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Examining the individual heterogeneity of tourists is fundamental to providing insights on tourist market segmentation, targeting potential markets and niches, and proposing suitable marketing strategies. However, most past studies failed to incorporate this individual heterogeneity in an integral way. This study utilizes a latent class duration model to investigate the latent segments of tourists regarding the preference of length of stay (LOS) in a destination. The study unveils a substantial amount of latent heterogeneity across the sample, and our empirical results identify two latent classes of tourists, namely short-duration and long-duration tourists. These classes share distinct LOS preferences, and information sources and travel partners have no significant influences in predicting the LOS of short-duration tourists. Therefore, the “one-fit-all” solution from the conventional duration model could be misleading, and this highlighted heterogeneity provides destination marketing organizations (DMOs) with the incentive to segment the tourists and offer specific tourism products and bundles.
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* School of Tourism and Hospitality Management, Temple University,
Philadelphia, PA 19122, United States
Phone: (01) 215-204-8701; Fax: (01) 215-204-8705
Department of Land Resources and Tourism Sciences, Nanjing University,
Nanjing, 210093, China
Corresponding Author: Dr. Hong-Lei Zhang
Acknowledgement: This research was financially supported by National Natural Science Foundation of
China (No. 41301134) and Grants from Ministry of Education in China Project of Humanities and Social
Sciences (No. 13YJC790193).
Please cite as:
Yang, Y. and Zhang, H-L. (2015). Modeling tourists’ length of stay: Does one model fit all? Tourism
Analysis, 20(1), 13-23.
Abstract: Examining the individual heterogeneity of tourists is fundamental to providing
insights on tourist market segmentation, targeting potential markets and niches, and
proposing marketing strategy. However, most past studies failed to incorporate this individual
heterogeneity in an integral way. This study utilizes a latent class duration model to
investigate the latent segments of tourists regarding the preference of length of stay (LOS) in
a destination. The study unveils a substantial amount of latent heterogeneity across the
sample, and our empirical results identify two latent classes of tourists, namely,
short-duration and long-duration tourists. These classes share distinct LOS preferences, and
information sources and travel partners have no significant influences in predicting the LOS
of short-duration tourists. Therefore, the “one-fit-all” solution from the conventional duration
model could be misleading, and this highlighted heterogeneity provides destination marketing
organizations (DMOs) with the incentive to segment the tourists and offer specific tourism
products and bundles.
Keywords: length of stay; latent class; duration model; individual heterogeneity
1. Introduction
Length of stay (LOS) is an essential index for measuring the level of tourists consumption
and an important indicator for monitoring the growth of a tourism area (Archer & Shea,
1975). A large body of literature has been devoted to modeling LOS data of tourists (Alegre,
Mateo, & Pou, 2011; Barros & Machado, 2010; Gokovali, Bahar, & Kozak, 2007; Thrane,
2012; Yang, Wong, & Zhang, 2011). The analysis and investigation of the tourists LOS
preferences is important for several reasons. First, by understanding the determinants of the
LOS, tourism administrative units and organizations can allocate the necessary resources to
cater to tourists needs more efficiently. Second, the LOS modeling is beneficial for market
segmentation. Destination marketing organizations (DMOs) can therefore target more
specific marketing efforts toward a particular market and design featured tourism products
and services that increase market share. Third, private tourism sectors can increase revenue
after understanding factors associated with a longer LOS, as projected by the empirical model,
such as discrete choice model (Alegre & Pou, 2006, 2007; Yang, et al., 2011) and duration
model (Barros, Correia, & Crouch, 2008; Martínez-Garcia & Raya, 2008). Finally, by
targeting tourists with a predicted higher LOS at a destination, neighboring tourist
destinations could benefit from spillover effects due to the increased possibility of tourists
undertaking multi-destination tours to nearby areas (Yang & Wong, 2012).
Different econometric methods have been used to model tourists’ LOS. By assuming different
data generation processes of LOS, there are generally four types of models adopted in
tourism research: linear regression model (Fleischer & Pizam, 2002), count data model
(Alegre, et al., 2011; Salmasi, Celidoni, & Procidano, 2012), discrete choice model (Alegre &
Pou, 2006, 2007; Yang, et al., 2011), and duration model (survival analysis) (Barros, et al.,
2008; Barros & Machado, 2010; Martínez-Garcia & Raya, 2008). Among these four, the
duration model has been heavily applied over the last decade, and it is able to formulate a
survival function and a hazard function as well as incorporate censored observations and
time-varying covariates (Gokovali, et al., 2007).
In the conventional LOS model, it is assumed that sampled tourists are homogeneous, and a
single model is good enough to explain their duration within a particular destination.
However, understanding the individual heterogeneity of tourists is fundamental to providing
insights on market segmentation of tourists, targeting potential markets, and proposing
tourism destination marketing strategies. As suggested by Allenby and Rossi (1998), one of
the greatest challenges in marketing is to understand the diversity of consumers preferences
and provide differentiated products to market segments and niches with distinct preferences.
In the past literature, the individual heterogeneity of customers could be captured either by
assuming a continuous variation or a discrete variation across different customers. In
particular, as a method to incorporate the discrete individual heterogeneity, the latent class
methodology assumes that a sample of observations arises from a number of underlying
classes and regards the overall sample as a mixture of different segments (Wedel & DeSarbo.,
Although the latent class modeling strategy has been applied in tourism studies (Alegre, et al.,
2011; Mazanec & Strasser, 2007; Wu, Zhang, & Fujiwara, 2011), to the best of our
knowledge, no known research has utilized the latent class model to look into tourists’ LOS
under the framework of the duration model (survival analysis). Unlike the conventional
model, which estimates a single set of coefficients across all observations, a latent class
duration model is able to unveil the individual heterogeneity by estimating different sets of
regression coefficients for different segments simultaneously. More importantly, the latent
class model provides a formal statistical procedure to identify latent segments, allowing us to
recognize and characterize various preference groups. A global and universal duration model
might mask the individual heterogeneity of tourists and provide misleading results, especially
when consumer preferences and sensitivities become more diverse (Allenby & Rossi, 1998).
