ISSN: 1543-9518|Vol. 24

Black Women “DO” Workout!

Sherldine Tomlinson

### Abstract

Many studies cite that women of African descent have lower physical activity levels and/or are more sedentary, than White counterparts. The lack of exercise among Black women results in them experiencing compromised life quality and reduced life expectancy. To combat the striking rates of cardiovascular-related diseases and to increase habitual exercise, health promotion interventions have been initiated designed for Black populations. Female participants in Project Joy, a church-based cardiovascular education programme, reported weight loss and lower blood pressure. This paper reviews a similar initiative; Black Women “DO” Workout! (BWDW), which makes innovative use of social media to encourage physical activity (PA) among Black women.

**Key Words:** women of African descent; exercise; social media

### Introduction

Numerous studies indicate that women of African descent have lower physical activity levels, and/or are more sedentary, than their White counterparts. A 2006 national health survey on physical activity levels in Canada found that when compared to Caucasian Canadian females, both African Canadian and South Asian Canadian women less moderately active (Bryan, Tremblay, Pérez, Ardern & Katzmarzyk, 2004). In a similar American study looking at Black, White, Hispanic and Asian women, the data revealed that only 8.4% of African American women completed the recommended level of regular physical activity (Eyler, Matson-Koffman, Young, Wilcox, Wilbur, Thompson, Sanderson & Evenson, 2003). Unfortunately, this lack of exercise participation among Black women contributes to a significantly increased health risk of cardiovascular-related complications such as hypertension, type 2 diabetes and obesity (Flegal, Carroll, Ogden & Curtin, 2010). A lack of active activities also results in Black women experiencing compromised life quality and reduced life expectancy.

In an effort to combat these striking rates of cardiovascular-related diseases and complications among women of African descent, and to increase their habitual exercise involvement, a number of health promotion interventions have been initiated across North America. These include offerings of free exercise sessions especially designed for Black populations. Evaluative studies of these types of exercise programmes suggest they produce appreciably positive outcomes. The female participants in Project Joy, for instance, an African American church-based cardiovascular education programme, reported weight loss and improvement in blood pressure after participating in the included exercise sessions (Jakicic, Lang & Wing, 2010). This paper reviews a similar programme, Black Women “DO” Workout! (BWDW), which makes innovative use of social media to encourage exercise among women of African descent.

The BWDW initiative was created and founded by Crystal Adell, a fitness enthusiast and personal trainer. Adell uses Facebook as a tool to encourage regular exercise participation among African American women. She describes BWDW as a grassroots movement for championing weight loss and healthy living, a crusade she says is much needed to address the sobering statistics that show 49% of African American women are obese, while approximately 66% are overweight (US Dept of Health and Human Services 2000). Adell notes that using Facebook, which allows her to facilitate communication between Black women, is her “personal attempt to work with a collective who are more than willing to share their fitness goals, services and lifestyle changes towards healthier living”(personal communication, 2010). Information included on the site covers topics from exercising, body image, healthy eating habits and eating disorders to the importance of fitness and nutrition during pregnancy. Adell suggests that the success of BWDW is based on “information sharing and by showing praise, encouragement, inspiration and support in the way of sisterhood and by championing individuals for their fitness goals, which ultimately keep others motivated in to want to do the same”(C. Adell, personal communication, 2010).

There is little doubt that BWDW is a success. Thus far the site boasts more than 85,000 members, mainly women of African descent, many of whom regularly visit and post to the site. While African American women make up the largest block of BWDW users, the site also attracts international members from Canada, England, African and the Caribbean. Launching an online social media page as a means to promote exercise adherence and encourage healthy lifestyles among Black women is clearly a new, unique and successful approach. In addition to being innovative, the strategy is also in accordance with the American Healthy People 2010 mandate to (1) increase quality and years of healthy life and (2) eliminate health disparities that are associated with race, ethnicity and social economic status (US Dept of Health and Human Services 2000). One of Healthy 2010 physical activity and fitness objectives is to increase physical activity levels among Africa Americans as disparities in exercise and/or physical activity levels continue to exist with this group and other populations including Hispanics, the elders and people with disabilities (US Dept of Health and Human Services 2000). Indeed, the Black Women “Do” Workout social media campaign offers the opportunity for women of African descent to make regular exercise and a healthy lifestyle a part of their daily routine.

The BWDW web page is attractive, functional, and perhaps most importantly, interactive. Members are encouraged to participate through such means as submitting healthy recipes to the ‘Chef de Cuisine’ e-cookbook and posting images to the photo album which showcases before and after pictures. There are also announcements about the monthly BWDW ‘meet-ups’ held in locations across the United States for women who want to connect in person, as well as a service that informs members about personal trainers available in their area of the country. And the site has become a space of promotion for several members who now compete in fitness and body building competitions after experiencing significant body transformations via exercise and through healthy eating. In addition, a range of BWDW merchandise are available for sale on the site.

Health policy makers and promoters across North America have acknowledged the need for a better understanding of Black women’s exercise behaviour as a basis for improving their traditionally low physical activity rates. The BWDW programme offers an opportunity for those in the health field to learn from, and about, Black women and provides a potential avenue for the dissemination of health information. Adell herself notes these opportunities, commenting that she would like to see collaboration between BWDW and “organisations like the American Heart Association, Go Red For Women, the African American churches and corporate organisations” (C. Adell, personal communication, 2010). She believes these kinds of partnerships “will allow for an enhancement of services to local African American areas and communities that statistically have a high demand for wellness, health and fitness related support” (C. Adell, personal communication, 2010).

The BWDW programme presents a best practises model for building supportive and effective health networks within communities of African descent. The site has proven to be a powerful tool for increasing exercise rates and thus helping to address the troubling prevalence of cardiovascular-related and other diseases that continue to plague women of African descent. It is hoped the BWDW programme will inspire ongoing dialogue about finding other effective means of supporting Black women to become active, whether via other social media software, or in more traditional in-person venues.

### References

1. Adell, C. (November 2010). Telephone interview with author.
2. Bryan, S.N., Tremblay, M.S., Pérez ,C.E,, Ardern, C.I., Katzmarzyk, P.T. (2006, Jul/Aug). Physical Activity and Ethnicity: Evidence from the Canadian Community Health Survey. Can J Public Health. 2006 Jul-Aug; 97(4):271-6.
3. Eyler, A.A., Matson-Koffman, D., Young, D.R., Wilcox, S., Wilbur, J., Thompson, J.L., Sanderson, B., Evenson, K.R. Quantitative study of correlates of physical activity in women from diverse racial/ethnic groups: The Women’s Cardiovascular Health Network Project–summary and conclusions Am J Prev Med. 2003 Oct;25(3 Suppl 1):93-103.
4. Flegal, K.M., Carroll, M.D., Ogden, C.L., Curtin, L.R. Prevalence and Trends in Obesity Among US Adults, 1999–2008. JAMA. 2010 Jan 20; 303(3):235-41.
5. Jakicic, J.M., Lang, W., Wing, R.R. Do African-American and Caucasian overweight women differ in oxygen consumption during fixed periods of exercise? Int J Obes Relat Metab Disord. 2001 Jul; 25(7):949-53.
6. US Dept of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2000 Washington, DC: Government Printing Office.

### Corresponding Author

Sherldine Tomlinson, MSc.
2-440 Silverstone Drive,
Toronto, Ontario,
M9V 3K8,
<srtomlinson@students.ussa.edu>
416 749-7723

2013-11-22T22:58:08-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology, Women and Sports|Comments Off on Black Women “DO” Workout!
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Effects of Augmented Visual Feedback and Stability Level on Standing Balance Performance using the Biodex Balance System

Steven F. Pugh; Robert J. Heitman, John E. Kovaleski; Chris M. Keshock; Shelley H. Bradford. University of South Alabama, Mobile, Alabama

### Abstract

This study compared the effects of visual feedback and stability level on standing balance performance using the Biodex Balance System. The analysis was performed on a 2 x 2 factorial design for the purpose of testing the main effects of the type of feedback (augmented visual feedback or none) and balance condition (less stable – Biodex level 2 or more stable – Biodex level 7). Four randomly assigned groups performed nine 20-second dynamic balance trials at stability level 2 or at level 7, depending on group assignment. The dependent variable was the mean stability index calculated as an average of the nine 20-seconds trials. A significant feedback by stability level interaction was found (P = .04). At stability level 7, augmented visual feedback mean stability index scores were better when compared to no augmented visual feedback (P < .001). No significant differences were found at stabilty level 2. Our data indicate that when balancing on a Biodex Balance System, as the degree of difficulty increases the effect of concurrent augmented visual-feedback is reduced.

**Key words:** balance, visual feedback, posture, augmented

### Introduction

Dynamic balance is critical for the acquisition and execution of motor skills. Balance training is used for injury rehabilitation, fall reduction, and sport and motor skill development. One commercial device used to quantify the degree of dynamic balance is the Biodex Balance System (4). The Biodex Balance System is an instrumented device that allows the tilting of a circular flat platform. The degrees of tilt from horizontal are measured and used to calculate an overall stability index (1). This index is a quantitative estimate used for the evaluation of an individual’s neuromuscular control as it pertains to the ability to maintain postural stability on an unstable surface (4).

One feature unique to the Biodex Balance System is that the stability of the balance platform can be increased or decreased, thus enabling control of the level of difficulty of the standing balance task. Biodex platform stability levels range from 1 to 8, with 8 being the most stable or least difficult to perform. Another feature of the Biodex Balance System is an attached LCD monitor that provides augmented visual feedback. The monitor provides information, via a screen tracing, concerning the subject’s ability to balance on the platform as the subject tries to maintain the cursor in the middle of the screen’s grid (4).

#### Theoretical Rationale

We were unable to find studies that compared the efficacy of augmented visual feedback at different levels of balance difficulty. As the stabilometer platform becomes less stable and thus more difficult, the ability to effectively process both intrinsic and augmented visual feedback may become increasingly difficult. This would be caused by a decrease in the amount of time available to process feedback information while balancing (11). The increase in time constraints as balance difficulty increases may also bring about a change in the type of motor control strategy used, i.e., open versus closed loop. During open looped motor control, the movement is executed entirely by the motor program without the use of sensory feedback (5,6). During closed looped motor control, an initial command is sent to the muscles which start the movement. The actual execution of closed loop movements, then, depends on sensory feedback which is used to monitor the movement (6). As the balance task becomes increasingly difficult, information processing demands may be increased because of the greater number and rate of balance adjustments that must be monitored. The less stable platform also brings about the rapid initiation of postural responses which limits the effectiveness of feedback mechanisms because of the inherent time delays (11). Horack and Nashner (1986) suggests that rapid postural actions are organized by a limited repertoire of open looped motor programs which do not require feedback for execution. Open looped strategies simplify the process of complex movement by incorporating knowledge of past experiences into motor programs enabling anticipation of events and reducing reliance on the slower feedback mediated responses associated with closed looped monitoring (6).
The purpose of this study was to determine the effects of concurrent augmented visual feedback and balance condition on standing balance performance using the Biodex Balance System. We postulate that concurrent augmented visual feedback will not be as effective at the less stable condition (Biodex stability level 2) when compared to the more stable condition (Biodex stability level 7). This hypothesized difference in the effects of visual feedback at the different levels of stability will be demonstrated in the form of a feedback by stability level statistical interaction.

### Methods

#### Participants

Forty healthy, male university students (age = 21.4 ± 3.6 years, mass = 70.3 ± 14.3 kg, height = 170 ± 3.1 cm) volunteered to participate in this study. No participants reported any sensory impairment or physical injury that hindered performance of the balance task, nor did any of the participants have previous experience with balance training on the Biodex Balance System. The study was approved by the university’s institutional review board, and informed consent was obtained from each individual before testing.

