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2010 ttra International Conference
Comparing the Active and on-Active LeisureTourism Connection: Leisure Involvement, Leisure
Habit, Tourism Motivation, and Tourism Behavior
Seohee Chang
Department of Hospitality and Tourism Grand Valley State University
Heather Gibson
Department of Tourism, Recreation and Sport Management University of Florida
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Comparing the Active and on-Active Leisure-Tourism Connection: Leisure Involvement,
Leisure Habit, Tourism Motivation, and Tourism Behavior
Seohee Chang
Department of Hospitality and Tourism
Grand Valley State University
and
Heather Gibson
Department of Tourism, Recreation and Sport Management
University of Florida
ABSTRACT
Researchers have suggested that leisure and tourism are closely related to each other. While the
leisure-tourism connection has been observed indirectly in previous research, as yet, few studies
have empirically investigated the psychological connection between leisure and tourism activity
choices. Moreover, little attention has been given to the relationship between physically active
and non-active leisure and tourism choices. Therefore, this study examined the leisure-tourism
connection of both physically active (n=316) and non-physically active respondents (n=258) by
using four latent variables, leisure involvement, leisure habit, tourism motivation, and tourism
behavior. The leisure-tourism causal relationship was found to be statistically significant among
the physically active respondents and as such supports trends evident in the existing literature.
Keywords: leisure-tourism connection, leisure involvement, leisure habit, tourism motivation,
tourism behavior.
ITRODUCTIO
Researchers have suggested that leisure and tourism are closely connected to each other.
Tourism has been defined as a special form of leisure (Butler, 1999; Cohen, 1974) and frequently
shares behavioral and psychological (Mannell & Iso-Ahola, 1987), social (Colton, 1987), spatial
(Jensen-Verbeke & Dietvorst, 1987), and managerial characteristics (Harris, McLaughlin, & Ham,
1987). Developing this line of thought, several researchers have postulated that tourism behaviors
are rooted in leisure lifestyles (Fedler, 1987; Moore, Cushman, & Simmons, 1991). Even though
tourism behavior may be more novel and hedonic-oriented, behavioral choices are frequently
shaped by an individual’s leisure values and lifestyles (Currie, 1997), and there is a strong
relationship with the attitudinal and habitual components of leisure (Carr, 2002). Indeed,
researchers have found choice of tourism activities is related to everyday attitudes and lifestyles
(Chon & Singh, 1995; Hallab, Yoon, & Uysal, 2003). Brey and Lehto (2007) found that
individuals who take part in active leisure pursuits such as golf or jogging are more likely to
participate in these activities on vacation than those individuals who take part in more passive
leisure activities. Similar findings have been observed in other active-leisure contexts such as
recreational road runners and their participation in running-related tourism (McGehee, Yoon, &
Cardenas, 2003), bicycling tours (Ritchie, 1998), and recreational climbing (Woratschek,
Hannich, & Ritchie, 2007). Few studies exist exploring the connection between non-physically
active leisure pursuits and tourism choices and none have compared active and non-active leisure
and tourism pursuits. Thus, the purpose of this study was to investigate the psychological
connection for individuals between active and non-active leisure pursuits and their choice of
tourism behavior.
LITERATURE REVIEW
Involvement
According to Carr’s (2002) Leisure-Tourism Continuum, attitudinal and habitual
components are considered important in explaining the impact of leisure lifestyles on tourism
behaviors. In the leisure literature concepts such as recreation specialization (Bryan, 1977) and
enduring involvement (McIntyre, 1989) have been used to understand individuals’ continued
attachment to and participation in their chosen leisure activities. Much of this work has used
psychological involvement and behavioral frequency to investigate the developmental
progression of leisure behavior for individuals within a leisure but not a tourism context (e.g.,
Iwasaki & Havitz, 2004; Kim, Scott, & Crompton, 1997; Kyle, Graefe, Manning, & Bacon 2004a,
2004b; McIntyre, 1989). The reason that involvement has been often used to understand
behavioral consistency within lifestyles is because involvement is considered critical in the
development of a narrow range of attitudes, thereby resulting in behavioral consistency (Sherif &
Cantril, 1947). More specifically, social judgment theory (Sherif & Cantril, 1947) assumes that
people’s existing attitudes refer to lenses through which to judge new messages. The more
involved individuals are with an attitude or an activity the less likely they are to accept new ideas
or messages.
