Effective Security Management of University Sport Venues

Abstract

The purpose of this study was to identify standards for effective security
management of university sport venues. Standards were developed through
a series of interviews and a three-round Delphi study. Purposeful sampling
was used to select participants for both the interviews and Delphi panel.
Four sport security personnel participated in the interview process and
an initial set of standards were developed and used for the Delphi study.
The twenty-eight member Delphi panel included the athletic facility manager,
campus police chief, local sheriff, and local emergency management director
responsible for game day security operations at seven state-supported
universities in Mississippi. Importance ratings for developed standards
were assessed on a five-point Likert scale during Round 2 and 3. This
study identified 134 standards in eleven categories: Perimeter Control,
Access Control, Credentialing, Physical Protection Systems, Risk Management,
Emergency Management, Recovery Procedures, Communications, Security Personnel,
Training, Modeling, and Simulation, and WMD – Toxic Materials Protection.

Introduction

“The homeland is secure when the home town is secure”
Former Secretary Tom Ridge, Department of Homeland Security

Large public gatherings, such as sports events that celebrate American
popular culture, are considered to be potential terrorist targets (Hurst,
Zoubek, & Pratsinakis, n.d.). In March 2005, the Department of Homeland
Security (DHS) identified a dozen possible strikes it viewed most devastating,
including a truck bombing of a sports arena (Lipton, 2005). Since 9/11,
the American sports industry has increased security at major sporting
venues and high profile events such as the Super Bowl, World Series, and
Olympics. University sport programs must also take necessary steps to
secure their stadiums and campuses against potential threats. College
sport stadiums provide a perfect target for mass casualties and catastrophic
economic impact.

Assessing risk, reducing vulnerabilities, and increasing the level of
preparedness will help minimize potential threats to university sport
venues nationwide. The major goal of this study was to develop standards
for effective security management of university sport venues and assess
the level of importance for those standards according to individuals responsible
for sport venue security. Identifying standards will assist university
sport security management teams in their quest to provide a safe environment
for sport patrons and will help provide consistency in security practices
among sport venues nationwide. The two primary research questions that
spearheaded this research project were:

1. What standards are needed for effective security management of university
sport venues?
2. What is the perceived level of importance for the security standards?

Review of Literature

Sport is a multibillion dollar industry in the United States and large
sporting events such as the Super Bowl, NASCAR, or collegiate football
bowls provide an attractive stage for terrorists to communicate their
messages of evil and hatred for society. “Al-Qaeda’s Manual
of Afghan Jihad proposed football stadiums as a possible terrorist attack
site, and the FBI issued an alert in July (2002) warning that people with
links to terrorist groups were downloading stadium images” (Estell,
2002, p. 8). Unfortunately, the sporting world has already been victim
to terrorist attacks. At the 1972 Munich Games, a Palestinian group seized
Israeli athletes inside the Olympic village. In 1996, a domestic terrorist
bombed The Centennial Olympic Park at the Atlanta Games, killing one person
and injuring more than one hundred (CNN.com, 1996). University sports
venues are no exception to these terrorist threats. In October, 2005,
an Oklahoma University student killed himself by prematurely detonating
a bomb strapped to his body outside an 84,000 packed stadium (Hagmann,
2005). The intercollegiate game-day environment meets the criteria for
a perfect strike with high consequences. According to NCAA attendance
records, approximately forty-three million people attended collegiate
football games during the 2004 season (Official NCAA Football Records
Book, 2005).

In the aftermath of 9/11, most leagues, teams, and venues conducted threat
assessments and updated security practices (Hurst, Zoubek, & Pratsinakis,
n.d.). The National Football League developed a “best practices
guide” of recommended security measures for NFL teams. The NFL also
made a request to the Federal Aviation Administration to restrict airspace
above all NFL stadiums (Mason, 2001). Collegiate athletic programs in
particular stepped up security on many levels. The Federal Aviation Administration
accepted a request from the University of Michigan to declare a no-fly
zone over the Wolverines stadium for their game against Western Michigan
in September, 2001 (Bagnato, 2001). Michigan also locked down its stadium
several days before game day and used bomb sniffing dogs to sweep the
premises the morning before kick-off. The Penn State Nittany Lions no
longer allowed re-entry to the stadium, and illegally parked cars were
towed. The Mississippi State Bulldogs officially banned backpacks, and
like many other college stadiums in the country, Nebraska’s Memorial
stadium had a greater security presence inside and outside the grounds
(2001).

However, Pantera et al. (2003) findings indicate there is much room for
improvement in security at college sporting venues. Implications discussed
by Pantera et al., (2003) include the need for effective communication
and scrutinization of game plans in advance of game time and practiced
disaster/emergency responses with support of local, state, and federal
first responders. Furthermore, all game-day staff members must be familiar
with their roles and responsibilities (2003). According to Goss, Jubenville,
& MacBeth (n.d., “Training: our best kept secret”), “To
be ready to preempt or react to terror strikes, venue workers at entry
level must receive timely security training.” Training must be a
continuous element to facility worker’s duties. Outsourcing security
personnel just to present a security presence is no longer adequate. Many
venues have chosen to develop and maintain their own in-house security
response teams that are familiar with the venue (n.d). In today’s
unstable environment, with the threat of terrorist attacks, sport organizations
need to “institutionalize security measures in policy and procedure
guidelines, train personnel on the guidelines and stage exercises to drill
and test incident response plans” (Hurst, Zoubek, & Pratsinakis,
n.d., p. 4).

The NCAA has issued a “best practices” planning options guide
for institutions to review and the International Association of Assembly
Managers (IAAM) has identified key security practices for public venues.
Furthermore, the DHS developed a Target Capabilities List (TCL) which
identifies thirty-six capabilities that the Nation needs in order to address
major incidents. The DHS also created a Universal Task List (UTL) that
describes tasks to be performed to prevent, protect, respond, and recover
from incidents of national significance (Universal Task List, 2004). According
to Hurst, Zoubek, & Pratsinakis (n.d.), regardless of the analysis
conducted after an incident, “the fundamental question will always
be whether or not reasonable steps were taken to protect against an incident
in light of the availability of security measures, the industry “standards’
for security, and the potential threat of terrorism” (p. 5). Standards
are defined by Marshall Thurber (1993) as “a written, or visual
measurable guideline describing expected behavior, performance, product
or service.” A lack of industry standards for university sport venue
security in America may result in varying security policies, procedures,
and guidelines among institutions. After an extensive review of literature,
Homeland Security threat/risk assessment training, and experience working
on sports event security management grant projects, the researcher was
able to identify common categories of security measures to be used in
the research study. These included: Perimeter Control, Access Control,
Credentialing, Physical Protection Systems, Risk Management, Emergency
Management, Recovery Procedures, Communications, Security Personnel, Training,
Modeling, and Simulation, and WMD –Toxic Materials Protection.

Methodology

Participants

Participants in this study were qualified experts in the field of security
and/or sports event
security. Two sets of participants were used for this study – interview
participants and Delphi study participants. The researcher interviewed
six experts (n=6) in the field of sports event security management. These
experts worked in various disciplines and offered unique perspectives
on security management. They included: 1) a FBI agent with extensive experience
in conducting vulnerability assessments of sport venues; 2) a Homeland
Security Officer who oversees the implementation of risk management practices;
3) an Emergency Management Director; 4) a professional sports security
officer; 5) a professional sport management officer, and 6) an NCAA Division
I collegiate athletic administrator responsible for game-day security
planning and operations.

Delphi study participants (n=28) included the athletic facility manager,
local sheriff, campus police chief, and the local county emergency management
director responsible for game-day security at seven public universities
in Mississippi. The sample population reflected NCAA Division I, Division
I AA, and Division II, and four different Athletic Conferences.

Procedures

Approval to conduct the research was obtained by the Institutional Review
Board. All interviews were delivered via email during the fall of 2005.
A panel of experts reviewed the questionnaire to ensure face validity.
Six security experts were interviewed first to obtain a preliminary set
of standards critical to the effectiveness of university sports event
security management. Interview questionnaires included a definition and
example of a standard. Participants were asked to generate responses to
the question, “What standards, under the following categories, do
you perceive to be important in effectively securing sport venues?”
Security categories were provided by the researcher. The preliminary list
of standards was used for the Delphi study.

A three-round Delphi study was conducted during spring 2006 to gain feedback
on the preliminary list of standards and to reach consensus among sports
event security management professionals. Each Delphi questionnaire was
reviewed by a panel of experts to ensure face validity. Round 1 Delphi
asked the panel to review the preliminary list of standards and add/edit/comment
accordingly. Round 2 Delphi was sent to those who responded to the first
round. Participants were asked to rate the importance of each standard
on a five-point Likert Scale (1 = very low; 2 = moderately low; 3 = average;
4 = moderately high; 5 = very high). Round two results were compiled and
reformulated for Round 3 Delphi. Round 3 Delphi was sent to participants
who responded to round two. Round three again asked participants to rate
the importance of each standard. They were provided descriptive information
on how the group responded in round two and were asked to consider the
group response and then re-rate the items.

Data Analysis

Upon interview completion, standards were consolidated under each category
and as much as possible of the participants’ original wording was
retained. Some standards were suggested by more than one participant,
but were only listed once to avoid duplication. A peer examination enhanced
the researcher’s analysis and provided a “devil’s advocate”
point of view to enhance credibility.

