Examining the dimensions of a lifestyle tourism destination

jasminepvk · Feb 1, 2016

Description
To develop and test a measurement model for the combined study of involvement and
place attachment in a tourism context

International Journal of Culture, Tourism and Hospitality Research
Examining the dimensions of a lifestyle tourism destination
Michael J . Gross Chris Brien Graham Brown
Article information:
To cite this document:
Michael J . Gross Chris Brien Graham Brown, (2008),"Examining the dimensions of a lifestyle tourism
destination", International J ournal of Culture, Tourism and Hospitality Research, Vol. 2 Iss 1 pp. 44 - 66
Permanent link to this document:http://dx.doi.org/10.1108/17506180810856130
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Ana M. González, Laurentino Bello, (2002),"The construct “lifestyle” in market segmentation: The
behaviour of tourist consumers", European J ournal of Marketing, Vol. 36 Iss 1/2 pp. 51-85 http://
dx.doi.org/10.1108/03090560210412700
Sameer Hosany, Yuksel Ekinci, Muzaffer Uysal, (2007),"Destination image and destination personality",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 1 Iss 1 pp. 62-81 http://
dx.doi.org/10.1108/17506180710729619
J oseph S. Chen, Dogan Gursoy, (2001),"An investigation of tourists’ destination loyalty and preferences",
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dx.doi.org/10.1108/09596110110381870
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Examining the dimensions
of a lifestyle tourism destination
Michael J. Gross, Chris Brien and Graham Brown
University of South Australia, Adelaide, South Australia
Abstract
Purpose – To develop and test a measurement model for the combined study of involvement and
place attachment in a tourism context.
Design/methodology/approach – The study was conducted in South Australia, a state that has
positioned itself as a lifestyle tourism destination. Tourism involvement was conceptualised as a
multidimensional construct consisting of centrality to lifestyle, attraction, self-expression, and food
and wine. Place attachment was also conceptualised as a multidimensional construct consisting of
place identity and place dependence. Exploratory and con?rmatory factor analyses were used to
develop and test a measurement model using survey data from tourists in South Australia.
Findings – A six factor measurement model was developed and found to have a reasonable ?t with
the data.
Research limitations/implications – The present study ?ndings suggest that a viable theoretical,
practical, and methodological basis can be established to measure the relationships among the
involvement and place attachment constructs in a tourism context. This establishes a sound
foundation for further examination of the predictive nature of the relationships between the constructs.
Practical implications – A better understanding of involvement dimensions and the extent to
which tourism experiences are rooted in place may be of invaluable assistance in the marketing of
tourism destinations.
Originality/value – Involvement and place attachment have received considerable study as
individual constructs in tourism contexts, however their study in combination has been undertaken
only recently, and almost exclusively in leisure and recreation contexts. This study extended the scope
of the combined examination of involvement and place attachment into a tourism context.
Keywords Tourism, Lifestyles, Measurement, Modelling, Australia
Paper type Research paper
Introduction
The relationship between the interest that consumers attach to the consumption of
goods, services, and experiences and their affective bond with speci?c places has been
of growing interest to researchers in the leisure, recreation, and tourism ?elds. The two
concepts used to measure the relationship, involvement and place attachment, have
received considerable study as individual concepts in leisure and tourism contexts,
however their study in combination has been undertaken only recently, and almost
exclusively in leisure and recreational contexts (Kyle et al., 2003). This study extends
the scope of the combined examination of involvement and place attachment
dimensions into tourism, using tourism experiences as the involvement attitude object
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1750-6182.htm
This research is an outcome of a project funded by the Sustainable Tourism Cooperative
Research Centre (STCRC), established by the Australian Commonwealth Government. The study
is a collaboration between the STCRC, the University of South Australia, and the South
Australian Tourism Commission.
IJCTHR
2,1
44
Received February 2006
Revised March 2007
Accepted April 2007
International Journal of Culture,
Tourism and Hospitality Research
Vol. 2 No. 1, 2008
pp. 44-66
qEmerald Group Publishing Limited
1750-6182
DOI 10.1108/17506180810856130
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and ?ve tourism regions in South Australia as the place references. The aim was to
develop a measurement model that could be used to examine the relationship between
involvement and place attachment dimensions in a tourism context. It is proposed that
the model will be of value to destination marketing managers in helping them to
recognise how an understanding of tourists’ attitudes towards involvement and place
attachment may constitute an element of competitive advantage in destination
marketing strategies.
Involvement and place attachment
Consumer involvement can be de?ned as the perceived personal importance and/or
interest consumers attach to the acquisition, consumption, and disposition of a good,
service, or an idea (Mowen and Minor, 1998, p. 64). From the early conceptual work
(Bloch and Bruce, 1984; Selin and Howard, 1988) linking leisure with involvement, most
leisure involvement research has focused on activity contexts (Dimanche and Havitz,
1994). Speci?c tourism involvement studies have been less prevalent, and include
grouped touristic activities (Dimanche et al., 1991); tourist pro?les (Gursoy and Gavcar,
2003); opinion leadership (Jamrozy et al., 1996) and travel motivation and destination
selection (Josiam et al., 1999). The reason for wide research interest in leisure
involvement is that, relative to other products and services, touristic activities tend to
engender high levels of both enduring and situational involvement (Havitz and Howard,
1995). In a paper reviewing 52 leisure involvement data sets over a ten-year period,
Havitz and Dimanche (1999) concluded that involvement has proven to be a reasonably
good variable for explaining and predicting leisure behavior. The same authors have
also af?rmed that the consumer involvement pro?le (CIP) multidimensional scale
originally developed by Laurent and Kapferer (1985) has proved reliable and valid in
touristic contexts (Dimanche and Havitz, 1994). Consistent with these ?ndings, the CIP
scale was selected for use in this study, which examined the applicability of a modi?ed
version of the CIP scale, using the attitude object of tourism experiences to better
understand the nature of tourists’ involvement.
