Does sponsorship work in the same way across non-masculine societies and unsuccessful team seasons?

Mario Calderón Galdos

GRA 1900

Does sponsorship work in the

same way across non-masculine

societies and unsuccessful team

seasons?

-Case: Heineken and the UEFA Champions League

Hand-in date:

01.09.2011

Campus:

BI Norwegian Business School - Oslo

GRA 1900

MSc Thesis

Professor Erik L. Olson

Master of Science in Strategic Marketing Management

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Management Summary

The present research aims to examine the moderating effects of masculine societies and successful contexts on a global brand’s sponsorship effectiveness framework. Indeed, current research’s main objective is to prove that High-Masculine societies and successful seasons lead to more favorable context, in which the targeted audiences will depict significantly more lenient judgments towards event-sponsor fit, and ultimately more positive responses.

The study uses a panel data of 14.359 respondents, which was collected in a three-year span across seven European countries. Moreover, the questionnaire uses a prominent global brand and a world-class sport event to measure the effectiveness of the cross-contextual sponsorship framework. The data analysis consists of a three-stage approach, where Confirmatory Factor Analysis, Partial Least Squares and Multi-group Analysis are the statistical methods to be used.

The result of the research partly proved sponsorship effectiveness’ context sensitivity. Indeed, people showing higher sports involvement are expected to depict more lenient judgments towards event-sponsor fit and ultimately more positive responses towards the sponsoring brand (High-Masculine societies). Moreover, successful seasons seem to be a better context for sponsorship agreements to have a significantly more positive impact on audience responses. Another finding confirms that the audience’s profile matches the global brand’s targeted audience. Consequently, audiences depicting higher sports involvement are more likely to have heavier product class consumption in comparison to the lower ones. Hence, the results confirmed industries’ best practices, where sponsorship agreements are the means for global brands to reach the aimed target audience.

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Acknowledgments

“The longest journey starts with a single step”

It seems like yesterday when I started the Master’s journey. Although I was fully determined and confident about pursuing it, I have to admit that the road has been challenging, and nonetheless the most rewarding and compelling experience I have ever had.

The achievement of this would not have been possible without my family’s constant support and encouragement. Thus, I am eternally grateful to them for making my dreams of studying abroad possible. In addition, I thank my friends for making this experience even more enjoyable.

I also would like to thank the people whom collaborated with me at the academic level. First, I would like to thank Professor Erik L. Olson for his relevant and insightful advice. Second, I would like to thank PhD. student Merel Walraven for providing me access to the data and for her further feedback. Finally, I would like to thanks Dr. Liane Voerman for her constant support in my endeavors to pursue this double degree.

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Content

2.1SPORTS SPONSORSHIP NATURE AND ITS EFFECTIVENESS MEASUREMENT... 7

2.2MAIN PREDICTORS ON SPORTS SPONSORSHIP EFFECTIVENESS ... 8

2.3PRODUCT CLASS INVOLVEMENT AND BRAND PREFERENCE ... 9

2.4AUDIENCE RESPONSES TO SPORTS SPONSORSHIP ... 10

2.4.1COGNITIVE AND AFFECTIVE LEVELS ... 11

2.4.2CONATIVE LEVEL ... 12

2.5THE IMPACT OF MASCULINE SOCIETIES AND SUCCESSFUL SEASONS CONTEXTS ... 13

3.1UEFACHAMPIONS LEAGUE AND HEINEKEN SPONSORSHIP AGREEMENT ... 16

3.2DATA COLLECTION AND DATA CLEANING ... 17

3.3RESEARCH METHODS... 18

3.4QUESTIONNAIRE DESIGN ... 19

3.4.1.AUDIENCE CHARACTERISTICS ... 20

3.4.2.SPONSORSHIP PERCEPTIONS ... 20

3.4.3AUDIENCE RESPONSES ... 21

3.4.4MASCULINE SOCIETIES AND SUCCESSFUL SEASONS AS MODERATORS ... 22

3.4.4.1MASCULINE SOCIETIES ... 22

3.4.4.2SUCCESSFUL SEASONS ... 22

4. DATA ANALYSIS AND RESULTS ... 25

4.1.DESCRIPTIVE STATISTICS ... 25

4.1.1.DESCRIPTIVE STATISTICS OVERALL SAMPLE ... 25

4.1.2.DESCRIPTIVE STATISTICS FOR MASCULINE SOCIETIES AND SUCCESSFUL CONTEXTS ... 26

4.2.MEASUREMENT MODEL ... 28

4.2.STRUCTURAL MODEL FOR THE OVERALL SAMPLE ... 29

4.3.MULTI-GROUP ANALYSIS FOR MASCULINE SOCIETIES AND SUCCESSFUL CONTEXTS ... 30

4.3.1.DIFFERENCE BETWEEN MASCULINE SOCIETIES ... 31

4.3.2DIFFERENCES BETWEEN SUCCESS CONTEXTS ... 34

5. CONCLUSIONS AND IMPLICATIONS ... 37

5.1CONCLUSIONS AND DISCUSSION ... 37

5.1.1AGGREGATED AND SUBSAMPLE MODELS... 37

5.1.2CROSS-CONTEXTUAL SPONSORSHIP EFFECTIVENESS ... 39

5.2LIMITATIONS AND FURTHER RESEARCH ... 41

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List of Appendixes, Tables and Figures

Appendixes

Appendix 1 – SURVEY (HEINEKEN EXTRACT )……….…43

Appendix 2 - SUCCESS RATIO CALCUL ATION………... ... 48

Append ix 3 - DAT A COLLECTION – COUNTRY AND W AVE DIS TRIB UTION.49 Appendix 4 - EXP LORATORY FACTOR A NALYSIS – FIRST ST AGE …………..50

Appendix 5 - MEASUREMENT MODEL……… ………... 50

Appendix 6 – PRELIMINARY THESIS REPORT………. 60

Figures Figure 1 - PROPOSED RESEARCH MODEL...8

Figure 2 - RESULTS OF PLS ANALYSIS WITH PATH COEFFICIENTS – OVERALL MODEL...30

Figure 3 - RESULTS OF PLS ANALYSIS –LOW VS HIGH MASCULINE SOCIETIES...31

Figure 4 - RESULTS OF PLS- ANALYSIS – UNSUCCESSFUL VS SUCCESSFUL SEASONS...34

Tables Table 1 - DATA- COUNTRY AND WAVE...18

Table 2 - MASCULINITY INDEX AND SUCCESSFUL RATIOS...22

Table 3 - CONSTRUCTS AND MEASURES...24

Table 4 - PARTICIPANTS’ PROFILE...27

Table 5 - DESCRIPTIVE STATISTICS OF CONSTRUCTS...27

Table 6 - CONFIRMATORY FACTOR ANALYSIS RESULTS...28

Table 7 - HYPOTHESISED PATH COEFFICIENTS, R2 AND T-VALUES ACROSS LOW-MASCULINE AND HIGH-MASCULINE SEASONS...33

Table 8 - MULTI-GROUP COMPARISON - LOW-MASCULINE AND HIGH-MASCULINE SOCIETIES...33

Table 9 - HYPOTHESISED PATH COEFFICIENTS, R2 AND T-VALUES ACROSS SUCCESSFUL AND UNSUCCESSFUL SEASONS...36

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1. Introduction

1.1Field of Study

Sponsorships’ growth is leading other marketing activities (IGE, 2010), and therefore becoming a popular marketing vehicle (Cornwell, 2008). Sponsorships are defined as investments in causes or events to support either corporate (e.g. by enhancing corporate image) or marketing objectives (e.g. increase brand awareness) (Gardner and Shuman, 1987). The worldwide investment for sponsorships totaled $46.3 billion in 2010. Furthermore, the forecasts for the year 2011 are favorable; in fact, a 5.2% growth rate is estimated, representing a total investment of $48.7 billion (IGE, 2010). This scenario seems optimal for the current year, especially when markets have started to show signs of recovery after a remarkable downturn in the global economy.

