Master Thesis

Masters Thesis:

Motor sport and the image

of a car brand.

Stijn Vermeulen

I160342

August 2007

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Acknowledgements

The realization of this work has been thanks to the assistance from my

parents, my sister, my uncles and aunts and my cousin. I also acknowledge

the invaluable input of my supervisor, Lieven Quintens. I would like to thank

anybody else who has enabled me to complete this thesis.

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Executive Summary

This thesis sets out to investigate the effect of participation in motor sports on

the image of a company. The focus in this investigation lies on the effect for

companies that participate in Formula 1. The research looks at three different

items that can influence the image: brand fit, brand involvement and popularity

of the sport. Another factor investigates the moderating effect that the

influence of the image of the sport has on the three items under investigation.

These issues where investigated using an online questionnaire. The results

show that a correct brand fit and a high brand involvement are the issues that

will ensure a positive change on the image of a company. In addition,

companies need to choose a sport with a positive image in order to create an

image transfer regime that is positive for both parties.

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Table of contents

Acknowledgements..........................................................................................2

Executive Summary .........................................................................................3

Table of contents .............................................................................................4

Chapter 1 – Introduction ..................................................................................5

Chapter 2 – Literature Review .......................................................................10

Brand Loyalty..........................................................................................10

Brand Involvement and Brand Fit ...........................................................11

Sports Marketing.....................................................................................14

Towards a model of the perceived image of the brand Model ................17

Marketing in Motor sports .......................................................................21

Brand Fit and the Effect on Company Image..........................................23

Brand Involvement and the Effect on Company Image...........................25

Popularity of the Sport and the Effect on Company Image .....................26

Image of the Sport and the Effect on Company Image ...........................27

Model......................................................................................................30

Chapter 3 – Research Design........................................................................31

Chapter 4 – Results .......................................................................................36

Chapter 5 – Discussion..................................................................................42

Chapter 6 – Conclusion .................................................................................46

Bibliography ...................................................................................................48

Appendix I – Questionnaire............................................................................... I

Appendix II – Demographic Data .................................................................. VII

Appendix III – Factor Analysis and Reliability Test ......................................... X

Appendix IV – Paired Sample T-Test............................................................ XL

Appendix V – Regression Analysis ............................................................ XLIII

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Chapter 1 – Introduction

The topic of this thesis focuses on participation in sports by a company.

More in particularly, we take a close look at the partnership created through

sponsorship between a company and a participant on to the next level. Two

different types of sport marketing exists, the first deals with the marketing of a

sport, the second the marketing of a company via a sport. This second type of

sport marketing is the focus of this thesis. A further division of this type of

sport marketing can be made: the sponsoring of an event, and the sponsoring

of a team through participation. This is something that applies for almost every

sport. It could be a tennis racket for a tennis player, shoes for a runner,

swimming goggles for a swimmer, skis for a skier or, as will be the case for

this thesis, a car for a racing driver. This thesis will therefore look at the

advantage that a car company receives from participating in car racing.

Although at first sight both investment types (sponsoring and

participation) appear to be rather similar, participation is much more

expensive, but when a race is won, the possible marketing benefits will also

be much higher. Given the high competitive pressure in many of today’s

markets, this topic is therefore of increasing importance to the business world.

But, as already mentioned, this thesis, which is a type case, will focus on the

effect for a car manufacturer, although the results will probably be similar in

any sports field. The relationship between a manufacturer and the motor sport

it functions in is based on four pillars:

• Popularity of the sport

• Changing profile of the car manufacturer

• Changing profile of the car

• Entry barriers

Firstly, the popularity of the sport, car racing has been popular since

the creation of cars. Automobiles have been raced to see which one was the

fastest or the most reliable. Today, car racing is still a sport that offers

entertainment to people all around the world in many forms and ways such as

circuit racing, rallying, off road racing and drag racing. Although the

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preference of the audience might not always be the same all around the

world, one or more forms of racing are always popular. In Europe, for

example, Formula 1 and Rallying is popular, whereas in the United States

NASCAR and Indy car racing together with drag racing seem to have the

upper hand. This means that any car company can find a sport in which it can

participate and sponsor.

The second reason is linked to the changing profile of the car

manufacturer. During the last couple of years, car companies seem to have

different ideas about the effectiveness of motor racing. Not too long ago, the

three large Detroit car manufacturers (Ford, General Motors and Chrysler)

used “Win on Sunday, Sell on Monday” as a motto. Currently however, Ford,

which was one of the top three Detroit car manufacturers, for example, is

pulling out of most of the car racing fields in which it was present. At the same

time, however, Renault has become more involved in car racing through the

creation of the Renault World Series which features racing cars in many

different classes.

Another important cause is the change in the profile of the car itself.

Cars are becoming more and more a commodity in the current market. This

means that car companies have to compete on price which in turn reduces, or

in some instances takes it away completely, profits. Car racing at the top level

is a very expensive exercise and this thesis will therefore focus on the

questions if spending millions of euros is still a worthwhile investment for car

manufacturers or if this money could be put to better use within the company.

The last motive is the high entry barrier that manufacturers have when

entering into motor sports. Due to the high cost involved in motor sports, many

car companies only have one chance to be successful because they are only

present with a factory sponsored team in one form of motor racing. This is

completely different from other types of companies, for example Nike, who are

present in many different sports, but also present with many different

participants in each sport. In this way, the participation that companies like

Nike do resembles sponsoring very closely.

Keeping in mind the above factors, it is important for companies to

know how effective their presence in motor sport is. Because of the very

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limited information that exists and because of the importance for car

manufacturers, the thesis has the following research question:

How does the involvement of a car brand in motor sports affect

/influence customer’s brand perception of the brand in question?

To provide an answer to this question, the problem needs to be divided

into several, more researchable sub-questions. These sub-questions are the

basis for the upcoming hypotheses.

• How is motor sports defined by car manufacturers?

Car manufacturers probably have a slightly different definition of motor

sports than their customers have, to know their definition will help guiding the

research in the right direction so that the right questions are asked.

• What are the main differences between actively

participating and sponsoring?

The problem that needs to be solved here should take a step towards

answering the issue of why a manufacturer should chose for actively

participating instead of just sponsoring. The answer to this question should

give one a clearer view about the possible rewards in both forms of getting in

touch with the target audience.

• What are the important aspects of an image for a

brand?

To identify if motor sports has an effect on the image of a brand, one

needs to know what aspects of an image are important for a brand. If one

does not know these, it is impossible to research if motor sports have an

effect on the image of a brand. For example, do companies focus on quality,

luxury, comfort or the environment when they broadcast their image to

potential customers?

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• What is the perceived effect of sport on a brand’s

image?

Because very little literature exists on the specific effect of motor sports

on a car brand’s image, a broader view needs to be taken into account, that of

sport in general on a non-specific brand. This will help to see if there is an

effect and if this effect has changed for motor sports in recent times.

• What is the effect of not participating in sports for a

brand?

This question will take into account the effect on the image of a brand

when a company is not present in sports. While answering this question, the

focus will be on how this specifically affects brands that are not present in the

form of sports in which their product is used. For example, a brand of tennis

rackets not being used by any famous tennis player, a brand of running shoes

not being used by any prominent athlete or like the issue I’m looking at, a car

brand not participating in motor sports.

• What is the targeted audience by type of sport at the

different levels?

The issue at hand looks at why a company should be present at

different levels of a sport. These levels are two fold. On the one side, level

could mean the different age, or in motor sports, engine categories. Age

would apply to different sports such as soccer, basketball and athletics, where

participants are often divided into different teams according to their age. This

ensures fair competition; the same is done in motor sport with engine

categories, so that the different participants compete with the same

advantages and disadvantages. On the other hand, level also means the

different championships that range from local over national to world.

One of the benefits that companies can take away from this study will

be that although the actual cost of participation will not change. Companies

who have never participated before will have to deal with the high costs for the

first time, companies who are already participating, will still spend the same

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amount of money on participating, but might spend it on a sport that fits them

better. Companies will be able to see the tradeoffs between participation and

non-participation on the perceived image of their company. This will allow

them to make decisions on whether to participate and if participation seems

viable, in which field they should participate to obtain the best cost-benefit

ratio.

This thesis will provide companies with an insight on how their image

would be perceived whether they are sponsoring teams or if they would be

participating in the sport. These results, which are based on the perception of

consumers, will allow companies to see the differences of non-participation or

wrong participation, compared to the benefits, an area that before has always

been difficult to determine.

The marketing departments from any company participating in a sport

will be able to determine what is important for the customers and who can be

influenced by the results their product achieves in the sport. For example,

customers who participate in the same sport or whose perceived needs are

close to those of participants.

The thesis will exist of several sections. First, the limited existing

literature will be consulted to find information on the research questions in

order for hypothesis to be formed. After this is done, the research design will

be explained as well as the data that was collected. The next step will be to

analyze and discuss this data so that a conclusion can be found. In the end,

the limitations and possibilities for future studies will also be explored.

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Chapter 2 – Literature Review

Brand Loyalty

“The term "marketing" usually conjures images of consumer packaged

goods and advertising campaigns” (Olivia, 2007) but in reality this is only a

very small part of marketing. To ensure full understanding of the topic

discussed, a short overview of marketing possibilities will be given.

It is generally understood that the more brand loyal customers are, the

better the results will be for a firm. High brand loyalty leads to lower marketing

costs because the companies do not need to spend money on convincing

these customers to purchase their product, they can focus on retention which

is a lot cheaper. Chauduri et al (2001) highlight the importance of brand

loyalty in the last decades. One of the first to recognize this importance were

Howard and Sheth (1969). Since then, many have discussed the role of

loyalty in the brand equity process. His findings indicate that brand loyalty

could create marketing advantages, in this respect, reduced marketing costs,

an increase of new customers and greater trade leverage. Other advantages

from loyalty include favorable word of mouth and an increase in the resistance

among loyal customers toward competitive strategies (Dick and Basu, 1994).

Brand loyalty, however, is not as straight forward as it seems. Chauduri

et al (2001) define brand loyalty “as a deeply held commitment to re-purchase

or re-patronize a preferred product/service consistently in the future”, this

behavior will cause same-brand or same brand-set buying behavior while

ignoring the situational influences and marketing efforts meant to cause

switching behavior. This definition, just like many of the previous works on this

topic, shows the two different aspects of brand loyalty clearly – behavioral and

attitudinal. Purchase loyalty, behavioral, is focused on repeated purchases of

the brand, attitudinal brand loyalty is more focused on the dispositional

commitment with regards to the unique value associated with the brand

(Chauduri et al (2001)).

This issue, of brand loyalty, is closely linked to any type and area of

marketing. Any company that wants to survive needs to ensure that

customers keep coming back and keep spending money. This is obviously the

case for Business to Consumers (B2C) marketing, but also for Business to

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Business (B2B) marketing because B2B firms not always have many

customers. Some B2B companies even have 70% to 80% of their business

from one or two customers (for example, a company producing bumpers for a

car usually only does this for one or two different car manufacturers).

Two different aspects exist with regard to brand equity – from the

viewpoint of the firm and that of the consumer. “The firm-related side of brand

equity emphasizes such brand-related outcomes as relative price and market

share, whereas customer based brand equity appears to hinge at its core on

psychological associations with the brand” (Keller, 1993). In addition to Keller,

several authors suggest that psychological associations to a brand name lead

to brand equity results such as larger market share or differential consumer

responses to marketing-mix variables such as relative price. The findings from

Chaukuri (2001) show that brand trust and brand affect can be seen as

separate constructs that come together to establish two different types of

brand loyalty – purchase loyalty and attitudinal loyalty. In turn, these have an

influence on outcome-related aspects of brand equity like market share and

relative price (Chaukuri et al, 2001).

Donvaband (2007) shows similar results in his study, he finds that the

ultimate achievement when building brands is what he calls emotional brand

loyalty. The result is that consumers who are emotionally branded to a brand

will stay loyal, repeat purchase or cross-purchase, recommend the brand to

others and only settle for that brand. The results for a company – increased

sales and lower acquisition costs – means a high rate of success.

According to Donvaband (2007, p. 36), brand loyalty consists out of

four broad categories: shared values, customer care, product quality and

simplified decisions. A consumer will use one or several of these categories

on the road to becoming brand loyal. This means that companies also need to

ensure that the emotional needs of their customers are being met each time

an interaction takes place.

Brand Involvement and Brand Fit

Any company doing marketing also needs to keep the following two

things in mind: brand involvement and brand fit. Brand involvement meaning

the level at which the brand is committed to its marketing contribution. Brand

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fit showing how well the campaign and the company fit together, in other

words, “both have to be pushing in the right direction” (Music Week, 2006) in

order to create a successful pairing.

These two issues, brand involvement and fit, are closely linked to brand

loyalty. Without the right amount of involvement and the correct fit, companies

can never achieve the amount of brand loyalty necessary to increase profit

and sales and reduce acquiring costs.

Brand involvement is a principle that has steadily increased over the

last years, mainly due to an increase of technology. Companies who, in the

past, wanted to become involved on the highest levels with, for examples, a

music band would find it difficult to use the same band all over the world. The

popular bands in most countries used to be local bands only known in one or

two countries. Due to the increase of technology, more specifically TV and

internet, a company can find a band that is popular world wide in any music

genre. Looking at a research conducted by EMR in 2006 and published in

Music Weekly, results show that brand involvement is received as positive or

very positive by 51% of all consumers. Compared to those consumers who

viewed brand involvement as negative, it is four times higher (12%).This

number even increases if one looks at consumers under 25 years of age.

Teenagers don’t just live with it, they embrace it. This group of consumers is

also seen as the group which influences family purchases most.

All is not as easy as it sounds however, customers do have a preferred

choice about which type of involvement companies choose. Not every event

that a brand is involved in gives a company the same benefit. It is logical that

the reach of being involved in a local event is smaller than when a company is

involved in a national or international event. The reach of the event is not the

only issue that companies have to keep in mind, however. Companies should

also ensure they are spending their precious marketing budget on the right

event.

