Athlete burnout has been a continued topic of interest for over two decades (Gustafsson et al., 2017a). Despite its prominence, the prevalence of athlete burnout has been difficult to establish due to inconsistent diagnostic criteria. Nevertheless, burnout prevalence estimates that between 1-11% of athletes may be affected by burnout symptoms with 1-2% of burnout symptoms being severe (Gustafsson et al., 2007; Raedeke & Smith 2009). Due to limitations of previous models, Gustafsson et al. (2011) developed an integrated model of athlete burnout based upon previous empirical research and conceptual frameworks, which guided this research.
In response to calls for a greater empirical understanding of athlete burnout (Lu et al., 2016), the current study built upon the literature by analyzing key antecedents (i.e., patterns of sport club involvement, athlete social involvement) and consequences (i.e., athlete satisfaction with individual and team performance) of athlete burnout amongst collegiate sport club athletes. Athlete burnout has been studied within a variety of sport contexts (i.e., elite, college, youth sport; team, individual sport) (Gabana et al., 2017; Gustafsson et al., 2017a); however, there is a dearth of literature on athlete burnout within collegiate sport clubs (Allen, 2018). Given that collegiate sport club athletes engage in approximately 5-7 hours per week on sport club activities (Lower et al., 2015), on top of academic responsibilities, job schedules, and social lives (Cosh & Tully, 2015), this unique environment poses stressors that may exacerbate athlete burnout.
The purpose of the current study is to examine the antecedents and consequences of athlete burnout within collegiate sport clubs through an integrated model of athlete burnout. More specifically, the following research questions were explored: (a) What are the patterns of sport club involvement among collegiate sport club athletes?; (b) How are the patterns of sport club involvement and athlete social involvement associated with athlete burnout?; and (c) How is athlete burnout associated with athlete satisfaction with individual and team performance?
Review of Literature
While athlete burnout does not have a universally accepted definition, three key dimensions can be found across these definitions: a reduced sense of accomplishment (RSA), emotional and physical exhaustion (EPE), and devaluation (DEV) (Raedeke, 1997; Raedeke & Smith, 2001). RSA encapsulates feelings of inadequacy, unfulfilled goals, and evaluating oneself poorly in terms of ability and performance (Raedeke et al., 2002). EPE consists of physical and emotional fatigue resulting from the physical and psychological demands associated with competing and training in sport (Raedeke, 1997). DEV signifies a detached, negative perspective toward sport which is demonstrated through a diminished interested in sport and its achievements (Raedeke et al., 2002).
While scholars have employed a number of conceptual models to study athlete burnout (e.g., Smith's  cognitive-affective stress model; Raedeke's  commitment model), few models take into consideration the multiple influences of the environment on the individual (Goodger, et al., 2007). Moreover, many conceptual models examining athlete burnout reflect a reductionist approach, breaking down the phenomena into constituent parts of a greater whole for investigation (Flood, 2010). Gould and Whitley (2009) call for scholars to consider multiple theoretical perspectives of burnout simultaneously for a more comprehensive investigation. A holistic conceptual model--reflective of a systems approach --can account for the unique contributions of different theoretical perspectives of burnout to effectively address the complex problem involving multiple interacting agents in a dynamic environment (Peters, 2014).
Recognizing the limitations, yet complementary features, of conceptual models of athlete burnout, Gustafsson et al. (2011) used established frameworks and empirical research to develop an integrated model of athlete burnout. A major premise of the integrated model is the complex process of developing athlete burnout which can lead to negative outcomes. The integrated model accounts for 1) major antecedents contributing to athlete burnout; 2) entrapment, personality, coping, and environmental factors influencing the risk of burnout; 3) early signs and key dimensions of experiencing burnout; and 4) maladaptive consequences of burnout.
Within the integrated model (Gustafsson et al., 2011), major antecedents of athlete burnout include excessive training, school/work demands, stressful social relations, negative performance demands, lack of recovery, and early athletic success. Individual factors that increase an athlete's risk of burnout are organized into two categories: entrapment; and personality, coping, and environment. Entrapment reflects the reasons why athletes persist in sport to the point of burnout (e.g., unidimensional athlete identity, high investment), while personality, coping, and environment reflect areas of vulnerability that may increase athletes' risk of experiencing burnout (e.g., perfectionism, low social support). Combined, these environmental and individual factors may contribute to an athlete's early signs of burnout, such as elevated cortisol levels and/or performance decrement. If unaddressed, these early signs of athlete burnout may transform into RSA, EPE, and DEV.
According to the integrated model (Gustafsson et al., 2011), fully developed athlete burnout may result in a variety of maladaptive consequences associated with an athlete's physiology (e.g., chronic inflammation), psychology (e.g., loss of motivation), and sport participation (e.g., withdrawal from sport). The degree to which an athlete experiences these maladaptive consequences vary based upon an athlete's unique combination of RSA, EPE, and DEV. For example, scholarship examining athlete burnout levels within college sports has found athletes experience EPE the most and DEV the least (Allen, 2018; DeFreese & Smith, 2014; Martignetti et al., 2020). In a longitudinal study of athlete burnout, Isoard-Gautheur et al. (2015a) demonstrated EPE remains stable over time, while RSA decreases and DEV increases over time. Severe cases of athlete burnout are often characterized by high levels of all three burnout dimensions, whereas less severe cases may consist of high levels of individual burnout dimensions or low to moderate levels across burnout dimensions (Gustafsson et al., 2007).
The identification of factors connected to athlete burnout has been at the forefront of burnout research in sport (Gustafsson et al., 2017a). Systematic reviews examining burnout in sport reveal the majority of scholarship has focused on the psychological antecedents of burnout (e.g., athlete identity, psychological needs, motivation) (Bicalho & Costa, 2018; Goodger et al., 2007). To extend the literature, the authors of the current manuscript explored situational factors that may directly exacerbate or mitigate athlete burnout (i.e., patterns of sport club involvement; athlete social involvement), and indirectly- -through athlete burnout--influence athlete satisfaction in the context of collegiate sport clubs.
