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Impact of Market Demand and Game Support Programs on Consumption Levels of Professional Team Sport Spectators as Mediate...

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Title: Impact of Market Demand and Game Support Programs on Consumption Levels of Professional Team Sport Spectators as Mediated by Perceived Value
Physical Description: 1 online resource (183 p.)
Language: english
Creator: Byon, Kun-Wung
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: analysis, behavioral, confirmatory, demand, equation, factor, game, intentions, market, modeling, perceived, professional, programs, spectator, sport, structural, support, value
Health and Human Performance -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this study was to examine the structural relationship of market demand variables and game support programs to the consumption of professional team sport games while taking into consideration the mediating influence of perceived value. This study simultaneously incorporated market demand (core service) and game support (peripheral service) factors into one study and examined their direct and indirect relationships with game consumption behaviors. A questionnaire that measured market demand of professional team sport games, game support programs, perceived value, consumption intentions, and sociodemographics was responded by a total of 453 research participants at various metropolitan areas and locations, following a community intercept sampling approach (Brenner, 1996). The data set was randomly split into two halves: one for exploratory factor analyses and the other for confirmatory factor analyses and tests of structural relationships among these sets of variables. As a result of the factor analyses, five factors were confirmed for the market demand variables including Home Team, Opposing Team, Game Promotion, Economic Consideration, and Schedule Convenience. A three-factor model of game support programs was generated that consisted of Game Amenities, Ticket Service, and Venue Quality. Furthermore, a unidimensional model was derived for the perceived value (i.e., Perceived Value for the Cost) and consumption intentions (Behavioral Intentions) sections from the factor analyses, respectively. All measures displayed good psychometric properties in terms of validity and reliability. In the structural relationship analyses, Home Team, Opposing Team, Game Promotion, Game Amenities, and Perceived Value for the Cost were found to be significantly related to Behavioral Intentions for professional team sport games. Venue Quality was the only factor that was found to have an indirect relationship with Behavioral Intentions through Perceived Value for the Cost. The findings of this study revealed the importance for professional sport teams to build a strong and high-quality home team, highlight the merits and competitiveness of both home and opposing teams in their game promotions, adopt multiple means of marketing campaigns, formulate exciting entertainment elements for pre-game, during-game, and post-game shows, and price game tickets in a reasonable manner to ensure consumer affordability.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kun-Wung Byon.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Zhang, Jianhui.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022643:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022643/00001

Material Information

Title: Impact of Market Demand and Game Support Programs on Consumption Levels of Professional Team Sport Spectators as Mediated by Perceived Value
Physical Description: 1 online resource (183 p.)
Language: english
Creator: Byon, Kun-Wung
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: analysis, behavioral, confirmatory, demand, equation, factor, game, intentions, market, modeling, perceived, professional, programs, spectator, sport, structural, support, value
Health and Human Performance -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this study was to examine the structural relationship of market demand variables and game support programs to the consumption of professional team sport games while taking into consideration the mediating influence of perceived value. This study simultaneously incorporated market demand (core service) and game support (peripheral service) factors into one study and examined their direct and indirect relationships with game consumption behaviors. A questionnaire that measured market demand of professional team sport games, game support programs, perceived value, consumption intentions, and sociodemographics was responded by a total of 453 research participants at various metropolitan areas and locations, following a community intercept sampling approach (Brenner, 1996). The data set was randomly split into two halves: one for exploratory factor analyses and the other for confirmatory factor analyses and tests of structural relationships among these sets of variables. As a result of the factor analyses, five factors were confirmed for the market demand variables including Home Team, Opposing Team, Game Promotion, Economic Consideration, and Schedule Convenience. A three-factor model of game support programs was generated that consisted of Game Amenities, Ticket Service, and Venue Quality. Furthermore, a unidimensional model was derived for the perceived value (i.e., Perceived Value for the Cost) and consumption intentions (Behavioral Intentions) sections from the factor analyses, respectively. All measures displayed good psychometric properties in terms of validity and reliability. In the structural relationship analyses, Home Team, Opposing Team, Game Promotion, Game Amenities, and Perceived Value for the Cost were found to be significantly related to Behavioral Intentions for professional team sport games. Venue Quality was the only factor that was found to have an indirect relationship with Behavioral Intentions through Perceived Value for the Cost. The findings of this study revealed the importance for professional sport teams to build a strong and high-quality home team, highlight the merits and competitiveness of both home and opposing teams in their game promotions, adopt multiple means of marketing campaigns, formulate exciting entertainment elements for pre-game, during-game, and post-game shows, and price game tickets in a reasonable manner to ensure consumer affordability.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kun-Wung Byon.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Zhang, Jianhui.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022643:00001


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IMPACT OF MARKET DEMAND AND GAME SUPPORT PROGRAMS ON
CONSUMPTION LEVELS OF PROFESSIONAL TEAM SPORT SPECTATORS AS
MEDIATED BY PERCEIVED VALUE

















By

KUN-WUNG BYON


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008




































O Kun-wung Byon


































To my family









ACKNOWLEDGMENTS

I would like to show my deepest appreciation to my advisor, Dr. James J. Zhang for his

endless support throughout my course of study in the Ph D. program. I would not have been able

to complete my dissertation without his patience, encouragement, insight, and support. Also, I

want to thank the members of my committee (Dr. Connaughton, Dr. Ko, Dr. Kim, and Dr. Lutz)

for their guidance and constructive advice. Many thanks also go to my Ph. D program colleagues

who encouraged me during this arduous process. Most importantly, I thank my lovely wife,

Young-woo, and my precious son and daughter, Connor and Kaylee, for their endless love and

unselfish sacrifice. I love them all! Also, I would like to thank my entire family in Korea for their

support and encouragement during my 6 years of education in the U.S. Above all else, I want to

thank God, Jesus Christ, who strengthens me whenever and wherever.












TABLE OF CONTENTS


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ................ ...............8............ ....


LIST OF FIGURES .............. ...............10....


LIST OF TERM S ............ ............ ...............11...


AB S TRAC T ............._. .......... ..............._ 12...


CHAPTER


1 INTRODUCTION ................. ...............14.......... ......


Statement of Problem ................. ...............24................

Hypothesized Research Model .............. ...............26....
Significance of the Study ................. ...............35................
Delimitations ................. ...............36.................
Limitations ................. ...............36.................


2 LITERATURE REVIEW ................ ...............38........... ....


Sport Spectator Consumption ................. ........... ...............38......
Definition of Sport Spectator Consumption ................. ...............38........... ...
Measurement of Behavioral Intentions ................ ................ ...............3
Overview of the Proposed Dimensions of Spectator Behavioral Intentions .................. .42
Repatronage intentions ................. ...............42.................
Recommending to others intentions ................. ...............43................
Service Quality .............. ............. ..............4
Definition of Service Quality ..................... ...............45.
Significance of Examining Service Quality .............. ...............46....
Measurement of Service Quality ................. ................ ...............47. ...
Overview of the Proposed Spectator Service Quality .......... ................ ...............53
Market Demand (Core Service Quality)................ .............. .......5
Proposed Dimensions of Market Demand (Core Service Quality) ........._._... ...............58
Hom e team .............. ...............58....

Opposing team............... ..... ..............5
Love of professional team sport ................. ...............60......__. ...
Economic consideration ................. ...............60........ .....
Game promotion............... ...............6
Schedule convenience ................ ........ ..... ...............6

Spectator Game Support Programs (Peripheral Service Quality) ............... ..................62
Proposed Dimensions of Spectator Game Support Programs (Peripheral Service
Q quality) .............. ...............64....













Ticket service .............. ...............65....
Game amenities ............. .....__. ...............66....
Stadium service .............. ...............66....

Stadium accessibility ........._.__....... .__ ...............67....
Perceived Value .........._.... ........._ __ ...............67.....
Definition of Perceived Value ........._.__........___ ...............69...

Importance of Examining Perceived Value ....._.__._ ...... ._._. ......._...........7
Measurement of Perceived Value..........._....... ......_.__......._. ...........7

Overview of the Proposed Dimensions of Perceived Value ........._._..... ...._._.........76
Perceived Value for the Cost ........._._.... .. ...._._............._._... ... ....... .....7

Relationship among Perceived Value, Service Quality, and Behavioral Intentions .......77
Summary ........._.__....... .__ ...............82....


3 METHODOLOGY ........._._._ ...._... ...............84.....


Participants .............. ...............84....
M easurement. ........._...... ...............86..___........
Market Demand ........._._.... ...............86..___.........

Game Support Programs............... ...............88
Perceived Value ........._._.... ...............89..___.........
Behavioral Intentions............... ...............9

Demographic Information .............. ...............90....
Proc edure s........._._.... ...............90..._ .........

Data Analyses .............. ...............92....


4 RE SULT S .............. ...............98....


Descriptive Statistics .............. ...............98....

Exploratory Factor Analyses .............. ...............99....
Market Demand ........._._.... ...............99..___.........

Game Support ........._....... .......__ .. ...............100.....
Perceived Value for the Cost ........._._.... ...............101..._........
Behavioral Intentions............... .. .. ................10

Measurement Models: Confirmatory Factor Analyses ...._.__... ..... ..___.. ........_........102
Market Demand ........._._.... ...............102.___.........

Game Support Programs ........._._.... ...............105._._.. ......
Perceived Value for the Cost ........._._.... ...............108..._........
Behavioral Intentions............... ..............10
Structural M odel ................. ...............110._._._.......


5 DI SCUS SSION ........._.._.. ...._... ..............._ 1 15..


Measurement Properties ........._.._.. ...._... ...............116....

Hypotheses Testing............... ...............124
Additional Suggestions ........._..... ...._... ...............132....











APPENDIX

A INFORMED CONSENT AND QUESTIONNAIRE ................. .............................163

LIST OF REFERENCES ................. ...............168.....___ ....

BIOGRAPHICAL SKETCH ........... ........... ...............183....










LIST OF TABLES


Table page

4-1 Frequency distributions for the sociodemographic variables (N = 453) ................... .......141

4-2 Descriptive statistics for the market demand variables (N = 453) ................. .................143

4-3 Descriptive statistics for the game support programs variables (N = 453) ................... ...145

4-4 Descriptive statistics for the perceived value for the cost variables (N = 453) ...............146

4-5 Descriptive statistics for the behavioral intentions variables (N = 453) ................... .......147

4-6 Factor pattern matrix for the market demand variables: alpha factoring with promax
rotation using first half data (n = 231) ................. ......... ...............148 .

4-7 Factor pattern matrix for the game support programs variables: alpha factoring with
promax rotation using first half data (n = 231) ................. ...............149...........

4-8 model fit comparison between the six-factor model and five-factor model of market
demand using second half data (n = 222) .............. .....................150

4-9 Model fit comparison between the five-factor model, four-factor model, and three-
factor model of game support programs using second half data (n = 222)...................... 151

4-10 Model fit comparison between the five-item model and three-item model of
perceived value for the cost using second half data (n = 222) ................. ................ ...152

4-11 Model fit comparison between the ten-item model and five-item model of behavioral
intentions using second half data (n = 222) ................ ...............153.............

4-12 Overall model fit indices for the measurement model of hypothesized structural
model using second half data (n = 222) ....._._ ................ .... ........... .... 15

4-13 Interfactor correlations from the confirmatory factor analysis of the market demand
using second half data (n = 222) ................. ...............155.......... ...

4-14 Interfactor correlations from the confirmatory factor analysis of the game support
programs using second half data (n = 222) ..........._...__.......... ....._._ .........15

4-15 Interfactor correlations, construct reliability, and average variance extracted from the
confirmatory factor analysis of the hypothesized structural model using second half
data (n = 222) ................. ...............157......... .....

4-16 Indicator loadings, critical ratios, cronbach's alpha, construct reliability, average
variance extracted for the market demand using second half data (n = 222) .................. 158










4-17 Indicator loadings, critical ratios, cronbach's alpha, construct reliability, average
variance extracted for the game support programs using second half data (n = 222) .....159

4-18 Indicator loadings, critical ratios, cronbach's alpha, construct reliability, average
variance extracted for the perceived value for the cost using second half data (n =
222) .............. ...............160....

4-19 Indicator loadings, critical ratios, cronbach's alpha, construct reliability, average
variance extracted for the behavioral intentions using second half data (n = 222)..........161

4-20 Maximum likelihood standardized loadings (P), critical ratios (cr), standard errors
(se), and t-values for the hypothesized structural model using second half data (n =
222) .............. ...............162....











LIST OF FIGURES


FiMr page

1-1 Conceptual framework of market demand, game support programs, perceived value,
and behavioral intentions ........._._.._........ ...............29....

1-2 Six dimensions of market demand ........._._._..... ._._ ...............30...

1-3 Four dimensions of game support programs ....._._.__ ............ ....___ ..........3

1-4 Uni-dimension of perceived value ................. ...............32........... ...

1-5 Two dimensions of behavioral intentions ................. ...............33...............

1-6 Proposed structural relationships among market demand, game support, perceived
value for the cost, and behavioral intentions .............. ...............34....

4-1 First-order confirmatory factor analysis for market demand ................. ............... .....135

4-2 First-order confirmatory factor analysis for game support programs ................... ...........136

4-3 First-order confirmatory factor analysis for perceived value for the cost .......................137

4-4 First-Order confirmatory factor analysis for behavioral intentions ................ ...............138

4-5 Tested structural model ................. ...............139...............

4-6 Tested structural model ................. ...............140......... .....









LIST OF TERMS


Affect


Psychological orientation that refers to the experience of
feeling.

Behavioral intentions as indications of an individual's
willingness toward a given task (Ajzen, 2005).

Psychological orientation that refers to the knowing, thought,
remembering, and reasoning (Gerrig & Zimbardo, 2002).

A complex psychological pattern of changes, including
physiological arousal, feelings created in response to a
situation perceived to be personally significant (Gerrig &
Zimbardo, 2002).

Entertainment and promotional activities provided by a team
during an event.

Controllable service attributes that are related to game
operation programs such as ticket services, stadium services,
game amenities, and accessibility to a stadium, all of which to
support the enj oyment of a game (Zhang et al., 1998a).

Sport consumers' expectations towards the main attributes of
the game itself (Zhang et al., 1995).

Indirect effect of an independent variable on a dependent
variable that passes through a mediator variable (Edwards &
Lambert, 2007, p. 1).
The consumer's overall assessment of the utility of a product
based on perceptions of what is received and what is given
(Zeithaml, 1988, p. 14).

A form of attitude that results from the comparison of prior
expectations with performance (Cronin & Taylor, 1992, p.
5 6).

An informal way of passing information by verbal means.


Behavioral Intentions


Cognition


Emotional Response




Game Amenities


Game Support Programs




Market Demand


Mediation


Perceived Value




Service Quality


Word-of-Mouth









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

IMPACT OF MARKET DEMAND AND GAME SUPPORT PROGRAMS ON
CONSUMPTION LEVELS OF PROFESSIONAL TEAM SPORT SPECTATORS AS
MEDIATED BY PERCEIVED VALUE

By

Kun-wung Byon

August 2008

Chair: James J. Zhang
Maj or: Health and Human Performance

The purpose of this study was to examine the structural relationship of market demand

variables and game support programs to the consumption of professional team sport games while

taking into consideration the mediating influence of perceived value. This study simultaneously

incorporated market demand (core service) and game support (peripheral service) factors into

one study and examined their direct and indirect relationships with game consumption behaviors.

A questionnaire that measured market demand of professional team sport games, game support

programs, perceived value, consumption intentions, and sociodemographics was responded by a

total of 453 research participants at various metropolitan areas and locations, following a

community intercept sampling approach (Brenner, 1996). The data set was randomly split into

two halves: one for exploratory factor analyses and the other for confirmatory factor analyses

and tests of structural relationships among these sets of variables. As a result of the factor

analyses, five factors were confirmed for the market demand variables including Home Team,

Opposing Team, Game Promotion, Economic Consideration, and Schedule Convenience. A

three-factor model of game support programs was generated that consisted of Game Amenities,

Ticket Service, and Venue Quality. Furthermore, a unidimensional model was derived for the










perceived value (i.e., Perceived Value for the Cost) and consumption intentions (Behavioral

Intentions) sections from the factor analyses, respectively. All measures displayed good

psychometric properties in terms of validity and reliability. In the structural relationship analyses,

Home Team, Opposing Team, Game Promotion, Game Amenities, and Perceived Value for the

Cost were found to be significantly related to Behavioral Intentions for professional team sport

games. Venue Quality was the only factor that was found to have an indirect relationship with

Behavioral Intentions through Perceived Value for the Cost. The findings of this study revealed

the importance for professional sport teams to build a strong and high-quality home team,

highlight the merits and competitiveness of both home and opposing teams in their game

promotions, adopt multiple means of marketing campaigns, formulate exciting entertainment

elements for pre-game, during-game, and post-game shows, and price game tickets in a

reasonable manner to ensure consumer affordability.









CHAPTER 1
INTTRODUCTION

According to Shank (2005), game attendance is the most traditional and important form of

sport consumption behavior in spectator sport, which is defined as any live sport event that is

played in front of spectators. Examples of spectator sport include, but are not limited to, baseball,

basketball, football, and ice hockey. Spectator sport is distinguished from participant sport in two

aspects. First, the main activity and motivation for spectators in a sport event lie in watching a

sport competition; whereas, the main activity and motivation for a participant sport is the actual

playing of the sport. Second, spectator sport requires some type of confined facilities (e.g.,

stadium, arena, gym, or Hield), where spectators can watch the athletic performance and

competition. Nevertheless, a chaste participant sport such as hunting does not necessarily require

any venue for paid spectators (Shank, 2005).

Chelladurai (1999) classified the sport industry into three segments: (a) sport economic

activities, (b) spectator sports, and (c) participant sports. This researcher noted that spectator

sport had been the fastest growing segment within the sport industry and further estimated that

this sector alone was a $50 billion industry in terms of annual business transactions. Other

researchers have also recognized the continued growth of spectator sport in North America by

pointing out that spectator sport has become an increasingly important type of leisure behaviors

of Americans (Ross & James, 2006; Trail, Anderson, & Fink, 2005). The rapid and vast growth

of professional sport teams in North America is also evidence of the immense interest in

spectator sports. According to Frank (2000), 67% of the U. S. population referred to themselves

as fans of the National Football League (NFL), 62% of the U. S. population indicated that they

were rooting for Maj or League Baseball (MLB), and 54% reported that they were National

Basketball Association (NBA) fans. Masteralexis, Barr, and Hums (2008) indicated that as of










2007, a total of 149 franchise teams belong to the five maj or professional sport leagues: MLB,

NFL, NBA, the National Hockey League (NHL), and Maj or League Soccer (MLS). This figure

does not include teams in less-prominent professional sport leagues such as the Arena Football

League (AFL), Women' s National Basketball Association (WNBA), and many other maj or and

minor league teams. The augmentation of spectator sports has been also confirmed through

attendance and media viewership rates. In the year of 2003-2004, approximately 476 million

people attended spectator sport events in North America. In 2003, NFL games were played in

front of more than 17 million fans that attended games at 95% of stadium capacity on average. A

similar trend has been observed in international competitions as well; for example, the 2002

World Cup soccer tournament in Korea and Japan was televised to over 200 countries during 30

days of competition, drawing approximately 28.2 billion cumulative viewers (Hyundai Economic

Institute, 2002). Brandt (2004) reported that approximately 137 million television viewers

watched the 2004 Super Bowl. The same phenomenon is also true in Division I men's basketball

and football, which are considered to be the two main revenue producers for collegiate athletic

departments (Fulks, 2003). In 2005, nearly 1.3 million people watched March Madness college

men's basketball games online (Rein, Kotler, & Shields, 2006). According to Fulks, the average

percentages that Division-I men' s basketball and football contributed to the income of Division I

athletic programs in 2003 were 70% ($13 million) and 23% ($4.3 million), respectively.

The increasing popularity of spectator sport has led to the establishments of new leagues, teams,

and multimedia outlets, which has not only provided more spectating options for sport

consumers but also created greater competitions among various leagues and teams for consumer'


s choice. Due to a crowded sport marketplace, sport consumers now have many options with

which to spend their time and discretionary dollars. As a result, professional sport organizations









have faced increasing competitions for gaining market share. Mullin, Hardy, and Sutton (2007)

stated that "competition for sport dollar is growing at the pace of a full-court press" (p. 7) to

describe the intensity of the competitive sport marketplace. A recent ESPN sports poll asked a

sample of residents in North America if they were still considered themselves a fan of the sports

of which they had originally become a fan. The poll indicated that eight out of 10 professional

sports were losing their fans drastically. The two sports that gained positive scores were auto

racing and golf (Mullin et al., 2007). According to Rein et al. (2006), there are five potential

reasons for the growing challenges of attracting and retaining sport consumers within the sport

industry. First, there are too many sport-related product options available to sport consumers. As

indicated above, there are numerous professional sports, intercollegiate sports, interscholastic

sports, and even youth sport events being held on a regular basis across the United States. The

second reason is due to constrained leisure time for people in America. Today, Americans spend

on average 19 hours per week for leisure activities in 2004 compared to its 26 hours in 1973. The

third reason is due to the expensive cost of becoming a sport fan. For instance, in order to attend

a professional sport event, a family of four people would typically spends $164 for a MLB game,

$247 for a NHL game, $263 for a NBA game, and $330 for a NFL game. The fourth reason is

due to the proliferation of increased media outlets. In addition to traditional media such as

television and radio, the Internet has become a mass medium. In 2005, more than two thirds of

all Americans were able to access the Internet at home. Moreover, increasing availability of

satellite and cable television allows sport consumers to enj oy watching maj or sport events at

home or sports bars and restaurants. Mullin et al. (2007) stated that "ESPN capitalized on this

niche programming by offering nothing but sports 24 hours a day" (p. 370). Lastly, people's

discretionary money is increasingly spent in recreational activities (e.g., bowling, skating, and










golf) and other entertainment activities such as sport video games, movies, and concerts rather

than professional sport events (Shank, 2005). This notion has also been confirmed by an

empirical study (Zhang et al., 1997b), which found substitute forms of other entertainment

businesses (e.g., movies, concerts, recreational activities, television, restaurants, and night clubs)

had considerable negative influences on game attendance at minor league hockey games.

As market competitions are becoming more intensified in professional sports, it is

important for both academicians and practitioners to understand game consumption related

variables so as to improve the quality of product offering and to enhance competitiveness of

sport products) and services. Previous studies examining game consumption related variables

have often been conducted from the following two perspectives: market demand (Zhang, Lam, &

Connaughton, 2003a; Zhang, Pease, Hui, & Thomas, 1995) and game support programs (Zhang,

Lam, Connaughton, Bennett, & Smith, 2005a; Zhang et al., 2004a; Zhang, Smith, Pease, & Lam,

1998a). In previous studies, some researchers captured these two concepts under a general

concept of sport service quality (Greenwell, Fink, & Pastore, 2002; Zhang, Connaughton, &

Vaughn, 2004b). With this collective approach, variables directly related to athlete/team

performance are termed as core service (Mullin et al., 2007) and variables related to event

operations and game promotions are referred as peripheral service (Van Leeuwen, Quick, &

Daniel, 2002). Another approach to study game consumption related variables has separated

variables of game support programs from those variables primarily related to athlete/team

performance (Zhang et al., 1995, 1998a). Although the two approaches are not drastically

different, the disparity is that the collective approach tends to solely rely on service quality

theories as the theoretical framework to examine all game consumption related variables.

Conversely, the separated approach adopts different theoretical concepts to study market demand









variables and game support programs. This approach focuses on in-depth and systematic

analyses of specific team performance and game operation variables for the purpose of guiding

the development of meticulous marketing and promotion strategies (Zhang et al., 2003a).

According to Mullin et al. (2007) and Zhang et al. (1995), the core product in spectator

sport is the game itself. Following an extensive literature review on factors influencing game

attendance variables, Schofield (1983) proposed four market demand categories including

demographic variables, economic variables, game attractiveness, and residual preference.

Greenstein and Marcum (1981) and Jones (1984) focused their studies on game production

functions and found that team performance variables, such as winning/losing record and

presence of star player, were related to game attendance. Synthesizing key game demand

variables and production functions, Zhang et al. (1995) proposed the systematic concept of

market demand, which was defined as the spectators' expectations towards the main attributes of

the core product (i.e., game itself). Braunstein, Zhang, Trail, and Gibson (2005) further explained

that market demand was a set of essential constructs associated with the game that a sport team

could offer to its existing and prospective consumers. Unlike other business merchandise, the

core product of sport games is unique in that team marketers and management personnel can

hardly control the core product once a sport team's roster is Einalized.

A theoretical justification for the market demand can be partially attributed to the Theory

of Reasoned Action proposed by Fishbein and Ajzen (1975). This theory postulated that human

behavior was a direct consequence of behavioral intentions, which were functions of attitude and

subjective norm. Several researchers have found that attitude construct was found to have more

explanatory power in accounting for behavioral intentions when compared to that of subj ective

norm (Stutzman & Green, 1982; Warshaw, Calantone, & Joyce, 1986). Other researchers have









also indicated that a strong attitude toward a certain obj ect or phenomenon could act as powerful

heuristics that positively direct consumer behavior (Fazio, Powell, & Williams, 1989). When a

sport consumer holds a positive attitude toward the attributes of game product such as home

team/athlete performance, and/or game schedule, the positive attitude tends to be transformed

into attendance and re-attendance behaviors.

Numerous studies on sport market demand have been conducted to examine the

predictability of game attendance (Zhang et al., 1995, 2003a, 2004a) and fan satisfaction

(Greenwell et al., 2002; Madrigal, 1995). Involving a sample of spectators ofNBA regular

season games, Zhang et al. (1995) found that four factors (home team, opposing team, game

promotion, and schedule convenience) were related to game attendance. Zhang et al. (2003a)

conducted a study to examine the general market demand variables associated with the

consumption of professional sport events. Game attractiveness and economic consideration

factors were found to be predictive of the general consumption of professional sport games. In a

study examining game consumption of a NFL expansion team, Zhang et al. (2004a) found that

game attractiveness, economic consideration, and game promotion factors were positively related

to game consumption. Madrigal (1995) found that through affective reactions such as Basking in

Reflected Glory (BIRG) and enj oyment, the quality of opponent had a positive relationship with

consumer satisfaction of the game. Likewise, in a study conducted by Greenwell et al. (2002),

home team and opposing team were found to exert positive influence on a spectator's overall

satisfaction of game attendance experience.

Zhang et al. (1998a) defined game support programs as controllable service attributes that

are related to game operations, such as ticket services, stadium services, game amenities, and

facility accessibility, all of which are to support the provision and enj oyment of a spectator event.










The quality of these event operation activities can usually be controlled by team management

and marketers before, during, and after the event. Zhang et al. (1998a, 2004c) indicated that the

game support programs often affect the consumption behavior of spectators. During game

operations, focusing on these controllable variables is apparently more important for the team

management in order to enhance the game experience of spectators (Mullin et al., 2007; Murray

& Howat, 2002).

Studying the quality of game support programs have usually followed various service

quality related theories, such as Groinroos' (1984) two-component theory of service quality and

Bagozzi's (1992) appraisal-emotional response-coping framework. Groinroos (1984) proposed

the 'Nordic model', which was a service quality model that consisted of two components:

technical quality and functional quality. Technical quality was related to the outcomes of the

service, reflecting the tangible aspects of service. Functional quality was related to intangible

aspects, such as consumers' perceptions of the delivery process. Bagozzi's (1992) appraisal-

emotional response-coping framework suggested that preliminary appraisal in the evaluation of

service quality lead directly to positive consumer behavior. The model posits that the relationship

between appraisal and behavior can also be mediated by emotional response derived from the

initial appraisal. When a sport consumer is satisfied with service encounters as he/she attends a

sport event, the positive evaluation tends to drive future attendance.

Despite the recognized importance of game support programs, only a small number of

studies have focused on these variables (Greenwell et al., 2002; Wakefield & Blodgett, 1996;

Zhang et al., 1998a, 2004b, 2004c). Zhang et al. (1998a) conducted a study to examine the

influence of game support programs on game attendance of minor league hockey games. The

researchers found that game amenities and ticket service factors were significantly (p < .05)









related to game attendance. Zhang et al. (2004b) examined the predictability of game support

programs of NBA regular season games on game attendance. The results of this study indicated

that game amenities, arena accessibility, audio visual, and ticket services were positively

predictive of game attendance. To investigate the influence of special programs and services for

NBA season ticket holders, Zhang et al. (2004c) studied those programs and services designed

for offering added values to season ticket holders. The researchers found that those special

programs and services were effective in retaining professional sport consumers of the highest

ticket/consumption levels. Wakefield and Blodgett (1996), in a study that examined the influence

of sportscape (stadium quality) on fan attendance intention, found that all of the game operation

variables had positive relationship with repatronage intention and customer retention. Greenwell

et al. (2002) supported Wakefield and Blodgett' s notion by finding that the perceptions of

stadium quality factors significantly predicted spectator' s overall satisfaction of minor league

hockey games.

A number of limitations have been identified in previous studies related to market demand

and game support programs. First, studies adopting the collective approach tended to examine

market demand and game support program variables in a general and superficial manner. Only a

small segment of variables were included in these studies and the included variables were usually

a part of a larger study that attempted to examine many, if not all, variables related to the

marketing of sport events. Although the findings of these studies have provided insights on the

importance of studying market demand variables and game support programs, the studies were

partial, non-systematic, and overall superficial. Specific marketing implications can hardly be

drawn from these studies. Second, although studies adopting the separated approach were more

systematic and in-depth, and provided specific information on team formation, team performance,










and game operations, variables related to the core product and the game support elements were

rarely examined simultaneously. Consequently, sport marketers' decisions tend to be made on


either provision of core product or game support programs, rarely both. Third, previous studies

overlooked the potential influence of other socio-psychological variables, such as perceived

value of game product, when studying the relationship between game product-related marketing

variables and game consumption (Murray & Howat, 2002). In recent years, a great number of

studies have been conducted to examine spectator consumption behavior from such socio-

psychological perspectives as fan motivation (Funk, Mahony, Nakazawa, & Hirakawa, 2001;

Pease & Zhang, 2001; Trail & James, 2001; Wann, 1995) and team identification (Heere &

James, 2007; Trail, Fink, & Anderson, 2003; Wann & Branscombe, 1993; Wann & Pierce, 2003).

Although market demand variables, game support programs, socio-psychological variables, and

sociodemographic variables have been found to explain about 50% variances collectively (Zhang

et al., 2007), a significant portion of game consumption variance remains unexplained.

Researchers have attempted to identify additional variables with explanatory power on

game consumption behavior, particularly those that may interact with market demand variables,

game support programs, and spectator motivation variables. Perceived value (Kwon, Trail, &

James, 2007; Murray & Howat, 2002) is one set of those variables that have been identified as a

salient variable for spectator consumption behavior. Zeithaml (1988) defined perceived value as

consumer' s overall assessment of the utility of a product (or service) based on perceptions of

what is received (quality and benefit) and what is given (perceived value for the cost and non-

monetary price). Netemeyer et al. highlighted that "perceived value for the cost was considered a

cornerstone of the most consumer-based-brand-equity frameworks" (p. 21 1). Perceived value has

been found to be one of the most important variables in predicting consumption behavior (Bolton









& Drew, 1991; Chang & Wildt, 1994; Dodds, Monroe, & Grewal, 1991; Zeithaml, 1988). Bolton

and Drew (1991) even indicated that perceived value is a richer measure of a customer' s

psychological evaluation than perception of service quality. These researchers suggested that

perceived value plays a key role in connecting the perceived service quality with behavioral

intentions. A number of studies have been conducted to examine the influence of perceived value

on consumption behavior in the general marketing and consumer research (Chang & Wildt,

1994; Parasuraman & Grewal, 2000). Often, perceived value was identified as a mediator in the

relationship between service quality and behavioral intentions (Oh, 1999; Zeithaml, 1988).

Chang and Wildt found a hierarchical relationship among perceived price, perceived service

quality, perceived value, and purchase intentions. Perceived value was found to be a direct

antecedent of purchase intentions. Parasuraman and Grewal supported the hierarchical

relationship by finding that perceived service quality directly influenced perceived service value,

which in turn affected customer loyalty. In an experimental study, Dodds et al. (1991) further

confirmed the hierarchical relationships among service quality, perceived value, and purchase

intentions, indicating that perceived value was positively related to the willingness to buy.

Overall, considerable evidence supports the important role of perceived value as an intervening

factor in the relationship between service quality and consumption behavior (Cronin, Brady,

Brand, Hightower, & Shemwell, 1997).

Despite the highly recognized importance of perceived value on consumption behavior,

little research attention has been devoted to examining the effect of perceived value on sport

consumption (Kwon et al., 2007; Murray & Howat, 2002). Murray and Howat were among the

first researchers to examine the effect of perceived value on future consumptive intentions for a

leisure center. The result of a path analysis revealed that the perceived value had a direct









relationship with future intentions as well as indirect relationship with the future intentions

through satisfaction. Recently, Kwon et al. (2007) examined the role of perceived value on

purchase intentions of team-licensed merchandise and found that perceived value played a

mediating role in the relationship between team identification and purchase intentions. These two

studies provided empirical support for including perceived value variables when studying sport

consumption behavior. Similarly, Tsuji, Bennett, and Zhang (2007) highlighted the need for

investigating the effect of perceived value when examining relationship between service quality

and behavioral intentions as well as indirect relationship with the future intentions through

sati sfacti on.

Statement of Problem

Kotler and Armstrong (1996) indicated that the cost for retaining existing customers is

generally five times lower than attracting prospective customers. One area that is in great need of

retaining spectators is professional sport teams, as teams have been losing their fans drastically

because the marketplace has become very competitive (Mullin et al., 2007; Rein et al., 2006). It

is imperative for team management and marketers to identify those variables that directly and

indirectly affect game consumption (Hansen & Gauthier, 1989; Zhang et al., 1995).

Understanding what makes spectators decide to return to the game, and how they refer the game

product and service received to others such as family members, friends, and community

constituents is important for teams to better understand spectator consumption behavior and

accordingly formulate an effective marketing mix (i.e., product, price, place, and promotion).

Findings of previous studies revealed that market demand variables and game support

programs were salient variables in explaining sport spectator consumption behavior (Kwon et al.,

2007; Murray & Howat, 2002; Wakefield & Blodgett, 1996; Zhang et al., 1995, 1998a, 2004b).

However, these two concepts have usually been studied independently (Cronin & Taylor, 1992;

24









Ko & Pastore, 2005; Parasuraman, Zeithaml, & Berry, 1998; Wakefield & Sloan, 1995; Zhang et

al., 1995, 2004c). Although previous researchers recognized the importance of market demand

variables and game support programs when marketing professional sport games, only a small

number of studies have examined both sets of variables simultaneously (Greenwell et al., 2002;

Tsuji et al., 2007; Zhang et al., 2004c). Of those studies containing both concepts, over-

simplicity was a maj or issue. Previous studies tended to adopt general measures derived from

consumer satisfaction studies in the context of main stream business, failing to take into

consideration special characteristics of professional sport events. In fact, context-specific

measures have been recommended (Carman, 1990). It is critical for a research investigation to

incorporate the uniqueness and special characteristics of the core product, product extensions,

and market environment (Mullen et al., 2007; Zhang et al., 2003b). Additionally, previous

studies have revealed that only a small portion of game attendance variance (i.e., less than 50%)

were explained by market demand variables and game support programs although their

importance were undoubtedly confirmed by numerous researchers (Greenwell et al., 2002; Tsuji

et al., 2007; Wakefield & Blodgett, 1996; Zhang et al., 1995, 1998a, 2004b). Low variance

explanation may be due to the overlook of the potential influence of some mediating variables,

such as perceived value, on the relationship between sport production and game consumption.

McDougall and Levesque (2000) provided a good explanation on the need to study perceived

value as an intermediate concept when conducting consumer behavior studies:

Consider the situation where customers may be "satisfied" with "what was delivered and

how the service quality was delivered, but may not have felt they got their money's worth.

If perceived value is a driver of intention and the managers exclude this measure in their









model, they would attempt to improve intention through improvements in only service

quality. The results of these tactics would have a minimal effect on intentions (p. 395).

Therefore, studying game product variables and perceived value simultaneously is critical to

gaining a more comprehensive understanding of what influences spectators to repatronage the

game and how they conduct word-of-mouth promotions. From an analytical perspective, Bagozzi

(1980) argued that one reason for model misspecification in marketing research is due to

omitting important variables from the model. To fill the void, the purpose of the study was to

examine the structural relationship of market demand variables and game support programs to

the consumption of professional team sport games while taking into consideration the mediating

influence of perceived value.

Hypothesized Research Model

Deducted from a comprehensive review of literature, this study examined the hierarchical

relationship among market demand, game support, perceived value, and behavioral intentions.

This conceptual model is illustrated in Figure 1. As in a number of previous studies, market

demand, game support programs, and behavioral intentions were conceptualized as multi-

dimensional measures. More specifically, the concept of market demand was represented by six

factors: Home Team, Opposing Team, Love of Professional Team Sport, Economic

Consideration, Game Promotion, and Schedule Convenience (Braunstein et al., 2005; Greenstein

& Marcum, 1981; Jones, 1984; Schofield, 1983; Zhang et al., 1995, 2003a). Game support

programs consisted of four factors: Ticket Service, Game Amenities, Stadium Service, and

Stadium Accessibility (Greenwell et al., 2002; Wakefield & Blodgett, 1996; Zhang et al., 1998a,

2004b, 2004c).

The concept of perceived value was represented by a unidimensional factor, Perceived

Value for the Cost, as suggested by previous researchers (Kwon et al., 2007; McDougall &

26









Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). While acknowledging its

multidimensional aspects, previous studies (i.e., Kwon et al., 2007; McDougall & Levesque,

2000; Murray & Howat, 2002; Netemeyer et al., 2004) have consistently found that the

utilitarian aspect, namely Perceived Value for the Cost, was the most relevant perceived value

factor that affected sport consumption behavior (Kwon et al., 2007). Furthermore, Netemeyer et

al. (2004) argued that Perceived Value for the Cost was overall the best candidate for

representing global perceived value measure in consumer behavior research. Kwon et al.

supported Netemeyer et al.'s notion by emphasizing that a sport consumer tended to weigh the

cost versus the benefit (i.e., Perceived Value for the Cost) to determine perceived value of team-

licensed product. Thus, to be consistent with the empirical evidence, the current study adopted

the unidimensional aspect (i.e., Perceived Value for the Cost) to measure perceived value.

Behavioral intentions were initially composed of two factors: Repatronage Intentions and

Recommend to Others, using Soiderlund (2006) and Zeithaml, Berry, and Parasuraman's (1996)

behavioral intentions scales. The importance and relevance of repatronage have been stressed by

numerous scholars (Kotler & Armstrong, 1996; Mullin et al., 2007; Rein et al., 2006). Zeithaml

et al. (2006) stated that "among the most important generic behavioral intentions is willingness to


recommend the service to others and repurchase intent" (p. 149).

All measurement models are presented in Figures 1-2, 1-3, 1-4, and 1-5, respectively. By

following the conceptual model in Figure 1-1 and related research findings of previous studies, a

structural model was proposed in the context of professional team sports, where market demand

factors (core service) and game support factors (peripheral service) were hypothesized to directly

influence behavioral intention factors. The market demand and game support factors were also

hypothesized to indirectly affect behavioral intention factors through the perceived value factor










(i.e., Perceived Value for the Cost). The structural model is presented in Figure 1-6, where

market demand and game support factors were allowed to be correlated. Specifically, the

following hypotheses were tested in this study:

* Hypothesis 1: Home Team would have a direct influence on the behavioral intention
factors.

* Hypothesis 2: Opposing Team would have a direct influence on the behavioral intention
factors.

* Hypothesis 3: Love of Professional Sport would have a direct influence on the behavioral
intention factors.

* Hypothesis 4: Economic Consideration would have a direct influence on the behavioral
intention factors.

* Hypothesis 5: Game Promotion would have a direct influence on the behavioral intention
factors.

* Hypothesis 6: Schedule Convenience would have a direct influence on the behavioral
intention factors.

* Hypothesis 7: Game Amenities would have a direct influence on the behavioral intention
factors.

* Hypothesis 8: Ticket Service would have a direct influence on the behavioral intention
factors.

* Hypothesis 9: Stadium Service would have a direct influence on the behavioral intention
factors.

* Hypothesis 10: Stadium Accessibility would have a direct influence on the behavioral
intention factors.

* Hypothesis 11: Perceived Value for the Cost would have a direct influence on the
behavioral intention factors.

* Hypothesis 12: Market demand factors would have an indirect influence on the behavioral
intention factors through Perceived Value for the Cost.

* Hypothesis 13: Game support factors would have an indirect influence on the behavioral
intention factors through Perceived Value for the Cost.











































Figure 1-1. Conceptual framework of market demand, game support programs, perceived value, and behavioral intentions
















































Figure 1-2. Six dimensions of market demand
















































Figure 1-3. Four dimensions of game support programs












































Figure 1-4. Uni-dimension of perceived value




































Figure 1-5. Two dimensions of behavioral intentions


















































Figure 1-6. Proposed structural relationships among market demand, game support, perceived value for the cost, and behavioral
intentions










Significance of the Study

As market competitions increase in professional team sports, team management and

marketers need to develop strategic marketing plans that are based on in-depth understanding of

consumers. It is critical for them to identify those variables contributing to sport consumption

and how they function together to accomplish a team' s marketing obj ectives. In recent studies,

market demand, game support, and perceived value factors have been found to have significant

effects on customer's repatronage intentions and referral behaviors (Kwon et al., 2007; Murray &


Howat, 2002; Wakefield & Sloan, 1995, Zhang et al., 1995, 2004c). However, these concepts

have primarily been examined fragmentarily, making the practical implications partial and of

limited usage (Cronin & Taylor, 1992; Parasuraman et al., 1998; Wakefield & Sloan, 1995). This

study incorporated all of these sets of constructs altogether and in the meantime; their interactive

relationships were also examined. By taking into considerations the unique aspects of

professional team sports and the multidimensionality of these concepts, it was expected that

research Eindings would have a greater applicability to the marketing of professional team sport

events. Many researchers suggested that greater insight would be achieved for practitioners from

utilizing the multidimensional constructs (Greenwell et al., 2002; Parasuraman et al., 1988,

Zhang et al., 1995).

It was anticipated that the research Eindings would fill the void in the literature by building

linkages from market demand and game support to perceived value, and then to behavioral

intentions. When these relationships were found to exist, they would serve as a foundation for

researchers to establish a hierarchical theory that supports the notion that successful game

product and high service quality offered by a professional sport team enhances perceived value,

and eventually leads to repeating consumption behaviors of sport consumers. Gaining an in-










depth understanding of the relationships among these constructs would also enable team

management to identify specific constructs that have the most impact on spectator consumption

behaviors and thus to formulate and implement plans to adjust and improve team formation,

game tactics, event operations, and promotional strategies. The current study was initiated based

on a premise that the main goal of sport organizations is to offer quality game product and high

service quality to satisfy consumers' experiences. This provision would help sport consumers to

form a positive perceived value of the game products and services in order to enhance the

probability that those sport consumers would engage in repatronage and recommendation of the

game products and services.

Delimitations

The study was completed within the following delimitations:

* Research participants were those who attended a professional team sporting event within
the past 12 months of the time that the survey was conducted and had purchased the game
ticket.

* Research participants were those who resided in southeastern states in U.S.

* Research participants involved men and women over the age of 18.

* The study was conducted via a paper-and-pencil questionnaire.

* Research participation in the study was voluntary.

* Data were collected in the summer of 2008.

Limitations

The following limitations are recognized by the researcher, which might have affected the

internal and external validity of the study:

* Although all research participants were asked to respond to the questionnaires with
sincerity and honesty, their actual level of cooperativeness could not be fully controlled by
the researcher.









* The generalizability of the study findings might be limited to only two southeastern states
(i.e., Florida and Georgia) in U.S.

* Voluntary participation, instead of a random selection of research participants, may affect
the generalizability of the research findings.

* Although sample size of the current study was adequate for SEM (Wetson & Gore, 2006),
factor structures and causal relationships derived were not cross-validated by additional
independent sample.









CHAPTER 2
LITERATURE REVIEW

Sport Spectator Consumption

Consumers' behavioral loyalty is often shown via product/service consumption (Baker &

Crompton, 2000), having a direct impact on an organization's Einancial profitability (Zeithaml et

al., 1996). Sport consumption behavior is not an exception. Broadly speaking, two forms of sport

spectator consumption have been identified: active and passive sport consumption. Active sport

spectator consumption takes the form of game attendance (Zhang et al., 1995, 1997b) and the

purchasing of licensed merchandise products (Kwon et al., 2007). On the other hand, passive

sport spectator consumption refers to consumption activities through modes of various media

such as game watching, game listening, and game reading (Fink, Trail, & Anderson, 2002; Gantz,

1981). In the following section, more elaboration on defining sport spectator consumption and

how sport spectating has been measured will be presented.

Definition of Sport Spectator Consumption

In the Hield of sport management, there have been two views in defining a sport

consumer: micro-view and macro-view. In the micro-view, sport consumers are divided into two

categorizations: spectators and fans (Sloan, 1989; Trail, Robinson, Dick, & Gillentine, 2003).

Sloan separated the term spectator from fan by defining spectator as an individual who is merely

a game observer, whereas a fan is an individual who enthusiastically follows his/her favorite

teams. In the macro-view, a sport spectator is defined as an individual who attends a sport venue

to watch a sport event. Therefore, the term, sport spectator, is an encompassing word that

consists of sport fans as well (Funk & James, 2001). Based on the macro-view, sport spectator

consumption is defined as the act of attending a sport event for the specific purpose of watching

the sport event in a given venue (Parks et al., 2007). However, it has been argued that accurately










measuring actual consumption behavior is a challenging task because surveys can hardly be

made at the moment of purchase (Cronin, Brady, & Hult, 2000). As an alternative measure,

researchers have used a construct of behavioral intentions (Eggert & Ulaga, 2002; Fink et al.,

2002; Oh, 1999; Petrick & Backman, 2002a). Various researchers have found that measuring

behavioral intentions allows a highly accurate prediction of ensuing behaviors (Ajzen, 1971;

Conner, Sheeran, Norman, & Armitage, 2000; Sheeran, Orbell, & Trafimow, 1999). Ajzen

(2005) defined behavioral intentions as indications of an individual's willingness toward a given

task. Thus, it could be said that the stronger the intention an individual has, the more likely the

individual is to perform the intended action.

Measurement of Behavioral Intentions

Although behavioral intentions may change over time due to unforeseeable events or time

intervals, in general, behavioral intentions have been regarded as an immediate antecedent of

actual behavior in the fields of marketing (Cronin et al., 1997; Grewal, Monroe, & Krishnan,

1998b; Patterson & Spreng, 1997; Zeithaml, et al., 1996), tourism and hospitality (Baker &

Crompton, 2000; Lee, Yoon, & Lee, 2006; Oh, 1999; Petrick, 2003, 2004a; Petrick & Backman,

2002a), and sport management (Kwon et al., 2007; Murray & Howat, 2002; Trail et al., 2003;

Tsuji et al., 2007; Wakefield & Blodgett, 1996; Wakefield, Blodgett, & Sloan, 1996; Wakefield

& Sloan, 1995).

In marketing and consumer behavior research, two forms of measuring behavioral

intentions have been identified: unidimensional and multi-dimensional measurement. In terms of

the unidimensional measurement, variables such as purchase intentions repurchase intentions,

and/or word-of-mouth intentions, have been frequently used as either a multi-item or single-item

measure. For instance, Cronin et al. (1997, 2000) measured purchase intentions using three items









to examine the relationships among service quality, perceived value, and purchase intentions in

the context of six service industries. Patterson and Spreng (1997) adopted the unidimensional

approach to measure repurchase intentions in the context of business-to-business service. In an

attempt to predict golf travelers' consumption behavior, Petrick and Backman (2002a) measured

intentions to revisit using two items. The same measurement was shown by Petrick (2003,

2004a) to predict cruise passengers' repurchase behavior. Grewal, Krishnan, Baker, and Borin

(1998a) measured purchase intentions using three items to understand how consumers in a retail

store form purchase intentions toward durable goods.

In sport management research, Murray and Howat (2002) measured future purchase

intentions towards joining a leisure center using a single item. In an attempt to predict behavioral

intentions of an action sport event, Tsuji et al. (2007) also used a single item measure. Kwon et

al. (2007) measured purchase intentions of team licensed-apparel using a unidimensional

construct. Trail et al. (2003) also measured sport consumers' future behavior employing a

unidimensional approach that consisted of four items. Researchers have justified the use of either

a single-item or unidimensional measure by arguing that the method may reduce respondent' s

fatigue as well as research cost (Oh, 1999). However, single-item or unidimensional

measurement tends to lose considerable variances from the construct being examined (Churchill,

1979; Hair, Black, Babin, Anderson, & Tatham, 2005). Thus, a multi-dimensional measure

should be utilized whenever a construct is theoretically identified as having multi-dimensional

characteristics. In the context of spectator sport, Wakefield and Sloan (1995) and Wakefield and

Blodgett (1996) viewed behavioral intentions as a two-dimensional construct, measuring desire

to stay and repatronage intentions. In order to examine the influence of the physical environment

on customers' affective responses and subsequent behavioral intentions, Wakefield et al. (1996)









measured repatronage intentions and recommending to others as assessing behavioral intentions.

Petrick (2004a) also used a two-dimensional model of behavioral intentions that consisted of

repurchase intentions and recommending to others. In addition to repurchase intentions and

recommending to others, Eggert and Ulaga (2002) added another dimension of behavioral

intentions to their model, the search for an alternative.

Based on the literature review regarding behavioral intentions, it is suggested that

behavioral intentions are a multi-dimensional construct, and the most commonly identified sub-

dimensions are repatronage intentions and recommending to others. To support the above notion,

Zeithaml et al. (2006) stated that "among the most important generic behavioral intentions is


willingness to recommend the service to others and repurchase intent" (p. 149). A study

conducted by Soiderlund (2006) also empirically supported each factor' s unidimensionality,

indicating that the two factors were complementary but distinct. To compare aggregation and

disaggregation methods for examining the behavioral intentions construct measured by

repatronage intentions and word-of-mouth intentions, Soiderlund (2006) compared two models.

The first model was an aggregated model in which the two factors were combined into one factor,

and the second model was a disaggregation model in which the two factors were independent of

each other. As a result of Confirmatory Factor Analysis (CFA), the author found that the two-

factor model showed better model fit than the aggregated model. In addition, the two-factor

model demonstrated good discriminant validity, indicating that the two factors were distinct

factors. Therefore, the two dimensions (i.e., repatronage intentions and recommend to others)

have been proposed as spectator behavioral intentions for the current study.









Overview of the Proposed Dimensions of Spectator Behavioral Intentions

Building on the view of behavioral intentions as a multi-dimensional construct (Zeithaml

et al., 2006), a two-factor model of spectator behavioral intentions have been proposed. The two

factors are Repatronage Intentions and Recommending to Others. In the following section,

definitions, supporting empirical evidence, and justifications of using the two factors will be

discussed.

Repatronage intentions

Repatronage Intentions are defined as an indication of a consumer' s desire to repurchase

the product/service that the consumer once used/received (Ajzen, 2005). Repatronage intentions

"have to do with moving one's body in a physical sense to get in contact with a supplier"

(Soiderlund, 2006, p. 81). This construct has been used as one of the common outcome variables

in marketing and consumer behavior research (Zeithaml et al., 2006). Furthermore, repatronage

intentions have been found to be a direct consequence of such variables as customer satisfaction

(Eggert & Ulaga, 2002; Oh, 1999; Petrick & Backman, 2002a), perceived value (Grewal et al.,

1998b; Oh, 1999; Petrick, 2003, 2004a; Petrick & Backman, 2002a), service quality (Cronin et

al., 2000; Petrick, 2004b), and store image (Grewal et al., 1998a). In sport management research,

Wakefield and Blodgett (1996) found that repatronage intentions were directly influenced by

spectator satisfaction. In an attempt to examine the influence of the service environment on

behavioral intentions, Wakefield et al. (1996) also found that repatronage intentions were

positively related to minor league hockey spectators' perceived service quality (cognition) and

excitement (affect). Given its significant relationships with various customer variables such as

service quality and perceived value, the Repatronage Intentions factor has been operationalized

as a sub-dimension of spectator behavioral intentions in the current study.









Recommending to others intentions

Recommending to Others is referred to as the degree to which a consumer recommends a

service/product that they received/used to others (Zeithaml et al., 2006). This interpersonal

behavior has to do with communication with others (Soiderlund, 2006). Along with repatronage

intentions, the recommending to others factor has been found to be the most generic construct of

behavioral intentions in consumer behavior research (Zeithaml et al., 2006). Various researchers

have found that the recommending to others factor was a robust behavioral intention construct

directly predicted by perceived value (Oh, 1999), satisfaction (Lee et al., 2006; Oh, 1999), and

perceived service quality (Wakefield et al., 1996). Based on previous studies, it appears as

though the recommending to others factor is an important predictor of behavioral intentions.

Interestingly, few researchers have conceptualized repatronage intentions as a direct antecedent

of the recommending to others construct (Oh, 1999; Petrick, 2004b). In general, these two

constructs have not been separated to form a causal relationship with each other. However, Oh

(1999) and Petrick (2004b) differentiated between repatronage intentions and recommending to

others by arguing that consumers tend to recommend a product/service after forming an intention

to repurchase the product/service. For the current study, the Recommend to Others factor has

been conceptualized as a sub-dimension of spectator behavioral intentions since examining a

causal relationship between two constructs (Recommend to Others and Repatronage Intentions)

was not the purpose of this current study. The focal point of the current study is to measure

spectator behavioral intentions as a multi-dimensional construct as suggested by previous studies

in order to assess more holistic spectator behavioral intentions influenced by market demand,

game support programs, and perceived value.









In order to enhance sport spectators' consumption behavior, it is imperative for sport

marketers to identify key influencing factors. Extant literature has reported several key

antecedents of spectator behavioral intentions such as market demand (core service), game

support programs (peripheral service) (Tsuji et al., 2007; Zhang et al., 2004c; Zhang et al., 1995;

Zhang et al., 1998a), and perceived value (Kwon et al., 2007; Murray & Howat, 2002). In the

following sections, a literature review on general service quality, market demand, game support

programs, and perceived value as they relate to marketing, consumer research, and spectator

sport will be presented.

Service Quality

Today's sport organizations face increasing competition for gaining market share. In an

empirical study, Zhang et al. (1997b) found that substitute forms of other entertainment had

considerably negative influences on game attendance for minor league hockey games. Thus,

retaining existing consumers rather than attracting new consumers seems more imperative for the

financial stability of sport organizations. Indeed, research has shown that retaining consumers is

approximately five times less expensive for a service business than attracting prospective

consumers (Kotler & Armstrong, 1996). Therefore, it is important for sport organizations to

understand the underlying causes and antecedents of variables that may influence repatronage

intentions (e.g., game attendance) (Hansen & Gauthier, 1989; Zhang et al., 1995). The perception

of service quality has been identified as one of the most salient variables that may affect not only

customer retention but also attraction in the marketing literature (Brady & Cronin, 2001; Cronin

& Taylor, 1992; Groinroos, 1984; Parasuraman, Zeithaml, & Berry, 1985; Parasuraman et al.,

1988). The service quality construct has been widely utilized in other contexts including

hospitality (Choi & Chu, 2001); fitness, leisure, and recreation services (Alexandris, Grouios,









Tsorbatzoudis, & Bliatsou, 2001; Chelladurai & Chang, 2000; Hill & Green, 2000; Kim & Kim,

1995; Ko & Pastore, 2005; Lam, Zhang, & Jansen, 2005; Murray & Howat, 2002), and spectator

services (Greenwell et al., 2002; Tsuji et al., 2007; Wakefield & Sloan, 1995; Wakefield et al.,

1996; Zhang et al., 1998a, 2004b, 2004c, 2005b). Some of the identified consequences derived

from good service quality include customer loyalty (Petrick & Backman, 2001), repatronage

intentions (Wakefield et al., 1996), word-of-mouth (Wakefield & Boldgett, 1999), and

satisfaction (McDougall, & Levesque, 2000; Tsuji et al., 2007), which in turn, help generate

long-term profitability of an organization.

Definition of Service Quality

According to Kotler & Armstrong (1996) a service is defined as "any act or performance

one party can offer to another that is essentially intangible and does not result in the ownership

of anything" (p. 455). The above definition implies an important distinction between a service

and a product. A service deals with intangibility, which consumers cannot see or feel before the

consumption stage. In addition to the distinguishing aspect of intangibility a service is also

inseparable, perishable, and variable (Bitran & Hoech, 1990; Kotler & Armstrong, 1996; Sasser,

Olsen, & Wyckoff, 1978). Therefore, service quality can only be measured by an individual's

perceptions toward a service received (Parasuraman et al., 1985), whereas tangible products can

be more obj ectively measured based on their qualities, such as toughness, durability, or defects

(Crosby, 1979).

In service marketing, the term 'service quality' has been more frequently used than a

general term 'service' when it comes to assessing the 'service' from the consumer's perspective

(Para suram an et al., 1 98 8; Z eithaml et al., 1 9 96). B based on the confi rmati on -di sconfi rm ati on

paradigm (Oliver, 1980), Parasuraman et al. (1988) defined service quality as the comparison of









a consumer' s evaluation of the service performance to their pre-expectation of the service. This

definition of the gap model between expectation and perception has been widely adopted in the

marketing literature (Alexandris et al., 2001; Brown, Churchill, & Peter, 1993; Carman, 1990;

McDonald, Sutton, & Milne, 1995). However, due to lack of predictive validity and

measurement reliability, this gap model has been criticized (Cronin & Taylor, 1992; Buttle, 1996,

Zhang et al., 2004b), and researchers have recommended using a performance-only model by

viewing service quality as an attitudinal construct (Crompton & Love, 1995; Cronin & Taylor,

1992; VanDyke, Kappelmen, & Prybutok, 1997; Zeithaml et al., 1996). Empirically, Cronin and

Taylor compared the performance-only measure with the gap model and found that the

performance-only measure was superior to all four industries to which the measurement was

applied. Based on the performance-only measure, service quality is operationalized as a

consumer's perceptions towards a service performance received by the consumer.