Place attachment is conceived as an affective bond or link between people and
speci?c places (Hidalgo and Hernandez, 2001). Leisure researchers have studied place
attachment primarily as a psychological element of recreation experiences (Williams,
2002). The place attachment construct has been de?ned as having two distinct
dimensions: place identity, which refers to a symbolic or affective attachment to a
place, and place dependence, which refers to a functional attachment to a place
(Backlund and Williams, 2003).
The study of involvement and place attachment in combination is an emerging
stream in leisure research. There is indirect evidence suggesting involvement with
activities leads to attachment to settings (Kyle et al., 2004a). The use of the place
attachment and involvement constructs in combination has occurred only recently in
leisure studies, and in the context of recreation. Moore and Graefe (1994) used the
conceptual frameworks of activity specialisation and place attachment to study
recreation trail users, ?nding predictive relationships that were moderated by frequency
of use. Bricker and Kerstetter (2000) studied whitewater recreationists, using
involvement to measure levels of specialisation and levels of place attachment to a
particular river. A relationship was noted between dimension levels of specialisation
and place attachment. Moore and Scott (2003) used commitment and place attachment to
Lifestyle
tourism
destination
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study users of a trail in a park, and found predictive relationships between the
dimensions. Kyle et al. (2003) investigated the relationship between activity involvement
and place attachment through a study of hikers on a particular trail. Arelationship was
also noted between dimension levels of involvement and place attachment, along with
some predictive properties of a proposed model. Their analysis of data gathered from
hikers on the Appalachian Trail in the eastern USAhas provided the basis for a number
of studies along similar lines of enquiry, all of which have contributed insights into the
underlying motivations for recreationists’ engagement in speci?c leisure pursuits and
visitation to speci?c recreation settings (Kyle et al., 2004b). Hwang et al. (2005) sampled
groups of national park visitors in Taiwan, ?nding that both involvement and place
attachment had positive effects on perceived service quality and satisfaction. These
results suggested the value of combining involvement and place attachment as
measures in the current study of tourismexperiences. By combining examination of the
dimensions of involvement with those of place attachment, this study sought to assess
the suitability of measuring both in a tourism context.
Research method
The objective of the study was to develop and test a measurement model for the
constructs hypothesised to be measured by the instrument employed in this study
(Byrne, 2001, p. 98). A survey was conducted of tourists visiting ?ve regions of South
Australia. Exploratory factor analysis (EFA) was performedto check the dimensionality
of the instrument before using con?rmatory factor analysis (CFA) to establish a model
for the manner in which the instrument measures the constructs designed to measure
tourists’ involvement and place attachment in tourism experiences. Tourism
involvement was conceptualised as a multidimensional construct consisting of
centrality to lifestyle, attraction, and self-expression. Facing increasing competition to
attract tourists, the South Australian Tourism Commission – SATC (2002), the
destination management organisation, has developed a strategy using lifestyle as a tool
for destination marketing. As South Australia markets itself using the food and wine
aspects of the lifestyle of the destination as a point of difference, a lifestyle dimension
was also included that attempted to measure tourists’ attitudes towards how food and
wine feature as elements in their tourismexperiences in the state. Place attachment was
also conceptualised as a multidimensional construct consisting of place identity and
place dependence.
Questionnaire design
The questionnaire consisted of multiple-item scales using a ?ve point Likert-type
response format (1 – strongly disagree to 5 – strongly agree). The scale items were
based on prior research from the involvement and place attachment literature,
including the research by Kyle et al. (2003), who combined involvement and place
attachment measurements in their study of hikers on the Appalachian Trail, and Kim
et al. (1997), from whose study on bird watching in Texas additional centrality to
lifestyle items were drawn. Respondents were informed that they would be asked to
consider issues surrounding their tourism experiences. The ?rst section of the
questionnaire was designed to measure the consumer involvement construct of
attraction (?ve items), centrality to lifestyle (ten items), and self expression (?ve items).
The second section was designed to measure the place attachment construct of place
IJCTHR
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identity (four items) and place dependence (four items). The third section was designed
to measure possible elements of lifestyle tourism around themes related to lifestyle of
the destination, and how food and wine feature in tourism experiences (seven items).
The last section captured demographic data including gender, residence, age, marital
status, education, employment, income, method of travel, and accommodation.
Pilot survey
A pilot test of the questionnaire was conducted in August 2004 in the Adelaide and
Barossa tourism regions of South Australia. A combination of interviewer- and
self-administered methods was used to gather data in order to test the suitability of the
instrument to be executed by both methods, which was con?rmed through analysis of
the data. The researcher and four student research assistants attended the survey sites
to gather data, and 163 valid questionnaires were completed during the pilot test. EFA
and reliability tests of the pilot data resulted in modi?cation of the instrument to
produce the questionnaire for the main survey.
Sample
This study examined ?ve tourism regions in South Australia: Adelaide, Barossa,
Flinders & Outback, Kangaroo Island, and McLaren Vale/Fleurieu. The choice of these
regions was guided by advice received from local tourism industry professionals, who
were asked to nominate a variety of types of locations suf?cient to provide
representative coverage of the tourism regions of the state (Gross, 2005). A map of
South Australia’s twelve tourism regions as de?ned by the South Australian Tourism
Commission for destination marketing purposes is provided in the Appendix. The
sample frame consisted of adult tourists, staying at least one night away from home
and attending a Visitor Information Centre (VIC) or an attraction (e.g. wine cellar door
or resort) in one of the ?ve regions. Considerations that guided sample size included
attaining a minimum ratio of ?ve cases to every variable, with not less than 100 cases
for EFA (Gorsuch, 1983, p. 332). Data gathering produced a ratio of 13.6 cases per
variable, with 476 cases, and a range of 33-100 cases per survey location.
