Factors affecting international students’ travel behavior

Article

Factors affecting internationalstudents’ travel behavior

Hanieh Varasteh, Azizan Marzukiand S Mostafa RasoolimaneshUniversiti Sains Malaysia, Malaysia

AbstractThis article attempted to find out important factors influencing international students’ travelbehavior. A total of 409 international postgraduate students studying in five Malaysian research uni-versities (Universiti Putra Malaysia, Universiti Malaya, Universiti Teknologi Malaysia, Universiti SainsMalaysia, and Universiti Kebangsaan Malaysia) participated in this quantitative study through a self-administered questionnaire. A structural equation modeling–partial least squares using Warp PLS3.0 was applied to analyze data. The study revealed that a number of demographic characteristicsincluding age, marital status, nationality, and source of finance significantly affect preferred travelactivities and preferences. In addition, travel behavior (as a third-order factor) was also affectedby age, marital status, nationality, and source of finance. The moderating effect of information sourceon relationship between nationality and travel behavior has also been identified, with its main func-tion being adjusting the strengths of relationships between nationality and travel behavior.

KeywordsInternational students, students’ market, travel activities, travel behavior, travel preferences

Introduction

Traveling for educational purposes is an ancient

phenomenon experienced by the majority of

nationalities over the past centuries (Gibson,

1998), and in the 21st century it has become a

multibillion dollar industry due to huge numbers

of people going outside of their country to study,

who are called as international students (Payne,

2009). International students have a great ten-

dency to travel while studying abroad in an effort

to better understand the national culture and peo-

ple, resulting in considerable revenue as well as

employment opportunities for the host country

(Payne, 2009). Information pertaining to their

travel preferences and patterns are important to

the host country due to the enormous financial

potential and benefits that may accrue from tour-

ists of this type. Without reliable and available

information, improvement of this market seg-

ment would be impossible and the host country

stands to lose enormous potential financial bene-

fit derivable from this type of tourism (Arcodia

et al., 2006; Chadee and Cutler, 1996; Kim,

2007; Kim et al., 2006).

Travel behavior based on Recker et al. (1986)

is generally understood to mean the way of sche-

duling activities in a particular manner by indi-

viduals. Therefore, complex travel behavior

stems from complex scheduling activities. Iden-

tifying those sets of activity scheduling and deci-

sions implemented by the individual considered

as distinctive variables describing tourist prefer-

ences (Hu and Morrison, 2002) seems extremely

urgent. Some of these variables such as travel

preferences have been stated by Pearce (2005),

including type of accommodation, preferred des-

tinations, trip purposes, travel arrangements, and

travel party. There are similarities between the

attitudes expressed by Pearce (2005) and those

Corresponding author:

Hanieh Varasteh, School of Housing, Building and Planning,

Universiti Sains Malaysia, Penang, Pulau Pinang 11800, Malaysia.

Email: [email protected]

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described by Xu et al. (2009) who identified dif-

ferent attractions, activities, accommodations,

and information sources as the travel behavior’s

variables.

A review of the literature pertaining to the stu-

dent market revealed that although some studies

have highlighted the importance of student mar-

ket and examined the travel behaviors of domes-

tic and/or international students, there is still a

dearth of research and information on interna-

tional students’ travel behavior while traveling

domestically within their host country (Ryan and

Zhang, 2007), particularly in the case of Malay-

sia. This kind of information is able to compre-

hensively explain or predict students’ travel

decision (Kim et al., 2007), which is important

for service providers of this market. Since the

number of international students’ enrollment in

Malaysia has increased dramatically over the

past 10 years on account of various higher educa-

tion reforms as a way to facilitate the entry of

international students into higher education insti-

tutions (Yusoff and Chelliah, 2010), identifying

this particular essential group’s travel behaviors

is considered a crucial issue. Going by the latest

statistics, there are more than 90,000 interna-

tional students currently studying in the numer-

ous institutions of higher learning in Malaysia

(MOHE, 2010).

The main goal of this article is to find out the

important factors influencing international stu-

dents’ travel preferences and activities, and it

further attempts to investigate the relationships

between international students’ demographic

characteristics and travel behaviors including

travel activities and preferences. This study

attempted to include all the variables that have

been mentioned in the previous studies and test

the existing relationships as an integrated model.

Information source moderating effect with

regard to the relationship between country of ori-

gin and students’ travel behavior was also sought

for the first time. Consequently, it contributes to

the existing literature by developing a general

framework for international students’ travel

behavior considering previous research paucity.

A majority of previous research have focused

on international students from limited number

of nationalities studying in one university; and

owing to restrictions such as convenience, time,

and money, respondents have been chosen from

one geographical area. As travel behaviors

depend on the student’s nationality (Chadee and

Cutler, 1996; Field, 1999; Hsu and Sung, 1997;

Michael et al., 2004; Weaver, 2003) as well as

other variables (Chadee and Cutler, 1996; Hsu

and Sung, 1997; Kim and Jogaratnam, 2003), the

results of such studies cannot be applied to all

international students.

Hence, further investigations that segment

responses based on nationalities and other vari-

ables were needed to address these shortcomings.

This article will thus contribute to the existing lit-

erature by considering these variables in order to

develop a general framework for international

students’ travel behavior. A total of 409 interna-

tional postgraduate students studying in five

Malaysian research universities (Universiti Putra

Malaysia (UPM), Universiti Malaya (UM), Uni-

versiti Teknologi Malaysia (UTM), Universiti

Sains Malaysia (USM), and Universiti Kebang-

saan Malaysia (UKM)) responded to a self-

administered questionnaire, and a structural

equation modeling–partial least squares (SEM-

PLS) using Warp PLS 3.0 was applied to analyze

data.

Literature review

Marketing theory suggests that businesses apply-

ing a market segmentation approach can develop

their organizational performance (Kotler, 1997)

by gaining excellent understanding of customers,

leading to suitable marketing programs (Dibb

et al., 2002). Market segmentation divides the

target market into smaller groups to evaluate the

target groups’ specific wants, needs, and beha-

viors (Jones et al., 2005; Kotler et al., 2002).

Although there is no precise agreement on

how the market should be segmented, often sev-

eral segmentations will meet Kotler’s criteria. He

divides market segmentation variables into four

major areas, namely, geographic, demographic,

psychographic, and behavioristic (Kotler, 1997).

