Image Transfer Between Brands in the Broadcasting System

Image transfer between brands in the broadcasting system

An explorative research on image transfer and spillover effects between brands in the broadcasting system

Student name: Sander Borghouts

Student number: 10659099

Supervisor: Roger Pruppers

Second corrector: Jorge Labadie

Amsterdam Business School, Faculty of Economics and Business

University of Amsterdam

Master Thesis

Date: August 8 2014

1 Table of Contents

1. SO MANY BRANDS, SUCH AN OBTUSE PASTIME ...... 4 1.1 BRANDS IN THE BROADCASTING SYSTEM ...... 4 1.2 BROADCASTING BRANDS IN THE CURRENT EVENTS ...... 5 1.3 PROBLEM DEFINITION ...... 6 1.4 CONTRIBUTIONS ...... 7 1.5 GENERAL OVERVIEW ...... 8 2. BROADCASTING COMPONENTS AS FULL-FLEDGED CONSUMER BRANDS ...... 10 2.1 CLASSIC BRAND DEFINITION ...... 10 2.2 BRAND IMAGE BUILDING IN PRACTICE ...... 11 2.3 BRAND EQUITY SELLS ...... 12 2.4 POSITIVE EVALUATIONS CREATE IRREPLACEABLE BRAND EQUITY ...... 13 2.5 BROADCASTING COMPONENTS FIT THE BRAND DEFINITION ...... 14 3. BRANDS AS AN ASSOCIATIVE NETWORK ...... 15 3.1 THE BASICS OF ASSOCIATIVE NETWORKS ...... 15 3.2 APPLICATION OF THE BRAND CONCEPT MAP AND ITS FIT TO BROADCASTING BRANDS ...... 17 3.3 THE STRONGER THE ASSOCIATION, THE GREATER THE IMPACT ON IMAGE ...... 18 3.4 HOW FAVORABILITY AND UNIQUENESS IMPACT ASSOCIATIONS ...... 19 4. THE CONSUMER'S DISPOSITION TO PROCESS MULTIPLE BRAND CUES...... 22 4.1 BRAND ARCHITECTURE ...... 22 4.2 STRENGTH OF LINKAGES AND SPILLOVER EFFECTS ...... 25 4.3 SPILLOVER EFFECTS AND IMAGE TRANSFER ...... 27 5. PROPOSITIONS ...... 29 5.1 PRIMARY VS. SECONDARY ASSOCIATIONS ...... 30 5.2 PERCENTAGE OF RESPONDENTS MENTIONING >1 BRANDS (STATION AND PRESENTER AS DRIVER ROLE) ...... 32 5.3 TWO-WAY SIMILARITY OF % MENTIONING CORRESPONDING BRAND ...... 33 5.4 CORRECT INCOMING ASSOCIATIONS ...... 34 6. DATA EN METHOD ...... 36 6.1 CUE DEVELOPMENT ...... 37 6.2 PRE-TESTING ...... 41 6.3 SCENARIO DEVELOPMENT ...... 42 6.4 QUESTIONNAIRE DEVELOPMENT ...... 43 6.5 DATA STRUCTURING AND METHOD OF ANALYSIS ...... 44 7. RESULTS ...... 46 7.1 PRIMARY VS. SECONDARY ASSOCIATIONS ...... 46 7.2 PERCENTAGE OF RESPONDENTS MENTIONING >1 BRANDS (STATION AND PRESENTER AS DRIVER ROLE) ...... 52 7.3 TWO-WAY SIMILARITY OF % MENTIONING CORRESPONDING BRAND ...... 57 7.4 INCOMING ASSOCIATIONS ...... 59 8. DISCUSSION ...... 63 8.1 INTERPRETATION OF RESULTS ...... 63 8.2 INTERESTING FINDINGS APART FROM THE PROPOSITIONS ...... 67 8.3 THEORETICAL IMPLICATIONS ...... 69 8.4 MANAGERIAL IMPLICATIONS ...... 70 9. CONCLUSIONS ...... 73 9.1 LIMITATIONS ...... 74 9.2 FUTURE RESEARCH DIRECTIONS ...... 75 10. REFERENCES ...... 77 11. APPENDICES ...... 81

3 1. So many brands, such an obtuse pastime

Millions of people around the world end their working day by watching some television as a relaxing and enjoyable pastime, a staggering 3 hours and 39 minutes a day according to Eurodata TV.

Relaxing as it may be, viewers get to process an enormous amount of information while watching television. Four basic factors with which viewers are confronted while watching television, apart from commercials, are: the broadcasting station, broadcasting channel, program and the presenter.

Making a leap forward, one could argue that the viewer gets to process four brands simultaneously.

This makes it a case of intense and complex brand processing for the consumer and it would be intriguing to know how the consumer processes all four brands at the same time. Moreover what effects do these four broadcasting elements have on each other's image after being processed simultaneously?

1.1 Brands in the broadcasting system

The American Marketing Association (1960) defines a brand as “A name, term, design, symbol, or a combination of them, intended to identify the goods or services of one seller or group of sellers and to differentiate them from competitors.”, and by this definition one could argue that all of the mentioned four broadcasting elements are brands. Take the memorable HBO jingle as an example, even when the television show is aired by a different (foreign) channel, the well-known sound of the HBO jingle alone is enough to quickly prime people that it is an HBO show. Or consider Simon

Cowell, who is known for being a member of the jury in virtually every talent show from Pop Idol,

Il Divo, The X Factor till the Got Talent shows. He obviously is very good at branding himself as the 'jury member every show must have'. Oprah Winfrey takes it to an even higher level, as fans are willing to purchase merchandise such as Oprah t-shirts, pillows, bumper stickers, coasters and even

Oprah wigs. The former examples demonstrate the variety of broadcasting brands in play when watching a television program. Although some research has been done on how people process the exposure to multiple brands at the same time, no research has been done on this phenomenon in the

4 broadcasting context specifically. It would be very relevant to find out how different broadcasting elements affect each other's image as there are many current debates going on with regards to the broadcasting system. Knowledge on how different broadcasting elements affect each others brand image will help to formulate educated opinions on some relevant phenomena of which the following two paragraphs will depict a couple.

1.2 Broadcasting brands in the current events

The Netherlands has a government-subsidized public broadcasting system with 3 broadcasting channels and 21 broadcasting stations. Each of these broadcasting stations has its own characteristics and is founded on a certain set of beliefs and principles, which may be of a religious, political or social nature. Due to government deficiencies, the Dutch government has decided to cut back its subsidies towards the public broadcasting stations. As a result of these budget cuts, the 21 public broadcasting stations have to merge into just 8 broadcasting stations (www.rijksoverheid.nl). Con- sequently a fair debate has been going on concerning the effects on the image of the merged broadcasting stations. Do viewers actually know and care that the popular talkshow 'De Wereld Draait

Door' is aired by broadcasting station BNN/VARA or might it as well be aired by KRO-NCRV? By understanding how consumers process the broadcasting brands, one might be able to formulate a sensible picture on what effect these mergers have on the image of the concerning broadcasting stations. Moreover, one could to argue whether broadcasting stations brands matter at all.

At the same time, a fierce debate has been going on in the Netherlands on how much presenters at the public broadcasting should earn since they are paid with money collected by taxes. This debate raises questions concerning the value of a presenter because how much does a presenter actually affect or contribute to image of the broadcasting station or channel? An answer to this question might help with evaluating how much a presenter should earn. In order to answer this question one has to know how important the presenter is for the viewer count of a program or broadcasting station. In other words, do people actually watch a certain television program because of a certain pre-

5 senter and would they stop watching the program if the presenter were traded in for another, per- haps less-known, presenter. This might be an indicator of the net worth of a presenter and its com- petitive position. This phenomenon is greatly illustrated by the late night talkshow war in the U.S. where the three highest earning late night talkshow hosts John Stewart, Jay Leno and David Letter- man earn $30 million, $20 million and $20 million a year respectively (Dailymail, 2013). Also, a great race took place in 2013 on who could become Jay Leno's successor on the Tonight Show, which was won by Jimmy Fallon who is said to earn up to $12 million dollars for presenting the

Tonight Shows (Pomerantz, 2013).

These topical examples and questions come down to the general issue: How do the four major broadcasting brands interact and affect each other? One would need to know how these four elements connect in the mind of the consumer. Aaker (1991) describes consumer brand associations as those perceptions, preferences, and choices in memory linked to a brand. By this definition, we can argue that emotions and attitudes towards the different four elements might overlap or contradict. In either way, the assumption is that they do influence each other. In order to investigate this coher- ence, one has to understand what exact mental associations the viewer makes with regards to the broadcasting system. These so-called associate networks are actively researched and used, however little research has been conducted in this field with the broadcasting system specifically as a sub- ject. In order to understand how viewers process all four brands, associative networks seem to be a powerful tool in trying to understand the mental connections a person makes and how the television environment is portrayed in the consumers mind.

1.3 Problem Definition

As discussed before, recent debates around the broadcasting system raise a lot of interesting questions. Answers to these questions would be especially valuable to both the governments and broadcasting stations but would also provide interesting new insights into branding theory. The underly-

6 ing question that would ultimately help answer a lot of these questions is: How do the images of the four major broadcasting brands (broadcasting channel, station, program and presenter) affect one another in the mind of the consumer when displayed together?

There are several preceding questions this paper aims to answer in order to finally answer the before mentioned research question. First we need to find out whether general branding literature or theory can be applied to the four elements in the broadcasting system; do they behave as conventional brands? Consecutively, we want to identify what specific mental associations the viewer makes with all the four brands separately and how strong these connections are. By weaving this separate information together we want to analyze what the similarities and differences are in the images of four parallel or corresponding brands. With this information we hope to answer to what extent the images are impacted by one another.

In short, this paper will try to provide answers on the effects of simultaneous processing of the four major broadcasting brands and determine how they affect each other's image. However, this paper will not investigate why people watch certain programs nor will it discuss how to alter or improve the associations. Also this paper will not try to provide definitive answers on ongoing debates about the broadcasting system structure.

1.4 Contributions

Theoretical Contributions

This paper adds to academic literature by providing insight into the branding of elements in the broadcasting system (broadcasting channel, broadcasting station program and presenter); it will evaluate whether these elements actually behave like conventional brands. Also it sheds light on the specific associative networks that consumers possess on the broadcasting system. After analysis of study results, this paper will help provide insight into what associations connect different brand

7 elements in the broadcasting system. This will unravel how the different brands in the broadcasting system are connected and affect each other's image. Moreover this paper will provide valuable insight into the interdependence of 'brands' in the broadcasting system.

Managerial Contributions

This paper provides substantial managerial value as it caters a foundation for governments worldwide in deciding whether merging public broadcasting stations is viable option and what impact it will have in the broadcasting system. It would help them understand how strong the connections that viewers have with different broadcasting stations really are. Also, this paper will provide valuable insights for management of broadcasting stations as it displays the interdependence of their broadcasting station with all other brand elements in the broadcasting system. Moreover it will provide answers on the effect that the four factors (broadcasting channel, broadcasting station, program and presenter) have on each other's image. This will help management decide which of the elements to focus on and what elements are worthwhile to invest into.

1.5 General overview

This paper will commence by reviewing literature and concepts that are relevant to the research that will be conducted in this paper. First, the paper will investigate what the concept of ‘a brand’ is and whether the four broadcasting components can be classified as such according to current definitions. Next, it will investigate the concept of 'brand associative networks' in order to better understand how brands are connected and therefore might affect each others image. Subsequently, existing literature on the processing of multiple brands cues will be discusses. Current literature on brand architecture will be used to interpret current knowledge on how consumers respond to simultaneous brand cues. Then we will introduce our research proposal and methods whereupon the research procedure will be exhibited. The paper will conclude by a discussion of the finding of the

8 conducted study after which an overview of implications and limitations will bring this paper on broadcasting brands to a closure.

9 2. Broadcasting components as full-fledged consumer brands

In order to understand the impact and importance of the four main elements in the broadcasting system (broadcasting channel, broadcasting station, program and presenter) one has to know how to analyze them. A first and important step would be to understand whether these four elements behave like, and can be defined as conventional brands. Therefore it is vital to start by researching what the concept 'brand' entails according to existing branding literature and we will thus start by setting out some of the definitions. Accordingly, some examples will be provided with every definition to evaluate whether the broadcasting components can be considered brands according to the given definitions.

2.1 Classic brand definition

As noted earlier, the American Marketing Association (1960) defines a brand as “A name, term, design, symbol, or a combination of them, intended to identify the goods or services of one seller or group of sellers and to differentiate them from competitors.” According to this very basic definition, a brand is a mere visual or text element to recognize or identify the origins of a certain product or service in order to know the consumer is purchasing from. By this description it would actually be extremely easy to establish a 'brand' because a seller would only need to come up with a name or logo and one would have established a brand. Actual awareness or recognition by the consumer nor the market value of the brand plays any role in creating a brand according to this definition by the

American Marketing Association.

