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Market Demand 1
TITLE:
General Market Demand Variables Associated with Professional Sport
Consumption
AUTHORS:
1 James J. Zhang
2 Eddie T. C. Lam
3 Daniel P. Connaughton
AFFILIATION:
1&3 Department of Exercise and Sport Sciences
University of Florida
2 Department of HPERD
Cleveland State University
CORRESPONDENCE:
Dr. James J. Zhang
Department of Exercise and Sport Sciences
P. O. Box 118205
University of Florida
Gainesville, FL 32611-8205
Tel. 352-392-0584-ext 1274
Fax 352-392-5262
E-mail jamesz@hhp.ufl.edu
Market Demand 2
General Market Demand Variables
Associated with Professional Sport Consumption
Abstract
The purpose of this study was to examine the relationship between general market demands and
consumption levels of professional sport consumers. This study was accomplished through: (a)
validating the theoretical constructs of general market demand variables by conducting a
confirmatory factor analysis, (b) examining the predictability of general market demand factors
to consumption levels of live and televised sporting events, and (c) investigating the relationships
between sociodemographic and general market demand factors. Five hundred and twenty-five
residents of a major southern U.S. city were interviewed using a questionnaire that included eight
sociodemographic variables, 12 market demand variables under three factors (Game
Attractiveness, Economic Consideration, and Marketing Promotion), and 10 professional
sporting event consumption variables. The factor structure of the general market demand
variables was confirmed. Regression analyses revealed that market demand factors were
positively (p < .05) predictive of professional sport consumption. Sociodemographic variables
were significantly (p < .05) related to the market demand factors. The findings imply that
professional sport teams should highlight the market demand variables and adopt differential
marketing procedures for various sociodemographic segments in their marketing practice.
Key Words: market demands, game consumption, professional sports
Market Demand 3
Introduction
Professional sport teams have two primary product markets, ticket sales and broadcasting
rights, which account for over 80% of team revenue. Teams also have secondary revenue
producers such as parking, concessions, programs, endorsements, uses of team logos, and media
productions (Leonard, 1997; Noll, 1991). The relationship between live and televised events is
reciprocal. Each has influenced and depended on the other for its popularity and commercial
success (Jhally, 1989; Whannel, 1992). Spectator attraction and retention at both live and
televised events are very critical to the financial success of teams. Nevertheless, spectator
retention is the most common problem facing the sport industry (Sawyer & Smith, 1999).
Sport games are the core product function of professional sport teams. In recent years,
there has been an increased interest in studying consumer demands of the core products.
Researchers (Greenstein & Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983; Zhang,
Pease, Hui, & Michaud, 1995) have generally grouped variables affecting spectator game
consumption into the following categories: game attractiveness (e.g., athlete skills, team records,
league standing, record-breaking performance, closeness of competition, team history in a
community, schedule, convenience, and stadium quality); marketing promotions (e.g., publicity,
special events, entertainment programs, and giveaways); and economic considerations (e.g.,
ticket price, substitute forms of entertainment, income, and competition of other sport events).
The majority of previous studies have focused on game attractiveness variables, while the other
two areas have been studied to a lesser extent (e.g., Baade & Tiehen, 1990; Marcum &
Greenstein, 1985; Noll, 1974, 1991; Whitney, 1988; Zak, Huang, & Siegfried, 1979).
Game attractiveness and marketing promotion variables have generally been found to be
positively related to game consumption (Baade & Tiehen, 1990; Becker & Suls, 1983; Hansen &
Gauthier, 1989; Jones, 1984; Marcum & Greenstein, 1985; Noll, 1991; Whitney, 1988; Zhang et
al., 1995). For economic variables, income and ticket discounts have consistently been shown to
be positively related to game consumption, while ticket price, substitute forms of entertainment,
and competition from other sport events have generally been shown to be negatively related to
game consumption (Baade & Tiehen, 1990; Bird, 1982; Hansen & Gauthier, 1989; Noll, 1974;
Siegfried & Eisenberg, 1980; Zhang & Smith, 1997; Zhang, Smith, Pease, & Jambor, 1997).
To facilitate research activities and professional practice, a limited number of researchers
have attempted to formulate measurement scales to assess the main areas of market demand
Market Demand 4
variables. For examples, Hansen and Gauthier (1989) intended to develop a scale of 40 items
although the factors and items did not converge well enough to be interpretable. Zhang et al.
(1995) developed the Spectator Decision-Making Inventory for professional basketball games, in
which four factors with 15 items were retained: Game Promotion, Home Team, Opposing Team,
and Schedule Convenience. Overall, few quality measures exist for assessing general market
demand variables of professional sports.
Several weaknesses associated with the previous studies are recognized. First, many of
them were based on reviews of literature and just categorized the market demand variables
derived from various research studies (e.g., Noll, 1974, 1991; Schofield, 1983; Siegfried &
Eisenberg, 1980; Siegfried & Hinshaw, 1977). The qualitative nature of these studies may limit
their usefulness. Second, most of the studies focused on just one particular sport, for example,
professional basketball (e.g., Noll, 1991; Zak et al., 1979; Zhang et al., 1995) and professional
football (e.g., Doyle, Lewis, & Malmisur, 1980; Siegfried & Hinshaw, 1977). According to
Thomas and Nelson (2001) and Baumgartner and Jackson (1999), research findings are
population specific. The findings from a specific sport setting lack generalizable application in
the sport market place. Third, of the studies related to a wide range of professional sports, either
team or league cohort data were used as the unit of analysis. Marketing directors of teams,
instead of the direct consumers, were typically studied (e.g., Hansen & Gauthier, 1989; Noll,
1974; Whitney, 1988). Finally, exploratory statistical procedures were the primary form of
analysis in many previous studies, which was data-driven. The resolved scales could be biased
toward the samples involved in the studies. According to Disch (1989), Nunnally (1978), and
Stevens (1996), when developing an assessment instrument in the affective domain, it would be
advantageous to have a large sample size while applying both exploratory and confirmatory
analytical procedures, such as a confirmatory factor analysis that is more theory-driven.