Therefore, this global model may provide misleading results for marketing implications. To
fill this research gap, the paper applies the latent class duration model to identify the number
of classes with homogeneous LOS preferences and tourists’ memberships to each class.
Hence, the study represents the first application of the latent class duration model in tourists’
LOS studies and sheds light on the segmentation of tourists with regard to their LOS
This paper is organized as follows: Section 2 reviews the literature that applies various
duration models in modeling tourists LOS data. Section 3 details the specifications of the
models and describes the data set used in this study. In Section 4, the empirical results of the
models are presented and explained. Section 5 concludes the paper with implications.
2. Modeling Length of Stay of Tourists
LOS data can be modeled under a range of data generation mechanisms. Four major types of
micro-econometric models have been applied for modeling tourists LOS; these are linear
regression model (Fleischer & Pizam, 2002), count data model (Alegre, et al., 2011), discrete
choice model (Alegre & Pou, 2006, 2007), and duration model (Barros, et al., 2008; Barros &
Machado, 2010; Martínez-Garcia & Raya, 2008). By regarding LOS as continuous data, a
linear regression model assumes a normality of LOS distribution and a simple linear
relationship between the expectation of LOS and explanatory variables. Because LOS can be
treated as a number of days or months, the count data model, which assumes a specific
discrete distribution of the dependent variable as a count number, is an inherent alternative to
model tourists LOS (Hellström, 2006; Hellström & Nordström, 2008). Through treating all
possible duration outcomes within a choice set, a discrete choice model explains tourists’
decision among a set of durations. Finally, in a duration model, the time it takes before
tourists leave a destination is usually specified as the dependent variable (Barros, et al., 2008;
Gokovali, et al., 2007; Gomes de Menezes, Moniz, & Cabral Vieira, 2008; Martínez-Garcia
& Raya, 2008; Thrane, 2012). The duration model is particularly powerful in modeling
duration data, as the LOS data are always skewed, censored, or truncated (Cleves, Gould, &
Gutierrez, 2003).
In micro-econometrics and bio-statistics, duration models are specifically used to model the
time elapsed before the occurrence of certain events. Generally, there are two
parameterizations of the duration model: the proportional hazards (PH) model and the
accelerated failure-time (AFT) model. In the PH-metric model, the hazard rate is the
dependent variable, which denotes the possibility that a tourist will leave the destination in
the next infinitesimal time period conditional on the fact that the tourist has already stayed
there beyond that particular moment. In contrast, the AFT-metric model treats the logarithm
of LOS as a dependent variable and asserts an interest on possible factors associated with it.
To estimate duration models, both parametric and semi-parametric methods have been
proposed. In the parametric model, one can specify a particular distribution for the hazard and
related functions, such as the Weibull model and the Gamma model (De Menezes & Moniz,
2011), whereas in the semi-parametric model, such as the Cox model, prior information on
the baseline distribution is not necessary (De Menezes, Moniz, & Cabral Vieira, 2008).
Some specific types of duration models are very similar to the other three types of LOS
models. For the AFT-metric model that assumes a log-normal distribution of LOS, it is
statistically equivalent to the linear regression on the logarithm of LOS. The AFT-metric
model is estimated by maximum likelihood estimation (MLE), which is asymptotically
equivalent to ordinary least squares. Moreover, some semi-parametric duration models can be
estimated under the count data model framework (Hilbe, 2011) or the ordered discrete choice
model framework (Greene & Hensher, 2010).
Considering the individual heterogeneity of tourists, various duration models with
unobservable heterogeneity have been introduced in LOS studies by specifying a prior
distribution of the individual effect (frailty) and estimating the combined model with a
mixture distribution (Barros, Butler, & Correia, 2010; Barros, et al., 2008). Barros, et al.
(2010) argued that overlooking this heterogeneity results in inconsistent estimates in the
duration model. In general, the application of the frailty duration model improves the overall
goodness-of-fit and provides information on the extent of the heterogeneity over the sampled
tourists. However, this modeling strategy restricts heterogeneity to model intercepts, and
further investigation on the heterogeneity of slope coefficients is more intriguing.
A major debate over the use of duration models centers on the applicability of hazard
function in the context of tourists LOS decision making. Thrane (2012) argued that because
tourists generally determine their durations before their trips, the hazard rate should be
constant over the duration, and the analysis based on the hazard rate tends to be meaningless.
However, in various AFT-metric models, it is not necessary to use such concepts as hazard
rate and survival function to understand the model and interpret the results.
3. Methodology and Data
3.1. Model specification
Because the interpretation of the estimated coefficients is more intuitive in AFT-metric
duration models (Cleves, et al., 2003), we focus on various AFT-metric duration models. By
assuming different distributions of error terms, we estimated a series of AFT-metric models
with latent classes, including the exponential model, the Weibull model, the log-normal
model, the log-logistic model, and the gamma model. After that, we estimated a probit model
to explain the membership of individual tourists to each identified latent class.
A general AFT-metric duration model is specified as follows:
ln i i i
where i indexes the observation;
is the LOS of observation i;
is a vector of explanatory
variables; and
is a vector of coefficients. In particular,
denotes the error term of the
model. The difference between the AFT-metric duration model and the typical regression
model is that the error term of the former is not necessarily normal and involves one or more
shape parameters. If the error term follows a normal distribution with a fixed variance, the
model is labeled as the log-normal model, and if it follows a logistic distribution, the model
becomes a log-logistical model. Moreover, in the exponential model, the error term follows a
standard Gumbel (extreme value) distribution, whereas in the Weibull model, the error term
follows a Gumbel distribution with a particular shape parameter.
In this study, the latent class modeling strategy is introduced to unveil the potential
heterogeneity in factors determining tourists’ LOS. The regression coefficients in the model
are specified to be the same for observations within the same latent class while varying across
difference classes. Equation 1 then becomes
( ) ( )
ln j
i j i i j
where j indexes the latent class, j = 1, …, J, and
is varying across different classes.
