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Determinants of Length of Stay for Domestic Tourists: Case Study of Yixing

December 2011
Asia Pacific Journal of Tourism Research 16(6):619-633

DOI:10.1080/10941665.2011.610144

Authors:

Yang Yang

Temple University

Jie Zhang

The University of Manchester

The length of stay of a tourist is one of the most important factors indicating consumption levels and revenue generation for certain tourist destinations. This study employs data from a tourist survey in Yixing, China, to investigate potential factors influencing a tourist's length of stay. Applying an ordered logit model, it is found that distance, age, organized tour, transportation, motivation, past visits and assessment of accommodation are some of the major determinants of a tourist's length of stay. The results indicate that traveling distance and the assessment of accommodation are positively associated with the length of stay. In addition, tourists with different modes of transportation, motivations and past visits have different durations of stay. Based on the estimation results from subsamples, it is also found that there are differences in determinants of length of stay between organized tourists and individual tourists, and among different age groups.

Location Map of Yixing

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Summary of Explanatory Variables

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A Descriptive Statistics of Continuous Variables

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B Descriptive Statistics of Discrete Variables

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Estimation Results of Ordered Logit Model with Different Specifications

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Figures - uploaded by Yang Yang

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DETERMINANTS OF LENGTH OF STAY FOR DOMESTIC

TOURISTS: CASE STUDY OF YIXING

Yang Yang

University of Florida

Kevin. K.F Wong

The Hong Kong Polytechnic University

Jie Zhang

Nanjing University

Yang Yang, (Department of Geography, University of Florida, Gainesville, FL,

32611, U.S.A, E-mail: yang.yang@ufl.edu, phone: 1-352-8716187), is a Ph.D student

in Department of Geography, University of Florida.

Kevin K.F Wong, PhD (E-mail: hmkevinw@inet.polyu.edu.hk, phone:

852-2766-6341), is an associate professor in the School of Hotel and Tourism

Management, the Hong Kong Polytechnic University. His address is School of Hotel

and Tourism Management, the Hong Kong Polytechnic University, Hung Hom,

Kowloon, Hong Kong.

Jie Zhang, PhD (e-mail: jiezhang@nju.edu.cn), is a professor in Institute of

Tourism Studies, Nanjing University, PRC.

Yang, Y., Wong, K.and Zhang, J. (2011). Determinants of length of stay

for domestic tourists: Case study of Yixing. Asia Pacific Journal of

Tourism Research, 16(6), 619-633.

DETERMINANTS OF LENGTH

OF STAY FOR DOMESTIC TOURISTS:

CASE STUDY OF YIXING

The length of stay of a tourist is one of the most important factors indicating consumption levels

and revenue generation for certain tourist destinations. This study employs data from a tourist

survey in Yixing, China to investigate potential factors influencing a tourist’s length of stay.

Applying a (generalized) ordered logit model, it is found that distance, age, organized tour,

transportation, motivation, past visits, and assessment of accommodation are some of the major

determinants of a tourist’s length of stay. The results indicate that traveling distance and the

assessment of accommodation are positively associated with the length of stay. In addition,

tourists with different modes of transportation, motivations, and past visits have different

durations of stay. Based on the estimation results from sub-samples, it is also found that there are

differences in determinants of length of stay between organized tourists and individual tourists,

and among different age groups.

KEYWORDS: length of stay; generalized ordered logit model; Yixing

INTRODUCTION

In the tourism industry, one of the most important indices in evaluating tourists’ demands and

experiences is the length of stay. A length of stay index for a tourist provides a clear and reliable

indicator for the level of consumption (and revenue) for tourist destinations. According to Rugg

(1973), the utility of traveling is obtained from staying at certain destinations for some period of

time, rather than the possessing or consuming destinations directly. Therefore, this index is often

used to measure the consumption level of tourists in a particular destination. As an indicator

reflecting the demands of individual tourists, the length of stay also shows the extent of tourism

demand at a particular destination. This index is of great value for destination management and

marketing. Generally, the longer tourists stay, the more services and goods they are expected to

consume. Therefore, visits of long duration are considered to be beneficial to local economies

through the multiplier effect of the tourism industry (Archer & Shea, 1975).

