Managing Leisure 13, 139 –161 (July – October 2008)
A focused service quality, benefits, overall
satisfaction and loyalty model for public
aquatic centres
Gary Howat, Gary Crilley and Richard McGrath
Centre for Tourism and Leisure Management, School of Management, University of South
Australia, Mawson Lakes Campus, South Australia, Australia
This study supports a parsimonious range of key service quality dimensions that have a strong
influence on customer loyalty at public aquatic centres using data from two major centres in
Australia (n 5 367 and 307). Confirmatory factor analysis (CFA) supported a model that included
three process services quality dimensions and two benefits (outcomes) dimensions. Using structural
equation modelling (SEM), it was found that one of the outcome dimensions (relaxation) and two
process dimensions (staffing and facility presentation) significantly influenced overall satisfaction,
which mediated significant relationships with three attitudinal loyalty variables. The data from the
second centre provided a validation sample to confirm the potential to replicate the model for a different respondent profile. The parsimonious set of dimensions identified in this research could provide a
common core suitable for inclusion in service quality research for a range of contexts.
INTRODUCTION
In recent years, competition for customers
has been increasing due to the number of
new or refurbished public and commercial
aquatic and fitness centres in many Australian
cities (Benton, 2003; Howat et al., 2005a; King,
2004; Whittaker, 2004). Consequently, retention of customers and measuring customer
loyalty are increasingly important issues for
facility managers. Indicators of customer
loyalty include customer service quality as
well as customer satisfaction (Bernhardt
et al., 2000; Brady and Robertson, 2001;
Ganesh et al., 2000; Howat and Crilley, 2007;
Philip and Hazlett, 1997; Voss et al., 2004).
Linking specific service quality dimensions
with loyalty measures allows facility managers to identify strengths and areas for
improvement in attributes of the service that
they can manage to help improve their
competitive advantage.
This study examines the relationships
between service quality, overall satisfaction
and loyalty measures at Australian public
aquatic centres (Figure 1). Satisfaction
appears to be a combination of emotional
and cognitive responses (Oliver, 1997;
Wong, 2004; Zeithaml et al., 2006), while
service quality, as an antecedent to overall
satisfaction, appears to be mainly a customer’s cognitive assessment of a service
(Cronin, 2003). Perceptions of service
quality affect feelings of satisfaction, which,
in turn, influence customers’ likely future
support for that service (Alexandris et al.,
2004; Bernhardt et al., 2000; Ganesh et al.,
2000; Howat et al., 1999; Howat and Murray
2002; Voss et al., 2004).
Customer Loyalty
Customer loyalty is the level of continuity in
the customer’s relationship with a brand or
Managing Leisure ISSN 1360-6719 print/ISSN 1466-450X online # 2008 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/13606710802200829
140
Howat et al.
Fig. 1. A proposed model of the relationships between service quality, overall satisfaction and attitudinal
loyalty at public aquatic centres
service provider (Soderlund, 2006). The
behavioural view of loyalty includes repeat
purchasing or frequency of attendance
(Pritchard et al., 1992) and the duration of
the customer –service provider relationship
(Soderlund, 2006). The attitudinal view of
loyalty includes two major indicators of
customer retention – customers’ intention
to repurchase, and their willingness to
recommend the service to other prospective customers (Rundle-Thiele, 2005; Voss
et al., 2004; Zeithaml et al., 2006). ‘Among
the most important generic behavioural
intentions are willingness to recommend
the service to others and repurchase intent’
(Zeithaml et al., 2006: p. 149).
Soderlund (2006), however, asserted that
few researchers clearly distinguish between
repatronage intentions and word-of-mouth
intentions (e.g., Cole and Illum, 2006; Seiders
et al., 2005). Word-of-mouth intentions link
the customer to other prospective customers,
while repatronage intentions relate to a
potential future relationship between the customer and the service provider, which may be
constrained by such factors as cost and
A focused service quality, benefits, overall satisfaction and loyalty model
physical access to the service. In contrast, an
issue such as physical access may have less
influence on willingness to recommend a
service to others. For example, self-drive
travellers may be willing to recommend a
holiday route to other people, but their own
repatronage intentions may be relatively low
because of the distance from their home
location and the costs (time, travel and
accommodation) (Howat et al., 2006).
Location also had an important influence on
levels of visitation with the frequent or
‘heavy users’ of a lunch restaurant being
customers whose residence or work place
was nearby (Lehtinen and Lehtinen, 1991).
Satisfaction
Satisfaction as an emotional state of mind
reflects the benefits (Cole and Illum, 2006)
or outcome of an experience (Baker and
Crompton, 2000) along with other influences
such as process service quality. External
issues may also influence the individual’s
psychological state and thus the feelings of
satisfaction attributed to that experience
(Oliver, 1997; Zeithaml et al., 2006). Examples
of negative issues include feeling unwell
(e.g., due to a cold or tiredness from
working long hours) or external factors
such as foul weather or unpleasant personal
relationships. In contrast, positive life issues,
such as a recent promotion or other personal successes, could moderate satisfaction
in a positive direction.
Overall satisfaction can be considered as a
post-service representation of the customer’s overall feelings toward a service
(Choi and Chu, 2001) based on cumulative
experiences with that service (Gustafsson
et al., 2005; Homburg et al., 2005; Seiders
et al., 2005; Skogland and Siguaw, 2004).
Oliver (1997) maintained that the aggregated
satisfaction episodes from a series of consumption experiences result in ‘overall’ satisfaction, which appears to be one
determinant of loyalty. Overall satisfaction,
141
based on the cumulative experiences with
the same service provider, is expected to
have a stronger relationship with outcome
variables (e.g., future purchase behaviour,
willingness to recommend) than a single
consumption experience (Anderson et al.,
1997; Homburg et al., 2005; Jones and
Suh, 2000; Olsen and Johnson, 2003; Rust
et al., 1995).
However, there is also a caution that industry differences limit the extent to which links
between customer satisfaction and behavioural intentions can be extrapolated
from one context to another (de Ruyter
et al., 1998; Parasuraman et al., 1994). For
example, the satisfaction – repurchase intentions relationship appears to be servicespecific as concluded from a study of two
mid-price hotels in the USA Midwest where
there was only a weak link between customer
satisfaction and intention to revisit the hotel
(Skogland and Siguaw, 2004). Possible explanations include the low cost for customers to
switch to alternative service providers.
