**1. Introduction**

Tourism is a major force in global trade that plays a vital role in the social, cultural, and economic development of most nations (Smith 1995). According to statistics compiled by the World Travel and Tourism Council, in 2019, the scale of the global tourism industry reached USD 8.9 trillion, with a contribution rate of 10.3% to the world's gross domestic product. At the same time, the industry employed 330 million people worldwide, accounting for approximately 10% of global employment. A country's tourism market generally consists of two markets with different customer sources, namely, inbound and domestic tourism. The domestic tourism market gradually expands with economic growth, increases in residents' income, and adjustments to vacation arrangements. According to the World Tourism Organization, the scale of the domestic tourism market is 10 times that of the international market (Page et al. 2001). Therefore, domestic tourism contributes significantly to a country's tourism revenue.

If one considers the example of Taiwan, in 2019 the number of inbound tourists reached 11.86 million, of which 90% were from within Asia, and the tourism revenue amounted to USD 14.411 billion (Tourism Bureau, Ministry of Transportation and Communications 2020). There were 169 million domestic travelers, 14.24 times the number of inbound tourists, although the tourism revenue was USD 12.698 billion, or 88 percent of that for the inbound tourism market. The key reason for the substantial disparity in the number of tourists despite identical revenue levels was the differences in tourist behavior between the two tourism markets. The average length of stay of inbound tourists was 6.20 nights, whereas that of domestic tourism was mainly 1.51 days, with 66% choosing to return the same day without staying in accommodation facilities. The low level of demand for accommodation

**Citation:** Chen, Tzong-Shyuan, and Chaang-Iuan Ho. 2022. The Application of a Two-Stage Decision Model to Analyze Tourist Behavior in Accommodation. *Economies* 10: 71. https://doi.org/10.3390/ economies10040071

Academic Editor: Aleksander Panasiuk

Received: 16 February 2022 Accepted: 21 March 2022 Published: 23 March 2022

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was the main reason why the performance of domestic tourism failed to surpass that of inbound tourism. Therefore, understanding the factors influencing the demand for accommodation on the part of domestic tourists in order to increase the duration of stay is an important topic when it comes to expanding the domestic tourism market.

When establishing an econometric model to discuss the factors influencing the demand of domestic tourists for accommodation, the first issue is to deal with a large influx of tourists who do not spend any money on accommodation. The traditional least squares method assumes that dependent variables have continuity and can be measured. If this approach is used to estimate model parameters when observed values are constrained by censored data, it may result in such parameters being biased and inconsistent (Maddala 1983; Judge et al. 1988). As tourism is not necessary for livelihood, the phenomenon of zero expenditure widely exists in research on tourism spending (Dardis et al. 1994; Hong et al. 1996; Cai 1999; Lee 2001; Zheng and Zhang 2013; Weagley and Huh 2004; Nicolau and Màs 2005; Jang and Ham 2009; Alegre et al. 2013; Bernini and Cracolici 2015; Sun et al. 2015). This fact makes the choice of appropriate econometric techniques crucial for the consistency of the empirical results (Maddala 1983; Amemiya 1984). With regards to zero expenditure in tourism, the models commonly used by scholars include the doublehurdle (DH) model (Cragg 1971) and the Heckit model (Heckman 1979). Unlike traditional economic models that consider the purchase and consumption decisions of consumers to occur simultaneously, these two models divide consumer behavior into two decisionmaking processes, i.e., whether to buy and how much to buy—also referred to as the two-stage decision model. According to the two-stage decision model that is in line with the theory of consumer behavior, consumers will collect information before purchasing products and will use that information as a reference to decide whether or not to buy, and then decide how much to spend once they have made their purchase decision.

Past studies on tourism expenditure reveal that a few of the discussions focus on the demand for tourist accommodation, for example, Hong et al. (1996) and Cai (1999). However, while both studies have adopted the Tobit model that considers zero expenditure as no consumption (Su and Yen 1996), they neglect the fact that no consumption may be the result of a lack of willingness to participate. Thus, using the Tobit model to analyze tourist accommodation expenditure may have certain limitations, resulting in an inability to grasp different influencing factors between the intention to make use of and the decision to actually spend money on tourist accommodation. More recently, a few studies have discussed this issue by using a different approach. For example, Masiero et al. (2015) utilized a quantile regression model to analyze the relationship between key travel characteristics and the price paid to book the accommodation. Ismail et al. (2021) adopt a two-step Chi-square automatic interaction detection (CHAID) procedure to segment spending on accommodation for visitors according to demographic, trip-related, and psychographic factors.

Accommodation is a major component of tourist expenditure (Laesser and Crouch 2006). However, in the case of domestic tourism, accommodation may not be made use of by everyone, i.e., not all individuals participate in this expenditure activity, thus reporting values of expenditure equal to zero. Therefore, the analytical tool should be adequate to account for a large proportion of observations with a value of accommodation expenditure equal to zero. This study considers a data-oriented approach, employs the nonnested test method and selects an appropriate two-stage decision model to discuss the factors influencing the consumer behavior of domestic tourists in regard to accommodation. By estimating the double-hurdle model, the effects of the associated determinants on the intention to use tourist accommodation and expenditure decisions can be identified. Furthermore, despite numerous empirical studies that examine the determinant factors of total tourism expenses, a particular determinant factor may have varying impacts on a specific expenditure type. The research results may help to improve the economic benefits of the domestic tourism market and serve as valuable reference for relevant businesses in developing marketing strategies.