For example, in case of the research conducted by EMR, involvement

in the branding of a music chart shows a 50% positive reaction, but also a

16% negative reaction. On the other hand, when companies become involved

in the branding of live music events, the positive reaction climbs to 64%, while

the negative reactions drop to about 8% of the population. This shows that the

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reaction to a company being involved in any type of event it is received as

positive, the finding of EMR show that a lot of brands really take this seriously.

They just don’t ask themselves the question “How does this process work?”

(Music Weekly, 2006). This means that the benefits of an involvement should

not just be assumed but thoroughly investigated. This will often show

interesting results that could help save a company time and money while still

building a stronger image.

This all sounds very appealing, but a successful brand involvement

strategy can only be achieved if the brand fit is correct. If customers feel that

the link between the brand and the event is not realistic or does absolutely not

make any sense, it will not have the desired effect. According to the research

by EMR, positive results can always be obtained, no matter what the

perceived brand fit is. This sounds contradictory, but looking further the

research shows that the required investment is much higher, while the

benefits for a company’s image are not as high. In other words, the profit

gained from the campaign will be lower while the effort is considerably higher.

Brand fit is an issue that is important in all types of marketing. In

Cause-related marketing (CRM) for example, a study by Pracejus et al (2003)

demonstrates the importance of finding the right fit between the brand and the

charity. Companies experienced that donations to a high fit charity achieved

results that where 5 to 10 times the value than donations to a low fit charity.

The drawback in this study is that the value of CRM does not justify the cost in

with regards to short-term sales.

Both brand involvement and brand fit are two of the most popular topics

for research in the marketing field yet the focus of the research does not often

seem to fall on the sports marketing field. This is an add occurrence if one

knows that sports marketing is an industry worth US$ 250-billion. This

includes every type of sports marketing like athlete endorsements, facility

construction, sporting goods, licensed merchandise, sports-related advertising

and venue signage, event management and marketing services, ticket sales,

sponsorship, media broadcast rights, and multimedia – including websites,

magazines, books and video games. (Wikipedia 2006)

This is supported by the continuous growing interest in professional

sports. This means that sport is seen as big business, supported by the multi-

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million dollar payrolls, new and larger facilities with all possible add-ons, and

the costs of sports franchises escalating. Television contracts for any

professional sports already run into billions of dollars. (Shannon, 1999)

These two examples emphasize the importance sports marketing, with

regard to marketing a company, currently has all over the world. Sports

marketing opens the doors do new sets of potential customers and creates a

long term relationship with these customers. Sports marketing is useful for a

company until the customers have become habitual purchasers.

Sports Marketing

As mentioned earlier, sports marketing applies to two different areas,

marketing a sport, or marketing anything else through the use of sport. The

focus of this thesis is on the latter part, using sport as a means to market an

event, a company, a product or service.

Just like ‘normal’ marketing, sports marketing wants to ensure that a

company increases its profit; this can be achieved in many ways. The obvious

way is by increasing direct sales of one particular product as a result of the

campaign, but often companies prefer to build a long term partnership to

increase brand awareness, which will result in an increased sales of the entire

product range. “Perhaps the most notable distinction that sport has is in the

relationship it has with its consumers” (Whannel, 1992). One cannot deny that

watching sport offers aesthetic pleasures, but the real attraction when

watching a sport is being able to identify with an individual or team as they

battle to win. (Whannel, 1992) This occurrence has helped to make sport the

big business that is today, as well as making sport an ideal tool to promote

corporate interests. “When professional team sport emerged in the nineteenth

century, the relationship between sports teams and fans was sustained by

reliance upon community ownership and involvement” (Taylor, 1992). With the

increasing company involvement in sports, this idea is changing to that of

private ownership which means that it has repercussions for many leagues

(Nauright and Philips, 1997) (Mason, 1999).

Very simply put, sports marketing is marketing that only uses sport

players, sport teams, sport events or the sport itself to achieve the

predetermined goals of a company. In other words, sports marketing is the

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use of marketing ideology and methods applied to sport products and the

marketing of non-sports products, but by association with sport (Wikipedia,

2006).

Using sport players is a common practice in every type of sport. David

Beckham with Gillette is one of many well known examples. This type of

sponsorship is purely for the benefit of Beckham and Gillette and can

continue, even when Beckham changes soccer team. Other examples are

Michael Schumacher and Omega Watches, who continue to pair up even after

the retirement of Schumacher from Formula 1. This allows companies to

create a long term relationship with the one player they see as the best fit with

their product, without having to pull along a whole team.

Sport sponsorship is different from sports participation, although the

effect on the target market is the same; the way a company looks at it is

different. Companies view sponsorship as a way to show off how much they

care about the supporters, more than how involved they are with the team. In

addition, “sponsorships are being used strategically inside companies to

motivate employees or facilitate a major structural change, such as a merger.”

(Farrelly and Greyser, 2007).

Sports marketing and Sport events

Sports marketing through the use of sport events is another large part

of the marketing pie. One often hears about it when the events are large, like

the World Cup Soccer or the Olympic Games, but in reality one can safely say

that every sport event has a title sponsor. Every national soccer series has a

sponsor, every tennis game has a sponsor, every Formula 1 race has a

sponsor and for so many other sports like golf, swimming or athletics the

same applies. A few examples are Omega sponsoring the Olympic games,

IBM and the Wimbledon tennis tournament and even in fairly new markets like

China this practice is already widely spread, with the Formula 1 and Moto GP

races being names the Sinopec Chinese Grand Prix and the Sinopec Great

Wall Lubricants Grand Prix of China. The Sinopec Great Wall Lubricants

Grand Prix of China is at the same time a good example of how companies

need to be careful when using this form of marketing because too many

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sponsors will just result in confusion amongst the spectators and an unofficial

rename to the Grand Prix of China.

Richard Shannon (1999) states that “sports marketing is a large and

growing industry, both in the USA and throughout the world. The Super Bowl,

for example, is an event which has always attracted a large amount of

attention, both from spectators and companies. Companies often use is as a

marketing tool on a domestic level, while other use the Super Bowl to start off

major ad campaigns. The main reason that this is possible, is because of the

size and concentration of the market around the Super Bowl. Because of the

money that companies invest in other events, they are quickly becoming as

big or even bigger. “The Olympics, if we look at overall attendance at,

participation in, and viewership of, will easily surpass the Super Bowl.

(Shannon, 1999)” The popularity of the Super Bowl in the United States, is

surpassed by the worldwide popularity of the World Cup soccer. The

increased popularity of soccer in the USA means that this event is also

growing larger in the US market, which will increase its marketing popularity

for companies looking to operate world wide. Other large scale event, like

NASCAR racing, golf tournaments, and many other professional sports are

attracting large audiences continuously, both in person and on television.

“Literally millions of fans now attend NASCAR races annually, and hundreds

of millions more watch these races on television. These are, indeed,

tremendous marketing opportunities. (Shannon, 1999)”

Sports marketing and a Team

Another area that falls under sports marketing is sponsoring the sport

itself. This is the case when one sponsor does not take an interest in one

particular party involved, but rather sponsors the sport as a whole. This is

often done in running races or during sport championship at lower levels. For

example Hotel Lika who sponsors every Rally cross race in Belgium, or Ethias

who sponsors the Belgium Basketball by providing the referees with the

necessary equipment.

The last part of sports marketing is the involvement with a team. This is

the option with the broadest possibilities for sponsors, while it also allows

sponsors who do not have anything to do with sports to create a sporting

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image. The biggest advantage created when getting involved with a sports

team, any sports team, is that different packages are available and a company

can sponsor on different levels depending on how much they want to spend

and the goals they want to achieve with their participation.

If one, for example, looks at a popular sport like soccer, which is played

at many different levels, local companies can relatively cheaply sponsor a

team at a low level. Another option would be to become a smaller sponsor in a

higher ranked team. This applies for any sport, giving companies access to

many different markets and allowing for many different sponsors to be present

in sports marketing. The way that sports leagues organize themselves to

produce and sell the products linked to their league, show that sports

marketers have to know the unique nature and the potential of professional

sports products. (Mason, 1999)

Another possibility when becoming involved with team sports is to own

a team. Both the costs and the risks are much higher than through regular

sponsoring, but the rewards are also much higher. A company that just

sponsors a team can terminate the contract fairly easily when something goes

wrong (like corruption) without too much image damage. This, however, is not

the case when a company is involved in ownership. If a negative event would

take place, the owning company will have a much harder time combating the

results of a worsened image. They cannot pretend not knowing what was

going on, because they are the owners and carry ultimate responsibility.

When everything happens according to plan and a team wins, the

reward for the company is much higher than with regular sponsoring. Potential

customers directly make a link between the win and the company. This link

becomes even stronger when a company owns a team that is in line with the

business, in other words, companies that own a team that can use experience

from their daily business practice. These pairings can most often be found in

the motorized sports such as Formula 1, Rally, MotoGP and so many others

forms of motorized sports that exist all over the world.

Towards a model of the perceived image of the brand Model

The way a company image is perceived cannot easily be determined.

Company image has many factors that together create the perceived

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company image. According to Kapferer (1997), “companies seek to better

fulfill the expectations of specific customers; they concentrate on providing the

latter, consistently and repeatedly with the ideal combination of attributes –

tangible and intangible, functional and hedonistic, visible and invisible – under

viable economic conditions for their business.” The main goal for companies is

to put their mark on the different sectors they are present in as well put their

imprint on their products. Customers view a brand as being the collection of

the different products providing a long-lasting and stable reference while at

the same time providing a future. It is for this reason that a brand cannot be

seen as a static reality, consumers that buy a product do this because of the

brand. The larger and important brands communicate what they mean, what

they are made of and in which direction they are heading. This is especially

important for hi-tech goods, it shows that consumers the direction the

company is heading into with regards to research, innovation and overall

efforts. (Kapferer, 1997)

Model 1 – Company Image

Brands are not just regarded as a product or a service but also as a

symbol or a person. A strong symbol can provide a structure to the brand

identity and make it more recognizable to people. According to Kapferer

(1997), a conceptual model of how the company image is formed looks like

model 1. This model applies to this case because sport is one of the areas in

which everybody wants to come with the latest scoop. These scoops are often

totally incorrect, but once the consumers have heard the information, it will

start to live a life of its own, with no stopping it. This model clearly shows the

different steps and where a company would need to focus on in order to

combat false scoops and other incorrect information that is spread around by

competitors and media.

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This model shows the different aspects that create a company image.

The first aspect is the way people perceive the different products that a

company creates and the image that these products send out to the world.

This means that every product that is created by a company needs to belong

to the same image in order not to confuse customers. Companies like Procter

& Gamble have solved this problem by creating different brands which allow

them to sell the same product type at different prices without creating

confusion. This concept is also used by car manufacturers. The Volkswagen

group is a good example. Brands belonging to this group are Skoda, Seat,

Volkswagen, Audi, Bentley, Bugatti and Lamborghini. These brands allow

Volkswagen to build and sell to different customers without the need to work

under one central image. The same is done by Ford, Fiat, BMW, Mercedes-

Benz, Chrysler, General Motors and so many others.

The other part that builds up the sender area of model 1 is called other

sources. Not really a clear concept name, but that is simply because it is

impossible to give a clear name to this concept. The other sources that create

a company image are stock prices, product quality, customer service, world

wide presence and so on. This is often forgotten by companies as they tend to

focus on the identity of the different products rather than on what else can

influence the image. These other concepts are equally important as the actual

identity.

Also included in the other sources concept are those sources which are

not linked to the company, but report about the company and its products.

Magazines, newspapers, the Internet, television and consumer reporting

agencies all have their own way of communicating what they think about the

brand and the products and what they think you should do with that

information.

This means that a company can only control a small part of the image

building that happens in the mind of customers. To ensure that the company

controlled signals are successful, they need to choose the appropriate

medium of communication. For many companies the medium of choice will be

the classic ways such as radio, newspapers and magazines, television and

some in store publicity and sponsoring. Few companies spend the majority of

the media budget on sponsoring and communicate only the necessary info via

20

the traditional ways. Volvo, for example, sponsors sailing races all over the

world, but only uses the classic communication ways during the launch of a

new model or when special discounts are given. For Volvo, this way of

creating a brand image seems to work very well if one looks at the increased

sales figures and the changing image of Volvo from being a car for a ore

mature public into a car that is now also for hip, young consumers.

Another example is Ferrari who only communicates through motor

sports sponsorship and participation. They hardly ever use advertising in the

classical way. In addition, Ferrari uses a large assortment of merchandising

product ranging from key chains to laptops. This indirect form of image

transferal is extremely successful if one looks at the sales figures and

popularity of these gadgets.

The ‘signals transmitted’ comes also from sources that companies

cannot control. Although some companies find that there is no such thing as

negative press, many consumers disagree, especially in the short run. The

Mercedes A-class which flipped over during the moose test (The moose test,

also known as the Elk test, has been used in Sweden for decades to test how

a certain vehicle, usually an automobile, acts when avoiding a sudden danger,

such as a moose. Wikipedia, 2007) did not hinder it from selling one million

vehicles in six years after rectifying the problem (baby-benz.com, 2004). This

shows a company can still turn around negative press to become successful

in the long run. This change is indeed easier to achieve when product

problems occur once. The image problems are only problematic in the long

run when they occur with a few of the company’s products in a short

timeframe. For example, if the failing of the moose test had also occurred with

the new M-Class and new C-Class instead of just with the A-Class, Mercedes

would have had image problems and a reduced sale instead of a range of

successful products. Because of the once-off problem at Mercedes, press and

customers soon forgot about the moose test and went on buying the car.

The next part in the model is with regard to the filter that exists between

the signal and the receiver. This filter is present because of the large amount

of information sent to customers at any given point of the day. For example, if

BMW would have sent out a recall of all 3-series on the same day than the

reports about the failed moose test of the Mercedes A-Class, customers

21

would not have been aware that this problem existed. Unfortunately, the same

is also true if something positive would have happened, like BMW winning an

important race.