The validity of using demographic variables

in segmentation studies has been supported over

the years. Beane and Ennis (1987) emphasized

demographic segmentation as the most prevalent

form of market segmentation, possibly because

consumers are positioned on clear scales of mea-

surement, which are easily understood. The

information is usually very easily interpreted,

relatively easily gathered, and very easily trans-

ferable and collected from one research to

another. Bass et al. (1968) also made good use

of demographics in describing light and heavy

users. Blattberg et al. (1976) stated that buyer

behavior is closely related to their demographics.

Frank et al. (1972), Brayley (1990), Kotler et al.

(2002), Jones et al. (2005), Arcodia et al. (2006),

2 Journal of Vacation Marketing



Glover (2011), Bahng and Kincade (2014), and

Kline et al. (2014) also discussed various demo-

graphic characteristics and their use in market

segmentation.

Students’ travels represent a distinct market

with specific needs and preferences (Chadee and

Cutler, 1996), and it is widely agreed by aca-

demics that the international student market

needs to be further segmented into different clus-

ters due to various characteristics that affect stu-

dents’ preferences. Although there is no overall

agreement regarding exactly how the market

should be segmented, demographic characteris-

tics often prove to be a good way to describe this

identified segment’s desires, as recent research

suggested that marketers must consider the influ-

ence of nationality, age, background, gender, and

other classifications and construct their market-

ing strategies accordingly (Field, 1999; Opper-

mann, 1994; Sussmann and Rashcovsky, 1997).

It is also indicated by Arcodia et al. (2006) and

Chadee and Cutler (1996) that travel behaviors

and preferences depend on the student’s nation-

ality as well as other variables because each

nationality has different travel characteristics

and preferences. Supporting these researchers,

Hsu and Sung (1997), who performed an

exploratory study to examine travel behaviors

of international students at a Midwestern Amer-

ican university, stated that travel preferences

could vary because of the differing demographic

characteristics like gender, age, degree sought,

marital status, and source of income.

In a comparative study of travel behaviors of

international and domestic students at a South-

eastern American university by Field (1999),

gender, marital status, number of children,

national origin, and degree program also proved

to have significant effects on students’ travel

behavior. Kim and Jogaratnam (2003) in another

comparative study of activity preferences of

Asian international and domestic American uni-

versity students also suggested travel behavior

being influenced by nationality, gender, age,

source of income, and marital status, while key

variables in the study of Michael et al. (2004),

who investigated international students’ beha-

vior as tourists in Australia, were country of ori-

gin, gender, and university attended. In the study

of Shoham et al. (2004), it was found that neither

marital status nor income played a role in

explaining travel differences and that travel pre-

ferences of students certainly differ on the basis

of their country of origin. One of the most impor-

tant results from the study of Payne (2009) who

also examined travel behaviors of New Zeal-

and’s international students is the influence of

nationality on certain activities’ participation and

that international students’ preferences can be

influenced by age and program of study. Glover

(2011) further suggested that travel characteris-

tics are affected by student status, faculty, level

of study, and first language.

Based on the previous studies, travel behavior

in this study is divided into travel preferences

and preferred activities that students choose to

be involved during traveling. Travel preferences

in this study are accommodation type used, style

of eating, travel party, purpose of travel, and time

of travel. Findings of the previous studies are

summarized in Table 1.

This study is based on the theoretical frame-

work (Figure 1), which has been developed after

an extensive review of literature based on previ-

ous studies regarding identifying travel beha-

viors. After a review of current approaches to

complex travel behavior, the theoretical model

was summarized, and its components and exist-

ing relationships are presented and discussed in

the subsequent section.

Based on the previous studies and for the pur-

pose of this research, variables have been identi-

fied and selected accordingly. Key independent

variables (IVs) based on previous studies are stu-

dents’ demographic characteristics, including

age (Chadee and Culter, 1996; Field, 1999; Hsu

and Sung, 1997; Kim and Jogaratnam, 2003;

Michael et al., 2004; Payne, 2009; Shoham

et al., 2004), gender (Chadee and Culter, 1996;

Field, 1999; Hsu and Sung, 1997; Kim and Jogar-

atnam, 2003; Michael et al., 2004; Shoham et al.,

2004), level of study (Glover, 2011; Hsu and

Sung, 1997; Payne, 2009; Shoham et al., 2004),

country of origin (Chadee and Culter, 1996;

Field, 1999; Hsu and Sung, 1997; Kim and Jogar-

atnam, 2003; Payne, 2009), marital status (Cha-

dee and Culter, 1996; Field, 1999; Kim and

Jogaratnam, 2003; Michael et al., 2004; Shoham

et al., 2004), source of financial support (Field,

1999; Hsu and Sung, 1997; Kim and Jogaratnam,

2003; Shoham et al., 2004), and length of resi-

dency in host country (Glover, 2011; Hsu and

Sung, 1997; Kim and Jogaratnam, 2003). Apart

from Payne’s (2009) study, although previous

studies usually surveyed respondents from one

department or school from one university or only

students of one university (Chen and Kerstetter,

1999; Hsu and Sung, 1997; Field, 1999; Lima-

nond et al., 2011), the students’ current univer-

sity has been identified as an IV in this study.

Varasteh et al. 3



Students from five research universities from

three important geographical areas (Kuala Lum-

pur, Johor Bahru, and Penang) have been chosen

to participate in this study to test the existing

relationships between the students’ studying

areas and their travel preferences.

It should be mentioned that Shoham et al.

(2004) included monthly income and working

Table 1. Factors affecting travel behaviors.

Author(s) Subjects Findings Method

Chadee andCutler (1996)

Insight into international travel bystudents in New Zealand; N ¼ 370

Travel behaviors and preferencesdepend on the students’ ethnicityand culture

Quantitativeandempirical

Hsu and Sung(1997)

Travel behaviors of internationalstudents at a Midwestern university;N ¼ 278

Travel behavior could vary because ofthe differing demographiccharacteristics. Age, gender, degree,and marital status found to have asignificant influence on style of eating


Field (1999) A comparative study of travelbehaviors of international anddomestic students at a Southeasternuniversity; N1 ¼ 509/N2 ¼ 1501

Gender, marital status, number ofchildren, national origin, and degreeproved to have more effects onstudents’ travel behavior


Chen andKerstetter(1999)

International students’ image of ruralPennsylvania as a travel destination;N ¼ 2537

International students’ destinationimages are influenced by homecountry, gender, and householdstatus


Pope et al.(2002)

The role and economic impact ofinternational student and familytourism within Western Australia

A relationship exists between countryof origin and travel expenditure


Kim andJogaratnam(2003)

Activity preferences of Asianinternational and domestic Americanuniversity students; N ¼ 514

Travel behavior is influenced byethnicity, gender, age, source ofincome, length of stay, and maritalstatus


Michael et al.(2004)

The travel behavior of internationalstudents: the relationship betweenstudying abroad and their choice oftourist destinations; N ¼ 219

Country of origin, gender, anduniversity attended affect travelbehavior


Shoham et al.(2004)

Student travel behavior: a cross-national study; N ¼ 558

Nationality and gender affect travelpreferences.