Additionally, previous researchers suggested that game consumption levels were
associated with various sociodemographic variables, such as individual age, gender, ethnicity,
marital status, household, income, education level, occupation, and community size (population)
(Greenstein & Marcum, 1981; Mullin, Hardy, & Sutton, 2000; Noll, 1974; Pitts & Stotlar, 2002;
Scully, 1974; Siegfried & Eisenberg, 1980; Simmons Market Research Bureau, 2000; Whitney,
1988). However, these studies typically examined the relationships between sociodemographics
and game consumption levels by computing correlation coefficients or comparing mean
Market Demand 5
differences between these two variables. While it is useful to describe and understand the
existing relationships, individual sociodemographic characteristics cannot usually be
manipulated. Vital information on where, why, and how relationships exist is unknown. Thus,
results from these studies cannot be directly used in formulating marketing strategies. Perhaps,
the association between sociodemographics and game consumption is due to some intermediary
variables that are controllable, such as market demand variables. Zhang et al. (1995) found that
market demand variables for professional basketball games should be differentiated with respect
to spectator sociodemographics. Likewise, Trail, Anderson, and Fink (2002) found that spectator
satisfaction with sport venues was related to gender. Nonetheless, it is still unknown if market
demand variables mediate the relationships between sociodemographics and sport consumption.
The purpose of this study was to examine the relationships of general market demands
and consumption levels of professional sport consumers, through an investigation with the
following three components: (a) validating the theoretical structure of general market demand
variables by the use of a confirmatory factor analysis (CFA), (b) examining the predictability of
general market demand factors to consumption levels of professional sport consumers, and (c)
examining the relationships between sociodemographic and general market demand factors.
Specifically, three research questions guided this study:
1. Will the three general market factors (Game Attractiveness, Marketing Promotion,
and Economic Consideration) be confirmed by CFA?
2. Will the general market demand factors be predictive of consumption levels of live
and televised events?
3. Will sociodemographic variables be related to the general market demand factors?
Methodology
Sample
A total of 525 individuals, who resided in a greater metropolitan city area located in the
southern United States, voluntarily participated in the interview survey. Only those of 18 years of
age and older were interviewed. The data were collected in eight shopping malls (N=225), six
food courts (N=175), five restaurants (N=40), one sporting event (N=25), and by telephone using
a systematic sampling procedure (N=60).
Descriptive statistics of the sociodemographic variables are presented in Table 1. The
characteristics of the respondents were generally consistent with those of major league sport
Market Demand 6
consumers (Simmons Market Research Bureau, 2000). When compared to the characteristics of
the greater metropolitan population and the U.S. population, certain discrepancies were
recognized although consistency still existed in several aspects (U.S. Census Bureau, 2002).
About 65% of the subjects were males, which over represented the male proportions of 49.01-
49.76% in the local and national population. The sample also over represented the younger age
groups, with the majority of them (85%) between 18 and 45 years old. Although the ethnic
proportions represented the greater metropolitan population well, with close to 60% being White
and 21% being African Americans, the composition under represented White by 10% and over
represented African Americans by 10% when compared to the national population. Although
annual household income was quite normally distributed with about 80% being between $25,000
and $75,000, these proportions over represented the local and national norms of income level by
20%. Households with under $25,000 income were under represented by about 15% and
households with over $100,000 income were under represented by 5%.
Characteristics related to marital status and household size were basically consistent with
the greater metropolitan and U.S. population. One half of them were married and 41% were
single. Household sizes with 2 to 4 people were the majority (65%). Most of the respondents had
a household entertainment budget between 5 to 20% of their annual income. Television (TV) and
newspaper were the primary sources of entertainment information.
Questionnaire Development
Development of the questionnaire was based upon a review of literature (Greenstein &
Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995) and interviewing
five administrators of one professional basketball team and one professional hockey team located
in the greater metropolitan area where this study took place. The questionnaire included eight
sociodemographic background variables (gender, age, ethnicity, marital status, household
income, household size, household budget on entertainment, and prime information source of
entertainment); 12 market demand variables under three factors: Game Attractiveness (home
team’s win/loss record, team history in the community, closeness of competition, love of the
sport, record-breaking performance, and schedule convenience), Marketing Promotions
(publicity, advertising, and special promotional programs/giveaways), and Economic
Consideration (ticket price, choice of substitute entertainment forms, and entire event costs for
the individual or the group/family); and 10 professional sporting event consumption variables
Market Demand 7
with five of them related to event attendance (National Basketball Association (NBA) games,
Women’s National Basketball Association (WNBA) games, Major League Baseball (MBL)
games, arena football games, and minor league hockey games) and the remaining five related to
TV viewing (NBA games, MLB games, National Football League (NFL) games, National
Hockey League (NHL) games, and minor league hockey games).