#### Instrumentation

The Biodex Stability System 945-300 (Biodex Medical Systems; Shirely, New York) was used to quantify bilateral standing balance (4). The system consists of a multi-axial tilting platform interfaced with a computer which records and calculates stability indices of standing balance. The platform stability can be varied by adjusting the resistance applied to the platform via one of 8 stability settings controlled by the system’s microprocessor-based actuator (14). Setting 1 represents the least stable platform and setting 8 the greatest platform stability. An 11.5 x 8.5 cm LCD display screen, located at eye level, provides visual feedback via a circular grid that visually shows a cursor tracing of the subject’s stability performance. The goal of dynamic balance testing on the Biodex Balance System during the augmented visual feedback condition is for the subject to maintain the cursor on the center of the circular grid for as long as possible during the test trial (8). During the no augmented visual feedback condition the goal was to keep the balance platform in a horizontal position while focusing straight ahead on a covered LCD screen. The Biodex Balance System has been shown to have high reliability.(8,14).

#### Procedures

Four randomly assigned groups with ten subjects in each group performed nine 20-second dynamic balance trials. The platform balance task required the subject to stand barefooted in a comfortable upright position with feet shoulder width apart with arms at sides. Groups 1 and 3 received augmented visual feedback during the balance task, while groups 2 and 4 received no augmented visual feedback. During the augmented visual feedback trials, the subject was instructed to keep the cursor directly in the middle of the screen while balancing on the platform. Group 1 performed the balance task at platform stability level 2 with augmented visual feedback. Group 2 performed at stability level 2 with no augmented visual feedback, which involved the subject performing the balance task while focusing straight ahead on a covered screen. Group 3 performed the balance task at stability level 7 with identical augmented visual feedback as used with group 1. Group 4 performed at stability level 7 with no augmented visual feedback which involved performing the balance task while focusing on a covered screen.

A familiarization session was conducted in which the participants were introduced to the testing protocol. Four 20-second practice trials were performed either at stability level 2 or at level 7, depending on group assignment. A 20-second rest period was allowed between trials. Participants assigned to the augmented vision condition practiced the balance task while being allowed to watch the balance tracing on the screen. Participants assigned to the no visual feedback condition practiced the balance task while viewing a covered screen.

Prior to the data acquisition trials, all subjects achieved a stable upright stance by positioning their feet shoulder width apart on the center of platform while looking straight ahead. The screen was either left uncovered or covered which was dependent on the assigned treatment group. The platform was then unlocked, requiring balance at the given stability level. Nine 20-second dynamic balance trials were performed. The same examiner (S.F.P.) administered the balance task in a non-distracting environment. If a participant lost control of balance that required grabbing the handrail, the trial was repeated. Three participants repeated one trial each. Two participants lost balance control more than once and were not included in the data analysis.

Platform stability levels 2 and 7 were chosen based on testing recommendations found in the literature and from pilot data (12). In addition, enough disparity between groups in terms of balance difficulty was necessary in order to ensure that statistical differences between feedback groups, if it in fact existed, could be found. A previous study reported that approximately nine 20-second trials could be safely performed in one practice session before participants reported fatigue (12). No participant was told their stability index scores or given any other information concerning their performance other than that given in the visual feedback conditions.

#### Statistical Analysis

A 2 x 2 factorial design was used to examine the effects of feedback and balance condition on dynamic balance performance using the Biodex Balance System. The first independent variable was type of feedback with two levels (augmented visual and no augmented visual). The second independent variable was balance condition with two levels (stability level 2 or stability level 7). The dependent variable was the mean stability index calculated as an average of the nine 20-seconds trials. The stability index is determined from the amount of platform tilt in degrees from a zero-centered balance-point (level). The index was calculated as the standard deviation of the platform displacement from horizontal obtained from each 20-second trial (4). A low stability index score indicates good dynamic stability or balance, whereas a high stability index scores indicates poor balance control.

A two-way univariate analysis of variance was conducted to examine the effects of the type of feedback and balance condition for the stability index score data. The α level was set a priori at .05. We used SPSS (version 18; SPSS Inc, Chicago, IL) to analyze the data.

### Results

Means and standard deviations for stability index scores are presented in Table 1. A significant main effect was found for balance condition (F1,36 = 105.134, P = .001), which means participants assigned to the easier balance condition had better balance scores than those assigned to the more difficult balance task. No significant differences were found for the type of feedback groups (F1,36 = 2.145, P = .152). More importantly, a significant feedback by balance condition statistical interaction was found (F1,36 = 4.107, P = .04). At stability level 7, augmented visual feedback stability index scores were better when compared to no augmented visual feedback stability index scores (P < .001). However, for stability level 2, no difference was found between the feedback and no feedback conditions (P = .778).

### Discussion

We propounded the question of whether or not concurrent augmented visual feedback influences balance on the Biodex Balance System at different stability levels. The results supported our postulation that concurrent augmented visual feedback did not influence balance at the more unstable level (Biodex stability level 2). The importance of vision on postural control has long been known (2), however, the effect of concurrent augmented visual feedback on postural control while balancing on an unstable surface is equivocal. Most of the reported clinical studies that examined the effects of augmented visual feedback on postural control have involved stroke patients (7,11). Barclay-Goddard et al (2009) conducted a meta-analysis of the efficacy of concurrent augmented feedback using force platform standing balance in stroke patients. Their results showed no clear evidence that the use of force platform visual feedback improved standing balance. O’Connor et al (2008) compared the effects of different visual cues on postural sway in healthy older and younger adults. The older adults were able to habituate to repeated visual perturbations, however, it took more exposures compared to the younger adults. This finding suggests that aging impacts the ability to quickly modify augmented visual feedback for postural control. Hlavackova et al (2009) studied the effects of concurrent mirror feedback on upright stance control in elderly transfemoral amputees. Their results showed mirror feedback improved upright stance control.

Normal postural sway and equilibrium produced while standing on a flat stable surface may be controlled by lower level closed-looped feedback corrections. Standing balance on a stable surface primarily involves activating automatic postural reactions that are based on reflex actions rather than conscious control (12). The Biodex Balance System is unique in that it uses a moveable platform to create different levels of stability. Our rationale was that at the more difficult stability level 2 the influence of augmented visual feedback would be reduced as a result of change in motor control strategies. As platform stability decreased, open-looped strategies may have been used in an effort to maintain the platform in a horizontal position. Gutierrez et al (2009) in their clinical review state that during dynamic balance, open-looped mechanisms operate faster than closed looped mechanisms when perturbations to balance are imposed. This contention is supported by the study of Horak and Nasher (1986) who investigated the extent to which standing automatic postural reactions are controlled by motor programs. They adduce the theory that postural actions are organized by a limited repertoire of central programs selected in advance of movement. Organization of movements into motor programs simplifies the process of modifying movement by reducing reliance on concurrent sensory feedback. Our data suggest that the motor control strategies used when balancing on the Biodex Balance System may not be universal at all levels of difficulty.

### Conclusion

The learning/relearning of balance is a primary goal in many types of sport and wellness rehabilitation. Because of the importance of balance, there is a constant need for the identification of efficient and successful methods of balance testing and training as well as the delineation of variables that influence balance. We conclude that when balancing on the Biodex Stabilometer, the way feedback is administered is important because it significantly affects balance performance. Our study implies that, when balancing on a Biodex Balance System, as the degree of difficulty increases the influence of concurrent augmented visual-feedback is mitigated.

### Application in Sport

During the early stages of balance training, where the stabilometer tasks are performed at the more stable (less difficult) levels, augmented visual feedback may improve the performance of the balance task. However, as task difficulty increases the ability to use augmented visual feedback to guide postural reactions may decrease. These results infer that during Biodex stability training both open and closed looped motor control strategies are being used depending on the stability level being practiced. Under these conditions, previous research (12) has shown that variable practice, where several difficulty levels are practiced in a random order during any given training session, is a more efficient means of balance training when compared to constant practice where only one stability level is practiced during a training session. Variable practice has been shown to be more efficient in the development of open loop motor programs where rapid movements are required (6). Therefore, when doing Biodex balance training for sport a protocol that involves practicing several different levels of difficulty during one training session would be recommended. Future studies need to examine additional variables such as disability, injury and age in order to determine the most appropriate rehabilitation protocols.

### Tables

#### Table 1
Mean (± SD) Stability Index Scores Averaged Across Nine 20-Second Trials

Type of Feedback Level 7 Level 2
Augmented Visual 1.62 ± .41 11.58 ± 4.54
No Augmented Visual 4.45 ± .83a 11.12 ± 2.20

a. Difference between type of feedback at level 7 (P < .001).

### References

1. Arnold, B.L., Gansneder, B.M., & Perrin, D.H.(2005). Research Methods in Athletic Training. Philadelphia, PA: F.A. Davis.
2. Asakawa, K., Ishikawa H., Kawamorita T., Fuiyama Y., Shoji N., & Uozato H. (2007). Effects of ocular dominance and visual input on body sway. Jpn J Ophathalmol.,51:375-378.
3. Barclay-Goddard, R.E., Stevenson, T.J., Poluha, W., & Taback,S.P. (2009). Force platform feedback for standing balance training after stroke : The Cochrane Collaboration. New York, NY:Wiley.
4. Biodex Medical Systems. Balance System Operations and Service Manual. Shirley, NY: Biodex Medical Systems; 2003.
5. Davids K., Button C., & Bennett S. (2008). Dynamics of Skill Acquisition: A Constraints Approach. Champaign, IL: Human Kinetics.
6. Gutierrez, G.M., Kaminski, T.W., & Douex, A.T. (2009). Neuromuscular control and ankle instability: A clinical review. Phys Med Rehabil.,1(4):359-365.
7. Hartveld, A., & Hegarty, J.R. (1996). Augmented feedback and physiotherapy practice: Review report. Physiotherapy., 82(8):480-490.
8. Hinman, M. (2009). Factors affecting reliability of the biodex balance system: A summary of four studies. J Sport Rehabil., 9:240-252.
9. Hlavackova, P., Fristios, J., Cuisinier, R., Pinsault, N., Janura, M., & Vuillerme, N. (2009). Effect of mirror feedback on upright stance control in elderly transfemoral amputees. Arch Phys Med Rehabil., 90(11):1960-1963.
10. Horak, F.B., & Nashner, L.M. (1986). Central programming of postural movements: Adaptation to altered support-surface configurations. J Neurophysiol., 55(6):1369-1381.
11. Horak, F.B., Diener, H.C., & Nashner, L.M. (1989). Influence of central set on human postural responses. J Neurophysiol. ,62(4):841-853.
12. Kovaleski, J.E, Heitman, R.J, & Gurchiek L.R. (2009). Improved transfer effects on biodex balance system. Athletic Training & Health Care .,1(2):74-78.
13. O’Connor, K.W., Loughlin, P.J., Redfern, M.S., & Sparto,P.J. (2008). Posturaladaptations to repeated optic flow stimulation in older adults. Gait Posture., 28(3):385-391.
14. Schmitz R, Arnold B. (1998). (Intertester and intratester reliability of a dynamic balance protocol using the Biodex Stability System. J Sport Rehabil.,7:95-101.

