The author holds an M.S. Exercise Science degree from West Chester University of Pennsylvania and an MBA from Drexel University.
Inner Excellence, by Jim Murphy, was published in 2020 but entered the broader public conversation several years later. During a nationally televised Wild Card game in January 2025, A.J. Brown of the Philadelphia Eagles was spotted reading the book on the sideline, a moment that drew widespread attention. His postgame praise helped propel the book to the number one spot on Amazon by the following morning.While Brown’s visibility expanded its audience, Murphy’s work ultimately stands on its own as a clear, accessible exploration of the mental habits that separate elite performers and extend beyond sport.
At its core, Inner Excellence challenges the idea that achievement alone creates a meaningful life, instead placing value on growth that begins internally through self-awareness and personal development rather than external validation. Murphy encourages readers to move away from chasing approval or outcomes and toward building a steadier foundation rooted in purpose and perspective. Central to this shift is his caution against ego, which he describes as one of the greatest obstacles people face. In Murphy’s view, excessive self-focus distorts perspective, limits growth, and distracts from long-term development.
That same mindset shapes how Murphy frames the book itself. He invites readers to approach Inner Excellence not as a destination to reach, but as an ongoing process rather than a final achievement. In doing so, he challenges conventional definitions of success, suggesting that exceptional leaders measure it through effort, growth, and a sense of purpose extending beyond oneself. This reframing feels relevant in a culture fixated on contracts, awards, and social media validation. Importantly, Murphy delivers these ideas without an authoritative tone, writing instead as a guide alongside the reader and inviting reflection rather than dictating conclusions. Although many examples draw from athletics and the language of sport, the lessons extend well beyond athletes, translating naturally to everyday life and professional pursuits through references to figures such as Theodore Roosevelt, Ralph Waldo Emerson, and J.K. Rowling.
Murphy draws on his background as a former member of the Chicago Cubs to illustrate how closely his sense of identity and self-worth became tied to performance. That dynamic may feel familiar to many readers and raises a natural question about how often personal worth becomes linked to results alone. I recognize that tendency in myself, particularly in the ways confidence and self-perception can become tethered to external outcomes.
Inner Excellence is organized into eleven chapters that explore a broad range of ideas, including performance, emotional control, presence, overcoming mental blocks, maintaining poise under pressure, and leadership. Across these chapters, Murphy also weaves in topics such as the subconscious, visualization, self-awareness, the difference between dreams and goals, and the development of courage. Among them, Chapter 3, “The Greatest Opponents You’ll Ever Face,” stood out most to me. In it, Murphy identifies three internal adversaries that undermine performance and growth: the Critic, the Monkey Mind, and the Trickster. The Critic delivers negative judgments, the Monkey Mind fuels relentless mental noise, and the Trickster fixates on past failures to reinforce self-doubt. What gives the chapter its weight for me is how it reflects my own experience, where progress is often limited less by external obstacles than by the internal narratives I carry.
Murphy brings further clarity to this idea by framing life as a series of choices between two paths: the path of freedom and the path of comfort. One requires risk, growth, and a willingness to confront fear, while the other favors safety and familiarity, often at the cost of long-term development. I am frequently drawn toward the option that offers less resistance, only to recognize how easily comfort can delay growth. In this way, Inner Excellence moves beyond athletic performance and becomes a personal challenge, prompting an honest reckoning with how rarely meaningful progress occurs without deliberate discomfort.
Inner Excellence is not a book meant to be rushed, and for some readers, myself included, it can feel overwhelming if approached too quickly. There is a great deal of material, but readers should not feel pressured to absorb or apply everything at once. Instead, the book benefits from a slower, more intentional pace, with chapters best read in smaller sections and revisited as needed. Murphy’s approach relies on reflection, lived experience, illustrative examples, and quotations rather than formal research or statistical validation, which may not appeal to readers who prefer strictly data-driven frameworks. That said, the book is intended as a practical resource rather than an academic text. Each chapter concludes with clearly defined “Key Points,” along with follow-up questions and activities that encourage application. In this way, Inner Excellence functions less as a book to be read straight through and, for me, more as a personal toolkit, one that invites readers to return to specific chapters and apply the lessons most relevant to their lives. The writing is clear, making the ideas easy to understand and apply in everyday settings.
Murphy draws a parallel between physical and mental training, emphasizing that mental development deserves the same consideration we give physical preparation. Inner Excellence offers not a finish line, but a framework meant to be revisited, applied, and lived over time. It reads less as a prescription for immediate change and more as an invitation to return, reflect, and apply its ideas with patience and intention, making it a book I would strongly recommend and one that at times feels akin to a personal form of therapy.
Purpose: The culture of National Collegiate Athletics Association (NCAA) Division I (DI) athletics can stimulate a culture that appears to “greedy” placing high demands on the time and energy of those working within the sport organization. These intense demands create the potential for experiences of work addiction, burnout, and work-family conflict among sport professionals. We aimed to examine the overall experiences of work addiction, burnout, and work-family conflict within the NCAA DI sport organization. Methods: We used an online cross-sectional survey (Qualtrics, Provo, UT) composed of demographics, measurement tools for work addiction, burnout, and work-family conflict. Each of the scales have strong internal consistency as reported by Cronbach’s alpha scores. The study was distributed to certified athletic trainers (AT), coaches, and sport performance coaches (SPC) working full-time in their position at an NCAA DI institution. Results: There was no significant difference in reported scores on the BWAS between athletic trainers and coaches (U = 3952.00, p = .160), and no significant difference was found between sport performance coaches and athletic trainers (U = 5894.00, p = .879). A significant difference of burnout levels between athletic trainers and coaches was revealed (U = 3559.50, p = .017) andno significant difference discovered in the reported levels of burnout between athletic trainers and sport performance coaches (U = 5483.00, p = .313). There was no significant difference between athletic trainers and coaches for work-family conflict (U = 4483.00, p =.939), or sport performance sport performance coaches and athletic trainers (U= 5576.50, p = .416). Conclusions: Our results indicate that work addiction and work-family conflict are experienced similarly across the sport organization. Athletic trainers were found to experience higher levels of burnout compared to coaches, but similar levels to sport performance coaches. Application in Sport: Implementing policies that address work and family strain coaches, athletic trainers, and sport performance coaches can face working in sport is important. Although overall burnout was low, athletic trainers were at greater risk; thus addressing the factors causing them to have greater levels of burnout than other 2 stakeholder groups is important.
Key Words: stress, role strain, workplace dynamics, organization conflict
INTRODUCTION
Working within a collegiate sport organization places high demands on an individual, regardless of the role they play within that organization. The demands of the individual working in sport can include long working hours (+40 hours a week) that extend into nights and weekends (Laskowski & Ebben, 2016; Mazerolle et al., 2011; Scriber & Alderman, 2005; Singe et al., 2023b). Working hours are often accompanied by the need to be physically present, limiting flexibility and autonomy over work scheduling (Laskowski & Ebben, 2016; Mazerolle et al., 2011; Scriber & Alderman, 2005; Singe et al., 2023b). Organizational culture represents the underlying beliefs, values, and assumptions within an organization (Schein, 2010). The culture within sport organization has been described as one that is influenced by commercialization which has led to pressures to win at all costs due to the financial implications (Pope & Pope, 2014). Coaches, athletic trainers, and others working in sport organizations can feel the pressures associated with this culture, which can increase their stress, and influence their perceptions of work saliency, work-family conflict, and burnout.
Work addiction is a preoccupation with work (Andreassen, 2014; Robinson, 1999); and can be conceptualized as an individual who prioritizes their work over other responsibilities, which can lead to work-family conflict (Eason et al., 2022). Working in sport may have an influence on experiences of work addiction, particularly if the expectations around success and commitment hinge on prioritizing work. Coaches, athletic trainers, and sport performance coaches all contribute to the mission of the sport organization yet have very different and unique roles. Thus, the level of work addiction each of these individuals working in sport may demonstrate could vary, as well as the influence it may have on burnout and work-family. Research has examined experiences of burnout and work-family conflict among coaches and athletic trainers, independently, but not simultaneously (Graham & Smith, 2021; Singe et al., 2022). Organizational factors unique to sport are perhaps keys to understanding why burnout and work-family conflict occur, and better understanding if the role assumed in the sport organization can contribute.
LITERATURE REVIEW
Working Within the Sport Organization
The National Collegiate Athletic Association (NCAA) is the governing body that administers intercollegiate athletics in the United States. The NCAA is subdivided into three different divisions to create a fair playing field where teams are competing with schools at a similar level. Many factors separate the three subdivisions including media attention, airtime, and of course resources centered around finances and scholarship (Overview, n.d.). The NCAA Division I (DI) schools typically house the largest student bodies and possess the greatest number of athletic scholarship opportunities largely attributed to their large athletic budgets. Working within the NCAA DI setting comes with increased pressures and stress (Singe et al., 2022; Taylor et al., 2019) , particularly for coaches as they must produce through wins as well as retain students in their programs (Norris et al., 2017; Singe et al., 2022). The NCAA DI programs have large budgets which has the potential to play a significant role in the pressures and stress faced by those who are employed in the division.
At the NCAA Division II (DII) level student-athletes are offered scholarships to participate, but the number per sport is much less than the NCAA DI setting (Our Division II Story, n.d.).The expectations of those student-athletes participating at this level are somewhat less than the NCAA DI level, as time demands are slightly less (Our Division II Story, n.d.). The overall philosophy of the NCAA DII setting is one about balance, in which student-athletes are pushed to excel in their sport, but also in the classroom and campus community (Our Division II story, n.d.).
The NCAA Division III (DIII) level does not award scholarships generated from athletic participation (Our Three Divisions, n.d.), and has been described as a setting that encourages student first, and athlete second. Since there are no athletic scholarships offered, the budgets within these programs are much less than the other two divisions. The demands and expectations within the NCAA DIII setting are much less than and considered to be the most well-balanced collegiate experience (Our Division III Story, n.d.).
Working in the intercollegiate setting has been described as high-pressure, demanding, and one that can increase feelings of stress. Work addiction, burnout, and challenges with work-life balance have been found to occur for those working in intercollegiate sport, including coaches, athletic administrators, sports information specialists, and athletic trainers (Dixon & Bruening, 2005; Eason et al., 2022; Graham & Smith, 2022; Hatfield & Johnson, 2012). Causative factors linked to these challenges of working in sport include culture expectations within the workplace, time demands, inflexible work schedules, travel, and role incongruence. Sport is founded on the premise of teamwork and each member of the team has a critical role to support team success. Coaches, athletic trainers, and sport performance coaches are key members within the intercollegiate setting with unique roles supporting the student-athlete. Each has different roles, responsibilities, and expectations, and evidence that suggests those working in the intercollegiate setting are challenged to push beyond their work saliency leaving them vulnerable to work addiction, burnout, and work-family conflict.
Work Addiction and Sport
Workaholism is conceptualized as something that occurs when a person becomes completely engulfed in their work, investing their time and energy in their work life (McMillan et al., 2003). Those who display characteristics of a workaholic are prone to experiences of increased stress, burnout, and work-family conflict (Clark et al., 2016; Eason et al., 2022). One’s career has been associated with higher experiences of workaholism, such as sport as the culture is one of sacrifice, expectations to put in long work hours, and choosing work over one’s personal life (Dixon & Bruening, 2005; Graham & Dixon, 2014). Workaholics have a high involvement in their work (i.e. working long hours), have a hard time disengaging from work, and feel compelled or driven to work (McMillan et al., 2003). Working harder than perhaps their job requires workaholics will then start neglecting their lives outside of their jobs (Schaufeli et al., 2008).
Coaches, athletic trainers, and sport performance coaches all must work long hours; in fact, athletic trainers have reported working 60+ hour work weeks, extending into nights and weekends (Bruening & Dixon, 2007; Singe et al., 2023b; Snarr & Beasley, 2022). These long working hours reported by individuals working in sport have been attributed to burnout and work-family conflict (Eason et al., 2022), and recently have been suggested to be perhaps driven by work addiction ( Eason et al., 2022) or associated with it (Taylor et al., 2019). Work addiction can be explained as an individual factor that can be attributed to one’s experiences of work-family conflict or burnout, and job demands such as long hours can be an organizational construct that influences work-family conflict or burnout (Cayton & Valovich McLeod, 2020; Eason et al., 2022). What is unknown is the aspects such as the navigation of long working hours and personal attributes of a coach, athletic trainer, or sport performance coach necessary to be successful members working in intercollegiate athletics.
Work addiction has seven core components or symptoms: salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems. These symptoms have been developed into a scale, the Bergen Work Addiction Scale (BWAS) as outlined by Andreassen et al. (2014) salience (the activity dominates thinking and behavior), tolerance (increasing amounts of the activity are required to achieve initial effects), mood modification (the activity modifies/improves mood), relapse (tendency for reversion to earlier patterns of the activity after abstinence of control), withdrawal (occurrence of unpleasant feelings when the activity if discontinued or suddenly reduced), conflict (the activity comes into conflict with personal life, needs, and relationships), and problems (caused by being greatly engaged in the activity).
Experiences of Burnout in Athletics
Burnout is one of the many identified stressors of those working in athletics largely attributed to the long working hours, high workloads, and demands (Singe et al., 2023b). Burnout has been defined as the degree of physical and psychological fatigue experienced by a person that can be attributed to personal, work, or client-related stress (Cairns et al., 2023; Kristensen et al., 2005). Organizational factors have been identified in being the greatest influence over experiences of burnout (Barrett et al., 2016). Individual factors such as personality have also been observed to influence burnout as well. Burnout has been positively associated with role strain, neuroticism, and work-family conflict (Barrett et al., 2016; Cayton & Valovich McLeod, 2020). The demanding environment of athletics involves high emotional involvement, stress, responsibility, and time restraints (Cayton & Valovich McLeod, 2020; Mazerolle et al., 2008). Furthermore, the organization commonly inadequately compensates their employees while still expecting them to work long hours with inadequate numbers of staff, a lack of control over scheduling, and limited time off (Bruening & Dixon, 2007; Cayton & Valovich McLeod, 2020). The combination of these factors places those working within the sport organization at an increased risk of experiencing burnout. Positive relationships have been observed between burnout, work-family conflict, and intention to leave, while negative relationships have been observed with job and life satisfaction for those experiencing burnout (Mazerolle et al., 2008).
Due to the predispositions those working in sport face, burnout has been widely studied in sport. Those working in sports have been shown to experience moderate levels of burnout (Cairns et al., 2023; Singe et al., 2023a; Snarr & Beasley, 2022). However, there have been slight fluctuations in reported levels of burnout since the pandemic with levels of burnout lessening (Cairns et al., 2023). Sport professionals also tend to report high levels of personal and work-related burnout (Singe et al., 2023a; Taylor et al., 2019). Levels of personal burnout have a positive relationship with working hours and a negative relationship with hours of sleep (Singe et al., 2023a). Men and women report similar levels of burnout, suggesting that gender is not a significant predictor of experiences of burnout (Cairns et al., 2023). Incorporating coping strategies such as social support, continuing education, and self-care in addition to organizational support have all been associated with decreased levels of burnout in sport (Singe et al., 2023a; Snarr & Beasley, 2022).
