The international journal of tourism research tập 12, số 04, 2010 07 + 08

As expected, uncertainty avoidance was positively related to the extent of information search in all three country samples, whereas risk avoidance was not.. The present research attem

Traditionally, many studies have attempted

to use economic demand models This paper

stresses on the infl uence of non-economic

factors on tourism demand Some

researchers have suggested that tourists

from different origins have various cultural

and nationalistic backgrounds, and they

may interpret visual imagery and

experiences differently Aligning with this

suggestion, we have investigated different

underlying factors of tourism demand from

four continents (Asia, the Americas, Europe

and Oceania) Statistical data are collected

from international organisations and 135

countries were covered Our results showed

that there are differences and similarities

among the factors in determining the

tourism demand Copyright © 2009 John

Wiley & Sons, Ltd.

Received 18 November 2008; Revised 25 June 2009; Accepted

30 June 2009

Keywords: tourism demand; non-economic

determinants; holistic approach

INTRODUCTION

International tourism today has social,

cul-tural and political signifi cance, as well as

substantial economic benefi ts In the last 50

years, tourism has emerged as one of the largest

and fastest growing industries in the world

(Eadington and Redman, 1991; WTO, 1992)

According to the World Tourism Organization

(WTO), the number of international tourists worldwide increased from 25 million in 1950

to 160 million in 1970, 429 million in 1990, 689 million in 2001, 846 million in 2006 and 1.6 billion by 2020 International tourism has expe-rienced an overwhelming boom over the last two decades and has now been called the largest industry in the world As a result of the rise in the number of tourists, and the impor-tance of the tourism sector for many countries which have begun to channel their resources into its development (Balaguer and Cantavella-Jorda, 2002), tourism demand analysis has become increasingly important

In general, the international tourism demand model, which is based on classical economic theory, is typically estimated as a function of tourists’ income, tourism prices in a destina-tion relative to those in the origin country, tourism prices in the competing destinations (i.e substitute prices), exchange rates, trans-portation cost between destination and origin,

as well as dummy variables on various special events and deterministic trends (e.g Barry and O’Hagan, 1972; Loeb, 1982; Stronge and Redman, 1982; Uysal and Crompton, 1984; Smeral, 1988; Di Matteo and Di Matteo, 1993; Crouch, 1994; Lim, 1999; Croes, 2000; Vanegas

and Croes, 2000; Song et al., 2003; Chu, 2004; Li

et al., 2005; Song and Witt, 2006; Wong et al.,

2007; Chu, 2008; Song and Li, 2008) It lates that factors of income and price are likely to play a central role in determining the demand for international tourism As interna-tional tourism is generally regarded to be a luxury commodity or service, it is not sur-prising that the study of such variables has dominated past research

postu-There are three reasons why the discussed economic framework needed to be extended First, from the consumers’ perspective, travel-ling overseas is one of the many options for

INTERNATIONAL JOURNAL OF TOURISM RESEARCH

Int J Tourism Res 12, 307–320 (2010)

Published online 30 July 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/jtr.749

A Study of the Non-economic

Determinants in Tourism Demand

Vincent Cho*

Department of Management and Marketing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

*Correspondence to: V Cho, Department of Management

and Marketing, The Hong Kong Polytechnic University,

Hung Hom, Hong Kong.

E-mail: msvcho@polyu.edu.hk

308 V Cho

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 307–320 (2010)

DOI: 10.1002/jtr

them Once a decision to travel has been made,

a consumer (tourist), faced with different

alter-natives, chooses a destination to maximise

utility The tourist derives utility from

spend-ing time in a particular destination The utility

stems from destinational attributes such as an

agreeable climate, beautiful scenery and/or

socio-cultural features These attributes are

consumed along with other goods and services

available at the destination The tourist’s utility

function represents the preferences for

travel-ling abroad along with other goods and

services This suggests that the choice of

destinations is a typical consumer choice

problem (Rugg, 1973; Divisekera, 1995) In this

vein, Naude and Saayman (2005) have devised

a utility function based on hotel capacity, air

distance, political stability, urbanisation rate,

etc to estimate the tourist arrivals to Africa It

was done using the regression analysis on a

cross-sectional data of fi ve-year averages from

1996 to 2002

Second, based on the theories of the

behaviour-intention model, including the

theory of planned behaviour and the theory of

reasoned actions, it states that the perceived

value and consequence of an action will affect

the behaviour of a person (Ajzen and Fishbein,

1980; Ajzen, 1991) Thus, the perceived image

of a destination will have an infl uence on the

intention and actions of a person (tourist) to

visit a destination Empirically, Var et al (1985)

showed that destination image of a convention

venue is directly proportional to the number

of delegates going to the convention

Third, according to Sauran (1978), the main

difference between the economic and

non-eco-nomic types of factors is that econon-eco-nomic

vari-ables generally account for the total demand of

an origin country and that the role of

non-eco-nomic variables has more to do with the types

of tourism For instance, tourists in Thailand

may probably go for shopping and relaxation,

tourists in Europe may look for the historical

heritages In this paper, we suggest to broaden

the investigation on the non-economic factors

based on the antecedent studies on destination

image to study tourism demand This study

addresses the following research problems:

(1) to identify the potential factors infl uencing

the tourism demand;

(2) to fi nd out the signifi cant underlying factors of tourism demand; and

(3) to understand the tourism demand from four continents (the Americas, Europe, Asia and Oceania)

The organisation of this paper is as follows First, we review on the literature relating to the potential antecedents of destination image and formulate the framework for this study Second, we describe our data collection proce-dures and related analysis Cross-sectional data relating to tourism of 135 destination countries are collected in this study By apply-ing regression and neural network analyses, signifi cant factors are sorted out These factors help to identify the most important factors behind tourism demand Interesting fi ndings and discussions are presented, and fi nally there is a conclusion section

LITERATURE REVIEW

According to Gearing et al (1974), Ritchie and

Zins (1978) and Schmidt (1979), destination image refers to an aggregated perception of attributes which make the specifi c location appealing as a potential destination to travel-lers Leading image attributes identifi ed are nice climate, inexpensive goods and services, safety, similar lifestyles, etc To further under-stand the nature of destination image, we have reviewed the literature as follows

Gearing et al (1974) have established an

overall measure of destination image for a given region These researchers proposed eight factors including (i) accessibility of a region, (ii) attitudes towards tourists, (iii) infrastruc-ture of a region, (iv) price levels, (v) shopping and commercial facilities, (vi) sport, recreation and education facilities, (vii) natural beauty and climate, and (viii) cultural and social char-acteristics By combining the score relating to the importance and actual perception of these factors by tourists, an overall value of destina-tion image can be derived

Ritchie and Zins (1978) have conducted a study on the importance of cultural and social impact on destination image using a survey on

135 respondents They identify four sions of cultural image of a tourism region:

dimen-Non-economic Determinants in Tourism Demand 309

elements of daily life, remnants of the past,

good life and work habit

Var et al (1985) have studied the destination

image on convention tourism and found two

important factors that determine the number

of delegates The fi rst one is accessibility on

how close a convention is to the hometown of

a delegate, and the second one is the

attractive-ness of the convention location

Lew (1987) attempted to group the factors

behind a destination image into three different

perspectives: (i) ideographic nature of a

loca-tion focusing on its concrete descriploca-tion; (ii)

organisational nature stressing on the spatial,

capacity and temporal characteristics of a

loca-tion; and (iii) cognitive nature describing the

perceptions and experience of tourists

Getz (1993) applied the framework of

desti-nation image on business tourism He

com-pared the tourism business districts in Niagara

Falls (Ontario and New York) using

underly-ing factors such as location, accessibility,

design, attractions and services He concluded

that in order to be a good district for business

tourism, it should have three essential

ele-ments: (i) core attractions; (ii) central business

district functions; and (iii) supporting

services

Utilising multidimensional scaling, Kim

(1998) determines the relative positions of fi ve

well-known Korean national parks in terms of

selection criteria and the tourists’

psychologi-cal reception to the areas He derived six

features namely seasonal and cultural

attrac-tiveness, clean and peaceful environment,

quality of accommodations and relaxing

facili-ties, family-oriented amenities and safety,

accessibility and reputation, and entertainment

and recreational opportunities as the most

important factors infl uencing the destination

image

Chen and Hsu (2000) measured the

per-ceived image of South Korean tourists and

found that travel cost, destination lifestyle,

quality restaurants, freedom from language

barriers and availability of interesting places to

visit affects the destination choice behaviour of

a Korean tourist

Recently, Russo and Borg (2002) used a case

study to analyse the destination image for

cul-tural tourism in four European cities (Lyon,

Lisbon, Rotterdam and Turin) They found

that these four cities, besides their own tures to attract culture tourists, should pay attention to those intangible elements, such as transportation facilities, information centre and quality of human capital in order to enhance location attractiveness

fea-Getz and Brown (2006) explored the lying factors for a region on wine tourism Using an extensive survey on the perception

under-on the importance of different features such as

‘the wine region is close to home’, and ‘the region is popular with wine tourists like me’, they found out there are fi ve emerging factors: (i) core wine product; (ii) core destination appeal; (iii) core cultural product; (iv) variety; and (v) tourist oriented, and that these factors would defi ne the image of a wine region.Relating to the economic environment, Han

et al (2006) found that price competitiveness is

an important factor infl uencing Americans travelling to France, Italy and Spain, but not the UK However, as US expenditure rises, the market shares of Spain and the UK decline, while France and Italy benefi t Last, but not

least, Gallarza et al (2002) presented an

exten-sive review on destination image and proposed

a more comprehensive framework of tion image which contains cognitive elements, time elements and distance elements

destina-RESEARCH FRAMEWORK

In our review on the literature, we classify these attributes into fi ve categories: (i) attitude towards tourism; (ii) richness of tourism prod-ucts/services; (iii) tourism support; (iv) envi-ronmental factors; and (v) economic factors The attitude of people in the destination towards tourists and their social index are under the fi rst factor — attitude towards tourism Richness of tourism products/services includes the natural and cultural heritage of a destination, and the entertainment and recreational facilities in the destination Tourism support relates to ade-quacy of accommodation facilities, accessibility, road network infrastructure and safety of a des-tination The fourth category concerns the envi-ronmental factors such as seasonality of a destination The economic factors consist of price levels of a destination as well as the gross domestic product (GDP) of the source coun-tries Table 1 shows the summaries of related

310 V Cho

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 307–320 (2010)

DOI: 10.1002/jtr

studies investigating the underlying factors

affecting destination image We postulate that

these factors are also infl uential to tourism

demand

As this study concerns tourism demand from

four different regions (Americas, Europe, Asia

and Oceania) instead of from individual

coun-tries, it is hard to estimate the GDP or the

exchange rates in those regions as a whole Thus,

the GDP and the exchange rates are not

consid-ered in this study Moreover, it is hard to compare

the consumer price index (CPI) of all 135

coun-tries because different places have their own

preferences of goods and services as well as on

the weightings of those goods and services Thus,

we also exclude the CPI in this study

Neverthe-less, without the economic factors, our fi ndings

would focus on the non-economic aspects and

would have limited implications

DATA COLLECTION

In this study, we sourced reliable secondary

data from different international organisations

such as the WTO and World

Develop-ment Indicators (WDI) from the World Bank

Initially, statistical data in 2005 from 214 tries and territories were collected These achieved data were reported in the yearbooks from different international organisations in

coun-2007 However, there are 29 countries or tories that did not have tourist arrival data from four continents, they are Afghanistan, Canary Islands, Ceuta, Channel Islands, Cote d’Ivoire, Democratic Peoples Republic of Korea, Democratic Republic of Timor-Leste, Djibouti, Equatorial Guinea, Falkland Islands, French Guiana, Gibraltar, Greenland, Guern-sey, Isle of Man, Jersey, Liberia, Madeira Island, Mauritania, Mayotte, Melilla, Netherlands Antilles, Saint-Pierre and Miquelon, San Marina, Solomon Islands, Somali Democratic Republic, Turkmenistan and Western Sahara Also, there are 25 countries or territories that did not have tourist arrival data from at least one continent (mainly from Oceania), they are Anguilla, Antigua and Barbuda, Argentina, British Virgin Islands, Cape Verde, Republic of the Congo, Curacao, Gambia, Guadeloupe, Guyana, Haiti, Luxembourg, Martinique, Mexico, Montserrat, Namibia, Norway, Qatar, Reunion, Saba, Saint Helena, Saint Maarten,

terri-Table 1 Underlying antecedents of destination image

Attitude

towards

tourism

Attitude towards tourists (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979;

Gallarza et al., 2002; Getz and Brown, 2006)

Social factors (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979)

Tourism

products/

services

Natural heritage (Lew, 1987; Getz and Brown, 2006; Getz, 1993; Gearing et al., 1974; Ritchie

and Zins, 1978; Schmidt, 1979)

Cultural heritage (Getz and Brown, 2006; Kim, 1996; Gearing et al., 1974; Ritchie and Zins,

support

Accommodation (Kim, 1996) Accessibility of a region (Russo and Borg, 2002; Var et al., 1985; Kim, 1998; Lew, 1987; Getz,

