Reducing absenteeism and turnover among part-time labor in community sport settings: A case study example and project guidelines for sport management students

Authors: Michael J. Diacin1

1Department of Kinesiology, Health, and Sport Sciences, University of Indianapolis, Indianapolis, IN, USA

 

Corresponding Author:

Michael J. Diacin, Ph.D.

1400 E. Hanna Ave.

Indianapolis, IN 46227

(317)791-5703

[email protected]

Michael J. Diacin, Ph.D., is an Associate Professor in the sport management program at the University of Indianapolis. His research interests focus on sport management pedagogy, experiential learning, and consumer incentives within spectator and participatory sport organizations.

ABSTRACT 

Part-time employees are critical to the daily operation at many sport and recreation focused businesses. Managers at many sites regularly deal with turnover and absenteeism among part-time workers. Absenteeism among the part-time workforce is problematic when less than a full staff is present to perform critical tasks. It negatively impacts customers through longer wait times and employees through increased workload. Therefore, managers in these settings should be making attempts to retain quality employees for as long as possible and offset the detrimental consequences of absenteeism. Managers could develop initiatives to ensure attendance from employees scheduled to work at times of peak customer presence as well as incentivize employees to replace absent workers on short notice. Therefore, the purpose of this work is to provide students with a case study situated within the possible employment setting of community-based sport and recreation facilities and complexes and have them develop initiatives to improve attendance and longevity of employment among part-time workers.

The application to sport management is that current students could likely work in businesses that employ part-time, seasonal workers. Commercial sport and recreation facilities and complexes exist in many locations; therefore, there is a strong likelihood that current sport management students will be working in these settings after graduation. Furthermore, they could benefit from imagining themselves overseeing a labor force of part-time workers and developing initiatives aimed at those part-time workers ranging from high school aged students to senior citizens. As future managers in these settings, students could be challenged to find ways to reduce absenteeism, fill staff shortages created by absenteeism on short notice, and retain quality workers for longer durations. The efficiency and effectiveness of the operation is highly dependent upon part-time workers; as a result, it would be worthwhile to develop initiatives to best ensure the operation is running at a maximum level of efficiency and effectiveness.

KEYWORDS: management, incentives, employees

INTRODUCTION 

Commercial sport and recreation businesses may range from single buildings to expansive multi-sport complexes. These complexes might be referred to as “sports campuses.” The size of these sites could range from an indoor facility measuring 50,000 square feet to a larger complex measuring hundreds of acres. The activities that take place within could include any assortment of team-based and individual activities. Basketball, hockey, tennis, gymnastics, soccer, flag football, cornhole, and pickleball are among the activities conducted at these sites. Regarding ownership and management of these facilities and complexes some might be owned by a municipality and managed by the municipality’s sport and recreation division. Some municipalities choose to outsource the daily management to a private company while other facilities and complexes are privately owned.

At many of these sites, a core of full-time managers directs the overall operation. The quantity of full-time employees could vary based upon the size and scope of the operation. A common aspect within these facilities and complexes is that the full-time managerial core depends on a team of part-time employees who execute many significant tasks related to customer service and maintenance. The part-time staff includes people from different age groups ranging from high school aged students to senior citizens. They receive an hourly wage, and some might receive fringe benefits such as free use of the facility (e.g., swimming pool, fitness equipment). With rare exception, part-time employees do not receive health insurance, retirement contributions, and/or other benefits that are often provided to full-time workers.

An operation in which part-time employees are heavily relied upon presents challenges to the management. Despite being counted on to execute important tasks, part-time workers are not highly compensated, nor do they receive the same benefits given to full-time staff. Unlike full-time staff, the job might not be their primary focus nor primary source of income. This population could be more likely to leave if other opportunities become available or not report for duty if other circumstances arise. Consequently, reliability and retention of part-time employees have consistently been identified as a critical issue facing managers that work in commercial sport and recreation settings (McCole, Jacobs, Lindley, & McAvoy, 2012). Consequences resulting from frequent absenteeism and rapid turnover of part-time employees could negatively impact the operation in numerous ways; therefore, management should attempt to be proactive to best mitigate the negative effects associated with frequent absenteeism and rapid turnover.

Although turnover is an inevitable aspect associated with operating any business, lessening the amount of turnover can be beneficial. The cost associated with turnover can be significant. McKinney, Bartlett, and Mulvaney (2007) identified the consumption of time and financial resources as consequences of turnover. First, there could be a cost to announce vacancies through sites that charge for posting them (e.g., classified listings in the local newspaper, websites targeting job seekers). In addition, there would be a cost associated with additional wages being paid out because a new hire could be working alongside another employee to learn the job. Since that new hire is earning a wage while working alongside another employee earning a wage, the aspect of paying two wages to do one job exists until the new hire has been fully trained and able to do a job on their own.

In addition, the cost of time spent by management on screening and interviewing candidates could be significant. Although part of the job, these activities command time, and frequent turnover means that the managerial staff is frequently spending time on screening and interviewing activities to fill vacancies. If management consistently spends time on these activities, the time spent on other aspects of the operation decreases. In a setting where there are small quantities of managerial staff and each manager “wears many hats,” retention of part-time workers would benefit management because less of their time would be dedicated to finding replacements for departed employees.

Frequent absenteeism and turnover could be especially problematic because of the negative impact to an operation when inexperienced staff is working shorthanded. For example, absenteeism could add to the workload and stress to the employee who did show up for work. In addition, there could also be a negative consequence for customers, as staff shortages could result in negative outcomes such as longer lines and wait times. If customers repeatedly have negative experiences, they might be motivated to go elsewhere to pursue their leisure interests.

On the other hand, a fully staffed operation with an experienced workforce benefits coworkers and customers. When a full contingent of experienced employees is working, no one is placed in a position of having to cover for the absent worker. In addition, the accumulation of experience increases efficiency and effectiveness within the operation. Shorter lines and shorter wait times benefit the customer. Ensuring the customer has a positive experience is critical to securing their ongoing patronage. Although absenteeism and turnover will occur, management should strive to incentivize those employees to work when scheduled as well as remain for an entire busy season (McCole et al., 2012). Management could establish various initiatives to minimize absenteeism and turnover. The details of those initiatives are expanded upon in the following section.

INITIATIVES TO REDUCE TURNOVER AND OFFSET STAFF SHORTAGES 

Commercial sport and recreation facilities are highly reliant on part-time labor to execute many important tasks. There are many circumstances that would cause these employees to miss their scheduled shift on short notice or leave the job altogether. Regardless of the legitimacy of the reason for absenteeism, such occurrences negatively impact both part-time and managerial staff, as well as customers. Therefore, a full complement of staff is needed to ensure maximum efficiency and effectiveness occurs on any given day.

These facilities and complexes are also potential employment settings for sport management students. Graduates may begin as mid-level managers in community-based sport and recreation facilities and complexes as a first job in the sport industry after graduation. Because sport management students could be working in a setting where turnover and absenteeism could be frequent, it would be worthwhile for them to engage in an exercise before entering the setting that would challenge them to think proactively and create a program designed to reduce incidents of frequent turnover and absenteeism. Although they will never eliminate absenteeism and turnover, they should be thinking proactively to minimize absenteeism as well as increase longevity among part-time employees.

Therefore, the purpose of this case study exercise is to provide students with an opportunity to engage in a managerial challenge within the possible employment setting of community-based sport and recreation facilities/complexes. It is designed to help students understand the challenges of working in settings where there is a high level of reliance upon part-time labor as well as challenge them to create a proposal designed to entice potential part-time workers to stay for a particular duration, fulfill their scheduled shifts, and/or assist in situations of absenteeism by filling shifts left open by an absent employee. The initiative could focus on a period as short as a single day to an entire peak season lasting several months. The proposal might also include focus on performance-based initiatives. For this case study exercise, the student could take the role of a mid-level manager. This mid-level manager would supervise part-time staff and reports to a higher-level full-time staff member, such as a General Manager. The proposal would be presented to the General Manager (the course instructor and/or an invited guest such as a manager of a local facility or complex).

Although it would take time and effort to create and manage such initiatives, the benefit to colleagues, customers, and the business resulting from fewer incidents of absenteeism and turnover could make the initiative worth the effort and expense. These types of facilities and complexes could generate revenues in the hundreds of thousands to several million dollars. Expenses such as utilities, maintenance, personnel, and equipment/supplies will use up most of the revenues. Therefore, the financial resources available would be limited as the quantity of dollars available for this case study exercise would be $12,000 to $18,000 annually ($1,000 to $1,500 monthly), with the fiscal year starting September 1 and ending August 31 the following year.

“Survive the Day” Initiatives

This initiative is designed to offset staffing shortages that occur when a part-time worker calls off on short notice or does not show up without any notice given. It is intended to ensure enough employees are present to execute various tasks. This initiative could be focused upon accomplishing two ideals. They are to 1) incentivize the people who are scheduled that day to show up for their shift and 2) if someone must call off, incentivize someone who wasn’t originally scheduled to take the place of the worker who called off on short notice or did not show up for work (e.g., “no call, no show”).

“Survive the Season” Initiatives

Although open for business year-round, the amount of customer activity within commercial sport and recreation facilities and complexes fluctuates based on the season. The greatest amount of customer traffic occurs during the winter months (early December through late February). Ice surfaces have been booked from the late afternoon (4pm) until late night (1am) on weekdays and booked from 6am to 1am on Saturday and Sunday. Youth association and high school hockey teams are conducting their games in the early evening. Adult leagues occupy the latter hours. In addition to the presence of these user groups, youth and high school games bring a greater amount of spectator traffic as friends, classmates, and family members of the participants attend the contests. It is also the period when public skating attendance peaks. As many as 300 customers could be admitted for a two-hour session on a Saturday or Sunday afternoon.

The ice surfaces are booked for similar hours during the months of September and October. Practice and scrimmages are typically conducted. These activities bring user groups but do not bring spectator traffic. Public skating is offered but would bring a fraction of the traffic seen during the winter months. To ensure employees are present to cover the hours in which user groups are present, a “survive the season” initiative could be designed to incentivize part-time employees to stay with the job from September through February. Contingencies could also be added. For example, employees would need to work a specified quantity of shifts/hours (especially on weekends). In addition, limits to the number of times an employee is absent from a scheduled shift, especially weekends, could be implemented.

Recognition for Performance Initiatives

This initiative would focus on rewarding employees for engaging in certain behaviors outside of the attendance-based actions. Employees who engage in quality work would be rewarded for doing so. Support for recognizing employees was revealed by Kellison, Kim, and Magnusen (2013) as they surveyed 522 part-time college aged (18-23 years old) campus recreation center employees from eleven universities to gain insight regarding factors that influenced their intentions to continue working in a part-time capacity at their respective university recreation centers. Recognition was identified as a key factor that positively influenced intentions to remain with the job/organization. Because many of the part-time workers in this case study exercise are in the age range of 18-23, these findings lend support to attempting recognition-based initiatives that have potential to retain employees.

Many organizations have a performance-based initiative in place, commonly referred to as an “employee of the month” program. This is often a competition-based system where one person is selected from the entire staff and receives the award. Various challenges to implementing initiatives where an employee is rewarded in this fashion exist. First, there is a challenge to objectively measuring and documenting the employee’s work. Because many of the part-time support staff members working in commercial sport and recreation settings do not engage in tasks that are easily quantifiable, measuring “good work” could be subject to opinion and perspective. Second, there are different employee groups, each engaging in different tasks. For instance, some of the workers are front of the house workers who are frequently interacting with customers. Others would be considered back of the house workers who do not regularly engage with customers. Consequently, there would be difficulty in comparing the performance of front of the house to back of the house workers because of the differences in their jobs. As a result, it would be the responsibility of the manager to establish parameters, standards, and/or benchmarks for each employee group.

Although an initiative for rewarding good deeds/good work is well-meaning, a system that relies on opinion, relationships, and other subjective criteria could result in more employees feeling less valued if they perceive they earned the reward but were passed over. Instead of having a competition among all employees working different jobs, an alternative is to establish the initiative so that each employee would be able to “control their own destiny.” That means each employee could receive the reward if certain benchmarks and/or standards are reached. If the commitment is made to proceed with such an initiative, an objective system of measurement is needed so that the employee could clearly understand what is expected to obtain the reward. Otherwise, employees could perceive the initiative as subjective, biased, and/or arbitrary.Regardless of the initiative(s) chosen, the proposal should include the following content:

  • The parameters/standards/actions that the employee will take (e.g., filling in for an absent employee, working “x” number of peak busyness shifts over a particular period) to receive the reward.
  • The rewards that will be given.
  • The costs are associated with implementing the initiative.
  • Argument behind why this initiative is feasible in this setting and with this workforce.
  • Identification of potential obstacles for success; why could this initiative be implemented and still not provide the desired results?

PROJECT DETAILS

The following sections for this case study exercise include further description of the setting, operating schedule, manager and part-time worker job descriptions and categories. The quantity of part-time workers hired for each area and the quantity of workers from each category that is on duty at a given time is provided. In addition, the times of day and days of week they typically work as well as the duration of their shifts are indicated.