Antecedents and Consequences of Athlete Burnout
An athlete's sport training (e.g., training load, intensity, activities) has been identified as a situational antecedent of burnout (DeFreese et al., 2015; Markati et al., 2019). Athletes experiencing an imbalance in high levels of training yet low levels of recovery and time away from sport are considered at increased risk of burnout. However, empirical studies exploring this association have produced inconsistent findings. Some scholars have found a positive association between training demands and burnout (e.g., Cresswell & Eklund, 2006; Russell, 2021), whereas other scholars have found a negative association (e.g., DeFreese & Smith, 2013a; Smith et al., 2010) or no association (e.g., Gustafsson et al., 2007; Moen et al., 2017). These mixed findings may be due to other factors that potentially contribute to athlete burnout, such as social support discussed below. Another explanation is the assumption that the relationship between sport training and athlete burnout is linear within empirical studies, illuminating the need to consider potential non-linear relationships to clarify how sport training influences burnout (Smith et al., 2010). To date, sport training has predominantly been assessed through training load (e.g., frequency/hours of training activities) (Markati et al., 2019; Moen et al., 2017). The current study adds to the literature by exploring patterns of sport club involvement that account for different types of sport activities that may influence athletes' vulnerability to burnout.
Athlete burnout can occur when situational demands outweigh available resources (Markati et al., 2019). Social resources often have a buffering effect, with social agents providing support that help athletes cope with the demands of their sport experience and enhance their perceptions of accomplishment and sport valuation (Dubuc et al., 2010; Gabana et al., 2017). Social support has consistently been found an indirect predictor of athlete burnout (e.g., DeFreese & Smith, 2013b; Isoard-Gautheur et al., 2015b; Russell, 2021), but has largely been studied within the confines of the sport team (e.g., coach, teammate) (di Luzio et al., 2020; Pacewicz, 2019) without consideration for the role of social support outside the team context. In a study of athlete burnout, Kentta et al. (2001) found 35% of athletes were unsatisfied with the limited amount of time they had available to spend with family and friends due to an excess of training, likely affecting athletes' satisfaction with their sport experience. To extend this line of inquiry, the current study explored athletes' social involvement outside of sport clubs and its relation to athlete burnout.
Athletes who experience burnout are likely to experience consequences linked to their physiology, psychology, or sport participation (Gustafsson et al., 2011). While cases of athlete burnout can range along a continuum of severity, athletes with severe cases of burnout are more likely to experience maladaptive consequences. Athlete satisfaction can be explained broadly in terms of both antecedent and outcome variables such that, "positive affective state resulting from a complex evaluation of the structures, processes, and outcomes associated with the athletic experience" (Chelladurai & Riemer, 1997, p. 135). When considering individual versus team sport athletes, Riemer and Chelladurai (1998) partitioned athlete satisfaction further by distinguishing satisfaction with one's own personal performance from satisfaction with one's team performance. Reduced athlete satisfaction has been identified as a potential consequence of athlete burnout, with Altahayneh (2003) finding significant negative correlations between athlete satisfaction (individual performance satisfaction [IPS]; team performance satisfaction [TPS]) and athlete burnout (RSA, EPE, DEV). Athlete satisfaction has the potential to influence retention, not only within athletes' sport participation but within college itself (Gabana et al., 2017; Le Crom et al., 2009). Thus, an athlete experiencing decreased levels of athlete satisfaction as a result of burnout may discontinue sport participation and other campus involvement. However, outside factors such as life satisfaction, personal stress, and academic stress may be mitigating variables in the decision to discontinue sport participation in addition to or in conjunction with athlete satisfaction (Lee et al., 2017).
While scarce, research exploring the relationship between athlete burnout and satisfaction has demonstrated a negative association (Lee et al., 2017). For example, Goodger et al. (2007) found athletes experiencing a high degree of burnout reported negative reflections on their athletic playing careers and involvement in sport. Scholars have proposed athlete's negative psychological state (e.g., burnout) can adversely influence other psychological states (e.g., satisfaction) (Lee et al., 2017). Therefore, if an athlete is experiencing RSA, EPE, and/or DEV, their satisfaction and perceived sport experience may decline as well. Satisfaction is often conceptualized as an antecedent of burnout based on the supposition that individuals who experience greater satisfaction in an activity will increase their involvement, which has the potential to lead to burnout if the situational demands of increased involvement outweigh an individual's available resources (Markati et al., 2019; Onyett et al., 1997). In order to extend the literature, the authors explored athlete satisfaction with their individual and team sport performance as consequences of athlete burnout, as athlete satisfaction with their sport performance may have long-term implications for athletes (Gould & Whitley, 2009).
It is critical to consider the sport context when examining athlete burnout. From an achievement goal theory perspective, athletes enter the sport environment (achievement setting) with dispositional tendencies to demonstrate competence and/or avoid demonstrating incompetence through mastering tasks (mastery: task-goal), improving personal skills (mastery: self-goal), or outperforming others (performance: other-goal) (Elliot et al., 2011). The sport motivational climate--emphasizing mastery- and/or performance-based goals--can also be reinforced by the coaching staff and teammates (Keegan et al., 2014). Motivational theorists propose achievement goals adopted by athletes and the motivational climate of the sport environment impact athlete burnout (Isoard-Gautheur et al., 2013). Moreover, athletes who place high value on interpersonal competition, engage in social comparison, expect punishment for mistakes, and play on a team with a win at all cost mentality (i.e., performance orientation) are vulnerable to experiencing burnout (Lemyre et al., 2008). Whereas, athletes who place high value on effort, focus on the learning process, and play on a team where every member has an important role (i.e., mastery orientation) are less likely to experience burnout (Daumiller et al., 2021). Achievement goal theorists assert elite, competitive sport contexts are likely characterized by performance goals/climate due to the inherent emphasis on winning (Duda et al., 1995), and therefore may have a greater prevalence of athlete burnout.
Burnout has often been studied among elite athletes, who reach national, international, and/or Olympic levels of competition, due to the competitive sport environment and demands of competing at the highest level (Bicalho & Costa, 2018; Gustafsson et al., 2018). However, the majority of sport participants do not reach elite levels of competition. In the United States (U.S.), of the 7.2 million athletes who participate in high school sport, only 7% are estimated to play college athletics and less than 0.14% are estimated to play professionally (PlayToday, 2021). Many athletes interested in sport competition past high school pursue competitive sport clubs, with an estimated 2 million college students participating in sport clubs in the U.S. (Blumenthal, 2009).
