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Motivation, satisfaction, and behavioral intentions: segmenting youth participants at the Interamnia World Cup 2012.

Introduction

Understanding motivation of sport consumers is complex (Funk & Bruun, 2007). Sport consumers are a heterogeneous population, either active (participating) or passive (watching) (Tokuyama & Greenwell, 2011; Weed & Bull, 2004). In the sport marketing literature, the motivation of spectators/fans (passive consumers) has been prioritized over participants (Caro & Garcia, 2007; Dwyer, Shapiro, & Drayer, 2011). Gibson (1998a) suggests that sport consumers may also engage in some form of touristic activities (e.g., visiting sport stadiums) when away from home, giving rise to the phenomenon of sport tourism. Several definitions of sport tourism exist and the motivation of sport tourists is reasonably well examined (Deery, Jago, & Fredline, 2004). Existing studies not only suggest that the motives of sport participants may differ from watchers (Lera-Lopez & Rapun-Garate, 2011; Tokuyama & Greenwell, 2011), but also no single motive can account for the variety of multiple and shared motives prevalent at any one time among sport tourists (Robinson & Gammon, 2004).

"There is little research which has specifically focused on segmentation strategies in the sport marketing literature" (Dwyer et al., 2011, p. 130). Market segmentation is a key concept in the sport marketer effort to understand the needs of specific groups of consumers and the development of targeted marketing mixes (Mullin, Hardy, & Sutton, 2007). It is necessary for sport marketers to understand the behavior of sport consumers on psychological variables (Alexandris & Tsiotsou, 2012). Accordingly, several studies segment the motives of either sport consumers (e.g., Dwyer et al., 2011; Luna-Arocas & Tang, 2005; Rohm, Milne, & McDonald, 2006) or sport tourists (e.g., Alexandris, Kouthouris, Funk, & Giovani, 2009; Hallmann, Feiler, & Breuer, 2012). The resulting motivational profiles are often used to enhance promotional and marketing strategies (Wann, Grieve, Zapalac, & Pease, 2008). For sporting event organizers, segmentation offers the opportunity to better understand event attendees' motives, attitudes, and behaviors (Hallmann et al., 2012) while fine-tuning event marketing and positioning (Caro & Garcia, 2007).

Marketing strategies built around an understanding of sport tourist motivation and satisfaction can be effective at boosting sport event attendance and loyalty. In the tourism literature, several studies suggest that tourist motivation influences post-consumption behaviors such as satisfaction (e.g., Devesa, Laguna, & Palacios, 2010; Yoon & Usyal, 2005) and behavioral intentions (Prayag, 2012; Schofield & Thompson, 2007). However, limited studies have examined the relationship between motivation and post-consumption behaviors among sport tourists. Existing sport tourism studies prioritize the event satisfaction and behavioral intentions relationship (e.g., Kaplanidou & Gibson, 2010; Shonk & Chelladurai, 2008). With the exception of Caro and Garcia's (2007) study, the influence of motivation on satisfaction and behavioral intentions has received scant attention in the sport marketing literature. Luna-Arocas and Tang (2005) confirm that clusters of sport consumers grouped by motivations can have diverse levels of satisfaction.

The two research questions this study, hence, address are: (1) Can different motivational profiles of youth participants at the IWC be identified? (2) Are the identified motivational profiles different on the basis of demographics, satisfaction, and behavioral intentions? The contribution of this study to the sport tourism and marketing literature is two-fold. First, we identify the motivation of youth participation in a sporting event and evaluate whether subgroups of youth participants exist. There is a lack of studies on youth participants' motives for attending sporting events despite the importance of this segment to the sport and tourism industry (Hinch & Higham, 2011). Lim and Turco (1999) observe that the sport marketing literature focuses mainly on adult participants rather than youngsters. Taks and Scheerder (2006) argue that youth sport market segmentation studies are uncommon. The sheer size of this segment, commonly referred to as the Generation Y market, makes it a desirable target of corporations, events, and sport marketing professionals (Bennett & Lachowetz, 2004). The motivational patterns of sport participation for this segment suggest strong individualistic (e.g., competition, fitness, and status/recognition) and affiliation (e.g., friendship and fun) components (Daniels & Lawton, 2003). Second, despite motivation being an attractive variable for segmentation (Caro & Garcia, 2007), few studies (e.g., Caro & Garcia, 2007; Luna-Arocas & Tang, 2005) examine how motivational profiles differ on the basis of post-consumption behaviors such as satisfaction and behavioral intentions. Existing studies mainly ascertain the satisfaction and behavioral intention relationship (Kaplanidou & Gibson, 2010; Shonk & Chelladurai, 2008), omitting motivation as a critical component of event participation (Caro & Garcia, 2007).

Theoretical Framework

Segmentation Theory

Consumer motivation and market segmentation represent core marketing principles. Yet the integration of these two essential concepts with regard to sport consumers in general is limited (Dwyer et al., 2011). Segmentation consists of dividing a market into smaller and homogeneous groups based on the assumption that customers in such markets are heterogeneous and that a differentiated market offering can satisfy them (Kruger, Saayman, & Ellis, 2011). No absolutely "correct" segmentation method exists (Tkaczynski & Rundle-Thiele, 2010) because the underlying relationships among the units (customers and segmentation variables) may have a different structure and the researcher has to find the best segmentation method to capture the hidden structure (Dolnicar et al., 2008). It is not uncommon in marketing and tourism literature to segment motivation as part of the broader "benefit segmentation" approach (see Frochot & Morrison, 2001). In the sport marketing literature, segmentation studies generally focus on segmenting the motivation of sport fans/participants in a specific sport (Dwyer et al., 2011; Rohm et al., 2006) or different sports (Wann et al., 2008) and sport tourists (Alexandris et al., 2009; Hallmann et al., 2012).