Other filters that come up often do not interfere with the company or the

field it is present in. Any news that is seen as more important will halt the flow

of the signal to the receiver for whom it is intended.

The last part of the model is that of the receiver. The receiver is the

intended target group of a company. With all the information that a receiver

absorbs, an image is formed about the brand and company.

Marketing in Motor sports

This part of the thesis will deal with the first two sub-questions, ‘how is

motor sports defined by car manufacturers?’ and ‘what are the main

differences between actively participating and sponsoring?’

After discussing the definition of sports marketing and the importance

of loyalty, the main issue of this thesis will be discussed, marketing in motor

sports. This will focus on sports marketing that want to sell a company or

product, rather than the sport itself.

If one puts all the information together, one can see that due to the

special nature of the relationship and the lack of previous research between

sports participation and the effect it has on the image of a company, there is

need for further research. This is a strange occurrence when one considers

the importance of sports marketing. The lack of previous research means that

an investigation pertaining to sports participation is necessary to clarify certain

issues in the sports marketing field.

This lack of previous knowledge uncovers a new problem: the lack of a

benchmark that can be used as a comparison. An additional issue is the

difficulty in finding research that can be used as a stepping stone to start

investigating the effect of sports participation by a car brand in motor sports

on the customer’s brand perception.

According to Renault executives, “the company’s participation in

Formula 1 is to turn this success [winning the world title] into a tool for

enhancing the image and awareness of the Renault brand.” (Automotive

Industries, 2005) If this statement is compared to the press releases from

22

other car manufacturers in Formula 1 (BMW, Mercedes, Ferrari, Spyker,

Honda and Toyota) and press releases relating to other motor sports, very

similar participation goals come forward, most having to do with either brand

awareness or brand image and perception.

According to recent figures, U.S. companies spent $10.52 billion on

sponsorship, of which 69% is categorized as sport sponsorship (Stotlar,

David, 2004). This shows that despite changes in the overall economic

environment, a sport is still seen as the best way to showcase a company to

potential customers. In Formula 1, a similar trend can be seen, although the

amount of teams has been reduced steadily over the past decade, more

manufacturers have entered, or re-entered Formula 1.

A good example is Spyker, a relatively unknown and small Dutch car

company that has recently committed to Formula 1, not just by supplying a

chassis or engine, but by purchasing a whole team. Though this is not their

first endeavor into motor sports, the participation in Formula 1 brings them

onto a whole new level. Since they have branched out into motor sports, they

have become a profitable company rather than a small brand that is only

known by some car specialists.

Similar actions to that of Spyker have been undertaken by both Honda

and Toyota in the past five years with both companies building their own

teams. Mercedes ownership in McLaren seems to increase every year turning

two companies’ from working together as equal partners to Mercedes owing

the majority share of McLaren. BMW has left behind a very successful

partnership with the Williams team because it could takeover the Sauber

team. This has allowed BMW to run its own team and be involved with the

whole package as opposed to just supplying the engines.

Looking at the above examples, one can only assume that life in the

motor sport arena is good. Although stories of failure can easily be found,

these examples illustrate that when a company wants to be successful in

racing, they can do so, even if their name or budget does not match up to the

best teams.

One has to question whether the increase in manufacturer supported

teams is not just fueled by looking at the advancement of competitors, rather

than changes that provide a beneficial transformation of the image. To

23

investigate this, it is best to use the ‘sponsorship evaluation model’ as

proposed by Stotlar (2004). If one compares these goals of participation to the

goals set by many companies when it comes to sponsorship, as shown by

Stotlar (2004) in his Sponsorship Evaluation Model, it becomes clear that the

end result for the potential customers is the same. In other words, both

sponsorship and participation (having an own team) want to achieve an

increased positive or more fitting brand image.

It can be assumed, therefore, that if both sponsorship and participation

want to achieve alike result, testing if these results have been achieved can

also occur in a similar manner, in the same way that the data that applies to

sport sponsorship can also be used for the topic of sport participation.

Brand Fit and the Effect on Company Image

This part of the thesis is closely linked to the third sub-question, ‘What

are the important aspects of an image for a brand?’

The model (Model 2) has four main parts that every company has to

evaluate in order to move forward in its objectives.

• Step 1: Input step

• Step 2: Filter step

• Step 3: Activated Components Step

• Step 4: Evaluation Protocol Step

This model is also used as the basis for the model used in this thesis.

The model presented by Stotlar (2004) shows the different steps needed to

evaluated the sponsorship. Because the idea of this thesis is to evaluate the

participation of a company, several parts of this model can be applied to the

participation model. Although the model for this thesis is not a stepwise

model, the theories included in the Stotlar (2004) steps can also be applied in

the participation model. Especially the filter step and the evaluation protocol

step are of importance.

It begins with the ‘Input’ step, what the company wants to achieve. In

the case of motor sport participation, one can safely assume from the

statements given by many of the participants, it is to increase the awareness

of the brand which would lead to a more positive image for the company. This

24

positive image can cover a large range of aspects; some companies go for a

luxury image, others might prefer a reliable image.

Model 2: Sponsorship Evaluation Model (Stotlar, 2004)

The second step is the ‘Filter’ and the third step is the “Activated

Components”, “the Filter section of the model consists of the inventory that the

sport property has to offer through which the sponsor objectives may be

realized” while the “Activated Components in any sponsorship would be

ultimately determined by the corporate inputs and the property’s filter of viable

inventory” (Stotlar, 2004). In the case of participating in motor sports this will

be target market access and media coverage. These will be the two main

points of contact between the sport and the potential customers that need to

be influenced.

While the past three steps are pretty clear in motor sport, it is the last

step that should help companies determine if they are really creating

maximum benefits from the marketing tools they have at their disposal. This

step is the “Evaluation Protocol”. In this step one has to measure the reaction

of the public on ones efforts in motor sport.

25

This model is mainly used to find the best way for a company to show

its presence in the (sports) market. The need for such a model means that a

company’s marketing campaign is more effective if it is directed towards the

right market, in this case the sports market, in other words, the fit connection

between the participation and the company. Although everybody with enough

money can become the owner of a (motor) sport team, the connection

between the owner and the sport should be clear. If this is not the case, the

message will be totally lost. The previously mentioned example by Pracejus et

al shows that in the case of charity, a fit needs to be seen between the

company and the charity. For example, if makes more sense to consumers if

a dog food manufacturer creates a campaign to support the local animal

shelter as opposed to the local orphanage. Looking at what this means in this

investigation leads to the first hypothesis:

H1: A better brand fit positively influences the effects on image

Brand Involvement and the Effect on Company Image

This section of the research will focus on the fourth and fifth sub-

question, ‘What is the perceived effect of sport on a brand’s image?’ and

‘What is the effect of not participating in sports for a brand?’

The next issue that is particularly applicable to sport sponsorship is the

level of commitment, in other words, the brand involvement of a company. As

mentioned before, the car manufacturers that are present in the Formula 1 at

this moment in time are moving away from being suppliers and are going

towards becoming owners. This would suggest that being more involved is

more beneficial to brands.

With regards to the brand involvement of a company, Cornwell et al

(2001) found that “anything that causes the consumer to ‘experience’ or be

exposed to the brand has the potential to increase familiarity and awareness.”

This seems to be somewhat contradictory to the current marketing strategies

of many companies sponsoring or participating in sports. AIG, for example,

has spent the highest amount of money in soccer to become the main

sponsor of Manchester United. This, combined with the above examples from

Formula 1 would suggest that ‘anything’ is not good enough anymore to really

26

make an impact on consumers. Consumers are bombarded with an infinite

amount of messages every day so the only way to be noticed is to stand

above the competition. Previous studies by Cobb-Walgren et al (1995) found

that companies with larger advertising budgets accomplish higher levels of

brand awareness and brand image.

Cornwell et al (2001) investigated this issue by questioning corporate

sponsorship managers on their views. Cornwell (2001) only found partial

support for this issue (although the main reason that this hypothesis was not

fully accepted was that the respondents had to choose between two areas in

which high sponsorship leverage could be important: for either brand equity

elements or by adding financial value to the brand). This clear divide in the

marketing papers means that it is not redundant to test the following

hypothesis:

H2: A higher brand involvement positively influences the effects on

image

Popularity of the Sport and the Effect on Company Image

This piece of the research deals with the last sub-question ‘What is the

targeted audience by type of sport at the different levels?’

Formula 1 is a sport that is popular all around the world, with the

exception of the United States of America. This seems quite obvious looking

at the sponsors and brands present in Formula 1; the majority is non-

American. The recent change in tobacco advertising legislation after which

tobacco companies withdrew from sporting events in Europe is still fresh in

people’s minds. The United States, however, has had a similar law in place for

many years, in which a tobacco company could only sponsor one major

sporting event/team a year. This rule showed how unpopular Formula 1 was

in the United States, because many Formula 1 teams had to remove tobacco

advertising from their cars and uniforms during the United States Grand Prix

only. Tobacco companies preferred to sponsor different sports than Formula 1

in the US sports market.

The sports fans in the United States have always preferred NASCAR or

Indy car racing. Although this last form of racing has lost some popularity

27

recently with more and more fans turning up at the NASCAR race events.

During the 2003 season, NASCAR’s different divisions accounted for 2200

races. These races attracted a mind boggling thirteen million tickets sold. The

larger events that fall under the NASCAR flag, attract nearly 190 000 visitors.

In 2003, NASCAR had become the second most popular sport in terms of

sports-viewing size, right behind the NFL. (Amato et al, 2005)

Any sport and sponsor or participant can be satisfied with this amount

of attention from fans, but this does not necessarily mean that these fans will

become customers. Recent study in the NASCAR field showed that “72% of

racing fans report they consciously purchase NASCAR sponsors’ products,

and 40% say they would switch to brand that become official promoters”

(Amato 2005), while other “studies have shown that fans are three times as

likely to try and purchase NASCAR sponsors’ products and services than

those of non-sponsor” (Parry, 2005) simply because of their changed image

since they began to sponsor.

This shows how important the choice is to participate in the correct

sport. If the sport chosen has similar numbers compared to NASCAR, almost

any company will be successful when they sponsor or participate in it. These

numbers came from all sponsors put together, but it stands to reason that the

sponsors present in NASCAR which are linked to cars can achieve even

higher numbers. Brands should therefore always look at sport familiarity and

sport popularity before deciding on which sport to spend money. This means

that to actively participate in a sport, the best choice is the most popular one,

therefore:

H3: A more popular sport positively influences the effects on image

Image of the Sport and the Effect on Company Image

When talking about sponsorship and participation, two fit related issues

come to mind. The first is that of functional fit, this “describes the thematic

relatedness between a sponsor and an event” (Grohs et al, 2004).

The second fit, however is an image related fit. This “encompasses the

attributes associated with a sponsor and a sponsored event” (Grohs et al,

2004). This means that sponsorship and participation will be more effective

28

when the sport is perceived to fit the company better. In other words, it makes

more sense for Nissan to participate in Paris-Dakar than it does for Lancia.

Grohs et al (2004) tested this effect and found that it is significant.

Looking at the above two fit related issues, one immediately comes up

with a third issue: that of image fit. Looking in the world of sport marketing it is

obvious that some sports are more popular than others, but this does not

necessarily mean that this sport has a positive image. While Formula 1 or the

World Rally Championship (WRC) attracts many of the top companies to

sponsor or participate, the same can not be said for a race like the Gumball

3000. Both Formula 1 and the Gumball 3000 attract a large amount of

spectators from all over the world, yet those companies sponsoring the

Gumball 3000 are not present in any other motor sports. The main difference

between these two categories is image. Although the Gumball is portrayed as

an official long distance rally, it has an aura of illegality around it, enforced by

the many accidents and arrests. Although it would be cheaper and easier to

sponsor a race like the Gumball, few companies want to be associated with

this rally due to its negative image in the media and in the minds of the

majority of the consumers. If this is the case then:

H4a: A positive image of the sports event moderates the influences of

brand fit on the image of the brand.

Another issue with regards to image is the image transfer between the

sport and the brand. This transfer will depend on the involvement level of the

brand within the sport. According to Grohs (2004) “image transfer in sports is

defined as the transfer of associations attributed to the activity to the brand.”

The main goal is to create positive feelings and attitudes by ensuring a close

link between the sponsor and the event. This will especially be effective if it is

an event that the customer values highly. To put it simply, the image of the

event should transfer to that of the sponsors. (Grohs, 2004) This needs to be

evaluated to ensure that the investment by the brand was worth it. This can be

tested by looking at how the image of the brand has changed over the course

of the event. Grohs et all (2004) explored this issue and found that event

29

image had a significant influence on the post-event sponsor image. For this

study, it means the following should be tested:

H4b: A positive image of the sports event moderates the influences of

brand involvement on the image of the brand

According to Willins (2004) “sports like baseball and football are

traditional, but racing among today’s youth is catching fire”. This means that in

order to create a positive image of a brand, being present in the right sport is

crucial. In order to create a familiarity with a sport, families should be able to

experience the sport as soon as possible. This is often the case with ball

sports like basketball or football, due to the easy access to these sports. If one

can offer easy access to families to something that seems far away like motor

sport events, the impact of a marketing campaign in more advanced racing

series will be higher.

If one looks at the demographics of racing, it shows that one in four of

the racers is between 10 and 21 years old, while more than one in two is

between 22 and 35 years old (Willins, 2004), it is clear that this is a very large

group of potential customers. Although many people may assume many of

these racers are professional racing drivers, this is not the case. For the most

part, these participants see it as their hobby. If one combines this information

with the conclusions from Willins that families need to be introduced to the

racing sport as swift as possible, it is clear that the sooner and the more

familiar consumers become with the sporting event, the larger the impact. It

will also help to bring families close together around the sport if everybody

views the sport in a positive light. In other words, if the sport has a positive

image, more people will be familiar with it, or will do their best to familiarize

themselves with the sport. The best way to familiarize customers is by

allowing them to participate, therefore:

H4c: A positive image of the sports event moderates the influence of

sports familiarity on the image of the brand

30

Model

Creating a model of these hypotheses shows the relationship between

the different aspects such as brand fit, brand involvement, sport familiarity and

image of the sport. The model illustrates the direct effects that brand fit, brand

involvement and familiarity of the sport have on the effects of participating in

sports on the brand perception of a company. This model also shows the

moderating aspects that the image of the sport has on the relationship

between brand fit, brand involvement and sports familiarity respectively on the

effects of participating in sports on the brand perception of a company.

