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Determinant Factors of Tourist Expenses
Sangwon Park, PhD
Associate Professor
School of Hotel and Tourism Management
Hong Kong Polytechnic University
Kowloon, Hong Kong
Tel: +852-3400-2262
e-mail: sangwon.park@polyu.edu.hk
Mina Woo
Graduate School of Business
Sogang University
35 Baekbeomro, Mapogu, Seoul ,04107, Korea.
Tel: 82 11 9248 5313
Fax:82 2 511 8605
e-mail: drminawoo@gmail.com
Juan L. Nicolau, PhD*
J.Williard and Alice S. Marriott Professor of Revenue Management
Department of Hospitality and Tourism Management
Pamplin College of Business
Virginia Tech
Blacksburg VA 24061
Phone 540-231-8426
e-mail: jncolau@vt.edu
* corresponding author
Citation information
Park, S., Woo, M., & Nicolau, J. L. (2020). Determinant factors of tourist expenses. Journal
of Travel Research, 59(2), 267-280.
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Determinant Factors of Tourist Expenses
ABSTRACT
Tourist expenditure has been recognized as a key element of destination income, and research
has focused on this central component of destinations’ revenue. However, analyses across
and within expense types are needed for both theoretical advances and practical
improvements because determinant factors could vary their effects, depending on both the
type and the level of expense. Accordingly, the empirical application examines the
determinant factors of total expenses, controlling for potential endogeneity, and relies on
quantile regression to analyze the effects of information search behavior (via information
sources) on the distribution of total expenses as well as accommodation, shopping, food and
beverages, and local transportation expenses. The role of information sources in predicting
travel spending behaviors is a new dimension in the literature on expenses. The use of a
sample of 48,113 travelers visiting South Korea has led to the detection of effects of variables
with relevant managerial implications (for example, while the Korean office (or information
center) shows positive and significant parameters at the upper levels of accommodation
expenses, it presents null effects at the highest levels of shopping expenses) as well as
theoretical implications (special attention should be drawn to the variable length of stay
which, after being controlled by endogeneity, completely loses its significance).
Keywords: expenditures; travel decisions; Heckit model; quantile regression; endogeneity.
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Introduction
Tourist expenditure provides a substantial contribution to economic growth at the
national and regional levels (Marrocu, Paci, and Zara, 2015). For example, the World Travel
and Tourism Council (WTTC, 2015) reported that the direct contribution of travel and
tourism to the gross domestic product (GDP) in South Korea was 2.0% in 2014 and is
expected to rise by 2.9% per annum between 2015 and 2025. In terms of total contribution—
direct and indirect—income generated from travel and tourism is estimated to be 5.8% of the
GDP in 2014 and is forecast to reach 6.0% in 2025 (WTTC, 2015).
The study of tourist expenditure is crucial because “tourism is an expenditure-driven
economic activity” and “the consumption of tourism is at the center of the economic
measurement of tourism and the foundation of its economic impacts” (Mihalic, Sharpley, and
Telfer, 2002, p. 88), which helps to clarify the gross added value that destinations generate
(Eugenio-Martin and Campos-Soria, 2014; Eugenio-Martin and Inchausti-Sintes, 2016). In
particular, identifying the factors that affect tourist consumption behaviors and estimating the
effect of these factors on tourist expenditure patterns are of the utmost importance. From the
destination marketing perspective, this knowledge could help to discern “profitable tourists,”
who stay relatively longer and spend more during their trips, and to develop effective
marketing strategies and policies contingent on viable market segmentations (Nicolau and
Mas, 2005; Lin, Mao, and Song, 2015).
While macroeconomic approaches provide global understanding of tourist
expenditure patterns (Jang and Ham, 2009; Wu, Zhang, and Fujiwara, 2013; Lin et al., 2015;
Serra, Correia, and Rodrigues, 2015; Konstantakis, Soklis, and Michaelides, 2017), these
aggregated expense analyses do not fully consider product-specific issues (Laesser and
Crouch, 2006). Moreover, the aggregation of macrodata averages out individual
idiosyncrasies, and thus provides less valuable information to tourism marketers (Wang and
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Davidson, 2010a). Today, analyzing microdata by examining individual consumption is
clearly needed, which allows consideration of the diversity and heterogeneity of travel
behaviors and preferences (Lin et al., 2015). Furthermore, with regard to the nature of
tourism, it is vital to consider many facets of travel decisions because travel is not a single
product but a number of interrelated subproducts (Fesenmaier and Jeng, 2000).
Indeed, travelers are required not only to make a destination decision but also to
arrange numerous subset decisions, such as accommodation, restaurants, and transportation
(Park, Nicolau, and Fesenmaier, 2013). It becomes evident that, on account of the different
nature of these subdecisions, a particular determinant factor is not expected to show the same
effect on all of them (across expense-category analyses). Moreover, that determinant factor
may have varying impacts on a specific expenditure type, depending on its level (within
expense-category analysis). Consequently, the effect of prices can be different between
accommodation and restaurants, but it can vary within accommodation as well, depending on
whether the level of expenditures in accommodation is high or low. Accordingly, this paper
uses four sets of determinant factors (demographics, tripographics, prices, and information
sources) to explain, first, their effects on the total amount of tourist expenditure; and second,
the varying effects of these information sources on expenses for accommodation, shopping,
food and beverages (F&B), and local transportation (Park et al., 2013).
Determinant factors of tourism expenses
The effects of the determinant factors on purchasing behaviors can vary across and
within many facets of a trip because travel decisions have distinct levels of complexity
(Nysveen, 2003) and flexibility (or centrality) (Fesenmaier and Jeng, 2000) that are
contingent on the products or services involved, shaping the traveler’s engagement and/or
commitment to planning the decisions (Park and Fesenmaier, 2014). Consequently, the
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dimensions that lead consumers to purchase specific travel products vary according to the
product type (accommodation, restaurant, etc.). Expense is a dimension in which this
variability can be notably evidenced as it is a manner whereby tourists show their
consumption patterns quantitatively.
Across expense-type analysis. Considering the different nature of the aforementioned
subdecisions, the ease or complexity on which people base their expenditure decisions—for
example, accommodation and theater tickets—changes substantially as not only does the
amount of money required vary, but so does the duration of the service (two hours of
dissatisfaction in the theater can be less painful and easier to recover from than two days of
dissatisfaction during a stay in a hotel). Therefore, the factors that have a significant impact
on the level of expenses may vary from one decision to another as well as the size, if any, of
such impact.