The sociodemographic variables were phrased into multiple-choice questions. The market
demand variables were phrased into a Likert 5-scale statement with 5 being ‘most important’ and
1 being ‘least important.’ Variables related to attendance frequency of professional sport events
only included five types of events available in the greater metropolitan area where this study was
conducted. These variables were phrased into Likert 5-scale statements (5 = over 6 times; 4 = 5-6
times; 3 = 3-4 times; 2 = 1-2 times; 1 = 0 time in the past 12 months). The TV viewing variables
were also phrased into Likert 5-scale statements (5 = 10 or more times; 4 = 7-9 times; 3 = 4-6
times; 2 = 1-3 times; 1 = 0 time in the past 12 months). It is necessary to note that although
adopting the Likert scales for the game consumption variables enhanced cooperation from the
residents to participate in the study (Stotlar, 1989), certain accuracy may have been lost. An
open-ended scale could have been a better option. The findings of this study, particularly in the
proportion of variance explained, may have been somewhat affected by this research limitation.
Content validity of the questionnaire was attained through a panel of experts including
five administrators from the professional teams, three university professors in sport management,
and a group of university students (n=12) who were enrolled in sport marketing classes at the
graduate and undergraduate levels. The panel members were asked to examine the relevance,
clarity, and representativeness of the items in the questionnaire. The questionnaire was approved
with minor revisions that were mainly related to formatting and clarity.
Procedures
The interviews took place in eight shopping malls, six food courts, five restaurants, and
one sport event, as well as via telephone. For the shopping malls and the sport event, face-to-face
interviews took place at the entrance areas. For the food courts and restaurants, face-to-face
interviews took place in seating sections. Telephone interviews were conducted in the evenings.
Two trained researchers conducted the interviews.
Regardless of the place or format, a standard procedure was followed during all
interviews. The average time for the interviews was about 15 minutes. After an individual agreed
Market Demand 8
to participate in the study, he/she usually answered all of the questions with sincerity. For the
telephone interviews, a local residential telephone directory was used to conduct a systematic
selection. Every 100th resident in the phone book was selected. A total of 600 residential
households were contacted during the evening. A majority of the households (approximately
70%) did not answer the telephone call. Of those who answered the calls, 60 people representing
their households agreed to participate in the study. Overall, a total of 525 individuals responded
to the survey. The entire data collection was completed within two months.
Data Analyses
The procedures from Version 10.0 of the SPSS for Windows (SPSS, 1999), Windows
PRELIS 2 (Jöreskog & Sörbom, 1993), and Windows LISREL 8.12 (Jöreskog & Sörbom, 1993)
computer programs were utilized to conduct data analyses. Descriptive statistics were calculated
for the sociodemographic, market demand, and professional sport consumption variables. The
market demand variables were subject to a CFA. For the purpose of data reduction, an
exploratory factor analysis (EFA) was conducted separately for the consumption variables of live
and televised professional sports. Regression analyses were conducted to examine the
relationships among the market demand factors and the professional sport consumption factors.
General linear model (GLM) regression analyses were conducted to examine the relationships
between the sociodemographic variables and the market demand factors.
Results
Descriptive Statistics
Because the market demand variables were subject to a CFA, detailed descriptive
statistics were calculated (Table 2). Out of the 12 variables, 10 were significantly (p < .05)
skewed and all 12 had significant (p < .05) kurtosis. The basic assumption of multivariate
normality was not met. Descriptive statistics for the sport event attendance and TV viewing
variables are presented in Table 3. The most attended sport events were MLB and NBA games,
whereas the most watched sport events on TV were NFL and NBA games.
Factor Analyses
Because the basic assumption of multivariate normality was not met for the market
demand variables, the Weighted Least Squares (WLS) estimation method was used for the CFA
(Byrne, 1998). The chi-square statistics of the model was significant (i.e.,
2 = 246.74, df = 51, p
< .01). The
2 value is unrealistic in most structural equation modeling (SEM) empirical research
Market Demand 9
because of its limitations as a descriptive index of model fit (Loehlin, 1998). For this reason, the
2 statistic was used in this study for comparing the fit between models (i.e.,
2) rather than as a
fit index. The goodness-of-fit indexes of the model were in the uppermost ranges. For example,
Goodness-of-Fit Index (GFI) = .97, Adjusted Goodness-of-Fit Index (AGFI) = .95, and
Comparative Fit Index (CFI) = .95. According to Hu and Bentler (1995), a value of .95 or larger
indicates an acceptable fit to the data. The selection of these fit indexes was because of their
performance and stability over a variety of settings (Bentler, 1990; Bollen & Long, 1993;
Browne & Cudeck, 1993; Hoyle & Panter, 1995; McDonald & Marsh, 1990; Marsh, Balla, &
McDonald, 1988). CFI was chosen because its value would not fall outside the "normed" range
(i.e., within 0 to 1 range) and it has "only small downward bias (3% to 4%), even under severely
nonnormal conditions" (West, Finch, & Curran, 1995, p. 74).
(INSERT FIGURE 1 AROUND HERE)
Overall, the fit indexes indicated that the model provided a good fit to the data. The
lambda values (i.e., intercorrelation coefficients between indicators and a latent variable) of all
the items were .71 or higher. The phi coefficients (i.e., intercorrelation coefficients among latent
variables) ranged from .74 to .82, indicating that there were moderate intercorrelations among
the three latent variables. Because of these high correlations, a one-factor model was examined.