Collegiate sport clubs are a popular outlet for sport competition, social connection, community engagement, and health and wellness on college campuses (Czekanski & Lower, 2019; Lower et al., 2013). Broadly defined, a collegiate sport club is "a group of students that voluntarily organize to further their common interests in a [sport] activity through participation and competition" (Roberts et al., 2003, p. 11). A collegiate sport club team typically focuses on one sport, with participants (i.e., club athletes) engaged in regular sport training and competition (Lifschutz, 2012). Research estimates sport club athletes spend five to seven hours per week (on average) engaged in club activities, with some athletes reporting upwards of 20 hours per week due to the unique demands of club membership (Lower et al., 2015). Unlike other college sport offerings (e.g., varsity athletics, intramural sports), club athletes are responsible for coordinating all club operations (e.g., scheduling competitions, complying with university policies / procedures, coordinating team practices / meetings, coaching the sport; Flosdorf et al., 2016), managing club resources (e.g., revenue, expenses, facility space, equipment; Roberts, 2008), and organizing club social events (Lower-Hoppe et al., 2020a)--essentially conducting the club as a non-profit sport organization while participating in the sport as a club athlete. Sport club athletes have reported difficulty in balancing club commitments (i.e., training, meetings, travel, competition) with other obligations such as school and work (Rundio & Buning, 2021).
Collegiate sport clubs range from instructional/social clubs (whose main purpose is to provide instruction, participation and/or social opportunities for club athletes) to competitive clubs (whose main purpose is to develop and display athletic ability in the context of competition; e.g., The Ohio State University, 2021). Highest athlete involvement has been found in competitive individual or team sport clubs that do not have an equivalent varsity athletic sport offered (Helms & Moiseichik, 2018). For example, within the United States crew and rugby are primarily delivered at the club level, yet operate similar to a varsity team. For example, at a large public university in the Midwest the crew club maintains an operating budget of over $191,000 to fund their staff (three paid coaches; one athletic trainer), regattas (15 events), equipment (20 boats, repairs, uniforms), boat house, and insurance (boat, rowing, auto; R. Rathjens, personal communication, November 29, 2021). Participating in this crew club is not only a financial commitment for club athletes but also a time commitment, with athletes engaged in 5-6 club practices per week (S. Gaines, personal communication, December 10, 2021) and reporting an average of 18 hours per week engaged in club activities (SD = 4.0) (Author et al., 2020b). Scholars examining the point of diminishing returns in collegiate recreational sport have suggested intense participation in one activity (such as sport clubs) can contribute to burnout (Lower-Hoppe et al., 2020c).
Collegiate sport clubs are considered a recreational sport alternative to varsity athletics, with scholars recognizing similarities in the competitive sport environments and student outcomes (Lower et al., 2020; Warner et al., 2012). When considering athlete burnout, research consistently demonstrates moderate levels of burnout among varsity athletes. For example, through use of Raedeke and Smith's (2001) Athlete Burnout Questionnaire (ABQ; 5-point Likert scale), DeFreese & Smith (2014) found a mean burnout range of 2.24 to 2.92 and Martignetti et al. (2020) found a mean burnout range of 2.22 to 2.77 among varsity athletes. The few studies examining perceived burnout among collegiate sport club athletes have produced inconsistent findings. For example, Daniels et al. (2021) found a non-significant difference between athlete burnout reported by varsity versus club athletes, suggesting a similar prevalence of burnout in the club context. However, Allen (2018) found collegiate sport club athletes reported low levels of burnout (Mean range of 1.62 to 1.93) when completing the ABQ. With scant research examining burnout among this critical population, the current study fills this gap in the literature to inform strategies to mitigate athlete burnout and promote persistence within collegiate sport.
Participants and Procedures
A descriptive survey research design was employed to answer the study's research questions. The study was conducted at a large, public university (University A) and midsized, private university in the U.S (University B). Approval was received from the institutional review board and sport club programs investigated. Convenience sampling was employed, with a recruitment email sent to 65 sport clubs (across University A and B). At University A, 11 sport clubs indicated interest in the study and permitted the authors to attend a team practice or meeting to administer the questionnaire to club athletes. At University B, 21 sport clubs indicated interest, though due to geographic constraints the authors administered the questionnaire to club athletes online via Qualtrics. Informed consent was obtained before study participants gained access to the questionnaire. To incentivize participation, one athlete from each sport club investigated was randomly selected to receive a $15 gift card.
A total of 191 sport club athletes completed the questionnaire, representing 32 sport clubs across University A and B. Participant reported gender identity consisted of 107 woman/female (56%), 74 man/male (39%), 2 non-binary (1%), 2 "other" (1%), 1 transgender man (< 1%), and 5 non-response (3%). The majority of participants reported White / European American ethnicity (n = 150, 78%), with 9 Latinx/Hispanic (5%), 8 Asian (4%), 8 Multiple Races (4%), 4 African American / Black or African descent (2%), 3 Hawaiian / Pacific Islander (2%), 2 "other" (1%), and 7 non-response (4%). Academic year consisted of 44 first year students (23%), 47 second year (25%), 45 third year (23%), 44 fourth year (23%), 1 fifth year or beyond (< 1%), 5 graduate students (3%), and 5 non-response (3%). The mean cumulative grade point average (GPA) of the sample was 3.5 (SD = 0.4). Participants reported an average of 9.6 years of sport participation across the lifetime (SD = 5.8), and an average of 3.68 total academic semesters of collegiate sport club participation while at college (SD = 2.52). Of the 191 sport club athletes, 71 self-identified as serving on their club's executive board which is responsible for managing the club (37%).
The questionnaire consisted of 39 items and was designed to take 10-15 minutes to complete. Demographic questions solicited club athletes' gender, ethnicity, academic year, GPA, participation in sport clubs (number of semesters), and role in sport clubs (e.g., executive board).
To assess the type and frequency of activities sport clubs engaged in during their active season (i.e., sport club involvement; SCI), the authors developed five open- ended questions. Study participants were asked, "During the club's active season ...," 1) "approximately how many hours per week does your team practice?"; 2) "approximately how many hours on a typical week are you involved in sport club activities?"; 3) "how many in-season competitions does your team participate in?"; 4) "how many times does your team travel outside of your campus for club activities?"; and 5) "how many times does your team stay overnight in hotels for club activities?", with examples given. These items were reviewed by a panel of five experts in research methods and collegiate recreation, and pilot tested with 201 sport club athletes (not recruited for the current study) with the data found normally distributed (Author et al., 2020).