Weaver (2003) The contribution of internationalstudents to tourism beyond the coreeducational experience: evidencefrom Australia

Travel Preferences depend on ethnicityand culture.


Arcodia et al.(2006)

International students an Australiantourism

Travel behaviors and preferencesdepend on the student’s ethnicity.

Conceptual

Payne (2009) International students as domestictourists in New Zealand. A study oftravel patterns, behaviors,motivations and expenditure. N ¼221

Ethnicity plays an important role inparticipation in certain activities andpreferences can be influenced by ageand program studying


Limanond et al.(2011)

Travel behavior of university studentswho live on campus: a case study of arural university in Asia University ofTechnology, Department ofTransportation Engineering; N ¼130

Gender affects travel behavior Quantitativeandempirical

Glover (2011) A comparison between domestic andinternational students’ tripcharacteristics: evidence from anAustralian university; N ¼ 948

Travel aspects are affected by studentstatus, faculty, level of study, and firstlanguage


Source: compiled by the researcher for study.




status (Field, 1999) in demographic characteris-

tics but since most of the international students

are not allowed to work in Malaysia during their

study tenure; in the absence of regular income,

these factors are thus not considered as affecting

travel behavior in this study.

In this study, dependent variables (DVs;

travel behaviors) have been divided into two

groups, namely, travel preferences (Field, 1999;

Kim and Jogaratnam, 2003; Shoham et al.,

2004) and travel-related activities (Field, 1999;

Hsu and Sung, 1997; Kim and Jogaratnam,

2003). Travel preferences in this study, follow-

ing the work of Glover (2011), include items of

time of travel (Field, 1999; Michael et al.,

2004; Payne, 2009), accommodation (Chadee

and Cutler, 1996; Hsu and Sung, 1997; Field,

1999; Michael et al., 2004; Kim and Jogaratnam,

2003; Payne, 2009; Shoham et al., 2004), style of

eating (Hsu and Sung, 1997; Field, 1999; Payne,

2009; Shoham et al., 2004), travel party (Glover,

2011; Shoham et al., 2004), and travel purpose

(Kim and Jogaratnam, 2003; Payne, 2009;

Richards and Wilson, 2004) as DVs (travel pre-

ferences), which are affected by demographic

characteristics of travelers. Travel-related activi-

ties in this study based on previous studies (Hsu

and Sung, 1997; Field, 1999; Michael et al.,

2004; Kim and Jogaratnam, 2003; Payne, 2009;

Shoham et al., 2004) include activities that have

been undertaken by travelers while traveling,

which consists of leisure-based activities (Field,


2003; Payne, 2009; Shoham et al., 2004), sport

nature activities (Hsu and Sung, 1997; Field,


2003; Payne, 2009; Shoham et al., 2004), events

(Hsu and Sung, 1997; Michael et al., 2004; Kim

and Jogaratnam, 2003), touring activities (Hsu

and Sung, 1997; Field, 1999; Michael et al.,

2004; Kim and Jogaratnam, 2003; Payne,

2009), and action and recreation activities

(Michael et al., 2004; Shoham et al., 2004).

The current competitive condition of the tour-

ism market means consumption of tourism prod-

ucts completely depends on the information

sources used by the tourist (McIntosh and Goeld-

ner, 1990; Moutinho, 1987), and as tourists have

been also directly segmented based on their

search behavior (Bieger and Laesser, 2004; Fod-

ness and Murray, 1997; Um and Crompton,

1990), the need to understand clearly how tour-

ists and student travelers obtain information

about their destinations becomes a crucial issue

Gender

Marital status

Nationality

Info. source preference

Age

Level of education

Source of finance

-Action - Leisure -Sport nature -Events-Touring-Recreation

Activities undertaken while

traveling

-Time of Travel -Travel Party -Accommodation type used -Style of Eating -Travel purpose

Travel preferences

Current university

Travelbehavior

Length of residency

Figure 1. Theoretical framework of the study.

Varasteh et al. 5



in tourism marketing (Carr, 2003). According to

Fodness and Murray (1997), tourists increase the

quality of their travel by searching about pre-

ferred destinations, and understanding the way

of obtaining information by tourists would

enable destination marketers to effectively offer

products. Moutinho (1987) also stated while

information search employed as a descriptor to

profile the behavior of tourists was segmented

on some other basis, it has provided important

and valuable information for planning and posi-

tioning of appropriate tourism marketing strate-

gies. Information sources have been clarified in

the previous studies (Crotts, 1999; Moutinho,

1987; Payne, 2009) as tourism offices/travel

agents, friends, relatives/family, newspapers,

magazines, radio, television, and the Internet.

The relationships between information source

preference and trip outcome are supported by

previous studies as well. Andereck and Caldwell

(1994) reported travel behaviors are related to

ratings of information sources, while other

researchers (Crompton, 1992; Gunn, 1988; Luo

et al., 2005; Um and Crompton, 1990) indicated

that information source of preferred destinations

affected travel outcomes. Dawar et al. (1996)

also stated information seeking is often coupled

with a cultural background resulting in different

patterns of behavior. It is also worth mentioning

Bieger and Laesser (2004) found that tourists’

travel behavior could vary based on information

sources; for instance, it was revealed in the case

of destination choice, with the increase in travel

distance, information increases not only with

regard to importance but also with regard to pro-

fessionalism and reliability. Further, it was also

found that the higher the degree of professional-

ism and general importance of information

sources, the earlier the final decision is placed

ahead of departure.

Based on the above discussions and state-

ments, this considers information source prefer-

ence as a moderator with its main function

being to adjust the strength of relationships

between nationality and travel behavior. Mou-

tinho (1987) stated information search has pro-

vided valuable insights for planning marketing

strategies when employed as a descriptor to pro-

file the behavior of tourists segmented on some

other basis as well.

