The Impact of Service Quality of Public Sports Facilities on Citizens’ Satisfaction, Image, and Word-of-mouth Intention

 

Abstract

The purpose of this study was to find the impact of the service quality of public sports facilities on citizen’s satisfaction, image, and word-of-mouth intention. To accomplish the purpose of this study, 354 citizens using a public skating rink were surveyed by means of the revised questionnaires from the prior studies (Hur, 1997; Jang & Bae, 2003; Kang et al., 2002; Lee & Shin, 2004). The content validity and reliability of the questionnaire were determined by conducting a pilot study. The reliability coefficient for the questionnaire was found to be α=.670-.786. The questionnaire utilizing a five-point Likert scale was employed to measure the degree of satisfaction, image, and word-of-mouth intention. The statistical methods in this study included frequency analysis, factors analysis, t-test, one-way ANOVA, and multiple regression analysis. For all the analyses, statistical significance was set at an alpha level of .05. The major findings obtained from this study were as follows: First, it was found that there was a significant difference in the perception of service quality of public sports facilities according to demographic characteristics, such as gender, marital status, educational level, age, occupation, and household income. Second, the operating service, event and program service and safety service had significant effects on citizen satisfaction. Third, the operating service, event and program service, safety service and use service had significant effects on their image. Finally, the results of this study also indicated that the operating service and safety service had significant effects on their word-of-mouth intention.

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Static Stretching Versus Dynamic Warm Up: The Effect on Choice Reaction Time as Measured by the Makoto Arena II

ABSTRACT

Purpose: The purpose of the study was to determine whether a dynamic warm up or static stretching had a greater impact on choice reaction time. Methods: Nine recreationally trained subjects (5 males, 4 females) performed single-step choice reaction time trials using the Makoto Arena II testing device, following either a dynamic warm up or static stretching protocol chosen at random for all participants. The static stretching (SS) and dynamic warm up (DWU) protocols the subjects performed lasted ten minutes in duration and were preceded with baseline testing of a sit and reach and a single-step choice reaction time trial. Results: Results of a dependent t-test (p < .05) on sit and reach indicated a significant difference for both baseline to SS (p = .007) and baseline to DWU (p = .000), but not when compared to each other, SS to DWU (p = .246). Dependent t-test results for choice reaction time showed significance(p < .05) for all three categories: baseline to SS (p = .023), baseline to DWU (p = .003) and SS to DWU (p = .009). However, it should be noted that although both SS and the DWU resulted in significance, the greatest difference in the speed for the choice reaction time was found with the baseline to DWU. Conclusion: DWU had a greater impact on a single step choice reaction time and thus should be considered as an element to be incorporated into any athletic training program to enhance athletic achievement.

INTRODUCTION

Prior to working out, training, or any physical activity, athletes typically will warm up the body in preparation for the activity to follow. Throughout the past couple of decades, warm up routines have evolved as more and more scrutiny has been leveled at training modalities in the pursuit of physical excellence.The possibility of improved performance is sought in supplements, training regimens, nutrition, and even the rest periods. Within the past couple of decades multiple studies addressed the effects standard stretching routines have on performance (2-4, 6, 8-10, 11, 13, 14). Because of the continuous quest for improvement through research, stretching and warming up are now effectively considered different modalities and are not just semantically different. Statics stretching (SS) is the more traditional form of preparation for physical activity while dynamic warm up (DWU) is a progressive buildup of the same physical movements required in the exercise the individual will be participating in. Past research has shown that DWUs will have more impact on power production, flexibility, and agility of the muscles while SS reduces explosive muscular output (2-4, 6, 8-10, 11, 13, 14). The research has overwhelmingly demonstrated in physical activity requiring short bursts of power and speed as opposed to long sustained muscle recruitment, a DWU should be utilized to improve athletic performance for multiple individual and team sports (1, 2, 4-6, 8, 9, 11-14). Although DWU has been demonstrated to improve speed and power, very little research has been done to show a DWU has the same effect with reaction time, and no research has utilized a single step choice reaction format. Our intent was to determine if the superiority of DWU versus SS in power production would also hold true for choice reaction time; thus making it much more applicable for sport training purposes. Multiple sport activities require the athlete to react quickly to a stimuli and the speed of the reaction can make a difference in being successful or failing. Therefore any method to enhance the ability to quickly assess and react to the stimuli should be addressed by the coaches in their efforts for attaining peak performance; thus presenting the need for research to study actual choice reaction and not just reaction from a force plate. Therefore with the convincing literature regarding DWU and SS, our hypothesis was that the DWU would produce a quicker choice reaction time as opposed to a traditional SS procedure. Due to the lack of literature in the area of actual choice reaction time it became apparent a pilot study needed to be conducted in order to develop an adequate methodology to allow for future research.

METHODS

Subjects

Subjects were recruited from the United States Sports Academy staff and students. The study included nine subjects, five males and four females, ages ranging from 24-56 years old. Each participant was recreationally active and gave informed consent. The subjects participated in a variety of sport backgrounds including basketball, volleyball, track and field, swimming, badminton, tennis, weightlifting and bowling. The study was approved by an Institutional Review Board for human subjects.

Study Design

Participants arrived and were given consent forms to review and sign. The Makoto Arena II was turned on and allowed time to heat up. Directions for all testing protocols were then explained in detail. Using the Sit & Reach box (Novel Products, Rockton Illinois) to measure flexibility, students were instructed to sit down on the floor with shoes off and put the base of their feet against the box. A researcher put a hand just above the subject’s knees to ensure the knees stayed flat. Subjects put one hand on top of the other one and extended over the box as far as they could reach. Measurements were taken at the tip of the middle finger when the subject was able to hold the stretch. Baseline sit and reach testing was completed in a non-stretched state and recorded in centimeters (cm). Subjects were allowed to do a practice trial and then performed an additional trial as their baseline. The subjects were then instructed to put shoes back on and move over to the Makoto Arena II for demonstration and explanation. The Makoto Arena II uses audio and/or visual cues to test choice reaction time. For the purposes of testing reaction time, a lateral single-step procedure that utilized two of the three towers was employed. Each subject stood behind a line that was exactly equal distance between the two towers and 1.2 m from the edge of the device. Subjects positioned their body in an athletic stance in preparation for movement. The subjects were then given the direction to take one step laterally and hit the target as quickly as possible with the same hand as the direction of the step. The target height was 122 cm from the floor (7). Each subject was given a few practice trials to ensure directions were adequately explained. Then scores were recorded until the participant had completed two tests stepping to their right and two tests stepping to their left to account for true athletic movement. The Makoto Arena II has built in software that both calculates the reaction speed and randomly selects the tower used for each trial; therefore each test had a fifty-fifty chance of being to the left or the right of the subject. Due to the randomness of the trials we settled on recording two scores stepping right and two stepping left for a minimum of 4 trials to ensure an accurate average of reaction time. By utilizing this procedure, no two subjects were alike and each subject had an equal number of trials recorded. Once baseline scores for both the sit and reach and the choice reaction tests were recorded, subjects randomly chose which set of stretches they would perform first by drawing sticks labeled with a D (dynamic) or S (static). Stretching protocols were explained for static and dynamic stretches. The duration for each protocol was 10 minutes. Static stretches were held for 12 seconds, and the same stretch was duplicated on the opposite limb being stretched. SS and DWU protocols are found in Tables 1 and 2. Time was kept using a stopwatch by one of the testers. Each stretch was independent and each subject determined their own levels of discomfort and stretch limitations. DWUs were performed downstairs in a fitness room, approximately 90 seconds from the human performance lab, therefore not impacting the effects of the DWU on the sit and reach or choice reaction tests. Following the SS or DWU protocols, the subjects returned and performed the sit & reach test. Measurements were taken following each testing procedure of SS and DWU and recorded on the subject’s data sheet. Once all subjects’ results were written down, researchers then repeated the same lateral one step choice reaction time testing protocol for each subject, following the second protocol of either SS or DWU, which was done on a separate day.

