Sports Management – Page 74 – The Sport Journal

A Model of the Factors Contributing to Fan Support at Small-College Athletic Events

Submitted by: Alan Brokaw , Dr. George W. Stone & Michael A. Jones

Introduction

A great deal has been written in both academic and popular periodicals
about the value of college athletic programs. While some argue that the
net outcome of college athletic programs is favorable in terms of benefits
to the institution, detractors often view these programs as financially
debilitating to the welfare of the institution (Weeth, 1994). An issue
of controversy for many institutions is the value of the benefits versus
the cost associated with operating intercollegiate athletic programs (Lehnus
and Miller, 1996). The dilemma for administrators is often more pressing
at the small-college level because funding is usually limited and the
programs themselves generally prove to be unprofitable (Helitzer, 1996).
One of the more pressing problems for many small-college athletic programs
is the lack of fan attendance, because attendance can influence support
from alumni and the administration of the school. The present study examines
what factors are key in explaining attendance at small-college sporting
events.

Factors Affecting Attendance

Much research effort has been dedicated to the study of fan attendance
in an attempt to assess fan motivation and other related factors predicting
fan attendance (Wakefield, 1995; Mawson and Coan, 1994; Baade and Tiehen,
1990; Noll, 1974). A number of conceptual and empirical studies have been
directed in the area of sports-fan identity with the team as a future
predictor of attendance (Fisher and Wakefield, 1998; Wann and Schrader,
1997: Zhang, Smith, and Pease, 1996; Pol and Pak, 1994; Yeagin, 1986).
These investigations build upon earlier consumer research in such areas
as group involvement and group identification. Additional streams of sports
marketing research address sports promotion (Helitzer, 1996; Graham, Goldblatt,
and Delpy, 1995; and Wilkenson, 1993) as part of the attendance model.
None of these articles, however, specifically address the promotion of
attendance at small-college athletic programs. Wells et al. (2000) is
one of the few studies that address attendance at small-college sporting
events. They studied small-college football attendance using nine determinants
from DeSchriver’s (1996) model as well as fourteen additional determinants
from a literature review of fan attendance to develop their model. The
significant variables in their analysis were time and season of the game,
winning percentage of the team, promotional effort, prices, whether or
not the school had a sport marketing position, student enrollment, and
the existence of booster clubs.

Data Collection and Analysis

Data were collected at intercollegiate basketball games involving three
small schools in the South and Midwest at approximately the same time
of the season. The questionnaire that was used incorporated much of what
is known or understood to be the salient factors affecting attendance
while including additional factors that were derived from a series of
focus group studies with fans of various sports teams from several small
colleges. It included thirty-nine Likert scale questions (See Exhibit
I for a list of the Likert questions). 492 questionnaires were completed.
Missing data reduced the number of usable questionnaires to 404.

The thirty-nine Likert scale statements (1 = strongly disagree to 5 =
strongly agree) were analyzed using factor analysis to determine their
basic, underlying structure. As described by Hair et al. (1995), eight
of the variables were excluded from the factor analysis because of low
correlations with the other variables. Six factors were extracted, based
on the criterion of having eigenvalues greater than one. The six factors
represented slightly over 55% of the variability in the data. The factor
loadings, after varimax rotation, for the remaining thirty-one variables
on the six factors are shown in Exhibit I; the eight variables not included
in the factor analysis are also described.

Based on the pattern of factor loadings, Factor 1 is labeled “College
Affiliation.” Factor 2 is labeled “Entertainment.” Factor
3 measures the “Affiliation with the Sport.” Factor 4 is “Time
Constraints.” Factor 5 is a measure “Team Familiarity.”
Finally, Factor 6 is “Lack of Awareness.”

The purpose of the factor analysis was to use the results in a regression
model to explain attendance. As described by Hair et al. (1995) surrogate
variables, summated scales, or factor scores might be used for this purpose.
For this study, factor scores were used. The independent variables in
the model were therefore the six factors described above, using the corresponding
factor scores, and a number of dummy variables: GENDER (the gender of
the respondent; 0 = male, 1 = female), MARITAL (marital status; 0 = single,
1 = married), and CHILDREN (whether the respondent has children; 0 = no,
1 = yes). Finally, the eight Likert scale variables that were eliminated
from the factor analysis were included.

The dependent variable, which is the number of home games attended (GAMES),
is a series of discrete values from 1 to 5 (1 = first home game, 2 = 2
home games, 3 = 3 or 4 home games, 4 = 5 to 7 home games, inclusive, and
5 = 8 or more home games). The distribution of GAMES is shown below.

GAMES	Frequency
1 = 1st game	65
2 = 2nd game	50
3 = 3rd or 4th game	61
4 = 4 to 7 games	77
5 = 8 or more games	151

An appropriate regression procedure when the dependent variable is ordinally
scaled is ordered probit. Therefore, in order to examine the effects of
the independent variables on attendance, Minitab’s? ordered probit
procedure was used with GAMES as the dependent variable and with the factor
scores for the six factors and the other independent variables as described
above. The results were that only the six factors were statistically significant.
Therefore, another ordered probit model was created using only the six
factors; the results are shown below. The model is statistically significant
based on the G statistic, which follows a ?2 distribution with the degrees
of freedom equal to the number of independent variables (Hosmer and Lemeshow,
1989).

Predictor	Coefficient	P-Value
Const(1)	-1.42704	0.000
Const(2)	-0.81649	0.000
Const(3)	-0.20946	0.004
Const(4)	0.50139	0.000
factor1	-0.56369	0.000
factor2	0.22179	0.000
factor3	-0.30048	0.000
factor4	0.26738	0.000
factor5	-0.61468	0.000
factor6	0.29300	0.000

Log-likelihood = -495.180
Test that all slopes are zero: G = 239.220, DF = 6, P-Value = 0.000

Factors 1 through 6 are all significant using a 5% alpha value. Because
of the way Minitab? calculates the coefficients in ordered probit analysis,
the reported negative coefficients indicate that an increase in the independent
variable tends to be associated with a greater attendance. The pattern
of coefficients is as one would expect.

In linear regression, the estimated coefficients can be interpreted as
marginal effects. In ordered probit, the marginal effects must be calculated
using the coefficients, and are reported as probabilities. The marginal
effects were calculated and resulted in importance ranking of the factors
that were the same as the absolute value of each factor’s coefficient.
Therefore, the importance ranking of the six factors, from most to least,
is Factor 5 (Team Familiarity), Factor 1 (College Affiliation), Factor
3 (Affiliation with the Sport), Factor 6 (Lack of Awareness), Factor 4
(Time Constraints), and Factor 2 (Entertainment).

Discussion

Factor 1: College Affiliation

Research within the social science discipline indicates that peer group
affiliation creates a sense of belonging and identity (Parsons, 1993).
While secondary group affiliation plays a smaller role in the individual’s
identity and affiliation in terms of group dynamics, individual membership
and a sense of belonging are important to the formation of organizational
cultures. Larger organizational groupings do tend to play a major role
in the development of the type of organizational culture thought to exist
on many college campuses. Secondary group membership has been closely
linked with both organizational culture and the development of esprit
de corps within the organizational structure (Hunt, Wood, and Chonko,
1989; Tajfel, 1981). As Wakefield (1995) has indicated, attending a sporting
event is a highly social event, and thus the effects of reference group
acceptance may be considered a determining factor in patronage intentions.
Murrell and Dietz (1992) have also indicated that fans who maintain a
strong identity with a university as their relevant institution, will
manifest that identification in greater support for the school’s
sports teams. In the present study, Factor 1 (College Affiliation) was
the second most important factor influencing attendance, suggesting that
individual association with a school has a powerful effect on attendance
at school sponsored sporting events.