Within expense-type analysis. The determinant factors may also have varying effects
on a specific expense type, depending on whether its cost is high or low. In other words, the
determinant factors may have a non-constant effect on the distribution of a specific expense
type, in such a way that a factor may have a null effect at one region of the distribution (e.g.,
the lowest level of expenses) and a positive (or negative) effect in another part (e.g., the
highest level of expenses). To analyze these varying effects, we focus on the variable
“information search behavior,” whose role in predicting travel spending behaviors is a new
dimension used in the analysis of the determinants of expenditure.
Determinant factors. To carry out analyses across and within expense-types,
according to the literature, four sets of determinant factors are investigated—demographics,
tripographics, prices, and information search behavior (Wang, Rompf, Severt, and
Peerapatdit, 2006; Brida and Scuderi, 2013). It is important to note that the relevant literature
offers inconsistent empirical evidence for the influence of sociodemographic and trip-related
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characteristics on tourist expenditure (Wang and Davidson, 2010a). The consideration of
information-search behavior has been largely limited in understanding expenditure patterns,
although it has been recognized as a crucial aspect of travel decision-making behaviors (Choi,
Lehto, Morrison, and Jang, 2012). The following paragraphs discuss these four categories of
determinants: sociodemographics (age and income), tripographics (type of travel
arrangement, length of stay, visit to additional destinations, purpose of trip, travel
companions, and type of accommodation), prices, and information search behavior (types of
information sources).
Regarding sociodemographic characteristics, age is regarded as a vital demographic
dimension in explaining travel behaviors and expenditure (Pearce, 2013). The findings of
previous studies examining the relationship between age and travel expenditure do not seem
to be consistent. On the one hand, age has a significant influence on travel expenditure. Either
old travelers are more likely to overspend than relatively young travelers (Thrane, 2002;
Jang, Bai, Hong, and O’Leary, 2004), or tourist expenditure declines with age (Dardis,
Derrick, Lehfeld, and Wolfe, 1981; Mok and Iverson, 2000). Jang and Ham (2009) attribute
the different behavioral patterns of travel expenditure to the social and political environments
people experienced between temporal generations. Another group of studies indicates that the
age variable does not affect the trip budget (Chhabra, Sills, and Rea, 2002; Wu et al., 2013).
Walsh, John, McKean, and Hof (1992) demonstrate a nonlinear relationship where middle-
aged travelers are more likely to spend more on their travel expenditure than young and old
travelers (i.e., a concave relationship).
Level of income. The literature regards income as a personal budget restriction that
conditions people’s purchasing capacity (Crawford and Godbey, 1987; Marrocu et al., 2015)
such that higher income levels bring about higher consumption levels (Davis and Mangan,
1992; Middleton, 1994). Information on income is not always available—as is the case for
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this article—so two standard proxies are used in the literature (Fleischer and Felsenstein,
2004; Marcussen, 2011): occupation and education. Regarding occupation, it might reflect
social class (Wang et al., 2006), so it would be expected that travel expenditure would
increase with the level of occupational prestige (Hong, Morrison, and Cai, 1996). In other
words, white-collar professionals are likely to spend more on trips than other types of
occupations in general (see Jang et al., 2004). The literature also finds a positive relationship
between education and expenses (Parker, 1976; Nicolau and Mas, 2005).
Concerning tripographics, the following variables are considered to have an effect on
travel expenditure (Abbruzzo, Brida, and Scuderi, 2014):
Types of travel arrangement. Thanks to the development of information technology,
travelers have many different channels to book travel products. The advancement of online
travel agencies (e.g., Expedia and Booking.com), in particular, enables individuals to
organize the journey themselves. Alternatively, tour operators offer dynamic travel packages
so that travelers facilitate managing diverse facets of the entire trip (Money and Crotts, 2003).
These various types of booking methods lead to different levels of travel expenditure (Brida
and Scuderi, 2013). Accordingly, travelers who organize their entire trip with tour operators
tend to spend more than those who do not make any reservations in advance and reserve
partial elements of the trip (e.g., transportation and accommodation) (Perez and Juaneda,
2000).
Length of stay. Duration of stay is regarded as one of the crucial elements determining
travel expenditure. The longer travelers stay at the destination, the greater amount of the total
budget is spent (Mok and Iverson, 2000; Jang et al., 2004; Wang et al., 2017). One reason is
that people who stay longer at hotels are more likely to order food and beverages and obtain
transportation services and entertainment activities (Downward and Lumsdon, 2004; Driml et
al., 2017; Vu et al., 2017). In contrast, Thrane and Farstad (2011) indicate that in domestic
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travel, the positive magnitude declines as the length of stay increases. Some studies also
identify a nonlinear relationship between length of stay and travel spending. Roehl and
Fesenmaier (1996), for instance, demonstrate a diminishing positive effect of length of stay
on expenditures, which, at a certain duration point, becomes negative.
Number of destinations. Many vacations include multiple destinations or touring in
nature (Lue, Crompton, and Fesenmaier, 1993). Given that current travelers have greater
mobility, they often visit more than one destination. The relevant studies found that travelers
who visit multiple destinations tend to be higher spenders than those going to a single
destination (Wang and Davidson, 2010b). It is recognized that travelers can achieve variety in
their travel experiences by visiting multiple destinations, enhancing individual levels of
arousal (Lue et al., 1993). Thus, the patterns of travel behavior and spending would be
different between travelers with a single destination and those with multiple destinations.
Purpose of trip. Laesser and Crouch (2006) propose a segmentation method using
travel expenditure patterns and identify heterogeneity in travel purposes across the segmented
groups. Travel purpose inherently represents travelers’ needs and motivations when visiting a
destination. As a result, the different purposes shape different amounts of expenditure to
achieve the desired levels of satisfaction (Serra et al., 2015). Laesser and Crouch’s study
(2006) finds that travelers whose main purpose is attending a conference at the destination
appear to have relatively higher expenditure. In contrast, travelers visiting friends and
relatives (VFR) spend less compared to general leisure travelers. Other tourism studies
demonstrate consistent results which show that business travelers tend to spend almost twice
as much as VFR travelers (Jang, Yu, and Pearson, 2003). When focusing on specific
shopping expenditure, however, the opposite pattern is identified. Leisure travelers have the
highest shopping expenditure, followed by VFR and business travelers (Lehto, Cai, O’Leary,
and Huan, 2004).