### Corresponding Author

Dr. Steven Pugh, PhD.
HPELS Dept
University of South Alabama
HPE Building, RM 1016
171 Jaguar Drive
Mobile, Alabama 36688

<sfpugh@usouthal.edu>
(251) 461-8231

2013-11-22T22:58:23-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off on Effects of Augmented Visual Feedback and Stability Level on Standing Balance Performance using the Biodex Balance System
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The Effects of Conference Realignment on National Success and Competitive Balance: The Case of Conference USA Men’s Basketball

Martin M. Perline, G. Clayton Stoldt, Mark C. Vermillion

### Abstract

Collegiate athletic conferences serve multiple functions, including providing regular opportunities for members to compete in a relatively equitable environment and contributing to the financial well being of member institutions. Many conferences have undergone realignment in recent years, and the effects of those changes may impact the degree to which conferences realize those desired outcomes. The purpose of this paper is to assess how the churning of various institutions (i.e., changes in conference membership as institutions leave or are added) within Conference USA over a 10-year period affected the conference’s men’s basketball programs in regard to success at the national level and competitive balance within the conference. Both national success and competitive balance within the conference can significantly impact the financial well-being of the conference. Results of the study indicate decreases in both the competitive success of the men’s basketball programs at the national level and the in-conference competitive balance between the 2000-2001 through 2004-2005 and the 2005-2006 through 2009-1010 time periods.

**Key Words:** college athletics, competitive balance, conference realignment, basketball, conference USA

### Introduction

While amateur athletic conferences serve many functions for the individual member institutions, one important purpose is to attempt to enhance the financial status of their members. Although there are numerous ways this can be achieved, two important ways include (1) an attempt to accumulate a group of conference teams that are successful nationally against teams from rival conferences, and (2) an effort to insure teams are somewhat evenly matched within the conference—what is referred to as competitive balance.

Both winning against non-conference opponents and competitive balance are important as they tend to enhance the financial status of conference members. Indeed, “everyone loves a winner,” and is willing to attend games featuring successful teams more often and pay more to attend. Likewise, while people want their teams to win, fans like the games to be exciting and not a foregone conclusion as to the winner (5, 9, 12, 17, and 18).

Almost all major college athletic conferences have experienced changes in their membership within the last six years. These changes—commonly referred to as churning as members come and go—impact conferences in many ways. Competitive success at the national level and in-conference competitive balance are among the desired outcomes commonly impacted.

The purpose of this study was to assess how churning within Conference USA over a 10-year period has affected the conference’s men’s basketball programs in regard to success at the national level and competitive balance within the conference. The study is important because it assesses the impact of churning on two key but unrelated dimensions. A conference may be well balanced competitively but have negligible success at the national level. Conversely, a conference may be highly unbalanced, but the few teams who win consistently in-conference, may also enjoy considerable success at the national level. This can provide considerable financial rewards for the conference.

Competitive success at the national level and the financial well-being of conference members are inextricably linked because the number of teams a conference places in the NCAA national championship tournament and the number of victories those teams accrue determine the NCAA’s payout to participating conferences. Other studies have examined the effects of churning on competitive balance (see, for example, 13-15, 18) or the relationship between realignment and program revenue (8). This project is the first to combine both considerations, allowing for a more comprehensive assessment of churning outcomes.

### Related Literature

College conferences are comprised of college and universities that have established an association, one of the purposes of which is regular athletic competition (1). In 2011, Staurowsky and Abney (20) stated conferences “establish rules and regulations that support and sustain a level playing field for member institutions, while creating in-season and postseason competitive opportunities” (p. 149). And Rhoads (18) has observed that “(i)t is reasonable that conferences should be quite active in ensuring optimal levels of competitive balance” (p. 5).

Sustained competition among equitable teams is not the sole purpose of athletic conferences, however. Depken (4) observed:

> Sport leagues exist, in part, to insure profitability of their member franchises. Although the NCAA specializes in amateur sports, in which players do not receive direct salaries for their athletic performance, it is readily apparent that the schools that comprise the NCAA are often anxious to earn as much profit as possible from the sports programs (p. 4).

College athletic conferences contribute to their member institutions’ revenue by distributing rights fees from media agreements, corporate sponsorships, licensing and other forms of revenue received by the league (7). One source of revenue for NCAA Division I conferences are distributions from the annual Division I Men’s Basketball Championships. Payouts to conferences are based on financial values linked to units, which are accrued each time a conference member plays a game in the tournament (22). For example, a conference member advancing to the third round (i.e., “Sweet Sixteen”) is valued at three units. Payments to conferences are based on six-year averages of the financial values associated with units accrued (22).

#### Conference Churning

As illustrated in Table 1, 10 of the 11 conferences in the NCAA Division I’s Football Bowl Subdivision (FBS) experienced membership changes between 2005 and 2011. Additional changes at the FBS level are planned for 2012, and Quirk (16) has observed similar instability among non-FBS Division I conferences. Fort and Quirk (6) argued that football is the predominant consideration when institutions change conference affiliations. Competitive imbalance in existing conferences often results in churning because enhanced competitive balance is linked to desirable financial outcomes. Other scholars (5, 9, and 17) support that argument, observing that consumer uncertainty of a game’s outcome is linked to increased demand. Rhoads (18) specifically linked competitive balance with increased ticket sales and enhanced television rights fees.

Little scholarly attention has been devoted to effects of conference churning on competitive success against non-conference opponents. Minimal research has been devoted to evaluating conference realignment in terms of financial outcomes. One exception is Groza (8), who found FBS teams that changed conferences enjoyed an increase in attendance, even controlling for increased quality in competition. Of course, ticket sales (i.e., attendance) is only one of many financial factors that may be impacted by churning. Others include, but are not limited to, BCS and other bowl related revenue, NCAA tournament payouts; media rights fees, athletic donations, and corporate sponsorship fees.

Several studies have been conducted assessing the effects of conference churning on competitive balance within select sport programs. Rhoads (18) examined the Western Athletic and Mountain West conferences and found that membership changes in those conferences had resulted in enhanced competitive balance in football. The changes had no impact on competitive balance in men’s basketball however. Perline and Stoldt (13-14) conducted two studies focusing on competitive balance before and after the Big 8 Conference expanded to become the Big 12. Their first study focused on men’s basketball, for which they concluded that competitive balance within the sport decreased after the conference’s expansion (13).Their second study centered on football, for which they concluded that competitive balance improved after the merger (14). The same scholars also examined competitive balance in women’s basketball before and after the merger between the Gateway Collegiate Athletic Conference and Missouri Valley Conference (15). Multiple methods of assessing of competitive balance produced mixed results, with more measurements indicating more competitive balance after the merger.

#### Conference USA: History and evolution

Conference USA (C-USA) was formed in 1995 during a time of great upheaval in college athletics, which included the dissolution of the Southwest Conference and the formation of the Big XII in 1996 (21). C-USA is a Division I-A league that is divided into two competitive divisions: East and West. In the eastern division members include East Carolina University, Marshall University, the University of Memphis, Southern Mississippi University, University of Alabama- Birmingham, and the University of Central Florida. The western division includes the University of Houston, Rice University, Southern Methodist University, Tulane University, the University of Tulsa, and the University of Texas- El-Paso (2).

Since its inception in 1995, C-USA has endured much change. In the beginning the conference consisted of the University of North Carolina-Charlotte, the University of Cincinnati, DePaul University, the University of Houston (starting competition in 1996), Marquette University, the University of Memphis, Tulane University, St. Louis University, University of Alabama- Birmingham, and the University of Southern Florida. Mike Slive was appointed as the first commissioner, but left to become the commissioner of the Southeastern Conference in 2002 (19), leaving C-USA to appoint Britton Banowsky as its new commissioner. Additionally, in 2002, the C-USA headquarters moved from Chicago to Irving, Texas (2).

The major realignment of C-USA in 2005 was set in motion by larger conference realignment issues. The Atlantic Coast Conference’s (ACC) desire for football prestige triggered a mass reordering of conferences (23). Specifically, the ACC invited the University of Miami (FL), Virginia Polytechnic and State University, and Boston College to join their conference, thereby depleting the Big East Conference. In order to reestablish its conference, the Big East invited C-USA members the University of Cincinnati, DePaul University, Marquette University, the University of Louisville, and the University of South Florida (11). Additionally, four other institutions relinquished their C-USA memberships in 2005. Texas Christian University left to join the Mountain West Conference, the University of North Carolina-Charlotte and St. Louis University left to join the Atlantic 10 Conference, and the U.S. Military Academy (aka Army) became independent [11). Figure 1 lists the various institutions that have been members of C-USA, the dates of their memberships, and their current conference affiliations.

Crytzer (3) noted the unusual current geographical size of C-USA (over 1,500 miles separate the eastern most and western most schools) is a barrier for many of the member schools, which range in student population from 5,000 to 50,000. Additionally, conference defections over the past 15 years helped fuel speculation that future NCAA conference realignments could render C-USA obsolete.

### Methods

The purpose of this paper was to assess how churning within Conference USA over a 10-year period has affected the conference’s men’s basketball programs in regard to success at the national level and competitive balance within the conference. We employed two tactics each in evaluating winning success nationally and competitive balance.

#### Winning Success

In order to measure winning success, we measured the success of Conference USA teams against outside competition before the departure of teams in the 2004-05 season and after the addition of teams in the 2005-06 season. While the conference mean will always be .500, the non-conference mean could vary. We also measured the number of Conference USA teams that participated in the NCAA post-season tournament in both periods. The latter was a major source of revenue to the conference and ultimately to each team. The value of each appearance in the tournament varied from $94,086 in 2001 to $222,206 in 2010 and has continued to grow in magnitude over time. These values were paid annually for six years. Thus one appearance in 2001 would be worth $564,516 to the conference and one appearance in 2010 would be worth $1,333,236 to the conference over the six-year period. It is, therefore, readily apparent that the more appearances a conference makes in the tournament, the more revenue it receives.

#### Measuring Competitive Balance

There were several methods used in measuring competitive balance. The most appropriate of these methods depended on what the researcher was attempting to specifically measure (9). Methods most appropriate for measuring competitive balance within a given season may be different from those used to measure competitive balance between seasons (10). To measure competitive balance within a given year, we rely on the standard deviation of winning percentages and to measure competitive balance between seasons, we use the Hirfindahl-Hirschman Index (HHI).

##### Standard Deviation of Winning Percentages

Possibly the method most often used to measure competitive balance within a conference in a given season is the standard deviation of winning percentages. Since there will, outside of a tie, always be one winner and one loser for each game, the average winning percentage for the conference will always be .500.

In order to gain insight into competitive balance, we would need to measure the dispersion of winning percentages around this average. To do this we can measure the standard deviation. This statistic measures the average distance that observations lie from the mean of the observations in the data set. The formula for the standard deviation is:

![Formula 1](/files/volume-14/441/formula-1.jpg)

The larger the standard deviation, the greater is the dispersion of winning percentages around the mean, and thus the less competitive balance.

#### Championship Imbalance

While using the standard deviation as a measure of competitive balance provides a good picture of the variation within a given season, it does not indicate whether it is the same teams winning every season, or if there is considerable turnover among the winners, i.e., whether there is between season variation. Therefore, another method economists have used to measure imbalance is the Hirfindahl-Hirschman Index (HHI), which was originally used to measure concentration among firms within an industry ([10). We determine the HHI by counting the number of times a team won a championship during a given period, summing those values and then dividing by the number of years in the period considered.

![Formula 2](/files/volume-14/441/formula-2.jpg)

Using this method, the greater the number of teams that achieve championship status over a specific time period, the greater would be the competitive balance.

### Results

#### Winning Success

Table 3 gives the winning percentages for Conference USA teams against non-conference opponents in the two periods under consideration. For the earlier period the mean winning percentage was .606 and for the latter period it was .577—an approximate 5% differential favoring the earlier period. It should be noted that the highest winning percentage over this total period was .638 (2003-04) and the lowest was .539 (2005-06). The data suggest that Conference USA was more successful against outside competition in the earlier period.

Table 4 reflects the number of Conference USA members participating in the NCAA post-season tourney, the unit value of each appearance and the dollars received in each year from conference participation. The data in Table 4 indicates that in the 2001-05 period the conference received $30,722,250, and in the 2006-10 period the conference receipts were only $21,269,388. These numbers reflect a participation of 39 appearances in the earlier period and 19 in the latter period. Consequently, even though the dollars per unit were considerably higher in the latter period, the conference earned almost $10 million more in the earlier period.