Factor 2: Entertainment

The Entertainment factor was the least important influence on attendance.
Entertainment included special events, prizes, and sales promotions designed
to increase excitement and attendance. Research on the actual effect of
promotional activities on sport attendance is varied even though promotion
of sporting events is considered an essential element of success for any
sport franchise. Promotional activities, however, have been demonstrated
to produce mixed results. While some teams experience increased sport
attendance figures throughout the season as a result of the team’s
promotional activities, other teams have discovered that much of what
constitutes an “increase” is in fact temporal. The net effect
of season long stimulation for the purpose of increasing patronage is
that that a marketing barrage only affects those people who attend solely
for the purpose of receiving the sort of novelty item being offered at
a “special event” (Pitts and Stotlar, 1996). Hence, there
is a fine line between drawing attention to the team (or to the sporting
event) and interrupting the normal attendance schedule through promotional
activities. Promotions can either be considered an effective method of
demonstrating appreciation to the everyday sport consumer, or they can
mask serious deficiencies in actual fan support.

Factor 3: Affiliation with the Sport

One of the more obvious reasons why individuals would choose to attend
a sporting event is because they enjoy the sport itself. People who are
fans of a sport have developed a fondness for the intricacies of the game
and are more likely to choose to further their own participation in the
sport by becoming fans. Krohn and Clarke (1998) indicate that people who
attend sporting events can be characterized either as spectators or fans.
While spectators fulfill their enjoyment by casually viewing the sport
and not getting caught up in the logistics of the event, most true fans
attend sporting events because of some deep involvement in what the authors
describe as “the almost religious rituals” one sometimes associates
with the sporting event itself. While there are many ways of developing
an interest in a sport, one of the principal methods of developing deep
knowledge of a sport is through participation, either as a player or as
a spectator.

Factor 4: Time Constraints

This was the fifth most important factor in the model. In order for sporting
events to become attractive enough so that they become an integral part
of the fan’s schedule, the scheduling of the events should coincide
with the lifestyle and schedule of the primary attendees. The timing of
a sporting event is important in that if it is not conducive to the time
constraints and scheduling conflicts of the primary fan base, then the
event will not be well attended. However, it could be argued that time
conflicts are an excuse for not attending. A true fan would find how to
attend in spite of conflicts.

Factor 5: Team Familiarity

This was the most important influence on attendance in the model. Fan
identification with players of a particular sports team is an area in
which personal commitment and emotional involvement by the fan often occurs.
In rare cases, fans have so closely identified themselves with an organization’s
players that they begin to define themselves in terms of the attributes
of those players (Mael and Ashforth, 1992). Wann and Branscombe (1993)
have found that high fan identification with a team and its players relates
to additional involvement with the team, which in turn relates to greater
attendance at home games. In general, sport as a whole is thought to differ
significantly from other forms of entertainment because sports tend to
evoke a higher level of emotional attachment and identification from its
fans (Sutton, et al., 1997). As Lever (1983) indicates, sport not only
promotes communication among people, it tends to involve diverse groups
of people by providing common symbols and a collective sense of solidarity
for both the players and the sports organization.

Factor 6: Lack of Awareness

College athletic departments share the common need of promoting their
own product, in this case, the sporting event itself. Ironically, advertising
the event and promoting the general awareness of the scheduled time of
play and the opponent during the contest is not listed as the top perceived
priority of athletic department marketing personnel. Instead, college
athletic department marketing personnel list the job of selling corporate
sponsorships as their top priority. The second most important job responsibility
(as identified by 52% of athletic directors) is the planning and implementation
of individual game promotions, followed closely (at 48%) by planning and
directing season-ticket campaigns (Lehnus and Miller, 1996).

Respondents mentioned the general lack of awareness and knowledge of
the time of the sporting event and lack of awareness and knowledge about
the identity of the opponent as possible factors for why fans failed to
show for the game but, as with time conflicts, this may simply be an excuse.
Real fans would learn about the schedule.

Conclusions and Strategy Recommendations

An interesting outcome of this study is the relatively low importance
of win/loss records in explaining attendance. Only one of the Likert questions
(Q37: “I would not attend <SCHOOL> basketball games if the
team were not winning) was used in the factor analysis, and it loaded
(loading = 0.398) on the Entertainment factor. The three other questions
concerning the records of the teams (Q36: “One of the main reasons
I attend <SCHOOL> basketball games now is because of the team’s
record,” Q38: “I am attending <SCHOOL> basketball games
lately because of the team’s national small college ranking,”
and Q39: “The team’s record does not really affect my attendance
level”) were not significant in explaining attendance in the original
model.

That identification with players (Team Familiarity) resulted in being
the most important factor is not surprising for a smaller college. For
current students, the chances of knowing a player are likely to be greater
at smaller colleges.

Based on this sample, encouraging connections to players (Factor 5: Team
Familiarity) and the college (College Affiliation), in that order, will
have the greatest impact on encouraging heavy use. The results suggest
the following guidelines, roughly in order of importance, for encouraging
heavy users in small college basketball. These suggestions should be viewed
as complementary to the findings of Wells et al. (2000). Although their
study involved small-college football and our study basketball, we suspect
the same would be true for other sports.

• Make team members accessible to fellow students and community
members. Do not have special dormitories, etc. which would separate student
athletes from fellow students. Also, encourage other participants in the
sporting event (e.g., cheerleaders, members of the pep band, etc.) to
interact with students and the community.
• Encourage identification of the community and students with the
college.
• Help potential fans understand basketball better in an attempt
to convert people to true fans. Sessions with coaches and players in which
past games are analyzed and current strategy is discussed might be helpful.
These sessions would help with the previous two bullets as well.
• Ensure awareness of the times and dates of games. Merely printing
a schedule is not enough. Market segments must be identified in terms
of how best to aggressively inform them of the times and dates.
• Schedule college events to avoid conflicts with the sports schedule.
• Use promotions and other activities to improve the excitement
and entertainment value of the sporting event, taking care to make sure
that these activities are complementary to the event and do not detract
from it.

Exhibit I

Likert Scale Variables and Highest Factor Loadings
(1 = Strongly Disagree, 5 = Strongly Agree)

Variable		Factor	Loading
Q1:	One of the main reasons I go to basketball games here is because I want to support the <school> basketball program.	1	0.735
Q2:	I am a fan of <SCHOOL> basketball.	1	0.729
Q3:	I do not care whether the <SCHOOL> team wins the game.	1	-0.521
Q4:	It is important for me to support the <SCHOOL> basketball teams.	1	0.778
Q5:	If I could attend the similar sporting events elsewhere I would still choose to support <SCHOOL> sports.	1	0.759
Q6:	I attend sporting events here primarily because I love to watch basketball.	3	0.713
Q7:	The primary reason I attend basketball games here at <SCHOOL> is because I love to watch the sport itself.	3	0.803
Q8:	The basketball game itself is the most important reason I attend games here at <SCHOOL>.	3	0.829
Q9:	The basketball game itself is not the main reason I attend games at <SCHOOL>.	3	-0.658
Q10:	The special events (e.g., games at which cash or prizes are given) are main reasons I attend <SCHOOL> basketball games.	2	0.734
Q11:	I would attend <SCHOOL> basketball games even if there were no prizes given out during the games.	Not factored
Q12:	The prizes given out at <SCHOOL> basketball games are more important to me than attending for the sport itself.	2	0.783
Q13:	The prizes given out during the game are more important to me than supporting the <SCHOOL> basketball team.	2	0.811
Q14:	I attend basketball sporting events at <SCHOOL> primarily because they are very inexpensive.	Not factored
Q15:	I usually have scheduling conflicts at the same time that the games are being played.	4	0.752
Q16:	I would rather watch basketball on television than attend the games at <SCHOOL>.	1	-0.586
Q17:	Fraternity and sorority functions often interfere with my attendance at games.	2	0.475
Q18:	I would rather spend my time engaged in attending religious activities than attending <SCHOOL> basketball games.	Not factored
Q19:	I would rather play basketball than watch the game being played.	1	-0.485

Factor Labels:
Factor 1 = College Affiliation, Factor 2 = Entertainment, Factor 3 = Affiliation
with the Sport
Factor 4 = Time Constraints, Factor 5 = Team Familiarity, Factor 6 = Lack
of Awareness

Exhibit I (continued)