Round 1 Delphi questionnaires were analyzed through summarization and
identification of new standards suggested by the Delphi panel. Round 2
and 3 Delphi results were analyzed using SPSS. Descriptive statistics
(mean, median, and standard deviation) for importance ratings were provided
for each standard. The researcher set an elimination level at three or
below, indicating an average to low importance rating. No standard was
assigned a mean importance score low enough to warrant elimination. “The
equivalent terms for reliability and validity for qualitative data are
credibility, dependability, and confirmability. With the Delphi study,
credibility is directly related to the selection of the panel of experts
who must fit the area of inquiry,” (Doerries & Foster, 2005,
p. 260) as did the selected panel in this study. Athletic facility managers,
local sheriffs, campus police chiefs, and local county emergency management
directors are key players in the planning and preparation of security
operations at intercollegiate sports events. These experts provided valuable
insights into the coordination of security protocol on game day. To further
enhance credibility, transferability, dependability, and ‘confirmability’
of this study, the researcher utilized triangulation, peer debriefing,
and member checks.

Results

Interview

Four interview participants (n=4) successfully completed the interview
questionnaire. A total number of 206 standards were suggested from all
four participants. The standards were consolidated under each category
and as much as possible of the participants’ original wording was
retained. Some standards were suggested by more than one participant,
but were only listed once to avoid duplication. A total number of 141
standards under eleven security categories were used for round one of
the Delphi study.

Delphi Study

Twenty-two of the twenty-eight participants successfully completed all
three rounds of the Delphi Study (78.6%). Table 1 highlights the overall
participation rates and main purpose for each Delphi Round.

Table 1: Participation Rates for the Delphi Study

Round Main Purpose # of Experts Asked to Participate # of Complete Returns % Completed
1 Feedback on standards created through interviews 28 26 92.6
2 Rating of importance 26 23 82.1
3 Updating of previous ratings 23 22 78.6

Delphi Round 1 participants were asked to review the list of 141 standards
created by the interview panel. After Delphi Round 1 analysis, 134 standards
were listed in Round 2 and 3 Delphi for assessment of importance ratings.
The following is a summary of results after completion of the third and
final Delphi Round:

Perimeter Control
The panel of experts indicated the importance of locking down the stadium
(M=4.36), police patrolling before and after events (M=4.36), establishing
a secure inner perimeter (M=4.36) and securing vulnerable systems with
locks and seals (M=4.36). Security should also establish a 500-foot outer
perimeter around the stadium (M=4.09). However, the panel clearly felt
that the use of bomb dog teams for inspection (M=3.62) was not as important.

Access Control
The Delphi panel highlighted the prohibition of certain items such as
coolers, large backpacks, weapons, etc. as highly important with a mean
score of 4.76. Several other standards in this category proved to be important
including: publicizing inspections and prohibited items (M=4.73), locating
security personnel at each entry point (M=4.64), locating law enforcement
at each entry point (M=4.45), identification of coaches and players entering
locker rooms and restricted areas (M=4.50), and the right to inspect any
deliveries to event area (M=4.45). Electronic scanning of tickets (M=3.64)
was of least importance to the panel.

Credentialing
The panel indicated that credentials should be worn at all times (M=4.50)
and should be substantially different from those used in prior seasons
(M=4.45). Maintaining a record of persons issued credentials for control
purposes (M=4.36) was also important. All team bench staff, except players
in uniform, should wear a game credential (M=4.36). Requiring background
checks for vendors, employees, contractors, students, and volunteers received
a mean score of 3.91.

Physical Protection Systems
Standards in this category were assigned mean scores ranging from 3.86
(bomb removal equipment on site) to 4.59 (enhanced lighting of gated areas
and digital security system monitored by command center). Establishing
a 100-foot inner perimeter (M=4.41), utilizing barriers (M=4.27), and
having digital camera monitoring capabilities (M=4.27) were highly rated.
The stadium and press box should be equipped with an Integrated Security
Management System (ISMS) consisting of CCTV, access controls, and alarms
(M=4.41). Having portable hazmat smart stripes and detection equipment
on site received one of the lowest mean scores (M=3.91) in this category.

Risk Management
Developing risk management plans for athletic department events and completing
these plans in conjunction with local law enforcement were assigned mean
scores of 4.45 and 4.48 respectively. Weekly game management meetings
addressing risk management issues should be conducted (M=4.25). Risk management
training should also be conducted with all game day staff (M=4.36).

Emergency Management
Standards in this category were assigned means scores ranging from 4.33
to 4.73. Emergency management appears to be a critical area in the security
management of university sport venues, especially the development of an
Emergency Response Plan, Evacuation Plan, Disaster Plan, and an Emergency
Medical Plan. Emergency Response Plans should be coordinated with local,
state, and federal emergency management agencies (M=4.68). A primary and
secondary security command and control center should be established (M=4.55),
and it should have a view of the playing field to facilitate decision-making
(M=4.36).

Recovery Procedures
Identifying security needs (M=4.67) and having written contracts or mutual
aid agreements in effect with local and out of state emergency responders
(M=4.43) were assigned the highest mean importance ratings by the panel
of experts. Contracts should be in place for immediate restoration and
secondary locations identified to hold event bookings. Identifying insurance
needs received a mean score of 3.90.

Communications
Identifying a chain of command (M=4.76), providing a sequence of notification
(M=4.67), having access to hand held radios (M=4.52), and having reliable
communication systems with backups in place (M=4.62) were assigned some
of the highest importance scores. Hand held radios should have a minimum
of ten channels and be independent in case there is a breach of security
(M=4.67). The command center should have direct access to the emergency
communication system (M=4.57) and have reliable communications with the
PA/video staff in order to authorize emergency scripts and messages (M=4.68).
Communications must be checked with all emergency responders prior to
the sporting event (M=4.64).

Security Personnel
The panel of experts believes security personnel should be included in
all training and planning activities to ensure they are aware of their
duties and responsibilities (M=4.64), and the panel believes that security
personnel are provided by licensed and certified providers (M=4.55). All
personnel must have a background check was also highly rated with a mean
score of 4.45.

Training, Modeling, and Simulation
Training should be provided in several areas including: 1) inspection
procedures to security staff, 2) credential recognition to access control
personnel, and 3) security awareness to ushers, vendors, and volunteer
(M=4.59). Conducting evacuation simulations (M=4.14), practicing emergency
drills prior to season (M=4.55), and conducting table top exercises (M=4.41)
were highly important. During training scenarios, planners should test
the chain of command, decision making process, primary/secondary communications
and emergency use of the PA and video systems (M=4.55).

WMD – Toxic Materials Protection
The panel of experts indicated with the highest mean score of 4.59 that
all potentially dangerous chemicals or materials be permanently removed
from the sport stadium. Toxic materials protection and decontamination
should be part of the Emergency Response and Evacuation Plans (M=4.45).
Campus police and safety officers need to be trained to the Weapons of
Mass Destruction/Hazmat awareness level (M=4.32).

Discussion

The outcome of this study has been a consensus of best security practices
by key personnel responsible for security operations at university sports
events in the state of Mississippi. University sport security personnel
may utilize these standards to prioritize security measures according
to importance, especially those organizations with limited funding and
imminent need to harden their facilities. Standards in the Credentialing
Emergency Management, Risk Management, and Communication and Training,
Modeling, and Simulation categories were assigned some of the highest
mean importance scores. This finding was consistent with highlighted areas
in the review of literature. University sport programs need to ensure
these key areas are addressed sufficiently. The NCAA has issued “planning
options” for athletic department events but do not have standards
in place for institutions to adhere to and be held accountable for. Therefore,
security practices at university sports venues may vary between institutions.
Industry standards need to be established forcing compliance among members
to ensure the sporting public that reasonable measures are in place for
safety.

It is extremely critical for security staff to work as a team in the
coordination of security operations during university sports events and
to have in place effective communication systems. Athletic department
staff, hired security staff, and all other game day staff (ushers, vendors,
ticket takers, etc.) must be properly trained and aware of security policies
and practices. Emergency response and evacuation plans must be developed
and updated on a continuous basis. Disaster scenarios/exercises need to
be executed at least once before the sport season begins, involving all
emergency response services ensuring multi-agency collaboration. Sport
venue managers must be qualified in the area of sport event security management
(SESM) and aware of DHS security initiatives. A new market emerges for
educational institutions across the nation to offer curriculum and certification
programs in the SESM area for aspiring sport venue managers and professionals
already in the field.

Future research may focus on determining implications of new security
standards on sport consumers, sport marketers, sport financial officers,
and the potential legality issues for intercollegiate athletic departments
and universities. With increasing pressure to enhance security efforts
at university sports events, there may be some concern about the adverse
affect on the sport consumer’s experience. Sport organizations may
be hesitant to spend extra dollars on security upgrades; therefore, an
economic impact analysis of an incident at a high consequence sports event
would provide data for organizations to consider their return on investment
in security.