Among the various sponsorship activities, sports sponsorships play the most important role in North America and probably in the entire world. Indeed, in North America these investments accounted for the 68% of the total expenditures within sponsorships in 2010 (IEG, 2010). Hence, it is undeniable that sports sponsorship has become a pivotal marketing activity. Unsurprisingly, its popularity is justified by its flexible nature, broad reach and high level of either brand or corporate exposure gained through the agreement (Kropp et al. 1999).

A greater concern for marketing expenditure activities’ accountability has arisen in boardrooms (Verhoef and Leeflang, 2009) . Thus, sponsorships as high profile marketing activities, have been under the spotlight of researchers, especially when it comes to measuring the achievement of high-level communication goals. Conversely, managers have mainly focused their attention on measuring logo exposure time during coverage of a sponsored event (Cornwell et al. 2005; Meenaghan, 2001). Clearly, this remains as an inappropriate methodology for evaluating consumer responses such as attitude or behavioral change (Walraven, 2010).

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Conative Level (e.g. purchase intention, purchase commitment and behavior). Following this scheme, it is feasible to conclude that a sponsorship agreement will turn successful when the mindset and behavior of the specified target audience is positively affected. Moreover, these changes should ultimately translate into improved market and financial performance (Walraven, 2010).

Global brands frequently use sports sponsorships agreements as communication vehicles. Indeed, these agreements generally involve sports events that either reach global audiences, such as the FIFA World Cup and The Olympic Games, or more local oriented events, such as the national football leagues. To that extent, it is arguable whether these brands need to follow a standardization approach by means of sponsoring global sports events, or to apply a so-called “glocal” strategy through a set of more local oriented sponsorship agreements. On one hand, a standardized communication strategy is supported by the rise of a “global culture” or so-called “global village”, which is characterized by sharing common symbols, lifestyles and universal values (De Mooij, 2004). Indeed, economies of simplicity also suggest standardization as the most cost efficient strategy (De Mooij, 2004; Holt et. al, 2004). On the other hand, those who advocate for a “glocal” strategy believe that “the rise of a global culture doesn’t mean that consumers share the same tastes or values” (Holt et. al, 2004, pg. 2). Therefore, they are inclined to deliver communication strategies that are customized to the local preferences (Holt et al. 2004).

Difficulties arise when marketing managers decide to pursue expensive sponsorship agreements with global sport events. Arguably, firms should seek to canalize these resources into building a sponsorship portfolio that prioritizes prominent local events and sports, and therefore, follow a “glocal” strategy. Nonetheless, the broad audience and wide reach of global sports events, alongside with the “global villa” paradigm, seem to influence marketing managers on pursuing expensive sponsorship agreements with global sport events. In order to tackle this issue, managers should determine whether the target audience’s responses to global sports event’s sponsorships hold across different countries.

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sponsored sport event is reaching the right audience. This is supported by advertising theory (Percy and Elliot, 2002), which notes a maximum leverage of brand equity when the sport event’s audience profile matches the sponsoring brand’s regular consumer.

A review of sponsorship literature evidences that most researches took place in countries where the real (or fictitious) events were held, and therefore the researches were constrained to a particular context. Hence, resulting frameworks are not useful when it comes to determining whether an audience’s responses are context sensitive.

The contribution of the current research is as follows. First, it will validate the most cited sponsorship effectiveness predictors in an international context. Second, it will determine whether the studied global brand is reaching the right audience. Finally, its main contribution addresses to what extent sponsorship effectiveness’s main predictors are context sensitive. In the future, these results can be used as a guideline for global brands to build sponsorship portfolios, and therefore consider audience’s contextual sensitivity as moderators for sponsorship effectiveness frameworks. The implementation of a cross-contextual research framework is highly motivated by findings suggesting that there is no such thing as a homogeneous global market, and therefore the need for multi-local communication strategies has become normative (De Mooij, 2004). The model is tested using a prominent sport event and a global brand. The data have been gathered during a three-year time span across seven European countries.

1.2 Problem Statement

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especially when global brands’ managers need to decide whether to pursue sponsorships with global sports events or to pursue sponsorship agreements with more local oriented sport events.

The first limitation of previous studies is that several of them are constrained to on-site data gathering. This means that the sport event’s audience was merely surveyed on the event surroundings. A second limitation is that within the context of international sport events, most of the effectiveness measurement was conducted in the host country (e.g. Grohs et al. 2004, measured image transferring across the host country), thus overlooking the fact that these events were broadcasted to international audiences. A third limitation is that several studies used fictitious events, and therefore provided limited exposure to artificial sponsorship stimuli. As a result, neither image transfer nor attitude change is expected in such short time spans. On comparison to real brands, fictitious brands suffer the lack of exposure to press articles, television ads or any other surrounding stimuli related to the event (Olson, 2010).

A fourth limitation is that several of the attempts for developing a comprehensive framework have been limited to the use students as subjects (Olson, 2010). It is feasible to infer that students are not necessarily to be considered as a representative sample of the aimed target audiences. A fifth limitation is found in researches that have addressed the influence of cultural differences on audience responses. Despite the fact that only countries with contrasting cultural differences took part of them, in most of the cases, the data collection was constrained to two countries.

In spite of the extensive research regarding audience’s responses to sponsorships, the literature fails to address whether these responses hold across different countries. Moreover, it is yet not possible to determine whether effectiveness’ main predictors and audience responses are context sensitive. The aim of the current research is to validate previous sponsorship effectiveness frameworks in a cross-contextual fashion.

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1.3 Research Questions

Although previous researches have attempted to design a reliable framework for tracking sponsorship effectiveness, to the best of my knowledge there is no existence of a reliable cross-contextual framework. Thus, the accepted frameworks rely on the target audience’s cross-contextual homogeneity. Nevertheless, compelling studies in the field have achieved consensus in identifying significant predictors of audience’s responses to sponsorships. Indeed, an international set of literature reviews on this field conducted by Cornwell and Maignan (1998), Walliser (2003), and Speed and Thompson's (2000) have been able to shed light into the measurement of sponsorship effectiveness. However, top management’s reluctance to develop more compelling tracking schemes, supported by their merely reliance on brand exposure measurement, have deterred further development in the field. As a result, the available literature shows a rather significant lack of external validity.