To assess club athletes' social involvement outside of sport clubs (i.e., athlete social involvement; ASI), the authors employed Gropel's (2006) 6-item work-life balance scale. The tool measures perceived sufficiency of time available for a social life in relation to one's work. Three of the six items were slightly modified to be appropriate for the sport club context. For example, "Because of my work, I neglect my family or friends" was revised to "Because of my membership on this club, I neglect my family or friends." The tool's 6-point Likert-type scale ranging from 1 (completely disagree) to 6 (completely agree) was adopted. Construct validity of the measure has been supported through confirmatory factor analysis (CFA) (Gropel, 2006), with reliability testing demonstrating acceptable internal consistency ([alpha] = .81; Gropel & Kuhl, 2009).
To assess athlete burnout among club athletes, Raedeke and Smith's (2001) ABQ was used. The tool consists of 15 items designed to measure RSA, EPE, and DEV (5 items per dimension). Please note the athlete burnout latent variable will be referenced as ABO. The items were slightly modified to be appropriate for the sport club context (i.e., 'swimming' was replaced with 'sport club'). The tool's 5-point Likert-type scale of 1 (almost never), 2 (rarely), 3 (sometimes), 4 (frequently), and 5 (almost always) was adopted. Construct validity of the 3-factor measure has been supported through CFA [[chi square] (87) = 188.9, p < .01; GFI = .90; NNFI = .94; CFI = .95; RMSEA = 0.69], with reliability testing demonstrating acceptable internal consistency ([alpha] = 0.84-0.88) (Raedeke & Smith, 2001). The ABQ is the most widely used measure of athlete burnout (Lin et al., 2021) and has demonstrated validity and reliability in many studies (e.g., Cresswell & Eklund, 2006; Lemyre et al., 2006; Londsdale et al., 2009).
To assess club athletes' satisfaction with their individual performance (IPS; 3 items) and team performance (TPS; 3 items), the 6-item athlete satisfaction questionnaire (Riemer & Chelladurai, 1998) was employed. As sport club seasons vary by sport (e.g., cross country --fall season; softball--spring season), the authors modified two items to reflect past and present tense to accommodate the diverse sport clubs represented. For example, "The extent to which the team is meeting its goals for the season" was revised to "The extent to which the team is meeting (has met) its goals for the season." All items were prefaced with the phrase, "I am satisfied with ...", and used a 6-point Likert-type scale ranging from 1 (not at all satisfied) to 6 (extremely satisfied). Construct validity of the two- factor measure has been supported through CFA [[chi square] (1379) = 2631.47, p < .01; TLI = .93; BFI = .94; RMSEA = 0.45], with reliability testing demonstrating acceptable internal consistency ([alpha] = 0.85-0.95) (Riemer & Chelladurai, 1998). Validity and reliability of the athlete satisfaction questionnaire has been demonstrated in numerous studies (e.g., Aoyagi et al., 2008; Li et al., 2021; Riemer & Toon, 2001).
To address the first research question, we conducted K-means cluster analyses to identify the specific SCI profiles associated with athlete burnout and satisfaction. Specifically, drawing from the inconsistent findings of the relationship between training demands and burnout (e.g., Cresswell & Eklund, 2006; DeFreese & Smith, 2013a; Gustafsson et al., 2007; Moen et al., 2017; Russell, 2021; Smith et al., 2010), the authors do not assume linear relationships between the five indicators of SCI and athlete burnout and satisfaction. Empirically, the authors found only two significant pairs out of the 25 cases in bivariate correlation between the five items for SCI measures and the five endogenous variables (i.e., RSA, DEV, EPE, IPS, and TPS); but the correlation size of the two pairs was weak (.211 and . 155, respectively) and may be due to method (Podsakoff et al., 2012). Thus, to examine the inter-construct relationships, researchers should identify what specific patterns of SCI are vulnerable to burnout and beneficial to athletic satisfaction (Richardson et al., 2017). The clustering analyses were conducted with the standardized scores of the five items due to their different scales. After conducting a K-mean clustering analysis using SPSS 27, the authors compared the Z-scores of the five items to label the cluster using a one-way ANOVA with Bonferroni test. To combine the results with structural equation modeling (SEM) using R, the memberships were coded as dummy variables and used to examine the relationships between SCI and athlete burnout and satisfaction.
SEM was conducted to address the second and third research questions. Before computing the structural model, the authors evaluated the measurement constructs with CFA. The criteria of the measurement model evaluation include four indices (Hu & Bentler, 1999; MacCallum et al., 1996): (1) minimum fit function chi-square (CMIN < 3), (2) comparative fit index (CFI > .90), (3) standardized root mean square residual (SRMR <. 08), and (4) root-mean-square error of approximation (RMSEA < .08). Additionally, consistent with Hair et al. (2010), construct validity was assessed. For convergent validity, the average variance extracted (AVE) should be greater than .05, and for discriminant validity the square root of the AVE should be greater than inter-construct correlations and AVE should be greater than maximum shared variance (MSV). Then, the authors constructed a mediation model--guided by the integrated model of athlete burnout (Gustafsson et al., 2011)--with the four clusters of SCI and ASI as independent variables, three sub-dimensions of burnout as mediators, and two dimensions of athlete satisfaction as dependent variables. Following Hayes' (2018) recommendation, the mediating effects of the structural model were examined based on 95% bias-corrected bootstrapping confidence intervals (Iteration=5,000).
Patterns of Sport Club Involvement
Elbow method was computed to decide the optimal number of clusters, K. This technique looks at the total within-cluster sum of square (WSS) to assess the suggested clustering model and is useful to validate the proposed model and evaluate its performance and effectiveness (Asri et al., 2019). Its basic algorithm is to choose the K for which relatively large changes of WSS appear. With the five items to measure SCI, the result of the Elbow method suggested four clusters. For Cluster 1, 19.9% of respondents (n = 38) were labeled as "Practice-Oriented" and spent considerably more time in club practices (SCII) than other clusters (p < .001). For Cluster 2, 7.9% of respondents (n = 15) were labeled as "Travel-Oriented" who engaged in the most travel outside of campus (SCI4) for club activities (p < .001). Cluster 3, consisting of 26.7% of respondents (n = 51), was designated as "Competition-Oriented". They tended to participate in more club competitions (SCI3) than any other clusters (p < .001). Lastly, Cluster 4, consisting of 45.5% of respondents, was labeled "Low-Involved". This cluster consisted of those who tended to have relatively lower scores across the five indicators of sport club involvement (i.e., club practice, individual club involvement, in-season competitions, travel for club activities, overnight hotel stays for club activities).