Correspondence concerning this article should be addressed to:
Dr. Stacey Hall
The University of Southern Mississippi
School of Human Performance and Recreation
118 College Drive #5142
Hattiesburg, MS 39406
E-mail: Stacey.A.Hall@usm.edu
Work Phone: 601-266-6183
Fax: 601-266-4445

References

  1. Bagnato, A. (2001, September 17). Stadium security bolstered nationally; FAA bans aircraft from flying over Michigan’s game. Chicago Tribune. Retrieved September 29, 2005, from www.ebscohost.com
  2. CNN.com. (1996, July 27). Sources: arrest in Olympic bombing could occur within days. Retrieved September 15, 2005, from http://www.cnn.com/US/9607/27/blast.am/index.html
  3. Doerries, D.B. & Foster, V.A. (2005). Essential skills for novice structural family therapists: A Delphi Study of experienced practitioners’ perspectives. The Family Journal: Counseling and Therapy for Couples and Families, 13 (3), p259-265. [On-Line]. Available: http://tfj.sagepub.com/cgi/reprint/13/3/259
  4. Estell, L. (2002). A banner year for stadiums? Security concerns could put an end to stadium fly-overs. Incentive, 176 (12), 8. Retrieved September 29, 2005, from www.ebscohost.com
  5. Goss, B.D., Jubenville, C.B., & MacBeth, J.L. (n.d.). Primary principles of post-9/11 stadium security in the United States: Transatlantic implications from British practices. [On-Line]. Available: www.iaam.org/CVMS/Post%20911%20Stadium%20Security.doc
  6. Hagmann, D.J. (2005, October 30). Black hole in America’s heartland. Northeast Intelligence Network. Retrieved July 20, 2006, from http://www.homelandsecurityus.com/site/modules/news/article.php?storyid=16
  7. Hurst, R., Zoubek, P., & Pratsinakis, C. (n.d.). American sports as a target of terrorism: The duty of care after September 11th. [On-Line]. Available: www.mmwr.com/_uploads/UploadDocs/publications/American%20Sports%20As%20A%20Target%20Of%20Terrorism.pdf
  8. Lipton, E. (2005, March 16). U.S. Report Lists Possibilities for Terrorist Attacks and Likely Toll. New York Times, Section A, Page 1, Column 2.
  9. Mason, A. (2001, September 20). Security: NFL’s “No. 1 priority.” NFL.com. Retrieved September 13, 2005, from http://www.nfl.com/news/2001/security_092001.html
  10. Official 2006 NCAA Football Records Book. [On-line]. Available: http://www.ncaa.org/library/records/football_records_book/2006/2006_d1_football_records.pdf
  11. Pantera, M.J., et. al. (2003). Best practices for game day security at athletic & sport venues. The Sport Journal, 6 (4). [On-Line]. Available: http://www.thesportjournal.org/2003Journal/Vol6-No4/security.asp
  12. Thurber, M. (May 26-28, 1993). The Essence of Deming 3 Day Workshop, Meta-Quality Institute, San Francisco.
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2018-01-24T07:56:00+00:00September 4th, 2006|Contemporary Sports Issues, Sports Facilities, Sports Management|Comments Off on Effective Security Management of University Sport Venues

An Exploration of Female Athletes’ Experiences and Perceptions of Male and Female Coaches

Abstract

Gender may be a mediating factor for relationship effectiveness between
athletes and coaches (Lirgg, Dibrezzo, & Smith, 1994; Medwechuk &
Crossman, 1994). Ironically, with the increase in participation of female
athletes and sports that has occurred since Title IX, there has been a
decrease in the number of female coaches over the past 30 years (Felder
& Wishnietsky, 1990; Freeman, 2001; Pastore, 1992). The purpose of
this study was to explore twelve female athletes’ perceptions and
experiences of being coached by women and men. Semi-structured interviews
revealed four major themes: discipline and structure, personal relationships,
passivity and aggressiveness, and coach preference. Specifically, eight
of the participants stated a preference for male coaches, yet differences
were found when comparing various coaching qualities. Results are discussed
in regards to overall sport experiences.

Introduction

The coach-athlete relationship has been shown to have a profound effect
on an athlete’s satisfaction, performance, and quality of life (Greenleaf,
Gould, & Dieffenbach, 2001; Kenow & Williams, 1999; Vernacchia,
McGuire, Reardon, & Templin, 2000; Wrisberg, 1996) and several factors
may influence this relationship (Burke, Peterson, & Nix, 1995; Grisaffe,
Blom, & Burke, in press). Olympic athletes from the 1996 Summer Games
who did not perform as well as expected felt that conflict with the coach,
receiving inaccurate technical information, the coach’s inability to handle
selection controversy, and lack of focus on team climate played significant
roles in lower-level performances (Greenleaf, Gould, & Dieffenbach,
2001). Trust, friendship, and feedback from the coach had a positive impact
on the performances of athletes who met or exceeded expectations. Athletes
experiencing burnout have cited the coach as a negative influence due
to the coaches’ lack of belief in the athlete, extreme pressure,
and/or unrealistic expectations (Udry, Gould, Bridges, & Tuffey, 1997).
Stewart and Taylor (2000) found that athletes’ perceptions of coaching
competence and coaching behaviors were contributing factors to performance.

Numerous studies have examined the impact of gender on the coach-athlete
relationship. Athlete preferences for same-sex or opposite-sex coaches
have been examined, and factors taken into consideration have included
level of knowledge and ability to motivate, (Medwechuk & Crossman,
1994; Parkhouse & Williams, 1986), level of athlete’s comfort in disclosure
(Molstad & Whitaker, 1987; Sabock & Kleinfelter, 1987; Simmons,
1997), and capability of being a role model (Lirgg, Dibrezzo, & Smith,
1994). Molstad and Whitaker (1987) found that female basketball players
ranked female coaches as superior in the coaching qualities of relating
well to others and understanding athletes’ feelings (two of the three
most important rated qualities), while no difference was found among other
characteristics. Conversely, a strong sex bias favoring male coaches was
found in male and female high school basketball athletes who rated males
as more knowledgeable, more likely to achieve future success, more desirable
to play for, and having a greater ability to motivate (Parkhouse &
Williams, 1986). Overall, 89% of male athletes and 71% of female athletes
preferred a male coach. Previous research investigations have not shown
a clear consensus for coach gender for female athletes (Lirgg, Dibrezzo,
& Smith, 1994).

Although female athletic participation has increased since the passage
of Title IX, there has been a decrease in the number of female coaches
over the past thirty years (Carpenter & Acosta, 1991; Freeman, 2001;
Pastore, 1992). According to Felder and Wishnietsky (1990), the percentage
of females coaching high school teams has dropped as much as 50% between
the mid-1970’s and early 1980’s. Similarly, females coached
90% of collegiate teams in 1972 while only 47.3% of teams were coached
by women in 1990 (Carpenter & Acosta, 1991).

Osborne (2002) suggested that although male and female athletes share
many attributes such as the desire to win, willingness to sacrifice time
and energy, and enjoyment of competition, athletes need to be coached
differently. Factors to consider include training methods, coaching philosophy,
motivation tactics, communication style, and ability to relate on a personal
level. The majority of research that has examined the impact of coach
gender on the female athlete has been conducted quantitatively and has
used hypothetical coaches (Frankl & Babbitt, 1998; Medwechuk &
Crossman, 1994; Molstad & Whitaker, 1987; Williams & Parkhouse,
1988). The present study utilized a qualitative approach to explore female
athletes’ experiences with actual male and female coaches. Further,
Carron and Bennett (1977) noted the importance of gaining the athlete’s
perspective of coach-athlete compatibility, while Osborne (2002) pointed
out that very little is known about the extent to which female athletes
prefer a same-sex or opposite-sex coach. Thus, the purpose of this study
was to obtain a first-person perspective of the female athlete’s
experiences of playing for a male and female coach.

Method

Participants

The participants in this investigation were twelve NCAA Division I female
athletes. All athletes were Caucasian and had participated in basketball,
golf, cross country, track and field softball, or soccer. The sample was
derived from two different southeastern NCAA Division I universities.
Four athletes had junior academic classification, four athletes had senior
academic classification, and four athletes had graduate academic classification.
These athletes were chosen for this study as a purposeful sample (Glesne,
1999) because they had the potential to provide a rich description of
the experience of being coached by both a male and female and had a recent
memory of this experience.

Procedure

The process of bracketing one’s own presuppositions was developed
from Husserl’s concept of reduction in the method of phenomenology
(Glesne, 1999). Before initiating the present study, a bracketing interview
was conducted to clarify the interviewer’s personal experiences
of having a male coach and to explore potential biases. Themes from this
interview included preference for organization, winning attitude, and
enjoyment of the game.

Semi-structured interviews were then employed to collect information
about the athletes’ experiences and perceptions of having both male
and female coaches. All participants were invited to participate in the
study by personal or telephone contact, and those expressing interest
were interviewed. Participants were informed that involvement was voluntary,
and were advised of the ability to terminate participation at any time.
To ensure confidentiality, the participants were informed that pseudonyms
would be used for actual names and any team affiliations. The interviews
were conducted in person and lasted approximately forty minutes in length.
After the interview, participants were given an opportunity to review
the transcript and suggest changes. No changes were suggested by the participants.

Interview Protocol

Questions posed to the participants were designed to achieve a comprehensive
understanding of the experiences of being coached by men and women. The
interviewer initially gathered information about coach history, as well
as the sport and level of competition. Participants were then asked questions
related to differences or similarities experienced with each coach in
training methods, encouragement and motivation, personal relationships,
level of sport knowledge, and the coach preferred. The interview guide
is provided in the Appendix.

Analysis

Interviews were transcribed verbatim and a research team of five individuals
derived themes using a combination of phenomenological approaches. The
procedures for analyzing were adapted more directly from those developed
by Barrell (1988), Goodrich (1988), Hawthorne (1989), Ross (1987), and
Henderson (1992). More specifically, the following steps of: Approaching
the interview (Transcribing the interview, Obtaining a grasp of the interview
through an interpretive group), Focusing the data (Clearing the text,
Grouping the text), Summarizing the interviews (Preparing a summary, Verifying
the summary), and Releasing meanings (Forming categories, Determining
themes, and Describing themes) were utilized to analyze the information.