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Travel companions. Since travel is often a highly social event, travel companions play
an important role in determining not only travel behaviors but also expenditure (Gitelson and
Kerstetter, 1995; Park and Fesenmaier, 2014). Several approaches can be used to assess the
composition of travel groups, such as party size, presence of companion, number of adults
and children (Wang and Davidson, 2010), and the specific composition of a travel party
(Serra et al., 2015). The literature appears to show heterogeneous results according to the
different measurements used and specific travel context investigated (inbound vs. outbound
travelers). For example, Wang et al. (2006) show the positive effect of number of adults on
travel expenditure, while Jang et al. (2004) demonstrate the unimportance of travel party size
(Wu et al., 2013). A negative sign of travel party size is associated with the travel budget per
person (Mok and Iverson, 2000). Serra et al. (2015) examine the arrangement of travel groups
and conclude that family travelers spend more on travel expenditures than other types,
including people who travel alone or with friends.
Types of accommodation. The analysis of accommodation types is important at certain
destinations, such as South Korea, that involve diverse forms of accommodation. Previous
literature consistently shows that travel expenditure varies depending on the type of
accommodation. Agarwal and Yochum (1999) indicate that hotel accommodation is
associated with higher expenditure compared to other accommodations, such as cottages,
camping sites, and condos or apartments. This proposition is consistent with the results of
other travel studies (e.g., Nicolau and Mas, 2005; Laesser and Crouch, 2006; Marrocu et al.,
2015), implying that relatively higher room rates in hotels are linked with higher travel
expenditure.
Prices. The generalized finding in the literature on prices shows that the demand for
tourism products behaves as an ordinary good, so that price increases reduce consumption
(Smith, 1995). As Morrocu et al. (2015) indicate, observing specific prices for tourism
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products is not always feasible. However, considering the international character of this
research, it seems to be appropriate to follow Eymann and Ronning’s (1992) proposal, which
puts forth that the adequate procedure to show the prices of a tourist market is to observe
destination prices vis-à-vis the home market’s prices. Accordingly, these authors employ
purchase parity differentials between the origin and respective destinations, measured by the
corresponding consumer price indexes.
Information search. Finally, the information search-related factor is expressed as an
information source. The information sources used by travelers represent information search
strategy, inherently characterizing the information environment (Fodness and Murray, 1998).
Evidence has been found for systematic relationships between information search strategies
and individual and situational characteristics of the travelers (Choi et al., 2011; Park, Wang,
and Fesenmaier, 2011). More pertinently, several researchers explore the link between
information search behaviors and travel outcomes, including trip expenditure (Kambele, Li,
and Zhou, 2015). Murphy and Olaru (2009) classify travelers according to their information
foraging strategies: (1) sharks, who are active and have high information needs and (2)
spiders, who are passive and mostly rely on personal experience. Based upon information
foraging theory (Pirolli and Card, 1999), the difference is equivalent to a well-recognized
distinction in behavioral ecology between widely-foraging predators, such as sharks, and sit-
and-wait foragers, such as spiders. The former voraciously seeks a wide range of information
sources and contents, while the latter wanders a few convenient sources.
As expected, the study by Murphy and Olaru (2009) shows that travelers categorized
as sharks are likely to use more varied and up-to-date sources of information than those
categorized as spiders. In terms of travel behaviors, the shark group tends to have larger
travel budgets than the spider group. Consistently, different levels of entertainment
expenditure are recognized according to different clusters that use different information
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search strategies. Travelers who visit the local tourist office or check travel guides tend to
spend more than other groups (Fodness and Murray, 1999).
Apart from examining the search strategy, some studies investigate the associations of
specific information sources used to reach travel outcomes. The results show that travelers
who use the Internet to obtain information are likely to incur higher spending than those who
utilize other sources (e.g., destination sources, travel agents, and recommendations from
friends/relatives) (Luo, Feng, and Cai, 2005). Also, travel expenditure increases when TV is
used as a main information source as opposed to not considering information from TV
(Taylor, Fletcher, and Clabaugh, 1993).
Methodology
Data analysis
To carry out the analysis of the determinants of total expenses, we apply different
estimation procedures: ordinary least squares (OLS), two-stage least squares (2SLS), the
Heckit model, and quantile regression (QR). We start by getting the most basic estimates via
OLS. In particular, the following standard linear relation is formulated:
where yi is the expenses per person (we apply the log-transformation so that the resulting
parameters are directly interpreted as semi-elasticities), α is a constant term, βk is the
coefficient of the k-th independent variable related to demographic characteristics DCki, γk is
the coefficient of the k-th independent variable associated with trip-related characteristics
TRCki, δk is the coefficient of the k-th independent variable related to information search
behavior ISBki, θ is the coefficient of the price-related variable Pricei, and ε is an error that
follows a normal distribution.
On estimating this model, however, a potential endogeneity issue might arise, as the
causality of the decisions “how much to spend,” “how long to stay,” and “where to stay” is
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not straightforward. To handle this potential endogeneity, we resort to the two-stage least
squares (2SLS) regression to explicitly deal with the variable “length of stay” and the Heckit
model to control for the potential effect of “accommodation type” on other variables. The
2SLS estimation requires the use of instrumental variables. In line with Thrane (2015), the
variable length of stay can be instrumented via “number of previous visits to the destination”
and “level of satisfaction”.
As for the Heckit model, we split the consumer choice process into the “expense
decision” and the “accommodation decision”, so that the model takes the following form:
observed only if
dt*>0
where the disturbances ui and
i follow a bivariate normal distribution with a zero mean,
variances
u and
respectively, and covariance
u. di is a dichotomic variable, which takes
a value of one when the latent variable di*>0, and a value of zero when di*<0. In this way,
di=1 indicates the decision to stay in a hotel and di=0 in any other type of accommodation.
We use maximum likelihood to estimate the model.
Finally, we use quantile regression to enrich the results and find out whether the effect
of the determinant factors is constant over the range of the dependent variable (expenses), or
varies depending on the level of expenses. In particular, we will focus on information search
behavior, which represents one of this article’s main contributions to the literature. In this
way, we test the potential differentiated effects of each characteristic over the distribution of
the variable “overall expenses,” along with “accommodation,” “shopping,” “food and
beverages,” and “local transportation.”
The advantage of QR over OLS is that the former attempts to model the conditional
mean of the dependent variable, while the latter tries to model the conditional τth quantile of
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the dependent variable, being τ (0, 1). It is standard in the literature that the 10th, 25th,
50th, 75th, and 90th quantiles are estimated, as it encompasses the whole distribution of the
variable (Marrocu et al., 2015). Thus, QR permits the detection of potential varying impacts
of “information sources” (that capture the information search behavior) on the whole range of
the variable “expenses”.
Data collection
A series of surveys was used to collect data representing the behaviors of international
travelers who visited South Korea from 2011 to 2014. The subjects were over 18 years old
and stayed in South Korea for more than a day and less than a year. Four international
airports, including Incheon, Gimpo, Gimhae, and Jeju Island, as well as two international
harbors (Incheon and Busan), were selected to contact the respondents at the end of their
trips. This study utilized stratified sampling method according to origin destinations.
