Differentiating mass participant sport event consumers: traditional versus non-traditional events.
Buning, Richard J. ; Walker, Matthew
Introduction
Undeterred by the sometimes lofty financial costs and personal
sacrifices required for participation, running races across the United
States included approximately 19 million finishers at more than 28,000
events in 2013 (Running USA, 2014a). Exemplified by these data, mass
participant sporting events (MPSEs) have increased in popularity due to
an array of offerings that range from 5k running races to marathons.
These events attract a variety of participants and offer challenges for
a variety of skill levels and age groups. Murphy and Bauman (2007)
identified three categories of mass physical activity-related events:
(1) elite sporting events (e.g., Olympic Games), (2) non-elite mass
events with the potential for community-wide participation, and (3)
major population-level health promotion events. An emerging body of
literature has focused on understanding the unique participant
experiences of non-elite mass events (Funk, Jordan, Ridinger, &
Kaplanidou, 2011; Kaplanidou & Vogt, 2010). Additional research on
MPSEs has shown these events can catalyze physical activity (Funk et
al., 2011; Crofts, Dickson, Schofield, & Funk, 2012), provide
benefits to the host city through active sport tourism (Kaplanidou,
Jordan, Funk, & Ridinger, 2012), and provide economic impacts to the
host region (Coleman & Ramchandani, 2010).
Since running events have become increasingly popular, and the
marketplace is becoming ever more crowded, event organizers must
differentiate their offerings. Among the more novel approaches are
nontraditional events, which are typically organized around a central
theme such as obstacle races or color runs. Non-traditional events tend
to offer a less competitive environment focused more on fun, fitness,
camaraderie, and achievement compared to traditional events that focus
only on running a specified distance for a desired time (Running USA,
2014b). Due to their unique appeal, non-traditional events often attract
a large proportion of first-time participants with some events being
comprised of up to 60% of entrants with no previous running event
experience (Running USA, 2014b). This segment of the US running industry
has grown in recent years as the number of finishers since 2009 has
doubled annually to an estimated four million in 2014. These data affirm
that traditional events (i.e., half marathons and marathons) are
becoming less popular with only 2.5 million finishers over the same time
period (Running USA, 2014b). Still, the running event market remains
dominated by 5k and 10k distance events, which recorded a combined 9.78
million finishers in 2013 (Running USA, 2014a). One of the more popular
non-traditional events are obstacle races, in which participants
complete a 3-10 mile run over obstacles in muddy conditions. We might
assume that consumers of these events have different motives for
participation that focus on leisure and social bonding, rather than
personal achievement due to the differences in organization and
environment. However, research has yet to identify if differences
actually exist.
The obstacle race industry is relatively new with modern events
beginning in 1999 and current industry leaders beginning operations as
recently as 2010 (Babbitt, 2012; Keneally, 2012). According to one
source, in 2013 more than 100 companies organized obstacle races,
resulting in 3.4 million event finishers and $290 million in revenue
(Obstacle Race World, 2014). Still, an argument can be made against
obstacle races as a legitimate sporting event. However, Loy (1972)
contended that a commonly accepted description of sport is often
debatable and difficult to interpret. Obstacle races possess several
attributes akin to more general sport options since they test athletic
skills, have documented rules, employ standard distances, and are
time-based contests (Desena & Weinberg, 2012).
Given the relative infancy of obstacle race events, little (if any)
research has examined participant motives in this context. Understanding
the motives specific to different MPSEs is an important area of inquiry
as event promoters seek to differentiate their events in a congested
marketplace. Although numerous articles have been published on MPSEs
(e.g., Funk et al., 2011; Ridinger, Funk, Jordan, & Kaplanidou,
2012; Murphy & Bauman, 2007; Sato, Jordan, Kaplanidou, & Funk,
2014), the existing scholarly attention has focused primarily on
traditional running and multisport events and overlooked potential
differences between event contexts. Different events may attract
dissimilar individuals seeking varied motivational experiences. Thus, a
better understanding of the motivational profiles of event participants
will aid event organizers in event planning in leveraging the outcomes
of event participation. The purpose of the study was to assess the
validity of a traditional running motivation scale in a modern context
and examine if motivational differences exist across event categories
(i.e., obstacle races and half marathons).
Theoretical Framework
Defined as an internal factor that arouses, directs and integrates
a person's behavior" (Murray, 1964, p. 7), motivation has been
explored in a variety of fields but largely stems from psychology.
According to Maslow (1970), human motivation is directed at the
fulfillment of fundamental needs (i.e., physiological, safety,
belonging,) to advanced needs (i.e., esteem, self-actualization).