Results

Table 1 gives a description of each participant and her history of having
both male and female coaches. All participants played at the college level
for at least two years and have played competitively for at least four
years. It is important to note that three of the participants’ experiences
of the female coach were from high school experiences. Four major themes
emerged from the interviews.

Discipline and Structure

The participants indicated that male coaches were more structured and
organized. Carmen stated, “[the male coach] was much more together,
he knew structure. He knew exactly where we needed to be, what time and
what time we needed to start.” Differences were notably significant
in the practice setting. The male coaches would develop practice plans
and execute every detail needed to make them work. Kelli M. confirmed
this by stating, “I know [the male coach] would sit down before
a game and write down every possible thing the other team could do to
beat us; and then write down next to it exactly what we could do to defend
them.” Drills that were done at practice had a purpose, whether
it was fundamentals, offense, defense, or conditioning. The male coaches
were seen as being harder on the athletes and “expected more”
from the players than the female coaches. The males tended to coach from
an authoritarian perspective and enforced the concept of “no excuses,
this is the rule and we’re going to stick with this rule,”
according to Kelli M. Many of the athletes felt there would be more consequences
to face in practices under the male coach if they did not pay attention
or were not serious. Some of the athletes in this study responded favorably
to the male coaches’ disciplinary tactics, as it aided in keeping
them focused; however the male coach was also considered to be “too
strict” by others in the study.

Four of the participants felt that the female coaches were unorganized and
non-authoritative. The female coaches tended to run late at times and
would not get the players prepared for the game. Practices were not structured,
nor on a time schedule. These athletes perceived that the female coaches
had a harder time trying to accomplish tasks in practice, and did not
have similar discipline compared to experiences with the male coaches.

With the female coach, she had different stuff everyday. It would take
her five minutes to explain what we’re supposed to do and then it
wouldn’t really work very well. So, we would just look at each other.
When we did the drill, we didn’t do it full out because we knew
she wasn’t keeping score or we weren’t on a time limit. We
knew we weren’t going to really be disciplined. (Kelli M.)

Female coaches were more likely to forget details in practice, such as
not keeping score of games, which led to lack of motivation during practice.
Participants indicated that female coaches would consider individual situations
instead of sticking to certain rules and consequences. For example, if
an athlete was late to practice, a male coach would have a set rule regarding
this behavior and if any player broke the rule, regardless of the reason,
she would have to face the consequences. However, a female coach would
listen to the athlete’s reason and then decide what type of consequence
the player should face.

Personal Relationships

All of the participants felt that female coaches had a greater ability
to relate to them. Jennifer C. stated, “[the female coaches] know
sometimes what [female athletes] going through, different life cycles
and stages of their life. They can relate to how girls change differently
than boys.” The participants indicated that the female coach understood
how to “deal with” the athletes and could sympathize with
them when it came to “girl stuff.” The female coaches had
a greater tendency toward being friends with the players and getting to
know them more than the male coaches did. Kelli C. stated, “[the
female coach] was more on our level. She wanted to “chit-chat”
with us. Like get to know us rather than having to be stern.” This
sometimes caused problems though, because the female coach would develop
emotional ties with the players and would construct feelings of whom she
liked and did not like. This made a difference in some of the participants’
experiences because the coach would “characterize a couple of players
as being similar to the way [the female coach] played and/or worked in
high school or college. So people with different work ethics were considered
different” (Sam). The players began to see differences in coaching
as favoritism. Mistakes made by some players would be overlooked, but
similar mistakes would be made into ‘an issue’ with other
players.

So, in practice a lot of the people knew that if they made a mistake
then the female coach tended to focus on that one mistake. But if another
person made a mistake, she would focus on something else, like just ignore
it. Like if somebody in a game continuously threw the ball out of bounds
or in the bleachers she wouldn’t really look at that. She would
look at it as a negative that somebody else who’s not getting the
rebounds or not playing good defense or something like that. She would
pick and choose which mistakes mattered and which ones didn’t, with
a lot of different kinds of players, depending on what she thought of
you already. (Kelli M.)

The athletes did experience a lot of positive feedback and encouragement
from the female coaches. Many of the participants believed this came naturally
from the female coaches. Emily stated, “in general, you are going
to have a female that’s better at [encouraging and motivating] just
because females are more encouraging in general.” Others, such as
Carmen, felt the bond shared with the female coach is what helped motivate
and encourage performance. “She was a girl and girls can relate
to girls. And when they encourage you and you’re friends with them
you feel better.” The female coaches were more inclined than the
male coaches to say positive statements to encourage players. Female coaches
tended to first point out the positive tasks the athletes did before saying
what could be improved.

The personal relationships between the female athletes and male coaches
were very different from the relationships with female coaches. Many of
the female athletes were intimidated by the male coaches. The female players
knew that they could discuss ‘most anything’ about the sport,
certain plays or tactics with the male coaches, but nothing outside of
practice or the game was “allowed to be discussed.” Whereas
the athletes felt a variety of issues could be discussed with the female
coaches. Carmen stated, “If I had a [personal] problem with my male
coach, I wouldn’t say anything about it.” There was no bond,
per se, like the one she had with the female coach. If something was bothering
a player, the male coach would simply punish the player for not paying
attention. In similar situations with a female coach, Carmen thought that,
“she would have asked ‘hey are you okay.’ She would
have known something was bothering me and said “hey let’s
play or practice.”

Four of the athletes indicated the biggest difference between the relationships
with the male and female coaches came from a lack of encouragement and
positive reinforcement. The males tended to correct and point out the
mistakes more often and hesitated to use compliments as motivation. Sam
stated, “My male coach always told us what we were doing wrong.
After a while in practice, he could tell it was getting to us so he would
throw in a compliment. But, everyone knew he had to think about it before
he said it.”

Passivity and Aggressiveness

The mentality of the male coach compared to the female coach was a major
theme throughout the interviews. The males seemed to be more aggressive
and demanding. The males’ mentality was “you gotta go out
and get it” and they wanted to “win, win, win,” which
made practices hard and strict. A typical mindset was that if the female
athletes would make a mistake or, as Kelli M. stated, “If we took
too long, or if we were loafing around and it took us more than ten to
fifteen seconds to get in a drill, we had to get on the line and run.
It was like clockwork. It made us a better team and I am thankful for
that.”

With female coaches, a more laid back approach was utilized. The tone
was much lighter and practice proceeded in a more calm and non-aggressive
fashion. Carmen stated, “The female coach I had, we always got things
done but it was in a lighter tone. Like we’d do what she said and
we’d follow what she wanted us to do but we could be playful at
the same time.” The pressure of doing something wrong or making
a mistake and having to face consequences was not as prevalent with a
female coach. Only one of the participants had a positive outlook towards
this mentality, as Emily explained, “we may not had to have done
[a drill] four hundred times like we did with the males, but the end result
was the same.”

Coach Preference

When asked which coach they preferred the most, eight participants responded
favorably toward the male coach for various reasons. The athletes believed
that to be a good coach, the coach must have respect from the players.
According to Kelli C., “demonstrating their (coaches) soccer knowledge,
ability to control the team, and to enforce discipline,” were all
key elements in gaining the respect of players. Jennifer C. thought, “some
coaches you just respect because they know how to make you respect them.”
Along with respect, the female athletes viewed a good coach as one who
was able to perform the skill and have more than adequate knowledge about
the sport. Carmen stated that “[the male coach] was the one that
knew the most about soccer. He knew the most and challenged me the most.
I grew as a player when I was with him.” Further, Kelli M. stated,
“the males assumed to know more about the basics and the fundamentals.
Everything that’s required for a successful team.” The female
athletes considered an ideal coach to be a good leader, teacher, friend,
and motivator. Specifically, Sam thought a coach should “challenge
players to become better physically, mentally, tactically, and technically,”
while Emily felt that coaches should “teach [athletes], prepare
them for any kind of obstacles that they’re going to have to come
into contact with. Teaching them basics like discipline, punctuality,
getting to practice on time, dealing with other people, teamwork, and
good sportsmanship.” Four of the female participants believed that
a coach should be a good example and help in the teaching of life lessons.
Sam felt that a coach should be “a little bit of everything.”

Discussion

The purpose of the present investigation was to explore a group of female
athletes’ experiences of having female and male coaches. This comparison
demonstrated that four of the six female athletes preferred a male coach,
including various differences of opinions of each coach.

Discipline and Structure

While men were reported to be more detailed in instruction and structured,
the women were more lenient disciplinarians. This finding coincides with
Masin’s (1998) results, which found that 75% of female athletes
preferred male coaches because of more perceived organization. The desire
for this quality might exist because many female athletes want to be pushed
physically, challenged in skill development, and feel the need for competition,
and they believe this can be achieved through a structured environment
(Osborne, 2002). Five of the female athletes in this study expressed a
positive perception of the discipline enforced by the male coaches.

Personal Relationships

A female athlete may benefit from a personal connection with the coach.
When coaching females, there is the need for warmth, empathy, and a sense
of humor (Burke, Peterson, & Nix, 1995; Grisaffe, Blom, & Burke,
in press) with the players (Osborne, 2002). Female high school and college
basketball players ranked the coaching qualities of “relating well
to athletes” and understanding athletes’ feelings” as
two of the top three desirable characteristics, and female coaches rated
significantly higher than male coaches in demonstrating these qualities
(Molstad & Whitaker, 1987). Sabock and Kleinfelter (1987) and Simmons
(1997) found that female athletes were more inclined to disclose personal
information to a female coach. Many of the athletes in the present study
experienced these traits from female coaches. Female coaches in this study
were better at relating and more likely to establish a friendship. Although
the athletes expressed a desire to bond with the coach, they indicated
did not want favoritism to be shown toward any players. Further, many
female athletes thrive on self-satisfaction and the belief they are capable
of doing a certain task or drill, and can best achieve this through encouragement
from the coach (Osborne, 2002). The present findings indicated that female
coaches were viewed as more encouraging and motivating through a greater
use of positive feedback.