Data were gathered from November 2004 through May 2005 by self-administered
questionnaire. Survey forms were distributed by site staff and the researcher at the ten
survey sites. An information sheet describing the study was made available to all
respondents, and no contact details were requested, thus ensuring anonymity.
Completed surveys were returned to the researcher in the reply-paid envelope
provided, either posted directly by the respondent or forwarded by site staff. A total of
1,338 survey forms were distributed from the ten survey sites, and 494 completed
forms were received for a return rate of 37 percent. Of the 494 completed responses
received, 476 were usable (96 percent). Half of 1 percent (0.51 percent) of the data were
missing and were replaced with means. Completed usable surveys received by location
are shown in Table I.
Characteristics of respondents
A total of 57 percent of the respondents (n ¼ 476) were female, 60 percent resided in
Australia, and the local state of South Australia accounted for the largest domestic
share of visitors at 32 percent. The largest share of international visitors (70 percent)
was from Europe. The mean age of respondents was in the 40-49 range, 56 percent
Lifestyle
tourism
destination
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were married, and 53 percent had either an undergraduate and/or postgraduate degree.
About 62 percent of respondents were employed either full time (46 percent) or part
time (16 percent). Respondents reported a mean annual household income in the
$60,001-80,000 Australian dollar range. Travel to the destination by automobile was
the largest mode of transport used (47 percent), and 47 percent of respondents stayed at
a hotel/motel/bed and breakfast type of accommodation (Table II).
Results
As samples were drawn from two types of survey locations, attractions and VIC’s, the
question arose as to whether data generated from both locations were equivalent and
could be pooled for analysis, or whether they differed such that they must be analysed
separately (Gorsuch, 1983, p. 334). To answer this question, a comparison of factorial
structures was performed using SPSS 12. With comparison as the goal, similar
procedures were used at the various stages of EFA with each data set. Extractional
(principal axis factoring – PAF) and rotational (oblique) techniques were the same, as
were criteria for determining the number of factors. Careful inspection of the loading
matrices for the attractions and VIC’s revealed clear similarities. Both groups
generated the same number of factors, almost the same items loaded highly on the
respective factors, and the same labels could reasonably be used to name the factors for
both groups. This provided evidence to satisfy the conditions necessary for
preliminary acceptance of the invariance of the two factorial structures (Tabachnick
and Fidell, 1989, p. 642). Con?rmation of factorial invariance was further addressed as
a hypothesis in the development of a measurement model.
Once the comparability of attractions and VIC’s factorial structures was
established, the data were pooled and the 35 items of the scale were subjected to
EFA, using SPSS 12, in which PAF was used to obtain the initial solution. Prior to
performing PAF, the suitability of data for factor analysis was assessed. Inspection of
the correlation matrix revealed the presence of many coef?cients of 0.3 and above. The
Kaiser-Meyer-Olkin value was 0.903, exceeding the recommended minimum value of
0.6 and the Bartlett’s test of sphericity reached statistical signi?cance (0.000),
supporting the factorability of the correlation matrix (Field, 2000, p. 457).
PAF revealed the presence of six factors with eigenvalues of 1 or greater, explaining
a total of 63 percent of the variance, with contributions from Factor 1 (29 percent),
Survey location
Visitor Information
Centres (VIC) Attractions
Total usable
surveys Percent
Adelaide VIC 33 33 6.9
Adelaide attraction 50 50 10.5
Barossa VIC 38 38 8.0
Barossa Attraction 100 100 21.0
Flinders & Outback VIC 42 42 8.8
Flinders & Outback attraction 49 49 10.3
Kangaroo Island VIC 47 47 9.9
Kangaroo Island Attraction 33 33 6.9
McLaren Vale/Fleurieu VIC 37 37 7.8
McLaren Vale/Fleurieu attraction 47 47 9.9
Total 197 279 476 100.0
Table I.
Completed usable
surveys by location
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Variable No. Percent
Gender
Male 204 43.1
Female 269 56.9
For domestic: residential postcode
Australian Capital Territory 5 1.8
New South Wales 56 19.9
Northern Territory 2 0.7
Queensland 28 10.0
South Australia 91 32.4
Tasmania 6 2.1
Victoria 71 25.3
Western Australia 22 7.8
For international: country of residence
Asia/Paci?c 22 11.6
Europe 132 69.5
North America 35 18.4
South America 1 0.5
Age
18-29 105 22.2
30-39 80 16.9
40-49 72 15.2
50-59 108 22.8
60-69 86 18.1
70 þ 23 4.9
Marital status
Married 261 55.8
Not married 207 44.2
Highest level of education completed
Some high school 43 9.3
High school 84 18.2
Some university/TAFE 92 20.0
Undergraduate 119 25.8
Postgraduate 123 26.7
Employment status
Full time 217 45.9
Part time or casual 76 16.1
Not working 39 8.2
Student 24 5.1
Full time homemaker 14 3.0
Retired 103 21.8
Annual household income in Australian dollars
Up to $20,000 33 8.0
$20,001-40,000 56 13.5
$40,001-60,000 84 20.3
$60,001-80,000 69 16.7
$80,001-100,000 52 12.6
$100,000 þ 120 29.0
Method of travel to the region
Personal car 219 46.5
Rented car 90 19.1
(continued)
Table II.