A sweeping case to illustrate this definition is BBC's Top Gear with Jeremy Clarkson as the lead presenter of the program. This is a popular TV show about cars with an estimated worldwide viewer audience of 350 million (Metro News 2010). In the UK, the BBC is both the broadcasting channel and station of this British television show. The BBC has a discernible combination of a name and symbol in order to differentiate them from competitors. Also the Top Gear program itself has a rec-

10 ognizable name, logo and jingle in order to differentiate the show from competition in the television landscape. Last of the four components is the presenter of the show Jeremy Clarkson, who obviously has a name which discerns him from other presenters. Even more so, one could argue he even has

'terms' or slogans to characterize himself such as: "and on that bombshell, it is time to end the show" with which he closes every episode of the show.

2.2 Brand image building in practice

Obviously, the former definition by the American Marketing Association might be outdated because it was created in 1960, in which time the definitions of a brand were generally rather simplistic in nature. The consumers were often seen as passive variables in the branding context when brands were described as descriptive, administrative and legal uses of the seller. However, one definition of the concept 'brand' that we particularly like was formulated even earlier in 1955 and might have been ahead of its time. This definition is by Gardner And Levy (1955) "A brand name is more than the label employed to differentiate among manufacturers of a product. It is a complex symbol that represents a variety of ideas and attributes. It tells the consumer many things, not only by the way it sounds (and its literal meaning if it has one) but, more important, via the body of associations it has built up and acquired as a public object over a period of time." (p.35). Gardner and Levy describe a brand much more holistically and basically describe it as a complex image that has been built up in the consumer's mind over a substantial period of time.

A proper example to illustrate this definition of a brand in the broadcasting context might be the

German detective television show Tatort. Tatort has been running on German television for over four decades at the moment and has developed into a pop-culture symbol during this time (Kim- melman, 2009). The first episode was broadcasted in 1970 and adopted the old-fashioned blueprint of two detectives trying to decipher a murder mystery; the German 'Krimi'. There are 15 stand-alone versions of "Tatort" produced by regional divisions of the broadcasting station ARD, all of which

11 feature a specific region of Germany as its murder decor. Although Tatort is obviously a brand name with its own adoption in each region of Germany, it has become much more than that. Be- cause it has been aired for such a long time with so many regional adaptations, it has become a be- loved concept with which many Germans have numerous of personal associations. This image of

Tatort, which has been built up over time is what makes Tatort a legitimate brand according to

Gardner And Levy (1955).

2.3 Brand Equity sells

Keller (2002) much later also argues that a marketer should not only create a name or logo but actually has to create a certain amount of awareness, reputation and prominence in the marketplace.

This corresponds with the more contemporary perspective that a brand is established when it inter- acts with the consumer; with the consumer being an active participant in the creation of the brand.

Generally one could say that the favorability of the response to the brand determines the brand equity. This concept of brand equity is defined by Aaker (1996) as “the set of assets and liabilities linked to a brand’s name and symbol that adds to or subtracts from the value provided by a product or service to a firm and/or that firm’s customers”. One could see that this is a rather complex definition that is clearly rooted in the definition of the American Marketing Association but builds on it by stating that the brand is not merely a name or symbol but exists by having a balance of assets and liabilities. In other words, there is a certain substance or value behind the brand name that makes the brand name a brand. Consecutively he subdivides the set of assets and liabilities into four categories: brand awareness, perceived quality, brand associations and brand loyalty.

The widely popular talent show The Voice Of can be considered as a brand that has a certain set of assets and liabilities to its brand name. Apart from the brand name, logo and jingle it has undoubt- edly built up a certain amount of awareness, reputation and prominence in the marketplace. The concept of The Voice is that contestants participate in 'blind auditions' so that the jury members

12 have to judge merely by the voice of the contestant. This 'blind audition' element is what sets this talent show apart from others and helped it to build this reputation and prominence in the marketplace. The fact that the television brand 'The Voice' possesses a certain set of assets is demonstrated by the fact that producer Talpa sold the format to 45 countries around the globe (talpa.com). Again, this indicates that broadcasting components fit the existing definition of a brand in the current branding literature.

2.4 Positive evaluations create irreplaceable brand equity

Building on the previous section, Keller (2008) later supplements the definition by Aaker (1996) by stating in a substantially easier to grasp fashion that “A brand has positive customer-based brand equity when consumers react more favorably to a product and the way it is marketed when the brand is identified than when it is not”. What Keller actually describes with this definition is the word 'assets' in the definition given by Aaker. He notes that the recognition of the brand and a subsequent more positive valuation of the product can be defined as the brand having equity; it evokes a positive reaction. Brand equity should not be confused with brand value, the latter refers to the financial valuation of a brand and brand equity is one of the building blocks of this value. As we learn from Aaker's definition, brand equity is the factor that makes people evaluate product 'A' more favorably than the completely identical product 'B' because of the positive mental associations to brand 'A", which can not necessarily be expresses in financial value.

The last example in this section to illustrate the existence of brands in the broadcasting system is one of a presenter. 'All you need is love' is a popular Dutch television show bringing together loved ones by a plethora or original surprises. This TV show has been running for over two decades, more importantly however, all 22 seasons of this show have been presented by Robert ten Brink. The show has moved between three broadcasting channels (Veronica, SBS6 and RTL4) but the presenter always stayed the same. Apparently, Robert ten Brink has become such an important brand name

13 that the show would not be the same without him and thus all three broadcasting channels contract- ed Robert ten Brink along with the acquisition of the show. This demonstrates how presenter (A)

'king of love' has more positive mental associations than any other presenter (B) and thus caries a certain amount of brand equity.

2.5 Broadcasting components fit the brand definition

It is important to stress that this overview of the definition of a 'brand' and its fusion into 'brand equity' is just a very concise and abridged version on the evolving discussion by branding scholars on the definition of a brand. This is in large part because scholars have not yet agreed on a universal definition. De Chernatony and Leslie (1998) have given a very complete overview of twelve main themes that represent the broad range of definitions of 'a brand' given by marketing scholars: i) legal instrument; ii) logo; iii) company; iv) shorthand; v) risk reducer; vi) identity system; vii) image in the consumers' minds; viii) value system; ix) personality; x relationship; xi) adding value; and xii) evolving entity. By this enumeration it might become clear that the concept of a brand has been tried to grasp by many but that a consensus has not been reached.

However, this section provided examples to illustrate how the four broadcasting components fit many of the most common brand definitions. Therefore it is important to conclude that although an agreed upon definition of a brand does not exists, it seems fair to conclude that all four broadcasting components seem to fit all common definitions of a brand.

14 3. Brands as an associative network

The previous section gave a broad overview of the meaning of a brand, which has indicated that in large part the existence of a brand is formed by the mental associations of the consumer. It therefore seems beneficial to review what these so-called associative networks are in order to get a better understanding of what these mental connections to a brand look like. Moreover, it is particularly valuable to understand associative network theory, as this seems to be a fit theory or method to research the propositions in this paper.

3.1 The basics of associative networks

Henderson, Iacobucci and Calder (1998) have studied previous work on associative networks and conclude that researchers generally agree on the fact that knowledge is represented as "associative networks" and that such a network structure is made up of concept nodes and prepositional links.

These nodes are units of information such as an object, place, person, time and emotion. The prepositional links are the connecting lines between these units of information, creating a network of thoughts around the central node. This definition will be illustrated in the following paragraph.

An associative network can be constructed with every possible unit of information as a starting node and by no means has to be a brand. However for the purpose of this paper let us consider the below example by Aaker (1996) where the brand McDonalds is the central point or node of origin in the associative network. This can be considered an example of a person being asked what he or she thinks of when thinking of the brand McDonalds. This person comes up with a total of 10 nodes that he or she associates with the McDonalds. These are all sorts of associations such as a certain setting (social environment), evaluation (value) or experience (service). Often many nodes are not direct associations with the brand but derived associations, in this case one thinks of meals and consecutively of products and burgers.

15 Figure 3.1: Brand associative network

(Source: Aaker 1996)

Aaker (1991) describes the associative network in the context of brands in particular as being those perceptions, preferences, and choices in memory linked to a brand. He also notes that brand associations, in particular but not necessarily positive ones, build value for the brand as this is a necessary condition to have any brand awareness or reputation as discussed by Keller (2002). The knowledge on what associations consumers have with their brand could potentially very valuable for companies to have. It contains information on what connections the consumer makes with the brand and therefore it could easily be used to verify if these associations are the ones that the company intended to induce by means of their branding strategy. Some insight into this process is provided by Henderson, Iacobucci and Calder (1998) who performed empirical research in order to demonstrate how the representation of consumer brand perceptions in an associative network are a

16 valuable tool in analyzing the effects of branding. They set out a multiple measures and means of analysis to gather and interpret the brand associative networks with which a company could set up a profile of brand effects and strategies.

3.2 Application of the brand concept map and its fit to broadcasting brands

In an optimal situation, companies should be able to measure such a network of brand association in order to obtain a clear overview of the important brand associations but also how these associations are connected to the brand and to one another (John, Loken, Kim and Basu Monga, 2006). Having this knowledge, a company would be able to roughly determine why consumers have particular associations as the network contains information on how associations are spread by identifying specific links. It is the spreading activation process from a brand name to brand associations recalled from memory which produces a mental map of the brand (Dobni & Zinkhan, 1990; John et al.,

2006). Consequently, these maps of brand associations reflect the structure of consumer memory on which consumer based brand equity is based (French &Smith, 2013).

The latest major adaptation of the associative network model is the Brand Concept Model developed by John, Loken, Kim and Basu Monga, 2006. This model is very similar to the associative network as exemplified earlier and refines the methodology of free associations that consumers can make by recalled brand associations. As long as the consumer is familiar with the brand, these brand concept maps can be used to draw maps on any fmcg, consumer durable, service, corporate and or product brands (French & Smith, 2013). Even more so French and Smith note that the brand concept model technique may be applied in any market sector where consumers possess associative networks of the brands therein. It is important that French and Smith verify that the brand concept map can be extended to virtually every brand variation as this makes it easier for us to safely stretch the concept to research our four brand variations of focus (broadcasting channels, broadcasting stations, programs and presenters).

17 3.3 The stronger the association, the greater the impact on brand image

Krishnan (1996) makes an important case on the strength of brand associations. He argues that the more associations the associative network map contains, the stronger the brand is as this increases the number of pathways between associations and thus the likelihood that the brand will be activated or recalled when one of these associations is activated. This could prove to be an interesting phenomenon in our research on the broadcasting system as some of the components might evoke many more associations than others. This could potentially be key to understanding how certain brands in the broadcasting system influence the reputation of others. If brand 'A' has a large network with a lot of associations and brand 'B' has an equally large associative network, it is self- evident that the odds are far greater that some of the associations are overlapping than when both had limited associative networks. When brand A and B have overlapping associations, this would mean that brand A could activate brand B and the other way around. Equally this could mean that brand A and brand B can influence each other’s image as they are somehow connected in the brand network of the consumer.

Also, van Rekom, Jacons and Verlegh (2006) emphasize that associations that are connected directly to the brand are more important than more distant connections. In other words the closer the connections are to the brand the more they define the basic image that the consumer has of the brand.

Although this may seem obvious, it is meaningful to re-emphasize this point as this might help us to understand the effect on image later on in this paper; direct links might affect a broadcasting brand's image far stronger than distant links.

To illustrate the theory in the previous example in the broadcasting context let's consider the following example: The Dutch broadcasting channel RTL 4 might have a extensive brand concept map of which one of the associations is the program 'Miljoenenjacht' , 'Miljoenenjacht' itself has a network of associations as well of which one might be the presenter of the show Linda de Mol. By this

18 strain of associations RTL4, Miljoenenjacht and Linda de Mol are all connected and as a result one of these brands might activate the other two. One could argue that if these are strong connections that the image of RTL4 might influence the image of Linda de Mol as these are connected by prepositional links in the associative network through which image could transfer. This is a crucial assumption that this paper is going to explore in larger depth. On a side node, it seems apt to mention that 'RTL4' and 'Linda de Mol' can be linked by multiple associations simultaneously. The two brands might also connect by a node 'entertaining' and 'leisure time', an increase of mutual associations strengthens the connection between the brands.

3.4 How favorability and uniqueness impact associations

This paper examines the impact that broadcasting brands can have on each other's image, therefore one should recognize that this can entail both positive and negative impact. The number of associations evoked by a brand is therefore not the only relevant criterion to examine the 'image impact' phenomenon. As consumer build up an extensive network of associations, many of these associations might be negative and therefore it is important to assess the relative presence of positive ver- sus negative associations. (Krishnan, 1996). Dacin and Smith (1994) indeed argue that "the favorability of consumers" predispositions toward a brand may well be the most elemental of all brand associations and is at the core of many conceptualizations of brand strength/equity. Dacin and

Smith elaborated on this by stating that brand associations must be strong but also favorable for brand equity enhancement.

The last relevant concept to cover in this chapter is the so-called uniqueness of a brand association.