Industries with low switching costs (e.g.,
amusement parks and fast food) tend to
have a weaker service quality –loyalty link
than those with higher switching costs
(e.g., opera houses) (de Ruyter et al., 1998).
Switching costs include extra travel costs,
foregoing accrued loyalty benefits, and
psychological costs associated with the
uncertainty of dealing with different staff or
attending a new venue.
The customer– loyalty relationship is
much more emphatic for completely satisfied customers compared to those who
record lower satisfaction levels (Jones and
Sasser, 1995). This is dependent on the competitiveness of the particular market such as
the alternative markets available to the customer for that product. And even for
markets with little competition, Jones and
Sasser (1995) cautioned against assuming
that customers display true long-term
loyalty compared to false loyalty (spurious
loyalty) where customers who are not
142
completely satisfied only remain loyal
because they do not have convenient
access to alternatives.
Service Quality
A popular conceptualization of service
quality involves comparing a customer’s
evaluation of the perceived performance of
specific attributes of a service to their prior
expectations (Parasuraman et al., 1988;
Zeithaml et al., 2006). A relatively small
number of high priority attributes tend to
have a dominant influence on the customers’
perception of a service’s overall quality
(Hartline et al., 2003; Howat and Crilley,
2007). For example, expectation ratings indicated the relative importance that respondents gave to service quality attributes in a
study of fitness centres (Afthinos et al.,
2005). The relatively high importance of the
physical attributes of the service (clean, comfortable and modern facilities), and staffing
attributes were supported in other research
for sports and fitness centres (Papadimitriou
and Karteroliotis, 2000) and for public
aquatic centres (Howat and Crilley, 2007).
For over two decades, research involving
dimensions of service quality has been
guided by either the American perspective
as represented by the SERVQUAL instrument
and its adaptations (Parasuraman et al., 1985,
1988, 1991, 1993, 1994; Zeithaml et al., 2006) or
the European (Nordic) approach (Grönroos
1984, 1993, 2005; Lehtinen and Lehtinen,
1991). The latter includes two broad dimensions of service quality: the technical or
outcome dimension and the functional or
process dimension.
In the Nordic model technical quality of
the outcome is what the customer receives,
or ‘. . . what the customer is left with, when
the service production process and its
buyer-seller interactions are over’ (Grönroos,
2005: p. 63). Outcomes include the meal
received at a restaurant, the room and bed
provided by a hotel, the bank loan and
Howat et al.
transportation for the airline passenger
(Grönroos, 2005). However, outcomes that
have longer-term impacts, such as health
and fitness benefits, tend to be lag indicators
that may not yield benefits until some future
time (de Bruijn, 2002; Howat et al., 2005b;
Robinson and Taylor, 2003). Therefore,
such deferred benefits will be more difficult
for the customer to evaluate (Asubonteng
et al., 1996) as well as to credit directly to a
particular service.
Functional quality of the process is how
the customer receives the service. The way
in which the technical quality or the
outcome transfers to the customer involves
interactions between the service provider
and the customer (relational quality).
Additional to relational quality, the functional quality dimension includes physical
quality (Grönroos, 2005), where the service
is delivered or the ‘servicescape’ (Bitner,
1992). Functional quality attributes, rather
than technical quality attributes, are more
likely to influence customers’ overall satisfaction with a service, if the customer has a
choice of service providers (Grönroos,
1984; Swan and Combs, 1976).
The SERVQUAL instrument categorized
service quality attributes into five dimensions (Parasuraman et al., 1988). The first
four dimensions were process dimensions
that the customer experiences during delivery of the service (how the service is delivered). Tangibles include the ‘. . . appearance
of physical facilities, equipment, personnel,
and communication materials’ (Zeithaml
et al., 2006: p. 120). Tangible clues (evidence)
such as the facility cleanliness and staff
appearance help the customer to judge
a service before using or purchasing it
(Shostack, 1977).
Responsiveness includes employees being
willing to assist with customers’ requests
and resolve their problems, along with
promptness of service such as waiting
times. Assurance involves staff projecting
confidence to customers as indicated in the
A focused service quality, benefits, overall satisfaction and loyalty model
ability of staff and their experience and
knowledge. Empathy focuses on how the
customer is treated, such as personalized
attention, using first names, knowing their
preferences, and includes staff friendliness.
Reliability includes providing the core
service as promised such as teaching the
correct concepts in a university class, accurate medical diagnoses and stroke correction in a swim school. A criticism of the
SERVQUAL instrument is that it focuses
mainly on the functional or process dimensions of quality, with less attention to outcomes (Alexandris et al., 2004; Brady and
Cronin, 2001; Kang and James, 2005).
The SERVQUAL instrument has been
adapted to numerous tourism and leisure contexts including hotels (Alexandris et al., 2002;
Choi and Chu, 2001; Diaz-Martin et al., 2000;
Juwaheer, 2004), sports and leisure services
(Chelladurai and Chang, 2000; Crompton
et al., 1991; Hill and Green, 2000; Kim and
Kim, 1995; Howat et al., 1999; Papadimitriou
and Karteroliotis, 2000) and restaurants
(Heung et al., 2003; Knutson et al., 1995,
Soriano, 2002). Most of these adaptations of
SERVQUAL include context-specific tailoring
of the instrument, to ensure that service
quality attributes and their resultant dimensions are relevant to the specific service
being evaluated (Asubonteng et al., 1996;
Carman, 1990; Howat and Murray, 2002; Kim
and Kim, 1995; Imrie et al., 2002; Laroche
et al., 2004; Parasuraman et al., 1993; Richard
and Allaway, 1993).
The nature of sports and leisure services
reinforces the importance of context-specific
tailoring of service quality attributes. Such
services vary considerably in terms of such
factors as the level of involvement of the customer in the production and consumption of
the service, the length of time spent in
receiving the service, and the extent to
which the service is shared with others. For
example, fitness services often involve
habitual participants who spend relatively
long periods sharing equipment and fitness
143
leadership with other clients (Chang and
Chelladurai, 2003; Lehtinen and Lehtinen,
1991).
