Carolina League PowerPoint for Charles Town City Council

Directions: Week 2 Assignment:
After doing some research on the Carolina League, create a
PowerPoint presentation to the City Council of Charles Town
which specifically and thoroughly addresses the following
components:
· Overview of the Carolina League. This includes current
teams/cities/MLB affiliations, staff needed (list all positions
from the top to the bottom of the organization, coaches (list all
coaching positions needed for this new organization), and
facilities (list and explain what type of facilities will be
needed).
· Historical perspective of the Carolina League.
· Explanation of how a team in Charles Town, WV would
benefit the Carolina League and the city of Charles Town.
· Organizational goals of the Charles Town expansion team.
· Please create a mission statement and list the core values of
the Charles Town expansion team.
· Community relations and outreach opportunities for the team.
· SWOT analysis of having a minor League baseball team in
Charles Town.
· Proposed timeline for implementation, with specific details
either by month, or by quarter, based on the length of time
recommended.
Please create a PowerPoint presentation covering the areas
listed above for presentation to the City Council of Charles
Town, WV. Please be sure to review the "PowerPoint
Presentation Do's and Don’ts" document that’s attached.
Remember, the PowerPoint must be in bullet format and must
include a notes section. The notes section should be a summary
of the bullet points in your presentation, such that if you were
unable to make the presentation, someone else could make the
presentation for you by reading what you’ve written in the notes
section.
Remember, you will be handing this to the president and board

of trustees for review. Be sure to carefully proof your work, and
follow APA format throughout. Please include a title slide that
includes your name and the assignment topic, as well as a
reference slide at the end of your PowerPoint presentation
which includes a minimum of three (3) scholarly sources. Don't
forget that every source should be correctly cited throughout
your presentation on the appropriate slide.
Length of this section of your marketing plan: This assignment
should be approximately 10-12 PowerPoint Slides (not
including title page and references).
Johns Hopkins Nursing Evidence-Based Practice
Appendix E
Research Evidence Appraisal Tool
Evidence level and quality rating:
Article title:
Number:
Author(s):
Publication date:
Journal:
Setting:
Sample (composition and size):
Does this evidence address my EBP question?
Yes
No-Do not proceed with appraisal of this evidence
Is this study:
QuaNtitative (collection, analysis, and reporting of numerical
data)
Measurable data (how many; how much; or how often) used to
formulate facts, uncover patterns in research, and generalize

results from a larger sample population; provides observed
effects of a program, problem, or condition, measured precisely,
rather than through researcher interpretation of data. Common
methods are surveys, face-to-face structured interviews,
observations, and reviews of records or documents. Statistical
tests are used in data analysis.
Go to Section I: QuaNtitative
QuaLitative (collection, analysis, and reporting of narrative
data)
Rich narrative documents are used for uncovering themes;
describes a problem or condition from the point of view of those
experiencing it. Common methods are focus groups, individual
interviews (unstructured or semi structured), and
participation/observations. Sample sizes are small and are
determined when data saturation is achieved. Data saturation is
reached when the researcher identifies that no new themes
emerge and redundancy is occurring. Synthesis is used in data
analysis. Often a starting point for studies when little research
exists; may use results to design empirical studies. The
researcher describes, analyzes, and interprets reports,
descriptions, and observations from participants.
Go to Section II: QuaLitative
Mixed methods (results reported both numerically and
narratively)
Both quaNtitative and quaLitative methods are used in the study
design. Using both approaches, in combination, provides a
better understanding of research problems than using either
approach alone. Sample sizes vary based on methods used. Data
collection involves collecting and analyzing both quaNtitative
and quaLitative data in a single study or series of studies.
Interpretation is continual and can influence stages in the
research process.
Go to Section III: Mixed Methods
Appendix E

Page 6 of 10
Appendix E
The Johns Hopkins Hospital/ The Johns Hopkins University
5
Section I: QuaNtitative
Level of Evidence (Study Design)
Is this a report of a single research study?
A
· Yes
· No
Go to B
1. Was there manipulation of an independent variable?
· Yes
· No
2. Was there a control group?
· Yes
· No
3. Were study participants randomly assigned to the
intervention and control groups?
· Yes
· No
If Yes to questions 1, 2, and 3, this is a randomized controlled
trial (RCT) or experimental study.
LEVEL I
If Yes to questions 1 and 2 and No to question 3orYes to

question 1 and No to questions 2 and 3, this is quasi-
experimental.
(Some degree of investigator control, some manipulation of an
independent variable, lacks random assignment to groups, and
may have a control group).
LEVEL II
If No to questions 1, 2, and 3, this is nonexperimental.
(No manipulation of independent variable; can be descriptive,
comparative, or correlational; often uses secondary data).
LEVEL III
Study Findings That Help Answer the EBP Question
Skip to the Appraisal of QuaNtitative Research Studies section
Section I: QuaNtitative (continued)
Is this a summary of multiple sources of research evidence?
· Yes
Continue
· No
Use Appendix F
1. Does it employ a comprehensive search strategy and rigorous
appraisal method?
If this study includes research, nonresearch, and experiential
evidence, it is an integrative review (see Appendix F).
· Yes
Continue
· No
Use Appendix F
2. For systematic reviews and systematic reviews with meta-
analysis
(see descriptions below):
B
a. Are all studies included RCTs?
LEVEL I
b. Are the studies a combination of RCTs and quasi-
experimental, or quasi-experimental only?

LEVEL II
c. Are the studies a combination of RCTs, quasi-experimental,
and nonexperimental, or nonexperimental only?
LEVEL III
A systematic review employs a search strategy and a rigorous
appraisal method, but does not generate an effect size.
A meta-analysis, or systematic review with meta-analysis,
combines and analyzes results from studies to generate a new
statistic: the effect size.
Skip to the Appraisal of Systematic Review (With or Without a
Meta-Analysis) section
Appraisal of QuaNtitative Research Studies
Does the researcher identify what is known and not known
about the problem and how the study will address any gaps in
knowledge?
· Yes
· No
Was the purpose of the study clearly presented?
· Yes
· No
Was the literature review current (most sources within the past
five years or a seminal study)?
· Yes
· No
Was sample size sufficient based on study design and rationale?
· Yes
· No
If there is a control group:
· Were the characteristics and/or demographics similar in both
the control and intervention groups?

· Yes
· No
N/A
· If multiple settings were used, were the settings similar?
· Yes
· No
N/A
· Were all groups equally treated except for the intervention
group(s)?
· Yes
· No
N/A
Are data collection methods described clearly?
· Yes
· No
· Yes
· No
N/A
Was instrument validity discussed?
· Yes
· No
N/A
If surveys or questionnaires were used, was the response
rate > 25%?
· Yes
· No
N/A
Were the results presented clearly?
· Yes
· No
If tables were presented, was the narrative consistent with the
table content?

· Yes
· No
N/A
Were study limitations identified and addressed?
· Yes
· No
Were conclusions based on results?
· Yes
· No
Complete theQuality Rating for QuaNtitative Studiessection
Appraisal of Systematic Review (With or Without Meta-
Analysis)
Were the variables of interest clearly identified?
· Yes
· No
Was the search comprehensive and reproducible?
· Key search terms stated
· Yes
· No
· Multiple databases searched and identified
· Yes
· No
· Inclusion and exclusion criteria stated
· Yes
· No
Was there a flow diagram that included the number of studies
eliminated at each level of review?
· Yes
· No
Were details of included studies presented (design, sample,
methods, results, outcomes, strengths, and limitations)?
· Yes
· No

Were methods for appraising the strength of evidence (level and
quality) described?
· Yes
· No
Were conclusions based on results?
· Yes
· No
· Results were interpreted
· Yes
· No
· Conclusions flowed logically from the interpretation and
systematic review question
· Yes
· No
Did the systematic review include a section addressing
limitations andhow they were addressed?
· Yes
· No
Complete theQuality Rating for QuaNtitative Studies section
(below)
Quality Rating for QuaNtitative Studies
Circle the appropriate quality rating below:
A High quality: Consistent, generalizable results; sufficient
sample size for the study design; adequate control; definitive
conclusions; consistent recommendations based on
comprehensive literature review that includes thorough
reference to scientific evidence.
B Good quality: Reasonably consistent results; sufficient
sample size for the study design; some control, and fairly
definitive conclusions; reasonably consistent recommendations
based on fairly comprehensive literature review that includes
some reference to scientific evidence.
C Low quality or major flaws: Little evidence with inconsistent
results; insufficient sample size for the study design;
conclusions cannot be drawn.

Appendix E
Section II: QuaLitative
A
Is this a report of a single research study?
· Yes
this is
Level III
· No
go to II B
Complete theAppraisal of Single QuaLitative Research
Studysection(below)
Appraisal of a Single QuaLitative Research Study
Was there a clearly identifiable and articulated:
· Purpose?
· Yes
· No
· Research question?
· Yes
· No
· Justification for method(s) used?
· Yes
· No
· Phenomenon that is the focus of the research?
· Yes

· No
Were study sample participants representative?
· Yes
· No
Did they have knowledge of or experience with the research
area?
· Yes
· No
Were participant characteristics described?
· Yes
· No
Was sampling adequate, as evidenced by achieving saturation of
data?
· Yes
· No
Data analysis:
· Was a verification process used in every step by checking and
confirming with participants the trustworthiness of analysis and
interpretation?
· Yes
· No
· Was there a description of how data were analyzed (i.e.,
method), by computer or manually?
· Yes
· No
Do findings support the narrative data (quotes)?
· Yes
· No
Do findings flow from research question to data collected to
analysis undertaken?
· Yes
· No
Are conclusions clearly explained?
· Yes

· No
Skip to theQuality Rating for QuaLitative Studiessection
For summaries of multiple quaLitative research studies (meta-
synthesis), was a comprehensive search strategy and rigorous
appraisal method used?
B
· Yes
Level III
· No
go to Appendix F
Complete the Appraisal of Meta-Synthesis Studies section
(below)
Appraisal of Meta-Synthesis Studies
Were the search strategy and criteria for selecting primary
studies clearly defined?
· Yes
· No
Were findings appropriate and convincing?
· Yes
· No
Was a description of methods used to:
· Compare findings from each study?
· Yes
· No
· Interpret data?
· Yes
· No
Did synthesis reflect:
· Yes
· No