Motivation Theories in Tourism and Sport Tourism

Motivation is a critical component of behavioral models of tourism consumption (Gnoth, 1997). Various theories exist to understand tourist motivation. Examples include Maslow's (1943) hierarchy of needs, Plog's (1974) allocentric-psychocentric typology, expectancy-value theories (Lewin, 1938), goal directed behavior (Bettman, 1979), travel career ladder (Pearce & Lee, 2005), and the push-pull framework (Crompton, 1979; Klenosky, 2002). Yet there is no agreement on how to define travel motivation and the best way to measure it (Pearce & Lee, 2005). Generally, the tourist motivation for travel represents a compromise between multiple motivators (Swarbrooke & Horner, 1999).

Likewise, sport tourism researchers continue to struggle with definitional, measurement, and lack of replication in motivation studies (Weed, 2005). Intrinsic motivators refer to engaging in a sporting activity purely for pleasure and satisfaction derived from participation, while extrinsic motivators represent goals such as material rewards or avoidance of external constraints (Deci, 1975). Four categories of travel motivators may be relevant in the sport tourism context (McIntosh, Goelder, & Ritchie, 1996). Physical motivators, the first category, are directly related to physical needs and drives such as fitness and sports. The second category, cultural motivators, is linked to traditions and heritage that may include visiting sports museums, stadiums, halls of fame, and historic sites. The third category, interpersonal motivators, includes socialization with friends and other sport participants. The last category, status and prestige motivators, is linked to people traveling to high-profile destinations and distinctive sports events (Kurtzman & Zauhar, 2005). Other motives that may be associated with participants' motivation in sport tourism include relaxation, cultural learning, and experience (Funk, Toohey, & Bruun, 2007).

In addition, each destination hosting a sporting event has its own specificities, including sport tourists, making the comparison of motives across events difficult (Funk & Bruun, 2007). The same person may intentionally travel to a sporting event to participate either passively or actively in the event (Deery et al., 2004). This dichotomy has given rise to the terms "sport tourism" and "tourism sport," depending on the motives of the tourist. Indeed, some sport customers travel with the primary purpose of participating to the event (sport tourism) while others travel primary to visit the destination and taking part in the event is a secondary motive (Ottevanger, 2007).

Motivation of Sport Event Tourists

The motives for sport activities are highly diverse and have been categorized in different ways (Caro & Garcia, 2007; Wann et al., 2008). The motives of sport tourists also differ by type of sport attraction, experience desired (Gibson, 1998b), country (Won & Kitamura, 2007), gender, race, and social class (Gibson, 2004; Funk et al., 2007). Sport event tourists are generally classified as passive attendees (McDonald, Milne, & Hong, 2002; Kim & Chalip, 2004), active participants (Filo, Funk, & O'Brien, 2011; Taylor & Shanka, 2008; Xu & Pegg, 2007), and volunteers (Doherty, 2009; Jarvis & Blank, 2011). Researchers have not yet reached a consensus on whether sport participants (active) and sport fans (passive) share similar consumer characteristics related to motivation (Tokuyama & Greenwell, 2011). However, recent evidence suggests that sport participation and attendance are two distinct segments driven by different factors, and each segment has its own socio-demographic features and sport motivations (Lera-Lopez & RapunGarate, 2011). The motives of sport tourists (e.g., escape, relaxation, and socialization) can also be similar to those of pleasure travelers (Crompton, 1979; Pearce & Lee, 2005), but some sport-specific motives such as the need to compete, a desire to win, and the opportunity to develop current skill levels (Weed & Bull, 2004) are not necessarily relevant to other types of tourists.

In the context of active sport tourists, few studies exist on the youth segment (e.g., Daniels & Lawton, 2003; Taks & Scheerder, 2006) and local participants may have motives different from international participants. Xu and Pegg (2007) studied the motivations of students engaging in the Australian University Games. The results revealed that the primary purpose of participation was recreational but other factors such as competing with other athletes, going away from home (escape), participating in an organized event (achievement), and prestige in representing their university were also valued. Gillet (2011) studied the motives of participants to the 2003 Australian Master Games and found that the five key psychological motives were competition, socializing, camaraderie, achievement, and athletic motives. However, local and non-local participants differed in the intensity of their motivational factors. Non-local participants had a higher social motive score than local participants. Socialization and achievement are strong motives of participation in sporting events that include young participants (Daniels & Lawton, 2003; Taylor & Shanka, 2008). Overall, the findings suggest that young sport tourists may have different motives (e.g., need for excitement) and/or attach different levels of importance to motives (e.g., socialization, escape, and achievement) identified for the general sport consumer.