Work-family Conflict
Work-family conflict defined as a form of inter-role conflict. The conflict occurs when the general demands of, time devoted to, and strain created by the job interfere with performing family-related responsibilities (Netemeyer et al., 1996). With the high demands concerning time and presence associated with working in sport, work-family conflict is a prominent area of interest within the sport organization. Work-family conflict has been framed as a complex construct that is explained by individual, organizational/structural, and socio-cultural factors (Dixon & Bruening, 2005). This integrated approach to the exploration of work-family conflict within sport is increasingly important as studies have shown the presence of work-family conflict across the sport organization regardless of factors such as job, age, sex, or family/marital status. (Bruening & Dixon, 2007; Mazerolle et al., 2008) .
While work-family conflict is experienced regardless of demographic factors, there have been increased levels of work-family conflict associated with marital and parental statuses. Those who are married with children are more likely to experience greater levels of work-family conflict (Singe et al., 2022). Setting has also been seen to play a role in the experiences of work-family conflict with those working in collegiate athletics reporting higher levels than those in the secondary setting (Mazerolle et al., 2015). Experiences of work-family conflict among those working in the sport organization have also been seen to be above average (Mazerolle et al., 2015). Previous research has also suggested that working within the NCAA DI setting increases experiences of work-family conflict (Singe et al., 2022). This is supported by findings that those working in the NCAA DI setting report greater levels of work-family conflict compared to those working in the NCAA DIII setting which could likely be attributed to the increased demand of the DI setting (Singe et al., 2022). Beyond intense professional demands, long working hours, lack of control over work schedules, and unbalanced workloads were all also related to increased conflict at the DI level (Mazerolle et al., 2011). Within the sport organization, four types of conflict have been found attributing to work-family conflict: time, energy, attention, and emotional spillover (Graham & Smith, 2022). However, several organizational and personal strategies help establish work-family balance. As an organization, the implementation of staffing policies and the creation of a supportive work environment help in reducing experiences of work-family conflict (Mazerolle et al., 2011). Individual management strategies can be broken down into personal factors and individual strategies on the professional level. Individual strategies involve the incorporation of teamwork, boundary setting, prioritization, and integration of family with work (Mazerolle et al., 2011). Personal factors focus greatly on the separation and work and life as well as the establishment of a support network (Mazerolle et al., 2011).
Purpose
Despite the growing body of research dedicated to the examination of these constructs within the sport organization, there remains a need for a better understanding of the varied experiences held by different stakeholders within the organization. Additionally, the exploration of work-addiction within the sport organization is novel. Therefore, the purpose of this study was to examine overall experiences of burnout, work addiction, and work-family conflict within sport organizations. Additionally, this study seeks to compare these experiences among the various stakeholders within the sport organization. Given this information, we hypothesized the following:
H1a– Coaches will report greater levels of work addiction compared to athletic trainers.
H2b– Athletic trainers will report greater levels of work addiction compared to sport performance coaches.
H2a– Athletic trainers will report greater levels of burnout compared to coaches.
H2b– Athletic trainers will report greater levels of burnout compared to sport performance coaches.
H3a– Athletic trainers will report greater levels of work-family conflict compared to coaches.
H3b– Athletic trainers will report greater levels of work-family conflict compared to sport performance coaches.
H4a– Work addiction and work-family conflict will have a positive relationship.
H4b– Work addition and burnout will have a positive relationship.
METHODS
Study design
The study design is a web-based cross-sectional study (Qualtrics, Provo, UT). Data was collected using a self-reported online questionnaire evaluating sleep, self-care, work-family conflict, work addiction, and burnout among NCAA Division I collegiate athletic trainers, coaches, and sport performance coaches. Approval for this study was obtained from the institutional review board (IRB) prior to data collection, which occurred over a four-week period in the Fall of 2023.
Procedures
Prior to survey distribution, we completed a face validity process; 3 athletic trainers took the survey for the purposes of the process. No changes were made to the survey based on the face validity feedback. Two email reminders were sent at the 1-week and 3-week marks, reminding participants to complete the survey.
Participants
The target population for the current study were NCAA Division I (DI) athletic trainers, sport performance coaches, and coaches. A list of all NCAA DI institutions was created using the NCSA college recruiting website (n = 363). From the list of institutions offering NCAA DI athletics programs, the individual athletics websites were accessed to create a list of emails for those individuals identified as an athletic trainer, sport performance coach, or a head or assistant coach. We were able to identify 13,412 email addresses across the 3 stakeholder groups. Our power analysis indicated a requirement of 258 respondents, which resulted in 86 participants from each stakeholder (group). Strata randomization was utilized since we did not have a complete list of all possible participants, thus phases of distribution were utilized and represented in Figure 1.
Figure 1. Recruitment and Data Screening
Sample
A total of 153 athletic trainers (51.5%), 59 coaches (19.9%), and 78 sports performance coaches (26.3%) completed this research study. Of the participants, 166 were female (55.9%), 121 male (40.7%), and 2 preferred not to answer (0.7%). The mean age of the participants in this study was 33 ± 9, with ages ranging from 22 – 70 years. Participants on average had 10 ± 9 years of experience, with an average of 5 ± 6 years working at their current institution. On average, participants worked 55 ± 16 hours per week. Complete demographic data is shown in Table 1.
Table 1. Participant Demographics
Demographic
Score
Gender, n (%)
Male
121 (40.7)
Female
166 (55.9)
Prefer not to answer
2 (0.7)
Highest level of education, n (%)
Bachelor’s Degree
48 (16.2)
Master’s Degree
237 (79.8)
Doctorate
5 (1.7)
Primary Role, n (%)
Head Coach
18 (6.1)
Associate Coach
9 (3.0)
Assistant Coach
34 (11.4)
Head Athletic Trainer
15 (5.1)
Associate Athletic Trainer
37 (12.5)
Staff/Assistant Athletic Trainer
99 (33.3)
Director, Sport Performance (Conditioning)
23 (7.7)
Strength and Conditioning Coach
51 (17.2)
Marital status, n (%)
Single
137 (46.1)
Cohabitating
28 (9.4)
Married
117 (39.4)
Separated
2 (0.7)
Divorced
3 (0.7)
Widowed
1 (0.3)
Engaged
3 (1.0)
Spouse employment status n (%)
Employed, full-time
210 (70.7)
Employed, part-time
18 (6.1)
Does not work/stay at home
25 (8.4)
Children, n (%)
0
202 (70.0)
Currently Pregnant
8 (2.7)
1
21 (7.1)
2
33 (11.1)
3+
27 (8.8)
Group Identity, n (%)
Single Female
112 (37.7)
Single Male
33 (11.1)
Married Female
43 (14.5)
Married Male
80 (26.9)
This does not apply to me
20 (6.7)
Instrumentation
The online survey was hosted in Qualtrics and included 36-items not including the demographic questions. Participants completed 13 demographic questions, prior to the 3 scales (i.e. Copenhagen Burnout Inventory (CBI), Bergen Work Addiction Scale, and Work-Family Conflict), which were not altered as they are valid instruments.
Burnout. Burnout was measured using the CBI as a tool that demonstrates reliability (α=.85-.87) and had been used previously to measure burnout among athletic trainers (α=.88) (Kristensen et al., 2005; Naugle et al., 2013). The scale included 3 subscales: personal (n=6-items), work-related (n=7-items), and client-based burnout (n=6-items). Participants use a 5-point Likert scale 0 (never/almost never/low degree), 25 (seldom/low degree), 50 (somewhat or sometimes), 75 (often/high degree), and 100 (always/high degree). The scale is summed for an overall burnout score, with a higher score indicating a higher level of burnout (0 is low, 100 is severe).
Work addiction. The Bergen Work Addiction Scale (BWAS) was used to measure work addiction (α=.78) among our sample. The scale has 7-items, each representing an aspect, or symptom of work addiction (salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems – Table 6). The 7-items are assessed using a 5-point Likert scale, 1 (never) to 5 (always). The responses are summed (range 7 to 35), and a score of 4 (often) or 5 (always) on 4 of 7 items indicates a high risk for work addiction.
Work-family conflict. Work-family conflict scale was assessed using the scale previously validated by Netemeyer et al. (α=.90). The 10-item scale evaluates the bi-directional nature of the construct; 5-items for work-family conflict (WFC) and 5-items for family-work conflict (FWC). Participants indicated their responses on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Sample questions include: “WFC®The demands of my job interfere with my home and family life,” and “FWC®The things I want to do at home do not get done because of the demands of my job.”
Data analyses
The data collected via Qualtrics was transferred to Excel by Microsoft Corporation. Following the completion of data collection, it underwent a filtration process to remove incomplete responses, defined as those failing to complete the required scales or the survey itself as per the scale validation. Subsequently, the filtered data was imported into SPSS, version, etc., for statistical analysis. Demographic information such as age, gender, and marital status were obtained through specific questions, and these demographic variables were subjected to descriptive and frequency analyses. The outcomes are presented as mean and standard deviation or frequency. Validated scales were assessed using means due to the non-parametric nature of the data analysis at hand, and Cohens d is reported for effect size.
RESULTS
Participant Demographics
Participants were 51.5% athletic trainers (n = 153), 19.9% were coaches (n=59), and 26.3% were sports performance coaches (n = 78). The average age of the participants was 33 ± 9 and they had been working in their respective roles for an average of 11± 9. They self-reported working 55 ± 17 hours per week (at the time of data collection).
Stakeholders and Work-Addiction
The mean score on the BWAS across all three stakeholder groups was 20.71 ± 4.57. Table 2 represents the mean scores on the BWAS, reported by each stakeholder group. Athletic trainers reported a score of 20.84 ± 4.51, whereas coaches reported a mean score of 20.05 ± 4.85. There was no significant difference in reported scores on the BWAS between athletic trainers and coaches (U = 3952.00, p = .160, d= 0.11). Additionally, sport performance coaches reported a mean score of 20.96 ± 4.50, and no significant difference was found between sport performance coaches and athletic trainers (U = 5894.00, p = .879, d= -0.010). Furthermore, across all three stakeholders, 80 were found to be workaholics while 210 (38%) were found not to be work addicted. Among athletic trainers, 45 of the 153 (29%) respondents were found to be workaholics. Of coaches, 12 of the 59 (20%) respondents were found to be workaholics. Among sport performance coaches, 23 of 78 (29%) respondents were found to be workaholics.
Stakeholders and Burnout
Across all three stakeholder groups, participants reported low levels (46.27 ± 16.04) on the CBI, additionally mean scores of 54.9 5 ± 17.24 on the personal-related subscale, 49.99 ± 18.87 on the work-related subscale, and 33.25 ± 18.67 on the client-related subscale. Table 2 represents the mean scores on the CBI and subscales, reported by each stakeholder group. Athletic trainers reported a mean score of 48.07 ± 16.42 on the CBI, while coaches reported a mean score of 41.99 ± 15.89 on the CBI. A significant difference of burnout levels between athletic trainers and coaches was revealed (U = 3559.50, p = .017, d= -0.16).Additionally, sport performance coaches reported a mean score of 45.97 ± 14.92. There was no significant difference discovered in the reported levels of burnout between athletic trainers and sport performance coaches (U = 5483.00, p = .313, d= -0.06).
Table 2: Comparison of Reported Scale Scores by Stakeholder
Stakeholder
CBI (Mean±SD)
BWAS (Mean±SD)
WFC (Mean±SD)
Athletic Trainers
48.07±16.42
20.84±4.51
37.66±9.26
Coaches
41.99±15.89
20.05±4.85
37.64±10.52
Sports Performance
45.97±14.92
20.96±4.50
37.86±9.49
Stakeholders and Work-Family Conflict
The mean score across all stakeholders on the WFC scale was 37.71 ± 9.56. Athletic trainers reported a mean of 37.66 ± 9.26, whereas coaches reported a mean of 37.64 ± 10.52. There was no significant difference between athletic trainers and coaches (U = 4483.00, p =.939, d= -0.05).Furthermore, sport performance coaches reported a mean of 37.86 ± 9.49, and no significant difference was found between sport performance coaches and athletic trainers (U= 5576.50, p = .416, d= -0.06).
Variable relationships
Correlation matrices revealed a moderate positive correlation (.507) between work addiction and work-family conflict. Work addiction and burnout also resulted in a moderate positive relationship (.573).
DISCUSSION
Inferences has been made that working in sport can lead to experiences of burnout and work-family conflict, as well as that to be a productive member of the team one must be addicted to their role. Our purpose was to explore the experiences of work-addiction, burnout, and work-family conflict among athletic trainers, coaches, and sport performance coaches. This aim was directed at better understanding around one’s role in the sport organization and experiences of these constructs. As predicted work addiction, regardless of stakeholder position, leads to increased levels of burnout and work-family conflict. Uniquely, athletic trainers and coaches experience higher levels of burnout than sport performance coaches.
Stakeholders and Work-Family Conflict
We did not find any significant differences among our samples and experiences of WFC. The total mean score on the WFC scale is comparative to other studies examining WFC among athletic trainers work in the sport industry (Mazerolle et al. 2011; Pitney et al. 2011; Singe et al. in press). Our sample was largely represented by those who do not have children (70%); which could explain why we did not find any differences among our sample regarding experiences of WFC. Time is often a large facilitator of WFC, despite our sample reporting 55 hours per week, many did not have children another facilitator of WFC (Mazerolle et al., 2008; Pitney et al., 2011; Singe et al., 2023a). Perhaps working long hours has less of an impact on the individual when additional family responsibilities are not present, and one can focus on work and personal interests.
Stakeholders and Burnout
Overall, this sample of individuals working in the sport organization are experiencing low levels of burnout. Low levels of burnout does not imply that our sample is not experiencing it; however quantifiably it is lower. The literature over the last 5 years has suggested that coaches and athletic trainers are experiencing higher levels of burnout (Goodger et al., 2007; Singe et al., 2024; Singe et al., 2023a). We found that athletic trainers reported higher levels of burnout compared to coaches, but similar levels of burnout to sport performance coaches. Moderate levels of burnout have recently been reported among athletic trainers (Singe et al., 2023a); however, fluctuations in experiences have been observed over the past 3 years with levels varying between moderate and low (Cairns et al., 2023; Oglesby et al., 2020; Singe et al., 2023a). Sport performance coaches have yet to be identified within the literature regarding burnout; our sample reported similar levels of burnout as athletic trainers. Similar to athletic trainers, sport performance coaches have high demands placed upon them, and they are invested in the success of their athletes as well as log long hours in the workplace (Bentzen et al., 2016; Olusoga et al., 2019).
Stakeholders and Work-Addiction
Our overall sample is not classified as a workaholic; however, both athletic trainers and sport performance coaches demonstrate a larger sample (29%) of those who would be classified as such. Workaholics may work long hours but that is by choice and perhaps not as a necessity (Andersen et al., 2023). Although our sample reports working excessive hours (55), they do not self-identify as workaholics. Moreover, we did not find significant differences between stakeholders. These findings suggest that work addiction is likely an individualized factor, and not necessarily an outcome of working in sport organization. As detailed in the work-family conflict framework of Bruening and Dixon (2005, 2007), there are individual, organizational, and sociocultural outcomes of experiences of work-family conflict.