A substantial amount of literature has reported investigations of tourism demand using various

methods (Lim, 1997; Song & Li, 2008). However, most of these studies focused on macro tourism

demand with aggregated data. They regarded tourists in certain groups as homogeneous. Few

studies have concentrated on micro tourism demand analysis, which examined the tourism

demand function of the individual tourist. The socio demographic attributes of each tourist can be

taken into consideration by analyzing individual demand models, which are more reliable and

appropriate. Among studies on micro tourism demand analysis, many have selected destination

choice (Correia, Barros, & Silvestre, 2007; Hong, Kim, Jang, & Lee, 2006; Huybers, 2005) or

expenditures (Gong-Soog, Fan, Palmer, & Bhargava, 2005) as major focuses. In contrast, only a

few papers have investigated the issue of length of stay for tourism demand analysis.

To model the length of stay, several econometric methods have been adopted. In early papers,

the two stage square least square (2SLS) model was frequently used, considering the simultaneous

causality between tourists’ expenditure and length of stay (Silberman, 1985; Uysal, McDonald, &

O'Leary, 1988; Walsh & Davitt, 1983). More recently, to model the duration of stay, the Tobit

model (Fleischer & Pizam, 2002; J. Mak & Moncur, 1979), which focuses on the data generation

process for censored data, and survival analysis, which is powerful in dealing with censored

duration data (Barros, Correia, & Crouch, 2008; Gokovali, Bahar, & Kozak, 2007; Gomes de

Menezes, Moniz, & Cabral Vieira, 2008; Martínez-Garcia & Raya, 2008), are applied. However,

in China, most tourists survey questionnaires follow the template from CNTA’s national domestic

tourists survey in which case the options of “duration of stay” are listed in ordinal scale. This

means that the options are dichotomous and ranked. Therefore, to appropriately use or apply this

data, an ordered logit model is more efficient and suitable, and has been chosen in this study.

Another contribution of this research to the current body of tourism marketing literature is the

introduction of generalized ordered logit model. Some authors have applied ordered logit model to

examine tourists’ preference to culture heritage sites (van Leeuwen & Nijkamp, 2010), consumer

meal preference (Myung, Feinstein, & McCool, 2008), visitors’ duration of theme park activities

(Kemperman, Borgers, Oppewal, & Timmermans, 2003), intended duration of students’

international travel (Chadee & Cutler, 1996), and tourists’ evaluation of different products (Cuccia

& Cellini, 2007). However, since the validity of ordered logit estimation relies heavily on the

parallel regression assumption of parameters, and this assumption is highly likely to violate in

empirical studies (Long & Fresse, 2003), more robust model specifications should be introduced

to alleviate this problem. The generalized ordered logit model, which relaxes this assumption, is a

natural alternative to the traditional ordered logit model (Williams, 2006).

The main purpose of this article is to identify the determinants of length of stay for tourists in

Yixing, a famous tourist destination in Eastern China, and to discuss the traveling behavior of

Chinese domestic tourists. Once the determinants are identified, more specific and efficient

marketing strategy can be applied accordingly. Since little research has focused on Chinese

domestic tourists’ behavior, especially research utilizing quantitative analysis, this paper will

provide one of the first attempts to do so, and to examine the differences between Chinese

domestic tourists and those in western countries. Furthermore, since a tourist’s duration of stay is

influenced by destination attributes, destinations and tourism enterprises could improve their

products and services according to the results of such research to influence tourists to extend their

stay, leading to improved revenue for local economies.

This paper is organized as follows: Section 2 discusses factors contributing to the length of stay.

Section 3 details the specifications of the model and describes the data set used in this study. In

Section 4, the empirical results of the model and interpretations are explained. Finally, concluding

remarks are presented in Section 5.

FACTORS CONTRIBUTING TO THE LENGTH OF STAY

To further discuss the past research on length of stay, several categories of determinants of

length of stay are respectively discussed.

Economic Variables According to the traditional demand theory, consumers’ income and the price

of a product determine the amount that an individual consumes. People with higher disposable

income are more likely to consume more commodities. Similarly, regarding the daily experience

at a particular destination as a single product, people with higher disposable income are inclined to

purchase more products, and therefore to stay longer in the destination (Fleischer & Pizam, 2002;

Gokovali, et al., 2007; J. Mak & Moncur, 1979; James Mak, Moncur, & Yonamine, 1977). On the

other hand, the price of the product has a negative effect on the duration of stay for tourists

(Alegre & Pou, 2006, 2007; Gomes de Menezes, et al., 2008; J. Mak & Moncur, 1979; James Mak,

et al., 1977; Silberman, 1985; Walsh & Davitt, 1983).

Distance The distance from the tourist’s residence to the destination is another important factor

influencing the length of stay. In order to obtain higher utility from a trip, a rational tourist is

expected to balance the proportion between the fixed cost and the varied cost (Smith, 1995).