· New insights?
· Yes
· No
· Discovery of essential features of phenomena?
· Yes
· No
· A fuller understanding of the phenomena?
· Yes
· No
Was sufficient data presented to support the interpretations?
· Yes
· No
Complete the Quality Rating for QuaLititative Studies section
(below)
Quality Rating for QuaLitative Studies
Circle the appropriate quality rating below:
No commonly agreed-on principles exist for judging the quality
of quaLitative studies. It is a subjective process based on the
extent to which study data contributes to synthesis and how
much information is known about the researchers’ efforts to
meet the appraisal criteria.
For meta-synthesis, there is preliminary agreement that quality
assessments should be made before synthesis to screen out poor-
quality studies1.
A/B High/Good quality is used for single studies and meta-
syntheses2.
The report discusses efforts to enhance or evaluate the quality
of the data and the overall inquiry in sufficient detail; and it
describes the specific techniques used to enhance the quality of
the inquiry.
Evidence of some or all of the following is found in the report:
· Transparency: Describes how information was documented to
justify decisions, how data were reviewed by others, and how

themes and categories were formulated.
· Diligence: Reads and rereads data to check interpretations;
seeks opportunity to find multiple sources to corroborate
evidence.
· Verification: The process of checking, confirming, and
ensuring methodologic coherence.
· Self-reflection and self-scrutiny: Being continuously aware of
how a researcher’s experiences, background, or prejudices
might shape and bias analysis and interpretations.
· Participant-driven inquiry: Participants shape the scope and
breadth of questions; analysis and interpretation give voice to
those who participated.
· Insightful interpretation: Data and knowledge are linked in
meaningful ways to relevant literature.
CLower-quality studies contribute little to the overall review of
findings and have few, if any, of the features listed for
High/Good quality.
1
https://www.york.ac.uk/crd/SysRev/!SSL!/WebHelp/6_4_ASSE
SSMENT_OF_QUALITATIVE_RESEARCH.htm
2 Adapted from Polit & Beck (2017).
Section III: Mixed Methods

You will need to appraise both the quaNtitative and quaLitative
parts of the study independently, before appraising the study in
its entirety.
1. Evaluate the quaNitative part of the study using Section I.
Level
Quality
Insert here the level of evidence and overall quality for this
part:
2. Evaluate the quaLitative part of the study using Section II.
Level
Quality
Insert here the level of evidence and overall quality for this
part:
3. To determine the level of evidence, circle the appropriate
study design:
· Explanatory sequential designs collect quaNtitative data first,
followed by the quaLitative data; and their purpose is to explain
quaNtitative results using quaLitative findings. The level is
determined based on the level of the quaNtitative part.
· Exploratory sequential designs collect quaLitative data first,
followed by the quaNtitative data; and their purpose is to
explain quaLitative findings using the quaNtitative results. The
level is determined based on the level of the quaLitative part,
and it is always Level III.
· Convergent parallel designs collect the quaLitative and
quaNtitative data concurrently for the purpose of providing a
more complete understanding of a phenomenon by merging both
datasets. These designs are Level III.
· Multiphasic designs collect quaLitative and quaNtitative data
over more than one phase, with each phase informing the next
phase. These designs are Level III.

Complete the Appraisal of Mixed Methods Studies section
(below)
Appraisal of Mixed Methods Studies3
Was the mixed-methods research design relevant to address the
quaNtitative and quaLitative research questions (or objectives)?
· Yes
· No
· N/A
Was the research design relevant to address the quaNtitative and
quaLitative aspects of the mixed-methods question (or
objective)?
· Yes
· No
· N/A
For convergent parallel designs, was the integration of
quaNtitative and quaLitative data (or results) relevant to
address the research question or objective?
· Yes
· No
· N/A
For convergent parallel designs, were the limitations associated
with the integration (for example, the divergence of quaLitative
and quaNtitative data or results) sufficiently addressed?
· Yes
· No
· N/A
Complete the Quality Rating for Mixed-Method Studies section
(below)
3 National Collaborating Centre for Methods and Tools. (2015).
Appraising Qualitative, Quantitative, and Mixed Methods
Studies included in Mixed Studies Reviews: The MMAT.

Hamilton, ON: McMaster University. (Updated 20 July, 2015)
Retrieved from http://www.nccmt.ca/ resources/search/232
Quality Rating for Mixed-Methods Studies
Circle the appropriate quality rating below
A High quality: Contains high-quality quaNtitative and
quaLitative study components; highly relevant study design;
relevant integration of data or results; and careful consideration
of the limitations of the chosen approach.
B Good quality: Contains good-quality quaNtitative and
quaLitative study components; relevant study design;
moderately relevant integration of data or results; and some
discussion of limitations of integration.
C Low quality or major flaws: Contains low quality
quaNtitative and quaLitative study components; study design
not relevant to research questions or objectives; poorly
integrated data or results; and no consideration of limits of
integration.
Requirements
Description of the Assignment
The critique will involve writing a two-page analysis of an
article as well as completing the Johns Hopkins Research
Appraisal Tool that is applicable to the article (qualitative).
You will critique a qualitative research article.
Criteria for Content
1. Introduction: Provide introduction to article topic/focus,
authors and specific aim of assignment.
2. Critique of Article (Body):
a. Identify the type of qualitative method of the study.
b. Content of critique should include at a minimum:
i. participant sampling,
ii. questionnaires/tools,

iii. ethics,
iv. analysis of findings,
v. limitations,
vi. discussion section,
vii. Summary: Application (translation) to practice specialty,
and future implications.
c. Refer to and complete the Johns Hopkins Research Appraisal
Tool.
Article Review Steps
Step 1: Select a qualitative research article on your topic of
interest published within the last three (3) years.
Step 2: Write a two-page critique of the article in a Word Doc
supported by course readings.
Step 3: Complete the Johns Hopkins Quantitative Research
Appraisal Tool. No credit for partially completed sections of
The Appraisal Tool.
Preparing the Assignment:
1. APA Format according to current edition.
2. Word Doc
3. Word Doc Format:
Cover page, no abstract, introduction (no heading per APA),
body of the paper/review, reference list, appendix with Johns
Hopkins appraisal doc. For review sections refer to your
readings and the Johns Hopkins Research Appraisal Tool.
List should include the chosen article and other resources used
to construct the review, such as Johns Hopkins Evidence Based
Practice: Model and Guidelines, and How to Read a Paper by
Greenhalgh (2014).
Rubric
Criteria
Ratings
Pts
This criterion is linked to a Learning OutcomeIntroduction
Required content for this section includes:

• Introduction to chosen article
• Succinct overview of assignment focus.
10.0 pts
Excellent
Content includes well-written, succinct, information that
includes: Article topic/focus, authors and specific aim of
assignment.
9.0 pts
V. Good
Content is well-written but omits or is thin in one area.
8.0 pts
Satisfactory
Section content is basic in its explanation of the article
(overview) and the purpose of the assignment but lacks specific
detail and depth.
5.0 pts
Needs Improvement
All content is included but difficult to piece together in its
explanation of the article (overview) and the purpose of the
assignment OR a piece of the content is missing, for example,
overview of assignment focus, yet what is written is well stated.
0.0 pts
Unsatisfactory
Missing OR Section content is vague in its introduction of the
article (overview) and the purpose of the assignment is missing
OR article overview is missing, and purpose of the assignment
is vague.)
10.0 pts
This criterion is linked to a Learning OutcomeCritique of
Article
Required content for this section includes:
• Methodological review specific to type (non-research versus
research): (use text and resources)
• Ethical review (not always present with guidelines or
systematic reviews)

• Analysis of findings
• Limitations
• Discussion
• Application to practice (translation)
• Future implications
50.0 pts
Excellent
All content is included in the critique with comprehensive
definitions, examples and with in-text citations that support the
article evaluation with depth.
46.0 pts
V. Good
All content is included in the critique. One or two sections may
be included without depth: For example, Definitions, examples
and with in-text citations that support the article evaluation
with depth. Or: All content has explanatory depth of analysis
including definitions, examples and in-text citations supporting
the analysis, however, a content area may be missing (such as
ethical review or limitations)
42.0 pts
Satisfactory
Two or three content areas are missing, or all content areas are
included but there is inconsistent depth/ integration of
definitions, examples and in-text citations that support the
article evaluation with depth
25.0 pts
Needs Improvement
Four or more content areas are missing, or all content areas are
included but there is little to no depth/ integration of
definitions, examples and in-text citations that support the
article evaluation with depth.
0.0 pts
Unsatisfactory
Critique is vague, without structure, without discernible
integration of definitions, examples, and in-text citations that
support the writing.

50.0 pts
This criterion is linked to a Learning OutcomeJohns Hopkins
Appraisal Tool
50.0 pts
Excellent
All sections of the Appraisal Tool are completed for the correct
article review (for example, the non-research tool is used for
guidelines, the qualitative tool is used for qualitative review).
46.0 pts
V. Good
Tool is included, is the correct tool, and is missing: A. Non-
Evidence Tool: 1 of the 6 sections B. Evidence Tool: 1 section
missing
42.0 pts
Satisfactory
Tool is included, is the correct tool, and is missing: A. Non-
Evidence Tool 2 or 3 of the 6 sections B. Evidence Tool: 2
sections missing
25.0 pts
Needs Improvement
Tool is included and is missing: A. Non-Evidence Tool 4 or
more of the 6 sections B. Evidence Tool – 3 more sections
missing.
0.0 pts
Unsatisfactory
Tool is missing or the wrong tool is used.
50.0 pts
This criterion is linked to a Learning OutcomeOrganization &
Format
Requirements:
• Cover (title) page
• No abstract
• Introduction
• Body of paper and reference page must follow APA guidelines

as found in the current edition of the manual. This includes the
use of headings for each section of the paper except for the
introduction where no heading is used.
15.0 pts
Excellent
All aspects of paper follow APA guidelines (cover, no abstract,
introduction, headings (not on introduction), body of paper and
reference page
14.0 pts
V. Good
1-3 APA errors
12.0 pts
Satisfactory
4-5 APA errors
8.0 pts
Needs Improvement
6-9 APA errors
0.0 pts
Unsatisfactory
10 or greater APA errors
15.0 pts
Segmentation in Sports - Analyzing the Behavior
of the Sport's Consumer
Mihaela Constantinescu
Bucharest University of Economic Studies, Marketing Faculty
Abstract: Sport is a multifarious domain that can range from
100% involvement as a player to buying
sports equipment for everyday life activities, therefore the
sport's consumer can have an active involvement

