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Tourism Destination Attractiveness: Attractions, Facilities, and People as Predictors

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This article examines the influence of tourist attractions, destination support services, and people related factors on the attractiveness of a tourism destination. A sample consists of 275 tourists visiting major tourism destinations. Through moderated regression of models the study identifies the main contributors to destination attractiveness. Destination attractions are found to be the core determinants of the attractiveness; destination support facilities and services, and people-related factors are the secondary determinants. Support facilities and services and people-related factors explain equivalent variances suggesting they are complementary rather than substitutes.
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Tourism Analysis, Vol. 14, pp. 621–636 1083-5423/09 $60.00 +.00
Printed in the USA. All rights reserved. DOI: 10.3727/108354209X12597959359211
Copyright 2009 Cognizant Comm. Corp. www.cognizantcommunication.com
TOURISM DESTINATION ATTRACTIVENESS: ATTRACTIONS,
FACILITIES, AND PEOPLE AS PREDICTORS
SEBASTIAN VENGESAYI,* FELIX T. MAVONDO,† and YVETTE REISINGER‡
*School of Management, Faculty of Business, University of Tasmania, Hobart Campus, Tasmania, Australia
†Department of Marketing, Faculty of Business and Economics,
Monash University, Clayton Campus, Melbourne, Australia
‡School of Tourism and Hospitality Management, Temple University, Philadelphia, PA, USA
This article examines the influence of tourist attractions, destination support services, and people
related factors on the attractiveness of a tourism destination. A sample consists of 275 tourists
visiting major tourism destinations. Through moderated regression of models the study identifies
the main contributors to destination attractiveness. Destination attractions are found to be the core
determinants of the attractiveness; destination support facilities and services, and people-related
factors are the secondary determinants. Support facilities and services and people-related factors
explain equivalent variances suggesting they are complementary rather than substitutes.
Key words: Destination attractiveness; Attractions; Support services; People
Introduction nation attributes by attempting to group the desti-
nation attributes into broad categories that destina-
tion managers could use to prioritize the allocationThe study of destination attractiveness is lim-
ited (Formica, 2002) in that attempts to measure of resources for the benefit of both tourists and
destination operators. The study contributes to mar-or assess the attractiveness of tourism destinations
are ad hoc and therefore of little use to most stake- keting theory and practice by developing testable
models that show which groups of destination at-holders at these destinations. The existing destina-
tion studies seek to identify the most popular des- tributes are important, how much each group adds
to explaining destination attractiveness and whattination attributes, that is, attractions and activities
within a destination that are popular among tour- the managerial implications are for each group.
ists. Little attempt is however made to highlight,
group, and rank these attributes in a way that Literature Review
could help destinations to allocate resources and
prioritize the development of destination facilities. The attractiveness of a tourism destination is
often referred to the opinions of visitors about theThis study goes beyond identifying popular desti-
Address correspondence to Yvette Reisinger, School of Tourism and Hospitality Management, Temple University, 1810 N. 13th
Street, Speakman Hall, Philadelphia, PA 19122. Tel: 215-204-7139; Fax: 215-204-8705; E-mail: yvette.reisinger@temple.edu.
621
622 VENGESAYI, MAVONDO, AND REISINGER
destination’s perceived ability to satisfy their needs. There are significant spatial differences in
terms of resource availability and tourists’ percep-Research has shown that attractiveness studies are
necessary for understanding the elements that en- tions of the ability of these resources to deliver
individual benefits (Formica & Uysal, 2006). Identi-courage people to travel (Formica, 2002). The
more a destination is able to meet the needs of fying and understanding the main resources which
contribute to tourists’ perceptions of their abilitytourists, the more the destination is perceived to
be attractive and the more the destination is likely to deliver individual benefits that is destination at-
tractiveness is of importance because it could beto be chosen in preference to competing destina-
tions. Thus, the major value of destination attrac- used as a decision-making tool in planning, market-
ing, and developing appropriate resource alloca-tiveness is the pulling effect attractiveness has on
tourists (Kim & Lee, 2002). tion strategies.
A number of studies identify the attributes thatMayo and Jarvis (1981) define destination at-
tractiveness as “the relative importance of individ- tourists consider as important in evaluating the at-
tractiveness of a destination (Gearing, William,ual benefits and the perceived ability of the desti-
nation to deliver these individual benefits” (p. Swart, & Var 1974; Kim, 1998; Meinung, 1995).
For example, Middleton (1989) examines three at-201). This ability is enhanced by the specific attri-
butes of a destination that makeup the destination tributes of destination attractiveness: facilities,
prices of venues, and transport networks. How-such as attractions, infrastructure or services and
people providing these services. According to Hu ever, these attributes explain only a small propor-
tion of destination attractiveness. Gartner (1989)and Ritchie (1993), a tourism destination is there-
fore a combination of destination attributes, mostly identifies several other attributes of destination at-
tractiveness, including historic and cultural sites,tourist facilities and services. In an assessment of
the attractiveness of a destination tourists evaluate nightlife, liquor, outdoor life, natural environment,
and receptiveness among others. Meinung (1995)the perceived ability of the destination attributes
to meet their needs (Mayo & Jarvis, 1981). It is argues that scenery is one of the most important
attributes in attracting tourists, while cultural attri-generally believed that the attractiveness of a des-
tination is enhanced the more attributes the desti- butes are growing in importance in the global de-
mand for tourism. In a study of Korean destina-nation has. In order to attract visitors destinations
develop facilities and services to enhance its at- tions, Kim (1998) lists several other factors
affecting the attractiveness of a destination. Thesetractiveness. The attractiveness of a destination di-
minishes in the absence of these attributes. More- are clean and peaceful environment, quality of ac-
commodation facilities, family-oriented amenities,over, in the absence of destination attractiveness
tourism would not exist and there could be little safety, accessibility, reputation, entertainment, and
recreational opportunities. According to Hu andor no need for tourist facilities and services (Kim
& Lee, 2002). However, some destinations such Ritchie (1993), the attractiveness of tourism desti-
nations depends on the context of the vacation ex-as isolated tropical islands or small coastal towns
offer a limited range of facilities and services and perience and, in particular, educational and recre-
ational travel context. An important finding fromare very successful.