In contrast to the global dimensions
proposed in SERVQUAL and in the Nordic
Model, Brady and Cronin (2001) proposed a
multilevel service-quality model with each
of three dimensions consisting of three
sub-dimensions containing three items
each. The three subdimensions for interaction quality or customer-employee interaction were employee attitudes (e.g., staff
friendliness), employee behaviour (e.g.,
staff responding to customer needs) and
employee expertise (e.g., staff knowledge).
The three subdimensions for physical
environment quality or service environment
were ambient conditions of the facility (e.g.,
the atmosphere of the facility), design of
the facility (e.g., facility layout) and social
factors (e.g., other customers’ behaviours).
The outcome quality dimension (similar to
Grönroos’s technical quality) was a combination of three subdimensions: waiting
time, tangibles (e.g., cleanliness of rides)
and valence (customer’s feeling of wellbeing during the experience).
Service Quality Models for Sports and
Leisure Centres
Service quality models for sports and leisure
centres tend to vary considerably by context
and have seldom been validated by other
researchers (Lam et al., 2005). A consistent
issue with these models is that by including
a comprehensive range of attributes they
result in only a few dimensions that include
up to nine attributes (and marginal construct
reliability), while other models include up to
11 dimensions some with only one or two
attributes (Kim and Kim, 1995; Howat et al.,
1999; Papadimitriou and Karteroliotis, 2000).
In an attempt to provide a comprehensive
service-quality model for health-fitness
clubs, a six dimension (31 attributes)
‘Service Quality Assessment Scale’ (SQAS)
144
was proposed (Lam et al., 2005). The SQAS
model was ‘a synthesis of’ previous general
service-quality models (e.g., Brady and
Cronin, 2001; Parasuraman et al., 1988) and
more specific models designed for sport
and recreation contexts (e.g., Howat et al.,
1999; Kim and Kim, 1995; Papadimitriou and
Karteroliotis, 2000). Two issues identified
with this model were the lack of outcome
dimensions and the generally broad range
of attributes within some dimensions. For
example, the childcare dimension included
such attributes as quality of staff, cleanliness
of equipment, hours of operation and diversity of experience provided. The variability
of attributes (items) within a dimension
(factor) reduces the ability of facility managers to accurately identify specific aspects
of their service that require attention,
especially when relying on dimension-level
means ‘determined by averaging all the
scores of those . . . items within the . . . factor’ (Lam et al., 2005).
If dimensions-level results are to drive
diagnostic decision-making by managers, it
is important that all attributes in a dimension
meet stringent face validity as well as construct validity criteria. To ensure a relatively
comprehensive service quality review of a
service, data collection instruments (e.g.,
questionnaires) must include a range of
high priority attributes. However, as
researchers we face the dilemma of minimizing respondent fatigue by reducing replication of attributes, versus including several
attributes that mean exactly same thing to
increase the potential of good model fit for
structural models. The challenge is to identify key service attributes that load with
only a few closely related attributes to comprise robust service quality models that
explain significant levels of variance in
outcome variables such as loyalty.
Even the replication of a simplified version
of Brady and Cronin’s (2001) threedimension model resulted in dimensions of
up to nine attributes for a health club,
Howat et al.
some with factor loadings below 0.70
(Alexandris et al., 2004). The three service
quality dimensions were interaction quality
(personnel), physical environment quality,
and outcomes quality. The latter two dimensions had direct effects on overall satisfaction, which in turn significantly influenced
word of mouth recommendation and
psychological commitment (Alexandris
et al., 2004).
Earlier service quality research on sports
and recreation services tended to include
relatively generic outcome measures based
on the SERVQUAL reliability dimension
(e.g., Crompton and MacKay, 1990; Crompton et al., 1991) or completely excluded
outcome attributes (e.g., Howat et al., 1999;
Lam et al., 2005; Papadimitriou and
Karteroliotis, 2000; Wright et al., 1992).
In a comprehensive model of service
quality in fitness clubs, outcomes at a
general level were attributes such as ‘I
receive the service I requested’, and ‘The
service outcome meets my expectation’
(Chelladurai and Chang, 2003). In contrast,
relatively specific outcomes of exercise participation in a health club context included
positive health-related consequences such
as ‘increase my energy’, ‘improve my health’,
improve my mood’, ‘improve my psychological
well-being’, and ‘improve my fitness level’
(Alexandris et al., 2004). Other research on
benefits from participating in physical
activity included such mental health benefits
as feelings of empowerment and autonomy,
improvements in self-esteem and reduced
depression (Paluska and Schwenk, 2000).
Service Quality Models for Australian
Sports and Leisure Centres
The CERM PI research program initiated in
the early 1990s did not include outcome
dimensions. This earlier CERM PI research
involving aggregated data from aquatic as
well as dry leisure centres yielded a threedimension model: personnel, core services
A focused service quality, benefits, overall satisfaction and loyalty model
and peripheral services (Howat et al., 1999).
To facilitate diagnostic decision making for
facility managers a range of high priority
attributes for customers was the focus of
the CERM PI questionnaire. Concerns for
respondent fatigue meant that several attributes (e.g., value for money) were single
item ‘dimensions’ that did not logically load
with other attributes. The aquatic centres
in the CERM PI research program ranged
from large multi-purpose centres that
included indoor and outdoor pools as well
as aerobics rooms, fitness gyms and in
some cases sports halls. Consequently, the
research population was relatively heterogeneous ranging from recreational swimmers and lap swimmers to fitness gym and
sports court participants. In contrast,
service quality models for private health
clubs involve more homogeneous research
populations participating in a focused
range of activities and age groups (e.g.,
Alexandris et al., 2004).
In an attempt to define more stable dimensions for Australian public aquatic centres
sport court participants were excluded
from aggregated data from 28 centres collected in 2006, which included aquatics and
fitness gym respondents (n ¼ 6104) (Howat,
2007). Ranking of the expectations means
for 21 process service quality attributes confirmed that the highest priority attributes
were personnel and facility presentation
attributes with peripheral or support services (Howat et al., 1999) ranked lower.
These rankings of attributes were similar to
the results in other CERM PI research for
1999 to 2005 (Howat and Crilley, 2007).