The entire brand concept map is made up of a complex array of associations, some of which are unique to the brand and some of which are shared with other brands in the product category. Some of the brand associations may be shared with the brand HBO such as "entertainment" but may as well be shared with competitors in the category such as CBS and FOX. This is an example of a

19 shared association whereas "blockbuster series" might be an association that is unique to the brand

HBO. Krishnan (1996) amplifies that a brand needs some shared associations with the category in order to be classified as a brand that belongs in the relevant category, misconception on what category the brand belongs to can be catastrophic for the brand's image. Too many shared associations and the brand becomes generic, therefore a healthy balance of shared and unique associations greatly strengthens the brand. The figure below exemplifies an unbalanced situation (A) where there is just one unique connection and a variety of shared associations, it also depicts a balanced situation

(B) with a variety of shared and unique associations.

Figure 3.2: Uniqueness of the associative network

20 This chapter provided an overview on the concept 'associative brand networks' and covered the are- as of the concept that might prove relevant to understand how the image of one broadcasting brand might influence the image of another broadcasting brand. By understanding how brands are connected and what elements play an important part in connecting them, one should be more able to analyze when and how brand images can be affected by one-another.

21 4. The consumer's disposition to process multiple brand cues.

This paper aims to provide a better understanding of the way in which the broadcasting brands are structured in the perception of the consumer. Moreover, it explores how the brands are connected and therefore influence each other’s image. As the consumer is exposed to four broadcasting brands simultaneously, it is critical to understand how the consumer processes the exposure to multiple brands. As discussed before in chapter two, by the definition of Gardner And Levy (1955) and many others, broadcasting brands (program, presenter, station and channel) behave and therefore can be treated as traditional brands. Therefore, it is critical to explore present knowledge on how consumers process the exposure to multiple brands simultaneously. Current research on brand architecture, brand hierarchy and spillover effects tap into this topic and might help us gain a broader understanding of brand structures. Correspondingly, this chapter will discuss and highlight important research on the previously mentioned topics.

4.1 Brand architecture

Brand architecture literature helps us better understand the structure of brands when multiple brands are connected to each other. Understanding this brand structure, or the ‘brand relationship spectrum’ (Aaker and Joachimsthaler, 2000), might help us gain a clearer understanding of how the image transfer between multiple brands takes place. According to Aaker and Joachimsthaler, brand architecture can be described as the organizing structure of the brand portfolio in order to specify brand roles and the nature of relationships between brands. They developed a brand architecture tool called the brand relationship spectrum (figure 4.1), which is intended to classify different brand strategies. The main distinction they make between four basic brand strategies is amongst house of brands, endorsed brands, sub brands and the branded house. These four basic strategies are then further categorized into sub-strategies as can be seen in figure 4.1. Without diving to deep into the specifics of every strategy, the following paragraph will highlight relevant brand strategies in the broadcasting system and explain how these could influence image transfer.

Figure 4.1: The brand relationship spectrum.

By applying the brand relationship spectrum to the broadcasting system we have two make a distinction between the public broadcasting system and the commercial broadcasting system. The public broadcasting structure consists of four layers: the broadcasting channel, broadcasting station, program and presenter. The commercial broadcasting system excludes the layer of the separate broadcasting stations, or one might argue that the channel also serves as the station.

When employing the brand relationship spectrum for the broadcasting system, by the definitions of

Aaker and Joachimsthaler the structure would look like figure 4.2. According to their definition, a branded house involves an independent set of stand-alone brands, each maximizing the impact on a particular market. This closely describes the overarching function that a channel has over the broadcasting stations. Further specifying the function of the channel one could describe it as a shadow 23 endorser. The shadow endorser is defined as not being connected visibly to the endorsed brand, but many consumers know about the link. The broadcasting stations are most closely described by the definition of a token endorser. This strategy entails the endorsement of an established master brand

(station) to provide substance to the offering but it usually plays a minor driver role as the endorsed brand will be featured. The token endorser can be indicated by a logo, statement or other device. A broadcasting station is featured by a logo in the corner of the screen when a program (featuring the presenter) is aired. Some programs are also endorsed by a statement like ‘KRO’s detectives’. The station does however only play a minor driver role and thus can be considered a token endorser.

The channel can be considered a token endorser of the programs and presenters as well, as the channel’s logo is also visible when watching a program (featuring a presenter). In case of the commercial broadcasting system, the channel also plays the role of a broadcasting station and therefore adheres to the definition of a token endorser.

Figure 4.2: Brand relationship spectrum applied to the (public) broadcasting system.

House of brands: Token - Shadow Endorsement Endorser Program (Boer zoekt Vrouw)

Station Presenter (KRO) (Yvonne Jaspers)

Channel (Nederland 1) Program (Studio Sport) Station (NOS) Presenter (Tom Egberts)

24 Understanding the brand relationship spectrum in context of the broadcasting system is important because it could help us explain the linkage that consumers make between brands (Rangaswamy,

Burke and Olivia, 1993). This linkage that consumers could in turn help us explain the direction or ease of image transfer. As the parent brands (channel and station) play a minor driver role it might appear plausible that it is harder to retrieve the parent brands when confronted with the endorsed brands (channel and presenter).

4.2 Strength of linkages and spillover effects

The previous section discussed how linkages could potentially influence image transfer between brands; the concept of spillover effects builds upon this very theory. Studies (eg., John, Loken, and

Joiner 1998; Roehm and Tybout 2006) demonstrate that the strength of linkages between the parent brands and their subbrands or brand portfolio is a good predictor of the magnitude of spillover. De- ploying previously discussed associative network theory, Collins and Loftus (1975) found that linkages between two concepts can point in both directions. In other words, this means that the directionality of linkages suggests a pattern of spillover that cannot be predicted on the basis of non- directional strength of the association alone. Using an example by Lei, Dawar and Lemmink (2008) this means that negative information about Special K might not affect evaluations of Corn Flakes to the same extent as the same information about Corn Flakes affects the evaluations of Special K. In the context of our research we could replace Special K with a broadcasting station (KRO) and Corn

Flakes with a certain program (Boer zoekt Vrouw).

A typical brand portfolio consists of a parent brand (station or channel) and subbrands (program and presenter) acting as nodes with linkages of varying strengths. Farquhar and Herr (1993) proposed and Lei, Dawar and Lemmink (2008) further examined the spillover effects in these situations. Lei et al. (2008) constructed an example of an associative brand network representing a brand portfolio

(figure 4.3) to illustrate these spillover effects. They describe that the cognitive linkage between brands may not be symmetric in strength because “the direction processed more frequently develops

25 stronger relations” (Barsalou and Sewell 1985, p.650). In other words, the direction in which the brand link is processed influences the strength of the relationship and therewith the likelihood of image transfer. Similar results have been found by Morrin (1999) and Nedungadi (1990); both confirmed that the strength of association between brands is reflected in the probability of brand retrieval and the level of activation of the destination brands. Using figure 4.3 and the theory from

Barsalou and Sewell (1985) to explain this concept, when the consumer is often exposed to subbrand C when consuming parent brand P this link may be strong. The reverse could be true for the link in the contrary direction when the consumer is exposed in a small amount to parent brand P when consuming subbrand C. As a consequence the strength (probability of retrieval) of the relationship from parent brand P to subbrand C might be strong while the strength of the relationship from subbrand C to parent brand P might be weak. According to Lei et al. (2008) the same effects take place between different subbrands on the same brand level (program and presenter).

Figure 4.3: An example of associative brand network representing a brand portfolio

(Source: Lei, Dawar and Lemmink 2008 p.113)

26 4.3 Spillover effects and image transfer

Research (Lei, Dawar and Lemmink 2008) shows that previously discussed differences in directional strength can critically influence spillover in such a way that a strong relationship increases the likelihood of spillover effects and a weak relationship decreases the likelihood of spillover effects. These spillover effects are at the source of image transfer which is confirmed by Olson and

Zanna (1993) who found that when strength of association is retrieved, in most cases may determine the extent of updating of these brands.

The previously discussed theory on strength of linkages, spillover effects and updating of brand image has profound implications for the explorative research of this paper. These theories provide a solid theoretical background to develop our propositions and expectations on how broadcasting brands influence each other’s image.

The way in which consumers are exposed to multiple brands simultaneously determines the strength of the link, or number of associations, between these brands (Barsalou and Sewell 1985; Morrin

1999 and Nedungadi 1990). The simultaneous exposure to multiple brands is determined by the brand strategy or level of endorsement as previously discussed by means of the brand relationship spectrum (Aaker and Joachimsthaler, 2000). In context of the broadcasting system this is essential information to understand image transfer. A program is the main brand that is consumed since this is the primary focus of watching television. When watching this program, the consumer is to a greater extent exposed to the presenter of the program and to a lesser extent to the channel and station airing the program. The program is endorsed by two small logos of the station and channel in the corners of the screen.

Applying theory, when the consumer is processing the channel or station (watching the logo), the consumer is mostly consuming the program or presenter at the same time. According to Barsalou and Sewell (1985) this link, or probability of retrieval, should therefore be relatively strong which

27 promotes the possibility image transfer (Lei, Dawar and Lemmink 2008; Olson and Zanna 1993).

Reversely, when the consumer is processing the program, which is the main focus, or the presenter, which can be considered the secondary focus, the consumer is exposed to a very minor extent to the channel or station. Employing this theory, this link, or probability of retrieval, should be relatively weak resulting in a low possibility of image transfer. Building upon this knowledge, the next section will introduce the propositions for our research.

28 5. Propositions

For this paper we made an explicit choice to make use of propositions instead of hypothesis. The motivation for this choice is twofold. First of all this paper is an explorative research in which interesting findings with regards to image transfer in the broadcasting system are the primary focus and the fact that we were able to gather a large amount of quantifiable data is a big bonus. Since the research is explorative, hypothesis would have limited the explorative freedom of the paper. A major secondary reason that has everything to do with this limitation of freedom is the fact that to the best of our knowledge, no statistical test exists to statistically verify the majority of our propositions

(P2,3,4,5, 6 and 7). During the analysis stage of our research this remarkable fact came to light as no statistical test seems to be equipped to prove that ‘there are more 1 dummies in a certain group

(variable) than 1 dummies in another group (variable)’ in the same sample. We approached many professors and professionals in the field of statistics who all confirmed that that this statistical test does not exist. As our collected data is qualitative and our only means to quantify this data is trans- forming them into dummies, propositions seem to be the right choice. With the choice for propositions we are still able to use our bonus of quantifiable data to highlight interesting patterns without the need to statistically confirm hypothesis. We did however statistically test the first set of propositions P1 as these are different and can be tested by a repeated measures ANOVA test.

Now that we explained our deliberate choice for propositions we will now elaborate on the actual propositions. Taking into account the literature covered in the previous chapter we can derive a set of propositions that seems appropriate. These propositions are based upon the premise that brand hierarchy that applies to traditional brands also applies to brands in the broadcasting system. In traditional brand hierarchy literature, a distinct difference between a parent brand (endorser) and sub brand is made. In our propositions we try to grasp the essence of the associations, the relative strengths of the associations and the direction of associations between brands in the broadcasting system as these will ultimately determine the likelihood of image transfer.

29 A key concept used in the below propositions is the distinction between a program, a presenter, a broadcasting channel and a broadcasting station. These four brands are all presented as stimuli to a set of respondents. Next to each of these four brands being a cue, they can also be mentioned as an association to one of the cues. Another key concept that we use is the distinction between primary associations and secondary associations. This distinction is based on the type of association that a respondent has with a certain cue. Primary associations are qualities or equity inherently possessed by the brand. In other words they are associations directly describing the feelings or qualities attributed to a brand without naming another brand. Contrary, Secondary associations transfer the equity of another brand to the brand that the respondent is cued with. In our research we only classify associations that directly mention another brand as a secondary association. Because some respondents might mention brands other than the four brands that we are interested in we have used a sub category ‘miscellaneous secondary associations’ in our classification system. We leave these miscellaneous secondary associations out of consideration in our propositions. To rehash, wherever we use the classification secondary associations, we are talking about program, presenter, channel or station type associations.

5.1 Primary vs. Secondary associations

As discussed in the previous section, the broadcasting channel and station can be considered endorser brands in our set of brands, while programs and presenters can be considered sub-brands. In previous literature we have found that consumers are more opinionated about sub brands than about the parent company. Because consumers are more opinionated about sub brands we expect the respondents to have relatively many primary associations as these types of associations carry more personal opinions, values and qualities. Secondary associations convey less personal opinions, values and qualities but borrow these from the secondary associations that one has with the brand. In other words, these qualities are borrowed from the brands that the respondent associates the cued brand with. As discussed in previous brand literature, consumers tend to have relatively more brand

30 (secondary) associations with a parent brand.

As we consider the program and presenter to be sub brands, we derive our propositions P1a and P1b from the previously discussed theory. Embracing the theory that the level of exposure determines the strength of the link between brands (Barsalou and Sewell 1985), we expect the program and presenter to have relatively few brand (secondary) associations. As a result we expect the viewers to have a relatively large proportion of primary associations and propose the following:

P1a: Viewers have more primary associations than secondary associations with the brand ‘program’.

P1b: Viewers have more primary associations than secondary associations with the brand ‘presenter’.