This theoretical assumption guided consumer behavior research focused on understanding
the buying behaviors of consumers. Consumer behavior researchers in the 1970s defined
involvement as interest (Day, 1970), belief strength (Robertson, 1976), the internal state
including arousal and interest (Mitchell, 1979), or the perceived importance of values (Lastovicka,
1979). Furthermore, in the 1980s, its definitions tended to be categorized into different types of
involvement such as situational and enduring (Richins & Bloch, 1986). Laurent and Kapferer
(1985) developed the multidimensional Likert scale, Consumer Involvement Profile (CIP)
presenting five dimensions comprising importance, sign value, interest, emotional values, and
perceived risk. Connecting these five dimensions to two types of involvement (i.e., enduring and
situational involvement), Assael (1998) suggested that sign values and risks are situational
involvement combined with a short-term behavior that is easily changed by external situations,
whereas importance, interest, and emotional values are enduring involvement associated with a
long-term activity with a more focus of its quality.
Researchers who have studied leisure behaviors have tended to be more interested in
enduring involvement than situational involvement because leisure involvement tends to be
associated with quality of life through enjoyable leisure experiences (Backman & Crompton,
1989, 1991; Buchanan, 1985; Csikszentmihalyi, 1975, 1990; Kyle et al., 2004a 2004b; Mannell
& Iso-Ahola, 1987; McIntyre, 1989). Bryan (1977) is considered to be the first scholar to
introduce the concept of involvement to the leisure field. Bryan investigated the behavioral
involvement of sport anglers and found that there are different levels from general to specialized.
The more specialized group was characterized by those who spent more money on
equipments/magazines, participate more frequently in fishing trips, and most of their social
interaction is with other anglers. Bryan provided a good foundation for others to engage in more
in-depth studies of leisure involvement. Buchanan (1985) and McIntyre (1989) followed and
investigated the psychological components of involvement invoking the concepts “leisure
commitment” and “enduring leisure involvement,” respectively. In particular, enduring leisure
involvement (McIntyre, 1989; McIntyre & Pigram, 1992) included the psychological components
of self-expression, attraction, and centrality using the CIP as a foundation (Laurent & Kapferer,
1985).
Enduring involvement seems to be a valuable concept when studying the relationship
between leisure and tourism because it encapsulates ideas associated with an individual’s overall
life style (Carr, 2002; Gross, Brien, & Brown, 2008; Mannell & Iso-Ahola, 1987; Moore,
Cushman, & Simmons, 1991). For example, if people are engaged in an active lifestyle
participating in active leisure, they are more likely to maintain their active lifestyles while on
vacation because their involvement with physical activity may be an important part of their
identity (Carr, 2002; Sherif & Cantril, 1947). Even though previous studies have not directly
investigated the leisure-tourism connection through involvement with activities, many relevant
examples can be observed in studies examining the consistent leisure participation patterns in
downhill skiing, golf, competitive running (Dimanche, Havitz, & Howard, 1991), fishing (Perdue,
1993), white water recreation (Bricker & Kerstetter, 2000), road racing (McGehee et al., 2003),
and hiking (Kyle et al., 2004a 2004b). However, most of these studies have indirectly focused on
active leisure and tourism activities. Brey and Lehto (2007) also found that running and playing
golf are the activities that are most likely to transfer to the tourism context, however, there are
indications within their study that non-active leisure activities may not transfer as consistently as
active leisure to the tourism context. Moreover, Brey and Lehto used behavioral involvement as
their key concept. Few studies, if any have used the concept of enduring involvement to examine
the leisure-tourism connection.
Habit
Habit should be considered along with enduring involvement to investigate the leisuretourism connection. Habit is an important concept providing insights into the unconscious aspects
that influence behavior and as such complements involvement which focuses on the conscious
aspects of behavior (Bentler & Speckart, 1979; Triandis, 1977; Verplanken & Aarts, 1999). In
leisure studies, habits have tended to be operationalized as behavioral frequency, whereas, habit
is more complex than this. Verplanken and Orbell (2003) suggested that habit is characterized as
a structure which includes unconsciousness, automaticity, efficiency, and regularity components.