Descriptive statistics
of tourist sample
demographics (n ¼ 476)
Lifestyle
tourism
destination
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Factor 2 (13 percent), Factor 3 (7 percent), Factor 4 (7 percent), Factor 5 (5 percent), and
Factor 6 (3 percent) of the variance, respectively. An inspection of the scree plot showed
a break after the sixth factor. Using Cattell and Vogelmann’s (1977) scree test, it was
decided to retain the six factors for further investigation. To aid in the interpretation of
these six factors, oblique rotation was performed. The rotated solution reveals the
presence of simple structure (Tabachnick and Fidell, 2001, p. 623), with all six factors
showing a number of strong loadings, and most variables loading substantially on
only one factor. The presence of negative values for factors 4, 5, and 6 was noted, which
was attributed to the oblique rotation’s method of factor groupings on the rotation grid
in its search for simple structure, and was consistent with the practical signi?cance of
factor interpretation (Hair et al., 1998, p. 113). However, ?ve variables displayed
ambiguous loadings on two or more factors. Using 0.40 as a threshold value
(Guadagnoli and Velicer, 1988; Hinkin, 1995) three of the ?ve ambiguous variables
were eliminated, and two others (item 7 “Tourism experiences have a central role in my
lifestyle” and item 6 “I prefer tourism experiences to any other leisure activity”) were
retained as they had reasonably strong loadings on Factor 4, marginally suf?cient
loadings on Factor 1, and had content of high value to the study. This process of
elimination reduced the total number of scale items from 35 to 32.
The ?nal EFA (Table III) yielded a six factor solution with variables loading
substantially on only one factor except for items 6 and 7 as noted above. The ?nal six
factor solution explained a total of 65.8 percent of the variance, with contributions from
Factor 1 (29.4 percent), Factor 2 (13.7 percent), Factor 3 (7.4 percent), Factor 4
(7.1 percent), Factor 5 (5.0 percent), and Factor 6 (3.2 percent) of the variance,
respectively. The factors were given the respective labels of centrality to lifestyle (six
items), place dependence (four items), food and wine (four items), attraction (eight
items), self expression (six items), and place identity (four items).
Reliability analysis was conducted on the six factors, yielding Cronbach a statistics
in the acceptable range of 0.843 for centrality to lifestyle, 0.915 for place dependence,
0.800 for food and wine, 0.881 for attraction, 0.851 for self expression, and 0.866 for
place identity. Effects of eliminating any of the items from the factors were examined,
and indicated that all items contributed to high reliability, and that none of the items
Variable No. Percent
Motor home/caravan 31 6.6
Bus 18 3.8
Air 56 11.9
Ferry 14 3.0
Organised tour 30 6.4
Train 10 2.1
Other 3 0.6
Where staying
Friends/relatives 100 21.2
Caravan/camping 93 19.7
Hotel/motel/B&B 219 46.5
Backpacker/hostel 33 7.0
Other 26 5.5 Table II.
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(
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W
i
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0
2
3
.
5
6
0
.
7
1
(
c
o
n
t
i
n
u
e
d
)
Table III.
Exploratory factor
analysis results of scale
items (n ¼ 476)
Lifestyle
tourism
destination
51
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
2
:
0
5

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
F
a
c
t
o
r
1
:
c
e
n
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l
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F
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r
2
:
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l
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o
r
3
:
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4
:
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5
:
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6
:
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.
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o
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4
4
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.
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o
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8
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3
.
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n
g
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g
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0
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2
2
0
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2
0
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3
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0
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8
6
6
.
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p
r
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f
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r
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r
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y
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0
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4
3
0
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2
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2
0
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4
7
0
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4
2
0
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0
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3
.
2
1
0
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8
7
9
.
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o
f
t
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n
d
i
s
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u
s
s
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o
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r
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s
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y
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d
s
0
.
2
8
2
0
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1
4
0
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1
2
2
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4
3
2
0
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1
2
0
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0
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3
.
9
4
0
.
8
8
1
9
.
W
h
e
r
e
I
e
n
g
a
g
e
i
n
t
o
u
r
i
s
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e
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n
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m
2
0
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1
2
2
0
.
0
2
2
0
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0
4
0
.
0
3
2
0
.
9
8
0
.
0
2
3
.
3
2
0
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8
0
1
8
.
M
y
c
h
o
i
c
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o
f
t
o
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r
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s
m
e
x
p
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r
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e
n
c
e
s
s
a
y
s
a
l
o
t
a
b
o
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t
w
h
o
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a
m
2
0
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0
4
2
0
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0
4
0
.
0
4
0
.
0
3
2
0
.
8
5
2
0
.
0
2
3
.
3
7
0
.
8
1
(
c
o
n
t
i
n
u
e
d
)
Table III.
IJCTHR
2,1
52
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
2
:
0
5

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
F
a
c
t
o
r
1
:
c
e
n
t
r
a
l
i
t
y
t
o
l
i
f
e
s
t
y
l
e
F
a
c
t
o
r
2
:
p
l
a
c
e
d
e
p
e
n
d
e
n
c
e
F
a
c
t
o
r
3
:
f
o
o
d
a
n
d
w
i
n
e
F
a
c
t
o
r
4
:
a
t
t
r
a
c
t
i
o
n
F
a
c
t
o
r
5
:
s
e
l
f
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x
p
r
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s
s
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n
F
a
c
t
o
r
6
:
p
l
a
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d
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M
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f
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d
2
0
.
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5
5
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8
4
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.
W
h
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0
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2
6
0
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1
6
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e
Table III.