However, when the one-factor model was tested, the fit indexes dropped (e.g., GFI = .95 and CFI
= .92). Furthermore, the one-factor model was significantly (p < .01) inferior to the three-factor
model when examining the changes in chi-square and the changes in degrees of freedom. A
comparison of the changes in the goodness-of-fit indexes and model-fit statistics between the
three-factor model and the one-factor model are depicted in Table 4. In the table, the Expected
Cross-Validation Index (ECVI) and the Akaike Information Criterion (AIC) are cross-validation
indexes that are used to measure the fit across samples. The ECVI and AIC have no
predetermined range of values (Byrne, 1998). Both the ECVI and AIC were used to compare two
or more models with smaller values representing a better fit of the hypothesized model (Hu &
Bentler, 1995). In this study, both the ECVI and AIC statistics for the three-factor model were
substantially smaller than the one-factor model, suggesting that the three-factor model is better.
The factor structure coefficients, interfactor correlations, and errors of measurement
estimated by the CFA of the three-factor model are presented in Figure 1. The parameter
estimates between the indicators and latent variables ranged from .71 to .96. The interfactor
Market Demand 10
correlations ranged from .74 between Game Attractiveness and Economic Consideration to .82
between Game Attractiveness and Marketing Promotion. The errors of measurement extended
from .09 (advertising and publicity) to .49 (event cost to the group).
The composite reliability for the three-factor model, which is an internal consistency
reliability measure that accounts for measurement errors, were .91, .95, and .87 for the Game
Attractiveness, Marketing Promotion, and Economic Consideration factors, respectively, which
were above the .70 acceptable standard (Fornell & Larcker, 1981). Reasonable variances were
extracted by the constructs. The variance extracted was .62, .87, and .69 for the factors,
respectively, which were considered acceptable when compared to the minimum requirement of
.50 (Fornell & Larcker, 1981). The factor structure of the market demand variables was
consistent with theoretical indications and previous research findings.
Additionally, for the purpose of reducing the data sets and calculating factor scores, an
EFA was conducted for the professional sport attendance and TV watching variables,
respectively (Table 5). Conducting separate EFA was based upon the consideration that
attending live events and watching TV programs are two divergent forms of consumption. For
the attendance variables, two factors emerged from the principal component extraction and the
varimax rotation techniques, explaining a total of 55% of the variance. A factor loading equal to
or greater than .40 without double loading was used to retain a variable (Disch, 1989; Nunnally,
1978; Stevens, 1996). All 5 variables were retained under the two factors, which were named as
Traditional Sports (3 items – MLB, NBA, and minor league hockey games) and New Sports (2
items – WNBA and arena football games). Naming was based on the primary variables in the
factors. Alpha reliability coefficients for the factors were .67 and .61, respectively. Similarly,
two factors were extracted for professional sport TV watching variables, explaining a total of
almost 73% of the variance. The two factors were named as Big Three Sports (3 items – NBA,
NFL, and MLB games) and Hockey Events (2 items – NHL and minor league hockey games).
Alpha reliability coefficients for the factors were .76 and .72, respectively.
Overall, the market demand and professional sport consumption variables displayed
acceptable measurement properties in terms of validity (Disch, 1989; Nunnally, 1978; Stevens,
1996; Tabachnick & Fidell, 1996) and reliability (Baumgartner & Jackson, 1999; Nunnally,
1978) in order for this study to proceed. Factor scores were calculated for the consumption
factors and were utilized in hypothesis testing.
Market Demand 11
Regression Analyses
Multiple regression analyses were conducted to examine the relationships between the
market demand and the consumption factors (Table 6). Game Attractiveness and Economic
Consideration were significantly (p < .05) predictive of Traditional Sports attendance and
Hockey Events TV viewing. Game Attractiveness was significantly (p < .05) predictive of New
Sports attendance, whereas Game Attractiveness and Marketing Promotion were significantly (p
< .05) predictive of Big Three Sports TV viewing. The findings have stressed the value of
incorporating market demand variables in sport marketing studies and practice. Potential
marketing implications derived from these findings are discussed in the following section.
Because the sociodemographic variables were categorical, GLM regression analyses were
conducted to examine the relationships of these variables to the market demand and the
consumption factors (Table 7). The GLM analyses utilized the following formula (Stevens,
1996), where (r2/k) is the group mean square and (1- r2)/(N-k-1) is the residual mean square:
(r2/k)
(1- r2)/(N-k-1)
Due to the differences in the number of categories, two sociodemographic variables with
the same r2 values may end up with different findings. Five sociodemographic variables (gender,
age, ethnicity, marital status, and entertainment budget) were significantly (p < .05) related to
Game Attractiveness. Four sociodemographic variables (age, ethnicity, marital status, and
household income) were significantly (p < .05) related to Marketing Promotion. Only age was
significantly (p < .05) related to Economic Consideration, which was of a negative trend.