1993; Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Gallarza et al., 2002)

Road network infra-structure of a region (Getz, 1993; Gearing et al., 1974; Ritchie and Zins,

Non-economic Determinants in Tourism Demand 311

Saint Vincent and the Grenadines, and Sao

Tome and Principe Owing to government

policy, 25 countries have not recorded some

variable data such as social index, tourism

openness index, etc They are American Samoa,

Andorra, Aruba, Bermuda, Bonaire, Cayman

Islands, Cook Islands, Dominica, Eritrea,

French Polynesia, Grenada, Guam,

Microne-sia, Marshall Islands, New Caledonia, Niue,

North Mariana Island, Palau, Palestine, Puerto

Rico, Saint Kitts and Nevis, Saint Lucia, Turks

and Caicos Islands, Tuvalu, and United States

Virgin Islands Those 79 countries or territories

were neglected from the data set, which means

135 countries in total remained (as indicated in

appendix A) The collected data of the 135

countries were analysed in order to sort out the

determinants of tourism demand

Tourism demand statistics

Most destinations have used the same

destina-tion images or enticements to attract tourists

regardless of their country of origin (Bonn et

al., 2005) Previous research has suggested that

nationals of various geographic regions

inter-pret visual imagery and experiences

differ-ently dependent on their country of origin

(Berlyne, 1977; Britton, 1979; Thurot and

Thurot, 1983) In order to investigate any

dif-ferences among tourists from different origins,

we collect the data on tourism demand from

four continents: the Americas, Asia, Europe

and Oceania The tourist arrival statistics of

135 countries in 2005 were reported in the

yearbook of tourism statistics published in

2007 by the WTO The WTO is a leading

inter-national organisation in the fi eld of tourism

and serves as a global forum for tourism policy

issues and a practical source of tourism

know-how

Due to the functional form of the demand

model usually in terms of powers on those

underlying factors, we transformed the data

using the natural logarithm, which is a common

practice on most tourism demand studies In

the last three decades, many studies have

assumed a multiplicative form of model made

linear by a logarithmic transformation of the

variables (Loeb, 1982; Stronge and Redman,

1982; Summary, 1983; Arbel and Ravid, 1985;

Witt and Martin, 1987; Poole, 1988; Croes, 2000;

Vanegas and Croes, 2000; Song et al., 2003; Song and Witt, 2006; Wong et al., 2007; Song

of those 135 countries in 2005 On the other hand, data on safety and crime rate are only available in a few countries and factors such as lifestyle, government policy and intervention cannot be easily measured in a numerical sense Hence, these factors were neglected in this study The details of the independent variables are elaborated as follows

Accessibility

Accessibility is a signifi cant attribute to

desti-nation image (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979; Var et al., 1985; Lew, 1987; Getz, 1993; Kim, 1998; Gallarza et al.,

2002; Russo and Borg, 2002) In this paper, accessibility by air is proxy by the takeoffs abroad of air carriers registered in the country The unit of registered carrier departures world-wide in this study is the number of carriers The data were collected from the WDI which

is the premier data source on the global economy from the World Bank It contains sta-tistical data for over 550 development indica-tors and time series data from 1960 onwards for over 220 countries and country groups with populations of more than 1 million, as well as for China and Taiwan Natural logarithm was applied before fi tting into the demand model.Accessibility by road is proxy by the total road network which includes motorways, highways, and main or national roads, second-ary or regional roads, and all other roads in a country The unit of total road network in this study is kilometre (km) The data were col-lected from the WDI Natural logarithm was used before fi tting into the demand model

Environmental condition

Kim (1998) has indicated that environmental condition is a signifi cant attribute to destina-tion image In this vein, we have included the variable Carbon dioxide (CO2) emissions which

312 V Cho

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 307–320 (2010)

DOI: 10.1002/jtr

are those stemming from the burning of fossil

fuels and the manufacture of cement They

include carbon dioxide produced during

con-sumption of solid, liquid, and gas fuels and gas

fl aring The unit of Carbon dioxide (CO2)

emis-sions in this study is kilotons (kt) The data

were collected from the WDI Natural

loga-rithm was taken before fi tting into the demand

model

Travelling cost

In this study, we include the distance between

the origin region and the destination country,

which is a proxy for transport cost and effort

(Tremblay, 1989) Laber (1969) found that

dis-tance between ‘origin’ and ‘destination’ plays

a signifi cant role as a determinant of tourism

demand As our collected statistics only report

the tourism arrivals from a region — Asia,

Europe, the Americas or Oceania, thus we

need to manipulate the average distance of a

country from a region First, we assume that

the distance between two countries is the

dis-tance between two countries’ capitals From

the haversine formula as shown in Equation

(1) (Sinnott, 1984), let φs, λs; φf, λf be the

geo-graphical latitude and longitude of two points

respectively, and Δλ be the longitude

differ-ence Hence, Δθ is the (spherical) angular

The shape of the Earth more closely

resem-bles a fl attened spheroid with extreme values

for the radius of arc of 6335.437 km at the

equator (vertically) and 6399.592 km at the

poles, and having an average great-circle

radius of 6372.795 km (3438.461 nautical miles)

Using a sphere with a radius, r, of 6372.795 km,

thus results in an error of up to about 0.5% and

the distance between two points of the Earth is

equal to r Δ θ

A matrix with 135 rows and 135 columns is

formed containing the distances among the 135

countries Then we group the countries

accord-ing to their continent and take the mean

dis-tance of the countries on the same continent

For instance, the distance from England to Asia

is calculated by averaging the distance between London (England’s capital city) and all the 38 countries such as China, Hong Kong and Japan

in Asia using the locations of their capital cities Appendix A shows our list of countries (38 countries in Asia, 23 countries such as USA, Canada and Mexico in the Americas, 7 coun-tries such as Australia, New Zealand and Fiji

in Oceania, and 33 countries such as Hungary, Latvia, Norway and the UK in Europe) in dif-ferent origins for the manipulation of average distance

Cultural and natural heritages

Cultural and natural heritages are found to be important attributes on destination images

(Gearing et al., 1974; Ritchie and Zins, 1978;

Schmidt, 1979; Lew, 1987; Kim, 1996; Getz and Brown, 2006) The numbers of cultural and natural world heritage were collected from the United Nations Educational, Scientifi c and Cultural Organization (UNESCO) World Heritage Committee, which consists of repre-sentatives from 21 of the States Party to the Convention elected by their General Assembly for terms up to six years It determines whether

a property is inscribed on the World Heritage List which includes 644 cultural, 162 natural and 24 mixed properties with outstanding uni-versal value Similar to the effect of taking natural logarithm, Table 2, which is referenced from Wikipedia, was used to transform the heritage counting

Seasonality and climate

Seasonality is a well-documented issue in the literature, particularly in relation to cold-water regions of Europe and North America (Aguiló

and Sastre, 1984; Snepenger et al., 1990; Donatos

and Zairis, 1991; Jeffrey, 1999; Kennedy, 1999; Baum and Lundtorp, 2001) Although the reasons for a signifi cant variation in demand are also well documented (the climate, institu-tional patterns like school or calendar holidays, lifestyles, special events, etc.), there are a few studies on tourism seasonality (Butler, 2001) Belen-Gomez-Martin (2005) suggested that weather and climate are signifi cant in explain-ing tourism demand

Non-economic Determinants in Tourism Demand 313

Tyndall Centre, which brings together

tists, economists, engineers and social

scien-tists, who together are working to develop

sustainable responses to climate change, stored

the daily mean temperature data by country

In advance, they calculated the monthly mean

temperature by taking the average value of

daily mean temperature in every month We

can browse the monthly mean temperature

data from their website by country The unit of

monthly mean temperature in this study is in

Celsius The data were collected from the

Tyndall Centre for Climate Change Research

Standard deviation of monthly mean

tempera-ture was calculated by taking the standard

deviation of monthly mean temperature in 12

months If the standard deviation is large, this

would imply the destination country has a

wide spread of temperature within a year or a

clear seasonality

Social Index

As identifi ed by Gearing et al (1974), Ritchie

and Zins (1978) and Schmidt (1979), social

factor is an important attribute on destination

image Social Index, which is an aggregate

social index, combining the Human

Develop-ment Index, Newspaper Index, Personal

Com-puter Index, and Television Index, was collected

from the World Travel and Tourism Council

Population

Population counts all residents regardless of

legal status or citizenship — except for

refu-gees not permanently settled in the country of

asylum as they are generally considered part

of the population of their country of origin

With higher population, which would mean a

larger coverage in area and more tourists,

hence we attempt to control this variable so as

to reveal the signifi cance of the other infl

uen-tial factors The data were collected from the WDI Natural logarithm was used before fi tting

it into the demand model In sum, Table 3 highlights the above factors which would be grouped into the relevant category as indicated

in Table 1

ANALYSIS AND FINDINGSRecently, Lim (1997), Morley (1996, 1998, 2000),

Turner et al (1998) and Turner and Witt (2001)

surveyed more than 100 international tourism demand studies that have attempted to model the demand for tourism Most studies are time series econometric models such as the almost ideal demand system and autoregressive dis-tributed lag model estimated using multiple least-squares regression, which is appropriate for stationary time series data (Kulendran and Witt, 2001) As this study tries to investigate the tourism demand from another perspective using the cross-sectional data from 135 coun-tries, we applied regression and neural network for the analysis to determine the most infl uen-tial factors on tourism demand

As the data are cross-sectional, common time series models are not applicable Regres-sion is a simple yet robust linear analysis method that is capable of identifying impor-tant factors, which are assumed to be linearly related to the dependent variable However, it

is not appropriate if the underlying ship is non-linear Because of this limitation, neural network analysis, a well-developed technique which would handle both linear and non-linear data in artifi cial intelligence studies,

relation-is also applied to check the reliability of the results from linear regression The results of regression and neural network are shown in Tables 4 to 7 Those signifi cant factors in the regression are sorted according to the weight-ing from the result of the neural network anal-ysis and those insignifi cant factors are shown

Table 2 Transformation on the number of cultural and natural heritages

Cultural heritage Number 1–4 5–19 20–29 30+

Level 1 2 3 4Natural heritage Number 1 2–3 4–9 10+

Level 1 2 3 4

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DOI: 10.1002/jtr

at the end of the list From Tables 4 to 7, the

signifi cant factors from the regression are

rather consistent with those of high relative

importance in the neural network analysis All

the insignifi cant factors have relative

impor-tance of less than 0.2 in the neural network

analysis (except the CO2 emission on the tourist

arrival from Americas) Hence, our results are

deemed to be reliable and trustworthy

DISCUSSION

From the regression and data mining analyses

as shown in Tables 4 to 7, there are two common

factors among the tourists from the four nents — distance from the origin (all betas are negative and signifi cant) and aircraft depar-ture (all betas are positive and signifi cant) That is, most tourists prefer to visit proximal countries with good accessibility In this regard,

conti-we suspect that travelling to proximal tries, which are usually associated with less time and fi nancial effort, would be a dominant factor in selecting a destination to visit It is nice to have a mix of short and long haul trips during a year Usually, the number of short hauls would be greater than the number of long hauls for a normal traveller, unless

coun-Table 3 Potential factors behind tourism demand

Infl uential factors Independent variables

Demographic of the destination country Population

Accessibility by air (Russo and Borg, 2002; Var et al., 1985; Kim, 1998;

Lew, 1987; Getz, 1993; Gearing et al., 1974; Ritchie and Zins, 1978;

Schmidt, 1979; Gallarza et al., 2002)

Registered aircraft departures

Cultural heritage (Getz and Brown, 2006; Kim, 1996; Gearing et al.,

1974; Ritchie and Zins, 1978; Schmidt, 1979)

Cultural world heritageNatural heritage (Lew, 1987; Getz and Brown, 2006; Getz, 1993;

Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt, 1979)

Natural world heritageEnvironmental condition (Kim, 1998) Carbon dioxide emissions,

distance

Infrastructure on road network (Getz, 1993; Gearing et al., 1974;

Ritchie and Zins, 1978; Schmidt, 1979)

Total road network

Social factor (Gearing et al., 1974; Ritchie and Zins, 1978; Schmidt,

1979)

Social index

Seasonality and Climate (Lew, 1987; Kim, 1998; Gallarza et al., 2002) Average of monthly mean

temperature, Standard deviation

of monthly mean temperature

Table 4 Regression and neural network analyses on tourist arrival from Americas

Regression

(R2= 0.727) ANN

(Accuracy = 90.1)Relative importanceTourist arrival from the Americas Standard coeffi cient Signifi cance

Registered Aircraft Departures 0.374 0.000 0.298

Population 0.395 0.000 0.251

Social Index 0.342 0.000 0.235

Average distance from the Americas −0.334 0.000 0.223

Standard Deviation (Temp) −0.252 0.046 0.173

Roads, total network 0.186 0.086 0.080

CO2 Emissions 0.142 0.316 0.116

Natural world heritage 0.088 0.148 0.122

Cultural world heritage −0.051 0.488 0.069

Mean (Temp) −0.050 0.581 0.007

Non-economic Determinants in Tourism Demand 315

Table 5 Regression and neural network analyses on tourist arrival from Asia

Regression

(R2= 0.765) ANN

(Accuracy = 89.2)Relative importanceTourist arrival from Asia Standard coeffi cient Signifi cance