Thirteen higher-priority attributes (perceptions of performance) underwent
exploratory factor analysis (EFA), using principal axis factor analysis with oblique
rotation. There were several reasons for
excluding other attributes from the EFA.
First, pool water cleanliness and pool water
temperature were not relevant for up to 40%
of public aquatic centre customers who
145
primarily use the fitness gym or aerobics
classes. Second, several peripheral attributes (Howat et al., 1999) tended to be of a
lower priority and generally recorded
smaller P – E (perceptions of performance –
expectations) gaps for most centres (e.g.,
food and drink, child minding and information
available). Two attributes were discarded
because they loaded on more than one
factor (centre well organized and well run
and centre physically comfortable and pleasant). Finally, one attribute (value for
money) did not meet face validity for
inclusion in a particular factor. Taking into
account the large sample size (n ¼ 6104)
loadings above 0.7 were deemed appropriate
(Stevens, 2002) thus resulting in three dimensions: personnel (four attributes), facility
presentation (three attributes) and parking
(two attributes). EFA on aggregated data
from similar centres in 2003, 2004 and 2005
indicated relative stability of the three
dimensions for Australian public aquatic
centres (Howat, 2007). This three-dimension
service-quality
model
however,
only
included process attributes that management can influence directly and the customer can evaluate immediately.
Thirteen outcome (benefits) attributes
included in recent CERM PI service-quality
research for Australia public aquatic
centres were based on a series of customer
focus groups (McGrath, 2007) as well as
direction from the literature (Alexandris
et al., 2004; Paluska and Schwenk, 2000).
Using confirmatory factor analysis (CFA)
two outcome dimensions were combined
with the three process dimensions. In
testing our model, item deletion for several
outcomes variables improved the factor
model fit in the CFA (Anderson and Gerbling,
1988). Deleted items such as success in competition (two attributes) were important for
small segments of the research samples
(e.g., competitive swimmers), but did not
improve the model. Likewise, social benefits
(three attributes), while important for
146
Howat et al.
specific customer segments (e.g., friends
participating in leisure swimming), weakened the model. Contrary to the findings
of Alexandris et al. (2004) two other
outcome attributes (improved health and
well-being, and improved physical fitness)
also did not improve the model.
Using structure equation modelling (SEM)
two outcome dimensions were combined with
three process dimensions in a structural
model that included overall satisfaction as an
intervening variable and three loyalty
measures as dependent variables (Figure 1).
The main objective of this study was to identify
a parsimonious range of core service-quality
dimensions that have a significant influence
on customer loyalty at public aquatic centres.
METHOD
The Samples
Respondents were repeat customers of two
public aquatics centres in different
Australian state capital cities. Centre A was
in a suburban location (n ¼ 307) and Centre
B was in a near central city location
(n ¼ 367). To maximize the potential of
obtaining a representative sample a schedule
using next available customer sampling (Veal,
2006) guided distribution of self-administered
questionnaires to centre customers during a
14-day period. There were some similarities
in the respective respondent profiles. A
majority of respondents were female (61%),
almost two-thirds (65% and 63%) had been
using the centre for 2 years or more and
similar percentages attended the centre
for swim lessons (24% and 23%) and lap
swimming (22% and 16%).
However, there were also major differences in the respondent profiles including a
much higher percentage of fitness gym customers at Centre A, 27% compared to 7%
for Centre B. Recreational swimming was
the predominant activity for Centre B
respondents, 35% compared to only 8% for
Centre A. The mean age of the respondents
was 44 (SD ¼ 14.9) years for Centre A compared to 33 (SD ¼ 14.2) years for Centre
B. Over half of the respondents at Centre A
(53%) visited the centre three or more
times per week compared to Centre B
where a large percentage (40%) visited less
than once a week. A majority of Centre A
respondents (61%) were centre members,
compared to only 20% for centre
B. Inclusion of centres with relatively different respondent profiles was important to
test the potential to replicate the model.
Questionnaire and Measures
The questionnaire used in this study was
adapted from a service quality questionnaire designed for public sports and leisure
centres (Howat et al., 2002). Sections of
the questionnaire included customer
service quality, customer demographics,
customer use characteristics, overall satisfaction, a measure of the overall experience
and several attitudinal loyalty measures.
Service Quality
Using the disconfirmation approach to
measure service quality, customers’ expectations (E) were compared with how they perceived (P) each attribute of the service
performed (Parasuraman et al., 1988;
Zeithaml et al., 2006). A positively biased sixpoint interval scale, ranging from 1 (disagree)
to 6 (very strongly agree) was used to record
customers’ expectations for each of the 20
attributes (Crompton et al., 1991; Howat
et al., 1999; Kim and Kim, 1998). The same
scale was also used to record customers’ perceptions of how well the service actually performed for each of the service-quality
attributes. A ‘not applicable’ option was also
included. Perceptions of performance
measures were used in the structural model
discussed in this paper, because these are
output measures that can be compared
A focused service quality, benefits, overall satisfaction and loyalty model
directly with other output measures such as
overall satisfaction and attitudinal loyalty.
Outcomes (Benefits)
Outcomes were framed as benefits, which
were rated for importance as well as attainment on separate five-point interval scales,
ranging from 1 (very low) to 5 (very high). A
‘not applicable’ option was also included.
The benefit attainment measures were used
in the structural model. Thirteen outcome
(benefits) attributes were included. These
were derived from customer focus groups
and pilot testing with aquatic centres respondents (McGrath, 2007) as well as from other
research (e.g., Alexandris et al., 2004).
Overall Satisfaction
A global measure of overall satisfaction was
captured with a single question asking
respondents to rate their overall satisfaction
with the service on a seven-point Likert scale
ranging from 1 (very dissatisfied ) to 7 (very
satisfied ) (Choi and Chu, 2001; Diaz-Martin
et al., 2000; Drolet and Morrison, 2001;
Howat and Murray, 2002; Skogland and
Siguaw, 2004; Voss et al., 2004). The customers’ ‘overall experience’ was measured on
a seven-point scale ranging from 1 (displeased) to 7 ( pleased ) (Jones and Suh,
2000). These two measures were aggregated
into the ‘overall satisfaction’ scale used in
the model with construct reliability of 0.84
(Centre A) and 0.82 (Centre B) (Table 1).