Variable		Factor	Loading
Q20:	I would rather watch movies or television than attend <SCHOOL> basketball games.	1	-0.575
Q21:	I would rather spend my time doing homework or studying than attending <SCHOOL> basketball games.	2	0.400
Q22:	I am familiar with many of the players on the <SCHOOL> basketball teams.	5	0.677
Q23:	I attend basketball games at <SCHOOL> because I like many of the players.	5	0.709
Q24:	I don’t attend many basketball games at <SCHOOL> because I am not familiar with any of the players.	2	0.406
Q25:	<SCHOOL> basketball players don’t interest me in the least.	2	0.310
Q26:	I’ve become familiar with many of the players on the <SCHOOL> basketball team through my attendance.	5	0.503
Q27:	I attend basketball games at <SCHOOL> because I like the cheerleaders.	Not factored
Q28:	The cheerleaders, the pep band, and the dance team greatly influence my attendance at <SCHOOL> basketball games.	Not factored
Q29:	I would go to a <SCHOOL> basketball games just to watch the cheerleaders and dance team.	2	0.483
Q30:	If the games were held at a different time I would attend more <SCHOOL> basketball games.	4	0.779
Q31:	I generally have too many other time conflicts on the days that <SCHOOL> basketball games are played.	4	0.782
Q32:	If the games were played earlier I would attend more <SCHOOL> basketball games.	4	0.622
Q33:	I’d attend more basketball games if I knew when they were being played.	6	0.641
Q34:	I’m not always aware of when the games are being played.	6	0.684
Q35:	I generally know about the basketball games in advance.	6	-0.509
Q36:	One of the main reasons I attend <SCHOOL> basketball games now is because of the team’s record.	Not factored
Q37:	I would not attend <SCHOOL> basketball games if the team was not winning.	2	0.398
Q38:	I am attending <SCHOOL> basketball games lately because of the team’s national small college ranking.	Not factored
Q39:	The team’s record does not really affect my attendance level.	Not factored

Factor Labels:
Factor 1 = College Affiliation, Factor 2 = Entertainment, Factor 3 = Affiliation
with the Sport
Factor 4 = Time Constraints, Factor 5 = Team Familiarity, Factor 6 = Lack
of Awareness

References

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U.S. Sports Academy2015-03-27T13:30:32-05:00September 1st, 2006|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

Performance Enhancement Drugs: Knowledge, Attitude, And Intended Behavior Among Community Coaches In Hong Kong

Submitted by: Lena Fung

Abstract

The purpose of the study was to elucidate the perceived knowledge, actual knowledge, attitude, and intended behavior of community coaches with respect to performance enhancement drugs (PED). The Theory of Planned Behavior was used as a guiding framework to structure the questionnaire used for data collection. Results of the analyses suggested that community coaches under-estimated their own knowledge about PED. Most respondents are supportive to the anti-doping movement in terms of both attitude and behavior intent. Results of the present study also partially agreed with the Theory of Planned Behavior, perceived knowledge, actual knowledge, and attitude towards PED were found to be significantly related to behavioral intent. Implications of the results were discussed.

Introduction

The Athlete should not be the only person to be blamed in case of a positive drug test. Numerous studies have pointed out that an athlete’s use of drugs in sport could be attributed to a complex interaction of personal and environmental factors (Nicholson and Agnew, 1989; Tricker, Cook, and McGuire, 1989). Possible contributing environmental factors include attitudes of peer group and parents, accessibility to drugs, and cultural norms and values (Polich, Ellichson, Reuter, and Kahan, 1984; Tricker and Connolly, 1997).

In the coaching literature, coaches are viewed as having a strong influence in regulating athletes’ behavior and attitude (Anshel, 1990; Orlick, 1990). For example, Dieffenbach, Gould, and Moffett (2002) suggested that coaches play crucial roles in influencing quality of coach-athlete relationship, developing achievement goals for the athletes, mentoring athletes’ development and indirectly model the positive skills and characteristics athletes need for success. Therefore, it is argued that coaches could be one of the more important agents in preventing drug use among athletes and should be included in any doping prevention campaigns (Dubin, 1990).

For coaches to function optimally as role models and in assisting young athletes to formulate correct attitudes against doping, they must also possess accurate knowledge and appropriate attitude on doping and drug use. Although coaches can gain information about drug use and drug abuse through various channels, seminars and information packages are the media more favored by Hong Kong community coaches. In Hong Kong, the Sports Federation and Olympic Committee, Hong Kong, China and the Hong Kong Coaching Committee are the major stakeholders to provide such information to community coaches. In order for these agencies to develop appropriately sequenced knowledge, some understanding of the current status of coaches’ knowledge and attitude on drug use and drug abuse is necessary. Therefore, one of the purposes of the present study was to assess the perceived knowledge, actual knowledge, attitude, subjective norms, and behavioral intent related to performance enhancement drug (PED) among Hong Kong community coaches.

In developing this study and in constructing the questionnaire for data collection, the Theory of Planned Behavior (Ajzen and Fishbein, 1988) was used as a guiding framework. According to this theory, a person’s behavior is mainly determined by his/her behavioral intent which, in turn, is influenced by attitude towards the behavior, subjective norms, and perceived behavioral control. As the theory has been successfully used to predict recreational drug use (McMillan and Conner 2003; Orbell, Blair, Sherlock, and Conner, 2001), intentions to use PEDs among collegiate athletes (Allemeier, 1996) and in adolescents (Lucidi, Grano, Leone, Lombardo, and Pesce, 2004), we were confident that it could provide a meaningful structure for the study.

Methods

Participants

A total of 114 community coaches attending a coach education class during the data collection period were invited to take part voluntarily in the study. The sample is comprised of 93 male and 21 female (Age: 29.3 ± 8.1; mean ± SD). Among the participants, 28% are university graduates, 11% were university students, the remaining 61% are secondary school graduates.

Instrument

The questionnaire used for data collection was developed by the authors from literature review and consultation with experts working in the area of doping and drug use. The questionnaire is comprised of 61 items. Apart from the demographic section, all other items were designed to elucidate perceived knowledge on PED, actual knowledge about PED, attitude, subjective norms, and behavior intent on drug use in sports. A combination of response types was employed, including likert-type scale and binominal scale. As the possible total scores from items related to perceived knowledge on PED and from items related to actual knowledge about PED differs, the raw score from each category was transformed to allow for parallel comparison. In transforming the scores, the maximum of 100 points was used as the reference.

Results

A summary of means and standard deviations of key constructs examined in this study is presented in Table 1. The score mean for perceived knowledge on PED was 23.7 whereas the score mean for actual knowledge on PED reached 66.1.

Scores on attitude, subjective norm, and intent behaviour were computed in a way that positive scores represent preferred attitude, norm and intentional behavior that support the anti-doping movement. Negative scores, on the other hands, represent the support of the use of doping to take advantage over other athletes. The scores in attitude, subjective norm, and behavioral intent are 1.21 ± 0.91, –0.16 ± 1.01, and 1.37 ± 1.4 respectively. Both attitude and behavioral intent of the Hong Kong community coaches are supportive of the anti-doping movement. However, the score on subjective norm was negative and this suggests that they perceive doping as a problem in the sporting community. Table 2, 3 and 4 show the response pattern of participants to questions on attitude, subjective norm, and behavioral intent, respectively.

In terms of attitude, majority of the respondents agreed (86.2% agreed or highly agreed) that doping is not only a problem in sport but also a social problem. Most respondents did not have strong feeling on whether sanction imposed on doping cases is stringent or not (57.9% have no comment on the issue). The majority disagreed (63.7% disagreed or highly disagreed) that athletes can use drugs to enhance performance if it does not hurt his/her health. Most respondents did not believe (70.1% respondents disagreed or highly disagreed) that refusal to take PEDs equals to refraining from being an elite athlete. Respondents are slightly biased to disagree (43.8% disagreed or highly disagreed and 35.1% had no comment) that scientific research should develop drugs that can pass tests of doping control.

Questions in elucidating subjective norm of the respondents found out that most respondent disagreed (47.4% disagreed or highly disagreed) that most achievement records in sport are related to doping. The majority respondents agreed (73.6% agreed or highly agreed) that doping is a serious problem in international sports. On the other hands, most respondents disagreed (51.8% disagreed or highly disagreed) that doping is a serious problem in Hong Kong sports.

