Brand resonance: A scale validation

Student Jori van den Bosch | 6131670

Supervisor Dr. Karin A. Venetis

Master thesis MSc Business Studies | Marketing

Date April 2014

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Abstract

This research draws from the theory on Customer-based brand equity as proposed by

Keller (2009) and aimed to validate the theoretical concept Brand resonance as a

metric to indicate Brand performance. Brand resonance would be better able to

capture all relevant dimensions in the relationship between customers and brands than

current widely used unilateral Brand performance indicators like the Net promotor

score do. Steps in validation were taken to establish Content-, Construct-, and

Criterion validity. Experts with academic and practical backgrounds were involved in

the process of composing items for the metric and for multiple studies samples in nine

countries were collected to further assess the validity of the model. Results show

support for a clear single factor solution of a six item Brand resonance scale which

behaves as expected within the nomological net and shows better initial results as a

brand performance indicator than the Net promotor score. The Brand resonance

metric is valuable in both building and maintaining brands and is able to identify a

broad and deep relationship between customers and brands. Further research has to be

conducted to validate the metric in other product categories and the service industry.

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Contents

Abstract ................................................................................................................... 1

1. Introduction ......................................................................................................... 4

Background ........................................................................................................... 4

Research questions ................................................................................................ 8

Approach and outline of the thesis ......................................................................... 9

2. Literature review ............................................................................................... 10

Introduction ......................................................................................................... 10

Customer-based brand equity............................................................................... 10

Brand awareness and image ................................................................................. 12

The CBBE model ................................................................................................ 15

Brand resonance .................................................................................................. 21

3. Research method ............................................................................................... 26

Introduction ......................................................................................................... 26

Content validity ................................................................................................... 27

Construct validity ................................................................................................ 28

Criterion validity ................................................................................................. 29

Research design ................................................................................................... 29

Content validity ............................................................................................... 29

Sample collection ............................................................................................ 30

Construct validity ............................................................................................ 31

Criterion validity ............................................................................................. 32

4. Results ................................................................................................................ 33

Introduction ......................................................................................................... 33

Content validity ................................................................................................... 33

Behavioral Loyalty .......................................................................................... 34

Attitudinal attachment ..................................................................................... 36

Sense of community ........................................................................................ 36

Active engagement .......................................................................................... 37

Brand resonance .............................................................................................. 38

Construct validity ................................................................................................ 38

Phase 1 - Exploratory factor analysis ............................................................... 40

Conclusion ...................................................................................................... 43

Phase 2 - Exploratory factor analysis – replication ........................................... 43

Conclusion ...................................................................................................... 45

Phase 3 – Confirmatory factor analysis ............................................................ 45

Conclusion ...................................................................................................... 49

Criterion validity ................................................................................................. 49

Phase 4 – The predictive power on Brand preference ....................................... 50

Conclusion ...................................................................................................... 54

Phase 5 – The predictive power on Share-of-wallet .......................................... 54

Conclusion ...................................................................................................... 57

Phase 6 - The predictive power of the Net promotor score ............................... 57

Conclusion ...................................................................................................... 61

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5. Conclusion & Discussion ............................................................................... 62

Implications ......................................................................................................... 63

Limitations and future research ............................................................................ 64

References .............................................................................................................. 65

Appendix 1 – Brand trust and Brand affect scales .............................................. 71

Appendix 2 – Structural equation model CFA - standardized estimates ............ 72

Appendix 3 – Countries and gender/age distributions Phase 4 ........................... 73

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1. Introduction

Background

With the development of marketing as a more serious activity for companies in the

1960’s, the need to measure outcomes arose and more academics became interested in

the research area. Marketing metrics were developed to keep better track of results

from marketing investments. The metrics were financially based, focusing on profit,

sales revenue and cash flow and were well able to capture the short term results of

marketing campaigns. According to Clark (1999, p.713): “Early work in the firm-

level measurement of marketing performance was largely directed at examining the

productivity of a firm’s marketing efforts at producing positive financial outputs”.

Although having metrics was a good step in the right direction, over the years

it became clear that investments in marketing also influenced other aspects than direct

financial results. When investments were made in the right manner, incremental value

was added to the brand. The traditional performance measures could not cover all

aspects of marketing performance anymore, and researchers developed new metrics in

an attempt to capture all relevant drivers of performance.

Two of the leading performance indicators in this area became customer

satisfaction and customer loyalty. The measures were able to support the traditional

financial performance indicators and helped developing a better marketing strategy.

By focusing on aspects like service, the number of satisfied customers was expected

to go up. Highly satisfied customers should in turn buy more products (in depth and

breadth) from the same brand, making it loyal customers. “A loyal customer base, it is

argued, should lower marketing costs; current customers are cheaper to retain, and

word-of-mouth from current customers should make new customers easier to acquire”

(Clark, 1999; Aaker, 1991; Dick and Basu, 1994).

During the 1980’s the term ‘brand equity’ was born to give name to the added

value of a brand and at the same time treat it as a credible asset. The first motivation

to study brand equity according to Keller was financially based: “..to estimate the

value of a brand more precisely for accounting purposes or for merger, acquisition, or

divestiture purposes.” The other reason was to improve marketing productivity:

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“Given higher costs, greater competition and flattening demand in many markets,

firms seek to increase the efficiency of their marketing expenses” (Keller, 1993, p.1).

This call for more efficiency especially came from strategy-level. More insight in

consumer behavior was needed to make better decisions about the marketing mix,

portfolio management etc.

During that time, building and maintaining a brand became more and more

important and CEO’s learned that the benefits created by strong brands make a big

difference in a company’s financial performance. Therefore, for most companies,

branding became a key marketing priority (Aaker David & Joachimsthaler, 2000;

Kapferer, 2005).

A number of methods to measure brand equity were developed by scholars

and companies. Interbrand Group, for example, measures and manages brand value

for numerous clients. The company uses its brand valuation tool to conduct the ‘Best

Global Brands’ study every year by estimating the financial value of brands. In the

1980s, this world leading verdict was based on the assessment of seven brand

dimensions: leadership, stability, internationality, trend, support, protection and

market stability. Years later, Aaker (1996) also conceptualized Brand equity and

compiled a set of measures called ‘The Brand Equity Ten’ with the dimensions:

Loyalty, Perceived quality/Leadership, Associations/Differentiation, Awareness and

Market Behavior. The consumer perspective was also taken into account and there is

some overlap with the dimensions used by the Interbrand Group in those days. Both

concepts were developed to value brand equity. Due to the fact that it was clear how

brand equity was measured, automatic focus points arose when a brand was build or

maintained. Brand managers now knew better on which aspects to focus and used the

measures as brand building tools.

Nowadays more variables are taken into account wherein the viewpoint of the

consumer receives even more attention. One of the reasons for this development could

very well be the influence of Kevin Lane Keller, who introduced a Brand equity

concept from consumers’ perspective in 1993: Customer-based brand equity.

According to Keller (1993; 2009) “Customer-based brand equity is defined as the

differential effect of brand knowledge on consumer response to the marketing of the

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brand.” Customer-based brand equity occurs when “the consumer is familiar with the

brand and holds some favorable, strong and unique associations in memory.”

The author states that Customer-based brand equity consists of two dimension;

brand awareness and brand image (Keller, 1993; Keller, 2009). The dimension Brand

awareness splits up in Brand recognition (aided) and Brand recall (unaided).

Consumers must know the brand first to have an opinion about it or even commit to it.

Therefore Brand awareness needs to be build first as a basis for further development

of the brand. Brand image refers to the set of associations linked to the brand that

consumers hold in memory. The stronger the desired associations are, the better a

brand grows or sustains.

During the years, multiple practical models were created to guide marketeers

and managers through the process of developing their brand. Aaker and Keller among

others contributed a lot to the brand building literature. Keller developed from his

consumer-based perspective one of the most recent, managerially relevant and

academically backed models. The Customer-Based Brand Equity (CBBE) model

emphasizes the importance of understanding consumer brand knowledge structures

(Keller, 2009). The author describes four stages in the achievement of branding

objectives in the CBBE pyramid (see Figure 1.1).

First, a deep, broad brand awareness has to be

developed (Salience). The customer can

recognize and recall the brand, knows what

it stands for and which needs it can fulfill.

Second, points of parity and difference

(Imagery and Performance) must be

established in the right way. To do

this, the brand is targeted on

certain user profiles or usage

situations (Imagery) and the

functional aspects like price, features, product reliability and style and design

(Performance) are communicated and shown. It is assumed that the customer takes

every conscious or subconscious observation that involves the brand into account.

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This varies from controlled ATL campaigns to uncontrolled bad experiences with a

local dealer acting in name of the brand. The obvious goal is to elicit positive and

accessible reactions. The model distinguishes two important dimensions when it

comes to those reactions. The first dimension is judgment-based and is all about

quality, credibility etc. The second dimension touches the emotional side and is about

the feelings (warmth, fun, excitement etc.) that are triggered when the customer thinks

about the brand. The final stage in the CBBE pyramid is Brand resonance. Brand

resonance, according to Keller (2009, p.144) refers to “the nature of the relationship

customers have with the brand and the extent to which they feel they’re ‘in sync’ with

the brand.” In the ideal situation a brand’s customers show an intense and active form

of loyalty. The ultimate goal is therefore to create high levels of brand resonance that

represent a combination of behavioral (loyalty, engagement) and affective

(attachment, sense of community) aspects of commitment toward a brand.

Keller’s CBBE concept is a helpful model for building a brand but

unfortunately not all parts are tested thoroughly and some are not even tested at all.