#### Competitive Balance

##### Standard Deviation of Winning Percentages

Tables 5 and 6 display the winning percentage for men’s basketball for the years 2000-01 through 2004-05 and for 2005-06 through 2009-10. Table 7 displays the standard deviations for both time periods.

As shown in Table 7, the mean standard deviation was .208 for 2000-01 through 2004-05, and it was .250 for 2005-06 through 2009-10. As indicated above, the lower the standard deviation the greater the competitive balance. This is a 20.3% difference favoring competitive balance in the earlier period. It should also be pointed out that not only was the mean standard deviation lower for the earlier period, but the lowest standard deviation for the period, .173 (2000-01), was lower than the lowest standard deviation for the later period, .238 (2006-07). Likewise the highest standard deviation for the later period, .261 (2009-10) was higher than the highest standard deviation, .236 (2003-04) in the earlier period. As a matter of fact the standard deviation was lower every year of the earlier period than for the later period.

Why the standard deviation was lower for the earlier period can also be seen by the range of the means in the two periods. As indicated in Table 5 (the earlier period) the range was a high of .725 (Cincinnati) and a low of .266 (East Carolina). This was a range of .459 from top to bottom of the standings. On the other hand, and as indicated Table 6 (the latter period), the means ranged from a high of .948 (Memphis) to a low of .216 (East Carolina). This was a range of .732 from top to bottom. Indeed in this period Memphis had a perfect record of 16-0 in three of the five years investigated, while two teams, East Carolina and SMU, had losing records all five years.

##### Championship Imbalance

Using the data from Table 8 to construct the HHI to measure competitive balance between the two periods we find the results are consistent with the results found when using the standard deviation. Using the regular season standings we find that during the 2000-01 through 2004-05 period (see Table 8), three teams–Cincinnati, Marquette and Louisville–won the championship once each. Multiple teams shared the title for two seasons–2001-02 when Cincinnati and Southern Mississippi tied and 2003-04 when there was a five-team (DePaul, Memphis, Cincinnati, UAB and Charlotte) tie for first. If we give one point for each outright championship, .5 for a two-team tie, and .2 for a five-team tie, we find:

HHI = 1.72 + 12 + 12 + .52 + .22 + .22 + .22 + .22= 2.89 + 1 + 1 + .25 + .04 + .04 + .04 + .04 = 5.3/5 = 1.06

When measuring the HHI over the 2005-06 through 2009-10 period (see Table 8), we find considerably less competitive balance. During this period one team, Memphis, won the regular season championship four times and another team, UTEP, won the championship the other year. Measuring these results we find:

HHI= 42 + 12 = 16 + 1 = 17/5 = 3.4

These calculations indicate less competitive balance during the 2005-06 through 2009-10 period.

### Conclusions

The results of this study offer strong evidence that the churning that occurred in C-USA over the 10-year period 2000-2001 through 2009-2010 had negative effects for men’s basketball in terms of both competitive success at the national level and competitive balance within the conference. Both of the indicators of national success—winning percentage against non-conference opponents and revenue derived from member appearances in the national championship tournament—were better during the earlier period than the latter. In addition both measures of competitive balance within the conference—standard deviation of winning percentages and the HHI—indicate more competitive balance in the earlier period.

It is also important to note that while this study examined the financial ramifications of C-USA’s success, or lack thereof, in the men’s basketball national championship tournament, that revenue stream was but one of several that determine the overall financial well-being of the conference and its members. However, Crytzer (3) has observed that as the financial benefits of the C-USA’s success in men’s basketball from 2003-2005 in particular run out, the conference’s long-term viability may be at risk. Clearly, multiple factors relating to a variety of sport programs will affect whether C-USA is susceptible to additional churning and/or will even survive. However, the findings of this study pertaining to one flagship sport, men’s basketball, indicate the conference faces significant challenges in the near future.

### Applications In Sport

While the results of this study are not to be generalized to other sports programs or other conferences, they do align with the findings of other studies that have examined the effects of conference churning on competitive balance in men’s basketball. While Rhoads (9) found realignment in the Western Athletic and Mountain West conferences had enhanced competitive balance in football, it did not have the same positive effect in men’s basketball. And two studies on the effects of churning in the Big 12 found improved competitive balance in football (14) but diminished competitive balance in men’s basketball (13). Since football is recognized as the primary factor in conference realignment (6), it may be that conference churning commonly results in desirable outcomes for that one sport program while others (i.e., men’s basketball) do not enjoy the same benefits. Given the potential for revenue generation in men’s basketball, and perhaps a few other sport programs aside from football (depending on the institution), the appeal of competitive success on a national level, and the importance of in-conference competitive balance, university and college leaders are well advised to consider likely ramifications for multiple sport programs when considering conference affiliation options.

### Tables

Conference Last Change Description
Atlantic Coast Conference 2005 Boston College joins
Big East Conference 2011 Texas Christian joins
Big Ten Conference 2011 Nebraska joins
Big 12 Conference 2011 Two institutions withdraw
Conference USA 2005 Five institutions join, four withdraw
Mid-American Conference 2007 Temple joins as football-only member
Mountain West Conference 2011 Two institutions withdraw, Boise State joins
Pac-10 Conference 2011 Two institutions join
Southeastern Conference 1990 Two institutions join
Sun Belt Conference 2010 New Orleans withdraws
Western Athletic Conference 2011 Boise State withdraws

#### Table 2
Evolution of C-USA, 1995-2011

Conference Last Change Description
UNC Charlotte 1995-2005 Atlantic 10
Cincinnati 1995-2005 Big East
DePaul 1995-2005 Big East
Houston 1995-Present C-USA
Louisville 1996-Present C-USA
St. Louis 1995-2005 Atlantic 10
Southern Miss 1995-Present C-USA
Tulane 1995-Present C-USA
Alabama, Birmingham 1999-Present C-USA
Southern Florida 1995-2005 Big East
Central Florida 2005-Present C-USA
Texas Christian 1999-2005 Mountain West1
East Carolina 1996-Present C-USA
Army 1997-2005 Independant
Marshall 2005-Present C-USA
Rice 2005-Present C-USA
Southern Methodist 2005-Present C-USA
Tulsa 2005-Present C-USA
Texas, El-Paso 2005-Present C-USA

1. Moving to the Big East in 2011-2012 season

#### Table 3
Conference Winning Percentage in Games Against Non-Conference Opponents

Year Winning Percentage
2000-01 .550
2001-02 .622
2002-03 .607
2003-04 .638
2004-05 .615
5-Year Mean .606
2005-06 .539
2006-07 .590
2007-08 .585
2008-09 .589
2009-10 .583
5-Year Mean .577

#### Table 4
NCAA Tournament Appearances and Related Revenue

Year NCAA Appearances Unit Volume ($) Yearly Value ($) 6 Year Value ($)
2001 5 94,086 470,430 2,822,580
2002 4 100,672 402,688 2,416,128
2003 9 130,697 1,176,273 7,057,638
2004 11 140,964 1,550,604 9,303,624
2005 10 152,038 1,520,380 9,122,280
5-Year Totals 39 618,457 5,120,375 30,722,250
2006 5 163,981 819,905 4,919,430
2007 4 176,864 707,456 4,244,736
2008 5 191,013 955,065 5,730,390
2009 3 206,020 618,060 3,708,360
2010 2 222,206 444,412 2,666,472
5-Year Totals 19 960,084 3,544,898 21,269,388

#### Table 5
Winning Percentage for Men’s Basketball Teams, 2000-01 through 2004-05

Year Cin Char Marq StL Lou DeP SouM Mem USF UAB Hou Tul ECar TCU
2000-01 0.688 0.625 0.563 0.5 0.5 0.25 0.688 0.625 0.563 0.5 0.375 0.125
2001-02 0.875 0.688 0.813 0.563 0.5 0.125 0.25 0.75 0.5 0.375 0.563 0.313 0.313 0.375
2002-03 0.562 0.5 0.875 0.562 0.688 0.5 0.313 0.813 0.438 0.5 0.375 0.5 0.188 0.188
2002-04 0.75 0.75 0.5 0.563 0.563 0.75 0.375 0.75 0.063 0.75 0.188 0.25 0.313 0.438
2004-05 0.75 0.75 0.438 0.375 0.875 0.625 0.25 0.563 0.313 0.625 0.563 0.25 0.25 0.5
Mean 0.725 0.663 0.638 0.513 0.625 0.45 0.375 0.700 0.375 0.55 0.413 0.288 0.266 0.375

#### Table 6
Winning Percentage for Men’s Basketball Teams for 2005-06 through 2009-10

Year Memphis UAB UTEP Hou UCF Tulsa Rice Tulane Marshall SMU So.Miss E.Car.
2005-06 0.929 0.857 0.786 0.643 0.5 0.429 0.429 0.429 0.357 0.286 0.214 0.143
2006-07 1 0.438 0.375 0.625 0.688 0.563 0.5 0.563 0.438 0.188 0.563 0.063
2007-08 1 0.75 0.5 0.688 0.563 0.5 0 0.375 0.5 0.25 0.563 0.313
2008-09 1 0.688 0.625 0.625 0.438 0.75 0.25 0.438 0.438 0.188 0.25 0.313
2009-10 0.813 0.688 0.938 0.438 0.375 0.625 0.063 0.188 0.688 0.438 0.5 0.25
Mean 0.948 0.684 0.645 0.604 0.512 0.573 0.248 0.399 0.484 0.27 0.418 0.216

#### Table 7
Standard Deviation for Winning Percentages

Year SD
2000-01 0.173
2001-02 0.223
2002-03 0.202
2003-04 0.236
2004-05 0.205
5-Year Mean SD 0.208
2005-06 0.253
2006-07 0.238
2007-08 0.256
2008-09 0.243
2009-10 0.261
5-Year Mean SD 0.250

#### Table 8
Regular Season Conference Champions, 2000-01 through 2004-05

Year Champion(s)
2000-01 Cincinnati, Southern Mississippi
2001-02 Cincinnati
2002-03 Marquette
2003-04 DePaul, Memphis, Cincinnati, UAB, Charlotte
2004-05 Louisville
2004-05 Louisville
2005-06 Memphis
2006-07 Memphis
2007-08 Memphis
2008-09 Memphis
2009-10 UTEP