Variable relationships
Positive relationships were found between work addiction and both burnout and work-family conflict. The correlations found between the experiences of these constructs are consistent with those observed in previous studies examining these constructs in athletic trainers (Eason et al., 2022). These results make it apparent that experiences of work addiction, work-family conflict, and burnout occur at the same time. Previously stated, work-addiction can be attributed to experiences of work-family conflict and burnout. In this case all stakeholders are experiencing all three constructs.
We predicted there to be positive relationships between work addiction, burnout, and work-family conflict. Work addiction is yet another construct that is experienced by those working in the sport organization. This study adds to the literature that there are no differences in work-family conflict and burnout across athletic trainers, coaches, and SPCs. Yet, there are notable differences when it comes to burnout. Coaches and SPCs are experiencing work-family conflict, and work-addiction similarly to athletic trainers. This speaks to the sport organization as a whole; all employees are encountering these constructs. We suggest the sport organization investigate and assess reasons employees are work-addicted and have work-family conflict, to improve job and life satisfaction.
ATs experienced higher levels of burnout compared to coaches, and SPCs. There are many reasons this may be, the number of athletes per employee, responsibilities, and medical roles. However, in this sample athletic trainers reported low levels of burnout, though higher than coaches and SPCs, not quite as high as levels in recent literature (Barrett et al., 2016).
Consideration for Future Research and Study Limitations
The findings of this study expand upon the growing body of literature examining the constructs of work-addiction, burnout, and work-family conflict within the sport organization, yet limitations on these findings remain. Our study received 297 usable responses, which is a lower response rate than anticipated. Due to these factors, we recognize that these findings may not represent the experiences of all of those working within the sport organization. Our database was established using publicly available information therefore a complete list of all athletic trainers, coaches, and sports performance coaches at the DI level was unable to be obtained. Therefore, the results of this study may not represent the experiences of the entirety of NCAA DI athletic trainers, coaches, and SPCs. Our study also only examined those working within the NCAA DI setting; thus, those working in the DII, DIII, NAIA, or other collegiate levels may not have similar experiences with these constructs. Furthermore, those working in secondary schools or other settings also may not identify with the findings of this study.
Further research should include the investigation of work-addiction, burnout, and work-family conflict at all levels of collegiate athletics as well as those in secondary schools and alternate settings. Currently, the literature has examined these constructs within the sport organization solely focused on the experiences of athletic trainers, creating a need for future research among coaches and sports performance coaches on these constructs. Additionally, the study of work addiction within the sport organization is a novel issue, so further research is necessary to gain a better understanding of work addiction within athletics.
CONCLUSION
This study sought to further our knowledge of the experiences of athletic trainers, coaches, and sport performance coaches in the DI setting, regarding work-addiction, burnout, and work-family conflict. Experiences were nearly universal across the sport organization except for athletic trainers experiencing greater levels of burnout compared to coaches. Positive relationships were also observed between levels of work addiction and both burnout and work-family conflict. The findings of this study suggest that these constructs are prominent issues across the sport organization. Given the prevalence across the sport organization, increased implication of both personal and organizational strategies may be necessary as a means of mitigating the impact of these issues (Cairns et al., 2023; Singe et al., 2022). This study serves as a preliminary exploration into the variance of experiences of work addiction, burnout, and work-family conflict across the sport organization stakeholders.
APPLICATIONS IN SPORT
Athletic trainers reported significantly different levels of burnout compared to coaches and sport performance coaches; thus we believe that understanding the specific role stressors for the athletic trainer can help address potential programs to prevent burnout. For example, wellness programs or a workload redistribution may be warranted for athletic trainers. We did not find any differences among work-family conflict among any of the grups, which suggests more broad based policies that are family-friendly may help athletic trainers, coaches, and sport performance coaches (family-leave, time-off policies). Work addiction was a risk factor for both burnout and work-family conflict among our stakeholders, thus individuals and supervisors should be aware of the signs of burnout, but also encourage stress and boundary management, as well as healthy work habits to prevent issues around burnout and conflicts between work and home.
REFERENCES
Andersen, F. B., Djugum, M. E. T., Sjåstad, V. S., & Pallesen, S. (2023). The prevalence of workaholism: A systematic review and meta-analysis. Frontiers in Psychology, 14, 1252373. https://doi.org/10.3389/fpsyg.2023.1252373
Andreassen, C. S. (2014). Workaholism: An overview and current status of the research. Journal of Behavioral Addictions, 3(1), 1–11. https://doi.org/10.1556/JBA.2.2013.017
Barrett, J., Eason, C. M., Lazar, R., & Mazerolle, S. M. (2016). Personality Traits and Burnout Among Athletic Trainers Employed in the Collegiate Setting. Journal of Athletic Training, 51(6), 454–459. https://doi.org/10.4085/1062-6050-51.7.08
Bentzen, M., Lemyre, P.-N., & Kenttä, G. (2016). Development of exhaustion for high-performance coaches in association with workload and motivation: A person-centered approach. Psychology of Sport and Exercise, 22, 10–19. https://doi.org/10.1016/j.psychsport.2015.06.004
Bruening, J. E., & Dixon, M. A. (2007). Work–Family Conflict in Coaching II: Managing Role Conflict. Journal of Sport Management, 21(4), 471–496. https://doi.org/10.1123/jsm.21.4.471
Cairns, A. H., Singe, S. M., & Eason, C. M. (2023). Perceived Stress as an Indicator of Work–Family Conflict and Burnout Among Secondary School Athletic Trainers. International Journal of Athletic Therapy and Training, 28(4), 215–220. https://doi.org/10.1123/ijatt.2021-0112
Cayton, S. J., & Valovich McLeod, T. C. (2020). Characteristics of Burnout Among Collegiate and Secondary School Athletic Trainers: A Systematic Review. Athletic Training & Sports Health Care, 12(5), 227–234. https://doi.org/10.3928/19425864-20190529-01
Clark, M. A., Michel, J. S., Zhdanova, L., Pui, S. Y., & Baltes, B. B. (2016). All Work and No Play? A Meta-Analytic Examination of the Correlates and Outcomes of Workaholism. Journal of Management, 42(7), 1836–1873. https://doi.org/10.1177/0149206314522301
Dixon, M. A., & Bruening, J. E. (2005). Perspectives on Work-Family Conflict in Sport: An Integrated Approach. Sport Management Review, 8(3), 227–253. https://doi.org/10.1016/S1441-3523(05)70040-1
Eason, C. M., Gilgallon, T. J., & Singe, S. M. (2022). Work-Addiction Risk in Athletic Trainers and Its Relationship to Work-Family Conflict and Burnout. Journal of Athletic Training, 57(3), 225–233. https://doi.org/10.4085/JAT0348-20
Goodger, K., Gorely, T., Lavallee, D., & Harwood, C. (2007). Burnout in Sport: A Systematic Review. The Sport Psychologist, 21(2), 127–151. https://doi.org/10.1123/tsp.21.2.127
Graham, J. A., & Dixon, M. A. (2014). Coaching Fathers in Conflict: A Review of the Tensions Surrounding the Work-Family Interface. Journal of Sport Management, 28(4), 447–456. https://doi.org/10.1123/jsm.2013-0241
Graham, J. A., & Smith, A. B. (2021). Work and Life in the Sport Industry: A Review of Work-Life Interface Experiences Among Athletic Employees. Journal of Athletic Training, 57(3), 210–224. https://doi.org/10.4085/1062-6050-0633.20
Graham, J. A., & Smith, A. B. (2022). Work and Life in the Sport Industry: A Review of Work-Life Interface Experiences Among Athletic Employees. Journal of Athletic Training, 57(3), 210–224. https://doi.org/10.4085/1062-6050-0633.20
Hatfield, L. M., & Johnson, J. T. (2012, April 9). Work-Family Conflict and Related Theories in NCAA Division II Sports Information Professionals. The Sport Journal. https://thesportjournal.org/article/work-family-conflict-and-related-theories-in-ncaa-division-ii-sports-information-professionals/
Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The Copenhagen Burnout Inventory: A new tool for the assessment of burnout. Work & Stress, 19(3), 192–207. https://doi.org/10.1080/02678370500297720
Laskowski, K. D., & Ebben, W. P. (2016). Profile of Women Collegiate Strength and Conditioning Coaches. Journal of Strength and Conditioning Research, 30(12), 3481–3493. https://doi.org/10.1519/JSC.0000000000001471
Mazerolle, S. M., Bruening, J. E., & Casa, D. J. (2008). Work-Family Conflict, Part I: Antecedents of Work-Family Conflict in National Collegiate Athletic Association Division I-A Certified Athletic Trainers. Journal of Athletic Training, 43(5), 505–512. https://doi.org/10.4085/1062-6050-43.5.505
Mazerolle, S. M., Bruening, J. E., Casa, D. J., & Burton, L. J. (2008). Work-Family Conflict, Part II: Job and Life Satisfaction in National Collegiate Athletic Association Division I-A Certified Athletic Trainers. Journal of Athletic Training, 43(5), 513–522. https://doi.org/10.4085/1062-6050-43.5.513
Mazerolle, S. M., Eason, C. M., Pitney, W. A., & Mueller, M. N. (2015). Sex and Employment-Setting Differences in Work-Family Conflict in Athletic Training. Journal of Athletic Training, 50(9), 958–963. https://doi.org/10.4085/1052-6050-50.2.14
Mazerolle, S. M., Pitney, W. A., Casa, D. J., & Pagnotta, K. D. (2011). Assessing Strategies to Manage Work and Life Balance of Athletic Trainers Working in the National Collegiate Athletic Association Division I Setting. Journal of Athletic Training, 46(2), 194–205. https://doi.org/10.4085/1062-6050-46.2.194
McMillan, L. H. W., O’Driscoll, M. P., & Burke, R. J. (2003). Workaholism: A Review of Theory, Research, and Future Directions. In C. L. Cooper & I. T. Robertson (Eds.), International Review of Industrial and Organizational Psychology 2003 (1st ed., pp. 167–189). Wiley. https://doi.org/10.1002/0470013346.ch5
Naugle, K. E., Behar-Horenstein, L. S., Dodd, V. J., Tillman, M. D., & Borsa, P. A. (2013). Perceptions of Wellness and Burnout Among Certified Athletic Trainers: Sex Differences. Journal of Athletic Training, 48(3), 424–430. https://doi.org/10.4085/1062-6050-48.2.07
Netemeyer, R. G., Boles, J. S., & McMurrian, R. (1996). Development and validation of work–family conflict and family–work conflict scales. Journal of Applied Psychology, 81(4), 400–410. https://doi.org/10.1037/0021-9010.81.4.400
Norris, L. A., Didymus, F. F., & Kaiseler, M. (2017). Stressors, coping, and well-being among sports coaches: A systematic review. Psychology of Sport and Exercise, 33, 93–112. https://doi.org/10.1016/j.psychsport.2017.08.005
Oglesby, L. W., Gallucci, A. R., & Wynveen, C. J. (2020). Athletic Trainer Burnout: A Systematic Review of the Literature. Journal of Athletic Training, 55(4), 416–430. https://doi.org/10.4085/1062-6050-43-19
Olusoga, P., Bentzen, M., & Kentta, G. (2019). Coach Burnout: A Scoping Review. International Sport Coaching Journal, 6(1), 42–62. https://doi.org/10.1123/iscj.2017-0094
Our Division II Story. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2021/2/16/our-division-ii-story.aspx
Our Division III Story. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2021/2/16/our-division-iii-story.aspx
Our Three Divisions. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2016/1/7/about-resources-media-center-ncaa-101-our-three-divisions.aspx
Overview. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2021/2/16/overview.aspx
Pitney, W. A., Mazerolle, S. M., & Pagnotta, K. D. (2011). Work–Family Conflict Among Athletic Trainers in the Secondary School Setting. Journal of Athletic Training, 46(2), 185–193. https://doi.org/10.4085/1062-6050-46.2.185
Pope, D. G., & Pope, J. C. (2014). Understanding College Application Decisions: Why College Sports Success Matters. Journal of Sports Economics, 15(2), 107–131. https://doi.org/10.1177/1527002512445569
Robinson, B. E. (1999). The Work Addiction Risk Test: Development of a Tentative Measure of Workaholism. Perceptual and Motor Skills, 88(1), 199–210. https://doi.org/10.2466/pms.1999.88.1.199
Schaufeli, W. B., Taris, T. W., & Van Rhenen, W. (2008). Workaholism, Burnout, and Work Engagement: Three of a Kind or Three Different Kinds of Employee Well‐being? Applied Psychology, 57(2), 173–203. https://doi.org/10.1111/j.1464-0597.2007.00285.x
Schein, E. H. (2010). Organizational Culture and Leadership, 4th Edition (4th ed.). Jossey-Bass.
Scriber, K. C., & Alderman, M. H. (2005). The Challenge of Balancing Our Professional and Personal Lives. Athletic Therapy Today, 10(6), 14–17. https://doi.org/10.1123/att.10.6.14
Singe, S., Cairns, A., & Eason, C. (2024). Examining Burnout Among Collegiate Athletic Trainers: The Relationship with Age, Gender, and Years of Experience. Internet Journal of Allied Health Sciences and Practice, 22(3). https://nsuworks.nova.edu/ijahsp/vol22/iss3/8
Singe, S. M., Cairns, A., Eason, C. M., & Marion, P. E. (2022). Work-Family Conflict and Guilt among Athletic Trainers: Influences of Family Role Performance, Years of Experience and National Collegiate Athletic Association Level. Journal of Issues in Intercollegiate Athletics.
Singe, S. M., Mydosh, C. G., Cairns, A., & Eason, C. M. (2023a). Working Hours, Sleep, and Burnout Among Athletic Trainers Employed in College Athletics: A Cross-Sectional Study. The Internet Journal of Allied Health Sciences and Practice. https://doi.org/10.46743/1540-580x/2023.2430
Singe, S. M., Rodriguez, M., Cairns, A., Eason, C. M., & Rynkiewicz, K. (2023b). Work-Family Conflict and Family Role Performance Among Collegiate Athletic Trainers. Journal of Athletic Training, 58(4), 381–386. https://doi.org/10.4085/227.22
Snarr, R. L., & Beasley, V. L. (2022). Personal, Work-, and Client-Related Burnout Within Strength and Conditioning Coaches and Personal Trainers. Journal of Strength and Conditioning Research, 36(2), e31–e40. https://doi.org/10.1519/JSC.0000000000003956
Taylor, E. A., Huml, M. R., & Dixon, M. A. (2019). Workaholism in Sport: A Mediated Model of Work–Family Conflict and Burnout. Journal of Sport Management, 33(4), 249–260. https://doi.org/10.1123/jsm.2018-0248
Jeffrey Fountain, Ph.D., and Peter Finley, Ph.D., are Professors of Sport Management at the H. Wayne Huizenga College of Business and Entrepreneurship at Nova Southeastern University. Their research interests focus on issues in college athletics.