The lower three levels of the model (see Figure 1.1), until the Judgments and

Feelings, have been researched a lot but especially the top level, Brand resonance,

needs more examination. The four cornerstones within Brand resonance: Loyalty,

Attachment, Community and Engagement have received individual attention. As

described earlier, (customer) Loyalty as one of the separate dimensions of Brand

resonance has been widely used as a performance indicator. The relationship between

Loyalty and Satisfaction was an important research topic for a long time because,

satisfaction is believed to be one of the most important reasons why a customer would

be loyal (Chandrashekaran, Rotte, Tax, & Grewal, 2007; Cronin Jr & Taylor, 1992;

Fornell, 1992; Lai, Griffin, & Babin, 2009). Chaudhuri & Holbrook (2001) defined

two aspects of the construct brand loyalty: purchase loyalty and attitudinal loyalty.

They argue that purchase loyalty leads to greater market share and attitudinal loyalty

to a higher price tolerance. Carlson, Suter, & Brown (2008) write about the social

processes that underlie customers’ involvement in brand communities and Park,

MacInnis, Priester, Eisingerich, & Iacobucci (2010) developed a measure for brand

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attachment. Engagement received much less attention in literature, but the NPS (Net

promotor score) metric is a widely used performance measure that fits this dimension.

So far the different behavioral and affective aspects have not yet been

integrated in one construct. By combining several unilateral known measures more

aspects of the connection between a brand and a customer are taken into account. By

treating the Brand resonance construct as a single measure, it could be a more precise

brand performance indicator than current measures, covering more aspects of the

relationship between a customer and a brand. The NPS metric for example is a holy

performance indicator for numerous companies. Although a positive recommendation

is said to be the best form of marketing, its effect still depends on who recommends

you as a brand and why. The British clothing brand Lonsdale was originally

positioned in the boxing segment, but turned out to be worn and widely recommended

among neo-Nazi’s. In reaction, Lonsdale started to sponsor gay festivals in an attempt

to lose this unwanted group of paying customers and stop them from recommending

their brand to other neo-Nazi’s. Another example could be a cheap mobile network

operator that only has loyal customers due to its low priced services. If the operator

would only look at repeat purchases to measure brand attachment they could get the

idea that they are a strong operator brand with a large share of loyal customers. While

in reality, the brand would quickly lose market share if competition would lower its

prices.

Research questions

The brand resonance concept derives strength from the fact that it is based on multiple

pillars that prevents short-sighted and wrong conclusions that could result from a

single minded focus on NPS or behavioral loyalty only. The theoretical versatility and

synergy of the different dimensions included in Brand resonance give this metric

power and can help to provide better guidance to support strategy. Furthermore, the

absence of empirical measures for a valuable theoretical construct from one of the

most influential text books, Strategic Brand Management (Keller, 2009), on brand

building gives reason to conduct this research.

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Main question: How can Brand resonance be measured?

In this research, the focus will be on the validation of a brand performance measure.

Before the main question can be addressed, the following sub-questions need to be

answered:

Sub-question 1: How do we define Brand resonance and its dimensions?

Sub-question 2: What are the stages of validating a scale?

Sub-question 3: How do the dimensions of Brand resonance relate to each other

and can they form a valid and reliable construct?

Sub-question 4: What is the relationship between Brand resonance and other

brand performance indicators?

Approach and outline of the thesis

This research is conducted as part of an internship at a market research agency called

Epiphany, which collects data for clients in consumer lifestyle products and the automotive

industry. To be developed items of the Brand resonance scale will be included in this research

for the purpose of its validation. In the next chapter, the literature on the Brand resonance and

its dimensions will be reviewed, followed by a roadmap to marketing scale validation in the

next chapter. In the last chapters an attempt on validation will be described and results

discussed.

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2. Literature review

Introduction

In this chapter, the literature on Brand resonance and its background will be reviewed

and the construct defined. First Customer-based brand equity will be addressed as it is

the building ground for the CBBE model proposed by Keller (2009) in which Brand

resonance plays the lead. Then the basics of the CBBE model, Brand awareness and

Brand image are described, followed by the CBBE pyramid itself. Finally the

literature on the dimensions of the top level of the pyramid, Brand resonance will be

assessed and the measure defined.

Customer-based brand equity

As discussed in the introduction, the term Brand equity arose in the 1980s to give

meaning to the added value of a brand. Multiple frameworks were developed around

this concept, but the ones taking perspectives of the consumer into account, were

developed by Aaker (1996a) and Keller (1993). Keller (1993, p.02) defines Brand

equity as “the differential effect of brand knowledge on consumer response to the

marketing of the brand”. In his view, CBBE is a process whereby CBBE occurs

“when the consumer is familiar with the brand and holds some favorable, strong, and

unique brand associations in memory” (Keller, 1993, p.02). Before building those

associations, Brand awareness is a requirement. Before associations are made and

Brand image can be build, the consumer has to be aware of the brand to the extent that

he or she can recognize and recall the brand. According to Aaker (1996a) there are

certain assets attached to a brand which subtract or add value from a customer’s

perspective. A customer perceives Brand equity as the “value added” to the product

by associating it with a brand name. Cornerstones in his research resulting in the

‘Brand Equity Ten’ are Loyalty, Perceived quality/Leadership,

Association/Differentiation, Awareness and Market behavior. Where the proposed

framework of Keller (1993) was still largely theoretically about Brand knowledge

which holds the Awareness and Image dimensions, Aaker (1996a) made an attempt to

develop a measure, making the theory more usable in a business environment by

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actually giving an estimation of the added value of a brand that lies in the mind of

consumers.

In the following years, researchers build on their work and attempted to

validate measures of facets of CBBE or its underlying association characteristics. Yoo

and Donthu (2001) argue that the structural validity of the measurement remains

unanswered in the work of Keller (1993) and Aaker (1996a). They developed a

multidimensional scale of CBBE and assessed its psychometric properties and cross-

cultural generalizability. Findings suggest a potential causal order among the

measured dimensions in which Brand awareness and associations precede Perceived

quality and Perceived quality precedes brand loyalty. Yoo and Donthu (2001) argue

that their research needs more attention to higher external generalizability, but that the

measure they developed is parsimonious and therefore useful for practitioners.

Netemeyer et. al. (2004) present four studies that attempt to measure “core/primary”

facets of CBBE. The chosen facets are Perceived Quality, Perceived value for costs,

Uniqueness and the willingness to pay a price premium for a brand. They conclude

that the dimensions show high internal consistency and results also suggest that

Perceived quality, Perceived value for costs and Uniqueness are potential direct

antecedents of the Willingness to pay a price premium, which in turn precedes

purchase behavior. Other research regarding CBBE tested the use of the concept

under different circumstances. Washburn, Till and Priluck (2004) examined CBBE in

light of brand alliances and the equity value before and after the collaboration of

brands using the scale Yoo and Donthu (2001) proposed. Punj and Hillyer (2004)

identify four basic components of CBBE: Global brand attitude, Strength of

preference, Brand knowledge and Brand heuristic. The components are tested on two

frequently purchased product categories and results indicate that the four components

are all important determinants of CBBE. Bauer and Sauer (2005) conducted research

wherein the different CBBE models were consulted and refined to a model that would

fit the team sport industry. Because their sample existed mainly of respondents who

were well known with the researched brands, Keller’s (1993) framework of

Awareness and Image could only be confirmed for the second part: “If consumers are

extremely highly involved and knowledgeable they are believed to both recall and

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recognize the majority of available brand. Thus, the brand awareness dimension

cannot contribute to a better understanding of Brand equity.” (Bauer and Sauer, 2005,

p.509) Furthermore, they found a significant effect of CBBE on economic success of

the sport teams. In conclusion, CBBE is in a developing stage and more and more

research is conducted on the topic. In the next paragraph, Keller’s framework (1993)

leading to the CBBE model (Keller, 2009) will be discussed to provide understanding

of its background and theoretical base.

Brand awareness and image

In Keller’s (1993) conceptualization of CBBE, building and managing brands is

discussed. Brand awareness and Brand image are two important factors within Brand

knowledge in the proposed framework and build the foundation of his later work on

the CBBE-model (Keller, 2009), which encompasses the CBBE-pyramid in the form

of brand building blocks.

A lot is written about Brand awareness and its effects on performance. Brand

awareness is defined as the strength of the Brand node or trace in memory, as

reflected by consumers’ ability to identify the brand under different conditions

(Rossiter and Percy, 1987). Awareness can be split up in Recognition and Recall.

Brand recognition “relates to consumers’ ability to confirm prior exposure to the

brand when given the brand as a cue” (Keller 1993, p.03). Recognizing a brand alone

can influence preference especially when the consumer is in a low-involvement

setting. Hoyer and Brown (1990) demonstrate that subjects tend to choose brands they

recognize over unknown brands. Even if they are informed about the higher quality

those unknown brands have. Repeat-purchase products in supermarkets are a good

example when it comes to the importance of Brand recognition. Brand recall is the

second step in which a consumer can identify the brand, but also remembers it in the

right context. Brand recall is defined as “the consumers ability to retrieve the brand

when given the product category, or some type of probe as a cue” Keller (1993, p.03).

An example is asking someone for car manufacturers that come to mind. The brands

recalled are thus stored in memory and linked to the right setting. When the

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respondent can name the brand, a good chance exists that also other associations are

stored in memory linked to that brand.

The associations consumers have with brands are often seen as nodes (small

pieces of information) and linkages connecting them. A node can be a brand, product

or attribute. For example the brand ‘Ford’ with as product ‘Cars’ and attribute ‘Fuel

saving’. Links between the brand and one or both of the other nodes suggest an

association in a consumers mind called linkages. When managing a brand, this is an

important base to start from, because negative associations strongly linked to a brand

need to be identified and handled whenever possible. At the same time, positive nodes

not yet linked to the brand need work if they should be part of the association network

of that particular brand. Krishnan (1996) uses a memory network model to identify

various association characteristics underlying CBBE. His research indicates that the

number of associations, valence, uniqueness and origin of the associations have a

predictive power on Brand equity. The number of associations connected to a brand

needs to be high and therefore the association network as extensive as possible. The

associations can consist of brand or product attributes, but also of experiences the

consumer had with the brand. Coming back to the example of the car manufacturer

Figure 2.1 - Possible Association network Ford

Ford, you can see in Figure 2.1 that the brand is connected with a number of nodes.