(practicing a sport) or a passive one (taking part in sports as a
spectator or watching sport on TV), fact
that brings the necessity of a segmentation process in order to
better target the individual with the right
marketing tools. This paper presents the results of a marketing
research on the Romanian market that
analyses the behavior of the spbrt's consumer, his involvement
in the sports industry and the way sports
influences his life decisions.
Keywords: sports marketing, sport consumer, sport events,
segmentation
1. Introduction
Sport is a multifarious do-
main that can range from 100%
involvement as a player to buy-
ing sports equipment for every-
day life activities, therefore the
sport's consumer can have an
active involvement (praetieing
a sport) or a passive one (taking
part in sports as a speetator or
watehing sport on TV), faet that
brings the neeessity of a seg-
mentation proeess in order to
better target the individual with
the right marketing tools
To highlight the importance
of eonsumer behavior analysis,
espeeially for the marketing
strategy, it is sufficient to pres-
ent the reasons identified by Ch.
Miehon (2010), depending on

the time period eovered: short
term (to explain the behavior
and eonsumer attitudes towards
a produet or brand), long term
(to identify trends, design new
produets and to find suitable
message to target the audienee).
Before taking any deeision
about the market or consumers,
marketers must get familiar
with their needs and expecta-
tions, and how the individual re-
acts to various external stimuli,
as well as the process of reaching
a deeision on aetion taken and
purchase made, whether ratio-
nal or emotional.
In the process of buying and
eonsumption, the individual
may have multiple roles, all
equally important for market-
ers, in their attempt to make the
offer more attractive, the ulti-
mate goal being the purehase of
products offered. In some cases,
these roles are overlapping for
the same person, but usually
the marketing strategy must
take into account several target
audiences, all with influential
role in the purehase decision.
Once we have identified

the categories of population
with impact on consumer be-
havior, we carr~give a defini-
tion of the concept. According
_to the authors I. Cätoiu and N.
Teodorescu (1997), consumer
behavior is represented by all
acts, attitudes and decisions on
the use of his revenues for pur-
chases of goods, services and
for savings. M.Solomon (2005)
defines eonsumer behavior as
represented by the proeesses
involved in the seleetion, pur-
chase, use or abandonment of
goods, services, ideas or experi-
enees by individuals or groups
to satisfy their needs or wants.
The definition emphasizes that
consumer behavior should be
seen as a eontinuous proeess,
not limited to speeifie time ex-
change that takes place between
the customer and manufacturer.
Starting from these general
definitions given to eonsumer
behavior, we ean define sports
consumer as that person or enti-
ty that benefits from the offer of
produets and serviees from the
sports market, as a praetitioner,
spectator, viewer or sponsor.

2. Segmentation of the
sports market
Segmentation is a phase in
the marketing management
proeess (KotlerV 2008) and it re-
fers to dividing the market and
identifjang eonsumei^-segment&-
with similar behavior or needs.
On the sports market, the
segmentation proeess is based
on a series of soeio-demograph-
ic and behavioral criteria with
high relevance for the offer of
sports produet and services.
There is no universally accepted
opinion regarding the identifica-
tion of these criteria, primarily
due to the diversity that charac-
terizes the sport domain and the
38
motives for which an individual
or organization is involving it-
self in sports.
In his book "Sports Marketing.
A Strategic Perspective ", M.D.
Shank (2005) identifies the fol-
lowing categories of criteria for
segmentation: demographic

(gender, age, ethnicity or fam-
ily life cycle), socioeconomic
(income, education, occupa-
tion), psychographics (lifestyle,
personality, interests and opin-
ions), geographical (continent
and country of origin, city of
residence, chmate), behavioral
(purchase frequency, size pur-
chase loyalty) and criteria relat-
ed to the expected benefit (con-
sumer needs, desired features of
sports products or services).
Thierry Lardinoit and
Emmanuelle Le Nagard-Assayag
(2004) have developed a segmen-
tation according to the sport's
values, with emphasis on moder-
nity and creativity.
Tapp and Glowes (2002) pro-
pose two major criteria for this
process: the expected benefits
by the client (victory vs. enter-
tainment) and its hehavioral
commitment.
As can be seen, the authors
mentioned above only apply
market segmentation within the
business to consumer (B2C) sec-
tor, forgetting a very important
sports market segment - the or-
ganizations. Thus, the segmen-
tation process should start with

the legal status of the consum-
er, leading to the identification
of two categories: individual
consumers and organizational
consumers (Constantinescu,
2009). The second category of
consumers is mainly represent-
ed through sponsorships, but
there are situations where the
demand for products and ser-
vices is based on the sport needs
of an organization (such as the
need to organize sports competi-
tions between employees or the
need for easier access to services
offered by a gym for employees).
The segmentation process
deepens within the first catego-
ry - individual consumers, which
can be categorized in three cat-
egories according to the way
they are involving themselves
in sports: practitioner, spectator
and viewer.
Another criterion for seg-
mentation is based on reasons
that determine the sport behav-
ior of an individual, identify-
ing the following categories: 1)
those who like moving, not hav-
ing a favorite sport; 2) those who
are fans of a particular sport; 3)
those who are fans of a team; 4)

those who are fans of a athlete.
One can ohserve a relation of in-
clusion between these four cat-
egories. Thus, those who admire
a particular athlete automati-
cally hecome fans of the team for
which that athlete is playing,
also developing a special attach-
ment to the sport in which acti-
vates the respective team. And
those who develop a sense of ad-
miration for a sports team have
an inclination towards sports
and movement.
The literature provides many
methods of segmentation, but
their generality leads to a de-
crease in the degree of applica-
bihty in the market, if they are
not linked to a series of direct
researches through which the
particularities of the sport con-
sumer behavior can be identified
for each country or region. This
paper highlights" the results^
of such a research, conducted
within the urban population of
Romania.
3. Research methodology
To collect the necessary in-
formation, a nationwide survey
has been organized which aimed
to analyze the sport consumer

behavior, detailed in the follow-
ing objectives:
a) The percentage of popula-
tion that practices sport;
h) What sports are practiced;
c) The percentage of popula-
tion that are participating
in sports events as specta-
tors;
d) What type of sports events
are they participating in;
e) The percentage of popula-
tion that watches sports
events on TV;
f) What type of sports events
are they watching.
The survey was conducted on
a sample of 385 people, within
the Romanian urban popula-
tion, the probability of guaran-
teeing the research results is
95% (for which the t coefficient
is 1.96), with a margin of error
of ± 5%. The sampling process
used a stratified method, tak-
ing into account gender, age and
income criteria that ensured a
sample structure corresponding
to the Romanian population.

4. Research results
In order to highlight the par-
ticularities of the sports mar-
ket segmentation process in
Romania, research results will
be presented both in a univari-
ate analysis and bivariatej3ne__
for each research objective.
4.1. The percentage of
population that practices
sport
While in Europe the percent-
_age of those who practice sport
is 2/3, the results of the present
study show a share of only 41.7%
for Romania, which explains to
some extent the social problems
that our country is fighting -
physical inactivity and ohesity.
But this lack of sport in
Romania should not be gener-
alized, given that the decision
to practice sport is infiuenced
hy a number of socio-demo-
graphic characteristics, leading
to differences between certain
RRM-4/2013
39

segments of the population. For
example, the proportion of men
who play sports is 47.8%, while
for women is only 34%. The dif-
ferences are more visible in re-
gard to age categories, as can
be seen from Figure 1, where
the variation is from 64.8% for
those aged between 18 and 25
years to 12.3% for those over
55. Although it seems large, the
percentage recorded for young
people should not surprise us,
actually we expect it to be high-
er, given that they are in that
period of their life when sport
is part of the school curriculum.
Unfortunately, more and more
young people opt for exemp-
tion from these activities within
the education system, which
can only lead to a worsening of
the social problems mentioned
above (physical inactivity and
obesity), along with social inte-
gration and education problems.
The education level also has
a major influence on the deci-
sion to carry out sports activi-
ties, higher education leading
to greater number for those
practicing sports (from 11.1%
for those who have just gradu-

ated from middle school up to
61.5% for postgraduates).
4.2. Types of sports practiced
by Romanian population
As can be seen from Table 1,
the most commonly practiced
sport in Romania is football
(46.4% of respondents mention-
ing it). Somewhat odd is posi-
tioning tennis second, but the
explanation may be related to
the period of data collection -
summer, when the number of
places to practice this sport is
much higher. In the next two
places we find a category of
sports that are related to the
individual needs of body main-
tenance (aerobics, fitness, body-
building - 28.6% and running,
jogging - 23.6%). A sport that is
gaining more and more follow-
ers is basketball, and principal-
ly because of street competitions
for amateur players in formula
3 on 3 (a variant of the classic
sport that FIBA wishes to pro-
pose even as an Olympic sport).
Tahle 1. Types of sports
practiced hy Romanian
population (%)

Sport
soccer
tennis
aerobic, fitness,
bodybuilding
running, jogging
swimming
basketball
volleyball
table tennis
others
Percentage
46,4
37,9
28,6
23,6
22,1
21,4
20,0
17,1
41,4
The choice of sport is influ-
enced by a number of socio-de-
mographic characteristics of the
individual or the family they
come from, among which the
most important are gender, age
and level of education.

The correlation with gender
highlights major differences be-
tween men and women, the most
practiced sports by men being
football (70.6%), tennis (43.5%),
basketball (28.2%), swimming
(21.2 %) and table tennis (21.2%),
while women most often sports
practice body care sports, such as
100%
90%
80%
70%
60%
50%
40%
30%
: 20%
i 10%
I 0%
Figure 1. Percentage of population that practices sports,
hased on the age categories (%)
35,2%