As we consider the broadcasting channel and station to be parent brands (Aaker and Joachimsthaler,

2000), building upon this very same theory, as discussed in section 4.3 of this paper, we expect viewers to have more secondary than primary associations with both the channel and station. We propose the following:

P1c: Viewers have more secondary associations than primary associations with the brand ‘channel’.

P1d Viewers have more secondary associations than primary associations with the brand ‘station’

31 5.2 Percentage of respondents mentioning >1 brands (Station and Presenter as driver role)

Having covered our basic interest in whether consumers have more primary or secondary associations with the broadcasting brand, we would like to get a more detailed picture of the secondary brand associations. The secondary brand associations will tell us exactly how the broadcasting brands are connected and how they might affect each other’s image. In order to better understand what brand associations the consumer has, we want to measure the percentage of respondents that has an association with brand B, C or D when cued with brand A etc.

There is one caveat, for some of the brands it is far easier to have multiple brand associations while for some of the brands one cannot really have more than one association. To illustrate, when cued with a program, often one can only have one presenter association, as one person presents the program. Contrary, when cued with a channel, one can have many program associations as one could potentially mention all of the channel’s programs. Therefore we are not interested in the number of associations that respondents have but the percentage of respondents mentioning one or more associations. This will negate the before mentioned bias which would give a distorted view on reality.

We expect the level of exposure to determine the strength of the link between brands (Morrin 1999;

Nedungadi 1990), we therefore we expect people to more easily recall sub brands when cued with a parent brand and to more easily recall equal level sub brands than upward associations to a parent brand.

In order to find support for these theories on the strength and direction in the broadcasting system, we propose the following:

P2a: When cued with a ‘channel’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘station’.

P2b: When cued with a ‘channel’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘presenter’. 32 P2c: When cued with a ‘station’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘presenter’.

P2d: When cued with a ‘station’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘channel’.

P2e: When cued with a ‘presenter’, the percentage of respondents mentioning one or more associations with ‘program’ will be larger than with a ‘channel’.

P2f: When cued with a ‘presenter’, the percentage of respondents mentioning one or more associations with ‘program’ will be larger than with a ‘station’.

P3a: When cued with a ‘program’, the percentage of respondents mentioning one or more associations with a ‘presenter’ will be larger than with a ‘station’.

P3b: When cued with a ‘program’, the percentage of respondents mentioning one or more associations with a ‘presenter’ will be larger than with a ‘channel’

5.3 Two-way similarity of % mentioning corresponding brand

To further clarify and stress the differences in the directionality of the brand associations we have developed another set of propositions. This set of propositions directly talks about the two-way (dis) similarity (Lei, Dawar and Lemmink 2008) between brand associations in the broadcasting system.

This is specifically interesting because of the managerial implications this might have as when proven right, one brand is able to do more good or harm to another brand than the other way around.

P4a. When cued with a ‘channel’, the percentage of people having one or more associations with a

33 ‘program’ is higher than the other way around.

P4b. When cued with a ‘station’, the percentage of people having one or more associations with a

‘program’ is higher than the other way around.

5.4 Correct incoming Associations

So far we have presented propositions that talk about outgoing brand associations. In other words, what connections do consumer make when we present them with brand X. In order to further understand the brand associative network in the broadcasting system we also want to look at incoming connections. In our research we make use of four different cues (program, presenter, channel, station), which in all cases are relating sets in real life. That means that when we cue respondents with a program, we cue another set of respondents with the corresponding presenter of this program and the same goes for the channel and station. This means that every cued brand has the potential to be a secondary association in the other two or three corresponding cues (depending on whether it is aired by a broadcasting station). In other words, there are two or three ways to land on one of our cued brands. This phenomenon is exactly what Holden and Lutz (1992) describe when stressing that it is not only important to know what the brand can evoke, but to also know what evokes the brand.

These are the incoming associations and we expect that we can apply the previously discussed brand literature in section 4 of this paper. This literature implies that the extent of connection exposure strongly influence the strength of the associations, or probability of retrieval (Collins and

Loftus 1975; Barsalou and Sewell 1985). Based on the knowledge discussed in the literature section of this paper, we propose the following:

P5: The 'Program' has a greater percentage of matching incoming associations than the 'Station'

P6: The 'Program' has a greater percentage of matching incoming associations than the 'Channel'.

34 P7: A 'Program' has a greater percentage of matching incoming associations from the 'Presenter' than the other way around.

P8: The commercial channel RTL4 tends receives a greater percentage of incoming connections than the public channel or stations.

By means of the 18 propositions introduced in this section of the paper we are able to identify the strength of associations between brands in the broadcasting system. By uncovering the strength of these different links we will be able to discuss the probability of spillover effects as discussed by

Lei, Dawar and Lemmink (2008) and Morrin (1999). As a result we are able to identify the probability and direction of image transfer (lson and Zanna 1993) between brands in broadcasting system.

35 6. Data en Method

Although the nature of our study is explorative, we were able to base our studies on a quantitative approach of data collection to support our propositions. This will help with and reinforce the conclusions that we will subsequently draw concerning the propositions in this research (Saunders et al., 2012). In order to better understand the mental connections that consumers make between brands in the broadcasting system, we need to better understand their associative network. As discussed in section 3 of this paper, associative network mapping analysis is a good method to understand these associative network as French & Smith (2013) found it to be one of the best research methods for any market sector where consumers possess associative networks of the brands therein.

Therefore, to be able to get a thorough understanding of the associative networks of consumers, a free association response study was the best viable method to collect the necessary data. In a free association response study, respondents get cued with a predetermined brand or other stimulus and subsequently get the opportunity to write down all the associations or thoughts that come to mind when thinking about this brand.

As we want to find out the specific connections that people make between the program, presenter, broadcasting channel and station, we had to come up with a set of stimuli consisting out of these four brands. In order to gather a set of the data that could be used to analyze different angles, it was evident that it would be best to create matching sets of cues. This means that for every program (ea.

Boer zoekt Vrouw), we also developed the corresponding stimulus presenter (ea. Yvonne Jaspers), channel (ea. Ned1) and station (ea. KRO) in our set of stimuli. By having these matching pairs we are able to analyze propositions 5,6 and 7 and are able to talk about the specific interaction and dy- namics between matching brands.

In the online questionnaire we wanted to cue respondents with several brands, however, as we wanted to measure corresponding sets of brands we had to take the priming effect into account. If

36 we would cue the respondent with the program first and consecutively with the corresponding presenter, the respondent would have been primed with the program already. As a result, the respondent would most likely fill out the program, which would be biased because of the priming effect.

Therefore we needed to develop several sets of stimuli that were unrelated so that a priming effect would not take place. Another advantage of developing multiple sets of stimuli is that we are able to measure moderation and interaction affects. Just measuring one corresponding set of stimuli would give very narrow results. By using multiple sets of stimuli we are able to measure the differences between different programs, presenters, channels and stations. As a result we are able to analyze whether there is a bias in one of the stimuli or whether one of the stimuli seems to show abnormali- ties. Next to taking the bias out of our research this will also provide us with interesting results to talk about in the discussion section of this paper.

6.1 Cue Development

The next step in the process was the development of the specific stimuli for our research. There were several factors in the cue development that we wanted to keep in mind. Firstly, our research takes place in The Netherlands and therefore we need to develop a set of stimuli from the Dutch broadcasting system. The Dutch broadcasting system consists of two types of channels: public and commercial. We wanted to take both into account; however only the public channels have the synergy of different channels and stations (ea. Ned1/KRO) while for the commercial channels, the channel and station are the same (ea. RTL4). Secondly, we wanted to keep the survey relatively short (under 10 minutes) in order to attain a high response rate. This meant that we were aiming to cue the respondents with a maximum of three stimuli. Lastly, it was important that all stimuli were quite popular or known in the Dutch television landscape so that most of the respondents indeed have associations with the stimuli.

37 When developing the specific stimuli, we wanted to incorporate a good amount of variety between the different sets of stimuli. On the other hand, we had the keep the amount of sets in mind as every set needs a minimum response rate in order provide valid information. As we expected to reach 130 to 180 people with the survey and wanted a minimum response rate of 30 per set so we decided on four different sets. This gave us enough margin to gather a high enough response rate while still getting a great variety in stimuli. As elaborated on in section 6.3, we had to make some strategic choices as to the specific cue sets. When cueing people with different scenarios, we wanted to present them with independent cues to prevent form any priming effect taking place.

As we wanted to maximize the sets to four, but limit the number of cues to three, we had to make sure that some of the program cues are aired by the same channel. Therefore we picked two channels, the most popular public channel (NED1) and the most popular commercial channel (RTL4) and carefully picked two programs from each channel. Maximizing the likelihood for people to have associations, we picked from the most watched programs in the Netherlands. Also, we opted to have a wide variety in the kind of programs and therefore chose the most popular sports news, a reality dating show, a live showbiz news broadcasting and a talent show. We considered a large amount of cues before arriving to our final selection of cues. Among our rejected considerations were Linda de Mol, Wie is de Mol, TROS, AVRO, Alberto Stegerman, Ik Hou van Holland, Art

Rooijakkers, Red mijn vakantie and SBS6. Many of the stimuli we considered were dismissed because of sub-optimal conditions. An example of this is that many presenters recently switched between several channels or stations, which would promote confusion resulting in difficulty to classify associations. Another example would be the popular reality show ‘Wie is de Mol’ were the presenter plays to little of a role to facility a fair amount associations. After studying the Dutch television landscape we came up with four specific sets of stimuli that are popular or well-know and show a great amount of variety. All of the following stimuli adhere to our before mentioned criteria.

38 Cue set 1

1. Program: Studio Sport

Studio Sport is the most watched sports news program in the Netherlands.

2. Presenter: Tom Egberts

Tom Egberts is the Presenter of Studio Sport.

3. Channel: Nederland 1

Nederland 1 is one of the three Dutch public broadcasting channels and airs many programs

among which ‘Studio Sport’.

4. Station: NOS

NOS is one of the broadcasting stations in the Dutch public broadcasting system. NOS

mostly airs news and current affairs related programs among which ‘Studio Sport’.

Cue set 2

1. Program: Boer zoekt Vrouw

‘Boer zoekt Vrouw’ is a dutch reality TV show which evolves around the dating life of sev-

eral farmers. ‘Boer zoekt Vrouw’ currently is the TV show with the highest viewer rankings.

2. Presenter: Yvon Jaspers

Yvon Jaspers is the presenter of the program ‘Boer zoekt Vrouw’.

3. Channel: Nederland 1

Nederland 1 is one of the three Dutch public broadcasting channels and airs many pro-

grams among which ‘Boer zoekt Vrouw’.

4. Station: KRO

KRO is one of the broadcasting stations in the Dutch public broadcasting system. This

broadcasting station was founded on catholic principle but this doesn’t have a profound in-

fluence on the TV shows it airs currently. One of the programs in KRO’s portfolio is ‘Boer

zoekt Vrouw’.

39 Cue set 3

1. Program: RTL Boulevard

RTL Boulevard is the leading daily gossip news program in The Netherlands. It is a live

program with a variety of items and reports on showbiz, crime, royalty and lifestyle topics.

2. Presenter: Albert Verlinde

Albert Verlinde is one of the presenters of ‘RTL Boulevard’. A panel of presenters presents

RTL Boulevard but Albert Verlinde is the founder of the format and has been a main pre-

senter since the introduction of the show.

3. Channel (and station): RTL 4

RTL 4 is the most popular commercial TV channel in the Netherlands. Dutch commercial

channels don’t have different stations on the channel. RTL4 is both the channel and produc-

er of the show and therefore might be considered the station as well. One of the programs

that RTL 4 airs is ‘RTL Boulevard’.

Cue set 4

1. Program: The Voice of Holland

‘The voice of Holland’ is a very popular talent show in the Netherlands. It is a singing con-

test where they are looking for the most talented singer (voice) of The Netherlands. The

show is presented by a duo of two presenters and the contestants are assessed by a profes-

sional jury of four.

2. Presenter: Martijn Krabbé

Martijn Krabbé is one of the two presenters presenting The Voice of Holland. He is known

to host or present a variety of programs and talent shows in The Netherlands

3. Channel (and station): RTL 4

RTL 4 is the most popular commercial TV channel in the Netherlands. Dutch commercial

channels don’t have different stations on the channel. RTL4 is both the channel and produc-

40 er of the show and therefore might be considered the station as well. One of the programs

that RTL 4 airs is ‘RTL Boulevard’.