Specifically, after identifying international visitors across countries in previous years, the
target number of samples for each country could be calculated at a confidence level of 95%.
Furthermore, to control for the seasonality effect, the data collection was made over all
twelve months consistently with about 1,000 respondents across four years. As a result, the
total number of respondents used in this data analysis was 48,113, consisting of 12,038 in
2011; 12,021 in 2012; 12,030 in 2013; and 12,024 in 2014. Tables 1 and 2 show the
descriptive statistics of the sample for the categorical and continuous variables, respectively.
[Insert Tables 1 and 2 around here]
Measurements
There were two sections in this visitor survey. The first part asked the international
travelers about their behaviors while visiting South Korea: types of travel arrangement
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( independent or package tour), length of stay, number of destinations (just visiting South
Korea or visiting other destinations), purpose of trip (participants were asked to choose up to
three of the following purposes: leisure, recreation, and holiday; health, medical treatment;
religion or pilgrimage; shopping; VFR; business or professional activities; and education),
information sources (participants could choose the three sources they relied on the most:
travel agencies, relatives and friends, the Internet, traveler’s guides, media, tourist offices,
airlines, or hotels), travel companion (alone, family and relatives, friends, coworkers, and
others), types of accommodation (hotel, guesthouse, condominium, family/relatives,
school/dormitory, temple, and other), (Yoon and Shafer, 1997; Park et al., 2013) and travel
expenditures (accommodation, shopping, food and beverages, local transportation,
entertainment, expenses in travel agencies in Korea, cultural activities, and sports activities)
(Wang et al., 2006; Abbruzzo et al., 2014). The last part includes demographic questions,
such as age, educational level, and occupation. As for prices, in keeping with Eymann and
Ronning (1992), we use consumer price index differentials among home markets and
destinations, published by the World Bank, which show the cost of living in each place of
origin and destination.
Results
Profiles of respondents
Over half of respondents are 40 years or below and have obtained colleague/university
degrees. Checking the distribution of occupation and nationality, it shows a wide coverage of
the sample, as opposed to bias toward specific categories. With regard to travel
characteristics, 73% of travelers have carries out a type of individual trips and 88% of them
have visited only Korea as their travel destination. In terms of information sources, as
expected, Internet has been recognized as the most frequently used source (see Table 1).
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[Please insert Table 1 about here]
Table 2 shows numbers of sample size and mean/standard deviation of total travel expenses
as well as four different travel facets. It also presents that there is limited concern related to
bias of sample size and distribution of outcome variables.
[Please insert Table 2 about here]
Model Estimation
Before estimating the models, the potential existence of collinearity is tested. Two variance
inflation factors (VIF) are larger than the recommended value of 10 (Neter et al., 1989; Hair
et al., 1995): “health, medical treatment” and “religion or pilgrimage”. As both items
represent a very small proportion of the sample (1% and 0.8%, respectively), we have
integrated them into the reference alternative. After confirming that all VIFs are still below
10, we proceed with the estimation of the models.
Table 3 shows the results of the four models estimated: OLS, 2SLS, Heckit,
1
and QR.
According to the procedure outlined in the methodology section, in order to guarantee that
the parameters are not affected by endogeneity, we rely on those parameters that are
significant in all models and have the same sign.
[Please insert Table 3 about here]
Regarding occupation, “self-employed” and “student” have a positive effect (the largest) and
a negative effect (the smallest) respectively, compared to the alternative reference (“other”).
Concerning education, “college” and “graduate school” have a significant and positive impact
1
For the sake of space, only the response equation that analyzes the expenses is shown. The selection equation
is available upon request to the authors.
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in comparison with “other.” As proxies for income, both variables show that income has, as
expected, a positive impact on expenditures.
As for countries, China, Singapore, Taiwan, Malaysia and Saudi Arabia present
positive and significant parameters, while Japan shows a negative and significant parameter
compared to the alternative “other.”
Regarding length of stay, it is important to comment on this variable as it has been
instrumented to control for endogeneity. Interestingly, while in all other models it is
significant, when the 2SLS estimation is applied, its size is reduced and its significance
disappears. This reduction in size is in line with the results obtained by Thrane (2015), as the
other models do not control for endogeneity, and consequently, attribute the whole effect of
length of stay on expenses to this variable.
“Visiting only Korea”—compared to visiting multiple destinations—leads to greater
total expenses as the parameter is positive and significant. This result is contrary to the
existing empirical results (Wang and Davidson, 2010b); it seems that, for the same number of
days, tourists tend to spend more if they only visit Korea. Considering the distinction between
vacationers (who remain in one destination during their vacation in order to “experience” in
detail the characteristics of the place) and sightseers (who visit various destinations in order
to see, on a superficial level, their main sights), it seems that the former try to make the most
of the destination, which leads to higher expenses.
Regarding purposes of trip, the top spenders in total expenses are those whose
purposes are “leisure, recreation, and holiday” and “shopping” compared to the reference
“other,” and those who “visit friends and relatives” and do “business or professional
activities” spend less than “others,” in line with the results of Jang et al. (2003). Concerning
travel companions, when traveling with family and relatives and with friends, people tend to
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spend more; conversely, traveling alone or with a coworker leads to lower total expenses.
This outcome is in line with Wang et al. (2006) and Serra et al. (2015).
In total expenses, active information search is generally associated with greater
spending; that is, “sharks” spend more than “spiders,” in line with the terminology and results
found by Murphy and Olaru (2009). In other words, travelers who use up-to-date information
sources incurred more travel expenditure than those who sought other sources (Luo et al.,
2004).
The consumer price index differential is significant and positive, so when the country
of origin has higher prices, people tend to spend more in Korea, which is in accordance with
the prevailing negative relationship between price and demand (Smith, 1995).
Table 4 shows the effects of information sources on total expenses per person and
Table 5 on accommodation, shopping, F&B and local transportation, which are QR
parameters estimated at the 10th, 25th, 50th, 75th, and 90th quantiles. These tables show the
significance of each quantile parameter for the information sources.
[Please insert Tables 4 and 5 about here]
Regarding total expenses, it is observed, with no exception, that the effect of any information
source at the low level of expenses is higher than the reference variable “other,” and at high
levels of expense it is lower than this reference variable. This decrease in the size of the effect
might become null for the top level of expenses, as in “media,” or even negative, as in
“relatives and friends” and “traveler’s guides.” Note that “airlines and hotels” as information
sources have no effect on the lower levels of expenses and an increasing negative effect on
higher levels of expenses.