18-25
years
26-35
years
36-45
years
46-55
years
Over 55
years
Total
Practicing sports a Not practicing sports
40
aerobics, fitness or bodybuilding
(52.8%), running/jogging (34%),
followed by tennis (28.3%), vol-
leyball (28.3%) and swimming
(22.6%).
The sports market segmenta-
tion process must take into ac-
count the age of practitioners,
given that bivariate analysis
between these two variables

showed that young (18-35 years
old) choose team sports, through
which they satisfy the need for so-
cialization, as well as the compet-
itive one (most frequently men-
tioned sports being football and
basketball), while adults (36-55
years) and elderly (over 55 years)
are oriented towards body care
sports, such as running or fitness.
Regardless of education level,
soccer remains first in the prefer-
ences of individuals, but decreas-
es as a percentage from 52.9% for
those with secondary education
to 29.2% for those with graduate
studies. The influence of educa-
tion level is felt when choosing
body care sports (aérobies, fit-
ness, jogging), whieh is much
more present in the lives of those
with higher education.
The choice of football as
the most practiced sport by
Romanian is not influenced ei-
ther by income. The difference
between income categories are
although accounted for sports
that require investment such
as fitness, aerobics and tennis,
which are more often practiced
by those with high incomes (over
2000 RON / month). This finan-
cial barrier can~ be overcome

through public policies that fa-
cilitate the access of population
to gyms or places in public areas
equipped with appliances and
sports equipment.
population that are
participating in sports
events as spectators
The number of passive par-
ticipants to sporting events
(viewers) does not differ much
from that of active participants
(practitioners/athletes), re-
search results showing a per-
centage of 44.7% of the urban
adult population participating
in sports events as spectator.
This low percentage is a nation-
al issue with major impact on
the sports industry in the first
place, but also on the popula-
tion's quality of life. The nega-
tive influence on sport industry
is highlighted by several indica-
tors with disastrous results in
recent years in our country:
- the income of clubs and
sports arenas is dimin-
ished by the lack of inter-
est from the public to par-
ticipate directly in sport-

ing events
- the attractiveness of the
event for potential sponsors
decreases because there
isn't a target audience as-
sociated with that event
- the revenues from the
sales of promotional prod-
ucts with team logos are
at the borderline of sur-
vival on the Romanian
market, not to mention
the sales for products sold
in association with sport-
ing events whieh individu-
als can purchase when
arriving to the stadium/
arena (refreshments and
snaeks); in the U.S. mar-
ket, this range of produets
assoeiated with participa-
tion in a sporting event
determines the American
consumer to spend an av-
erage of $ 115, according
to the index "Fan Cost Ex-
perience" conducted annu-
ally by Team Marketing
Report (http://fancostex-
perience.com/pages/fcx/
blog_pdfs/entry0000025_
pdf002.pdf).
Regarding the negative im-

pact on quality of life, this is re-
lated to the need for socialization
of the individual and to the vari-
ety of ways in terms of leisure,
dimensions of quality of life that
on the Romanian sports market
are not related in any way to at-
tending sporting events.
The deeision to attend sport-
ing events is influeneed by indi-
vidual eharaeteristics, the big-
gest difference being noticed be-
tween men and women (56.8%
of men compared with 30.1% of
women that said they partici-
pate in sporting events).
Another influenee on this de-
eision is related to the sport na-
ture of the individual; there is a
tendeney for those who praetice
sport to participate in a larg-
er number at sporting events
(61.9%) compared to those
who do not play sport (32.3 %).
Market segmentation based on
this correlation helps to identify
the message that will be sent to
persuade the target audience to
attend sporting events, for those
non-sporting types is not indi-
cated the association with move-
ment, because it isn't an impor-
tant motivating factor for them.

4.4. Types of sports events
Romanian are participating
in as spectators
The sports market segmen-
tation in Romania must take
account the attractiveness
of the events from this mar-
ket. Figure 2 shows that most
Romanians are participating in
domestie ehampionships events
(81.3%), the pereentage being
eut in half when it eomes to
the partieipation in European
or international ehampion-
ships (40.3%). This differenee
ean be explained primarily by
lower frequency of international
events in sports in general, but
also by low purehasing power
that characterizes our country
(knowing that the price of tick-
ets to such events is mueh high-
er than for national ehampion-
ship matches).
RRM-4/2013
41
Figure 2. Types of sports events tbat Romanian people are
participating
in as spectators (%)

100,0 1
80,0 -
60,0
40,0
20,0
0,0
81,3
Domestic championship
40,3
^1
European/World championship
9,7
Local events
Local events have the lowest
degree of attractiveness - 9.7%,
which indicates a lack of cohe-
sion in the local community re-
garding the sports dimension.
Although these events are prob-
ably the most affordable, it does
not enjoy a proper promotion,
hence the very low presence of
spectators. Their accessibility
makes the percentage of women

among the spectators to be high-
er (18.6%) than men (6.06%).
Another explanation for the pre-
ponderance of women is the fact
that local events are usually or-
ganized for junior athletes who
come with their parents.
Segmentation by age brings
again a difference in terms of
participation in local events
"where we have a higher pro-
portion of adolescents and el-
_derljL. One explanation for this
result may lie in the much less
available for these categories of
people to travel to other areas
for national or international
events, correlated with their
lower income.
Correlation with marital sta-
tus shows that participation as
spectator to events within the
domestic championship is most
common in the case of unmar-
ried (88.9%), while the mar-
ried participates, mainly, to
Romania's representative team
matches (45.2%)), local events
being preferred by divorced/wid-
owed, in correlation with their
older age (28.6%).

Regarding the sports cor-
responding to events involv-
ing Romanians as spectators,
whether national or interna-
tional, the flrst two are soccer
and handball. The difference be-
tween attending national sport-
ing events or international ones
is represented by gymnastics, a
sport for which our country has
a long and notable performance
worldwide, hence the greater
share of the audience when it
comes to international competi-
tions for this sport.
population that watches
sports events on TV
Although does not imply di-
rect participation in sporting
events, the consumer of sport-
ing events represent a very im-
portant market, for which me-
dia trusts invest millions in TV
broadcast and firms turn into
sponsors. This category of sport
consumers represent the major-
ity of fans, taking into account
that traveling to sports events
is not always possible, hence
the desire to watch the event
through media.

Precisely because it does not
require an equally big effort as
for practitioners and specta-
tors, the percentage of those
who said they are watching
sporting events on television is
quite high - 76%. We must how-
ever make a distinction between
the types of viewers, first of all
based on their involvement in
sport, because the decision to
watch sporting events on televi-
sion is influenced by the sport
behavior of the individual. The
research results showed that-
87.9% of those who play sports
also watch sporting events on
television, while the percentage
for those that do not practice
sport is 67.4%. This influence
can be continued with the corre-
lat ion with the spectator status_
- 96.6% of those participating
in sporting events as spectators
are also watching these events
on television, while the percent-
age for non-spectators is 60.2 %.
The segmentation process
can continue using the socio-de-
mographic characteristics such
as gender or education level. It is
expected to have a difference be-
tween men and women in terms
of watching sporting events on

television, as can be seen from
42 Mihaela Constantinescu
Figure 3. Percentage of population watching sports events on
TV,
based on the gender categories (%)
1 "
120,0
100,0
80,0
60,0
40,0
20,0
n n
87,1
62,7
Men Women Total
I Viewer B Non-̂ ewer
Eigure 3, the percentage of men
being 87.1% compared to 62.7%

for women.
The correlation with educa-
tion shows that with increasing
level of education increases also
the percentage of those who fol-
low sports events on television,
ranging from 50% for those who
have completed only middle
school to 92.3% for those with
graduate studies.
4.6. Types of sports events
watching on TV
Viewers should be segmented
not only by soeio-demographie
charaeteristics, but also by ex-
pectations and preferences in
terms sports watched on TV.
Although most often Romanians
attends the domestic champi-
onship matches, on television
they are watehing at a higher
frequeney European and world
ehampionships, as ean be seen
from Eigure 4. The explanation
for this inversion is in the much
higher attraetiveness of sueh
international events, whieh
inereases the number of view-
ers, to those who usually follow
sports events on television add-
ing the oeeasional ones. The lat-
ter represents a large pereent-

age in the ease of the Olympies,
an event watehed by 66.8% of
Romanian population.
Figure 4. Type of sport event watched on TV (%)
European and world
championship 75,1
Olympic Games
Chompionships from other
countries
Other e«nts I
67,6
66,8
43,9
0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 80,0
RRM-4/2013 43
Figure 5. Type of sport event watched on TV, based on the
gender categories (%)
Domestic Chompionships European and Olympic Games
championship from other world

countries championship
Other e«nts
I Men I Women
The correlation between
the status of spectator and the
viewer comes to uphold the idea
that people who participate in
sporting events as spectators
have a higher tendency to watch
these events also on TV, espe-
cially when it comes to domestic
championship matches.
Another possibility for view-
ers segmentation is based on
gender, observing from Figure
5 that both domestic champi-
onships and those from other
countries are pursued mostly by
men, the percentages balancing
in terms of European and world
championships, for Olympic
Games women's share being
even higher than men's (77, 1%
compared to 60.6%).
^Regarding the sports-
watched on TV, the present re-
search has shown that there are
no differences hetween the do-
mestic championships and those
from other countries, football
dominating all the time (as can

be seen from Table 2). The same
sport is refiected in first place
for European or world champi-
onships, but here also appears a
sport for which Romania is still
well represented - gymnastics.
For the Olympic Games,
Romanians don't have very
clearly defined preferences,
watching any broadcast of the
event, but there are certain seg-
ments of the population with
a predisposition towards gym-
nastics (38.7%) and athletics
(22.6%).
Table 2. Type of sport event
watched on TV, based on
competition type (%)
- Sport Percentage
soccer
handball
basketball
91,8
20,5
5,8
Championship from other
countries

soccer
handball
basketball
83,5
9,6
4,3
European and world
championship
soccer
haiidball
gymnastics
73,9
16,5
14,9
Olympics Games
an3rthing
gymnastics
athletics
45,2
38,7
22,6
5. Conclusions
The sports market segmenta-
tion can lead to identifying the

sports consumer categories on
every level, their characteris-
tics can be used to better match
the marketing strategy with the
needs and desires of individuals.
Thus we can make a portrait of
the consumer in the three steps
of his involvement in sports
activities:
Procesul de segmentare al
pieêi sportive poate conduce
la identificarea consumatorului
de sport pe fiecare nivel, car-
acteristicile acestuia putând
fi folosite pentru o mai buna
corelare a strategiei de market-
ing cu nevoile §i dorinêle indi-
vizilor. Astfel cä putem realizâ_
un portret al consumatorului pe
cele trei trepte de implicare în
activitäîle sportive:
- Practitioner - the main
segment is the singles
men aged 18 to 35 years,
who have postgraduate
studies and most often
play football, this segment
can be used as a factor
promoter for the segments
with a lower frequency of
practicing sports activi-
44

ties such as women or the
elderly, but we must keep
in mind that each seg-
ment can be motivated
by another factor in the
decision to do sports, so is
not recommended a single
message for all segments
identified;
Spectator - this type of
sport consumer includes
the one above, taking into
account that most specta-
tors are men and there is a
direct correlation between
sport and attending events
in this area; this segment
of fans attend more often
domestic championship
events, while women and
older people prefer local
events;
Viewer - this segment is
represented by both wom-
en and men, the differ-
ence standing in the type
of sporting event watched:
men are oriented towards
national championships
(either from us or from
another country), while
women are more receptive
to large events such as the