Therefore, the tourist traveling from a more distant place of residence with a higher fixed cost will

increase the varied cost in destinations for a longer duration, to optimize the proportion between

the two components (Thunberg & Crotts, 1994; Walsh & Davitt, 1983).

Social-demographic Variables Several social-demographic variables are associated with the length

of stay. Tourists of different ages have different propensities on the length of their stay, and the

relationship between the length of stay and age is non-linear (Alegre & Pou, 2006; Fleischer &

Pizam, 2002; Gokovali, et al., 2007; James Mak, et al., 1977). For example, Fleischer and Pizam

(2002) found that for Israel seniors between the ages of 55 and 65, an increase of leisure time and

household income resulted in an increase in the number of vacation days. For those older than 65,

the declination of income and the deterioration of health results in a decrease in the number of

vacation days. Furthermore, family life cycle influences tourists’ duration choice (Oppermann,

1995). Lawson (1991) found that young singles (younger than twenty-five) stay the longest time,

while young couples (no children) and people in the "Empty Nest I" stage (still working, no

children) have short duration.

Past Traveling Experience Past traveling experience of the tourist plays an indispensable role in

determining the duration of stay. First, familiarity gained from past traveling experience influences

the decision of duration. Repeat visitors always stay longer in a single destination than first-time

visitors (Oppermann, 1997; Uysal & McDonald, 1989). Wang (2004) stated that, due to a higher

level of awareness and familiarity of the destination, repeat visitors tend to have different

itineraries or possible emotional attachment to a place, resulting in longer duration. Furthermore,

annual traveling frequency has also been reported as a determinant of length of stay (Fleischer &

Pizam, 2002). As the experience of tourism increases, tourists are more inclined to stay longer in a

destination (Gokovali, et al., 2007).

Trip Characteristics Different traveling motivations result in distinct duration. For different

purposes, tourists participate in different activities and make different itineraries for trips, which

require different duration (Andreu, Kozak, Avci, & Cifter, 2005; Kim & Prideaux, 2005; Seaton &

Palmer, 1997). For example, tourists traveling for VFR purposes stay longer in a destination than

those traveling for other purposes (Hsu & Kang, 2007; Sung, Morrison, Hong, & O’Leary, 2001),

while the tourist on a sight-seeing trip stays for a shorter period of time. Furthermore, it has been

found that tourists on an organized package tour have a greater possibility of staying longer than

individual tourists (Alegre & Pou, 2007). Other vacation characteristics that influence the length

of stay include traveling party size (Alegre & Pou, 2006), accommodation type (Gokovali, et al.,

2007), and type of board (Alegre & Pou, 2006, 2007).

Destination Related Variables Gokovali, Bahar and Kozak (2007) emphasized the importance of

satisfaction during the stay in a destination. It was argued that, as long as tourists make the

decision to stay longer, they become more aware of the facilities and services at their destination.

Also, the perceived attractiveness and image of the destination affect the duration of stay

(Gokovali, et al., 2007). Moreover, through a study of travelers in Colorado, Tierney (1993) found

that a welcome center contributes to a longer length of stay. Other destination attributes, such as

annual rainfall (J. Mak & Moncur, 1979) and crowding (Uysal, et al., 1988), also exert influences

on the length of stay for tourists in particular destinations.

METHODOLOGY

Specified Model and Data

After reviewing previous research, this study proposes an empirical model for analyzing the

potential factors which may influence a tourists’ duration of stay. The model is as follows:

), ,,,,,,( 2assessmentvisitpastmotivationtiontransportaorganizeddistanceageageflength 

In this model, the dependent variable is the length of stay for each tourist. This variable is coded

as: 1 for a one-day stay, 2 for a two-day stay, 3 for a three-day stay, and 4 for a four-day or longer

stay. The selection of explanatory variables is based on the relevant findings in past research.

Three categories of variables are considered. The first category includes variables representing

individual attributes, such as age. It is assumed that age exerts a non-linear influence on the

duration of tourists’ stays, and the squared term of age is included in the model. This means that

below a certain age, the duration of stay increases as age increases. However, above a certain age,

the duration declines as age increases. The second category of variables is comprised of trip

characteristic variables, such as distance, organized, transportation, motivation, and past visit. The

third category includes destination related variables. In the model assessment is the perceived

assessment of a particular type of service in the destination, such as the assessment of

accommodation, transportation, tour guides, entertainment, and shopping. Table 1 summarizes

variables used in this study and their reference categories.