Therefore, the factors are assumed to contribute to tourists’ LOS for different classes in a
different way. The latent class model specifies the density of the dependent variable, lny, as a
linear combination of J different densities. To estimate the proposed latent class duration
model, the total density becomes
(ln | , , ) (ln | , ), 0 1, =1
j j j j j
i i i i i i i
f y f y
 
 
where i indexes the observation and j indexes the latent class, j = 1, …, J.
(ln | , )
is the density of jth class(component), and
is the probability of being jth class for
observation i. To determine the empirical optimal value of J (the total number of classes) in
the latent class model, a common way is to compare the information criteria associated with
different J values from MLE (Bhatnagar & Ghose, 2004; Clark, Etilé, Postel-Vinay, Senik, &
Van der Straeten, 2005). However, according to Swait and Sweeney (2000), the selection of
models should also be based on judgment, experience, and statistical considerations. In
general, lower values of information criteria measures characterize optimal solutions. In this
paper, we report four of these, namely, the Akaike Information Criterion (AIC), the ‘finite
sample’ version of AIC (FSAIC), the Bayes Information Criterion (BIC), and the Hannan and
Quinn Information Criterion (HQIC). They are specified as follows:
2ln 2AIC L K 
2 ( 1)
2ln lnBIC L K N 
2ln 2 ln(ln )HQIC L K N 
where lnL is the log likelihood value, K is the number of parameters, and N is the sample
In past literature, several types of factors were found to determine tourists LOS. The most
used factors are tourists social-demographic variables, such as age, income, level of
education, and frequency of travel (Barros, et al., 2010; Barros & Machado, 2010; Gokovali,
et al., 2007; Martínez-Garcia & Raya, 2008). Trip-characteristic-related factors are another
important group of determinants, including distance to destination, motivation, party size,
package tour, transport, daily cost, information source, activities participated, accommodation
type, and past visit (Barros, et al., 2008; Barros & Machado, 2010; Machado, 2010).
According to the previous literature, we set the length of stay of a tourist to be a function of
tourists age, motivation, past visit to the destination, travel distance from home, education
level, number of attractions visited, information source, accommodation type, whether the
tourist comes from the same city as the destination city, and type of travel partners.
3.2. Data description
To estimate the proposed model, we use the data from a province-wide domestic tourist
survey in Jiangsu Province of China. Jiangsu, located in the Yangtze River Delta, is one of the
most developed regions in China, with a GDP per capita of 7,945 USD in 2010. Seeking to
enjoy the high-quality infrastructure and exceptional tourist attractions, 350 million tourists
visited Jiangsu in 2010, bringing in a total revenue of 468.5 billion RMB Yuan. The Jiangsu
domestic tourist survey is conducted by the Jiangsu Tourism Administration, and the
questionnaires are distributed to domestic tourists at scenic spots and hotels around 13 cities
in Jiangsu. In this survey, various questions cover individual social-demographic information,
trip characteristics, and trip satisfaction. As far as we are concerned, this survey is one of the
most comprehensive domestic tourist surveys in China, considering its sample size, the scope
and variety of questions, and the heterogeneity of surveyed tourists. The variables we are
interested in are described in Table 1. The table demonstrates that in the overall sample of
27,709 observations, 62% of tourists were aged between 25 and 44 years, and 42% visited the
cities with the purpose of sightseeing. In terms of traveling partners, 34% tourists traveled
with friends and relatives, and 29% traveled alone. A further examination of the correlation
matrix of these independent variables indicates that most pairwise Pearson correlation
coefficients are below 0.3. Only two exceed 0.5, and they are the correlation coefficient
between info1 and partner3 (0.546) and between info3 and partner1 (0.557). This suggests
that tourists traveling with colleagues usually obtain tourism information from their work
affiliation, whereas those organized by travel agencies resort to those agencies to collect the
information related to their tours.
(Please place Table 1 about here)
In particular, we investigate the distribution of the LOS. As shown in Figure 1, the data are
heavily left skewed, as more than 70% of tourists choose two or three day stays in Jiangsu
destinations, and very few tourists spend more than seven days.
(Please place Figure 1 about here)
4. Empirical Results
At the outset, we have to choose the underlying distribution of the duration model and the
number of latent classes. Table 2 presents the values of information criteria for different
models. Different information criteria measures, such as AIC, FSAIC, BIC, and HQIC, are
particularly useful in comparing different latent class model solutions based on their model fit
and parsimony (Magidson & Vermunt, 2004). To select the best-fit underlying distribution of
the duration model, we compare the measures of different duration models. The results
suggest that the log-logistical model consistently outperforms others, no matter how many
latent classes are specified. Moreover, we compare these information criteria measures to
determine the optimal number of latent classes. Table 2 shows that adding a third or fourth
class does not decrease the information criteria measures. Therefore, as highlighted by the
lowest values of AIC, FSAIC, BIC, and HQIC, a log-logistical model with two latent classes
is selected, and among all alternatives, this model offers the optimal balance between
goodness-of-fit, parsimony, and explanatory power. Figure 2 demonstrates the distribution of
LOS within each latent class. For latent class 1, LOS ranges from one to six days, and all
one-day tourists belong to this class. For latent class 2, LOS ranges from two to twenty-one
days, and the average LOS is much larger than latent class 1. Therefore, we labeled latent
class 1 as “short-duration tourists” and latent class 2 as “long-duration tourists”. The result of
these two unveiled latent classes is similar to the findings from Alegre, et al. (2011), who also
recognized two latent classes with different durations based on count data models.