Model 3

31

Chapter 3 – Research Design

To test the hypotheses, thorough research needs to be done. This

survey should follow several steps. The first step is to determine the sample

needed for the study. The main idea behind sampling is that by choosing a

small part of the total population, conclusions can be made about the entire

population. The population is the subject that is used to take the

measurement. “It is the unit of study… A population is the total collection of

elements about which we wish to make some inferences.” (Blumberg et al,

2005) For this research, a simple random sample will be used.

The next step is to determine which tool should be used to conduct the

survey. Many options are available ranging from face-to-face, via telephone

and arriving at internet based surveys. Because this research should be set

up in such a way that a large number of potential respondents are contacted

in a short time period, it is best to use a questionnaire based research. Using

this type of survey also gives the added advantage of being able to use the

NetQuestionnaire community that exists at the Universiteit Maastricht. This

online questionnaire tool allows collecting data from several different groups

without having to take the time to travel and visit them personally. This means

that the results will represent the entire market as opposed to one province or

country. Using an online questionnaire also makes collecting the data

considerably cheaper and faster. Respondents can fill out the research when

they find the time to complete it, as opposed to when a researcher contacts

them in person. According to Coomber (1997), the Internet and electronic mail

has allowed an easier form of communication, especially among different

people investigating the same thing. The electronic communication methods

have allowed people to discuss from different areas of the world while

continuing to work in their areas of expertise. Another important aspect of

electronic communications is that one can reach individual research subjects.

“In particular, there may be significant research benefits to be learned where

the group being researched is normally difficult to reach and/or the issues

being researched are of a particularly sensitive nature” (Coomber, 1997).

32

One drawback when using an experienced panel of respondents is

that responding becomes a routine. One can assume this is not the case for

this research due to its special topic.

The market that is targeted by constructors when they participate in a

race series is anybody, male and female, from a very young age. It is

however, not feasible to contact this group, due to the type of questions being

asked about their personal opinion and perception. These questions are

difficult to answer by minors children because they require some experience

with different car brands. This means a sample should be created. When

using random sampling, or probability sampling, every combination of items

from the population can occur, but the chance of them occurring is not equal

for all the items. The problem when using any type of sampling is that there is

a risk the sample does not represent the population properly. The advantage

of random sampling is that it is been used and researched a lot before, which

means that it is easy to choose the correct sample size. In addition, as soon

as the sample has been taken, the sampling error can be calculated. This is

not the case for non-random sampling. While the latter method may be

cheaper, it does not offer any way to determine the quality of the results.

(Wikipedia, 2007) A simple random sample is described by Blumberg et al

(2005) as a sample in which “each population element has an equal chance of

being selected into the sample”. This research therefore focuses on a sample

of respondents that are both male and female, with an age between 18 and

65. This age group is chosen because it can be assumed that this is the age

group that at some point in their lives they will purchase a car. The

respondents should also live in a market in which Formula 1 is broadcasted,

as well as in a sales market for Fiat and Renault.

It was not necessary to only contact specialists in the field to complete

this research, because they only make up a small percentage of the target

market of the constructors. Constructors also want to make an impact on

those consumers that are not necessarily specialists or full-time fans. The

non-specialist group is a much larger potential customer group.

The respondents are chosen according to a random sample of

participants in NetQuestionnaire. The actual number of respondents contacted

is not available due to the fact that this is chosen by the NetQuestionnaire

33

crew. The respondents received an e-mail in the first half of 2007 and had a

week to complete the questionnaire.

From all the respondents contacted, 43 choose to start the

questionnaire and 37 completed the questionnaire. These 37 respondents

finished the questionnaire completely without omitting any questions. This low

number of respondents can be explained by the busy period that people

experience right before the start of the holidays.

The questionnaire consisted of 13 questions, the first ten with regards

to testing the hypotheses, the next three gathered demographic information

from the respondents with regards to age, nationality and gender.

Due to the unconventional topic of this research, it was not always

possible to use standardized questions. The questions with regard to the

popularity of motor sports (Hypothesis 3) and those with regard to the image

of Formula 1 (Hypothesis 4a, 4b and 4c) were tested by using eight

dimensions on a seven point Likert scale as well as by testing four statements

that needed to be evaluated on a seven point Likert scale ranging from

Strongly Disagree to Strongly Agree. The four questions that where used in

addition to test hypotheses 3, 4a, 4b and 4c are an adoption of questions that

are linked to image and trust.

The eight dimensions come from a list of 34 dimensions (Bruner et al,

2001) that have been successfully used in a wide range of different

questionnaires. “A wide variety of bipolar adjectives has been used over the

years to measure brand attitude. No one set of items has been declared the

optimal scale.” “Respondents typically complete the scale as part of a longer

instrument administered in a survey or experimental context. Subjects are

asked to evaluate a specific good or service using some set of bi-polar

adjectives and marking the scales appropriately. The overwhelming majority

of scales have employed seven-point response alternatives. Scores on the

overall scale can be calculated as the sum or the mean of numeric responses

to the individual items.” (Bruner et al, 2001)

The next set of questions that appeared on the questionnaire was in

relation to hypothesis 1. This hypothesis tests the brand fit between the brand

and the sport. In the case of this questionnaire, Fiat and Renault where taken

as examples and Formula 1 was the sport that was used in this example. To

34

test this hypothesis a set of questions from Sengupta et al (1997) was used.

“Sengupta, Goodstein and Boninger (1997) proposed that different kinds of

low-involvement cues lead to varying degrees or attitude persistence. As

noted, endorser/product fit was measured in a pretest before the second main

study as a manipulation check, namely to confirm that the target population

viewed one endorser as being significantly more related to a product than

another.” (Bruner et al, 2001) The questions where changed in such a way

that the essence of the question remained, but the wording was more

applicable for this research. The questions had to be rated on a seven point

Liker scale.

The next questions that needed to be completed in the questionnaire

were asked to test hypothesis 2 which asks for the level of brand involvement.

To test this, respondents had to answer four questions per brand (Renault and

Fiat) by rating them on a seven point Likert scale. The questions asked found

their basis in a previously tested questionnaire that looked for a personal

opinion on product involvement.

The last questions that needed to be answered in order to be able to

test all the hypotheses were questions that asked for the opinion respondents

had on Renault and Fiat as companies. These questions needed to be rated,

just like all the others, on a seven point Likert scale by the respondents.

For the complete questionnaire, please refer to Appendix 1.

Due to the similar nature of the questions (all scaled on a seven point

Likert scale), the way they are tested will also be similar. The questions will be

tested by using factor analysis, the analysis of the Cronbach’s alpha and

through using a regression analysis.

A factor analysis is a group name for several computational techniques.

These techniques are created to reduce the collected data to a more

manageable number of variables that are linked together by having

overlapping characteristics. “The predictor-criterion relationship that was

found in the dependence situation is replaced by a matrix of intercorrelations

among several variables, none of which is viewed as being dependent on

another. For example, one may have data on 100 employees with scores on

six attitude scale items.” (Blumberg et al, 2005)

35

The main reason for using a Cronbach’s alpha analysis is that

“reliability measures, such as Cronbach’s alpha, do not ensure

unidimensionality, but they do detect whether the indicators of a construct

have an acceptable fit on a single factor model.” (Blumberg et al, 2005)

A regression analysis is a statistical technique for investigating and

modeling the relationship between variables. A regression analysis

investigates the relationship of the dependent variable to specified

independent variables. The result of this mathematical model of the

relationship is called the regression equation. The dependent variable is

reproduced as a random variable due to the uncertainty of the value. The

regression equation is build up out of at least one regression parameters,

which quantitatively link the dependent and independent variables. “Uses of

regression include prediction, modeling of causal relationships, and testing

scientific hypotheses about relationships between variables. Once a

regression model has been constructed it is important to confirm the

goodness of fit of the model and the statistical significance of the estimated

parameters. Commonly used checks of goodness of fit include R-squared,

analysis of the pattern of residuals and construction of an ANOVA table.

Statistical significance is checked by an F-test of the overall fit, followed by t-

tests of individual parameters.” (Wikipedia, 2007)

36

Chapter 4 – Results

The respondents of this research were mostly male (59,5%) and 50%

of the respondents were 30 years or younger. The youngest respondent was

18 and the oldest respondent being 64 years old. The majority of the

respondents have the Belgian nationality, followed by the Dutch and the

German. A more detailed break down of the demographic data can be found

in appendix 2.

Table 1: Item-Total Statistics question 1 – Popularity of the sport

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

Q1 A - Dislike 29,43 145,308 ,854 ,965

Q1 B - Unpleasant 29,32 141,392 ,893 ,963

Q1 C - Foolish 29,81 150,380 ,877 ,964

Q1 D - Common 29,27 159,592 ,711 ,972

Q1 E - Unlikable 29,24 145,078 ,920 ,961

Q1 F - Unattractive 29,00 142,167 ,910 ,962

Q1 G - Unenjoyable 29,00 140,444 ,922 ,961

Q1 H - Unappealing 29,32 141,114 ,921 ,961

Table 2: Item-Total Statistics question 3 – Image of the sport event

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

Q3 A - Dislike 28,97 161,083 ,905 ,964

Q3 B - Unpleasant 28,81 161,435 ,941 ,962

Q3 C - Foolish 29,27 171,925 ,811 ,969

Q3 D - Common 28,54 181,866 ,624 ,978

Q3 E - Unlikable 28,86 161,953 ,951 ,962

Q3 F - Unattractive 28,70 160,548 ,931 ,963

Q3 G - Unenjoyable 28,65 160,790 ,936 ,962

Q3 H - Unappealing 28,81 159,880 ,926 ,963

The next step in the research was to ensure the reliability of the data.

This is best done by a combination of a factor analysis and reliability test. The

results of these calculations showed that in the case of four questions, a

higher Cronbach’s Alpha can be achieved if certain sub-questions where not

used in further calculations. For the question linked to the popularity of the

sport (Question 1), Cronbach’s Alpha is 0,968, proving a high reliability. An

increase of the Cronbach’s Alpha would be possible, but due to the already

high value it is not necessary anymore. This would be more useful if the

Cronbach’s Alpha was around 6 or 7. Full Cronbach’s Alpha result are shown

in table 1.

37

The results for the issue with regards to the image of the sport event

(Question 3), showed a similar trend, achieving a Cronbach’s Alpha of 0,970 if

all the items are included. After the omission of item D, common-uncommon,

the value increased to 0,978 (as is shown in table 2).

Hypothesis 1 (question 5 and 6) which deals with the issue of brand fit,

a similar result was found when the reliability was calculated. When the

respondents were asked about their opinion on the sponsorship of Fiat

towards Ferrari, the original Cronbrach’s Alpha was 0,831, which would

increase to 0,875 if the results from sub-question A were not included (table

3). When the opinion of the participation from Renault in Formula 1 was

asked, the answers resulted in a Cronbach’s Alpha of 0,909, increasing to

0,928 when sub-question A was discarded (table 4). For full results including

all the other questions, please refer to appendix 3.

Table 3: Item-Total Statistics question 5 – Brand fit

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

Q5 A - Fiat sponsor F1 Sport category 12,19 21,602 ,479 ,875

Q5 B - Fiat sponsor F1 Fit 11,65 18,790 ,759 ,740

Q5 C - Fiat sponsor F1 Relevant 11,46 18,866 ,814 ,716

Q5 D - Fiat sponsor F1 Appropriate 11,16 22,417 ,631 ,802

Table 4: Item-Total Statistics question 6 – Brand fit

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Cronbach's Alpha if Item Deleted

Q6 A - Renault sponsor F1 Sport category 13,81 20,880 ,682 ,928

Q6 B - Renault sponsor F1 Fit 13,92 20,188 ,825 ,872

Q6 C - Renault sponsor F1 Relevant 13,81 20,380 ,882 ,853

Q6 D - Renault sponsor F1 Appropriate 13,59 21,914 ,817 ,878

In all four the cases it was not absolutely necessary to discard the sub-

questions because they all had an extraction value that was higher than 0,5.

The sub-questions were not used in the mean calculation because enough

other items were available after the removal to still ensure a conclusion that is

based on enough different items. In other words, not using these four items

does not mean that the results of this thesis are based on only one item.

38

After all the questions were tested, a factor analysis and reliability test

was run using all the different sub-questions. The results show that the

Cronbach’s Alpha of 0,976 would only decrease if any of the items were taken

out of the calculation. It also showed that in the case of the dependent

variable (question 9 and 10), the effect on the image of a car manufacturer,

the Cronbach’s Alpha would remain the same when these items were deleted.

This shows that these items make up the dependent variable and do not

interfere with the model itself.

Table 5: Paired Samples Correlations

N Correlation Sig.

Pair 1 Popularity of the sport & Popularity of the sport 37 ,853 ,000

Pair 2 Sport event & Sport event 37 ,870 ,000

Pair 3 Brand fit & Brand fit 37 ,522 ,001

Pair 4 Brand involvement & Brand involvement 37 ,673 ,000

Pair 5 Image of a company & Image of a company 37 ,454 ,005

Pair 6 Popularity of the sport & Sport event 37 ,840 ,000

Pair 7 Popularity of the sport & Sport event 37 ,896 ,000

In addition to the Cronbach’s Alpha, a t-test was conducted. Although

this was not really necessary because of the good Cronbach’s Alpha results, it

was run just to be sure. To run the test, the questions used to measure each

hypothesis and the independent factor were paired. For example, the two

questions dealing with motor sports (question 1 and 2) because together they

are used to test hypothesis 3. The results of these tests show that the view of

the respondents across the paired questions remains the same, while the

standard deviation is comparable as well. This means that the answers to the

questions are similar, with a maximum difference of 0,5 between answers,

showing that the questions measure the same thing. Even in the case of a t-

test on two similar questions that dealt with motor sports and Formula 1

(question 1 and 3 and question 2 and 4) the results show no significant

difference in the answers or standard deviation.