Destination attractiveness is distinguished from their study is that “certain potentially negative at-
tributes of destination are more acceptable for cer-destination competitiveness. While the attractive-
ness of a destination depends on the relationship tain types of vacation (educational) than others
(recreational)” (p. 34).between the availability of existing attractions and
the perceived importance of such attractions (For- The studies of destination image highlight the
importance of destination attributes in developingmica & Uysal, 2006) and their ability to deliver
benefits to tourists (demand), destination competi- positive destination images. Different destinations
have different images and thereby attract differenttiveness depends on the availability of resources
and a destination ability to use these resources ef- people (Gartner, 1989; Haahti, 1986). Goodrich
(1978) concludes that there is a direct associationfectively over the long term to attract visitors (sup-
ply) (Ritchie & Crouch, 2003). between tourists’ perceptions of a destination and
TOURISM DESTINATION ATTRACTIVENESS 623
preferences for that destination. The more favor- identification of the core destination attributes
able the perception of a destination, the more at- should be a priority for destination researchers,
tractive and preferred that destination tends to be. given the need for destination managers and mar-
Fakeye and Crompton (1991), who examine the keters to allocate scarce developmental resources.
image differences between visitors to Texas, con- Because of the limited resources available to
clude that the following destination attributes de- the tourism industry for developmental purposes,
termine its attractiveness: social opportunities, nat- not all destination attributes can be developed si-
ural and cultural attractions, amenities, transport, multaneously. Conventional wisdom dictates that
and entertainment. certain attributes should be developed earlier than
In an attempt to capture the major determinants others. The resources that should be allocated first
of destination attractiveness, Genest and Legg to the development of a tourism destination are
(2003) identify three dimensions of attractiveness: those that are perceived to provide the greatest en-
product, performance, and futurity. They argue that hancement to its attractiveness. For this to be pos-
a premier destination is the one that provides a sible, the attributes should be categorized into
quality tourist product and experience. The perfor- groups.
mance dimension is measured by visitation levels, Many researchers have categorized destination
and the futurity dimension is sustained by market- attributes into groups (Ferrario, 1979; Lew, 1987;
ing, product renewal and effective management of Leiper, 1990; Ritchie & Zins, 1978). The grouping
destination capacities. Though this evaluative of destination attributespredictors of destination
analysis of a destination helps to assess its attrac- attractiveness—has its roots in the study by Ferra-
tiveness, there are various questions that remain rio (1979). According to Ferrario, for a destination
unanswered, such as 1) How each dimension is to be attractive there should be something very
evaluated and what is more important: the product special within it (i.e., an attraction). Thus, attrac-
dimension or the performances dimension; 2) From tions represent the first important group or cate-
what perspective are the destinations being ana- gory of destination attractiveness. This assertion is
lyzed—supply perspective or demand perspective? supported by Crouch and Ritchie (1999), who note
Also, high visitation levels do not automatically that attractions are the primary factors that pull
transform to high destination attractiveness since people to visit a destination and thus destination
visitation levels may be influenced by other fac- attractions are the main factors of destination at-
tors like the destination proximity to the source tractiveness. In order for tourism to flourish there
market (Prideaux, 2000a) and market accessibility should be attractions within a destination; other at-
(McKercher, 1998). tributes are complementary. The second group of
Further, although destination attractiveness destination attributes that predict its attractiveness
studies (see Echtner & Ritchie, 1993; Hu & Ritchie, is represented by destination support services and
1993; Klenosky, 2002; Lee, 2001; Meinung, 1995) facilities. According to Dwyer, Livaic, and Mellor
highlight attributes that determine the attractive- (2003) and Ritchie and Crouch (2000), destination
ness of a tourism destination, the magnitude and support services and facilities play a complimen-
strength of each attribute are not being explored. tary role in predicting the success of a destination.
Only few attempts are made to categorize the attri- However, without attractions within a destination,
butes that are important to destinations and inves-
support services become irrelevant. The third group
tigate their magnitude, strength, and contribution
of destination attractiveness predictors includes
to destination attractiveness.
people-related factors. People-related factors com-
This study extends prior studies through cate-
pliment the role destination attractions play in de-
gorizing (and ranking) the attributes of destination
termining the attractiveness of a destination. On
attractiveness as perceived by those attracted to a
their own, people related-factors are not useful;
destination (i.e., the tourist). A review of literature
they require the existence of attractions and sup-
suggests that destinations are multiattribute and
port facilities and services to which people can
thus the identification of the various categories of
these attributes becomes important. Further, the add value.
624 VENGESAYI, MAVONDO, AND REISINGER
Purpose of the Study 2000). These factors contribute much to the char-
acter of many coastal, island, and isolated destina-
This study attempts to empirically examine and tions. According to Ferrario (1979), the real ele-
categorize the attributes that are predictors of des- ment that determines destination attractiveness is
tination attractiveness and establish their relative the presence of “something interesting or unusual
contributions to attractiveness. The classification to see or do” (p. 18).
and ranking of attractiveness predictors help desti- There are different classifications and catego-
nations to allocate their scarce resources. This ries of destination attractions (Crouch & Ritchie,
study attempts to integrate previous studies on 1999; Ferrario, 1979; Formica, 2001; Ritchie &
destination attractiveness by incorporating three Crouch, 1993). Destination attractions come in
groups of destination predictors such as attrac- several different shapes, sizes, and forms (Walsh-
tions, support services, and people-related factors, Heron, 1990). For example, Leask (2003) and
as indicated in the literature. The study then em- Swaarbrooke (1995) classify attractions into two
pirically examines the contribution of each of broad classes, namely man-made and natural.
these groups to the perceived attractiveness of a Man-made attractions are created by human be-
destination. A brief description of each group of ings (e.g., historical monuments or theme parks)
attributes that predict destination attractiveness is and the examples of natural attractions are the un-
discussed in the following sections. usual flora and fauna and spectacles such as Victo-
ria Falls (Holloway, 1998). Some classify attrac-
Destination Attractions
tions into site and events, with a site being the
Destination attractions are the fundamental destination that appeals to visitors (e.g., a national
tourism core assets that tourism destinations pos- park) while an event being something that draws
sess. These attractions define the framework people because of what is taking place (e.g., Soc-
within which visitors enjoy their vacations. They cer World Cup tournament). Goeldner, Ritchie,
include all forms of natural and created (man- and McIntosh (2000) categorize attractions into
made) resources, culture, heritage, history, cus- five main groups such as cultural, natural, events,
toms, architectural features, traditional artwork, recreation, and entertainment. Attractions may also
cuisine, music, and handicrafts that attract travel- include landscapes, activities, and experiences.