(Please place Table 2 about here)
(Please place Figure 2 about here)
Table 3 presents the estimation results for these two latent classes. In the first column for
latent class 1, short-duration tourists, several variables are estimated to be statistically
significant. The effect of the explanatory variable xj in the AFT-metric duration model is to
change the LOS by a factor of exp(xjβj). The estimated coefficient of age4, which is -0.032,
indicates that tourists aged 65 years and above stay for 3.1% less time than tourists aged
between 24 and 44 years, which is set as the reference category. In terms of motivations, the
results indicate that business tourists have the longest LOS, followed by those with other
purposes, then followed by sightseers. motivation4 is estimated to be 0.358, suggesting that
business tourists tend to stay 43.0% longer than tourists with a vacation motivation (reference
category) in latent class 1. The negative and statistically significant coefficient of motivation3
notes that VFR tourists have the shortest stay in latent class 1. This result is contradictory to
findings from previous literature (Hsu & Kang, 2007; Sung, Morrison, Hong, & O’Leary,
2001), highlighting the different LOS preferences of this tourist segment. Moreover, distance,
attraction, and hnr are estimated to be statistically significant and positive, and these results
indicate that tourists who traveled longer to the destination, visited more attractions, and
stayed in hotels are more likely to stay longer. However, several other explanatory variables
are found to be insignificant for latent class 1, such as pastvisit, educate, and samecity, as
well as grouped variables of information sources and travel partners.
(Please place Table 3 about here)
Column 2 in Table 3 provides the estimation results for latent class 2, long-duration tourists.
These estimates are substantially different from those for latent class 1, suggesting the
noticeable heterogeneity in LOS preference between short-duration and long-duration tourists.
First, in a set of age dummies, age3 is statistically significant and estimated to be -0.016,
implying that tourists aged between 45 and 64 years stay for 1.59% less time than those aged
between 25 and 44 years. Second, although all motivation variables are significant as in the
latent class 1 estimates, the estimates of these variables flipped the sign in latent class 2. The
results show that sightseeing, business, and other-motivation tourists are likely to stay shorter
than vacationing tourists, whereas VFR tourists stay longer. motivation4 is estimated to be
-0.027, suggesting that VFR tourists tend to stay for 2.7% less time than tourists with a
vacation motivation in latent class 2. Third, the grouped variables of information sources and
travel partners become statistically significant for latent class 2. We find that tourists
collecting information from friends and relatives, affiliations, and other sources have a longer
LOS than those obtaining information from travel agencies; furthermore, tourists traveling
alone have a shorter LOS than those traveling with colleagues. Finally, variables pastvisit,
educate and samecity are estimated to be statistically significant only in the latent class 2
model, suggesting that tourists with more past visits, higher levels of education, and
residency in the destination city are associated with a longer LOS for long-duration tourists.
We further fit a probit model to understand the membership of the two latent classes. Column
3 in Table 3 presents these estimates, and the dependent variable is whether the observation
belongs to latent class 2. The results show that, compared to latent class 1, latent class 2
consists of fewer aged tourists, fewer frequent travelers to the destination, more long-haul
tourists, and more well-educated tourists. In terms of motivation, there are more VFR,
business, and other-motivation tourists than vacationing tourists, but there are fewer
sightseeing tourists in latent class 2. Moreover, for those belonging to latent class 2, fewer
tourists collect information from work affiliation or media, and fewer travel with family and
friends or alone.
We also fit an ordinary log-logistic model without considering latent heterogeneity. Keep in
mind that this log-logistic model can also be regarded as a frailty model and it is a Weibull
model with an exponential heterogeneity term. Column 4 in Table 3 presents the estimation
results of this model. The results are quite different from the estimates of either latent class
(Columns 1 and 2 in Table 3). For example, in the global model, VFR tourists (motivation3)
are found to have the longest duration, which is consistent with the estimate of latent class 2,
whereas business and other-motivation tourists (motivation4 and motivation5) are found to
stay longer than vacationing tourists, which are similar to the estimates of latent class 1.
Therefore, our results suggest that a model that does not consider latent heterogeneity masks
the substantial individual heterogeneity, especially the heterogeneity of slope coefficients.
5. Conclusion
To account for latent heterogeneity in the tourists’ LOS model, we employed a latent class
modeling strategy that allows for multiple segments varying in model estimates. In the
estimated model, slope coefficients are different across segments. We highlighted a
substantial amount of latent heterogeneity across the sample, and our empirical results
unveiled two latent classes of tourists, namely, short-duration and long-duration tourists.
They share distinct LOS preferences. The estimation results suggested that information
source and travel partners have no significant influences in predicting the LOS of
short-duration tourists, whereas for long-duration tourists, those who obtain information from
friends and relatives, work affiliations, and other sources and who do not travel alone are
likely to stay longer.
We observed significant differences regarding the LOS preferences of tourists. Therefore, the
“one-fit-all” solution from the global duration model could be misleading. Tourists differ in
determining their LOS, and this diversity provides DMOs with the incentive to segment the
tourists and offer specific tourism products. Therefore, a well-rounded understanding and
analysis of individual heterogeneity in LOS modeling enables practitioners to identify the
proper segments and consider product differentiation to maximize revenue. To increase this
revenue, specific schedules and activities should be provided to cater to the needs of these
tourist segments.
Our results show that long-haul tourists are more likely to stay longer. Hence, we recommend
the improvement of visitor information centers in major transportation hubs where long-haul
travelers can be found, such as airports and railway stations. Furthermore, as suggested by the
results of latent class duration model, to encourage tourists to stay longer in Jiangsu, DMOs
should increase marketing efforts targeting hotel guests for the short duration segment (latent
class 1) and VFR tourists for the long-duration segment (latent class 2). To further lengthen
the stay of long-duration VFR tourists, feasible undertakings include offering all-inclusive
package discounts for groups, providing multi-day tickets of major attractions with little
additional charge, and giving free tickets to local residents when traveling with a group of
friends or relatives. Moreover, our results suggested that keeping a high level of satisfaction
is important. As highlighted in the latent class 2 estimates, those tourists obtaining
information from relatives and friends are more likely to stay longer, and the word-of-mouth
effect heavily relies on past visitors’ satisfaction (Aktaş, Çevirgen, & Toker, 2010). Therefore,
it is important to consistently improve service quality to guarantee a high level of satisfaction.
In this paper, we did not correct for possible sample selection (Barros & Machado, 2010) or
consider simultaneous decision making between LOS and other trip characteristics (Machado,
2010). Moreover, our research is based on data from domestic tourists in China, which cover
a relatively narrow range of LOS values and might be distinct from data from Western
tourists in terms of duration patterns. Therefore, we believe that future studies should
incorporate more sophisticated duration models with latent classes and apply this modeling
framework with other LOS datasets.