Facility Setting and Description

The facility that will be utilized for this case study is a multi-purpose facility in which the terms “ice arena” or “hockey arena” might be used. The activities that commonly take place would be ice sports such as hockey, figure skating, and recreational skating. The facility is approximately 180,000 square feet. Two arenas that each house an ice surface of 85×200 feet are the primary activity spaces. When the ice is removed, activities can be conducted on the concrete floor. During off-peak months, various events and programs such as trade shows, exhibitions, and circuses could be conducted.

Each arena consists of stationary spectator seating in the form of metal bleachers with a seating capacity of 1,000. Each arena has six locker rooms (four for hockey teams, one for referees and one additional room to be used on an “As Needed” basis (e.g., for girls participating on boys’ youth hockey teams). There are storage areas and a large garage area where the ice resurface machines are housed. Other areas not accessible to the public include mechanical rooms where the ice cooling equipment is housed. Public areas would consist of a large lobby in which numerous benches and tables are present for the convenience of the patrons. Accessible from the lobby is the concession stand, pro shop (equipment and merchandise sales), arcade, office space, fitness center, restrooms, and two multi-purpose rooms where staff meetings, birthday parties, and team banquets could be held.

Facility Operating Schedule

Many sport and recreation related businesses are open for business seven days a week and typically see most customer activity during weeknights (after 5pm, Monday through Friday) and weekends. On weekends, activity could start as early as 6am and continue as late as midnight or 1am during the peak season. This is when staff is most needed to cover these hours. The amount of customer activity will be at its peak from early December until early March. This is the peak period for youth hockey games, which increases the amount of spectator traffic as family members attend the contests. High school programs could rent space for their practices and games as well. Their games bring additional spectator traffic. It is also peak season for public skating sessions. A public skating session on a weekend afternoon during the winter months could attract as many as 300 paying customers for a two-hour window of skating time. 

Regular business hours (e.g., Monday-Friday from 8am-5pm) are typically the periods with the least amount of customer activity. During this time, most maintenance and cleaning activities occur. Deliveries from vendors also occur during this time. Therefore, there is a need for management and custodial personnel to be present during times of minimal customer activity.

Full-Time Manager Descriptions

The facility is overseen by a general manager and additional full-time, salaried assistant managers. The general manager and assistant managers participate in various aspects of the operation. It is not uncommon for each assistant manager to not only have a primary responsibility regarding some managerial aspect, but also “wear many hats” and participate in other aspects of the operation. For example, one of the assistant managers might be responsible for overseeing tasks in connection with human resources. This person would be responsible for writing and disseminating job descriptions, screening applicants, and conducting interviews. The other assistant managers could be responsible for overseeing facility maintenance/cleanliness, the concessions operation, the pro shop/retail operation and/or marketing/programming. In addition, full-time managers participate in other aspects of the operation as they should be able to step in and assist anywhere on an “as needed” basis. This would include driving the ice resurface machine, operating cash registers, distributing rental equipment, and spot cleaning.

At least one of the full-time, salaried staff members are present when the building is open for business. This would include coverage during regular business hours as well as weeknights and weekends. It is possible that during peak times of business, more than one manager could be present. It would not be uncommon for 4-5 full-time management members to be employed at this type of facility.

Part Time Staff Descriptions

Perry (2018) identified different categories of employees that seek part-time employment in commercial sport and recreation facilities and complexes. The first category consists of individuals who are looking for some work to keep busy and gain supplementary income. A retired individual, perhaps a senior citizen, would fall into this category. The second category consists of post-college aged workers with full-time jobs who want or perhaps need a second job to help pay bills, accumulate extra savings, etc. The third category would consist of high school and college aged individuals who are looking to gain work experience and obtain income. This demographic is typically working around their schooling.

Regardless of the demographic, these jobs are often not the primary focus in the employee’s life. Other aspects are higher on the priority scale; therefore, employees might not alter other life aspects (e.g., primary job, school, family commitments) to work these jobs. Because the employee is not intending to make a career out of the part time job in this setting, this could have an impact upon attendance and performance.

These part-time workers fulfill “front of the house” and “back of the house” positions. In this setting, front of the house positions consists of duties such as cash handling/cash register operation, serving food products, collecting participation fees, distributing rental equipment, monitoring customer conduct, and being present in the event customers have questions and/or need assistance. Front of the house positions that often exist in the setting include concessions, skate staff, pro shop/merchandise sales, and front desk workers/receptionists.

Back of the house employees largely contribute to the cleanliness and upkeep of the facility. In this setting, custodians and ice resurfacing machine drivers/building attendants are common types of back of the house workers. They have little interaction with customers and in the case of custodians, often work when few to no customers are present in the facility.

These employees are paid an hourly wage and could work as little as 10 hours a week or as many as 40 hours a week. Accumulated hours are monitored so that the employee does not exceed 40 hours a week. If 40 hours in a week are exceeded, overtime compensation of one and a half times the employee’s standard hourly wage would be paid. In many cases, the hourly wage could be at or slightly above the locally mandated minimum wage.

For this case study exercise, seven part-time worker categories exist, consisting of several front and back of the house positions. Several people are on the roster within each worker category. Not everyone who has been hired and appears on the roster is working at the same time. Saturday and Sunday will be the busiest days requiring the greatest amount of part-time worker participation. Key duties, the time of day and quantity of hours per shift that employees within each of these categories are typically scheduled, and worker demographics are provided.

Concessions

Concession stand workers are responsible for preparing and serving food and beverages. “Quick serve” foods are usually prepared and then held in a warming bin or warming rollers (e.g., pizza, popcorn, hot dogs). Some facilities might possess a deep fryer, which would allow workers to prepare items such as fries, mozzarella sticks, etc. Concessions workers work when customer traffic is heaviest (evenings and weekends), except for periods when school is out of session such as winter break. High school and college aged employees are common. Post college aged adults working part-time, perhaps around another full-time day job, also staff the concessions operation. One person will be on duty most of the time. During the periods of peak customer traffic, such as public skating sessions during the winter months, two people could be scheduled to work at the same time. Shift duration is commonly 4-6 hours. There could be 6-8 employees on the roster in this area.

Skating staff

The skate staff would consist of counter/desk workers who are responsible for collecting admission fees and distributing “skate passes” to patrons participating in public skating sessions. Skate passes are often colored stickers the patron can wear on their clothing so that staff can easily see they paid their admission fee for that public skating session. They would also distribute rental skates to patrons who do not own their own set of skates. The other type of worker in the skating staff category is the “skate guard.” These individuals ensure those who enter the ice have paid their admission, indicated by the skate pass they are wearing. They also watch for and report any injuries or incidents of dangerous behavior to management. This worker group commonly consists of high school and college age individuals. Their work schedule aligns with public skating sessions, which are typically on Saturday and/or Sunday. With a two-hour skating session, for instance, workers could be scheduled for a 3.5 to 4-hour shift. This duration allows for them to be on duty before customers arrive and allows for post-session cleanup, putting skates away, etc. The roster could consist of 4-8 employees in this category. 1-2 skate guards would be on duty for each session (2 during the busiest winter sessions) as well as 1-2 counter/desk workers (2 during the busiest winter sessions).

Pro shop staff

Merchandise such as tape, water bottles, mouthguards, sticks, helmets, and other equipment is commonly sold in the “pro shop.” These workers are responsible for operating the register and assisting customers. Some light cleaning within the area is periodically assigned. In some facilities, skate sharpening is offered, and the pro shop employees will sharpen customers’ skates. During slow periods, the pro shop staff often is charged with sharpening the rental skates that will be used during the public skating sessions. The pro shop would be open during the evening and weekends. This worker group commonly consists of high school and college age individuals. The shift duration could last from 4-6 hours. On weeknights, one person would likely be on duty. During the weekends, especially the times around public skating sessions, two people could be on duty. There could be 4-6 people on the roster within this worker category.

Fitness center workers

The facility in this case study has a fitness center on site. The fitness center would include equipment that would commonly be found at commercial fitness centers, such as treadmills, elliptical trainers, and free weights. Monthly and/or annual memberships could be sold. This area could be open from early morning until late evening (6am-11pm) seven days a week. Fitness center workers would ensure members have checked in, engage in light cleaning duties and conduct minor troubleshooting of equipment. These workers do not conduct personal training sessions. During the weekday mornings and afternoons, the workers would mostly consist of senior citizens or other post-college aged adults. High school and college aged individuals would typically work evening and weekend hours. Because this area is staffed seven days a week from early morning to late evening, 6-8 individuals could be on the roster for this position. One worker would be working at a time for a shift typically lasting 4-6 hours.

After hours reception desk

These workers would be on duty after regular business hours on weekdays and on weekends. This person would likely distribute keys for the locker rooms to hockey teams, provide information to basic inquiries (e.g., assigned locker room number), answer phone calls, and serve as a point of contact for patrons who report a circumstance in need of attention, such as cleaning up a spill, restocking paper products in restrooms, etc. The desk worker would contact the manager on duty and/or other worker groups to address the need. In some facilities, the desk worker might assume duties such as collecting fees and distributing passes for public skating patrons. Workers in this group could range from high school or college students to post college aged individuals and senior citizens. One person at a time would be on duty and there could be 3-4 people on the roster in this category.

Building attendant/ice resurface machine driver

Building attendants are responsible for resurfacing the ice for each new user group. In between ice resurfacing duties, they are responsible for surface cleaning in locker rooms, restrooms, spectator seating areas, and lobbies/foyers. Restocking restrooms and mopping up spills are among cleaning and light maintenance duties that a building attendant would be expected to perform. They are scheduled during times when user groups are present in the building; therefore, the schedule consists of mostly evening and weekend work. Building attendants are required to be a minimum of 18 years old because the job includes operation of the ice resurfacing machine. Middle-aged individuals working around a primary job could also be working in this role. One person would be scheduled to work in this capacity on a weeknight; however, two people could be scheduled to work on weekends during peak times. The shift duration would likely be 7-8 hours. There could be 4-6 employees on the roster.

Custodial

Custodians are responsible for the overall cleanliness of the facility. Much of their time is spent cleaning and restocking restrooms and locker rooms, emptying trash bins and cleaning spectator seating areas. These employees typically work when the building is not full of customers so that they can engage in deep cleaning activities. Working during regular business hours (e.g., Monday-Friday 8am-5pm) is common. They could also be scheduled for late night/early mornings on Friday night into Saturday morning and Saturday night into Sunday morning as the facility will typically be full of customers when the doors open on weekend mornings. Worker demographics could vary, ranging from post college age to semi-retired individuals. Some of these employees might be working this job along with another job. There could be 2-4 people employed in this category, with one person on duty at a time. A typical shift duration could be 4-8 hours. If a special cleaning or maintenance project is planned, more than one worker from this category could be scheduled.

APPLICATION TO SPORT MANAGEMENT

Regarding the application of this work to the educational setting, sport management students could find this case study exercise useful because it gets them to imagine themselves working in a setting and engaging in challenges they could face once they enter the workforce as a full-time, managerial employee. Commercial sport and recreation facilities and complexes exist all over the world; therefore, there is at least some likelihood that some will work in these settings. Therefore, it is important to expose students to situations they could experience within possible employment settings. Having students generate content that could be used in an actual setting would be useful because many could be overseeing part-time workers from various demographics and life situations at their respective workplaces.

Students who work in these settings will not only be faced with challenges related to staffing but could also be working in settings where there are not large amounts of financial resources available to them. Many of these facilities and complexes are smaller “mom and pop” businesses that do not generate massive amounts of revenue. Therefore, they will have to find ways to address a challenge with a limited amount of money (in this case study $1,000-$1,500 monthly limit) at their disposal.

The content students create in association with this case study could take the form of a written proposal and/or an oral presentation. In order to give them the opportunity to create the most extensive proposal possible, it is suggested that students create content for each of the three initiatives (survive the day, survive the season, and performance). As a middle level manager who was hired by a superior, the student would report to that individual or perhaps several individuals who occupy a higher position in the organizational chart. For this case, the student could present the content to the instructor of the course who would represent the upper-level member of management. It is suggested that if feasible, managers from a local facility or complex be invited to participate in the presentation of the proposal. Their presence and scrutiny would add an additional layer of authenticity to the endeavor. Furthermore, the instructor might wish to reach out to managers of local facilities and complexes to see if they would like for students to create a proposal specifically for their operation. The manager would possibly first appear as a guest speaker and share details of the operation with the students. Students could use that visit to ask questions and gain a better understanding of the operation and then develop a plan for that manager.

Regarding the execution of the students’ proposal in the “real world,” it is likely that costs and personnel limitations would be presented as reasons as to why these ideas would not reach the execution stage. Therefore, part of the challenge for students is to create a plan that would be financially feasible for a small business as well as a plan that could be executed by a single manager or perhaps a small managerial team of 2-4 people. In closing, it is hoped that this case study exercise will benefit faculty seeking content to add to their courses. This case study could be executed within courses focusing on human resources management, facility management, and/or financial management. Because a large quantity of these operations exists, it is possible that students will secure employment in them. Therefore, this endeavor can help to further prepare students for managing a predominately part-time workforce within commercial sport and recreation facilities and complexes.