ers (Crouch & Ritchie, 1999; Goeldner & Ritchie, For example, Crouch and Ritchie (1999) divide at-
2003; Walsh-Heron, 1990). The most ideal desti- tractions into six categories: physiography, culture
nation attractions are those that are rare, inimita- and history, market ties, activities, events, and the
ble, and only available at a particular destination tourism superstructure. This categorization, how-
or at very few destinations. Flagestad and Hope ever, appears to be contrary to the recommenda-
(2001) argue that destination attractions ought not tions of Ferrario (1979), in which there should be
to be limited to physical attractions only. How- a clear distinction between the unusual elements
ever, Flagestad and Hope’s assertion can be criti- that determine the character of a destination (cul-
cized in that the nonphysical attractions are found ture, history, or events) and other supporting ele-
to play only a supporting role to destination’s core ments like market ties and tourism superstructure.
attractions (Goeldner & Ritchie, 2003). Lew (1987), however, correctly observes that it is
Destination attractions are the primary elements
difficult to differentiate between attractions and
of destination appeal; they are the key motivators
nonattractions as some forms of infrastructure
for tourist visitation and the fundamental reasons
such as transportation (e.g., cruise liners), accom-
why prospective visitors choose one destination
modations (e.g., resorts), and other services (e.g.,
over another (Crouch & Ritchie, 1999). Several
restaurants) can themselves be attractions. Even
other destination factors that contribute to and en-
tourists can become attractions (MacCannell,
hance the attractiveness of a tourist destination
1976). For example, MacCannell notes that virtu-
have been identified such as climate (Hu & Ritchie,
ally anything can become a tourist attraction. Not
1993), communication facilities (Falk, 2002), and
favorable exchange rates (Dwyer, Forsyth, & Rao, only the historical and natural sites but also ser-
TOURISM DESTINATION ATTRACTIVENESS 625
vices and facilities that cater for tourists can be around and in which destination services (e.g. roads,
water, sewage) are developed and maintained toincluded in attractions.
Tourists can also be attracted to a destination high standards. The major destination support ser-
vices are provided by accommodation, transporta-by being “involved and active” in the attractions
like white water rafting, hunting safaris (Ferrario, tion, and communication facilities. These support
services enable tourism destinations to develop as1979), bush trails, and bicycle rides. Crouch and
Ritchie (1999) suggest that the challenge to desti- well as to monitor negative aspects of this devel-
opment and take corrective actions in order to re-nations is to develop active attractions that take
advantage of the natural physiography of the desti- main sustainable.
The important role of the support services andnation while remaining consistent with the local
culture and value. facilities in predicting destination attractiveness is
highlighted by Crouch and Ritchie (1999). SomeWhatever the classification of destination at-
tractions it is evident that different attractions at- theoretical studies attempt to relate the adequacy
of support services to the number of visitors thetract different types of tourists to destinations and
satisfy different needs. For example, destinations destination receives. However, there appears to be
no empirical investigation that underpins the theo-that offer an entertaining nightlife may appeal to
young travelers, whereas destinations that offer retical relationship between destination support
services and destination attractiveness. This studyeasily accessible children-friendly facilities would
be of value to families with young children. Also, attempts to provide this evidence.
those destinations that have a relatively easy and
low cost transportation to and from the destination People-Related Factors
may be more attractive to senior travelers. Tourism is a labor-intensive industry that fre-
One can conclude that, at least for most desti- quently involves the interaction between people
nations, the more diverse attractions a destination (Wright, Dunford, & Snell, 2001). The social in-
has, the more attractive that destination is to the teraction between tourists and local people is cru-
tourist market (Goeldner et al., 2000). Many suc- cial in attracting people to a destination (Smith,
cessful tourism destinations that offer a wide vari- 2000). Tourists usually meet and interact with lo-
ety of popular attractions cater to numerous target cals while visiting a destination. This social inter-
markets. However, some of the most successful action plays an important role in developing tour-
mass tourism destinations have a limited range of ists’ perceptions of how attractive the destination
attractions such as sun, sand, and sea. Further, be- is to a tourist. The most attractive and reputable
cause the uniqueness of an attraction is a major tourism destinations are well known for the friend-
pull factor that determines the visitation level to a liness of their residents. Thus, local residents,
destination, the more unique an attraction is the whether they are employees of the tourism and
more attractive is the destination (Swaarbrooke, hospitality industry or the general public, influ-
1995). ence the attractiveness of a destination.
The role that a destination’s people play in en-
Destination Support Services hancing the destination attractiveness is widely
documented in literature (e.g., Baum, 1995, 1996;The development of a destination requires care-
ful planning and management (Prideaux, 2000b). Crouch & Ritchie, 1999). Some studies explain
the importance of human resources to the tourismLiterature abounds with examples of destinations
that have been haphazardly developed thus result- industry (Conlin & Titcombe, 1995; Esichaikul &
Baum, 1998). The availability of adequately anding in major problems to the locals (Prideaux,
2000b). Destinations need to be nurtured and their professionally trained staff is reported to be an es-
sential component of today’s destinations withoutdevelopment monitored if they are to maintain an
acceptable growth pattern (Gearing et al., 1974). which the destination product becomes disadvan-
taged. Well-trained personnel are required in allNurturing a destination involves the creation of an
environment in which visitors feel free to move the service establishments at a destination (Bri-
626 VENGESAYI, MAVONDO, AND REISINGER
guglio & Vella, 1995). Various studies discuss the jor cause for concern for the local tourism indus-
try. It was felt necessary to examine the country’srelationship between destination employees and its
attractiveness (Baum, 1993; Cameron & Harvey, attractiveness as a tourism destination and identify
its major predictors so destination managers could1994; Jafari & Fayos-Sola, 1995; Lucas & Perrin,
1994). prioritize the allocation of resources for the benefit
of both tourists and business operators.