Discussion
The resolved construct of the market demand variables in this study was consistent with
the theoretical dimensions suggested by previous researchers (Greenstein & Marcum, 1981;
Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995). The CFA in this study supported
the market demand factors and their respective items. Most measurement validation procedures
in sport marketing have only employed EFA. This study utilized a CFA to validate the
psychometric properties of the market demand variables and to examine their robustness,
resulting in a greater generalizability of the findings. It is believed that having the statistically
confirmed inventory is timely and important for enhancing the quality of marketing research and
practice in professional sports. The commonality feature of the resolved market demand
Market Demand 12
variables warrants them to be more applicable in various professional sport settings, particularly
when comparisons across different sporting events need to be made. Understanding the common
features may also help to set a stage for researchers and practitioners to further examine specific
issues that are unique to a particular sporting event. Researchers and practitioners may
incorporate the market demand factors into their marketing studies. However, even when a
model fits the data well, one should not ignore the presence of other equivalent models
(MacCallum, 1995). The moderate intercorrelation among the three latent constructs implies that
they are possibly influenced by another latent variable. Future studies should examine this.
The market demand factors were found to be predictive of the consumption of live and
televised professional sporting events. These findings were consistent with previous research
findings (Baade & Tiehen, 1990; Becker & Suls, 1983; Hansen & Gauthier, 1989; Jones, 1984;
Marcum & Greenstein, 1985; Noll, 1991; Whitney, 1988) and they have revealed the importance
of the market demand factors (i.e., Game Attractiveness, Economic Consideration, and
Marketing Promotion) when formulating the marketing mix (i.e., product, price, place, and
promotions). In practice, specific marketing procedures may be formulated with respect to a 4x3
interactive grid between the four marketing mix elements and the three market demand factors.
Contingency factors that are unique to various sports and competition levels can also be reflected
in different situations. Some general marketing implications are briefly discussed below.
Of the three market demand factors, Game Attractiveness was the strongest predictive
factor of game consumption. This factor is composed of six items: winning/loss record, team
history, closeness of competition, love of sport, record breaking performance, and schedule.
According to Zillmann, Bryant, and Sapolsky (1989), sport consumers tend to establish implicit
alliances with renowned athletes and/or teams. Watching an allied team succeed enhances the
affective bond with that team. Greater spectator enjoyment occurs when an allied team defeats a
very competitive opponent. Obviously, building and promoting a winning team should be a goal
of all professional sport teams. Highlighting the previous achievements of a team in game
presentations, game promotions, and stadium/arena decorations would enhance game
attractiveness, which is particularly important when a team is not winning at a desirable level.
According to Irwin, Sutton, and MacCarthy (2002), the appearance of previous team stars or
other related celebrities and formulating team rituals such as cheering/ fighting songs or slogans
are valuable forms of promotion for building team tradition. Researchers (e.g., Baade & Tiehen,
Market Demand 13
1990; Hansen & Gauthier, 1989; Jones, 1984; Marcum & Greenstein, 1985; Noll, 1991; Zhang et
al., 1995) have consistently found that record breaking performances, of both individual athletes
and teams, generate public interests for game consumption. Various advertising, publicity, and
public relation channels may be used to inform the public of a likely record breaking
performance to promote the game. Zhang, Wall, and Smith (2000) found that sport fans are more
likely to attend games that are expected to be close, dramatic, and/or when the visiting team has
a superstar(s). Publicizing the profiles and statistics of opposing teams and athletes, as well as
making reasonable comparisons between the home and opposing teams, may be useful
procedures to attract spectators to attend more games. According to Schwartz (1973), Sloan
(1989), and Zillmann and Paulus (1993), spectators are attracted to a team by identifying with
the achievement of others, sharing success, gaining knowledge, and satisfying their own needs.
Zhang et al. (1997) suggested that providing spectators and the community with a link to the
team's short-term and long-term goals may help to maintain and improve spectator support. It is
important to make sport consumers feel that they have contributed to a team’s success. Effective
communication, education, public and media relations, and community image building are
usually predictive of fan identification with a team. Improvement of team loyalty is also helpful
for eliminating the negative consideration of an opposing team (e.g., one that may be extremely
strong, extremely weak, and/or lack star players) when making decisions to consume games.
Mullin et al. (2000) indicated that the marketing of sport products requires an approach
that may at times lie beyond the approaches of mainstream business marketing. Sport has certain
characteristics in its core, extensions, and presentation that make the product unique. Each game
has its uniqueness in terms of game nature, composition, form, skills, and tactics. Understanding
the game elements that contribute to spectator enjoyment of a sport would be helpful to sport
marketers when attempting to promote the games with specificity. Love of a sport is one
indication of the commitment level between fans and a local sport team. Several researchers
suggest that love of a sport is generally formed at a young age (e.g., Smith, 1995; Zhang, Smith,
Pease, & Mahar, 1996). Logically, events such as theme nights and tie-ins with popular events
that are targeted at youth can be used to promote a sport to youth. Fostering grassroots program,
sport educational programs, sponsoring youth, school, and community sports, and promoting an
active lifestyle through sports are necessary practices that professional sport teams should
conduct to generate love and passion towards a specific sport. Promotions should also include
Market Demand 14
using multimedia sources and developing sound public and media relations. Besides game
winning elements, spectators’ love of sport also derives from effective and daring play. Spectator
enjoyment is positively related to the perceived riskness, implicit contempt, skill involved, and
gusty play that lead to success. Additionally, spectators in general prefer weekend games. When
a game is scheduled for a weekend, it should be publicized in the promotional messages.