### References

1. Abbott, C. (1990). College athletic conferences and American regions. Journal of American Studies, 24, 220-221.
2. C-USA: Official site of Conference USA. (2011). About Conference USA. Retrieved March 21, 2011 from <http://conferenceusa.cstv.com/ot/about-c-usa.html>
3. Crytzer, J. (2009, August 30). The future of college football and the death of Conference USA 1995-2011 [Web log post]. Retrieved from <http://bleacherreport.com/articles/245204-the-future-of-college-football-and-the-death-of-conference-usa-1995-2011>
4. Depken II, C.A. (2011). Realignment and profitability in Division IA college football. Unpublished paper. Retrieved April 2, 2011 from <http://www.belkcollege.uncc.edu/cdepken/P/confsize.pdf>
5. Depken, C.A., & Wilson, D. (2005). The uncertainty outcome hypothesis in college football. Department of Economics, University of Texas-Arlington.Paper under review.
6. Fort, R., & Quirk, J. (1999). The college football industry. In J. Fizel, E. Gustafson and L. Hadley (Eds.) Sports economics: Current research (pp. 11-26). Westport, CT: Praeger.
7. Grant, R.R., Leadley, J., & Zygmont, Z. (2008). The economics of intercollegiate sports. Mountain View, CA: World Scientific.
8. Groza, M.D. (2010). NCAA conference realignment and college football attendance.Managerial and Decision Economics, 31, 517-529.
9. Humpreys, B. (2002). Alternative measures of competitive balance. Journal of Sports Economics, 3, (2), 133-148.
10. Leeds, M., & von Allmen, P. (2005).The Economics of Sports.Boston: Pearson-Addison Wesley.
11. Nunez, T. (2010, June 6). Conference realignment will have ripple effect on Conference USA. The Times-Picayune. Retrieved from <http://www.nola.com/tulane/index.ssf/2010/06/conference_realignment.html>
12. Paul, R.J., Wachsman, Y., & Weinbach, A. (2011). The role of uncertainty of outcome and scoring in the determination of satisfaction in the NFL. Journal of Sports Economics, 12, 213-221.
13. Perline, M.M., & Stoldt, G.C. (2007a). Competitive Balance and the Big 12. The SMART Journal, 4 (1), 47-58.
14. Perline, M.M., & Stoldt, G.C. (2007b). Competitive balance and conference realignment: The case of Big 12 football. The Sport Journal, 10 (2). <http://www.thesportjournal.org/2007Journal/Vol10-No2/Perline08.asp>.
15. Perline, M.M., & Stoldt, G.C. (2008). Competitive balance in women’s basketball: The Gateway Collegiate Athletic Conference and Missouri Valley Conference merger.Women in Sport and Physical Activity Journal, 17 (2), 42-49.
16. Quirk, J. (2004).College football conferences and competitive balance. Journal of Managerial and Decision Economics, 25, 63-75.
17. Rein, I., Kotler, P., & Shields, B. (2006). The elusive fan.New York: McGraw-Hill.
18. Rhoads, T.A. (2004). Competitive balance and conference realignment in the NCAA. Paper presented at the 74th Annual Meeting of Southern Economic Association, New Orleans, LA.
19. SECSports.com (2011). About the SEC. Retrieved March 21, 2011 from http://www.secdigitalnetwork.com/SECSports/Home.aspx
20. Staurowsky, E.J., & Abney, R. (2011). Intercollegiate athletics. In P.M. Pedersen, J.B. Parks, J. Quarterman, & L. Thibault (Eds.) Contemporary sport management (4th ed., pp. 142-163). Champaign, IL: Human Kinetics.
21. The State of Conference Realignment. (ND). The national championship issue: Perspectives on college football. [Web log post]. Retrieved March 22, 2011 from <http://thenationalchampionshipissue.blogspot.com/2008/01/state-of-conference-realignment.html
22. Where the money goes. (2010). Champion. Retrieved April 2, 2011 from http://www.ncaachampionmagazine.org/Exclusives/WhereTheMoneyGoes.pdf>
23. Wieberg, S. (2005, June 29). Conference shakeup continues as schools seek right fit. USA Today. Retrieved March 22, 2011 from <http://www.usatoday.com/sports/college/2005-06-28-conference-hopscotch_x.htm>

### Corresponding Author

G. Clayton Stoldt
Wichita State University
Department of Sport Management
1845 Fairmount
Wichita, KS 67260-0127
clay.stoldt@wichita.edu
P: (316) 978-5441

Martin Perline is a professor and Bloomfield Foundation fellow in the Department of Economics at Wichita State University. G. Clayton Stoldt is chair and professor in the Department of Sport Management at Wichita State University. Mark Vermillion is an assistant professor in the Department of Sport Management at Wichita State University.

2015-11-08T07:40:19-06:00January 3rd, 2012|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology|Comments Off on The Effects of Conference Realignment on National Success and Competitive Balance: The Case of Conference USA Men’s Basketball
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Ticket Price Comparison of Double-A and Triple-A Affiliate Baseball Leagues

Liette B. Ocker, Don B. Melrose, Frank Spaniol, Randy Bonnette

### Abstract

As the economy continues to decline, sport managers realize that discretionary spending is limited. As such, sport managers are giving more consideration to price strategies within their own marketing mix as well as their comparison to other sport teams. The purpose of this study was to conduct a cross-sectional pricing investigation of individual teams by region within a Class-AAA and Class-AA league from the minor league baseball system. Data were obtained for ticket prices and fees from baseball team websites and phone interviews. Multivariate analysis of variance was examined for both Double-A and Triple-A leagues divided into regions. This study found no significant F (1,6) = .09, p = .77 differences for the highest ticket prices, F (1,6) = .09, p = .78, or the lowest ticket prices, and F (1,6) = .07, p = .80 for the groups within the Double-A Affiliate Texas League. However, a significance F (2,13) = 8.08, p = .00 was found in lowest ticket price within the Triple-A Affiliate Pacific Coast League, unlike highest ticket prices and fees which were not measurably different. Most minor league sport managers could consider this advantageous for promoting their entertainment as a good economic value.

**Key Words:** Baseball, Ticket Prices, Minor League

### Introduction

In light of recent economic times, sport organizations are faced with the challenge of maintaining a competitive marketplace while keeping a close eye on the bottom line. At the same time, the economic market watch (9) indicates consumers are becoming more selective with discretionary spending. Since sport consumption is not a fundamental cost of living, sport organizations have had to take a hard look at their strategic placement in the market. Pricing is a fundamental component of the marketing mix (6). Economic strategists have recommended complex formulas to establish pricing structures (2), while many sport organizations are opting for simplicity (6). In fact, Mullin, Hardy, and Sutton (6) said “the core issues in any pricing situation are cost, value, and objectives” (p. 215). In keeping with the simple pricing strategies that are the focus today, many sport franchises have utilized price comparisons as a simple and effective method of determining where a sport organization “fits” in the regional and league markets.

#### Price Comparisons

Determining the best fit in the sport consumer marketplace and how pricing strategies align with peer teams has become an emphasis for sport managers within the minor league baseball industry. As baseball ticket prices increase (1) and pricing strategies become complex (7), baseball consumers may look to alternative discretionary spending investments. Price comparison consumption behaviors have increased exponentially with the convenience of the Internet (5). Websites like Pricerunner, Amazon, and Shop.com have allowed potential consumers to price shop merchandise with several companies at the same time. Sport organizations have not considered price comparisons as a major influence on strategic pricing, due to the uniqueness offered in sport consumption. For example, sporting events occur sometimes great distances apart whereby a potential consumer may traditionally only be willing to travel 30 miles (4) and therefore do not offer a competitive risk to the local sport organization. However, as seen in recent articles (e.g., 3, 8) with a click of a button, price comparisons are made. In today’s tumultuous economy, many sport organizations have elected to market their event as a “value” within the discretionary spending category. This marketing technique is not only being utilized in relation to their direct sport competition, but also with discretionary spending businesses in general (e.g. cinema, concerts, other types of sporting events).

It was hypothesized that there was a significant (p < .05) difference between Texas and Non-Texas regions when comparing ticket pricing (highest price, lowest price, and ticketing fee) for minor league baseball Class-AA Texas League, as well as a significant (p < .05) difference among West, South, and Central regions when comparing ticket pricing (highest price, lowest price, and ticketing fee) for minor league baseball Class-AAA Pacific Coast League. Additionally, it was hypothesized that there was a significant (p < .05) difference between Double-A and Triple-A affiliate leagues when comparing ticket pricing (highest price, lowest price, and ticketing fee). This study examined price comparisons of minor league professional baseball teams segmented by league and region. A selection criterion was based upon geographic region of the minor league baseball teams as well as a comparison between Class-AA and Class-AAA organizations. Tables 1 and 2 represent the teams included in the study organized by league and region.

### Methods

#### Procedures

The data were collected through a variety of methods. Most of the information was collected through individual team websites. Some information was obtained though cold-calling via landline phones, and remaining data were provided through personal interviews. Once the data were collected, they were entered into a Microsoft Excel spreadsheet and reserved for future reference. Collection of data occurred over several months during the 2009 baseball season.

#### Data Analyses

Descriptive statistics, specifically means and standard deviations, were initially reviewed and reported for the Leagues, regions, and individual teams. The data obtained for the purpose of determining the research hypotheses were analyzed using MANOVA statistical methods. The independent variables were regions within the Texas League (Texas and Non-Texas regions) and Pacific Coast League (West, South, and Central regions). The dependent variables were the ticket pricing (highest ticket price, lowest ticket price, and ticketing fee), concession pricing (draft beer and hot dogs), and price for a family of four. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 17.0.

### Results And Discussion

#### Descriptive Statistics

Descriptive statistics were reported on all the dependent variables for the team, region and league. Table 3 provides the means and standard deviations found for highest ticket price, lowest ticket price, and ticketing fees.

Further examination of the ticket pricing established by the respective franchises indicates that the Triple-A teams (i.e. Pacific Coast League) have the greatest price point means. Specifically, the West region is higher than all other regions examined across all three price point values $22.63, $7.56, and $3.66 respectively. Inversely, the Southern region within the same league offers considerably lower price points for the highest ticket ($12.00) and lowest ticket ($5.50). All regions studied included fees into their ticket prices, particularly when utilizing online purchasing websites. Most regions were consistently adding approximately $2.00 to the overall price of the ticket.

Figures 1 and 2 show graphical comparisons between the highest and lowest ticket prices for the Texas League teams and Pacific Coast League teams respectively. As shown in both the Texas League and the Pacific Coast League price comparisons, there is great variability among teams when comparing the highest ticket prices; however within both leagues all the franchises have a relatively similar low cost for tickets.

The aforementioned price points did not include additional fees traditionally included in ticket prices for sporting events. As an example of how prices fluctuate with fees included in the price, Figures 3 and 4 show the price increase for the highest ticket price per franchise within both the Texas League and Pacific Coast League. Previously noted within Table 3, the West region of the Pacific Coast League had the highest ticket prices and once again that is reflected in Figure 4 as the fees are also the greatest among several West coast baseball franchises.

#### MANOVA Hypotheses Testing

The three hypotheses were tested by applying MANOVA to the data with SPSS software. The first group analyzed was the Texas League regions (Texas and Non-Texas) as the independent variable and the ticket prices (highest, lowest, and fees) as the dependent variables. As indicated in Table 4, there were no significant F (1,6) = .09, p = .77 differences for the highest ticket prices, F (1,6) = .09, p = .78, and the lowest ticket prices, F (1,6) = .07, p = .80, or the ticketing fees between Texas teams and Non-Texas teams within the Double-A Affiliate Texas League baseball.

As shown in Table 5, the same statistical principles were applied to the Pacific Coast League. The three regions, West, South, and Central, were the independent variables and the ticket prices (highest, lowest, and fees) were the dependent variables. There was a significant F (2,13) = 8.08, p = .00 difference in lowest ticket price between Pacific Coast League when divided by region. A Scheffe post hoc analysis revealed that the South region was significantly different from both the West (p = .00) and the Central (p = .04) regions. The South region had the lowest of the low ticket prices with an average of $5.50 as compared to the West which was $7.56 and the Central at $7.25.

Table 6 shows the difference between the Double-A Texas League and the Triple-A Pacific Coast League ANOVA source table. As noted in the Table, there were no significant F (1,22) = 1.91, p = .18 differences for the highest ticket prices, F (1,22) = 4.11, p = .06, the lowest ticket prices, and F (1,22) = .66, p = .42, or the ticketing fees.

### Conclusions

As more sport franchises compete in this challenging economic market, the need to maintain a positive public image is imperative. Baseball ticket pricing has increased substantially (1) and complex ticket prices could potentially confuse the consumer (7). As a means of determining the best fit in the sport consumer marketplace and how pricing strategies align with peer teams, leagues are examining ticketing price points. This is a simple marketing approach in line with sport marketing professionals (6). Since the advent of the internet price comparison shopping, consumers are able to make buying decisions with a simple click of a button (3, 8). With that said, sport franchises are more conscious than ever of how their ticket prices compare to their competitors’. This research determined, through mainly website analysis, that most of the ticket prices within Double-A and Triple-A baseball affiliate leagues were similar to competition franchises located within their regions. The only exception was found in the Triple-A Pacific Coast League where the South region had substantially lower low-end ticket prices (more similar to that of the Double-A Texas League). As the consumer becomes savvier with online price comparisons, and as economic discretionary spending continues to decline (9), knowing where a team fits within the market offers a greater promotional advantage. Future research may consider examining the impact of how price comparisons can improve sport franchise marketing potential (e.g. illustrating the “value” of minor league entertainment) and measure spectator attitudes toward region price comparisons.