This number would preferably be high according to Krishnan (1996), but it is also

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important that the net positive associations are higher than the negative ones. In this

example of possible associations connected to Ford, we see that the most are positive

and only one (‘Bad for employees’) is negative. Looking at the length of the linkage,

this is also the node that is the least related to Ford. The valence of this network

would therefore still be considered good. When it comes to uniqueness, the node

‘Value for costs’ is closely and thus strongly linked to Ford. Furthermore it is not

connected to competition making it unique and usable as USP in marketing

communications. On the other hand, the node ‘Fuel saving’ is more closely linked to

Volkswagen, which means consumers see Volkswagen as a manufacturer of more

economical cars then they see Ford. Thus the node is more unique to Volkswagen and

Ford is better off focusing on other nodes to differentiate in the category. In terms of

origin, some sources of nodes are more impactful than others. Logically, when

associations emerge from own experience, they will be stronger than the ones

proposed in a commercial. The complete associative network of nodes and linkages

that a brand is part of, is called Brand image.

Brand image is defined as “perceptions about a brand as reflected by the brand

associations held in consumer memory” (Keller, 1993, p.03). Brand image is the

second stage in the Brand knowledge framework of Keller (1993) and exists of three

components: Attributes, Benefits and Attitudes. Attributes can be product-related and

non-product-related. Product-related attributes are the needed functions of the product

or service to perform as was intended. For example the electrical engine that makes a

new car a hybrid car. Non-product-related attributes are price information, packaging

or product information, user imagery and usage imagery (Keller, 1993). The price of a

product represents: “a necessary step in the purchase process but typically does not

relate directly to the product performance or service function” (Keller, 1993, p.04).

This attribute is fairly important because consumers directly relate price to the value

of a brand. Also the packaging is most times not directly related to the performance

attributes of the product. User and usage imagery attributes are formed through own

experience or other sources of information. “Associations of a typical brand user may

be based on demographic-, psychographic and other factors” (Keller, 1993, p.03).

Usage imagery refers to possible typical moments or situations the product is used in.

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The second pillar within Brand image is formed by the functional, experiential and

symbolic benefits of a product or service that result in personal value for the

consumer (Keller, 1993). Functional benefits refer to the intrinsic advantages that

solve problems or prevent potential ones. “A brand with a functional concept is

defined as one designed to solve externally generated consumption needs” (Park,

Jaworski and MacInnis, 1986, p.136). Experiental benefits relate to what it feels like

to use the product or service and Symbolic benefits satisfy extrinsic values. An

example of Symbolic benefits could be the acquired social approval when wearing

branded clothes. Consumers may value the prestige, exclusivity or fashionability of a

brand because of how it relates to their self-concept (Solomon, 1983). Finally, Brand

attitudes form the overall evaluations of a brand (Wilkie, 1994) wherein all attributes

are evaluated and valued by the consumer. The conceptual basis of CBBE as

described by Keller (1993), existing of Brand knowledge and its dimensions

Awareness and Image, is an important step towards the CBBE model. In the coming

paragraph, this model will be elaborated and defined.

The CBBE model

Keller (2009) introduces the Customer-based brand equity model with the intention to

guide marketers in building and managing their brand in a dramatically changing

communications environment: “Traditional approaches to branding that put emphasis

on mass media techniques seem questionable in a marketplace where customers have

access to massive amounts of information about brands, products and in which social

networks have, in some cases, supplanted brand networks” (Keller, 2009, p.140). The

past decades more emphasis has been put on below the line communications, because

mass media seems to get less and less efficient and effective. In the 1960s, an

advertiser could reach 80% of American women with a single 30-second ad

broadcasted simultaneously on the three available channels. Nowadays, to reach the

same effect, the ad has to run on 100 channels (Keller, 2009). Because of the increase

in advertising, consumers tend to ignore ads. To make marketing communications

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more effective, integration of all efforts is highly recommended to reach synergy and

thus the optimal effect.

“The basic premise of CBBE is that the power of a brand lies in what

customers have learned, felt, seen and heard about the brand as a result of their

experience” (Keller, 2009, p.143). While building or managing a brand, those

experiences with the brand cannot always be controlled for, but for the bigger part

they can be influenced. The aim is to link the desired thoughts, feelings, images,

beliefs, perceptions and opinions of the brand in the mind of the target audience.

When a brand has built a positive Customer-based brand equity, consumers might be

more willing to accept brand extensions, are less sensitive to price increases and more

willing to seek the brand in a new distribution channel (Keller, 2009). The CBBE

model has its groundwork in the Brand knowledge framework as put forward in

previous paragraph. In Figure 2.2 the CBBE model is shown with on the left side a

visualized integration of this framework. The Brand knowledge framework is mainly

answering the question what makes a brand a strong brand. The sequel that Keller

(2009; 2012) proposes is focused on how a strong brand can actually be developed.

The CBBE model exists of four ascending steps to building a brand (Keller,

2012). The steps are visualized on the right in Figure 2.2. First, the Brand identity has

to be established. Associations have to be built in customers’ minds wherein he or she

is aware of the brand and places it in the right product class.

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Second, the association network around the brand needs to be build further in a

strategic way, to link the right tangible and intangible nodes to the brand. Third, the

right responses need to be triggered in terms of Brand identification and meaning. In

other words, strong, unique and positive nodes need to be linked to the brand which in

turn should result in favorable responses. In the last stage, the outcome or brand

response must be converted to an intense and active (loyal) relationship between the

customers and the brand (Keller, 2012). These steps are also formulated as follows:

who are you, what are you, what about you and what about you and me (Keller, 2012,

p.65)? As mentioned, the obvious order in these steps is necessary, because a

customer needs to be familiar with the brand before any associations can be

established and an identity created. Associations with a brand must exist before

consumers can give meaning to them and an intense and active relationship will only

follow when the proper responses are given.

To give more structure to this theory, Keller (2009) created the CBBE

pyramid (see also Fig. 2.2), which exists of six Brand building blocks that follow the

structure of the basic model. The first step in the CBBE pyramid is Brand Salience.

Salience is referring to identification of the brand and “customers’ ability to recall and

recognize the brand, as reflected by their ability to identify the brand” (Keller, 2012,

p.67) and linking the accompanying logo and symbol with the brand name.

Furthermore, the question of purpose to the customer needs to be answered. For a new

insurance company this means telling potential customers that the brand is selling

insurances. In the case of product extensions for a well-established brand this means

telling new and existing customers that the brand also sells product in another

category. In the pyramid the author defines the breadth and depth as two additional

dimensions to this first building block. The breath of awareness is the range of

purchase situations a brand element comes to mind. The depth of awareness refers to

the likelihood and ease that a brand element comes to mind. Consider for example an

ice cream brand. The sales of ice cream are higher in warmer seasons because the

customers’ need for cold refreshments is more likely to emerge in this period. To

generate more revenue the brands strategy is to communicate more possible moments

and situations of consumption for its products, like the consumption as an everyday

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desert after dinner or in celebratory situations. When the strategy succeeds, the breath

of awareness becomes higher, because when customers get groceries for a birthday,

ice cream now comes to mind. The ease to which the ice-cream brand in this example

and not another ice-cream brand comes to mind or gets recognized when standing in

the supermarket is the depth of awareness. Another aspect to keep in mind is the

structure of product categories in customers’ minds and the way of

reasoning when deciding which kind of product to buy

independent from brand choice. Keller (2012) gives an

example of the drinks category hierarchy wherein a

customer first decides to go for water or a flavored drink.

When choosing for a flavored drink a decision is made

between alcoholic and non-alcoholic drinks. When

non-alcoholic is chosen, hot drinks, soft drinks,

milk or juices etc. are all options within this

category. As can be imagined, the situations

and the likelihood that a brand of water

comes to mind are both higher than a

certain brand of milk all the way down in the category hierarchy. “A salient brand is

one that has both depth and breadth of Brand awareness, so that customers always

make sufficient purchases and always think of the brand across a variety of settings in

which it could possibly be employed or consumed” (Keller, 2012, p.70). When

salience is established, the next building blocks in the CBBE pyramid can be

developed: Performance and Imagery.

Brand performance and imagery are located at the same level in the CBBE

pyramid and represent both the functional and affective associations that can be linked

to a brand in the process of brand building. In earlier research Keller (1993) refers to

this and the next level of building blocks in the CBBE pyramid as the Brand image

dimension. Brand performance relates to the functional needs that a brand attempts to

fulfill for the customer. There are five important attributes and benefits when it comes

to Brand performance (See also Figure 2.3). The first holds the primary ingredients

and supplementary features. Customers expect certain features of a product or service

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they intend to buy. This could be the basis features to meet the standard in the

category (in case of for example a lighter it is just the function to light) or special or

patented features as promised in advertisements (in case of for example a new TV: a

new kind of 3D technology). Second, product reliability, durability and serviceability

refer to the consistency of performance over time and after repurchase, the expected

economic life and the ease of getting service when needed in case of defects. Other

components of service form the third set of attributes and benefits: service

effectiveness, efficiency and empathy. To what extend was the repair effective, quick

and is the service provider seen as trusting, caring and having the customer’s interests

in mind? (Keller, 2012). Fourth, style and design can have an important role in certain

product or service categories and last is the price. As mentioned in the previous

paragraph, pricing can have great influence on the perception of value of the brand.

Pricing also influences customers’ estimation of the level (low, medium, high) in the

product category.

