publications included in the analysis, respectively, and C = [ C xn ] denotes the N × M citation matrix. C xn is the number of citations given by publication n for researcher x. The formula for calculating a n as the total number of citations given by publication n to all researchers is: $$\:{a}_{n}={\sum\:}_{x=1}^{N}{C}_{xn}$$ Co-citation network analysis formula 1 With a n > 1 for each publication n. W xy : co-citation matrix and equal to the number of co-citation links between researchers x and y. $$\:{W}_{xy}=\:{\sum\:}_{n=1}^{M}{C}_{xn}{C}_{yn}$$ Co-citation network analysis formula 2 Co-occurrence analysis, on the other hand, focuses on the frequency with which keywords appear together in documents. This method is particularly useful for identifying emerging research themes and understanding how different concepts are related. In a co-occurrence network, each node represents a keyword, and edges between nodes indicate that the keywords co-appear in the same documents. The strength of these connections is proportional to the frequency of co-occurrence, helping to visualize the thematic structure of a research field. Different colors in a co-occurrence network typically represent different clusters of related keywords, showing how concepts are grouped within the literature. By analyzing these clusters, researchers can gain insights into the dominant themes and potential gaps in the existing research. 4. Empirical Results 4.1. Descriptive statistics The data analysis of 5,474 documents from the Web of Science, covering the period from January 1, 1990, to January 13, 2023, provides valuable insights into AI research in the Tourism and Hospitality industry (Table 1 ). The dataset reveals that 62.3% of documents are “Article in Progress,” highlighting ongoing research, while 36.4% are fully published articles. The average citation count of 32.67 per article indicates a significant academic impact. The prevalence of in-progress articles suggests a dynamic field with active exploration of AI applications. Future analyses should focus on publication trends, citation distributions, and the impact of different document types to further understand research developments and guide future studies. Table 1 Data Description Statistics Time January 1, 1990 - January 13, 2023 Document 5474 articles Average total number of citations per article 32.67 Article types Article in progress 3412 Article 1991 Review article 103 The article is in early access 92 Chapter article 71 Other genres 42 Based on the research keywords, this study examines key metrics such as the data collection timeline, publication volume, average citations per article, and article genres. The data indicates that from 1990 to 2003, the number of articles published annually was notably low, with fewer than 10 articles per year. In contrast, the period from 2004 to 2022 shows a marked increase in publication volume. Specifically, the years 2004–2007 exhibited relatively low article counts, ranging from 10 to 36 per year. Notably, the period from 2007 to 2012 displayed considerable fluctuations: article counts surged dramatically in 2008, increasing more than sixfold compared to 2007, before decreasing in 2009 to 79 articles. The volume then rose significantly in 2010, reaching 204 articles, representing a more than 2.5-fold increase from 2009. However, the number of articles in 2011 and 2012 was halved compared to 2010. From 2012 to 2015, there was a substantial rise in publications, with a peak of 951 articles in 2015—more than nine times the number published in 2012. This increase aligns with advancements in AI processing technologies, particularly the introduction of GPUs, which enhanced computational efficiency and cost-effectiveness. During this period, IEEE emerged as a leading publisher, contributing approximately 100 articles in 2015. Subsequently, from 2015 to 2018, there was a significant decline in publication volume, with 392 articles in 2016, 348 in 2017, and a low of 276 in 2018. However, from 2019 to 2022, there was a gradual increase in the number of publications, rising annually from just over 30 articles to 130 articles by 2022. Additionally, this research identifies the top 10 publishers contributing to the field of AI applications in Tourism and Hospitality. Chart 2 illustrates that the IEEE (Institute of Electrical and Electronics Engineers) is the leading publisher, with a total of 1,553 articles. IEEE, known for its commitment to advancing technology for societal benefit, has published numerous studies in this domain. Notably, the 2019 article "Technology in the Hospitality Industry: Prospects and Challenges," featured in the IEEE Consumer Electronics journal, explores cutting-edge technologies currently employed in the hospitality sector. The study highlights how these innovations enhance guest experiences and transform service delivery, while also addressing key challenges that must be resolved to ensure sustainable and future-proof solutions in the industry. Following IEEE, Springer Nature is the second-largest publisher, contributing 1,158 articles, while Elsevier ranks third with 781 articles. A notable study published by Elsevier, titled “Research On Information Technology In The Hospitality Industry,” examines the impact of technology on guest decision-making in hotels and underscores the significance of information security in guest satisfaction. Other significant publishers include MDPI with 201 articles, Emerald Group Publishing with 156 articles, Taylor and Francis with 142 articles, and IOP Publishing Ltd with 132 articles. Additionally, journals such as IOS Press, American Physical Society, World Scientific, Wiley, and Association for Computing Machinery contribute a smaller number of articles, ranging from 44 to 126. Researchers often compile citation frequency lists to highlight the most cited journals within their research scope (Hoffmann and Doucette, 2012 ). 4.3 Author Productivity Author co-citation analysis (ACA) has established itself as a critical method for elucidating the intellectual framework of a research domain (Jeong et al., 2014 ). By examining the frequency with which different authors' works are co-cited, ACA reveals the underlying connections and collaborative dynamics within a field (Bayer et al., 1990 ). In this study, ACA was applied to a substantial dataset of 97,275 authors, with the criterion that each author must have at least 20 citations. This rigorous selection process refined the dataset to 448 authors, of whom 442 were ultimately included in the analysis. The results, as illustrated in Fig. 3 , reveal four distinct clusters of co-cited authors, each reflecting a different aspect of artificial intelligence (AI) research in Tourism and Hospitality. The red cluster, comprising 206 authors, is the largest and most diverse. Notable researchers in this cluster include Kumar et al. ( 2016 , 2018 ) and Wei ( 2019 ). Kumar et al.'s studies on chatbot technologies, differentiating between text-based and voice-based interactions, are instrumental in understanding the enhancement of customer service through AI tools. Wei ( 2019 ) offers a comprehensive review of virtual reality (VR) and augmented reality (AR) in the tourism sector, providing a theoretical framework for integrating these technologies into strategic planning for Tourism and Hospitality. The green cluster, which includes 109 authors, focuses on the application of AI during the COVID-19 pandemic. Key contributions from Chi et al. (2012), Ivanov and Webster ( 2019 ), and Gursoy et al. ( 2019 ) examine the role of technologies such as the Internet of Things (IoT), big data, and AI-driven service robots in transforming service delivery and minimizing direct human interaction. These studies, referenced by Li et al. ( 2021 ), emphasize AI's critical role in adapting to pandemic-related challenges and propose four modes of AI service encounters in the hospitality sector. The blue cluster, consisting of 58 authors, is led by prominent researcher Buhalis, 2000 . Buhalis and Amaranggana ( 2015 ) advocate for the utilization of big data to develop smart tourist destinations that offer personalized services. Sigala et al. (2018) further explore how technology reshapes tourism ecosystems, highlighting the dynamic interactions between traditional and technological actors. These studies are cited in Sampaio et al. ( 2021 ), which investigates travel agents' perspectives on AI's impact on enhancing tourism services amidst the pandemic. The yellow cluster, also comprising 58 authors, is centered on AI-based tourism demand forecasting techniques. Significant contributions include Law's (2000) innovative use of back-propagation neural networks, which outperform traditional forecasting models, and Witt's (1995) discussion on econometric models and their empirical accuracy. These studies underscore the evolution of forecasting methodologies and their implications for understanding tourism trends. ACA provides a nuanced view of the research landscape surrounding AI applications in Tourism and Hospitality. The identified clusters not only highlight significant advancements but also offer a comprehensive perspective on how AI technologies are influencing and shaping the future trajectory of the industry. 4.3 Institute Productivity The co-authorship analysis reveals valuable insights into the collaborative dynamics and research productivity of prominent organizations, providing a clearer understanding of how these institutions are shaping the future of AI in Tourism and Hospitality. The analysis focused on 4,877 organizations that have contributed to the field, selecting 34 organizations that meet the criterion of publishing at least 20 works annually. The results are illustrated in Fig. 4 . Co-Authorship Analysis: Key Findings and Organizational Connections Notably, the Chinese Academy of Sciences emerges as a significant player in the light blue cluster, demonstrating substantial collaboration with other institutions such as National Sun Yat-sen University, Taiwan. The Chinese Academy of Sciences leads with a total link strength of 76, indicating extensive collaborative efforts in the field. National Sun Yat-sen University follows with a total link strength of 43, underscoring its active participation in AI research in Tourism and Hospitality. Significant Contributions and Research Impact A notable study by Feng et al. ( 2019 ) from the Chinese Academy of Sciences highlights the application of web search data and big data technology for forecasting tourism demand. This research exemplifies the innovative approaches being explored by leading organizations and their impact on advancing AI applications in the sector. 