Measurement and Structural Equation Model
The authors performed an initial CFA with 27 observed items composed of six first-order latent variables (i.e., ASI, RSA, DEV, EPE, IPS, and TPS). The results of the CFA indicated an unacceptable fit [[chi square] (309) = 666.848, [chi square]/df = 2.158, p < .001; RMSEA = .078; CFI = .888, SRMR = .062] as its CFI was less than .9. In addition, the authors found two critical issues: inadmissibly low factor loadings of two items (ASI I and ASI5 < .4) and lack of discriminant validity between RSA and DEV due to their high correlation (.87). To remedy the issues, the authors determined to use a second-order factor of Athlete Burnout (ABO) (Gerber et al., 2018; Isoard-Gautheur et al., 2010, 2018) and delete two items for each subdimension (i.e., RSA I and 2, DEV 2 and 3, and EPE I and 5) to enhance fit (Larson et al., 2019). In the second CFA that entails 21 observed items consisting of three first-order (i.e., ASI, IPS, and TPS) and one second-order latent variables (i.e., ABO), the results displayed an acceptable fit and satisfactory construct validity: [chi square] (143) = 285.257, [chi square]/df = 1.99, p < .001 ; RMSEA = .074; CFI = .931, SRMR = .075. Table 1 and Table 2 presents the details of the CFA results.
Next, SEM was conducted to examine the relationships between the four clusters of SCI and latent variables with the two control variables (i.e., sport club executive board and participation year). The final structural model indicated an acceptable fit [[chi square] (223) = 405.790, [chi square]/df = 1.820, p < .001; RMSEA = .066; CFI = .915, SRMR = .073] (Hu & Bentler, 1999; MacCallum et al., 1996). The results of the structural model displayed significant interrelationships. First, competition-oriented SCI was significantly associated with IPS and TPS, fully mediated through the integrated burnout (indirect [effect.sub.IPS] = .248, BootLLCI = .090, BootULCI = .559 and indirect [effect.sub.TPS] = .331, BootLLCI = .120, BootULCI = .733). Second, results indicated ASI had a significantly positive association with IPS and TPS, fully mediated by ABO (indirect [effect.sub.TPS] = .522, BootLLCI = .288, BootULCI = 1.006 and indirect [effect.sub.TPS] = .697, BootLLCI = .401, BootULCI = 1.358). Lastly, the authors found a significant direct relationship between team-oriented SCI and TPS ([beta] = .151). In addition, this model demonstrated a large effect size ([R.sup.2.sub.ABO]=58.8%; [R.sup.2.sub.IPS]=43.7%; and [R.sup.2.sub.TPS]=42.6%). Table 3 presents detailed path coefficients and explained variances and Figure 2 presents the final mediated structural model.
This study explored the patterns of sport club and social involvement among collegiate sport clubs and how this involvement is associated with burnout and athlete satisfaction at the individual and team level. Results of SEM indicated ABO mediated SCI and ASI (antecedents) and athlete satisfaction with individual and team performance (consequences). Additionally, practice-oriented sport club involvement had a direct and positive relationship with TPS.
Measurement Dimensions and Modifications Patters of Sport Club Involvement
K-means cluster analysis yielded a meaningful four-cluster solution (i.e., practice-oriented, travel-oriented, competition-oriented, and low-involved) representing varying patterns of sport club involvement and expectations. These ranged from sport club athletes that reported more club practices but less club competitions--seemingly more socially and developmentally driven--to athletes that reported heavy club involvement with in-season competitions or travel. The National Intramural and Recreational Sports Association (NIRSA) suggests different classifications for collegiate sport clubs are necessary to ensure opportunities are available that appeal to individuals with varied interests (Roberts, 2008). These classification types are often labeled social, instructional, and competitive and are delineated by a series of factors that include athlete expectations and abilities, leadership, facility and equipment availability, and financial support (Mull et al., 2005; Roberts, 2008).
The four-cluster solution had similarities to other collegiate sport club classification structures (e.g., NIRSA) in that categories associated with competition and instruction were distinguished. However, this survey contained variables specific to practices, competitions, and team travel while excluding factors such as equipment availability or financial support, which accounts for differences in this classification structure (e.g., four categories instead of three) comparatively to others. Nonetheless, these distinct clusters indicate there are different club types in the recreational sport system--despite sharing the unifying categorization of "sport club"--each with unique characteristics and functions. Accounting for variation in patterns of involvement is important when studying athlete burnout to better understand how training acts as a situational correlate of burnout (Markati et al., 2019; Moen et al., 2017).
When establishing the first-order latent variables for the SEM, three of which were the dimensions of athlete burnout (i.e., RSA, DEV, and EPE), critical measurement issues were encountered related to RSA and DEV. In this study, there was a lack of discriminant validity between RSA and DEV that resulted from their high correlation, bringing into question the existence of three distinct burnout dimensions among the sport club population. This was surprising given the ABQ is a psychometrically sound measure (Raedeke & Smith, 2001) that has been validated in other languages (e.g., French; Isoard-Gautheur et al., 2010) and is implemented across a variety of sports and populations (e.g., Cresswell & Eklund, 2006; Gustafsson et al., 2017b). However, other empirical research has encountered concerns with discriminant validity between the ABQ dimensions. For example, Gerber et al. (2018) confirmed the three-factor structure but noted that athletes demonstrating clinically relevant burnout symptoms also scored low on DEV items, suggesting limited accuracy of this dimension. Another concern with the DEV dimension is that it does not distinguish between devaluing sport and amotivation toward sport (Holmberg & Sheridan, 2013; Londsdale & Hodge, 2011). Despite being conceptually similar constructs, devaluation incorporates negative emotions toward sport while amotivation involves questioning one's continued participation in sport.
Recognizing these nuances is important for scholars studying burnout in different athletic contexts and populations. Perhaps the salient dimensions of athlete burnout are dependent on the environmental influence(s) and athletic context(s) studied. Sport club athletes are not contractually committed to a single team or coach, and there are fewer financial ramifications associated with a sport club athlete's participation. Thus, these athletes may perceive more autonomy and less pressure about continuing their team membership. Furthermore, sport club athletes may also view their sport participation as recreational and developmental comparatively to the competitive narrative that is dominant among elite athletes. As such, these athletes may be less likely to differentiate between the ABQ constructs, making it necessary to assess whether supplementary burnout measures are required for a specific context (e.g., sport club athletes vs. collegiate athletes). The ABOS (Isoard-Gautheur et al., 2018) has reconceptualized dimensions of athlete burnout and might be more sensitive to how sport club athletes perceive their sport experiences. These findings also demonstrate that a systems approach is needed to determine the appropriate measure of athlete burnout among sport club athletes (Peters, 2014).