Loyalty
Soderlund (2006: p. 90) concluded that
measuring more than one dimension of
loyalty ‘. . . is likely to provide a richer
picture of customer behaviour’. Accordingly,
147
the following two attitudinal measures were
included: (1) To what extent would you recommend this centre to others using a sevenpoint scale ranging from 1 (very strongly not
recommend) to 7 (very strongly recommend );
and (2) Do you intend visiting this centre
again in the near future? measured on a
seven-point scale ranging from 1 (definitely
not) to 7 (definitely). As they are discrete constructs, repatronage intentions and word-ofmouth intentions were not aggregated into
an overall loyalty scale (Soderlund, 2006). A
loyalty measure that acknowledges the influence of access to alternative facilities If there
was another centre available to you, would you
be likely to use it instead of this centre also
used a seven-point scale ranging from 1 (definitely not) to 7 (definitely).
While Soderlund (2006) recommended
more than one measure for each construct,
single item loyalty measures have been
used previously (Howat et al., 1999; Spreng
et al., 1995). Reichheld (2003) claimed that
one customer loyalty question is best for
predicting repeat purchases or referrals
How likely is it that you would recommend
[company X] to a friend or colleague?
RESULTS
Building on findings from exploratory factor
analysis (Howat, 2007) CFA was conducted
using maximum likelihood (ML) extraction
in AMOS 7.0 to assess the construct reliability
of the factors and to check that the model was
a satisfactory fit to the data. Missing values
were replaced via imputation using ML
estimation (Switzer and Roth, 2002).1
Mardia’s multivariate kurtosis indicated that
the multivariate normality assumptions had
1
Missing data for Centre A ranged from 0 to 13% across items and for Centre B missing data ranged from 0 to 11%
across items. Little’s MCAR test confirmed that data was missing completely at random for Centre A (Chi-square
384.57; df 382; p ¼ 0.453). Hence, ML imputation was an appropriate substitution method (Switzer and Roth,
2002). However, for Centre B data was not missing completely at random (Chi-square 1319.11; df 991; p ¼ 0.000).
A comparison between a listwise deletion sample (n ¼ 232) and the imputed sample (n ¼ 367) showed minimal differences in parameters. Thus, the imputed sample for Centre B was used in further analysis.
148
Howat et al.
Table 1 Goodness-of-fit statistics
Centre A
CFI
IFI
SRMR
RMSEA
Chi square
df
Normed chi-square (Chi square/df)
Centre B
CFA
SEM
CFA
SEM
0.957
0.957
0.060
0.075
182
67
2.7
0.958
0.958
0.055
0.059
283
137
2.1
0.952
0.953
0.037
0.076
210
67
3.1
0.951
0.951
0.044
0.061
323
137
2.3
been violated for both samples (normalized
kurtosis for Centre A ¼ 42.82 and for Centre
B ¼ 27.60). While all other variables had skew
values ,1.25 and kurtosis ,1.70, ‘Intention to
revisit’ was not univariate normal. However,
this was expected as the respondents were
mostly regular and long-term customers of
the centre. Therefore, it was appropriate to
proceed with ML as it is relatively robust to violations of this assumption (McDonald and Ho,
2002). Also, ML is preferable to other extraction
techniques such as ADF because of the small
sample size, and performs better than GLS
and WLS under conditions of non-normality
(Olsson et al., 2002). However, bootstrapped
parameter and standard error estimates were
used to further compensate for violation of
this assumption for both samples.
The following goodness-of-fit indicators
were considered as a guide to acceptable
model fit: CFI (Comparative Fit Index) and
IFI (Incremental Fit Index) .0.95; SRMR
(Standardized root mean squared residual)
,0.08; and RMSEA (Root mean square error
approximation) ,0.06 (Hu and Bentler,
1999; Schreiber et al., 2006).
Confirmatory Factor Analysis
Three process service quality dimensions
(personnel, facility presentation and parking)
and two outcome dimensions (relaxation
and personal accomplishment) were included
in the CFA initially with data from Centre
A. The overall fit of the model was acceptable
(Table 1). The SRMR (0.060) was below
the recommended cut-off of 0.08, although
the RMSEA (0.075) exceeded the ,0.06 recommended by Hu and Bentler (1999), but
was less than 0.08 deemed as acceptable by
Byrne (1998). She considered that values up
to 0.08 indicate reasonable errors of approximation in the population. A normed chisquare of less than 3 was achieved (2.7).
A validation sample from Centre B underwent CFA, and produced almost identical
results when compared to Centre A
(Table 1). The overall fit of the model was
acceptable for Centre B. The normed chisquare (3.1) for Centre B was slightly above
the recommended maximum of 3 (Table 1).
Standardized coefficients for all attributes
exceeded 0.7 for Centre A (Table 2) and the
squared multiple correlations (SMC) were
above 0.5 indicating item reliability. The
standardized coefficients and construct
reliability for Centre B closely matched
those for Centre A.
Correlations between each of the five
dimensions (latent variables) were low to
moderate for Centre A (Table 3), and most
were similar to those for Centre B (Table 4).
Item means, standard deviations and a
correlation matrix for all attributes are
presented in Appendix A (Centre A) and
Appendix B (Centre B).
149
A focused service quality, benefits, overall satisfaction and loyalty model
Table 2 Standardized CFA coefficients and construct reliability
Centre A
Standardized
coefficients
Dimensions and attributes
Personnel
Staff experienced and knowledgeable
Staff responsive
Staff friendly
Staff presentable and easily identified
0.87
0.91
0.87
0.80
Facility presentation
Facilities well maintained
Facilities clean
Equipment high quality and well maintained
0.98
0.83
0.75
Parking
Safe and secure parking
Parking area suitable
0.73
0.72
Centre B
Construct
reliability
Standardized
coefficients
0.92
0.91
0.84
0.92
0.85
0.78
0.89
0.89
0.95
0.78
0.83
0.69
Relaxation benefits
Relaxation
Escaping pressures of daily life
(e.g., reduced stress)
0.72
0.73
0.77
0.84
0.82
0.88
0.83
0.76
0.92
Personal accomplishment
A sense of personal accomplishment and
success
Improved self-esteem
Improved skill level
0.80
0.82
0.83
0.80
0.79
0.81
Total satisfaction
Overall satisfaction
Overall experience
0.84
0.87
0.85
0.84
0.84
The Structural Equation Model
The structural model (Figure 1) was tested
using ML estimation based on data from
Centre A with the AMOS 7.0 statistical
Construct
reliability
0.82
0.84
0.83
package. The five service quality dimensions
validated in the CFA ( personnel, facility presentation, parking, relaxation and personal
accomplishment) were included in the SEM
Table 3 Correlations between latent variables from CFA (Centre A)
1. Personnel
2. Facility presentation
3. Parking
4. Relaxation benefits
5. Personal accomplishment
Note: significant at p , 0.01.