6.2 Pre-testing

In order to make sure that we chose a proper set of stimuli, we conducted a pretest with the following objectives: (1) to assess baseline familiarity with all of the selected stimuli (2) to assess whether there are no major variations in familiarity between the several sets of stimuli. This base level of familiarity is necessary for the respondents to be able to have and write down free associations in our subsequent main research. Since the pre-test had the simple objective of measuring familiarity with the stimuli, a simple questionnaire asking whether the person was familiar with the brand yes or no met the criteria. We set up a questionnaire asking the respondent ‘do you know X?’ on which they could answer yes or no on all 12 individual stimuli. The panel for this pretest consisted out of

31 respondents, which were chosen on a convenience-sampling basis. The results of the pretest were as follows:

Stimulus Familiar: YES Familiar: NO Yes % No %

Studio Sport 31 0 100 0

Boer zoekt Vrouw 31 0 100 0

RTL Boulevard 30 1 96.8% 3.2%

The Voice of Holland 30 1 96.8% 3.2%

Tom Egberts 26 5 83.9% 16.1% Yvon Jaspers 28 3 90% 10%

Albert Verlinde 29 2 93.5% 6.5%

Martijn Krabbé 27 4 87% 13%

Ned1 31 0 100% 0%

RTL4 31 0 100% 0%

NOS 31 0 100% 0% KRO 30 1 96.8% 3.2

41 As can be observed in the above table all of the 12 stimuli have a very high awareness rate. This awareness rate only tells whether the respondent has ever heard of the particular stimulus. This is all the information that we required at the point of pre-testing as the specific associations are only of our interest in the main research in this paper. The high familiarity rate tells us that most of the respondents will be able to relate or mention at least some associations when queued with our stimuli.

With the knowledge that at all of the stimuli were familiar to at least 83.9% of the respondents we felt confident to proceed with the scenario development with our chosen stimuli.

6.3 Scenario development

As discussed earlier we were aiming to queue the respondent with three different stimuli. It was important that the respondent did not get primed with any linked stimuli. This means for example that the respondent should not be cued with both the program ‘The Voice of Holland’ and the channel ‘RTL4’. The respondent would then have been primed with ‘The Voice of Holland’ and most certainly mention this program when consecutively queued with ‘RTL4’. Therefore we developed scenario’s consisting out of non-linked sets. All of the respondents will be randomly presented with one out of four scenarios. All of the scenario’s consist out of three stimuli that are non linked and all four scenario consist out of a (1) program, (2) presenter and (3) channel or station. The scenarios that we used in our research are as follows:

Scenario 1:Studio Sport/ Martijn Krabbe/ Nederland 1

Scenario 2: Boer zoekt Vrouw / Tom Egberts/ RTL 4

Scenario 3: RTL Boulevard/ Yvonne Jaspers / NOS

Scenario 4: The Voice of Holland / Albert Verlinde / KRO

42 6.4 Questionnaire Development

The data for this paper has been collected through the online Qualtrics survey software. In total, 173 respondents fully completed the survey and thus the results are based on these 173 complete data sets. For the survey we made use of the previously discussed ‘free association’ response type (Hen- derson, Iacobucci and Calder 1998; Aaker (1996). We presented respondents with three main questions which were formulated as follows: ‘Please write down everything that comes to mind when you think of xxxxx, there are no right or wrong answers and you don’t need to finish all 10 response options. However, please make sure that you keep on going until nothing comes to mind anymore.’

For every question each respondent got 10 response fields in which they could note down their free associations. We used four different scenarios and each respondent was randomly presented with one of four scenarios. For our explorative research, these questions provide us with a good understanding of the links in the associative networks of consumers. Next to the three main questions in the online questionnaire, we developed several questions that are designed to provide us with control variables. These questions asked the respondents about their gender, age, weekly hours of TV watching and their favorability towards public and commercial television. Age and weekly hours of

TV watching were measured with free response fields. Favorability towards public and commercial television were both measured on a 7-point likert scale (Preston and Colman 2000; Cox 1980). We identified these variables as variables that could potentially influence the network of associations that one has. These control variables enable us to measure whether there is an even demographic group distribution over the different scenarios. This means that over the four different scenarios, the groups look roughly the same in terms of gender, age, weekly hours of watching TV and favorability. Also with these control variables we are able to analyze whether there are differences between associative networks when we take the above mentioned control variables into account.

43 6.5 Data structuring and method of analysis

The survey data was imported into SPSS, which is the software we use to process the data and run the analysis. In order to run analysis on the collected data, the data needs to be quantitative, however, free response data is qualitative by nature. Therefore we had to recode every single free response given into a dummy response. Key to analyzing the propositions is the classification of the responses into brand categories. In other words, for every single we response we are interested in knowing whether this is a association related to a program, presenter, channel, station or an other response.

For the first set of propositions we need to make the distinction between primary and secondary associations. Consequently, we also developed a dummy to accommodate a ‘primary’ response. The four brand dummies together form a new variable: secondary associations. To be able to rightfully classify every response and analyze our propositions we created seven dummy categories which are: 0: Primary, 1: Program, 2: Presenter, 3: Channel, 4: Station, 5 Miscellaneous Secondary, 6:

Miscellaneous. To be able to categorize and add all different combinations of associations we created a total of 379 individual variables in SPSS. This enables us to get a usable data set on which we can run the appropriate analysis.

Each of the roughly 2.500 free associations were manually assigned to one of the before mentioned categories. Clearly, this involves a certain degree of arbitrariness, which cannot be avoided. We did however categorize all the responses in a very consistent manner. Also logic of category allocation and consistency were checked by a secondary corrector. All responses relating to a program were assigned to the corresponding ‘program’ category. The same logic is applies to responses related to a presenter, channel, or station. Also, as described earlier, we assigned primary responses relating directly to the cue to the category ‘primary’. Two other categories were created which are labeled

‘Miscellaneous Secondary’ and ‘Miscellaneous’. The former category was created to accommodate secondary associations to the cue that cannot be assigned to any four of the brands. To illustrate, one can think of associations to the producer of the show, the location of the show, or a family

44 member of the presenter. The last category ‘Miscellaneous’ is designed to accommodate associations that have no logical link to the cue at all. Examples of answers that were allocated to this category are ‘my grandfather’, ‘Philips ambilight’ and ‘dinner on the couch’.

We then created dummies for every single response, which are coded as 0 or 1. These dummies describe for every single response option whether or not they belong to a certain category. These dummies are labeled as follows: VAR1_Resp1_Cat1, VAR1_Resp1_Cat2, VAR1_Resp1_Cat3,

VAR1_Resp1_Cat4, VAR1_Resp1_Cat5, VAR1_Resp1_Cat6, VAR1_Resp1_Cat7 and the same variables are created for all 10 responses that the respondent can give to one single cue. Each response can only belong to one category so one of the dummies has the outcome 1 and the other 6 dummies therefore must have the outcome 0. This is verified by a separate variable, which must add up to 1 for every single response. We then created variables that sum up the number of dummies for every category. This variable (varX_catX_amount) indicates for every cue how many of the free associations belong in category 1,2,3,4,5,6 or 7. These variables are used to run the repeated measures ANOVA tests on propositions P1a, P1b, P1c and P1d.

For proposition P2, P3 and P4 we need to know the percentage of respondents that has a minimum response number of one for each category. Therefore we needed to make dummies to classify whether for every cue the respondent has at least one association in one of the seven categories.

These variables are named: varX_cat1_yesno, varX_cat2_yesno, varX_cat3_yesno, varX_cat4_yesno, varX_cat5_yesno, varX_cat6_yesno and varX_cat7_yesno. These dummy variables are used to run the chi-square tests for proposition 2,3 and 4. To the best of our knowledge, no statistical test can provide us with a P-value to prove that the chi square values are significant. In other words, no statistical test exists that is able to test whether there are more ‘0’ or ‘1’ dummies in one category compared to a secondary category of dummies. Therefore we will use the chi-square test outcomes to support or invalidate our explorative propositions.

45 7. Results

This chapter will lay out the results gained from the analysis of the 173 associative networks that we collected. The quantitative results will be accompanied by comprehensive interpretations of the results, which are subdivided into the different sections or sets of propositions.

7.1 Primary vs. Secondary associations

P1a: Viewers have more primary associations than secondary associations with the brand ‘program’. SUPPORTED

P1b: Viewers have more primary associations than secondary associations with the brand ‘presenter’. SUPPORTED

For both ‘program’ and ‘presenter’, with a 95% significance, PrimvsSec has a P-value of .000 (Ta- ble 7.1 or appx. 7.1.1 and appx. 7.1.3), which means that there is a major difference between the number of primary and secondary associations. This fact alone just means there is a significant difference between the number of primary and secondary associations but does not tell us whether the viewers indeed have more primary than secondary associations or the other way around. Therefore we have run the analysis in separate graphs (figure 7.1.1 and 7.1.2) that neatly depicts the fact that for both the program and presenter cue, the respondents have more primary than secondary associations. The combination of the significant P-values and the analysis of the graph support the propositions P1a and P1b.

PrimSec*VAR1 and PrimSec*VAR2 are also significant (Table 7.1 or appx. 7.1.1 and appx. 7.1.3) which means that the ratio between number of primary and secondary associations varies between the different stimuli. Taking a closer look at the graph (figure 7.1.1), we see that in the program analysis, the most extreme seems to be ‘Boer zoekt Vrouw’. This cue has a slightly steeper slope than the other three cues, which means the ratio between the number of primary and secondary as-

46 sociations is more extreme. The possible and speculative explanations for this will be further highlighted in the discussion section of this paper. In the presenter analysis one cue seems to be deroga- tory which is the cue ‘Martijn Krabbe’. This cue has a slope that is relatively flat compared to the other three cues which seems to be the primary reason that PrimvsSec*VAR2 is significant. The underlying reasons that respondents have relatively more secondary associations with ‘Martijn

Krabbe’ compared to the other three stimuli might be the fact that compared to the other presenters,

Martijn Krabbe presents a larger number of programs, which can be mentioned as secondary cues.

VAR1 (appx. 7.1.2) is also significant which means that for the cue program, the individual stimuli are significantly spread. This means that the amount of associations seems to differ between the cues in VAR1. We can see that the stimulus ‘The voice of Holland’ receives a greater number of associations that the stimulus ‘Studio Sport’.

47 Table 7.1: Core ANOVA statistics on Program and Presenter

F P N^2

Prim vs, Sec (program) 312,877 ,000 ,649

PrimSec*Program 2,892 ,037 ,049

Program 5,283 ,002 ,086

Prim vs. Sec (presenter) 110,863 ,000 ,396

PrimSec*Presenter 3,141 ,027 ,053

Presenter 1,814 ,147 ,031

Note: Refer to the appendices (11) for the detailed ANOVA tables

Figure 7.1.1: Number of Prim vs. Sec: Program

48 Figure 7.1.2: Number of Prim vs. Sec: Presenter

P1c: Viewers have more secondary associations than primary associations with the brand ‘channel’. NOT SUPPORTED

P1d: Viewers have more secondary associations than primary associations with the brand ‘station’

NOT SUPPORTED

The P-value for PrimvsSec for the variable Channel is .962 (Table 7.2 or appx. 7.1.5), which means that proposition P1c is far from being supported. Taking a closer look at the graph (Figure 7.1.3), we can observe that the reason being is that the graphs for ‘Ned1’ and ‘RTL4’ seem to be exactly

49 opposite. However, if we observe the average number of associations on the y-axis, we see that the cases are not as extreme as the graph might depict. The mean differences are much smaller than for the variables ‘program’ and ‘presenter. It is however certain that P1c is not supported for the variable ‘Channel’. The potential underlying reasons for the differences between ‘Ned1’ and ‘RTL4’ will be discussed in the discussion section.

The P-value of PrimvsSec for the variable Station is .088 (Table 7.2 or appx. 7.1.7), which means that proposition P1d is also rejected. The p-value is not very extreme which means that it is rather close to supporting the proposition. By looking at the graph (figure 7.1.4) we see that the stimulus

‘NOS’ seems to have a relatively flat slope. This means there is little difference between the number of primary and secondary associations for this particular stimulus. Taking into consideration both P-values, the only fair conclusion is that propositions P1c and P1d are rejected.

Table 7.2: Core ANOVA statistics on Channel and Station

F P N^2

Prim vs, Sec (channel) ,002 ,962 ,000

PrimSec*Channel 1,820 ,181 ,023

Channel ,191 ,663 ,002

Prim vs. Sec (station) 2,978 ,088 ,032

PrimSec*Station 1,583 ,212 ,017

Station 4,013 ,048 ,043

Note: Refer to the appendices (11) for the detailed ANOVA tables

50 Figure 7.1.3: Number of Prim vs. Sec: Channel

Figure 7.1.4: Number of Prim vs. Sec: Station

51 7.2 Percentage of respondents mentioning >1 brands (Station and Presenter as driver role)

P2a: When cued with a ‘channel’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘station’. SUPPORTED

P2b: When cued with a ‘channel’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘presenter’. SUPPORTED

In order to explore the validity of all P2 and P3 propositions, we ran chi-square tests to get better insights into the distributions of people mentioning one or more associations. For P2a and P2b, we ran the analysis with VAR3 for Ned1 and RTL4, which encompass half of the respondents because the other half of the respondents got cued with the stations ‘NOS and ‘KRO’ in VAR3. In the first chi-square we cross tabulate with the number of respondents that have at least one ‘Program’ response. In this case we see that this percentage is 77.8% (Figure 7.2.1 or appx. 7.2.1). For the second chi-square test we cross tabulate with number of respondents that has at least one ‘presenter’ response, which we find is 17.3% (Figure 7.2.1 or appx. 7.2.2). For the third chi-square test we cross tabulate the number of people that has at least one station response. In this case we can only run for ‘Ned1’ since ‘RTL4’ has no corresponding station. The percentage of people mention a station is 27.5% (Figure 7.2.1 or appx. 7.2.3). 77.8% is a remarkably higher percentage than 17.3% and 27.5% and therefore we appear to find support for propositions P2a and P2b.