Average distance from Asia −0.385 0.000 0.316Population 0.385 0.000 0.302Registered aircraft departures 0.350 0.000 0.288

CO2 emissions 0.319 0.018 0.225Roads, total network 0.337 0.002 0.210Social index 0.245 0.005 0.145Natural world heritage 0.148 0.010 0.099Cultural world heritage 0.153 0.025 0.081Standard deviation (Temp) −0.049 0.541 0.102Mean (Temp) 0.050 0.553 0.036

Table 6 Regression and neural network analyses on tourist arrival from Europe

Regression

(R2= 0.801) ANN

(Accuracy = 90.6)Relative importanceTourist arrival from Europe Standard coeffi cient Signifi cance

CO2 emissions 0.436 0.000 0.423Average distance from Europe −0.374 0.000 0.293Mean (Temp) −0.188 0.016 0.242Cultural world heritage 0.172 0.008 0.130Standard deviation (Temp) −0.158 0.027 0.113Registered aircraft departures 0.188 0.015 0.108Social index 0.108 0.178 0.108Population 0.004 0.968 0.101Natural world heritage 0.047 0.373 0.043Roads, total network 0.147 0.128 0.021

Table 7 Regression and neural network analyses on tourist arrival from Oceania

Regression

(R2= 0.614) ANN

(Accuracy = 88.3)Relative importanceTourist arrival from Oceania Standard coeffi cient Signifi cance

Registered aircraft departures 0.571 0.000 0.325Average distance from Oceania −0.264 0.000 0.223Social index 0.231 0.036 0.203Population 0.287 0.027 0.093

CO2 emissions −0.283 0.093 0.113Cultural world heritage 0.145 0.104 0.107Mean (Temp) −0.086 0.538 0.062Standard deviation (Temp) −0.042 0.664 0.052Natural world heritage 0.010 0.891 0.052Roads, total network 0.087 0.616 0.036

Registered aircraft departure is a signifi cant

and important factor to tourists from the four

continents We can see that the accessibility of

countries is the main concern to most

interna-tional tourists According to the World Bank

Group, there are about 48.84% (11 816 848

air-crafts) of total aircraft takeoffs, domestic or

abroad, registered in the Americas in 2004 We

can see that tourists from the Americas are

likely to travel by aircraft This fi gure is

coher-ent to our fi nding, registered aircraft

depar-tures is a signifi cant factor to tourists from the

Americas Geographically, Oceania is

sepa-rated from other continents by Ocean If

Oceania tourists travel to other continents,

they need to travel by air or ship Oceania

tour-ists are much more likely to go to countries

that have airline connections from their home

countries By investing in infrastructure on

airport facilities, a country seems to secure a

niche position for tourists from the Americas

and Oceania For European tourists, they

usually take the aircrafts for their travel

Nev-ertheless, the total road network of a country

is not a signifi cant factor except for those Asian

tourists (as shown in Tables 4 to 7) This is due

to the fact that most travellers visit the major

cities of a country which are usually well

con-nected by air Thus, as a traveller, one does not

need to worry about the details of its road

network within a country

Social factor is found to be another

impor-tant factor for tourists from Americas, Asia

and Oceania This implies social culture, which

embeds the culture of hospitality, is an

impor-tant issue in attracting tourism People in a

sociable country would act hospitable, that is,

the reception and entertainment of guests,

visitors or strangers, with liberality and

goodwill

Regarding the cultural and natural heritages,

our analyses found that cultural heritage, as

indicated in Tables 5 and 6, is a signifi cant

factor to Asian and European tourists, and

referring to Table 5, countries with natural

heritages seem to attract Asian tourists It is

rather reasonable to say that Europe tourists

are concerned about cultural world heritage

Countries in Europe had 52% (315 heritages)

of cultural heritages in the world during 2007

(UNESCO World Heritage Committee) ally, Europeans are proud of their own culture They pay much attention and put a lot of effort into protecting their cultural world heritages Even in travel, they also view cultural heritage

Actu-as the most signifi cant factor in selecting a country to visit

For seasonality, tourists from the Americas and Europe like to visit those destinations with mild variations of temperature or seasonal changes (refer to Tables 4 and 6) According to our result, large temperature variation or dis-tinctive seasons in a country have a negative impact on the total tourist arrivals in a year Usually people may like to visit a place during its best season in terms of its climate and natural beauty For example, if a country only has warm weather for a few months in summer, tourist may likely visit there during these few months As a result, there is a heavy slump of tourist arrivals in other seasons On the other hand, as from Table 6, the average temperature

of a country has a signifi cant and negative impact on European tourist arrivals That is, the lower the average temperature of a country, the number of tourists arriving from Europe would be higher This would be because most Europeans like to go skiing

Concerning the environmental condition,

CO2 emission, as shown in Tables 5 and 6, is a positive and signifi cant factor for both Euro-pean and Asian tourists in selecting a country

to visit Certainly, a person may not like to visit

a country that is heavily polluted Given the fact that most people do not recognise the

fi gures of CO2 emission of a country, instead, they usually have perceptions on whether a country is a fabulous one or not Naturally, some tourists may like to visit a country that

is economically strong, which is somehow positively associated with its CO2 emission This is due to the consumption of gasoline in the production and transportation process In those economically renowned cities which are usually characterised by busy traffi c, the CO2emissions from automobiles are also signifi -cant In this regard, we suspect that both Euro-pean and Asian tourists would like to visit some countries that are economically strong This would explain why Hong Kong, Singa-pore and other economically developed coun-tries attract so many Asians and Europeans

Non-economic Determinants in Tourism Demand 317

every year However, the CO2 emissions in

those places are also relatively high In sum,

this paper has contributed to the literature by

including the non-economic factors to study

tourism demand and identifi ed factors of

tourism demand from different continents

LIMITATION AND DIFFICULTIES

The availability of tourist arrival data from the

Middle East is much lower than other

conti-nents A lot of countries do not further

differ-entiate their tourist arrival from the Middle

East; some of them will mark those arrivals

from the Middle East as ‘other area’ Therefore,

tourist arrival data from the Middle East are

not suitable to be included in this study

We may think that the factors behind

loca-tion attractiveness may vary according to the

different purposes of tourists Most people

travel abroad on holiday, to visit their friends

or family members, to study and for business

(UNWTO) At the beginning, we wanted to

further subdivide the tourist arrival data,

according to their purpose, as a dependent

variable However, those data were not

avail-able in UNWTO

The aircraft departure, which may not be a

truly exogenous variable, would limit the

interpretation of this study In some sense,

tourism demand is determined by aircraft

departure; nevertheless, it may also be true

that it determines the aircraft departure

Somehow, aircraft departure is affected by the

government policy in a country According to

Bieger and Wittmer (2006), aircraft departure

often is limited by geographic restriction, or by

ensuring local environment is maintained

Some governments would promote tourism

intensively by various policies such as

exempt-ing visa requirement and sales tax rebate

These policies would probably inject a higher

number on aircraft departure and bring along

high number of tourists Idealistically,

govern-ment policy, if it can be quantifi ed, would be

used as an instrumental variable to elaborate

the impact of aircraft departure to tourism

demand in a more accurate way In the future

study, we should identify some instrumental

variables, which would infl uence both aircraft

departure and tourism demand, and make

a better estimate of the impact of aircraft

departure to tourism demand Right now, this

is a limitation in our study

On the other hand, it would make more sense if the future study could estimate the model using panel data approach with units being the individual source markets By doing this, the problem associated with the aggregate models may disappear and the GDP and price variable (CPI) would be considered in the model Right now, the demand models, which focus on the non-economic factors, are not comparable with the demand models in the literature

CONCLUSIONThis paper has collected data from 135 coun-tries and investigated the non-economic under-lying factors on tourism demand from four continents Rigorous data pre-processing has been done so as to make the data normalised Both the regression analysis and neural network have shown consistent results and our fi ndings showed there are different signifi -cant factors based on tourism demand from different continents We have contributed to the tourism theories in two aspects First, besides those well-studied economic factors, the non-economic factors are also signifi cant

to tourism demand Second, the underlying factors of tourism demand are different for tourists from different origins

In terms of practice, government and tourism bureaus in different countries and territories view tourism as an important industry; they invest lots of resources so as to attract more tourist arrivals This may include the preserva-tion and restoration of their heritage, build tourist-related infrastructure (e.g airport, road, rail and wharf), provide attractive conditions

to enterprises building theme parks and hotels

or promote their location through marketing campaigns around the world This paper has identifi ed that people from different regions have their own preference in selecting a desti-nation While Europeans and Asians like to visit a destination for its cultural heritage, Asians also prefer a destination with a natural heritage Tourists from the Americas like to visit a proximal country with a sociable envi-ronment that would be accessible by air In general, investments on building accessibility

318 V Cho

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 307–320 (2010)

DOI: 10.1002/jtr

by air, maintaining heritage would secure

tourism demand in a destination Last, but not

least, tourism operators should pay attention

to the origins of tourists and take care of their

preferences

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While studies in and out of tourism contexts

have explored risk and/or uncertainty

avoidance’s impact on information search,

few have clarifi ed whether the two

constructs impact differentially on

information search To examine this issue,

data were collected from large online panels

in Australia, China and Japan The risk and

uncertainty avoidance scales were reliable,

had convergent and discriminant validity

and were invariant across the three country

samples As expected, uncertainty avoidance

was positively related to the extent of

information search in all three country

samples, whereas risk avoidance was not

This suggests that the constructs are distinct

and may impact at different stages of

decision-making Copyright © 2009 John

Wiley & Sons, Ltd.

Received 22 April 2008; Revised 4 September 2009; Accepted

25 September 2009

Keywords: consumer decision-making; risk

and uncertainty avoidance; information

search

INTRODUCTION

Consumer decision-making is uncertain

and risky as it involves choices that may

or may not deliver expected benefi ts

The tourism industry is very vulnerable to direct and indirect events, over which they often have little control (commonly known as crises), that threaten travellers’ assurance and safety Since 2000, a number of events have negatively impacted on tourism around the

globe (Law, 2006; Kozak et al., 2007) and have

infl uenced the way potential tourists respond

to fi nancial, performance, psychological, social, physical and time risk, as well as to the un-certainty associated with travel (Simpson and Siguaw, 2008)

Consumers averse to risk and uncertainty are likely to engage in risk and uncertainty-reducing strategies, such as looking for quality

assurances (Sweeney et al., 1999) and searching

extensively for information (Vogt and maier, 1998) This is likely to be more pro-nounced in tourism because of its ‘intangible and experiential nature’ (Sirakaya and Wood-side, 2005, p 816), which leads people to ‘search for information and move back and forth between search and decision-making stages’

Fesen-(Jun et al., 2007, p 267) However, it is diffi cult

to untangle the differential infl uence of risk and uncertainty avoidance, as researchers have often used the terms interchangeably The present research attempted to shed some light

on this issue by examining differences between risk and uncertainty avoidance and seeing how the two constructs impact on tourists’ information search First, we clarify some con-ceptual and operational differences between risk and uncertainty to help integrate the frag-mented literature in these two areas Then, we examine the potentially distinct infl uences risk and uncertainty avoidance have on the extent

of information search undertaken by tourists using data collected in three countries

INTERNATIONAL JOURNAL OF TOURISM RESEARCH

Int J Tourism Res 12, 321–333 (2010)

Published online 28 October 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/jtr.753

Tourists’ Information Search:

the Differential Impact of Risk

and Uncertainty Avoidance

Vanessa Ann Quintal1,*, Julie Anne Lee2 and Geoffrey N Soutar2

1 School of Marketing, Curtin University of Technology, Bentley, Western Australia, Australia

2 Business School, University of Western Australia, Crawley, Western Australia, Australia

*Correspondence to: Dr V A Quintal, School of

Market-ing, Curtin University of Technology, Kent Street, Bentley

6102, Western Australia, Australia.

E-mail: Vanessa.Quintal@cbs.curtin.edu.au

322 V A Quintal, J A Lee and G N Soutar

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 321–333 (2010)

DOI: 10.1002/jtr

A LITERATURE REVIEW

Risk avoidance versus

uncertainty avoidance

Risk and uncertainty can be distinguished by

the probabilities of their outcomes Risk exists

when the probabilities of outcomes are known,

while uncertainty exists when the probabilities

of outcomes are not known (Knight, 1948;

Savage, 1954) This distinction underlies many

conceptual risk and uncertainty avoidance

defi nitions For instance, Weber and Bottom

(1989, p 128) defi ned risk avoidance (RA) as

‘whether, ceteris paribus, a decision maker has

a tendency to be attracted or repelled by

alter-natives that he or she perceives as more risky

over alternatives perceived as less risky’, while

Hofstede (1991, p 113) and other researchers

(e.g Reisinger and Turner, 2003) defi ned

uncer-tainty avoidance (UA) as ‘the extent of feeling

threatened by uncertain or unknown

situa-tions’ People in high UA cultures have lower

tolerance for ambiguity (Hofstede, 2001) and

are more likely to prefer structures that make

events more easily interpretable and

predict-able (Reisinger and Turner, 1999) In contrast,

people in low UA cultures are relatively more

comfortable with ambiguity and are more

likely to seek novelty and convenience (Lee

et al., 2007).