Similarly, describing habit as an automatic behavioral sequence associated with a particular
situation, Triandis postulated that future behaviors can be predicted more accurately when they
are measured as both attitudinal (i.e., involvement) and habitual. Likewise, several scholars
suggested that habits help attitudes to actualize as behaviors because habit accounts for variance
that cannot be explained by attitudes alone in predicting future behaviors (Anderson, 1990; Kahle,
1984). The cognitive formation behind habit is a well learned schema that encourages people to
group numerous objects into several subjective categories and makes it easy to automatically
extract the necessary information from the appropriate category. This schema is efficiencyoriented and easily guides behavior.
In some situations, when attitudes are too weak that it is hard to make a decision to
behave, habits facilitate behavior or when attitudes are strong, habitual schema are not that
influential (Ouellette & Wood, 1998). However, it does not mean that habit and attitudes always
act in opposition. In some situations, habits result from consistent attitudes and may contribute to
certain behaviors without much deliberate effort on the part of the individual (Kahle, 1984;
Triandis, 1977). Therefore, whenever individuals face new situations and have less information,
they might be assisted by habits held in the subconscious developed from past experiences and
insights (Bentler & Speckart, 1979; Triandis, 1977). Therefore, when attitudes (conscious) and
habits (unconscious) are combined, they may exert more influence on consistent behavior than
when acting alone (Triandis, 1977).
Habits have been found to be an important variable predicting health, exercise, and
physical activities (Davis, Brewer, & Ratusny, 1993; Valois, Desharnais, & Godin, 1988; Wood,
Tam, & Guerrero-Wit, 2005). Wood et al. found that habits encourage people to maintain the
same exercise patterns despite environmental change. Aarts and Dijksterhuis (2000) found
habitual cyclists were faster to react to bicycling-related destination information than other
transportation-related destination information. Similarly, Verplanken, Aarts, and Knippenberg
(1997) found that people with strong bicycling habits apply simpler rules in their decision making
process, and Verplanken, Aarts, Knippenberg, and Moonen (1998) also found that these people
do not tend to solicit new information and alternative options. These findings show that habits are
similar to involvement in the acceptance and rejection latitude process associated with social
judgment theory (Sherif & Cantril, 1947). Accordingly, it seems that attitudes and habits have a
synergetic relationship with each other and may be valuable in understanding the leisure-tourism
connection within lifestyle patterns.
Motivation
The leisure-tourism link is also likely to be mediated by tourism motivation. Motivation
has been found to guide tourism behavior (Fodness, 1994; Iso-Ahola, 1982; Pearce & Caltabiano,
1983; Ryan & Glendon, 1998). Eagly and Chaiken (2005) defined motivation as an engine that
guides thoughts and behaviors to satisfy their needs. Indeed, Dann (1977, 1981) and Crompton
(1979) formulated a tourism motivation theory based on Murray’s (1938) manifest needs theory.
Murray postulated that needs occur from lack of balance and people are motivated to pursue a
balanced state, and needs that are more manifest (i.e., salient) drive behavior. Dann and
Crompton suggested that travel needs consist of push and pull motivations. Push motivations are
internal needs whereas, pull motivations are induced by external factors such as destination
attractions. Crompton identified a range of push factors such as escape from the routine,
exploration of self, relaxation, prestige, regression, enhancement of kinship, and facilitation of
social interaction.
Another approach to motivation also based on Murray (1938) and Maslow (1943) is
Beard and Ragheb’s (1983) Leisure Motivation Scale (LMS). This scale identifies four
motivational dimensions: relaxation, social, intellectual, and mastery. Ryan and Glendon (1998)
applied this scale to the tourism contexts and found good reliability and validity. Thus, if leisure
motivation and tourism motivations appear to be similar, this may mean that leisure and tourism
are not only psychologically connected (Mannell & Iso-Ahola, 1987), but are part of an overall
lifestyle (Moore et al., 1995). The connection between leisure and tourism needs may manifest
itself in push and pull factors. An individual’s leisure needs connected to their favorite leisure
activities may make certain destinations more attractive to an individual as the pull factors
associated with that destination may include opportunities to participate in favored leisure
activities. The potential dynamic relationship between leisure and tourism motivation, push and
pull factors may provide additional insights along with involvement and habit to provide a more
in-depth understanding of the leisure-tourism psychological and behavioral connection.
