A Bibliometric Analysis of Artificial Intelligence Research in Urban Tourism

Introduction

The rapid growth of urban populations has created environmental and managerial challenges. By leveraging technologies such as the Internet of Things (IoT), blockchain, and Artificial Intelligence (AI), smart cities aim to address these challenges. AI, which was once overlooked due to computational limitations, now plays a key role in urban management, smart transportation, and energy systems and has even transformed urban tourism spaces. Digital technologies, especially in the areas of marketing, destination management, and enhancing visitor experiences, have contributed to the growth of sustainable tourism. Although numerous studies have explored the application of AI in urban tourism, a noticeable gap remains in systematic reviews in this field. This research simultaneously utilized data from both the Web of Science and Scopus databases and employed two software tools, R and VOSviewer, to conduct a bibliometric analysis, providing a fresh perspective on trends and research gaps in this domain.



Methodology

This study is descriptive-applied research that employs a systematic review approach to address the challenge of organizing a large volume of scientific studies and effectively analyzing and synthesizing their data (Akhter et al., 2019). It simultaneously investigates research on artificial intelligence and urban tourism by analyzing publications from two major databases, Scopus and Web of Science, which are known for their comprehensive and high-impact content (Kraus et al., 2022). Following the five-step procedure suggested by Lim et al. (2024), this research was conducted as follows.

1. Data Search: Using a combined protocol based on PRISMA and SPAR-4-SLR, keywords related to AI (e.g., “artificial intelligence,machine learning,augmented reality”) and urban tourism were used to retrieve documents up to May 3, 2025, from both databases.

2. Data Extraction: Relevant data were downloaded in the CSV format for Scopus and BibTeX for Web of Science.

3. Data Integration: The bibliometric data from both sources were merged and de-duplicated using the bibliometrix package in RStudio, creating a unified dataset for analysis.

4. Data Cleaning: Manual cleaning involved removing duplicates based on DOIs and standardizing synonyms across key fields, such as titles and keywords.

5. Finalization: The cleaned dataset was saved in formats compatible with both Bibliometrix and VOSviewer software, which were subsequently used for bibliometric mapping and trend analysis.

Results and discussion

This paper presents a comprehensive bibliometric analysis of the emerging field of artificial intelligence (AI) applications in urban tourism, covering scientific publications from 2006 to 2025. The data extracted from the Web of Science and Scopus databases reveal that 76 scholarly documents have been published across 73 journals during this period, reflecting a steady annual growth rate of approximately 8.84%. The average age of these documents is 4.82 years, and each article has been cited on average 7.65 times. The relatively modest citation count suggests the novelty of this interdisciplinary research domain, which is still evolving and gaining recognition in the academic community.

Publication trends indicate a gradual increase in research output, with limited activity before 2010, followed by a slow but consistent increase between 2011 and 2017. A significant surge in publications was observed from 2018 onwards, peaking in 2023 and 2024, with 11 and 13 papers, respectively. This acceleration likely corresponds to the increasing integration of AI technologies into urban tourism management, including applications in tourist behavior analysis, intelligent destination management, and optimization of urban services tailored for tourists.

Author collaboration patterns highlighted a total of 230 contributing researchers, with an average of 3.25 authors per paper and limited international co-authorship at 10.53%. The dominance of single-country collaborations, particularly within China, which accounts for 47.4% of total publications, indicates a strong domestic research focus. However, countries such as the United Kingdom and Japan demonstrate higher proportions of international collaboration, reflecting varying degrees of global scientific networking across regions.

The analysis of key contributors identifies prolific authors, such as LI J and ZHANG H, with fractionalized contribution metrics revealing differences in author engagement across multiple publications. Citation impact metrics further distinguish researchers, such as ANAND S and KARTHIKEYA M, who have attained higher average citations per year shortly after publication, suggesting their influential role in advancing the field.

Co-authorship network visualization using VOSviewer shows a balanced and interconnected cluster of leading authors, indicating collaborative synergy, although overall international collaboration remains limited. This presents an opportunity to expand cross-border partnerships to enhance knowledge exchange and innovation.

In summary, bibliometric evidence underscores that AI in urban tourism is a rapidly developing research area characterized by interdisciplinary diversity and growing scholarly interest. The identified trends and collaboration patterns provide valuable insights for future research, emphasizing the need for increased international cooperation and deeper exploration of AI’s potential to transform urban tourism ecosystems.

Conclusion

The field of artificial intelligence (AI) in urban tourism is currently transitioning from its nascent stage to scientific maturity. The rapid growth in scholarly output reflects increasing academic and practical interest in leveraging advanced technologies for the management and development of urban tourism destinations. However, to fully realize the potential of this interdisciplinary domain, greater emphasis must be placed on enhancing the qualitative aspects of research, improving citation impact, and fostering deeper international collaboration.

The inherently interdisciplinary nature of this field offers abundant opportunities for innovation but simultaneously demands the establishment of robust multidisciplinary and cross-border collaborative infrastructures. Moreover, prioritizing the scientific impact of researchers over mere publication volumes can guide the sustainable and practical advancement of knowledge. Considering the global trends in technology and smart cities, it is anticipated that AI in urban tourism will emerge as a central interdisciplinary research hub, intersecting digital humanities, urban management, and technological innovation, thereby playing a pivotal role in the design and implementation of novel tourism policies and strategies.

Nonetheless, the present study is limited by its reliance on only two bibliographic databases and exclusive use of quantitative methods. Future research could benefit from employing a systematic review approach that integrates both qualitative and quantitative analyses, providing complementary insights to enrich our understanding of this evolving field