The behaviour intent of the respondents is in general supportive to the anti-doping movement. Most respondents (65.8%) claimed that they would take positive actions against his/her friends or relatives who are on banned substance. The respondents slightly biased towards not working with medical team to produce high quality banned substance (44.3% disagreed or highly disagreed and 41.6 had no comment). The majority of the respondents (62.8%) claimed that they would not find ways to assist his/her friends or relatives to get hold of banned substance.

Table 5 shows the Pearson correlation coefficients among the key constructs of the study. Behavioral intent is significantly correlated to perceived knowledge (r = -.270, p = .004), actual knowledge (r = .304, p = .002), and attitude (r =.335, p = .000) but not to subjective norm (r = .065, p = .493).

Two other significant correlations were identified, namely the correlation between actual knowledge and perceived knowledge (r = -.263, p = .007), and between attitude and actual knowledge (r = .233, p = .018).

Discussion

According to the Theory of Planned Behavior (Ajzen and Fishbein, 1988), a person’s behavior is mainly determined by his/her behavioral intent which, in turn, is influenced by attitude towards the behavior, subjective norms, and perceived behavioral control. Result of the present study finds partial agreement with the Theory, namely the level of intentions to perform a particular behaviour depends on the individual’s attitude on the behaviour. However, the relationship between subjective norm and behavioral intent was not significant in our study. One of the possible reasons for this discrepancy is that the participants are community coaches who may not perceive themselves as having any significant influence or involvement with the doping problem more commonly found in elite level athletes. The three items used to elucidate information on the subjective norms were biased towards drug use among elite level athletes. Therefore, even though the respondents might have agreed to the presence of doping problem at

the elite level, the items were not sufficiently sensitive to capture their opinions on drug use issues on their day-to-day settings. Further investigation on this issue with refined items would be needed.

The present study also aims at elucidating the Hong Kong community coaches’ current status of knowledge and attitude on PEDs. This group of coaches was found to be relatively supportive to the anti-doping movement according to their attitude (1.21 ± 0.91) and behaviour intent (1.37 ± 1.4) scores. A survey on Norwegian coaches found that coaches have strong and unequivocal attitudes against doping (Figved, 1992). Laure, Thouvenin, and Lecerf (2001) also found that 98.1% of the France coaches consider that they have a role to play to flight against doping. The present respondents’ actual knowledge on PEDs, reached the mean value of 66.1, was fair and yet had rooms for further improvement. This baseline measurement could also be used for monitoring the effectiveness of any intervention programs in the future.

It is interesting to notice that there is a huge discrepancy between the respondents’ perceived knowledge (mean = 23.7) and actual knowledge (mean = 66.1). Participants tend to under-estimate their knowledge in PED and doping control. This conclusion is further supported by the negative correlation between the perceived knowledge and actual knowledge (r = -.263, p = .007). The more knowledgeable they are, the greater their under-estimation. It is possible that the more they know about PED and the doping control system, the more they understand that the problem of drug in sport is more complicated than presented. This implies that any education program designed for the coaches on PEDs could be more effective if it is mandatory. As the individuals with the least knowledge is likely to perceived that they have enough knowledge about the issue.

It is also interesting to note that the low perceived knowledge on doping among coaches was also found in a survey on France coaches. 80.3% of the France coaches consider themselves badly trained in the prevention of doping (Laure, et al., 2001).

Unlike the Hong Kong community coaches, the Norwegian coaches believed that they are well informed about doping (Figved, 1992). This can be due to the fact that the education about PEDs for coaches was more structured and successful in Norway than that in Hong Kong. Furthermore, the difference on cultural background may have lead to the under-estimation of the Hong Kong coaches’ knowledge on PEDs as discussed in the previous paragraph.

Currently, seminars on PEDs are few and infrequent in Hong Kong. A systematic curriculum on doping is also lacking. According to Figved’s study (1992), most coaches believed that seminars, courses, and evening sessions were the best ways of changing attitudes and increasing knowledge. Given the important role of coaches in influencing the direction of fair play in sports and the findings from this study, we suggest the need to develop a systematic and spirally progressive education program on drug use and drug abuse. Furthermore, incentives such as certifications and fee waivers could be developed to encourage coaches to such courses so as to work towards knowledge and attitude development in the area of PED.

References

Allemeier, M.F. (1996). CIAU athletes’ use and intentions to use performance enhancing drugs: a study utilizing the theory of planned behaviour. Thesis (M.A.) University of British Columbia, Eugene, Ore: Microform Publications Int’l Inst for Sport & Human Performance, University of Oregon.
Anshel, M.H. (1990). Sport psychology: From theory to practice. Gorsuch Scarisbrisk: Scottsdale, AZ.
Ajzen, I. & Fishbein, M. (1988). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice Hall.
Dieffenbach, K., Gould, D., & Moffett, A. (2002). The coach’s role in developing champions. International Journal of Volleyball Research, 5(1), 30-32.
Dubin, C. (1990). Commission of inquiry into the use of drugs and banned practices intended to increase athletic performance. Ottawa, ON: Canadian Government Publishing Centre, 615.
Figved, S.E. (1992). Drug education programs for coaches and leaders. Science Periodical on Research and Technology in Sport, 12(4), 5-9.
Laure, P., Thouvenin, F., and Lecerf, T. (2001). Attitudes of coaches towards doping. J Sports Med Phy Fit, 41, 132-136.
Lucidi, F., Grano, C., Leone, L., Lombardo, C., & Pesce, C. (2004). Determinants of the intention to use doping substances: An empirical contributions in a sample of Italian adolescents. International Journal of Sport Psychology, 35, 133-148.
McMillan, B. & Conner, M. (2003). Applying an extended version of the theory of planned behavior to illicit drug use among students. Journal of Applied Social Psychology, 33, 1662-1683.
Nicholson, N. & Agnew, M. (1989). Education strategies to reduce drug use in sport. Sports Coach, 13(1), 38-41.
Orbell, S., Blair, C., Sherlock, K., & Conner, M. (2001). The theory of planned behavior and ecstasy use: Roles for habit and perceived control over taking versus obtaining substances. Journal of Applied Social Psychology, 31, 31-47.
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Parish, C.A. (1973). An epidemiological survey of drug use among secondary school students in grades eight and eleven in eastern South Dakota. Doctoral dissertation, University of South Dakota.
Polich, J.M., Ellichson, P.L., Reuter, P., & Kahan, J.P. (1984). Strategies for controlling adolescent drug use. The Rand Publication Series, Ca.
Tricker, R. & Connolly, D. (1997). Drugs and the college athlete: An analysis of the attitudes of student athletes at risk. Journal on Drug Education, 26, 275-287.
Tricker, R., Cook, D., & McGuire, R. (1989). Issues related to drug abuse in college athletics: Athletes at risk. Sport Psychologist, 2(1), 155-165.

Acknowledgement

This study was supported by the Faculty Research Grant of the Hong Kong Baptist University.

Table 2

U.S. Sports Academy2015-03-27T13:13:57-05:00June 7th, 2006|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

A New Market Research Approach in Sport-Data Mining

Submitted by: Chen-Yueh Chen & Yi-Hsiu Lin

Introduction

Numerous organizations in the field of business have shown that great success and lucrative outcomes can be accomplished through implementing data mining. For example, Wal-Mart used data mining and found a link between the sales of babies’ diapers and beer. Based on this result, Wal-Mart placed beer close to the babies’ diapers, which resulted in a significant increase in terms of beer sales (Saban, 2001). Another salient example is American Express. American Express built a data mining model to examine millions of data and calculated “purchase scores”—customer’s propensity to make purchases, which not only provided merchants with valuable information, but also reduced American Express’ marketing expenses (Saban, 2001). As a result, research efforts made in data mining are warranted due to numerous successes accomplished while utilizing it.