Furthermore, the results of the t-test show that there is no significant

correlation between the paired questions (table 5). More in-depth results can

be found in appendix 4.

39

The next step in the research is hypothesis testing which is done

through a regression analysis. All six hypotheses were tested in a regression

analysis, as a dependent variable the data collected with regards to the

personal opinion of respondents on Renault and Fiat as companies (question

9 and 10) is used (Image of a Company, Table 6). For full regression analysis

results, please refer to Appendix 5.

The hypothesis dealing with brand fit (Hypothesis 1: A better brand fit

positively influences the effects on image) is accepted. This is the case

because the t-value of 2,173 combined with a significance level of 95%.

(Table 9: Coefficients)

Hypothesis 2, A higher brand involvement positively influences the

effects on image, concerning with brand involvement illustrates a high

absolute t-value, the significance level of brand involvement is 90% meaning

that this hypothesis is accepted. (Table 9: Coefficients)

Popularity of the sport was the topic of hypothesis 3, A more popular

sport positively influences the effects on image. The low t-value of 1 combined

with a significance level of less than 70% means that this hypothesis will be

rejected. (Table 9: Coefficients)

The next three hypotheses test a moderating effect. Hypothesis 4a, A

positive image of the sports event moderates the influences of brand fit on the

image of the brand, which looks at the connection between sport event and

brand fit, yields a high absolute t-value and has a significance level of 90%,

this means that hypothesis 4a is accepted. (Table 9: Coefficients)

The moderating effect of sporting event on brand involvement is

hypothesis 4b, A positive image of the sports event moderates the influences

of brand involvement on the image of the brand. The results show a t-value of

2,658 and a significance level of 95% meaning that this hypothesis should be

accepted. (Table 9: Coefficients)

The last moderating hypothesis, 4c, A positive image of the sports

event moderates the influences of brand involvement on the image of the

brand, dealt with the link between sport event and the popularity of the sport.

A low t-value and a significance level of less than 75% leads to a rejection of

this hypothesis. (Table 9: Coefficients)

40

In addition to being able to control for acceptation or rejection of

hypothesis, a regression analysis also shows the performance of the entire

model. Table 7 shows that the adjusted R squared value is 0,225. This means

that 22,5% of all the cases can be explained by this model. Although this is

not extremely high, it is enough to accept this model as having significance in

the market.

Table 6: Descriptive Statistics

Mean Std. Deviation N

Image of a Company 3,6486 1,17476 37

Brand Fit 4,3333 1,34026 37

Brand Involvement 3,1014 1,27334 37

Popularity of the Sport 3,8880 1,65993 37

Sport event - Brand fit 18,7121 12,12106 37

Sport event - Brand Involvement 13,9896 10,34150 37 Sport event - popularity of the sport 18,0597 13,25764 37

Table 7: Model Summary(b)

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 ,595(a) ,354 ,225 1,03444

a Predictors: (Constant), Sport event - popularity of the sport, Brand Fit, Brand Involvement, Popularity of the Sport, Sport event - Brand fit, Sport event - Brand Involvement b Dependent Variable: Image of a Company Table 8: ANOVA(b)

Model Sum of Squares df Mean Square F Sig.

1 Regression 17,580 6 2,930 2,738 ,030(a)

Residual 32,102 30 1,070

Total 49,682 36

a Predictors: (Constant), Sport event - popularity of the sport, Brand Fit, Brand Involvement, Popularity of the Sport, Sport event - Brand fit, Sport event - Brand Involvement b Dependent Variable: Image of a Company Table 9: Coefficients(a)

Model Unstandardized Coefficients

Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 2,387 ,799 2,987 ,006

Brand Fit ,803 ,370 ,916 2,173 ,038

Brand Involvement -1,259 ,621 -1,365 -2,029 ,051

Popularity of the Sport ,357 ,356 ,504 1,001 ,325

Sport event - Brand fit -,164 ,086 -1,688 -1,898 ,067

Sport event - Brand Involvement ,364 ,137 3,207 2,658 ,012

Sport event - popularity of the sport -,096 ,086 -1,084 -1,118 ,273

a Dependent Variable: Image of a Company

41

Table 10: Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N

Predicted Value 2,5086 5,9085 3,6486 ,69881 37

Residual -1,89985 2,20717 ,00000 ,94431 37

Std. Predicted Value -1,631 3,234 ,000 1,000 37

Std. Residual -1,837 2,134 ,000 ,913 37

a Dependent Variable: Image of a Company

42

Chapter 5 – Discussion

The general line of the findings is that image and fit are the areas which

have the highest impact on the image of a company. In other words, the items

that a company should focus on when deciding in which sport they want to

participate.

Brand fit, an area which has always been seen as very important when

it comes to marketing a company, seems to have the same level of

importance for motor sports marketing. The hypothesis was set out to prove

that to participate in motor sports and to have a positive effect on your image,

a company needed to select its field carefully. In other words, companies

dealing with bicycles should focus on cycling races, car manufacturers should

focus on car races, boat manufacturers on boat races and so on.

These results with regard to brand fit (Hypothesis 1) are in line with

what is usually established in marketing research. EMR (2004) and Prajecus

et al (2003) found similar results in their respective research. Their research

shows that the benefits for a company using its marketing budget to sponsor

events or activities that fall within its natural scope of business will receive

much higher benefits.

This result is contradictory to what has been previously witnessed in

motor sports. For example, British American Tobacco (BAT) which is different

from the other tobacco companies present in the Formula 1 because it is an

owner of the British American Racing team. BAT had to make sure, however,

that it’s other presence in motor sport, Reynard Racing Cars, was not

forgotten. That is the main reason why BAT named the Formula 1 team British

American Racing, rather than sticking to Reynard, and the created a logo that

was very close to BAT’s logo. (Carlyle et al, 2004)

The next hypothesis, dealing with brand involvement finds that the

higher the brand involvement, the more positive the effect on the image of a

participating company. This means that taking the step from sponsoring to

ownership of a team is not just something that is done to create a good moral

in the company, taking the step from sponsoring to ownership and active

participation will positively influence a company’s image.

43

This result explains why many teams in motor sports have gone from

being a private team to a factory team. Usually, a company will first become a

sponsor, than a supplier and move gradually to full ownership to reap the full

benefits of being present in the sport. Some exceptions exist, however, such

as Spyker, the Dutch sports car manufacturer, who has skipped every step in

between and has straight gone from no activity in Formula 1, and nearly no

activity in motor sports in general to owning a Formula 1 team.

This is a finding that is supported by most of the research that is done

in the marketing field. EMR (2004) has found that the same result is found in

view of music events: the larger the event, the higher the impact on the image

of the company. This is also supported by previous research in the motoring

industry. For example, the Land Rover expeditions that are exclusively for

Land Rover owners received positive feedback from the participants. Land

Rover could also report that the popularity of this event continued to stay high,

even after it was organized several times.

The third hypothesis deals with the popularity of the sport. This

hypothesis was rejected and therefore shows that companies do not always

need to focus on the highest level of a sport in order to gain positive results

taking into account a changing company image.

A popular sport, like Formula 1, which is known all over the world,

every race watched by millions of people and with drivers from many different

nationalities usually has one drawback: price. Although Formula 1, for

example, seems to be a near perfect fit as a sport for any company searching

for a activity with a high brand involvement, the biggest problem with this

perfect solution is that the costs involved are extremely high. Cost is the one

factor that keeps most companies from getting involved in motor sports. This

research shows that cost does not necessarily have to be a limiting factor

anymore, companies can choose a sport that falls within their budget while

still having a beneficial result.

To go back to the British American Racing example, they have not

produced a competitive car until the 2004 season, yet, BAT was already

satisfied with the effect BAR had on the key brands and adult smokers under

30. At the same time, BAT also was able to create a world wide appeal of the

main brands by using the broadcast media coverage of Formula 1 that was

44

directed at young people. In addition, they also used merchandising proposals

and activities that where aimed at children and young people and increased

their race sponsorships in the emerging markets of Asia. (Carlyle et al, 2004)

The idea of continuing to be present on television via Formula 1 after

the advertising bans was the biggest attraction for BAT. This allowed BAT to

develop Lucky Strike and State Express 555 as global brands. “Formula 1

was seen as particularly valuable because TV coverage is massive around

the world for each of the sixteen races and there is a genuine association with

the team, vital for image building” (Carlyle et al, 2004). Although this result

only shows the effects of Formula 1, BAT was simultaneously also active in

rallying, MotoGP and other forms of motor sports. It is therefore impossible to

conclude that the benefits for BAT only came from participation in Formula 1.

The last hypothesis tested in this research looked at the moderating

effect of the image of the sports event on the image of the brand and was split

in to three parts. The hypothesis that deals with the moderating effect of a

sporting event on brand fit and the hypothesis that looks at the effect of a

sporting event on brand involvement were accepted. The third part of the

hypothesis, with regards to the effect of the sport event on the popularity of a

sport, the hypothesis was rejected.

These results mean that a company wanting to start in motor sports

marketing has to also look at the image of the sport. Companies should be

aware of the responsibilities and risks involved with the effect the image of the

sports event has. If anything goes wrong, the participating companies will also

suffer with regards to their company’s image. According to Grohs et al (2004)

“recent surveys find that while managers favored issues of media coverage

not more than ten years ago, now they rate sponsor awareness and image

transfer from the sponsored event to the sponsor as the main reasons for

engaging in sport C.” Further study by Grohs et al (2004) shows that this

holds true for all sponsors, no matter what type of industry they are present in.

The results of this research show that one factor is extremely important

in motor sports marketing: perception. The perception people have of the

image of the sport, the perception people have with regards to the brand fit

and the perception people have on what a high brand involvement entails will

45

have a high impact on how people perceive your company and thus which

image they will form of your company.

Sports in an ideal marketing tool to create a better image for a

company. Motor sports in particular has certain special aspects that result in a

stronger image for the brand. The whole world of motor sports is based upon

the image it portrays. Motor sports like to keep people guessing about how it

works and what it entails. From this research, it is clear that the higher and

more important people view a sport to be, the higher the impact on your

brand. Motor sports create the same feeling by charging people a very high

amount of money for attending. And when they attend an event, they cannot

freely walk around and view everything, spectators are restricted to public

areas without being able to come close to the drivers and the cars. This keeps

the special image of motor sports alive and creates a transfer of this image to

the companies involved.

In the case of British American Tobacco, it was particularly important to

be able to project a particularly dynamic, young and international imagery,

especially with a larger presence in Formula One. During the 1985 season,

the presence of one driver, Ayrton Senna, who drove the Lotus John Player

Special, revitalized that brand in Brazil. “Research confirms that it has a

younger image than before, is more dynamic, more human and credible and

quite clearly international” (Carlyle et al, 2004).

Furthermore, Formula 1 would allow BAT to build corporate goodwill,

as teams and organisers have generally displayed few qualms about receiving

money from tobacco companies. The role of BAT as a race host has helped

them in creating a relationship with many important decision makers. A

Formula 1 race provides the perfect setting to build relationships, close deals

and generate goodwill for the company. (Carlyle et al, 2004)

46

Chapter 6 – Conclusion

From this study we can conclude that motor sports marketing is not so

different from any other type of marketing. A company needs to ensure a

good brand fit and a high brand involvement in order to have a positive effect

on the image. A company, furthermore, needs to ensure that, whichever sport

they choose, a sport with a positive image is chosen. These factors together

will ensure a positive change on the image of a company.

For companies looking to enter into motor sports marketing through

ownership of a team this research will help in making the decisions necessary

to be successful. Management now knows that image transfer is an important

area and something that needs to be taken into consideration.

Also the difference that can be found when companies participate at

different levels of motor sports is important. This is an issue that is closely

linked to commitment from a company. If a company wants to be present in

the motor sports for many years, it would probably be good to progress from a

lower level to a higher level in order to learn the way things are done in motor

sports.

Furthermore, this research shows that a company to get the best image

transfer should go to the highest possible level of the most popular sport with

the best image. It is clear that not every company has the resources to buy a

Formula 1 team, and that is understandable. A company needs to take the

findings of this research and merge them with what they want to achieve. A

national company does not need to sponsor an international event because

the majority of the viewers will not be potential customers.

One of the limitations of this research is the few respondents that found

time to complete the questionnaire. If more respondents had completed the

questionnaire, the results would carry a higher importance. Although more

respondents would probably give a very similar result, it would probably have

assisted in increasing the level of significance for certain hypotheses.

This research also does not look at the geographic differences that

exist in the market. Certain countries, like the United Kingdom, Germany and

Italy are well known for their racing heritage, while other countries like

47

Belgium and to a certain extent the Netherlands are not considered to be

racing crazed countries. This is a limitation, but also a possibility for future

research to determine if companies need to look at the geographical

possibilities of a motor sports.

If a similar research is conducted in the future, it would probably be

wise to test the long term effect that participation has on the image of a

company. Why does, for example, BMW have an image that reflects their

racing past, while Mercedes, who started racing much earlier, has an image

that is more focused on comfort?

Another area that could be tested is the effect participation has on the

image of a company if the company has nothing to do with technology used in

the sport, like Red Bull who currently own two Formula 1 teams.

Another possibility for future research would be to not make the link

between a car brand and the sport they participate in, but rather only give the

respondent a car brand and see the recall they have to the sport.

Two additional questions that companies need to ask when they want

to look into this topic further is firstly what companies can do to leverage the

image of the sports. Secondly, how can one increase the impact on the

general public of the sport?

48

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I

Appendix I – Questionnaire

I am a marketing student at the Universiteit Maastricht and I would like your cooperation for a

research that I am conducting in connection with my thesis. This research is solely used for

academic purposes and will not be made public. I would appreciate 10 minutes of your time to

help me by completing this questionnaire.