World Championships or
Olympic Games.
The segmentation must be
followed by an adaptation of the
supply from the sports market to
the needs and preferences that
result from sport consumer be-
havior analysis, whether prac-
titioner, spectator or viewer. At
the microeconomic level, this
translates into a market offer
better adapted, individualized
when possible (especially for
sports services). At the macro-
economic level, arises the need
to implement a national strate-
gy that promotes the positive ef-
fects of practicing sport on qual-
ity of life (especially for dimen-
sions such as health and educa-
tion), together with the social
effects that attending sporting
events has even as a spectator.
References
1. Cätoiu, L, Teodorescu, N. (1997),
Consumer Behaviour. Teory and
practice. Económica Publishing
House, Bucharest
2. Constantinescu, M. (2009), Sports
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al approach to a relationship

perspective, ASE Publishing
House, Bucharest
3. Kotier, Ph., Keller, K.L. (2008),
Marketing Management, 5*'' Edi-
tion, Teora Publishing House,
Bucharest
4. Lardinoit, T., Le Nagard-Assay-
ag, E. (2004), Comment le mar-
keting sportif peut-il contribuer
au succès des nouveaux produ-
its?. Decisions Marketing, nr. 35,
p. 61-74
5. Michon, Ch. (2010), Le Marke-
teur, 3" Edition, Pearson Educa-
tion France, Paris
6. Shank, M.D. (2005), Sports Mar-
keting. A Strategic Perspective,
3rd edition, Pearson Education
International, New Jersey
7. Solomon, M., Tissier-Desbordes,
E., Heilbrunn, B. (2005), Com-
portement du consommateur,
6e Edition, Pearson Education
France, Paris
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carefree casuals to football anor-
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an Journal of Marketing, vol 36,

nr. 11, p. 1248-1270
9.http://fancostexperience.coiTi/pag-
es/fcx/blog_pdfs/entry0000025_
pdf002.pdf
RRM-4/2013
45
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Official Journal of NASSM
www.JSM-Journal.com
ARTICLE
130
Journal of Sport Management, 2013, 27, 130-145
© 2013 Human Kinetics, Inc.
Shapiro and Ridinger are with the Dept. of Human Movement
Sciences – Sport Management Program, Old Dominion Uni-
versity, Norfolk, VA. Trail is with the Dept. of MSAL, Seattle
University, Seattle, WA.

An Analysis of Multiple Spectator Consumption
Behaviors, Identification, and Future Behavioral Intentions
Within the Context of a New College Football Program
Stephen L. Shapiro and Lynn L. Ridinger
Old Dominion University
Galen T. Trail
Seattle University
The growth of college sport over the last several years,
combined with increased competition for the sport
consumer dollar, has created a need to understand spectator
consumption behavior. In addition, the impact of a
new football program can generate interest that influences
future spectator spending decisions. Using identity
theory as a framework, the current study examined the
differential effects of past sport consumer behaviors on
various future sport consumer intentions within the context of a
new college football program. Consumption
intentions included attendance, sponsor support, and
merchandise purchases. Furthermore, this investigation
helped to determine how much variance past behaviors would
explain in behavioral intentions after controlling
for nine points of attachment. Data were collected from
spectators of a Football Championship Subdivision
(FCS) football program located in the Mid-Atlantic region. The
findings suggest past behavior predicted
future intentions; however, the amount of variance explained
varied dramatically depending on specific past
behaviors and points of attachment. These results can help sport
marketers develop strategies to capitalize on
the interest generated through new athletic programs.

College athletic departments have continued to
increase generated revenues over recent years. According
to Fulks (2011), National Collegiate Athletic Association
(NCAA) Division I Football Bowl Subdivision (FBS)
schools, which is the highest level of college football
competition in the Unites States, saw a 9.5% increase
from 2009 to 2010 in median generated revenue. NCAA
Division I Football Championship Subdivision (FCS)
schools, which are one level below FBS schools in regard
to football competition, experienced even larger median
revenue increases (14%) over the same time period. How-
ever, total expenses have increased at approximately the
same rate. Only 22 college athletic programs reported a
profit in 2010 (Fulks, 2011).
In the current financial landscape of college sport,
revenue growth is essential to cover the increase in costs.
The primary areas of college athletic revenue, which
include ticket sales, charitable contributions, sponsorship,
broadcasting rights, and merchandise purchases (Fulks,
2011) are primarily spectator driven. Fans purchase tick-
ets and merchandise, make annual contributions, support
program sponsors, and consume games through mediated
channels (i.e., television, team/league websites, social
media). Past fan consumption behavior through various
means helps determine how likely fans are to engage in
future sport consumption (Trail, Anderson, & Fink, 2000;
Trail, Fink & Anderson, 2003). This is further supported
by role identity and identity theory which suggests that
identity with certain activities influences behavior related
to those activities. According to Callero (1985), identi-
ties by their very nature, imply action. The relationship
between previous behaviors, identification, and future
behavioral intentions become particularly important in
a college athletics environment where spectator behav-

iors drive revenue production. Therefore, it is important
to understand spectator behavior and specifically, the
factors that may have an influence on future spectator
consumption intentions.
There is a wealth of literature examining the influ-
ence of identification on sport consumer behavior.
Previous research has focused on the development of
identification measures (Robinson & Trail, 2005; Wann
& Branscombe, 1993), the influence of team identification
on attendance (Laverie & Arnett, 2000; Trail, Anderson,
& Lee, 2006; Wakefield, 1995), and the influence of team
identification on various future consumption behaviors
(James & Trail, 2008; Trail et al., 2000, 2003, 2006; Trail,
Anderson, & Fink, 2005). Team identification has been
http://www.nassm.com/
http://www.JSM-Journal.com
An Analysis of Multiple Consumption Behaviors 131
shown to significantly influence consumption intentions
related to attending games and purchasing merchandise
(Trail et al., 2003, 2005, 2006), two vital revenue sources
in college athletics.
However, the literature examining the differential
effects of past consumption behaviors and identification on
future consumption intentions is limited. Only Trail et al.
(2006) focused solely on these relationships. The authors
argued that the influence of previous behavior and team
identification on future behavioral intentions is imperative
because consuming an event and establishing a connection
with a program helps move consumers up the fan commit-
ment escalator; which Mullin, Hardy, and Sutton (2007)

suggest increases overall consumption activity.
In addition, examining only one form of previous
consumption behavior (attendance), one facet of identi-
fication (team), and limited future intentions (attendance
and merchandise purchases) is only a piece of the puzzle.
Previous research supports the use of multiple facets
of identification and the importance of other revenue
sources (fundraising, sponsorship, broadcasting rights)
in addition to attendance and merchandise (Fulks, 2011;
Robinson & Trail, 2005; Trail, Robinson, Dick, & Gil-
lentine, 2003). Research examining multiple categories
of previous consumption behavior, identification, and
future intentions combined, is nonexistent.
It is especially important to examine multiple facets
of identification in the context of a new team. New sport
teams do not have a history of achievement or well
established traditions through which to attract fans. Thus,
the facets that influence the formation of identification
with a new team may be different from those that affect
identification with an existing team (Lock, Taylor, &
Darcy, 2011). Lock et al. (2011) found that the forma-
tion of new team identification was driven primarily by
identification with the sport, rather than with the specific
team. These authors encouraged the inclusion of multiple
points of attachment in future research on identification
with new sport teams.
As leagues expand and new teams emerge (Tierney,
2009), a better understanding of the identity fans develop
with a new team and its impact on consumptive behav-
iors may help maximize marketing opportunities. To be
viable in a competitive sports environment, a new team
must attract, develop, and maintain a relationship with
a substantial number of sport consumers (James, Kolbe,

& Trail, 2002). In college football, as schools look to
develop stronger connections with students, alumni, and
the community, the growth of new programs has been
sizeable. This includes 42 new college football programs
in the 1980s, 22 in the 1990s, and 28 in the 2000s (Tier-
ney, 2009). Furthermore, 25 additional college football
programs are slated to begin by 2014 (McGuire, 2011).
Research on consumer attitudes and behavior within
the context of a new program is scant. Conceptual and
theoretical development of sport consumer identification
has focused on established teams, largely ignoring how
identification might vary for a new team or league (Lock
et al., 2011).
Therefore, the purpose of this study was to examine
the relationship between previous fan/spectator behavior,
identification, and future behavioral intentions, while
incorporating multiple facets of each of these variables
within the context of a new college football program.
Review of Literature
Early research in sport consumer behavior focused on
the factors that influence attendance (Demnert, 1974;
Hansen & Gauthier, 1989; Noll, 1974; Whitney, 1988)
or the development of economic models to predict atten-
dance (Baade & Tiehen, 1990; Greenstein & Marcum,
1981). However, these studies failed to examine con-
sumer behavior factors that influence future consumption
intentions. Individual factors such as consumer attitudes,
feelings, and emotions influence how sport fans think and
consume sport-related products and services (Mullin et
al., 2007). Therefore, as an extension to this early work,
later studies explored relationships between spectator
identification and consumption behaviors (Laverie &
Arnett, 2000; Madrigal, 1995; Wakefield, 1995; Wann
& Branscombe, 1993). Wann and Branscombe devel-

oped an instrument to measure team identification and
examined the impact of identification on fan behavior.
The results provided evidence that fans with high levels
of team identification appear to be more involved with
their team and more willing to invest time and resources
into being a fan. Madrigal (1995) extended this research
through an examination of the relationship between team
identification and fan satisfaction. Team identification
was found to have a dominant influence on fan satisfac-
tion. However, actual consumption was not measured
in this model. Wakefield (1995) also examined team
identification, but focused specifically on repatronage
intentions as an outcome variable. The author found a
positive relationship between team identification and
future intentions providing some of the first empirical
evidence regarding the influence of identification on
future consumption intentions.
The previous studies demonstrate the significant
role team identification plays in fan behavior. Fan iden-
tity can be further explained through identity theory
(Stryker, 1968, 1980; Stryker & Burke, 2000). Accord-
ing to Stryker (1980), identity theory is focused on the
concept that individuals develop identities through social
experiences and relationships. Multiple aspects of identi-
fication are internalized through these social exchanges.
It has been hypothesized that the higher the salience of
these identities, “the greater the probability of behavioral
choices in accord with the expectation attached to that
identity” (Stryker & Burke, 2000, p. 186). This is further
supported by Callero (1985), who stated that the most
discernable consequence of identity salience relates to
actual behavior. The relationship between identification
and behavior is apparent in both theoretical and practi-
cal terms. Stronger identity salience leads to increased
actions. The relationship between social experiences,

