img
img
Predicting tourism competitiveness with innovation: a machine learning approach  
Yazarlar (3)
Doç. Dr. Hasan Evrim ARICI Doç. Dr. Hasan Evrim ARICI
Kastamonu Üniversitesi, Türkiye
Mehmet Ali Köseoglu
Metropolitan State University, United States
Levent Altinay
Oxford Brookes Business School, United Kingdom
Devamını Göster
Özet
This study introduces an analytical model that establishes a connection between the factors that promote innovation in a country and the competitiveness of its tourism destinations. Invoking the international strategic competitiveness theory, this study is among the first to propose and empirically test the predictive roles of innovation facilitators on tourism competitiveness. Utilising longitudinal data from multiple countries from 2013 to 2022, we ran machine learning algorithms. The results show that several innovation facilitators, such as research and development and trade, diversification, and market scale, significantly predict competitiveness in tourism destinations. The results of this investigation enhance our knowledge of innovation and competitiveness in tourism locations globally.
Anahtar Kelimeler
Competitiveness | innovation | longitudinal study | machine learning algorithms | tourism destination
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale
Dergi Adı Current Issues in Tourism
Dergi ISSN 1368-3500 Wos Dergi Scopus Dergi
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 01-2025
Sayı 1
Doi Numarası 10.1080/13683500.2025.2550657
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Predicting tourism competitiveness with innovation: a machine learning approach

Paylaş