(Insert Table 1 here)

This study chose Yixing, a city in Jiangsu Province in Eastern China (Figure 1), to conduct the

empirical work. There are several reasons. First, as the members of Yixing tourism master planning

group, it is convenient for us to conduct the tourists’ survey. Second, Yixing is located in the center

of the Yangtze River Delta, which is the most developed areas in China and a great domestic

tourism market. Therefore, through the study of Yixing, we can know about the traveling behavior

of tourists from this market. Third, Yixing is a representative tourist destination in China with

diverse tourist attractions which attracts distinctively heterogeneous tourists. Famous tourist

attractions in Yixing include ‘Shanjuan Cavern’, a large limestone cavern; ‘Sea of Bamboo’, which

is renowned for natural sights, and many historic sites for historical events and ancient kiln sites.

According to the statistics from Yixing Tourism Bureau, the motivation of tourists to Yixing covers

a large spectrum, and its source market covers a broad geographical area. The heterogeneity of

tourists provides an ideal sample to estimate the empirical model.

(Insert Figure 1 here)

The research data was gathered from tourist survey in major tourist destinations in Yixing within

the period of July-August 2004. The final sample included the valid responses from 417 visitors

who finished the questionnaire. The traveling distance of tourists is obtained by measuring the

road distance from Yixing to tourist’s residence reported in the questionnaire through ArcGIS@

software. Descriptive statistics of variables are provided in Tables 2-A and 2-B. In Table 2-A, the

average age of tourists is 35.57 and the average traveling distance is 432.37 kilometers. All

destination attribute variables for the assessment of service quality are lower than 3

(corresponding to the level of “good”), indicating that tourist satisfaction is not very high. As

shown in Table 2-B, more than 60% of tourists to Yixing are one-day travelers, while only about

7% of tourists stay in Yixing more than four days. They come to Yixing mainly by coach/bus or in

their own vehicles (self-driving). Moreover, most tourists, accounting for nearly 80% of total

tourists, travel for sight seeing or vacation. Also, more than 80% of tourists have traveled to Yixing

less than two times.

(Insert Table 2-A here)

(Insert Table 2-B here)

Econometric Method

As previously mentioned, the ordered logit model and its extension, the generalized ordered

logit model, will be estimated in this research. This model is commonly presented as a latent

variable model. Deﬁning y∗ as a latent variable ranging from −∞ to +∞, the structural model is



 i

where i is the observation (individual tourist) and ε is a random error (Long & Fresse, 2003). The

measurement model for divide y

∗

into J ordinal categories:

myi

mm y





*

for

1m

where the cut-points τ 1 through τJ−1 are estimated. We assume τ0 = −∞ and τJ = +∞.

In this paper, responses in the model are: 1 for a one-day stay, 2 for a two-day stay, 3 for a

three-day stay, and 4 for a four-day or longer stay. Hence, J = 4 in this model. The continuous

latent variable can be thought of as the propensity to stay longer in the destination. To include

independent variables, we can rewrite the probability of duration as a function of explanatory

variables:

)()()|Pr( 1



 mm FFmy

where F(.) is a logit function. To further explain the results from ordered logit model, the concept

of odds is introduced to interpret the coefficient:

)exp(

)|Pr( )|Pr(

)(











 mmm my my

odds

The generalized ordered logit model, as an extension to the traditional ordered logit model, allows

to differ for each of the J-1 comparisons (Williams, 2006). That is,

)()()|(Pr* 11   mmmm FFmy



In this case, the odds is no longer a constant among different outcomes in the dependent variable,

and it becomes:

)exp(

)|(Pr* )|(Pr*

)(* |mmmm my my

odds













which varied between different outcomes of dependent variables.

EMPIRICAL RESULTS

First, ordered logit models with different specifications are first estimated using Stata@ 12.0

package. Table 3 summarizes results from specified models which include different assessment

variables. The result shows that nearly all variables included in the models have significant effects

on the length of stay and their signs are generally in line with findings reported in previous studies.

In terms of their explanatory power, both equations are satisfactory when the likelihood ratio (LR)

test and log likelihood diagnostic statistics are similarly acceptable. Moreover, the estimated

coefficients in each model vary little, demonstrating the ideal robustness of each model.