Concerning expenses for accommodation, different patterns are found depending on
the level of expenses. While travel agencies, relatives and friends, and traveler’s guides have
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positive effects at the middle levels of expenses, their effects become negative at the upper
levels (75th and 90th quantiles). In fact, only the Korean office (or information center) and
hotels show positive and significant parameters at these upper levels. As for expenses on
shopping, a general decreasing effect is found as the level of expenses rises. It is interesting
to see that traveler’s guides and the Korean office have negative and null effects,
respectively, at the top level of expenses. For F&B, the sources associated with higher
expenses seem to be relatives and friends, with positive parameters for the 50th, 75th and 90th
quantiles. It is relevant to note that the internet, traveler’s guides, media, and the Korean
office have positive effects at the middle levels of expenses but null or negative impacts at
the top level. Regarding expenses for local transportation, all sources except for travel
agencies have positive effects at the upper levels of expenses. Airlines and hotels have no
significant effects.
Discussion and Conclusions
Given that recognizing the importance of tourism to local economies and understanding
expenditure behaviors of international travelers are crucial for tourism business and
destination marketing organizations (Lin et al., 2015), this study analyzes expenditure
patterns of a sample of 48,113 international travelers who visited South Korea between 2011
and 2014. To increase the generalizability of the findings, a stratified sampling method based
on original destinations and a controlled seasonality effect with consistent numbers of survey
responses across twelve months are applied.
According to the methodology used that attempts to consider potential endogeneity,
the determinant factors that have a positive effect on expenditures are occupation (self-
employed); education (college and graduate schools); originating from China, Singapore,
Taiwan, Malaysia or Saudi Arabia; visiting Korea only, trip purposes (“leisure, recreation,
19
and holiday” and “shopping”), traveling with family and relatives and with friends,
information sources, and consumer price index differential.
Regarding the quantile regression estimates, the fact that a diversity of effects is found
for a particular variable depending on the level of expenses implies that the use of quantile
regression is relevant to detecting potential intricacies in the determinants of expenses. This
article has focused on information search behavior, whose role in predicting travel spending
behaviors analyzed in the application represents new possibilities for analyzing the
determinants of expenditure. The analysis has found that, for total expenses, a decreasing
effect occurs over the distribution of the variable expenses, with higher effects at the low
levels of expense and lower impact at the high levels. For the specific expenses analyzed,
some interesting results are found: for example, while the Korean office shows positive and
significant parameters at the upper levels of accommodation expenses, it presents null effects
at the top levels of shopping expenses. Even more intricate are the cases of traveler’s guides
and media that show positive, negative or null effects at the top level of expenses depending
on the specific type of expenses. In particular, traveler’s guides have null effects on
accommodation, negative effects on shopping and F&B, and positive effects on local
transportation. Media has a null impact on accommodation, a positive impact on shopping
and local transportation, and a negative impact on F&B.
As for theoretical implications, special attention should be drawn to the variable
length of stay which, after controlling for endogeneity, loses its significance. While some
previous literature has tackled this issue, this empirical result reinforces the idea that
controlling for endogeneity is not a minor issue. It is not a mere reduction in the size of the
effect but a complete cancellation of the effect.
As for managerial implications, several issues can be considered: i) for destination
marketing organizations, policies that favor a general pattern of expenditures can be
20
misleading if they do not consider that the same variable can have different effects on tourist
expenses, depending on the product or service purchased. This is illustrated by the
aforementioned cases of the Korean office, traveler’s guides and media; ii) similarly, for
DMOs and decision-makers in tourism firms, a specific variable not having the same effect
over the range of the variable expenditures opens up new courses of action for segmentation,
as heavy and light spenders are influenced differently by their information search behavior