1
2
3
4
–
0.48
0.63
0.49
0.36
–
0.55
0.27
0.21
–
0.39
0.29
–
0.69
150
Howat et al.
Table 4 Correlations between latent variables from CFA (Centre B)
1.
2.
3.
4.
5.
Personnel
Facility presentation
Parking
Relaxation benefits
Personal accomplishment
1
2
3
4
–
0.51
0.38
0.30
0.25
–
0.62
0.30
0.23
–
0.14
0.12
–
0.62
Note: significant at p , 0.05; significant at p , 0.01.
along with overall satisfaction and three
loyalty measures (Figure 1). The overall fit
of the model was reasonable to good
(CFI ¼ 0.958,
IFI ¼ 0.958,
SRMR ¼ 0.055,
RMSEA ¼ 0.059, normed chi-square ¼ 2.1)
(Table 1).
Data from Centre B provided validation for
the structural model, with goodness-of-fit
statistics being almost identical to those for
Centre A (Table 1). The overall fit of the
model was reasonable to good (CFI ¼ 0.951,
IFI ¼ 0.951, SRMR ¼ 0.044, RMSEA ¼ 0.061,
normed chi-square ¼ 2.3).
For Centre A the strongest relationship
was between overall satisfaction and the
loyalty measure willingness to recommend
with a standardized regression weight (R)
of 0.80 and a squared multiple correlation
(R 2) of 0.65 (p , 0.01) (Figure 2). Therefore,
overall satisfaction accounted for 65% of the
variance explained in willingness to recommend. Overall satisfaction accounted for
25% of the variance explained in intention to
revisit. There was a negative relationship
between overall satisfaction and likelihood
of attending another centre instead of this
one (R 2 ¼ 0.19, p , 0.05).
As for Centre A, the strongest relationship
for Centre B was between overall satisfaction
and willingness to recommend (R ¼ 0.76,
R 2 ¼ 0.58, p , 0.01) (Figure 2). Therefore,
overall satisfaction accounted for 58% of the
variance explained in willingness to recommend. Overall satisfaction also accounted
for 13% of the variance explained in intention
to revisit. There was a negative relationship
between overall satisfaction and likelihood
of attending another centre instead of this
one (R 2 ¼ 0.17).
The personnel dimension recorded the
strongest relationship with overall satisfaction (R ¼ 0.33) for Centre A followed by facility presentation (R ¼ 0.26). The results were
reversed for Centre B with facility presentation recording the strongest relationship
with overall satisfaction (R ¼ 0.34) followed
by personnel (R ¼ 0.23). The relaxation
dimension was also positively related to
overall satisfaction for Centre A (R ¼ 0.19),
and Centre B (R ¼ 0.16) (Figure 2).
An alternative model with direct links
between the five service quality dimensions
and the three loyalty variables was relatively
weak for both Centre A and Centre B.
DISCUSSION AND CONCLUSIONS
The major aim of this study was to identify a
parsimonious set of core service quality
dimensions that have a significant influence
on customer loyalty at public aquatic
centres. The SEM applied to Centre A data
and validated by a second database for
Centre B supported the structural model
(Figure 1). While further testing is required,
the relatively diverse nature of the two
samples is an indicator of the potential to
replicate the model in other public aquatic
centres. Compared to Centre B, Centre A
respondents were significantly older,
attended more regularly, included more
fitness centre patrons attending alone and
A focused service quality, benefits, overall satisfaction and loyalty model
151
Fig. 2. Standardized regression weights for Centre A [Centre B]
Note: significant at p , 0.05; significant at p , 0.01.
included a high percentage of members. A
contribution of this research to the relative
literature is the support for a set of three
core service quality dimensions relevant to
sports and leisure centres. Each of the
three dimensions includes only a small
number of similar attributes thus allowing
facility managers to focus on relatively
specific aspects of service quality in their
diagnostic decision making.
Two process dimensions (personnel and
facility presentation) and one outcome
dimension (relaxation) exerted significant
amounts of variance on overall satisfaction,
which in turn strongly influenced three
loyalty measures, in particular willingness
to recommend the centre to others. The
links between overall satisfaction and willingness to recommend, intention to revisit and
likelihood of attending another centre were
far stronger than the direct links between
the service-quality dimensions and the
three loyalty variables. This indicates a mediating role of overall satisfaction between
service quality and customer loyalty as
indicated in other service quality research
152
(Baker and Crompton, 2000; Alexandris et al.,
2004) and loyalty research (Kuenzel and
Yassim, 2007).
The respondent profile for Centre A indicates a regular and long-term customer
base, where membership commitments
may have influenced some loyalty measures.
Independent sample t-tests indicated that
members and non-members did not vary significantly on either overall satisfaction or
willingness to recommend the centre to
others. Members and non-members indicated a similar willingness to recommend
the centre to others possibly because this
did not necessarily affect their own future
behaviour directly.
However, there were significant differences between the two groups for intention
to return to the centre ( p , 0.01) for both
Centre A and Centre B and likelihood of visiting another centre ( p , 0.01) for Centre
A. The financial commitment already
made in the form of memberships probably
influenced members’ loyalty in terms of
their intention to revisit the centre in the
future rather than visit another centre,
even though other alternatives were available. An implication for this finding is the
influence of memberships on loyalty. For
example, to reduce cognitive dissonance,
paid-up memberships tend to strengthen
attitudinal loyalty, thus maintaining consistency between the customer’s attitudes
and behaviour. Facility management therefore, should consider the use of memberships as a means to increase attitudinal
loyalty.
These results support the value of measuring several attitudinal loyalty constructs.