Thomas Wuerzer, Ph.D., is Professor in the Department of Public Administration & Real Estate Development at Nova Southeastern University. His research focus is on Geographic Information Systems.
ABSTRACT
Recruiting college football players to come play for a National Collegiate Athletic Association (NCAA) Power-5 school is highly competitive, with each school inviting recruits nationwide on official campus visits. By estimating the carbon emissions generated, this study examined the environmental impact of official recruiting visits (n = 7,045) to Power-5 schools from 2013 to 2020. Using Geographic Information Systems (GIS) to geocode recruits’ hometowns and calculate travel distances, a Recruit Visit Carbon Footprint (RVCF) was calculated to approximate the CO2eq emissions for each visit. The analysis focused on the 23 Power-5 schools with over 250 reported official visits. The findings revealed substantial variability in RVCF among schools, with 15 of the 23 schools increasing their carbon footprint in the latter years of the study. Still, the higher-spending athletic departments tended to have lower RVCFs. The findings provide valuable insights into the environmental impact of recruiting activities and highlight the importance of addressing this overlooked aspect of college sports’ carbon emissions.
KEYWORDS:Carbon Footprint, Power-5, Recruiting, Official Visit, College Football
INTRODUCTION
As societal awareness of the environmental impact of both mega sporting events and routine contests (regular season games) has increased, many sports organizations, teams, and sponsors have come to understand the need to assess the carbon footprint they create (10). As noted by Dolf et al. (13), several researchers have stressed that sports events are worth investigating to leverage broader change because of the high-profile nature of such events, because they are capable of creating real and meaningful action (11, 19). Several athletic departments have promoted their initiatives throughout the last decade and publicized their efforts to reduce and offset their environmental impact by tracking and reducing carbon dioxide-equivalent emissions (CO2eq). The typical path toward claiming to be carbon neutral for college athletic departments is to assess the environmental impact of the day-to-day operations, home game operations, and off-campus travel for official tournaments and games. However, it is important to recognize that the carbon footprint begins long before sporting events are played; for college sports, this goes back to the initial recruitment phase of the athletes, which typically requires them to travel as part of the recruiting process.
In 2020, the Power-5 conferences included the Atlantic Coast Conference (ACC), Big 12 Conference, Big Ten Conference, Pacific 12 Conference, and the Southeastern Conference (SEC). Over the years, the number of Power-5 schools increasing their investment in recruiting athletes has grown, with 38 of the 52 public Power-5 schools reporting a significant growth in overall athletic department recruiting expenditures (37). One extreme example was the University of Georgia’s athletic department, which increased its overall recruiting budget from $308,000 in 2005 to $4.5 million by 2022 (23).
Recruiting
Each recruit is permitted one official visit per school, extendable only if there is a change in the coaching staff, with each visit lasting no more than 48 hours or one weekend (29). Visits are classified by the funding source; when the host school covers expenses such as transportation, lodging, meals, and entertainment for the recruit and their parents or guardians, it is deemed an official visit (29). Historically, recruits were limited to five official visits; however, this cap was removed in 2023, allowing unlimited visits while maintaining the “only one visit per school” rule (30).
College football recruiting visits often feature expensive, extravagant events designed to attract recruits (12, 24, 36). The financial commitment to a recruiting weekend at Clemson University in the fall of 2019, during which the Tigers brought eleven prospects to campus (they would eventually sign ten of them), ended with a total bill of $85,000 (32). While the NCAA prohibits media from attending recruiting events or interacting with prospects while on campus, the expenditures from that weekend provided insight into the itinerary, which included travel by professional car service to and from local airports, flights to Greenville-Spartanburg, and transportation to the campus, about 40 miles away. In addition, two charter buses were used to transport prospects and their families to the finest restaurants in the area, including a steakhouse about 45 minutes from campus (32). Another example was the University of Texas spending over $280,000 during a single weekend in June 2022 to host nine recruits, including highly touted quarterback Arch Manning (20).
Carbon Footprinting
The concept and measurement of an “ecological footprint” was introduced by Wackernagel and Rees (34) and originally quantified the land and sea area necessary to support human populations. Subsequent adaptations of this concept have focused on the “carbon footprint,” which estimates the land required to sequester CO2 emissions attributable to human activities. This notion has evolved into broader assessments such as the “life cycle impact,” which evaluates the environmental impact of products and services throughout their life cycles (31).
Research by Čuček et al. (9) and Pandey et al. (31) expanded the scope of assessment to include calculating sustainability metrics and measuring energy, water, and ecological impacts. These studies defined a carbon footprint as “the quantity of Greenhouse Gases (GHGs), expressed in terms of CO2 equivalents, emitted by an individual, organization, process, product, or event within a specified boundary” (31) and as “a quantitative measurement describing the appropriation of natural resources by humans,” (9). This study adopted these definitions to evaluate the carbon footprint of prospective college football players while making their official recruiting visits to college campuses.
Attempts to measure carbon footprint related to sports have historically focused on major events and the travel of sports teams. Examples include the findings that approximately 560 tons of CO2eq was created during the 2004 Football Association (FA) Cup Final in the United Kingdom (one soccer game) (4), 1,260 tons of CO2eq for the 2004 Wales Rally (an Autosport’s event over four days) (5), and 144,120 tons of CO2eq for the stages of the Tour de France held in the United Kingdom in 2007 (the Prologue and Stage One) (6). Most studies focused solely on the carbon footprint of spectators, though a limited number of studies examined participants, such as teams and staff members.
The environmental impact of all college activities, including collegiate sports has garnered significant attention (28). However, there appears to be no available research that has explicitly focused on the environmental impact (carbon footprint) produced throughout the college football recruiting season. Therefore, the researchers sought to explore and determine the approximate carbon emissions produced during official college football recruiting visits from Power-5 schools. This study utilized the reported official recruiting visits between 2013 and 2020. Using Geographic Information Systems (GIS) to conduct spatial analysis of multimodal travel, including car and plane trips, the researchers were able to calculate the approximate travel distances and corresponding carbon footprint of each recruit.
The Recruit Visit Carbon Footprint (RVCF) was created as a proxy measure utilizing prior carbon footprinting research of sport tourism. This approach enabled a systematic exploration of three primary research questions.
RQ1: Which Power-5 schools generated the largest RVCF between 2013 and 2020?
RQ2: Did RVCF totals increase or decrease over time?
RQ3: Was there a correlation between each school’s financial, recruiting, and performance variables and their RVCF?
METHODS
Data Collection
Data on official recruiting visits, published by 247sports.com, was collected for the years 2013 to 2020. Previous research has utilized data from 247sports.com, recognizing it as a well-established source of college football recruiting information (21, 27, 35). The dataset included dates of official school visits and recruits’ hometowns. Prior research also utilized GIS to geocode locations such as athletes’ hometowns or high school locations for analysis (1, 26, 38). GIS geocoding takes a specific location, such as addresses or towns, and references it as a mapped location. Therefore, this study geocoded each football recruit’s hometown, the location of each college visited, and the closest major airport to calculate the approximate travel distances for spatial analysis.
The study utilized ESRI ArcPRO 3.5 (Esri, Redlands, CA, USA) software with the Business Analyst extension to geocode the dataset. To focus on the highest-producing RVCF programs and to make the data set more manageable, a minimum threshold of 250 visits was established. Of the 64 Power-5 schools, 23 (35.9%) met the 250-visitor threshold, totalling 7,045 reported official visits. The travel routes for each visit were then calculated using GIS to determine the most efficient mode of travel. Driving directly to the school was the most efficient mode for 1,636 visits. Typically, these distances were around 200 miles or less to the campus. For recruits living over 200 miles from the visiting campus, if their distance from their home to an airport necessitated a long drive followed by a flight, driving was deemed more efficient due to the extensive travel time involved in flying to the campus. For the remaining 5,409 visits, air travel was deemed the most efficient mode. For these visits, three travel distances were calculated: 1) the drive from the recruits’ hometown to the nearest major airport, 2) the flight miles from that airport to the nearest major airport to the campus they visited, and 3) the drive from that airport to the campus. These distances were doubled to account for the return trip and integrated into a travel matrix to approximate CO2eq emissions from transportation.
Additionally, financial data for athletic departments (i.e., Football Revenue, Football Recruiting was sourced from the Knight-Newhouse College Athletics database (25), team performance was sourced from ESPN.com (16). The descriptions and summary statistics for these variables are provided in Table 1. Utilizing these variables allowed for additional analysis to explore potential correlations between an athletic department’s RVCF and financial data, performance data, and recruiting data.
Table 1Descriptive Analysis of Variables: Mean and Standard Deviation
Variable
Description
Mean
SD
FB_TotalRev
Total Revenue from Football
$66,518,526
$25,205,244
Mens_TotalRev
Total Revenue from all Men’s Sports (including Football)
$84,428,967
$25,300,581
FB_MensRev%
Football’s Revenue as a Percentage of all Men’s Sports Revenues
77.40%
11.17%
Dept_TotalRev
Total Revenue from the entire Athletic Department
$125,143,966
$31,108,327
FB_DeptRev%
Football’s Revenue as a Percentage of the entire Athletic Department Revenues
52.50%
13.10%
Mens_RecruitExp
Total Recruiting Expenses from all Men’s Sports (including Football)
$1,391,362
$704,861
Dept_RecruitExp
Total Recruiting Expenses from the entire Athletic Department
$1,878,962
$855,080
FB_OpsExp
Total Operation Expenses for Football
$5,683,499
$2,558,649
Mens_OpsExp
Total Operation Expenses for all Men’s Sports (including Football)
$8,800,193
$4,035,500
Dept_OpsExp
Total Operating Expenses for the entire Athletic Department
$12,787,529
$5,068,156
FB_TotalExp
Total Expenses for the entire Football Program
$33,846,192
$11,218,516
Mens_TotalExp
Total Expenses for all Men’s Sports Programs (including Football)
$53,035,310
$13,927,935
FB_MensExp%
Football Expenses as a Percentage of all Men’s Sports Expenses
63.18%
7.58%
Dept_TotalExp
Total Expenses for the entire Athletic Department
$116,141,712
$27,071,219
FB_DeptExp%
Football Expenses as a Percentage of the entire Athletic Department Expenses
63.18%
7.58%
Win_Percentage
Football teams Win Percentage
62.43%
19.97%
Recruit Visit Carbon Footprint
Calculating CO2eq emissions from travel can vary depending on the methods and formulas used. In this study, the researchers approximated the RVCF utilizing established methods from prior sport tourism carbon footprint research. The framework by Franchetti and Apul (18) required three boundaries. 1) Temporal Boundary, which refers to the period used for analysis, which, in this study, included Power-5 official recruiting visits from 2013 to 2020. 2) Organizational Boundary, which defines the measured entity, ensuring that only emissions produced from the designated entity are included. Here, it refers to the travel for a single recruit’s official visit to a Power-5 school. 3) Operational Boundary, which is based on the scope of emissions, including direct emissions, indirect emissions, and indirect products. The operational boundary was set at direct emissions only for this study.
In order to operationalize the boundaries, calculations were used to approximate each recruit’s carbon footprint as they travelled from their hometown to their selected school for an official recruiting visit. Cooper’s (2020) approximation of the University of Tennessee’s football gameday tourism carbon footprint was used as a framework for this study. The method for approximating the carbon footprint of sport tourism was applied to the dataset to calculate the approximated total amount of CO2eq emissions produced by each recruiting visit. The total carbon footprint of each visit was calculated by considering direct emissions from transportation (car and flight miles), food consumption per day, waste per day, and hotel stays (8, 14). The EPA formula for the average gasoline-powered passenger vehicle (3.91 × 10^-4 metric tons CO2eq per mile) was applied and converted into kilograms (15). For air travel emissions, the formula (air miles × 0.24 × 1.891) combined the Blue Sky Model formula and the Carbon Fund’s radiative forcing factor (1.891) to provide a total CO2eq per person per pound figure, which was then converted to kilograms (2, 3). Hotel accommodation emissions were calculated using Filimonau’s (17) factor of 11.65 kg CO2eq per night, multiplied by two to account for the typical two-night stay during a recruiting visit. For food and waste emissions, factors from Cooper’s (7) study were used: 7.4 kg CO2eq per person per day for food and 1.1 kg CO2eq per day for waste, multiplied by two for the typical 48-hour visit. Utilizing these formulas allowed the researchers to approximate the RVCF for each reported recruiting visit.
RESULTSAND DISCUSSION
Over the eight years, the top 23 highly-visited schools collectively emitted 2.3 million kg of CO2eq, averaging 328 kg CO2eq per recruiting visit. For context, the global average annual CO2eq emission per person is approximately 4.7 tons (4,263 kg), according to the IEA (22). Thus, the CO2eq for a single 48-hour recruiting visit represents about 7.7% of the average person’s global annual CO2eq footprint.
Table 2 provides a breakdown of RVCF variables along with the means and totals for all 23 schools to address RQ1, “Which Power-5 schools generated the largest RVCF between 2013 and 2020?” Washington State (n = 276) reported the highest total RVCF at 171,489.84 kg CO2eq, and the highest mean RVCF at 621.34 kg CO2eq. In contrast, the University of South Carolina (n = 263) had the smallest carbon footprint, with a total RVCF of 55,621.71 kg CO2eq and an average RVCF per visit of 211.49 kg CO2eq. All official visits to Washington State and South Carolina are depicted using GIS maps in Figure 1, which shows Washington State attracted several recruits from the Midwest, Florida, and Texas. At the same time, South Carolina only invited a few recruits who required a long-distance flight to visit Columbia, South Carolina.