The second brand building block is Brand imagery, which represents the

affective associations that can be built around the brand node. As defined by Keller

(2012, p.72): Brand imagery deals with the extrinsic properties of the product or

service, including the ways in which it attempts to meet customers’ psychological or

social needs”. It refers to the more intangible associations that can be created directly

through own experience or indirectly through advertising or for example word of

mouth. Within Brand imagery, four categories can be distinguished (see Figure 2.3.)

First, certain user profiles can be associated with a brand. This means that in the mind

of customers, a type of customer is linked to the brand as one or more of the typical

users. The user characteristics can be both demographic and psychographic. A typical

example is the brand Rolex, which mostly sells luxury watches to men who have

enough money to buy a Rolex product and at the same time are willing to show this to

other people. In this example both demographic variables (age, income) and

psychographic (attitudes towards possessions) variables contribute to the user profile

of Rolex. The second category holds purchase and usage situations and refers to the

place and time the products of a certain brand are bought. The ice-cream example

earlier in this paragraph also refers to usage situations and the Breath of awareness is

20

closely related to this topic, but now the usage and purchase situations are seen as

more typical for the Brand and not optional. Thus, the ice-cream brand is seen as a

brand especially for celebrations or desert. The third dimension exists of personality

and values. A brand can adopt human traits like caring, modern or exotic (Keller,

2012), which facilitates identification with the brand. Aaker (1997, p. 347) defines

brand personality as "The set of human characteristics associated with a brand" and

Sung and Kim (2010) adapt the Big Five personality scale to brand personality and

define its dimensions: competence, sophistication, excitement, ruggedness and

sincerity. The dimensions can all have a positive influence on the relationship

between customer and brand. Aaker, Fournier and Brasel (2004) find for example that

Sincerity encourages stronger relationships, similar to close friendships in the

interpersonal relationship. The fourth and last dimension of Brand imagery as stated

by Keller (2009;2012) exists of the history, heritage and experiences linked to a

brand, which can help to enrich the brand, to build credit or to differentiate from other

brands. Imagine a brand that sells espresso coffee makers and obviously

communicates it has its foundation in Italy, where espresso is always of a high

quality.

In conclusion, in both the Performance and Imagery building block, the right

associations need to be established in customers’ minds. In the beginning of this

chapter CBBE was defined and described as existing when strong, unique and

desirable associations were connected to the brand in customers’ minds. In this stage

of the brand building pyramid, also this order has to be hold in thought, because

without strength and uniqueness, a desirable association will never become connected

to a brand.

The next building blocks in the CBBE pyramid are the Brand judgments and

feelings that are the result of the Performance and Imagery associations associated

with a brand. Brand judgments are the opinions and evaluations on the brand wherein

four types are particularly important: quality, credibility, consideration and superiority

(Keller 2009;2012). The perceived quality of a brand is closely depending on the

attributes and benefits that are related to a brand. A Rolex watch thus cannot have a

cheap and underperforming clockwork because of the segment it is positioned in.

21

Credibility refers to the expertise, trustworthiness and likeability of the brand and is in

essence the extent to which customers “see the company or organization behind the

brand as good at what they do, concerned about their customers or just easy to like”.

Third, Brand consideration is about the relevance the brand has for the customer and

if the brand will be part of the consideration set when choosing between products or

services in a certain category. Last, Superiority related to the extent to which the

customer perceives the brand as unique and superior to other brands in the category

and consideration set. Brand judgments are mostly the result of a more objective way

of reasoning about attributes and benefits, but Brand feelings are the emotional

response that a brand elicits. Important pillars within this brand building block are:

warmth, fun, excitement, security, social approval and self-respect. In conclusion, all

that matters according to Keller (2012) is that the judgments and feeling are positive

and come easily to mind when consumers think about the brand. When this happens,

the last block of the CBBE pyramid, Brand resonance, will be easier to develop. As

Brand resonance is the main topic of this thesis, it will be reviewed in more depth in

the next paragraph.

Brand resonance

As stated in the introduction of this thesis, Brand resonance is defined as “the nature

of the relationship customers have with the brand and the extent to which they feel

they’re ‘in sync’ with the brand” (Keller, 2009, p.144). Brand resonance consists of

four pillars that form a single construct. When levels of the four pillars Behavioral

loyalty, Attitudinal attachment, Sense of community and Active engagement are all

high, the customer experiences an intense and deep relationship with the brand

wherein he or she actively shows a level of loyalty that includes repeat purchases,

seeking for more information about the brand and connecting with other loyal

customers (Keller, 2009; 2012). In this paragraph, the separate pillars of Brand

resonance will be defined and explained to a larger extent on the basis of available

research.

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First, Behavioral loyalty is defined as the behavior wherein a customer keeps

purchasing the available products or services the brand has to offer over time. “To

make a profit, the brand must be bought often and in volume” (Keller, 2012, p.79).

When consumers stay loyal to a brand, this has multiple positive implications. Aaker

(1991) finds that loyalty can result in more new customers, greater trade leverage and

reduced marketing costs. Dick and Basu (1994) find that favorable word of mouth and

a greater resistance to competing strategies are also results of Brand loyalty. In

addition to this, Pessemier (1959), Jacoby and Chestnut (1978), Reichheld (1996) and

Chaudhuri and Holbrook (2001) find evidence for the acceptance of a higher price for

products and services. Reichheld (1996) also proposes that satisfaction, which was for

a long time seen as the most important driver of loyalty, is not always performing as

predicted. He shows that although 90% of the car buyers are satisfied or very satisfied

about their purchase, less than half will buy the same brand of car next time. As stated

by Keller (2009): “Creating greater loyalty requires deeper Attitudinal attachment,

which can be generated by developing marketing, products and services that fully

satisfy consumer needs”. Furthermore, Behavioral loyalty is necessary but not

sufficient for Resonance to occur. Customers can be loyal due to the fact it is the only

brand in the product category or because it is the only affordable brand.

Attitudinal attachment is the second pillar of Brand resonance and relates to

the extremely positive feelings a customer can have towards a brand. “Customers with

a great deal of Attitudinal attachment to the brand may state that they ‘love’ the brand,

describe it as one of their favorite possessions or view it as a ‘little pleasure’ that they

look forward to” (Keller, 2012, p.79). Closely related to Attitudinal attachment is

Brand affect, which was the subject of research for multiple scholars. Brand affect is

defined as “a brand's potential to elicit a positive emotional response in the average

consumer as a result of its use” (Chaudhuri and Holbrook, 2001, p. 82). Sung and

Kim (2010) found that three of the five dimensions in their Brand personality scale

(Excitement, Sophistication and Competence) had a positive effect on Brand affect

and Chaudhuri and Holbrook (2001) demonstrate that Brand affect is an antecedent of

Loyalty. Where Brand affect is a more temporary result of product use, Attitudinal

attachment can be seen as a long lasting state wherein consumers feel a personal

23

attachment and shared identity with the brand independent from product use.

Therefore we define Attitudinal attachment as a sense of belonging the customer feels

towards a brand.

Sense of community is the third pillar of Brand resonance and an important

factor existing when “customers feel a kinship or affiliation with other people

associated with the brand” (Keller, 2009, p.145). Furthermore a positive attitude

towards the brand is required for this pillar. When Sense of community occurs, Brand

communities can arise wherein groups of customers actively communicate and

sometimes regularly meet to share their affiliation with the brand. A good example of

a Brand community is the Land Rover club with its numerous sub-clubs wherein

owners of Land Rovers meet, exchange maintenance information, have fun by driving

their car with others and share a common love: their Land Rover. As stated by

McAlexander, Shouten and Koenig (2002), a brand may take on a broader meaning to

the customer in terms of community. One of their findings is that marketers can

strengthen brand communities by facilitating shared customer experiences. Schau,

Muniz Jr. and Arnould (2009) also find that brand communities create value for a

brand both in the real world and online. Brodie et. al. (2011) investigate virtual

communities and state that “engaged consumers exhibit enhanced consumer loyalty,

satisfaction, empowerment, connection, emotional bonding, trust and commitment

(Brodie et. al., 2011, p.38).

The last pillar of Brand resonance is Active engagement. Keller (2012, p.80)

states that Active engagement arises “when customers are willing to invest time,

energy, money or other resources in the brand beyond those expended during

purchase or consumption of the brand”. According to Keller (2012), customers with

higher engagement become brand evangelists and ambassadors who are helping to

communicate about the brand and help strengthen the brand ties of others. The pillar

involves participation in discussions on brand-related websites, chat rooms etc. The

author also states that when the level of Attitudinal attachment is high, Active

engagement is more likely to occur. Brodie et. al. (2011) found multiple definitions of

engagement in which loyalty, commitment and empowerment are involved. In this

thesis we define Active engagement as the extent to which customers act as an

24

ambassador for the brand and are willing to invest time, energy, money or other

resources for his cause.

In conclusion, the dimensions of Brand resonance are closely related to each

other, but at the same time contribute with their own unique properties to the

construct. As stated before, this multidimensional measure could be a more precise

Brand performance indicator than current measures, since it covers multiple aspects of

the relationship between a customer and a brand. The more behavioral pillars

Behavioral loyalty and Active engagement cover a part of the construct wherein all

active processes from customers towards and about a brand are included. Attitudinal

attachment and Sense of community are the more affective pillars covering the

feelings customers can have and express towards the brand and other users of the

brand. The brand resonance concept derives strength from the fact that it is has a

broad base. This prevents short-sighted and wrong conclusions that could result from

a single minded focus on one specific dimension of brand-customer relationships such

as NPS or behavioral loyalty only. Many managers have adopted the Net Promoter

metric because they believe that solid science shows its superiority over other metrics.