In differentiating between risk and

uncer-tainty avoidance, Hofstede (2001, p 148)

argued UA is not the same as RA, even though

many people ‘interpreted “uncertainty

avoid-ance” as “risk avoidavoid-ance” — for example, in

business decisions’ He observed that UA is

related to structure and escape from ambiguity

and not necessarily to RA Indeed, people may

engage in risky behaviour in order to reduce

ambiguity, ‘such as starting a fi ght with a

potential opponent rather than sitting back

and waiting’ (Hofstede, 2001, p 148)

In spite of the distinction made between

risk and uncertainty avoidance, much of the

research has used these constructs

interchange-ably (Hofstede, 2001) This has led to some

confusion, with cases of RA being attributed to

UA and vice versa For instance, Steenkamp

et al (1999, p 59) described consumers from

high uncertainty-oriented cultures as ‘resistant

to change from established patterns and

focused on risk avoidance and reduction’ They examined UA using Hoppe’s (1990) updated country ratings that extensively validated Hofstede’s (1980) results and found innova-

tiveness was lower in UA cultures Bao et al

(2003) used Hofstede and Bond’s (1984, p 419)

UA defi nition to defi ne risk aversion as ‘the extent to which people feel threatened by ambiguous situations, and have created beliefs and institutions that try to avoid these’ Despite their conceptual defi nition of RA being UA, they measured risk aversion using a scale adapted from Raju (1980) and concluded that risk aversion appeared to contribute to differ-ent decision-making styles in the USA and China Similarly, Money and Crotts (2003, p 191) suggested that UA was ‘a measure of intolerance for risk’

The conceptual distinction between risk and uncertainty is especially important, as the con-structs are likely to be correlated This implies that at least some people who avoid risk may

also avoid uncertainty and vice versa Thus,

when researchers only measure one of the structs, the infl uence of the other may be incor-rectly attributed to the measured construct, especially in cases where the theoretical rea-soning confuses the two constructs, such as in previous examples This is especially problem-atic when researchers use culture level con-structs, such as Hofstede’s (1980) country level uncertainty avoidance index (UAI) to predict individual level behaviour, as people within a country differ widely in their tolerance for risk and uncertainty In the current paper, we clarify the issue by examining the infl uence risk and uncertainty avoidance have on the extent of information search, which is theoreti-cally related to only one of the avoidance con-structs, at an individual level To address this objective, we used tourists from three coun-tries that differ on Hofstede’s country level UAI scores

con-Information search

Information search is an important aspect of tourism decision-making; as such deci-sions are likely to be a high cost and high-

involvement purchase (Bonn et al., 1998) and

the search process is often seen as an enjoyable part of the travel experience Information

Tourists’ Information Search 323

search in a travel context has been studied in

relation to the amount of searching (Fodness

and Murray, 1997; Bai et al., 2004; Öörni, 2004;

Lehto et al., 2006); the sources of information

used (Fodness and Murray, 1997; Chen and

Gursoy, 2000; Cai et al., 2004; Kerstetter and

Cho, 2004; Mourali et al., 2005; Lehto et al.,

2006), including online searching (Bai et al.,

2004; Jang, 2004; Luo et al., 2004; Öörni, 2004;

Beldona, 2005; Lehto et al., 2006); the search

process itself (Fodness and Murray, 1997;

Bieger and Laesser, 2004; Gursoy and McCleary,

2004; Kim et al., 2006; Pan and Fesenmaier,

2006); situational factors (Fodness and Murray,

1997; Gursoy and Chen, 2000; Bieger and

Laesser, 2004; Luo et al., 2004); consumer

involvement (Cai et al., 2004; Gursoy and

McCleary, 2004; Lehto et al., 2006);

demo-graphic differences (Fodness and Murray,

1997; Luo et al., 2004; Kim et al., 2006); and

cultural differences (Webster, 1992; Reisinger

and Turner, 1998; Chen and Gursoy, 2000;

Gursoy and Chen, 2000; Money and Crotts,

2003; Litvin et al., 2004) Most of these studies

have focused on destination choice (Chen and

Gursoy, 2000; Money and Crotts, 2003; Cai et

al., 2004; Kerstetter and Cho, 2004; Luo et al.,

2004; Lehto et al., 2006) and the most commonly

used sources seem to include the Internet,

bro-chures and pamphlets, family and friends and

travel agents (Chen and Gursoy, 2000; Gursoy

and Chen, 2000; Kerstetter and Cho, 2004)

Information search and risk and

uncertainty avoidance

While considerable research has examined the

infl uence UA has on aspects of information

search, it has usually examined the sources of

information used, rather than the extent of

information search, across countries that differ

on Hofstede’s (1980) UAI scores For instance,

Money and Crotts (2003) and Litvin et al (2004)

compared people from a high UA culture

(Japan) with people from lower UA cultures

(Germany and the USA) They found that

people from the higher UA culture were more

likely to select travel information sources

related to the channel (e.g travel agent) over

personal or mass media sources, compared

with people from lower UA cultures In the

current paper, we argue that an individual’s

level of UA, rather than RA, infl uences the extent of their information search

People are likely to react differently to tions with inherent ambiguity, depending on their tolerance for uncertainty The early stages

situa-of decision-making are more uncertain, ing people to look for information on available products or services that might fulfi l recognised

prompt-needs (Comegys et al., 2006) During the

infor-mation search stage, people are unlikely to sider precise probabilities of potential outcomes, which would infer the existence of risk The calculation of probabilities is more likely to come at a later evaluation stage when only a few alternatives are compared At the evaluation stage, the probability of a loss from one alterna-tive can be weighed against the probability of a loss from other alternatives Consequently, we suggest people’s level of UA is more likely to be related to the extent of their information search than RA It is expected people with higher UA, will search more sources of information than will people with lower UA, holding RA con-stant Conversely, it can be inferred that RA, will not infl uence the search for information at this early stage, holding UA constant, which suggests:

con-Hypothesis 1: UA will be positively ciated with extent of information search, holding RA constant

asso-Hypothesis 2: RA will not be associated with extent of information search, holding

UA constant

METHODOLOGYThe present study assessed the differential infl uences RA and UA had on the extent of potential tourists’ information search Samples were obtained in three countries that vary on Hofstede’s (2001) national UAI This country index is high in Japan (UAI score = 92), medium

in Australia (51) and low in China (30) Samples

in each country were obtained from cial online panels and chosen to refl ect the populations’ gender and age characteristics However, the Japanese sample was also screened to only include people who had travelled internationally in the last fi ve years

commer-or who intended to travel internationally in the

324 V A Quintal, J A Lee and G N Soutar

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 321–333 (2010)

DOI: 10.1002/jtr

next fi ve years This international travellers’

sample was added to see whether RA and UA

functioned in a similar manner for more

expe-rienced travellers It was expected that

inter-national travellers would be less risk and

uncertainty avoidant than the general

popula-tion, as such, Japan (a high UAI culture) was

chosen The questionnaires in Australia were

administered in English, while those in China

and Japan were translated into Mandarin

and Japanese respectively The procedures

used followed the translation-back-translation

method recommended by Brislin (1976)

The survey was electronically administered

to adult members of large online consumer

panels in Australia (State of Victoria), three

major Chinese cities (Beijing, Shanghai and

Guangzhou) and three major Japanese cities

(Tokyo, Osaka and Nagoya) The sample sizes

were 200 in Australia, 443 in China and 342 in

Japan, which refl ected response rates of 93%,

77% and 57% respectively The samples

approxi-mated their respective populations on gender

(55% male in Australia, 49% male in China and

56% male in Japan) The median age was 43

years in Australia, 33 years in China and 42

years in Japan Both age and gender were

reasonably representative in each country (e.g

Australian median age = 37, China = 33 and

Japan = 43), according to the CIA World Factbook

(CIA, 2006) Annual household incomes in

excess of A$60 000 were reported by 50% of

Australian respondents, 61% of Japanese

respondents but by less than half in China

RA

Donthu and Gilliland’s (1996) three-item scale

was used to measure RA as the scale had

reasonable measurement properties in past

research (e.g α = 0.78) and is relevant to

pur-chase behaviour The RA items were measured

on a seven-point Likert-type scale ranging

from strongly disagree (1) to strongly agree (7)

The items were averaged to produce a

composite measure of RA for each country

sample

UA

Yoo and Donthu’s (2002) fi ve-item scale was

used to measure UA as this scale had

reason-able measurement properties in past research (e.g α = 0.88) and is based on Hofstede’s (1980)

UA items The UA items were also measured

on a seven-point Likert-type scale ranging from strongly disagree (1) to strongly agree (7) Again, the items were averaged to produce a composite measure of UA for each country sample

Extent of information search

Respondents were asked whether or not they used 14 sources of information when making four types of tourism decisions (tourism destinations, pricing, accommodation and

fl ight decisions) The information sources included personal sources (relatives, friends, travel agents, tour operators), traditional travel sources (travel guidebooks, newspapers, TV travel programmes, travel magazines, airline telephones, tourism offi ce brochures) and web sources (Internet, airline websites, online newsletters, travel agent websites) and were adapted from prior tourism information studies (e.g Fodness and Murray, 1997; Chen and Gursoy, 2000; Gursoy and Chen, 2000).FINDINGS

The reliability and validity of the risk and uncertainty avoidance scales as well as their measurement invariance across the three country samples are discussed fi rst Next, the results of the Rasch analysis that was used to obtain a unidimensional measure of the extent

of search across the 14 sources of information are reported Finally, the infl uence risk and uncertainty avoidance had on the extent of information search is outlined

The measurement properties of the risk and uncertainty avoidance scales

The AMOS 16 software package (Arbuckle, 2007) was used to estimated the RA and UA scales; fi rst as a pooled sample and then sepa-rately in the three country samples The good-ness of fi t indices for the RA and UA scales are shown in Table 1, while the parameter esti-mates of the RA and UA items, as well as the means and standard deviations of the summed scores, are shown in Table 2

Tourists’ Information Search 325

As there were only three items in the RA

scale, model fi t could not be estimated because

there were no degrees of freedom However,

an examination of the results suggested two of

the error variances were very similar and could

be constrained to be equal to provide the

degrees of freedom needed to examine the RA

construct’s measurement properties As can be

seen in Table 1, the chi-square statistic and the

other indices suggested that the modifi ed

three-item RA model had a good fi t to the

pooled sample and to the data in each of the

three country samples

The chi-square statistic in the pooled sample

was signifi cant for the fi ve-item UA model

(χ2 = 31.06; d.f = 5; p ≤ 0.001) However, an

examination of the modifi cation indices

sug-gested that the poor fi t was because of a

cor-relation between two of the error terms

Consequently, the item with the lower

stan-dardised estimate (instructions for operations are

important) was excluded and the model was

re-estimated As can be seen in Table 1, the

four items fi tted the data well Further, the

cor-relation between the fi ve-item scale and the

revised four-item scale was 0.99, which gests nothing would be lost by using the four-

sug-item scale (Thomas et al., 2001).

As can be seen in Table 2, the scales had acceptable reliabilities in each case, with the

RA scale’s reliabilities ranging from 0.72 to 0.79 and the UA scale’s reliabilities from 0.87

to 0.88 Further, the magnitude, direction and statistical signifi cance of the estimated param-eters were consistent, which suggests that con-vergent validity can be assumed (Steenkamp and van Trijp, 1991) Convergent validity was also examined by computing average variance extracted (AVE) scores for each construct, which should be equal to or greater than 0.50 (Fornell and Larcker, 1981) The RA and UA scales for the pooled sample had AVE scores

of 0.56 and 0.62 respectively, while the AVE scores for the two scales in the three country samples ranged from 0.53 to 0.64, suggesting that the scales had convergent validity in each country

Discriminant validity was investigated in two ways First, the correlations between the

RA items and the UA items were computed

Table 1 The RA and UA scale goodness of fi t indices

Risk avoidance Australia

n = 200 n China= 443 n Japan= 342 Pooled samplen = 985

Goodness of fi t indices

p value >0.05 >0.05 >0.05 >0.05 RMSEA 0.06 0.03 0.01 0.01 NNFI 0.99 1.00 1.00 1.00

Uncertainty avoidance Australia

n = 200 n China= 443 n Japan= 342 Pooled samplen = 985

Goodness of fi t indices

p value >0.05 >0.05 >0.05 >0.05 RMSEA 0.06 0.05 0.01 0.01 NNFI 0.99 1.00 1.00 0.99

CFI, comparative fi t index; d.f., degrees of freedom; GFI, goodness of fi t index; NNFI, non-normed fi t index; RA, risk

avoidance; RMSEA, root mean square error of approximation; χ 2 , chi-square; UA, uncertainty avoidance.