### Applications In Sport

As the present economy is depressed and the future market is unpredictable, discretionary spending on sport entertainment may continue to decline. As such, sport managers within the minor league structure are determining the best approach to continue financial feasibility. This study revealed a common price point for minor league baseball organizations with similar attributes. Most importantly, however, this study revealed that the lowest ticket price in most minor league venues is still relatively affordable. This offers a unique marketing perspective for the increased demand for discretionary spending and sport management organizations should capitalize on this marketing opportunity.

### Acknowledgments

The authors would like to thank graduate research assistant, Lindsey Eidner, and undergraduate research assistant, Nick Garcia, for their invaluable contributions to data collection and analyses of this research endeavor.

### Tables

#### Table 1

Texas League teams organized by region.

Texas League
Double-A Affiliate
Texas Team Non-Texas Team
Corpus Christi Hooks Arkansas Travelers
Frisco Rough Riders Northwest Arkansas Naturals
Midland Rockhounds Springfield Cardinals
San Antonio Missions Tulsa Drillers

#### Table 2

Pacific Coast League teams organized by region.

Pacific Coast League
Triple-A Affiliate
West South Central
Albuquerque Isotopes Nashville Sounds Oklahoma City Redhawks
Fresno Grizzles Memphis Redbirds Colorado Springs Sky Fox
Las Vegas 51’s New Orleans Zephyrs Iowa Cubs
Portland Beavers Round Rock Express Omaha Royals
Salt Lake City Bees
Reno Aces
Sacramento River Cats
Tacoma Rainers

#### Table 3

Descriptive statistics (mean ± standard deviation) for the baseball teams separated by region.

Ticket Prices*
Highest Lowest Fees**
Texas League 13.13 ± 5.40 6.06 ± 0.56 2.13 ± 1.25
  Texas 12.50 ± 4.51 6.13 ± 0.85 2.00 ± 1.08
  Non-Texas 13.75 ± 6.84 6.00 ± 0.00 2.25 ± 1.55
Pacific Coast League 18.13 ± 9.43 6.97 ± 1.19 2.72 ± 1.86
  West 22.63 ± 10.70 7.56 ± 1.05 3.66 ± 2.26
  South 12.00 ± 4.00 5.50 ± 0.58 2.00 ± 0.82
  Central 15.25 ± 6.85 7.25 ± 0.50 1.58 ± 0.15

* Ticket prices are in US dollars
** Fees were team specific, examples included online convenience charges, facility improvement fees, and taxes

#### Table 4

MANOVA source table for the Texas League by region.

Source of Variation df SS MS F
Highest Tickets Between Groups 1 3.13 3.13 0.09
Within Groups 6 201.25 33.54
Total 7 204.38
Lowest Tickets Between Groups 1 0.03 0.03 0.09
Within Groups 6 2.19 0.37
Total 7 2.22
Fees Between Groups 1 0.13 0.13 0.07
Within Groups 6 10.75 1.79
Total 7 10.88

* p < .05

#### Table 5

MANOVA source table for the Pacific Coast League by region.

Source of Variation df SS MS F
Highest Tickets Between Groups 2 345.13 172.56 2.27
Within Groups 13 990.13 76.16
Total 15 1335.25
Lowest Tickets Between Groups 2 11.77 5.88 8.08±
Within Groups 13 9.47 0.73
Total 15 21.23
Fees Between Groups 2 14.33 7.17 2.46
Within Groups 13 37.81 2.91
Total 15 52.14

* p < .05

#### Table 6

MANOVA source table for the Double-A Texas League compared to the Triple-A Pacific Coast League.

Source of Variation df SS MS F
Highest Tickets Between Groups 1 133.33 133.33 1.91
Within Groups 22 1539.63 69.98
Total 23 1672.96
Lowest Tickets Between Groups 1 4.38 4.38 4.11
Within Groups 22 23.45 1.07
Total 23 27.83
Fees Between Groups 1 1.90 1.90 0.66
Within Groups 22 63.02 2.86
Total 23 64.92

* p < .05

### Figures

#### Figure 1

Texas League individual franchise highest and lowest ticket prices.

![figure 1](/files/volume-14/437/figure-1.jpg “figure 1”)

#### Figure 2

Pacific Coast League individual franchise highest and lowest ticket prices.

![figure 2](/files/volume-14/437/figure-1.jpg “figure 2”)

#### Figure 3

Texas League highest ticket price with franchise-specific fees included.

![figure 3](/files/volume-14/437/figure-1.jpg “figure 3”)

#### Figure 4

Pacific Coast League highest ticket price with franchise-specific fees included.

![figure 4](/files/volume-14/437/figure-1.jpg “figure 4”)

### References

1. Alexander, D.L. (2001). Major league baseball: Monopoly pricing and profit-maximizing behavior. Jounal of Sports Economics, 2, 341-355.
2. French, C.W. (2002). Jack Treynor’s ‘Toward a theory of market value of risky assets’. Social Science Research Network. Retrieved April 09, 2010, from <http://papers.ssrn.com/so13/papers.cfm>.
3. Henderson, Dan. (2007, December 13). Online or offline, price comparison tools help consumers shop smart. The Free Library. (2007). Retrieved April 09, 2010, from <http://www.thefreelibrary.com/Online or Offline, Price Comparison Tools Help Consumers Shop Smart-a01073766340>.
4. Jallai, T. (2008). Development of fan loyalty questionnaire for a Double-A minor league baseball affiliate (Master thesis, Texas A&M University-Kingsville, 2008).
5. Lake, C. (2006). Shopping comparison engines market worth £120m-£140m in 2005, says E-consultancy. UK & Global News Distribution. Retrieved April 09, 2010, from <http://www.ukprwire.com/Detailed/Computer_Internet_Shopping_Comparison_Engines_market_worth>.
6. Mullin, B.J., Hardy, S., & Sutton, W.A. (2007). Pricing Strategies. In Human Kinetics (3rd), Sport Marketing (pp. 213-230). Champaign, IL: Human Kinetics.
7. Rascher, D.A., McEvoy, C.D., Magel, M.S., & Brown, M.T. (2007). Variable ticket pricing in major league baseball. Journal of Sport Management, 21, 407-437.
8. Simonds, M. (2009, April 29). Online price comparisons: Easing off your shopping experience. Articlesbase. (2009). Retrieved April 09, 2010, from <http://articlesbase.com/shopping-articles/online-price-comparison-easing-off-your-shopping-experience>.
9. U.S. Bureau of Labor Statistics. (2008). The consumer expenditure survey: Thirty years as a continuous survey.

### Corresponding Author

Liette B. Ocker, Ph.D.
Department of Kinesiology
Texas A&M University – Corpus Christi
6300 Ocean Drive, Unit 5820
Corpus Christi, TX 78412-5820
O (361) 825-2670 F (361) 825-3708
<Liette.Ocker@tamucc.edu>

2013-11-25T14:51:21-06:00December 2nd, 2011|Contemporary Sports Issues, Sports Facilities, Sports Management|Comments Off on Ticket Price Comparison of Double-A and Triple-A Affiliate Baseball Leagues
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Super Bowl Commercial and Game Consumption for the College Demographic

James E. Johnson, Donghun Lee

### Abstract

The Super Bowl is the largest annual sporting event in America in terms of single-game television viewership (5). In addition to the game, a tremendous amount of entertainment is intertwined into the Super Bowl telecast via commercials that can cost as much as $3 million for 30 seconds of air time (16). The consumption of the game and commercials is well documented. However, there is little evidence as to how the Super Bowl telecast is consumed by various demographic subgroups. College students, an often overlooked demographic for major sport marketing campaigns, are one group that appear to be an ideal target market for Super Bowl advertising due to their ability for discretionary spending (13) and affinity for popular culture. Therefore, the purpose of this study was to determine the commercial and game consumption patterns for college students during the Super Bowl. A sample of 651 traditional-aged college students at a mid-size Midwestern university was surveyed within 48 hours of Super Bowl XLIV to determine such patterns. Results indicated students watched in large numbers, watched in group settings, identified humor as a primary factor in commercial enjoyment, were interested most in the game itself, identified a different favorite commercial than the USA Today Ad Meter, are strong sport fans, and demonstrated different viewing consumption patterns by gender. It can be concluded from these results that college students resemble the average adult consumer identified in previous research (1,25) in some of their game and commercial consumption patterns (e.g., watching the Super Bowl in large groups and identifying humor as a primary attribute they enjoyed in commercials), but differed in their commercial preferences, their higher level of sport fanship, and their gender differences. Sport marketers can utilize this information to create strategies that appeal to this important demographic.

**Key words:** Super Bowl, commercials, marketing, sport consumption

### Introduction

On February 7th, 2010 the New Orleans Saints defeated the Indianapolis Colts in Super Bowl XLIV. This event, as nearly all Super Bowls before it, came with a tremendous amount of media attention, marketing savvy, and fanfare. In fact, this particular Super Bowl was the second most-watched single-game television program in American history behind Super Bowl XLV (5).

In addition to the football game, the Super Bowl television broadcast offers a definitive glimpse into the competitive and creative world of sport marketing and advertising. Consumers anxiously anticipate new commercials unveiled during the Super Bowl. These commercials, which cost as much as $3 million per 30 seconds for Super Bowl XLIV (16), have become a cultural phenomenon that create nearly as much buzz as the game itself (1,3,5,16). In essence, the commercials are considered part of the overall Super Bowl entertainment package. McAllister (22), as well as Apostolopoulou, Clark, and Gladden (1), illustrated the integration of Super Bowl commercials into popular culture by examining their content and relative importance. McAllister (2) found that the discourse surrounding the pre, during, and post-Super Bowl advertising led to special status for Super Bowl commercials. Specifically, Super Bowl commercials “often have characteristics more in line with entertainment media messages than stereotypical commercial media messages” (p. 421). Additionally, McAllister explained that Super Bowl commercials are more likely to include celebrities, are much more expensive, and are more thoroughly scrutinized by the public when compared to non-Super Bowl commercials. Blackshaw and Beard (4), echoed these sentiments by noting the uniqueness of Super Bowl advertising is partly due to their entertainment value, their ability to create a “free media” dividend, their high anticipation levels, and their growing ability to engage consumers beyond television (e.g., internet, telecommications, etc.).

Given the elevated status associated with Super Bowl entertainment, Apostolopoulou et al. (1) surveyed 1,101 Super Bowl viewers and NFL database subscribers to determine what elements of the Super Bowl contributed most to their enjoyment. Not surprisingly, the primary contributor to viewer’s enjoyment was the competitiveness of the game itself. The second largest contributor was the specific teams competing, indicating more enjoyment is based on the level of fanship towards a specific team. The third largest contributor was the Super Bowl commercials. The commercials were rated higher than the pre-game show, the celebrity coin toss, the national anthem, the team introductions, the halftime entertainment, and the post-game show. Furthermore, Elliot (9) reported that approximately 4% of the Super Bowl viewing audience watches the Super Bowl only for the commercials. Results from Apostolopoulou et al. (1) and Elliot (9) suggest that beyond the game, commercials have a tremendously powerful influence on Super Bowl viewing patterns.