Researchers investigating motivation have often relied on the
self-determining aspects of individual volitional behavior to drive
motivations. Self-determination theory (SDT) is thought to be derived
from intrinsic (e.g., interests) and extrinsic sources (e.g., rewards),
which are influenced by personal and cultural forces associated with
autonomy, competence, and relatedness (Deci & Ryan, 2002). Autonomy
describes an individual's ability to govern his or her own
behavior, whereas competence refers to an individual's perceived
ability to control an outcome, and relatedness is concerned with the
desire to be connected to others (Deci & Ryan, 2000).
As conditions are created that support an individual's
autonomy, competence, and relatedness, an increased motivation that
leads to higher engagement in an activity such as increased performance,
persistence, and creativity is witnessed (Deci & Ryan, 2000). This
idea is especially pronounced in the context of leisure activities since
performing them reflects and reinforces the characteristics of SDT.
Accordingly, leisure motivation is considered to be a need, reason, or
satisfaction that catalyzes an individual's involvement in a
leisure activity, and an internal factor that subsequently drives
behavior (Crandall, 1980; IsoAhola, 1982).
Early research on leisure motivation by Beard and Ragheb (1983)
identified four motivational components. First, the intellectual
component is associated with motivation to participate in an activity
that encompassed mental activities such as exploring, learning,
discovering, creating, and/or imagining. Second, the social component
pertains to social motivation, driven by needs for friendship and
relationships, and esteem of other individuals. The third component,
competence-mastery, pertains to individual desires to achieve, master,
challenge, and compete. Lastly, the stimulus-avoidance component is
associated with the intent to escape and get away from over-stimulating
life situations.
Leisure motives vary based on individual circumstances and are
often linked to event preferences. For instance, using a push-pull
motivation approach, Snelgrove and Wood (2010) found motives of
supporting others, learning about a destination, and identity predicted
event choice. Push motives are considered internal needs that create a
desire to participate, while pull motives are related to external
factors such as an event attractiveness (Crompton, 1979; Dann, 1977,
1981). Further, Snelgrove and Wood (2010) argued that although
first-time event visitors might be initially motivated by physical event
characteristics, this motivation may decline over time as they create a
stronger identity related to the sport.
Based on the unique aspects of non-traditional events and
considering the potential differences between the participants attracted
to traditional and non-traditional events the concept of serious leisure
provides us a fundamental understanding of the range of leisure
involvement. Stebbins (1992) argued that leisure involvement ranged from
casual to serious and that serious leisure is "... the systematic
pursuit of an amateur, hobbyist, or volunteer core activity that is
highly substantial, interesting, and fulfilling and where, in the
typical case, participants find a career in acquiring and expressing a
combination of its special skills, knowledge, and experiences" (p.
3). Perhaps individuals that participate in traditional events are
focused on the core product of running and competing, while
non-traditional event participants are more concerned with the ancillary
event products (e.g., obstacles and themes) making them less
"serious." Research on traditional running events supports
this idea by linking serious leisure to marathon running (Shipway &
Jones, 2007, 2008).
[FIGURE 1 OMITTED]
The MOMS Scale
The Motivations of Marathoners Scale (MOMS), developed by Masters,
Ogles, and Jolton (1993), is one of the most comprehensive and widely
used scales for measuring endurance event participant motives. Although
many leisure motives apply to these event types (Beard & Ragheb,
1983), Masters et al. (1993) suggested a comprehensive measure was
needed to assess the specific motives of individuals participating in
and training for running events. Based on previous distance running
research (Carmack & Martens, 1979; Masters & Lambert, 1989;
Curtis & McTeer, 1981), Masters et al. (1993) identified four broad
categories of running motives, each containing two or more
subdimensions: (1) physical health (i.e., general health orientation and
weight concern), (2) social motives (i.e., affiliation and recognition),
(3) achievement (i.e., competition and personal goal achievement), and
(4) psychological motives (i.e., psychological coping, self-esteem, and
life meaning). Masters et al. found the motives for health, personal
achievement, and self-esteem to be more important than social motives
for event participation. Subsequent research using the MOMS has
continued to provide evidence of the validity and reliability of the
related motives (Masters & Ogles, 1995; Ogles, Masters, Richardson,
1995; Ogles & Masters, 2000, 2003; Havenar & Lochbaum, 2007),
but researchers have yet to empirically examine the nuanced differences
among different event types.