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Table 1. Description of Independent Variables
age 15-24
age 25-44
age 45-64
age 65 and above
vacation motivation
sightseeing motivation
VFR motivation
business motivation
other motivations
info from agency
info from friends and relatives
info from affiliations
info from media
other info sources
indicator for tourists whose residence is in
the destination city
with colleagues
with friends and relatives
with travel agency
1=no past visit; 2=1-2 past visits; 3=3-4
past visits; 4=5 and more past visits
distance from residence (in 1,000 km)
education level: 1= college and above; 2=
associate diploma; 3=senior high
school/secondary vocational school;
4=junior high school; 5=elementary
school and below.
number of attractions visited
proportion of nights in hotel
Table 2. Goodness-of-fit Measures of Different Models
AFT-metric model
Number of
latent classes
Table 3. Estimation Results of Duration Models
Log-logistic model
(latent class 1)
Log-logistic model
(latent class 2)
Probit model
of membership
Sample size
Pseudo R-squared
(Notes: * indicates p<0.10, ** indicates p<0.05, *** indicates p<0.01.The significance of some auxiliary parameters is
tested in logarithm. Standard errors are estimated by Huber/White/sandwich estimator of the variance in parenthesis.)
Figure 1. Histogram of length of stay
010 20 30 40
0 5 10 15 20
Length of Stay
Figure 2. Histogram of length of stay in different latent classes
0.2 .4 .6 .8 1
0 5 10 15 20
LOS (latent class 1)
0.2 .4 .6 .8 1
0 5 10 15 20
LOS (latent class 2)
... Strategies to cope with unobserved heterogeneity are diverse. Researchers have opted for continuous survival models that allow unobserved heterogeneity (Barros et al., 2008;Thrane 2012;Santos et al, 2015), the random-logit model (Nicolau and Más, 2009), the mixed-logit model (Grigolon et al., 2014) or the utilization of latent class models (Alegre et al., 2011;Yang and Zhang, 2015). ...
... Neither gender nor education level attained exert significant impacts, whereas age extends length of stay for the oldest population group given the fact that they face fewer time constraints 2 . This latter result is consistent with most of the research in this area (Santos et al, 2015), although some previous work suggests the opposite results (Barros et al, 2008;Yang and Zhang, 2015). The type of accommodation emerges as a decisive factor. ...
... The daily price of each of the accommodation options is the underlying factor that accounts for this highly significant impact of the different types of accommodations (Fleischer and Byk, 2009). According to Menezes and Moniz (2011), Grigolon et al. (2014) or Yang and Zhang (2015), party size has been analysed from a party composition perspective. All three models signal that the greatest number of nights spent at the destination corresponds to trips accompanied by adult family members, followed by trips undertaken with friends. ...
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We analysed the determinants of the length of stay for tourists arriving in a Mediterranean coastal destination by means of high-speed rail (HSR) service. This study is based on data obtained from a survey completed by HSR passengers returning from holiday in Costa Daurada (Catalonia). The empirical analysis is based on estimations made using a survival model. The influence of the availability of HSR service on tourists’ destination choices together with the tourists’ profiles, party structure or accommodation characteristics was used as explanatory variables. Results revealed that the existence of HSR services played a minor role in tourists’ decision of whether to visit the Costa Daurada. Also, evidence suggests that the existence of the HSR station would only affect the length of stay of those tourists who stay overnight in second residences.
... The analysis employs two sets of econometric models applied to Irish national level data; the logit model to analyze the socio-demographic factors influencing the decision to take a day trip or an overnight stay and the truncated Travel Cost Models (TCM) to analyze the factors associated with the number of days or nights spent in a marine or coastal location. Although some of the economic impacts of day-trips (Downward et al., 2020;Prayaga 2017) and longer stays (Canavan, 2013;Canavan, 2016) in marine and coastal areas have been studied, comparative research is less common (Hynes et al., 2017;Yang and Zhang 2015) By providing a comparative analysis of day-trippers and overnight tourists, valuable insight into how to best grow and service the market can be obtained. Further to this, a comparative analysis allows for a better understanding of the relative importance of day trips and overnight stays in relation to the marine tourism economy and the benefits derived by users from both types of trips. ...
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Domestic marine and costal tourism has increased in importance over the last number of years due to the impacts of international travel, environmental concerns, associated health benefits and COVID-19 related travel restrictions. Consequently, this paper conceptualizes domestic marine and coastal tourism within an economic framework. Two logit models examine the factors that influence participation in the coastal day trips and overnight stays markets, respectively. Two truncated travel cost models are employed to explore trip duration, one analyzing the number of day trips taken and the other examining the number of nights spent in marine and coastal areas. Although a range of variables predict participation, no one variable had a significant and consistent affect in every model. A division in access to domestic marine and coastal tourism is also observed based on variation in household income. The results also indicate a vibrant day trip market and large consumer surpluses. The decision to use logit participation models and travel cost models applied to day trips and overnight stays is a direct result of the audiences this paper aims to inform. Firstly, by presenting the decision making process for domestic marine and coastal tourism in this depth, evidence based decision makers can gather a better understanding of how domestic tourist decided to participate in marine and coastal tourism, who the larger beneficiary are of the different types of marine and coastal tourism and how policy focused solely on overnight stays can adversely affect particular segment of society, often those less well off financially. Secondly, the academic literature has presented a dearth of information comparing day trip participation to overnight stays in marine and coastal tourism, as such, this paper provides a valuable source of information.
... Events are known to also create supplementary demand during the regular season of a destination, thereby aiding to generate additional revenue (Connell et al., 2015). More so, regular tourists may extend their stay in a destination, just to wait for the occurrence of an event that they did not previously plan to attend (Sotiriadis, 2015;Yang & Zhang, 2015). Events therefore become critically important in positioning destinations as viable investment alternative in tourism marketplace, as events do significantly contribute towards destination marketing (Arnegger & Herz, 2016). ...
