Which dimensions of AI development shape tourism’s direct contribution to GDP? Evidence from a multi-country panel

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Type of the article: Research Article

Whether national artificial intelligence (AI) ecosystem development shapes tourism’s contribution to GDP is an open empirical question, particularly given the multidimensional nature of modern AI ecosystems and the heterogeneous reliance of countries on tourism. This study identifies which dimensions of national AI ecosystem development drive within-country changes in tourism’s direct GDP share, using panel data from 33 countries over 2017–2023. Fixed-effects estimation with clustered standard errors is applied to both the composite Stanford HAI AI Vibrancy Score and its seven constituent pillars, complemented by lagged, dynamic, and interaction specifications. The aggregate AI Vibrancy Score shows no significant within-country effect on tourism’s GDP share after controlling for macroeconomic factors (β = 0.061, p = 0.622), indicating that overall AI vibrancy alone does not measurably move tourism’s economic contribution. The pillar decomposition reveals, however, that this null result masks two significant positive drivers of tourism’s GDP share – AI-related R&D (β = 1.811, p = 0.005) and Policy and Governance (β = 0.353, p = 0.037) – both robust to alternative standard errors and two-way fixed effects. The Talent pillar exerts a significant positive effect on tourism’s GDP share with a one-year lag (β = 0.183, p = 0.025), indicating that the human-capital channel requires time to materialize. The COVID-19 pandemic reduced tourism’s GDP share by approximately 37% (β = –0.455, p < 0.001), and AI development did not moderate this decline. The findings imply that targeted AI policies – particularly in R&D and governance – can strengthen tourism’s economic contribution, while aggregate AI metrics obscure heterogeneous pillar-level effects.

 
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    • Figure 1. Tourism GDP share dynamics by income group (2017–2023)
    • Figure 2. Effect of AI Vibrancy on ln(Tourism GDP share) across model specifications
    • Figure 3. AI Vibrancy pillar effects on ln(Tourism GDP share)
    • Figure 4. Robustness of the AI Vibrancy coefficient across specifications
    • Figure B1. Correlation heatmap
    • Figure C1. Country fixed effects on ln(Tourism GDP share)
    • Figure F1. Correlation between AI Vibrancy pillars
    • Figure F2. Mean AI Vibrancy pillar scores by country (2017–2023)
    • Figure F3. AI Vibrancy pillar profiles: top-5 vs bottom-5 tourism GDP share countries
    • Figure F4. Tourism GDP share distribution by COVID period
    • Figure F5. Country-level tourism GDP share trajectories (2017–2023)
    • Figure F6. AI Vibrancy Score by country and year (2017–2023)
    • Table 1. Descriptive statistics of main variables (2017–2023)
    • Table 2. Fixed effects regression results: aggregate AI Vibrancy Score
    • Table 3. FE vs RE comparison and Hausman test
    • Table 4. Diagnostic tests for the FE model (M2)
    • Table 5. Descriptive statistics of AI Vibrancy pillars (raw values)
    • Table 6. Fixed effects regressions with AI Vibrancy pillars (Yeo-Johnson transformed)
    • Table 7. Robustness checks for the pillar model (selected coefficients)
    • Table A1. Countries included in the sample, with income group, region, and temporal coverage
    • Table B1. Pairwise correlation coefficients (regression sample, N = 166)
    • Table C1. Estimated country fixed effects on ln(Tourism GDP share), ranked by magnitude
    • Table D1. Summary of the AI Vibrancy coefficient across all specifications
    • Table E1. Variable definitions and data sources
    • Conceptualization
      Farhad Rahmanov, Anar Azizov , Elnara Samedova, Murad Bagirzadeh
    • Formal Analysis
      Farhad Rahmanov, Murad Bagirzadeh
    • Methodology
      Farhad Rahmanov, Anar Azizov , Elnara Samedova, Murad Bagirzadeh
    • Resources
      Farhad Rahmanov, Gunel Isayeva
    • Supervision
      Farhad Rahmanov, Anar Azizov , Murad Bagirzadeh
    • Validation
      Farhad Rahmanov, Elnara Samedova, Gunel Isayeva
    • Writing – original draft
      Farhad Rahmanov, Anar Azizov , Elnara Samedova, Murad Bagirzadeh, Gunel Isayeva, Taleh Aghazada, Abdulla Abdullayev
    • Writing – review & editing
      Farhad Rahmanov, Anar Azizov , Elnara Samedova, Murad Bagirzadeh, Gunel Isayeva, Taleh Aghazada, Abdulla Abdullayev
    • Funding acquisition
      Anar Azizov , Elnara Samedova
    • Project administration
      Anar Azizov , Elnara Samedova
    • Software
      Murad Bagirzadeh, Taleh Aghazada, Abdulla Abdullayev
    • Data curation
      Gunel Isayeva, Taleh Aghazada, Abdulla Abdullayev
    • Investigation
      Gunel Isayeva, Taleh Aghazada, Abdulla Abdullayev
    • Visualization
      Gunel Isayeva, Taleh Aghazada, Abdulla Abdullayev
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Knowledge and Performance Management
Knowledge and Performance Management