Ogles and Masters (2000) found that marathon runner motives
differed based on age, where older runners were more motivated by health
concerns and affiliation, while younger runners were more motivated by
achievement. Later, Ogles and Masters (2003) discovered that runners
could be grouped into similar clusters based on motivation, and these
clusters differed based on running experience, training patterns, and
demographics. Havenar and Lochbaum (2007) used the MOMS to assess
differences among first-time marathoners and found social motives to be
higher for individuals who dropped out of competition compared to event
finishers. Further, the MOMS has been used to assess motivation in a
variety of other sporting contexts including cycling (LaChausse, 2006),
5k running (Bell & Stephenson, 2014), ultrarunning (Krouse,
Ransdell, Lucas, & Pritchard, 2011), adventure races (Doppelmayr
& Molkenthin, 2004), and triathlons (Rundio, Heere, & Newland,
2014). Although other scales have been developed to measure motives
related to sport participation, the MOMS is the most comprehensive and
applicable to running events. Other scales measuring sport participant
motivation such as the Leisure Motivation Scale (Beard & Ragheb,
1983), the Sport Motivation Scale (Pelletier, Fortier, Vallerand, Tuson,
& Bilas, 1995), and the Behavioral Regulation in Sport Questionnaire
(Lonsdale, Hodge, & Rose, 2008) are quite general and fail to
adequately measure motives related to health, weight loss, goals, and
competition.
Recently, Rundio et al. (2014) compared the motives of participants
from cause-related events and non-cause related events using the MOMS.
The authors revealed that cause-related event participants rated motives
related to self-esteem, personal goal achievement, competition, and
recognition/approval significantly higher than the participants from the
non-cause related events. Interestingly, the limited significance of
social motives uncovered in previous research (Havenar & Lochbaum,
2007; Ogles & Masters, 2003; Masters et al., 1993) may be indicative
of long-time participant samples in which attitudes toward participation
were directly tied to competitive preferences (e.g., winning and best
times). Recent shifts in participant motivation factors have suggested a
need to reexamine this complex element of sport participant psychology.
The discovery of a wider range of motives for MPSE participants (Funk et
al., 2011) may be evidence of broadening leisure pursuits attached to
MPSE participation (e.g., social bonding and getting out in nature).
Funk et al. (2011) reported MPSE participants were propelled by a range
of motives, but reiterated that social motives were relatively low among
marathon participants.
Based on the lack of research on non-traditional participant sport
events and the need to better understand participant motivation for
modern MPSEs, the following research questions were developed to guide
the study:
RQ 1: Does the Motivation of Marathoners Scale factor structure
apply to traditional and non-traditional mass participant sport events?
RQ 2: Do participant motives differ across traditional and
non-traditional mass participant sport events?
Method
Data Collection
To assess the research questions, data were collected using an
online questionnaire distribution system. Four MPSEs in the Midwest
United States throughout 2011 served as the research contexts. The event
owners and researchers agreed to not have the events named in the
research. Data were collected approximately two weeks prior to each
event in order to remove potential bias from the influence of the actual
event experience. Prospective respondents were invited to participate in
the study via an emailed questionnaire link from the event organizers.
Emails were obtained by the event organizers from the participant
registration database. Following the recommendations of Dillman, Smyth,
and Christian (2009), a three-contact strategy (i.e., initial invitation
and two follow-up invitations) was utilized to encourage responses.
Since the emails were confidential and protected by the event owner, the
actual number distributed is unknown. Thus, an accurate response rate
could not be calculated.
Instrumentation
The questionnaire was comprised of three main sections: (1) event
evaluation items, (2) participant motivation items, and (3)
demographics. The event evaluation items were included in the
questionnaire as a requirement imposed by the event promoter.
Participant motivations were measured using the multidimensional MOMS
comprising 56 items under nine motivational factors (Masters et al.,
1993): health orientation (six items), weight concern (four items),
affiliation (six items), recognition (six items), competition (four
items), personal goal achievement by (six items), psychological coping
(nine items), self-esteem (eight items), and life meaning by (seven
items). All of the items were rated on a seven-point scale (1 = not a
reason to 7 = most important reason), as to the degree the participant
considered the item a reason for event participation. Since the MOMS was
administered to four different events, the wording of select individual
scale items and the questionnaire directions were adapted to fit each
event context. For example, the item "to share a group identity
with other runners" was modified to "to share a group identity
with others" for the non-traditional group. Finally, demographic
items were used to assess participant gender and location. Traditional
event data were collected at one half marathon, while the
non-traditional event data were collected at three obstacle races. The
obstacle races were a series of events held at multiple locations. All
four events included in the study were produced and owned by the same
event management company and were the primary offerings in their event
portfolio.