Passivity and Aggressiveness

Female athletes tended to be more acceptable of the male coaches’
mentality than that of the female coaches’ mentality. Nine participants
in this study approved the authoritarian style of coaching utilized by
the male coaches. Women may prefer this style of coaching due to cultural
expectations of men in authority positions, male dominance in women’s
sports, or the lack of female coaches as role models (Osborne, 2002).
As with male athletes, female athletes want to be trained hard and challenged.
However, if coaches use an extreme “in your face” mentality,
such as constant yelling, the female athlete may be less receptive to
this style (Osborne, 2002).

Coach Preference

Nine of the female athletes in the present study expressed a preference
for male coaches, citing factors such as a greater level of knowledge,
knowing what it takes to be successful, and having more respect for him.
Previous research (Parkhouse & Williams, 1986) has not shown a clear
consensus as to whether female athletes prefer a male or a female coach
(Lirgg, Dibrezzo, & Smith, 1994; Osborne, 2002). Some of the literature
has claimed that athletes may be more comfortable with male authority
figures who could explain their perceptions (Frankl & Babbitt, 1998;
Osbourne, 2002; Whitaker & Molstad, 1985). Similarly, since men have
held coaching positions for a longer period of time, athletes may have
more confidence in their knowledge levels and coaching abilities (Sabock
& Kleinfelter, 1987). In the late 1980’s and early 1990’s,
much of the literature stated that female athletes preferred a male coach
because there was simply a lack of women in the profession (Osborne, 2002).
Further, coach preference may depend on the gender of the athletes’
present coaches (Medwechuk & Crossman, 1994; Sabock & Kleinfelter,
1987). Since the majority of coaches have been male, this could help to
explain the female athletes’ preference toward male coaches.

Caution must be taken in assuming that coach preference is due only
to gender.
Additional factors exist that may influence athletes’ perceptions
of coaches such as the success of the team (Williams & Parkhouse,
1988) or influence of current coach (Parkhouse & Williams, 1986).
Female athletes who exhibited higher trait anxiety, higher state cognitive
and somatic anxiety, and lower state self-confidence have been shown to
have more negative perceptions of coaches (Kenow & Williams, 1992;
1999). Lirgg, Dibrezzo, & Smith (1994) found that female athletes
coached by females reported a greater desire to become head coaches than
those coached by male coaches. Other personal attributes such as athlete
age (Burke, Peterson, & Nix, 1995; Whitaker & Molstad, 1988),
socioeconomic status, ethnicity, and the athletes’ level of skills
and abilities (Williams & Parkhouse,1988) may also impact athletes’
experiences with coaches. Longitudinal studies should be employed to more
thoroughly examine the influences that male and female coaches have on
athletes.

References

  1. Burke, K. L., Peterson, D., & Nix, C. L. (1995). The effects of the coaches’ use of humor on female volleyball players’ evaluation of their coaches. Journal of Sport Behavior, 18, 83-90.
  2. Carpenter, L. J. & Acosta, V. (1991). Back to the future: Reform with a woman’s voice. Academe, 23-27.
  3. Carron, A. V. & Bennett, B. B. (1977). Compatibility in the coach-athlete dyad. Research Quarterly, 48, 671-679.
  4. Felder, D. & Wishnietsky, D. (1990). Role conflict, coaching burnout, and the reduction in the number of female interscholastic coaches. The Physical Educator, 47, 7-13.
  5. Frankl, D. & Babbitt, D. G. (1998). Gender bias: A study of high school track & field athletes’ perceptions of hypothetical male and female head coaches. Journal of Sport Behavior, 21, 396-407.
  6. Freeman, W. H. (2001). Physical Education and Sport. Boston: Allyn and Bacon.
  7. Glesne, C. (1999). Becoming Qualitative Researchers. New York: Addison Wesley Longman.
  8. Greenleaf, C., Gould, D., & Dieffenbach, K. (2001). Factors influencing Olympic performance: Interviews with Atlanta and Nagano U.S. Olympians. Journal of Applied Sport Psychology, 13, 154-184.
  9. Grisaffee, C., Blom, L. C., & Burke, K. L. (in press). The Effects of Head and Assistant Coaches’ Uses of Humor on Collegiate Soccer Players’ Evaluation of Their Coaches. Journal of Sport Behavior.
  10. Kenow, L. J. & Williams, J. M. (1992). Relationship between anxiety, self-confidence, and evaluation of coaching behaviors. The Sport Psychologist, 6, 344-357.
  11. Kenow, L. & Williams, J. M. (1999). Coach-athlete compatibility and athlete’s perception of coaching behaviors. Journal of Sport Behavior, 22, 251 – 259.
  12. Lirgg, C. D., Dibrezzo, R., & Smith, A. N. (1994). Influence of gender of coach on perceptions of basketball and coaching self-efficacy and aspirations of high school female basketball players. Women, Sport, and Physical Activity Journal, 3, 1-14.
  13. Masin, H. L. (1998). Men coaching women…..Coach and Athletic Director, 68, 16.
  14. Medwechuk, N. & Crossman, J. (1994). Effects of gender bias on the evaluation of male and female swim coaches’. Perceptual and Motor Skills, 78, 163-169.
  15. Molstad, S. & Whitaker, G. (1987). Perceptions of female basketball players regarding coaching qualities of males and females. Journal of Applied Research in Coaching and Athletics, 2, 57-71.
  16. Osborne, B. (2002). Coaching the female athlete. In John M. Silva III & Diane E. Stevens (Eds)., Psychological foundations of sport (pp. 428 – 437). Boston: Allyn and Bacon.
  17. Parkhouse, B. L. & Williams, J. M. (1986). Differential effects of sex and status on evaluation of coaching ability. Research Quarterly for Exercise and Sport, 57, 53-59.
  18. Pastore, D. L. (1992). Two-year college coaches of women’s teams: Gender differences in coaching career selections. Journal of Sport Management, 6, 179-190.
  19. Sabock, R. J. & Kleinfelter, E. R. (1987). Should coaches be gendered? Coaching Review, 10, 28-29.
  20. Simmons, C. D. (1997). The effects of gender of coach on the psychosocial development of college female student-athletes. Unpublished master’s thesis, University of Louisville.
  21. Stewart, C. & Taylor, J. (2000). Why female athletes quit: Implications for coach education. Physical Educator, 57, 170.
  22. Udry, E., Gould, D., Bridges, D., & Tuffey, S. (1997). People helping people? Examining the social ties of athletes coping with burnout and injury stress. Journal of Sport and Exercise Psychology, 19, 368-395.
  23. Vernacchia, R. A., McGuire, R. T., Reardon, J. P., & Templin, D. P. (2000). Psychosocial characteristics of Olympic track and field athletes. International Journal of Sport Psychology, 31, 5-23.
  24. Whitaker, G. & Molstad, S. (1985). Male coach/female coach: A theoretical analysis of the female sport experience. Journal of Sport and Social Issues, 9, 14-25.
  25. Whitaker, G. & Molstad, S. (1988). Role modeling and female athletes. Sex Roles, 18, 555-566.
  26. Williams, J. M. & Parkhouse, B. L. (1988). Social learning theory as a foundation for examining sex bias in evaluation of coaches. Journal of Sport & Exercise Psychology, 10, 322-333.
  27. Wrisberg, C. A. (1996). Quality of life for male and female athletes. Quest, 48, 392-408.

Table 1
Mean Demographic Data of Female Athletes

Participant
(Pseudonym)
Sport(s) Years of Experience Years coached by a male Years coached by a female
Kelli C. Basketball Soccer and
Softball
10 7 3
Kelli M. Basketball 11 7 4
Carmen Soccer 13 10 3
Emily Soccer 12 9 3
Jennifer C. Golf and Basketball 13 6 ½ 6 ½
Sam Soccer and Basketball 12 8 4
Lekeisha Basketball 10 7 3
Tyler Cross Country 11 8 3
Misha Soccer 9 4 5
Kylie Softball 10 5 5
Alexis Basketball 8 3 5
Natalie Track and Field 9 7 2
Carmen Soccer 13 10 3

Appendix

Interview Guide
The initial question posed to participants: “What do you think the role of a coach should be?”