The tourism industry in Zimbabwe is mostlyHypotheses
focused on its natural attractions; most visitors are
The study hypothesizes that the attractiveness attracted by Victoria Falls, the Great Zimbabwe
of a tourist destination is primarily dependent on Ruins, and wildlife that is in abundance. Zim-
the attractions available at a destination and that babwe has also eight national parks that are spread
different destination attributes have different pull- throughout the country. These national parks are
ing effects on tourists. Because attractions are the home to a variety of animals including the big
core predictors of each tourism destination’s at- five.
tractiveness they are treated as the control vari-
ables in this study. Destination support services Method
and people-related factors provide support to the
Sample
attractions in making destination attractive. Hence,
they are hypothesized to play a secondary comple- The primary unit of analysis in this study is a
mentary role in destination attractiveness. This study tourist. Tourists are selected because they are ca-
predicts that the total variance explained by desti- pable of identifying what attracts them to one des-
nation attractions in destination attractiveness will tination over another. Tourists’ opinions of what
be much higher than the variance explained by attracts them to a destination are important in de-
destination support services and people-related fac- termining the attractiveness of a destination.
tors. Although the sample used in the study con-
sisted mostly of international visitors to Zim-
The Study Context babwe, a limited number of Zimbabwe nationals
were also included. However, only those Zimbab-
This study was conducted in Zimbabwe located weans who were not residents in the local areas
in the southern part of Africa with a current popu- and who were visiting the area for the purposes of
lation of about 11 million people. Today African vacationing were included in the study. In order
countries attract an increasing number of interna- to improve the representativeness of the sample,
tional tourists who search for new and authentic visitors were sampled from different geographical
experiences. Since attaining its independence in locations and at different types of attractions (see
1980, the tourism industry in Zimbabwe grew by data collection procedure). A total of 300 tourists
an average rate of 6% per year. By 1995 the coun- were surveyed.
try received a high of 2.5 million visitors and di-
rectly employed almost 100,000 people; tourism Development of Measures
became the fastest growing sector in the country’s
economy (Zimbabwe Tourism Authority, 2005). A self-completion questionnaire was used as
the data collection instrument. A survey instru-Unfortunately, in the last several years the tourism
industry in Zimbabwe has experienced a down ment included the factors that were postulated to
influence the attractiveness of tourism destina-turn. In 1999 the tourism industry contributed 15%
to the GDP; yet in 2005 tourism contributed only tions. Because no universal set of items that mea-
sure destination attractiveness exists, the items2% to the GDP (Zimbabwe Tourism Authority,
2005). The decline in Zimbabwean tourism is used in this study derived from previous destina-
tion attractiveness and image studies (Ferrario,attributed to the negative publicity the country
receives from the international community for po- 1979; Haahti, 1986; Hu & Ritchie, 1993; Kim,
1998; Middleton, 1989; Tang & Rochananond,litical reasons. This decline in attractiveness of
Zimbabwe as a tourism destination becomes a ma- 1990).
TOURISM DESTINATION ATTRACTIVENESS 627
Table 1
Despite the identification of the individual de-
Factor Analysis and Reliability Statistics
terminants of destination attractiveness in the ex-
for Destination Attractions
tant tourism literature, a close examination of
these determinants revealed three clearly identifi-
Reliability
able groups of factors: destination attractions, des-
(Cronbach’s
Attractions Loadings Alpha)
tination support services, and people-related fac-
tors. The items used in this study were also
Overall destination attractions 0.892
clustered into three main groups based on Crouch
Cultural and historical attrac-
tions 0.826
and Ritchie’s (1999) and Enright and Newton’s
Historical sites 0.692
(2004) categorization. The third factor was called
Cultural sites 0.769
“people-related factors” in recognizing the impor-
Architectural sites 0.622
Traditional arts 0.770
tant role that local people play in making a desti-
Cultural arts 0.700
nation attractive.
Natural attractions 0.776
Exploratory factor analysis was used to deter-
National parks 0.800
Wildlife 0.763
mine the dimensions of the three major groups of
Natural wonders/scenery 0.751
destination attractiveness. Orthogonal rotation was
Recreation facilities 0.655
chosen to maximize the differences among the di-
Pleasant weather 0.661
Water-based activities 0.644
mensions extracted. Only factors with eigenvalue
Recreational activities 0.532
greater than 1 (unit) and items with factor loadings
Unique attractions 0.742
greater than 0.6 were retained for further analysis.
Variety of attractions 0.652
Shopping facilities 0.608
Tables 1, 2, and 3 show the factors and factor
Accessibility of attractions 0.824
loadings of the respective variables.
Unique attractions 0.546
Table 1 shows the dimensions of attractions
Created attractions 0.837
Tours 0.533
that were identified through exploratory factor
Opportunities for sport 0.761
analysis (EFA) of the destination attractions items.
Special events 0.682
Factor analysis resulted in the five dimensions of
Sports facilities 0.757
Nature-based activities 0.770
destination attractions namely historical, natural,
unique and created attractions, and recreational
and recreation. They were found to determine the
character of a destination and provide something Prior to the major field study, a series of steps
interesting and/or unusual to see or do, as sug- was taken to refine the questionnaire and to ensure
gested by Ferrario (1979). Recreation has been a smooth conduct of the study. The questionnaire
classified under attractions because recreation can was pretested on 20 graduate students and faculty
be attractions on their own, though they can members to determine the quality of the question-
equally be classified under support services. How- naire, absence of ambiguous questions, time it
ever, it is believed they are more of attractions took to complete the questionnaire, and the gen-
than support services, especially for families with eral flow and layout of the questionnaire.
children and sport/fitness fanatics. The classifica-
tion of recreation under destination attractions is Instrument and Measures
further supported by the need for more active and
involving consumptive tourism activities. In this study, destination attractiveness was
conceptualized as attractiveness relative to com-Table 2 shows the dimensions of destination
support services as identified by factor analysis. peting destinations. The concept was measured by
asking 12 questions on how attractive various as-These dimensions are accommodation facilities,
destination utilities, communication facilities, and pects of the destination were relative to the alter-
native the tourists would have chosen. A “destina-destination accessibility. Table 3 shows the di-
mensions of people-related factors that include at- tion” was conceptualized and operationalized at
the level of the study site. The questions clearlytitude of local people to tourists, physical risk,
health risk, and customer service. stated “for this particular destination site NOT for
628 VENGESAYI, MAVONDO, AND REISINGER
Table 2 tent to which these contribute to destination attrac-
Factor Analysis and Reliability Statistics tiveness. The physical environment was added to
for Destination Support Services the attraction factors presented in Table 1.