identification, and behavior has been supported in areas
132 Shapiro, Ridinger, and Trail
such as student involvement in university organizations
(Serpe & Stryker, 1987) and commitment to religious
activities (Stryker & Serpe, 1982).
Within the context of sport, Laverie and Arnett (2000)
examined spectator identification and behavior based on
identity theory. It was suggested that role identities are
formed through past sport-related experiences. High levels
of identity salience influence current attitudes and future
behavioral intentions. The authors found support for the
relationship between team identification and attendance.
However, the outcome variable used was past attendance.
No other behaviors or behavioral intentions were consid-
ered. Past behaviors have been shown to influence future
behavioral intentions in a variety of contexts (Ouellette
& Wood, 1998). However, to fully understand the role
identification plays on consumption behavior through an
identity theory framework, various past behaviors and
various future intentions should be examined.
Trail et al. (2005) extended the work of Laverie
and Arnett (2000) through the development of multiple
models examining relationships between team identifi-
cation, disconfirmation/confirmation of expectancies,
mood, self-esteem, and future behavioral intentions
(i.e., attendance, merchandise purchasing, overall team
support). These models were created from previous
theoretical (Trail et al., 2000) and empirical (Trail et
al., 2003) studies that combined multiple determinants
of spectator consumption in an effort to further under-

stand fan behavior. The findings provided evidence of
a direct relationship between team identification and
future behavioral intentions along with an indirect rela-
tionship between these two variables, influenced by fan
self-esteem.
In addition, team identification may influence spe-
cific consumption behaviors differently. Trail et al. (2003)
found that the relationships between team identification
and two types of consumption intentions (attendance and
merchandise purchasing) were different, as indicated
by the difference in factor loadings on the second-order
latent variable (future behavior; Trail et al., 2003). These
findings are further supported in the literature (James &
Trail, 2008).
Trail et al. (2006) developed a model based on
identity theory that focused exclusively on past atten-
dance, team identification, future intentions, and actual
attendance. This study was the first sport consump-
tion examination that included previous consumption
behavior, identification, and future behavior. The
authors proposed that past attendance would predict
preseason team identification, intentions to attend
games, and actual game attendance. Findings showed
that number of games attended explained approxi-
mately 21% of the variance in team identification
and past attendance and team identification combined
explained 48% of the variance in future intentions.
Although Trail et al. (2006) did not specifically test for
mediation in their model; there certainly is the poten-
tial for team identification to mediate the relationship
between past attendance and future attendance. These
results offer further support regarding the influence
of identification on future consumption behavior. In

addition, this study highlights the impact that previous
behavior can have on team identification.
In summary, the previous literature provides
empirical support for two distinct relationships, (1)
team identification and various spectator consumption
behaviors and (2) past behaviors and future intentions.
Additional research is needed to further understand these
relationships. First, various consumption behaviors, both
previous and future, in addition to attendance should
be considered (i.e., merchandise purchases, mediated
consumption, sponsor purchases). As mentioned previ-
ously, merchandise purchases, sponsorship agreements,
charitable contributions, and broadcasting contracts
generate significant revenue in college athletics. In 2010,
these areas accounted for approximately 28.4% of gener-
ated revenue for FBS schools and approximately 33.7%
of generated revenue for FCS schools (Fulks, 2011).
Furthermore, attendance is only part of the complete fan
experience. Many fans are not able to attend live games
due to cost, location, or other obligations. Still, these fans
can build identification through many of the alternative
consumption methods previously mentioned.
Second, other forms of identification in addition to
team identification must be considered. Previous research
has shown support for multiple points of attachment or
facets of identification (e.g., player identification, sport
identification, coach identification; Robinson & Trail,
2005; Trail et al., 2003; Woo, Trail, Kwon, & Anderson,
2009). Previous consumption experiences may differen-
tially influence various points of attachment and these
points of attachment may differentially affect aspects
of future intentions. Examining only team identification
limits the opportunity to reach other segments of the
fan market, which identify with alternative facets of the

organization.
However, the literature examining the connection
between past behaviors, multiple facets of identifica-
tion, and various future intentions is underdeveloped.
Trail et al. (2006) provided empirical evidence that past
behavior and identification combined provided a more
thorough explanation of the variance in future behav-
ioral intentions. However, this was the only study that
provided evidence of this relationship, and both previous
and future consumption behaviors were measured only
through attendance.
This becomes even more important when dealing
with a new program where team identification may not
yet have been established. The impact of a new football
program is a unique consumption experience which may
have an effect on various levels of identification and
future consumption behavior. There has been a substan-
tial growth of new sport teams in general and college
football programs in particular in recent times. A better
understanding of past behaviors and fan identification
effects on future intentions for sport consumption can
help cultivate a fan base for these new teams which is
vital to their existence.
However, only a few studies examined identification
in a new sport environment. James et al. (2002) found
the reasons for purchasing season tickets for a new Major
League Baseball team differed based on psychological
connection to the team. Lock, Darcy, and Taylor (2009)
examined member identification with a new club soccer

team in Australia and concluded that age and income
were related to identity strength. Lock et al. (2011) used
a mixed-method approach to understand key themes
driving the formation of new team identification for fans
of Sydney FC, a soccer team in the newly developed
Australian A-League. Their findings suggest that to attract
fan support, a new team should leverage existing social
identities such as identification with the sport or with
the city where the new team plays. None of these studies
looked at the impact of identification on future intentions.
The current study had two objectives. The primary
objective was to test the differential effects of various past
behaviors on multiple behavioral intentions. A second-
ary goal within this objective was to examine how much
variance past behaviors would explain in behavioral
intentions after controlling for points of attachment.
This methodology provides an evaluation of the total
amount of variance explained by points of attachment,
and helps to determine whether the points of attachment
entirely subsume (mediate) the variance explained by past
behaviors. The results will allow marketers and managers
to understand whether it is necessary to take into account
both past behaviors and points of attachment when trying
to ascertain the determinants of future sport consumer
behaviors. However, this type of analysis assumes that
points of attachment potentially mediate the relationship
between past behavior and behavioral intentions. Based on
identity theory and the previous literature on identification
and spectator consumption behavior noted above, this is
a valid assumption, but it should be tested in the current
data. Thus, the second objective was to test for mediation.
Method
Research Setting
The context for this study was a large public university in

the Mid-Atlantic region with an enrollment of approxi-
mately 23,000 students. It is the largest among several
colleges and universities in a metropolitan community
with a population of 1.5 million citizens, but has been
considered a commuter school for many years. Foot-
ball was essential to the university’s goal of shedding
its commuter image and developing a greater sense of
community on campus (Sander, 2010). After confirming
student, alumni, and community interest in 2005, the
Board of Visitors unanimously approved a plan to begin
playing football at the FCS level and the inaugural home
opener occurred on September 5, 2009.
Participants
Data were collected from a random sample of fans
(season ticket holders and students) who attended at least
one home football game during the inaugural season.
Interest in the new team resulted in 73% of the seats
being sold as season tickets. Another 20% of the seats
were reserved for students, 5% were complimentary
tickets for the athletic department, and 2% were provided
to the visiting team, half of those on consignment. The
only tickets available for purchase on game day were
those unsold by the visiting team. Thus, the two largest
groups, season ticket holders and students, were targeted
for this study. The sample was selected from a list of
season ticket holders and student ticket holders during the
inaugural season. A total of 3,000 season ticket holders
were randomly selected from a list of 14,450. In addition,
2,616 students were randomly selected from a database
which included all students who registered for tickets
and attended at least one game during the season. Online
surveys were sent to a total of 5,616 fans and 1,092 usable
surveys were returned for a response rate of 19.4%.

Instrumentation
The questionnaire used for the current study consisted
of four sections with a total of 55 items. The first section
had 12 items related to demographics to profile the typi-
cal respondent. The second section had items measuring
various forms of past consumption behavior including
attendance, television viewership, radio listenership, print
media consumption, merchandise purchases, member
status and length of membership in the annual donor
club, tailgating, and other mediated consumption (e.g.,
web content, Facebook, Twitter; see Table 1 for the list of
the items/scales used in this research and how they were
measured). These measures were adapted from earlier
investigations examining previous behavior (Trail et al.,
2003, 2005). Some of the questions were open-ended to
collect continuous numeric data (e.g., How many home
games did you attend this past season?). Other questions
(7; e.g., I listened to the weekly football coach’s show)
were measured on a 7-point Likert-type scale with end
points ranging from Strongly Disagree (1) to Strongly
Agree. Means and standard deviations along with reliabil-
ity measures are listed in Table 1. The internal consistency
was satisfactory for all multi-item past behaviors (alpha
values ranging from .85 to .86). The correlations among
the past behavior items/scales indicated that they could
not be reduced into higher order factors so they were used
as 11 distinct independent variables.
The third section of the survey included 27 items
measuring identification. The Points of Attachment Index
(PAI), a scale developed to measure facets of identifica-
tion with a sport program (Robinson & Trail, 2005), was
used to measure various aspects of attachment to the new
football program. The PAI consists of nine categories
of attachment (player, team, coach, university, sport,

community, athletic department, general sport fan, level
of sport), which have shown past reliability and validity
related evidence, with alpha scores ranging from .70 to
.88 (Robinson & Trail, 2005; Robinson, Trail, & Kwon,
2004; Woo et al., 2009) and Average Variance Extracted
134 Shapiro, Ridinger, and Trail
Table 1 Means (M) and Standard Deviation (SD) Values for the
Past Behaviors and Behavioral
Intentions
Item/Scale M (SD)
Past Behaviors
Television Consumption (mean score of two items: I watched
sports broadcasts on the local TV news for
information about the team; I watched TV for news about the
team—α= .86)
4.89 (1.84)
Print Media Consumption (I read about the (TEAM NAME)
football team in the daily sport pages.) 5.53 (1.79)
Radio Consumption (mean score of four items: I listened to the
weekly (TEAM NAME) football coach’s
show; I got my information about (TEAM NAME) football from
radio stations; I listened to the pregame
shows on the radio; I listened to the postgame shows on the
radio— α = .85)
3.31 (1.66)

Website (I read about the (TEAM NAME) football team on the
(TEAM NAME) website.) 5.26 (1.80)
Past Attendance (How many home (TEAM NAME) football
games did you attend this season? 0–7.) 6.10 (1.81)
Tailgating (7-point Likert-type scale, Very negative influence
on my attendance (-3) to Very positive
influence on my attendance (+3))
5.66 (1.39)
Facebook (I got information about (TEAM NAME) football
from Facebook.) 2.51 (1.88)
Twitter (I got information about (TEAM NAME) football
through Twitter.) 1.53 (1.28)
Annual Donor Club (6-point scale: How long have you been a
member of the (Donor) club? (1) Less than
a year, (2) 1–2 years, (3) 3–5 years, (4) 6–10 years, (5) 11–20
years, (6) More than 20 years)
2.58 (1.58)
Web Broadcast (I was aware that I could watch (TEAM NAME)
home games online at odusports.com.) 3.76 (2.43)
Merchandise Purchase (fill-in-the-blank item: Please estimate
the total dollar amount (if any) that you
spent during this current season on (TEAM NAME) football
team merchandise and paraphernalia for
yourself and others.)
$136.61 (194.87)