In the light of developing more compelling frameworks, it is necessary to acknowledge the fact that although there are global brands, there may not be global motivations for buying them (De Mooij, 2004). Hence, the correct recognition of factors driving different motivations across target audiences remains a challenge. Moreover, it is expected that certain factors would potentially lead to different contexts (audience wise) where sponsorship effectiveness frameworks will not necessarily work the same way. In this vein, the cultural dimensions framework has gained relevance when it comes to understand differences across audiences, and therefore develop “glocal” communication strategies.

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Additionally, product class involvement and brand preference are considered crucial factors to profile event’s target audience. This is motivated by product class’ unique strong link with lifestyle and sense of identity (Quester and Lin, 2003), which is further expected to influence both audience perceptions and responses. Furthermore, sports sponsorship common practices show that global brands seek to leverage their equity through associations with affinity sports. The current research essays the following main research question, and sub-questions.

To what extent do masculine societies and successful contexts create significantly different context to a sponsorship effectiveness framework?

Research sub-question 1: Do successful contexts influence an audience’s responses to sponsorships?

Research sub-question 2: Do masculine societies react differently to sports sponsorships?

Research sub-question 3: Do product consumption and/or brand preference influence audiences’ responses and/or perceptions?

1.4 Structure

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research, and possible managerial implications to global brands when it comes to leverage their equity through sponsorship agreements.

2. Theoretical Framework

2.1 Sports sponsorship nature and its effectiveness measurement

Throughout the last thirty years, academics have attempted to define sponsorship. However, there is so far no consensus about it, neither across researches nor across countries (Walliser 2003). Therefore, for the current research purposes, the most western and widely spread definitions are to be revised and adopted. Meenaghan (1983, pg. 9) defined sponsorship as “the provision of assistance either financial or in-kind to an activity by a commercial organization for the purpose of achieving commercial objectives”. Although, this definition implies a counter service mediated by a financial exchange, commercial objectives remain broad within it. In the light of developing a clear understanding of sponsorships, Gardner and Shuman (1987, pg. 11) clarified the commercial nature of it. Thus, it was stated “sponsorship may be defined as investments in causes or events to support corporate objectives (e.g. by enhancing corporate image) or marketing objectives (e.g. increase brand awareness)".

Furthermore, when it comes to draw boundaries between advertising and other communication activities (e.g. sponsorships), Speed and Thompson (2000, pg. 226) stated the following: “the involvement of a second party, that is, the activity sponsored, distinguishes sponsorship from advertising, and the commercial motivation distinguishes sponsorship from altruism.”

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2.2 Main predictors on sports sponsorship effectiveness

Using a literature review as a background, a model dealing with effects on audience characteristics on sponsorship perceptions, and finally on the audience’s responses is to be elaborated. Moreover, paths’ strengths are to be compared across different contexts, hence it will possible to determine whether the paths are moderated by contextual factors. A graphical representation of the research framework is presented in Figure 1. This framework is further elaborated in the current section (Hypotheses H1-H13 have been extensively tested in earlier research, hence they are only depicted in figure 1).

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sports involvement, as the extent, to which people identify with, and are motivated by, their engagement and affiliation with a particular leisure (sport) activity. According to Laurent and Kapferer (1985), involvement engage people in a number of behaviors, hence, when it comes to sports sponsorship literature, sports involvement turn into a facilitator for explaining remarkably different audience responses.

Several studies have found sports involvement as a prominent predictor for attitudinal change (affective and conative). Indeed, a sport/sponsored-activity relationship leads a positive emotional orientation toward the sponsor who confers on the consumer-favored activity (Meenaghan, 2001; Olson, 2010). With regards to conative responses, Yong et al. (2008) found that sports involvement has a significant direct effect on intention to purchase sponsor’s products. Levin et al. (2008) and Alexandris et al. (2007) confirmed these results across different sport events’ settings. Additionally, the current framework will explore the influence of sports involvement on the event-sponsor fit. This potential link is clearly justified by audience depicting high sports involvement, and thereby being keen on lenient judgments against event-sponsor degree of congruence.

The degree of fit between sport events and sponsoring brands has been subject of study throughout several researches in the field. Scholars such as Keller (1993) and Gwinner (1997) suggested that sponsorship follows the same scheme as celebrity endorsers advertising, where the perceived match (or mismatch) of brand attitudes with endorsers’ attributes, influences audiences’ responses. In the same way, a higher degree of fit between sport events and sponsoring brands leads to a wide range of positive outcomes for the latter, such as a more positive attitude towards the brand, which may lead to a change in behavior (Grohs et al. 2004; Gi-Yong Koo et al. 2006; Pestana et al. 2006; Johan and Pham, 1999; Wakefield et al. 2007; Speed and Thompson, 2000; Olson, 2010). In addition, Gi-Yong Koo et al. (2006) and Gwinner and Eaton (1999) reported a significant relationship between the event-sponsor fit and the transfer of corporate image.

2.3 Product Class Involvement and Brand Preference

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lifestyle. Indeed, product class involvement is characterized by ongoing commitment on the part of consumer with regard to thoughts, feelings and behavioral responses to a product category (Miller and Marks, 1996; Gordon et al. 1998). According to Quester and Lin (2003), the more important a product class is to an individual’s ego or sense of identity, the stronger the psychological attachment. Consequently, it is feasible to recognize that certain product classes (e.g. beer) are more likely to create unique psychological attachments, especially those that are in line with consumer’s identity.

In product classes where consumers develop higher degree of involvement, depicting a brand preference is to be seen as a strong psychological and behavioral attachment. Indeed, it is inferable that a higher attachment to a specific product class and preference towards the sponsor brand, will lead consumers to depict a substantially better brand image. As a result, consumers are also expected to be more lenient when it comes to assess the degree of fit between the sponsoring brand and the event.

In addition, common practices in sports sponsorship suggest that certain product classes have built up particular bonds with specific sports. To this extent, a simple screening across main sport events (in the football domain), confirms that beer and football, as one of the most common links throughout the annals of sports sponsorships (UEFA, 2011). Arguably, this recurrent condition is supported in the audience and consumer’s common lifestyles and shared values. Although this link has not been explored in previous sponsorship literature, its validation would rather confirm this common business practice, and advice for their maintenance.

2.4 Audience responses to sports sponsorship

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commitment, and behavior). Thus, making a parallel with Keller’s Customer-Based Equity (CCBE) pyramid (Keller, 2008), salience and imagery, feelings and resonance, are to be accounted for the cognitive, affective and behavioral level respectively.

In line with Keller’s framework, and mainly based on Cornwell’s levels of consumer responses; the current research is to assume a three-level progression previously suggested by Percy and Elliot (2002) in the advertising field. Indeed, this widely accepted framework depicts brand awareness, brand attitude and brand purchase intention as the three fundamental communication goals. The aforementioned scheme is applied to sponsorship agreements as follows. First, the target audience gain awareness and formulates a set of associations regarding the sponsoring brand. Second, attitudes towards the agreement are to be aroused. Finally, behavioral responses are to be expected in favor of the sponsoring brand. A critical caveat is that this sequence does not necessarily imply causality. In fact, a certain degree of correlation is expected among the audience responses’ constructs. In the following section, the three levels of audience responses are addressed. Moreover, potential relationships among them are explored.