Statistical Analyses

Baselines for both the reaction time protocols and the sit and reach were analyzed against the two tests of SS and DWU. A timed measurement of the lateral single-step choice reaction time within the Makoto Arena II device was completed following a ten minute session of the SS or DWU protocol. The mean, mean difference, and standard deviation were then calculated for each variable. Dependent t-tests were used to compare the baseline reaction times to both reaction times following the SS protocol and the DWU protocol. An alpha level of p < 0.05 was used to establish significance. Sit and reach data analysis followed the same procedures mentioned above.

RESULTS

The mean and mean differences were calculations done manually by a calculator and the significance (p < .05) was found through the use of IBMSPSS Statistics 19 software. The means for sit and reach testing are as follows: baseline: 27.1 cm, SS: 30.4 cm, DWU: 32.0 cm. The mean differences were baseline to SS: -3.28cm, baseline to DWU: -4.89cm, and SS to DWU: -1.61 cm. Results indicated a significant difference for both baseline to SS (p = .007)and baseline to DWU (p = .000), but not when compared to each other, SS to DWU(p = .246). The mean for the baseline reaction time was .872 s, the mean following the SS protocol was .833 s and the mean following the DWU protocol was .796 s. The difference in the means for reaction time was baseline to SS:.039 s, baseline to DWU: .077 s, and SS to DWU: .038 s. Choice reaction testing for all three categories showed significance (p < .05): baseline to SS (p =.023), baseline to DWU (p = .003), and SS to DWU (p = .009). However, it should be noted that although both SS and the DWU resulted in significance, the greatest difference in the speed for the choice reaction time was found with the baseline to DWU. All results can be found in Tables 3 and 4.

DISCUSSION

At least one study has shown no effect on muscle force production (11), while the majority of studies have shown that a bout of SS produces an inhibitory effect on the contractile force production of a muscle (4,10,11,13). The studies reaching these conclusions were applied to outputs of power such as sprinting and agility drills. From these studies, we hypothesized that the same physiological responses affiliated with SS and DWU would produce similar results in a single-step choice reaction time. We hypothesized that a static stretch prior to a choice reaction timed test would not affect reaction time, whereas a DWU prior to testing would result in a quicker reaction time. Our hypothesis regarding the DWU was supported; however, the static stretching also produced a quicker time compared to the baseline choice reaction time. Results taken from the sit and reach test also showed a significant improvement for both SS and the DWU. From our findings, since both the SS and DWU produced an increase in flexibility from a non-stretched to post stretching protocol, the theory of stretched muscle fibers inhibiting muscle contraction force and thus reaction time is not fully supported. To account for both the SS and DWU producing a faster choice reaction time, there must be some other form of physiological adaptation occurring. It is possible that the concept of postactivation potentiation (PAP), which is defined by Behm and colleagues (2004) as an increase in the efficiency of the muscle to produce submaximal force after a voluntary contraction (4) is the rationale for both protocols producing positive effects. It is possible that the duration of the SS protocol was not long enough to inhibit the force-producing cross bridges that may develop with lower frequency stimulation but enough of a stimulation to actually form a greater number of these cross bridges, which would then result in an ability to create more force similar to the DWU (4). Because the DWU had a greater effect on increasing the choice reaction time than the SS we can infer that a DWU as opposed to a simple static stretch routine for a typical warm up for sports participation would be of a greater benefit. However, a short duration of SS coupled with a DWU certainly would not inhibit performance. Although the results support our hypothesis because this was a pilot study with a diverse and limited number of participants it cannot be generalized. Further research with a larger participant pool of males and females; trained and untrained athletes of varying sports would need to be tested under similar conditions to reach conclusive evidence.

CONCLUSION

The same physiological factors a DWU produces for speed, namely greater force of the muscle contraction, is also prominent with choice reaction time. In this small pilot study a one-step choice reaction utilizes the same physiology of muscle force production as a sprint; the effects of a DWU are similar, resulting in a quicker choice reaction time when compared to a standard static stretch protocol. Therefore those professionals responsible for preparing athletes in sports requiring quick reactions might want to consider incorporating a DWU as part of the athlete or teams’ development and preparation. Since this study was so limited in participants we suggest future research test entire athletic teams of males and females in sports dependent on reaction times. These teams should range in ages and skill level from interscholastic to the professional levels. With this larger pool of participants this hypothesis would be tested adequately allowing for the results to be more generalized, till then it is simply a pilot study with too few participants to conclusively generalize the results.

APPLICATION TO SPORT

Athletes at all levels are trying to develop and gain an edge in their performance, with sports that require a quick explosive movement, a few tenths of a second can mean the difference in getting to the ball first, blocking an attempt at a goal, digging a spike; the difference between success and failure. Personnel responsible for preparing athletes whether it is the coach, the strength coach, or a trainer must be cognizant of how to best prepare for training or competition. The warm up has become a critical component of preparation for athletes and teams dependent on quick, explosive, and reactive movements. Unlike a static stretching protocol, DWU’s has been shown to enhance and better prepare athletes for performance by not stretching the muscles past the point where they can quickly recoil and exert their maximal force. The DWU incorporates an increase in body temperature as well as functional stretching of the muscles. This state of higher body temperature and a slightly stretched muscle has demonstrated better speed and agility times. Therefore, athletes and coaches responsible for their preparation should be utilizing a DWU as a part of their daily training protocol for better athletic performance.

REFERENCES

1. Aguilar, A. J., DiStefano, L. J., Brown, C. N., Herman, D. C., Guskiewicz, K. M., & Padua, D. A. (2012). A dynamic warm-up model increases quadriceps strength and hamstring flexibility. Journal of Strength and Conditioning Research, 26(4), 1130-1141.

2. Alpkaya, U., & Koceja, D. (2006). The effects of acute static stretching on reaction time and force. The Journal of Sports Medicine and Physical Fitness, 47(2), 147-150.

3. Amiri-Khorasani, M., Sahebozamani, M., Tabrizi, K., & Yusof, A.(2010). Acute effect of different stretching methods on illinois agility test in soccer players. The Journal of Strength and Conditioning Research,24(10), 2698-2704.

4. Behm, D., Bambury, A., Cahill, F., & Power, K. (2004). Effect of acute static stretching on force, balance, reaction time and movement time.Medicine and Science in Sports and Exercise, 36(8), 1397-1402.

5. Chaouachi, A., Castagna, C., Chtara, M., Brughelli, M., Turki, O., Galy,O., Chamari, K., & Behm, D. (2010). Effects of warm-ups involving static or dynamic stretching on agility, sprinting, and jumping performance in trained individuals. Journal of Strength and Conditioning Research, 24(8),2001-2011.