Following questions:

  1. What sport do you play?
  2. When were you coached by a male and a female?
  3. How many years were you coached by a male and a female?
  4. In what setting did you have the male and female coach?
  5. Which coach did you prefer the most?
  6. Who do you think knew more about the sport? Why?
  7. If you had daughters, whom would you want them to be coached by?
    Why? Were there any differences/ similarities between the male and female
    coaches in regards to:
  8. Training practices and evaluation performance?
  9. Encouragement and motivation?
  10. Punishments and commands?
  11. Helping with personal problems and enjoyment?
  12. Encouraging after mistakes and correcting behavior?
  13. Coaching methods?
  14. In an ideal world, what would you like to see in the world of female
    sports in regards to coaching?
  15. In general, what are your thoughts about males and females coaching
    female athletes?
2015-03-27T13:38:02+00:00September 3rd, 2006|Contemporary Sports Issues, Sports Coaching, Sports Management, Sports Studies and Sports Psychology, Women and Sports|Comments Off on An Exploration of Female Athletes’ Experiences and Perceptions of Male and Female Coaches

A Study of Gambling Activity in a NCAA Division II Institution

Abstract

The purpose of this study was to examine both the overall and the sports
specific gambling activity among athletes and non-athletes enrolled in
a Southern, regional National Collegiate Athletic Association (NCAA) Division
II university. The findings were contrasted to the results of a 2003 NCAA
Sports Wagering study. The instrument utilized in this particular study
was an adaptation of the survey used in the NCAA 2003 study. Gambling
by athletes at NCAA member schools is a growing concern, and there are
indicators that gambling by college athletes may be more prevalent today
than described in the 2003 study as gambling activity among student-athletes,
male and female, in Division II seems to have increased dramatically from
2003 to 2006.

Specific to this study, respondents from a Southern, regional, NCAA Division
II college, the University of West Georgia, indicated a much higher rate
of gambling as contrasted to the 2003 overall NCAA II findings. Interestingly,
the prevalence of gambling activity among the subjects of this study seemed
to be most prevalent within two sports: women’s basketball and men’s
football. The reported activity among the other nine sports was practically
non-existent. The increase in gambling activity reported by the 2006 student-athletes
as contrasted to the 2003 student-athletes might reflect a change in recreational
lifestyle, ease of access to gambling via the intranet, a rapidly changing
set of sports morays, or an aberration associated with one particular
NCAA II college.

Introduction

Gambling of all types is on the rise in the United States. In 1999, thirty-seven
states and the District of Columbia had lotteries, as compared to thirteen
in 1976 (Claussen & Miller, 2001). In this same year, Nevada hosted
142 bookmaking sites (National Gambling Impact Study Commission, 1999b).
Casino growth has paralleled this expansion of gambling. The approval
rate for using gambling as a way to raise state funds for government programs
and/or education has also dramatically increased. A Gallup Poll conducted
in 1989 indicated that 55% of Americans approved of this type of fund
raising. Ten years later, Goldin (1999) noted that the approval rate grew
to 92% of Americans.
Early acceptance of widespread gambling was evident in The United States
as early as the late 1980s through the passing of the Indian Gaming Regulatory
Act (IGRA). This law gave American Indian Tribes the right to host gambling
activities on reservation grounds as long as the activities were not against
state or national law (Goldin, 1999). Since the passing of this regulatory
act, revenue from gambling has grown from $212 million in 1988 to $6.7
billion in 1999 (NGISC, 1999b). This growth has continued despite disasters
such as “911” and Hurricane Katrina. According to the Mississippi
Gaming Commission (2006), three Gulf Coast casinos alone were able to
generate net revenue of sixty-four million dollars in January of this
year, even after the effects of Hurricane Katrina.

Both private business and governments associated with gambling have responded
to the above phenomena by creating additional opportunities for involvement
with gambling. These include allowing water-based casinos to relocate
to land-based operations; a growth in state lotteries, animal racing,
charitable gambling, video poker machines, sports betting, and internet
gambling (Claussen & Miller, 2001).

Improved technology has created an opportunity for the formation of internet
gambling sites, which lures today’s internet savvy students. Lowry
(1999) reported there were approximately 280 online sites that offer internet
gambling. These online sites generated 1.5 billion dollars revenue in
the year 2000 (Woodruff & Gregory, 2005). Revenue from this type of
gambling will continue to increase as the internet becomes more accessible.
Internet opportunities, along with an increased public acceptance of gambling,
make activities such as betting on an athletic competition more appealing
and much easier (Doocey, 1996; Udovicic, 1998). In fact, sports gambling
has grown to a greater than $100 billion industry (Udovivic, 1998). This
is partially due to the fact that more information (via the Internet)
is available describing sports teams, which allows people to feel more
informed in predicting outcomes.

According to a meta-analysis of gambling habits among university students
by Labrie, Shaffer, LaPlante, & Wechsler (2003), it was reported that
41.9% of students indicated involvement in gambling activity within the
past year, while 23% indicated participation in the activity within the
past week. Additionally, 5.6% of these students met the criteria for pathological
gambling as compared to the rates of 0.2 to 2.1% for the general population
(Labrie, Shaffer, LaPlante, & Wechsler, 2003). This was the result
of a study of college students which utilized findings from the South
Oaks Gambling Screen study (Lesieur & Blume, 1987).

Another study by Engwall, Hunter, & Steinberg (2004) reported similar
findings to the Labrie, et al (2003) meta-analysis. They found that 42%
of college students reported at least one gambling episode in the past
year and 3% of the respondents gambled at least once a week. Labrie, et
al (2003) found that playing the lottery was the most common gambling
activity reported among college students. He found that gambling activity
among college men was significantly greater than college women. Engwall,
et.al (2003) also noted that gambling appears to be related to behavioral
characteristics in college students such as (a) increased television viewing,
(b) computer use for non-academic reasons, (c) spending less time studying,
(d) earning lower grades, (e) participation in intercollegiate athletics,
and (f) binge drinking.

Ironically, alcohol use is a strong predictor of college student gambling
behavior, regardless of gender. Labrie, et al (2003) reported that college
students who had used alcohol within the past year were 2.4 times more
likely to engage in gambling behavior than those who had abstained from
alcohol. It also appears that the variables associated with gambling vary
by gender. For example, among Caucasians, being a male was a strong predictor
of gambling, as contrasted to being a female (Labrie, et.al, 2003). Labrie,
et al (2003) also noted that college female gamblers were more likely
to work for wages, be single, and view community service as less than
very important. Unlike the female gamblers, males who gambled were more
likely to view sports and physical activity as very important.

College students who participated in sports gambling in particular were
more likely to gamble on golf than any other activity according to the
National Collegiate Athletic Association (NCAA) study (Petr, Paskus, &
Dunkle, 2003). Perhaps this is due to the extensive history of gambling
in golf that involves players betting with large sums of money. This has
been documented as far back as 1870 (LeCompte, 2005). The United States
Golf Association (USGA) does not object to gambling that does not interfere
with the game (LeCompte, 2005). This is in contrast to the NCAA policy
that prohibits any type of gambling in the context of athletics.

In a statement to the Senate Commerce Committee, Senator John McCain
noted that college gambling was “reaching epidemic proportions”
(McCain, 2003). Senator McCain made this statement after results from
the National Gambling Impact Study Commission Report (NGISC) indicated
that college students spend more money on gambling activities than alcohol
(NGISC, 1999b).

Gambling on sports by amateur athletes has been added to the list of
behavioral issues addressed by the NCAA. Even though the NCAA prohibits
sports gambling in general, the primary concern has been with participating
athletes betting on games and then shaving points to influence outcomes.
Point shaving has been defined as the deliberate refusal of an athlete
to score in exchange for monetary resources from a book master or “bookie”
(Petr, et al, 2003).

The NCAA utilized the Petr et al. (2003) study to examine the gambling
behaviors of student athletes from all NCAA divisions. The majority of
the activities in which these athletes admitted gambling activity included
playing cards or board games for money, betting on games of personal skill,
purchasing lottery tickets, using slot or electronic poker machines, trading
sports cards, and entering football pools (Petr, et al., 2003).

The overall prevalence of gambling among NCAA student athletes was reported
to be 35 % among males and 10 % among females. Division III athletes were
found to have the greatest prevalence of gambling (Petr, et al, 2003).
In Division I, point shaving was more prevalent among football players
than male basketball players. Just over 1% of football players reported
that they had played poorly in a game in exchange for money, compared
to ½% of the basketball players (Petr, et al., 2003). Golf had
the highest percentage of participants reporting gambling behavior: 8.4
% for females and 48.6% for males.

There appears to be an inverse relationship between knowledge of the
NCAA policy on gambling and the frequency of the behavior. Athletes in
Division III had the highest overall rates of gambling and the least reported
knowledge concerning the NCAA policy on gambling. Only 43.5% of male athletes
in Division III were aware that the NCAA had rules and regulations that
discourage gambling (Petr, et al., 2003), despite the release of the NCAA
publication, Don’t Bet on It. This suggests that this NCAA publication
and the information contained in it may not be disbursed by all member
schools to athletes.

The personality characteristics that produce excellent athletes are also
present in pathological gamblers. These characteristics include feeling
in control of situations and outcomes, a large ego, and optimism (Naughton,
1998). Just as a great athlete is confident in his or her ability to win
competitions, a pathological gambler is confident in accumulating wealth
from gambling. This link alone may account for some of the gambling activity.

There will always be athletes who engage in gambling behaviors despite
being forewarned of the repercussions. The motives have been widely documented;
however, the top stated reasons for gambling by student-athletes have
been reported as “for fun,” “to win money,” and
“for excitement” (Petr, et al., 2003).

Prior to the NCAA sports wagering study conducted in 2003, no data had
been collected specifically looking at the gambling habits of non-Division
I NCAA athletes (Copeland, 2004). The majority of the research on gambling
among athletes has focused on the activities of NCAA Division I men’s
football and basketball players. However, the results of the 2003 NCAA
sports wagering study indicate that additional research on gambling should
be expanded to include athletes in classifications such as Division II
and III. The NCAA study found that 66.5% of Division II athletes, as compared
to 63.4% in Division I, had participated in some form of gambling within
the past year (Petr, et al., 2003). Furthermore, 33.5% of Division II
athletes, as compared to 28.8% of athletes in Division I, admitted participation
in sports wagering within the past year. This is significant, as sports
wagering is prohibited by the NCAA, and results in an athlete losing one
year of eligibility to compete in his or her respective sport if convicted.