326 V A Quintal, J A Lee and G N Soutar

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 321–333 (2010)

DOI: 10.1002/jtr

Table 2 The RA and UA scale item parameter estimates, means and standard deviations

Risk avoidance items Australia

n = 200 n China= 443 n Japan= 342 Pooled samplen = 985

I would rather be safe than sorry 0.83

5.23(1.53)

0.79

5.39(1.55)

0.73

4.52(1.38)

0.78

5.05(1.54)

I want to be sure before I purchase anything 0.60

5.24(1.56)

0.67

5.49(1.52)

0.55

4.72(1.34)

0.65

5.17(1.51)

I avoid risky things 0.75

4.55(1.64)

0.77

5.39(1.49)

0.76

4.39(1.42)

0.78

4.88(1.57)Reliability 0.77 0.79 0.72 0.78Uncertainty avoidance items Australia

n = 200 n China= 443 n Japan= 342 Pooled samplen = 985

It is important to have instructions spelled out in detail

so I always know what I am expected to do

0.77

4.60(1.60)

0.75

5.32(1.39)

0.79

4.99(1.21)

0.76

5.06(1.40)

It is important to closely follow instructions and

procedures

0.81

4.92(1.50)

0.81

5.02(1.40)

0.84

4.80(1.12)

0.82

4.92(1.33)Rules and regulations are important because they tell me

what is expected of me

0.81

4.79(1.48)

0.83

5.09(1.43)

0.73

4.68(1.13)

0.80

4.89(1.36)Standardised work procedures are helpful 0.76

5.03(1.47)

0.81

5.26(1.37)

0.78

4.91(1.19)

0.79

5.09(1.34)Reliability 0.87 0.88 0.87 0.87

Note: Parameter estimates are shown in bold, mean scores are shown in the second row in each case and standard

devia-tions are shown in parentheses.

All of the correlations were small (less than

0.50) and well below 0.80, which has been

suggested as the level at which discriminant

validity issues become problematic Second,

Fornell and Larcker’s (1981) discriminant

validity test was undertaken As was noted

earlier, the AVE scores for the RA and UA

constructs for the pooled data were 0.56

and 0.63 respectively Because both values

exceeded the square of the correlation between

the constructs (0.46) in the pooled data,

discriminant validity was supported The

highest squared correlation in the three

country samples was 0.49, while the lowest AVE score was 0.53, suggesting that discrimi-nant validity could also be assumed in each country sample

The measurement invariance of the risk and uncertainty avoidance scales

To see whether the RA and UA scales were equivalent across the three country samples, confi gural invariance and metric invariance were examined Steenkamp and Baumgartner (1998) suggested that confi gural and at least partial metric invariance are necessary for the

Tourists’ Information Search 327

analysis that was used in the present study

These forms of measurement invariance are

nested models as each test is nested in the

pre-ceding model As one of the goals of the study

was to test the basic meaning and structure of

the RA and UA measures across countries,

partial metric invariance was seen as the

minimal requirement

Although the results for each country sample

reported in Table 2 provided indications of

confi gural invariance (the fi rst level of

mea-surement invariance), the pooled sample with

mean structure is the most effi cient and

appro-priate means of testing measurement

invari-ance (Wang and Waller, 2006) Consequently,

the measurement invariance of the RA and UA

scales was examined using the pooled data

from Australia, China and Japan and the results

obtained are shown in Table 3 As can be seen

in the Table, the goodness of fi t indices for the

multiple group RA model were acceptable

These results, coupled with the fact that the

hypothesised loadings were all signifi cant in

the pooled sample, suggested that the RA scale

had confi gural invariance The metric

invari-ance of the RA scale (M2), in which the

load-ings were set to be invariant across the three

country samples, was examined As can be

seen in Table 3, the increase in the chi-square

statistic between M1 and M2 (Δχ2(6) = 10.79)

was not signifi cant at the 10% level, suggesting

that the RA scale had full metric invariance

As can also be seen in Table 3, the goodness

of fi t indices for the multiple group UA model

were acceptable Again, these results, coupled with the fact that the hypothesised factor load-ings were all signifi cant in the pooled sample, suggested that the UA scale had confi gural invariance The metric invariance of the UA scale (M2) was also tested As can be seen in Table 3, the increase in the chi square statistic between M1 and M2 (Δχ2(6) = 10.49) was not

signifi cant at the 10 % level, suggesting that the

UA scale also had full metric invariance

The acceptance of confi gural invariance gested that the basic meaning and structure was similar for both scales across the three country samples (Wang and Waller, 2006), and the full metric invariance of the RA and UA scales suggested the scales had the degree

sug-of measurement invariance necessary for meaningful comparisons of cross-country differences (Steenkamp and Baumgartner, 1998) Consequently, this issue was examined

in the subsequent analysis

The extent of information search

A descriptive analysis of the sources of information was conducted, prior to undertaking a Rasch analysis (Andrich, 1988)

to develop a unidimensional extent of information search scale As was noted earlier, respondents were asked whether they consulted

14 different sources of information for four types of tourism decisions (tourism destinations, pricing, accommodation and fl ight decisions) The sources of information refl ected four

Table 3 Assessment of measurement invariance and latent mean differences across the pooled sample from Australia, China and Japan

Model specifi cation

RA χ2 d.f Models

compared Δχ2 p value RMSEA NNFI GFI CFI χ2/d.f.M1: Confi gural invariance* 0.00 0 — — — — — — — —M2: Full metric invariance 10.79 6 M2 vs M1 10.79 0.10 0.03 0.99 0.99 0.99 1.80Model specifi cation

UA χ2 d.f Models

compared Δχ2 p value RMSEA NNFI GFI CFI χ2/d.f.M1: Confi gural invariance 7.75 6 — — 0.01 0.02 1.00 1.00 1.00 1.29M2: Full metric invariance 18.24 12 M2 vs M1 10.49 0.11 0.02 0.99 0.99 1.00 1.52

* There are no degrees of freedom in this case as there are only 3 items in the scale and the error variances have been freed to allow the cross-country comparison to be made

CFI, comparative fi t index; d.f., degrees of freedom; GFI, goodness of fi t index; NNFI, non-normed fi t index; RA, risk

avoidance; RMSEA, root mean square error of approximation; χ 2 , chi-square; UA, uncertainty avoidance.

328 V A Quintal, J A Lee and G N Soutar

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 321–333 (2010)

DOI: 10.1002/jtr

personal sources, six traditional sources and

four web-based sources, and the frequency

of their use in each country can be seen in

Table 4

Chi-square tests demonstrated some

similarities in the sources used by the majority

of respondents in each country sample For

destination decisions, all three source categories

were used by a majority of respondents in each

country For the other decisions (pricing,

accommodation and fl ights), web-based

sources were more common and traditional

sources less common However, for Chinese

respondents, personal sources were commonly

used for all decisions except fl ights

Despite these similarities in patterns of

information sources used, most of the

frequencies for the sources differed signifi cantly

across the three countries, as can be seen in

Table 4 (based on a calculation of percentage

difference Z scores) Very few of the sources

were consulted in similar proportions across

the three country samples, as only two of the

traditional sources for destination decisions

(newspapers and tourism offi ce brochures)

and one traditional (travel guidebooks) and

one web-base source for accommodation

decisions were the same across the three

country samples

Some of these differences support prior

research For instance, Australian and Chinese

respondents used personal sources, such as

relatives and friends more often than the more

experienced Japanese respondents This

sup-ported Moutinho’s (1987) and Urbany et al.’s

(1989) suggestion that experienced travellers

depend less on personal information sources

Further, Chinese and Japanese respondents

were more likely than Australians to consult

certain printed information sources, such as

travel guidebooks and travel magazines, which

was consistent with Chen’s (2000), Money and

Crotts’ (2003) and Nishimura et al.’s (2006)

results Finally, web sources were used

inten-sively in each country, particularly for

destina-tion, pricing and fl ight information However,

the Internet was consulted signifi cantly more

often in Australia than in China or Japan

As the purpose of this paper was to examine

the extent of search, rather than the types of

sources consulted, the 56 items (14 sources × 4

decisions) were analysed using the Rasch

model to see whether a unidimensional order existed (Andrich, 1988) A few of the information

search items (e.g consulting tourism offi ce brochures for destination decisions) did not fi t the

Rasch model as their associated chi-square

statistics were signifi cant well beyond the p ≤

0.001 level (Soutar and Cornish-Ward, 1997) The information search items with a poor fi t to the Rasch model were removed iteratively

to improve overall fi t After removing fi ve

information search items (tourism offi ce chures for destination, pricing, and fl ight decisions, relatives living in the region and airline websites for accommodation decisions), an acceptable fi t

bro-was obtained (χ2 = 124.6; p ≤ 0.10) This

suggested that the remaining items fi tted the Rasch model and that the 51 sources of infor-mation could be combined to form an extent of information search construct

Risk and uncertainty avoidance’s effects on information search

The two hypotheses were examined using the AMOS 16 program’s multiple-group structural equation modelling procedure UA and RA were modelled as correlated exogenous vari-ables and regressed on the extent of search score developed through the Rasch analysis

As can be seen in Table 5, the structural models were equivalent across the three country samples (χ2 = 2.07, p ≤ 0.71; RMSEA = 0.01;

AGFI = 0.99) That is, the impact the two structs had on information search was similar

con-in the three countries As expected, UA had a signifi cant positive effect on the extent of infor-mation search in Australia (b = 0.27, p ≤ 0.01), China (b = 0.18, p ≤ 0.01) and Japan (b = 0.17,

p ≤ 0.01), while RA was unrelated to the extent

of information search in each sample lia = −0.06, not signifi cant (ns); China = 0.09, ns; Japan = 0.01, ns), supporting Hypothesis 1 and

(Austra-Hypothesis 2 In addition, the correlations between UA and RA in the three country samples were all signifi cant at the 0.001 level (Australia = 0.58; China = 0.72; Japan = 0.55)

If we had examined RA and UA ally, we may have attributed the effect of UA

individu-to RA, at least in China The correlation between RA and the extent of information

search was signifi cant for China (r = 0.22, p ≤ 0.001), but not for Australia (r = 0.10, ns) or

Tourists’ Information Search 329

330 V A Quintal, J A Lee and G N Soutar

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 321–333 (2010)

DOI: 10.1002/jtr

Japan (r = 0.09, ns) Thus, this result is better

explained by the higher correlation between

UA and RA for China, rather than the distinct

infl uence of RA

CONCLUSIONS

The present study clarifi ed the differences

between RA and UA by assessing the

cross-country applicability of existing scales and

their differential effects in the information

search phase of travellers’ decision-making

processes The results suggested that the RA

and UA scales were unidimensional and

reli-able, and had convergent, discriminant and

predictive validity Both scales had full metric

equivalence across the three countries in which

data were collected, allowing an examination

of cross-cultural differences Further, the

results illustrated a remarkable amount of

sim-ilarity in the effect of UA and RA on the extent

of information search, despite differences in

the actual sources of information consulted

across countries As expected, the UA construct

was positively related to the extent of

informa-tion search in all three country samples,

whereas the RA construct was not This

pro-vides some evidence that the RA and UA

con-structs are distinct and are likely to operate in

different stages in travellers’ decision-making

uncer-sumers (Drollinger et al., 2006) Future research

is needed to assess the expected infl uence RA has in the later evaluation stage of decision-making, when specifi c alternatives with prob-able outcomes are being compared

Some general and more specifi c dations can be made by examining the fre-quency of information used in Table 4 For instance, it appears that a very wide variety of sources are consulted for destination decisions

recommen-in all three country samples, suggestrecommen-ing that destination marketers need to consider presenting consistent information about their destination in a wide variety of information sources Information about other travel deci-sions, such as pricing, accommodation and

fl ights, are more likely to be sought from sonal sources, such as travel agents and web-based sources The importance of personal

per-Table 5 Standardized path coeffi cients and correlationsPaths Australia China Japan

Hypothesis 1: UA → Info search 0.27** 0.18** 0.17**

Hypothesis 2: RA → Info search −0.06 0.09 0.01Model fi t statistics

AGFI, adjusted goodness of fi t index; CMIN, chi-square; RA, risk avoidance;

RMSEA, root mean square error of approximation; UA, uncertainty avoidance.