Significance of Examining Service Quality

Theoretically, one of the most important reasons to examine service quality is due to its

high explanatory power on outcome variables, such as purchase intentions (Petrick & Backman,

2001; Reichheld & Sasser, 1990; Tsuji et al., 2007), cost (Crosby, 1979), profitability (Buzzell &

Gale, 1987; Rust & Zahorik, 1993), customer satisfaction (Bolton & Drew, 1991; Cronin &

Taylor, 1992), and word-of-mouth (Petrick & Backman, 2001). The practical importance of

investigating service quality lies in the fact that a high quality of service will produce a

competitive edge, which will be directly related to revenue generation (Zhang et al., 1998a,

2004c). Furthermore, accurate and periodic assessment would provide management with

feedback by pointing out areas in which management should improve.









Service quality research also has significance in the field of sport management. According

to Wakefield and Sloan (1995), study on service quality has been a largely undeveloped area

compared to areas such as psychology (i.e., team identification and motivation) and socio-

demographic variables (gender, ethnicity, income, and education) in spectator attendance

research. Zhang et al. (2004c) argued that services in relation to a sporting event can be extended

to the game support/operation programs, which are considered extensions of the core product

(game itself). Thus, examining service quality of those game support/operation programs would

provide information for immediate attention by sport marketers. Moreover, the attributes relevant

to game support/operation programs can be controlled and manipulated by a sport marketer,

whereas the game itself cannot. Therefore, examining satisfaction toward game

support/operation programs would have much practical relevance and value for game

management (Baker & Crompton, 2000; Zhang et al., 2004c, 2005).

Measurement of Service Quality

For the past two decades, service quality research has been guided by two theoretical

perspectives: (a) the American point of view that is represented by the SERVQUAL scale

(Parasuraman et al., 1988) and its numerous modifications (Brown et al., 1993; Carman, 1990;

MacKay & Crompton, 1990; McDonald et al., 1995; Wright, Duray, & Goodale, 1992) and (b)

the European viewpoint, referred to as the Nordic model, developed by Groinroos (1984). Both

scales were developed based upon Oliver' s (1980) disconfirmation paradigm. Parasuraman and

his colleagues (1985) proposed a conceptual model that included 10 factors related to service

quality. The 10 factors were as follows: Reliability, Responsiveness, Competence, Access,

Courtesy, Communication, Credibility, Security, Understanding/Knowing the customer, and

Tangibles. Later, Parasuraman et al. (1988) conducted two studies to empirically test the above









10 factors to determine whether the dimensions were representing various service settings. The

first study was conducted using 200 customers recruited by a mall-intercept method. As a result,

a preliminary 34-item scale was developed. To further validate the initial scale, the researchers

collected 800 customers from four nationally known firms, including a bank, a credit-card

company, an appliance repair and maintenance firm, and a long-distance telephone company. For

each firm, 200 customers were sampled. As a result of alpha reliability, exploratory factor

analysis (EFA), and regression, the 10-factor model was collapsed into a five-factor model,

called SERVQUAL, which included Reliability, Assurance, Tangibles, Empathy, and

Responsiveness. Reliability referred to how dependably and accurately the service was

performed. Assurance was defined as the courtesy, knowledge, and trust of employees. Tangibles

were related to the appearance of physical facilities and communication items. Empathy was

defined as the offering of caring and attention to customers. Responsiveness referred to the

extent to which a service firm displayed a willingness to help and provide timely service to

customers (Parasuraman et al., 1988). Numerous studies in the fields of leisure and sport

management have adopted the theoretical framework of the SERVQUAL scale (Chelladurai &

Chang, 2000; Howat, Murray, & Crilley, 1999; Kim & Kim, 1995; Lam et al., 2005;

Papadimitriou & Karteroliotis, 2000). In the context of fitness centers in Korea, Kim and Kim

(1995) developed a scale of Quality Excellence of Sports Centers (QUESC) that included 33

items under 12 dimensions that measured perceptions of service quality. The twelve dimensions

derived from an EFA were as follows: Ambiance, Employee Attitude, Employee Reliability,

Social Opportunity, Information Available, Programs Offered, Personal Considerations, Price,

Privilege, Ease of Mind, Stimulation, and Convenience. However, it was found that most of the

factors showed low reliability. In an attempt to apply the QUESC scale to Greek private fitness









centers, Papadimitriou and Karteroliotis (2000) conducted a study using 487 actual users of the

fitness centers. Although the authors failed to confirm the factor structure of the QUESC,

Papadimitriou and Karteroliotis (2002) proposed a parsimonious and sound a 24-item four-factor

model that consisted of Program Availability, Other Services, Instructor Quality, and

Facility/Attraction Operations. In the context of Austrian recreation centers, Howat, Absher,

Crilley, and Milne (1996) developed a scale that contained 15-items under five factors, including

Core Services, Staff Quality, General Facility, Secondary Services, and Knowledge. In an

attempt to define more parsimonious dimensions, Howat et al. (1999) tested the five-factor

model, which was collapsed into a three-factor model that contained Core, Peripheral, and

Personnel. The three-factor model has shown stable psychometric properties in other applications

(Howat & Crilley, 2007; Howat et al., 2002). Based on an extensive literature review,

Chelladurai and Chang (2000) developed a five-factor model that may generally pertain to the

recreation and fitness industry. The factors were: Core Service, Interaction between Employee

and Client, Interaction between Client and Client, Context, and Client Participation. While the

proposed factors seem relevant to recreation and fitness industry, the conceptual model has not

yet been empirically validated. All of the above empirical studies were modeled upon the result

of EFA except for Howat and Crilley's (2007), which utilized CFA.

Adopting Oliver' s (1980) disconfirmation paradigm, Groinroos (1984) proposed a two-

dimensional model that included technical quality and functional quality. Technical quality was

defined as the outcomes of the service, which reflects tangible aspects. Groinroos (2005)

elaborated that technical quality is "what the customer is left with, when the service production

process and its buyer-seller interactions are over" (p. 63). Functional quality was related to

intangible aspects, such as the consumers' perception as to how the service is delivered. An









important aspect when defining a service is the interaction between the service provider and the

customer that takes place while the service is delivered (Brady & Cronin, 2001). McDougall &

Levesque (2000) used the term 'relational quality' as they defined the functional quality while

taking into consideration the interaction aspect of the service. Using 447 church members, the

authors tested a conceptual model to examine the relative importance of service quality and

perceived value on customer satisfaction, which was hypothesized to directly affect behavioral

intentions. The results of the study indicated that core service quality, relational service quality,

and perceived value were found to be directly related to customer satisfaction, which, in turn

influenced behavioral intentions, which were measured by switching intentions and intentions to

remain loyal. Based on the Groinroos' (1984) two-component model, Zhang et al. (1998a)

developed the Spectator Satisfaction Inventory (SSI) that contained 24-items under five factors,

including Satisfaction with Ticket Service (STS), Satisfaction with Audio Visuals (SAV),

Satisfaction with Accessibility and Parking (SAP), Satisfaction with Arena Staff (SAS), and

satisfaction with Event Amenities (SEA). As a result of a test of Cronbach' s alpha coefficient

and EFA, the scale showed good reliability and construct validity. To the best of the author' s

knowledge, the SSI scale was the first instrument empirically tested for measuring spectator

service quality toward game support programs. In addition, the SSI scale has been adapted to the

contexts ofNBA professional basketball games (Zhang et al., 2004c) and minor league hockey

games (Zhang et al., 2005b) for the purpose of further validations.

In addition to these two theoretical frameworks (i.e., SERVQUAL and Groinroos' two-

component model), Brady and Cronin (2001) identified two additional recent conceptualizations

of service quality, which were Rust and Oliver' s (1994) 'three-component model' and Dabholker,

Thorpe, and Rentz's (1996) 'multilevel model.' In fact, the three-component model was









developed based on Groinroos' (1984) two-component model (Rust & Oliver, 1994). Rust and

Oliver proposed Service Product, Service Delivery, and Service Environment as the three

components of the model. The service product dimension was related to Groinroos' (1984)

technical quality, and the service delivery was in relation to the functional quality. Although Rust

and Oliver did not empirically test the three-component model, the model's efficacy has been

found in McDougall and Levesque' s (1994) study in which the authors found a three-factor

model, including Service Outcome, Service Process, and Physical Environment.

Dabholker et al. (1996) were the first researchers who viewed service quality as a

hierarchical and multilevel concept, consisting of three levels: (a) overall perceptions of service

quality, (b) primary dimensions, and (c) sub-dimensions. The authors employed three facets of

qualitative measures to derive the initial items thought to be relevant to service quality in retail

settings. As a result, the authors proposed the Retail Service Quality Scale (RSQS), which

included Overall Service Quality as the third-order factor, five primary dimensions (Physical

Aspects, Reliability, Personal Interaction, Problem Solving, and Policy), and six sub-dimensions

(Appearance, Convenience, Promises, Doing It Right, Inspiring Confidence, and Courteous).

Adopting the multilevel concept, Brady and Cronin (2001) validated the Dabholker et al.'s

(1996) model using the sample drawn from four different service industries including fast food,

photograph, amusement parks, and dry cleaning. As a result of CFA, the authors proposed the

third-order model that included one third order (Service Quality), and three second-order factors

(Interaction Quality, Physical Environmental Quality, and Outcome Quality), with each second-

order factor containing three first-order sub-dimensions. This hierarchical theory of service

quality has been adapted to the field of sport management (Ko & Pastore, 2004, 2005). Utilizing

the data collected from a university recreation center, Ko and Pastore (2005) also tested their









conceptual model consisting of one third-order factor (Service Quality) and four second-order

factors (Program Quality, Interaction Quality, Outcome Quality, and Physical Environmental

Quality), with each second-order factor containing three sub-dimensions, except for Interaction

Quality, which consisted of two sub-dimensions. CFA and SEM analyses also confirmed its

sound reliability and validity.

In sum, three important considerations have been identified based on the literature review

regarding the measurement of service quality. First, there has been general consensus that service

quality is multi-dimensional in nature (Brady & Cronin, 2001; Carman, 1990; Cronin & Taylor,

1992; Greenwell et al., 2002; Howat et al., 1999, 2002; Ko & Pastore, 2005; Lam et al., 2005;

Parasuraman et al., 1988; Zhang et al., 1998a, 2004c, 2005). Second, it is suggested that core

service and peripheral service quality be measured simultaneously in order to assess the service

quality comprehensively, regardless of using any of the theoretical frameworks reviewed

(Greenwell et al., 2002; Howat et al., 1999, 2002, 2007; McDougall & Levesque, 2000; Murray

& Howat, 2002; Tsuji et al., 2007; Van Leeuwen et al., 2002). Finally, the attributes measuring

service quality should be those relevant to the context in which the service is to be employed. In

fact, the applicability of the SERVQUAL scale to other contexts has been criticized by

researchers (Carman, 1990; Cronin & Taylor, 1992) even when the SERVQUAL scale was

developed simply for generic use (Parasuraman et al., 1988). For instance, Carman (1990)

applied the SERVQUAL to measure service quality towards hospitals. The author failed to

confirm the proposed five factors (Reliability, Assurance, Tangibles, Empathy, and

Responsiveness). Instead, the researcher derived nine factors representing the perceptions of

service quality toward hospitals. As a result of applying the SERVQUAL scale to a retail apparel

store, Gagliano and Hathcote (1994) found 19 items under four factors: Reliability, Tangibles,









Personal Attention, and Convenience. In the Hield of sport management, numerous researchers

also supported the notion of developing the industry-specific factors of service quality due to the

different nature of service among sport organizations. In fact, services in spectator sport are more

likely to deal with intangibles than services in durable goods (Greenwell, et al., 2002; Lam et al.,

2005; Murray & Howat, 2002; Zhang et al., 1998a, 2004c). In the end, the original authors of the

SERVQUAL scale, Parasuraman, Berry, and Zeithaml (1993) also acknowledged that the scale

should be modified to be relevant to the context in which it is being examined.

Overview of the Proposed Spectator Service Quality

For developing a service quality scale that is pertinent to spectator sport, the current study

will adopt the three criteria suggested above: (a) service quality should be treated as multi-

dimensional in nature, (b) core service quality and peripheral service quality should be measured

simultaneously, and (c) attributes related to core service quality and peripheral service quality

should be context-specific. In terms of the multi-dimensional scale, a variety of scales have been

developed in the Hield of sport management (Greenwell, et al., 2002; Ko & Pastore, 2005; Lam et

al., 2005; McDonald et al., 1995; Murray & Howat, 2002; Tsuji et al., 2007; Wakefield et al.,

1996; Zhang et al., 1998a, 2004c, 2005). With regards to measuring both core service quality and

peripheral service quality simultaneously, Greenwell et al. (2002) examined the influence of the

sportscape (physical sport facility) on customer satisfaction within the context of minor league

hockey games. In the study, service quality was divided into two dimensions: (a) core product,

which was measured as quality of home team and opposing team (Zhang et al., 1995), and (b)

peripheral (service personnel) which was measured by staff responsiveness, presentation,

knowledge, and behavior of officials. The authors suggested that greater insight would be

achieved for team marketers if they know the relative influences of the core and peripheral









service quality on customer satisfaction in relation to game attendance. Recently, Tsuji et al.

(2007) investigated spectators' satisfaction with action sport events as a predictor of future game

attendance intentions. As an antecedent to satisfaction, the authors operationalized service

quality as core and peripheral, suggested by Van Leeuwen et al. (2002).

The last criterion is related to developing a context-specific measurement of service quality.

In spectator sport, the game itself is considered the core product (Mullen et al., 2007; Zhang et

al., 2003b), which is related to the set of attributes that may affect consumers' perceptions of the


quality of the game (Greenwell et al., 2002). The factors comprising the core product are

generally categorized as Home Team, Opposing Team, Game Promotion, Love of Sport,

Economic Consideration, and Schedule Convenience (Branustein et al., 2005; Greenstein &

Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995). The uniqueness

related to core product is that the team marketing and management personnel can hardly control

the core product once it has been set up for the game and season. However, Zhang et al. (2004b)

described consumers' expectations towards peripheral service product by stating that "sport fans

expect more than the core game product when they attend a sport event. Thus, the quality of

game support programs and relational services plays an important role in maintaining and

increasing spectator attendance levels" (p. 100). Furthermore, Zhang et al. (1998a) argued that

the peripheral service product is an extension of the core product, which team marketers and

management can manipulate if necessary during the season. Factors representing the peripheral

service quality are Ticket Service, Game Amenities, Stadium Service, and Stadium Accessibility

(Zhang et al., 2005b).

Unfortunately, there is no scale that incorporates aspects of both core service quality and

peripheral service quality in the context of spectator sport. While Greenwell et al.'s (2002) scale









seems the most comprehensive in terms of adopting necessary attributes that are relevant to

spectator sport, two weaknesses have been identified. First, only two factors, home team and

opponent team' quality, were represented by core service quality. As suggested by previous

studies (Greenstein & Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al.,

1995), more aspects, such as game schedule, should be added when measuring core service

quality. Second, their scale pertaining to peripheral service quality focused on only physical

environment aspects (e.g., layout, seating comfort, and scoreboard) and perceptions of service

staff (e.g., responsiveness). The peripheral service quality scale should have reflected game

operation variables, such as ticket service and game amenities (Zhang et al. 1998a, 2004c, 2005).

In the field of spectator sport, variables featuring core product (game itself) have been well

discussed (Braunstein et al., 2005; Greenstein & Marcum, 1981; Hansen & Gauthier, 1989;

Schofield, 1983; Zhang et al., 1995, 2003a, 2003b, 2004a). In addition, attributes related to

peripheral service, which includes aspects of game support (operation) programs, have also been

identified (Greenwell et al., 2002; Tsuji et al., 2007; Zhang et al. 1998a, 2004c, 2005). In the

following section, the concept and relevant variables representing core product service quality as

well as peripheral service quality in relation to spectator sport will be discussed, followed by the

dimensions of service quality related to spectator sport that will be proposed for the current study.

Market Demand (Core Service Quality)

As Mullin et al. (2007) and Zhang et al. (1995) noted, the core product in spectator sport is

the game itself. Following an extensive literature review on factors influencing game attendance

variables, Schofield (1983) proposed four demand categories: Demographic Variables,

Economic Variables, Game Attractiveness, and Residual Preference. Greenstein and Marcum

(1981) and Jones (1984) also proposed game production function that was related to team










performance variables such as winning/losing record and star player thought to account for

factors affecting game attendance. Synthesizing demand categories and production function,

Zhang et al. (1995) proposed a concept of market demand, which was defined as the spectators'

expectation towards the main attributes of the game itself. Furthermore, Braunstein et al. (2005)

argued that market demand is a construct associated with the game that a team can offer to its

existing and new consumers. In a sense, market demand variables are comprised of the attributes

of core service quality.

A number of studies have been conducted to develop scales that measure market demand

(Braunstein et al., 2005; Zhang et al., 1995, 2003a. 2003b, 2004a). For the purpose of examining

variables that influence spectator' s game attendance of NBA games, Zhang et al. (1995)

developed the Spectator Decision Making Inventory (SDMI) through data collected from six

second-half NBA games (N = 861). Initially 20 items were developed based on an extensive

literature review and interviews with administrators. The 20 items were sent to a panel of experts

for a test of content validity, which resulted in 17 items. Following an EFA, 15 items were

retained that included four dimensions such as Home Team, Opposing Team, Game Promotion,

and Schedule Convenience. The SDMI displayed high psychometric properties. This study was

unique because it was the first study that developed a scale of market demand in a systematic

way to examine factors affecting game attendance in the context of sport management. However,

an EFA was used to validate the factor structure of the SDMI. In an attempt to re-examine the

SDMI's factor structure using a CFA, Zhang et al. (2003b) collected a total of 685 surveys from

spectators of five NBA games. The initial SDMI scale included 15 items under four factors:

Home Team, Opposing Team, Game Promotion, and Schedule Convenience. Following the CFA,

items were further reduced to 13 under the same factors. The CFA revealed that the revised-









SDMI displayed good psychometric properties. The resolved model was consistent with the

theoretical dimensions proposed by previous researchers (Greenstein & Marcum, 1981; Hansen

& Gauthier, 1989; Schofield, 1983). The SDMI has been adapted to a MLB spring training game

(Braunstein et al., 2005). The data were split into half. The result of CFA on the first set of data

was found to have poor psychometric properties, so it was subj ect to an EFA with a direct

oblimin rotation, which resulted in 29 items under the eight factors, which was confirmed by

another CFA using the second data set. The developed scale was named as Spectator Decision

Making Inventory-Spring Training (SDMI-ST). The identified eight factors were: Home Team,

Opposing Team, Game Promotion, Vacation Activity, Economic Consideration, Schedule

Convenience, Nostalgic Sentiment, and Love of Baseball. However, some factors such as

Nostalgic Sentiment and Love of Baseball showed poor loadings but were retained due to the

theoretical relevance to the study context.

In an attempt to apply market demand to the general setting where home and opposing

teams cannot be distinguished, Zhang et al. (2003a) conducted a study to examine the

relationship between general market demand variables and sport consumption of professional

sports. Using 525 subjects recruited by a community intercept method (Brenner, 1996), the

authors derived a 12-item instrument of market demand under three factors: Game Attractiveness,

Marketing Promotion, and Economic Consideration. The concept of general market demand has

been adapted to a National Football League (NFL) expansion team (Zhang et al., 2004a) in

which the authors developed an 18-item instrument of general market demand variables that

loaded onto four factors (Game Attractiveness, Marketing Promotion, Economic Consideration,

and Socializational Opportunity). As a result of an EFA and Cronbach's alpha coefficient, the

scale displayed good reliability and validity. This was the first examination to identify the extent









to which market demand factors explain NFL expansion team's product consumption and team

identification.

As a result of the literature review on market demand variables, it was found that when the

home team and opposing team can be differentiated, the Game Attractiveness factor should be

split into two dimensions: Home Team and Opposing Team (Braunstein et al., 2005; Zhang et al.,

1995, 2003b). However, the Game Attractiveness factor should remain combined when home

and opposing teams cannot be distinguished (Zhang et al., 2003a, 2004a).

Proposed Dimensions of Market Demand (Core Service Quality)

The Theory of Reasoned Action (Fishbein & Ajzen, 1975) as a primary theoretical

framework and based on empirical findings of previous market demand studies (Braunstein et al.,

2005; Zhang et al., 1995, 2003a, 2003b, 2004a), a six-factor model of market demand (core

service quality) was proposed in the current study. The six factors are: Home Team, Opposing

Team, Game Promotion, Economic Consideration, Love of Professional Team Sport, and

Schedule Convenience. A discussion and rationale of selecting each factor will be presented in

the following section.

Home team

The first dimension in the proposed model, Home Team, is defined as the perceived

quality of the home team that is represented by such attributes as home team performance,

presence of superstar, quality of home team players, home win/loss record, home team reputation,

and/or home team league standing. Previous studies found that the home team had a positive

relationship with game attendance of NBA basketball (Zhang et al., 1995) and minor league

hockey (Zhang et al., 1997a). Zhang et al. (1995) found that home team's win/loss records,


league standing, presence of superstars, and home team's performance had positive relationship









with NBA game attendance. Zhang, Wall, and Smith (2000) found that win/loss record was

positively related to NBA season ticket holder' s game attendance. Bird (1982) in his football

attendance study, found that league standing had a direct relationship with game attendance.

Zhang et al. (1997a), in their minor league hockey study, found that home team history,

reputation, league standing, the presence of star players, and home team quality were

contributing variables to game attendance. Given its significance on game attendance, Home

Team would be an important factor representing market demand as it relates to professional team

sport.

Opposing team

The second dimension in the proposed model, Opposing Team, refers to the perceived

quality of the opposing team that is featured by such variables as opposing team performance,

quality of opposing team, overall quality of opposing team players, opposing team history and

tradition, opposing team league standing, opposing team as a rivalry, and/or superstar. Madrigal

(1995) found that quality of opponent was related to affective reactions (enj oyment and BIRG),

both of which had a direct relationship with spectator satisfaction. In the context of the NHL,

Jones (1984) found that the presence of star players was what motivated sport consumers to

attend hockey games. Quality of opposing team, opposing team history, league standing, and

presence of superstar were consistently found to be contributing variables to game attendance

(Greenwell et al., 2002; Zhang et al., 1995, 1997a). Based on the predictive validity of the

Opposing Team factor, it should be measured as a sub-dimension of market demand as it relates

to professional team sport.









Love of professional team sport

The third dimension in the proposed model, Love of Professional Team Sport, is defined as

the perceived quality of professional team sports. A wide range of attributes have been identified

as influencing variables. These may include, but are not limited to, closeness of competition,

popularity of professional team sport, duration of game, high level of skills, best players in a

sport, and/or speed of game (Braunstein et al., 2005; Ferreira & Armstrong, 2004; Zhang et al.,

2003a). Braunstein et al. found that love of professional baseball was identified as an important

factor representing SDMI-ST. The same finding was discovered in Zhang et al.'s (2003a) general

market demand associated with professional sport consumption study. Furthermore, the authors

found that love of professional sports was positively related to game attendance and media

consumption. In an attempt to examine attributes that influence college students game attendance,

Ferreira and Armstrong (2004) found that such variables as the duration of event, the popularity

of sport, high level of skill displayed, and speed of game were revealed as salient attributes

influencing game attendance. Given its significance on game attendance, the Love of

Professional Team Sport would be an important factor representing market demand as it relates

to professional team sport.

Economic consideration

The fourth dimension in the proposed model, Economic Consideration, is defined as an

individual's perceptions towards economic variables, including ticket price, ticket affordability,

good seats, and/or ticket discounts. Previous studies have shown conflicting results regarding the

impact of economic consideration on game attendance. Baade and Tiehen (1990), in their

longitudinal study on maj or league baseball attendance, found that economic consideration was

negatively related to game attendance. Similar findings were found in Bird's (1982) football










study and the general professional sports study conducted by Hansen and Gauthier (1989). On

the other hand, Zhang et al. (1995) found that ticket discounts, good seats, and group ticket cost

were positively associated with attendance of NBA game. In numerous studies, economic

consideration was found to exert a substantial influence on game attendance (Zhang et al., 1997a,

2003a, 2004a). Given its significant contribution in accounting for game attendance, the

Economic Consideration would be an important factor representing market demand as it relates

to professional team sport.

Game promotion

The fifth dimension in the proposed model, Game Promotion, is defined as the specific

mixture of marketing tools that the sport organization can use for persuasion (Kotler &

Armstrong, 1996). The Game Promotion factor can be represented by such attributes as

advertising, direct mail and notification, publicity, and web information. This factor should be

separated from in-game entertainment amenities (Zhang et al., 2005b), which can be manipulated

by team marketers on a game basis. Previous studies indicate that the game promotion factor was

positively related to game attendance in the NBA (Zhang et al., 1995, 2000), minor league

hockey (Zhang et al., 1997a), general professional sports (Zhang et al., 2003a), and an NFL

expansion team (Zhang et al., 2004a). Based on the Eindings of the previous studies, the Game

Promotion factor should be treated as an influencing variable to form market demand as it relates

to professional team sport.

Schedule convenience

The sixth dimension in the proposed model, Schedule Convenience, is defined as the

assigned time and day in which a sport game is held. Zhang et al. (1995) found that schedule

convenience was related to only past game attendance. Hill, Madura, and Zuber (1982) found









that schedule convenience was positively related to game attendance with weekend and season

ending games, but not afternoon games in the MLB. Zhang (1998b) examined minor league

hockey spectators' preferred time for game attendance. The author found that spectators

preferred evening times (7:00 pm) for weekday and Saturday games, and late afternoon times

(4:00 pm) for Sunday games. This factor seems to have lesser predictive validity on game

attendance compared to the two other factors of Home Team and Opposing Team. However, in

various scale development studies, the Schedule Convenience factor emerged as an important

sub-dimension of market demand (Braunstein et al., 2005; Zhang et al., 1995, 2003b). Thus, this

factor should be considered as a contributing factor of market demand as it relates to professional

team sport.

Spectator Game Support Programs (Peripheral Service Quality)

Zhang et al. (1998a; 2004c) argued that the peripheral service quality that is related to

game support programs often affects the consumption levels of spectators. Furthermore, it has

been suggested that utilizing manipulated variables such as the game support programs may be

more important than the core product in terms of game consumption (Mullin et al., 2007; Murray

& Howat, 2002). Despite the significance of game support programs on game consumption, few

studies have been conducted to develop a scale that is pertinent to consumers' perceptions

towards the game support programs in the context of spectator sport (McDonald et al., 1995;

Zhang et al., 1998a, 2004b, 2004c, 2005b).

Adopting the SERVQUAL scale, McDonald et al. (1995) developed the TEAMQUALTM

scale, which included 39 items under five factors, the same as the SERVQUAL scale. Although

the TEAMQUALTM Scale has never been empirically validated, the authors took into

consideration the nature of spectator sport and management of a sport event when developing the









scale. Based on a sample of 181 spectators from three minor league hockey games, Zhang et al.

(1998a) developed the Spectator Satisfaction Inventory (SSI) that measured game support

programs related to peripheral services of spectator sport. The SSI included 24 items under five

factors: Satisfaction with Ticket Service (STS), Satisfaction with Audio Visuals (SAV),

Satisfaction with Accessibility and Parking (SAP), Satisfaction with Arena Staff (SAS), and

Satisfaction with Event Amenities (SEA). In an attempt to apply the SSI scale to the NBA

context, Zhang et al. (2004c) examined spectators' satisfaction towards game support programs

offered by a professional basketball team and its relationship with game attendance. Based on

Groinroos' (1984) two-component model (technical and functional) of service quality and the

characteristics of professional basketball games, the researchers developed the Spectator

Satisfaction Scale (SSS) that included 18 items under four factors, including Satisfaction with

Ticket Service (STS), Satisfaction with Amenities of Game (SAG), Satisfaction with Audio

Visuals (SAV), and Satisfaction with Accessibility Condition (SAC). An EFA, Cronbach's alpha,

stepwise multiple regression, and Kruskal-Wallis indicated that the SSS scale showed good

measurement properties and predictive validity (16% variances explained in game attendance).

Utilizing a more advanced factor analysis method (CFA), Zhang et al. (2005b) developed

the Scale of Game Support Programs (SGSP) to measure spectator satisfaction associated with

game operation of minor league hockey games. A preliminary scale consisting of 28 broad game

support activities was developed through an extensive literature review, field observations, and

interviews with administrators. Following an EFA, the data were reduced to 23 items under four

factors, including six items for Satisfaction with Ticket Service (STS), six items for Satisfaction

with Game Amenities (SGA), six items for Satisfaction with Arena Service (SAS), and four

items for Satisfaction with Arena Accessibility (SAA). Following a CFA, the items were reduced









to 22, retaining the same factors. Furthermore, the CFA revealed that the SGSP had sound

psychometric properties. This study was an extension of the previous study (Zhang et al., 1998a)

that developed a game operation scale (SSI). However, two aspects were improved: (a) the study

utilized a much larger sample size than 1998 study, and (b) both EFA and CFA applications

were utilized to confirm the scale's factor structure.

Using Groinroos' (1984) two-component model and Oliver' s (1980) expectancy

disconfirmation theory as theoretical frameworks, Zhang et al. (2004b) examined the role of

special programs and services perceived by NBA season-ticket holders to predict their sport

consumption. A total of 350 season ticket holders answered a questionnaire that included six

items measuring demographic variables, 15 items measuring special programs and services, and

eight items measuring game consumption. Following an EFA, four factors emerged in the special

programs and services variables: Representative, Benefit, Opportunity, and Socialization in the

Expectation and Perception dimensions. Additionally, an EFA extracted three factors that were

related to sport consumption: Event Viewing, Ticket Type, and Ticket Level. As a result of a

stepwise multiple regression analysis, the authors found that apart from the Benefit factor, the

special programs and services factors were found to have significant influences on the sport

consumption factors. Because the Congruence factor did not show good explanatory power, the

authors suggested that a performance-only measure be employed, as proposed by various authors

(Crompton & Love, 1995; Cronin & Taylor, 1992).

Proposed Dimensions of Spectator Game Support Programs (Peripheral Service Quality)

Using the two component model (Groinroos, 1984) as a primary theoretical framework and

specific characteristics concerning game support programs related to professional team sports

(Zhang et al., 1998a, 2004a, 2005b), a four-factor model represents the Game Support Programs










of professional team sport in this study. The four factors are as follows: Ticket Service, Game

Amenities, Stadium Service, and Stadium Accessibility. In the following section, justification for

using each dimension will be discussed.

Ticket service

Ticket Service is defined as the various channels of ticket sale services, including phone

order, mail order, box onfce, ticket personnel friendliness, web order procedures, convenience of

ticket sale locations, and/or will call. Providing effective ticket services are imperative for sport

organizations in order to enhance the perceptions of service quality of sport consumers. Because

ticketing is necessary for all spectators to get into the venue, the ticket onfce is usually the first

contact place for most spectators (Mulrooney & Farmer, 1996). Previous studies concerning

ticket service revealed a positive relationship with game consumption (Zhang et al., 1998a;

Zhang et al., 2004a). As a result of a stepwise multiple regression, Zhang et al. (1998a) found

that ticket service was positively predictive of future game attendance of minor league hockey.

In an attempt to predict NBA season-ticket holders' sport consumption, Zhang et al. (2004a)

examined the roles of special programs and services. The authors found that the Representative

factor, which was comprised of ticket service attributes, was positively related to media

consumption, which included items such as watching games, game attendance, and visiting team

website. Contrary to the above findings, Zhang et al. (2004c) found that ticket services were not

a statistically significant predictor of NBA spectators (Zhang et al., 2004c). However, the result

may have been attributed to online ticket shopping or ticket purchase through a nation-wide

ticket distribution company, such as Ticketmaster (Zhang et al., 2004c). Thus, online ticketing

should be taken into consideration in the measurement of game support programs. Given the










significance of its predictive validity on game consumption, the Ticket Service factor will be

included as a sub-dimension of game support programs as related to professional team sport.

Game amenities

Game amenities are defined as entertainment and promotional activities offered during the

course of a game. Music, public announcements, scoreboard, promotions, pre, half, and post-

game entertainments, dance/cheerleading activities, and music selection have been identified as

contributing variables of game amenities. Furthermore, this factor has been found to be related to

game consumption (Greenwell et al., 2002; Wakefield et al., 1996; Zhang et al., 1998a; Zhang et

al., 2004c; Zhang et al., 2005b). Wakefield et al. (1996) found that scoreboard was related to

affective reaction (pleasure), which in turn, influenced game consumption in the context of

college football and minor league baseball. Greenwell et al. (2002) also supported the above

findings when the result of their study revealed that scoreboard quality was positively related to

minor league hockey spectator satisfaction. Zhang et al. (1998a) found that game amenities were

an important predictor of game attendance of minor league hockey games. The same finding was

discovered in the case of NBA spectators (Zhang et al., 2004c). Zhang et al. (2005), in their NBA

season ticket holder study, found that in-game entertainment amenities were positively related to

game consumption of NBA games. Based on the findings of the previous studies, the Game

Amenities factor should be treated as an influencing variable to form game support programs as

related to professional team sport.

Stadium service

Stadium service is defined as the physical surroundings of service encounters that

spectators can experience as a part of game spectating. Variables identified are concession,

cleanliness, restroom, and/or staff courtesy. Previous studies concerning the stadium service









revealed a positive relationship with game consumption (Wakefield & Sloan, 1995; Zhang et al.,

2004c). In the context of college football spectators, Wakefield and Sloan (1995) found that

parking, food, and cleanliness were statistically significant predictors of the desire to stay longer

variable. Zhang et al. (2004c) also found that the stadium service factor had a positive

relationship with game attendance ofNBA spectators. Similar findings were discovered in the

context of minor league hockey (Zhang et al., 1998a). Previous findings provide supporting

evidence that Stadium Service could be an important sub-factor representing game support

programs as related to professional team sport.

Stadium accessibility

Stadium Accessibility refers to the degree of convenience to stadium access. A wider range

of variables have been identified as significant attributes. These may include, but are not limited

to, parking, niceness of venue, security, ticket takers, ushers, and/or ease of entrance. Wakefield

et al. (1996) found that stadium access had a positive relationship with pleasure of college

football spectators. Furthermore, Zhang et al. (2004c) also found that the stadium accessibility

factor was positively related to game attendance ofNBA spectators. The stadium accessibility

factor in Zhang et al.'s (1998a) minor league hockey consumption study was extracted as a

significant factor by an EFA. Given its significance of predictive validity on spectator' s affective

reaction and game consumption, this factor has been included as an influencing variable to form

game support programs as related to professional team sport.

Perceived Value

Over the last two decades, perceived value has received increasing attention as one of the

most salient variables in predicting consumption behavior in the marketing literature (Bolton &

Drew, 1991; Chang & Wildt, 1994; Zeithaml, 1988). Holbrook (1994) argued that the









fundamental marketing basis cannot be explained without considering perceived value. Ravald

and Groinroos (1996) supported Holbrook' s (1994) argument by mentioning that the perceived

value construct has been found to be a strong predictor for repurchase intentions, word-of-mouth,

and customer loyalty.

Various terms representing perceived value have been used by different researchers. These

include, but are not limited to: customer value (Eggert & Ulaga, 2002; Oh, 1999), consumption

value (Sheth, Newman, & Gross, 1991), service value (Cronin et al., 1997, 2000; Lee, Petrick, &

Crompton, 2007), and perceived value (Petrick, & Backman, 2002a; Petrick, 2003; Lee et al.,

2006; Moliner, Sanchez, Rodriguez, & Callarisa, 2007; Patterson, & Spreng, 1997 Sweeney, &

Soutar, 2001; Zeithaml, 1988).

Although perceived value has been considered one of the important constructs in service

marketing and consumer behavior research, a widely accepted consensus on the definition of

perceived value has yet to emerge (McDougall & Levesque, 2000). One of the main reasons

behind this may be the dynamic nature of the perceived value construct. That is, perceived value

varies between customer characteristics (Bolton & Drew, 1991; Parasuraman, 1997), types of

product or service (Zeithaml, 1988), and different time periods such as pre-purchase, at the

moment of purchase, at the time of use, and post-purchase (Parasuraman & Grewal, 2000;

Ravald & Groinroos, 1996). Kortge and Okonkwo (1993) argued that perceived value is a

subj ective construct. Zeithamal (1988) also contended that there are individual differences in

terms of possessing perceived value. For example, consumers may form good perceived value

when the price is low, regardless of quality. Additionally, consumers may shape the perceived

value when there is a balance between quality and price. This theoretical proposition has been










empirically validated by Sheth et al. (1991) and Sweeney & Soutar (2001), who argued that there

were relative influences of value dimensions on consumption behavior.

Definition of Perceived Value

In marketing and consumer research, perceived value is often defined from the consumer's

standpoint. Conventionally, researchers viewed perceived value as a two-dimensional construct

consisting of product/service quality received and price paid for the quality (Buzzell & Gale,

1987; Monroe, 1990). Holbrook (1994) supported this view by arguing that perceived value is

the discrepancy between the benefits of a product or service in relation to its costs. In the same

vein, Sawyer and Dickson (1984) understood perceived value as a comparison of get dimension

(i.e., product/service quality) and give dimension (price/cost). McDougall and Levesque (2000)

also agreed with the two-dimensional view, in which they defined perceived value as consumers'

cognitive evaluation of what they have received for what they have given. To support the above

view, Patternson and Spreng (1997) submitted a definition of perceived value as "a cognitive-

based construct which captures any benefit-sacrifice discrepancy" (p. 4). This definition has been

widely used as the most common definition in marketing and consumer research (Boj anic, 1996;

Dodds & Monroe, 1985). However, Grewal et al. (1998b) defined perceived value as having only

a 'get' dimension, comprised of acquisition value and transaction value. Acquisition value was

defined as the physical gain that a consumer directly obtains from the service or product,

whereas transaction value is the psychological gain that a consumer gets from the product or

service use and the feeling that one received a good deal. Grewal et al.'s (1998b) definition has

been adopted by various domains in both hospitality (Al-Sabbahy, Ekinci, Riley, 2004; Jayanti &

Ghosh, 1996) and tourism (Petrick & Backman, 2002a). However, the two-dimensional

definition, which consists of 'get' and 'give' or trade-off between quality received and price










paid' has been more widely used as the most common definition in marketing and consumer

research (Bojanic, 1996; Dodds & Monroe, 1985; Dodds et al., 1991).

However, Bolton and Drew (1991) argued that treating perceived value as just trade-off

between quality and cost is unsophisticated as it provides a lack of usefulness of understanding

the construct. In line with the above notion, Woodruff (1997) contended that perceived value

should be conceptualized more than just quality in relation to cost. He suggested perceived value

be understood as the relationship between total benefits and total sacrifice. Total benefits may

include not only quality received but also emotion derived from the purchase of product. Total

sacrifice consists of monetary sacrifice as well as non-monetary sacrifice, such as time, effort,

and risk (Woodruff, 1997). Employing qualitative measures such as focus groups and in-depth

interviews, Zeithaml (1988) proposed one of the most comprehensive models that depict the

relationship among price, quality, perceived value, and purchase intentions. In her study, she

elaborated 'get' and 'give' in a similar manner to which Woodruff (1997) defined them. She

argued that the 'get' included such aspects as perceived quality, intrinsic attributes, and extrinsic

attributes. The intrinsic attributes were related to the feeling that a consumer gets from the

purchase of the product. The extrinsic attributes were represented by the reputation of the

product purchase. In addition, Zeithaml (1988) divided the 'give' dimension into two

components, including perceived value for the cost and perceived sacrifice (effort made to buy).

Based on the above thesis, Zeithaml (1988) defined perceived value as "the consumer' s overall

assessment of the utility of a product based on perceptions of what is received and what is given"

(p. 14).

It should be noted that due to the historically unclear consensus regarding the definition of

perceived value (Dodds et al., 1991; McDougall & Levesque, 2000), the literature demonstrated









some confusion in discriminating the terms perceived value and personal value (Ledden,

Kalafatis, & Samouel, 2007) and perceived value and satisfaction (Patternson & Spreng, 1997;

Ravald & Groinroos, 1996; Woodruff & Gardial, 1996). These terms are complementary, but

clearly distinct, from each other. In terms of the difference between perceived value and personal

value, Sheth et al. (1991) pointed out that perceived value is the individual perceptions formed

by pre- and post-consumption of products or services. On the other hand, personal value is an

individual's enduring beliefs that direct his/her behavior in their ordinary life (Rokeach, 1968).

Rokeach argued that "values have to do with modes of conduct and end-states of existence. More

formally, if a person 'has a value' is to say that he has an enduring belief that a particular mode

of conduct or that a particular end-state of existence is personally and socially preferable to

alternative modes of conduct or end-states of existence" (p. 550). Therefore, personal value can

be derived from without any consumption-related situation, whereas perceived value cannot be

formed unless an individual is related to the consumptive state.

With regards to the conceptual differences between perceived value and satisfaction,

perceived value can occur at various stages of purchasing (Parasuraman & Grewal, 2000; Ravald

& Groinroos, 1996), whereas satisfaction is considered as post-consumption evaluation, which

occurs only at the post-consumption stage (Oliver, 1981). Second, perceived value can be formed

as a function of both cognitive and affective attitudinal orientations (Bolton & Drew, 1991;

Petrick, 2002a; Sheth et al., 1991; Sweeney & Soutar, 2001; Zeithaml, 1988). However,

satisfaction has been conceptualized as purely an affective evaluation (Oliver, 1996).

Importance of Examining Perceived Value

Over the two decades, perceived value has received growing academic attention due to its

theoretical and practical significance. In the context of marketing, Parasuraman (1997) argued









that in addition to service quality, perceived value has been considered one of the most

influential constructs for achieving a competitive edge. Cronin et al. (2000) pointed out that

perceived value has gained increased attention to marketing managers and researchers because of

its high explanatory power in outcome variables such as customer satisfaction and behavioral

intentions (Eggert & Ulaga, 2002). Furthermore, Parasuraman and Grewal (2000) supported the

importance of examining perceived value by mentioning that the construct has been found to be

the most significant predictor of repurchase intentions. Woodruff (1997) suggested in his study

that by recognizing the relationship of perceived value with other variables such as service

quality, satisfaction, and behavioral intentions, managers will be able to more efficiently allocate

their marketing resources.

Despite the highly recognized importance of perceived value in understanding consumer

decision-making process, few studies on the perceived value construct have been conducted in

the field of sport management. The literature review only identified two studies that have used

the perceived value construct (Kwon et al., 2007; Murray & Howat, 2002). Murray and Howat

were among the first researchers to examine the effect of perceived value on the prediction of

future intentions in the context of sport and leisure center. Utilizing stratified random sampling,

218 surveys were collected to examine the relationships among service quality, satisfaction,

perceived value, and future intentions. The authors conceptualized service quality as a two-

dimensional construct composed of core service quality and relational service quality. A path

analysis revealed that both the core and relational service quality constructs were found to have a

direct relationship with perceived value (r = .59, and r = .63) respectively. In addition, they

found that perceived value was related to satisfaction and future intentions. More specifically,

perceived value had a direct relationship with future intentions as well as an indirect relationship









with future intentions via satisfaction. As the authors tested a comprehensive model that

considered the mediating role of perceived value, the authors used perceived value for the cost of

perceived value using a single item.

To predict purchasing team-licensed apparel, Kwon et al. (2007) examined the mediating

role of perceived value in the relationship between team identification and purchase intentions.

Using a small student sample (N = 1 10), the authors found that perceived value was indeed a key

mediating variable between team identification and purchase intentions, explaining nearly 43%

of the variance. The results of the study indicated that team sport marketers need to take into

consideration perceived value when developing marketing strategy, because the study found that

team identification alone was not sufficient to influence consumer's purchase intentions. In this

study, perceived value was measured as a unidimensional construct related to value-for-money.

Measurement of Perceived Value

Despite the recognition of Zeithaml's (1988) multi-dimensional conceptualization of

perceived value and Bolton and Drew' s (1991) empirical results, a maj ority of the research

concerning perceived value has operationalized the factor as a single-item measure (Cronin et al.,

1997; Eggert & Ulaga, 2002; McDougall & Levesque, 2000; Murray & Howat, 2002; Oh, 1999;

Patternson & Spreng, 1997), which purported to measure overall perceived value of a product in

terms of value-for-money.

Some of the problems associated with using a single-item measure have been well

documented in the literature (Al-Sabbahy et al., 2004; Bolton & Drew, 1991; Petrick, 2002a). In

line with the notion by Bolton and Drew (1991) that components representing perceived value

are more than just value-for-money, Al-Sabbahy et al. (2004), in their hospitality marketing

research, argued that the use of a single-item measure does not address the concept of perceived









value because it is proposed with multiple dimensions (Petrick, 2002a; Sweeney & Soutar, 2001;

Zeithaml, 1988). Another problem associated with a single-item measure of perceived value is

identified by Petrick (2002a), who proposed the SERV-PERVAL scale that was constructed as a

multi-dimensional perceived value of a service. The researcher pointed out that the problem with

using a single-item measure is that theoretically "it assumes that consumers have a shared

meaning of value" (p. 122). Practically, Petrick (2002a) also claimed that the single-item

measure "results in the knowledge of how well one is rated for perceived value, but give no

specific direction on how to improve perceived value" (p. 122). Sheth et al. (1991) also pointed

out that the choice decision is a function of multiple perceived consumption values. Holbrook

(1994) argued that purchase behavior derived directly from perceived value can be categorized

into two attitudinal dimensions: a) cognition and b) affect. More specifically, in a buying context,

the cognitive components are in relation to a conventional view of perceived value in which a

consumer tends to compare what they receive for what they give up. The affective components

are generated when consumers consider how the purchasing is viewed by others or how this

buying makes them feel good or bad. Havlena and Holbrook (1986) supported the argument of

Holbrook (1994) by suggesting that affective aspects be entered into the equation of perceived

value for two reasons: (a) "emotional benefits affect choice behavior between instrumental

alternatives that are functionally equivalent in other aspects" (p. 394), and (b) perceived value is

considered a dynamic variable (Bolton & Drew, 1991; Parasuraman, 1997), which means that

consumers could form perceived value after the consumption as a post-hoc evaluation that may

include subj ective or emotional reactions such as fear, anger, and happiness that are caused by

the purchase (Bolton & Drew, 1991; Havlena & Holbrook, 1986; Sweeney & Soutar, 2001). The

affective and hedonic aspects of consumption may be more relevant to sport consumers. Sport









consumer behavior can hardly be understood solely from a cognitive view, as sport consumers

may be attracted to a sport game not only for mental benefits (i.e., cognition) but also for the

pleasure, positive arousal, sensation, satisfaction, and feeling associated with winning, which are

related to affective aspects (Sloan, 1989). Due to the above reasons, it is suggested that perceived

value be formed through not only cognitive processes (e.g., belief and thinking) but also

affective processes (feeling and emotion), which justifies multi-dimensional aspects of perceived

value (Bolton & Drew, 1991; Petrick, 2002a; Sheth et al., 1991; Sweeney & Soutar, 2001;

Zeithaml, 1988). In tourism marketing research, Sanchez, Callarisa, Rodriguez, and Moliner

(2006) also pointed out that developing a scale measuring perceived value should reflect both

functional (cognitive) and affective dimensions. This multi-dimensional view may attenuate the

conventional perspective that perceived value is related to cognitive response to a service

experience (Cronin et al., 2000; McDougall & Levesque, 2000; Patternson & Spreng, 1997).

While acknowledging multi-dimensional aspects of perceived value, cognitive aspects, which are

related to economic utility variables such as perceived value for the cost and quality,

outperformed hedonic aspects of perceived value in previous literature (Cronin et al., 1997;

Eggert & Ulaga, 2002; McDougall & Levesque, 2000; Oh, 1999). Furthermore, when perceived

value is measured with other variables such as quality simultaneously, the two constructs tend to

form a causal relationship, in which perceived value for the cost is positively related to perceived

value (Oh, 1999; Sweeney & Soutar, 1997; Zeithaml, 1988). Thus, the sole use of perceived

value for the cost as measuring perceived value has been suggested when perceived quality is

incorporated into the same model. Following Churchill's (1979) multi-item measure, several

items assessing perceived value for the cost have been recommended.










Overview of the Proposed Dimensions of Perceived Value

In this current study, perceived value is represented by a unidimensional factor, Perceived

Value for the Cost, as suggested by previous research (Kwon et al., 2007; McDougall &

Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). Zeithaml (1988) defined

perceived value as a consumer' s overall assessment of the utility of a product (or service) based

on perceptions of what is received (quality and benefit) and what is given (perceived value for

the cost and non-monetary price). While acknowledging its multi-dimensional aspects, previous

studies have consistently found that utilitarian aspect such as perceived value for the cost

accounted for more variance explained in consumption behavior (Kwon et al., 2007; Netemeyer

et al., 2004). Netemeyer et al. argued that "perceived value for the cost was considered a

cornerstone of the most consumer-based-brand-equity frameworks" (p. 21 1). Kwon et al.

supported Netemeyer' s rationale by suggesting that a sport consumer tends to weigh the cost

versus the benefit to determine perceived value of team-licensed product. Therefore, perceived

value for the cost could be overall the well-representing global measure of perceived value

construct. Furthermore, a unidimensional measure of perceived value using perceived value for

the cost can certainly be overarching approach. Consistent with the theoretical relevance and

empirical suggestions, the current study adopted a unidimensional aspect (i.e., perceived value

for the cost) to measure perceived value, even though the measurement may exclude the

potential importance of hedonic aspects of perceived value such as emotional response.

Perceived Value for the Cost

Perceived Value for the Cost refers to the perceived price paid for a service (i.e., a ticket

for game attendance). This is one of the most important factors that distinguish perceived value

from other theoretically related factors such as service quality, personal value, and satisfaction

(Bolton & Drew, 1991; Ledden et al., 2007; Zeithaml, 1988). Many scholars view perceived









value as a trade-off between what consumers paid for what they received (McDougall &

Levesque, 2000; Patternson & Spreng, 1997; Sawyer & Dickson, 1984). Zeithaml (1988) found

that perceived value for the cost was indirectly related to perceived value through perceived

quality. Oh (1999) also confirmed the above relationship by finding that perceived value for the

cost was directly related to perceived value in the context of choosing a luxury hotel. This would

be the same case for attending a sporting event for fans. Studies have found that ticket price for a

sport event was, in general, negatively related to game attendance (Bird, 1982; Demmert, 1973,

Noll, 1974). However, this negative relationship would be moderated by psychological

orientations such as involvement and team identification (Wann & Branscobe, 1993). Therefore,

the Perceived Value for the Cost factor has been included as the dimension representing

perceived value of professional team sport. This factor will be measured by perceptions

regarding price reasonability, economic worth, and/or money worth.

Relationship among Perceived Value, Service Quality, and Behavioral Intentions

As Parasuraman (1997) and Woodruff (1997) indicated, perceived value should be

considered for gaining a competitive advantage in marketing. In numerous empirical studies,

perceived value has been found to have considerable impact on behavioral intentions (Petrick,

2003; Zeithaml, 1988). Furthermore, Cronin et al. (1997) and Oh (1999) found that perceived

service quality was the most significant predictor of perceived value. Zeithaml's (1988) finding

supported the positive relationship between perceived service quality and perceived value.

Therefore, a conceptual framework that posits the hierarchical relationship among service quality,

perceived value, and behavioral intentions has gained increased attention over the past decade in

marketing and consumer research (Cronin et al., 1997, 2000; Dodds et al., 1991; Gallarza &









Saura, 2006; Grewal et al., 1998b; Lee et al., 2006; Murray & Howat, 2002; Oh, 1999; Petrick,

2004a, 2004b; Zeithaml, 1988).

Using a 5x3x3 between-subj ects factorial design, Dodds et al.'s (1991) study examined the

effects of price, brand, and store information on consumers' product evaluations. The authors

conceptualized perceived value as a trade-off between perceived service quality and perceived

sacrifice, which was measured by monetary and non-monetary orientations. The results of the

study indicated that perceived service quality positively led to perceived value, which in turn,

positively influenced willingness to buy.

In the context of purchasing a durable product, Grewal et al. (1998a) developed a

conceptual model of the consumer decision-making process and tested the effects of store name,

brand name, and price discounts on consumers' psychological evaluations, such as store image,

perceived brand quality, and internal reference prices, which in turn, may influence consumer' s

perceived value and purchase intentions. Using a 2x2x2 between-subj ects design, the author

found that perceived value was positively related to purchase intentions. Furthermore, perceived

value was directly influenced by brand quality, internal reference price, and perceived brand

quality. However, in this study, perceived value was operationalized as a unidimensional model.

In an attempt to understand the effect of price on perceived value, which in turn, may affect

willingness to buy and search intentions, Grewal et al. (1998b) developed a two-dimensional

perceived value model, which included acquisition value and transaction value. The results

showed that both values had a positive effect on willingness to buy durable goods. Also, both

values were found to be negatively related to search intentions.

To better understand consumers' decision-making process of choosing an upscale hotel,

Oh (1999) developed a conceptual model to test the relationship among price, perceptions of










performance, perceived service quality, customer perceived value, satisfaction, intentions to

repurchase, and word-of-mouth. The results of the study indicated that perceived value was

found to be directly related to repurchase intentions as well as word-of-mouth, and perceived

value was also indirectly related to repurchase intentions through satisfaction. Perceived service

quality had a direct relationship with perceived value. Furthermore, perceived service quality

also had an indirect relationship with repurchase intentions as well as word-of-mouth by means

of perceived value. The author also found that perceived value for the cost was a direct

antecedent of perceived value, which in turn, influenced repurchase intentions as well as word-

of-mouth. While this study was recognized as the first holistic approach to examine decision-

making process in the context of hospitality, the limitation associated with this study was the use

of a single-item measure for all constructs except for the perception of performance. Because of

the single-item measures, the construct's validity and reliability have been questioned.

Cronin et al. (2000) examined the relationships among service quality, service value,

satisfaction, and behavioral intentions across the six service industries that were characterized as

hedonic vs. utilitarian, tangible vs. intangible, and primary vs. secondary. As a result of SEM

analysis, the authors found that service quality was directly related to perceived service value,

satisfaction, and behavioral intentions and had indirect relationships with behavioral intentions

through perceived service value and satisfaction. Contrary to Oh' s (1999) Einding, perceived

sacrifice was found not to be related to perceived service value. In terms of the effect of

perceived service value on behavioral intentions and satisfaction, the results revealed that

perceived service value was directly related to behavioral intentions as well as satisfaction. Since

indirect effects on behavioral intentions have been scarce in service marketing research, the

researchers suggested incorporating both direct and indirect effects of quality on behavioral









intentions. From the findings of Cronin et al. (2000), it should be noted that perceived value

played not only a role of direct predictor of behavioral intentions but also a mediating role

between service quality and behavioral intentions.

The above theoretical relationship of perceived value has been verified in various contexts,

including spectating, recreation sport, health care and communication (Cronin et al., 1997);

festival attendance (Lee et al., 2007); cruise travel (Petrick, 2004a, 2004b); university students'

travel behavior (Gallarza & Saura, 2006); durable goods (Grewal et al., 1998a); and use of a

leisure center (Murray & Howat, 2002).

Cronin et al. (1997) examined the hierarchical relationship among service quality,

perceived value, and purchase intentions in six service industries including three hedonic

services such as recreation sport, spectator sport, and entertainment businesses, and three

utilitarian services businesses such as health care, communication, and food. As a result of path

analysis, the authors found that there was a considerable increment of the variance explained (on

average of 39%) in purchase intentions by adding the perceived value construct to the service

quality and intention model. In addition, service quality was found to be directly, as well as

indirectly, related to purchase intentions via perceived value.

In order to predict festival attendees' future behavioral intentions, Lee et al. (2007)

investigated the roles of service quality, perceived value, and satisfaction on behavioral

intentions. In this study, perceived value and satisfaction have been treated as mediating factors.

The result of SEM analysis indicated that service quality and perceived value were found to be

the significant predictors of behavioral intentions. In particular, perceived value was revealed to

be the best predictor of behavioral intentions. The authors also confirmed the theoretical

proposition suggested by Sheth et al. (1991), Sweeney and Soutar (2001), and Petrick (2002a,









2003) that consumption values have differential impacts (relative influences) on consumption

behavior.

Petrick (2004a) replicated Cronin et al.'s (2000) study to examine whether the proposed

theoretical relationship (i.e., service quality-perceived value-purchase intentions) would hold up

in the context of cruise travel. As a result of SEM analysis, the author concluded that service

quality was shown to have direct and indirect relationships with repurchase intentions through

perceived value, and repurchase intentions were found to be directly related to word-of-mouth.

Later, Petrick (2004b) conducted another study to examine the extent to which Petrick' s (2002a)

five dimensions of perceived value would have predictive validity on cruise passengers'

repurchase intentions and to compare the differences of the effect of perceived value on

repurchase intentions between first timers and repeaters. As a result of SEM analysis, the author

found that that quality was directly related to repurchase intentions, and indirectly related to

repurchase intentions through perceived value. These effects existed in both groups. An

interesting finding was that the perceived value for the cost was found to be the best predictor of

perceived value, whereas quality in generally was the best predictor in other studies (Bolton &

Drew, 1991; Jayanti & Ghosh, 1996). It appears that behavioral price was related to perceived

value only in first timers. Finally, it was revealed that reputation was a good predictor of quality

but not a good predictor of perceived value, which was consistent with the finding by Zeithaml

(1988).

Recently, Gallarza and Saura (2006) explored the relationships among consumer

perceived value, satisfaction, and loyalty in the context of university students' travel behavior.