METHOD
The purpose of this study was to examine the leisure-tourism causal connection using four
latent variables for physically active and non active leisure and tourism participants. Leisure
involvement and leisure habits are considered as exogenous variables and tourism motivation and
tourism behavior are deemed endogenous variables. The same hypotheses are applied to both the
active and non-active participants: H1) Leisure involvement and leisure habit are correlated with
each other, H2) Leisure involvement has a direct effect on tourism motivation, H3) Leisure habit
has a direct effect on tourism motivation, H4) Tourism motivation has a direct effect on tourism
behavior, H5) Leisure involvement has an indirect effect on tourism behavior, and H6) Leisure
habit has an indirect effect on tourism behavior.
For instrumentation, a questionnaire consisting of fixed choice and open-ended items was
used. Three sections were used for this particular study: Leisure (i.e., a type of favorite leisure
activity, leisure involvement, leisure habit), tourism (type of favorite tourism activity, tourism
motivation, tourism behavior), and demographics. The open-ended questions asking about type of
leisure and tourism activities were used to distinguish the respondents who take part in physically
active leisure and tourism activities from those who participate in non-physically active leisure
and tourism activities. The leisure involvement items (18 items) were adapted from those used by
Kim et al. (1997), Gahwiler and Havitz (1998), Pritchard, Havitz, and Howard (1999), and Kyle
et al. (2004a & b); the leisure habit items (10 items) were adapted from the self-reported habit
index (SRHI) developed by Verplanken and Orbell (2003); the tourism motivation items were
adapted from the Leisure Motivation Scale (LMS) (18 items) developed by Beard and Ragheb
(1983); and the tourism behavior items (11 items) were adapted from the leisure participation
scale used by Ragheb and Tate (1993) and the self-report leisure behavior items used by StantonRich and Iso-Ahola (1998). All of scales were measured using a 7 point Likert type format. The
wording of all the scale items was modified and a few items were created for the purpose of this
study. Content validity was established using a panel of 12 experts during a pilot study.
For data collection and analysis, the study population comprised alumni members of a
university. The scope of the study necessitated access to a population who were likely to be
diverse in their leisure and tourism interests. A university educated population was also deemed
to be appropriate as higher levels of are associated with higher levels of discretionary income
(Griliches & Mason, 1972) and so, these individuals are likely to be able to extend participation
in their favorite leisure activities by traveling to different environments. The targeted population
size (() (i.e., the total number of the university alumni members) was 200,000. The sample size
(n) with 2% margin of error at the 99% confidence level was calculated at 4,063 (Kish, 1995).
The survey method was on-line and as undeliverable email addresses and low response rates
associated with web-surveys were anticipated (Dillman, 2000), 80% of the total sample size was
added and the ultimate sample size was 7,313. According to systematic sampling procedures,
every 25th name as the sample interval was selected from the sampling frame. A final usable
sample of 703 participants was obtained, yielding a response rate of 23% (n= 703 out of 3082;
4231 e-mails were undeliverable). Descriptive statistics, Cronbach’s alpha coefficients (α), and
independent T-test were used to analyze the data using SPSS 17.0, and confirmatory factor
analysis (CFA) was used to test the fit of the measurement model and structural equation
modeling (SEM) was used to test the causal relationship between latent variables using LISREL
8.80/PRELIS 2.80. Both the measurement and structural equation model had three broad steps,
model specification, model assessment, and model respecification.
For sample, of the 703 respondents, 316 engaged in both physically active leisure
activities and physically active tourism activities, and 258 engaged in both non-physically active
leisure activities and non-physically active tourism activities. For those in the physically active
group, 55.3% were male and the average age was 45.18 years (SD = 12.69) ranging from 18 to 77
years. Two-thirds (64.7%) of those non-active respondents were female with an average age of
47.74 (SD = 14.48) ranging from 19 to 87 years.