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This study assessed the role played by the existing marketing strategies in terms of positioning Dundee July rural horse racing event (in uMzinyathi District Municipality, KwaZulu-Natal Province, South Africa) on the tourism map, both within the province, and in South Africa at large. This study adopted the exploratory qualitative research method using purposive semi-structured interviews to collect data for analyses; hence its results are not conclusive. There is a further need to collect quantitative data from current and prospective attendees, on a larger scale, to validate the study findings and model customer expectations (customer orientation). However, this study’s exploratory findings revealed that the current marketing strategies and tools are not effectively promoting this event to attract a larger number of spectators and participants. As a feasible solution, this paper puts forward practical recommendations, emphasising on conditions, new marketing strategies and the right combination of both traditional and digital marketing tools to attract a critical mass of attendees to this event. Emphasis was also made on the best way to utilise appropriate marketing mix to position this event. The experience-setting must align to the original rustic nature of this event to optimise its authenticity.
... The moderating role of length of stay The length of stay is an essential factor in tourist destination management. The length of a tourist's visit is related to the tourism industry, increasing the total time spent on leisure and travel increases traveler experience (Scholtz et al., 2015;Thrane, 2016;Yang and Zhang, 2015). The study by Barros et al. (2010) indicates the length of travel has a beneficial impact on the travel experiences when visitors have the opportunity to contact the service providers. ...
Purpose This paper aims to investigate the impact of gastronomic experience on sharing experiences, as well as place attachment as a mediator and length of stay as a moderator. Design/methodology/approach Quantitative method was used in this study. The paper conducted an online survey from 717 international tourists who visited Phuket, a city of gastronomy. Findings The result revealed that four dimensions of gastronomic experience – entertainment, escapist, education and aesthetic – affected sharing experience. The mediating role of place attachment was documented. As expected, the length of stay moderated the effect of the gastronomic experience on sharing experience through place attachment as a mediator. Practical implications These results could help destination managers to develop tourist experiences and enhance customers' length of stay and place attachment. On the other hand, this research contributes to the understanding of the factors that affect sharing experience in the tourism industry with a special focus on the city of gastronomy. Originality/value Prior research shows that tourism experience provides a future tourist behavior based on effective attitudinal variables. At the present, this research provides researchers with information on how to narrow the behavior gap through a range of marketing. This study gives additional insights into the indications of what visitors will transfer into behavior and why an area that has not been addressed previously in this context.
... They identified two segments containing individuals showing a preference for either short or longer stays. More recently, Yang and Zhang (2015) also applied a latent class duration model to analyse the determinants of tourism length of stay in different segments and reached similar conclusions to those of Alegre et al. (2011). Alén, Nicolau, Losada, and Domínguez (2014), supported by a negative binomial model, analysed the factors determining tourism length of stay of Spanish seniors. ...
The length of stay for tourists is shrinking for traditional tourism destinations, with tourists instead opting for short breaks to multiple destinations. The reasons for these changes include the increasing number of low cost airlines reduces the cost per journey, alongside heightened disposable income and strong marketing strategies by competing destinations. Madeira Island is well placed in this study as it faces a typical issue of declining length of stay, meanwhile acquires rich data in carrying out thorough analyses in the factors that explain length of stay in Madeira Island-Portugal by five different econometric approaches, further policy implications of the research findings, particularly those that could potentially prove useful to increase the length of stay, are also discussed.
... Events can also create supplementary demand in the regular season of the destination, thus generating additional revenue (Connell, Page and Meyer, 2015). Furthermore, regular tourists can possibly extend their stay in the destination, awaiting the occurrence of a given event that they may not have necessarily planned to attend (Sotiriadis, 2015, Yang andZhang, 2015;Dimanche, 2002). The inclination to make an investment in a given destination relies on the attractiveness of the destination, as well as how sufficiently it is branded (Dinnie, 2015;McDonnell and Gebhardt, 2002). ...
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In recent times there has been an increase in the global use of events for marketing and branding destinations. The strategic marketing of events enhances the destination’s attractiveness and subsequently draws more visitors in the highly contested tourist marketplace. This study investigates how Nelson Mandela Bay (NMB), South Africa can market events to augment its attractiveness as a competitive destination. The study used a quantitative research method, where questionnaires were distributed to NMB residents. A sample of 3 359 responses were obtained. The findings suggest that the strategic bundling, placement, promotion and positioning of events is critical when proceeding with an event-marketing initiative. The proposed event-marketing framework developed in the study can be used as a blueprint for informing the strategic development of event concepts for NMB. The successful establishment of event offerings will consequently strengthen the destination attractiveness of NMB.
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Domestic marine and coastal tourism has increased in importance over the last number of years due to the impacts of environmental concerns connected with international travel, the associated health benefits and COVID-19 related travel restrictions. Consequently, this paper analyses the determinants of demand for domestic day trips and overnight stays by Irish residents to marine and coastal areas. Two logit models examine the factors that influence participation in the coastal day trip and overnight stay markets, respectively. Two truncated travel cost models are employed to explore trip duration, one analyzing the number of day trips taken and the other examining the number of nights spent in marine and coastal areas. The results suggest a division amongst those who can and cannot access marine and coastal tourism. In particular, those who are financially better off have a greater level of access to Irish marine and coastal tourism. Additionally, although generally disregarded in tourism policy and marketing, the results indicate a vibrant day trip market that commands high per person consumer surplus.
This paper analyses the determinants of tourist length of stay in sharing accommodation. Methodologically, the paper proposes a new approach to account for the observed bimodality of length of stay. More specifically, the determinants of length of stay are analyzed by modeling both the conditional mean and conditional modal frequencies, which represent shorter and longer tourist stays, unlike previous contributions on length of stay where modeling has been based exclusively on the conditional mean. The empirical analysis examines the length of stay in sharing accommodation lodgings (Airbnb and Homeaway) of tourists visiting the Canary Islands (Spain) before and after the outbreak of the COVID-19 pandemic. The results show a bimodal distribution of length of stay: 1 and 7 nights in the pre-pandemic period, reduced to 1 and 2 nights in the pandemic period. The results also indicate that the model approach followed in this paper is preferable to other previous bimodality models in terms of estimation simplicity and fit. In addition, the model allows an analysis of the determinants of length of stay and differentiation of the influence of each determinant on shorter and longer stays.
