Analysis of factors affecting inbound tourism demand in Thailand

Abstract

This study aims to investigate the factors affecting inbound tourism demand in Thailand from both the “traditional” and “emerging” research perspectives. Given the characteristics of non-normality and asymmetry in the tourism data, a copula method is an important tool for modeling the dependence structure. Five main origin countries accounting for 51.9% of inbound tourism demand in Thailand in 2019 are selected for this study including China, Malaysia, South Korea, Laos, and Japan. Quarterly and monthly data from January 2004 to March 2021 are used for this analysis. Firstly, from a “traditional” research perspective, this study analyzes the long-term and short-term relationships between inbound tourism demand and influencing factors in Thailand through the ARDL and ECM models. Secondly, from an “emerging” research perspective of high interest, i.e., investigating the relationship between air pollution and inbound tourism demand. The copula-based on ARMA-GARCH model is used to capture the nonlinear dynamic relationship between air pollution and inbound tourism demand in Thailand. PM10 concentration as an indicator is used to capture the air pollution situation. Subsequently, this study also innovates the h-function forecasting method to predict inbound tourism demand. Lastly, from another “emerging” research perspective on the tourism economy, this study aims to identify the link between expected macroeconomic economic conditions and inbound tourism demand in Thailand through modelling their nonlinear dynamic dependence using a copula-based GARCH approach. To capture the expected economic conditions, the Thailand term structure of interest rate is used. The following main conclusions are obtained from the empirical analysis results. First, there is a long-term and short-term relationship between inbound tourism demand and influencing factors in Thailand. The word-of-mouth effect is an important factor that affects the long-term relationship of tourists’ behavioral persistence. And the income level of tourists is another important determinant of tourist travel to Thailand. Second, all pairwise correlations between air pollution and inbound tourism demand are time varying. There is a directional nonlinear causality from air pollution to inbound tourism demand for global, indicating that air pollution has an impact on tourism demand. By comparing the predictable values and actual values, the MAPE of the h-function forecasting method is in the acceptable range. Third, there is a time-varying dependence between expected economic growth and inbound tourism from all countries except South Korea. Besides this, the empirical evidence supports the tourism-oriented economic growth hypothesis, the economic-driven-tourism growth hypothesis, and the neutral hypothesis, respectively. It is also shown that the tourism-economic growth dependence is influenced by extreme events (global financial crisis, Thai floods, Thai coup, and China-United States trade conflicts). Finally, basing on the empirical results of this study, policy recommendations are made for the development of inbound tourism in Thailand from three perspectives: tourism products, brand image building, and economic development. And enlightenments also provided for the economic recovery of Thailand after the pandemic of COVID-19 in the aspect of the tourism-economic growth relationship.