2.4.1 Cognitive and Affective levels

In principle, the current research will refer to sponsoring brand image and sponsorship attitude, as constructs that will represent the cognitive and affective levels respectively.

Keller (1993, pg. 3) defined brand image as “perceptions about a brand as reflected by the brand associations held in the consumer memory”. Among other relevant image definitions, it is relevant to mention Kotler and Armstrong (2010), who defined image as the set of beliefs consumers hold about a particular object; and Aaker (1991), who described them as a set of associations organized in a significant way.

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Hoyer (2007) defined attitude as “an overall evaluation that expresses how much one likes or dislikes an object, issue, person, or action”. To that extent, attitudes are learned, and tend to persist over time. In addition, they are expected to reflect our overall evaluation of something based on the set of associations linked to it. Within the sports sponsorship literature, attitudes are the evaluations made by the audience based upon stimulus surrounding the sport event.

It is plausible to argue that the image and attitude are intimately related. In fact, Hoyer (2007) states: “Attitudes are important because they (1) guide our thoughts (cognitive function), (2) influence our feelings (affective function), and (3) affect our behavior (conative function).” Hence, the current research is to assume a two-way relationship between brand image and attitude towards the sponsorship. This is supported by Stipp and Schiavone's (1996) findings, where positive attitude towards the sponsorship lead to a brand image enhancement. Additionally, previous studies found that customers with a more favorable image of a sponsorship were more likely to purchase the sponsoring brand (Pope and Voges, 2000; Yong et al. 2008; Turco, 1995).

2.4.2 Conative level

Arguably, sports sponsorships’ ultimate goal is to influence consumers’ behaviors (conative level). Hence, the likelihood to recommend a particular brand is considered as the ultimate desired effect. Indeed, the so-called “Net Promoter Score” (NPS) (Reichheld, 2003) has been used as a straightforward and reliable way to measure the extent to which consumers will be willing to act as promoters and seek to influence other’s purchase intentions (Reichheld, 2003).

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2.5 The impact of Masculine societies and successful seasons contexts

The main contribution of this research is to explore whether the main predictors of the sponsorship perceptions and audience responses are contextual sensitive. To this extent, cultural differences and sports success are appointed to function as discrete moderators variables for the sponsorship effectiveness framework (Figure 1). Indeed, these variables are expected to affect the strength of either independent and/or dependent variables (Baron and Kenny, 1986). Within the model this is manifested through a set of expected influences in both sponsorship perceptions and audience responses. The motivation for their inclusion and further influence in the models is discussed below.

Certainly, National culture manifests itself in various forms (Kroeber and Kluckhohn, 1963). According to Hofstede (1980) it refers to culturally-programmed behavioral beliefs, values, and predisposition that are collectively shared by members of a nation. Indeed, one of these dimensions is masculinity, and it is manifested in nations where men occupy the more dominant roles, and are further expected to be assertive and highly competitive (Hofstede, 1980). Furthermore, masculinity has been linked to several societies’ behaviors. Among those behaviors, sports engagement and alcohol consumption have been appointed as activities that reaffirm male’s sense of masculinity (Lemle and Mishkind, 1989; Connell and James, 2005). Thus, they are to be considered relevant linkages for the current research purposes.

A first linkage comes from the so-called hegemonic masculinity, which is grounded in men’s attempt to validate their dominant role in society (Connell and James, 2005), and has been constantly represented in professional sports. Indeed, media’s tendency to depict representations of masculinity contributes to the prevalence of men’s hegemony. For instance, it is common to see magazine covers portraying contact confrontational sports, exemplars of masculinity (e.g. professional sports stars) or symbols of authority; despite the fact that an average man will not fully live up to them (Messner and Sabo, 1990).

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where manly stereotypes have been stressed to promote alcohol sales (Finn and Strickland, 1982). In fact, a recurrent association between alcohol and masculinity are constantly depicted in beer commercials by means of interjoining the product class with athletic events (Lemle and Mishkind, 1989). This is further supported by documented evidence through the common practice of airing beer commercials during sport activities (Cafiso et al. 1982; Singer, 1985).

From the above described literature background, it is inferable that High-Masculine societies will show higher involvement in sports where hegemonic-masculinity is projected, and therefore depict a stronger consumption of alcohol (e.g. beer) as a mean of symbolizing masculinity. In this vein, the strength of product class involvement and sports involvement paths (H1, H3, H4, H6, and H7) is expected to be significantly stronger in High-Masculine societies. Hence, the following hypotheses are withdrawn.

H14: The positive relationship between product class involvement and sports involvement is stronger in High-Masculine societies.

H15: The positive relationship between product class involvement and event-sponsor fit is stronger in High-Masculine societies.

H16: The positive relationship between sports involvement and event-sponsor fit is stronger in High-Masculine societies.

H17: The positive relationship between sports involvement and sponsorship attitude is stronger in High-Masculine societies.

H18: The positive relationship between sports involvement and intention to recommend purchase is stronger in High-Masculine societies.

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A particular scenario is given by sports where fans depict a higher degree of involvement. Certainly, football is one of those sports where fans are characterized for having stronger demands on success (King, 2003). Indeed, football support is largely concerned about gaining recognition from rivals through success on the pitch (King, 2003). In this sense, success demands are even stronger when local teams take part of international events, something particularly evidenced in international competitions, such as the FIFA World Cup or the UEFA Champions League.

Recognition and success gained by local teams performing in international events are also expected to be extensive to the whole Nation. This is supported by the national degree of identification and attachment to local sport teams (Wann and Branscombe, 1993). Moreover, mass media coverage, and therefore exposure to stimuli tend to increase when sport teams go through the final stages of international tournaments (e.g. football and the UEFA Champions League).

Sponsorship literature addressing the impact of success has found that the impact of sports success goes beyond fan’s mood and recognition gained on the pitch. Indeed, Cornwell et al. (2001) studied the influence of successful performance (NASCAR) at the firm level, and concluded that winning teams have a positive influence in sponsoring brands’ share-prices. More recently, a research by Ngan et al. (2011) concluded that successful team performance has a positive influence on consumer’s intention to purchase the sponsoring brand.

As previously discussed, sports sociology and sponsorship literature shows that successful sports performance has a positive impact on both a society mood and sponsorship agreements’ effectiveness. Thus, the current research will expect successful seasons as different contexts, where audience responses paths are moderated by local teams’ success or failure in international sport events. To this extent, audience responses’ paths (H11, H12 and H13) are expected to be significantly stronger in successful seasons. Hence the following hypotheses are withdrawn.

H19: The positive relationship between sports brand image and sponsorship attitude is stronger in successful seasons.

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H21: The positive relationship between sponsor brand image and intention to recommend purchase is stronger in successful seasons.