Data Analysis
The data analysis consisted of several sequential steps. First,
given the shift toward a broader definition of MPSE participant, the
MOMS factor structure was assessed across the two event contexts via
Mplus7.31 statistical modeling software. Due to the ordinal nature of
the data and response distributions, a robust weighted least squares
approach (WLSMV) was used to estimate the measurement model (Brown,
2006). Accordingly, single-group confirmatory factor analysis (CFA)
models were examined for each group (i.e., traditional and
non-traditional) to ensure the MOMS was a robust measure for both
contexts. Second, a multi-group confirmatory factor analysis (MG-CFA)
was performed to evaluate the MOMS structure between event types and to
assess evidence of discriminant and convergent validity of the
individual dimensions.
In order to test overall model fit for the individual CFA models
and the MG-CFA, [chi square] goodness-of-fit, Comparative Fit Index
(CFI), Tucker Lewis-Index (TLI), and Root Mean Square of Approximation
(RMSEA) were calculated. Recommend cutoff values for fit indices vary.
For example, Hu and Bentler (1999) suggest CFI and TLI values should be
close to .95 and RMSEA should be close to .06 to decrease Type II error.
Other sources suggest lower cutoff values as MaCallum, Browne, and
Sugawara (1996) recommend RMSEA values of .01, .05, and .08 indicate
excellent, good, and mediocre model fit, respectively, while Bentler and
Bonnett (1980) suggest CFI and TLI scores greater than .90 designate
good fit. When evaluating RMSEA the PCLOSE statistic is often evaluated
concurrently. PCLOSE is a test for closeness of fit that RMSEA is below
.05 in the population and should be greater than .05 (Browne &
Cudek, 1993). However, Hair, Black, Babin, and Anderson (2010) argued
that model fit should be assessed in regards to group size and the
number of observed variables as simpler models with smaller samples
should be subject to a stricter evaluation than complex models with
larger samples.
Following the recommendations by Hair et al. (2010), for a model
with more than 30 observed variables and a group size less than 250,
goodness of fit is demonstrated by a significant [chi square] p-value,
CFI and TFI above .92, and RMSEA less than .08. Internal consistencies
of the nine factors were assessed with composite reliability scores (CR)
while average variance extracted (AVE) values were used to assess
evidence of convergent validity between latent variables (Hair et al.,
2011; Fornell & Larcker, 1981). For descriptive purposes, summary
statistics were calculated for the individual MOMS items and the nine
motivational factors. Finally, to further compare the group differences
between traditional and non-traditional events, a one-way multivariate
analysis of variance (MANOVA) was conducted via SPSS 22.0 to examine the
potential differences between each of the motivation factors based on
event type (i.e., traditional and non-traditional).
Results
A total of 408 participants responded to the pre-event
questionnaire from both event types, which exceeded the recommendations
of Muthen, du Toit, and Spisic (1997) and Flora and Curran (2004) for
the WLSMV estimation procedure. Responses from the divided sample based
on event type consisted of non-traditional (n=275) and traditional
(n=133) event participants. The total sample was 35.2% male and 64.8%
female. The traditional event subsample was comprised of 63.5% females
and 36.5% males, whereas the non-traditional event subsample was
comprised of 65.4% females and 34.6% males. The highest-rated single
item for the traditional sample was "to push myself beyond my
current limits" (M=5.90, SD=1.47) and the lowest-rated single item
was "to get a faster time than my friends" (M=2.26, SD=1.70).
The non-traditional sample rated the single item "to participate
with my family or friends" (M=5.84, SD=1.68) the highest and the
single item "to have time alone in the world" (M=1.55,
SD=1.19) the lowest.
Factor Analysis
For the first step of the factor analysis, separate CFAs were
performed for each event type. Model fit criteria were based on the
recommendations cited above. The traditional and non-traditional groups
demonstrated mixed support for the hypothesized models (see Table 1 for
all model fit statistics). The fit of the traditional group (CFI=.93,
TLI=.92, RMSEA=.07) and the nontraditional group (CFI=.91, TLI=.90,
RMSEA=.07) can be considered mediocre to good. Thus, single-step post
hoc model modifications were performed to respecify the models by
examining the modification indices and expected parameter change
statistics (Kline, 2011; Kaplan, 1990, 1991; Boomsa, 2000). This
procedure resulted in the deletion of three items (i.e., "to
participate with family or friends," "to visit with
friends," and "to beat someone I've never beaten
before") due to low factor loadings and implausible modification
indices.
The respecification process also resulted in three items (i.e.,
"to compete with myself," "to improve my sense of
self-worth," and "to have time alone with the world")
being set to load on different factors than originally designed by
Masters et al. (1993). The subsequent 53-item multi-group model provided
evidence of good model fit (CFI=.94, TLI=.94, RMSEA=.06). Although the
PCLOSE statistic was not above the preferred value of .05, the
multi-group model provided evidence of acceptable model fit according to
the recommendations by Hair et al. (2011) among others.
