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Sport Spectator Conative Loyalty

Permanent Link: http://ufdc.ufl.edu/UFE0019460/00001

Material Information

Title: Sport Spectator Conative Loyalty A Comparison of Latino Subgroups and Non-Latino Consumers
Physical Description: 1 online resource (149 p.)
Language: english
Creator: Harrolle, Michelle Gacio
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: behavior, comparison, conative, consumer, latino, marketing, sport
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: Latinos are both the largest ethnic group in the United States and the fastest growing ethnic minority group in the U.S. Over a five year period (2000 2004), Latino sport fandom in the United States has increased for some sports, and this growth is expected to continue as the Latino population in the U.S. grows. While an escalating amount of consumer research within the marketing literature has studied the Latino population, the sport marketing and management field has neglected to study this particular market segment. This research has four main purposes that were encapsulated in three studies. In the first study, the Models of Sport Spectator Conative Loyalty (Models A, B, & C) were tested on a Latino and Non-Latino population to examine the models on these two market segments. For the second purpose, additional constructs (ethnic identity, identification with the dominant group, vicarious achievement, and past behaviors) were added to the Models of Sport Spectator Conative Loyalty (Model A & B) in order to see if any additional variance was explained in conative loyalty by the addition of these constructs to the original models. In the second study for the third purpose, data from four subgroups (Cubans, Puerto Ricans, Columbians, and Non-Latinos) were compared to determine if differences exist among Latino subgroups and Non-Latinos on identification with specific sports (American football, baseball, basketball, hockey, and soccer) and on sport consumer behaviors (attendance, media, and merchandise). In the third study for the fourth purpose, pre and post differences in participants conative loyalty were examined before and after a sporting event to determine the effect of the outcome of the game (win v. loss) on conative loyalty while controlling for identification with a team. Comprehensively, the results indicated differences do exist not only between Latinos and Non-Latinos, but also among Latino subgroups. This study provided the first stage of sport management research into the Latino market segments.
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 Michelle Gacio Harrolle.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Trail, Galen T.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0019460:00001

Permanent Link: http://ufdc.ufl.edu/UFE0019460/00001

Material Information

Title: Sport Spectator Conative Loyalty A Comparison of Latino Subgroups and Non-Latino Consumers
Physical Description: 1 online resource (149 p.)
Language: english
Creator: Harrolle, Michelle Gacio
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: behavior, comparison, conative, consumer, latino, marketing, sport
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: Latinos are both the largest ethnic group in the United States and the fastest growing ethnic minority group in the U.S. Over a five year period (2000 2004), Latino sport fandom in the United States has increased for some sports, and this growth is expected to continue as the Latino population in the U.S. grows. While an escalating amount of consumer research within the marketing literature has studied the Latino population, the sport marketing and management field has neglected to study this particular market segment. This research has four main purposes that were encapsulated in three studies. In the first study, the Models of Sport Spectator Conative Loyalty (Models A, B, & C) were tested on a Latino and Non-Latino population to examine the models on these two market segments. For the second purpose, additional constructs (ethnic identity, identification with the dominant group, vicarious achievement, and past behaviors) were added to the Models of Sport Spectator Conative Loyalty (Model A & B) in order to see if any additional variance was explained in conative loyalty by the addition of these constructs to the original models. In the second study for the third purpose, data from four subgroups (Cubans, Puerto Ricans, Columbians, and Non-Latinos) were compared to determine if differences exist among Latino subgroups and Non-Latinos on identification with specific sports (American football, baseball, basketball, hockey, and soccer) and on sport consumer behaviors (attendance, media, and merchandise). In the third study for the fourth purpose, pre and post differences in participants conative loyalty were examined before and after a sporting event to determine the effect of the outcome of the game (win v. loss) on conative loyalty while controlling for identification with a team. Comprehensively, the results indicated differences do exist not only between Latinos and Non-Latinos, but also among Latino subgroups. This study provided the first stage of sport management research into the Latino market segments.
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 Michelle Gacio Harrolle.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Trail, Galen T.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0019460:00001


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434d4771de65a41c9404268342a52bcd10f6eab2







SPORT SPECTATOR CONATIVE LOYALTY: A COMPARISON OF LATINTO
SUBGROUPS AND NON-LATINTO CONSUMERS




















By

MICHELLE GACIO HARROLLE


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

2007




































O 2007 Michelle Gacio Harrolle





























To my father, Peter E. Gacio










ACKNOWLEDGMENTS

I would like to thank all of the wonderful people that helped me finish this dissertation.

First off, I would like to thank my advisor, Dr. Galen Trail, for all of his tremendous help,

support, and advice. Next, I would like to thank my Mom and Dad. I know that you are both very

proud of me. I would like to thank my husband, best friend, and partner, Ryan Eric Harrolle.

Also, I would like to thank my committee, Dr. James Zhang, Dr. Dan Connaughton, and

Dr. Efrain Barradas. Finally, I would like to thank the wonderful people at the University of

Florida and the College of Health and Human Performance. Go Gators!












TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. ...............4.....


LI ST OF T ABLE S ................. ...............8................


LI ST OF FIGURE S .............. ...............9.....


AB S TRAC T ............._. .......... ..............._ 10...


CHAPTER


1 INTRODUCTION ................. ...............12.......... ......


Growth of the Latino Population ................ ...............12........... ...
Growth of the Latino Sport Fan ................... ............ ...............13.....
Sport Marketers focusing on the Latino Market ................. ...............13........... ..
M marketing to Latinos. ................. ......... ..... ... .......... ............1
Lack of Sport Marketing Research Pertaining to Latinos .............. ...............15....
Overview of the Research Problem ................ ...............15........... ...

Purpose .............. ...............16...
Theory Development ................. ....... ........ ........... .............1
Development of Sport Consumer Behavior Research ................. ............... ......... ...17
Practical Implications .............. ...............18....
Delimitations ................. ...............19...............
Limitations ................. ...............19.................
Definitions of Terms ................. ...............19........... ....
Overview ................. ...............20.................


2 LITERATURE REVIEW ................. ...............22................


Theoretical Background for the Model of Sport Spectator Conative Loyalty (Study 1/
Purpose 1) .............. ...............22....
Social Identity Theory .............. ...............23....
Identity Theory .............. .... ...... ..... ..........2
Identity Theory and Social Identity Theory............... ...............27.
Self-Esteem Theory .............. ... ............... ... .........2
Link between Identity Theory and Self-Esteem Theory ................. ........................__30
Satisfaction Theory ............... .... .... .._ _. .... _. ............3
Di sconfirmation/Confirmation of Expectancies ................. .. .. ......... ....... ........... .....33
Affective Responses (Positive Affect, Negative Affect, and Satisfaction) ................... ..35
Conative Loyalty ............... .. ............. .... .................3
Model of Sport Spectator Conative Loyalty (Study 1/Purposel)............... ...............3
Team Identification ............... .. .. .. ............. .. .. ... ........3
Disconfirmation or Confirmation of Expectations within a Sport Context.. .................. .41












Affective Responses (Positive Affect, Negative Affect, and Satisfaction) within a
Sport Context ....................... .. .. ........... .......4
Self-esteem Responses (BIRGing and CORFing) ................. .............................43
Conative Loyalty within a Sport Context ............................. ............... 45. ...
Results of the Model of Sport Spectator Conative Loyalty ................. ... .......................45
Revised Model of Sport Spectator Conative Loyalty (Study 1/Purpose 2) ................... .........46
Ethnic Identity ................ .. ........ .... .................4
Identification with the Dominant Society (Acculturation) ........._..__.... ...._.._.........49
Vicarious Achievement ........._... ......___ ...............5 1....
Past Behaviors ................... ....... ...... ... ..... .. .......5
Differences among Latinos Subgroups and Non-Latinos (Study 2/Purpose 3)......................52
Pre and Post Testing of Conative Loyalty (Study 3/Purpose 4) ............... ...................5


3 M ETHODS .............. ...............62....


Study 1 .............. ...............62....
Participants .............. ...............62....
Procedure ........._..._.._ ...._._. ...............62.....
In strmentati on..........._..... ..... ........ .. ......._..... .... ...... .......6
Points of Attachment Index and Team Identification Index ................. ...............64

(Dis)Confirmation of Expectancies Scale ............ ..... ........._........65
Affective State Index............... ...............66.
Self-Esteem Responses .............. .......... ....... ........6
Intentions for Sport Consumption Behavior Scale............... ...............67.
Multi group Ethnic Identity Measure ................ ............. ............... 68. ....
Abbreviated Multi dimensional Acculturation Scale ................. ... ...__...........69
Vicarious Achievement Sub scale................... ...... ...........6
Past Behaviors and Estimated Future Sport Consumption Behaviors Index ...........70
Data Analy sis............... ...............70
Study 2 ................. ...............71.......... .....
Participants .............. ...............71....
Procedure ................. ...............72.................
Data Analysis............... ...............73
Study 3 .............. ...............74....
Participants .............. ...............74....
Procedure ................. ...............75.................
Data Analysis............... ...............76


4 RE SULT S .............. ...............88....


M odel Results (Study 1/Purpose 1) ................. ...... ..............................8
Psychometric Properties of the Scales (Measurement Model for MSSCL Models A,
B & C)............... ...............88...
M S SCL M odel A............... ................ .................8
Latent Variable Relationships in MSSCL Model A............... ...............89...
M S SCL M odel B.............. .............. ........ ...............8
Latent Variable Relationships in MSSCL Model B .............. ...............90....












M S SCL M odel C.............. .............. ........ ...............9
Latent Variable Relationships in MSSCL Model C .............. ...............91....
Model Results (Study 1/Purpose 2) ................. ...... ........... .... ..........9
Psychometric Properties of the Scales (Measurement Model for Revised MSSCL
M odels A & B)............... ...............91...
Revised M S SCL M odel A............. ............ ..... ......................9
Latent Variable Relationships in Revised MSSCL Model A............... ...................9
Revised M S SCL M odel B .............. .. ............... ......... .......9
Latent Variable Relationships in Revised MSSCL Model B .............. .....................9
Results (Study 2/Purpose 3).................................. ......................9
Psychometric Properties of the Scale (Points of Attachment Index) ........._.... .............94
MANOVA Results .............. ...............95....
Results (Study 3/Purpose 4)............... ...............96...

5 DI SCUS SSION ................. ................. 125........ ....


Purpose 1/Study 1.............. ...............125...
Purpose 2/Study 1 .............. ...............129....
Purpose 3/Study 2 ............... ...............132...
Purpose 4/Study 3 .............. .. .. .... ............. ...............13
Recommendations for Future Research and Alternative Models ................. ................ ...134
Summary ................. ...............135................


LIST OF REFERENCES ................. ...............137................


BIOGRAPHICAL SKETCH ................. ...............149......... ......










LIST OF TABLES


Table page

4-1 Fit Measures for Measurement Model (Original Model Data & Revised Model Data)....97

4-2 Factor Loadings (P), Cronbach's Alpha (u), and Average Variance Extracted Values
(AVE) for Team Identification Index, (Dis)Confirmation of Expectancies Scale,
Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for
Sport Consumption Behavior Scale ..........___... ...._ ...............98..

4-3 Means and Standard Deviations (SD) for the Subscales [Team Identification Index,
(Dis)Confirmation of Expectancies Scale, Affective State Index, Self-Esteem
Maintenance Behavioral Scale, Intentions for Sport Consumption Behavior Scale,
Multigroup Ethnic Identity Measure, Abbreviated Multidimensional Acculturation
Scale, Vicarious Achievement Subscale, and Past Attendance] for the Total, Latino,
and Non-Latino groups .......... __. ..... ._ ...............100..

4-4 Correlation Matrix for the Latent Variables Included in the Measurement Model for
Original M odels A, B, & C............... ...............101...

4-5 Fit Measures for Model A, Model B, Model C, Revised Model A, & Revised Model
B for the Total Sample, Latino Sample, and Non-Latino Sample ................. ................102

4-6 Factor Loadings (P), Cronbach's Alpha (u), and Average Variance Extracted Values
(AVE) for Team Identification Index, (Dis)Confirmation of Expectancies Scale,
Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for
Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure,
Abbreviated Multidimensional Acculturation Scale, Vicarious Achievement
Sub scale, and Past Sport Consumption Behaviors Index .............. .....................0

4-7 Correlation Matrix for the Latent Variables Included in the Measurement Model for
Revised Models A & B ................ ...............105........... ...

4-8 Factor Loadings (P), Cronbach's Alpha (u), and Average Variance Extracted Values
(AVE) for Points of Attachment Index ................. ...............106......... ...

4-9 Correlation Matrix for the Latent Variables Included in Study 2 (Group
Comparisons) ........._ ...... .... ...............107...

4-10 Mean Scores (M) and Standard Deviations (SD) for Identification with American
Football, Baseball, Basketball, Hockey, and Soccer across Each Group (Non-
Latinos, Puerto Ricans, Cubans, and Colombians) ................. ................ ......... .108

4-11 Mean Scores (M) and Standard Deviations (SD) for Past Consumption Behaviors
(Attendance, Media, and Merchandise) Across Each Group (Non-Latinos, Puerto
Ricans, Cubans, and Colombians) .............. ...............109....











LIST OF FIGURES

figure page

2-1 Model A: Model of Sport Spectator Conative Loyalty ................. .......... ...............56

2-2 Model B: Model of Sport Spectator Conative Loyalty ................. .......... ...............57

2-3 Model C: Model of Sport Spectator Conative Loyalty ................. .......... ...............58

2-4 Modified Model A: Revised Model of Sport Spectator Conative Loyalty ................... .....59

2-5 Modified Model B: Revised Model of Sport Spectator Conative Loyalty ........................60

2-6 Sport Attendance Model .............. ...............61....

4-1 Model A for the Total Sample ................. ...............110.......... ..

4-2 Model A for the Latino Sample ................. ...............111.......... ..

4-3 Model A for the Non-Latino Sample ................. ...............112........... ..

4-4 Model B for the Total Sample ................. ...............113.......... ..

4-5 Model B for the Latino Sample ................. ...............114.......... ..

4-6 Model B for the Non-Latino Sample .............. ...............115............ ...

4-7 Model C for the Total Sample ................. ...............116.....__ ...

4-8 Model C for the Latino Sample ................. ...............117._.._ ..

4-9 Model C for the Non-Latino Sample .............. ...............118..._.__ ...

4-10 Revised Model A for the Total Sample ................. ......... ...............119

4-11 Revised Model A for the Latino Sample .............. ...............120....

4-12 Revised Model A for the Non-Latino Sample ........._. ............ ........._. .....2

4-13 Revised Model B for the Total Sample............... ...............122

4-14 Revised Model B for the Latino Sample............... ...............123

4-15 Revised Model B for the Non-Latino Sample .............. ...............124....









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

SPORT SPECTATOR CONATIVE LOYALTY: A COMPARISON OF LATINTO
SUBGROUPS AND NON-LATINTO CONSUMERS

By

Michelle Gacio Harrolle

August 2007

Chair: Galen T. Trail
Maj or: Health and Human Performance

Latinos are both the largest ethnic group in the United States and the fastest growing

ethnic minority group in the U.S. Over a Hyve year period (2000-2004), Latino sport fandom in

the United States has increased for some sports, and this growth is expected to continue as the

Latino population in the U.S. grows. While an escalating amount of consumer research within

the marketing literature has studied the Latino population, the sport marketing and management

Hield has neglected to study this particular market segment. This research has four main purposes

that were encapsulated in three studies. In the first study, the Models of Sport Spectator Conative

Loyalty (Models A, B, & C) were tested on a Latino and Non-Latino population to examine the

models on these two market segments. For the second purpose, additional constructs (ethnic

identity, identification with the dominant group, vicarious achievement, and past behaviors) were

added to the Models of Sport Spectator Conative Loyalty (Model A & B) in order to see if any

additional variance was explained in conative loyalty by the addition of these constructs to the

original models. In the second study for the third purpose, data from four subgroups (Cubans,

Puerto Ricans, Columbians, and Non-Latinos) were compared to determine if differences exist

among Latino subgroups and Non-Latinos on identification with specific sports (American

football, baseball, basketball, hockey, and soccer) and on sport consumer behaviors (attendance,









media, and merchandise). In the third study for the fourth purpose, pre and post differences in

participants' conative loyalty were examined before and after a sporting event to determine the

effect of the outcome of the game (win v. loss) on conative loyalty while controlling for

identification with a team. Comprehensively, the results indicated differences do exist not only

between Latinos and Non-Latinos, but also among Latino subgroups. This study provided the

first stage of sport management research into the Latino market segments.









CHAPTER 1
INTTRODUCTION

Growth of the Latino Population

The Latino population in the United States was estimated to be 42.7 million, constituting

approximately 14% of the nation' s total population as of July 1, 2005. Moreover, Latinos are

both the largest ethnic group in the United States and the fastest growing ethnic minority group.

It is proj ected that the Latino population will grow to 106.7 million by July 1, 2050. With this

rate of growth, Latinos will constitute approximately 24% of the nation' s total population by

2050 (U. S. Census, 2006). Consequently, this growth will lead to an increase in buying power

for Latinos.

Not only did Latinos control over $700 billion in spending power in 2005, but over a 20-

year period (1990-2010), their purchasing power is anticipated to increase 413% to $1,087

billion (Humphreys, 2005). The buying power of Latinos living in the United States will also

grow faster than any of the following minority groups: African American (222%), Asian

Americans (251%), and Native American (397%). In 2010, Latinos will account for

approximately 9.2% of the total buying power in the United States (Humphreys).

The median income of Latino households was $35,967 in 2005 which is 38% less than

the national median income ($57,278; Humphreys, 2005). However, Latinos spend more money

on average than Non-Latinos on "groceries, telephone services, furniture, maj or appliances,

men's and boy's clothing, and footwear" (p. 8) and less money than Non-Latinos on "health care,

entertainment, education, and personal insurance" (p. 8). These figures are based on Latinos as a

whole; however, spending patterns may differ based on a Latino's country of origin

(Humphreys).









Subsequently, marketers have become increasingly aware of the growth and purchasing

power of the Latino consumer (Berkowitz, Bao, & Allaway, 2005; Mulhern & Williams, 1994).

Therefore, numerous United States' companies have begun to increase their marketing

campaigns directed toward the Latino market in an effort to capture their attention (Berkowitz et

al., 2005).

Growth of the Latino Sport Fan

Over a five year period (2000-2004), Latino sport fandom in the United States has

increased for some sports, and this growth is expected to continue as the Latino population in the

U.S. grows ("The changing," 2006). For example, Latino fandom for collegiate football and

collegiate basketball has increased 3.1% and 4.4% respectively over the past year. However,

some sports have seen a decrease in the percentage of U. S. Latinos indicating that they are fans

[e.g., National Basketball Association (NBA) 4.6% decrease, professional boxing 6.4%

decrease]. As a whole in 2006, 36% of all Latinos reported themselves as avid fans of at least

one sport compared to 29.8% of the total population ("World Congress," 2006, pp. 34-3 5).

Sport Marketers focusing on the Latino Market

Recognizing this potential market segment, professional sports have been actively

marketing to the Latino population. For example, National Association for Stock Car Auto

Racing (NASCAR) has been aggressively recruiting the Latino fan base with promotional

activities and incentives. Additionally, NASCAR has been recruiting Latino drivers, employees,

and business partners (Smith, 2006). For the 2006 World Baseball Classic, Maj or League

Baseball (MLB) developed different marketing strategies to target Latinos from multiple Latin

American countries (King, 2006). In order to attract the Latino market, National Football League

(NFL) teams celebrated Hispanic Heritage Month with festivities at multiple games (NFL News,









2006). For a grassroots marketing initiative, the NBA has partnered with McDonalds@ and

Discovery Kids en Espahiol to help tackle childhood obesity (Hispanic PR Wire, 2006).

Professional teams are also using Spanish to market to Latinos. MLB has not only

doubled their Spanish-language websites (Fisher, 2006), but they also have White Sox manager,

Ozzie Guillen, speaking in Spanish on a Comcast SportNet Chicago radio program (Marchand,

2006). In order to expand into the Spanish language television market in the U. S., Maj or League

Soccer (MLS) committed to an eight year, $80 million deal with Univision in 2006 (Mickle,

2006). However, using Spanish to communicate to consumers should not be the only

consideration when sport organizations are sending marketing messages to Latinos.

Marketing to Latinos

From a sociological perspective, Davila (2001) argued that "Hispanic marketing" in the

early 1970s stemmed from the ideas within corporate America to market to the "Hispanic"

population. The profitability of the Latino market has dictated an accompanying increase in

marketing campaigns toward Latinos, thus numerous Hispanic advertising agencies have

developed within large Hispanic populations (Davila). Based on her exploration of the political

and economic implications of marketing to Latinos, Davila argued that current marketing

practices to Latinos as a homogeneous group and using stereotypical representations will

inevitably perpetuate marketing campaigns stereotyping ethnic consumers.

Even though Latinos are labeled through common names (i.e., Latinos, Hispanics), they

are a very heterogeneous group with multiple cultural and geographical backgrounds (Davila,

2001; Oboler, 1998; Torres-Saillant, 2005). The larger ethnic group of Latinos is comprised of

different nuances, which can be broken into numerous categories (e.g., culture, religion, music,

family values, attitudes, holidays) based on their nationality (Braus, 1993), and these differences

may dictate their marketing preferences and potential identification with specific sports or teams.









Because Latinos are a complex, heterogeneous group, sport marketers and managers should

strive to understand the cultural distinctions between the subgroups within the Latino community

(e.g., Cubans, Puerto Ricans, Mexicans, Dominicans) that have distinctive cultural backgrounds,

national histories, demographic profies, and levels of progress (Valdes, 2000). Braus (1993)

stated that many marketers attempt to place all Latinos into one target market, while neglecting

the fact that considerable cultural and demographic diversity exist.

Typically, marketers will use Spanish as a common dominator when marketing to Latinos

as a homogeneous group (Korzenny & Korzenny, 2005). Nevertheless, the Latino market

segment is more than a group of individuals that speak a common language, and marketers

should Eind better ways to understand Latinos and market to them accordingly. The content of

the marketing message should be tailored to the Latino segment and any specific Latino

subgroups.

Lack of Sport Marketing Research Pertaining to Latinos

As companies have increasingly recognized the need to market to Latinos, marketing

research efforts have consequently increased (Deshpande, Hoyer, & Donthu, 1986; Mulhern &

Williams, 1994; Shepherd, Tsalikis, & Seaton, 2002). While an escalating amount of consumer

research within the marketing literature has studied the Latino population (e.g., Berkowitz et al.,

2005; Donthu & Cherian, 1994; Korgaonkar, Karson, & Lund, 2000; Mulhern & Williams, 1994;

Torres & Briggs, 2005; Torres & Gelb, 2002), the sport marketing and management Hield has

neglected to study this particular market segment.

Overview of the Research Problem

When searching through the EB SCOhost Research Database, specifically in Academic

Search Premier, Business Source Premier, and SPORTDiscus Select, using any combination of

the search terms Hispanic, Latino, consumer, and sport, not one academic journal article is










available in the search results. A lack of sport research examining the Latino fan is evident. This

study would be the first comprehensive study that examined Latino sport consumption behavior.

Additionally, this study attempts to examine the conative loyalty of the Latino fan by testing the

Models of Sport Spectator Conative Loyalty (MSSCL; Trail, Anderson, & Fink, 2005) on a

Latino sample and to compare the sport consumption behaviors among Latinos subgroups and

Non-Latinos.

Purpose

This research has four main purposes that were encapsulated in three studies. In the first

study, the Models of Sport Spectator Conative Loyalty (MSSCL Models A, B, & C; Trail et al.,

2005) were tested on a Latino and Non-Latino population to examine the models on these two

market segments. For the second purpose, additional constructs (ethnic identity, identification

with the dominant group, vicarious achievement, and past behaviors) were added to the Models

of Sport Spectator Conative Loyalty (Model A & B) in order to see if any additional variance

was explained in conative loyalty by the addition of these constructs to the original models. In

the second study for the third purpose, data from four subgroups (Cubans, Puerto Ricans,

Columbians, and Non-Latinos) were compared to determine if differences exist among Latino

subgroups and Non-Latinos on identification with specific sports (American football, baseball,

basketball, hockey, and soccer) and on sport consumer behaviors (attendance, media, and

merchandise). In the third study for the fourth purpose, pre and post differences in participants'

conative loyalty were examined before and after a sporting event to determine the effect of the

outcome of the game (win v. loss) on conative loyalty while controlling for identification with a

team .









Theory Development

The results of this study would contribute to theory development by further examining

and testing previous theories including identity theory, self-esteem theory, social identity theory,

and acculturation theory. The MSSCL was developed by combining the different perspective of

both identity theory and satisfaction theory (Trail et al., 2005), while the additional variables in

the revised models are explained through acculturation theory, self-esteem theory, and social

identity theory. The results of this research would support the development of these theories in

two ways: (1) verify the structure of the theories, and (2) explain sport consumption behaviors

through the tested models. By combining these theories and developing new models, the results

of this study could help to explain more variance in sport consumption behaviors, thus

supporting the theories.

Development of Sport Consumer Behavior Research

This research would contribute to the sport management literature in four primary ways.

First, the MSSCL would be replicated using an additional population, moreover the replicated

population would represent a minority segment in the United States (Latinos). The findings

would not only test the MSSCL psychometrics and model fit, but the model would also facilitate

the understanding of conative loyalty for Latino sport consumers. Second, in an effort to extend

the previous research by Trail et al. (2005), additional variables would be added to the MSSCL

(Models A & B), thus attempting to increase the variance explained in conative loyalty. Third,

Gacio Harrolle and Trail (2007) suggested future research should segment the Latino population

and test Latino subgroups in order to examine the possible differences in sport identification and

sport consumption among these groups. The findings of the subgroup comparisons would be the

first of its kind within the sport management literature, and the results would facilitate the

understanding of group differences among different subgroups within one ethnic group. Fourth,










the pre and posttest of conative loyalty would provide researchers with a guide to understanding

the relationship between the outcome of the game (win or loss) and an individual's sport

consumption intentions while controlling for an individual's identification with a specific team.

To further the sport management literature, this study would test the relationships among

the numerous variables (confirmation/di confirmation of expectancies, positive affective

responses, negative affective responses, satisfaction, BIRGing, CORFing, identification with a

team, conative loyalty, past behaviors, vicarious achievement, ethnic identity, and identification

with the dominant group). By understanding these relationships and by further testing models of

conative loyalty (MSSCL & modified MSSCL), this study would expand the knowledge and

theory development of the sport management and marketing literature.

Practical Implications

Sport marketers and managers have been blindly marketing to the Latino sport fan based

on demographic information. Because previous sport management researchers (Fink, Trail,

Anderson, 2002; Funk, Ridinger, & Moorman, 2003; Robinson & Trail, 2005; Trail, Robinson,

Dick, & Gillentine, 2003; Wann, Schrader, & Wilson, 1999) have explained more variance in

sport identification and fan identification using variables other than demographics (e.g., motives

and psychographics), the findings of this study would provide sport marketers with more

effective tools for marketing strategies.

Sport managers would possess an efficient way of determining the relationships as to

why Latinos consume sport and a more appropriate model to follow when marketing to this

ethnic group. As managers market to their Latino segments more appropriately, the potential for

revenue growth also increases. Additionally, the results of this study would enable sport

marketers to have a better understanding of the differences between Latinos, their subgroups, and

Non-Latinos.










Delimitations

Thi s study was delimited to the di sconfirmation/confirmation of expectations,

identification with a team, mood, self-esteem responses, conative loyalty, ethnic identity,

identification with the dominant group, motives, and past behaviors of Latino/a and Non-

Latino/a students at one southeastern National Collegiate Athletic Association (NCAA) Division

I university. Therefore, the results of the study may not be generalizable to other students or

individuals in the United States. Because the sample was collected before and after one

collegiate football game, the results may not be generalizable to other sporting events. In

addition, other factors (e.g., gender, income) may influence the differences between or within the

groups.

Limitations

As with any type of survey-based research, limitations are inevitable. The convenience

sample approach for the students in the study is a limitation of this study, such that the Latino/a

and Non-Latino/a students were members of Latino organizations, Latin American Studies

classes, and the College of Health and Human Performance. The results of this study may differ

from other universities. The Latino participants in the study were representative of the Latino

community in Florida; however, it may not be representative of the entire U. S. Latino

population. A low response rate from emails sent to university students may have harmed the

results of the group comparisons within the Latino sub-groups.

Definitions of Terms

As all of the terms tested in this study are numerous and complex, the definitions for the

terms are placed in Chapter 2. Confirmation/di sconfirmation of expectancies, positive affective

responses, negative affective responses, satisfaction, self-esteem responses such as BIRGing and

CORFing, identification with a team, conative loyalty, past behaviors, vicarious achievement,









ethnic identity, and identification with the dominant group are defined in detail in the following

chapter.

Overview

Chapter 1 introduces the scope of the study. This chapter contains background

information on the Latino sport market in the United States, the lack of sport marketing research

of the Latino population, and the purpose of the study.

In Chapter 2, the theoretical framework for the study is presented. Initially, the theories

supporting the Model of Sport Spectator Conative Loyalty are discussed. A thorough literature

review of social identity theory, identity theory, self-esteem theory, and acculturation theory is

included. Then, each construct of the MSSCL is examined including the relevant sport

management literature. Subsequently, the constructs that will be added to the original MSSCL

model are explained including ethnic identity, identification with the dominant group

(acculturation), vicarious achievement, and past behaviors. Lastly, group differences among

Latino subgroups and differences between Latinos and Non-Latinos are discussed along with the

pre and post testing of conative loyalty.

The material and methods of the research are located in Chapter 3. The participants,

instrumentation, procedures, and data analysis for each of the three studies are presented. The

statistical analysis procedures for each study includes descriptive statistics of the participants and

psychometric properties of the scales including the confirmatory factor analysis (CFA) of the

measurement model, the construct reliability, internal consistency, and discriminant validity of

the constructs. For the first study, the model fit for the original Model of Sport Spectator

Conative Loyalty (Models A, B, & C) on the samples and the model fit for the modified Model

of Sport Spectator Conative Loyalty (Models A and B) on the samples are presented. For the

second study, a MANOVA to compare the sport identification and sport behaviors among the










subgroups is included. For the third study, the methods section includes an ANCOVA to

compare the pre and post test results for conative loyalty on the total sample while controlling for

team identification.

In Chapter 4, the results of the statistical analysis are presented. The first section contains

the results of the first study including the psychometric properties of the scales, the confirmatory

factor analysis (CFA) of the measurement model, the construct reliability, internal consistency,

discriminant validity of the constructs, and the model fit for the original Model of Sport

Spectator Conative Loyalty (Models A, B, & C) on the Latino and Non-Latino sample. The

second section contains the results for purpose 2 including the psychometric properties of the

scales, the confirmatory factor analysis (CFA) of the measurement model, the construct

reliability, internal consistency, discriminant validity of the constructs, and the model fit for the

modified Model of Sport Spectator Conative Loyalty (Models A and B) on the Latino and Non-

Latino sample. In the third section, the results for the second study, the comparisons of sport

identification and sport behaviors among the subgroups, are presented. Finally, the fourth section

contains the results of the third study, the pre and post test results for conative loyalty on the total

sample.

The final chapter, Chapter 5, contains the discussion of the results. Additionally, the

recommendations for future research and practical applications for sport managers are included.









CHAPTER 2
LITERATURE REVIEW

The goal of Chapter 2 is to provide the reader with a thorough background of the research

and theory related to the four main purposes of this study. This literature review begins with the

theoretical background of the MSSCL including identity theory, satisfaction theory, and self-

esteem theory along with the relevant social identity theory. Then, a detailed explanation of the

MSSCL (Models A, B, and C) is explained along with the pertinent sport management literature.

Next, the results of Trail, Anderson, and Fink' s (2005) original study are provided, along with

the revised MSSCL (Models A and B) including the additional constructs (ethnic identity,

dominant identity, vicarious achievement, and past behaviors) added to the models. After that,

the need to examine group differences among Latino subgroups and differences between Latinos

and Non-Latinos was explored. Finally, the need for pre and post testing of conative loyalty are

explained.

Theoretical Background for the Model of Sport Spectator Conative Loyalty (Study 1/
Purpose 1)

Trail et al. (2005) proposed and tested three distinct, but related, sport consumption

behavior models (Models A, B, & C) that depicted the relationships among

disconfirmation/confirmation of expectancies, identification with a team, mood, self-esteem

responses, and conative loyalty. Their research and models were based on three primary theories:

identity theory, self-esteem theory, and consumer satisfaction theory. This literature review

begins with a thorough exploration of the theoretical framework of the original models and the

related theory of social identity theory. Trail et al. (2005) did not utilize social identity theory

when forming their models. However, the discussion of social identity theory within this

literature review is due to the commonalities between social identity theory and identity theory,









the use of both in the sport consumer literature, and the confusion created by the distinction

between the two theories.

Social Identity Theory

Tajfel and Turner (1986) defined social groups "as a collection of individuals who

perceive themselves to be members of the same social category, share some emotional

involvement in this common definition of themselves, and achieve some degree of social

consensus about the evaluation of their group and of their membership in it" (p. 15). Tajfel and

Turner (1986) argued that social groups provide their members with a way of identifying

themselves based on the social dynamics of the group and facilitate a way for their members to

create and define their place in society. Originally developed by Tajfel and Tumner (1979), social

identity theory (SIT) deals specifically with how individuals become members of a group, how

individuals see themselves in that group, and how individuals in one group compare themselves

with another group (Brown, 2000; Stets & Burke, 2000). Tajfel (1981) described social identity

"as that part of an individual's self-concept which derives from his knowledge of his

membership of a social group (or groups) together with the value and emotional significance

attached to that membership" (p. 255). The framework of social identity theory contained several

stipulations regarding group membership: (a) individuals maintain or seek out memberships in

social groups if these groups provide a positive aspect to the individual's social identity (self-

esteem); (b) an individual may leave a group if his/her membership does not satisfy a need for

positive identity; (c) if an individual can not depart from a group membership, then he/she will

adapt, justify a lower status, or simply accept the situation; and (d) all groups in society live

among other groups (Tajfel, 1981, p. 256).

Originally, SIT was explored and developed as a supplemental theory to Sherif s (1967)

realistic group conflict theory (RCT; Tajfel & Tumner, 1986). Tajfel and Turner argued that RCT









did not examine the identification with the in-group, thus failing to explain the process of

development and maintenance of group identity. Therefore, this neglect of RCT perpetuated

inconsistencies in the theory and empirical research of social group theories. Tajfel and Turner

proposed SIT as a way to explain the psychological aspects of social psychology and social

conflict. Moreover, they attempted to answer the question of psychological processes involved in

the development of positive group identity along with the question of the conditions involved

with the enhancement or reduction of group conflict.

Within social groups, intergroup behaviors exist. These intergroup behaviors are defined

as "any behavior displayed by one or more actors toward one or more others that is based on the

actors' identification of themselves and the others as belonging to different social categories"

(Tajfel & Turner, 1986, p. 15). According to Brown (2000), this concept of intergroup behaviors

and dynamics developed by Sherif (1967) and Tajfel and Turner (1986) has made significant

contributions to SIT in the form of relevant research in response to inequality between groups,

ingroup bias, stereotyping, and changing attitudes as contact with other groups increases.

Tajfel (1981) stipulated that through these social memberships and relationships, an

individual strives to achieve a favorable level of self-esteem or self-image. Additionally, a

favorable group comparison between ingroups and outgroups can promote higher levels of self-

esteem as long as the comparisons are made between comparable groups (Brown, 2000).

Social identity theory and identity theory are related such that both theories deal with the

self within a social context. Moreover, Stryker and Burke (2000) and Stets and Burke (2000)

hypothesized that these two theories were connected and overlapped due to their connection with

the overall concept of the self.









Identity Theory

Stryker and Burke (2000) defined identity as the meanings that individuals attach to the

multiple roles that are played in a contemporary society. Burke (2003) stated that "identities are

the meanings that individuals hold for themselves what it means to be who they are" (p. 196).

Identity theory can be traced back to Mead's (1934) framework and formulas stating that society

shapes "self' and that each person shapes social behavior. Originally, identity theory was

developed as a way to conceptualize the ideas of"society" and "self" and to develop ways to

research these concepts (Stryker & Burke, 2000). As one accepts Mead's hypothesis that "self

reflects society," then self would be considered "multifaceted, made up interdependent and

independent, mutually reinforcing and conflicting parts" (Stryker & Burke, p. 286).

The first assumption when examining identity is that there is a reciprocal relationship

between society and the self (Stryker, 1980). An individual influences society through actions

and through "creation of groups, organizations, networks, and institutions. Reciprocally, society

influences the self through its shared language and meanings that enable a person to take the role

of the other, engage in social interaction, and reflect on oneself as an obj ect" (Stets & Burke,

2003, p. 128). This interaction between self and society requires researchers to not only

understand the self, but also study and understand the parts of self (identities) within the context

and influences of society as a whole (Stryker, 1980).

Burke and Tully (1977) stated that identities are based on the meanings that an individual

attributes to himself/herself as an obj ect in a social role or social situation. Moreover, these

meanings to the roles occur through interaction with others. Within identity theory, Stets and

Burke (2000) suggested the essence of "identity is the categorization of the self as an occupant of

a role, and the incorporation" (p. 225) of the meanings associated with that role. They further

explained that as an individual has a particular role identity a need exists ensuring that the









individual will fulfill the expectations associated with that role. Therefore, researchers can use

the concept of identity to examine individuals in terms of each network of relationships or roles

in which an individual occupies.

Based on the concepts of "self' and "society," Stryker and Burke (2000) stated that

previous identity theory research examined identity from two different perspectives or strands:

how social interactions affect one's identities and the process of internal self-verifieation of

one's identity. They suggested that identity theory is based on both internal self-verification

processes and social constructs. Interactions between individuals are typically not between all

aspects of a "whole person," rather interactions between individuals are between specific aspects

of each person within the context of a particular role or group membership (Stets & Burke,

2003). That is, the interaction is between each person's role identity specific to that situation

(e.g., an interaction between an individual in the role identity of teacher and an individual in the

role identity of student).

Individuals have multiple role identities at the same time (McCall, & Simmons, 1978;

Stryker, 1980; Stets & Burke, 2003), and these multiple roles may be good for the self (Stets &

Burke, 2003). McCall and Simmons (1978) suggested that multiple role identities provide an

individual with a sense of meaning and purpose, therefore providing a positive aspect to his/her

life. However, the kinds of role identities may have an effect on their positive influence on the

self (Stets & Burke, 2003). For example, stressful role-identities may not be beneficial to the

self, whereas a role-identity as a friend with little or no stress would be beneficial.

In society, identities are organized in a hierarchical fashion, such that the salience of the

identity dictates the importance of the identity (Burke & Tully, 1977; Stryker & Burke 2000).

Based on the situation, an individual will possess a particular identity and act accordingly.









Identity is an internal process, whereas a role is an external component. An individual may

internalize the expectations and meanings of specific roles. Moreover, social situations can

connect multiple roles based on the situation and the social group (Stryker & Burke, 2000). For

example, students partake in numerous roles simultaneously through interaction in numerous

networks including social groups with other students, interaction with professors, interaction

with their local communities, and other social groups connected to their university, all at the

same time.

Moreover, researchers hypothesized that as the level of salience increases for a particular

identity, behavioral decisions attached to that particular identity will also increase (Stryker &

Burke, 2000). These different roles may elicit different magnitudes of identification. For

example, students may have stronger ties to their universities than to their local communities.

Previous research found that individuals with higher levels of commitment to a particular group

maintained their identity with the group and worked harder to maintain their identity (Burke &

Reitzes, 1991).

Stryker and Burke (2000) stated that the goal of identity research should be to examine

and improve the knowledge concerning the relationships between society and self. Additionally,

they suggested that emotions and mood needed to be explored within identity research.

Identity Theory and Social Identity Theory

Identity theory and social identity theory are similar in many respects and recently have

been combined to create one inclusive theory of the self. Stets and Burke (2000) stated that in

both social identity theory and identity theory an individual can classify, categorize, or name

himself/herself in relation to other social groups, and both theories deal specifically with

structured societies. Within social identity theory, this process is called self-categorization

(Turner, Hogg, Oakes, Reicher, & Wetherell, 1987), while in identity theory this process is









called identification (McCall & Simmons, 1978). Moreover, an identity is formed through the

processes of self-categorization or identification (Stets & Burke, 2000). Stryker and Burke

(2000) along with Stets and Burke (2000) hypothesized that these two theoretical strands were

related and converged at the behavioral level and during interactions with others. Burke (2000)

combined three primary concepts/theories on identity to define the essence of identity: (a) role

identities or roles that an individual plays (e.g., being a teacher, a student, or a friend); (b) social

identities or social group memberships that provide meaning to an individual's identity (e.g.,

being a college graduate or an American); (c) personal identities or personal characteristics that

are not necessarily shared with others (e.g., being honorable, confident, or responsible).