Studies scrutinizing the tangible and intangible factors regarding the length of stay (LOS) in a destination are rare. The emotional factors have not always been integrated into this analysis. We have contributed to fill this gap in the literature considering the degree of happiness with the tourist destination. We used a sample of 1253 tourists and three regression models were estimated (OLS regression model, a Weibull survival model and a zero-truncated negative binomial) to study the LOS. We verified that the emotional factor related with happiness affects the LOS. Furthermore, regarding the managerial/practical implications it is important to highlight that the tourists who intend to visit the city have gastronomic and wine experiences, and, through their contact with the cultural heritage of the city, they will stay for a longer period of time. In addition to economic factors, as expenditures, there are also emotional and experiential aspects that influence LOS and these have to be included in the communication of a tourist destination. The attributes of a destination are not enough to influence the LOS. The destination must also be a set of experiences that will increase the happiness of the tourist with the destination.
We present the determinants of the length of stay (LOS) of Porto Street Stage that integrated the program of the 52nd edition of Rally de Portugal. Sport events assume an important role in the marketing of tourism destinations. However, when we compare them with other segments in tourism, it still remains underexplored. This study represents an opportunity to contribute to the literature, and it could become a significant toll for the organizers, public entities and other stakeholders. We contemplate a set of information and data that may improve the management of the future editions in a more rigorous and effective way. As we are dealing with an international event, it is an occasion to enlist tourists and promote the tourist destination. We applied a quantitative analysis and considered the sociodemographic characteristics of the spectators, factors that influenced the trip, expenses per day in the city, level of satisfaction with the event, and the intention to return. An OLS regression model, a Weibull survival model and a zero-truncated negative binomial regression model were estimated, and the results were compared. On the LOS determinants it is not common to consider the influence of each item of expenditure during the stay and the satisfaction levels with the event and different effects were observed. The travel and accommodation expenditures present a negative effect on the LOS. In the opposite side the satisfaction level and intention to return, both present a positive effect on the LOS. The sociodemographic characteristics have diverse impacts on the LOS.
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The main purpose of this research is to investigate and estimate the spillover effects in inbound and domestic tourism flows to 341 cities in mainland China. In conjunction with this, the key determinants of tourism flows are also studied in the spatial econometric model. The results confirm the existence of spillover effects in both inbound and domestic tourism flows, and suggest that physical infrastructure factors, tourist attractions, and the SARS outbreak are significant determinants of inbound and domestic tourism flows. In addition, it is found that although the degree of openness to inbound tourists is important for inbound tourism flows, a city’s income is the key to enhancing domestic tourism flows. Significant differences in spillover effects and determinants of tourism flows are also discovered between cities in different regions.
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The statistical modeling of tourists’ length of stay at destinations is a topic that recently has received much attention from tourism scholars. In this regard, so-called “survival models” have been introduced as a means of studying how a set of independent variables explain variation in the number of days tourists spend at destinations. This paper provides a critical look at these studies. There are two main findings. (1) The various justifications offered for favoring the survival models over the traditional OLS regression do not hold up under closer scrutiny. (2) An empirical study shows that the OLS regression model describes the association between a set of independent variables and length of stay at least as effectively as a battery of survival models. In line with the principle of parsimony it is concluded that future studies on tourists’ length of stay should abandon survival models if they are conducted along similar lines as the ones to date.
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Economic theory generally treats the duration of a vacation as a constraint on demand imposed by available time. In contrast, in this paper, it is shown that the length of stay is a determinant of destination demand rather than a demand characteristic. The length of stay is largely explained by the socio-demographic profile of the tourist, and moderated by the perceived characteristics of the destination. The length of stay is also found to have sample selection. Moreover, previous research that does not take sample selection into account is inadequate. Policy implications of the research findings, particularly those to increase the length of stay, are discussed.
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This paper analyses the relationship between the image of a destination and demand duration, focusing specifically on Madeira. A seemingly unrelated discrete-choice duration model is adopted, with data from a questionnaire survey undertaken in 2008 on a sample of homeward-bound foreign individuals departing from Madeira's Funchal Airport. The paper discusses the policy implications of the research findings.