(remember that the effect of the information sources at low levels of expense is higher than at
high levels of expense).
Several limitations of this study could be addressed in future research. While this
research examines a variety of factors reflecting travel behaviors, some others would also be
relevant, such as perceptions and motivations as well as other sensorial external effects such
as the impact of weather on expenditures (Wilkins et al., 2017). The analysis conducted on
firms—rather than destinations—would allow the detection of different effects across types
of firms; for example, a hotel with different levels of service for specific types of rooms
might find (and quantify) that a change in a specific variable may have distinct impacts on
expenditures, depending on the level of those expenditures.
21
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25
Table 1. Descriptive statistics (categorical variables)
Variable
Sample
size
Proportion
Variable
Sample
size
Proportion
Demographic characteristics
Canada
1726
3.6
up to 30 (reference category)
15541
32.3
UK
1685
3.5
31-40
13220
27.5
Germany
1644
3.4
41-50
8987
18.7
France
1440
3.0
51-60
5520
11.5
Russia
1792
3.7
over 60
2301
4.8
Arab countries
1734
3.6
Public official/Armed forces
3105
6.5
India
1439
3.0
Business person/Manager
8744
18.2
Other (reference category)
2441
5.1
Office employee/Technician
7625
15.8
Travel related characteristics
Sales/Service worker
4727
9.8
Independent
35114
73.0
Professional
5224
10.9
Only Korea
42311
87.9
Manufacturer/Engineer/Laborer
1718
3.6
Leisure, recreation, holiday
19798
41.1
Self-employed
3704
7.7
Health, medical treatment
499
1.0
Student
6175
12.8
Religion or pilgrimage
388
0.8
Housewife
2099
4.4
Shopping
3829
8.0
Retiree
908
1.9
Visiting friends and relatives
4877
10.1
Unemployed
589
1.2
Business or professional
activities
16029
33.3
Other (reference category)
2761
5.7
Alone
18141
37.7
Elementary
6576
13.7
Family and relatives
13688
28.4
College
28030
58.3
Friends
10877
22.6
Graduate school
10933
22.7
Coworker
6475
13.5
Other (reference category)
1809
3.8
Other (reference category)
608
1.3
Japan
8205
17.1
Hotel
35270
73.3
China
7436
15.5
Information search behaviors
Hong Kong
2821
5.9
Travel agency
3340
6.9
Singapore
1911
4.0
Relatives and Friends
8022
16.7
Taiwan
3407
7.1
Internet
10330
21.5
Thailand
2717
5.6
Traveler's guides
3699
7.7
Malaysia
2064
4.3
Media (TV, radio, newspaper)
2916
6.1
Australia
1720
3.6
Korean office (tourist office,
embassy)
1083
2.3
America
3931
8.2
Airlines, hotels
968
2.0
26
Table 2. Descriptive statistics (continuous variables)
Variable
Sample size
Mean
Std. Deviation
Total travel expenses
47347
1904.86
23610.81
Accommodation
34489
543.55
7931.38
Shopping
38424
838.92
20745.88
Food and beverage
47405
386.01
3729.47
Local transportation
47416
100.90
1234.86
Consumer price index differentials
45672
1.005
0.05
Number of visits
48113
1.79
1.14
Overall travel satisfaction
48113
4.27
0.77
27
Table 3. Determinant factors of total expenses per person (OLS, 2SLS, Heckit and QR)
Variables
OLS
OLS with
CPId
2SLS
2SLS with
CPId
Heckit
Heckit
with CPId
QR
QR with
CPId
Demographic
characteristics
C
6.0315a
(0.0461)
5.0203a
(0.1019)
6.3966a
(0.1419)
4.8913a
(0.1528)
6.4226a
(0.0517)
5.1973a
(0.1162)
6.1613a
(0.0454)
4.3832a
(0.1075)
31-40
0.0620a
(0.0123)
0.0465a
(0.0128)
0.0197
(0.0197)
0.0259
(0.0222)
0.0296b
(0.0146)
0.0118
(0.015)
0.0599a
(0.0109)
0.0377a
(0.0116)
41-50
0.1179a
(0.0141)
0.0688a
(0.0146)
0.0202
(0.0265)
0.0415
(0.028)
0.0615a
(0.0164)
0.0051
(0.0168)
0.1089a
(0.0133)
0.0519a
(0.014)
51-60
0.1268a
(0.0166)
0.0500a
(0.0172)
-0.0051
(0.0265)
0.0271
(0.0266)
0.0677a
(0.0192)
-0.0213
(0.0197)
0.1341a
(0.0153)
0.0534a
(0.0163)
over 60
0.0778a
(0.0242)
-0.0431
(0.025)
-0.1079a
(0.0347)
-0.0675b
(0.0332)
0.0269
(0.027)
-0.1035a
(0.0286)
0.1120a
(0.0240)
0.0038
(0.028)
Public official/Armed
forces
0.0434
(0.0244)
0.0237
(0.0252)
0.0170
(0.0252)
0.0250
(0.0256)
-0.0025
(0.0287)
-0.0225
(0.0292)
0.0527b
(0.0210)
0.0413
(0.0226)
Business
person/Manager
0.1259a
(0.0204)
0.1129a
(0.0213)
0.0948a
(0.0277)
0.0823b
(0.0344)
0.0423
(0.0240)
0.0189
(0.0247)
0.1327a
(0.0198)
0.1326a
(0.0214)
Office
employee/Technician
0.0127
(0.0201)
-0.0068
(0.0208)
-0.0384
(0.0253)
-0.0262
(0.027)
-0.0332
(0.0238)
-0.0572b
(0.0243)
0.0291
(0.0176)
-0.0068
(0.0192)
Sales/Service worker
0.0582a
(0.0218)
0.0594a
(0.0225)
0.0234
(0.0301)
0.0307
(0.0339)
0.0092
(0.0258)
0.0187
(0.0264)
0.0422b
(0.0184)
0.0214
(0.021)
Professional
0.0371
(0.0218)
0.0084
(0.0228)
0.0180
(0.0227)
0.0119
(0.0233)
0.0022
(0.0258)
-0.0271
(0.0268)
0.0474b
(0.0210)
0.0207
(0.0227)
Manufacturer/Engine
er/Laborer
-0.0387
(0.0290)
-0.0330
(0.0304)
-0.0321
(0.0311)
-0.0241
(0.0318)
-0.0608
(0.0343)
-0.0279
(0.0356)
-0.0256
(0.0264)
-0.0224
(0.0313)
Self-employed
0.3313a
(0.0232)
0.3971a
(0.0242)
0.3703a
(0.0249)
0.3861a
(0.0263)
0.2954a
(0.0278)
0.3511a
(0.0286)
0.2777a
(0.0221)
0.3392a
(0.0258)
Student
-0.1120a
(0.0222)
-0.1221a
(0.0231)
-0.1080a
(0.0253)
-0.1063a