Tourists’ Information Search 331

sources of information for destination, pricing

and accommodation decisions is clear,

espe-cially for less-experienced travellers This

rein-forces the importance of word-of-mouth, with

referrals being likely to play a crucial role

in many travel decisions Further, Chinese

respondents were more likely to consult

rela-tives or friends who had visited or lived in the

region and tour operators as sources for

pricing, accommodation and fl ights than were

Australian or Japanese respondents As China

is a developing economy in which

interna-tional travel is an emerging activity, it is likely

that travellers will seek advice from

experi-enced or expert personal sources to help them

allay their uncertainties about travel

The present study has some limitations It

used existing RA and UA sales to measure

these constructs at an individual level that

were based on Hofstede’s scales which

measured uncertainty in a workplace

environment A scale that focuses on uncertainty

in other contexts might lend additional insight

into how consumers respond to uncertainty in

everyday decisions The development of new

UA and RA scales is likely to help tourism

researchers gain a better understanding of the

impact cultural backgrounds have on tourists’

decision-making

Gender, age and income were appropriately

represented in the samples drawn from

Austra-lia, China and Japan However, while the

analy-sis was undertaken on general population

samples in Australia and China, the Japanese

respondents were international travellers, a

requirement of a larger study in which the

constructs were relevant Further, budget

constraints limited the sample sizes to 200 in

Australia, 443 in China and 342 in Japan These

factors may have impacted on the

generalisabil-ity of the fi ndings (Wang and Waller, 2006)

In fact, we purposefully chose a

non-repre-sentative sample from Japan, to see whether

UA and RA operated in the same manner

in a more experienced traveller sample As

expected, Japanese respondents had lower RA

and UA individual scores than did the

Austra-lian and Chinese respondents, differing from

Hofstede’s (2001) national UAI (Japan = 92;

Australia = 51; China = 30) Further, the Chinese

respondents reported higher RA and UA

scores than did Australian respondents This

illustrates the variability between country level and individual level scores It also reinforces the need to measure variables of interest at an individual level when predicting individual level behaviour

The determinants of information search also require further research It would be interesting

to examine the impact prior knowledge (familiarity, expertise and past experience) has on information search (extent and type) The relationship between prior knowledge and information search has been suggested to be

positive (Jacoby et al., 1978), negative (Simonson

et al., 1988) and to have an inverted U effect

(Johnson and Russo, 1984) Clarifying this relationship would improve our understanding

of important variables that impact on people’s use of information (Kersetter and Cho, 2004).While some effort was made to profi le the samples according to their cultural responses

to risk and uncertainty, differences in economic development may account for some of the

variation (Bao et al., 2003) For instance, while

Australia and Japan are developed economies, China is a developing economy It seems likely that consumer decisions are infl uenced by both their economic and their cultural circum-stances, but further research is needed to assess this in more detail

Subsequent research is also needed to examine the multiple and dynamic infl uences risk and uncertainty have on the decision-making process To date, no study has

attempted to untangle the effects of generalised attitudes towards risk and uncertainty and situation-specifi c perceptions of risk and

uncertainty on people’s behaviour A making model that integrates these four constructs is likely to shed considerable light

decision-on their differential impacts decision-on tourists’ decision-making processes

ACKNOWLEDGEMENTThis research was supported by an Australian Research Council grant in conjunction with Tourism Western Australia

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This research explores the extent to which

VFR travellers utilise commercial

accommodation in the Sunshine Coast,

Australia, and profi les the characteristics

and behaviours of this particular type of

VFR traveller The research indicated that

26% of the VFRs stayed in commercial

accommodation (CVFRs) This closely

aligned with research using the same

method in a comparative destination,

Ballarat, Australia, indicating that 22% of

VFRs stayed in commercial accommodation

CVFR travellers occupied similar types of

commercial accommodation as non-VFRs

and engaged in similar tourism activities

However, they came from different

generating regions and used different

sources of information for planning their

trip Copyright © 2009 John Wiley &

Sons, Ltd.

Received 10 July 2008; Revised 17 September 2009; Accepted

25 September 2009

Keywords: VFR; visiting friends and

relatives; commercial accommodation

INTRODUCTION

Scholarly interest into visiting friends and

relatives (VFR) travel developed in the

mid-1990s after Jackson’s (1990) seminal

paper suggested that this type of tourism was

much larger than offi cial estimates suggested

VFR travel was originally developed as a

residual category for trips that could not be classifi ed into other categories (Hay, 2008) Only a minority of countries identify VFR as a distinct category (Jackson, 2003), thereby making it diffi cult to know the size of VFR travel on a global level

Despite the size of VFR, it is ‘one of the most neglected areas of study’ (Page and Connell,

2009, p 94) In comparison to its size, there has been little research into VFR travellers, their motivations, behaviours and characteristics, and the factors that infl uence their choices This has led to their lack of recognition as a market segment and an assumption that they contribute little to local economies and tourism industries While VFR travel is one of the largest and most signifi cant forms of travel,

‘VFR travel remains well-known but not known well’ (Backer, 2009, p 2)

In Australia, offi cial tourism data present

VFR in two ways — by purpose of visit or by

accommodation type As can be seen in Table

1, the numbers of visitor nights for Australia and for each state in Australia are always lower

if purpose of visit rather than accommodation type is used as the basis of VFR data VFR travel represented 30% of the total size of domestic visitor nights in Australia in 2007 based on purpose of visit, and 37.5% based on accommodation

The categories presented in Table 1 cannot be regarded as complete representations of VFR travel, because of each type leaving out one group of VFR travellers, therefore understating the size of this form of travel This problem was raised by Seaton and Palmer (1997) who high-lighted that VFR trips ‘defi ned by form of accommodation’ (p 353) were more than double the size as those that had been defi ned by purpose of visit (Seaton and Palmer, 1997).The difference in these fi gures can be based

on several reasons First, not all travellers staying with friends or relatives will self-

Copyright © 2009 John Wiley & Sons, Ltd.

INTERNATIONAL JOURNAL OF TOURISM RESEARCH

Int J Tourism Res 12, 334–354 (2010)

Published online 3 November 2009 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/jtr.754

Opportunities for Commercial

Accommodation in VFR Travel

Elisa Rose Backer

School of Business, University of Ballarat, Ballarat, Victoria, Australia

*Correspondence to: E R Backer, Lecturer in Tourism,

School of Business, University of Ballarat, Mt Helen

Campus, University Drive, Mt Helen, PO Box 663,

Ballarat, Victoria 3353, Australia.

E-mail: e.backer@ballarat.edu.au

VFR Travel 335

classify themselves as VFR, instead identifying

themselves as a holidaymaker (Jackson, 2003)

In addition, not all travellers staying with

friends or relatives will have a VFR purpose

of visit Therefore, the data considering the

number of travellers staying with friends or

relatives will necessarily underestimate the

size of VFR travel Similarly, not all VFR

travel-lers stay with the friends or relatives they have

travelled to see (Seaton, 1994; Braunlich and

Nadkarni, 1995; Lehto et al., 2001; Backer, 2009)

Therefore, data presenting VFR by

accommo-dation type will also underestimate the size of

VFR travel This poses defi nitional problems

Neither VFR tourism nor VFR travel is

commonly defi ned in the literature This has

resulted in a defi nitional problem for VFR

travel (Seaton and Palmer, 1997), which is still

largely apparent despite the research that has

been undertaken in the fi eld This suggests an

underlying assumption that VFR travel is so

well understood that the reader will

under-stand the group that is being discussed In a

number of cases (e.g Hu and Morrison, 2002;

Lee et al., 2005), no defi nition is provided but

the authors state that data were collected by

purpose of visit, which reveals an assumed

defi nition for VFR travel in this manner While

it is reasonable to assume that readers will

have an overall understanding of what VFR

stands for, it does overlook the data collection problem that VFR is commonly categorised by purpose of visit, but it can also be categorised

by accommodation type (Seaton and Palmer, 1997) Different percentages will be attained depending on which classifi cation is used, and neither should be considered a comprehensive defi nition

While acknowledging the defi nitional problem, Seaton and Palmer’s (1997) research utilised United Kingdom Tourism Survey (UKTS) statistics As a result, this led to the adoption of the UKTS statistical parameter of defi ning a VFR traveller as someone whose primary purpose of trip is to visit friends or

relatives Yuan et al (1995) defi ned a VFR

trav-eller in such a way, stating that a ‘VFR travtrav-eller

is one who reported visiting friends and tives as the major purpose for the trip’ (p 19) Similarly, McKercher (1995) stated ‘that the primary purpose of most participants in this type of travel is to visit with their friends and relatives is axiomatic’ (p 246)

rela-However, VFR travel has also been classifi ed

in terms of accommodation King (1994) stated that VFR travel is categorising visitors by the type of accommodation that they used Boyne

et al (2002) proposed that ‘a VFR tourism trip

is a trip to stay temporarily with a friend of relative away from the guest’s normal place of residence, that is, in another settlement or, for travel within a continuous settlement, over

15 km one-way from the guests’ home’ (p 246) They admitted that this defi nition ‘largely avoids rather than confronts some of the key conceptual issues’ (pp 246–247) Similarly,

Kotler et al (2006) state that ‘VFR, as the name

suggests, are people that stay in the homes of friends and relatives’ (p 748) These sugges-tions reinforce the implied notion that VFR travellers do not stay in commercial accom-modation In fact, according to Navarro and Turco (1994), the perceptions that VFR travel-lers make little use of commercial accommoda-tion and do not tend to frequent restaurants, cafes, pubs and clubs is why VFR travel has not been clearly defi ned

The fact that VFR travellers do utilise mercial accommodation was raised by Braun-lich and Nadkarni (1995) Subsequent studies

com-have confi rmed this point (e.g Morrison et al., 2000; Moscardo et al., 2000; Lehto et al., 2001;

Table 1 Relationship between VFR typologies for

domestic travel by Australians: 2007

VFR by accommodation (%)

VFR by purpose of visit (%)Sunshine Coast 29 25

336 E R Backer

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 334–354 (2010)

DOI: 10.1002/jtr

Pennington-Gray, 2003; Bischoff and

Koenig-Lewis, 2007) However, while studies have

affi rmed the point that VFRs do use

commer-cial accommodation, to date, no comparative

analysis has been undertaken to assess the

pro-fi les and characteristics of those VFRs staying

in commercial accommodation relative to

non-VFRs As raised by Lehto et al (2001), ‘further

research on the VFRs who use commercial

accommodation is needed’ (p 211) As such,

the purpose of this research is to contribute

to the knowledge in the area of VFR travel,

specifi cally with respect to understanding the

characteristics and profi les of VFRs that stay in

commercial accommodation

LITERATURE REVIEW

VFR travel is an area of research that was

largely neglected until Jackson’s (1990) seminal

paper This led to a wave of interest in the

mid-1990s, resulting in an international conference

(VFR Tourism: Issues and Implications, 1996)

and a special edition of an international journal

(The Journal of Tourism Studies, 1995) being

dedicated to this subject area This special issue

combined research on VFR travellers

under-taken in Australia, the USA, Canada, the

Netherlands and Northern Ireland, to provide

a broad analysis of VFR from various parts of

the world All studies (Braunlich and

Nad-karni, 1995; Meis et al., 1995; Morrison et al.,

1995; Seaton and Tagg, 1995; Yuan et al., 1995)

found that VFR travel represented a signifi cant

part of the overall travel market in those parts

of the world The interest that resulted in the

immediate years following Jackson’s (1990)

paper resulted in a realisation that VFR travel

had been overlooked and underestimated

(Jackson, 1990, 2003; McKercher, 1994, 1995;

Seaton, 1994; Braunlich and Nadkarni, 1995;

Morrison et al., 1995; Seaton and Tagg,

1995; Hay, 1996, 2008; King, 1996; Seaton and

Palmer, 1997; Backer, 2007, 2008, 2009)

Since those earliest contributions were made,

research advanced in the early 2000s through

segmented analysis, resulting in an improved

understanding in the fi eld Morrison et al (2000)

considered the perceptions of VFR travel by

Destination Marketing Organisations (DMOs)

and highlighted the importance of offering

spe-cifi c marketing campaigns targeting VFR

travel-lers Moscardo et al (2000) raised the notion of

various typologies within VFR travel, which resulted in a number of important studies (e.g

Lehto et al., 2001; Hu and Morrison, 2002; Pennington-Gray, 2003; Lee et al., 2005) that con-

sidered this research approach Other tation studies (Lockyer and Ryan, 2007; Hay, 2008) furthered the earlier work (Seaton, 1994; Seaton and Tagg, 1995) that fi rst highlighted the differences between those people who are visit-ing friends (VFs) and those who are visiting relatives (VRs) By the mid-2000s, VFR travel was being recognised in the literature as provid-ing substantial benefi ts to many regions

segmen-This more recent literature also attempted to study the fi eld in more detail MacEachern (2007) considered that VFR travel can be a motive, activity and typology, resulting in additional complexities when studying it

Similarly, Morrison et al (2000, p 103) stated

that VFR travel can be considered ‘from at least four different perspectives’ It can be consid-ered from a travel purpose, travel motivation, activity and as a form of accommodation

(Morrison et al., 2000) This typology can be

considered ‘as an extended defi nition of the phenomenon’ of VFR travel (Pearce and Moscardo, 2006, p 49)

Their typology model highlighted the tinct groups that exist within VFR travel Some VFR travellers will stay with their friends or relatives, and are accommodated solely with them (AFR) As there is a perception that VFR travel is ‘all AFR or predominantly AFR’

dis-(Moscardo et al., 2000, p 252), the distinction

is made in the model Moscardo et al (2000)

state that other VFRs are accommodated, at least in part, through commercial accommoda-tion (NAFR)