The results of the study indicated that service quality was found to be directly related to

perceived value and indirectly related to loyalty via perceived value and satisfaction. While the










exploration and confirmation of the relationship among service quality, perceived value, and

behavioral intentions has received considerable attention in marketing, hospitality, and tourism

domains, little attention has been paid to the field of sport management. Thus far, only two

studies have appeared in maj or sport management j ournals (Kwon et al., 2007; Murray & Howat,

2002). Kwon et al. (2007) confirmed the mediating role of perceived value in the relationship

between team identification and purchase intentions in the context of team-licensed merchandise

consumption. Murray and Howat' s (2002) study was the first exploration of the relationship

among quality, value, and intentions in the field of sport management. Their findings were

consistent with the previous studies in which the authors found that both core service quality and

relational service quality were directly related to perceived value, which in turn, influenced

future intentions.

Summary

For professional sport teams, ticket sales and media contracts are considered as two main

revenue producers (Zhang et al., 1995). Also, sport teams have secondary revenue generators,

such as parking, concessions, and the sale of team-licensed products (Zhang et al., 1997a), which

are regarded as by-products of ticket sales (game attendance). However, media contracts are

generally decided based on unique factors, such as population size in which the team is located,

team performance, and the presence of star players or coaches. Due to the requirement of those

unique factors, broadcast rights are often enjoyed by select teams and conferences. Therefore, it

is essential for team marketers to identify variables that influence game attendance in order to

enhance the level of consumption towards game products/services.

In the consumer research domain, constructs such as market demand, game support

programs, and perceived value have been shown to be good predictors of behavioral intentions,









which are considered as an immediate antecedent of consumption. Furthermore, those constructs,

except for perceived value (Perceived Value for the Cost), have been conceptualized as multi-

dimensional in nature (Groinroos, 1984; Petrick, 2002a, Parasuraman et al., 1998; Zhang et al.,

1995, 1998a), which has more practical implications than a unidimensional conceptualization.

According to Petrick (2002a) and Zhang et al. (2004c), a multi-dimensional scale is desirable

because the results would pinpoint areas that need immediate attention. In addition, management

can identify the relative performance of each area in which it is succeeding and failing. Cronin et

al., (2000) and Oh (1999) suggested a holistic approach of analysis that measures service quality,

perceived value, and behavioral intentions simultaneously to better understand why people

decide to repurchase or spread word-of-mouth concerning their experience with the

products/services. Therefore, adopting its holistic approach, this current study measured the

influence of market demand (core service), game support programs (peripheral service), on

spectator behavioral intentions as mediated by perceived value.









CHAPTER 3
IVETHODOLOGY

The method of this study is presented in the following four sections: (a) participants, (b)

measurement, (c) procedures, and (d) data analyses. A survey design was conducted to assess the

influence of market demand and game support factors on spectator behavioral intentions as

mediated by perceived value in the context of professional team sports.

Participants

For the purpose of including professional team sport spectators from diverse backgrounds,

the current study employed a community intercept sampling method to recruit research

participants. A community intercept method is a modified method of the traditional mall

intercept. While a traditional mall intercept is only conducted at shopping malls, community

intercept method can be conducted at various public places where sampling can be more

representative of the residents in the community, such as grocery stores, shopping malls,

churches, movie theaters, sports bars, and mass transportation waiting areas (Brenner, 1996).

Participation in this survey was voluntary, and a participant had to be 18 years of age or

older. Research participants were those who resided in the southeastern metropolitan cities or

within close proximity, where one or more professional sport teams were franchised, at the time

when this survey was conducted. To qualify for participating in the current study, an individual

must have experienced attending and also paid for at least one professional team sport event

within the past 12 months. These sampling conditions would enable the research participants to

be familiar with the game products and services for which they paid (Petrick, 2002a). Thus, the

following screening questions were included in the survey form: "Have you attended a

professional team sport event within the past 12 months?" Because of the presence of the

'Perceived Value for the Cost' factor (Petrick, 2002a; Zeithaml, 1988), it was necessary to









include the second screening question: "If so, did you or your family pay for the game ticket?"

Only those people who answered positively to the screening questions were included in the study.

Thus, this study was delimited to only those residents who attended at least one professional

team sport event and had purchased the game ticket.

In terms of sample size required for advanced statistical analyses (i.e., confirmatory factor

analysis and structural equation modeling), Kline (2005) suggested that at least 10 respondents

are desirable for each observed variable, which was also recommended by Hair et al. (2005).

Considering that the market demand section had a total of 46 observed variables, this study

targeted on a minimum number of 460. Contiguous to this obj ected sample size, a total of 453

respondents from four maj or metropolitan areas and their proximity communities (Atlanta,

Jacksonville, Tampa, and Miami) in the states of Florida and Georgia responded to the

questionnaire. These responses were resulted from data collections at seven sport bars, three

malls, one grocery store, one community park, and one college campus.

Of the sample, 60.5% were male and 39.5% were female. Nearly 72% of the participants

were between 23 and 40 years old, and close to 20% were over 40 years old. The sample

included predominantly Caucasians (about 60%). Hispanic (about 20%), African Americans

(13.5%), and Asians (about 9%) were among the remaining ethnic groups. Approximately, 55%

respondents came from families with 3-4 or 5-6 people in the household; whereas, a single-

person household accounted for 20% of the sample. Household income level was widely

distributed among the income categories; with about 50% respondents from families of $60,000

or more and 10% from families of $100,000 or more annual income, representing an upper-

middle and upper levels among professional team sport consumers. In terms of marital status,

single was somewhat more dominant (53%) over the married (43%). The respondents were of










good education background, with close to 80% possessing an undergraduate or an advanced

degree. Occupation categories were widespread among the respondents, with a maj ority of them

in the management, professional, or educational fields. These characteristics of respondents were

consistent with those general backgrounds of professional sport consumers as described by

Simmons Market Research Bureau (2007). The consistency would enhance the relevance and

applicability of this study to the population of professional team sport game consumers; thus, it

was appropriate for this study to proceed.

In terms of the most recent game that the respondents attended, 44.8% of the respondents

indicated that they attended a NFL game, followed by an NBA game (25.8%), a MLB game

(21.9%), an NHL game (4%), an AFL game (3.3%), and a MLS game (0.2%). Among the sport

franchise teams, Jacksonville Jaguars, Tampa Bay Buccaneers, and Miami Dolphins games were

the most popularly attended, followed Orlando Magic and Miami Heat games and Tampa Bay

Rays and Florida Marlins games (Table 4-1).

Measurement

A questionnaire was formulated based on a comprehensive review of literature and a test of

content validity. This preliminary questionnaire included the following five sections: (a) market

demand, (b) game support programs, (c) perceived value, (d) behavioral intentions, and (e)

demographic information (Appendix A).

Market Demand

Sport games are the core product function of professional sport team. Market demand is

related to consumer expectations towards the attributes of the core product (Zhang et al., 2003a).

Essentially, it is a cluster of pull factors associated with the game that a professional sport team

can offer to its new and returning spectators (Braunstein et al., 2005; Hansen & Gauthier, 1989;

Schofield, 1983; Zhang et al., 1995). The market demand section was developed primarily based









on Spectator Decision-Making Inventory (SDMI) (Zhang et al., 1995, 2003b), which originally

consists of four dimensions (Home Team, Opposing Team, Game Promotion, and Schedule

Convenience). Two additional factors, Economic Consideration and Love of Professional Team

Sports, were added based on the indications by numerous researchers such as Braunstein et al.

(2005), Hansen and Gauthier (1989), and Schofield (1983). A maj ority of the items were derived

from direct adoptions and modifications of the SDMI and other existing scales. A very small

number of items (< 10%) were generated from review of other published literature. In particular,

all adoptions and modifications took into consideration the unique product and service features

of professional team sports, the general nature of this study with an attempt to include all

professional team sports, and validity and reliability evidence of related factors and items. These

were consistent with Zhang et al.'s (2003b) indications that when a scale is adopted in settings

that are different from the original study, revision and validation are necessary. Variables related

to the uniqueness of the sporting event need to be included in such revisions.

Previous scales by Braunstein et al. (2005), Hansen and Gauthier (1989), and Zhang et al.

(1995, 2003a, 2003b) followed rigorous measurement procedures in their development, usually

including a comprehensive review of literature, formulation of a theoretical framework,

qualitative study components such observations and interviews, test of content validity,

exploratory and confirmatory factor analyses, and tests of reliability. Thus, adopting items from

these previous studies were appropriate. A total of 46 items were included for the six market

demand factors: Home Team (10 items), Opposing Team (10 items), Love of Professional Team

Sport (10 items), Economic Consideration (6 items), Game Promotion (4 items), and Schedule

Convenience (6 items). These items were preceded with the following statement: 'please rate the

following variables that might have influenced your decision making to attend the most recent










professional team sport event within the past 12 months.' A Likert 5-point scale was adopted,

ranging from 1 = 'Not at All' to 5 = 'Very Much.'

Game Support Programs

Game support programs were operationalized as the controllable service activities that

were related to game operations such as ticket services, stadium accessibility, stadium services,

and game amenities to support the production of the core product. Following similar

measurement procedures outlined in the market demand section, factors and items for the game

support programs were formulated mainly based on three scales, including Spectator Satisfaction

Inventory (Zhang et al., 1998a), Spectator Satisfaction Scale (Zhang et al., 2004c), and the Scale

of Game Support Programs (Zhang et al., 2005b). These scales were generally developed

through appropriate and systematic measurement procedures, usually including a comprehensive

review of literature, formulation of a theoretical framework, qualitative study components such

observations and interviews, test of content validity, exploratory and confirmatory factor

analyses, and tests of reliability. Once again, all adoptions and modifications took into

consideration the unique product and service features of professional team sports, the general

nature of this study with an attempt to include all professional team sports, and validity and

reliability evidence of related factors and items.

A total of 38 items related to game support activities were included in this section, which

were under four factors: Ticket Services (10 items), Game Amenities (12 items), Stadium

Services (6 items), and Stadium Accessibility (10 items). The items were preceded with the

following statement: 'With respect to the professional team sport event that you most recently

attended, please rate the following statements that assess your perceptions of game operation

related activities during your attendance.' A Likert 5-point scale was adopted as in the original

scales, ranging from 1 = 'Very Unsatisfied' to 5 = 'Very Satisfied.'









Perceived Value

In the current study, perceived value was represented by a unidimensional factor,

Perceived Value for the Cost, as suggested by previous researchers (Kwon et al., 2007;

McDougall & Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). Zeithaml

(1988) defined perceived value as consumer' s overall assessment of the utility of a product (or

service) based on perceptions of what is received and what is given. While acknowledging its

multi-dimensional aspects, previous studies have consistently found that utilitarian aspect such as

perceived value for the cost was found to be more related to consumption behavior (Kwon et al.,

2007; Netemeyer et al., 2004). Netemeyer et al. argued that "perceived value for the cost was

considered a cornerstone of the most consumer-based-brand-equity frameworks" (p. 21 1). Kwon

et al. supported Netemeyer' s rationale by suggesting that a sport consumer tends to weigh the

cost versus the benefit to determine perceived value of team-licensed product. Consistent with

the empirical suggestions, the current study adopted unidimensional aspect (i.e., perceived value

for the cost) to measure perceived value even if the measurement may exclude the potential

importance of hedonic aspects of perceived value such as emotional response. A total of five

items for the Perceived Value for the Cost factor were derived from Petrick' s (2002a) SERV-

PERVAL. Development of the SERV-PERVAL underwent rigorous measurement procedures.

The original items were slightly modified to be relevant to the professional sport game setting.

The items were preceded with the following statement: 'With respect to the professional team

sport event that you most recently attended, please rate the following statements that assess your

perceptions of game experience during your attendance.' A Likert 5-point scale as in the original

scale was adopted, ranging from 1 = 'Definitely False' to 5 = 'Definitely True.'










Behavioral Intentions

Items measuring a spectator's behavioral intentions were generated from Soiderlund (2006)

and Zeithaml et al.'s (1996) scales, which all followed proper measurement procedures. There


were two factors in this section: Repatronage Intentions and Recommend to Others, with each

factor having 5 items. The original items were slightly modified in order to be relevant to the

professional sport game setting. The items were preceded with the following statement: 'With

respect to the professional team sport event that you most recently attended, please rate the

following statements that assess your intentions for future attendance at the professional team

sport events.' A Likert 5-point scale was adopted, ranging from 1 = Strongly Disagree' to 5 =

' Strongly Agree.'

Demographic Information

For the purpose of sample description, demographic background variables were included

in the questionnaire, which consists of the following variables: gender, age, number of people in

the household, household income, marital status, education, ethnicity, and occupation. Questions

were phrased in close-ended multiple-choice format.

Procedures

Following the development of the scales, the preliminary questionnaire submitted to a

panel of 10 experts for content validity testing. The panel included five university professors and

five practitioners. For the university professors, one specializes in marketing, four in sport

management. Among the practitioners, four were event operation coordinators for a NFL team,

NHL team, MLB team, or a maj or intercollegiate athletic department, and one was associate

athletics director responsible for marketing and sponsorship programs at a maj or university

athletic program. Each panel member was requested to examine the relevance, representativeness,









and clarity, test format, and item content of the questionnaire and its associated sections.

Following the feedback of the panel of members, the preliminary questionnaire was modified,

revised, and improved, mainly in the areas of item adequacy, factor relevance, and wording

clarity. With the modified version of the questionnaire, a pilot study was conducted to sample of

sport consumers who had an experience of attending a professional team sport within the past 12

months (n = 32). The purpose of this pilot study was to further examine the content validity from

the perspective of targeted population. At this stage, suggested changes and improvements were

all minor and they were primarily related to wording clarifications. A survey packet was

prepared, which included the revised instrument, a cover letter explaining the purpose of the

study and requesting cooperation from a participant, and the Informed Consent form. Approval

from the Institutional Review Board for the Protection of Human Participants was then obtained

prior to the beginning of data collection.

The researcher first contacted targeted community locations to obtain permissions to

conduct the study. Only with permission from a location, a test administration was conducted.

Data were then collected at seven sport bars, three malls, one grocery store, one community park,

and one college campus in four metropolitan cities and their proximity communities (i.e., Atlanta,

Tampa, Jacksonville, and Miami) in the states of Florida and Georgia. All of these cities had at

least one professional team sport franchise. To ensure a good representation of professional team

sport consumers with different backgrounds in the sample, data collections were conducted on

both week days and weekend days. Four trained research assistants provided on-site support with

the data collection process. Throughout the data collections, a standardized 9-step procedure was

followed: (a) politely approaching all people regardless of gender, age, and race; (b) politely

presenting the screening questions to verify the study eligibility; (c) explaining the purpose of the










study; (d) explaining that participation would be voluntary and that participation would be

anonymous; (e) explaining that there would be no penalty for not participating or stopping

anytime during the survey; (f) presenting the informed consent form; (g) distributing the

questionnaire upon an individual agreed to participate; (h) collecting the completed

questionnaire; and (i) thanking the individual for his/her time and support for the study (Zhang et

al., 2004c). Completing a questionnaire, on average, took approximately 15 minutes. A total of

470 copies of the questionnaire were collected. Of those, 17 questionnaires were discarded due to

having non-sporadic missing values, following the suggestions made by Zhang, Pease, and Hui

(1996). Therefore, a total of 453 were included in subsequent data analyses and hypothesis

testing. Missing values were rarely spotted within the remaining sample of 453 respondents.

Among those occasional missing data point, there was no Not-Missing-At-Random (NMAR)

data (Rubin, 1987; Schafer & Graham, 2002) were found. Only few Missing-At-Random (MAR)

were detected. For those MAR data, mean substitution was applied.

Data Analyses

The total sample of 453 was randomly split into two halves. The first set (n = 23 1) was

used for conducting exploratory factor analyses (EFA) of the market demand, game support,

perceived value for the cost, and behavior intention variables, respectively. The second data set

(n = 222) was employed for conducting confirmatory factor analyses (CFA) of the measures and

a structural equation modeling (SEM) that examined the relationships among market demand,

game support, perceived value for the cost, and behavioral intentions. Procedures in SPSS

version 15.0 (SPSS, 2006) were carried out to calculate descriptive statistics for

sociodemographic, market demand, game support, perceived value for the cost, and behavioral

intentions variables.









Procedures in the SPSS program were employed for executing the EFA and calculating

reliability coefficients. Although the factors and items in the measures were adopted from one or

two maj or scales, information from other related studies was also incorporated in the revised

questionnaire. Due to this compilation process, an EFA was deemed necessary as the initial step

for examining the factor structure of the measures. The primary purpose of the EFA was to

identify unique and reliable simple factor structures that are of the potential to be generalized to a

universe of variables from a sample of variables, so as to reduce any redundant data. Following

an EFA, internal consistency reliability was examined by calculating the Cronbach's alpha

coefficients for the identified factors (Cronbach, 1951). In the EFA, alpha factoring extraction

(Kaiser & Caffrey, 1965) was applied, followed by promax rotation (Hendrickson & White,

1964) to identify factors. The promax rotation was developed by combining the advantages of

varimax orthogonall) and oblique rotation techniques (Zhang, Smith, Lam, Brimer, & Rodriquez,

2002). "The promax method is first started with an orthogonal solution; the factor matrix is then

rotated to the best least-square fit to the ideal solution by the procrustes procedure" (Hurley &

Cattell, 1962). Following criteria were used to determine the factors and their items: (a) a factor

had an eigenvalue equal to or greater than 1.0 (Kaiser, 1974), (b) an item had a factor loading

equal to or greater than .40 (Nunnally & Bernstein, 1994), (c) a factor had at least 3 items, and

(d) an identified factor and retained items must be interpretable in the theoretical context. The

scree plot test was also utilized to help make a determination on the number of extracted factors

(Cattell, 1966).

Procedures in the AMOS version 7.0 (Arbuckle, 2006) were executed for conducting the

CFA and the SEM for the retained market demand, game support programs, perceived value for

the cost, and behavioral intentions factors that were resolved from the exploratory factor analyses.









According to Bollen (1989) and Hair et al. (2005), executing a CFA needs to follow the

following fiye steps: (a) model specification, (b) model identification, (c) model estimation, (d)

testing model fit, and (e) model respecifieation. If the hypothesized model fits the data well, the

confirmed factor structure can be accepted. Model respecifieation would be needed if the

hypothesized model does not fit the data well. Following the suggestions of Hair et al. (2005),

several goodness of fit measures were adopted, which included chi-square statistic (X2), normed

chi-square (X 2/d~f), root mean square error of approximation (RMSEA), standardized root mean

residual (SRMR), comparative fit index (CFA), and expected cross validation index (ECVI)

(Bentler, 1990; Bollen, 1989; Hu & Bentler, 1999; Steiger, 1990). For the chi-square statistic

(X2), it is expected to have non-significant difference that indicates that there is no difference

between expected and observed covariance matrices. However, it has been criticized that chi-

square statistic is too sensitive to sample size (Kline, 2005). Thus, it is suggested that chi-square

statistic be used, along with other goodness of fit measures (Hair et al., 2005). Oftentimes,

normed chi-square is interchangeably called as the chi-square statistic per degrees of freedom

(X2/df) (Kline). Bollen (1989) suggested that cutoff values of less than 3.0 for the normed chi-

square are considered reasonable fit. Browne and Cudeck (1992) indicated that any RMSEA

values less than .05 show a close fit. Recently, Hu and Bentler (1999) suggested that RMSEA

value of .06 also indicates a close fit. Any values of RMSEA between .06 and .08 indicate

acceptable fit. Values of RMSEA between .08 and .10 show mediocre fit. Yet, any values greater

than .10 indicates unacceptable fit (Hu & Bentler). SRMR indicates how large residuals are.

Therefore, smaller values of SRMR show good fit. Any values less than .10 are considered

favorable fit (Kline, 2005). The comparative fit index (CFI) assesses "the relative improvement

in fit of the researcher' s model compared with a baseline model (i.e., null model)" (Kline, p.









140). A rule of thumb for CFI is that any values larger than .90 indicate an acceptable fit, and

values greater than .95 show a close fit. Lastly, the expected cross validation index (ECVI)

measures the fit across samples and has no set criteria. Generally, smaller values are considered

better fit of the model.

Three tests were employed to measure the reliability of the scales: Cronbach' s coefficient

alpha (u) values, construct reliability (CR), and average variance extracted (AVE). The

recommended .70 cut-off value were adopted to determine internal consistency (u) and CR

(Fornell & Larcker, 1981; Nunnally & Bernstein, 1994). The benchmark value for AVE was .50

suggested by Bagozzi and Yi (1988). Fornell and Larcker (1981) defined CR as an internal

consistency measure that accounts for the measurement errors of all indicators. Since the AMOS

program does not provide CR values, the researcher in the current study adopted the following

formula for the calculation of CR (Hair et al., 2005).

(C standardized loading)2 / (C Standardized loading)2 CE (1)

Where (C standardized loading)2 is the squared sum of the pattern coefficients between the

indicator and the latent variable within the construct; 1 4, is the sum of all measurement errors of

the indicators within the construct. Another way to determine reliability of the construct is to

evaluate AVE values, which is defined as an amount of variance that is accounted for by the

construct, relative to the amount of variance due to measurement errors of all indicators (Fornell

& Larcker). As with CR value, the AMOS program does not provide AVE value, therefore, the

following formula was used (Hair et al., 2005).

I (standardized loading )/ C (Standardized loading2) F (2)

A convergent validity test was conducted to ascertain this aspect of construct validity.

Convergent validity refers to a psychometric property test that measures how well items are









theoretically related to each other (Kline, 2005). To determine convergent validity, the researcher

evaluated indicator loadings and critical ratios for each indicator. Since convergent validity

refers to how well each indicator loads on a priori latent construct, item's high loading on the

respective latent construct indicates good convergent validity. Generally, an item loading value

equal to or greater than .707 (i.e., R2 value > .50) would be considered an acceptable loading for

good convergent validity, indicating that more than 50% of the variance is associated with

common variance (Anderson & Gerbing, 1988). Critical ratio is an alternative way to examine

convergent validity of the indicators. Regarding critical ratio value, any critical ratio value that

exceeds 2.58 for a two-tail test would be considered statistically significant at the .001 level

(Arbuckle, 2006). Additionally, discriminant validity was examined to measure how distinct the

constructs are one another. To establish discriminant validity, the researcher employed two

methods: (a) examination of the interfactor correlations; and (b) comparing squared correlation

of any of two latent constructs with AVE value (Fornell & Larcker, 1981). According to Kline

(2005), discriminant validity can be established when interfactor correlation is below .85. A

more robust way of measuring discriminant validity was suggested by Fornell and Larcker

(1981), referring that a squared correlation between two constructs should be lower than the

AVE for each construct.

Finally, a SEM test was conducted using the AMOS program to examine the hypothesized

structural relationships among the market demand, game support, perceived value for the cost,

and behavioral intentions factors. The same fit index criteria were employed to examine the

structural model as with the measurement model. Path coefficients were used to determine the

direct and indirect relationships among the sets of factors. The SEM analysis provides the basis









for accepting or rej ecting the hypothesized relationships among the latent constructs (Kline,


2005).









CHAPTER 4
RESULTS

The results of this study are presented in the following four sections: (a) descriptive

statistics, (b) exploratory factor analyses, (c) confirmatory factor analyses, and (d) structural

equation model analyses.

Descriptive Statistics

Descriptive statistics including mean and standard deviation for the market demand

variables are presented in Table 4-2. Of the 46 items, 3 8 had a mean score greater than 3.0 (i.e.,

midpoint on the 5-point Likert scale), indicating that overall market demand variables were

considered important when making a decision to attend a professional team sport event. Seven

items had a mean score that was lower than the midpoint. Of the all variables in the market

demand factor, 'love professional team sportss' item had the highest mean score (M~= 4.22; SD

= 0.95) and 'web information' item had the lowest mean score (M~= 2.42; SD = 1.20).

Descriptive statistics for the game support programs are reported in Table 4-3. Of the 3 8 items,

33 had a mean score greater than 3.0, the midpoint on the 5-point Likert scale, indicating that

overall game support variables were evaluated with satisfaction by the professional team sport

consumers when assessing their game attending experience. Five items had a mean score that

was lower than the midpoint. Of the all variables, 'scoreboard information' item had the highest

mean score (M~= 3.98; SD = 0.86), and 'mail order' had the lowest mean score (M~= 2.53; SD =

1.12).

Mean and standard deviation for the Perceived Value for the Cost factor are presented in

Table 4-4. All variables had a mean score greater than 3.0 midpoints on the 5-point Likert scale,

indicating that overall the game experience was considered valuable by the professional team

sport consumers. 'The game experience was worth the money' item had the highest mean score










(M~= 4.24; SD = 0.79) and 'the game experience was economical' had the lowest mean score (M\~

= 3.61; SD = 1.12). Descriptive statistics for the Behavioral Intention variables are reported in

Table 4-5. All variables had a mean score greater than 4.0 points on the 5-point Likert scale,

indicating that the level of intention to re-attend a professional team sport event and recommend

to others were very likely. Of the variables, 'I plan on attending more games) of this

professional sport in the future' item had the highest mean score (M~= 4.54; SD = 0.71) and 'I am

likely to say positive things about this professional sport game to other people' had the lowest

mean score (M~= 4.34; SD = 0.83).

Additionally, skewness and kurtosis for the items were examined. For the skewness cut-off

value, an absolute value of 3.0 would be considered extreme. For the kurtosis threshold value, an

absolute score greater than 3.0 would be considered extreme (Chou & Bentler, 1995). In this

study, all skewness and kurtosis values for the Market Demand, Game Support, Perceived Value

for the Cost, and Behavioral Intention variables were well within the acceptable threshold

(Tables 4-2 to 4-5).

Exploratory Factor Analyses

Market Demand

An EFA of the market demand variables was conducted for the purpose of data reduction

and identifying a simple structure (Stevens, 1996). The Kaiser-Meyer-Olkin (KMO) measure of

sampling adequacy value (Kaiser, 1974) was .845, suggesting that the sample was appropriate

for a factor analysis. Bartlett's Test of Sphericity (BTS) was 4521.27 (p < .001), indicating that

the hypothesis of the variance and covariance matrix of the variables as an identity matrix was

significantly rej ected. Hence, a factor analysis was deemed appropriate. In the EFA, six factors

emerged with 31 items meeting the retention criteria, explaining a total variance of 57.69%. The

scree plot test also suggested that a six-factor model was the most interpretable. The results of









the rotated pattern matrix from promax rotation are reported in Table 4.6. Based on the pre-

determined criterion of an item loading equal to or greater than .40, nine items were eliminated

(i.e., high level of performance, home team star playerss, support the home team, high level of

skills, weather condition, closeness of competition, opposing team as a rivalry, high level of

competitiveness, and good seats). Six other items were removed due to having only one or two

items loaded on the respective factors (i.e., home team record breaking performance, athleticism

of professional team sport, best players in a sport, location of venue, love professional team

sportss, and popularity of professional team sport). Consequently, the six factors were labeled as

Opposing Team (9 items), Home Team (6 items), Game Promotion (5 items), Economic

Consideration (4 items), Love of Professional Sport (4 items), and Schedule Convenience (3

items). Alpha coefficients for the factors were .93, .85, .86, .83, .70, and .75, respectively,

indicating that they were all internally consistent and reliable. The resolved factor structure was

overall consistent with the conceptual model for the market demand variable in this study.

Game Support

An EFA for the game support variables was also conducted for the purpose of data

reduction and identifying a simple structure (Stevens, 1996). KMO measure of sampling

adequacy value (Kaiser, 1974) was .862, suggesting that the sample was adequate for a factor

analysis. BTS was 1962.95 (p < .001), indicating that the hypothesis of the variance and

covariance matrix of the variables as an identity matrix was rej ected. Therefore, a factor analysis

was deemed appropriate. In the EFA, five factors emerged with 21 items retained, explaining a

total variance of 51%. The scree plot test also suggested that a five-factor model was the most

interpretable. The results of the rotated pattern matrix from promax rotation are reported in Table

4.7. Based on the pre-determined criterion of an item loading equal to or greater than .40, five

items were eliminated (i.e., give away/prize, ushers, food and drink quality, music volume, and

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ease of entrance). Due to lack of interpretability and relevance, three other items were removed

(i.e., newness of arena/stadium, efficiency of ticket office, and niceness of arena/stadium).

Furthermore, nine variables were also eliminated due to having only one or two items loaded on

the respective factors (i.e., replay screens, convenience of ticket sale locations, mail order, food

and drink price, ticket personnel friendliness, music selection, ticket agencies, public

transportation, and web (on-line) order procedures). Consequently, of the original 38 items for

game support programs, 21 items were retained under five factors: Game Amenities (6 items),

Arena/Stadium Services (5 items), Ticket Service (3 items), Arena/Stadium Convenience (4

items), and Arena/Stadium Accessibility (3 items). Alpha coefficients for the factors

were .88, .77, .73, .74, and .66, respectively, indicating that they were of acceptable internal

consistency. Although slightly different from the conceptual model for the game support

programs in this study, the factor structure was essentially consistent with the proposed

measurement model. The slight difference might be an indication that the current study examined

game operational activities of all maj or professional team sport events from a general perspective,

unlike previous studies that focused on a specific event.

Perceived Value for the Cost

An EFA was also conducted for the Perceived Value for the Cost factor for the purpose of

validating the unidimensionality (Stevens, 1996). KMO was .840, suggesting that the sample was

adequate for a factor analysis. BTS was 672.58 (p < .001), indicating that the hypothesis of the

variance and covariance matrix of the variables as an identity matrix was rejected. Therefore, a

factor analysis was appropriate. Following the EFA, all five items under the single factor were

retained, explaining a total variance of 61.9%. The scree plot test also suggested that a one-factor

model was the most interpretable. Items loadings were as follows: .702, .742,.774, .815,

and .888 for the items, respectively, Due to the single factor structure, the promax rotation was

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not needed. Alpha coefficient for the factor was .89, indicating that the factor was internally

consistent and reliable.

Behavioral Intentions

An EFA was conducted for the Behavioral Intention variables for the purpose of data

reduction and identifying simple structure (Stevens, 1996). KMO was .939, suggesting that the

sample was adequate for a factor analysis. BTS was 1916.44 (p < .001), indicating that the

hypothesis of the variance and covariance matrix of the variables as an identity matrix was

rej ected. Therefore, a factor analysis was deemed appropriate. In the EFA, one factor was

extracted with all 10 items retained, explaining a total variance of 64.74%. The scree plot test

also suggested that a one-factor model was the most interpretable. Items loadings were as

follows: .847 (Repurchase Intentions item 1), .874 (Repurchase Intentions item 2), .828

(Repurchase Intentions item 3), .816 (Repurchase Intentions item 4), .774 (Repurchase Intentions

item 5), .815 (Recommend to Others item 1), .762 (Recommend to Others item 2), .898

(Recommend to Others item 3), .717 (Recommend to Others item 4), and .691 (Recommend to

Others item 5). Due to the fact that only one factor was extracted, the promax rotation was not

needed. The factor was labeled as Behavioral Intentions. The factor structure resolved from the

EFA was not consistent with the original two-factor model proposed in this study. However, the

number of items (i.e., 10) was retained consistent with the proposed model. Alpha coefficients

for the factor was .95, indicating that it was internally consistent and reliable.

Measurement Models: Confirmatory Factor Analyses

Market Demand

The second data set for the market demand variables, that contained 31 items under six

factors, was submitted to a CFA, using ML estimation (Hair et al., 2005). Goodness of fit

indexes revealed that the six-factor and 31-item measurement model did not fit the data well

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(Table 4-8). The chi-square statistic was significant (72 = 1340.89, p < .001), indicating that the

hypothesized model and the observed model had statistically significant difference. Because chi-

square value is known to be sensitive to sample size (Kline, 2005), alternative fit indices were

further examined, including the normed chi-square, RMSEA, SRMR, CFI, and ECVI. A value of

the normed chi-square (72/df= 3 .20) was above the suggested cuf-off value (i.e., < 3.0; Bollen,

1989). The RM SEA value indicated that the six-factor model showed a poor fit (RM SEA = .10,

90% CI = .094 .106; Hu & Bentler, 1999). Although the value of SRMR (.077) was within the

range of acceptable fit (< .10; Kline, 2005), the CFI value of .78 was substantially lower than the

recommended cut-off ratio (>.90; Hu & Bentler, 1999), indicating an overall lack of fit to the

data. The model fit tests suggested a need for respecification. According to Tabachnick and

Fidell (2001), model respecification would be needed if the proposed model did not fit the data

well. Poor indicator loadings also supported a model respecification. Adopting a conservative

criterion in order for the scale to have good convergent validity, an indicator loading should be

equal to or greater than .707 (Anderson & Gerbing, 1988). In the current study, indicator

loadings ranged from .398 (group ticket cost) to .903 (advertising). Of 31 items, nine items were

below .707, indicating a lack of convergent validity. Therefore, the nine items were removed

(opposing team history and tradition, home team exciting play, web information, travel distance,

played that sportss, speed of game, group ticket cost, duration of the game, and home team

history and tradition). Furthermore, modification indexes suggested additional item elimination.

After careful consideration of both statistical and theoretical justifications, a decision was made

to remove five more items, which were highly double loaded (opposing team star playerss,

home team exciting play, opposing team league standing, publicity, and player charisma of

opposing team).









As a result of the model respecification, a five-factor model with 17 items was

conceptualized: Home Team (3 items), Opposing Team (5 items), Game Promotion (3 items),

Economic Consideration (3 items), and Schedule Convenience (3 items). This was consistent

with the recommendations made by Bollen (1989) in that each factor consisted of at least three

items. A five-factor model with 17 items was further submitted to a CFA. Overall goodness of fit

revealed that the five-factor model fit the data reasonably well (Table 4-8). Chi-square statistic

was significant (72 = 278.31i, p < .001). The normed chi-square (g2/df= 2.55) was lower than the

suggested cuf-off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the five-

factor model had an acceptable fit (RMSEA = .084, 90% CI = .072 .096; Hu & Bentler, 1999).

The SRMR (.054) was of a good value (< .10; Kline, 2005). CFI was .93, which was considered

acceptable (Kline). ECVI was 1.66, which indicated a much better fit than that of the six-factor

model. ECVI has no pre-determined range of values (Kline), but it is generally used to compare

models, with a smaller value indicating better model fit. Overall model fit of the five-factor

model improved drastically, indicating its acceptability.

The reliability of the factors and respective items was evaluated by Cronbach' s alpha, CR,

and AVE (Table 4-16). Cronbach's alpha values for the five-factor model indicated that all

factors were well above the acceptable threshold (i.e., greater than .70) suggested by Hair et al.

(2005), ranging from .80 (Schedule Convenience) to .91 (Opposing Team). The CR values for

the five constructs of market demand were above the recommended cut-off criterion (Fornell &

Larcker, 1981), ranging from .76 (Economic Consideration) to .82 (Opposing Team). All AVE

values were also above the suggested standard, ranging from .52 (Economic Consideration) to

.64 (Opposing Team). Based on the overall information of reliability, the determined factors

were deemed reliable.









A convergent validity test was conducted by evaluating indicator loadings and critical ratio

values. All of the indicator loadings were greater than the suggested standard of .707 (Anderson

& Gerbing, 1988) except for one item on Schedule Convenience ('day of the week' with a value

of .67). A decision was made to retain the item due to its theoretical relevance to the Schedule

Convenience factor and only slightly lower than .707 threshold. Critical ratio values ranged from

8.99 (home team reputation) to 16.79 (overall quality of opposing team players), indicating that

all values were statistically significant. Overall, the Hyve-factor of the market demand showed

excellent convergent validity (Table 4-16).

According to Kline (2005), discriminant validity can be established when interfactor

correlation is below .85. No interfactor correlations were above .85, ranging from .193 (between

Game Promotion and Economic Consideration) to .511 (between Economic Consideration and

Schedule Convenience), indicating very good discriminant validity. The Fornell and Larcker' s

test found that all squared correlations in the scale were less than AVE value for respective

construct, indicating excellent discriminant validity (Tables 4-13 for interfactor correlations and

4-16 for AVE). Thus, the Hyve-factor model was used for a subsequent SEM analysis. A graphical

representation of the Hyve-factor market demand model is presented in Figure 4-2.

Game Support Programs

Data for the game support programs that contained Hyve factors with 21 items were

submitted to a CFA, using ML estimation method (Hair et al., 2005). Goodness of fit indexes

revealed that the five-factor measurement model did not fit the data well (Table 4-9). Values of

model fit indices were as follows: X2= 482.84 (p < .001); X2/df= 2.70; RMSEA = .088, 90% CI =

.078 .97; SRMR = .077, CFI = .78, and ECVI = 2.66. The model fit tests suggested a need for

respecification. Poor indicator loadings also supported a model respecification. Only seven out of

21 variables had loadings above .707, a very high and conservative criterion (Anderson &

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Gerbing, 1988). Rather, a comparatively modest criterion was adopted to assess the relevance of

the items (Meyers, Gamst, & Guarino, 2006). Besides Meyers et al., other researchers (Nunnally

& Bernstein, 1994) have also suggested that if an indicator loading is equal to or greater than .50,

it indicates that the pattern coefficient achieves meaningful significance. Based on this criterion,

six items were eliminated (public address system, scoreboard information, traffic/crowd control,

seating directions, game calendar and schedule, and restroom cleanliness). Consequently, a four-

factor model with 15 items was respecified: Game Amenities (6 items), Ticket Service (3 items),

Stadium Service (3 items), and Stadium Accessibility (3 items). As recommended by Bollen

(1989), each factor consisted of at least three items. A four-factor model with 15 items was

further submitted to a CFA. Overall goodness of fit indexes revealed that the four-factor model

fit the data reasonably well (Table 4-9). Chi-square statistic was significant C2 = 212.44, p <

.001). The normed chi-square OC2/df= 2.53) was lower than the suggested cut-off value (i.e., <

3.0; Bollen, 1989). The RMSEA value indicated that the four-factor model had an acceptable fit

(RMSEA = .083, 90% CI = .069 .097; Hu & Bentler, 1999). The value of SRMR (.068) was of

a good value (< .10; Kline, 2005). CFI was .89, which was marginally acceptable (Meyers et al.,

2006). ECVI was 1.29, which indicated a much better fit than that of the five-factor model.

However, the interfactor correlation between Stadium Service and Stadium Accessibility

was excessively high (1.06), suggesting that the two factors be combined into one factor, labeled

Venue Quality. Therefore, a three-factor model was respecified. The overall model fit remained

almost the same as the four-factor model (72 = 2 19.04, p < .00 1; X2/df = 2. 52; RMSEA = .083,

90% CI = .069 .97; SRMR = .07, CFI = .89, and ECVI = 1.29. Since the three-factor model was

statistically more feasible in the current study, the three-factor model was used for subsequent

analyses (i.e., reliability, convergent, and discriminant validity).









The reliability of the factors and respective items was evaluated by Cronbach' s alpha, CR,

and AVE (Table 4-17). Cronbach's alpha values for the three-factor model indicated that all

factors were well above the acceptable threshold (i.e., greater than .70) suggested by Hair et al.

(2005), ranging from .74 (Ticket Service) to .85 (Game Amenities). The CR values for the all

three constructs of game support programs were above the recommended cut-off criterion

(Fornell & Larcker, 1981), ranging from .72 (Ticket Service) to .86 (Game Amenities).

However, two of the three AVE values were below the suggested standard (i.e., .47 for Ticket

Service; .41 for Venue Quality). These low AVE values might have been caused by low

indicator loadings of Ticket Service and Venue Quality factors. Considering the high Cronbach's

alpha and CR coefficients of these factors, the slightly low AVE values were not of great

concerns for the factors. A convergent validity test was conducted by evaluating indicator

loadings and critical ratio values. Nine indicator loadings out of 15 were lower than the

suggested standard of .707 (Anderson & Gerbing, 1988). However, all loadings were above the

modest criterion (Meyers et al., 2006) except for one item on Venue Quality ('ease of entrance'

with a value of .44). A decision was made to retain the item due to its theoretical relevance to the

Venue Quality factor. CR values ranged from 5.70 (ease of entrance) to 10.75 (intermission/half-

game entertainments), indicating that all values were statistically significant. Overall, the three-

factor model of the game support programs showed acceptable convergent validity (Table 4-17).

No interfactor correlations were above .85, ranging from .363 (between Ticket Service and

Venue Quality) to .634 (between Game Amenities and Venue Quality), indicating excellent

discriminant validity. The Fornell and Larcker' s test found that all squared correlations in the

scale were less than AVE value for respective construct, indicating robust discriminant validity

(Tables 4-14 for interfactor correlations and 4-17 for AVE). Therefore, the three-factor model









was used for a subsequent SEM analysis. A graphical representation of the three-factor game

support programs model is presented in Figure 4-3.

Perceived Value for the Cost

Data for the Hyve Perceived Value for the Cost variables were submitted to a CFA using

ML estimation method (Hair et al., 2005). Goodness of fit indexes revealed that the one-factor

measurement model did not fit the data well (Table 4-10). Values of the fit indices were as

follows: X2= 138.39 (p < .001); X2/df= 27.68; RM SEA = .347, 90% CI = .299 .399; SRMR =

.172, CFI = .76, and ECVI = .72. The model fit tests suggested a respecification. Poor indicator

loadings also supported a model respecification. Indicator loadings for two of the items were

.103 and .138, respectively, which were far below the recommended standard (Anderson &

Gerbing, 1988). As a result, the two items were eliminated (the game experience was a good buy

and the game experience was worth the money). A three-item model was respecified, which met

Bollen's (1989) suggestion that a factor for CFA should have at least three items. Overall

goodness of fit of the three-item model fit the data reasonably well (Table 4-10). All fit indices

achieved substantial improvement (72 = 2.79, p < .001; X2/df= 2.79; RMSEA = .090, 90% CI =

.000 .223; SRMR = .001; CFI = .99; and ECVI = .069).

The reliability of the factor and respective items was evaluated by Cronbach's alpha, CR,

and AVE (Table 4-18). Cronbach's alpha value (.90) for the three-item model was well above

the suggested threshold (i.e., greater than .70) suggested by Hair et al. (2005). The CR (.88) and

AVE (.71) values for the model were also well above the recommended cut-off criteria (Fornell

& Larcker, 1981). Based on the overall information of reliability, the three-item of Perceived

Value for the Cost model showed excellent reliability.

Convergent validity was conducted by evaluating indicator loadings and critical ratio

values. All indicator loadings were well above the conservative standard of .707 (Anderson &

108









Gerbing, 1988). Critical ratio value was 16.12 (the game experience was economical), indicating

that the value was statistically significant. Overall, the three-item model showed excellent

convergent validity (Table 4-18). Therefore, the three-item model was used for a subsequent

SEM analysis. A graphical representation of the three-item model is presented in Figure 4-4.

Behavioral Intentions

Data for the single Behavioral Intentions factor and its 10 items were submitted to a CFA,

using ML estimation method (Hair et al., 2005). Goodness of fit indexes revealed that the one-

factor measurement model did not fit the data well (Table 4-1 1). Values of the fit indices were as

follows: X2= 157.04 (p < .001); X2/df= 4.49; RM SEA = .126, 90% CI = .106 .146; SRMR =

.048, CFI = .92, and ECVI: .89. The model fit tests suggested a need for respecification. Two

items did not meet the .707 criterion (Anderson & Gerbing, 1988). In addition, modification

indexes indicated that specifying correlation between two latent error variables (Items 2 and 3

related to Repatronage Intentions) would yield a substantial impact in better fit. However, there

was no theoretical justification for an error variable correlation. Thus, after carefully considering

item contents, a decision was made for eliminating the two items since the other three items in

the same factor were measuring the same content. The same reason was adopted for item 2, 4,

and 5 that were related to Recommend to Others. Consequently, a one-factor model with five

items was respecified. All fit indices achieved substantial improvement (Table 4-11) (72= 14.99,

p < .001; X2/df= 3.00; RMSEA = .095, 90% CI = .042 .152; SRMR = .019; CFI = .99; and

ECVI= .20).

The reliability of the factor and respective items was evaluated by Cronbach's alpha, CR,

and AVE (Table 4-19). Cronbach's alpha value of .93 for the one-factor model was well above

the suggested threshold (Hair et al., 2005). The CR (.95) and AVE (.79) values for the model









were also well above the recommended cut-off criteria (Fornell & Larcker, 1981). Based on the

overall information of reliability, the one-factor model showed excellent reliability.

Convergent validity was conducted by evaluating indicator loadings and critical ratio

values. All indicator loadings were well above the conservative standard of .707 (Anderson &

Gerbing, 1988), ranging from .78 (I plan on attending more games) of this professional sport in

the future) to .89 (I am likely to re-attend games) next season). Critical ratio values ranged from

14.22 to 17.89, indicating that the values were statistically significant. Overall, the one factor,

Hyve-item model of the Behavioral Intentions showed excellent convergent validity (Table 4-19).

Therefore, the Hyve-item model was used for a subsequent SEM analysis. A visual representation

of the Hyve-item of Behavioral Intention model is presented in Figure 4-5.

Structural Model

The second data set was also used for conducting a SEM to test the hypotheses of this

study. Prior to estimating path coefficients for the hypothesized structural model, goodness of fit

indexes for the overall measurement model was first evaluated. The overall model fit was

reasonably well (Table 4-12). Chi-square statistic was significant (72 = 1544.33, p < .001), and

the normed chi-square (72/df = 2.22) was lower than the suggested cut-off value (i.e., < 3.0;

Bollen, 1989). The RMSEA value indicated that the structural model had an acceptable fit

(RMSEA = .070, 90% CI = .065 .074; Hu & Bentler, 1999). The value of SRMR (.067) was of

a good value (< .10; Kline, 2005). Only CFI was slightly below the suggested standard, with a

value of .86. According to Cheung and Rensvold (2002), CFI value tends to be sensitive to

model complexity, which may explain why the value of the CFI decreased, when compared to

the separate measurement model assessments for the market demand, game support, Perceived

Value for the Cost, and Behavioral Intentions variables. Although a respecification was needed

to improve the overall model fit, a decision was made not to modify due to two reasons: (a)

110










except for CFI value, most of the alternative model fit indices indicated good values and (b) it

might be possible to lose theoretical values from the specified model when a respecification was

initiated.

The reliability of the factors was evaluated by CR and AVE. Table 4-15 presents

interfactor correlations, CR, and AVE values. All values of CR were above the suggested

threshold, ranging from .72 (Ticket Service) to .92 (Perceived Value for the Cost and Behavioral

Intentions). All AVE values were above the suggested threshold (Hair et al., 2005) except for

two factors: .36 (Venue Quality) and .47 (Ticket Service). Notwithstanding the two low AVE

values, it can be concluded that all factors in the hypothesized structural model showed

acceptable reliability.

To determine convergent validity, the researcher evaluated item loadings and critical ratio

values for each indicator. As a result, all loadings were significant (p < .001). Item loadings

ranged from .498 to .922. Critical ratio values indicated that they were all above the cut-off

criterion, which was above 2.58 at the .001 level, ranging from 6.04 to 20.01. Based on the

results of loadings and critical ratio values, the hypothesized structural model showed good

convergent validity.

None of the interfactor correlations were above the suggested threshold (. 85; Kline, 2005),

ranging from .200 (between Home Team and Venue Quality) to .558 (between Game Amenities

and Venue Quality), indicating excellent discriminant validity (Kline, 2005). As a result of

Fornell and Larcker' s method, it was found that none of the squared correlations between any of

the two constructs in the structural model were above the AVE value of the respective construct,

which indicated strong discriminant validity of the model. Therefore, it can be concluded that the

hypothesized structural model showed strong discriminant validity on the sample data. Having









satisfied the psychometric properties of the measurement model, it was appropriate to proceed to

examine the structural relationships among the different set of factors.

The hypothesized structural model was estimated to examine the hypotheses with regard to

the effect of market demand and game support factors on Behavioral Intentions as mediated by

Perceived Value for the Cost (Table 4-20). The tested model included a total of 10 latent

constructs (Figure 4-5). More specifically, there were five latent variables representing market

demand and three latent variables for game support programs, a mediated latent variable of

Perceived Value for the Cost, and an endogenous latent variable of Behavioral Intentions.

The standardized direct effect of Home Team had a positive influence on Behavioral

Intentions (p = .281, p < .01), indicating that when perceptions towards Home Team increased by

one standard deviation, Behavioral Intentions also increased by .281 standard deviations.

Therefore, Hypothesis 1 was supported. The standardized direct effect of Opposing Team was

found to exert a positive influence on enhancing Behavioral Intentions (P = .246, p < .01), which

indicated that when perceptions regarding Opposing Team increased up by one standard

deviation, Behavioral Intentions increased also by .246 standard deviations. Therefore,

Hypothesis 2 was supported. Hypothesis 3 was related to the effect of Love of Professional Sport

on Behavioral Intentions. However, Hypothesis 3 was not estimated because the factor was

found to be a less relevant factor of market demand by means of CFA. The standardized direct

effect of Economic Consideration was found not to be related to Behavioral Intentions (P = .021,

p = .769). Therefore, Hypothesis 4 was not supported.

Hypothesis 5 dealt with the direct effect of Game Promotion on Behavioral Intentions.

The findings revealed that the direct effect of Game Promotion had an inverse relationship with

Behavioral Intentions (P = -.319, p < .01), indicating that when perceptions towards Game









Promotion increased by 1 standard deviation, Behavioral Intentions decreased by .319 standard

deviations. Although a relational direction was not supported (i.e., originally it was hypothesized

to have a positive effect), the influence was statistically significant (p < .01). Therefore,

Hypothesis 5 was partially supported. The standardized direct effect of Schedule Convenience

was not found to be related to Behavioral Intentions (P = -.215, p = .062). Hence, Hypothesis 6

was not supported.

With regard to the standardized direct effects of factors of game support programs on

Behavioral Intentions, only Game Amenities was found to be positively related to Behavioral

Intentions (p = .246, p < .05). As a result, Hypothesis 7 was supported. The remaining two

hypotheses that specified the effect of Ticket Service on Behavioral Intentions and the direct

effect of Venue Quality on Behavioral Intentions were not found to be statistically significant (P

= -. 161, p = .135) and (P = -.215, p = .075), respectively. Therefore, Hypothesis 8, 9, and 10

were not supported. However, the standardized direct effect of Perceived Value for the Cost on

Behavioral Intentions was found to be statistically significant (P = .240, p < .01), therefore,

Hypothesis 11 was supported.

One of the aims of this study was to examine the mediating effect of Perceived Value for

the Cost. A total of eight mediating analyses were conducted. It was found that Perceived Value

for the Cost played a mediating role only in the relationship between Venue Quality and

Behavioral Intentions (P = .083, p < .05). In terms of the calculation for the indirect effect, the

standardized indirect effect of Venue Quality on Behavioral Intentions through Perceived Value

for the Cost was estimated as the product of the standardized path coefficients for the paths of

Venue Quality to Perceived Value for the Cost (P = .346, p < .01) and Perceived Value for the

Cost to Behavioral Intentions (P = .240, p < .01), which yielded P = .083, p < .05). This result









indicated that Behavioral Intentions were expected to enhance by .083 standard deviations for

every increase in Venue Quality of one full standard deviation through its prior effect on

Perceived Value for the Cost. Therefore, Hypothesis 13 was supported. None of the market

demand factors were found to be indirectly related to Behavioral Intentions through Perceived

Value for the Cost, therefore, Hypothesis 12 was not supported.









CHAPTER 5
DISCUSSION

The discussion of this study is presented in the following three sections: (a) measurement

properties, (b) hypotheses testing, and (c) additional suggestions.

As market competition is becoming more intensified in professional team sports, it is

imperative for both researchers and practitioners to identify those variables that directly and

indirectly influence game consumption (Hansen & Gauthier, 1989; Zhang et al., 1995). An in-

depth understanding of what factors influence spectators to decide to return to the game, and how

they refer the game products and services to others, is crucial for professional teams to better

understand spectator consumption behavior.

Findings of previous studies revealed that market demand variables and game support

programs were important predictors of sport spectator consumption behavior (Kwon et al., 2007;

Murray & Howat, 2002; Wakefield & Blodgett, 1996; Zhang et al., 1995, 1998a, 2004b).

However, these two concepts have usually been studied fragmentarily (Cronin & Taylor, 1992;

Ko & Pastore, 2005; Parasuraman, Zeithaml, & Berry, 1998; Wakefield & Sloan, 1995; Zhang et

al., 1995, 2004c). Although previous researchers recognized the importance of market demand

variables and game support programs when marketing professional sport games, only a small

number of studies have examined both sets of variables simultaneously (Greenwell et al., 2002;

Tsuji et al., 200). Of those studies containing both concepts, over-simplicity was a maj or concern.

Furthermore, previous studies failed to consider unique features related to professional team

sport events. Therefore, it is essential for a study to incorporate the uniqueness and special

characteristics of the core product, product extensions, and market environment (Mullen et al.,

2007; Zhang et al., 2003b). Additionally, previous studies have revealed that only a small portion

of game attendance variance (i.e., less than 50%) were explained by market demand variables










and game support programs (Greenwell et al., 2002; Tsuji et al., 2007; Wakefield & Blodgett,

1996; Zhang et al., 1995, 1998a, 2004b). Low variance explanation may be due to the overlook

of the potential influences of some intervening variables, such as perceived value, on the

relationship between sport production and game consumption. Therefore, studying game product

variables and perceived value simultaneously is critical to gaining a more comprehensive

understanding of what influence spectators to repatronage the game and how they conduct word-

of-mouth promotions. The current study was designed to fi11 this void by examining the

structural relationships of market demand variables and game support programs to professional

team sport attendance; in the meantime, the mediating influence of perceived value was taken

into consideration. In this study, rigorous psychometric testing procedures were first conducted

for the four constructs (i.e., market demand, game support programs, perceived value for the cost,

and behavioral intentions). A SEM analysis was executed to test the hypotheses.

Measurement Properties

Systematic procedures were undertaken to formulate the preliminary questionnaire and its

sections, which included a comprehensive review of literature, interviews of sport industry

practitioners, and test of content validity by a panel of experts and a pilot study group of

consumers representing the targeted population. It was the intention of the researcher to enhance

research Einding generalizability of this study by adopting the community intercept approach.

Data collection was conducted at various locations in four maj or metropolitan areas, which also

supported this intention. Previous studies usually studied professional sport consumers at a

limited number of sport events in one geographic location. It was the intention of this study to

include consumers of comparatively more diverse backgrounds in terms of geographic locations

and sport types so as to improve the external validity of this study.









In this study, both EFA and CFA were conducted to ensure theoretical relevance,

generalizability, and usefulness of the resolved factor structures. For the market demand

variables, six factors with 31 items were retained in the EFA: (a) Opposing Team, (b) Home

Team, (c) Game Promotion, (d) Economic Consideration, (e) Love of Professional Sport, and (f)

Schedule Convenience. The derived factors from the EFA were consistent with the theoretical

dimensions suggested by previous researchers (Greenstein & Marcum, 1981; Hansen & Gauthier,

1989; Schofield, 1983; Zhang et al., 1995). However, the six-factor model did not fit the data

well in the initial CFA. After careful consideration of statistical and theoretical evidence, the

scale was revised to a five-factor model with a total of 17 items: Opposing Team (5 items),

Home Team (3 items), Game Promotion (3 items), Economic Consideration (3 items), and

Schedule Convenience (3 items). This respecified model exhibited much improved fit indexes.

As a result of the respecification, the Love of Professional Sport factor was eliminated, mainly

due to low indicator loadings and low critical ratio values. In previous studies, Love of Sport was

found to be a contributing variable to game attendance of college sports (Ferreira & Armstrong,

2004) and game consumption of professional sports (Zhang et al., 2003a). In Braunstein et al.'s

(2005) study, the researchers found that Love of Baseball was an important factor related to

MLB spring training; yet, the factor displayed poor psychometric properties. The factor was

eventually retained by the researchers based on the consideration that Love of Sport covers

detailed characteristics of sport events, such as closeness of competition, duration of game, high

level of skills, best players in a sport, and/or speed of game. Although the researchers were

reluctant to eliminate this factor, they did suggest the need for further studies of this factor.

Although the current study conducted rigorous procedures in item purification, test of content

validity, and a pilot study, similar findings occurred. As Braunstein et al. suggested, more









examinations are necessary for this factor in future studies. A key issue is how to keep Love of

Sport factor theoretically separated with Home Team and Opposing Team factors because the

factor analyses in the current study revealed that Love of Sport items were double loaded with

these two factors.

Although the number of factors was reduced to five, the resolved constructs of the market

demand were essentially consistent with previously suggested factors (Braunstein et al., 2005;

Schofield, 1983; Zhang et al., 1995; 2003b, 2004a). Schofield (1983) proposed four market

demand categories, including Demographic Variables, Economic Variables, Game

Attractiveness, and Residual Preference. Economic Variables were related to Game Promotion

and Economic Consideration, Game Attractiveness contained items relevant to athlete/team

performances, history, and reputation of Home Team and Opposing Team, and Residual

Preference in Schofield' s (1983) study consisted of variables related to Schedule Convenience.

Synthesizing Schofield's four factors, key game demand variables, and production functions,

Zhang et al. (1995, 2003b) developed a four-factor model of market demand (Home Team,

Opposing Team, Game Promotion, and Schedule Convenience) and included the factors in the

Spectator Decision Making Inventory (SDMI). In the context of MLB spring training, Braunstein

et al. (2005) developed an eight-factor model that consisted of Home Team, Opposing Team,

Game Promotion, Vacation Activity, Economic Consideration, Schedule Convenience, Nostalgic

Sentiment, and Love of Baseball. In an attempt to assess market demand of general professional

sport events, Zhang et al. (2003a) identified three factors: Game Attractiveness, Marketing

Promotion, and Economic Consideration. When the general market demand factors were applied

to a NFL expansion team, Zhang et al. (2004a) found consistent factor structure (i.e., Game

Attractiveness, Marketing Promotion, Economic Consideration, and Socializational Opportunity).









Overall, the resolved factor structure in the current study was consistent with the indications of

previous researchers.