52 Figure 7.2.1: Cued with Channel and having ≥ 1 association

Cued with Channel % ≥ 1 Association

100% 90% 80% 70% 60% 50% NED1 40% RTL4 30% 20% 10% 0% Program Presenter Station

P2c: When cued with a ‘station’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘presenter’. SUPPORTED

P2d: When cued with a ‘station’, the percentage of respondents mentioning one or more associations with a ‘program’ will be larger than with a ‘channel’. SUPPORTED

For propositions P2c and P2b, we ran the analysis with the stations for NOS and KRO, which again consists of half of the respondents since the other half of the respondents got cued with the channels

‘Ned1’ and ‘RTL4’ in VAR3. For the first chi-square we cross tabulate with the number of respondents that have at least one ‘Program’ response. In this case we see that this percentage is

84.4% (Figure 7.2.2 or appx. 7.2.4). By means of a second chi-square test we cross tabulate with the number of respondents that has at least one ‘presenter’ response, which we can see is 20.7% (Figure

53 7.2.2 or appx. 7.2.5). For the third chi-square test we cross tabulate the number of people that has at least one channel response. The percentage of people mentioning a channel is 19.6% (Figure 7.2.2 or appx. 7.2.6). Again 84.4% is a distinctly larger number than 20.7% and 19.6% and leads us to the conclusion that these numbers support propositions P2c and P2b.

Figure 7.2.2: Cued with Station and having ≥ 1 association

Cued with Station % ≥ 1 Association

100%

90%

80%

70%

60%

50% NOS KRO 40%

30%

20%

10%

0% Program Presenter Channel

P2e: When cued with a ‘presenter’, the percentage of respondents mentioning one or more associations with ‘program’ will be larger than with a ‘channel’. SUPPORTED

P2f: When cued with a ‘presenter’, the percentage of respondents mentioning one or more associations with ‘program’ will be larger than with a ‘station’. SUPPORTED

54 Similar to the previous four propositions, we now run the analysis for P2e and P2f with the second variable (presenter) for all four presenters that the respondents were cued with, which are ‘Martijn

Krabbe’, ‘Tom Egberts’, ‘Yvon Jaspers’ and ‘Albert Verlinde’. Firstly we cross tabulate the presenter cue with the number of respondents that have at least one ‘Program’ response. We find that the corresponding percentage is 60.1% (Figure 7.2.3 or appx. 7.2.7). The second chi-square test reveals the number of people having at least one ‘channel’ response amounts to 16.2% (Figure 7.2.3 or appx. 7.2.8). By means of the third test we cross tabulate with the number of people that has at least one station response. In this case we only take ‘Tom Egberts’ and ‘Yvon Jaspers’ into account because only these two presenters have a corresponding station. The percentage of people mentioning a station is 19.8%. 60.1% is notably larger than 16.2% and 19.8%, which means that we also seem to find support for propositions P2e and P2f.

Figure 7.2.3: Cued with Presenter and having ≥ 1 association

Cued with Presenter % ≥ 1 Association

100%

90%

80%

70%

60% Martijn Krabbé

50% Tom Egberts Yvon Jaspers 40% Albert Verlinde 30%

20%

10%

0% Program Channel Station

55 P3a: When cued with a ‘program’, the percentage of respondents mentioning one or more associations with a ‘presenter’ will be larger than with a ‘station’. SUPPORTED

P3b: When cued with a ‘program’, the percentage of respondents mentioning one or more associations with a ‘presenter’ will be larger than with a ‘channel’. SUPPORTED

Lastly, for propositions P3a and P3b, we analyze the results of the respondents that were cued with

VAR1 (program), which consists of ‘Studio Sport’, ‘Boer zoekt Vrouw’, ‘RTL Boulevard’ and

‘The Voice of Holland’. In the first chi-square we cross tabulate with the number of respondents that have at least one ‘Presenter’ response. In this case we see that this percentage is 45.1% (Figure

7.2.4). For the second chi-square test we cross tabulate with amount of respondents that has at least one ‘channel’ response, which we can see is 17.9% (Figure 7.2.4). The third chi-square test cross tabulates with the number of people that has at least one station response. In this case we only take

‘Studio Sport’ and ‘Boer zoekt Vrouw’ into account because only these two programs have a corresponding station. The percentage of people mention a station is 19.8% (Figure 7.2.4). In this case the difference is not as major as in the P2 propositions, which is in line with what we expected.

This expectation will be discussed in the discussion section of this paper. Still, one will notice that

45.1% is a remarkably higher percentage than 17.9% and 19.8%. The difference is still large enough to give us enough confidence to conclude that there seems to be support for propositions

P3a and P3b.

56 Figure 7.2.4: Cued with Program and having ≥ 1 association

Cued with Program % ≥ 1 Association

100%

90%

80%

70%

60% Studio Sport

50% Boer zoekt Vrouw RTL Boulevard 40% The Voice of Holland 30%

20%

10%

0% Pesenter Channel Station

7.3 Two-way similarity of % mentioning corresponding brand

P4a. When cued with a ‘channel’, the percentage of people having one or more associations with a

‘program’ is higher than the other way around. SUPPORTED

H4b. When cued with a ‘station’, the percentage of people having one or more associations with a

‘program’ is higher than the other way around. SUPPORTED

H4a and H4b are intended to clearly depict the interaction between certain variables. They are however based on the same premises and therefore variables as propositions P2 and P3. As a consequence the same chi-square outputs can be used that were produced for propositions P2 and P3. In order to validate P4a, we need to know what the percentage of respondents is that mention a channel or a program when cued with the respective opposites. By means of the two chi-square cross tabulations (Appx. 7.2.1 and 7.2.11), we find that the percentages corresponding to P4a are 77.8% 57 and 17.9% (Figure 7.3.1). These respective percentages depict a substantial difference, which seems to support the validity of propositions P4a.

Proposition P4b is similar to P4a only this time do we test the percentages corresponding to station and program responses. From the cross tabulation tables (appx. 7.2.4 and 7.2.12), we learn that these percentages are 84.8% and 19.8% (Figure 7.3.2). Again, the substantial difference in percentages seems to support the validity of proposition P4b.

Figure 7.3.1: Cued with Station/ Program and having ≥ 1 association

Cued with Channel Cued with Program % ≥ 1 Association with a % ≥ 1 Association with a program channel

100% 100% Studio Sport 80% 80%

60% 60% Boer zoekt NED1 Vrouw 40% RTL4 40% RTL Boulevard 20% 20% The Voice of 0% 0% Holland Program Channel

Figure 7.3.2: Cued with Station/Program and having ≥ 1 association

100% 100% 90% 90% 80% 80% 70% 70% 60% 60% Studio 50% Sport 50% NOS 40% Boer 40% KRO 30% zoekt 30% Vrouw 20% 20% 10% 10% 0% 0% Station Program

58 7.4 Incoming associations

The incoming associations are pictured in figures 7.4.2, 7.4.3, 7.4.4 and 7.4.5. To further illustrate the process of incoming and outgoing associations we have constructed figure 7.4.1These tables clearly depict the percentage of respondents mentioning the correct corresponding brand when cued with one of the three possible cues. These three cues are then distilled into the individual stimuli in order to identify any striking differences or similarities between the different stimuli.

Figure 7.4.2: Network of incoming and outgoing associations between broadcasting brands

Channel

Program Presenter

Station

59 P5: The 'Program' has a greater percentage of matching incoming associations than the 'Station'

SUPPORTED

The total percentage of correct incoming associations for the programs is 30% (figure 7.4.1) while the total percentage of correct incoming associations for stations is 18% (figure 7.4.4.). These figures seem to be in favor of proposition P5. More interestingly, the tables present striking differences between certain individual cues. When cued with a presenter for instance, the percentage of correct associations proves to be 51% for programs while the figure for the correct station amounts to 21%.

Lessons like the latter might have interesting practical implications that will be elaborated on in the discussion section of this paper. Furthermore, this section will highlight other specific observations from the four tables presented below.

P6: The 'Program' has a greater percentage of matching incoming associations than the 'Channel'.

SUPPORTED

The total percentage of correct incoming associations for the channels is 20% (figure 7.4.4). A 10% difference with the 30% that belongs to the programs table as observed in the previous proposition.

Again the 10% difference seems to favor proposition P6 to a reasonable extent. As for many of the propositions in this research, there is no statistical test to prove the significance of this difference.

The statistical significance is however not the main focus of our study as interesting finding are the primary focus. The 10% difference does give us enough confidence to conclude that proposition P6 appears to be right.

P7: A 'Program' has a greater percentage of matching incoming associations from the 'Presenter' than the other way around. SUPPORTED

60 As can be seen in figure 7.4.2, when cued with a presenter, 51% of the respondent matching program while the reverse number is 32% (figure 7.4.3). The difference of 19% seems to strongly support proposition P7. Again, several interesting explanations and implications as to why the strength of associations is not equal will be discussed in the discussion section. It is however clear at this point that distinct differences are present between the strengths of opposite incoming associations.

P8: The commercial channel RTL4 receives a greater percentage of incoming connections than the public channel or stations. SUPPORTED

As the commercial channel RTL4 is a unified channel and station in one we expect that this will strengthen the number of correct incoming associations compared to Nederland 1 where the channel and station are two separate brands. In figure 7.4.3 we learn that this is indeed an existing phenomenon as the percentages are 30% and 10% respectively. These results might have strong implications with regards to the legitimacy of the separation of the channels and station brands.

Figure 7.4.2: Percentage of respondents mentioning the correct Program. Mentioned Cued with Presenter Cued with Channel Cued with Station TOTAL Studio Sport 29% 15% 18% 22% Boer zoekt Vrouw 80% 5% 34% 41% RTL Boulevard 49% 10% 30% The Voice of Holland 45% 10% 27% Program 51% 10% 26% 30%

Figure 7.4.3: Percentage of respondents mentioning the correct Presenter. Mentioned Cued with Program Cued with Channel Cued with Station TOTAL Tom Egberts 13% 0% 0% 4% Yvon Jaspers 47% 0% 15% 19% Albert Verlinde 51% 2% 29% Martijn Krabbé 17% 2% 12% Presenter 32% 1% 8% 16%

61 Figure 7.4.4: Percentage of respondents mentioning the correct Channel. Mentioned Cued with Program Cued with Presenter Cued with Station TOTAL NED1 8% 0% 22% 10% 8% 0% 22% 10% RTL4 34% 26% 30% 34% 26% 30% Channel 21% 13% 22% 20%

Figure 7.4.5: Percentage of respondent mentioning the correct Station. Mentioned Cued with Program Cued with Presenter Cued with Channel TOTAL NOS 23% 29% 20% 24% KRO 16% 13% 8% 12%

Station 20% 21% 14% 18%

62 8. Discussion

This section will be dedicated to the interpretation and discussion of the results acquired in the previous section 7 of this paper. The first section will focus on the interpretation of the results after which we will discuss interesting findings from the analysis of the results apart from the propositions we constructed. The last two sections will be dedicated to the theoretical and managerial implications that these results and findings give rise to.

8.1 Interpretation of results

Primary vs. Secondary associations

Barsalou and Sewell (1985) found that the direction processed more frequently develops stronger relationships. Deploying the brand relationship spectrum (Aaker and Joachimsthaler 2000), we proposed that viewers have more primary than secondary associations with the brands ‘program’ and

‘presenter’. By analyzing the propositions we found this to be correct. We previously discussed that the strength of linkages between brands is a good predictor of the magnitude of spillover (eg., John,

Loken, and Joiner 1998; Roehm and Tybout 2006). The fact that viewers have relatively few associations with other brands in the broadcasting system when cued with a ‘program’ or ‘presenter’ means that the probability of spillover effects from other brands in the broadcasting system is relatively small (Morrin 1999; and Nedungadi 1990). In light of our research this means that the chances of image transfer from a another broadcasting brand to a ‘program’ or ‘presenter’ are fairly slim as Olson and Zanna (1993) found that when strength of association is retrieved, in most cases may determine the extent of updating of these brands.

We already discussed that Collins and Loftus (1975) found that linkages between two concepts or brands can point in both directions. We indeed expected the reverse pattern to be true for the endorser brands ‘channel’ and ‘station’ as the exposure to other broadcasting brands when consuming those brands is larger. By the theory of Barsalou and Sewell 1985 we expected the exposure to de-

63 termine the strength of the link between other broadcasting brands and as a result we expected the predomination of secondary associations. The results do not confirm these expectations, as the p- values from the analysis are insignificant. The underlying cause can either be attributed to methodological imperfections or to conceptual misinterpretations. Considering all the other confirmative results from the other 16 propositions into account, which build forth on the same theory, and examining the specific results, we strongly believe that the underlying cause is methodological in nature.