(0.0272)
-0.0610b
(0.0278)
-0.0704b
(0.0286)
-0.1002a
(0.0190)
-0.1252a
(0.0207)
Housewife
0.0744a
(0.0277)
0.0561b
(0.0284)
0.0393
(0.0289)
0.0533
(0.0288)
0.0742b
(0.0325)
0.0525
(0.0329)
0.0968a
(0.0218)
0.0556b
(0.0236)
Retiree
-0.1288a
(0.0388)
-0.083b
(0.0401)
-0.0281
(0.0481)
-0.0493
(0.0502)
-0.0831
(0.0461)
-0.0531
(0.0471)
-0.0833b
(0.0368)
-0.0448
(0.0391)
Unemployed
-0.0450
(0.0447)
-0.0427
(0.0464)
-0.0141
(0.0507)
-0.0122
(0.054)
-0.0123
(0.0568)
-0.005
(0.0579)
-0.0168
(0.0369)
-0.0424
(0.0421)
Elementary
0.0498
(0.0254)
0.0963a
(0.0264)
0.0794a
(0.0265)
0.1037a
(0.0275)
0.0507
(0.0277)
0.076a
(0.0286)
0.0407
(0.0217)
0.079a
(0.0255)
College
0.0818a
(0.0232)
0.1413a
(0.0241)
0.1360a
(0.0241)
0.1439a
(0.0245)
0.0971a
(0.0251)
0.1589a
(0.0259)
0.0649a
(0.0201)
0.1277a
(0.0239)
Graduate school
0.0612b
(0.0245)
0.0857a
(0.0255)
0.0826a
(0.0253)
0.0867a
(0.0258)
0.0618b
(0.0265)
0.0948a
(0.0273)
0.0520b
(0.0220)
0.100a
(0.0257)
Japan
-0.0777a
(0.0244)
-0.0813a
(0.0291)
-0.1789a
(0.0257)
China
0.5002a
(0.0245)
0.5651a
(0.0298)
0.4019a
(0.0260)
Hong_Kong
0.0942a
(0.0279)
0.0936a
(0.0328)
0.0331
(0.0278)
Singapore
0.3660a
(0.0300)
0.3206a
(0.0349)
0.3292a
(0.0299)
Taiwan
0.1392a
(0.0278)
0.2512a
(0.0334)
0.0562b
(0.0269)
Thailand
0.0942a
(0.0290)
0.1350a
(0.0340)
0.04062
(0.0273)
Malaysia
0.2215a
(0.0297)
0.2947a
(0.0352)
0.2292a
(0.0291)
Australia
-0.0233
(0.0306)
-0.097237a
(0.0363)
-0.0219
(0.0405)
USA
-0.0097
(0.0259)
0.0068
(0.0311)
-0.0361
(0.0308)
Canada
-0.0829a
-0.0919b
-0.0897b
28
(0.0315)
(0.038)
(0.0381)
UK
-0.0940a
(0.0311)
-0.1570a
(0.0366)
-0.0234
(0.0403)
Germany
-0.0678b
(0.0311)
-0.1261a
(0.0363)
-0.0700
(0.0390)
France
-0.0091
(0.0329)
-0.0720
(0.0391)
0.0176
(0.0370)
Russia
0.2519a
(0.0312)
0.1904a
(0.0376)
0.2557a
(0.0377)
Saudi Arabia
0.5309a
(0.0325)
0.4452a
(0.0412)
0.4824a
(0.0445)
India
-0.0068
(0.0348)
0.0321
(0.0412)
0.0270
(0.0490)
Travel related
characteristics
Independent
-0.1875a
(0.0125)
-0.2019a
(0.0122)
-0.1877a
(0.0193)
-0.1802a
(0.0227)
0.0249
(0.0148)
-0.0266
(0.0143)
-0.1859a
(0.0104)
-0.1802a
(0.0105)
Days
0.0293a
(0.0005)
0.0292a
(0.0006)
0.0087
(0.0128)
0.0093
(0.0174)
0.0539a
(0.0009)
0.053a
(0.001)
0.0303a
(0.0009)
0.0313a
(0.0008)
Only Korea
0.1533a
(0.0129)
0.2159a
(0.0133)
0.2370a
(0.0202)
0.2383a
(0.0238)
0.126a
(0.0152)
0.2030a
(0.0156)
0.1453a
(0.0200)
0.1742a
(0.0213)
Leisure, recreation,
holiday
0.1132a
(0.0089)
0.1581a
(0.0091)
0.1280a
(0.0199)
0.1291a
(0.027)
0.0384a
(0.0112)
0.0992a
(0.0113)
0.1110a
(0.0091)
0.1523a
(0.0102)
Shopping
0.2662a
(0.0137)
0.2782a
(0.0138)
0.2267a
(0.0285)
0.2417a
(0.0348)
0.2107a
(0.0163)
0.222a
(0.0163)
0.2497a
(0.0124)
0.2440a
(0.0131)
Visiting F&R
-0.1105a
(0.0131)
-0.1532a
(0.0135)
-0.1246a
(0.0245)
-0.1179a
(0.0337)
0.2809a
(0.0200)
0.2246a
(0.0207)
-0.1228a
(0.0146)
-0.1439a
(0.0153)
Business or
professional activities
-0.1933a
(0.0112)
-0.2045a
(0.0115)
-0.1556a
(0.0230)
-0.1771a
(0.0266)
-0.4540a
(0.0141)
-0.4660a
(0.0144)
-0.1671a
(0.0123)
-0.1832a
(0.0136)
Alone
-0.1130a
(0.0256)
-0.1435a
(0.0264)
-0.1267a
(0.0293)
-0.1233a
(0.032)
-0.0654b
(0.0301)
-0.0902a
(0.0306)
-0.0981a
(0.0236)
-0.1293a
(0.0224)
Family and relatives
0.1070a
(0.0235)
0.1146a
(0.0242)
0.1407a
(0.0248)
0.1176a
(0.0246)
0.0563b
(0.0272)
0.0742a
(0.0276)
0.1029a
(0.0198)
0.101a
(0.0178)
Friends
0.0681a
(0.0234)
0.0532b
(0.024)
0.0433
(0.0256)
0.0415
(0.0264)
0.0833a
(0.0270)
0.0797a
(0.0274)
0.0693a
(0.0198)
0.0354
(0.0181)
Coworker
-0.1151a
(0.0258)
-0.0975a
(0.0265)
-0.1045a
(0.0271)
-0.1044a
(0.0276)
-0.1036a
(0.0298)
-0.0719b
(0.0303)
-0.0622a
(0.0229)
-0.0542a
(0.0210)
Hotel
0.2879a
(0.0124)
0.2307a
(0.0128)
0.1267
(0.0658)
0.1344
(0.0852)
0.2882a
(0.0126)
0.2569a
(0.0132)
Information search
behaviors
Travel agency
0.1356a
(0.0185)
0.1070a
(0.0190)
0.1118a
(0.0194)
0.1130a
(0.0199)
0.1066a
(0.0211)
0.0840a
(0.0215)
0.0636a
(0.0153)
0.0534a
(0.0159)
Relatives and Friends
0.1227a
(0.0134)
0.0936a
(0.0139)
0.0989a
(0.0139)
0.0959a
(0.0142)
0.1165a
(0.0156)
0.0956a
(0.016)
0.0654a
(0.0117)
0.0518a
(0.0126)
Internet
0.1448a
(0.0124)
0.1119a
(0.0128)
0.1228a
(0.0140)
0.1212a
(0.0154)
0.1351a
(0.0146)
0.1032a
(0.015)
0.0891a
(0.0119)
0.071a
(0.0124)
Traveler's guides
0.1237a
(0.0179)
0.066a
(0.0185)
0.0792a
(0.0198)
0.0773a
(0.0211)
0.1083a
(0.021)
0.0559b
(0.0217)
0.0736a
(0.0149)
0.0255
(0.0148)
Media (TV, radio,
newspaper)
0.1123a
(0.0197)
0.0823a
(0.0204)
0.0872a
(0.0210)
0.0918a
(0.0223)
0.1028a
(0.0230)
0.0666a
(0.0235)
0.0637a
(0.0169)
0.0454a
(0.0175)
Korean office (tourist
office, embassy)
0.1610a
(0.0313)
0.1355a
(0.0329)
0.1719a
(0.0379)
0.1646a
(0.0419)
0.2207a
(0.0382)
0.198a
(0.0398)
0.1022a
(0.0253)
0.0698b
(0.032)
Airlines, hotels
0.0772b
(0.0316)
0.0060
(0.0333)
-0.0033
(0.0342)
-0.007
(0.0357)
0.0297
(0.0358)
-0.0382
(0.0373)
0.0430
(0.0360)
-0.0115
(0.0326)
Price
Consumer price
index differential
(CPId)
1.141a
(0.0923)
1.473a
(0.305)
1.3102a
(0.1069)
1.8361a
(0.1002)
Notes: a= prob < 1%;b= prob < 5%.