Repatronage intentions generally infer
future behaviour of the customer themselves, which may be constrained by such
factors as cost, physical access to the
service, and financial commitments. In contrast, word-of-mouth intentions involve the
customer influencing the likely future
behaviour of other prospective customers.
Howat et al.
For example, a respondent living some
distance from a service may indicate their
strong willingness to recommend that
service to other prospective customers,
even if they were unlikely to use the service
again themselves (Howat et al., 2006). In
contrast, access (e.g., distance from home
or work, opening times, entry fees) to an
alternative aquatic centre should influence
responses to the loyalty measure likelihood
of attending another centre.
Our finding that two process dimensions
exerted significantly more variance on
overall satisfaction than the outcome dimensions supports the claim that functional
(process) quality attributes, rather than
technical (outcome) quality attributes, are
more likely to influence customers’ overall
satisfaction with a service, if the customer
has a choice of service providers (Grönroos,
1984; Swan and Combs, 1976). The two
aquatic centres in this study were close to
alternative facilities. This finding reinforces
the importance of core service quality
dimensions (personnel and facility presentation) as management priorities, especially
where customers have access to a choice of
competing facilities.
Our model indicates that outcomes that
most directly influence overall satisfaction
(relaxation and escaping the pressures of
daily life) tend to be benefits that may be
experienced during and immediately following participation at an aquatic centre, and
appear to support Brady and Cronin’s (2001)
valence or the customer’s feeling of wellbeing during the experience. A reason why
two other outcomes attributes (improved
health and improved physical fitness) did
not improve the final structural model may
have been that these tend to be lag indicators
that may not yield benefits until some future
time (de Bruijn, 2002; Howat et al., 2005b;
Robinson and Taylor, 2003). The same
reason may help explain the weak relationship between overall satisfaction and the personal accomplishment dimension that
A focused service quality, benefits, overall satisfaction and loyalty model
included improved self-esteem and improved
skill level. Even though the majority of the
respondents in this study were long-term,
repeat customers, it appears that overall satisfaction is influenced more by the feelings
(e.g., escaping pressures of daily life, and
relaxation) that respondents can directly
attribute to their immediate participation at
the aquatic centre. A message for facility managers is that the on-site experience should not
be overlooked for potential longer-term
benefits.
A limitation of this study is that it
included data for only two Australian
public aquatic centres. Future research
could include replication of the research in
a wider range of public aquatic centres in
Australia as well as overseas, including comparisons with private health clubs. A second
limitation is the use of two items to capture
the overall satisfaction construct and single
items for each of the three loyalty constructs. While single-item overall satisfaction scales have been used previously
(e.g., Skogland and Siguaw, 2004; Voss
et al., 2004), future research with three
items for each construct should strengthen
the model fit. A third limitation is that comprehensive service quality reviews require
examination of additional attributes to
assist managers in identifying aspects of
their service in need of improvement. Consequently, service quality reviews should
include a comprehensive list of attributes
relevant to that context to facilitate diagnostic decision making for managers (Howat
and Crilley, 2007). However, the parsimonious set of dimensions identified in this
study should provide a common core for a
range of contexts.
ACKNOWLEDGEMENT
The authors wish to thank Dr Brianne Hastie
for advice on the statistics used in this
paper.
153
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158
Appendix A Correlation coefficients, means and standard deviations between attributes and dimensions (Centre A)
1
3
4
5
6
7
8
9
10
11
12
0.79
0.74
0.72
0.82
0.71
0.70
0.43
0.41
0.37
0.36
0.94
0.34
0.52
0.37
0.47
0.33
0.45
0.35
0.44
0.89
0.85
0.81
0.73
0.58
0.44
0.41
0.38
0.39
0.40
0.38
0.31
0.40
0.86
0.46
0.39
0.30
0.46
0.39
0.39
0.33
0.33
0.89
0.53
0.37
0.38
0.37
0.40
0.33
0.35
0.28
0.34
0.25
0.23
0.24
0.22
0.13
0.10
0.31
0.31
0.27
0.28
0.22
0.25
0.26
0.23
0.29
0.26
0.23
0.22
0.14
0.13
0.08
0.18
0.15
0.12
0.13
0.42
0.20
0.31
0.14
0.27
0.13
0.34
0.15
0.27
0.13
0.22
0.11
0.19
0.07
0.33
0.17
0.29
0.14
0.23
0.10
0.28
0.14
0.44
0.46
0.41
0.25
20.19
4.72
1.04
0.48
0.46
0.42
0.30
20.23
4.74
1.13
0.39
0.39
0.35
0.27
20.23
4.86
1.07
0.38
0.33
0.35
0.25
20.22
5.06
0.91
0.42
0.44
0.36
0.16
20.09
4.31
1.07
0.41
0.42
0.36
0.15
20.09
4.31
1.19
0.32
0.35
0.28
0.10
20.02
4.23
1.24
0.39
0.42
0.32
0.18
20.14
4.39
1.15
0.28
0.32
0.34
0.15
20.12
4.39
0.88
0.18
0.22
0.24
0.09
20.04
4.44
0.94
0.30
0.33
0.34
0.16
20.16
4.34
1.07
Howat et al.