Although it is impossible to arrange all season home games on the weekends, it is important to
maximize the number of weekend games. Game time of day should also be considered in order
to compensate for weekday games. In fact, game time is also important for weekend games. For
example, Zhang (1998) found that for weekday and Saturday games, 7:00 p.m. was a more
preferred time and for Sunday games, 4:00 p.m. was preferred. Certainly, time zone differences
and telecast schedules must be considered when scheduling games.
Economic Consideration is composed of three items (ticket price, choice of substitution
entertainment forms, entire cost of event for the individual or a group/family), and was predictive
of two of the four game consumption factors (Traditional Sports and TV Hockey Events). Yet,
the mean scores of the Economic Consideration items were the lowest among all items (see
Table 2), which were inconsistent with previous research findings that ticket price, substitute
forms of entertainment, and competition from other sport events were negatively related to game
consumption (e.g., Baade & Tiehen, 1990; Hansen & Gauthier, 1989; Siegfried & Eisenberg,
1980; Zhang & Smith, 1997; Zhang et al., 1997). The findings may have been influenced by the
following reasons: (a) Game Attractiveness was found to be the strongest predictive factor of
game consumption and thus it is the primary product element. Consumers are willing to pay a
higher ticket price when the primary product is very attractive; (b) in this study, spectator
consumption of both major and minor sports were included and thus economic issues were not as
significant as major league only studies; and (c) Economic Consideration was found to be the
second strongest predictor of game consumption factor in the regression analyses, which was an
inferential procedure and provided more generalizable information about the relevance and
importance of the factor to the marketing of professional sports. In any case, a professional sport
team should focus on providing a quality game package through the game itself and game
presentations to spectators at a reasonable price. According to Helitzer (1994), Irwin et al.
(2002), Mullin et al. (2000), and Pitts and Stotlar (2002), ticket discounts, such as buy one ticket
and get the second at half price, may be used when attendance is expected to be low. The same
Market Demand 15
type of promotion may also be used to maintain spectator support, for instance, when the team is
not performing well. Upgrading seat sections and/or occasionally providing better seats without
additional costs or with low costs are enormous incentives for spectators. Spectators are always
pleased to receive a giveaway, prize, and/or souvenir, for example, a mug or a poster.
Discounted group ticket plans should always be available. According to Melnick (1993),
sport events provide social opportunities for family members, friends, and organizational
members to come together to be entertained, enrich their social lives through socialization,
experience quasi-intimate relationships, and develop a sense of belonging. Sport arenas and
stadia are convenient places, and provide an ecological setting and social structure for people to
search for and conduct casual socialization. Through game consumption, people develop a sense
of commitment, support, and alliance, as well as share interests, knowledge, and excitement.
Melnick (1993) and Sloan (1989) agree that socialization among spectators adds to the
entertainment value of spectator sports. Additionally, according to Zhang et al. (1997), market
competitions to sporting events may come from movies, recreational sports, non-sport TV
programs, arts, and even dining and nightclubs. Professional sport teams should study their
market environment and identify market competitors and supporters in order to formulate
competitive or cooperative promotional strategies with organizations in their communities.
Marketing Promotion is composed of three items (publicity, advertising, and special
promotional programs). Although this factor appeared as the weakest predictor, it should not be
underrated. A number of researchers have emphasized the importance of promotional activities
to sport consumption (e.g., Helitzer, 1994; Irwin et al., 2002; Mullin et al., 2000; Pitts & Stotlar,
2002). Ticket deals and incentive activities should be promoted through advertising, publicity,
direct mailings and notification, and discounts/coupons. Direct mailings (i.e., personal letter,
congratulation card, newsletter, annual report, brochure, and flyer) are a popular and effective
tool in market promotions. The key to an effective direct mail program is the quality of the piece
mailed. Self addressed, stamped envelopes should be used to make it easier for customers to
respond. Direct mail may be routinely used to communicate with past and present season ticket
holders. Whenever possible, multimedia advertising should be considered, including traditional
forms (e.g., television, newspaper, radio) and less traditional forms (e.g., internet, telephone).
Good team and media/community relations are critical to a positive team image. A team's
media and community relations departments should actively seek to develop good team/game
Market Demand 16
publicity and community image. Cooperation from individuals, organizations, businesses, and
clubs in the local community are invaluable to game promotions since these groups convey their
positive team-related dealings/feelings to potential spectators. The availability of a team's
members (e.g., athletes, coach, and mascot) to the local community is a very important form of
public relations. Player appeal may be promoted by having favorite players appear (in picture,
description, or person) in the local media, community events, team program book, schedule,
pamphlet/flyers, mail-outs, and sponsorship. When possible, player appearances can be held
before, during, and immediately following games. Excitement of the games should also be
highlighted through printed and broadcasting media, replay screens, music selection, the public
address system, and flyers/mail outs. In addition to ticket discounts, give-aways, coupons, and
seat upgrades, promotional activities should include theme days, on-line information,
opportunities to communicate with the team, and participation in various team functions, such as
a practice session and draft parties (e.g., Helitzer, 1994; Irwin et al., 2002; Mullin et al., 2000).
Significant relationships between the sociodemographic and the market demand factors
suggest the need to further differentiate the demands of professional sport consumers by their
background characteristics. These findings are consistent with the suggestions by Trail et al.