6. Gabrett, T., Sheppard, J., Pritchard-Peschek, K., Leveritt, M., &Aldred, M. (2008). Influence of closed skill and open skill warm-ups on the performance of speed, change of direction speed, vertical jump, and reactive agility in team sport athletes. The Journal of Strength and Conditioning Research, 22(5), 1413-1415.

7. Hoffman, J., Kang, J., Ratamess, N., Hoffman, M., Tranchina, C., &Faigenbaum, A. (2009). Examination of a pre-exercise, high energy supplement on exercise performance. Journal of the International Society of Sports Nutrition, 6(2)

8. Kistler, B., Walsh, M., Horn, T., & Cox, H. (2010). The acute effects of static stretching on the sprint performance of collegiate men in the 60- and 100-m dash after a dynamic warm up. The Journal of Strength and Conditioning Research, 24(9), 2280-2284.

9. Makaruk, H., Makaruk, B., & Kedra, S. (2008). Effects of warm-up stretching exercises on sprint performance. Physical Education and Sport, 52, 23-26.

10. McMillian, D. J., Moore, J. H., Hatler, B. S., & Taylor, D. C.(2006). Dynamic vs. static- stretching warm up: the effect on power and agility performance. Journal of Strength and Conditioning Research, 20(3), 492-499.

11. Perrier, E. T., Pavol, M. J., & Hoffman, M. A. (2011). The acute effects of warm-up including static or dynamic stretching on counter movement jump height, reaction time, and flexibility. Journal of Strength and Conditioning Research, 25(7), 1925-1931.

12. Roca, J. (1980). Effects of warming-up on reaction time and movement in the lower extremities. International Journal of Sport Psychology, 11(3), 165-171.

13. Sayers, A., Farley, R., Fuller, D., Jubenville, C., & Caputo, J.(2008). The effect of static stretching on phases of spring performance in elite soccer players. The Journal of Strength and Conditioning Research, 22(5), 1416-1421.

14. Yamaguchi, T., Ishii, K., Yamanaka, M., & Yasuda, K. (2007). Acute effects of dynamic stretching exercise on power output during concentric dynamic constant external resistance leg extension. The Journal of Strength and Conditioning Research, 21(4), 1238-1244.

TABLES AND FIGURES

Table 1

Static Stretches Stretch Hold = 12 seconds 10 minutes
Standing was completed prior to moving onto seated stretches followed
by the stomach
Standing Stretches Sitting Stretches Laying on Stomach
Double Leg hamstring & gluteus. Feet together, bend over at the
waist keeping back straight
Double leg hamstring & gluteus stretch- seated keep back of knees
on ground and bend at the waist forward reaching to touch toes
Quadriceps stretch- with right hand grasp the heel of right leg and
pull to gluteus. Switch to left hand and left leg
Single Leg hamstring and gluteus – right leg over left leg & left
leg over right leg, bend at the waist keeping back straight
Single leg hamstring & gluteus- bend right leg to the inside of
left leg, leaving left leg straight in front, bend at waist forward to
touch toes. Repeat procedure with left leg bent and right forward
Outer quadriceps stretch- with right hand grasp foot of left leg and
pull to gluteus. Switch to left hand and right leg.
Legs spread wide- (right, left & center) Bend at the waist,
keeping back straight not rounded.
Butterfly stretch- bend knees so that feet are sole to sole in front
of body, place elbows on inside of both legs & press down
gently
Quadriceps stretch- leg bent behind try to pull heel to gluteus. Right hand right leg, left hand left leg. Legs spread out wide in front of body- bend at the waist trying to touch toes. Lean to the right, lean to the left and lastly forward or center
Outer quadriceps stretch- leg bent behind try to pull heel to gluteus. Right hand to left leg, left hand to right leg. Butterfly stretch- bend knees so that feet are sole to sole in front of body, place elbows on inside of both legs & press down gently
Gastrocnemius stretch- standing with hands pressed against wall & lower body angled away from wall, both feet, then right foot, followed by left foot. Gluteus stretch- in seated position with bent knee place right leg over the outstretched left leg. With both arms pull the bent knee to your chest, switch sides.

Table 2

Dynamic stretch/ warm ups
Enclosed room length of 44 feet
Order performed:
Jog down & back 2x
Back pedal
Jog down back pedal back
Skipping down & back 2x
High knees down & back 2x
Butt kicks down & back
High knees down butt kicks back
Skipping down & back 2x
Carioca down & back 2x (also known as grapevine)
Walking sumo squats down & back
Defensive slides down & back
Frankenstein walks down & back
Heel walks/toe walks down & back respectively – 2x
Wall assisted leg throws – facing wall 10 rt. leg
Wall assisted leg throws – side to wall 10 rt. leg
Frankenstein – keeping legs straight swing one at a time high up in front with your hands stretched out and chest high 10 rt. leg

Table 3

CHOICE REACTION TIME (Measured in seconds)
PAIRS N MEAN MEAN DIFFERENCE SD P
Pair 1 Baseline 9 .872 .039 .090 .023
Static 9 .833 .079
Pair 2 9 .872 .077 .090 .003
Baseline 9 .796 .073
Dynamic
Pair 3 9 .833 .038 .079 .009
Static 9 .796 .073
vs. Dynamic

Table 4

SIT & REACH
SR= Sit and Reach
Measured in centimeters (cm)
PAIRS N MEAN MEAN DIFFERENCE SD P
Pair 1 9 27.1 -3.28 7.69 .007
Baseline SR 9 30.4 7.77
Static SR
Pair 2 9 27.1 -4.89 7.69 .000
Baseline SR 9 32.0 5.94
Dynamic SR
Pair 3 9 30.4 -1.61 7.77 .246
Static SR vs. 9 32.0 5.94
Dynamic SR

 

Effect of Acute Massage on Delayed-Onset Muscle Soreness

ABSTRACT

The purpose of this study was to investigate the effects of acute massage on delayed-onset muscle soreness. A total of 20 subjects (5 men and 15 women; mean age 24 ± 3 years; height 1.7 ± 0.1 m; weight 71 ± 1.4 kg) were randomly assigned to either a massage treatment (MAS, n = 8) or control (CON, n = 12) group. Following preliminary data collection, muscle soreness was induced to both groups using identical protocols. The MAS group received a 10 min massage immediately following the muscle soreness protocol where the CON group did not. Data collected included signals from electromyography (EMG), mechanomyography (MMG), perceived muscles soreness, muscle circumference, and muscle torque. Data were collected for each subject prior to and on days 1, 2, 3, and 7 following the intervention. Repeated measures analysis of variance was used to determine significant differences in the research variables between the groups with p ≤ 0.05. A significant interaction was noted in MMG frequency during isokinetic muscle actions but all other data showed no significant interactions. Based on these data massage may not be beneficial following exercise that induces delayed onset muscle soreness.