4.4 Research Trend This study undertook an in-depth examination of the co-keyword network based on author keywords to identify emerging trends in the application of artificial intelligence (AI) within the Tourism and Hospitality sectors. Applying a 15 occurrences per keyword threshold, 56 out of 13,632 keywords met the criteria. Figure 5 visualizes the co-occurrence network, organized into seven distinct clusters, as detailed in Table 2 . Each cluster is color-coded to highlight key trends and insights. Cluster 1: AI's Dominant Role in Tourism and Hospitality The green cluster, prominently featuring the keyword “artificial intelligence” (330 occurrences), signifies AI’s pivotal role in shaping current research and practices. Since 1991, AI research has gained momentum, with a notable surge in publications and citations from 2018 onwards, reflecting its growing importance (Kong et al., 2022 ). The cluster encompasses significant keywords like “hospitality,” “hotel,” and “service quality,” illustrating the extensive use of AI to drive innovation and enhance service standards within the industry. The connection to “robots,” “robotics,” and “service robots” highlights AI’s integration with robotics to revolutionize business processes across Tourism and Hospitality (Mingotto et al., 2021 ). The inclusion of “COVID-19” (49 occurrences) further underscores the pandemic’s role in accelerating AI adoption and research (Wang et al., 2022 ). Cluster 2: Enhancing Operational Efficiency through AI The red cluster reveals keywords such as “computer vision,” “prediction,” “management,” “optimization,” and “destination image,” showcasing AI's impact on operational efficiency and strategic management. AI’s machine learning capabilities are increasingly utilized for risk prediction and revenue optimization in the hospitality sector (Rocha et al., 2020 ; Millauer, 2019 ). The emphasis on “destination image” reflects AI’s role in enhancing destination branding and travel experiences (Wang et al., 2020 ). This cluster also includes “tourism” (170 occurrences) and “deep learning” (123 occurrences), highlighting the growing reliance on AI and deep learning to harness big data and improve tourism services (Essien & Chukwukelu, 2022 ). Cluster 3: Advancing Sustainability in the Post-Pandemic Era The blue cluster, featuring “sustainable development” and “sustainability,” highlights the tourism industry's shift towards sustainable practices after the COVID-19 pandemic. This trend reflects a broader industry movement toward balancing economic, social, and environmental benefits (Gajdošík et al., 2019). The proximity of these keywords to “hospitality” indicates a growing focus on sustainable development as a key strategy for long-term profitability. Cluster 4: Smart Tourism and Technology Integration The yellow cluster, including “smart tourism,” illustrates the increasing demand for technology-driven, environmentally friendly tourism solutions. This trend emphasizes integrating advanced technologies with minimal environmental impact, aligning with consumer preferences for sustainable travel options (Han, 2021 ). The rise of smart tourism reflects a broader desire for innovative yet responsible travel experiences. Cluster 5: Revolutionizing Customer Experience with Voice Assistants The orange cluster focuses on “Voice Assistants,” a technology that recognizes and responds to human commands. This cluster highlights voice technology's significant role in enhancing hotel guest experiences and offering cost-effective solutions for personalized service (Buhalis & Moldavska, 2022 ). This trend points to the need for continuous innovation in service delivery, leveraging voice assistants to improve operational efficiency. Cluster 6: Machine Learning and Human-Robot Interaction The light blue cluster features “machine learning,” “human-robot interaction,” “education,” and “reinforcement learning,” emphasizing the growing importance of advanced learning and understanding of AI technologies. The frequent mention of these keywords underscores the need for education and improved interaction between humans and AI systems, addressing gaps in knowledge and enhancing customer and business experiences (Yörük et al., 2022). Cluster 7: Evolving Trends in AI and Its Implications The analysis of these clusters provides a comprehensive view of how AI technologies are evolving and impacting the Tourism and Hospitality sectors. Each cluster reflects AI's influence, from enhancing operational efficiency and sustainability to revolutionizing customer experiences and integrating advanced technologies. Table 2 Results of co-keyword analysis Keywords Occurrences Total Link strength Keywords Occurrences Total Link strength artificial intelligence 330 299 forecasting 28 26 tourism 170 161 service robots 26 35 machine learning 128 154 education 26 25 deep learning 124 86 sustainability 25 22 hospitality 49 72 robotics 23 27 big data 48 51 smart tourism 23 17 data mining 46 32 innovation 21 17 robots 40 50 Internet of things 21 15 covid-19 39 47 human-robot interaction 20 13 social media 32 36 management 20 10 5. Conclusion and implications 5.1. Conclusion This study provides a comprehensive analysis of the evolution and impact of artificial intelligence (AI) within the tourism and hospitality industries from 1990 to 2023. Utilizing data from Web of Science, the bibliometric analysis elucidates key research trends, influential journals and authors, citation dynamics, collaboration models, and the state of AI-related topics in the field. Research Productivity and Influential Authors The co-citation analysis identifies prominent contributors to AI research in tourism and hospitality, notably including Gursoy, Lv, Chi, Webster, and Ivanov. These scholars have significantly advanced the field, reflected in their extensive publication records and influential studies. Organizational Contributions Co-authorship analysis reveals that the Chinese Academy of Sciences is a leading organization in AI research, with the highest total link strength of 1,165 citations and 76 articles. This underscores its pivotal role in shaping the field and contributing valuable insights and innovations. Research Themes and Trends : Co-keyword analysis reveals that “artificial intelligence” is the most prominent keyword, with research clustered into four main areas: AI technology, technology acceptance, customer perception, and future trends. Other significant keywords include “tourism,” “machine learning,” “deep learning,” and “hospitality,” indicating a robust integration of AI technologies in operational and business practices. The impact of COVID-19 has accelerated the adoption of AI solutions, such as service robots and facial recognition technologies, addressing labor shortages and enhancing guest experiences. Long-Term Developments Research since 1990 has consistently demonstrated AI’s transformative potential in the tourism and hospitality industries. Advancements in AI technologies, including facial expression recognition, traffic forecasting, and smart recommendations, have modernized guest experience management. The growing adoption of integrated AI-based solutions, such as security systems and smart management tools, reflects a significant market expansion driven by operational efficiency and revenue growth. 5.2. Management implications For Government : One of the important goals of the Government is to develop artificial intelligence to improve the quality of service to organizations and people. In the context of the Fourth Industrial Revolution, artificial intelligence is becoming an important factor promoting social development, better meeting the increasing needs of management subjects in all fields. The State should create pilot programs on the use of AI technologies for various purposes, including the needs of Tourism and Hospitality by calling on Vietnamese and foreign enterprises, startups, etc. to participate in giving opinions as well as contributing in terms of technology and training as well as sharing experiences. Thereby, creating opportunities for people to access free data and the most advanced technology applications. From there, people will see the great benefits of applying AI in this field such as cost savings, environmental protection, and convenience. Gradually forming acceptance of using Tourism and Hospitality services with artificial intelligence technology application. As the analysis results of this study, European countries such as the United States, Germany, England, France have a large number of articles publishing research on AI in the field of Tourism and Hospitality, showing that the research trend is developing very strongly in these countries. These are also countries that invest and find useful applications from AI in many fields, so Vietnam should have investment in academia, cooperation with leading corporations and countries in the field of computing, artificial intelligence technology to be able to build an information technology ecosystem with high-quality resources for Vietnam, to receive transfer, apply and effectively develop the most advanced technologies in the world. From there, establish a foundation that would enable Vietnam to investigate novel applications of AI technology in the tourism and hospitality sectors. For businesses providing Tourism and Hospitality services : As a pioneer in digital transformation, the tourist industry is tasked with creating breakthrough improvements through the development of smart tourism on digital platforms. This research also pointed out that other research articles in most countries (especially China - Asia) when referring to AI in the Tourism and Hospitality industry are very interested in topics about “Smart Tourism”, “Sustainable Development”; “Robots” or “Education”. As a result, this study proposes the following solutions for industry businesses: Firstly, not only focus on developing modern technologies to support the provision of the best service quality to customers, but also pay attention to the trend of sustainable tourism development, touchless tourism, etc. Secondly, the tourism business community in our country is mostly small and medium-sized, with limited resources. Therefore, in order to support businesses, the technology products of the Vietnam National Authority of Tourism (VNAT) need to be designed in the direction of forming a shared digital platform, helping businesses have the opportunity to exploit information, access the market, and improve business management efficiency. For example, the Vietnam Travel Guide application serves travel businesses and tour guides; Vietnam Tourism Yellow Pages supports businesses in promoting products and services and connecting with tourists; in addition, there is an electronic ticket system, vending machines, electronic payment tools, etc. Lastly, businesses need to create conditions for customers to access and provide detailed instructions on how to use artificial intelligence technologies applied when they go sightseeing or use Tourism and Hospitality products and services. 