Furthermore, Burke expressed a need for a unification of these three theoretical identity sources

(role, social, and personal), thus serving as a better measurement of identity. Additionally, due to

the broader concept of the self, researchers (Ervin & Stryker, 2001; Owen & Serpe, 2003;

Stryker & Burke, 2000) have expressed the need for a linkage between identity theory and self-

esteem theory.

Self-Esteem Theory

Rosenberg (1979) defined self-esteem as the "positive or negative evaluation of the self"

(p. 31). Rosenberg, Schooler, Schoenbach, and Rosenberg (1995) expressed self-esteem in terms

of an attitude containing two main distinctions. First, they postulated that self-esteem was an

attitude, such that individuals have attitudes about obj ects as a whole (global self-esteem) and

specific attitudes about that the characteristics of the obj ects (specific self-esteem). Secondly,

they stated that attitudes have both cognitive and affective elements. In addition, as with other

attitudes, Rosenberg et al. also stated that self-esteem may contain both negative and positive

elements .









The current views on self-esteem theory were developed primarily from the theories of

self-concept developed in the late 1970s by Morris Rosenberg (Elliott, 2001). Rosenberg (1979)

defined self-concept as "the totality of the individual's thoughts and feelings having reference to

himself as an obj ect" (p. 7). Furthermore, self-concept was defined using three broad concepts:

"the extant self (how an individual sees himself); the desired self (how he would like others to

see himself; and the presenting self (how he shows himself to others)" (p. 9). Elliott (2001)

argued that Rosenberg' s theoretical and empirical studies were the most influential of all self-

esteem theorists in the second half of the twentieth century.

Three main principles define self-esteem theory: (a) individuals want to be happy with

themselves and increase their self-esteem; (b) individuals want to maintain a consistent self-

image, even if this image is negative; and (c) thoughts about oneself and feelings about oneself

are intertwined concepts (Ervin & Stryker, 2001). During self examination, self-esteem refers to

the affective responses to oneself, such that an individual evaluates who they are (Rosenberg,

1979; Rosenberg et al., 1995). Self esteem stabilizes a group, because individuals are motivated

to maintain relationships that ultimately verify identities (Cast & Burke, 2002).

More recently, Rosenberg et al. (1995) conducted a longitudinal study of high school

students in order to examine the nature of self-esteem as containing both a global and specific

self-esteem (academic self-esteem) and in order to clarify previous self-esteem research. Their

findings supported their claims that global and specific self-esteem are distinct and Non-

interchangeable, and the claims that specific self-esteem has a greater effect on the dependent

variable than does global self-esteem. Therefore, because self-esteem is an attitude, self-esteem

specific to a role, as apposed to global self-esteem, should more accurately predict relevant

behavior (Rosenberg et al., 1995).









Rosenberg (1965) designed a self-esteem scale (Rosenberg Self-Esteem Scale: RSES)

that was meant to measure self-esteem as a unidimensional model. His measurement model was

designed to be easily administered, to be economic and efficient, to contain face validity, and to

be unidimensional (Owen & King, 2001). However, through examining previous research, Owen

and King (2001) argued that self-esteem contains both a positive dimension and negative

dimension and have shown that a bidimensional measurement model of self-esteem nearly

always fits the data better than a unidimensional model. Through confirmatory factor analysis,

they showed that a two-factor model fit the data better than a one-factor model. Owen and King

concluded that self-esteem should be examined not as a strictly positive dimension, but should

also include the negative dimension of self-esteem (e.g., happiness and unhappiness). This

concept of bidimensional self-esteem models have also used to examine self-esteem responses

within the sport literature.

Link between Identity Theory and Self-Esteem Theory

Previous researchers (e.g., Cast & Burke, 2002; Ervin & Stryker, 2001; Stryker & Burke,

2000) have suggested that self-esteem theory can stem from the theoretical framework of identity

theory. Additionally, Cast and Burke (2002) stated that self-esteem is an outcome of a successful

self-verification process. Their findings supported the idea that self-esteem is a central

component of the identity process, such that as individuals self-verify their social group identities

they also build self-esteem. For example, as an individual self-verifies an identity then he/she

subsequently produces a feeling of competency and worth, thus increasing self-esteem.

Additionally, Ervin and Stryker (2001) suggested that self-esteem fits logically into

identity theory formation and defined self-esteem as the affective attachment to one's self and

outlined an argument for this linkage. Ervin and Stryker (2001) argued that both self-esteem and

identity are "aspects of self" (p. 32). They argued that identity theory has developed to include









the concepts of social interaction interactionall commitment) and the affective responses to those

social interactions (affective commitment). Ervin and Stryker (2001) hypothesized that the

concepts of identity lead to self-esteem constructs and role performance, while self-esteem

constructs lead to identity salience and role performance. For example, highly identified sport

fans increase their self-esteem through social interactions and self-verification, and this higher

self-esteem leads to a stronger and more salient role as a sport fan. Overall, Cast and Burke's

results supported the notion that self-esteem should be incorporated into identity theory.

As previously discussed, Trail and colleagues' (2005) conative loyalty model was based

on identity theory, self-esteem theory, and consumer satisfaction theory. Once an individual

creates a fan role-identity and starts consuming a sport product, the next progression within the

consumption behavior process would be the individual's evaluation of that product and the

subsequent satisfaction or dissatisfaction with his/her purchase of a good and/or service (Oliver,

1997).

Satisfaction Theory

Based on the need to fully understand consumers' needs and subsequently offer

appropriate products and services, American businesses placed a high priority on the

measurement of consumer satisfaction in the late 1990s (Vavra, 1997). However, within the

business literature, consumer satisfaction began to develop as a legitimate field of study in the

early 1970s (Churchill & Surprenant, 1982). Cardozo's (1965) pioneering research into customer

efforts, expectations, and satisfaction along with previous researchers in the early 1970s (e.g.,

Anderson, 1973; Olshavsky & Miller, 1972) formed the foundation of the first decade of the

development of consumer satisfaction research (Vavra, 1997). Within the political arena, Pfaff

(1972) reported the first measurement problems and opportunities of consumer satisfaction by

developing the U.S. Department of Agriculture' s Index of Consumer Satisfaction for policy









makers. Moreover, the majority of these initial research studies on customer satisfaction

contained "the disconfirmation paradigm which holds that satisfaction is related to the size and

direction of the disconfirmation experience, where disconfirmation is related to the person' s

initial expectations" (Churchill & Surprenant, p. 491).

A lack of consensus exists within consumer satisfaction research on an appropriate

definition for satisfaction (Giese & Cote, 2000; Peterson & Wilson, 1992; Yu & Dean, 2001). At

the beginning stages of consumer satisfaction research, Anderson (1973) defined

satisfaction/di satisfaction based on the levels of discrepancy between expectations and

perceived performance of a product. Taking into consideration previous literature and research

results, Oliver (1997) defined satisfaction as "the consumer's fulfillment response. It is a

judgment that a product or service feature, or the product or service itself, provided (or is

providing) a pleasurable level of consumption-related fulfillment, including levels of under- or

overfulfillment" (p. 13). This fulfillment implies that a goal exists and should be filled. Hence,

fulfillment and satisfaction can be a judgment or a comparison by an individual with reference to

some standard. Thus, satisfaction is a consumer' s response to a level of fulfillment that is

unpleasant or pleasant (Oliver, 1997). Additionally, Giese and Cote (2000) found that previous

research contained common elements when defining satisfaction. Three general components

were "identified: 1) consumer satisfaction is a response (emotional or cognitive); 2) the response

pertains to a particular focus (expectations, products, consumption experience, etc.); and 3) the

response occurs at a particular time (after consumption, after choice, based on accumulated

experience, etc.)" (Giese & Cote, p. 1). In an effort to provide a meaningful definition of

satisfaction, Giese and Cote identified the concepts within satisfaction and proposed an outlined

process of satisfaction, such that comparisons could be made across studies. Through a detailed









literature review and validation through group and personal interview data, Giese and Cote

defined consumer satisfaction as a summary affective response of varying intensity, with a time-

specific point of determination, directed toward the focal aspects of product acquisition and/or

consumption (p.14-15).

Previous authors have defined satisfaction in two fundamental ways: as either an outcome

or a process (Vavra, 1997). From an output perspective, Churchill and Surprenant (1982) defined

satisfaction as "an outcome of a purchase and use resulting from the buyer' s comparison of the

rewards and costs of the purchase in relation to the anticipated consequences" (p. 493). From a

process perspective, Tse and Wilton (1988) defined satisfaction "as the consumer's response to

the evaluation of the perceived discrepancy between prior expectation" (p. 204) and actual

performance after consumption. The core process of satisfaction is the comparison between what

is expected by the consumer and the actual performance of the product (the

confirmation/disconfirmation process; Vavra, 1997). Oliver (1980) was the first researcher to

suggest that expectations served as a frame of reference for measuring a consumer' s experience

(Vavra, 1997). Oliver' s (1997) cycle of satisfaction was comprised of four factors (a)

performance (b) disconfirmation/confirmation of expectancies, (c) affective responses (positive

attitude, negative attitude, and satisfaction), and (d) behavioral intentions (conative loyalty).

Disconfirmation/Confirmation of Expectancies

Expectations are important to the satisfaction response, because they provide a guideline

for future judgments of product performances (Oliver, 1997). Expectancy

disconfirmation/confirmation [(dis)confirmation] is defined as the result of a comparison

between what an individual expects to happen and what is observed (Oliver, 1997).

(Dis)confirmation has three components: the event, probability of occurrence, and its

(un)desirability (Oliver). For example in sport, the event would be a sport competition outcome










(win vs. loss); the probability of occurrence would be the likelihood of a win or a loss (e.g., a

very talented team competing against a less talented team); and the desire for a win or a loss

based on previous experience or knowledge.

Oliver (1981) defined disconfirmation as "a mental comparison of an actual state of

nature with its anticipated probability. In common usage, these states of nature may be perceived

as worse than expected (negative confirmation), better than expected (positive disconfirmation),

or just as expected (zero disconfirmation)" (p. 35). Positive disconfirmation occurs when

performance exceeds expectations. For example, a fan expects their preferred team to lose, but

the team actually wins. Conversely, negative disconfirmation occurs when performance drops

below expectations. For example, a fan expects their team to win, but the team actually loses.

Zero confirmation occurs when high-probability events take place and low-probability events do

not take place. For example, a fan expects their team to win, and the team wins.

Vavra (1997) argued that Oliver's (1980) use of"disconfirmation" to describe both

instances when performance exceeds expectations and when performance falls below

expectations is confusing. In an attempt to reduce misunderstanding and provide a clear

definition of the di sconfirmation/confirmation process, Vavra (1997) used the directional terms

of "affirmed," "confirmed," and "disconfirmed" to express the size and directionality of the

(dis)confirmation experience. Thus, "expectations will be considered as confirmed when

perceived performance meets them; expectations will be considered affirmed (reinforced by

positive disconfirmation) when perceived performance exceeds them; and expectations will be

considered disconfirnzed (failed by negative disconfirmation) when perceived performance falls

short of them" (p. 42).









Previous research has shown that (dis)confirmation of expectancies correlates with an

individual's satisfaction (Oliver, 1997). Oliver (1980) tested the relationships between

confirmation/disconfirmation and satisfaction, the relationship between satisfaction and attitude

change, and the relationship between attitude and purchase intentions. His results within two

samples supported the relationships of satisfaction influencing attitude, and attitude influencing

intention. Additionally, the results along with theoretical satisfaction framework showed that

(dis)confirmation appeared to influence satisfaction, attitude, and intention with the most

immediate impact of (dis)confirmation influencing satisfaction.

Oliver and Swan (1989) studied the disconfirmation effects of consumers on product

satisfaction for the automobile industry. Their results showed that disconfirmation of

expectancies with a salesperson were significantly related to satisfaction with a salesperson. In

addition, they showed a significant relationship between disconfirmation of expectancies with a

dealer and satisfaction with a dealer. Oliver and Swan stated that disconfirmation of expectancies

was a significant predictor of satisfaction, thus strengthening previous research (Bearden & Teel,

1983; Oliver, 1980; Oliver, Rust, & Varki, 1997).

Affective Responses (Positive Affect, Negative Affect, and Satisfaction)

Oliver (1997) believed that satisfaction should be placed with affect in terms of cognitive

perspectives, such that research examining positive and negative affective states should also

include an individual's perspective on satisfaction. As defined by Oliver (1997), "affect refers to

the feeling side of consciousness, as opposed to thinking, which taps the cognitive domain" (p.

294). An affect state or feeling refers to a psychological sensation such as happiness, sadness,

pleasure, or displeasure. Whereas, moods are a temporary state of affect and are based on their

short duration. Satisfaction is a component of consumption that can result in an emotion or

temporary mood. Previous research in both psychology and in consumer satisfaction has shown









that negative and positive affective orientations influence future affective judgments (Oliver,

1997).

Within the service management literature, previous researchers (Colgate & Stewart,

1998; Hocutt, 1998; Patterson & Spreng, 1997) have found that consumer satisfaction is

significantly related to consumer loyalty, however within these studies, satisfaction did not

contain an affective component (Yu & Dean, 2001). Consequently, using a convenience sample

from an undergraduate campus in Australia, Yu and Dean explored the role of emotions in

customer satisfaction and re-tested the satisfaction-loyalty relationship while including the

emotional component of satisfaction. Their results showed a significant relationship between the

emotional component of satisfaction and consumer loyalty and suggested that the emotional

component of satisfaction is a better predictor of consumer loyalty than the cognitive component

(expectancy disconfirmation model) of satisfaction.

Within the retailing industry, Oliver, Rust, and Varki (1997) tested the concept of

"customer delight" at a wildlife theme park and symphony concert. They proposed and tested a

Model of Delight and Satisfaction, and subsequently modified the original model in order to

increase model fit. They found a significant relationship between (dis)confirmation and positive

affect, such that 8% of the variance in positive affect was explained by (dis)confirmation. Their

results for the modified model supported the relationship between disconfirmation of

expectancies and positive affect.

Conative Loyalty

Oliver (1999) theorized that consumers first become loyal to a brand or product on a

cognitive level, then on an affective level, then later on a conative level, and finally on a

behavioral level. The first stage, cognitive loyalty, is based on recently acquired information

(e.g., price, features) and is a shallow level of loyalty. The second stage, affective loyalty, is









determined by an attitude toward a brand based on previous satisfying usages (i.e., "I buy it

because I like it," p. 36). This stage contains both a level of cognition and affect; however it is

also a shallow level of loyalty, thus consumers are subject to switching brands. The next stage,

conative loyalty, is influenced by positive, repeated exposures to a brand and is considered

loyalty to an intention to purchase. This stage involves a specific commitment to repurchase a

particular brand and is similar to a motivation. However, an intention or desire to purchase a

product does not always translate to an actual purchase. The Einal stage, action loyalty, occurs

when intentions to purchase are transformed into actual purchases (Oliver, 1999).

Morris, Woo, Geason, and Kim (2002) studied the relationships among cognitive,

affective, and conative attitudes in response to numerous advertisements (television, radio, and

print). The results of the structural equation modeling showed the model to have good fit

(RM SEA = .03). A significant relationship existed between the affective attitude construct and

conative loyalty construct, such that the affective construct explained 24% of the variance in the

conative loyalty.

Harris and Goode (2004) examined Oliver's (1997) sequential loyalty chain (cognitive-

affective-conative-action) and tested a conceptual framework of loyalty, trust value, satisfaction,

and service quality model. Data was collected for two groups through online data collection from

individuals who purchased books and individuals who purchased online airline flights.

Hypothesis 1 stated "that there are four sequential levels of loyalty (respectively; cognitive,

affective, conative, and action loyalty; p. 146)." Using two data sets, Harris and Goode tested

Oliver' s (1997) loyalty chain by analyzing 24 possible structural model combinations (48 models

for the two data sets). Their results showed that the original model was the most robust and valid.

Hypothesis 3 stated that "satisfaction would have a positive influence on loyalty" (p. 142). For









the online book consumers, a significant relationship existed between satisfaction and conative

loyalty, such that 8% of the variance in conative loyalty was explained by satisfaction. However,

no significant relationship existed between satisfaction and conative loyalty for the online flight

data. Even though these results are contradictory, Harris and Goode suggested that the findings

from the online flight purchases may be attributed to the market dynamics of airplane flight

purchases.

Recently within the retail literature, Da Silva and Alwi (2006) examined the influence of

satisfaction on loyalty intentions of consumers (online and within stores) at two bookstores. The

loyalty intention construct was comprised of future purchases, recommendations to friends, and

future business. They found a significant relationship between satisfaction and intentions, such

that 19% of the variance in loyalty intentions was explained by satisfaction.

The previous sections have reviewed the theoretical background of the MSSCL (Models

A, B, and C). The following sections will explain the pertinent sport management literature

pertaining to the Models including a discussion of the MSSCL variables (team identification,

disconfirmation or confirmation of expectations, affective responses pertaining to positive affect,

negative affect, and satisfaction, self-esteem responses pertaining to BIRGing and CORFing, and

conative loyalty) along with the supporting sport management research.

Model of Sport Spectator Conative Loyalty (Study 1/Purpose 1)

Trail et al. (2005) used identity theory, self-esteem theory, and satisfaction theory to

propose three different conative loyalty models (Models A, B & C). Model A (Figure 2-1) was

theoretically justified through Oliver' s (1997) consumer satisfaction theory and Ervin and

Stryker' s (2001) identity theory. Trail et al. (2005) postulated that in Model A (dis)confirmation

of expectancies influenced the mood of an individual (satisfaction, negative mood, and positive

mood), and mood influenced conative loyalty. Furthermore, the authors postulated that in Model










A, identification with a team lead to self-esteem responses (basking in reflected glory [BIRGing]

and cutting off reflected failure [CORFing]) and then lead to conative loyalty. Similarly, Model

B (Figure 2-2) was based on consumer satisfaction theory and identity theory, but additionally

was guided by sport consumer behavior research. Model B "posited that (dis)confirmation leads

to mood, then mood and identification both directly lead to self-esteem responses (BIRGing and

CORFing), and self-esteem responses lead to conative loyalty" (p. 107). Using previous research

(e.g., Kwon & Trail, 2001; Laverie & Arnett, 2000; Trail, Anderson, & Fink, 2002; Wakefield,

1995) that tested the independent relationships between the variables in the models, Trail et al.

(2005) also proposed and tested Model C (Figure 2-3), a direct effects model. Within Model C,

they allowed all of the independent variables (di sconfirmation/confirmation of expectancies,

identification with a team, mood, and self-esteem responses) to lead directly to conative loyalty.

Team Identification

Within a sport context, previous researchers (Trail, Anderson, & Fink, 2000; Trail et al.,

2005) have used identity theory as a framework to study identification, in particular, team

identification. However, some researchers have not specifically stated the theoretical genesis of

their research, items, constructs, and ideas, but these concepts may stem from either social

identity theory (e.g., Mahony, Howard, & Madrigal, 2000; Milne & McDonald, 1999) or identity

theory (e.g., Wann, 2000). Trail, Anderson, and Fink (2000) defined "identification as an

orientation of the self in regard to other obj ects including a person or group that results in

feelings or sentiments of close attachment" (p. 165-166). Moreover, Madrigal (1995) defined

team identification as "the extent to which a person feels a psychological attachment to a

particular sports team" (p. 269). In regard to sport consumer behavior, team identification is an

important concept due to its moderating effects on cognitive, affective, and behavioral responses

(Trail et al., 2000).









Researchers have shown that the level of team identification has moderated cognitive

involvement such as the expectations of a team's success (Trail, Fink, & Anderson, 2003; Wann,

1994; Wann & Dolan, 1994). For example, Wann and Dolan showed that fans who were highly

identified with their teams showed high levels of bias toward their preferred teams. The largest

contrast between lowly and highly identified spectators was shown when the preferred team lost.

Accordingly, spectators at a sporting event, who had differing levels of team identification (high

versus low) perceived "different games," and highly identified fans changed their attributes after

a loss in order to protect their self esteem (Wann & Dolan, 1994). However, Trail, Fink, and

Anderson found that team identification was not significantly related to expectancies about team

performance or team success.

A fan' s level of team identification has also been shown to have an influence on affective

states before, during, and after a sporting event (Trail et al., 2000). Madrigal (1995) argued that

team identification and a spectator's disposition toward a specific team serve as a basis for an

emotional reaction, thus satisfying a motive (e.g., achievement seeking, self-esteem) for

watching sporting events. Moreover, he found that team identification, rather than

disconfirmation/confirmation of expectancy or quality of opponent, explained the most variance

(25%) in enj oyment (an affective state). Wann, Dolan, McGeorge, and Allison (1994) predicted

that fans who were highly identified with their preferred teams would show more intense

affective responses after a competitive performance. Highly identified fans were more likely to

exhibit a significant increase in positive emotions when their preferred team won and a

significant decrease in positive affect when their preferred team lost. Individuals with low levels

of team identification exhibited minimal emotional changes after a win or a loss.









When examining team identification and self-esteem responses (BIRGing and CORFing),

Madrigal (1995) found that team identification had a significant influence on the self-esteem

response (BIRGing), such that 3 1% of the variance in BIRGing was explained through team

identification. More recently, the relationship between team identification and self-esteem

responses was studied by Trail et al. (2005), and they found that identification with a team

explained 27% of the variance in self-esteem responses.

The relationship between team identification and behavioral intentions or involvement

might be the most important relationship in sport spectator consumption behavior (Trail et al.,

2000). However, Trail et al. (2005) found that team identification only explained 2% of the

variance in conative loyalty, but Trail, Anderson, and Lee (2006) found that team identification

explained a little less than 10% of the variance in attendance intentions. Wann and Branscombe

(1993) showed that team identification had an influence on several sport consumption behaviors.

For example, highly identified fans attended more home and away games than lower identified

fans (Wann & Branscombe).

Disconfirmation or Confirmation of Expectations within a Sport Context

An affective response from a fan may be more intense when his/her expectations about

the outcome of the game are disconfirmed, either positively or negatively (Madrigal, 1995).

Based on Madrigal's (1995) research, Trail, Fink, and Anderson (2003) stated that the

(dis)confirmation process may be conceptualizedd as a continuum with negative disconfirmation

on one end, followed by negative confirmation, then positive confirmation, and anchored on the

other end by positive disconfirmation" (p. 10). Thus, (dis)confirmation would influence the

affective state of sport fans.

Madrigal (1995) studied the cognitive and affective determinants of fan satisfaction by

examining spectators at women's Division I-A basketball games. He examined the relationship









between (dis)confirmation and enj oyment (mood/emotions), and found that (dis)confirmation

explained 15% of the variance in enj oyment. Madrigal's best fitting Model of Fan Satisfaction

supported a disconfirmation-affect-sati sfaction hierarchical model. More recently, Trail, Fink,

and Anderson (2003) and Trail et al. (2005) tested the relationship between disconfirmation of

expectancies and affective state (mood) and found much better results; (dis)confirmation

explained 3 1% and 37%, respectively, of the variance in mood.

Trail et al. (2000) stated that based on the level of disconfirmation or confirmation of

expectations of a team's performance or the outcome of a game, individuals may often respond

with self-esteem behaviors such as basking in reflective glory (BIRGing) or cutting off reflected

failure (CORFing). Madrigal (1995) examined the relationship between (dis)confirmation and

BIRGing and found that only 6% of the variance in BIRGing was explained by

(dis)confirmation. More recently, Trail, Fink, and Anderson (2003) tested the relationship

between (dis)confirmation and self-esteem responses and found a non-significant relationship,

such that only 2% of the variance in self-esteem responses was explained by (dis)confirmation.

Affective Responses (Positive Affect, Negative Affect, and Satisfaction) within a Sport
Context

In order to examine affective responses as a one complete construct, Trail et al. (2005)

utilized Oliver, Rust, and Varki's (1997) findings of a significant relationship between

satisfaction and positive affect. The MSSCL combined the variables of positive affect, negative

affect and satisfaction into one affective state (mood). Moreover, Madrigal (1995) also believed

that enj oyment and satisfaction should be measured at the same time but as distinct constructs

that measure an affective state.

Madrigal (1995) hypothesized a fan satisfaction model based on the research conducted

by Oliver (1993) and Westbrook (1987) on consumer satisfaction. He found a significant









relationship between affective responses (enj oyment) and satisfaction, such that enj oyment

(mood/emotions) explained 12% of the variance in satisfaction. Based on Madrigal's results,

Trail, Fink, and Anderson (2003) tested the relationship of affective state on intention behaviors,

and their results showed that affect only explained 1 1% of the variance in future fan behavior. In

order to further explain more variance in self-esteem responses and to correct the "theoretical

and empirical deficiencies" (p.99) with Trail, Fink, and Anderson's (2003) model, Trail et al.

(2005) hypothesized and tested the relationship between affective state (mood) and self-esteem

responses. Their results showed that mood explained 27% of the variance in self-esteem

responses.

Recently, Caro and Garcia (2006) tested the framework of the consumer satisfaction

process after a sporting event. They tested the relationship between emotions and satisfaction and

between satisfaction and conative loyalty. After modifications to the original model of the

satisfaction process, the model fit was adequate (RM SEA = .078), and their results showed a

significant relationship between the emotion construct (pleasure) and satisfaction, and a

significant relationship between satisfaction and loyalty. These previous results from a sport

context confirm the relationships within the MSSCL.

Self-esteem Responses (BIRGing and CORFing)

Generally, previous research examining self-esteem behavior was focused on the

achievement seeking theory of sport spectating. Based on the situation or outcome of the game,

some sport fans watch sports to fulfill achievement needs or to feel more apart of the team.

However, some fans may distance themselves from a team when their teams are not successful

(Trail et al., 2000). On the basis on self-esteem theory, in order to increase self-esteem an

individual may want to associate with a winning team (e.g., bask in another's success) or in order









to maintain self-esteem an individual may want to cut off an association with an unsuccessful

team (e.g., cut off all association).

Basking in reflective glory (BIRGing) refers to the phenomena where an individual will

publicize an association or connection with another person or organization that is successful.

"That is, people appear to feel that they can share in the glory of a successful other with whom

they are some way associated; one manifestation of this feeling is the public trumpeting of the

association" (Cialdini et al., 1976, p. 366). Moreover, BIRGing can occur even if an individual

has no influence on the other's success (e.g., sports fans).

Cutting off reflected failure (CORFing) refers to the behavior where an individual

distances himself from another person or organization that is not successful (Snyder, Higgins, &

Stucky, 1983). CORFing "can be understood as an image protection tactic: that is, the severing

of association with others who have failed, in the interest of avoiding a negative evaluation by

others (and oneself)" (Snyder, Lassegard, & Ford, 1986, p. 383).

Previous sport consumer researchers (Madrigal, 1995; Wann & Branscombe, 1990, 1993;

Wann, Dolan, McGeorge, & Allison, 1994) have examined BIRGing and CORFing, but only

recently have researchers examined the relationship between the concepts of self-esteem

responses (BIRGing and CORFing) and conative loyalty (purchase intentions). Trail et al. (2000)

hypothesized that BIRGing and CORFing behaviors were influenced by achievement motive,

team identification, and disconfirmation of expectancy, while BIRGing and CORFing influenced

affective state and future behavior intentions. Recently, Trail et al. (2005) tested the relationship

between self-esteem responses and purchase intentions, and found that 48% of the variance in

purchase intentions was explained by self-esteem responses. The relationship between mood and

conative loyalty is mediated by self-esteem responses (Trail et al., 2005).









Conative Loyalty within a Sport Context

Previous sport researchers (Kwon, Trail, & Anderson, 2005; Miloch & Lambretcht, 2006;

Trail et al., 2005; Trail, Fink, & Anderson, 2003; Wann & Branscombe, 1993) have examined

numerous variables (e.g., team identification, self-esteem responses, and affective responses) on

conative loyalty (behavioral intentions/purchase intentions). Due to the difficulty of

longitudinally studying behavioral loyalty of sport consumers, the present study focused on the

conative loyalty stage of consumer behavior.

Typically, the best predictor of consumption is behavior intentions (Trail et al., 2005).

Trail, Anderson, and Lee (2006) proposed a sport consumption model in which preseason

purchase intentions lead to consumption behaviors (attendance). Their results showed a

significant relationship between purchase intentions and attendance, such that 45% of the

variance in attendance was explained by preseason purchase intentions.

Results of the Model of Sport Spectator Conative Loyalty

As previously discussed the MSSCL (Models A, B, & C) was hypothesized based on

consumer satisfaction theory, identity theory, and sport consumer research. Trail and colleagues'

(2005) focus was to study three competing models of conative loyalty using sport spectators. The

results of the CFA showed that the measurement model fit the data well (RM~SEA = .055), and

each of the items loaded significantly onto its designated factor. The Cronbach' s alpha

coefficients for each factor exceeded .70 indicating satisfactory internal consistency for social

science subscales (Nunnally & Bernstein, 1994).

Model A showed fair, but not close fit (RM~SEA = .065); Model B showed close fit

(RM~SEA = .060); and Model C had fair fit as well (RM~SEA = 0.78). Model B explained more

variance in conative loyalty (49%) than Model A (41%) and Model C (35%). Based on the

RMSEA values and the much lower amount of variance explained by Model C, Trail et al.









(2005) chose to eliminate it from further analysis. Although Models A and B did not differ

significantly, Trail et al. suggested that because Model B intertwined identity theory and

satisfaction theories with sport spectator consumption behavior, it might be the better model.

Trail et al. (2005) indicated that future research should test both Models A and B using

additional data. Based on the authors' suggestions and the need to understand the conative

loyalty of Latinos in the United States, this study tested both Models A and B on the Latino

population.

Revised Model of Sport Spectator Conative Loyalty (Study 1/Purpose 2)

As previously stated, the variables of ethnic identity, identification with the dominant

group (acculturation), vicarious achievement, and past behaviors were added to Models A and B

in an effort to increase the variance explained in conative loyalty. Trail et al. (2005), the authors

of the original MSSCL, noted that 5 1% of the variance of conative loyalty was not explained in

the model. They suggested future research should study variables that could be added to the

model. Thus, in the newly proposed models, both Model A and B contain a direct path from

ethnic identity to conative loyalty and from identity with the dominant culture to conative loyalty

(Figures 2-4 & 2-5). Past behaviors are depicted to have a direct path to identification with a

team and to vicarious achievement, while vicarious achievement is depicted to have a direct path

to team identification (Figures 2-4 & 2-5). The theoretical justification for the four additional

variables (ethnic identity, dominant identity, vicarious achievement, and past behaviors) added to

the MSSCL (Models A & B) is presented in the next section.

Ethnic Identity

Ethnic identity has been defined in numerous ways by many authors (Phinney, 1990).

Isajiw (1992) defined ethnic identity as an individually experienced phenomenon where an

individual identifies with an ethnic group. When defining an ethnic group, Bernal, Knight,









Ocampo, Garza, and Cota (1993) recognized the need to acknowledge a personal ownership and

knowledge about one's ethnic group. For this study, ethnic identity was defined as an

individual's identification with an ethnic group or ethnicity (Bernal et al., 1993; Isajiw, 1992;

Phinney, Horenczyk, Liebkind, & Vedder, 2001), while an ethnic group referred to the members

of the non-dominant group in the society (Phinney, 1996). Moreover, Phinney et al. (2001) stated

the need for future research to examine various aspects of ethnic identity "including self-

identifieation, feelings of belongingness and commitment to a group, a sense of shared values,

and attitudes toward one's own ethnic group" (p. 496). Individuals may identify with more than

one social or ethnic group (Korzenny & Korzenny, 2005; Phinney, 1990; Tsai, Ying, & Lee,

2000) and therefore have more than one ethnic identity.

Based on social identity theory (Tajfel, 1981) and Erikson's (1968) development theories,

Phinney (1992) examined the concepts of ethnic identity across diverse ethnic groups. She

suggested that future ethnic identity research should examine ethnic identity as a psychological

construct not merely a demographic variable (Phinney, 1996). Moreover, individuals seeking an

identity with their ethnic group and a social group identity will inevitably strive for a positive

self-esteem (Phinney, 1992) leading to the linkage of ethnic identity with self-esteem theory.

Phinney et al. (2001) expressed the need for research to delve into the various aspects of

ethnic identity "including self-identifieation, feelings of belongingness and commitment to a

group, a sense of shared values, and attitudes toward one's own ethnic group" (p. 496).

Researchers hypothesized that ethnic identity contains "various aspects including a sense of

belonging, positive evaluation of the group, preference for the group, ethnic interest and

knowledge, and involvement in activities associated with the group" (Phinney, 1996, p. 923),

commitment to the group, positive attitudes toward the group (Phinney et al., 2001), ethnic









origin, and sense of shared values (Naylor, 1997). These concepts are precursors to ethnic

identity and are necessary antecedents for ethnic identity to form. Additionally, Gacio Harrolle

and Trail (2007) proposed ethnic identity is a cognitive commitment to one's ethnicity.

Gacio Harrolle and Trail (2007) examined the relationships among ethnic identity,

acculturation (dominant identity), and sport identification to provide insight into the sport

identification of Latinos in the U. S. In an effort to clarify the constructs related to ethnic

identity, acculturation, and sport identification, Gacio Harrolle and Trail provided detailed

definitions of the previously mentioned terms and the terms of nationality, racial identity,

culture, and ethnicity. They concluded that ethnic identity was the most appropriate way to

examine an individual's level of identification with their ethnic group (e.g., Latinos). Gacio

Harrolle and Trail used the affirmation-belonging-commitment factor of the revised Multigroup

Ethnic Identity Measure (MEIM; Roberts et al., 1999), originally developed by Phinney (1992),

to examine ethnic identity of Latinos in the U. S. Their findings stated that participants studied

were highly identified with their self-identified ethnic group. However, ethnic identity did not

influence their identification with sports in general or identification with specific sports (e.g.,

American football, baseball, basketball, hockey, and soccer).

Gacio Harrolle and Trail (2007) suggested that future research look into the relationships

between ethnic identity and sport consumption behaviors or intentions. Additionally, Asbridge,

Tanner, and Wortley's (2005) results showed ethnic identity had an influence on consumption

behaviors for youth. Thus, an attachment to an ethnic group may influence one's sport consumer

behaviors. The addition of ethnic identity to the modified MSSCL (models A & B) is based on

this previous research.









Identification with the Dominant Society (Acculturation)

Stephenson (2000) defined acculturation as "a complex, multidimensional process of

learning that occurs when individuals and groups come into continuous contact with different

societies" (p. 77). Furthermore, individuals may form additional group identities as they come in

constant contact with other social groups (e.g., dominant society; Berry, 2001). Acculturation

theory as discussed by Berry (2001) suggests that an individual's exposure to a dominant culture

will influence their role-identities, and subsequently this identity may influence their

consumption behaviors. Acculturation is based on identification with the dominant or larger

group/society; however, it does not limit identification with an ethnic group (Berry, 2001). Over

days, weeks, years, and even generations (Berry & Kim, 1988), the acculturation process may

involve transformations in identities (Greenland & Brown, 2005). An individual may have a

strong identification with the dominant culture while also maintaining a strong identification

with an ethnic group. The relationship between these two identities is not negatively correlated,

such that the identification may be related but are independent of each other (Berry, 2001). For

example, an increase in identification with the dominant American society may not necessarily

cause a decrease in an individual's identification with his/her ethnic group. As individuals

interact with the dominant society, an identity with the dominant American society may begin to

develop (i.e., acculturation).

Recently, acculturation theorists have begun to study a bidimensional model of

acculturation as apposed to a unidimensional model (LaFromboise, Coleman & Gerton, 1993;

Phinney, 1990; Zea, Asner-Self, Birman, & Buki, 2003). Within the past decade, previous

research examining acculturation has measured numerous minority groups in a variety of distinct

ways including identity, language competence, and cultural competence (Zea et al., 2003);

cultural domains of language, social affiliation, activities, attitudes, media, exposure, and food










(Tsai et al., 2000); ethnic versus dominant immersion (Stephenson, 2000); comfort with

dominant and non-dominant language, food, media, and traditions (Gomez & Fassinger, 1994);

and language ability and perceived cultural distance (Greenland & Brown, 2005). If an

individual who is identified with an ethnic group is also identified with the dominant

culture/societal group (i.e., the culture in the United States), this individual's level of

identification with both groups may have an influence on identification as a sport fan in general

and identification with certain sports (Pons, Laroche, Nyeck, & Perreault, 2001).

Gacio Harrolle and Trail (2007) tested the relationships between ethnic identity and

identification with the dominant culture (acculturation) and found that a moderate relationship

existed between ethnic identity and acculturation (r = .412). Within their sample, Gacio Harrolle

and Trail (2007) also found that Latinos in the Southeastern U.S. were highly identified with the

dominant culture acculturatedd). Their results defended the theoretical justification for a bi-

dimensional acculturation model.

Gacio Harrolle and Trail (2007) suggested that future research include both ethnic

identity and acculturation variables when examining ethnic groups in the U.S. Their results

showed that level of identification with the dominant culture did not influence identification with

sports in general or with specific sports. Therefore, they recommended that future research

examine the relationships between identification with the dominant culture and sport

consumption behaviors or intentions. Additionally, Asbridge and colleagues' (2005) results

showed acculturation had an influence on consumption behaviors for youth. Thus, identification

with the dominant group may influence one's sport consumer behaviors. The addition of

acculturation to the modified MSSCL (Models A & B) is based on this previous research.









Vicarious Achievement

Vicarious achievement is defined as a need to gain success or feelings of accomplishment

vicariously through another (Sloan, 1989). Based on Maslow' s (1970) hierarchy of needs, Sloan

(1989) stated that individuals seeking a need to obtain prestige may engage in sports

spectatorship to fi11 these needs. Thus, spectators may increase self-esteem through vicarious

achievement of a successful team (Fisk, 1992). In order to fulfill an achievement need, Cialdini

et al. (1976) suggested that fans could fulfill a need to achieve through basking in the reflected

glory of their vicarious teams.

Vicarious achievement stems from self-esteem theory such that as individuals want to be

happy and increase their self-esteem they also want to associate with a successful other in order

to increase or maintain a consistent self-image. Sloan (1989) argued that through an association

with a successful sport team fans may seek out vicarious achievement. The concept of vicarious

achievement may provide a fan with an avenue for building self-esteem thorough an association

with a successful other.

Within sport management literature, previous researchers have examined vicarious

achievement as a motive for being a fan (e.g., Fink et al., 2002; Funk, Mahony, & Ridinger,

2002; Mahony, Nakazawa, Funk, James, & Gladden, 2002; Trail et al., 2005; Trail & James,

2001; Wann, 1994). Wann (1994) found a significant correlation between self-esteem responses

(success through others) and other motives for fandom. Mahony et al. (2002) measured the

vicarious achievement of spectators at a Japanese Professional Soccer League game. Their

results showed that vicarious achievement was positively associated with number of games

attended.

Recently, Trail, Kwon, and Lee (2007) looked beyond vicarious achievement as a motive

and have examined the relationships among vicarious achievement, team identification, and









BIRGing/CORFing. Data were collected before and after the 2003 MLB World Series. They

found that vicarious achievement explained approximately 15% of the variance in team

identification. Vicarious achievement was also found to be positively correlated with CORFing

in a failure situation (losing team New York Yankees). However, vicarious achievement was

hypothesized to be positively correlated with BIRGing in the success situation (winning team -

Florida Marlins), but this relationship was not evident in the data. The variable of vicarious

achievement was added to Models A and B based on identity theory, self-esteem theory, and

previous vicarious achievement research (Sloan, 1989; Trail et al., 2007; Figure 2-4 & 2-5).

Past Behaviors

Recently, Trail et al. (2006) have shown that past behaviors influenced team

identification and conative loyalty. Based on identity theory and previous research, Trail et al.

(2006) proposed a model in which past attendance predicted preseason team identification,

preseason intentions to attend, and actual attendance during the season. Their model (Figure 2-6)

contained a path from past behaviors (number of games attended previous years) to preseason

team identification and purchase intentions (number of games planning to attend this year). Past

behaviors predicted 21% of the variance in team identification and 24% of the variance in

purchase intentions. Based on this previous research, the revised MSSCL (Models A & B) were

tested to see whether more variance could be explained in behavioral intentions when past

behaviors influenced both team identification and purchase intentions (Figures 2-4 & 2-5).

Differences among Latinos Subgroups and Non-Latinos (Study 2/Purpose 3)

Gacio Harrolle and Trail's (2007) Eindings showed that ethnic identity and identity with

the U. S. American society explained very little variance in identification with sports in general

or identification with specific sports (e.g., American football, baseball, basketball, hockey, and

soccer). Previous researchers (Berkowitz et al., 2005; Gacio Harrolle & Trail, 2007) suggested









that future research should focus on specific subgroups within the Latino ethnic group (e.g.,

Puerto Ricans, Columbians, Cubans, etc.) instead of the Latino population as a whole to

determine if market segmentation would be the most appropriate marketing strategy.

