Factors contributing to tourism demand at major Japanese hot springs

Abstract

Hot springs are a major tourism resource in nature-based tourism, and the hot springs market is one of the biggest sectors in wellness tourism markets. In the present study, we examine factors contributing to tourism demand for major hot spring resorts in Japan using ordinary least squares regression models and generalized linear mixed models, and compare the estimation results. The results show that significant factors in most of our models are quality of accommodations and the degree of dependence on inbound demand. Furthermore, the number of non-Japanese languages supported on websites of hot spring resorts has a significant impact on inbound demand. Since the results of the present study cover more than 80 hot spring sites, the results highlight common important factors for hot spring resorts. Such widely applicable factors have been missing in previous studies, and the present study fills this research gap.

Identifying the determinants of tourism receipts of Thailand and relevant determinant-determinant interactions

“…Two GLMM models were considered. The baseline GLMM model involved a log link function (see also [ 21 ]) and was given by: In Eq ( 1 ) the variable receipts pt describes the receipts of province p at time t , where t measures time in calendar quarters, β (0) denotes the fixed intercept, indices k and l count the m independent variables of the final pool, x (k) pt with k = 1,…, m denote the independent variables of that pool measured for province p at time t . Moreover, β ( k ) and β ( kl ) denote the regression coefficients of the main and interaction effects of factor k and factors kl , respectively.…”

“…Likewise, Raharti et al [ 25 ] showed for data from Yogyakarta city, Indonesia, for the period of 2005 to 2018 that the number of tourists had a significant effect on the revenues coming from the tourism sector. More recently, Medai et al [ 21 ] showed that roughly speaking the number of visitors had a positive effect on the tourism revenues obtain from hot spring areas of Japan when focusing on the year 2018 and considering the major ten Japanese hot springs. When studying factors determining the number of foreign visitors, it has been suggested to take a measure for the cost of living for tourists at their destinations into account.…”

“…In order to study determinants of tourism demand, time series models have been utilized as well [ 27 , 50 ]. Finally, a few studies have used generalized linear mixed models and panel analysis models to identify those tourism demand determinants [ 21 , 41 ]. Overall, the methods that have been applied are quite diverse.…”

“…In this context, a generalized linear mixed modeling (GLMM) approach to identify not only interactions but also the presence of unknown factors and to determine the possibility of a fine-grained characteristics underlying tourism demand has not yet been applied to data from Thailand. In general, in the field of tourism research the power of this kind of modeling analysis has not been fully appreciated (for an exception, see, e.g., [ 21 ]). Previous studies on tourism demand in Thailand have primarily focused on Thailand as a whole.…”

Research on Industry Transformation Models and Influencing Factors of Hot Spring Villages from the Perspective of Sustainable Development: A Case Study of Fujian, China

“…China is rich in hot spring resources, with an estimate of over 2000 hot spring spots in rural areas [5]. The hot spring industry provides about two million jobs each year, creating an output value of about USD 200 billion [6]. The development of hot spring resources plays an important and positive role in increasing the employment of villagers, improving rural financial income, promoting the sustainable development of rural industries, and achieving common prosperity [7,8].…”