3. Research Design

The structure of the section is as follows. First, the study case is elaborated. This section addresses the communication goals set up for the sponsorship deal, and paves the way on how the sponsor aims to track sponsorship effectiveness. Second, the data cleaning and collection procedures are explained. Third, the research methods applied to analyze the data are explained. This section addresses the importance of using Partial Least Squares (PLS), and explains the three-stage approach used in the data analysis. The fourth section provides a detailed explanation of how the questionnaire was designed. Moreover, it elaborates on how the success ration and the masculine index are obtained.

3.1 UEFA Champions League and Heineken sponsorship agreement

The prominence of football is undeniable; actually it is by far the most popular sport, with over 200 million participant’s worldwide (Junge et al. 2004). Furthermore, within the football domains, the UEFA Champions League (UCL) has been placed as one of the most representative and universal competitions. Indeed, the UCL has become one of the most successful and most watched sports programs in history (FIBA Assist Magazine, 2003). Indeed, UCL’s wide range of non European football players and the permanent presence of well known Football superstars ensure its global reach, popularity, and hence high ratings. For instance, the estimated audiences for 2009 UCL’s final were 110 million viewers (bbc.co.uk, 2010) (Spyreport, 2010).

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“The UCL has become truly global and fits perfectly with the Heineken brand, which is the world’s most international premium beer” (Heineken.com, 2010; Sportspromedia Magazine, 2010). In this regards, Hans Tuijt comments, “We track what happens to the brand in terms of awareness; not only do we track the awareness, we track Heineken drinkers and non-Heineken drinkers. Then we track whether they are aware of the sponsorship or not aware of the sponsorship. And then you can track how much they consume” (Sportspromedia Magazine, 2010). From his words, it is plausible to withdraw that Heineken – UCL sponsorship’s ultimate goal is to trigger consumption through leveraging image and attitude among Heineken’s target audience.

It has been six years (2005-2011) since Heineken has become an official sponsor of the UCL, therefore, a fair question would arise: To what extent Mr. Tuijt’s expectation has been fulfilled? Even though, a complete answer cannot be essayed yet, it is fair to recognize that Heineken has obtained a significant progress towards the achievement of certain goals. For example, the SPORT+MARKT report “European Football Brands Top 20 2009/10” has listed Heineken for the very first time in the 20th place. Therefore, it is plausible to notice that Heineken’s attempt to increase awareness, and leverage associations with world-class football, has started to pay off.

3.2 Data collection and data cleaning

“Blauw Research” (Market Research Company) collected the main data set between December 2006 and December 2009. The data collection took part in the following countries: France, Italy, Netherlands, Spain, Greece, England, Argentina, Poland and Thailand (Table 1). Nevertheless, since neither Argentina nor Thailand, are represented in UEFA Champions League, they were not longer considered within the final data set. New data used to create the subsample splits, such as societies’ degree of masculinity and success seasons, were collected through the internet.

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those questions regarding Heineken and the UCL sponsorship arrangement were considered for the purposes of the current research (see Appendix 1).

The final data set entails a sample of 14.359 cases. This final number of cases was obtained after carrying out a data cleaning process that is further elaborated. First, cases with a larger percentage (50%) of missing data were deleted, as suggested by Hair et al. (1998). Second, cases depicting rather acceptable levels of missing data (and no opinion answers) were treated trough a mean substitution imputation method. Fourth, since high-level communication effects are not accountable when the audience cannot correctly identify the sponsoring brand (Olson, 2010), respondents who were neither aware (spontaneous or prompted) of Heineken, nor about the UCL were deleted (777 cases). Finally, respondents who were least interest in football (sports involvement=1) were not considered any longer, because they were not allow to fill out questions regarding particular constructs (Event-sponsor fit and sponsor brand image).

3.3 Research methods

The research method will use a three-stage approach. The first stage entails a Confirmatory Factor Analysis (CFA) that aims to validate the prespecified relationships (Hair et al. 1998) depicted in the research framework (Figure 1).

The second stage uses PLS (Partial least squares) as the main methodology for modeling and testing the sponsorship framework. This approach was chosen because it simultaneously examines the structure of inter relationships expressed in a series of equations, similar to a series of multiple regressions equations (Hair

Dec. 2006 May 2007 Dec. 2007 May 2008 Dec. 2008 May 2009 Dec. 2009 301 311 286 279 190 178 175 1720 319 321 338 322 225 209 206 1940 386 387 376 379 247 239 237 2251 299 300 293 301 175 181 172 1721 416 437 379 415 249 257 259 2412 305 305 290 279 286 284 281 2030 302 301 505 283 299 279 316 2285 2328 2362 2467 2258 1671 1627 1646 14359

Table 1. Data - Country and Wave Country

Wave

Total

Note: The sample was collected across seven different countries through seven different waves.

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et al. 1998). Moreover, PLS is a more suitable approach when the model is rather complex (e.g. large number of latent and/ or manifest variables; Wold, 1995) or, there are violations of usual statistical assumptions (e.g. normality) of latent variable modeling (Cassel et al., 1999). Indeed, the use of soft distributional assumptions from the data, will not lead to improper or nonconvergent results (Henseler et al. 2009). Moreover, since the data neither fulfills fundamental latent modeling assumptions, nor some paths were depicted after sound theory, SmartPLS was the chosen statistical package (Ringle et al. 2005; Barclay et al. 1995, Gefen et al. 2000).

The third stage involves a multi-group analysis across the four subsamples; this approach was chosen due to its simplicity and effectiveness (Chin, 1998). In this vein, following Esposito et al.’s (2010) advice, a society’s degree of masculinity and successful seasons were treated as discrete variables, and hence were used to divide into a group of subsamples. Although the split was aimed to obtain group subsamples with a rather comparable extension, existing differences across them are not expected to influence the results (Esposito et al. 2010).The cut-off index for the MAS sample is established in 66 (2). The United Kingdom, Italy and France, encompass the group so-called “High-Masculine societies” (n=6,073), whereas The Netherlands, Spain, Poland and Greece the group so-called “Low-Masculine societies” (n=8,286). The cut-off index for team performance is established in 0.43 (it ranges from 0.10 to 0.68). Overall, the most successful season for a country was 2006-2007, where the both an Italian and an English team achieved the champion and runner-up positions respectively. On the other hand, Polish teams had the worst performance in every single season; depicting a 0.10 successful and not going through the qualification stage (Table 2). In this last stage, Henseler’s et al. (2009) PLS-MGA free distribution approach to multi-group analysis is implemented for testing significant differences across the four subsamples’ paths.

3.4 Questionnaire design

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experience in tracking other sporting events. Moreover, discrete variables used to split the data in subsamples are further elaborated by the end of the section.

3.4.1. Audience characteristics

Audience’s characteristics are to be considered as important elements to predict future responses to sponsorships agreements. Indeed, consumer’s habits and consumption patterns enable top managers to correctly profile the event’s audience, and therefore reach them with the right message. The three scales used to profile the audience are as follows.