REFERENCES 

  1. Kellison, T. B., Kim, Y. K., & Magnusen, M. J. (2013). The work attitudes of millennials in collegiate recreational sports. Journal of Park and Recreation Administration, 31(1), 78-97.
  2. McCole, D., Jacobs, J., Lindley, B., & McAvoy, L. (2012). The relationship between seasonal employee retention and sense of community: The case of summer camp employment. Journal of Park and Recreation Administration, 30(2), 85-101.
  3. McKinney, W. R., Bartlett, K. R., & Mulvaney, M. A. (2007). Measuring the costs of turnover in Illinois Public Parks and Recreation Agencies: An exploratory study. Journal of Park and Recreation Administration, 25(1), 50-74.
  4. Perry, P. M. (2008). Finding great part-time workers. NSGA Retail Focus, 61(2), 10-11, 22.

2025-12-05T10:58:04-06:00June 3rd, 2026|Contemporary Sports Issues, General, Leadership, Research, Sports Management, Sports Studies|Comments Off on Reducing absenteeism and turnover among part-time labor in community sport settings: A case study example and project guidelines for sport management students

An Analysis of Carbon Emissions from College Football Recruiting Visits

Authors: Jeffrey J. Fountain1, Thomas Wuerzer2, & Peter S. Finley1

1Department of Management, Nova Southeastern University, Fort Lauderdale, FL, USA

2Department of Public Administration and Real Estate Development, Nova Southeastern University, Fort Lauderdale, FL, USA

 

Corresponding Author:

Jeffrey J. Fountain, Ph.D.

3301 College Avenue

Fort Lauderdale, FL 33314

[email protected]

954-262-8129

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 1 Descriptive Analysis of Variables: Mean and Standard Deviation
VariableDescriptionMeanSD
FB_TotalRevTotal Revenue from Football$66,518,526$25,205,244
Mens_TotalRevTotal 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 Revenues77.40%11.17%
Dept_TotalRevTotal 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 Revenues52.50%13.10%
Mens_RecruitExpTotal Recruiting Expenses from all Men’s Sports (including Football)$1,391,362$704,861
Dept_RecruitExpTotal Recruiting Expenses from the entire Athletic Department$1,878,962$855,080
FB_OpsExpTotal Operation Expenses for Football$5,683,499$2,558,649
Mens_OpsExpTotal Operation Expenses for all Men’s Sports (including Football)$8,800,193$4,035,500
Dept_OpsExpTotal Operating Expenses for the entire Athletic Department$12,787,529$5,068,156
FB_TotalExpTotal Expenses for the entire Football Program$33,846,192$11,218,516
Mens_TotalExpTotal 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 Expenses63.18%7.58%
Dept_TotalExpTotal Expenses for the entire Athletic Department$116,141,712$27,071,219
FB_DeptExp%Football Expenses as a Percentage of the entire Athletic Department Expenses63.18%7.58%
Win_PercentageFootball teams Win Percentage62.43%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.

RESULTS AND 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 2   RVCF by school for all reported official visits from 2013 to 2020  
Schooln% Drove (No Flight)Car (No Flight)Car  (To/From Airport)FlightHotelFoodWasteMSDTotal
Washington State2762.17%564.4821,782.22137,796.796,405.954,084.79855.60621.34362.98171,489.84
Oregon2813.20%667.817,322.00150,332.796,522.004,158.79871.10604.54323.99169,874.50
Nebraska3735.90%1,437.039,203.29131,283.868,657.325,520.391,156.30421.60171.96157,258.20
Alabama37817.99%6,138.0121,504.7481,842.828,773.375,594.391,171.80330.34219.30125,025.12
Minnesota32815.55%1,543.639,279.4494,019.657,612.874,854.391,016.80360.75190.85118,326.78
Louisville3437.87%1,814.239,356.0792,166.147,961.025,076.391,063.30342.38189.87117,437.16
Oklahoma31521.27%7,627.739,318.5385,483.067,311.144,661.99976.50364.79187.59115,378.96
Tennessee35614.89%5,866.3411,129.2172,691.178,262.755,268.791,103.60293.04213.71104,321.87
Texas A&M32746.18%15,370.2818,162.7656,491.357,589.664,839.591,013.70314.50223.19103,467.35
Washington25122.31%2,329.335,626.1481,958.425,825.713,714.80778.10399.33250.68100,232.49
Ohio State30126.58%7,676.345,596.1469,715.536,986.204,454.79933.10316.82223.4995,362.10
Arkansas32515.38%4,563.0713,222.0664,152.597,543.244,809.991,007.50293.23166.9795,298.46
Indiana27315.38%3,307.9013,293.6462,085.006,336.324,040.39846.30329.34169.1389,909.57
Florida33328.53%8,658.696,782.7656,932.067,728.924,928.391,032.30258.45179.7686,063.13
Miami30139.53%3,920.904,735.8663,566.686,986.204,454.79933.10280.01263.3084,597.54
Florida State31714.20%3,929.026,600.1159,945.347,357.564,691.59982.70262.44182.3083,506.32
Auburn31335.14%9,914.5514,971.1242,452.127,264.724,632.39970.30256.25161.7180,205.21
Georgia27033.70%6,709.3215,279.3141,800.056,266.693,995.99837.00276.78196.7074,888.37
Penn State25429.53%9,210.7316,663.5938,471.375,895.343,759.20787.40289.49192.6574,787.61
Mississippi State29356.31%17,224.2017,413.1326,745.766,800.524,336.39908.30250.61176.9073,428.32
Kentucky27419.71%4,331.997,066.4249,895.236,359.534,055.19849.40264.81146.5772,557.76
LSU30038.00%7,465.215,491.1341,783.316,962.994,439.99930.00222.53146.7367,072.63
South Carolina26332.70%7,526.485,361.7631,921.566,104.233,892.40815.30211.49128.8055,621.71
Total7,04523.57%137,797.27255,161.411,633,532.66163,514.32104,265.8721,839.48328.91203.012,316,111.00
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 3 Largest Mean Difference in RVCF between the two time periods
 2013-2016 2017-2020 
SchoolsnTotalM nTotalM DifferenceMD
Ohio State15643,804.86280.80 14551,557.24355.57 7,752.3974.77
Penn State10225,847.68253.41 15247,683.11313.70 21,835.4460.30
Oregon12270,128.43574.82 15999,746.07627.33 29,617.6552.51
Florida St.16546,656.85282.77 15236,535.52240.37 -10,121.32-42.40
Arkansas15349,311.35322.30 17245,987.11267.37 -3,324.24-54.93
Miami15146,944.88310.89 15037,339.37248.93 -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.

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2025-10-13T15:18:13-05:00May 27th, 2026|Contemporary Sports Issues, Research, Sports Studies, Sports Studies and Sports Psychology|Comments Off on An Analysis of Carbon Emissions from College Football Recruiting Visits

Identifying Self-Awareness of Leadership Abilities Using 360 Degree Feedback Method: A Case Study of Collegiate Rowers

Authors: Stephen Cadoux1, Kimberly Shaffer2

1Department of Clinical Psychology, Antioch University New England, Keene, NH, USA

2Department of Sport & Exercise Science, Barry University, Miami, FL, USA

 

Corresponding Author:

Kimberly Shaffer

[email protected]

Stephen Cadoux, MS, is a Clinical Psychology Doctoral student at Antioch University New England. His research interests focus on sports neuropsychology, leadership development, and neurocognitive effects of stress.

Kimberly Shaffer, Ph.D., CMPC is an Associate Professor and program director of the Sport, Exercise & Performance Psychology Program at Barry University. Kimberly’s areas of research interest include athlete identity, transition from sport & core values of performers. 

ABSTRACT 

Self-awareness is one of the most vital characteristics to effective leadership, yet it is a trait rarely measured within leaders. Without self-awareness, leaders place themselves in a position of weakness that can negatively impact their team’s performance. Using a Female NCAA Division II Rowing Team (n= 7), and their coaches (n=2) this study aimed to identify if captains of a collegiate athletic team are self-aware of their leadership abilities. The study was conducted via the Multifactor Leadership Questionnaire (MLQ) and a research technique known as the 360-degree feedback method. Teammates and coaches completed the MLQ about their team captain(s) leadership abilities. Meanwhile, the captain(s) rated their own leadership using the MLQ. Results from the three participant groups were compared to evaluate self-awareness (S-A) of the captain(s). S-A was determined if the Captain(s) self-reported scores are within the standard deviation of the scores of the Coaches and Teammates. Results suggested differences in the S-A of the two captains is, such that Captain X scores were found to be outside the SD of either the Coaches and/or the teammates in six of the twelve leadership subcategories, while Captain Y self-reported scores outside of their coaches and/or teammates SD on 4 different subcategories. The applied nature of this study is valuable for creating leadership programs within collegiate athletic departments and provides a quantitative model for assessing self-awareness in leadership.

Keywords: coaching, NCAA athletics, peer assessment

INTRODUCTION 

Leaders are critical to the functioning of any group, team, or organization. For teams to be successful, they require motivation, hard work, social and task cohesion, and swift decision making (1, 3, 8, 17). Over the past 60 years, there have been over 60 different leadership theories; each aiming to define leadership into distinct and unique concepts (8, 17). 

Presently, the most validated and widely used theory is the Transactional and Transformational Leadership Theory (TTLT) (3). The TTLT involves dividing leadership into two areas: Transactional leadership and Transformational leadership (3). Avolio and Bass modified TTLT to include Passive/ Avoidance behavior (1).

According to TTLT Avoidant/ Passive leaders are more passive and reactive. Avoidant leaders “avoid specifying agreements, clarifying expectations, and providing goals and standards” (1). Individuals with this style can negatively impact those around them and hurt the team’s overall performance. Within Avoidant behavior, are two sections: Management-by-Exception: Passive (MBEP) and Laissez-Faire (LF). Individuals who are high in MBEP wait until an issue arises before acting while leaders high in LF go one step further and fail to ever intervene in issues (1).

The second major category of leadership within the TTLT is Transactional leadership. Transactional leadership is based on exchanging rewards for goal completion, good performances, and desired behavior (3). These leaders clearly lay out the expectations they have for their subordinates, and they encourage their followers to perform to the best of their abilities (1). Transactional leadership is based on Contingent Reward (CR) and Management-by- Exception: Active (MBEA) (Avolio & Bass, 2004). Leaders’ high in CR offer rewards in exchange of one’s service; celebrating the accomplishments of their team and its members to reinforce positive behavior teams accomplishments Conversely, MBEA minded leaders focus on past failures, mistakes, and irregularities. These leaders set a specific standard that all members must meet and any deviation from this standard is confronted (1).

The third category of leadership in the TTLT is Transformational. Transformational leaders are viewed as the highest level of leaders (3). These individuals “connect with followers and appeal to their strengths in order to best challenge them to be more productive” (14, p. 62).

Avolio and Bass added “5 I’s” under the Transformational leadership (1). The 5 I’s are Idealized Attributes (IA), Idealized Behaviors (IB), Inspirational Motivation (IM), Intellectual Stimulation (IS), and Individual Consideration (IC; 1). Both IA and IB fall under the subset of Idealized Influence. Leaders with high Idealized Influence are leaders who consider others needs before their own and are people who others want to emulate (1). Inspirational Motivation (IM) leaders use their leadership to motivate those around them. Intellectual Stimulation (IS) leaders help fuel their follower’s intellectual mental efforts. They help their followers to be more innovative and creative as well as stimulate new ideas, thoughts, and solutions. Lastly, Individual Consideration (IC) leaders focus on their groups need for achievement and growth. They accomplish this by acting as a peer mentor and coaching figure to those around them (1).

The increase in leadership research has been primarily led by Industrial-Organizational psychology (I/O), focusing on improving for-profit businesses, personnel, and staff (5). In contrast, the field of sports has not received comparable levels of research attention or financial investment (16). This disparity has created several gaps in sport leadership research, particularly within the sub-category of leader self-awareness.

Self-awareness is arguably the most important aspect of leadership (9). Despite extensive leadership research in sports, self-awareness is rarely measured (7). Most leaders are not self-aware of their own abilities or talents (7). Without self-awareness, captains are at a disadvantage when it comes to leading their teams to victory. With the amount of money, time, and energy put into these sports teams, captains cannot have large flaws in their leadership.

While there are many ways to measure self-awareness, the 360 Degree Feedback method is not as widely used as it requires more empirical evidence. The 360 Degree Feedback method was designed for the use of providing business managers and executives more accurate feedback on their performance (5). This method involves having the leader (ratee) score their abilities on a survey or questionnaire. The organization then has several staff, peers, and supervisors anonymously complete that same survey about the ratee. This provides the organization with not only how the leader views themselves, but also how the rest of the organization and team view them. The organization can then provide the leader with structured feedback. Using 360-degree feedback has been found to provide more accurate feedback, enhance self-awareness, and can increase self-perceptions in individuals (4).

While the 360 Degree Feedback model is being utilized within the business world, the use of this method has also branched into other academic areas, including sport psychology.  Consultant groups, such as Amplos, have applied the method to identify development within coaches and athletes at various Power 5 athletic institutions (15). Although the method has proven successful in applied settings, it lacks validity in the scientific community and needs empirical evidence to further support its success.