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Evidence for a Curvilinear Relationship between Burnout and Years of Coaching Experience

ABSTRACT

The purpose of this study was to determine if the relationship between burnout, as measured by the Maslach Burnout Inventory (MBI), and years of coaching experience was curvilinear for male high school coaches. Hierarchical regression found a significant quadratic component for the MBI subscales of Emotional Exhaustion (p<.05) and Depersonalization (p<.05). No significant linear or quadratic relationships were found for the Personal Accomplishment subscale. These results suggest that two categories of burnout as measured by the MBI (Emotional Exhaustion and Depersonalization) do not increase in a linear fashion with coaching experience rather a curvilinear shape was found. Male high school coaches with fewer years of experience suffered more emotional exhaustion and depersonalization than those with more years of experience.

INTRODUCTION

Burnout has been studied across a variety of occupations including sport coaching. A commonly used operational definition of the construct of burnout is supplied by Maslach and colleagues (5)(11)(12)(13). They have identified the major components of burnout as emotional exhaustion, depersonalization, and personal accomplishment. Further, their work has provided not only a more fully developed conceptual framework of burnout, but a psychometrically sound instrument for the measurement of burnout, the Maslach Burnout Inventory(MBI).

Rationale

In order to avoid burnout in sport coaches it is important to determine which factors are associated with this undesirable phenomenon. Investigator shave studied numerous variables (e.g. gender, age, type sport, marital status, etc.) in order to determine if an association with burnout exists. Onedemographic variable which has been studied is years of coaching experience. Results of studies investigating the relationship between years of coaching and burnout have been equivocal. Investigations have found either no association or a significant, but low negative association between burnout and experience (1)(2)(8)(15). Several studies have found less experienced coaches, both male and female, to have higher perceived burnout than coaches with more experience (1)(4)(8)(10). Drake and Herbert (3) found, in a qualitative study of burnout among collegiate coaches, that the level of stress and burnout were high during early years of coaching, then, decreased with experience. These findings parallel those of Kelley and Gill (8) who found higher levels of burnout in less experienced collegiate coaches. To date, studies have only tested for linear associations between coaching experience and burnout. It is conceivable that experience is related to burnout in a curvilinear way. It may be that in the early stages of coaching burnout is high, but decreases or levels off with experience influencing the linear association. A larger amount of variation might be accounted for in the quadratic component of the regression of burnout on years of coaching experience. This study investigated the relationship of years of coaching experience and burnout, as measured by the three MBI subscales, and whether this relationship is curvilinear.

METHODS

Participants

The sample consisted of 205 male head varsity high school coaches from two states in the Southeastern United States who voluntarily completed the subscales and demographic information. The mean age of the participants was 42.9±9.76 with a range of 23 to 68 years. The number of years as a head varsity high school coach ranged from 1 to 37 years with a mean of 10.92±8.52 years. Each respondent was informed of the purpose and requirements of the study according to institutional guidelines and implied consent by completing the survey.

Instrumentation

The MBI Form Ed (14) developed for educators was used to measure burnout. The MBI is the most widely used instrument in the study of burnout for serving professions (12, 13). The MBI uses a liker t-type scale to measure the frequency of experienced feelings on the subscales of Emotional Exhaustion, Depersonalization, and Personal Accomplishment. Scores range from 0 (never) to 6 (everyday). The 9-item Emotional Exhaustion (EE) scale measures a person’s feeling of being emotionally exhausted by the work of their profession. The Depersonalization (DP) scale is a 5-item scale measuring the frequency of feelings of uncaring and impersonal attitudes toward those being served. The Personal Accomplishment scale (PA) is an 8-item scale describing feelings of accomplishment and satisfaction with ones job. In contrast to the EE and DP subscales, lower scores on the PA subscale correspond to higher degrees of burnout. The scores on each subscale are considered separately and are not combined into a single aggregate score. Validity and reliability of the instrument have been documented (12)(13). Permission to use the instrument was obtained from the publisher, Consulting Psychologist Press.

Statistical Analysis

The IBM PASW statistical Package (Version 18.0) was used for analyses. Linear and quadratic relations between Years of Experience and the three MBI subscales were each tested separately. Years of Experience scores were firs tmean centered and then squared to create the quadratic term. Using hierarchical regressions, the three MBI subscale scores were separately regressed onto the linear centered Years of Experience in step 1, in step 2 the quadratic centered Years of Experience was sequentially added (16). Alpha for all analyses was setat p<.05. Means and Standard Deviations are in Table 1.

Table 1

Means and Standard Deviations for the three MBI subscales

Subscale M (SD)
Emotional Exhaustion 21.55 11.94
Depersonalization 7.59 6.01
Personal Accomplishment 37.31 6.96

RESULTS

Sequential hierarchical regression examined whether the quadratic component of the relation between Years of Experience and Burnout explains more variance over and above the linear effect as measured by significance of R square change (7). For the EE subscale, when Years of Experience was regressed onto the EE subscale during step 1, there was a significant amount of variance explained, F(1,203)=7.266, p=.007, adjusted R2=.024. However,as indicated by the R2, only 2.4% of the variance in Emotional Exhaustion was explained by Years of Experience. When the Quadratic component for Years of Experience was added into the equation in step 2, there was a significant increase in the variance explained by the regression, R2change=.014, F change= F(1,203)=3.776, p=.053 and the linear Years of Experience was no longer significant, B=.190,t=.984, p=.326, with a significant quadratic component,B=-.374, t=-1.943, p=.053. The positive coefficient for the linear effect and negative coefficient for the quadratic effect suggests a gradually flattening convex shape of the curve (7). For the DP, when Years of Experience was regressed onto the DP subscale in step 1 there was significance, F(1,203)=7.858, p=.005, adjusted R2=.026 explaining 2.6% of the variance. When the quadratic component for Years of Experience was entered during step 2, there was a significant increase in the variance explained, R2 change=.018, F change=F(1.203)=4.795, p=.029 and, as with EE, the linear effect of Years of Experience from step 1 was no longer significantB=.227, t=1.18, p=.238 while the quadratic component entered on step 2 was significant, B=-.421, t=-2.190,p=.029. These results also suggest a convex curvilinear function. For PA, no significant regression coefficients were found for the linear effect entered on step 1, F(1,203)=1.031, p=.311 or the quadratic component entered on step 2, F change=F(1,203)=.202, p=.654. This result infers no relationship between feelings of Personal accomplishment,as measured by the MBI, and how many years someone has been coaching.

DISCUSSION

Coaching is considered by many to be a stressful occupation. Burnout is a result of constant stressors over prolonged periods of time (9). In order to avoid burnout, it is necessary to identify the stressors that most influence the phenomenon. Once identified, appropriate measures can be taken in order to alleviate the problem. One demographic variable that has been studied is the relationship between the years someone has been coaching and the degree of burnout. Results of studies that have used coaching experience as a variable to explain burnout have found conflicting results. We postulate that one of the reasons for contradictory findings is the possibility of a curvilinear relationship between burnout and years of coaching experience. Our results partially support the hypothesis that the relationship between burnout, as measured by the three MBI scales, and coaching experience is curvilinear. Significant quadratic components were found for Emotional Exhaustion, and Depersonalization, but no significant findings were found for Personal Accomplishment. The significant findings do support the notion put forth by several authors (4)(8)(10) that burnout is more prevalent in less experienced coaches compared to more experienced coaches at least as far as Emotional Exhaustion and Depersonalization are concerned. Our findings are also consistent with the findings of Caccese and Mayerberg (1) and Kelly and Gill(8) who found that the pattern of means across age and experience levels does not clearly suggest a linear increase in burnout as a function of time. However, one limitation of this study is that posed by Weinberg and Gould (17). It may be that coaches who experienced high levels of stress are no longer coaching with only those who possess adequate coping skills remaining in the profession and available for investigation. Future investigations may want to include former coaches who are still teaching but left the coaching profession.