1. Please rate the following statements, relating to Motorsport, on a 1-7 scale.

A. Dislike Like

1 2 3 4 5 6 7

B. Unpleasant Pleasant

1 2 3 4 5 6 7

C. Foolish Wise

1 2 3 4 5 6 7

D. Common Distinctive

1 2 3 4 5 6 7

E. Unlikable Likable

1 2 3 4 5 6 7

F. Unattractive Attractive

1 2 3 4 5 6 7

G. Unenjoyable Enjoyable

1 2 3 4 5 6 7

H. Unappealing Appealing

1 2 3 4 5 6 7

2. Please rate the following statements, relating to motor sport, on a 1-7 scale, 1 being strongly

disagree and 7 being strongly agree

A. I always have a favorable impression of motor sport

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. Motor sport projects a better image than other sports branches

Strongly disagree Strongly agree

1 2 3 4 5 6 7

II

C. Motor sport is a sport branch that I trust

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. Motor sport offers high quality events

Strongly disagree Strongly agree

1 2 3 4 5 6 7

3. Please rate the following statements, relating to Formula 1, on a 1-7 scale.

A. Dislike Like

1 2 3 4 5 6 7

B. Unpleasant Pleasant

1 2 3 4 5 6 7

C. Foolish Wise

1 2 3 4 5 6 7

D. Common Distinctive

1 2 3 4 5 6 7

E. Unlikable Likable

1 2 3 4 5 6 7

F. Unattractive Attractive

1 2 3 4 5 6 7

G. Unenjoyable Enjoyable

1 2 3 4 5 6 7

H. Unappealing Appealing

1 2 3 4 5 6 7

4. Please rate the following statements, relating to Formula 1, on a 1-7 scale, 1 being strongly

disagree and 7 being strongly agree

A. I always have a favorable impression of formula 1

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. Formula 1 projects a better image than other sports

Strongly disagree Strongly agree

1 2 3 4 5 6 7

III

C. Formula 1 is a sport that I trust

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. Formula 1 offers high quality events

Strongly disagree Strongly agree

1 2 3 4 5 6 7

5. Please rate the following statements with regard to Fiat’s sponsorship of the Ferrari team in

Formula 1 by rating the following statement on a 1-7 scale, 1 being strongly disagree and 7

being strongly agree

A. When I think of Fiat as a sponsor in motor sports, Formula 1 is one of the first sport

categories I think about

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. The idea of Fiat sponsoring Ferrari in Formula 1 represents a very good fit

Strongly disagree Strongly agree

1 2 3 4 5 6 7

C. I think Fiat is a relevant sponsor in Formula 1

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. I think Fiat is an appropriate sponsor in Formula 1

Strongly disagree Strongly agree

1 2 3 4 5 6 7

6. Please rate the following statements with regard to Renault’s participation in Formula 1 by

rating the following statement on a 1-7 scale, 1 being strongly disagree and 7 being strongly

agree

A. When I think of Renault as a participant in motor sports, Formula 1 is one of the first sport

categories I think about

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. The idea of Renault participating in Formula 1 represents a very good fit

Strongly disagree Strongly agree

1 2 3 4 5 6 7

IV

C. I think Renault is a relevant participant in Formula 1

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. I think Renault is an appropriate participant in Formula 1

Strongly disagree Strongly agree

1 2 3 4 5 6 7

7. Please rate the following statements with regard to Fiat by rating the following statement on

a 1-7 scale, 1 being strongly disagree and 7 being strongly agree

A. Fiat’s image as a car manufacturer is largely dependent on the participation in motor sport.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. For Fiat it is important that they participate in circuit racing.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

C. When I think of motor sport, Fiat comes to mind.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. Motor sport without Fiat would make for a less interesting sport.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

8. Please rate the following statements with regard to Renault by rating the following statement

on a 1-7 scale, 1 being strongly disagree and 7 being strongly agree

A. Renault’s image as a car manufacturer is largely dependent on the participation in motor

sport.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. For Renault it is important that they participate in circuit racing.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

V

C. When I think of motor sport, Renault comes to mind.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. Motor sport without Renault would make for a less interesting sport.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

9. Please rate the following statements with regard to Renault by rating the following statement

on a 1-7 scale, 1 being strongly disagree and 7 being strongly agree

A. I always have a favorable impression of Renault.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. Renault projects a better image than its competition.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

C. Renault is a company I trust.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. Renault offers high quality products.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

10. Please rate the following statements with regard to Fiat by rating the following statement on

a 1-7 scale, 1 being strongly disagree and 7 being strongly agree

A. I always have a favorable impression of Fiat.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

B. Fiat projects a better image than its competition.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

VI

C. Fiat is a company I trust.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

D. Fiat offers high quality products.

Strongly disagree Strongly agree

1 2 3 4 5 6 7

Please complete the following questions.

11. How old are you?

_________________

12. What is your gender (please circle the correct one)?

Male Female

13. What nationality do you have?

o Belgian

o Dutch

o German

o Other ________________________

Thank you for completing this questionnaire. If you have any further questions, feel free to

contact me.

VII

Appendix II – Demographic Data How old are you?

Frequency Percent Valid Percent Cumulative Percent

18 1 2,7 2,8 2,8

23 5 13,5 13,9 16,7

24 1 2,7 2,8 19,4

25 3 8,1 8,3 27,8

26 3 8,1 8,3 36,1

28 2 5,4 5,6 41,7

29 1 2,7 2,8 44,4

30 2 5,4 5,6 50,0

32 1 2,7 2,8 52,8

33 1 2,7 2,8 55,6

34 3 8,1 8,3 63,9

35 1 2,7 2,8 66,7

47 1 2,7 2,8 69,4

48 2 5,4 5,6 75,0

51 1 2,7 2,8 77,8

53 1 2,7 2,8 80,6

55 1 2,7 2,8 83,3

58 1 2,7 2,8 86,1

61 1 2,7 2,8 88,9

62 3 8,1 8,3 97,2

64 1 2,7 2,8 100,0

Valid

Total 36 97,3 100,0

Missing 9999999 1 2,7

Total 37 100,0

18 23 24 25 26 28 29 30 32 33 34 35 47 48 51 53 55 58 61 62 64

Age

0

1

2

3

4

5

Freq

uenc

y

How old are you?

VIII

What nationality do you have?

Frequency Percent Valid Percent Cumulative Percent

Belgian 17 45,9 45,9 45,9

Botswana 2 5,4 5,4 51,4

Dutch 13 35,1 35,1 86,5

German 4 10,8 10,8 97,3

South African

1 2,7 2,7 100,0

Valid

Total 37 100,0 100,0

Belgian

Botswana

Dutch

German

South African

What nationality do you have?

IX

What is your gender?

Frequency Percent Valid Percent Cumulative Percent

Male 22 59,5 59,5 59,5

Female 15 40,5 40,5 100,0

Valid

Total 37 100,0 100,0

Male Female

Gender

0

10

20

30

40

50

60

Perc

en

t

What is your gender?

X

Ap

pe

nd

ix I

II –

Fa

cto

r A

na

lys

is a

nd

Re

lia

bil

ity T

es

t F

acto

r A

naly

sis

Mo

tor

sp

ort

(Q

1)

C

om

mu

na

liti

es

Initial

Extraction

Q1 A - Dislike

1,000

,789

Q1 B - Unpleasant

1,000

,846

Q1 C - Foolish

1,000

,822

Q1 D - Common

1,000

,591

Q1 E - Unlikable

1,000

,882

Q1 F - Unattractive

1,000

,869

Q1 G - Unenjoyable

1,000

,887

Q1 H - Unappealing

1,000

,884

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

6,570

82,123

82,123

6,570

82,123

82,123

2

,537

6,712

88,835

3

,357

4,464

93,298

4

,197

2,463

95,761

5

,165

2,065

97,826

6

,085

1,062

98,888

7

,066

,829

99,717

8

,023

,283

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q1 A - Dislike

,888

Q1 B - Unpleasant

,920

Q1 C - Foolish

,907

Q1 D - Common

,769

Q1 E - Unlikable

,939

Q1 F - Unattractive

,932

Q1 G - Unenjoyable

,942

Q1 H - Unappealing

,940

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XI

Reli

ab

ilit

y T

est

Mo

tor

sp

ort

(Q

1)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,968

8

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q1 A - Dislike

29,43

145,308

,854

,965

Q1 B - Unpleasant

29,32

141,392

,893

,963

Q1 C - Foolish

29,81

150,380

,877

,964

Q1 D - Common

29,27

159,592

,711

,972

Q1 E - Unlikable

29,24

145,078

,920

,961

Q1 F - Unattractive

29,00

142,167

,910

,962

Q1 G - Unenjoyable

29,00

140,444

,922

,961

Q1 H - Unappealing

29,32

141,114

,921

,961

XII

Facto

r A

naly

sis

Mo

tor

sp

ort

s (

Q2)

C

om

mu

na

liti

es

Initial

Extraction

Q2 A - Favorable

1,000

,791

Q2 B - Image

1,000

,711

Q2 C - Trust

1,000

,800

Q2 D - Quality events

1,000

,797

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3,099

77,480

77,480

3,099

77,480

77,480

2

,393

9,833

87,313

3

,294

7,350

94,663

4

,213

5,337

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q2 A - Favorable

,889

Q2 B - Image

,843

Q2 C - Trust

,894

Q2 D - Quality events

,893

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XIII

Reli

ab

ilit

y T

est

Mo

tor

sp

ort

s (

Q2)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,903

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q2 A - Favorable

10,65

24,790

,797

,869

Q2 B - Image

11,46

27,200

,728

,894

Q2 C - Trust

10,92

24,021

,805

,866

Q2 D - Quality events

10,11

24,432

,803

,867

XIV

Facto

r A

naly

sis

Sp

ort

Even

t (Q

3)

C

om

mu

na

liti

es

Initial

Extraction

Q3 A - Dislike

1,000

,865

Q3 B - Unpleasant

1,000

,915

Q3 C - Foolish

1,000

,723

Q3 D - Common

1,000

,470

Q3 E - Unlikable

1,000

,932

Q3 F - Unattractive

1,000

,903

Q3 G - Unenjoyable

1,000

,911

Q3 H - Unappealing

1,000

,898

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

6,617

82,714

82,714

6,617

82,714

82,714

2

,684

8,545

91,259

3

,289

3,611

94,870

4

,243

3,038

97,908

5

,073

,916

98,824

6

,041

,506

99,330

7

,031

,386

99,716

8

,023

,284

100,000

Extraction Method: Principal Component Analysis.

Co

mp

on

en

t M

atr

ix(a

)

Component

1

Q3 A - Dislike

,930

Q3 B - Unpleasant

,957

Q3 C - Foolish

,851

Q3 D - Common

,685

Q3 E - Unlikable

,965

Q3 F - Unattractive

,950

Q3 G - Unenjoyable

,954

Q3 H - Unappealing

,948

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XV

Reli

ab

ilit

y T

est

Sp

ort

Even

t (Q

3)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,970

8

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q3 A - Dislike

28,97

161,083

,905

,964

Q3 B - Unpleasant

28,81

161,435

,941

,962

Q3 C - Foolish

29,27

171,925

,811

,969

Q3 D - Common

28,54

181,866

,624

,978

Q3 E - Unlikable

28,86

161,953

,951

,962

Q3 F - Unattractive

28,70

160,548

,931

,963

Q3 G - Unenjoyable

28,65

160,790

,936

,962

Q3 H - Unappealing

28,81

159,880

,926

,963

XVI

Facto

r A

naly

sis

Sp

ort

Even

t (Q

4)

C

om

mu

na

liti

es

Initial

Extraction

Q4 A - Favorable

1,000

,817

Q4 B - Image

1,000

,822

Q4 C - Trust

1,000

,909

Q4 D - Quality events

1,000

,812

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3,360

84,005

84,005

3,360

84,005

84,005

2

,270

6,757

90,762

3

,245

6,133

96,896

4

,124

3,104

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q4 A - Favorable

,904

Q4 B - Image

,907

Q4 C - Trust

,953

Q4 D - Quality events

,901

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XVII

Reli

ab

ilit

y T

est

Sp

ort

Even

t (Q

4)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,936

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q4 A - Favorable

11,68

31,725

,829

,923

Q4 B - Image

11,92

31,910

,833

,922

Q4 C - Trust

12,00

30,667

,912

,896

Q4 D - Quality events

11,03

32,027

,824

,925

XVIII

Facto

r A

naly

sis

Bra

nd

Fit

Fia

t (Q

5)

Co

mm

un

aliti

es

Initial

Extraction

Q5 A - Fiat sponsor F1 Sport

category

1,000

,427

Q5 B - Fiat sponsor F1 Fit

1,000

,800

Q5 C - Fiat sponsor F1 Relevant

1,000

,850

Q5 D - Fiat sponsor F1 Appropriate

1,000

,645

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

2,722

68,046

68,046

2,722

68,046

68,046

2

,689

17,216

85,261

3

,426

10,657

95,918

4

,163

4,082

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q5 A - Fiat sponsor F1 Sport category

,653

Q5 B - Fiat sponsor F1 Fit

,895

Q5 C - Fiat sponsor F1 Relevant

,922

Q5 D - Fiat sponsor F1 Appropriate

,803

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XIX

Reli

ab

ilit

y T

est

Bra

nd

Fit

Fia

t (Q

5)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,831

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q5 A - Fiat sponsor F1

Sport category

12,19

21,602

,479

,875

Q5 B - Fiat sponsor F1 Fit

11,65

18,790

,759

,740

Q5 C - Fiat sponsor F1

Relevant

11,46

18,866

,814

,716

Q5 D - Fiat sponsor F1

Appropriate

11,16

22,417

,631

,802

XX

Facto

r A

naly

sis

Bra

nd

Fit

Ren

au

lt (

Q6)