Although the popularity of Super Bowl commercials is well-documented (1, 4, 16, 22), there is limited evidence to suggest whether a return on investment (ROI) is realized by the companies producing such commercials. Because the Super Bowl is an isolated event that has limited advertising time, in addition to the extreme cost, it is logical to question if ROI is attainable. O’Reilly, Lyberger, McCarthy, Séguin, and Nadeau (25) found a tremendous amount of volatility surrounding the influence of Super Bowl sponsorship. This volatility is caused by the many extraneous factors influencing advertising during this unique event (e.g., presponsorship awareness levels, existing brand associations, increased clutter in the marketplace, etc.). Despite this instability in the marketplace, there has been evidence for increased intent to purchase sponsored products, as well as a willingness on the part of consumers to pay higher prices for goods advertised during the Super Bowl (17, 25, 28). Additionally, an increasing trend for sponsors is to evaluate consumers’ intent to purchase, which ultimately impacts ROI. Blackshaw and Beard (4) noted a clear latency effect on advertised brands during the Super Bowl whereby brand opinion increased 16% and purchase consideration increased 13% in the week following the Super Bowl. Furthermore, the timing of Super Bowl commercials influenced intent to purchase and ROI, where commercials shown closer to the beginning of the game scored higher on nearly every positive advertising measure. Dotterweich and Collins (8) concluded that consumers’ intent to purchase was also impacted by the ratio of value and prestige for any given product. Achieving prestige is often accomplished by repeated brand recognition, and nearly impossible for new companies given the limited television time afforded during the Super Bowl. Therefore, ROI is likely to be greater for companies that are established and have identified a specific target audience versus start-up companies searching for their ideal demographic.

Findings from Dotterweich and Collins (8), as well as O’Reilly et al. (25), are consistent with the results of the USA Today Ad Meter. The Ad Meter is a real-time evaluation of Super Bowl commercials conducted by USA Today whereby participants’ reactions to Super Bowl commercials are measured using a hand-held device. The 2010 Ad Meter gathered information from 250 adult volunteers from San Diego, California and McLean, Virginia. The winning commercial from the 2010 Ad Meter featured famous actress Betty White in a Snickers advertisement. Consistent with findings from Dotterweich and Collins (8), Snickers is an established brand with a certain amount of prestige. Likewise, Anheuser-Busch and its well-known Budweiser commercials have been the Ad Meter champion a record ten times (16). Besides the prestige and brand recognition associated with these Ad Meter winners, there are some other qualities that impact affect toward the advertisement. Kelly and Turley (18) investigated all of the Super Bowl commercials between 1996 and 2002, and used the Ad Meter scores as a dependent variable. Content analysis revealed advertising for goods (i.e., products) was more effective than services, and the use of humor, animals, sports themes, children, and emotional appeal resulted in high levels of affect.

Although the preceding literature helps to contextualize the Super Bowl as a unique advertising opportunity, there is a gap in the literature pertaining to Super Bowl commercial consumption for specific demographic groups. Ad Meter research, as well as general research investigating viewing and consumption patterns have mostly focused their efforts on the general consumer, or on specific commercial content. For example O’Reilly et al. (25) identified respondents from 10 to 94 years of age when assessing their intent to purchase Super Bowl information, and Apostolopoulou et al. (1) investigated adults aged 25 to 44 when examining many forms of Super Bowl entertainment. However, evidence from Zhang, Lam, and Connaughton (30) suggest a need to differentiate demands of various sociodemographic groups during the marketing process. They found the most active sport consumer profile includes individuals that are relatively young (18-25 years of age), single, have low household income, and have a medium entertainment budget. Traditional college students fit this description well.

Given the similarities between the most active sport consumers and traditional college students, the current study attempted to isolate and examine the viewing patterns and perceptions of college students during the most watched sporting event of all time (5). To date, no research has attempted to ascertain the perceptions and consumption patterns of the Super Bowl for this important demographic audience. However, as consumers college students are a powerful force. Oftentimes this demographic, who are currently referred to as millennials (born between 1979 and 1994), are overlooked in marketing outcome research (2). “Considering that college students wield $200 billion in buying power each year, it may be time to set aside any preconceived notions about these coeds and start thinking of them as serious consumers” (13, p. 18). When evaluated individually, it was estimated that the average college student had $287 in discretionary spending per month, which totals $3,444 per year. Additionally, in 2002 over 99% of college students visited the internet a few times per week. Given the nearly exponential growth of the internet, as well as the increase in social and marketing websites, the number of college students who visit the internet regularly continues to rise. It is these technology-savvy college students (2) who are easily reached by the supplemental advertising offered via the Internet, and are often targeted in Super Bowl advertising (e.g., Twitter, Facebook, smart phone applications, etc.).

Within the college student market, as with most forms of marketing research, it is prudent to examine differences in gender consumption patterns. This was a secondary goal of the current research. Developments such as Title IX, female youth sports, and women’s professional leagues have the current generation of sport marketers realizing females are a viable and relevant group of sport consumers with different wants and needs than their male counterparts (24). Females, in general, have demonstrated an affiliation for the feelings of others while fostering communal relationships (27). Work from Chodorow (7) and Gilligan (14) suggests women are more likely to see “morality as emerging from the experience of social connections and value the ethic of responsibility and care.” (6, p. 609). Additionally, female athletes report they most value feelings of belonging, being part of a university community, exercise benefits, and team affiliation (10). These attributes guiding female consumption patterns lend themselves to various marketing strategies, particularly during the Super Bowl when it is common for group viewing to occur, and especially when one considers that nearly half of the viewers of Super Bowl XLIV were female (20). Furthermore, Beasley, Shank, and Ball (3) found that women’s attention levels were higher for Super Bowl commercials than they were for the game itself. According to Zhang et al. (30), “females represent the greatest market potential for professional sports, and identifying their expectations and interests are vital to the future of professional sport organizations” (p. 50).

In contract, males have generally been found to be motivated by an internal self-guided impulse whereby thoughts and behaviors are created by a particular level of self-efficacy or achievement (23). Furthermore, males have been found to be more physically and verbally aggressive (26), more competitive (12), and value autonomy (6). Similarly, male athletes were found to value competition and winning more than the social aspects offered by competitive sport (10), as well as display higher levels of athletic identity (21). From a purely marketing standpoint, males have been found to consume sport more frequently and value specific sport market demands (e.g., win/loss record, team history, close competition, love of the sport, ticket prices, etc.) more than females (30). These gender differences, combined with the unique and powerful college student demographic, may shed light on consumption patterns during the most watched annual sporting event in modern history (5).

### Methods

The purpose of this study was to determine the commercial and game consumption patterns for college students during the Super Bowl.

#### Sample

A sample of 651 traditional college students (mean age = 20.9 years) from a state-funded mid-sized Midwestern university agreed to participate. The original sample consisted of 656 participants, but five surveys were not used due to incomplete or missing answers. A total of 424 males and 227 females were included in the sample. Based on Frankel and Wallen’s (11) sampling methodology, 651 participants was a large enough sample to be representative of the entire University (approximately 20,000 students) at the 99% confidence level.

#### Procedures

Participants located at high-traffic areas on campus (e.g., food courts, busy common areas) completed a nine-item survey designed to determine; 1) The number of Super Bowl commercials watched; 2) Their favorite Super Bowl commercial; 3) The characteristic they enjoyed most about their favorite commercial; 4) Their intent to spend money on any products advertised during the Super Bowl due to the commercial; 5) The number of people they were with when watching the Super Bowl; 6) Their primary interest during the Super Bowl broadcast; 7) Their level of fanship; 8) Gender; and 9) Age. These variables were chosen in an attempt to initiate new lines of inquiry regarding college student consumer behavior s, as well as replicate some of the previous studies on Super Bowl consumption (e.g., 1, 22). Participants were surveyed using a convenience sample of on-campus college students within 48 hours of the completed Super Bowl telecast.

#### Data Analysis

Data analysis was conducted using PASW (version 18). Frequencies and measures of central tendency were used to evaluate all relevant data. Descriptive statistics, Pearson correlations, and multivariate analysis of variance were utilized to determine significance among appropriate categories.

### Results

Research findings are presented in the following four sections: (a) frequencies (b) descriptive statistics, (c) correlations, and (d) MANOVA results.

#### Frequencies

Table 1 represents the frequencies reported by category for each item investigated, and is divided into commercial information and viewing patterns. For the categories identifying favorite commercial and the characteristics of their favorite commercial, the top three answers are provided. Responses for the amount of Super Bowl commercials watched, number of people students were watching with, and level of sport fanship were coded as a number for purposes of further statistical investigation. For example, the amount of Super Bowl commercials watched were coded into four groups where group 1 = none, 2 = a few, 3 = most, 4 = all. These numbers were then used in the following descriptive, correlational, and MANOVA calculations. Age is the only category listed as a mean score.

#### Descriptive Statistics

Table 2 displays descriptive statistics for the number of commercials watched, the number of people present to watch commercials, and the level of fanship. The highest mean score was found for the number of people students were with when they watched the Super Bowl (M = 3.28, SD = .842), indicating the majority of the respondents watched the 2009-2010 Super Bowl within a group setting. Most of the respondents also indicated they watched the majority of all commercials during the Super Bowl (M = 3.14, SD = .697). A high mean score (M = 3.11, SD = .859) for the level of sport fanship indicated the majority of the respondents identified themselves as a ‘big’ or ‘huge’ sports fans. Skewness and kurtorsis values ranged from |.229| to |.553| and |.625|to |.837|, respectively, which were within the criterion of +2.0 indicating establishment of normality among the variables (15).

#### Correlations

Table 3 displays Pearson correlation coefficients for the number of commercials watched, the number of people students were with during the Super Bowl, the level of fanship, gender, and age. A significant positive correlation (r = .21) was found between the number of Super Bowl commercials watched and the level of sport-fanship, indicating individuals who considered themselves big or huge fans were more likely to watch the Super Bowl commercials. A significant negative correlation (r = -.12) was found between the number of commercials watched and gender, indicating male respondents were more likely to watch the Super Bowl commercials. Gender was also negatively and significantly correlated with the level of sport-fanship (r = -.38), indicating male participants identified themselves as stronger sports fans than female respondents. A significant positive correlation between age and the number of commercials watched indicated older students were more likely to watch Super Bowl commercials. Similarly, older students were more strongly identified as a sport fan than younger students (r = .11). As evidenced by a significant positive correlation of .11, the level of sport-fanship was interrelated with the number of people present to watch commercials. This finding indicates that bigger fans were more likely to watch the Super Bowl commercials with people other than students who identified themselves as having a low level of fanship (r = -.09). The highest correlation among the variables was .38, which is lower than the suggested criterion of .85 to establish discriminant validity (19).

### MANOVA Results

To examine gender differences among the factors (the number of commercials watched, the number of people present to watch commercials and the level of sport-fanship – see descriptive statistics across gender in Table 4), a multivariate analysis of variance (MANOVA) was conducted. The results of the MANOVA presented in Table 5 indicate that significant gender differences exist in all factors. The descriptive statistics for the three factors indicated males had higher mean scores in all factors, suggesting males were more likely to be influenced by the three motivational factors listed in the current study. In other words, males were more likely to 1) watch Super Bowl commercials, 2) watch Super Bowl commercials in the presence of others, and 3) display stronger sport fanship. Wilks’ Lambda value of .851 indicated that the three-factor model explained a total variance of approximately 15 percent. However, R squared values at the univariate level indicated that only a small amount of variance (ƞ2 = .014 and .008, respectively) was explained by the number of commercials watched and the number of people present to watch commercials, while a greater amount of variance (ƞ2 = .145) was explained by the level of sport fanship.

