AN ANALYSIS OF GLOBAL COUNTRIES IN TERMS OF TOURISM INDICATORS USING MULTIVARIATE ANALYSIS

Tourism performance is commonly evaluated using outcome indicators such as tourist arrivals and tourism receipts; however, these indicators alone are insufficient to capture its multidimensional nature. This study aims to rank and classify 45 countries from different world regions according to tourism performance using seven indicators derived from 2024 data. It also examines the effect of data standardization on country rankings and cluster structures. Data on tourist arrivals, tourism receipts, number of rooms, number of bed-places, occupancy rate, average length of stay, and the share of tourism receipts in gross domestic product were compiled primarily from the World Tourism Organization and national statistical sources. Principal component analysis was applied to both non-standardized (original) and standardized (z-score) data to construct a tourism development index, followed by cluster analysis to classify countries. Based on the non-standardized data, the top five countries were the United States, Spain, the United Kingdom, France, and Japan, whereas under standardized data this ranking changed to the United States, Spain, France, China, and Japan. A three-cluster solution offered the most consistent classification for both datasets. Non-standardized clusters largely reflected differences in tourism volume, whereas standardized clusters revealed more detailed differences in occupancy rate, length of stay, and economic dependence on tourism, with countries such as Greece, Jamaica, and Jordan showing the strongest tourism-GDP dependence. These findings demonstrate that country rankings and classifications are highly sensitive to data standardization, suggesting that tourism performance should be evaluated within a multidimensional framework rather than relying solely on absolute indicators.