The results in Table 3 suggest that distance is a significant determinant of duration. Tourists

from source markets more distant from the destination tend to stay longer. With respect to age, as

hypothesized, both age and the squared term of age (age_squared) are significant. It indicates the

non-linear relationship between age and duration: the length of stay would not keep on rising as

the age of the tourist increases; the possibility for long duration would decline as the age increases

above a certain threshold value. For multiple response questions in the model, after setting one

variable as the reference, others are coded into dummy variables. In this study, the Wald test is

applied to test the overall significance of such multiple response questions as transportation,

motivation, and past visit. The result shows that they are all significant at a 0.05 level. Therefore,

we can conclude that transportation, motivation, and past visit are significantly associated with

tourists’ duration of stay. Given the estimated magnitude of the coefficient of each dummy

variable, we can infer which categories of tourists stay significantly longer than others. With

respect to transportation, tourists traveling by train and airplane have greater lengths of stay in

Yixing than those traveling by coach/bus and self-driving. Also, tourists with different motivations

have different lengths of stay. Visitors for research purpose stay longest while those with

sight-seeing purposes stay shortest, and visitors with VFR and study purposes exhibit greater

lengths of stay than those visiting for vacation. Moreover, with respect to past visit, tourists who

have been to Yixing more than five times stay significantly longer than first-time visitors.

Furthermore, in Table 3, Models 1 to 5 are compared to investigate which assessment variable is

more important for determining tourists’ duration of stay. It is suggested that assessment of

accommodation is the only factor that exerts influence on the length of stay based on the

significance of the coefficient and the goodness-of-fit indices, such as log likelihood and LR value.

Therefore, it is suggested that accommodation assessment is a statistically significant determinant

of length of stay.

(Insert Table 3 here)

Attention is now turned to the duration model for different tourists. In Table 4, Models 6 and 7

estimate the ordered logit model for organized tourists and individual tourists respectively; while

Models 8, 9, and 10 estimate the model for tourists aged less than 30 (Age group 1), between 31

and 40 (Age group 2), and more than 41 (Age group 3). In each model, the only variable included

is the “assessment of accommodation” so as to avoid the problem of multi-collinearity which

occurred when all assessment variables were employed. Looking first at Models 6 and 7,

interestingly, it shows that the age and accommodation assessment are not determinants of the

length of stay for organized tourists. This may reflect the fact that organized tourists have much

less flexible schedules for traveling, and their itineraries are determined by others such as the

travel agency or the group that organized the trip. Besides, individual tourists stay significantly

longer for VFR purposes than for sight-seeing purposes; while organized tourists do not. On the

other hand, organized tourists on vacation stay longer than sight-seeing organized tourists, while

their counterparts, traveling individually, do not.

In regard to models for different age groups, the group of motivation variables is jointly

significant in Models 8 and 10, while not in Model 9. For those in Age Groups 1 and 3, VFR

tourists stay significantly longer than sight-seeing tourists, while it is not true in Age Group 2.

Furthermore, past visit to Yixing would significantly increase tourists’ probabilities to stay longer

for tourists in Age Groups 1 and 3. However, in Age Group 2, the difference of the length of stay

only exists between first-time visitors and repeated visitors, but not between frequently visited

tourists (3 times and above) and less frequently visited tourists (1 time to 2 times). Moreover, the

result suggests that there is not significant difference in the length of stay for organized and

individual tourists in Age Group 3. This may be explained by the fact that tourists in this age

group are more likely to undertake longer organized visit either by purchasing package tour with

longer duration or by attending tours organized by affiliations as incentive tours which is

characterized by longer durations. As a result, tourists in organized tours may not stay

significantly shorter than individual ones. Also, the accommodation assessment is not associated

with the duration for tourists in this age group. This can be contributed by the fact that aged

domestic tourists in China are not demanding in regard to accommodation service and facility due

to their life experience in early days with poor living conditions, especially in the period before

the opening-up.

(Insert Table 4 here)

However, as indicated in Table 3, the approximate LR test for parallel regression assumption

suggests that the parallel regression assumption is violated in previous models. The Wald test by

Brant (1990) is used to test the parallel regression assumption for each variable individually. The

result of the test argues that the largest violations are for organized visitation and assessment of

accommodation, which implies that these two variables are mis-specified in the traditional ordered

logit model. Therefore, the generalized ordered logit model, not imposing the constraints of

parallel regressions for these two variables, is adopted to further explore potential determinants of

length of stay in Model 11. In Model 11, age, square of age, transportation variables, motivation

variables, and past visitation variables are constrained with the parallel assumption, which means

the coefficients of these variables are the same for each outcome category (each category of length

of stay). For the unconstrained variables, organized visitation and assessment of accommodation,

the marginal effects for each outcome category are not the same, and coefficients are estimated for

each category.