Table 2RVCF by school for all reported official visits from 2013 to 2020
School
n
% Drove (No Flight)
Car (No Flight)
Car (To/From Airport)
Flight
Hotel
Food
Waste
M
SD
Total
Washington State
276
2.17%
564.48
21,782.22
137,796.79
6,405.95
4,084.79
855.60
621.34
362.98
171,489.84
Oregon
281
3.20%
667.81
7,322.00
150,332.79
6,522.00
4,158.79
871.10
604.54
323.99
169,874.50
Nebraska
373
5.90%
1,437.03
9,203.29
131,283.86
8,657.32
5,520.39
1,156.30
421.60
171.96
157,258.20
Alabama
378
17.99%
6,138.01
21,504.74
81,842.82
8,773.37
5,594.39
1,171.80
330.34
219.30
125,025.12
Minnesota
328
15.55%
1,543.63
9,279.44
94,019.65
7,612.87
4,854.39
1,016.80
360.75
190.85
118,326.78
Louisville
343
7.87%
1,814.23
9,356.07
92,166.14
7,961.02
5,076.39
1,063.30
342.38
189.87
117,437.16
Oklahoma
315
21.27%
7,627.73
9,318.53
85,483.06
7,311.14
4,661.99
976.50
364.79
187.59
115,378.96
Tennessee
356
14.89%
5,866.34
11,129.21
72,691.17
8,262.75
5,268.79
1,103.60
293.04
213.71
104,321.87
Texas A&M
327
46.18%
15,370.28
18,162.76
56,491.35
7,589.66
4,839.59
1,013.70
314.50
223.19
103,467.35
Washington
251
22.31%
2,329.33
5,626.14
81,958.42
5,825.71
3,714.80
778.10
399.33
250.68
100,232.49
Ohio State
301
26.58%
7,676.34
5,596.14
69,715.53
6,986.20
4,454.79
933.10
316.82
223.49
95,362.10
Arkansas
325
15.38%
4,563.07
13,222.06
64,152.59
7,543.24
4,809.99
1,007.50
293.23
166.97
95,298.46
Indiana
273
15.38%
3,307.90
13,293.64
62,085.00
6,336.32
4,040.39
846.30
329.34
169.13
89,909.57
Florida
333
28.53%
8,658.69
6,782.76
56,932.06
7,728.92
4,928.39
1,032.30
258.45
179.76
86,063.13
Miami
301
39.53%
3,920.90
4,735.86
63,566.68
6,986.20
4,454.79
933.10
280.01
263.30
84,597.54
Florida State
317
14.20%
3,929.02
6,600.11
59,945.34
7,357.56
4,691.59
982.70
262.44
182.30
83,506.32
Auburn
313
35.14%
9,914.55
14,971.12
42,452.12
7,264.72
4,632.39
970.30
256.25
161.71
80,205.21
Georgia
270
33.70%
6,709.32
15,279.31
41,800.05
6,266.69
3,995.99
837.00
276.78
196.70
74,888.37
Penn State
254
29.53%
9,210.73
16,663.59
38,471.37
5,895.34
3,759.20
787.40
289.49
192.65
74,787.61
Mississippi State
293
56.31%
17,224.20
17,413.13
26,745.76
6,800.52
4,336.39
908.30
250.61
176.90
73,428.32
Kentucky
274
19.71%
4,331.99
7,066.42
49,895.23
6,359.53
4,055.19
849.40
264.81
146.57
72,557.76
LSU
300
38.00%
7,465.21
5,491.13
41,783.31
6,962.99
4,439.99
930.00
222.53
146.73
67,072.63
South Carolina
263
32.70%
7,526.48
5,361.76
31,921.56
6,104.23
3,892.40
815.30
211.49
128.80
55,621.71
Total
7,045
23.57%
137,797.27
255,161.41
1,633,532.66
163,514.32
104,265.87
21,839.48
328.91
203.01
2,316,111.00
Note: Car, Flight, Hotel, Food, Waste, Mean, Standard Deviation, and Total are in kg CO2eq
To explore the second research question, “Did RVCF totals increase or decrease over time?” the dataset needed to be segmented. During this time period college football programs did not get an entirely new roster of players each year; consequently, examining each year’s change would yield varying results based on how many recruits the school needed that year. Rosters typically turn over every 4 to 5 years. Therefore, with eight years of data available, the dataset was subdivided into two four-year periods (2013-2016 and 2017-2020) to better examine changes over a longer period of time.
Table 3 shows the schools with the largest changes in their mean RVCFs. Fifteen schools experienced an increase in mean RVCF between the two time periods. Ohio State had the largest increase in mean difference (MD = 74.77 kg CO2eq), with its mean RVCF rising from 280.80 kg CO2eq in 2013-2016 to 355.57 kg CO2eq in 2017-2020. Oregon saw the largest overall increase in total RVCF, increasing 29,617.65 kg CO2eq during the latter period. Figure 2 utilizes GIS maps to depict all recruiting visits to Ohio State for each period, highlighting an expanded recruiting range that targeted more players from Texas and the Western United States. Conversely, eight schools showed a reduction in mean RVCF between the two time periods, with the University of Miami experiencing the largest decrease in mean difference (MD = -61.96 kg CO2eq). Although Washington State’s mean reduction was not as considerable as the bottom three schools, it had the largest total reduction in RVCF, decreasing by 19,562.28 kg CO2eq between the two periods.
Table 3Largest Mean Difference in RVCF between the two time periods
2013-2016
2017-2020
Schools
n
Total
M
n
Total
M
Difference
MD
Ohio State
156
43,804.86
280.80
145
51,557.24
355.57
7,752.39
74.77
Penn State
102
25,847.68
253.41
152
47,683.11
313.70
21,835.44
60.30
Oregon
122
70,128.43
574.82
159
99,746.07
627.33
29,617.65
52.51
Florida St.
165
46,656.85
282.77
152
36,535.52
240.37
-10,121.32
-42.40
Arkansas
153
49,311.35
322.30
172
45,987.11
267.37
-3,324.24
-54.93
Miami
151
46,944.88
310.89
150
37,339.37
248.93
-9,605.51
-61.96
Note: Totals and Means are in kg CO2eq
Wuerzer et al. (38) identified county-level geographical hotspots in the United States overproducing elite college football talent, necessitating migration to other states to find available roster spots on Power-5 football teams. Consequently, Power-5 schools in regions with minimal elite talent and far from these hotspots must expand their recruiting efforts, increasing their RVCF. Schools that rely heavily on air travel for recruiting will naturally have a larger carbon footprint, as air travel is the primary contributor to total RVCF. This is evident from the top three schools with the highest total RVCF also have the lowest percentages of recruits visiting within driving distance to their campuses (Washington State (2.17%), Oregon (3.20%), and Nebraska (5.90%)). Despite this, schools still make strategic choices in their recruiting practices. For example, as shown in Figure 1, Washington State invited several recruits from Florida, a state with prominent county-level recruiting hotspots, instead of focusing on nearby regions or closer recruiting hotspots in California and Texas.
A Pearson correlation coefficient analysis was conducted to address research question three: “Were there any correlations between schools’ financial, recruiting, and performance variables and their RVCF?” The analysis identified two significant correlations, both negative: Total RVCF and Athletic Department Total Annual Revenue [r(176) = -.202, p = .007] and Athletic Department Total Annual Expenses [r(176) = -.198, p = .008]. These findings suggest that athletic departments with higher revenues and expenses tend to have lower RVCFs. This could be attributed to the fact that Power-5 programs with substantial financial resources often have well-established and highly regarded football programs, enabling them to attract top recruits from within a closer geographical range. Consequently, these programs would be less dependent on long-distance recruiting, which typically requires greater air travel, the primary contributor to a school’s RVCF, thereby lowering their overall RVCF.
Overall, these findings highlight the multifaceted nature of college football recruiting, shaped by a complex interplay of positional needs, recruits’ availability, and recruits’ geographical location. The competitive nature of Power-5 college football recruiting requires substantial time and effort to build top-tier recruiting classes, prompting many schools to expand their recruiting reach over time, which subsequently increases their RVCF. The findings show that 15 of the 23 schools increased their RVCF over the two periods. Given the fierce competition for elite talent, it is unlikely that any football program would willingly reduce its recruiting-related carbon emissions if it jeopardizes on-field performance. This creates a significant challenge for universities wanting to adopt more sustainable operations.
CONCLUSION
This study provides a substantial initial assessment of the carbon footprint associated with college football recruiting. By utilizing GIS for recruits’ hometowns, college locations, and nearest major airports to calculate travel distances, the researchers provided an approximation of each school’s RVCF Recruiting Visit Carbon Footprint (RVCF). The findings reveal substantial variability in RVCF among schools, highlighting the different levels of environmental impact of recruiting. The study also found that higher-spending athletic departments tended to have lower RVCFs, suggesting that successful programs may not need to extend their recruiting reach as widely.
However, several limitations must be acknowledged. The data for this study came from a third-party recruiting website, thus allowing for only an approximate carbon footprint for each official visit. Also, various models and formulas can be used to estimate CO2eq emissions from travel, but each carries assumptions and biases. Moreover, policy changes during the study period, such as the NCAA’s 2016 rule change allowing schools to cover travel costs for up to two parents or guardians accompanying a recruit, could result in a higher actual carbon footprint than the reported RVCF from this study (33). More detailed research is essential for a more accurate and comprehensive understanding of the carbon emissions associated with college football recruiting. Unfortunately, without a governing body mandating standardized reporting of recruiting carbon emissions using consistent formulas, it will remain difficult to fully assess and compare the carbon emissions of different athletic departments.
APPLICATIONS IN SPORT
For universities aiming to reduce their athletic department’s carbon footprint, including all recruiting activities in their calculations is crucial. A comprehensive approach would enable the development of effective strategies that promote sustainability without sacrificing athletic success. Athletic departments can better incorporate sustainability into their planning and decision-making processes by understanding the true carbon footprint generated by each sport, school, and conference.
ACKNOWLEDGMENTS This research was supported by a college-level seed grant focused on sustainability issues from the Huizenga College of Business and Entrepreneurship’s Societal Impact Seed Grant program.
REFERENCES
1. Andris, C., Measuring geographic pull power: A case study of US college athletic teams. The Professional Geographer, 2018. 70(3): p. 476-490.
4. Collins, A., et al., Assessing the environmental consequences of major sporting events: The 2003/04 FA Cup Final. Urban studies, 2007. 44(3): p. 457-476.
5. Collins, A., C. Jones, and M. Munday, Assessing the environmental impacts of mega sporting events: Two options? Tourism management, 2009. 30(6): p. 828-837.
6. Collins, A., M. Munday, and A. Roberts, Environmental consequences of tourism consumption at major events: An analysis of the UK stages of the 2007 Tour de France. Journal of Travel Research, 2012. 51(5): p. 577-590.
7. Cooper, J.A., Making orange green? A critical carbon footprinting of Tennessee football gameday tourism. Journal of Sport & Tourism, 2020. 24(1): p. 31-51.
8. Cooper, J.A. and D.H. Alderman, Cancelling March Madness exposes opportunities for a more sustainable sports tourism economy. Tourism Geographies, 2020. 22(3): p. 525-535.
9. Čuček, L., J.J. Klemeš, and Z. Kravanja, A review of footprint analysis tools for monitoring impacts on sustainability. Journal of Cleaner Production, 2012. 34: p. 9-20.
10. Daddi, T., et al., Environmental management of sport events: a focus on European professional football. Sport, Business and Management: An International Journal, 2022. 12(2): p. 208-232.
11. Death, C., ‘Greening’the 2010 FIFA World Cup: Environmental sustainability and the mega-event in South Africa. Journal of Environmental Policy & Planning, 2011. 13(2): p. 99-117.
13. Dolf, M. and P. Teehan, Reducing the carbon footprint of spectator and team travel at the University of British Columbia’s varsity sports events. Sport Management Review, 2015. 18(2): p. 244-255.
14. El Hanandeh, A., Quantifying the carbon footprint of religious tourism: the case of Hajj. Journal of cleaner Production, 2013. 52: p. 53-60.
17. Filimonau, V., et al., Reviewing the carbon footprint analysis of hotels: Life Cycle Energy Analysis (LCEA) as a holistic method for carbon impact appraisal of tourist accommodation. Journal of cleaner production, 2011. 19(17-18): p. 1917-1930.
18. Franchetti, M.J. and D. Apul, Carbon footprint analysis: concepts, methods, implementation, and case studies. 2012: CRC press.
19. Getz, D., Policy for sustainable and responsible festivals and events: Institutionalization of a new paradigm. Journal of Policy Research in Tourism, Leisure and Events, 2009. 1(1): p. 61-78.
20. Glasspiegel, R., Texas spent $280,000 on Arch Manning recruitment weekend. New York Post, 2022.
21. Huml, M.R., et al., If we build it, will they come? The effect of new athletic facilities on recruiting rankings for power five football and men’s basketball programs. Journal of Marketing for Higher Education, 2019. 29(1): p. 1-18.
23. Jeyarajah, S., College football recruiting expenses by conference: SEC stands out as Georgia spends big for success in 2022, in CBSSports. 2023: CBSSports.com.
24. Jude, A., Ice carvings and lavish dinners: A look at the cost of football recruiting at Washington. Spokesman, 2020.
26. Macaulay, C., J. Cooper, and S. Dougherty, High school football and the athletic-market economy: Recruiting, producing, and manufacturing talent. Sociology of Sport Journal, 2019. 36(3): p. 203-212.
27. Mankin, J.A., J. Rivas, and J.J. Jewell, The effectiveness of college football recruiting ratings in predicting team success: A longitudinal study. Mankin, J., Rivas, J. & Jewell, J.(2021) The Effectiveness of College Football Recruiting Rankings in Predicting Team Success: A Longitudinal Study. Research in Business and Economics Journal, 2021. 14: p. 4-22.
28. Natural Resources Defense Council, Collegiate game changers: How campus sport is going green. 2013, Natural Resources Defense Council.
31. Pandey, D., M. Agrawal, and J.S. Pandey, Carbon footprint: current methods of estimation. Environmental monitoring and assessment, 2011. 178(1): p. 135-160.
32. Raynor, G., Behind the scenes of Clemson’s biggest recruiting weekend. New York Times, 2020.
33. VanHaaren, T., NCAA allows schools to pay for two guardians to accompany recruits on official visits, in ESPN. 2016.
34. Wackernagel, M. and W. Rees, Ecological footprint: Reducing human impact on the earth. New Society, Gabriola Island, BC, Canada, 1996.
35. Watkins, J. and K. Slater, Talent level and major distribution in “Power Five” conference football programs. Journal for the Study of Sports and Athletes in Education, 2021. 15(2): p. 150-170.
36. Wiltfong, S., New LSU 2024 QB commit Colin Hurley takes you inside an epic weekend. 247Sports, 2022.
37. Wittry, A., An Analysis Of College Football Recruiting Costs, in Athletic Director U. 2022: https://athleticdirectoru.com.
38. Wuerzer, T., J.J. Fountain, and P.S. Finley, An Analysis of the Geographic Origins and Migration Patterns of Elite College Football Players. Geographical Review, 2023. 114(2): p. 157-179.
Joshua S. Greer. https://orcid.org/0009-0005-2890-1673
We have no known conflict of interest to disclose.
ABSTRACT
This explanatory mixed-methods case study explored the relationship between accreditation, curriculum design, and student performance in sport sales education within undergraduate sport management programs. Using archival data from the 2024–2025 National Collegiate Sports Sales Championship (NCSSC), the study compared outcomes among 25 institutions, including COSMA- and non-accredited programs. Quantitative analysis found no significant relationship between accreditation status and Top-10 finishes in either the Ticket Sales or Corporate Partnerships divisions (p > .05). Qualitative findings indicated that student performance was more closely associated with experiential learning depth, faculty expertise, and the integration of customer relationship management and analytics tools. Grounded in Experiential Learning Theory, Competency-Based Education, Human Capital Theory, and Communities of Practice, the study concludes that accreditation provides useful structure but does not independently predict competitive success. Program-level factors such as applied pedagogy, simulation-based learning, and industry partnerships appear to be stronger indicators of professional readiness and employability in sport sales.
KEYWORDS:Experiential Learning Theory, Competency-Based Education, Human Capital Theory, Communities of Practice
INTRODUCTION
The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011). The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:
Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.
At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected. Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.
LITERATURE REVIEW
Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).
Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).
Coach-Athlete Relationship
Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.
Peer Cohesion
Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020). Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.
Parental Involvement
While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.
Study Aims
In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.
METHODS
Participants
Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (Mage = 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.
Measures
Coach-Athlete Relationship Questionnaire (CART-Q)
To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”
Youth Sport Environment Questionnaire (YSEQ)
Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.