Motivation Measurement for Sport Tourists

There are several frameworks to measure the motivation of sport consumers from the spectator/fan perspective (Wann et al., 2008), but these may not always be relevant to sport tourists. One of the earliest scales is Wann's (1995) Sport Fan Motivation Scale (SFMS) that measures eight factors represented by motives such as eustress, self-esteem, escape from daily life, entertainment, economic factors, aesthetics, group affiliation, and family needs. Milne and McDonald (1999) proposed the Motivations of the Sport Consumer (MSC) scale, which includes 12 motivation factors (stress release, skill mastery, aesthetics, self-esteem, self-actualization, value development, social facilitation, affiliation, achievement, risk-taking, aggression, and competition). Trail and James (2001) developed the Motivational Scale for Sport Consumption (MSSC) that measures nine motives (achievement, knowledge, aesthetics, drama, escape, family, physical attraction, physical skills, and social) of sport consumption for mainly sport spectators using 27 items. Funk et al. (2002) augmented the Sport Interest Inventory (SII), which they developed a year earlier that included 10 factor and 30 items to explain fan motivations associated with women's soccer in the United States. Trail, Anderson, and Fink (2000) specified additional factors such as event expectations, disconfirmation or confirmation of expectations, and affective reactions that may also influence sport consumption. Overall, existing research suggests that self-esteem, aesthetics, drama, escape, entertainment, and social interaction demonstrate applicability across a wide range of sport settings (Dwyer & Kim, 2011; Wann et al., 2008).

Given the plethora of motives uncovered in previous studies, it is not surprising that researchers have tried to bring parsimony to the study of motivation (Funk et al., 2009; Kim et al., 2008). Recent studies typically consider a wider array of motives, refine existing scales, and put forward different scales for measuring different types of sports or spectators (Won & Kitamura, 2007). Existing frameworks are often perceived as burdensome and complex by practitioners (Funk et al., 2009) but nevertheless the multi-dimensionality of motivation is confirmed. For example, Funk et al. (2009) developed the SPEED scale that measures five facets of sport attendance (socialization, performance, excitement, esteem, and diversion). These dimensions represent a convergence of constructs from previous instruments.

Motivation, Satisfaction, and Behavioral Intentions

Attracting new and retaining existing tourists require an understanding of tourist motivation (Prayag, 2012), management of satisfaction, and the generation of favorable behavioral intentions through satisfied tourists (Eusebio & Vieira, 2013). Tourist satisfaction is a central concept in tourism (Chen & Tsai, 2007) and its measurement has been operationalized using the disconfirmation theory, whereby satisfaction arises when consumers compare their perceptions with initial expectations (Baker & Crompton, 2000). A distinction is made also between overall satisfaction and satisfaction with individual components of the holiday (Spreng et al., 1996). Overall satisfaction is a broader concept that includes an evaluation of the accumulated experiences of a tourist's expectations, purchase, and consumption experiences (Andreassen, 1995). Overall satisfaction is, hence, a holistic impression after the purchase and consumption of a holiday (Fornell, 1992). A single global measure of satisfaction may be a better measure than using the disconfirmation theory (Baloglu et al., 2003).

Behavioral intention is an outcome of mental processing that leads to an action that transforms motivation into future behavior (Jang et al., 2009). Two proxies, revisit and recommend intentions, have been used to measure the construct of behavioral intentions in tourism (Baloglu et al., 2003; Bigne et al., 2001; Prayag, 2009). The general consensus is that a positive relationship exists between tourist satisfaction and behavioral intentions (Bigne et al., 2001; Yoon & Uysal, 2005), and motivation and behavioral intentions (Prayag, 2012). Fulfillment of motives generally leads to high satisfaction (Prayag, 2012), in turn leading to favorable behavioral intentions about the destination (Baker & Crompton, 2000). However, some studies have shown that the relationship between satisfaction and revisit intention does not always hold true (Bigne et al., 2001) and that not all fulfilled motives are capable of generating satisfaction (Schofield & Thompson, 2007).

In the sport marketing and tourism literatures, several studies (e.g., Biscaia et al., 2012; Kaplanidou & Gibson, 2010; Kaplanidou & Vogt, 2007; Shonk & Chelladurai, 2008) suggest a positive relationship between satisfaction and behavioral intentions. However, it is not clear whether satisfaction with the event leads to positive behavioral intentions for the event only and/or the destination. Kaplanidou and Vogt (2007) found that satisfaction with the event did not significantly influence intentions to revisit the destination for sporting activities. In another study, Kaplanidou and Gibson (2010) showed that satisfaction with the event directly influenced intentions to participate in the event again. Biscaia et al. (2012) found that satisfaction with team games increased the likelihood of attending and recommending future games to other people. When the event is recurring and in the same place, intention to return may pertain to both the place and the event (Shonk & Chelladurai, 2008).

Yet, limited studies (e.g., Caro & Garcia, 2007; Taylor & Shanka, 2008) examine the relationship between motivation and satisfaction or behavioral intentions in the sport marketing context. Taylor and Shanka (2008) found that overall satisfaction with a not-for-profit sporting event was significantly predicted by the motive "to get fit/health benefits" but not by the motive "to have fun." In the same study, intention to participate in future sporting events was not predicted by motivation but rather by overall satisfaction. Hence, beyond understanding how satisfaction leads to post-consumption behaviors of sport tourists (Kaplanidou & Vogt, 2007; Taylor & Shanka, 2008; Shonk & Chelladurai, 2008), it may be necessary to understand the role of motivation in shaping satisfaction and behavioral intentions. Such an understanding is critical for effective event and destination marketing strategies. Hence, the main research questions for this study are whether different motivational profiles of youth participants at the IWC can be identified and whether such profiles differ on demographic characteristics, satisfaction, and behavioral intentions.