(2002) and Zhang et al. (1995). When implementing market demand variables in the marketing
practice, sport marketers should consider the sociodemographic backgrounds of consumers. In
order to effectively reach the targeted consumer segments, the identified differences should be
incorporated in all aspects of the marketing mix. Although males and females are similar in their
demands of Marketing Promotion and Economic Consideration, males have greater demands on
Game Attractiveness than females. Younger people and minority groups have greater
expectations on the market demand factors than older people and Whites. Similarly, those who
are single, have lower household income, and have a medium entertainment budget tend to have
greater expectations on the market demand factors. Using the market demand factors in the
marketing mix would be more effective in attracting males, the young, singles, and minority
consumers. Except for the ethnic minority variable, males, the young, and singles represent the
core characteristics of professional sport consumers (Simmons Market Research Bureau, 2000).
Apparently, the market demand factors are primary expectations and demands of the core
consumers of professional sports, which further highlights the importance of incorporating the
market demand variables in the marketing practices of professional sport organizations.
Market Demand 17
Although the market demand factors were found to be comparatively less important by females,
older consumers, the married, and the White, they are still relevant to these segments. Other
sport consumption related aspects, such as socio-motivation (Milne & McDonald, 1999; Wann,
1995) and service quality (McDonald, Sutton, & Milne, 1995; Wakefield & Sloan, 1995), that
were not included in this study, should be examined to determine if they are more effective in
marketing professional sports to these consumer segments.
Generally speaking, promotional procedures should be formulated to meet the high
market demands of those who are male, young, minority, single, with a lower household income,
and with a medium entertainment budget. Creative procedures are necessary to make
professional sports more attractive to those who are female, older (seniors), White, married or
divorced, with a higher income level, and with a very low or very high entertainment budget.
According to Simmons Market Research Bureau (2000) and the findings of this study, Whites
and people with a high income and entertainment budget account for the greatest portions of
professional sport consumers and they tend to consume at a greater level than the other groups.
Consumers become dull to the same marketing efforts. Formulating quality and new programs is
vital to make the professional sport product continuously attractive to repeating consumers.
Females represent the greatest market potential for professional sports, and identifying their
expectations and interests are vital to the future of professional sport organizations. Creating
special promotional programs geared toward women, such as a woman’s theme night, is an
example of such practice. Similarly, special marketing procedures should be formulated to make
the marketing mix more attractive to those who are older, married, or divorced. Older people
may have more discretionary money and time for entertainment; however, they may have
different expectations than the younger generation does. Product adaptability and proper
promotions are key to attracting them to events. Affordability and family oriented activities are
important to attract those who are married and with children; whereas increased social
opportunities and networks would be of more interest to those who are single or divorced.
In summary, this study examined the general constructs of market demand variables and
their relationships with professional sport consumption levels. The findings imply that
professional sport teams should highlight the three market demand factors (Game Attractiveness,
Economic Consideration, and Marketing Promotion) when formulating marketing strategies and
adopt differential promotional procedures for various sociodemographic segments.
Market Demand 18
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Market Demand 23
Table 1. Descriptive Statistics for the Sociodemographic Variables (N=525)
_____________________________________________________________________________________________
Variable Category N % Cumulative %
_____________________________________________________________________________________________
Gender Male 345 65.71 65.71
Female 180 34.29 100.00
Age 18-25 120 22.86 22.86
26-35 211 40.19 63.05
36-45 114 21.71 84.76
46-55 53 10.10 94.86
56-65 15 2.86 97.72
Over 65 12 2.29 100.00
Ethnicity White 313 59.62 59.62
African-American 111 21.14 80.76
Hispanic 52 9.90 90.66
Asian 17 3.24 93.90
Other 32 6.10 100.00
Marital Status Single 216 41.14 41.14
Married 264 50.29 91.43
Divorced 32 6.10 97.53
Other 13 2.48 100.00
Household Income Under $25,000 63 12.00 12.00
$25,00-49,999 175 33.33 45.33
$50,000-74,999 138 26.29 71.62
$75,000-100,000 102 19.43 91.05
Over $100,000 47 8.95 100.00
Household Size One 102 19.49 19.49
Two 161 30.71 50.20
Three or Four 186 35.43 85.63
Five or Six 69 13.14 98.77
Seven or More 7 1.33 100.00
Household Budget Less than 5% 72 13.71 13.71
on Entertainment 5-10% 192 36.57 50.28
11-20% 180 34.29 84.57
Over 20% 81 15.43 100.00
Information Source Local TV 250 47.62 47.62
of Entertainment Cable TV 114 21.71 69.33
Newspaper 110 20.95 90.28
Radio 27 5.14 95.42
Internet 13 2.48 97.90
Other 11 2.10 100.00
_____________________________________________________________________________________
Market Demand 24
Table 2. Descriptive Statistics of the Market Demand Variables
Item
M
SD
Skewness
z
p
Kurtosis
z
p
win/loss record
3.49
1.50
-0.76
-3.43
0.00
-0.69
-4.89
0.00
team history
2.60
1.46
0.01
0.12
0.45
-1.17
-16.07
0.00
close competition
3.22
1.53
-0.51
-2.97
0.00
-0.97
-9.46
0.00
love the sport
2.57
1.56
0.07
0.92
0.18
-1.27
-23.62
0.00
record breaking
3.29
1.61
-0.63
-3.22
0.00
-0.99
-9.89
0.00
schedule
2.79
1.61
-0.15
-1.57
0.06
-1.34
-38.00
0.00
ticket price
1.82
1.37
0.83
3.54
0.00
-0.34
-1.77
0.04
substitute forms
1.93
1.39
0.70
3.34
0.00
-0.58
-3.73
0.00
group cost
1.92
1.48
0.77
3.46
0.00
-0.63
-4.22
0.00
publicity
2.91
1.66
-0.20
-1.86
0.03
-1.37
-49.80
0.00
advertising
3.19
1.70
-0.42
-2.73
0.00
-1.34
-37.65
0.00
promotional
3.00
1.56
-0.37
-2.60
0.01
-1.17
-16.30
0.00
Skewness: Z-score = 29.57 (p < .00); Kurtosis: Z-score = 18.87 (p < .00)
Multivariate Normality: 2 = 1,230 (p < .00).