29
Table 4. Effect of information sources on total expenses per person (QR)
Information sources
Quantile
0.1
0.25
0.5
0.75
0.9
Travel agency
0.777a
(0.0428)
0.5178
(0.0211)
0.2384a
(0.0151)
0.1000a
(0.0209)
0.0187
(0.0289)
Relatives and Friends
0.6098a
(0.028)
0.3731a
(0.0192)
0.1613a
(0.0135)
0.0896a
(0.0165)
-0.0043
(0.0224)
Internet
0.5008a
(0.031)
0.338a
(0.0178)
0.1375a
(0.0127)
0.1018a
(0.0151)
0.0498b
(0.0222)
Traveler's guides
0.6684a
(0.0322)
0.3671a
(0.0213)
0.1186a
(0.0164)
0.0461b
(0.0202)
-0.0552b
(0.026)
Media (TV, radio, newspaper)
0.6601a
(0.0396)
0.4254a
(0.0261)
0.1794a
(0.0179)
0.0518b
(0.0216)
0.0000
(0.0329)
Korean office (tourist office, embassy)
0.5596a
(0.0639)
0.3595a
(0.0441)
0.2023a
(0.0329)
0.1655a
(0.0284)
0.1600b
(0.0741)
Airlines, hotels
-0.0619
(0.0561)
-0.0791
(0.0591)
-0.0812b
(0.0368)
-0.1022b
(0.0403)
-0.1619a
(0.0465)
Notes: a= prob < 1%;b= prob < 5%.
30
Table 5. Effect of information sources on accommodation, shopping, F&B and local transportation
expenses per person (QR)
Information sources
Quantile
Accommodation
0.1
0.25
0.5
0.75
0.9
Travel agency
0.0000
(0.0163)
4.0775a
(0.1589)
0.1778a
(0.048)
-0.1285a
(0.0373)
-0.1106b
(0.0493)
Relatives and Friends
0.0000
(0.0094)
4.2047a
(0.0873)
0.2068a
(0.0308)
-0.0667b
(0.0271)
-0.0223
(0.0297)
Internet
0.0000
(0.0065)
0.0000
(0.0119)
0.102a
(0.032)
-0.0649a
(0.0249)
0.0116
(0.0269)
Traveler's guides
0.0000
(0.0135)
4.4308a
(0.0833)
0.239a
(0.0374)
-0.115a
(0.0325)
-0.0609
(0.0373)
Media (TV, radio, newspaper)
0.0000
(0.0148)
4.143a
(0.1761)
0.2585a
(0.0437)
0.0185
(0.0379)
0.0151
(0.0438)
Korean office (tourist office, embassy)
0.0000
(0.0203)
4.1897a
(0.2854)
0.375a
(0.0685)
0.1229b
(0.0537)
0.1517a
(0.0545)
Airlines, hotels
0.0000
(0.02)
3.6889a
(0.4158)
0.2852a
(0.0742)
0.1244b
(0.0581)
0.1276b
(0.0596)
Shopping
Travel agency
4.0943a
(0.1384)
1.3419a
(0.068)
0.5228a
(0.0269)
0.2375a
(0.0305)
0.1132a
(0.0313)
Relatives and Friends
3.7769a
(0.0647)
1.1929a
(0.0556)
0.3857a
(0.024)
0.1938a
(0.0231)
0.0714b
(0.028)
Internet
3.419a
(0.1271)
1.0516a
(0.057)
0.3526a
(0.024)
0.2015a
(0.0214)
0.0828a
(0.0281)
Traveler's guides
4.1431a
(0.1126)
1.1911a
(0.0591)
0.3526a
(0.0303)
0.1087a
(0.0264)
-0.0896b
(0.0359)
Media (TV, radio, newspaper)
4.3438a
(0.069)
1.3027a
(0.0623)
0.3857a
(0.0321)
0.1913a
(0.0328)
0.0912b
(0.0445)
Korean office (tourist office, embassy)
3.312a
(0.4396)
1.0403a
(0.1138)
0.3526a
(0.0618)
0.1938a
(0.0607)
0.0443
(0.0663)
Airlines, hotels
0.0000
(0.0252)
-0.0991
(0.1189)
-0.3075a
(0.0561)
-0.264a
(0.0564)
-0.3612a
(0.057)
F&B
Travel agency
0.0000
(0.006)
0.0000
(0.0098)
-0.0943
(0.05)
0.0000
(0.0411)
-0.1005b
(0.0439)
Relatives and Friends
0.0000
(0.0057)
0.0000
(0.0093)
0.2852a
(0.0295)
0.2517a
(0.0287)
0.0797a
(0.0294)
Internet
0.0000
(0.0052)
0.0000
(0.0085)
0.3001a
(0.0266)
0.2199a
(0.0255)
0.0428
(0.0276)
Traveler's guides
0.0000
(0.0086)
2.9596a
(0.5599)
0.3365a
(0.0338)
0.0892a
(0.0312)
-0.1159a
(0.0379)
Media (TV, radio, newspaper)
0.0000
(0.0079)
0.0000
(0.0128)
0.2546a
(0.0498)
0.2082a
(0.0368)
-0.0508
(0.0427)
Korean office (tourist office, embassy)
0.0000
(0.0177)
3.8067a
(0.156)
0.6366a
(0.0576)
0.4627a
(0.0529)
0.0524
(0.0614)
Airlines, hotels
0.0000
(0.016)
2.1972a
(0.6995)
0.0198
(0.0619)
-0.036
(0.0562)
-0.2985a
(0.0755)
Local transportation
Travel agency
0.0000
(0.0032)
0.0000
(0.0049)
0.0000
(0.0069)
-0.5319a
(0.0584)
-0.2169a
(0.0514)
Relatives and Friends
0.0000
(0.0027)
0.0000
(0.0042)
0.0000
(0.006)
0.2029a
(0.0305)
0.2311a
(0.0364)
Internet
0.0000
(0.0025)
0.0000
(0.0039)
1.7918
(1.0222)
0.2231a
(0.0289)
0.4055a
(0.0287)
Traveler's guides
0.0000
(0.0042)
0.0000
(0.0065)
2.9957a
(0.1107)
0.2231a
(0.0382)
0.2546a
(0.045)
Media (TV, radio, newspaper)
0.0000
(0.0042)
0.0000
(0.0064)
0.0000
(0.0091)
0.1927a
(0.0435)
0.2546a
(0.0576)
Korean office (tourist office, embassy)
0.0000
(0.0076)
0.0000
(0.0118)
3.2661a
(0.1962)
0.5878a
(0.0809)
0.4731a
(0.0615)
Airlines, hotels
0.0000
(0.0069)
0.0000
(0.0106)
0.0000
(0.015)
0.1187
(0.0653)
0.0000
(0.0753)