Although data mining has been widely and successfully used in the domain of business operations, data mining in sport is just in its infancy (Fielitz & Scott, 2003; Lefton, 2003). In other words, the sports industry has generally been a poor and light user of data mining (Jutkins, 1998). It turned out that few papers related to data mining in the area of sport were found in sport journals. However, Lewis (2004) pointed out that data mining will become a critical component of selling and marketing sports teams. Similarly, the concept of data mining will become main stream in sports as an effective complementary marketing tool in the future (Martin, 2005). As a result, data mining warrants sport marketing researchers’ attention and efforts.

The purpose of the present article was to advocate the data mining approach to be utilized in the sport industry in order to effectively achieve sport organizations’ marketing goals. The organization of the current article is as follows: first, definitions and benefits of data mining were discussed; second, successful cases of application of data mining in sport were illustrated; third, proposed techniques of data mining that are appropriate and potentially useful in the sport industry were described followed by discussions and conclusions.

Definitions and Benefits of Data Mining

Data mining is a process of extracting previously unknown, valid, actionable, and ultimately comprehensible information from large databases and then using the information to make crucial business decisions (Cabena et al., 1998). From a different perspective, Kotler (2003) described data mining as “involving the use of sophisticated statistical and mathematical techniques such as cluster analysis, automatic interaction detection, predictive modeling, and neural networking” (p.54). Most of the definitions of data mining fall into these two aforementioned categories. As a result, from the combination of the two definitions, data mining is the process of using sophisticated mathematical or statistical models to extract valuable, valid, and actionable information from a database to accomplish an organization’s goals. (For similar definitions, also see Berry & Linoff, 2004; Hair, Anderson, Tatham, & Black, 1998).

The benefits of executing data mining are as follows: implementing up-selling, increasing season-ticket sales, monitoring season-ticket usage, raising transplanted-fan ticket sales, and executing cross-selling (James, 2004). Additionally, other benefits include (a) retaining current customers, (b) determining customers’ lifetime value, (c) developing relationships with customers, (d) improving delivery of sales promotion, (e) reinforcing consumers purchase decisions, (f) customizing consumer services, (g) facilitating marketing research, (h) profiling the customers, and (i) identifying the best customers for an organization (Aaker, Kumar, & Day, 2000; “Happy Customer,” 2004; Kotler, 2003).

Cases of Executing Data Mining in Sport

Although data mining has not been as widely employed in sport as it has in business, various successful applications in sport still exist. The following observations demonstrate how effective data mining can be for sport organizations and how sport organizations benefit from implementing data mining.

Dick and Sack (2003) conducted a study about effective marketing techniques in the NBA. They contended that a more effective and efficient way to ensure that advertising messages are received by the target markets was to use data mining. Several NBA teams such as the Cleveland Cavaliers, the Seattle SuperSonics, the Portland Trail Blazers, and the Miami Heat have successfully utilized data mining. The Cleveland Cavaliers created a database that includes customers’ names, addresses, telephone numbers, and other detailed information on the products purchased. By analyzing that database, the Cleveland Cavaliers consistently gave a follow-up call to those who bought tickets from Ticketmaster to determine whether they were interested in other games or events (Bonvissuto, 2005). The Seattle SuperSonics also developed a data mining program to raise its revenues and increase its season ticket holders. Additionally, the Portland Trail Blazers analyzed their customer database to help forecast advertising revenues and spot ticket-sale trends (Whiting, 2001). Finally, Miami Heat officials contend that data mining delivers an even more effective targeted audience than traditional advertising or traditional mass-media marketing. By using data mining, the overall Miami Heat season-ticket renewal rate in 2005 was expected to be around eight-five percent (Lombardo, 2005).

Data mining can also be applied by coaches to identify player patterns that box scores do not reveal, which helps win games by extracting relevant information from the database. In a 1997 playoff series, the Orlando Magic discovered Darrell Armstrong’s talent through data mining and inserted him into the starting lineup. The coach increased Armstrong’s responsibility in this series because the data showed that if Armstrong was on the court, the probability of an Orlando Magic win increased. Finally, the Orlando Magic won two consecutive games, and Armstrong personally won the Sixth Man Award in 1999 (Restivo, 1999). In addition, Brian James, assistant coach of the Toronto Raptors, employed a data mining application to know what kinds of plays opponents will use. Utilizing data mining in this way makes it easier for coaches to make decisions about when and how to position their players for maximum effect (Baltazar, 2000). Francett (1997) and Hudgins-Bonafield (1997) stated that data mining applications help analyze a huge amount of data to reveal winning player combinations for coaches. Moreover, the data mining approach to postgame analysis and improvement takes much less time than the traditional approach—forever rewinding the videotape. Namely, data mining makes analysis more efficient.

In summary, not only have the major league teams adopted data mining to increase ticket sale revenues and season-ticket holders’ renewal rates, but also sport coaches have utilized data mining to achieve their goals and objectives. Information extracted from records about players’ performance enables coaches to position and direct their players in a game. Consequently, data mining is a powerful technique with flexible applicability in sport.

Proposed Techniques for Data Mining in Sport

This section briefly presents an overview of the frequently used statistical models or techniques for data mining in terms of marketing, sales, and customer relationship management. The tasks that have been performed in the area of data mining are as follows: classification, estimation, prediction, and profiling (Berry & Linoff, 2004). The overview will include the definitions and the properties of the models along with the circumstance under which a model should be used. These models include (a) Discriminant Analysis, (b) Logistic Regression, (c) Decision Trees, (d) Artificial Neural Networks, (e) Collaborative Filtering, (f) Market Basket Analysis, and (g) Survival Analysis.

Discriminant Analysis

Discriminant analysis is a statistical method using linear functions to distinguish groups based on the independent variables. Discriminant analysis is the appropriate statistical technique when the dependent variable is categorical and the independent variable is continuous (Hair, Jr., Anderson, Tatham, & Black, 1998; Tabachnick & Fidell, 2001). It is an old and extensively used parametric statistical approach in classification. It works by comparing a weighted sum of the input variables to a constant value in the weights, and the constants are determined in such a way that the least square error of misclassification is minimized (Tabachnick & Fidell, 2001). Sport organizations can use it, for example, to classify customers as high-, medium-, or low-value customers in terms of their monetary contribution to the sport organization. This enables a sport organization to allocate marketing resources more effectively.

Logistic Regression

Logistic regression is a widely used technique for classifying subjects into two mutually exclusive exhaustive categories (Ratner, 2003). In logistic regression, the maximum likelihood estimation is employed to estimate the probability of classifying a subject into a group. The logistic regression is often used as a benchmark in the field of data mining when comparing the accuracy of model prediction. Professional sport teams can employ it to investigate the characteristics of the season ticket holders who end up terminating season ticket purchases and predict the probability of terminating season ticket purchases.

Decision Trees

Decision trees are one of the most popular methods in data mining and are frequently used for data exploration (Borisov, Chikalov, Eruhimov, & Tuv, 2005). Decision trees are a data mining technique that can be used to divide or partition a large collection of heterogeneous data into successively smaller sets of homogeneous data by using a sequence of simple decision rules with respect to a selected target variable (Berry & Linoff, 2004). In essence, decision trees are utilized to partition the data by employing independent variables to identify the subgroups that contribute most to the dependent variable (Chakrapani, 2004). This technique can also be used to classify and/or predict in the sport settings.

Artificial Neural Networks

Artificial neural networks (ANNs) are computer-intensive computational techniques that simulate the function of neural activity in a human brain (Chakrapani, 2004). To put it differently, ANNs are the computational tools for data exploration and model development to help identify patterns or structures in the data (Smith & Gupta, 2002). ANNs consist of three layers of processing units: input layer, hidden layer, and output layer. Since the final decision is binary (0 or 1), the value for the output layer is the predicted value of the decision. If the output value is 0.5 or above, then the decision is assumed to be an acceptance, while if it is 0.5 or below, then the decision is a rejection (Kumar & Olmeda, 1999). Compared to the traditional statistical methods, which are usually linear-based, ANNs use the non-linear approach (Cho & Ngai, 2003) and do not depend on a set of specified procedures. ANNs have been widely used in recent years as a classification technique and have been applied to a variety of business fields including bond rating, bankruptcy prediction, and stock market prediction. ANNs are superior over the regression-type models because of their ability to detect non-linear relationships and to adapt to changing input (Chakrapani, 2004). Sport organizations can use it to predict and classify their customers to better allocate marketing resources, i.e., more accurately segmenting the market and targeting custoemrs.