Testing for measurement invariance was conducted to determine
whether motivation was measured similarly across groups. Three
measurement invariance tests were conducted as described by (Dimitrov,
2010): configural invariance (equal factor structure), metric invariance
(invariant factor loadings), and scalar invariance (invariant factor
loadings and intercepts).
As shown in Table 1, model fit remained stable as constraints were
added, supporting measurement invariance across groups.
Reliability and Validity
We tested for evidence of convergent and discriminant validity.
First, convergent validity was assessed based on three criteria: (1) the
size of the individual factor loadings (average [lambda] = .81), (2) for
each scale dimension the composite reliability score should exceed .70,
and (3) the AVE score for each scale dimension should exceed .50 (Hair
et al., 2010; Fornell & Larcker, 1981). For all nine motivation
factors all of these criteria were successfully met for both groups.
Each of the nine factors for both groups demonstrated high internal
consistency with composite reliability scores ranging from .795-.967,
which were above the recommended criterion of greater than .70 (Hair et
al., 2010). With regard to evidence of convergent validity, all nine
factors had acceptable AVE scores according to recommendations by Hair
et al. (2010).
We next examined the multi-group model for discriminant validity,
which is particularly challenging for multi-dimensional scaling because
of the strong relationships between dimensions. Therefore, and
considering some high correlations between dimensions, one additional
test was performed to ensure the dimensions were independent. Following
the suggestion of Fornell and Larcker (1981), if the square root of each
construct's AVE is greater than the inter-construct correlations,
evidence of discriminant validity is present. The results revealed this
final condition was met (see Table 3). Correlations and descriptive
statistics are presented in Table 3. The highest rated motivation factor
for the overall participant sample was self-esteem (M=4.59, SD=1.54),
followed by affiliation (M=3.76, SD=1.58), personal goal achievement
(M=3.73, SD=1.65), health-orientation (M=3.55, SD=1.78), weight concern
(M=2.98, SD=2.03), life meaning (M=2.83, SD=1.77), competition (M=2.80,
SD=1.55), recognition (M=2.64, SD=1.53), and psychological coping
(M=2.34, SD=1.38).
Traditional vs. Non-Traditional Events
To answer RQ2, a MANOVA with event type (i.e., traditional vs.
non-traditional) as the independent variable and the revised MOMS
factors as the dependent variables revealed a statistically significant
omnibus main effect (Pillai's Trace=.436, F (9, 398)=34.231,
p<.001, [[eta].sup.2]=.436). Thus, significant differences between
event participants based on the nine motivation factors were present
(see Table 4). Traditional event participants rated personal goal
achievement (M=4.93, SD=1.46), health orientation (M=4.85, SD=1.59),
self-esteem (M=4.62, SD=1.46), and weight concern (M=4.29, SD=1.80) as
the most important motives, whereas the least important motives were
recognition (M=2.80, SD=1.55) and psychological coping (M=3.14,
SD=1.61). The traditional event participants were moderately motivated
by competition (M=3.64, SD=1.32), affiliation (M=3.50, SD=1.64), and
life meaning (M=3.30, SD=1.78). The non-traditional event participants
rated affiliation (M=3.89, SD=1.54) and self-esteem (M=3.83, SD=1.59) as
the most important motives for participation, while psychological coping
(M=2.14, SD=1.18), life meaning (M=2.23, SD=1.42), recognition (M=2.40,
SD=1.49), and weight concern (M=2.43, SD=1.64) were the least important.
Other moderately important motives included health orientation (M=3.10,
SD=1.63), personal goal achievement (M=3.19, SD=1.46), and competition
(M=3.36, SD=1.43).
Subsequent post hoc analysis revealed that the motivations
significantly differed by event type on several factors (see Table 4).
In addition to the statistically significant results, we examined the
data for practical significance. Cohen (1988) noted that a value of
[[eta].sup.2]=0.01 is a small effect, a value of [[eta].sup.2]=0.06 is a
moderate effect, and a value of [[eta].sup.2]=0.14 is considered a large
effect. Traditional event participants were more highly motivated than
non-traditional event participants on health orientation (p<.001,
[[eta].sup.2]=.205), weight concern (p<.001, [[eta].sup.2]=.211), and
personal goal achievement (p<.001, [[eta].sup.2]=.250), which were
all large effects according to Cohen (1988). The small to moderate
effects included psychological coping (p<.001, [[eta].sup.2]=.112),
life-meaning (p<.001, [[eta].sup.2]=.097), and self-esteem
(p<.001, [[eta].sup.2]=.054). However, non-traditional event race
participants were more strongly motivated by affiliation (p<.05,
[[eta].sup.2]=.013) while no significant difference between the motives
of competition and recognition was found between groups.