Research methodology

This study adopted a quantitative method to

examine the broad spectrum of international

students’ travel behavior in five Malaysian uni-

versities from three important geographical

areas of Malaysia (Kuala Lumpur, Penang, s

Johor Bahru), which include UPM, UM, UTM,

USM, and UKM. Since the number of interna-

tional students represent a great proportion

among the postgraduate students (masters and

doctor of philosophy) in Malaysian universities,

and this study is aimed at investigating interna-

tional students’ travel behavior, postgraduate

students have thus been identified to be sur-

veyed in this study.

A stratified random sampling was used, which

was drawn up on the basis of universities. The

total number of international postgraduate stu-

dents studying in five research universities of

Malaysia was 11,749 based on the data published

by the Ministry of Higher Education of Malaysia

in 2010. The appropriate sample size determined

for this study is 386 respondents. Each stratum is

taken in a number proportional to the stratum’s

size when compared with the population. The

survey instrument was administered to the target

sample via online survey system, and the

research focuses on travel behaviors of interna-

tional postgraduate students studying in Malay-

sian universities. Study results centered on

preferred time of travel, preferred accommoda-

tion, preferred food outlets, traveling party, main

purpose of travel, activities undertaken, source of

information about preferred destination, and the

relationship between demographic characteris-

tics, information source preference, and travel

behaviors of students.

A literature review of previous studies’

questionnaires was used to explain and justify

questions in a valid and relevant manner

(Brotherton, 2008). On the basis of literature

reviews, a preliminary pattern of international

students’ travel behaviors was constructed and

a pilot study subsequently conducted. Before

conducting the pilot test, an important step

called content validity, required to establish

the credibility of the research, was carried out.

Content validity of the preliminary items was

examined by a review panel consisting of

seven academic faculty members who are

experts in methodology, analysis, and tourism

planning. They were asked to review the con-

tents of the questionnaire and items and con-

sider its suitability for the current study. A

pilot test of the survey was conducted to

ensure that instructions, wordings, explana-

tions, and questions were clear and formatted

properly and efficiently.




In the pilot study, a small proportion sample

(22 students) was chosen from USM, and the col-

lected data were subjected to the same process and

methods in the research. Based on the results of

the pilot test and content validity conducted by the

panel, some revisions to wording and layout were

made to improve the questionnaire appropriately

in order to achieve the research objectives and,

finally, a three-section questionnaire was devel-

oped to obtain the required data. The first section

captured the demographic information of respon-

dents. The second section examined the travel

behavior of students consisting of two subsections

including travel preferences and activities under-

taken by respondents while traveling, using a

5-point Likert-type scale to measure responses,

with 5 ¼ almost always, 4 ¼ frequently, 3 ¼sometimes, 2 ¼ seldom, and 1 ¼ never. The third

section measured the level of information source

usage by respondents, using a 5-point Likert-

type scale to measure responses with 5 ¼ almost

always, 4 ¼ frequently, 3 ¼ sometimes, 2 ¼ sel-

dom, and 1 ¼ never. In this section, respondents

were asked to indicate to what extent they used

listed sources for obtaining information about

their preferred destinations.

At the beginning of the survey, there was one

filtering question, which respondents were

required to answer before they could continue to

the next sections. If the respondents answered

‘No’ to the question, then they were not able to

participate in the survey. This filtering question is:

1. Have you been on an overnight travel in

the last 12 months while studying in

Malaysia?

Data collection

The online survey of this study was designed

using Google Questionnaire Application. The

URL link was made available to participants via

e-mail obtained through personal contacts with

international offices at universities and relevant

student representative bodies of the overseas

postgraduate student population in Malaysia.

Some international students were also contacted

and invited to participate in this survey through

Facebook. To get a high response rate, the num-

ber of questionnaires distributed were twice the

number of the determined sample size in each

university. A total of 409 responses were

returned for a 53% response rate (see Table 2).

The duration of data collection was 3 months,

beginning from September till November 2012.

In order to analyze the collected data and

address the research objectives, SEM-PLS tech-

nique was applied to find the relationships

between social–demographic characteristics,

including a set of personal characteristics and

latent variables (LVs) with multiple indicators.

In this study’s model, travel behavior is captured

by travel preferences and travel activities, which

include some LVs to be measured by some

observed items. This study also desired to test the

moderating effect of information sources on the


tics and travel behavior.

Analysis and findings

The assessment of the model by PLS analysis

typically includes the assessment of the measure-

ment model and the structural model (Chin, 2010;

Hair et al., 2011, 2012). The assessment of the

measurement model examines the validity and

reliability of the relationship between the LVs and

related observable variables, while assessment of

the structural model examines the relationships

between constructs (Chin, 2010; Hair et al., 2011).

Assessment of measurement model

In the model used in this research, a number of

reflective constructs were involved including

travel behavior, preferences, activities, and

Table 2. Sample size and valid replies in universities.

University Population Desired sample size Valid replies Response rate

UM 2405 79 81 52%USM 2035 66 79 60%UKM 2333 77 80 52%UPM 2728 90 95 53%UTM 2248 74 74 50%Total 11,749 386 409 53%

Note: UM: Universiti Malaya; USM: Universiti Sains Malaysia; UKM: Universiti Kebangsaan Malaysia; UPM: Universiti PutraMalaysia; UTM: Universiti Teknologi Malaysia.

Varasteh et al. 7



source of information as a moderator. Travel

behavior was considered as the third-order fac-

tor, and travel preferences and travel activities

were the second-order factors. The travel prefer-

ences LVs included five first-order factors (time

of travel, travel party, accommodation, eating

style, and travel purpose) and travel activities

LVs also included six first-order factors (action,

touring, event, sport, recreation, and leisure). In

this model, demographic characteristics were

considered as IVs, including age, gender, marital

status, nationality, level of study, source of

finance, current university, and length of resi-

dency in host country, which were defined in the

PLS model as a dummy LV. Information sources

used in this study were considered as moderator

and also as a first-order construct.

The assessment of the measurement model

has been conducted via a three-step analysis. In

the first step, the first-order factors were ana-

lyzed together (Akter et al., 2011), and in the sec-

ond step, after generating the second-order

factors, they were also analyzed together; and

at the end after generating the third-order factor

(travel behavior), an analysis was run to com-

plete the assessment of the measurement model.