The estimation result of Model 11 is presented in Table 5. The estimated coefficients of most

constrained variables do not differ too much from the result in Table 3. The greatest difference is

the estimated coefficients of unconstrained variables. Organized visitation is only significant for

the outcome categories "1 day vs. 2 days and more" and "1-2 day vs. 3 days and more." Therefore,

it is suggested that organized visitation only influences the length of stay for tourists with short

duration. In the same way, judging from the estimated coefficient, longer duration tends to be

more sensitive to the assessment of accommodation. This reveals the fact that tourists’ assessment

of accommodation is a vital factor for stimulating longer duration of stay.

(Insert Table 5 here)

To make the interpretation more straightforward, the estimation result of each coefficient is

explained based on the concept of odds. Based on the result from Model 11, for a unit increase in

distance, the odds of a shorter length of stay outcome compared with a longer length outcome are

changed by the factor exp(-0.0004)=0.9996, holding all other variables constant. For nominal

variables, the change of transportation from coach/bus to self-driven would contribute to a

exp(-((-2.342)-(-1.707)))=1.887 change of odds of a shorter length of stay outcome compared with

a longer length, while a change from train to self-driven would cause a exp(-(-0.353-0))=1.423

change. Likewise, a change of motivation from VFR to vacation will cause an

exp(-(0.688-1.687))=2.716 change of those odds, and a change of past visitation from first-time

visit to 3 time or 4 times will contribute to an exp(-((-1.309)-( -1.872)))=0.569 change.

To further elaborate the estimation, graphing predicted probabilities for each outcome is

employed. Figure 2 plots the predicated probabilities of each outcome of length of stay with the

distance change from Model 11. It emphasizes the positive effect of distance on length of stay,

showing that the probability of a 1 day stay decreases dramatically as traveling distance increases,

while the probabilities of longer durations increase respectively. Since parallel regression

assumption is imposed on the coefficient of distance, the probability line in the graph is straight,

suggesting that the marginal effects of distance on each duration category is constant.

(Insert Figure 2 here)

The graphic interpretation is also useful for explaining the non-linear effects in the model with

age and age_squared. Figure 3 demonstrates the non-linear effect of age on the length of stay

based on the result from Model 11. It shows that there is a turning point in the probability curve at

the age of 45. Therefore, it is suggested that when age is less than 45, the probability of long

duration decreases while the probability of a one day additional stay increases with age. However,

the situation is inverse when the age of the tourist is more than 45. This result is consistent with

findings from Alegre and Pou (2006), Fleischer and Pizam (2002), Gokovali, Bahar and Kozak

(2007).

(Insert Figure 3 here)

Figure 4 depicts the non-parallel marginal effect of assessment of accommodation on duration.

Due to the relaxation of parallel constraints, the probability line in the graph is no longer a strict

strait line (in contrast to Figure 2); instead, there are several minor turning points on the line. In

this figure, it is suggested that when the assessment of accommodation changes from ‘good’ to

‘fairly good,’ the probability of a ‘4 days or more’ stay increases significantly. As previously

discussed, longer duration is considered to be more sensitive to the assessment of accommodation.

(Insert Figure 4 here)

An important advantage of the (generalized) ordered logit models is that they can be utilized for

simulation after the estimation. By holding other variables at average value, the effect of

explaining a variable on the probability can be drawn. Table 6 shows several simulation results in

diverse scenarios. For example, if a tourist’s traveling distance changes from 100km to 300km,

his/her probability of a 1 day stay would decrease 1.770%, while the probability of a 2 days stay, 3

days stay, and 4 days or more stay would increase 1.070%, 0.480% and 0.220%, respectively,

holding other variables constant. In the same way, as shown in Table 6, if a tourist changes the

motivation from sight-seeing to VFR, the probability of a 1 day stay would decline by 39.15%,

and at the same time the probability of 2 days stay, 3 days stay and 4 days or more stay would

increase 9.410%, 3.700% and 1.600%.

(Insert Table 6 here)

CONCLUSION

The purpose of this study is to examine the effect of various factors on a tourist’s length of stay.

The ordered logit model is utilized to estimate potential determinants. The findings of this study

show traveling distance, age (with the squared term of age), organized visits, transportation,

motivation, past visits, and assessment of accommodation to be influential factors. Furthermore,

some differences in duration models are found between organized tourists and individual tourists.