Additionally, Gacio Harrolle and Trail suggested that a comparison should be made between the

various ethnic groups of Latinos and Non-Latinos to see if any significant differences existed in

terms of identification with specific sports (American football, baseball, basketball, boxing,

hockey, and soccer) and sport consumer behaviors (attendance, media, and merchandise).

Previous research (Singh, Kwon, & Pereira, 2003) within the Hield of marketing and

psychology examined group differences among three ethnic groups (Asian Americans,

Hispanics, and African Americans) in terms of personal and media influences. They found that

for young adults significant differences existed for their socialization influences. When

Berkowitz et al. (2005) studied differences between Hispanic and Non-Hispanic consumers, they

found no significant differences in terms of store brand versus national brand purchases.

However, their results showed a significant difference existed between Hispanics and Non-

Hispanics when buying specific product types (i.e., utilitarian versus hedonistic). Previous sport

consumer research has not looked at the differences among ethnic groups or between ethnic

groups.

This study examined the differences among the subgroups of Latinos and between

Latinos and Non-Latinos. Four hypothesizes were proposed: (a) significant differences would

exist among the subgroups of Latinos on identification with specific sports; (b) significant

differences would exist between Latinos and Non-Latinos on identification with specific sports;

(c) no significant differences would exist among the subgroups of Latinos on sport consumption









behaviors; and (d) significant differences would exist between Latinos and Non-Latinos on sport

consumption behaviors.

Pre and Post Testing of Conative Loyalty (Study 3/Purpose 4)

Within the advertising and business literature, numerous researchers (e.g., Chandon,

Morwitz, & Reinartz, 2004; Da Silva & Alwi, 2006; Morris et al., 2002) have examined purchase

intentions, however very few have examined pre and post effects on repurchase intentions.

Within the field of advertising, Tipps, Berger, and Weinberg (2006) studied pre and post effects

of advertising exposure on conative loyalty. Their results showed that level of involvement with

a publication lead to more favorable perceptions and a greater likelihood of being persuaded by

the advertisement. A positive significant difference existed between the pre and posttest results

on purchase intentions, thus the participants purchase intentions of the product increased

significantly after being exposed to an advertisement. Conversely, within the restaurant industry,

Kwun and Oh (2004) tested pre and post behavioral intentions and did not find a significant

difference in behavioral intentions for the participants. Their results showed that attending the

restaurant (independent variable) did not have a significant effect on future intentions to attend

the restaurant. These conflicting results showed that repurchase intentions may or may not be

affected by initial purchase behavior.

Similarly within the sport literature, researchers (e.g., Miloch & Lambrecht, 2006; Trail

et al., 2005; Trail, Fink, & Anderson, 2003) have examined purchase intentions, however

previous sport consumer research has not examined conative loyalty before and after a sporting

event. The results of this study will provide further understanding of the effects of watching a

game on purchase intentions. Additionally, this study hypothesized that based on the outcome of

the game (win vs. loss) individuals with low levels of team identification would be more likely to










show a larger increase or decrease in conative loyalty than those with high levels of team

identification.































Figure 2-1. Model A: Model of Sport Spectator Conative Loyalty





































Figure 2-2. Model B: Model of Sport Spectator Conative Loyalty






























Figure 2-3. Model C: Model of Sport Spectator Conative Loyalty


































Figure 2-4. Modified Model A: Revised Model of Sport Spectator Conative Loyalty



































Figure 2-5. Modified Model B: Revised Model of Sport Spectator Conative Loyalty






































Figure 2-6. Sport Attendance Model









CHAPTER 3
METHOD S

Study 1

Participants

The participants were comprised of Latinos and Non-Latinos [N= 3 13; Latinos (n = 127

and Non-Latinos (n = 186)]. The total sample exceeded the recommended value of 195 to

achieve a power of .80 for structural equation modeling (MacCallum, Browne, & Sugawara,

1996). The participants were attendees of a professional Maj or League Baseball game in Florida.

The sample consisted of 54% (n = 169) male respondents and 46% (n = 144) female respondents.

The average age of the participants was 44 years-old, and 65% were married. During the

collection process, 80 individuals declined to participate in the study, and 108 individuals did not

completely fill out the questionnaire, resulting in a 62.5% completion rate.

Procedure

Using an intercept method, all attendees at three out of six entrances of the stadium were

asked to fill out the questionnaire. Attendees were asked to complete selected sections of the

survey before and after the baseball game. After agreeing to participate in the study, participants

were given brief instructions on which specific sections to fill out before and after the game.

Participants were asked to complete the first portion of the questionnaire (demographic

information, Points of Attachment Index, Team Identification Index, Vicarious Achievement

Index, Ethnic Identity Scale, Multigroup Ethnic Identity Measure, Abbreviated Multidimensional

Acculturation Scale, Intentions for Sport Consumption Behavior Scale, and Past Behaviors)

before the baseball game. Participants were also instructed to complete the second portion of the

survey [Team Identification Index, (Dis)Confirmation of Expectancies Scale, Affective State

Index, Self-Esteem Maintenance Behavioral Scale, Intentions for Sport Consumption Behavior









Scale, and Estimated Future Sport Consumption Behaviors] at the conclusion of the baseball

game. Surveys were collected at the stadium exits starting at the end of the seventh inning, so

that participants could turn in their surveys.

The purpose of the study and the instructions for completing the questionnaire were

included in the questionnaire. In accordance with the IRB approval process at the university, the

participants had an opportunity to decline to participate in the questionnaire at any time. No

known physical or psychological risks were associated with completing the survey.

Instrumentation

The questionnaire was comprised of the following ten scales: Team Identification Index

(TII; Trail & James, 2001), (Dis)Confirmation of Expectancies Scale (DCES; Trail et al., 2005),

Affective State Index (ASI; Trail, Fink, & Anderson, 2003), Self-Esteem Maintenance

Behavioral Scale (SEMBS; Trail, Fink, & Anderson, 2003), Intentions for Sport Consumption

Behavior Scale (ISCBS; Trail, Fink, & Anderson, 2003), Multigroup Ethnic Identity Measure

(MEIM; Roberts et al., 1999), Abbreviated Multidimensional Acculturation Scale (AMAS; Zea

et al., 2003), Vicarious Achievement Subscale (VAS; Trail & James, 2001), Points of

Attachment Index (PAI; Robinson & Trail, 2005), and Past Behaviors and Estimated Future

Sport Consumption Behaviors Index. Eight of the ten scales had a 7-point response format

ranging from "Strongly Disagree" (1), to "Neutral" (4), to "Strongly Agree" (7): Team

Identification Index, Affective State Index, Self-Esteem Maintenance Behavioral Scale,

Intentions for Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure,

Abbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale, and Points

of Attachment Index. The items for each of these scales were measured using the same response

format and were randomly placed in sections on the corresponding pre and post surveys. The

(Dis)Confirmation of Expectancies Scale had a 7-point response format ranging from "Much









worse than I expected" (1), to "As expected" (4), to "Much better than I expected" (7); therefore,

the scale was placed in a separate section of the questionnaire. The Past Behaviors and Estimated

Future Sport Consumption Behavior Index were comprised of open-ended questions and were

placed in separate sections on the survey. At the end of the questionnaire, various demographic

variables (e.g., ethnicity, age, gender) were also included (Table 3-1).

Points of Attachment Index and Team Identification Index

The complete Points of Attachment Index, developed by Trail, Robinson, et al. (2003)

was based on the Team Identification Index originally developed by Trail and James (2001) as a

test of concurrent validity for motivation items (the actual TII items were first reported in Trail,

Fink, & Anderson, 2003). The Team Identification Index, a subscale of the PAI, was comprised

of three items (Table 3-2) and measures an individual's level of attachment with a specific team.

Fink et al. (2002) defined identification with a particular sport team (e.g., Gators) as "the

orientation of self in regard to other obj ects (the team) that results in feeling of close attachment"

(p. 198). Previous research results (Fink et al., 2002; Robinson & Trail, 2005; Trail et al., 2005;

Trail & James, 2001; Trail, Robinson, et al., 2003) have shown that the TII has good construct

reliability (AVE values ranging from .62 to .69) and internal consistency (Cronbach's alpha

values ranging from .83 to .88). This scale was measured before and after the sporting event

occurred to examine any possible pre and post differences in the TII.

The PAI measures seven constructs: identification with the players, the coach, the

community, the sport, the university, the team (the TII), and the level of the sport (e.g.,

intercollegiate not professional). This study used two dimensions of the PAI in addition to the

TII (Table 3-3): overall attachment to sports (e.g., "I am a fan of sports") and the attachment to a

specific sport for six different sports (American football, basketball, baseball, boxing, soccer,

and hockey). Previous authors (Gacio Harrolle & Trail, 2007; Robinson & Trail, 2005; Trail,









Robinson, et al., 2003) showed that attachment to a particular sport, a sub scale of the PAI, had

good internal consistency (Cronbach's alpha ranging from .75 to .94) and had good Average

Variance Extracted values as well (AVE = .50 .84). Gacio Harrolle and Trail found that

identification as a sport fan in general had good internal consistency (a = .80) and construct

reliability (AVE = .67). Due to the fact that these items were not based on the outcome of the

game, they were tested before the game occurred.

(Dis)Confirmation of Expectancies Scale

Trail, Fink, and Anderson (2003) first developed the (Dis)Confirmation of Expectancies

Scale based on Madrigal's (1995) two expectancy disconfirmation items measuring the play and

quality of the game. The response format for the items ["much worse than I expected" (1) to

"much better than I expected" (7)] used by Trail, Fink, and Anderson (2003) and Madrigal

(1995) was originally developed by Oliver (1980; 1981). Previous researchers (Trail et al., 2005;

Trail, Fink, & Anderson, 2003) have shown the scale to have construct reliability (AVE ranging

from .60 to .67) and internal consistency (Cronbach's alpha ranging from .81 to.91).

Gacio Harrolle, Trail, and Anderson (2007) revised the DCES and divided the scale into

two subscales (quality of the play and outcome of the game). The latest version of the DCES

(Table 3-4) contains three new items measuring quality of play ("the quality of the game"; "the

overall quality of play"; and "the performance of the teams") and two additional items measuring

the outcome of the game ("the outcome of the game" and "the conclusion of the game"). Both

sub scales, quality of play (AVE = .67; a = .86) and outcome of the game (AVE = .91; a = .97)

had good internal consistency and construct reliability. Additionally, the participants were either

confirming or disconfirming what they expect to happen at the game, therefore this scale was

tested after the sporting event occurred.









Affective State Index

The Affective State Index originally developed by Trail, Fink, and Anderson (2003) was

comprised of three subscales: positive mood, negative mood, and satisfaction. The subscales

were based on Madrigal's (1997) three items which measured overall satisfaction with a decision

to attend a sporting event. Trail, Fink, and Anderson's (2003) original subscales were each

comprised of three items respectively. Trail, Fink, and Anderson (2003) showed that the positive

affect (AVE = .83; a = .93) and negative affect (AVE = .69; a = .87) had good construct

reliability and internal consistency. However, satisfaction had adequate internal consistency (a =

.63), but the construct reliability value fell below the recommended .50 (AVE = .47). After Trail,

Anderson, and Fink (2005) modified the satisfaction subscale, the Cronbach's alpha increased to

.75, and the AVE value increased to .57.

Trail, Fink, and Anderson's (2003) three subscales were revised by Gacio Harrolle et al.

(2007) in an effort to increase the reliability and validity of the scales. The modified Positive

Affect sub scale (Table 3-5) contained five items and has shown good internal consistency (a =

.91) and construct reliability (AVE = .70). The modified Negative Affect sub scale (Table 3-5)

also contained five items and has shown good internal consistency (a = .89) and construct

reliability (AVE = .65). The modified Satisfaction (Table 3-5) sub scale was divided into two

sub scales (Satisfaction with Play and Satisfaction with the Outcome). These two new sub scales

have shown a vast improvement in internal consistency (Satisfaction with Play: a = .89;

Satisfaction with the Outcome: a = .93) and construct reliability (Satisfaction with Play: AVE =

.69; Satisfaction with the Outcome: AVE = .84). By revising the Affective State Index into four

subscales, Gacio Harrolle et al. have improved the overall measurement of the construct. In









addition, these subscales measure the affective state of the participant after the outcome of the

game, and thus were tested after the game.

Self-Esteem Responses

Previous researchers (Trail et al., 2005; Trail, Fink, & Anderson, 2003) have studied self-

esteem responses by examining BIRGing and CORFing. The Self-Esteem Maintenance Behavior

Scale (Trail, Fink, & Anderson, 2003) was comprised of two subscales with three items for each

subscale and were generated from Madrigal's (1995) one item measuring BIRGing and one item

measuring CORFing. Trail, Fink, and Anderson (2003) found that the subscales both had

adequate internal consistency (BIRGing: a = .80; CORFing: a = .79) and construct reliability

(BIRGing: AVE = .58; CORFing: AVE = .56). However, Trail et al. (2005) found that the

sub scales were not as internally consistent (BIRGing: a = .77; CORFing: a = .72), and the AVE

scores were very close to or below the accepted .50 value (BIRGing: AVE = .54; CORFing:

AVE = .46).

In order to increase the reliability and validity of the subscale, Trail et al. (2007) added

one additional item for BIRGing ("I would like to let others know about my association with the

team") and two new items for CORFing ("I do not want to be associated with the team" and "I

do not wish to be a fan of the team"). These new sub scales (Table 3-6) have improved internal

consistency (BIRGing: a = .91; CORFing: a = .87) and construct reliability (BIRGing: AVE =

.78; CORFing: AVE = .71). In addition, the participants either BIRG or CORF depending on the

outcome of the game, therefore, this scale was tested after the sporting event occurred.

Intentions for Sport Consumption Behavior Scale

Trail, Fink, and Anderson (2003), as a means to measure future intentions to consume

sport, developed the Intentions for Sport Consumption Behavior Scale (Table 3-7). This scale









attempted to measure the conative loyalty of sport consumers and included items measuring

future behavior to consume merchandise, future attendance at sporting events, and future overall

support of a particular team examined. Previous research (Trail et al., 2005; Trail, Fink, &

Anderson, 2003) has shown this scale to have good construct reliability (AVE ranging from .58

to .59) and internal consistency (a = .84).

The participants in this study were watching the football game on television, therefore an

additional item measuring future intention to watch more football games (i.e., "I am more likely

to watch future games") was added to the scale. This scale was tested before and after the game

in order to see if the outcome of the game changed an individual's conative loyalty.

Multigroup Ethnic Identity Measure

The Multigroup Ethnic Identity Measure, originally developed by Phinney (1992),

measured an individual's ethnic identity. Higher scores indicated stronger levels of ethnic

identity. Previous researchers (Phinney, 1992; Roberts et al., 1999) have shown this scale to be

internally consistent (Cronbach's alpha ranging from .81 to .90). Gacio Harrolle and Trail (2007)

used the affirmation-belonging-commitment factor (Roberts et al., 1999) to examine ethnic

identity and initially tested six items of the MEIM. However, due to high kurtosis values (2.8 to

10.4) three items were eliminated from their final analysis. The final three items of the ethnic

identity measure had good construct reliability (AVE = .65) and internal consistency (a = .89).

This current study tested the six original items (Table 3-8) tested by Gacio Harrolle and Trail

(2007) in order to reexamine all six ethnic identity items for internal consistency, kurtosis, and

reliability. These items were measured before the game.









Abbreviated Multidimensional Acculturation Scale

One portion of the Abbreviated Multidimensional Acculturation Scale (Zea et al., 2003)

represented the level of identity with the United States and was originally developed from the

American Identity Questionnaire (Phinney & Devich-Navarro, 1997). Zea and colleagues' results

showed that the six items had good factor loadings for the sample (P = .78 to .89) and internal

consistency (a = .96). Gacio Harrolle and Trail (2007) tested the six items (Table 3-9) measuring

identity with American society. Their results showed that two of the identity with American

society items (e.g., "I am proud to be a U.S. American" and "I feel good about being a U.S.

American") had high kurtosis values and were eliminated from their final analysis. The final

questionnaire contained four items that showed good construct reliability (AVE = .61) and

internal consistency (a = .88). Additionally, Gacio Harrolle and Trail (2007) found that higher

levels of identity with the U.S. indicated higher levels of acculturation. Before the game

occurred, this study tested the six original items tested by Gacio Harrolle and Trail (2007) in

order to reexamine the internal consistency, kurtosis, and reliability of all items measuring

identity with U.S. America.

Vicarious Achievement Subscale

Trail and James (2001) originally developed the vicarious achievement items from

Sloan's (1989) theories on sport fan behavior and needs for achievement. These items constituted

the Vicarious Achievement Subscale (Table 3-10) and measured an individual's need to

associate with a successful team. Moreover, the VAS is a subscale of the Motivation Scale for

Sport Consumption (Trail & James, 2001). Previous researchers (James & Ridinger, 2002;

Robinson & Trail, 2005; Trail & James, 2001; Trail, Robinson et al., 2003) have shown the VAS









to have good construct reliability (AVE ranging from .59 to .74) and internal consistency

(Cronbach's alpha ranging from .82 to .89). These items were tested before the game occurred.

Past Behaviors and Estimated Future Sport Consumption Behaviors Index

The Past Behaviors and Estimated Future Sport Consumption Behavior Index (Table 3-

1 1) were comprised of open-ended questions measuring an individual's past and future sport

consumption behaviors. The items asked for information about previous sport consumption

behaviors (media, attendance, and merchandise) and future intentions to consume sport (media

consumption, attendance, and merchandise consumption). The media and attendance

consumption items were measured by number of games, while the items measuring the

consumption of merchandise were measured by dollar amounts. The past behaviors were asked

before the game, and the future behaviors were asked after the game concluded.

Data Analysis

The descriptive statistics of the participants' demographic and socio-demographic

variables were calculated using SPSS 14.0 (George & Mallery, 2003). The Structural Equation

Modeling (SEM) technique, available in EQS 6.1 (Bentler & Wu, 1995), was used to test the fit

of the confirmatory factor analysis (CFA) of the measurement model, to calculate the construct

reliability (average variance extracted: AVE) and to test the discriminant validity (correlations).

SPSS 14.0 was used to test internal consistency (Cronbach's alpha coefficients: ot). When

examining internal consistency for social science subscales, alpha coefficients greater than .70

are assumed to be adequate (Nunnally & Bernstein, 1994), while AVE values greater than .50 are

good (Hair, Anderson, Tatham, & Black, 2002) and correlations between constructs less than .85

indicate discriminant validity for the constructs (Kline, 2005).

After the CFA was conducted on the measurement model, the five structural models

[original MSSCL (Models A, B, and C) and the revised MSSCL (Models A and B)] were tested









for goodness of fit using EQS (2005). The chi-square test statistic per degrees of freedom Cdjf

and the root-mean-square-error (RMSEA, represented by ea) were used. The RMSEA fit index

and associated confidence intervals were used to compare the revised Model A and the revised

Model B. Other fit indices (e.g., CFI and TLI) were not included in the analysis, because large

numbers of items and/or factors in models deleteriously affect the maj ority of goodness-of-fit

indices except for RMSEA and chi-square test statistic per degrees of freedom (Cheung &

Rensvold, 2002).

RMSEA values equal to or less than .06 indicates close fit (Hu & Bentler, 1999), while

values between .06 and .08 indicate reasonable fit, values between .08 and .10 indicate mediocre

fit, and values greater than .10 indicate poor fit (Browne & Cudeck, 1992). If the model fits the

data exactly, then the RMSEA value will equal zero. To indicate whether the model would fit

well within the population and because RMSEA is a point estimate, Browne and Cudeck (1992)

suggested that the 90% confidence interval should be used. Bollen (1989) suggested that

acceptable values for chi-square per degree of freedom should range from 2.0 to 3.0, however

there is no consensus on what constitutes an acceptable value and an acceptable value can be as

large as 5.0. When less than 10% of the residuals are greater than .10, Bagozzi and Yi (1988)

suggested that the model has adequate fit.

Study 2

Participants

Cubans (31.1%) and Puerto Ricans (18.0%) are the largest Latino groups in the State of

Florida (U.S. Census, 2006) and in the university [Cubans (49.0%), Columbians (33.0%), and

Puerto Ricans (15%); L. Martinez, University of Florida Institute of Hispanic-Latino Cultures,

personal communication, April 1, 2007]. Mexicans, the highest percentage (66.9%) of Latinos










within the total United States population, account for only 13.6% of the Latinos in the state of

Florida (U.S. Census, 2006) and approximately 8% of the Latino students associated with the

Latino student organizations at the university (L. Martinez, University of Florida Institute of

Hispanic-Latino Cultures, personal communication, April 1, 2007). Due to the composition of

the population at the university and in the state of Florida, Mexicans were not included in the

group comparisons. The participants for study 2 were comprised of Latinos [N = 353;

Columbians (n = 105), Cubans (n = 139), and Puerto Ricans (n = 109)] and Non-Latino (N=

23 1) participants, which exceeded the recommended value of 284 to achieve a power of .95 for a

MANOVA with four groups and five dependent variables (Faul, Erdfelder, Lang, & Buchner, in

press). The age of the participants ranged from 18 to 75 with an average age of 27. 15. Among

the participants, university students represented 62.9% [Latinos (N= 176): Puerto Ricans (n =

50), Cubans (n = 84), and Columbians (n = 42), and Non-Latinos (N= 187)] of the participants

and individuals from the general population represented 37.8% (n = 221) of the total data set for

study 2. Forty-five percent of the participants were male, and 55% were female. Ages of the

participants ranged from 18 to 75 years, with a mean of 27. 15. Based on participants' answers to

demographic questions (ethnicity, ethnic group, country of birth, and parents' country of birth),

participants were place into appropriate groups (Puerto Ricans, Cubans, Columbians, and Non-

Latino). During the collection process, 2295 individuals were sent emails requesting

participation in the study, and 405 were approached to fill out the questionnaire. Of the emails

sent, 1,478 individuals did not respond, 32 declined to participate in the study, and 519 of the

questionnaires were not completely filled out, resulting in a 22% completion rate.

Procedure

Among the student participants, Latino students were recruited from the Hispanic student

organizations at the university and students registered for Latin American Studies and Spanish









classes. The Non-Latino participants were recruited from the general student population at the

university. Using the Hispanic Student Organization email database, email databases on campus,

and email addresses from participants who signed-up for the study, students received an email

containing a website link to the Internet address for an online survey. The participants were

asked to fill out the following scales/items: demographic information, Points of Attachment

Index (identification with American football, baseball, basketball, hockey, and soccer) and Past

Sport Consumption Behaviors.

Data collection for the non-students took place in various community locations in Florida

including a Latino outdoor music festival, three Latino style restaurants, and a Latino doctor' s

office waiting room. The general population participants were asked to fill out demographic

information and Points of Attachment Index (identification with American football, baseball,

basketball, hockey, and soccer).

The purpose of the study and the instructions for completing the questionnaire were

included in the questionnaire. In accordance with the IRB approval process at the university, the

participants had an opportunity to decline to participate in the questionnaire at any time. No

known physical or psychological risks were associated with completing the survey.

Data Analysis

The descriptive statistics of the participants' demographic and socio-demographic

variables were calculated using SPSS 14.0 (George & Mallery, 2003). The Structural Equation

Modeling (SEM) technique, available in EQS 6.1 (Bentler & Wu, 1995), was used to test the fit

of the confirmatory factor analysis (CFA) of the measurement model, to calculate the construct

reliability (average variance extracted: AVE) and to test the discriminant validity (correlations).

SPSS 14.0 was used to test internal consistency (Cronbach's alpha coefficients: ot) and to









conduct a Multivariate Analysis of Variance (MANOVA) to compare the identification with

specific sports (American football, baseball, basketball, hockey, and soccer) among the

subgroups (Cubans, Puerto Ricans, Columbians, and Non-Latinos) of the total sample and to

compare sport consumption behaviors (media, attendance, and merchandise) among the student

population subgroups.

Study 3

Participants

The participants (N = 1 14) in study 3 were comprised of Latinos (12%), Caucasians

(68%), Asians (6%), African Americans (10%), and others (4%) which exceeds the

recommended value of 54 to achieve a power of .95 for a repeated measures ANCOVA (Faul,

Erdfelder, Lang, & Buchner, in press). The Latino participants (n = 14) were students recruited

from the Hispanic student organizations at the university and students registered for Latin

American Studies and Spanish classes. The Non-Latino (n = 100) participants were recruited

from the general student population at the university. A large maj ority of the participants were

single (96.5%) with an average age of 21.4.

Marketers have begun marketing to the next big group of consumers, Generation Y (i.e.,

Echo Boomers and Millennium Generation), who were born between 1979 and 1995. Generation

Y is very similar to the Baby Boomers in size and purchasing power, however they are different

in almost every other way. They are a more diverse group (i.e., one in three is Non-Caucasian),

where one in three lives with one parent and three out of four have a working mother. With such

large consumption potential, companies need to understand the nuances of this market

(Neuborne & Kerwin, 1999). As college students constitute approximately the upper 60% of the

Generation Y spectrum, university students are an appropriate sample for this study.









Additionally, the Generation Y students are Internet savvy and spend hours a day on the

computer (Neuborne & Kerwin), and this worked well for this study conducted online.

Procedure

Students were asked to complete the survey before and after a collegiate football game.

Using the Hispanic Student Organization email database and email addresses from participants

who signed-up for the study, students received an email containing a website link to the Internet

address for the online survey one week before the game occurred. The first portion of the

questionnaire (demographic information, Team Identification Index, and Intentions for Sport

Consumption Behavior Scale) was posted as an online survey before the football game. Three

days before the game, emails with the survey's web address were sent out reminding students to

complete the survey before the game. After the participants completed the survey, they received

a thank you notice and a reminder to complete the second portion of the questionnaire after the

game finished. At the conclusion of the game, the second portion of the survey [Points of

Attachment Index (Team Identification Index) and Intentions for Sport Consumption Behavior

Scale] was posted as an online survey on a network, and emails with the survey's web address

were again sent out to participants asking them to complete the second portion of the

questionnaire. After the participants completed the second portion of the survey, they received a

thank you notice. After the participants completed the surveys, the data were stored on the

network and later downloaded at the completion of the data collection.

The purpose of the study and the instructions for completing the questionnaire were

included in the emails and on the online survey. In accordance with the IRB approval process at

the university, the participants had an opportunity to decline to participate in the survey at any

time during the questionnaire. No known physical or psychological risks were associated with

completing the online survey.









Data Analysis

The descriptive statistics of the participants' demographic and socio-demographic variables

were calculated using SPSS 14.0 (George & Mallery, 2003). The psychometric properties of the

scales were tested in the previous studies. A repeated Analysis of Covariance (ANCOVA) was

conducted using SPSS 14.0 to compare the pre and posttest results for conative loyalty on the

total sample while controlling for team identification.










Table 3-1. Demographic Items
Demographic Items
Ethnicity
Gender
Age
Marital status
Highest level of education
Household income
Marital Status
Number of Children
Country of Origin
Country of Origin for Mother
Country of Origin for Father
Years in the United States
Generation in the United States
Ethnic Group










Table 3-2. Team Identification Index
Team Identifieation Index
I consider myself to be a real fan of the (specific sport) team
I would experience a loss if I had to stop being a fan of the team
Being a fan of the (specific sport) team is very important to me









Table 3-3. (Dis)Confirmation of Expectancies Scale
(Dis)Confirmation ofExpectancies Scale
Quality of Play
The quality of the game
The overall quality of play
The performance of the teams
Outcome of the Game
The result of the game
The outcome of the game
The conclusion of the game









Table 3-4. Points of Attachment Index
Points of Attachment Index
American Football
First and foremost I consider myself an American football fan.
American football is my favorite sport.
Of all sports, I prefer American football.
Baseball
First and foremost I consider myself a baseball fan.
Baseball is my favorite sport.
Of all sports, I prefer baseball.
Basketball
First and foremost I consider myself a basketball fan.
Basketball is my favorite sport.
Of all sports, I prefer basketball.
Hockey
First and foremost I consider myself a hockey fan.
Hockey is my favorite sport.
Of all sports, I prefer hockey.
Soccer
First and foremost I consider myself a soccer fan.
Soccer is my favorite sport.
Of all sports, I prefer soccer.









Table 3-5. Affective State Index
Affective State Index
Positive Mood
I feel happy
I feel cheerful
I feel delighted
Negative Mood
I feel disappointed
I feel upset
I feel irritated
Satisfaction with Play
I am satisfied with the quality of (name of sport) played in the (game, series, etc.)
I was satisfied with the overall quality of play
I am satisfied with the performance of both teams
Satisfaction with Outcome
I was satisfied with the result of the (game, series, etc.)
I am satisfied with the outcome of the (game, series, etc.)
I was satisfied with the conclusion of the (game, series, etc.)









Table 3-6. Self-Esteem Maintenance Behavioral Scale
Self-Esteem Maintenance Behavioral Scale
BIRGing
I would like to let others know about my association with the (name of the team)
I plan to discuss the game with other people
I would like to publicize my connection with (the name of the team)
I would like to tell others about my association with this team
CORFing
I do not want to be associated with the (name of the team)
I do not wish to be a fan of the (name of the team)
I plan to avoid talking about the game with others
I would like to disconnect myself from (the name of the team)









Table 3-7. Intentions for Sport Consumption Behavior Scale
Intentions for Sport Consumption Behavior Scale
I am more likely to attend future games.
I am more likely to purchase the team's merchandise.
I am more likely to buy (team name) clothing.
I am more likely to support the (team name).
I am more likely to watch games highlights with other people.









Table 3-8. Multigroup Ethnic Identity Measure
Multigroup Ethnic Identity Measure
I am happy that I am a member of my ethnic group.
I have a strong sense of belonging to my own ethnic group.
I understand pretty well what my ethnic group membership means to me.
I have a lot of pride in my ethnic group and its accomplishments.
I feel a strong attachment to my own ethnic group.
I feel good about my cultural or ethnic background.










Table 3-9. Abbreviated Multidimensional Acculturation Scale
Abbreviated Multidimensional Acculturation Scale
I think of myself as being U.S. American.
I feel good about being U.S. American.
Being U.S. American plays an important part of my life.
I feel that I am a part of U. S. American culture.
I have a strong sense of being U.S. American.
I am proud of being a U.S. American.










Table 3-10. Vicarious Achievement Scale
Vicarious Achievement Index
I feel personal sense of achievement when the team does well
I feel like I have won when the team wins
I feel proud when the team plays well









Table 3-11. Past Behaviors & Estimated Future Sport Consumption Behaviors Index
Past Behaviors & Estimated Future Sport Consumption Behaviors
Please estimate the total number of (name of team) games that you intend to watch on television
this season:
Please estimate the total number of (name of team) games that you expect to attend this season:
How many (name of team) games (if any) have you already watched on television during this
season?
How many (name of team) games (if any) have you already attended during this season?
Please estimate the total dollar amount (if any) that you have spent already this season on (name
of team) merchandise and paraphernalia for yourself. $
Please estimate the total dollar amount (if any) that you expect to spend this season on (name of
team) merchandise and paraphernalia for yourself. $_









CHAPTER 4
RESULTS

Model Results (Study 1/Purpose 1)

Psychometric Properties of the Scales (Measurement Model for MSSCL Models A, B, & C)

The results of the CFA on the measurement model for Models A, B, and C (Table 4-1)

showed close fit (RMSEA, E, = 0.062; CI = 0.057, 0.067; //df= 1072.64/482 = 2.22).

Additionally, only 7.7% of the residuals in the residual matrix exceeded .10, indicating that the

actual sample correlations and the reproduced correlation matrix for the items differed only

slightly. The Cronbach's alpha coefficients were good for all constructs in all ten scales ranging

from .75 to .95, and the AVE values ranged from .47 to .86 (Table 4-2). For all subscales, each

manifest variable loaded significantly onto its first-order latent variable and all factor loadings

ranged from .578 to .942 (Table 4-2). The means and standard deviations for the subscales of the

total, Latino, and Non-Latino groups are presented in Table 4-3. The correlations among the

variables did not exceed .85 (-.40 to .79; Table 4-4), therefore showing discriminant validity.

MSSCL Model A

When the total sample was studied, the structural model for Model A (Table 4-5) showed

reasonable fit (RMSEA, E, = 0.073; CI = 0.068, 0.077; //df= 1312.36/510 = 2.57), but 27. 1%

of the residuals in the residual matrix exceeded .10 indicating that the fit could be improved

(Bagozzi & Yi, 1988). When the Latino sample was studied, the structural model for Model A

showed mediocre fit (RMSEA, = 0.094; CI = 0.086, 0. 102; //df= 967.72/510 = 1.90) and

21.1% of the residuals exceeded .10. When the Non-Latino sample was studied, the structural

model for Model A showed reasonable fit (RMSEA, E, = 0.076; CI = 0.069, 0.082; //df=

1044.77/5 10 = 2.05) and 31i.1% of the residuals exceeded .10.









Latent Variable Relationships in MSSCL Model A

For the total, Latino, and Non-Latino samples in Model A, all of the first-order latent

variables loaded significantly on the second order latent variables except for the relationship

between Satisfaction (third-order latent variable) and Affective Responses (second-order latent

variable) which had a boundary parameter violation ( = 1.0). For the total sample (Figure 4-1),

Team Identification explained 32.5% of the variance in Self-Esteem, and (Dis)confirmation

explained 66.4% of the variance in Affective Responses. The latent variables Self-Esteem (B=

.69) and Affective Responses (B= .18) were significantly associated with Conative Loyalty and

5 1.2% of the variance in Conative Loyalty was explained by the variables in the model. For the

Latino sample (Figure 4-2), Team Identification explained 23.2% of the variance in Self-Esteem,

and (Dis)confirmation explained 63.5% of the variance in Affective Responses. The latent

variables Self-Esteem (B= .83) and Affective Responses (B= .14) were significantly associated

with Conative Loyalty and 71.2% of the variance in Conative Loyalty was explained by

variables in the model. For the Non-Latino sample (Figure 4-3), Team Identification explained

46.5% of the variance in Self-Esteem, and (Dis)confirmation explained 64.5% of the variance in

Affective Responses. The latent variables Self-Esteem (fl = .65) and Affective Responses (B=

.14) were significantly associated with Conative Loyalty and 44. 1% of the variance in Conative

Loyalty was explained by variables in the model.

MSSCL Model B

When the total sample was studied, the structural model for Model B (Table 4-5) showed

reasonable fit (RMSEA, E, = 0.072; CI = 0.067, 0.076; //df= 1306.57/510 = 2.56), but 21.0%

of the residuals in the residual matrix exceeded .10 indicating that the fit could be improved

(Bagozzi & Yi, 1988). When the Latino sample was studied, the structural model for Model B

showed mediocre fit (RMSEA, E, = 0.094; CI = 0.085, 0. 101; //df= 957.58/510 = 1.88) and









17.3% of the residuals exceeded .10. When the Non-Latino sample was studied, the structural

model for Model B showed reasonable fit (RMSEA, E = 0.075; CI = 0.068, 0.081; //df =

1041.22/5 10 = 2.04) and 25.2% of the residuals exceeded .10.

Latent Variable Relationships in MSSCL Model B

For the total, Latino, and Non-Latino samples in Model B, all of the first-order latent

variables loaded significantly on the second order latent variables except for the relationship

between Satisfaction (third-order latent variable) and Affective Responses (second-order latent

variable) which had a boundary parameter violation ( = 1.0). For the total sample (Figure 4-4),

(Dis)confirmation explained 65.8% of the variance in Affective Responses. Affective Responses

(B= .37) and Team Identifieation (B= .52) explained 39.6% of the variance in Self-Esteem. The

latent variable Self-Esteem (B= .76) was significantly associated with Conative Loyalty and

57.2% of the variance in Conative Loyalty was explained in the model. For the Latino sample

(Figure 4-5), (Dis)confirmation explained 62.7% of the variance in Affective Responses.

Affective Responses (B= .34) and Team Identification (B= .50) explained 32.6% of the variance

in Self-Esteem. The latent variable Self-Esteem (B= .86) was significantly associated with

Conative Loyalty and 73.5% of the variance in Conative Loyalty was explained in the model.

For the Non-Latino sample (Figure 4-6), (Dis)confirmation explained 64.0% of the variance in

Affective Responses. Affective Responses (B= .37) and Team Identification (B= .61) explained

50.8% of the variance in Self-Esteem. The latent variable Self-Esteem (B= .67) was significantly

associated with Conative Loyalty and 44.5% of the variance in Conative Loyalty was explained

by variables in the model.

MSSCL Model C

When the total sample was studied, the structural model for Model C (Table 4-5) showed

mediocre fit (RMSEA, E, = 0.084; CI = 0.079, 0.088; //df= 1640.46/510 = 3.22), but 35.4% of









the residuals exceeded .10, indicating poor fit (Bagozzi & Yi, 1988). When the Latino sample

was studied, the structural model for Model C showed poor fit (RMSEA, E = 0. 102; CI = 0.094,

0. 109; //df = 1034.85/510 = 2.03) and 29.3% of the residuals in the residual matrix exceeded

.10. When the Non-Latino sample was studied, the structural model for Model C showed

mediocre fit (RMSEA, E, = 0.088; CI = 0.081, 0.094; //df= 1284.61/510 = 2.52) and 39.0% of

the residuals exceeded .10.

Latent Variable Relationships in MSSCL Model C

For the total, Latino, and Non-Latino samples in Model C, the relationships between

Satisfaction and Affective Responses ( = 1.0) and between (Dis)confirmation with the Quality

of the Game and the (Dis)confirmation latent construct (B= 1.0) had boundary parameter

violations. For the total sample (Figure 4-7), Self-Esteem (B= .64), Team Identification (B=

.10), (Dis)confirmation (B= .19), and Affective Responses (B= .05) explained 45.8% of the

variance in Conative Loyalty. For the Latino sample (Figure 4-8), Self-Esteem (B= -.71), Team

Identification (B= .17), (Dis)confirmation (B= .20), and Affective Responses (B= -.02)

explained 57.4% of the variance in Conative Loyalty. For the Non-Latino sample (Figure 4-9),

Self-Esteem ( = .61), Team Identification ( = .03), (Dis)confirmation ( = .15), and Affective

Responses (B= .10) explained 40.0% of the variance in Conative Loyalty.

Model Results (Study 1/Purpose 2)

Psychometric Properties of the Scales (Measurement Model for Revised MSSCL Models A
& B)

The results of the CFA on the measurement model for Revised Models A and B (Table 4-

1) showed close fit (RMSEA, E, = 0.055; CI = 0.052, 0.059; //df= 2329.68/1286 = 1.81).

Additionally, only 5.9% of the residuals in the residual matrix exceeded .10. With the exception

of the past behavior construct, the Cronbach' s alpha coefficients were good for 13 of the 14









constructs ranging from .75 to .97, and the AVE values ranged from .42 to .84 (Table 4-6). The

Cronbach's alpha coefficients for the past behavior construct was .41, and the AVE was .12.

With the exception of the past behavior construct, each manifest variable loaded significantly

onto its first-order latent variable and all factor loadings ranged from .533 to .963 (Table 4-6).

The factor loadings for the manifest variables explaining past behavior ranged from .437 to .179.

Due to the poor construct validity of the Past Behavior latent variable and the data were collected

at a home baseball game, only the manifest variable of past attendance was used to represent past

behavior. The means and standard deviations of the sub scales for the total, Latino, and Non-

Latino groups are presented in Table 4-3. The correlations among all of the variables did not

exceed .85 (-.35 to .81), therefore indicating discriminant validity (Table 4-7).

Revised MSSCL Model A

When the total sample was studied, the structural model for Revised Model A (Table 4-5)

showed good fit (RMSEA, E,= 0.060; CI = 0.057, 0.063; //df = 2456. 16/1 152 = 2. 13), but

28.0% of the residuals exceeded .10 indicating that the fit could be improved (Bagozzi & Yi,

1988). When the Latino sample was studied, the structural model for Revised Model A showed

mediocre fit (RMSEA, E, = 0.091; CI = 0.085, 0.095; //df= 1974.83/1 152 = 1.71) and 26.0% of

the residuals exceeded .10. When the Non-Latino sample was studied, the structural model for

Revised Model A showed reasonable fit (RMSEA, E,= 0.074; CI = 0.068, 0.079; //df=

1697.23/1 152 = 1.47) and 23.1% of the residuals exceeded .10.