Examining figure 7.1.1, we find that the difference between the number of primary and secondary associations is only 0.5 for both channels, which is a fairly small number. One can also observe that the two channels produce opposite patterns. For the public channel ‘Nederland 1’ the results are in correspondence with our expectations. For the commercial channel ‘RTL4’ the patterns seems to be contrary to our expectation by a small amount. This may be due to the fact that RTL4 plays a dual role as both a channel and a station and might in practice be regarded and behave more as a station.

This brings us to the fact that the proposition on the ‘station’ produces opposite results to our expectations. When examining figure 7.1.2 we see that this contrary result is noticeably greater for the station ‘KRO’ than the station ‘NOS’. As the slope of the two different stimuli is different in gradi- ent, it might be a result of the stimuli selection. Some factor might cause the slope of the specific station ‘KRO’ to deviate from our expectations. The P-value is .088 and thus not far from supporting our proposition.

Percentage of respondents mentioning >1 brands (Station and Presenter as driver role)

The propositions on this topic are based on the premise that the level of exposure determines the strength of the link between brands (Morrin 1999; Nedungadi 1990). As explained in section 4.3 in this paper, we therefore we expected people to more easily recall sub brands when cued with a parent brand and to more easily recall equal level sub brands than upward associations to a parent

64 brand. We seem to find support for all 8 propositions belonging to this topic. These propositions talk about the individual links between specific brands in the broadcasting system. John, Loken, and

Joiner (1998) and Roehm and Tybout (2006) demonstrated that the strength of linkages between the parent brand and their subbrands or brand portfolio is a good predictor of the magnitude of spillover. With the results on the strength and direction of the specific linkages between broadcasting brands we can now draw some conclusions on the probability of image transfer between specific broadcasting brands (Olson and Zanna 1993).

As can be seen in figure 7.2.1 and confirmed by the propositions the in the brand hierarchy down- ward link, from the channel to the primary focus brand ‘program ‘ is found to be far stronger than the link with the station or presenter. Deploying the literature we can therefore conclude that the probability of image transfer from the program to the channel is considerably larger than from the station or presenter to the channel. Put differently, it seems very likely that the channel borrows many associations from the programs, as this link is very strong. The presenter or station can do very little to influence the image of a channel as these links are seemingly weak. Very similar conclusions can be drawn when we swap the station and channel. Again the likelihood of image transfer from the program to a station is larger than from the presenter or channel to a station.

Figure 7.2.3 clearly depicts that the horizontal link from presenter to program seems to be much stronger than to the parent brands ‘station’ and ‘channel’. Therefore we can conclude that the probability of image transfer from the program to a presenter in considerably larger than from a station or channel to a presenter. In other words, the likelihood of the station or channel impacting the presenter’s image, positively or negatively, is fairly slim. Lastly, figure 7.2.4 highlights that the horizontal link from the program to the presenter is far stronger than the upward link to the parent brands ‘station’ or ‘channel’. The percentages in this case are however slightly lower than in the other cases. Therefore we conclude that the probability of image transfer from the program to the

65 presenter is reasonably larger than from the program to the ‘channel’ or ‘station’. These findings might have very interesting managerial implications that will be discussed in section 8.4.

Two-way similarity of % mentioning corresponding brand

These results (figure 7.3.1) are based on the same premises and results as the P2 and P3 propositions. These propositions are however constructed to emphasize the dissimilarity in direction of the strengths of one link as discussed by Collins and Loftus (1975). The first proposition indeed con- firms our expectation that the link from a channel to a program is far stronger that the reverse link, from a program to a channel. Deploying the literature we can verify the phenomenon wherein the probability of image transfer from a program to a channel is far greater than the other way around.

Put differently, the program can do far more to influence a channel’s image than the channel can for the program. The results in figure 7.3.2 confirm the exact same effect for the brands station and program. Considering the strength of the links, the probability of a program influencing a station’s image is far greater than the other way around.

Percentage of respondents mentioning the correct Program

The last set of propositions tells us something about the specific incoming associations to the four brands. The first learning is that the 'Program' has a greater percentage of matching incoming associations than the 'Station' or ‘Channel’. The second learning is that the same applies to the presenter, which is on a horizontal sub brand level with the program. In essence this means that the strength of the link from the other three brands to the program is stronger than from the other three brands to either the channel or station. Also the program proves to affect the presenter a lot more than the presenter affecting the program. These are important findings as these confirm our expectations on directionality playing a large role in the strength between different broadcasting brands. In practice this means that ‘Station’, ‘Channel’ and ‘Presenter’ borrow more associations from programs than the other way around (eg., John, Loken, and Joiner 1998; Roehm and Tybout 2006).

66 Lastly the most speculative proposition, which is based on the premise that the commercial channel fulfills a dual role of both a station and a channel, is confirmed. We proposed that the commercial channel RTL4 receives a greater percentage of incoming connections than the public channel or stations. This is a meaningful finding as this means that a unified channel/station is a stronger brand in the retrieval of consumers than when the station and channel brands are separated. Consumers have an easier time retrieving, and therefore have more incoming associations with a commercial channel when compared to a public channel. This of course builds a stronger brand, which has interesting managerial implications to be discussed in section 8.4.

8.2 Interesting findings apart from the propositions

Whilst interpreting the results, several interesting findings came to light that are not accounted for by the propositions. These interesting finding might help to gain a broader understanding of the network of associations of the broadcasting system. Although not accounted for by the propositions, they might have some managerial implications and we will therefore highlight the most interesting side finding in the next paragraphs.

Firstly, in figure 7.2.4 one can see that when cued with a program, respondents have distinctly more channel associations with the commercial programs ‘RTL Boulevard’ and ‘The voice of Holland’ than with the public programs ‘Boer zoekt Vrouw’ and ‘Studio Sport’. On first sight one might argue that is because part of the channel name RTL4 comes back in the program name RTL Boule- vard. This is however not the case for the program The voice of Holland, where a very similar result is found. This is interesting as this means that people have an easier time remembering the corresponding channel when cued with a commercial program than with a public program. In other words, people have almost no awareness of the channel when watching a commercial program. This is interesting as in the same figure (7.2.4), one can see that the awareness of the broadcasting station is at the same level as the awareness of the channel for public programs. This is finding is on par

67 with proposition P8. This means that the commercial channels play a similar role as public broadcasting stations. One could therefore argue that having both a channel and a station is very redun- dant but we will elaborate on this in the managerial implications.

Secondly, in figure 7.4.1, we can observe that the total percentage of correct program associations is noticeably higher for ‘Boer zoekt Vrouw’ and noticeably lower for ‘Studio Sport’. This observation is magnified in the percentage of correct program associations when cued with the presenters. Put differently, when cued with ‘Yvon Jaspers’, 80% of the respondents mention the correct program

‘Boer zoekt Vrouw’ while only 29% of the respondents mention ‘Studio Sport’ when cued with its presenter ‘Tom Egberts’. Applying associative network theory by Rekom, Jacons and Verlegh

(2006), one might therefore conclude that the program ‘Boer zoekt Vrouw’ greatly defines the image of Yvon Jaspers as almost every respondent directly associates her with the program. Also, half of the respondents directly names Yvon Jaspers to be the presenter when cued with the program

‘Boer zoekt Vrouw’. On the other hand, ‘Studio Sport’ reflects on Tom Egberts far less and in figure 7.4.2 we can see that he influences the image of Studio Sport even less as only 13% of the respondents recalls him as the presenter. In general the patterns as described by the propositions seem to be correct. It is however interesting that when looking at the individual cues in great detail, interesting differences can be found. This means that not only the particular brand (program and presenter) seems to play a role, but also the type of program and presenter seem to influence the results. For a certain type of program, in this case a reality dating soap, the synergy or match between program and presenter seems to be significantly more important that for other programs, in this case a sports news broadcasting.

68 8.3 Theoretical implications

Our explorative research and findings carry important implications to existing literature as no research has been done before to embed the broadcasting system into existing brand knowledge. By means of this paper we have applied several theoretical brand concepts to the broadcasting system which adds to the general knowledge on how brand literature can be applied to the broadcasting system. We depicted how several elements of the broadcasting system can be regarded as brands by applying traditional brand and brand equity definitions (Aaker 1996; Chernatony and Leslie 1998;

Keller 2008). We therefore added to existing branding literature by contemplating that a television program, presenter, channel and program can all be considered as brands. This implies that existing brand literature can be applied to analyze but also to understand and build better brands in the broadcasting system. By applying traditional brand literature, one can better understand how to build brand awareness, reputation and prominence in the broadcasting landscape as discussed by

Keller (2002).

Moreover, our findings show that brand architecture literature as by ea. Aaker and Joachimsthaler

(2000), applies to associative networks in the broadcasting system. Our results show that the channel and station can be considered as parent brands or endorser brands while the program and presenter can be considered as subbbrands. This is particularly important as Rekom, Jacons and Ver- legh (2006) describe that direct links in the associative network influence the image of the connected node. This ads another dimension to existing brand architecture literature as we now know that such literature can be applied outside of the spectrum of traditional brands, in this case specifically on brands in the broadcasting system.

Also, our propositions shed light on the fact that the level of exposure (Morrin 1999; Nedungadi

1990; Barsalou and Sewell 1985) significantly impacts the number of associations that one has. Our extensive research on this topic has deeply embedded the concept of exposure into associative net-

69 work theory. We found it to be correct that brands that receive the most prominent exposure receive generally a greater amount of associations. Also, we found that these brands (program and presenter) receive remarkably more primary associations. In context of brands in the broadcasting system, this adds to existing literature by proving that people possess a greater associative network on brands that are most prominent in the consumption of multiple brands simultaneously. In other words, we exhibited that people more easily develop primary associations about broadcasting brands that form the main source of consumption.

Finally, our findings add to existing literature by demonstrating how spillover effects as proposed by Farquhar and Herr (1993) and Lei, Dawar and Lemmink (2008) can take place outside of the traditional brand spectrum. Although no formal or traditional brand hierarchy is in place in the broadcasting system, we found that directionality (Collins and Loftus, 1975) plays a crucial role in the structure of broadcasting brands. This is demonstrated by our finding such as the fact that when cued with a ‘channel’, the percentage of people having one or more associations with a ‘program’ is higher than the other way around. The same was found to be true for a broadcasting station and a program.

8.4 Managerial Implications

Recent budget cuts by the Dutch government have forced the public broadcasting system to merge its 21 broadcasting stations into a total 8 stations. The three public channels that together form the platform for the broadcasting stations remain to exist. Our research has provided interesting insights into the differences in associations between broadcasting stations, public and commercial channels.

When cued with a program from the public broadcasting system, people have extremely little awareness (8%) of the channel airing the program. The percentages (16% and 23%) of people that directly associate the program to the correct broadcasting station are noticeably higher. It is howev-

70 er highly interesting to notice that number of people (34%) that associates a commercial program to the correct channel is on par or higher than the public stations and channels combined.

This is interesting as this might raise awareness from managers that the commercial channels seem to fulfill a similar role as both the channel and station do in the public broadcasting system. Howev- er they seems to be able to focus their branding better which creates a higher lever of awareness and probability of image transfer. Moreover, one could conclude that the dual public system seems to be superfluous. It shows that just branding the channel right, as per the commercial channels, seems to create at least the same level of awareness of the brand broadcasting the program. Decreasing the number of Dutch broadcasting stations therefore seems to be only a partial step in the right direction. This paper shows that choosing to merge the broadcasting stations and channels into unified brands seems a logical decision. Not only would this significantly cut back costs to maintain and brand all the individual stations and channels, it would also help focus branding efforts and therewith decrease the dispersion of awareness between multiple brands.

Our most interesting finding is the fact that the ‘program’ has by far the largest likelihood to transfer its image onto one of the other three broadcasting brands. The likelihood of image transfer from the presenter, station or channels to any one of the four broadcasting brands is distinctly lower. This finding could have many interesting implications. It could potentially influence current debates on the salaries of presenters. In the Netherlands fierce debates were raised on how much presenters at the public broadcasting stations should earn as they are paid with tax money. To answers this question one has to know how much unique value the presenter actually adds to a program, station and channel. In other words, how high is the likelihood that unique image properties from the presenter transfer to the program, station or presenter which in turn could influence quality and viewer ratings.

71 Our research has shown that viewers rarely recall the presenter when cued channel or station. The results of presenter recall when cued with a program varied widely. We found that some presenters are often recalled (Yvon Jasper and Albert Verlinde) while others (Martijn Krabbe and Tom Eg- berts) are almost never recalled when cued with the program. Therefore managers should research how much the presenter does to impact the image of the specific program and to what extent the presenter is replaceable. As we now know that his varies greatly, this is an important factor that has the potential to influence the debate on how much presenters should be paid. Image transfer, and therefore potential unique value transfer, from the presenter to the broadcasting station or channel is very indirect. People rarely recall them as being connected to a certain station or channel, therefore they only have the potential to add value to those brands through their influence on programs who in turn can define the image of a broadcasting station or channel. As channels and stations can be viewed as companies that should reach certain targets, our research suggests that is seems wise to put investments towards programs instead of presenters as the former impacts the image of the channels and stations the most.

