An Integrated Model of Value Equity in Spectator Sports: Conceptual Framework and Empirical Results Daniel Robert Sweeney

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2008 An Integrated Model of Value Equity in Spectator Sports: Conceptual Framework and Empirical Results Daniel Robert Sweeney

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AN INTEGRATED MODEL OF VALUE EQUITY IN SPECTATOR SPORTS:

CONCEPTUAL FRAMEWORK AND EMPIRICAL RESULTS

DANIEL ROBERT SWEENEY

A Dissertation submitted to the Department of Sport Management, Recreation Management, and Physical Education in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded Spring Semester, 2008

The members of the committee approved the Dissertation of Daniel Robert Sweeney defended on March 6, 2008.

______Jeffrey D. James Professor Directing Dissertation

______J. Joseph Cronin, Jr. Outside Committee Member

______R. Aubrey Kent Committee Member

______Steven McClung Committee Member

Approved:

______Cheryl Beeler, Chair, Department of Sport Management, Recreation Management, and Physical Education

The Office of Graduate Studies has verified and approved the above named committee members.

iii

To my remarkable wife Jamie –

for her infinite patience, understanding, and love. She has made significant sacrifices over the last five years to see me pursue my dreams and accomplish my goals.

iv ACKNOWLEDGEMENTS

I am especially indebted to Dr. Jeffrey James, my committee chair and program advisor, for his guidance throughout the research process as well as the last four years. His continued support through difficult times made all the difference. I also thank my committee members: Dr. Aubrey Kent, Dr. Joe Cronin, and my pinch hitter Dr. Steven McClung, who stepped in midway through the process when asked. The committee members provided valuable insight and I appreciate their time and effort. I would also like to thank Dr. Harry Kwon for his early involvement in the project. His comments and critiques during the proposal stage of this project were very helpful. My PhD colleagues, past and present, were an inspiration and a source of support to me. I also thank everyone past and present in the SMRMPE office, including Cynthia Bailey, Kerry Behnke, Harriet Kasper, and Shannon Barksdale for their help. The little things really meant a lot to me. This research would not have been possible without the cooperation of Ben Zierden, Director of Ticket Operations for the Florida State University Department of Athletics and Kirk Goodman, former General Manager of the Jacksonville Suns. I thank them for providing me access to their facilities, and more importantly to their valued customers. I would be remiss if I did not acknowledge and thank Jamie, Carly, Derek, Justin, Birgit, Masa, Young, Yuko, Katie, and Sean for giving up their time to help distribute surveys. My family was very much a part of this success. I thank my mother and father who instilled in me from a young age the value of an education. My sister Carly deserves considerable praise for unknowingly pushing me to stay one-step ahead of her;). Thanks to my gramma Eleanor, for the unshakeable confidence she has that her grandson Dani can do no wrong (except for wet towels left in a pile on the floor of her apartment of course!). I also want to thank the Metz’s and Crumley’s, my new family,

v for supporting me throughout this process as I continue to drag their daughter across the country. Finally, I wish that my grandmother Reva and grandfathers Bernie and Matthew were here to see this moment. I know they would have enjoyed it and somewhere up there, I know they are proud of me and happy for me.

vi TABLE OF CONTENTS

List of Tables...... x List of Figures...... xii Abstract...... xiii

1. INTRODUCTION...... 1 Statement of the Problem...... 2 Purposes of the Study...... 6 Research Questions...... 11 Limitations of the Research...... 11 Contributions of the Research...... 11 Definition of Terms ...... 12 Organization and Structure of the Dissertation...... 13 2. LITERATURE REVIEW...... 14 Customer Equity...... 14 Components of Customer Equity ...... 21 Value Equity ...... 22 Brand Equity...... 45 Relationship Equity...... 58 Scale Development ...... 67 Initial Data Collection...... 72 Purification of the Measure...... 72 Reliability...... 72 Validity...... 74 3. METHODOLOGY & PILOT STUDY ...... 80 Research Objectives ...... 80 Research Design...... 81 Steps 1 and 2: The Specification of the Domains of Construct and Generation of Sample Items ...... 81 Entertainment Value...... 82 Social Value ...... 83 Service Quality ...... 87 Perceived Price ...... 87 Epistemic Value...... 91

vii Satisfaction...... 91 Step 3 – First Data Collection: Pilot Study ...... 92 Introduction...... 92 Population and Sample ...... 92 Data Collection ...... 93 Instrument Development ...... 93 Step 4 – Reliability and Validity Assessment of First Data Collection ...... 94 Data Analysis ...... 94 Results ...... 96 Pilot Study Discussion...... 124 4. METHODOLOGY...... 135 Step 6 – First Data Collection of the Main Study...... 135 Target Population and Sample Design...... 135 Step 7 – Assessment of Reliability and Validity...... 137 Data Analysis Procedures ...... 137 Step 8 – Development of Norms ...... 146 5. RESULTS...... 147 6. DISCUSSION AND CONCLUSIONS ...... 184 Introduction ...... 184 Discussion of the Results...... 185 Entertainment Value...... 185 Perceived Service Quality ...... 196 Perceived Price ...... 198 Knowledge as Value...... 199 Satisfaction as and Outcome of Value...... 202 Research Implications...... 202 Limitations and Future Research ...... 206 Conclusions...... 215 APPENDIX A Letters Seeking Organizational Participation ...... 216 APPENDIX B Human Subjects Committee Approval ...... 220 APPENDIX C Florida State Seminoles Baseball Questionnaire...... 222 APPENDIX D Item Codes for Pilot Study Questionnaire...... 228 APPENDIX E Jacksonville Suns Questionnaire ...... 233 APPENDIX F Item Codes for Jacksonville Suns Questionnaire...... 238 APPENDIX G Student Sample Questionnaire...... 242

viii APPENDIX H Student Sample Questionnaire Item Codes...... 247 REFERENCES...... 251 BIOGRAPHICAL SKETCH...... 275

ix LIST OF TABLES

Table 2.01. Literature on the Antecedents of Customer Equity...... 18 Table 3.01. Dimensions and Items of Entertainment Value...... 84 Table 3.02. Dimensions and Items of Social Value...... 86 Table 3.03. Dimensions and Items of Service Quality...... 88 Table 3.04. Dimensions and Items of Perceived Price...... 90 Table 3.05. Dimensions and Items of Epistemic Value ...... 91 Table 3.06. Dimensions and Items of Satisfaction ...... 92 Table 3.07. Demographic Characteristics of the Pilot Sample ...... 97 Table 3.08. Reliability Estimates of Entertainment Value Factors...... 98 Table 3.09. Reliability Estimates of Social Value Factors...... 99 Table 3.10. Reliability Estimates of Service Quality Factors ...... 100 Table 3.11. Reliability Estimates of Perceived Price ...……………………………….…101 Table 3.12. Reliability Estimates of Epistemic Value ....………………………………..101 Table 3.13. Reliability Estimates of Satisfaction...... 101 Table 3.14. Descriptive Statistics for Entertainment Value Items...... 104 Table 3.15. Eigenvalues for Entertainment Value Factors ...... 105 Table 3.16. Rotated Pattern Matrix for Entertainment Value...... 107 Table 3.17. Factor Correlation Matrix for Entertainment Value ...... 108 Table 3.18. Descriptive Statistics for Social Value Items...... 108 Table 3.19. Eigenvalues for Social Value Factors...... 109 Table 3.20. Rotated Pattern Matrix for Social Value ...... 111 Table 3.21. Factor Correlation Matrix for Social Value...... 111 Table 3.22. Descriptive Statistics for Service Quality Items ...... 112 Table 3.23. Eigenvalues for Service Quality Factors...... 113 Table 3.24. Model Fit Results for Varying Number of Service Quality Factors...... 115 Table 3.25. Rotated Pattern Matrix for Two-Factor Model of Service Quality...... 116 Table 3.26. Rotated Pattern Matrix for Three-Factor Model of Service Quality...... 117 Table 3.27. Factor Correlation Matrix for Two-Factor Model of Service Quality...... 118

x Table 3.28. Descriptive Statistics for Perceived Price Items ...... 118 Table 3.29. Eigenvalues for Perceived Price Factors...... 119 Table 3.30. Rotated Pattern Matrix for Perceived Price ...... 121 Table 3.31. Factor Correlation Matrix for Perceived Price...... 121 Table 3.32. Descriptive Statistics for Epistemic Value Variables...... 122 Table 3.33. Eigenvalues for Epistemic Value ...... 123 Table 3.34. Factor Matrix for Epistemic Value...... 124 Table 5.01. Demographic Characteristics of the Confirmatory Sample...... 148 Table 5.02. CFA for the Value Equity Factors and Items ...... 152 Table 5.03. Fit Indices for the 16-Factor Model with 74 Indicators...... 155 Table 5.04. Factor Correlations for First Data Collection...... 156 Table 5.05. Discriminant Validity Analysis for Model AVE’s ...... 157 Table 5.06. X2 difference test for One- and Two-Factor Models of Service Quality.... 157 Table 5.07. CFA for the RESPECIFIED Value Equity Factors and Items...... 166 Table 5.08. Factor Correlations for Respecified Model ...... 168 Table 5.09. Fit Statistics for Respecified Model ...... 169 Table 5.10. Demographic Characteristics of the Validation Sample...... 174 Table 5.11. Chi Square Analysis – Relationship between length of time following consumption and manifest variable mean scores...... 175

Table 5.12. CFA Validation Sample ...... 176 Table 5.13. Factor Correlations for Third Data Collection ...... 177 Table 5.14. Fit Indices for Validation Sample of 14-Factor Model with 64 Items...... 179 Table 5.15. Fit Statistics for Validation Sample Second-Order CFA ...... 181 Table 5.16. Second-Order CFA for the Validation Sample...... 181 Table 5.17. Validation Sample Second-Order Factor Correlations...... 182 Table 5.18. Validation Sample Second-Order psi Estimates...... 182

xi LIST OF FIGURES

Figure 1.01. Conceptual Model for Sport Customer Equity ...... 7 Figure 2.01. Drivers of Customer Equity (Rust, Zeithaml & Lemon, 2000)...... 16 Figure 2.02. CUSAMS Framework (Bolton, Lemon, & Verhoef, 2004)...... 19 Figure 2.03. Behavioral and Financial Consequences of Service Quality Model (Zeithaml, Berry, & Parasuraman, 1996) ...... 27

Figure 2.04. Service Quality Model (Grönroos, 1984) ...... 29 Figure 2.05. Model of Retail Service Quality (Dabholkar, Thorpe, and Rentz, 1996) ... 32 Figure 2.06. Brady and Cronin’s (2001) Hierarchical Model of Service Quality...... 33 Figure 2.07. Framework of Value Equity in Spectator Sports ...... 45 Figure 2.08. Crawford’s (2005) integrated model of competence...... 56 Figure 2.09. Model of Customer-Based Brand Equity for Team Sport Services...... 59 Figure 2.10. Framework of Relationship Equity in Team Sport Services...... 68 Figure 2.11. Churchill’s (1979) Procedure for Developing Better Measures...... 70 Figure 3.01. Scree Plot for Entertainment Value ...... 106 Figure 3.02. Scree Plot for Social Value...... 110 Figure 3.03. Scree Plot for Service Quality ...... 114 Figure 3.04. Scree Plot for Perceived Price ...... 120 Figure 3.05. Scree Plot for Epistemic Value...... 123 Figure 3.06. Post Pilot Study Model of Value Equity ...... 134 Figure 5.01. Competing models of service quality for the X2 difference test ...... 158 Figure 5.02. Respecified Model of Value Equity...... 164

xii ABSTRACT

The current research was undertaken to propose a model of the components of customer equity in a spectator team sport setting and to identify and empirically test measures to assess one part of the model, namely: value equity. Value equity refers to the portion of a firm’s customer equity derived from customers’ perceived value or worth of that firm’s product offerings. The measurement of customer perceived value is essential in assessing current services and for the development of further ones, because customer segments may have different motives to use services and thus perceive different value in them (Pura, 2005). This study, which is a first attempt to measure Value Equity within a spectator sport context, presents a conceptualization of Value Equity derived from a combination of the frameworks proposed by Sheth, Newman, and Gross (1991), Rust, Zeithaml, and Lemon (2000), and Sweeney and Soutar (2001), and includes six dimensions: 1) entertainment value; 2) social value; 3) service quality; 4) perceived price; 5) epistemic value; and 6) Satisfaction. A pilot study involving a sample (n = 254) of consumers at a NCAA Division I baseball game was conducted to provide an initial test of the items in the measurement scales to establish preliminary validity and reliability. Descriptive statistics, internal consistency reliability, and exploratory factor analysis were utilized in the data analysis. The first-order factor structure of Value Equity comprised of five dimensions, 16 first- order latent variables, and 75 indicator variables was tested in five separate exploratory factor analyses, one for each of the dimensions of Value Equity, to explain the variance in the observed variables in terms of underlying latent factors. The results of the pilot study indicated that the data fit the model reasonably well, though room for improvement remained. Modifications to the model resulted in a first-order model of Value Equity comprised of 16 dimensions and 75 items, which was tested in the next phase of the study. The main phase of the study was comprised of two separate data collections. The first data collection involved a convenience sample (n = 376) of spectators in attendance at a ‘Double A’ minor league baseball game. Based on the analysis of the

xiii results of the pilot study, a 16-factor model was tested using all 75 items, and internal tests and confirmatory factor analysis (CFA) were performed. The results indicated the 16-factor model of Value Equity in Spectator Sports Scale did not adequately fit the data. The 16-factor model with 75 indicators needed to be modified to provide the best fit to the data based on suggestions from the tests of model estimations and fit of the internal structure. Based on Bollen’s (1989) criteria for model respecification, the model was modified accordingly. The modifications resulted in the testing of a 14-factor, 64- item model. The psychometric properties of the respecified measurement model were acceptable, as were the assessment of the global and internal fit indices. Given the favorable results, the researcher proceeded to a second data collection, which was used to validate the results of the respecified model. The second data collection of the main study comprised a sample (n = 285) of undergraduate and graduate students at a large Southeastern university. The analysis of the results of the second data collection served to confirm the revised model from the analysis of the results from the first data collection. Finally, a second order HCFA was conducted to test the relationship between the proposed higher order factors on the first order latent variables. While the results of the first-order CFA provided support for discrimination among the first-order factors, the results of the HCFA presented in this chapter indicate the predicted higher- model may not be appropriate for the current population from which the sample was drawn. The final chapter presents a discussion of the findings and reported results, as well as content discussing the implications and limitations of the current research project. Suggestions for future research are also provided.

xiv CHAPTER ONE

INTRODUCTION

“In the brutal competition for American leisure time, sports franchises have found themselves in a good old-fashioned donnybrook for share of wallet” (Ferguson, 2005, p. 5).

Sport spectatorship has become an increasingly prominent form of entertainment as well as an important part of the American economy in contemporary society. The sports business is one of the largest and fastest growing industries in the United States. A recent Plunkett Research (2007) report estimated the size of the entire U.S. sports industry to be $410 billion as of 2007. Concerning sport spectatorship, Street & Smith’s SportsBusiness Journal (2007) reported spectator spending at sporting events reached an estimated $32.06 billion last year. The growth of sporting events as a form of entertainment has led to an increase in competition among sport organizations for the consumer entertainment dollar. Sport teams must also compete with other entertainment providers, such as restaurants, movie theaters, home television viewing, and video games, for a share of peoples’ time and discretionary income. Because of the highly competitive entertainment environment, James, Kolbe, and Trail (2002), suggested teams must attract, develop, and maintain relationships with a substantial number of consumers in order to create adequate income streams. However, in an era where marketers are under increasing pressure from organizational stakeholders and shareholders to be financially accountable, they must be increasingly careful in how they use their finite resources (McDonald & Milne, 1997). Therefore, attracting, developing, and maintaining relationships with the right customers, those that are most valuable, and offer the greatest return on marketing expenditures, is paramount.

1 Statement of the Problem

Despite the mounting pressure and recognized need to effectively employ limited resources, Rust, Lemon, and Zeithaml (2004) noted that far too often, marketing executives view marketing expenditures as short-term costs rather than long-term investments and tend to rely on intuition and instinct when making strategic decisions. This type of behavior undermines an organization’s ability to attract, develop, and maintain relationships with valuable consumers and hinders the ability to receive the greatest return possible on marketing expenditures. One reason many sport organizations may rely on intuition and instinct is they have not developed, or do not have access to a unified, data-driven system from which to make broad, strategic- marketing decisions. For some sport organizations, not having a system for collecting information about existing and potential consumers may be a result of not having the resources (i.e., number of personnel or budget) needed for establishing such a system. For example, many smaller collegiate athletic programs throughout the United States, whether at the Division 1, Division 2, or the Division 3 level, are constrained by small budgets and too few personnel charged with marketing related responsibilities. For example, one metropolitan university with a small, Division 1, collegiate athletic program in the Southern United States has one individual responsible for developing, implementing, and evaluating marketing strategies and initiatives for the entire intercollegiate athletic program. Furthermore, this person’s other responsibilities include fundraising, the recruitment of new donors, the recruitment of new sponsors, as well as overseeing all season ticket sales. With such few resources, it is possible to understand why marketing strategies based on intuition and instinct can supplant those supported by concrete data. For other sport organizations, such as professional sport franchises, adequate resources may not be the sole impediment to the effective use of data-driven marketing strategy development and implementation. Rather, barriers to the development, implementation, or use of an effective system for managing consumer data may stem from the prevailing culture within the team, front office, or league. The marketing

2 strategies and activities of many minor league baseball teams serves as an example of how the prevailing culture within a league or team front office affects marketing orientation and perspective. Review of the promotional schedules for most, if not all, minor league baseball teams, for instance, illustrates that the teams rely heavily on promotions as a strategy for attracting consumers. Rust et al., (2004) would likely argue that promotions are short-term marketing investments that do not reflect a desire by teams to understand the needs and wants of existing and potential consumers, thus undermining the potential to develop meaningful relationships with those consumers. Basing strategic marketing decisions on instinct and intuition rather than consumer driven data indicates that an organization has not shifted its marketing perspective from a product orientation to one that is customer oriented. The product orientation reflects a management philosophy whereby an organization assumes that customers will purchase a product so long as they can afford it (Tuckwell, 1991). According to Kotler (2002), product oriented companies seek to gain a competitive advantage by attempting to increase the attraction of their product through the addition of extra features, or the use of modern technology, while neglecting to specify consumer’s needs and wants and a manner to serve these specific needs and wants better than the competition. In the context of the marketing of spectator sport, many minor league baseball teams, for example, focus on the provision of new facilities, program schedules, and promotions in their efforts to attract consumers. In other words, they have focused on the physical components of their service offering, and not necessarily on the needs and wants of the consumers who use the service. In addition to not considering consumer needs, a critical weakness of the “if you build it, they will come” approach is that many other service providers are able to provide the same, if not better physical service components. A product-focused approach to marketing prevents organizations from gaining a true understanding of the needs and wants of its existing and potential consumers, which limits their ability to develop meaningful relationships with these consumers. Rust, Ambler, Carpenter, Kumar, and Srivastava (2004) lamented that a product focused approach to marketing serves to undermine marketing’s credibility, threatens its

3 standing in the organization, and even threatens its existence as a distinct capability within an organization. A new paradigm that has emerged in the general marketing literature to help guide managers to build strong and profitable relationships with consumers is customer equity. Recognizing the value potential of current and future customers, Blattberg and Deighton (1996) were the first to coin the term ‘customer equity’, and over the past decade, marketing academicians and practitioners have begun to alter their perspectives on marketing from the product-focused concept of brand equity to the more consumer-oriented concept of customer equity. According to Hogan, Lemon, and Rust (2002), the adoption of a customer-centered orientation has been spurred by several major marketplace transformations: mounting pressure on managers to be more accountable to financial stakeholders as a result of increased marketplace competition; greater access to vast amounts of detailed customer information; and the emergence of sophisticated technologies, which has raised consumers’ levels of expectations regarding the possibilities of individual level marketing efforts by the organization. A result of the organizational pressures and marketplace changes is that organizations have had to adapt to, as well as develop and implement alternative strategies leading to sustainable profits. A customer equity orientated marketing approach necessitates that marketing expenditures be viewed as investments in customer assets that lead to long-term value for the firm (Hogan at al., 2002). In viewing customers as assets, firm’s are able to identify the most appropriate marketing actions to acquire, maintain and enhance customer assets and thereby maximize financial returns (Berger, Bolton, Bowman, Briggs, Kumar, Parasuraman, & Terry, 2002). As the customer is viewed as a financial asset, Blattberg and Deighton (1996) suggested that a firm’s customer equity should therefore be managed like any other financial asset. According to Blattberg, Getz, and Thomas (2001), the effective management of the asset value of customer relationships requires a total marketing system based on integrative business strategies. In other words, the authors noted that organizations must develop and implement strategies that concurrently manage products as well as customers throughout the customer lifecycle and “reframe brand and product strategies

4 within the context of their effect on customer equity” (p. 3). The proposed benefits associated with the transition to a customer equity marketing system include: an increased ability to make informed decisions regarding marketing investments in relation to acquisition, retention and add-on selling; the capability to adjust the level or depth of investment level in each of these activities as needed; a maximization of profitability through the organization of processes and structures around acquisition, retention, and add-on selling; and the utilization of customer interactions to reinforce relationships and acquire new customers. Blattberg and Deighton (1996) argued that one of the primary benefits of adopting a customer equity approach to marketing is that it enables firms to compute the asset value of customers so that informed decisions can be made regarding marketing spending on customer acquisition, retention, and add-on selling, thus maximizing the profitability of each over the course of their customer lifecycle. This calculation enables competing marketing strategy options to be traded off on the basis of projected financial return. Viewed from this perspective, the customer equity paradigm may be defined as a “management approach for acquisition and retention, geared to individual lifetime values of current and future customers with the aim of continuously increasing customer equity” (Bayon, Gutsche, & Bauer, 2002, p. 214). Many sport organizations continue to subscribe to a product-oriented approach to marketing, and do not base strategic marketing decisions on hard consumer data. As a result, the field of sport marketing is in need of an integrated framework that enables sport marketing academicians and practitioners to amalgamate existing and future knowledge about consumers into a framework permitting organizations to examine and identify the most profitable segments of one’s consumers, as well as the ones that are most likely to have an impact on the bottom line thus adding value to the organization and its shareholders. It is this perspective that guides the purpose of the current research project.

5 Purposes of the Study

The identification of appropriate marketing actions is important for the enhancement of customer equity. However, the first step towards the development of strategies aimed at growing customer equity is for researchers and practitioners to gain an understanding of, and to identify the specific components of customer equity for a given industry, organization, and market. In doing so, firms will be able to effectively tailor their marketing actions to the orientations of the consumers they seek to acquire, retain, and up-sell. An understanding of how to grow and manage customer equity is crucial to an organization’s long-term success (Peppers & Rogers, 2004). As a part of their proposed customer equity marketing management system, Bayon et al. (2002) posited that the management of customer equity be viewed as a process involving analysis, planning, implementation, and control. Analysis, which is the first step in the process, involves the determination of the industry specific components of customer equity. Analyzing customer equity and its actionable components enables firms to identify the strategic initiatives that will have the greatest impact on the long-term profitability of its customer base, and provides an overall framework for effectively focusing strategic resources (Rust, Zeithaml, & Lemon, 2000). An understanding of the important components of the most profitable segments of one’s consumer base, as well as a potential consumer base, will enable a firm to focus its resources on attracting and managing its most important consumer segments. The purposes of this study are to propose a model of the components of customer equity and to identify and empirically test measures to assess selected elements within the model for spectator team sport. As a theoretical framework, customer equity is applicable in a wide variety of market contexts and industries as it provides direction for firms to become customer oriented (Rust et al., 2004). The conceptual framework for assessing customer equity in spectator team sport is shown in Figure 1.01. The proposed model was created to introduce the concept of customer equity in spectator team sport, as well as to begin to develop an understanding of its components and outcomes as applied to this setting.

Figure 1.01. Conceptual Model for Sport Customer Equity

7 The current research project consists of the identification of the components of customer equity in a spectator team sport setting and testing one part of that framework - value equity. The ensuing section will review the concept of consumer value. It is proposed in this research that sport teams must develop a customer-oriented approach to marketing in order to attract and retain new and existing customers. One of the ways for a sport team to become more customer-oriented is to acquire an understanding of what existing customers perceive to be of value in the consumption experience.

Consumer Value

Consumer value is defined as “the ratio of benefits to the sacrifice necessary to obtain those benefits” (Naumann, 1995, p. 102). Consumer value is perceptual as it is customers who define and determine the benefits and sacrifices associated with a service or good. Woodruff (1997) defined customer value as “a customer’s perceived preference for and evaluation of those product attributes, attribute performance, and consequences arising from use that facilitate (or block) achieving the customer’s goals and purposes in use situations” (p. 142). Based on these definitions of customer value, value equity can be defined as “a consumer’s objective assessment of the utility of a brand, based on perceptions of what is given up for what is received” (Rust et al., 2000, p. 68). Bolton and Drew (1991) suggested that perceived service value involves a trade-off between a customer’s evaluation of the benefits of consuming a service and its cost. Because these assessments of value are dependent upon sacrifice and the customer’s frame of reference, differences in customers’ assessments of service value will be due to differences in monetary and non-monetary costs, customer tastes and customer characteristics (Bolton & Drew, 1991). Several models of customer value have been proposed in the marketing literature (Sheth, Newman, & Gross; 1991; Sweeney & Soutar, 2001). Sheth et al. (1991) developed a theory explaining the basic values of consumption that guide consumers when they make choices. Based on conceptual frameworks from a diverse set of literature, the authors identified five consumption values thought to influence consumer choice behavior, namely: 1) functional value, 2) social value, 3) emotional

8 value, 4) epistemic value, and 5) conditional value. Functional value is related to economic utility, which indicates the benefits associated with possessing the service as in economic person theory, and underlies the performance of the object in terms of a series of salient attributes including price, reliability and durability. Interestingly, in several studies, these cues have been identified as determining quality (Peterson & Wilson, 1985; Parasuraman, Zeithaml, & Berry, 1991). Social value refers to the utility derived from customers' associations with certain social groups. The association that a customer has with members of important reference groups is thought to significantly influence his or her evaluation of provided services (Park & Lessig, 1977). Emotional value represents the capacity of a service to arouse feelings or affective states, and is measured in terms of a set of feelings toward its object. Epistemic value is the capacity of a service to provide novelty or satisfy a desire for knowledge. Schiffman and Kanuk (1987) suggested that consumer behavior is normally driven by the epistemic value of a product with novel, curious, complicated or unique factors. Conditional value is described as the set of situations faced by a customer when making a decision. In other words, a consumer’s choice is contingent on the presented set of circumstances or situation. Levitt (1980) argued that a product represents "a complex cluster of value satisfactions" to buyers, who attach value to the product according to its perceived ability to meet their needs. Fundamental to Sheth et al.’s (1991) proposed theory of marketplace behavior were the notions that each of the identified values make differential contributions in any given choice situation and that the values are independent of one another. Differential contributions referred to the notion that in a given choice situation, different values will contribute more or less than other values. Independence among values referred to the idea that each of the five values are independent from one another, meaning that a change in a consumer’s perception of one of the values will not affect his or her perception of any of the others. Using their framework as a foundation, Sweeney and Soutar (2001) extended Sheth et al.’s (1991) value constructs and developed a scale consisting of four distinct value dimensions in order to assess consumer perceived value of durable goods at the brand level. The four value dimensions identified by Sweeney and Soutar (2001) were

9 emotional value, social value, quality and price. The authors cited Sheth et al.’s (1991) conceptualization of functional value as the reason for the need for a new framework. They argued that the inclusion of attributes such as reliability, durability, and price to measure functional value was ill conceived. They noted that the reliability and durability are aspects of quality, and that in many value models quality and price are held to have separate influences on perceived value. Furthermore, they argued that price and quality are subfactors that contribute separately to perceived value and thus should be measured separately. Additionally, Sweeney and Soutar (2001) did not include epistemic value and conditional value in their framework. Epistemic value was not included because a product’s ability to arouse curiosity, offer novelty, or satisfy a desire for knowledge was thought to be less important to consumers in the consumption of durable goods. Conditional value was not included in the framework because of its nature of being a temporary and specific case of functional and social value. The current study presents a conceptualization of value equity that is derived from a combination of the frameworks presented by Sheth et al. (1991) and Sweeney and Soutar (2001), and includes six dimensions, namely: (1) entertainment value; (2) social value; (3) service quality; (4) price; (5) epistemic value; and (6) satisfaction. The dimension emotional value is renamed in the current study as entertainment value, given that the pleasure derived from the event is likely a function of the entertaining aspects of the event. Epistemic value is included in the current conceptualization of value equity as recent research on the motivations of sport consumers to attend sporting events supports the idea that people are motivated to attend sporting events because of the novelty of the event as well as the knowledge that can be gained from consumption (James, Kolbe, & Trail, 2002; Trail & James, 2001). This suggests that consumers place a certain amount of value on these dimensions in the sport service consumption experience. These six dimensions are selected because they are deemed appropriate in a spectator sport service context. A detailed review and explanation of each of the dimensions of Value Equity is presented in Chapter 2.

10 Research Questions

Following from the statement of the problem, several research questions were posed to guide the formulation of a tool to assess the components of value equity in team sports: 1. Are the evaluations of the examined components of value equity for spectator team sport reliable within their respective factors? 2. How well does the hypothesized measurement model involving first-, and second-order factors fit the observed data?

Limitations of the Research

As with any research study, limitations must be acknowledged: 1. The predictive validity of self-reported purchase behavior is not always high (Morwitz, Steckel, & Gupta, 1997). Bickart (1993) reported that reliance on self- reports may lead to overestimation of correlations between marketing activities, perceptions of these activities, and behavior due to common-method variance problems. 2. Not all of the proposed value equity dimensions can be universally applied to all spectator sport situations. Dimensions are likely to change over time and according to context, which means they are likely to rank differently among different consumers and different consumer experiences. 3. It is likely that the proposed components of value equity do not represent an exhaustive list, since a number of other unexplored variables could potentially act as indirect antecedents of value equity in a spectator sport setting.

Contributions of the Research

The ultimate goal of this stream of research is the refinement of the framework so that individual sport organizations can use it to analyze their own customer equity and the specific antecedents that drive that equity. The drivers of customer equity have been shown to positively relate to customer capital as the drivers represent the potential

11 of a firm to increase its customer equity. Towards this effort, the current study makes significant contributions to sport marketing theory and practice. First, the development of a reliable and valid instrument to measure the indirect drivers of customer equity is a critical first step towards the identification of spectator sport specific drivers of customer equity. This will enable sport organizations to develop effective, efficient, and measurable marketing strategies that maximize customer value and increase organizational stakeholder value. Additionally, the reexamination of existing conceptualizations of various constructs in the sport literature (such as brand equity) through the identification and empirical testing of measures adopted from outside literature streams, provides an opportunity to expand the scope of our understanding of the factors likely to affect consumer intentions and behavioral outcomes on a spectator sport setting.

Definition of Terms

Brand Equity: “The customer equity gained from the subjective and intangible appraisal of the brand beyond its objectively perceived value” (Rust, Lemon, & Zeithaml, 2000). Consumer Value: “A customer’s perceived preference for and evaluation of those product attributes, attribute performance, and consequences arising from use that facilitate (or block) achieving the customer’s goals and purposes in use situations” (Woodruff, 1997, p. 142). Customer Lifetime Value: “The present value of the future cash flows attributed to the customer relationship” (Pfeiffer, Haskins, & Conroy, 2005, p.17). Customer Equity: “The total of the discounted lifetime values summed over all of the firm’s customers” (Rust, Lemon, & Zeithaml, 2004, p.110). Customer Equity Management: “Management approach for acquisition and retention, geared to individual lifetime values of current and future customers with the aim of continuously increasing customer equity” (Bayon, Gutsche, & Bauer, 2002, p. 214).

12 Relationship Strength: “The customer’s tendency to stick with a product above and beyond the objective and subjective assessments of the product” (Rust et al., 2001, p. 95). Service Quality: A customer’s perceptions of the quality of at least one of the following situations: (1) the interactions with the organization; (2) perceptions of the quality of the physical environment in which the service is consumed and produced; and (3) the perceptions of the quality of the outcome (Brady & Cronin, 2001). Social Value: The objective assessment of the utility of the social interactions derived from consumption of the sport product. Validity: “The extent to which an empirical measure adequately reflects the real meaning of the concept under consideration” (Babbie, 2004, p. 143).

Organization and Structure of the Dissertation

This dissertation is presented in six chapters. Chapter two begins with a review of the literature on customer equity and the differing perspectives on the concept. This chapter also presents an overview of the various components of customer equity, including: 1) value equity, 2) brand equity, and 3) relationship equity. Chapter three presents the methodology and results of a pilot study designed to explore the constructs examined in the study. Chapter four presents a detailed discussion and explanation of methods of inquiry for the main study. Chapter five presents the results of the main study. Finally, Chapter six offers a discussion of the results and recommendations for future research.

13 CHAPTER TWO

LITERATURE REVIEW

Chapter two presents a review of the pertinent research examining customer equity, along with the literature related to the components of customer equity. First, a review of the customer equity literature is provided to explain the paradigm in detail and to provide justification for its application in a team sport setting. Next, the relevant components of customer equity are reviewed. These components include value equity, brand equity, and relationship equity. Finally, a review of the literature pertaining to scale development is included in this chapter. The main objective of the literature review is to provide a background and justification for the application of a customer equity marketing orientation in the context of the consumption of professional team sports. Customer Equity

Geared towards growing the long-term value of the firm, customer equity is a competitive marketing strategy referring to the value of the resources that customers invest in particular organizations (Dorsch & Carlson, 1996). Rust et al., (2004) defined customer equity as “the total of the discounted lifetime values summed over all of the firm’s current and potential customers” (p. 110). From this perspective, customer equity is viewed as a critical asset of the organization, as customer-generated revenues and the investments made to generate those revenues are the basis of a firm’s cash flows (Hansotia, 2004), and represent an important metric of corporate value (Bauer, Hammerschmidt, & Braehler, 2003). As such, the ability of an organization to acquire and retain customers of value is crucial for its success in today’s competitive marketplace. As a theoretical framework, customer equity is applicable in a wide variety of market contexts and industries as it provides direction for firms to become customer oriented (Rust et al., 2004). Firms adopting a customer equity approach to marketing

14 recognize that customer equity is a “management approach for acquisition and retention, geared to individual lifetime values of current and future customers with the aim of continuously increasing customer equity” (Bayon, et al., 2002, p. 214). Customer equity provides a strategic perspective for management decisions by providing managers with a basis for identifying those customers or customer types they wish to seek out, retain, avoid, or even abandon (Dorsch, Carlson, Raymond, & Ranson, 2001). A key component of all customer equity-marketing initiatives is a determination of the specific antecedents, or drivers, of customer equity. Recognizing that marketing budgets are finite and that not all efforts to improve the drivers of customer equity are profitable, Rust, et al. (2004) suggested that it is imperative for organizations to be able to differentiate between driver improvement strategies that are profitable and those that are not. Despite the recognition by researchers of the need to identify industry specific drivers of customer equity, there has been only a limited amount of the research directed towards this accomplishment. It is only very recently that researchers have sought to identify the specific drivers of customer equity. A summary of this research is presented in Table 2.01. A review of each of these studies will ensue. Rust, Zeithaml, and Lemon (2000) introduced a strategic framework revealing the key drivers thought to increase a firm’s customer equity. Three key drivers of a firm’s customer equity, namely: value equity, brand equity, and retention equity were proposed (see Figure 2.01). Retention equity was later re-conceptualized as relationship equity, given that the degree to which an organization is able to retain its customers is a reflection of the strength of the relationship that it has with those customers (Rust et al., 2004). Rust et al. (2000) defined value equity as the customer’s objective assessment of the utility of a brand. This definition of value equity places emphasis on the rational and objective assessments of a firm’s offerings. Improvements in quality, price, and convenience were theorized to be the most useful methods for driving the value equity of a firm. Rust et al. (2000) characterized brand equity as a customer’s subjective assessment of the brand and was proposed to be shaped by the firm’s marketing strategy and tactics and influenced by the customer through life experiences with the brand. Finally, Rust et al. (2000) described retention equity as the propensity of an individual to continue to patronize or consume a given

Figure 2.01. Drivers of Customer Equity (Rust, Zeithaml & Lemon, 2000)

brand. The focus of retention equity is on that part of the relationship between the customer and the firm that is based on the actions taken by the firm and the customer to establish, build, and maintain a relationship. Building upon existing models (Blattberg & Deigthon, 1996; Rust et al., 2000), Bayon, et al. (2002) presented a detailed process framework for understanding customer equity. They proposed a customer equity marketing system comprised of four primary, sequential stages each containing several specific actions. The four stages

16 included: 1) analysis; 2) planning; 3) implementation; and 4) control. A key component of the analysis stage involved the recognition that a determination of the industry- specific direct and indirect drivers of customer equity is an important first activity in which firms must engage. Only then is it possible for firms to model and predict the value of specific consumer segments. Although the authors did not specifically attempt to identify precisely what the indirect drivers of customer equity are, their work is significant because it recognized that an identification of industry specific indirect variables is a crucial component towards the eventual calculation of a firm’s customer equity. Dias, Pilhens, and Ricci (2002) introduced the concept and benefits of fusion analysis in understanding the drivers of customer value, and its relevance to customer profitability and shareholder value. Fusion analysis was described as “the analysis of the influence of macroenvironmental variables, such as brand and market drivers, at the microbehavioural level” (Dias et al., 2002, p. 271). The researchers specifically tested the differential impact of various brand drivers, such as pricing and advertising, on various customer segments. Three particular insights were reported from the findings, namely: 1) customer segments were found to differ on their current and potential value to the brands within a category; 2) the contribution of different marketing variables have varying impacts on different consumer segments; and 3) brand loyal customers are les less motivated by promotions, but are influenced by advertising; and brand switchers were found to be motivated purely by promotions. The results of this study are significant because they demonstrate that knowing accurately how sales respond to demand drivers at the customer level informs planning and decision making at the marketing strategy development stage. Bolton et al. (2004) proposed an integrated framework, called Customer Asset Management of Services (CUSAMS) to understand and influence the value of customer assets and to understand the influence of marketing instruments on those assets (see Figure 2.02). The authors proposed that the foundation of the CUSAMS framework is the specification of key customer behaviors that reflect the length, depth, and breadth of the customer-service organization relationship: duration, usage, and cross- buying.

17 Table 2.01. Literature on the Antecedents of Customer Equity

Reference Key Focus Lemon, Rust, and Presented a strategic framework revealing the key Zeithaml (2001); Rust, drivers thought to increase a firm’s customer equity. Zeithaml, and Lemon, Three key drivers of a firm’s customer equity, namely: (2000); Rust, Zeithaml, value equity, brand equity and retention equity were and Lemon (2004); proposed. Rust, Zeithaml, and Narayandas (2004). Bayon, Gutsche, and Overview of customer equity as a process in marketing. Bauer (2002) Discussed management process, modeling, and segmentation. Dias, Pilhens, and Examined the impact of marketing activities on customer Ricci (2002) profitability and shareholder value.

Bolton, Lemon, and Proposed an integrated framework, called CUSAMS Verhoef (2004) (customer asset management of services) to understand and influence the value of customer assets.

Chang and Tseng An exploration of the mediating roles of the drivers of (2005) customer equity in the effect of relationship marketing activities on customer per capita revenue.

Voorhees (2006) Tested the efficacy of the customer equity drivers in predicting actual customer behaviors.

18 The framework was intended to be a starting point for a set of propositions regarding how marketing instruments influence customer behavior within the relationship, thereby influencing the value of the customer asset.

Figure 2.02. CUSAMS Framework (Bolton, Lemon, & Verhoef, 2004)

Rust et al. (2004) presented a unified strategic framework enabling competing marketing strategy options to be traded off on the basis of projected financial return, and operationalized as the change in a firm’s customer equity relative to the incremental expenditure necessary to produce the change. The authors illustrated their approach with data collected from customers of five industries. The authors identified what they termed three strategic investment categories, namely: 1) perceived value, 2) brand equity, and 3) relationship management. The three categories were selected because they were identified as spanning all major marketing expenditures (Rust, Zeithaml, & Lemon 2000).

19 Chang and Tseng (2005) provided a theoretic framework of the development of customer capital and empirically verified that framework using data collected from Taiwanese organizations. The authors specifically explored the mediating roles of the drivers of customer equity in the effect of relationship marketing activities on customer per capita revenue. Relationship marketing activities were measured using Gruen, Summers, and Acito’s (2000) five dimensions of relationship marketing. The dimensions included: core service performance, recognition for contributions, membership interdependence enhancement, dissemination of organizational knowledge, and reliance on external membership requirements. The authors employed the drivers of the customer equity framework proposed by Lemon et al. (2001) to measure the mediating role of customer equity. Value equity was measured with perceived reasonableness of product price, perceived product quality, and convenience. Brand equity was measured using brand awareness, brand attitude, and ethical image of the firm. Finally, relationship equity was measured by asking respondents to express their attitudinal and behavioral loyalty, their identification with the mission of the firm, their participations of brand-community activities, their knowledge sharing with other members, and their special treatments and recognition received from the firm. The variables were measured using five point Likert scales, with eight, nine, and twelve items for value, brand, and relationship equity drivers respectively. The results suggested that all of the marketing relationship activities except for reliance on external membership requirements were found to have significant influences on relationship equity. Relationship equity in turn was found to affect customer capital. This study is particularly poignant because it is the first to develop measures and empirically test the model proposed by Lemon et al. (2001). Recently, Voorhees (2006) conducted a study in which he tested the efficacy of the customer equity drivers in predicting customer behaviors using a bi-criterion clusterwise logistic regression application. Voorhees (2006) also utilized Lemon et al.’s (2001) model of the customer equity drivers to operationalize customer equity. The author proposed a model examining the effects of six drivers of value equity (service quality, physical goods quality, convenience, satisfaction, price, and value), six drivers of brand equity (brand awareness, attitude toward the provider, service provider image,

20 corporate citizenship behaviors, corporate ethics, and brand equity), and six drivers of retention equity (trust, enduring commitment, affective commitment, switching costs, preferential treatment, and quality of the loyalty programs). While researchers have begun to empirically investigate the antecedents of customer equity, to date, there have not been any studies that have identified or empirically measured specific drivers of customer equity in the managed team sport industry. Using the main driver categories of customer equity proposed by Rust et al. (2004) and by Rust, Zeithaml, and Lemon (2000, 2001), the specific focus of this study is to identify and test the specific actionable drivers of customer equity in a sport team context. A conceptualization of the drivers of customer equity is presented next.

Components of Customer Equity

Chang and Tseng (2005) defined drivers of customer equity as “the state of a firm that enables a firm to increase its customer equity” (p. 259). According to Bayon et al. (2002) the drivers of a firm’s customer equity can either be direct or indirect. Direct drivers refer to the variables that are included directly in the calculation of customer equity. According to Bayon et al. (2002) specific direct drivers of customer equity include the following variables: volume of base transaction, repeat purchase frequency, extent of cross-buying, value of word of mouth activities, costs for acquisition mail shots, and complaint management. Essentially, the direct drivers of customer equity are those variables that lead directly to a firm’s customer generated revenues. The indirect drivers were proposed to influence customer equity as a result of their effect on the direct drivers (Bayon et al., 2002). Rust et al.’s (2000) framework of customer equity identifying value equity, brand equity, and relationship equity are examples of indirect drivers according to Bayon et al. (2002). Within each of these drivers are actionable factors that sport-marketing managers can manipulate, in order to increase overall customer equity. An elaboration of the specific indirect drivers of customer equity will ensue.