Although the current study reconfirmed the factor structure of the market demand

suggested by previous studies, findings of this study were likely improved and more

generalizable when considering the following three aspects: (a) a more representative sample

was involved, (b) a comprehensive study was designed and carried out in the study, including

various statistical analyses such as EFA and CFA, and (c) better psychometric properties were

obtained. In previous studies, data were usually collected on-site in arenas or stadiums (Zhang et

al., 1995, 2003b), where only spectators of one sport event participated in the study and they

might be under temporal influence due to an instant moment of winning or losing. The

respondents of the current study were current sport consumers who indicated that they attended a

professional team sport event within past 12 months. Descriptive statistics indicated that a total

of six premier professional team sport leagues were attended by the respondents, which may help

improve the generalizability of findings. Additionally, Zhang et al. (2003b) suggested that

besides EFA and CFA, other types of construct validity, including convergent and discriminant

validity tests be utilized to improve the factor structure. These suggestions made by previous

researchers were materialized in the current study.

The current study retained at least three items per factor through the CFA. One of the

limitations found in Zhang et al.'s (2003b) study was that the Opposing Team and Schedule

Convenience factors were measured by only two items. When using CFA and SEM analyses, the

number of items per factor is important for measurement precision, based upon the following

two important points: (a) optimal number of items per factor, and (b) meaningfulness of the

factor. In terms of an optimal number of items per factor, Bollen (1989) argued that two items









could cause an estimation problem with a small sample size (i.e., less than 100). Although Zhang

et al.'s (2003b) study had a large sample size (N= 685), based on the Eindings from previous

studies on optimal number of indicators per factor, three items per factor are considered ideal

(Bollen, 1989; Kline, 2005; Marsh, Balla, & McDonald, 1988). The possible reason that two

items of Opposing Team and Schedule Convenience factors were consistently used in previous

studies may be due to the use of EFA as the primary item selection method, which is data-driven.

In this regard, the current study improved in that Hyve items and three items related to Opposing

Team and Schedule Convenience, respectively, were retained by factor analyses. The items of

the Opposing Team represented overall performance, quality of opposing teams, quality of

players, opposing team's exciting play, and team reputation. Schedule Convenience was

represented by such attributes as game time of the day, convenient game schedule, and day of the

week. Nonetheless, more work to validate the items related to Opposing Team and Schedule

Convenience factors is necessary in future studies.

In addition to measuring market demand that is related to core product function (i.e., the

game itself), this study assessed professional team sport consumers' perceptions towards

peripheral service quality and examined how their satisfaction with event operation activities

would affect their future consumption behaviors. Taking into consideration the unique aspects

that were related to professional team sports; this study adopted, modified, and revised existing

scales measuring game support programs of professional team sports (Zhang et al., 1998a, 2004,

2005b). Unlike previous studies that measured game support programs related to specific

professional games (e.g., minor league hockey games), the current study attempted to assess

game support programs that could be generalized to all professional team sports. In addition to

adopting existing scales (SSI, SSS, and SGSP), other relevant items were incorporated into the









measurement of the game support programs. Zhang et al., (2003b) stressed that having a reliable,

valid, and generalizable scale must be a priority when studying service quality issues associated

with specific areas of event operations. Following this notion, rigorous measurement procedures

were conducted in this study to develop a measure for game support programs, including a

thorough review of literature, test of content validity, and examinations of construct validity.

Five factors with 21 items were retained in the EFA: (a) Game Amenities, (b) Arena/Stadium

Services, (c) Arena/Stadium Convenience, (d) Ticket Service, and (e) Arena/Stadium

Accessibility. These factors were consistent with the theoretical dimensions suggested by

previous studies (Groinroos, 1984; Zhang et al., 1998a, 2005b). However, the five-factor model

did not fit the data well in the initial CFA. Following careful statistical and theoretical

considerations, the scale was respecified to a three-factor model with 15 items: Game Amenities,

Ticket Service, and Venue Quality. The current model showed much improved fit indexes, along

with convergent and discriminant validity, and reliability.

One noticeable factor solution that emerged in this study was that due to high interfactor

correlation, two separate factors, Arena/Stadium Services and Arena/Stadium Accessibility, were

combined into a single construct, Venue Quality. Although the number of factors was reduced to

three, all of the items in the four-factor model were retained. Although somewhat different from

the findings of previous studies, the resolved factor structure in this study still well reflected

those factors derived in previous studies (Zhang et al., 1998a, 2005b). For instance, in a minor

league hockey study, Zhang et al.'s (2005b) found high interfactor correlations between

Arena/Stadium Services and Arena/Stadium Accessibility; thus, the researchers commented that

"the two factors can be merged to form one single construct or they may be influenced by

another latent variable" (p. 64). Another possible explanation may be related to respondents'









memory decay. Unlike previous studies, the current study recruited respondents who reported

that they attended a professional team sporting event within the past 12 months at the time when

the survey was conducted. With the passing of time, consumers might have had a difficult time

to clearly distinguish between the Arena/Stadium Service and Arena/Stadium Accessibility

factors particularly when both of these factors assessed attributes related to services and

accessibility. Nevertheless, further studies are suggested to confirm the factor structure of the

two latent constructs. In future studies, it may be worthwhile to have several competing models

for game support programs, including a Hyve-factor model (Zhang et al., 1998a), four-factor

model (Zhang et al., 2005b), three-factor model suggested by the current study, and a second-

order model. Although the current study showed a slight difference compared to previous studies

with regard to factor structure of the game support programs, findings of this study has its

uniqueness in that the scale of the current study extended its viability to general professional

team sports. There have been no scales measuring general game support programs related to

professional team sports. The existing two scales (SSI and SGSP) by Zhang et al. (1998a, 2005b)

were specifically designed to measure minor league hockey games. The sample characteristics in

the current study represented sport consumers of six professional sports leagues (NFL, NBA,

MLB, NHL, AFL, and Soccer). Thus, the factors and respective items derived from the current

study can be used in more general professional sport settings.

In the current study, a unidimensional construct of perceived value as represented by

Perceived Value for the Cost was tested for its mediating effect (Kwon et al., 2007; McDougall

& Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). While acknowledging the

importance of multidimensional aspects, previous studies have consistently found that utilitarian

aspect such as Perceived Value for the Cost was the primary factor that was related to









consumption behavior (Kwon et al., 2007; Netemeyer et al., 2004). Although the EFA retained

all five items measuring Perceived Value for the Cost in the current study, two items (the game

experience was a good buy and the game experience was worth the money) could not be retained

in the CFA due to extremely low indicator loadings. The three-item model showed good model

fit. Various scholars have proposed multidimensional aspects of the perceived value model by

arguing that consumers' decision making is a function of multiple perceived values (Sheth et al.,

1991; Sweeney & Soutar, 2001). To some extent, these claims were empirically validated in

previous studies (Gallarza & Saura, 2006; Lee et al., 2007; Petrick, 2002a; Sanchez et al., 2006).

Thus, it is suggested that future research attention on conceptualizing the perceived value

construct is a viable option by adopting multidimensional aspects.

The current study initially proposed a two-dimensional model of behavioral intentions

represented by Repatronage Intentions and Recommend to Others. However, both EFA and CFA

consistently yielded a one-factor model due to high interfactor correlation. The Einding of a

unidimensional factor had its merit and made practical sense when considering the fact that one

positive intention in one behavioral domain usually leads to another positive intention in the

same behavioral domain. Nonetheless, this finding was inconsistent with previous studies

(Soiderlund, 2006; Zeithaml et al., 2006), which suggested that the most frequently utilized

behavioral intention constructs were Willingness to Recommend the Service to Others and

Repurchase Intentions. Multidimensional aspects of behavioral intentions have been consistently

suggested and would provide better practical implications. For instance, Soiderlund (2006) found

satisfaction influenced both Repurchase Intentions and Recommend to Others factors but with

unequal strengths. This finding implied that mere selection of one intention construct over

another may cause a misunderstanding about the role of satisfaction (antecedent) as a









determinant of intentions. Therefore, more careful conceptualization is required to distinguish

the Repurchase Intentions factor from the Recommend to Others constructs in order to reduce

high interfactor correlation in future studies. Furthermore, more behavioral intentions constructs

such as Desire to Stay (Wakefield & Blodgett, 1996) or Intentions to Switch Product/Service

(Eggert & Ulaga, 2002) need to be incorporated into the measurement in order to better

understand the effects of antecedents (e.g., market demand and/or game support programs) on

sport consumption behaviors.

Hypotheses Testing

Of paramount interest to the current study was to examine the structural relationships of

market demand variables and game support programs to professional team sports attendance-

related variables, while taking into consideration the mediating influence of Perceived Value for

the Cost. To achieve this, a series of hypotheses testing were conducted by means of SEM.

Consequently, it was found that Home Team, Opposing Team, Game Amenities, and Perceived

Value for the Cost were positively related to Behavioral Intentions, whereas Game Promotion

was negatively related to Behavioral Intentions. Additionally, Perceived Value for the Cost was

found to be the only mediating role in the relationship between Venue Quality and Behavioral

Intentions.

The finding that Home Team had a positive influence on Behavioral Intentions was

consistent with previous studies (Noll, 1991; Schofield, 1983; Zhang et al., 1995, 1997a, 2003a,

2004a). Home Team factor was comprised of the following variables: win/loss record, league

standing, and team reputation in the current study. Various scholars have stressed that winning is

the ultimate goal for a professional sport team due to its enormous impact on the success of the

sport organization (Milne & McDonald, 1999; Zhang et al., 2003a). However, constant winning

seems impossible in professional sports. Equally important as winning is making home fans

124










psychologically connected with the home team. Fans with high identification with a team are not

likely to reduce their game consumption levels even though the team may not be winning, as can

be seen with the Chicago Cub's supportive fans despite their propensity for losing and Boston

Red Sox fans prior to their World Series victory in 2004. This phenomenon has been empirically

validated by various researchers (Wann & Branscombe, 1993; Zhang et al., 2004a). Regardless

of winning and losing, professional sport team marketers should formulate strategies to increase

fan identification with their team. Likewise, the finding that Opposing Team had a positive

influence on Behavioral Intentions was consistent with Eindings from previous studies (Madrigal,

1995; Zhang et al., 1995, 2000). The Opposing Team factor was comprised of such variables as

opposing team's overall performance, quality of opposing team, opposing team exciting play,

opposing team reputation, and overall quality of opposing team players. Essentially, Home Team

and Opposing Team make up the maj or elements of a game. Both of them were relevant and are

important to the marketing of the games. The relative influences that Home Team and Opposing

Team had on Behavioral Intentions implies that professional team sports fans tend to consider

home team and opposing team separately as they make a decision regarding game attendance.

This notion is also consistent with previous studies (Braunstein et al., 2005; Zhang et al., 1995).

Thus, sport marketers should utilize marketing activities to promote the aspects of home team

and opposing team separately. For instance, sport marketers should emphasize home team while

focusing on such aspects as current league standing and home team reputation, but should

highlight opposing team, not only on their current performance and reputation, but also the

presence of star players.

This study found that Game Promotion had a negative influence on Behavioral

Intentions, which was in contrast to the Eindings of previous studies (Zhang et al., 1995, 2003a).









The Game Promotion factor was comprised of three variables: advertising, direct mail and

notification, and sales promotions. Although the findings were different from previous studies, it

might make practical sense that when a consumer focused on Game Promotion variables instead

of other maj or game related issues such as home and opposing teams, he/she would be less likely

to attend future games. It is a testimony that a true fan focuses on elements directly related to

team performances on the court. Additionally, it is necessary to point out that the Game

Promotion factor was the weakest predictor of sport consumption behaviors in Zhang et al.'s

(2003a) general market demand study. In Zhang et al.'s (1995) study, Game Promotion was

represented by a larger number of variables, including good seats, giveaway/prize, and ticket

discount, which were found to be all positively related to sport consumption (Zhang et al., 2000,

2003a). The current study initially consisted of these variables, but they were subsequently

eliminated in the EFA and CFA procedures. In addition, Game Promotion factor was not found

to be a significant predictor of sport consumption in some of the previous studies (Zhang et al.,

1997a, 2003a). Furthermore, it was pointed out that an excessive persuasion attempt employed

by direct mail, notification, and/or e-mail, which was not requested by a consumer, could create

a negative reaction to the organization, as the consumer may feel an invasion of privacy due to

unwanted solicitation (Groinroos, 2005). This phenomenon may occur more frequently in people

with low team identification. Because the current study did not incorporate the effect of team

identification into the measurement, this speculation could not be confirmed. Future studies

should examine a moderating effect of team identification in the relationship between Game

Promotion and Behavioral Intentions

Two factors, Economic Consideration and Schedule Convenience, were not found to be

statistically significant predictors of Behavioral Intentions in the SEM. However, descriptive









statistics, the EFA, and the CFA indicated that these two factors were important factors to be

considered by professional sport teams when formulating marketing strategies. The Economic

Consideration factor was primarily comprised of ticket-related variables (personal ticket price,

ticket affordability, and ticket discount), which were found to be contributing variables to game

attendance and media consumption in previous studies (Zhang et al., 2003a). When a team is not

playing well, team marketers should consider such strategies as ticket discounts or buy one get

the second one at half off, along with well-planned in-game amenities so that fans can be

satisfied with the game products and services. The Schedule Convenience factor was comprised

of three variables in the current study (game time of the day, convenient game schedule, and day

of the week). Previous studies found that the Schedule Convenience factor was an important

predictor of game attendance (Hill et al., 1982; Zhang, 1998b). Hill et al. (1982) found that

weekend games and season ending games were positively related to MLB game attendance.

Zhang (1998b) found that spectators of minor league hockey preferred an evening time (7:00

pm) for weekday and Saturday games, and an afternoon time (4:00 pm) for Sunday games.

Although team marketers cannot have complete control over the game schedule, they should

make efforts to make the game schedule as convenient as possible.

The Einding that Game Amenities had a positive influence on Behavioral Intentions was

consistent with the Eindings of previous studies (Zhang et al., 1998a, 2004c, 2005b). Zhang et al.

(2005a) found that various in-game amenities and music were important predictors of game

consumption for NBA season-ticket holders. The Game Amenities factor was comprised of six

variables (during game shows/entertainments, post-game shows/entertainments, pre-game

shows/entertainments, intermission/half-game entertainments, dance cheerleading activities, and

concourse entertainment activities). Based on the Eindings, professional team sport marketers










should pay attention to in-game amenities in order to enhance entertainment value for spectators.

Today's professional sport events are considered to be not only competitive sports but also

family-oriented entertainment events, which can be enjoyed by people of various backgrounds.

For instance, the Pittsburg Pirates, a team considered by most people to be not very competitive,

was ranked #1 in offering in-game promotional activities, including fireworks nights and bobble

head giveaways (Sutton, 2008). Getting selected fans involved in the half-time activities for

prizes can also positively promote the game entertainment value. Additionally, any concourse

fun activities by a team mascot or staff members would add entertainment value to fans who go

to the restroom or concession area. Offering unique and enj oyable activities may keep spectators

stay longer at the game even if the team is not playing well. In NBA and NFL games,

cheerleaders play an important role in enhancing entertainment value. Some cheerleading teams

such as the Dallas Cowboy's and Laker Girls have been well branded and have their own fan

bases.

In this study, Ticket Service and Venue Quality factors were not found to be significant

predictors of Behavioral Intentions. However, when formulating marketing strategies, these two

areas should be considered as important factors as they were found to be significant predictors of

sport consumption in previous studies (Zhang et al., 1998a; 2005b). In terms of ticket service, the

variables representing the factor (phone order, will call, and ticket exchange programs) were

found to be more relevant to season-ticket holders (Zhang et al., 2000). One possible explanation

for why the Ticket Service factor was not found to be related to Behavioral Intentions was due in

part to the characteristics of the respondents in this study, who were recruited from various areas

(sports bars, malls, and/or grocery stores). To a certain extent, the respondents may have

different types of tickets, which imply that they may have received different services. As a result,









the different service experiences may have cancelled out other service experiences received by

people who had variant ticket types. This issue deserves future study.

In terms of Venue Quality, the variables representing the factor were staff courtesy,

restroom availability, arena/stadium cleanliness, ease of entrance, security, and parking. These

were found to be important predictors of sport consumption in previous studies (Wakefield &

Blodgett, 1996; Wakefield & Sloan, 1995; Zhang et al., 2004c). For instance, Wakefield and

Sloan (1995) found that parking and cleanliness were significant predictors of the desire to stay

longer at college football games. Zhang et al. (2004c) also found that the Stadium Service factor

had a positive influence on NBA game attendance. Wakefield et al. (1996) found that Stadium

Accessibility had a positive relationship with emotional reaction of college football game

spectators. Based on these previous findings, Venue Quality issues deserve to be considered

when formulating marketing strategies for professional team sports.

The current study found that Perceived Value for the Cost had a positive influence on

Behavioral Intentions. This finding is consistent with the findings of previous studies (Cronin et

al., 1997; Oh, 1999; Zeithaml, 1988). The current study utilized a unidimensional factor of

perceived value (i.e., Perceived Value for the Cost), which was related to judging game

experience in terms of money value. In previous studies, Perceived Value for the Cost was

consistently found to be positively related to consumption behavior in the field of marketing

(Bolton & Drew, 1991; Netemeyer et al., 2004). This same relationship was found in the context

of sport consumption behaviors (Kwon et al., 2007; Murray & Howat, 2002). Thus, team

marketers should pay particular attention to providing quality products/services in order to

enhance perceived value for the money that spectators spend at the games, which in turn may

positively influence Behavioral Intentions.









Of interest to this study was to examine the mediating role of Perceived Value for the

Cost in the relationship of market demand and game support to Behavioral Intentions. It was

found that Perceived Value for the Cost mediated the relationship between Venue Quality and

Behavioral Intentions (P = .083, p < .05). This finding is unique in that there was no direct effect

of Venue Quality on Behavioral Intentions. However, a significantly indirect effect occurred

when Perceived Value for the Cost was incorporated into the equation. This result indicates that

Venue Quality could be a significant predictor of Behavioral Intentions only through Perceived

Value for the Cost. This is consistent with previous studies (Kwon et al., 2007; Murray & Howat,

2002), which found the mediating role of perceived value (Perceived Value for the Cost) on the

relationships of team identification (Kwon et al.) and service quality (Murray & Howat) to

Behavioral Intentions. At times, human consumption behaviors are complex and can hardly be

explained by one-way direct relationships (Ajzen, 2005; Baggozi et al., 1999). Thus, it has been

suggested to identify mediating and moderating effects that may influence the direct relationship

in order to better understand the complexity of human consumption behaviors. Essentially,

studying the perceived value construct as either a mediating or moderating variable in the

relationships among market demand, game support programs, and sport consumption behaviors

was worth the effort.

According to Mullin et al. (2007), there are six general characteristics associated with the

core product in spectator sport event (i.e., the game itself), which can separate the core product

of spectator sport event from the general business products. These are: unpredictable, intangible,

perishable, variable, inseparable, and uncontrollability (Mullin et al., 2007). Due to these special

natures, sport consumers may not be well conscious about intangible delivery of services

received such as market demand factors. Instead, sport consumers tend to be more judgmental










about tangible products such as parking, cleanliness of venue and restroom, and ease of entrance

that they have tangible experience, leading to the direct connection with perceived value for the

cost, which in turn influences positive consumption behavior. Therefore, team marketers should

be more cognizant to the functional service quality (i.e., tangible aspects of service) as they

formulate marketing and service strategies so as to enhance the consumption level of spectators.

Essentially, findings of this study displayed promise of explanation power of perceived

value to sport consumption behaviors. This study chose to focus on the most salient aspect of

perceived value, Perceived Value for the Cost. Considering that multidimensional aspects of

perceived value have been suggested by a number of researchers (Petrick, 2002a; Sheth et al.,

1991; Sweeney & Soutar, 2001), future studies need to look into the variability of this

suggestion.

In the current study, several theoretical frameworks have been fully or partially

incorporated, including the Theory of Reasoned Action (Fishbein & Ajzen, 1975), the Appraisal-

Emotional Response-Coping framework (Bagozzi, 1992), and the Nordic Model (Groinroos,

1984). These frameworks suggest that initial positive evaluation of product/service would

directly or indirectly lead to positive consumer behavior (e.g., game attendance). The findings of

the current study confirmed the suggested theoretical frameworks in the context of professional

team sports. Cognitive-based constructs (i.e., market demand, game support, and perceived value

for the cost) were found to exert conative consumption, which indicates that the cognitive-based

constructs used in the current study are indeed superordinate decision criteria for professional

sport consumers. Therefore, sport marketers in professional team sports need to consider how to

best present this information, in which consumers are likely to use in their decision making

process.









Additional Suggestions

Suggestions for future studies have been made through the discussions about the research

findings. A few points need to be emphasized here. In the current study, the initially proposed

Love of Professional Sport factor was not included in the SEM because this factor did not exhibit

acceptable measurement properties in the CFA. However, the factor was characterized by such

attributes as closeness of competition, duration of game, and/or high level of skills, which appear

to be essential, relevant, and important to consumption of professional team sports. Thus, more

measurement studies on this factor are necessary in future studies.

The current study failed to show discriminant validity for Arena/Stadium Service and

Arena/Stadium Accessibility factors. However, the two factors seem theoretical distinct as they

were found to be separate factors in previous studies (Zhang et al., 1998a; 2005b). Thus, future

studies are necessary to examine the factor structure of the two factors. The same procedure can

be suggested for the Behavioral Intentions factor that was initially represented by two factors,

Repatronage Intentions and Recommend to Others in the current study. Future studies should

also examine other mediating and moderating variables that may influence the relationships

among market demand, game support programs, and behavioral intentions. These may include,

but are not limited to, team identification, involvement, fan motivation, and socio-demographic

variables.

In the current study, Bagozzi's (1992) Appraisal-Emotional Response-Coping framework

has been partially utilized. That is, only a direct relationship between Appraisal (positive

attitude) and Coping (behavioral intentions) has been confirmed. However, the model can be

mediated by the emotional response derived from the initial appraisal (Bagozzi, 1992), which

was not examined by the current study. Therefore, future studies should examine affective

constructs in the equation of cognition (i.e., market demand and game support programs) to

132









conation (behavioral intentions) to better understand sport consumer behaviors related to

professional team sports.

In terms of sample size, only half of the entire data (n = 222) was used for CFA and

SEM. Although Wetson and Gore (2006) suggested that a minimum sample size of 200 was

adequate for SEM, the small sample size might have negatively influenced model fit for the

structural model (i.e., CFI) in the current study (Cheung & Rensvold, 2002). According to

MacCallum, Ronznowski, and Necowitz (1992), any model respecification should have an

additional independent sample for cross-validation for the respecified model in order to avoid

capitalizing on chances of variation. Although, measurement and structural models in the current

study displayed good psychometric properties, more attempts to validate factor structures and

causal relationships are recommended.

In the current study, no effort was made to examine if differences exist between die-hard

and fair-weather fans in terms of the structural relationships among market demand, game

support, perceived value, and consumption intentions. As a matter of fact, spectators can at least

be categorized as die-hard fans and fair-weather fans according to their consumption levels and

socio-motivations (Wann & Branscombe, 1990). Die-hard fans generally are of higher team

identification, involvement, and consumption levels than fair-weather fans; and are likely to

support a team when the team does not performs well (Heere & James, 2007; Trail, Fink, &

Anderson, 2003). Perhaps, die-hard fans pay more attention to core product attributes (e.g.,

win/loss, level of performance, and/or the presence of star players); whereas, fair-weather fans

pay more attention to peripheral attributes (e.g., venue, promotion, and/or entertainment). These

speculations need further examination by assessing invariance issues with respect to the

consumption level of spectators. When doing this, a number of socio-psychological variables,










such as team identification and consumer involvement level, may be incorporated into the study.

Invariance analyses are also needed to examine the structural relationships with respect to

different professional sport leagues. Data in this study were collected through a community

intervention approach; thus, research participants were those who attended professional sport

events somewhat in the past. Due to the decay of memory, some of the respondents might not be

able to provide their responses with specificity. Hence, future studies should also examine the

invariance issues between on-site and recall settings. Finally, this study was delimited to

variables directly related to professional teams and their management (i.e., market demand and

game support programs). According to Mullin et al. (2007) and Zhang et al. (1997), there are

many marketing environment variables (e.g., substitute forms of entertainments, availability of

recreational activities, economics, and income) that may simultaneously interact with these

market demand and game support programs. Future studies should take into consideration these

environmental variables and their interactions with market demand, game support programs, and

perceived value variables and how they function together to influence spectator consumption

behaviors.



























































Figure 4-1. First-order confirmatory factor analysis for market demand




135
























































Figure 4-2. First-order confirmatory factor analysis for game support programs







136









































Figure 4-3. First-order confirmatory factor analysis for perceived value for the cost













































Figure 4-4. First-Order confirmatory factor analysis for behavioral intentions

















138


















































Figure 4-5. Tested structural model













139






















































Note. Dashed lines represent non-signifieant paths
** Path significant at the .01 level
* Path significant at the .05 level


Figure 4-6. Tested structural model










Table 4-1. Frequency distributions for the sociodemographic variables (N = 453)
Variables Category Frequency (%) Cumulative %
(N = 453)
Gender Male 274 (60.5) 60.5
Female 179 (39.5) 100.0


Age


18-22
23-30
31-40
41-50
51-65


41 (9.0)
175 (38.6)
151 (33.3)
58 (12.8)
28 (6.2)

90 (19.9)

112 (24.7)
179 (39.5)
62 (13.7)
7 (1.5)
3 (0.7)

23 (5.1)
79 (17.4)
128 (28.3)
81 (17.9)
56 (12.4)
42 (9.3)
27 (6.0)
17 (3.8)

241 (53.2)
195 (43 .0)
17 (3.8)

1 (0.2)
47 (10.4)
45 (9.9)
265 (58.5)
95 (21.0)

259 (57.2)
61 (13.5)
87 (19.2)
40 (8.8)
2 (0.4)
2 (0.4)
2 (0.4)


9.0
47.7
81.0
93.8
100.0

19.9

44.6
84.1
97.8
99.3
100.0

5.1
22.5
50.8
68.7
81.0
90.3
96.2
100.0

53.2
96.2
100.0

0.2
10.6
20.5
79.0
100.0

57.2
70.6
89.8
98.7
99.1
99.6
100.0


Number of People in
Household


2
3-4
5-6
7-8
9 or more


Household Income









Marital Status



Education


Below $20,000
$20,000-39,999
$40,000-59,999
$60,000-79,999
$80,000-99,999
$100,000-149,999
$150,000-199,999
Above $200,000

Single
Married
Divorced

In School Now
High School Graduate
In College Now
College Graduate
Advanced Degree

Caucasian
African American
Hispanic
Asian/Pacific Islander
American Indian
Interracial
Other


Ethnicity





Table 4-1. Continued
Occupation










Attended Game


Management
Technical
Professional
Sales
Clerical
Education
Skilled Worker
Non-Skilled Worker
Other

AFL
MLB
NBA
NFL
NHL
SOCCER


79 (17.4)
28 (6.2)
128 (28.3)
60 (13.2)
12 (2.6)
111 (24.5)
30 (6.6)
3 (0.7)
2 (0.4)

15 (3.3)
99 (21.9)
117 (25.8)
203 (44.8)
18 (4.0)
1 (0.2)


17.4
23.6
51.9
65.1
67.8
92.3
98.9
99.6
100.0

3.3
25.2
51.0
95.8
99.8
100.0










Table 4-2. Descriptive statistics for the market demand variables (N = 453)
Variable MSD Aknws Kurtosis
1. Home team win/loss record (HT1) 3.5938 1.22408 -.691 -.485
2. Home team star players) (HT2) 3.9294 1.18782 -1.001 .047
3. Home team record breaking performance (HT3) 3.2723 1.37517 -.280 -1.133
4. Overall quality of home team players (HT4) 4.0243 .93451 -.947 .953
5. Home team reputation (HT5) 4.1634 1.03470 -1.234 .908
6. Home team league standing (HT6) 3.8274 1.13915 -.893 .144
7. Home team history and tradition (HT7) 4.0000 1.11506 -.962 .023
8. Home team exciting play (HT8) 4.0067 .97873 -1.080 1.029
9. Support the home team (HT9) 4.1637 1.02086 -1.199 .850
10. High level of skills (HT10) 3.7704 1.24457 -.934 -.074
11i. Opposing team's overall performance (OT1) 3.2318 1.15278 -.444 -.590
12. Opposing team star players) (OT2) 3.2274 1.27569 -.471 -.851
13. Opposing team history and tradition (OT3) 3.4194 1.07907 -.459 -.275
14. Opposing team reputation (OT4) 3.2500 1.09580 -.326 -.472
15. Overall quality of opposing team players (OT5) 3.3488 1.14530 -.517 -.455
16. Opposing team league standing (OT6) 3.2434 1.15906 -.468 -.526
17. Quality of opposing team (OT7) 3.4658 1.05880 -.634 -.188
18. Opposing team as a rivalry (OT8) 3.6927 1.14210 -.718 -.124
19. Opposing team exciting play (OT9) 3.1239 1.22028 -.349 -.833
20. Player charisma of opposing team (OT10) 3.1969 1.22427 -.461 -.760
21. Played that sports) (LS1) 2.8940 1.47014 .029 -1.334
22. Closeness of competition (LS2) 3.3920 1.08592 -.538 -.296
23. Popularity of professional team sport (LS3) 3.8234 1.20826 -.844 -.175
24. Duration of the game (LS4) 2.8702 1.29197 -.047 -1.145
25. High level of skills (LS5) 3.8825 1.03348 -.898 .374
26. Best players in a sport (LS6) 3.7870 1.23264 -.797 -.415
27. Speed of game (LS7) 3.2062 1.29654 -.382 -.931
28. Athleticism of professional team sport (LS8) 3.6372 1.08947 -.520 -.401
29. High level of competitiveness (LS9) 3.9400 1.05111 -.901 .251
30. Love professional team sports) (LS10) 4.2235 .94592 -1.137 .631
31. Personal ticket price (EC1) 3.1715 1.26718 -.175 -.943
32. Ticket affordability (EC2) 3.3177 1.21467 -.384 -.609
33. Good seats (EC3) 3.6659 1.14562 -.613 -.416
34. Group ticket cost (EC4) 2.6705 1.30453 .103 -1.188
35. Ticket discount (EC5) 3.1723 1.36950 -.268 -1.093
36. Sales Promotions (EC6) 2.9219 1.29863 -.146 -1.169
37. Advertising (GP1) 2.8742 1.19533 .002 -.907
38. Direct mail & notification (GP2) 2.5044 1.31069 .239 -1.230
39. Publicity (GP3) 3.2208 1.18052 -.305 -.783
40. Web information (GP4) 2.4224 1.19629 .379 -.887
41. Game time of the day (SC1) 3.6637 1.04848 -.982 .720










Table 4-2 Continued
42. Convenient game schedule (SC2) 3.7533 .93319 -.818 .861
43. Weather condition (SC3) 3.3518 1.32460 -.426 -.893
44. Day of the week (SC4) 3.6592 1.01558 -.763 .261
45. Travel distance (SC5) 3.1499 1.19267 -.112 -.727
46. Location of venue (SC6) 3.5919 1.24954 -.658 -.486
Note. HT = home team; OT = opposing team; LS = love of professional sport; EC = economic consideration;
GP = game promotion; SC = schedule convenience.










Table 4-3. Descriptive statistics for the game support programs variables (N = 453)
Variable MSD Aknws Kurtosis
1. Phone order service (TS1) 2.8381 1.05699 -.002 -.211
2. Will call service (TS2) 3.0703 1.01743 .019 -.188
3. Ticket exchange program (TS3) 3.0740 1.08765 -.245 -.217
4. Ticket agencies (TS4) 2.9046 1.05939 -.205 -.355
5. Game calendar and schedule (TS5) 3.9508 .81864 -.467 -.140
6. Ticket personnel friendliness(TS6) 3.7562 .92849 -.457 -.139
7. Convenience of ticket sale locations (TS7) 3.5258 .98321 -.486 .201
8. Web (on-line) order procedures (TS8) 2.9748 1.00301 -.094 -.191
9. Mail order (TS9) 2.5324 1.12279 .111 -.715
10. Efficiency of ticket office (TS 10) 3.5442 .96554 -.185 -.544
11. Music selection (GA1) 3.6777 .91804 -.283 -.259
12. Public address system (GA2) 3.7020 .98289 -.505 -.266
13. Replay screens (GA3) 3.5366 1.12114 -.499 -.452
14. During game shows/entertainments (GA4) 3.5982 .93704 -.384 -.330
15. Post-game shows/entertainments (GA5) 3.2993 1.14694 -.314 -.591
16. Give away/prize(GA6) 3.2345 1.10624 -.179 -.660
17. Music volume (GA7) 3.8407 .91028 -.687 .429
18. Scoreboard information (GA8) 3.9779 .86446 -.639 .242
19. Pre-game shows/entertainments (GA9) 3.4568 .96155 -.484 -.096
20. Intermission/half-game entertainments (GA10) 3.4181 .96392 -.384 -.112
21. Dance/cheerleading activities (GAll) 3.5565 1.00805 -.507 -.245
22. Concourse entertainment activities (GA12) 3.3166 .88871 -.089 -.039
23. Food and drink quality (SS1) 3.5022 .91267 -.252 .137
24. Arena/Stadium cleanliness (SS2) 3.7345 .91707 -.434 -.114
25. Restroom cleanliness (SS3) 3.2301 .98672 -.211 -.497
26. Food and drink price (SS4) 3.0310 1.20615 -.174 -.898
27. Restroom availability (SS5) 3.6049 .89772 -.372 -.124
28. Staff courtesy (SS6) 3.7439 .88285 -.251 -.474
29. Parking (SA1) 2.9467 1.18688 .056 -.891
30. Newness of arena/stadium (SA2) 3.5398 1.02435 -.486 -.075
31. Security (SA3) 3.7450 .88207 -.448 .019
32. Ticket takers (SA4) 3.6991 .90851 -.489 -.028
33. Traffic/crowd control (SA5) 3.4204 1.08299 -.382 -.431
34. Public transportation (SA6) 3.1327 1.10767 -.271 -.388
3 5. Niceness of arena stadium (SA7) 3.8514 .89621 -.444 -.437
36. Ushers (SA8) 3.4614 .83689 -.014 -.246
37. Ease of entrance (SA9) 3.5762 1.01605 -.373 -.454
38. Seating directions (SA10) 3.7441 .88886 -.575 .331
Note. TS = ticket services: GA = game amenities: SS = stadium services: SA = stadium accessibility










Table 4-4. Descriptive statistics for the perceived value for the cost variables (N = 453)
Variable MSD Aknws Kurtosis
1. The game experience was a good buy (MP 1) 4.2230 .80441 -1.015 1.192
2. The game experience was worth the money (MP2) 4.2362 .78949 -.743 -.003
3. The game experience was fairly priced (MP3) 3.9029 .97958 -.755 .227
4. The game experience was reasonably priced (MP4) 3.8609 .97563 -.580 -.134
5. The game experience was economical (MP5) 3.6093 1.12084 -.521 -.449
Note. MP = perceived value for the cost










Table 4-5. Descriptive statistics for the behavioral intentions variables (N = 453)
Variable MSD Aknws Kurtosis
1. I am likely to attend more games as soon as the sport is in 4.4004 .86028 -1.563 2.133
season (REP1)
2. I am likely to re-attend games) next season (REP2) 4.4636 .78516 -1.518 2.129
3. I have a high likelihood of re-attending the games) next 4.3664 .83768 -1.523 2.539
season (REP3)
4. I plan on attending more games) of this professional sport 4.5366 .70105 -1.466 1.868
in the future (REP4)
5. The probability that I will re-attend this professional sport 4.5077 .79992 -1.718 2.633
game is high (REP5)
6. I will recommend this professional sport game to other 4.3642 .83468 -1.433 2.092
persons (REC1)
7. I am likely to recommend this professional sport game to 4.3775 .83408 -1.606 2.994
nw family (REC2)
8. I am likely to recommend this professional sport game to 4.3731 .76417 -1.255 1.751
nw friends (REC3)
9. I am likely to say positive things about this professional 4.3355 .83215 -1.435 2.471
sport game to other people (REC4)
10. I will talk about this professional sport game with other 4.4238 .78230 -1.431 2.031
people (REC5)
Note. REP = repurchase intentions: REC = recommend to others











Table 4-6. Factor pattern matrix for the market demand variables: alpha factoring with promax
rotation using first half data (n = 231)
Fl F2 F3 F4 F5 F6


Opposing Team (9 items)
Quality of opposing team
Overall quality of opposing team players
Opposing team exciting play
Opposing team star players)
Opposing team reputation
Player charisma of opposing team
Opposing team league standing
Opposing team's overall performance
Opposing team history and tradition

Home Team (6 items)
Home team win/loss record
Home team league standing
Home team reputation
Home team history and tradition
Overall quality of home team players
Home team exciting play

Game Promotion (5 items)
Advertising
Sales Promotions
Direct mail & notification
Publicity
Web information

Economic Consideration (4 items)
Ticket affordability
Travel distance
Ticket discount
Personal ticket price

Love ofProfessional Sport (4 items)
Group ticket cost
Speed of game
Duration of the game
Played that sports)


.862 -.085 -.112 -.005 -.002 .118
.827 -.095 -.006 -.001 -.036 .118
.822 .065 .092 .141 -.108 -.154
.818 -.167 .076 -.103 .056 .021
.814 .074 -.187 -.001 .120 -.017
.765 -.144 .237 -.097 -.026 .054
.725 .141 .023 .138 -.106 -.093
.717 .065 .025 .159 -.077 -.037
.641 .243 -.161 -.360 .215 .000



-.153 .773 .103 -.049 .086 -.029
.014 .738 .056 -.071 .019 -.019
-.122 .722 -.042 .004 -.003 .137
.078 .695 -.072 .005 -.182 .029
.085 .634 .062 .048 .031 .051
.083 .565 .040 .133 .022 -.077


-.007 .035 .902 -.131 .097 -.033
-.142 .007 .873 .087 -.018 .082
.036 -.003 .817 -.129 -.011 -.012
.204 .023 .494 .056 -.098 .230
.093 .122 .471 .106 .059 -.164


-.040 .014 -.081 .948 -.121 .060
.007 .074 .091 .709 -.067 -.037
-.090 -.069 -.078 .630 .312 .028
.126 -.037 -.078 .590 .037 .176


-.181 .107 -.042 -.089 .651 .140
.087 .016 .027 .302 .569 -.182
.105 -.081 .146 .213 .513 .039
.120 -.120 .059 -.103 .509 .016


Schedule Convenience (3 items)
Day of the week .011 -.005 -.061 .075 -.005 .659
Convenient game schedule .111 .167 -.041 .055 .063 .620
Game time of the day -.024 -.008 .131 .054 .066 .616
Note. F1 = opposing team; F2 = home team; F3 = game promotion: F4 = economic consideration:
F5 = love of professional sport; F6 = schedule convenience.











Table 4-7. Factor pattern matrix for the game support programs variables: alpha factoring with
promax rotation using first half data (n = 231)
Fl F2 F3 F4 F5
Game Amenities (6 itents)
Pre-game shows/entertainments .856 .036 -.059 .031 -.083
Post-game shows/entertainments .795 .080 -.149 -.136 -.017
Dance/cheerleading activities .720 -.184 -.005 .072 .096
During game shows/entertainments .668 -.036 .027 .248 -.015
Intermission/half-game entertainments .627 .042 .105 .070 .159
Concourse entertainment activities .600 .129 .205 -.066 -.088

Arena/Stadium Services (5 itents)
Arena/Stadium cleanliness .034 .673 -.211 .084 .056
Restroom availability -.140 .656 -.117 .205 .085
Restroom cleanliness .005 .653 .018 -.086 .188
Parking .163 .628 .031 -.222 .063
Ease of entrance -.118 .444 .360 .246 -.252

Ticket Service (3 itents)
Ticket exchange program .083 -.049 .769 -.098 -.058
Will call sen ice -.221 -.099 .756 .138 .116
Phone order sen ice .127 -.055 .635 -.136 .048

Arena/Stadium Convenience (4 itents)
Scoreboard information -.001 -.058 -.108 .704 .125
Game calendar and schedule -.039 .006 -.054 .598 .077
Security .185 -.070 .111 .565 -.053
Staff courtesy .096 .161 .075 .550 -.090

Arena/Stadium Accessibility (3 itents)
Public address system .020 .025 .401 .078 .539
Traffic/crowd control -.054 .183 .169 -.125 .535
Seating directions .049 .074 -.197 .236 .525
Note. F1 = game amenities: F2 = arena/stadium services: F3 = ticket service: F4 = arena/stadium convenience:
F5 = arena/stadium accessibility.










Table 4-8. model fit comparison between the six-factor model and five-factor model of market
demand using second half data (n = 222)
Model x" /~fXI~f RMSEA RMSEA CI SRMR CFI ECVI
Six-Factor Model 1340.89 419 3.20 .10 .094-. 106 .077 .78 6.76
(31 items)
Five-Factor 278.31 109 2.55 .084 .072-.096 .054 .92 1.66
Model
(17 items)
CI = confidence interval










Table 4-9. Model fit comparison between the five-factor model, four-factor model, and three-
factor model of game support programs using second half data (n = 222)
Model x" df X'/df RMSEA RMSEA CI SRMR CFI ECVI

Five-Factor Model 482.84 179 2.70 .088 .078-.097 .077 .82 2.66
(21 items)
Four-Factor Model 212.44 84 2.53 .083 .069-.097 .068 .89 1.29
(15 items)
Three-Factor Model 219.04 87 2.52 .083 .069-.097 .070 .89 1.29
(15 items)
CI = confidence interval










Table 4-10. Model fit comparison between the five-item model and three-item model of
received value for the cost using second half data (n = 222)
Model x" /~fXI~f RMSEA RMSEA CI SRMR CFI ECVI
Five-Item 138.39 5 27.68 .347 .299-.399 .172 .76 .72
Model
Three-Item 2.79 1 2.79 .090 .000-.223 .001 .99 .06
Model
CI = confidence interval










Table 4-1 1. Model fit comparison between the ten-item model and five-item model of behavioral
intentions using second half data (n = 222)
Model D" f /dL~f RMSEA RMSEA CI SRMR CFI ECVI
Ten-Item 157.04 35 4.60 .126 106-. 146 .048 .92 .89
Model
Five-Item 14.99 5 3.00 .095 .042-. 152 .019 .99 .20
Model
CI = confidence interval










Table 4-12. Overall model fit indices for the measurement model of hypothesized structural
model using second half data (n = 222)
Model x" /~fXI~f RMSEA RMSEA CI SRMR CFI ECVI
Structural 1545.33 695 2.22 .70 .065-.074 .067 .86 7.15
Model
CI = confidence interval














OT 1.0
HT .308*** 1.0
GP .464*** .501*** 1.0
EC .308*** .200* .193* 1.0
SC .444*** .460*** .461*** .511*** 1.0
Note. OT = opposing team; HT = home team; GP = game promotion; EC = economic consideration; SC = schedule
convenience.
*** Correlation significant at the .001 level
Correlation significant at the .05 level


Table 4-13. Interfactor correlations from the confirmatory factor analysis of the market demand
using second half data (n = 222)
OT HT GP EC SC










Table 4-14. Interfactor correlations from the confirmatory factor analysis of the game support
programs using second half data (n = 222)
GA TS VQ
GA 1.0
TS .634*** 1.0
VQ .491*** .363*** 1.0
Note. GA = game amenities; TS = ticket services; VQ = venue quality
*** Correlation significant at the .001 level











Table 4-15. Interfactor correlations, construct reliability, and average variance extracted from the
confirmatory factor analysis of the hypothesized structural model using second half
data (n = 222)
OT HT GP EC SC GA TS VQ MP BI
OT .87(.57)
HT .263*** .75(.51)
GP .476*** .414*** .83(.63)
EC .321*** .064 .120 .76(.52)
SC .477*** .346*** .345*** .360*** .77(.53)
GA .167* .228** .480*** .020 .331*** .90(.60)
TS .364*** .031 .437*** .167* .394*** .462*** .72(.47)
VQ .028 .200* .073 -.020 .503*** .558*** .461*** .77(.36)
MP .001 .299*** .095 .045 .301*** .344*** .204* .487*** .92(.81)
BI .061 .315*** -.069 -.014 .116 .278*** -.004 .367*** .406*** .92(.69)
Notel. OT = opposing team; HT = home team: GP = game promotion: EC = economic consideration:
SC = schedule convenience: GA = game amenities: TS = ticket service: VQ = venue quality: MP = perceived value
for the cost: BI = behavioral intentions.
Note2. Interfactor correlations are in lower triangle; construct reliabilities are in diagonal; and average variance
extracted values are in parentheses.
*** Correlation significant at the .001 level
** Correlation significant at the .01 level
Correlation significant at the .05 level












Variables Indicator Critical Cronbach's Construct Average
Loadings Ratios Alpha Reliability Variance
Extracted
OppoosingP Team (5 itents) .91 .82 .64


_I


Table 4-16. Indicator loadings, critical ratios, cronbach's alpha, construct reliability,
usinn second half data (n 222)


average variance extracted for the market demand


Opposing team's overall performance
Opposing team reputation
Overall quality of opposing team players
Quality of opposing team
Opposing team exciting play

Home Team (3 itents)
Home team win/loss record
Home team reputation
Home team league standing

Game Promotion (3 itents)
Adverti sing
Direct mail & notification
Sales Promotions

Economic Consideration (3 itents)
Personal ticket price
Ticket affordability
Ticket discount

Schedule Convenience (3 itents)
Game time of the day
Convenient game schedule
Day of the week


13.52
16.79
16.43
13.91


8.99
11.56


14.84
15.31


10.92
10.41


10.91
9.54












Variables Indicator Critical Cronbach's Construct Average
Loadings Ratios Alpha Reliability Variance
Extracted
Game Amenities (6 items) .85 .86 .52


Table 4-17. Indicator loadings, critical ratios, cronbach's alpha, construct reliability,
programs usinn second half data (n 222)


average variance extracted for the game support


During game shows/entertainments
Post-game shows/entertainments
Pre-game shows/entertainments
Intermission/half-game entertainments
Dance/cheerleading activities
Concourse entertainment activities


8.20
10.54
10.75
9.24
9.60


Ticket Service (3 items)
Phone order service
Will call service
Ticket exchange program

Venue Quality (6 items)
Staff courtesy
Restroom availability
Arena/Stadium cleanliness
Ease of entrance
Security
Parking


7.54
8.18


8.70
8.90
5.70
7.76
6.69









Table 4-18. Indicator loadings, critical ratios, cronbach's alpha, construct reliability, average variance extracted for the perceived
value for the cost using second half data (n = 222)
Variables Indicator Critical Cronbach's Construct Average
Loadings Ratios Alpha Reliability Variance
Extracted
Perceived Value for the Cost (3 items) .90 .88 .71
The game experience was fairly priced .90
The game experience was reasonably priced .91
The game experience was economical .79 16.117









Table 4-19. Indicator loadings, critical ratios, cronbach's alpha, construct reliability, average variance extracted for the behavioral
intentions using second half data (n = 222)
Variables Indicator Critical Cronbach's Construct Average
Loadings Ratios Alpha Reliability Variance
Extracted
Behavioral Inztentions (5 items) .93 .95 .79
I am likely to attend more games as soon as the sport is in season .86
I am likely to re-attend games) next season .89 17.89
I plan on attending more games) of this professional sport in the.7142
future
I will recommend this professional sport game to other persons .86 16.69
I am likely to recommend this professional sport game to my friends .85 16.25










Table 4-20. Maximum likelihood standardized loadings (P), critical ratios (cr), standard errors
(se), and t-values for the hypothesized structural model using second half data (n =
222)
Path Coefficients between Factors (3 CR SE t
Direct Effect

Behavioral Intentions f Home Team (S) .281 3.277 .071 .231**
Behavioral Intentions f Opposing Team (S) .246 2.778 .073 .204**
Behavioral Intentions e Economic Consideration (NS) .021 .294 .061 .018
Behavioral Intentions f Game Promotion (PS) -.319 -2.896 .082 -.238**
Behavioral Intentions f Schedule Convenience (NS) -.215 -1.863 .127 -.237
Behavioral Intentions + Game Amenities (S) .246 2.453 .099 .243*
Behavioral Intentions f Ticket Service (NS) -.161 -1.484 .122 -.180
Behavioral Intentions f Venue Quality (NS) .257 1.779 .240 .426
Behavioral Intentions e Perceived Value for the Cost (S) .240 3.199 .073 .234**
Perceived Value for the Cost + Home Team (S) .237 2.758 .073 .200**
Perceived Value for the Costt Venue Quality (S) .36 2.358 .250 .589**

Path Coefficients between Factors (3 P
Indirect Effect

Behavioral Intentions f Perceived Value for the Cost f
.083 .028
Venue Quality (Game Support) (PS)
Behavioral Intentionsf Perceived Value for the Cost +
057 133
Home Team (Market Demand) (NS)
Note. S = significant: PS = partially significant: NS = not significant
** Correlation significant at the .01 level
* Correlation significant at the .05 level









APPENDIX
INFORMED CONSENT AND QUESTIONNAIRE

Dear Participants:

Purpose of Study: The purpose of this study is to examine the impact of market demand, game
support programs on consumption levels of professional team sport spectators as mediated by
perceived value.

What you will be asked to do in the study: The questionnaire consists of items that are
designed to measure market demand, game support programs, perceived value, and attendance
intentions. By using these items, we are attempting to develop a model that explains what
influences sport spectators' re-attend intentions towards a professional team sport.

Time required, Risks and Benefits, & Compensation: The survey will take approximately 10
minutes to complete. There are no known risks and we do not anticipate that you will benefit
directly by participating in this study. There is no compensation for participating in this study.

Confidentiality: Your identity will be kept confidential to the extent provided by law. Your
responses will be anonymous and will only be used for the current research purposes. In
addition, there will be no identifying markers that will link you to the questionnaire you
complete, as the results will be reported as group results.

Voluntary participation: Your participation in this research is totally voluntary and there is no
penalty for not participating.

Right to withdraw from the study: You have the right to withdraw from the study at any time
without consequence.

Whom to contact if you have questions about the study: Dr. James Zhang (advisor), Dept. of
Tourism, Recreation, & Sport Management, 1 86A Florida Gym, j amesz@hhp.ufl.edu, 392-4042
x 1274

Whom to contact about your rights as a research participant in the study: UFIRB Office,
Box 112250, University of Florida, Gainesville, FL 32611-2250; phone 392-0433

Agreement: I have read the procedure described above. I voluntarily agree to participate in the
procedure and I have received a copy of this description.

Participant: Date:

Principal Investigator: Date:

To contact: Kunwung Byon, Florida Gym 300. PO. BOX. 118208. Gainesville, FL, 32611-
8208, Phone (392-4042 xl309), E-mail (kbyon@hhp.ufl.edu)














PURPOSE: This survey is for a marketing study on professional team sports. The collected information will be solely used for research. Your identity will be
kept confidential to the fullest extent provided by law, and your responses will be anonymous. There is no right or wrong answers. Your participation is
voluntary, and your honest response is greatly appreciated. THANK YOU!

SCREEN QUESTIONS:
1. Have you attended one or more professional team sport events within the past 12 months? Yes No
2. If so, did you or your family pay for the game ticket? Yes No
Please specify the game that you attended ( )

-- If you answered No to #1 or #2, you are finished with the survey. Thank you!
-- If you answered Yes to both #1 and #2, please continue.

DECISION MAKING: Please rate the following variables that might have influenced your decision making to attend the most recent professional team
sport event (1=Not at All to 5 = Very Much).

Home Team, Favorite Team, or Team A (1=Not at All to 5 = Very Much):
1. Home team win/loss record 1 2 3 4 5 6. Home team league standing 1 2 3 4 5
2. Home team star players) 1 2 3 4 5 7. Home team history and tradition 1 2 3 4 5
3. Home team record breaking performance 1 2 3 4 5 8. Home team exciting play 1 2 3 4 5
4. Overall quality of home team players 1 2 3 4 5 9. Support the home team 1 2 3 4 5
5. Home team reputation 1 2 3 4 5 10. High level of skills 1 2 3 4 5
Opposing Team, Visiting Team, or Team B (1=Not at All to 5 = Very Much):
1. Opposing team's overall performance 1 2 3 4 5 6. Opposing team league standing 1 2 3 4 5
2. Opposing team star players) 1 2 3 4 5 7. Quality of opposing team 1 2 3 4 5
3. Opposing team history and tradition 1 2 3 4 5 8. Opposing team as a rivalry 1 2 3 4 5
4. Opposing team reputation 1 2 3 4 5 9. Opposing team exciting play 1 2 3 4 5
5. Overall quality of opposing team players 1 2 3 4 5 10. Player charisma of opposing team 1 2 3 4 5
Love of Professional Team Sport (1=Not at All to 5 = Very Much):
1. Played that sports) 1 2 3 4 5 6. Best players in a sport 1 2 3 4 5
2. Closeness of competition 1 2 3 4 5 7. Speed of game 1 2 3 4 5
3. Popularity of professional team sport 1 2 3 4 5 8. Athleticism of professional team sport 1 2 3 4 5
4. Duration of the game 1 2 3 4 5 9. High level of competitiveness 1 2 3 4 5
5. High level of performance 1 2 3 4 5 10. Love professional team sport(s) 1 2 3 4 5
Economic Consideration (1=Not at All to 5 = Very Much):
1. Personal ticket price 1 2 3 4 5 4. Group ticket cost 1 2 3 4 5
2. Ticket affordability 1 2 3 4 5 5. Ticket discount 1 2 3 4 5
3. Good seats 1 2 3 4 5 6. Sales Promotions 1 2 3 4 5


Marketing Survey Questionnaire for Professional Team Sports











Game Promotion (1=Not at All to 5 = Very Much):
1. Advertising 1 2 3 4 5 3. Publicity 1 2 3 4 5
2. Direct mail & notification 1 2 3 4 5 4. Web information 1 2 3 4 5
Schedule Convenience (1=Not at All to 5 = Very Much):
1. Game time of the day 1 2 3 4 5 4. Day of the week 1 2 3 4 5
2. Convenient game schedule 1 2 3 4 5 5. Travel distance 1 2 3 4 5
3. Weather condition 1 2 3 4 5 6. Location of venue 1 2 3 4 5

ATTENDANCE INTENTION: With respect to the professional team sport event that you most recently attended, please rate the following statements
that assess your intentions for future attendance at the professional team sport events (1 = Strongly Disagree to 5 = Strongly Agree).

Re-patronage Intentions
1. I am likely to attend more games as soon as the sport is in season 1 2 3 4 5
2. I am likely to re-attend games) next season 1 2 3 4 5
3. I have a high likelihood of re-attending the games) next season 1 2 3 4 5
4. I plan on attending more games) of this professional sport in the future 1 2 3 4 5
5. The probability that Iwill re-attend this professional sprt gaeis hih 1 2 3 4 5
Recommendation to Others
1. I will recommend this professional sport game to other persons 1 2 3 4 5
2. I am likely to recommend this professional sport game to my family 1 2 3 4 5
3. I am likely to recommend this professional sport game to my friends 1 2 3 4 5
4. I am likely to say positive things about this professional sport game to other people 1 2 3 4 5
5. I will talk about this professional spr aewith other pole 1 2 3 4 5

SERVICE QUALITY: With respect to the professional team sport event that you most recently attended, please rate the following statements that
assess your perceptions of game-operation related activities during your attendance (1= Very Unsatisfied to 5 = Very Satisfied).

Ticket Service (1= Very Unsatisfied to 5 = Very Satisfied)
1. Phone order service 1 2 3 4 5 6. Ticket personnel friendliness 1 2 3 4 5
2. Will call service 1 2 3 4 5 7. Convenience of ticket sale locations 1 2 3 4 5
3. Ticket exchange program 1 2 3 4 5 8. Web (on-line) order procedures 1 2 3 4 5
4. Ticket agencies 1 2 3 4 5 9. Mail order 1 2 3 4 5
5. Game calendar and schedule 1 2 3 4 5 10. Efficiency of ticket office 1 2 3 4 5
Game Amenities (1= Very Unsatisfied to 5 = Very Satisfied)
1. Music selection 1 2 3 4 5 7. Music volume 1 2 3 4 5
2. Public address system 1 2 3 4 5 8. Scoreboard information 1 2 3 4 5
3. Replay screens 1 2 3 4 5 9. Pre-game shows/entertainments 1 2 3 4 5
4. During game shows/entertainments 1 2 3 4 5 10. Intermission/half-game entertainments 1 2 3 4 5
5. Post-game shows/entertainments 1 2 3 4 5 11. Dance/cheerleading activities 1 2 3 4 5
6. Give awayprze 1 2 3 4 5 12. Concourse entertainment activities 1 2 3 4 5











Arena/Stadium Services (1= Very Unsatisfied to 5 = Very Satisfied)
1. Food and drink quality 1 2 3 4 5 4. Food and drink price 1 2 3 4 5
2. Arena/Stadium cleanliness 1 2 3 4 5 5. Restroom availability 1 2 3 4 5
3. Restroom cleanliness 1 2 3 4 5 6. Staff courtesy 1 2 3 4 5
Arena/Stadium Accessibility (1= Very Unsatisfied to 5 = Very Satisfied)
1. Parking 1 2 3 4 5 6. Public transportation 1 2 3 4 5
2. Newness ofarena/stadium 1 2 3 4 5 7. Niceness of arena stadium 1 2 3 4 5
3. Security 1 2 3 4 5 8. Ushers 1 2 3 4 5
4. Ticket takers 1 2 3 4 5 9. Ease of entrance 1 2 3 4 5
5. Traffic/crowd control 1 2 3 4 5 10. Seating directions 1 2 3 4 5

COST AND BENEFIT: With respect to the professional team sport event that you most recently attended, please rate the following statements that
assess your overall perceptions of game experience during your attendance (1= Definitely False to 5 = Definitely True).