Organized tourists are not influenced by age and assessment of accommodation, while individual

tourists are. Moreover, tourists with different ages determine their length of stay differently:

organized visitation and assessment of accommodation are not factors affecting the duration for

tourists above 41 years of age. To elaborate the estimation result, the study analyzes the

relationship between age and length of stay by using the probability the model calculated, and the

non-linear effect is demonstrated.

Based on the model, some marketing suggestions and implications can be proposed. To begin

with, extra attention should be paid to individual tourists to attract them to stay longer in a given

destination. Also, to attract VFR visitors, and to make them stay longer in Yixing, more family

activities and products are recommended. In addition, since repeat visitors are very important,

extra effort should be allocated on how to improve service quality and develop tourists’

destination loyalty, especially for older tourists. Finally, as indicated by the model, the assessment

of accommodation is crucial for tourists’ length of stay, especially for stays of longer duration. It is

necessary for local government and industry to improve both the hard and soft infrastructure of

accommodation facilities.

Finally, the limitation of this study should be stated. Due to the unavailability of data, variables

such as income and daily expenditures could not be obtained and included in the estimated model.

Moreover, since this research only focuses on a single destination, further studies should be

undertaken to investigate how the various attributes in different destinations would influence the

length of stay for tourists. Understanding these factors allows those responsible for an area’s

tourism to make a destination more inviting to tourists, and this could bring about a higher rate of

trade and improved revenue for local businesses located in close proximity to the destination.

REFERENCES

Alegre, J., & Pou, L. (2006). The length of stay in the demand for tourism. Tourism Management, 27(6),

1343-1355.

Alegre, J., & Pou, L. (2007). Microeconomic determinants of the duration of stay of tourists. In Á.

Matias, P. Nijkamp & P. Neto (Eds.), Advances in Modern Tourism Research (pp. 181-206).

Heidelberg: Physica-Verlag

Andreu, L., Kozak, M., Avci, N., & Cifter, N. (2005). Market segmentation by motivations to travel:

British tourists visiting Turkey. Journal of Travel & Tourism Marketing, 19(1), 1-14.

Archer, B. H., & Shea, S. (1975). Length of stay problems in tourist research. Journal of Travel

Research, 13(3), 8-10.

Barros, C. P., Correia, A., & Crouch, G. (2008). Determinants of the length of stay in Latin American

tourism destinations. Tourism Analysis, 13(4), 329-340.

Chadee, D. D., & Cutler, J. (1996). Insights into international travel by students. Journal of Travel

Research, 35(2), 75-80.

Correia, A., Barros, C. P., & Silvestre, A. L. (2007). Golf tourism repeat choice behaviour in the

Algarve: a mixed logit approach. Tourism Economics, 13(1), 111-127.

Cuccia, T., & Cellini, R. (2007). Is cultural heritage really important for tourists? A contingent rating

study. Applied Economics, 39(2), 261-271.

Fleischer, A., & Pizam, A. (2002). Tourism constraints among Israeli Seniors. Annals of Tourism

Research, 29(1), 106-123.

Gokovali, U., Bahar, O., & Kozak, M. (2007). Determinants of length of stay: a practical use of

survival analysis. Tourism Management, 28(3), 736-746.

Gomes de Menezes, A., Moniz, A., & Cabral Vieira, J. (2008). The determinants of length of stay of

tourists in the Azores Tourism Economics, 14(1), 205-222.

Gong-Soog, H., Fan, J. X., Palmer, L., & Bhargava, V. (2005). Leisure Travel Expenditure Patterns by

Family Life cycle Stages. Journal of Travel & Tourism Marketing, 18(2), 15-30.

Hong, S.-k., Kim, J.-h., Jang, H., & Lee, S. (2006). The roles of categorization, affective image and

constraints on destination choice: An application of the NMNL model. Tourism Management,

27(5), 750-761.

Hsu, C. H. C., & Kang, S. K. (2007). CHAID-based segmentation: International visitors' trip

characteristics and perceptions. Journal of Travel Research, 46(2), 207-216.

Huybers, T. (2005). Destination choice modelling: what's in a name? Tourism Economics, 11(3),

329-350.

Kemperman, A., Borgers, A., Oppewal, H., & Timmermans, H. (2003). Predicting the duration of

theme park visitors' activities: an ordered logit model using conjoint choice data. Journal of

Travel Research, 41(4), 375-384.

Kim, S. S., & Prideaux, B. (2005). Marketing implications arising from a comparative study of

international pleasure tourist motivations and other travel-related characteristics of visitors to

Korea. Tourism Management, 26(3), 347-357.

Lawson, R. (1991). Patterns of tourist expenditure and types of vacation across the family life cycle.