The classical demand theory overlooks the peculiarities of tourist services (Quandt, 1970; Rugg, 1973; Papatheodorou, 2001). An analysis of the tourism demand should recognize that this is a time-consuming activity. In this paper we discuss the discrete/continuous choice model proposed by Dubin and McFadden (1984) and Hanemann (1984) as an alternative way of analysing the tourism demand. Our model focuses on one of the main characteristics of a tourist trip, the length of stay. Although the evolution of the length of stay has critical implications for tourist destinations, particularly on the income generated3 but also on tourists' spatial distribution throughout a destination (Opperman, 1994) and destinations' seasonality (Alegre and Pou (2003), it has received little attention in literature.4 For that purpose, an empirical model is estimated using data from one of the Mediterranean's lead-ing sun and sand destinations, the Balearic Islands, applying Pollak's (1969, 1971) conditional demand function. Lancaster (1966) modified the classical demand theory, reformulating it as a theory of the demand for attributes. In his simpler version, Lancaster (1966, p.137) proposes a technology of consumption that relates attributes (from which consumers derive direct utility) with a group of goods. Lancaster's model of consumption (1966) has been applied to the demand for tourism by Rugg (1973), Morley (1992), Papatheodorou (2001), Seddighi and Theocharous (2002) and Huybers (2003 a), among others. These authors assume that the utility generated by some relevant characteristics of the trip increases with the time spent on holiday. In Rugg (1973) it is assumed that tourism generates a vector z, the elements of which are quantities of destinational characteristics. The values of this vector are dependent on the length of stay at different destinations, z = B t, where t is the vector of the lengths of stay and B is the matrix of coefficients that generates the quantities of characteristics z. This function describes the production of characteristics by commodities, that is, the days spent visiting each destination (Rugg, 1973). The above approach has several drawbacks. Firstly, no direct utility is derived from the length of stay, since it only affects the values of the consumed amounts of holiday characteristics. Following Gorman (1980), it can be assumed that the consumed amount of a good or service itself generates utility. This implies that the length of time a tourist spends on holiday contributes directly to his satisfaction with the trip and, by extension, that the length of stay should be treated as a holiday characteristic. Secondly, the empirical applications of the model should, on the one hand, explicitly define the technology of consumption (i.e. the coefficients of matrix B) and, on the other, justify the product characteristics dependent on the length of stay. None of the previous authors complies with these two requirements. The discrete choice or random utility model proposed by McFadden (1974) and Manski (1977) has also been applied to recreational or tourism demand.5 The model assumes that consumers compare the utility of alternative choices, selecting the one that maximises their utility. These choices can concern any of the trip's different characteristics. However, in literature on leisure and tourism demand where discrete choice models are used, only the discrete characteristics of the trip have been analysed.6 The aim of this study is to use the discrete/continuous model of consumer demand proposed by Dubin and McFadden (1984) and Hanemann (1984) for modelling tourism consumption. These authors present a consumer choice model from which demand functions for the discrete or continuous characteristics of a good or service can be derived. Dubin and McFadden (1984) apply the discrete/continuous model to the household demand for energy. They suggest that domestic appliances are chosen according to their characteristics and the amount and type of energy that they consume. Households must weigh up the benefits that each appliance offers against expectations of future use and future energy prices. A similar approach can be taken to the demand for a length of stay at a holiday destination. Consumers weigh up the benefits of different holiday choices, bearing in mind the cost of each one and the length of stay they can afford, given their budget and time constraints. The model integrates the concept of Pollak's conditional demand function (1969, 1971), according to which consumers assign optimal quantities of some goods, dependent on another part of their consumption having already been determined. One advantage of this function is the fact that the conditioning goods need not be explicitly modelled. Moreover, the demand system will be correctly specified, whether the conditioning goods are chosen optimally or not (Browning and Meghir, 1991). In the case of the demand for length of stay, the tourist is assumed to choose the optimal length of stay, conditioned on the remaining holiday characteristics he has chosen (i.e. the destination, type of accommodation etc). The model for the length of stay is applied to tourists visiting the Balearic Islands, one of the Mediterranean's leading sun and sand destinations. 7 The data was drawn from the Tourist Expenditure Survey (TES) conducted by the Regional Government of the Balearic Islands in collaboration with the University of the Balearic Islands. The TES provides information about the tourists' sociodemographic profile as well as holiday characteristics, including the length of stay. According to the TES, the average length of stay fell by just over three days between 1989 and 2003 (from 13.14 to 9.89 days), representing a cumulative average fall of 2% per year. This trend is common to most European holiday destinations as well as to the main issuing countries (Tourism Intelligence International, 2000 a and 2000 b). Figure 1 shows the percentage of British and German tourists who visited the Balearic Islands on a holiday of up to a week, from 8 to 14 days and for over two weeks. It clearly highlights the importance of the downward trend in the Balearic Islands. Whilst in 1989, 17.1% of tourists spent up to a week in the Balearics, 74.6% spent between eight and fourteen days and 8.3% spent over two weeks, in 2003 the respective percentages were 47.6%, 46.5% and 5.8%. The outline of the paper is as follows. Section 2 describes the discrete/continuous choice model applied to the tourism demand and proposes a conditional demand function for the length of stay. Section 3 discusses the econometric specification and the data used. The results of the empirical model are outlined in Sect. 4. Finally, Sect. 5 contains the main conclusions.
Perceptions-based analysis is a general framework for analyzing tourist choice alternatives involving large sets of perceived attributes. Its development in tourism research was encouraged as tourism is characterized by multifaceted choice alternatives such as destinations and other experiential “products” that the tourists cannot succinctly evaluate with a small number of attributes. PBA differentiates between the generic perceptions of a class of choice alternatives (destinations, product/service providers; e.g., “metropolitan city”; “tour operator”), the perceptual profiles of a specific choice alternative (“San Francisco”; “Thomas Cook”), and the profiles of the choice alternatives preferred or actually selected. It condenses the attribute profiles into distinguished perceptual positions and analyzes their competitive relationships by diagnosing the perceptual strengths and weaknesses of the choice alternatives. Further analysis reveals the number of choice decisions made in favor of a uniquely positioned choice alternative or one sharing its position with others. An empirical example for tour operators illustrates the various data processing steps and discusses their policy implications.
This study examined the effect of income, travel expenditures, and demographic, socio-cultural, and trip-related characteristics on four trip types of the U.S. households. Using data from the U.S. Consumer Expenditure Survey, it was found that households with different trip types varied in their income, demographic, and sociocultural and trip-related characteristics, and exhibited different expenditure patterns on their taking of pleasure/personal trips. The results emphasized the importance of trip type as a key measure for segmenting the U.S. travel markets.
Awareness of tourists’ length of stay and the factors which determine that is an essential element for good planning and management at tourist destinations. This article analyses to what extent the personal characteristics of the low-cost tourist, those of the trip and stay and those of the destination itself are significant in determining the duration of a trip. To this end an econometric duration model is estimated. The results obtained show that the effect of time restrictions seem to be relevant for explaining the observed differences in length of stay, as well as the effects of the tourist's spending capacity, prices and the differences between urban and “sun and sand” destinations. Furthermore, the model also allows us to analyse changes in the likelihood of the stay being ended at a specific point in time (hazard) associated with changes in the explanatory variables, and to obtain predicted survival times for different groups of tourists.
This study segments inbound travelers to Hong Kong with a CHi-square Automatic Identification Detector (CHAID) technique. Seven predictors are used to derive market segments based on their likelihood of revisiting Hong Kong. The CHAID analysis produces six segments based on respondents’ travel purpose, age, income, and repeat visit status. Each segment is described according to trip characteristics, including length of stay, travel party size, total expenditure, frequency of visits, mode of travel, and post-trip perceptions. Suggestions are made based on findings from the study, and marketing implications for resultant segments are discussed.