Integrated Model of Athlete Burnout
The final SEM specified ABO as the mediator between burnout antecedents (i.e., SCI and ASI) and consequences (i.e., ISP and TSP). This mediation model was developed using cross-sectional data, therefore, the unique causal and temporal relationships that are discussed could be further supported and confirmed with a longitudinal mediation model. Two antecedents demonstrated significant negative relationships with the ABO meditator variable: 1) ASI; and 2) competition-oriented involvement. ASI negatively predicted ABO which indicates social support is important for athletes to maintain positive feelings toward their sport endeavors and participation, supporting findings that adequate social support can buffer athlete burnout and overwhelming situational demands (Dubuc et al., 2010). Social support is a broad concept that includes an individual's social support system (e.g., friends, family, group memberships) as well as specific functions that support system fulfills (e.g., providing personal advice) (Uchino, 2004). According to the integrated model of athlete burnout (Gustafsson et al., 2011), low social support constitutes a vulnerability factor for burnout which provides theoretical support for ASI negatively predicting ABO.
In this study, ASI was related to time spent with friends and family which indicates that an athlete's social structure expands beyond individuals directly associated with the team. There is likely overlap between teammates and friends, but this is an important contribution to the literature since social support is often studied solely in relation to intrateam support (DeFreese & Smith, 2013b; di Luzio et al., 2020; Isoard-Gautheur et al., 2015b; Pacewicz, 2019; Russell, 2021). This is also an important finding as it highlights the influence of social systems on sport participation. Elite sport programs often focus on performance outcomes rather than social development or personal assessment (Coakley, 1992). As such, reframing burnout as a problem within the social construction of sport as opposed to individual character flaws or ineptitudes may more accurately identify the root causes of athlete burnout (Coakley, 1992). Since ASI has a negative correlation with ABO among sport club athletes, the social organization of collegiate sport clubs may be a salient buffer to athlete burnout whereas in other sport contexts, the social support system may contribute to the development of burnout. The negative relationship between ASI and ABO may also indicate the demands of being a sport club athlete do not overwhelmingly interfere with social relationships since the ASI items were primarily about having enough time for family and friends. This contrasts findings from Kentta et al. (2001) where 35% of young elite athletes sampled were dissatisfied with the time they could spend on important relationships.
Competition-oriented involvement demonstrated a significant negative relationship with ABO and was the only pattern of sport club involvement significantly related to ABO. This finding was surprising as performance achievement goals and motivational climate have been associated with higher levels of athlete burnout (Daumiller et al., 2021; Lemyre et al., 2008). The negative relationship between competition-oriented involvement and ABO advances the inconsistent findings regarding sport training demands and burnout. Specifically, none of the involvement types were positively associated with ABO, suggesting the time and training demands required by collegiate sport clubs is low enough to avoid contributing to the development of burnout (Moen et al., 2017) and surpassing a point of diminishing returns related to recreational sport participation (Lower et al., 2018; Lower-Hoppe et al., 2020a). The law of diminishing returns posits that as inputs are invested into a system, the outputs will increase monotonically until a certain point, at which time any additional input results in decreased outputs. Applying the law of diminishing returns to the current study findings, sport club athletes may value competition and be able to invest time and energy into the team (input) at a level that avoids passing the point of diminishing returns and experiencing negative psychological or physiological symptoms of burnout, such as increased stress, decreased motivation, and mood or behavioral disturbances (output).
Finally, practice-oriented involvement was a significant positive predictor of TPS, but not significantly associated with ABO. This finding was unexpected because TPS incorporated an observed variable related to win-loss records, but the practice- oriented teams reported a low number of competition experiences. One explanation may be the level of competition, as clubs can perform at a high level (leading to TPS) yet still experience a loss when competing against a skilled team. The other two observed variables in TPS were related to the team's overall performance and meeting team goals, which were not defined specifically in terms of winning. Instead, the practice-oriented teams could interpret these questions based on other meaningful aspects of their sport experience. For example, team cohesion (Ona? & Tepeci, 2014) and leadership behaviors among coaches and players (Fouraki et al., 2020) are important factors that improve team member satisfaction. From an achievement goal theory perspective, sport clubs that engaged in more practices and less competitions (i.e., practice-oriented involvement) are likely characterized by a mastery-orientation, with a focus on the learning process. Research has found mastery achievement goals associated with lower levels of athlete burnout (Daumiller et al., 2021), suggesting athletes focused on developing competencies are interested in intrapersonal challenges and therefore more resilient in stressful and/or exhausting achievement situations that can lead to burnout.
In terms of burnout consequences, ABO had a significant negative relationship with athlete satisfaction at the individual and team level which expands on the maladaptive consequences of experiencing burnout. This finding furthers our understanding of the relationships between burnout and satisfaction. Not only is satisfaction an antecedent of burnout, it is also a consequence. While low satisfaction may lead to RSA and burnout (Markati et al., 2019), this study showed that experiencing burnout may also lower satisfaction. Further, burnout can negatively impact the perception of satisfaction with the team's performance as well as individual performance, supporting previous research (Altahayneh, 2003). Even though the observed items associated with TPS were all related to perceptions of team outcomes, if an individual is feeling RSA, DEV, or EPE toward their sport, this may negatively affect their perceptions of performance broadly.
The current study has implications for collegiate recreation professionals and sport club executive boards responsible for the administration of sport clubs. While prior empirical scholarship has demonstrated the presence of athlete burnout when certain antecedents occur (e.g., excessive training demands, low perceived social support) (Russell, 2021), the results of this study suggest that burnout is not a major factor for the sport club athletes surveyed. This is undeniably a good thing that can be used to leverage sport club participation. Moreover, collegiate recreation professionals should market this competitive sport offering as a favorable alternative to varsity athletics and promote the sport club environment where athletes have less likelihood of experiencing burnout.