RESULTS
The active group reported that they prefer outdoor-related sports, water-related sports,
golf/ski, fitness, and team sports for leisure and tourism activities. In the CFA conducted as the
first step to confirm the reliability and validity of those items comprising the four main variables,
the following results were generated using a respecification process. For the Active Group,
Leisure Involvement has five factors (Social, α=.67, CR=.66, AVE=.41, Self-Identity, α=.83,
CR=.86, AVE=.68, Social Identity, α=.78, CR=.81, AVE=.70, Hedonic, α=.83, CR=.85,
AVE=.75, Central, α=.86, CR=.88, AVE=.75); Leisure Habit has three factors (Automatic, α=.72,
CR=.77, AVE=.46; Resistant, α=.71, CR=.76, AVE=.51; Regular, α=.79, CR=.86, AVE=.76);
Tourism Motivation has four factors (Social α=.84, CR=.88, AVE=.59; Physical α=.87, CR=.91,
AVE=.78; Relaxation α=.79, CR=.85, AVE=.56; Intellectual α=.87, CR=.91, AVE=.72); and
Tourism Behavior has two factors (Participation α=.83, CR=.81, AVE=.56; Decision α=.81,
CR=.73, AVE=.68). As a further step, the mean values of each factor per each main variable
were generated. These were used as the new observed variables (i.e., total 14 items) for the initial
SEM measurement model. However, this initial measurement model failed to obtain a good fit.
After the removal of Social in the respecification process, the final measurement model yielded
an acceptable fit (Minimum Fit Function Chi-Square χ2/df = 156.52/57 = 0.00; RMSEA = 0.074
with CI, 0.060-0.088; SRMR = 0.070; NNFI = 0.91; CFI= 0.93). The Cronbach’s alphas (ranging
from .74 to .88), CR (.75 to .87), and AVE (.55 to .63) of each latent variable showed good
reliability and validity except tourism motivation (α=.42, CR=.46, AVE=.19). Tourism
motivation consists of four different components to explain various internal push factors to travel.
The four components may not be strongly correlated with each other. In the aforementioned CFA
model, socializing, physical, and relaxation were significantly correlated with each other, but
intellectual was not significantly correlated with the other three factors. The reason that
intellectual was distant from other motivations may be because it yielded much higher mean
values than the other factors. Tourism motives may be diverse and show a wide range of factor
loadings. Accordingly, low factor loadings, reliability and validity do not mean that the item is
not valuable in measuring the factor. Finally, all four of the tourism motivation items were
employed in the SEM. For the structural portion of the SEM, the correlation between
involvement and habit was significant (φ=.57). The direct effect of leisure involvement on
tourism motivation was significant (γ=.34) as well as the direct influence of leisure habit on
tourism motivation was significant (γ=.22). The direct influence of tourism motivation on tourism
behavior appeared significant as well (β=.45). The indirect effects of leisure involvement
(indirect=0.15; total=0.15) and leisure habit (indirect=.10; total=.10) on tourism behavior were
identified as significant.