Market Demand 25
Table 3. Descriptive Statistics for Sport Event Attendance and TV Viewing Variables
Likert 5-Scale Response %
Variable ____________________________ M SD
5 4 3 2 1
Event Attendance
MLB games 10.3 17.0 29.1 43.4 0.2 2.43 1.27
NBA games 7.1 12.6 31.1 49.0 0.2 2.21 1.16
Minor league hockey games 4.4 5.6 13.1 76.8 0.2 1.71 0.98
WNBA games 1.9 2.7 11.3 83.9 0.2 1.53 0.74
Arena football games 0.0 0.6 2.3 96.9 0.2 1.29 0.28
TV Viewing
NBA games 44.1 19.3 26.0 10.7 0.0 3.71 1.33
NFL games 42.6 17.7 22.3 17.4 0.0 3.56 1.44
MLB games 27.9 11.3 30.7 30.2 0.0 2.96 1.48
NHL games 8.2 6.5 24.8 60.5 0.0 2.63 1.16
Minor league hockey games 3.2 3.2 19.8 73.7 0.0 2.36 0.88
Market Demand 26
Table 4. A Comparison of the Three-Factor Model and the One-Factor Nested Model
Model
GFI
AGFI
ECVI
AIC
2
df
2
df
3-Factor
.97
.95
0.58
301
246.74
51
1-Factor
.95
.93
0.80
417
368.99
54
122.25*
3
* p < .01
Market Demand 27
Table 5. Exploratory Factor Analyses with Principal Component Extraction and Varimax
Rotation for Sport Event Attendance and TV Viewing Variables
Factor Loading
Variable ________________________________________________
Factor 1 Factor 2
Event Attendance Traditional Sports New Sports
MLB games .809 -.120
NBA games .636 .296
Minor league hockey games .664 -.085
WNBA games .261 .716
Arena football games -.090 .775
TV Viewing Big Three Hockey Events
NBA games .869 -.035
NFL games .808 .158
MLB games .743 .294
NHL games .172 .871
Minor league hockey games .138 .874
Market Demand 28
Table 6. Regression Analyses Examining the Relationship Between Market Demand and
Consumption Factors
______________________________________________________________________________
Factor r r2 b SE B ß t p
______________________________________________________________________________
DV – Traditional Sports
Game Attractiveness .366 .134 .364 .040 .366 9.029 .000
Marketing Promotion .017 .000 .016 .040 .017 0.409 .683
Economic Consideration .098 .010 .010 .040 .098 2.420 .016
DV – New Sports
Game Attractiveness .136 .018 .135 .043 .136 3.143 .002
Marketing Promotion .075 .006 .075 .043 .075 1.734 .083
Economic Consideration .006 .000 .006 .043 .006 0.132 .895
DV – Big Three Sports
Game Attractiveness .469 .220 .469 .039 .469 12.175 .000
Marketing Promotion .075 .006 .075 .039 .075 1.958 .051
Economic Consideration .037 .001 .037 .039 .037 0.968 .334
DV – Hockey Events
Game Attractiveness .162 .026 .162 .043 .162 3.768 .000
Marketing Promotion .015 .000 .015 .043 .015 0.337 .736
Economic Consideration .103 .011 .103 .043 .103 2.398 .017
______________________________________________________________________________
Market Demand 29
Table 7. General Linear Model Regression Analyses Examining the Relationships of
Sociodemographic Variables with Market Demand Factors
_____________________________________________________________________________________________
Sociodemographic Market Demand Factor
______________________________________________________
Variable Attractiveness Promotion Economics
r ŋ2 r ŋ2 r ŋ2
______________________________________________________________________________
Gender .161 .026* .055 .003 .055 .003
Age .192 .037* .155 .024* .145 .021*
Ethnicity .134 .018* .281 .079* .063 .004
Marital Status .138 .019* .155 .024* .032 .001
Household Income .063 .004 .200 .040* .077 .006
Household Size .183 .033 .148 .022 .145 .021
Entertainment Budget .148 .022* .077 .006 .063 .004
Entertainment Information .141 .020 .176 .031 .089 .008
______________________________________________________________________________
Total .423 .179 .479 .229 .261 .068
______________________________________________________________________________
* Significant at .05 level.
Market Demand 30
Figure 1 Caption
Figure 1. The Standardized Factor Structure Coefficients, Interfactor Correlations, and Errors of
Measurement of The Three-Factor 12-Item Model
.45
A1
.74
.41
A2
.77
.82
.33
A3
.82
.33
A4
.83
.31
A5
.72
.48
A6
.82
.96
.09
P1
.74
.96
.09
P2
53
.88
.22
P3
.77
.16
S1
.92
.84
.29
S2
.71
.49
S3
Socioeconomic
Promotion
Attractiveness