Lifestyle
tourism
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seriously reduced the value of the coef?cient a by being removed from the factor
(Pett et al., 2003, p. 195).
The hypothesised measurement model
Consistent with the study objective to develop and test a measurement model for the
constructs to be measured by the instrument employed in this study, and based on the
EFA ?ndings, ?ve a priori hypotheses were proposed. The hypotheses were drawn
from a combination of theoretical, practical, and methodological considerations as
noted for each hypothesis below.
The CFA model proposed for the ?rst hypothesis that:
H1. the relationship between involvement and place attachment in a tourism
setting could be explained by six factors.
The scale items were largely adapted from previous studies in the involvement and
place attachment literature, which suggests that relationships can be established
between the dimensions of the two latent constructs in a leisure and recreation context.
The present study sought support for those relationships in a tourism context.
The second hypothesis was that:
H2. Each item would have a nonzero loading on the factor it was designed to
measure, and zero loadings on all other factors.
This hypothesis was necessary to test the methodological principle that a model
satisfying this hypothesis, along with H3, is considered to be the ideal measuring
instrument so that a reliable and valid instrument would be expected to ful?ll these
hypotheses.
The third hypothesis was that:
H3. The measurement error terms would be uncorrelated.
Like H2 this hypothesis represents an aspect of an ideal measuring instrument.
Although knowledge developed from previous studies suggested an overlapping
nature of some of the involvement and place attachment items in a leisure and
recreation context, it was still necessary to test to what extent this may hold true in a
tourism context.
The fourth hypothesis was that:
H4. The six factors would be correlated.
In CFA, correlated factors are usually expected and almost always provide a better ?t
to the data (Thompson, 2004, p. 118).
The ?nal hypothesis was that:
H5. The measurement models for the attractions and VIC’s samples would be the
same.
This hypothesis sought to answer the question: do the items comprising the
measurement instrument operate equivalently across the two populations (Byrne,
2001, p. 173)? This step was taken to con?rm the validity of results generated from
EFA by showing the models for the two data sets to be invariant. Knowledge of
factorial structure may assist in interpreting results in a destination marketing context,
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as understanding is gained about the degree to which attitudes of tourists sampled
from different places compare. The same measurement model for both populations
would be testimony to the accuracy of the interpretation of homogeneity of tourism
experience attitudes towards involvement and place attachment between attractions
and VIC’s respondents (Loehlin, 2004, p. 147).
The hypothesised measurement model is shown in Figure 1, where ovals represent
latent variables, rectangles represent observed variables, and circles represent
measurement error associated with observed variables. Absence of a line connecting
variables implies no hypothesised direct effect. A six factor measurement model of
Figure 1.
Hypothesised
measurement model
Attraction
q9centr err9
1
1
q6centr err6
1
q7centr err7
1
q4attrac err4
1
q3attrac err3
1
q2attrac err2
1
q1attrac err1
1
q5attrac err5
1
Centrality to
Lifestyle
q14centr err14
q8centr err8
q15centr err15
q10centr err10
q11centr err11
q12centr err12
1
1
1
1
1
1
1
Self
Expression
q33lifst err33
q16expre err16
q17expre err17
q20expre err20
q18expre err18
q19expre err19
1
1
1
1
1
1
1
Food &
Wine
q30lifst err30
q31lifst err31
q34lifst err34
q35lifst err35
1
1
1
1
1
Place
Dependence
q28depen err28
q25depen err25
q27depen err27
q26depen err26
1
1
1
1
1
Place
Identity
q29lifst err29
q23ident err23
q22ident err22
q21ident err21
1
1
1
1
1
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centrality to lifestyle (six items), attraction (eight items), self expression (six items),
food and wine (four items), place dependence (four items), and place identity (four
items) was hypothesised.
Model estimation
A CFA was performed using AMOS 5 on the 32 scale items of involvement and place
attachment. Maximum likelihood estimation was used to estimate all models. In
assessing models we followed Byrne (2001, pp. 79-88) and did not rely on the overall
x
2
. Rather, we also employed the comparative ?t index (CFI) and root mean square
error of approximation (RMSEA) in assessing model ?t. The independence model that
tests the hypothesis that all variables are uncorrelated was easily rejectable,
x
2
¼ 9,512 (n ¼ 476, df ¼ 496, p , 0.01). The hypothesised model was tested next
and limited support was found for the hypothesised model, x
2
¼ 1,844 (n ¼ 476,
df ¼ 449, p , 0.01), CFI ¼ 0.845; RMSEA ¼ 0.081. A x
2
difference test indicated a
signi?cant improvement in ?t between the independence model and the hypothesised
model.