The purpose of the proposed study is to use the Multifactor Leadership Questionnaire (MLQ; 1), and the 360 Degree Feedback Method (6) to identify if collegiate team captains are self-aware (S-A) of their leadership abilities. This study explored three hypotheses: (1)Captains would rate themselves as having higher Transformational and Transactional Leadership as compared to the scores of the coaches and teammates. (2) Captains would rate themselves as having lower Avoidant Leadership as compared to the scores of the coaches and teammates. (3) Captains would have an inverse relationship between the scores of MBEA and MBEP.

METHODS 

Participants

Participants consisted of both male (n=1) and females (n=8) involved in a NCAA Division II rowing team located in South Florida. Ages varied within the three participant categories as both young collegiate athletes and older coaches participated in this study. The Coaches (n=2) had a mean age of 33.50 (SD= ±12.02), the Captains (n=2) had a mean age of 21.50 (SD= ±2.12), and the Teammates (n=5) had a mean age of 21.60 (SD= ±2.30). The Teammate group consisted of 5 participants; however, each Captain rated the other captain and were thus included in the “Teammate” participant group during data collection. With the captains included in the Teammate participant group, the Teammates (n=7) had a mean age of 21.14 (SD= ±2.03).

Procedures

The study began with participant recruitment. Recruitment was conducted via email. Upon recruitment of the rowing team, individual athletes, captains, and coaches were recruited as well. Once recruitment had completed, the study was conducted virtually via an online video call explanation session in which participants received all directions verbally. The PI gave a brief explanation of the purpose of the study, following initial instructions, the PI explained the directions for the consent form, the demographic questionnaire and the MLQ questionnaire (all of which were provided via an online Qualtrics survey link). Participants were instructed to complete one MLQ questionnaire form for each of their participating team captains. After completion of the study, participants were thanked for their time.

 Instruments

Demographic Questionnaire

Demographic questionnaires were created by the PI and were administered to all study participants. Each participant group had its own distinct demographic questionnaire. These questionnaires were used to gather additional data about the participants that the MLQ does not specifically ask for. This data included both personal and athletic information.

Multifactor Leadership Questionnaire

The shortened version of the Multifactor Leadership Questionnaire (MLQ) was used (11). This 45-item self-reporting questionnaire is designed to assess an individual’s leadership abilities, leadership style, and the outcomes of their leadership (11).

The MLQ measures leadership by dividing the subject into three categories: Transactional Leadership, Transformational Leadership, and Passive/ Avoidant Leadership Within these three categories, the MLQ measures these styles using twelve subcategories. Transactional Leadership is divided into CR and MBEA (11). Transformational Leadership is made up of IA, IB, IM, IS, and IC (1). Passive/ Avoidant Leadership is divided into MBEP and LF (1). The last area that the MLQ measures is the outcomes of leadership; this is separated into Extra Effort (EE), Effectiveness (EFF), and Satisfaction (SAT). The MLQ uses a five point-Likert scale ranging from zero (Not at all) to four (Frequently, if not always). The questionnaire’s Cronbach’s coefficient alphas range from 0.63 to 0.92 with an internal consistency above 0.70.

Data Analyses

All data was analyzed using the IBM SPSS Statistics program. A descriptive analysis was conducted to find the means and standard deviations of the self-reported scores. S-A is determined if the captain’s self-reported scores are within the standard deviation of the scores collected from their Coaches and Teammates (1, 11).

RESULTS

Captains
The two captains tested in this study will be labelled as “Captain X” and “Captain Y”. Captain X is an American citizen who has been rowing for 10 years. She has been Captain of her team for 1 year and was also the Captain of her High School rowing team. She believes that her team is highly successful and believes that she has directly influenced the performances of her team. She also describes herself as self-aware of her abilities. Captain Y is an international student studying in the United States. Captain Y has been rowing for only two years, not having rowed in high school. Captain Y also believes her team is highly successful and her leadership abilities directly influence the team’s overall results. She also describes herself as self-aware of her leadership abilities.


Coaches
The coaching staff consisted of a male, American head coach with 12 years of coaching experience and a female, Eastern European assistant coach with four years’ experience. Both Coaches have Coached Captain X for three years and Captain Y for two years. Both Coaches also believe that their team is having a successful season and that their Team Captains are a direct result of that success.


Captain X
As seen below in table 1, Captain X’s self-reported scores were found to be outside the SD range of the scores of their Coaches and/or Teammates in six of twelve leadership subcategories. The first is IM. Captain X (m=4, ±0) self-reported themselves as higher than the scores of the teammates (m=3.30, ±0.48), while the Coaches (m=3.12, ±1.24) rated Captain X between the two groups. Within Intellectual Stimulation, Captain X (m=3.75, ±0) rated themselves higher than both the Coaches (m=2.87, ±0.53) the Teammates (m=3.30, ±0.44). In CR, Captain X (m=3.50, ±0) rated themselves as higher than the Coaches (m=2.25, ±0) while their teammates (m=3.05, ±0.51) scored between them. In MBEA, Captain X (m=2.25, ±0) ranked themselves as higher than the Coaches (m=1.87, ±0.17) but were not outside the scores provided by the Teammates (m=1.65, ±1.16). In EE, Captain X (m=4.00, ±0) scored higher than the rankings of the Teammates (m=3.13, ±0.69) while the Coaches (m=3.16, ±1.17) scored between both of the groups. The last category is EFF, where Captain X (m=4.00, ±0) rated themself higher than the SD of the Teammates (m=3.30, ±0.48). The Teammates scores were not outside the SD range of the Coaches (m=3.37, ±0.88).

Table 1

Mean scores and Standard Deviation’s for Captain X’s MLQ 360-Degree Feedback Test

 IA (SD)IB (SD)IM* (SD)IS* (SD)IC (SD)CR* (SD)MBEA* (SD)MBEP (SD)LF (SD)EE* (SD)EFF* (SD)SAT (SD)
Captain X3.50 (0)3.50 (0)4.00 (0)3.75 (0)2.75 (0)3.50 (0)2.25 (0)1.00 (0)0.25 (0)4.00 (0)4.00 (0)4.00 (0)
Coaches (n=2)3.12 (0.88)3.37 (0.88)3.12 (1.24)2.87 (0.53)2.75 (0.70)2.25 (0)1.87 (0.17)1.25 (1.76)1.00 (1.41)3.16 (1.17)3.37 (0.88)3.25 (1.06)
Teammates (n=6)3.35 (0.57)3.50 (0.46)3.30 (0.48)3.30 (0.44)3.30 (0.77)3.05 (0.51)1.65 (1.16)1.08 (0.61)0.60 (0.57)3.13 (0.69)3.30 (0.48)3.40 (0.65)
Note: *Captains scores are outside the SD for one or both groups

Table 2

Mean scores and Standard Deviation’s for Captain Y’s MLQ 360-Degree Feedback Test

 
 IA (SD)IB* (SD)IM (SD)IS (SD)IC (SD)CR (SD)MBEA* (SD)MBEP* (SD)LF (SD)EE (SD)EFF (SD)SAT* (SD)
Captain Y2.75 (0)4.00 (0)3.50 (0)3.00 (0)3.00 (0)2.75 (0)2.25 (0)0.25 (0)0.75 (0)3.00 (0)3.25 (0)4.00 (0)
Coaches (n=2)3.25 (0.70)3.37 (0.53)3.50 (0.70)3.12 (0.17)3.12 (0.17)3.25 (0.70)2.87 (0.53)0.75 (1.06)0.50 (0.70)3.50 (0.70)3.50 (0.70)3.00 (2.00)
Teammates (n=6)2.91 (0.54)3.33 (0.30)3.08 (0.78)2.70 (0.96)3.33 (0.46)3.04 (0.88)2.54 (1.30)1.00 (0.61)0.62 (0.41)3.27 (0.57)3.33 (0.43)3.08 (0.37)
Note: *Captains scores are outside the SD for one or both groups

Figure 1

Captain X 360-Degree Feedback Data

Figure 2

Captain Y 360-Degree Feedback Data

Captain Y

As seen in Table 2, Captain Y’s self-reported scores are outside the SD range of the reported scores of the Coaches and/or Teammates in only four of twelve leadership subcategories. The first is IB. Captain Y (m=4, ±0) rated themselves higher than both their Teammates (m=3.33, ±0.30) and Coaches (m=3.37, ±0.53). In MBEA, Captain Y (m=2.25, ±0) rated themselves below the SD of the Coaches (m=2.87, ±0.53). Another category of difference is MBEP. Captain Y (m=0.25, ±0) rated themselves lower than the SD of the teammates (m=1.00, ±0.61). Neither group’s scores were outside the SD provided by the Coaches (m=0.75, ±1.06). The last difference is in the subcategory of SAT. Captain Y (m=4.00, ±0) self-reported scores higher than the SD of both the Coaches (m=3.00, ±0) and Teammates (m=3.08, ±0.37).

DISCUSSION

The collected data suggests Captain Y and Captain X differ in their leadership strengths and level of S-A. Captain X scores were found to be outside the SD of either the Coaches and/or the teammates in six of the twelve leadership subcategories, while Captain Y self-reported scores outside of their coaches and/or teammates SD on 4 different subcategories. Captain X’s scores were outside the SD of both the Coaches and Teammates for only one subcategory, Leadership. While Captain Y had two subcategories, Idealized Behavior and Satisfaction, that were outside the SD range of both the Teammates and Coaches scores.

Most interesting is the evaluation of SD of scores. The SD for several Coach and Teammate scores varied greatly. An example of this wide-ranging SD can be found on Table 1 with the Coaches having a SD of 1.76 (m=1.25) on MBEP and on Table 2 with the Teammates having a SD of 1.30 (m= 2.53) on MBEA. These wide-ranging SD display a divide in the perspective the Coaches and Teammates have on the Captains. Captain X and Y scored different than the mean scores both the Coaches and Teammates in almost all of the Leadership subcategories. However, the large SDs kept the Captains within the range to be labeled “self-aware” according to Avolio and Bass (1). These large SDs argue neither the Coaches or Teammates were unified in their beliefs of the Captains. Some participants within their groups believed that their captains were excellent leaders who provided crucial support to their team. While some participants saw their captains as less effective and, sometimes, borderline detrimental to their teams. It furthers interest that the Coaches, with a group size of 2, were also divided on their Captains in several categories. While the data suggests that these Captains are self-aware of their leadership, this self-awareness does not come without scrutiny. This can be best seen in Figures 1 and 2.

Another interesting point is within Captain X and Y’s belief in the Outcomes of their Leadership. Represented in the MLQ as EE, EFF, and SAT, Captain X rated herself as a “4” for all three categories, while Captain Y rated herself as the following: 3 (EE), 3.25 (EFF), and 4 (SAT). While Captain X has stronger belief that their leadership causes more positive outcomes for their team than Captain Y, they each rated themselves as a “4” in satisfaction. Meaning, they each believe their Teammates and Coaches are satisfied with their leadership abilities. However, this cannot be the case due to the wide-ranging SD’s found in many subcategories. It can be inferred, even without major differences from both their Teammates and Coaches in the SAT category, Captains may be incorrect about their teammate’s opinions of their leadership. They believe their team celebrate their leadership, while there is not a unified belief on their abilities. In addition, a high level of perceived satisfaction may inhibit captains’ motivation to grow or further develop their leadership abilities, as they may mistakenly believe their current performance is sufficient. This tendency aligns with patterns of social loafing, where individuals reduce effort or avoid self-improvement when they perceive their contributions as adequate and unchallenged (2, 10).

While the MLQ does not label the leadership style of Captains, it does infer trends and likelihoods. Within the scores collected, Captain X views themselves as a Transformational leader who directly, and positively, influences their teams’ performances. While Captain Y does not fit directly into Transformational, Transactional, or Avoidant Leadership. Captain Y rated herself as an amalgamation of both transformational and transactional leadership styles, specializing in having a strong moral code who may occasionally act as a parental figure to many of their teammates (IB).

As stated previously, this study had three hypotheses. The first hypothesis was that the Captains would rate themselves as having higher Transformational and Transactional Leadership when compared to the scores of the Coaches and Teammates. This hypothesis was not true with either Captains. The second hypothesis was the Captains would rate themselves as having lower Avoidant Leadership when compared to the scores of the Coaches and Teammates. This hypothesis was true only for Captain Y. The last hypothesis was that Captains will have an inverse relationship between the scores of MBEA and MBEP. This was found to be true in both Captains.

Limitations & Future Directions

While this study had several strengths, the main being the first empirical test of the 360 Feedback method, it of course is not without weakness. The first being a small sample size. While the MLQ does not give a specific sample size to use to make it effective, merely using one team (n=9) is small nonetheless. Future studies of this nature should look to include various teams from different sport types, genders, age and experience levels. To ensure validity, the items of the MLQ were not re-worded for each distinct participant group. All items of the MLQ were phrased “I am…”. While the items were worded correctly for the captains, all coaches and teammates had to reword the items in their heads as they were not responding to these questions about themselves. Furthermore, the MLQ is not a sport specific questionnaire. While it is a statistically valid and reliable questionnaire, it was designed to be used with a general population base. It was not specifically designed for athletes.