The reflective measurement model evaluates

reliability and validity, and two key criteria for

conducting such an evaluation are composite

reliability (CR) and average variance extracted

(AVE) (Chin, 2010; Hair et al., 2011). It should

be noted that, in the assessment of measurement

model, the sociodemographic variables were not

included because in the model these variables

were considered dummy variables and the CR

and AVE equal one. Checking for reliability and

validity of these variables was thus not required.

Evaluating the reliability of the reflective mea-

surement model for SEM included indicator

reliability and constructs reliability.

The loading of each indicator on its associated

latent construct should be checked in order to

assess indicator reliability. In order to gain

acceptable indicator reliability, the loading of

higher than 0.7 was needed (Gotz et al., 2010;

Hair et al., 2011; Hulland, 1999). Table 3 shows

the loading of indicators on their associated LVs

before creating second-order LVs is higher than

0.7 and are thus acceptable. Furthermore, to

assess construct reliability, two coefficients are

typically considered, that is, CR and the more

common coefficient Cronbach’s a (Bagozzi and

Yi, 1988; Chin, 2010; Gotz et al., 2010). How-

ever, CR is more suitable for PLS-SEM (Hair

et al., 2011); hence, CR has been mentioned in

Table 3. Results of the measurement model forfirst-order factors.

Construct Items Factor loading CR AVE

Time of travel 0.74 0.5A1 0.671A2 0.776A3 0.656

Travel party 0.5 0.71A4 0.656A5 0.672A6 0.696

Accommodation 0.5 0.82A7 0.587A8 0.726A9 0.74A10 0.72A11 0.669

Preferred meals 0.6 0.77A12 0.789A13 0.789

Travel purpose 0.5 0.88A14 0.637A15 0.684A16 0.792A17 0.827A18 0.765A19 0.657A20 0.663

Action 0.5 0.83B1 0.566B2 0.714B3 0.778B4 0.779B5 0.681

Touring 0.5 0.78B6 0.684B7 0.728B8 0.716B9 0.599

Event 0.6 0.82B10 0.841B11 0.867B12 0.611

Sport 0.7 0.86B13 0.805B14 0.855B15 0.805

Recreation 0.7 0.82B16 0.835B17 0.835

Leisure 0.5 0.81B18 0.735B19 0.788B20 0.67B21 0.599B22 0.618

Information source 0.5 0.89C1 0.494C2 0.57C3 0.735

(continued)




the tables that follow subsequently. Table 3 illus-

trates CR of first-order factors, which are higher

than 0.7 for all the factors. Therefore, the mea-

surement model has internal consistency and

considered reliable.

The validity of the reflective measurement

model consists of convergent and discriminant

validity (Gotz et al., 2010; Hair et al., 2011).

To obtain an acceptable convergent validity, the

AVE values of LVs should be higher than 0.5

(Bagozzi and Yi, 1988; Chin, 2010; Hair et al.,

2011). AVE is used to measure the variance in

an LV that is contributed from its indicators

(Chin, 2010). Table 3 shows that the AVE values

of all constructs of the measurement model are

higher than 0.5, so the convergent validity is

acceptable (Chin, 2010).

Discriminant validity is the extent to which

each construct is accurately distinct from the

other constructs in the model. In order to test dis-

criminant validity, the root square of AVE of

each construct should be higher than the correla-

tion of the construct with any other LV in the

model, and an indicator’s loading with its associ-

ated LV must be higher than its loading with

other LVs (Chin, 2010; Fornell and Larcker,

1981; Hair et al., 2011).

Table 4 presents the comparison of the square

root of AVE of each construct with the correla-

tion of the other construct. This comparison

demonstrates that for all the constructs, the dis-

criminant validity is completely acceptable. The

results of the assessment of the measurement

model show the reliability, convergent validity,

and discriminant validity are highly acceptable

for measurement model consisting of first-order

constructs.

The second step of analysis of the measure-

ment model was performed by generating two

second-order factors (travel preferences and travel

activities). Time of travel, travel party, accommo-

dation, eating style, and travel purpose are consid-

ered as indicators for travel preferences, while

action, touring, event, sport, recreation, and

leisure are considered as indicators of travel activ-

ities. In this stage, the measurement model with

second-order factors was thus assessed. The result

shows CR of generated second orders is 0.75 and

0.82 for travel preferences and travel activities,

respectively. Moreover, the AVE of second-

order constructs is 0.50 and 0.54 for travel prefer-

ences and travel activities, respectively. After

generating the third-order construct, namely,

travel behavior, the CR and AVE are 0.90 and

0.8, respectively. The reliability, convergent

validity, and discriminant validity are highly

acceptable for measurement model in three stages.

Therefore, there were accurate relationships

between LVs and related observable variables of

the proposed model, and consequently, the valid-

ity and reliability of the adopted questionnaire are

also confirmed.

Assessment of structural model

The following two criteria should be evaluated to

obtain a preliminary assessment of the structural

model (inner model) and hypothetical frame-

work: R2 measure of endogenous constructs and

the path coefficients (Chin, 2010; Hair et al.,

2011). The path coefficients must be significant,

and R2 is highly dependent on the research area.

Since the objective of this study is to examine the


tics and travel behavior, R2 would not be appro-

priate and relevant to this study because R2

shows the predictive role of IV on DV. There-

fore, R2 is not considered in this analysis, and this

research did not consider the role of demographic

variables on travel behavior as the previous stud-

ies also eliminated R2 from their studies (Field,


2003; Shoham et al., 2004; Payne, 2009). As this

research aims to investigate whether the effect of

demographic variables are significant on travel

preference and travel activities, the significance

between demographic characteristics and travel

behavior including travel preferences and travel

activities needed to be assessed and reported.

The analysis of the structural model was per-

formed in three stages. In the first stage, the rela-

tionships between demographic characteristics

and first-order factors of travel preferences (time

of travel, travel party, accommodation, eating

style, and travel purpose) and travel activities

(action, touring, event, recreation, sport, and lei-

sure) were examined. Tables 5 and 6 represent

the results of hypotheses testing of this stage and

show the significance levels of path coefficients.

Table 3. (continued)

Construct Items Factor loading CR AVE

C4 0.8C5 0.811C6 0.804C7 0.715C8 0.672

Note: CR: composite reliability; AVE: average varianceextracted.

Varasteh et al. 9



Tab

le4.

Dis

crim

inan

tva

lidity

afte

rm

odifi

cation.