21 Value Equity Value in consumer research. There are two general ways in which the value concept is conceived of in the marketing literature – the value of a customer to an organization and a customer’s perceived value of the benefits derived from a company’s service offerings. The former concept is a measure of a customer’s value to a service provider. As discussed above, this conceptualization of value is commonly measured using the customer lifetime value (CLV) concept and is a measure of that service provider’s customer equity when all of the individual customers’ lifetime values are summed. In the latter case, a customer’s perceived value refers to the customer’s perceived worth of a service offering. The relationship between the two concepts may be thought of as being synergistic in that the more value customers attach to a firm’s products the more likely they are to purchase said products thus increasing their value to the firm (Ambler, 2000). In other words, the ability of an organization to deliver superior value to its customers is thought to be one of the most successful mechanisms for establishing service differentiation as well as for maintaining a competitive advantage in a crowded marketplace (Ravald & Grönroos, 1994). Pura (2005) noted the measurement of customer perceived value is essential in assessing current services and for the development of further ones, because customer segments may have different motives to use services and thus perceive different value in them. From this perspective, the value equity concept can be conceived of as the portion of a firm’s customer equity derived from customers’ perceived value or worth of that firm’s product offerings. Value equity constructs. Theories of consumption values and concepts like utility and value creation are well-established concepts in the marketing literature, and depict the factors influencing purchase decisions and future use of products and services. Following is a review of the components of value equity. Entertainment Value Sweeney and Soutar’s (2001) model of consumer value identified emotional value as a factor of consumer value. The authors defined emotional value as “the utility derived from the feelings or affective states that a product generates” (p. 211). In defining emotional value as the enjoyment derived from a product or service, it is

22 possible to view emotional value in terms of the hedonic benefits consumers seek from the consumption experience. The Costello (2000) defined hedonism as “the pursuit of or devotion to pleasure, especially to the pleasures of the senses; the ethical doctrine holding that only what is pleasant or has pleasant consequences is intrinsically good; the doctrine holding that behavior is motivated by the desire for pleasure and the avoidance of pain” (p. 629). Hirschman and Holbrook (1982) defined hedonism in relation to the consumption behaviors of consumers as “those facets of consumer behavior that relate to the multi- sensory, fantasy, and emotive aspects of one’s experiences with products” (p. 92). Spangenberg, Voss, and Crowley (1997) refined this definition of hedonic consumption through additional construct and scale development. They defined the hedonic dimension as stimulated “internal imagery and emotional arousal based on externally sensed, product related stimuli” (p. 235). Recently, Voss, Spangenberg, and Grohmann (2003) developed a scale to measure the conceptualization of attitudes. They defined hedonic consumption as resulting from “sensations derived from functions performed by products” (p.310). Recognizing that consumer behavior is influenced by both hedonic and utilitarian factors, germinal conceptualizations of hedonism in the marketing literature developed out of a recognized need to view consumption outside the scope of rational choice (Dhar & Wertenbroch, 2000). This acknowledgement enabled researchers to consider the impact of consumption factors such as perceptual pleasures, leisure pursuits, and emotive responses (Holbrook & Hirschman, 1982). Babin, Darden, and Griffin (1994) conducted a study to develop a scale to measure both the hedonic and utilitarian values obtained from the consumption experience of shopping. The results demonstrated that distinct hedonic and utilitarian shopping value dimensions exist and are related to a number of consumption variables. Fiore, Jin, and Kim (2005) examined the hedonic value of stimulating experiences and emotional pleasure and arousal in an on-line apparel store setting. The authors also examined the role that hedonic value played in the approach responses toward the online store. The results indicated that emotion is a key link in the consumption

23 experience approach to consumer behavior. The path-analysis model revealed significant paths between hedonic value and resulting emotional pleasure and arousal variables. A pattern of significant paths was also found between these three variables and global attitude, willingness to purchase, and willingness to patronize the on-line store. The results demonstrated that hedonic value from the consumption experience is a contributor to approach responses toward the on-line store that have a direct impact on viability of the firm. In spectator sport, the product is comprised of the core and extended components of the service experience that involve the event (Mullin, Hardy, & Sutton, 2000). The fact that the consumption of sport has hedonic aspects indicates that for many people sport spectatorship is a form of entertainment whose value is derived from the enjoyment associated with the event. Sloan (1989) speculated that part of the attraction to the sport product is its entertainment value. As such, a spectator’s emotional value may be defined as the utility he or she derives from the feelings or affective states that the sport product generates. The elements that motivate people to attend sporting events involve the activities that produce the entertainment value for which individuals are willing to pay (Westerbeek & Shilbury, 2003). Hightower, Brady, and Baker (2002) suggested that the servicescape has become a focal point in the delivery of pleasure, as organizations compete against an ever-expanding competitor base. The authors further suggested that sport teams have revised their missions from providing a quality product on the field of play to the embrace of a more widespread entertainment objective. As evidence, sport marketers have attempted to enhance the sports experience and provide entertainment through a variety of activities such as pre-game, half-time, and post game shows, providing tailgating opportunities, as well as through game day promotions and giveaways. To date, researchers have only just begun to develop an understanding of what particular hedonistic aspects of the sport event spectators find entertaining, or pleasurable. A look to the work of Trail and James (2001) and Funk, Mahony, and Ridinger (2002) indicates that sport may provide hedonistic value as a result of its aesthetic appeal, the physical skill of the athletes, the drama that is inherent in its

24 simultaneous production and consumption, or the escape one experiences from attending the event. James, Sun, and Lukkarinen (2004) conducted a study of sport consumers to ascertain what they find enjoyable about attending a sport event. The results indicated that consumers find the following aspects of the event to be entertaining: the amusement derived from attending the event, the excitement of being in a crowd, the opportunity to party, game immersion, the escape one is afforded from everyday life, the aesthetic appeal, and the drama inherent in the experience. As a result, it may be postulated that the perceptions of the entertainment value consumers derive from a sporting event are likely to affect the value equity of an organization and thus indirectly impact consumption behavior and ultimately the customer equity of the team. Simply stated, the more enjoyment that people derive from attending sporting events, the more likely they are to continue to purchase tickets and merchandise, and to speak favorably of the experience to others, thus increasing their value to the organization. Social Value Social values are abstract beliefs about behaviors or end states of existence that transcend specific situations and guide the selection or evaluation of events (Rokeach, 1973). Kahle and Kennedy (1989) suggested that by linking a specific good, service or idea to an abstract value, the ease with which specific items can be stored and remembered should increase. Sheth et al. (1991) defined social value as “the perceived utility acquired by an alternative as a result of its association with one or more specific social groups” (p. 19). Social value results from a psychological connection with a positively or negatively stereotyped demographic, socioeconomic, and cultural- ethnic groups and products that are consumed in public are attributed to such value (Sheth et al., 1991). Social Value in Spectator Sport Part of the social value derived from spectator sport is that it acts as an important symbolic sociological vehicle through which people seek to connect with other people in order to satisfy innate needs for community belonging. Individuals attach to spectator sport to overcome feelings of loneliness and disconnectedness in a society where consumerism and individualism are emphasized and valued. The social value derived

25 from sport spectating may be thought of as a function of one’s objective assessment of the utility of the social interactions derived from consumption of the sport product. A sport organization’s ability to provide equitable social value to consumers will be dependent on whether its product offering corresponds to what the consumer expects and perceives social value to be. Melnick (1993) suggested people attend sporting events to satisfy deep-rooted needs for sociability, needs that are often otherwise left unfulfilled in contemporary society. Sport spectating, it was proposed, also allows individuals to develop a close identification with a group of other fans and promotes a sense of camaraderie (Duncan, 1983). An examination of the extant literature investigating the motives of sport consumption (Funk, Mahony, & Ridinger, 2002; Funk, Ridinger, & Moorman, 2003; James & Ross, 2004; Mehus, 2005; Trail, Fink, & Anderson, 2003; Trail & James, 2001; Wann, 1995) provides an indication of which groups of other fans individuals wish to develop connections with, and subsequently which groups of other fans spectators might determine to be of value. Specifically, the literature suggests that valuable social interactions at sporting events are likely to include 1) family members (Funk et al., 2002; Funk et al., 2003; James & Ross, 2004; Mehus, 2005; Trail & James, 2001), 2) friends (Funk et al., 2002; Funk et al., 2003; James & Ross, 2004; McDonald, Milne, & Hong, 2002; Mehus, 2005; Trail, Fink, & Anderson, 2003; Trail & James, 2001, Wann, 1995), 3) non-acquaintances (Funk et al., 2002; Funk et al., 2003; Robinson, Trail, & Kwon, 2004; Trail & James, 2001; Trail et al., 2003; Wann, 1995) and 4) business associates/clients (James, Kolbe, & Trail, 2002). Sheth et al. (1991) further commented that consumers’ decisions to purchase or not to purchase are influenced by social value in that consumers perceive various product classes as either congruent or incongruent with the norms of the reference group to which they belong or aspire. Thus, the preceding drivers of social value can influence product type and brand choices. Service Quality Service quality is considered to be a critical factor impacting the success of service companies. It is a critical measure of efficiency and effectiveness in organizational performance and has become an important topic to scholars and

26 practitioners in the service marketing area (Jensen & Markland, 1996). The management of service quality is thought to be one of the major methods by which a service firm can differentiate itself from its competition in order to gain a competitive advantage in the marketplace (McDougall, Kotler, & Armstrong, 1992). As such, it is thought that firms providing a higher level of service quality will gain long-term profitability as a result of higher market share and higher return on investment (Ghobadian, Speller, & Jones, 1994). Service quality researchers have found a positive link between consumer perceptions of a company’s service quality and various consumer related outcomes, including: behavioral intentions (Bitner, 1990; Bolton & Drew, 1991; Cronin & Taylor, 1994) and actual consumer behaviors (Anderson, Fornell, & Lehman, 1994; Rust, Zahorik, & Keiningham, 1995; Zeithaml, Berry, & Parasuraman, 1996) such as consumer spending, cross-buying, up-buying, and word of mouth activities. Favorable perceptions of service quality have also been positively linked to organizational outcome measures such as firm profitability (Fornell, 1992). Zeithaml et al.’s (1996) comprehensive study on the behavioral consequences of service quality highlights the link between service quality, behavioral intentions, behaviors, and organizational outcomes. The authors demonstrated that behavioral intentions are intervening variables between service quality and organizational financial performance measures. Figure 2.03 depicts Zeithaml et al.’s (1996) model.

Figure 2.03. Behavioral and Financial Consequences of Service Quality Model (Zeithaml, Berry, & Parasuraman, 1996)

27 The service quality literature lends support for its conceptualization as a component of a firm’s customer equity. Specifically, researchers (Anderson et al., 1994; Fornell, 1992; Rust, et al., 1995; Zeithaml et al., 1996) have shown that consumer perceptions of service quality ultimately affect the value that those consumers bring to an organization. Given this link, a review of the literature regarding service quality is conducted in the section that follows. The review will be organized in the following manner: 1) conceptualization of service quality; 2) service quality models; and 3) research on service quality in the sport literature. Conceptualization of Service Quality There is a lack of consensus in the literature about the conceptualization of service quality. The majority of definitions of quality in the contemporary marketing literature focus on the consumer’s perception of service excellence (Bitner & Hubbert, 1994; Grönroos, 1984; Parasuraman, Zeithaml, & Berry, 1985). For example, Bitner and Hubbert (1994) defined service quality as “the consumer’s overall impression of the relative inferiority/superiority of the organization and its services” (p. 77). However, this definition is in stark contrast to the traditional approach towards defining service quality. Many scholars have viewed perceived service quality as a comparison of consumer expectations with actual service performance (Grönroos, 1984; Parasuraman et al., 1985; Zeithaml, Parasuraman, & Berry, 1990), or the degree and direction resulting from comparing consumer expectations with actual perceptions of service performance (Parasuraman, Zeithaml, & Berry, 1988). These definitions of service quality are based on the disconfirmation paradigm, which is operationalized as the comparison between consumers’ expectations and the perceived performance of the service. Research associated with the disconfirmation paradigm is largely based on product satisfaction. The function of comparison produces three outcomes of service quality evaluation, namely: 1) confirmation; 2) positive disconfirmation; and 3) negative disconfirmation. Confirmation occurs when perceived performance and expectations of performance match. Positive disconfirmation occurs when perceived performance surpasses one’s expectations of performance, resulting in consumer satisfaction. Finally, negative disconfirmation occurs when expectation

28 surpasses perceived performance, resulting in consumer dissatisfaction (Oliver, 1993; Parasuraman et al., 1988, 1991). Based on this paradigm, Grönroos (1984) proposed the Service Quality Model. This model was developed as an attempt to understand how customers perceive the quality of a given service. It divides the customer's perception of any particular service into two dimensions, namely: 1) technical quality (what the consumer receives, the technical outcome of the process); and 2) functional quality (how the consumer receives the technical outcome, what Grönroos (1984) calls the "expressive performance of a service," (p. 39). Grönroos (1984) suggested that, in the context of services, functional quality is generally perceived to be more important than technical quality, assuming that the service is provided at a technically satisfactory level. He also points out that the functional quality dimension can be perceived in a very subjective manner (see Figure 2.04).

Figure 2.04. Service Quality Model (Grönroos, 1984)

Parasuraman et al. (1988) developed the SERVQUAL instrument to measure consumers’ perceptions of service quality. The authors regarded service quality as a form of attitude, related but not equivalent to satisfaction, resulting from a comparison of expectations with perceptions of performance. To measure service quality, the SERVQUAL consisted of 22 items in two different forms, namely: 1) for measuring

29 consumer expectations relating to various aspects of service quality; and 2) for measuring actual consumer perceptions of service quality. Additionally, the SERVQUAL consisted of five dimensions, including: 1) tangibles; 2) reliability; 3) responsiveness; 4) assurance; and 5) empathy. The SERVQUAL instrument has been used in many studies, and has been applied as a universal scale to assess consumers’ perceptions of the service quality of various service firms (Babakus & Boller, 1992; Woodside, Frey, & Daly, 1989). However, despite the widespread use of the SERVQUAL instrument, researchers have critiqued the SERVQUAL instrument ability to measure service quality (Carman, 1990; Cronin & Taylor, 1992; McDougall & Levesque, 1994). Carman (1990) argued the SERVQUAL instrument does not accurately measure service quality across industries. In a study of four different service settings (dental school patient clinic, business school placement center, tire store, and an acute care hospital), the author found that various items did not load on the same component when compared across each of the service settings. Another criticism levied against the SERVQUAL instrument is that it is not appropriate for measuring service quality because the disconfirmation paradigm is designed for measuring satisfaction (Cronin & Taylor, 1992). Service quality and satisfaction are proposed to be distinct constructs and as a result, the SERVQUAL, is a more appropriate measure of satisfaction as opposed to service quality (Bitner, 1990; Bolton & Drew, 1991; Cronin & Taylor, 1992, 1994). Other conceptualizations, models, and measures of service quality have been proposed in the literature. Among these include models put forth by Rust and Oliver (1994), McDougall and Levesque (1994), Dabholkar, Thorpe, and Rentz (1996), and Brady and Cronin (2001). Rust and Oliver (1994) developed the Three-Component Model of service quality that is based on three distinct elements, namely: 1) service product; 2) service delivery; and 3) service environment. In the model, service product referred to the consumer’s cumulative perception of the service and any and all additional elements that are associated with the service. Service delivery involves the perception of how the service is delivered in a specific interaction. Third, the service environment is comprised of both internal and external environments. The internal environment deals with the organizational culture and business philosophy inherent in

30 the service delivery by a firm’s employees, whereas the external environment referred to the physical ambience of the setting in which the service is provided. While this model was not empirically tested, each of the three factors was thought to contribute to the consumer’s subjective evaluation of the service encounter. The three-component model put forth by Rust and Oliver (1994) is similar to McDougall and Levesque’s (1994) tri-dimensional model of service. Alternative labels for the service quality elements of service product, service delivery, and service environment were service process, service outcomes, and tangible aspects of service quality. McDougall and Levesque (1994) tested and found support for the three- dimensional model using data from the retail-banking sector of the economy. A study by McAlexander, Kaldenberg, and Koenig (1994) also tested and found support for a three-dimensional model of service quality in the health care industry. Dabholkar, Thorpe, and Rentz (1996) proposed an alternative approach to looking at service quality (see figure 2.05). The authors put-forth a hierarchical model of service quality in retail stores suggesting that service quality is a multi-level and multidimensional construct. The constructs of the model comprise three distinct levels, namely: 1) an overall level; 2) a dimension level; and 3) a sub-dimension level. The overall level reflected consumers’ overall perceptions of service quality. The dimension level contained five distinct yet highly correlated dimensions, including: 1) physical aspects; 2) reliability; 3) personal interaction; 4) problem solving; and 5) policy. Finally, the authors proposed a sub-dimensional level, which recognized the multifaceted nature of the service quality dimensions. Based on their empirical validation of Rust and Oliver’s (1994) three-component conceptualization of service quality, as well as through the incorporation of various elements from each of the other models described above, Brady and Cronin (2001) proposed a hierarchical model of service quality involving both primary and secondary dimensions (see figure 2.06). The authors defined service quality as a customer’s perceptions of the quality of at least one of the following situations: 1) the interactions with the organization; 2) perceptions of the quality of the physical environment in which the service is consumed and produced; and 3) the perceptions of the quality of the outcome. Each of these dimensions has three secondary dimensions. Interaction

Figure 2.05. Model of Retail Service Quality (Dabholkar, Thorpe, & Rentz, 1996)

quality includes consumer perceptions of employees’ attitudes, behaviors, and expertise. The quality of the physical environment is comprised of consumers’ evaluations of ambient conditions, design, and social factors. Finally, outcome quality includes consumers’ perceptions of waiting time, tangibles, and valence. The following sections contain a review of each of the identified dimensions. Interaction Quality. Brady and Cronin (2001) posited that because of the intangible and inseparable nature of services, the interpersonal interactions that take place during the service delivery are likely to have the greatest impact on customers’ service quality perceptions. The authors viewed this interaction as an exchange between a firm’s customers and employees, whereby customers evaluate the quality of their interaction based on their perceptions of the attitudes, behaviors, and expertise of the employees. Physical Environment Quality. Due to the intangible nature of services and the fact that consumers must often consume said services in the same location as they are provided, Brady and Cronin (2001) suggested that consumer perceptions of the surrounding environment can also significantly influence consumer perceptions of the overall service encounter. Identified in the marketing literature as “the servicescape” (Bitner, 1992), Brady and Cronin (2001) identified three factors that influence the

Figure 2.06. Brady and Cronin’s (2001) Hierarchical Model of Service Quality

perceived quality of the service environment, namely: ambient conditions, design, and social factors. Ambient conditions were identified as the non-visual aspects of the service environment such as temperature, music, and sounds. Facility design comprises such aspects as facility layout or architecture and can include both functional and aesthetic elements. Finally, social conditions were identified as referring to consumer perceptions of the amount and type of people present during consumption. Outcome Quality. Outcome quality refers to the technical quality of the service outcome. Brady and Cronin (2001) stated that outcome quality affects perceived service quality. Three attributes were identified as comprising outcome quality, namely: waiting time, tangible evidence, and valence. Waiting time refers to a consumer’s favorable or unfavorable perception of the amount of time to receive the service. The authors proposed that perceptions of waiting time that are more favorable are associated with enhanced outcome quality perceptions. Tangible evidence refers to those elements of the service outcome that individuals can point to in order to judge the quality of the service performance. Finally, the authors found that valence is also a determinant of outcome quality. Valence refers to “the essence of the service outcome above and beyond waiting time and intangibles” (Brady & Cronin, p. 40). Valence

33 perceptions include those factors that are largely outside the control of the organization but that influence consumer perceptions of the service outcome. The authors used the example of a customer’s evaluation of the service quality of a sporting event to illustrate how valence can impact consumer perceptions of the quality of the service encounter. A consumer may have a positive perception of waiting time and tangibles, however, may negatively perceive the quality of service because his or her team lost the game. Actual service performance, in terms of waiting time and tangibles, may be consistent but the loss contributes to the spectator’s assessment of the service quality. In much the same way, a team win may forgive unfavorable perceptions of waiting time and tangibles. Service Quality in Sport Research The study of service quality has received increasing attention in the sport literature over the course of the last decade. Research on service quality in this industry has focused on various issues relating to the topic, including: the development of models and scales to measure service quality in team (McDonald, Sutton, & Milne, 1995) and recreational (Kim & Kim, 1995; Ko & Pastore, 2005; Lam, Zhang, & Jensen, 2005) sport settings; the use of existing service quality models from the general marketing literature to examine the relationship between service quality and various outcome measures, such as customer satisfaction (Alexandris, Zahariadis, Tsorbatzoudis, & Grouios, 2004; Chang & Lee, 2004; Gonzalez & Brea, 2005; Kouthouris & Alexandris, 2005; Murray & Howat, 2002; Tian-Cole & Crompton, 2003; Westerbeek & Shilbury, 2003), psychological commitment (Alexandris, Zahariadis, Tsorbatzoudis, & Grouios, 2004), behavioral intentions (Chang & Lee, 2004; Gonzalez & Brea, 2005; Kouthouris & Alexandris, 2005; Theodorakis, Goulimaris, & Gargalianos, 2003; Tian-Cole & Crompton, 2003), and actual consumer behaviors (Alexandris, Kimitriadis, & Kasiara, 2001). Researchers have also examined the effect of various antecedent variables on perceptions of service quality, such as consumer demographics and participation rates (Westerbeek, 2000) as well as cultural differences in service quality perceptions (Tsaur, Lin, & Wu, 2005). Although the study of service quality has received increasing attention in the sport literature in recent years, the vast majority of studies have been conducted within

34 recreational and/or leisure settings, such as: health/fitness clubs and spas (Alexandris, Kimitriadis, & Kasiara, 2001; Alexandris, Zahariadis, Tsorbatzoudis, & Grouios, 2004; Chang & Lee, 2004; Gonzalez & Brea, 2005; Lam, Zhang, & Jensen, 2005), sport tourism sites (Kouthouris & Alexandris, 2005; Tian-Cole & Crompton, 2003; Thwaites & Chadwick, 2005; Tsaur, Lin, & Wu, 2005), school sport facilities (Chen & Ryder, 2006; Hung & Su, 2005; Hung, Su, & Wu, 2004), public recreational sport settings (Alexandris & James, 2003), corporate recreational sport/fitness programs (Chang, Chen, & Hsu, 2002), general community based recreational leisure centers (Howat & Murray, 2002; Kim & Kim, 1995; Ko & Pastore, 2005; Murray & Howat, 2002; Sifkus, Howat, & Crilley, 2005), athletic camps (Costa, Tsitskari, Tzetzis, & Goudas, 2004), and at traditional cultural performances (Theodorakis, Goulimaris, & Gargalianos, 2003). Although the number and breadth of studies examining topics related to service quality has increased in recent time, to date, only a handful of researchers have examined service quality in the context of spectator sports. A review of the research related to the study of service quality in spectator sport will follow. Writing on effective event management strategies, Brown, Sutton, and Duff (1993) suggested that the quality of the services offered at an event are a critical component of a consumer’s event experience. The authors noted that even those consumers with a highly vested interest in the event are likely to form perceptions of the quality of service provided. As examples, the authors suggested that consumers can become discouraged with the lengthy periods of waiting or poor procedures in place for ticket procurement, unfriendly or inefficient interactions with service personnel such as ticket takers, ushers, or concessionaires, as well becoming discouraged with the quality of the venue itself in terms of access and cleanliness issues. In one of the first empirical studies investigating service quality in a team sport setting, McDonald (1996) examined the relationship between customer ratings of overall service quality and lifetime value, which was defined as the measure of the strength of the relationship between an individual and a professional franchise. Incidentally, in conducting this study, McDonald (1996) was also the first researcher to examine the concept of lifetime value in a sport setting. The study developed and tested a model of lifetime value to measure the strength of the customer firm relationship. Customers

35 were then segmented into lifetime value deciles and each segment then compared on their ratings of overall service quality, which was captured with the SERVQUAL instrument developed by Parasuraman et al. (1988). The results of the study revealed that perceptions and expectations of service quality increased with higher levels of customer investment. In other words, customers demonstrating a stronger relationship with a sport team (as defined by a higher lifetime value) are more likely than individuals with weaker relationships to perceive that the team delivers higher service quality. In addition, these customers were shown to expect an increased level of service quality. Highlighting a need to develop measures of service quality specifically relevant to the professional team sports industry, McDonald, Sutton, and Milne (1995) developed the TEAMQUAL scale. Modeled after the SERVQUAL (Parasuraman et al., 1988), the scale consisted of 39-items measuring five dimensions of service quality. The dimensions were based on the idea that quality judgments are made on the gap between customer expectations and perceptions. The TEAMQUAL scale has not been used in the sport literature to measure perceptions of service quality. Presumably, this is due to the same reasons identified for the SERVQUAL scale as discussed above. Much of the research that guides our understanding of the value that sport fans obtain from their perceptions of the service quality of the sport consumption experience is derived from research on sport consumption that has focused primarily on the topic of sport demand. In particular, sport consumers often assess the service quality of residual preference factors such as the stadium environment, accessibility, cleanliness, and so on. Research investigating demand based spectator motives for attendance to sporting events were the first to suggest that the context, or setting, in which spectator sport services are provided are a significant predictor of attendance (Hansen & Gauthier, 1995; Wakefield & Sloan; 1995; Westerbeek, 2000). For example, Westerbeek (2000) examined the influence of attendance frequency and spectator age on “place” specific dimensions of service quality at Australian Rules Football matches. With respect to attendance frequency, the researcher found that heavy users place importance on the stadium characteristic that make them feel at home so that they can “fanatically support their team” (p. 194) in comfort. Significant differences for age were also found. Older spectators were found to place more importance on those

36 characteristics of the stadium environment that make them feel at home than did younger spectators. Additionally, the opportunity to engage in casual social conversation was also rated to be more important and of greater value for older spectators than for younger ones. Younger spectators placed more importance on stadium atmosphere elements such as smells and sounds. Westerbeek’s (2000) findings highlight the importance of the stadium environment in spectator perceptions of the quality of the services provided. These findings suggest that consumers make evaluations related to the quality of the environment in which the service is provided in deciding whether to attend a professional team-sporting event. They are consistent with, and lend support for Brady and Cronin’s (2001) physical environment dimension to be included in the study of service quality in a spectator sport setting. Further parallels between Brady and Cronin’s (2001) conceptualization of service quality and work in the sport management literature are observed. For example, Chelladurai and Chang (2000) proposed a framework for analysis of quality in sport services in which specific targets of service quality were identified. Targets included: service co-production, the sportscape and the core sports product. The sportscape refers to the physical environment including the facility, seating comfort, layout accessibility, and facility cleanliness, and is thus equivalent to Brady and Cronin’s (2001) physical environment construct. Service co-production refers to the interactions that occur between customers and service employees as well as between customers. Service co-production is similar to the dimension of interaction quality in Brady and Cronin’s (2001) model. Finally, the core sports product refers to those elements that relate directly to those aspects of the core service, such as the game. The core sports product is analogous to the outcome quality construct identified by Brady and Cronin (2001). It is important to note that sport consumers are likely to form an overall perception of a sport team’s service quality on the basis of a combined evaluation of the organization’s performance at multiple levels. Conceptualizing quality in this manner enables managers of sport teams to gain a greater understanding of how their consumers assess the quality of their sport spectating service experiences.

37 The major contribution of the service quality literature has been the development of an understanding of how many marketing functions contribute to the value of a customer and subsequently, to shareholder value (Hogan et al., 2002). Research examining the relationship between customer satisfaction and service quality has made a significant contribution to the understanding of the relationship between service quality and customer profitability as it has identified probable linkages between antecedents of service quality and components of customer lifetime value (Anderson et al., 1994; Bolton & Drew, 1991; Duncan & Elliott, 2002; Zeithaml et al., 1996). Price A fourth dimension of value equity is the perception that consumers have of the price associated with the consumption of services. Dodds (1991) suggested that price is both an objective and subjective stimulus that affects buyers’ product evaluations and attitudes. As such, pricing may be viewed as a dynamic process whereby firms design pricing structures that change over time to account for different consumers and different situations. It is important for organizations to consider perceptions of price and how those perceptions affect buying decisions. Han, Gupta, and Lehmann (2001) suggested price to have a significant influence on consumers’ purchase behavior and consequently on a firm’s sales and profits. In the decision to purchase a product, consumers exchange something of value (price) in return for something else of value, such as the benefits associated with having or consuming the product. Customer oriented pricing strategies involve an awareness of the actual and perceived value that consumers place on the benefits they receive from the product. Zeithaml (1988) defined perceived price as “what is given up or sacrificed to obtain a product” (p. 10). From this perspective, the overall perceived price may be conceived as a combination of both monetary and non-monetary aspects, including such factors as money, time, search costs, and convenience. In a study to develop a multi-dimensional scale for measuring the perceived value of a service, Petrick (2002) expanded on Zeithaml’s (1988) conceptualization of perceived price in labeling non- monetary price as behavioral price. Evidence from the literature of other fields, such as economics and marketing, support the proposition that other costs such as time, effort, search, and psychic are important to consumers (Zeithaml 1988).

38 Monroe and Della Bitta (1978) suggested that theoretical explanations for the influence of price on consumer decision-making may be broadly classified into two categories, economic and psychological. The economic research stream presumes that consumers behave rationally, while the psychological stream attempts to explain the irrational behavior of consumers. Monroe and Krishman’s (1985) price-perceived quality model defined the direct influence of price on buyers' perceptions of product quality/benefits and monetary sacrifice, with further secondary effects on perceived value and willingness to buy. They found that as long as consumer perceptions of quality and benefit are greater than the perception of sacrifice, consumers will have a net positive perceived value of the product. The model also implies a positive relationship between consumer perceptions of value and consumer willingness to buy a product, meaning that the higher a consumer's perceived value, the higher the consumer's willingness to buy. In the context of spectator sports, perceived price is particularly hard to operationalize since there are a number of assorted services and products for which a spectator could potentially transact during his or her attendance at a sport event. Most salient among these products is likely the price of the ticket to the event since this is the primary method through which one gains access to an event. Sport managers must therefore examine consumers’ reasons for purchasing a product and set the price according to their perceptions of the product’s value. In spectator sports, organizations often set their prices on buyers’ perceptions of value. From this orientation, non-price marketing variables are used to build up perceived value in the minds of buyers and price is set to match the perceived value. Epistemic Value Often, consumers may find value in a product simply because it is something that they have not tried before, or because they wish to increase their knowledge of that product. The value derived from this type of motivation is defined in the literature as epistemic value. Epistemic value refers to the perceived utility of a good or service resulting from its “ability to arouse curiosity, provide novelty, and/or satisfy a desire for knowledge” (Sheth et al., 1991, p. 21).

39 In his germinal work on curiosity, Berlyne (1960) considered the idea of epistemic curiosity to be a temporary, situation dependant information or knowledge-seeking activity stimulated when people are confronted with information that challenges their beliefs, attitudes, or knowledge in one way or another. Individuals are then motivated to explore their environment for information that will resolve the resulting conceptual conflict, ultimately culminating in the acquisition of new learning and knowledge. Thus, curiosity has been described as the degree to which an individual reacts positively to new elements in his/her environment by exploring them and examining stimuli in order to know more about them. Stell and Paden (1999) described curiosity as "the degree to which an individual reacts positively to new elements in his or her environment by exploring them and examining stimuli in order to find out more" (p. 334). With respect to individual learning, Reio and Wiswell (2000) stated that the fostering of curiosity is an important strategy for enhancing the motivation to learn. In a study examining the direct effects of Sheth et al.’s (1991) perceived value dimensions on attitudinal and behavioral components of loyalty, Pura (2005) suggested that epistemic value may be the trigger to use a service for the first time. Berlyne (1960) also conducted extensive study on the effects of perceiving novelty on peoples’ behavior. He identified several dimensions for differentiating types of novelty that involve the epistemic properties of novel stimuli. Epistemic novelty was defined in relation to knowledge. That is, the epistemic novelty of a stimulus is based on a comparison of the meaningful associations that it connects with in the minds of consumers. As such, epistemic novelty requires the stimulus be recognized as being of a certain type before the novelty can be appreciated. McQuiston (1989) conducted a study examining the purchase situation attribute of novelty as a causal determinant of participation and influence in an industrial purchase decision. A review of the literature suggested to McQuiston (1989) that the novelty of the purchase to an organization may affect various aspects of organizational buying behavior. Purchasing organizations were defined as a group of individuals within an organization who have varying degrees of experience with similar purchase situations. His review further indicated that the less experience people have the more novel the purchase is to them. McQuiston (1989) subsequently defined novelty as “the

40 lack of experience of individuals in the organization with similar purchase situations” (p. 69). The results of his study indicated that novelty provides a plausible typology for describing participation and influence. Babin, Darden, and Griffin (1994) suggested that a product’s capacity to arouse curiosity, offer novelty, or to satisfy a desire for knowledge is particularly important for consumers considering new experiences such as in the case of experiential services like holidays, adventures, and shopping trips. Sheth et al. (1991) noted that many leisure activities in the service sector, such as movies, concerts, and sporting events, are pursued for the sake of curiosity and knowledge. The results of the research of Berlyne (1960), McQuiston (1989), and Babin et al. (1994) provides support for the study of novelty and knowledge as dimensions of epistemic value in a spectator sport setting. First, as Berlyne (1960) noted, epistemic value requires that the stimulus be recognized as being of a certain type before the novelty can be appreciated. For spectator sport, the recognition is inherent in the activity of being a spectator, for without any recognition of the stimulus (i.e., the game, the team, etc.), spectatorship is non-existent. Second, although the population of interest in McQuiston’s (1989) study was industrial buyers, the same processes are likely to exist for individual consumers of sport services. Sport consumers may be defined as spectators of a particular team or event who have varying degrees of experience with similar purchase situations. Furthermore, it is likely that the less experience people have spectating sporting events, the more novel the purchase is to them. Finally, it is possible to classify spectator sport as an experiential service. A review of the current literature on the motivations to attend sporting events suggests that for many, the consumption of sporting events through spectatorship may be a result of the epistemic value that is derived from the consumption experience (James, Kolbe, & Trail, 2002; Trail & James, 2001). Satisfaction The sixth dimension of value equity examined in this study is customer satisfaction. According to Szymanski and Henard (2001), customer satisfaction represents an important foundation for organizations adopting a customer-oriented approach to marketing and an important objective for both the organization and the

41 customer. From an organizational perspective, researchers have reported evidence for a strong relationship between customer reported satisfaction and financial performance as customer satisfaction is a significant determinant of repeat sales, positive word-of- mouth, and decreased incidences of switching (Anderson, Fornell, & Lehmann, 1994; Anderson, Fornell, & Rust, 1997; Gruca & Rego, 2005; Yi, 1990). From a consumer perspective, satisfaction represents the customer’s sense that consumption has provided pleasant outcomes against a standard of pleasure/displeasure (Oliver, 1999). Oliver (1997) defined satisfaction as a customer’s evaluation of pleasurable fulfillment of some need, desire, or goal. The key concepts inherent in this definition are ‘pleasure’ and ‘evaluation’. Oliver (1997) noted that for satisfaction to occur, the fulfillment of needs, desires, or goals must be pleasurable. It is not enough for the need to simply be fulfilled, he noted, as the fulfillment of certain needs, such as paying taxes and doing laundry, are unpleasant. A consumer’s evaluation of the fulfillment of needs is also important as the literature suggests two processes by which consumers evaluate the fulfillment of service outcomes, namely: 1) disconfirmation of expectations, and 2) equity theory. Disconfirmation of expectations views customer satisfaction as a response to an evaluation of service performance compared to expectations (Woodruff & Gardial, 1996). This paradigm asserts that when the perceived performance of a service provider meets, or confirms, some preceding standard, the customer is satisfied. When service performance does not meet prior expectations, the consumer is dissatisfied. Finally, if performance exceeds a customer’s preceding standard he or she will be very satisfied. According to Wirtz and Mattila (2000) the disconfirmation of expectations paradigm is a widely accepted process by which consumers evaluate their levels of satisfaction and dissatisfaction. Equity theory is another paradigm which researchers have pointed to as an explanation for consumer satisfaction or dissatisfaction. Equity theory is a social justice stream of research that combines the notions of social comparison and cognitive evaluation into a formula that assesses the fairness of social relationships (Blau, 1964). The equity formula is represented as a ratio of one’s inputs to outcomes as compared to the ration of inputs to outcomes of a referent other. When the ratios are deemed equal,

42 equity is perceived. Any discrepancy between the two ratios will cause the individual to perceive inequity. In the context of consumer satisfaction, consumers will compare their input/output ratio to that of the organization. A ratio perceived to be fair will result in a satisfied consumer. In a test of equity theory in a consumer satisfaction context, Fisk and Young (1985) found that perceptions of inequity lead to dissatisfaction and decreased repurchase intentions. A great deal of research on consumer value has focused on the analysis of the relationship between value and satisfaction (Babin & Kim, 2001; Bolton & Drew, 1991; Cronin, Brady, & Hult, 2000; Day & Crask, 2000; Eggert & Ulaga, 2002; Fornell, Johnson, Anderson, Cha, & Bryant, 1996; Oliver, 1999; Spreng, Dixon, & Olshavsky, 1993; Sweeney & Soutar, 2001; Westerbeek & Shilbury, 2003; Woodruff & Gardial, 1996). The majority of these studies have demonstrated that consumer perceived value exerts a positive influence on consumer satisfaction (Babin & Kim, 2001; Cronin et al., 2000; Fornell et al., 1996; Spreng et al., 1993). These studies suggest that satisfaction is an outcome of perceived value. Thus, if the consumer’s overall assessment of the utility of the service based on perceptions of what is received and what is given is favorable, than the customer will be satisfied. That is, perceived value gives satisfaction. In contrast to the research described above, there are a small number of studies suggesting that perceived value may also be viewed as an outcome of satisfaction (Bolton & Drew, 1991; Petrick, Morais, & Norman, 2001). Oliver (1999) noted that satisfaction provides value to customers because being satisfied leaves the customer in a satisfied state, which is valuable in and of itself. Thus, the end-state of being satisfied is of value to consumers. As such, the current study has included satisfaction as a component of value within the value equity framework. The literature related to the dimensions of value equity suggests that the six identified components can have significant effects on consumers’ evaluations of a service relationship as well as on organizational outcomes. Researchers (Parasuraman & Grewal, 2000) have established a relationship between customer value perceptions and willingness to buy, as well as between value perceptions and a decrease in the intention to search for alternatives. Furthermore, customer perceived value has gained

43 a significant amount of attention recently as a stable predictor of consumer buying behavior (Anderson & Srinivasan, 2003; Dodds, Monroe, & Grewal, 1991; Sweeney, Soutar, & Johnson, 1999). Figure 2.07 illustrates the components of value equity identified in the preceding sections. In summary, value equity was conceptualized as a hierarchical structure comprised of five latent second-order factors (entertainment value, social value, service quality, perceived price, and epistemic value), 19 latent first-order variables, and 91 indicators.

Figure 2.07. Framework of Value Equity in Spectator Sports

45 Brand Equity

While the primary focus of the current research project is the identification, testing, and validation of measures of value equity, the researcher has also undertaken the task of proposing a framework identifying the components of customer equity in spectator team sports. This section will review the literature supporting the notion that brand equity is a component contributing to the customer equity of sport organizations. The customer equity concept has been born out of the transition of the global economy to a service-based economy and the spread of service-based industries. One of the challenges and sources of confusion faced by scholars of customer equity has been to define and describe it in comparison to brand equity, as well as in explaining the nature of the relationship between the two concepts. In commenting on the field of marketing management, Ambler, Bhattacharya, Edell, Keller, Lemon, and Mittal (2002) stated “there remains much confusion regarding the definitions of brand equity and customer equity and the extent to which the two are related or distinct” (p. 14). In the following section I will review the concept of brand equity; review the literature on brand equity and explain how it relates to customer equity; review the brand equity research that has been conducted on professional sport, and examine the antecedents of brand equity within a customer equity framework. Aaker (1991) defined brand equity as “a set of assets and liabilities linked to a brand, its name and symbol, that add to or subtract from the value provided by a product or service to a firm and/or that firm’s customers” (p. 15). Subsequently, Keller (1993) defined brand equity as “the differential effect of brand knowledge on consumer response to the marketing of a brand” (p. 2). Essentially, a company will have higher brand equity when the reactions of consumers towards their marketing efforts are more favorable than they are to the reactions to a fabricated or unnamed version of the product or service. Ambler et al. (2002) defined brand equity as “everything that exists in the minds of consumers, with respect to a brand – thoughts, feelings, experiences, images, perceptions, beliefs and attitudes” (p. 15). An underlying principle associated with brand equity and brands is that a brand’s foundations are comprised of “peoples’ intangible mental associations about it…(and) the value placed on the brand is really the value of the strength and resilience of those associations” (Dyson, Farr, & Hollis,

46 1996, p. 9). The resulting value is the financial value that attaches to a brand name as a result of consumers’ perceptions and attitudes regarding the assets associated with that brand name. The numerous definitions of brand equity reveal that there exists little consensus as to the exact meaning of brand equity. An examination of the available literature suggests that definitions of brand equity can be broadly classified into two categories, namely: 1) those based on a financial perspective that stress the value of a brand to the firm (Brasco, 1988; Simon & Sullivan, 1993); and 2) those that are based on a consumer-perspective which views brand equity as the value of a brand to the consumer (Aaker, 1991; Keller, 1993). With respect to the customer perspective, brand equity is commonly referred to as customer-based brand equity (CBBE) (Netemeyer, Krishnan, Pullig, Wang, Yagci, Dean, Ricks, & Wirth, 2004; Pappu, Quester, & Cooksey, 2005; Punj & Hillyer, 2004; Yoo & Donthu, 2001). In reference to spectator sports, customer-based brand equity has been identified as spectator-based brand equity (Ross, 2006). Under this perspective, Punj and Hillyer (2004) noted that brand equity is largely attitudinal in scope, comprised of beliefs, affect, and other subjective experiences related to the brand. Keller (2003) noted that “the power of the brand lies in what resides in the minds of customers” (p. 59). Relationship between customer-based brand equity and customer equity. In explaining the nature of the relationship between brand equity and customer equity, Rust et al. (2000) defined brand equity as “that portion of customer equity attributable to the customer’s perceptions of the brand. (Furthermore), brand equity represents the customer’s subjective and intangible assessment of the brand, above and beyond its objectively perceived value” (p. 81). This definition is comprised of several key components that must be examined. First, brand equity is considered to be a portion of customer equity, thus indicating that it is only partially responsible for the overall determination of the customer equity of a firm. Second, brand equity is a subjective assessment of the brand, and not an objective evaluation. Thus, the role of brand equity within a customer equity framework is far more focused than with previously put forth conceptualizations of brand equity.

47 Brand equity influences customer equity as the brand provides a strong tie between the customer and the firm, thus strengthening the value of customers to that firm. Hogan et al. (2002) suggested that brands significantly affect an organization’s customer equity by impacting on the acquisition, retention, and add-on selling efforts of the firm. With regard to retention and add-on selling, brands provide an opportunity for the firm to obtain a greater share of wallet from an existing customer through additional purchases of current brands or products or through purchases of new brands or product extensions. Second, brands also provide the opportunity to attract new customers through the strength of the overall perception of the brand in the marketplace or through the development of new brands or brand extensions that attract new customers. Brand equity and customer equity are synergistic in that the more a company builds its brand, the more customer equity it adds (Peppers & Rogers, 2004). Brand equity focuses on how the customer sees the characteristics of the products offered by the organization, recognizing that the characteristics assume meaning only when the organization and consumer interact (Ambler et al., 2002). Firms with a clear sense of those customers that are most relevant to the bottom line (i.e., those who attend more games and purchase more merchandise) would engage in brand building activities, but among the right set of customers – those that are most profitable (Ambler et al., 2002). The existing research on the relationship between brand equity and economic success supports Rust et al.’s (2000) conceptualization of brand equity as a component driver of a firm’s customer equity. An examination of the dimensions and measurements of customer-based brand equity in the current study will now be presented. Dimensionality of customer-based brand equity. Brand equity is a multidimensional concept whose dimensions have been subjected to empirical testing in the literature. Two of the more commonly cited models are those of Aaker (1991) and of Keller (1993). There has been considerable discussion in the literature concerning the dimensionality of the brand equity concept, both in content and in definition, however only a limited number of studies have been conducted to empirically test its constructs. Cobb-Walgren, Ruble, and Donthu (1995) conceptualized and measured customer-based brand equity on four dimensions, including brand awareness, brand

48 associations, perceived quality, and brand loyalty. Using confirmatory factor analytic methods to measure customer-based brand equity, Yoo, Donthu, and Lee (2000) considered it to be a three-dimensional construct, combining brand awareness and brand associations into one dimension. Yoo and Donthu (2001) developed a scale for customer-based brand equity and tested its psychometric properties. The researchers observed three dimensions for customer-based brand equity similar to Yoo et al. (2000). Keller (2003) viewed CBBE as a two-dimensional construct comprised of brand awareness and brand image. In a recent study of car brands and television brands, Pappu, Quester, and Cooksey (2005) found support for the four dimensional conceptualization of customer based brand equity originally put forth by Cobb-Walgren et al. (1995). Rust et al. (2001) and Rust, Lemon, and Narayandas (2004) each proposed that brand equity is comprised of three dimensions considered to be important measures of a consumer’s mind set, namely: brand awareness, brand attitudes, and perception of corporate ethics. The dimensions put forth by these authors differ slightly to those put forth by Keller (2003). While each of the works identified brand awareness as a driver of customer based brand equity, Rust et al. (2001) suggested that brand attitudes and corporate ethics are important while Keller (2003) indicated that the associations that consumers have of the brand is key. Within the realm of spectator sports, Gladden, Irwin, and Sutton (2001) suggested that sports teams build and maintain brand equity through the enhancement of customer relationships. Specifically, it was proposed that this could be achieved by developing an enhanced understanding of the consumer, increasing the interactions between consumers and the brand, reinforcing and rewarding loyalty to the team brand, and consistently integrating marketing communication to reinforce key brand associations. Recent research has been conducted to examine the dimensionality of brand equity in team sports as well (Bauer, Sauer, & Schmitt, 2005; Gladden & Funk, 2002; Ross, 2006). Drawing on Gladden and Funk’s (2002) model of brand associations in team sport, and on Keller's (1993) brand equity model, Bauer, Sauer, and Schmitt (2005) presented and tested a model of brand equity for the team sport industry, the

49 Brand Equity model in Team Sports (BETS), to examine the importance of brand equity in European professional sport. The purpose of the study was twofold: 1) to refine existing models of customer-based brand equity in professional team sports using data from spectators of the German professional soccer league, the Bundesliga; and 2) to use economic data to demonstrate the impact of brand equity based on direct consumer responses on company success. To achieve the first objective, the researchers empirically tested a hypothesized BETS model consisting of two dimensions (brand awareness and brand image), five factors and a total of 16 indicators using exploratory and confirmatory factor analysis. The results of the study did not lend support for a two dimensional model of brand equity. The final model, consisted of one dimension (brand image), with four factors and fourteen indicators. As an explanation, the researchers noted that awareness might not be a factor that adds a lot of value to the understanding of brand equity when confronted with a product category that is quite well known by consumers. As such, it was suggested that future researchers devote special attention to the awareness dimension when researching product categories in which consumers possess a high degree of knowledge, such as professional team sport. Ross (2006) proposed a conceptual framework for understanding spectator- based brand equity in the spectator sport services environment in which brand-equity is comprised of brand awareness and brand associations. The conceptualization by Ross (2006) is consistent with Keller’s (2003) proposed dimensions of brand equity. Keller (2003) indicated that customer based brand equity occurs when “the consumer has a high level of awareness and familiarity with the brand and holds some strong, favorable, and unique brand associations in memory” (p. 67). Brand Associations Aaker (1991) described brand associations as “anything linked in memory to a brand” (p. 109). Additionally, existing brand associations were described as having a level of strength which varies depending on the experiences and exposures that consumers have to varying communications. Keller (2003) noted that the favorability, strength, and uniqueness of brand associations that consumers have can result from a variety of sources, including: marketer controlled information, direct experience, outside commercial non-partisan sources, word of mouth, assumptions or inferences from the

50 brand itself, or from the identification of the brand with a company, country, channel of distribution, or some particular person, place, or event. Because of the existence of numerous sources from which associations of a brand are created, marketing communication programs attempt to create strong brand associations and recalled communication effects through a variety of means (Keller, 2003). Another key characteristic of brand associations is that they represent perceptions, which may or may not reflect objective reality. A significant amount of research has been conducted demonstrating that a well-positioned brand with strong associations can have a significant positive impact on consumer behavior and consumer decision making (Feinberg, Kahn, & McAlister, 1992; Grover & Srinivasan, 1992; Rangaswamy, Burke, & Oliva, 1993; Sheinin, 2000) and subsequently on organizational outcomes (Bucklin, Gupta, & Han, 1995; Sethuraman, 1996) which is crucial for companies wishing to gain a competitive advantage in the marketplace. Brand Associations in Spectator Team Sports It is only in the last five years or so that researchers have begun to empirically examine brand associations in a team sport context. Gladden and Funk (2002) investigated the dimensionality of team brand associations in sport through the development of the Team Association Scale (TAM). A review of the sport literature provided the authors with 16 potential dimensions of brand equity. The 16 dimensions were derived from the Keller’s (1993) categorization of brand associations which was comprised of attributes, benefits, and attitudes. The attribute category comprised eight associations, including: success, head coach, star player, management, stadium, logo design, product delivery, and tradition. Benefits associations included the following five constructs: identification, nostalgia, pride in place, escape, and peer group acceptance. Finally, attitude was comprised of importance, knowledge, and affect. Results of a factor analysis provided support for the 16 distinct construct proposed to underlie brand associations in sports. Recently, Ross, James, and Vargas (2006) developed the Team Brand Association Scale (TBAS), to measure professional sport team brand associations. A free though listing technique was employed to elicit information from individuals regarding their favorite sports team. In all, 11 dimensions underlying professional sport

51 team brand associations were identified, including: non-player personnel, team success, team history, stadium community, team play characteristics, brand mark, commitment, organizational attributes, concessions, social interaction, and rivalry. The purpose for developing the scale was to aid sport marketers to manage their brands in order to create favorable associations to attract or retain consumers. Subsequent studies involving the TBAS have been conducted by Ross (2007) and Ross, Bang, and Lee (2007). Ross (2007) utilized the TBAS to identify segments of spectators for a professional sport team. The author discovered that two significantly different groups emerged based upon the perceptions of the sport brand. The differences were in terms of the frequency and nature of which brand association dimensions were elicited from memory. Additionally, demographic differences were observed between members of the two segments in terms of gender, education level, and household income. Ross, Bang, and Lee (2007) examined the applicability and psychometric properties of the TBAS in the context of intercollegiate ice hockey. The results of the study revealed that the instrument was applicable and valid in the examined setting. Previously Unexplored Brand Associations Corporate Social Responsibility. Another association that consumers are likely to have about an organization is its ethical orientation. Rust et al. (2004) proposed that consumer attraction to a firm is based in part on perceptions of corporate ethics and citizenship behaviors. One concept that encompasses corporate ethics and citizenship behavior is corporate social responsibility (CSR). CSR involves the ethical and moral issues related to corporate decision-making and behavior (Branco & Rodrigues, 2006). The concept of corporate social responsibility has been the subject of much study in recent decades, and researchers have presented numerous definitions of CSR. Mosley, Pietri, and Megginson (1996) defined CSR as management’s obligation to set policies, make decisions and follow courses of action beyond the requirements of the law that are desirable in term of the values and objectives of society. Kok, Weile, McKenna, and Brown (2001) viewed CSR as the obligation of the firm to use its resources in ways to benefit society through committed participation as a member of society at large, independent of direct gains of the company. Essentially, CSR is to be

52 understood as a broad concept, since it takes in the whole range of philosophical and normative issues relating to the role of business in society and all the moral obligations that enhance the positive impact and diminish the negative impact of the firm on its social environment (Salmones, Crespo, & Bosque, 2005). These issues and obligations include a business’s contribution to the welfare of society in the longer term as well as its relationship with its customers in the short-term. The study of corporate social responsibility has been the subject of much research in recent decades. One of the focuses of this line of research has been the examination of the effect of CSR on the financial performance of firm. Pava and Krausz (1996) undertook a review of 21 studies conducted between 1972 and 1992 to investigate the relationship. Of these studies, 12 reported a positive relationship between CSR and financial performance, one reported a negative relationship, and 8 reported no relationship. The authors concluded that CSR appeared to have a weak but positive relationship with financial performance. While the preponderance of CSR research has involved the study of the construct from an internal perspective, recognizing the likely relationship between CSR and firm financial performance, Sen and Battacharya’s (2001) stressed the need for researchers and firms to gain a better understanding of how and why various consumer segments are apt to respond to specific CSR initiatives. In this vein, research has examined the effects of CSR actions on consumer behavior and consumption. These studies have found that CSR activities have a positive effect on consumers’ attitudes toward a firm and its products (Brown & Dacin, 1997; Creyer, 1997; Ellen, Mohr, & Webb, 2000; Stodder, 1998). Brown and Dacin (1997) conducted three studies to examine the impact of a firm’s CSR activities on consumers’ evaluations of the firm and its products. The results indicated that CSR’s effect on consumers’ preference for a company’s product occurs through consumers’ evaluations of the company. Stodder (1998) reported that a Walker Information survey (1994) of consumers found that forty-seven per cent of those polled responded that they would be much more likely to buy from a "good" company given parity in quality, service, and price. Additionally, 70% of the consumers answered that they would not do business with a firm that was not socially responsible, regardless of price.

