The Role of AI in Shaping Future Tourism and Hospitality Trends

doi:10.21203/rs.3.rs-5280180/v1

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The Role of AI in Shaping Future Tourism and Hospitality Trends

https://doi.org/10.21203/rs.3.rs-5280180/v1

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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

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.

2.1. 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

3.1 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.

4.1. 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

5.1. 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.

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

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No competing interests reported.

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The Role of AI in Shaping Future Tourism and Hospitality Trends

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Abstract

Figures

1. Introduction

2. Literature Review

2.1. Artificial Intelligence (AI)- A Technological Revolution

2.2. The Role of Bibliometric Methods and Artificial Intelligence in Advancing Tourism and Hospitality

3. Methodology

3.1 Research data

3.2 Research methods

4. Empirical Results

4.1. Descriptive statistics

4.3 Author Productivity

4.3 Institute Productivity

4.4 Research Trend

5. Conclusion and implications

5.1. Conclusion

5.2. Management implications

5.3 Limitations and development directions of the research

Declarations

Author Contribution

References

Additional Declarations

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