Parental Involvement in Sport Questionnaire (PISQ)
The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”
Youth Experience Survey for Sport (YES-S)
Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.
Procedure
Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season. Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.
The dataset contained 0.3% missingness, and results of an MCAR test were not significant (X2(1386) = 0.00, p = 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (MT2 – MT1) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (r = 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.
Table 1
Sample Characteristics and Descriptive Statistics
CART-Q
YSEQ
PISQ
YES-S Social Skills
YES-S Cog. Skills
YES-S Goal Setting
YES-S Initiative
Variable
n
%
T1 – M(SD)
T2 – M(SD)
T1 – M(SD)
T2 – M(SD)
T1 – M(SD)
T2 – M(SD)
T2 – M(SD)
T2 – M(SD)
T2 – M(SD)
T2 – M(SD)
Age
10
3
7.3
5.61(1.24)*
5.97(1.47)*
4.25(2.01)*
5.08(1.98)*
2.39(0.18)
2.91(0.45)
3.58(0.52)
3.67(0.58)
4.25(0.58)
4.58(0.52)
11
9
22.0
5.46(1.73)
6.36(0.39)
4.74(1.52)
5.53(0.83)
3.02(0.60)
3.13(0.52)
4.00(0.60)
3.69(1.05)
4.00(0.85)
4.50(0.45)
12
20
48.8
6.10(0.40)
5.96(0.85)
5.10(0.75)
5.30(0.91)*
2.92(0.60)
3.25(0.74)*
4.17(0.75)
3.53(1.16)
3.93(0.90)
4.25(0.59)
13
9
22.0
5.71(1.04)*
5.15(1.26)* †
4.69(1.40)
4.89(1.18)
3.16(0.69)
3.30(0.58)
4.03(0.57)
3.56(0.69)
4.25(0.57)
4.43(0.66)
Gender
Boy
25
61.0
6.03(0.61)
6.17(0.69)†
4.91(1.03)*
5.26(0.89)*
2.92(0.62)*
3.24(0.66)*
4.12(0.66)
3.72(0.85)
4.11(0.71)
4.43(0.41)
Girl
16
39.0
5.56(1.43)
5.41(0.99)†
4.81(1.42)
5.22(1.25)
3.01(0.62)
3.17(0.62)
3.96(0.89)
3.35(1.19)
3.90(0.94)
4.27(0.76)
Race
White
27
65.9
5.77(1.13)
5.97(0.83)
4.75(1.21)*
5.24(1.03)*
2.95(0.61)
3.14(0.57)
4.07(0.69)
3.52(1.04)
3.99(0.85)
4.43(0.54)
Black
1
2.4
Asian
4
9.8
5.50(1.38)
5.41(1.85)
4.77(1.85)*
5.30(1.64)*
2.74(0.90)
3.29(0.90)
4.00(0.35)
3.94(0.43)
3.94(0.43)
3.94(0.66)
Hispanic
4
9.8
6.27(0.45)
5.86(1.12)
5.50(0.89)
5.55(0.74)
2.99(0.57)
3.41(0.83)
4.50(0.41)
4.25(0.54)
4.69(0.47)
4.63(0.32)
Other
5
12.2
6.13(0.31)
5.65(1.20)
5.05(0.83)
4.99(1.04)
2.99(0.45)
3.31(0.52)
3.80(0.89)
3.15(1.29)
3.80(0.94)
4.15(0.74)
Total
41
100.0
5.84(1.02)
5.87(0.99)
4.87(1.18)*
5.24(1.03)*
2.96(0.62)*
3.21(0.64)*
4.06(0.67)
3.58(1.00)
4.03(0.80)
4.37(0.57)
Notes. n = 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points; †Difference is significant between groups.
RESULTS
The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (R2 = 0.454, F(5,34) = 5.664, p < 0.001).
Regression Results for Perceptions of Social Skills Gained by Athletes
95% CI
Variable
b
b
SE
LL
UL
p
Intercept
0.774
1.268
-1.802
3.350
0.546
Gender
-0.129
-0.175
0.184
-0.550
0.199
0.348
Age
0.369
0.291
0.110
0.067
0.515
0.012
Race
-0.024
-0.008
0.042
-0.094
0.078
0.858
DCART-Q
0.482
0.250
0.074
0.099
0.400
0.002
DPISQ
0.326
0.382
0.160
0.580
0.707
0.022
Notes. n = 41; R2= 0.454, F(5,34) = 5.664, p < 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
**When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (R2 = 0.317, F(4,35) = 4.063, p = 0.008).
Within this model, both changes to the coach-athlete relationships (b= 0.482, p = 0.002) and changes to parental involvement (b= 0.326, p = 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b = 0.369, p = 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (R2 = 0.251, F(5,34) = 2.275, p = 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403, p = 0.022), whereas changes to parental involvement was not (b= 0.158, p = 0.330).
Table 3
Regression Results for Perceptions of Cognitive Skills Gained by Athletes
95% CI
Variable
b
b
SE
LL
UL
p
Intercept
2.048
2.221
-2.465
6.561
0.363
Gender
-0.155
-0.315
0.323
-0.972
0.342
0.337
Age
0.143
0.169
0.193
-0.224
0.561
0.389
Race
-0.066
-0.032
0.074
-0.182
0.119
0.670
DCART-Q
0.403
0.312
0.130
0.048
0.576
0.022
DPISQ
0.158
0.277
0.280
-0.292
0.845
0.330
Notes. n = 41; R2= 0.251, F(5,34) = 2.275, p = 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
The models predicting goal setting skills (R2 = 0.183, F(5,34) = 1.528, p = 0.207) and initiative (R2 = 0.185, F(5,34) = 1.542, p = 0.203) were not statistically significant.
DISCUSSION
The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.
While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).
The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.
Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.
Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.
Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research. As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.
CONCLUSION
The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.
APPLICATIONS IN SPORT
In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:
Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).
Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).
REFERENCES
1. Adie, J. W., & Jowett, S. (2010). Meta‐perceptions of the coach–athlete relationship, achievement goals, and intrinsic motivation among sport participants. Journal of Applied Social Psychology, 40(11), 2750-2773. https://doi.org/10.1111/j.1559-1816.2010.00679.x
2. Barnett, N. P., Smoll, F. L., & Smith, R. E. (1992). Effects of enhancing coach-athlete relationships on youth sport attrition. The Sport Psychologist, 6(2), 111-127. https://doi.org/10.1123/tsp.6.2.111
3. Benson, P. L., Scales, P. C., Hamilton, S. F., & Sesma Jr., A. (2006). Positive youth development so far: Core hypotheses and their implications for policy and practice. Search Institute Insights & Evidence 3(1), 1-13.
4. Bruner, M. W., McLaren, C. D., Sutcliffe, J. T., Gardner, L. A., Lubans, D. R., Smith, J. J., & Vella, S. A. (2021). The effect of sport-based interventions on positive youth development: A systematic review and meta-analysis. International Review of Sport and Exercise Psychology, 16(1), 368-395. https://doi.org/10.1080/1750984X.2021.1875496
5. Burns, L., Weissensteiner, J. R., & Cohen, M. (2019). Supportive interpersonal relationships: a key component to high-performance sport. British Journal of Sports Medicine, 53(22), 1386-1389. https://doi.org/10.1136/bjsports-2018-100312
6. Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis. Journal of Sport and Exercise Psychology, 24(2), 168–188. https://doi.org/10.1123/jsep.24.2.168
7. Carron, A. V., Spink, K. S., & Prapavessis, H. (1997). Team building and cohesiveness in the sport and exercise setting: Use of indirect interventions. Journal of Applied Sport Psychology, 9(1), 61–72. https://doi.org/10.1080/10413209708415384
8. Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships, 35(1), 32-58.
9. Catalano, R. F., Toumbourou, J. W., & Hawkins, J. D. (2008). Positive youth development in the United States: History, efficacy, and links to moral and character education. In L. Nucci & T. Krettenauer (Eds.) Handbook of moral and character education. Routledge. https://doi.org/10.4324/9780203114896
10. Christiansen, E. D., & von Eye, A. (2005). Positive youth development, participation in community youth development programs, and community contributions of fifth-grade adolescents: Findings from the first wave of the 4-H study of positive youth development. The Journal of Early Adolescence, 25(1), 17-71. https://doi.org/10.1177/0272431604272461
10. Clonan, S. M., Chafouleas, S. M., McDougal, J. L., & Riley-Tillman, T. C. (2004). Positive psychology goes to school: Are we there yet? Psychology in the Schools, 41(1), 101–110. https://doi.org/10.1002/pits.10142
11. Danioni, F., Barni, D., & Rosnati, R. (2017). Transmitting sport values: The importance of parental involvement in children’s sport activity. Europe’s Journal of Psychology, 13(1), 75–92. https://doi.org/10.5964/ejop.v13i1.1265
12. Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development. Educational Psychologist, 38(4), 207-234. https://doi.org/10.1207/S15326985EP3804_2
13. Davis, L., & Jowett, S. (2014). Coach–athlete attachment and the quality of the coach–athlete relationship: Implications for athlete’s well-being. Journal of Sports Sciences, 32(15), 1454–1464. https://doi.org/10.1080/02640414.2014.898183
14. Davis, L., Jowett, S., & Tafvelin, S. (2019). Communication strategies: The fuel for quality coach-athlete relationships and athlete satisfaction. Frontiers in Psychology, 10, 2156. https://doi.org/10.3389/fpsyg.2019.02156
15. Dong, Y., Wang, H., Luan, F., Li, Z., & Cheng, L. (2021). How children feel matters: Teacher–student relationship as an indirect role between interpersonal trust and social adjustment. Frontiers in Psychology, 11, 581235. https://doi.org/10.3389/fpsyg.2020.581235
16. Dorsch, T. E., Wright, E., Eckardt, V. C., Elliott, S., Thrower, S. N., & Knight, C. J. (2021). A history of parent involvement in organized youth sport: A scoping review. Sport, Exercise, and Performance Psychology, 10(4), 536. https://doi.org/10.1037/spy0000266
17. Edwards, J. R. (1994). Regression analysis as an alternative to difference scores. Journal of Management, 20(3), 683-689.
18. Erikson, E. H. (1950). Childhood and society. W. W. Norton & Co.
19. Erikstad, M. K., Martin, L. J., Haugen, T., & Høigaard, R. (2018). Group cohesion, needs satisfaction, and self-regulated learning: A one-year prospective study of elite youth soccer players’ perceptions of their club team. Psychology of Sport and Exercise, 39, 171–178. https://doi.org/10.1016/j.psychsport.2018.08.013
20. Eys, M., Loughead, T., Bray, S. R., & Carron, A. V. (2009). Development of a cohesion questionnaire for youth: The Youth Sport Environment Questionnaire. Journal of Sport and Exercise Psychology, 31(3), 390-408. https://doi.org/10.1123/jsep.31.3.390
21. Felber Charbonneau, E., & Camiré, M. (2020). Parental involvement in sport and the satisfaction of basic psychological needs: Perspectives from parent–child dyads. International Journal of Sport and Exercise Psychology, 18(5), 655–671. https://doi.org/10.1080/1612197X.2019.1570533
22. Filho, E., Dobersek, U., Gershgoren, L., Becker, B., & Tenenbaum, G. (2014). The cohesion–performance relationship in sport: A 10-year retrospective meta-analysis. Sport Sciences for Health, 10(3), 165–177. https://doi.org/10.1007/s11332-014-0188-7
23. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58(6-7), 466. https://doi.org/10.1037/0003-066X.58.6-7.466
24. Hampson, R., & Jowett, S. (2014). Effects of coach leadership and coach–athlete relationship on collective efficacy. Scandinavian Journal of Medicine & Science in Sports, 24(2), 454-460. https://doi.org/10.1111/j.1600-0838.2012.01527.x
25. Holt, N. L., Neely, K. C., Slater, L. G., Camiré, M., Côté, J., Fraser-Thomas, J., MacDonald, D., Strachan, L., & Tamminen, K. A. (2017). A grounded theory of positive youth development through sport based on results from a qualitative meta-study. International Review of Sport and Exercise Psychology, 10(1), 1-49. https://doi.org/10.1080/1750984X.2016.1180704
26. Jones, M. I., Dunn, J. G., Holt, N. L., Sullivan, P. J., & Bloom, G. A. (2011). Exploring the ‘5Cs’ of positive youth development in sport. Journal of Sport Behavior, 34(3). https://psycnet.apa.org/record/2011-17095-003
27. Jowett, S. (2017). Coaching effectiveness: The coach–athlete relationship at its heart. Current Opinion in Psychology, 16, 154–158. https://doi.org/10.1016/j.copsyc.2017.05.006
28. Jowett, S., & Cockerill, I. M. (2003). Olympic medalists’ perspective of the athlete–coach relationship. Psychology of Sport and Exercise, 4(4), 313–331. https://doi.org/10.1016/S1469-0292(02)00011-0
29. Jowett, S., & Ntoumanis, N. (2004). The Coach–Athlete Relationship Questionnaire (CART‐Q): Development and initial validation. Scandinavian Journal of Medicine & Science in Sports, 14(4), 245-257. https://doi.org/10.1111/j.1600-0838.2003.00338.x
30. Kipp, L. E., & Bolter, N. D. (2020). Motivational climate, psychological needs, and personal and social responsibility in youth soccer: Comparisons by age group and competitive level. Psychology of Sport and Exercise, 51.https://doi.org/10.1016/j.psychsport.2020.101756
31. Knight, C. J., Neely, K. C., & Holt, N. L. (2011). Parental behaviors in team sports: How do female athletes want parents to behave? Journal of Applied Sport Psychology, 23(1), 76-92. https://doi.org/10.1080/10413200.2010.525589
32. Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55(1), 170–183. https://doi.org/10.1037/0003-066X.55.1.170
33. Lee, M., & MacLean, S. (1997). Sources of parental pressure among age group swimmers. European Journal of Physical Education, 2(2), 167-177. https://doi.org/10.1080/1740898970020204
34. Lerner, R. M. (2005). Promoting positive youth development: Theoretical and empirical bases. Washington, DC: National Academies of Sciences.
35. Lerner, R. M., Almerigi, J. B., Theokas, C., & Lerner, J. V. (2005). Positive youth development A view of the issues. The Journal of Early Adolescence, 25(1), 10–16. https://doi.org/10.1177/0272431604273211
36. Lerner, R. M., Lerner, J. V., Almerigi, J. B., Theokas, C., Phelps, E., Gestsdottir, S., Naudeau, S., Jelicic, H., Alberts, A., Ma, L., Smith, L. M., Bobek, D. L., Richman-Raphael, D., Simpson, I., 37.
37. Lerner, R. M., Lerner, J. V., Lewin-Bizan, S., Bowers, E. P., Boyd, M. J., Mueller, M. K., Schmid, K. L., & Napolitano, C. M. (2011). Positive youth development: Processes, orograms, and problematics. Journal of Youth Development, 6(3), 38–62. https://doi.org/10.5195/JYD.2011.174
38. MacDonald, D. J., Côté, J., Eys, M., & Deakin, J. (2012). Psychometric properties of the youth experience survey with young athletes. Psychology of Sport and Exercise, 13(3), 332-340. https://doi.org/10.1016/j.psychsport.2011.09.001
39. Massey, W. V., Veliz, P. T., Zarrett, N., & Farello, A. (2024). Thriving through sport: The transformative impact on girls’ mental health. Women’s Sport Foundation.