53 The results of the aforementioned studies suggest that consumers are likely to form associations about a firm’s activities with respect to corporate social responsibility. Applying the rationale that brand associations influence purchase behavior and intentions, which in turn influence firm financial performance, it is appropriate to view CSR as a measure of brand equity within a customer equity framework. Carroll’s (1979, 1991) three-dimensional framework of CSR is one of the most widely used and accepted frameworks for explaining the construct. Building upon previous definitions, Carroll (1979) defined corporate social responsibility as encompassing “the economic, legal, ethical, and discretionary expectations that society has of organizations at a given point in time” (p. 500). According to this definition, organizations have economic, legal, ethical and discretionary (philanthropic) obligations that society expects organizations to assume, and these four dimensions comprise CSR. Economic responsibility reflects the understanding that organizations have a responsibility to produce goods and services that consumers and society wants or needs, and to be profitable in the process. Legal responsibility refers to the laws and regulations under which firms are expected to operate in fulfillment of their economic responsibilities. Ethical responsibility reflects society’s expectation of a firm over and above its legal requirements. It refers to business practices not codified by formal law but nonetheless expected of a firm by society. Finally, discretionary responsibility reflects the volitional or philanthropic activities of a firm for which society has no set guidelines or expectations. They are society’s expectation that firms assume social roles over and above their legal, ethical, or economic responsibilities, including philanthropic contributions and engagement in community enhancement programs. While Rust et al.’s (2004) framework only identified corporate ethics as a driver of brand equity; the literature on corporate social responsibility provides support for the inclusion of Carroll’s (1979) other three dimensions of CSR (economic, legal, and discretionary) as measures of the associations consumers might hold towards a firm. Organizational Competence. Another organizational association that consumers may have with respect to a brand is the level of competence of the organization’s management and coaching staff. The management literature has differentially defined organizational competence. Pitt and Clarke (1999) defined organizational competence

54 as a “coordinated, collective skill or capacity” (p. 302). Webster (1991) defined an organization’s distinctive competence as the attention focused on the customers served, the nature of the needs that are satisfied and the role of the firm's product and services that are offered in satisfying that set of needs. In attempting to meet the needs and wishes of their customers, Winter (1987) proposed competence be viewed as a strategic asset that, when appropriately deployed, enhances an organization’s adaptation to competitive and other environmental contingencies over time. Citing Chandler’s (1982) longitudinal study of factors impacting the organizational growth and survival of U.S. business enterprises, Reimann (1982) noted that it is the top decision makers within an organization that specifically play a key role in the organization’s competence in dealing with its environment. Johnson, Zinkhan, and Ayala (1998) conducted a study to investigate the relationship between consumers’ perceptions of organizational competency and their behavioral intentions. The authors proposed and tested a model to explain consumers’ willingness to recommend a service provider. The model considered four predictors of willingness to recommend, including: affect, outcome, competency and courtesy. Path analyses revealed that competency was found to influence the likelihood that the consumers would recommend a particular service provider. Based on the review of the literature presented in the preceding sections, it is likely that sport consumers will form brand associations that are related to the competencies of its personnel which are separate from their perceptions of the quality of the interactions that occur during service delivery/consumption. Whereas interaction quality refers the subjective appraisal of the quality of the interaction one has with an organization’s personnel during the delivery and consumption of the service, organizational competence refers to the associations that consumers have about the capacity of the upper level personnel, or decision makers (e.g., general manager, head coach, owner) to effectively perform in their roles. For example, anecdotal evidence suggests that fans of the Florida State Seminoles football team had negative associations related to the competence of the coaching staff during the 2006 season. Based on numerous conversations between the researcher and Florida State Seminoles ticket holders while the researcher was employed as a Graduate Assistant in

55 the Florida State Athletics ticket office, it is the conclusion of the researcher that the coaching changes made during the following off-season seem to have altered the valence of those associations. For now, Seminole Football fans seem to associate a higher level of competence to the current staff. According to Snehota (1990), the development of an organization's competence is the result of the linking together of its internal skills, activities, and resources to those of some external actors. This perspective is consistent with a resource-based view of the firm which equates capability, or competence, with an organization’s exploitation of both tangible and intangible value-generating assets and resources (Wernerfelt, 1984). Intangible assets refer to the personal knowledge of individuals as well as the collective knowledge of the firm. Crawford (2005) developed a framework for identifying and measuring aspects of competence related to senior management perceptions of project management competence (see Figure 2.08). The purpose of the study was to reconcile

Figure 2.08. Crawford’s (2005) integrated model of competence two disparate conceptualizations of competency in the literature, namely attribute-based and performance-based competency. The attribute-based approach defines

56 competency as individual characteristics related to job or situation performance (Crawford, 2005). Competency attributes are comprised of the knowledge, skills and experience, personality traits, attitudes, and behaviors of firm members. They are represented in the model as input competencies (knowledge, skills, and experience) and personal competencies (personality traits, attitudes, and behaviors). Performance based competency is an evaluation of “the ability of an individual to perform the activities within an occupational area to the levels of performance expected in employment” (Crawford, 2005, p. 9). Performance-based competency is represented in the model as output competencies. Crawford (2005) noted that together, input and output competencies account for the various aspects of competence that are addressed in the literature. Consumer evaluations of organizational competence have not been examined in the sport literature. In addition, researchers have not examined this construct in terms of an association that consumers might form. However, the extant general management literature demonstrating a link between perceived competence and behavioral intentions, as well as between perceived competence and organizational success suggests that competence should be examined as an association that is held by sport consumers. Crawford’s (2005) dimensions operationalize competency as a brand association in the current conceptualization of brand equity. In summary, brand equity refers to the associations and attitudes that consumers have about a brand. It is important to note that the conceptualization of brand equity presented here, that is, as an indirect driver of customer equity, does not comprise many of the dimensions of brand equity presented in the literature outside of a customer equity framework. Rust et al. (2000) noted that conceptualizations of brand equity within the scope of a customer equity framework do not include dimensions relating to the performance of the brand, nor the customer experience with the brand. The value equity driver of customer equity instead captures these dimensions. Additionally, the current conceptualization of brand equity does not include elements of a consumer’s perceptions of the relationship he or she has with an organization as this falls under the scope of relationship equity.

57 Figure 2.09 illustrates the components of brand equity identified in the preceding sections. In summary, brand equity was conceptualized as a hierarchical structure comprised of two latent second-order factors (brand attitudes and brand associations), 12 latent first-order variables, and 40 indicators. A review of relationship equity will now ensue. Relationship Equity The third and final indirect driver of customer equity identified by Rust et al. (2001) is relationship equity. Relationship equity refers to “the customer’s tendency to stick with a brand, above and beyond objective and subjective assessments of the brand” (Rust et al., 2001 p. 95). Relationship equity represents the strength of the relationship between the firm and the customer. The concept is rooted in the relationship marketing literature. To examine the relationship between relationship marketing and relationship equity, as well as how this construct impacts customer equity, it is necessary to briefly examine the relationship phenomenon. The phenomenon of the relationship between a consumer and a company can be considered as the interaction between two parties (Kelley, 1979). In order for a relationship to exist the parties must, at a minimum, have a mutual orientation and that they must have relevant context or meaning for the relationship (Kelley, 1979). Morgan and Hunt (1994) defined relationship marketing as referring to all of the marketing activities and strategies directed towards the creation, expansion, and maintenance of successful relational exchanges. Grönroos (2004) indicated that the relationship marketing perspective is based on the notion that the existence of a relationship between two parties creates additional value for the customer and also for the supplier or service provider. Thus, relationship marketing focuses on developing long-term relationships with an organization’s best customers. According to Bejou and Iyer (2006) there is a growing recognition that relationships with customers are a major source of a firm’s intangible assets. The intangible assets the authors are referring to are synonymous with the indirect drivers of customer equity that were identified above. Therefore, relationship-marketing activities may be viewed as serving a significant role

Figure 2.09. Model of Customer-Based Brand Equity for Team Sport Services

59 in enhancing the relationship-based equity of an organization as well as its financial performance and customer equity. The fundamental premise driving relationship marketing theory and practice is that an organization’s relationships with its customers enhances customer satisfaction and loyalty to the organization and that this loyalty contributes to higher profitability for the organization (Iyer, Sharma, & Bejou, 2006). The outcomes of relationship marketing strategies are referred to in the customer equity literature as relationship equity, and academics and practitioners have recently focused on customer relationship management (CRM) and customer equity management on organization profitability (Iyer et al., 2006). Relationship equity is thus identified as the outcome of relationship marketing strategies and it takes into account the customer equity developed and the processes that contribute to strengthening the relationship with customers (Iyer et al., 2006). As the focus of relationship equity is on customer assessments and evaluations of the direct relationship efforts of the firm, the goal of relationship marketing, at least from an organizational perspective, is to identify and retain long-term customers. In order to do so, it is important for firms to develop knowledge about how relationships are developed. According to Holmes (1991) the relationship paradigm has several phases of development, including: 1) awareness; 2) exploration; 3) expansion; 4) commitment; and 5) dissolution. The awareness phase is the launching of the relationship, and is characterized by the absence of any formal interaction. The exploration phase involves a search by the consumer for additional information with perhaps even a trial of the market offering. The expansion phase is typified by increased transaction and exchange and the development of evaluatory notions in the mind of the consumer, such as satisfaction and trust. The fourth stage is commitment, where the consumer demonstrates a level of selectivity and may have abandoned alternative market relationship offerings. The final phase of relationship development is the dissolution phase, where there is disengagement from the relationship on the part of one or both of the parties. The rate of development at which the customer-firm relationship progresses is influenced by several factors, including the nature and type of the goods and/or services

60 offered. Rust et al. (2001) suggested that relationship equity is key when the community associated with the product or service is as important as the product or service itself. According to the authors, certain products or services “have the added benefit of building a strong community of enthusiasts” (p. 25). Shani (1997) posited that spectator sports offer an ideal context for the practice of relationship marketing as spectator sports are generally consumed by highly involved consumers with a desire to associate with a team on a long-term basis. In examining the development of consumer relationships with sport service providers such as professional sport teams, it is possible to examine the phases of development in the context of the stages of adoption through which consumers’ progress in the development of their psychological connections to sport teams, as proposed in the Psychological Continuum Model by Funk and James (2001). As individuals progress from awareness through attraction, attachment, and allegiance, the relationship they have with a particular sport team strengthens. In using the PCM model as a framework for understanding how consumer-firm relationships develop and strengthen, it is possible to assert that as one’s psychological connection to an organization deepens, or strengthens, the relationship with that organization also strengthens. As a result, a major focus of organizations is to engage in activities and develop strategies that strengthen the psychological connection that individuals have with them thus strengthening the relationship. In field of marketing these activities are collectively referred to as relationship marketing. Given the relationship between relationship marketing and customer equity, it is important to underscore the role of relationship equity in understanding customer equity. Rust et al. (2004) argued that in today’s competitive service marketplace where consumers are presented with numerous choice opportunities, a strong brand that consistently meets customers’ expectations may not be enough to retain individuals as customers. Rather, it is often the strength of the customer-organization relationship that will determine a customer’s likelihood of remaining a frequent or repeat customer. Prior research in the area of CRM reveals that the key drivers of relationship equity are loyalty programs, affinity programs, community building programs, and knowledge building programs (Dowling & Uncles, 1997). According to Rust et al. (2001),

61 organizations can augment relationship equity through the manipulation of these drivers. Antecedents of relationship equity. Recognizing that a strong brand and the ability to meet customer expectations may not be enough to hold customers, many sport organizations are beginning to recognize the importance in developing and maintaining strong relationships with customers and are beginning to implement focused relationship marketing programs. As stated by Sheth and Parvatiyar (1995), a relationship marketing orientation has resulted in a number of novel marketing techniques geared at customer retention (e.g. membership programs, after-marketing). Rust et al. (2000) identified relationship building techniques to include loyalty and affinity programs, community building programs, knowledge building programs, and special recognition and treatment programs. Advances in technology over the last fifteen years or so have led to the adoption by many business organizations of customer loyalty programs. Uncles, Dowling, and Hammond (2003) identified two primary objectives that companies seek to achieve in adopting a loyalty program: First, organizations seek to increase sales revenue by increasing customer usage level and/or by increasing the range and products purchased. Second, organizations also seek to prevent customer defection by building a closer bond between the brand and current customers. The way that loyalty programs work is typically by rewarding customers for their patronage by offering financial and relationship rewards to those customers. The adoption of loyalty programs is becoming increasingly popular among sport teams and leagues. For example, in March of 2005 Major League Baseball launched their first loyalty club. A fee based membership club, the Players Choice Platinum Club provides members with various benefits, including: access to special exclusive events where they can interact with players. Special events include invitations to parties hosted by the players, meet and greet opportunities, player signings, golf tournaments, camps and clinics, and special road trip packages. The Los Angeles Dodgers, for example, rolled out their Think Blue Rewards loyalty program at the start of Major League Baseball’s 2006 spring training season. The program rewards loyal Dodgers fans by allowing them to earn points whenever they buy tickets, merchandise or

62 concessions at the ballpark or through coalition sponsors such as area restaurants, hotels, theatres, retail stores, and online retailers. The points can be redeemed for game tickets, merchandise, and special club events (Ferguson, 2005). Through this program the Dodgers are hoping to provide an incentive for their customers to attend more games and purchase more goods, as well as to have the Dodgers be the primary consideration for leisure spending. According to Rust et al. (2001), two key concepts are of important concern for loyalty programs to have a positive influence on customer equity. First, the benefits the customer associates with the firm’s loyalty program must be significantly greater than the actual “cash value” of the benefits received. Second, the value consumers seek from a loyalty program presents a solid opportunity for firms to strengthen relationship equity by creating a strong incentive for the customer to return to the firm for future purchases. Despite their increasing popularity, however, debate exists in the literature regarding the effectiveness of loyalty programs to achieve the outlined objectives. Some researchers question the effectiveness of loyalty programs in generating and sustaining customer loyalty (Barnes, 1997; Uncles, 1994). These researchers have each suggested that loyalty programs do not contribute very much to an organization’s bottom line, and thus are actually more of drain on company resources. Others (Rust et al., 2000), however, have suggested that a key motivation for firms to adopt customer loyalty programs does not relate to increasing customer loyalty at all. Rather, a primary function of loyalty program adoption is the facilitation of the collection of critical marketing information, such as demographic, psychographic, geographic, and contextual data about consumers in order to improve the efficiency and effectiveness of marketing initiatives. The adoption of affinity programs are another type of organizational approach to strengthening the emotional connection customers have with a firm. Macchiette and Roy (1992) defined affinity as “an individual’s level of cohesiveness, social bonding, identification, and conformity to the norms and standards of a particular reference group” (p.48). Thus, affinity marketing was defined as “a unique exchange process in which value-expressive products an services are marketed to reference groups with cohesiveness, common

63 interests, and/or values, usually offering shared incentives, in return for the group’s endorsement as a marketing leverage to its individual members or constituency” (p. 48). The authors identified three crucial distinguishing features of affinity marketing, namely: third party endorsement, shared incentive, and enhancement package. Rust et al. (2001) stated that affinity programs seek to imbed the firm’s product or service in some strong interest or emotional link of the customer so that the firm, or the firm’s product or service becomes an integral part of the customer. Affinity programs are different from loyalty programs in that affinity programs seek to build long-lasting relationships with external companies that can provide a direct benefit to the organization while enhancing the value of the customer-firm relationship to customer through special promotions. One of the most commonly recognized type of affinity programs involves the credit card industry. Most affinity programs involving credit cards are set up so that the card issuer (i.e., Visa, MasterCard, and American Express) gives a percentage of the face of the credit card to a sponsoring organization to display their logo. Cardholders can build up points that can be traded in for various rewards (such as points towards the purchase of a car, vacation, or merchandise). Additionally, a small portion of each card holder’s purchase amount is then returned to the sponsoring organization. For example, Major League Baseball fans can sign up for MLB Extra Bases MasterCard cards offered by Fia Card Services N.A. There is a card for each of the Major League Baseball teams to choose from, and cardholders can build up points towards autographed memorabilia by past and current players, VIP access, field-level game tickets, travel rewards and cash rewards. Sport organizations also offer their customers affinity programs with other businesses as well. For example, in becoming members of U.S. Lacrosse, consumers are eligible to receive special discounts and savings with US Lacrosse’s national partners, who include: Confertel, Hertz, and the Positive Coaching Alliance. In addition to receiving special discounts, members also indirectly support U.S. Lacrosse, as a portion of sales received from members are returned to that organization.

64 Customer community programs are another link between strong relationships and being a part of a community. Archol and Kotler (1999) defined a customer community as “a body of consumers who are involved with a company in a social relationship. They are involved because the product represents a significant aspect of their lifestyle and because they can enhance their satisfaction by participating in information- and experience-rich exchanges with the company and among themselves. The key feature is the ability of customers to interact among themselves” (p. 160). Rust, Zeithaml, and Lemon (2000) suggested that successful customer community building programs can increase consumer perceived switching costs. That is, the customer perceives that the whole community must switch to maintain the benefit of consumption. The authors further stated that for community building programs to be effective, customers must value the idea of community prior to the implementation of such a program. McAlexander, Schouten, and Koenig (2002) suggested that from a customer- experiential perspective, a brand community that is formed around a particular brand represents a fabric of relationships in which the customer is situated. The authors noted that a brand community is comprised of several crucial relationships, including those between a customer and other fellow customers. As was noted in the review of the literature on social value above, one of the primary motives for sport consumption is the motive to be a part of a community around a particular team. Thus, community building programs that foster stronger ties to a particular sport consumption community are also strengthening the relationship customers have with the firm, which ultimately will lead to an increase in customer equity. In team sports, these customer communities can include: fan clubs such as D.C. United’s Screaming Eagles, booster organizations such as the Seminole Boosters at Florida State University, and Internet web sites providing fans a place to chat or blog with other fans, such as Planet Orange, the Phoenix Suns’ official social network website. Rust et al. (2000) noted that the key to a customer community is that the organizations find a way in which to increase customers’ perceived cost of switching to

65 a competitor. The switching cost refers to the community that customers would lose if they stopped doing business with the organization. Thus, it is crucial for marketers to facilitate shared customer experiences in order to strengthen the communities that involve their brand. Knowledge programs also contribute to the relationship equity of a firm. Alba and Hutchinson (1987) proposed consumer knowledge to be an important consumer construct as it influences how consumers gather and organize information, and ultimately what products consumers buy and how they use them. Rust, Lemon, and Narayandas (2004) stated that knowledge building programs increase relationship equity by “creating structural bonds between the customer and the firm, making the customer less willing to recreate the customer firm relationship with an alternative provider” (p. 26). Many sport organizations have recognized the value that offering knowledge programs have in strengthening relationships with existing and potential customers. For example, in an effort to attract more women to football games, the NFL and its member franchises have offered classes entitled “football 101 for women” to educate women about the rules and nuances of football. Football 101 is a knowledge building program designed to educate women on the fundamentals of football. The class is typically taught by current and former players of member teams, and participants learn about the history of their team and the NFL, rules, and positions. The classes also usually involve a tour of the facilities as well as question and answer sessions. In this example, NFL teams used knowledge building programs as a fun and educational way to increase knowledge about their product in order to strengthen their relationships with a targeted segment of their market. Finally, special recognition and special treatment programs as a relationship equity marketing strategy are another important aspect to creating, developing, and maintaining successful marketing relationships (Lacey, 2003). Preferential treatment is defined as “the practice of giving selective customers’ elevated social status recognition and/or additional or enhanced products and services above and beyond standard firm value propositions and customer service practices” (Lacey, Suh, & Morgan, 2007, p. 242). These types of programs seek to provide value added services to customers in

66 recognition of their loyalty to the firm (Rust et al., 2000). The authors further noted that these value added services foster an emotional attachment to the brand/firm. Zabin and Brebach (2004) noted that an increase in the popularity of preferential treatment programs has been spawned by the emergence of relationship marketing, as firms seek more strategic and effective approaches for retaining valued customers. Incentives for firms to develop these types of programs are highlighted be recent research findings suggesting that preferential treatment of customers can potentially contribute to important relational outcomes valued by organizations, including: increased purchases, share of customer, word-of-mouth, customer feedback, and customer relationship commitment (Lacey et al., 2007). Figure 2.10 illustrates the components of relationship equity identified in the preceding sections. In summary, relationship equity was conceptualized as being comprised of 5 latent first-order variables (loyalty programs, affinity programs, product knowledge, customer community, and preferential treatment) and 20 indicators.

Scale Development

The purpose of the current research project is to identify and empirically test measures to assess the components of value equity among sport spectators. Although the testing of the entire model of customer equity is the ultimate goal of this line of research, the researcher has chosen to focus on only one dimension of customer equity. Given that measures for many of the constructs have not been well developed, the researcher thought it prudent to limit the focus of this research to developing reliable and valid measures of one dimension so that the entire model can ultimately be tested. Surveys, or self-report scales, are popular measurement tools used by marketing researchers to collect a wide variety of information about consumers. The collected data enables the description and comparison of people’s attitudes, knowledge, perceptions, motivations, and behaviors and are therefore often used to make important decisions concerning marketing strategy. Because of the central role that self-report scales have assumed in the acquisition of knowledge about consumers, it is critical that researchers adhere to proper methodologies in their development. Stressing the

Figure 2.10. Framework of Relationship Equity in Team Sport Services

68 importance of ‘good’ scale development, McIntire and Miller (2000) suggested that good surveys share similar essential characteristics. These included specific and measurable objectives; straightforward and easily understood questions; subjection to a pretest or pilot to ensure question clarity; administration to an adequate population or sample so that generalizations are possible; subjection to appropriate analysis; an accurate reporting of results; and they are reliable and valid. The following section presents a review of the scale development literature used to inform the development of the scale used in the current research project. The purpose of this section is to describe the rationale for the scale development approach utilized to construct a measure of value equity in spectator sports and to specify the sequence of steps used to develop the measurement instrument. Churchill (1979) presented one the most informative works involving the development of measures within the field of marketing. His seminal article, entitled ‘A paradigm for developing better measures of marketing constructs,’ has informed numerous subsequent writings on scale development in marketing, including works by Gerbing and Anderson (1988), DeVellis (1991), Spector (1992), Flynn and Pearcy (2001), and Netemeyer, Bearden, and Sharma (2003). Contending a need for, and lamenting the lack of adequate measures in marketing, Churchill (1979) proposed an eight-step procedure for developing better measures (see Figure 2.11). The first step involves a specification of the domain of construct. In this stage the researcher explicitly delineates what is included and what is excluded in the definitions of the dimensions of interest (Churchill, 1979). Step two is comprised of a generation of items, which specifically capture the specified domains. This typically involves the use of techniques helpful in exploratory research, including literature searches, experience surveys, and insight-stimulating examples (Churchill, 1979). The third step involves a first round of data collection. Churchill (1979) indicated that the type of data collected is dependant on the type of scale used to measure the construct. Step 4 of Churchill’s (1979) scale development progression entails a purification of the measure. A second round of data collection was called for in the fifth step of the procedure. The second data collection was followed by assessments of reliability and validity in the sixth and seventh steps. The final step of Churchill’s (1979)

69 Recommended Coefficients or Techniques

1. Specify domain Literature search of construct

2. Generate sample Literature search of items Experience survey Insight stimulating examples Critical incidents Focus groups 3. Collect data

4. Purify Coefficient alpha measure Factor analysis

5. Collect data

6. Assess Coefficient alpha reliability Split-half reliability

7. Assess Multitrait-multimethod matrix validity Criterion validity

8. Develop Average and other statistics norms summarizing distribution of scores

Figure 2.11. Churchill’s (1979) Procedure for Developing Better Measures

70 scale development process model consisted of the development of norms. An in-depth review of the major steps in scale development is presented in the following sections. Specification of the Domain of the Construct The specification of the domain of construct is one of the most critical steps of the scale development process, as the definitions generated at this stage serve as the basis for developing items at the formative stages of the process and are important for determining validity during the latter. Mackenzie (2003) commented that the failure to adequately define the constructs of interest imposes palpable difficulties in the second step of Churchill’s (1979) process, namely the generation of sample items. He argued that improper construct definition makes it difficult for researchers to develop measures that accurately represent its domain, and difficult to specify how the construct should relate to its measures. Clark and Watson (1995) noted that writing out a brief, formal description of a construct of interest can also be useful in crystallizing one’s conceptual model and increases the likelihood that the resulting scale will make a substantial contribution to the literature. Generation of Sample Items The second step in Churchill’s (1979) paradigm is the generation of a sample of items, or measures, which capture the domain as specified. The creation of an initial pool, or sample, of items is a critical stage in scale construction. Clark and Watson (1995) stated that the principal goal at this stage is to systematically sample all content that is potentially relevant to the target construct. To emphasize their point, the authors cited Loevinger’s (1957) articulation of the principal, which read: “The items of the pool should be chosen so as to sample systematically all contents which might comprise the putative trait according to all known alternative theories of the trait” (p. 659). Mackenzie (2003) indicated the goal to developing measures is to ensure that all key aspects of the conceptual definition are reflected in the measures, the items are not contaminated by the inclusion of things that are not part of the conceptual domain, and that the items are properly worded. Churchill (1979) noted that techniques typically productive at helping to accomplish these goals are those that are often used in exploratory research, including literature searches, experience surveys, and insight stimulating examples. It was emphasized that theses techniques be employed towards

71 the goal of developing a set of items which tap each of the dimensions of the construct at issue. Furthermore, Churchill (1979) advised the inclusion of items with slightly different shades of meaning as the original list will be refined to produce the final measure. Initial Data Collection The third step in Churchill’s (1979) paradigm is an initial round of data collection. This first round of data collection is typically conducted in the form of a pilot-test, or pilot study. Clark and Watson (1995) noted that it can be very helpful for a researcher to do some preliminary pilot-testing on moderately sized samples before launching a main study. A pilot study is a smaller version of a study that is carried out before the actual investigation is done. More specifically, it is a scientific investigation of the new test’s reliability and validity for its specified purpose. The pilot test process involves the administration of the initial test to a sample of the test’s target audience and analyzing the data obtained (McIntire & Miller, 2000). As indicated in the last statement, a key determinant of the success of the pilot test administration is the sample to which the test is given. Pilot study respondents ought to be as similar as possible to the target population of interest and the test should be given in a situation that matches the actual circumstances in which the test will be used (McIntire & Miller, 2000). This is important as the information gathered at this stage is used in the refinement and modification of the research methodologies. At this stage it is also likely that basic content item decisions will made, decisions that will shape the future empirical and conceptual development of the scale (Clark & Watson, 1995). Purification of the Measure Babbie (2004) noted that the construction and evaluation of measurements requires social scientists to pay particular attention to the technical considerations of reliability and validity. Reliability. Reliability refers to “that quality of a measurement method that suggests that the same data would have been collected each time in repeated observations of the same phenomenon” (Babbie, 2004, p. 141). A desired characteristic of any measure is its ability to capture information about a given construct consistently (Kline, 2005). Reliability is the degree to which measures are free from

72 random error and yield consistent results (Dillon, Madden, & Firlte, 1994). In other words, the more reliable a test, or instrument is, the more confidence the researcher will have that the measurement will give close to the same result every time the same property is measured (Reaves, 1992). Thus, reliability is one of the most important standards for determining how trustworthy data derived from a survey instrument are (McIntire & Miller, 2000). The use of a multi-item measurement scale requires internal consistency be measured for reliability. McIntire and Miller (2000) defined internal consistency as “the internal reliability of a measurement instrument; the extent to which each test question has the same value of the attribute the test measures” (p. 572). One of the most commonly used methods for establishing the internal consistency of a measure is the coefficient alpha, or Cronbach’s alpha. In fact, Churchill (1979) emphatically endorsed the use of Cronbach’s alpha as the preferred measure of the internal consistency of a set of items when he wrote: “Coefficient alpha absolutely should be the first measure one calculates to assess the quality of the instrument” (p. 68, emphasis in original). He noted that that the alpha coefficient is awash with meaning because of Nunnally’s (1978) observation that the square root of the coefficient alpha is the estimated correlation of the k-item test with errorless true scores. Thus, an alpha equal to 1.0 occurs when all items measure only the true score and there is no error component. By convention, a commonly accepted cut-off indicating an adequate scale is .70 (Nunnally, 1978; Nunnally & Bernstein, 1994). An alpha coefficient that is lower than .70 indicates that the sample of items do not adequately capture the construct of interest, while higher scores indicate that the measures do correlate well with the true scores. Another measure of internal reliability consistency is construct reliability. The coefficient of construct reliability is based on a definition of reliability as an assessment of the variance in the indicators explained by the common underlying latent construct. The calculation of the construct reliability is a more appropriate measure of reliability than Cronbach’s alpha when the items have unequal reliabilities (Anderson & Gerbing, 1988). Gerbing and Anderson (1988) noted computing alpha for items with unequal relaibilities leads to an underestimation of the reliability of the composite score. The authors recommended using the following formula to calculate construct reliability:

Another third measure of internal reliability consistency is the item-total correlation. The calculation of the item-total correlation represents a good way in which to assess the contribution of a single item to overall consistency. The item-total correlation coefficient symbolizes the strength and direction of the relation between the way the test takers responded to one item and the way they responded to all of the items as a whole (McIntire & Miller, 2000). The stronger the positive correlation of an item, the more it will contribute to internal consistency. Item-total correlations with values greater than .50 are considered acceptable and may be retained in the scale (Robinson, Shaver, & Wrightsman, 1991). Those falling short of this criterion are inadequately correlated with the overall scale and may be dropped. Additionally, a negative item-total correlation signifies the need for an individual item to be reverse- coded or coded in the opposite direction. If this happens, the reliability analysis should be recomputed. Validity. Reliability, as noted by Kline (2005), is also a vital element of the validity of an instrument as valid tests are, in practice, highly consistent. Thus, a primary goal of scale development is also the creation of a valid measure of an underlying construct. Babbie (2004) formally defined validity as “the extent to which an empirical measure adequately reflects the real meaning of the concept under consideration” (p. 143). Simply put, a measure is valid if it measures what it claims to measure. Unlike reliability, however, the calculation or estimation of validity is not straightforward and cannot derive from the meeting of an established set of criteria. Rather, Kline (2005) described the determination of whether or not a test is valid as being dependent on a matter of opinion in light of the evidence about its validity. Kline (2005) noted there are various ways of demonstrating test validity with each contributing facets of its meaning. The primary types of validity of concern in for scale development include face validity, content validity, and construct validity (Babbie, 2004).

74 Face Validity Face validity, refers to the quality of an indicator that makes it seem a reasonable measure of some variable (Babbie, 2004). Kline (2000) noted that a test is face valid if “it appears to measure what it purports to measure, especially to subjects” (p. 4). It is important to note that in the field of motivation and attitude testing, face validity displays little resemblance to true validity (Kline, 2000). This is because tests of this kind require subjects to be truthful, cooperative and insightful (Kline, 2000). Truthful refers to the degree to which peoples’ responses reflect actual behavior. Cooperative represents the degree to which the subject is willing to take the test seriously and think carefully about his or her responses. Insightfulness requires the individual to have actually acquired a modicum of experience with the subject matter under investigation. Kline (2000) argued that face validity is only of importance because people will generally not cooperate on tests that lack-face validity. Content Validity Similar, but not to be confused with face validity, content validity refers to “how much a measure covers the range of meanings included within a concept” (Babbie, 2004, p. 145). A test is said to have content validity when the instrument is shown to represent the phenomena under study. As is the case with face validity, there are no statistical procedures used in determining the content validity of a test instrument. Rather, a panel of experts or focus groups of representative subjects are typically asked to determine content validity (Dempsey & Dempsey, 2000). Finally, Dempsey and Dempsey (2000) revealed that content validity is especially useful when developing instruments that measure specific areas of knowledge. Construct Validity According to Kline (2000), construct validity is a powerful method of demonstrating the validity of tests for which the establishment of a single criterion is difficult. It is defined as the degree to which a measure relates to other variables as expected within a system of theoretical relationships (Babbie, 2004). Construct validity involves accumulating a body of evidence suggesting that a test is based on sound psychological theory and then describing the extent to which inferences can legitimately be made from the operationalizations in one’s study to the theoretical constructs on

75 which those operationalizations were based (Trochim, 1999). Two strategies exist for demonstrating construct validity, namely convergent and discriminant validity. Convergent validity demonstrates that constructs that should be related to the test scores are indeed related, while discriminant validity shows that constructs that should not be related to the test scores are in fact not related (McIntire & Miller, 2000). It is recommended that researchers establish both of the two main types of construct validity for their constructs. Convergent Validity. Convergent validity is a measure of the degree to which different indicators of theoretically similar or overlapping constructs are strongly interrelated (Brown, 2006). The concept refers to a convergence among different methods designed to measure the same construct (Campbell & Fiske, 1959). According to Campbell and Fiske (1959), the evaluation of construct validity requires an examination of the correlation of the measure being evaluated with the variables that are known to be related to the construct allegedly measured by the instrument being evaluated. Correlations fitting the expected pattern are thought to contribute to evidence of convergent validity, and thus construct validity. Alternatively, convergent validity can also be established through an examination of the average variance extracted (AVE) scores. AVE is a measure of the amount of variance explained by a construct relative to the amount of variance attributed to measurement error. AVE scores are computed by summing the squared item loadings for a given construct and then dividing the output by the number of items in the scale. According to Fornell and Larcker (1981) convergent validity is evidenced when AVE scores exceed .50. Values above .50 suggest that less than half of the total variance of a construct is derived from measurement error (Fornell & Larcker, 1981). Further evidence of convergent validity results from the establishment of external validity. External validity refers to the degree to which the conclusions in one’s study are generalizable to other places and at other times (Trochim, 1999). Shadish, Cook, and Campbell (2004) stated an experiment possesses external validity if the experiment’s results hold across different experimental settings, procedures, and participants. Three general conditions have been described as facilitators of the assurance of external validity, namely: 1) statistical generalizability; 2) conceptual

76 replicability; and 3) realism. Ferber (1977) noted that the generalizability of experimental results to the larger population of interest is dependent upon the appropriate use of probability sampling procedures. Realism refers to the degree to which the tasks, stimuli, and settings employed were realistic, as the more realistic the experiment is, the more generalizable the results are thought to be (Berkowitz & Donnerstein, 1982). Discriminant Validity. Discriminant validity is a measure of the uniqueness of the constructs. Discriminant validity refers to the distinctiveness of constructs, demonstrated by the divergence of measures of different constructs (Campbell & Fiske, 1959). According to Brown (2006), discriminant validity is indicated when the results show that indicators of theoretically distinct constructs are not highly intercorrelated. Brown (2006) further commented that discriminant validity is present when “behaviors purported to be manifestations of different types of delinquency load on separate factors, and the factors are not so highly correlated as to indicate that a broader construct has been erroneously separated into two or more factors” (p. 3). In multifactorial CFA solutions, discriminant validity is established through an examination of the size of factor correlations. Factor correlations approaching 1.0 provide strong evidence that the latent factors may not represent distinct constructs. According to Kline (2000), in applied research, factor correlations exceeding .85 are indicative of poor discriminant validity. A second method for establishing discriminant validity is the factor-analytic method proposed by Fornell and Larcker (1981). In this method, the researcher compares the construct’s average variance extracted to their shared variance. The square root of the average variance extracted for each construct should be greater than the absolute value of the standardized correlation of the given construct with any other construct in the analysis. In their discussion of theoretical principles, practical issues, and pragmatic decisions to aid researchers pursuing scale development, Clark and Watson (1995) provided four recommendations to aid researchers to maximize the construct validity of scales and subscales. First, it was noted that an essential task for researchers is to have a clear conceptualization of the target constructs. This involves the writing of brief,

77 formal descriptions of the constructs used in one’s conceptual model. The authors stated that by thinking about the theoretical issues prior to the actual process of scale construction, the likelihood that the resulting scale will make a substantial contribution to the literature is increased. Second, it was advised that the content of the initial item pool should be over inclusive. The logic behind this proposition is that subsequent psychometric analysis will identify weak, unrelated items that should be dropped from the emerging scale but are powerless to detect content that should have been included but was not. Additionally, Clark and Watson (1995) noted that in addition to sampling a sufficient breadth of content, researchers must ensure that there exists an adequate sample of items within each of the major content areas comprising the broadly conceptualized domain so that one or more areas are not underrepresented in the final scale. Next, Clark and Watson (1995) proposed the item pool be tested, along with the variables that assess closely related constructs, on a heterogeneous sample representing the entire range of the target population. Finally, it was advocated that in selecting scale items, the goal is to achieve unidimensionality. Unidimensionality as determined through factorial analysis provides an indication of whether the scale items assess a single underlying factor or construct. Criterion Validity Another approach used for establishing the validity of a quantitative measuring instrument is criterion-related validity. This type of validity refers to the relationship of the measuring instrument to an already known external criterion or other valid instrument (Dempsey & Dempsey, 2000). Two-types of criterion-related validity are used to examine the relationship of the instrument to the external criterion: 1) predictive validity; and 2) concurrent validity. Predictive validity refers to the ability of the instrument to predict one’s behavior at a future point in time. The behavior is later compared with the prediction to determine whether the measurement was related to the correct property (Reaves, 1992). Concurrent validity is a measure of how well a measurement instrument correlates with another similar instrument that is known to be valid. Reaves (1992) noted that the establishment of concurrent validity typically involves “giving the measurement to two groups, one of which is not to have more of the

78 property of interest than the other, and looking for a corresponding difference in the measurement” (p. 348). External Validity Cook and Campbell (1979) defined external validity as “the approximate validity with which we can infer that the presumed causal relationship can be generalized to and across alternate measures of the cause and effect and across different types of persons, settings, and times” (p. 37). External validity refers to the generalizability or applicability of inferences obtained in a study to other individuals or entities, other settings or situations, other time periods, or other methods of observation/measurement (Tashakkori & Teddlie, 1998).

Summary

Taken together, the components of customer equity presented in the literature review provide opportunities for sport teams to maximize the likelihood of customer repurchase, maximize the value of a customer’s future purchases, and reduce the likelihood of a customer switching to another provider. This research project encompasses the proposal and testing of a part of the conceptual framework of customer equity in spectator sports and provides an initial place for further research investigation. The conceptual framework presented in the literature review provided an opportunity to describe the components that may contribute to customer equity in spectator sport and identify theory to help measure its impact. The literature supports the notion that the ability to measure the components of customer equity will enable sport managers to realize a number of potential monetary and non-monetary benefits, including increased up-selling, reduced process or transaction costs, reduced customer attrition and decreased customer complaints. The next chapter outlines the methods utilized in this study to achieve the identified objectives.

79 CHAPTER THREE

METHODOLOGY & PILOT STUDY

The purpose of the current study was to develop a valid and reliable instrument to measure the domains comprising value equity in the context of spectator sports. This chapter presents the methodology used to develop the scale to measure the multidimensional construct of value equity. Specifically, discussion of the research objectives, research design, population and sample, sampling method, data collection, instrument development, and data analysis are presented. The chapter also includes a reporting of the results of a pilot study.

Research Objectives

To date, researchers have not empirically investigated the factor structure of the components of value equity in spectator sports. The present study comprises the following three objectives: 1) define the concept of sport related customer equity and propose dimensions to account for the various latent constructs; 2) develop a valid and reliable instrument that captures the value equity dimension of sport related customer equity and its various facets; and 3) validate the Value Equity in Spectator Sports Scale (VESSS) in different contexts. Towards these objectives, two research questions directed the data collection and analysis on sport consumers perceptions and evaluations of the components of value equity. 1. Are the evaluations of the proposed components of value equity for team spectator team sport reliable within their respective factors? 2. How well does the hypothesized measurement model involving second-order and first-order factors fit the observed data?

80 Research Design

This study utilized a quantitative research design using cross-sectional survey instrument data to develop a valid instrument to explore the dimensionality of value equity in spectator sports. The research design was based on Churchill’s (1979) suggested procedures for developing better measures. Churchill (1979) proposed a series of eight sequential steps to be performed in developing measures of marketing constructs. The suggested procedures include: 1) the specification of the domains of the constructs; 2) the generation of a sample of items; 3) first data collection; 4) the purification of the measure; 5) a second data collection; 6) an assessment of the reliability; 7) an assessment of the validity; and 8) the development of norms. The literature review presented in chapter two informed steps one and two of Churchill’s (1979) suggested procedure for developing better measures in this research project, and enabled the researcher to propose the model of Value Equity in Spectator Sport Scale (VESSS) presented in Figure 1.01. In Step 1, the domains of six value equity factors were defined. They included: 1) entertainment value; 2) social value; 3) service quality; 4) perceived price; 5) epistemic value; and 6) satisfaction. In Step 2, sample items for each domain were developed and an initial instrument was prepared. A pilot study was conducted to satisfy stages three and four of Churchill’s (1979) process. In Step 3, the data collection for the pilot study involving spectators of a National Collegiate Athletic Association (NCAA) Division I collegiate baseball team was conducted to test the survey instrument. Step 4 involved the purification of the proposed items. An assessment of the reliability and validity of the data collected in Step 3 using various statistical analysis techniques and exploratory factor analysis was conducted. A second round of data collection involving the spectators of a Minor League Baseball team was undertaken in Step 5. In Steps 6 and 7 reliability and validity assessment were conducted for the second round of data collection using assorted statistical measures and confirmatory factor analysis. Steps 1 and 2: Specification of the Domains of Construct and Generation of Sample Items

Steps one and two in Churchill’s (1979) process involve the specification of the domains of construct and a generation of sample items. The following section presents

81 the specification of domains of value equity and a generation of sample items that were informed from the review of the literature. Rust, Zeithaml, and Lemon (2000) defined value equity as “the consumer’s objective assessment of the utility of a brand based on perceptions of what is given up for what is received” (p. 68). Several scales have been proposed in the literature to measure consumer perceived value. Sweeney and Soutar (2001) developed and tested the PERVAL scale, a multiple item scale to assess consumers’ perceptions of the value of consumer durable goods based on Sheth et al.’s (1991) model of perceived value. As outlined in the literature review, the authors conceived of consumer perceived value as being comprised of four dimensions, namely: 1) emotional value, 2) social value, 3) functional value (price/value for money), and 4) functional value (performance/quality). The drivers of value equity in the current study were derived from the dimensions of Sheth et al. (1991) and Sweeney and Soutar (2001) with modifications to enhance clarity and understanding. Additionally, the current study did not employ the measures developed and tested by Sweeney and Soutar (2001). Instead, measurement of each of the drivers of value equity were proposed using scales that are either well established in the literature, or are developed in this research project. The rationale for selecting measures other than those presented by Sweeney and Soutar (2001) is that their model was developed to assess the perceived value of consumer durable goods. The measures contained herein are specific to the spectator sport services industry. A description of the individual measures of the dimensions of value equity employed in the current study follows. Entertainment value. Sweeney and Soutar (2001) defined emotional value as “the utility derived from the feelings or affective states that a product generates” (p. 211). Their definition of emotional value is synonymous to Hirschman and Holbrook’s (1982) characterization of hedonic consumption, defined as “those facets of consumer behavior that relate to the multi-sensory, fantasy and emotive aspects of one’s experience with products” (p. 92). The congruence between the two definitions indicates that for many people, the consumption of sport services may be a form of entertainment whose value is derived from the enjoyment associated with an event. For the current research project, entertainment value is considered an indirect driver of customer equity and a component of consumer value. Research presented by James,

82 Sun, and Lukkarinen (2004) provides insight into the measurement of the entertaining aspects of attending a sporting event. Modified items developed by those authors were included as a measure of entertainment value. Table 3.01 shows the original dimensions and measures of entertainment value accompanied by the revised items used in the current stage of the study. Social value. Social value may be conceptualized in two ways. Sweeney and Soutar (2001) viewed social value as “the utility derived from the product’s ability to enhance social self-concept” (p. 211). This definition suggests that from a social perspective the consumption experience is socially valuable only for those who have a negative or weak self-concept. This definition of social value is limited in that it precludes, or does not account for consumers whose self-concept is not lacking. Additionally, it is important to note that the authors were interested in the social value of durable consumer goods. The consumption of services, particularly the consumption of spectator sports, is different from the consumption of durable goods, in that the former are typically consumed and experienced simultaneously with other consumers, thus forming a consumption community of sorts. An examination of the writings of Duncan (1983) and of Melnick (1993) suggests that the social value derived from the spectator sport consumption experience requires a broader definition. These authors proposed that people attend sport events to satisfy deep-rooted needs for sociability, which allows individuals to develop a close identification with a group of other fans and promotes a sense of camaraderie. Although there are no existing scales appropriate for measuring the social value derived from the consumption of sporting events, research on the various motives thought to drive sport consumption (e.g. Funk, Mahony, & Ridinger, 2002; Funk, Ridinger, & Moorman, 2003; James, Kolbe, & Trail, 2002; James & Ross, 2004; Trail, Fink, & Anderson, 2003; Trail & James, 2001; Wann, 1995) provide some ideas as to where social value lies. These various works provide valuable information to draw upon in delineating the attributes that sport consumers might deem to be socially valuable. Table 3.02 contains a list of both the original and modified dimensions and measures of social value in a spectator sport setting.

83 Table 3.01. Dimensions and Items of Entertainment Value

Original Items* Dimension (James, Sun, & Lukkarinen, 2004) Revised Items

1. I value the special events that are 1. of the special events that are organized organized by the team. by the team 2. The special activities going on before 2. of the special activities going on at the games are important to me. game before and during games Amusement 3. The special promotions that are a part of 3. of the special promotions that are a the team name games are meaningful to part of the team name games me. 4. of the special activities going on during 4. The special activities going on during the a game game are important to me.

1. It just wouldn't be a team name game if I 1. I like to party at the game. It just didn't party wouldn't be a team name game if I 2. There is a party atmosphere at team name didn't party games. 2. of the party atmosphere 3. Team name games provide me an 3. of the opportunity to party, which is opportunity to party. more interesting than watching the 4. I drink alcohol at the game, which is a big Partying game part of watching baseball games. 4. I drink alcohol at the game, which is a 5. I like that people can get a little drunk if big part of watching team name games they choose to at team name games. 5. people can get a little drunk if they 6. The team name baseball experience choose to enables people to drink heavily. 6. the team name experience enables 7. Partying at team name games is more people to drink heavily interesting than watching the games.

1. there is something special about being 1. There is something special about being in in a crowd at the stadium a crowd at name of stadium. 2. I love the feeling of being surrounded 2. I love the feeling of being surrounded by all Crowd by thousands of fans of the fans. Experience 3. I feed off of the excitement of the 3. I feed off of the excitement of the crowd at crowd at team name games team name games. 4. the excitement among the fans at team 4. The excitement among the fans at team name games is exhilarating. name games is exhilarating.

Note: * The leading statement, “I would attend a team name game because...” preceded the original questionnaire items.