The attractiveness of a destination may depend
Reliability
on the quality of the physical environment. The
(Cronbach’s
Supporting Services Loadings Alpha)
quality of the natural environment is related to the
attractiveness of destinations (Butler, 2000; Hu &
Overall destination supporting
Ritchie, 1993). Mihalic (2000) argues that a well-
services 0.897
Accommodation facilities 0.825
managed destination environment is a requirement
Quality of facilities 0.709
if the attractiveness of a destination is to be en-
Variety of facilities 0.647
Upmarket facilities 0.781
hanced. Go and Govers (2000) and Hu and Ritchie
Value for money 0.523
(1993) note that the quality of the physical envi-
Destination utilities 0.749
ronment is one of the most mentioned attractive-
Clean water 0.784
Electricity 0.683
ness determinants. Also, the environment commu-
Communication facilities 0.760
nicates an image of the destination, signals its
Direct flights 0.624
market positioning and differentiates it from com-
Modern communication
facilities 0.796
petitors. Although some may question whether
Internet 0.716
destination attractiveness depends on environment
Destination accessibility 0.818
Quality of road system 0.709
Traffic congestion 0.747
Car rental facilities 0.755
Range/cars for rent 0.701
Adequate transport networks 0.538
Table 3
Adequate internal transportation 0.565
Factor Analysis and Reliability Statistics
Accessibility of attraction sites 0.599
for People-Related Factors
Loadings less than 0.40 were suppressed. Reliability
(Cronbach’a
People-Related Factors Loadings Alpha)
Overall people-related factors 0.763
the country.” The list of items respondents were
Attitude of locals to tourists 0.757
asked about to assess destination attractiveness is
Willingness of locals to help 0.419
Attitude of employees to tour-
presented in Table 4.
ists 0.702
On exploratory factor analysis this produced
Attitude of locals to tourists 0.717
three factors that the authors interpreted as (a)
Friendliness of locals 0.496
Physical risk 0.709
attractiveness of destination physical atmosphere
Peaceful environment 0.804
(α=0.732); (b) friendliness of the people (α=
Availability of police patrols 0.836
832); and (c) variety of activities (α=0.757). Des-
Safe/secure parks 0.745
Political stability 0.507
tination attractiveness was therefore operational-
Health risk 0.723
ized as a second-order factor of these three latent
Hygiene standard 0.682
variables. The overall Chronbach was α=0.769.
Risk of illness 0.879
Personal health safety 0.496
The confirmatory model had strong fit measures
Modern medical facilities 0.729
and the average variance extracted (AVE) was
Customer service 0.785
0.695. For computation into the regression models
Appearance of employees 0.641
Ability to speak English 0.678
destination attractiveness was computed as the av-
Other foreign languages skills 0.538
erage of the three dimensions represented by the
Tour guiding skills 0.534
above latent factors.
Employees’ knowledge of lo-
cal attractions 0.692
A structured questionnaire was used to measure
Attitude of immigration offi-
the constructs. Respondents were asked to rate the
cials 0.536
importance of various attractions, services and
facilities, and people-related factors, and the ex-
Loadings less than 0.40 were suppressed.
TOURISM DESTINATION ATTRACTIVENESS 629
Table 4
Destination Attractiveness Measures
Friendliness of local people 1234567
Attitude of local people towards tourists 1234567
Your ability to communicate with local people 1234567
The willingness of residents to help tourists 1234567
The quality of the natural environment 1234567
The level of safety provided by the destination 1234567
Availability and quality of local transportation 1234567
The facilities available for tourists to use 1234567
The level of prices 1234567
The professionalism of service staff 1234567
Availability of information about the destination 1234567
The quality of information about the destination 1234567
(Bramwell et al., 1996), most tourists are now Data Collection Procedure
convinced of the need to conserve the physical en- The questionnaire was administered by the
vironment and are attracted to destinations that main researcher. Visitors were approached at the
preserve the environment (Holden, 2000). major tourist destinations in Zimbabwe such as
Residents’ support for tourism was added to the Victoria Falls (46% of the sample), Zimbabwe Ru-
people-related factors presented in Table 3. The ins in Masvingo (18%) and Matopo (15%) region,
residents’ support for tourism is important because Great Zimbabwe National Park, the Eastern High-
destinations may not be attractive without the sup- lands (12%), and Kariba (9%). Respondents were
port of the local residents and government. The requested to complete the questionnaire while visit-
strength of the residents’ support for tourism is ing a particular attraction site. This method ensured
an important determinant of how welcomed and that the responses were current and not dependent
needed tourists feel at a destination. The feelings on memory recall. The completed questionnaires
of welcoming and hospitality make a particular were collected by the researcher. Only those re-
destination attractive to tourists. spondents who were older than 18 years were
In addition, respondents were requested to rate asked to participate in the study. Only one partner
the attractiveness of the destination in which the in a couple was interviewed. It took about 20 min-
research was conducted. The measuring items utes to complete the questionnaire.
were adapted from previous studies (e.g., Gearing Out of 300 returned questionnaires, 25 were not
et al., & Var, 1974; Hu & Ritchie, 1993; Klen- usable; they had missing data. As a result, 275
osky, 2002; Tang & Rochananond, 1990). The completed questionnaires were used for further
same questions were asked at different sites. Fi- analysis, giving an effective response rate of 91.7%.
nally, the questionnaire asked the general informa-
tion about the respondents pertaining to their na- Respondent Demographics
tionality, age, and gender.