Conclusion

These results suggest that two categories of burnout as measured by the Maslach Burnout Inventory (Emotional Exhaustion and Depersonalization) do not increase in a linear fashion with experience. After early increases in Emotional Exhaustion and Depersonalization scores, a point is reached where the scores tend to decrease or level off as Years of Experience continues to increase. No association was found for Personal Accomplishment. These results are interpreted to mean less experienced high school coaches have more emotional exhaustion and depersonalization than more experienced coaches.

Application in Sport

The results underline a significant curvilinear function between years of experience and level of burnout experienced by male high school varsity coaches. High school administrative personnel (e.g. principals, athletic directors, superintendents) may consider implementing mentoring programs for inexperienced coaches that address topics such as job responsibilities, administrative tasks (e.g. fundraising, scheduling, contest contracts, etc.)and stress management. Research on burnout in coaching has identified three major areas of stressors. One, demographic variables (e.g., gender, marital status, age, etc.), two, support variables (e.g., administrative support, work overload, role clarification, etc.) and three, personal variables (e.g.,leadership styles, trait anxiety, etc.) (9)(16). Preventative measures that address coping with these types of stressors may help reduce the level of burnout experienced by male varsity high school coaches. Burnout has a number of consequences that negatively influence not only the coach, but the athletes also. Future studies may want to investigate the influence of variables such as gender, coaching status, and personality traits on this curvilinear function.

References

1. Caccese, T.M., & Mayerberg, C.K. (1984). Gender differences in perceived burnout of college coaches. Journal of Sport Psychology, 6,279-288.

2. Dale, J., & Weinberg, R.S. (1990). Burnout in sport: A review and critique. Journal of Applied Sport Psychology, 2, 67-83.

3. Drake, D., & Herbert, E.P. (2002). Perceptions of occupational stress and strategies for avoiding burnout: Case studies of two female teacher-coaches. The Physical Educator, 59(4), 170-176.

4. Goodger, K., Gorely, T., Lavallee, D., & Harwood, C. (2007). Burnout in sport: A systematic review. The Sport Psychologist, 9(2), 127-151.

5. Jackson, S.E., Scwab, R.L., & Schuler, R.S. (1986). Toward an understanding of the burnout phenomenon. Journal of Applied Psychology, 71, 630-640.

6. Karabatsos, G., Malousaris, G., & Apostolidis, N. (2006). Evaluation and comparison of burnout levels in basketball, volleyball, and track and field coaches. Studies in Physical Culture and Tourism, 13(1), 79-83.

7. Keith, T. (2006). Multiple regression and beyond. New York, NY:Pearson.

8. Kelley, B.C., & Gill, D.L. (1993). An examination of personal/situational variables, stress appraisal, and burnout in collegiate teacher-coaches. Research Quarterly for Exercise and Sport, 64(1),94-102.

9. Kelley, B.C. (1994). A model of stress and burnout in collegiate coaches:Effects of gender and time of season. Research Quarterly for Exercise and Sport, 65(1), 48-58.

10. Koustelios, A. (2010). Burnout among football coaches in Greece.Biology of Exercise, 6(1), 5-12.

11. Maslach, C. (1976). Burned- out, Human Behavior, 5, 16-22.

12. Maslach, C., & Jackson, S.E. (1981). The measurement of experienced burnout. Journal of Occupational Behavior, 2, 99-83.

13. Maslach, C., & Jackson, S.E. (1986). Maslach Burnout Inventory:Manual: Palo Alto, CA: Consulting Psychologist Press.

14. Schwab, R. (1986). Burnout in education. In C. Maslach, & S.E.Jackson (Eds.), Maslach Burnout Inventory: Manual (pp18-22). PaloAlto, CA: Consulting Psychologist Press.

15. Taylor, A.H. , Daniel, J.V., Leith, L., & Burke, R.J. (1990).Perceived stress, psychological burnout and paths to turnover intentions among sport coaches. Journal of Applied Sport Psychology, 2, 84-97.

16. Taylor, J. (1992). Coaches are people too: An applied model of stress management for sport coaches. Journal of Applied Sport Psychology, 4,27-50.

17. Weinburg, R.S., & Gould, D. (2007). Foundations of Sport and Exercise Psychology. 4th ed., Human Kinetics: Champaign, IL. p503.

 

College Choice Factors for Division I Athletes at an Urban University

ABSTRACT

Purpose: Recently there has been much research attention focused on the college and university choice factors of potential student-athletes. Kankey and Quarterman (2007) developed a questionnaire, which was tested on Division I softball players, and advocated for more research utilizing different athlete populations to further analyze college and university choice factors among student athletes. As a result, the purpose of this research is to apply Kankey and Quarterman’s (2007) questionnaire to one athletic department with student athlete respondents from all sports funded by a Division I athletic department in order to ascertain: What factors are important to these Division I athletes when choosing to attend their present school? Methods: Division I student athletes were surveyed regarding the importance of certain factors influencing their decisions to attend this particular urban-serving institution. Online surveys were solicited through sport programs for volunteers. Student athletes took the online survey, which was used to develop an electronic database for analysis. Surveys with missing or skipped information were discarded leaving a sample of 101 respondents (n=101). Results: Statistical analyses indicate the most important choice factor to be the coaching staff. Other important—and highly rated factors—include personal relationships, financially based reasons, and academics/ career development. The least important factors included media related issues, technology outlets, and past coaches. Conclusion: Hossler and Gallagher’s (1987) student choice model is integrated with Symbolic Interactionism in order to understand results. It appears that a variety of factors are important to student athletes, which illustrates the multifaceted identities of student athletes. Applications in Sport: Collegiate sport practitioners and/or coaches working with constrained student development programming and/or recruiting budgets are better able to streamline these processes with a better understanding of student athlete choice factors. Knowing which factors to emphasize during the choice stage of choosing a college/university will better assist urban-serving universities during program development or recruiting.

INTRODUCTION

A sizable proportion of colleges and universities within the United States support athletic opportunities for their respective student bodies (Kankey& Quarterman, 2007). One common notion is those athletic programs supported by colleges/universities are integral to the overall college experience for potential and/or current students. Indeed, Coakley (2007) articulated the common perception that student athletes positively impact universities because sport programs increase student enrollment and revenue generating opportunities. Another potential expense to colleges or universities is the process of bringing those student athletes to campus, which can be a costly venture. Urban serving institutions of higher education tend to have constrained financial resources, which mirror the social inequities of urban public schools (Jordan, 2007). Athletic departments within these institution scan benefit greatly from understanding how to efficiently recruit potential student athletes. Finally, “conducting research regarding college or university choice factors, especially when organized within a social framework,helps both practitioners and academics in understanding the identities of student-athletes by illustrating what is important to them during the recruiting process” (Vermillion, 2010, p. 1). Indeed, previous research identified the need for examining how student athletes view their identities,academic careers, and the factors influencing them to attend specific institutions of higher education. (For example, see Letawsky, Schneider,Pedersen, & Palmer, 2003; Kankey & Quarterman, 2007; Vermillion,2010).