1. Personnel
2. Staff experienced and
0.90
knowledgeable
3. Staff responsive
0.93
4. Staff friendly
0.91
5. Staff presentable and
0.86
easily identified
6. Facility presentation
7. Facilities well
0.44
maintained
8. Facilities clean
0.39
9. Equipment quality
0.52
and well maintained
10. Parking
11. Safe and secure
0.45
parking
12. Parking area suitable
0.44
13. Relaxation benefits
14. Relaxation
0.38
15. Escaping pressures
0.41
of daily life
16. Personal
accomplishment
17. A sense of personal
0.28
accomplishment
18. Improved self-esteem
0.37
19. Improved skill level
0.17
20. Total satisfaction
21. Overall satisfaction
0.47
22. Overall experience
0.46
23. Recommend centre
0.42
24. Revisit centre
0.30
25. Visit other centre
20.24
Means
4.84
Standard deviations
0.98
2
14
15
16
17
18
19
20
21
22
23
24
25
0.21
0.23
0.29
0.18
20.14
3.86
0.76
0.94
0.92
0.73
0.45
20.40
5.83
1.07
0.74
0.65
0.44
20.37
5.76
1.21
0.71
0.40
20.37
5.91
1.07
0.42
20.37
5.81
1.19
20.31
6.66
0.80
3.46
1.67
0.72
0.46
0.51
0.87
0.49
0.40
0.54
0.42
0.87
0.89
0.63
0.67
0.67
0.34
0.32
0.43
0.22
20.22
3.92
0.77
0.34
0.33
0.37
0.29
20.21
4.03
0.76
0.30
0.30
0.36
0.25
20.18
3.92
0.66
0.26
0.27
0.32
0.24
20.21
4.05
0.74
0.31
0.28
0.35
0.25
20.12
3.85
0.76
A focused service quality, benefits, overall satisfaction and loyalty model
13
1. Personnel
2. Staff experienced and
knowledgeable
3. Staff responsive
4. Staff friendly
5. Staff presentable and
easily identified
6. Facility presentation
7. Facilities well
maintained
8. Facilities clean
9. Equipment quality
and well maintained
10. Parking
11. Safe and secure
parking
12. Parking area suitable
13. Relaxation benefits
14. Relaxation
0.93
15. Escaping pressures
0.93
of daily life
16. Personal
accomplishment
17. A sense of personal
0.52
accomplishment
18. Improved self-esteem
0.56
19. Improved skill level
0.44
20. Total satisfaction
21. Overall satisfaction
0.37
22. Overall experience
0.35
23. Recommend centre
0.43
24. Revisit centre
0.27
25. Visit other centre
20.23
Means
3.97
Standard Deviations
0.71
Note: significant at p , 0.05, significant at p , 0.01
159
160
Appendix B Correlation coefficients, means and standard deviations between attributes and dimensions (Centre B)
1
3
4
5
6
7
8
9
10
11
12
0.77
0.65
0.75
0.82
0.68
0.65
0.44
0.39
0.39
0.35
0.94
0.43
0.44
0.42
0.41
0.40
0.39
0.39
0.34
0.87
0.88
0.75
0.79
0.60
0.27
0.25
0.30
0.23
0.44
0.41
0.42
0.37
0.87
0.26
0.25
0.23
0.18
0.47
0.45
0.38
0.45
0.90
0.57
0.25
0.22
0.22
0.26
0.17
0.26
0.13
0.17
0.28
0.24
0.28
0.24
0.23
0.19
0.23
0.22
0.14
0.09
0.09
0.07
0.16
0.10
0.15
0.18
0.22
0.11
0.17
0.17
0.14
0.16
0.09
0.12
0.05
0.21
0.19
0.17
0.16
0.21
0.21
0.16
0.15
0.15
0.20
0.17
0.21
0.14
0.14
0.11
0.19
0.05
0.10
0.08
0.14
0.02
0.05
0.36
0.42
0.31
0.17
20.22
4.75
1.05
0.32
0.40
0.30
0.12
20.25
4.68
1.09
0.30
0.35
0.27
0.12
20.22
4.66
1.06
0.27
0.33
0.27
0.16
20.16
4.95
1.06
0.41
0.48
0.34
0.00
20.17
4.22
1.09
0.42
0.47
0.37
0.00
20.17
4.22
1.25
0.31
0.40
0.27
20.02
20.14
4.21
1.20
0.37
0.42
0.28
0.01
20.14
4.23
1.18
0.22
0.26
0.21
0.08
20.12
3.99
1.05
0.22
0.25
0.18
0.13
20.14
4.07
1.09
0.17
0.21
0.18
0.02
20.07
3.9
1.28
Howat et al.
1. Personnel
2. Staff experienced and
0.89
knowledgeable
3. Staff responsive
0.92
4. Staff friendly
0.88
5. Staff presentable and
0.87
easily identified
6. Facility presentation
7. Facilities well
0.44
maintained
8. Facilities clean
0.46
9. Equipment quality
0.44
and well maintained
10. Parking
11. Safe and secure
0.30
parking
12. Parking area suitable
0.26
13. Relaxation benefits
14. Relaxation
0.22
15. Escaping pressures
0.26
of daily life
16. Personal
accomplishment
17. A sense of personal
0.19
accomplishment
18. Improved self-esteem
0.21
19. Improved skill level
0.20
20. Total satisfaction
21. Overall satisfaction
0.35
22. Overall experience
0.42
23. Recommend centre
0.32
24. Revisit centre
0.16
25. Visit other centre
20.24
Means
4.76
Standard deviations
0.95
2
Note:
14
15
16
17
18
19
20
21
22
23
24
25
0.22
0.22
0.21
0.20
20.16
3.58
0.91
0.92
0.92
0.69
0.32
20.37
5.57
1.07
0.70
0.65
0.33
20.34
5.55
1.14
0.62
0.25
20.35
5.59
1.17
0.35
20.30
5.53
1.13
20.16
6.27
1.10
4.05
1.67
0.70
0.32
0.49
0.88
0.43
0.35
0.52
0.39
0.86
0.89
0.63
0.69
0.64
0.30
0.26
0.27
0.18
20.09
3.7
0.82
0.25
0.28
0.27
0.18
20.10
3.74
0.86
0.22
0.21
0.24
0.20
20.19
3.58
0.79
0.19
0.17
0.23
0.14
20.19
3.62
0.89
0.18
0.17
0.19
0.20
20.15
3.55
0.91
A focused service quality, benefits, overall satisfaction and loyalty model
13
1. Personnel
2. Staff experienced and
knowledgeable
3. Staff responsive
4. Staff friendly
5. Staff presentable and
easily identified
6. Facility presentation
7. Facilities well
maintained
8. Facilities clean
9. Equipment quality
and well maintained
10. Parking
11. Safe and secure
parking
12. Parking area suitable
13. Relaxation benefits
14. Relaxation
0.92
15. Escaping pressures
0.93
of daily life
16. Personal
accomplishment
17. A sense of personal
0.44
accomplishment
18. Improved self-esteem
0.52
19. Improved skill level
0.41
20. Total satisfaction
21. Overall satisfaction
0.30
22. Overall experience
0.30
23. Recommend centre
0.29
24. Revisit centre
0.19
25. Visit other centre
20.10
Means
3.72
Standard deviations
0.77
significant at p , 0.05, significant at p , 0.01.
161