Perceived Value of Game Experience (1= Definitely False to 5 = Definitely True)
1. The game experience was a good buy 1 2 3 4 5
2. The game experience was worth the money 1 2 3 4 5
3. The game experience was fairly priced 1 2 3 4 5
4. The game experience was reasonably priced 1 2 3 4 5
5. The gae eprience was economical 1 2 3 4 5











DEMOGRAPHICS: Please provide the following information by circling an answer or filling a blank.
1. Gender: a. male b. female

2. Age: a. 10 years or younger b. 11-17 years old c. 18-22 years old d. 23-30 years old
e. 31-40 years old f. 41-50 years old g. 51-65 years old h. 66 years or older

3. Number of people in your household: a. 1 b. 2 c. 3-4 d. 5-6 e. 7-8 f. 9 or more

4. Household income: a. below $ 20,000 b. $20,000-$39,999 c. $40,000-$59,999 d. $60,000-$79,999
e. $80,000-$99,999 f. $100,000-$149,999 g. $150,000-$199,999 h. above $200,000
5. Marital Status: a. single b. married c. divorced d. widowed e. Other

6. Education: a. in school now b. high school graduate c. in college now
d. college graduate e. advanced degree f. Other (be specific)

7. Ethnicity: a. Caucasian b. Mfrican American c. Hispanic
d. Asian/Pacific Islander e. American Indian f. Interracial g. Other

8. Occupation: a. management b. technical c. professional d. sales e. clerical
f. education g. skilled worker h. non-skilled worker i. Other










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BIOGRAPHICAL SKETCH

Kun-wung Byon was originally from South Korea and spent the last 6 years pursuing a

master' s and Ph D. in sport management at the Slippery Rock University of Pennsylvania and

University of Florida, respectively. He received his Bachelor' s of Art degree in Japanese

literature at Hannam University in South Korea in 1998. He completed his Master of Science

(specialization: sport management) in August 2004. Finally, Kun-wung earned his Ph D. in

health and human performance (sport management) and a minor in research evaluation and

methodologies in 2008.





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1 IMPACT OF MARKET DEMAND AND GAME SUPPORT PROGRAMS ON CONSUMPTION LEVELS OF PROFESSI ONAL TEAM SPORT SPECTATORS AS MEDIATED BY PERCEIVED VALUE By KUN-WUNG BYON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 Kun-wung Byon

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3 To my family

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4 ACKNOWLEDGMENTS I would like to show m y deepes t appreciation to my advisor, Dr. James J. Zhang for his endless support throughout my cour se of study in the Ph D. program. I would not have been able to complete my dissertation without his patien ce, encouragement, insight, and support. Also, I want to thank the members of my committee (D r. Connaughton, Dr. Ko, Dr. Kim, and Dr. Lutz) for their guidance and constructive advice. Many thanks also go to my Ph. D program colleagues who encouraged me during this arduous process. Most importantly, I thank my lovely wife, Young-woo, and my precious son and daughter, Connor and Kaylee, for their endless love and unselfish sacrifice. I love them all! Also, I would like to thank my entire family in Korea for their support and encouragement during my 6 years of edu cation in the U.S. Above all else, I want to thank God, Jesus Christ, who strengt hens me whenever and wherever.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES.......................................................................................................................10 LIST OF TERMS...........................................................................................................................11 ABSTRACT...................................................................................................................................12 CHAP TER 1 INTRODUCTION..................................................................................................................14 Statement of Problem........................................................................................................... ..24 Hypothesized Research Model............................................................................................... 26 Significance of the Study........................................................................................................35 Delimitations...........................................................................................................................36 Limitations.................................................................................................................... ..........36 2 LITERATURE REVIEW.......................................................................................................38 Sport Spectator Consumption.................................................................................................38 Definition of Sport Spectator Consumption.................................................................... 38 Measurement of Behavioral Intentions........................................................................... 39 Overview of the Proposed Dimensions of Spectator Behavioral Intentions ................... 42 Repatronage intentions............................................................................................. 42 Recommending to others intentions......................................................................... 43 Service Quality................................................................................................................ .......44 Definition of Service Quality..........................................................................................45 Significance of Examining Service Quality.................................................................... 46 Measurement of Service Quality..................................................................................... 47 Overview of the Proposed Spectator Service Quality..................................................... 53 Market Demand (Core Service Quality).......................................................................... 55 Proposed Dimensions of Market Demand (Core Service Quality)................................. 58 Home team............................................................................................................... 58 Opposing team..........................................................................................................59 Love of professional team sport...............................................................................60 Economic consideration........................................................................................... 60 Game promotion.......................................................................................................61 Schedule convenience.............................................................................................. 61 Spectator Game Support Programs (Peripheral S ervice Quality)................................... 62 Proposed Dimensions of Spectator Game Support Program s (Peripheral Service Quality)....................................................................................................................... .64

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6 Ticket service...........................................................................................................65 Game amenities........................................................................................................66 Stadium service........................................................................................................ 66 Stadium accessibility................................................................................................ 67 Perceived Value......................................................................................................................67 Definition of Perceived Value......................................................................................... 69 Importance of Examining Perceived Value..................................................................... 71 Measurement of Perceived Value.................................................................................... 73 Overview of the Proposed Dimensions of Perceived Value...........................................76 Perceived Value for the Cost........................................................................................... 76 Relationship among Perceived Value, Servi ce Quality, and Behavioral Intentions .......77 Summary.................................................................................................................................82 3 METHODOLOGY................................................................................................................. 84 Participants.............................................................................................................................84 Measurement.................................................................................................................... .......86 Market Demand............................................................................................................... 86 Game Support Programs..................................................................................................88 Perceived Value...............................................................................................................89 Behavioral Intentions.......................................................................................................90 Demographic Information............................................................................................... 90 Procedures..................................................................................................................... ..........90 Data Analyses.........................................................................................................................92 4 RESULTS...............................................................................................................................98 Descriptive Statistics......................................................................................................... .....98 Exploratory Factor Analyses.................................................................................................. 99 Market Demand............................................................................................................... 99 Game Support................................................................................................................100 Perceived Value for the Cost......................................................................................... 101 Behavioral Intentions.....................................................................................................102 Measurement Models: Confirm atory Factor Analyses......................................................... 102 Market Demand............................................................................................................. 102 Game Support Programs................................................................................................105 Perceived Value for the Cost......................................................................................... 108 Behavioral Intentions.....................................................................................................109 Structural Model............................................................................................................... ....110 5 DISCUSSION.......................................................................................................................115 Measurement Properties....................................................................................................... 116 Hypotheses Testing............................................................................................................. ..124 Additional Suggestions......................................................................................................... 132

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7 APPENDIX A INFORMED CONSENT AND QUESTIONNAIRE........................................................... 163 LIST OF REFERENCES.............................................................................................................168 BIOGRAPHICAL SKETCH.......................................................................................................183

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8 LIST OF TABLES Table page 4-1 Frequency distributions for the socio demographic variables ( N = 453).......................... 141 4-2 Descriptive statistics for the market demand variables ( N = 453) ...................................143 4-3 Descriptive statistics for the gam e support programs variables ( N = 453)...................... 145 4-4 Descriptive statistics for the perceive d value for the cost variables (N = 453) ............... 146 4-5 Descriptive statistics for the behavioral intent ions variables ( N = 453) .......................... 147 4-6 Factor pattern matrix for the market de m and variables: alpha factoring with promax rotation using firs t half data ( n = 231)............................................................................. 148 4-7 Factor pattern matrix for the game suppor t program s variables: alpha factoring with promax rotation using first half data ( n = 231)................................................................149 4-8 model fit comparison between the six-factor m odel and five-factor model of market demand using second half data (n = 222)........................................................................150 4-9 Model fit comparison between the five-factor m odel, four-factor model, and threefactor model of game support programs using second half data ( n = 222)......................151 4-10 Model fit comparison between the fi ve-item model and three-item model of perceived value for the cost using second half data (n = 222)......................................... 152 4-11 Model fit comparison between the ten-item model and five -item model of behavioral intentions using second half data ( n = 222)..................................................................... 153 4-12 Overall model fit indices for the meas urem ent model of hypot hesized structural model using second half data ( n = 222)........................................................................... 154 4-13 Interfactor correlations from the confirma tory factor analysis of the m arket demand using second half data ( n = 222)......................................................................................155 4-14 Interfactor correlations from the confirma tory factor analysis of the gam e support programs using second half data ( n = 222)...................................................................... 156 4-15 Interfactor correlations, construct reliabi lity, and average v ariance extracted from the confirmatory factor analysis of the hypothesized structural m odel using second half data ( n = 222)................................................................................................................... 157 4-16 Indicator loadings, critical ratios, cro nbachs alpha, construct reliability, average variance extracted for the market demand using second half data ( n = 222).................. 158

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9 4-17 Indicator loadings, critical ratios, cro nbachs alpha, construct reliability, average variance extracted for the game suppor t programs using second half data ( n = 222)..... 159 4-18 Indicator loadings, critical ratios, cro nbachs alpha, construct reliability, average variance extracted for the perceived value fo r the cost using second half data (n = 222)..................................................................................................................................160 4-19 Indicator loadings, critical ratios, cro nbachs alpha, construct reliability, average variance extracted for the behavioral intentions using second half data (n = 222).......... 161 4-20 Maximum likelihood standardized loadings ( ), c ritical ratios (cr), standard errors (se), and t-values for the hypothesized stru ctural model using second half data ( n = 222)..................................................................................................................................162

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10 LIST OF FIGURES Figure page 1-1 Conceptual framework of market demand, ga me support programs, perceived value, and behavioral intentions................................................................................................... 29 1-2 Six dimensions of market demand..................................................................................... 30 1-3 Four dimensions of game support programs...................................................................... 31 1-4 Uni-dimension of perceived value..................................................................................... 32 1-5 Two dimensions of behavioral intentions..........................................................................33 1-6 Proposed structural relationships amo ng m arket demand, game support, perceived value for the cost, and behavioral intentions..................................................................... 34 4-1 First-order confirmatory factor analysis for market demand........................................... 135 4-2 First-order confirmatory factor analysis for gam e support programs.............................. 136 4-3 First-order confirmatory factor anal ysis for perceived value for the cost .......................137 4-4 First-Order confirmatory factor analysis for behavioral intentions .................................138 4-5 Tested structural model.................................................................................................... 139 4-6 Tested structural model.................................................................................................... 140

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11 LIST OF TERMS Affect Psychological orientation th at refers to the experience of feeling. Behavioral Intentions Behavi oral intentions as indica tions of an individuals willingness toward a given task (Ajzen, 2005). Cognition Psychological orientation that refers to the knowing, thought, remembering, and reasoning (Gerrig & Zimbardo, 2002). Emotional Response A complex psychological patt ern of changes, including physiological arousal, feelings created in response to a situation perceived to be pe rsonally significant (Gerrig & Zimbardo, 2002). Game Amenities Entertainment and promotional activities provided by a team during an event. Game Support Programs Controllable service attributes that are related to game operation programs such as ticket services, stadium services, game amenities, and accessibility to a stadium, all of which to support the enjoyment of a game (Zhang et al., 1998a). Market Demand Sport consumers expectations towards the main attributes of the game itself (Zhang et al., 1995). Mediation Indirect effect of an indepe ndent variable on a dependent variable that passes through a mediator va riable (Edwards & Lambert, 2007, p. 1). Perceived Value The consumer s overall assessment of th e utility of a product based on perceptions of what is received and what is given (Zeithaml, 1988, p. 14). Service Quality A form of attitude that results from the comparison of prior expectations with performance (Cronin & Taylor, 1992, p. 56). Word-of-Mouth An informal way of passing information by verbal means.

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12 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy IMPACT OF MARKET DEMAND AND GAME SUPPORT PROGRAMS ON CONSUMPTION LEVELS OF PROFESSI ONAL TEAM SPORT SPECTATORS AS MEDIATED BY PERCEIVED VALUE By Kun-wung Byon August 2008 Chair: James J. Zhang Major: Health and Human Performance The purpose of this study was to examine the structural relationship of market demand variables and game support progra ms to the consumption of professional team sport games while taking into consideration the mediating influenc e of perceived value. This study simultaneously incorporated market demand (cor e service) and game support (per ipheral service) factors into one study and examined their direct and indirect relati onships with game consumption behaviors. A questionnaire that measured market demand of professional team sport games, game support programs, perceived value, consumption intent ions, and sociodemographics was responded by a total of 453 research participan ts at various metropolitan areas and locations, following a community intercept sampling approach (Brenne r, 1996). The data set was randomly split into two halves: one for exploratory fa ctor analyses and the other for confirmatory factor analyses and tests of structural relationships among these sets of variables. As a result of the factor analyses, five factors were confirmed for th e market demand variable s including Home Team, Opposing Team, Game Promotion, Economic C onsideration, and Schedule Convenience. A three-factor model of game support programs was generated that consisted of Game Amenities, Ticket Service, and Venue Quality. Furthermor e, a unidimensional model was derived for the

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13 perceived value (i.e., Perceived Value for the Cost) and consumption in tentions (Behavioral Intentions) sections from th e factor analyses, respectivel y. All measures displayed good psychometric properties in terms of validity and reli ability. In the structural relationship analyses, Home Team, Opposing Team, Game Promotion, Ga me Amenities, and Perceived Value for the Cost were found to be significantly related to Be havioral Intentions for professional team sport games. Venue Quality was the only factor that wa s found to have an indirect relationship with Behavioral Intentions through Perceived Value for the Cost. The findings of this study revealed the importance for professional sport teams to build a strong and high-quality home team, highlight the merits and competitiveness of both home and opposing teams in their game promotions, adopt multiple means of marketi ng campaigns, formulate exciting entertainment elements for pre-game, during-game, and post-game shows, and price game tickets in a reasonable manner to ensure consumer affordability.

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14 CHAPTER 1 INTRODUCTION According to Shank (2005), gam e attendance is the most traditional and important form of sport consumption behavior in spectator sport, wh ich is defined as any li ve sport event that is played in front of spectators. Examples of spect ator sport include, but ar e not limited to, baseball, basketball, football, and ice hocke y. Spectator sport is distinguishe d from participant sport in two aspects. First, the main activity and motivation for spectators in a sport event lie in watching a sport competition; whereas, the main activity a nd motivation for a participant sport is the actual playing of the sport. Second, spectator sport requires some type of confined facilities (e.g., stadium, arena, gym, or field), where spectat ors can watch the athletic performance and competition. Nevertheless, a chaste participant sport such as hunting does not necessarily require any venue for paid spectators (Shank, 2005). Chelladurai (1999) classified the sport indus try into three segments: (a) sport economic activities, (b) spectator sports, and (c) participan t sports. This researcher noted that spectator sport had been the fastest growing segment within the sport industry and further estimated that this sector alone was a $50 billion industry in terms of annual busines s transactions. Other researchers have also recognized the continued growth of spect ator sport in North America by pointing out that spectator sport has become an increasingly importa nt type of leisure behaviors of Americans (Ross & James, 2006; Trail, Anderson, & Fink, 2005) The rapid and vast growth of professional sport teams in North America is also evidence of the immense interest in spectator sports. According to Frank (2000), 67% of the U.S. population referred to themselves as fans of the National Football League (NFL), 62% of the U.S. population indicated that they were rooting for Major League Baseball (MLB), and 54% reported that they were National Basketball Association (NBA) fa ns. Masteralexis, Barr, and Hums (2008) indicated that as of

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15 2007, a total of 149 franchise teams belong to the five major prof essional sport leagues: MLB, NFL, NBA, the National Hockey League (NHL), and Major League Soccer (MLS). This figure does not include teams in less-prominent professi onal sport leagues such as the Arena Football League (AFL), Womens National Basketball As sociation (WNBA), and many other major and minor league teams. The augmentation of spect ator sports has been also confirmed through attendance and media viewership rates. In the year of 2003-2004, approximately 476 million people attended spectator sport events in Nort h America. In 2003, NFL games were played in front of more than 17 million fans that attended games at 95% of stadium capacity on average. A similar trend has been observed in internati onal competitions as well; for example, the 2002 World Cup soccer tournament in Korea and Japa n was televised to over 200 countries during 30 days of competition, drawing approximately 28.2 billion cumulative viewers (Hyundai Economic Institute, 2002). Brandt (2004) reported th at approximately 137 million television viewers watched the 2004 Super Bowl. The same phenomenon is also true in Division I mens basketball and football, which are considered to be the two main revenue producers for collegiate athletic departments (Fulks, 2003). In 2005, nearly 1.3 m illion people watched March Madness college mens basketball games online (Rein, Kotler, & Sh ields, 2006). According to Fulks, the average percentages that Division-I mens basketball and football contributed to the income of Division I athletic programs in 2003 were 70% ($13 m illion) and 23% ($4.3 million), respectively. The increasing popularity of spectator sport has led to the establishments of new leagues, teams, and multimedia outlets, which has not only pr ovided more spectating options for sport consumers but also created greater competitions among various leagues and teams for consumer s choice. Due to a crowded sport marketplace, sport consumers now have many options with which to spend their time and discretionary dollars As a result, professional sport organizations

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16 have faced increasing competitions for gaining market share. Mullin, Hardy, and Sutton (2007) stated that competition for sport dollar is growing at the pace of a full-court press (p. 7) to describe the intensity of the competitive sport marketplace. A recent ESPN sports poll asked a sample of residents in North America if they were still considered themselves a fan of the sports of which they had originally be come a fan. The poll indicated that eight out of 10 professional sports were losing their fans drastically. The two sports that gained positive scores were auto racing and golf (Mullin et al., 2007 ). According to Rein et al. (2006), there are five potential reasons for the growing challenges of attracting and retaining sport cons umers within the sport industry. First, there are too many sport-related product options avai lable to sport consumers. As indicated above, there are numerous professional sports, intercolle giate sports, interscholastic sports, and even youth sport events being held on a regular basis across the United States. The second reason is due to constrai ned leisure time for people in America. Today, Americans spend on average 19 hours per week for leisure activiti es in 2004 compared to its 26 hours in 1973. The third reason is due to the expensive cost of beco ming a sport fan. For instance, in order to attend a professional sport event, a fa mily of four people would typi cally spends $164 for a MLB game, $247 for a NHL game, $263 for a NBA game, and $330 for a NFL game. The fourth reason is due to the proliferation of incr eased media outlets. In addition to traditional media such as television and radio, the Internet has become a mass medium. In 2005, more than two thirds of all Americans were able to access the Internet at home. Moreover, increasing availability of satellite and cable television allows sport cons umers to enjoy watching major sport events at home or sports bars and restaurants. Mullin et al. (2007) stated that ESPN capitalized on this niche programming by offering not hing but sports 24 hours a day (p. 370). Lastly, peoples discretionary money is increasingly spent in recreational activities (e.g., bowling, skating, and

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17 golf) and other entertainment activities such as sport video games, movies and concerts rather than professional sport events (Shank, 2005). Th is notion has also been confirmed by an empirical study (Zhang et al., 1997b), which found substitute forms of other entertainment businesses (e.g., movies, concerts, recreational activ ities, television, restaurants, and night clubs) had considerable negative influences on game attendance at minor league hockey games. As market competitions are becoming more intensified in professional sports, it is important for both academicians and practitioners to understand game consumption related variables so as to improve the quality of product offering and to enhance competitiveness of sport product(s) and services. Pr evious studies examining game consumptions related variables have often been conducted from the following two perspectives: market demand (Zhang, Lam, & Connaughton, 2003a; Zhang, Pease, Hui, & Thomas, 1995) and game support programs (Zhang, Lam, Connaughton, Bennett, & Smith, 2005a; Zhang et al., 2004a; Zhang, Smith, Pease, & Lam, 1998a). In previous studies, some researcher s captured these two concepts under a general concept of sport serv ice quality (Greenwell, Fink, & Pastore, 2002; Zhang, Connaughton, & Vaughn, 2004b). With this collective approach, vari ables directly related to athlete/team performance are termed as core service (Mullin et al., 2007) and variab les related to event operations and game promotions are referred as peripheral service (Van Leeuwen, Quick, & Daniel, 2002). Another approach to study game consumption re lated variables has separated variables of game support programs from those variables prim arily related to athlete/team performance (Zhang et al., 1995, 1998a). Although the two approaches are not drastically different, the disparity is that the collective appr oach tends to solely rely on service quality theories as the theoretical framework to exam ine all game consumption related variables. Conversely, the separated approach adopts different theoretical c oncepts to study market demand

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18 variables and game support programs. This a pproach focuses on in-depth and systematic analyses of specific team performance and ga me operation variables fo r the purpose of guiding the development of meticulous marketing and pr omotion strategies (Zhang et al., 2003a). According to Mullin et al. (2007) and Zhang et al. (1995), the core product in spectator sport is the game itself. Following an extensive literature review on f actors influencing game attendance variables, Schofield (1983) proposed four market demand categories including demographic variables, economic variables, ga me attractiveness, a nd residual preference. Greenstein and Marcum (1981) and Jones (1984) focused their studies on game production functions and found that team performance va riables, such as winning/losing record and presence of star player, were related to game attendance. Synthesizing key game demand variables and production functions, Zhang et al (1995) proposed the systematic concept of market demand, which was defined as the spectators expectations towards the main attributes of the core product (i.e., game itself). Braunstein, Zhang, Trail, and Gibson (2005) further explained that market demand was a set of essential construc ts associated with the game that a sport team could offer to its existing and prospective co nsumers. Unlike other bus iness merchandise, the core product of sport games is unique in that team marketers and management personnel can hardly control the core product once a sport teams roster is finalized. A theoretical justification for the market demand can be partially attributed to the Theory of Reasoned Action proposed by Fishbein and Ajze n (1975). This theory postulated that human behavior was a direct consequence of behavioral intentions, which were functions of attitude and subjective norm. Several researchers have found th at attitude construct was found to have more explanatory power in accounting for behavioral intentions when compared to that of subjective norm (Stutzman & Green, 1982; Warshaw, Calant one, & Joyce, 1986). Other researchers have

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19 also indicated that a strong att itude toward a certain object or phenomenon could act as powerful heuristics that positively direct consumer be havior (Fazio, Powell, & Williams, 1989). When a sport consumer holds a positive attitude toward the attributes of game product such as home team/athlete performance, and/or game schedule, the positive attitude te nds to be transformed into attendance and re-attendance behaviors. Numerous studies on sport market demand have been conducted to examine the predictability of game attendance (Zha ng et al., 1995, 2003a, 2004a) and fan satisfaction (Greenwell et al., 2002; Madrigal, 1995). Involving a sample of spectators of NBA regular season games, Zhang et al. (1995) found that four factors (home team, opposing team, game promotion, and schedule convenience) were relate d to game attendance. Zhang et al. (2003a) conducted a study to examine the general mark et demand variables a ssociated with the consumption of professional sport events. Game attractiveness and economic consideration factors were found to be predictiv e of the general consumption of professional sport games. In a study examining game consumption of a NFL ex pansion team, Zhang et al. (2004a) found that game attractiveness, economic consideration, and game promotion factors were positively related to game consumption. Madrigal (1995) found that through affective reactions such as Basking in Reflected Glory (BIRG) and enjoyment, the qual ity of opponent had a positive relationship with consumer satisfaction of the game. Likewise, in a study conducted by Gr eenwell et al. (2002), home team and opposing team were found to exer t positive influence on a spectators overall satisfaction of game attendance experience. Zhang et al. (1998a) defined game support progra ms as controllable service attributes that are related to game operations, such as ticket services, stadium services, game amenities, and facility accessibility, all of which are to support the provision and enjoyment of a spectator event.

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20 The quality of these event operation activities can usually be controlled by team management and marketers before, during, and after the event. Zhang et al. (1998a, 2004c) indicated that the game support programs often affect the cons umption behavior of spectators. During game operations, focusing on these controllable variable s is apparently more important for the team management in order to enhance the game experi ence of spectators (Mulli n et al., 2007; Murray & Howat, 2002). Studying the quality of game support program s have usually followed various service quality related theories, such as Grnroos (198 4) two-component theory of service quality and Bagozzis (1992) appraisal-em otional response-coping framew ork. Grnroos (1984) proposed the Nordic model, which was a service quality model that consisted of two components: technical quality and functional quality. Technical quality was related to the outcomes of the service, reflecting the tangible aspects of servic e. Functional quality wa s related to intangible aspects, such as consumers perceptions of the delivery proc ess. Bagozzis (1992) appraisalemotional response-coping framework suggested that preliminary appraisal in the evaluation of service quality lead directly to positive consumer behavior. The m odel posits that the relationship between appraisal and behavior can also be me diated by emotional response derived from the initial appraisal. When a sport consumer is satisfi ed with service encounte rs as he/she attends a sport event, the positive evaluation te nds to drive future attendance. Despite the recognized importance of game support programs, only a small number of studies have focused on these variables (Gr eenwell et al., 2002; Wakefield & Blodgett, 1996; Zhang et al., 1998a, 2004b, 2004c). Zhang et al. (1998a) conducted a study to examine the influence of game support programs on game attendance of minor league hockey games. The researchers found that game amenities and ti cket service factors were significantly ( p < .05)

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21 related to game attendance. Zhang et al. (2004b) examined the predictability of game support programs of NBA regular season games on game atte ndance. The results of this study indicated that game amenities, arena accessibility, audio visual, and ticket services were positively predictive of game attendance. To investigate the influence of special programs and services for NBA season ticket holders, Zhang et al. (2004c) studied those programs and services designed for offering added values to season ticket hold ers. The researchers f ound that those special programs and services were effective in retain ing professional sport c onsumers of the highest ticket/consumption levels. Wakefield and Blodgett (1996), in a study that examined the influence of sportscape (stadium quality) on fan attendance intention, found that all of the game operation variables had positive relationship with repatrona ge intention and customer retention. Greenwell et al. (2002) supported Wakefiel d and Blodgetts notion by findi ng that the perceptions of stadium quality factors significantly predicted spectators overall satisfaction of minor league hockey games. A number of limitations have been identified in previous studies rela ted to market demand and game support programs. First, studies adop ting the collective approach tended to examine market demand and game support program variables in a general and superficial manner. Only a small segment of variables were included in these studies and the included variables were usually a part of a larger study that attempted to exam ine many, if not all, va riables related to the marketing of sport events. Although the findings of these studies have provided insights on the importance of studying market demand variables and game support programs, the studies were partial, non-systematic, and overa ll superficial. Specific marketing implications can hardly be drawn from these studies. Second, although studies adopting the separated approach were more systematic and in-depth, and provi ded specific information on team formation, team performance,

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22 and game operations, variables related to the core product and the game support elements were rarely examined simultaneously. Consequently, sport marketers decisions tend to be made on either provision of core product or game support programs, rarely both. Third, previous studies overlooked the potential influence of other socio-psychological va riables, such as perceived value of game product, when studying the relati onship between game product-related marketing variables and game consumption (Murray & Howat, 2002). In recent years, a great number of studies have been conducted to examine spec tator consumption behavior from such sociopsychological perspectives as fan motiva tion (Funk, Mahony, Nakazawa, & Hirakawa, 2001; Pease & Zhang, 2001; Trail & James, 2001; Wan n, 1995) and team identification (Heere & James, 2007; Trail, Fink, & Anderson, 2003; Wann & Branscombe, 1993; Wann & Pierce, 2003). Although market demand variables, game support programs, sociopsychological variables, and sociodemographic variables have been found to e xplain about 50% variances collectively (Zhang et al., 2007), a significant portion of game consumption variance remains unexplained. Researchers have attempted to identify a dditional variables with explanatory power on game consumption behavior, partic ularly those that may interact with market demand variables, game support programs, and spectator motivatio n variables. Perceived value (Kwon, Trail, & James, 2007; Murray & Howat, 2002) is one set of those variables that have been identified as a salient variable for spectator consumption beha vior. Zeithaml (1988) defined perceived value as consumers overall assessment of the utility of a product (or service) based on perceptions of what is received (quality and be nefit) and what is given (perce ived value for the cost and nonmonetary price). Netemeyer et al. highlighted that perceived valu e for the cost was considered a cornerstone of the most consumer-based-brand-equity frameworks (p. 211). Perceived value has been found to be one of the most important vari ables in predicting consumption behavior (Bolton

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23 & Drew, 1991; Chang & Wildt, 1994; Dodds, M onroe, & Grewal, 1991; Zeithaml, 1988). Bolton and Drew (1991) even indicated that perceived value is a rich er measure of a customers psychological evaluation than perception of se rvice quality. These resear chers suggested that perceived value plays a key role in connecting the perceived service quality with behavioral intentions. A number of studies have been conducted to examine the influence of perceived value on consumption behavior in the general marke ting and consumer research (Chang & Wildt, 1994; Parasuraman & Grewal, 2000). Often, perceived value was identified as a mediator in the relationship between service quality and behavioral inten tions (Oh, 1999; Zeithaml, 1988). Chang and Wildt found a hierarch ical relationship among perceive d price, perceived service quality, perceived value, and pur chase intentions. Perceived value was found to be a direct antecedent of purchase intentions. Parasuraman and Grewal supported the hierarchical relationship by finding that perceive d service quality directly influe nced perceived service value, which in turn affected customer loyalty. In an experimental study, Dodds et al. (1991) further confirmed the hierarchical relationships among service quality, perceived value, and purchase intentions, indicating that perceived value wa s positively related to the willingness to buy. Overall, considerable evidence s upports the important ro le of perceived value as an intervening factor in the relationship be tween service quality and consum ption behavior (Cronin, Brady, Brand, Hightower, & Shemwell, 1997). Despite the highly recognized importance of perceived value on c onsumption behavior, little research attention has been devoted to ex amining the effect of perceived value on sport consumption (Kwon et al., 2007; Murray & Ho wat, 2002). Murray and Howat were among the first researchers to examine the effect of perceived value on future consumptive intentions for a leisure center. The result of a path analysis re vealed that the perceived value had a direct

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24 relationship with future intentions as well as i ndirect relationship with the future intentions through satisfaction. Recently, Kwon et al. (2007) examined the role of perceived value on purchase intentions of team-licensed merchandi se and found that perceived value played a mediating role in the relationship between team identification and purchase intentions. These two studies provided empirical support for including perceived value variables when studying sport consumption behavior. Similarly, Tsuji, Bennett and Zhang (2007) highlighted the need for investigating the effect of perceived value wh en examining relationship between service quality and behavioral intentions as we ll as indirect relationship with the future in tentions through satisfaction. Statement of Problem Kotler and Ar mstrong (1996) indicated that the cost for retaining existing customers is generally five times lower than attracting prospectiv e customers. One area that is in great need of retaining spectators is pr ofessional sport teams, as teams have been losing their fans drastically because the marketplace has become very competit ive (Mullin et al., 2007; Rein et al., 2006). It is imperative for team management and marketers to identify those variables that directly and indirectly affect game consumption (Han sen & Gauthier, 1989; Zhang et al., 1995). Understanding what makes spectators decide to retu rn to the game, and how they refer the game product and service received to others such as family members, friends, and community constituents is important for teams to bette r understand spectator consumption behavior and accordingly formulate an effective marketing mix (i.e., product, price, place, and promotion). Findings of previous studies revealed th at market demand variables and game support programs were salient variables in explaining sport spectator cons umption behavior (Kwon et al., 2007; Murray & Howat, 2002; Wakefield & Bl odgett, 1996; Zhang et al., 1995, 1998a, 2004b). However, these two concepts have usually been studied independently (Cronin & Taylor, 1992;

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25 Ko & Pastore, 2005; Parasuraman, Zeithaml, & Berry, 1998; Wakefield & Sloan, 1995; Zhang et al., 1995, 2004c). Although previous researchers recognized the importance of market demand variables and game support programs when marketing professional sport games, only a small number of studies have examined both sets of variables simultaneously (Greenwell et al., 2002; Tsuji et al., 2007; Zhang et al., 2004c). Of t hose studies containing both concepts, oversimplicity was a major issue. Previous studies te nded to adopt general measures derived from consumer satisfaction studies in the context of main stream bus iness, failing to take into consideration special characteri stics of professional sport even ts. In fact, context-specific measures have been recommended (Carman, 1990). It is critical for a research investigation to incorporate the uniqueness and sp ecial characteristics of the co re product, product extensions, and market environment (Mullen et al., 2007; Zhang et al., 2003b). A dditionally, previous studies have revealed that only a small portion of game attendance variance (i.e., less than 50%) were explained by market demand variables and game support programs although their importance were undoubtedly confirmed by numerous researchers (Greenwell et al., 2002; Tsuji et al., 2007; Wakefield & Bl odgett, 1996; Zhang et al., 1995, 1998a, 2004b). Low variance explanation may be due to the ove rlook of the potential influence of some mediating variables, such as perceived value, on the relationship between sport production and game consumption. McDougall and Levesque (2000) provided a good explanation on the need to study perceived value as an intermediate concept when conducting consumer behavior studies: Consider the situation where customers may be satisfied with what was delivered and how the service quality was delivered, but may not have felt they got their moneys worth. If perceived value is a driver of intention a nd the managers exclude this measure in their

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26 model, they would attempt to improve in tention through improvements in only service quality. The results of these ta ctics would have a minimal effect on intentions (p. 395). Therefore, studying game product variables and pe rceived value simultaneously is critical to gaining a more comprehensive understanding of what influences spectators to repatronage the game and how they conduct word-of-mouth promotions From an analytical perspective, Bagozzi (1980) argued that one reason for model misspeci fication in marketing research is due to omitting important variables from the model. To fill the void, the purpose of the study was to examine the structural relationship of market demand variables and game support programs to the consumption of professional team sport games while taking into consideration the mediating influence of perceived value. Hypothesized Research Model Deducted f rom a comprehensive review of literature, this study examined the hierarchical relationship among market demand, game support, pe rceived value, and behavioral intentions. This conceptual model is illustrated in Figure 1. As in a number of previous studies, market demand, game support programs, and behavioral intentions were conceptualized as multidimensional measures. More specifically, the co ncept of market demand was represented by six factors: Home Team, Opposing Team, Love of Professional Team Sport, Economic Consideration, Game Promotion, and Schedule Co nvenience (Braunstein et al., 2005; Greenstein & Marcum, 1981; Jones, 1984; Schofield, 1983 ; Zhang et al., 1995, 2003a). Game support programs consisted of four factors: Ticket Se rvice, Game Amenities, Stadium Service, and Stadium Accessibility (Greenwell et al., 2002; Wakefield & Blodgett, 1996; Zhang et al., 1998a, 2004b, 2004c). The concept of perceived value was represented by a unidimensional factor, Perceived Value for the Cost, as suggested by previous researchers (Kwon et al., 2007; McDougall &

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27 Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). While acknowledging its multidimensional aspects, previous studies (i.e., Kwon et al., 2007; McDougall & Levesque, 2000; Murray & Howat, 2002; Netemeyer et al ., 2004) have consiste ntly found that the utilitarian aspect, namely Perceived Value for th e Cost, was the most relevant perceived value factor that affected sport consumption behavior (Kwon et al., 2007). Furthermore, Netemeyer et al. (2004) argued that Perceived Value for th e Cost was overall the best candidate for representing global perceived value measure in consumer be havior research. Kwon et al. supported Netemeyer et al.s notion by emphasizing that a sport consumer tended to weigh the cost versus the benefit (i.e., Perceived Value for the Cost) to determine perceived value of teamlicensed product. Thus, to be consistent with the empirical evidence, the current study adopted the unidimensional aspect (i.e., Perceived Valu e for the Cost) to measure perceived value. Behavioral intentions were in itially composed of two factor s: Repatronage Intentions and Recommend to Others, using Sderlund (2006) and Zeithaml, Berry, and Parasuramans (1996) behavioral intentions scales. Th e importance and relevan ce of repatronage have been stressed by numerous scholars (Kotler & Armstrong, 1996; Mullin et al., 2007; Rein et al., 2006). Zeithaml et al. (2006) stated that among the most important generic behavi oral intentions is willingness to recommend the service to others an d repurchase intent (p. 149). All measurement models are presented in Fi gures 1-2, 1-3, 1-4, and 1-5, respectively. By following the conceptual model in Figure 1-1 and related research findings of previous studies, a structural model was proposed in the context of professional team sports, where market demand factors (core service) and game s upport factors (peripheral service) were hypothesized to directly influence behavioral intention factors. The mark et demand and game support factors were also hypothesized to indirectly affect behavioral intention factors through the perceived value factor

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28 (i.e., Perceived Value for the Cost). The struct ural model is presente d in Figure 1-6, where market demand and game support factors were allowed to be correlated. Specifically, the following hypotheses were tested in this study: Hypothesis 1 : Home Team would have a direct in fluence on the behavioral intention factors. Hypothesis 2 : Opposing Team would have a direct influence on the behavioral intention factors. Hypothesis 3 : Love of Professional Sport would have a direct influence on the behavioral intention factors. Hypothesis 4 : Economic Consideration would have a direct influence on the behavioral intention factors. Hypothesis 5 : Game Promotion would have a direct influence on the behavioral intention factors. Hypothesis 6 : Schedule Convenience would have a direct influence on the behavioral intention factors. Hypothesis 7 : Game Amenities would have a direct influence on the behavioral intention factors. Hypothesis 8 : Ticket Service would have a direct influence on the behavioral intention factors. Hypothesis 9 : Stadium Service would have a direct influence on the behavioral intention factors. Hypothesis 10: Stadium Accessibility would have a direct influence on the behavioral intention factors. Hypothesis 11: Perceived Value for the Cost would have a direct influence on the behavioral intention factors. Hypothesis 12: Market demand factors would have an indirect influence on the behavioral intention factors through Percei ved Value for the Cost. Hypothesis 13: Game support factors would have an indirect influence on the behavioral intention factors through Percei ved Value for the Cost.

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29 Figure 1-1. Conceptual framework of mark et demand, game support programs, perceive d value, and behavi oral intentions Behavioral Intentions Perceived Value Game Support Programs Market Demand

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30 Figure 1-2. Six dimensions of market demand Opposing Team Market Demand Economic Consideration Love of Professional Sport Schedule Convenience Home Team Game Promotion

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31 Figure 1-3. Four dimensions of game support programs Ticket Service Stadium Accessibility Game Amenities Game Support Programs Stadium Service

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32 Figure 1-4. Uni-dimension of perceived value Perceived Value for the Cost Perceived Value for the Cost 1 Perceived Value for the Cost 2 Perceived Value for the Cost 3 Perceived Value for the Cost 4 Perceived Value for the Cost 5 E1 E2 E3 E4 E5

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33 Figure 1-5. Two dimensions of behavioral intentions Repatronage Intentions Behavioral Intentions Recommend To Others

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34 Figure 1-6. Proposed structural re lationships among market demand, game support, perceived value for the cost, and behavioral intentions Perceived Value for the Cos t Opposing Team Love of Sport Economic Consideration Home Team Game Promotion Stadium Accessibility Stadium Service Ticket Service Repurchase Intentions Schedule Convenience Game Amenities Recommend to Others

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35 Significance of the Study As m arket competitions increase in profe ssional team sports, team management and marketers need to develop stra tegic marketing plans that are based on in-depth understanding of consumers. It is critical for them to identify those variables contributi ng to sport consumption and how they function together to accomplish a teams marketi ng objectives. In recent studies, market demand, game support, and perceived valu e factors have been found to have significant effects on customer s repatronage intentions and referral behaviors (Kwon et al., 2007; Murray & Howat, 2002; Wakefield & Sloan, 1995, Zhang et al., 1995, 2004c). However, these concepts have primarily been examined fragmentarily, ma king the practical implications partial and of limited usage (Cronin & Taylor, 1992; Parasuraman et al., 1998; Wakefield & Sloan, 1995). This study incorporated all of th ese sets of constructs altogether an d in the meantime; their interactive relationships were also exam ined. By taking into consider ations the unique aspects of professional team sports and the multidimensiona lity of these concepts, it was expected that research findings would have a greater applicability to the mark eting of professional team sport events. Many researchers suggested that greater insight would be achieved for practitioners from utilizing the multidimensional co nstructs (Greenwell et al., 2002; Parasuraman et al., 1988, Zhang et al., 1995). It was anticipated that the research findings would fill the void in the literature by building linkages from market demand and game support to perceived value, and then to behavioral intentions. When these relations hips were found to exist, they would serve as a foundation for researchers to establish a hierarchical theory that supports the notion that successful game product and high service quality o ffered by a professional sport team enhances perceived value, and eventually leads to repeating consumption behaviors of sport consumers. Gaining an in-

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36 depth understanding of the relationships among th ese constructs would also enable team management to identify specific constructs that have the most impact on spectator consumption behaviors and thus to formulat e and implement plans to adjust and improve team formation, game tactics, event operations, and promotional st rategies. The current st udy was initiated based on a premise that the main goal of sport organiza tions is to offer quality game product and high service quality to satisfy consumers experiences This provision would help sport consumers to form a positive perceived value of the game pr oducts and services in order to enhance the probability that those sport consumers would enga ge in repatronage and recommendation of the game products and services. Delimitations The study was com pleted within the following delimitations: Research participants were those who attend ed a professional team sporting event within the past 12 months of the time that the surv ey was conducted and had purchased the game ticket. Research participants were those who resi ded in southeastern states in U.S. Research participants involved men and women over the age of 18. The study was conducted via a paper-and-pencil questionnaire. Research participation in the study was voluntary. Data were collected in the summer of 2008. Limitations The f ollowing limitations are recognized by the researcher, which might have affected the internal and external validity of the study: Although all research participants were as ked to respond to the questionnaires with sincerity and honesty, their actual level of c ooperativeness could not be fully controlled by the researcher.

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37 The generalizability of the st udy findings might be limited to only two southeastern states (i.e., Florida and Georgia) in U.S. Voluntary participation, instead of a random sel ection of research participants, may affect the generalizability of the research findings. Although sample size of the current study wa s adequate for SEM (Wetson & Gore, 2006), factor structures and causal relationships derived were not cross-validated by additional independent sample.

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38 CHAPTER 2 LITERATURE REVIEW Sport Spectator Consumption Consum ers behavioral loyalty is often s hown via product/service consumption (Baker & Crompton, 2000), having a direct impact on an orga nizations financial prof itability (Zeithaml et al., 1996). Sport consumption behavior is not an exception. Broadly speaking, two forms of sport spectator consumption have been identified: active and passive sport consumption. Active sport spectator consumption takes the form of ga me attendance (Zhang et al., 1995, 1997b) and the purchasing of licensed merchandise products ( Kwon et al., 2007). On the other hand, passive sport spectator consumption refers to consum ption activities through mo des of various media such as game watching, game listening, and game reading (Fink, Trail, & Anderson, 2002; Gantz, 1981). In the following section, mo re elaboration on defining sport spectator consumption and how sport spectating has been measured will be presented. Definition of Sport Spectator Consumption In the field of sport management, there have been two views in defining a sport consumer: micro-view and macro-view. In the mi cro-view, sport consumers are divided into two categorizations: spectators and fans (Sloan, 1989; Trail, Robinson, Di ck, & Gillentine, 2003). Sloan separated the term spectator from fan by de fining spectator as an individual who is merely a game observer, whereas a fan is an individual who enthusiastically follows his/her favorite teams. In the macro-view, a sport spectator is de fined as an individual w ho attends a sport venue to watch a sport event. Therefore, the term, sport spectator, is an encompassing word that consists of sport fans as well (Funk & James, 2001). Based on the macro-view, sport spectator consumption is defined as the act of attending a sport event for the specific purpose of watching the sport event in a given venue (Parks et al., 2007 ). However, it has been argued that accurately

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39 measuring actual consumption behavior is a cha llenging task because surveys can hardly be made at the moment of purchase (Cronin, Bra dy, & Hult, 2000). As an alternative measure, researchers have used a construct of behavioral intentions (Eggert & Ulaga, 2002; Fink et al., 2002; Oh, 1999; Petrick & Backman, 2002a). Vari ous researchers have found that measuring behavioral intentions allows a highly accurate prediction of ensuing behaviors (Ajzen, 1971; Conner, Sheeran, Norman, & Armitage, 2000; Sheeran, Orbell, & Trafimow, 1999). Ajzen (2005) defined behavioral intenti ons as indications of an indi viduals willingness toward a given task. Thus, it could be said that the stronger th e intention an individual has, the more likely the individual is to perfor m the intended action. Measurement of Behavioral Intentions Although behavioral in tentions may change over time due to unforeseeable events or time intervals, in general, behavioral intentions have been regarded as an immediate antecedent of actual behavior in the fields of marketing (Cronin et al., 1997; Grew al, Monroe, & Krishnan, 1998b; Patterson & Spreng, 1997; Zeithaml, et al ., 1996), tourism and hospitality (Baker & Crompton, 2000; Lee, Yoon, & Lee, 2006; Oh, 1999; Petrick, 2003, 2004a; Petrick & Backman, 2002a), and sport management (Kwon et al., 200 7; Murray & Howat, 2002; Trail et al., 2003; Tsuji et al., 2007; Wakefield & Blodgett, 1996 ; Wakefield, Blodgett, & Sloan, 1996; Wakefield & Sloan, 1995). In marketing and consumer behavior rese arch, two forms of measuring behavioral intentions have been identified: unidimensional and multi-dimensional measurement. In terms of the unidimensional measurement, variables such as purchase intentions repurchase intentions, and/or word-of-mouth intentions, have been freque ntly used as either a multi-item or single-item measure. For instance, Cronin et al. (1997, 2000) m easured purchase intentions using three items

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40 to examine the relationships among service quality, perceived value, and purchase intentions in the context of six service i ndustries. Patterson and Spreng (1 997) adopted the unidimensional approach to measure repurchase intentions in the context of business-to-business service. In an attempt to predict golf travel ers consumption behavior, Petr ick and Backman (2002a) measured intentions to revis it using two items. The same meas urement was shown by Petrick (2003, 2004a) to predict cruise passengers repurchase behavior. Grewal, Krishnan, Baker, and Borin (1998a) measured purchase intentio ns using three items to understand how consumers in a retail store form purchase intentions toward durable goods. In sport management research, Murray and Howat (2002) measured future purchase intentions towards joining a leisure center using a single item. In an attemp t to predict behavioral intentions of an action sport even t, Tsuji et al. (2007) also used a single item measure. Kwon et al. (2007) measured purchase intentions of team licensedapparel using a unidimensional construct. Trail et al. (2003) also measured sport consumers future behavior employing a unidimensional approach that consisted of four item s. Researchers have jus tified the use of either a single-item or unidimensional measure by arguing that the method may reduce respondents fatigue as well as research cost (Oh, 1999). However, single-item or unidimensional measurement tends to lose considerable variance s from the construct bei ng examined (Churchill, 1979; Hair, Black, Babin, Anderson, & Tatham 2005). Thus, a multi-dimensional measure should be utilized whenever a construct is theo retically identified as having multi-dimensional characteristics. In the context of spectator spor t, Wakefield and Sloan (1 995) and Wakefield and Blodgett (1996) viewed behavioral intentions as a two-dimensiona l construct, measuring desire to stay and repatronage intentions. In order to examine the influence of the physical environment on customers affective responses and subsequent behavi oral intentions, Wake field et al. (1996)

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41 measured repatronage intentions and recommending to others as assessing behavioral intentions. Petrick (2004a) also used a two-dimensional mode l of behavioral intenti ons that consisted of repurchase intentions and recommending to others. In addition to repur chase intentions and recommending to others, Eggert and Ulaga ( 2002) added another dimension of behavioral intentions to their model, the search for an alternative. Based on the literature review regarding behavioral inten tions, it is suggested that behavioral intentions are a multi-dimensional construct, and the most commonly identified subdimensions are repatronage intentions and r ecommending to others. To support the above notion, Zeithaml et al. (2006) stated that among the most important generi c behavioral intentions is willingness to recommend the service to others and repurchase intent (p. 149). A study conducted by Sderlund (2006) also empirically supported each factors unidimensionality, indicating that the two factors were complementary but dis tinct. To compare aggregation and disaggregation methods for examining the beha vioral intentions construct measured by repatronage intentions and word-of-mouth inte ntions, Sderlund (2006) compared two models. The first model was an aggregated model in which the two factors were combined into one factor, and the second model was a disaggregation model in which the two factors were independent of each other. As a result of Confirmatory Factor Analysis (CFA), the author found that the twofactor model showed better model fit than the aggregated model. In addition, the two-factor model demonstrated good discriminant validity, indicating that the two factors were distinct factors. Therefore, the two dimensions (i.e., re patronage intentions and recommend to others) have been proposed as spectator behavior al intentions for the current study.

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42 Overview of the Proposed Dimensions of Spectator Behavioral Intentions Building on the view of behavioral intentions as a multi-dimensional construct (Zeithaml et al., 2006), a two-factor model of spectator behavioral inte ntions have been proposed. The two factors are Repatronage Intentions and Reco mmending to Others. In the following section, definitions, supporting empirical evidence, and jus tifications of using th e two factors will be discussed. Repatronage intentions Repatronage Intentions are defined as an indication of a consum ers desire to repurchase the product/service that the consumer once used /received (Ajzen, 2005). Repatronage intentions have to do with moving ones body in a physical sense to get in contact with a supplier (Sderlund, 2006, p. 81). This construct has been us ed as one of the common outcome variables in marketing and consumer behavior research (Zeithaml et al., 2006). Furthermore, repatronage intentions have been found to be a direct conseq uence of such variables as customer satisfaction (Eggert & Ulaga, 2002; Oh, 1999; Petrick & Back man, 2002a), perceived value (Grewal et al., 1998b; Oh, 1999; Petrick, 2003, 2004a; Petrick & Back man, 2002a), service quality (Cronin et al., 2000; Petrick, 2004b), and store image (Grewal et al., 1998a). In sport management research, Wakefield and Blodgett (1996) found that repatronage intentions were directly influenced by spectator satisfaction. In an attempt to examin e the influence of the service environment on behavioral intentions, Wakefiel d et al. (1996) also found that repatronage intentions were positively related to minor league hockey specta tors perceived service quality (cognition) and excitement (affect). Given its significant relationships with various customer variables such as service quality and perceived value, the Repatronage Intentions factor has been operationalized as a sub-dimension of spectator behavioral intentions in the current study.

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43 Recommending to others intentions Recommending to Others is refe rred to as th e degree to which a consumer recommends a service/product that they received/used to ot hers (Zeithaml et al., 2006). This interpersonal behavior has to do with communication with others (Sderlund, 2006). Along with repatronage intentions, the recommending to others factor has been found to be the most generic construct of behavioral intentions in consum er behavior research (Zeithaml et al., 2006). Various researchers have found that the recommending to others factor was a robust behavioral intention construct directly predicted by perceived value (Oh, 1999) satisfaction (Lee et al., 2006; Oh, 1999), and perceived service quality (Wakefield et al., 1996 ). Based on previous st udies, it appears as though the recommending to others factor is an important predictor of behavioral intentions. Interestingly, few researchers have conceptualized repatronage intentions as a direct antecedent of the recommending to others construct (O h, 1999; Petrick, 2004b). In general, these two constructs have not been separated to form a cau sal relationship with each other. However, Oh (1999) and Petrick (2004b) diffe rentiated between repatronage intentions and recommending to others by arguing that consumers tend to recommend a product/service after forming an intention to repurchase the product/service For the current study, the Recommend to Others factor has been conceptualized as a sub-dimension of spect ator behavioral intent ions since examining a causal relationship between two constructs (Rec ommend to Others and Re patronage Intentions) was not the purpose of this current study. The fo cal point of the current study is to measure spectator behavioral intentions as a multi-dimens ional construct as suggested by previous studies in order to assess more holistic spectator beha vioral intentions infl uenced by market demand, game support programs, and perceived value.

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44 In order to enhance sport sp ectators consumption behavior it is imperative for sport marketers to identify key influencing factors. Extant literature has reported several key antecedents of spectator behavioral intentions such as market demand (core service), game support programs (peripheral serv ice) (Tsuji et al., 2007; Zhang et al., 2004c; Zhang et al., 1995; Zhang et al., 1998a), and perceived value (Kwon et al., 2007; Murray & Howat, 2002). In the following sections, a literature review on gene ral service quality, market demand, game support programs, and perceived value as they relate to marketing, consumer research, and spectator sport will be presented. Service Quality Todays sport organizations face increasing co mpetition for gaining market share. In an empirical study, Zhang et al. ( 1997b) found that substitute form s of other entertainment had considerably negative influences on game atte ndance for minor league hockey games. Thus, retaining existing consumers rather than attrac ting new consumers seems more imperative for the financial stability of sport organizations. Indeed, research has shown that retaining consumers is approximately five times less expensive for a service business than attracting prospective consumers (Kotler & Armstrong, 1996). Therefore, it is important for sport organizations to understand the underlying causes and antecedents of variables that may influence repatronage intentions (e.g., game attendance) (Hansen & Ga uthier, 1989; Zhang et al., 1995). The perception of service quality has been identified as one of th e most salient variables th at may affect not only customer retention but also attraction in th e marketing literature (B rady & Cronin, 2001; Cronin & Taylor, 1992; Grnroos, 1984; Parasuraman, Zeithaml, & Berry, 1985; Parasuraman et al., 1988). The service quality construct has been wi dely utilized in othe r contexts including hospitality (Choi & Chu, 2001); fitn ess, leisure, and recreation services (Alexandris, Grouios,

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45 Tsorbatzoudis, & Bliatsou, 2001; Chelladurai & Chang, 2000; Hill & Green, 2000; Kim & Kim, 1995; Ko & Pastore, 2005; Lam, Zhang, & Jans en, 2005; Murray & Howat, 2002), and spectator services (Greenwell et al., 2002; Tsuji et al., 2007; Wakefield & Sloan, 1995; Wakefield et al., 1996; Zhang et al., 1998a, 2004b, 2004c, 2005b). Some of the identified consequences derived from good service quality include customer loyalty (Petrick & Backman, 2001), repatronage intentions (Wakefield et al ., 1996), word-of-mouth (Wakef ield & Boldgett, 1999), and satisfaction (McDougall, & Levesque, 2000; Tsuji et al., 2007), which in turn, help generate long-term profitability of an organization. Definition of Service Quality According to Kotler & Armstrong (1996) a serv ice is defined as any act or performance one party can offer to another th at is essentially intangible and does not result in the ownership of anything (p. 455). The above definition implie s an important distinction between a service and a product. A service deals with intangibility, which consumers cannot see or feel before the consumption stage. In addition to the distinguishing aspect of in tangibility a service is also inseparable, perishable, and variable (Bitran & Hoech, 1990; Kotler & Armstrong, 1996; Sasser, Olsen, & Wyckoff, 1978). Therefore, service qual ity can only be measured by an individuals perceptions toward a service received (Parasuram an et al., 1985), whereas tangible products can be more objectively measured based on their qual ities, such as toughness, durability, or defects (Crosby, 1979). In service marketing, the term service quality has been more frequently used than a general term service when it comes to assessin g the service from the consumers perspective (Parasuraman et al., 1988; Zeithaml et al., 1996). Based on the conf irmation-disconfirmation paradigm (Oliver, 1980), Parasuraman et al. (1988) defined service quality as the comparison of

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46 a consumers evaluation of the se rvice performance to their pre-e xpectation of the service. This definition of the gap model between expectation and perception has been widely adopted in the marketing literature (Alexandr is et al., 2001; Brown, Churchill, & Peter, 1993; Carman, 1990; McDonald, Sutton, & Milne, 1995). However, due to lack of predictive validity and measurement reliability, this gap model has been criticized (Cronin & Taylor, 1992; Buttle, 1996, Zhang et al., 2004b), and researchers have recommended using a performance-only model by viewing service quality as an attitudinal construct (Crompton & Love, 1995; Cronin & Taylor, 1992; VanDyke, Kappelmen, & Prybutok, 1997; Zeith aml et al., 1996). Empi rically, Cronin and Taylor compared the performance-only meas ure with the gap model and found that the performance-only measure was superior to all four industries to which the measurement was applied. Based on the performance-only measure, service quality is operationalized as a consumer s perceptions towards a service perf ormance received by the consumer. Significance of Examining Service Quality Theoretically, one of the m ost important reasons to examine service quality is due to its high explanatory power on outcome variables, such as purchase intentions (Petrick & Backman, 2001; Reichheld & Sasser, 1990; Tsuji et al., 2007) cost (Crosby, 1979), prof itability (Buzzell & Gale, 1987; Rust & Zahorik, 1993) customer satisfaction (B olton & Drew, 1991; Cronin & Taylor, 1992), and word-of-mouth (Petrick & Backman, 2001). The practical importance of investigating service quality lies in the fact that a high quality of service will produce a competitive edge, which will be directly related to revenue generation (Zhang et al., 1998a, 2004c). Furthermore, accurate and periodic assessment would provide management with feedback by pointing out areas in which management should improve.