A 7-point Likert-type scale was used to mea- The sample was balanced in terms of gender;
there was 52% male and 48% female respondents.sure the responses that ranged from 1 =not impor-
tant at all to 7 =very important. The 7-point Likert The age distribution was as follows: nearly 11%
of the respondents were younger than 20 years,scale was chosen because it has advantages in
terms of increasing reliability (Zikmund, 2000). 36.4% were in the 21–29 age brackets, nearly
50% were between 30–49 years of age, and onlyThis scale is also common in empirical studies
(Mavondo, 1999; Slater & Narver, 1993). The use 3% were older than 50 years. Clearly, the majority
of the respondents were middle-aged tourists. Theof an odd number of response options provides a
midpoint, which represents a point of neutrality on majority of the respondents were from Europe
(45.5%), followed by other African countries (18.5%),a scale. Few sections of the questionnaire also
used nominal scales to generate categorical data. and Australasia (14.2%). Australasia is a region
630 VENGESAYI, MAVONDO, AND REISINGER
Table 5
Internal Consistency for Destination Attractions Construct
Internal
Construct Consistency CA HA UA NA REC PE
Created attractions 0.8456 0.7160
Historical attractions 0.8161 0.436 (0.36) 0.6780
Unique attractions 0.7480 0.446 (0.49) 0.428 (0.41) 0.6525
Natural attractions 0.7925 0.159 (0.14) 0.374 (0.46) 0.283 (0.37) 0.7400
Recreation 0.5787 0.455 (0.62) 0.347 (0.42) 0.348 (0.57) 0.324 (0.51) 0.5567
Physical environment 0.8003 0.309 (0.25) 0.342 (0.30) 0.267 (0.37) 0.392 (0.48) 0.287 (0.39) 0.6640
Values in parentheses represent correlations; values in bold represent the square root of average variance extracted.
of Oceania that includes New Zealand, Australia, The use of average variance shared between a
construct and its measures, or AVE was suggestedPapua New Guinea, and neighboring islands in the
Pacific Ocean. A small percentage (8%) of the to- by Fornell and Larcker (1981) for assessment of
discriminant validity.
tal sample was represented by tourists from the
Tables 5–7 show the coefficient alpha scores
US and Canada. The composition of the respon-
of the measures used in this study. Churchill ar-
dents in this study mirrors official statistics for
gues that the reliability of a developing measure-
these destinations suggesting the sample was fairly
ment instrument should be assessed by calculating
representative of the visitors to Zimbabwe.
coefficient of alpha (Churchill, 1979; Cronbach,
1951). The guideline is that the coefficient alpha
Data Analysis
should be 0.70 or above (De Vaus, 2001). The co-
In order to examine if factors in the three efficient of alpha score for all the measures used
groups of destination attractiveness factors (attrac- in this study ranged from 0.56 to 0.74. Churchill
tions, support services, people-related factors) (1979) recommends the deletion of some items in
were not measuring the same attributes, discrimi- a multi-item measure in order to improve the mea-
nant validity of the factors in all groups was run; sure’s reliability. Confirmatory factor analysis did
the results are shown in Tables 5–7. The reliability not support any deletion of items from these mea-
of each of the constructs used in this study was sures (Smith, 1999). Though some of the alpha
recalculated using Fornell and Larcker’s (1981) scores appear low they were accepted for this
internal consistency formula but using the notation study, based on Nunally’s (1967) suggestion that
in Mavondo and Conduit (2000) as shown below: reliabilities around 0.60 will suffice during the
early stages of scale development.
Because the aim of the study was to investigate
Variable (ξ)=(Σλ)
2
(Σλ)
2
(δ)the relationship between destination attractions,
destination support services, people-related factors,
and destination attractiveness, a series of regres-where λis the regression weight and δ=(1 −λ
2
).
Table 6
Internal Consistency for Destination Support Services Construct
Internal
Construct Consistency AF DU CF AD
Accommodation facilities 0.8367 0.7100
Destination utilities 0.8142 0.560 (0.68) 0.7175
Communications facilities 0.6923 0.482 (0.66) 0.483 (0.56) 0.6500
Accessibility of destination 0.7093 0.509 (0.64) 0.458 (0.52) 0.434 (0.61) 0.6125
Values in parentheses represent correlations; values in bold represent the square root of average
variance extracted.
TOURISM DESTINATION ATTRACTIVENESS 631
Table 7
Internal Consistency for People-Related Factors Construct
Internal
Construct Consistency AT PR HR CS RST
Attitude to tourists 0.5855 0.6400
Physical risk 0.7765 0.280 (0.44) 0.6800
Health risk 0.7515 0.468 (0.66) 0.537 (0.65) 0.6550
Customer service 0.7893 0.343 (0.61) 0.544 (0.68) 0.506 (0.71) 0.5885
Residents’ support for tourism 0.8188 0.457 (0.32) 0.377 (0.32) 0.280 (0.37) 0.458 (0.61) 0.6802
Values in parentheses represent correlations; values in bold represent the square root of average variance extracted.
sion models was run involving attractions, support tions and destination support services. Model 4
seeks to establish the total variance of destinationservices, people factors, and destination attractive-
ness. First, destination attractions and destination attractiveness that is explained by the three groups
attractiveness were run in a regression to assess of factors (i.e., destination attractions, destination
the predictive power of the different types of at- support services, and people-related factors).
tractions on destination attractiveness. Second, the
different types of destination support services were Results and Discussion
entered into a regression with destination attrac- Model 1 investigates the relationship between
tions to assess their combined contribution to des- destination attractions and the attractiveness of a
tination attractiveness. Third, destination attrac- tourist destination. As mentioned previously at-
tions and people-related factors were then entered
tractions are treated as control variables for the
into a regression. Finally, destination attractions,
other models. This is because attractions are as-
destination support services and people-related fac-
sumed to be the most important factors determin-
tors were entered into a moderated regression with
ing the attractiveness of a destination. The results
destination attractiveness. Destination attractions
presented in Table 8 are based on one-tail test be-
were used as the control variable because attrac-
cause specific directions of the relationships are
tions were hypothesized to be the primary factors
hypothesized.
of any tourism destination attractiveness. Without
The results of Model 1 show that destination
attractions, the other variables become irrelevant
attractions explain 32% (R
2
=0.316) of the total
and there is no tourism to talk about (Formica,
variance of destination attractiveness. The strong-
2002). A place might have world class support ser-
est predictor of destination attractiveness is unique
vices in terms of roads or accommodation facili-
attractions (0.32, t=5.25, p<0.001), followed by
ties and high level of customer service but if the
physical environment (0.17, t=2.87, p<0.01),
place does not have the attractions for tourists to
historical attractions (0.16, t=2.54, p<0.01), and
visit, it cannot be called a tourist destination. The
created attractions (0.15, t=2.30, p<0.05).
results are shown in Table 8.