40. McBride, A. M., Johnson, E., Olate, R., & O’Hara, K. (2011). Youth volunteer service as positive youth development in Latin America and the Caribbean. Children and Youth Services Review, 33(1), 34–41. https://doi.org/10.1016/j.childyouth.2010.08.009
41. Nikolina, K., & Đorić, V. (2023). Relations of coach-athlete relationship quality and athlete psychosocial functioning: A systematic review. SportLogia, 19(1). https://doi.org/10.7251/SGIA2319002K
42. Riggio, R. E., Throckmorton, B., & Depaola, S. (1990). Social skills and self-esteem. Personality and Individual Differences, 11(8), 799-804. https://doi.org/10.1016/0191-8869(90)90188-W
43. Salavera, C., Usán, P., & Quilez-Robres, A. (2022). Exploring the effect of parental styles on social skills: The mediating role of affects. International Journal of Environmental Research and Public Health, 19(6), 3295. https://doi.org/10.3390/ijerph19063295
44. Sancassiani, F., Pintus, E., Holte, A., Paulus, P., Moro, M. F., Cossu, G., Angermeyer, M. C., Carta, M. G., & Lindert, J. (2015). Enhancing the emotional and social skills of the youth to promote their wellbeing and positive development: A systematic review of universal school-based randomized controlled trials [Special issue]. Clinical Practice and Epidemiology in Mental Health,11, 21-40. https://doi.org/10.2174/1745017901511010021
45. Senécal, J., Loughead, T. M., & Bloom, G. A. (2008). A season-long team-building intervention: Examining the effect of team goal setting on cohesion. Journal of Sport and Exercise Psychology, 30(2), 186-199. https://doi.org/10.1123/jsep.30.2.186
46. Sheridan, D., Coffee, P., & Lavallee, D. (2014). A systematic review of social support in youth sport. International Review of Sport and Exercise Psychology, 7(1), 198-228. https://doi.org/10.1080/1750984X.2014.931999
47. Sibthorp, J., & Morgan, C. (2011). Adventure‐based programming: Exemplary youth development practice. New Directions for Youth Development, (130), 105-119. https://doi.org/10.1002/yd.400
48. Smith, A. L., & Ullrich-French, S. (2020). Peers and the sport experience. In G. Tenenbaum & R. C. Eklund (Eds.) Handbook of sport psychology (pp. 410–428). John Wiley & Sons, Inc. https://doi.org/10.1002/9781119568124.ch19
49. Storm, M. K., Williams, K. N., Shetter, E. M., Kaminsky, J., Lowery, C. M., Caldas, S. V., & Winch, P. J. (2017). Sink or swim: Promoting youth development through aquatics programs in Baltimore, Maryland. Journal of Park & Recreation Administration, 35(1), 66-79. https://doi.org/10.18666/JPRA-2017-V35-I1-7295
50. Sullivan, P. J., LaForge-MacKenzie, K., & Marini, M. (2015). Confirmatory factor analysis of the Youth Experiences Survey for Sport (YES-S). Open Journal of Statistics, 5(05), 421. https://doi.org/10.4236/ojs.2015.55044
51. Sung, Y. Y., & Chang, M. (2010). Which social skills predict academic performance of elementary school students. Journal on Educational Psychology, 3(3), 23-34. https://doi.org/10.26634/jpsy.3.3.1078
52. Turman, P. D. (2003). Coaches and cohesion: The impact of coaching techniques on team cohesion in the small group sport setting. Journal of Sport Behaviour, 26(1), 86-104. https://psycnet.apa.org/record/2003-01497-007
53. White, R. L., & Bennie, A. (2015). Resilience in youth sport: A qualitative investigation of gymnastics coach and athlete perceptions. International Journal of Sports Science & Coaching, 10(2-3), 379-393. https://doi.org/10.1260/1747-9541.10.2-3.379
54. Whitley, M. A., Massey, W. V., Camiré, M., Boutet, M., & Borbee, A. (2019). Sport-based youth development interventions in the United States: A systematic review. BMC Public Health, 19, 1-20. https://doi.org/10.1186/s12889-019-6387-z
55. Worker, S. M., Iaccopucci, A. M., Bird, M., & Horowitz, M. (2019). Promoting positive youth development through teenagers-as-teachers programs. Journal of Adolescent Research, 34(1), 30-54. https://doi.org/10.1177/0743558418764089
Appendix A Supplemental Materials
Table 4
Correlation Matrix of Study Variables
Variables
1
2
3
4
5
6
7
1. Age
2. CART-Q
-0.34*
3. PISQ
0.15
0.23
4. YSEQ
-0.14
0.66**
0.31*
5. Social Skills
0.14
0.62**
0.35*
0.47**
6. Cognitive Skills
-0.05
0.40*
0.16
0.16
0.66**
7. Goal Setting
0.03
0.43**
0.13
0.42**
0.57**
0.70**
8. Initiative
-0.10
0.53**
0.18
0.47**
0.51**
0.40*
0.70**
Notes. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships
Table 5
Regression Results for Perceptions of Goal Setting Skills Gained by Athletes
95% CI for B
Variable
b
SE
LL
UL
b
p
Intercept
2.053
1.862
-1.731
5.836
0.278
Gender
-0.228
0.271
-0.779
0.322
-0.140
0.405
Age
0.186
0.162
-0.143
0.515
0.196
0.259
Race
0.011
0.062
-0.115
0.137
0.028
0.863
DCART-Q
0.230
0.109
0.008
0.451
0.369
0.042
DPISQ
0.175
0.235
-0.302
0.651
0.124
0.462
Notes. R2= 0.183, p = 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Table 6
Regression Results for Perceptions of Initiative Gained by Athletes
95% CI for B
Variable
b
SE
LL
UL
b
p
Intercept
4.000
1.315
1.328
6.671
-0.120
3.043
Gender
-0.138
0.191
-0.527
0.250
0.062
-0.723
Age
0.042
0.114
-0.191
0.274
0.035
0.365
Race
0.010
0.044
-0.079
0.099
0.389
0.221
DCART-Q
0.171
0.077
0.015
0.327
0.087
2.224
DPISQ
0.086
0.166
-0.251
0.423
-0.120
0.520
Notes. R2= 0.185, p = 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Jim P. Arnold https://orcid.org/0009-0004-2282-1915 William V. Massey, Ph.D. https://orcid.org/0000-0002-4002-3720 We have no known conflicts of interest to disclose.
ABSTRACT
Purpose. Research on positive youth development (PYD) through sport remains unclear and speculative (Whitley et al., 2019). It has been suggested that sport-based PYD can occur implicitly through positive relationships (Holt et al., 2017). The present pilot study examined the impact of changes in the coach-athlete relationship, peer cohesion, and parental involvement on PYD outcomes across a sport season in a sample of youth soccer participants (N = 41, Mage = 11.85, 61% boys).
Methods. Athletes responded to surveys rating their relationships with coaches, parents, and peers at two time points, and additionally reported their perceptions of developmental skills gained across the sport season. A difference score was calculated for each relationship variable to measure change across the season. Four developmental outcomes (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) were regressed on changes in relationship quality across the season, while controlling for age, race, and gender.
Results. Changes to the coach-athlete relationship (b= 0.482, p = 0.002) and parental involvement (b= 0.326, p = 0.022) were significant predictors of perceptions of social skill development (R2 = 0.454, F(5,34) = 5.664, p < 0.001), supporting a relationship-based model of PYD in sport. Significant age and gender differences in ratings of the coach-athlete relationship were also discovered.
Conclusions. The present study not only offers partial support to a Holt and colleagues’ (2017) theory of implicit PYD through sport but also highlights the need important developmental role of relationship building in in the sport context.
Applications in Sport. Organizations should prioritize positive sport relationships through education, training, and programming, as poor or absent relationships may undermine the envisioned benefits of sport. In particular, the present study highlights the need for positive parental involvement, which may require stakeholders to work with parents to define their role expectations.
KEYWORDS:youth sport, positive youth development, sport relationships, coaching, parental involvement
INTRODUCTION
The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011). The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:
Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.
At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected. Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.
LITERATURE REVIEW
Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).
Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).
Coach-Athlete Relationship
Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.
Peer Cohesion
Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020). Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.
Parental Involvement
While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.
Study Aims
In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.
METHODS
Participants
Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (Mage = 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.
Measures
Coach-Athlete Relationship Questionnaire (CART-Q)
To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”
Youth Sport Environment Questionnaire (YSEQ)
Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.
Parental Involvement in Sport Questionnaire (PISQ)
The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”
Youth Experience Survey for Sport (YES-S)
Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.
Procedure
Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season. Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.
The dataset contained 0.3% missingness, and results of an MCAR test were not significant (X2(1386) = 0.00, p = 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (MT2 – MT1) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (r = 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.
Table 1
Sample Characteristics and Descriptive Statistics
CART-Q
YSEQ
PISQ
YES-S Social Skills
YES-S Cog. Skills
YES-S Goal Setting
YES-S Initiative
Variable
n
%
T1 – M(SD)
T2 – M(SD)
T1 – M(SD)
T2 – M(SD)
T1 – M(SD)
T2 – M(SD)
T2 – M(SD)
T2 – M(SD)
T2 – M(SD)
T2 – M(SD)
Age
10
3
7.3
5.61(1.24)*
5.97(1.47)*
4.25(2.01)*
5.08(1.98)*
2.39(0.18)
2.91(0.45)
3.58(0.52)
3.67(0.58)
4.25(0.58)
4.58(0.52)
11
9
22.0
5.46(1.73)
6.36(0.39)
4.74(1.52)
5.53(0.83)
3.02(0.60)
3.13(0.52)
4.00(0.60)
3.69(1.05)
4.00(0.85)
4.50(0.45)
12
20
48.8
6.10(0.40)
5.96(0.85)
5.10(0.75)
5.30(0.91)*
2.92(0.60)
3.25(0.74)*
4.17(0.75)
3.53(1.16)
3.93(0.90)
4.25(0.59)
13
9
22.0
5.71(1.04)*
5.15(1.26)* †
4.69(1.40)
4.89(1.18)
3.16(0.69)
3.30(0.58)
4.03(0.57)
3.56(0.69)
4.25(0.57)
4.43(0.66)
Gender
Boy
25
61.0
6.03(0.61)
6.17(0.69)†
4.91(1.03)*
5.26(0.89)*
2.92(0.62)*
3.24(0.66)*
4.12(0.66)
3.72(0.85)
4.11(0.71)
4.43(0.41)
Girl
16
39.0
5.56(1.43)
5.41(0.99)†
4.81(1.42)
5.22(1.25)
3.01(0.62)
3.17(0.62)
3.96(0.89)
3.35(1.19)
3.90(0.94)
4.27(0.76)
Race
White
27
65.9
5.77(1.13)
5.97(0.83)
4.75(1.21)*
5.24(1.03)*
2.95(0.61)
3.14(0.57)
4.07(0.69)
3.52(1.04)
3.99(0.85)
4.43(0.54)
Black
1
2.4
Asian
4
9.8
5.50(1.38)
5.41(1.85)
4.77(1.85)*
5.30(1.64)*
2.74(0.90)
3.29(0.90)
4.00(0.35)
3.94(0.43)
3.94(0.43)
3.94(0.66)
Hispanic
4
9.8
6.27(0.45)
5.86(1.12)
5.50(0.89)
5.55(0.74)
2.99(0.57)
3.41(0.83)
4.50(0.41)
4.25(0.54)
4.69(0.47)
4.63(0.32)
Other
5
12.2
6.13(0.31)
5.65(1.20)
5.05(0.83)
4.99(1.04)
2.99(0.45)
3.31(0.52)
3.80(0.89)
3.15(1.29)
3.80(0.94)
4.15(0.74)
Total
41
100.0
5.84(1.02)
5.87(0.99)
4.87(1.18)*
5.24(1.03)*
2.96(0.62)*
3.21(0.64)*
4.06(0.67)
3.58(1.00)
4.03(0.80)
4.37(0.57)
Notes. n = 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points; †Difference is significant between groups.
RESULTS
The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (R2 = 0.454, F(5,34) = 5.664, p < 0.001).
Regression Results for Perceptions of Social Skills Gained by Athletes
95% CI
Variable
b
b
SE
LL
UL
p
Intercept
0.774
1.268
-1.802
3.350
0.546
Gender
-0.129
-0.175
0.184
-0.550
0.199
0.348
Age
0.369
0.291
0.110
0.067
0.515
0.012
Race
-0.024
-0.008
0.042
-0.094
0.078
0.858
DCART-Q
0.482
0.250
0.074
0.099
0.400
0.002
DPISQ
0.326
0.382
0.160
0.580
0.707
0.022
Notes. n = 41; R2= 0.454, F(5,34) = 5.664, p < 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
**When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (R2 = 0.317, F(4,35) = 4.063, p = 0.008).
Within this model, both changes to the coach-athlete relationships (b= 0.482, p = 0.002) and changes to parental involvement (b= 0.326, p = 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b = 0.369, p = 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (R2 = 0.251, F(5,34) = 2.275, p = 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403, p = 0.022), whereas changes to parental involvement was not (b= 0.158, p = 0.330).
Table 3
Regression Results for Perceptions of Cognitive Skills Gained by Athletes
95% CI
Variable
b
b
SE
LL
UL
p
Intercept
2.048
2.221
-2.465
6.561
0.363
Gender
-0.155
-0.315
0.323
-0.972
0.342
0.337
Age
0.143
0.169
0.193
-0.224
0.561
0.389
Race
-0.066
-0.032
0.074
-0.182
0.119
0.670
DCART-Q
0.403
0.312
0.130
0.048
0.576
0.022
DPISQ
0.158
0.277
0.280
-0.292
0.845
0.330
Notes. n = 41; R2= 0.251, F(5,34) = 2.275, p = 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
The models predicting goal setting skills (R2 = 0.183, F(5,34) = 1.528, p = 0.207) and initiative (R2 = 0.185, F(5,34) = 1.542, p = 0.203) were not statistically significant.
DISCUSSION
The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.
While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).
The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.
Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.
Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.
Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research. As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.
CONCLUSION
The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.
APPLICATIONS IN SPORT
In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:
Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).
Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).
REFERENCES
1. Adie, J. W., & Jowett, S. (2010). Meta‐perceptions of the coach–athlete relationship, achievement goals, and intrinsic motivation among sport participants. Journal of Applied Social Psychology, 40(11), 2750-2773. https://doi.org/10.1111/j.1559-1816.2010.00679.x
2. Barnett, N. P., Smoll, F. L., & Smith, R. E. (1992). Effects of enhancing coach-athlete relationships on youth sport attrition. The Sport Psychologist, 6(2), 111-127. https://doi.org/10.1123/tsp.6.2.111
3. Benson, P. L., Scales, P. C., Hamilton, S. F., & Sesma Jr., A. (2006). Positive youth development so far: Core hypotheses and their implications for policy and practice. Search Institute Insights & Evidence 3(1), 1-13.