84 Table 3.01 (continued) – Dimensions and Items of Entertainment Value

Original Items* Dimension (James, Sun, & Lukkarinen, 2004) Revised Items

1. Watching team name games is a very 1. watching team name games is a very intense experience for me intense experience for me 2. I really get into the game when I watch 2. I really get into the game when I watch team name games. 3. I concentrate very hard on the action on 3. I concentrate very hard on the action on the field the field. 4. I feel as much a part of the game as the 4. I feel as much a part of the game as the Game players players. Intensity/ 5. the action on the field is most important 5. The action on the field is most important to Immersion to me me. 6. when I am at the game, nothing else 6. When I am at the game, nothing else matters but the game matters but the game. 7. my focus is on the game, and not the 7. My focus is on the game, and not the other other activities at the stadium activities at the stadium. 8. the game is the most important thing at 8. The game is the most important thing at the stadium the stadium.

1. the distraction that a team name game 1. Team name games provide me with a provides from my everyday activities. distraction my everyday activities. 2. it provides me with a distraction from my 2. Team name games provide me with a Escape daily life for a while. distraction from my daily life for a while. 3. I could get away from the tension in my 3. Team name games allow me to get away life. from the tension in my life.

1. I like team name games because of the 1. of the natural elegance of the game. natural elegance of the game of sport. 2. of the gracefulness associated with the Aesthetics 2. I like the gracefulness associated with the game. game of sport. 3. of the beauty and grace of sports. 3. I like the beauty and grace of sports.

1. of the uncertainty of a close team name 1. I like the uncertainty of a close game. game. 2. I like team name games where the 2. I like team name games where the outcome is uncertain. outcome is uncertain. Drama 3. A close game involving team name is more 3. close game involving team name is more enjoyable than a blowout. enjoyable than a blowout. 4. I prefer watching a close game rather than 4. a close game is more enjoyable than a a one-sided game. one-sided game.

Note: * The leading statement, “I would attend a team name game because...” preceded the original questionnaire items.

85 Table 3.02. Dimensions and Items of Social Value.

Dimension Original Items Revised Items

1. Being with my family is why I enjoy sport games (James & Ross, 2004). 1. I enjoy spending time with my family at 2. I enjoy team name games because team name games. Family they are a good family activity 2. I enjoy team name games because (James & Ross, 2004). they are a good family activity. 3. Attending team name games gives 3. Team name games give me a chance me a chance to bond with my family to bond with my family. (Funk et al., 2003).

1. I enjoy team name games because they provide an opportunity to be with 1. I enjoy team name games because my friends (James & Ross, 2004). they provide an opportunity to be with 2. Having a chance to see friends is one my friends Friends thing I enjoy about sport games 2. Having a chance to see friends is one (James & Ross, 2004). thing I enjoy about team name games. 3. Attending games gives me a chance 3. Being at team name games gives me a to bond with my friends (Funk et al., chance to bond with my friends. 2003).

1. Games are great opportunities to 1. Team name games give me a great socialize with other people (Trail & opportunity to socialize with other James, 2001). people. 2. I like to talk to other people sitting Non 2. I like to talk to other people sitting near near me during the games (Trail & Acquaintances me during team name games. James, 2001). 3. Interacting with other fans is a very 3. Interacting with other fans is a very important part of being at team name important part of being at games games. (Trail & James, 2001). 1. Team name games provide me with a great opportunity to entertain my clients. 1. The opportunity to entertain clients at Business 2. Team name games give me a chance to sport games (James et al., 2002). Opportunitiesa socialize with people from my work.

3. Team name games give me the opportunity to entertain potential clients. Note: a The researcher developed two additional items to ascertain the social value derived from business opportunities.

86 Service quality. Based on their empirical validation of Rust and Oliver’s (1994) three-component conceptualization of service quality, Brady and Cronin (2001) proposed a hierarchical model of service quality involving both primary and secondary dimensions. This model was used to measure service quality perceptions within professional spectator sport. Given that Brady and Cronin (2001) have formally developed measures of service quality according to Churchill’s (1979) guidelines for scale development, the current research represents an opportunity to employ their scale to measure perceptions of service quality in the spectator sport industry. The authors defined service quality as a customer’s perceptions of the quality of at least one of the following situations: 1) the interactions with the organization; 2) perceptions of the quality of the physical environment in which the service is consumed and produced; and 3) the perceptions of the quality of the outcome. Each of these dimensions has three secondary dimensions. Interaction quality includes consumer perceptions of employees’ attitudes, behaviors, and expertise. The quality of the physical environment is comprised of consumers’ evaluations of ambient conditions, design, and social factors. Finally, outcome quality includes consumers’ perceptions of waiting time, tangibles, and valence. The current study employs the measures developed by Brady and Cronin (2001). Each of the measures and items are identified in Table 3.03. Perceived price. Dickson and Sawyer (1990) suggested that consumers often encode prices in ways that are meaningful to them because they have difficulty remembering the exact price that they pay for a product or service. The subjective nature of price perceptions suggests that it is imperative for organizations determine the prices that customers perceived that they paid. Zeithaml (1988) defined perceived price as “what is given up or sacrificed to obtain a product” (p. 10). Kashyap and Bojanic (2000) stated that this sacrifice is a combination of monetary and non-monetary price, including such factors as search costs and convenience. Several multi-item scales have been developed to measure customers’ perceptions of the monetary dimension of perceived price (Sweeney & Soutar, 2001; Voorhess, 2006; Yoo et al., 2000). Items from each of these scales were adopted in the current study to assess the monetary dimension of perceived price.

87 The scales identified, however, do not incorporate non-monetary measures of perceived price. Thus, to accurately assess this dimension of perceived price, the current study also incorporates items from Berry, Seiders, and Grewal’s (2002) conceptualization of service convenience as measures of the non-monetary dimension of perceived price. Specifically, five items suggested by the authors are adopted in the current research (see Table 3.04).

Table 3.03. Dimensions and Items of Service Quality

Original Items Dimension Revised Items (Brady & Cronin, 2001).

Attitude Attitude 1. You can count on the employees of the 1. You can count on the ballpark employees to be team name to be friendly. friendly. 2. The attitude of the team name employees 2. The attitude of the ballpark staff demonstrates demonstrates their willingness to help me. their willingness to help me. 3. The attitude of the team name employees 3. The attitude of the ballpark employees shows shows me that they understand my needs. me that they understand my needs. Behavior Behavior 1. I can count on team name’s employees 1. I can count on the event staff taking actions to taking actions to address my needs. address my needs. Interaction 2. Team name’s employees respond quickly 2. The ballpark employees respond quickly to my Quality to my needs needs 3. The behavior of team name’s employees 3. The behavior of the event staff indicates to me indicates to me that they understand my that they understand my needs. needs. Expertise Expertise 1. You can count on the ballpark employees 1. You can count on team name’s employees knowing their jobs. knowing their jobs. 2. The ballpark staff is able to answer my 2. Team name’s employees are able to questions quickly. answer my questions quickly. 3. The event staff understands that I rely on their 3. The employees understand that I rely on knowledge to meet my needs. their knowledge to meet my needs.

88 Table 3.03 (continued) – Dimensions and Items of Service Quality

Dimension Original Items Revised Items

Ambient Conditions Ambient Conditions 1. At team name’s games, you can rely on 1. At team name’s games, you can rely on there being a good atmosphere. there being a good atmosphere. 2. Team name’s ambience is what I am 2. The ambience at Team name’s games is looking for at a game. what I am looking for at a game. 3. Team name understands that its 3. The baseball staff understands that the atmosphere is important to me. atmosphere is important to me. Design Factors Design Factors 1. The team name’s stadium/arena layout 1. The team name’s stadium/arena layout never fails to impress me. never fails to impress me. Service 2. Team name’s stadium/arena layout serves 2. The layout of stadium name serves my Environment my purposes. purposes. Quality 3. The team name understands that the 3. The team name understands that the design design of its facility is important to me. of its facility is important to me. Social Factors Social Factors 1. I find that team name’s other customers 1. The team name’s other fans consistently consistently leave me with a good leave me with a good impression of service. impression of service. 2. The other spectators do not affect the staff’s 2. Team name’s other customers do not ability to provide me with good service. affect its ability to provide me with good 3. The employees at the ballpark understand service. that the other fans affect my perceptions of 3. Team name understands that the other service. patrons affect my perceptions of service

Waiting Time Waiting Time 1. Waiting time for service at team name games 1. Waiting time at team name is predictable. is predictable. 2. Team name tries to keep my waiting time to 2. The staff tries to keep my waiting time for a minimum. service to a minimum. 3. Team name understands that waiting time 3. The ballpark staff understands that waiting is important to me. time is important to me. Tangibles Tangibles 1. I am consistently pleased with the _____ at 1. I am consistently pleased with the at team team name. name games. Outcome 2. I like team name because it has the _____ 2. I like team name sport because they have the Quality that I want. service I want. 3. Team name knows the kind of ______its 3. The event staff knows the kind of service its customers are looking for. customers are looking for. Valence Valence 1. When I leave team name games, I usually 1. When I leave team name games, I usually feel like I had a good experience. feel like I had a good experience. 2. I believe that team name tries to give me a 2. I believe that team name tries to give me a good experience good experience 3. I believe that team name knows the type of 3. The event staff knows the type of experience its customers want. experience its customers want.

89 Table 3.04. Dimensions and Items of Perceived Price

Dimension Original Items Revised Items

1. The prices charged by this service 1. The price of team name games is high provider are very high compared to their compared to their competitors. competitors (Voorhees, 2006). 2. The price of team name games is low 2. The price of Xa is lowr (Yoo et al., 2000). (reverse coded). Monetary 3. X is expensive (Yoo et al., 2000). 3. Team name games are expensive. 4. This product is reasonably priced 4. Team name games are reasonably (Sweeney & Soutar, 2001). priced. 5. The price of X is high (Yoo et al., 2000). 5. The price of team name games is high.

Original Items Dimension Revised Items (Berry, Seiders, & Grewal, 2002) 1. It takes minimal time to get the 1. It took minimal time to get the information information I need about team name needed to choose a service provider. games. 2. It was easy to get the information I 2. It is easy to get the information I need needed to decide which service provider about team name games. to use. Non- 3. It is easy to contact the team name 3. It was easy to contact the service Monetary when I need to. provider. 4. I am able to get to stadium name 4. I was able to get to the service provider’s quickly for team name games. location quickly. 5. The athletics department makes it easy 5. They made it easy for me to conclude my for me to get tickets to team name purchase. games. Note: a X refers to the focal brand r reverse coded

90 Epistemic value. Epistemic value refers to the perceived utility of a good or service resulting from its “ability to arouse curiosity, provide novelty, and/or satisfy a desire for knowledge” (Sheth et al., 1991, p. 21). Thus, products selected because of their epistemic value are chosen because they are either new or different. Epistemic value is measured using two-factor, six-item scale that is based on the research of Trail, Fink, and Anderson (2003), James, Kolbe, and Trail (2002), and Trail and James (2001) concerning the motivations of individuals to attend sporting events (see Table 3.05).

Table 3.05. Dimensions and Items of Epistemic Value

Dimension Original Items Revised Items

1. I increase my knowledge about sport 1. Team name games allow me to name at the game (Trail, Fink, & increase my knowledge of sport. Anderson, 2003). 2. Team name games enable me to 2. I increase my understanding of sport increase my understanding of sport Knowledge name strategy by watching the game strategy. (Trail et al., 2003). 3. Team name games allow me to learn 3. I can learn about the technical about the technical aspects of sport. aspects of sport name by watching

the game (Trail et al., 2003).

1. of the opportunity to do something I 1. Team name games provide me the haven’t done before (Sheth, opportunity to do something I haven’t Newman, & Gross, 1991). done before. Novelty 2. I am interested in experiencing new 2. I am interested in experiencing new things (Sheth et al., 1991). things. 3. of the chance to experience 3. Team name games give the chance to something different (Sheth et al., experience something different. 1991).

91 Satisfaction. Satisfaction is an evaluation by consumers based on the overall purchase and consumption experiences with a particular product over time (Johnson & Fornell, 1991). Wang, Lo, and Yang (2004) noted that satisfaction is more fundamental and useful in predicting consumer behaviors and organizational performance than transaction-specific consumer satisfaction because cumulative customer satisfaction motivates a firm’s investment in customer satisfaction. Satisfaction is measured using three items developed by Voorhees (2006) that were based on a study by Bolton (1998) (see Table 3.06).

Table 3.06. Dimensions and Items of Satisfaction

Original Items Dimension (Voorhees, 2006) Revised Items

1. Overall, I am very satisfied with the 1. Overall, I am very satisfied with the services that I receive from this services that I receive from team name. provider. 2. Overall, I am satisfied with my Satisfaction 2. Overall, I am happy that I do business experience at team name games. with this provider. 3. I truly enjoy myself at team name 3. I have truly enjoyed doing business games. with this service provider.

Step 3 – First Data Collection: Pilot Study Introduction. A pilot study provides an initial test of the items in a measurement scale in order to establish preliminary validity and reliability. The pilot study provided an opportunity to evaluate and modify all methods, instructions, instruments, and data collection procedures. Additionally, the pilot study enabled the researcher to assess and modify the measurement tool based on its psychometric properties. According to Churchill (1979), the purification stage assesses the properties of the items in order to identify the most adequate ones to retain for the next stage. Population and Sample. The population for the pilot study consisted of the spectators at a NCAA Division I collegiate baseball game. The subjects for the pilot study were a stratified sample of spectators in attendance at Dick Howser Stadium on

92 the campus of Florida State University for the May 11, 2007, Atlantic Coast Conference baseball game between the Clemson Tigers and the Florida State Seminoles. There were 4,397 spectators in attendance for the contest. Data collection. For the administration of the questionnaire, two sources of permission were solicited. First, necessary approval was obtained from the Florida State University Institutional Review Board (see Appendix C). The researcher also sought and received approval to collect data at Dick Howser Stadium from the athletics department at the University (see Appendix B). Dick Howser Stadium has a seating capacity of approximately 5,000 distributed throughout ten seating sections. In order to ensure stratified sampling, a team of six researcher aids comprised of graduate students enrolled in the Sport Management and Communications programs at Florida State University distributed surveys to persons sitting in aisle seats from field level to the top of the stadium in seven pre-designated areas. Spectators sitting in aisle seats were surveyed for two reasons: 1) to ensure that a stratified sample of the population in attendance at the game was surveyed; and 2) survey distribution and collection was made easier as survey administrators did not have to lean over non-survey participants to administer the instrument. In total, 350 forms were prepared, however 17 were not distributed and no specific reason was given for the failure to distribute. Thus, 333 forms were handed out, of which 254 useable surveys were returned, for a return rate of 76.27%. There were four reasons why the 79 forms were deemed unusable and thus omitted from the analysis: 1) the forms were not returned (n = 43); 2) the forms were filled out incorrectly (n = 12); 3) there were too many (> 10%) missing values (n =22); and 4) participants were under the age of 18 (n = 2). Instrument development. The primary task of this investigation was to empirically measure the components of value equity in spectator sports. This section describes the instrument that was used to collect the data. Based upon the theoretical frameworks and measurement scales described in steps one and two, an initial version of the Value Equity in Spectator Sports Scale (VESSS) questionnaire was developed. The VESSS is a self-administered paper-and-pencil questionnaire, which includes closed-ended questions used to gather detailed information assessing the components of value equity (See Appendix E). The instrument developed for this study is comprised

93 of two sections. The first section includes the items examining the components of value equity in spectator team sport. Value equity is comprised of the following six first-order dimensions: 1) entertainment value, 2) social value, 3) perceived service quality, 4) perceived price, 5) epistemic value, and 6) satisfaction. The second section of the VESSS included items that captured various demographic data. Respondents were asked to identify their gender, age, marital status, household income, education level, and ethnicity. This information was used in the descriptive analysis of the sample. An initial draft of the questionnaire was subjected to an examination of content validity. Content validity refers to the degree to which a measure covers the range of meanings included within a concept (Babbie, 2004). The researcher and an associate professor with expertise in sport marketing in the Department of Sport Management, Recreation Management, and Physical Education at Florida State University assessed content validity at this stage. Step 4 – Reliability and Validity Assessment of First Data Collection Data analysis. The data for the pilot study were analyzed using the Statistical Package for the Social Sciences (SPSS) 11.5. Descriptive statistics, internal consistency reliability, and exploratory factor analysis were utilized in the data analysis assessment. Descriptive statistics involve tabulating, depicting, and describing sets of data. Descriptive statistics are used to classify and summarize numerical data, and to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures, and are used to present quantitative descriptions in a manageable form. Internal consistency reliability of survey instruments is a measure of the reliability of different survey items intended to measure the same characteristic. Two measures of internal consistency reliability were computed: Cronbach’s alpha coefficient and the item-to-total correlations. One of the most commonly calculated measures of internal consistency reliability is the Cronbach’s alpha coefficient. Cronbach’s alpha is an estimate of the proportion of variance in the test scores that can be attributed to true score variance. It is used to estimate the proportion of variance that is systematic or consistent in a set of test scores (Cronbach, 1951). The Cronbach’s alpha coefficients were calculated to provide an initial assessment of the reliability of the VESSS. An alpha coefficient equal to or above 0.70

94 is generally considered an acceptable measurement (Nunally, 1978). Another measure of internal consistency reliability is the item-to-total correlation. Item-to-total correlation measures the correlation of each of the items to the total scale. Items with a correlation of less than 0.50 (Hair, Anderson, Tatham, & Black, 1998) were deleted from the scale. In total, five separate exploratory factor analyses were conducted, one for each of the first order latent variables, to explain the variance in the observed variables in terms of the underlying latent factors. The researcher chose to compute separate EFA’s for each dimension, as opposed to a global EFA comprised of all dimensions because of the exploratory nature of the study. Rummel (1970) proposed that in a new domain of scientific interest, where complex interrelations of phenomena have undergone little systematic investigation, the unknown domains may be explored through factor analysis. Factor analysis, he noted enables the researcher to separate different sources of variation, and to partial out or control for undesirable influences on the variables of concern. Thus, because many of the constructs proposed to measure value equity in this study are relatively unknown and have undergone little systemic investigation, it was necessary to test each first-order latent variable individually before proceeding to a global analysis of the value equity construct. Towards the development of a valid and reliable scale of value equity, it is important to ensure that the scales measuring each of the dimensions of value equity proposed in the model are both valid and reliable. Using confirmatory factor analysis, the fit of the overall model of value equity is assessed and validated in subsequent stages of the study. For each of the six EFA’s, a factor analysis was utilized to examine the dimensionality of the factor model. Factor analysis is an appropriate technique to use when the goal of the research is to reveal any latent variables that cause the manifest variables to covary (Costello & Osbourne, 2005). A Maximum Likelihood (ML) extraction procedure was used as it allows the computation of various indices of goodness-of-fit as well as the significance of loadings and correlations between factors (Fabrigar, Wegener, MacCallum, & Strahan, 1999). Oblique (OBLIMIN) rotation was selected as the rotation method as it is thought to produce a better estimate of true factors and a better simple structure when the latent variables are correlated (Fabrigar, et al., 1999). According to Hair et al. (1998), item loadings of ±.40 or greater are

95 considered to meet a threshold of practical significance and may be retained in the factor. Results. The following sections report the results of the statistical procedures utilized in the pilot phase of this study. Demographic Characteristics Six demographic classification characteristics were measured in the survey instrument for the pilot study, namely: gender, age, marital status, household income, ethnicity, and education. As indicated in Table 3.07, 52.3% of the respondents were male. In terms of age, more than half of the sample was older than 50 years of age. A large majority of the sample (69.6%) reported being married. With respect to ethnicity, 94.9% were white/caucasian. Finally, over 60% of the sample had an undergraduate degree. Internal Consistency Reliability Cronbach’s alpha and item-to-total correlations served as measures of internal consistency reliability. Decisions to eliminate or retain items were based on these statistics. A Cronbach’s alpha coefficient of .70 (Nunnally, 1978) and a corrected item- total correlation of .50 (Hair et al., 1998) were used as cut-off values for determining acceptable levels. For the entertainment value dimensions, the Cronbach’s alphas ranged from .7453 to .8464; the corrected item-total correlation coefficients ranged from .4101 to .7791 (see Table 3.08). For the social value dimensions, the Cronbach’s alpha coefficients ranged from .7634 to .8493; the corrected item-total correlation coefficients ranged from .5754 to .7837 (see Table 3.09). For the service quality dimensions, the Cronbach’s alpha coefficients ranged from .8424 to .9505; the corrected item-total correlation coefficients ranged from .4677 to .8833 (see Table 3.10). For the perceived price dimensions, the Cronbach’s alpha coefficients ranged from .7228 to .8368; the corrected item-total correlation coefficients ranged from .2837 to .7780 (see Table 3.11). For the epistemic value dimension, the Cronbach’s alpha coefficient was .7434; the corrected item-total correlation coefficients ranged from .3270 to .6360 (see Table 3.12). The Cronbach’s alpha coefficient for the satisfaction dimension was .6673; the corrected item-total correlation coefficients ranged from .4377 to .5436 (see Table 3.13).

96 Table 3.07. Demographic Characteristics of the Pilot Sample

Valid Cumulative Demographic Variables Frequency Percent Percent Gender Female 122 47.7 47.7 Male 134 52.3 100.0 Total 256 100.0 System Missing 3 Age 18-34 58 24.2 24.2 35-49 59 24.6 48.8 50-64 75 31.3 80.0 65+ 48 20.0 100.00 Total 240 100.0 System Missing 19 Marital Status Married 179 69.6 69.6 Single 52 20.2 89.9 Divorced 14 5.4 95.3 Widowed 9 3.5 98.8 Other 3 1.2 100.00 Total 257 100.00 System Missing 2 Household Income < $20,000 29 12.7 12.7 $20,000 - $39,999 15 6.6 19.3 $40,000 - $59,999 42 18.4 37.7 $60,000 - $79,999 31 13.6 51.3 $80,000 - $99,999 42 18.4 69.7 $100,000+ 69 30.3 100.0 Total 228 100.00 System Missing 31 Ethnicity Black/African American 4 1.6 1.6 Native American 1 0.4 2.0 White/Caucasian 243 94.9 96.9 Latina/Latino 6 2.3 99.2 Other 2 0.8 100.0 Total 256 100.0 System Missing 3 Education High School 48 19.1 19.1 Trade/Professional 9 3.6 22.7 Junior College 33 13.1 35.9 Undergraduate Studies 100 39.8 75.7 Masters Studies 52 20.7 96.4 Doctoral Studies 9 3.6 100.0 Total 251 100.0 System Missing 8

97 Table 3.08. Reliability Estimates of Entertainment Value Factors.

Corrected Squared Alpha if Factors Item-total Multiple item Correlation Correlation deleted Amusement Amuse1 .567 .344 .851 Amuse2 .744 .559 .777 Amuse3 .749 .565 .775 Amuse4 .677 .503 .807 Cronbach's alpha coefficient for Amusement = .846 Partying Party1 .620 .531 .820 Party2 .416 .275 .851 Party3 .723 .641 .802 Party4 .617 .485 .825 Party5 .691 .560 .808 Party6 .550 .425 .830 Party7 .631 .433 .819 Cronbach's alpha coefficient for Partying = .845 Crowd Experience Crowd1 .460 .224 .729 Crowd2 .579 .338 .660 Crowd3 .536 .320 .685 Crowd4 .583 .358 .663 Cronbach's alpha coefficient for Crowd Experience = .743 Game Intensity/Immersion GamInt1 .574 .438 .821 GamInt2 .606 .446 .821 GamInt3 .671 .465 .811 GamInt4 .410 .262 .848 GamInt5 .607 .500 .821 GamInt6 .662 .472 .810 GamInt7 .587 .526 .820 GamInt8 .608 .499 .818 Cronbach's alpha coefficient for Game Intensity/Immersion = .841 Escape Esc1 .556 .313 .744 Esc2 .648 .427 .649 Esc3 .614 .399 .675 Cronbach's alpha coefficient for Escape = .769 Aesthetics Aes1 .681 .495 .791 Aes2 .779 .607 .689 Aes3 .649 .449 .822 Cronbach's alpha coefficient for Aesthetics =.836 Drama Drama1 .633 .426 .812 Drama2 .624 .410 .815 Drama3 .695 .604 .784 Drama4 .733 .631 .767 Cronbach's alpha coefficient for Drama = .838

98 Table 3.09. Reliability Estimates of Social Value Factors Corrected Squared Alpha if Factors Item-total Multiple item Correlation Correlation deleted Non-Acquaintances NonAcq1 .616 .402 .717 NonAcq2 .575 .340 .760 NonAcq3 .682 .468 .645 Cronbach's alpha coefficient for Non-Acquaintances =.785 Family Fam1 .606 .368 .672 Fam2 .583 .340 .708 Fam3 .631 .398 .660 Cronbach's alpha coefficient for Family = .763 Business Opportunities BusOpp1 .657 .507 .782 BusOpp2 .612 .416 .831 BusOpp3 .784 .622 .652 Cronbach's alpha coefficient for Business Opportunities = .825 Friends Friend1 .627 .393 .876 Friend2 .774 .628 .736 Friend3 .784 .637 .725 Cronbach's alpha coefficient for Friends =.849

99 Table 3.10. Reliability Estimates of Service Quality Factors Corrected Squared Alpha if Factors Item-total Multiple item Correlation Correlation deleted Interaction Quality IQ1 (A1) .588 .437 .955 IQ2 (A2) .847 .731 .942 IQ3 (A3) .883 .819 .940 IQ4 (B1) .741 .560 .948 IQ5 (B2) .841 .732 .942 IQ6 (B3) .868 .832 .941 IQ7 (E1) .815 .705 .944 IQ8 (E2) .821 .707 .943 IQ9 (E3) .817 .731 .944 Cronbach's alpha coefficient for Interaction Quality = .951 Service Environment Quality SEQ1 (AC1) .491 .294 .833 SEQ2 (AC2) .494 .277 .833 SEQ3 (AC3) .656 .462 .816 SEQ4 (DF1) .616 .448 .819 SEQ5 (DF2) .661 .502 .815 SEQ6 (DF3) .586 .452 .822 SEQ7 (SF1) .469 .287 .835 SEQ8 (SF2) .594 .405 .821 SEQ9 (SF3) .476 .301 .836 Cronbach's alpha coefficient for Service Environment Quality = .842 Outcome Quality OutQual1 (WT1) .467 .266 .915 OutQual2 (WT2) .786 .685 .891 OutQual3 (WT3) .778 .655 .891 OutQual4 (T1) .796 .695 .890 OutQual5 (T2) .728 .603 .896 OutQual6 (T3) .765 .669 .892 OutQual7 (V1) .552 .574 .908 OutQual8 (V2) .590 .594 .906 OutQual9 (V3) .791 .690 .891 Cronbach's alpha coefficient for Outcome Quality = .909

100 Table 3.11. Reliability Estimates of Perceived Price Squared Alpha if Factors Item-total Multiple item Correlation Correlation deleted Monetary Mon1 .417 .209 .871 Mon2 .626 .465 .780 Mon3 .772 .668 .807 Mon4 .653 .470 .801 Mon5 .778 .677 .768 Cronbach's alpha coefficient for Monetary = .837 Non-Monetary NonMon1 .550 .415 .651 NonMone2 .656 .520 .608 NonMon3 .472 .281 .680 NonMon4 .284 .095 .764 NonMon5 .510 .275 .666 Cronbach's alpha coefficient for Non-Monetary = .723

Table 3.12. Reliability Estimates of Epistemic Value Squared Alpha if Factors Item-total Multiple item Correlation Correlation deleted Knowledge Know1 .704 .524 .883 Know2 .843 .715 .757 Know3 .761 .629 .835 Cronbach's alpha coefficient for Knowledge = .879 Novelty Nov1 .347 .239 .616 Nov2 .268 .225 .599 Nov3 .605 .390 .138 Cronbach's alpha coefficient for Novelty = .560

Table 3.13. Reliability Estimates of Satisfaction Squared Alpha if Factors Item-total Multiple item Correlation Correlation deleted Sat1 .438 .193 .672 Sat2 .544 .321 .492 Sat3 .499 .284 .569 Cronbach's alpha coefficient for Satisfaction = .667

101 The results of the internal consistency reliability tests revealed that the Cronbach’s alpha value for satisfaction did not meet the 0.70 cut-off value. Additionally, several items did not meet the 0.50 cut-off-value established for acceptable corrected item-total correlation coefficients. Items not meeting the 0.50 benchmark included: party2, crwdexp1, and gamint4 for entertainment value; ambcon1, ambcon2, socfac1, socfac3, and waittime1 for service quality; mon1, nonmon3, and nonmon4 for perceived price; nov4 and nov5 for epistemic value; sat1 and sat3 for satisfaction. Exploratory Factor Analysis Separate factor analyses were conducted for each of the components of value equity in the model. Maximum Likelihood (ML) was selected as the factor extraction method to test the factor loadings and correlations among factors (Costello & Osbourne, 2005). Communalities, eigenvalues, and the scree plot were examined to reveal the underlying factor structure and to decide how many factors to retain for rotation. The communality is the proportion of the variance of the test that has been accounted for by the factors extracted. An eigenvalue is the total test variance accounted for by a particular factor. The scree plot provides a graphic image of the eigenvalue for each component extracted. The factor structures for each EFA were rotated using the oblique oblimin with Kaiser Normalization procedure. Missing values were treated using a listwise deletion method for each of the EFA’s. Entertainment Value Table 3.14 displays descriptive statistical information for the measures of entertainment value. The mean scores range from a low of 1.57 for party4 to a high of 6.25 for gamint2. Following a listwise deletion, the sample for the EFA for entertainment value was n = 209. Total Variance Explained Table 3.15 displays information about the entertainment value factors that were extracted with the factor analysis. Seven eigenvalues exceeded 1.00. Following Kaiser’s criterion, factors with an eigenvalue of less than one were excluded from further analysis. The proportion of the total test variance accounted for by each factor before rotation ranged from a high of 17.042 to a low of 2.530. The total variance accounted for by the seven factors was 59.238%.

102 The Scree Plot The scree plot is a graphic representation of the eigenvalue for each component extracted. The scree plot for entertainment value reveals that the amount of variance accounted for by successive components initially plunges sharply as successive components are extracted. It can be seen that the ‘scree’ “break at the seventh factor. Considering the eigenvalues and scree plot, it was reasonable to suggest that a seven- factor model of entertainment value be retained for the next step. Rotated Factor Matrix Following the selection of a seven factor-model, a maximum likelihood extraction method and oblique oblimin with Kaiser normalization rotation method was used to compute a rotated pattern matrix in order to arrive at a new position for the axes that is easier to interpret. The resulting rotated pattern matrix is displayed in Table 3.16. Factor loadings for the entertainment value components range from 0.456 to 1.010. Items with a factor loading less than 0.40 were deemed unacceptable (Hair et al., 1998). In total, seven factors were identified, including: aesthetics (factor 1), escape (factor 2), drama (factor 3), partying (factor 4), amusement (factor 5), game intensity/immersion (factor 6), and a seventh factor comprised of one item from crowd experience (crowd2) and two items from game intensity/immersion (gamint1 and gamint2). Of the 30 items included in the EFA, two (crowd3, crowd4) did not load on any factor. Additionally, one measure of aesthetics (aes2) loaded above 1.0. Factor Correlation Matrix The factor correlation matrix displaying the correlation estimates for the seven factors is presented in Table 3.17. The correlation estimates between factors ranged from -.368 to .420. The low correlations among the seven factors suggest discrimination among the factors.

103 Table 3.14. Descriptive Statistics for Entertainment Value Items.

Mean Std. Deviation Analysis N AMUSE1 5.18 1.475 209 AMUSE2 4.50 1.656 209 AMUSE3 4.61 1.629 209 AMUSE4 4.53 1.554 209 PARTY1 2.34 1.683 209 PARTY3 2.62 1.828 209 PARTY4 1.57 1.219 209 PARTY5 2.12 1.837 209 PARTY6 1.90 1.498 209 PARTY7 1.82 1.491 209 CROWD2 6.02 1.098 209 CROWD3 5.88 1.105 209 CROWD4 5.86 1.007 209 GAMINT1 5.47 1.294 209 GAMINT2 6.25 .934 209 GAMINT3 5.81 1.070 209 GAMINT5 6.15 .928 209 GAMINT6 5.00 1.546 209 GAMINT7 5.53 1.334 209 GAMINT8 6.00 1.114 209 ESC1 5.67 1.444 209 ESC2 5.90 1.191 209 ESC3 5.54 1.337 209 AES1 5.65 1.278 209 AES2 5.58 1.310 209 AES3 5.86 1.141 209 DRAMA1 5.49 1.594 209 DRAMA2 5.17 1.525 209 DRAMA3 5.55 1.454 209 DRAMA4 5.34 1.492 209

104 Table 3.15. Eigenvalues for Entertainment Value Factors

Factor Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 7.434 24.781 24.781 5.113 17.042 17.042 2 4.082 13.605 38.386 4.281 14.270 31.312 3 2.755 9.184 47.570 2.130 7.100 38.413 4 2.233 7.445 55.015 2.871 9.569 47.981 5 1.544 5.148 60.163 1.655 5.517 53.498 6 1.286 4.286 64.449 .963 3.210 56.708 7 1.126 3.752 68.201 .759 2.530 59.238 8 .872 2.906 71.107 9 .847 2.822 73.930 10 .723 2.411 76.340 11 .644 2.148 78.489 12 .626 2.088 80.576 13 .577 1.924 82.500 14 .492 1.641 84.142 15 .483 1.610 85.751 16 .461 1.536 87.287 17 .422 1.408 88.695 18 .406 1.354 90.049 19 .372 1.240 91.289 20 .340 1.134 92.424 21 .330 1.099 93.523 22 .286 .954 94.476 23 .281 .937 95.414 24 .256 .853 96.266 25 .242 .806 97.072 26 .218 .726 97.798 27 .200 .668 98.466 28 .181 .604 99.070 29 .147 .490 99.560 30 .132 .440 100.000 Extraction Method: Maximum Likelihood. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

105 Scree Plot 8

0 Eigenvalue 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Factor Number

Figure 3.01. Scree Plot for Entertainment Value

106 Table 3.16. Rotated Pattern Matrix for Entertainment Value

Factor 1 2 3 4 5 6 7 AMUSE1 -.043 .178 -.040 -.011 -.480 .054 .166 AMUSE2 .072 -.104 .015 -.048 -.789 -.006 .057 AMUSE3 .032 .071 .019 .035 -.814 -.020 -.080 AMUSE4 -.031 -.046 .013 -.037 -.831 .010 -.072 PARTY1 .111 -.017 -.021 -.670 -.112 -.085 .204 PARTY3 .060 -.012 -.107 -.732 -.132 -.040 .215 PARTY4 -.081 .016 .018 -.752 .012 .105 -.102 PARTY5 -.005 .000 .068 -.845 .097 .080 -.039 PARTY6 -.013 .033 .034 -.706 .053 -.018 -.156 PARTY7 -.016 -.002 -.023 -.715 -.072 -.104 .053 CROWD2 .085 .153 .222 -.064 -.147 -.105 .456 CROWD3 -.062 .360 .118 .005 -.176 .090 .324 CROWD4 .024 .125 .114 .060 -.237 .208 .357 GAMINT1 .019 .077 -.117 -.057 -.010 .277 .468 GAMINT2 .082 .157 .029 .020 -.086 .182 .533 GAMINT3 .309 -.043 .057 .043 -.016 .492 .190 GAMINT5 .108 .008 .076 .107 -.022 .660 .037 GAMINT6 .042 .218 -.077 -.019 -.084 .523 .097 GAMINT7 .032 -.095 .020 -.040 .087 .807 .044 GAMINT8 -.024 .104 .025 -.017 -.075 .791 -.127 ESC1 .063 .618 -.070 -.003 .092 .025 .139 ESC2 .026 .698 .066 .012 -.003 .050 .034 ESC3 .022 .917 -.007 -.051 -.081 -.035 -.168 AES1 .673 .030 -.005 .026 3.639E-05 -.022 .103 AES2 1.010 -.061 .002 .040 -.002 .003 -.108 AES3 .640 .119 .063 -.084 -.027 .118 -.124 DRAMA1 .043 .066 .651 -.051 .086 -.023 .114 DRAMA2 .019 -.034 .643 .002 -.045 -.013 .072 DRAMA3 .003 -.026 .849 -.027 -.067 .007 -.096 DRAMA4 -.029 -.032 .910 .068 .008 .082 -.112 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization.

107 Table 3.17. Factor Correlation Matrix for Entertainment Value

Factor 1 2 3 4 5 6 7 1 1.000 2 .260 1.000 3 .287 .112 1.000 4 -.017 -.159 -.024 1.000 5 -.212 -.368 -.207 .219 1.000 6 .420 .385 .164 .066 -.133 1.000 7 .260 .386 .053 -.076 -.346 .274 1.000 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization.

Social Value Table 3.18 displays descriptive statistics information for the measures of social value. The mean scores range from a low of 3.14 for busopp3 to a high of 6.02 for fam1. Following a listwise deletion, the sample size for the EFA for social value was n=227.

Table 3.18. Descriptive Statistics for Social Value Items

Mean Std. Deviation Analysis N NONACQ1 5.22 1.479 227 NONACQ2 5.32 1.447 227 NONACQ3 5.04 1.445 227 FAM1 6.02 1.310 227 FAM2 6.00 1.101 227 FAM3 5.32 1.554 227 BUSOPP1 3.37 1.600 227 BUSOPP2 3.69 1.773 227 BUSOPP3 3.14 1.677 227 FRIEND1 5.78 1.240 227 FRIEND2 4.96 1.601 227 FRIEND3 4.96 1.617 227

108 Total Variance Explained Table 3.19 displays information about the social value factors that were extracted with the factor analysis. Three eigenvalues exceeded 1.00. The proportion of the total test variance accounted for by each factor before rotation ranges from a high of 35.893 to a low of 10.114. The total variance accounted for by the three factors is 59.233%. The Scree Plot The scree plot for the social value factors (see Figure 3.02) reveals that the amount of variance accounted for by successive components initially plunges sharply as successive components are extracted. The leveling of the graph begins between factors three and four. Considering the eigenvalues and scree plot, it is reasonable to suggest a three-factor model be retained for the ensuing step.

Table 3.19. Eigenvalues for Social Value Factors

Factor Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % 1 5.137 42.812 42.812 4.307 35.893 35.893

2 1.669 13.912 56.724 1.587 13.225 49.118

3 1.421 11.844 68.567 1.214 10.114 59.233

4 .752 6.266 74.834

5 .572 4.768 79.601

6 .518 4.315 83.916

7 .462 3.851 87.767

8 .441 3.675 91.442

9 .326 2.713 94.155

10 .272 2.264 96.419

11 .235 1.957 98.376

12 .195 1.624 100.000 Extraction Method: Maximum Likelihood. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

109 Scree Plot 6

0 Eigenvalue 1 2 3 4 5 6 7 8 9 10 11 12

Factor Number

Figure 3.02. Scree Plot for Social Value

Rotated Factor Matrix A maximum likelihood extraction method using oblique oblimin with Kaiser Normalization rotation method was used to compute a pattern matrix. The resulting rotated pattern matrix is displayed in Table 3.20. Factor loadings for the social value components range from .536 to .997. In total, three factors were identified, including: friends/non-acquaintances (factor 1), business opportunities/networking (factor 2), and family (factor 3). As indicated in the table, the items for friends and the items for non- acquaintances loaded together. Factor Correlation Matrix The factor correlation matrix displaying the correlation estimates for the three factors is presented in Table 3.21. The correlation estimates between factors ranged from .399 to .454. The low correlations among the three factors suggest discrimination among the factors.

110 Table 3.20. Rotated Pattern Matrix for Social Value

Factor

1 2 3 NONACQ1 .710 .012 .118

NONACQ2 .536 .045 .041

NONACQ3 .698 .006 .058

FAM1 -.056 .019 .859

FAM2 .027 -.040 .650

FAM3 .141 -.125 .607

BUSOPP1 -.018 -.717 .069

BUSOPP2 .210 -.570 .038

BUSOPP3 -.087 -.997 -.014

FRIEND1 .687 .050 .157

FRIEND2 .883 -.079 -.123

FRIEND3 .872 -.123 -.186 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 7 iterations.

Table 3.21. Factor Correlation Matrix for Social Value

Factor 1 2 3

1 1.000

2 -.454 1.000

3 .399 -.332 1.000

Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization.

111 Service Quality Table 3.22 displays descriptive statistics information for the measures of service quality. The mean scores range from a low of 4.91 for wait2 and tang2 to a high of 6.26 for val1. Following a listwise deletion, the sample size for the EFA for service quality was n=212.

Table 3.22. Descriptive Statistics for Service Quality Items

Mean Std. Deviation Analysis N ATT1 5.94 1.155 212 ATT2 5.37 1.199 212 ATT3 5.25 1.288 212 BEHAV1 5.22 1.228 212 BEHAV2 5.29 1.345 212 BEHAV3 5.15 1.318 212 EXPERT1 5.61 1.240 212 EXPERT2 5.16 1.263 212 EXPERT3 5.03 1.352 212 AMBCON3 5.47 1.137 212 DESFAC1 6.08 1.181 212 DESFAC2 5.99 1.066 212 DESFAC3 5.49 1.237 212 SOCFAC2 5.46 1.237 212 WAIT2 5.28 1.365 212 WAIT3 4.91 1.410 212 TANG1 5.37 1.316 212 TANG2 4.91 1.501 212 TANG3 5.07 1.368 212 VAL1 6.26 .752 212 VAL2 6.12 .821 212 VAL3 5.14 1.308 212

112 Total Variance Explained Table 3.23 displays information about the service quality factors extracted by the factor analysis. Three eigenvalues exceeded 1.00. The proportion of the total test variance accounted for by each factor before rotation ranges from a high of 57.852 to a low of 3.116. The total variance accounted for by the three factors is 66.435%.

Table 3.23. Eigenvalues for Service Quality Factors

Extraction Sums of Factor Initial Eigenvalues Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % 1 13.075 59.433 59.433 12.727 57.852 57.852 2 1.591 7.231 66.664 1.203 5.466 63.318 3 1.024 4.653 71.317 .686 3.116 66.435 4 .834 3.791 75.107 5 .588 2.672 77.779 6 .568 2.583 80.362 7 .507 2.305 82.667 8 .470 2.137 84.804 9 .450 2.046 86.851 10 .416 1.889 88.740 11 .377 1.713 90.453 12 .296 1.347 91.800 13 .279 1.266 93.067 14 .254 1.155 94.221 15 .233 1.059 95.280 16 .226 1.026 96.306 17 .185 .841 97.147 18 .167 .761 97.908 19 .159 .724 98.633 20 .120 .545 99.178 21 .105 .477 99.655 22 .076 .345 100.000 Extraction Method: Maximum Likelihood. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

113 The Scree Plot The scree plot for the service quality factors reveals that the amount of variance accounted for by successive components plunges sharply as successive components are extracted. The “break” in the graph occurs at the second factor, suggesting a two- factor model. Considering the eigenvalues and scree plot, it is reasonable to suggest that service quality may be comprised of either a two- or three-factor model.

Scree Plot 14

0 Eigenvalue 1 3 5 7 9 11 13 15 17 19 21 23

Factor Number

Figure 3.03. Scree Plot for Service Quality

Although the analysis of the eigenvalues indicated that service quality may be captured by three factors, an assessment of the scree plot suggests service quality may be either a two- or three-factor construct. Thus, it was necessary to compute multiple factor analyses while setting the number of factors to retain manually. A maximum likelihood procedure was computed to assess the adequacy of model fit for two- and three-factor models. This procedure aided the researcher in determining the dimensionality of the factor model. Based on the results (see Table 3.24), a three-factor

114 model was retained. The following reports a summary of the results of the model fit for the three factor model: 1) the three-factor model accounted for 66.435% of the explained variance; 2) the communalities ranged from .361 to .894, indicating that the individual variables explained the model reasonably well; 3) 32 (13%) of the residuals exceeded .05.

Table 3.24. Model Fit Results for Varying Number of Service Quality Factors

Number of Explained Range of Number of p-value for goodness Factors Variance Communalities Residuals >.05 of fit test 2 63.121 .328 ~ .842 49 (21%) .000

3 66.435 .361 ~ .894 32 (13%) .000

Rotated Factor Pattern Matrix A maximum likelihood extraction method using oblique oblimin with Kaiser Normalization rotation method was used to compute a rotated pattern matrix for both the two- and three-factor model. Although the fit-indices provided evidence for the acceptance of a three-factor model of service quality, a comparison of the rotated pattern matrices revealed that a two-factor model best fit the data. The two-factor model has a cleaner factor structure, fewer item cross-loadings, and no factors with fewer than three items. For comparison purposes, the rotated pattern matrices for the two- and three-factor models are presented below (Tables 3.25 and 3.26). The rotated pattern matrix for the two-factor model presented in Table 3.25 resulted in factor loadings for the service quality components ranging from 0.463 to .978. In total, two factors were identified. Factor one comprised all nine items for interaction quality, two items from service environment quality (ambient conditions3 and social factors2), and all but three outcome quality items (wait2, wait3, tang1, tang2, tang3, and val3). Factor 2 is comprised of all three design factor items and the first two valence items.

115 Table 3.25. Rotated Pattern Matrix for Two-Factor Model of Service Quality

Factor

1 2 ATT1 .463 .184

ATT2 .905 -.062

ATT3 .884 .037

BEHAV1 .727 .019

BEHAV2 .900 -.009

BEHAV3 .871 .067

EXPERT1 .788 .028

EXPERT2 .846 -.036

EXPERT3 .869 -.027

AMBCON3 .524 .312

DESFAC1 .061 .531

DESFAC2 .282 .465

DESFAC3 .143 .489

SOCFAC2 .541 .152

WAIT2 .844 -.031

WAIT3 .920 -.136

TANG1 .840 -.015

TANG2 .808 -.028

TANG3 .774 .091

VAL1 -.132 .978

VAL2 .032 .798

VAL3 .790 .050 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 4 iterations.

116 Table 3.26. Rotated Pattern Matrix for Three-Factor Model of Service Quality

Factor

1 2 3 ATT1 .415 .168 .319

ATT2 .873 -.052 .130

ATT3 .887 .062 -.083

BEHAV1 .694 .035 .097

BEHAV2 .863 .019 .066

BEHAV3 .904 .093 -.206

EXPERT1 .727 .019 .376

EXPERT2 .806 -.016 .118

EXPERT3 .876 .001 -.121

AMBCON3 .532 .336 -.107

DESFAC1 -.012 .539 .345

DESFAC2 .250 .476 .131

DESFAC3 .146 .504 -.048

SOCFAC2 .479 .153 .317

WAIT2 .811 -.011 .078

WAIT3 .909 -.101 -.085

TANG1 .814 .006 .055

TANG2 .770 .000 .079

TANG3 .801 .123 -.218

VAL1 -.095 .955 -.056

VAL2 .063 .788 -.084

VAL3 .809 .084 -.189 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 5 iterations.

117 Factor Correlation Matrix The factor correlation matrix displaying the correlation estimates for the two factors is presented in Table 3.27. The correlation estimate between the two factors was .674.

Table 3.27. Factor Correlation Matrix for Two-Factor Model of Service Quality

Factor 1 2 1 1.000 .674

2 .674 1.000 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization.

Perceived Price Table 3.28 displays descriptive statistics information for the measures of perceived price. The mean scores range from a low of 5.06 for mon2 to a high of 5.80 for nonmon5. Following a listwise deletion, the sample size for the EFA for service quality was n=246.

Table 3.28. Descriptive Statistics for Perceived Price Items

Mean Std. Deviation Analysis N MON2 5.06 1.476 246 MON3 5.54 1.387 246 MON4 5.43 1.350 246 MON5 5.50 1.422 246 NONMON1 5.78 1.186 246 NONMON2 5.62 1.198 246 NONMON5 5.80 1.194 246

118 Total Variance Explained Table 3.29 displays information about the perceived price factors extracted by the factor analysis. Two eigenvalues exceeded 1.00. The proportion of the total test variance accounted for by each factor before rotation ranges from a high of 39.996 to a low of 19.780. The total variance accounted for by the two factors is 59.776%.