Model 2 is a moderated regression that seeks to
Model 1 in Table 8 investigates the relationship
establish the increment in total variance explained
between attractions and destination attractiveness.
by adding support services into Model 1. The re-
Model 2 seeks to establish the incremental contri-
sults show that Model 2 explains 38% (R
2
=0.376)
bution made by destination support services to ex-
of the total variance in destination attractiveness.
plaining destination attractiveness. Destination sup-
The incremental variance R
2
is significant F-ratio
port services are expected to increase the total
(p<0.001) showing that significantly more total
variance explained by destination attractions. Model
variance in destination attractiveness is explained
3 seeks to establish the incremental contribution, if
by adding support services. The factors with the
any, made by people-related factors to destination
strongest and significant influence on destination
attractiveness. People-related factors are expected
to increase the total variance explained by attrac- attractiveness are destination accessibility (0.22,
632 VENGESAYI, MAVONDO, AND REISINGER
Table 8
Destination Attractions, Destination Support Services, and People-Related Factors as Determinants
of Destination Attractiveness
Models of Destination Attractiveness
Construct (n=275) Direction Model 1 Model 2 Model 3 Model 4
Destination attractions
Created attractions +0.15 (2.30)* 0.16 (2.50)** 0.17 (2.71)** 0.17 (2.63)**
Historical attractions +0.16 (2.54)** 0.15 (2.41)** 0.19 (3.19)*** 0.15 (2.41)**
Unique attractions +0.32 (5.25)*** 0.15 (2.13)* 0.19 (2.86)** 0.22 (1.99)*
Natural attractions +0.05 (0.89) 0.048 (0.85) 0.06 (1.06) 0.03 (0.56)
Recreation facilities +−0.10 (1.66) 0.12 (1.97)* 0.14 (2.38)** 0.14 (2.43)**
Physical environment +0.17 (2.87)** 0.12 (2.15)* 0.08 (1.38) 0.08 (1.39)
Destination support services
Accommodation facilities +0.20 (2.91)** 0.14 (1.76)*
Destination utilities +−0.04 (0.62) 0.02 (0.37)
Communication facilities +0.01 (0.19) 0.01 (0.13)
Destination accessibility +0.22 (3.50)*** 0.20 (3.27)***
People-related factors
Attitude to tourists +−0.09 (1.34) 0.11 (1.71)*
Physical risk −−0.02 (0.36) 0.05 (0.68)
Health risk 0.05 (0.67) 0.1 (0.16)
Customer service +0.20 (2.95)** 0.18 (2.55)**
Residents’ support for tourism +0.13 (2.39)** 0.13 (2.23)*
R
2
0.32 0.38 0.39 0.43
Adj. R
2
0.30 0.34 0.38 0.39
R
2
0.06 (19%) 0.079 (25%) 0.12 (37%)
F-ratio 6.59*** 7.03*** 6.14***
Degrees of freedom F4, 27 5, 27 9, 27
The values in the table are standardized regression weights; the values in parentheses are t-values. Destination attractions
were two-tail tests while support services and people-related factors were one-tail t-tests.
*p<0.05, **p<0.01, ***p<.001.
t=3.50, p<0.001), accommodation facilities <0.01), and residents’ support for tourism (0.13,
t=2.39, p<0.01). The negative significant rela-(0.20, t=2.91, p<0.01), followed by created at-
tractions (0.16, t=2.50, p<0.01), historical at- tionship of recreation with destination attractive-
ness may be explained by the nature-based type oftractions (0.15, t=2.41, p<0.01), unique attrac-
tions (0.15, t=2.13, p<0.05), and recreation a destination under investigation in which recre-
ation is not perceived by tourists as important to(0.12, t=−1.97, p<0.05).
Model 3 is a moderated regression that seeks to destination attractiveness and additionally may re-
flect the lack of adequate recreational facilities inestablish the increment in variance explained by
adding people factors into Model 1. The results developing economies such as Zimbabwe.
Model 4 incorporates all the three groups ofshow that this model explains 40% (R
2
=0.39) of
the total variance in destination attractiveness. The factors to produce the integrated model. This pro-
vides a holistic model of destination attractivenessincremental R
2
is significant F-ratio (p<0.001),
again showing that significantly more variance in as a function of destination attractions, support
services, and people-related factors. This modeldestination attractiveness is explained by adding
people factors. The strongest and significant influ- explains 43% (R
2
=0.43) of the total variance in
destination attractiveness. In Model 4 the factorsence on destination attractiveness have customer
service (0.20, t=2.95, p<0.01), historical attrac- from attractions that remain significant include
created attractions (0.17, t=2.63, p<0.01), his-tions (0.19, t=3.19, p<0.001), unique attractions
(0.19, t=2.86, p<0.01), created attractions (0.17, torical attractions (0.15, t=2.41, p<0.01), unique
attractions (0.22, t=1.99, p<0.05), and recreationt=2.71, p<0.01), recreation (0.14, t=−2.38, p
TOURISM DESTINATION ATTRACTIVENESS 633
(0.14, t=−2.43, p<0.01). The factors from sup- the attitude of the Zimbabwean government to
tourists as positive. This is consistent with interna-port services that remain significant are destina-
tion accessibility (0.20, t=3.27, p<0.001) and tional media portrayal of the government as anti-
Western, undemocratic, and intolerant of free speech.accommodation facilities (0.14, t=1.76, p<0.05).