This distinction is important This aspect

was highlighted by Lehto et al (2001) and

Pen-nington-Gray (2003) who explored the VFR typologies of sector, scope and accommoda-

tion, testing the Moscardo et al (2000) model

These studies concluded that there were tinct differences between the VFR typologies, but that the model had limitations and that further refi ning would be advantageous (Lehto

dis-et al., 2001) While Lehto dis-et al (2001) suggested

incorporating additional variables such as demographics, information sources and trip planning, a simplifi ed model was proposed by

VFR Travel 337

Backer (2009) for the purpose of being a way

of defi ning VFR travel as shown in Figure 1

This VFR Travel Defi nitional Model cements

those typology distinctions highlighted in the

literature by defi ning VFR travel through a

simple matrix This follows a similar structure

as presented by Pennington-Gray (2003) and

concentrates on the same distinct typologies

The model describes that VFR travel can fall

into three VFR typologies (Backer, 2009),

shown by those categories with a tick inside

The residual category represents those who are

non-VFR travellers The model is intended to

visually represent the defi nition put forward

that ‘VFR travel is a form of travel involving a

visit whereby either (or both) the purpose of

the trip or the type of accommodation involves

visiting friends and/or relatives’ (Backer, 2007,

p 369)

The top left-hand category depicts a ‘pure’

VFR who stays with friends or relatives and

also states a VFR purpose of visit (Backer,

2009) The category underneath these pure

VFR travellers are those who are also staying

with a friend or relative but have come to the

destination for a different main purpose that is

not VFR In the top right-hand corner are those

VFR travellers that have travelled to the

desti-nation with a VFR purpose of visit, but are

staying in commercial accommodation

There can be a range of reasons why people

would select commercial accommodation in

preference to staying with the friends and

rela-tives they are travelling to visit The reasons

why people are motivated to travel are broad

(Leiper, 2004) and people are often motivated

to travel by a culmination of reasons rather than a singular reason Travel often involves the need to escape, to rest and to relax (Cromp-ton, 1979; Leiper, 2004) And as such, if one of the primary motivations of a VFR traveller is relaxation and escape, he/she may choose to stay in commercial accommodation to enable him/her to fi nd a balance between meeting his/her main objectives of relaxations and escape, while still fi nding time to be with his/her friends and relatives at his/her own leisure Other reasons can include the lack of facilities

at the hosts’ home to accommodate the travel party This can be especially the case for families with a greater number of children or with small children

Given that VFR travel has been shown to provide benefi ts across a broad range of cate-gories, including commercial accommodation,

it is surprising that it remains neglected by many tourism operators There have been various reasons put forward as to why VFR travel tends to be neglected despite its size Jackson (1990, 2003) suggests it is largely a classifi cation problem, while Hay (1996, 2008) highlights the lack of lobbying for it being a problem Despite marketing organisations failing to champion it or undertake dedicated marketing strategies to capture these travel-

lers, Lee et al (2005) feel that marketing

organi-sations cannot afford to marginalise this form

of tourism because of the fact that it is ‘bouyant’ (p 35) Paci (1994) blames the ‘poorly docu-mented’ (p 36) data for the neglect in this fi eld, which Hay (2008) also recognises Seaton and Palmer (1997) considered three perception problems causing the neglect of VFR travel These three perceptions are: low economic impact, that it cannot be infl uenced by tourism planners and that it cannot be infl uenced by marketing Backer (2007) highlighted seven reasons for VFR travel being neglected, which incorporate a number of those reasons pro-vided by others together with additional reasons As such, these can be used as a basis for considering a total summary of reasons why VFR is neglected

(1) Absence of a defi nition This aspect of defi nition was discussed previously and high-lighted that there have been few attempts

-Figure 1 VFR Travel Defi nitional Model

Source: Backer, 2009, p 11.

338 E R Backer

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 334–354 (2010)

DOI: 10.1002/jtr

to defi ne VFR travel and this has led to a

lack of understanding concerning this fi eld

There is an implied notion that VFR

travel-lers are staying with friends and relatives

Although, despite this ‘common

percep-tion’ (Morrison et al., 1995, p 48), VFR

travellers do not necessarily stay with the

friends and relatives they have travelled to

visit This perception reinforces the lack of

consideration of VFR as a potential market

segment for commercial accommodation

operators

(2) Discrepancy with existing data As there

are different VFR typologies, data

depicting the size of VFR travel can vary

depending on the measurement tool As

shown in Table 1, the size of VFR in terms

of purpose of visit is not the same as the

size of VFR by accommodation

Com-pounding this problem is the point that

Jackson (1990, 2003) raised with visitors

needing to self-classify themselves As

purpose of visit relies on self-assessment,

it assumes that visitors have one central

reason for travel However, VFRs may not

necessarily classify themselves as VFRs

VFR can be a component of ‘hybrid

activ-ity’ (King, 1996, p 86) and VFR travellers

may consider that they are on holiday, and

classify themselves accordingly, resulting

in an underestimation of the entire

cate-gory By referring to VFR by purpose of

visit data, which is less than the VFR by

accommodation data, an underestimation

of VFR size results

(3) Diffi culties with measurement VFR travel

can be a diffi cult segment to measure As

discussed previously, VFR travel is a

com-position of different types and as such VFR

travellers cannot be sourced through one

form alone VFR travellers might be staying

in commercial accommodation or with

friends and relatives It can be

resource-intensive to gather adequate data to

measure this There is also the issue of the

host being part of VFR travel As by its

very nature, VFR centrally involves hosts,

the role that the host has in measuring VFR

travel is also crucial to enable a complete

understanding In terms of offi cial data

that measure tourist expenditure only, VFR

research tends to overlook the additional

tourist dollars expended by residents hosting their friends and relatives The

‘emphasis on gathering data from commercial accommodation houses’ (King,

1996, p 87) necessarily under-reports VFR travel as well This contributes to VFR being underestimated and neglected

(4) Lack of lobbying First raised by Hay (1996), VFR travel is without a voice to champion

it Many DMOs are infl uenced heavily by accommodation providers, which often take up the majority of seats on boards and represent a substantial membership com-position There is no place on a board of directors for a DMO for a representative to champion VFR travel The strategic direc-tion of the marketing efforts will be geared towards other areas by those in a position

to champion other causes

King (1996) states that there is actually lobbying against research into VFR travel

He claims that in some countries there is lobbying, primarily by the accommodation sector, against using public funds to under-take research in this area The perceived secondary status of VFR travel within tourism has probably contributed to the fact that there has been little championing

of it In fact, there is no level in which championing of VFR travel occurs No one speaks on behalf of VFR travel because it

is often perceived in terms of being linked with private homes; and private homes are not a market

Tourism Organisations are well placed

to lobby for VFR travel though, as they are chartered with the responsibility of encour-aging more visitations, more exploration of the region and longer length of stays They are in the business of promoting the region which in turn supports the underlying businesses that are involved either directly

or indirectly in tourism Without lobbying for resources to be dedicated towards research and/or promotional campaigns in this area, little comprehensive consider-ation is likely to be presented

(5) Perceived minor economic impact VFR travel tends to hold secondary status within

tourism (Lehto et al., 2001), with VFR

travel-lers considered to be visitors that partake in few tourism activities, spend nothing on

VFR Travel 339their lodgings and as such of negligible

value to local communities (Seaton, 1994;

Bull, 1995) The average daily expenditures

by VFRs tends to be reported as low

(Denman, 1988; Bull, 1995; Lehto et al., 2001)

and this offers little incentive to marketing

organisations to pursue the VFR segment

However, such reports neither typically fail

to account for the broad level in which VFR

expenditures occur (McKercher, 1994), nor

do they include the expenditures made by

hosts during VFR trips or the cost of gifts

for hosts made by VFRs or contributions to

household expenditures, which can be

con-siderable (McKercher, 1995; Morrison et al.,

1995; King, 1996; Backer, 2007) In addition,

a lower daily spend can be compensated by

VFR length of stay, resulting in spending

over a longer period (Seaton, 1994; Hay,

1996)

The role that local residents have through

hosting VFR travellers was found to be

sig-nifi cant in a study undertaken in Central

Queensland (Litster, 2007) The study

indi-cated that over 170 000 people in Central

Queensland, out of a population of around

250 000, hosted VFR travellers in the

previ-ous year (Litster, 2007) It also concluded

that the region hosted more than 1 million

VFR travellers within that period (Litster,

2007) There is also evidence that VFR

travellers stimulate additional spending

by the local resident within the local

economy they visit (Beioley, 1997)

Economic impact by VFR travel, has also

been discussed in respect to international

students International education has

expe-rienced substantial growth over the past

20 years (Taylor et al., 2004) and leads to

tourism arising from friends and

particu-larly family who are visiting these

inter-national students This is estimated to

be worth between AU$10.3 million and

AU$17.4 million each year to Australia’s

State of Western Australia alone (Taylor

et al., 2004) Similarly, in a study

under-taken in the United Kingdom, the ‘vast

majority of students’ (Bischoff and

Koenig-Lewis, 2007, p 478) received visits from

friends or family Furthermore, the average

frequency of these visits was shown to be

‘fairly high’ (Bischoff and Koenig-Lewis,

2007, p 478), resulting in a ‘signifi cant’ (Bischoff and Koenig-Lewis, 2007, p 478) contribution to the local economy

VFR travel also has economic tance because it tends to have a stabilising effect on an economy and is less vulnerable

impor-to market fl uctuations King (1994) stressed this point by presenting data showing that between 1980 and 1990, ‘VFR travel grew

by 87%, a higher fi gure than the one utable to holiday travel of 69%’ (p 175) He also pointed out that VFR travel ‘experi-enced a further growth of 22% between

attrib-1990 and 1991 whilst outbound holiday numbers declined’ (King, 1994, p 175).Because of this, King (1996) believes that targeted VFR strategies may be appropri-ate during economic declines to help buffer against business downturns In saying this,

he refers to the fact that during the sion, VFR travel held up better than holiday travel in Australia’s state of Victoria, particularly in Victoria’s rural areas

reces-Furthermore, VFR travel is said to be less susceptible than other forms of tourism

to seasonality issues (Denman, 1988; McKercher, 1994; Bull, 1995; Seaton and Tagg, 1995; Hay, 1996; Seaton and Palmer, 1997; Weaver and Lawton, 2010), also com-pounding its stabilising effect In fact, according to Seaton and Palmer (1997), VFR travel is not only spread more evenly throughout the year than other tourism segments, but it may also peak in times that are traditionally low off-season times This illustrates a major economic benefi t

of VFR travel in that it may serve as an economic stabiliser (McKercher, 1994; King, 1996; Seaton and Palmer, 1997; Lehto

et al., 2001).

(6) Tourism textbooks Despite its size, VFR travel is given at best, a cursory mention in tourism text books Despite the research efforts that have been published over the past two decades, VFR has failed to make any real entry in texts (Backer, 2009) Present by way of a column in a table only,

or a few paragraphs at best, VFR barely makes it to the index of many current tourism educational books (e.g Leiper, 2004; Weaver and Lawton, 2006; Hall,

2007; Collier, 2008; Cooper et al., 2008;

340 E R Backer

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 334–354 (2010)

DOI: 10.1002/jtr

O’Shannessy et al., 2008) and does not even

rate a place in the index of others (e.g Hsu

et al., 2008; Richardson and Fluker, 2008)

With tourism textbooks serving as a critical

reading and learning tool for future tourism

managers undertaking degrees, and serving

as the basis for teaching template in

tertiary education, VFR is regularly left off

the teaching syllabus resulting in the

con-tinuation of VFR being neglected (Backer,

2009)

(7) VFR travellers are diffi cult to infl uence

The seventh reason that contributes to

VFR’s neglected status is the issue of how

to infl uence VFR travellers VFRs have

been regarded as a form of travel that

comes naturally and as such cannot be

infl uenced (Morrison et al., 2000) However,

VFR hosts are considered to hold a highly

infl uential role concerning what activities

are undertaken by VFRs (Hume, 1986;

McKercher, 1995; Meis et al., 1995; Yuan

et al., 1995; Jackson, 2003) and therefore it

would seem logical that VFR travellers can

be readily infl uenced at the local host level

As McKercher (1995) points out, the role

that the host has in infl uencing VFR

activi-ties does not seem to have been

investi-gated and as such, researching this issue

‘will help to address the concern of whether

VFR travellers can be infl uenced or not,

and if so, at what level’ (Backer, 2007,

p 372)

This aspect was considered in a

com-parative analysis (Backer, 2008) that

high-lighted the differences in VFR behaviours

in two different destinations The average

length of stay in the highly popular coastal

destination of the Sunshine Coast in

Australia’s state of Queensland was around

fi ve times greater than that of the

destina-tion of Albury-Wodonga, situated inland

straddling the Australian state borders of

Victoria and New South Wales The point

made by Backer (2008) was that if VFR is

purely about visiting the host, there should

not be such a signifi cant difference in the

length of stay Backer (2008) concluded

that the attractiveness of the destination,

and not just the attractiveness of the host,

infl uenced VFR travel behaviour The

destination attraction factor may even be

responsible for initiating the trip to begin with This point was also highlighted by Carmichael and Smith (2006), who stated that friends and family may sometimes visit because the local resident lives in an attractive destination