Latent Variable Relationships in Revised MSSCL Model A

For the total, Latino, and Non-Latino samples in Revised Model A, the relationship

between Satisfaction and Affective Responses (B= 1.0) had a boundary parameter violation. For

the total sample (Figure 4-10), Team Identification (B= .21) and Vicarious Achievement (B=









.46) explained 40. 1% of the variance in Self-Esteem, and (Dis)confirmation explained 66.3% of

the variance in Affective Responses. Past attendance (B= -.01), Self-Esteem (B= .72), Ethnic

Identity (B= -.01), U.S. Identity (B= .01), and Affective Responses (B= .16) explained 54.0%

of the variance in Conative Loyalty. For the Latino sample (Figure 4-1 1), Team Identifieation (

=-.00) and Vicarious Achievement (B= .57) explained 32.3% of the variance in Self-Esteem,

and (Dis)confirmation explained 63.3% of the variance in Affective Responses. Past attendance

(B= .07), Self-Esteem (B= .77), Ethnic Identity (B= .01), U.S. Identity (B= .12), and Affective

Responses (B= .12) explained 63.2% of the variance in Conative Loyalty. For the Non-Latino

sample (Figure 4-12), Team Identifieation (B= .53) and Vicarious Achievement (B= .17)

explained 45.7% of the variance in Self-Esteem, and (Dis)confirmation explained 59.0% of the

variance in Affective Responses. Past attendance (B= -.04), Self-Esteem (B= .72), Ethnic

Identity (B= -.09), U.S. Identity (B= .04), and Affective Responses (B= .09) explained 53.0%

of the variance in Conative Loyalty.

Revised MSSCL Model B

When the total sample was studied, the structural model for Revised Model B (Table 4-5)

showed good fit (RMSEA, E, = 0.060; CI = 0.057, 0.063; //df = 244 1.24/1 152 = 2. 12), but

26.0% of the residuals exceeded .10 indicating that the fit could be improved (Bagozzi & Yi,

1988). When the Latino sample was studied, the structural model for Revised Model B showed

mediocre fit (RMSEA, E, = 0.090; CI = 0.085, 0.095; //df= 1964.26/1 152 = 1.71) and 25.0% of

the residuals exceeded .10. When the Non-Latino sample was studied, the structural model for

Revised Model B showed reasonable fit (RMSEA, E, = 0.073; CI = 0.067, 0.079; //df =

1698.3 1/1 152 = 1.47) and 21.3% of the residuals in the residual matrix exceeded .10.









Latent Variable Relationships in Revised MSSCL Model B

For the total, Latino, and Non-Latino samples in Revised Model B, the relationship

between Satisfaction and Affective Responses (B= 1.0) had a boundary parameter violation. For

the total sample (Figure 4-13), Team Identifieation (B= .24) and Vicarious Achievement (B=

.39) explained 43.2% of the variance in Self-Esteem; (Dis)confirmation explained 65.6% of the

variance in Affective Responses; and Affective Responses explained 7.8% of the variance in

Self-Esteem. Past attendance (B= -.02), Self-Esteem (B= .77), Ethnic Identity (B= .01), and

U. S. Identity (B= .00) explained 59.4% of the variance in Conative Loyalty. For the Latino

sample (Figure 4-14), Team Identifieation (B= .07) and Vicarious Achievement (B= .48)

explained 36.2% of the variance in Self-Esteem; (Dis)confirmation explained 62.4% of the

variance in Affective Responses; and Affective Responses explained 7.3% of the variance in

Self-Esteem. Past attendance (B= .06), Self-Esteem (B= .79), Ethnic Identity (B= -.02), and

U. S. Identity (B= .09) explained 63.2% of the variance in Conative Loyalty. For the Non-Latino

sample (Figure 4-15), Team Identifieation (B= .52) and Vicarious Achievement (B= .13)

explained 45.5% of the variance in Self-Esteem; (Dis)confirmation explained 57.8% of the

variance in Affective Responses; and Affective Responses explained 5.7% of the variance in

Self-Esteem. Past attendance (B= -.04), Self-Esteem (B= .74), Ethnic Identity (B= -.10), and

U.S. Identity (B= .04) explained 55.0% of the variance in Conative Loyalty.

Results (Study 2/Purpose 3)

Psychometric Properties of the Scale (Points of Attachment Index)

The results of the CFA on the measurement model for the group comparisons showed

reasonable fit (RMSEA, Ea = 0.063; CI = 0.055, 0.071; / df= 264.70/80 = 3.31). Additionally,

none of the residuals in the residual matrix exceeded .10. The Cronbach' s alpha coefficients were

good for all of the constructs ranging from .89 to .95, and the AVE values ranged from .73 to .86









(Table 4-8). Each manifest variable loaded significantly onto its first-order latent variable and all

factor loadings ranged from .758 to .947 (Table 4-8). The correlations among all of the variables

did not exceed .28, therefore indicating discriminant validity (Table 4-9).

MANOVA Results

For the total sample in study 2, a MANOVA was conducted to examine group differences

among four subgroup (Cubans, Puerto Ricans, Columbians, and Non-Latinos) on five dependent

variables (identification with five specific sports: American football, baseball, basketball,

hockey, and soccer). MANOVA results showed a significant difference among the groups on the

combined set of dependent variables [Wilks' A = .73, F (15, 1590.50) = 12.69, p < .001, r12

.099]. Group differences were significant for identification with baseball [F (3, 580) = 10.923, p

< .001, r12 = .053], for identification with basketball [F (3, 580) = 5.594, p = .001, r12 = .028], for

identification with American football [F (3, 580) = 13.677, p < .001, r12 = .066], for identification

with hockey [F (3,580) = 2.941, p = .033, r12 = .015], and for identification with soccer [F (3,580)

= 32.111l, p < .001, r12 = .142]. The group means, significant mean differences, and standard

deviations for identification with each sport across groups are presented in Table 4-10.

For the student sample in study 2, a MANOVA was also conducted to examine group

differences among four subgroups (Cubans, Puerto Ricans, Columbians, and Non-Latinos) on

three sport consumption behaviors: attendance, media, and merchandise. The multivariate results

showed a significant difference among the groups on the combined set of dependent variables

[Wilks' A = .85, F (9, 869) = 6.901, p < .001, r12 = .054]. The univariate results indicated that

group differences were significant for past attendance [F (3,359) = 16.356, p < .001, r12 = .120],

but not for past media consumption [F (3,359) = .463, p = .708, r12 = .004] and for past

merchandise consumption [F (3,359) = 1.382, p = .248, r12 = .011]. The group means, significant









mean differences, and standard deviations for past consumption behaviors across groups are

presented in Table 4-11.

Results (Study 3/Purpose 4)

A repeated measures ANCOVA was conducted to examine Conative Loyalty (purchase

intentions) across time, while controlling for Team Identification. The results showed no

significant difference [F (1, 1 12) = 1.23 8, p = .268, r12 = .01 1] between the Conative Loyalty

before the sporting event (Time 1; M = 6.06, SD = 1.25) and the Conative Loyalty after the

sporting event (Time 2; M = 5.55, SD = 1.28), when controlling for Team Identification [F (1,

112) = 5.127, p = .025, r12 = .044].





Table 4-1. Fit Measures for Measurement Model (Original Model Data & Revised Model Data)


Model df a RMSEA CI L X2/dSf Res > .10
Measurement Model (Original:
A, B, & C) 482 .062 (.057,.067) 1072.64 2.22 7.7%
Measurement Model (Revised
A & B) 1286 .055 (.052,.059) 2329.68 1.81 5.9%
Note. df- degrees of freedom; E -RMSEA; RMSEA CI =90% confidence intervals for RMSEA;
X2 chi square test statistic; X2/df = chi square divided by the degrees of freedom.









Table 4-2. Factor Loadings (P), Cronbach's Alpha (ot), and Average Variance Extracted Values
(AVE) for Team Identification Index, (Dis)Confirmation of Expectancies Scale,
Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for
Sport Consumption Behavior Scale
Factor and Item p a A VE
Team Identification .855 0.69
I consider myself to be a real fan of the baseball team .833
I would experience a loss if I had to stop being a fan of the team .774
Being a fan of the baseball team is very important to me .884
(Dis)Confirmation of Expectancies (Outcome) .945 0.86
The result of the game .923
The outcome of the game .928
The conclusion of the game .925
(Dis)Confirmation of Expectancies (Quality) .946 0.80
The quality of the game .845
The overall quality of play .897
The performance of the teams .940
Positive Affect .800 0.60
I feel happy .756
I feel cheerful .809
I feel delighted .766
Negative Affect .797 0.62
I feel disappointed .652
I feel upset .824
I feel irritated .869
BIRGing .877 0.62
I would like to let others know about my association with the (name of the .829
team)
I plan to discuss the game with other people .606
I would like to publicize my connection with (the name of the team) .825
I would like to tell others about my association with this team .874
CORFing .746 0.47
I do not want to be associated with the (name of the team) .657
I do not wish to be a fan of the (name of the team) .728
I plan to avoid talking about the game with others .606
I would like to disconnect myself from (the name of the team) .729
Satisfaction with Quality .866 0.70
I am satisfied with the performance of both teams .766
I am satisfied with the quality of baseball played in the game .818
I was satisfied with the overall quality of play .914
Satisfaction with Outcome .931 0.81
I am satisfied with the outcome of the game .868
I was satisfied with the conclusion of the game .942
I was satisfied with the result of the game .895









Table 4-2. Continued
Factor and Item /7 a A VE
Intentions for Sport Consumption Behavior .861 0.59
I am more likely to attend future games .588
I am more likely to purchase the team's merchandise .947
I am more likely to watch the game highlights with other people .578
I am more likely to buy (team name) clothing .950
I am more likely to support the (team name) .690









Table 4-3. Means and Standard Deviations (SD) for the Subscales [Team Identification Index,
(Dis)Confirmation of Expectancies Scale, Affective State Index, Self-Esteem
Maintenance Behavioral Scale, Intentions for Sport Consumption Behavior Scale,
Multigroup Ethnic Identity Measure, Abbreviated Multidimensional Acculturation
Scale, Vicarious Achievement Subscale, and Past Attendance] for the Total, Latino,
and Non-Latino groups
Total Sample Latinos Non-Latinos
Team Identification 5.69(1.40) 5.90(1.37) 5.55(1.41)
(Dis)Confirmation of Expectancies (Outcome) 6.01(1.13) 6.19(1.07) 6.16(1.04)
(Dis)Confirmation of Expectancies (Quality) 5.94(1.02) 6.15(1.00) 6.01(.98)
Positive Affect 6.27(.92) 6.42(.84) 6.17(.96)
Negative Affect 1.59(1.12) 1.64(1.33) 1.56(.95)
BIRGing 5.28(1.40) 5.60(1.31) 5.06(1.41)
CORFing 1.69(1.18) 1.94(1.50) 1.51(.86)
Satisfaction with Quality 6.12(.97) 6.27(6.40) 6.01(.98)
Satisfaction with Outcome 6.25(1.05) 6.38(1.04) 6.16(1.05)
Intentions for Sport Consumption Behavior 5.29(1.38) 5.82(1.18) 4.93(1.39)
Ethnic Identity 6.06(1.18) 6.38(.90) 5.84(1.29)
US Identity 6.40(1.21) 6.47(1.06) 6.36(1.30)
Vicarious Achievement 5.76(1.28) 6.14(1.19) 5.51(1.27)
Past Attendance 12.02(17.11) 12.25(14.67) 11.86(18.63)
Note: The subscales had a 7-point response format ranging from "Strongly Disagree" (1), to
"Neutral" (4), to "Strongly Agree" (7). There were 82 possible home games for Past
Attendance.










Table 4-4. Correlation Matrix for the Latent Variables Included in the Measurement Model for Original Models A, B, & C
1 2345678910
1 Team ID 1.0
2 (Dis)confirmation of Outcome .275 1.0
3 (Dis)confirmation of Quality .350 .756 1.0
4 BIRGing .539 .245 .336 1.0
5 CORFing -.395 -. 114 -.095 -.270 1.0
6 Conative Loyalty .451 .292 .411 .713 -.063 1.0
7 Negative Affect -.111 -.213 -.200 -.088 .647 -.032 1.0
8 Positive Affect .391 .514 .564 .434 -.278 .401 -.370 1.0
9 Satisfaction with Outcome .214 .684 .561 .292 -.230 .310 -.379 .737 1.0
10 Satisfaction with Quality .328 .618 .727 .398 -.210 .395 -.292 .794 .769 1.0









Table 4-5. Fit Measures for Model A, Model B, Model C, Revised Model A, & Revised Model B
for the Total Sample, Latino Sample, and Non-Latino Sample
Model df' a RMSEA CI X2 X2/df Res >.10
Model A
Total sample 510 .073 (.068, .077) 1312.36 2.57 27.1%
Latino sample 510 .094 (.086, .102) 967.72 1.90 21.1%
Non-Latino sample 510 .076 (.069, .082) 1044.77 2.05 31.0%
Model B
Total sample 510 .072 (.067, .076) 1306.57 2.56 21.0%
Latino sample 510 .094 (.085, .101) 957.58 1.88 17.3%
Non-Latino sample 510 .075 (.068, .081) 1041.22 2.04 25.2%
Model C
Total sample 510 .084 (.079, .088) 1640.46 3.22 35.4%
Latino sample 510 .102 (.094, .109) 1034.85 2.03 29.3%
Non-Latino sample 510 .088 (.081, .094) 1284.61 2.52 39.0%
Revised Model A
Total sample 1152 .060 (.057, .063) 2456.16 2.13 28.0%
Latino sample 1152 .091 (.085, .095) 1974.83 1.71 26.0%
Non-Latino sample 1152 .074 (.068, .079) 1697.23 1.47 23.1%
Revised Model B
Total sample 1152 .060 (.057, .063) 2441.24 2.12 26.0%
Latino sample 1152 .090 (.085, .095) 1964.26 1.71 25.0%
Non-Latino sample 1152 .073 (.067, .079) 1698.31 1.47 21.3%
Note. df- degrees of freedom; E -RMSEA; RMSEA CI =90% confidence intervals for RMSEA;
X2 chi square test statistic; X2/df = chi square divided by the degrees of freedom.









Table 4-6. Factor Loadings (P), Cronbach's Alpha (ot), and Average Variance Extracted Values
(AVE) for Team Identification Index, (Dis)Confirmation of Expectancies Scale,
Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for
Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure,
Abbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale,
and Past Sport Consumption Behaviors Index
Factor and Item p a A VE
Team Identification .855 0.67
I consider myself to be a real fan of the baseball team .839
I would experience a loss if I had to stop being a fan of the team .752
Being a fan of the baseball team is very important to me .869
(Dis)Confirmation of Expectancies (Outcome) .945 0.87
The result of the game .908
The outcome of the game .956
The conclusion of the game .934
(Dis)Confirmation of Expectancies (Quality) .946 0.78
The quality of the game .829
The overall quality of play .900
The performance of the teams .925
Positive Affect .800 0.64
I feel happy .763
I feel cheerful .819
I feel delighted .821
Negative Affect .797 0.60
I feel disappointed .557
I feel upset .844
I feel irritated .878
BIRGing .877 0.65
I would like to let others know about my association with the .855
(name of the team)
I plan to discuss the game with other people .653
I would like to publicize my connection with (the name of the .796
team)
I would like to tell others about my association with this team .890
CORFing .746 0.42
I do not want to be associated with the (name of the team) .667
I do not wish to be a fan of the (name of the team) .626
I plan to avoid talking about the game with others .533
I would like to disconnect myself from (the name of the team) .745
Satisfaction with Quality .866 0.72
I am satisfied with the performance of both teams .791
I am satisfied with the quality of baseball played in the game .827
I was satisfied with the overall quality of play .914









Table 4-6. Continued
Factor and Item /7 a A VE
Satisfaction with Outcome .931 0.83
I am satisfied with the outcome of the game .859
I was satisfied with the conclusion of the game .936
I was satisfied with the result of the game .938
Intentions for Sport Consumption Behavior .861 0.62
I am more likely to attend future games .611
I am more likely to purchase the team's merchandise .963
I am more likely to watch the game highlights with other people .958
I am more likely to buy (team name) clothing .699
I am more likely to support the (team name) .622
Ethic Identity .926 0.72
I feel good about my cultural or ethnic background .672
I have a strong sense of belonging to my own ethnic group .843
I feel a strong attachment to my own ethnic group .862
I understand pretty well what my ethnic group membership means .868
to me
I have a lot of pride in my ethnic group and its accomplishments .910
I am happy that I am a member of my ethnic group .918
Acculturation (Dominant Identity) .967 0.84
I am proud of being a U.S. American .932
I think of myself as being U.S. American .911
I feel that I am a part of U. S. American culture .879
Being U.S. American plays an important part of my life .866
I feel good about being U.S. American .937
I have a strong sense of being U.S. American .956
Vicarious Achievement .878 0.70
I feel personal sense of achievement when the team does well .745
I feel like I have won when the team wins .888
I feel proud when the team plays well .872
Past Behaviors .407 0.12
How many Marlins' baseball home games (if any) have you .337
already attended during this season?
How many Marlins' baseball away games (if any) have you .179
already attended during this season?
How many Marlins' baseball games (if any) have you already .424
watched on television during this season?
How many Marlins' baseball games (if any) have you already .323
listened to on the radio during this season?
Please estimate the total dollar amount (if any) that you have spent .437
already this season on Marlins' baseball merchandise and
paraphernalia for yourself.










Table 4-7. Correlation Matrix for the Latent Variables Included in the Measurement Model for Revised Models A & B
1 2 3 4 5 6 7 8 9 10 11


12 13 14


1 Vic. Achievement
2 Ethnic ID
3 Team ID
4 US ID
5 Past Behavior
6 (Dis)con. of Outcome
7 (Dis)con. of Quality
8 BIRGring
9 CORFing
10 Conative Loyalty
11 Negative Affect
12 Positive Affect
13 Sat. with Outcxne
14 Sat. with Quality


1.0
.381 1.0
.811 .316 1.0
.363 .566 .392 1.)
.673 .323 .758 .306 1.0
.312 .083 .236 -.011 .141 1.0
.351 .043 .261 -.022 .213 .774 1.0
.538 .125 .540 .077 .679 .236 .363 1.0
-.351 -.188 -.472 -.166 -.331 -.140 -.113 -.318 1.0
.456 .091 .445 .080 .551 .291 .442 .718 -.172 1.0
-.104 -.064 -.113 -.055 .040 -.157 -. 133 -.102 .713 -.023 1.0
.399 .159 .295 .044 .342 .533 .606 .442 -.349 .406 -.352 1.0
.262 .070 .157 -.028 .075 .702 .601 .302 -.233 .304 -.297 .797 1.)
.328 .084 .266 .012 .277 .635 .743 .427 -.277 .410 -.280 .827 .814 1.)










Table 4-8. Factor Loadings (P), Cronbach's Alpha (ot), and Average Variance Extracted Values
(AVE) for Points of Attachment Index
Factor and Item /7 a A VE
Identification with American football .940 .84
First and foremost, I consider myself an American football fan. .885
American football is my favorite sport. .928
Of all sports, I prefer American football. .936
Identification with baseball .945 .85
First and foremost, I consider myself a baseball fan. .915
Baseball is my favorite sport. .919
Of all sports, I prefer baseball. .933
Identification with basketball .916 .79
First and foremost, I consider myself a basketball fan. .831
Basketball is my favorite sport. .901
Of all sports, I prefer basketball. .924
Identification with hockey .887 .73
First and foremost, I consider myself a hockey fan. .758
Hockey is my favorite sport. .906
Of all sports, I prefer hockey. .893
Identification with soccer .947 .86
First and foremost, I consider myself a soccer fan. .917
Soccer is my favorite sport. .950
Of all sports, I prefer soccer. .911










Table 4-9. Correlation Matrix for the Latent Variables Included in Study 2 (Group Comparisons)
1 2 3 4 5
1 Identification with baseball 1.0
2 Identification with basketball .190 1.0
3 Identification with American football .179 .227 1.0
4 Identification with hockey .226 .173 .180 1.0
5 Identification with soccer .016 -.040 -.165 .279 1.0









Table 4-10. Mean Scores (M) and Standard Deviations (SD) for Identification with American Football, Baseball, Basketball, Hockey,
and Soccer across Each Group (Non-Latinos, Puerto Ricans, Cubans, and Colombians)
Factor Non-Latinos Puerto Cubans Colombians
Ricans
Identification with American football 4.74(1.87) a 4.03(2. 12) b 4.33(1.90) ab 3.33(1.83)
Identification with baseball 2.79(1.86) a 3.66(2.07) b 3.63(2.05) b 2.63(1.57) a
Identification with basketball 3.53(1.86) a 4.00(1.79) a, b 3.46(1.71) a 3.01(1.66)" a,
Identification with hockey 2.24(1.46) a 1.83(1.18) a 2.17(1.43) a 1.92(1.23) a
Identification with soccer 2.97(1.91) a, 2.48(1.62) b 3.20(1.90) a 4.81(2.03) "
Note: Means with different superscripts are statistically different across each group at the .05 level.









Table 4-11. Mean Scores (M) and Standard Deviations (SD) for Past Consumption Behaviors
(Attendance, Media, and Merchandise) Across Each Group (Non-Latinos, Puerto
Ricans, Cubans, and Colombians)
Factor Non-Latinos Puerto Ricans Cubans Colombians
Attending games 4.87(2.98) a 2.68(3.27) b 2.68(2.97) b 2.57(2.91)b
Television consumption 4.97(3.16) a 4.66(3.34) a 4.76(2.89) a 4.36(4.03) a
Merchandise purchases 58.53(77.28) a 74.54(110.56) a 75.69(94.40) a 50.95(55.55) a
Note: Means with different superscripts are statistically different across each group at the .05
level .


































Figure 4-1. Model A for the Total Sample



































Figure 4-2. Model A for the Latino Sample



































Figure 4-3. Model A for the Non-Latino Sample



































-26


Figure 4-4. Model B for the Total Sample



































-52


Figure 4-5. Model B for the Latino Sample



































-63


Figure 4-6. Model B for the Non-Latino Sample






























Figure 4-7. Model C for the Total Sample































Figure 4-8. Model C for the Latino Sample































Figure 4-9. Model C for the Non-Latino Sample


































Figure 4-10. Revised Model A for the Total Sample


































Figure 4-11. Revised Model A for the Latino Sample



































Figure 4-12. Revised Model A for the Non-Latino Sample



































-26


Figure 4-13. Revised Model B for the Total Sample



































-16


Figure 4-14. Revised Model B for the Latino Sample



































-.54


Figure 4-15. Revised Model B for the Non-Latino Sample










CHAPTER 5
DISCUSSION

Due to the lack of research on Latinos within the sport management literature, the focus

of this study was to provide an initial examination of the Latino market segment and conative

loyalty. This study explored the conative loyalty of Latinos and Non-Latinos and made group

comparisons among Latino subgroups and Non-Latinos. The MSSCL (Models A, B, & C) was

tested on both Latino and Non-Latino samples, and additional constructs were included in a

proposed revised MSSCL (Models A & B) in an effort to explain more variance in conative

loyalty. Furthermore, identification with five sports and three sport consumption behaviors

(attendance, media, and merchandise) were compared across four groups (Non-Latinos,

Colombians, Cubans, and Puerto Ricans). In order to examine fully sport consumption

intentions, conative loyalty was tested before and after a sporting event to determine the effects

of the outcome of the game on conative loyalty. Through testing the models, comparing the

groups, and testing the progression of conative loyalty, this study thoroughly investigated the

Latino market segment and conative loyalty.

Purpose 1/Study 1

For the total, Latino, and Non-Latino samples, Model A and Model B were very similar

in terms of model fit, and both fit reasonably well; however, the fit indices for Model C were

worse. Additionally, the variance explained in Conative Loyalty in Model B was slightly higher

than Model A, and both Model A and B were higher than Model C for all three samples. Based

on these results and Trail and colleagues' (2005) previous findings, Model C was removed from

any further analysis. Model B was chosen for further analysis over Model A because of a greater

theoretical justification and support for the relationships in previous sport research. However,









from a statistical standpoint, both Models A and B are good fitting models and are not

statistically distinct in this data set.

Oliver (1997) stated that within the satisfaction cycle, performance (e.g., outcome of the

game) would lead to an individual's disconfirmation/confirmation of expectancies then lead to

affective responses. The significant relationship between (Dis)confirmation and Affective

Responses (66.0% of the variance explained) supported Oliver' s satisfaction theory, Oliver and

Swan's (1989) previous research, and previous sport management research (Madrigal, 1995;

Trail et al., 2005; Trail, Fink, & Anderson, 2003). After a spectator or fan disconfirms or

confirms his/her expectations on the quality and/or outcome of the game, he/she has either a

positive or a negative affective response, and a certain level of satisfaction or dissatisfaction with

the quality and/or outcome of the game.

The significant relationship between Affective Responses and Self-Esteem supported

self-esteem theory and previous sport management research (Caro & Garcia, 2006; Cialdini et

al., 1976; Madrigal, 1995; Snyder et al., 1986; Trail et al., 2005; Trail et al., 2000). However, the

variance explained in Model B (13%) was not as large as the variance (27%) explained in Self-

Esteem in the original MSSCL (Trail et al., 2005). Previous self-esteem researchers (Owen &

King, 2001; Rosenberg, 1979; Rosenberg et al., 1995) have suggested that self-esteem is a

bidimensional construct explained by both negative and positive responses. The moderate

negative relationship between BIRGing and CORFing (r = -.32) supported the bidimensional

model of self-esteem.

Latinos and Non-Latinos both exhibited a strong relationship between Team

Identification and the Self-Esteem Responses of BIRGing and CORFing, thus confirming Ervin

and Stryker' s (2001) identity theory, which stated that self-esteem fits logically into identity









formation, and Cast and Burke's (2002) self-esteem theory, in which individuals build self-

esteem through verifying their social group identities. These results are also consistent with the

findings of Trail et al. (2005) and Madrigal (1995).

Trail et al. (2005) hypothesized that self-esteem responses (BIRGing and CORFing)

mediated the relationship between affective responses and conative loyalty (48% of the variance

explained). By reproducing Trail and colleagues' previous model results and explaining more

variance in conative loyalty (57.2%) for the total sample, this research extended the research of

Trail et al. (2005) and Madrigal (1995). In addition, the results supported Oliver' s (1997) stages

of conative loyalty in which the affective stage of loyalty leads to conative loyalty (purchase

intentions).

Based on the significant relationship found between satisfaction and positive affect in

Oliver and colleagues' (1997) research, Trail et al. (2005) combined the variables of positive

affect, negative affect, and satisfaction into the affective response latent variable. However, a

parameter violation (p = 1.0) occurred between Satisfaction and Affective Responses. Two main

reasons may explain the parameter violation. First, the low correlation between Negative Affect

and Positive Affect (r = -.352) in this study has typically been shown to be much higher in

previous research (e.g., r = -.83; Trail et al., 2005). Because of this low correlation, Satisfaction

played a more central role in explaining Affective Responses, thus possibly causing the

parameter violation. Second, the two first-order latent variables (Satisfaction with the Quality of

Play and Satisfaction with the Outcome of the Game) loaded onto a second-order latent variable

(Satisfaction) that loaded onto a third-order latent variable (Affective Responses). In statistical

situations such as this, a parameter violation may occur. Future research should either add an

additional first-order latent variable to explain Satisfaction or eliminate Satisfaction as a second-









order latent variable and allow the two first-order latent variables (Satisfaction with the Quality

of Play and Satisfaction with the Outcome of the Game) to load directly onto Affective

Responses.

Generally, the paths in Model B among the latent constructs for both the Latino and Non-

Latino samples were very similar except for the relationships between Self-Esteem and Conative

Loyalty, and between Team Identification and Self-Esteem. Latinos (P = .86) demonstrated a

stronger relationship between Self-Esteem and Conative Loyalty than Non-Latinos (P = .67).

Non-Latinos (P = .61) demonstrated a stronger relationship between Team Identification and

Self-Esteem than Latinos (P = .50). This may be due to the cultural differences between the

groups. As a minority group, Latinos face racial prejudice and thus might posses lower self-

esteem (Wells, 2001), therefore Latinos may have a stronger need than Non-Latinos to increase

their self-esteem through BIRGing.

Interestingly, for Latinos the (Dis)confirmation of the Quality of Play (P = .94) was more

central in explaining the (Dis)confirmation construct than (Dis)confirmation of the Outcome of

the Game (p = .81). In addition, Satisfaction with the Quality of Play (P = .98) was more central

in explaining the construct of Satisfaction than Satisfaction with the Outcome of the Game (P =

.79). Conversely, for Non-Latinos the (Dis)confirmation of the Outcome of the Game (P = .89)

was more central in explaining the (Dis)confirmation construct than (Dis)confirmation of the

Quality of Play (P = .83). Furthermore, Satisfaction with the Outcome of the Game (P = .90) was

slightly more central in explaining the construct of Satisfaction than Satisfaction with of the

Quality of Play (P = .87). From a practical standpoint, sport managers should focus on

controlling the quality of play by hiring talented, hard working players who will provide quality









entertainment through their athletic skills. When marketing to Latinos an appropriate slogan for

advertising the quality of play could read, "We don't give up until the final strike!"

Purpose 2/Study 1

For the total, Latino, and Non-Latino samples, the model fit analysis for Revised Model

A and Revised Model B were almost exactly the same in terms of model Sit. The variance

explained in Conative Loyalty in Revised Model B was slightly higher than Revised Model A for

the total sample (59.4% and 54.0%, respectively) and Non-Latino samples (55.0% and 53.0%,

respectively). For the Latino sample, the variance explained in Conative Loyalty in the Revised

Model A and Revised Model B were equal (63.2%). Because Model B was slightly better in

terms of variance explained for the three samples, Model B was used in further analysis.

Based on ethnic identity theory, acculturation theory, and Gacio Harrolle and Trail's

(2007) research, the additional constructs of ethnic identity and U.S. identity were added to Trail

and colleagues' (2005) original models. The non-significant relationships between Ethnic

Identity and Conative Loyalty and between U.S. Identity and Conative Loyalty confirmed Gacio

Harrolle and Trail's (2007) results that showed ethnic identity and U.S. identity did not influence

sport behavioral intentions. Conversely, these Eindings did not support Asbridge and colleagues'

(2005) previous research in which acculturation influenced consumption behaviors. Future

models examining Latinos and sport consumption behaviors should not use ethnic identity or

identity with the dominant culture as a predictive variable of sport fandom or conative loyalty. In

the past fifteen years, acculturation research has developed a bidimensional model of

acculturation (LaFromboise et al., 1993; Phinney, 1990; Zea et al. 2003). The strong correlation

between Ethnic Identity and U.S. Identity (r = .60) within the findings confirmed the

bidimensional model of acculturation and Gacio Harrolle and Trail's (2007) previous research.









The strong positive relationships between Vicarious Achievement and Team

Identification and between Vicarious Achievement and Self-Esteem supported self-esteem

theory and previous sport management research (Sloan, 1989; Trail et al., 2007). The addition of

vicarious achievement to the revised model contributed significantly to the additional variance

explained in Conative Loyalty, thus extending the research of Trail et al. (2005).

The past behaviors variable was added to the models based on Trail and colleagues'

(2006) findings that showed past behaviors influenced both identification with the team and

conative loyalty. The relationship between past behaviors (i.e., attendance) and conative loyalty

was not significant (only 3% variance explained) in the Revised Model B, thus contradicting

Trail and colleagues' (2006) previous findings that showed past attendance accounted for 24% of

the variance in purchase intentions. This study was conducted during a special Hispanic weekend

event; consequently, this may have been the only game participants attended this season, thus

distorting the findings. Past attendance should be included in future research to test these

relationships.

When comparing the Latino and Non-Latino samples on Revised Model B, generally the

paths between the latent constructs were very similar except for the relationships between Team

Identification and Self-Esteem as previously depicted in the original Model B. However, Latinos

(p = .48) demonstrated a much stronger relationship between Vicarious Achievement and Self-

Esteem than Non-Latinos (P = .13). For both Latinos and Non-Latinos, as the need for vicarious

achievement increases so does identification with the team. For Non-Latinos, the relationship

between Vicarious Achievement and Self-Esteem may be fully mediated by Team Identification.

As identification with the team increases, Non-Latinos are more likely to BIRG and less likely to

CORF, thus Non-Latinos may be more loyal fans. For Latinos, as the need for vicarious









achievement increases so do self-esteem responses and the likelihood of BIRGing behaviors, but

CORFing behaviors do not necessarily vary. Additionally, BIRGing and CORFing responses do

not vary based on the level of identification with the team, thus Latinos may be more likely to

become "bandwagoners."

The amount of variance explained in Conative Loyalty for the Revised Model B for all of

the samples was considerably higher than the 49% of variance explained in the original model

research (Trail et al., 2005) and the 11% of variance explained in Trail and colleagues' (2003)

conative loyalty model. The differences in the amount of variance explained may be due to the

additional variables in the revised models.

Marketers need to be aware of the differences in the relationships between the variables

for Latinos and Non-Latinos. Regardless of their level of identification with the team, Latinos

want to BIRG, thus marketers should provide opportunities for BIRGing behaviors. For

example, photo opportunities should be arranged before and after the game with a famous player

or mascot thus increasing identification with the team and providing a BIRGing opportunity for

the fan. Using current technology, marketers could go one step further and take digital photos

and send them via email to the fan, therefore reinforcing identification with the team through

emails received in the future. Additionally, tangible items that fans can take home that express

BIRGing behaviors could improve conative loyalty. For example, providing t-shirts that

associate a fan with the successful team (e.g., T-shirt slogan: "I am a member of the 2007

National Championship team").

Sport marketers should provide outlets for social interaction with fans and players. Social

networking online has become a strong trend within the sport industry and sport marketing

(Mickle, 2007). Teams should not only provide more opportunities for fans to engage with










players online through team websites, but also through social websites (e.g., facebook.com,

myspace. com).

Purpose 3/Study 2

As hypothesized, significant differences existed among the subgroups of Latinos on

identification with specific sports, and significant differences existed between Latinos and Non-

Latinos on identification with specific sports. Furthermore, as hypothesized, no significant

differences existed among any of the subgroups of Latinos on sport consumption behaviors, but

significant differences existed between Latinos and Non-Latinos on past attendance. The

findings in study 2 supported the concepts shaped by identity theory such that social situations or

ethnic groups (Burke & Tully, 1977) may influence an individual's particular role identity (e.g.,

fan of baseball). The lack of differences among the groups on merchandise consumption

supported the previous ethnic group comparison research by Berkowitz et al. (2005). Berkowitz

et al. found that in general Latinos and Non-Latinos do not differ on the quantity of store brand

purchases.

Colombians had the lowest level of identification with American football (significantly

lower than all other groups, 10% variance explained by group differences), while Non-Latinos

had a higher level of identification than Puerto Ricans, but not Cubans. Relative to level of

identification with baseball (5% variance explained by group differences), Puerto Ricans and

Cubans had significantly higher mean scores than both Non-Latinos and Colombians. For

identification with basketball (3% variance explained by group differences), Puerto Ricans had a

significantly higher mean score than Colombians but Non-Latinos and Cubans did not differ

from either the Puerto Ricans or Columbians. Identification with hockey (2% variance explained

by group differences) was low across all groups and there were no significant group differences.

Specific to the identification with soccer (14% variance explained by group differences),









Colombians had a significantly higher mean score than all other groups, and Cubans has a

significantly higher mean score than Puerto Ricans.

Interestingly, when sport consumption behaviors were examined, Non-Latinos and

Latinos only differ on past attendance behaviors, with Non-Latinos going to two more games per

season on average than the other three groups. These differences may be due to cultural

differences between the groups. Latinos may enj oy watching the games on television as a group

as opposed to actually attending the game. The differences may also be due to the cost associated

with attending the games or the hassle associated with the complicated student lottery system.

From a practical standpoint, sport marketers should realize that differences may or may

not exist within Latino segments dependent upon the sport. In addition, differences may or may

not exist between Latinos and Non-Latinos by sport and by behavior. Thus, marketers should

research their potential Latino subgroups and market them accordingly. When higher levels of

variance are explained by the group differences (e.g., identification with soccer, 14%), marketers

should consider marketing to the Latino subgroups with the highest levels of identification. For

example, any level of soccer organization (e.g., professional, youth) who desires to market to the

Latino segment might want to focus first on the Colombian market segment, because they were

by far the highest level of soccer fans within the Latino groups studied.

As professional sports have focused their attention on marketing to Latinos, many of

them have pursued Latinos as a homogeneous group (Gacio Harrolle & Trail, 2007; Korzenny &

Korzenny, 2005). Professional teams need to understand that the Latino market is diverse even

though they speak a common language (i.e., Spanish). Marketers should focus their attention on

specific groups based on the subgroups in their local geographical areas (e.g., Cubans in Miami)

and on the groups more likely to consume their products (e.g., Puerto Ricans who possess the










highest level of identification with baseball). Marketers should spend extra time and Einancial

resources researching the Latino subgroups in their marketing areas.

Purpose 4/Study 3

While controlling for identification with the team, the Eindings showed no significant

difference between conative loyalty before and after the sporting event. The results of study 3

confirmed Kwon and Oh's (2004) previous findings in which future intentions were not affected

by attending the restaurant. On the other hand, the results disconfirmed Tipps and colleagues'

(2006) previous research in which conative loyalty showed a positive significant difference after

being exposed to advertising.

As expected, fans with high levels of team identification will be less influenced by the

outcome of the game and will typically consume sport products. From a practical standpoint,

marketers can not control the outcome of the game, but they can provide opportunities for fans to

maintain high levels of team identification. For example, marketers can provide opportunities for

fans to interact with the team or provide social opportunities before and after the games.

Recommendations for Future Research and Alternative Models

Many future research endeavors are possible. The primary suggestion for future research

is to retest Revised Models A and B on larger Latino and Non-Latino samples. Future research

should also examine Trail and colleagues' (2005) original models and the revised models on

additional populations such as other ethnic groups (e.g., Asians, Europeans). Even though a large

percentage of conative loyalty was explained in the model results, a percentage of conative

loyalty has not yet been explained. Future research should incorporate additional variables (e.g.,

environmental variables) in the models. Based on Trail and colleagues' (2007) findings and the

correlation between Team Identification and Conative Loyalty for the total sample (r = .45),

future research should consider adding a path between Team Identification and Conative










Loyalty. Within the group comparisons, future research should include other Latino ethnic

groups (e.g., Mexicans), and samples from multiple states (e.g., California, New York, Florida,

Texas, etc.) in the U.S. should be examined in future studies. As the favored team in the sporting

event won the contest, future research should attempt to examine pre and post conative loyalty

after a loss. A more comprehensive and difficult suggestion for future research would be to

pursue a longitudinal study that examined both actual behaviors and conative loyalty instead of

just conative loyalty.

Summary

In summary, the Revised Models (A & B) explained more variance in Conative Loyalty

thus providing a stronger motivation for marketers to include those variables in their marketing

campaigns. Within the Revised Models (A & B), Ethnic Identity and U.S. Identity did not help in

explaining conative loyalty and should be removed from further research. Model B fit the best

and worked adequately well for the three samples (Latino, Non-Latino, and total). A few of the

relationships among the variables in Model B differed by degree for Latinos and Non-Latinos.

These differences indicate that Latino and Non-Latino groups should possibly be marketed

differently based on these varied relationships. Furthermore, within the Latino market,

differences existed among Latino subgroups on identification with specific sports. In terms of

identification with different sports and attendance consumption behaviors, Latinos differ from

Non-Latinos. Lastly, conative loyalty was not influenced by the outcome of the game after taking

into account identification with the team. Comprehensively, these results indicate differences do

exist not only between Latinos and Non-Latinos, but also among Latino subgroups. This study

provided the first stage of sport management research into the Latino markets. Further research

should expand and develop this area for sport managers and marketers to advance the

understanding of conative loyalty, especially in Latino market segments.
































































136










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BIOGRAPHICAL SKETCH

Michelle Gacio Harrolle, PhD graduated from the University of Florida in August 2007.

Her research interests focus on consumer behavior in sport, and lifestyle marketing and

promotion through ethnic cultures. Her research has been published in the International Journal

of Sports Marketing and Sponsorship. She has presented numerous presentations at the North

American Society for Sport Management and Sport Marketing Association conferences.

Additionally, she has taught Administration of Sport, Moral and Ethical Issues in Sport, Sport

and Sociology, and Introduction to Sport. Dr. Michelle Gacio Harrolle is currently an Assistant

Professor at North Carolina State University.





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1 SPORT SPECTATOR CONATIVE LOYA LTY: A COMPARISON OF LATINO SUBGROUPS AND NONLATINO CONSUMERS By MICHELLE GACIO HARROLLE 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 2007

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2 2007 Michelle Gacio Harrolle

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3 To my father, Peter E. Gacio

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4 ACKNOWLEDGMENTS I would like to thank a ll of the wonderful peopl e that helped me fini sh this dissertation. First off, I would like to thank my advisor, Dr Galen Trail, for all of his tremendous help, support, and advice. Next, I would like to tha nk my Mom and Dad. I know that you are both very proud of me. I would like to thank my husband, best friend, and partner, Ryan Eric Harrolle. Also, I would like to thank my committee, Dr. James Zhang, Dr. Dan Connaughton, and Dr. Efrain Barradas. Finally, I would like to th ank the wonderful people at the University of Florida and the College of Health and Human Performance. Go Gators!