Given these ?ndings of inadequate ?t, post hoc model modi?cations were made in
an attempt to develop a better ?tting measurement model. The statistical signi?cance
of parameter estimates for the measurement model was examined through the critical
ratio (c.r.) test statistic. Using a threshold range of ^1.96, all c.r.’s for regression
weights, covariances and variances for the measurement model fell within the
acceptable range. In an effort to identify further areas of mis?t, the standardised
residuals and modi?cation indices (MI) were examined. An examination of the
standardised residuals identi?ed three possible items that were candidates for either
respeci?cation or deletion. These were lifestyle item 30 “The distinctive food of the
region is something that attracted me here,” self expression item 16 “When I engage in
tourism experiences I can really be myself,” and centrality to lifestyle item 6 “I prefer
tourism experiences to any other leisure activity,” all of which displayed multiple
covariance discrepancies well exceeding a threshold value of ^2.58, which is
considered to be large (Byrne, 2001, p. 89). The MI were examined to determine what, if
any, action should be taken on items 30, 16, and 6. Examination of the regression
weights suggested that substantial parameter improvements could be made by
moving item 30 from the food and wine factor to either place identity (MI ¼ 57.11, par
change ¼ 0.584) or place dependence (MI ¼ 47.83, par change ¼ 0.414), however the
lack of clarity about which factor should contain the item, combined with little
theoretical reason for making the change suggested no action. Similarly, no action was
taken for item 16, for which parameter improvements could be made by moving the
item from self expression to centrality to lifestyle, however little theoretical reason
existed to change, and parameter improvements would be marginal (MI ¼ 16.42, par
change ¼ 0.253). Item 6 was moved from the attraction factor to the centrality to
lifestyle factor based on parameter improvements (MI ¼ 36.62, par change ¼ 0.359),
as well as theoretical grounds that the item is a well-tested measure for centrality to
lifestyle in the literature, and was originally intended to measure centrality to lifestyle
in the survey instrument. Additionally, further examination of the regression weights
showed that parameter improvements could be made by moving centrality item 7
“Tourism experiences have a central role in my lifestyle” from attraction to centrality
to lifestyle (MI ¼ 47.16, par change ¼ 0.437). The item was moved based on the
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theoretical grounds that the item is also a well-tested measure for centrality to lifestyle
in the literature, and was also originally intended to measure centrality to lifestyle in
the survey instrument.
Next, attention was turned to the MI’s representing error covariances. Model ?t
improvements were indicated for allowing a number of error terms to correlate, and,
based on maximum contribution to ?t and no objections on theoretical or practical
grounds, nine correlated error terms were added to improve the model ?t. Errors
between the following item pairs were allowed to correlate freely: 10/11, 10/12, 11/12,
12/15, 14/15, 16/17, 18/19, 29/30, and 31/34.
The ?nal measurement model, including coef?cients in standardised form, is shown
in Figure 2.
All of the measures in the model possessed acceptable psychometric properties. In
summary, the difference in the x
2
was 553.581, the difference in the degrees of freedom
(df) was 9, and p , 0.01. Therefore, model respeci?cation resulted in an improved
model ?t as demonstrated by Table IV.
Indicators of reliability (i.e. internal consistency) and variance-extracted measures
were also used to assess the adequacy of the measurement model. Each construct was
subjected to reliability computations which showed that all factors exceeded the
recommended level of 0.70 (Hair et al., 1998, p. 624). Each construct was also subjected to
variance-extracted computations which showed that all but two of the factors exceeded
the recommended level of 50 percent. This indicates that, for the two factors of centrality
to lifestyle and self expression, less than half of the variance for the observed variables
was accounted for by the construct. However, even though the variance extracted
values of two of the factors were somewhat lower than the threshold, the observed
variables can be regarded as suf?cient in terms of how the overall measurement model
was speci?ed (Hair et al., 1998, p. 636). A summary of reliabilities and variances
extracted for the measurement model is provided in Table V.
Con?rmation of invariance
The multiple group analysis function in AMOS 5 was used to test for group-invariant
factor patterns for the two samples of attractions and VIC’s data. Using the ?nal
measurement model, the path diagram speci?ed for attractions and VIC’s was the
same, and cross-group constraints were ?xed for the two-group factor analysis model
(Arbuckle, 2003, p. 57). Five nested models (Bollen, 1989, p. 291) were compared using a
series of increasingly restrictive parameter constraints:
(1) unconstrained;
(2) measurement weights;
(3) measurement intercepts;
(4) structural covariances; and
(5) measurement residuals.
In which each model contained all the constraints of its predecessor. Table VI shows
the likelihood ratio x
2
statistic for each ?tted model. As for the model ?tted to the
complete set of data, in all cases there are signi?cant departures from the ?tted model.
However, the unconstrained model has an RMSEA value of 0.049 with a 90 percent
con?dence interval of (0.046, 0.052) that indicates a good ?t for this model. On the other
Lifestyle
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Figure 2.
Final measurement model
Attraction
q9centr err9
q6centr err6
q7centr
err7
q4attrac
err4
0.63
q3attrac
err3
0.78
q2attrac err2
0.76
q1attrac
err1 0.77
q5attrac
err5
0.80
Centrality to
Lifestyle
q14centr err14
q8centr err8
q15centr err15
q10centr err10
q11centr
err11
q12centr err12
0.53
0.80
0.64
0.50
0.59
0.47
Self
Expression
q33lifst
err33
q16expre err16
q17expre err17
q20expre err20
q18expre err18
q19expre err19
0.61
0.67
0.65
0.68
0.72
0.76
Food &
Wine
q30lifst err30
q31lifst
err31
q34lifst
err34
q35lifst
err35
0.51
0.80
0.81
0.79
Place
Dependence
q28depen
err28
q25depen err25
q27depen
err27
q26depen err26
0.72
0.86
0.90
0.94
Place
Identity
q29lifst err29
q23ident
err23
q22ident err22
q21ident err21
0.42
0.85
0.91
0.86
0.74
0.67
0.29
0.37
0.49
0.26
0.18
0.26
0.31
0.24
0.23
0.68
0.78
0.83
0.47
0.51
0.29
–0.75
0.27
0.21
0.38
0.37
0.51
0.58
0.09
0.07
0.08
Model x
2
df x
2
/df p-Value CFI RMSEA 90 percent CI RMSEA
Initial CFA 1,844.611 449 4.108 0.000 0.845 0.081 (0.077, 0.085)
Final CFA 1,291.030 440 2.934 0.000 0.906 0.064 (0.060, 0.068)
Table IV.