This research focuses exclusively on Division I student athletes in an urban-serving institution and attempts to extend Kankey and Quarterman’s(2007) findings regarding factors influencing the university choice of NCAA Division I softball players by utilizing their questionnaire for student athletes of all sports. As a result, the purpose of this project is to readily identify what college or university factors influence Division I student athletes to attend their present urban-serving schools. To accurately ground this project within the previous literature, a brief background discussion of factors influencing the college or university choice of the general student body, student athletes, and sport specific student athletes is summarized.Vermillion (2010) noted the usefulness of amalgamating social theory with other education theories in order to develop a holistic, interdisciplinary framework for discussing college choice factors with student athletes. As a result,Hossler and Gallaher’s (1987) model, and Symbolic Interactionism (Blumer,1969) are combined in order to explain or describe not only the data collected,but also the results and recommendations.

Background

There has been a relatively constant stream of college and university choice factors research for the last 50 years (for example, see Astin, 1965, Gorman,1976, Kealey & Rockel, 1987, Lourman & Garman, 1995, and Hu &Hossler, 2000). Summarizing this research, several key college or university choice factors—regarding the general student body—have been identified. These key factors include academic reputation of the institution,friendship influences, proximity to family, financial aid availability, the location of the institution, and program availability. Kankey and Quarterman(2007) noted the increase of research being conducted regarding college or university choice factors as related to student athletes. The emerging line of scholarly inquiry includes, but is not limited to, research regardingwomen’s athletics (Nicodemus, 1990), male athletes in general (Fielitz,2001), male, sport-specific athletes (Ulferts, 1992; Kraft & Dickerson,1996), freshmen male athletes (Fortier, 1986), and Division III male athletes and non-athletes (Giese, 1986). Common conclusions from the aforementioned studies and other research indicates the head coach, opportunity for participation, various academic factors and amount of available scholarships are important factors influencing student athletes. However, Letawsky,Schneider, Pedersen, and Palmer (2003) noted while athletic -based factors are important to student athletes’ decisions to attend colleges or universities, non-athletic factors also contribute to the decision to attend apresent college or university. To our knowledge, there has been little to no exploration of college choice factors of student athletes in one athletic department with respondent representation of all athletic programs.Additionally, there has been very little research done examining urban-serving institutions and their respective athletic departments. In order to adequately understand college choice factors and urban serving schools’ athletics, a theoretical framework is needed to guide not only research questions, but also interpretation of the descriptive statistical results.

Conceptual Framework

The original conceptual framework utilized by Kankey and Quarterman (2007)to organize and represent their data and findings was Hossler and Gallaher’s (1987) model. Hossler and Gallaher’s model has also been adapted to better understand this research. Specifically, it is a three-stagemodel that identifies and describes the college selection process of individuals and is composed of three stages: predisposition, search, and choice stages. The predisposition stage is the time when students decide if they want to continue into higher education by pursuing colleges or universities, while the search stage encompasses the individual’s evaluations of college or universities, which includes large amounts of interaction. Finally, the choice stage focuses on the submission of application to a targeted pool of colleges or universities.Regarding sport, Kankey and Quarterman (2007) focused primarily on the last stage within Hossler and Gallagher’s (1987) model, which is when the student athlete develops serious intentions about a select few colleges or universities. The student athlete engages in a cost-benefit analysis in order to determine the positives and negatives of each college or university and attempts to make a sound decision. For student athletes, this stage could encompass not only being recruited, but also critically examining the factors that are the most pertinent to their specific situation and taking official visits. Focusing on the “choice stage” is also salient for this project, which addresses college athletes attending an urban serving institution. Understanding why some student athletes choose to attend one college or university over another competitor is important for understanding student athletes’ educational, athletic, and social motivations to attend institutions of higher education.

Symbolic Interactionism

Vermillion (2010) noted Symbolic Interactionism (SI)—a sociological theory focusing on identity, social interaction, and symbolinterpretation—is easily applied to many areas within the institution of sport. Using a micro level of analysis, SI provides a description or explanation of the constructed reality of spectators, athletes, or coaches(Coakley, 2007). Additionally, Cunningham (2007) noted SI understands how people give meaning to their participation or consumption of daily activities.Recently, SI has been used by a variety of scholars to examine a wide variety of sport-based social dynamics, including student athlete choice factors. Some of this research includes, but is not limited to: understanding sport subcultures and the resulting socialization process of rugby players and rock climbers (Donnelly & Young, 1999); examining the role of athletics in gay or lesbian athletes’ lives (Anderson, 2005); explaining the disproportionate lack of women in sport organization leadership positions(Sartore & Cunningham, 2007); understanding how students interpret and consume indigenous sport imagery (Vermillion, Friedrich, & Holtz, 2010); or examining the college choice factors important for influencing community college softball players to attend their current school (Vermillion, 2010).

SI is composed of three basic assumptions. Hughes and Kroehler (2005)summarize Blumer (1969) and Fine (1993) and stated the following theory tenets:1) we interact with things in our social environment based upon shared meanings, 2) these meanings are not inherent, but rather, are social constructions, and 3) shared meanings are in a perpetual state of change and evolution. Interactions and communication within a specific social environment adheres to the aforementioned assumptions and helps to form an individual’s “constructed reality,” which is an individual’s interpretation of the social world and dynamics around them(Eitzen & Sage, 2009). When combined with Hossler and Gallagher’s(1987) choice model, we are better able to understand the social psychological processes interacting within the decision to attend or not attend a specific urban -serving institution.

Explaining or describing choice factors important to athletes in urban-serving institutions is important by highlighting the social psychological processes associated with the decision to attend a specific institution of higher education. SI’s focus on the “meaning”athletes give to their participation is useful for examining the power the“athlete role” has on not only the identity of the student athlete,but also the decisions that student athlete makes. Stryker (1980) addressed oneof SI’s limitations—lack of a focus on social structure (Ritzer,2000)—by combining SI with role theory. This adapted version of SI identifies the importance of social roles within the lives of individuals,which are forms of social structure. Student athletes, for example, have multiple roles that they “play” throughout the day, including being a student, university representative, son/daughter, sibling, friend, and athlete. Examining the social-psychological process of how impactful these roles are upon the individual in question provides practitioners insight into the programs, services, or infrastructures that should be emphasized during the costly process of student athlete recruitment. As previously noted urban-serving, institutions tend to suffer from constrained fiscal environments, which are similar to those constraints faced by urban public schools (Jordan, 2007). SI’s usefulness lies in the fact it understands individuals are decision-makers, and provides a structured, analytical way for highlighting how the decisions student athletes make impact not only their social environments (Hughes & Kroehler, 2005), but also the colleges oruniversities they attend (Vermillion, 2010).

Significance

This research project is significant in a number of ways. First, there is very little research done examining the choice factors of: 1) all sports (and resulting athletes) in one athletic department, and 2) athletes from an urban-serving institution. The purpose of this research is to address these gaps in the previous literature. Secondly, the research would also be useful to college or university athletic programs. Specifically, the research will help to streamline the recruiting process for many athletic departments—ofsimilar composition—by addressing the most important choice factors for student athletes in these types of schools. As a result, a better and more efficient allocation of recruiting funds may be developed in order to maximize shrinking recruiting budgets. Moreover, this research is particularly timely as athletic departments attempt to build relationships with other university,academic-based programs. If certain academic programs are identified as particularly salient to potential student athletes, then athletic department personnel can work with other academic administrators in order to: 1) bridge the increasing division and distance between academic programs/the campus community and athletic departments, and 2) demonstrate a commitment to a holistic student athlete experience, which includes the social, athletic, and professional/academic development of the student athlete.