Other limitations to consider, are the social pressures of collegiate teammates. Despite the confidential and anonymous nature of the study, teammates may have felt unconscious pressure to identify their captains as having higher levels of positive leadership to avoid drama, feelings of guilt, or confrontations from the team (2).

Outside of adjustments to sample size, and inclusion of a sport specific questionnaire, future research should include a qualitative component to capture nuances of leadership, as well as a debriefing session with both coaches and captains. This level of transparency about how the captain is doing in the coaches and teammates eyes could provide a mechanism for change and promote open dialogue between all parties.

Lastly, the population used in this study were proficient in the English language, it was not their first language. With many international students and coaches used in this study, it is unknown if there were any difficulties understanding, reading, or comprehending the items they were tasked with completing.

CONCLUSION 

This study provides an empirical look at leadership and perceptions of different stakeholders about how team captain’s lead. Ultimately, one of the biggest takeaways is the large variance in opinions about the captains. Not just the difference in perception from the captains themselves to the ratings of the athletes and coaches, but the differences of how each individual teammate viewed the ability of the captain.  While the goal was to analyze the self-awareness of collegiate sport captains, the take home was more centered around the unique perception and individual nature to each athlete of what makes a great leader. This is supported in various studies regarding the notion that there is no one-size-fits-all approach to leadership (9, 12, 13, 17) Simply because an individual is elected, or selected, as a captain, that does not automatically make them an excellent leader and unanimously beloved.

APPLICATIONS IN SPORT

Applied implications of this study are vast within the realms of research and consulting practices. First, it provides a framework for future 360-Degree Feedback Method studies to take place. As previously stated, this method of research is underutilized in the realm of Sport Psychology research. Additionally, the data collected from this study may be used to update leadership education programs, creating importance for Self-Awareness training and identification within students, athletes, and leaders. Use of this data can also be used to stress the importance of team building and team cohesion. This study’s data found that the team’s coaches and teammates had dramatically different opinions on the leadership of their captains. This dramatic difference within the groups can be harmful to a team’s cohesion and performance, stressing the importance of this research study.

REFERENCES 

  1. Avolio, B. J., & Bass, B. M. (2004). Multifactor leadership questionnaire. Mind Garden.
  2. Bratton, V. K., Dodd, N. G., & Brown, F. W. (2011). The impact of emotional intelligence on accuracy of self‐awareness and leadership performance. Leadership & Organization Development Journal, 32(2), 127–149. https://doi.org/10.1108/01437731111112971
  3. Burns, J. M. (1978). Leadership. Harper & Row.
  4. Carlson, M. S. (1998). 360-degree feedback: The power of multiple perspectives. Popular Government, 63(2), 38–49.
  5. Carson, M. (2006). Saying it like it isn’t: The pros and cons of 360-degree feedback. Business Horizons, 49(5), 395–402. https://doi.org/10.1016/j.bushor.2006.01.004
  6. Drew, G. (2009). A “360” degree view for individual leadership development. Journal of Management Development, 26(7), 581–592. https://doi.org/10.1108/02621710910972698
  7. Eurich, T. (2017, September). Increase your self-awareness with one simple fix [Video]. TEDxMileHigh. https://www.ted.com/talks/tasha_eurich_increase_your_self_awareness_with_one_simple_fix
  8. Fleishman, E. A., Mumford, M. D., Zaccaro, S. J., Levin, K. Y., Korotkin, A. L., & Hein, M. B. (1991). Taxonomic efforts in the description of leader behavior: A synthesis and functional interpretation. The Leadership Quarterly, 2(4), 245–287. https://doi.org/10.1016/1048-9843(91)90016-U
  9. George, B., Sims, P., McLean, A. N., & Mayer, D. (2007). Discovering your authentic leadership. Harvard Business Review, 85(2), 1–8.
  10. Ghaleb, B. (2024). Social loafing: Understanding, mitigating, and enhancing group performance. International Journal of Scientific Multidisciplinary Research, 2(9), 1321-1328. https://doi.org/10.55927/ijsmr.v2i9.10975
  11. Muenjohn, N., & Armstrong, A. (2008). Evaluating the structural validity of the Multifactor Leadership Questionnaire (MLQ), capturing the leadership factors of transformational-transactional leadership. Contemporary Management Research, 4(1), 3–14. https://doi.org/10.7903/cmr.704
  12. Northouse, P. G. (2016). Leadership: Theory and practice (7th ed.). SAGE Publications.
  13. Pienaar, J., & Nel, P. (2017). A conceptual framework for understanding leader self-schemas and the influence of those self-schemas on the integration of feedback. SA Journal of Human Resource Management, 15, 1–11. https://doi.org/10.4102/sajhrm.v15i0.772
  14. Robbins, J. E., & Madrigal, L. (2017). Sport, exercise, and performance psychology: Bridging theory and application. Springer Publishing Company.
  15. Shaffer, J. (2018). 360 review: Self, teammate, and coach evaluation for personal development. Synergy Performance: A Division of Synergy Group.
  16. Wagstaff, C. R. D., Fletcher, D., & Hanton, S. (2012). Positive organizational psychology in sport: An ethnography of organizational functioning in a national sport organization. Journal of Applied Sport Psychology, 24(1), 26-47. https://doi.org/10.1080/10413200.2011.589423
  17. Warrick, D. (2011). The urgent need for skilled transformational leaders: Integrating transformational leadership and organization development. Journal of Leadership, Accountability, and Ethics, 8(5), 11–26.
2026-04-15T11:28:29-05:00May 6th, 2026|General, Sport Education, Sports Coaching, Sports Studies|Comments Off on Identifying Self-Awareness of Leadership Abilities Using 360 Degree Feedback Method: A Case Study of Collegiate Rowers

Fundraising in Sports: A case study

Author: Francisco J. Quevedo1

Corresponding Author:

Francisco J. Quevedo

72 Maple Street

Watchung, NJ, 07069

[email protected]

929-208-5289 


1Department of Marketing, Rutgers, The State University of New Jersey, Newark, NJ 

Dr. Quevedo is an Assistant Professor of Marketing at Rutgers University. A UMass Amherst ’78 graduate, he got his doctorate, MBA, and CAGSB at Pace University. He taught there, and at NYU before joining Rutgers full-time in 2020. He worked corporate and developed his family’s businesses in insurance, tourism, sports, and agriculture for 33 years until returning to academia. He has taught college for 15 years and done consulting for Fortune 100 firms, NGOs, and governments in nine countries. He has worked with nonprofits for 20 years. He researches brand management and nonprofit marketing, publishing 12 articles and chapters since 2019. He received an Award for Teaching Innovation in 2023 and coordinates the CM3A consulting center at Rutgers. 

ABSTRACT

Nonprofits in general long for fundraising guidance, market and donor research, and strategic planning support from academia. Within this sector, US amateur sports could represent a $60.5 billion segment, which receives but a small portion of total donations. To help close the gap, this paper presents a case study that can serve as a model to optimize nonprofit performance based on an amateur sports organization, which combines three related studies: a time-series analysis of nonprofits in the US showing that revenues depend largely on awareness and income, and points to the need to choose the right target and put the message out to raise funds; a donor survey which showed that, individually, decisions to give are based mostly on pride, pity, PR, personal interest, and pleasure, and points to the need to craft the right appeal; and a cross-sectional, six-country analysis of a proposed structure and processes that represents the underlying theory for this paper, which showed how networking, fiscal leveraging, and a coherent narrative, supported by the proper strategy and organization, generate external influence and revenues, thus emphasizing the need to follow proper procedure to achieve the desired results. A deep dive into the scientific literature sets the stage to analyze 17 years of experience in the WSKF Sports Foundation, part of a worldwide organization that spans over 110 countries and a million members, and raised up to $3.3 million at its peak in 2015, winning 266 world medals between 2007 and 2017, thereby providing a blueprint for fundraising in sports that can extend to most nonprofit organizations.

Key Words: sponsorship, strategy, process, model, medals, nonprofit, WSKF, foundation

INTRODUCTION

This paper points to the most pressing needs of nonprofit organizations. An unpublished survey of the Center for Marketing Advantage, Advancement, and Action of Rutgers University, working with the membership of the NJ Center for Nonprofits, pinpointed the demands of private foundations; fundraising, marketing and donor research stand out as the most urgent requirements of NGOs, followed by specifics like digital marketing and communications, market research, and strategic planning. Tracking 17 years of nonprofit research and amateur sports experience, we aim to present a tested and proven model to optimize nonprofit performance with the support of three specific research studies and a wide search of the literature.

The proposed model is supported by a cross-sectional test of Koschmann, Khun & Pfaerrer’s theory (23) done by Quevedo (33), a time series analysis of the US nonprofit sector by Quevedo & Quevedo-Prince (36), and a national survey that studied the driving motives to donate by Quevedo and Lee (35), which extended prior research by Quevedo and Gopalakrishna (34) on consumer preferences applying them in the nonprofit field.

The WSKF Venezuela Sports Foundation, part of a Japanese karate federation, the World Shotokan Karate-do Federation, that spans over 20,000 clubs and over a million members in more than 100 countries, served as the basis for a six-country analysis that showed how networking, leveraging, and a coherent narrative, deployed on the shoulders of the proper strategy, organization and processes, generate external influence (press coverage and lobbying power), and lead to substantially more revenues for the organization.

These studies and experiences showed that choosing the right target, designing the right appeal, and following the right approach, strategy and processes, will boost press coverage and drive fundraising. It is not just about saying and doing the right things, nonprofits must do the right things correctly.

A key paradox in amateur sports is whether funding follows medals or medals follow funding. In the case of the WSKF Sports Foundation, winning seemed to be the key to fundraising. Winning in one championship leveraged the next championship cycle. Looking at other causes, however, we must ask, should they generate social benefits to raise funds or raise funds to generate benefits? This chicken-and-the-egg paradox (Illustration 1) is paramount in sports, since medals increase media coverage and provide bragging rights to get more funds, but then funds, and training of course, are the means to get those medals, but it may not be necessarily true in other scenarios.

Illustration # 1: Medals and Funds – A Virtuous Circle in Amateur Sports

BACKGROUND

The youth and amateur sports industry is booming. The sector’s direct spending impact was valued at $39.7 billion in 2021, says a Sports ETA’s industry report signed by Clement (6). Wintergreen Research predicted that this market would grow at a compound annual growth rate of 8.9% until 2028. The NCAA generated a record $1.22 billion of revenue in 2022 from March Madness ticket sales, merchandise and television broadcast rights. Indeed, CBS and Turner Sports will pay the NCAA up to $19.6 billion over a 22-year contract term said Morones (31). These elements can add up to a $41 billion industry which depends in good part on fundraising to survive. However, sports are but a minuscule part of the philanthropic market and dynamics, so small that they do not make the charts. Certainly, more research support is needed to develop the sector. Unfortunately, marketing literature is unable to provide meaningful guidance because scant research attention has hampered a fuller understanding of why people help, as Bendapudi, Singh & Bendapudi found (2).

Chart 1: Nonprofit Revenues in the US

The professional sports market on the other hand is projected to reach close to $85 billion this year and that may not consider royalties for branded sports clothing and memorabilia according to Statista (39). Based on these figures, we could be looking at an umbrella sports market of $126 billion in the US alone, and perhaps as much as $500 billion worldwide by extrapolation (based on US vs. world GDP). 

METHODS

Sargeant and Shang (2010) emphasized that the need for a comprehensive model for fundraising has never been greater (37). Accordingly, we aim to provide a blueprint for funding amateur sports based on both theory and practice, leaning on three specific research studies, a deep dive into the scientific literature, and 17 years of successful fundraising experience with the WSKF Venezuela Sports Foundation, and 20 years of foundational work overall. Furthermore, we aimed to answer the question “will the right target and message, the right appeal and the right approach drive fundraising success, or do we need credentials and credibility upfront to attract sponsors?”

Illustration # 2: Kushman’s et al (2012) Model for Nonprofits

The WSKF Venezuela Sports Foundation raised up to $3.3 million (at the official rate of exchange) in its peak year, 2015, when its national team won 66 world medals in Tokyo, and received 73 press mentions which reverberated throughout the web internationally. These results speak for themselves. Its model was in use since 2008, and was replicated in Japan, the US, Canada, Panama, Spain, Ireland and other countries where the organization is present. A cross-sectional study, covering six countries, tested how much a gap in the execution of the appropriate model will affect  fundraising results.

Data Analyses

Statistical analyses were performed using SPSS version 29.0.2.0 (IBM). Multiple regression was combined with factor analysis in the time series modeling of the US nonprofit sector. Pearson correlation coefficients were calculated, as were the significance and p-values once the best fitting variables were identified. The donor decision model was determined through multinomial logistic regression, considering the extensive use of categorical variables. Cronbach’s alpha, Pseudo-R2 coefficients, significance and Chi-square values were calculated as well. Compare means was used in the cross-sectional analysis of six countries represented in the WSKF Sports Foundation to validate variations in their results. 