72 9. Conclusions

This paper was written to extend existing and traditional brand literature to elements in the broadcasting system. This paper showed a specific interest in how the program, presenter, broadcasting station and channel are connected in the associative network of the consumer. Specifically we wanted to know how the images of the four major broadcasting brands (broadcasting channel, station, program and presenter) affect one another when displayed together? In order to find an answer to this question we first needed to know whether these four elements could indeed be considered as brands according to traditional brand concepts. The well known brand concepts of brand literature proved to be very applicable to the broadcasting system and therefore we could proceed to apply more branding theory to the broadcasting brands in order to answer our research question.

By means of free response research we were able to map the associative networks of the different brands in the broadcasting system. First of all we found that the program and the presenter evoke considerably more primary than secondary (brand) associations. The associative network of the channel and station however seem to rely more on secondary associations which mainly consist of program associations. Moreover we found that directionality between the different brands plays a major role in the strength of associations between the brands. We found that consumers, when cued with a presenter, station or channel, have significantly more associations with programs than with any of the other three brands. When cued with a program, consumers seem to have far more presenter associations in their associative network than channel or station associations. Both these phenomena can be explained by brand architecture literature by which we can depict the channel and station as parent or endorser brands and the program and presenter can be described as subbrands.

By deploying existing research on spillover effects (ea. Lei, Dawar and Lemming 2008), we were able to elaborate on the likelihood of image transfer between the different broadcasting brands. Our conclusions based on the results of our research are that the likelihood of image transfer from the

73 program to either a presenter, station or channel seems to be high. The chances of the presenter affecting the image of a program seem to be very likely as well. Any other directions of image transfer seem to be highly unlikely as these connections are so weak that the odds of spillover effects seem negligible. This means that the broadcasting station and channel can do relatively little to add any value to the associative network, or image, of any of the broadcasting brands. The same goes for the presenter with the herefore mentioned exception of a television program. The television program on the other hand has the potential to impact the image of the other broadcasting brands tremendously, in a good or in a bad way.

Coming back to our research question we may conclude that consumers process the four broadcasting brands in different manners when displayed together. Therefore the linkages in the associative networks of consumers are not equal and thus the likelihood of image transfer between the different broadcasting brands is not equal. As described in the previous paragraph, it is mainly the program and to a lesser extent the presenter that plays a major role in the consumers associative network. As described earlier this has implications for existing branding literature but especially from a managerial point of view these findings bear valuable insights.

9.1 Limitations

Some limitations were present during the course of our research of which we will highlight the most important ones. First of all, for our research we made use of the snowball sampling survey technique, which means that the survey was handed out to people in our network who in turn were asked to further distribute the survey. This is a very common method of sampling, it does however not assure a clean cross section of the population as some groups, in this case relatively high educated individuals, are over represented in the sample. We did however account for age and gender dispersion, which was evenly distributed, and we expect the bias to certain population groups does not heavily influence the results but may or may not alter them to a smaller extent.

74 An important limitation in our research surfaced during the analysis of our study as we then became aware of the fact that to the best of our knowledge and other statistics professors, no existing statistical test is equipped to test ‘whether there are significantly more dummy variables in one group compared to another group’. To simplify, we needed to measure whether the number of ‘1’ dummies in the first category is significantly higher than the number of ‘1’ dummies in the other category. This appears to be a relatively simplistic statistical dilemma, however no test was able to account for it. Therefore we could draw conclusions on the percentages that chi-square tests were able to generate, we could however not statistically prove that one group was significantly larger than the other.

A last limitation that provides a great opportunity for further research is the fact that our study was not set up to enable us to measure the actual image transfer. Our research focused on understanding if, how and in which direction image transfer takes place between broadcasting brands. We did however not have the tools in this research to measure the actual image transfer that has taken place between the different brands in the broadcasting system and how this impacts the perceptions and associative networks of consumers. In order to better understand this actual image transfer one would need to come up with an extensive research framework and method to measure the actual and concrete spillover effects.

9.2 Future Research Directions

By means of our research we are able to measure and express the likelihood of image transfer or spillover effects. Our study was however not setup to measure the actual spillover effects as described in the limitations. It would be very interesting to see how and what kind if image qualities are borrowed from one brand to another by means of spillover effects. In other words, when we know that the likelihood of image transfer from a program to a broadcasting station is high, what kind of unique qualities tend to be transferred and what is their effect on the image of the destina-

75 tion brand. We also suggest to include direction of the associations into future research in order to explore whether positive or negative associations tend to have a higher likelihood of spillover. With this knowledge, managers would be able to put in place strategies that could promote and prevent the transfer of unique qualities of certain brands to another.

This paper focused on the associative networks of brand in the broadcasting system and how the composure of these associative networks might influence or promote image transfer. We mainly highlighted and discussed how the strength of associations between the brands influences spillover effects. In order to understand even better how image transfer can be influenced it would be very interesting if future research would be done on how the composure and exposure to multiple brands simultaneously could influence the strength of associations. To illustrate, Dutch broadcasting channels only deploy a small logo in the corner of the screen to connect the program to the channel.

Channels like CNN brand the channel far more explicitly by means of multiple and larger logos and banners that cover the screen. Insights into if and how this alters the likelihood of image transfer would add an interesting dimension to understanding spillover effects in the broadcasting system.

Finally, we strongly suggest specialists in the field of statistics to solve the statistical hurdle that surfaced in our research. Although the statistical test to prove that the number of dummies in one category is significantly higher than in another category seems rather simple, no statistical test appears to facilitate this application. We expect that this statistical test would benefit other research with extensive usage of dummy variables tremendously. We therefore encourage statisticians to dive deeper into this demand to find out whether any such test has ever been introduced and if not, this would provide a fantastic opportunity to develop a new statistical test.

76 10. References

Aaker, D.A., 1996. Building Strong Brands. Free Press, New York

Aaker, D. A. (1996) Measuring brand equity across products and markets. California Management

Rev. 38(Spring): 102-120.

Aaker, D. A. (2009). Managing brand equity Simon and Schuster.

Aaker, D. A., & Joachimsthaler, E. (2000). The brand relationship spectrum: The key to the brand

architecture challenge. California Management Review, 42(4), 8.

Aaker, D. A. 1. (1991). Managing brand equity : Capitalizing on the value of a brand name / david

A. aaker. New York; Toronto; New York etc.: New York : Free Press ; Toronto : Maxwell

Macmillan Canada ; New York etc.] : Maxwell Macmillan International.

American Marketing Association (1960), Marketing Definitions: A Glossary of Marketing Terms,

AMA, Chicago, IL.

Barsalou, Lawrence and Daniel Sewell (1985), “Contrasting the Representation of Scripts and Cat-

egories,” Journal of Memory and Language, 24 (6), 646–65.

Collins, Allan M. and Elizabeth F. Loftus (1975), “A Spreading- Activation Theory of Semantic

Processing,” Psychological Bulletin, 82 (6), 407–428.

Cox III, E. P. (1980). The optimal number of response alternatives for a scale: A review. Journal of

Marketing Research, 17. 407-422.

Dacin, P.A. and Smith, D.C. (1994), “The effect of brand portfolio characteristics on consumer

evaluations of brand extensions”, Journal of Marketing Research, Vol. 31, May, pp. 229-242.

Dailymail (2013) Jon Stewart eclipses late night rivals Jay Leno and David Letterman with $30m

annual salary as highest paid TV stars are revealed | Mail Online. (n.d.). Retrieved from 77 http://www.dailymail.co.uk/tvshowbiz/article-2398253/Jon-Stewart-eclipses-late-night-rivals-

Jay-Leno-David-Letterman-30m-annual-salary-highest-paid-TV-stars-revealed.html

De Chernatony, L., & Dall'Olmo Riley, F. (1998). Defining a" brand": Beyond the literature with

experts' interpretations. Journal of Marketing Management, 14(5), 417-443.

Dobni, D. and Zinkhan, G.M. (1990), “In search of brand image: a foundation analysis”, Advances

in Consumer Research, Vol. 17, pp. 110-119.

Farquhar, Peter H. and Paul M. Herr (1993), “The Dual Structure of Brand Associations,” in Brand

Equity and Advertising: Advertising’s Role in Building Strong Brands, David A. Aaker and

Alexander L. Biel, eds. Hillsdale, NJ: Lawrence Erlbaum Associates, 263–77.

French, A., & Smith, G. (2013). Measuring brand association strength: A consumer based brand

equity approach. European Journal of Marketing, 47(8), 1356-1367.

Gardner, Burleigh, B. and Sidney J. Levy (1955), “The Product and the Brand,” Harvard Business

Review, March-April , 33-9.

Henderson, G. R., Iacobucci, D., & Calder, B. J. (1998). Brand diagnostics: Mapping branding ef-

fects using consumer associative networks. European Journal of Operational Research,

111(2), 306-327.

Holden, Stephen J.S. and Richard J. Lutz (1992), “Ask Not What the Brand Can Evoke; Ask What

Can Evoke the Brand?” in Advances in Consumer Research, Vol. 19, John F. Sherry Jr. and

Brian Sternthal, eds. Provo, UT: Association for Consumer Research, 101–107.

John, D. R., Loken, B., Kim, K., & Monga, A. B. (2006). Brand concept maps: A methodology for

identifying brand association networks. Journal of Marketing Research, 43(4), 549-563.

John, Deborah Roedder, Barbara Loken, and Christopher Joiner (1998), “The Negative Impact of

78 Extensions: Can Flagship Products Be Diluted?” Journal of Marketing, 62 (January), 19–32.

Keller, K. L. (2002). Branding and brand equity. Handbook of Marketing, , 151-178.

Keller, K.L., 2008. Strategic Brand Management. Pearson Press

Kimmelman, M. (2009, August 26). Jimmy Fallon Could Double His Payday With 'Tonight Show'

Move. Retrieved from

http://www.nytimes.com/2009/08/27/arts/television/27abroad.html?pagewanted=all&_r=0

Krishnan, S.H. (1996), “Characteristics of memory associations: a consumer-based brand equity

perspective”, International Journal of Research in Marketing, Vol. 13, pp. 389-405.

Lei, J., Dawar, N., & Lemmink, J. (2008). Negative spillover in brand portfolios: Exploring the

antecedents of asymmetric effects.(report). Journal of Marketing, 72(3), 111.

Metro News. (2010, October 28). Top Gear presenters pull in record ratings for US TV show. Re-

trieved from http://metro.co.uk/2010/10/28/top-gear-presenters-pull-in-record-ratings-for-us-

tv-show-60-minutes-565646/

Morrin, Maureen (1999), “The Impact of Brand Extensions on Parent Brand Memory Structures

and Retrieval Processes,” Journal of Marketing Research, 36 (November), 517–25.

Nedungadi, Prakash (1990), “Recall and Consumer Consideration Sets: Influencing Choice Without

Altering Brand Evaluations,” Journal of Consumer Research, 17 (December), 263–76.

Olson, James M. and Mark P. Zanna (1993), “Attitudes and Attitude Change,” Annual Review of

Psychology, 44, 117–54.

Pommerants. (2013, March 26). Jimmy Fallon Could Double His Payday With 'Tonight Show'

Move. Retrieved from http://www.forbes.com/sites/dorothypomerantz/2013/03/26/jimmy-

fallon-could-double-his-payday-with-tonight-show-move/ 79 Preston, C.C., & Colman, A.M. (2000). Optimal number of response categories in rating scales:

Reliability, validity, discriminating power, and respondent preferences. Acta Psychologica,

104, 1-15.

Rangaswamy, A., Burke, R. R., & Oliva, T. A. (1993). Brand equity and the extendibility of brand

names. International Journal of Research in Marketing, 10(1), 61-75.

Roehm, Michelle and Alice M. Tybout (2006), “When Will a Brand Scandal Spillover, and How

Should Competitors Respond?” Journal of Marketing Research, 43 (August), 366–73.

Saunders, M., Lewis, P. & Thornhill, A. 2012. Research Methods for Business Students. 6th ed. FT

Prentice Hall.

van Rekom, J., Jacobs, G. and Verlegh, P.W.J. (2006), “Measuring and managing the essence of

abrand personality”, Marketing Letters, Vol. 17, pp. 181-192.

80 11. Appendices

Table 7.1.1: Program (VAR1) primary vs. secondary associations.

Table 7.1.2: Program (VAR1) type of brand.

Table 7.1.3: Presenter (VAR2) primary vs. secondary associations.

Table 7.1.4: Presenter (VAR2) type of brand.

81 Table 7.1.5: Channel (VAR3) primary vs. secondary associations.

Table 7.1.6: Channel (VAR3) type of brand.

Table 7.1.7: Station (VAR3) primary vs. secondary associations.

Table 7.1.8: Station (VAR3) type of brand.

82 Table 7.2.1

Table 7.2.2

Table 7.2.3

83 Table 7.2.4

Table 7.2.5

Table 7.2.6

Table 7.2.7

Table 7.2.8

Table 7.2.9

85 Table 7.2.10

Table 7.2.11

Table 7.2.12