While the NCAA (2003) study noted the prevalence of gambling among Division
II athletes, it did not provide data on the specific gambling preferences
of this group nor did it segment the various types of Division II colleges,
such as small-private, large state, or other strata. Therefore, the purpose
of this study was to reexamine the NCAA findings and collect additional,
more current information on the gambling preferences of NCAA Division
II student athletes and non-athletes, with a focus on a NCAA II regional,
rural, state university.

Methods

The subjects selected were all enrolled at the University of West Georgia
(UWG), a NCAA Division II college, during the spring of 2006. This particular
university is a regional school within the University of Georgia System.
The enrollment at the time of the study was approximately 10,800 students.
The subject pool was divided into two groups: non-athlete students and
student-athletes. From each of these two groups a random sample was identified
using alphabetical ordering and then a selection of a predetermined number
of participants based on the total subject pool. The number of athletes
selected was 141 and a 63.1% response rate was obtained thus yielding
fifty-one female and thirty-eight male respondents. The number of non-athlete
students in the initial random sample pool was 220. Eighty-nine or 40.5%
of the subjects agreed to participate, thus yielding a response pool of
fifty-three females and thirty-six males. The predetermined numbers for
the initial subject pools were obtained using the recommendations of Magnani
(1997).

Both student-athletes and non-athlete students completed the survey instrument
in the presence of research assistants. Complete anonymity was guaranteed
and names were not associated with the collected questionnaires. Permission
to conduct the study was granted by the IRB at the University of West
Georgia.

The number of NCAA Division II student athletes participating in the
NCAA Wagering Study conducted by Petr el al (2003) was 1798 females and
2957 males. This data were frequently used for comparison with the findings
of this particular study.

The instrument utilized in this particular study was an adaptation of
the survey used in the NCAA Wagering Study in 2003. The instrument took
between ten and fifteen minutes for the subjects to complete. The survey
instrument identified the prevalence and extent of gambling behaviors
among students within the most recent twelve-month period. Survey items
not completed were labeled by the researchers as “not stated”.
The definition of “not stated” therefore implied the refusal
of the respondent to answer a particular question. In addition to examining
habits, the instrument also identified problem and pathological gambling
behaviors using the South Oaks ten item screening tool as a guide (SOGS).

Results

The findings of this study were tabulated consistent with the format
of the 2003 NCAA wagering study. This allowed for comparisons between
the various categories of respondents. The results were quantified and
examined for observed differences among NCAA II female and male athletes,
UWG female and male athletes, and UWG female and male non-athletes.

As seen in Table 1, among both athletes and non-athletes and females
and males alike, the UWG population in this particular study reported
a higher rate of total gambling activity than the findings in the NCAA
II (2003) female and male athlete population. The UWG population was 18%
to 31% more active in gambling activity in general. However the specific
rate of gambling on college sports at UWG was less than the NCAA II rate
for both females and males.

Table 1

Involvement with Gambling in the Past 12 Months

Any Gambling On Collegiate Sport
Female Male Not Stated Female Male Not Stated
NCAA Division II athletesc 51.0% 66.5% 15.5% 5.8% 21.0% 73.2%
UWG student-athletesb 70.6% 97.3% NA 2.0% 7.9% NA
UWG students (non-athletes)a 67.9% 86.1% NA 1.9% 8.3% NA

Note. All NCAA statistics are from Petr et al (2003).
an= 51 females, 38 males.
bn= 53 females, 36 males.
cn= 1798 females, 2957 males.

The findings presented in Table 2 reinforced the sports gambling prevalence
of UWG students and student-athletes. This was particularly true when
wagering on all sports, not just college sports, was considered. Both
UWG females and males were twice as likely to gamble on sports as contrasted
to the total population of NCAA II female and male athletes.

Table 2

Students and Athletes Who Wagered on any Sport by Gender

Category Female Male Not Stated
NCAA Division II athletesc 10.6% 33.5% 55.9%
UWG student-athletesa 21.6% 60.5% NA
UWG studentsb 15.1% 61.1% NA

Note. All NCAA statistics are from Petr et al. (2003).
a n= 51 females, 38 males.
b n= 53 females, 36 males.
c n= 1798 females, 2957 males.

The findings in Table 3 depicted a wide array of gambling activity by
UWG students and student-athletes. This diversity of gambling activity
was evident in the overall NCAA II athlete population as well. Specifically,
non-athlete, female UWG students reported a higher degree of gambling
using card games, whereas male non-athlete UWG students were more likely
to utilize casino table games for gambling purposes. Both female and male
UWG non-athletes were more likely to be involved with craps and dice games
than athletes. The prevalence of gambling activity involving personal
skill was higher among both UWG male and female athletes as contrasted
to non-athletes. Male UWG athletes were twice as likely as any group in
this study to utilize internet gambling options. Utilizing on campus bookies
was three times higher among UWG male athletes as contrasted to all other
groups. The use of off-campus bookies was similar among all groups, except
UWG non-athlete males, who were twice as likely to use an off-campus bookie
compared to the other groups. Female and male UWG students, athletes,
and non-athletes were twice as likely to purchase lottery tickets compared
to the total NCAA Division II group.

Table 3

Students Engaging in Specific Gambling Activities in the Past 12 Months

Males Females
Non-
Gambling Pursuit
NCAA Division IIc UWG Student Athletesa UWG Non Athletesb NCAA Division IIc UWG Athletesa UWG Student Athletesb
Played card or board games for money 42.5% 81.6% 66.7% 19.2% 27.5% 34%
Table games at casino 19.1% 34.2% 11.1% 9.3% 2.0% 5.7%
Games of personal skill 35.1% 73.7% 61.1% 16.3% 27.5% 18.9%
Stock market/commodities 9.1% 15.8% 16.7% 3.6% 5.9% 1.9%
Commercial bingo 6.9% 7.9% 8.3% 6.7% 9.8% 20.8%
Played dice/craps 12.2% 36.8% 27.8% 3.8% 13.7% 7.5%
Internet gambling 7.2% 23.7% 13.9% 2.0% 7.8% 5.7%
Sports cards, football pools, or parlays 19.0% 52.6% 52.8% 7.0% 13.7% 9.4%
Bet on horse or dog races 8.9% 26.3% 13.9% 4.8% 9.8% 7.5%
Bet on intercollegiate games with campus bookie 2.4% 7.9% 8.3% 0.4% 0.0% 0.0%
Bet on intercollegiate games with off-campus bookie 4.6% 2.6% 8.3% 0.9% 2.0% 1.9%
Lottery tickets 37.0% 76.3% 72.2% 31.9% 52.9% 62.3%
Slot or electronic poker machines 20.0% 34.2% 33.3% 14.6% 15.7% 26.4%
Some other type of gambling 22.8% 44.7% 38.9% 8.0% 19.6% 15.9%

 

Note. All NCAA statistics are from Petr et al. (2003).
a n= 51 females, 38 males.
b n= 53 females, 36 males.
c n= 1798 females, 2957 males.

As seen in Table 4, both UWG female and male athletes were nearly twice
as likely to say they had no knowledge of the NCAA gambling rules as contrasted
to the overall NCAA II population responses.

Table 4
Athletes Knowledgeable of the NCAA Rules Concerning Gambling:

Males Females
Know Rules NCAA Division IIb UWG Athletesa NCAA Division IIb UWG Athletesa
Yes 50.1% 15.7% 39.1% 9.8%
No 19.6% 26.3% 20.4% 43.1%
Not sure 30.3% 57.9% 40.6% 35.3%

Note. All NCAA statistics are from Petr et al. (2003).
a n= 51 females, 38 males.
b n= 1798 females, 2957 males.

As seen in Table 5, both female and male athletes at UWG expressed a
similar frequency of problem or pathological characteristics as compared
to those in the NCAA II 2003 study. However, there were a disproportionately
high percentage of non-athlete UWG students whose responses were consistent
with potential problem gambling issues. This group was four times as likely
to indicate potential problem gambling characteristics.

Table 5

Students Who Indicate a Problem or Pathology Concerning Gambling:

Males Females
Screening Outcome NCAA Division IIc UWG Studentsb UWG Athletesa NCAA Division IIc UWG Studentsb UWG Athletesa
Non-Gambler 35.3% 27.8% 26.3% 60.1% 41.5% 47.1%
No problem 48.3% 30.6% 50% 35.7% 45.3% 37.3%
Potential problem gambler 11.3% 41.7% 10.5% 3.8% 9.4% 13.7%
Pathological gambler 1.7% 2.8% 7.9% 0.1% 0.0% 0.0%
Not stated (but still gambles) info not provided 0.0% 5.3% info not provided 0.0% 5.9%

Note. All NCAA statistics are from Petr et al. (2003).
a n= 51 females, 38 males.
b n= 53 females, 36 males.
c n= 1798 females, 2957 males.

The authors found that sports gambling athletes from only two sports
among the UWG population displayed significant gambling activity of any
type during the recent twelve months. The sports were women’s basketball
and men’s football. The reported prevalence of gambling activity
among the other nine sports at UWG was not significant.
Discussion

As previously noted, gambling by athletes at NCAA member schools is a
growing concern. The NCAA obviously senses a problem as evidenced by their
focus on the issue. There are indicators that the problem may be larger
than described in the 2003 NCAA study. For example, the fact that 73.2%
of NCAA II athletes in the 2003 NCAA Wagering Study refused to make a
statement about their gambling activity a matter of concern. This could
indicate a fear of being forthright due to concerns about retribution
and conviction.