Prior Research Studies

Traditionally, the largest source of charitable giving in the US are individuals, not corporations, with $268.28 billion in donations which represent 71% of total giving, followed by foundations ($57.19 billion or 16%), bequests ($28.72 billion or 9%), and corporations ($18.46 billion or 5%). The average annual household contribution to nonprofits stood at $2,974, according to Statista (42). The majority of charitable dollars go to churches (32%), schools and colleges (15%), human services (12%), grant-making foundations (11%), and hospitals in general (8%). Sports does not make the Top 5 in this report.

List says that the nonprofit market revolves around three major players: (1) the donors, who provide the resources to charities. These can be corporations, public institutions, individuals, and non-government organizations (NGOs); (2) charitable organizations, which attract and allocate resources; and (3) the government, which decides on the fiscal framework for individual, corporate and NGO contributions, shapes the supply of grants to the various charities, and decides which public goods it will provide directly (28).

This proposal feeds from three research studies and 17 years of fundraising experience with the WSKF Sports Foundation. First, a predictive model of the US Nonprofit Sector based on time-series analysis showed that Nonprofit Revenues (NPR) depend largely on Public Awareness, as measured by TV coverage, and on Disposable Personal Income (DPI), specifically: NPR = – 4401.542 + 528.327(DPI) +23.121(TV Coverage) + Ɛ (36). Pearson’s R came up to 0.935, significance levels were at 0.001. Confirmatory Factor Analysis reaffirmed the fit of the equation, with an R² of 0.87. These findings indicate that nonprofits must first choose their targets well. Then fundraisers must put the message out, if they wish to get funds.

The question is “what should nonprofits say?” The second reference comes from a survey of 615 respondents, using their alma mater, the ASPCA, St. Jude’s Hospital for Children, a local homeless shelter, and their church as references; considering pride, pity, PR, personal interest, and pleasure as the driving motives, testing which appeal worked best to communicate a Nonprofit Organization’s message to generate funds. These were called “The 5-Ps of Fundraising” (35). Based on the pseudo-R2 coefficients generated by Multinomial Logistic Regression, the model reflected a predictive ability of 49.7%. All criteria were statistically significant. The pleasure of giving was the strongest driver, coming out as an underlying motivator in the donating decision. Different social causes respond differently to alternate fundraising appeals, therefore, determining which appeal works best is key to success. Ignoring the key drivers in the decision to donate may lead to being both ineffective and inefficient. These findings tell fundraisers how to craft the right appeal.

The third study would show how to deliver the right appeal to the right target, and how to operate a nonprofit organization successfully. Looking into the literature, Curry, Rodin and Carlson proposed that organizations that operate on transformational approaches to fundraising have fared significantly better than those which operate on a more transactional basis, and that the greater physical proximity of the donor base of an organization would positively impact fundraising (7). Wallace said that predictive modeling has concentrated on big-donor analytics, largely aimed at the identification of potential donors (43). Nonetheless, Koschman et al. (23) presented a more detailed model for optimizing the performance of Nonprofit Organizations (Illustration 2), which in hindsight, was being used by the organization under study years before it was published. Their model became thereby the underlying theory for this case study.

Indeed, Harris says that case analysis is a valid learning tool for research in fundraising for sports (15). Accordingly, we tested the Koschman et al. (23) model on the WSKF Venezuela Sports Foundation, part of a Japanese federation that spans over 20,000 clubs and more than one million members in more than 100 countries throughout all the continents except Antarctica, using six countries (the US, Panama, Spain, Ireland, Canada, and Venezuela) to find cross sectional illustrations of how the “meaningful participation” of members, the “centripetal forces” generated by the organization and its environment, and the consolidation of an institutional image through a “coherent narrative,” worked on the basis of “authoritative texts,” to use the original labels (23), generated “external influences” and led to substantially more revenues for the organization (33). These findings in sum tell fundraisers to follow proper procedure, a solid strategy, detailed plans and professional processes to achieve the desired results, given the choice of the right target and an appropriate message and appeal.

Although a better understanding of nonprofit dynamics and of the factors that affect fundraising efficiency is essential to charity managers, policy makers, and private donors, research has focused more on the micro than the macro view, says Yi (46), and not quite on the “how to” of organizational performance. Guy and Patton say that nonprofit marketing should begin with a basic understanding of motivations and donor behavior rather than merely adopting prefabricated marketing techniques (14). Sure enough, to be competitive, charitable organizations must rely on carefully formulated promotional programs, but there is an urgent need for research to identify the prevalence and effectiveness of different messages, according to Leonhardt and Peterson (27), who add that more than 55% of all NGOs appeal to selfless consumer motives (i.e., altruism), which is appropriate. However, an important experiment revealed that appealing to more selfless vs. less selfless (i.e., reputation) motives results in consumers having a more favorable attitude toward the charitable organization. So, there is more to donating than just the desire to help, and there is more to fundraising than just asking for money to those who have it. Consumer involvement, for instance, is found to have an important effect on the decision to donate; selfless appeals promote a more positive attitude among consumers with low involvement, but not for those with high involvement with a charitable cause (e.g., animal welfare).

Furthermore, Cao  found that psychological involvement with charities affects donation intentions; seeing a picture of a sad vs. a happy person increased intentions to give among participants with lower levels of psychological involvement, whereas the reverse was true for highly involved participants (3), hence the importance for NGOs and CSR executives to understand the nature and behavioral context of their operations. Huber, Van Boven, & McGraw combine what they call the internal and external influences on donor behavior (18), pointing in the direction of this paper and related research. Donor behavior has been disaggregated by researchers like Fajardo, Townsend, and Bolander into two components: donation choice and donation amount. Donor-related appeals have a greater effect on choice, while organization-related appeals have a greater effect on the amount pledged or donated. This could lead one to conclude that presenting both types of appeals in a solicitation is ideal (10).

On an individual level, the vast majority of donors are enthusiastic and positive about the organizations they give to, and about charities in general says Wooden (45). Leonhardt says that people give money to feel the “glow” associated with being the kind of person who helps a worthy cause (26). Kemp, Kennett-Hensel, and Kees studied emotions like pride and pity in charitable appeals, focusing on sex and gender as potential emotional collateral variables (21). Utility-based models that focus on the effects of lifetime, recency, seasonality, and appeals also show that fundraising attempts should emphasize commitment rather than amount, as stated by Kim, Gupta, and Lee, (22). Sectorial research by Kamatham, Pahwa, Jiang and Kumar focused on education’s 75% success rate studied how different appeals affect fundraising; sophistication of the appeal has a positive effect on fundraising and the amount donated. Providing information on the state of a project has a positive effect on donations, corroborating reinforcement models of donor behavior; individuals share a burden when supporting charitable causes and donate at least as much as the minimum donated (20). At the strategic level, Krug and Weinberg’s Merit Axis Model links the mission of the organization, the money raised, and merit as a standard for nonprofit management (24). Pride, pleasure, and personal interest were linked by Third to the legacy effect in the college and universities context, pointing to relational fundraising and the application of CRM to nonprofit marketing (41). A unified conceptual, behavioral, and econometric framework for optimal fundraising can combine approaches from Economics, Marketing, Psychology, and Sociology, said Haruvy, Popkowski,  Leszczyc, Allenby, Belk, Eckel, Fisher, Li, Ma, Wang, and List (16), which is the intention of this paper, considering the need for developing a comprehensive model of giving behavior and nonprofit organization performance.

Although the marketization of nonprofit activities, given by the introduction of marketing practices like sales of POP and different goods and services, competing for consulting contracts, donor relations management (the philanthropic version of CRM), and social entrepreneurship has drawn criticism, according to Eikenberry and Drapal (8), fierce competition for funds and a tighter economy have given rise to innovative fundraising methods like web-based crowdfunding and what is called Cause Related Marketing or CRAM by Chaney and Dolli (5).

Little research has been published about the perhaps circular correlation between medals and funds raised. Slater’s study relates medals and press coverage (38) which in turn supports fundraising. A cross-sectional study covering Belgium, Finland, Japan, the Netherlands, and the United Kingdom by Funahashi, Shibli, Sotiriadou, Mäkinen, Dijk, and De Bosscher relates funding with sporting success (12), which seems logical. Funds allow athletes and teams to train and eat, even to rest properly, and of course to compete and classify, thereby increasing their chances of success in top-tier events. Another report by Hogan and Norton, published through the National Institutes of Health found a high direct correlation between medals and funds (17). Although correlation does not imply causation, definitely the more funds, the more medals (and vice-versa, we would add).

Fundraising will continue to be vital for sports programs and facilities to operate. However, the climate for fundraising has become more competitive as more organizations chase the same discretionary dollars, and donors become more demanding. In order to cope, fundraisers will need to readjust their strategies. Fundraisers must understand all fundraising-related elements such as the event’s purpose, target markets and donors, and methods and strategies to be employed, said a 1996 editorial in the Journal of Social Marketing. Indeed, Stier and Schneider claim that fundraising is one of the major responsibilities of sport managers in the 21st century (40).

The Case of the WSKF Sports Foundation

As mentioned, prior research showed that the secret to fundraising success lies on selecting the right target and getting the message out there (36), based on the right appeal (35), to set in motion the most effective model of nonprofit performance (33). Indeed, Koschmann et al. (23) suggested that a proper combination of networking, leveraging and communication, based on a clear strategy, and following well-targeted processes, will generate optimal press coverage and influence, and -of course- funds.

Illustration # 3: The Winning Strategy

At the WSKF Venezuela Sports Foundation, applying the Koschmann et al. (23) model, something it did four years before it was ever published, meant (1st) leaning on the athletes and their parents to network and target corporations to gain access to their Corporate Social Responsibility (CSR) programs, (2nd) leveraging fundraising efforts on the Law for the Development of Sports which created a 0.5% sports tax on profits and allowed corporations to channel half of that directly to projects accredited by the Ministry of Sports, and (3rd) appealing to pride and PR interests, considering that Charity Sport Event (CSE) fundraisers are often confronted by the donors’ lack of interest, even though those events can provide participants with a meaningful experience, as stated by Filo, Fechner and Inoue (11). The message was carried by a top-of-the-line institutional DVD presentation, a quarterly newsletter, a website, direct and digital marketing efforts, and through an aggressive media management strategy that used timely press-releases, many of them sent from Tokyo, the common championship site, to gain immediate exposure.

This strategy, born out of a Shihan-kai meeting in Cyprus in 2010, blended well with Kaplan and Norton’s (19) map format, which kicks off from an organization that strove to muster the  support of parents, athletes, and instructors to execute the fundraising process, by reaching out to the right target with the proper appeal and press support, and achieve the desired financial results, as seen on Illustration 3. The leading KPIs (Key Performance Indicators) were medals won and funds raised primarily, but press coverage was extremely important for fundraising, since it reinforced the pride and PR appeal, as were the dimensions of the donors’ database. Donor relationship management leaned on the newsletter, BUDOtips, and as many as 73 media mentions per championship cycle.

The fundraising process was detailed, starting with the identification of all possible sources of funds, since it is not all about sponsorship. Indeed, McKeever and Pettijohn stressed that nonprofit organizations derive half of their revenues quid-pro-quo (30), as Graph 1 shows; in terms of sports organizations, this 50% may come from ticket sales, broadcasting rights, advertising, memorabilia and fees charged, among other internal sources. Additional funding may come from government or NGO grants, private and corporate donors, even multilaterals; depending on a single source is myopic as Levitt (25) would most likely define it. Accordingly, the first question that nonprofit managers must ask themselves is “are we doing the things we need to do to get money, or should we be getting money for the things we do?” Some nonprofits miss this benchmarking and go straight to asking for donations without considering the monetization of things that they can do or sell to generate funds. In case of WSKF, this meant monthly fees, sales of sporting goods and memorabilia, special training sessions, and events like national and regional championships.

Chart 3: Structure of Nonprofit Revenues

Based on a clear understanding of nonprofit market dynamics and the supply of funds, and considering the Sports Law, corporate and government targets were identified, and a unique appeal was tailored for each segment. The operational planning began when all decisions had been made and defined, otherwise it could have turned into a map without destination. The organization would pursue its financial objectives through traditional fundraising means, grants, events, and crowdfunding. The technical arm, the WSKF organization, would be the one to charge fees and hold events, collecting money from attendance and participation, under foundational guidelines.

Illustration # 4: The WSKF Fundraising Process

A growing database of corporate donors was informed and nurtured with a newsletter called BUDOtips which circulated throughout the organization. A survey of athletes, parents, and instructors generated the structure of the magazine which was then tested against donors’ expectations. Four sections were created: “Budo,” dealing with principles, for the parents who sought discipline and principles for their children, and who represented over two-thirds of the membership; “Technique” for the athletes who wanted to improve their performance; “Management” for the instructors who wanted to run their clubs profitably; and “News” for the donors and for everyone; the Editorial was just an introduction and an invitation to read, as seen on the cover page below.