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47 Service quality research also has significance in the field of sport management. According to Wakefield and Sloan (1995), study on servic e quality has been a largely undeveloped area compared to areas such as psychology (i.e., team identification and motivation) and sociodemographic variables (gender, ethnicity, inco me, and education) in spectator attendance research. Zhang et al. (2004c) argued that services in relation to a sporting event can be extended to the game support/operation programs, which ar e considered extensions of the core product (game itself). Thus, examining service quality of those game support/o peration programs would provide information for immediate attention by sport marketers. More over, the attributes relevant to game support/operation programs can be controlled and manipulated by a sport marketer, whereas the game itself cannot. Therefore, examining satisfaction toward game support/operation programs would have much practical relevance and value for game management (Baker & Crompton, 2000; Zhang et al., 2004c, 2005). Measurement of Service Quality For the pas t two decades, service quality rese arch has been guided by two theoretical perspectives: (a) the American point of view that is repr esented by the SERVQUAL scale (Parasuraman et al., 1988) and its numerous modifications (Brown et al., 1993; Carman, 1990; MacKay & Crompton, 1990; McDonald et al., 19 95; Wright, Duray, & Goodale, 1992) and (b) the European viewpoint, referred to as the Nordic model, de veloped by Grnroos (1984). Both scales were developed based upon Olivers (198 0) disconfirmation paradigm. Parasuraman and his colleagues (1985) proposed a conceptual model that included 10 factors related to service quality. The 10 factors were as follows: Relia bility, Responsiveness, Competence, Access, Courtesy, Communication, Credibility, Securit y, Understanding/Knowing the customer, and Tangibles. Later, Parasuraman et al. (1988) conduct ed two studies to empirically test the above

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48 10 factors to determine whether the dimensions were representi ng various service settings. The first study was conducted using 200 customers recr uited by a mall-intercept method. As a result, a preliminary 34-item scale was developed. To furt her validate the initial scale, the researchers collected 800 customers from four nationally known firms, including a bank, a credit-card company, an appliance repair and maintenance firm, and a long-distan ce telephone company. For each firm, 200 customers were sampled. As a re sult of alpha reliability, exploratory factor analysis (EFA), and regression, the 10-factor m odel was collapsed into a five-factor model, called SERVQUAL, which included Reliabili ty, Assurance, Tangibles, Empathy, and Responsiveness. Reliability referred to how dependably and accurately the service was performed. Assurance was defined as the courte sy, knowledge, and trust of employees. Tangibles were related to the appearance of physical f acilities and communication items. Empathy was defined as the offering of cari ng and attention to customers. Responsiveness referred to the extent to which a service firm displayed a will ingness to help and provide timely service to customers (Parasuraman et al., 1988). Numerous studies in the fields of leisure and sport management have adopted the theoretical fram ework of the SERVQUAL scale (Chelladurai & Chang, 2000; Howat, Murray, & Crilley, 1999; Kim & Kim, 1995; Lam et al., 2005; Papadimitriou & Karteroliotis, 2000). In the cont ext of fitness centers in Korea, Kim and Kim (1995) developed a scale of Quality Excellence of Sports Centers (QUESC) that included 33 items under 12 dimensions that measured percepti ons of service quality. The twelve dimensions derived from an EFA were as follows: Ambiance, Employee Attitude, Employee Reliability, Social Opportunity, Information Available, Programs Offered, Personal Considerations, Price, Privilege, Ease of Mind, Stimulation, and Conve nience. However, it was found that most of the factors showed low reliability. In an attempt to apply the QUESC scale to Greek private fitness

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49 centers, Papadimitriou and Karteroliotis (2000) conducted a study using 4 87 actual users of the fitness centers. Although the authors failed to confirm the factor structur e of the QUESC, Papadimitriou and Karteroliotis (2002) proposed a parsimonious and sound a 24-item four-factor model that consisted of Progr am Availability, Other Servi ces, Instructor Quality, and Facility/Attraction Operations. In the context of Austrian recreation centers, Howat, Absher, Crilley, and Milne (1996) developed a scale that contained 15-items under five factors, including Core Services, Staff Quality, General Facilit y, Secondary Services, and Knowledge. In an attempt to define more parsimonious dimensions Howat et al. (1999) tested the five-factor model, which was collapsed into a three-factor model that contained Core, Peripheral, and Personnel. The three-factor model has shown stable psychometric properties in other applications (Howat & Crilley, 2007; Howat et al., 2002). Based on an extensive literature review, Chelladurai and Chang (2000) developed a five-fact or model that may generally pertain to the recreation and fitness industry. The factors were: Core Service, Interaction between Employee and Client, Interaction between C lient and Client, Context, and Client Participation. While the proposed factors seem relevant to recreation and fitness industry, the co nceptual model has not yet been empirically validated. All of the above empirical studies were modeled upon the result of EFA except for Howat and Cril leys (2007), which utilized CFA. Adopting Olivers (1980) disconfirmation paradigm, Grnroos (1984) proposed a twodimensional model that included technical quality and functional quality. Technical quality was defined as the outcomes of the service, whic h reflects tangible asp ects. Grnroos (2005) elaborated that technical quality is what the customer is left with, when the service production process and its buyer-seller inte ractions are over (p. 63). Fu nctional quality was related to intangible aspects, such as the consumers pe rception as to how the service is delivered. An

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50 important aspect when defining a service is the interaction between the service provider and the customer that takes place while the service is delivered (Brady & Cronin, 2001). McDougall & Levesque (2000) used the term relational quality as they defi ned the functional quality while taking into consideration the interaction aspect of the service. Using 447 church members, the authors tested a conceptual model to examine the relative importance of service quality and perceived value on customer satisfaction, which wa s hypothesized to directly affect behavioral intentions. The results of the study indicated that core service quality, rela tional service quality, and perceived value were found to be directly related to customer satisfaction, which, in turn influenced behavioral intentions, which were meas ured by switching intentions and intentions to remain loyal. Based on the Grnroos (1984) tw o-component model, Zhang et al. (1998a) developed the Spectator Satisfactio n Inventory (SSI) that containe d 24-items under five factors, including Satisfaction with Ticket Service (STS), Satisfactio n with Audio Visuals (SAV), Satisfaction with Accessibility and Parking (SAP), Satisfaction with Arena Staff (SAS), and satisfaction with Event Amenities (SEA). As a result of a test of Cronbachs alpha coefficient and EFA, the scale showed good reliability and c onstruct validity. To the best of the authors knowledge, the SSI scale was the first instrument empirically tested for measuring spectator service quality toward game support programs. In addition, the SSI scale has been adapted to the contexts of NBA professional basketball game s (Zhang et al., 2004c) and minor league hockey games (Zhang et al., 2005b) for the purpose of further validations. In addition to these two theoretical fram eworks (i.e., SERVQUAL and Grnroos twocomponent model), Brady and Cronin (2001) identif ied two additional recen t conceptualizations of service quality, which were Rust and Olive rs (1994) three-component model and Dabholker, Thorpe, and Rentzs (1996) multilevel model. In fact, the three-component model was

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51 developed based on Grnroos (1984) two-com ponent model (Rust & O liver, 1994). Rust and Oliver proposed Service Product, Service De livery, and Service Environment as the three components of the model. The service produc t dimension was related to Grnroos (1984) technical quality, and the service delivery was in relation to th e functional quality. Although Rust and Oliver did not empirically test the threecomponent model, the models efficacy has been found in McDougall and Levesques (1994) study in which the authors found a three-factor model, including Service Outcome, Servic e Process, and Physical Environment. Dabholker et al. (1996) were the first researchers who viewed service quality as a hierarchical and multilevel concept, consisting of three levels: (a) overall perceptions of service quality, (b) primary dimensions, and (c) sub-di mensions. The authors employed three facets of qualitative measures to derive th e initial items thought to be releva nt to service quality in retail settings. As a result, the authors proposed the Retail Service Quality Scale (RSQS), which included Overall Service Quality as the third-order factor, five primary dimensions (Physical Aspects, Reliability, Personal In teraction, Problem Solving, and Policy), and six sub-dimensions (Appearance, Convenience, Promises, Doing It Right, Inspiring Confidence, and Courteous). Adopting the multilevel concept, Brady and Cr onin (2001) validated the Dabholker et al.s (1996) model using the sample drawn from four different service indust ries including fast food, photograph, amusement parks, and dry cleaning. As a result of CFA, the authors proposed the third-order model that included one third order (S ervice Quality), and three second-order factors (Interaction Quality, Physical Environmental Qu ality, and Outcome Quality), with each secondorder factor containing three firs t-order sub-dimensions. This hierarchical theory of service quality has been adapted to the field of sport management (Ko & Pastore, 2004, 2005). Utilizing the data collected from a university recreation cen ter, Ko and Pastore (2005) also tested their

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52 conceptual model consisting of one third-order factor (Service Quality) and four second-order factors (Program Quality, Inte raction Quality, Outcome Qualit y, and Physical Environmental Quality), with each second-order factor containing three sub-dimensions, except for Interaction Quality, which consisted of two sub-dimensions CFA and SEM analyses also confirmed its sound reliability and validity. In sum, three important considerations have been identified based on the literature review regarding the measurement of serv ice quality. First, there has been general consensus that service quality is multi-dimensional in nature (Bra dy & Cronin, 2001; Carman, 1990; Cronin & Taylor, 1992; Greenwell et al., 2002; Howat et al., 1999, 2002; Ko & Pastore, 2005; Lam et al., 2005; Parasuraman et al., 1988; Zhang et al., 1998a, 2004c, 2005). Second, it is suggested that core service and peripheral service quali ty be measured simultaneously in order to assess the service quality comprehensively, regard less of using any of the theo retical frameworks reviewed (Greenwell et al., 2002; Ho wat et al., 1999, 2002, 2007; McDouga ll & Levesque, 2000; Murray & Howat, 2002; Tsuji et al., 2007; Van Leeuwen et al., 2002). Finally, the attributes measuring service quality should be those rele vant to the context in which th e service is to be employed. In fact, the applicability of the SERVQUAL scal e to other contexts has been criticized by researchers (Carman, 1990; Cr onin & Taylor, 1992) even when the SERVQUAL scale was developed simply for generic use (Parasuraman et al., 1988). For in stance, Carman (1990) applied the SERVQUAL to measure service qual ity towards hospitals. The author failed to confirm the proposed five factors (Reliab ility, Assurance, Tangibles, Empathy, and Responsiveness). Instead, the res earcher derived nine factors re presenting the perceptions of service quality toward hospitals. As a result of applying the SERV QUAL scale to a retail apparel store, Gagliano and Hathcote (1994) found 19 items under four f actors: Reliability, Tangibles,

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53 Personal Attention, and Convenience. In the fiel d of sport management, numerous researchers also supported the notion of devel oping the industry-specific factors of service quality due to the different nature of service among sport organizations In fact, services in spectator sport are more likely to deal with intangibles than services in durable goods (G reenwell, et al., 2002; Lam et al., 2005; Murray & Howat, 2002; Zhang et al., 1998a, 2004c ). In the end, the original authors of the SERVQUAL scale, Parasuraman, Berry, and Zeitham l (1993) also acknowle dged that the scale should be modified to be relevant to the c ontext in which it is being examined. Overview of the Proposed Spectator Service Quality For developing a service quality scale that is pe rtinent to sp ectator sport, the current study will adopt the three criteria suggested above: (a ) service quality should be treated as multidimensional in nature, (b) core service quality and pe ripheral service quality should be measured simultaneously, and (c) attributes related to core service quality and peri pheral service quality should be context-specific. In terms of the multi-dimensional scale, a variety of scales have been developed in the field of sport management (Green well, et al., 2002; Ko & Pastore, 2005; Lam et al., 2005; McDonald et al., 1995; Murray & Howat, 2002; Tsuji et al., 2007; Wakefield et al., 1996; Zhang et al., 1998a, 2004c, 2005). With regards to measuring both core service quality and peripheral service quality simultaneously, Greenwell et al. (2002) examined the influence of the sportscape (physical sport facility) on customer satisfaction within the c ontext of minor league hockey games. In the study, service quality was divided into two dimensions: (a) core product, which was measured as quality of home team and opposing team (Zhang et al., 1995), and (b) peripheral (service personnel) which was measured by staff responsiveness, presentation, knowledge, and behavior of officials. The aut hors suggested that greater insight would be achieved for team marketers if they know the re lative influences of th e core and peripheral

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54 service quality on customer satisfaction in relation to game attendance. Recently, Tsuji et al. (2007) investigated spectators satisfaction with action sport events as a predictor of future game attendance intentions. As an antecedent to sa tisfaction, the authors operationalized service quality as core and peripheral, sugg ested by Van Leeuwen et al. (2002). The last criterion is related to developing a context-specific measurement of service quality. In spectator sport, the game its elf is considered the core product (Mullen et al., 2007; Zhang et al., 2003b), which is related to the set of attributes that may affect consumers perceptions of the quality of the game (Greenwell et al., 2002) The factors comprising the core product are generally categorized as Home Team, Opposi ng Team, Game Promotion, Love of Sport, Economic Consideration, and Schedule Convenien ce (Branustein et al., 2005; Greenstein & Marcum, 1981; Hansen & Gaut hier, 1989; Schofield, 1983; Zha ng et al., 1995). The uniqueness related to core product is that the team marketing and mana gement personnel can hardly control the core product once it has been set up for the game and season. However, Zhang et al. (2004b) described consumers expectations towards peripheral service pr oduct by stating that sport fans expect more than the core game product when th ey attend a sport event. Thus, the quality of game support programs and relational services plays an important role in maintaining and increasing spectator attendance levels (p. 100). Furthermore, Zhang et al. (1998a) argued that the peripheral service product is an extension of the core product, which team marketers and management can manipulate if necessary during the season. Factors representing the peripheral service quality are Ticket Service, Game Amenitie s, Stadium Service, and Stadium Accessibility (Zhang et al., 2005b). Unfortunately, there is no scale that incorporates aspects of both core service quality and peripheral service quality in the context of spectat or sport. While Greenwell et al.s (2002) scale

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55 seems the most comprehensive in terms of adopti ng necessary attributes that are relevant to spectator sport, two weaknesses have been identi fied. First, only two factors, home team and opponent team quality, were represented by core service quality. As suggested by previous studies (Greenstein & Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995), more aspects, such as game schedule, s hould be added when measuring core service quality. Second, their scale pertaining to peri pheral service quality focused on only physical environment aspects (e.g., layout, seating comfort, and scoreboard) and perceptions of service staff (e.g., responsiveness). The peripheral serv ice quality scale should have reflected game operation variables, such as ticket service and game amenities (Zhang et al. 1998a, 2004c, 2005). In the field of spectator sport, variables featuring core product (game itself) have been well discussed (Braunstein et al ., 2005; Greenstein & Marcum, 19 81; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995, 2003a, 2003b, 2004a ). In addition, attributes related to peripheral service, which includes aspects of game support (ope ration) programs, have also been identified (Greenwell et al., 2002; Tsuji et al., 2007; Zhang et al. 1998a, 2004c, 2005). In the following section, the concept and relevant variables representing core product serv ice quality as well as peripheral service quality in relation to spectator sport will be discussed, followed by the dimensions of service quality related to spectator s port that will be proposed for the current study. Market Demand (Core Service Quality) As Mullin et al. (2007) and Zha ng et al. (1995) n oted, the core product in spectator sport is the game itself. Following an extensive literature review on factors influe ncing game attendance variables, Schofield (1983) proposed four demand categories: Demographic Variables, Economic Variables, Game Attr activeness, and Residual Prefer ence. Greenstein and Marcum (1981) and Jones (1984) also proposed game pr oduction function that was related to team

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56 performance variables such as winning/losing reco rd and star player t hought to account for factors affecting game attendance. Synthe sizing demand categories and production function, Zhang et al. (1995) proposed a concept of mark et demand, which was defi ned as the spectators expectation towards the main attributes of the ga me itself. Furthermore, Braunstein et al. (2005) argued that market demand is a construct associated with the game that a team can offer to its existing and new consumers. In a sense, market demand variables are comprised of the attributes of core service quality. A number of studies have been conducted to develop scales that measure market demand (Braunstein et al., 2005; Zha ng et al., 1995, 2003a. 2003b, 2004a). For the purpose of examining variables that influence spectat ors game attendance of NBA games, Zhang et al. (1995) developed the Spectator Deci sion Making Inventory (SDMI) th rough data collected from six second-half NBA games ( N = 861). Initially 20 items were developed based on an extensive literature review and interviews with administrato rs. The 20 items were sent to a panel of experts for a test of content validity, which resulted in 17 items. Following an EFA, 15 items were retained that included four dimensions such as Home Team, Opposing Team, Game Promotion, and Schedule Convenience. The SD MI displayed high psychometric properties. This study was unique because it was the first st udy that developed a scale of market demand in a systematic way to examine factors affecting game attendance in the context of sport management. However, an EFA was used to validate the factor structure of the SDMI. In an attempt to re-examine the SDMIs factor structure using a CFA, Zhang et al. (2003b) co llected a total of 685 surveys from spectators of five NBA games. The initial SDMI scale included 15 items under four factors: Home Team, Opposing Team, Game Promotion, a nd Schedule Convenience. Following the CFA, items were further reduced to 13 under the same factors. The CFA revealed that the revised-

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57 SDMI displayed good psychometric properties. The resolved mode l was consistent with the theoretical dimensions proposed by previous re searchers (Greenstein & Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983). The SDMI has been adapted to a MLB spring training game (Braunstein et al., 2005). The data were split into half. The re sult of CFA on the first set of data was found to have poor psychometric properties, so it was subject to an EFA with a direct oblimin rotation, which resulted in 29 items under the eight factors, which was confirmed by another CFA using the second data set. The de veloped scale was named as Spectator Decision Making Inventory-Spring Traini ng (SDMI-ST). The identified ei ght factors were: Home Team, Opposing Team, Game Promotion, Vacation Ac tivity, Economic Consideration, Schedule Convenience, Nostalgic Sentiment, and Love of Baseball. However, some factors such as Nostalgic Sentiment and Love of Baseball showed poor loadings but were retained due to the theoretical relevance to the study context. In an attempt to apply market demand to the general setting where home and opposing teams cannot be distinguished, Zhang et al (2003a) conducted a study to examine the relationship between general market demand vari ables and sport consumption of professional sports. Using 525 subjects recruited by a co mmunity intercept method (Brenner, 1996), the authors derived a 12-item instrume nt of market demand under three factors: Game Attractiveness, Marketing Promotion, and Economic Consideratio n. The concept of general market demand has been adapted to a National F ootball League (NFL) expansion team (Zhang et al., 2004a) in which the authors developed an 18-item instrume nt of general market demand variables that loaded onto four factors (Gam e Attractiveness, Marketing Promotion, Economic Consideration, and Socializational Opportunity). As a result of an EFA and Cr onbachs alpha coefficient, the scale displayed good reliability and validity. This wa s the first examination to identify the extent

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58 to which market demand factors explain NFL e xpansion teams product consumption and team identification. As a result of the literature review on mark et demand variables, it was found that when the home team and opposing team can be differentia ted, the Game Attractiveness factor should be split into two dimensions: Home Team and Opposi ng Team (Braunstein et al., 2005; Zhang et al., 1995, 2003b). However, the Game Attractiveness f actor should remain combined when home and opposing teams cannot be distingui shed (Zhang et al., 2003a, 2004a). Proposed Dimensions of Market Demand (Core Service Quality) The Theory of Reasoned Action (Fishbein & Ajzen, 1975) as a prim ary theoretical framework and based on empirical findings of previous market dema nd studies (Braunstein et al., 2005; Zhang et al., 1995, 2003a, 2003b, 2004a), a sixfactor model of market demand (core service quality) was proposed in the current st udy. The six factors are: Home Team, Opposing Team, Game Promotion, Economic Consideratio n, Love of Professional Team Sport, and Schedule Convenience. A discussion and rationale of selecting each factor will be presented in the following section. Home team The first dimension in the proposed model, Home Team, is defined as the perceived quality of the home team that is represented by such attributes as home team performance, presence of superstar, quality of home team pl ayers, home win/loss record, home team reputation, and/or home team league standing. Previous studies found that the home team had a positive relationship with game attendan ce of NBA basketball (Zhang et al., 1995) and minor league hockey (Zhang et al., 1997a). Zhang et al. (1995) found that home team s win/loss records, league standing, presence of superstars, and home team s performance had positive relationship

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59 with NBA game attendance. Zhang, Wall, and Smith (2000) found that win/loss record was positively related to NBA season ticket holders game attendance. Bird (1982) in his football attendance study, found that league standing had a direct relati onship with game attendance. Zhang et al. (1997a), in their minor lea gue hockey study, found that home team history, reputation, league standing, the presence of star players, a nd home team quality were contributing variables to game attendance. Gi ven its significance on ga me attendance, Home Team would be an important factor representing ma rket demand as it relates to professional team sport. Opposing team The second dimension in the proposed model, Opposing Team, refers to the perceived quality of the opposing team that is featured by such variables as opposing team performance, quality of opposing team, overall quality of oppos ing team players, opposing team history and tradition, opposing team league standing, opposing team as a rivalry, and/or superstar. Madrigal (1995) found that quality of opponent was related to affective reactions (enjoyment and BIRG), both of which had a direct relationship with sp ectator satisfaction. In the context of the NHL, Jones (1984) found that the presen ce of star players was what motivated sport consumers to attend hockey games. Quality of opposing team, opposing team history, league standing, and presence of superstar were consistently found to be contributing variables to game attendance (Greenwell et al., 2002; Zhang et al., 1995, 1997a). Based on th e predictive validity of the Opposing Team factor, it should be measured as a sub-dimension of market demand as it relates to professional team sport.

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60 Love of professional team sport The third dim ension in the proposed model, Love of Professional Team Sport, is defined as the perceived quality of professional team sports. A wide range of attributes have been identified as influencing variables. Thes e may include, but are not limited to, closeness of competition, popularity of professional team sport, duration of game, high level of skills, best players in a sport, and/or speed of game (Braunstein et al., 2005; Ferreira & Arms trong, 2004; Zhang et al., 2003a). Braunstein et al. found that love of professional baseball was identified as an important factor representing SDMI-ST. Th e same finding was discovered in Zhang et al.s (2003a) general market demand associated with professional sp ort consumption study. Furthermore, the authors found that love of professional sports was positively related to game attendance and media consumption. In an attempt to ex amine attributes that influence college students game attendance, Ferreira and Armstrong (2004) found that such variables as the duration of ev ent, the popularity of sport, high level of skill displayed, and speed of game were revealed as salient attributes influencing game attendance. Given its signi ficance on game attendance, the Love of Professional Team Sport would be an important factor representing market demand as it relates to professional team sport. Economic consideration The fourth dim ension in the proposed model, Economic Consideration, is defined as an individuals perceptions towards economic variables, including ticke t price, ticket affordability, good seats, and/or ticket discounts. Previous studies have shown conflicting results regarding the impact of economic consideration on game a ttendance. Baade and Tiehen (1990), in their longitudinal study on major league baseball atte ndance, found that economic consideration was negatively related to game attendance. Simila r findings were found in Birds (1982) football

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61 study and the general professiona l sports study conducted by Hans en and Gauthier (1989). On the other hand, Zhang et al. (1995) found that tic ket discounts, good seats, and group ticket cost were positively associated with attendance of NBA game. In numerous studies, economic consideration was found to exer t a substantial influe nce on game attendance (Zhang et al., 1997a, 2003a, 2004a). Given its significant contributio n in accounting for game attendance, the Economic Consideration would be an important f actor representing market demand as it relates to professional team sport. Game promotion The fifth dim ension in the proposed model, Ga me Promotion, is defined as the specific mixture of marketing tools th at the sport organization can use for persuasion (Kotler & Armstrong, 1996). The Game Promotion factor can be represented by such attributes as advertising, direct mail and notif ication, publicity, and web inform ation. This factor should be separated from in-game entertainment amenities (Z hang et al., 2005b), which can be manipulated by team marketers on a game basis. Previous studie s indicate that the game promotion factor was positively related to game attendance in th e NBA (Zhang et al., 1995, 2000), minor league hockey (Zhang et al., 1997a), gene ral professional sports (Zha ng et al., 2003a), and an NFL expansion team (Zhang et al., 2004a). Based on th e findings of the previous studies, the Game Promotion factor should be treated as an influenc ing variable to form market demand as it relates to professional team sport. Schedule convenience The sixth dim ension in the proposed model, Schedule Convenience, is defined as the assigned time and day in which a sport game is held. Zhang et al. ( 1995) found that schedule convenience was related to only past game attendance. Hill, Madura, and Zuber (1982) found

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62 that schedule convenience was positively related to game attendance with weekend and season ending games, but not afternoon games in the MLB. Zhang (1998b) examined minor league hockey spectators preferred time for game a ttendance. The author found that spectators preferred evening times (7:00 pm) for weekday a nd Saturday games, and late afternoon times (4:00 pm) for Sunday games. This factor seems to have lesser predictive validity on game attendance compared to the two other factors of Home Team and Opposing Team. However, in various scale development studies, the Schedule Convenience factor emer ged as an important sub-dimension of market demand (Braunstein et al., 2005; Zhang et al., 1995, 2003b). Thus, this factor should be considered as a contributing factor of market demand as it relates to professional team sport. Spectator Game Support Programs (Peripheral Service Quality) Zhang et al. (1998a; 2004c) argued that the peri pheral service quality that is related to gam e support programs often affects the consump tion levels of spectators. Furthermore, it has been suggested that utilizing manipulated variab les such as the game support programs may be more important than the core pr oduct in terms of game consumption (Mullin et al., 2007; Murray & Howat, 2002). Despite the significance of game support programs on game consumption, few studies have been conducted to develop a scale that is pertinent to consumers perceptions towards the game support programs in the context of spectator sport (McDonald et al., 1995; Zhang et al., 1998a, 2004b, 2004c, 2005b). Adopting the SERVQUAL scale, McDonald et al. (1995) developed the TEAMQUALTM scale, which included 39 items under five factors, the same as the SERVQUAL scale. Although the TEAMQUALTM scale has never been empirically validated, the au thors took into consideration the nature of spectator sport and management of a sport event when developing the

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63 scale. Based on a sample of 181 spectators from three minor league hockey games, Zhang et al. (1998a) developed the Spectator Satisfaction Inventory (SSI) that measured game support programs related to peripheral services of spect ator sport. The SSI included 24 items under five factors: Satisfaction with Ticket Service (S TS), Satisfaction with Audio Visuals (SAV), Satisfaction with Accessibility and Parking (SAP), Satisfaction with Arena Staff (SAS), and Satisfaction with Event Amenities (SEA). In an attempt to apply the SSI scale to the NBA context, Zhang et al. (2004c) examined spectators satisfactio n towards game support programs offered by a professional basketball team and its relationship with game attendance. Based on Grnroos (1984) two-component model (technical and functional) of se rvice quality and the characteristics of professional basketball games, the researchers developed the Spectator Satisfaction Scale (SSS) that in cluded 18 items under four factors, including Satisfaction with Ticket Service (STS), Satisfaction with Amen ities of Game (SAG), Satisfaction with Audio Visuals (SAV), and Satisfaction with Accessibility Condition (SAC). An EFA, Cronbachs alpha, stepwise multiple regression, and Kruskal-Wa llis indicated that the SSS scale showed good measurement properties and predic tive validity (16% variances e xplained in game attendance). Utilizing a more advanced factor analysis method (CFA), Zhang et al. (2005b) developed the Scale of Game Support Programs (SGSP) to measure spectator satisfa ction associated with game operation of minor league hockey games. A preliminary scale consisting of 28 broad game support activities was developed through an extensive literature review, field observations, and interviews with administrators. Following an EFA, the data were reduced to 23 items under four factors, including six items for Satisfaction with Ticket Service (STS), six items for Satisfaction with Game Amenities (SGA), six items for Satisf action with Arena Service (SAS), and four items for Satisfaction with Arena Accessibility (SAA). Following a CFA, the items were reduced

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64 to 22, retaining the same fact ors. Furthermore, the CFA revealed that the SGSP had sound psychometric properties. This st udy was an extension of the prev ious study (Zhang et al., 1998a) that developed a game operation scale (SSI). Ho wever, two aspects were improved: (a) the study utilized a much larger sample size than 1998 study, and (b) bo th EFA and CFA applications were utilized to confirm the scales factor structure. Using Grnroos (1984) two-component m odel and Olivers (1980) expectancy disconfirmation theory as theore tical frameworks, Zhang et al. (2004b) examined the role of special programs and services perceived by NB A season-ticket holders to predict their sport consumption. A total of 350 season ticket holders answered a questionnaire that included six items measuring demographic variables, 15 item s measuring special programs and services, and eight items measuring game consumption. Following an EFA, four factors emerged in the special programs and services variables: Representative, Benefit, Opport unity, and Socialization in the Expectation and Perception dimensions. Additionall y, an EFA extracted three factors that were related to sport consumption: Ev ent Viewing, Ticket Type, and Ti cket Level. As a result of a stepwise multiple regression analysis, the author s found that apart from the Benefit factor, the special programs and services factors were f ound to have significant influences on the sport consumption factors. Because the Congruence fa ctor did not show good e xplanatory power, the authors suggested that a performance-only measur e be employed, as proposed by various authors (Crompton & Love, 1995; Cronin & Taylor, 1992). Proposed Dimensions of Spectator Game Suppo rt Programs (Peripheral Service Quality) Using the tw o component model (Grnroos, 1984) as a primary theoretical framework and specific characteristics c oncerning game support programs relate d to professional team sports (Zhang et al., 1998a, 2004a, 2005b), a four-factor model represents the Game Support Programs

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65 of professional team sport in th is study. The four factors are as follows: Ticket Service, Game Amenities, Stadium Service, and Stadium Accessibili ty. In the following section, justification for using each dimension will be discussed. Ticket service Ticket Service is defined as the various cha nnels of ticket sale se rvices, including phone order, mail order, box office, ticket personnel frie ndliness, web order procedures, convenience of ticket sale locations, and/or will call. Providing ef fective ticket services are imperative for sport organizations in order to enhance the perceptions of service quality of sport consumers. Because ticketing is necessary for all spectators to get in to the venue, the ticket office is usually the first contact place for most spectators (Mulrooney & Farmer, 1996). Previ ous studies concerning ticket service revealed a positiv e relationship with game consumption (Zhang et al., 1998a; Zhang et al., 2004a). As a result of a stepwi se multiple regression, Zhang et al. (1998a) found that ticket service was positively predictive of future game attendance of minor league hockey. In an attempt to predict NBA season-ticket holders sport consumption, Zhang et al. (2004a) examined the roles of special programs and services. The authors found that the Representative factor, which was comprised of ticket servi ce attributes, was positively related to media consumption, which included items such as watchi ng games, game attendance, and visiting team website. Contrary to the above findings, Zhang et al. (2004c) found that tic ket services were not a statistically significant predictor of NBA spect ators (Zhang et al., 2004c). However, the result may have been attributed to online ticket s hopping or ticket purchase through a nation-wide ticket distribution company, such as Ticketmast er (Zhang et al., 2004c). Thus, online ticketing should be taken into consideration in the m easurement of game support programs. Given the

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66 significance of its predictive validity on game consumption, the Ticket Service factor will be included as a sub-dimension of game support programs as related to professional team sport. Game amenities Gam e amenities are defined as entertainment and promotional activities offered during the course of a game. Music, public announcements, scoreboard, promotions, pre, half, and postgame entertainments, dance/cheerleading activities, and music selection have been identified as contributing variables of game amenities. Furthermor e, this factor has been found to be related to game consumption (Greenwell et al., 2002; Wakefield et al., 1996; Zhang et al., 1998a; Zhang et al., 2004c; Zhang et al., 2005b). Wake field et al. (1996) found that scoreboard was related to affective reaction (pleasure), which in turn, in fluenced game consumption in the context of college football and minor league baseball. Gr eenwell et al. (2002) al so supported the above findings when the result of their study revealed th at scoreboard quality was positively related to minor league hockey spectator satisfaction. Zhang et al. (1998a) found that game amenities were an important predictor of game attendance of mi nor league hockey games. The same finding was discovered in the case of NBA spectators (Zhang et al., 2004c). Zhang et al (2005), in their NBA season ticket holder study, found that in-game ente rtainment amenities were positively related to game consumption of NBA games. Based on the findings of the previous studies, the Game Amenities factor should be treated as an influe ncing variable to form game support programs as related to professional team sport. Stadium service Stadium service is defined as the physical surroundings of service encounters that spectators can experience as a part of game spectating. Variables identified are concession, cleanliness, restroom, and/or st aff courtesy. Previous studies concerning the stadium service

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67 revealed a positive relationship with game cons umption (Wakefield & Sloan, 1995; Zhang et al., 2004c). In the context of college football sp ectators, Wakefield and Sloan (1995) found that parking, food, and cleanliness were st atistically significant predictors of the desire to stay longer variable. Zhang et al. (2004c) also found that the stadium service factor had a positive relationship with game attendan ce of NBA spectators. Similar fi ndings were discovered in the context of minor league hockey (Zhang et al., 1998a). Previous findi ngs provide supporting evidence that Stadium Service could be an im portant sub-factor representing game support programs as related to prof essional team sport. Stadium accessibility Stadium Accessibility refers to the degree of convenience to stadium access. A wider range of variables have been identified as significant attributes. These may include, but are not limited to, parking, niceness of venue, secu rity, ticket takers, ushers, and/ or ease of entrance. Wakefield et al. (1996) found that stadium access had a positi ve relationship with pleasure of college football spectators. Furthermore, Zhang et al. (2004c) also found that th e stadium accessibility factor was positively related to game attendance of NBA spectators. The stadium accessibility factor in Zhang et al.s (1998a ) minor league hockey consump tion study was extracted as a significant factor by an EFA. Give n its significance of predictive validity on spectators affective reaction and game consumption, this factor has been included as an influencing variable to form game support programs as relate d to professional team sport. Perceived Value Over the last two decades, perceived value ha s received increasing atte n tion as one of the most salient variables in predic ting consumption behavior in th e marketing literature (Bolton & Drew, 1991; Chang & Wildt, 1994; Zeithaml, 1988). Holbrook (1994) argued that the

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68 fundamental marketing basis canno t be explained without consider ing perceived value. Ravald and Grnroos (1996) supported Ho lbrooks (1994) argument by me ntioning that the perceived value construct has been found to be a strong predictor for repurchase intentions, word-of-mouth, and customer loyalty. Various terms representing perceived value have been used by different researchers. These include, but are not limited to: customer valu e (Eggert & Ulaga, 2002; Oh, 1999), consumption value (Sheth, Newman, & Gross, 1991), service value (Cronin et al., 1997, 2000; Lee, Petrick, & Crompton, 2007), and perceived value (Petrick, & Backman, 2002a; Petrick, 2003; Lee et al., 2006; Moliner, Sanchez, Rodriguez, & Calla risa, 2007; Patterson, & Spreng, 1997 Sweeney, & Soutar, 2001; Zeithaml, 1988). Although perceived value has been considered on e of the important constructs in service marketing and consumer behavior research, a widely accepted consensu s on the definition of perceived value has yet to emerge (McDougall & Levesque, 2000). One of the main reasons behind this may be the dynamic nature of the per ceived value construct. Th at is, perceived value varies between customer characteristics (B olton & Drew, 1991; Parasuraman, 1997), types of product or service (Zeithaml, 1988), and different time periods such as pre-purchase, at the moment of purchase, at the time of use, a nd post-purchase (Parasur aman & Grewal, 2000; Ravald & Grnroos, 1996). Kortge and Okonkwo (1993) argued that perceived value is a subjective construct. Zeithamal (1988) also cont ended that there are in dividual differences in terms of possessing perceived value. For example, consumers may form good perceived value when the price is low, regardless of qualit y. Additionally, consumers may shape the perceived value when there is a balance between quality an d price. This theoretic al proposition has been

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69 empirically validated by Sheth et al. (1991) and Sweeney & Soutar (2001), who argued that there were relative influences of value dimensions on consumption behavior. Definition of Perceived Value In m arketing and consumer research, perceived value is often defined from the consumers standpoint. Conventionally, research ers viewed perceived value as a two-dimensional construct consisting of product/service quality received and price paid for the quality (Buzzell & Gale, 1987; Monroe, 1990). Holbrook (1994) supported this view by arguing that perceived value is the discrepancy between the benefits of a product or service in relation to its costs. In the same vein, Sawyer and Dickson (1984) understood perceived value as a comparison of get dimension (i.e., product/service quality) a nd give dimension (price/cost) McDougall and Levesque (2000) also agreed with the two-dimens ional view, in which they defined perceived value as consumers cognitive evaluation of what they have received for what they have given. To support the above view, Patternson and Spreng (1997) submitted a de finition of perceived value as a cognitivebased construct which captures a ny benefit-sacrifice discrepancy (p. 4). This definition has been widely used as the most common definition in marketing and consumer research (Bojanic, 1996; Dodds & Monroe, 1985). However, Grewal et al. (1998b) defined perceived value as having only a get dimension, comprised of acquisition valu e and transaction value. Acquisition value was defined as the physical gain that a consumer di rectly obtains from the service or product, whereas transaction value is th e psychological gain that a consumer gets from the product or service use and the feeling that one received a good deal. Grewal et al.s (1998b) definition has been adopted by various domains in both hospitality (Al-Sabbahy, Ekinci, Riley, 2004; Jayanti & Ghosh, 1996) and tourism (Petrick & Backma n, 2002a). However, the two-dimensional definition, which consists of get and give or trade-off between quality received and price

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70 paid has been more widely used as the most common definition in marketing and consumer research (Bojanic, 1996; Dodds & M onroe, 1985; Dodds et al., 1991). However, Bolton and Drew (1991) argued that treating perceived value as just trade-off between quality and cost is unsophisticated as it provides a lack of usefulness of understanding the construct. In line with the above notion, Woodruff (1997) contended that perceived value should be conceptualized more than just quality in relation to cost. He suggested perceived value be understood as the relationship between total bene fits and total sacrific e. Total benefits may include not only quality received but also emotion derived from the purchase of product. Total sacrifice consists of monetary s acrifice as well as non-monetary s acrifice, such as time, effort, and risk (Woodruff, 1997). Employi ng qualitative measures such as focus groups and in-depth interviews, Zeithaml (1988) proposed one of the most comprehensive mode ls that depict the relationship among price, quality, perceived value, and purchase intentions. In her study, she elaborated get and give in a similar ma nner to which Woodruff (1997) defined them. She argued that the get included such aspects as pe rceived quality, intrinsic a ttributes, and extrinsic attributes. The intrinsic attributes were related to the feeling that a consumer gets from the purchase of the product. The extrinsic attribut es were represented by the reputation of the product purchase. In addition, Zeithaml (1988) divided the give dimension into two components, including perceived value for the cost and perceived sacrifice (effort made to buy). Based on the above thesis, Zeithaml (1988) define d perceived value as t he consumers overall assessment of the utility of a product based on percep tions of what is received and what is given (p. 14). It should be noted that due to the historically unclear consensus regarding the definition of perceived value (Dodds et al., 1991; McDougall & Levesque, 2000), th e literature demonstrated

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71 some confusion in discriminating the terms perceived value and personal value (Ledden, Kalafatis, & Samouel, 2007) and perceived value and satisfaction (Pa tternson & Spreng, 1997; Ravald & Grnroos, 1996; Woodruff & Gardial, 1996). These terms are complementary, but clearly distinct, from each other. In terms of the difference between perceived value and personal value, Sheth et al. (1991) pointed out that perceived value is th e individual perceptions formed by preand post-consumption of products or se rvices. On the other hand, personal value is an individuals enduring beliefs that direct his/her behavior in th eir ordinary life (Rokeach, 1968). Rokeach argued that values have to do with modes of conduct and end-states of existence. More formally, if a person has a value is to say that he has an enduring belief that a particular mode of conduct or that a particular e nd-state of existence is persona lly and socially preferable to alternative modes of conduct or en d-states of existence (p. 550) Therefore, personal value can be derived from without any consumption-relate d situation, whereas perceived value cannot be formed unless an individual is related to the consumptive state. With regards to the conceptual differences between perceived value and satisfaction, perceived value can occur at va rious stages of purchasing (Par asuraman & Grewal, 2000; Ravald & Grnroos, 1996), whereas satisfaction is consid ered as post-consumption evaluation, which occurs only at the post-consumption stage (Olive r, 1981). Second, perceived value can be formed as a function of both cognitive and affective at titudinal orientations (Bolton & Drew, 1991; Petrick, 2002a; Sheth et al., 1991; Sweeney & Soutar, 2001; Zeithaml, 1988). However, satisfaction has been conceptu alized as purely an affective evaluation (Oliver, 1996). Importance of Examining Perceived Value Over the tw o decades, perceived value has received growing academic attention due to its theoretical and practical signifi cance. In the context of mark eting, Parasuraman (1997) argued

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72 that in addition to service qua lity, perceived value has been considered one of the most influential constructs for achieving a competitive edge. Cronin et al. (2000) pointed out that perceived value has gained increased attention to marketing managers and researchers because of its high explanatory power in ou tcome variables such as custom er satisfaction and behavioral intentions (Eggert & Ulaga, 2002). Furthermore, Parasuraman and Grewal (2000) supported the importance of examining perceived value by menti oning that the construct has been found to be the most significant predictor of repurchase intentions. Woodru ff (1997) suggested in his study that by recognizing the relationshi p of perceived value with othe r variables such as service quality, satisfaction, and behavioral intentions, managers will be able to more efficiently allocate their marketing resources. Despite the highly recognized importance of perceived value in understanding consumer decision-making process, few studies on the perceived value construct have been conducted in the field of sport management. The literature revi ew only identified two studies that have used the perceived value construct (Kwon et al ., 2007; Murray & Howat, 2002). Murray and Howat were among the first researchers to examine the effect of perceived va lue on the prediction of future intentions in the contex t of sport and leisure center. Ut ilizing stratified random sampling, 218 surveys were collected to examine the rela tionships among service quality, satisfaction, perceived value, and future intentions. The authors conceptual ized service quality as a twodimensional construct composed of core service quality and re lational service quality. A path analysis revealed that bo th the core and relationa l service quality constructs were found to have a direct relationship with perceived value ( r = .59, and r = .63) respectively. In addition, they found that perceived value was related to satisfaction and future intentions. More specifically, perceived value had a direct relationship with future intentions as well as an indirect relationship

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73 with future intentions via satisfaction. As th e authors tested a comprehensive model that considered the mediating role of perceived value, the authors used perceived value for the cost of perceived value using a single item. To predict purchasing team-li censed apparel, Kwon et al. (2 007) examined the mediating role of perceived value in the relationship be tween team identification and purchase intentions. Using a small student sample (N = 110), the authors found that perceived value was indeed a key mediating variable between team identification and purchase intentions, explaining nearly 43% of the variance. The results of th e study indicated that team sport marketers need to take into consideration perceived value when developing marketing strategy, because the study found that team identification alone was not sufficient to in fluence consumers purchase intentions. In this study, perceived value was measured as a unidimensional construct related to value-for-money. Measurement of Perceived Value Despite the recognition o f Zeithamls (1988) multi-dimensional conceptualization of perceived value and Bolton and Drews (1991) em pirical results, a majority of the research concerning perceived value has operationalized the factor as a singleitem measure (Cronin et al., 1997; Eggert & Ulaga, 2002; McDougall & Le vesque, 2000; Murray & Howat, 2002; Oh, 1999; Patternson & Spreng, 1997), which purported to measure overall pe rceived value of a product in terms of value-for-money. Some of the problems associated with using a single-item measure have been well documented in the literature (Al-Sabbahy et al ., 2004; Bolton & Drew, 1991; Petrick, 2002a). In line with the notion by Bolton and Drew (1991) that components representing perceived value are more than just value-for-money, Al-Sabbahy et al. (2004), in their hospitality marketing research, argued that the use of a single-item measure does not address the concept of perceived

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74 value because it is proposed with multiple dimensions (Petrick, 2002a; Sweeney & Soutar, 2001; Zeithaml, 1988). Another problem associated with a single-item measure of perceived value is identified by Petrick (2002a), who proposed th e SERV-PERVAL scale that was constructed as a multi-dimensional perceived value of a service. Th e researcher pointed out that the problem with using a single-item measure is that theoretica lly it assumes that consumers have a shared meaning of value (p. 122). Pr actically, Petrick (2002a) also claimed that the single-item measure results in the knowledge of how well one is rated for perceived value, but give no specific direction on how to impr ove perceived value (p. 122). Sh eth et al. (1991) also pointed out that the choice decision is a function of multiple perceived consumption values. Holbrook (1994) argued that purchase behavior derived di rectly from perceived value can be categorized into two attitudinal dimensions: a) cognition and b) affect. More specifically, in a buying context, the cognitive components are in re lation to a conventional view of perceived value in which a consumer tends to compare what they receive for what they give up. The affective components are generated when consumers consider how the purchasing is viewed by others or how this buying makes them feel good or bad. Havlena an d Holbrook (1986) suppor ted the argument of Holbrook (1994) by suggesting that affective aspects be entered in to the equation of perceived value for two reasons: (a) emotional benefits affect choice behavior between instrumental alternatives that are f unctionally equivalent in other aspects (p. 394), and (b) perceived value is considered a dynamic variable (Bolton & Dr ew, 1991; Parasuraman, 1997), which means that consumers could form perceived value after the consumption as a post-hoc evaluation that may include subjective or emotional reactions such as fear, anger, and happi ness that are caused by the purchase (Bolton & Drew, 1991; Havlena & Holbrook, 1986; Sweeney & Soutar, 2001). The affective and hedonic aspects of consumption may be more relevant to sport consumers. Sport

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75 consumer behavior can hardly be understood sole ly from a cognitive view, as sport consumers may be attracted to a sport game not only for ment al benefits (i.e., cogni tion) but also for the pleasure, positive arousal, sensation, satisfaction, and feeling a ssociated with winning, which are related to affective aspects (Slo an, 1989). Due to the above reasons it is suggested that perceived value be formed through not only cognitive processes (e.g., belief and thinking) but also affective processes (feeling and em otion), which justifies multi-di mensional aspects of perceived value (Bolton & Drew, 1991; Pe trick, 2002a; Sheth et al., 199 1; Sweeney & Soutar, 2001; Zeithaml, 1988). In tourism marketing research, Sanchez, Callarisa, Rodriguez, and Moliner (2006) also pointed out that de veloping a scale measuring per ceived value should reflect both functional (cognitive) and affective dimensions. This multi-dimensional view may attenuate the conventional perspective that perceived value is related to cognitive response to a service experience (Cronin et al., 2000; McDougall & Levesque, 2000; Patternson & Spreng, 1997). While acknowledging multi-dimensional aspects of perceived value, cognitive aspects, which are related to economic utility va riables such as perceived va lue for the cost and quality, outperformed hedonic aspects of perceived value in previous literature (Cronin et al., 1997; Eggert & Ulaga, 2002; McDougall & Levesque, 2000; Oh, 1999). Furthermore, when perceived value is measured with other variables such as quality simultaneously, the two constructs tend to form a causal relationship, in which perceived value for the cost is positively related to perceived value (Oh, 1999; Sweeney & Soutar, 1997; Zeitham l, 1988). Thus, the sole use of perceived value for the cost as measuring perceived value has been suggested when perceived quality is incorporated into the same model. Following Ch urchills (1979) multi-item measure, several items assessing perceived value for the cost have been recommended.

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76 Overview of the Proposed Dimensions of Perceived Value In this current study, perceived value is repr esented by a unidim ensional factor, Perceived Value for the Cost, as suggested by previ ous research (Kwon et al., 2007; McDougall & Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). Zeithaml (1988) defined perceived value as a consumers overall assessment of the utility of a prod uct (or service) based on perceptions of what is received (quality and benefit) and what is given (perceived value for the cost and non-monetary price). While acknowle dging its multi-dimensional aspects, previous studies have consistently found th at utilitarian aspect such as perceived value for the cost accounted for more variance explained in consum ption behavior (Kwon et al., 2007; Netemeyer et al., 2004). Netemeyer et al. argued that perceived value fo r the cost was considered a cornerstone of the most consumer-based-brand-equity frameworks (p. 211). Kwon et al. supported Netemeyers rationale by suggesting that a sport consumer tends to weigh the cost versus the benefit to determine perceived value of team-license d product. Therefore, perceived value for the cost could be overall the well-re presenting global measure of perceived value construct. Furthermore, a unidimensional measur e of perceived value using perceived value for the cost can certainly be overa rching approach. Consistent with the theoretical relevance and empirical suggestions, the curren t study adopted a unidimensional aspect (i.e., perceived value for the cost) to measure perceived value, even though the measurement may exclude the potential importance of hedonic aspects of perceived value such as emotional response. Perceived Value for the Cost Perceiv ed Value for the Cost refers to the per ceived price paid for a service (i.e., a ticket for game attendance). This is one of the most im portant factors that dis tinguish perceived value from other theoretically related factors such as service quality, personal value, and satisfaction (Bolton & Drew, 1991; Ledden et al., 2007; Ze ithaml, 1988). Many scholars view perceived

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77 value as a trade-off between what consumers paid for what they received (McDougall & Levesque, 2000; Patternson & Spreng, 1997; Sawyer & Dickson, 1984). Zeithaml (1988) found that perceived value for the cost was indirect ly related to perceived value through perceived quality. Oh (1999) also confirmed the above rela tionship by finding that perceived value for the cost was directly related to perceived value in the context of choosing a luxury hotel. This would be the same case for attending a sporting event for fans. Studies have found that ticket price for a sport event was, in general, negatively related to game a ttendance (Bird, 1982; Demmert, 1973, Noll, 1974). However, this negative relationship would be moderated by psychological orientations such as involvement and team id entification (Wann & Branscobe, 1993). Therefore, the Perceived Value for the Cost factor has been included as the dimension representing perceived value of professional team sport. Th is factor will be measured by perceptions regarding price reasona bility, economic worth, and/or money worth. Relationship among Perceived Value, Serv ice Quality, and Behavioral Intentions As Parasuraman (1997) and Woodruff (1997) indicated, perceived value should be considered for gaining a competitive advantage in marketing. In numerous empirical studies, perceived value has been found to have considerab le impact on behavioral intentions (Petrick, 2003; Zeithaml, 1988). Furthermore, Cronin et al (1997) and Oh (1999) found that perceived service quality was the most significant predicto r of perceived value. Zeithamls (1988) finding supported the positive relationship between perc eived service quality and perceived value. Therefore, a conceptual framework that posits th e hierarchical relationship among service quality, perceived value, and behavioral intentions has ga ined increased attention over the past decade in marketing and consumer research (Cronin et al., 1997, 2000; Dodds et al., 1991; Gallarza &

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78 Saura, 2006; Grewal et al., 1998b; Lee et al., 2006; Murray & Howat, 2002; Oh, 1999; Petrick, 2004a, 2004b; Zeithaml, 1988). Using a 5x3x3 between-subjects fact orial design, Dodds et al. s (1991) study examined the effects of price, brand, and store information on consumers product evaluations. The authors conceptualized perceived value as a trade-off between perceived servic e quality and perceived sacrifice, which was measured by monetary and non-monetary orientations. The results of the study indicated that perceived se rvice quality positively led to perceived value, which in turn, positively influenced willingness to buy. In the context of purchasing a durable pr oduct, Grewal et al. (1998a) developed a conceptual model of the consumer decision-making process and test ed the effects of store name, brand name, and price discounts on consumers psychological evaluations, such as store image, perceived brand quality, and intern al reference prices, which in turn, may influence consumers perceived value and purchase intentions. Us ing a 2x2x2 between-subject s design, the author found that perceived value was positively related to purchase intentions. Furthermore, perceived value was directly influenced by brand quality, internal reference price, and perceived brand quality. However, in this study, perceived value was operationalized as a unidimensional model. In an attempt to understand the e ffect of price on perceived valu e, which in turn, may affect willingness to buy and search intentions, Grewal et al. (1998b) devel oped a two-dimensional perceived value model, which included acquisi tion value and transaction value. The results showed that both values had a positive eff ect on willingness to buy durable goods. Also, both values were found to be negatively re lated to search intentions. To better understand consumers decision-maki ng process of choosing an upscale hotel, Oh (1999) developed a conceptual model to test the relationship among price, perceptions of

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79 performance, perceived service quality, customer perceived value, satis faction, intentions to repurchase, and word-of-mouth. The results of the study indicated that perceived value was found to be directly related to repurchase intentions as well as word-of-mouth, and perceived value was also indirectly relate d to repurchase intentions thro ugh satisfaction. Perceived service quality had a direct relationship with perceived value. Furtherm ore, perceived service quality also had an indirect relationship with repurchase intentions as well as word-of-mouth by means of perceived value. The author also found that perceived value for the cost was a direct antecedent of perceived value, which in turn, infl uenced repurchase intentions as well as wordof-mouth. While this study was recognized as the first holistic approach to examine decisionmaking process in the context of hospitality, the limitation associat ed with this study was the use of a single-item measure for all constructs except for the percep tion of performance. Because of the single-item measures, the constructs validity and reliability have been questioned. Cronin et al. (2000) examined the relations hips among service quality, service value, satisfaction, and behavioral inten tions across the six service industr ies that were characterized as hedonic vs. utilitarian, tangible vs intangible, and primary vs. s econdary. As a result of SEM analysis, the authors found that service quality wa s directly related to perceived service value, satisfaction, and behavioral inten tions and had indirect relationsh ips with behavioral intentions through perceived service value and satisfaction. Contrary to Ohs (1999) finding, perceived sacrifice was found not to be rela ted to perceived service value. In terms of the effect of perceived service value on behavi oral intentions and satisfaction, the results revealed that perceived service value was directly related to behavioral intenti ons as well as satisfaction. Since indirect effects on behavioral in tentions have been scarce in service marketing research, the researchers suggested incorporating both direct and indirect effects of quality on behavioral

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80 intentions. From the findings of Cronin et al. (2000), it should be noted that perceived value played not only a role of direct predictor of behavioral inten tions but also a mediating role between service quality and behavioral intentions. The above theoretical relationship of perceived value has been verified in various contexts, including spectating, recreation sport, health care and communi cation (Cronin et al., 1997); festival attendance (Lee et al ., 2007); cruise travel (Petric k, 2004a, 2004b); university students travel behavior (Gallarza & Saura, 2006); durab le goods (Grewal et al., 1998a); and use of a leisure center (Murray & Howat, 2002). Cronin et al. (1997) examined the hierarchical relationship among service quality, perceived value, and purchase intentions in six service industries in cluding three hedonic services such as recreation sport, spectator sport, and entertainmen t businesses, and three utilitarian services busi nesses such as health care, communi cation, and food. As a result of path analysis, the authors found that there was a consid erable increment of the variance explained (on average of 39%) in purchase inte ntions by adding the perceived value construct to the service quality and intention model. In addition, servic e quality was found to be directly, as well as indirectly, related to purchase in tentions via perceived value. In order to predict festival attendees fu ture behavioral intent ions, Lee et al. (2007) investigated the roles of se rvice quality, perceived value, and satisfaction on behavioral intentions. In this study, perceive d value and satisfaction have been treated as mediating factors. The result of SEM analysis indicated that serv ice quality and perceived value were found to be the significant predictors of beha vioral intentions. In particular, perceived value was revealed to be the best predictor of behavioral intentions. The authors also confirmed the theoretical proposition suggested by Sheth et al. (1991), Sweeney and Soutar (2001), and Petrick (2002a,

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81 2003) that consumption values have differentia l impacts (relative influences) on consumption behavior. Petrick (2004a) replicated Cronin et al.s (2000) study to examine whether the proposed theoretical relationship (i.e., se rvice quality-perceived value-pur chase intentions) would hold up in the context of cruise travel. As a result of SEM analysis, the author concluded that service quality was shown to have direct and indirect relationships w ith repurchase intentions through perceived value, and repurchase intentions were found to be directly re lated to word-of-mouth. Later, Petrick (2004b) conducted another study to examine the extent to which Petricks (2002a) five dimensions of perceived value would ha ve predictive validity on cruise passengers repurchase intentions and to compare the diffe rences of the effect of perceived value on repurchase intentions between first timers and repeaters. As a result of SEM analysis, the author found that that quality was directly related to repurchase intentions, and indirectly related to repurchase intentions through perceived value. These effects existed in both groups. An interesting finding was that the perceived value for the cost was f ound to be the best predictor of perceived value, whereas quality in generally wa s the best predictor in other studies (Bolton & Drew, 1991; Jayanti & Ghosh, 1996). It appears that behavioral price was related to perceived value only in first timers. Finall y, it was revealed that reputati on was a good predictor of quality but not a good predictor of perceived value, wh ich was consistent with the finding by Zeithaml (1988). Recently, Gallarza and Saura (2006) expl ored the relationships among consumer perceived value, satisfaction, and loyalty in the context of university students travel behavior. The results of the study indicate d that service quality was found to be directly related to perceived value and indirectly related to loyalty via perceived value a nd satisfaction. While the

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82 exploration and confirmation of the relationship among service quality, perceived value, and behavioral intentions has receiv ed considerable attention in ma rketing, hospitality, and tourism domains, little attention has b een paid to the field of sport management. Thus far, only two studies have appeared in major sport management journals (Kwon et al., 2007; Murray & Howat, 2002). Kwon et al. (2007) confirmed the mediating role of perceived value in the relationship between team identification and purchase intentions in the context of team-licensed merchandise consumption. Murray and Howats (2002) study wa s the first exploration of the relationship among quality, value, and intentions in the fiel d of sport management. Their findings were consistent with the previous studies in which the authors found that both core service quality and relational service quality were directly related to perceived value, which in turn, influenced future intentions. Summary For professional sport teams, ticket sales and media contracts are considered as two main revenue producers (Zhang et al., 1995). Also, sp ort teams have secondary revenue generators, such as parking, concessions, and the sale of t eam-licensed products (Zhang et al., 1997a), which are regarded as by-products of ticket sales (game attendance). However, media contracts are generally decided based on unique factors, such as population size in which the team is located, team performance, and the presence of star players or coaches. Due to the requirement of those unique factors, broadcast rights are often enjoyed by select teams a nd conferences. Therefore, it is essential for team marketers to identify variab les that influence game attendance in order to enhance the level of consumption towards game products/services. In the consumer research domain, constr ucts such as market demand, game support programs, and perceived value have been shown to be good predictors of behavioral intentions,

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83 which are considered as an immediate antecedent of consumption. Furthermore, those constructs, except for perceived value (Perceived Value for the Cost), have been conceptualized as multidimensional in nature (Grnroos, 1984; Petrick, 2002a, Parasuraman et al., 1998; Zhang et al., 1995, 1998a), which has more practical implicatio ns than a unidimensional conceptualization. According to Petrick (2002a) and Zhang et al. (2004c), a multi-dimensional scale is desirable because the results would pinpoint areas that ne ed immediate attention. In addition, management can identify the relative performa nce of each area in which it is succeeding and failing. Cronin et al., (2000) and Oh (1999) suggested a holistic appro ach of analysis that measures service quality, perceived value, and behavioral intentions simultaneously to better understand why people decide to repurchase or spread word-ofmouth concerning their experience with the products/services. Therefore, a dopting its holistic approach, th is current study measured the influence of market demand (core service), ga me support programs (peripheral service), on spectator behavioral intentions as mediated by perceived value.