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES................................................................................................................ .........9 ABSTRACT....................................................................................................................... ............10 CHAPTER 1 INTRODUCTION..................................................................................................................12 Growth of the Latino Population............................................................................................12 Growth of the Latino Sport Fan..............................................................................................13 Sport Marketers focusing on the Latino Market.....................................................................13 Marketing to Latinos........................................................................................................... ....14 Lack of Sport Marketing Res earch Pertaining to Latinos......................................................15 Overview of the Research Problem........................................................................................15 Purpose........................................................................................................................ ...........16 Theory Development............................................................................................................. .17 Development of Sport Cons umer Behavior Research............................................................17 Practical Implications......................................................................................................... ....18 Delimitations.................................................................................................................. .........19 Limitations.................................................................................................................... ..........19 Definitions of Terms........................................................................................................... ....19 Overview....................................................................................................................... ..........20 2 LITERATURE REVIEW.......................................................................................................22 Theoretical Background for the Model of S port Spectator Conative Loyalty (Study 1/ Purpose 1)..................................................................................................................... ......22 Social Identity Theory......................................................................................................... ...23 Identity Theory................................................................................................................ .......25 Identity Theory and Social Identity Theory............................................................................27 Self-Esteem Theory............................................................................................................. ...28 Link between Identity Theory and Self-Esteem Theory.........................................................30 Satisfaction Theory............................................................................................................ .....31 Disconfirmation/Confirmation of Expectancies..............................................................33 Affective Responses (Positive Affect, Negative Affect, and Satisfaction).....................35 Conative Loyalty.............................................................................................................36 Model of Sport Spectator Conativ e Loyalty (Study 1/Purpose1)...........................................38 Team Identification.........................................................................................................39 Disconfirmation or Confirmation of Exp ectations within a Sport Context.....................41

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6 Affective Responses (Positive Affect, Negati ve Affect, and Satisfaction) within a Sport Context...............................................................................................................42 Self-esteem Responses (BIRGing and CORFing)...........................................................43 Conative Loyalty within a Sport Context........................................................................45 Results of the Model of Spor t Spectator Conative Loyalty....................................................45 Revised Model of Sport Spectator C onative Loyalty (Study 1/Purpose 2)............................46 Ethnic Identity................................................................................................................ .46 Identification with the Domina nt Society (Acculturation)..............................................49 Vicarious Achievement...................................................................................................51 Past Behaviors.................................................................................................................52 Differences among Latinos Subgroups and Non-Latinos (Study 2/Purpose 3)......................52 Pre and Post Testing of Conativ e Loyalty (Study 3/Purpose 4).............................................54 3 METHODS........................................................................................................................ .....62 Study 1........................................................................................................................ ............62 Participants................................................................................................................... ...62 Procedure...................................................................................................................... ...62 Instrumentation................................................................................................................63 Points of Attachment Index and Team Identification Index....................................64 (Dis)Confirmation of Expectancies Scale................................................................65 Affective State Index................................................................................................66 Self-Esteem Responses............................................................................................67 Intentions for Sport Consumption Behavior Scale...................................................67 Multigroup Ethnic Identity Measure........................................................................68 Abbreviated Multidimensional Acculturation Scale................................................69 Vicarious Achievement Subscale.............................................................................69 Past Behaviors and Estimated Future Sport Consumption Behaviors Index...........70 Data Analysis.................................................................................................................. .70 Study 2........................................................................................................................ ............71 Participants................................................................................................................... ...71 Procedure...................................................................................................................... ...72 Data Analysis.................................................................................................................. .73 Study 3........................................................................................................................ ............74 Participants................................................................................................................... ...74 Procedure...................................................................................................................... ...75 Data Analysis.................................................................................................................. .76 4 RESULTS........................................................................................................................ .......88 Model Results (Study 1/Purpose 1)........................................................................................88 Psychometric Properties of the Scales (M easurement Model for MSSCL Models A, B, & C)........................................................................................................................ .88 MSSCL Model A.............................................................................................................88 Latent Variable Relationships in MSSCL Model A........................................................89 MSSCL Model B.............................................................................................................89 Latent Variable Relationships in MSSCL Model B........................................................90

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7 MSSCL Model C.............................................................................................................90 Latent Variable Relationships in MSSCL Model C........................................................91 Model Results (Study 1/Purpose 2)........................................................................................91 Psychometric Properties of the Scales (Measurement Model for Revised MSSCL Models A & B).............................................................................................................91 Revised MSSCL Model A...............................................................................................92 Latent Variable Relationships in Revised MSSCL Model A..........................................92 Revised MSSCL Model B...............................................................................................93 Latent Variable Relationships in Revised MSSCL Model B..........................................94 Results (Study 2/Purpose 3)....................................................................................................94 Psychometric Properties of the Scal e (Points of Attachment Index)..............................94 MANOVA Results..........................................................................................................95 Results (Study 3/Purpose 4)....................................................................................................96 5 DISCUSSION..................................................................................................................... ..125 Purpose 1/Study 1.............................................................................................................. ...125 Purpose 2/Study 1.............................................................................................................. ...129 Purpose 3/Study 2.............................................................................................................. ...132 Purpose 4/Study 3.............................................................................................................. ...134 Recommendations for Future Rese arch and Alternative Models.........................................134 Summary........................................................................................................................ .......135 LIST OF REFERENCES.............................................................................................................137 BIOGRAPHICAL SKETCH.......................................................................................................149

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8 LIST OF TABLES Table page 4-1 Fit Measures for Measurement Model (Ori ginal Model Data & Revised Model Data)....97 4-2 Factor Loadings ( ), Cronbachs Alpha ( ), and Average Variance Extracted Values (AVE) for Team Identification Index, (D is)Confirmation of Expectancies Scale, Affective State Index, Self-Esteem Maintena nce Behavioral Scale, Intentions for Sport Consumption Behavior Scale...................................................................................98 4-3 Means and Standard Deviations (SD) for the Subscales [Team Identification Index, (Dis)Confirmation of Expectancies Scal e, Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure, A bbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale, a nd Past Attendance] for the Total, Latino, and Non-Latino groups....................................................................................................100 4-4 Correlation Matrix for the Latent Variab les Included in the Measurement Model for Original Models A, B, & C..............................................................................................101 4-5 Fit Measures for Model A, Model B, Model C, Revised Model A, & Revised Model B for the Total Sample, Latino Sample, and Non-Latino Sample...................................102 4-6 Factor Loadings ( ), Cronbachs Alpha ( ), and Average Variance Extracted Values (AVE) for Team Identification Index, (D is)Confirmation of Expectancies Scale, Affective State Index, Self-Esteem Maintena nce Behavioral Scale, Intentions for Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure, Abbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale, and Past Sport C onsumption Behaviors Index................................................103 4-7 Correlation Matrix for the Latent Variab les Included in the Measurement Model for Revised Models A & B....................................................................................................105 4-8 Factor Loadings ( ), Cronbachs Alpha ( ), and Average Variance Extracted Values (AVE) for Points of Attachment Index............................................................................106 4-9 Correlation Matrix for the Latent Variables Included in Study 2 (Group Comparisons)...................................................................................................................107 4-10 Mean Scores (M) and Standard Deviati ons (SD) for Identification with American Football, Baseball, Basketball, Hock ey, and Soccer across Each Group (NonLatinos, Puerto Ricans, C ubans, and Colombians)..........................................................108 4-11 Mean Scores (M) and Standard Deviati ons (SD) for Past Consumption Behaviors (Attendance, Media, and Merchandise) Across Each Group (Non-Latinos, Puerto Ricans, Cubans, and Colombians)...................................................................................109

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9 LIST OF FIGURES Figure page 2-1 Model A: Model of Spor t Spectator Conative Loyalty......................................................56 2-2 Model B: Model of Spor t Spectator Conative Loyalty......................................................57 2-3 Model C: Model of Spor t Spectator Conative Loyalty......................................................58 2-4 Modified Model A: Revised Model of Sport Spectator Conative Loyalty........................59 2-5 Modified Model B: Revised Model of Sport Spectator Conative Loyalty........................60 2-6 Sport Attendance Model....................................................................................................61 4-1 Model A for the Total Sample.........................................................................................110 4-2 Model A for the Latino Sample.......................................................................................111 4-3 Model A for the Non-Latino Sample...............................................................................112 4-4 Model B for the Total Sample.........................................................................................113 4-5 Model B for the Latino Sample.......................................................................................114 4-6 Model B for the Non-Latino Sample...............................................................................115 4-7 Model C for the Total Sample.........................................................................................116 4-8 Model C for the Latino Sample.......................................................................................117 4-9 Model C for the Non-Latino Sample...............................................................................118 4-10 Revised Model A for the Total Sample...........................................................................119 4-11 Revised Model A for the Latino Sample.........................................................................120 4-12 Revised Model A for the Non-Latino Sample.................................................................121 4-13 Revised Model B for the Total Sample............................................................................122 4-14 Revised Model B for the Latino Sample..........................................................................123 4-15 Revised Model B for the Non-Latino Sample.................................................................124

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10 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 SPORT SPECTATOR CONATIVE LOYA LTY: A COMPARISON OF LATINO SUBGROUPS AND NONLATINO CONSUMERS By Michelle Gacio Harrolle August 2007 Chair: Galen T. Trail Major: Health and Human Performance Latinos are both the largest ethnic group in the United States and the fastest growing ethnic minority group in the U.S. Over a five year period (2000), Latino sport fandom in the United States has increased for some sports, a nd this growth is expected to continue as the Latino population in the U.S. grows. While an es calating amount of consumer research within the marketing literature has studied the Latino population, the sport marketing and management field has neglected to study this particular market segment. This research has four main purposes that were encapsulated in three studies. In the first study, the Models of Sport Spectator Conative Loyalty (Models A, B, & C) were tested on a Latino and Non-Latino population to examine the models on these two market segments. For the second purpose, additional constructs (ethnic identity, identification w ith the dominant group, vicarious achiev ement, and past behaviors) were added to the Models of Sport Spectator Conative Loyalty (Model A & B) in order to see if any additional variance was explained in conative loyalty by the addition of these constructs to the original models. In the second study for the third purpose, data from four subgroups (Cubans, Puerto Ricans, Columbians, and Non-Latinos) were compared to determin e if differences exist among Latino subgroups and Non-Latinos on identif ication with specific sports (American football, baseball, basketball, hockey, and socce r) and on sport consumer behaviors (attendance,

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11 media, and merchandise). In the third study for the fourth purpose, pre and post differences in participants conative loyalty were examined before and after a sporting event to determine the effect of the outcome of the game (win v. loss) on conative loyalty while controlling for identification with a team. Comprehensively, the results indicated differences do exist not only between Latinos and Non-Latinos, but also am ong Latino subgroups. This study provided the first stage of sport management research into the Latino market segments.

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12 CHAPTER 1 INTRODUCTION Growth of the Latino Population The Latino population in the United States wa s estimated to be 42.7 million, constituting approximately 14% of the nations total populati on as of July 1, 2005. Moreover, Latinos are both the largest ethnic group in the United States and the fastest grow ing ethnic minority group. It is projected that the Latino population w ill grow to 106.7 million by July 1, 2050. With this rate of growth, Latinos will constitute appr oximately 24% of the nations total population by 2050 (U.S. Census, 2006). Consequently, this growth will lead to an increase in buying power for Latinos. Not only did Latinos control over $700 billi on in spending power in 2005, but over a 20year period (1990), their purchasing power is anticipated to increase 413% to $1,087 billion (Humphreys, 2005). The buying power of La tinos living in the United States will also grow faster than any of the following mi nority groups: African American (222%), Asian Americans (251%), and Native American (397%). In 2010, Latinos will account for approximately 9.2% of the total buying power in the United States (Humphreys). The median income of Latino households was $35,967 in 2005 which is 38% less than the national median income ($57,278; Humphreys, 2005). However, Latinos spend more money on average than Non-Latinos on groceries, tele phone services, furniture, major appliances, mens and boys clothing, and f ootwear (p. 8) and less money th an Non-Latinos on health care, entertainment, education, and pers onal insurance (p. 8). These fi gures are based on Latinos as a whole; however, spending patterns may differ based on a Latinos country of origin (Humphreys).

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13 Subsequently, marketers have become increas ingly aware of the growth and purchasing power of the Latino consumer (Berkowitz, Bao, & Allaway, 2005; Mulhern & Williams, 1994). Therefore, numerous United States companie s have begun to increase their marketing campaigns directed toward the Latin o market in an effort to captu re their attention (Berkowitz et al., 2005). Growth of the Latino Sport Fan Over a five year period (2000), Latino sport fandom in the United States has increased for some sports, and this growth is e xpected to continue as the Latino population in the U.S. grows (The changing, 2006). For example, Latino fandom for collegiate football and collegiate basketball has increase d 3.1% and 4.4% respectively over the past year. However, some sports have seen a decrease in the percenta ge of U.S. Latinos indicating that they are fans [e.g., National Basketball Association (N BA) 4.6% decrease, professional boxing 6.4% decrease]. As a whole in 2006, 36% of all Latinos reported themselves as avid fans of at least one sport compared to 29.8% of the tota l population (World Congress, 2006, pp. 34-35). Sport Marketers focusing on the Latino Market Recognizing this potential market segment, professional sports have been actively marketing to the Latino population. For exampl e, National Association for Stock Car Auto Racing (NASCAR) has been aggr essively recruiting the Latino fan base with promotional activities and incentives. Add itionally, NASCAR has been recrui ting Latino drivers, employees, and business partners (Smith, 2006). For the 2006 World Baseball Classic, Major League Baseball (MLB) developed different marketing stra tegies to target Latinos from multiple Latin American countries (King, 2006). In order to attract the Latino ma rket, National Football League (NFL) teams celebrated Hispanic Heritage Month with festivities at multiple games (NFL News,

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14 2006). For a grassroots marketi ng initiative, the NBA has part nered with McDonalds and Discovery Kids en Espaol to help tackle childhood obesity (His panic PR Wire, 2006). Professional teams are also using Spanish to market to Latinos. MLB has not only doubled their Spanish-language webs ites (Fisher, 2006), but they al so have White Sox manager, Ozzie Guillen, speaking in Spanish on a Comcas t SportNet Chicago radio program (Marchand, 2006). In order to expand into the Spanish language television market in the U.S., Major League Soccer (MLS) committed to an eight year, $80 million deal with Univision in 2006 (Mickle, 2006). However, using Spanish to communicat e to consumers should not be the only consideration when sport organizations ar e sending marketing messages to Latinos. Marketing to Latinos From a sociological perspective, Dvila (2001) argued that Hispanic marketing in the early 1970s stemmed from the id eas within corporate America to market to the Hispanic population. The profitability of the Latino market has dictated an accompanying increase in marketing campaigns toward Latinos, thus numerous Hispanic advertising agencies have developed within large Hispanic populations (Dvila). Based on he r exploration of the political and economic implications of ma rketing to Latinos, Dvila argued that current marketing practices to Latinos as a ho mogeneous group and using stereotypical representations will inevitably perpetuate marketing campai gns stereotyping ethnic consumers. Even though Latinos are labeled through commo n names (i.e., Latinos, Hispanics), they are a very heterogeneous group with multiple cultural and geographical backgrounds (Dvila, 2001; Oboler, 1998; Torres-Saillan t, 2005). The larger ethnic group of Latinos is comprised of different nuances, which can be broken into nu merous categories (e.g., cu lture, religion, music, family values, attitudes, holidays) based on thei r nationality (Braus, 1993) and these differences may dictate their marketing preferences and potentia l identification with specific sports or teams.

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15 Because Latinos are a complex, heterogeneous group, sport marketers and managers should strive to understand the cultural distinctions between the subgroups within the Latino community (e.g., Cubans, Puerto Ricans, Mexicans, Dominican s) that have distinct ive cultural backgrounds, national histories, demographic profiles, and levels of progress (Val ds, 2000). Braus (1993) stated that many marketers attempt to place all La tinos into one target market, while neglecting the fact that considerable cultural and demographic diversity exist. Typically, marketers will use Spanish as a co mmon dominator when marketing to Latinos as a homogeneous group (Korzenny & Korzenny 2005). Nevertheless, the Latino market segment is more than a group of individuals that speak a common language, and marketers should find better ways to understand Latinos an d market to them accordingly. The content of the marketing message should be tailored to the Latino se gment and any specific Latino subgroups. Lack of Sport Marketing Rese arch Pertaining to Latinos As companies have increasingly recognized th e need to market to Latinos, marketing research efforts have consequently increas ed (Deshpande, Hoyer, & Donthu, 1986; Mulhern & Williams, 1994; Shepherd, Tsalikis, & Seaton, 2002) While an escalating amount of consumer research within the marketing literature has st udied the Latino population (e.g., Berkowitz et al., 2005; Donthu & Cherian, 1994; Korgaonkar, Karson, & Lund, 2000; Mulhern & Williams, 1994; Torres & Briggs, 2005; Torres & Gelb, 2002), the sport marketing and management field has neglected to study this particular market segment. Overview of the Research Problem When searching through the EBSCOhost Rese arch Database, specifically in Academic Search Premier, Business Source Premier, and SPORTDiscus Select, using any combination of the search terms Hispanic, Latino, consumer, an d sport, not one academic journal article is

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16 available in the search results. A lack of sport research examining the Latino fan is evident. This study would be the first comprehensive study that examined Latino sport consumption behavior. Additionally, this study attempts to examine the conative loyalty of the Latino fan by testing the Models of Sport Spectator Conative Loyalty (MSSCL; Trail, Anderson, & Fink, 2005) on a Latino sample and to compare the sport cons umption behaviors among Latinos subgroups and Non-Latinos. Purpose This research has four main purposes that were encapsulated in three studies. In the first study, the Models of Sport Specta tor Conative Loyalty (M SSCL Models A, B, & C; Trail et al., 2005) were tested on a Latino and Non-Latino p opulation to examine the models on these two market segments. For the second purpose, additi onal constructs (ethnic identity, identification with the dominant group, vicarious achievement, a nd past behaviors) were added to the Models of Sport Spectator Conative L oyalty (Model A & B) in order to see if any additional variance was explained in conative loyalty by the addition of these constructs to the original models. In the second study for the third purpose, data from four subgroups (Cubans, Puerto Ricans, Columbians, and Non-Latinos) were compared to determine if differences exist among Latino subgroups and Non-Latinos on identification with specific sports (American football, baseball, basketball, hockey, and soccer) and on sport consumer behaviors (attendance, media, and merchandise). In the third study for the fourth pur pose, pre and post differe nces in participants conative loyalty were examined before and after a sporting event to determine the effect of the outcome of the game (win v. loss) on conative lo yalty while controlling for identification with a team.

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17 Theory Development The results of this study would contribute to theory development by further examining and testing previous theories including identity th eory, self-esteem theory, social identity theory, and acculturation theory. The MSSCL was develope d by combining the different perspective of both identity theory and satisfaction theory (Trail et al., 2005), while the additional variables in the revised models are explained through accultura tion theory, self-esteem theory, and social identity theory. The resu lts of this research would support th e development of these theories in two ways: (1) verify the structur e of the theories, and (2) expl ain sport consumption behaviors through the tested models. By combining these th eories and developing ne w models, the results of this study could help to explain more va riance in sport consum ption behaviors, thus supporting the theories. Development of Sport Cons umer Behavior Research This research would contribute to the sport management literature in four primary ways. First, the MSSCL would be replicated using an additional population, moreover the replicated population would represent a minority segment in the United States (Latinos). The findings would not only test the MSSCL psychometrics and m odel fit, but the model would also facilitate the understanding of conative loyalt y for Latino sport consumers. Second, in an effort to extend the previous research by Trail et al. (2005), additional variable s would be added to the MSSCL (Models A & B), thus attempting to increase th e variance explained in conative loyalty. Third, Gacio Harrolle and Trail (2007) suggested future research should segment the Latino population and test Latino subgroups in order to examine the possible differences in sport identification and sport consumption among these groups. The findi ngs of the subgroup comparisons would be the first of its kind within the sport management literature, and the results would facilitate the understanding of group differences among differen t subgroups within on e ethnic group. Fourth,

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18 the pre and posttest of conative loyalty would provide researcher s with a guide to understanding the relationship between the outco me of the game (win or loss) and an individuals sport consumption intentions while cont rolling for an individuals identification with a specific team. To further the sport management literature, th is study would test the relationships among the numerous variables (confirmation/disconfirmation of expectancies, positive affective responses, negative affective responses, satisf action, BIRGing, CORFing, identification with a team, conative loyalty, past behaviors, vicari ous achievement, ethnic id entity, and identification with the dominant group). By understanding these re lationships and by further testing models of conative loyalty (MSSCL & modified MSSCL), this study would expand the knowledge and theory development of the sport management and marketing literature. Practical Implications Sport marketers and managers have been blin dly marketing to the Latino sport fan based on demographic information. Because previous sport management researchers (Fink, Trail, Anderson, 2002; Funk, Ridinger, & Moorman, 2003; Robins on & Trail, 2005; Trail, Robinson, Dick, & Gillentine, 2003; Wann, Schrader, & Wils on, 1999) have explained more variance in sport identification and fan identi fication using variables other th an demographics (e.g., motives and psychographics), the findings of this st udy would provide sport marketers with more effective tools for marketing strategies. Sport managers would possess an efficient wa y of determining the relationships as to why Latinos consume sport and a more appropri ate model to follow when marketing to this ethnic group. As managers market to their Latino segments more appropri ately, the potential for revenue growth also increases. Additionally, th e results of this study would enable sport marketers to have a better understanding of the differences between Latinos, their subgroups, and Non-Latinos.

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19 Delimitations This study was delimited to the disconfir mation/confirmation of expectations, identification with a team, mood, self-esteem responses, conative loyalty, ethnic identity, identification with the domina nt group, motives, and past be haviors of Latino/a and NonLatino/a students at one southeastern National Collegiate Athletic Asso ciation (NCAA) Division I university. Therefore, the results of the study ma y not be generalizable to other students or individuals in the United States. Because the sample was collected before and after one collegiate football game, the re sults may not be gene ralizable to other sporting events. In addition, other factors (e.g., gender, income) may in fluence the differences between or within the groups. Limitations As with any type of survey-based resear ch, limitations are inevitable. The convenience sample approach for the students in the study is a limitation of this study, such that the Latino/a and Non-Latino/a students were members of La tino organizations, Latin American Studies classes, and the College of Hea lth and Human Performance. The re sults of this study may differ from other universities. The Lati no participants in the study were representative of the Latino community in Florida; however, it may not be representative of the entire U.S. Latino population. A low response rate from emails sent to university students may have harmed the results of the group comparisons within the Latino sub-groups. Definitions of Terms As all of the terms tested in this study ar e numerous and complex, the definitions for the terms are placed in Chapter 2. Confirmation/disc onfirmation of expectancies, positive affective responses, negative affective res ponses, satisfaction, self-esteem responses such as BIRGing and CORFing, identification with a te am, conative loyalty, past behavi ors, vicarious achievement,

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20 ethnic identity, and identification with the dominan t group are defined in de tail in the following chapter. Overview Chapter 1 introduces the scope of the study. This chapter contains background information on the Latino sport market in the Unit ed States, the lack of sport marketing research of the Latino population, and the purpose of the study. In Chapter 2, the theoretical framework for the study is presented. In itially, the theories supporting the Model of Sport Spectator Conative Loyalty are discusse d. A thorough literature review of social identity theor y, identity theory, self-esteem th eory, and acculturation theory is included. Then, each construct of the MSSCL is examined including the relevant sport management literature. Subsequently, the construc ts that will be added to the original MSSCL model are explained including ethnic identit y, identification with the dominant group (acculturation), vicarious achievement, and past behaviors. Lastly, group differences among Latino subgroups and differences between Latinos and Non-Latinos are discussed along with the pre and post testing of conative loyalty. The material and methods of the research are located in Chapter 3. The participants, instrumentation, procedures, and data analysis for each of the three st udies are presented. The statistical analysis procedures fo r each study includes descriptive stat istics of the participants and psychometric properties of the scal es including the confirmatory factor analysis (CFA) of the measurement model, the construct reliability, inte rnal consistency, and discriminant validity of the constructs. For the first study, the model f it for the original Mode l of Sport Spectator Conative Loyalty (Models A, B, & C) on the samp les and the model fit for the modified Model of Sport Spectator Conative Loyalty (Models A and B) on the samples are presented. For the second study, a MANOVA to compare the sport identification and sport behaviors among the

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21 subgroups is included. For the third study, th e methods section includes an ANCOVA to compare the pre and post test results for conative loyalty on the total sample while controlling for team identification. In Chapter 4, the results of th e statistical analysis are presen ted. The first section contains the results of the first study including the psychometric properties of the scales, the confirmatory factor analysis (CFA) of the measurement model, the construct reliability, internal consistency, discriminant validity of the constructs, and th e model fit for the orig inal Model of Sport Spectator Conative Loyalty (Models A, B, & C) on the Latino and Non-Latino sample. The second section contains the results for purpose 2 including the psychometric properties of the scales, the confirmatory factor analysis (C FA) of the measurement model, the construct reliability, internal consistency, di scriminant validity of the cons tructs, and the model fit for the modified Model of Sport Spectator Conative Lo yalty (Models A and B) on the Latino and NonLatino sample. In the third section, the result s for the second study, the comparisons of sport identification and sport behaviors among the subgr oups, are presented. Finall y, the fourth section contains the results of the third study, the pre and post test results for conative loyalty on the total sample. The final chapter, Chapter 5, contains the discussion of the resu lts. Additionally, the recommendations for future research and practical applications for sport managers are included.

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22 CHAPTER 2 LITERATURE REVIEW The goal of Chapter 2 is to provide the reader with a thorough background of the research and theory related to the four main purposes of th is study. This literature review begins with the theoretical background of the M SSCL including identity theory, satisfaction theory, and selfesteem theory along with the relevant social iden tity theory. Then, a deta iled explanation of the MSSCL (Models A, B, and C) is explained along with the pertinent sport management literature. Next, the results of Trail, Anderson, and Fink s (2005) original study are provided, along with the revised MSSCL (Models A and B) including the additional constructs (ethnic identity, dominant identity, vicarious achie vement, and past behaviors) a dded to the models. After that, the need to examine group differences among Lati no subgroups and differences between Latinos and Non-Latinos was explored. Fi nally, the need for pre and post testing of conative loyalty are explained. Theoretical Background for the Model of Sport Spectator Conative Loyalty (Study 1/ Purpose 1) Trail et al. (2005) proposed and tested thr ee distinct, but related, sport consumption behavior models (Models A, B, & C) that depicted the relationships among disconfirmation/confirmation of expectancies, identification with a team, mood, self-esteem responses, and conative loyalty. Th eir research and models were based on three primary theories: identity theory, self-esteem theo ry, and consumer satisfaction th eory. This literature review begins with a thorough exploration of the theoretical framework of the original models and the related theory of social identity theory. Trail et al. (2005) did not utilize social identity theory when forming their models. However, the discus sion of social identity theory within this literature review is due to the commonalities between social ident ity theory and identity theory,

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23 the use of both in the sport consumer literatu re, and the confusion created by the distinction between the two theories. Social Identity Theory Tajfel and Turner (1986) defined social groups as a collection of individuals who perceive themselves to be members of the same social category, share some emotional involvement in this common definition of them selves, and achieve some degree of social consensus about the evaluation of their group and of their membersh ip in it (p. 15). Tajfel and Turner (1986) argued that soci al groups provide their memb ers with a way of identifying themselves based on the social dynamics of the group and facilitate a way for their members to create and define their place in society. Originally developed by Tajfel and Turner (1979), social identity theory (SIT) deals spec ifically with how individuals become members of a group, how individuals see themselves in that group, and how individuals in one group compare themselves with another group (Brown, 2000; St ets & Burke, 2000). Tajfel (1981) described social identity as that part of an indivi duals self-concept which deri ves from his knowledge of his membership of a social group (or groups) togeth er with the value and emotional significance attached to that membership (p. 255). The framew ork of social identity theory contained several stipulations regarding group memb ership: (a) individuals maintain or seek out memberships in social groups if these groups provi de a positive aspect to the indi viduals social identity (selfesteem); (b) an individual may leave a group if his/her membership does not satisfy a need for positive identity; (c) if an individual can not de part from a group membership, then he/she will adapt, justify a lower status, or simply accept th e situation; and (d) all groups in society live among other groups (Tajfel, 1981, p. 256). Originally, SIT was explored a nd developed as a supplemental theory to Sherifs (1967) realistic group conflict theory (RCT; Tajfel & Turner, 1986). Tajf el and Turner argued that RCT

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24 did not examine the identification with the in-g roup, thus failing to explain the process of development and maintenance of group identity. Therefore, this neglect of RCT perpetuated inconsistencies in the theory a nd empirical research of social group theories. Tajfel and Turner proposed SIT as a way to explain the psycholog ical aspects of social psychology and social conflict. Moreover, they attempte d to answer the question of ps ychological processes involved in the development of positive group identity along with the question of the conditions involved with the enhancement or reduction of group conflict. Within social groups, intergroup behaviors ex ist. These intergroup behaviors are defined as any behavior displayed by one or more actors to ward one or more others that is based on the actors identification of themselves and the others as belonging to different social categories (Tajfel & Turner, 1986, p. 15). According to Brown (2000), this concept of intergroup behaviors and dynamics developed by Sherif (1967) and Tajf el and Turner (1986) has made significant contributions to SIT in the form of relevant re search in response to in equality between groups, ingroup bias, stereotyping, and changing attitude s as contact with other groups increases. Tajfel (1981) stipulated that through these social memberships and relationships, an individual strives to achieve a favorable level of self-esteem or self-image. Additionally, a favorable group comparison between ingroups and outgroups can promote higher levels of selfesteem as long as the comparisons are made between comparable groups (Brown, 2000). Social identity theory and identity theory are related such that both th eories deal with the self within a social context. Moreover, Str yker and Burke (2000) a nd Stets and Burke (2000) hypothesized that these two theori es were connected and overlappe d due to their connection with the overall concept of the self.

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25 Identity Theory Stryker and Burke (2000) defined identity as th e meanings that individuals attach to the multiple roles that are played in a contemporary society. Burke (2003) stated that identities are the meanings that individuals hold for themselves what it means to be who they are (p. 196). Identity theory can be traced b ack to Meads (1934) framework a nd formulas stating that society shapes self and that each person shapes social behavior. Originally, identity theory was developed as a way to conceptualize the ideas of society and self and to develop ways to research these concepts (Stryke r & Burke, 2000). As one accepts Meads hypothesis that self reflects society, then self would be consid ered multifaceted, made up interdependent and independent, mutually reinforcing and conflic ting parts (Stryker & Burke, p. 286). The first assumption when examining identity is that there is a reciprocal relationship between society and the self (Stryker, 1980). An individual influences society through actions and through creation of groups, orga nizations, networks, and instit utions. Reciprocally, society influences the self through its sh ared language and meanings that en able a person to take the role of the other, engage in social interaction, and reflect on oneself as an object (Stets & Burke, 2003, p. 128). This interaction between self and society requires resear chers to not only understand the self, but also study and understand the parts of self (identitie s) within the context and influences of society as a whole (Stryker, 1980). Burke and Tully (1977) stated th at identities are based on the m eanings that an individual attributes to himself/herself as an object in a social role or social situation. Moreover, these meanings to the roles occur through interaction w ith others. Within identity theory, Stets and Burke (2000) suggested the essence of identity is th e categorization of the se lf as an occupant of a role, and the incorporation ( p. 225) of the meanings associated with that role. They further explained that as an individual has a particular role identity a need exists ensuring that the

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26 individual will fulfill the expectations associated with that role. Therefore, researchers can use the concept of identity to examine individuals in terms of each network of relationships or roles in which an individual occupies. Based on the concepts of self and socie ty, Stryker and Burke (2000) stated that previous identity theory research examined identi ty from two different perspectives or strands: how social interactions affect ones identities and the process of internal self-verification of ones identity. They suggested that identity theo ry is based on both internal self-verification processes and social constructs. Interactions between individuals are typically not between all aspects of a whole person, rather interactions between indivi duals are between specific aspects of each person within the context of a particular role or group membership (Stets & Burke, 2003). That is, the interaction is between each persons role identity specific to that situation (e.g., an interaction between an individual in the ro le identity of teacher and an individual in the role identity of student). Individuals have multiple role identities at the same time (McCall, & Simmons, 1978; Stryker, 1980; Stets & Burke, 2003), and these mu ltiple roles may be good for the self (Stets & Burke, 2003). McCall and Simmons (1978) suggested that multiple role identities provide an individual with a sense of meaning and purpose, therefore providing a positive aspect to his/her life. However, the kinds of role identities may have an effect on their positive influence on the self (Stets & Burke, 2003). For example, stressful role-identities may not be beneficial to the self, whereas a role-identity as a friend with little or no stress would be beneficial. In society, identities are organi zed in a hierarchical fashion, such that the salience of the identity dictates the importanc e of the identity (Burke & Tully, 1977; Stryker & Burke 2000). Based on the situation, an individual will posse ss a particular identity and act accordingly.

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27 Identity is an internal process, whereas a role is an external compone nt. An individual may internalize the expectations and meanings of specific roles. Mo reover, social situations can connect multiple roles based on the situation an d the social group (Stryker & Burke, 2000). For example, students partake in numerous roles si multaneously through inte raction in numerous networks including social groups with other students, interaction with professors, interaction with their local communities, and other social gr oups connected to their university, all at the same time. Moreover, researchers hypothesized that as the level of salience increa ses for a particular identity, behavioral decisions attached to that particular identity will also increase (Stryker & Burke, 2000). These different roles may elicit different magnitudes of identification. For example, students may have stronger ties to thei r universities than to their local communities. Previous research found that indi viduals with higher levels of commitment to a particular group maintained their identity with th e group and worked harder to maintain their identity (Burke & Reitzes, 1991). Stryker and Burke (2000) stated that the goal of identity research should be to examine and improve the knowledge concer ning the relationships between society and self. Additionally, they suggested that emotions and mood needed to be explored within identity research. Identity Theory and Social Identity Theory Identity theory and social identity theory are similar in many respects and recently have been combined to create one inclusive theory of the self. Stets and Burke (2000) stated that in both social identity theory and identity theory an individual can classif y, categorize, or name himself/herself in relation to other social groups, and both theories deal specifically with structured societies. Within social identity th eory, this process is called self-categorization (Turner, Hogg, Oakes, Reicher, & Wetherell, 1987), while in iden tity theory this process is

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28 called identification (McCall & Simmons, 1978). Mo reover, an identity is formed through the processes of self-categorizati on or identification (Stets & Burke, 2000). Stryker and Burke (2000) along with Stets and Burk e (2000) hypothesized that these two theoretical strands were related and converged at the behavioral level a nd during interactions w ith others. Burke (2000) combined three primary concepts/theories on identity to define the essence of identity: (a) role identities or roles that an indi vidual plays (e.g., being a teacher, a student, or a friend); (b) social identities or social group memberships that pr ovide meaning to an individuals identity (e.g., being a college graduate or an Am erican); (c) personal identities or personal characteristics that are not necessarily shared with others (e.g., being honorable, confident, or responsible). Furthermore, Burke expressed a need for a unifica tion of these three theoretical identity sources (role, social, and personal), thus serving as a better measurement of identity. Additionally, due to the broader concept of the self, researchers (Ervin & Stryker, 2001; Owen & Serpe, 2003; Stryker & Burke, 2000) have expres sed the need for a linkage betw een identity th eory and selfesteem theory. Self-Esteem Theory Rosenberg (1979) defined self-esteem as the positive or negative ev aluation of the self (p. 31). Rosenberg, Schooler, Sc hoenbach, and Rosenberg (1995) e xpressed self-esteem in terms of an attitude containing two ma in distinctions. First, they post ulated that self-esteem was an attitude, such that individuals have attitudes about objects as a whole (global self-esteem) and specific attitudes about that the characteristics of the objects (specific self-esteem). Secondly, they stated that attitudes have both cognitive and affective elemen ts. In addition, as with other attitudes, Rosenberg et al. also stated that self-esteem may contain both negative and positive elements.

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29 The current views on self-esteem theory were developed primarily fr om the theories of self-concept developed in the la te 1970s by Morris Rosenberg (Elliott, 2001). Rosenberg (1979) defined self-concept as the totality of the indi viduals thoughts and feel ings having reference to himself as an object (p. 7). Furthermore, self -concept was defined usin g three broad concepts: the extant self (how an indivi dual sees himself); the desired self (how he would like others to see himself; and the presenting self (how he s hows himself to others) (p. 9). Elliott (2001) argued that Rosenbergs th eoretical and empirical studies were the most influential of all selfesteem theorists in the second half of the twentieth century. Three main principles define self-esteem th eory: (a) individuals want to be happy with themselves and increase their self-esteem; (b) individuals want to maintain a consistent selfimage, even if this image is negative; and (c) thoughts about oneself and feelings about oneself are intertwined concepts (Ervin & Stryker, 2001). During self exam ination, self-esteem refers to the affective responses to oneself, such that an individual evaluates w ho they are (Rosenberg, 1979; Rosenberg et al., 1995). Self esteem stab ilizes a group, because individuals are motivated to maintain relationships that ultimately verify identities (Cast & Burke, 2002). More recently, Rosenberg et al. (1995) conducted a longitudinal study of high school students in order to examine the nature of se lf-esteem as containing both a global and specific self-esteem (academic self-esteem) and in order to clarify previ ous self-esteem research. Their findings supported their claims that global a nd specific self-esteem are distinct and Noninterchangeable, and the claims that specific se lf-esteem has a greater effect on the dependent variable than does global self-esteem. Therefore, because self-esteem is an attitude, self-esteem specific to a role, as apposed to global self -esteem, should more accura tely predict relevant behavior (Rosenberg et al., 1995).