In light of the diversity of collegiate sport club programs (e.g., social, instructional, competitive clubs) (Mull et al., 2005; Roberts, 2008), there is a possibility that competitive clubs--that operate more closely to their varsity counterparts (e.g., more competition, financial support, and commitment levels) (Helms & Moiseichik, 2018)--would be more susceptible to athlete burnout. Collegiate recreation professionals are in an advantageous position to help mitigate the demands of sport club participation that may contribute to sport club athlete burnout. Practically, professionals can use the findings to be more aware of the nuanced differences between sport clubs at the same institution to effectively tailor their support to meet the needs of club executive boards and athletes.
For example, in this study four classifications of clubs emerged (i.e., practice-oriented, travel-oriented, competition-oriented, and low involved), which require varying levels of administrative support to function (Mull et al., 2005). Travel-oriented clubs might require guidance with university travel policies, procedures, and paperwork, while practice-oriented clubs might benefit from discussions about facility reservations and coordinating sport clinics (Lower & Czekanski, 2019). Comparatively, low involved clubs may need mentorship to increase engagement in practice and competition which is associated with positive outcomes (Hall-Yannessa & Forrester, 2005). Tracking sport club activities using a compliance checklist or through consistent meetings with club executive boards can enable collegiate recreation professionals to tailor support to individual clubs (Author et al., 2020).
As the integrated model of athlete burnout demonstrated a negative relationship between ASI and ABO, collegiate recreation professionals and sport club executive boards are encouraged to foster an environment that cultivates social support inside and outside the club. Collegiate recreation professionals may utilize club executive board trainings to educate student leaders on the value of facilitating social activities for club athletes (e.g., team dinners, team-building events, or community service activities) while protecting time for athletes to engage in social interactions with family and friends outside of practice and competition (Lower-Hoppe et al., 2020a). By increasing social interactions between players, families, and friends throughout the club season, club executive boards can foster social support and positive sport experiences for their athletes to help reduce burnout and increase satisfaction (Dubuc et al., 2010; Gabana et al., 2017).
Based on the negative relationship between competition-oriented sport clubs and ABO, promoting engagement in competitive events may benefit club athletes. Collegiate recreation professionals could facilitate a point system to incentivize competitive sport activities (Czekanski & Lower, 2019). For example, by engaging in local, regional, and national competitions, clubs could earn points that inform the allocations (i.e., funding, facility space, priority scheduling) they receive from the university. Due to mixed findings regarding sport training and athlete burnout, collegiate recreation professionals and sport club executive boards should note a potential point of diminishing returns in which greater sport involvement may result in diminished positive outcomes (Lower et al., 2018). As such, recreation professionals may advise club executive boards to develop club goals (e.g., number of in-season competitions, travel to competitions, post-season play) that consider athlete values, capacity, and internal/external resources. The focus of these achievement goals should not be performance driven (i.e., wins vs. losses, placement at event)--which has been associated with athlete burnout (Isoard-Gautheur et al., 2013). Rather, achievement goals should be task oriented, accounting for the experience of preparing for competition, traveling to and engaging in competitions, and building friendships.
Practice-oriented sport clubs demonstrated a positive relationship with TPS, which though surprising--presents important implications. While sport competitions provide club athletes opportunities to perform and experience accomplishment, satisfaction with sport performance is often dependent upon developing individual and team sport skills that collectively contribute to team performance. As athlete satisfaction has been linked to reduced burnout (Gabana et al., 2017) and increased retention (Le Crom et al., 2009), sport club executive boards should facilitate regular weekly practices during the club season to provide opportunities for skill development and team improvement. Furthermore, as satisfaction with team performance accounts for the extent to which the team is meeting its season goals (Riemer & Chelladurai, 1998), sport club executive boards may facilitate team goal setting early in the club season, which can motivate goal striving to lead to goal attainment, resulting in satisfaction (Smith et al., 2007).
It is also worth noting the negative impact athlete burnout can have on club athletes' sport experience. Scholars have argued burnout occurs "as a potential (but not the sole) outcome for an athlete who is unable to effectively cope with the chronic psychosocial stress involved in sport training and competition" (Eklund & DeFreese, 2015, p. 64). Accordingly, collegiate recreation professionals may consider a process for monitoring sport club athletes' coping skills, symptoms of burnout, and overall sport experience. On-campus psychological, counseling, and/or health and wellness services can be a valuable tool for preventing and addressing athlete burnout, and collegiate recreation professionals can create a referral program with these providers for club athletes to learn how to cope with stress and manage burnout symptoms (Kroshus & DeFreese, 2017). Moreover, trained professionals can be invited to educate sport club coaches, athletic trainers, and executive boards on the signs and symptoms of burnout to help identify and prevent burnout in club athletes (Neal, 2016). As mental health concerns rise across university campuses, collegiate recreation departments can advocate for the positive role sports clubs play in reducing burnout based on the results of this study.
Limitations and Future Recommendations
Findings should be interpreted with the limitations in mind. First, this study used cross-sectional data, presenting correlations between variables. Future research should be conducted with longitudinal data to examine causal relationships between patterns of sport club involvement, athlete social involvement, burnout, and satisfaction. Second, convenience samples limit external validity to generalize findings, suggesting that future researchers might employ random sampling across the broader population of sport club athletes to promote generalizability. Third, the use of reverse coded items for ASI and ABO might have resulted in common method bias (i.e., poor factor loadings, reliability, validity, and fit) in our initial measurement model (Weijters et al., 2013). As a result, an item deletion procedure was conducted with careful consideration. Future researchers can minimize the potential problems of reverse coded items by preventing careless responding and reducing reversal ambiguity (see Weijters et al., 2013). Fourth, testing the ABQ revealed issues of validity, suggesting an alternative athlete burnout measure be considered in future research (e.g., Athlete Burnout Scale, Isoard-Gautheur et al., 2018).
To extend this line of inquiry, researchers are encouraged to incorporate individual factors that influence an athlete's risk of burnout (i.e., entrapment; personality, coping, and environment) for a more comprehensive investigation of athlete burnout. Researchers may also draw comparisons across college sport offerings (e.g., collegiate athletes, sport clubs, intramural sports) to identify environmental factors that exacerbate or mitigate burnout among sport participants. To reduce burnout in sport, researchers should consider partnering with mental health specialists to test psychological interventions that increase athletes' awareness of burnout symptoms, coping skills, and ability to manage burnout.