Method

Study Setting

The Interamnia World Cup (IWC) is an international event described as the "world's largest junior handball tournament" (IWC Organizing Committee, 2011), held in the same location (Teramo, Italy), on the same date (July 4-10) every year since 1973. The event normally draws participants from more than 100 nations (www.interamniaworldcup.com). IWC is different from other youth sporting events in two main ways. First, a key theme of the event over the past few years has been the promotion of peace between participating nations with a clear commitment toward supporting athletes from countries in conflict or engaged in post-war reconstruction. Second, every year a specific theme is chosen (e.g., Leaders of Tomorrow) that fits with the "peace" theme, which then becomes the focus of debate and consideration among town/city mayors of participating cities/towns/countries. The event also encourages youth participants to discover the city of Teramo and Italy as a tourist destination, with trips to Rome and Venice being a regular feature. In many respects, the city of Teramo has become the gateway for youth from politically and racially torn countries to participate in sporting events and compete peacefully with others, earning the city's recognition as "Teramo, citta aperta al mondo" (Teramo, city open to the world) by UNICEF in 1989.

Measures

To operationalize the motivation concept in this study, a mixed method (Rohm et al., 2006) was utilized. Four team managers were conveniently selected prior to the event and semi-structured interviews were carried out to find out the motives and satisfaction of participants. Only two of the team managers interviewed had previous participation in IWC. The four team managers were selected on the basis of their knowledge about the event and the fact that they were also the coach of their team. Numerous studies have highlighted the role of the coach in positively or negatively influencing youth sporting experiences (Fraser-Thomas et al., 2005). A coach can also influence the motivation of participants as well as the drivers of performance of the team (Keegan et al., 2010). Hence, it is reasonable to assume that the team managers are knowledgeable about participants' motives for attending IWC. The main motives identified from the interviews were: socialization, learning and discovery of Italy, competing with other participants, prestige, escape, and excitement for participants. Also of particular interest is the motive of participation related to the promotion of peace between participating countries. These broad motives were used to refine the list of motives identified from the literature (Dwyer & Kim, 2011; Funk & Bruun, 2007; Funk et al., 2007; Funk et al., 2009; Gillet, 2011; Taylor & Shanka, 2008; Xu & Pegg, 2007). A final list of 14 motivation items ([alpha] = 0.88) was retained and measured on a 5-point scale (1 = Strongly Disagree and 5 = Strongly Agree). In general, item wording was based on these studies (e.g., Gillet, 2011; Taylor & Shanka, 2008; Xu & Pegg, 2007).

Overall satisfaction was operationalized using one item adapted from Chen and Li (2010), "I am globally satisfied with my participation at the 2012 IWC," and measured on a 5-point scale (1 = Strongly Disagree and 5 = Strongly Agree). Given the recurring nature of the event, intentions to revisit and recommend were focused on the event rather than the destination. Revisit and recommendation intentions were operationalized using one item each adapted from the following studies (Caro & Garcia, 2007; Chen & Li, 2010; Shonk & Chelladurai, 2008; Taylor & Shanka, 2008). The items "I will probably attend the IWC next year" and "I will recommend the IWC to my friends and family" were measured on a 5-point scale (1 = Strongly Disagree and 5 = Strongly Agree). Several demographics and travel characteristics were measured including age group, gender, country of residence, and previous participation at the event. The survey instrument was originally designed in English and translated in French and Italian. The method of back translation (Brislin, 1970) was used to ensure that the translated versions reflected the meanings and intent of the original questionnaire. The questionnaire was pre-tested on 20 potential participants to the event.

Sample and Data Collection

The target population for the study was all international registered participants of the 2012 Interamnia World Cup. Each country represented at the event was accompanied by a team manager who signed consent forms on behalf of participants who were less than 18 years old. The total number of participants at the event was estimated at 3,100, representing 48 different countries, and aging from 10 to 30 years old. Of participants, an estimated 1,862 were from Italian cities/regions other than Teramo. Of these, 1,161 were international participants and the survey instrument was systematically included in every welcome pack for this group. Once completed the questionnaires were returned either directly to the organizing committee office or handed to official volunteers at the event. Since the focus of the study was on youth participants, only questionnaires from respondents aged 10 to 21 years old were considered useable. Data collection lasted for the duration of the game and resulted in 265 usable surveys. The demographic profile of the sample showed that the survey polled more males (56.2%) than females (43.8%). The age group of respondents was as follows: 12-14 (13.1%), 15-17 (70.9%), and 1821 (16%). The survey polled participants with different country of residence, including Denmark (17.8%), Cyprus (9.1%), France (11.2%), Bosnia & Herzegovina (8.7%), Belgium (9.1%), Chile (7%), and Algeria (5.8%), among many others. The majority of participants (61.4%) were at their first participation to the event.

Data Analysis

The data were analyzed in three stages. In stage one, a two-step cluster analysis (Punj & Stewart, 1983) was used to classify respondents on their motivation scores. Missing data led to the exclusion of another 23 surveys for clustering purposes (n = 242). Ward's (1963) hierarchical clustering method with squared Euclidean distances was performed on a sub-sample generated randomly following the procedures recommended by Punj and Stewart (1983) to identify potential clusters in the data. The agglomeration schedule suggested the presence of three to five clusters. A non-hierarchical k-means clustering algorithm developed three, four and five cluster solutions. An examination of group membership, group sizes, and the associated dendograms indicated the four-cluster solution was the best. In stage two, the validity of the four-cluster solution was assessed using discriminant analysis and the final stage consisted of profiling the clusters using socio-demographic characteristics of respondents, satisfaction, and behavioral intentions.

