A spatial analysis on the determinants of tourism performance in Japanese Prefectures

Abstract

Assuming tourism as a place-oriented activity where tourist flows often cross regional borders, local and global indicators of spatial autocorrelation can be useful tools in order to identify and to explain different patterns of regional tourism dynamics and their determinants. These techniques recently became widely used in applied economic studies, as a result of their useful insights to understand spatial phenomena and benefiting from the existence of geo-referenced data and adequate software tools. This tendency is also observed in the tourism sector in the last few years. In this work, an exploratory spatial analysis and a spatial econometric model are applied to the case of Japanese Prefectures, leading to the identification of the specific spatial aspects prevailing in Japanese regional tourism dynamics. Spatial heterogeneity and agglomeration processes are identified, with a view on policy and managerial recommendations, offering a contribution to explore potential synergies arising from inter-regional cooperation in crucial aspects of tourism development. The results reveal the existence of such spatial effects, reflecting the importance for tourism of central areas of Japan, while revealing that competition effects among Japanese Prefectures prevail over positive regional spinoffs identified in other countries. It was also possible to observe that regions where tourism plays a more prominent role in terms of its importance within regional employment do not present a relatively high performance in terms of economic impact and benefits. The results suggest that a more balanced regional economic structure and higher levels of education of the work force contribute for improvements in tourism value added. Finally, the important role of foreign tourism boosting regional tourism performance is revealed.

Territorial dynamics of tourism in Romania: a long-term perspective (1990 – 2016)

“…For example, banking on this method, Yang & Wong (2013) identified significant tendencies of concentration for inbound and domestic tourism flows in China during the period 1999-2006 and investigated the tourism hot-spot areas for both types of tourists through local indicators of spatial autocorrelation. A second and more complex approach is switching from uni to bivariate ESDA, which allows for detecting the potential factors for the spatial patterns identified (Romao & Saito, 2017;Majewska, 2015, Luo & Yang, 2013. For example, Luo & Yang (2013) employ ESDA measures for identifying and explaining the spatial patterns of hotel located in Chinese cities, by making use of both univariate and bivariate Moran Statistics.…”

“…Majewska (2015) added spatially weighted location quotient, Herfindahl index, and tree-clustering analysis to the results obtained through spatial auto-correlation in order to address the issue of the relation with the neighbourhood. Romao & Saito (2017) employed a regression model in order to provide an evaluation of the relations between the spatial patterns of tourism and a series of variables related to the tourism industry and economic development in Japan. A fourth category of methodological approaches incorporates methods aimed at studying tourism regional spillovers.…”

Spatial Variation in Tourism and Investment Potential in the Context of Sustainable Development—A Case Study of Staszowski County

“…The spatial consequences of these influences, however, depend on the type of tourism as well as on the specific character of each place or region. Therefore, these differences in the positive influences of various places or regions substantiate the need for detailed spatial analyses of the distribution of tourist traffic, as they can be sensitive to local conditions [26]. Moreover, a specific spatial pattern of tourism was observed at borderlands, which were often analysed as separate research areas [27].…”

ICT, infrastructure, and tourism development in Africa

“…A vast literature has investigated the determinants of tourism demand (Adeola et al, 2018; De Castro et al, 2015; Dogru et al, 2017; Habibi, 2017; Muchapondwa and Pimhidzai, 2011; Porto et al, 2018; Romão and Saito, 2017; Seetanah et al, 2010; Tavares and Leitão, 2017). Potentially significant drivers of tourism demand that have been identified in the literature include infrastructure (Naudé and Saayman, 2005), foreign direct investment (FDI; Tang et al, 2007), prices of alternative destinations (Saayman and Saayman, 2008; Song and Wong, 2003), changes in tourists’ tastes (Lim, 2004; Muchapondwa and Pimhidzai, 2011), habit persistence in travel preferences (Peng et al, 2014), income (Muchapondwa and Pimhidzai, 2011; Saayman and Saayman, 2008), travel cost (Muchapondwa and Pimhidzai, 2011; Saayman and Saayman, 2008), the real effective exchange rate (Ibrahim, 2011), and trade openness (Ibrahim, 2011).…”

“…Most analyses have largely concentrated on developed country destinations (Gios et al, 2006; Gretzel et al, 2015; Reino et al, 2013; Romão and Saito, 2017; Vietze, 2012). Recent exceptions are Buigut and Amendah (2016) and Adeola et al (2018).…”