5.3 Limitations and development directions of the research This study has several limitations. First, it relies on bibliometric data from Web of Science, which may not include all relevant research from other databases or grey literature, potentially missing emerging trends. Second, the analysis is based on a specific set of keywords, which might not capture all relevant developments in AI applications within tourism and hospitality. The geographic and institutional focus, notably on the United States and the Chinese Academy of Sciences, may also skew the findings, potentially overlooking contributions from other regions and institutions. Additionally, the rapid evolution of AI technology means the findings might become outdated as new developments occur. To address these limitations, future research should integrate data from multiple bibliometric databases and expand keyword sets to cover emerging concepts. Including a broader range of geographic regions and institutions would offer a more balanced view of global trends. Longitudinal studies and interdisciplinary approaches could provide deeper insights into the long-term impact of AI. Empirical case studies could also help evaluate the practical implications of AI technologies in real-world settings. Declarations Author Contribution Quoc-Loc Nguyen did conceptualization; formal analysis; investigation; writing original draft preparation; writing review and editing; supervision; project administrationPhi-Phung Tran did conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; writing original draft preparation; writing review and editing References Bayer, A.E. et al. (1990), “Mapping intellectual structure of a scientific subfield through author cocitations”, Journal of the American Society for Information Science. Vol. 41 No. 6, pp.433-443. Bowen, J., & Whalen, E. (2017), “Trends that are changing travel and tourism ”, Worldwide Hospitality and Tourism Themes, Vol. 9 No. 6, pp.592–602. Buhalis, D. (2000), “Marketing the competitive destination of the future”, Tourism management , Vol. 21 No. 1, pp.97-116. Buhalis, D., & Amaranggana, A. 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(2018), “Automated sentiment analysis in tourism: Comparison of approaches ”, Journal of Travel Research , Vol. 57 No. 8, pp.1012–1025. Kılıçhan, R., & Yılmaz, M. (2020), “Artificial intelligence and robotic technologies in tourism and hospitality industry”, Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi , No. 50, pp.353-380. Kong, H., Wang, K., Qiu, X., Cheung, C., & Bu, N. (2022), “30 years of artificial intelligence (AI) research relating to the hospitality and tourism industry”, International Journal of Contemporary Hospitality Management, Vol. 35 No. 6, pp.2157-2177.. Köseoglu, M.A., Okumus, F., Putra, E.D., Yildiz, M., & Dogan, I.C. (2018), “Authorship trends, collaboration patterns, and co-authorship networks in lodging studies (1990–2016)”, Journal of Hospitality Marketing and Management , Vol. 27 No. 5, pp.561–582. Kumar, R., Li, A., & Wang, W. (2018), “Learning and optimizing through dynamic pricing”, Journal of Revenue and Pricing Management , Vol. 17 No. 2, pp.63-77. Kumar, V. M., Keerthana, A., Madhumitha, M., Valliammai, S., & Vinithasri, V. (2016), “Sanative chatbot for health seekers”, International Journal Of Engineering And Computer Science , Vol. 5 No. 03, pp.16022-16025. Law, R. (2000), “Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting”, Tourism Management , Vol. 21 No. 4, pp.331-340. Li, M., Yin, D., Qiu, H., & Bai, B. (2021), "A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations”, International Journal of Hospitality Management , Vol. 95, pp.102930. Loureiro, S. M. C. (2022), “Technology and luxury in tourism and hospitality”, In The Emerald Handbook of Luxury Management for Hospitality and Tourism (pp. 273-284). Emerald Publishing Limited. Mariani, M., & Borghi, M. (2021), “Customers' evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics”, International Journal of Contemporary Hospitality Management, Vol. 33 No. 11, pp.3956-3976. Millauer, T., & Vellekoop, M. (2019), “Artificial intelligence in today's hotel revenue management: opportunities and risks”, Research in Hospitality Management , Vol. 9 No. 2, pp.121–124. Mingotto, E., Montaguti, F., & Tamma, M. (2021), “Challenges in Re-Designing Operations and Jobs to Embody AI and Robotics in Services. Findings from a Case in the Hospitality Industry”, Electronic Markets , Vol. 31 No. 3, pp.493-510. Norris, M., & Oppenheim, C. (2007), “Comparing alternatives to the Web of Science for coverage of the social sciences' literature”, Journal of Informetrics , Vol. 1 No. 2, pp.161–169. Okumus, B., Koseoglu, M.A., & Ma, F. (2018), "Food and gastronomy research in tourism and hospitality: A bibliometric analysis ”, International Journal of Hospitality Management , Vol. 73, pp.64–74. Okumus, F., Köseoglu, M.A., Putra, E.D., Dogan, I. C., & Yildiz, M. (2019), “A bibliometric analysis of lodging-context research from 1990 to 2016”, Journal of Hospitality and Tourism Research , Vol. 43 No. 2, pp.210–225. Perianes-Rodriguez, A., Waltman, L., & Van Eck, N.J. (2016), “Constructing bibliometric networks: A comparison between full and fractional counting”, Journal of informatics , Vol. 10 No. 4, pp.1178-1195. Prentice, C., Dominique Lopes, S., & Wang, X. (2020), “The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty”, Journal of Hospitality Marketing & Management , Vol. 29 No. 7, pp.739-756. Reis, J., Melão, N., Salvadorinho, J., Soares, B., & Rosete, A. (2020), “Service robots in the hospitality industry: The case of Henn-na hotel, Japan”, Technology in Society , Vol. 63 , pp.101423. Rocha, Á., Abreu, A., de Carvalho, J.V., Liberato, D., González, E.A., & Liberato, P. (Eds.). (2020), “Advances in Tourism, Technology and Smart Systems”, Smart Innovation, Systems and Technologies, pp.307-317. Ruhanen, L., Weiler, B., Moyle, B.D., & McLennan, C.J. (2015), "Trends and patterns in sustainable tourism research: A 25-year bibliometric analysis”, Journal of Sustainable Tourism , Vol. 23 No. 4, pp.517–535. Ruiz-Real, J.L., Uribe-Toril, J., Valenciano, J. de P., & Gázquez-Abad, J.C. (2020), “Rural tourism and development: Evolution in scientific literature and trends”, Journal of Hospitality and Tourism Research , pp.1–25. Sampaio, H.A., Correia, A.I., Melo, C., Brazão, L., & Shehada, S. (2021), “Analyzing Tourism Agents' Perceptions of the Use of Artificial Intelligence”, In Advances in Tourism, Technology and Systems: Selected Papers from ICOTTS20 , Volume 1 (pp. 245-254). Springer Singapore. Sánchez, A.D., de la Cruz Del Río Rama, M., & García, J. Á. (2017), "Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS”, European Research on Management and Business Economics , Vol. 23 No. 1, pp.8–15. Shahrizoda, S., & Nargiza, A. (2024), “The Influence of Modern Technology and Artificial Intelligence in Tourism Industry”, Science and innovation , No. 3 Special Issue 28, pp.640-644. Sigala, M. (2018), “New technologies in tourism: From multi-disciplinary to anti-disciplinary advances and trajectories”, Tourism management perspectives , Vol. 25, pp.151-155. Van Eck, N.J. and Waltman, L. (2009), “Software survey: VOSviewer, a computer program for Bibliometric mapping ”, Scientometrics , Vol. 84 No. 2, pp. 523–538. Wang, K., Kong, H., Bu, N., Xiao, H., Qiu, X., & Li, J. (2022), “AI in health tourism: developing a measurement scale”, Asia Pacific Journal of Tourism Research, Vol. 27 No. 9, pp.954-966. Wang, R., Luo, J., & Huang, S. S. (2020), “Developing an artificial intelligence framework for online destination image photos identification”, Journal of Destination Marketing & Management , Vol. 18, pp.100512. Wei, W. (2019), “Research progress on virtual reality (VR) and augmented reality (AR) in tourism and hospitality: A critical review of publications from 2000 to 2018”, Journal of Hospitality and Tourism Technology, Vol. 10 No. 4, pp.539-570. Witt, SF, & Witt, CA (1995), “Forecasting tourism demand: A review of empirical research”, International Journal of forecasting , Vol. 11 No. 3, pp.447-475. Yörük, T., Akar, N., & Özmen, N. V. (2024), “Research trends on guest experience with service robots in the hospitality industry: a bibliometric analysis”, European Journal of Innovation Management , Vol. 27 No. 6, pp.2015-2041. Zhang, B., Li, N., Shi, F., & Law, R. (2020), “A deep learning approach for daily tourist flow forecasting with consumer search data”, Asia Pacific Journal of Tourism Research, Vol. 25 No. 3, pp.323–339. Zheng, W., Liao, Z., & Lin, Z. (2020), “Navigating through the complex transport system: A heuristic approach for city tourism recommendation”, Tourism Management, Vol 81, pp.104162. