Mapping the integration of Industry 4.0 technologies in sustainable tourism: a bibliometric study

Purpose The role of Industry 4.0 technologies in developing sustainable tourism has been gaining importance progressively. This study aims to investigate the potential role of Industry 4.0 technologies (immersive technologies, artificial intelligence, cloud computing, robotics, big data analytics, etc.) in developing and promoting sustainable tourism. Design/methodology/approach The database was extracted from Scopus, and 1,037 articles were selected and analyzed in this bibliometric study. RStudio was used to conduct the descriptive analysis to assess the top-performing countries, authors, journals and institutions in this area of research. The keywords co-occurrence analysis was applied using VOSviewer software to identify the most influential topics. Findings The results of this study reveal that the role of Industry 4.0 technologies has emerged as the key player in achieving sustainability targets in the tourism industry. The results revealed that China is the key contributor to this field of research. The major themes that emerged are: digital destination competitiveness and technology-enabled tourism; digital transformation and smart destination management; integration of the latest technology with transportation systems; technology-supported environmental conservation and ecological tourism; and machine learning–enabled heritage preservation and tourism experience. Originality/value There is a lack of prior bibliometric study investigating how Industry 4.0 technologies contribute to developing sustainable tourism. This study contributes to the field’s understanding in a number of ways. First, this study offers a thorough bibliometric mapping of Industry 4.0 technologies in sustainable tourism, highlighting important trends and thematic groupings that have never been thoroughly examined before. Second, it provides methodological rigor and repeatable findings by combining quantitative bibliometric analysis (Biblioshiny) with network visualization (VOSviewer). To direct future academic research and real-world applications, this study concludes by identifying gaps within each cluster and suggesting an organized research plan.