(8) VFR is not ‘sexy’ There should also be an eighth reason added to why VFR tends to

be neglected That reason is that VFR is not regarded as being a sexy area of marketing International marketing is often regarded

as more higher level and prestigious, and falls under the obvious charter of National Tourism Organisations Marketing to ‘Aunt Betty’ is not as glamorous In fact, even domestic tourism is often considered the neglected and ‘poor cousin’ of the more glamorous international tourism market (Pearce, 1993; Scheyvens, 2007) As such, VFR travel, which has even less appeal and lower status, is even more so the poor cousin in tourism terms

These eight reasons highlight the reasons

behind the neglect of VFR travel Lee et al

(2005) suggests two main areas of future research to assist in developing the fi eld of VFR One was for the need to have ‘a model that incorporates motivational factors in the VFR context’ (p 355) The second was for further segmentation studies in VFR This fi rst point, considering a model outlining motiva-tional factors, has been provided by Backer (2009) This model (Figure 2) describes that the purpose of visit by VFRs may not necessarily

be VFR It may be leisure, business, VFR, other

or indeed a combination of these elements In addition, VR and VF are disaggregated because

of the statistically signifi cant differences in purpose of visit established between these two

sub-groups (Backer, 2009) Lee et al.’s (2005)

second recommendation concerning further segmentation is where this research contributes

VFR Travel 341

Pacifi c Ocean to the east Small inland

com-munities such as Nanango, Kilcoy and

Chin-chilla are to the west To the north of the

Sunshine Coast is Fraser Island and

Mary-borough To the south are Caboolture and

further south, Brisbane The Sunshine Coast

region has a population that exceeds 260 000

people (Sunshine Coast Australia, 2007;

Aus-tralian Bureau of Statistics, 2008; Sunshine Coast Regional Council, 2009), which is the 10th largest population area in Australia (Sun-shine Coast Australia, 2009) It is one of Aus-tralia’s most popular holiday destinations (Weaver and Lawton, 2006) and ranks in the top 10 destination regions in Australia for inbound visitors (Weaver and Lawton, 2006)

Figure 3 The case study area: the Sunshine Coast

Source: Greenwich Meantime, 2007; TRA, 2008.

342 E R Backer

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 334–354 (2010)

DOI: 10.1002/jtr

and in the top fi ve regions in Australia ranked

by expenditure (Tourism Research Australia

(TRA), 2009)

VFR travel is a large segment of tourism

for the Sunshine Coast (Table 2); however, as

a proportion, is smaller than for Queensland

and Australia This could be because of the

popularity of the Sunshine Coast, creating a

proportionately large holiday/leisure market

Nevertheless, VFR travel to the Sunshine Coast

by purpose of visit comprised 2 704 000 visit

nights, representing 25% of the total number

of visitor nights spent in the region during

2007 In terms of accommodation, 3 184 000

visit nights were spent by travellers staying

with friends or relatives, which represented

29% of the total proportion of visitor nights in

the Sunshine Coast during 2007 While the

average length of stay for domestic VFR

travel-lers to the Sunshine Coast is three nights, the

international VFR traveller stays an average of

12 nights on the Sunshine Coast (TRA, 2008)

The average total length of stay for all

domes-tic visitors to the Sunshine Coast is four nights

and for international visitors the total average

is nine nights (TRA, 2008) Thus, domestic VFR

travellers stay fewer nights but international

VFR travellers stay longer

Data collection

Quantitative research was used to gather data

on VFRs, non-VFRs and VFR hosts, and took

the form of two different questionnaires

con-ducted by personal questionnaire Data were

collected over a fi ve-month period between

January and May 2002, in order to capture data

from across peak, shoulder and off-peak times

While not part of this research study, this same

survey instrument has been used to collect

VFR data in a different region, Ballarat,

Victoria (near Melbourne) as a comparative analysis Some results from that study are pre-sented in this study as they were used as a test platform for key aspects

The fi rst questionnaire was a visitors survey, designed to capture information on VFRs and non-VFRs visiting the Sunshine Coast The second questionnaire was a local resident survey designed to capture information on local residents to ascertain their role in hosting VFRs in terms of expenditures, activities and frequency of visit The combination of these surveys would serve to form a full spectrum of VFR versus non-VFR trips to the Sunshine Coast Convenience surveying was the method selected Seven main street survey locations were selected for personal interviewing to occur to endeavour to reduce survey bias Locations included both inland townships and coastal townships within the Sunshine Coast

to capture both inland visitors and residents as well as coastal residents and visitors

In total, 831 visitor surveys were collected and 625 local resident surveys were collected The response rate for the residents’ survey was 43% While accepting that visitors need not stay in a location overnight to be considered a visitor to that area, for the purposes of under-taking meaningful comparative analysis, day visitors were excluded, resulting in 738 surveys representing overnight visitors The response rate for the visitors’ survey was 45%

The visitor questionnaire contained profi ing questions to establish characteristics such

l-as length of stay, attractions visited and diture As this particular research was inter-ested in understanding the percentage and profi le of VFRs staying in commercial ac-commodation, the visitor data were further disaggregated to separate visitors staying in commercial accommodation Those visitors

expen-Table 2 Domestic VFR visitor nights in 2007

Purpose of visit VFR visitor nights ’000 (%)

Accommodation VFRvisitor nights ’000 (%)Sunshine Coast 2704 (25) 3184 (29)Queensland 21 953 (28) 27 329 (35)Australia 87 211 (30) 108 180 (37)

VFR, visiting friends and relatives.

Source: derived from TRA, 2008.

VFR Travel 343

with a VFR purpose of visit staying in

com-mercial accommodation were then compared

with other visitors staying in commercial

accommodation whose purpose of visit was

non-VFR A comparative analysis of the

com-mercial accommodation data would ascertain

whether VFR travellers staying in commercial

accommodation exhibit different profi les and

characteristics to non-VFR travellers

Sample sizes for the three VFR typologies

and non-VFRs can be seen in Table 3, applied

using the VFR defi nitional model confi

gura-tion (Backer, 2009) as depicted in Table 1 The

three VFR categories are depicted by the

grey-shaded area Those VFR travellers staying in

commercial accommodation (CVFRs) are

rep-resented by the darker shaded zone The

non-VFR category is depicted by the non-shaded

box The number of VFR travellers staying in

commercial accommodation totalled 60 out of

229 VFR travellers (26%)

As a comparison, VFR data collected in

Ballarat between April and June 2009 were

disaggregated using the identical method, and

similar proportions were found As shown in

Table 4, the proportion of VFR travellers in

Ballarat staying in commercial

accommoda-tion (CVFRs) was 22%

Data analysis

By comparing the VFRs with non-VFRs that were staying in commercial accommodation, it revealed a number of similarities and differ-ences between the two trip-purpose types Sample sizes for the respective categories based

on the VFR Travel Defi nitional Model are as described in Table 3 By comparing the responses from VFR travellers staying in commercial accommodation with non-VFRs, an understand-ing of VFR behaviour in commercial accommo-dation was established This was done through using a variety of statistic tests Chi-square tests

at the 95% confi dence level were performed in the majority of cases to assess overall differ-ences between the two groups Fisher’s exact test was performed where cell levels were too

low t-Tests and z-tests were also performed at

the 95% level to compare rows Where outliers skewed the data and violated the assumption of normality, data were converted to logarithmic functions and tests were run on log data

RESEARCH FINDINGSThe proportion of VFR travellers that used commercial accommodation in the Sunshine

Table 3 VFR category sample sizes based on VFR Travel Defi nitional Model: Sunshine Coast

Accommodation:

friends/family

Accommodation:

commercialPurpose of visit:

VFR, visiting friends and relatives.

Table 4 VFR category sample sizes based on VFR Travel Defi nitional Model: Ballarat

Accommodation:

friends/family

Accommodation:

commercialPurpose of visit:

344 E R Backer

Copyright © 2009 John Wiley & Sons, Ltd Int J Tourism Res 12, 334–354 (2010)

DOI: 10.1002/jtr

Coast was 26% As a percentage of all visitors

who were staying in commercial

accommoda-tion, this represented 10.5% being VFR

travel-lers Because VFR travellers represent an

important segment to commercial

accommo-dation operators in size, a comparative

analy-sis of the commercial accommodation data was

undertaken to ascertain whether VFR

travel-lers staying in commercial accommodation

exhibit different profi les and characteristics to

non-VFR travellers As in size they are

impor-tant, if they also behave in a similar way to

other travellers, this highlights an important

segment for commercial accommodation

operators to market to This CVFR typology

was then profi led using a whole tourism

systems approach (fi ve elements being: tourist,

transit route, generating region, industries,

destination) to explore the characteristics and

behaviours of these visitors using comparative

analysis As the fi fth element, the destination

was pre-determined by the region selected

(Sunshine Coast); these research fi ndings

describe the other four whole tourism system

elements

Whole tourism system element one: Tourist

The whole tourism system (WTS) element of

tourist was considered with respect to

con-sidering the profi les and characteristics of VFR

travellers and non-VFR travellers staying in

commercial accommodation at the Sunshine

Coast This involved considering the

compara-tive expenditures, frequency of visitation,

travel party size, information sources and their

respective length of stay

Length of stay The length of stay in commercial

accommodation properties was unaffected by

visitor types, with both CVFR travellers and

non-VFR travellers staying for similar

dura-tions of time While the descriptive data

indi-cated that CVFR travellers stay in commercial

accommodation longer than non-VFR

travel-lers, a t-test of the logarithmic data to test for

signifi cance indicated there was no signifi cant

difference at the 95% level Both the raw means

and log means are reported in Table 5

Expenditures In total, CVFR travellers spent

$2,824.58 over the duration of their stay in

the Sunshine Coast while non-VFRs spent

$2023.30 Like non-VFR travellers, CVFR ellers were shown to expend considerable funds on accommodation, as well as across a broad range of categories, particularly restau-rants and cafes, retail stores and tourist attrac-tions There was no statistical difference between the two groups, indicating that in terms of levels of expenditure, CVFR travel-lers are as important as non-VFR travellers for every category of expenditure including accommodation (Table 6)

trav-Information source Based on a chi-square test at

the 95% confi dence level, there was an overall statistical difference between CVFR and non-VFR travellers for what source of information was used for planning the trip (Table 7) Indi-

vidual z-tests were run to understand what

was driving this overall difference Fisher’s exact test was used for testing travel shows, as the cell counts were low Based on these tests, word of mouth was signifi cantly greater for CVFR travellers than non-VFR travellers at the 95% confi dence level While four-fi fths (79.7%)

of CVFR travellers relied on word of mouth as

a source of information to plan their trip, less than two-thirds (64.8%) of non-VFR travellers relied on word of mouth Other information sources such as information centres, travel agents, travel (consumer) shows, Internet and media were all more popular for non-VFR travellers compared with CVFR travellers, although at an individual pairwise level, were not signifi cantly different at the 95% confi -dence level This highlights the particular importance of word of mouth for VFR travel

in which, local residents would be expected to

be a major contributing source Despite staying

Table 5 Relationship between purpose of visit and length of stay for visitors staying in commercial accommodation

n Mean Log mean Standard

deviationCVFR 60 14.3 81 50Non-VFR 509 7.8 73 34

Levene statistic = 12.48 (p < 0.05); t(65) = 1.17 (p > 0.05).

CVFR, VFR travellers staying in commercial tion; VFR, visiting friends and relatives.

n

χ2 p

Word of mouth 79.7 47 64.8 320 5.606 0.018*Internet 10.2 6 19.4 96 2.864 0.091Travel agent 10.2 6 11.7 58 0.105 0.746Media 3.4 2 10.1 50 2.722 0.099Information centre 1.7 1 7.1 35 2.458 0.117Travel show 0 0 2.2 11 1.322 0.616

* Signifi cant at the 95% level.

χ 2= 15.024; d.f = 6; p < 0.05.

CVFR, VFR travellers staying in commercial accommodation; VFR, visiting friends and relatives.

Table 8 Relationship between purpose of visit and travel party size for visitors staying in commercial accommodation

CVFRMean (Log/SD)

Non-VFRMean (Log/SD)

CVFR, VFR travellers staying in commercial accommodation; ns, not signifi cant; SD, standard deviation; VFR, visiting friends and relatives.

in commercial accommodation, this source

was still critical for CVFR travellers’

planning

Travel party As shown in Table 8, there was no

statistically signifi cant overall difference in the

size of the travel parties for visitors staying

in commercial accommodation regardless of purpose of visit Neither, the mean number of adults, or children, or the total travel party size was found to be signifi cantly different between the two groups

Table 6 Relationship between purpose of visit and trip expenditures for visitors staying in commercial accommodation

CVFR Mean (Log/SD) Non-VFR Mean (Log/SD) Levene’s F-test t-value p

* Signifi cant at the 95% level.

CVFR, VFR travellers staying in commercial accommodation; SD, standard deviation; VFR, visiting friends and relatives.