The factors from people-related factors that re- The results suggest that destination attractions
are the main determinants and predictors of desti-main significant are customer service (0.18, t=
2.55, p<0.01), residents’ support for tourism nation attractiveness as supported by all models in
Table 3. Destination attractions alone account for(0.13, t=2.23, p<0.05) and attitude of local peo-
ple to tourists (0.11, t=−1.71, p<0.05). 32% of the total variance in destination attractive-
ness. When support services and people relatedAcross all models (from Model 1 to Model 4)
factors that have changed in significance include factors are added to the regression, the total vari-
ance explained increased by 12%, to 43% suggest-created attractions ( p<0.01), historical attractions
(p<0.01), recreation (p<0.01), attitude to tourists ing attractions are the most significant predictors
of destination attractiveness. Without attractions(( p<0.05), unique attractions (p<0.05), accom-
modation facilities (p<0.05), and residents’ sup- tourism destinations cannot exist; attractions are
the basis for visitation (Formica, 2002). Theseport for tourism (p<0.05). Factors that did not
change in significance are destination accessibility findings are consistent with Ritchie and Crouch
(2000) suggesting that attractions are the main rea-( p<0.001) and customer service ( p<0.01). From
these results one can compare the two incremental son people visit certain destinations and not oth-
ers. Another support for the hypothesis comesR
2
values from Model 2 and Model 3. The results
suggest that the bigger increment occurs between from the secondary role that support services play
in making a destination more attractive (Crouch &Model 1 and Model 3 (25%) than the one between
Model 1 and Model 2 (19%). Ritchie, 1999; Dwyer et al., 2003). Crouch and
Ritchie (1999) argue that while attractions are the
primary reasons for visitation, supporting services
Destination Attractions, Destination Support
play a secondary role in making a tourism destina-
Service, and People-Related Factors:
tion more attractive by providing the necessary
Integrated Model
facilities for the comfort of tourists.
Destination attractions, destination support ser-
vices, and people-related factors (Model 4) ac- Conclusion and Implications
count for 43% of the total variance in destination
attractiveness. Most of the destination attraction The multiple regressions models give an indi-
cation of the potential explanatory power of thetypes are positively and significantly related to
destination attractiveness (created, historical, unique). various destination factors on destination attrac-
tiveness. While the results show that attractions,Destination accessibility and accommodation facili-
ties are also significantly associated with destina- support services, and people-related factors influ-
ence the attractiveness of a destination, these threetion attractiveness. Surprisingly, accessibility is
the strongest predictor of destination attractiveness groups of factors have different effects on destina-
tion attractiveness. Destination attractions are thein Model 4. The other types of destination support
services such as destination utilities and communi- core determinants of destination attractiveness,
providing support to earlier studies by Formicacation facilities are not significantly related to des-
tination attractiveness. As shown in Model 4 the (2002) and Meinung (1995). The incremental con-
tribution of supporting services and people-relatedpeople-related factors that are significantly associ-
ated with destination attractiveness are customer factors to the contribution of attractions is rela-
tively small (19% and 25%, respectively). People-service, residents’ support for tourism, and attitude
to tourists. The attitude to tourists is significantly related factors make a higher contribution than
supporting services, implying that people-relatedand negatively associated with destination attrac-
tiveness. Although unexpected this result may factors are more highly regarded by tourists in in-
fluencing the attractiveness of a destination thansuggest that tourists to Zimbabwe do not perceive
634 VENGESAYI, MAVONDO, AND REISINGER
supporting services. Interestingly, all the destina- Limitations
tion factors (Model 4) explain more total variance The findings may be country specific and may
in destination attractiveness (43%), making an in- not be generalizable. The circumstances in which
cremental contribution of 37%. This implies that the tourism industry in Zimbabwe finds itself is
developing a destination should involve providing symptomatic of a much wider problem. The econ-
supporting services and developing positive hu- omy has been shrinking over the last decade. The
man relations. country is perceived as unfavorable for foreign in-
From a destination attractiveness perspective, vestment and for attracting international tourists.
these results suggests that as long as destination The country is perceived as undemocratic and de-
attractions are popular, tourists will visit it; how- void of free speech. The contribution of the tour-
ever, the attractiveness of the destination is further ism industry has significantly declined from its
enhanced by availability of support services and highs in 1990s. Thus, the results may reflect a spe-
the attitude of the locals to tourists. People-related cial case.
factors are perceived as more important in deter- This is a study of users, or visitors to particular
mining destination attractiveness than support ser- sites in Zimbabwe. Nonusers (nonvisitors) or those
vices. This might explain why tourists are pre- who have not been attracted to Zimbabwe have
pared to use “substandard” facilities to visit highly not been included in the survey and their ranking
attractive and popular destinations. Destination of the destination factors has not been considered.
managers are therefore encouraged to develop and Perhaps those who assign greater weight to factors
manage destination attraction facilities and edu- such as political instability have decided the coun-
cate local residents on the importance of tourism. try is insufficiently attractive to visit. Sampling
Destination managers are also encouraged to per- only those who have decided to visit is a limitation
suade local residents to be friendly to tourists if a of this study; managers wanting to develop the in-
destination is to remain attractive. This suggests a dustry also need to know why others (nonvisitors)
prioritization of the destination attractions devel- do not find the place attractive to visit.
opment and management, when allocating resources. However, Zimbabwe has some of the interna-
The results might be of great interest to tourism tional attractions; the industry is highly sophisti-
managers and marketers. The study identifies des- cated in terms of products on offer, expertise, and
tination attributes that should be developed and
quality of hotels and services. Those have allowed
marketed to make a destination attractive to tour-
the industry to survive despite the broader geopo-
ists. The study is the first one which empirically
litical issues. The results indicate the resilience of
examined the contribution of each group of the
an industry and adverse macroenvironmental con-
destination attractiveness. The study can be repli-
ditions. One main limitation, common to many
cated in other settings and results compared.
survey studies, is the common method bias. How-
Lastly, the variance explained in Model 4
ever, all the potential checks for this do not sug-
(43%) may appear too low, but when considering
gest this was a problem but this must be accepted
other factors that people think of when selecting a
as a potential limitation. On the more positive
tourism destination and the multifaceted nature of
side, limited research from southern Africa makes
tourism destinations, capturing 43% of the vari-
this a unique and important contribution to tour-
ance appears a big starting point. Other factors
ism studies. The findings of the study are consis-
that people take into consideration when choosing
tent with extant theory and to that extent the con-
a destination may include destination reputation,
textual issues are of much less importance.
safety and security, the costs to and at the destina-
tion, or the distance between their homes and the
destination country. Obviously many people prefer
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