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Driving tourism growth through AI adoption in G-7 economies: a quantile regression approach

Author

Listed:
  • Shuai Zhao

    (Shandong Xiehe University, School of Business)

  • Muhammad Adil Javed

    (University of Management and Technology
    Sichuan University of Arts and Science)

  • Xinxiang Gao

    (Sichuan University of Arts and Science)

Abstract

G-7 nations are struggling to drive tourism growth through AI. While AI holds transformative potential for enhancing tourism services through personalization, automation, and predictive analytics, its implementation is hindered by regulatory constraints, infrastructure limitations, and market volatility. This study employs Method of Moments Quantile Regression (MMQR) from 2004 to 2024 to analyze how AI adoption influences tourism growth, economic growth, CO₂ emissions, consumer prices index (CPI), and Information Communication Technology development (ICT). The results indicate that AI adoption significantly and positively contributes to tourism growth by facilitating smart destination management, enabling real-time service personalization, and enhancing traveler digital experiences. Economic growth is positively associated with AI adoption, while CO₂ emissions exhibit a negative relationship, underscoring the tension between industrial activity and sustainable digital transformation. ICT development is identified as the strongest enabler of AI, underscoring the essential role of digital infrastructure in smart tourism systems. Additionally, AI adoption correlates positively with the CPI, suggesting that smart technologies contribute to more responsive and adaptive pricing mechanisms. This study provides novel empirical evidence for policymakers and industry stakeholders on harnessing AI and digital innovations in tourism to achieve balanced technological advancement and sustainable tourism growth in advanced economies.

Suggested Citation

  • Shuai Zhao & Muhammad Adil Javed & Xinxiang Gao, 2026. "Driving tourism growth through AI adoption in G-7 economies: a quantile regression approach," Information Technology & Tourism, Springer, vol. 28(1), pages 1-24, June.
    • Handle: RePEc:spr:infott:v:28:y:2026:i:1:d:10.1007_s40558-025-00342-2
    DOI: 10.1007/s40558-025-00342-2
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