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84 CHAPTER 3 METHODOLOGY The m ethod of this study is presented in the following four sections: (a) participants, (b) measurement, (c) procedures, and (d) data anal yses. A survey design was conducted to assess the influence of market demand and game support factors on spectator behavioral intentions as mediated by perceived value in the context of professional team sports. Participants For the purpose of including professional team sport spectators from diverse backgrounds, the current study employed a community inter cept sampling method to recruit research participants. A community intercept method is a modified method of the traditional mall intercept. While a traditiona l mall intercept is only conduc ted at shopping malls, community intercept method can be conducted at various public places where sampling can be more representative of the residents in the comm unity, such as grocery stores, shopping malls, churches, movie theaters, sports bars, and mass transportation waiting areas (Brenner, 1996). Participation in this survey was voluntary, and a participant had to be 18 years of age or older. Research participants were those who re sided in the southeastern metropolitan cities or within close proximity, where one or more professional sport teams were franchised, at the time when this survey was conducted. To qualify for pa rticipating in the curr ent study, an individual must have experienced attending and also paid for at least one professional team sport event within the past 12 months. These sampling conditions would enable the research participants to be familiar with the game products and services for which they paid (Petrick, 2002a). Thus, the following screening questions were included in the survey form: Have you attended a professional team sport event within the past 12 months? Because of the presence of the Perceived Value for the Cost factor (Petrick, 2002a; Zeit haml, 1988), it was necessary to

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85 include the second screening question: If so, di d you or your family pay for the game ticket? Only those people who answered positively to the screening questions were included in the study. Thus, this study was delimited to only those re sidents who attended at least one professional team sport event and had purchased the game ticket. In terms of sample size required for advanced st atistical analyses (i.e., confirmatory factor analysis and structural equation modeling), Klin e (2005) suggested that at least 10 respondents are desirable for each observed variable, whic h was also recommended by Hair et al. (2005). Considering that the market demand section ha d a total of 46 observed variables, this study targeted on a minimum number of 460. Contiguous to this objected sample size, a total of 453 respondents from four major metropolitan ar eas and their proximity communities (Atlanta, Jacksonville, Tampa, and Miami) in the stat es of Florida and Ge orgia responded to the questionnaire. These responses were resulted from data collections at seven sport bars, three malls, one grocery store, one commun ity park, and one college campus. Of the sample, 60.5% were male and 39.5% were female. Nearly 72% of the participants were between 23 and 40 years old, and close to 20% were over 40 years old. The sample included predominantly Caucasians (about 60%). Hispanic (about 20%), African Americans (13.5%), and Asians (about 9%) were among the remaining ethnic groups. Approximately, 55% respondents came from families with 3-4 or 56 people in the household; whereas, a singleperson household accounted for 20% of the sample. Household income level was widely distributed among the income categories; with about 50% respondents from families of $60,000 or more and 10% from families of $100,000 or more annual income, representing an uppermiddle and upper levels among professional team s port consumers. In terms of marital status, single was somewhat more dominant (53%) over the married (43%). The respondents were of

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86 good education background, with close to 80% possessing an undergraduate or an advanced degree. Occupation categories were widespread among the respondents, with a majority of them in the management, professional, or educational fields. These char acteristics of respondents were consistent with those general backgrounds of professional sport consumers as described by Simmons Market Research Bureau (2007). The consistency would enhance the relevance and applicability of this study to th e population of professi onal team sport game consumers; thus, it was appropriate for this study to proceed. In terms of the most recent game that the respondents attended, 44.8% of the respondents indicated that they attended a NFL game, followed by an NBA game (25.8%), a MLB game (21.9%), an NHL game (4%), an AFL game (3.3 %), and a MLS game (0.2%). Among the sport franchise teams, Jacksonville Jaguars, Tampa Ba y Buccaneers, and Miami Dolphins games were the most popularly attended, followed Orlando Magic and Miami Heat games and Tampa Bay Rays and Florida Marlins games (Table 4-1). Measurement A questionn aire was formulated based on a comprehensive review of literature and a test of content validity. This preliminary questionnaire included the following five sections: (a) market demand, (b) game support programs, (c) perceive d value, (d) behavioral intentions, and (e) demographic information (Appendix A). Market Demand Sport gam es are the core product function of professional sport team. Market demand is related to consumer expectations towards the attributes of the core product (Zhang et al., 2003a). Essentially, it is a cluster of pull factors associat ed with the game that a professional sport team can offer to its new and returning spectators (B raunstein et al., 2005; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995 ). The market demand section was developed primarily based

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87 on Spectator Decision-Making Inventory (SDMI) (Zhang et al., 1995, 2003b), which originally consists of four dimensions (Home Team, Opposing Team, Game Promotion, and Schedule Convenience). Two additional factors, Economic Consideration and Love of Professional Team Sports, were added based on the indications by nume rous researchers such as Braunstein et al. (2005), Hansen and Gauthier (1989) and Schofield (1983). A majority of the items were derived from direct adoptions and modi fications of the SDMI and other existing scales. A very small number of items (< 10%) were generated from revi ew of other published lite rature. In particular, all adoptions and modifications took into consid eration the unique produc t and service features of professional team sports, the general nature of this study w ith an attempt to include all professional team sports, and validity and reliabil ity evidence of related fa ctors and items. These were consistent with Zhang et al.s (2003b) indica tions that when a scale is adopted in settings that are different from the original study, revision and validation are nece ssary. Variables related to the uniqueness of the sporting event need to be included in such revisions. Previous scales by Braunstein et al. (2005), Hansen and Gauthier (1989), and Zhang et al. (1995, 2003a, 2003b) followed rigorous measurement pr ocedures in their development, usually including a comprehensive revi ew of literature, formulati on of a theoretical framework, qualitative study components such observations and interviews, test of content validity, exploratory and confirmatory fact or analyses, and tests of reliab ility. Thus, adopting items from these previous studies were appropriate. A total of 46 items were included for the six market demand factors: Home Team (10 items), Opposing Team (10 items), Love of Professional Team Sport (10 items), Economic Consideration (6 it ems), Game Promotion (4 items), and Schedule Convenience (6 items). These items were preceded with the following statement: please rate the following variables that might have influenced y our decision making to attend the most recent

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88 professional team sport event within the past 12 months. A Likert 5point scale was adopted, ranging from 1 = Not at Al l to 5 = Very Much. Game Support Programs Ga me support programs were ope rationalized as the controlla ble service activities that were related to game operations such as ticket services, stadium accessibility, stadium services, and game amenities to support the production of the core product. Following similar measurement procedures outlined in the market demand section, factors and items for the game support programs were formulated mainly based on three scales, including Spectator Satisfaction Inventory (Zhang et al., 1998a), Sp ectator Satisfaction Scale (Zhang et al., 2004c), and the Scale of Game Support Programs (Zhang et al., 2005b). These scales were generally developed through appropriate and systematic measurement procedures, usually including a comprehensive review of literature, formulation of a theoreti cal framework, qualitative study components such observations and interviews, test of content validity, explorat ory and confirmatory factor analyses, and tests of reliab ility. Once again, all adoptions and modifications took into consideration the unique product an d service features of professi onal team sports, the general nature of this study with an attempt to include all professional team sports, and validity and reliability evidence of related factors and items. A total of 38 items related to game support ac tivities were included in this section, which were under four factors: Ticket Services ( 10 items), Game Amenities (12 items), Stadium Services (6 items), and Stadium Accessibility (10 items). The items were preceded with the following statement: With respect to the professi onal team sport event th at you most recently attended, please rate the following statements that assess your perceptions of game operation related activities dur ing your attendance. A Likert 5-point sc ale was adopted as in the original scales, ranging from 1 = Very Unsa tisfied to 5 = Very Satisfied.

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89 Perceived Value In the current study, perceived value wa s represented by a unidim ensional factor, Perceived Value for the Cost, as suggested by previous researchers (Kwon et al., 2007; McDougall & Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). Zeithaml (1988) defined perceived value as consumers overall assessment of the utility of a product (or service) based on perceptions of what is rece ived and what is given. While acknowledging its multi-dimensional aspects, previous studies have c onsistently found that util itarian aspect such as perceived value for the cost was found to be more related to consumption behavior (Kwon et al., 2007; Netemeyer et al., 2004). Netemeyer et al. ar gued that perceived value for the cost was considered a cornerstone of the most consume r-based-brand-equity frameworks (p. 211). Kwon et al. supported Netemeyers rati onale by suggesting that a sport consumer tends to weigh the cost versus the benefit to determine perceived va lue of team-licensed product. Consistent with the empirical suggestions, the current study adopte d unidimensional aspect (i.e., perceived value for the cost) to measure perceived value even if the measurement may exclude the potential importance of hedonic aspects of perceived value such as emotional response. A total of five items for the Perceived Value for the Cost fact or were derived from Petricks (2002a) SERVPERVAL. Development of the SERV-PERVAL u nderwent rigorous measurement procedures. The original items were slightly modified to be relevant to the profe ssional sport game setting. The items were preceded with the following statement: With respect to the professional team sport event that you most recently attended, please rate the following statements that assess your perceptions of game experience dur ing your attendance. A Likert 5-point scale as in the original scale was adopted, ranging fr om 1 = Definitely False to 5 = Definitely True.

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90 Behavioral Intentions Item s measuring a spectators behavioral inte ntions were generated from Sderlund (2006) and Zeithaml et al. s (1996) scales, which all followed proper measurement procedures. There were two factors in this section: Repatronage Intentions and R ecommend to Others, with each factor having 5 items. The original items were slightly modified in order to be relevant to the professional sport game setting. The items were preceded with the following statement: With respect to the professi onal team sport event that you most recently attended, please rate the following statements that assess your intentions for future attendance at the professional team sport events. A Likert 5-point scale was adopted, ranging from 1 = Strongly Disagree to 5 = Strongly Agree. Demographic Information For the purpose of sam ple description, dem ographic background variables were included in the questionnaire, which consists of the follo wing variables: gender, age, number of people in the household, household income, marital status, education, ethnicity, and occupation. Questions were phrased in close-ended multiple-choice format. Procedures Following the develop ment of the scales, the preliminary questionnaire submitted to a panel of 10 experts for content va lidity testing. The panel included five university professors and five practitioners. For the unive rsity professors, one specializes in marketing, four in sport management. Among the practitioners, four were event operation coordinators for a NFL team, NHL team, MLB team, or a major intercollegiat e athletic department, and one was associate athletics director responsible for marketing and sponsorship programs at a major university athletic program. Each panel me mber was requested to examine the relevance, representativeness,

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91 and clarity, test format, and item content of th e questionnaire and its associated sections. Following the feedback of the pa nel of members, the preliminar y questionnaire was modified, revised, and improved, mainly in the areas of it em adequacy, factor relevance, and wording clarity. With the modified version of the questionnaire, a pilot study was conducted to sample of sport consumers who had an experience of attendi ng a professional team s port within the past 12 months ( n = 32). The purpose of this pilot study was to further examine the content validity from the perspective of targeted population. At this stage, suggested changes and improvements were all minor and they were primarily related to wording clarifications. A survey packet was prepared, which included the revised instrument a cover letter explai ning the purpose of the study and requesting cooperation from a particip ant, and the Informed Consent form. Approval from the Institutional Review Board for the Protection of Human Participants was then obtained prior to the beginning of data collection. The researcher first contacted targeted co mmunity locations to obtain permissions to conduct the study. Only with permission from a location, a test administration was conducted. Data were then collected at seven sport bars, th ree malls, one grocery stor e, one community park, and one college campus in four metropolitan cities and their proximity communities (i.e., Atlanta, Tampa, Jacksonville, and Miami) in the states of Florida and Georgia. All of these cities had at least one professional team sport franchise. To ensure a good representati on of professional team sport consumers with different backgrounds in th e sample, data collectio ns were conducted on both week days and weekend days. Four trained research assistan ts provided on-site support with the data collection process. Throughout the data collections, a standardized 9-step procedure was followed: (a) politely approachi ng all people regardless of gende r, age, and race; (b) politely presenting the screening questions to verify the study eligibility; (c) explaining the purpose of the

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92 study; (d) explaining that partic ipation would be voluntary and that participation would be anonymous; (e) explaining that there would be no penalty for not participating or stopping anytime during the survey; (f) presenting the informed consen t form; (g) distributing the questionnaire upon an individual agreed to participate; (h) coll ecting the completed questionnaire; and (i) thanking th e individual for his/her time a nd support for the study (Zhang et al., 2004c). Completing a questionna ire, on average, took approximately 15 minutes. A total of 470 copies of the questionnaire were collected. Of those, 17 questi onnaires were discarded due to having non-sporadic missing values, following th e suggestions made by Zhang, Pease, and Hui (1996). Therefore, a total of 453 were included in subsequent data analyses and hypothesis testing. Missing values were rarely spotted wi thin the remaining samp le of 453 respondents. Among those occasional missing data point, th ere was no Not-Missing-At-Random (NMAR) data (Rubin, 1987; Schafer & Graham, 2002) were found. Only few Missing-At-Random (MAR) were detected. For those MAR data, mean substit ution was applied. Data Analyses The total sample of 453 was randomly split into two halves The first set ( n = 231) w as used for conducting exploratory factor analyses (EFA) of the market demand, game support, perceived value for the cost, and behavior intention variables, re spectively. The second data set ( n = 222) was employed for conducting confirmatory factor analyses (CFA) of the measures and a structural equation modeling (SEM) that examined the relationships among market demand, game support, perceived value for the cost, a nd behavioral intentions Procedures in SPSS version 15.0 (SPSS, 2006) were carried out to calculate descriptive statistics for sociodemographic, market demand, game support, perceived value for the cost, and behavioral intentions variables.

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93 Procedures in the SPSS program were employe d for executing the EFA and calculating reliability coefficients. Although the factors and ite ms in the measures were adopted from one or two major scales, information from other related studies was also incorporated in the revised questionnaire. Due to this compilation process, an EFA was deemed necessary as the initial step for examining the factor structure of the m easures. The primary purpose of the EFA was to identify unique and reliable simple factor structures that are of the potential to be generalized to a universe of variables from a sample of variables, so as to re duce any redundant data. Following an EFA, internal consistency reliability was examined by calculating the Cronbachs alpha coefficients for the identified factors (Cronbach, 1951). In the EFA, alpha factoring extraction (Kaiser & Caffrey, 1965) was applied, followe d by promax rotation (Hendrickson & White, 1964) to identify factors. The pr omax rotation was developed by combining the advantages of varimax (orthogonal) and oblique rotation technique s (Zhang, Smith, Lam, Brimer, & Rodriquez, 2002). The promax method is first started with an orthogonal solution; the factor matrix is then rotated to the best least-square fit to the id eal solution by the procrust es procedure (Hurley & Cattell, 1962). Following criteria were used to de termine the factors and their items: (a) a factor had an eigenvalue equal to or greater than 1.0 (K aiser, 1974), (b) an item had a factor loading equal to or greater than .40 (Nunnally & Bernst ein, 1994), (c) a factor had at least 3 items, and (d) an identified factor and retained items must be interpreta ble in the theoretical context. The scree plot test was also utilized to help make a determination on the number of extracted factors (Cattell, 1966). Procedures in the AMOS version 7.0 (Arbuckle, 2006) were executed for conducting the CFA and the SEM for the retained market deman d, game support programs, perceived value for the cost, and behavioral intentions factors that were resolved from the exploratory factor analyses.

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94 According to Bollen (1989) and Hair et al (2005), executing a CFA needs to follow the following five steps: (a) model specification, (b ) model identification, (c ) model estimation, (d) testing model fit, and (e) mode l respecification. If the hypothesized model fits the data well, the confirmed factor structure can be accepted. Mo del respecification would be needed if the hypothesized model does not fit the data well. Following the s uggestions of Hair et al. (2005), several goodness of fit measur es were adopted, which included chi-square statistic ( 2), normed chi-square ( 2/ df ), root mean square error of approxima tion (RMSEA), standardized root mean residual (SRMR), comparative fit index (CFA), and expected cross validation index (ECVI) (Bentler, 1990; Bollen, 1989; Hu & Bentler, 1999; Steiger, 1990). For the chi-square statistic ( 2), it is expected to have non-si gnificant difference that indica tes that there is no difference between expected and observed covariance matric es. However, it has been criticized that chisquare statistic is too sensitive to sample size (K line, 2005). Thus, it is suggested that chi-square statistic be used, along with other goodness of fit measures (Hair et al., 2005). Oftentimes, normed chi-square is interchangeably called as th e chi-square statistic per degrees of freedom ( 2/ df ) (Kline). Bollen (1989) suggested that cutoff values of less than 3.0 for the normed chisquare are considered reasonabl e fit. Browne and Cudeck (1992) indicated that any RMSEA values less than .05 show a close fit. Recentl y, Hu and Bentler (1999) suggested that RMSEA value of .06 also indicates a close fit. Any values of RMSEA be tween .06 and .08 indicate acceptable fit. Values of RMSEA between .08 and .10 show mediocre fit. Yet, any values greater than .10 indicates unacceptable fit (Hu & Bentle r). SRMR indicates how large residuals are. Therefore, smaller values of SRMR show good f it. Any values less than .10 are considered favorable fit (Kline, 2005). The comparative fit index (CFI) assesses the relative improvement in fit of the researcher s model compared with a baseline model (i.e., null model) (Kline, p.

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95 140). A rule of thumb for CFI is that any values larger than .90 indicate an acceptable fit, and values greater than .95 show a close fit. Lastly, the expected cross validation index (ECVI) measures the fit across samples and has no set crite ria. Generally, smaller values are considered better fit of the model. Three tests were employed to measure the reliability of the scales: Cronbachs coefficient alpha ( ) values, construct reliability (CR), a nd average variance extracted (AVE). The recommended .70 cut-off value were adopted to determine internal consistency ( ) and CR (Fornell & Larcker, 1981; Nunna lly & Bernstein, 1994). The benchmark value for AVE was .50 suggested by Bagozzi and Yi (1988). Fornell an d Larcker (1981) defined CR as an internal consistency measure that accounts for the measurem ent errors of all indi cators. Since the AMOS program does not provide CR values, the research er in the current study adopted the following formula for the calculation of CR (Hair et al., 2005). ( standardized loading)2 / ( standardized loading)2 + j (1) Where ( standardized loading)2 is the squared sum of the patte rn coefficients between the indicator and the latent variable within the construct; j is the sum of all measurement errors of the indicators within the construct. Another way to determine reliability of the construct is to evaluate AVE values, which is defined as an amount of variance that is accounted for by the construct, relative to the amount of variance due to meas urement errors of all indicators (Fornell & Larcker). As with CR value, the AMOS program does not provide AVE value, therefore, the following formula was used (Hair et al., 2005). (standardized loading2 )/ (standardized loading2) + j (2) A convergent validity test was conducted to ascertain this aspect of construct validity. Convergent validity refers to a psychometric pr operty test that meas ures how well items are

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96 theoretically related to each other (Kline, 2005). To determin e convergent validity, the researcher evaluated indicator loadings and critical ratios for each indicator. Since convergent validity refers to how well each indicator loads on a prio ri latent construct, items high loading on the respective latent construct indicates good converg ent validity. Generally, an item loading value equal to or greater than .707 (i.e., R2 value .50) would be considered an acceptable loading for good convergent validity, indicating that more th an 50% of the variance is associated with common variance (Anderson & Gerbi ng, 1988). Critical ratio is an alternative way to examine convergent validity of the indicators. Regarding cri tical ratio value, any cr itical ratio value that exceeds 2.58 for a two-tail test would be considered statistically significant at the .001 level (Arbuckle, 2006). Additionally, discriminant valid ity was examined to measure how distinct the constructs are one another. To establish disc riminant validity, the researcher employed two methods: (a) examination of the interfactor co rrelations; and (b) comparing squared correlation of any of two latent constructs with AVE value (Fornell & Larc ker, 1981). According to Kline (2005), discriminant validity can be established when interfactor corr elation is below .85. A more robust way of measuring discriminant validity was suggested by Fornell and Larcker (1981), referring that a squared correlation between two constructs should be lower than the AVE for each construct. Finally, a SEM test was conducted using the AMOS program to examine the hypothesized structural relationships among the market deman d, game support, perceived value for the cost, and behavioral intentions factor s. The same fit index criteria were employed to examine the structural model as with the measurement model. Path coefficients were used to determine the direct and indirect relationships among the sets of factors. The SEM analysis provides the basis

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97 for accepting or rejecting the hypothesized relati onships among the latent constructs (Kline, 2005).

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98 CHAPTER 4 RESULTS The results of this study are presented in the following f our sections: (a) descriptive statistics, (b) exploratory factor analyses, (c) confirmatory fact or analyses, and (d) structural equation model analyses. Descriptive Statistics Descriptive statistics including mean and standard deviation for the market demand variables are presented in Table 4-2. Of the 46 items, 38 had a mean scor e greater than 3.0 (i.e., midpoint on the 5-point Likert scale), indicating that overall market demand variables were considered important when making a decision to attend a professional team sport event. Seven items had a mean score that was lower than th e midpoint. Of the all va riables in the market demand factor, love professional team sport(s) item had the highest mean score ( M = 4.22; SD = 0.95) and web information item had the lowest mean score ( M = 2.42; SD = 1.20). Descriptive statistics for the game support prog rams are reported in Table 4-3. Of the 38 items, 33 had a mean score greater than 3.0, the midpoint on the 5-point Likert scale, indicating that overall game support variables were evaluated with sa tisfaction by the prof essional team sport consumers when assessing their game attending e xperience. Five items ha d a mean score that was lower than the midpoint. Of th e all variables, sco reboard information item had the highest mean score ( M = 3.98; SD = 0.86), and mail order had the lowest mean score ( M = 2.53; SD = 1.12). Mean and standard deviation for the Perceive d Value for the Cost factor are presented in Table 4-4. All variables had a m ean score greater than 3.0 midpoint s on the 5-point Likert scale, indicating that overall the game experience was considered valu able by the prof essional team sport consumers. The game experience was wort h the money item had the highest mean score

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99 ( M = 4.24; SD = 0.79) and the game experience was ec onomical had the lowest mean score ( M = 3.61; SD = 1.12). Descriptive statistics for the Behavi oral Intention variables are reported in Table 4-5. All variables had a m ean score greater than 4.0 points on the 5-point Likert scale, indicating that the level of intention to re-atte nd a professional team s port event and recommend to others were very likely. Of the variables, I plan on attending more game(s) of this professional sport in the future it em had the highest mean score ( M = 4.54; SD = 0.71) and I am likely to say positive things about this profession al sport game to other people had the lowest mean score ( M = 4.34; SD = 0.83). Additionally, skewness and kurtosi s for the items were examined. For the skewness cut-off value, an absolute value of 3.0 would be consider ed extreme. For the kurto sis threshold value, an absolute score greater than 3.0 would be cons idered extreme (Chou & Bentler, 1995). In this study, all skewness and kurtosis values for the Market Demand, Game Support, Perceived Value for the Cost, and Behavioral In tention variables were well within the acceptable threshold (Tables 4-2 to 4-5). Exploratory Factor Analyses Market Demand An EFA of the market demand variables wa s conducted for the purpose of data reduction and identifying a simple structure (Stevens, 1996). The Kaiser-Meyer-Olk in (KMO) measure of sampling adequacy value (Kaiser, 1974) was .845, suggesting that the sample was appropriate for a factor analysis. Bartletts Test of Sphericity (BTS) was 4521.27 ( p < .001), indicating that the hypothesis of the variance and co variance matrix of the variable s as an identity matrix was significantly rejected. Hence, a factor analysis was deemed appropriate. In the EFA, six factors emerged with 31 items meeting the retention cr iteria, explaining a tota l variance of 57.69%. The scree plot test also suggested th at a six-factor model was the most interpretable. The results of

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100 the rotated pattern matrix from promax rota tion are reported in Ta ble 4.6. Based on the predetermined criterion of an item loading equal to or greater than .40, nine items were eliminated (i.e., high level of performance, home team star player(s), support the home team, high level of skills, weather condition, closeness of compe tition, opposing team as a rivalry, high level of competitiveness, and good seats). Six other items were removed due to having only one or two items loaded on the respective factors (i.e., home team record breaking performance, athleticism of professional team sport, best players in a sport, location of venue, love professional team sport(s), and popularity of professional team sport) Consequently, the six factors were labeled as Opposing Team (9 items), Home Team (6 items), Game Promotion (5 items), Economic Consideration (4 items), Love of Professiona l Sport (4 items), and Schedule Convenience (3 items). Alpha coefficients for the fact ors were .93, .85, .86, .83, .70, and .75, respectively, indicating that they were all internally consistent and reliable. The resolv ed factor structure was overall consistent with the conceptual model for the market demand variable in this study. Game Support An EFA for the game support variables wa s also conducted for the purpose of data reduction and identifying a simple structur e (Stevens, 1996). KMO measure of sampling adequacy value (Kaiser, 1974) was .862, suggesting that the sample was adequate for a factor analysis. BTS was 1962.95 ( p < .001), indicating that the hyp othesis of the variance and covariance matrix of the variables as an identity matrix was rejected. Therefore, a factor analysis was deemed appropriate. In the EFA, five fact ors emerged with 21 items retained, explaining a total variance of 51%. The scree plot test also suggested that a five-factor model was the most interpretable. The results of the rotated pattern ma trix from promax rotation are reported in Table 4.7. Based on the pre-determined criterion of an it em loading equal to or greater than .40, five items were eliminated (i.e., give away/prize, ushers, food and drink qua lity, music volume, and

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101 ease of entrance). Due to lack of interpretability and relevance, three other items were removed (i.e., newness of arena/stadium, efficiency of ticket office, and niceness of arena/stadium). Furthermore, nine variables were also eliminat ed due to having only one or two items loaded on the respective factor s (i.e., replay screens, convenience of ticket sale locations, mail order, food and drink price, ticket personnel friendline ss, music selection, ticket agencies, public transportation, and web (on-line) order procedures ). Consequently, of the original 38 items for game support programs, 21 items were retained under five factors: Game Amenities (6 items), Arena/Stadium Services (5 items), Ticket Service (3 items), Arena/Stadium Convenience (4 items), and Arena/Stadium Accessibility (3 items). Alpha coefficients for the factors were .88, .77, .73, .74, and .66, respectively, indicating that they were of acceptable internal consistency. Although slightly different from the conceptual model for the game support programs in this study, the factor structure was essentially consistent with the proposed measurement model. The slight difference might be an indication that the current study examined game operational activities of all major professional team sport events from a general perspective, unlike previous studies that focused on a specific event. Perceived Value for the Cost An EFA was also conducted for the Perceived Va lue for the Cost factor for the purpose of validating the unidim ensionality (Stevens, 1996). KMO was .840, s uggesting that the sample was adequate for a factor analysis. BTS was 672.58 ( p < .001), indicating that the hypothesis of the variance and covariance matrix of the variables as an identity matrix was rejected. Therefore, a factor analysis was appropriate. Following the EF A, all five items under the single factor were retained, explaining a total varian ce of 61.9%. The scree plot test al so suggested that a one-factor model was the most interpretable. Item s loadings were as follows: .702, .742, .774, .815, and .888 for the items, respectively, Due to the si ngle factor structure, the promax rotation was

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102 not needed. Alpha coefficient for the factor was .89, i ndicating that the fact or was internally consistent and reliable. Behavioral Intentions An EFA was conducted for the Behavioral In tention variables for the purpose of data reduction and identifying simple structure (S tevens, 1996). KMO was 939, suggesting that the sample was adequate for a factor analysis. BTS was 1916.44 (p < .001), indicating that the hypothesis of the variance and covariance matrix of the variables as an identity matrix was rejected. Therefore, a factor analysis was d eemed appropriate. In the EFA, one factor was extracted with all 10 items retained, explaining a total variance of 64.74%. The scree plot test also suggested that a one-factor model was the most interpretable. Items loadings were as follows: .847 (Repurchase Intentions item 1), .874 (Repurchase Intentions item 2), .828 (Repurchase Intentions item 3) .816 (Repurchase Intentions item 4), .774 (Repurchase Intentions item 5), .815 (Recommend to Others item 1), .762 (Recommend to Others item 2), .898 (Recommend to Others item 3) .717 (Recommend to Others item 4), and .691 (Recommend to Others item 5). Due to the fact that only one factor was extracted, the promax rotation was not needed. The factor was labeled as Behavioral Inte ntions. The factor structure resolved from the EFA was not consistent with the original two-factor model propos ed in this study. However, the number of items (i.e., 10) was retained consistent with the proposed model. Alpha coefficients for the factor was .95, indicating that it wa s internally consistent and reliable. Measurement Models: Confirmatory Factor Analyses Market Demand The second data set for the m arket demand variables, that contained 31 items under six factors, was submitted to a CFA, using ML estimation (Hair et al., 2005). Goodness of fit indexes revealed that the sixfactor and 31-item measurement model did not fit the data well

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103 (Table 4-8). The chi-square statistic was significant ( 2 = 1340.89, p < .001), indicating that the hypothesized model and the observed model had stat istically significant di fference. Because chisquare value is known to be sensitive to sample size (Kline, 2005), altern ative fit indices were further examined, including the normed chi-squa re, RMSEA, SRMR, CFI, and ECVI. A value of the normed chi-square ( 2/ df = 3.20) was above the suggested cu f-off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the six-factor model s howed a poor fit (RMSEA = .10, 90% CI = .094 .106; Hu & Bentler, 1999). Alth ough the value of SRMR (.077) was within the range of acceptable fit ( .10; Kline, 2005), the CFI value of .78 was substantially lower than the recommended cut-off ratio (>.90; Hu & Bentler, 1999), indicating an overal l lack of fit to the data. The model fit tests suggested a need for respecification. According to Tabachnick and Fidell (2001), model respecificati on would be needed if the propos ed model did not fit the data well. Poor indicator loadings also supported a model respecification. Adopting a conservative criterion in order for the scal e to have good convergent validity, an indicator loading should be equal to or greater than .707 (Anderson & Gerbing, 1988). In the current study, indicator loadings ranged from .398 (group ticket cost) to .903 (advertising). Of 31 items, nine items were below .707, indicating a lack of convergent valid ity. Therefore, the nine items were removed (opposing team history and tradition, home team exciting play, web information, travel distance, played that sport(s), speed of game, group ticket cost, duration of the game, and home team history and tradition). Furtherm ore, modification indexes sugges ted additional item elimination. After careful consideration of bot h statistical and theoretical jus tifications, a decision was made to remove five more items, which were highly double loaded (opposing team star player(s), home team exciting play, opposing team league standing, publicity, and player charisma of opposing team).

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104 As a result of the model respecifica tion, a five-factor model with 17 items was conceptualized: Home Team (3 items), Opposing Team (5 items), Game Promotion (3 items), Economic Consideration (3 items), and Schedule Convenience (3 items). This was consistent with the recommendations made by Bollen (1989) in that each factor consisted of at least three items. A five-factor model with 17 items was furt her submitted to a CFA. Overall goodness of fit revealed that the five-factor model fit the data reasonably well (Table 4-8) Chi-square statistic was significant (2 = 278.31, p < .001). The normed chi-square (2/ df = 2.55) was lower than the suggested cuf-off value (i.e., < 3.0; Bollen, 1989) The RMSEA value indicated that the fivefactor model had an acceptable fit (RMSEA = .084, 90% CI = .072 .096; Hu & Bentler, 1999). The SRMR (.054) was of a good value ( .10; Kline, 2005). CFI was .93, which was considered acceptable (Kline). ECVI was 1.66, which indicated a mu ch better fit than that of the six-factor model. ECVI has no pre-determined range of valu es (Kline), but it is generally used to compare models, with a smaller value indicating better m odel fit. Overall model fit of the five-factor model improved drastically, i ndicating its acceptability. The reliability of the factors and respectiv e items was evaluated by Cronbachs alpha, CR, and AVE (Table 4-16). Cronbachs alpha values for the five-fact or model indicated that all factors were well above the accepta ble threshold (i.e., greater than .70) suggested by Hair et al. (2005), ranging from .80 (Schedule Convenience) to .91 (Opposing Team). The CR values for the five constructs of market demand were above the recommended cut-off criterion (Fornell & Larcker, 1981), ranging from .76 (Economic Cons ideration) to .82 (Opposing Team). All AVE values were also above the s uggested standard, ranging from .52 (Economic Consideration) to .64 (Opposing Team). Based on the overall informa tion of reliability, th e determined factors were deemed reliable.

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105 A convergent validity test was conducted by eval uating indicator loadings and critical ratio values. All of the indicator load ings were greater than the su ggested standard of .707 (Anderson & Gerbing, 1988) except for one item on Schedule Convenience (day of th e week with a value of .67). A decision was made to retain the item due to its theoretical re levance to the Schedule Convenience factor and only sligh tly lower than .707 threshold. Cr itical ratio values ranged from 8.99 (home team reputation) to 16.79 (overall quality of opposing team play ers), indicating that all values were statistically significant. Overal l, the five-factor of the market demand showed excellent convergent validity (Table 4-16). According to Kline (2005), discriminant vali dity can be established when interfactor correlation is below .85. No interfactor correla tions were above .85, ranging from .193 (between Game Promotion and Economic Consideration) to .511 (between Economic Consideration and Schedule Convenience), indicati ng very good discriminant validity. The Fornell and Larckers test found that all squa red correlations in the scale were less than AVE value for respective construct, indicating excellent discriminant valid ity (Tables 4-13 for interf actor correlations and 4-16 for AVE). Thus, the five-factor model was us ed for a subsequent SEM analysis. A graphical representation of the five-factor market demand model is presented in Figure 4-2. Game Support Programs Data for the gam e support programs that contained five factor s with 21 items were submitted to a CFA, using ML estimation met hod (Hair et al., 2005). Goodness of fit indexes revealed that the five-factor measurement model di d not fit the data well (Table 4-9). Values of model fit indices were as follows: 2 = 482.84 ( p < .001); 2/ df = 2.70; RMSEA = .088, 90% CI = .078 .97; SRMR = .077, CFI = .78, and ECVI = 2.66. The model fit tests suggested a need for respecification. Poor indicator lo adings also supported a model re specification. Only seven out of 21 variables had loadings above .707, a very high and conservative cr iterion (Anderson &

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106 Gerbing, 1988). Rather, a comparatively modest cr iterion was adopted to assess the relevance of the items (Meyers, Gamst, & Guarino, 2006). Besi des Meyers et al., othe r researchers (Nunnally & Bernstein, 1994) have also suggest ed that if an indicator loading is equal to or greater than .50, it indicates that the pattern coefficient achieves meaningful significance. Based on this criterion, six items were eliminated (public address system scoreboard information, traffic/crowd control, seating directions, game calendar and schedule, and restroom cleanliness) Consequently, a fourfactor model with 15 items was respecified: Game Amenities (6 items), Ticket Service (3 items), Stadium Service (3 items), and Stadium Accessi bility (3 items). As recommended by Bollen (1989), each factor consisted of at least thr ee items. A four-factor model with 15 items was further submitted to a CFA. Overall goodness of fit indexes revealed that the four-factor model fit the data reasonably well (Table 4-9). Chi-square statistic was significant ( 2 = 212.44, p < .001). The normed chi-square ( 2/ df = 2.53) was lower than the s uggested cut-off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the four-factor model had an acceptable fit (RMSEA = .083, 90% CI = .069 .097; Hu & Bentle r, 1999). The value of SRMR (.068) was of a good value ( .10; Kline, 2005). CFI was .89, which was ma rginally acceptable (Meyers et al., 2006). ECVI was 1.29, which indicated a much better fit than that of the five-factor model. However, the interfactor correlation between Stadium Service and Stadium Accessibility was excessively high (1.06), suggesting that the tw o factors be combined into one factor, labeled Venue Quality. Therefore, a three-factor model was respecified. The overall model fit remained almost the same as the four-factor model ( 2 = 219.04, p < .001; 2/ df = 2.52; RMSEA = .083, 90% CI = .069 .97; SRMR = .07, CFI = .89, and EC VI = 1.29. Since the three-factor model was statistically more feasible in the current study, the th ree-factor model was used for subsequent analyses (i.e., reliability, convergent, and discriminant validity).

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107 The reliability of the factors and respectiv e items was evaluated by Cronbachs alpha, CR, and AVE (Table 4-17). Cronbachs alpha values for the three-f actor model indicated that all factors were well above the accepta ble threshold (i.e., greater than .70) suggested by Hair et al. (2005), ranging from .74 (Ticket Service) to .85 (Game Amenities). The CR values for the all three constructs of game support programs were above the recommended cut-off criterion (Fornell & Larcker, 1981), ranging from .72 (Ticket Service) to .86 (Game Amenities). However, two of the three AVE values were below the suggested standard (i.e., .47 for Ticket Service; .41 for Venue Quality). These low AVE values might have been caused by low indicator loadings of Ticket Se rvice and Venue Quality factors. Considering the high Cronbachs alpha and CR coefficients of these factors, th e slightly low AVE values were not of great concerns for the factors. A convergent valid ity test was conducted by evaluating indicator loadings and critical ratio values. Nine indi cator loadings out of 15 were lower than the suggested standard of .707 (Anderson & Gerbing, 1988). However, all loadings were above the modest criterion (Meyers et al., 2006) except fo r one item on Venue Quality (ease of entrance with a value of .44). A decision was made to retain the item due to its theoretical relevance to the Venue Quality factor. CR values ranged from 5. 70 (ease of entrance) to 10.75 (intermission/halfgame entertainments), indicating th at all values were statistically significant. Overall, the threefactor model of the game support programs showed acceptable convergent validity (Table 4-17). No interfactor correlations were above .85, ra nging from .363 (between Ticket Service and Venue Quality) to .634 (between Game Amenities and Venue Quality), indicating excellent discriminant validity. The Fornell and Larckers test found that all squa red correlations in the scale were less than AVE value for respective cons truct, indicating robust discriminant validity (Tables 4-14 for interfactor corr elations and 4-17 for AVE). Therefore, the three-factor model

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108 was used for a subsequent SEM analysis. A gra phical representation of the three-factor game support programs model is presented in Figure 4-3. Perceived Value for the Cost Data f or the five Perceived Value for the Cost variables were submitted to a CFA using ML estimation method (Hair et al., 2005). Goodness of fit indexes revealed that the one-factor measurement model did not fit the data well (Tab le 4-10). Values of the fit indices were as follows: 2 = 138.39 ( p < .001); 2/ df = 27.68; RMSEA = .347, 90% CI = .299 .399; SRMR = .172, CFI = .76, and ECVI = .72. The model fit tests suggested a respecification. Poor indicator loadings also supported a model respecification. Indicator loadings for two of the items were .103 and .138, respectively, which were far below the recommended standard (Anderson & Gerbing, 1988). As a result, the two items were eliminated (the game experience was a good buy and the game experience was worth the money) A three-item model was respecified, which met Bollens (1989) suggestion that a factor for CFA should have at least three items. Overall goodness of fit of the three-item mo del fit the data reasonably we ll (Table 4-10). All fit indices achieved substantial improvement ( 2 = 2.79, p < .001; 2/ df = 2.79; RMSEA = .090, 90% CI = .000 .223; SRMR = .001; CFI = .99; and ECVI = .069). The reliability of the factor and respectiv e items was evaluated by Cronbachs alpha, CR, and AVE (Table 4-18). Cronbachs alpha value (.90) for the th ree-item model was well above the suggested threshold (i.e., greater than .70) su ggested by Hair et al. (2005). The CR (.88) and AVE (.71) values for the model were also well above the recommended cu t-off criteria (Fornell & Larcker, 1981). Based on the overall information of reliability, the thr ee-item of Perceived Value for the Cost model showed excellent reliability. Convergent validity was conducted by evaluatin g indicator loadings and critical ratio values. All indicator loadings were well above the conservative standard of .707 (Anderson &

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109 Gerbing, 1988). Critical ratio va lue was 16.12 (the game experien ce was economical), indicating that the value was statistically significant. Ov erall, the three-item model showed excellent convergent validity (Table 4-18). Therefore, the three-item model was used for a subsequent SEM analysis. A graphical representation of the three-item model is presented in Figure 4-4. Behavioral Intentions Data f or the single Behavioral Intentions fact or and its 10 items were submitted to a CFA, using ML estimation method (Hair et al., 2005). G oodness of fit indexes revealed that the onefactor measurement model did not f it the data well (Table 4-11). Values of the fit indices were as follows: 2 = 157.04 ( p < .001); 2/ df = 4.49; RMSEA = .126, 90% CI = .106 .146; SRMR = .048, CFI = .92, and ECVI: .89. The model fit tests suggested a need for respecification. Two items did not meet the .707 criterion (Anders on & Gerbing, 1988). In addition, modification indexes indicated that specifying correlation between two latent error variables (Items 2 and 3 related to Repatronage Intentions ) would yield a substantial impact in better fit. However, there was no theoretical justification for an error variab le correlation. Thus, after carefully considering item contents, a decision was made for eliminati ng the two items since the other three items in the same factor were measuring the same content. The same reason was adopted for item 2, 4, and 5 that were related to Recommend to Others Consequently, a one-factor model with five items was respecified. All fit indices achieve d substantial improveme nt (Table 4-11) ( 2 = 14.99, p < .001; 2/ df = 3.00; RMSEA = .095, 90% CI = .042 .152; SRMR = .019; CFI = .99; and ECVI = .20). The reliability of the factor and respectiv e items was evaluated by Cronbachs alpha, CR, and AVE (Table 4-19). Cronbach s alpha value of .93 for the one-factor model was well above the suggested threshold (Hair et al., 2005). Th e CR (.95) and AVE (.79) values for the model

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110 were also well above the recommended cut-off criteria (Fornell & Larcker, 1981). Based on the overall information of reliab ility, the one-factor model show ed excellent reliability. Convergent validity was conducted by evaluatin g indicator loadings and critical ratio values. All indicator loadings were well above the conservative standard of .707 (Anderson & Gerbing, 1988), ranging from .78 (I plan on attending more game(s) of this professional sport in the future) to .89 (I am likely to re-attend game(s) next season). Critical ratio values ranged from 14.22 to 17.89, indicating that the valu es were statistically significan t. Overall, the one factor, five-item model of the Behavioral Intentions showed excellent c onvergent validity (Table 4-19). Therefore, the five-item model was used for a su bsequent SEM analysis. A visual representation of the five-item of Behavioral Intention model is presented in Figure 4-5. Structural Model The second data set was also used for conducting a SEM to test th e hypotheses of this study. Prior to estim ating path coefficients for the hypothesized structural model, goodness of fit indexes for the overall measurement model wa s first evaluated. The overall model fit was reasonably well (Table 4-12). Chi-s quare statistic was significant ( 2 = 1544.33, p < .001), and the normed chi-square ( 2/ df = 2.22) was lower than the suggested cut-off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the structural model had an acceptable fit (RMSEA = .070, 90% CI = .065 .074; Hu & Bentle r, 1999). The value of SRMR (.067) was of a good value ( .10; Kline, 2005). Only CFI was slightly below the suggested standard, with a value of .86. According to Cheung and Rensvold (2002), CFI value tends to be sensitive to model complexity, which may explain why the value of the CFI decreased, when compared to the separate measurement model assessments fo r the market demand, game support, Perceived Value for the Cost, and Behavioral Intentions variables. Although a respecification was needed to improve the overall model fit, a decision was made not to modify due to two reasons: (a)

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111 except for CFI value, most of the alternative model fit indices indicated good values and (b) it might be possible to lose theoretical values fr om the specified model wh en a respecification was initiated. The reliability of the factors was eval uated by CR and AVE. Table 4-15 presents interfactor correlations, CR, and AVE values. All values of CR were above the suggested threshold, ranging from .72 (Ticket Service) to .9 2 (Perceived Value for th e Cost and Behavioral Intentions). All AVE values we re above the suggested threshol d (Hair et al., 2005) except for two factors: .36 (Venue Quality) and .47 (Ticke t Service). Notwithstanding the two low AVE values, it can be concluded that all factors in the hypothesized structural model showed acceptable reliability. To determine convergent validit y, the researcher evaluated item loadings and critical ratio values for each indicator. As a re sult, all loadings were significant ( p < .001). Item loadings ranged from .498 to .922. Critical ratio values indicated that they were all above the cut-off criterion, which was above 2.58 at the .001 le vel, ranging from 6.04 to 20.01. Based on the results of loadings and critic al ratio values, the hypothesized structural model showed good convergent validity. None of the interfactor correlations were above the suggested threshold (.85; Kline, 2005), ranging from .200 (between Home Team and Ve nue Quality) to .558 (between Game Amenities and Venue Quality), indicating excellent discriminant validity (Kline, 2005). As a result of Fornell and Larckers method, it was found that none of the squa red correlations between any of the two constructs in the struct ural model were above the AVE value of the respective construct, which indicated strong discriminant validity of the model. Therefore, it can be concluded that the hypothesized structural model s howed strong discriminant valid ity on the sample data. Having

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112 satisfied the psychometric propertie s of the measurement model, it was appropriate to proceed to examine the structural relationships among the different set of factors. The hypothesized structural model was estimated to examine the hypotheses with regard to the effect of market demand and game support fa ctors on Behavioral Intentions as mediated by Perceived Value for the Cost (Table 4-20). Th e tested model included a total of 10 latent constructs (Figure 4-5). More spec ifically, there were five latent variables representing market demand and three latent variable s for game support programs, a mediated latent variable of Perceived Value for the Cost, and an endogenous la tent variable of Behavioral Intentions. The standardized direct eff ect of Home Team had a posit ive influence on Behavioral Intentions ( = .281, p < .01), indicating that when perceptions towards Home Team increased by one standard deviation, Behavioral Intentions also increa sed by .281 standard deviations. Therefore, Hypothesis 1 was suppor ted. The standardized direct effect of Opposing Team was found to exert a positive in fluence on enhancing Behavioral Intentions ( = .246, p < .01), which indicated that when percepti ons regarding Opposing Team increased up by one standard deviation, Behavioral Intenti ons increased also by .246 stan dard deviations. Therefore, Hypothesis 2 was supported. Hypothesis 3 was related to the effect of Love of Professional Sport on Behavioral Intentions. However, Hypothesis 3 was not estimated because the factor was found to be a less relevant factor of market demand by means of CFA. The standardized direct effect of Economic Consideration was found not to be related to Behavioral Intentions ( = .021, p = .769). Therefore, Hypothesis 4 was not supported. Hypothesis 5 dealt with the di rect effect of Game Promo tion on Behavioral Intentions. The findings revealed that the direct effect of Game Promotion had an inverse relationship with Behavioral Intentions ( = -.319, p < .01), indicating that when perceptions towards Game

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113 Promotion increased by 1 standard deviation, Behavioral Intentions decreased by .319 standard deviations. Although a relational direction was not supported (i.e., originally it was hypothesized to have a positive effect), the influence was statistically significant ( p < .01). Therefore, Hypothesis 5 was partially supporte d. The standardized direct e ffect of Schedule Convenience was not found to be related to Behavioral Intentions ( = -.215, p = .062). Hence, Hypothesis 6 was not supported. With regard to the standardized direct e ffects of factors of game support programs on Behavioral Intentions, only Game Amenities was found to be positively related to Behavioral Intentions ( = .246, p < .05). As a result, Hypothesis 7 was supported. The remaining two hypotheses that specified the effect of Ticket Se rvice on Behavioral Intentions and the direct effect of Venue Quality on Behavioral Intentions were not found to be sta tistically significant ( = -.161, p = .135) and ( = -.215, p = .075), respectively. Therefore, Hypothesis 8, 9, and 10 were not supported. However, the standardized di rect effect of Perceive d Value for the Cost on Behavioral Intentions was found to be statistically significant ( = .240, p < .01), therefore, Hypothesis 11 was supported. One of the aims of this study was to examine the mediating effect of Perceived Value for the Cost. A total of eight mediating analyses we re conducted. It was found that Perceived Value for the Cost played a mediating role only in the relationship between Venue Quality and Behavioral Intentions ( = .083, p < .05). In terms of the calculation for the indirect effect, the standardized indirect effect of Venue Quality on Behavioral Intentions through Perceived Value for the Cost was estimated as the product of the standardized path coefficients for the paths of Venue Quality to Perceived Value for the Cost ( = .346, p < .01) and Perceived Value for the Cost to Behavioral Intentions ( = .240, p < .01), which yielded = .083, p < .05). This result

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114 indicated that Behavioral Inten tions were expected to enhanc e by .083 standard deviations for every increase in Venue Quality of one full standard deviation through its prior effect on Perceived Value for the Cost. Therefore, H ypothesis 13 was supported. None of the market demand factors were found to be indirectly related to Behavior al Intentions through Perceived Value for the Cost, therefore, Hypothesis 12 was not supported.

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115 CHAPTER 5 DISCUSSION The discussion of this study is presented in the following three sec tions: (a) m easurement properties, (b) hypotheses testing, a nd (c) additional suggestions. As market competition is becoming more inte nsified in professional team sports, it is imperative for both researchers a nd practitioners to id entify those variables that directly and indirectly influence game consumption (Hanse n & Gauthier, 1989; Zhang et al., 1995). An indepth understanding of what factors influence spectators to decide to return to the game, and how they refer the game products and services to othe rs, is crucial for professional teams to better understand spectator consumption behavior. Findings of previous studies revealed that market demand variables and game support programs were important predictors of sport sp ectator consumption beha vior (Kwon et al., 2007; Murray & Howat, 2002; Wakefield & Blodge tt, 1996; Zhang et al., 1995, 1998a, 2004b). However, these two concepts have usually been studied fragmentarily (Cronin & Taylor, 1992; Ko & Pastore, 2005; Parasuraman, Zeithaml, & Berry, 1998; Wakefield & Sloan, 1995; Zhang et al., 1995, 2004c). Although previous researchers recognized the importance of market demand variables and game support programs when marketing professional sport games, only a small number of studies have examined both sets of variables simultaneously (Greenwell et al., 2002; Tsuji et al., 200). Of those stud ies containing both concepts, over-s implicity was a major concern. Furthermore, previous studies failed to consider unique features related to professional team sport events. Therefore, it is essential for a study to incorporate the uniqueness and special characteristics of the core product, product exte nsions, and market environment (Mullen et al., 2007; Zhang et al., 2003b). Additionally, previous st udies have revealed that only a small portion of game attendance variance (i.e., less than 50%) were explained by market demand variables

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116 and game support programs (Greenwell et al., 2002; Tsuji et al., 2007; Wakefield & Blodgett, 1996; Zhang et al., 1995, 1998a, 2004b). Low variance explanation may be due to the overlook of the potential influences of some interveni ng variables, such as perceived value, on the relationship between sport produc tion and game consumption. Therefore, studying game product variables and perceived value simultaneously is critical to gaining a more comprehensive understanding of what influence spectators to repatronage the game and how they conduct wordof-mouth promotions. The current study was de signed to fill this void by examining the structural relationships of ma rket demand variables and game support programs to professional team sport attendance; in the meantime, the me diating influence of perceived value was taken into consideration. In this study, rigorous psycho metric testing procedures were first conducted for the four constructs (i.e., market demand, game support programs, perceived value for the cost, and behavioral intentions). A SEM analys is was executed to test the hypotheses. Measurement Properties Systematic procedures were undertaken to formulate the preliminar y questionnaire and its sections, which included a comprehensive review of literature, interviews of sport industry practitioners, and test of content validity by a panel of experts a nd a pilot study group of consumers representing the targeted population. It was the intention of the researcher to enhance research finding generalizability of this study by adopting the community intercept approach. Data collection was conducted at various locations in four major metropolitan areas, which also supported this intention. Previous studies usually studied professional sport consumers at a limited number of sport events in one geographic location. It was the intention of this study to include consumers of comparatively more divers e backgrounds in terms of geographic locations and sport types so as to improve the external valid ity of this study.

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117 In this study, both EFA and CFA were conduc ted to ensure theoretical relevance, generalizability, and usefulness of the resolved factor stru ctures. For the market demand variables, six factors with 31 items were retained in the EFA: (a) Opposing Team, (b) Home Team, (c) Game Promotion, (d) Economic Consider ation, (e) Love of Prof essional Sport, and (f) Schedule Convenience. The derived factors from the EFA were consistent with the theoretical dimensions suggested by previous researchers (Greenstein & Marcum, 1981; Hansen & Gauthier, 1989; Schofield, 1983; Zhang et al., 1995). However, the six-factor model did not fit the data well in the initial CFA. After careful considerat ion of statistical and theoretical evidence, the scale was revised to a five-factor model with a total of 17 items: Opposing Team (5 items), Home Team (3 items), Game Promotion (3 it ems), Economic Consider ation (3 items), and Schedule Convenience (3 items). This respecified model exhibited much improved fit indexes. As a result of the respecifica tion, the Love of Professional Spor t factor was eliminated, mainly due to low indicator loadings and low critical ratio values. In prev ious studies, Love of Sport was found to be a contributing variab le to game attendance of colle ge sports (Ferreira & Armstrong, 2004) and game consumption of professional sports (Zhang et al., 2003a). In Braunstein et al.s (2005) study, the researchers found that Love of Baseball was an important factor related to MLB spring training; yet, the factor displayed poor psychometri c properties. The factor was eventually retained by the researchers based on the consideration that Love of Sport covers detailed characteristics of sport events, such as closeness of competition, duration of game, high level of skills, best players in a sport, and/ or speed of game. Alt hough the researchers were reluctant to eliminate this factor, they did suggest the need for further studies of this factor. Although the current study conducte d rigorous procedures in item purification, test of content validity, and a pilot study, similar findings occu rred. As Braunstein et al. suggested, more

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118 examinations are necessary for this factor in futu re studies. A key issue is how to keep Love of Sport factor theoretically separated with Home Team and Opposing Team factors because the factor analyses in the current study revealed that Love of Spor t items were double loaded with these two factors. Although the number of factors was reduced to fi ve, the resolved constructs of the market demand were essentially consiste nt with previously suggested factors (Braunstein et al., 2005; Schofield, 1983; Zhang et al., 1995; 2003b, 2004a). Schofield (1983) proposed four market demand categories, including Demographic Variables, Economic Variables, Game Attractiveness, and Residual Preference. Economi c Variables were related to Game Promotion and Economic Consideration, Game Attractiveness contained items relevant to athlete/team performances, history, and reputation of Ho me Team and Opposing Team, and Residual Preference in Schofields (1983) study consisted of variables related to Schedule Convenience. Synthesizing Schofields four factors, key game demand variables, and production functions, Zhang et al. (1995, 2003b) developed a four-fact or model of market demand (Home Team, Opposing Team, Game Promotion, and Schedule C onvenience) and included the factors in the Spectator Decision Making Inventory (SDMI). In th e context of MLB spring training, Braunstein et al. (2005) developed an eight-factor model that consisted of Home Team, Opposing Team, Game Promotion, Vacation Activity, Economic C onsideration, Schedule Convenience, Nostalgic Sentiment, and Love of Baseball. In an attempt to assess market demand of general professional sport events, Zhang et al. (2003a) identified th ree factors: Game Attr activeness, Marketing Promotion, and Economic Consideration. When the general market demand factors were applied to a NFL expansion team, Zhang et al. (2004a) found consistent factor structure (i.e., Game Attractiveness, Marketin g Promotion, Economic Consideration, and Socializational Opportunity).