Results

Cluster Identification and Description

The results (Table 1) indicated that cluster 1 (n = 48) comprised participants that were on average either mostly "disagree" or "neutral" on the motives measured, except for the item (SOC3) "to share an international experience with my friends" (M = 4.08), indicative of the importance of socialization for youth event participation. The hedonistic aspects of the trip such as (LEAR2) "to discover and experience Italy as a tourist destination" (M = 1.94) and (ESC1) "to enjoy being away from home" (M = 2.35) scored low with this group. Hence, this group was named "Indifferent" given that they were not strongly motivated by any particular item, except for socialization. Cluster 2 (n = 98) scored on average highly all the motives except for the item (ESC1) "to enjoy being away from home" (M = 3.24), indicative of various motives such as participation, competing, socialization, learning, and prestige being valued. This was the only cluster driven by the motive of "peace" (M = 4.49). Hence, this cluster was named "Enthusiast" based on their high scores for most items. Cluster 3 (n = 60) scored highly on average only the motives (PAR1) "to take part in an organized youth sporting event" (M= 4.04), (SOC1) "to meet new people and make friends" (M = 4.15) and (SOC3) "to share an international experience with my friends" (M = 4.10), indicative of socialization being the most valued motive of participation. This cluster was, thus, named "Socializer." Cluster 4 (n = 36) assigned the highest scores on average to the motives of prestige (PRES1) "to take part in a prestigious event for people who follow handball" (M = 4.81), (COMP1) "to compete with other athletes" (m = 4.69), (COMP2) "to compete with strong opponents in handball" (M= 4.78), and (PAR1) "to take part in an organized youth sporting event" (M = 4.67). This cluster was named "Competitive" due to their high scores on such motives. Overall, the results indicate that motives of socialization, competition, and participation are highly valued by most clusters while escape (ESC1) is not valued highly by any of the clusters.

A discriminant analysis was performed to determine the accuracy of the four cluster solution. Using the saved cluster membership as the dependent variable and the motives as the predictors (Dwyer et al., 2011; Luna-Arocas & Tang, 2005), three statistically significant canonical discriminant functions were extracted (Table 2), explaining the majority of variance in the dependent variable. Wilk's lambda and chi-square tests (Table 2) show that the groups are statistically significantly different (p<0.001). The canonical correlations between the groups .92 (function 1), .73 (function 2), and .48 (function 3) are high and significant (p < 0.001), indicative of a significant relationship between the functions and cluster membership (Hosany & Prayag, 2013). The eigenvalue can be interpreted as the proportion of variance accounted for by the correlation between the respective canonical variates (Alexandris & Tsiotsou, 2012). The classification matrix showed that 95.5% of the original grouped cases were correctly classified, with cluster 2 achieving the highest percentage of correct classification (98%) and cluster three the lowest (90%). The results confirm the external validity of the four cluster solution.

Cluster Profiling

To profile the clusters, cross tabulations with demographic variables (age, gender, and country of residence) and travel characteristics (previous participation at IWC) were undertaken. The results (Table 3) indicated statistically significant differences between the clusters on gender, country of residence, and previous participation only. Cluster 4 had a high percentage of male participants (86.1%) while cluster 1 had a higher percentage of female participants (54.2%). Country of residence was recoded in seven groups (North Africa, Middle East, Western Europe, Northern Europe, Asia, South America, and Eastern Europe). A high percentage of participants in cluster 1 were from Northern Europe (58.3%). Cluster 2 had a high percentage of Eastern European participants (45.9%). Cluster 4 had a high percentage of both Western European (47.2%) and from North African (33.3%) participants. Cluster 4 had the highest percentage (77.8%) of first-time participants while cluster 2 had a high percentage (12.5%) of previous participation (4 times or more). Cluster 1 had a significant percentage of participants (23.4%) who had attended the event once before.

The one item for satisfaction, revisit intention, and recommendation intention was used to further profile the clusters. ANOVA tests with Tukey post-hoc comparisons identified differences between the four clusters on all three variables (Table 4). Cluster 2 had on average the highest (M = 4.57) while cluster 1 had the lowest (M = 3.34) overall satisfaction with the event. Cluster 2 had on average the highest revisit (M = 4.31) and recommendation (M = 4.63) intentions. Cluster 1 had on average the lowest revisit (M = 2.65) and recommend (M = 3.08) intentions. Overall, the results suggest that the clusters had significantly different satisfaction and behavioral intentions based on the motivational profiles identified.

To further understand the relationship between clusters and post-consumption behaviors, four logistic regression models were estimated. Cluster membership was the dependent variable (1 = belongs to the cluster; 0 = belongs to other clusters) while post-consumption behaviors (satisfaction, revisit intention, and recommendation intention) were specified as the independent variables. Table 5 indicates that the clusters are different on the basis of the post-consumption behaviors. Cluster 1 (Indifferent) is less likely than other clusters to consist of sport tourists that are satisfied with the event based on the odd ratio of Exp(B) = .404. Odd ratios greater than 1.0 show the increase in odds of an outcome of '1,' whereas odd ratios less than 1.0 show the decrease in odds of that outcome (Hair et al., 2005). The negative beta coefficient for satisfaction ([beta] = -.905, p < 0.05) confirm the existence of a negative relationship between cluster membership and overall satisfaction. The logistic regression (Table 5) for cluster 2 (Enthusiast) suggests that participants in this cluster are most likely to be satisfied (Exp(B) = 1.97, [beta] = .679, p < 0.05) and recommend (Exp(B) = 1.93, [beta] = .656, p < 0.05) the event. As for cluster 3 (Socializers) none of the post-consumption behaviors could predict accurately cluster membership. Cluster 4 is most likely to have sport tourists that are satisfied with the event (Exp(B) = 2.07) but will most likely neither recommend nor revisit the event.