A growing database of corporate donors was informed and nurtured with a newsletter called BUDOtips which circulated throughout the organization. A survey of athletes, parents, and instructors generated the structure of the magazine which was then tested against donors’ expectations. Four sections were created: “Budo,” dealing with principles, for the parents who sought discipline and principles for their children, and who represented over two-thirds of the membership; “Technique” for the athletes who wanted to improve their performance; “Management” for the instructors who wanted to run their clubs profitably; and “News” for the donors and for everyone; the Editorial was just an introduction and an invitation to read, as seen on the cover page below.

Illustration # 5: The WSKF Newsletter

The results of these concerted efforts were evident. Formal fundraising began after a lack of funding left the 2005 championship cycle dry. 14 medals were won in 2007. The WSKF Venezuela Sports Foundation was created in 2008, leading to 24 world medals in Tokyo the following year. As the organization learned and matured, the medal count skyrocketed to record-breaking numbers, 50 in 2011, 42 in 2013, a record-breaking 66 in 2015, and 60 in the following cycle, 2017. Eight medals were won by a small team in the World Cup held in Cyprus in 2010. Winning led to press coverage which peaked at 73 TV, newspaper, radio and digital mentions in 2015, which reverberated throughout the web, nationally and internationally.

Chart 4: The WSKF Venezuela Medal Count

rage of 158 days younger than those athletes who win bronze medals.  Together, these results suggest that the results are generally consistent across males and females as well as Summer and Winter Games.    

DISCUSSION

The predictive model points fundraising and communicational efforts toward deep pockets (36), which implies choosing the right target and putting out the most appropriate message; research into donor choice (35) leads to crafting the right appeal to carry that message; and testing Koschmann et al.’s communicative framework (23, 33) guides nonprofits to follow the right strategy and proper processes, supported on networking, leveraging on legal and fiscal incentives, and on the proper media strategy. Indeed, the strategy of the WSKF Sports Foundation, knowingly or not, and ahead of its time, blended these three theories and put them into practice, combining this theoretical framework with the Kaplan and Norton’s (19) strategy map format by adapting the organizational perspective to create a network of athletes and parents to reach out to corporate donors, crafting fundraising and sports operations to leverage on the Law for the Development of Sports, and fitting the customer perspective to the media strategy, and vice-versa. The financial perspective was led by the Balanced Score Card with metrics like revenues and average sponsorship level per athlete. The Strategy Map represented in and of itself a vital authoritative paper, along with the fundraising process flowchart. Moreover, it added an interesting twist, using world championship success and feedback to fuel fundraising, as medals triggered press coverage which in turn attracted sponsors, and then their sponsorship allowed the teams and athletes to train, compete and win more medals. This created a virtuous cycle. To feed the flame, the Foundation added reverberance by hosting a “Dinner with the Champs” upon returning from Tokyo, where the press and the donors would share photo-ops with the athletes in their colors and with their medals, while receiving plaques for their support, which added more press coverage and PR opportunities.

The Foundation continued to multiply its branding efforts by adding non-sports philanthropy to its credentials, networking with several organizations like Mayor’s Offices, corporate programs (CSR), and private foundations to help the needy, thereby positioning its brand at a national level and squeezing the most out of the athletes’ medals’ appeal (Illustration 6). Again, this added more press coverage. Indeed, the WSKF Venezuela Sports Foundation showed that theory, when put into practice, gets the most out of the strategy.

CONCLUSIONS

Theory says choose your target well, craft the right appeal, and execute the right strategy correctly, following proper procedure, through a well laid out fundraising process. Strategizing will require a detailed situational analysis and brainstorm, blending the theory and the best practices into your initiatives. Choose your KPIs well; funds, medals, or outside of sports, social impact, and press coverage should be the strongest drivers; medals add leverage, they lead to press coverage, press coverage attracts sponsors and triggers pride and PR opportunities; and sponsorship allows athletes to train and participate in world events, which leads to medals, as the virtuous cycle makes another rotation. Be relentless and thorough in the execution of the strategy; and whenever and wherever possible, widen your networking circles. The more, the merrier!

Limitations and Further Research

Although the Pearson coefficient of the first study is outstanding, the donor choice research could use additional criteria like peer influence and personal commitment with the social cause to increase its predictive ability. This would make it “The 7-Ps of Fundraising” and should raise the model’s pseudo-R2. The cross-sectional study is pretty straightforward, but it also showed that not every country has such a favorable fiscal framework for sports as Venezuela, which enacted legislation that taxes corporate earnings to fund the development of sports. They finance the construction of sports complexes, sporting events, and national team competitions, both nationally and internationally. Corporate donors can channel one half of that tax directly to accredited projects; this benefits the leveraging aspect of Koschmann et al.’s model (23). Nonetheless, there are always tax incentives and breaks for donors and fundraisers in just about every country we analyzed; in the end, what donors are looking for are meaningful projects that are properly organized and well presented. Credibility is a must, and feelings and appearances matter.

It should be also mentioned that the Venezuelan socio-economic and political situation today may not be conducive to achieving the same 2007 ⎯ 2017 results that were analyzed here. Funding has been politicized, the economy has shrunk 80%, and the exchange rate has gone from Bs. 10 per US dollar, in August 2018, to Bs. 119,144,000,000,000 or 119.14 today, after the regime erased eleven zeroes from the currency to hide the mega-devaluation and hyper-inflation.

APPLICATIONS IN SPORT

Rarely has a combination of theory and practice been put together to recommend fundraisers how to balance strategy and operations; not one or two but three research studies support this paper; 20 years of foundational experience leverage them; raising up to $3.3 million a year in funds and winning 266 world medals in 10 years prove it right; an organization spanning over 110 countries and over one million members, make this a unique learning opportunity. The underlying theoretical model calls for networking among people and organizations, leveraging on legal and fiscal incentives, and communicating the right message to the right target, working on the shoulders of a clear strategy, a lean and mean organization, and a consistent fundraising process, to generate press coverage and lobbying power, and ⎯ultimately⎯ funds. The theory says choose wisely, and indeed strategy is all about choice: identify the right target, craft the right appeal, and do the right things correctly, which demands a fine-tuned organization and processes.

Now, to the question, “do we need to win medals to raise funds or raise funds to win medals?” Well, yes, credentials help fundraisers win support but in the absence of medals, the operational model and the right choices should cast a net that is wide enough to generate revenues and attract volunteers, but in the absence of results, in startup nonprofits, the founders’ accolades, and networks, can help. But appearances matter, that is why the WSKF Sports Foundation leaned on its website, a top-of-the-line DVD presentation, and its newsletter, all of which seemed bigger than life, to reach the target before the medal count skyrocketed and a virtuous cycle was created. Momentum did the rest.

It is important to remember that one half of nonprofit revenues are quid-pro-quo, coming from things nonprofit organizations do or sell (see Graph # 1). Hospitals recover medical costs, universities charge tuition, and the WSKF Sports Foundation collected fees from its membership. Income cannot depend solely on donations or grants. Nonprofits must make an effort to add to their revenue streams by monetizing their activities, something not always remembered, as our consulting efforts at Rutgers University have shown us. Private foundations struggle with lack of resources and specialized skills, but solutions are at an arm’s length.

Social Implications

The Nonprofit Sector in general, which represents 5.4% of the US economy, can benefit from  strategies that are supported by data and research, plus decades of fundraising experience at the same time. Amateur sports fundraising in particular, a $60 billion industry, can surely profit from a fresh perspective.

Eather, Wade, Pankowiak, et al.’s research suggests that community sports programs, supported by fundraising, can significantly enhance social capital and promote social cohesion by increasing trust, improving social networks, and fostering a stronger sense of community amongst participants, providing opportunities for community members –athletes, coaches, volunteers, and supporters– to interact, build relationships, and develop a shared identity (8)

Supporting fundraising in amateur sports through scientific research goes beyond securing financial resources. It fosters community spirit, enhances social connections, and provides numerous positive social and psychological benefits for both participants and volunteers. These benefits contribute to stronger, healthier, and more cohesive communities says Wheatley (44). Ultimately, if the nonprofit sector does indeed pick up the slack of governmental failure, Matsunaga and Yamauchi’s theory (29), then anything that benefits philanthropy will benefit society as a whole.

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2025-09-10T15:45:29-05:00January 21st, 2026|General, Olympics, Research, Sports Management, Sports Studies|Comments Off on Fundraising in Sports: A case study

Relationship Between the National Football League (NFL) Combine Measurables and Playing Time in the 2024 NFL Rookie Class

Authors: Greg A. Ryan, Kevin Harvey, Elijah Campbell, Mark Shoebridge, Landon Overby, Joshua Sauer, & Robert L. Herron

Corresponding Author:

Robert L. Herron, Ed.D., CSCS*D, ACSM-RCEP

75 College Drive

Montevallo, AL 35115

[email protected]

205-665-6118


Authors’ Affiliation: College of Health Professions, Department of Nursing & Health Sciences, University of Montevallo, Montevallo, AL, USA.

ABSTRACT

Purpose: This study investigated the relationship between anthropometric and performance measures collected at the 2024 National Football League (NFL) Combine and playing time (PT) during the 2024 NFL regular season. Methods: Data from four anthropometric (Body Mass Index; Hand Size; Arm Length; Wingspan) and seven performance tests (40-yard Dash; 10-yard Split; Vertical Jump; Broad Jump; 3-Cone Drill; 20-Yard Shuttle; 225lb Bench Press) of 315 players were standardized into average Anthropometric Z-Scores (AZ), Performance Z-Scores (PZ) and Total Z-Scores (TZ) for analyses. PT was calculated as a player’s total number of regular season snaps during their 2024 rookie season. Pearson correlations were used to investigate the relationships (α = 0.05) between AZ, PZ, and TZ to PT. Players were also analyzed for potential relationships within each position group. Results: A significant, weak, positive correlation existed between PZ and PT (r = 0.19, p < 0.01) and TZ and PT (r = 0.20, p < 0.01) for all players. No relationship existed for AZ and PT (r = 0.02; p = 0.73). Additionally, significant relationships existed among: Offensive Line  – PZ and PT (r = 0.33, p = 0.01) and TZ and PT (r = 0.35, p < 0.01); Wide Receiver – PZ and PT (r = 0.39, p = 0.03) and TZ and PT (r = 0.46, p < 0.01); Linebacker – TZ and PT (r = 0.39, p = 0.05). Conclusions: NFL Combine performance metrics may provide insight on PT, but anthropometric measurables were not related to PT. The lack of relationship within position groups indicates the NFL Combine may not be valuable in evaluating a rookie’s success on the field. Applications in Sport: Professionals who work with prospects may choose to train Combine specific techniques to maximize a prospect’s chances of playing in the NFL. However, individualized training that focuses on position specific demands or weaknesses that are not directly measured by NFL Combine tests may be more useful in increasing PT. The NFL Combine may be a useful supplement to all factors that go into an NFL team’s decision to draft a player.

Key Words: performance testing, predictive analytics, scouting, correlational analysis, American football

INTRODUCTION

The National Football League (NFL) hosts an annual Scouting Combine in Indianapolis, Indiana of elite college football players. Only about 3% of college football players are invited to the NFL Combine and therefore represent those with the highest chance of being drafted into the NFL (4). The purpose of the NFL Combine is to allow coaches, scouts, and other team personnel representing the 32 NFL teams the opportunity to assess hundreds of players from all divisions of collegiate football.

Football has position-specific skills that are needed to excel at the highest level. However, there are similarities between each position. All positions need vertical and horizontal power, agility, and strength. During this weeklong event, players participate in a multitude of tests. These tests include anthropometric measurements (Height; Weight; Wingspan; Arm Length; Hand Size) and performance tests (40-yard dash; 10-yard split; Vertical Jump; Broad Jump; 3 Cone Drill; 20 Yard Shuttle; 225lb Bench Press). All the events in the NFL Combine have been shown to have face validity (4). NFL player personnel departments use the NFL Combine data as part of their criteria to determine whether to select a player in the upcoming NFL Draft.

While the NFL Combine tests are designed to determine that aptitude to play at the next level, research is conflicted on the ultimate usefulness of the NFL Combine in determining player performance and playing time (PT). Kuzmits and Adams (4) found no consistent significant relationship between NFL Combine tests and player performance during the years of 1999 to 2004. Research also noted that the NFL Combine from 2013 to 2015 lacked the ability to predict game performance when specifically analyzing first year game performance (3). Teramoto, Cross, and Willick (12) looked at whether the NFL Combine could predict future performance of Running Backs (RB) and Wide Receivers (WR). The results of this study were that the time on 10-yard split was the most important predictor of yards per attempt for RB while vertical jump was significantly associated with receiving yards per reception for WR. However, the measures cannot explain a large part of the variance in the future performance of RBs and WRs. Vincent et al. (15) looked at NFL Combine participants from 2005 to 2010 who then played in the NFL. Significant relationships were found between at least one NFL Combine measure and on-field success. Even though significant relationships were found the authors stated that the NFL Combine tests are only modest predictors of future performance. More recently, investigation of six physical skill tests at the NFL Combine to try and predict draft placement in the 2022 NFL Draft and showed no significant difference between drafted and nondrafted players in any of the six physical tests analyzed (14).