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5280180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367398669,"identity":"a76e85c7-591b-41ea-b299-081ce4853051","order_by":0,"name":"Quoc-Loc Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHAD5gYGhgoGxgYQm4c4LSDFZwwYe0jTwthGhBZz/jVmEh93WCf2sx9sfPBz3h/Z/RIJjA/etjEkbsehxXLGGzPJmWfSE2f2JDYb9m4zMO6RSGA2nAvUsrMBuxaDG2fMpHnbDiduuMHYJs24zSARqIUNKMJgbHAAj5a/cC1zwFrYf+PVcr7HTJoRrqUBYgszUIscblvYii1729KNwX7pOWZs3HPmYbPknHMSuLWcP7zxxs82a9l+9sMHH/yokZNtb08++OFNmQ0PLi0MEgksEsCYRxYCJwAJHOqBgP8A8wc0LaNgFIyCUTAKUAEA+Cxdwllli5QAAAAASUVORK5CYII=","orcid":"","institution":"Ton Duc Thang University","correspondingAuthor":true,"prefix":"","firstName":"Quoc-Loc","middleName":"","lastName":"Nguyen","suffix":""},{"id":367398670,"identity":"85788aa3-733e-4579-94d0-7a093aa4d4ca","order_by":1,"name":"Phi-Phung Tran","email":"","orcid":"","institution":"Ton Duc Thang University","correspondingAuthor":false,"prefix":"","firstName":"Phi-Phung","middleName":"","lastName":"Tran","suffix":""}],"badges":[],"createdAt":"2024-10-17 06:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5280180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5280180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67721908,"identity":"eb0b43be-8a10-426d-b08d-4ea4786128ce","added_by":"auto","created_at":"2024-10-29 05:24:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDevelopment of research publications from 2004-2022\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5280180/v1/69c916076f4b9218eeabdf64.png"},{"id":67721906,"identity":"928b60f9-6012-4f23-89ca-d088e7657779","added_by":"auto","created_at":"2024-10-29 05:24:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTop 10 leading publishers in AI applications in tourism and hospitality\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5280180/v1/9e65e6d7abb1d45f64ccf23d.png"},{"id":67721907,"identity":"5ac7d8bd-0437-47d7-bf34-18197a6fedc2","added_by":"auto","created_at":"2024-10-29 05:24:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of co-citation analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5280180/v1/a4ce86b295995d0ffe6aa26d.png"},{"id":67721909,"identity":"0ebfe220-93a5-41ac-982b-42dcbb285c54","added_by":"auto","created_at":"2024-10-29 05:24:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResults of co-authorship analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5280180/v1/52bd1665a57db6d1268bdfba.png"},{"id":67721910,"identity":"7112aaae-c821-4044-a127-313bc4c091e9","added_by":"auto","created_at":"2024-10-29 05:24:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":288726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResearch tendency\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5280180/v1/562324e8c7c1f44579527a35.png"},{"id":69031362,"identity":"c2bae67c-6dce-4ed0-9042-43a980c7c7b8","added_by":"auto","created_at":"2024-11-14 19:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1111121,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5280180/v1/7f349160-13cc-4454-a818-ed58c20ad175.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of AI in Shaping Future Tourism and Hospitality Trends","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Tourism and Hospitality industry is undergoing rapid transformation, driven by technological advancements such as Artificial Intelligence (AI), robotics, and big data (Reis et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The integration of these technologies into core operational systems like property management, revenue management, and customer relationship management has been a game-changer, enhancing performance metrics and personalizing services (Mariani et al., 2018; Chi et al., 2020). AI's role in redefining the industry is profound, offering tools that improve service quality, customer satisfaction, employee engagement, and overall productivity (Prentice et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By disrupting traditional systems, AI allows customers to customize their travel experiences, from reservations to purchasing products and services, directly through digital platforms, bypassing traditional intermediaries like hotels and travel agencies (Chen, Y., \u0026amp; Prentice, C., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the increasing reliance on AI, many hospitality businesses have escalated their technological investments to boost revenue and stay competitive in a fiercely evolving market (Kilichan and Yilmaz, 2020; Loureiro, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite AI's transformative potential, there remains a gap in academic research that thoroughly examines AI's impact within the tourism context, particularly through the lens of bibliometrics (Gajdoš\u0026iacute;k and Marciš, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shahrizoda, S., \u0026amp; Nargiza, A., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While studies have explored AI tools in smart tourism and hospitality, including predictive models for room occupancy and resource management (Kirilenko et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), there is a notable absence of comprehensive bibliometric studies that map the evolution, impact, and future trends of AI in these sectors.\u003c/p\u003e \u003cp\u003eThis study aims to contribute significantly to the academic discourse on AI applications in the Tourism and Hospitality industries by addressing key research questions that have both theoretical and practical implications. The primary objectives include (1) The research seeks to identify the most influential journals, authors, and organizations that have shaped the discourse on AI in Tourism and Hospitality. Understanding the sources of significant contributions can guide future research and provide a comprehensive view of the knowledge landscape in this field; (2) By analyzing publication trends and keyword co-occurrence, the study aims to map the evolution of AI research in the Tourism and Hospitality sectors. This includes identifying emerging topics, shifts in research focus, and the development of new technological paradigms that are influencing industry practices; (3) The research also examines the geographical distribution of contributions to AI in Tourism and Hospitality, highlighting the countries that are leading in this domain. This global perspective is crucial for understanding the diversity of research approaches and the varying impacts of AI adoption across different regions; (4) The study delves into the specific applications of AI within the industry, from operational efficiencies to enhancing customer experiences. By exploring these applications, the research provides insights into how AI is being utilized to solve real-world problems in the Tourism and Hospitality sectors; (5) Finally, this research identifies gaps in the existing literature and suggests future research directions. By highlighting areas that require further exploration, the study aims to inspire more in-depth research that can drive innovation and improve industry practices.\u003c/p\u003e \u003cp\u003eThe bibliometric approach employed in this study is particularly valuable as it allows for a comprehensive analysis of the existing literature, providing a macro-level view of research developments over time. By combining co-authorship, co-citation, and co-occurrence analyses, this study offers a multidimensional perspective on AI's role in the Tourism and Hospitality industries, making it a critical resource for both researchers and practitioners. As the industry continues to evolve, the findings of this study will be essential for understanding how AI can be leveraged to enhance competitiveness, improve service quality, and meet the changing demands of consumers. The insights provided will not only contribute to the academic literature but also offer practical guidelines for industry stakeholders looking to integrate AI into their business strategies effectively.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Artificial Intelligence (AI)- A Technological Revolution\u003c/h2\u003e \u003cp\u003eArtificial intelligence (AI) is a term defined by Bulchand-Gidumal (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to describe machines or computers capable of replicating cognitive functions traditionally associated with human intelligence, such as learning and problem-solving. In the context of the Tourism and Hospitality industry, AI represents a significant leap forward, driven by advancements in Information and Communication Technology (ICT). The concept of e-tourism, enabled by ICT, has ushered in a new era where intelligent systems not only enhance the efficiency of evaluating tourist behavior but also enable the processing of vast datasets from tourists and destinations alike.\u003c/p\u003e \u003cp\u003eICT has fundamentally altered tourist behavior, influencing how they consume, purchase, and share their experiences (Buhalis, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Tourists and service providers now benefit from greater mobility, improved decision-making capabilities, and access to more precise and relevant information, all of which contribute to a more satisfying travel experience (Gretzel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As the Tourism and Hospitality industry continues to evolve, AI emerges as the next pivotal phase, building upon the foundations laid by ICT.