Discussion and Implications

The research questions for this study were related to the identification of different segments of youth participants on their motivation to attend a sporting event and the differences in motivational profiles based on demographic characteristics and post-consumption behaviors. The results indicate that four motivational profiles exist among active participants at the IWC (Indifferent, Enthusiast, Socializer, and Competitive). The identified clusters were profiled on demographic characteristics, which revealed that only gender, country-of-residence and previous participation to the event could significantly differentiate between the clusters. Profiling the clusters by overall satisfaction, revisit, and recommendation intentions revealed that statistically significant differences existed between the clusters on these post-consumption behaviors. Accordingly, the results hold important theoretical and managerial implications.

From a theoretical perspective, the study confirms that the youth segment of the sport tourism market is heterogeneous on their motives for attending sporting events. Similar to previous studies (e.g., Daniels & Lawton, 2003; Taylor & Shanka, 2008), the findings suggest that motives of socialization, participation, and competition are highly valued by young participants. However, unlike the study of Xu and Pegg (2007), the motive of escape is not necessarily a driver of event participation for all young sport tourists, as evidenced by the clusters of "Indifferent" and "Competitive." The "fun" and "excitement" motives are highly valued by one group (e.g., Enthusiast) but not necessarily a main motive of participation as suggested in previous studies (e.g., Gillet, 2011). Likewise, the motive of learning (Funk et al., 2007) is important for adult sport tourists but only valued by two of the clusters (Enthusiast and Competitive) when it is related to the sport (handball). When the motive of learning is related to discovery and experience of the destination (Italy), only cluster 2 assigned a high score to this motive.

Overall, the motives identified in this study are somewhat similar to past youth market sport studies (e.g., Daniels & Lawton, 2003; Gillet, 2011), highlighting that no single motive, but rather a combination of motives, drives event choice. This confirms the complexity (Funk & Bruun, 2007) and multi-dimensionality of the motivation construct as suggested in other studies (Caro & Garcia, 2007; Dwyer & Kim, 2011; Funk et al., 2009). The identified segments not only suggest that youth participants have different motives but also different demographic profiles. Gender, country of residence, and previous participation can also be used effectively as segmentation variables to differentiate between the youth participants at an event.

While several studies suggest that satisfaction can adequately predict behavioral intentions (Biscaia et al., 2012; Kaplanidou & Gibson, 2010; Kaplanidou & Vogt, 2007), our results confirm that motivation profiles are related to post-consumption behaviors. The segment of "Indifferent" is least likely to be satisfied with the event while the segment of "Enthusiast" is most likely to be satisfied and recommend the event. The differences that exist between the clusters on overall satisfaction, revisit, and recommend intentions suggest a potentially contradictory relationship between motivation and post-consumption behaviors. On the one hand, highly motivated youth participants are likely to be satisfied and recommend an event (e.g., Enthusiast) but on the other, relatively high scores on motivation do not necessarily lead to revisit and recommend intentions (e.g., Competitive). Hence, the positive relationship between motivation and event satisfaction is not guaranteed as implied in other studies (Caro & Garcia, 2007; Taylor & Shanka, 2008).

In addition, the results offer several implications for event organizers, sport marketers, and destination marketers to fine-tune marketing strategies. Understanding motivation is critical for implementing effective market segmentation and targeting strategies. Motives also inform successful promotion and advertising campaigns (Dwyer et al., 2011). Cluster 1 had the lowest scores on motivation, satisfaction, revisit, and recommend intentions suggesting that these youth participants are potentially disengaged with the event. It would be worthwhile for event organizers to improve communication with participants and team managers prior to the event and emphasize opportunities for socialization for future IWC events. The segment of "Enthusiast" is highly motivated and has positive post-consumption behaviors. Through relationship marketing, this segment can be transformed into advocates of the event that can stimulate interest in the event among both active and passive youth handball sport consumers using social media. Findings suggest the existence of sport tourist sub-groups with different motivational profiles. Recognizing these differences should enable IWC to better market the event to young sport tourists. The development of promotional campaigns around event and sport specific motives can aid in the marketing of IWC, thus driving attendance and consumption (Wann et al., 2008). Specifically, advertising themes of socialization and participation should be used to enhance the attractiveness of the event, along with the "peace" theme. Also, given that motives differed by country of residence and gender, communication strategies should be adapted to effectively communicate with international participants. Availability of event information through the official website in several languages would be a good starting point. Currently, website information is available only in English and Italian. Given that the event is organized around young participants, the website should also enhance the experience of potential participants by including more interactive components such as a virtual visit of the event site and local touristic attractions. These may create the desire to participate and, hence, boost event attendance.

Limitations and Future Studies

Despite the contribution of this study to the sport tourism and marketing literature, the study is not without limitations. First, the results are specific to one event and a small sample of international event attendees. The results cannot be generalized to all youth sporting events or IWC event attendees. Second, the sample size does not meet the recent conservative estimate of sample size requirements for effective segmentation. However, the sample is larger than the worst-case scenario suggested in the literature (Dolnicar et al., 2014). Third, the motivation items identified are specific to this event and the youth market. There is the possibility that other motives may drive sporting event attendance for the youth market. Fourth, k-means as a clustering method has several limitations (see Dolnicar, 2003). There is a possibility that sport tourists, similar to pleasure tourists, may belong to multiple clusters as suggested in other studies (Li et al., 2013).