C

om

mu

na

liti

es

Initial

Extraction

Q6 A - Renault sponsor F1 Sport

category

1,000

,646

Q6 B - Renault sponsor F1 Fit

1,000

,828

Q6 C - Renault sponsor F1 Relevant

1,000

,886

Q6 D - Renault sponsor F1 Appropriate

1,000

,828

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3,188

79,704

79,704

3,188

79,704

79,704

2

,482

12,058

91,762

3

,229

5,718

97,480

4

,101

2,520

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q6 A - Renault sponsor F1 Sport

category

,804

Q6 B - Renault sponsor F1 Fit

,910

Q6 C - Renault sponsor F1 Relevant

,941

Q6 D - Renault sponsor F1 Appropriate

,910

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XXI

Reli

ab

ilit

y T

est

Bra

nd

Fit

Ren

au

lt (

Q6)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,909

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q6 A - Renault sponsor

F1 Sport category

13,81

20,880

,682

,928

Q6 B - Renault sponsor

F1 Fit

13,92

20,188

,825

,872

Q6 C - Renault sponsor

F1 Relevant

13,81

20,380

,882

,853

Q6 D - Renault sponsor

F1 Appropriate

13,59

21,914

,817

,878

XXII

Facto

r A

naly

sis

Bra

nd

In

vo

lvem

en

t F

iat

(Q7)

C

om

mu

na

liti

es

Initial

Extraction

Q7 A - Fiat image

1,000

,758

Q7 B - Fiat important participate

1,000

,668

Q7 C - Fiat motor sport

1,000

,737

Q7 D - Fiat less interesting sport

1,000

,603

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

2,766

69,152

69,152

2,766

69,152

69,152

2

,542

13,561

82,713

3

,465

11,627

94,340

4

,226

5,660

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q7 A - Fiat image

,871

Q7 B - Fiat important participate

,817

Q7 C - Fiat motor sport

,858

Q7 D - Fiat less interesting sport

,777

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XXIII

Reli

ab

ilit

y T

est

Bra

nd

In

vo

lvem

en

t F

iat

Q7

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,845

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q7 A - Fiat image

8,89

19,266

,742

,786

Q7 B - Fiat important

participate

8,08

18,077

,663

,812

Q7 C - Fiat motor sport

9,03

17,305

,722

,786

Q7 D - Fiat less

interesting sport

8,78

17,841

,624

,832

XXIV

Facto

r A

naly

sis

Bra

nd

In

vo

lvem

en

t R

en

au

lt (

Q8)

C

om

mu

na

liti

es

Initial

Extraction

Q8 A - Renault im

age

1,000

,782

Q8 B - Renault im

portant participate

1,000

,632

Q8 C - Renault motor sport

1,000

,773

Q8 D - Renault less interesting sport

1,000

,678

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

2,865

71,618

71,618

2,865

71,618

71,618

2

,620

15,488

87,106

3

,273

6,816

93,922

4

,243

6,078

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q8 A - Renault im

age

,884

Q8 B - Renault im

portant participate

,795

Q8 C - Renault motor sport

,879

Q8 D - Renault less interesting sport

,824

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XXV

Reli

ab

ilit

y T

est

Bra

nd

In

vo

lvem

en

t R

en

au

lt (

Q8)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,867

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q8 A - Renault im

age

9,95

17,719

,781

,803

Q8 B - Renault

important participate

9,35

18,790

,648

,858

Q8 C - Renault motor

sport

9,92

17,743

,765

,810

Q8 D - Renault less

interesting sport

10,43

19,530

,680

,844

XXVI

Facto

r A

naly

sis

Co

mp

an

y I

mag

e F

iat

(Q9)

C

om

mu

na

liti

es

Initial

Extraction

Q9 A - Fiat favorable

1,000

,855

Q9 B - Fiat image

competition

1,000

,836

Q9 C - Fiat trust

1,000

,893

Q9 D - Fiat quality

1,000

,832

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3,416

85,391

85,391

3,416

85,391

85,391

2

,230

5,762

91,153

3

,215

5,377

96,530

4

,139

3,470

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q9 A - Fiat favorable

,925

Q9 B - Fiat image

competition

,914

Q9 C - Fiat trust

,945

Q9 D - Fiat quality

,912

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XXVII

Reli

ab

ilit

y T

est

Co

mp

an

y I

mag

e F

iat

(Q9)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,943

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q9 A - Fiat favorable

10,78

22,230

,865

,925

Q9 B - Fiat image

competition

11,03

21,971

,848

,929

Q9 C - Fiat trust

10,54

20,422

,898

,914

Q9 D - Fiat quality

10,54

21,477

,844

,931

XXVIII

Facto

r A

naly

sis

Co

mp

an

y I

mag

e R

en

au

lt (

Q10)

C

om

mu

na

liti

es

Initial

Extraction

Q10 A - Renault favorable

1,000

,790

Q10 B - Renault im

age

competition

1,000

,781

Q10 C - Renault trust

1,000

,855

Q10 D - Renault quality

1,000

,711

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3,136

78,412

78,412

3,136

78,412

78,412

2

,416

10,404

88,817

3

,253

6,331

95,147

4

,194

4,853

100,000

Extraction Method: Principal Component Analysis.

C

om

po

ne

nt

Ma

trix

(a)

Component

1

Q10 A - Renault favorable

,889

Q10 B - Renault im

age

competition

,884

Q10 C - Renault trust

,925

Q10 D - Renault quality

,843

Extraction Method: Principal Component Analysis.

a 1 components extracted.

XXIX

Reli

ab

ilit

y T

est

Co

mp

an

y I

mag

e R

en

au

lt (

Q10)

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,907

4

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q10 A - Renault favorable

11,30

12,992

,799

,878

Q10 B - Renault im

age

competition

11,46

13,811

,792

,879

Q10 C - Renault trust

11,19

12,880

,855

,856

Q10 D - Renault quality

10,73

15,314

,729

,902

XXX

Facto

r A

naly

sis

Co

mm

un

aliti

es

Initial

Extraction

Q1 A - Dislike

1,000

,896

Q1 B - Unpleasant

1,000

,846

Q1 C - Foolish

1,000

,908

Q1 D - Common

1,000

,876

Q1 E - Unlikable

1,000

,909

Q1 F - Unattractive

1,000

,939

Q1 G - Unenjoyable

1,000

,949

Q1 H - Unappealing

1,000

,926

Q2 A - Favorable

1,000

,888

Q2 B - Image

1,000

,695

Q2 C - Trust

1,000

,784

Q2 D - Quality events

1,000

,894

Q3 A - Dislike

1,000

,911

Q3 B - Unpleasant

1,000

,914

Q3 C - Foolish

1,000

,908

Q3 D - Common

1,000

,772

Q3 E - Unlikable

1,000

,959

Q3 F - Unattractive

1,000

,945

Q3 G - Unenjoyable

1,000

,962

Q3 H - Unappealing

1,000

,960

Q4 A - Favorable

1,000

,953

Q4 B - Image

1,000

,896

Q4 C - Trust

1,000

,907

Q4 D - Quality events

1,000

,898

XXXI

Q5 A - Fiat sponsor F1

Sport category

1,000

,809

Q5 B - Fiat sponsor F1 Fit

1,000

,860

Q5 C - Fiat sponsor F1

Relevant

1,000

,900

Q5 D - Fiat sponsor F1

Appropriate

1,000

,825

Q6 A - Renault sponsor F1

Sport category

1,000

,829

Q6 B - Renault sponsor F1

Fit

1,000

,894

Q6 C - Renault sponsor F1

Relevant

1,000

,901

Q6 D - Renault sponsor F1

Appropriate

1,000

,876

Q7 A - Fiat image

1,000

,869

Q7 B - Fiat important

participate

1,000

,941

Q7 C - Fiat motor sport

1,000

,841

Q7 D - Fiat less interesting

sport

1,000

,839

Q8 A - Renault im

age

1,000

,835

Q8 B - Renault im

portant

participate

1,000

,883

Q8 C - Renault motor sport

1,000

,881

Q8 D - Renault less

interesting sport

1,000

,779

Q9 A - Fiat favorable

1,000

,902

Q9 B - Fiat image

competition

1,000

,866

Q9 C - Fiat trust

1,000

,915

Q9 D - Fiat quality

1,000

,888

XXXII

Q10 A - Renault favorable

1,000

,859

Q10 B - Renault im

age

competition

1,000

,850

Q10 C - Renault trust

1,000

,900

Q10 D - Renault quality

1,000

,739

Extraction Method: Principal Component Analysis.

T

ota

l V

ari

an

ce

Ex

pla

ine

d

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Component

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

23,206

48,347

48,347

23,206

48,347

48,347

14,483

30,174

30,174

2

4,833

10,069

58,416

4,833

10,069

58,416

4,276

8,909

39,083

3

3,003

6,256

64,672

3,003

6,256

64,672

3,748

7,809

46,892

4

2,615

5,447

70,119

2,615

5,447

70,119

3,634

7,571

54,463

5

2,389

4,977

75,096

2,389

4,977

75,096

3,559

7,414

61,877

6

2,034

4,238

79,334

2,034

4,238

79,334

3,521

7,336

69,213

7

1,671

3,480

82,814

1,671

3,480

82,814

3,253

6,776

75,989

8

1,305

2,719

85,534

1,305

2,719

85,534

3,076

6,408

82,397

9

1,120

2,334

87,867

1,120

2,334

87,867

2,626

5,471

87,867

10

,776

1,617

89,485

11

,658

1,371

90,856

12

,589

1,227

92,083

13

,549

1,143

93,226

14

,458

,954

94,180

15

,405

,845

95,025

16

,375

,782

95,807

17

,353

,735

96,542

18

,254

,529

97,071

19

,201

,418

97,489

20

,196

,407

97,896

21

,182

,379

98,275

XXXIII

22

,160

,332

98,608

23

,128

,267

98,875

24

,101

,211

99,086

25

,080

,167

99,253

26

,070

,146

99,399

27

,061

,127

99,526

28

,052

,109

99,635

29

,046

,095

99,730

30

,039

,082

99,812

31

,027

,056

99,868

32

,024

,050

99,919

33

,017

,035

99,954

34

,010

,021

99,975

35

,007

,016

99,990

36

,005

,010

100,000

37

1,271E-15

2,647E-15

100,000

38

6,688E-16

1,393E-15

100,000

39

4,465E-16

9,301E-16

100,000

40

3,826E-16

7,970E-16

100,000

41

9,194E-17

1,915E-16

100,000

42

5,092E-17

1,061E-16

100,000

43

7,861E-18

1,638E-17

100,000

44

-9,753E-17

-2,032E-16

100,000

45

-1,770E-16

-3,687E-16

100,000

46

-3,040E-16

-6,334E-16

100,000

47

-4,342E-16

-9,045E-16

100,000

48

-9,426E-16

-1,964E-15

100,000

Extraction Method: Principal Component Analysis.

XXXIV

Co

mp

on

en

t M

atr

ix(a

)