Finally, urban-serving institutions, historically, are comprised of student populations that differ from institutions not classified as such. Urban-serving school districts have higher rates of poverty, racial/ethnic diversity, and equalized access to strong community and educational infrastructures (Howey,2008). As Jordan (2007) noted, urban-serving colleges or universities mirror many of the same inequality patterns found in urban, public school districts.As a result, more research is needed in order to understand collegiate athletics within an urban- embedded university context. It can be hypothesized that universities within urban settings—or designated as urban-serving institutions—have athletic departments that must recognize the relatively unique nature of these campus communities, which may manifest itself in unique athletic facilities, programs, and/or recruiting efforts and strategies.

Research Questions

The research question guiding this research was influenced by previous sport-based research centering on college or university choice factors for student athletes. Based upon Hossler and Gallagher’s (1987) model, are cognition of the uniqueness of urban-serving institutions of higher education, and utilized in conjunction with SI’s theoretical influence,the following research questions is posed: Which college and university choice factors are the most influential for having Division I athletes attend their present urban serving institution? That is, what factors are the most important to Division I student athletes when deciding to attend their present school?

METHODOLOGY

Participants

Respondents for the study were selected from the student athlete population of a large, state university located in the southern high plains of the United States. The university is designated as an urban-serving university and is embedded in an urban environment within a predominantly rural state. It is important to note the university is designated as a Division I (formerly known as Division I AAA) by the NCAA. This is the label given to Division I athletic departments that do not fund or field football teams. As a result, the potential survey population is slightly smaller as compared to FBS (FootballBowl Subdivision) or FCS (Football Championship Series) athletic departments,formerly known as Division I A and Division I AA respectively. Surveys we readministered as online surveys and once surveys were completed, responses were automatically entered into a spreadsheet, which was imported into SPSS 17.0 in order to develop an electronic database. Surveys with missing (skipped)questions or ambiguous answers were discarded and not included in the database.While not all student athletes responded fully, there was representation of all athletic programs administered by the athletic department at the time of data collection. After data collection a total of 101 usable surveys were included in the analysis (n=101).

In order to determine the demographics of the respondents, basic questions were asked to determine gender, academic status (freshman, sophomore, junior,and senior), country of origin, race or ethnicity and sport they participated in. The resulting sample included more females than males (65% vs. 35%) and was composed of freshmen (23.2%), sophomores (30.3%), juniors (29.3%), and seniors(17.2%). The majority of respondents listed white as their race/ ethnicity(64.6%) or African-American/Black (30.2%) and their country of origin as the United State (84.5%). Finally, table 1 illustrates the percent of respondents based upon sport.

Table 1

Percent of respondents by sport categories (n=101).

Sport Percent (%) N
Baseball 9.1 9
Softball 6.1 6
Women’s Basketball 10.1 10
Men’s Basketball 5.1 5
Volleyball 10.1 10
Men’s track 11.1 11
Women’s track 24.2 24
Men’s golf 4 4
Women’s golf 3 3
Women’s tennis 4 4
Men’s tennis 4 4
Cross Country 9.1 9

Measure

The data collection survey consisted of the aforementioned five demographic questions and college choice factors used by Kankey and Quarterman (2007). Permission was obtained by the primary researcher to use the Kankey and Quarterman factor list for additional research and was adapted to this research focusing on Division I student athletes. The possible answer choices regarding the importance of the college choice factors included “extremely important,” “very important,” “moderately important,” “slightly important,” and“unimportant,” which were numerically coded with “extremely important” rating a five (5) while “unimportant” was rated as one (1). As a result, the higher the rating, the more important the college choice factor was to the student athlete.

Procedure

Student athletes were asked by their coaches or athletic program administrators to complete the online survey. Additional follow-up contacts were made to specific programs to ensure that there was student athlete representation from all sponsored sports in the athletic department. Informed consent was done electronically with the disclaimer attached to the electronic version of the survey. Student athlete participation was not mandatory, but it was encouraged. All results are not simply confidential, but also anonymous because a detailed respondent record cannot be tracked or charted in the current electronic database. Surveys were taken by student athletes while coaches and staff were not present to avoid any influence or tainting of respondent self-reports. The gathered statistical information was shared with the athletic department in addition to being used for this research. Electronic survey information, which was saved in spreadsheet format, was imported into SPSS 17.0 for data analysis.

RESULTS

In keeping with Kankey and Quarterman (2007) a descriptive analysis is used to initially describe and identify the college choice factors associated with Division I athletes attending urban-serving institutions. Regarding the research question (what factors are the most important to Division I student athletes when deciding to attend their present school?), initial univariate responses indicate that 87% of the factors presented in this research were at or above the midpoint of the scale (M= 3.00). In addition, almost half of the factors (15 out of 32, or almost 47%) had means over 4.00 with over 70% of respondents rating these factors as ‘extremely’ or ‘very important’ to their choice to attend this urban-serving university. The seven most highly rated factors, which had mean scale scores at or above 4.25,included coaching staff (M=4.68, SD=0.66); amount of financial aid or scholarship offered (M=4.47, SD=078); support services offered to studentathletes (e.g. study hall, tutors, etc…)(M=4.44, SD= 0.74); availability of resources (money, equipment, etc…)(M=4.31, SD=0.75); opportunity to win conference or national championship (M=4.27, SD=0.83); availability of major (M= 4.25, SD=0.94); and social atmosphere of team (M=4.25, SD= 0.88). See table 2.

The means of only three factors were rated below the scale midpoint. These factors included amount of media coverage (M=2.96, SD=1.94); high school coach(M=2.87, SD= 1.44); and team’s website, Facebook, Twitter (M=2.66, SD=1.21). Only about 30% of the respondents rated these three factors as‘extremely’ or ‘very important’ in their decision to attend this particular urban-serving institution and participate in athletics.See table 2.

Table 2

Mean, Standard Deviation, and Percent (%) of Factor Choices Influencing Division I Student Athletes to attend their Urban-serving Institution(n=101).