The on-Active Group reported that they prefer passive leisure and tourism activities
such as watching TV, reading, relaxing, playing cards, creating arts, shopping, dining, visiting
museums, etc. Results were a product of the respecification process of the CFA. Leisure
Involvement consisted of five factors (Social, α=.71, CR=.80, AVE=.49, Self-Identity, α=.85,
CR=.89, AVE=.72, Social Identity, α=.82, CR=.86, AVE=65, Hedonic, α=.86, CR=.91,
AVE=.69, Central, α=.85, CR=.88, AVE=.59); Leisure Habit had three factors (Automatic, α=.73,
CR=.78, AVE=.46; Resistant, α=.73, CR=.78, AVE=.53; Regular, α=.76, CR=.85, AVE=.75);
Tourism Motivation comprised four factors (Social α=.90, CR=.92, AVE=.69; Physical α=.90,
CR=.91, AVE=.68; Relaxation α=.86, CR=.89, AVE=.67; Intellectual α=.92, CR=.94,
AVE=.81); Tourism Behavior had two factors (Participation α = .74, CR = .81, AVE =.54;
Decision α=.87, CR=.90, AVE=.80). The same14 observed variables as the active respondents
were generated, and the initial measurement portion also did not have a good fit. After the
deletion of social and participation with low factor loadings and relaxation with negative factor
loadings, the model had a reasonable fit (Minimum Fit Function Chi-Square χ2/df = 106.10/36 =
0.00; RMSEA = 0.086 with CI, 0.067-0.11; SRMR = 0.056; NNFI = 0.92; CFI= 0.95). The
Cronbach’s alphas (ranging from .84 to .90), CR (.88 to .95), and AVE (.65 to .95) of each latent
variable showed good reliability and validity. However, again, tourism motivation (α=.51,
CR=.53, AVE=.37) did not have good reliability and validity for the same reasons as that of the
active respondents. As with the active respondents, the tourism motivation factors remained in
the analysis. For the structural portion of the SEM, leisure involvement and habit were strongly
correlated with each other (φ=.66), but the direct effect of leisure involvement on tourism
motivation (γ =.25) as well as the direct effect of leisure habit on tourism motivation (γ=.12) was
not significant. Also, the direct effect of tourism motivation on tourism behavior appeared
insignificant (β=.41). However, leisure involvement (indirect=.15; total=.15) and leisure habit
(indirect=.10; total=.10) had a slightly significant indirect effect on tourism behavior.
In addition to the causal model comparison, independent t-tests were used to investigate if
the physically active and non-physically active respondent groups have different levels of leisure
involvement, leisure habit, tourism motivation, and tourism behavior. The results showed that the
active respondent group rated social (t= 3.721, p=.000), social identity (t=2.925, p=.004), and
centrality (t=2.127, p=.034) significantly higher than the non-active respondent group, whereas
the non-active respondent group were much higher in all of the leisure habit variables, regular
(t=3.798, p=.000), automatic (t=5.445, p=.000), and resistant (t=.2119, p=.034) than the active
respondent group. As expected, the active respondent group rated physical (t=6.802, p=.000) and
socializing (t=2.388, p=.017) significantly higher. The active respondent group was also
significantly higher in decision-making (t=6.476, p=.000).
DISCUSSIO AD COCLUSIO
Leisure involvement and habit were found to be significantly correlated for both the
active and non-active respondent groups, which empirically supports Triandis’ (1977) habit
theory, Kahle’s (1984) social adaptation theory, and Anderson’s (1990) thought theory. These
theories assume that consistent attitudes induce repeated behaviors, these repetitions form
habitual schema, and in turn, this schema contributes to the reinforcement of attitudes (Anderson,
1990; Kahle, 1984). More specifically, the results suggest that beyond simple behavioral
frequency, unconscious and automatic components play an important role in habits and interact
with conscious components. This seems to be very crucial in understanding people’s lifestyles
that cannot be merely explained by intentional and conscious components (Bentler & Speckart,
1979; Triandis, 1977; Verplanken & Aarts, 1999). However, the ratio of consciousness to
unconsciousness in each individual’s lifestyle is different, and this may influence who has active
or passive lifestyles. Likewise, this study found that physically active respondents showed
stronger involvement than non-physically active respondents, whereas the non-active respondents
had stronger habits.
Despite their significant correlation, the ratio of leisure involvement to leisure habit may
be different and this different ratio may be an important determinant to see whether leisure and
tourism will be connected or disconnected. For example, for active individuals who perceive
themselves as a runner in terms of their identity are likely to have very high involvement level
with running (Shipway & Jones, 2008). In turn, these runners might be motivated to participate in
marathons as an outcome of their involvement in running and also to reinforce their identity as a
runner by participating in competitive running events. Yet, even though they intend to take part
in a running competition or running event, it is hard to take part if they have not trained regularly,
the regularity of which is likely to be habitual. Thus, their running habit and involvement likely
motivates them to take part in running events some of which may involve travel away from home
(McGehee et al., 2003). However, for those non-active individuals, the combination of too much
automaticity of habit and too little involvement may make it hard to generate enough
motivational intention to travel to take part in associated leisure activities (Ouellette & Wood,
1998). In other words, although habitual leisure behaviors could be enhanced by the identity or
social aspects of leisure involvement, much stronger leisure habitual behaviors may not induce
individuals to plan travel that incorporates these activities directly. The habit of non-active leisure,
particularly activities such as TV watching, card playing, and shopping seem to be related to a
generally non-physically active lifestyle that manifests itself in tourism contexts in the same
manner, for example, relaxing by the pool, spending time with family and friends, cultural and
historical activities were reported as favored tourism pursuits by the non-physically active group.