Keiningham et. al. (2007) researched this metric, compared it to other Brand

performance indicators and show that it is not. The author was unable to replicate the

results that Reichheld (2003) found, likely because of the insufficient theoretical

ground the metric has. The Net promotor score has further received a lot of criticism

because ‘the one question’ only represents a willingness to recommend (part of Active

engagement), which on its own is no guarantee for a strong relationship between a

brand and a customer or brand performance. The problem that could occur because of

the one-dimensionality of this metric becomes evident in the example of Lonsdale.

Although the brand was recommended by a large group of customers the Brand

performance was damaged as a result since the promoters were neo-nazi’s who caused

other Lonsdale customers to distance themselves from the brand. Behavioral loyalty

alone is no accurate representation of the relationship between a customer and a brand

either. Customers don’t have to feel connected to a brand to buy it repeatedly.

Examples are situations in which there is only one brand in the category or when

purchase decisions are only based on a low price. If another brand enters the category

25

or starts to offer the product at a lower price, a lot of customers will defect from the

brand they initially showed ‘behavioral loyalty’ to. These examples show the danger

of using one aspect of the brand-customer relationships as a brand metric.

The pillars of Brand resonance form a synergetic, broad foundation for measuring

customer-brand relationships that can serve as a reliable indicator of Brand

performance. The focus of this research will be on developing a scale that includes

and integrates the four dimensions described in the previous paragraphs to build up to

Brand resonance as a transcending entity. To validate the Brand resonance model,

literature on scale validation will be reviewed in the next chapter, to define the steps

to follow through this process. Leading will be the literature written by Churchill Jr &

Iacobucci (2009). Their guidelines on methodological foundations in marketing

research form a solid basis for the research conducted in this study.

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3. Research method

Introduction

To validate a marketing scale, it has to be developed and tested according to the

criteria for reliability and validity. Reliability is the test to what extend the results of

certain measures are affected by irrelevant factors like item order or situational factors

when the measure is taken. Validity is about the degree the developed scale is

measuring what it is supposed to measure. There are different types of validity that

have to be taken into account like Content-, Construct-, and Criterion validity. Within

those types, a lot of sub-types of validity exist. The process and validation

components as which described by (Churchill Jr & Iacobucci, 2009) will be leading in

this research.

Before going into detail about the validation process, first two kinds of error

have to be addressed as well as the internal and external validity in a research

environment in general. The Systematic error (also known as Constant error) affects

the measure in a constant way. Thus when using a poorly calibrated instrument (like a

speedometer) to measure, the results will show a consistent deviation. The Random

error is the lack of consistency of the measure when repeated. For example when

respondents are asked to fill out an intelligence test and the questions in the test are

unclear and interpreted in multiple ways. The results are now not only depending on

intelligence, but also on the numerous ways of interpretation. Throughout the process

of scale validation, these two types of error can emerge and thus should be limited.

In a research process in general, also internal and external validity have to be

taken into account. Internal validity refers to our ability to attribute the effect of an

independent variable on a dependent variable. Are all variables involved controlled

and stable to know for sure the independent variable was the cause of the results

measured and no other factors? To achieve this, a labtest is often used to minimize

possible unaccounted influences. External validity refers to the generalizability of the

findings and if ‘real world’ factors are taken into account. Findings only replicable in

a testlab are in most cases a lot less valuable. Satisfaction, for example, is a predictor

of Loyalty. But are Trust and Service quality also predicting factors? Is Trust in every

27

situation a predictor, or only in a business to business environment and not when it

comes to the relationship between brands and consumers?

As described earlier, according to (Churchill Jr & Iacobucci, 2009) the first

focus in validation is on the content. Then is the stability of the construct tested,

followed by an assessment of the Criterion validity. The authors define the process of

scale validation in a marketing environment as follows. First, a large number of

statements has to be generated with the help of literature and experts covering as

many aspects as possible of the construct. Then a selection is made by deleting non-

relevant, ambiguous or awkward statements. A large sample of judges is then asked to

classify the statements by the degree of favorableness. After recoding, a check on

consistency in the response pattern and other checks regarding validity and reliability

are done. The researcher is left with a selection of questions that now would form a

valid scale to measure what is meant to be measured. In upcoming paragraphs the

different steps in this process of validation will be described in more detail followed

by the actual proposal to validate the Brand resonance construct.

Content validity

Content validity is defined as "the systematic examination of the test content to

determine whether it covers a representative sample of the behavior domain to be

measured" (Anastasi & Urbina, 1997, p.114). One of the most critical elements in

generating a content valid instrument is conceptually defining the domain of the

characteristic. The researcher has to specify what the variable is and what not. To do

that, the literature has to be examined to find out how the variable has been used and

defined. As not all definitions will be consistent, the researcher has to make a choice

that is applicable to the setting of interest (Churchill Jr & Iacobucci, 2009).

Next step in validation is formulating a collection of items that broadly

represent the variable as defined. Items from all the relevant dimensions of the

variable have to be included. Items with slightly different meaning can be included

since the list will be refined to produce the final measure. Face validity is part of

Content validity and defined as “the degree to which test respondents view the content

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of a test and its items as relevant to the context in which the test is being

administered” (Weiner & Craighead, 2010, p.637). To develop a measure, Face

validity is also used to appreciate the different items that are composed on a large

scale in the first phase. Different experts in the concerning area of research are

consulted to eliminate the irrelevant questions or add others to higher Content

validity. In general, Content validity can almost never be guaranteed because it partly

depends on the matter of judgment

Construct validity

Construct validity is largely about the degree in which the construct actually measures

what it theoretically should measure. Only behavior related to the construct can be

measured, unlike the construct itself. In essence the construct is then a set of

observables, which makes this type of validity most difficult to establish. “If a set of

items is really measuring some underlying trait or attitude, then the underlying trait

causes the covariation among the items. The higher the correlations, the better the

items are measuring the same underlying construct” (Bohrnstedt, 1970, p.80).

Consistency is a necessary but not sufficient condition for construct validity

(Churchill Jr & Iacobucci, 2009).

The process of refinement in which the internal consistency is tested, is done

by use of a statistical process. In this case factor analysis is one of the possible

statistical methods to seek for internal consistency and eliminate items that have no

valuable correlation in the construct.

After testing the construct on internal validity, the next thing to do is see how

the new construct relates to other theoretically linked models and see if it does it

behave as expected. Within Construct validity, Convergent-, Nomological- and

Discriminant validity can be distinguished. Convergent validity measures the

correlation between the tested scale and other measures that theoretically should be

correlated and Nomological validity is about the degree to which those measures

behave as expected within the system of related constructs. A diagram showing the

relationship among a set of constructs is called a Nomological net. When the

29

Nomological net behaves as expected, the Nomological validity is established.

Discriminant validity requires that a measure is not correlating too highly with other

constructs from which it is supposed to differ in theory (Campbell & Fiske, 1959).

When correlation is low enough, the construct is valid as a unique measure.

Criterion validity

In this stage, the focus lies on the predictive power of the construct on already

validated constructs that measure related concepts. The predictive validity is

determined strictly by examining the correlation between a dependent and

independent variable. Is for instance a lower price leading to a higher preference for a

brand? If correlation is high, the construct is said to have predictive validity. This

stage of the validation process is most times not the most important kind, because

most concerns are on the question if we measure what we want to measure, rather

than knowing if it predicts accurately or not (Churchill Jr & Iacobucci, 2009).

Concurrent validity can be tested by comparing the new construct to existing scales

that are already validated. An example could be a newly developed IQ test which is

compared to other IQ tests that already proved their accuracy.

Research design

In the following subparagraphs, the research design for each form of validation is

described in detail and sample collection and chosen analysis are discussed.

Content validity

In constructing the scale, items will be selected based on the current theory on Brand

resonance and that of the pillars it holds. In the process of selecting the right items, as

much of them as possible will be adopted from already validated scales in marketing

research. Also judges from both a practical and academic context will be consulted to

optimize Face validity. The first judge is Ph.D. Candidate at the Department of

Marketing & Supply Chain Management of the Maastricht University School of

Business and Economics. The second is the CEO of a marketing research company

30

and has 20 years of experience in the field with the focus on consultancy in marketing

and strategy. The last judge has 17 years of experience in marketing research. Above

three judges were selected to form a jury that would cover both the academic and

practical dimensions of the Brand resonance metric. Their experience and knowledge

will be used to define the domain of research and to higher content validity of the

items and ensure their appropriateness in the context in which they will be

administered.

Sample collection

The items for the Brand resonance scale will be included in different studies for

clients of a marketing research company. Confidentiality agreements prevent

elaboration on details of the clients further than the description of the type of markets

they are in. The construct will be tested in markets where involvement is high. These

kind of products increase the chances of developing a relationship with the brand and

form therefore an appropriate starting point of the process of validation. At the

moment no possibility exists to test the construct in markets with low-involvement

products and when the scale is composed this could be an interesting suggestion for

future research.

In the studies we conduct for this validation, respondents will be collected

with the use of online panels over a national representative sample. The respondents

are all subscribed to an online research platform and invited to different studies.

Based on demographics and current consumer product usage, invitations are send out

to participants and a small incentive (in the form of prize draw entries) is given when

a questionnaire is completed. Following Hair et. al. (2010), as a rule of thumb, the

ratio of observations to variables should be at least 10:1 for all studies.

In each study, the participants will be shown a selection of well-known

available brands of the product of topic and is asked to select the brands that are

known to them. Among the known brands, one will be assigned to the respondent and

inserted in the Brand resonance items and other scales used for validation. Apart from

the items in the Brand resonance scale, variables are included evaluating brand image,

product experience and the decision journey when buying the product of topic.

31

Construct validity

A strong part of this validation will lie in the Construct validity. A lot of resources are

available to cover the aspects of this part of the validation process. The scale is

included in different studies, administered at different points in time, covering

different countries and product categories.