Factor Mean SD % rated extremely or very important
Coaching staff 4.68 0.66 94%
Amt of financial aid/scholarship offered 4.47 0.78 86.2%
Support services offered to student athletes (e.g. study hall, tutors, etc…) 4.44 0.74 89.1%
Availability of resources (e.g. money, equipment, etc…) 4.31 0.75 85.1%
Opportunity to win conference or national championship 4.27 0.83 83.2%
Availability of anticipated major 4.25 0.94 84.2%
Social atmosphere of team 4.25 0.88 81.2%
Athletic facilities 4.21 0.83 83.2%
Career opportunities after graduation 4.20 0.95 78.2%
Team’s competitive schedule 4.20 0.80 84.2%
Meeting team’s members 4.12 0.98 74.2%
Amt of playing time 4.10 1.02 77.3%
Overall reputation of the college/university 4.10 0.90 80.2%
Academic reputation of the college/university 4.10 1.00 71.2%
Team’s overall win/loss record 4.03 0.86 73.3%
Team’s tradition 3.89 0.85 68.3%
Location of university 3.86 1.04 66.4%
Opportunity to play immediately 3.82 1.08 59.4%
Conference affiliation of team 3.82 0.96 61.4%
Cost of college/university 3.76 1.26 64.3%
My parents 3.76 1.37 59.5%
Housing 3.66 1.04 57.5%
Campus visit 3.64 1.13 62.4%
Fan support of the team 3.60 1.12 52.5%
Social life at the university 3.54 1.13 51.5%
Campus life at college/university 3.53 1.01 48.5%
My friends 3.26 1.39 46.5%
Size of the college/university 3.24 1.10 39.6%
Team sponsorships (e.g. Nike, Adidas, UnderArmor) 3.24 1.39 42.5%
Amt of media coverage 2.96 1.24 30.7%
High school coach 2.87 1.44 37.6%
Team’s website, Facebook, Twitter 2.26 1.21 34.7%

DISCUSSION

The purpose of this research was to identify the college choice factors mostsalient to Division I athletes attending urban-serving institutions. Table 2highlights the factors that were most readily identified by these studentathletes as impactful and relates to Hossler and Gallagher’s (1987)choice stage. Using symbolic interactionism (SI)—a social psychologicaltheory examining how sports are related to peoples’ choices and identities—may be beneficial for understanding the most and leastimportant factors for student athletes (Vermillion, 2010). As reported bystudent athletes, there are many factors that go into the choice to attend this particular urban-serving institution. Personal or social relationships (e.g.coaching staff, social atmosphere of team), career goals (e.g. support services, availability of major, career opportunities after graduation),finances (e.g. amount of financial aid/scholarship offered), and program success (e.g. opportunity to win conference or national championship) wereself-reported as influencing their decisions. Conversely, media coverage,technology outlets (e.g. website, Facebook, and Twitter), and previous headcoach had little to no impact upon their ultimate decision to attend thisuniversity.

These categories of factors illustrate how multi-faceted student athletes are regarding both their personal and athletic identities. Specifically, SI notes sports are important to an individual’s identity; with this information both academics and collegiate sport practitioners are able tobetter understand motives of student athletes when choosing colleges/universities and athletic departments/programs. In keeping with much previous research (e.g. Kankey & Quarterman, 2007), the importance of relationships—especially with coaches—tops the list of college choice factors. Indeed, Seifried (2006) noted the importance—on manylevels—of coaches within the lives of student athletes. Although the importance of “coaches” is not unexpected, additional results reveal the highly variegated nature of student athletes’ perceptions of themselves.

Athletic-related reasons, such as opportunity to win a conference ornational championships or the availability of resources, are still factors influencing the student athletes in this sample. However, Letawsky et al.(2003) noted the importance of non-athletic factors in deciding on a college/university. Regarding this sample, non-athletic factors appear salient,as well. For example, financial reasons (e.g. financial aid/scholarships) andpreparation for a professional career after sports (e.g. availability of major,support services offered to student athletes, and career opportunities after graduation) all had mean scores above 4.00, with almost 80% of respondents listing these non-athletic factors as ‘extremely’ or ‘very important’ in relationship to their decision to attend their urban-serving university.

Interpreting these findings from an SI framework would focus on the lack of role homogeneity within the sample. That is, these student athletes appear to“see” themselves as having multiple roles, which relates to amultifaceted or holistic identity. As a result, this research is in alignment with Letawsky et al.’s (2003) conclusions that non-athletic factors are important to student athletes, while simultaneously acknowledging that winning and athletic success is important to student athletes. Both of these models,i.e. student athlete development and performance and success, can be promoted effectively during recruiting processes.

CONCLUSION

The purpose of this research was to identify the most important college choice factors regarding Division I student athletes attending urban-serving institutions. Utilizing the college choice factors identified by Kankey and Quarterman (2007) and their analysis as a guide, student athletes were surveyedin an attempt to better understand their motives for attending an urban-serving institution. The research contributes to not only academic scholarship, but also advocates for the integration of social theory into athletic department data management strategies and recruiting. Streamlining the recruiting processis important in a collegiate athletic climate that is fiscally constrained and extremely competitive, especially at the Division I, FBS, and FCS levels.Smaller, less visible sports and/or athletic departments must find ways to become more efficient with student athlete recruitment. Additionally,common sensical or popular notions of funneling money into newer athletic facilities and media or technological outlets do not appear productive for all levels of collegiate sport; they are not a panacea for recruiting barriers nordo they automatically translate into traditional definitions of success. While these highly popular endeavors are important to maintaining a visible athletic department profile, this research hypothesizes—based upon the aforementioned results—they should not be viewed as the most productive recruiting tools. This research has identified how multifaceted student athletes may very well be, and that a commitment to a holistic student development model may be an efficient recruiting tool for student athletes,especially within Division I, urban-serving universities.

Limitations & future research

As with any research, there are limitations that should be identified.Firstly, the university student athlete population that was surveyed did notinclude a football team, which not only decreased the number of potentialsurvey respondents, but also limits the generalizability of the results. Additionally, using a Division I athletic department also decreases thegeneralizability of the research. Future research should extend the college choice factor scales to include FBS and FCS schools. Focusing on urban-serving institutions is a productive endeavor, but more research needs to be doneinvolving the athletic departments in these types of colleges/universities.According the Coalition of Urban Serving Universities, there are almost 50 nationally recognized urban-serving schools (Great cities, great universities,n.d.), many of which fund athletic programs.

Another limitation involves extrapolating group level summaries (such asmeans of college choice factors) to the individualistic level. SI recognizes the importance of group dynamics upon the individual. However, recruiting and the decision to attend one particular university is a decision that ultimately comes down to a single person, as evidenced in Hossler and Gallagher’s(1987) model, which focuses on the individualistic decision. Student athlete recruitment is a dynamic social psychological process that appears to be acombination of many factors. Sole reliance upon the factors identified in this research would be a disservice to not only collegiate sport practitioners, butalso the recruited student athletes.

APPLICATIONS IN SPORT

Division I student athletes see themselves as more than solely athletes;they have many “roles” to play throughout a given day, week,semester, or season. These roles include, but are not limited to: the athleterole (wanting program success), the social role with others (coachrelationships and social atmosphere of the team), and the student role(focusing on academics and preparing for a professional career after sports).It is important for collegiate sport practitioners involved in recruiting torealize that funneling resources exclusively into media/technology outlets orfacilities does not appear to be efficient or productive recruiting tools. Instead, these practitioners during recruiting efforts should focus on:programs for student success, professional preparation opportunities,highlighting the social and personal relationships within their athletic department/program, and programmatic success. The aforementioned focal pointsillustrate not only holistic student athlete development but also present athletic departments an opportunity for increasing campus wide collaborative efforts.

Of particular importance to urban-serving universities and athleticadministrators, the factor “location of the university” had a meanof 3.86 (midpoint of scale, M=3.00) with over 66% of respondents indicating itwas ‘extremely’ or ‘very important’ to them. It could be interpreted—cautiously, of course—that the stigma of the urban environment education as a disadvantage is unfounded and that, to some studentsor majors, the urban-serving mission and context could be perceived as a unique advantage.

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