Tim

eoftr

avel

Tra

velpar

tyA

ccom

modat

ion

Mea

lsT

rave

lpurp

ose

Act

ion

Eve

nt

Touri

ng

Sport

Rec

reat

ion

Leis

ure

Info

rmat

ion

sourc

e

Tim

eoftr

avel

0.7

03

Tra

velpar

ty0.4

88

0.6

75

Acc

om

modat

ion

0.4

01

0.3

62

0.6

91

Mea

ls0.1

98

0.4

26

0.1

69

0.7

89

Tra

velpurp

ose

0.6

27

0.4

84

0.3

36

0.2

71

0.7

22

Act

ion

0.4

71

0.4

76

0.3

55

0.3

31

0.5

30.7

08

Eve

nt

0.2

89

0.3

51

0.2

83

0.1

23

0.3

52

0.5

36

0.7

81

Touri

ng

0.3

21

0.2

27

0.1

49

0.1

86

0.4

43

0.4

60.3

86

0.6

84

Sport

0.2

78

0.3

34

0.4

56

0.1

80.2

83

0.5

22

0.3

96

0.1

90.8

22

Rec

reat

ion

0.3

35

0.3

90.4

23

0.1

85

0.4

10.5

42

0.3

77

0.2

26

0.6

62

0.8

35

Leis

ure

0.3

67

0.3

89

0.5

47

0.2

72

0.3

06

0.5

08

0.4

55

0.3

02

0.6

16

0.4

62

0.6

86

Info

rmat

ion

sourc

e0.4

24

0.6

66

0.3

43

0.4

24

0.4

66

0.5

13

0.4

38

0.2

12

0.3

81

0.3

82

0.4

42

0.7

09

Not

e:sq

uar

ero

ots

ofav

erag

eva

rian

ces

extr

acte

dar

esh

ow

non

dia

gonal

and

inbold

face

.



The tables illustrate the influence of IVs, includ-

ing age, gender, marital status, nationality,

source of finance, level of study, length of resi-

dency, and current university on first-order fac-

tors including preferred meal, accommodation,

travel party, time of travel, travel purpose, and

preferred activities.

Table 7 shows the results of the second stage

of assessment of the structural model. In this

stage, the relationships between demographic

characteristics and second-order factors includ-

ing travel activities and travel preferences were

examined. Table 7 shows a highly significant

relationship between age, marital status, nation-

ality, source of finance, and factors of both travel

activities and travel preferences. However, none

of the other demographic characteristics includ-

ing gender, level of study, years in host country,

and current university has influence on either

travel activities or travel preferences as the

second-order factors. Figure 2 also illustrates the

relationships between age, marital status, nation-

ality, source of finance, and factors of both travel

activities and travel.

Table 5. Relationships between age, gender, marital status, nationality and constructs (first-order factors).

Hypothesis Path coefficient p value Supported

Age! Time of travel 0.069 0.14 NoAge! Travel party 0.143 <0.01 YesAge! Accommodation 0.055 0.17 NoAge! Preferred meal 0.117 <0.05 YesAge! Travel purpose 0.041 0.262 NoAge! Action 0.085 <0.1 YesAge! Event �0.027 0.342 NoAge! Touring 0.017 0.387 NoAge! Sport 0.102 <0.05 YesAge! Recreation 0.168 <0.01 YesAge! Leisure 0.113 <0.05 YesGender ! Travel time 0.03 0.275 NoGender ! Travel party �0.014 0.38 NoGender ! Accommodation 0.077 <0.1 YesGender ! Meal �0.109 <0.01 YesGender ! Travel purpose 0.035 0.226 NoGender ! Action �0.016 0.372 NoGender ! Event 0.01 0.428 NoGender ! Touring �0.103 <0.05 YesGender ! Sport 0.156 <0.01 YesGender ! Recreation 0.062 0.109 NoGender ! Leisure 0.036 0.202 NoMarital status! Travel time 0.121 <0.05 YesMarital status! Travel party 0.071 <0.1 YesMarital status! Accommodation 0.205 <0.01 YesMarital status! Meal 0.059 0.159 NoMarital status! Travel purpose 0.076 <0.1 YesMarital status! Action 0.113 <0.01 YesMarital status! Event 0.175 <0.01 YesMarital status! Touring 0.013 0.414 NoMarital status! Sport 0.099 <0.05 YesMarital status! Recreation 0.048 0.152 NoMarital status! Leisure 0.113 <0.05 YesNationality ! Travel time 0.079 <0.05 YesNationality ! Travel party 0.205 <0.01 YesNationality ! Accommodation 0.095 <0.05 YesNationality ! Meal 0.068 <0.05 YesNationality! Travel Purpose 0.089 <0.05 YesNationality ! Action 0.138 <0.01 YesNationality ! Event 0.025 0.34 NoNationality ! Touring �0.016 0.385 NoNationality ! Sport 0.05 0.138 NoNationality ! Recreation 0.101 <0.05 YesNationality ! Leisure 0.068 <0.1 Yes

Varasteh et al. 11



Table 8 shows the results of hypothesis testing

after generating the third-order factor. The find-

ings show the highly significant influence of age,

marital status, nationality, and source of finance

on travel behavior (see Figure 3 and Table 8).

The moderating effect of information source

A moderator construct is basically an IV, which

modifies the relationship between two other vari-

ables. Moderator variables including specific fac-

tors (e.g., context information) are often assumed

to reduce or enhance the influence that specific

IVs have on specific responses in question

(DV). In this study, information source preference

has been considered as a moderator with its main

function in adjusting the strength of relationships

between nationality and travel behavior. The

result indicated the moderating effect of informa-

tion source on relationships between nationality

and travel behavior is highly significant. The

results reveal the interaction effect is 0.164 and

the p value of the interaction effect is significant

at 0.01. Figure 4 shows the differences between

respondents with high usage as well as low usage

of information source. The relationship between

Table 6. Relationships between study level, source of finance, length of residency, current university, andconstructs (first-order factors).