Given these limitations, several avenues for further research can be offered. The recurring nature of IWC and its focus on young participants suggest that a longitudinal study would be worthwhile to understand both active and passive attendees' behavior on site and post-consumption behaviors. Also, previous studies have shown differences between the motives of local and non-local participants (Gillet, 2011). It would be beneficial if future studies could compare the motives, attitudes, and behaviors of local and non-local participants to a recurring sporting event to understand the drivers of event (re)attendance. Future research can also consider other clustering methods such as fuzzy clustering, bagged clustering, and latent class analysis to improve cluster stability and reproducibility (Wedel & Kamakura, 2000). Finally, motivation is only one of many psychological variables that can be used to segment sport tourists. Future studies can consider using other psychological variables such as destination image (Kaplanidou & Gibson, 2012), event image (Kaplanidou & Vogt, 2007), and emotions (Biscaia et al., 2012) for segmentation purposes.

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Table 1 Results of Cluster Analysis of Motivation Items

Motivations                                     Cluster 1   Cluster 2
                                                (n = 48)    (n = 98)

ESC1: To enjoy being away from home               2.35        3.24
PAR1: To take part in an organized youth          3.56        4.64
  sporting event
PRES1: To take part in a prestigious event        3.23        4.58
  for people who follow handball
LEAR1: To learn something new about               2.42        4.62
  handball
PAR2: To participate in physical activities       3.35        4.41
  because it's fun
COMP1: To compete with other athletes             3.46        4.69
COMP2: To compete with strong opponents           3.21        4.70
  in handball
SOC1: To meet new people and make friends         2.48        4.78
SOC 2: To meet new people with similar            2.29        4.57
  interest as myself
SOC 3: To share an international experience       4.08        4.89
  with my friends
EXC 1: To experience excitement and               2.41        4.20
  nothing more
EXC 2: To enjoy the atmosphere                    3.21        4.62
  surrounding the event
ESC2: To feel in peace with myself and others     2.21        4.49
LEAR2: To discover and experience Italy as        1.94        4.58
  a tourist destination

Motivations                                     Cluster 3   Cluster 4
                                                (n = 60)    (n = 36)

ESC1: To enjoy being away from home               3.07        2.16
PAR1: To take part in an organized youth          4.04        4.67
  sporting event
PRES1: To take part in a prestigious event        3.60        4.81
  for people who follow handball
LEAR1: To learn something new about               3.77        4.33
  handball
PAR2: To participate in physical activities       3.68        2.78
  because it's fun
COMP1: To compete with other athletes             3.75        4.69
COMP2: To compete with strong opponents           3.82        4.78
  in handball
SOC1: To meet new people and make friends         4.15        4.00
SOC 2: To meet new people with similar            3.81        3.85
  interest as myself
SOC 3: To share an international experience       4.10        4.19
  with my friends
EXC 1: To experience excitement and               2.91        3.37
  nothing more
EXC 2: To enjoy the atmosphere                    3.77        4.08
  surrounding the event
ESC2: To feel in peace with myself and others     2.98        3.43
LEAR2: To discover and experience Italy as        3.60        1.78
  a tourist destination

Motivations                                      F *     Sample
                                                         Mean

ESC1: To enjoy being away from home              9.98    2.86
PAR1: To take part in an organized youth        30.39    4.28
  sporting event
PRES1: To take part in a prestigious event      47.81    4.10
  for people who follow handball
LEAR1: To learn something new about             66.73    3.93
  handball
PAR2: To participate in physical activities     24.72    3.78
  because it's fun
COMP1: To compete with other athletes           34.71    4.21
COMP2: To compete with strong opponents         43.79    4.20
  in handball
SOC1: To meet new people and make friends       102.22   4.05
SOC 2: To meet new people with similar          95.31    3.82
  interest as myself
SOC 3: To share an international experience     20.53    4.43
  with my friends
EXC 1: To experience excitement and             41.69    3.40
  nothing more
EXC 2: To enjoy the atmosphere                  32.26    4.05
  surrounding the event
ESC2: To feel in peace with myself and others   67.65    3.51
LEAR2: To discover and experience Italy as      147.32   3.40
  a tourist destination

Motivations                                     Sample
                                                SD

ESC1: To enjoy being away from home             1.32
PAR1: To take part in an organized youth        0.84
  sporting event
PRES1: To take part in a prestigious event      1.01
  for people who follow handball
LEAR1: To learn something new about             1.22
  handball
PAR2: To participate in physical activities     1.21
  because it's fun
COMP1: To compete with other athletes           0.98
COMP2: To compete with strong opponents         1.04
  in handball
SOC1: To meet new people and make friends       1.12
SOC 2: To meet new people with similar          1.13
  interest as myself
SOC 3: To share an international experience     0.84
  with my friends
EXC 1: To experience excitement and             1.23
  nothing more
EXC 2: To enjoy the atmosphere                  1.01
  surrounding the event
ESC2: To feel in peace with myself and others   1.32
LEAR2: To discover and experience Italy as      1.47
  a tourist destination

* all F values significant, p < 0.001; (COMP: Competing with other
participants; ESC: Escape; EXC: Excitement; LEAR: Learning &
Discovery; PAR: Participation; PRES: Prestige; and
SOC: Socialization)

Table 2
Results of Discriminant Analysis

Discriminant   Eigenvalues    Canonical    Wilks'   Chi-square   Sig.
Functions                    Correlation   Lambda

1                 5.39          0.92        0.06      667.40     0.00
2                 1.15          0.73        0.36      236.88     0.00
3                 0.29          0.48        0.77      59.47      0.00

Standardized canonical   Function 1   Function 2   Function 3
discriminant function
coefficients