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30 Rosenberg (1965) designed a self-esteem s cale (Rosenberg Self-Esteem Scale: RSES) that was meant to measure self-esteem as a uni dimensional model. His measurement model was designed to be easily administere d, to be economic and efficient, to contain face validity, and to be unidimensional (Owen & King, 2001). However, through examining previous research, Owen and King (2001) argued that se lf-esteem contains both a pos itive dimension and negative dimension and have shown that a bidimensiona l measurement model of self-esteem nearly always fits the data better than a unidimensional model. Through confirmatory factor analysis, they showed that a two-factor model fit the data better than a one-factor model. Owen and King concluded that self-esteem should be examined not as a strictly positive dimension, but should also include the negative dime nsion of self-esteem (e.g., happi ness and unhappiness). This concept of bidimensional self-esteem models have also used to examine self-esteem responses within the sport literature. Link between Identity Theo ry and Self-Esteem Theory Previous researchers (e.g., Cast & Burke, 2002; Ervin & Stryker, 2001; Stryker & Burke, 2000) have suggested that self-esteem theory can stem from the theoretical framework of identity theory. Additionally, Cast and Burke (2002) stated that self-esteem is an outcome of a successful self-verification process. Thei r findings supported the idea that self-esteem is a central component of the identity process, such that as individuals self-verify thei r social group identities they also build self-esteem. For example, as an individual self-verifies an identity then he/she subsequently produces a feeling of competency and worth, thus increasing self-esteem. Additionally, Ervin and Stryker (2001) suggested that self-e steem fits logically into identity theory formation and de fined self-esteem as the affectiv e attachment to ones self and outlined an argument for this linkage. Ervin and Stryker (2001) argued that both self-esteem and identity are aspects of self (p. 32). They argued that identity theory has developed to include

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31 the concepts of social interac tion (interactional commitment) and the affective responses to those social interactions (affective commitment). Er vin and Stryker (2001) hypothesized that the concepts of identity lead to self-esteem cons tructs and role performance, while self-esteem constructs lead to identity salience and role performance. For example, highly identified sport fans increase their self-esteem through social in teractions and self-verif ication, and this higher self-esteem leads to a stronger and more salient role as a sport fan. Overall, Cast and Burkes results supported the notion that self-esteem s hould be incorporated in to identity theory. As previously discussed, Tra il and colleagues (2005) cona tive loyalty model was based on identity theory, self-esteem theory, and c onsumer satisfaction theory. Once an individual creates a fan role-identity and st arts consuming a sport product, the next progression within the consumption behavior process would be the in dividuals evaluation of that product and the subsequent satisfaction or dissatisfaction with his/her purchase of a good and/or service (Oliver, 1997). Satisfaction Theory Based on the need to fully understand c onsumers needs and subsequently offer appropriate products and services, American businesses placed a high priority on the measurement of consumer satisfaction in the late 1990s (Vavra, 1997). However, within the business literature, consumer satisfaction began to develop as a legitimate field of study in the early 1970s (Churchill & Surprenant, 1982). Cardozo s (1965) pioneering research into customer efforts, expectations, and satis faction along with previous rese archers in the early 1970s (e.g., Anderson, 1973; Olshavsky & Miller, 1972) formed the foundation of the first decade of the development of consumer satisfaction research (Vavra, 1997). Within the political arena, Pfaff (1972) reported the first measurement problems and opportunities of c onsumer satisfaction by developing the U.S. Department of Agriculture s Index of Consumer Satisfaction for policy

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32 makers. Moreover, the majority of these initi al research studies on customer satisfaction contained the disconfirmation paradigm which holds that satisfaction is related to the size and direction of the disconfirmation experience, wh ere disconfirmation is related to the persons initial expectations (Churc hill & Surprenant, p. 491). A lack of consensus exists within consum er satisfaction resear ch on an appropriate definition for satisfaction (Gie se & Cote, 2000; Peterson & Wils on, 1992; Yu & Dean, 2001). At the beginning stages of consumer sati sfaction research, Anderson (1973) defined satisfaction/dissatisfaction based on the levels of discrepancy between expectations and perceived performance of a product. Taking into c onsideration previous li terature and research results, Oliver (1997) defined satisfaction as the consumers fulfillment response. It is a judgment that a product or serv ice feature, or the product or service itself, provided (or is providing) a pleasurable level of consumption-re lated fulfillment, including levels of underor overfulfillment (p. 13). This fulfillment implies th at a goal exists and should be filled. Hence, fulfillment and satisfaction can be a judgment or a comparison by an individual with reference to some standard. Thus, satisfaction is a consume rs response to a level of fulfillment that is unpleasant or pleasant (Oliver, 1997). Additionally, Giese and Cote (2000) found that previous research contained common elements when de fining satisfaction. Thre e general components were identified: 1) consumer satisfaction is a response (emotional or cognitive); 2) the response pertains to a particular focus (expectations, products, consumpti on experience, etc.); and 3) the response occurs at a particular time (after consumption, after choice, based on accumulated experience, etc.) (Giese & Cote, p. 1). In an effort to provide a meaningful definition of satisfaction, Giese and Cote identified the concep ts within satisfaction and proposed an outlined process of satisfaction, such that comparisons c ould be made across stud ies. Through a detailed

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33 literature review and validati on through group and personal inte rview data, Giese and Cote defined consumer satisfaction as a summary affec tive response of varying intensity, with a timespecific point of determination, directed toward the focal aspects of pr oduct acquisition and/or consumption (p.14-15). Previous authors have defined satisfaction in two fundamental ways: as either an outcome or a process (Vavra, 1997). From an output perspective, Churchill and Surprenant (1982) defined satisfaction as an outcome of a purchase and use resulting from the buyers comparison of the rewards and costs of the purchas e in relation to the anticipated consequences (p. 493). From a process perspective, Tse and W ilton (1988) defined satisfaction as the consumers response to the evaluation of the perceived discrepancy be tween prior expectati on (p. 204) and actual performance after consumption. Th e core process of satisfaction is the comparison between what is expected by the consumer and the actual performance of the product (the confirmation/disconfirmation proces s; Vavra, 1997). Oliver (1980) was the first researcher to suggest that expectations served as a frame of reference for me asuring a consumers experience (Vavra, 1997). Olivers (1997) cycle of satis faction was comprised of four factors (a) performance (b) disconfirmation/confirmation of e xpectancies, (c) affectiv e responses (positive attitude, negative attitude, and sa tisfaction), and (d) behavioral in tentions (conative loyalty). Disconfirmation/Confirma tion of Expectancies Expectations are important to the satisfacti on response, because they provide a guideline for future judgments of product perf ormances (Oliver, 1997). Expectancy disconfirmation/confirmation [(dis)confirmation] is defined as the result of a comparison between what an individual expects to ha ppen and what is observed (Oliver, 1997). (Dis)confirmation has three components: the ev ent, probability of occurrence, and its (un)desirability (Oliver). For example in sport, the event would be a sport competition outcome

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34 (win vs. loss); the proba bility of occurrence w ould be the likelihood of a win or a loss (e.g., a very talented team competing against a less tale nted team); and the desire for a win or a loss based on previous experience or knowledge. Oliver (1981) defined disconfirmation as a mental comparison of an actual state of nature with its anticipated probability. In common usage, these states of nature may be perceived as worse than expected (negative confirmation), better than expected ( positive disconfirmation), or just as expected (zero disconfirmation) ( p. 35). Positive disconfirmation occurs when performance exceeds expectations. For example, a fan expects their preferred team to lose, but the team actually wins. Convers ely, negative disconfirmation occu rs when performance drops below expectations. For example, a fan expects th eir team to win, but the team actually loses. Zero confirmation occurs when high-probability ev ents take place and lowprobability events do not take place. For example, a fan expect s their team to win, and the team wins. Vavra (1997) argued that Oliver s (1980) use of disconfirmation to describe both instances when performance exceeds expect ations and when performance falls below expectations is confusing. In an attempt to reduce misunderstanding and provide a clear definition of the disconfirmation/confirmation pro cess, Vavra (1997) used the directional terms of affirmed, confirmed, and disconfirmed to express the size and directionality of the (dis)confirmation experience. Thus, e xpectations will be considered as confirmed when perceived performance meets them; expectations will be considered affirmed (reinforced by positive disconfirmation) when perceived performance exceeds them; and expectations will be considered disconfirmed (failed by negative disconfirmati on) when perceived performance falls short of them (p. 42).

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35 Previous research has shown th at (dis)confirmation of expect ancies correlates with an individuals satisfaction (Oliv er, 1997). Oliver (1980) tested the relationships between confirmation/disconfirmation and sa tisfaction, the relationship betw een satisfaction and attitude change, and the relationship betw een attitude and purchase inten tions. His results within two samples supported the relationships of satisfaction influencing att itude, and attitude influencing intention. Additionally, the results along with th eoretical satisfaction framework showed that (dis)confirmation appeared to influence satisf action, attitude, and inte ntion with the most immediate impact of (dis)confir mation influencing satisfaction. Oliver and Swan (1989) studied the disc onfirmation effects of consumers on product satisfaction for the automobile industry. Thei r results showed that disconfirmation of expectancies with a salesperson were significantly rela ted to satisfaction wi th a salesperson. In addition, they showed a significan t relationship between disconfirma tion of expectancies with a dealer and satisfaction with a deal er. Oliver and Swan stated that disconfirmation of expectancies was a significant predictor of sa tisfaction, thus strengthening prev ious research (Bearden & Teel, 1983; Oliver, 1980; Oliver Rust, & Varki, 1997). Affective Responses (Positive Affect Negative Affect, and Satisfaction) Oliver (1997) believed that satisfaction shoul d be placed with affect in terms of cognitive perspectives, such that research examining pos itive and negative affec tive states should also include an individuals perspectiv e on satisfaction. As defined by O liver (1997), affect refers to the feeling side of consciousness, as opposed to thinking, which taps the cognitive domain (p. 294). An affect state or feeling refers to a ps ychological sensation such as happiness, sadness, pleasure, or displeasure. Whereas moods are a temporary state of affect and are based on their short duration. Satisfaction is a component of consumption that can result in an emotion or temporary mood. Previous research in both psyc hology and in consumer satisfaction has shown

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36 that negative and positive affective orientations influence future affective judgments (Oliver, 1997). Within the service management literature, previous researchers (Colgate & Stewart, 1998; Hocutt, 1998; Patterson & Spreng, 1997) have found that consumer satisfaction is significantly related to consumer loyalty, however within these studies, satisfaction did not contain an affective component (Yu & Dean, 2001). Consequentl y, using a convenience sample from an undergraduate campus in Australia, Yu and Dean explored the role of emotions in customer satisfaction and re-tested the satisf action-loyalty relationship while including the emotional component of satisfaction. Their resu lts showed a significant relationship between the emotional component of satisfaction and consum er loyalty and suggested that the emotional component of satisfaction is a be tter predictor of consumer loyalty than the cognitive component (expectancy disconfirmation model) of satisfaction. Within the retailing industr y, Oliver, Rust, and Varki ( 1997) tested the concept of customer delight at a wildlife theme park and symphony concert. They proposed and tested a Model of Delight and Satisfacti on, and subsequently modified th e original model in order to increase model fit. They found a significant re lationship between (dis)c onfirmation and positive affect, such that 8% of the variance in positiv e affect was explained by (dis)confirmation. Their results for the modified model supported th e relationship between disconfirmation of expectancies and positive affect. Conative Loyalty Oliver (1999) theorized that consumers fi rst become loyal to a brand or product on a cognitive level, then on an affective level, then later on a conative level, and finally on a behavioral level. The first stage, cognitive l oyalty, is based on recently acquired information (e.g., price, features) and is a sh allow level of loyalty. The sec ond stage, affective loyalty, is

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37 determined by an attitude toward a brand base d on previous satisfyi ng usages (i.e., I buy it because I like it, p. 36). This stage contains both a level of cognition and affect; however it is also a shallow level of loyalty, thus consumers are subject to sw itching brands. The next stage, conative loyalty, is influenced by positive, rep eated exposures to a brand and is considered loyalty to an intention to purch ase. This stage involves a specific commitment to repurchase a particular brand and is similar to a motivation. However, an intention or desire to purchase a product does not always translate to an actual purchase. The final stage, action loyalty, occurs when intentions to purchase are transfor med into actual purchases (Oliver, 1999). Morris, Woo, Geason, and Kim (2002) studied the relationships among cognitive, affective, and conative attitudes in response to numerous advertisements (television, radio, and print). The results of the structural equati on modeling showed the model to have good fit (RMSEA = .03). A significant relationship existed between the affective a ttitude construct and conative loyalty construct, such that the affective construct explained 24% of the variance in the conative loyalty. Harris and Goode (2004) examined Olivers (1997) sequential loya lty chain (cognitiveaffective-conative-action) and test ed a conceptual framework of l oyalty, trust value, satisfaction, and service quality model. Data was collected for two groups through online data collection from individuals who purchased books and individuals who purchas ed online airline flights. Hypothesis 1 stated that there are four sequential levels of loyalty (respectively; cognitive, affective, conative, and action l oyalty; p. 146). Using two data sets, Harris and Goode tested Olivers (1997) loyalty chain by analyzing 24 possi ble structural model combinations (48 models for the two data sets). Their results showed that the original model was the most robust and valid. Hypothesis 3 stated that satis faction would have a positive in fluence on loyalty (p. 142). For

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38 the online book consumers, a significant relations hip existed between sa tisfaction and conative loyalty, such that 8% of the variance in conativ e loyalty was explained by satisfaction. However, no significant relationship existed between satisf action and conative loyalty for the online flight data. Even though these results are contradictory, Harris and Goode suggest ed that the findings from the online flight purchases may be attribut ed to the market dynamics of airplane flight purchases. Recently within the reta il literature, Da Silva and Alwi (2006) examined the influence of satisfaction on loyalty intentions of consumers ( online and within stores ) at two bookstores. The loyalty intention construct was comprised of fu ture purchases, recommen dations to friends, and future business. They found a significant relations hip between satisfaction and intentions, such that 19% of the variance in loyalty intentions was explained by satisfaction. The previous sections have reviewed the theoretical background of the MSSCL (Models A, B, and C). The following sections will expl ain the pertinent sport management literature pertaining to the Models includ ing a discussion of the MSSCL variables (team identification, disconfirmation or confirmation of expectations, a ffective responses pertaining to positive affect, negative affect, and satisfaction, self-esteem responses pertaining to BIRGing and CORFing, and conative loyalty) along with the supporting sport management research. Model of Sport Spectator Conative Loyalty (Study 1/Purpose 1) Trail et al. (2005) used identity theory, se lf-esteem theory, and sa tisfaction theory to propose three different conative l oyalty models (Models A, B & C). Model A (Figure 2-1) was theoretically justified through Olivers (1997) consumer sa tisfaction theory and Ervin and Strykers (2001) identity theory. Tr ail et al. (2005) postulated th at in Model A (dis)confirmation of expectancies influenced the mood of an i ndividual (satisfaction, negative mood, and positive mood), and mood influenced conative loyalty. Furthermore, the aut hors postulated that in Model

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39 A, identification with a team l ead to self-esteem responses (bas king in reflected glory [BIRGing] and cutting off reflected failure [CORFing]) and then lead to conative loyalty. Similarly, Model B (Figure 2-2) was based on consumer satisfactio n theory and identity theory, but additionally was guided by sport consumer behavior research Model B posited that (dis)confirmation leads to mood, then mood and identificat ion both directly lead to self -esteem responses (BIRGing and CORFing), and self-esteem responses lead to conative loyalty ( p. 107). Using previous research (e.g., Kwon & Trail, 2001; Laverie & Arnett, 2000; Trail, Anderson, & Fink, 2002; Wakefield, 1995) that tested the independent relationships be tween the variables in th e models, Trail et al. (2005) also proposed and tested Mo del C (Figure 2-3), a direct effects model. Within Model C, they allowed all of the indepe ndent variables (disconfirmation/ confirmation of expectancies, identification with a team, mood, an d self-esteem responses) to lead directly to conative loyalty. Team Identification Within a sport context, previous researcher s (Trail, Anderson, & Fi nk, 2000; Trail et al., 2005) have used identity theory as a framewor k to study identification, in particular, team identification. However, some researchers have not specifically stated the theoretical genesis of their research, items, constructs and ideas, but these concepts may stem from either social identity theory (e.g., Mahony, Howard, & Madrigal 2000; Milne & McDonald, 1999) or identity theory (e.g., Wann, 2000). Trail, Anderson, and Fink (2000) defined identification as an orientation of the self in regard to other obj ects including a person or group that results in feelings or sentiments of close attachment (p.165-166). More over, Madrigal (1995) defined team identification as the extent to which a person feels a psychologi cal attachment to a particular sports team (p. 269). In regard to sp ort consumer behavior, team identification is an important concept due to its mode rating effects on cognitive, affec tive, and behavioral responses (Trail et al., 2000).

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40 Researchers have shown that the level of team identification has moderated cognitive involvement such as the expectations of a teams success (Trail, Fink, & Anderson, 2003; Wann, 1994; Wann & Dolan, 1994). For example, Wann a nd Dolan showed that fans who were highly identified with their teams showed high levels of bias toward their preferred teams. The largest contrast between lowly and highly identified spect ators was shown when the preferred team lost. Accordingly, spectators at a sporting event, who ha d differing levels of team identification (high versus low) perceived different games, and highly identified fans changed their attributes after a loss in order to protect their self esteem (Wann & Dolan, 1994). However, Trail, Fink, and Anderson found that team identific ation was not significan tly related to expect ancies about team performance or team success. A fans level of team identification has also been shown to have an influence on affective states before, during, and after a sporting event (Trail et al., 2000). Ma drigal (1995) argued that team identification and a spectator s disposition toward a specific team serve as a basis for an emotional reaction, thus satisfying a motive (e .g., achievement seeking, self-esteem) for watching sporting events. More over, he found that team id entification, rather than disconfirmation/confirmation of expectancy or qu ality of opponent, explained the most variance (25%) in enjoyment (an affective state). Wann, Dolan, McGeorge, and Allison (1994) predicted that fans who were highly identified with th eir preferred teams woul d show more intense affective responses after a competitive performan ce. Highly identified fans were more likely to exhibit a significant increase in positive emotions when th eir preferred team won and a significant decrease in positive affect when their preferred team lost. Individuals with low levels of team identification exhibited minimal emotional changes after a win or a loss.

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41 When examining team identification and se lf-esteem responses (BIRGing and CORFing), Madrigal (1995) found that team identification had a significan t influence on the self-esteem response (BIRGing), such that 31% of the vari ance in BIRGing was explained through team identification. More recently, the relationship between team identification and self-esteem responses was studied by Trail et al. (2005), a nd they found that identif ication with a team explained 27% of the variance in self-esteem responses. The relationship between team identification and behavioral intent ions or involvement might be the most important relationship in spor t spectator consumption behavior (Trail et al., 2000). However, Trail et al. (2005) found that team identificati on only explained 2% of the variance in conative loyalty, but Trail, Anderson, and Lee (2006) found that team identification explained a little less than 10% of the varian ce in attendance intentions. Wann and Branscombe (1993) showed that team identification had an in fluence on several sport consumption behaviors. For example, highly identified fans attended more home and away games than lower identified fans (Wann & Branscombe). Disconfirmation or Confirmation of Expectations within a Sport Context An affective response from a fan may be more intense when his/her expectations about the outcome of the game are disconfirmed, eith er positively or negatively (Madrigal, 1995). Based on Madrigals (1995) research, Trail, Fink, and Anderson ( 2003) stated that the (dis)confirmation process may be conceptualized as a continuum with negative disconfirmation on one end, followed by negative confirmation, then positive confirmation, and anchored on the other end by positive disconfirmation (p. 10). Thus, (dis)confirmation would influence the affective state of sport fans. Madrigal (1995) studied the c ognitive and affective determinan ts of fan satisfaction by examining spectators at womens Division I-A basketball games. He examined the relationship

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42 between (dis)confirmation and enjoyment (mood /emotions), and found that (dis)confirmation explained 15% of the variance in enjoyment. Madrigals best fitting Model of Fan Satisfaction supported a disconfirmation-affect-s atisfaction hierarchical mode l. More recently, Trail, Fink, and Anderson (2003) and Trail et al. (2005) tested the relation ship between disconfirmation of expectancies and affective state (mood) and found much better results; (dis)confirmation explained 31% and 37%, respectively, of the variance in mood. Trail et al. (2000) stated that based on th e level of disconfirmation or confirmation of expectations of a teams performance or the outcome of a game, individuals may often respond with self-esteem behaviors such as basking in re flective glory (BIRGing) or cutting off reflected failure (CORFing). Madrigal (1995) examined the relationship between (dis)confirmation and BIRGing and found that only 6% of the variance in BIRGing was explained by (dis)confirmation. More recently, Trail, Fink, and Anderson (2003) tested the relationship between (dis)confirmation and self-esteem res ponses and found a non-sign ificant relationship, such that only 2% of the variance in self-est eem responses was explained by (dis)confirmation. Affective Responses (Positive Affect, Negativ e Affect, and Satisfaction) within a Sport Context In order to examine affective responses as a one complete c onstruct, Trail et al. (2005) utilized Oliver, Rust, and Varkis (1997) fi ndings of a significant relationship between satisfaction and positive affect. The MSSCL comb ined the variables of positive affect, negative affect and satisfaction into one affective stat e (mood). Moreover, Madrigal (1995) also believed that enjoyment and satisf action should be measured at the same time but as distinct constructs that measure an affective state. Madrigal (1995) hypothesized a fan satisfaction model base d on the research conducted by Oliver (1993) and Westbrook (1987) on c onsumer satisfaction. He found a significant

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43 relationship between affective responses (enjoy ment) and satisfaction, such that enjoyment (mood/emotions) explained 12% of the variance in satisfaction. Based on Madrigals results, Trail, Fink, and Anderson (2003) tested the relati onship of affective state on intention behaviors, and their results showed that affect only explaine d 11% of the variance in future fan behavior. In order to further explain more va riance in self-esteem responses a nd to correct the theoretical and empirical deficiencies (p.99) with Trail, Fink, and Andersons (2003) model, Trail et al. (2005) hypothesized and tested the relationship between affective state (mood) and self-esteem responses. Their results showed that mood e xplained 27% of the variance in self-esteem responses. Recently, Caro and Garca (2006) tested the framework of the consumer satisfaction process after a sporting event. They tested the relationship between emotions and satisfaction and between satisfaction and conative loyalty. After modifications to the original model of the satisfaction process, the model fit was adequate (RMSEA = .078), and their results showed a significant relationship between the emotion c onstruct (pleasure) and satisfaction, and a significant relationship between sa tisfaction and loyalty. These pr evious results from a sport context confirm the relationships within the MSSCL. Self-esteem Responses (BIRGing and CORFing) Generally, previous research examining self-esteem behavior was focused on the achievement seeking theory of sport spectating. Based on the situation or outcome of the game, some sport fans watch sports to fulfill achieveme nt needs or to feel mo re apart of the team. However, some fans may distance themselves fr om a team when their teams are not successful (Trail et al., 2000). On the basi s on self-esteem theory, in orde r to increase self-esteem an individual may want to associat e with a winning team (e.g., bask in anothers success) or in order

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44 to maintain self-esteem an individual may want to cut off an associati on with an unsuccessful team (e.g., cut off all association). Basking in reflective glory (BIRGing) refers to the phenomena wher e an individual will publicize an association or conne ction with another person or or ganization that is successful. That is, people appear to feel that they can sh are in the glory of a su ccessful other with whom they are some way associated; one manifestation of this feeling is the public trumpeting of the association (Cialdini et al., 1976, p. 366). Moreover, BIRGing can occur even if an individual has no influence on the others success (e.g., sports fans). Cutting off reflected failure (CORFing) refe rs to the behavior where an individual distances himself from another person or organi zation that is not succes sful (Snyder, Higgins, & Stucky, 1983). CORFing can be understood as an im age protection tactic: that is, the severing of association with others who have failed, in the interest of avoiding a negative evaluation by others (and oneself) (Snyder, Lassegard, & Ford, 1986, p. 383). Previous sport consumer researchers (M adrigal, 1995; Wann & Branscombe, 1990, 1993; Wann, Dolan, McGeorge, & Allison, 1994) have examined BIRGing and CORFing, but only recently have researchers examined the relatio nship between the concepts of self-esteem responses (BIRGing and CORFing) a nd conative loyalty (purchase in tentions). Trail et al. (2000) hypothesized that BIRGing and CORFing behavior s were influenced by achievement motive, team identification, and disconfir mation of expectancy, while BI RGing and CORFing influenced affective state and future behavior intentions. Recently, Trail et al. (2005) tested the relationship between self-esteem responses and purchase inte ntions, and found that 48% of the variance in purchase intentions was explained by self-est eem responses. The relationship between mood and conative loyalty is mediated by self-e steem responses (Trail et al., 2005).

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45 Conative Loyalty within a Sport Context Previous sport researchers (Kwon, Trail, & Anderson, 2005; Miloch & Lambretcht, 2006; Trail et al., 2005; Trail, Fi nk, & Anderson, 2003; Wann & Branscombe, 1993) have examined numerous variables (e.g., team identification, self-esteem respons es, and affective responses) on conative loyalty (behavioral intentions/purch ase intentions). Due to the difficulty of longitudinally studying behavioral loyalty of s port consumers, the present study focused on the conative loyalty stage of consumer behavior. Typically, the best predictor of consumption is behavior in tentions (Trail et al., 2005). Trail, Anderson, and Lee (2006) proposed a s port consumption model in which preseason purchase intentions lead to consumption beha viors (attendance). Their results showed a significant relationship between purchase intentions and attenda nce, such that 45% of the variance in attendance was explaine d by preseason purchase intentions. Results of the Model of Sport Spectator Conative Loyalty As previously discussed the MSSCL (Mode ls A, B, & C) was hypothesized based on consumer satisfaction theory, identity theory, an d sport consumer research. Trail and colleagues (2005) focus was to study three competing models of conative loyalty using sport spectators. The results of the CFA showed that the m easurement model fit the data well ( RMSEA = .055), and each of the items loaded significantly onto its designated factor. The Cronbachs alpha coefficients for each factor ex ceeded .70 indicating satisfactory internal consistency for social science subscales (Nunnally & Bernstein, 1994). Model A showed fair, but not close fit ( RMSEA = .065); Model B showed close fit ( RMSEA = .060); and Model C had fair fit as well ( RMSEA = 0.78). Model B explained more variance in conative loyalty (4 9%) than Model A (41%) and M odel C (35%). Based on the RMSEA values and the much lower amount of variance explained by Mo del C, Trail et al.

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46 (2005) chose to eliminate it from further an alysis. Although Models A and B did not differ significantly, Trail et al. suggested that becau se Model B intertwined identity theory and satisfaction theories with sport spectator consum ption behavior, it might be the better model. Trail et al. (2005) indicated th at future research should test both Models A and B using additional data. Based on the authors sugges tions and the need to understand the conative loyalty of Latinos in the Unite d States, this study tested bot h Models A and B on the Latino population. Revised Model of Sport Spectator Cona tive Loyalty (Study 1/Purpose 2) As previously stated, the variables of ethni c identity, identification with the dominant group (acculturation), vicarious ach ievement, and past behaviors we re added to Models A and B in an effort to increase the variance explained in conative loyalty. Trail et al. (2005), the authors of the original MSSCL, noted that 51% of the va riance of conative loyalty was not explained in the model. They suggested future research shou ld study variables that could be added to the model. Thus, in the newly proposed models, bot h Model A and B contain a direct path from ethnic identity to conative loyalty and from identity with the dominant culture to conative loyalty (Figures 2-4 & 2-5). Past behavior s are depicted to have a direct path to identification with a team and to vicarious achievement, while vicarious achievement is depicted to have a direct path to team identification (Figures 2-4 & 2-5). The theoretical justification for the four additional variables (ethnic identity dominant identity, vicarious achieve ment, and past behaviors) added to the MSSCL (Models A & B) is pr esented in the next section. Ethnic Identity Ethnic identity has been defined in nu merous ways by many authors (Phinney, 1990). Isajiw (1992) defined ethnic id entity as an individually experienced phenomenon where an individual identifies with an ethnic group. Wh en defining an ethnic group, Bernal, Knight,

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47 Ocampo, Garza, and Cota (1993) recognized the n eed to acknowledge a personal ownership and knowledge about ones ethnic group. For this study, ethnic identity was defined as an individuals identification with an ethnic group or ethnicity (B ernal et al., 1993; Isajiw, 1992; Phinney, Horenczyk, Liebkind, & Vedder, 2001), while an ethnic group referred to the members of the non-dominant group in th e society (Phinney, 1996). Moreover, Phinney et al. (2001) stated the need for future research to examine various aspects of ethnic identity including selfidentification, feelings of belongingness and comm itment to a group, a sense of shared values, and attitudes toward ones own ethnic group (p. 496). Individuals may identify with more than one social or ethnic group (Korzenny & Ko rzenny, 2005; Phinney, 1990; Tsai, Ying, & Lee, 2000) and therefore have more than one ethnic identity. Based on social identity theory (Tajfel, 1981) and Eriksons (1968) development theories, Phinney (1992) examined the concepts of et hnic identity across diverse ethnic groups. She suggested that future ethnic identity research should examine ethnic iden tity as a psychological construct not merely a demographic variable (Phinney, 1996). Moreover, individuals seeking an identity with their ethnic group and a social group identity will inevitably strive for a positive self-esteem (Phinney, 1992) leading to the linkage of ethnic identity with self-esteem theory. Phinney et al. (2001) expressed the need for re search to delve into the various aspects of ethnic identity including self-i dentification, feelings of be longingness and commitment to a group, a sense of shared values, and attitude s toward ones own et hnic group (p. 496). Researchers hypothesized that ethnic identity co ntains various aspect s including a sense of belonging, positive evaluation of the group, pref erence for the group, ethnic interest and knowledge, and involvement in activities asso ciated with the group (Phinney, 1996, p. 923), commitment to the group, positive attitudes toward the group (Phinne y et al., 2001), ethnic

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48 origin, and sense of shared values (Naylor, 1997). These concepts are precursors to ethnic identity and are necessary antecedents for ethni c identity to form. Additionally, Gacio Harrolle and Trail (2007) proposed ethnic identity is a cognitive commitment to ones ethnicity. Gacio Harrolle and Trail (2007) examined the relationships among ethnic identity, acculturation (dominant identity), and sport iden tification to provide insight into the sport identification of Latinos in the U. S. In an effo rt to clarify the constructs related to ethnic identity, acculturation, and spor t identification, Gaci o Harrolle and Trail provided detailed definitions of the previously mentioned terms and the terms of nationality, racial identity, culture, and ethnicity. They conc luded that ethnic identity was the most appropriate way to examine an individuals level of identification with their ethnic group (e.g., Latinos). Gacio Harrolle and Trail used the affirmation-belongin g-commitment factor of the revised Multigroup Ethnic Identity Measure (MEIM; Roberts et al., 1999), origina lly developed by Phinney (1992), to examine ethnic identity of Latinos in the U.S. Their findings stated that participants studied were highly identified with th eir self-identified ethnic group. Ho wever, ethnic identity did not influence their identification with sports in general or identifi cation with specific sports (e.g., American football, baseball, ba sketball, hockey, and soccer). Gacio Harrolle and Trail (2007) suggested that future research look in to the relationships between ethnic identity and sport consumption behaviors or intentions. Additionally, Asbridge, Tanner, and Wortleys (2005) resu lts showed ethnic identity ha d an influence on consumption behaviors for youth. Thus, an attachment to an ethnic group may influence ones sport consumer behaviors. The addition of ethnic identity to th e modified MSSCL (models A & B) is based on this previous research.

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49 Identification with the Domi nant Society (Acculturation) Stephenson (2000) defined acculturation as a complex, multidimensional process of learning that occurs when individuals and groups come into continuous contact with different societies (p. 77). Furthermore, individuals may form additional group identi ties as they come in constant contact with other social groups (e .g., dominant society; Berry, 2001). Acculturation theory as discussed by Berry (2001) suggests that an individuals exposure to a dominant culture will influence their role-identities, and subs equently this identity may influence their consumption behaviors. Acculturation is based on identification with the dominant or larger group/society; however, it does not limit identification with an et hnic group (Berry, 2001). Over days, weeks, years, and even generations (Berry & Kim, 1988) the acculturation process may involve transformations in id entities (Greenland & Brown, 2005) An individual may have a strong identification with the dominant culture while also maintaining a strong identification with an ethnic group. The relationship between th ese two identities is not negatively correlated, such that the identification may be related but are independent of each other (Berry, 2001). For example, an increase in identification with th e dominant American society may not necessarily cause a decrease in an individua ls identification with his/he r ethnic group. As individuals interact with the dominant society, an identity with the dominant American society may begin to develop (i.e., acculturation). Recently, acculturation theorists have begun to study a bidimensional model of acculturation as apposed to a unidimensional model (LaFromboise, Coleman & Gerton, 1993; Phinney, 1990; Zea, Asner-Self, Birman, & Buki 2003). Within the past decade, previous research examining acculturation has measured nume rous minority groups in a variety of distinct ways including identity, language competence, and cultural competence (Zea et al., 2003); cultural domains of language, social affiliation, activities, attitudes, media, exposure, and food

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50 (Tsai et al., 2000); ethnic versus dominant immersion (Stephenson, 2000); comfort with dominant and non-dominant language, food, medi a, and traditions (Gomez & Fassinger, 1994); and language ability and perceived cultural distance (Greenland & Brown, 2005). If an individual who is identified with an ethnic group is also identified with the dominant culture/societal group (i.e., the culture in the United States), this i ndividuals level of identification with both groups may have an influe nce on identification as a sport fan in general and identification with certain sports (Pons, Laroche, Nyeck, & Perreault, 2001). Gacio Harrolle and Trail (2007) tested the re lationships between ethnic identity and identification with the dominant culture (accult uration) and found that a moderate relationship existed between ethnic id entity and acculturation ( r = .412). Within their sample, Gacio Harrolle and Trail (2007) also found that La tinos in the Southeastern U.S. were highly identified with the dominant culture (acculturated). Their results defended the theoretical justification for a bidimensional acculturation model. Gacio Harrolle and Trail (2007) suggested that future research include both ethnic identity and acculturation variables when examin ing ethnic groups in the U.S. Their results showed that level of identification with the domin ant culture did not influence identification with sports in general or with specific sports. Ther efore, they recommended that future research examine the relationships between identification with the dominant culture and sport consumption behaviors or intentions. Additiona lly, Asbridge and colleagues (2005) results showed acculturation had an influence on consum ption behaviors for yout h. Thus, identification with the dominant group may influence ones sp ort consumer behavior s. The addition of acculturation to the modified MSSCL (Models A & B) is based on this previous research.

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51 Vicarious Achievement Vicarious achievement is defined as a need to gain success or feeli ngs of accomplishment vicariously through another (Sloan, 1989). Based on Maslows (1970) hierarchy of needs, Sloan (1989) stated that individuals seeking a need to obtain prestige may engage in sports spectatorship to fill these needs. Thus, spect ators may increase self-esteem through vicarious achievement of a successful team (Fisk, 1992). In or der to fulfill an achievement need, Cialdini et al. (1976) suggested that fans could fulfill a need to achieve through basking in the reflected glory of their vicarious teams. Vicarious achievement stems from self-esteem th eory such that as i ndividuals want to be happy and increase their self-esteem they also want to associate with a suc cessful other in order to increase or maintain a consistent self-image Sloan (1989) argued that through an association with a successful sport team fa ns may seek out vicarious achiev ement. The concept of vicarious achievement may provide a fan with an avenue for building self-esteem thorough an association with a successful other. Within sport management literature, previ ous researchers have examined vicarious achievement as a motive for being a fan (e .g., Fink et al., 2002; Funk, Mahony, & Ridinger, 2002; Mahony, Nakazawa, Funk, James, & Gladden, 2002; Trail et al., 2005; Trail & James, 2001; Wann, 1994). Wann (1994) found a significant correlation between se lf-esteem responses (success through others) and other motives for fandom. Mahony et al. (2002) measured the vicarious achievement of spectators at a Japa nese Professional Socce r League game. Their results showed that vicarious achievement was positively associated with number of games attended. Recently, Trail, Kwon, and Lee (2007) looked beyond vicarious achievement as a motive and have examined the relationships among vicar ious achievement, team identification, and

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52 BIRGing/CORFing. Data were collected before and after the 2003 MLB World Series. They found that vicarious achievement explained approximately 15% of the variance in team identification. Vicarious achievement was also fo und to be positively correlated with CORFing in a failure situation (losing team New York Yankees). However, vicarious achievement was hypothesized to be positively correlated with BI RGing in the success situation (winning team Florida Marlins), but this relati onship was not evident in the da ta. The variable of vicarious achievement was added to Models A and B base d on identity theory, self-esteem theory, and previous vicarious achievement research (Slo an, 1989; Trail et al., 2007; Figure 2-4 & 2-5). Past Behaviors Recently, Trail et al. (2006) have shown that past behaviors influenced team identification and conative loyalty. Based on identity theory and pr evious research, Trail et al. (2006) proposed a model in which past atte ndance predicted preseas on team identification, preseason intentions to attend, a nd actual attendance during the s eason. Their model (Figure 2-6) contained a path from past be haviors (number of games attende d previous years) to preseason team identification and purchase intentions (numbe r of games planning to attend this year). Past behaviors predicted 21% of the variance in team identification and 24% of the variance in purchase intentions. Based on this previous rese arch, the revised MSSCL (Models A & B) were tested to see whether more variance could be e xplained in behavioral intentions when past behaviors influenced both team identification and purchase inten tions (Figures 2-4 & 2-5). Differences among Latinos Subgroups a nd Non-Latinos (Study 2/Purpose 3) Gacio Harrolle and Trails (2007) findings show ed that ethnic identity and identity with the U. S. American society explained very little variance in identification with sports in general or identification with specific sports (e.g., American footba ll, baseball, basketball, hockey, and soccer). Previous researchers (Berkowitz et al., 2005; Gacio Harrolle & Trail, 2007) suggested

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53 that future research should focus on specifi c subgroups within the Latino ethnic group (e.g., Puerto Ricans, Columbians, Cubans, etc.) in stead of the Latino popul ation as a whole to determine if market segmentation would be the most appropriate marketing strategy. Additionally, Gacio Harrolle and Trail suggested that a comparis on should be made between the various ethnic groups of Latinos and Non-Latinos to see if any si gnificant differences existed in terms of identification with sp ecific sports (American footba ll, baseball, basketball, boxing, hockey, and soccer) and sport consumer behavi ors (attendance, media, and merchandise). Previous research (Singh, Kwon, & Pereira, 2003) within the field of marketing and psychology examined group differences among three ethnic groups (Asian Americans, Hispanics, and African Americans) in terms of personal and media influences. They found that for young adults significant differences existe d for their socialization influences. When Berkowitz et al. (2005) st udied differences between Hispanic and Non-Hispanic consumers, they found no significant differences in terms of st ore brand versus national brand purchases. However, their results showed a significant difference existed between Hispanics and NonHispanics when buying specific product types (i.e., ut ilitarian versus hedonistic). Previous sport consumer research has not looked at the di fferences among ethnic groups or between ethnic groups. This study examined the differences am ong the subgroups of Latinos and between Latinos and Non-Latinos. Four hypothesizes were proposed: (a) signifi cant differences would exist among the subgroups of Latinos on identifi cation with specific sports; (b) significant differences would exist between La tinos and Non-Latinos on identi fication with specific sports; (c) no significant differences would exist among th e subgroups of Latinos on sport consumption

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54 behaviors; and (d) significant differences woul d exist between Latinos and Non-Latinos on sport consumption behaviors. Pre and Post Testing of Conative Loyalty (Study 3/Purpose 4) Within the advertising and business literat ure, numerous researchers (e.g., Chandon, Morwitz, & Reinartz, 2004; Da Silva & Alwi, 2006; Morris et al., 2002) have examined purchase intentions, however very few have examined pre and post effects on repurchase intentions. Within the field of advertising, Tipps, Berger, and Weinberg (2006) studied pre and post effects of advertising exposure on conativ e loyalty. Their results showed th at level of involvement with a publication lead to more favor able perceptions and a greater likelihood of being persuaded by the advertisement. A positive significant difference existed between the pre and posttest results on purchase intentions, thus the participants purchase intentions of the product increased significantly after being exposed to an advertisement. Conversely, within the restaurant industry, Kwun and Oh (2004) tested pre and post behavi oral intentions and di d not find a significant difference in behavioral intentions for the partic ipants. Their results showed that attending the restaurant (independent variable) did not have a significant effect on future intentions to attend the restaurant. These conflicting results showed that repurchase intentions may or may not be affected by initial purchase behavior. Similarly within the sport literature, res earchers (e.g., Miloch & Lambrecht, 2006; Trail et al., 2005; Trail, Fink, & A nderson, 2003) have examined purchase intentions, however previous sport consumer research has not examin ed conative loyalty before and after a sporting event. The results of this study will provide fu rther understanding of the effects of watching a game on purchase intentions. Additionally, this study hypothesized that based on the outcome of the game (win vs. loss) individuals with low levels of team identification would be more likely to

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55 show a larger increase or decrease in conative loyalty than those with high levels of team identification.