4. Bruner, M. W., McLaren, C. D., Sutcliffe, J. T., Gardner, L. A., Lubans, D. R., Smith, J. J., & Vella, S. A. (2021). The effect of sport-based interventions on positive youth development: A systematic review and meta-analysis. International Review of Sport and Exercise Psychology, 16(1), 368-395. https://doi.org/10.1080/1750984X.2021.1875496
5. Burns, L., Weissensteiner, J. R., & Cohen, M. (2019). Supportive interpersonal relationships: a key component to high-performance sport. British Journal of Sports Medicine, 53(22), 1386-1389. https://doi.org/10.1136/bjsports-2018-100312
6. Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis. Journal of Sport and Exercise Psychology, 24(2), 168–188. https://doi.org/10.1123/jsep.24.2.168
7. Carron, A. V., Spink, K. S., & Prapavessis, H. (1997). Team building and cohesiveness in the sport and exercise setting: Use of indirect interventions. Journal of Applied Sport Psychology, 9(1), 61–72. https://doi.org/10.1080/10413209708415384
8. Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships, 35(1), 32-58.
9. Catalano, R. F., Toumbourou, J. W., & Hawkins, J. D. (2008). Positive youth development in the United States: History, efficacy, and links to moral and character education. In L. Nucci & T. Krettenauer (Eds.) Handbook of moral and character education. Routledge. https://doi.org/10.4324/9780203114896
10. Christiansen, E. D., & von Eye, A. (2005). Positive youth development, participation in community youth development programs, and community contributions of fifth-grade adolescents: Findings from the first wave of the 4-H study of positive youth development. The Journal of Early Adolescence, 25(1), 17-71. https://doi.org/10.1177/0272431604272461
10. Clonan, S. M., Chafouleas, S. M., McDougal, J. L., & Riley-Tillman, T. C. (2004). Positive psychology goes to school: Are we there yet? Psychology in the Schools, 41(1), 101–110. https://doi.org/10.1002/pits.10142
11. Danioni, F., Barni, D., & Rosnati, R. (2017). Transmitting sport values: The importance of parental involvement in children’s sport activity. Europe’s Journal of Psychology, 13(1), 75–92. https://doi.org/10.5964/ejop.v13i1.1265
12. Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development. Educational Psychologist, 38(4), 207-234. https://doi.org/10.1207/S15326985EP3804_2
13. Davis, L., & Jowett, S. (2014). Coach–athlete attachment and the quality of the coach–athlete relationship: Implications for athlete’s well-being. Journal of Sports Sciences, 32(15), 1454–1464. https://doi.org/10.1080/02640414.2014.898183
14. Davis, L., Jowett, S., & Tafvelin, S. (2019). Communication strategies: The fuel for quality coach-athlete relationships and athlete satisfaction. Frontiers in Psychology, 10, 2156. https://doi.org/10.3389/fpsyg.2019.02156
15. Dong, Y., Wang, H., Luan, F., Li, Z., & Cheng, L. (2021). How children feel matters: Teacher–student relationship as an indirect role between interpersonal trust and social adjustment. Frontiers in Psychology, 11, 581235. https://doi.org/10.3389/fpsyg.2020.581235
16. Dorsch, T. E., Wright, E., Eckardt, V. C., Elliott, S., Thrower, S. N., & Knight, C. J. (2021). A history of parent involvement in organized youth sport: A scoping review. Sport, Exercise, and Performance Psychology, 10(4), 536. https://doi.org/10.1037/spy0000266
17. Edwards, J. R. (1994). Regression analysis as an alternative to difference scores. Journal of Management, 20(3), 683-689.
18. Erikson, E. H. (1950). Childhood and society. W. W. Norton & Co.
19. Erikstad, M. K., Martin, L. J., Haugen, T., & Høigaard, R. (2018). Group cohesion, needs satisfaction, and self-regulated learning: A one-year prospective study of elite youth soccer players’ perceptions of their club team. Psychology of Sport and Exercise, 39, 171–178. https://doi.org/10.1016/j.psychsport.2018.08.013
20. Eys, M., Loughead, T., Bray, S. R., & Carron, A. V. (2009). Development of a cohesion questionnaire for youth: The Youth Sport Environment Questionnaire. Journal of Sport and Exercise Psychology, 31(3), 390-408. https://doi.org/10.1123/jsep.31.3.390
21. Felber Charbonneau, E., & Camiré, M. (2020). Parental involvement in sport and the satisfaction of basic psychological needs: Perspectives from parent–child dyads. International Journal of Sport and Exercise Psychology, 18(5), 655–671. https://doi.org/10.1080/1612197X.2019.1570533
22. Filho, E., Dobersek, U., Gershgoren, L., Becker, B., & Tenenbaum, G. (2014). The cohesion–performance relationship in sport: A 10-year retrospective meta-analysis. Sport Sciences for Health, 10(3), 165–177. https://doi.org/10.1007/s11332-014-0188-7
23. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58(6-7), 466. https://doi.org/10.1037/0003-066X.58.6-7.466
24. Hampson, R., & Jowett, S. (2014). Effects of coach leadership and coach–athlete relationship on collective efficacy. Scandinavian Journal of Medicine & Science in Sports, 24(2), 454-460. https://doi.org/10.1111/j.1600-0838.2012.01527.x
25. Holt, N. L., Neely, K. C., Slater, L. G., Camiré, M., Côté, J., Fraser-Thomas, J., MacDonald, D., Strachan, L., & Tamminen, K. A. (2017). A grounded theory of positive youth development through sport based on results from a qualitative meta-study. International Review of Sport and Exercise Psychology, 10(1), 1-49. https://doi.org/10.1080/1750984X.2016.1180704
26. Jones, M. I., Dunn, J. G., Holt, N. L., Sullivan, P. J., & Bloom, G. A. (2011). Exploring the ‘5Cs’ of positive youth development in sport. Journal of Sport Behavior, 34(3). https://psycnet.apa.org/record/2011-17095-003
27. Jowett, S. (2017). Coaching effectiveness: The coach–athlete relationship at its heart. Current Opinion in Psychology, 16, 154–158. https://doi.org/10.1016/j.copsyc.2017.05.006
28. Jowett, S., & Cockerill, I. M. (2003). Olympic medalists’ perspective of the athlete–coach relationship. Psychology of Sport and Exercise, 4(4), 313–331. https://doi.org/10.1016/S1469-0292(02)00011-0
29. Jowett, S., & Ntoumanis, N. (2004). The Coach–Athlete Relationship Questionnaire (CART‐Q): Development and initial validation. Scandinavian Journal of Medicine & Science in Sports, 14(4), 245-257. https://doi.org/10.1111/j.1600-0838.2003.00338.x
30. Kipp, L. E., & Bolter, N. D. (2020). Motivational climate, psychological needs, and personal and social responsibility in youth soccer: Comparisons by age group and competitive level. Psychology of Sport and Exercise, 51.https://doi.org/10.1016/j.psychsport.2020.101756
31. Knight, C. J., Neely, K. C., & Holt, N. L. (2011). Parental behaviors in team sports: How do female athletes want parents to behave? Journal of Applied Sport Psychology, 23(1), 76-92. https://doi.org/10.1080/10413200.2010.525589
32. Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55(1), 170–183. https://doi.org/10.1037/0003-066X.55.1.170
33. Lee, M., & MacLean, S. (1997). Sources of parental pressure among age group swimmers. European Journal of Physical Education, 2(2), 167-177. https://doi.org/10.1080/1740898970020204
34. Lerner, R. M. (2005). Promoting positive youth development: Theoretical and empirical bases. Washington, DC: National Academies of Sciences.
35. Lerner, R. M., Almerigi, J. B., Theokas, C., & Lerner, J. V. (2005). Positive youth development A view of the issues. The Journal of Early Adolescence, 25(1), 10–16. https://doi.org/10.1177/0272431604273211
36. Lerner, R. M., Lerner, J. V., Almerigi, J. B., Theokas, C., Phelps, E., Gestsdottir, S., Naudeau, S., Jelicic, H., Alberts, A., Ma, L., Smith, L. M., Bobek, D. L., Richman-Raphael, D., Simpson, I., 37.
37. Lerner, R. M., Lerner, J. V., Lewin-Bizan, S., Bowers, E. P., Boyd, M. J., Mueller, M. K., Schmid, K. L., & Napolitano, C. M. (2011). Positive youth development: Processes, orograms, and problematics. Journal of Youth Development, 6(3), 38–62. https://doi.org/10.5195/JYD.2011.174
38. MacDonald, D. J., Côté, J., Eys, M., & Deakin, J. (2012). Psychometric properties of the youth experience survey with young athletes. Psychology of Sport and Exercise, 13(3), 332-340. https://doi.org/10.1016/j.psychsport.2011.09.001
39. Massey, W. V., Veliz, P. T., Zarrett, N., & Farello, A. (2024). Thriving through sport: The transformative impact on girls’ mental health. Women’s Sport Foundation.
40. McBride, A. M., Johnson, E., Olate, R., & O’Hara, K. (2011). Youth volunteer service as positive youth development in Latin America and the Caribbean. Children and Youth Services Review, 33(1), 34–41. https://doi.org/10.1016/j.childyouth.2010.08.009
41. Nikolina, K., & Đorić, V. (2023). Relations of coach-athlete relationship quality and athlete psychosocial functioning: A systematic review. SportLogia, 19(1). https://doi.org/10.7251/SGIA2319002K
42. Riggio, R. E., Throckmorton, B., & Depaola, S. (1990). Social skills and self-esteem. Personality and Individual Differences, 11(8), 799-804. https://doi.org/10.1016/0191-8869(90)90188-W
43. Salavera, C., Usán, P., & Quilez-Robres, A. (2022). Exploring the effect of parental styles on social skills: The mediating role of affects. International Journal of Environmental Research and Public Health, 19(6), 3295. https://doi.org/10.3390/ijerph19063295
44. Sancassiani, F., Pintus, E., Holte, A., Paulus, P., Moro, M. F., Cossu, G., Angermeyer, M. C., Carta, M. G., & Lindert, J. (2015). Enhancing the emotional and social skills of the youth to promote their wellbeing and positive development: A systematic review of universal school-based randomized controlled trials [Special issue]. Clinical Practice and Epidemiology in Mental Health,11, 21-40. https://doi.org/10.2174/1745017901511010021
45. Senécal, J., Loughead, T. M., & Bloom, G. A. (2008). A season-long team-building intervention: Examining the effect of team goal setting on cohesion. Journal of Sport and Exercise Psychology, 30(2), 186-199. https://doi.org/10.1123/jsep.30.2.186
46. Sheridan, D., Coffee, P., & Lavallee, D. (2014). A systematic review of social support in youth sport. International Review of Sport and Exercise Psychology, 7(1), 198-228. https://doi.org/10.1080/1750984X.2014.931999
47. Sibthorp, J., & Morgan, C. (2011). Adventure‐based programming: Exemplary youth development practice. New Directions for Youth Development, (130), 105-119. https://doi.org/10.1002/yd.400
48. Smith, A. L., & Ullrich-French, S. (2020). Peers and the sport experience. In G. Tenenbaum & R. C. Eklund (Eds.) Handbook of sport psychology (pp. 410–428). John Wiley & Sons, Inc. https://doi.org/10.1002/9781119568124.ch19
49. Storm, M. K., Williams, K. N., Shetter, E. M., Kaminsky, J., Lowery, C. M., Caldas, S. V., & Winch, P. J. (2017). Sink or swim: Promoting youth development through aquatics programs in Baltimore, Maryland. Journal of Park & Recreation Administration, 35(1), 66-79. https://doi.org/10.18666/JPRA-2017-V35-I1-7295
50. Sullivan, P. J., LaForge-MacKenzie, K., & Marini, M. (2015). Confirmatory factor analysis of the Youth Experiences Survey for Sport (YES-S). Open Journal of Statistics, 5(05), 421. https://doi.org/10.4236/ojs.2015.55044
51. Sung, Y. Y., & Chang, M. (2010). Which social skills predict academic performance of elementary school students. Journal on Educational Psychology, 3(3), 23-34. https://doi.org/10.26634/jpsy.3.3.1078
52. Turman, P. D. (2003). Coaches and cohesion: The impact of coaching techniques on team cohesion in the small group sport setting. Journal of Sport Behaviour, 26(1), 86-104. https://psycnet.apa.org/record/2003-01497-007
53. White, R. L., & Bennie, A. (2015). Resilience in youth sport: A qualitative investigation of gymnastics coach and athlete perceptions. International Journal of Sports Science & Coaching, 10(2-3), 379-393. https://doi.org/10.1260/1747-9541.10.2-3.379
54. Whitley, M. A., Massey, W. V., Camiré, M., Boutet, M., & Borbee, A. (2019). Sport-based youth development interventions in the United States: A systematic review. BMC Public Health, 19, 1-20. https://doi.org/10.1186/s12889-019-6387-z
55. Worker, S. M., Iaccopucci, A. M., Bird, M., & Horowitz, M. (2019). Promoting positive youth development through teenagers-as-teachers programs. Journal of Adolescent Research, 34(1), 30-54. https://doi.org/10.1177/0743558418764089
Appendix A Supplemental Materials
Table 4
Correlation Matrix of Study Variables
Variables
1
2
3
4
5
6
7
1. Age
2. CART-Q
-0.34*
3. PISQ
0.15
0.23
4. YSEQ
-0.14
0.66**
0.31*
5. Social Skills
0.14
0.62**
0.35*
0.47**
6. Cognitive Skills
-0.05
0.40*
0.16
0.16
0.66**
7. Goal Setting
0.03
0.43**
0.13
0.42**
0.57**
0.70**
8. Initiative
-0.10
0.53**
0.18
0.47**
0.51**
0.40*
0.70**
Notes. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships
Table 5
Regression Results for Perceptions of Goal Setting Skills Gained by Athletes
95% CI for B
Variable
b
SE
LL
UL
b
p
Intercept
2.053
1.862
-1.731
5.836
0.278
Gender
-0.228
0.271
-0.779
0.322
-0.140
0.405
Age
0.186
0.162
-0.143
0.515
0.196
0.259
Race
0.011
0.062
-0.115
0.137
0.028
0.863
DCART-Q
0.230
0.109
0.008
0.451
0.369
0.042
DPISQ
0.175
0.235
-0.302
0.651
0.124
0.462
Notes. R2= 0.183, p = 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
Table 6
Regression Results for Perceptions of Initiative Gained by Athletes
95% CI for B
Variable
b
SE
LL
UL
b
p
Intercept
4.000
1.315
1.328
6.671
-0.120
3.043
Gender
-0.138
0.191
-0.527
0.250
0.062
-0.723
Age
0.042
0.114
-0.191
0.274
0.035
0.365
Race
0.010
0.044
-0.079
0.099
0.389
0.221
DCART-Q
0.171
0.077
0.015
0.327
0.087
2.224
DPISQ
0.086
0.166
-0.251
0.423
-0.120
0.520
Notes. R2= 0.185, p = 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement
** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.