Also, this study found a much higher rate of gambling among UWG students,
as contrasted to the 2003 overall NCAA Division II population. This could
be more than an aberration associated with one NCAA Division II college.
It could reflect a rapid growth of gambling among college students which
could be related to the widening social acceptance of gambling, the expansion
of internet gambling, or perhaps other issues. However, there is always
the possibility that the limitation due to the smaller number of respondents
among the 2006 UWG population groups, as contrasted to the 2003 NCAA Division
II group, could have skewed the data.

At this point in time however, the UWG population of both athletes and
non-athletes appeared to have a comparatively high rate of gambling involvement.
If one were to assume the rate of involvement among NCAA Division II athletes
has remained constant over the three years since the NCAA study, then
one would have to question whether a regional, rural, relatively large,
state university might have a consistently higher rate of gambling involvement.
This issue alone might merit future study.

Interestingly, the prevalence of gambling activity among UWG athletes
in particular seemed to reside exclusively within two sports, women’s
basketball, and men’s football. The reported activity among the
other nine sports at UWG was practically non-existent. This finding may
be inferable or it might have been the result of a reluctance of athletes
from other sports to express activity among teams. This question also
merits further investigation.

Several other questions associated with gambling among college athletes
merits future study. Is there a link between expressed gambling activity
among student-athletes and graduation rates? Are there athletes from specific
sports that have higher gambling activity rates as indicated in this particular
study? Do non-athlete students actually have a higher gambling activity
rate than the student-athlete population?

Obviously, if gambling becomes an interference with fair
sports competition, the development of the student-athlete, graduation
rates, or the integrity of any aspect of higher education, it deserves
attention. At this point in time, it appears that this determination is
still in question and thus deserves additional research. Additionally,
other universities might consider replicating thus study in order to provide
a basis for comparison and analysis.

References

  1. Copeland, J. (2004). Sports wagering survey focuses attention on the high rates of misbehavior in Divisions II, III. The NCAA News. December 6, 2004, Retrieved April 6, 2006 from http://www.ncaa.org/wps/portal/newsdetail
  2. Claussen, C.L. & Miller, L.K. (2001). The gambling industry and sports gambling: A stake in the game? Journal of Sport Management, 15, 350-363.
  3. Doocey, P. (1996). The case for legal sports betting. International Gaming & Wagering Business, 17 (4), 1, 40-41.
  4. Engwall, D., Hunter, R., & Steinberg, M. (2004). Gambling and other risk behaviors on university campuses. Journal of American College Health, 52(6), 245-255.
  5. Goldin, N.S. (1999). Casting a new light on tribal casino gaming: Why Congress should curtail the scope of high stakes Indian gaming. Cornell Law Review (Note), 84, 798-849.
  6. LaBrie, R., Shaffer, H., LaPlante, D., & Wechsler, H. (2003). Correlates of College Student Gambling in the United States. Journal of American College Health, (52), 53-62.
  7. LeCompte, T. (2005). Gambling and golf a match made in heaven. American Heritage, 56 (4), 64.
  8. Lesieur, H. & Blume, S. (1987). The South Oaks gambling screen (SOGS): a new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144(9): 1184-1188.
  9. Magnani, R. (1997). Sampling guide. IMPACT Food Security and Nutrition
  10. Monitoring Project. Arlington, Va.
  11. McCain, J. (2003). Statement of Senator John McCain, Commerce Committee
  12. Hearing on Sports Gambling and S.2267, the Amateur Sports Integrity Act.
  13. National Gambling Impact Study Commission. (1999b). Final Report. Washington, DC: Author. The Mississippi Gaming Commission. (2006). Monthly Reports. Jackson, MS: Retrieved from www.mstc.state.ms.us/taxareas/misc/gaming/stats/GamingGrossRevenues.pdf April 4, 2006.
  14. Naughton, J. (1998). Why athletes are vulnerable to gambling. The Chronicle of Higher Education, 44 (32), A51.
  15. Petr, T., Paskus, T.S. & Dunkle, J.B. (2003). NCAA national study on collegiate sports wagering and associated behaviors. National Collegiate Athletic Association, 1-62.
  16. Udovicic, A. (1998). Sports and gambling a good mix? I wouldn’t bet on it. Marquette Sports Law Journal, 8 (2), 401-427.
  17. Woodruff, C. & Gregory, S. (2005). Profile of Internet Gamblers: Betting on the Future. UNLV Gaming Research and Review Journal. 9 (1), 1-14.
2015-03-27T13:34:24+00:00September 2nd, 2006|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on A Study of Gambling Activity in a NCAA Division II Institution

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

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

  1. Baade, Robert A. and Laura J. Tiehen (1990). “An Analysis of Major League Baseball Attendance, 1969-1987.” Journal of Sport and Social Issues. v.14(1), pp.14-32.
  2. Graham, Peter J. (1994). “Characteristics of Spectators Attending Professional Tennis Tournaments in Two Regions of the U.S.” Sports Marketing Quarterly, v.3(3), pp.38-44.
  3. Graham, Stedman, Joe Jeff Goldblatt, and Lisa Delpy (I 995). In The Ultimate Guide to Sport Event Management & Marketing. Richard D. Irwin Inc. Helitzer, Melvin (1996). “In The Dream Job: Sports Publicity, Promotion and Marketing.”
  4. University Sports Press. The E.W. Scripps School of Journalism, Ohio University. Athens, Ohio.
  5. Hunt, Shelby D., Van R. Wood, & Lawrence B. Cainca (1998). “Corporate Ethical Values and Organizational Commitment in Marketing.” Journal of Marketing 53(July), pp.79-90.
  6. Krohn, Franklin B. and Clarke, Mark (1998). “Psychological and Sociological Influences on Attendance At Small College Sporting Events.” In College Student Journal. v.32 (2), June. pp.277-287.
  7. Lehnus, Darryl L. and Glenn A. Miller (1996). “The Status of Athletic Marketing in Division IA Universities.” in Sport Marketing Quarterly v.5 (3), pp. 31-48.
  8. Mael, F. & B.E. Ashforth (1992). “Alumni and Their Alma Mater: A Partial Test of the Reformulated Model of Organizational Identification.” Journal of  Organizational Behavior Behavior, 13, pp.103-123.
  9. Mawson, L. Marlene, and Edward d. Coan (1994). “Marketing Techniques Used by NBA Franchises to Promote Home Game Attendance.” Sport Marketing
  10. Quarterly v.3(1), pp.37-45.
  11. Murrell, Audrey J. and Beth Dietz (1992). “Fan Support of Sport Teams: The Effect of a Common Group Identity.” Journal of Sport & Exercise Psychology v.14, pp. 28-39.
  12. Noll, Roger (1974). “Attendance and Price Setting.” in Government and the Sports Business. The Brookings Institute Washington, D.C. pp.115-157.
  13. Parsons, Patricia H. (1993). “Framework for Analysis of Conflicting Loyalties” Public Relations Review. 19(l), pp.45-57.
  14. Pitts, Brenda G. and David K. Stotlar (1996). In Fundamentals of Sport Marketing, Fitness Information Technology Inc., Morgantown, WV.
  15. Pol, Louis G. and Sukgoo Pak (1994). “The Use of Two-Stage Survey Design for Gathering Data From People Who Attend Sporting Events.” Sport Marketing Quarterly v.3, pp. 9-12.
  16. Sutton, William A., Mark A. McDonald & George R. Milne (1997). “Creating and Fostering Fan Identification in Professional Sports.” Sport Marketing Quarterly_ v. 6(1), pp.15-22.
  17. Tajfel, H. (1981). in Human Groups and Social Categories: Studies in Social Psychology. Cambridge, England: Cambridge University Press.
  18. Wakefield, Kirk (1995). “The Persuasive effects of Social Influence on Sporting Event Attendance.” Journal of Sports & Social Issues, v. 19 (4) November. pp.335-352.
  19. Wann, Daniel L. and Michael P. Schrader (1997). “Team Identification and the Enjoyment of Watching A Sporting Event” in Perceptual and Motor Skills, pp. 84-954.
  20. Wann, D.L. & N.R.A. Branscombe (1993).”Sports Fans: Measuring Degree of Identification With Their Team.” International Journal of Sport Psychology, v. 24, pp. 1-17.
  21. Weeth, Charles (1994). “Fan Loyalty: Rose Bowl Boosts University of Wisconsin’s Revenue.” In Amusement Business. (Mar) v. 106 (10), pp. 7-13.
  22. Wilkenson,D.(1993). In Sponsorship Marketing:A Practical Reference Guide for Corporation’s in the 1990 ‘s. The Wilkenson Group; Sunnydale, CA.
  23. Zhang, James J., Dennis W. Smith, and Dale G. Pease (1996). “Spectator Knowledge of Hockey as a Significant Predictor of Game Attendance.” Sport  Marketing Quarterly v 5 (3), pp.41-48.
2015-03-27T13:30:32+00:00September 1st, 2006|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off on A Model of the Factors Contributing to Fan Support at Small-College Athletic Events

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

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

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Acknowledgement

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

Table 1

Table 2

Table 3

Table 4

Table 5

2015-03-27T13:13:57+00:00June 7th, 2006|Contemporary Sports Issues, Sports Coaching, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off on Performance Enhancement Drugs: Knowledge, Attitude, And Intended Behavior Among Community Coaches In Hong Kong