Table 3.29. Eigenvalues for Perceived Price Factors

Factor Initial Eigenvalues Extraction Sums of Squared Loadings % of Total % of Variance Cumulative % Total Variance Cumulative % 1 3.352 47.892 47.892 2.800 39.996 39.996 2 1.546 22.079 69.971 1.385 19.780 59.776 3 .704 10.057 80.029 4 .496 7.083 87.112 5 .378 5.401 92.513 6 .334 4.776 97.289 7 .190 2.711 100.000 Extraction Method: Maximum Likelihood. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

The Scree Plot The scree plot for the perceived price factors revealed that the amount of variance accounted for by successive components plunges sharply as successive components are extracted (see Figure 3.04). The “break” in the graph begins between factors two and three. Considering the eigenvalues and scree plot, it is reasonable to suggest that perceived price is comprised of a two-factor model.

119 Scree Plot 4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.0 Eigenvalue 1 2 3 4 5 6 7

Factor Number

Figure 3.04. Scree Plot for Perceived Price

Rotated Factor Pattern Matrix The rotated pattern matrix for the two-factor model presented in Table 3.30 resulted in factor loadings for the perceived price components ranging from .448 to .977. In total, two factors were identified: monetary (factor 1) and non-monetary (factor 2). Factor Correlation Matrix The factor correlation matrix displaying the correlation estimates for the two factors of perceived price is presented in Table 3.31. The correlation estimate between the two factors was -.367.

120 Table 3.30. Rotated Pattern Matrix for Perceived Price

Factor

1 2 MON2 .677 -.032

MON3 .877 .023

MON4 .685 -.047

MON5 .921 .063

NONMON1 -.014 -.680

NONMON2 -.110 -.977

NONMON5 .196 -.448 Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 5 iterations.

Table 3.31. Factor Correlation Matrix for Perceived Price

Factor 1 2

1 1.000 -.367

2 -.367 1.000

Extraction Method: Maximum Likelihood. Rotation Method: Oblimin with Kaiser Normalization.

121 Epistemic Value Table 3.32 displays descriptive statistics information for the measures of epistemic value. The mean scores range from a low of 5.04 for know3 to a high of 5.36 for know1. Following a listwise deletion, the sample size for the EFA for epistemic value was n = 253.

Table 3.32. Descriptive Statistics for Epistemic Value Variables

Mean Std. Deviation Analysis N

KNOW1 5.36 1.366 253

KNOW2 5.23 1.510 253

KNOW3 5.04 1.494 253

Total Variance Explained Table 3.33 displays information about the epistemic value factors extracted by the factor analysis. One eigenvalue exceeded 1.00. The total test variance accounted for by that factor is 72.01. The Scree Plot The scree plot for epistemic value revealed that the amount of variance accounted for by successive components plunges sharply as successive components are extracted (see Figure 3.05). The “break” in the graph occurs at factor two, however the eigenvalue of factor two is well below 1.0. Thus, it is reasonable to suggest that epistemic value was comprised of one factor.

122 Table 3.33. Eigenvalues for Epistemic Value

Initial Eigenvalues Extraction Sums of Squared Loadings

Factor Total % of Variance Cumulative % Total % of Variance Cumulative % 1 2.418 80.603 80.603 2.160 72.011 72.011

2 .395 13.176 93.779

3 .187 6.221 100.000 Extraction Method: Maximum Likelihood.

Scree Plot

2.5

2.0

1.5

1.0 Eigenvalue 0.5

0.0

1 2 3 Factor Number

Figure 3.05. Scree Plot for Epistemic Value

123 Factor Matrix The factor matrix for the one factor model is presented in Table 3.33 The factor loading for epistemic value ranges from .747 to .965.

Table 3.34. Factor Matrix for Epistemic Value

Factor 1 KNOW1 .747 KNOW2 .965 KNOW3 .820 Extraction Method: Maximum Likelihood. 1 factors extracted. 9 iterations required.

Satisfaction An EFA was not performed for satisfaction as the reliability estimate for this factor was below 0.70 and two of the corrected item-to-total correlations were below 0.50.

Pilot Study Discussion

A pilot study was conducted to examine the factor structure of the proposed dimensions of the VESSS and to begin the purification process based on the psychometric properties of the individual items. At this stage, internal consistency reliabilities were examined and five separate exploratory factor analyses were conducted, one for each of five dimensions in the proposed model, excluding satisfaction. This was done to provide an initial test of the reliability for the dimensions of value equity prior to an assessment of the overall model properties. Additionally, by conducting five separate EFA’s, the sample size of the pilot study (n = 254) was not a concern. Had the entire 75 item scale been tested together there would have been a subject-to-variable ratio of 3:1, well below the minimum ratio of 5:1 suggested by Gorsuch (1983) and Hair et al. (1998). Instead, the author was able to employ a listwise

124 deletion technique for each of the EFA’s and subject-to-item ratio’s for each of the analyses were as follows: entertainment value (209:30, 6.97:1); social value (227:12, 18.92:1); service quality (212:22, 9.64:1); perceived price (246:7, 35.14:1); epistemic value (253:4, 63:1). The results of the pilot study were used as the criteria to determine the dimensionality of the factor model and for item reduction. First, Cronbach’s alpha and corrected item-to-total correlations indicated the items to retain for additional analysis. Cronbach’s alpha coefficients lower than .70 indicates that the sample of items do not adequately capture the construct of interest, while higher scores indicate that the measures do correlate well with the true scores (Nunnally, 1978; Nunnally & Bernstein, 1994). Corrected item-to-total correlations were computed to assess the contribution of each item to overall consistency. According to Hair et al. (1998), corrected item-to-total correlations with values greater than .50 are considered acceptable and may be retained in the scale. With regard to the exploratory factor analyses that were computed for each of the identified components of value equity, factor loadings lower than ±.40 were deleted or modified from the item pool for that component. A discussion of the results of the reliability tests and factor analysis for each factor is presented in the following sections. Entertainment Value The results of the reliability analysis and EFA indicated that, for the most part, the data fit the model reasonably well. Each of the Cronbach’s alpha coefficients exceeded the .70 threshold. An examination of the corrected item-to-total correlation revealed three items did not meet the recommended .50 threshold. Consequently, the following items were deleted from further analysis: party2, crowd1, and gamint4. An exploratory factor analysis using a maximum likelihood procedure with oblique oblimin rotation revealed some problems with the proposed structure. First, the results did not support the existence of a crowd experience variable. Two of the three measures of crowd experience (crowd3 and crowd4) failed to load on any factor based on the established loading criterion of .40. The Crowd Experience measure which did load above .40 (crowd2) loaded together with two Game Intensity/Immersion measures, namely: gamint1 and gamint2.

125 The remainder of the items for Game Intensity/Immersion (gamint3, gamint5, gamint6, gamint7, and gamint8) did load together on the Game Intensity/Immersion factor. However this factor was relabeled Game Immersion given that each of the items remaining in the Game Intensity/Immersion scale refer to aspects pertaining to the importance of the game on the field relative to other activities at the stadium, as opposed to the affective experience derived from being at the game. The word intensity refers to how strong one feels, or how passionate one is about the experience, while immersion refers to the importance of specific aspects or features of the experience. The renaming of this factor to Game Immersion reflects the understanding that each of the remaining items are a measure of how important it is for spectators to immerse themselves in the game as it is played on the field. The rest of the items in the entertainment value scale did load well on their respective factors. The only other issue with the factor loadings was with the Aesthetics factor. While all three items loaded above the .40 threshold, one of the measures (aes2) loaded above 1.0. According to Jöreskog (1999) such a result is not necessarily associated with model misspecification. He notes that when factors are correlated, or (oblique), the factor loadings are regression coefficients and not correlations, and as such, they can be larger than one in magnitude. Thus, aes2 was retained as a measure of Aesthetics and was included in subsequent analyses. The results of the EFA did not entirely support the researcher’s initial conceptualization of Entertainment Value. Specifically, the results did not support the existence of a Crowd Experience variable, as the items for this construct loaded in a non-specified manner. Rather than blindly accept the results of the statistics, the researcher used the results of the pilot to reexamine the conceptualization of these constructs in order to elucidate understanding. Specifically, the researcher conducted a reexamination of the wording of the items for each construct as well as a reexamination of the literature related to the motivation of individuals to consume sporting events. Initially thought to be conceptually distinct, the results suggested that the two measures of Game Intensity/Immersion, along with the one Crowd Experience measure, perhaps comprise a previously unidentified dimension of Entertainment Value. The literature examining measures of entertainment related to the sport consumption

126 experience is in its infancy and continues to evolve. To date, there have not been any studies which have empirically tested the reliability and validity of measures associated with game intensity, game immersion, and crowd experience. The initial conceptualization of the constructs in the current study was based on the work presented by James, Sun, and Lukkarinen (2004). An examination of the wording of each of the three items suggested that they are indeed conceptually similar in that they each deal with the intensity of the emotional or affective feelings the experience of being at an event provides. For example, crowd2 read, “I love the feeling of being surrounded by all of the fans.” This item deals with the emotions associated with experiential aspect of being at the game. Similarly, gamint1 is also associated with the affective features of the experiential consumption experience. This item read, “watching team name games is a very intense experience for me.” Finally, gamint2, which read, “I really get into the game when I watch team name games” may also be thought of as referring to the affective experience of being at the event. The words “I really get into” deals with the affective component of the experience, while “the game” may be interpreted as meaning both the core and peripheral aspects of the event. Based on a reexamination of the literature, as well as the wording of the items the researcher chose to accept the results of the pilot study statistics and test a modified conceptualization of Entertainment Value in subsequent stages of the current research. The three measures were retained together for the confirmatory factor analysis stage of the study and were labeled Experience Intensity. Social Value The results of the reliability analysis and EFA for social value indicated that the data fit the model well. Each of the Cronbach’s alpha coefficients exceeded the .70 threshold. An examination of the corrected item-to-total correlation revealed each of the items also exceeded the .50 threshold. Consequently, each of the twelve items were included in the EFA. The exploratory factor analysis using maximum likelihood extraction with oblique oblimin rotation revealed that the data generally fit the model well. However, the results did fail to discriminate between non-acquaintances and friends as each of the sets of three items loaded together. After reviewing the items, it is likely that spectators do not

127 perceive there to be a difference between “others” and “friends”, especially when presented with questions about family in the survey. Thus, the merged factor containing the six measures was renamed non-family. There is a lack of consensus in the sport consumer motivation literature regarding the distinction between friends and non-acquaintances (also referred to as others in the literature) dimensions from both the conceptual and empirical perspectives. In a study examining the factors believed to motivate sport fandom Wann (1995) proposed and tested a three-item dimension called Group Affiliation, whose content included motivations to be with friends, others, and a large group of people. Although critiqued later (Trail & James, 2001) for various reasons, including the wording of items, the dimension was nonetheless found to be reliable and valid. Similar to Wann (1995), James, Kolbe, and Trail (2002) tested a dimension they labeled Social, which also included items containing information about other people and friends. This dimension, however, did not display an acceptable level of validity and reliability. Trail and James (2001) also examined a dimension labeled Social in their study assessing the psychometric properties of the Motivation Scale for Sport Consumption (MSSC). The items used by these authors contained general language referring simply to other people at the game. The dimension was found to be both reliable and valid. The authors did not provide a rationalization for why the Social dimension did not include items assessing friends as a motivation. One explanation is that by ‘other people’ the authors were also referring to friends as well. This explanation is particularly possible given that the MSSC explored the family dimension, but did not explore one for friends. In contrast to Trail and James (2001), James and Ross (2004) proposed and tested a dimension labeled Social Interaction comprised of measures containing language specific to friends. In this study, the authors did not consider social interaction with other people, or non-acquaintances as motives for sport consumption. The dimension also proved to be reliable and valid. Of all the studies that examined the social motivations for sport consumption, only Funk, Ridinger, and Moorman (2003) have examined friends, others, and family as separate dimensions. Each of the dimensions were found to be reliable and valid.

128 Additionally, although they did discriminate, the dimensions capturing friends and others were highly correlated. Due to the lack of consensus regarding the measurement of socialization and the impact of different groups, the researcher thought it prudent to conceptualize and test a scale for Social Value treating Friends and Non-Acquaintances as distinct. Given the results of the pilot study, and considering the previous research, the decision to merge Friends and Non-Acquaintances into one dimension seems justified. It is unlikely that spectators perceive a difference between friends and others. Service Quality The results of the reliability analysis and EFA for service quality revealed that the model could be improved. The Cronbach’s alpha coefficients for each of the three Service Quality factors (Interaction Quality, Service Environment Quality, and Outcome Quality) exceeded the .70 threshold. An examination of the corrected item-to-total correlation revealed four measures of Service Environment Quality (seq1, seq2, seq7, and seq9) and one measure of Outcome Quality (outqual1) did not meet the recommended .50 threshold. These measures were thus not included in the EFA. An exploratory factor analysis using a maximum likelihood procedure with oblique oblimin rotation revealed several problems with model fit. First, the results supported the existence of a two-factor model of Service Quality. The first factor is comprised of all nine Interaction Quality measures, two Service Environment Quality measure (seq3 and seq8), and six Outcome Quality measures (outqual2 through outqual6 and outqual9). Although Seq3 is an item that deals with the ambient conditions, it is designed to elicit information about spectators’ perceptions of how well employees understand that the atmosphere is important to the survey respondent. The degree to which a spectator is able to assess this understanding is likely dependent on the interaction that he or she has with the baseball employees. Similarly, although seq8 deals with social factors, the item assesses survey respondents’ perceptions of how the other spectators affect the staff’s ability to provide good service. There is an interaction component to the item. Each of the six Outcome Quality measures that loaded on the first factor also had an interaction orientation in the content of the item. As a result, the first factor contained 17 items. This factor was labeled Interaction Quality.

129 The second factor comprised the three Service Environment Quality items dealing with the design and layout of the stadium (seq4, seq5, and seq6) and two Outcome Quality items (outqual7 and outqual8) dealing with the valence associated with the game. It is unclear to the author why two measures designed to assess outcome quality loaded together with the items measuring consumer perceptions of the quality of the design and layout of the venue. One suggestion is that consumer perceptions of facility design and layout are really an evaluation of outcome quality. Items such as “the team name’s stadium/arena layout never fails to impress me” and “the layout of the stadium serves my purposes” reflect consumer evaluations of the service environment in terms of what is actually received from the service encounter. As a result, these measures were retained together for the confirmatory factor analysis stage of the study and were labeled Outcome Quality. The results of the exploratory factor analysis suggest that service quality is a two- dimensional construct. The results do not support the researcher’s initial conceptual argument that service quality is a three-dimensional construct. Rather it may be more appropriate to conceive of service quality as being comprised of two-dimensions rather than three. A review of the work of several service quality researchers (Grönroos, 1984; Lehtinen & Lehtinen, 1991; Rust & Oliver, 1994) indicates that quality perceived by a customer has both functional and technical elements. Grönroos (1984) explained functional quality as the way a service is delivered to a consumer, or the customer’s perception of the service interaction that occurs during the service encounter. Technical quality was described as being what the customer actually receives from a service or service encounter. It refers to the customer’s evaluation of the quality of the service outcome. Given the empirical results of the pilot study and the support from the literature, the decision was made to alter the conceptualization of service quality in the current study from a three-dimensional construct to a two-dimensional construct. Perceived Price The results of the reliability analysis and EFA for perceived price indicated the data fit the model reasonably well. Each of the Cronbach’s alpha coefficients exceeded the .70 threshold. An examination of the corrected item-to-total correlation revealed three items, monetary1, non-monetary3, and non-monetary4, did not meet the

130 recommended .50 threshold. As such, these items were not included in the EFA for perceived price. An exploratory factor analysis using maximum likelihood extraction with oblique oblimin rotation offered support for a two-factor model of Perceived Price. Each of the four monetary measures loaded together above the 0.40 threshold, as did the three non-monetary items. A seven measure, two-factor model of Perceived Price was examined in subsequent stages. Epistemic Value The results of the reliability analysis and EFA for Epistemic Value showed that the data did not fit the model reasonably well. While the Cronbach’s alpha coefficient for knowledge exceeded the .70 threshold, it was well below .70 for novelty. Additionally, an examination of the corrected item-to-total correlation revealed that two of the novelty items (novel1 and novel2) did not meet the recommended .50 threshold. A review of the measures of novelty employed in the current study suggests the wording of the measures may be problematic. The first problem related to the wording of novelty2, which was “I am interested in experiencing new things.” The wording of this item did not relate the interest of experiencing new things to the consumption experience of being at a game. An accurate assessment of the extent to which consumers value the novelty associated with a given sporting event requires the questions to be specific to the event in question. For, a high rating on the measure outlined above does not provide any indication that the new thing subjects are interested in experiencing is in fact the team’s games. In addition to conducting a reexamination of the wording of the items, the researcher further examined the novelty concept to elucidate understanding. McQuiston (1989) defined novelty as “the lack of experience of individuals in the organization with similar purchase situations” (p. 69). This definition suggests that sport consumers must not have had any prior experience attending the sporting event in question for the novelty of the experience to be of value in the consumption experience. A review of the sport marketing literature revealed that novelty has only been examined with respect to the motivations to attend the game of a new team in a new stadium (James, Kolbe, & Trail, 2002). The Florida State Seminoles baseball team is not a new team and does not play in a new stadium. Additionally, each of the approximately 5,000

131 grandstand seats at the stadium is available for purchase as season tickets only. Given, these circumstances, it is unlikely that novelty is of value to these fans. Furthermore, given that the subjects sampled in subsequent data collection efforts for this research projects are consumers of teams that that are not new and that do not play in new stadiums, it was decided to omit novelty from further testing. Based on the results of the reliability analysis and a review of the novelty concept in the literature, the researcher made the decision to drop the novelty dimension from the model and not to include novelty in the EFA for Epistemic Value. Thus, the results of the exploratory factor analysis offered support for a three-measure, one-factor model of Epistemic Value. The three measures that loaded together above the .40 threshold each dealt with aspects of the knowledge acquired from a team’s games. Satisfaction As reported in the results section of the pilot study, the model of Satisfaction did not fit the data. The reliability estimate for this factor was below 0.70 and two of three measures (satisfaction1 and satisfaction3) did not meet the corrected item-to-total threshold of 0.50. Consequently, an EFA could not be computed. However, the item satisfaction3 had a corrected item-to-total correlation of .499, just below the .50 cut-off. A review of the literature indicates that satisfaction has been measured using a single measure (Babakus & Boller, 1992; Bolton & Drew, 1991; Cronin & Taylor, 1992; Spreng & Mackoy, 1996), and the results of the reliability analysis suggest that a single item may capture the construct. However, researchers (Fornell et al., 1996; Yi, 1990) have noted that there are many shortcomings to measuring a construct with a single item. Fornell et al. (1996) noted single item measures often fail to capture the richness and complexity of a theoretical construct or latent variable that is not directly measurable. Yi (1990) remarked that single-item scales cannot assess or average out the variance due to random errors, specific items, and method factors. As such, rather than delete the two underperforming measures of satisfaction, these items were reworded and a multiple-item measure of satisfaction was tested in subsequent stages of this study.

132 Conclusion

The results of the pilot study suggest that although there is evidence to indicate that the data fit the model reasonably well, there remains room for improvement. Six substantial modifications to the proposed model were made as a result of the findings of the pilot study, including: 1) the elimination of the Crowd Experience dimension; 2) the merging of factors from Game Intensity/Immersion and Crowd Experience to form a new dimension named Experience Intensity; 3) given the content of the measures loading together, the Game Intensity/Immersion dimension was relabeled Game Immersion; 4) Non-Acquaintances and Friends were merged to form the newly named Non-Family dimension; 5) a two-factor model of Service Quality was identified; and 6) the items measuring Satisfaction were reworded. The described changes resulted in a first-order Value Equity model comprised of 16 dimensions and 74 items (see Figure 3.06), which was tested in the next phase of the study.

133

Figure 3.06. Post Pilot Study Model of Value Equity

134 CHAPTER IV

METHODOLOGY

Introduction

The purpose of the current study was to develop a valid and reliable instrument to measure the domains comprising value equity in the context of spectator sports. The main study involved two separate data collections. The analysis of the first set of data provided information used to test the model obtained from the pilot phase of the research. The analysis from the results of the second data collection served to confirm the revised model from the analysis of the results from the first data collection. The current chapter describes the methodology used to conduct the main part of the study.

Main Study

Step 6 – First Data Collection of the Main Study Target population and sample design. The population for the main study included spectators at a Jacksonville Suns minor league baseball game. The main study consisted of a convenience sample of spectators in attendance at the Baseball Grounds of Jacksonville for a selected game during the early part of the 2007 season. The survey form was distributed to spectators in their seats starting one hour prior to the commencement of the game and were collected prior to the first pitch and in between innings of play. A cover letter outlining the purpose of the study as well as providing instructions on how to complete the survey was distributed with the survey form. A combination of undergraduate students, graduate students, and university graduates helped to administer the instrument. The selection of a sample is an important concern in any research. The methods used to determine how large a sample size should be, and the manner in which it is to be selected from the population of study, is of interest. Many recommendations exist in the literature related to the sample size in the use of factor analysis, and researchers

135 have suggested a wide variety of guidelines for estimating adequate sample size in factor analysis. Guidelines typically involve either a recommendation of a minimum number of subjects needed to perform a factor analysis (Anderson & Gerbing, 1988; Boomsma, 1983), or a determination of sample size in terms of a ratio to the number of measured variables being analyzed (Cliff & Hamburger, 1967; Gorsuch, 1983; Hatcher, 1998; Nunally, 1978). Minimum recommendations for a satisfactory sample size when constructing structural equation models range from between 100 and 150 (Anderson & Gerbing, 1988) to a minimum of 400 (Boomsma, 1983). The rules of thumb for a determination of sample size in relation to the number of measured variables being analyzed, typically ranges from 5 to 25 subjects per variable. For example, Cliff and Hamburger (1967) suggested a minimum ratio of 20 subjects per measured variable to ensure stable estimates. Nunnally (1978) recommended that the minimum ratio should be ten to one, while Gorsuch (1983) and Hatcher (1998) suggested a minimum ratio of 5 subjects per measured variable. In recent research Gagne and Hancock (2006) reported that sample size recommendations in confirmatory factor analysis (CFA) have shifted away from observations per variable or per parameter toward consideration of model quality. In a study designed to determine the extent to which CFA model convergence and parameter estimation are affected by n as well as by construct reliability, which is a measure of measurement model quality derived from the number of indicators per factor (p/f) and factor loading magnitude, the authors found that model convergence and accuracy of parameter estimation were affected by n and by construct reliability within levels of n. Thus, their sample size recommendations were presented as a function of a researcher’s relevant design characteristics. Whichever recommendations are followed, increases in sample size (n) have at least three well known potential benefits: (a) likelihood of proper model convergence increases; (b) accuracy of parameter estimates and estimated standard errors can be enhanced; and (c) statistical power to reject/retain null hypotheses regarding entire models, or regarding parameters within those models, increases with sample size (Gagne & Hancock, 2006).

136 Taking into account the recommendations to determine sample size, the minimal number of subjects in the sample should be no less than five times the number of variables being analyzed. Following the results of the reliability analysis and EFA, 74 items remained in the model. Thus, the minimum number of subjects in the sample should total no less than n = 370. A convenience sampling procedure was used to distribute 550 survey questionnaires to persons sitting in the seating throughout each of the sections of the stadium. Initially, the researcher intended to collect a stratified sample of customers sitting in aisle seats in predetermined sections. However due to a late arriving crowd, it was necessary to use a convenience sample employing an unguided selection criterion. In total, a team of six individuals aided in the distribution and collection of the survey questionnaire. Survey team members included two doctoral students, one master’s student, one undergraduate student, and two friends of the undergraduate student. Each survey team member distributed surveys in a predetermined section of the stadium. Each survey team member approached spectators appearing to be 18 years of age or older as they arrived in their seats. This method of sampling yielded a return of 376 useable surveys for a return rate of 68.36%. There were five reasons why the 174 forms were deemed unusable and thus omitted from the analysis: 1) the forms were not returned (n = 87); 2) the forms were filled out incorrectly (n = 21); 3) the returned forms had at least one complete page of questions left blank (n = 19); 4) there were too many (> 10) missing values (n = 41); and 5) participants were under the age of 18 (n = 6).

Step 7 – Assessment of Reliability and Validity Data analysis procedures. The statistical procedures utilized to analyze the data are outlined in this section. The methodology utilized to test the model was confirmatory factor analysis (CFA). The purpose of CFA is to identify latent factors that account for the variation and covariation among a set of indicators (Brown, 2006). CFA is an appropriate analysis technique to use in later phases of scale development or construct validation after the underlying structure has been tentatively established by prior empirical analysis using EFA (Brown, 2006). An attractive feature of CFA models

137 is that they distinguish between indicators and the underlying latent variable that the indicators are presumed to measure. The Lisrel 8.80 statistical software application (Jöreskog & Sörbom, 2006) was used to analyze the data and test the factor structure of the model. A first-order confirmatory factor model was evaluated with the aim of testing the existence of the dimensionality of the first-order variables. CFA is an appropriate technique to use when a priori underlying dimensions are operationalized through observed measures (Maruyama, 1997). Following the recommendation by Brown (2006), the next section of this chapter describes the methodology used for the development of a good-fitting, conceptually valid first-order CFA solution for Value Equity. First-Order CFA Most researchers agree conducting a proper CFA involves several sequential steps, including: 1) specification; 2) identification; 3) estimation; 4) evaluation; and 5) model revision (Brown, 2006; Long, 1983; Schumacker & Lomax, 2004; Tate, 1998). The following section explains the steps for the first order CFA. Specification An important first step in the analysis of a confirmatory factor model is the specification of a measurement model that is well grounded in prior empirical evidence and theory (Brown, 2006). CFA specification is based on a strong conceptual framework and on prior research that is more exploratory in nature (Brown, 2006). Model specification is important because many different relationships among a set of variables can be postulated with many different parameters being estimated. As such, many different factor models can be postulated on the basis of different hypothesized relationships between the observed variables and the factors (Schumacker & Lomax, 2004). According to Long (1983), the specification of the confirmatory factor model requires the researcher to make formal and explicit statements about the following six items: 1) the number of common factors; 2) the number of observed variables; 3) the variances and covariances among the common factors; 4) the relationships among observed variables and latent factors; 5) the relationships among unique factors and

138 observed variables; and 6) the variances and covariances among the unique factors (Long, 1983). In the current study, the researcher is evaluating the latent structure for a model of value equity in spectator sports. Substantive theory and prior exploratory factor analysis involving a separate data set suggest that the latent structure of the current stage of the study is predicted to be characterized by 16 first-order factors that represent 16 distinctive ways in which spectators derive value from the consumption of spectator sports: Amusement, Partying, Game Immersion, Escape, Aesthetics, Drama, Experience Intensity, Family, Non-Family, Business Opportunities, Epistemic Value, Monetary, Non-Monetary, Interaction Quality, Outcome Quality, and Satisfaction. Identification Once a confirmatory factor model has been specified, the researcher must determine whether the model is identified. Model identification is concerned with whether the parameters of the model are uniquely determined. A model is identified when there are an adequate number of observed variances and covariances to estimate all of the unknowns (Tate, 1998). The t-rule is a useful test for identification. The t-rule is satisfied if the number of variances and covariances of the observed variables (p[p+1]/2 where p is the number of x’s) is equal to or greater than the number of model parameters to be estimated. The model parameters to be estimated include the covariances of the latent variables, the factor loadings, and the measurement error variances. In the current study a count of the free parameters is as follows: • 74 factor loadings • 74 measurement error variances • 16 correlations among the latent variables A total of 164 free parameters were estimated. The number of distinct values in the matrix S is equal to 74(74+1)/2 = 2775. The number of values in S, 2775, is greater than the number of free parameters, 164, with the difference being the degrees of freedom for the specified model, df = 2775 – 164 = 2611. According to the order condition, the current model is overidentified because there are more values in S than parameters to be estimated (Schumacker & Lomax, 2004). As a result, a test of the model is possible (Tate, 1998).

139 Estimation Once identification has taken place, it is necessary to conduct an estimation of the confirmatory factor model. The purpose for estimating the factor model is to find estimates of the parameters that reproduce the sample matrix of variances and covariances of the observed variables as closely as possible in some well-defined sense (Long, 1983). Several different procedures can be used to estimate the parameters of a confirmatory factor model, including: maximum likelihood (ML), generalized least squares (GLS), unweighted least squares (ULS), weighted least squares (WLS), and robust maximum likelihood. According to Brown (2006), the use of maximum likelihood estimation is most appropriate in situations where there is a sufficient sample size and indicators approach interval level scales. Given these recommendations, maximum likelihood (ML) was used to estimate the parameters of the factor model for Value equity. Evaluation Model evaluation is the next important step in the scale development process. At this stage, all items were tested in the same model and were restricted to load on their particular factors. The acceptability of the CFA solution was evaluated on the basis of the goodness of fit statistics, the presence or absence of localized areas of strain, and the interpretability, size, and statistical significance of the model’s parameter estimates. First, the goodness-of-fit of the model was evaluated using a selection of goodness-of-fit indices recommended by Brown (2006) including: chi-square statistic, standardized root mean square residual (SRMR), root mean square error of approximation (RMSEA), comparative fit index (CFI), and the Tucker-Lewis index (TLI). The TLI is referred to in LISREL 8.1 (Jöreskog & Sörbom, 2006) as the non-normed fit index (NNFI). The chi-square statistic is a measure of the overall or absolute fit of a model. The resulting chi-square value is compared to a critical value for a selected alpha level for statistical significance. A statistically significant chi-square value supports the hypothesis that the model estimates do not sufficiently reproduce the sample variances and covariance meaning the model does not fit the data the well (Brown, 2006). Thus, a statistically insignificant result is desirable. However, Brown (2006) noted that the chi- square index is rarely used as a sole index of model fit because it is often inflated by

140 sample size, where large N solutions are commonly rejected on its basis even when differences between model and sample variance and covariances are negligible. Another measure of the absolute fit of a model is the SRMR. The SRMR is a measure of the average discrepancy between the correlations observed in the input matrix and the correlations predicted by the model. The SRMR, which is derived from the residual correlation matrix, is calculated by summing the elements of the residual correlation matrix and dividing the sum by the number of elements in the matrix and then taking the square root of this result (Brown, 2006). Ranging in value from 0.0 to 1.0, with 0.0 indicating a perfect fit, Hu and Bentler (1999) suggested that an SRMR close to .08 or below is indicative of a reasonably good fit. The RMSEA, according to Brown (2006), is a measure of absolute model fit that incorporates a penalty function in its calculation for poor model parsimony. With a relative insensitivity to sample size, the RMSEA is an index that assesses the extent to which a model fits reasonably well in the population. As with the SRMR, RMSEA values of 0.0 indicate perfect fit. The upper value of the RMSEA are unbounded, however, Brown (2006) noted it is rare for RMSEA values to exceed 1.0. Numerous recommendations exist in the literature regarding the evaluation of the cut-off criteria for determining the fit of a model. Hu and Bentler (1999) suggested values close to .06 or below signify a reasonably good fit. Browne and Cudeck (1993) provided descriptive anchors for various ranges of fit. For example, the authors proposed that RMSEA values of less than 0.05 are indicative of good model fit, values less than .08 suggest adequate model fit, and RMSEA values greater than 1.0 should be rejected. Finally McCallum, Browne, and Sugawara (1996) proposed that values between .08 and 1.0 suggest a mediocre fit of the model to the data. The CFI and NNFI are referred to as comparative fit indices (Brown, 2006), or incremental fit indices (Hu & Bentler, 1998). These indices evaluate the fit of a researcher-specified solution in relation to a more restricted, nested baseline model. The baseline model is a “null” or “independence” model in which the indicator covariances are fixed to zero and the variances are left unconstrained (Brown, 2006). The NNFI differs slightly from the CFI in that it includes a penalty function for adding freely estimated parameters that do not markedly improve the fit of the model. Hu and

141 Bentler (1990) suggested that CFI and NNFI values that are close to .95 or greater are indicative of reasonably good fit. Bentler (1990) proposed that CFI and NNFI values in the range of .90-.95 are suggestive of acceptable model fit. An understanding of how the various fit indices are calculated is crucial for the identification of localized areas of strain in a poor fitting model. After the assessment of the overall fit of the model, the researcher examined various measures of internal fit to identify potential sources of misspecification. A powerful indicator of the internal structure of a model is the fitted residuals (Bagozzi & Yi, 1988). An examination of the fitted residuals is an appropriate method for analyzing local areas of strain in a model (Brown, 2006) and for identifying sources of misspecification (Anderson & Gerbing, 1988). Researchers have suggested that residual values that are greater than .15 indicate an issue of misspecification (McDonald, 2002). In addition to the evaluation of model fit and localized strain, the standardized factor loadings, confidence intervals, standard errors, t-values, construct reliabilities, and average variance extracted (AVE) scores for the 16 constructs were calculated. For the purposes of the current research project, convergent and discriminant validity are two validation processes applicable for providing evidence of the construct validity of psychological measures. Convergent validity was evaluated through an examination of the significance of t-values and the average variance extracted (Fornell & Larcker, 1981). Discriminant validity was tested using the correlation threshold of .85 recommended by Kline (2000). Finally, the researcher did not evaluate criterion-related validity in the current study. Based on the definition of criterion validity presented earlier, this type of validity is used to demonstrate the accuracy of a measurement instrument by comparing it with another procedure, which has been demonstrated to be valid. Two-types of criterion- related validity are predictive validity and concurrent validity. Predictive validity was not assessed, as the purpose of this research was to develop a valid and reliable instrument to assess consumers’ perceptions of value vis-à-vis the sport consumption experience. At this stage, the researcher was not concerned with whether the instrument could accurately predict future behaviors such as attendance, word-of-

142 mouth, and switching. Future studies designed to measure the effect of perceptions on future consumption habits would be concerned with the establishment of predictive validity in order to use the developed instrument with confidence to discriminate between consumers based on the measured outcomes. Recall that concurrent validity refers to the degree to which a measurement instrument correlates with an established and tested measure of the same construct. Thus, if the results are compared and have a high correlation with an established (tested) measurement, one could say that the measure has concurrent validity and is valid. As there have not been any previous efforts to develop a comprehensive measure of value equity in spectator sports, it is not possible to compare the results of this study to an established measure, and thus it is not possible to establish concurrent validity.

Second Data Collection – Main Study

Based on the results of the first data collection of the main study, a modified 14- factor, 64-item survey form was administered to 350 students enrolled at a Southeastern University during the summer semester of 2007. The modifications to the scale involved the rewording of the items to contain non-team specific language (see Appendix H). Additionally, the tense of certain items was changed from present to past- tense to account for the fact that students were asked about their previous consumption experiences. Students were sampled from several disciplines across campus, including: the College of Business, the College of Communications, Athletic Training, and the Lifetime Activities Program. The survey administration resulted in a total of 285 useable questionnaires for a return rate of 81.4%. There were four reasons why the 65 forms were deemed unusable and thus omitted from the analysis: 1) the forms were not returned (n = 32); 2) the forms were filled out incorrectly (n = 8); 3) the returned forms had at least one complete page of questions left blank (n = 9); and 4) there were too many (> 10%) missing values (n =16). The sample size is considered to be appropriate given the literature on the recommendations regarding sample size in factor analysis. In terms of the recommended guidelines for the minimum necessary absolute sample size, Kline (2000) and Gorsuch (1983) recommended that N should be at least 100. Guilford

143 (1954) proposed that N should be at least 200, while Cattell (1978) argued the minimum desirable N to be 250. Regarding the recommendations for the minimum ratio of sample size, N to the number of variables being analyzed, p, Cattell (1978) believed this ratio should be in the range of three to six, while Gorsuch (1983) argued for a minimum ratio of five. The N:p ratio for this data collection was 4.45:1. The researcher chose to survey a sample of students at this stage to further examine the psychometric properties of the Value Equity in Spectator Sports Scale across sports. The previous two data collections involved surveys of baseball spectators. As the current research is a study of sport consumers, students were asked to identify the last collegiate or professional sporting event they had attended. Students were then instructed to respond to each question in the survey while thinking about that consumption experience. Students were also asked to report the date on which they attended that game, as well as the number of times they have attended games of the identified team. The majority of the students reported that a college sporting event had been the last they attended, while more than half of the sample reported having attended their last game inside of six months. Additionally, a majority of the sample reported that they had attended games of the indicated team more than one time. The purposes of the qualification questions were twofold: First, it was necessary to provide participants with a subject from which perceptions of value could be made. Second, the responses to these questions provided evidence that the students sampled were consumers of sporting events. Recognizing that the length of time after consumption may adversely impact perceptions, a chi-square difference test between those who had last attended a game within six months and those who last attended a game more than six months ago was computed on the mean scores for the manifest variables. This test was conducted to establish that length of time after consumption did not affect perceptions. The results of the chi-square analysis, which are presented in the next chapter, indicated there were not any statistically significant differences between groups. As such, responses from the two groups were combined in order to test the scale. Finally, the same procedures that were used to test the factor-structure from the first data collection of the main study were used to test the first-order factor structure for

144 the second data collection. Additionally, following the testing of the first-order factor structure, the researcher chose to examine the second-order factor structure at this stage as well. Marsh (1987) noted that the existence of a well-defined, a priori, first- order factor structure is a precondition for testing higher order structures as successive higher order models are based on it and its goodness of fit is the upper limit for the goodness of fit for higher order models. Therefore, a second-order model was calculated to test the notion that individual second-order dimensions integrate the first- order variables. Specifically, a second-order hierarchical CFA (HCFA) utilizing a two-step procedure was computed to assess the Value Equity in Spectator Sport Scale (VESSS). Hierarchical CFA models are used to examine hierarchical relationships between constructs through the specification of higher-order factors with presumed direct causal effects on lower order factors (Kline, 2005). Marsh and Hocevar (1988) noted three differences between HCFA and traditional CFA models. First, estimates of measurement error in the HCFA model are based on the agreement among multiple measured variables (items or combined items). Second, HCFA allows for an a priori factor structure hypothesized to underlie the multiple indicators of each scale to be formally examined because HCFA is based on item-level estimation. Traditional CFA models do not provide statistical testing information to evaluate or modify a priori structures. Finally, HCFA models are capable of testing the assumption that error- uniqueness for the items used to represent each scale are truly not correlated, which is not available in traditional CFA models. According to Marsh (1987) the purpose of HCFA is to explain covariation among the first-order factors with one or more higher- order factors. Higher-order factor analysis, or hierarchical factor analysis, is a theory driven procedure in which the researcher imposes a more parsimonious structure to account for the interrelationships among factors established by the CFA. According to Brown (2006) the general sequence of a CFA-based higher-order factor analysis is as follows: 1) develop a good-fitting, conceptually valid, first-order CFA solution; 2) examine the magnitude and pattern of correlations among factors in the first-order solution; and 3) fit the second-order factor model, as justified on conceptual and empirical grounds.

145 Step 8 – Development of Norms The establishment of norms in psychological measurement refers to the formalization of processes by making implicit standards explicit (Churchill, 1979). In an effort to provide a preliminary reference for scale norms, range, means, and standard deviations were reported for the scale scores of the main study and subsequently compared to scores on the same measures found in the pilot study.

Summary

The current chapter presented the methodology employed in the current research to conduct the main part of the study. Churchill’s (1979) eight-step procedure for developing better measures was used to develop a multi-dimensional measure for assessing the factors thought to comprise the value in attending sporting events. The results of the study are presented in Chapter Five.

146 CHAPTER V

RESULTS

The main study consisted of two separate data collections. The first data collection (the second overall) for the main study was used for the initial confirmatory factor analysis to test the reliability and validity of the modified subscales and to assess model fit. The second data collection (the third overall) was used for subsequent validation of the Value Equity in Spectator Sports Scale. The results of the two data collections are presented in this chapter.

First Data Collection – Main Study

Based on the results of the pilot study, a modified survey questionnaire was administered to spectators in attendance at a Jacksonville Suns’ baseball game at the Baseball Grounds of Jacksonville in Jacksonville, Florida. The Suns are the ‘Double A” minor league baseball affiliate of the Los Angeles Dodgers. The questionnaires were distributed to 550 spectators situated throughout six different areas of the ballpark. A total of 376 useable questionnaires were returned for a return rate of 68%. The sample size for analysis met the minimum cases per variable ratio of five to one recommended by Gorsuch (1983) and Hatcher (1998). Characteristics of the Sample Six demographic classification characteristics were measured in the survey instrument for the main study: gender, age, marital status, household income, ethnicity, and education. As indicated in Table 5.01, 55.4% of the respondents were male. In terms of age, nearly 40% of the sample were between the ages of 35 and 49. Seventy one percent of the sample was married, over 93% were Caucasian, and just over 40% had completed at least an undergraduate degree. Finally, for household income, the largest group was those earning more than $100,000 per year (28.4%) followed by

147 Table 5.01. Demographic Characteristics of the Confirmatory Sample Valid Cumulative Demographic Variables Frequency Percent Percent Gender Female 166 44.6 Male 206 55.4 Total 372 100.0 System Missing 4 Age 18-34 82 29.0 29.0 35-49 111 39.2 68.2 50-64 70 24.7 92.9 65+ 20 7.1 100.0 Total 283 100.0 System Missing 93 Marital Status Married 262 71.0 Single 65 17.6 Divorced 27 7.3 Widowed 10 2.7 Other 5 1.4 Total 369 100.0 System Missing 7 Household Income < $20,000 24 7.3 7.3 $20,000 - $39,999 43 13.1 20.4 $40,000 - $59,999 64 19.5 39.9 $60,000 - $79,999 63 19.2 59.1 $80,000 - $99,999 41 12.5 71.6 $100,000+ 93 28.4 100.0 Total 328 100.0 System Missing 48 Ethnicity Black/African American 15 4.1 Native American 3 .8 Asian / Pacific Islander 4 1.1 White/Caucasian 339 93.1 Latina/Latino 2 .5 Other 1 .3 Total 364 100.0 System Missing 12 Education High School Diploma 106 29.5 29.5 Trade/Professional 45 12.5 42.1 Junior College Diploma 60 16.7 58.8 Undergraduate Degree 99 27.6 86.4 Masters Degree 37 10.3 96.7 Doctoral Degree 12 3.3 100.0 Total 359 100.0 System Missing 17

148 those earning between $40,000 and $59,999 (19.5%) and earners making between $60,000 and $79,999 (19.2%). Model Evaluation The 16-factor model was tested using all 74 items retained from the exploratory factor analysis (see Appendix D). The standardized factor loadings (), confidence intervals (90% CI), standard errors (SE), t-values (t), Construct Reliabilities (CR), and average variance extracted for the 16 constructs are presented in Table 5.02. The factor loadings ranged from.629 to .834 in amusement; .154 to .861 in partying; .603 to .762 in experience intensity; .119 to .766 in game immersion; .357 to .884 in escape; .593 to .754 in aesthetics;.297 to .868 in drama; .532 to .800 in non family; .765 to .861 in family; .738 to .799 in business opportunities; .813 to .927 in epistemic value; .376 to .815 in monetary; .799 to .890 in non monetary; .586 to .825 in satisfaction; .439 to .880 in interaction quality; .434 to .914 in outcome quality. Model Fit As described in chapter four, several fit indices were used to verify the sub-scale structure of the instrument. The measures of model fit employed in the current study are presented in Table 5.03. The 2 value was 10286.05 (p < 0.00), which was not satisfactory. Additional measures of model fit included: SRMR (.089), RMSEA (.089), NNFI (.906), and CFI (.913). The model fit indices suggest that the fit of the data to the model is adequate to mediocre. Internal Consistency Reliability The 74 items were examined for internal consistency based on an assessment of the construct reliabilities. Construct reliabilities are reported in Table 5.02. The construct reliability scores for each of the subscales were as follows: amusement (.84); partying (.85); experience intensity (.74); game immersion (.75); escape (.75); aesthetics (.73); drama (.77); non family (.86); family (.86); business opportunity (.83); epistemic value (.90); monetary (.78); non monetary (.87); satisfaction (.79); interaction quality (.96); and outcome quality (.90). Each of the 16 subscales scored above .70, indicating good internal consistency.

149 Construct Validity Convergent validity. Five of the sixteen constructs did not meet the .50 threshold recommended by Fornell and Larker (1981) indicating poor construct validity. The results of the test for convergent validity are presented in Table 5.02. Constructs with low AVE scores included: experience intensity (.49); game immersion (.41); aesthetics (.48); drama (.49); and monetary (.49). An examination of the residual matrix of the current model (not included in the results) revealed that 15.11% of the residuals were greater than .15. Items with the highest residuals were associated with the following four factors: 1) Game Immersion; 2) Drama; 3) Escape; and 4) Interaction Quality. Five items specifically accounted for 54.28% of the high residuals - gamint8 (16.1%), drama4 (14.78%), esc3 (11.7%), party6 (7%), and iq15 (4.7%). Discriminant validity. Table 5.04 shows the correlations among the 16 latent variables. The correlations among the sixteen dimensions ranged from .002 to .986. The correlation between interaction quality and outcome quality (.986) was above the threshold specified by Kline (2000), as were the correlations between satisfaction and interaction quality (.904); and between satisfaction and outcome quality (.853). The results of the test for discriminant validity are presented in Table 5.05. The results indicate that the test failed to discriminate among several constructs, suggesting a lack of differentiation among the constructs. The constructs that proved to be the most difficult were Satisfaction, Interaction Quality, and Outcome Quality. Each of these constructs correlated with several other factors. The results revealed that the initial measurement model failed to discriminate between the two service quality constructs: interaction quality and outcome quality. As such, a chi-square difference test was conducted on two competing models of service quality to determine whether service quality is best represented as a one-factor or two- factor construct. Using a chi-square difference test, the chi-square score from Model A is subtracted from Model B's chi-square score. This difference represents the value of chi-square difference that is tested. Model A's degrees of freedom are then subtracted from Model B's degrees of freedom. This difference should correspond to the number of paths that Model B lacks relative to Model A, and this is the degrees of freedom for

150 the difference test. The chi-square difference value is then applied to a chi-square table, using the computed degrees of freedom. If the test is significant, it suggests that deleting the parameters in Model B adversely affected the fit of the model. Figure 5.01 depicts the two models of service quality. The results of the chi-square difference test of the two models are reported in Table 5.06. The difference of chi-square statistics for the two models was 12.77 with one degree of freedom. The results indicated that there existed a significant difference between the chi-square values of the two models at the .05 probability level, suggesting that the two-factor model of service quality provided a better fit to the data than did the one-factor model. Discussion of First Data Collection – Main Study The primary objective of this study was to develop a reliable and valid measurement model of value equity in spectator sports. The current discussion will focus on the results from the first data collection. Specifically, issues related to overall model fit, internal structure fit, and model validity are discussed. Initial testing of the components of value equity in the exploratory phase of the research resulted in a reduction of the items in the measurement pool from 91 to 74. Consequently, the first confirmatory factor analysis was conducted to identify the latent factors thought to account for variation and covariation among the set of indicators specified by the exploratory factor analysis conducted in the pilot phase of the research. Overall model fit. A 16-factor, 74-item measurement model of value equity was evaluated for overall fit using several indices of fit recommended by Brown (2006). The overall model fit indices indicated that while the fit of the data was not terrible, there was plenty of room for improvement. Although the 2 value was not satisfactory, Brown (2006) noted that the chi-square index is rarely used in applied research as a sole index of model fit as it can be sensitive to sample size. It was recommended that other fit indices be relied upon more heavily in the evaluation of model fit. While the two measures of comparative fit did fall within the .90 -.95 range indicating acceptable fit (Bentler, 1990), the SRMR (.0892) and the RMSEA (.0892) were above the thresholds specified by Hu and Bentler (1999) and Browne and Cudeck (1993) for acceptable fit.