Collaborative Filtering

Collaborative filtering (CF) is a new technique in the area of data mining, assisting people to make choices based on other people’s choices. Similarly, Berry and Linoff (2004) described collaborative filtering as an approach to making and providing personalized recommendations. The collaborative filtering approach starts with evaluating a history of customer product preferences as well as demographics and ends up with determining similarities so that people who may like the same products will be put together (Berry & Linoff, 2004). Namely, this approach employs the reactions or preference of others within the database as well as their similarity to generating recommendations. Professional sport teams can utilize it to make recommendations or promote sporting events/sport merchandise to their customers based on what other customers purchased or consumed.

Market Basket Analysis

Market basket analysis is a data mining technique aiming to understand point-of-sale transaction data (Berry & Linoff, 2004). In other words, market basket analysis deals with such business problems as which products tend to be purchased together as well as which are most appropriate to promotion (Berry & Linoff, 2004). To perform basket analysis, three levels of market basket data are required: customers, orders, and items. Customer data refers to customer information including customer’s IDs, names, addresses and so on. Order data represents a single purchase event by a customer including the total amount of the purchase, payment type and whatever other data is related to and relevant to this transaction. Item data contains the price paid for the purchased item and the number of items (Berry & Linoff, 2004). Sport organizations can acquire benefits, such as deciding which product should have a promotion, the segmentation of customers, and the identification of the relationships among product items by using this technique.

Survival Analysis

Survival analysis method, also known as Event History Analysis, Reliability Analysis, Time to Failure and Duration Analysis, is developed mainly to deal with the probability that a certain event will occur but also deals with when it will occur (Harrison & Ansell, 2002). Namely, it deals with the time between events (Drye, Wetherill, & Pinnock, 2001). For example, survival analysis can identify when existing customers will re-attend professional sporting events based on their past game attendance records, which provides valuable information for professional sport teams in terms of promotional decision-making.

Discussions and Conclusions

Both advances in information technology and organizations’ needs have facilitated the upsurge of data mining. Even though a data mining approach has been successfully adopted to accomplish a number of organizations’ marketing goals and objectives in business, it is still in the infancy stage in the domain of sport. However, lack of use of data mining in the sport business does not mean that it is not applicable or important in the sport business. Instead, it is a great opportunity for sport businesses to adapt data mining and benefit from implementing it. With correct and appropriate use of data mining, sports organizations can benefit from the strategies and tactics developed from analyzing customer databases.

The development of models or algorithms in the area of data mining is upsurging to fulfill a variety of problems in practice. Various models have been commonly and successfully employed to solve real world problems. Tasks that are performed vary from model to model. Consequently, no rule of thumb exists that explains which model is the best model in solving a practical problem. In other words, the selection of the model depends heavily on the type of problems, the data structure an organization possesses, and the objective of an organization. Therefore, it is critical to have a thorough examination of organizational goals and data structure before choosing data mining techniques.

Reference

Aaker, D. A., Kumar., V., & Day, G. S. (2000). Marketing research (7th ed.). NY: John Wiley & Sons, Inc.
Baltazar, H. (2000). NBA coaches’ latest weapon: Data mining. PC Week, 17(10), 69.
Berry, M. J. A., & Linoff, G. S. (2004). Data mining techniques: For marketing, sales, and customer relationship management (2nd ed.). Indiana: Wiley Publishing, Inc.
Bonvissuto, K. (2005). Cavaliers forge fan friendships with strategic database use. Crain’s Cleveland Business, 26(8).
Borisov, A., Chikalov, I., Eruhimov, V., & Tuv, E. (2005). Performance and scalability analysis of tree-based models in Large-Scale Data-Mining Problems. International Technology Journal, 9(2), 143-151.
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., & Zanasi, A. (1998). Discovering data mining: From concept to implementation. NJ: Prentice Hall.
Chakrapani, C. (2004). Statistics in market research. New York: Oxford University Press Inc.
Cho, V., & Nagi, E. (2003). Data mining for selection of insurance sales agents. Expert Systems, 20(3), 123-132.
Dick, R., & Sack, A.L. (2003). NBA marketing directors’ perceptions of effective marketing techniques: A longitude perspective. International Sports Journal, 7(1), 88-99.
Drye, T., Wetherill, G., & Pinnock, A. (2001). When are customers in the market? Applying survival analysis to marketing challenges. Journal of Targeting, Measurement, and Analysis for Marketing, 10(2), 179-188.
Fielitz, L., & Scott, D. (2003). Prediction of physical performance using data mining. Research Quarterly for Exercise and Sport, 74(1), 25.
Francett, B. (1997). The NBA gets a jump on data mining. Software Magazine, 17(9).
Hair, Jr., J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). New Jersey: Upper Saddle River: Prentice Hall.
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Harrison, T., & Ansell, J. (2002). Customer retention in the insurance industry: Using survival analysis to predict cross-selling opportunities. Journal of Financial Service Marketing, 6(3), 229-239.
Hudgins-Bonafield, C. (1997). Data mining software scores high with the NBA. Network Computing, 8(11).
James, V.L. (2004). Build fan base from your database. Street & Smith’s SportsBusiness Journal. Retrieved June 24, 2005, from SportsBusiness Journal archive database.
Jutkins, R. (1998). Direct marketing: A new strategy in the sports industry. Direct Marketing, 60(10), 34-35.
Kotler, P. (2003). Marketing management. (11th ed.). New Jersey: Upper Saddle River Pearson, Education, Inc.
Kumar, A., & Olmeda, I. (1999). A study of composite or hybrid classifiers for knowledge discovery. Journal on Computing, 11(3), 267-277.
Lefton, T. (2003). NFL flexes database marketing muscle to sell tix, boost viewership. Street & Smith’s SportsBusiness Journal, Retrieved June 24, 2005, from SportsBusiness Journal archive database.
Lewis, J. (2004). Career spotlight: Tom Glick. Street & Smith’s SportsBusiness Journal. Retrieved September 24, 2005, from SportsBusiness Journal archive database.
Lombardo, J. (2005). A new ball game in South Florida: Heat puts twist on marketing. Street & Smith’s SportsBusiness Journal, Retrieved June 24, 2005, from SportsBusiness Journal archive database.
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U.S. Sports Academy2015-03-27T11:53:07-05:00June 4th, 2006|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

Letter to the Editor

Submitted by: Don Anthony

Thinking About Olympism and the USA

Editor’s Note: Don Anthony, a noted Olympic historian from Great Britain and long-time friend of the United States Sports Academy, wrote this article for USSA President Dr. Thomas P. Rosandich. The Sport Journal is publishing this piece as a letter to the editor.

25 years ago Dr. William Renato Jones died. He was a Springfield graduate. Born in Rome and speaking Italian as a first language, he had British nationality, a Swiss mother, a Scottish father, and spent much of his working life in Germany! His first job on leaving Springfield was for the YMCA in Adana, Turkey. Later, for the YMCA, he organized a global congress in Paris which brought him to the attention of Unesco. This UN body appointed him
director of the Unesco Youth Institute in Gauting, near Munich. Concurrently, as a volunteer, he founded and became general secretary, of the world body
for basketball – FIBA. Furthermore, he was the obvious candidate to lead – again as general secretary – Unesco’s consultative body for sport – ICSPE
(the International Council for Sport and Physical Education) in l960. On retirement from his basketball post, he was invited to be honorary life president.
He refused!