SOC2                       .461 *
ESC2                       .388 *
LEAR1                      .372 *
EXC1                       .300 *
EXC2                       .269 *
LEAR2                                   -.608 *
PRES1                                    .447 *
COMP2                                    .356 *
COMP1                                    .331 *
PAR1                                     .278 *
ESC1                                     .204 *
SOC1                                                  -.517 *
SOC3                                                   .440 *
PAR2                                                   .345 *

* Largest absolute correlation between each variable and
any discriminant function

Table 3
Cluster Profiling by Demographic and Travel Characteristics

Variables              Indifferent   Enthusiast   Socializer
                          (CL1)        (CL2)        (CL3)

Age                         %            %            %
12-14 yrs                  4.2          18.8         8.6
15-17 yrs                 81.3          66.7         75.9
18-21 yrs                 14.5          14.5         15.5

Gender                      %            %            %
Male                      45.8          54.1         50.0
Female                    54.2          45.9         50.0

Country of residence        %            %            %
North-Africa               --           16.3         10.0
Middle East                2.1          4.1          5.0
Western Europe            31.3          10.2         26.7
Northern Europe           58.3          2.0          40.0
Asia                       4.2          5.1          5.0
South America              4.2          16.3         1.7
Eastern Europe             --           45.9         11.7

Previous                    %            %            %
  Participation
0 time                    63.8          50.0         66.7
1 time                    23.4          13.6         17.5
2 times                   12.8          18.2         7.0
3 times                    --           5.7          7.0
>=4 times                  --           12.5         1.8

Variables              Competitive           Significance
                          (CL4)                 level

Age                         %
12-14 yrs                 17.1       [chi square] = 9.57, p > 0.05
15-17 yrs                 60.0
18-21 yrs                 22.9

Gender                      %
Male                      86.1       [chi square] = 16.29, p < 0.01
Female                    13.9

Country of residence        %
North-Africa              33.3       [chi square] = 149.79, p < 0.001
Middle East
Western Europe            47.2
Northern Europe            8.3
Asia                       2.8
South America              8.3
Eastern Europe             --
Previous                    %
  Participation
0 time                    77.8       [chi square] = 32.15, p < 0.01
1 time                    19.4
2 times                    -3
times                     2.8
>=4 times                  --

Table 4
Cluster Profiling by Overall Satisfaction, Revisit, and
Recommendation Intentions

Variables       Mean/SD     Indifferent   Enthusiast   Socializer
               (CL1) (a)       (CL2)        (CL3)        (CL4)

Overall        4.14/ 0.92      3.34          4.57         3.85
satisfaction

Revisit        3.71/1.47       2.65          4.31         3.52
intention

Recommend      4.03/ 1.18      3.08          4.63         3.68
intention

Variables      Competitive   Significance
                             level

Overall           4.53       F = 32.66, p < 0.001,
satisfaction                 CL1-CL2 *, CL1-CL3 *,
                             CL1-CL4 *, CL2-CL3 *,
                             CL3-CL4 *
Revisit           3.81       F = 16.83, p < 0.001,
intention                    CL1-CL2 *, CL1-CL3 *,
                             CL1-CL4 *, CL2-CL3 *

Recommend         4.25       F = 27.76, p < 0.001,
intention                    CL1-CL2 *, CL1-CL3 *,
                             CL1-CL4 *, CL2-CL3 *,
                             CL3-CL4 *

(a) CL=Cluster, * indicate statistical significant differences
between clusters

Table 5
Results of Logistic Regression on Clusters

Post-consumption             Indifferent
behaviors              Wald    Exp(B)     [beta]

Overall Satisfaction   17.07   0.404      -.905 *
Revisit intention      3.192   0.757     -.279 (n.s)
Recommend              3.118   0.715     -.336 (n.s)
  intention
Nagelkerke             0.344
  R-square
Hosmer &               [chi square]=6.21, p>0.05
  Lemeshow
  GoF test
% correctly                                85.1
  classified

Post-consumption              Enthusiast
behaviors              Wald    Exp(B)     [beta]

Overall Satisfaction   10.41    1.97      .679 *
Revisit intention      0.863    1.14    .127 (n.s)
Recommend              9.55     1.93      .656 *
  intention
Nagelkerke             0.312
  R-square
Hosmer &               [chi square] = 8.93, p>0.05
  Lemeshow
  GoF test
% correctly                     75.2
  classified

Post-consumption              Socializers
behaviors              Wald    Exp(B)     [beta]

Overall Satisfaction   3.477   0.713    -.338 (n.s)
Revisit intention      0.913   1.147    .137 (n.s)
Recommend              2.97    0.738    -.303 (n.s)
  intention
Nagelkerke             0.064
  R-square
Hosmer &               [chi square] = 22.22, p<0.05
  Lemeshow
  GoF test
% correctly                     74.8
  classified

Post-consumption       Competitive
behaviors              Wald    Exp(B)     [beta]

Overall Satisfaction   6.252    2.07      .726 *
Revisit intention      0.521    0.89    -1.17 (n.s)
Recommend              0.029    1.04    .039 (n.s)
  intention
Nagelkerke             0.195
  R-square
Hosmer &               [chi square] = 4.96, p>0.05
  Lemeshow
  GoF test
% correctly                     85.1
  classified

* p < 0.01; ** p < 0.05, (n.s) = not significant
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Title Annotation:Sport Tourism
Author:Prayag, Girish; Grivel, Elsa
Publication:Sport Marketing Quarterly
Article Type:Report
Geographic Code:4EUIT
Date:Sep 1, 2014
Words:9476
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