### Discussion

The frequency and statistical information presented in Tables 1-5 offers several important insights into the viewing and consumption patterns of college students during Super Bowl XLIV. First, the majority (536 out of 651 participants) indicated they watched most or all of the commercials during the Super Bowl telecast, with males indicating they watched slightly more commercials (M = 3.21) than females (M = 3.04). Given the tremendous popularity of Super Bowl commercials as entertainment (1, 4, 16, 22), it is no surprise college students overwhelmingly engaged in viewing these commercials. However, it is surprising that males watched more commercials than females because males have traditionally been found to be more interested in attributes of the game itself (30), and female attention levels during the Super Bowl were previously found to be higher during commercials (3). Perhaps the Super Bowl is such a mega-special-event (25) that male media desensitization is erased due to the powerful affiliation the commercials seem to have with the game experience. From a practical standpoint, it is important for marketers to acknowledge that male college student demographic is watching the Super Bowl commercials at a greater rate than female college students.

Second, college students were asked to list their favorite Super Bowl commercial and describe the characteristic they enjoyed most about that commercial. With over 70 commercials aired during the Super Bowl XLIV telecast, there were a variety of options. Out of 651 responses, 420 students indicated they had the same favorite commercial. The commercial was from Doritos and it depicted a young boy warning a potential suitor for his mother to “keep your hands off my mama, and keep your hands off my Doritos.” This commercial, although not the Ad Meter winner, was reported by TiVo Inc. to be the commercial that was stopped and played back the most on digital video recorders. Approximately 15% of homes replayed this commercial (5). The results for the Ad Meter ranked this Doritos commercial as the 11th most popular, falling behind another Doritos commercial depicting a dog placing a shock collar on a human to gain access to the man’s Doritos. The second and third rated commercials in the current study (Bud Light ‘house of cans’ and Snickers ‘Betty White football game’) were ranked third and first respectively for the Ad Meter. These results are particularly important for marketers because it suggest a rather large gap (11 spots) between the favorite commercial for college students versus adults who participated in the Ad Meter. This difference validates a marketing strategy that would attempt to isolate particular characteristics of various commercials to be most effective on a specific target audience (e.g., college students).

Although there were different favorite commercials reported for this study and the USA Today Ad Meter, the characteristics that make a commercial enjoyable appear similar. The top three characteristics most enjoyed by college students were humor, creativity, and originality. These characteristics could be used to describe many of the Super Bowl commercials, and reinforces the work of Kelly and Turley (18) who found that humor, sports themes, animals, children, and music increase the likelihood of positive affect towards advertisements. Specifically, the top ranked commercial in this study (i.e., Doritos) used humor and children. The second ranked commercial (i.e., Bud Light house) used humor and music. The third ranked commercial in this study, and first for the Ad Meter (i.e., Betty White football game) used humor and a sports theme. The use of humor is the recurring factor that appears in the top three commercials ranked in this study, and the characteristic chosen by 420 of the 651 participants (64.5%) that made their favorite commercial more enjoyable. Although it is not a secret that humor is an effective advertising tool (also see 29), confirming the importance of this factor within the college student population is a key point for marketers, especially given the overwhelming number of students who reported the importance of humor as the reason they enjoyed their favorite commercial.

Third, this study investigated college students’ intent to spend money on any products advertised during the Super Bowl, commonly referred to as intent to purchase (25). Results confirmed only 11.4% of students indicated they were likely to spend money on any of the products advertised during the Super Bowl. These numbers offer a less than optimistic view for marketers given the intent to purchase is a moderate to strong indicator of ROI (17, 25, 28). Perhaps college students believe the products and services advertised are not important to their lifestyles. Or, perhaps the $3,444 of discretionary spending reported by Gardyn (13) are being applied to products or services that are not advertised during the Super Bowl, but that are important to college students (e.g., laundry, other entertainment, etc.). If this is the case, marketing executives would be wise to understand the general acceptance by college students of Super Bowl advertising, and produce advertising that would peak their interest levels to influence their individual spending behavior, or choose advertising that targets a different demographic altogether.

Fourth, 518 of 651 participants (79.6%) watched the Super Bowl with at least four or more other people, and only 11 students reported watching the Super Bowl by themselves. This finding demonstrates that college students, much like society at large, enjoys the Super Bowl in a communal setting. Furthermore, correlational data demonstrates that males and college students who report themselves as big or huge fans are more likely to watch the Super Bowl with others. The finding that college students watch the Super Bowl in groups is consistent with literature that identifies the Super Bowl as a mega-special-event (25) that focuses on game and surrounding advertisement as a large entertainment package (1). However, the finding that male college students watch the Super Bowl with more people than females is counterintuitive to previous gender literature which suggested females are more attracted to communal relationships and social connections (6, 27). It is possible that the social environment afforded by campus living fosters a more communal context for which males can gather. It is also possible that given the importance of the largest annual sporting event in world, the fans that care about sports most (i.e., males) may find it more important than normal to gather in groups.

Fifth, this study supports the findings by Apostolopoulou et al., (1) which demonstrated that the game itself is the primary point of interest during the Super Bowl. Of the 651 participants, 458 (70.3%) reported they were most interested in the game. This finding is important because it reveals traditional college students are the same as other adults in their primary interest of the game. Marketers can use this interest to incorporate various advertising that might involve the flow of the game (e.g., commercials that air depending on the current score, or commercials that utilize the stars of each team, etc.). Furthermore, the current study found that only 66 of the 651 participants (approximately one percent) tuned into the Super Bowl telecast specifically for the commercials. This finding is noticeably lower than the four percent of the entire Super Bowl viewing audience found by Elliot (9), and implies that college students tune in to specifically watch the commercials at a lower rate than society at large. One must be careful to conclude this result implies college students do not watch commercials. In fact, this study found that most college students do watch and evaluate the majority of Super Bowl commercials, but watching those commercials are not as much of a priority as watching the game itself. Additionally, this study included more males than females, and males have been found to focus more on the aspects of the game (30).

Finally, this study attempted to identify the level of general sport-fanship, and its relationship to viewing patterns. Most students in this study (75.4%) identified themselves as big or huge fans. Males identified themselves as significantly bigger fans than females, and correlational results revealed bigger fans watched more commercials, and watched with more people. These findings allow marketers to begin construction of a blueprint for the average college student sport consumer whereby bigger fans will tend to be males, watch more commercials, and watch with more people. It is not a surprise that males considered themselves bigger fans than females given that males have been found to consume sport more frequently (30), be more competitive (12), value winning more (10), and display higher levels of athletic identity (21). It is also expected that bigger fans would watch more commercials and watch with more people given the large scale entertainment value surrounding the Super Bowl (1, 16). Implications for application are apparent. Marketers should attempt to identify the biggest fans and plan their advertising strategies accordingly. However, one must be mindful that the Super Bowl is a unique event with unusually high consumption, and offers a wide variety of consumers that may not routinely watch sporting events (20). Identifying marketing and advertising strategies for new consumers with varying levels of fanship is a challenging task during any sporting event, but particularly so during the Super Bowl.

### Conclusions

This study evaluated the viewing patterns and perceptions of commercials during Super Bowl XLIV for college students. The results suggest that although college students resemble the average Super Bowl viewer in many ways, they have specific differences that make them an important demographic for marketers to consider. The similarities between college students and the general population include watching the Super Bowl in large numbers, watching the Super Bowl with groups of other people, identifying humor as an enjoyable attribute in advertising, and being interested in the game over other factors (e.g., entertainment). These patterns of behavior lend themselves to specific market segmentation strategies, including advertising that appeals to groups and that contain humor. The differences found between college students and society at large highlight the gap in previous literature that has neglected to isolate this important demographic segment. Specifically, college students displaying a different preference for their favorite commercial (i.e., Doritos house rules vs. Betty White football game) appear to be extremely strong sport fans, and differ greatly by gender. The gender differences are of particular importance because previous literature would lead one to believe that females would be more interested than males in the entertainment portion of the Super Bowl, as well as gathering with large groups of people. This was not the case. The fact that males watched more commercials, and watched in larger groups, implies that male college students are a group of particular importance to marketers. These findings necessitate the need for further research into Super Bowl consumption patterns. Furthermore, given the Super Bowl is the most viewed annual sporting event in the world (5), identifying other sociodemographic consumption patterns is a key for effective marketing strategies.

### Applications in Sports

The results of this study make a strong case for differential game and commercial consumption patterns of college students during the Super Bowl telecast. If sport marketers choose to target the college student demographic in their design of commercials, they would be wise to focus their efforts on strategies which emphasize the importance of the game, using humor as a theme, products that college students would be most likely to use, and concepts that appeal to males. The fact that males are bigger fans, watch both the game and commercials more than females, and gather in groups more so than females, makes the male college student an ideal target market during the Super Bowl. Furthermore, if marketers understand that a majority of college students do not plan to spend money on products or services advertised during the Super Bowl, they may choose to ignore this demographic altogether.

### Tables

Table 1: Total Responses by Category

Variables Category Frequency
Commercial information
Number of Super Bowl commercials watched All 210
Most 326
A Few 115
None 0
Top three favorite Super Bowl commercials 1. Doritos 290
2. Bud Light 72
3. Snickers 45
Top three characters enjoyed most about favorite commercial 1. Funny or humorous 420
2. Creative 24
3. Original 21
Intent to spend money on a product due to a Super Bowl commercial Yes 74
No 489
Not sure 236
Viewing patterns
Number of people students were with when watching the Super Bowl 8 or more 323
4-7 195
1-3 122
0 11
Primary interest during the Super Bowl Game 458
Commercials 66
People students were with 113
Place viewed 14
Level of general sport fanship Huge fan 255
Big fan 236
Somewhat fan 138
Not a fan 22

Table 2: Descriptive Statistics

M SD Skewness Kurtosis
Statistic Statistic Statistic Std. Error Statistic Std. Error
Number of commercials watched 3.14 .697 -.229 .095 -.837 .190
Number of people students were with during the Super Bowl 3.28 .842 -.433 .095 .699 .191
Level of sport-fanship 3.11 .859 -.553 .096 -.625 .191

Table 3: Correlations

(1) (2) (3) (4) (5)
Number of commercials watched (1) 1.0
Number of people present to watch commercials (2) .07 1.0
Level of sport-fanship (3) .21** .11** 1.0
Gender (4) -.12 -.09 -.38 1.0
Age (5) .11** .04 .11** -.04 1.0

* p < .05
** p < .01

Table 4: Descriptive Statistics across Gender Groups

Variables Gender M SD n
Number of commercials watched Female 3.04 .72 227
Male 3.21 .67 424
Number of people present to watch commercials Female 3.18 .91 227
Male 3.33 .80 424
Level of sport-fanship Female 2.67 .81 227
Male 3.35 .78 424

Table 5: Multivariate Analysis of Variance

Source Dependent Variables SS df MS F Sig.a
Gender Number of commercials watcheda 4.270 1 4.270 9.031 .003
Number of people present to watch commercialsb 3.519 1 3.519 4.987 .026
Level of sport-fanshipc 69.142 1 69.142 110.282 .000
Error Number of commercials watched 306.867 649 .473
Number of people present to watch commercials 458.038 649 .706
Level of sport-fanship 406.895 649 .627
Total Number of commercials watched 6754.00 651
Number of people present to wach commercials 7470.00 651
Level of sport-fanship 6775.00 651

Note: Wilks’ Lambda Value = .851; F(3, 647) = 37.635; p < .01
(a) R2 = .014 (Adjusted R2 = .012)
(b) R2 = .008 (Adjusted R2 = .006)
(c) R2 = .145 (Adjusted R2 = .144)

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### Corresponding Author

James E. Johnson, Ed.D.
HP 223A, Ball State University
2000 W. University Ave.
Muncie, IN, 47306
jejohnson1@bsu.edu
765-285-0044

### Author Bio

James Johnson and Donghun Lee are Assistant Professors in the School of Physical Education, Sport, and Exercise Science at Ball State University.

2017-03-21T08:12:37-05:00October 6th, 2011|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on Super Bowl Commercial and Game Consumption for the College Demographic
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