Component

1

2

3

4

5

6

7

8

9

Q1 A - Dislike

,842

-,100

-,108

,085

,345

-,061

,037

,098

-,154

Q1 B - Unpleasant

,830

-,070

-,129

-,055

,216

-,209

-,069

,008

-,191

Q1 C - Foolish

,858

-,096

-,073

-,170

,166

-,178

,056

-,039

-,255

Q1 D - Common

,675

,091

-,210

-,194

,068

-,528

,097

,169

-,094

Q1 E - Unlikable

,826

-,071

-,315

-,169

,248

-,052

-,155

,068

-,035

Q1 F - Unattractive

,872

-,216

-,212

-,094

,011

-,039

-,208

-,095

-,155

Q1 G - Unenjoyable

,828

-,126

-,377

-,174

,156

,058

-,209

,003

-,069

Q1 H - Unappealing

,856

-,063

-,331

-,140

,105

,003

-,217

-,037

,017

Q2 A - Favorable

,872

-,206

-,203

-,098

,152

,025

-,002

-,067

-,077

Q2 B - Image

,792

,031

,101

,041

-,013

-,157

,064

-,004

,161

Q2 C - Trust

,824

-,086

-,012

,075

-,133

-,116

,221

-,024

,107

Q2 D - Quality events

,743

-,051

-,202

,108

-,002

-,445

,264

,012

,139

Q3 A - Dislike

,860

-,261

-,003

,287

,103

,077

-,062

,016

,024

Q3 B - Unpleasant

,870

-,307

,039

,225

,008

,073

-,069

,018

-,024

Q3 C - Foolish

,868

-,202

-,037

-,106

,093

-,129

-,073

-,234

-,124

Q3 D - Common

,772 -6,094E-05

,087

-,108

-,057

-,366

,099

,098

,004

Q3 E - Unlikable

,859

-,295

-,108

,151

-,160

,243

-,113

,007

,056

Q3 F - Unattractive

,818

-,267

-,161

,163

-,215

,193

-,253

-,058

,020

Q3 G - Unenjoyable

,857

-,267

-,167

,100

-,177

,243

-,152

,002

,070

Q3 H - Unappealing

,838

-,290

-,170

,099

-,208

,257

-,065

-,016

,143

Q4 A - Favorable

,881

-,310

-,053

,148

-,127

,159

-,021

-,016

,119

Q4 B - Image

,778

-,055

,115

,037

-,390

-,203

,158

,026

,233

Q4 C - Trust

,824

-,120

-,009

,122

-,257

-,034

,207

,060

,292

Q4 D - Quality events

,780

-,126

-,136

,165

-,043

-,290

,263

,040

,267

XXXV

Q5 A - Fiat sponsor F1

Sport category

,479

-,014

,609

,226

-,061

-,059

,082

,376

,044

Q5 B - Fiat sponsor F1 Fit

,567

,318

,417

-,134

,342

-,139

-,216

-,180

,170

Q5 C - Fiat sponsor F1

Relevant

,509

,211

,543

-,114

,341

-,117

-,386

,051

,085

Q5 D - Fiat sponsor F1

Appropriate

,510

,245

,211

,002

,300

,114

-,401

,076

,437

Q6 A - Renault sponsor F1

Sport category

,486

-,082

,352

-,424

,054

,171

,344

,344

-,117

Q6 B - Renault sponsor F1

Fit

,661

,111

,092

-,447

,132

,304

,318

-,155

,037

Q6 C - Renault sponsor F1

Relevant

,676

-,004

,184

-,371

,218

,393

,110

,230

-,073

Q6 D - Renault sponsor F1

Appropriate

,662

,065

,075

-,416

,266

,335

,190

,053

,182

Q7 A - Fiat image

,620

,281

,147

,019

-,545

,048

-,087

-,215

-,175

Q7 B - Fiat important

participate

,539

,454

,379

-,109

-,123

-,003

,062

-,515

-,065

Q7 C - Fiat motor sport

,512

,240

,420

,300

-,311

-,055

-,356

,035

-,164

Q7 D - Fiat less interesting

sport

,535

,234

,437

-,245

-,123

-,370

-,264

,141

-,073

Q8 A - Renault im

age

,743

,101

,258

,214

-,066

-,106

,228

-,234

-,194

Q8 B - Renault im

portant

participate

,678

,174

,223

-,045

,165

,196

,271

-,446

,055

Q8 C - Renault motor sport

,724

-,159

,225

,241

-,105

,287

,110

,074

-,335

Q8 D - Renault less

interesting sport

,666

-,011

,337

,107

-,265

,196

,066

,271

-,154

Q9 A - Fiat favorable

,433

,704

-,251

-,124

-,242

-,012

-,228

,170

-,021

Q9 B - Fiat image

competition

,345

,739

-,313

-,152

-,207

,095

-,035

,072

,149

Q9 C - Fiat trust

,339

,682

-,279

-,278

-,283

,257

,031

,176

-,049

Q9 D - Fiat quality

,298

,655

-,282

-,351

-,403

-,048

,001

-,051

-,010

XXXVI

Q10 A - Renault favorable

,367

,524

-,173

,490

,328

,105

,019

,104

-,223

Q10 B - Renault im

age

competition

,311

,595

,015

,492

,300

,197

,046

-,118

,109

Q10 C - Renault trust

,237

,648

-,181

,507

,256

,089

,164

,182

,030

Q10 D - Renault quality

,371

,594

-,217

,318

,196

-,109

,203

-,007

-,095

Extraction Method: Principal Component Analysis.

a 9 components extracted.

XXXVII

Reli

ab

ilit

y T

est

R

eli

ab

ilit

y S

tati

sti

cs

Cronbach's

Alpha

N of Items

,976

48

It

em

-To

tal

Sta

tis

tic

s

Scale Mean if

Item Deleted

Scale

Variance if

Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q1 A - Dislike

180,16

3315,529

,820

,975

Q1 B - Unpleasant

180,05

3308,608

,806

,975

Q1 C - Foolish

180,54

3338,977

,834

,975

Q1 D - Common

180,00

3384,222

,660

,975

Q1 E - Unlikable

179,97

3329,805

,801

,975

Q1 F - Unattractive

179,73

3307,703

,838

,974

Q1 G - Unenjoyable

179,73

3311,647

,798

,975

Q1 H - Unappealing

180,05

3306,219

,834

,975

Q2 A - Favorable

180,49

3318,757

,839

,975

Q2 B - Image

181,30

3345,659

,780

,975

Q2 C - Trust

180,76

3319,745

,802

,975

Q2 D - Quality events

179,95

3342,441

,715

,975

Q3 A - Dislike

180,24

3296,689

,830

,975

Q3 B - Unpleasant

180,08

3303,021

,838

,974

Q3 C - Foolish

180,54

3323,366

,835

,975

Q3 D - Common

179,81

3347,602

,755

,975

Q3 E - Unlikable

180,14

3309,342

,827

,975

Q3 F - Unattractive

179,97

3310,138

,784

,975

Q3 G - Unenjoyable

179,92

3302,521

,825

,975

XXXVIII

Q3 H - Unappealing

180,08

3302,132

,804

,975

Q4 A - Favorable

180,35

3300,734

,848

,974

Q4 B - Image

180,59

3322,970

,761

,975

Q4 C - Trust

180,68

3314,281

,803

,975

Q4 D - Quality events

179,70

3324,604

,752

,975

Q5 A - Fiat sponsor F1

Sport category

180,92

3392,465

,466

,976

Q5 B - Fiat sponsor F1 Fit

180,38

3377,908

,571

,975

Q5 C - Fiat sponsor F1

Relevant

180,19

3397,269

,507

,975

Q5 D - Fiat sponsor F1

Appropriate

179,89

3406,599

,514

,975

Q6 A - Renault sponsor F1

Sport category

179,65

3400,068

,462

,976

Q6 B - Renault sponsor F1

Fit

179,76

3370,967

,649

,975

Q6 C - Renault sponsor F1

Relevant

179,65

3376,623

,661

,975

Q6 D - Renault sponsor F1

Appropriate

179,43

3386,197

,649

,975

Q7 A - Fiat image

181,51

3397,868

,624

,975

Q7 B - Fiat important

participate

180,70

3391,826

,549

,975

Q7 C - Fiat motor sport

181,65

3397,568

,515

,975

Q7 D - Fiat less interesting

sport

181,41

3388,303

,534

,975

Q8 A - Renault im

age

180,95

3360,108

,733

,975

Q8 B - Renault im

portant

participate

180,35

3367,456

,670

,975

Q8 C - Renault motor sport

180,92

3364,965

,699

,975

Q8 D - Renault less

interesting sport

181,43

3382,252

,655

,975

XXXIX

Q9 A - Fiat favorable

180,70

3415,937

,465

,976

Q9 B - Fiat image

competition

180,95

3429,775

,376

,976

Q9 C - Fiat trust

180,46

3426,700

,365

,976

Q9 D - Fiat quality

180,46

3438,033

,319

,976

Q10 A - Renault favorable

180,62

3435,353

,383

,976

Q10 B - Renault im

age

competition

180,78

3448,452

,335

,976

Q10 C - Renault trust

180,51

3457,590

,264

,976

Q10 D - Renault quality

180,05

3446,830

,391

,976

XL

Ap

pe

nd

ix I

V –

Pa

ire

d S

am

ple

T-T

es

t

Pa

ire

d S

am

ple

s S

tati

sti

cs

Mean

N

Std. Deviation

Std. Error

Mean

Popularity of

the sport

4,1815

37

1,80471

,29669

Pair 1

Popularity of

the sport

3,5946

37

1,64396

,27027

Sport event

4,0772

37

1,92654

,31672

Pair 2

Sport event

3,8851

37

1,85367

,30474

Brand fit

4,0631

37

1,54927

,25470

Pair 3

Brand fit

4,6036

37

1,52315

,25040

Brand

involvement

2,8986

37

1,38376

,22749

Pair 4

Brand

involvement

3,3041

37

1,40081

,23029

Image of a

company

3,5743

37

1,53191

,25184

Pair 5

Image of a

company

3,7230

37

1,21733

,20013

Popularity of

the sport

4,1815

37

1,80471

,29669

Pair 6

Sport event

4,0772

37

1,92654

,31672

Popularity of

the sport

3,5946

37

1,64396

,27027

Pair 7

Sport event

3,8851

37

1,85367

,30474

XLI

P

air

ed

Sa

mp

les

Co

rre

lati

on

s

N

Correlation

Sig.

Pair 1

Popularity of the

sport &

Popularity of the

sport

37

,853

,000

Pair 2

Sport event &

Sport event

37

,870

,000

Pair 3

Brand fit &

Brand fit

37

,522

,001

Pair 4

Brand

involvement &

Brand

involvement

37

,673

,000

Pair 5

Image of a

company &

Image of a

company

37

,454

,005

Pair 6

Popularity of the

sport & Sport

event

37

,840

,000

Pair 7

Popularity of the

sport & Sport

event

37

,896

,000

XLII

P

air

ed

Sa

mp

les

Te

st

Paired Differences 95% Confidence Interval

of the Difference

Mean

Std. Deviation

Std. Error

Mean

Lower

Upper

t df

Sig. (2-tailed)

Pair 1

Popularity of the

sport -

Popularity of the

sport

,58687

,94744

,15576

,27098

,90276

3,768

36

,001

Pair 2

Sport event -

Sport event

,19208

,96489

,15863

-,12962

,51379

1,211

36

,234

Pair 3

Brand fit - Brand

fit

-,54054

1,50175

,24689

-1,04125

-,03983

-2,189

36

,035

Pair 4

Brand

involvement -

Brand

involvement

-,40541

1,12631

,18516

-,78094

-,02987

-2,189

36

,035

Pair 5

Image of a

company -

Image of a

company

-,14865

1,46185

,24033

-,63605

,33876

-,619

36

,540

Pair 6

Popularity of the

sport - Sport

event

,10425

1,06141

,17449

-,24964

,45814

,597

36

,554

Pair 7

Popularity of the

sport - Sport

event

-,29054

,82182

,13511

-,56455

-,01653

-2,150

36

,038

XLIII

Ap

pe

nd

ix V

– R

eg

res

sio

n A

na

lys

is

D

es

cri

pti

ve

Sta

tis

tic

s

Mean

Std. Deviation

N

Image of a Company

3,6486

1,17476

37

Brand Fit

4,3333

1,34026

37

Brand Involvement

3,1014

1,27334

37

Popularity of the Sport

3,8880

1,65993

37

Sport event - Brand fit

18,7121

12,12106

37

Sport event - Brand

Involvement

13,9896

10,34150

37

Sport event - popularity

of the sport

18,0597

13,25764

37

XLIV

Co

rre

lati

on

s

Image of a

Company

Brand Fit

Brand

Involvement

Popularity of

the Sport

Sport event

- Brand fit

Sport event -

Brand

Involvement

Sport event -

popularity of

the sport

Image of a Company

1,000

,368

,426

,344

,353

,433

,338

Brand Fit

,368

1,000

,659

,643

,782

,629

,618

Brand Involvement

,426

,659

1,000

,662

,745

,899

,710

Popularity of the Sport

,344

,643

,662

1,000

,856

,813

,931

Sport event - Brand fit

,353

,782

,745

,856

1,000

,913

,938

Sport event - Brand

Involvement

,433

,629

,899

,813

,913

1,000

,917

Pearson Correlation

Sport event -

popularity of the sport

,338

,618

,710

,931

,938

,917

1,000

Image of a Company

. ,012

,004

,019

,016

,004

,020

Brand Fit

,012

. ,000

,000

,000

,000

,000

Brand Involvement

,004

,000

. ,000

,000

,000

,000

Popularity of the Sport

,019

,000

,000

. ,000

,000

,000

Sport event - Brand fit

,016

,000

,000

,000

. ,000

,000

Sport event - Brand

Involvement

,004

,000

,000

,000

,000

. ,000

Sig. (1-tailed)

Sport event -

popularity of the sport

,020

,000

,000

,000

,000

,000

.

Image of a Company

37

37

37

37

37

37

37

Brand Fit

37

37

37

37

37

37

37

Brand Involvement

37

37

37

37

37

37

37

Popularity of the Sport

37

37

37

37

37

37

37

Sport event - Brand fit

37

37

37

37

37

37

37

Sport event - Brand

Involvement

37

37

37

37

37

37

37

N

Sport event -

popularity of the sport

37

37

37

37

37

37

37

XLV

V

ari

ab

les E

nte

red

/Re

mo

ve

d(b

)

Model

Variables Entered

Variables

Removed

Method

1

Sport event - popularity of the

sport, Brand Fit, Brand

Involvement, Popularity of the

Sport, Sport event - Brand fit,

Sport event - Brand

Involvement(a)

. Enter

a All requested variables entered.

b Dependent Variable: Image of a Company

M

od

el

Su

mm

ary

(b)

Model

R

R Square

Adjusted R

Square

Std. Error of

the Estimate

1

,595(a)

,354

,225

1,03444

a Predictors: (Constant), Sport event - popularity of the sport, Brand Fit, Brand Involvement, Popularity of the Sport, Sport event - Brand fit, Sport event - Brand Involvement

b Dependent Variable: Image of a Company

A

NO

VA

(b)

Model

Sum of

Squares

df

Mean Square

F

Sig.

1

Regression

17,580

6

2,930

2,738

,030(a)

Residual

32,102

30

1,070

Total

49,682

36

a Predictors: (Constant), Sport event - popularity of the sport, Brand Fit, Brand Involvement, Popularity of the Sport, Sport event - Brand fit, Sport event - Brand Involvement

b Dependent Variable: Image of a Company

XLVI

Co

eff

icie

nts

(a)

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B

Std. Error

Beta

1

(Constant)

2,387

,799

2,987

,006

Brand Fit

,803

,370

,916

2,173

,038

Brand Involvement

-1,259

,621

-1,365

-2,029

,051

Popularity of the Sport

,357

,356

,504

1,001

,325

Sport event - Brand fit

-,164

,086

-1,688

-1,898

,067

Sport event - Brand Involvement

,364

,137

3,207

2,658

,012

Sport event - popularity of the sport

-,096

,086

-1,084

-1,118

,273

a Dependent Variable: Image of a Company

R

es

idu

als

Sta

tis

tic

s(a

)

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

2,5086

5,9085

3,6486

,69881

37

Residual

-1,89985

2,20717

,00000

,94431

37

Std. Predicted Value

-1,631

3,234

,000

1,000

37

Std. Residual

-1,837

2,134

,000

,913

37

a Dependent Variable: Image of a Company

XLVII

-2-1

01

23

Reg

ress

ion

Sta

nd

ard

ized

Res

idu

al

024681012 Frequency

Mea

n = 2,46

E-16

Std. D

ev. =

0,913

N = 37

Dep

end

ent

Var

iab

le:

Imag

e o

f a

Co

mp

any

His

tog

ram

0,0

0,2

0,4

0,6

0,8

1,0

Ob

serv

ed C

um

Pro

b

0,0

0,2

0,4

0,6

0,8

1,0

Expected Cum ProbDep

end

ent

Var

iab

le:

Imag

e o

f a

Co

mp

any

No

rmal

P-P

Plo

t o

f R

egre

ssio

n S

tan

dar

diz

ed R

esid

ual

Ch

art

48