Summary of model ?t
statistics
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hand the CFI is 0.892 which indicates that the ?t may only be moderately good. The
maintenance of a relatively stable x
2
/df ratio through all ?tted models suggests that
the increase in the x
2
from one model to the next is never very different to the change
in the df and so it is unlikely that there will be signi?cant differences between them.
Differences between x
2
s from the table model ?t summary are presented in the
nested models comparisons (Table VII) and are used to formally compare the models.
Each x
2
tests whether the removal of the constraint would result in an improved ?t,
assuming all previous constraints are correct. As each of the ?rst three models in
Table VII is in turn nonsigni?cant ( p . 0.05) there is no evidence that the
measurement weights, measurement intercepts and structural covariances differ
between the attractions and VIC’s groups. However, the measurement residuals model
is signi?cant and so the constraint that the variance of the measurement residuals is
the same is not tenable.
Based on the overall results of the multiple group analysis, the ?nal measurement
model shown in Figure 2 differs between attractions and VIC’s only in the variances of
the residuals.
Factor Reliability Variance extracted
Attraction 0.87 0.53
Centrality to lifestyle 0.85 0.43
Self expression 0.84 0.47
Food and wine 0.82 0.55
Place dependence 0.92 0.74
Place identity 0.86 0.62
Table V.
Summary of reliabilities
and variances extracted
Model NPAR x
2
df p x
2
/df
Unconstrained 240 1,873.004 880 0.000 2.128
Measurement weights 214 1,910.887 906 0.000 2.109
Measurement intercepts 182 1,952.730 938 0.000 2.082
Structural covariances 161 1,972.828 959 0.000 2.057
Measurement residuals 120 2,040.914 1,000 0.000 2.041
Saturated model 1,120 0.000 0
Independence model 128 10,180.515 992 0.000 10.263
Table VI.
Model ?t summary – x
2
Model df x
2
p
Measurement weights 26 37.883 0.062
Measurement intercepts 32 41.843 0.114
Structural covariances 21 20.098 0.515
Measurement residuals 41 68.086 0.005
Table VII.
Nested models
comparisons
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Discussion
The objective of the study was to develop and test a measurement model for the
constructs hypothesised to be measured by the instrument employed in this study.
Goodness-of-?t indices were chosen on the basis of their common application in the
literature (Thompson, 2004, p. 128), and their variant approaches to ?t. It is considered
necessary that ?t indices similar to the CFI exceed 0.90 before a model can be viewed as
consistent with the observed data (Hoyle, 1995, p. 7). Similarly, a RMSEA value of 0.08
or less would indicate a reasonable error of approximation (Arbuckle and Wothke,
1999, p. 403), and minimum discrepancy (x
2
/df) ratio of 3-1 is indicative of an
acceptable ?t between the hypothetical model and the sample data (Arbuckle and
Wothke, 1999, p. 399). The ?t indices of the ?nal measurement model were within
acceptable ranges of measurement, indicating an overall acceptable ?t of the
measurement model. Five elements of the model were hypothesised.
H1 was supported by the data. Involvement and place attachment in a tourism
setting could be explained by six factors. This hypothesis was supported by the data.
The hypothesised six-factor model was identi?ed, however with some re-speci?cation
of the item loadings on the centrality to lifestyle and attraction factors. Fit indices
showed that items 6 and 7, both of which were indicated by EFA results to load on the
attraction factor, were better suited to load on the centrality to lifestyle factor. This was
supported on theoretical grounds from the literature, as both items were originally
intended to measure centrality to lifestyle. One new factor to emerge from the EFA was
food and wine. This is consistent with the expectation that tourists would be attracted
by the regions’ distinctive food and wine, and would consider food and wine to be
important features of their tourism experiences in South Australia. The state promotes
its food and wine as major components of tourism experiences (SATC, 2002), and two
of the regions surveyed, Barossa and McLaren Vale, are particularly well-known wine
regions in Australia.
H2 was supported by the data. Each item would have a nonzero loading on the
factor it was designed to measure, and zero loadings on all other factors. A model
satisfying this hypothesis, along with H3 is considered to be the ideal measuring
instrument so that a reliable and valid instrument would be expected to ful?ll these
hypotheses. H2 was supported by the data as all items loaded on only a single factor.
H3 was supported by the data. The measurement error terms would be uncorrelated.
This hypothesis was not fully supported by the data. Nine correlated error terms were
added to improve the model ?t, seven of which were intra-factor correlations that could
be explained by a high degree of overlap in itemcontent. We believe this to be true of the
survey instrument, as involvement scales may be characterised by groupings of items
that take a slightly different approach to the same question (Gursoy and Gavcar, 2003;
Havitz and Mannell, 2005) and this was noted by some respondents who found the style
of questions occasionally repetitive. Similar overlap of items has been found in place
attachment studies (Williams and Vaske, 2003). An example of this is between item 18
“My choice of tourism experiences says a lot about who I am” and item 19 “Where I
engage in tourismexperiences gives a glimpse of the type of person I am,” both of which
are intended to measure self expression. Given the similarity of the questions, it is
intuitive on practical and theoretical grounds to allowthe error terms for two such items
to correlate freely in order to give a more realistic picture of the contribution to variance
explained by the respective items and their error terms. One intra-factor correlation
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between items 31 and 34 yielded a negative value, indicating the possibility that
responses for the two items may be mutually exclusive. As item 31 is about wine and
item 34 is about food, this may suggest a separation in respondents’ attitudes towards
combining food and wine in the same tourism experience in some regions. Inter-factor
correlation between error terms for items 29 and 30 was allowed on the rationale that
both questions enquire about an aspect (food or lifestyle) of a region that attracted
respondents there. This decision was based on allowing for the connection that
respondents may make between the food and the lifestyle of a region, especially in a
place like SouthAustralia, where there is a determined marketing effort to establish such
connections in tourists’ minds.