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119 Overall, the resolved factor structure in the cu rrent study was consistent with the indications of previous researchers. Although the current study rec onfirmed the factor struct ure of the market demand suggested by previous studies, findings of this study were likely improved and more generalizable when considering the following thr ee aspects: (a) a more representative sample was involved, (b) a comprehensive study was de signed and carried out in the study, including various statistical analyses such as EFA and CF A, and (c) better psychometric properties were obtained. In previous studies, data were usually collected on-site in arenas or stadiums (Zhang et al., 1995, 2003b), where only spectators of one spor t event participated in the study and they might be under temporal influence due to an instant moment of winning or losing. The respondents of the current study were current sport consumers who indicated that they attended a professional team sport event within past 12 mont hs. Descriptive statistics indicated that a total of six premier professional team sport leagues were attended by the respo ndents, which may help improve the generalizability of findings. Add itionally, Zhang et al. (2003b) suggested that besides EFA and CFA, other types of construct validity, including convergent and discriminant validity tests be utilized to improve the factor structure. These sugges tions made by previous researchers were materialized in the current study. The current study retained at least three items per factor through the CFA. One of the limitations found in Zhang et al.s (2003b) st udy was that the Opposing Team and Schedule Convenience factors were measured by only two items. When using CFA and SEM analyses, the number of items per factor is important fo r measurement precision, based upon the following two important points: (a) optimal number of items per factor, and (b) meaningfulness of the factor. In terms of an optimal number of items per factor, Bollen (1989) argued that two items

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120 could cause an estimation problem with a small sample size (i.e., less th an 100). Although Zhang et al.s (2003b) study ha d a large sample size ( N = 685), based on the findings from previous studies on optimal number of indicators per factor, three items per factor are considered ideal (Bollen, 1989; Kline, 2005; Marsh, Balla, & McDonald, 1988). The possible reason that two items of Opposing Team and Schedule Convenience f actors were consistently used in previous studies may be due to the use of EFA as the primary item selection method, which is data-driven. In this regard, the current study improved in that five items and three items related to Opposing Team and Schedule Convenience, respectively, were retained by factor analyses. The items of the Opposing Team represented overall performance, quality of opposing teams, quality of players, opposing teams exciting play, and team reputation. Schedule Convenience was represented by such attributes as game time of the day, convenient game schedule, and day of the week. Nonetheless, more work to validate the items related to Opposing Team and Schedule Convenience factors is necessary in future studies. In addition to measuring market demand that is related to core product function (i.e., the game itself), this study assessed professional team sport consumers perceptions towards peripheral service quality and examined how th eir satisfaction with event operation activities would affect their future consumption behavior s. Taking into consider ation the unique aspects that were related to professional team sports; this study adopted, modifi ed, and revised existing scales measuring game support programs of pr ofessional team sports (Zhang et al., 1998a, 2004, 2005b). Unlike previous studies that measured game support programs related to specific professional games (e.g., minor league hockey ga mes), the current study attempted to assess game support programs that coul d be generalized to all professi onal team sports. In addition to adopting existing scales (SSI, SSS, and SGSP), other relevant items were incorporated into the

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121 measurement of the game support programs. Zhang et al., (2003b) stressed th at having a reliable, valid, and generalizable scale must be a priority when studying serv ice quality issues associated with specific areas of event operations. Following this notion, rigorous measurement procedures were conducted in this study to develop a m easure for game support programs, including a thorough review of literature, test of content validity, and examinations of construct validity. Five factors with 21 items were retained in the EFA: (a) Game Amenities, (b) Arena/Stadium Services, (c) Arena/Stadium Convenience, (d) Ticket Serv ice, and (e) Arena/Stadium Accessibility. These factors were consistent with the theoretical dimensions suggested by previous studies (Grnroos, 1984; Zhang et al ., 1998a, 2005b). However, the five-factor model did not fit the data well in the initial CFA. Following careful statis tical and theoretical considerations, the scale was respecified to a th ree-factor model with 15 items: Game Amenities, Ticket Service, and Venue Quality. The current model showed much improved fit indexes, along with convergent and discrimina nt validity, and reliability. One noticeable factor solution th at emerged in this study was that due to high interfactor correlation, two separate factors, Arena/Stadium Services and Arena/Stadium Accessibility, were combined into a single construct, Venue Qua lity. Although the number of factors was reduced to three, all of the items in the four-factor model were retained. Although so mewhat different from the findings of previous studies, the resolved factor structure in this study still well reflected those factors derived in previous studies (Zhang et al., 1998a, 2005b). For instance, in a minor league hockey study, Zhang et al.s (2005b) found high interfac tor correlati ons between Arena/Stadium Services and Arena/Stadium Accessibility; thus, the resear chers commented that the two factors can be merged to form one single construct or they may be influenced by another latent variable (p. 64). Another possible explanation ma y be related to respondents

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122 memory decay. Unlike previous studies, the cu rrent study recruited re spondents who reported that they attended a professional team sporting event within the pa st 12 months at the time when the survey was conducted. With the passing of ti me, consumers might have had a difficult time to clearly distinguish between the Arena/Stad ium Service and Arena/Stadium Accessibility factors particularly when both of these factor s assessed attributes re lated to services and accessibility. Nevertheless, further studies are sugge sted to confirm the factor structure of the two latent constructs. In future studies, it may be worthwhile to have several competing models for game support programs, including a five-fact or model (Zhang et al., 1998a), four-factor model (Zhang et al., 2005b), three-factor mode l suggested by the current study, and a secondorder model. Although the current study showed a slight difference compared to previous studies with regard to factor structure of the game support programs findings of this study has its uniqueness in that the scale of the current study extended its viab ility to general professional team sports. There have been no scales meas uring general game support programs related to professional team sports. The existing two scales (SSI and SGSP) by Zhang et al. (1998a, 2005b) were specifically designed to measure minor league hockey games. The sample characteristics in the current study represented sport consumers of six professional sports leagues (NFL, NBA, MLB, NHL, AFL, and Soccer). Thus, the factors a nd respective items derived from the current study can be used in more genera l professional spor t settings. In the current study, a unidimensional constr uct of perceived value as represented by Perceived Value for the Cost was tested for it s mediating effect (Kw on et al., 2007; McDougall & Levesque, 2000; Murray & Howat, 2002; Netemeyer et al., 2004). While acknowledging the importance of multidimensional aspect s, previous studies have cons istently found that utilitarian aspect such as Perceived Value for the Cost was the primary factor that was related to

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123 consumption behavior (Kwon et al., 2007; Nete meyer et al., 2004). Although the EFA retained all five items measuring Perceived Value for the Cost in the current study, two items (the game experience was a good buy and the game experience wa s worth the money) could not be retained in the CFA due to extremely low indicator load ings. The three-item model showed good model fit. Various scholars have proposed multidimensiona l aspects of the perc eived value model by arguing that consumers decision ma king is a function of multiple pe rceived values (Sheth et al., 1991; Sweeney & Soutar, 2001). To some extent, these claims were empirically validated in previous studies (Gallarza & Sa ura, 2006; Lee et al., 2007; Petrick, 2002a; Sanchez et al., 2006). Thus, it is suggested that futu re research attention on concep tualizing the perceived value construct is a viable option by a dopting multidimensional aspects. The current study initially proposed a two-di mensional model of behavioral intentions represented by Repatronage Intentions and R ecommend to Others. However, both EFA and CFA consistently yielded a one-factor model due to high interfactor correlation. The finding of a unidimensional factor had its merit and made prac tical sense when consider ing the fact that one positive intention in one behavioral domain usua lly leads to another positive intention in the same behavioral domain. Nonetheless, this find ing was inconsistent with previous studies (Sderlund, 2006; Zeithaml et al., 2006), which sugge sted that the most frequently utilized behavioral intention constructs were Willingne ss to Recommend the Service to Others and Repurchase Intentions. Multidimensional aspects of be havioral intentions have been consistently suggested and would provide bett er practical implications. Fo r instance, Sderlund (2006) found satisfaction influenced both Repurchase Intentio ns and Recommend to Others factors but with unequal strengths. This finding implied that mere selection of one in tention construct over another may cause a misunderstanding about th e role of satisfaction (antecedent) as a

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124 determinant of intentions. Theref ore, more careful conceptualiza tion is required to distinguish the Repurchase Intentions factor from the Reco mmend to Others constructs in order to reduce high interfactor correlation in future studies. Furt hermore, more behavioral intentions constructs such as Desire to Stay (Wakefield & Blodgett 1996) or Intentions to Switch Product/Service (Eggert & Ulaga, 2002) need to be incorporated into the measurement in order to better understand the effects of antecedents (e.g., mark et demand and/or game support programs) on sport consumption behaviors. Hypotheses Testing Of para mount interest to the current study was to examine the structural relationships of market demand variables and game support progra ms to professional team sports attendancerelated variables, while taking into consideratio n the mediating influence of Perceived Value for the Cost. To achieve this, a se ries of hypotheses testing were conducted by means of SEM. Consequently, it was found that Home Team, Op posing Team, Game Amenities, and Perceived Value for the Cost were positively related to Be havioral Intentions, whereas Game Promotion was negatively related to Behavioral Intentions Additionally, Perceived Value for the Cost was found to be the only mediating role in the rela tionship between Venue Quality and Behavioral Intentions. The finding that Home Team had a positive influence on Behavioral Intentions was consistent with previous studies (Noll, 1991; Schofield, 1983; Zhang et al., 1995, 1997a, 2003a, 2004a). Home Team factor was comprised of the following variables: win/loss record, league standing, and team reputation in th e current study. Various scholars have stressed that winning is the ultimate goal for a professional sport team due to its enormous impact on the success of the sport organization (Milne & Mc Donald, 1999; Zhang et al., 2003a). However, constant winning seems impossible in professional sports. Equally important as winning is making home fans

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125 psychologically connected with the home team. Fans with high iden tification with a team are not likely to reduce their game consumption levels even though the team may not be winning, as can be seen with the Chicago Cubs supportive fans despite their propensity for losing and Boston Red Sox fans prior to their World Series vict ory in 2004. This phenomenon has been empirically validated by various researchers (Wann & Bran scombe, 1993; Zhang et al., 2004a). Regardless of winning and losing, professional s port team marketers should formulate strategies to increase fan identification with their team. Likewise, the finding that Opposing Team had a positive influence on Behavioral Intentions was consistent with findings fr om previous studies (Madrigal, 1995; Zhang et al., 1995, 2000). The Opposing Team f actor was comprised of such variables as opposing teams overall performance, quality of opposing team, opposing team exciting play, opposing team reputation, and overall quality of opposing team players. E ssentially, Home Team and Opposing Team make up the major elements of a game. Both of them were relevant and are important to the marketing of the games. The re lative influences that Home Team and Opposing Team had on Behavioral Intentions implies that professional team sports fans tend to consider home team and opposing team separately as they make a decision regarding game attendance. This notion is also consistent with previous st udies (Braunstein et al., 2005; Zhang et al., 1995). Thus, sport marketers should uti lize marketing activities to prom ote the aspects of home team and opposing team separately. For instance, spor t marketers should emphasize home team while focusing on such aspects as current league standing and home team reputation, but should highlight opposing team, not only on their curr ent performance and reputation, but also the presence of star players. This study found that Game Promotion ha d a negative influence on Behavioral Intentions, which was in contrast to the findings of previous studies (Zhang et al., 1995, 2003a).

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126 The Game Promotion factor was comprised of three variables: advertising, direct mail and notification, and sales promotions Although the findings were diffe rent from previous studies, it might make practical sense that when a consum er focused on Game Promotion variables instead of other major game related issues such as hom e and opposing teams, he/she would be less likely to attend future games. It is a testimony that a true fan focuses on elements directly related to team performances on the court. Additionally, it is necessary to point out that the Game Promotion factor was the weakest predictor of sport consumption behaviors in Zhang et al.s (2003a) general market demand study. In Zhang et al.s (1995) study, Game Promotion was represented by a larger number of variables, including good seat s, giveaway/prize, and ticket discount, which were found to be all positively related to sport consumption (Zhang et al., 2000, 2003a). The current study initiall y consisted of these variables, but they were subsequently eliminated in the EFA and CFA procedures. In addition, Game Promoti on factor was not found to be a significant predictor of sport consumption in some of th e previous studies (Zhang et al., 1997a, 2003a). Furthermore, it was pointed out that an excessive persuasion attempt employed by direct mail, notification, and/or e-mail, whic h was not requested by a consumer, could create a negative reaction to the organization, as the co nsumer may feel an inva sion of privacy due to unwanted solicitation (Grnroos, 200 5). This phenomenon may occur more frequently in people with low team identification. Because the curren t study did not incorporate the effect of team identification into the measurement, this spec ulation could not be confirmed. Future studies should examine a moderating effect of team id entification in the rela tionship between Game Promotion and Behavioral Intentions Two factors, Economic Consid eration and Schedule Convenience, were not found to be statistically significant predictors of Behavioral Intentions in the SEM. However, descriptive

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127 statistics, the EFA, and the CFA indicated that these two factors were important factors to be considered by professional sport teams when formulating marketing strategies. The Economic Consideration factor was primarily comprised of ticket-related variables (personal ticket price, ticket affordability, and ticket discount), which were found to be contributing variables to game attendance and media consumption in previous studies (Zhang et al., 2003a). When a team is not playing well, team marketers shoul d consider such strategies as ticket discounts or buy one get the second one at half off, along with well-pl anned in-game amenities so that fans can be satisfied with the game products and services. The Schedule Convenience factor was comprised of three variables in the current study (game time of the day, c onvenient game schedule, and day of the week). Previous studies found that the Schedule Convenience factor was an important predictor of game attendance (Hill et al., 1982; Zhang, 1998b). Hill et al. (1982) found that weekend games and season ending games were positively related to MLB game attendance. Zhang (1998b) found that spectators of minor league hockey pref erred an evening time (7:00 pm) for weekday and Saturday games, and an afternoon time (4:00 pm) for Sunday games. Although team marketers cannot have complete c ontrol over the game schedule, they should make efforts to make the game schedule as convenient as possible. The finding that Game Amenities had a positive influence on Behavioral Intentions was consistent with the findings of previous studi es (Zhang et al., 1998a, 2004c, 2005b). Zhang et al. (2005a) found that various in-game amenities and music were important predictors of game consumption for NBA season-ticket holders. The Game Amenities factor was comprised of six variables (during game shows/ entertainments, post-game shows/entertainments, pre-game shows/entertainments, intermission/half-game en tertainments, dance cheerleading activities, and concourse entertainment activities). Based on th e findings, professional team sport marketers

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128 should pay attention to in-game am enities in order to enhance entertainment value for spectators. Todays professional sport events are considered to be not only competitive sports but also family-oriented entertainment events, which can be enjoyed by people of various backgrounds. For instance, the Pittsburg Pirates, a team considered by most people to be not very competitive, was ranked #1 in offering in-game promotional activities, including fireworks nights and bobble head giveaways (Sutton, 2008). Getting selected fans involved in the half-time activities for prizes can also positively promote the game en tertainment value. Additionally, any concourse fun activities by a team mascot or staff member s would add entertainment value to fans who go to the restroom or concession area. Offering unique and enjoyabl e activities may keep spectators stay longer at the game even if the team is not playing well. In NBA and NFL games, cheerleaders play an important role in enhancing entertainment value. Some cheerleading teams such as the Dallas Cowboys and Laker Girls ha ve been well branded and have their own fan bases. In this study, Ticket Service and Venue Qua lity factors were not found to be significant predictors of Behavioral Inten tions. However, when formulating marketing strate gies, these two areas should be considered as important factors as they were found to be significant predictors of sport consumption in previous studies (Zhang et al., 1998a; 2005b). In terms of ticket service, the variables representing the factor (phone order, will call, and ticket exchange programs) were found to be more relevant to season-ticket holde rs (Zhang et al., 2000). On e possible explanation for why the Ticket Service factor was not found to be related to Be havioral Intentions was due in part to the characteristics of the respondents in this study, who we re recruited from various areas (sports bars, malls, and/or groc ery stores). To a certain extent, the respondents may have different types of tickets, which imply that they may have received different services. As a result,

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129 the different service experiences may have cance lled out other service experiences received by people who had variant ticket types. This issue deserves future study. In terms of Venue Quality, the variables re presenting the factor were staff courtesy, restroom availability, arena/stadium cleanliness, ease of entrance, security, and parking. These were found to be important predic tors of sport consumption in previous studies (Wakefield & Blodgett, 1996; Wakefield & Sloan, 1995; Zhang et al., 2004c). For instance, Wakefield and Sloan (1995) found that parking and cleanliness were signifi cant predictors of th e desire to stay longer at college football games. Zhang et al. (2 004c) also found that the Stadium Service factor had a positive influence on NBA game attendance. Wakefield et al. (1996) found that Stadium Accessibility had a positive relationship with emotional reaction of college football game spectators. Based on these previ ous findings, Venue Quality issues deserve to be considered when formulating marketing strategies fo r professional team sports. The current study found that Perceived Value for the Cost had a positive influence on Behavioral Intentions. This finding is consistent with the findings of previous studies (Cronin et al., 1997; Oh, 1999; Zeithaml, 1988). The current study utilized a unidimensional factor of perceived value (i.e., Perceived Value for th e Cost), which was related to judging game experience in terms of money value. In previo us studies, Perceived Va lue for the Cost was consistently found to be positivel y related to consumption behavior in the field of marketing (Bolton & Drew, 1991; Netemeyer et al., 2004). Th is same relationship was found in the context of sport consumption behaviors (Kwon et al., 2007; Murray & Howat, 2002). Thus, team marketers should pay particular attention to pr oviding quality products/s ervices in order to enhance perceived value for the money that spect ators spend at the games, which in turn may positively influence Behavioral Intentions.

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130 Of interest to this study was to examine th e mediating role of Perceived Value for the Cost in the relationship of market demand and game support to Behavioral Intentions. It was found that Perceived Value for the Cost mediated the relationship between Venue Quality and Behavioral Intentions ( = .083, p < .05). This finding is unique in that there was no direct effect of Venue Quality on Behavioral Intentions. Howe ver, a significantly indi rect effect occurred when Perceived Value for the Cost was incorporat ed into the equation. This result indicates that Venue Quality could be a significant predictor of Behavioral Intentions only through Perceived Value for the Cost. This is consistent with prev ious studies (Kwon et al., 2007; Murray & Howat, 2002), which found the mediating role of perceived value (Perceived Value for the Cost) on the relationships of team identific ation (Kwon et al.) and servi ce quality (Murray & Howat) to Behavioral Intentions. At times, human consumption behaviors are complex and can hardly be explained by one-way direct relationships (Ajz en, 2005; Baggozi et al., 1999). Thus, it has been suggested to identify mediating and moderating e ffects that may influence the direct relationship in order to better understand the complexity of human consumption behaviors. Essentially, studying the perceived value construct as either a mediating or moderating variable in the relationships among market demand, game support programs, and sport consumption behaviors was worth the effort. According to Mullin et al. (2007), there are si x general characteristics associated with the core product in spectator sport event (i.e., the game itself), whic h can separate the core product of spectator sport event from the general busines s products. These are: unpr edictable, intangible, perishable, variable, inse parable, and uncontrollability (Mullin et al., 2007). Due to these special natures, sport consumers may not be well conscious about intangible delivery of services received such as market demand factors. Instea d, sport consumers tend to be more judgmental

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131 about tangible products such as parking, cleanliness of venue and restroom, and ease of entrance that they have tangible experien ce, leading to the direct connec tion with perceived value for the cost, which in turn influences positive consump tion behavior. Therefore, team marketers should be more cognizant to the functional service quality (i.e., tangible aspects of service) as they formulate marketing and service strategies so as to enhance the consumption level of spectators. Essentially, findings of this study displayed promise of explanati on power of perceived value to sport consumption behaviors. This study chose to focus on the most salient aspect of perceived value, Perceived Value for the Cost. Considering that multidimensional aspects of perceived value have been suggested by a numbe r of researchers (Petri ck, 2002a; Sheth et al., 1991; Sweeney & Soutar, 2001), future studies n eed to look into the variability of this suggestion. In the current study, several theoretical frameworks have been fully or partially incorporated, including the Theory of Reasoned Action (Fishbein & Ajzen, 1975), the AppraisalEmotional Response-Coping framework (Bagoz zi, 1992), and the Nordic Model (Grnroos, 1984). These frameworks suggest that initial positive evaluation of product/service would directly or indirectly lead to positive consumer behavior (e.g., game a ttendance). The findings of the current study confirmed the suggested theoretic al frameworks in the context of professional team sports. Cognitive-based constructs (i.e., ma rket demand, game support, and perceived value for the cost) were found to exer t conative consumption, which indi cates that the cognitive-based constructs used in the current study are indeed superordinate d ecision criteria for professional sport consumers. Therefore, sport marketers in pr ofessional team sports n eed to consider how to best present this information, in which consum ers are likely to use in their decision making process.

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132 Additional Suggestions Suggestions for future studies have been m a de through the discussions about the research findings. A few points need to be emphasized he re. In the current study, the initially proposed Love of Professional Sport factor was not included in the SEM because this factor did not exhibit acceptable measurement properties in the CFA. Ho wever, the factor was characterized by such attributes as closeness of competition, duration of game, and/or high level of skills, which appear to be essential, relevant, and important to cons umption of professional team sports. Thus, more measurement studies on this factor ar e necessary in future studies. The current study failed to show discriminant validity for Arena/Stadium Service and Arena/Stadium Accessibility factors. However, the two factors seem theoretical distinct as they were found to be separate fact ors in previous studies (Zhang et al., 1998a; 2005b). Thus, future studies are necessary to examine the factor struct ure of the two factors. The same procedure can be suggested for the Behavioral Intentions factor that was initially represented by two factors, Repatronage Intentions and Recommend to Othe rs in the current study. Future studies should also examine other mediating and moderating va riables that may influence the relationships among market demand, game support programs, a nd behavioral intentions. These may include, but are not limited to, team identification, invol vement, fan motivation, and socio-demographic variables. In the current study, Bagozzis (1992) Appr aisal-Emotional Response-Coping framework has been partially utilized. Th at is, only a direct relations hip between Appr aisal (positive attitude) and Coping (behavioral intentions) has been confirmed. However, the model can be mediated by the emotional response derived from the initial appraisal (Bagozzi, 1992), which was not examined by the current study. Therefor e, future studies should examine affective constructs in the equation of cognition (i.e., market demand and game support programs) to

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133 conation (behavioral intentions ) to better understand sport consumer behaviors related to professional team sports. In terms of sample size, only half of the entire data ( n = 222) was used for CFA and SEM. Although Wetson and Gore (2006) suggested that a minimum sample size of 200 was adequate for SEM, the small sample size might have negatively influenced model fit for the structural model (i.e., CFI) in the current study (Cheung & Rensvold, 2002). According to MacCallum, Ronznowski, and Necowitz (1992), any model respecification should have an additional independent sample for cross-validatio n for the respecified model in order to avoid capitalizing on chances of variation. Although, measurement and structural models in the current study displayed good psychometric properties, more attempts to validate factor structures and causal relationships are recommended. In the current study, no effort was made to ex amine if differences exist between die-hard and fair-weather fans in terms of the stru ctural relationships am ong market demand, game support, perceived value, and consumption intentions As a matter of fact, sp ectators can at least be categorized as die-hard fans and fair-weather fans according to their consumption levels and socio-motivations (Wann & Branscombe, 1990). Di e-hard fans generally are of higher team identification, involvement, and consumption levels than fair-weather fans ; and are likely to support a team when the team does not perfor ms well (Heere & James, 2007; Trail, Fink, & Anderson, 2003). Perhaps, die-hard fans pay more attention to core pr oduct attributes (e.g., win/loss, level of performance, and/or the presen ce of star players); whereas, fair-weather fans pay more attention to periphera l attributes (e.g., venue, promoti on, and/or entertainment). These speculations need further examination by assess ing invariance issues with respect to the consumption level of spectators. When doing th is, a number of socio-psychological variables,

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134 such as team identification and consumer involve ment level, may be incorporated into the study. Invariance analyses are also needed to examine the structural relationships with respect to different professional sport lea gues. Data in this study were collected through a community intervention approach; thus, rese arch participants were those who attended professional sport events somewhat in the past. Due to the decay of memory, some of the respondents might not be able to provide their responses with specificity. Hence, future studies should also examine the invariance issues between on-site and recall settings. Finally, this study was delimited to variables directly related to professional team s and their management (i.e., market demand and game support programs). According to Mullin et al. (2007) and Zhang et al. (1997), there are many marketing environment variables (e.g., substitute forms of entertainments, availability of recreational activities, economics and income) that may simulta neously interact with these market demand and game support programs. Future studies should take into consideration these environmental variables and their interactions with market demand, game support programs, and perceived value variables and how they function together to influence spectator consumption behaviors.

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135 OT HT GP EC SC OT1 e1 OT4 e2 OT5 e3 OT7 e4 OT9 e5 HT1 e6 HT5 e7 HT6 e8 GP1 e9 GP2 e10 EC6 e11 EC1 e12 EC2 e13 EC5 e14 CN1 e15 CN2 e16 CN4 e17 Figure 4-1. First-order confirmatory f actor analysis for market demand

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136 GA GA4 e1 GA5 e2 GA9 e3 TS TS1 e7 TS2 e8 TS3 e9 SSA SA1 e10 SA3 e11 SA9 e12 GA10 e4 GA11 e5 GA12 e6 SS2 e13 SS5 e14 SS6 e15 Figure 4-2. First-order confirmatory factor analysis for game support programs

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137 Monetary Price MP3 e1 MP4 e2 MP5 e3 Figure 4-3. First-order confirmatory factor analysis for perceived value for the cost

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138 Behavioral Intentions REP1 e1 REP2 e2 REP4 e3 REC1 e4 REC3 e5 Figure 4-4. First-Order confirmatory factor analysis for behavioral intentions

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139 Figure 4-5. Tested structural model Perceived Value for the Cost Venue Quality Behavioral Intentions Ticket Service Game Amenities Home Team Opposing Team Economic Consideration Game Promotion Schedule Convenience

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140 Note. Dashed lines represent non-significant paths ** Path significant at the .01 level Path significant at the .05 level Figure 4-6. Tested structural model Perceived Value for the Cost Venue Quality Behavioral Intentions Ticket Service Game Amenities Home Team Opposing Team Economic Consideration Game Promotion Schedule Convenience .246** .021 -.319** .281** -.215 .240** .246* -.161 .257 .083*

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141 Table 4-1. Frequency distributions fo r the sociodemographic variables ( N = 453) Variables Category Frequency (%) Cumulative % ( N = 453) Gender Male 274 (60.5) 60.5 Female 179 (39.5) 100.0 Age 18-22 41 (9.0) 9.0 23-30 175 (38.6) 47.7 31-40 151 (33.3) 81.0 41-50 58 (12.8) 93.8 51-65 28 (6.2) 100.0 Number of People in Household 1 90 (19.9) 19.9 2 112 (24.7) 44.6 3-4 179 (39.5) 84.1 5-6 62 (13.7) 97.8 7-8 7 (1.5) 99.3 9 or more 3 (0.7) 100.0 Household Income Below $20,000 23 (5.1) 5.1 $20,000-39,999 79 (17.4) 22.5 $40,000-59,999 128 (28.3) 50.8 $60,000-79,999 81 (17.9) 68.7 $80,000-99,999 56 (12.4) 81.0 $100,000-149,999 42 (9.3) 90.3 $150,000-199,999 27 (6.0) 96.2 Above $200,000 17 (3.8) 100.0 Marital Status Single 241 (53.2) 53.2 Married 195 (43.0) 96.2 Divorced 17 (3.8) 100.0 Education In School Now 1 (0.2) 0.2 High School Graduate 47 (10.4) 10.6 In College Now 45 (9.9) 20.5 College Graduate 265 (58.5) 79.0 Advanced Degree 95 (21.0) 100.0 Ethnicity Caucasian 259 (57.2) 57.2 African American 61 (13.5) 70.6 Hispanic 87 (19.2) 89.8 Asian/Pacific Islander 40 (8.8) 98.7 American Indian 2 (0.4) 99.1 Interracial 2 (0.4) 99.6 Other 2 (0.4) 100.0

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142 Table 4-1. Continued Occupation Management 79 (17.4) 17.4 Technical 28 (6.2) 23.6 Professional 128 (28.3) 51.9 Sales 60 (13.2) 65.1 Clerical 12 (2.6) 67.8 Education 111 (24.5) 92.3 Skilled Worker 30 (6.6) 98.9 Non-Skilled Worker 3 (0.7) 99.6 Other 2 (0.4) 100.0 Attended Game AFL 15 (3.3) 3.3 MLB 99 (21.9) 25.2 NBA 117 (25.8) 51.0 NFL 203 (44.8) 95.8 NHL 18 (4.0) 99.8 SOCCER 1 (0.2) 100.0

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143 Table 4-2. Descriptive statistics fo r the market demand variables (N = 453) Variable M SD Skewness Kurtosis 1. Home team win/loss record (HT1) 3.5938 1.22408 -.691 -.485 2. Home team star player(s) (HT2) 3.9294 1.18782 -1.001 .047 3. Home team record brea king performance (HT3) 3.2723 1.37517 -.280 -1.133 4. Overall quality of home team players (HT4) 4.0243 .93451 -.947 .953 5. Home team reputation (HT5) 4.1634 1.03470 -1.234 .908 6. Home team league standing (HT6) 3.8274 1.13915 -.893 .144 7. Home team history and tradition (HT7) 4.0000 1.11506 -.962 .023 8. Home team exciting play (HT8) 4.0067 .97873 -1.080 1.029 9. Support the home team (HT9) 4.1637 1.02086 -1.199 .850 10. High level of skills (HT10) 3.7704 1.24457 -.934 -.074 11. Opposing teams overall performance (OT1) 3.2318 1.15278 -.444 -.590 12. Opposing team star player(s) (OT2) 3.2274 1.27569 -.471 -.851 13. Opposing team history and tradition (OT3) 3.4194 1.07907 -.459 -.275 14. Opposing team reputation (OT4) 3.2500 1.09580 -.326 -.472 15. Overall quality of opposing team players (OT5) 3.3488 1.14530 -.517 -.455 16. Opposing team league standing (OT6) 3.2434 1.15906 -.468 -.526 17. Quality of opposing team (OT7) 3.4658 1.05880 -.634 -.188 18. Opposing team as a rivalry (OT8) 3.6927 1.14210 -.718 -.124 19. Opposing team exciting play (OT9) 3.1239 1.22028 -.349 -.833 20. Player charisma of opposing team (OT10) 3.1969 1.22427 -.461 -.760 21. Played that sport(s) (LS1) 2.8940 1.47014 .029 -1.334 22. Closeness of competition (LS2) 3.3920 1.08592 -.538 -.296 23. Popularity of professional team sport (LS3) 3.8234 1.20826 -.844 -.175 24. Duration of the game (LS4) 2.8702 1.29197 -.047 -1.145 25. High level of skills (LS5) 3.8825 1.03348 -.898 .374 26. Best players in a sport (LS6) 3.7870 1.23264 -.797 -.415 27. Speed of game (LS7) 3.2062 1.29654 -.382 -.931 28. Athleticism of professional team sport (LS8) 3.6372 1.08947 -.520 -.401 29. High level of competitiveness (LS9) 3.9400 1.05111 -.901 .251 30. Love professional team sport(s) (LS10) 4.2235 .94592 -1.137 .631 31. Personal ticket price (EC1) 3.1715 1.26718 -.175 -.943 32. Ticket affordability (EC2) 3.3177 1.21467 -.384 -.609 33. Good seats (EC3) 3.6659 1.14562 -.613 -.416 34. Group ticket cost (EC4) 2.6705 1.30453 .103 -1.188 35. Ticket discount (EC5) 3.1723 1.36950 -.268 -1.093 36. Sales Promotions (EC6) 2.9219 1.29863 -.146 -1.169 37. Advertising (GP1) 2.8742 1.19533 .002 -.907 38. Direct mail & notification (GP2) 2.5044 1.31069 .239 -1.230 39. Publicity (GP3) 3.2208 1.18052 -.305 -.783 40. Web information (GP4) 2.4224 1.19629 .379 -.887 41. Game time of the day (SC1) 3.6637 1.04848 -.982 .720

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144 Table 4-2 Continued 42. Convenient game schedule (SC2) 3.7533 .93319 -.818 .861 43. Weather condition (SC3) 3.3518 1.32460 -.426 -.893 44. Day of the week (SC4) 3.6592 1.01558 -.763 .261 45. Travel distance (SC5) 3.1499 1.19267 -.112 -.727 46. Location of venue (SC6) 3.5919 1.24954 -.658 -.486 Note. HT = home team; OT = opposing team; LS = love of professional sport; EC = economic consideration; GP = game promotion; SC = schedule convenience.

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145 Table 4-3. Descriptive statistics for th e game support programs variables ( N = 453) Variable M SD Skewness Kurtosis 1. Phone order service (TS1) 2.8381 1.05699 -.002 -.211 2. Will call service (TS2) 3.0703 1.01743 .019 -.188 3. Ticket exchange program (TS3) 3.0740 1.08765 -.245 -.217 4. Ticket agencies (TS4) 2.9046 1.05939 -.205 -.355 5. Game calendar and schedule (TS5) 3.9508 .81864 -.467 -.140 6. Ticket personnel friendliness(TS6) 3.7562 .92849 -.457 -.139 7. Convenience of ticket sale locations (TS7) 3.5258 .98321 -.486 .201 8. Web (on-line) order procedures (TS8) 2.9748 1.00301 -.094 -.191 9. Mail order (TS9) 2.5324 1.12279 .111 -.715 10. Efficiency of ticket office (TS10) 3.5442 .96554 -.185 -.544 11. Music selection (GA1) 3.6777 .91804 -.283 -.259 12. Public address system (GA2) 3.7020 .98289 -.505 -.266 13. Replay screens (GA3) 3.5366 1.12114 -.499 -.452 14. During game shows/entertainments (GA4) 3.5982 .93704 -.384 -.330 15. Post-game shows/en tertainments (GA5) 3.2993 1.14694 -.314 -.591 16. Give away/prize(GA6) 3.2345 1.10624 -.179 -.660 17. Music volume (GA7) 3.8407 .91028 -.687 .429 18. Scoreboard information (GA8) 3.9779 .86446 -.639 .242 19. Pre-game shows/entertainments (GA9) 3.4568 .96155 -.484 -.096 20. Intermission/half-game entertainments (GA10) 3.4181 .96392 -.384 -.112 21. Dance/cheerleading activities (GA11) 3.5565 1.00805 -.507 -.245 22. Concourse entertainment activities (GA12) 3.3166 .88871 -.089 -.039 23. Food and drink quality (SS1) 3.5022 .91267 -.252 .137 24. Arena/Stadium cleanliness (SS2) 3.7345 .91707 -.434 -.114 25. Restroom cleanliness (SS3) 3.2301 .98672 -.211 -.497 26. Food and drink price (SS4) 3.0310 1.20615 -.174 -.898 27. Restroom availability (SS5) 3.6049 .89772 -.372 -.124 28. Staff courtesy (SS6) 3.7439 .88285 -.251 -.474 29. Parking (SA1) 2.9467 1.18688 .056 -.891 30. Newness of arena/stadium (SA2) 3.5398 1.02435 -.486 -.075 31. Security (SA3) 3.7450 .88207 -.448 .019 32. Ticket takers (SA4) 3.6991 .90851 -.489 -.028 33. Traffic/crowd control (SA5) 3.4204 1.08299 -.382 -.431 34. Public transportation (SA6) 3.1327 1.10767 -.271 -.388 35. Niceness of arena stadium (SA7) 3.8514 .89621 -.444 -.437 36. Ushers (SA8) 3.4614 .83689 -.014 -.246 37. Ease of entrance (SA9) 3.5762 1.01605 -.373 -.454 38. Seating directions (SA10) 3.7441 .88886 -.575 .331 Note. TS = ticket services; GA = game amenities; SS = stadium services; SA = stadium accessibility

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146 Table 4-4. Descriptive statistics for the percei ved value for the cost variables (N = 453) Variable M SD Skewness Kurtosis 1. The game experience was a good buy (MP1) 4.2230 .80441 -1.015 1.192 2. The game experience was worth the money (MP2) 4.2362 .78949 -.743 -.003 3. The game experience was fairly priced (MP3) 3.9029 .97958 -.755 .227 4. The game experience was reasonably priced (MP4) 3.8609 .97563 -.580 -.134 5. The game experience was economical (MP5) 3.6093 1.12084 -.521 -.449 Note. MP = perceived value for the cost

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147 Table 4-5. Descriptive statistics for th e behavioral intent ions variables ( N = 453) Variable M SD Skewness Kurtosis 1. I am likely to attend more games as soon as the sport is in season (REP1) 4.4004 .86028 -1.563 2.133 2. I am likely to re-attend game(s) next season (REP2) 4.4636 .78516 -1.518 2.129 3. I have a high likelihood of re-attending the game(s) next season (REP3) 4.3664 .83768 -1.523 2.539 4. I plan on attending more game(s) of this professional sport in the future (REP4) 4.5366 .70105 -1.466 1.868 5. The probability that I will re-attend this professional sport game is high (REP5) 4.5077 .79992 -1.718 2.633 6. I will recommend this professional sport game to other persons (REC1) 4.3642 .83468 -1.433 2.092 7. I am likely to recommend this professional sport game to my family (REC2) 4.3775 .83408 -1.606 2.994 8. I am likely to recommend this professional sport game to my friends (REC3) 4.3731 .76417 -1.255 1.751 9. I am likely to say positive things about this professional sport game to other people (REC4) 4.3355 .83215 -1.435 2.471 10. I will talk about this professional sport game with other people (REC5) 4.4238 .78230 -1.431 2.031 Note. REP = repurchase intentions; REC = recommend to others

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148 Table 4-6. Factor pattern matrix for the market demand variables: alpha factoring with promax rotation using firs t half data ( n = 231) F1F2F3F4 F5F6 Opposing Team (9 items) Quality of opposing team .862 -.085 -.112 -.005 -.002 .118 Overall quality of opposing team players .827 -.095 -.006 -.001 -.036 .118 Opposing team exciting play .822 .065 .092 .141 -.108 -.154 Opposing team star player(s) .818 -.167 .076 -.103 .056 .021 Opposing team reputation .814 .074 -.187 -.001 .120 -.017 Player charisma of opposing team .765 -.144 .237 -.097 -.026 .054 Opposing team league standing .725 .141 .023 .138 -.106 -.093 Opposing teams overall performance .717 .065 .025 .159 -.077 -.037 Opposing team history and tradition .641 .243 -.161 -.360 .215 .000 Home Team (6 items) Home team win/loss record -.153 .773 .103 -.049 .086 -.029 Home team league standing .014 .738 .056 -.071 .019 -.019 Home team reputation -.122 .722 -.042 .004 -.003 .137 Home team history and tradition .078 .695 -.072 .005 -.182 .029 Overall quality of home team players .085 .634 .062 .048 .031 .051 Home team exciting play .083 .565 .040 .133 .022 -.077 Game Promotion (5 items) Advertising -.007 .035 .902 -.131 .097 -.033 Sales Promotions -.142 .007 .873 .087 -.018 .082 Direct mail & notification .036 -.003 .817 -.129 -.011 -.012 Publicity .204 .023 .494 .056 -.098 .230 Web information .093 .122 .471 .106 .059 -.164 Economic Consideration (4 items) Ticket affordability -.040 .014 -.081 .948 -.121 .060 Travel distance .007 .074 .091 .709 -.067 -.037 Ticket discount -.090 -.069 -.078 .630 .312 .028 Personal ticket price .126 -.037 -.078 .590 .037 .176 Love of Professional Sport (4 items) Group ticket cost -.181 .107 -.042 -.089 .651 .140 Speed of game .087 .016 .027 .302 .569 -.182 Duration of the game .105 -.081 .146 .213 .513 .039 Played that sport(s) .120 -.120 .059 -.103 .509 .016 Schedule Convenience (3 items) Day of the week .011 -.005 -.061 .075 -.005 .659 Convenient game schedule .111 .167 -.041 .055 .063 .620 Game time of the day -.024 -.008 .131 .054 .066 .616 Note. F1 = opposing team; F2 = home team; F3 = game promotion; F4 = economic consideration; F5 = love of professional sport; F6 = schedule convenience.

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149 Table 4-7. Factor pattern matrix for the game su pport programs variables: alpha factoring with promax rotation using first half data ( n = 231) F1F2F3 F4F5 Game Amenities (6 items) Pre-game shows/entertainments .856 .036 -.059 .031 -.083 Post-game shows/entertainments .795 .080 -.149 -.136 -.017 Dance/cheerleading activities .720 -.184 -.005 .072 .096 During game shows/entertainments .668 -.036 .027 .248 -.015 Intermission/half-game entertainments .627 .042 .105 .070 .159 Concourse entertainment activities .600 .129 .205 -.066 -.088 Arena/Stadium Services (5 items) Arena/Stadium cleanliness .034 .673 -.211 .084 .056 Restroom availability -.140 .656 -.117 .205 .085 Restroom cleanliness .005 .653 .018 -.086 .188 Parking .163 .628 .031 -.222 .063 Ease of entrance -.118 .444 .360 .246 -.252 Ticket Service (3 items) Ticket exchange program .083 -.049 .769 -.098 -.058 Will call service -.22 1 -.099 .756 .138 .116 Phone order service .127 -.055 .635 -.136 .048 Arena/Stadium Convenience (4 items) Scoreboard information -.001 -.058 -.108 .704 .125 Game calendar and schedule -.039 .006 -.054 .598 .077 Security .185 -.070 .111 .565 -.053 Staff courtesy .096 .161 .075 .550 -.090 Arena/Stadium Accessibility (3 items) Public address system .020 .025 .401 .078 .539 Traffic/crowd control -.054 .183 .169 -.125 .535 Seating directions .049 .074 -.197 .236 .525 Note. F1 = game amenities; F2 = arena/stadium services; F3 = ticket service; F4 = arena/stadium convenience; F5 = arena/stadium accessibility.

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150 Table 4-8. model fit comparison be tween the six-factor model and five-factor model of market demand using second half data (n = 222) Model 2 df 2/ df RMSEA RMSEA CISRMR CFI ECVI Six-Factor Model (31 items) 1340.89 419 3.20 .10 .094-.106 .077 .78 6.76 Five-Factor Model (17 items) 278.31 109 2.55 .084 .072-.096 .054 .92 1.66 CI = confidence interval

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151 Table 4-9. Model fit comparison be tween the five-factor model, four-factor model, and threefactor model of game support programs using second half data ( n = 222) Model 2 df 2/ df RMSEA RMSEA CI SRMR CFI ECVI Five-Factor Model (21 items) 482.84 179 2.70 .088 .078-.097 .077 .82 2.66 Four-Factor Model (15 items) 212.44 84 2.53 .083 .069-.097 .068 .89 1.29 Three-Factor Model (15 items) 219.04 87 2.52 .083 .069-.097 .070 .89 1.29 CI = confidence interval

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152 Table 4-10. Model fit comparis on between the five-item mode l and three-item model of perceived value for the cost using second half data (n = 222) Model 2 df 2/ df RMSEA RMSEA CISRMR CFI ECVI Five-Item Model 138.39 5 27.68 .347 .299-.399 .172 .76 .72 Three-Item Model 2.79 1 2.79 .090 .000-.223 .001 .99 .06 CI = confidence interval

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153 Table 4-11. Model fit comparison be tween the ten-item model and fi ve-item model of behavioral intentions using second half data ( n = 222) Model 2 Df 2/ df RMSEA RMSEA CISRMR CFI ECVI Ten-Item Model 157.04 35 4.60 .126 .106-.146 .048 .92 .89 Five-Item Model 14.99 5 3.00 .095 .042-.152 .019 .99 .20 CI = confidence interval

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154 Table 4-12. Overall model fit indices for the measurement model of hypothesized structural model using second half data ( n = 222) Model 2 df 2/ df RMSEA RMSEA CISRMR CFI ECVI Structural Model 1545.33 695 2.22 .70 .065-.074 .067 .86 7.15 CI = confidence interval

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155 Table 4-13. Interfactor correlations from the confir matory factor analysis of the market demand using second half data ( n = 222) OT HT GP EC SC OT 1.0 HT .308*** 1.0 GP .464*** .501*** 1.0 EC .308*** .200* .193* 1.0 SC .444*** .460*** .461*** .511*** 1.0 Note. OT = opposing team; HT = home team; GP = game promotion; EC = economic consideration; SC = schedule convenience. *** Correlation significant at the .001 level Correlation significant at the .05 level

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156 Table 4-14. Interfactor correlations from the conf irmatory factor analysis of the game support programs using second half data ( n = 222) GA TS VQ GA 1.0 TS .634*** 1.0 VQ .491*** .363*** 1.0 Note. GA = game amenities; TS = ticket services; VQ = venue quality *** Correlation significant at the .001 level

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157 Table 4-15. Interfactor correlations, construct reli ability, and average vari ance extracted from the confirmatory factor analysis of the hypothesized structural m odel using second half data ( n = 222) OT HT GP EC SC GA TS VQ MP BI OT .87(.57) HT .263*** .75(.51) GP .476*** .414*** .83(.63) EC .321*** .064 .120 .76(.52) SC .477*** .346*** .345*** .360*** .77(.53) GA .167* .228** .480*** .020 .331*** .90(.60) TS .364*** .031 .437*** .167* .394*** .462*** .72(.47) VQ .028 .200* .073 -.020 .503*** .558*** .461*** .77(.36) MP .001 .299*** .095 .045 .301*** .344*** .204* .487*** .92(.81) BI .061 .315*** -.069 -.014 .116 .278*** -.004 .367*** .406*** .92(.69) Note1. OT = opposing team; HT = home team; GP = game promotion; EC = economic consideration; SC = schedule convenience; GA = game amenities; TS = ticket service; VQ = venue quality; MP = perceived value for the cost; BI = behavioral intentions. Note2. Interfactor correlations are in lower triangle; construct reliabilities are in diagonal; and average variance extracted values are in parentheses. *** Correlation significant at the .001 level ** Correlation significant at the .01 level Correlation significant at the .05 level

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158Table 4-16. Indicator loadings, cr itical ratios, cronbachs alph a, construct reliability, averag e variance extracted for the ma rket demand using second half data ( n = 222) Variables Indicator Loadings Critical Ratios Cronbachs Alpha Construct Reliability Average Variance Extracted Opposing Team (5 items) .91 .82 .64 Opposing teams overall performance .90 Opposing team reputation .74 13.52 Overall quality of opposing team players .84 16.79 Quality of opposing team .83 16.43 Opposing team exciting play .75 13.91 Home Team (3 items) .81 .80 .58 Home team win/loss record .92 Home team reputation .80 8.99 Home team league standing .82 11.56 Game Promotion (3 items) .88 .82 .61 Advertising .94 Direct mail & notification .60 14.84 Sales Promotions .77 15.31 Economic Consideration (3 items) .83 .76 .52 Personal ticket price .71 Ticket affordability .91 10.92 Ticket discount .77 10.41 Schedule Convenience (3 items) .80 .80 .57 Game time of the day .81 Convenient game schedule .79 10.91 Day of the week .67 9.54

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159Table 4-17. Indicator loadings, cr itical ratios, cronbachs alph a, construct reliability, averag e variance extracted for the ga me support programs using second half data ( n = 222) Variables Indicator Loadings Critical Ratios Cronbachs Alpha Construct Reliability Average Variance Extracted Game Amenities (6 items) .85 .86 .52 During game shows/entertainments .71 Post-game shows/entertainments .60 8.20 Pre-game shows/entertainments .78 10.54 Intermission/half-game entertainments .79 10.75 Dance/cheerleading activities .68 9.24 Concourse entertainment activities .70 9.60 Ticket Service (3 items) .74 .72 .47 Phone order service .69 Will call service .65 7.54 Ticket exchange program .76 8.18 Venue Quality (6 items) .77 .80 .41 Staff courtesy .70 Restroom availability .70 8.70 Arena/Stadium cleanliness .72 8.90 Ease of entrance .44 5.70 Security .61 7.76 Parking .52 6.69

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160Table 4-18. Indicator loadings, cr itical ratios, cronbachs alph a, construct reliability, averag e variance extracted for the pe rceived value for the cost using second half data (n = 222) Variables Indicator Loadings Critical Ratios Cronbachs Alpha Construct Reliability Average Variance Extracted Perceived Value for the Cost (3 items) .90 .88 .71 The game experience was fairly priced .90 The game experience was reasonably priced .91 The game experience was economical .79 16.117

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161Table 4-19. Indicator loadings, cr itical ratios, cronbachs alph a, construct reliability, averag e variance extracted for the be havioral intentions using second half data ( n = 222) Variables Indicator Loadings Critical Ratios Cronbachs Alpha Construct Reliability Average Variance Extracted Behavioral Intentions (5 items) .93 .95 .79 I am likely to attend more games as soon as the sport is in season .86 I am likely to re-attend game(s) next season .89 17.89 I plan on attending more game(s) of this professional sport in the future .78 14.27 I will recommend this professional sport game to other persons .86 16.69 I am likely to recommend this professiona l sport game to my friends .85 16.25

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162 Table 4-20. Maximum likelihood standardized loadings ( ), critical ratios (cr), standard errors (se), and t-values for the hypothesized stru ctural model using second half data ( n = 222) Path Coefficients between Factors CR SE t Direct Effect Behavioral Intentions Home Team (S ) .281 3.277 .071 .231** Behavioral Intentions Opposing Team (S) .246 2.778 .073 .204** Behavioral Intentions Economic Consideration (NS) .021 .294 .061 .018 Behavioral Intentions Game Promotion (PS ) -.319 -2.896 .082 -.238** Behavioral Intentions Schedule Convenience (NS) -.215 -1.863 .127 -.237 Behavioral Intentions Game Amenities (S) .246 2.453 .099 .243* Behavioral Intentions Ticket Service (NS) -.161 -1.484 .122 -.180 Behavioral Intentions Venue Quality (NS) .257 1.779 .240 .426 Behavioral Intentions Perceived Value for the Cost (S) .240 3.199 .073 .234** Perceived Value for the Cost Home Team (S ) .237 2.758 .073 .200** Perceived Value for the Cost Venue Quality (S) .346 2.358 .250 .589** Path Coefficients between Factors p Indirect Effect Behavioral Intentions Perceived Value for the Cost Venue Quality (Game Support) (PS ) .083 .028 Behavioral Intentions Perceived Value for the Cost Home Team (Market Demand) (NS) .057 .133 Note. S = significant; PS = partially significant; NS = not significant ** Correlation significant at the .01 level Correlation significant at the .05 level

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163 APPENDIX INFORMED CONSENT AND QUESTIONNAIRE Dear Participants: Purpose of Study: The purpose of this study is to exam ine the impact of market demand, game support programs on consumption levels of profe ssional team sport spectators as mediated by perceived value. What you will be asked to do in the study: The questionnaire cons ists of items that are designed to measure market demand, game suppor t programs, perceived value, and attendance intentions. By using these items, we are attemp ting to develop a model that explains what influences sport spectators re-attend inten tions towards a professional team sport. Time required, Risks and Benefits, & Compensation: The survey will take approximately 10 minutes to complete. There are no known risks a nd we do not anticipate that you will benefit directly by participating in th is study. There is no compensation for participating in this study. Confidentiality: Your identity will be kept confidential to the extent provided by law. Your responses will be anonymous and will only be used for the current research purposes. In addition, there will be no identifying markers that will link you to the questionnaire you complete, as the results will be reported as group results. Voluntary participation: Your participation in this research is totally voluntary and there is no penalty for not participating. Right to withdraw from the study: You have the right to withdr aw from the study at any time without consequence. Whom to contact if you have questions about the study: Dr. James Zhang (advisor), Dept. of Tourism, Recreation, & Sport Ma nagement, 186A Florida Gym, jamesz@hhp.ufl.edu, 392-4042 x 1274 Whom to contact about your rights as a research participant in the study: UFIRB Office, Box 112250, University of Florida, Gainesville, FL 32611-2250; phone 392-0433 Agreement: I have read the procedur e described above. I voluntarily agree to partic ipate in the procedure and I have received a copy of this description. Participant:____________________________________ Date:___________________ Principal Investigat or:___________________________ Date:___________________ To contact: Kunwung Byon, Florida Gym 300. PO. BOX. 118208. Gainesville, FL, 326118208, Phone (392-4042 x1309), E-mail (kbyon @hhp.ufl.edu)

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164Marketing Survey Questionnaire for Professional Team Sports PURPOSE: This survey is for a marketing study on professional team sports The collected information will be solely used for research. Y our identity will be kept confidential to the fullest extent provided by law, and yo ur responses will be anonymous. Th ere is no right or wrong answe rs. Your participation is voluntary, and your honest response is greatly appreciated. THANK YOU! SCREEN QUESTIONS: 1. Have you attended one or more professional team sport events within the past 12 months? Yes No 2. If so, did you or your family pay for the game ticket? Yes No Please specify the game that you attended ( ) -If you answered No to #1 or #2, you a re finished with the survey. Thank you! -If you answered Yes to both #1 and #2, please continue. DECISION MAKING: Please rate the following variables that might have influenced your d ecision making to attend the most recent professional tea m sport event (1=Not at All to 5 = Very Much). Home Team, Favorite Team, or Team A (1=Not at All to 5 = Very Much): 1. Home team win/loss record 1 2 3 4 5 6. Home team league standing 1 2 3 4 5 2. Home team star player(s) 1 2 3 4 5 7. Home team history and tradition 1 2 3 4 5 3. Home team record breaking performance 1 2 3 4 5 8. Home team exciting play 1 2 3 4 5 4. Overall quality of home team players 1 2 3 4 5 9. Support the home team 1 2 3 4 5 5. Home team reputation 1 2 3 4 5 10. High level of skills 1 2 3 4 5 Opposing Team, Visiting Team, or Team B (1=Not at All to 5 = Very Much): 1. Opposing teams overall performance 1 2 3 4 5 6. Opposing team league standing 1 2 3 4 5 2. Opposing team star player(s) 1 2 3 4 5 7. Quality of opposing team 1 2 3 4 5 3. Opposing team history and tradition 1 2 3 4 5 8. Opposing team as a rivalry 1 2 3 4 5 4. Opposing team reputation 1 2 3 4 5 9. Opposing team exciting play 1 2 3 4 5 5. Overall quality of opposing team players 1 2 3 4 5 10. Player charisma of opposing team 1 2 3 4 5 Love of Professional Team Sport (1=Not at All to 5 = Very Much): 1. Played that sport(s) 1 2 3 4 5 6. Best players in a sport 1 2 3 4 5 2. Closeness of competition 1 2 3 4 5 7. Speed of game 1 2 3 4 5 3. Popularity of professional team sport 1 2 3 4 5 8. Athleticism of professional team sport 1 2 3 4 5 4. Duration of the game 1 2 3 4 5 9. High level of competitiveness 1 2 3 4 5 5. High level of performance 1 2 3 4 5 10. Love professional team sport(s) 1 2 3 4 5 Economic Consideration (1=Not at All to 5 = Very Much): 1. Personal ticket price 1 2 3 4 5 4. Group ticket cost 1 2 3 4 5 2. Ticket affordability 1 2 3 4 5 5. Ticket discount 1 2 3 4 5 3. Good seats 1 2 3 4 5 6. Sales Promotions 1 2 3 4 5

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165Game Promotion (1=Not at All to 5 = Very Much): 1. Advertising 1 2 3 4 5 3. Publicity 1 2 3 4 5 2. Direct mail & notification 1 2 3 4 5 4. Web information 1 2 3 4 5 Schedule Convenience (1=Not at All to 5 = Very Much): 1. Game time of the day 1 2 3 4 5 4. Day of the week 1 2 3 4 5 2. Convenient game schedule 1 2 3 4 5 5. Travel distance 1 2 3 4 5 3. Weather condition 1 2 3 4 5 6. Location of venue 1 2 3 4 5 ATTENDANCE INTENTION: With res pect to the professional team sport event that you most recently attended, please rate the follow ing statements that assess your intentions for future attendance at the professional team sport events (1 = Strongly Disagree to 5 = Strongly Agree). Re-patronage Intentions 1. I am likely to attend more games as soon as the sport is in season 1 2 3 4 5 2. I am likely to re-attend game(s) next season 1 2 3 4 5 3. I have a high likelihood of re-attending the game(s) next season 1 2 3 4 5 4. I plan on attending more game(s) of this professional sport in the future 1 2 3 4 5 5. The probability that I will re-attend this professional sport game is high 1 2 3 4 5 Recommendation to Others 1. I will recommend this professional sport game to other persons 1 2 3 4 5 2. I am likely to recommend this professional sport game to my family 1 2 3 4 5 3. I am likely to recommend this professional sport game to my friends 1 2 3 4 5 4. I am likely to say positive things about this professional sport game to other people 1 2 3 4 5 5. I will talk about this professional sport game with other people 1 2 3 4 5 SERVICE QUALITY: With respect to the professional team sport ev ent that you most recen tly attended, please rate the following s tatements that assess your perceptions of game-operation rel ated activities durin g your attendance (1= Very Unsatisfied to 5 = Very Satisfied) Ticket Service (1= Very Unsatisfied to 5 = Very Satisfied) 1. Phone order service 1 2 3 4 5 6. Ticket personnel friendliness 1 2 3 4 5 2. Will call service 1 2 3 4 5 7. Convenience of ticket sale locations 1 2 3 4 5 3. Ticket exchange program 1 2 3 4 5 8. Web (on-line) order procedures 1 2 3 4 5 4. Ticket agencies 1 2 3 4 5 9. Mail order 1 2 3 4 5 5. Game calendar and schedule 1 2 3 4 5 10. Efficiency of ticket office 1 2 3 4 5 Game Amenities (1= Very Unsatisfied to 5 = Very Satisfied) 1. Music selection 1 2 3 4 5 7. Music volume 1 2 3 4 5 2. Public address system 1 2 3 4 5 8. Scoreboard information 1 2 3 4 5 3. Replay screens 1 2 3 4 5 9. Pre-game shows/entertainments 1 2 3 4 5 4. During game shows/entertainments 1 2 3 4 5 10. Intermission/half-game entertainments 1 2 3 4 5 5. Post-game shows/entertainments 1 2 3 4 5 11. Dance/cheerleading activities 1 2 3 4 5 6. Give away/prize 1 2 3 4 5 12. Concourse entertainment activities 1 2 3 4 5

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166Arena/Stadium Services (1= Very Unsatisfied to 5 = Very Satisfied) 1. Food and drink quality 1 2 3 4 5 4. Food and drink price 1 2 3 4 5 2. Arena/Stadium cleanliness 1 2 3 4 5 5. Restroom availability 1 2 3 4 5 3. Restroom cleanliness 1 2 3 4 5 6. Staff courtesy 1 2 3 4 5 Arena/Stadium Accessibility (1= Very Unsatisfied to 5 = Very Satisfied) 1. Parking 1 2 3 4 5 6. Public transportation 1 2 3 4 5 2. Newness of arena/stadium 1 2 3 4 5 7. Niceness of arena stadium 1 2 3 4 5 3. Security 1 2 3 4 5 8. Ushers 1 2 3 4 5 4. Ticket takers 1 2 3 4 5 9. Ease of entrance 1 2 3 4 5 5. Traffic/crowd control 1 2 3 4 5 10. Seating directions 1 2 3 4 5 COST AND BENEFIT: With respect to the prof essional team sport event th at you most recently attended, please rate the following statements that assess your overall perceptions of gam e experience during your attendance (1= Definitely False to 5 = Definitely True) Perceived Value of Game Experience (1= Definitely False to 5 = Definitely True) 1. The game experience was a good buy 1 2 3 4 5 2. The game experience was worth the money 1 2 3 4 5 3. The game experience was fairly priced 1 2 3 4 5 4. The game experience was reasonably priced 1 2 3 4 5 5. The game experience was economical 1 2 3 4 5

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167DEMOGRAPHICS: Please provide the following information by circling an answer or filling a blank. 1. Gender: a. male b. female 2. Age: a. 10 years or younger b. 11-17 years old c. 18-22 years old d. 23-30 years old e. 31-40 years old f. 41-50 years old g. 51-65 years old h. 66 years or older 3. Number of people in your household: a. 1 b. 2 c. 3-4 d. 5-6 e. 7-8 f. 9 or more 4. Household income: a. below $ 20,000 b. $20,000-$39,999 c. $40,000-$59,999 d. $60,000-$79,999 e. $80,000-$99,999 f. $100,000-$149,999 g. $150,000-$199,999 h. above $200,000 5. Marital Status: a. single b. married c. divorced d. widowed e. other 6. Education: a. in school now b. high school graduate c. in college now d. college graduate e. advanced degree f. other (be specific) _____ 7. Ethnicity: a. Caucasian b. African American c. Hispanic d. Asian/Pacific Islander e. American Indian f. Interracial g. other 8. Occupation: a. management b. technical c. professional d. sales e. clerical f. education g. skilled worker h. non-skilled worker i. other

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183 BIOGRAPHICAL SKETCH Kun-wung Byon was originally from South Ko rea and spent the last 6 years pursuing a m asters and Ph D. in sport management at th e Slippery Rock University of Pennsylvania and University of Florida, respectively. He receive d his Bachelors of Art degree in Japanese literature at Hannam University in South Korea in 1998. He completed his Master of Science (specialization: sport management) in Augus t 2004. Finally, Kun-wung earned his Ph D. in health and human performance (sport manageme nt) and a minor in research evaluation and methodologies in 2008.


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