151 Table 5.02. CFA for the Value Equity Factors and Items: Item Loadings (), Confidence Intervals (CI), Standards Errors (SE), t-values (t), Construct Reliabilities (CR); and Average Variance Extracted (AVE)

Factors and Items 90% CI SE t CR AVE

Amusement .84 .57 Amuse1 .629 .548-.710 .049 12.80 Amuse2 .838 .764-.912 .045 18.80 Amuse3 .697 .618-.776 .048 14.58 Amuse4 .834 .760-.908 .045 18.69 Partying .85 .52 Party1 .861 .790-.932 .043 20.16 Party3 .775 .701-.849 .045 17.22 Party4 .790 .716-.864 .045 17.72 Party5 .692 .615-.769 .047 14.71 Party6 .154 .065-.243 .054 2.85 Party7 .792 .718-.866 .045 17.77 Experience Intensity .74 .49 Expint1 .603 .521-.685 .050 12.02 Expint2 .731 .652-.810 .048 15.32 Expint3 .762 .685-.839 .047 16.16 Game Immersion .75 .41 Gamint3 .717 .640-.794 .047 15.14 Gamint5 .692 .613-.771 .048 14.43 Gamint6 .660 .579-.741 .049 13.57 Gamint7 .766 .690-.842 .046 16.57 Gamint8 .119 .029-.209 .055 2.16 Escape .75 .53 Esc1 .884 .808-.960 .046 19.27 Esc2 .827 .752-.906 .047 17.73 Esc3 .357 .268-.446 .054 6.66 Aesthetics .73 .48 Aes1 .727 .650-.804 .047 15.36 Aes2 .754 .677-.831 .047 16.10 Aes3 .593 .511-.675 .050 11.91 Drama .77 .49 Drama1 .639 .560-.722 .049 13.11 Drama2 .839 .767-.911 .044 18.92 Drama3 .868 .796-.940 .044 19.89 Drama4 .297 .208-.386 .054 5.54

152 Table 5.02 (cont.) Confirmatory Factor Analysis for the Value Equity Factors and Items: Item Loadings (), Confidence Intervals (CI), Standards Errors (SE), t- values (t), Construct Reliabilities (CR); and Average Variance Extracted (AVE)

Factors and Items 90% CI SE t CR AVE

Non Family .86 .51 Nonfam1 .775 .701-.849 .045 17.14 Nonfam2 .591 .510-.672 .049 11.97 Nonfam3 .532 .450-.614 .050 10.57 Nonfam4 .800 .726-.874 .045 17.95 Nonfam5 .771 .697-.845 .045 17.01 Nonfam6 .757 .681-.833 .046 16.57 Family .86 .67 Fam1 .834 .762-.906 .044 18.91 Fam2 .861 .790-.932 .043 19.83 Fam3 .765 .689-.841 .046 16.72 Business Opportunity .83 .62 Busopp1 .799 .723-.875 .046 17.25 Busopp2 .738 .659-.817 .048 15.23 Busopp3 .818 .742-.894 .046 17.81 Epistemic Value .90 .76 Know1 .813 .741-.885 .044 18.64 Know2 .927 .860-.994 .041 22.81 Know3 .867 .798-.936 .042 20.51 Monetary .78 .49 Mon2 .690 .611-.769 .048 14.49 Mon3 .815 .741-.889 .045 18.26 Mon4 .812 .738-.886 .045 18.18 Mon5 .376 .289-.463 .053 7.12 Non Monetary .87 .70 Nonmon1 .890 .819-.961 .043 20.68 Nonmon2 .799 .725-.873 .045 17.75 Nonmon5 .811 .737-.885 .045 18.10 Satisfaction .79 .56 Sat1 .809 .737-.809 .044 18.43 Sat2 .825 .753-.897 .044 18.96 Sat3 .586 .505-.667 .049 12.04

153 Table 5.02 (cont.) Confirmatory Factor Analysis for the Value Equity Factors and Items: Item Loadings (), Confidence Intervals (CI), Standards Errors (SE), t- values (t), Construct Reliabilities (CR); and Average Variance Extracted (AVE)

Factors and Items 90 %CI SE t CR AVE

Interaction Quality .96 .57 Iq1 (A1) .701 .627-.775 .045 15.52 Iq2 (A2) .656 .580-.732 .046 14.23 Iq3 (A3) .666 .590-.742 .046 14.51 Iq4 (B1) .817 .748-.886 .042 19.27 Iq5 (B2) .841 .770-.908 .042 20.15 Iq6 (B3) .606 .529-.683 .047 12.87 Iq7 (E1) .842 .773-.842 .042 20.18 Iq8 (E2) .779 .708-.850 .043 17.95 Iq9 (E3) .837 .768-.906 .042 20.00 Iq10 (AC3) .811 .742-.880 .043 19.07 Iq11 (SF2) .797 .726-.866 .043 18.58 Iq12 (WT2) .779 .708-.850 .043 17.96 Iq13 (WT3) .880 .813-.947 .041 21.66 Iq14 (T1) .846 .777-.915 .042 20.36 Iq15 (T2) .439 .357-.521 .050 8.87 Iq16 (T3) .631 .554-.708 .047 13.54 Iq17 (V3) .704 .630-.778 .045 15.61 Outcome Quality .90 .66 OQ1 (DF1) .841 .772-.910 .042 20.08 OQ2 (DF2) .874 .807-.941 .041 21.39 OQ3 (DF3) .914 .848-.980 .040 23.06 OQ4 (V1) .434 .352-.516 .050 8.72 OQ5 (V2) .884 .817-.951 .041 21.77

154 Table 5.03. Fit Indices for the 16-Factor Model with 74 Indicators

Index Value Rule of Thumb

10286.05 Chi-square (X2) p >.05 or .01 (p < 0.00)

Chi-square / Degrees of 3.99 < 3.0 (Kline, 2000) Freedom (X2/df)

Standardized Root Mean Close to .08 or below = reasonably good .0892 Square Residual (SRMR) fit (Hu & Bentler, 1999)

<.08 = adequate; > 1.0 = rejected Root Mean Square Error of .0892 (Browne & Cudeck, 1993; MacCallum, Approximation (RMSEA) 1996)

Non-Normed Fit Index .906 .90 – .95 = acceptable fit (Bentler, 1990) (NNFI)

Comparative Fit Index (CFI) .913 .90 – .95 = acceptable fit (Bentler, 1990)

155 Table 5.04. Factor Correlations for First Data Collection

156 Table 5.05. Discriminant Validity Analysis for Model AVE’s

Factor Non-Discriminating Factors

Satisfaction Interaction Quality, Outcome Quality

Interaction Quality Satisfaction, Outcome Quality

Outcome Quality Interaction Quality, Satisfaction

Table 5.06. X2 difference test for One- and Two-Factor Models of Service Quality

Goodness of Fit Test of Invariance

Model 2 2 X difference with the X df p-value CFI p-value baseline model

Model A 1428.36 188 .000 .96

Model B 1441.14 189 .000 .96 X2 difference(1) = 12.77 <.05

*The critical value of X2 with one degree of freedom is 3.841 at the .05 probability level.

157

Figure 5.01. Competing models of service quality for the X2 difference test

158 Following the assessment of the overall fit of the model, the researcher examined various measures of internal fit to identify potential sources of misspecification. Assessment of internal fit. Based on the examination of indices, the following observations were made and subsequent actions taken. The item loading for party6 (.154) suggested that this item did not load well on the Partying construct. The fitted residuals also revealed this item to be problematic. Thus, party6 was removed from further analysis. Another item that merited inspection was gamint8. This item did not load well on the Game Immersion construct as its item loading of .119 was well below the recommended .70. Also, this item was associated with the greatest percentage of problematic residuals. More than 16% of the problematic residuals were found with this item. Thus, it was decided to eliminate this item for further analysis in the respecified model in order to remain consistent in the application of item removal criteria. Other items that did not merit inclusion in the respecified model were drama4, mon5 and iq15. These items did not load well on their respective constructs. The item esc3 was retained as a measure of Escape despite not loading well on the construct as the reliability and AVE score met the established cut-off values and it was important to retain three measures of Escape. Construct validity. Two measures of construct validity were examined in the current research project, convergent validity and discriminant validity. Convergent Validity The results indicated that the AVE scores for five of the sixteen constructs did not meet the .50 threshold recommended by Fornell and Larker (1981), including experience intensity, game immersion, aesthetics, drama, and monetary. Discriminant Validity An examination of the correlation matrix for the current model revealed that the correlations between Interaction Quality and Outcome Quality (.986); Satisfaction and Interaction Quality (.904); and between Satisfaction and Outcome Quality (.853) each exceeded the .85 threshold specified by Kline (2000).

159 Measurement Model Respecification

The 16-factor model with 74 indicators needed to be modified to provide the best fit to the data based on suggestions from the tests of model estimations and fit of the internal structure. Bollen (1989) proposed two criteria for model respecification: 1) the exclusion of items with poor fit to the data, and 2) the preservation of a minimum of three items per construct. Based on the results of the first data collection of the main study several modifications were made. First, due to their high correlation, the initial measurement model failed to discriminate between the two service quality constructs, namely: interaction quality and outcome quality. In order to solve the problem the researcher considered two scenarios: combining the two factors together resulting in one construct, and the retention of the two separate factors. Two different models of service quality were tested to determine whether service quality is best represented as a one-factor or two- factor construct. The chi-square difference test was used to make a direct comparison between the nested models (Bagozzi & Phillips, 1982). The results indicated there was a significant difference between the chi-square values of the two models at the .05 probability level, suggesting that the two-factor model of service quality provided a better fit to the data than did the one-factor model. As such, each of the dimensions of service quality were retained in the model. The decision to retain both factors was also supported by the literature, which views service quality as a two-dimensional construct comprised of technical and functional elements (Grönroos, 1984). Next, the item loadings for the Partying dimension were considered. The item loading for party6 was .154. A low item loading indicates that the item does not load well on the construct. Additionally, the fitted residual for party6 was high. High fitted residuals indicate there exists a difference between the observed covariance matrix S and the model-implied covariance matrix ∑ and that the model is misspecified. An appropriate course of action for dealing with low item loadings is to drop the item from the model. Misspecified items may also be dropped from the model. Thus, the researcher decided to delete the item party6 from further analysis. Another item that merited inspection was gamint8. This item did not load well on the Game Immersion

160 construct as its item loading of .119 was well below the recommended .70. Also, this item was associated with the greatest percentage of problematic residuals. More than 16% of the problematic residuals were found with this item. Thus, this item was also eliminated from further analysis. The respecified model did not include gamint8 as a measure of Game Immersion. Finally, the AVE score for Game Immersion (.41) was well below the recommended threshold of. 50 indicating that more than half the total variance of Game Immersion was derived from measurement error. The decision to keep this construct in the measurement model was justified using the rationale that the elimination of the underperforming measurement item (gamint8) would cause the average variance extracted to increase sufficiently. Other items that were not included in the respecified model due to low item loadings were drama4, mon5 and iq15. Each of these items did not load well on their respective constructs. There were also problems with the Escape construct. The item esc3 did not load well on the construct. However, this item was retained as a measure of Escape as the construct reliability and AVE score met the established cut-off values. Next, an examination of the reliability and validity of the ‘Experience Intensity’ construct revealed that this construct was reliable. The reliability (.74) was above the established threshold, while the AVE (.49) score was just below the recommended cut- off threshold of .50. The results of the reliability and validity analysis should have led the researcher to make a decision to include this construct in subsequent testing, however this was not done. Rather, the researcher erroneously chose to eliminate this construct from the model of value equity. See the footnote at the bottom of the page for a detailed explanation for this decision.1 Next, an examination of the AVE scores for each of the scales indicated that five of the sixteen construct did not meet the .50 threshold recommended by Fornell and Larker (1981). The rationale for retaining or excluding experience intensity and game immersion based on their respective AVE scores has been addressed. It was decided

1 Initially, the researcher computed Cronbach’s alpha scores as a measure of reliability and based on that information removed the items and proceeded with the second data collection. Much later, the researcher realized that a more appropriate measure of reliability in confirmatory factor analysis is a calculation of the construct reliabilities and not Cronbach’s alpha. As a result, the researcher acknowledges that the three items should have been retained, and future research should examine the construct to determine its viability in the model.

161 by the researcher to keep the constructs of drama and monetary for subsequent testing because the elimination of underperforming individual items within each of those scales was expected to improve the AVE scores of those constructs. Finally, while the reworded items for the Satisfaction construct did result in the construct obtaining adequate reliability and AVE score, a decision was made to drop Satisfaction from the model because of its failure to discriminate from the three other constructs. This identification of non-discrimination was based on an examination of the correlation matrix and on Kline’s (2000) threshold for discriminant validity. Satisfaction was defined as a customer’s evaluation of pleasurable fulfillment of some need, desire, or goal (Oliver, 1997). The relationship between satisfaction and value has been the subject of much study. There has been general support in the literature for satisfaction being an outcome of value. Woodruff (2003) noted that satisfaction is a customer’s feelings, or emotional response, to cognitive evaluations of one or more use experiences with a product. As indicated in the literature review, a majority of researchers have empirically found support for viewing satisfaction as an outcome of value as opposed to an antecedent (Brady, Cronin, & Hult, 2000). The failure of the satisfaction construct to discriminate from two other factors, including, Interaction Quality and Outcome Quality led the researcher to contend that satisfaction should be regarded as an outcome of value. Each of the other measures of value assess consumers’ cognitive evaluations of their experiences with the sport service. As such, the researcher chose to eliminate satisfaction from the model. Testing of the Modified Measurement Model Model specification. Based on the modifications described above, a respecified model comprised of 14 latent variables and 64 indicators was identified and tested using the same data set. Figure 5.02 illustrates the relationships between the second-order variables, latent variables, and indicators in the modified measurement model. Entertainment Value comprises six latent variables measured by 22 indicators: amusement (four indicators), partying (five indicators), game immersion (4 indicators), escape (three indicators), aesthetics (three indicators), and drama (three indicators). Social Value comprises three latent variables measured by 12 indicators: family (three indicators), non-family (six indicators), and business opportunities (three indicators).

162 Service quality comprises two latent variables measured by 21 indicators: interaction quality (16 indicators) and outcome quality (five indicators). Perceived Price comprises two latent variables measured by six indicators: monetary (three indicators) and non- monetary (three indicators). Finally, knowledge is a manifest measure of Epistemic Value. Consequently, the three measures of knowledge are run directly to epistemic value. Model identification. A count of the free parameters for the respecified model is as follows: • 64 factor loadings • 64 measurement error variances • 14 correlations among the latent variables A total of 142 free parameters were estimated. The number of distinct values in the matrix S was equal to 64(64+1)/2 = 2080. The number of values in S, 2080, was greater than the number of free parameters, 142, with the difference being the degrees of freedom for the specified model, df = 2080 – 142 = 1938. According to the order condition, the current model is overidentified because there are more values in S than parameters to be estimated (Schumacker & Lomax, 2004). As a result, a test of the model was possible (Tate, 1998). Model estimation and evaluation. LISREL 8.80 (Jöreskog & Sörbom, 2006) was utilized to conduct the confirmatory factor analysis (CFA) of the Value Equity in Spectator Sports Scale. At this point, the researcher used CFA as the analysis procedure as opposed to HFCA as the general sequence of a CFA-based higher-order factor analysis is to first develop a good-fitting, conceptually valid, first-order CFA solution and then fit the second-order hierarchical factor model, as justified on conceptual and empirical grounds (Brown, 2006). The respecified 14-factor, 64 item model was tested for reliability and validity. The results of the respecified model of the Value Equity in Spectator Sports Scale are presented in the following section. The standardized factor loadings (), standard errors (SE), construct reliabilities (CR), and average variance extracted for the 14 constructs are presented in Table 5.07. The factor loadings for the respecified model ranged from.621 to .845 in amusement; .687 to .863 in partying; .598 to .796 in game immersion; .650 to .944 in escape; .631 to .818 in

163

Figure 5.02. Respecified Model of Value Equity aesthetics; .662 to .810 in drama; .529 to .793 in non family; .756 to .874 in family; .742 to .819 in business opportunities; .810 to .926 in epistemic value; .690 to .833 in monetary;.756 to .833 in non monetary; .445 to .900 in interaction quality; .595 to .831

164 in outcome quality. Table 5.08 shows the correlations among the 14 latent variables. The values among the 14 dimensions ranged from .022 to .810. Model fit. The measures of model fit for the respecified model are presented in Table 5.09. The 2 value was 6525.70 (p < 0.00), which was not satisfactory. The chi- square/df was 3.5, the SRMR was .0647, the RMSEA was .0819, the NNFI was .926, and the CFI was .932. Based on the rule of thumb criteria identified in the literature, the model fit indices indicate that the fit of the data to the model is acceptable. Reliability. The 64 items were examined for internal consistency based on an assessment of the construct reliabilities. Construct reliability scores are reported in Table 5.07. The construct reliability scores for each of the subscales were as follows: amusement (.84); partying (.89); game immersion (.80); escape (.84); aesthetics (.78); drama (.79); non family (.86); family (.86); business opportunity (.83); epistemic value (.90); monetary (.82); non monetary (.86); interaction quality (.97); and outcome quality (.86). Each of the 14 subscales scored above .70, indicating good internal consistency. Construct validity. Two measures of construct validity were examined for the respecified model, namely, convergent validity and discriminant validity. Convergent Validity As illustrated in Table 5.02, the AVE scores ranged from .50 to .76. The AVE score for each of the 14 constructs met the established .50 threshold. An examination of the residual matrix of the respecified model revealed that 50 of 954 residuals (5.24%) were greater than .15. According to McDonald (2002) residual values below .15 are acceptable, while values greater than .15 indicate a possible misspecification of the model. Items with the highest residuals were associated with the following three factors: 1) Interaction Quality; 2) Partying; and 3) Amusement. Four of the items accounted for over 50% of the high residuals, namely: iq11 (16%), party3 (14%), party7 (11%), and amuse1 (10%). Discriminant Validity. An examination of the correlation matrix presented in Table 5.08 revealed that none of the correlations exceeded the .85 threshold specified by Kline (2000). The results present sufficient evidence that the latent factors represent distinct constructs.

165 Table 5.07. CFA for the RESPECIFIED Value Equity Factors and Items: Item Loadings (), Standards Errors (SE), Standard Deviations (SD), Construct Reliabilities (CR), and Average Variance Extracted (AVE)

Factors and Items Mean SD SE CR AVE

Amusement .84 .57 Amuse1 5.15 1.308 .621 .049 Amuse2 4.46 1.590 .845 .045 Amuse3 4.72 1.430 .703 .048 Amuse4 4.68 1.469 .827 .045

Partying .89 .61 Party1 3.13 1.881 .863 .043 Party3 4.04 1.866 .777 .045 Party4 2.96 2.124 .790 .045 Party5 2.81 1.936 .687 .047 Party7 3.64 1.670 .790 .045

Game Immersion .80 .50 GamInt3 5.24 1.096 .796 .046 GamInt5 5.57 1.532 .793 .046 GamInt6 4.44 1.394 .616 .049 GamInt7 4.91 1.334 .598 .050 Escape .84 .64 Esc1 5.54 1.340 .650 .048 Esc2 5.65 1.231 .944 .042 Esc3 5.40 1.323 .787 .046 Aesthetics .78 .54 Aes1 5.80 1.159 .631 .049 Aes2 5.41 1.205 .818 .045 Aes3 5.68 1.094 .739 .047

Drama .79 .56 Drama1 5.74 1.326 .810 .047 Drama2 5.63 1.138 .767 .048 Drama3 5.69 1.296 .662 .050 Non Family .86 .51 Nonfam1 5.56 1.227 .793 .045 Nonfam2 4.69 1.590 .783 .045 Nonfam3 5.17 1.433 .763 .046 Nonfam4 4.97 1.465 .767 .045 Nonfam5 4.67 1.528 .592 .049 Nonfam6 4.98 1.607 .529 .050 Family .86 .67 Fam1 6.21 1.170 .824 .044 Fam2 5.98 1.062 .874 .043 Fam3 5.62 1.413 .756 .046

166 Table 5.07 (cont.) CFA for the RESPECIFIED Value Equity Factors and Items: Item Loadings (), Standards Errors (SE), Standard Deviations (SD), Construct Reliabilities (CR), and Average Variance Extracted (AVE)

Factors and Items Mean SD SE CR AVE

Business Opportunity .83 .62 Busopp1 3.88 1.565 .793 .047 Busopp2 4.21 1.680 .742 .048 Busopp3 3.59 1.657 .819 .046 Epistemic Value .90 .76 Know1 4.87 1.521 .810 .044 Know2 4.67 1.524 .926 .041 Know3 4.68 1.564 .871 .042 Monetary .82 .61 Mon2 4.70 1.773 .690 .048 Mon3 4.90 1.713 .816 .045 Mon4 5.35 1.398 .833 .044 Non Monetary .86 .67 Nonmon1 5.64 1.216 .756 .046 Nonmon2 5.58 1.141 .823 .045 Nonmon5 6.03 1.140 .870 .044 Interaction Quality .97 .64 Iq1 (A1) 5.51 1.306 .832 .042 Iq2 (A2) 5.51 1.204 .872 .041 Iq3 (A3) 5.38 1.227 .900 .040 Iq4 (B1) 5.09 1.350 .776 .044 Iq5 (B2) 5.32 1.243 .889 .040 Iq6 (B3) 5.25 1.229 .860 .041 Iq7 (E1) 5.38 1.238 .817 .042 Iq8 (E2) 5.18 1.259 .855 .041 Iq9 (E3) 5.11 1.436 .606 .047 Iq10 (AC3) 5.37 1.260 .871 .041 Iq11 (SF2) 4.99 1.528 .445 .050 Iq12 (WT2) 5.25 1.400 .827 .042 Iq13 (WT3) 4.93 1.379 .812 .043 Iq14 (T1) 5.71 1.180 .825 .042 Iq16 (T3) 5.14 1.262 .855 .041 Iq17 (V3) 5.48 1.164 .649 .046 Outcome Quality .86 .56 OQ1 (DF1) 5.85 1.307 .595 .049 OQ2 (DF2) 6.00 1.034 .787 .044 OQ3 (DF3) 5.54 1.105 .769 .045 OQ4 (V1) 6.03 0.894 .831 .043 OQ5 (V2) 6.00 1.050 .730 .046

167 Table 5.08. Factor Correlations for Respecified Model

168 Table 5.09. Fit Statistics for Respecified Model

Respecified Model Rule of thumb Criteria Fit Statistics (n=376)

Chi-square 6525.70

Chi-square / Degrees of 6525.70/1861 3:1 ratio indicates acceptable Freedom (X2/df) = 3.5 model fit (Kline, 1998). Close to .08 or below = Standardized Root Mean .065 reasonably good fit (Hu & Square Residual (SRMR) Bentler, 1999) <.08 = adequate; > 1.0 = rejected Root Mean Square Error of .082 (Browne & Cudeck, 1993; Approximation (RMSEA) McCallum et al., 1996) Non-Normed Fit Index .90 – .95 = acceptable fit .926 (NNFI) (Bentler, 1990)

.90 – .95 = acceptable fit Comparative Fit Index (CFI) .932 (Bentler, 1990)

169 Second Data Collection – Main Study

The analysis of the results of the CFA on the respecified model using the first data collection of the main study indicated that the model demonstrated acceptable levels of reliability and validity. Additionally, the assessment of overall model fit and of internal fit indicated an acceptable fit of the data to the measurement model. Therefore, the researcher conducted another data collection to further test, or validate, the Value Equity in Spectator Sports Scale (VESSS) across sports. At this stage, the VESSS is comprised of the same 14 latent variables and 64 indicators as examined in the previous stage of the research. Given the results of the respecification testing from the first data collection of the main study, the researcher did not modify the scale. For this data collection, the VESSS survey was administered to 350 students enrolled at Florida State University in the first summer session of 2007. Since the data collection did not take place at a sporting event, the wording of the items was modified. A table providing the modified wording is presented in Appendix H. The survey administration resulted in a total of 285 useable questionnaires for a return rate of 81.4%. As the current research is a study of sport consumers, students were asked to identify the last collegiate or professional sporting event they had attended. Students were then instructed to respond to each question in the survey while thinking about that consumption experience. Students were also asked to report the date on which they attended that game, as well as the number of times they have attended games of the identified team. The purposes of this battery of questions were twofold: First, it was necessary to provide participants with a subject from which perceptions of value could be made. Second, the responses to these questions provided evidence that the students sampled were indeed consumers of sporting events. As illustrated in Table 5.10, the majority of the students reported that a college sporting event had been the last they attended (84.2%). More than half of the sample reported having attended their last game inside of six months, while 48.6% indicated they last attended a sporting event more than six months ago. Given that the study was concerned with consumer perceptions, chi-square analysis was used to determine whether length of time following consumption of the event adversely influenced

170 perceptions. The chi-square analysis results showed that the mean scores for the manifest variables were similar across the two groups (See table 5.11). There were not any statistically significant differences between the two groups for each of the 14 manifest variables at the .01 level. At the .05 level of significance, chi-square analysis indicated that a significant difference existed between those who last attended a game within six months and those who last attended a game more than six months ago on perceptions of aesthetics (X2 = 33.077, p=.016). Given the results of the chi-square analysis, the decision was made to combine all of the responses for both groups for testing the scale. Characteristics of the sample. Three demographic classification characteristics were measured in the survey instrument for the validation sample, namely: gender, ethnicity, and college level classification. As indicated in Table 5.10, the sample was 49% male and 51% female. The majority of students were White/Caucasian. Model estimation and evaluation. LISREL 8.80 (Jöreskog & Sörbom, 2006) was utilized to conduct the confirmatory factor analysis (CFA) of the Value Equity in Spectator Sports Scale for the validation sample. As before, the researcher chose to use CFA as the analysis procedure as opposed to HFCA in order to first develop a good-fitting, conceptually valid, first-order CFA solution. Following the validation of an acceptable fit to the first-order solution the researcher then tested the second-order hierarchical factor model. The 14-factor model was tested using the 64 items retained from the respecified model in the previous stage of this research. A maximum likelihood technique was used to estimate the parameters of the factor model for value equity. The mean scores, standard deviations (SD), standardized factor loadings (), confidence intervals (90% CI), standard errors (SE), t-values (t), construct reliabilities (CR), and average variance extracted for the 16 constructs are presented in Table 5.12. Table 5.13 shows the correlation values among the 14 latent variables. The correlation among the 14 dimensions ranged from .018 for between business opportunities and non-family to .807 for between interaction quality and outcome quality.

171 Model fit. The fit indices used to verify the sub-scale structure of the instrument in the validation sample are the same as those utilized for the confirmatory sample. The measures of model fit employed in the validation sample are presented in Table 5.14. They include two measures of absolute fit (2 and SRMR), one measure of parsimony correction (RMSEA), and two measures of comparative fit (NNFI and CFI). The 2 value was 5417.53 (p < 0.00), 2/df = 2.91, SRMR (.078), RMSEA (.082), NNFI (.912), and CFI (.919). The model fit indices resulting form the validation sample show a slight improvement in model fit as compared to those found in the respecified confirmatory model. Specifically, the 2/df for the validation sample falls below the 3:1 ratio indicating an acceptable model fit (Kline, 1998). The remainder of the fit indices are similar to those found in the results from the first data collection. Internal consistency reliability. As in the previous stages, internal consistency among the respective items served as a measure of reliability for each of the dimensions in the model. The 64 items were examined for internal consistency based on an assessment of the construct reliabilities. As reported in Table 5.12, the construct reliabilities for each of the subscales were as follows: amusement (.85); partying (.90); game immersion (.85) escape (.91); aesthetics (.86); drama (.85); non-family (.89); family (.91); business opportunity (.85); epistemic value (.90); monetary (.84); non- monetary (.93); interaction quality (.97); and outcome quality (.85). Each of the subscales scored above .70, indicating good internal consistency. Construct validity. Two measures of construct validity were examined for the validation stage of the research project, namely: convergent validity and discriminant validity. Convergent Validity Appropriate tests of convergent validity are the assessment of the average variance extracted (AVE) scores and an evaluation of the standardized residual matrix. AVE is a measure of the amount of variance explained by a construct relative to the amount of variance attributed to measurement error. As illustrated in Table 5.12, the AVE scores ranged from .54 to .81. Each of the constructs exceeded this criterion, indicating acceptable construct validity. An examination of the residual matrix of the current model revealed that 12.45% (132/1060) of the residuals were greater than .15.

172 Items with the highest residuals were associated with the following four factors: 1) Interaction Quality; 2) Partying; 3) Amusement; and 4) Monetary. Five items specifically accounted for 73.08% of the high residuals, namely: party7, iq17, iq11, amuse1, and mon4. Discriminant Validity. An examination of the correlation matrix for the current model revealed that the correlations between the constructs were all below the .85 threshold specified by Kline (2000), indicating the latent factors represent distinct constructs. Second-Order Hierarchical Confirmatory Factor Analysis The general sequence of CFA-based hierarchical second-order factor analysis calls for the development of a well-behaved, good-fitting, and conceptually valid first- order CFA solution followed by an examination of the magnitude and pattern of correlations among factors in the first order solution. Only once these two steps have been satisfactorily completed is it possible for the researcher to fit the second-order model. The analysis of the results for the first order CFA in the validation sample indicated that the model demonstrated acceptable reliability and validity. Additionally, the assessment of overall model fit and of internal fit indicated an acceptable fit of the data to the measurement model. As such, the researcher proceeded with an assessment of the second-order constructs. The second-order HCFA was conducted for the purpose of evaluating the latent structure of the questionnaire measure of value equity in spectator sports. The latent structure was predicted to be characterized by 14 first-order factors that represent 14 sources of perceived value in the sport consumption experience. The 14 factors were presumed to be intercorrelated and four higher-order factors were predicted to account for the correlations among the first-order factors, namely: entertainment value, social value, perceived price, and service quality. Brown (2006) noted that the empirical feasibility of the higher-order model should be evidenced by the patterning of correlations among factors in the first-order model. In the current model, for example, the latent factors of amusement, partying, game immersion, escape, aesthetics, and drama should be more strongly correlated with one another than with other latent factors.

173 Table 5.10. Demographic Characteristics of the Validation Sample

Demographic Variables Frequency Valid % Cumulative % Gender Female 143 50.9 50.9 Male 138 49.1 100.0 Total 281 100.0 System Missing 4 Ethnicity Black/African American 34 12.5 Asian/Pacific Islander 15 5.5 White/Caucasian 182 67.2 Latina/Latino 35 12.9 Other 5 1.8 Total 271 100.0 System Missing 14 School Classification Freshman 2 0.7 0.7 Sophomore 23 8.2 8.9 Junior 60 21.4 30.2 Senior 157 55.9 86.1 Graduate Student 39 13.9 100.0 Total 281 100.0 System Missing 4 Department Business 123 43.2 Communications 27 9.5 Lifetime Activities 102 35.8 Athletic Training 33 11.6 Total 285 100.0 Event Type College Event 240 84.2 Professional Event 45 15.8 Total 285 100.0 Last Attended < 6 Months 142 51.4 51.4 > 6 Months 134 48.6 100.0 Total 276 100.0 System Missing 9 Attendance Frequency One Time 43 15.9 15.9 Multiple Times 228 84.1 100.0 Total 271 100.0 System Missing 14

174 Table 5.11. Chi Square Analysis – Relationship between length of time following consumption and manifest variable mean scores.

Manifest Variable 2 p-value

Amusement 19.245 .630

Partying 28.945 .520

Game Immersion 30.075 .117

Escape 18.077 .319

Aesthetics 33.077 .016**

Drama 15.723 .472

Non Family 38.528 .166

Family 21.075 .276

Business Opportunities 15.888 .600

Knowledge 19.822 .343

Monetary 28.983 .051

Non-monetary 21.682 .154

Interaction Quality 69.951 .513

Outcome Quality 21.173 .387 Note: **p-value <.05 n = 142 for < 6 months and 134 for > 6 months

175 Table 5.12. CFA Validation Sample: Means, Standard Deviations (SD), Item Loadings (), Confidence Intervals (CI), Standards Errors (SE), t-values (t), Construct Reliability (CR), and Average Variance Extracted (AVE).

Factors and Items Mean SD SE t CR AVE

Amusement .85 .58 Amuse1 5.19 1.518 .622 .056 11.02 Amuse2 4.25 1.627 .851 .050 16.89 Amuse3 4.59 1.569 .755 .053 14.21 Amuse4 4.65 1.553 .806 .052 15.61 Partying .90 .64 Party1 3.94 2.056 .861 .049 17.73 Party3 5.05 1.773 .844 .049 17.19 Party4 3.91 2.273 .807 .050 16.04 Party5 4.46 2.200 .786 .051 15.43 Party7 3.14 1.945 .703 .053 13.19 Game Immersion .85 .59 GamInt3 5.29 1.365 .871 .049 17.64 GamInt5 5.39 1.389 .810 .051 15.84 GamInt6 4.20 1.644 .620 .056 11.03 GamInt7 4.67 1.574 .748 .053 14.13 Escape .91 .76 Esc1 5.72 1.451 .757 .051 14.75 Esc2 5.79 1.230 .926 .046 20.04 Esc3 5.69 1.274 .928 .046 20.10 Aesthetics .86 .68 Aes1 5.00 1.555 .747 .053 14.17 Aes2 4.95 1.621 .873 .049 17.81 Aes3 5.09 1.647 .843 .050 16.88 Drama .85 .66 Drama1 5.84 1.500 .831 .052 16.11 Drama2 5.81 1.371 .861 .051 16.94 Drama3 5.95 1.359 .741 .054 13.79 Non Family .89 .57 Nonfam1 5.93 1.258 .814 .050 16.29 Nonfam2 5.42 1.370 .831 .049 16.82 Nonfam3 5.75 1.372 .811 .050 16.21 Nonfam4 5.65 1.344 .850 .049 17.44 Nonfam5 5.04 1.535 .637 .055 11.63 Nonfam6 5.03 1.713 .523 .057 9.15 Family .91 .76 Fam1 5.27 1.659 .848 .049 17.28 Fam2 5.07 1.598 .888 .048 18.55 Fam3 4.68 1.716 .881 .048 18.32

176 Table 5.12 (cont.) CFA Validation Sample: Means, Standard Deviations (SD), Item Loadings (), Standards Errors (SE), t-values (t), Construct Reliability (CR), and Average Variance Extracted (AVE).

Factors and Items Mean SD SE t CR AVE

Business Opportunities .85 .67 Busopp1 3.88 1.758 .820 .051 16.04 Busopp2 4.26 1.892 .680 .054 12.50 Busopp3 3.54 1.824 .928 .048 19.20 Epistemic Value .90 .76 Know1 4.94 1.627 .829 .050 16.67 Know2 4.79 1.614 .910 .047 19.27 Know3 4.75 1.518 .872 .048 18.01 Monetary .84 .64 Mon2 5.55 1.557 .781 .052 14.95 Mon3 5.34 1.734 .924 .049 19.01 Mon4 5.49 1.524 .680 .055 12.45 Non Monetary .93 .81 Nonmon1 5.59 1.349 .947 .045 20.91 Nonmon2 5.51 1.249 .828 .049 16.87 Nonmon5 5.56 1.529 .916 .046 19.76 Interaction Quality .97 .64 Iq1 (A1) 5.09 1.515 .759 .050 15.07 Iq2 (A2) 4.95 1.350 .931 .045 20.77 Iq3 (A3) 4.75 1.414 .915 .045 20.16 Iq4 (B1) 4.58 1.368 .861 .047 18.19 Iq5 (B2) 4.74 1.455 .928 .045 20.67 Iq6 (B3) 4.86 1.380 .884 .047 19.02 Iq7 (E1) 5.00 1.357 .837 .049 17.41 Iq8 (E2) 4.77 1.427 .862 .047 18.25 Iq9 (E3) 4.59 1.445 .653 .053 12.32 Iq10 (AC3) 5.11 1.331 .744 .051 14.64 Iq11 (SF2) 4.70 1.444 .484 .056 8.60 Iq12 (WT2) 4.95 1.469 .833 .048 17.27 Iq13 (WT3) 4.65 1.555 .881 .047 18.89 Iq14 (T1) 5.15 1.301 .834 .048 17.30 Iq16 (T3) 4.83 1.412 .874 .047 18.66 Iq17 (V3) 5.45 1.234 .444 .057 7.80 Outcome Quality .85 .54 OQ1 (DF1) 5.07 1.421 .675 .054 12.50 OQ2 (DF2) 5.28 1.381 .867 .048 17.94 OQ3 (DF3) 5.20 1.335 .691 .054 12.89 OQ4 (V1) 6.00 1.143 .656 .054 12.05 OQ5 (V2) 5.71 1.181 .776 .051 15.16 Table 5.13. Factor Correlations for Third Data Collection.

177

178 Table 5.14. Fit Indices for Validation Sample of 14-Factor Model with 64 Items

Index Value Rule of Thumb

2 5417.53 Chi-square (X ) p >.05 or .01 (p = 0.0)

Chi-square / Degrees of Freedom 2 2.91 < 3.0 (Kline, 1998) (X /df)

Standardized Root Mean Square Close to .08 or below = reasonably good fit .078 Residual (SRMR) (Hu & Bentler, 1999)

Root Mean Square Error of <.08 = adequate; > 1.0 = rejected .082 Approximation (RMSEA) (Browne & Cudeck, 1993; MacCallum, 1996)

Non-Normed Fit Index (NNFI) .912 .90 – .95 = acceptable fit (Bentler, 1990)

Comparative Fit Index (CFI) .919 .90 – .95 = acceptable fit (Bentler, 1990)

179 Utilizing a maximum likelihood method of extraction, the fit of the second-order model is as follows: X2 (1928) = 6168.66, p = 0.0, X2 /df = 3.2, SRMR = .109, RMSEA = .088, NNFI = .907, CFI = .911. A summary of the fit indices is presented in Table 5.15. The results of the analysis of model fit revealed that the second-order model did not adequately fit the data. The comparative fit indices indicated acceptable fit, as did the RMSEA (.088). The SRMR did not (.109). In addition to goodness of fit, the acceptability of the second-order model was evaluated with regard to the magnitude of the second-order parameters, namely, the size of the second-order factor loadings and second-order factor correlations. The factor loadings range from .163 to .996 (see Table 5.16). The correlation matrix representing the correlations between the second-order variables is presented in Table 5.17. The correlations between the second-order factors range from .594 to .972. The pattern of correlations indicated that the predicted relationships between the first- and second-order latent variables in the model are suspect. For example, of the six first-order variables predicted to measure the second- order variable Entertainment Value, amusement and partying did not correlate well with the other predicted measures of Entertainment Value. Similarly, business opportunities did not correlate highly with non-family and family, as measures of Social Value. Two first-order variables that did correlate highly were Interaction Quality and Outcome Quality as measures of the second-order variable Service Quality. Based on the examination of the correlation matrix, the factor correlations do not appear to show a clear pattern. Finally, the estimates provided in the psi matrix indicating the proportion of variance in the first-order factors that is not explained by the second-order factors are presented in Table 5.18. The results of the psi matrix indicated that the higher-order factors account for a wide range of variance in the first-order factors (from 2.4% to 99.3%).

180 Table 5.15. Fit Statistics for Validation Sample Second-Order CFA

Second-Order Model Fit Statistics (n = 285) Chi-square 6168.66

Chi-square / Degrees of Freedom (X2/df) 6168.66/1928 = 3.2

Standardized Root Mean Square Residual (SRMR) .109

Root Mean Square Error of Approximation (RMSEA) .088

Non-Normed Fit Index (NNFI) .907

Comparative Fit Index (CFI) .911

Table 5.16. Second-Order CFA for the Validation Sample: Item Loadings ()

Factors and Items

Entertainment Value Amusement .585 Partying .163 Game Immersion .418 Escape .669 Aesthetics .568 Drama .519 Social Value Non Family .639 Family .585 Business Opportunities .566 Perceived Price Monetary .779 Non Monetary .538

Service Quality Interaction Quality .810 Outcome Quality .996

181

Table 5.17. Validation Sample Second-Order Factor Correlations

Entertainment Social Epistemic Perceived Service Value Value Value Price Quality Entertainment 1.00 Value Social Value .972 1.00

Epistemic Value .718 .816 1.00

Perceived Price .897 .722 .692 1.00

Service Quality .931 .757 .594 .879 1.00

Table 5.18. Validation Sample Second-Order psi Estimates

% Variance in First-order Factors psi estimate First-Order Factors (1 – psi estimate)

Amusement .658 34.2

Partying .974 2.6

Game Immersion .825 17.5

Escape .552 44.8

Aesthetics .678 32.2

Drama .731 26.9

Non Family .591 40.9

Family .658 34.2

Business Opportunities .680 32.0

Knowledge .376 72.4

Monetary .393 70.7

Non-monetary .711 28.9

Interaction Quality .344 65.6

Outcome Quality .007 99.3

182 Summary of the Results

The current chapter provided the results from two separate data collections. The purpose of the first data collection was to test the model of value equity in spectator sports that was obtained from the pilot phase of the research. Based on the analysis of results from the pilot study, a 16-factor model was tested using all 74 items was assessed, and internal consistency tests and CFA were performed. The results indicated the 16-factor model of Value Equity in Spectator Sports Scale did not adequately fit the data. The current 16-factor model with 74 indicators needed to be modified to provide the best fit to the data based on suggestions from the tests of model estimations and fit of the internal structure. Based on Bollen’s (1989) criteria for model respecification, the model was modified accordingly. The modifications resulted in the testing of a 14-factor, 64-item model. The psychometric properties of the respecified measurement model were acceptable, as were the assessment of the global and internal fit indices. Given the favorable results, the researcher proceeded to a second data collection, which was used to validate the results of the respecified model. The analysis from the results of the second data collection served to confirm the revised model from the analysis of the results from the first data collection. Finally, a second order HCFA was conducted to test the relationship between the proposed higher order factors on the first order latent variables. While the results of the first-order CFA provided support for discrimination among the first-order factors, the results of the HCFA presented in this chapter indicate that the predicted higher-model may not be appropriate for the current population from which the sample was drawn. A more detailed discussion of the results of the second order HFCA is presented in the next chapter. The sixth and final chapter presents a discussion of the findings and reported results, as well as content discussing the implications and limitations of the current research project.

183 CHAPTER VI

DISCUSSION AND CONCLUSIONS

Introduction

The consumption of team sporting events through spectatorship represents an increasingly important place in contemporary society in the United States and abroad. Consumers spend a great deal of time and money to attend sporting events and consume sport-related services. One consequence of increased consumer interest in sport spectatorship as a leisure time activity has been a proliferation in the number of sport and non-sport outlets consumers have to choose from in deciding where to spend their discretionary entertainment dollar. In consideration of the crowded leisure-time marketplace, scholars (Ravald & Grönroos, 1994; Woodruff, 1997) have suggested that the ability of an organization to deliver superior value to its customers is an important means by which service differentiation and a competitive advantage are achieved. The current study tested an instrument to assess value equity within a team sport setting. The Value Equity in Spectator Sports Scale (VESSS) was developed from the models of value proposed by Sheth et al. (1991), Rust et al. (2000), and Sweeney and Soutar (2001), as well from a review of the literatures of various disciplines, including marketing, management, psychology, sociology, and sport management. The first- order factor structure of value equity comprised of sixteen first-order latent variables and 74 indicator variables was initially tested. The results of the study provided initial evidence of a valid and reliable scale comprised of fourteen first-order, five second- order, and 64 indicator variables of value equity in spectator sports. The current chapter provides a discussion of the findings and results of the current study conducted. The chapter is presented in the following five main sections: 1) Discussion of the Results; 2) Implications; 3) Limitations of the Research; 4) Future Research; and 5) Conclusions.

184 Discussion of the Results

The ensuing discussion comprises the researchers thoughts regarding the results in and attempt to infuse meaning and understanding. Specifically, the researcher addresses four outcomes of the study meriting additional discussion, namely: 1) the first- and second-order CFA results for Entertainment Value, 2) the Social Value dimensions (non-acquaintances and friends) loading together to form one dimension, 3) Service Quality as a 2-dimensional factor, and 4) knowledge as a dimension of Epistemic Value. The purposes of this study were to propose a model of customer equity, and to identify and empirically test measures to assess selected elements within the model in a spectator sport context. With respect to the proposed overall model, the current conceptualization of customer equity as being comprised of three primary dimensions (i.e., Value Equity, Brand Equity, and Relationship Equity), was based on the writings of Rust, Lemon, and Narayandas (2004), Rust, Lemon, and Zeithaml (2004), and Rust, Zeithaml, and Lemon (2000). These authors presented a strategic framework in which the three components described in this study were identified as the key components thought to increase an organization’s customer equity. Support for the development of a model of, and identification of specific components of customer equity within the context of the consumption of spectator sport services was based on the assertion by Bayon, Gutsche, and Bauer (2002) that a determination of industry-specific components is a critical first step in the analysis of a firm’s customer equity. With respect to the specific dimensions and measures of Value, Brand, and Relationship Equity that were included in the overall model, the researcher heeded the advice of Porter, Kongthon, and Lu (2002), by examining a wide variety of literature streams to identify, select, and develop appropriate dimensions and measures for the specific context of this study. Porter, et al. (2002) suggested that a broad scan of the contextual literature can extend the span of science by better linking efforts across research domains and thus aid in the discovery of topical relationships and research trends. The authors contended that these discoveries serve to facilitate research projects. Therefore, the proposed model of customer equity represents a synthesis of

185 existing research and theory on key concepts related to consumer value, hedonic consumption, service quality, motivation, perception, satisfaction, brand equity, corporate social responsibility, organizational competence, and relationship marketing. As reported, the major thrust of this research project was to empirically test the psychometric properties of the proposed model of customer equity. It was determined that an appropriate first step towards achieving this objective was to break apart the model into manageable components and test one dimension at a time. The rationale for this approach was due to the exploratory nature of the study. Given that the study of customer equity in a spectator sport context is a new domain of scientific interest, and that many of the constructs proposed to measure customer equity have undergone little systemic inquiry, it was deemed necessary to test the psychometric properties of each of the components of customer equity in turn, with the eventual goal of proceeding to an analysis of the entire model of customer equity. The focus of the current study was the development and testing of a scale to measure value equity in a spectator sport context. The ensuing paragraphs present discussion on how the results of the study relate to the review of the literature followed by content addressing the implications for a valid and reliable model of Value Equity. Proposed Model of Value Equity. Customer value is becoming an important focus among sport marketing researchers and practitioners as an essential element of an organization’s competitive strategy. As noted by Ravald and Grönroos (1994), the ability of an organization to deliver superior value to its customers is a key mechanism through which service differentiation is established and a competitive advantage is gained. Spurred by Pura’s (2005) stressing of the importance of measuring customer perceived value in the assessment of the provision of services, along with the lack of an adequate measure of perceived value in the context of the provision of spectator sport services, the current study developed and tested the psychometric properties of a model of Value Equity for Spectator Sport. An extensive review of the literature dealing with consumer perceptions of value in the consumption of products enabled the researcher to develop a testable framework for assessing consumer perceptions of value in spectator sport. The researcher presented an initial conceptualization of value equity derived from the works on

186 consumer value by Sheth et al. (1991) and Sweeney and Soutar (2001). The main dimensions of value equity identified were: 1) entertainment value, 2) social value, 3) service quality, 4) perceived price, 5) epistemic value, and 6) satisfaction. Specific discussion related to each of the dimensions of value equity will ensue. Entertainment Value First-order Results. Entertainment Value was included in the initial conceptualization of value equity based on a review of the literature, which indicated that consumers are likely to derive enjoyment or pleasure from the entertaining aspects of a sporting event. The initial model of entertainment value was comprised of 7 dimensions and 33 items. The initial dimensions of Entertainment Value included: 1) Amusement; 2) Partying; 3) Crowd Experience; 4) Game Intensity/Immersion; 5) Escape; 6) Aesthetics; and 7) Drama. Following the computation of an exploratory factor analysis and two confirmatory factor analyses, the final model for entertainment value contained 6-factors and 21-items. The six factors maintained were: 1) Amusement; 2) Partying; 3) Game Immersion; 4) Escape; 5) Aesthetics; and 6) Drama. A discussion of the results of the first-order factor analyses for Entertainment Value is presented in the following paragraphs, along with the logic used for eliminating dimensions not fitting within the initial conceptualization of Entertainment Value presented in Chapter 3 of this study. This discussion focuses on three specific issues, including: 1) the elimination of the Crowd Experience dimension from the model; 2) the refinement of the Game Intensity/Immersion dimension to include items relating specifically to the perceived value associated with being immersed in a game; and 3) the creation of, testing, and ultimate elimination of a previously unidentified dimension of Entertainment Value comprise of a combination of items from Crowd Experience and Game Intensity/Immersion. This dimension was labeled Experience Intensity. As noted above, the results of the analyses did not support all aspects of the researcher’s initial conceptualization of Entertainment Value. First, the results of the exploratory factor analysis that was conducted at the pilot phase of the study did not support the existence of a Crowd Experience variable, as the items for this construct loaded in a non-specified manner. To elucidate understanding, the researcher used the outcome of the analysis to conduct a reexamination of the conceptualization of Crowd

187 Experience. Specifically, the researcher conducted a reexamination of the wording of the items for Crowd Experience as well as a reexamination of the literature related to the motivation of individuals to consume sporting events. Initially thought to be conceptually distinct, the results suggested that a measure of Crowd Experience and two measures of Game Intensity/Immersion perhaps comprise a previously unidentified dimension of Entertainment Value. The literature examining measures of entertainment related to the sport consumption experience is in its infancy and continues to evolve. To date, there have not been any studies which have empirically tested the reliability and validity of measures associated with game intensity, game immersion, and crowd experience. The initial conceptualization of these constructs in the current study was based on work presented by James, Sun, and Lukkarinen (2004). An examination of the wording of each of the three items suggested that they are indeed conceptually similar in that they each deal with the intensity of the emotional or affective feelings the experience of being at an event provides. For example, crowd2 read, “I love the feeling of being surrounded by all of the fans.” This item deals with the emotions associated with experiential aspect of being at the game. Similarly, gamint1 is also associated with the affective features of the experiential consumption experience. This item read, “watching team name games is a very intense experience for me.” Finally, gamint2, which read, “I really get into the game when I watch team name games” may also be thought of as referring to the affective experience of being at the event. The words “I really get into” deals with the affective component of the experience, while “the game” may be interpreted as meaning both the core and peripheral aspects of the event. Based on the results of the EFA, a reexamination of the literature, and an examination of the wording of the items, the researcher contended it reasonable to view intensity and immersion as being separate testable concepts. The researcher tested a modified conceptualization of Entertainment Value in subsequent stages of the study. The modified model of Entertainment Value comprised two previously unidentified or unrefined dimensions, namely: 1) Experience Intensity, and 2) Game Immersion. The results of the first-order CFA on Entertainment Value for the first data collection of the main study indicated that the two new dimensions were reliable.

