Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

Abstract

Purpose

This study aims to determine and evaluate the artificial intelligence (AI) development and competitiveness of the top 20 countries that receive the highest number of tourists with the entropy technique for order of preference by similarity to the ideal solution (TOPSIS)-integrated method.

Design/methodology/approach

This study is based on Global AI Index data published by Tortoise Media. Based on this index, according to the World Tourism Organization (UNWTO) report, the top 20 destinations that will host the highest number of tourists in 2022 were evaluated in seven different subpillars, which are talent, infrastructure, operating environment, research, development, government strategy and commercial. These seven subpillars of the index were considered as criteria, and the top 20 tourist destinations were included in the research as decision alternatives.

Findings

The analysis results show that the three most important AI criteria are operating environment, infrastructure and government strategy. Furthermore, the first three countries with the best AI performance according to the weighted criteria were the USA, China and the UK, respectively.

Practical implications

Considering that AI technologies will direct tourist behavior in a world where technology is rapidly developing, it is recommended that the countries that receive the highest number of tourists improve their AI performance.

Originality/value

When the relevant literature is examined, there is a limited number of studies examining the AI development and competitiveness of the top tourist destinations and weighting the Global AI Index values. Therefore, this study contributes to the gap in the relevant literature.

Citation

Yılmaz, M., Ülker, M. and Ülker, P. (2024), "Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method", Worldwide Hospitality and Tourism Themes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/WHATT-03-2024-0068