Author Contributions: Conceptualization, L.L., S.B., A.R., and C. L.; methodology, L.L. and W.L.; validation, W.L.; formal analysis, W.L.; investigation, L.L., S.B., A.R., and C. L..; writing--original draft preparation, L.L., W.L., S.B., A.R., and C.L.; writing--review and editing, L.L., W.L., S.B., A.R., and C.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by The Ohio State University Sports and Society Initiative.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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Caption: Figure 1 presents the descriptive profiles of the five items of each cluster.
Caption: Figure 2. Mediating Structural Model.
Table 1. Summary of Measurement Items and Factor Loadings.Item Question Factor LoadingAthlete Social Involvement (ASI)ASI1 (a) I often visit my friends and -- acquaintances.ASI2 Because of my membership on .735 this club, I have no free time.ASI3 Because of my membership on .619 this club, I neglect my friends or family.ASI4 I have enough time for my .813 friends.ASI5 (a) In my free time I still deal -- with my club dutiesASI6 I have enough time for my .858 friends and family.Athlete Burnout (ABO) Reduced Sense of Achievement (RSA) .745RSA1 (a) I'm accomplishing many -- worthwhile things in my sport club.RSA2 (a) I am not achieving much in my -- sport club.RSA3 I am not performing up to my .759 ability in my sport club.RSA4 It seems that no matter what I .776 do, I don't perform as well as I should.RSA5 I feel successful at my sport .569 club. Devaluation (DEV) .964DEVI The effort I spend in my sport .723 club would be better spent doing other things.DEV2 (a) I don't care as much about my -- sport club performance as I used to.DEV3 (a) I'm not into my sport club -- like I used to be.DEV4 I feel less concerned about .699 being successful in my sport club than I used to.DEV5 I have negative feelings .781 toward my sport club. Emotional and Physical Exhaustion (EPE) .679EPEI (a) I feel so tired from my -- training that I have trouble finding energy to do other things.EPE2 I feel overly tired from my .776 sport club participation.EPE3 I feel "wiped out" from my .931 sport club.EPE4 I feel physically worn out .648 from my sport club.EPE5 (a) I am exhausted by the mental -- and physical demands of my sport club.Athlete Satisfaction: Individual Performance Satisfaction(IPS)IPSI The degree to which I have .718 reached (reach) my performance goals during the season.IPS2 The improvement in my .955 performance over the previous season.IPS3 The improvement in my skill .942 level.Athlete Satisfaction: Team Performance Satisfaction(IPS)TPSI The team's win/loss record .665 this season.TPS2 The team's overall performance .953 this season.TPS3 The extent to which the team .863 is meeting (has met) its goals for the season.(a) Removed items due to validity and model fit
Table 2. Confirmatory Factor Analysis Results: Correlations andConstruct Validity. Mean SD ASI ABO IPS TPS MSV CRASI 4.78 .64 .58 .49 .85ABO 1.92 .65 -.70 .65 .49 .82IPS 4.53 1.02 .34 -.57 .78 .32 .91TPS 4.56 1.26 .37 -.60 .51 .66 .36 .85Note. SD=standard deviation; MSV=maximum shared variance;CR=composite reliability; Numbers in bold on the diagonal representthe square root of AVE; AVE=average variance extracted; ASI=AthleteSocial Involvement; ABO=Athlete Burnout; IPS=Individual PerformanceSatisfaction; TPS=Team Performance Satisfaction; * p < .05.
Table 3. The Results of Structural Equation Modeling.Path [beta] BDirect effectsASI [right arrow] ABO -.700 -.539PO [right arrow] ABO .096 .154TO [right arrow] ABO -.038 -.089CO [right arrow] ABO -.178 -.256ABO [right arrow] IPS -.786 -.968ASI [right arrow] IPS -.208 -.197PO [right arrow] IPS .054 .106TO [right arrow] IPS .014 .042CO [right arrow] IPS -.038 -.067ABO [right arrow] TPS -.756 -1.294ASI [right arrow] TPS -.136 -.179PO [right arrow] TPS .151 .413TO [right arrow] TPS -.062 -.252CO [right arrow] TPS -.044 -.109Indirect effectsASI [right arrow] ABO [right arrow] IPS .550 .522PO [right arrow] ABO [right arrow] IPS -.076 -.149TO [right arrow] ABO [right arrow] IPS .030 .086CO [right arrow] ABO [right arrow] IPS .140 .248ASI [right arrow] ABO [right arrow] TPS .529 .697PO [right arrow] ABO [right arrow] TPS -.073 -.199TO [right arrow] ABO [right arrow] TPS .028 .115CO [right arrow] ABO [right arrow] TPS .134 .331[R.sup.2] ABO IPS TPS 58.8% 43.7% 42.6%Path Z-value or 95% CI (a)Direct effectsASI [right arrow] ABO -6.525 ***PO [right arrow] ABO 1.418TO [right arrow] ABO -.570CO [right arrow] ABO -2.525 *ABO [right arrow] IPS -4.483 ***ASI [right arrow] IPS -1.551PO [right arrow] IPS .759TO [right arrow] IPS .211CO [right arrow] IPS -.506ABO [right arrow] TPS -4.274 ***ASI [right arrow] TPS -1.016PO [right arrow] TPS 2.080 *TO [right arrow] TPS -.898CO [right arrow] TPS -.582Indirect effectsASI [right arrow] ABO [right arrow] IPS [.288, 1.006]PO [right arrow] ABO [right arrow] IPS [-.493, .100]TO [right arrow] ABO [right arrow] IPS [-.235, .412]CO [right arrow] ABO [right arrow] IPS [.090, .559]ASI [right arrow] ABO [right arrow] TPS [.401, 1.358]PO [right arrow] ABO [right arrow] TPS [-.665, .137]TO [right arrow] ABO [right arrow] TPS [-.332, .553]CO [right arrow] ABO [right arrow] TPS [.120, .733][R.sup.2] ABO IPS 58.8% 43.7%Note. [right arrow] represents standardized regression weights;[right arrow] represents unstandardized regression weights;ASI=Athlete Social Involvement; ABO=Athlete Burnout;IPS=Individual Performance Satisfaction; TPS=Team PerformanceSatisfaction; PO=practice-oriented; TO=travel-oriented;CO=competition-oriented; low involvement was used as areference; * p [right arrow] .05; ** p < .01; *** p < .001.(a) 95% bias corrected bootstrapping confidence interval;confidence intervals were constructed based on unstandardizedcoefficients
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