188 However, further examination of the new Experience Intensity dimension in subsequent analyses was not able to occur due to an error that was committed by the researcher in measuring the reliabilities of the constructs under investigation. As noted in a footnote on page 160 of this document, the researcher initially computed Cronbach’s alpha scores as a measure of the reliabilities of the constructs in the first data collection of the main study. The Cronbach’s alpha score for Experience Intensity was well below the .70 cutoff threshold specified by Nunnally (1978). This result led to a decision to not include Experience Intensity in the respecified model of Value Equity and therefore not to test its psychometric properties in subsequent analyses. At a much later time in this research project the researcher learned that a more appropriate measure of reliability in Confirmatory Factor Analysis is a calculation of construct reliability. As reported in the results chapter, the construct reliability for Experience Intensity was .74, and thus above Nunnally’s (1978) threshold. Although it is rather unfortunate that this error cost the researcher the chance to further examine the viability of this newly identified dimension within a model assessing the perceived value of the entertaining aspects thought to contribute to the consumption experience of a sporting event, the possible identification of a previously unexamined dimension of entertainment value is a significant finding of this study, one that merits further investigation in future studies. A brief review of the intensity construct is presented in the following paragraphs to elucidate how intensity may fit within a model of entertainment value. Within the context of the consumption of sport services, experience intensity has not yet been defined. However, Demangeot and Broderick (2006) espoused a definition of experiential intensity in the context of online shopping which can be used as a starting point towards a definition of experiential intensity for spectator sports. In their study exploring the experiential intensity of online shopping environments, Demangeot and Broderick (2006) defined experiential intensity as “the degree to which a web site is perceived as a rich mediated space, conducive to the consumer’s participation in a stimulating and memorable shopping visit” (p. 327). Following from this definition, the experiential intensity associated with the consumption of spectator sports may be defined as the degree to which a sporting event is perceived as an intensely

189 entertaining experience, conducive to the sport consumer’s participation in a stimulating and memorable stadium/venue visit. There is some questioning in the general management literature regarding the role of intensity as an entertaining component of consumption. In particular, researchers have examined the relationship of the intensity of the experience to consumer immersion in the event. Evidence suggests that intensity may act as an antecedent of immersion, rather than as a distinct and separate dimension of entertainment value. Research exploring factors related to the consumption of artistic events (Carù & Cova, 2005, 2006; Petkus, 2004) and information processing from advertisements (MacInnis & Jaworski, 1989) have considered the relationship of intensity and immersion. These researchers have found that intensity may influence consumer immersion in an event. Rather than being conceptualized as a dimension of entertainment value, these researchers have proposed that the intensity of the experience is a necessary condition, or precursor, for immersion to take place. While there have not been any studies in the sport marketing literature to examine this proposition, the studies mentioned above (Carù & Cova, 2005, 2006; MacInnis & Jaworski, 1989; Petkus, 2004) have offered some insight into the relationship that may be taking place between these two constructs within the context of the consumption of sporting events. For example, in a qualitative study assessing the impact of service elements on the artistic experience of consumers of classical music concerts, Carù and Cova (2005) proposed that the presence or absence of classical music concert consumers’ subjective assessments of intensity affected self-reports of immersion in the show. Defining immersion as “becoming one with the experience…(and a) total elimination of the distance between consumers and the situation, (where consumers are) plunged into a thematised and secure spatial enclave where they can let themselves go” (Carù & Cova, 2006, p. 5), Carù and Cova (2005) explained that intensity is an important criteria for immersion to occur because classical works presented within the confines of a designated environment, such as a concert hall, establish, from the outset, a certain distance from the public. As is the case for classical music concerts, the direct consumption of sporting events at stadiums or arenas places the consumer at a distance from the performance, or game. The following passage

190 from Carù and Cova’s (2005) study explains how perceived intensity may influence immersion. The researchers stated: “…the absence (or infrequency) of such moments of intensity led to frustration and/or a lack of appreciation for the experience” (p. 47). Similarly, in a study involving an analysis of dimensions related to the consumption experience of the arts, Petkus (2004) noted that the “variation in the sensory intensity of the experience may be necessary to avoid either boredom or burnout” (p. 51). In their research capturing and extending theory on information processing from advertisements, MacInnis and Jaworski (1989) proposed that the intensity of an experience causes respondents to attend to, rehearse, and recall central stimuli that are responsible for the experience to the detriment of stimuli that are unrelated to the experience. In the context of spectator sports, the more intense an experience, the more respondents, or spectators, are likely to immerse themselves in the central stimuli responsible for the experience. In spectator sport, the central stimulus responsible for the experience is the game. Thus, according to MacInnis and Jaworski’s (1989) proposition, the more intense the experience of a sporting event, the greater the immersion in the game will be. Based on the research described above, it is worth examining the relationship between the presence or absence of consumer perceptions of the intensity of the experience of being at a sporting event and self-reports of immersion in the game. The identification of a previously unexplored possible dimension of entertainment value in the consumption of spectator sports is an important finding in this study, one that merits further investigation. For, while the results of the study may have offered support for a distinct Experience Intensity dimension, further thinking, and study, is needed to determine if this dimension is an entertaining component of the sport consumption experience, or if it acts as a facilitator of other entertaining aspects of the event, such as immersion in the game. Second-order Results. The results of the second-order factor analysis revealed that the dimensions of Partying and Game Immersion did not load well with the other dimensions proposed to measure entertainment value. The factor loading for Partying was .163 while for Game Immersion it was .418. Although the factor loading for Game Immersion exceeded the .4 threshold recommended by Raubenheimer (2004), it did so

191 minimally. As factor loadings must be interpreted in the light of theory, and not by arbitrary cutoff levels (Garson, 2008), the following section presents a discussion of, and plausible explanation for the results of the second-order CFA for Entertainment Value. One possible explanation for the failure of the items described above to load well with the other dimensions of entertainment value is that Partying and Game Immersion do not refer to elements of the experience under the control of the organization, whereas the others do, be they by members of the front office or players and coaches on the field. Rooted in the idea that products provide a certain level of emotional value to consumers (Sheth, Newman, & Gross, 1991), entertainment value refers to the pleasure received from a sporting event and is a function of the entertainment that is derived from the event. In thinking about the six dimensions used to measure entertainment value in the current study, all but Partying and Game Immersion refer specifically to factors concerned with the game or other tangible aspects associated with the experience surrounding the game that the organization has some aspect of direct control over. For example, the Drama construct is concerned with the excitement inherent in uncertainty on the field of play. This construct assesses the value that sport consumers may place on the excitement and spectacle inherent in a close game or in specific game situations, such as a baseball player coming to the plate to bat in the bottom of the ninth inning of play with the bases loaded and the game tied; or a hockey game going to a sudden death overtime in the playoffs; or a quarterback leading his football team on an 80 yard drive, behind by a touchdown, with a minute to play. For some, close games may be more entertaining than games that are decided early in the contest. The television ratings for the final round of the 2007 NBA Playoffs are an excellent example of how sport consumers value drama, or lose interest in a sport contest when drama is lacking. Despite the presence of superstars on both rosters, the final series was the least watched of any final series, garnering an average Nielsen rating of 6.2, as the outcome of the series was decided in only four games (Associated Press, 2007). While it is recognized that organizations do not have specific control over the creation of specific situations during a sporting event that may be characterized as being dramatic, the control that they do exert over the creation of drama relates to the hiring of personnel that either place the team in dramatic situations (i.e., the hiring of

192 competitive and competent players and coaches), or are able to convey a sense of the dramatic through their communications with the consuming public (i.e., the manner in which a play-by-play announcer calls a game or sets up a potentially dramatic situation occurring in a game; the content of a press release or news story originating from the communications department within an organization). Organizations also have a degree of control over the aesthetic qualities of a sporting event. The aesthetic dimension deals with the artistic or visual aspects of the sport or game. For some sport consumers, the play on the field and the movement of the athletes is artistically appealing. To understand how a sport consumer may value the aesthetic appeal that a game, a sport, or a player may posses, one only needs to think about how millions of people flock to the Louvre museum in Paris, France each year to catch a glimpse of Mona Lisa’s famous smirk, and liken that experience to sport. For many, ballparks like Yankee Stadium, Fenway Park, Pro Player Stadium, and Wrigley Field assume the role of the Louvre. One can see such works of art as A-Rod’s swing, a Roger Clemens fastball, the green monster out in left field, the Marlins Mermaids dance team, or the piece of abstract art that is the play of the Chicago Cubs in these museums. The control exerted by organizations over the aesthetic dimension involves the decisions made regarding such things as stadium design, personnel selection, coaching strategy or philosophy, and marketing communications. Amusement refers to tangible aspects of the sport consumption experience that consumers can point to as being enjoyable. Recall that amusement refers to the events, activities, and promotions organized by the team. Examples that come to mind include, promotional giveaways such as seat cushions and rally towels, pre- or post- game music concerts, fireworks, on-field half-time contests, and other sorts of prize giveaways. Many organizations attempt to enhance perceived entertainment value through the manipulation of amusing activities. Whereas the dimensions described above are controlled, to varying degrees, by the organization, the Partying construct deals with aspects of the sport consumption experience that may not be perceived by consumers as being controlled by sport organizations. Although many organizations permit the purchase of alcohol at their events, it is feasible that spectators may not view drinking and partying as something

193 the organization provides or controls, rather, it may be that partying is perceived as something that individuals do, or create for themselves, while at a sporting event. A similar argument to that which was made for Partying may also be contemplated for Game Immersion. While it may be that immersing oneself in the game is pleasurable, and thus entertaining, being immersed in the game is out of the control of the organization. The degree to which sport consumers concentrate or focus on the game as opposed to the myriad of other diversions occurring during a sporting event is rather subjective and dependent on individual consumers. In fact, sport organizations have recognized that some consumers do not particularly care at all about what is happening in the game or on the field of play and have responded by enhancing those entertaining aspects of the event that are under some degree of control, such as amusement, aesthetics, and drama. Another explanation for the lack of fit at the level of the second-order factors involves an interpretation of the standard deviations for the individual items. Standard deviation is a measure of the spread, or variation of data points around the mean of a data set. Representing how tightly a set of data is grouped, standard deviation scores offer a form of evidence for how reliable the data may be. An analysis of the standard deviations of individual items enables the researcher to describe the variation in a set of data, which is useful for finding out how similar all the parts of a group are. For instance, in the development of measurement scales, the standard deviation enables the researcher to examine how much variation there is with respect to various dimensions. By establishing the standard deviation for a set of scores, one is able to describe accurately how various scores differ from one another and from the mean. A small standard deviation means the data is close together, a large deviation means the data is wide spread. As the standard deviation begins to increase, it is possible for the researcher to suggest which items or measures are most problematic in terms of lower reliability. A useful tool for interpreting standard deviation scores is the 68-95-99 rule. According to the 68-95-99 rule, about 68% of all scores fall within one standard deviation of the mean, 95% of all scores fall within about two standard deviations of the mean, and 99.7% of all scores fall within three standard deviations from the mean. The

194 rule provides an indication that data more than two standard deviations from the mean is unusual and that the mean may not be a good indicator of the average score. Conversely, data falling within two standard deviations offers support that the mean is a good indicator of the average score and the data is thus more reliable. An examination of the standard deviations of the measures for each of the first- order latent variables of Value Equity was conducted using the validation sample data. Except for the measures of the Partying dimension, every one of the measures for the remaining 13 dimensions had standard deviations below two, indicating acceptable reliability. An examination of the standard deviation scores for the measures of Partying however, revealed that three of five measures had standard deviations above two, with a fourth measure just a shade below two at 1.945. These results indicate that there exists a great amount of variability in the self-report responses for this dimension. Several reasons may explain why a great amount of variability exists in the responses to the measures of partying, such as: 1) respondents did not take the question items seriously; 2) there was unacceptable inter-subject variability; and 3) the nature and type of event in which sport consumers were surveyed led to tremendous variability in responses to questions related to drinking and partying. Discussion related to this last explanation will ensue. Suppose that Raubenheimer’s (2004) .4 threshold is adhered to and Game Immersion is viewed as having loaded adequately on the Entertainment Value construct. This supposition results in a situation where the Partying dimension is the only dimension failing to load on the Entertainment construct. From this perspective, it is plausible that the nature of the event in which sport consumers are surveyed is another factor likely responsible for the failure of Partying to load with the other respective entertainment value dimensions in the second-order CFA. For example, a large majority (84.2%) of the college students sampled indicated that the last sporting event they had attended was a college game or event. Of that 84.2%, 42% had last attended a football game, 34% had last attended a basketball game, 21% had last attended a baseball game, and 3% had last attended some other type of collegiate sporting event. One reason why partying and drinking may not load with the other entertainment constructs is a result of FSU being a dry campus in that alcohol is not

195 sold at the football or baseball venues. Alcohol is available for purchase at basketball games as the venue where FSU’s games are played is operated by municipal and county governments, and not Florida State University. While it is acknowledged that some college students likely “smuggle” alcohol into FSU’s venues (particularly the football venue) for consumption purposes, many students do not. As alcohol is not available at games, it is possible to conclude that drinking and partying may not be a valuable part of the entertaining aspects of a collegiate sporting event at Florida State University. However, these results cannot be generalized to other collegiate settings where alcohol is permitted and sold at games. Additionally, it is quite possible, and likely, that a shift in policy permitting alcohol at Florida State athletic events will yield different results. The results of the second-order HCFA for partying suggest that the some dimensions of Entertainment Value may not be as salient for some sports or contexts as they are in others. For example, in a study examining sport consumers perceptions of the entertaining aspects of the sport consumption experience, James, Sun, and Lukkarinen (2004) identified the partying construct as a dimension of the entertaining aspects of football. The results of the current study suggest that consumers might not recognize Partying as being of value in terms of what is entertaining about a sporting event. Future research may consider distinguishing between consumer perceptions of the entertaining aspects of the game. Perceived Service Quality The proposed three-factor model of service quality did not hold up in the current study. Rather, the results of the study supported the existence of a two-factor model of service quality for team sports. The two factors were interaction quality and outcome quality. A two-factor model of service quality is consistent with the conceptualizations of service quality put forth by Grönroos (1984). He proposed service quality to be comprised of both functional and technical components. Another course of action that could have been taken in the current study would be to reword the measures of service quality in a sport context in order to maintain service quality as a three-factor construct, as conceptualized in the initial research model. Indicating that the importance and effects of the various dimensions of service quality may vary from industry to industry,

196 Brady and Cronin (2001) advocated that researchers modify the conceptualization of service quality to account for industry-specific factors. Perhaps stronger wording of the items providing a better reflection of the sport consumption experience would have been prudent. This is a limitation of the current study, and future research efforts should identify stronger measures of service quality for the sport consumption experience. Examples of measures that need to be revisited and strengthened for use in a sport consumption setting include waiting time, social factors, and tangibles. The measures of waiting time as written in Brady and Cronin’s (2001) model are too general for the sport consumption setting. The measures of waiting time in the current study were ‘Waiting time for service at Seminoles games is predictable,’ ‘The staff tries to keep my waiting time for service to a minimum,’, and ‘The ballpark staff understands that waiting time is important to me.’ In the sport consumption experience, there are many targets of service in which customers might perceive there to be a waiting time. Waiting time might refer to how long one must wait in line to purchase tickets or wait at will call. Perceptions of wait time can also refer to how long one must wait in line to use the restroom facilities or wait in line at the concession stand to purchase a hot dog and a beer. It is not surprising that as they are currently worded, measures of service quality loaded together. Consider the following example. One of the criticisms of the Bell Center in Montreal from consumers of Montreal Canadiens games is that the hallways on the concourse are two narrow and that there are not enough restrooms to accommodate the crowd. This in turn has resulted in increased wait time to use the restrooms as well as difficulty in returning to one’s seat in time for the recommencement of play in the next period. Perceptions of wait time in this instance are a result of the design factors of the stadium. The results of the current study suggest that service quality perceptions in the sport consumption experience are captured by two dimensions: 1) interaction quality; and 2) outcome quality. The results do not support the researcher’s initial conceptualization of service quality as a three-dimensional construct. Rather, as noted above, the final results lend support for Grönroos’ (1984) two-dimensional structure for service quality. However, it must be noted that the results of this study are far from conclusive, and future study should consider whether service quality might be measured

197 as a one-dimensional construct. The rationale behind this proposition is twofold. First, the results of the analyses for the first two data sets indicated a lack of discrimination between the two factors. While the results of a chi-square difference test indicated that a two-factor model of service quality provided a better fit to the data than did a one- factor model of service quality, the lack of discrimination suggests that the measures for the two dimensions may be capturing a single construct. Second, although the analysis of the last data set for the respecified model resulted in a correlation of (.82) between the two service quality dimensions – below the recommended .85 threshold for discrimination specified by Kline (2000) – the margin was very slim. It is evident from the results of the current study that more testing of the service quality construct is warranted in a sport consumption context to determine its true dimensionality. Perceived Price An important finding was that the 2-factor conceptualization of perceived price, comprised of both monetary and non-monetary aspects, was both reliable and valid. Recognizing that consumer perceptions of price are based on what is given up for what is received (Zeithaml, 1988), the conceptualization of perceived price in the current study comprised both monetary and non-monetary costs. The final perceived price scale included six items, three each for monetary and non-monetary cost respectively. These results are significant because studies (Campo & Yagüe, 2007; Munnukka, 2005) of perceived price continue to limit themselves to perceived monetary price, and do not include perceived non-monetary price. Measures of the monetary aspects of Perceived Price were selected from the scales of Voorhees (2006) and Yoo et al. (2000). The non-monetary dimension of perceived price incorporated items from Berry, Seiders, and Grewal’s (2002) service convenience scale. The content of the measures of perceived non-monetary costs in the current study included wording related to time, search costs, effort, and convenience. Measures of the monetary aspects of Perceived Price are well established in the literature, and the researcher is comfortable that the scale in the current study captures the elements of the monetary aspects of perceived price in the context of the consumption of spectator sports. In contrast, as discussed, very few studies have identified and tested measures of the perceived non-monetary costs of

198 consumption, particularly in relation to perceptions of value. Thus, although the scale proved to be reliable and valid, it is possible that the elements of the perceived non- monetary costs identified in the current study do not represent a comprehensive list of those elements. The researcher echoes Oh’s (1999) suggestion that future studies should consider perceived non-monetary price as a measure of the perceived price associated with the consumption of sporting events, and additional items of perceived non-monetary price should be identified and tested in future studies. Recommendations for how additional items should be identified are presented in subsequent sections in this chapter. Knowledge as Value To date, there has not been any research in the sport management literature examining the value of knowledge acquisition from a sport consumer perspective. Researchers in the general management literature have suggested that it is possible for knowledge to be valued from the perspective of the knowledge seeker (Gupta & Govindarajan, 2000). The general marketing literature suggests that consumers will value knowledge based on whether it possesses the ability help one reach his or her goals, and whether it helps achieve the desired consequences in specific use situations (Woodruff, 1997). The results of the current study provide preliminary evidence of support for a reliable and valid scale for the measurement of the value consumers place on the knowledge acquired from the consumption of a sporting event. First, the results of the pilot study revealed the reliability of the Knowledge construct to be .879. Additionally, each of the three measures of Knowledge had item-to-total correlation values above the .50 threshold specified by Hair et al. (1998), indicating an acceptable level of internal consistency reliability. Additionally, the results of the exploratory factor analysis on the Knowledge construct revealed that each of the three measures of Knowledge loaded together well above the .40 threshold specified by Hair et al. (1998) for practical significance. The item loadings ranged from .747 to .965. Next, the results of the Confirmatory Factor Analyses for each of the data collections of the main part of the study offered further evidence supporting the Knowledge construct as a reliable and valid scale for the measurement of value in the

199 consumption of spectator sport. The results of the CFA for the first data collection on Knowledge yielded factor loadings ranging from .813 to .927 and a construct reliability of .90, and an average variance extracted (AVE) score of .76. Lastly, the correlations among the Knowledge variable and each of the other 15 latent variables ranged from .012 to .520, which provided evidence of discriminant validity. The results of the CFA for the second data collection of the main study were similar to those found in the first data collection of the main study. The factor loadings ranged from .872 to .910, the construct reliability was .90, and the AVE was .76. Finally, the correlations between Knowledge and the remaining 15 latent variables ranged from .022 to .494. While there have not been any studies specifically examining knowledge from a value creation perspective in spectator sports, the knowledge construct has been studied in terms of knowledge as a function of consumer motivation to attend sporting events. Studies investigating the psychometric properties of the knowledge construct in the motivational literature have found similar results to the ones reported in this study (Funk, Ridinger, & Moorman, 2003; James, Kolbe, & Trail, 2002; James & Ridinger, 2002; Trail, Fink, & Anderson, 2003; Trail & James, 2001). Each of these studies found the subscale of Knowledge to have good internal consistency and construct validity. In the sport realm, consumers can have a variety of different goals for different use situations, which affect the value placed on knowledge acquisition. For example, many consumers of professional golf likely place a great deal of value on the knowledge acquired from watching the sport on TV or in person. The goals for watching golf for many are to learn how to improve one’s own golf game by improving one’s swing and learning what to do in specific situations on the golf course. Recognizing, the value that consumers place on knowledge acquisition from watching golf, the Island Lake Golf and Training Center in Minnesota has incorporated watching golf events on television into their teaching of the game of golf. The description of the class appearing on the Center’s website highlights the value that some consumers, and even businesses, place on the value of knowledge acquisition from sport consumption. The last line in the following promotional material is particularly telling. The class entitled “How to Better Watch Golf on TV” is described as follows:

200 “Classes will be given on the weekends in the clubhouse while golf is on TV. It will be a class designed to take advantage of what you see and learn on TV, what you can do while watching the actual event, and what to do during the commercials to get better at golf. This course will give you the opportunity to come and watch the event at the golf course where you can hit actual balls and putts while it’s on. We will show you efficient ways to practice your faults and give you exercises to help overcome your swing problems. Each student will be videotaped to begin the class, so you can have your own agenda during the telecasts. After you have participated at the course you will be ready to continue your education at home in the comfort of your living room, or up at the golf course to encourage practice and follow through” (islandlakegolf.com) For the consumption of team sports, where the consumer is less likely to acquire knowledge for the sake of improving one’s game, the goals and intended use situations for knowledge acquisition are likely very different. For example, individuals may wish to acquire knowledge simply to understand the game better so that it becomes more enjoyable. For others, the knowledge acquired from watching or attending an event may be used to appear more educated or knowledgeable about the sport or the team to referent others. Other consumers may use the knowledge acquired from use for sport gambling purposes or fantasy sports. The instrument developed in this study will enable organizations to learn to what extent consumers place value on acquiring knowledge from the consumption of their events. Future investigation is needed to examine what particularly consumers are interested in learning about from the event and for what purpose. As presented in the review of the literature, sporting events of all kinds include educational dimensions, such as: pre- or post-event write-ups in newspapers, in online content, or in game programs; the reporting of statistics related to player and team performance; in game announcements from the public address announcer or the referees indicating what has transpired on the field of play; and educational seminars to teach the game to different segments of the population. The potential for increasing the educational dimension of sporting events is great. By increasing the educational dimension, sport organizations

201 can train targeted audiences to become more involved in the sport and the team and therefore more likely to repeat and expand their patronage. Satisfaction as an Outcome of Value Satisfaction was defined as a customer’s evaluation of pleasurable fulfillment of some need, desire, or goal (Oliver, 1997). The relationship between satisfaction and value has been the subject of much study. There has been general support in the literature for satisfaction being an outcome of value. Woodruff (2003) noted that satisfaction is a customer’s feelings, or emotional response, to cognitive evaluations of one or more use experiences with a product. As indicated in the literature review, a majority of researchers have empirically found support for viewing satisfaction as an outcome of value as opposed to an antecedent (Brady, Cronin, & Hult, 2000). The failure of the satisfaction construct to discriminate from two other factors, including, Interaction Quality and Outcome Quality led the researcher to contend that satisfaction should be regarded as an outcome of value. Each of the other measures of value assess consumers’ cognitive evaluations of their experiences with the sport service. As such, the researcher chose to eliminate satisfaction from the model.

Research Implications

From the results of the current study, several noteworthy implications for practitioners and academicians have emerged. From a practical standpoint, many sport organizations are becoming interested in the analysis of customer value, which involves an exploration of the antecedent factors of perceived value to assess their relative importance in the perceptions of their customers. To market the range of their services effectively, sport marketing managers should understand the fundamental source of customer value for their services. There are several principal potential managerial benefits for assessing value equity, including: 1) the development of marketing strategy; 2) the identification of value creation opportunities; 3) the enhancement of service specifications; and 4) improved market research capabilities. Possible applications of the model in designing marketing strategy, recognizing new service opportunities,

202 enhancing service specifications, and improving the financial return on marketing activities are discussed. First, the recognition of the importance of the different dimensions of value will enable sport services marketers to develop sophisticated yet efficient and effective marketing strategies. Consistent with the works of Sheth et al. (1991), Rust et al. (2000), and Sweeney and Soutar (2001), the five dimensions of value identified in the model suggest various value creation strategies. This has substantial implications for marketing strategy. For example, sports teams such as the Cincinnati Bengals and Cleveland Cavaliers have identified improvements in service quality as a competitive marketing strategy (Milicia, 2007; Brunsman, 2006). These teams have invested in, and have placed emphasis on improving the quality of interaction with organizational personnel and outcome of service, as evidenced by the tailored or customized solutions offered to their customers. The Cincinnati Bengals implemented a program whereby Bengals customers attending a game at the Bengals home stadium can report unruly fan behavior by cell phone to a hotline number monitored by the Sheriff’s Deputy Department. Fans who do not heed initial warnings face possible ejection from the stadium, as well as run the risk of having their season tickets and personal seat licenses revoked. The efforts by the team to control unruly fan behavior could be construed as an effort to improve the quality of the services offered by the team and to make the product offered more valuable to prospective and current customers. The Cavaliers have sought to improve service quality by initiating an online ticket program called “Flash Seats” where consumers have access to the organization’s season ticket holder marketplace. Through this program, fans can buy and sell tickets that would otherwise go unused. Not only does this strategy serve to increase revenues through increases in sales of concessions and parking from the seats being used, but it also makes it easier for potential customers to acquire tickets to a game that they might otherwise not have been able to get. Teams like the Buffalo Bills have developed strategies to improve the knowledge that certain segments of the populations have about football. In recognition of the potential growth of the female segment, these teams have developed programs, such as Football 101, to educate consumers about the rules, strategies and nuances of the

203 game of football. This focus on epistemic value creating strategies demonstrates the understanding that some teams have regarding the effect that knowledge about one’s product may have on the maintenance of a competitive foothold in the marketplace. It is important to note that few sport organizations create just one type of value, and the current model extends the works of Sheth et al. (1991), Rust et al. (2000), and Sweeney and Soutar (2001) by suggesting the subtypes of value that can be created from different value creating processes. The current model can thus be used to describe the value creation strategy of an organization. Minor League Baseball (MiLB) teams, for example, create entertainment value through the numerous promotions and gimmicks provided on an almost daily basis (e.g., pie eating contests, dizzy-bat racing, costume and theme nights, and fan pitching contests). They create social value mainly via positioning themselves as a fun family activity where families can spend time together and by providing comfortable spaces where friends and colleagues can interact. MiLB teams create service quality value mainly via appropriate features and attributes (i.e., ushers wiping down seats, intimate stadium ambience). Finally, with respect to the price / sacrifice value, MiLB teams create economic value through the provision of affordable entertainment for the entire family. Another key managerial implication is that the model provides a relatively easy way for organizations to document their value creation strategies for specific services offered or for the organization as a whole. By delineating the value creation strategy of an organization using the model, sport marketers can clearly define service concepts. Sport organizations can use the information generated from the model to create additional value in each area. For example, emerging from the 2004 lockout, the National Hockey League identified the need to improve the value offered to hockey consumers through entertainment related processes. Following the lockout, the NHL altered its service offering to create greater entertainment value by manipulating the aesthetic appeal and drama dimensions of the VESSS in the game of hockey. The rule changes adopted during the season-long hiatus resulted in a game that now emphasizes speed and focuses on skating, puck movement, and scoring, as opposed to the clutch-and-grab style of play that predominated prior to the lockout. The adoption

204 of a shoot-out to decide contests added to the drama in the game by providing an element of finality to contests A third implication for managers is that in addition to helping sport organizations describe service concepts and positioning strategies, the VESSS also aids sport marketers to specify sources of competitive advantage. Teams will be able to identify and focus on the dimensions of value equity that create the value on which they plan to compete. For example, much of the value offered by more and more Major League Soccer (MLS) teams concerns the consumption environment as it relates to service outcome evaluation. There has been a frantic push by MLS to build soccer specific stadiums in as many of its markets as possible. The league has recognized that a large part of the value in attending soccer games involves the quality of the venues in which the games are played. A final practical implication of the current research is the contribution the VESSS provides towards the measurement of marketing effectiveness. An underlying premise of Value Equity is that scarce resources should be allocated to the highest-value customers. Therefore, another application of the VESSS is that the calculation of the costs associated with the implementation of various value creation strategies enables competing marketing strategy options to be traded off on the basis of projected financial return. A commonly identified weakness, or limitation of the marketing function within organizations is that it is difficult to assess the effectiveness of marketing strategies and thus difficult to hold marketing financially accountable. Rust, Lemon, and Zeithaml (2004) lamented that the lack of any form of financial accountability has caused marketing executives to rely on intuition and instinct in making strategic decision, and to view marketing expenditures as short-term costs rather than long-term investments. The need for financial accountability in the marketing function is highlighted by the proclamation of the Marketing Science Institute that assessing marketing productivity and marketing metrics is among the highest of priorities. Rust, Ambler, Carpenter, Kumar, and Srivastava (2004) argued that the perceived lack of accountability has undermined marketing’s credibility, threatened its standing in the organization, and even threatened marketing’s existence as a distinct capability within the organization. The importance of developing measures that assess

205 the effectiveness of marketing strategies is important for two interdependent reasons: (1) marketing budgets are finite; and (2) not all efforts to improve the drivers of customer equity (value equity, brand equity, relationship equity) are profitable. Organizations must therefore differentiate between driver improvement strategies that are profitable and those that are not. Applying current financial concepts to the marketing function enables organizations to view value enhancing marketing actions as investments, with these actions being viewed as profitable only if the return on the investment exceeds the cost of the initial investment. Thus, an advantage of the empirical investigation of the value equity of existing and potential customers is that the quantitative data involved in the process enables managers to base strategy decisions on data, rather than on intuition and hunches. In summary, the key consideration that sport marketers must adhere to in the implementation of the proposed model of value equity is that each of the identified dimensions of value equity makes differential contributions in any given choice situation and that the values are independent of one another (Sheth et al., 1991). In a sport context, different choice situations may include, but are not limited to: the day of the week of the game; the time of the game; the time in the season (i.e., playoffs versus regular season); the winning percentage of the team; and the weather. While many choice situations may be out of the control of sport marketers, the current model enables the analysis of each of the proposed value dimensions in any given choice situation. Thus, it is possible for a minor league baseball team’s sport marketer to investigate what the most salient value dimensions are for a weekday night game early in the season, versus a Sunday afternoon game in late July. This understanding will enable sport marketers to utilize their finite marketing resources in the most efficient and effective manner. One of the identified objectives of this dissertation was to propose a framework for assessing customer equity in spectator sports. The literature review reinforced the contention that the ability to measure the components of customer equity will enable sport managers to realize numerous benefits. Although only one component of the framework (Value Equity) was tested in this research, the ultimate objective of such a proposal is the testing and development of all components together. In light of the

206 overarching objective just stated, the following paragraphs present discussion of the components of the model that remain to be tested, namely: Brand Equity and Relationship Equity. Brand Equity The conceptualization of Brand Equity in the current study stemmed from Rust et al.’s (2000) definition of brand equity as the portion of customer equity attributable to the customer’s subjective and intangible assessments of a brand. Given its position as one component of customer equity, the conceptualization of brand equity in the current study is far more focused, or refined, than with previous conceptualizations of brand equity. Rust et al. (2000) argued that conceptualizations of brand equity as a part of a customer equity framework do not include dimensions related to the performance of a brand, nor to consumers’ experiences with the brand. As such, the current conceptualization of brand equity was comprised of two factors: 1) Brand Attitudes, and 2) Brand Associations. At the same time that the focus of brand equity narrows, viewing brand equity within the context of its position as a component of customer equity allows for the revisiting of the elements of various brand equity dimensions. For example, a major contribution of this study is the proposition of previously unexplored brand associations related to sport organizations, such as consumer perceptions of corporate social responsibility and consumer perceptions of organizational competence. Recall that brand associations relate to those things which are linked in memory to a brand and whose strength depends on the experiences and exposures that consumers have with a brand (Aaker, 1991). Brand associations also represent consumer perceptions, meaning they do not have to be rooted in objective reality. The significance of this last characteristic of brand associations is that consumers do not require an intimate knowledge of the inner workings of the organization to form perceptions about the way in which an organization operates. For example, consider Carroll’s (1979, 1991) four- dimensions of corporate social responsibility, which is comprised of the “economic, legal, ethical, and philanthropic expectations that society has of organizations at a given point in time” (1979, p. 500). While it is likely that most consumers do not have enough to information to realistically assess whether a sport organization operates in legal,

207 ethical, and economically responsible ways, consumers are likely to nonetheless make judgments or associations related to each of these areas that are based on the limited information that they do have. A similar argument is put forth for the identification of organizational competence as an association that sport consumers make about sport organizations. Relationship Equity The conceptualization of Relationship Equity in the current study stemmed from Rust et al.’s (2001) identification of relationship equity as the third component of customer equity. Rust et al. (2001) defined relationship equity as “the customer’s tendency to stick with a brand, above and beyond objective and subjective assessments of the brand” (p. 95). Grounded in the relationship marketing literature, this component is representative of the strength of the relationship between a consumer and a sport organization. Relationship equity is a component of customer equity because the strength of the customer-organization relationship often determines a customer’s likelihood of continuing to purchase an organization’s products. From this perspective, a review of literature relating to the mechanisms through which organizations develop and strengthen relationships served as a platform from which the dimensions of relationship equity in a spectator sport context were identified. These dimensions included such things as consumer loyalty programs, affinity programs, community building programs, knowledge programs, and special recognition and special treatment programs. Present day, sport organizations are increasingly recognizing the importance in developing and maintaining strong relationships with customers and are beginning to implement various programs, such as the ones described, towards achieving these strong relationships. This Relationship Equity scale will enable sport organizations to assess the importance of each of the various relationship equity building programs on their relationship building efforts and ultimately an understanding of which programs create the most value for the organization. The combination of the results of this study and the process of conducting this research has taught the researcher valuable lessons that should be considered in the testing of the remaining drivers of customer equity and their components. First, it is imperative that a reliable and valid first-order factor structure is developed before

208 proceeding to the testing of the second-order factor structure. Second, each of the other drivers of customer equity must be studied in turn. Before the psychometric properties of the full model of customer equity is examined, it will be important to ensure that parsimonious scale for each of the three components have been developed. Third, as was done in this study, decisions to retain or eliminate items and dimensions of Brand and Relationship equity must be based on an analysis of both the statistical results and a theoretical and conceptual rationale. Finally, it is important that to test the models of brand and relationship equity on actual consumers of a sporting event. While student samples may be convenient, asking respondents to report on their memories of their past consumption experiences is not ideal.

Limitations and Future Research

It is recognized that the current study has several limitations which point to a necessity for future research. Specifically, the researcher has identified five particular limitations. First, the predictive validity of self-reported purchase behavior is not always high (Morwitz, Steckel, & Gupta, 1997). Bickart (1993) reported that reliance on self- reports may lead to overestimation of correlations between marketing activities, perceptions of these activities, and behavior due to common-method variance problems. Second, it is likely that the proposed components of value equity do not represent an exhaustive list, since a number of other unexplored variables could potentially act as indirect antecedents of value equity in a spectator sport setting. A number of unexplored variables and measures could potentially represent what consumers find to be of value in the sport consumption experience. The list of variables and measures examined in the current research were generated from a literature search. The author did not seek to include measures or variables suggested by sport consumers. Future research might conduct interviews with sport consumers to obtain additional dimensions or measures of value in the sport consumption experience. A third limitation is that surveys of spectators for the exploratory and confirmatory samples were conducted prior to the start of the games. Research in strategic

209 marketing suggests that customer value should be evaluated from the consumer's total consumption experience (Parasuraman, 1997; Woodruff, 1997). Sport consumers who had not attended a prior game, or who had not done so in quite some time, are unlikely to have experienced enough of a game to offer an adequate evaluation of all of the value measures in the questionnaire. Therefore, future research can expand on this work by examining such issues as how sport consumers' post purchase experience might influence their perceived customer value. The fourth limitation is the results of this study were generated primarily from spectators’ value perceptions for the sport of baseball. While the validation sample assessed value across sports, the exploratory and purification samples dealt with collegiate and professional baseball respectively. Additional studies in other sports and at other levels of sport may strengthen the generalizability of the proposed constructs and framework. Another limitation of the current study is that a cross-sectional sampling design was employed. Only those persons in attendance at the game on the day of the game were surveyed. Thus, the results are not generalizable beyond the game that where surveys were distributed. The researcher recognizes that what consumers perceive as being valuable for weekend games may be vastly different from what is perceived as having value during the week. For example, spending time with family and friends may be a tremendous source of social value for many sport consumers on weekends, while social value may be derived from entertaining business clients or networking with potential clients for a game that occurs during the week. Similarly, waiting time for service may be far more important during the week when people have to go to work the next day than it is on the weekend when individuals may have greater amounts of discretionary time. Additionally, a cross-sectional sample does not allow the researcher to study the dimensions and measures of Value Equity over the course of a full sport season. Through a full sport season, there is likely tremendous variation in the different dimensions. For example, it is likely that perceptions of the dimensions of value equity will vary based on many factors. For example, consider the intensity of experience construct. It is likely that perceptions of this dimension will vary based on who the opponent is (hated rival versus non-conference opponent), the day of the week, and

210 whether or not it is the pre-season, regular season, or post-season. A cross-section sample does not allow for assessment of any variation in this dimension and its impact on customer equity over time. Additional research may find it desirable to employ a longitudinal design to examine the factor structure of the proposed model. A longitudinal research design will enable researchers to track the variability of specific cohorts of consumers over the course of the season. This type of design will provide a more accurate assessment of consumer perceptions of sources of value than does a cross-sectional design. Finally, a major limitation of this research that likely negatively affected the fit of the model involves the methods that were employed to collect the data. Due to the limited availability of resources, the researcher elected to survey spectators at the selected games prior to the commencement of the game as opposed to following the conclusion of the game. This method for collecting data presented two problems which could have affected the results. First, asking participants to rate their perceptions of the value derived from the consumption experience prior to experiencing the event in its totality is problematic. In the face of not being able to accurately assess the subject of the items presented in the survey, many respondents may randomly circle a response. A second limitation to surveying spectators prior to the commencement of a game is that for many, taking the time to fill out a survey detracts from the experience of being at the game. In reluctantly agreeing to complete a survey, many participants may circle responses without reading the content of the items carefully or at all. A more appropriate method for collecting data about consumer perceptions of sporting events would be to survey willing participants with a predetermined time following the event. One way to do this is to simply ask participants at the game if they would be willing to participate at a future point in time. Email addresses can be collected and an electronic survey can be emailed to those agreeing to participate. Notwithstanding the limitations described above, the current research provided the stimulus for additional future research and testing opportunities. First, an evaluation of the impact of the dimensions of value equity on various consumer and organizational outcomes is warranted. Woodruff (1997) noted that evidence of the impact of value

211 enhancing strategies on firm performance is crucial if firms are to adopt value specific strategies towards achieving a competitive advantage. Another way in which the current study can be extended is by using the current scale as a part of a study examining how desired value changes over time. It has already been suggested that the setting or sport is likely to affect the salience of each of the dimensions of value. However, it would be interesting and useful to understand the relevance of the individual value factors as consumers move through personal and product life-cycles and as experience with the product increases. Also, as noted above, and in the section in the first chapter of this document describing the limitations of the research, not all of the proposed and tested dimensions of value equity can be universally applied to all spectator sport contexts. Specifically, the psychometric properties of the scale need to be continually improved. To increase the external validity of the model it would be beneficial to direct future research towards the examination of the application of the model in marketing contexts other than those explored in the current study. Future research should be directed to testing the scale on consumers in a variety of sport settings and levels. For example, the scale could be tested on consumers: 1) of different types of professional team sports (i.e., professional hockey, professional football, professional soccer, and professional baseball); 2) of different levels of sport (i.e., minor leagues and 2nd tier sports such as Arena Football 2, AAFL, Central Hockey League); 3) of amateur sport (i.e., college athletics and Major Junior Hockey in Canada); 4) of major special events occurring once per year or as one-off events (i.e., NCAA Tournament, Super Bowl, U.S. Open of Golf, U.S. Open of Tennis, major Marathons such as the New York City Marathon or the Chicago Marathon); 5) of individual sports (i.e., tennis, golf, and beach volleyball); 6) of women’s team sports (i.e., WNBA, Women’s Professional Football, and Women’s Professional Soccer); of alternative sports (i.e., super motocross, and the X-Games); and in different geographical areas (i.e., Canada). As noted, the scale is currently only generalizable to the population sampled in this study. In addition to directing future research to testing the scale on consumers in a variety of sport settings and levels, researchers may find it informative to examine and compare the application of the model to other entertainment settings outside of

212 spectator sport. At the outset of chapter one of this document the researcher acknowledged that as a major form of entertainment in contemporary society, sport teams compete for consumer dollars with other entertainment providers such as movie theatres, theme parks, restaurants, and so forth. A comparison of consumer perceptions of value in each of these settings to the value derived from the consumption of various spectator sport settings would provide a plethora of information related to perceptions of value equity in the consumption of both sport services and services in general. It is possible to think of several examples of where there might be differences in the various drivers in the context of sport services as compared to services in general. One such example might involve consumer perceptions of drama as a source of value in the consumption experience. Drama relates to the uncertainty of the outcome of the core product. For most entertainment services, there is a high degree of outcome certainty associated with the provision of such services. In most entertainment settings, consumers rely on the expectation that the outcome will be certain. For example, at amusement parks, consumers expect that each ride will conclude by delivering them safely to solid ground. It is likely that consumers do not place a high value on the possibility that the ride may fail. Similarly, most consumers of restaurants likely expect there to be a level of consistency in the restaurant consumption experience regarding the core aspects of the service, namely the food and service. In contrast, much of the literature related to the economics of sport indicates that sports teams, and league, do much better from a financial perspective when there is competitive balance, or parity, between teams (Leeds & Von Allmen, 2005). These studies have found that consumers are more willing to spend money to attend sporting events when there is a likelihood that either team has a chance to win. In the study of value equity in spectator sports, competitive balance may be used as a proxy for drama. The more balanced the teams, the more drama there is and the more uncertain the outcome. Another example of where there might be a difference in the salience of certain drivers of value equity within sport and non-sport entertainment services involves the value placed on the opportunities to increase one’s knowledge about the product. It is possible that consumers will place greater value on the knowledge acquired from the

213 consumption of sport services than of other entertainment related services. This assertion relates directly to the plethora of information is currently available to consumers of sport entertainment services. For the consumption of most non-sport related entertainment services, such as amusement parks, restaurants, and movie theatres, most consumers likely just want to be entertained, and are not interested in learning about how the rides are constructed, where the vegetables were grown, or how many consecutive movies an actor participated in without going through rehab. A final objective of this study was the development of a scale that can be universally applied to each of the settings described above. In order to accomplish this task it will be necessary for future researchers to identify a more comprehensive, even exhaustive list of possible dimensions and measures of Value, Brand, and Relationship Equity in various settings. One strategy for developing a more exhaustive list of value equity components is to follow the strategy used by Ross, James, and Vargas (2006) in their research developing a scale for the measurement of professional sport team brand associations. The authors used a mixed-methods design in the identification, development, and testing of measures of professional sport team brand associations. Specifically, a free-thought listing technique was used to develop measures originating from respondents perceptions as opposed to measures and categories identified by the researchers. The rationale for the use of this technique was that “constructs and items assessing brand associations developed by the researcher might not accurately represent the thoughts of sport consumers. Such measures would assess a consumer’s evaluation of what the researcher believes are team brand associations” (p. 265). In the current study, it was the researcher who identified the constructs and measures that were used in the scale for assessing consumer perceptions of value equity in spectator sports. Although these constructs and measures were developed based on a thorough and exhaustive review of the relevant literature, they did not originate from the respondents. Thus, there exists the possibility that the constructs and dimensions identified and tested in the current study do not completely, or adequately comprise all potential measures of consumers perceptions of value equity in spectator sports. In the future, research should be

214 directed towards the identification of possible additional dimensions and measures of value equity.

Conclusions

Customer value driven strategies are the foundation of a customer’s relationship with an organization as they provide an important direction for creating and maintaining a competitive advantage in the sport services industry. Rust et al. (2000) noted that a firm’s products or services must meet the value needs and expectations of its customers or the best brand strategies and strongest relationship building strategies will not work. Recognizing the competitive advantage superior value delivery provides, Woodruff (1997) identified, nearly a decade ago, a need for the improvement and development of tools to help organizations compete on superior customer value delivery. Heeding the call, the purpose of this study was to develop and test a framework for analyzing and explaining the value of the sport services consumption experience to the customer. This model, which was built on the strengths of existing frameworks, is a useful tool for sport marketers to contemplate when exploring ways to distinguish themselves, in the eyes of the customer, from others in the marketplace. If customer perceptions of value are based on what consumers’ perceive they get for what they perceive is given up, then the creation of value for customers is a critical task for marketers. An understanding of the components of value equity enables sport marketers to focus on how to improve an organization's competitive position, attract and retain targeted customers, and create shareholder/stakeholder value.