“I will accept the title honorary life secretary” he said – and so it was – “anyone in sport knows it is the secretary who is the kingpin.”
Surrounding Jones were other formidable Springfield people such as Frank Hepp from Hungary. There was Ernst Jokl from Kentucky University; and the
President of ICSPEP, Philip Noel-Baker. Philip became unique – an Olympian – silver in the 1500 metres in l920 at Antwerp -and Nobel Peace Prize in
l959 for his work on disarmament. Philip too, had USA roots: he began his academic career in l907 at Haverford – then the major Quaker (Society of Friends) academy in North America. Another polyglot with a Canadian father and a Scottish mother, he spoke well in French, German, Greek and Italian. This last language he learned on the Serbian front in World War I when he worked as a pacifist with the Friends Ambulance Unit. The Italian government awarded him their highest honour for “bravery in battle.” Despite this, no matter how hard I tried, the organizers of the recent Turin Winter Olympic Games ignored my pleas that they should honor his unique qualifications for mention and commemoration! I had the wonderful pleasure of working closely with Jones and Philip for some 20 years from l960 onwards. Every moment with them was “an education and a joy” I once explained to my wife – to excuse my constant absences on duty for global physical education and sport. Strangely enough, the educational energies of the IOC were rather dormant between and 1960.and l980. In this time, Unesco began to lead matters and eventually the World Conference of Ministers of Sport came into being.

I look again at my profession of “physical education” – with its modern roots in the USA. These were summed-up marvellously by John Lucas in his article
in November 2004, for the Journal of the International Society of Olympic Historians (ISOH): “The Great Gathering of Sport Scientists; the l904 St. Louis
Olympic Games Exposition Fair Physical Education Lectures.” I think of that other significant Springfield man Harold Friermood who was “always there” as world P.E. blossomed. Harold even attended the first meeting of the Amateur Volleyball Association of Great Britain, founded in London at the YMCA
headquarters in l955.

As my researches grew in Olympic matters, I discovered Charles Waldstein (Columbia, I think) and then Heidelberg and Cambridge (England) ending up as Slade Professor of Art at Cambridge and concurrently Director of the Fitzwilliam Museum in that city, and Director of the American School of Classical Studies in Athens. A confidant of Coubertin, he arranged meetings with the Royal Princes in Athens in l896. He first met Coubertin during the young Frenchman’s studies of English schools and universities in l886. They met again in l896 when Waldstein pistol-shot for the USA in the Games. Charles (then Walston and a British national) was chairing the Arts Commission of the l924 Paris Olympic Games near the end of his life. His grandson, Oliver, still farming near Cambridge, was persuaded (by me) to donate his grandfather’s archives to the IOC Museum in Lausanne where they still lie. Again I was startled by the Athens American School of Classical Studies attitude in 2004 – when they showed no interest in honouring their previous Director!

In l889, at the famous Boston Conference on Physical Training, Coubertin delivered a paper. On the same platform was the Earl of Meath from England, the man who got PE on to the British education statute books in Parliament. In l89l both were elected members of the Wenlock Olympian Society (WOS) – in Shropshire, England. Brookes, the founder of the WOS was keenly aware of new developments in the USA, and also in Russia, France, Germany and elsewhere. David Young of Florida wrote a major book on this Olympian root in his distinctive l996 book “The modern Olympics – a struggle for survival”.

Coubertin’s major USA friend and colleague was of course William Sloane (Columbia and Princeton). Charles Battle tells me that Atlanta created a sculpting of Sloane for presentation to the IOC. Its whereabouts need research in Lausanne? It was the same with a sculpting of Philip Noel-Baker “Man of Sport- Man of Peace” given in l986 to the IOC as part of the Birmingham (England) bid. This lurked in the cellars at Lausanne until recent years when it was fished out and now graces the entrance of the Sports Court of Arbitration in Lausanne – a fitting place for one who was a master in international law! In the intervening years a copy of the sculpting was purchased by the University of Hiroshima in Japan. Philip attended the atom-bomb memorial meeting in August at Hiroshima whenever he could. At his last attendance, he was given only one minute to speak before the sounding of the peace gong in a square packed with 200,000 people. Pushing away his prepared speech Philip said “Say after me – no more war – no more Hiroshima!” Boom!!! What a man – what a brain – at nearly 90 years of age. At his last major speech, at the IOC Congress in Baden-Baden (l981), he was granted only 3 minutes under any other business. Taking 11 minutes, much to the consternation of the organizers, he got a three minute mass standing ovation for his last words -“If the IOC…can bring sport for all to the whole world – especially the developing world – I will nominate them for the Nobel Peace Prize!” Indeed I have a letter from the Nobel Committee, saying that such a nomination was made prior to his death in l982. I was left his two ice-axes (he was a keen mountaineer). I gave one to the Winter Sports Museum of Sarajevo and the second to the Winter Sports Museum of Pyongchaeng in Korea. I hope the latter might one day help to rebuild the former. Indeed plans are afoot to try to restore the energy of the Inter-University Centre in Dubrovnik which, organized seminars in the l980’s to celebrate Philip’s work for sport, peace, and development – and to look again at the feasibility of the Noel Baker Medals for outstanding examples of sport and international understanding. The first medals were awarded in l984 to the Sarajevo Winter Olympic Games Organizing Committee – and to President of the IOC at that time, Juan Antonio Samaranch.

For a moment, back to Atlanta: Local man George Hirthler, working with the Pierre de Coubertin International Committee (CIPC), managed to fund the erection of a delightful monument to Coubertin in a main square in his city. I wonder if it is still there and whether its purpose is still recognised and celebrated.

Further thoughts about the USA-Olympic links begin to flood back: John Lucas one asked me to investigate the sermon given in St. Paul’s cathedral by the “Bishop of Pennsylvania” (in Coubertin’s memoirs). It turned out that it was indeed a Bishop – but of Central Pennsylvania – one Ethelbert Talbot. It was this sermon which inspired Coubertin’s own description of the true Olympic ethos “The honor is less in winning than in taking part.” How we need to restate, and restate, and restate, this message – in today’s Olympic world consumed as it is by medal mania. The name John Lucas appears everywhere – even as Honorary Consul for Albania! What a wonderful testament for Olympism in the USA, and the world, his life has been – and still is. I thank him for much inspiration and education. It was much to him that the USOC National Olympic Academy flourished. Indeed in the early l980’s we were so taken with this model that we started the British National Olympic Academy (l982 – a one day affair). This is now institutionalised and we regularly have the problem of over-subscription – a long weekend – 120 people maximum. Two regular USA friends have been, and are, Elizabeth Hanley of Penn State and Robert Merchikoff of Seattle.

My own studies in physical education were nourished by USA texts starting in 1946. I remember Thomas Cureton visiting us at Loughborough in the tests – and- measurement days. When we started the British volleyball association we lacked knowledge of the high-level game. Fortuitously, one Victor Tseirov of the USSR Embassy rang me one day to say that “I am scientific attaché at the Embassy – but mainly because my father pushed me; in my heart I am one percent scientific attaché and 99 percent volleyball enthusiast.” At roughly the same time one John Gay (USA national team player) contacted us from the London Ruislip USAF base to offer us his expertise – and facilities! We now look forward to preparing British volleyball teams for the London 2012 Olympic Games. Such are the fortuitous chances of life – serendipity. Let us glory in it and use it more. In my own field, I have long thought that – with USA leadership – we could enrich the recently restored Great Library of Alexandria. The ancient library linked Pergamon with Alexandria. The revived library could be aided to establish a sports section to record the whole area of African sport – and its possible contribution to health and development in that continent – the one of greatest need. I write this thinking of my ancient namesake giving the Pergamon library to Cleopatra (as a love token!).

Out of all this nostalgia springs a thought. That 2007 will be the centenary of Philip Noel-Baker’s entry into academic life at Haverford – USA. Can we not celebrate this in different ways – e.g. a U.S. seminar to record and examine the USA’s past role in Olympic matters – its current status – and its future potential at one of the USOC’s centers?

On the global level to assist Dubrovnik in its recovery – a session at the IUC could be held. An initial 2006 step could be a small planning meeting at the IUC to also embrace the life and work of William Jones – who’s chosen Crown Prince in FIBA. Boris Stankovic – now of Olympic rank – could play an appropriate role.

I often remind audiences that the Olympic idea came out of moves to strengthen physical education; of the first 12 members of the IOC, six were educationists; Coubertin was the foremost comparative physical educationist of his time. Today in the fight against the dumbing-down of sport these values need urgently to be restored and the major world-power, the USA, has a special responsibility to us all.