Exploratory Factor analysis

To test for internal consistency, Exploratory factor analysis will be used to examine

the correlation among the measured items and the scale’s dimensionality. For this

procedure, we will use Principal axis factoring as recommended by (Netemeyer,

Bearden, & Sharma, 2003). A first sample in the German automatic espresso market

will be collected to facilitate an initial analysis. As recommended by (Hair et. al.,

2010), the analysis will be done over different samples for comparison of results. For

a second analysis, after six months, the study will be repeated in Germany and also a

sample in the US will be collected.

Confirmatory factor analysis

A selection of other constructs will be part of the studies to build and validate the

Nomological net of the brand resonance scale under study and to ensure Convergent

validity as well as Discriminant validity. A Brand trust and a Brand affect scale are

included that were used by Chaudhuri and Holbrook (2001) in their work to measure

the effect of both concepts on Brand performance. The included items in the scales

are listed in Appendix 1. Chaudhuri and Holbrook (2001) found a moderate effect on

Purchase- and Attitudinal loyalty and therefore it is hypothesized that a correlation

will exist between both scales and the Brand resonance scale, but should differ

enough to distinguish three different constructs.

To assess Nomological-, Convergent- and Discriminant validity, we will use

Confirmatory factor analysis. For this analysis SPSS AMOS Graphics is used to

perform Structural equation modeling. The three scales will be included in a study

about the automotive industry in the US.

32

Criterion validity

Criterion validity will be examined by use of a regression analysis of the predictive

power of Brand resonance on previously used brand performance indicators.

Brand preference is a widely used brand performance measure and thus qualifies to

assess the predictive power of Brand resonance. As no other Brand resonance scale is

validated yet, we are unable to examine concurrent validity of the metric. However

we can compare the predictive power of Brand resonance with other scales used as a

brand performance indicator. For this comparison, the NPS metric will be included as

it is widely used in a business environment.

Binary logistic regression

As the Brand preference variable is dichotomous (answer options preferred/non-

preferred), Binary logistic regression will be used which is a type of analysis

especially designed for this kind of variables. NPS, Brand preference and Brand

resonance are included in the study regarding espresso machines over nine countries

worldwide: Brazil, France, Germany, Italy, Netherlands, Poland, Russia, South Korea

and the US.

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4. Results

Introduction

In validating the Brand resonance measure, all practically feasible steps in the process

of validation are done as thoroughly as possible. In constructing the scale, items will

be selected based on the current theory on Brand resonance and that of the pillars it

holds: Behavioral loyalty, Attitudinal attachment, Sense of community and Active

engagement. The defined domain to be measured was covered extensively in the

literature review and will be referred to in the coming paragraphs.

Although we are aiming to validate a generalizable construct, the composed

statements should certainly be appropriate in the context of the high-involvement

markets the clients are in. In the questionnaire, multiple brands over a variety of

product categories in different countries will be evaluated by respondents. Following

the process and validation components as described by Churchill Jr & Iacobucci

(2009), first an attempt will be made to cover the aspects of the Content validity in the

process of composing the Brand resonance construct. After selecting the items, the

Construct validity and Criterion validity of the newly developed scale will be tested.

Content validity

In this paragraph, the selection of the Brand resonance scale items is covered and

described. Choices made in the process are explained and elaborated. First step in

Content validity is to define the domain of the characteristic. Brand resonance is “the

nature of the relationship customers have with the brand and the extent to which they

feel they’re ‘in sync’ with the brand” (Keller, 2009, p.144). The pillars Behavioral

loyalty, Attitudinal attachment, Sense of community and Active engagement together

form this construct definition and thus all four need to be represented by the items in

the scale. In the process of generating items, both an academic and practical

viewpoint are adopted. First the literature and current measures related to the different

pillars were reviewed and items per pillar generated. As discussed earlier, in the

context of Face validity, a judge with an academic background in the area of research

and two other judges with years of practical experience in marketing research were

34

consulted to give their expert opinions in the process of composing the Brand

resonance scale.

The commercial setting wherein this research is conducted, eliminates the

possibility to pretest the Brand resonance construct with a large collection of items.

We are aiming to cover each pilar of the Brand resonance construct, so at least 4 items

will be selected to form the scale. Kim and Mueller (1981) state that a construct

should be measured with at least three items and Bearden & Netemeyer (1999) agree

and only included scales with more than three items in their Handbook of marketing

scales. The final amount of items selected will depend on the theoretical coverage of

the construct and internal fit of the selected items.

Behavioral Loyalty

The different forms of loyalty have been researched for a long time and developed

measures have an attitudinal or behavioral base. Examples of loyalty metrics are

Brand preference (Yoo and Donthu, 2001), Recommendation (Lau and Lee, 1999),

Share-of-wallet (Berger et. al., 2002; Mägi, 2003) and Repurchase intention (Odin et.

al., 2001; Kressman et. al., 2006). Behavioral loyalty measured in this context is

defined as the behavior wherein a customer keeps purchasing the available products

or services the brand has to offer over time. In selecting the right statements to cover

this pillar, previously used measures were reviewed focusing on the aspects that come

closest to the actual behavior of purchasing products of a brand and the energy that is

put in obtaining them.

Within the frequently used dimensions of loyalty, Share-of-wallet seems the

first suitable measure that covers actual purchase behavior. “Share-of-wallet measures

the share of category expenditures spent on purchases at a certain company, which

integrates choice behavior and transaction sizes during a certain period into one single

measure” (Leenheer et. al., 2007, p.32). Taking in consideration that the scale will be

first tested within the consumer electronics market, the statement for inclusion will

then be as follows: “If you think of all consumer electronics products (e.g. TV, phone,

coffee machine, blender, shaver, etc.) that you have purchased in the last 12 months,

which percentage of those products are [BRAND] products?”. Although this measure

35

has the benefit that it is not based on intention, but actual purchase behavior, the risk

is that given the type of market concerning high-involvement products, respondents

did not buy a wide range of for example consumer electronics in the previous 12

months. Making the measure less accurate. Extending this period and thus including

more products will also lower accuracy because people tend to lack accuracy in

remembering things over longer periods of time. In making a Brand resonance scale

that is generalizable over different markets, the period stated in the item should be

adaptable to fit different circumstances. The item is measured with a range of 0-100%

on a 11-point scale.

In addition to the Share-of-wallet item, other behavioral related items are

found in literature, but most are referring to future intentions. Chaudhuri and

Holbrook (2001) measured the behavioral aspect, which they call Purchase loyalty

with the two statements: “I will buy this brand the next time I buy [PRODUCT

NAME]” and “I intend to keep purchasing this brand”. Also Kressman et. al. (2006)

and Odin et. al. (2001) measure behavioral loyalty on the basis of future intentions

with likewise statements adapted to different product categories. One of the

statements Odin et. al. (2001) used, could be suitable to cover the investment a

customer is willing to make to obtain products from a specific brand: “If the shop I

regularly visit has not got the brand of __ I usually buy, I go to another shop”. This

statement is not linked to a specific brand and does not include online purchases.

Keller (2009) proposes the following behavioral statement as a possible measure of

Behavioral loyalty: “I would go out of my way to use this brand”. As this statement

was marked by the judges as too vague and excluding the actual purchase, the

dimension of investment was covered in a more complete way. The following

statement was composed in consultation with the different experts involved: “I am

willing to invest a lot of time and energy to obtain [BRAND] products”. The item is

measured on a 7-point Likert scale.

As suggested by the judges, another dimension of behavioral loyalty was

included, covering active current behavior of customers wherein their interest in the

brand is uncovered in the run up to a purchase. It measures the extent to which

customers actively follow the brand and seek for new products or price reductions

36

within the current product portfolio of the brand: “I actively follow the latest news

about product introductions and promotions of [BRAND]”. This item is also

measured on a 7-point Likert scale.

Attitudinal attachment

We define attitudinal attachment as a sense of belonging the customer feels towards a

brand. As Keller (2012, p.79) states, “Customers with a great deal of Attitudinal

attachment to the brand may state that they ‘love’ the brand”. One of the items earlier

proposed by Keller (2009, p.79) to measure this concept is then logically: “I really

love this brand”. This item was approved by the judges and will be included in a way

multiple brands can be evaluated by the same respondent: “I really love [BRAND]”.

The item is measured on a 7-point Likert scale.

Another aspect not covered yet, is the long lasting state wherein customers

feel, apart from a personal attachment, also a shared identity with the brand. Brand

identification is researched by Curloo and Chamblee (1997), Bhattacharya and Sen

(2003) and others. Keller (2009, p.79) proposes three items from which the most to-

the-point one is: “This brand is more than a product to me”. As pointed out by the

judges, the proposed item can be interpreted in multiple ways though. Buying a

certain car brand could for example also be a way to show off, without the buyer

feeling really connected to the brand. It was decided that the most straightforward and

clear way to cover this aspect is as follows: “I can identify with [BRAND]”. The item

is measured on a 7-point Likert scale. The different experts gave their feedback in the

process of composing this item.

Sense of community

The pillar Sense of community is defined as a factor that is existing when: “customers

feel a kinship or affiliation with other people associated with the brand” (Keller, 2009,

p.145). Bhattacharya and Sankar (2003) suggest that identification with a brand

community involves both cognitive and affective components. The cognitive

component involves the awareness of the consumer to be part of the group. The

affective component is what is measured in this construct, focusing on the emotional

37

involvement with the group. Keller (2009, p.83) proposes a number of statements to

measure this affective component. Two statements that capture the definition best are:

“I really identify with people who use this brand” and “I feel a deep connection with

others who use this brand”. McAlexander, Shouten and Koenig (2002) find that

within brand communities, crucial relationships are those between the customer and

the brand, the customer and the firm, the customer and the product in use and among

fellow customers. The relationship between the customer and the brand is covered by

the Behavioral loyalty and Attitudinal attachment dimensions and the firm is not taken

into account since the construct to be validated is concentrated on the relationship

with brands. Following recommendations by McAlexander, Shouten and Koenig

(2002), the product in use should be integrated to cover this dimension. As ‘a kinship

or affiliation’ comes closer to identification than a deep connection, the first statement

proposed by Keller (2009, p.83) is taken as a base. Integrating the product in use

resulted in the following statement, taking into account that respondents are already

assigned to a product category before answering to this statement: “I identify with

people who use [BRAND] products”. The item is measured on a 7-point Likert scale.