Behavioral Intentions Mean (SD)
Support Sponsors of Football team (mean score of three items:
When I’m planning to purchase a
product, I would be more likely to choose a particular brand if
that company sponsors (TEAM NAME)
athletics; I will support companies that sponsor (TEAM NAME)
athletics when I have a choice between
two products; When a company sponsors (TEAM NAME)
athletics, I am more likely to purchase their
products/services when I have that option (α = .96).
4.86 (1.46)
Purchase Football team Merchandise (Please estimate the total
dollar amount (if any) that you intend
to spend next year on (TEAM NAME) football team
merchandise and paraphernalia for yourself and
others.)
$129.03 (192.23)
Attend Football Games (What is the number of (TEAM NAME)
football home game(s) that you intend to
attend next season?)
6.93 (1.79)
Attend Men’s Basketball Games (I am likely to attend (TEAM
NAME) men’s basketball games.) 5.51 (1.58)
Attend Women’s Basketball Games (I am likely to attend
(TEAM NAME) women’s basketball games.) 4.15 (1.82)
Note: All items were measured on a 7-point Likert-type scale

ranging from Strongly Disagree (1) to Strongly Agree (7) unless
otherwise noted.
(AVE) values ranging from .48 to .73 (Robinson & Trail,
2005; Robinson et al., 2004; Woo et al., 2009). The PAI
items were measured on a 7-point Likert-type scale and
indicated good internal consistency (alpha values rang-
ing from .82–.93) and construct reliability (AVE values
ranging from .615–.809; Table 2).
Finally, the fourth section of the survey consisted
of items measuring future intentions. These items were
measured by asking participants how likely they were
to attend future football games, attend men’s basketball
games, attend women’s basketball games, consume spon-
sor (of the football team) products (3-item scale), and
purchase football team merchandise in the future. Future
intentions were adapted from previous examinations of
identification and future behavior (James & Trail, 2008;
Trail et al., 2003, 2005). These items were measured
individually using a 7-item Likert-type scale and were
retained as single items (except for the sponsored prod-
ucts scale which showed satisfactory internal consistency,
a = .96), with each used as the dependent variable in the
different regression analyses (see Table 1 for means and
standard deviations).
Procedure
Questionnaires were administered through an online
format. Surveys were sent out one week after the final
home game during the inaugural season. Each potential

participant received an introductory e-mail explain-
ing the purpose of the study along with a link to the
web-based survey. A follow up e-mail was sent to all
nonrespondents ten days later in an effort to increase
response rate. In addition, incentives were offered to
respondents who completed the survey. Respondents
had the option to enter a drawing to win one of several
prizes. The information collected for the drawings was
kept separate from survey responses to maximize ano-
nymity and confidentiality.
Table 2 Factor Loadings (β), Confidence Intervals (CI),
Standard Errors (SE), and Average Variance
Explained (AVE) Values for the Points of Attachment Index
(PAI)
Factor and Item β CI SE α AVE
Identification with the players .91 .773
I am a fan of the individual players on the team .788 .767–.809
.013
I am a big fan of specific players .923 .910–.936 .008
I consider myself a fan of certain players .920 .907–.933 .008
Identification with the team .90 .752
Being a fan of (university) football team is very important to me
.819 .800–.838 .012
I am a committed fan of (university) football team .887 .873–
.902 .009

I consider myself to be a “real” fan of the (university) football
team .893 .879–.907 .009
Identification with the coach .87 .705
I am a big fan of (head coach) .788 .765–.810 .014
I would experience a loss if (head coach) was no longer the
coach .850 .831–.868 .011
Being a fan of (head coach) is very important to me .878 .861–
.895 .010
Identification with the university .85 .665
I feel connected to numerous …
Applied Economics, 2009, 41, 3209–3214
Attendance and promotions in
minor league baseball: the
Carolina League
Richard J. Cebula, Michael Toma* and Jay Carmichael
Economics Department, Armstrong Atlantic State University,
Savannah,
GA 31419, USA
This empirical study investigates determinants of attendance at

minor
league baseball games in the Carolina League in 2006. The
focus of the
analysis is on the effect of a wide variety of game-day
promotions on
attendance on a game-by-game basis, rather than aggregate
attendance
during the season. The Ordinary Least Square (OLS) results
imply that
attendance is positively a function of per capita income in the
city or
county hosting the team, runs scored by the home team, Friday
and
Saturday games, and promotions that provide cost-reduced food
or
beverages, low- and high-value merchandise and post-game
fireworks.
Attendance is negatively a function of home team errors,
Monday games
and possibly rainy conditions during the game. An unusual
finding with
respect to minor league baseball is that team performance
variables affect
attendance. However, home team runs scored and home team

errors
contribute to the overall entertainment experience for the home
team fans,
and thus yield plausible effects on attendance.
I. Introduction
The operation of Major League Baseball (MLB)
teams is a remarkably complex enterprise involving
the marketing of a diverse multi-dimensional enter-
tainment commodity (Demment, 1973; Scully, 1974,
1989; Baade and Tiehen, 1990; Quirk and Fort, 1992;
Zimbalist, 1992; Burger and Walters, 2003). Indeed,
as a consequence, there has developed rather sophis-
ticated theoretical as well as empirical literature
dealing not only with baseball but also with other
professional sports as well as amateur sports, partic-
ularly in the US (El-Hodiri and Quirk, 1971; Koch
and Leonard, 1978; Grimes and Chressanthis, 1994;
Vrooman, 1995; Solow and Krautmann, 2007).
1

At the uppermost level is the MLB franchise team’s
playing games in either the National League or
American League. This level of marketing involves
myriad forms of de facto ‘services’/‘commodities’,
especially the playing of MLB games (predominantly
in the form of regular season games), which generates
revenues not only through ticket sales, television
revenue and radio revenue but also through conces-
sion sales (soft drinks, beer, hot dogs, popcorn,
candy) and merchandising, for example, the sale of
team baseball caps, shirts of star players, baseballs,
bats, pennant flags and the like.
At another level of MLB is the multi-tiered system
of minor league teams, a mechanism through which
screening of players with greater potential for MLB
playing occurs and through which development of
players with talent occurs such that at least some
*Corresponding author. E-mail: [email protected]
1
The reader is also referred to the innovative survey by Fort and

Quirk (1995).
Applied Economics ISSN 0003–6846 print/ISSN 1466–4283
online � 2009 Taylor & Francis 3209
http://www.informaworld.com
DOI: 10.1080/00036840903286323
portion of minor league players eventually, and
sometimes quickly, are ‘called up’ to the MLB team
for a chance to make the MLB team roster.
Managers of minor league teams want to maximize
team success, as well as to help develop players to
reach their potential. Arguably, the most successful
minor league teams develop players through a com-
bination of coaching/direction, conditioning and
other means. Arguably, more successful minor
league teams help to avert their own extinction over
the long run by attracting larger crowds. Presumably,
these larger crowds serve to generate favourable
attendance data and revenues that make them less
of a financial burden to the MLB franchise. Teams
with poor attendance records are more likely to be
a financial burden and ultimately become candi-
dates for phasing out. Moreover, in theory, when
‘successful’ minor league teams attract larger crowds,
they can in effect use the ‘roar of the crowds’ to
encourage (‘psych’) young would-be MLB candidates
to respond to the crowd and play to their capacity
so as to attract the attention of their host MLB
team while becoming more accustomed to playing
in front of larger and perhaps more vocal audiences.
Indeed, learning to adjust to heckling may be yet

another side benefit of performing in front of larger
(and arguably more vocal) crowds.
Attendance at minor league games is the focus
of this study. In particular, the objective of this
study is to identify key factors that determine the
attendance record of minor league baseball teams.
To ensure greater comparability of data between
teams and hence relevance of the results, this
study focuses upon a single grouping of teams, the
Carolina League, and a single minor league baseball
season, 2006.
2
II. The Framework
The framework of analysis is one in which attendance
at minor league baseball games is largely a reflection
of factors influencing the demand for home team
tickets for game j. To begin this analysis, it is argued
that the higher the per capita income in the host
county (or host city) for a minor league team, the
greater the demand for tickets in that county, ceteris
paribus, as implied directly or indirectly in a number
of prior studies (Baade and Tiehen, 1990; Fort and
Quirk, 1995; Cebula and Belton, 1996; Solow and
Krautman, 2007). The term PCIj represents the 2005
per capita income in the host county or host city
where game j was played. Naturally, the demand for
minor league tickets is expected to be a decreasing
function of ticket price, ceteris paribus. The term TPj
represents the price of a general admission ticket on
game day for the home team’s j-th game. Team
performance has been argued/found to profoundly

affect the economic well-being of professional base-
ball teams (Baade and Tiehen, 1990; Fort and Quirk,
1995; Cebula and Belton, 1996; Solow and
Krautman, 2007). This study measures team perfor-
mance for the j-th minor league team in two ways: the
cumulative mean number of home team fielding
errors per game over the course of the season (ERRj);
and the mean number of runs scored per game by
the home team over the course of the season (RUNj).
The demand for tickets is expected to be a decreasing
function of ERRj, ceteris paribus, and an increasing
function of RUNj, ceteris paribus. Arguably, home
team fans prefer their team to make fewer errors
(manifest good fielding/defense) and score more runs
(manifest good offense). Next, minor league baseball
fans presumably prefer to attend games when the
weather is not rainy, ceteris paribus. The variable
RAINj is a binary variable indicating whether there
was precipitation present during the course of game j.
Arguably, the demand for minor league game
tickets might reflect various marketing efforts direc-
ted at attracting fans by making attendance a more
pleasurable family experience. General data reflecting
such marketing efforts for each of the teams in the
Carolina League assume the following four forms:
LOWVALj (a binary variable reflecting whether low
value merchandise was ‘given away’ upon entrance to
the stadium at game j, e.g. key chains or magnetized
team schedules),
3
HIGHVALj (a binary variable
indicating whether higher value items were given
away upon entry into the stadium at game j, e.g. hats,

jerseys or helmets),
4
FOOD/DRj (a binary variable
indicating whether discounts or specials on
2
The teams in the Carolina League (and their respective MLB
affiliations and county or city plus state where located) are as
follows: Frederick Keys (Baltimore Orioles, Frederick County,
MD); Kinston Indian (Cleveland Indians, Lenoir County,
NC); Lynchburg Hillcats (Pittsburgh Pirates, Lynchburg City,
VA); Myrtle Beach Pelicans (Atlanta Braves, Horry County,
SC); Potomac National (Washington Nationals, Prince William
County, VA); Salem Avalanche (Houston Astros, Salem City,
VA); Wilmington Blue Rocks (Kansas City Royals, New Castle
County, DE) and the Winston-Salem Warthogs (Chicago
White Sox, Forsyth County, NC).
3
Also included in this category of promotions are mugs, bobble
heads, calendars, water bottles, mouse pads, posters, team
photos, baseball cards and stadium replicas. Such items can, in
theory, tend to generate a degree of spectator loyalty.
4
Also include in this category are shirts, blankets, backpacks,
gym bags, baseball caps and more.
3210 R. J. Cebula et al.
concession items such as two-for-one hotdogs at or
before game time j were offered) and FIREWKSj