Sports’ involvement (SI) has been identified as an important dimension when it comes to understanding the sponsorship phenomena. This involvement is expected to be driven by consumers’ degree of engagement in a set of behaviors and attitudes towards particular objects. The current research’s scale adopts the so-called Sports Fan Index (SFI) developed by a Miller Lite Report on American attitudes towards sports, (Research and Forecasts, 1983) and named it the FanShip Level (FLS) model. This unique scale has been used to measure consumers’ degree of involvement in a particular object (football). Indeed, it measures objects’ follow up frequency on the media; attendance to object’s events and object’s degree of enjoyment (Blauw Research, 2011). As previously mentioned, least interested respondents (SI=1) were deleted from the final data set. A more elaborated description of the methodology could be found in Appendix 1.

Individual product consumption is considered a proxy for product class involvement (PCI). This unidimensional construct was developed through a consensus approach, and measures to what extent the consumer engage on the consumption of the product class category (beer).

The Sponsor Brand Preference (SBP) scale was developed using a consensus approach, and is determined by consumers’ consumption pattern of the sponsoring brand. In this unidimensional scale, consumers are asked to report sponsor brand’ consumption (yearly basis), which is reflected in a specific number of glasses out of 100 (glasses drank in the same product class).

3.4.2. Sponsorship perceptions

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sport event. Naturally, these judgments are made only if respondents show a previous awareness of both sponsoring brand and sport event (Table 4).

The Event-Sponsor Fit (ESF) construct, which is developed in a consensus fashion, encompasses two items which captures the extent to which the audience report the perceived fit.

3.4.3 Audience responses

Changing attitudes and behavior of the target audience is the cornerstone for every sponsorship agreement to become successful. Although specific communication goals may vary across different sponsoring brands, it is expected that a well-delivered sponsorship agreement, will lead to positively influence audiences’ responses (cognitive, affective and conative). Audience responses are measured as follow.

Sponsor brand image (SBI) was adopted from Aker’s (1997) brand personality scale. Nonetheless, for respondents who were not able to report an honest answer, a “no opinion” option was available.

The Sponsorship Attitude (SA) construct is an extended and adopted version from Becker-Olsen and Simmons (2002) original attitude scale. Indeed, nine items capture to what extent the target audience favors the sponsorship agreement.

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3.4.4 Masculine societies and Successful seasons as moderators

Current research’s main contribution is lead by a multi-group analysis that will enable managers to determine to what extent the sponsorship effectiveness framework is context sensitive. As a result, societies’ degree of masculinity and successful seasons (Table 2) are used as discrete scales to split the final data set and create the four different subsamples. This data were collected as follows.

3.4.4.1 Masculine Societies

Masculine Societies are measured with Hofstede’s Masculinity (MAS) scale. The latest update of this scale (Hofstede et al. 2010) captures the extent to which men occupy the more dominant roles and are expected to be assertive and highly competitive. The MAS index is a 110 points scale where the lowest extreme denotes societies with extremely low masculinity presence, whereas the highest extreme the opposite type.

3.4.4.2 Successful seasons

Successful seasons are determined by a success ratio calculation for teams taking part of the UCL. This ratio has been adapted following event’s general rules regarding point distribution. In fact, within the ratio calculation the obtained points are weighted based on each stage’s degree of difficulty. For instance, a victory at the round of 4, is considered twice as hard than a victory at the round of 8. The historical data were collected at http://www.eurocupshistory.com, and the approach used in the calculation is as follows. The complete list of success ratios could be seen in Appendix 2.

 Team performance is measured by an index that does take a number that goes from 0.00 until 1.00.

Country Masculinity Index (MAS) Success Ratio 2006-2007 Success Ratio 2007-2008 Success Ratio 2008-2009 Success Ratio 2009-2010 The Netherlands 14 0.47 0.40 0.36 0.33 Spain 42 0.54 0.49 0.44 0.44 Greece 57 0.31 0.39 0.17 0.17 Poland 64 0.5 0.10 0.10 0.10 England 66 0.64 0.61 0.54 0.59 Italy 70 0.58 0.57 0.50 0.55 France 86 0.47 0.43 0.40 0.41

Table 2. Masculinity Index and successful ratios

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 This index is a ratio of the total amount of points obtained divided by the total amount of points played throughout the competition (Points obtained/points played).

 Every team is granted 3 points for every victory, 1 point for every draw and 0 points for every loss.

 The degree of difficulty is increased at each sequential stage, and therefore following an opposite pattern to the qualification, each stage is weighted twice as the previous one.

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Construct Measure Scale

(SBI1) Heineken is a brand of high quality (SBI2) Heineken has an appealing taste (SBI3)Heineken is a brand one can trust (SBI4) Heineken is an innovative brand (SBI5) Heineken is a brand to be seen with (SBI6) Heineken is a prestigious brand (SBI7) Heineken is a great brand for me

(SBI8) Heineken is a brand that shares my interests (SBI9) Heineken is a brand worth paying more money for (SBI10) Heineken is a brand that is worth its price (SBI11) Heineken has good advertising

(SBI12) Heineken has attractive packaging (SBI13) Heineken is an original, unique brand

(SBI14) Heineken is really different from other beer brands (SBI15) Heineken is a cosmopolitan brand

(SBI16) Heineken is an intelligent brand (SBI17) Heineken is a self-confident brand

(SA1) I value the fact that Heineken sponsors the UEFA Champions league (SA2) Trough its sponsoring of the UEFA Champions league, I've become more interested in the Heineken brand

(SA3) Because Heineken sponsors the UEFA Champions League, I appreciate the brand more than I used to do

(SA4) Because Heineken sponsors the UEFA Champions league, I take this brand into consideration when it comes to purchasing beer

(SA5) Because Heineken sponsors the UEFA Champions league, I take this brand into consideration when it comes to purchasing beer

(SA6) Because Heineken sponsors the UEFA Champions League, it has really become a brand for me

(SA7) Because Heineken sponsors the UEFA Champions League, I prefer them to other beer brands

(SA8) Because Heineken sponsors the UEFA Champions League, I would definitely recommend the to friends and family

(SA9) The fact that Heineken sponsors the UEFA Champions League no effect on me at all in how I think about the brand

SBP: Sponsor

Brand Preference

(SBP1) Amount of Heineken drunk out of 100 glasses of beer.

6p Frequency Intervals 6= Most Preferred, 1= Not preferred at all PCI: Product Class Involvement

(PC1) Frequency drinking beer

6p Frequency Intervals 6= More

than 4 times a week, 1= Never

SI: Sports

Involvement (SI1) Degree of involvement with football

4p Scale 4=Football Addict, 1=Not interested in football IRP: Intention to Recommed Purchase

(IRP1) Likeliness to recommend Heineken to a relative or colleague

10p scale 10=Extremely

Likely, 1=Extremely

Unlikely

(ESF1) Degree of fit between Heineken and the UEFA Champions League

5p Scale 5=Strongly agree,

1= Strongly disagree (ESF2) Fit between Heineken and the UEFA Champions League 3p Scale 3= agree,

1 disagree

Note 1: The intervals on the SBP contruct are as follows (in number of glasses). 6=100, 5=76-99, 4=51-75, 3=26-50, 2= 1-25, 1=none.