Active engagement

The Active engagement pillar is defined as the extent to which customers act as an

ambassador for the brand and are willing to invest time, energy, money or other

resources for this cause. A statement proposed by Keller (2009, p.83) to cover this

construct is as follows: “I really like to talk about this brand to others”. Although this

statements covers the fact that the brand is being talked about or not, it is unknown if

the respondent likes to talk negatively or positively about the brand. The judges

proposed the widely used Net promotor score (Reichheld, 2003) metric which is all

about ambassadors of brands. Although this metric has received a lot of criticism as a

sole indicator of brand performance, the imperfections due to its one-dimensionality

would become less problematic when integrating the metric into the Brand resonance

construct. The statement proposed by Reichheld (2003), “How likely is it that you

would recommend our company to a friend, relative or colleague”, is adapted to fit the

other statements and the research setting. This means that the brand and product

38

category is added, resulting in the following item: “Based on your experience with

your [BRAND + PRODUCT CATEGORY], how likely are you to recommend

[BRAND + PRODUCT CATEGORY] to a friend, relative or colleague?”. The item is

like the NPS measured with an 11-point scale starting at “Extremely unlikely” and

ending at “Extremely likely”.

Brand resonance

All facets of the Brand resonance construct are represented by seven items in total and

developed with the greatest care, taking into consideration as much as dimensions of

Content validity:

1. If you think of all consumer electronics products (e.g. TV, phone, coffee

machine, blender, shaver, etc.) that you have purchased in the last 12 months,

which percentage of those products are [BRAND] products?

2. I am willing to invest a lot of time and energy to obtain [BRAND] products.

3. I actively follow the latest news about product introductions and promotions

of [BRAND].

4. I really love [BRAND].

5. I can identify with [BRAND.

6. I identify with people who use [BRAND] products.

7. Based on your experience with your [BRAND + PRODUCT CATEGORY],

how likely are you to recommend [BRAND + PRODUCT CATEGORY] to a

friend, relative or colleague?

In the next paragraphs, the construct- and criterion validity of the Brand resonance

construct will be tested in further development of the metric.

Construct validity

The scale is included in different studies, administered at different points in time,

covering different countries and multiple product categories. Also other constructs are

39

part of the studies to build and validate the Nomological net of the brand resonance

scale under study. To test for internal consistency, Exploratory factor analysis will be

used to examine the correlation among the measured items and the scale’s

dimensionality.

Hypothesis 1: Initial analysis on the selected items to form the Brand resonance

construct results in a clear single factor solution.

For this procedure, Principal axis factoring will be used as recommended by

Netemeyer, Bearden, & Sharma (2003). The factor structure of the construct can be

tested in multiple product contexts and multiple countries, which can provide a

stronger foundation for the scales validity due to comparability over different

countries and product categories. Furthermore, the scales reliability will be examined.

Other previously validated scales covering Brand affect and Brand trust are included

in one study and can be used to ensure Convergent validity as well as Discriminant

validity as much as possible. Brand affect is closely related to the Attitudinal

attachment pillar within Brand resonance and is therefore expected to correlate.

According to the commitment-trust theory (Morgan and Hunt, 1994), Brand trust is a

key variable in the development of an enduring desire to maintain a relationship in the

long term. Chaudhuri and Holbrook (2001) indeed found a significant correlation

between Brand trust and Purchase loyalty (.46) and Attitudinal loyalty (.33). Results

of Brand affect on both constructs were weaker but also significant with correlations

of .25 and .30 respectively. It is thus expected that both Brand affect and Brand trust

correlate high enough with Brand resonance to confirm the theoretical linkage, but

low enough to differentiate three different constructs in the analysis. Results will

show if Brand resonance behaves as expected within this Nomological net.

Hypothesis 2: Brand resonance correlates positively with Brand affect and Brand

trust.

40

Hypothesis 3: Brand resonance, Brand affect and Brand trust show discriminant

validity.

To assess above hypotheses, Confirmatory factor analysis will be used and the model

containing the three metrics will be tested in SPSS AMOS.

Phase 1 - Exploratory factor analysis

To test the first hypothesis, Principal axis factoring is applied on a dataset obtained

for commercial research in espresso machines in Germany. No rotation was applied

due to the fact that we do not expect to find multiple factors. The questionnaire was

conducted with the use of online panels over a national representative sample of 979

respondents from which 52% was male. The sample was spread over the age range

18-65 with an average of 40,80 (SD=12,09). Completion of the questionnaire took

roughly about 15-20 minutes. According to Hair et. al. (2010) as a rule of thumb, the

ratio of observations to variables should be at least 10:1. The ratio for used dataset is

around 139:1 and thus meets this criteria.

Table 4.1

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .893

Bartlett's Test of

Sphericity

Approx. Chi-Square 3646.417

df 21

Sig. .000

Note. N=979.

First, KMO and Bartlett’s test are performed. The measures tests for the probability

that at least some of the Brand resonance items correlate. KMO has a range from 0 to

1 showing the extent to which correlation within the construct is found. If Bartlett’s

test shows significant results, the items are indicated to form a valid construct (Hair

et. al., 2010). Results in Table 4.1 show that the correlations between pairs of

variables can be highly (.893) explained by the other variables. The Correlation

41

matrix is found significant as an Identity matrix (.000). Both results show that the

underlying construct correlates enough to continue factor analysis.

Note. Extraction Method: Principal Axis Factoring. EV=Eigenvalue.

Var=% of Variance explained. C=Cumulative %. N=979

Second, the amount of factors in the results are assessed. Using the Latent root

criterion, all factors with Eigenvalue more than 1.0 are considered significant (Hair et.

al., 2010). Results in table 4.2

show that in line with expectations

indeed only one significant factor

is extracted (Eigenvalue 4.206)

which is a first indication that a

single factor solution is

appropriate. Figure 4.1 shows a

scree plot of the extracted factors.

Combining the Latent root criterion

and the Scree test criterion,

wherein we look at the point where

the scree plot levels off and identify the point of inflexion, it is clear that indeed only

one factor stands out. The third column in Table 4.2 shows the total variance that is

explained by the factor. Hair et. al. (2010, p.109) state that “..in the social science,

where information is often less precise, it is not uncommon to consider a solution that

Table 4.2

Total Variance Explained

Factor

Initial Eigenvalues

EV Var C %

1 4.206 60.083 60.083

2 .901 12.866 72.950

3 .574 8.202 81.151

4 .406 5.794 86.945

5 .379 5.411 92.356

6 .290 4.146 96.502

7 .245 3.498 100.000

42

accounts for 60% of the total variance (and in some instances even less) as

satisfactory”. With a result of 60%, enough variance is explained in support of the

factor solution. In this light, for now we deem this percentage acceptable.

Note. N=979.

Looking at the correlation matrix in Table 4.3, we see all individual relationships

among the variables. For values of .000 no correlation exists, and for values of 1.000

a perfect correlation exists. Taking the sample size in account, correlations of .5 and

above are seen as satisfactory (Hair et. al., 2010). Examining the results, some

weaknesses are shown at NPS and Share-of-wallet.

Table 4.3

Correlation Matrix

1 2 3 4 5 6 7

1. NPS 1.000

2. Brand love .550 1.000

3. Brand identification .496 .732 1.000

4. Investment to obtain .424 .659 .671 1.000

5. Identification others .365 .574 .665 .643 1.000

6. Brand following .371 .559 .584 .681 .617 1.000

7. Share-of-wallet .198 .381 .369 .493 .439 .527 1.000

Table 4.4

Factor Matrix

Factor 1

NPS .537

Brand love .804

Brand identification .829

Investment to obtain .840

Identification others .767

Brand following .768

Share-of-wallet .535

Note. Extraction Method: Principal Axis Factoring.

a. 1 factors extracted. N=979.

43

This can be partly explained. Both the NPS and Share-of-wallet item were included

with an 11-point scale and therefore correlation with the other items could be lower.

The wider scope could thus be a good reason for shown results. The rest of the

correlations show a good positive fit.

Last, the factor matrix is shown in Table 4.4, wherein the factor loadings per

variable are shown which identify to what extend each variable explains the total

factor solution. The all positive loadings are deemed significant when exceeding .3

and considered as satisfactory results (Hair et. al., 2010). As expected after previous

results, the factor loadings shown in Table 4.4 are lower for NPS and Share-of-wallet.

Although lower, all loadings are still exceeding the minimum value of 0.3.

Conclusion

The first results are promising and confirm the existence of a clear single factor

structure explaining the latent variable Brand resonance. Lower scoring items are NPS

and Share-of-wallet. Additional tests are performed to decide on their inclusion in the

final scale. Highest scoring items are Brand love, Brand identification and Investment

to obtain with factor loadings starting at .804.

Phase 2 - Exploratory factor analysis – replication

In the second stage of this Exploratory factor analysis the previous research method is

duplicated into two other studies in the same product category. Again, German

respondents were asked to fill out an online questionnaire about espresso machines

and also a sample in the US was collected. Again respondents evaluated an espresso

Note. DE=Germany. US= United States.

Table 4.5

Comparison samples over base, gender and age

DE 1 DE2 US 1

Base 979 934 603

Male 52% 50% 56%

Mean age 40.83 41.79 34.36

SD age 13.09 12.09 11.44

44

machine brand that they were familiar with. The brands used in the US study were

partly different because of the availability of espresso machines from the brands in

that specific country. In Table 4.5 the samples are compared. Bases are comparable to

the first studies, with ratios of observations to variables of 133:1 (DE2) and 86:1

(US1) the earlier mentioned criterion of 10:1 is amply met. Further, the samples can

be called fairly equal when it comes to the distributions of gender and age.