U.S. Sports Academy2015-03-27T11:48:22-05:00June 2nd, 2006|Contemporary Sports Issues, Sports Management|Comments Off

Preferred Player Characteristics and Skills of Division I Men’s Basketball Coaches

Submitted by: William F. Stier Jr. & Robert C. Schneider

Abstract

A national survey of selected men’s basketball coaches, at the NCAA Division I level, revealed how essential the respondents felt certain work ethic characteristics were for successful basketball players on their team. The respondents also revealed how important specific skills or talents were for the success of men’s NCAA men’s Division I basketball programs. The survey was completed by means of a 36-item Likert scale questionnaire. This investigation determined to what degree NCAA Division I coaches should seek specific work ethic characteristics and physical skills/talents in their players.

Introduction

College basketball coaches seek athletes with high caliber skills and specific basketball talent as well as a good overall work ethic. Although there is plenty of antidotal information regarding the type of desirable skills and talent desirable in the world of basketball, there has been very little definitive research done in terms of determining exactly what skills, talent and examples of work ethic are highly rated by coaches of men’s basketball at the NCAA Division I level of competition (Stier, 1997).

Successful and effective coaching is a highly complex and multi-dimensional enterprise (Jones, Housner, and Kornspan, 1997). It is very important, according to Owens & Stewart (2003) to be able to understand individual squad members’ physical, emotional, social and cognitive needs if the team is to be successful, that is, win. In a study by Forman (1995), it was determined that college basketball coaches need to make a commitment to each player’s growth and improvement in the sport if the team, as a collective unit, is to emerge victorious in actual competition,

In a study of elite athletes by Mallet & Hanrahan (2004), it was determined that players recognize the need to train hard to be winners. Training hard implies working diligently to improve both individual and team performance in order to produce meaningful results when it counts, in actual competition (Laios and Theodorakis, 2002). This emphasis on both individual and team (collective) training is reinforced by Bursari, (2000).

Elite athletes exhibit significant effort in games as well as in practice and this dedication extends to off-season work habits (Adams, 1996). The ability and willingness to work hard as well as to work harder are important examples of an athlete’s ability that can lead to success on the proverbial playing field, especially if the coach believes in the athlete and is successful in motivating the individual player to work harder (Jowett, 2003).

Literature presented by Stier (1998) included several major factors that distinguish consistent winning teams from teams that consistently lose: (a) better skilled athletes and (b) better conditioned athletes. The importance of adequate strength and conditioning was emphasized by Laios and Theodorakis (2002). Dirks (2000) studied control variables of team performance representing elements of the coach and players’ talent. And, in 1999, Pascarella et al. looked at the topic of physical energy that is required of athletes in actual competition.

Purpose of the Study

The purposes of this study were two-fold. The first purpose was to determine the essentiality of selected work ethic characteristics on behalf of athletes. And, the second was to determine the importance of specific skills or talents of athletes to the success of men’s NCAA men’s Division I basketball programs. In summary, this investigation sought to determine to what degree coaches should seek in their Division I men’s basketball players’ specific work ethic characteristics and physical skills/talents.

Methods

The Questionnaire

A survey instrument was developed from the existing current literature related to work ethic characteristics of players as well as specific athletic skills and talents that might have an effect on the success or failure of NCAA Division I men’s basketball programs. An extensive literature search found basketball related articles in which work ethic characteristics and various athletic skills and talent for athletes engaged in basketball were identified and which served as the foundation for the 36 items included on the Likert scale statements of the questionnaire.

Of the total of 36 Likert scale statements, 15 related to work ethic characteristics while 21 related to athletic skills and talents that might have an impact upon the success or failure of Division I men’s basketball programs. For work ethic characteristics, respondents were instructed to circle the number that corresponded with the degree of essentiality they believed most accurately depicted the impact that selected work ethic characteristics have upon the success [winning games] of their basketball programs and had the following categories of essentiality from which to choose: 5 – Very Essential, 4 – Essential, 3 – Neither Essential nor Unessential, 2 – Unessential, and 1 – Very Unessential. For the second category, specific athletic skills and talents, the coaches had the following Likert scale options which included the following choices of importance from which they were asked to circle the corresponding number: 1 – Very Important, 2 – Important, 3 – Neither Important nor Unimportant, 4 – Unimportant, and 5 – Very Unimportant.

To help address content validity a draft of the survey questionnaire was completed by five Division I head basketball coaches who were determined to be expert coaches for the purpose of gaining feedback regarding the instrument. In order to be deemed an expert coach, the coaches were required to have coached men’s basketball at the Division I level for at least 10 years and won at least 75% of their games during that time. After receiving the feedback from the expert coaches, appropriate suggestions and recommendations were incorporated into the final version of the survey instrument which was then utilized in this national study. The University’s Internal Review Board reviewed the final, revised version of the instrument and gave its approval.

The subjects for this national survey included all 315 men’s NCAA Division I head basketball coaches whose names and addresses were provided by the NCAA national headquarters. Of these, 118 completed and returned usable surveys generating a return rate of 37.5%.

Results

Work Ethic – Training

The category of work ethic contained two general categories, (a) training and (b) effort.

Of the eight characteristics related to training, six pertained directly to players’ training; two pertained to sacrifices made by athletes and the remaining two dealt directly with the athletes’ state of physical conditioning. Training hard was deemed to be the single most essential characteristic for winning, according to the respondents. In fact, 74.6% of the coaches indicated that training hard was very essential to winning while 25.4% classified it as essential.

Strength and conditioning was likewise thought to be very essential by a large percentage of coaches (72.9%), and deemed essential by 25.4%. Individual training was the only other work ethic characteristic thought to be very essential by more than half of the coaches (52.5%), while another sizeable group of coaches (44.1%) also classified this characteristic as essential. Table 1 shows all eight work ethic characteristics and how the respondents classified each in terms of how essential they are to winning basketball games at the Division I level.

Work Ethic – Effort

Of the seven characteristics identified in the survey as being related to effort, three dealt directly with effort, two addressed the conditioning efforts of players, while the remaining two involved how essential were the players’ work habits—in the eyes of the responding coaches. Individual player’s effort, in general, was consistently valued very highly by coaches with five of the seven categories deemed to be very essential by more than sixty percent (64.4%) of the respondents. Only two categories relating to effort were deemed to be very essential by less than half of the coaches, and both related to off-season activities. These were (a) player’s off-season conditioning efforts (45.8%) and (b) player’s off-season work habits. Table 2 illustrates how essential the coaches viewed these seven work ethic characteristics that related to effort.

Athletic Skills and Talents – Performance and Abilities

The section on athletic skills and talents contained two general categories, (a) performance/skills and (b) basketball talent. Of the eight performance skills identified in the investigation, two related directly with performance, two pertained directly to individual and team play while three dealt with abilities of players. The remaining skill is related to the physical energy that a player exudes. Only three performance skills were deemed to be very important by more than half of the coaches responding to the survey: (a) game performance (83.1%), (b) team oriented play (67.8%), and (c) physical energy (66.1%). Table 3 illustrates how the respondents rated each of the eight performance/abilities in terms of their importance or unimportance to winning Division I basketball games.

Athletic Skills and Talents – Basketball Talent

Of the 21 basketball talent categories that the coaches rated in terms of importance, 7 items related to physical talent while the remaining 14 focused on specific basketball skills. Defense, with 57.6% of the coaches, and passing, with 55.5%, were the only talent items that more than half of the respondents rated as very important. Two other talent categories are worth noting in that both (a) overall fundamental base and (b) rebounding were the only two talent categories that all the respondents classified as very important or important. Table 4 shows how the coaches classified all of the categories of basketball talent relative to their importance of unimportance in terms of their impact upon winning.

Conclusions

This national investigation sheds light on how Division I basketball coaches view the essentiality of specific work ethic characteristics and the importance these coaches place on specific skills or talents identified as having impact upon winning in competition. The results have implications for coaches in respect to what qualities, characteristics, skills and talents to look for in terms of potential recruits as well as current team members.

References

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Forman, B. (1995). Factors of hiring head coaches in collegiate athletics. Unpublished master’s thesis, Ball State University, Muncie, Indiana.
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