\u003c/p\u003e \u003cp\u003eStudies by Bowen and Whalen (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Gajdoš\u0026iacute;k and Marciš (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Kazak et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlight AI's role in this transformation, underscoring its potential to reshape the industry. AI's advanced computational capabilities allow it to navigate complex relationships and problems across various concepts, particularly when working with large datasets\u0026mdash;a crucial factor in the increasingly data-driven tourism sector (Inanc-Demir and Kozak, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. The Role of Bibliometric Methods and Artificial Intelligence in Advancing Tourism and Hospitality\u003c/h2\u003e \u003cp\u003eBibliometric methods have become instrumental in evaluating the impact of scientific research on various aspects of the Tourism and Hospitality industry. These methods have provided insights into fields such as smart tourism (Johnson and Samakovlis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), food and beverage services (Okumus et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), accommodation services (K\u0026ouml;seoglu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Okumus et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), sustainable tourism (Ruhanen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), rural tourism (Ruiz-Real et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), wine tourism (S\u0026aacute;nchez et al., 2017) and the economic impact of tourism (Comerio and Strozzi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the late 1990s, Artificial Intelligence (AI) has been increasingly applied in the Tourism and Hospitality sectors to enhance predictive capabilities, such as forecasting hotel occupancy and tourism demand. Over time, AI's role has expanded, encompassing tasks like analyzing social media data and online reviews (Kirilenko et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), assessing tourist satisfaction through facial expression recognition (Gonz\u0026aacute;lez-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), forecasting traffic and tourist arrivals (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and generating intelligent recommendations (Zheng et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to a McKinsey Global report (Chui et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), AI can significantly outperform traditional analytical methods in the tourism industry, potentially increasing performance and driving revenue growth from 7\u0026ndash;11.6%. This positions the tourism industry as one of the primary beneficiaries of AI advancements.\u003c/p\u003e \u003cp\u003eThe findings from the most cited and co-cited articles highlight that AI is predominantly utilized for forecasting, demand analysis, and recommendation systems within the Tourism and Hospitality sectors. These applications underscore the transformative impact of AI and bibliometric analyses in driving innovation and enhancing service quality across the industry\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research data\u003c/h2\u003e \u003cp\u003eThe research data for this study were sourced from the Web of Science (WoS) database, a leading resource that offers comprehensive information on journals, articles, and cited references (Norris and Oppenheim, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The WoS database was selected due to its extensive coverage and reliable indexing of scholarly literature, making it an ideal foundation for a thorough bibliometric analysis.\u003c/p\u003e \u003cp\u003eTo gather the relevant literature, a targeted search strategy was employed using specific topic words and keywords within the WoS database. The keywords \"AI\" or \"artificial intelligence\" and \"hospitality\" or \"tourism\" were searched across all fields and content. The search syntax utilized was: \u003cb\u003e\u0026ldquo;AI\u0026rdquo; OR \u0026ldquo;artificial intelligence\u0026rdquo; (All fields) AND \u0026ldquo;hospitality\u0026rdquo; OR \u0026ldquo;tourism\u0026rdquo; (All fields).\u003c/b\u003e This approach ensured the inclusion of a diverse array of documents, including articles, book chapters, conference papers, reviews, records, notes, and letters, thereby providing a comprehensive overview of the existing scientific literature on the subject.\u003c/p\u003e \u003cp\u003eThe search was conducted without restrictions on document type or language, allowing for a broad and inclusive dataset. The final dataset comprised 5,474 documents published between January 1, 1990, and January 13, 2023, which were downloaded from WoS on January 13, 2023. These documents will be thoroughly analyzed in Chap.\u0026nbsp;4 - Research Results.\u003c/p\u003e \u003cp\u003eThe bibliographic data, including articles, authors, titles, keywords, and references, were downloaded in text format to facilitate subsequent analysis. The systematic analysis of AI-related articles in tourism and hospitality was conducted using VOSviewer software (version 1.6.20), which enabled the analysis and direct visualization of data in the form of networks (van Eck and Waltman, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research methods\u003c/h2\u003e \u003cp\u003eTo conduct this study, the articles were analyzed by keywords, bibliographic method was selected to evaluate and analyze the topic of artificial intelligence in the Tourism and Hospitality industry, through content analysis including co-authorship, co-citation and co-occurrence analysis. In general, this study investigates cited articles, author collaboration, co-citation, analysis of topic evolution through co-occurrence of keywords over time and popular keywords of AI in tourism research, respectively. Additionally, the results of this analysis contribute to answering the research question raised at the beginning of this study.\u003c/p\u003e \u003cp\u003eCo-authorship analysis is performed at the author and organization level. Collaboration networks describe clusters of papers comprising authors and organizations. These networks are a prominent feature of contemporary research because scholars tend to act as members of a group rather than as individual researchers (Gl\u0026auml;nzel and Schubert, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Evaluating co-authorship networks sheds light on how scientific knowledge is analyzed among authors and reveals prominent scholars, thus providing important insights into the future of the field.\u003c/p\u003e \u003cp\u003eAccording to Perianes et al. (2016), papers with only one author will not be able to provide any co-authorship links. Therefore, the paper assumes that each publication analyzed has at least two authors. This means t\u003csub\u003ej\u003c/sub\u003e \u0026gt; 1 for each publication j. We have the formula as below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{t}_{j}=\\:{\\sum\\:}_{x=1}^{M}{a}_{xj}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFormula for analyzing co-authorship networks 1\u003c/p\u003e \u003cp\u003eIn which:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eM: Number of researchers\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eN: Number of publications analyzed\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ea \u003csub\u003exj\u003c/sub\u003e = M \u0026times; N (a \u003csub\u003exj\u003c/sub\u003e equals 1 if researcher x is the author of publication j and equals 0 otherwise)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003et \u003csub\u003ej\u003c/sub\u003e : Number of authors of publication k\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAdd to that:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eq \u003csub\u003exy\u003c/sub\u003e : full co-authorship matrix (elements in the matrix equal all the number of co-authorship links between researchers x and y)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{q}_{xy}={\\sum\\:}_{k=1}^{B}{a}_{xk}{a}_{xk}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFormula for analyzing co-authorship networks 2\u003c/p\u003e \u003cp\u003eCo-citation analysis provides valuable insights into the interconnections and relationships among articles based on how frequently they are cited together. This method reveals how knowledge is accumulated and shared within a research field. When two articles are often cited together, it suggests a strong intellectual connection between them, often indicating that they share common methodologies, theoretical frameworks, or research questions.\u003c/p\u003e \u003cp\u003eIn bibliometric research, co-citation analysis is a crucial tool for mapping the intellectual structure of a field. By identifying clusters of co-cited papers, researchers can uncover the core topics and influential studies that shape a particular area of research. These clusters often represent different schools of thought or subfields within a broader discipline.\u003c/p\u003e \u003cp\u003eLet \u003cem\u003eN\u003c/em\u003e and \u003cem\u003eM\u003c/em\u003e be the number of researchers and publications included in the analysis, respectively, and \u003cem\u003eC\u003c/em\u003e = [\u003cem\u003eC\u003c/em\u003e\u003csub\u003exn\u003c/sub\u003e] denotes the \u003cem\u003eN \u0026times; M\u003c/em\u003e citation matrix. \u003cem\u003eC\u003c/em\u003e\u003csub\u003exn\u003c/sub\u003e is the number of citations given by publication n for researcher x.\u003c/p\u003e \u003cp\u003eThe formula for calculating \u003cem\u003ea\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e as the total number of citations given by publication n to all researchers is:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{a}_{n}={\\sum\\:}_{x=1}^{N}{C}_{xn}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCo-citation network analysis formula 1\u003c/p\u003e \u003cp\u003eWith a\u003csub\u003en\u003c/sub\u003e \u0026gt; 1 for each publication n.\u003c/p\u003e \u003cp\u003eW\u003csub\u003exy\u003c/sub\u003e: co-citation matrix and equal to the number of co-citation links between researchers x and y.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{W}_{xy}=\\:{\\sum\\:}_{n=1}^{M}{C}_{xn}{C}_{yn}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCo-citation network analysis formula 2\u003c/p\u003e \u003cp\u003eCo-occurrence analysis, on the other hand, focuses on the frequency with which keywords appear together in documents. This method is particularly useful for identifying emerging research themes and understanding how different concepts are related. In a co-occurrence network, each node represents a keyword, and edges between nodes indicate that the keywords co-appear in the same documents. The strength of these connections is proportional to the frequency of co-occurrence, helping to visualize the thematic structure of a research field.\u003c/p\u003e \u003cp\u003eDifferent colors in a co-occurrence network typically represent different clusters of related keywords, showing how concepts are grouped within the literature. By analyzing these clusters, researchers can gain insights into the dominant themes and potential gaps in the existing research.