However, the lack of involvement in these activities seems to be a key difference.
Thus, in this study, the leisure-tourism causal relationship was found to be significant
only for the active respondent group and as such supports the tenets of Carr’s (2002) LeisureTourism Continuum and Currie’s (1997) LIP behaviors framework explaining the impact of daily
behaviors on tourism behaviors. In addition, the findings support the idea of framing tourism
behaviors within overall lifestyles (Cohen, 1974; Moore et al., 1991; Pearce, 1988; Rapoport &
Rapoport, 1975). Those with active lifestyles showed a significant connection. Perhaps, as
assumed by Sherif and Cantril’s (1947) social judgment theory, those individuals seem to
formulate their identity around their leisure activities and are likely to find and confirm their
identity by taking part in associated leisure and tourism experiences (Haggard & Williams, 1992;
Shipway & Jones, 2008) Consistent with this assumption, in this study physically active
individuals revealed stronger identity with their leisure activities. Their identities may be tied to
active and healthy lifestyles and thus, their leisure and tourism behaviors show consistency
(Hallab & Gursoy, 2006; Hallab, Yoon, & Uysal, 2003). For the active group, the synergetic
effect between conscious and unconscious components seems to push them to pursue relevant
identity-projected tourism activities. This may also provide a more in-depth understanding by
incorporating the psychological meanings behind Brey and Lehto’s (2007) finding that frequent
participation in physically active leisure is more likely to be connected to tourism activities. On
the other hand, those non-active individuals may perceive leisure and tourism as unconnected.
Depending on individuals’ subjective perception (Hamilton-Smith, 1987; Kelly, 1983; Mannell &
Iso-Ahola, 1987), the ratio of involvement to habit seems to be important, with involvement level
in a leisure activity in particular predicting tourist activity choices. While it would be incorrect to
suggest that non-physically active leisure pursuits are not associated with tourism choices as
there were indications within the data that non-physically active leisure was associated with
lesser activity in tourism settings, it seems that the level of involvement in a particular leisure
activity is very important. Thus, in future research focus should be directed towards how high
leisure involvement needs to be in relation to leisure habits to encourage people to travel to
pursue their favorite leisure pursuits. Anecdotally, we know that people who are interested in
such leisure pursuits as science fiction, quilting or train spotting do travel to engage in these
activities. However, such findings were not evident for the non-active participants in this study.
For implications, it appears that a variety of physically active tourism forms rooted in
favorite leisure activities may be particularly attractive to physically active people. Information
about their favorite leisure activities and lifestyles might be very useful in developing more
specialized tourism products as well as increasing the industry’s ability to better predict their
behavioral patterns in vacation settings. As compared to the non-active respondent group, the
identity and social (i.e., social identity, social interaction, socializing) components of the
experience important to the physically active respondents should be built into any active tourism
programs and emphasized in their promotion (Gilbert & Hudson, 1998). Certainly for the active
tourism activities, promotion linked to leisure lifestyles might be very effective for tourism
marketing. Furthermore, there may be ways of linking active tourism to active lifestyles and the
goals for overall health promotion pertinent in many societies today. For example, harnessing the
dynamic relationship between leisure and tourism might help encourage people to maintain their
active and healthy lifestyles while they are on vacation and may provide a chance to enhance
their skills or reinforce their motivation in this regard. The novelty of the vacation context might
also be used among those people who are not physically active on a regular basis with a chance to
participate in physically active tourism pursuits. From these a physically active habit might be
developed that might encourage them to keep active once they return home. For passive leisure
more generally, subliminal messages associated with their favored leisure attributes of less
physically active, relaxing and socializing might be more effective rather than messages directly
connected to their leisure activities per se for tourism promotion.
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