Table 3. Constructs and Measures

Note 2: The SI (formerly called FanShipLevel) construct is the result of summing up the points of 4 questions regarding consumers habit. Points distribution is as follows. 0 Points= Not interested in football, 1-4 points= Interested in fotball, 5-7 points= football lover, 8-12 points= football addict.

Note 3: SA9 is a reverse-coded measure.

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4. Data Analysis and Results

The analysis and results are to be presented as follows. First, a descriptive analysis across the subsamples is to be elaborated. In this section potential constructs’ differences are appointed. Second, the validation of the measurement model for the overall sample if briefly described. Third, the research model’s (Figure 1) estimated paths are discussed. This is done in two steps, first the aggregated model, and later the subsample models (Masculine societies and successful contexts). Finally, a multi-group analysis is carried out across the subsamples, hence the results validate to what extent sponsorship effectiveness frameworks are context sensitive.

4.1. Descriptive Statistics

4.1.1. Descriptive Statistics overall sample

The final data set consists of 14.359 cases of cross-sectional (time and country) aggregated data (Table 1), where only respondents who were aware of the sponsorship agreement and sponsor brand completed the questionnaire (Table 4). Not surprisingly, the data show a larger sample of male respondents (62%) than their female (38%) counterparts do. In addition, it depicts a dominant presence of middle age people. Where 39.5 years is the mean age and 13.4 years the standard deviation. Regarding the education level, a large majority of respondents (87%) had obtained either a bachelor’s or master’s degree. The income level shows no significant variance across the five-level income brackets (except for medium-high income with 13%).

The overall data sample shows a rather balanced sample distribution among countries and collection waves (See appendix 3). Nonetheless, Italy and Greece depict larger shares (range 18%-20%) from December 2006 to December 2008. Furthermore, December 2008 and May 2009 yielded the smallest collection wave share, with 12% and 11% respectively.

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Furthermore, when it comes to report sponsorship perceptions, a rather positive fit is perceived by the audience (ESF1, M=3.55, SD= 1.11; ESF2, M= 2.40, SD=0.72).

Audience responses depict mix results. Whereas the sample showed a positive response towards the sponsor brand’s image (SBI, M=3.44, SD= 0.74) and a rather high intention to recommend it (IRP, M=7.01, SD=2.49), their attitude towards the sponsorship was rather neutral (SA, M=2.49, SD=1.03).

4.1.2. Descriptive Statistics for Masculine societies and Successful contexts

The subsamples show rather contrasting construct results across the sponsorship framework (Table 5). Clearly, the Low-Masculine subsample yields the highest indicators (5 out of 7 constructs) across the four subsamples (Table 5). Nevertheless, potential drivers for these differences will not be discussed extensively since their understanding is beyond the scope of current research.

Masculine society’s descriptive characteristics are as follows. Whereas High-Masculine societies depict a higher preference towards the sponsoring brand (SBP, M=2.74, SD=1.20) and degree of sports involvement (SI, M=3.11, SD= 0.79); Low-Masculine societies show a higher involvement in the product class (PCI, M= 3.99, SD= 1.36).

When it comes to report the event-sponsor fit, clearly Low-Masculine societies depict more lenient judgments (ESF1, M=3.70, SD= 1.08; ESF2, M= 2.49, SD=0.69), consequently they perceive a rather good fit. In terms of audience responses, High-Masculine societies show a more favorable attitude towards the sponsorship (SA, M=2.57, SD=1.05) and towards the sponsor brand (SBI,

M=3.55, SD= 0.77). Nonetheless, Low-masculine societies tend to be more likely

to engage positive word-of-mouth (IRP, M=7.26, SD=2.48).

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Dimension Mean Standard Deviation

Age 39,45 Years 13,57 Years

Male 62% Female 38% Low 14% Middle 40% High 47% Low _22% Low-Medium 22% Medium 20% Medium-High 13% High 22% 100%

Table 4. Participants' Profile

Income Level Education

Level Gender

Aware of Sponsor Brand

Note 1: Sample size = 14359. No missing data.

Note 2: Aware of Sponsor brand consider both prompted and spontaneous awareness.

In terms of audience responses, whereas successful seasons depict more favorable responses towards the sponsor brand image (SBI, M=3.51, SD= 0.78) and sponsorship attitude (SA, M=2.55, SD=1.06); unsuccessful seasons depict a considerably more positive intention to recommend purchase (IRP, M=7.17,

SD=2.52).

Mean SD Mean SD Mean SD Mean SD Mean SD

SBP 2.67 1.19 2.62 1.18 2.74 1.20 2.68 1.19 2.66 1.20 _6-p PCI 3.87 1.36 3.99 1.36 3.71 1.34 3.92 1.35 3.83 1.37 6-p SI 3.08 0.81 3.06 0.83 3.11 0.79 3.10 0.81 3.07 0.82 4-p ESF1 3.55 1.11 3.70 1.08 3.34 1.11 3.54 1.05 3.56 1.15 _5-p ESF2 2.40 0.72 2.49 0.69 2.27 0.74 2.39 0.69 2.40 0.74 3-p SBI 3.44 _0.74 3.36 _0.71 3.55 _0.77 3.51 _0.78 3.39 _{0.70 5-p} SA 2.49 1.03 2.43 1.00 2.57 1.05 2.55 1.06 2.44 0.99 _5-p IRP 7.01 2.49 7.26 2.48 6.68 2.46 6.81 2.42 7.17 2.52 10-p

Note: The highest values resulting of the comparison across the four subsamples are highlighted. Constructs

Table 5. Descriptive Statistics of Constructs

Overall Sample Low-Masculine High-Masculine Successful Unsuccessful

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4.2. Measurement Model

In order to validate whether the indicators of each constructs are correctly measuring what they are meant to measure, convergent validity and discriminant validity were tested for the overall sample. Indeed, both Exploratory Factor Analysis (Appendix 4) and Confirmatory Factor Analysis (Table 6) were able to confirm that the measurement model was found appropriate, and therefore enabled the research to proceed with the next stage. A detailed explanation of the measurement model’s validation could be seen in Appendix 3.

SBI SA ESF SBP IRP PCI SI

SBI1 0,80 SBI2 0,68 SBI3 0,78 SBI4 0,74 SBI5 0,71 SBI6 0,80 SBI7 0,72 SBI8 0,65 SBI9 0,53 SBI10 0,66 SBI11 0,74 SBI12 0,75 SBI13 0,73 SBI14 0,62 SBI15 0,71 SBI16 0,73 SBI17 0,80 SA2 0,88 SA3 0,90 SA4 0,92 SA5 0,91 SA6 0,91 SA7 0,90 SA8 0,88 ESF1 0,97 ESF2 0,97 SBP1 0,70 IRP1 0,80 PCI1 0,97 SI1 0,96

Note 2: This table only contains loadings that are higher than 0.5. Rotation converged in 7 iterations.

Table 6. Confirmatory Factor Analysis results

Note 1: SPSS was used for an exploratory factor analysis (EFA). Extraction Method: Principal Com ponent Analysis. Rotation Method: Varim ax with Kaiser Norm alization.