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56 Figure 2-1. Model A: Model of S port Spectator Conative Loyalty Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty (Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction

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57 Figure 2-2. Model B: Model of Sport Spectator Conative Loyalty Affective Responses (post game) SelfEsteem Team ID Dis ( confirmation ) Conative Loyalty (Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction

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58 Figure 2-3. Model C: Model of Sport Spectator Conative Loyalty Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty (Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction

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59 Figure 2-4. Modified Model A: Revised M odel of Sport Spectator Conative Loyalty Affective Responses (post game) SelfEsteem Team ID Dis (confirmation Conative Loyalty (Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Ethnic ID US ID Past Behaviors VACH

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60 Figure 2-5. Modified Model B: Revised M odel of Sport Spectator Conative Loyalty Ethnic ID Affective Responses (post game) SelfEsteem Past Behaviors Team ID Dis (confirmation) Conative Loyalty (Behavior Intentions) US ID BIRGING CORFING Negative Satisfaction Positive VACH

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61 Figure 2-6. Sport Attendance Model Preseason Team ID # of Games Attended Previous Year # of Games Planning to Attend This Year # of Games Attended Present Year

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62 CHAPTER 3 METHODS Study 1 Participants The participants were comprise d of Latinos and Non-Latinos [ N = 313; Latinos (n = 127 and Non-Latinos (n = 186)]. The total samp le exceeded the recommended value of 195 to achieve a power of .80 for stru ctural equation modeling (Mac Callum, Browne, & Sugawara, 1996). The participants were attende es of a professional Major League Baseball game in Florida. The sample consisted of 54% (n = 169) male re spondents and 46% (n = 144) female respondents. The average age of the participants was 44 years-old, and 65% were married. During the collection process, 80 individuals declined to participate in th e study, and 108 individuals did not completely fill out the questionnaire, resulting in a 62.5% completion rate. Procedure Using an intercept method, all attendees at thr ee out of six entrances of the stadium were asked to fill out the questionnair e. Attendees were asked to comp lete selected sections of the survey before and after the base ball game. After agreeing to partic ipate in the study, participants were given brief instructions on which specific sections to fill out before and after the game. Participants were asked to complete the fi rst portion of the questionnaire (demographic information, Points of Attachment Index, Team Identification Index, Vicarious Achievement Index, Ethnic Identity Scale, Multigroup Ethnic Id entity Measure, Abbreviated Multidimensional Acculturation Scale, Intentions for Sport Consumption Behavior Scale, and Past Behaviors) before the baseball game. Participants were also instructed to complete the second portion of the survey [Team Identification Index, (Dis)Confirma tion of Expectancies Scale, Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for Sport Consumption Behavior

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63 Scale, and Estimated Future Sport Consumption Behaviors] at the conclusion of the baseball game. Surveys were collected at the stadium exit s starting at the end of the seventh inning, so that participants could turn in their surveys. The purpose of the study and the instructi ons for completing the questionnaire were included in the questionnaire. In accordance with the IRB approval process at the university, the participants had an opportunity to decline to participate in the questionnaire at any time. No known physical or psychological risks were associated with completing the survey. Instrumentation The questionnaire was comprised of the follo wing ten scales: Team Identification Index (TII; Trail & James, 2001), (Dis)Confirmation of Expectancies Scale (D CES; Trail et al., 2005), Affective State Index (ASI; Trail, Fink, & Anderson, 2003), Self-Esteem Maintenance Behavioral Scale (SEMBS; Trail, Fink, & Ande rson, 2003), Intentions for Sport Consumption Behavior Scale (ISCBS; Trail, Fink, & Anders on, 2003), Multigroup Ethn ic Identity Measure (MEIM; Roberts et al., 1999), Abbreviated Multidimensi onal Acculturation Scale (AMAS; Zea et al., 2003), Vicarious Achievement Subscal e (VAS; Trail & James, 2001), Points of Attachment Index (PAI; Robins on & Trail, 2005), and Past Be haviors and Estimated Future Sport Consumption Behaviors Inde x. Eight of the ten scales had a 7-point response format ranging from Strongly Disagree (1), to Neu tral (4), to Strongly Agree (7): Team Identification Index, Affective State Index, Self-Esteem Main tenance Behavioral Scale, Intentions for Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure, Abbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale, and Points of Attachment Index. The items for each of these scales were measured using the same response format and were randomly placed in sections on the corresponding pre and post surveys. The (Dis)Confirmation of Expectanci es Scale had a 7-point respons e format ranging from Much

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64 worse than I expected (1), to As expected (4), to Much better than I ex pected (7); therefore, the scale was placed in a separate section of the questionnaire. The Past Behaviors and Estimated Future Sport Consumption Behavior Index were comprised of open-ende d questions and were placed in separate sections on the survey. At th e end of the questionnaire, various demographic variables (e.g., ethnicity, age, gender) were also included (Table 3-1). Points of Attachment Index and Team Identification Index The complete Points of Attachment Index, developed by Trail, R obinson, et al. (2003) was based on the Team Identification Index orig inally developed by Trail and James (2001) as a test of concurrent validity for motivation items (t he actual TII items were first reported in Trail, Fink, & Anderson, 2003). The Team Id entification Index, a subscale of the PAI, was comprised of three items (Table 3-2) and measures an indivi duals level of attachment with a specific team. Fink et al. (2002) defined identification with a particular sport team (e.g., Gators) as the orientation of self in regard to other objects (the team) that results in feeling of close attachment (p. 198). Previous research results (Fink et al., 2002; Robinson & Trail, 2005; Trail et al., 2005; Trail & James, 2001; Trail, Robinson, et al., 200 3) have shown that the TII has good construct reliability (AVE values ranging from .62 to .69) and internal consistency (Cronbachs alpha values ranging from .83 to .88). This scale was measured before and after the sporting event occurred to examine any possible pre and post differences in the TII. The PAI measures seven constructs: identif ication with the players, the coach, the community, the sport, the university, the team (the TII), and the level of the sport (e.g., intercollegiate not professional). This study used two dimensions of the PAI in addition to the TII (Table 3-3): overall attachment to sports (e.g., I am a fan of sports) and the attachment to a specific sport for six different sports (American football, bask etball, baseball, boxing, soccer, and hockey). Previous authors (Gacio Harrolle & Trail, 2007; Robinson & Trail, 2005; Trail,

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65 Robinson, et al., 2003) showed that attachment to a particular spor t, a subscale of the PAI, had good internal consistency (Cronb achs alpha ranging from .7 5 to .94) and had good Average Variance Extracted values as well (AVE = .50 .84). Gacio Harrolle and Trail found that identification as a sport fan in ge neral had good internal consistency ( = .80) and construct reliability (AVE = .67). Due to the fact that th ese items were not based on the outcome of the game, they were tested before the game occurred. (Dis)Confirmation of Expectancies Scale Trail, Fink, and Anderson (2003) first develo ped the (Dis)Confirmati on of Expectancies Scale based on Madrigals (1995) two expectancy disconfirmation items measuring the play and quality of the game. The response format for th e items [much worse than I expected (1) to much better than I expected (7)] used by Trail, Fink, and Anderson (2003) and Madrigal (1995) was originally developed by Oliver (1980; 1981). Previous researchers (Trail et al., 2005; Trail, Fink, & Anderson, 2003) have shown the scal e to have construct reliability (AVE ranging from .60 to .67) and internal consistency (Cronbachs alpha ranging from .81 to.91). Gacio Harrolle, Trail, and Anderson (2007) revised the DCES and divided the scale into two subscales (quality of the play and outcome of the game). The latest version of the DCES (Table 3-4) contains three new items measuring quality of play (the quality of the game; the overall quality of play; and the performance of the teams) and two additional items measuring the outcome of the game (the outcome of the ga me and the conclusion of the game). Both subscales, quality of play (AVE = .67; = .86) and outcome of the game (AVE = .91; = .97) had good internal consistency and co nstruct reliability. Additionally, the participants were either confirming or disconfirming what they expect to happen at the game, therefore this scale was tested after the sporting event occurred.

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66 Affective State Index The Affective State Index or iginally developed by Trail, Fink, and Anderson (2003) was comprised of three subscales: positive mood, negative mood, and satisfaction. The subscales were based on Madrigals (1997) three items whic h measured overall satisfaction with a decision to attend a sporting event. Trai l, Fink, and Andersons (2003) original subscales were each comprised of three items respectively. Trail, Fi nk, and Anderson (2003) sh owed that the positive affect (AVE = .83; = .93) and negative affect (AVE = .69; = .87) had good construct reliability and internal consistency. However, sa tisfaction had adequate internal consistency ( = .63), but the construct reliability value fell belo w the recommended .50 (AVE = .47). After Trail, Anderson, and Fink (2005) modified the satisfacti on subscale, the Cronbach s alpha increased to .75, and the AVE value increased to .57. Trail, Fink, and Andersons (2003) three subs cales were revised by Gacio Harrolle et al. (2007) in an effort to increase the reliability and validity of the scales. The modified Positive Affect subscale (Table 3-5) contained five items and has shown good internal consistency ( = .91) and construct reliability (AVE = .70). The m odified Negative Affect subscale (Table 3-5) also contained five items and has shown good internal consistency ( = .89) and construct reliability (AVE = .65). The modified Satisfacti on (Table 3-5) subscale was divided into two subscales (Satisfaction with Pl ay and Satisfaction with the Ou tcome). These two new subscales have shown a vast improvement in intern al consistency (Satisfaction with Play: = .89; Satisfaction with the Outcome: = .93) and construct reliability (Satisfaction with Play: AVE = .69; Satisfaction with the Outcome: AVE = .84). By revising the Affective State Index into four subscales, Gacio Harrolle et al have improved the overall measurement of the construct. In

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67 addition, these subscales measure the affective stat e of the participant af ter the outcome of the game, and thus were tested after the game. Self-Esteem Responses Previous researchers (Trail et al., 2005; Tr ail, Fink, & Anderson, 2003) have studied selfesteem responses by examining BIRGing and CO RFing. The Self-Esteem Maintenance Behavior Scale (Trail, Fink, & Anderson, 2003) was comprised of two subscales with three items for each subscale and were generated from Madrigals (1 995) one item measuring BIRGing and one item measuring CORFing. Trail, Fink, and Anderson (2003) found that the subscales both had adequate internal consistency (BIRGing: = .80; CORFing: = .79) and construct reliability (BIRGing: AVE = .58; CORFing: AVE = .56). Howe ver, Trail et al. (2005) found that the subscales were not as intern ally consistent (BIRGing: = .77; CORFing: = .72), and the AVE scores were very close to or below the accep ted .50 value (BIRGing: AVE = .54; CORFing: AVE = .46). In order to increase the relia bility and validity of the subs cale, Trail et al. (2007) added one additional item for BIRGing (I would like to let others know about my association with the team) and two new items for CORFing (I do not wa nt to be associated with the team and I do not wish to be a fan of the team). These ne w subscales (Table 3-6) have improved internal consistency (BIRGing: = .91; CORFing: = .87) and construct relia bility (BIRGing: AVE = .78; CORFing: AVE = .71). In addition, the particip ants either BIRG or CORF depending on the outcome of the game, therefore, this scale was tested after the sporting event occurred. Intentions for Sport Cons umption Behavior Scale Trail, Fink, and Anderson (2003), as a means to measure future intentions to consume sport, developed the Intentions for Sport Consum ption Behavior Scale (T able 3-7). This scale

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68 attempted to measure the conative loyalty of sport consumers and included items measuring future behavior to consume merc handise, future attendance at s porting events, and future overall support of a particular team examined. Previous research (Trail et al ., 2005; Trail, Fink, & Anderson, 2003) has shown this scale to have go od construct reliability (AVE ranging from .58 to .59) and internal consistency ( = .84). The participants in this study were watching the football game on television, therefore an additional item measuring future intention to watch more football games (i.e., I am more likely to watch future games) was added to the scale. This scale was tested before and after the game in order to see if the outco me of the game changed an individuals conative loyalty. Multigroup Ethnic Identity Measure The Multigroup Ethnic Identity Measure, originally developed by Phinney (1992), measured an individuals ethni c identity. Higher scores indica ted stronger levels of ethnic identity. Previous researchers (Phinney, 1992; Robe rts et al., 1999) have s hown this scale to be internally consistent (Cronbachs alpha ranging from .81 to .90). Gacio Harrolle and Trail (2007) used the affirmation-belonging-commitment fact or (Roberts et al., 1999 ) to examine ethnic identity and initially tested six items of the ME IM. However, due to high kurtosis values (2.8 to 10.4) three items were eliminated from their fina l analysis. The final th ree items of the ethnic identity measure had good construct reliabil ity (AVE = .65) and internal consistency ( = .89). This current study tested the six original items (Table 3-8) tested by Gacio Harrolle and Trail (2007) in order to reexamine all six ethnic identit y items for internal consistency, kurtosis, and reliability. These items were measured before the game.

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69 Abbreviated Multidimension al Acculturation Scale One portion of the Abbreviated Multidimensi onal Acculturation Scale (Zea et al., 2003) represented the level of identity with the United States and wa s originally developed from the American Identity Questionnaire (Phinney & Devi ch-Navarro, 1997). Zea and colleagues results showed that the six items had good f actor loadings for the sample ( = .78 to .89) and internal consistency ( = .96). Gacio Harrolle and Trail (2007) test ed the six items (Table 3-9) measuring identity with American society. Their results sh owed that two of the identity with American society items (e.g., I am proud to be a U.S. Am erican and I feel good about being a U.S. American) had high kurtosis values and were e liminated from their final analysis. The final questionnaire contained four items that show ed good construct reliability (AVE = .61) and internal consistency ( = .88). Additionally, Ga cio Harrolle and Trail (2007) found that higher levels of identity with the U.S. indicated hi gher levels of acculturation. Before the game occurred, this study tested the six original ite ms tested by Gacio Harro lle and Trail (2007) in order to reexamine the internal consistency, kurtosis, and reli ability of all items measuring identity with U.S. America. Vicarious Achievement Subscale Trail and James (2001) originally devel oped the vicarious achi evement items from Sloans (1989) theories on spor t fan behavior and needs for achi evement. These items constituted the Vicarious Achievement Subs cale (Table 3-10) and measur ed an individuals need to associate with a successful team. Moreover, th e VAS is a subscale of the Motivation Scale for Sport Consumption (Trail & James, 2001). Prev ious researchers (James & Ridinger, 2002; Robinson & Trail, 2005; Trail & Ja mes, 2001; Trail, Robinson et al., 2003) have shown the VAS

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70 to have good construct reliability (AVE rangi ng from .59 to .74) and internal consistency (Cronbachs alpha ranging from .82 to .89). These items were tested before the game occurred. Past Behaviors and Estimated Future Sport Consumption Behaviors Index The Past Behaviors and Estimated Future S port Consumption Behavior Index (Table 311) were comprised of open-ended questions meas uring an individuals pa st and future sport consumption behaviors. The items asked for in formation about previous sport consumption behaviors (media, attendance, and merchandise) a nd future intentions to consume sport (media consumption, attendance, and merchandise consumption). The media and attendance consumption items were measured by number of games, while the items measuring the consumption of merchandise were measured by do llar amounts. The past behaviors were asked before the game, and the future behavior s were asked after the game concluded. Data Analysis The descriptive statistics of the partic ipants demographic and socio-demographic variables were calculated using SPSS 14.0 (Geo rge & Mallery, 2003). The Structural Equation Modeling (SEM) technique, available in EQS 6.1 (Bentler & Wu, 1995), was us ed to test the fit of the confirmatory factor anal ysis (CFA) of the measurement m odel, to calculate the construct reliability (average variance extracted: AVE) and to test the discriminant validity (correlations). SPSS 14.0 was used to test internal consistency (Cronbachs alpha coefficients: ). When examining internal consistency for social scienc e subscales, alpha coefficients greater than .70 are assumed to be adequate (Nunnally & Bernst ein, 1994), while AVE values greater than .50 are good (Hair, Anderson, Tatham, & Black, 2002) and co rrelations between cons tructs less than .85 indicate discriminant validity for the constructs (Kline, 2005). After the CFA was conducted on the measurem ent model, the five structural models [original MSSCL (Models A, B, and C) and the revised MSSCL (Models A and B)] were tested

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71 for goodness of fit using EQS (2005). The chi-square test statistic per degrees of freedom (2/df ) and the root-mean-square-error (RMSEA, represented by a) were used. The RMSEA fit index and associated confidence intervals were used to compare the revised Model A and the revised Model B. Other fit indices (e.g., CFI and TLI) were not included in the analysis, because large numbers of items and/or factors in models de leteriously affect the majority of goodness-of-fit indices except for RMSEA and chi-square test statistic per degrees of freedom (Cheung & Rensvold, 2002). RMSEA values equal to or less than .06 i ndicates close fit (Hu & Bentler, 1999), while values between .06 and .08 indica te reasonable fit, values between .08 and .10 indicate mediocre fit, and values greater than .10 indicate poor fit (Browne & Cudeck, 1992). If the model fits the data exactly, then the RMSEA value will equal zero. To indicate whether the model would fit well within the population and because RMSEA is a point estimate, Browne and Cudeck (1992) suggested that the 90 % confidence interval should be us ed. Bollen (1989) suggested that acceptable values for chi-square per degree of freedom should range from 2.0 to 3.0, however there is no consensus on what constitutes an acce ptable value and an acceptable value can be as large as 5.0. When less than 10% of the residua ls are greater than .10, Bagozzi and Yi (1988) suggested that the model has adequate fit. Study 2 Participants Cubans (31.1%) and Puerto Ricans (18.0%) are the largest Latino groups in the State of Florida (U.S. Census, 2006) and in the university [Cubans (49.0 %), Columbians (33.0%), and Puerto Ricans (15%); L. Martinez, University of Florida Institute of Hi spanic-Latino Cultures, personal communication, April 1, 2 007]. Mexicans, the highest pe rcentage (66.9%) of Latinos

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72 within the total United States population, account for only 13.6% of the Latinos in the state of Florida (U.S. Census, 2006) and approximately 8% of the Latino students associated with the Latino student organizations at th e university (L. Martinez, Univer sity of Florida Institute of Hispanic-Latino Cultures, personal communicat ion, April 1, 2007). Due to the composition of the population at the university and in the state of Florida, Mexicans were not included in the group comparisons. The participants for study 2 were comprised of Latinos [ N = 353; Columbians ( n = 105), Cubans ( n = 139), and Puerto Ricans ( n = 109)] and Non-Latino ( N = 231) participants, which exceeded the recommende d value of 284 to achieve a power of .95 for a MANOVA with four groups and five dependent variables (Faul, Er dfelder, Lang, & Buchner, in press). The age of the participants ranged fr om 18 to 75 with an average age of 27.15. Among the participants, university stude nts represented 62.9% [Latinos ( N = 176): Puerto Ricans ( n = 50), Cubans ( n = 84), and Columbians ( n = 42), and Non-Latinos ( N = 187)] of the participants and individuals from the genera l population represented 37.8% ( n = 221) of the total data set for study 2. Forty-five percent of the participants were male, and 55% were female. Ages of the participants ranged from 18 to 75 years, with a mean of 27.15. Ba sed on participants answers to demographic questions (ethnicit y, ethnic group, country of birth, a nd parents country of birth), participants were place into appropriate groups (Puerto Ricans, Cubans, Columbians, and NonLatino). During the collection process, 2295 i ndividuals were sent emails requesting participation in the study, and 405 were approached to fill out the questionnaire. Of the emails sent, 1,478 individuals did not respond, 32 declined to participate in th e study, and 519 of the questionnaires were not completely filled out, resulting in a 22% completion rate. Procedure Among the student participants, Latino students were recruited from the Hispanic student organizations at the university and students registered for Latin American Studies and Spanish

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73 classes. The Non-Latino particip ants were recruited from the general student population at the university. Using the Hispanic St udent Organization email database, email databases on campus, and email addresses from participants who si gned-up for the study, students received an email containing a website link to the Internet address for an online survey. The participants were asked to fill out the following scales/items: de mographic information, Points of Attachment Index (identification with Ameri can football, baseball, basketball, hockey, and soccer) and Past Sport Consumption Behaviors. Data collection for the non-students took place in various community locations in Florida including a Latino outdoor music festival, three Latino style rest aurants, and a Latino doctors office waiting room. The general population participants were asked to fill out demographic information and Points of Attachment Index (ide ntification with Ameri can football, baseball, basketball, hockey, and soccer). The purpose of the study and the instructi ons for completing the questionnaire were included in the questionnaire. In accordance with the IRB approval process at the university, the participants had an opportunity to decline to participate in the questionnaire at any time. No known physical or psychological risks were associated with completing the survey. Data Analysis The descriptive statistics of the partic ipants demographic and socio-demographic variables were calculated using SPSS 14.0 (Geo rge & Mallery, 2003). The Structural Equation Modeling (SEM) technique, available in EQS 6.1 (Bentler & Wu, 1995), was us ed to test the fit of the confirmatory factor anal ysis (CFA) of the measurement m odel, to calculate the construct reliability (average variance extracted: AVE) and to test the discriminant validity (correlations). SPSS 14.0 was used to test internal consistency (Cronbachs alpha coefficients: ) and to

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74 conduct a Multivariate Analysis of Variance (MANOVA) to compare the identification with specific sports (American football, baseba ll, basketball, hockey, and soccer) among the subgroups (Cubans, Puerto Ricans, Columbians, a nd Non-Latinos) of the total sample and to compare sport consumption behaviors (media, attendance, and merchandise) among the student population subgroups. Study 3 Participants The participants (N = 114) in study 3 were comprised of Latinos (12%), Caucasians (68%), Asians (6%), African Americans (10%), and others (4%) which exceeds the recommended value of 54 to achieve a power of .95 for a repeated measures ANCOVA (Faul, Erdfelder, Lang, & Buchner, in pr ess). The Latino participants ( n = 14) were students recruited from the Hispanic student organizations at th e university and students registered for Latin American Studies and Spanish classes. The Non-Latino ( n = 100) participants were recruited from the general student population at the university. A large major ity of the participants were single (96.5%) with an average age of 21.4. Marketers have begun marketing to the next big group of consumers, Generation Y (i.e., Echo Boomers and Millennium Generation), wh o were born between 1979 and 1995. Generation Y is very similar to the Baby Boomers in size and purchasing power, however they are different in almost every other way. They are a more dive rse group (i.e., one in three is Non-Caucasian), where one in three lives with one parent and three out of four ha ve a working mother. With such large consumption potential, companies need to understand the nuances of this market (Neuborne & Kerwin, 1999). As college students constitute approximately the upper 60% of the Generation Y spectrum, university students ar e an appropriate sample for this study.

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75 Additionally, the Generation Y students are Internet savvy and spend hours a day on the computer (Neuborne & Kerwin), and this work ed well for this study conducted online. Procedure Students were asked to complete the survey before and after a collegiate football game. Using the Hispanic Student Organization email da tabase and email addresses from participants who signed-up for the study, students received an em ail containing a website link to the Internet address for the online survey one week before the game occurred. The first portion of the questionnaire (demographic information, Team Id entification Index, and Intentions for Sport Consumption Behavior Scale) was posted as an online survey before the football game. Three days before the game, emails with the surveys web address were sent out reminding students to complete the survey before the game. After the pa rticipants completed the survey, they received a thank you notice and a reminder to complete th e second portion of the questionnaire after the game finished. At the conclusion of the game the second portion of the survey [Points of Attachment Index (Team Identification Index) a nd Intentions for Sport Consumption Behavior Scale] was posted as an online survey on a netw ork, and emails with the surveys web address were again sent out to part icipants asking them to complete the second portion of the questionnaire. After the participan ts completed the second portion of the survey, they received a thank you notice. After the participants complete d the surveys, the data were stored on the network and later downloaded at the completion of the data collection. The purpose of the study and the instructi ons for completing the questionnaire were included in the emails and on the online survey. In accordance with the IRB approval process at the university, the participants ha d an opportunity to decline to pa rticipate in the survey at any time during the questionnaire. No known physical or psychological risks we re associated with completing the online survey.

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76 Data Analysis The descriptive statistics of the participants dem ographic and socio-demographic variables were calculated using SPSS 14.0 (George & Mall ery, 2003). The psychometric properties of the scales were tested in the previous studies. A repeated Analysis of Covariance (ANCOVA) was conducted using SPSS 14.0 to compare the pre and posttest results for conative loyalty on the total sample while controlling for team identification.

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77 Table 3-1. Demographic Items Demographic Items Ethnicity Gender Age Marital status Highest level of education Household income Marital Status Number of Children Country of Origin Country of Origin for Mother Country of Origin for Father Years in the United States Generation in the United States Ethnic Group

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78 Table 3-2. Team Identification Index Team Identification Index I consider myself to be a real fan of the (specific sport) team I would experience a loss if I had to stop being a fan of the team Being a fan of the (specific sport) team is very important to me

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79 Table 3-3. (Dis)Confirmation of Expectancies Scale (Dis)Confirmation of E xpectancies Scale Quality of Play The quality of the game The overall quality of play The performance of the teams Outcome of the Game The result of the game The outcome of the game The conclusion of the game

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80 Table 3-4. Points of Attachment Index Points of Attachment Index American Football First and foremost I consider myself an American football fan. American football is my favorite sport. Of all sports, I prefer American football. Baseball First and foremost I consider myself a baseball fan. Baseball is my favorite sport. Of all sports, I prefer baseball. Basketball First and foremost I consider myself a basketball fan. Basketball is my favorite sport. Of all sports, I prefer basketball. Hockey First and foremost I consider myself a hockey fan. Hockey is my favorite sport. Of all sports, I prefer hockey. Soccer First and foremost I consider myself a soccer fan. Soccer is my favorite sport. Of all sports, I prefer soccer.

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81 Table 3-5. Affective State Index Affective State Index Positive Mood I feel happy I feel cheerful I feel delighted Negative Mood I feel disappointed I feel upset I feel irritated Satisfaction with Play I am satisfied with the quality of (name of sport) played in the (game, series, etc.) I was satisfied with the overall quality of play I am satisfied with the performance of both teams Satisfaction with Outcome I was satisfied with the result of the (game, series, etc.) I am satisfied with the outcome of the (game, series, etc.) I was satisfied with the conclusion of the (game, series, etc.)

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82 Table 3-6. Self-Esteem Maintenance Behavioral Scale Self-Esteem Maintenance Behavioral Scale BIRGing I would like to let ot hers know about my association with the (name of the team) I plan to discuss the game with other people I would like to publicize my connecti on with (the name of the team) I would like to tell othe rs about my association with this team CORFing I do not want to be associated with the (name of the team) I do not wish to be a fan of the (name of the team) I plan to avoid talking ab out the game with others I would like to disconn ect myself from (the name of the team)

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83 Table 3-7. Intentions for Sport Consumption Behavior Scale Intentions for Sport Consumption Behavior Scale I am more likely to attend future games. I am more likely to purcha se the teams merchandise. I am more likely to buy (team name) clothing. I am more likely to s upport the (team name). I am more likely to watch game s highlights with other people.

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84 Table 3-8. Multigroup Ethnic Identity Measure Multigroup Ethnic Identity Measure I am happy that I am a me mber of my ethnic group. I have a strong sense of be longing to my own ethnic group. I understand pretty well what my et hnic group membership means to me. I have a lot of pride in my et hnic group and its accomplishments. I feel a strong attachment to my own ethnic group. I feel good about my cultura l or ethnic background.

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85 Table 3-9. Abbreviated Multidimen sional Acculturation Scale Abbreviated Multidimensional Acculturation Scale I think of myself as being U.S. American. I feel good about being U.S. American. Being U.S. American plays an important part of my life. I feel that I am a part of U.S. American culture. I have a strong sense of being U.S. American. I am proud of being a U.S. American.

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86 Table 3-10. Vicarious Achievement Scale Vicarious Achievement Index I feel personal sense of achievement when the team does well I feel like I have won when the team wins I feel proud when the team plays well

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87 Table 3-11. Past Behaviors & Estimated Fu ture Sport Consump tion Behaviors Index Past Behaviors & Estimated Futu re Sport Consumption Behaviors Please estimate the total number of (name of team) games that you intend to watch on television this season: Please estimate the total number of (name of team) games that you expect to attend this season: How many (name of team) games (if any) have you already watched on television during this season? How many (name of team) games (if any) have you already attended during this season? Please estimate the total do llar amount (if any) that you have spent already this season on (name of team) merchandise and pa raphernalia for yourself. $ __ Please estimate the total do llar amount (if any) that you expect to spend this season on (name of team) merchandise and paraphernalia for yourself. $ __

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88 CHAPTER 4 RESULTS Model Results (Study 1/Purpose 1) Psychometric Properties of the Scales (Measu rement Model for MSSCL Models A, B, & C) The results of the CFA on the measurement mo del for Models A, B, and C (Table 4-1) showed close fit (RMSEA, a = 0.062; CI = 0.057, 0.067; 2/df = 1072.64/482 = 2.22). Additionally, only 7.7% of the resi duals in the residual matrix ex ceeded .10, indicating that the actual sample correlations and the reproduced correlation matrix for the items differed only slightly. The Cronbachs alpha coe fficients were good for all constr ucts in all ten scales ranging from .75 to .95, and the AVE values ranged from .47 to .86 (Table 4-2). For all subscales, each manifest variable loaded significan tly onto its first-order latent va riable and all factor loadings ranged from .578 to .942 (Table 4-2). The means and standard deviations fo r the subscales of the total, Latino, and Non-Latino groups are pres ented in Table 4-3. The correlations among the variables did not exceed .85 (-.40 to .79; Table 44), therefore showing discriminant validity. MSSCL Model A When the total sample was studied, the struct ural model for Model A (Table 4-5) showed reasonable fit (RMSEA, a = 0.073; CI = 0.068, 0.077; 2/df = 1312.36/510 = 2.57), but 27.1% of the residuals in the residual matrix exceeded .10 indicating that the f it could be improved (Bagozzi & Yi, 1988). When the Latino sample wa s studied, the structural model for Model A showed mediocre fit (RMSEA, a = 0.094; CI = 0.086, 0.102; 2/df = 967.72/510 = 1.90) and 21.1% of the residuals exceeded .10. When the N on-Latino sample was st udied, the structural model for Model A showed reasonable fit (RMSEA, a = 0.076; CI = 0.069, 0.082; 2/df = 1044.77/510 = 2.05) and 31.1% of the residuals exceeded .10.

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89 Latent Variable Relationships in MSSCL Model A For the total, Latino, and Non-Latino samples in Model A, all of the first-order latent variables loaded significantly on the second orde r latent variables except for the relationship between Satisfaction (third-order latent variable) and Affectiv e Responses (second-order latent variable) which had a bounda ry parameter violation ( = 1.0). For the total sample (Figure 4-1), Team Identification explained 32.5% of the va riance in Self-Esteem, and (Dis)confirmation explained 66.4% of the variance in Affective Responses. The latent variables Self-Esteem ( = .69) and Affective Responses ( = .18) were significantly asso ciated with Conative Loyalty and 51.2% of the variance in Conative Loyalty was expl ained by the variables in the model. For the Latino sample (Figure 4-2), Team Identification explained 23.2% of the variance in Self-Esteem, and (Dis)confirmation explained 63.5% of the variance in Affective Responses. The latent variables Self-Esteem ( = .83) and Affective Responses ( = .14) were signifi cantly associated with Conative Loyalty and 71.2% of the vari ance in Conative Loyalty was explained by variables in the model. For the Non-Latino samp le (Figure 4-3), Team Identification explained 46.5% of the variance in Self-Est eem, and (Dis)confirmation explai ned 64.5% of the variance in Affective Responses. The latent variables Self-Esteem ( = .65) and Affective Responses ( = .14) were significantly associated with Conativ e Loyalty and 44.1% of th e variance in Conative Loyalty was explained by variables in the model. MSSCL Model B When the total sample was studied, the struct ural model for Model B (Table 4-5) showed reasonable fit (RMSEA, a = 0.072; CI = 0.067, 0.076; 2/df = 1306.57/510 = 2.56), but 21.0% of the residuals in the residual matrix exceeded .10 indicating that the f it could be improved (Bagozzi & Yi, 1988). When the Latino sample wa s studied, the structural model for Model B showed mediocre fit (RMSEA, a = 0.094; CI = 0.085, 0.101; 2/df = 957.58/510 = 1.88) and

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90 17.3% of the residuals exceeded .10. When the N on-Latino sample was st udied, the structural model for Model B showed reasonable fit (RMSEA, a = 0.075; CI = 0.068, 0.081; 2/df = 1041.22/510 = 2.04) and 25.2% of the residuals exceeded .10. Latent Variable Relationships in MSSCL Model B For the total, Latino, and Non-Latino samples in Model B, all of the first-order latent variables loaded significantly on the second orde r latent variables except for the relationship between Satisfaction (third-order latent variable) and Affectiv e Responses (second-order latent variable) which had a bounda ry parameter violation ( = 1.0). For the total sample (Figure 4-4), (Dis)confirmation explained 65.8% of the variance in Affective Responses. Affective Responses ( = .37) and Team Identification ( = .52) explained 39.6% of the variance in Self-Esteem. The latent variable Self-Esteem ( = .76) was significantly associ ated with Conative Loyalty and 57.2% of the variance in Conative Loyalty was e xplained in the model. For the Latino sample (Figure 4-5), (Dis)confirmation explained 62.7% of the variance in Affective Responses. Affective Responses ( = .34) and Team Identification ( = .50) explained 32.6% of the variance in Self-Esteem. The latent variable Self-Esteem ( = .86) was significant ly associated with Conative Loyalty and 73.5% of th e variance in Conative Loyalty was explained in the model. For the Non-Latino sample (Figur e 4-6), (Dis)confirmation explai ned 64.0% of the variance in Affective Responses. Affective Responses ( = .37) and Team Identification ( = .61) explained 50.8% of the variance in Self-Esteem. The latent variable Self-Esteem ( = .67) was significantly associated with Conative Loyalty and 44.5% of the variance in Conative Loyalty was explained by variables in the model. MSSCL Model C When the total sample was studied, the struct ural model for Model C (Table 4-5) showed mediocre fit (RMSEA, a = 0.084; CI = 0.079, 0.088; 2/df = 1640.46/510 = 3.22), but 35.4% of

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91 the residuals exceeded .10, indicating poor fit (Bagozzi & Yi 1988). When the Latino sample was studied, the structural model fo r Model C showed poor fit (RMSEA, a = 0.102; CI = 0.094, 0.109; 2/df = 1034.85/510 = 2.03) and 29.3% of the residua ls in the residual matrix exceeded .10. When the Non-Latino sample was studied, the structural model for Model C showed mediocre fit (RMSEA, a = 0.088; CI = 0.081, 0.094; 2/df = 1284.61/510 = 2.52) and 39.0% of the residuals exceeded .10. Latent Variable Relationships in MSSCL Model C For the total, Latino, and Non-Latino samples in Model C, the relationships between Satisfaction and Affective Responses ( = 1.0) and between (Dis)confirmation with the Quality of the Game and the (Dis)conf irmation latent construct ( = 1.0) had boundary parameter violations. For the total sample (Figure 4-7), Self-Esteem ( = .64), Team Identification ( = .10), (Dis)confirmation ( = .19), and Affective Responses ( = .05) explained 45.8% of the variance in Conative Loyalty. For the Lati no sample (Figure 4-8), Self-Esteem ( = -.71), Team Identification ( = .17), (Dis)confirmation ( = .20), and Affective Responses ( = -.02) explained 57.4% of the variance in Conative Loyalty. For the N on-Latino sample (Figure 4-9), Self-Esteem ( = .61), Team Identification ( = .03), (Dis)confirmation ( = .15), and Affective Responses ( = .10) explained 40.0% of the variance in Conative Loyalty. Model Results (Study 1/Purpose 2) Psychometric Properties of the Scales (Mea surement Model for Re vised MSSCL Models A & B) The results of the CFA on the measurement mo del for Revised Models A and B (Table 41) showed close fit (RMSEA, a = 0.055; CI = 0.052, 0.059; 2/df = 2329.68/1286 = 1.81). Additionally, only 5.9% of the resi duals in the residual matrix exceeded .10. With the exception of the past behavior construct, the Cronbachs alpha coefficients were good for 13 of the 14

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92 constructs ranging from .75 to .97, and the AVE values ranged from .42 to .84 (Table 4-6). The Cronbachs alpha coefficients for the past behavior construct was .41, and the AVE was .12. With the exception of the past behavior construc t, each manifest variable loaded significantly onto its first-order latent variab le and all factor loadings ranged from .533 to .963 (Table 4-6). The factor loadings for the manifest variables explaining past behavior ranged from .437 to .179. Due to the poor construct validity of the Past Beha vior latent variable and the data were collected at a home baseball game, only the manifest variable of past attendance was used to represent past behavior. The means and standard deviations of the subscales for the total, Latino, and NonLatino groups are presented in Table 4-3. The correlations among all of the variables did not exceed .85 (-.35 to .81), therefore indicating discriminant validity (Table 4-7). Revised MSSCL Model A When the total sample was studied, the struct ural model for Revised Model A (Table 4-5) showed good fit (RMSEA, a = 0.060; CI = 0.057, 0.063; 2/df = 2456.16/1152 = 2.13), but 28.0% of the residuals exceeded .10 indicating that the fit could be improved (Bagozzi & Yi, 1988). When the Latino sample wa s studied, the structural mode l for Revised Model A showed mediocre fit (RMSEA, a = 0.091; CI = 0.085, 0.095; 2/df = 1974.83/1152 = 1.71) and 26.0% of the residuals exceeded .10. When the Non-Latino sample was studied, the structural model for Revised Model A showed reasonable fit (RMSEA, a = 0.074; CI = 0.068, 0.079; 2/df = 1697.23/1152 = 1.47) and 23.1% of the residuals exceeded .10. Latent Variable Relationships in Revised MSSCL Model A For the total, Latino, and Non-Latino sample s in Revised Model A, the relationship between Satisfaction and Affective Responses ( = 1.0) had a boundary parameter violation. For the total sample (Figure 4-10), Team Identification ( = .21) and Vicarious Achievement ( =

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93 .46) explained 40.1% of the variance in Self-Est eem, and (Dis)confirmation explained 66.3% of the variance in Affective Re sponses. Past attendance ( = -.01), Self-Esteem ( = .72), Ethnic Identity ( = -.01), U.S. Identity ( = .01), and Affective Responses ( = .16) explained 54.0% of the variance in Conative Loyalty. For the Lati no sample (Figure 4-11), Team Identification ( = -.00) and Vicarious Achievement ( = .57) explained 32.3% of th e variance in Self-Esteem, and (Dis)confirmation explained 63.3% of the va riance in Affective Responses. Past attendance ( = .07), Self-Esteem ( = .77), Ethnic Identity ( = .01), U.S. Identity ( = .12), and Affective Responses ( = .12) explained 63.2% of the variance in Conative Loyalty. For the Non-Latino sample (Figure 4-12), Team Identification ( = .53) and Vicarious Achievement ( = .17) explained 45.7% of the variance in Self-Esteem, and (Dis)confir mation explained 59.0% of the variance in Affective Res ponses. Past attendance ( = -.04), Self-Esteem ( = .72), Ethnic Identity ( = -.09), U.S. Identity ( = .04), and Affective Responses ( = .09) explained 53.0% of the variance in Conative Loyalty. Revised MSSCL Model B When the total sample was studied, the struct ural model for Revised Model B (Table 4-5) showed good fit (RMSEA, a = 0.060; CI = 0.057, 0.063; 2/df = 2441.24/1152 = 2.12), but 26.0% of the residuals exceeded .10 indicating that the fit could be improved (Bagozzi & Yi, 1988). When the Latino sample wa s studied, the structural mode l for Revised Model B showed mediocre fit (RMSEA, a = 0.090; CI = 0.085, 0.095; 2/df = 1964.26/1152 = 1.71) and 25.0% of the residuals exceeded .10. When the Non-Latino sample was studied, the structural model for Revised Model B showed reasonable fit (RMSEA, a = 0.073; CI = 0.067, 0.079; 2/df = 1698.31/1152 = 1.47) and 21.3% of the residuals in the residual matrix exceeded .10.

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94 Latent Variable Relationships in Revised MSSCL Model B For the total, Latino, and Non-Latino sample s in Revised Model B, the relationship between Satisfaction and Affective Responses ( = 1.0) had a boundary parameter violation. For the total sample (Figure 4-13), Team Identification ( = .24) and Vicarious Achievement ( = .39) explained 43.2% of the variance in Self-Est eem; (Dis)confirmation explained 65.6% of the variance in Affective Responses ; and Affective Responses expl ained 7.8% of the variance in Self-Esteem. Past attendance ( = -.02), Self-Esteem ( = .77), Ethnic Identity ( = .01), and U.S. Identity ( = .00) explained 59.4% of the variance in Conative Loyalty. For the Latino sample (Figure 4-14), Team Identification ( = .07) and Vicarious Achievement ( = .48) explained 36.2% of the varian ce in Self-Esteem; (Dis)confir mation explained 62.4% of the variance in Affective Responses ; and Affective Responses expl ained 7.3% of the variance in Self-Esteem. Past attendance ( = .06), Self-Esteem ( = .79), Ethnic Identity ( = -.02), and U.S. Identity ( = .09) explained 63.2% of the variance in Conative Loyalty. For the Non-Latino sample (Figure 4-15), Team Identification ( = .52) and Vicarious Achievement ( = .13) explained 45.5% of the varian ce in Self-Esteem; (Dis)confir mation explained 57.8% of the variance in Affective Responses ; and Affective Responses expl ained 5.7% of the variance in Self-Esteem. Past attendance ( = -.04), Self-Esteem ( = .74), Ethnic Identity ( = -.10), and U.S. Identity ( = .04) explained 55.0% of the variance in Conative Loyalty. Results (Study 2/Purpose 3) Psychometric Properties of the Scal e (Points of Attachment Index) The results of the CFA on the measurement model for the group comparisons showed reasonable fit (RMSEA, a = 0.063; CI = 0.055, 0.071; 2/df = 264.70/80 = 3.31). Additionally, none of the residuals in the resi dual matrix exceeded .10. The Cronb achs alpha coefficients were good for all of the constructs ranging from .89 to .95, and the AVE values ranged from .73 to .86

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95 (Table 4-8). Each manifest variable loaded signif icantly onto its first-order latent variable and all factor loadings ranged from .758 to .947 (Table 4-8) The correlations among all of the variables did not exceed .28, therefore indicating di scriminant validity (Table 4-9). MANOVA Results For the total sample in study 2, a MANOVA was conducted to examine group differences among four subgroup (Cubans, Puerto Ricans, Colu mbians, and Non-Latinos) on five dependent variables (identification with five specific sp orts: American football, baseball, basketball, hockey, and soccer). MANOVA results showed a significant difference among the groups on the combined set of dependent variables [Wilks = .73, F (15, 1590.50) = 12.69, p < .001, 2 = .099]. Group differences were sign ificant for identification with baseball [F (3, 580) = 10.923, p < .001, 2 = .053], for identifica tion with basketball [F (3, 580) = 5.594, p = .001, 2 = .028], for identification with American football [F (3, 580) = 13.677, p < .001, 2 = .066], for identification with hockey [F (3,580) = 2.941, p = .033, 2 = .015], and for identificat ion with soccer [F (3,580) = 32.111, p < .001, 2 = .142]. The group means, significant mean differences, and standard deviations for identification with each sport across groups are presented in Table 4-10. For the student sample in study 2, a MANO VA was also conducted to examine group differences among four subgroups (Cubans, Puer to Ricans, Columbians, and Non-Latinos) on three sport consumption behaviors: attendance, media, and merchandise. The multivariate results showed a significant difference among the groups on the combined set of dependent variables [Wilks = .85, F (9, 869) = 6.901, p < .001, 2 = .054]. The univariate re sults indicated that group differences were significant for pa st attendance [F (3,359) = 16.356, p < .001, 2 = .120], but not for past media consumption [F (3,359) = .463, p = .708, 2 = .004] and for past merchandise consumption [F (3,359) = 1.382, p = .248, 2 = .011]. The group means, significant

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96 mean differences, and standard deviations fo r past consumption behaviors across groups are presented in Table 4-11. Results (Study 3/Purpose 4) A repeated measures ANCOVA was conducted to examine Conative Loyalty (purchase intentions) across time, while controlling for Team Identification. The results showed no significant difference [F (1, 112) = 1.238, p = .268, 2 = .011] between the Conative Loyalty before the sporting event (Time 1; M = 6.06, SD = 1.25) and the Conative Loyalty after the sporting event (Time 2; M = 5.55, SD = 1.28), when controlling for Team Identification [F (1, 112) = 5.127, p = .025, 2 = .044].