H4 was supported by the data. The six factors would be correlated. This hypothesis
was supported by the data. Although inter-factor correlation was not a focus of this
study, it was necessary to allow all factors to correlate to ensure that a priori restraints
would not restrict the measurement model’s ability to indicate which items loaded on
which factors as well as relationships between error terms.
H5 was supported by the data. The measurement models for the attractions and
VIC’s samples would be the same. This hypothesis was supported by the data. As an
exploratory step, the data were tested using multiple group analysis to determine the
extent to which the factorial structures between the two data sets in the model were
invariant. Multiple group analysis offered support for the invariance of the factorial
structures of the attractions and VIC’s data. This ?nding suggests that results
generated from analysis of the pooled two data sets may be regarded as equivalent,
and that tourists attending attractions and VIC’s display similarities in their attitudes
towards tourism experiences when measured by involvement and place attachment.
Such knowledge may assist in interpreting results in a destination marketing context,
as understanding is gained about the degree to which attitudes of tourists sampled
from different places compare. Destination managers may make decisions regarding
resource allocation and marketing strategies and tactics based on a more thorough
understanding of the who, where and how of reaching their target markets.
In a previous study (Gross and Brown, 2005) using a single sample of VIC’s visitors
in South Australia (n ¼ 189), a measurement scale of the four involvement dimensions
of attraction, self expression, centrality, and food and wine, and a single dimension of
place attachment was developed. The two dimensions of place attachment that are
commonly generated for data samples in the leisure and recreation literature did not
appear in the sample of VIC’s visitors. The present study combined a slightly increased
VIC’s sample (n ¼ 197) with a sample of tourists attending attractions (n ¼ 279) in
South Australia for comparison to determine the extent to which the nature of the
sampling location may have a bearing on the factorial structure of the data. EFA in
the present study yielded the two place attachment dimensions of place identity and
place dependence for both attractions and VIC’s. As the combination of extractional
and rotational techniques used for the previous study EFA were PCA/varimax, and
those used for the present study were PAF/oblique, this suggests that derivation of the
place identity and place dependence dimensions may be in?uenced by the choice of
techniques used.
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Conclusion and implications
Results from this study suggest that it is possible to identify and measure six factors
underlying involvement and place attachment, indicating that their use in the leisure
research environment can be extended to a tourism research environment. Owing to
the nascent stage of combined leisure and tourism involvement and place attachment
literature, opportunities for comparison of parameter values with similar studies are
limited. Studies that have used structural modeling tend to report results from full
model analysis, and do not report measurement model results separately. Two of the
studies fromwhich the present study drewprecedence (Table VIII) do however provide
a source of comparison of some ?t indices for reported measurement models:
Comparison of the results of these two previous studies suggests that ?t indices
found in the present study are consistent with those found in the emerging literature.
It is important to note that once respeci?cation of an originally hypothesised model
is undertaken, the process of post hoc model ?tting ceases to be purely con?rmatory
and assumes the exploratory nature of model generation, the most common type of
model analysis (Byrne, 2001, p. 8). Final models resulting from speci?cation searches
must be cross-validated before any real validity can be claimed (Bentler, 1980). We
were guided in our speci?cation search by the need to respect the theoretical integrity
of the measurement model (MacCallum, 1986), however the measurement model needs
to be further tested with other data sets.
Having established that six factors underlying involvement and place attachment
can be measured, and that the factorial structures for the attractions and VIC’s data are
invariant, further research will use structural equation modelling (SEM) to investigate
alternative structure models for the relationships among the factors of the combined
samples. The six factors will form the starting point for SEM model generation,
extending the measurement model that has been developed in the present study. The
present study ?ndings suggest that a viable theoretical, practical, and methodological
basis has been established to proceed to a full structural equation model that will
examine the predictive effects among the six factors, speci?cally between the latent
constructs of involvement and place attachment. Further research will seek to
determine the extent to which the nature of the relationships between involvement and
place attachment are consistent in a tourism context with those found by researchers
who have studied those relationships in a leisure and recreation context. The type of
knowledge generated by SEM analysis may have implications for destination
marketers for whom it is critical to be able to distinguish those attitudes that are
substitutable from those that are perceived to be unique to a particular place. A better
understanding of involvement dimensions and the extent to which tourism experiences
are rooted in place may be of invaluable assistance in the marketing of tourism
destinations.
Measurement model x
2
df p-Value CFI RMSEA Note
Kyle et al. (2004c) 2,780.15 504 0.000 0.92 0.057
Hwang et al. (2005) 1,654.89 165 0.000 0.95 0.060 Involvement construct
Hwang et al. (2005) 775.54 76 0.000 0.97 0.060 Place attachment construct
Present study 1,291.03 440 0.000 0.906 0.064
Table VIII.
Comparison of
measurement model ?t
statistics with similar
studies
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See Appendix ?gure on following page.
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Appendix. Tourism regions of South Australia
Corresponding author
Michael J. Gross can be contacted at: [email protected]
Figure A1.
Far North
Flinders Ranges
Eyre Peninsula
Yorke
Peninsula
Barossa
Valley
Mid
North
Riverland
Murraylands
Adelaide
Fleurieu
Peninsula
South
East
0 200
Kilometres
Source: Bureau of Tourism Research (1999), Australia
Kangaroo
Island
Coober Pedy
Roxby Downs
Woomera
Ceduna
Streaky Bay
Port Augusta
Port
Pirie
Renmark
Murray Bridge
Kingscote
Port Lincoln
Naracoorte
MT
Gambier
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