Manifest Variables

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4.2. Structural Model for the overall sample

PLS modeling is assessed through the percentage of explained variance (R2 scores) for the endogenous variables, while the structural paths assess structural model’s explanatory power. Results (Figure 1) show that the sponsorship framework accounts for 3 to 20 percent of the variances in the aggregated model. In order to calculate the paths coefficient’s significance, a nonparametric bootstrapping procedure (500 subsamples, 13.459 cases; no sign change) was applied to the overall sample (Davidson and Hinkley, 1997; Henseler et al. 2009). The resulting estimation shows 10 significant paths significant at the level of 0.01 and 1 at 0.05 (Figure 2). The overall fit of the model can considered as rather acceptable, nonetheless, the R2 of two endogenous variables (SA and SBI) depicts a good explanatory power with 16 and 20 percent respectively.

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4.3. Multi-group analysis for masculine societies and successful

contexts

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estimates are not significantly different when the resulting p-values are > 0.05 or < 0.95, on a 5% significance level, or p-values > 0.01 or < 0.99, on a 1% significance level.

4.3.1. Difference between Masculine Societies

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In order to test whether High-Masculine societies depict significantly more positive influences on sponsorship perception and audience responses, a multi-group comparison was carried out. Results presented in table 8 are elaborated as follows.

H14 was disconfirmed. In fact, product class involvement’s influence on sports involvement is weaker in High-Masculine societies (H1, β=0.19, p<0.05, Δ β= – 0.03). H15 and H18 were both confirmed. High-Masculine societies showed significantly stronger paths from sports involvements towards event-sponsor fit (H3, β=0.15, p<0.01, Δ β=0.05) and intention to recommend purchase (H7, β= – 0.01, p<0.05, Δ β=0.03). H16 and H18 were disconfirmed since none of them showed a significant difference.

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Path t-value Path t-value Δ Path P-value

H1: PCI -> SI 0.19 (17.51)** 0.16 (12.12)** – 0.03 0.04** H14 H2: SBP -> ESF – 0.04 (4.15)** – 0.06 (5.20)** – 0.02 0.11 H3: PCI -> ESF 0.10 (9.05)** 0.15 (12.84)** 0.05 0.99** H15 H4: SI -> ESF 0.15 (13.81)** 0.16 (12.41)** 0.01 0.72 H16 H5: SBP -> SBI 0.44 (50.89)** 0.45 (44.49)** 0.01 0.77 H6: SI -> SA 0.01 (0.53) 0.01 (1.23) 0.00 0.50 H17 H7: SI -> IRP – 0.04 (3.87)** – 0.01 (1.05) 0.03 0,97* H18 H8: ESF -> SBI – 0.03 (3.22)** 0.06 (5.19)** 0.09 1.00* H9: ESF -> SA – 0.01 (1.35) – 0.02 (1.94)* 0.01 0.27 H10: ESF -> IRP 0.28 (23.97)** 0.26 (19.17)** – 0.02 0.13 H11: SBI -> SA 0.39 (39.62)** 0.43 (36.79)** 0.04 0.99** H12: SBI -> IRP 0.07 (5.85)** 0.06 (3.90)** – 0.01 0.30 H13: SA -> IRP – 0.09 (7.74)** – 0.08 (5.69)** 0.01 0.71 Hypotheses

Table 8. Multi-group comparison - Low-Masculine and High-Masculine societies

Note 1: T- values are depicted in parenthesis. ** Significant at the 0.01 level. * Significant at the 0.05 level.

Note 2: Context difference shows the significant differences across their path coefficients. P-values -> ** Significant at the 0.01 level.*Significant at the 0.05 level.

Low-Masculine (n=8.286) High-Masculine (n=6.073) Context difference

Hypotheses Results 0.17 (21.71)** 0.19 (17.51)** 0.16 (12.12)** Confirmed – 0.06 (7.38)** – 0.04 (4.15)** – 0.06 (5.20)** Not Confirmed 0.14 (17.78)** 0.10 (9.05)** 0.15 (12.84)** Confirmed 0.15 (18.44)** 0.15 (13.81)** 0.15 (12.41)** Confirmed 0.45 (66.18)** 0.44 (50.89)** 0.45 (44.49)** Confirmed 0.01 (1.22) 0.00 (0.53) 0.01 (1.23) Not Confirmed – 0.04 (4.48)** – 0.04 (3.87)** – 0.01 (1.05) Not Confirmed – 0.00 (0.51) – 0.03 (3.22)** 0.06 (5.19)** Partly Confirmed – 0.02 (2.31)* – 0.01 (1.35) – 0.02 (1.94)* Not Confirmed 0.28 (33.65)** 0.28 (23.97)** 0.26 (19.17)** Confirmed 0.40 (52.28)** 0.40 (39.62)** 0.43 (36.79)** Confirmed 0.05 (6.08)** 0.07 (5.85)** 0.05 (3.90)** Confirmed – 0.08 (9.82)** – 0.09 (7.74)** – 0.08 (5.69)** Not Confirmed H1: Product Class Involvement will positively influence Sport's involvement

H2: Sport Brand Preference will positively influence Event-Sponsor Fit H3: Product Class Involvement will positively influence Event-Sponsor Fit

H4: Sports Involvement will positively influence Event-Sponsor fit H5: Sponsor Brand Preference will positively influence Sponsor Brand Image

Endogenous construct explained variance

H13: Sponsorship Attitude will positively influence Intention to Recommend Purchase

0.16 0.08 Event-Sponsor Fit R2

Sport's Involvement R2 Sponsor Brand Image R2

Sponsorship Attitude R2 Intention to Recommend Purchase R2

Note 1: T- values are depicted in parenthes is . ** Significant at the 0.01 level. * Significant at the 0.05 level.

(n=8,286)

High-Masculine (n=6,073)

H12: Sponsor Brand Image will positively influence Intention to Recommend Purchase

Path coefficients Overall Sample

(n=13,459)

H6: Sports involvement will positively influence Sponsorship Attitude H7: Sports involvement will positively influence Intention to Recommend Purchase

H8: Event- Sponsor Fit will positively influence Sponsor Brand Image H9: Event-Sponsor Fit will positively influence Sponsorship Attitude H10: Event-Sponsor Fit will positively influence Intention to Recommend Purchase

H11: Sponsor Brand Image will positively influence Sponsor Attitude

Table 7. Hypothesised path coefficients, R2 and t-values across Low-Masculine and High-Masculine societies

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4.3.2 Differences between Success Contexts

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A multi-group comparison was carried out across the successful subsamples. Results presented in table 10 are elaborated as follows. Sponsor brand image’s influence on sponsorship attitude shows to be significantly stronger (H11, β=0.45, p<0.01, Δ β=0.07) in successful seasons, hence H20 is supported. In the same vein, sponsorship attitude’s influence on intention to recommend purchase is significantly stronger (H13, β= – 0.04, p<0.01, Δ β=0.08) in successful seasons. Although H21 is supported, this influence is negative and yet does not support the original hypothesis. Noteworthy, results supporting the stronger influence of sponsorship attitude on successful seasons are to be considered valuable. Regarding H20, results clearly disconfirmed it.