Discussion
The motivational profiles of participants from modern MPSEs across
two different event contexts were examined. As such, the MOMS was
successfully adapted as a measure of MPSE motives outside a traditional
marathon setting, which extends it applicability to non-traditional
events. However, the results herein should be viewed cautiously since
many of the scale dimension mean scores were centered around the scale
midpoint. This means that while many of the motives are applicable to
modern events, there may be additional (more salient) motives that might
exist. That said, the results of the multi-group CFA and the generalized
linear model revealed three distinct participant motivational profiles
of MPSE consumers. We confirmed that regardless of event type,
individuals participated in a MPSE in order to maintain or enhance their
self-esteem, a motivational factor overlooked by Funk et al. (2011). The
self-esteem construct is comprised of items that exhibit positive
feelings about oneself such as confidence, pride, and achievement.
Similarly, Ogles and Masters (2000) reported in a comparison of older
and younger marathon participants that self-esteem was highly related as
a motive regardless of age. Further, Masters and Ogles (1995) found
event motivation was geared towards enhancing or maintaining
self-esteem.
The event contexts did, however, appear to have attracted
individuals with different motivational profiles. For example, the
traditional event participants were a more highly motivated group
compared to the non-traditional event participants. In the pursuit of
traditional event training and completion, participants were motivated
by health and weight concerns such as becoming physically fit, losing
weight, and staying/becoming physically attractive. Further, traditional
event participants were motivated by achieving personal goals (e.g.,
running faster, to perform better), mentally coping with daily life
(e.g., distraction from stress, to get away), and deriving life meaning
from their event involvement (e.g., adding purpose to life, to feel
whole). Non-traditional event participants were less interested in the
motivational factors associated with traditional MPSEs and placed
greater emphasis on socializing with others, sharing a mutual
identity/interest with other runners, and meeting new people. Thus, the
non-traditional events attracted a more socially motivated participant
compared to the traditional MPSE.
This finding is particularly salient in light of the work of
Havenar and Lochnaum (2007), who found that marathon dropouts are more
socially motivated than marathon finishers. Thus, other types of MPSEs
should seek to attract socially motivated participants. However, the
analysis revealed that competition against others through better times,
finishing position, and general competition along with gaining
recognition from others did not vary between event types. This suggests
that although these non-traditional events appear to be more socially
oriented, the participants were still as motivated compared to the
traditional event to compete against others and gain recognition. These
results are similar to research on spectator sport motivations since
motives to spectate sport events also differ based on event type (James
& Ross, 2004).
The researchers proposed that different MPSEs attract specific
participants, which has implications for managerial practice. James and
Ross (2004) argued that understanding interpersonal motives that
stimulate consumer interests allow sport marketers to target certain
motives. Results from the current research affirmed this point and
demonstrated how event marketers could gain a heightened understanding
of the MPSE consumption process, which could enable more effective
future marketing initiatives. Event organizers should focus their
marketing efforts toward the prospective MPSE participants specific to
their event. Event characteristics such as distance, obstacles, and
other unique aspects should be acknowledged as they attract participants
and satisfy specific motives. Ultimately, if traditional events seek to
compete with the growth of non-traditional events, organizers should
consider adding unique event characteristics to differentiate their
offering. Maximizing the efficiency of MPSE marketing efforts is
important, beyond cost savings, since these events foster health and
social outcomes (Funk et al., 2011).
Limitations and Future Research
The current study is not without limitations that necessitate
acknowledgement to improve future research efforts. First, as with any
model modification procedure, replication is needed to confirm the
proposed factors structure. As argued by Steiger (1990), replication is
an important step in ensuring the accuracy of the post hoc
modifications. Thus, future work should consider using a similar MOMS
factor structure to assess participant motivation to ensure validity.
Given the connection of expectations-importance behaviors identified in
previous research (Pritchard & Funk, 2010), continued examination
and a deeper understanding of post-event attitudes and behaviors is
recommended to further understand how participant pre-event psychology
affects post-event consumption. This can be accomplished through
interviews and focus groups with event participants, which would also
assuage concerns over common method bias for the current study. Second,
a pre-/post-event analysis would enhance our understanding of how the
motives of MPSE participants matched their post-event satisfaction
levels.