LaPlaca and McCullick (5) built on previous research looked at player performance from the years 2006 to 2018 and compared it to the NFL Combine from 2006 to 2016. They found that every position group, both offensive and defensive, had at least one NFL Combine test that was significantly correlated with player performance. The study made sure to disclose that even though they found significant correlations, the large sample size made it easier to find weaker correlations. A limitation that was discussed was that while the authors did use objective performance statistics such as Touchdowns scored, they also used a grading system through Pro Football Focus to determine player performance. This grading system was not purely objective because the grades are determined by multiple reviewers through the observation of game film. Therefore, the overall performance of each player was not entirely objective. Additionally, a robust study by Frank and colleagues (2) analyzed 20 years (2000-2018) of NFL Combine data and noted that for offensive positions, single measures often best predicted success, while various combinations of NFL Combine performance traits predicted success among defensive players. This study also suggested that NFL Combine data is best used in conjunction with scouting and personnel departments to supplement NFL draft decision making. Similarly, research was conducted looking at the impact of the NFL Combine on five-year performance data from the 2013-2017 NFL seasons and concluded that the NFL Combine lacked predictive ability during that timeframe (1). While historical research does exist in this field, each year provides another opportunity to determine the NFL Combine’s effectiveness in predicting success. Additionally, limited research exists discussing the relationship between NFL Combine Measurables and PT for first-year players. The primary purpose of this study was to determine if the anthropometric and performance measures of the athletes invited to the 2024 NFL Combine were related to PT during the 2024 NFL regular season.

METHODS

Participants

Participants for the data analysis in this study were college football players that participated in the 2024 NFL Combine (N = 315). Participants were also grouped by position for use of positional comparisons (Offensive Line [OL] (N = 70); Defensive Back [DB] (N = 67); Defensive Line [DL] (N = 50); Running Back [RB] (N = 29); Linebacker [LB] (N = 30); Quarterback [QB] (N = 14); Tight End [TE] (N = 16); Wide Receiver [WR] (N = 39)). All player positions were input based off their official designation at the time of the NFL Combine. Due to limited sample size (N = 6) and variations in specializations, NFL Combine athletes who were labeled Specialist (Kicker, Punter, Long Snapper) were excluded from analyses.

Procedures

Four anthropometric (Body Mass Index [BMI]; Hand Size; Arm Length; Wingspan) and seven performance measures (40-yard Dash; 10-yard Split; Vertical Jump; Broad Jump; 3-Cone Drill; 20-Yard Shuttle; 225lb Bench Press) were analyzed. BMI was calculated by the researchers using Height and Weight measurements taken at the NFL Combine. Full descriptions of the performance tests have been detailed previously by McShay (7).

The data from the NFL Combine was obtained from NFL.com/combine/tracker (8). Each participant’s scores were retrieved for every test that was completed. Standardization of data, via Z-scores, were created for every anthropometric and performance measure. The measures from the NFL Combine were standardized into averages for each player, taking each player’s combined Z-Score score and dividing by the number of NFL Combine events they participated in to account for players who did not complete every NFL Combine event. Standardized averages were created for Anthropometric Z-scores (AZ), consisting of the four anthropometric measures, Performance Z-scores (PZ), consisting of the seven performance measures, and Total Z-scores (TZ), consisting of all 11 NFL Combine measures, for analyses This method of standardization of NFL Combine data into Z-scores for analysis has previously been supported (1).

Once all NFL Combine data was standardized, researchers used Pro-football-reference.com (9) to retrieve offensive, defensive, and special teams snaps for each player during the 2024 NFL regular season. Each player’s total snap count was then combined to provide a single value to determine PT, which was used for analysis. Because of this study only requiring secondary analysis of data which is publicly available on web-based domains, which do not disclose individual’s health information, Institutional Review Board approval was not required, though the study was approved by the research institution.

Data Analyses

Pearson product moment correlations, using Statistical Product and Service Solutions (SPSS, v29.0, IBM Corporation, Armonk, NY), were used to determine the relationship (α = 0.05) between AZ, PZ, TZ to PT. Additionally, players were separated by position and Pearson product moment correlations (α = 0.05) were used to determine potential relationships within each group between AZ, PZ, and TZ, to PT. All data are presented as means ± standard deviation with 95% confidence intervals (95%CI) unless otherwise stated.

RESULTS

Descriptive Statistics

Of the 321 athletes whose data were collected, 315 were used for analysis. A total of six athletes were excluded from analysis due to their position of Specialist (punter, kicker, long snapper) because only anthropometric data was collected on this group. Of the 315 athletes used for analysis, 312 (99%) completed all anthropometric measurements. There was more variability in the performance testing, with 25 (8%) completing all seven performance events, and 263 (83.5%) completing at least one performance event. When broken down by event, 220 (69.8%) completed the 40yd (4.73 ± 0.31s) with a 10yd split (1.63 ± 0.11s), 227 (72.1%) completed the VJ (34.0 ± 4.3in), 220 (69.8%) completed the BJ (117.9 ± 9.0in), 78 (24.8%) completed the 3C (7.30 ± 0.40s), 89 (28.3%) completed the PRO (4.44 ± 0.28s), and 100 (31.8%) completed the BP (21.9 ± 5.6reps). When examining snaps played over the 2024 regular season, 239 (75.9%) players went on to play at least one snap, with 224 (71.1%) averaging more than one snap per game over the course of the season.

Anthropometric Correlation Analysis

The results of the correlation analysis for AZ and PT are presented in Figure 1. Pearson product moment correlation coefficients were calculated for the relationship between average AZ and PT for all players and separated by position group. No significant overall relationship existed for AZ and PT (n = 312; r = 0.02; p = 0.73).

Additionally, no significant relationships existed among position groups: OL (n = 70; r = 0.13; p = 0.29); RB (n = 29; r = 0.21; p = 0.29); WR (n = 37; r = 0.24; p = 0.16); TE (n = 16; r = 0.39; p = 0.14); QB (n = 13; r = 0.02; p = 0.95); DL (n = 50; r = -0.10; p = 0.52); LB (n = 30; r = 0.19; p = 0.33); DB (n = 67; r = -0.02; p = 0.89).

  Performance Correlation Analysis

The results of the correlation analysis for PZ and PT are presented in Figure 2. Pearson product moment correlation coefficients were calculated for the relationship between average PZ and PT for all players and separated by position group. A significant, weak, positive correlation existed between PZ and PT (n = 263; r = 0.19, 95%CI [0.07, 0.31]; p < 0.01). The positive direction of this relationship indicates that players who performed better at the NFL Combine played more snaps during the 2024 NFL regular season.

When separated by position groups, significant, positive relationships existed for the following groups: OL (n = 61; r = 0.33, 95%CI [0.09, 0.54]; p = 0.01); WR (n = 34; r = 0.39, 95%CI [0.06, 0.65]; p = 0.03). The positive direction of these relationships indicates that OL and WR who performed better at the NFL Combine accumulated more snaps during the 2024 NFL Regular season. No significant correlations were noted for: RB (n = 25; r = 0.31; p = 0.14); TE (n = 12; r = 0.07; p = 0.15); QB (n = 7; r = -0.39; p = 0.40); DL (n = 43; r = 0.30; p = 0.06); LB (n = 26; r = 0.31; p = 0.13); DB (n = 55; r = -0.02; p = 0.89).

Total Correlation Analysis

The results of the correlation analysis for TZ and PT are presented in Figure 3. Pearson product moment correlation coefficients were calculated for the relationship between average TZ and PT for all players and separated by position group. A significant, weak, positive correlation existed between TZ and PT (r = 0.20, 95%CI [0.08, 0.31]; p < 0.01) for all players. The positive direction of this relationship indicates that players who had higher average TZ scores played more snaps in the 2024 NFL regular season.

When separated by position groups, significant, positive relationships existed for the following groups: OL (n = 61; r = 0.35, 95%CI [0.11, 0.56]; p < 0.01); WR (n = 34; r = 0.46, 95%CI [0.15, 0.69]; p < 0.01); LB (n = 26; r = 0.39, 95%CI [0.01, 0.68]; p = 0.05). The positive direction of these relationships indicates that players in these position groups who had higher average AZ scores played more snaps in the 2024 NFL Regular season. No significant correlations were noted for: RB (n = 25; r = 0.31; p = 0.14); TE (n = 12; r = 0.07; p = 0.85); QB (n = 7; r = -0.24; p = 0.61); DL (n = 43; r = 0.30; p = 0.06); DB (n = 55; r = 0.06; p = 0.70).

Discussion

The main finding of this study is that PZ and TZ may have a weak relationship to PT in a player’s first year in the NFL. There was no relationship between a player’s AZ and subsequent PT across all athletes nor when separated by position group. The study did find a significant weak positive correlation between average PZ and PT for all players. However, when separated by position groups significant, positive relationships existed for OL and WR. Finally, there was a significant weak positive correlation between TZ and PT for all players. When separated by position groups, significant, positive relationships existed for OL, WR, and LB.

There could be many reasons why these relationships exist for WR, LB, and OL. Previous movement analysis research for NFL players by position found that WR had highest in-game velocity and highest total running volume by an offensive position (6). Therefore, the 40-yard dash and 10-yard split may carry more importance among WR. The same study showed that LB had the most high-velocity efforts and high-velocity distance in game compared to all other positions. LB also showed the largest variability across player-games which is likely due to the roles that LB perform which include rushing the QB, play in space and cover offensive players, or primarily to tackle an opponent. Additionally, OL noted a positive relationship in the current study, with better NFL Combine performances leading to more PT.  While previous research (11) has noted that OL have worse NFL Combine values compared to other positions, the nature of the OL position may lend itself to more direct relationships from NFL Combine performance, since these athletes require multidirectional power over limited space. The positional findings in the current study do support previous research that noted relationships between NFL Combine performance metrics and PT among WR (40-yard Dash, Vertical Jump), LB (40-yard Dash, 20-yd Shuttle) and OL (20-yard Shuttle, Vertical Jump) (1, 2).

The NFL is not the only sport that uses a combine to test and evaluate future players’ abilities. Teramoto et al. (13), investigated the National Basketball Association (NBA) scouting Combine to determine whether the NBA Combine could predict PT. The study showed that the NBA Combine metrics had minimal correlation with long-term performance. In the NBA, it was found that certain anthropometrics had slightly better predictive power than athletic tests, which contrasts with what researchers found about the 2024 NFL Combine. Both in the NFL and NBA Combine researchers have proposed that performance in college or in game is the biggest predictor of draft position and future performance (11, 13).

There are limitations associated with this study. As reported in the results only 25 (8%) of all prospects completed all seven performance events. Increasingly, players are opting out of some or all the NFL Combine process, due to injury concern, agent decision, recovering from an injury during the season, or to focus on performing well at individual workouts, where more variables can be controlled by that athlete. In the season being analyzed in this study, five of the first six picks in the NFL Draft did not participate in the NFL Combine process, which could impact these findings. A larger, more complete sample from all NFL Combine athletes would comprise a better representation of their athletic performance. Finally, players that played zero snaps their first year due to injury were included in analysis, due to limitations among researchers to determine the extent of every injury or whether a player was not on the field due to injury or coaching decisions. A player that may have had strong AZ, PZ, and TZ scores, but did not play during their rookie season because of injury, which would have impacted the relationship between those variables and PT.

CONCLUSIONS

Many studies have been conducted over the last 20 years to determine if and how NFL Combine measurables can predict performance in the NFL (1-6, 10, 12, 14, 15). These studies have found mostly found minimal relationships overall, though stronger relationships among certain position groups. Despite the general scientific consensus that the NFL Combine is not a strong predictor of future NFL success, a multitude of NFL Combine “prep courses” exist, with athletes paying for training specifically to improve in NFL Combine measurables. There has been scientific skepticism about these courses and their impact on performance at the NFL Combine and its translation to improved draft status or playing time. While these courses claim that they will improve an athlete’s chance of getting drafted, there is currently no scientific evidence to these claims (1, 4, 10). Training programs that focus on a prospect’s position specific demands or individual weaknesses that are not directly measured by NFL Combine tests may be more useful in increasing PT for that athlete. The results of the current study support the previous work in the literature, but do note that some position groups (OL, WR, LB) may benefit by improving NFL Combine-specific performance in the lead up to the NFL Combine and Draft.

APPLICATIONS IN SPORT

The results from the current study suggest PT among NFL rookies during the 2024 regular season could not be strongly predicted with data collected during the NFL Combine. However, due to the relationships that were found, specifically withing certain position groups, it may be important for athletes in those positions to train specifically for those performance tests to have a better chance at playing in their first year. The data can be important for NFL player personnel departments who may use data collected during the NFL Combine to influence drafting decisions. Due to the significant, but variable, nature of the relationships found in the current study, it appears that the NFL Combine may be a useful supplement to scouting, film analysis, interviews, and other factors that go into an NFL team’s decision to draft a player. However, it is apparent that there is more to determining PT during a rookie season than just superlative measurables collected during the NFL Combine.

REFERENCES 

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2025-09-05T08:46:38-05:00January 7th, 2026|General, Research, Sports Management, Sports Studies|Comments Off on Relationship Between the National Football League (NFL) Combine Measurables and Playing Time in the 2024 NFL Rookie Class
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