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe data analysis of 5,474 documents from the Web of Science, covering the period from January 1, 1990, to January 13, 2023, provides valuable insights into AI research in the Tourism and Hospitality industry (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dataset reveals that 62.3% of documents are \u0026ldquo;Article in Progress,\u0026rdquo; highlighting ongoing research, while 36.4% are fully published articles. The average citation count of 32.67 per article indicates a significant academic impact. The prevalence of in-progress articles suggests a dynamic field with active exploration of AI applications. Future analyses should focus on publication trends, citation distributions, and the impact of different document types to further understand research developments and guide future studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJanuary 1, 1990 - January 13, 2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocument\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5474 articles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage total number of citations per article\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArticle types\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArticle in progress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArticle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReview article\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe article is in early access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChapter article\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther genres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the research keywords, this study examines key metrics such as the data collection timeline, publication volume, average citations per article, and article genres. The data indicates that from 1990 to 2003, the number of articles published annually was notably low, with fewer than 10 articles per year. In contrast, the period from 2004 to 2022 shows a marked increase in publication volume.\u003c/p\u003e \u003cp\u003eSpecifically, the years 2004\u0026ndash;2007 exhibited relatively low article counts, ranging from 10 to 36 per year. Notably, the period from 2007 to 2012 displayed considerable fluctuations: article counts surged dramatically in 2008, increasing more than sixfold compared to 2007, before decreasing in 2009 to 79 articles. The volume then rose significantly in 2010, reaching 204 articles, representing a more than 2.5-fold increase from 2009. However, the number of articles in 2011 and 2012 was halved compared to 2010.\u003c/p\u003e \u003cp\u003eFrom 2012 to 2015, there was a substantial rise in publications, with a peak of 951 articles in 2015\u0026mdash;more than nine times the number published in 2012. This increase aligns with advancements in AI processing technologies, particularly the introduction of GPUs, which enhanced computational efficiency and cost-effectiveness. During this period, IEEE emerged as a leading publisher, contributing approximately 100 articles in 2015.\u003c/p\u003e \u003cp\u003eSubsequently, from 2015 to 2018, there was a significant decline in publication volume, with 392 articles in 2016, 348 in 2017, and a low of 276 in 2018. However, from 2019 to 2022, there was a gradual increase in the number of publications, rising annually from just over 30 articles to 130 articles by 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, this research identifies the top 10 publishers contributing to the field of AI applications in Tourism and Hospitality. Chart 2 illustrates that the IEEE (Institute of Electrical and Electronics Engineers) is the leading publisher, with a total of 1,553 articles. IEEE, known for its commitment to advancing technology for societal benefit, has published numerous studies in this domain. Notably, the 2019 article \"Technology in the Hospitality Industry: Prospects and Challenges,\" featured in the IEEE Consumer Electronics journal, explores cutting-edge technologies currently employed in the hospitality sector. The study highlights how these innovations enhance guest experiences and transform service delivery, while also addressing key challenges that must be resolved to ensure sustainable and future-proof solutions in the industry. Following IEEE, Springer Nature is the second-largest publisher, contributing 1,158 articles, while Elsevier ranks third with 781 articles. A notable study published by Elsevier, titled \u0026ldquo;Research On Information Technology In The Hospitality Industry,\u0026rdquo; examines the impact of technology on guest decision-making in hotels and underscores the significance of information security in guest satisfaction.\u003c/p\u003e \u003cp\u003eOther significant publishers include MDPI with 201 articles, Emerald Group Publishing with 156 articles, Taylor and Francis with 142 articles, and IOP Publishing Ltd with 132 articles. Additionally, journals such as IOS Press, American Physical Society, World Scientific, Wiley, and Association for Computing Machinery contribute a smaller number of articles, ranging from 44 to 126. Researchers often compile citation frequency lists to highlight the most cited journals within their research scope (Hoffmann and Doucette, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Author Productivity\u003c/h2\u003e \u003cp\u003eAuthor co-citation analysis (ACA) has established itself as a critical method for elucidating the intellectual framework of a research domain (Jeong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By examining the frequency with which different authors' works are co-cited, ACA reveals the underlying connections and collaborative dynamics within a field (Bayer et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). In this study, ACA was applied to a substantial dataset of 97,275 authors, with the criterion that each author must have at least 20 citations. This rigorous selection process refined the dataset to 448 authors, of whom 442 were ultimately included in the analysis. The results, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, reveal four distinct clusters of co-cited authors, each reflecting a different aspect of artificial intelligence (AI) research in Tourism and Hospitality.\u003c/p\u003e \u003cp\u003eThe red cluster, comprising 206 authors, is the largest and most diverse. Notable researchers in this cluster include Kumar et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Wei (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Kumar et al.'s studies on chatbot technologies, differentiating between text-based and voice-based interactions, are instrumental in understanding the enhancement of customer service through AI tools. Wei (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) offers a comprehensive review of virtual reality (VR) and augmented reality (AR) in the tourism sector, providing a theoretical framework for integrating these technologies into strategic planning for Tourism and Hospitality.\u003c/p\u003e \u003cp\u003eThe green cluster, which includes 109 authors, focuses on the application of AI during the COVID-19 pandemic. Key contributions from Chi et al. (2012), Ivanov and Webster (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Gursoy et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examine the role of technologies such as the Internet of Things (IoT), big data, and AI-driven service robots in transforming service delivery and minimizing direct human interaction. These studies, referenced by Li et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), emphasize AI's critical role in adapting to pandemic-related challenges and propose four modes of AI service encounters in the hospitality sector.\u003c/p\u003e \u003cp\u003eThe blue cluster, consisting of 58 authors, is led by prominent researcher Buhalis, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e. Buhalis and Amaranggana (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) advocate for the utilization of big data to develop smart tourist destinations that offer personalized services. Sigala et al. (2018) further explore how technology reshapes tourism ecosystems, highlighting the dynamic interactions between traditional and technological actors. These studies are cited in Sampaio et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which investigates travel agents' perspectives on AI's impact on enhancing tourism services amidst the pandemic.\u003c/p\u003e \u003cp\u003eThe yellow cluster, also comprising 58 authors, is centered on AI-based tourism demand forecasting techniques. Significant contributions include Law's (2000) innovative use of back-propagation neural networks, which outperform traditional forecasting models, and Witt's (1995) discussion on econometric models and their empirical accuracy. These studies underscore the evolution of forecasting methodologies and their implications for understanding tourism trends.\u003c/p\u003e \u003cp\u003eACA provides a nuanced view of the research landscape surrounding AI applications in Tourism and Hospitality. The identified clusters not only highlight significant advancements but also offer a comprehensive perspective on how AI technologies are influencing and shaping the future trajectory of the industry.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Institute Productivity\u003c/h2\u003e \u003cp\u003eThe co-authorship analysis reveals valuable insights into the collaborative dynamics and research productivity of prominent organizations, providing a clearer understanding of how these institutions are shaping the future of AI in Tourism and Hospitality. The analysis focused on 4,877 organizations that have contributed to the field, selecting 34 organizations that meet the criterion of publishing at least 20 works annually. The results are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCo-Authorship Analysis: Key Findings and Organizational Connections\u003c/em\u003e \u003c/p\u003e \u003cp\u003eNotably, the Chinese Academy of Sciences emerges as a significant player in the light blue cluster, demonstrating substantial collaboration with other institutions such as National Sun Yat-sen University, Taiwan. The Chinese Academy of Sciences leads with a total link strength of 76, indicating extensive collaborative efforts in the field. National Sun Yat-sen University follows with a total link strength of 43, underscoring its active participation in AI research in Tourism and Hospitality.