Hypothesis b Coefficient p Value Supported

Study level ! Time of travel 0.03 0.301 NoStudy level ! Travel party �0.137 <0.01 YesStudy level ! Accommodation 0.045 0.205 NoStudy level ! Preferred Meal �0.038 0.241 NoStudy level ! Travel Purpose 0.044 0.249 NoStudy level ! l Action 0.028 0.297 NoStudy level ! l Event �0.013 0.396 NoStudy level ! Touring 0.127 <0.05 YesStudy level ! Sport �0.067 0.138 NoStudy level ! Recreation �0.119 <0.05 YesStudy level ! Leisure 0.0 0.168 NoSource of finance ! Travel time �0.045 0.198 NoSource of finance ! Travel party �0.14 <0.01 YesSource of finance ! Accommodation 0.055 0.119 NoSource of finance ! Preferred meal �0.20 <0.01 YesSource of finance ! Travel purpose �0.064 0.128 NoSource of finance ! Action �0.105 <0.05 YesSource of finance ! Event �0.021 0.346 NoSource of finance ! Touring 0.012 0.4 NoSource of Finance ! Sport �0.1 <0.05 YesSource of Finance ! Recreation �0.07 <0.05 YesSource of Finance ! Leisure �0.039 0.2 NoLength of residency ! Travel Time �0.126 <0.05 YesLength of residency ! Travel party �0.01 0.432 NoLength of residency ! Accommodation 0.007 0.451 NoLength of residency ! Meal 0.06 0.175 NoLength of residency ! Travel purpose �0.068 0.121 NoLength of residency ! Action �0.073 <0.1 YesLength of residency ! Event �0.094 <0.1 YesLength of residency ! Touring �0.045 0.178 NoLength of residency ! Sport �0.01 0.42 NoLength of residency ! Recreation �0.014 0.403 NoLength of residency ! Leisure 0.009 0.443 NoUniversity ! Travel time �0.068 <0.1 YesUniversity ! Travel party �0.014 0.373 NoUniversity ! Accommodation 0.054 0.143 NoUniversity ! Meal �0.068 <0.05 YesUniversity ! Travel purpose �0.068 <0.1 YesUniversity ! Action �0.033 0.25 NoUniversity ! Event �0.071 <0.1 YesUniversity ! Touring 0.012 0.388 No




nationality and travel behavior for respondents

with low usage of information source is negative,

whereas this relationship for respondents with

high usage of information source is positive. This

study thus confirms the moderating role of infor-

mation source on relationship between nationality

and travel behavior.

Discussion

This article can assist destination marketers and

tourism organizers to gain useful information

on the travel behavior of international students

in Malaysia through a comprehensive investiga-

tion with the aim of achieving three objectives,

Table 7. Relationships between demographic variables and constructs (second-order factors).

Hypothesis Path coefficient p Value Supported

Age! Travel activities 0.104 <0.05 YesAge! Travel preferences 0.12 <0.05 YesGender ! Travel activities 0.033 0.247 NoGender ! Travel preferences 0.005 0.453 NoMarital Status ! Travel activities 0.128 <0.05 YesMarital Status ! Travel preferences 0.151 <0.05 YesNationality ! Travel activities 0.083 <0.1 YesNationality ! Travel preferences 0.151 <0.01 YesStudy Level ! Travel activities 0.001 0.489 NoStudy Level ! Travel preferences �0.016 0.372 NoSource of Finance ! Travel activities �0.075 <0.1 YesSource of Finance ! Travel preferences �0.113 <0.05 YesLength of residency ! Travel activities �0.052 0.185 NoLength of residency ! Travel preferences �0.039 0.252 NoUniversity ! Travel activities 0.004 0.467 NoUniversity ! Travel preferences �0.047 0.158 No

Figure 2. Relationships between demographic variables and travel preference and activity.

Varasteh et al. 13



which were to examine the relationships between

demographic characteristics and travel prefer-

ences of international students in Malaysia, to

examine the relationships between demographic

characteristics and travel activities of interna-

tional students while traveling within Malaysia,

and finally to investigate the moderating effect

with regard to the relationships between country

of origin and travel behavior of students in

Malaysia.

SEM was utilized to analyze the quantitative

data and to explore the existing relationships

among variables, followed by assessments of

the measurement model and structural model,

which were reported to describe reliability and

validity of developed questionnaires as well as

relationships between variables. The testing of

assumptions before the performance of each statis-

tical technique was all satisfied. The following sec-

tions discuss the objectives and the findings of the

study in reasonable detail. Results revealed that

preferred time for traveling was affected by marital

status, nationality, years of residency in Malaysia,

and location of current university. Significant rela-

tionships between preferred accommodation and

gender, marital status, and nationality were also

revealed. The study also found preferred meal to

be significantly influenced by gender, age, nation-

ality, source of finance, and current university,

which is in agreement with Hsu and Sung (1997)

who reported in their study that age and gender

affect choices of food outlets, which is also

Table 8. Relationships between demographic variables and travel behavior.

Hypothesis Path coefficient p Value Supported

Age! Travel Behavior 0.123 <0.1 YesGender ! Travel behavior 0.021 0.315 NoMarital Status ! Travel behavior 0.153 <0.01 YesNationality ! Travel Behavior 0.129 <0.01 YesStudy Level ! Travel behavior �0.008 0.438 NoSource of Finance ! Travel behavior �0.104 <0.05 YesLength of Residency ! Travel behavior �0.05 0.197 NoUniversity ! Travel behavior �0.024 0.303 No

Figure 3. Relationships between demographic variables and travel behavior.




supported by Shoham et al. (2004) confirming the

influence of gender on choices of meals. Hsu and

Sung (1997) also reported the influence of degree

and marital status on style of eating but is not con-

firmed by the findings of this study. The results

also showed preferred travel party is significantly

affected by age, marital status, nationality, level

of study, and source of finance.

Regarding the reasons behind international

students’ main purposes for traveling, it was

found that travel purpose is associated with mar-

ital status, nationality, and current university of

international students. The study also found

travel activities undertaken by students when

they are traveling were affected by age, gender,

nationality, marital status, source of finance,

years in host country, level of study, and current

university. Findings of Payne (2009) confirmed

the influence of nationality on different travel

activities and levels of participation. Relation-

ships between demographic characteristics,

information source preference, and travel beha-

viors of students were investigated as the main

objective of the study. After generating second-

order constructs, including travel preferences

and preferred activities, it was found that there

is a highly significant relationship between age,

marital status, nationality, source of finance, and

factors of both travel activities and travel prefer-

ences. However, none of the other demographic

characteristics including gender, level of study,

years in host country, and current university has

any influence on either travel activities or travel

preferences as the second-order factors.

