Mapping the Landscape of China's Digital Intelligence Tourism (DIT) in the Post-ChatGPT Era: A Bibliometric Synthesis and Trend Analysis
Abstract: This study employs a mixed-methods approach combining bibliometric analysis and empirical validation to comprehensively examine the innovative development pathways of China’s Digital Intelligence Tourism (DIT) in the post-ChatGPT era. Utilizing a dataset of 398 international publications indexed in the Web of Science, our Bibliometrix analysis confirms the significant positive impact of synergistic GenAI-IoT integration on enhancing tourism’s New Quality Productivity (path coefficient λ=0.72, p<0.01). We propose a five-tiered “Tourism New Quality Productivity Ecosystem” theoretical framework: Innovative Technology Layer; Tourism Products & Services Layer; Market Actor Layer; Supporting Element Layer; Environmental Element Layer. This framework elucidates the interdependencies between technological adoption and industrial collaboration. Four hypotheses are tested and validated:1.Technological convergence significantly enhances tourism’s new quality productivity;2.Drives digital transformation and operational efficiency optimization;3.Facilitates progress toward sustainable development goals;4. Establishes an Asian research paradigm in tourism research through a “Technology-Policy-Culture” synergistic mechanism, with Chinese scholars demonstrating leadership. Our findings reveal that Asian researchers, led by China, have established a novel paradigm in global tourism scholarship through context-specific innovation and theoretical advancements. This study not only delivers a robust framework and actionable guidelines for DIT development but also integrates Chinese perspectives into the global discourse on tourism transformation. Key words: Digital Intelligence Tourism (DIT); Generative Artificial Intelligence (GenAI); Internet of Things (IoT); Smart Tourism Platform; Bibliometric Synthesis; Bibliometrix R
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-05-04
- DOI
- https://doi.org/10.5281/zenodo.15336446
- Primary Topic
- Competitive and Knowledge Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00