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSignificant Contributions and Research Impact\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA notable study by Feng et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) from the Chinese Academy of Sciences highlights the application of web search data and big data technology for forecasting tourism demand. This research exemplifies the innovative approaches being explored by leading organizations and their impact on advancing AI applications in the sector.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Research Trend\u003c/h2\u003e \u003cp\u003eThis study undertook an in-depth examination of the co-keyword network based on author keywords to identify emerging trends in the application of artificial intelligence (AI) within the Tourism and Hospitality sectors. Applying a 15 occurrences per keyword threshold, 56 out of 13,632 keywords met the criteria. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e visualizes the co-occurrence network, organized into seven distinct clusters, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each cluster is color-coded to highlight key trends and insights.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 1: AI's Dominant Role in Tourism and Hospitality\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe green cluster, prominently featuring the keyword \u0026ldquo;artificial intelligence\u0026rdquo; (330 occurrences), signifies AI\u0026rsquo;s pivotal role in shaping current research and practices. Since 1991, AI research has gained momentum, with a notable surge in publications and citations from 2018 onwards, reflecting its growing importance (Kong et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The cluster encompasses significant keywords like \u0026ldquo;hospitality,\u0026rdquo; \u0026ldquo;hotel,\u0026rdquo; and \u0026ldquo;service quality,\u0026rdquo; illustrating the extensive use of AI to drive innovation and enhance service standards within the industry. The connection to \u0026ldquo;robots,\u0026rdquo; \u0026ldquo;robotics,\u0026rdquo; and \u0026ldquo;service robots\u0026rdquo; highlights AI\u0026rsquo;s integration with robotics to revolutionize business processes across Tourism and Hospitality (Mingotto et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The inclusion of \u0026ldquo;COVID-19\u0026rdquo; (49 occurrences) further underscores the pandemic\u0026rsquo;s role in accelerating AI adoption and research (Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 2: Enhancing Operational Efficiency through AI\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe red cluster reveals keywords such as \u0026ldquo;computer vision,\u0026rdquo; \u0026ldquo;prediction,\u0026rdquo; \u0026ldquo;management,\u0026rdquo; \u0026ldquo;optimization,\u0026rdquo; and \u0026ldquo;destination image,\u0026rdquo; showcasing AI's impact on operational efficiency and strategic management. AI\u0026rsquo;s machine learning capabilities are increasingly utilized for risk prediction and revenue optimization in the hospitality sector (Rocha et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Millauer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The emphasis on \u0026ldquo;destination image\u0026rdquo; reflects AI\u0026rsquo;s role in enhancing destination branding and travel experiences (Wang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This cluster also includes \u0026ldquo;tourism\u0026rdquo; (170 occurrences) and \u0026ldquo;deep learning\u0026rdquo; (123 occurrences), highlighting the growing reliance on AI and deep learning to harness big data and improve tourism services (Essien \u0026amp; Chukwukelu, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 3: Advancing Sustainability in the Post-Pandemic Era\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe blue cluster, featuring \u0026ldquo;sustainable development\u0026rdquo; and \u0026ldquo;sustainability,\u0026rdquo; highlights the tourism industry's shift towards sustainable practices after the COVID-19 pandemic. This trend reflects a broader industry movement toward balancing economic, social, and environmental benefits (Gajdoš\u0026iacute;k et al., 2019). The proximity of these keywords to \u0026ldquo;hospitality\u0026rdquo; indicates a growing focus on sustainable development as a key strategy for long-term profitability.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 4: Smart Tourism and Technology Integration\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe yellow cluster, including \u0026ldquo;smart tourism,\u0026rdquo; illustrates the increasing demand for technology-driven, environmentally friendly tourism solutions. This trend emphasizes integrating advanced technologies with minimal environmental impact, aligning with consumer preferences for sustainable travel options (Han, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The rise of smart tourism reflects a broader desire for innovative yet responsible travel experiences.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 5: Revolutionizing Customer Experience with Voice Assistants\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe orange cluster focuses on \u0026ldquo;Voice Assistants,\u0026rdquo; a technology that recognizes and responds to human commands. This cluster highlights voice technology's significant role in enhancing hotel guest experiences and offering cost-effective solutions for personalized service (Buhalis \u0026amp; Moldavska, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This trend points to the need for continuous innovation in service delivery, leveraging voice assistants to improve operational efficiency.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 6: Machine Learning and Human-Robot Interaction\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe light blue cluster features \u0026ldquo;machine learning,\u0026rdquo; \u0026ldquo;human-robot interaction,\u0026rdquo; \u0026ldquo;education,\u0026rdquo; and \u0026ldquo;reinforcement learning,\u0026rdquo; emphasizing the growing importance of advanced learning and understanding of AI technologies. The frequent mention of these keywords underscores the need for education and improved interaction between humans and AI systems, addressing gaps in knowledge and enhancing customer and business experiences (Y\u0026ouml;r\u0026uuml;k et al., 2022).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCluster 7: Evolving Trends in AI and Its Implications\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe analysis of these clusters provides a comprehensive view of how AI technologies are evolving and impacting the Tourism and Hospitality sectors. Each cluster reflects AI's influence, from enhancing operational efficiency and sustainability to revolutionizing customer experiences and integrating advanced technologies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of co-keyword analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeywords\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccurrences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Link strength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKeywords\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOccurrences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal Link strength\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eartificial intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eforecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eservice robots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emachine learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eeducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edeep learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esustainability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehospitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erobotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebig data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esmart tourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edata mining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003einnovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erobots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternet of things\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecovid-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehuman-robot interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esocial media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emanagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and implications","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Conclusion\u003c/h2\u003e \u003cp\u003eThis study provides a comprehensive analysis of the evolution and impact of artificial intelligence (AI) within the tourism and hospitality industries from 1990 to 2023. Utilizing data from Web of Science, the bibliometric analysis elucidates key research trends, influential journals and authors, citation dynamics, collaboration models, and the state of AI-related topics in the field.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Productivity and Influential Authors\u003c/strong\u003e \u003cp\u003eThe co-citation analysis identifies prominent contributors to AI research in tourism and hospitality, notably including Gursoy, Lv, Chi, Webster, and Ivanov. These scholars have significantly advanced the field, reflected in their extensive publication records and influential studies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOrganizational Contributions\u003c/strong\u003e \u003cp\u003eCo-authorship analysis reveals that the Chinese Academy of Sciences is a leading organization in AI research, with the highest total link strength of 1,165 citations and 76 articles. This underscores its pivotal role in shaping the field and contributing valuable insights and innovations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Themes and Trends\u003c/b\u003e: Co-keyword analysis reveals that \u0026ldquo;artificial intelligence\u0026rdquo; is the most prominent keyword, with research clustered into four main areas: AI technology, technology acceptance, customer perception, and future trends. Other significant keywords include \u0026ldquo;tourism,\u0026rdquo; \u0026ldquo;machine learning,\u0026rdquo; \u0026ldquo;deep learning,\u0026rdquo; and \u0026ldquo;hospitality,\u0026rdquo; indicating a robust integration of AI technologies in operational and business practices. The impact of COVID-19 has accelerated the adoption of AI solutions, such as service robots and facial recognition technologies, addressing labor shortages and enhancing guest experiences.