Clustering and country destination performance at a global scale: Determining factors of tourism competitiveness

Abstract

Our aim is to evaluate the efficiency of tourist destinations at a global scale, considering 140 countries and drawing on World Economic Forum 2019 data. The approach follows three stages. First, we try to solve the problem of sample heterogeneity through cluster analysis to obtain homogeneous groups of countries. Second, we apply data envelopment analysis to evaluate countries’ efficiency as tourist destinations, considering a territorially based virtual production function which optimizes the flow of revenue from international tourism grounded on a set of inputs such as accommodation capacity, employment of tourist sector and volume of tourist arrivals. Finally, we identify which external factors might determine tourism efficiency by using bootstrap truncated regression analysis. We obtain two groups of countries which evidence differential levels of competitiveness. Rather than natural resources, cultural heritage in a broad sense seems to act as factor that enhances tourism efficiency.

Cluster Identification of Agrotourism in Banten Province Indonesia

“…The regions in Banten Province consisting of four municipals and four regencies were grouped via cluster analysis based on the three variables. The regions clustering [7][8][9] was intended to identify potential regions w.r.t agrotourism. A distance-based clustering was applied in the cluster analysis where the k-medoids method was opted for due to its distance variation [17].…”

“…Clustering has been conducted for regions [7][8][9] and tourist destination sites [10][11]. The efficient indicators, important attributes, inefficient behaviour, and popular destinations can be obtained by this analysis so that cooperation among regions can increase tourism competitiveness.…”

Формування Туристичних Кластерів У Середземномор'ї

“…Дослідженням напрямків формування туристичних кластерів у Середземноморському регіоні займаються відомі зарубіжні науковці -К. Глипту [7], М. Гомес-Вега [8], С. Домі [6], Х. Дочай [6], Л. Ерреро-Пріето [8], Е. Кадіу [6], Н. Калогерас [7], М. Крамер [10], В. Лопес В, [8], А. Мандіч [9], Л. Петрич [9], С. Півчевич [9], М. Портер [10], Д. Скурас [7], І. Спіланіс [7], А. Терополларі [6].…”

Spatiotemporal evolution and mechanisms of tourism efficiency and its decomposition: Evidence from 63 counties in Zhejiang, China

“…Previous research has improved the input-output index system for evaluating tourism efficiency and analyzed its spatial spillover effects [28]. Go ´mez-Vega et al (2022) evaluated the tourism efficiency of 149 countries worldwide, analyzing external factors that may determine tourism efficiency [29]. By reviewing the existing literature on countyscale eco-efficiency, rural tourism impact and tourism industry specialization, research on the spatiotemporal pattern and evolutionary characteristics of tourism efficiency is of great value and should be further investigated to better amplify the socio-economic effects of county-scale tourism industry [30][31][32].…”