Future research could also be centered on developing an
MPSE-specific scale that can be applied to both traditional and
non-traditional event types. Given the relative ubiquity and mass appeal
of these events, it makes this point even more germane. Additionally,
since many of the mean scores in the MOMS were close to median values on
the Likert-type scale, the possibility exists that a more refined
version of the scale could be developed. Since the MOMS scale is
somewhat dated, it may not accurately capture all the motives for event
participation. Third, the data revealed high correlations between some
related factors in the MOMS (i.e., health orientation, weight concern,
and life meaning). Thus, future research should consider collapsing
these factors into "health orientation" and
"psychological" dimensions, which would improve instrument
parsimony. Understanding how the MOMS factors translate into patronage
intentions and actual purchasing behavior should also be considered.
Lastly, non-traditional events might be more apt to attract and change
an individual's physical activity level, as these events seem to
cater to a unique target market that consists of a high proportion of
individuals with no previous running experience. Thus, future research
should continue to investigate the thriving non-traditional event market
as well as acknowledge differences between event types.
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Richard J. Buning and Matthew Walker
Richard J. Buning, PhD, is an assistant professor in the Department
of Tourism, Conventions, and Event Management at Indiana
University-Purdue University Indianapolis. His research interests
include sport tourism, event management, participant sport events, and
sport development.
Matthew Walker, PhD, is an associate professor, sport management
division chair, and associate department head in the Department of
Health and Kinesiology at Texas A&M University. His research
interests include corporate citizenship, environmental responsibility,
social impact, and social responsibility.
Author Correspondence
Richard J. Buning
IU School of Physical Education and Tourism Management - IUPUI
Department of Tourism, Conventions, and Event Management 901 West New
York Street, Ste.. 250 Indianapolis, IN 46202 Email: rjbuning@iu.edu
Table 1 Model Comparisons
Models [chi square] df CFI TLI RMSEA PCLOSE
Traditional Group 2466.61 1394 .93 .92 .07 .000
Non-Traditional 3547.34 1394 .91 .90 .07 .000
Group
Multi-group Model 5233.16 2871 .94 .94 .06 .000
Configural 4815.78 2572 .94 .94 .06 .000
Invariance
Metric Invariance 4867.23 2616 .94 .94 .06 .000
Scalar Invariance 5233.16 2872 .94 .94 .06 .000
Table 2 Multi-group Confirmatory Factor Analysis (MG-CFA) Results
Factors and Items [lambda] [lambda]
(a) (b)
Weight Concern
To help me control my weight .754 .714
To reduce my weight .624 .724
To look leaner .927 .949
To stay physically attractive .951 .925
Health Orientation
To improve my health .788 .778
To prolong my life .903 .883
To become more physically fit .857 .884
To reduce my chance of having .870 .872
a heart attack
To stay in physical condition .824 .860
To prevent illness .918 .891
Personal Goal Achievement
To improve my running speed .888 .684
To try to run faster .802 .766
To beat someone I've never beaten .616 .747
before
To make my body perform better .558 .664
To feel mentally in control of my .881 .844
body
Competition
To compete with others .489 .594
To see how high I can place in races .768 .825
To get a faster time than my friends .871 .814
To compete with myself .700 .779
Recognition
To earn respect of peers .784 .727
To earn the respect of people in .790 .764
general
To make my family or friends proud .820 .862
of me
People look up to me .923 .897
Brings me recognition .911 .908
To get compliments from others .886 .883
Affiliation
To socialize with other runners .747 .543
Have something in common with other .947 .891
people
To meet people .798 .690
To share a group identity with .867 .659
other runners
Psychological Coping
To become less anxious .770 .750
To become less depressed .832 .787
To distract myself from daily .794 .801
worries
To improve my mood .866 .783
To have time alone to sort things .919 .866
out
To concentrate on my thoughts .912 .926
To solve problems .823 .842
To blow off steam .816 .746
To get away from it all .937 .727
To have time alone with the world .954 .874
Life-Meaning
To add a sense of meaning to life .828 .833
To make my life more purposeful .874 .897
To make myself feel whole .906 .915
To make my life more complete .899 .890
To feel a sense of belonging in .872 .834
nature
To feel at peace with the world .883 .891
To improve my sense of self-worth .960 .865
Self-Esteem
To improve my self-esteem .893 .785
To feel more confident about myself .859 .819
It is a positive emotional experience .700 .767
To feel proud of myself .775 .769
To feel a sense of achievement .676 .728
To feel mentally in control of my .798 .861
body
To feel like a winner .827 .801
Factors and Items AVE CR AVE CR
(a) (a) (b) (b)
Weight Concern .680 .892 .698 .901
To help me control my weight
To reduce my weight
To look leaner
To stay physically attractive
Health Orientation .742 .945 .743 .945
To improve my health
To prolong my life
To become more physically fit
To reduce my chance of having
a heart attack
To stay in physical condition
To prevent illness
Personal Goal Achievement .580 .870 .553 .860
To improve my running speed
To try to run faster
To beat someone I've never beaten
before
To make my body perform better
To feel mentally in control of my
body
Competition .519 .806 .576 .842
To compete with others
To see how high I can place in races
To get a faster time than my friends
To compete with myself
Recognition .730 .942 .711 .936
To earn respect of peers
To earn the respect of people in
general
To make my family or friends proud
of me
People look up to me
Brings me recognition
To get compliments from others
Affiliation .711 .907 .500 .795
To socialize with other runners
Have something in common with other
people
To meet people
To share a group identity with
other runners
Psychological Coping .747 .967 .660 .951
To become less anxious
To become less depressed
To distract myself from daily
worries
To improve my mood
To have time alone to sort things
out
To concentrate on my thoughts
To solve problems
To blow off steam
To get away from it all
To have time alone with the world
Life-Meaning .791 .964 .766 .958
To add a sense of meaning to life
To make my life more purposeful
To make myself feel whole
To make my life more complete
To feel a sense of belonging in
nature
To feel at peace with the world
To improve my sense of self-worth
Self-Esteem .629 .922 .626 .921
To improve my self-esteem
To feel more confident about myself
It is a positive emotional experience
To feel proud of myself
To feel a sense of achievement
To feel mentally in control of my
body
To feel like a winner
Note. Standardized estimates are presented.