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLong-Term Developments\u003c/strong\u003e \u003cp\u003eResearch since 1990 has consistently demonstrated AI\u0026rsquo;s transformative potential in the tourism and hospitality industries. Advancements in AI technologies, including facial expression recognition, traffic forecasting, and smart recommendations, have modernized guest experience management. The growing adoption of integrated AI-based solutions, such as security systems and smart management tools, reflects a significant market expansion driven by operational efficiency and revenue growth.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Management implications\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFor Government\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eOne of the important goals of the Government is to develop artificial intelligence to improve the quality of service to organizations and people. In the context of the Fourth Industrial Revolution, artificial intelligence is becoming an important factor promoting social development, better meeting the increasing needs of management subjects in all fields. The State should create pilot programs on the use of AI technologies for various purposes, including the needs of Tourism and Hospitality by calling on Vietnamese and foreign enterprises, startups, etc. to participate in giving opinions as well as contributing in terms of technology and training as well as sharing experiences. Thereby, creating opportunities for people to access free data and the most advanced technology applications. From there, people will see the great benefits of applying AI in this field such as cost savings, environmental protection, and convenience. Gradually forming acceptance of using Tourism and Hospitality services with artificial intelligence technology application.\u003c/p\u003e \u003cp\u003eAs the analysis results of this study, European countries such as the United States, Germany, England, France have a large number of articles publishing research on AI in the field of Tourism and Hospitality, showing that the research trend is developing very strongly in these countries. These are also countries that invest and find useful applications from AI in many fields, so Vietnam should have investment in academia, cooperation with leading corporations and countries in the field of computing, artificial intelligence technology to be able to build an information technology ecosystem with high-quality resources for Vietnam, to receive transfer, apply and effectively develop the most advanced technologies in the world. From there, establish a foundation that would enable Vietnam to investigate novel applications of AI technology in the tourism and hospitality sectors.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFor businesses providing Tourism and Hospitality services\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eAs a pioneer in digital transformation, the tourist industry is tasked with creating breakthrough improvements through the development of smart tourism on digital platforms. This research also pointed out that other research articles in most countries (especially China - Asia) when referring to AI in the Tourism and Hospitality industry are very interested in topics about \u0026ldquo;Smart Tourism\u0026rdquo;, \u0026ldquo;Sustainable Development\u0026rdquo;; \u0026ldquo;Robots\u0026rdquo; or \u0026ldquo;Education\u0026rdquo;. As a result, this study proposes the following solutions for industry businesses:\u003c/p\u003e \u003cp\u003eFirstly, not only focus on developing modern technologies to support the provision of the best service quality to customers, but also pay attention to the trend of sustainable tourism development, touchless tourism, etc.\u003c/p\u003e \u003cp\u003eSecondly, the tourism business community in our country is mostly small and medium-sized, with limited resources. Therefore, in order to support businesses, the technology products of the Vietnam National Authority of Tourism (VNAT) need to be designed in the direction of forming a shared digital platform, helping businesses have the opportunity to exploit information, access the market, and improve business management efficiency. For example, the Vietnam Travel Guide application serves travel businesses and tour guides; Vietnam Tourism Yellow Pages supports businesses in promoting products and services and connecting with tourists; in addition, there is an electronic ticket system, vending machines, electronic payment tools, etc.\u003c/p\u003e \u003cp\u003eLastly, businesses need to create conditions for customers to access and provide detailed instructions on how to use artificial intelligence technologies applied when they go sightseeing or use Tourism and Hospitality products and services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and development directions of the research\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, it relies on bibliometric data from Web of Science, which may not include all relevant research from other databases or grey literature, potentially missing emerging trends. Second, the analysis is based on a specific set of keywords, which might not capture all relevant developments in AI applications within tourism and hospitality.\u003c/p\u003e \u003cp\u003eThe geographic and institutional focus, notably on the United States and the Chinese Academy of Sciences, may also skew the findings, potentially overlooking contributions from other regions and institutions. Additionally, the rapid evolution of AI technology means the findings might become outdated as new developments occur.\u003c/p\u003e \u003cp\u003eTo address these limitations, future research should integrate data from multiple bibliometric databases and expand keyword sets to cover emerging concepts. Including a broader range of geographic regions and institutions would offer a more balanced view of global trends. Longitudinal studies and interdisciplinary approaches could provide deeper insights into the long-term impact of AI. Empirical case studies could also help evaluate the practical implications of AI technologies in real-world settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQuoc-Loc Nguyen did conceptualization; formal analysis; investigation; writing original draft preparation; writing review and editing; supervision; project administrationPhi-Phung Tran did conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; writing original draft preparation; writing review and editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBayer, A.E. et al. 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(2009), \u0026ldquo;Software survey: VOSviewer, a computer program for Bibliometric mapping\u003cem\u003e\u0026rdquo;, Scientometrics\u003c/em\u003e, Vol. 84 No. 2, pp. 523\u0026ndash;538.\u003c/li\u003e\n\u003cli\u003eWang, K., Kong, H., Bu, N., Xiao, H., Qiu, X., \u0026amp; Li, J. (2022), \u0026ldquo;AI in health tourism: developing a measurement scale\u0026rdquo;, \u003cem\u003eAsia Pacific Journal of Tourism Research, Vol. 27 \u003c/em\u003eNo. 9, pp.954-966.\u003c/li\u003e\n\u003cli\u003eWang, R., Luo, J., \u0026amp; Huang, S. S. (2020), \u0026ldquo;Developing an artificial intelligence framework for online destination image photos identification\u0026rdquo;, \u003cem\u003eJournal of Destination Marketing \u0026amp; Management\u003c/em\u003e, Vol. 18, pp.100512.\u003c/li\u003e\n\u003cli\u003eWei, W. (2019), \u0026ldquo;Research progress on virtual reality (VR) and augmented reality (AR) in tourism and hospitality: A critical review of publications from 2000 to 2018\u0026rdquo;, \u003cem\u003eJournal of Hospitality and Tourism Technology, Vol. 10 No. 4, pp.539-570.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eWitt, SF, \u0026amp; Witt, CA (1995), \u0026ldquo;Forecasting tourism demand: A review of empirical research\u0026rdquo;, \u003cem\u003eInternational Journal of forecasting\u003c/em\u003e, Vol. 11 No. 3, pp.447-475.\u003c/li\u003e\n\u003cli\u003eY\u0026ouml;r\u0026uuml;k, T., Akar, N., \u0026amp; \u0026Ouml;zmen, N. V. (2024), \u0026ldquo;Research trends on guest experience with service robots in the hospitality industry: a bibliometric analysis\u0026rdquo;, \u003cem\u003eEuropean Journal of Innovation Management\u003c/em\u003e, Vol. 27 No. 6, pp.2015-2041.\u003c/li\u003e\n\u003cli\u003eZhang, B., Li, N., Shi, F., \u0026amp; Law, R. (2020), \u0026ldquo;A deep learning approach for daily tourist flow forecasting with consumer search data\u0026rdquo;,\u003cem\u003e Asia Pacific Journal of Tourism Research, Vol. \u003c/em\u003e25 No. 3, pp.323\u0026ndash;339.\u003c/li\u003e\n\u003cli\u003eZheng, W., Liao, Z., \u0026amp; Lin, Z. (2020), \u0026ldquo;Navigating through the complex transport system: A heuristic approach for city tourism recommendation\u0026rdquo;, \u003cem\u003eTourism Management,\u003c/em\u003e Vol 81, pp.104162.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bibliometric Analysis, Smart Tourism, Sustainable Development, Technology Innovation, Digital Transformation, Trends","lastPublishedDoi":"10.21203/rs.3.rs-5280180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5280180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntegrating artificial intelligence (AI) and other advanced technologies is transforming the Tourism and Hospitality industry, reshaping operations and elevating service standards. This study offers a comprehensive bibliometric analysis of AI\u0026rsquo;s impact on the sector, providing valuable insights into key research trends, influential authors, and significant contributions from academia and industry. Using data from the Web of Science, the study examines the evolution of AI-related research from 1990 to 2023, highlighting its role in enhancing customer satisfaction, operational efficiency, and innovation. The findings reveal the industry's growing reliance on AI to address challenges such as those posed by the COVID-19 pandemic and advance smart tourism and sustainable development. This research not only maps the current landscape of AI in hospitality and tourism but also identifies future directions for technology-driven growth and innovation. 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