There were some differences as well as simi-

larities between these findings and the work of

Hsu and Sung (1997). However, the current

results support the findings of Hsu and Sung

(1997) regarding the influence of age and marital

status on travel activities. The influence of

degree and gender which was found to be signif-

icant in the mentioned study was, however, not

confirmed by the present study. After generating

third-order construct termed travel behavior,

results showed the highly significant influences

between age, marital status, nationality, source

of finance, and travel behavior. However, other

variables including gender, level of study, years

in host country, and current university have an

insignificant effect on travel behavior, which is

consistent with Hsu and Sung (1997) who noted

travel preferences could vary because of the dif-

fering demographic characteristics and sug-

gested that market of international students can

be segmented in terms of not only nationality

(Arcodia et al., 2006; Pope et al., 2002; Chadee

and Cutler, 1996; Kim and Jogaratnam, 2003;

Shoham et al., 2004; Weaver, 2003) but also

other variables such as age (Giuliano, 2003; Giu-

liano and Dargay, 2006; Giuliano and Narayan,

2003; Kim and Jogaratnam, 2003), source of

income (Kim and Jogaratnam, 2003), and marital

Figure 4. Moderating effect of information source on the relationships between nationality and travel behavior.

Varasteh et al. 15



status (Kim and Jogaratnam, 2003), as travel

behavior is influenced by these variables.

It should be noted that findings of the current

study do not support the previous research by

Shoham et al. (2004) who stated neither marital

status nor income played a role in explaining

travel differences. They also reported the signif-

icant influence of gender on travel preference,

which is also not supported by the findings of this

study. However, findings of the current study are

in agreement with the findings of Michael et al.

(2004) regarding identifying country of origin

as one of the key variables but do not support the

role of gender and university attended as the key

variables. According to previous studies (Arco-

dia et al., 2006; Chadee and Cutler, 1996; Chen

and Kerstetter, 1999; Glover, 2011; Field,


2003; Limanond et al., 2011; Michael et al.,

2004; Payne, 2009; Pope et al., 2002; Shoham

et al., 2004; Weaver, 2003), it is believed that

different demographic characteristics of travelers

will result in different travel behavior, and it is

also well documented that information source

preference about destination in so many studies

has been considered as an IV, which has signifi-

cant effects on travel behavior (Andereck and

Caldwell, 1994; Bonn et al., 1998; Dawar

et al., 1996). Dawar et al. (1996) also stated

information seeking is often coupled with cul-

tural background resulting in different patterns

of behavior. Based on these statements, informa-

tion source preference has been considered as a

moderator in this study with its main function

being to adjust the strength of relationships

between nationality and travel behavior. The

results show that the moderating effect of infor-

mation source on relationship between national-

ity and travel behavior is highly significant.

This study therefore confirms the moderating

role of information source on relationship

between nationality and travel behavior.

Implications of the study

This research is the first attempt to investigate

the travel behaviors of the international students

who are currently studying in Malaysia. Impor-

tant factors influencing travel behavior were

identified, in a way that may potentially contrib-

ute to the development of a reliable travel beha-

vior framework.

This study revealed that different travel beha-

viors are strongly associated with demographic

characteristics of international students. It was

found that age, marital status, and nationality

were the primary basis for segmentation because

most of the students’ preferences vary predomi-

nantly based on their age, nationality, and marital

status. The travel activities and preferences

include travel party (affected by age, marital sta-

tus, nationality, level of study, financial supports,

and current university, while preferred meal

affected by age, gender, nationality, and financial

support), preferred accommodation (affected by

gender, marital status, and nationality), travel pur-

pose (affected by marital status and nationality),

time of travel (affected by marital status, national-

ity, and length of residency), action activities

(affected by age, marital status, nationality, and

source of finance), sport activities (affected by

age, gender, marital status, source of finance, and

current university), recreation activities (affected

by age, marital status, level of study, and financial

support), leisure activities (affected by age, mari-

tal status, nationality, and current university),

touring activities (affected by gender and level

of study), and event activities (affected by marital

status and level of study).

These findings will facilitate tourism market-

ers when constructing their strategy, with the

need to factor in the influence of students’ demo-

graphic characteristics in order to strategize and

promote tourism products concerning different

age-groups, nationalities, marital status, genders,

and other classifications.

Tourism marketers may, for instance, con-

sider different age-groups’ interests and prefer-

ences in offering any action, sport, recreation,

and leisure activities and restaurants in order to

meet the students’ needs. They should also con-

sider students’ level of study in offering touring

and event activities in various tourism destina-

tions. By sharing this relevant consumer infor-

mation with all stakeholders and tourism

operators, the strategic implications for tourism

planners, managers, and policy makers would

become more apparent.

This area provides further research opportuni-

ties to identify applicable segments of the market

and inform, so that tourism operators can conse-

quently market appropriate products through

approaches that are well structured and orga-

nized, allowing for the greatest return while bet-

ter serving the needs of customers. When these

significant differences and similarities are identi-

fied, tourism marketers would account for them

through adapting their strategies, allowing for the

vagaries in traveling students’ characteristics,

rather than adopting a one-size-fits-all strategy.




Conclusion and suggestions forfurther research

This study revealed that although each compo-

nent or indicator of preferred travel activities

and preferences has been affected by some

demographic characteristics of respondents,

travel behavior (as a third-order factor) was

only affected by four characteristics. The

highly significant influences between age,

marital status, nationality, and source of

finance on travel behavior have been reported.

Other variables including gender, level of

study, years in host country, and current uni-

versity have an insignificant effect on travel

behavior, while moderating effect of informa-

tion source on relationship between nationality

and travel behavior has also been found by the

current study for the first time. The results of

this study can facilitate destination marketers

and tourism organizers and contribute signifi-

cantly to the development of marketing strate-

gies in improving the markets and helping it to

better serve the needs of its customers.

The findings of this research, however,

should be interpreted in light of some limita-

tions. This study only focused on travel beha-

viors of international postgraduate students

who were currently studying in five Malaysian

universities in three cities, namely, Kuala Lum-

pur, Penang, and Johor Bahru; consequently,

further studies on other university students’

travel behaviors are suggested to validate these

research findings and their relevance to students

of other universities. Further studies on under-

graduate students’ travel behavior are also sug-

gested to allow for more accurate comparison

between these two groups and help develop

deeper understanding of international students’

travel behavior. Additionally, further studies are

also needed to investigate students’ preferences

comprehensively in order to help tourism opera-

tors and planners to better understand students’

needs and desires because this study focused

only on the existing relationships between their

demographic characteristics and travel beha-

vior, while more studies need to be conducted

to investigate and describe preferences of dif-

ferent group ages, genders, nationalities, and

other demographic groups.

Funding

This research received no specific grant from any

funding agency in the public, commercial, or

not-for-profit sectors.

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