(a) Traditional group. (b) Non-Traditional group.
Table 3 Mean Scores, Standard Deviations, and Correlations
Construct M SD 1 2
1. Weight Concern 3.03 1.90 1.00 .616
2. Health Orientation 3.67 1.81 .785 ** 1.00
3. Personal Goal Achievement 3.76 1.63 .586 ** .742 **
4. Competition 3.45 1.40 .281 ** .457 **
5. Recognition 2.53 1.52 .414 ** .475 **
6. Affiliation 3.76 1.58 .150 ** .331 *
7. Psychological Coping 2.46 1.41 .546 ** .653 **
8. Life Meaning 2.58 1.63 .513 ** .610 **
9. Self-Esteem 4.09 1.59 .530 ** .662 **
Construct 3 4 5 6
1. Weight Concern .343 .079 .171 .023
2. Health Orientation .551 .209 .226 .110
3. Personal Goal Achievement 1.00 .426 .224 .081
4. Competition .653 ** 1.00 .193 .144
5. Recognition .473 ** .439 ** 1.00 .199
6. Affiliation .284 ** .379 ** .446 ** 1.00
7. Psychological Coping .568 ** .331 ** .549 ** .404 **
8. Life Meaning .498 ** .300 ** .649 ** .408 **
9. Self-Esteem .636 ** .452 ** .693 ** .463 **
Construct 7 8 9
1. Weight Concern .298 .263 .281
2. Health Orientation .426 .371 .438
3. Personal Goal Achievement .323 .248 .404
4. Competition .109 .09 .204
5. Recognition .301 .421 .480
6. Affiliation .163 .167 .214
7. Psychological Coping 1.00 .591 .464
8. Life Meaning .769 ** 1.00 .506
9. Self-Esteem .681 ** .711 ** 1.00
Note. ** Correlation is significant at the 0.01 level (2-tailed).
N=408. Above 1.00 are the squared correlations for each construct.
Table 4 Multiple Analysis of Variance (MANOVA) for Event Type Effects
Traditional Non-Traditional
(n=133) (n=275)
Motives M SD M SD df
Weight Concern 4.29 1.80 2.43 1.64 1
Health Orientation 4.85 1.59 3.10 1.63 1
Personal Goal Achievement 4.93 1.31 3.19 1.46 1
Competition 3.64 1.32 3.36 1.43 1
Recognition 2.80 1.54 2.40 1.49 1
Affiliation 3.50 1.64 3.89 1.54 1
Psychological Coping 3.14 1.61 2.14 1.18 1
Life Meaning 3.30 1.78 2.23 1.42 1
Self-Esteem 4.62 1.46 3.83 1.59 1
Motives F P [[eta].sup.2]
Weight Concern 310.95 .000 *** .211
Health Orientation 274.84 .000 *** .205
Personal Goal Achievement 269.23 .000 *** .250
Competition 7.11 .057 .009
Recognition 14.93 .11 .016
Affiliation 13.38 .020 * .013
Psychological Coping 91.09 .000 *** .112
Life Meaning 104.08 .000 *** .097
Self-Esteem 55.87 .000 *** .054
Note. Pillai's Trace = .436, F (9, 398) = 34.231, p<.001. All items
measured from 1 (not a reason) to 7 (a most important reason).
* p<.05; * p<.01; *** p<.001.