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97 Table 4-1. Fit Measures for Measurement Model (Original Model Data & Revised Model Data) Model df RMSEA CI 2/ df Res > .10 Measurement Model (Original: A, B, & C) 482 .062 (.057,.067) 1072.64 2.22 7.7% Measurement Model (Revised A & B) 1286.055 (.052,.059) 2329.68 1.81 5.9% Note. df = degrees of freedom; RMSEA; RMSEA CI =90% confidence intervals for RMSEA; = chi square test statistic; 2/ df = chi square divided by the degrees of freedom.

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98 Table 4-2. Factor Loadings ( ), Cronbachs Alpha ( ), and Average Variance Extracted Values (AVE) for Team Identification Index, (D is)Confirmation of Expectancies Scale, Affective State Index, Self-Esteem Maintena nce Behavioral Scale, Intentions for Sport Consumption Behavior Scale Factor and Item AVE Team Identification .8550.69 I consider myself to be a real fan of the baseball team .833 I would experience a loss if I had to stop being a fan of the team .774 Being a fan of the baseball team is very important to me .884 (Dis)Confirmation of Expect ancies (Outcome) .9450.86 The result of the game .923 The outcome of the game .928 The conclusion of the game .925 (Dis)Confirmation of Expect ancies (Quality) .9460.80 The quality of the game .845 The overall quality of play .897 The performance of the teams .940 Positive Affect .8000.60 I feel happy .756 I feel cheerful .809 I feel delighted .766 Negative Affect .7970.62 I feel disappointed .652 I feel upset .824 I feel irritated .869 BIRGing .8770.62 I would like to let others know about my associa tion with the (name of the team) .829 I plan to discuss the game with other people .606 I would like to publicize my connection with (the name of the team) .825 I would like to tell others about my association with this team .874 CORFing .7460.47 I do not want to be associated with the (name of the team) .657 I do not wish to be a fan of the (name of the team) .728 I plan to avoid talking about the game with others .606 I would like to disconnec t myself from (the name of the team) .729 Satisfaction with Quality .8660.70 I am satisfied with the performance of both teams .766 I am satisfied with the quality of baseball played in the game .818 I was satisfied with the overall quality of play .914 Satisfaction with Outcome .9310.81 I am satisfied with the outcome of the game .868 I was satisfied with the conclusion of the game .942 I was satisfied with the result of the game .895

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99 Table 4-2. Continued Factor and Item AVE Intentions for Sport Cons umption Behavior .8610.59 I am more likely to attend future games .588 I am more likely to purchase the teams merchandise .947 I am more likely to watch the game highlights with other people .578 I am more likely to buy (team name) clothing .950 I am more likely to supp ort the (team name) .690

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100 Table 4-3. Means and Standard Deviations (SD) for the Subscales [Team Identification Index, (Dis)Confirmation of Expectancies Scal e, Affective State Index, Self-Esteem Maintenance Behavioral Scale, Intentions for Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure, A bbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale, a nd Past Attendance] for the Total, Latino, and Non-Latino groups Total Sample Latinos Non-Latinos Team Identification 5.69(1.40) 5.90(1.37) 5.55(1.41) (Dis)Confirmation of Expectancies (Outcome)6.01(1.13) 6.19(1.07) 6.16(1.04) (Dis)Confirmation of Expectancies (Quality) 5.94(1.02) 6.15(1.00) 6.01(.98) Positive Affect 6.27(.92) 6.42(.84) 6.17(.96) Negative Affect 1.59(1.12) 1.64(1.33) 1.56(.95) BIRGing 5.28(1.40) 5.60(1.31) 5.06(1.41) CORFing 1.69(1.18) 1.94(1.50) 1.51(.86) Satisfaction with Qual ity 6.12(.97) 6.27(6.40) 6.01(.98) Satisfaction with Outcome 6.25(1.05) 6.38(1.04) 6.16(1.05) Intentions for Sport Consumption Behavior 5.29(1.38) 5.82(1.18) 4.93(1.39) Ethnic Identity 6.06(1.18) 6.38(.90) 5.84(1.29) US Identity 6.40(1.21) 6.47(1.06) 6.36(1.30) Vicarious Achievement 5.76(1.28) 6.14(1.19) 5.51(1.27) Past Attendance 12.02(17.11) 12.25(14.67) 11.86(18.63) Note: The subscales had a 7-point response form at ranging from Strongly Disagree (1), to Neutral (4), to Strongly Agree (7). There were 82 possible home games for Past Attendance.

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101 Table 4-4. Correlation Matrix for the Latent Variables Included in the Measurement Model for Original Models A, B, & C 1 2 3 4 5 6 7 8 9 10 1 Team ID 1.0 2 (Dis)confirmation of Outcome .2751.0 3 (Dis)confirmation of Quality .350.7561.0 4 BIRGing .539.245.3361.0 5 CORFing -.395-.114-.095-.2701.0 6 Conative Loyalty .451.292.411.713-.0631.0 7 Negative Affect -.111-.213-.200-.088.647-.0321.0 8 Positive Affect .391.514.564.434-.278.401-.370 1.0 9 Satisfaction with Outcome .214.684.561.292-.230.310-.379 .7371.0 10 Satisfaction with Quality .328.618.727.398-.210.395-.292 .794.7691.0

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102 Table 4-5. Fit Measures for Model A, Model B, Model C, Revised Model A, & Revised Model B for the Total Sample, Latino Sample, and Non-Latino Sample Model df RMSEA CI 2/ df Res > .10 Model A Total sample 510 .073(.068, .077) 1312.362.57 27.1% Latino sample 510 .094(.086, .102) 967.72 1.90 21.1% Non-Latino sample 510 .076(.069, .082) 1044.772.05 31.0% Model B Total sample 510 .072(.067, .076) 1306.572.56 21.0% Latino sample 510 .094(.085, .101) 957.581.88 17.3% Non-Latino sample 510 .075(.068, .081) 1041.222.04 25.2% Model C Total sample 510 .084(.079, .088) 1640.463.22 35.4% Latino sample 510 .102(.094, .109) 1034.852.03 29.3% Non-Latino sample 510 .088(.081, .094) 1284.612.52 39.0% Revised Model A Total sample 1152 .060(.057, .063) 2456.162.13 28.0% Latino sample 1152 .091(.085, .095) 1974.831.71 26.0% Non-Latino sample 1152 .074(.068, .079) 1697.231.47 23.1% Revised Model B Total sample 1152 .060(.057, .063) 2441.242.12 26.0% Latino sample 1152 .090(.085, .095) 1964.261.71 25.0% Non-Latino sample 1152 .073(.067, .079) 1698.311.47 21.3% Note. df = degrees of freedom; RMSEA; RMSEA CI =90% confidence intervals for RMSEA; = chi square test statistic; 2/ df = chi square divided by the degrees of freedom.

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103 Table 4-6. Factor Loadings ( ), Cronbachs Alpha ( ), and Average Variance Extracted Values (AVE) for Team Identification Index, (D is)Confirmation of Expectancies Scale, Affective State Index, Self-Esteem Maintena nce Behavioral Scale, Intentions for Sport Consumption Behavior Scale, Multigroup Ethnic Identity Measure, Abbreviated Multidimensional Acculturation Scale, Vicarious Achievement Subscale, and Past Sport Consumption Behaviors Index Factor and Item AVE Team Identification .855 0.67 I consider myself to be a real fan of the baseball team .839 I would experience a loss if I had to stop being a fan of the team .752 Being a fan of the baseball team is very important to me .869 (Dis)Confirmation of Expect ancies (Outcome) .945 0.87 The result of the game .908 The outcome of the game .956 The conclusion of the game .934 (Dis)Confirmation of Expect ancies (Quality) .946 0.78 The quality of the game .829 The overall quality of play .900 The performance of the teams .925 Positive Affect .800 0.64 I feel happy .763 I feel cheerful .819 I feel delighted .821 Negative Affect .797 0.60 I feel disappointed .557 I feel upset .844 I feel irritated .878 BIRGing .877 0.65 I would like to let ot hers know about my association with the (name of the team) .855 I plan to discuss the game with other people .653 I would like to publicize my conn ection with (the name of the team) .796 I would like to tell others about my association with this team .890 CORFing .746 0.42 I do not want to be associated with the (name of the team) .667 I do not wish to be a fan of the (name of the team) .626 I plan to avoid talking about the game with others .533 I would like to disconnec t myself from (the name of the team) .745 Satisfaction with Quality .866 0.72 I am satisfied with the performance of both teams .791 I am satisfied with the quality of baseball played in the game .827 I was satisfied with the overall quality of play .914

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104 Table 4-6. Continued Factor and Item AVE Satisfaction with Outcome .931 0.83 I am satisfied with the outcome of the game .859 I was satisfied with the conclusion of the game .936 I was satisfied with the result of the game .938 Intentions for Sport Cons umption Behavior .861 0.62 I am more likely to attend future games .611 I am more likely to purchase the teams merchandise .963 I am more likely to watch the game highlights with other people .958 I am more likely to buy (team name) clothing .699 I am more likely to supp ort the (team name) .622 Ethic Identity .926 0.72 I feel good about my cultural or ethnic background .672 I have a strong sense of belongi ng to my own ethnic group .843 I feel a strong attachment to my own ethnic group .862 I understand pretty well what my ethnic group membership means to me .868 I have a lot of pride in my ethni c group and its accomplishments .910 I am happy that I am a member of my ethnic group .918 Acculturation (Dominant Identity) .967 0.84 I am proud of being a U.S. American .932 I think of myself as being U.S. American .911 I feel that I am a part of U.S. American culture .879 Being U.S. American plays an important part of my life .866 I feel good about being U.S. American .937 I have a strong sense of being U.S. American .956 Vicarious Achievement .878 0.70 I feel personal sense of achievement when the team does well .745 I feel like I have won when the team wins .888 I feel proud when the team plays well .872 Past Behaviors .407 0.12 How many Marlins baseball hom e games (if any) have you already attended dur ing this season? .337 How many Marlins baseball aw ay games (if any) have you already attended dur ing this season? .179 How many Marlins baseball game s (if any) have you already watched on television during this season? .424 How many Marlins baseball game s (if any) have you already listened to on the radio during this season? .323 Please estimate the total dollar amount (if any) that you have spent already this season on Marlins baseball merchandise and paraphernalia for yourself. .437

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105 Table 4-7. Correlation Matrix for the La tent Variables Included in the Measur ement Model for Revised Models A & B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 Vic. Achievement 1.0 2 Ethnic ID .381 1.0 3 Team ID .811 .3161.0 4 US ID .363 .566.3921.0 5 Past Behavior .673 .323.758.3061.0 6 (Dis)con. of Outcome .312 .083.236-.011.1411.0 7 (Dis)con. of Quality .351 .043.261-.022.213.7741.0 8 BIRGing .538 .125.540.077.679.236.363 1.0 9 CORFing -.351 -.188-.472-.166-.331-.140-.113 -.3181.0 10 Conative Loyalty .456 .091.445.080.551.291.442 .718-.1721.0 11 Negative Affect -.104 -.064.113-.055.040-.157-.133 -.102.713-.0231.0 12 Positive Affect .399 .159.295.044.342.533.606 .442-.349.406-.3521.0 13 Sat. with Outcome .262 .070.157-.028.075.702.601 .302-.233.304-.297.7971.0 14 Sat. with Quality .328 .084.266.012.277.635.743 .427-.277.410-.280.827.8141.0

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106 Table 4-8. Factor Loadings ( ), Cronbachs Alpha ( ), and Average Variance Extracted Values (AVE) for Points of Attachment Index Factor and Item AVE Identification with American football .940.84 First and foremost, I consider myself an American football fan. .885 American football is my favorite sport. .928 Of all sports, I prefer American football. .936 Identification with baseball .945.85 First and foremost, I consider myself a baseball fan. .915 Baseball is my favorite sport. .919 Of all sports, I prefer baseball. .933 Identification with basketball .916.79 First and foremost, I consider myself a basketball fan. .831 Basketball is my favorite sport. .901 Of all sports, I prefer basketball. .924 Identification with hockey .887.73 First and foremost, I consider myself a hockey fan. .758 Hockey is my favorite sport. .906 Of all sports, I prefer hockey. .893 Identification with soccer .947.86 First and foremost, I consider myself a soccer fan. .917 Soccer is my favorite sport. .950 Of all sports, I prefer soccer. .911

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107 Table 4-9. Correlation Matrix for the Latent Va riables Included in Study 2 (Group Comparisons) 1 2 3 4 5 1 Identification with baseball 1.0 2 Identification with basketball .1901.0 3 Identification with American football .179.2271.0 4 Identification with hockey .226.173.1801.0 5 Identification with soccer .016-.040-.165.2791.0

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108 Table 4-10. Mean Scores (M) and Standard De viations (SD) for Identification with Amer ican Football, Baseball, Basketball, Hocke y, and Soccer across Each Group (Non-Latinos, Pu erto Ricans, Cubans, and Colombians) Factor Non-Latinos Puerto Ricans Cubans Colombians Identification with American football 4.74(1.87) a 4.03(2.12) b 4.33(1.90) a, b 3.33(1.83) c Identification with baseball 2.79(1.86) a 3.66(2.07) b 3.63(2.05) b 2.63(1.57) a Identification with basketball 3.53(1.86) a 4.00(1.79) a, b 3.46(1.71) a 3.01(1.66) a, c Identification with hockey 2.24(1.46) a 1.83(1.18) a 2.17(1.43) a 1.92(1.23) a Identification with soccer 2.97(1.91) a, b 2.48(1.62) b 3.20(1.90) a 4.81(2.03) c Note: Means with different superscr ipts are statistically different ac ross each group at the .05 level.

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109 Table 4-11. Mean Scores (M) and Standard Devi ations (SD) for Past Consumption Behaviors (Attendance, Media, and Merchandise) Across Each Group (Non-Latinos, Puerto Ricans, Cubans, and Colombians) Factor Non-Latinos Puerto Ricans Cubans Colombians Attending games 4.87(2.98) a 2.68(3.27) b 2.68(2.97) b 2.57(2.91) b Television consumption 4.97(3.16) a 4.66(3.34) a 4.76(2.89) a 4.36(4.03) a Merchandise purchases 58.53(77.28) a74.54(110.56) a75.69(94.40) a50.95(55.55) a Note: Means with different superscripts are st atistically different ac ross each group at the .05 level.

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110 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Sat. w/Quality Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality .69 .18 1.0* .97 -.27 .82 .57 .86 .88 -.36 .85 .92 .85 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .69 .18 1.0* .97 -.27 .82 .57 .86 .88 -.36 .85 .92 .85 Figure 4-1. Model A for the Total Sample

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111 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Sat. w/Quality Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality .83 .14 1.0* .91 -.15 .80 .48 .81 .93 -.25 .91 .98 .80 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .83 .14 1.0* .91 -.15 .80 .48 .81 .93 -.25 .91 .98 .80 Figure 4-2. Model A for the Latino Sample

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112 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Sat. w/Quality Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality .65 .14 1.0* .92 -.60 .80 .68 .89 .83 -.46 .83 .87 .89 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .65 .14 1.0* .92 -.60 .80 .68 .89 .83 -.46 .83 .87 .89 Figure 4-3. Model A for the Non-Latino Sample

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113 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .85 ..89 Sat. w/Quality Sat. w/Outcome.92 .85 .90 -.26 .76 .52 .85 -.36 1.0* .81 .36 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .85 ..89 Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome.92 .85 .90 -.26 .76 .52 .85 -.36 1.0* .81 .36 Figure 4-4. Model B for the Total Sample

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114 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .81 .94 Sat. w/Quality Sat. w/Outcome.98 .79 .90 -.52 .86 .50 .92 -.25 1.0* .79 .34 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .81 .94 Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome.98 .79 .90 -.52 .86 .50 .92 -.25 1.0* .79 .34 Figure 4-5. Model B for the Latino Sample

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115 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .89 .83 Sat. w/Quality Sat. w/Outcome.87 .90 .83 -.63 .67 .61 .83 -.46 1.0* .80 .37 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .89 .83 Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome.87 .90 .83 -.63 .67 .61 .83 -.46 1.0* .80 .37 Figure 4-6. Model B for the Non-Latino Sample

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116 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .76 1.0* Sat. w/Quality Sat. w/Outcome .89 .85 .19 .10 .05 .64 1.0* -.25 .93 -.39 .94 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .76 1.0* Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome .89 .85 .19 .10 .05 .64 1.0* -.25 .93 -.39 .94 Figure 4-7. Model C for the Total Sample

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117 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .76 1.0* Sat. w/Quality Sat. w/Outcome .95 .81 .20 .17 -.02 -.71 -1.0* .18 1.0 -.33 .91 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .76 1.0* Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome .95 .81 .20 .17 -.02 -.71 -1.0* .18 1.0 -.33 .91 Figure 4-8. Model C for the Latino Sample

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118 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .74 1.0* Sat. w/Quality Sat. w/Outcome .86 .87 .15 .03 .10 .61 1.0* -.51 .87 -.47 .99 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .74 1.0* Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome .86 .87 .15 .03 .10 .61 1.0* -.51 .87 -.47 .99 Figure 4-9. Model C for the Non-Latino Sample

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119 Affective Responses (post game) Self-Esteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Ethnic ID US ID Past Attendance VACH Sat. w/Quality Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality .86 .88 .85 -.36 1.0* .81 .92 .85 .16 .51 .01 -.01 .72 .16 .21 .81 .46 -.27 .94 -.01 Affective Responses (post game) Affective Responses (post game) Self-Esteem Self-Esteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Ethnic ID Ethnic ID US ID US ID Past Attendance VACH VACH Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .86 .88 .85 -.36 1.0* .81 .92 .85 .16 .51 .01 -.01 .72 .16 .21 .81 .46 -.27 .94 -.01 Figure 4-10. Revised Model A for the Total Sample

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120 Affective Responses (post game) Self-Esteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Ethnic ID US ID Past Attendance VACH Sat. w/Quality Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality .81 .93 .91 -.25 1.0* .80 .98 .80 .12 .60 .12 .01 .77 .18 -.00 .77 .57 -.20 .97 .07 Affective Responses (post game) Affective Responses (post game) Self-Esteem Self-Esteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Ethnic ID Ethnic ID US ID US ID Past Attendance VACH VACH Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .81 .93 .91 -.25 1.0* .80 .98 .80 .12 .60 .12 .01 .77 .18 -.00 .77 .57 -.20 .97 .07 Figure 4-11. Revised Model A for the Latino Sample

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121 Affective Responses (post game) Self-Esteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Ethnic ID US ID Past Attendance VACH Sat. w/Quality Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality .88 .87 .79 -.36 1.0* .77 .84 .91 .09 .44 .04 -.09 .72 .12 .53 .81 .17 -.53 .90 -.04 Affective Responses (post game) Affective Responses (post game) Self-Esteem Self-Esteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Ethnic ID Ethnic ID US ID US ID Past Attendance VACH VACH Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome Dis(con.) w/Outcome Dis(con.) w/Outcome Dis(con.) w/Quality Dis(con.) w/Quality .88 .87 .79 -.36 1.0* .77 .84 .91 .09 .44 .04 -.09 .72 .12 .53 .81 .17 -.53 .90 -.04 Figure 4-12. Revised Model A for the Non-Latino Sample

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122 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .85 .89 Sat. w/Quality Sat. w/Outcome.92 .85 .88 -.26 .77 .39 .85 -.36 1.0* .81 .28 Past Attendance VACH Ethnic ID US ID .16 .81 .24 .51 .01.00 -.02 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .85 .89 Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome.92 .85 .88 -.26 .77 .39 .85 -.36 1.0* .81 .28 Past Attendance VACH VACH Ethnic ID Ethnic ID US ID US ID .16 .81 .24 .51 .01.00 -.02 Figure 4-13. Revised Model B for the Total Sample

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123 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .81 .94 Sat. w/Quality Sat. w/Outcome.99 .79 .95 -.16 .79 .48 .91 -.24 1.0* .79 .27 Past Attendance VACH Ethnic ID US ID .18 .77 .07 .60 -.02.09 .06 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .81 .94 Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome.99 .79 .95 -.16 .79 .48 .91 -.24 1.0* .79 .27 Past Attendance VACH VACH Ethnic ID Ethnic ID US ID US ID .18 .77 .07 .60 -.02.09 .06 Figure 4-14. Revised Model B for the Latino Sample

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124 Affective Responses (post game) SelfEsteem Team ID Dis (confirmation) Conative Loyalty(Behavior Intentions) BIRGING CORFING Negative Positive Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .88 .87 Sat. w/Quality Sat. w/Outcome.85 .91 .86 -.54 .74 .13 .79 -.37 1.0* .76 .24 Past Attendance VACH Ethnic ID US ID .12 .81 .52 .44 -.10.04 -.04 Affective Responses (post game) Affective Responses (post game) SelfEsteem SelfEsteem Team ID Team ID Dis (confirmation) Dis (confirmation) Conative Loyalty(Behavior Intentions) Conative Loyalty(Behavior Intentions) BIRGING BIRGING CORFING CORFING Negative Negative Positive Positive Satisfaction Satisfaction Dis(con.) w/Outcome Dis(con.) w/Quality .88 .87 Sat. w/Quality Sat. w/Quality Sat. w/Outcome Sat. w/Outcome.85 .91 .86 -.54 .74 .13 .79 -.37 1.0* .76 .24 Past Attendance VACH VACH Ethnic ID Ethnic ID US ID US ID .12 .81 .52 .44 -.10.04 -.04 Figure 4-15. Revised Model B for the Non-Latino Sample

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125 CHAPTER 5 DISCUSSION Due to the lack of research on Latinos within the sport management literature, the focus of this study was to provide an initial examin ation of the Latino mark et segment and conative loyalty. This study explored th e conative loyalty of Latinos and Non-Latinos and made group comparisons among Latino subgroups and Non-Lati nos. The MSSCL (Models A, B, & C) was tested on both Latino and Non-Latino samples, a nd additional constructs were included in a proposed revised MSSCL (Models A & B) in an e ffort to explain more variance in conative loyalty. Furthermore, identification with five sports and three sport consumption behaviors (attendance, media, and merchandise) were compared across four groups (Non-Latinos, Colombians, Cubans, and Puerto Ricans). In order to examine fully sport consumption intentions, conative loyalty was tested before an d after a sporting event to determine the effects of the outcome of the game on conative loya lty. Through testing the models, comparing the groups, and testing the progression of conative lo yalty, this study thoroug hly investigated the Latino market segment and conative loyalty. Purpose 1/Study 1 For the total, Latino, and Non-Latino samples, Model A and Model B were very similar in terms of model fit, and both fit reasonably well; however, the fit indices for Model C were worse. Additionally, the variance explained in Conative Loyalt y in Model B was slightly higher than Model A, and both Model A and B were highe r than Model C for all three samples. Based on these results and Trail and colleagues (2005) previous findings, Model C was removed from any further analysis. Model B was chosen for furt her analysis over Model A because of a greater theoretical justification and suppor t for the relationships in prev ious sport research. However,

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126 from a statistical standpoint, both Models A and B are good fitting models and are not statistically distinct in this data set. Oliver (1997) stated that w ithin the satisfaction cycle, pe rformance (e.g., outcome of the game) would lead to an individu als disconfirmation/confirmation of expectancies then lead to affective responses. The significant relations hip between (Dis)confirmation and Affective Responses (66.0% of the variance explained) suppor ted Olivers satisfacti on theory, Oliver and Swans (1989) previous research, and previous sport management research (Madrigal, 1995; Trail et al., 2005; Trail, Fink, & Anderson, 2003). After a spectator or fan disconfirms or confirms his/her expectations on the quality and/or outcome of the game, he/she has either a positive or a negative affective response, and a certain level of satisfaction or dissatisfaction with the quality and/or outcome of the game. The significant relationship between Affective Responses and Self-Esteem supported self-esteem theory and previous sport manageme nt research (Caro & Ga rca, 2006; Cialdini et al., 1976; Madrigal, 1995; Snyder et al., 1986; Trail et al., 2005; Tr ail et al., 2000). However, the variance explained in Model B ( 13%) was not as large as the va riance (27%) expl ained in SelfEsteem in the original MSSCL (Trail et al., 2005). Previous self-esteem researchers (Owen & King, 2001; Rosenberg, 1979; Rosenberg et al., 1995) have suggest ed that self-esteem is a bidimensional construct explai ned by both negative and positive responses. The moderate negative relationship between BIRGing and CORFing ( r = -.32) supported the bidimensional model of self-esteem. Latinos and Non-Latinos both exhibited a strong re lationship between Team Identification and the Self-Esteem Responses of BIRGing and CORFing, thus confirming Ervin and Strykers (2001) identity theory, which stated that self-esteem fits logically into identity

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127 formation, and Cast and Burkes (2002) self-est eem theory, in which individuals build selfesteem through verifying their soci al group identities. These results are also consistent with the findings of Trail et al. (2005) and Madrigal (1995). Trail et al. (2005) hypothesized that self-esteem responses (BIRGing and CORFing) mediated the relationship between affective respons es and conative loyalty (48% of the variance explained). By reproducing Trail and colleagues previous mode l results and explaining more variance in conative loyalty (57.2%) for the total sample, this research extended the research of Trail et al. (2005) and Madrigal (1995). In additi on, the results supported Olivers (1997) stages of conative loyalty in which the affective stage of loyalty leads to conative loyalty (purchase intentions). Based on the significant relationship found be tween satisfaction and positive affect in Oliver and colleagues (1997) re search, Trail et al. (2005) comb ined the variables of positive affect, negative affect, and satisfaction into th e affective response latent variable. However, a parameter violation ( = 1.0) occurred between Satisfacti on and Affective Responses. Two main reasons may explain the parameter violation. First, the low correlation between Negative Affect and Positive Affect ( r = -.352) in this study has typically been shown to be much higher in previous research (e.g., r = -.83; Trail et al., 2005). Because of this low correlation, Satisfaction played a more central role in explaining Affective Responses, thus possibly causing the parameter violation. Second, the two first-order late nt variables (Satisfacti on with the Quality of Play and Satisfaction with the Outcome of the Ga me) loaded onto a second-order latent variable (Satisfaction) that loaded onto a third-order late nt variable (Affective Responses). In statistical situations such as this a parameter violation may occur. Fu ture research should either add an additional first-order latent vari able to explain Satisfaction or eliminate Satisfaction as a second-

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128 order latent variable and allow the two first-orde r latent variables (Satisfaction with the Quality of Play and Satisfaction with the Outcome of the Game) to load directly onto Affective Responses. Generally, the paths in Model B among the late nt constructs for both the Latino and NonLatino samples were very similar except for the relationships between Self-Esteem and Conative Loyalty, and between Team Identifi cation and Self-Esteem. Latinos ( = .86) demonstrated a stronger relationship between Self-Esteem and Conative Loyalty than Non-Latinos ( = .67). Non-Latinos ( = .61) demonstrated a stronger relati onship between Team Identification and Self-Esteem than Latinos ( = .50). This may be due to the cultural differences between the groups. As a minority group, Latinos face racial prejudice and thus might posses lower selfesteem (Wells, 2001), therefore Latinos may have a stronger need than Non-Latinos to increase their self-esteem through BIRGing. Interestingly, for Latinos the (Dis)confirmation of the Quality of Play ( = .94) was more central in explaining the (Dis)c onfirmation construct than (Dis)c onfirmation of the Outcome of the Game ( = .81). In addition, Satisfaction with the Quality of Play ( = .98) was more central in explaining the construct of Satisfaction than Satisfaction with the Outcome of the Game ( = .79). Conversely, for Non-Latinos the (Dis)conf irmation of the Outcome of the Game ( = .89) was more central in explaining the (Dis)confirmation construct than (Dis)confirmation of the Quality of Play ( = .83). Furthermore, Satisfaction with the Outcome of the Game ( = .90) was slightly more central in explai ning the construct of Satisfaction than Satisfaction with of the Quality of Play ( = .87). From a practical standpoint sport managers should focus on controlling the quality of play by hiring talente d, hard working players who will provide quality

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129 entertainment through their athletic skills. When marketing to La tinos an appropriate slogan for advertising the quality of play could rea d, We dont give up until the final strike! Purpose 2/Study 1 For the total, Latino, and Non-Latino samples, the model fit analysis for Revised Model A and Revised Model B were almost exactly the same in terms of mode l fit. The variance explained in Conative Loyalty in Revised Model B was slightly higher th an Revised Model A for the total sample (59.4% and 54.0%, respectively) and Non-La tino samples (55.0% and 53.0%, respectively). For the Latino sample, the variance explained in Conative Loyalty in the Revised Model A and Revised Model B were equal (63.2 %). Because Model B was slightly better in terms of variance explained for the three sample s, Model B was used in further analysis. Based on ethnic identity theory, acculturati on theory, and Gacio Ha rrolle and Trails (2007) research, the additional constr ucts of ethnic identity and U.S. identity were added to Trail and colleagues (2005) original models. The non-significant relationshi ps between Ethnic Identity and Conative Loyalty a nd between U.S. Identity and C onative Loyalty confirmed Gacio Harrolle and Trails (2007) results that showed et hnic identity and U.S. id entity did not influence sport behavioral intentions. C onversely, these findings did not s upport Asbridge and colleagues (2005) previous research in wh ich acculturation influenced consumption behaviors. Future models examining Latinos and sport consumption behaviors should not use ethnic identity or identity with the dominant culture as a predictive variable of sport fandom or conative loyalty. In the past fifteen years, acculturation resear ch has developed a bidimensional model of acculturation (LaFromboise et al., 1993; Phinne y, 1990; Zea et al. 2003). The strong correlation between Ethnic Identity and U.S. Identity ( r = .60) within the findings confirmed the bidimensional model of acculturation and Gacio Ha rrolle and Trails (2007) previous research.

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130 The strong positive relationships between Vicarious Achievement and Team Identification and between Vicarious Achiev ement and Self-Esteem supported self-esteem theory and previous sport management research (Sloan, 1989; Trail et al., 2007). The addition of vicarious achievement to the revised model cont ributed significantly to the additional variance explained in Conative Loyalty, thus extendi ng the research of Trail et al. (2005). The past behaviors variable was added to the models based on Trail and colleagues (2006) findings that showed past behaviors influenced both iden tification with the team and conative loyalty. The relationship between past be haviors (i.e., attendance ) and conative loyalty was not significant (only 3% vari ance explained) in the Revise d Model B, thus contradicting Trail and colleagues (2006) prev ious findings that showed past attendance accounted for 24% of the variance in purchase intentions. This study was conducted during a special Hispanic weekend event; consequently, this may have been the only game participan ts attended this season, thus distorting the findings. Past atte ndance should be included in futu re research to test these relationships. When comparing the Latino a nd Non-Latino samples on Revised Model B, generally the paths between the latent constructs were very similar except for the relationships between Team Identification and Self-Esteem as previously depict ed in the original Model B. However, Latinos ( = .48) demonstrated a much stronger relation ship between Vicarious Achievement and SelfEsteem than Non-Latinos ( = .13). For both Latinos and Non-La tinos, as the need for vicarious achievement increases so does identification w ith the team. For Non-Latinos, the relationship between Vicarious Achievement and Self-Esteem may be fully mediated by Team Identification. As identification with the team increases, Non-La tinos are more likely to BIRG and less likely to CORF, thus Non-Latinos may be more loyal fa ns. For Latinos, as the need for vicarious

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131 achievement increases so do self-esteem responses and the likelihood of BI RGing behaviors, but CORFing behaviors do not necessarily vary. A dditionally, BIRGing and CORFing responses do not vary based on the level of identification with the team, thus Latinos may be more likely to become bandwagoners. The amount of variance explained in Conative Loyalty for the Revised Model B for all of the samples was considerably higher than the 49% of variance explained in the original model research (Trail et al., 2005) and the 11% of va riance explained in Tra il and colleagues (2003) conative loyalty model. The differences in the am ount of variance explained may be due to the additional variables in the revised models. Marketers need to be aware of the differences in the relationships between the variables for Latinos and Non-Latinos. Regardless of their level of identification with the team, Latinos want to BIRG, thus marketers should provide opportunities for BIRGing behaviors. For example, photo opportunities should be arranged befo re and after the game with a famous player or mascot thus increasing identification with the team and providing a BIRGing opportunity for the fan. Using current technology, marketers could go one step further and take digital photos and send them via email to the fan, therefore re inforcing identification with the team through emails received in the future. Additionally, tangible items that fans can take home that express BIRGing behaviors could improve conative loya lty. For example, providing t-shirts that associate a fan with the successful team (e.g., T-shirt slogan: I am a member of the 2007 National Championship team). Sport marketers should provide outlets for soci al interaction with fans and players. Social networking online has become a strong trend w ithin the sport industry and sport marketing (Mickle, 2007). Teams should not only provide more opportunities for fans to engage with

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132 players online through team websites, but al so through social websites (e.g., facebook.com, myspace.com). Purpose 3/Study 2 As hypothesized, significant differences ex isted among the subgroups of Latinos on identification with specific spor ts, and significant differences existed between Latinos and NonLatinos on identification with specific sports. Furthermore, as hypothesized, no significant differences existed among any of the subgroups of Latinos on s port consumption behaviors, but significant differences existed between Latinos and Non-Latinos on pa st attendance. The findings in study 2 supported the con cepts shaped by identity theory su ch that social situations or ethnic groups (Burke & Tully, 1977) may influence an individuals particul ar role identity (e.g., fan of baseball). The lack of differences among the groups on merchandise consumption supported the previous ethnic gr oup comparison research by Berkow itz et al. (2005). Berkowitz et al. found that in general Latinos and Non-La tinos do not differ on the quantity of store brand purchases. Colombians had the lowest level of identification with American football (significantly lower than all other groups, 10% variance expl ained by group differences), while Non-Latinos had a higher level of identification than Puerto Ricans, but not Cubans. Relative to level of identification with baseball (5 % variance explained by group diffe rences), Puerto Ricans and Cubans had significantly higher mean scores than both Non-Latinos and Colombians. For identification with basketball (3% variance expl ained by group differences), Puerto Ricans had a significantly higher mean score than Colombians but Non-Lati nos and Cubans did not differ from either the Puerto Ricans or Columbians. Id entification with hockey (2% variance explained by group differences) was low across all groups a nd there were no significant group differences. Specific to the identification with soccer ( 14% variance explained by group differences),

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133 Colombians had a significantly higher mean sc ore than all other groups, and Cubans has a significantly higher mean scor e than Puerto Ricans. Interestingly, when sport consumption be haviors were examined, Non-Latinos and Latinos only differ on past attendance behaviors, with Non-Latinos going to two more games per season on average than the other three groups. These differences may be due to cultural differences between the groups. Latinos may enj oy watching the games on television as a group as opposed to actually attending the game. The differe nces may also be due to the cost associated with attending the games or the hassle associated with the complicated student lottery system. From a practical standpoint, sport marketers should realize that differences may or may not exist within Latino segments dependent upon the sport. In addition, differences may or may not exist between Latinos and Non-Latinos by sport and by behavior. Thus, marketers should research their potential Latino subgroups and market them accordingly. When higher levels of variance are explained by the gr oup differences (e.g., identification with soccer, 14%), marketers should consider marketing to the Latino subgroups w ith the highest levels of identification. For example, any level of soccer organization (e.g., prof essional, youth) who desires to market to the Latino segment might want to focus first on the Colombian market segment, because they were by far the highest level of soccer fans within the Latino groups studied. As professional sports have focused thei r attention on marketi ng to Latinos, many of them have pursued Latinos as a homogeneous group (Gacio Harrolle & Tr ail, 2007; Korzenny & Korzenny, 2005). Professional teams need to underst and that the Latino mark et is diverse even though they speak a common language (i.e., Spanish) Marketers should focus their attention on specific groups based on the subgroups in their local geographical areas (e.g., Cubans in Miami) and on the groups more likely to consume their products (e.g., Puerto Ricans who possess the

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134 highest level of identification with baseball). Marketers shoul d spend extra time and financial resources researching the Latino subg roups in their marketing areas. Purpose 4/Study 3 While controlling for identification with th e team, the findings showed no significant difference between conative loyalt y before and after the sporting event. The results of study 3 confirmed Kwon and Ohs (2004) previous findings in which future intentions were not affected by attending the restaurant. On the other hand, the results disconfirmed Tipps and colleagues (2006) previous research in which conative loya lty showed a positive significant difference after being exposed to advertising. As expected, fans with high levels of team identification will be less influenced by the outcome of the game and will typically consum e sport products. From a practical standpoint, marketers can not control the outcome of the game but they can provide o pportunities for fans to maintain high levels of team identification. Fo r example, marketers can provide opportunities for fans to interact with the team or provide social opportunities before and after the games. Recommendations for Future Rese arch and Alternative Models Many future research endeavors are possi ble. The primary suggestion for future research is to retest Revised Models A and B on larger Latino and Non-Latino samp les. Future research should also examine Trail and colleagues (2005 ) original models and the revised models on additional populations such as other ethnic groups (e.g., Asians, Europeans). Even though a large percentage of conative loyalty was explained in the model resu lts, a percentage of conative loyalty has not yet been explained. Future rese arch should incorporate additional variables (e.g., environmental variables) in the models. Based on Trail and colleagues (2007) findings and the correlation between Team Identification and Co native Loyalty for the total sample (r = .45), future research should consider adding a path between Team Identification and Conative

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135 Loyalty. Within the group comparisons, future research should include other Latino ethnic groups (e.g., Mexicans), and samples from multiple states (e.g., California, New York, Florida, Texas, etc.) in the U.S. should be examined in fu ture studies. As the favored team in the sporting event won the contest, future research should at tempt to examine pre and post conative loyalty after a loss. A more comprehens ive and difficult suggestion for fu ture research would be to pursue a longitudinal study that examined both act ual behaviors and conativ e loyalty instead of just conative loyalty. Summary In summary, the Revised Models (A & B) e xplained more variance in Conative Loyalty thus providing a stronger motivation for marketers to include those variab les in their marketing campaigns. Within the Revised Models (A & B), Et hnic Identity and U.S. Identity did not help in explaining conative loyalty and should be remove d from further research. Model B fit the best and worked adequately well for the three sample s (Latino, Non-Latino, and total). A few of the relationships among the variable s in Model B differed by degree for Latinos and Non-Latinos. These differences indicate that Latino and Non-Latino groups should possibly be marketed differently based on these varied relationshi ps. Furthermore, within the Latino market, differences existed among Latino subgroups on identif ication with specific sports. In terms of identification with different spor ts and attendance consumption be haviors, Latinos differ from Non-Latinos. Lastly, conative loya lty was not influenced by the outcome of the game after taking into account identification with the team. Comprehensively, thes e results indicate differences do exist not only between Latinos and Non-Latinos but also among Lati no subgroups. This study provided the first stage of sport management res earch into the Latino markets. Further research should expand and develop this area for spor t managers and marketers to advance the understanding of conative loyalty, esp ecially in Latino market segments.

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149 BIOGRAPHICAL SKETCH Michelle Gacio Harrolle, PhD graduated from the University of Florida in August 2007. Her research interests focus on consumer beha vior in sport, and lifestyle marketing and promotion through ethnic cultures. Her research has been published in the International Journal of Sports Marketing and Sponsorship. She has pr esented numerous presentations at the North American Society for Sport Management and Sport Marketing Association conferences. Additionally, she has taught Admini stration of Sport, Moral and Et hical Issues in Sport, Sport and Sociology, and Introduction to Sport. Dr. Michelle Gacio Harro lle is currently an Assistant Professor at North Carolina State University.