215

APPENDIX A

Letters Seeking Organizational Participation

216

March 23, 2007

Mr. Ben Zierden Director of Ticket Operations P.O. Box 2195 Tallahassee, FL 32306-2340

Dear Mr. Zierden:

By means of this letter I am writing to introduce myself as a Ph.D. student studying in the area of Sport Management at the Florida State University. I am writing to gain support for my dissertation study, which focuses on the areas of consumer behavior and marketing. More specifically, I am examining issues related to consumer value, perceptions of service quality, and customer satisfaction. The results generated from a study such as this will have implications for the way in which collegiate sport programs market their product.

In order to acquire the necessary data for this study, it is my intention to survey the spectators of Florida State Seminoles baseball on two separate occasions. I am interested in surveying spectators at Dick Howser Stadium during the 2007 baseball season. The survey will be short in length, easy to complete, and will not disturb fans while the game is in progress. Unfortunately I cannot provide you with a copy of the survey at this time, as I am in the process of constructing it. As soon as it is complete, I will send it to you for review. I have, however, enclosed a copy of the consent letter that will be presented to each spectator along with the survey questionnaire.

I hope that you feel a study such as this is both worthwhile and helpful in developing a better understanding of the preferences and perceptions of spectators of Florida State baseball. I am hoping that you will assist in the completion of my study by allowing me access to the spectators at Dick Howser Stadium. Rest assured that at the completion of this study I would provide Florida State Athletics with all of the results.

Please inform me if this proposal meets your approval. I will follow up this letter with a telephone call in two weeks time. Should you have any questions regarding any aspect of this study, please do not hesitate to call me at 850-284-8168, or by email at [email protected].

Thank you very much for your assistance.

Sincerely,

Dan Sweeney Ph.D. Candidate

DEPARTMENT OF SPORT MANAGEMENT, RECREATION MANAGEME NT, AND PHYSICAL EDUCATION 200 TULLY GYM · TALLAHASSEE FLORIDA · 32306-4280 PHONE: (850) 644-4814 00 · FAX: (850) 644-0975

217

May 11, 2007

Mr. Kirk Goodman General Manager Jacksonville Suns Baseball Club Baseball Grounds of Jacksonville 301 A. Philip Randolph Blvd. Jacksonville, FL 32202

Dear Mr. Goodman:

I am a current employee in with the Florida State University Athletics ticket office and a Ph.D. student studying in the area of Sport Management. I am writing to gain support for my dissertation study, which focuses on the areas of consumer behavior and marketing. More specifically, I am examining issues related to baseball spectators’ perceptions of value, service quality, and satisfaction.

In order to acquire the necessary data for this study, I am seeking permission to survey spectators at the Baseball Grounds of Jacksonville on Monday, May 21. The survey will be relatively short in length, easy to complete, and will not disturb fans while the game is in progress. I am currently conducting a similar study with the Seminoles baseball team. I have attached a copy of the survey that I am using with the Seminoles. The survey used to for Suns’ spectators will be similar.

The specific details of the study are as follows:

Purpose: My study involves a determination of sport consumer perceptions of the service experience at sporting events. For this study, I am interested in examining baseball spectators’ perceptions of value and service quality and their level of satisfaction with their experience at Suns’ games. I will also collect information about consumption habits (including amount of money spent at Suns’ games, attendance frequencies, media consumption habits, and merchandise consumption) and future attendance intentions. Additionally, various demographic classification indicators will be collected, including: age, sex, education level, household income level, marital status and ethnicity.

Dates: I am interested in distributing questionnaires on May 21, 2007 prior to the Suns final game versus the Carolina Mudcats.

Data Collection Procedure: The procedure for collecting the data will be similar to procedures that I have used to collect data for other teams including the Florida State Seminoles, Montreal Expos, Montreal Canadiens, Montreal Alouettes, and Tallahassee Titans. Through discussion with your organization prior to the event, six to eight areas of the Baseball Grounds of Jacksonville will be targeted for survey

DEPARTMENT OF SPORT MANAGEMENT, RECREATION MANAGEME NT, AND PHYSICAL EDUCATION 200 TULLY GYM · TALLAHASSEE FLORIDA · 32306-4280 PHONE: (850) 644-4814 00 · FAX: (850) 644-0975

218 distribution. These areas will be diverse in location so as to ensure that a cross-sample of individuals is selected to participate. Surveys and golf pencils will be handed out to spectators in their seats starting 45 minutes prior to the start of the game and will be collected before the game starts. Included with the survey will be an introductory letter to introduce the purpose of the study and to provide direction for its completion. In total, 500 surveys will be distributed. The survey should take respondents between 10 and 15 minutes to complete.

Miscellaneous: All costs associated with printing, copying and travel are to be assumed by the researcher. Requested of the organization are the following: (1) credentialing / ticketing for team members to gain entrance to the Baseball Grounds of Jacksonville; (2) One parking pass for research team members as deemed appropriate by the Suns

Follow-up: My intention is to have the results of the study ready for distribution to your offices by the middle of July.

I hope that you feel a study such as this is both worthwhile and helpful towards the development of a better understanding of the perceptions and preferences of Suns’ spectators. I am hoping that you will assist in the completion of my study by allowing me access to survey Suns’ spectators at the Baseball Grounds of Jacksonville. Rest assured that at the completion of this study I would provide the Suns with all of the results.

Please inform me if this proposal meets your approval. I will follow up this letter with a telephone call in the next week. Should you have any questions regarding any aspect of this study, please do not hesitate to call me at 850-284-8168, or by email at [email protected].

Thank you very much for your assistance.

Sincerely,

Dan Sweeney Ph.D. Candidate

219

APPENDIX B

Human Subjects Committee Approval

220 221

APPENDIX C

Florida State Seminoles Baseball Questionnaire

222

Hello,

We are Sports Marketing researchers from Florida State University. We are working in cooperation with Florida State Athletics to gain a better understanding of how fans evaluate their experiences at a Florida State baseball game.

Please take a few minutes and complete our survey. Participation is voluntary, and all results are anonymous and confidential to the extent allowed by the law. The survey should take about ten minutes to complete.

If you agree to participate, please answer each question to the best of your knowledge. You do not have to respond to any questions that you are not comfortable with. Sincere and honest responses to questions are greatly appreciated. Completion of the questionnaire is implied consent to use the data you have provided.

You will be asked to evaluate the experience using a series of scales and then to provide some brief background information. For each question below, please select the answer that best reflects your opinion by marking the appropriate circle or filling in the appropriate response.

You must be at least 18 years of age to participate. The data will be stored under lock and key on file on campus until one year after the study has been completed.

If you have any questions, please contact Daniel Sweeney at [email protected], Dr. Jeffrey James at [email protected], or The Florida State University IRB at 850.644.8633 located at the Office of Research, Innovation Park, 100 Sliger Building, Tallahassee, FL, 32306-2811.

Thank you in advance for your participation.

Sincerely,

Daniel Sweeney

223

Please rate the extent to which you DISAGREE or AGREE with each of the following items by circling the Disagree Neutral Agree appropriate number in the scale beside each statement.

1. It just wouldn‘t be a Seminoles‘ game if I didn‘t party. 1 2 3 4 5 6 7 2. There is something special about being in the crowd at Dick Howser Stadium. 1 2 3 4 5 6 7 3. Watching Seminoles‘ games is a very intense experience for me. 1 2 3 4 5 6 7 4. Seminoles‘ games provide me with a distraction from my everyday activities. 1 2 3 4 5 6 7 5. I value the special events that are organized by the team. 1 2 3 4 5 6 7

6. The Seminoles‘ other fans consistently leave me with a good impression of 1 2 3 4 5 6 7 service. 7. I like Seminoles‘ games because of the natural elegance of the game of baseball. 1 2 3 4 5 6 7 8. I enjoy spending time with my family at Seminoles‘ games. 1 2 3 4 5 6 7 9. You can count on the ballpark employees to be friendly. 1 2 3 4 5 6 7 10. The price of Seminoles‘ games is high compared to their competitors. 1 2 3 4 5 6 7

11. At Seminoles‘ baseball games, you can rely on there being a good atmosphere. 1 2 3 4 5 6 7 12. I love the feeling of being surrounded by all of the fans. 1 2 3 4 5 6 7 13. I really get into the game when I watch Seminoles‘ games. 1 2 3 4 5 6 7 14. I enjoy Seminoles‘ games because they provide an opportunity to be with my friends. 1 2 3 4 5 6 7 15. Seminoles‘ games provide me an opportunity to party. 1 2 3 4 5 6 7

16. Seminoles‘ games give me a great opportunity to socialize with other people. 1 2 3 4 5 6 7 17. It takes minimal time to get the information I need about Seminoles‘ games. 1 2 3 4 5 6 7 18. Seminoles‘ games allow me to increase my knowledge of baseball. 1 2 3 4 5 6 7 19. I can count on the event staff taking actions to address my needs. 1 2 3 4 5 6 7 20. Overall, I am very satisfied with the services that I receive from Seminoles baseball. 1 2 3 4 5 6 7

21. Seminoles‘ games provide me with a great opportunity to entertain my clients. 1 2 3 4 5 6 7 22. I like the uncertainty of a close game. 1 2 3 4 5 6 7 23. I concentrate very hard on the action on the field. 1 2 3 4 5 6 7 24. I like the gracefulness associated with the game of baseball. 1 2 3 4 5 6 7 25. Dick Howser Stadium‘s layout never fails to impress me. 1 2 3 4 5 6 7

26. The other spectators do not affect the staff‘s ability to provide me with good service. 1 2 3 4 5 6 7 27. You can count on the ballpark employees knowing their jobs. 1 2 3 4 5 6 7 28. The special activities going on before games are important to me. 1 2 3 4 5 6 7 29. Waiting time for service at Seminoles‘ games is predictable. 1 2 3 4 5 6 7 30. There is a party atmosphere at Seminoles‘ games. 1 2 3 4 5 6 7

224

Please rate the extent to which you DISAGREE or AGREE with each of the following items by circling the appropriate number in the scale beside each statement. Disagree Neutral Agree 31. Seminoles‘ games enable me to increase my understanding of baseball 1 2 3 4 5 6 7 strategy. 32. I feed off of the excitement of the crowd at Seminoles‘ games. 1 2 3 4 5 6 7 33. I feel as much a part of the game as the players. 1 2 3 4 5 6 7 34. I am consistently pleased with the service at Seminoles‘ games. 1 2 3 4 5 6 7 35. The price of Seminoles‘ games is low. 1 2 3 4 5 6 7

36. The ambience at Seminoles‘ baseball is what I am looking for at a game. 1 2 3 4 5 6 7 37. The staff tries to keep my waiting time for service to a minimum. 1 2 3 4 5 6 7 38. I like Seminoles‘ games where the outcome is uncertain. 1 2 3 4 5 6 7 39. The attitude of the ballpark staff demonstrates their willingness to help me. 1 2 3 4 5 6 7 40. Overall, I am satisfied with my experience at Seminoles‘ baseball games. 1 2 3 4 5 6 7

41. Seminoles‘ games give me the chance to socialize with people from my work. 1 2 3 4 5 6 7 42. I like Seminoles‘ baseball because they have the service that I want. 1 2 3 4 5 6 7 43. The action on the field is most important to me. 1 2 3 4 5 6 7 44. Seminoles‘ games provide me with an escape from my daily life for a while. 1 2 3 4 5 6 7 45. I drink alcohol at the game, which is a big part of watching baseball games. 1 2 3 4 5 6 7

46. Seminoles‘ games allow me to learn about the technical aspects of baseball. 1 2 3 4 5 6 7 47. It is easy to get the information I need about Seminoles‘ games. 1 2 3 4 5 6 7 48. I enjoy Seminoles‘ games because they are a good family activity. 1 2 3 4 5 6 7 49. The ballpark employees respond quickly to my needs. 1 2 3 4 5 6 7 50. The layout of Dick Howser Stadium serves my purposes. 1 2 3 4 5 6 7

51. Seminoles‘ baseball games are expensive. 1 2 3 4 5 6 7 52. The excitement among the fans at Seminoles‘ games is exhilarating. 1 2 3 4 5 6 7 53. When I am at a game, nothing else matters but the game. 1 2 3 4 5 6 7 54. Having a chance to see friends is one thing I enjoy about Seminoles‘ games. 1 2 3 4 5 6 7 55. The special promotions that are a part of Seminoles‘ games are meaningful to me. 1 2 3 4 5 6 7

56. I like to talk to other people sitting near me during Seminoles‘ games. 1 2 3 4 5 6 7 57. The ballpark staff is able to answer my questions quickly. 1 2 3 4 5 6 7 58. I prefer watching a close game rather than a one-sided game. 1 2 3 4 5 6 7 59. It is easy to contact the Seminoles‘ when I need to. 1 2 3 4 5 6 7 60. I like that people can get a little drunk if they choose to at the Seminoles‘ games. 1 2 3 4 5 6 7

225

Please rate the extent to which you DISAGREE or AGREE with each of the following items by circling the appropriate number in the scale beside each statement. Disagree Neutral Agree

61. Seminoles‘ games give me the opportunity to entertain potential clients. 1 2 3 4 5 6 7 62. The ballpark staff understands that waiting time for service is important to me. 1 2 3 4 5 6 7 63. My focus is on the game, and not the other activities at the stadium. 1 2 3 4 5 6 7 64. The event staff knows the kind of service its customers are looking for. 1 2 3 4 5 6 7 65. Seminoles‘ games are reasonably priced. 1 2 3 4 5 6 7

66. The baseball staff understands that the atmosphere is important to me. 1 2 3 4 5 6 7 67. Seminoles‘ baseball games provide me the opportunity to do something I haven‘t done before. 1 2 3 4 5 6 7 68. I am able to get to Dick Howser Stadium quickly for Seminoles‘ baseball games. 1 2 3 4 5 6 7 69. The attitude of the ballpark employees shows me they understand my needs. 1 2 3 4 5 6 7 70. I truly enjoy myself at Seminoles‘ baseball games. 1 2 3 4 5 6 7

71. A close game involving the Seminoles is more enjoyable than a blowout. 1 2 3 4 5 6 7 72. I like the beauty and grace of sports. 1 2 3 4 5 6 7 73. The game is the most important thing at the stadium. 1 2 3 4 5 6 7 74. Being at Seminoles‘ games gives me a chance to bond with my friends. 1 2 3 4 5 6 7 75. The Seminoles‘ baseball experience enables people to drink heavily. 1 2 3 4 5 6 7

76. Interacting with other fans is a very important part of being at Seminoles‘ games. 1 2 3 4 5 6 7 77. The event staff understands that I rely on their knowledge to meet my needs. 1 2 3 4 5 6 7 78. I am interested in experiencing new things. 1 2 3 4 5 6 7 79. The behavior of the ballpark staff indicates to me that understand my needs. 1 2 3 4 5 6 7 80. The special activities going on during the game are important to me. 1 2 3 4 5 6 7

81. The Seminoles understand that the design of their facility is important to me. 1 2 3 4 5 6 7 82. When I leave Seminoles‘ games, I usually feel like I had a good experience. 1 2 3 4 5 6 7 83. Partying at Seminoles‘ games is more interesting than watching the games. 1 2 3 4 5 6 7 84. Seminoles‘ games allow me to get away from the tension in my life. 1 2 3 4 5 6 7 85. I believe that the Seminoles‘ try to give me a good experience. 1 2 3 4 5 6 7

86. The employees at the ballpark understand that the other fans affect my perceptions of service. 1 2 3 4 5 6 7 87. The athletics department makes it easy for me to get tickets to Seminoles‘ baseball games. 1 2 3 4 5 6 7 88. Seminoles‘ games give me a chance to bond with my family. 1 2 3 4 5 6 7 89. Seminoles‘ games give me the chance to experience something different. 1 2 3 4 5 6 7 90. The price of Seminoles‘ games is high. 1 2 3 4 5 6 7 91. The event staff knows the type of experience its customers want. 1 2 3 4 5 6 7

226

For each item below please circle the number that best describes your behavior. Disagree Neutral Agree

92. I buy Seminoles-related merchandise. 1 2 3 4 5 6 7 93. I use the Internet to get information about the Seminoles Baseball team. 1 2 3 4 5 6 7 94. I watch the Seminoles play baseball on television. 1 2 3 4 5 6 7 95. I read newspaper articles/editorials about the team. 1 2 3 4 5 6 7 96. I wear clothing that is related to the Seminoles Baseball team. 1 2 3 4 5 6 7 97. How many Seminoles games have you attended at Dick Howser Stadium THIS season? ______98. How many Seminoles games do you think you will attend at Dick Howser Stadium NEXT season? ______99. How much money do you spend at a Seminoles Baseball game (excluding tickets)? $______per game

Please tell us a little about yourself by checking or writing the appropriate response to the items below. All information is confidential and will remain anonymous.

Gender: ___ Female ___ Male

Age: ______

Marital Status: ___ Married ___ Single ___ Divorced ___ Widowed ____ Other

Household Income: ___ less than $20,000 ___ $20,000 - $39,999 ___ $40,000 - $59,999

___ $60,000 - $79,999 ___ $80,000 - $99,999 ___ $100,000 +

Ethnicity: ___Black/African American (non-Hispanic) ___ Native American ___ Asian or Pacific Islander

___ White/Caucasian (non-Hispanic) ___ Latina/Latino ___ Other ______

Highest level of education you have completed:

___ High School ___ Professional / Trade School ___ Junior College

___Undergraduate Studies ___ Masters Studies ___ Doctoral Studies

Are you a Season Ticket Holder? ___ Yes ___ No

Thank you for taking the time to complete this questionnaire.

227

APPENDIX D

Item Codes for Pilot Study Questionnaire

228

Item Dimensions and Items for Pilot Study # Amusement I value the special events that are organized by the team. 5 The special activities going on before games are important to me. 28

The special promotions that are a part of the team name games are meaningful to me. 55 The special activities going on during the game are important to me. 80

Partying It just wouldn't be a team name game if I didn't party 1 There is a party atmosphere at team name games. 30 Team name games provide me an opportunity to party. 15 I drink alcohol at the game, which is a big part of watching baseball games. 45 I like that people can get a little drunk if they choose to at team name games. 60 The team name baseball experience enables people to drink heavily. 75 Partying at team name games is more interesting than watching the games. 83

Crowd Experience There is something special about being in a crowd at name of stadium. 2 I love the feeling of being surrounded by all of the fans. 12 I feed off of the excitement of the crowd at team name games. 32 The excitement among the fans at team name games is exhilarating. 52

Game Intensity/Immersion Watching team name games is a very intense experience for me 3 I really get into the game when I watch team name games. 13 I concentrate very hard on the action on the field. 23 I feel as much a part of the game as the players. 33 The action on the field is most important to me. 43 When I am at the game, nothing else matters but the game. 53 My focus is on the game, and not the other activities at the stadium. 63 The game is the most important thing at the stadium. 73

Escape Team name games provide me with a distraction my everyday activities. 4 Team name games provide me with a distraction from my daily life for a while. 44 Team name games allow me to get away from the tension in my life. 84

Aesthetics I like team name games because of the natural elegance of the game of sport. 7 I like the gracefulness associated with the game of sport. 24 I like the beauty and grace of sports. 72

229

Item Dimensions and Items for Pilot Study (continued) # Drama I like the uncertainty of a close game. 22 I like team name games where the outcome is uncertain. 38 A close game involving team name is more enjoyable than a blowout. 71 I prefer watching a close game rather than a one-sided game. 58

Social Value Family I enjoy spending time with my family at team name games. 8 I enjoy team name games because they are a good family activity. 48 Team name games give me a chance to bond with my family. 88

Friends I enjoy team name games because they provide an opportunity to be with my friends 14 Having a chance to see friends is one thing I enjoy about team name games. 54 Being at team name games gives me a chance to bond with my friends. 74

Non-Acquaintances Team name games give me a great opportunity to socialize with other people. 16 I like to talk to other people sitting near me during team name games. 56 Interacting with other fans is a very important part of being at team name games. 76

Business Opportunities Team name games provide me with a great opportunity to entertain my clients. 21 Team name games give me a chance to socialize with people from my work. 41 Team name games give me the opportunity to entertain potential clients. 61

Service Quality Interaction Quality Attitude You can count on the ballpark employees to be friendly. 9 The attitude of the ballpark staff demonstrates their willingness to help me. 39 The attitude of the ballpark employees shows me that they understand my needs. 69

Behavior I can count on the event staff taking actions to address my needs. 19 The ballpark employees respond quickly to my needs 49 The behavior of the event staff indicates to me that they understand my needs. 79

Expertise You can count on the ballpark employees knowing their jobs. 27 The ballpark staff is able to answer my questions quickly. 57 The event staff understands that I rely on their knowledge to meet my needs. 77

230

Item Dimensions and Items for Pilot Study (continued) # Service Environment Quality Ambient Conditions At team name‘s games, you can rely on there being a good atmosphere. 11 The ambience at Team name‘s games is what I am looking for at a game. 36 The baseball staff understands that the atmosphere is important to me. 66

Design Factors The team name‘s stadium/arena layout never fails to impress me. 25 The layout of stadium name serves my purposes. 50 The team name understands that the design of its facility is important to me. 81

Social Factors The team name‘s other fans consistently leave me with a good impression of service. 6 The other spectators do not affect the staff‘s ability to provide me with good service. 26 The employees at the ballpark understand that the other fans affect my perceptions of service. 86

Outcome Quality Waiting Time Waiting time for service at team name games is predictable. 29 The staff tries to keep my waiting time for service to a minimum. 37 The ballpark staff understands that waiting time is important to me. 62

Tangibles I am consistently pleased with the at team name games. 34 I like team name sport because they have the service I want. 42 The event staff knows the kind of service its customers are looking for. 64

Valence When I leave team name games, I usually feel like I had a good experience. 82 I believe that team name tries to give me a good experience 85 The event staff knows the type of experience its customers want. 91

Perceived Price Monetary The price team name games is high compared to their competitors. 10 The price of team name team name games is low (reverse coded) 35 Team name games are expensive. 51 Team name games are reasonably priced. 65 The price of team name games is high. 90

231

Item Dimensions and Items for Pilot Study (continued) # Non-Monetary It takes minimal time to get the information I need about team name games. 17 It is easy to get the information I need about team name games. 47 It is easy to contact the team name when I need to. 59 I am able to get to stadium name quickly for team name games. 68 The athletics department makes it easy for me to get tickets to team name games. 87

Epistemic Value Knowledge Team name games allow me to increase my knowledge of sport. 18 Team name games enable me to increase my understanding of sport strategy. 31 Team name games allow me to learn about the technical aspects of sport. 46 Novelty Team name games provide me the opportunity to do something I haven‘t done before. 67 I am interested in experiencing new things. 78 Team name games give the chance to experience something different. 89

Satisfaction

Overall, I am very satisfied with the services that I receive from team name. 20 Overall, I am satisfied with my experience at team name games. 40 I truly enjoy myself at team name games. 70

232

APPENDIX E

Jacksonville Suns Questionnaire

233

Hello,

We are Sports Marketing researchers from Florida State University. We are working in cooperation with the Jacksonville Suns to gain a better understanding of how fans evaluate their experiences at a Suns baseball game.

You must be at least 18 years of age to participate. The data will be stored under lock and key on file on campus until one year after the study has been completed.

Thank you in advance for your participation.

Sincerely,

Daniel Sweeney Florida State University

234

Please rate the extent to which you DISAGREE or AGREE

with each of the following items by circling the Disagree Neutral Agree appropriate number in the scale beside each statement.

1. Interacting with other fans is a very important part of being at Suns‘ games. 1 2 3 4 5 6 7 2. Overall, I truly enjoy the time I spend at Suns‘ baseball games. 1 2 3 4 5 6 7 3. Watching Suns‘ games is a very intense experience for me. 1 2 3 4 5 6 7 4. Suns‘ games provide me with a distraction from my everyday activities. 1 2 3 4 5 6 7 5. I value the special events that are organized by the team. 1 2 3 4 5 6 7

6. The Jacksonville Suns know the type of experience its customers want. 1 2 3 4 5 6 7 7. I like Suns‘ games because of the natural elegance of the game of baseball. 1 2 3 4 5 6 7 8. I enjoy spending time with my family at Suns‘ games. 1 2 3 4 5 6 7 9. The event staff understands that I rely on their knowledge to meet my needs 1 2 3 4 5 6 7 10. The price of Suns‘ games is high. 1 2 3 4 5 6 7

11. The special activities going on during the game are important to me. 1 2 3 4 5 6 7 12. I love the feeling of being surrounded by all of the fans. 1 2 3 4 5 6 7 13. I really get into the game when I watch Suns‘ games. 1 2 3 4 5 6 7 14. I enjoy Suns‘ games because they provide an opportunity to be with my friends 1 2 3 4 5 6 7 15. Suns‘ games provide me an opportunity to party. 1 2 3 4 5 6 7

16. Suns‘ games give me a great opportunity to socialize with other people. 1 2 3 4 5 6 7 17. It takes minimal time to get the information I need about Suns‘ games. 1 2 3 4 5 6 7 18. Suns‘ games allow me to increase my knowledge of baseball. 1 2 3 4 5 6 7 19. I can count on the event staff taking actions to address my needs. 1 2 3 4 5 6 7 20. Overall, I am very satisfied with the services I receive at Suns‘ games. 1 2 3 4 5 6 7

21. Suns‘ games provide me with a great opportunity to entertain my clients. 1 2 3 4 5 6 7 22. I like the uncertainty of a close game. 1 2 3 4 5 6 7 23. I concentrate very hard on the action on the field. 1 2 3 4 5 6 7 24. I like the gracefulness associated with the game of baseball. 1 2 3 4 5 6 7 25. The layout of the Baseball Grounds of Jacksonville never fails to impress me. 1 2 3 4 5 6 7 26. The other spectators do not affect the team‘s ability to provide me with good service. 1 2 3 4 5 6 7

27. You can count on the Suns‘ employees knowing their jobs. 1 2 3 4 5 6 7 28. The special activities going on before games are important to me. 1 2 3 4 5 6 7 29. Suns‘ games give me a chance to bond with my family. 1 2 3 4 5 6 7 30. It just wouldn‘t be a Suns‘ game if I didn‘t party. 1 2 3 4 5 6 7 31. Suns‘ games enable me to increase my understanding of baseball strategy. 1 2 3 4 5 6 7 32. I believe that the Suns‘ try to give me a good experience. 1 2 3 4 5 6 7

235

Please rate the extent to which you DISAGREE or AGREE

with each of the following items by circling the Agree appropriate number in the scale beside each statement. Disagree Neutral

33. The Suns‘ organization makes it easy for me to get tickets to Suns‘ baseball 1 2 3 4 5 6 7 games. 34. I am consistently pleased with the service at Suns‘ games. 1 2 3 4 5 6 7 35. The price of Suns‘ games is low. 1 2 3 4 5 6 7 36. The behavior of the event staff indicates to me that they understand my needs. 1 2 3 4 5 6 7 37. The staff tries to keep my waiting time for service to a minimum. 1 2 3 4 5 6 7 38. I like Suns‘ games where the outcome is uncertain. 1 2 3 4 5 6 7

39. The attitude of the ballpark staff demonstrates their willingness to help me. 1 2 3 4 5 6 7 40. Overall, I am satisfied with my experience at Suns‘ baseball games. 1 2 3 4 5 6 7 41. Suns‘ games give me the chance to socialize with people from my work. 1 2 3 4 5 6 7 42. I like Suns‘ baseball because they have the service that I want. 1 2 3 4 5 6 7 43. The action on the field is most important to me. 1 2 3 4 5 6 7 44. Suns‘ games provide me with an escape from my daily life for a while. 1 2 3 4 5 6 7

45. I drink alcohol at the game, which is a big part of watching baseball games. 1 2 3 4 5 6 7 46. Suns‘ games allow me to learn about the technical aspects of baseball. 1 2 3 4 5 6 7 47. It is easy to get the information I need about Suns‘ games. 1 2 3 4 5 6 7 48. I enjoy Suns‘ games because they are a good family activity. 1 2 3 4 5 6 7 49. The ballpark employees respond quickly to my needs. 1 2 3 4 5 6 7 50. The layout of the Baseball Grounds of Jacksonville serves my purposes. 1 2 3 4 5 6 7

51. Suns‘ baseball games are expensive. 1 2 3 4 5 6 7 52. Partying at Suns‘ games is more interesting than watching the games. 1 2 3 4 5 6 7 53. When I am at a game, nothing else matters but the game. 1 2 3 4 5 6 7 54. Having a chance to see friends is one thing I enjoy about Suns‘ games. 1 2 3 4 5 6 7 55. The special promotions that are a part of Suns‘ games are meaningful to me. 1 2 3 4 5 6 7

56. I like to talk to other people sitting near me during Suns‘ games. 1 2 3 4 5 6 7 57. The ballpark staff is able to answer my questions quickly. 1 2 3 4 5 6 7 58. I prefer watching a close game rather than a one-sided game. 1 2 3 4 5 6 7 59. Suns‘ games allow me to get away from the tension in my life. 1 2 3 4 5 6 7 60. I like that people can get a little drunk if they choose to at the Suns‘ games. 1 2 3 4 5 6 7

61. Suns‘ games give me the opportunity to entertain potential clients. 1 2 3 4 5 6 7 62. The ballpark staff understands that waiting time for service is important to me. 1 2 3 4 5 6 7 63. My focus is on the game, and not the other activities at the stadium. 1 2 3 4 5 6 7 64. The event staff knows the kind of service its customers are looking for. 1 2 3 4 5 6 7 65. Suns‘ games are reasonably priced. 1 2 3 4 5 6 7

236

Please rate the extent to which you DISAGREE or AGREE with each of the following items by circling the appropriate number in the scale beside each statement. Disagree Neutral Agree

66. The baseball staff understands that the atmosphere is important to me. 1 2 3 4 5 6 7 67. The Suns understand that the design of their facility is important to me. 1 2 3 4 5 6 7 68. When I leave Suns‘ games, I usually feel like I had a good experience. 1 2 3 4 5 6 7 69. The attitude of the ballpark employees shows me they understand my needs. 1 2 3 4 5 6 7 70. The Suns‘ baseball experience enables people to drink heavily. 1 2 3 4 5 6 7

71. A close game involving the Suns is more enjoyable than a blowout. 1 2 3 4 5 6 7 72. I like the beauty and grace of sports. 1 2 3 4 5 6 7 73. The game is the most important thing at the stadium. 1 2 3 4 5 6 7 74. Being at Suns‘ games gives me a chance to bond with my friends. 1 2 3 4 5 6 7 75. You can count on the ballpark employees to be friendly. 1 2 3 4 5 6 7

Please circle the number that best describes your behavior. Disagree Neutral Agree

76. I buy Suns-related merchandise. 1 2 3 4 5 6 7 77. I use the Internet to get information about the Suns Baseball team. 1 2 3 4 5 6 7 78. I watch the Suns play baseball on television. 1 2 3 4 5 6 7 79. I read newspaper articles/editorials about the team. 1 2 3 4 5 6 7 80. Including this game, how many Suns games have you attended this season? ______81. After this game, how many more Suns games do you think you will attend this season? ______82. About how much money do you spend at a Suns Baseball game (excluding tickets)? $______per game

Please tell us a little about yourself by checking or writing the appropriate response to the items below.

Gender: ___ Female ___ Male Age: ______Marital Status: ___ Married ___ Single ___ Divorced ___ Widowed ____ Other

Household Income: ___ less than $20,000 ___ $20,000 - $39,999 ___ $40,000 - $59,999

___ $60,000 - $79,999 ___ $80,000 - $99,999 ___ $100,000 +

Ethnicity: ___ Black/African American (non-Hispanic) ___ Native American ___ Asian or Pacific Islander

___ White/Caucasian (non-Hispanic) ___ Latina/Latino ___ Other ______

Highest level of educa tion completed : Are you a Season Ticket Holder?

___ High School ___ Professional / Trade School 75 ____ Yes ____ No

___ Junior College ___ Undergraduate Degree

Thank you for taking the time to complete this ___ Masters Degree ___ Doctorate Degree questionnaire!

237

APPENDIX F

Item Codes for Jacksonville Suns Questionnaire

238

Item Dimensions and Items for Main Study – Sample 1 # Entertainment Value Amusement I value the special events that are organized by the team. 5 The special activities going on before games are important to me. 28 The special promotions that are a part of the team name games are meaningful to me. 55 The special activities going on during the game are important to me. 11

Partying It just wouldn't be a team name game if I didn't party 30 There is a party atmosphere at team name games. --- Team name games provide me an opportunity to party. 15 I drink alcohol at the game, which is a big part of watching baseball games. 45 I like that people can get a little drunk if they choose to at team name games. 60 The team name baseball experience enables people to drink heavily. 70 Partying at team name games is more interesting than watching the games. 52

Experience Intensity I love the feeling of being surrounded by all of the fans. 12 Watching team name games is a very intense experience for me 3 I really get into the game when I watch team name games. 13

Game Immersion I concentrate very hard on the action on the field. 23 The action on the field is most important to me. 43 When I am at the game, nothing else matters but the game. 53 My focus is on the game, and not the other activities at the stadium. 63 The game is the most important thing at the stadium. 73

Aesthetics I like team name games because of the natural elegance of the game of sport. 7 I like the gracefulness associated with the game of sport. 24 I like the beauty and grace of sports. 72

239

Item Dimensions and Items for Main Study – Sample 1 (continued) # Drama I like the uncertainty of a close game. 22 I like team name games where the outcome is uncertain. 38 A close game involving team name is more enjoyable than a blowout. 71 I prefer watching a close game rather than a one-sided game. 58

Social Value Non Family I enjoy spending time with my family at team name games. 8 I enjoy team name games because they are a good family activity. 48 Team name games give me a chance to bond with my family. 29 Team name games give me a great opportunity to socialize with other people. 16 I like to talk to other people sitting near me during team name games. 56 Interacting with other fans is a very important part of being at team name games. 1

Perceived Price Monetary The price of team name team name games is low (reverse coded) 35 Team name games are expensive. 51 Team name games are reasonably priced. 65 The price of team name games is high. 10

Non-Monetary It takes minimal time to get the information I need about team name games. 17 It is easy to get the information I need about team name games. 47 The athletics department makes it easy for me to get tickets to team name games. 33

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Item Dimensions and Items for Main Study – Sample 1 (continued) # Service Quality Interaction Quality You can count on the ballpark employees to be friendly. 75 The attitude of the ballpark staff demonstrates their willingness to help me. 39 The attitude of the ballpark employees shows me that they understand my needs. 69 I can count on the event staff taking actions to address my needs. 19 The ballpark employees respond quickly to my needs 49 The behavior of the event staff indicates to me that they understand my needs. 36 You can count on the ballpark employees knowing their jobs. 27 The ballpark staff is able to answer my questions quickly. 57 The event staff understands that I rely on their knowledge to meet my needs. 9 The baseball staff understands that the atmosphere is important to me. 66 The other spectators do not affect the staff‘s ability to provide me with good service. 26 The staff tries to keep my waiting time for service to a minimum. 37 The ballpark staff understands that waiting time is important to me. 62 I am consistently pleased with the service at team name games. 34 I like team name sport because they have the service I want. 42 The event staff knows the kind of service its customers are looking for. 64 The event staff knows the type of experience its customers want. 6

Outcome Quality The team name‘s stadium/arena layout never fails to impress me. 25 The layout of stadium name serves my purposes. 50 The team name understands that the design of its facility is important to me. 67 When I leave team name games, I usually feel like I had a good experience. 68 I believe that team name tries to give me a good experience 32

Satisfaction Overall, I am very satisfied with the services I receive at team name games. 20 Overall, I am satisfied with my experience at team name baseball games. 40 Overall, I truly enjoy the time I spend at team name baseball games. 2

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APPENDIX G

Student Sample Questionnaire

242

Sport Consumer Survey

I am a Doctoral student in the Department of Sport Management, Recreation Management, and Physical Education at Florida State University. I am conducting a research study to gain a better understanding of how fans evaluate their experiences at sporting events.

Please take a few minutes and complete this survey. Participation is voluntary, and all results are anonymous and confidential to the extent allowed by the law. The survey should take about ten minutes to complete.

If you agree to participate, please answer each question to the best of your knowledge. You do not have to respond to any questions with which you are not comfortable. Sincere and honest responses to questions are greatly appreciated. Completion of the questionnaire is implied consent to use the data you have provided. You will be asked to evaluate your experiences using a series of scales and then to provide some brief background information. For each question below, please select the answer that best reflects your opinion by circling or writing the appropriate response in the space provided.

You must be at least 18 years of age to participate. The data will be stored under lock and key on file on campus until one year after the study has been completed.

Thank you in advance for your participation.

Sincerely,

Daniel Sweeney Ph.D. Candidate

Please answer the following questions related to your consumption of sporting events.

Question #1: What was the last professional or collegiate game you attended in person?

ANSWER: ______

Question #2: When was the event?

ANSWER: ______

Question #3: In what stadium, arena, or ballpark was the game played?

ANSWER: ______

Question #4: How many times have you attended games at this venue?

ANSWER: ______

- 243 -

Take a moment and think about your experiences at the last college/professional sporting event you listed on the previous page.

Now, please rate the extent to which you DISAGREE or AGREE with each of the following statements as they relate to the sporting event you are thinking about by circling the appropriate number in the scale. Disagree Agree 1. Interacting with other fans is a very important part of being at a game. 1 2 3 4 5 6 7 2. You can count on the venue employees to be friendly. 1 2 3 4 5 6 7 3. The staff understands that the atmosphere at a game is important to me. 1 2 3 4 5 6 7 4. The game provided me with a distraction from my everyday activities. 1 2 3 4 5 6 7 5. I value the special events that are organized by the team. 1 2 3 4 5 6 7 Disagree Agree 6. The team knows the type of experience its customers want. 1 2 3 4 5 6 7 7. I like a game because of the natural elegance in the game. 1 2 3 4 5 6 7 8. I enjoy spending time with my family at a game. 1 2 3 4 5 6 7 9. The event staff understands that I rely on their knowledge to meet my needs 1 2 3 4 5 6 7 10. When I left the game, I felt like I had a good experience. 1 2 3 4 5 6 7 Disagree Agree 11. The special activities going on during a game are important to me. 1 2 3 4 5 6 7 12. A close game is more enjoyable than a blowout. 1 2 3 4 5 6 7 13. The organization understands that the design of their facility is important to me. 1 2 3 4 5 6 7 14. I enjoy a game because it provides an opportunity to be with my friends 1 2 3 4 5 6 7 15. The game provided me an opportunity to party. 1 2 3 4 5 6 7 Disagree Agree 16. The game gave me a great opportunity to socialize with other people. 1 2 3 4 5 6 7 17. It takes minimal time to get the information I need about the games. 1 2 3 4 5 6 7 18. The game enabled me to increase my knowledge of the sport. 1 2 3 4 5 6 7 19. I can count on the venue staff taking actions to address my needs. 1 2 3 4 5 6 7 20. The attitude of the facility employees showed me they understood my needs. 1 2 3 4 5 6 7 Disagree Agree 21. The games provide me with a great opportunity to entertain my clients. 1 2 3 4 5 6 7 22. I like the uncertainty of a close game. 1 2 3 4 5 6 7 23. I concentrate very hard on the action on the field. 1 2 3 4 5 6 7 24. I like the gracefulness associated with the sport. 1 2 3 4 5 6 7 25. The layout of the venue never fails to impress me. 1 2 3 4 5 6 7 Disagree Agree 26. The other spectators do not affect the team‘s ability to provide me with good service. 1 2 3 4 5 6 7 27. You can count on the facility employees knowing their jobs. 1 2 3 4 5 6 7 28. The special activities going on before a game are important to me. 1 2 3 4 5 6 7 29. The game gave me a chance to bond with my family. 1 2 3 4 5 6 7 30. It just wouldn‘t be a game if I didn‘t party. 1 2 3 4 5 6 7 244

Thinking about your experiences at the game you identified, please rate the extent to which you DISAGREE or AGREE with each of the following statments as they relate to the sporting event you are thinking about by circling the appropriate number in the scale.

Disagree Agree 31. The game enabled me to increase my understanding of the strategy of the sport. 1 2 3 4 5 6 7 32. I believe that the organization tries to give me a good experience. 1 2 3 4 5 6 7 33. The organization makes it easy for me to get tickets to a game. 1 2 3 4 5 6 7 34. I am consistently pleased with the service at the games. 1 2 3 4 5 6 7 35. The price of the game was low. 1 2 3 4 5 6 7 Disagree Agree 36. The behavior of the event staff indicates to me that they understood my needs. 1 2 3 4 5 6 7 37. The facility staff tried to keep my waiting time for service to a minimum. 1 2 3 4 5 6 7 38. I like games where the outcome is uncertain. 1 2 3 4 5 6 7 39. The attitude of the venue staff demonstrates their willingness to help me. 1 2 3 4 5 6 7 40. Being at the game gave me a chance to bond with my friends. 1 2 3 4 5 6 7 Disagree Agree 41. The game gave me the chance to socialize with people from my work. 1 2 3 4 5 6 7 42. I like the beauty and grace of sports. 1 2 3 4 5 6 7 43. The action on the field is most important to me. 1 2 3 4 5 6 7 44. The game provided me with an escape from my daily life for a while. 1 2 3 4 5 6 7 45. I drank alcohol at the game, which is a big part of watching the games. 1 2 3 4 5 6 7 Disagree Agree 46. The game enabled me to learn about the technical aspects of the sport. 1 2 3 4 5 6 7 47. It is easy to get the information I need about a game. 1 2 3 4 5 6 7 48. I enjoy a game because it is a good family activity. 1 2 3 4 5 6 7 49. The venue employees responded quickly to my needs. 1 2 3 4 5 6 7 50. The layout of venue served my purposes. 1 2 3 4 5 6 7 Disagree Agree 51. A game is expensive. 1 2 3 4 5 6 7 52. Partying at a game is more interesting than watching a game. 1 2 3 4 5 6 7 53. When I am at a game, nothing else matters but the game. 1 2 3 4 5 6 7 54. Having a chance to see friends is one thing I enjoy about a game. 1 2 3 4 5 6 7 55. The special promotions that are a part of a game are meaningful to me. 1 2 3 4 5 6 7 Disagree Agree 56. I like to talk to other people sitting near me during a game. 1 2 3 4 5 6 7 57. The venue staff is able to answer my questions quickly. 1 2 3 4 5 6 7 58. The game was reasonably priced. 1 2 3 4 5 6 7 59. The game enabled me to get away from the tension in my life. 1 2 3 4 5 6 7 60. I like that people can get a little drunk if they choose to at a game. 1 2 3 4 5 6 7

245

Disagree Agree 61. The game gave me an opportunity to entertain potential clients. 1 2 3 4 5 6 7 62. The venue staff understood that waiting time for service is important to me. 1 2 3 4 5 6 7 63. My focus is on the game, and not the other activities at the stadium. 1 2 3 4 5 6 7 64. The event staff knows the kind of service its customers are looking for. 1 2 3 4 5 6 7

Please tell us a little about yourself by checking or writing the appropriate response:

Gender: ___ Female ___ Male

Age: ______

Ethnicity: ___ Black/African American ___ Native American ___ Asian or Pacific Islander

___ White/Caucasian ___ Latina/Latino ___ Other ______

Classification: ___ Freshman ___ Sophomore ___ Junior ___ Senior ___ Graduate

Thank you for taking the time to complete this questionnaire!

246

APPENDIX H

Student Sample Questionnaire Item Codes

247

Item Dimensions and Items for Main Study – Validation Sample # Entertainment Value Amusement I value the special events that are organized by the team. 5 The special activities going on before a game are important to me. 28 The special promotions that are a part of a game are meaningful to me. 55 The special activities going on during a game are important to me. 11

Partying It just wouldn‘t be a game if I didn‘t party. 30 The game provided me an opportunity to party. 15 I drank alcohol at the game, which is a big part of watching the games. 45 I like that people can get a little drunk if they choose to at a game. 60 Partying at a game is more interesting than watching a game. 52

Game Immersion I concentrate very hard on the action on the field. 23 The action on the field is most important to me. 43 When I am at a game, nothing else matters but the game. 53 My focus is on the game, and not the other activities at the stadium. 63

Escape The game provided me with a distraction from my everyday activities. 4 The game provided me with an escape from my daily life for a while. 44 The game enabled me to get away from the tension in my life. 59

Aesthetics I like a game because of the natural elegance in the game. 7 I like the gracefulness associated with the sport. 24 I like the beauty and grace of sports. 42

Drama I like the uncertainty of a close game. 22 I like games where the outcome is uncertain. 38 A close game is more enjoyable than a blowout. 12

248

Item Dimensions and Items for Main Study – Validation Sample (continued) # Social Value Family I enjoy spending time with my family at a game. 8 I enjoy a game because it is a good family activity. 48 The game gave me a chance to bond with my family. 29

Non-Family I enjoy a game because it provides an opportunity to be with my friends 14 Having a chance to see friends is one thing I enjoy about a game. 54 Being at the game gave me a chance to bond with my friends. 40 The game gave me a great opportunity to socialize with other people. 16 I like to talk to other people sitting near me during a game. 56 Interacting with other fans is a very important part of being at a game. 1

Business Opportunities The games provide me with a great opportunity to entertain my clients. 21 The game gave me the chance to socialize with people from my work. 41 The game gave me an opportunity to entertain potential clients. 61

Service Quality Interaction Quality You can count on the venue employees to be friendly. 2 The attitude of the venue staff demonstrates their willingness to help me. 39 The attitude of the facility employees showed me they understood my needs. 20 I can count on the venue staff taking actions to address my needs. 19 The venue employees responded quickly to my needs. 49 The behavior of the event staff indicates to me that they understood my needs. 36 You can count on the facility employees knowing their jobs. 27 The venue staff is able to answer my questions quickly. 57 The event staff understands that I rely on their knowledge to meet my needs. 9 The staff understands that the atmosphere at a game is important to me. 3 The other spectators do not affect the team‘s ability to provide me with good service. 26 The facility staff tried to keep my waiting time for service to a minimum. 37 The venue staff understood that waiting time for service is important to me. 62 I am consistently pleased with the service at the games. 34 The event staff knows the kind of service its customers are looking for. 64 The team knows the type of experience its customers want. 6

249

Item Dimensions and Items for Main Study – Validation Sample (continued) # Outcome Quality The layout of the venue never fails to impress me. 25 The layout of venue served my purposes. 50 The organization understands that the design of their facility is important to me. 13 When I left the game, I felt like I had a good experience. 10 I believe that the organization tries to give me a good experience. 32

Perceived Price Monetary The price of the game was low. 35 A game is expensive. 51 The game was reasonably priced. 58

Non-Monetary It takes minimal time to get the information I need about the games. 17 It is easy to get the information I need about a game. 47 The organization makes it easy for me to get tickets to a game. 33

Epistemic Value The game enabled me to increase my knowledge of the sport. 18 The game enabled me to increase my understanding of the strategy of the sport. 31 The game enabled me to learn about the technical aspects of the sport. 46

250

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BIOGRAPHICAL SKETCH

Name: Daniel Robert Sweeney

Place of Birth: Montréal, Québec, Canada

Date of Birth: June 25, 1975

Education: Wagar High School, 1992 Côte-St-Luc, Québec, Canada

Diplôme D’Études Collégiales, 1994 Dawson College Montréal, Québec, Canada

Bachelor of Education – Kinesiology, 1999 McGill University Montréal, Québec, Canada

Master of Human Kinetics – Sport Management, 2003 University of Windsor Windsor, Ontario, Canada

PhD – Sport Management, 2008 The Florida State University Tallahassee, Florida, USA

Personal: Dan is married to Jamie Michelle Sweeney (nee Metz) of St. Louis, Missouri. The two met in 2002 while they were both living in the Washington, D.C. area. They were married on June 24, 2006 in New Orleans, Louisiana – one of their favorite cities.

Employment: Dan is currently employed as an Assistant Professor of Sport Management at the University of Arkansas at Little Rock.

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