Journal of Travel Research, 29(4), 12-18.

Lim, C. (1997). Review of international tourism demand models. Annals of Tourism Research, 24(4),

835-849.

Long, S., & Fresse, J. (2003). Regression Models for Categorical Dependent Variables Using STATA

(Revised Edition ed.). Texas, USA: STATA Press.

Mak, J., & Moncur, J. (1979). The choice of journey destinations and length of stay: a micro analysis.

The Review of Regional Studies, 10(2), 38-48.

Mak, J., Moncur, J., & Yonamine, D. (1977). Determinants of visitor expenditures and visitor lengths

of stay: a cross-section analysis of U.S. visitors to Hawaii. Journal of Travel Research, 15(3),

5-8.

Martínez-Garcia, E., & Raya, J. M. (2008). Length of stay for low-cost tourism. Tourism Management,

29(6), 1064-1075.

Myung, E., Feinstein, A. H., & McCool, A. C. (2008). Using a discrete choice model to identify

consumer meal preferences within a prix fixe menu. Journal of Hospitality & Tourism

Research, 32(4), 491-504.

Oppermann, M. (1995). Travel life cycle. Annals of Tourism Research, 22(3), 535-552.

Oppermann, M. (1997). First-time and repeat visitors to New Zealand. Tourism Management, 18(3),

177-181.

Rugg, D. (1973). The choice of journey destination: a theoretical and empirical analysis. The Review of

Economics and Statistics, 55(1), 64-72.

Seaton, A., & Palmer, C. (1997). Understanding VFR tourism behaviour: the first five years of the

United Kingdom tourism survey. Tourism Management, 18(6), 345-355.

Silberman, J. (1985). A demand function for length of stay: the evidence from Virginia beach. Journal

of Travel Research, Spring, 16-23.

Smith, S. L. J. (1995). Tourism Analysis: A Handbook (2nd ed.). Harlow: Longman.

Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—a review of recent research

Tourism Management, 29(2), 203-220.

Sung, H. H., Morrison, A. M., Hong, G.-S., & O’Leary, J. T. (2001). The effects of household and trip

characteristics on trip types: a consumer behavioural approach for segmenting the US

domestic leisure travel market. Journal of Hospitality and Tourism Research, 25(1), 46-68.

Thunberg, E. M., & Crotts, J. C. (1994). Factors affecting travelers' overnight stay behavior. Journal of

Travel & Tourism Marketing, 3(1), 1-17.

Tierney, P. T. (1993). The influence of state traveler information centers on tourist length of stay and

expenditures. Journal of Travel Research, 31(3), 28-32.

Uysal, M., & McDonald, C. D. (1989). Visitor segmentation by trip index. Journal of Travel Research,

27(3), 38-42.

Uysal, M., McDonald, C. D., & O'Leary, J. T. (1988). Length of stay: a macro analysis for

cross-country skiing trips. Journal of Travel Research, 26(3), 29-31.

van Leeuwen, E., & Nijkamp, P. (2010). A microsimulation model for e-services in cultural heritage

tourism Tourism Economics, 16(2), 361-384.

Walsh, R. G., & Davitt, G. J. (1983). A demand function for length of stay on ski trips to Aspen. Journal

of Travel Research, 21(4), 23-29.

Wang, D. (2004). Tourist behaviour and repeat visitation to Hong Kong. Tourism Geographies, 6(1),

99-118.

Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent

variables The Stata Journal, 6(1), 58–82.

Table 1

Summary of Explanatory Variables

Name

Explanation

1. Individual attributes

age

Age of the reference person (in years)

age_squared

Squared term of age

income

Monthly income (in 1,000 RMB)

frequency

Number of domestic tour in last year (1 = 1-2 tours (reference),

2 = 3-5 tours, 3 = 6 tours and more)

2. Trip characteristics

distance

Distance between Yixing and the residence of tourist (in 100

kilometers)

partner

1 if alone (reference), 2 if with friends and relatives, 3 if with

colleagues, 4 if organized by travel agency

transportation

1 if self-driving (reference), 2 if coach/bus, 3 if airplane, 4. if

train, 5 if others

pastvisit

1 if no previous visit (reference), 2 if one to two times, 3 if

three to four times, 4 if five times and above

motivation1

1 if sight-seeing for motivation, 0 otherwise

motivation2

1 if vacation for motivation, 0 otherwise

motivation3

1 if VFR for motivation, 0 otherwise

motivation4

1 if business for motivation, 0 otherwise

3. Destination related variables

assessment of accommodation