(a binary variable indicating whether a fireworks
show/display occurred following the conclusion of
game j). In each of these four cases, the expected
impact of the marketing policy/tool is expected to be
positive, ceteris paribus.
Finally, there are the temporal control variables,
that is, variables that reflect the day during the week
when game j was played. Arguably, such a variable is
needed to control for the fact that families are more
likely to attend games on certain days of the week,
especially Friday and Saturday, when the working
adults in the family are relatively more available,
than other days. Accordingly, dummy variables to
reflect whether game j was played on Monday
(MONj), Tuesday (TUj), Thursday (THj), Friday
(FRj), Saturday (SATj) or Sunday (SUNj) are
included in the model.
III. Empirical Model
Based upon the arguments provided above, the
following reduced-form equation is to be estimated
PERCAPACITYj
¼ a0 þa1PCIj þa2TPj þa3ERRj þa4RUNj
þa5RAINj þa6LOWVALj þa7HIGHVALj
þa8FOOD=DRj þa9FIREWKSj
þa10MONj þa11TUj þa12THj þa13FRj
þa14SATj þa15SUNj þu ð1Þ

where
PERCAPACITYj the total attendance at game j,
expressed as a percentage of the
seating capacity of the stadium
where game j was played during
the 2006 season for all of the
games in the Carolina League,
j¼1, . . . , 975;
a0 constant term;
u stochastic error term.
The Carolina League consists of eight teams
that played 975 games during the 2006 season. The
effective demand for tickets is described as a per cent
of the stadium capacity in each of the venues where
Carolina League games were played. Expressing the
dependent variable thus permits comparison across
stadiums of different capacities. All variables are
for the year 2006. Table 1 provides the data sources,
and Table 2 provides basic descriptive statistics
for the variables in the model. Observe that the
mean percentage attendance at the 975 Carolina
League games in 2006 was 52.29%, with a SD
of 27.6%.
Based on the arguments in the previous section
of this study, the following are the expected signs on
the coefficients for the economic, team-performance,

weather and marketing variables
a1 4 0, a2 5 0, a3 5 0, a4 4 0, a5 5 0,
a6 4 0, a7 4 0, a8 4 0, a9 4 0 ð2Þ
As for the days-of-the-week control variables, it is
expected that
a13 4 0, a14 4 0 ð3Þ
based on the argument that families can most easily
‘get together’ on Fridays (especially during the
evenings) and on Saturdays, when working parents
are more available. Interestingly, the 2 days of the
Table 1. Data sources
Variable Source
PERCAPACITYj Ballparks of Baseball (2007),
http://www.ballparksofbaseball.
com/aballparks.htm
PCIj US Department of Commerce,
Bureau of Economic Analysis
(2005)
TPj Team contacts*
ERRj http://www.minorleaguebaseball.
com/milb/stats/

RUNj http://www.minorleaguebaseball.
com/milb/stats/
RAINj http://www.minorleaguebaseball.
com/milb/stats/
LOWVALj Team contacts*
HIGHVALj Team contacts*
FOOD/DRj Team contacts*
FIREWKSj Team contacts*
MONj Team contacts*
TUj Team contacts*
THj Team contacts*
FRj Team contacts*
SATj Team contacts*
SUNj Team contacts*
Notes: *Team contacts – Frederick Keys, Deanna Davis
(2006), Assistant General Manager of Ticket Operations;
Kinston Indians, Katrina Carter (2006), Director of Sales
and Promotions; Lynchburg Hillcats, Erica Marcum
(2006), Ticket Manager; Myrtle Beach Pelicans, Dan
Kurland (2006), Director of Ticket Sales and Services;
Potomac Nationals, Doug McConnell (2006), Box Office
Manager; Salem Avalanche, Jeanne Boester (2006),
Director of Ticket Operations; Wilmington Blue Rocks,
Jared Forma (2006), Director of Ticket Operations;
Winston-Salem Warthogs, Brian Shollenberger (2006),
Director of Ticket Operations.
Attendance and promotions in the Carolina League 3211
week having the highest percentages of Carolina
League games are Friday and Saturday. By contrast,

the signs on the control variables for MONj, TUj and
THj should not be significantly positive because
Mondays, Tuesdays and Thursdays are generally
working days for most working parents. The argu-
ment regarding SUNj is unclear because although
most employed parents are not working on Sunday,
the family often has other family obligations, reli-
gious attendance and related activities, and possibly
preparation for the coming workweek during the
evening and/or afternoon on Sundays.
IV. Empirical Results
Estimating Equation 1 by OLS, adopting the
White (1980) heteroscedasticity correction yields
Equation 4
PERCAPACITYj
¼ 1:35
ðþ0:22Þ
þ0:0012PCIj
ðþ8:00Þ
�0:109TPj
ð�0:12Þ
�7:831ERRj
ð�3:76Þ
þ2:071RUNj
ðþ3:65Þ
�15:89RAINj
ð�1:87Þ

þ9:579LOWVALj
ðþ6:23Þ
þ13:693HIGHVALj
ðþ4:87Þ
þ5:662FOOD=DRj
ðþ2:31Þ
þ31:696FIREWKSj
ðþ11:20Þ
�4:67MONj
ð�2:00Þ
�4:23TUj
ð�1:62Þ
þ4:25THj
ðþ1:65Þ
þ6:40FRj
ðþ2:29Þ
þ19:62SATj
ðþ7:25Þ
þ0:81SUNj
þ0:33ð Þ
;
R2 ¼ 0:39; adj: R2 ¼ 0:38; F ¼ 41:48 ð4Þ
where terms in parentheses beneath coefficients are

signed t-values. Seven of the nine noncontrol
variables exhibit the expected signs and are significant
at the 5% level or beyond; only the ticket-price
variable fails to be significant at an acceptable
(i.e., 5%) level. Among the control variables, the
estimated coefficients on FRj and SATj are both
positive and statistically significant at the three and
one percent levels, respectively. None of the other
control variables are positive and statistically signif-
icant at the 5% level. The coefficient on MONj is
actually negative and significant at the 5% level.
Overall, ceteris paribus, the results for the control
variables suggest that Friday and Saturday games are
the most likely to attract large turnouts whereas
Monday is perhaps a day to avoid scheduling games
(if feasible). The coefficient of determination is 0.39,
so that the model explains nearly two-fifths of the
variation in the attendance variable as defined.
The F-statistic is significant at the 1% level, attesting
to the overall strength of the model.
As shown in Equation 4, the coefficient on the
per capita income variable is positive and significant
at beyond the 1% level, suggesting strongly that
locating a team in a venue with a higher per capita
income acts to raise attendance. By contrast, the
coefficient on the ticket-price variable is not statisti-
cally significant, suggesting that in the proximal price
range of general admission tickets, the ticket price is
not a significant factor in determining ticket pur-
chases. As a comparison, the average price of a
general admission ticket is less than that of an adult
ticket to a movie theatre. As for the home team
performance variables, ERRj and RUNj, the esti-
mated coefficients are both statistically significant
at beyond the 1% level and, respectively, negative

(as hypothesized) and positive (as hypothesized).
Thus, attendance at/ticket purchases to Carolina
League games are inversely impacted by fielding
errors committed by the home team and positively
impacted by runs scored by the home team. Team
performance counts! However, the loyalty of
Carolina League fans may be tested by inclement
weather. Namely, the coefficient on the RAINj
variable is negative and marginally statistically
significant, implying that rainy weather conditions
may dampen attendance, indeed, by as much as 16%.
Lastly, there are the impacts of the marketing
mechanisms. The estimated coefficients on each
of the four marketing variables, LOWVALj,
HIGHVALj, FOOD/DRj and FIREWKSj are posi-
tive and statistically significant at beyond the 1%
level. Thus, in 2006, when Carolina League home
teams offered fans enticements that fell under the
umbrella of LOWVAL, attendance rose on the
Table 2. Descriptive statistics
Variable Mean SD
PERCAPACITYj 52.29 27.6
PCIj 21.880 3.379
TPj 6.46 0.86
ERRj 1.153 1.1479
RUNj 4.997 4.234
RAINj 0.008 0.09
LOWVALj 0.217 0.412
HIGHVALj 0.0636 0.244
FOOD/DRj 0.071 0.257
FIREWKSj 0.138 0.346
MONj 0.127 0.333

TUj 0.1456 0.353
THj 0.131 0.338
FRj 0.161 0.368
SATj 0.1589 0.366
SUNj 0.135 0.342
3212 R. J. Cebula et al.
average by roughly 10 percentage points for the game
in question, ceteris paribus. Alternatively, when home
teams in the Carolina League offered the enticements
that fell under the more costly umbrella of
HIGHVAL, attendance rose for the game in question
on average by roughly 14 percentage points, ceteris
paribus. Enticements falling under the umbrella of
FOOD/DRj on average for the game in question
acted to raise attendance by 6 percentage points,
ceteris paribus. Finally, home teams in the Carolina
League that chose to offer the costly displays of
fireworks (FIREWKSj) on average experienced a
32 percentage point boost in attendance for that
game, ceteris paribus. Thus, on average, taken one
at a time, each of these marketing options offered
by itself had a significant effect on attendance,
ceteris paribus.
In pursuing higher attendance levels, however,
management must be cognizant of increased operat-
ing costs associated with each option and must be
very circumspect as to how these (or other) marketing
tools might be optimally combined. Clearly, sound
marketing strategy would seem to require that
management simultaneously consider all of the
factors that influence ticket purchases, including

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