First, in all studies the Bartlett’s test is significant and KMO stays at a high

level (DE 1: .893 – DE 2: .837 – US 1: .892) confirming the existence of the factor.

Hereby we have to acknowledge Bartlett’s test is known to be less accurate in

detecting validity when sample sizes are high, meaning that a significant result can be

shown earlier when the sample grows. As the sample sizes in this study are

considered fairly high, we will interpret results of the Bartlett’s test as a good

indication.

Table 4.6

Factor Matrix – comparison

DE 1 DE 2 US 1

1. NPS .537 .707 .767

2. Brand love .804 .815 .831

3. Brand identification .829 .831 .818

4. Investment to obtain .840 .857 .892

5. Identification others .767 .766 .831

6. Brand following .768 .773 .714

7. Share-of-wallet .535 .402 .647

Note. Extraction Method: Principal Axis Factoring. a. 1 factors extracted.

DE=Germany. US= United States. N DE 1=979. N DE 2=934 N US=603

Looking at Table 4.6 two distinct developments are identified comparing the different

studies. The loadings of NPS on the factor has improved in the second Germany and

US study. This shows that the item seems to fit the model. On the other hand, the

Share-of-wallet variable shows a lower loading in the second Germany study and a bit

higher in the US study.

45

Conclusion

In the previous results a clear improvement was shown for NPS, but the factor

loadings of Share-of-wallet fluctuated. Compared to the factor loadings of the other

variables, results below .7 are considered unsatisfactory and thus Share-of-wallet will

be excluded in further analysis. With this modification a fairly strong single factor

solution is presented with sufficiently high factor loadings and a clear latent structure.

With this result, further evidence is given in support of Hypothesis 1 and validation of

the Brand resonance metric can continue.

Phase 3 – Confirmatory factor analysis

In this phase, Confirmatory factor analysis will be performed to test Nomological-,

Convergent- and Discriminant validity and the preconceived hypothesis from the EFA

will be supported or rejected. For this study another sample was used as

recommended by Hair et. al. (2010). The study covers the automotive industry in the

US. Respondents from online panels were asked to answer questions about their

current and previous cars and their opinions about car brands they were familiar with.

Within the sample, 51% of the 253 respondents is male and the age range is 21 to 85

with an average of 43,97 (SD=14,85). Completion of the questionnaire took about 15

to 20 minutes.

Note. US= United States. N=253

For this analysis SPSS AMOS Graphics was used to perform Structural equation

modeling. The latent constructs and relations were specified as seen in Appendix 2.

Table 4.7

Factor Matrix CFA

US 2

1. NPS .774

2. Brand love .846

3. Brand identification .846

4. Investment to obtain .881

5. Identification others .724

6. Brand following .600

46

First, the factor loadings of the Brand resonance construct will be reviewed again, as

the decision was made in Phase 2 to exclude Share-of-wallet. Results are shown in

Table 4.7. Again, all positive loadings are deemed significant when exceeding 0.3

(Hair et. al., 2010). Results show high factor loadings of all but one observed

variables. The Brand following item is performing lower than was measured during

the EFA (DE 1: .768 – DE 2: .773 – US 1: .714). Although the factor loading of this

item is the lowest (.600) in this sample, it still is high enough to see it as a satisfactory

result. It could be that given the product category, it is explicable that customers are

not continuously following news about product introductions and promotions of a car

brand because of the length of time between purchases and the financial magnitude of

them.

Note. CR=Composite Reliability. AVE=Average Variance Extracted. MSV=Maximum Shared Variance.

ASV=Average Shared Variance. R2=Squared correlation between constructs. N=253

Next the validity and reliability of the model will be examined. In Table 4.8 the

measures to ensure Convergent- and Discriminant validity as well as reliability are

shown. For above outcomes, the thresholds as suggested by Hair et. al. (2010) will be

used. To establish reliability, the values for CR have to be above .7. For Convergent

validity, CR must be greater than AVE and AVE greater than .5. For Discriminant

validity, MSV and ASV should be smaller than AVE. In addition, AVE should be

greater than the squared correlations between the constructs for satisfactory

Discriminant validity. Looking at the results in Table 4.8 we see that the Brand

resonance construct passes all thresholds as suggested by Hair et. al. (2010)

supporting the existence of Convergent- and Discriminant validity as well as

reliability. Correlations between Brand resonance and Brand trust and Brand affect

are .60 and .66 respectively, confirming the hypothesized linkage between the

Table 4.8

CFA – Validity and reliability

CR AVE MSV ASV R2

1. Brand trust .910 .716 .819 .588 BA= .800

2. Brand affect .915 .782 .819 .641

3. Brand resonance .897 .596 .464 .410 BT= .360 BA= .436

47

theoretical concepts. As highlighted in orange, problems arise in the Maximum shared

variances of the Brand trust and Brand affect factors, meaning that both latent factors

are better explained by variables of other factors within the model. In other words, the

metrics are too alike to be able to demonstrate their independence from each other.

This is well explained by a correlation of .9 between both factors. As we investigate

validity and reliability of the Brand resonance factor in this model, these outcomes are

less relevant and we can conclude that Convergent- and Discriminant validity as well

as Reliability is established. The next step in this Confirmatory factor analysis is the

assessment of model fit.

In Structural equating modeling no one perfect index exists to validate a model and

thus we will look at a range of Absolute fit indices which are shown in Table 4.9.

Absolute fit indices measure how well the proposed theory fits the data. The most

fundamental Absolute fit index is the Statistic which is the only statistically based

Structural equation modeling (SEM) fit measure (Hair et. al., 2010). “The implied null

hypothesis of SEM is that the observed sample and estimated covariance matrices are

equal, meaning that the model fits perfectly” (Hair et. al., 2010, p.666). When

differences are found between the matrices, the statistic increases. A significant p-

value shows a significant difference and therefore a high p-value is preferred for this

Table 4.9

CFA – Absolute fit indices

Result CI

CMIN 165.396

df 62

Statistic 2.668*

GFI .897

RMSEA .081* (.066, .96)

RMR .119

Note. CI=Confidence interval. CMIN=Chi-square. df=Degrees of freedom.

GFI=Goodness-of-fit index. RMSEA=Root mean square error of

approximation. RMR=Root mean square residual. *P < .01. N=253

48

test. The Goodness-of-fit index (GFI) has a range of 0 to 1 and values higher than .9

are considered good (Hair et. al., 2010). The Root mean square error of approximation

(RMSEA) is an Absolute fit index that attempts to correct for sample sizes larger than

500, as they tend to make the previous indices less reliable. Lower values for the

RMSEA are desirable and values ranging between .05 and .08 are considered

acceptable, although it is advised by Hair et. al. (2010) not to use a cutoff point. An

advantage of RMSEA is that a confidence interval can be given. The RMR (Root

mean square residual) index is the average of the residuals that are created by the

error in the prediction for each covariance term (Hair et. al., 2010). Results exceeding

4.0 are an indicator of a bad fit.

Looking at the results in Table 4.9, we see that the statistic is found

significant at .01 with a which shows bad fit. The GFI of .897 is close to .9 and thus

an indication of good fit can be confirmed. RMSEA of .081 is also deemed acceptable

with a 99% confidence interval between .066 and .096. Last we look at the result for

the RMR statistic and see that the value of .119 is ample below 4.0 and thus another

indication of good fit. In conclusion, the fast majoraty of the Absolute fit indices show

positive results in support of model fit. Last, we will look at a selection of Incremental

fit indices which measure how well the model fits compared to a baseline model,

which assumes all observed variables are uncorrelated (Hair et. al., 2010).

Note. NFI=Normed fit index. TLI=Tucker-Lewis index. CFI=Comparitive fit

index. *P < ,01. N=253

In Table 4.10, the first index is the Normative fit index. The NFI ranges between 0

and 1. A perfect fit would produce an NFI of 1. The TLI (Tucker-Lewis index) is

different from the NFI in a way that it is a comparison of the normed Chi-square

values. The scores can exceed 1.0, but a result around that number is deemed proper.

Table 4.10

CFA – Incremental fit indices

Result

NFI .937

TLI .949

CFI .959

49

The Comparative fit index is an improved version of the NFI due to its normalized

results. Also this index ranges from 0 to 1 and CFI values above .90 are usually

associated with a model that fits well (Hair et. al., 2010). Looking at the results in

Table 4.10, we see positive results. As the NFI of .937 is close to 1, this can be

considered a good result. The TLI of .949 is close to 1 and therefore also another

indication of good fit is shown. Best results are found for CFI with a value of .959 (far

above .9) which shows good fit.

Conclusion

Previous results show a validated model wherein Reliability and Discriminant- and

Convergent validity as well as model fit is well established. Almost all results on the

Absolute fit indices and all results on the Incremental fit indices showed good model

fit. The Brand resonance scale seems to behave as expected within the nomological

net containing Brand Trust and Brand affect and thus Hypothesis 2 and 3 are

supported. As was shown, Brand resonance clearly contributed to the model wherein

Brand trust and Brand affect hardly differentiated from each other. Nomological

validity can be underpinned better in the next paragraph wherein the Brand resonance

metric is tested on its predictive power.

Criterion validity

In this paragraph, we will test criterion validity by analyzing the predictive power of

Brand resonance construct on Brand Preference. Since the Share-of-wallet item was

excluded from the scale it will be used as a second brand performance indicator

within the nomological net. Logistic- Linear and regression will be used to assess this

part of the scale validation process. We expect to find a positive relationship between

Brand resonance and the two measures. When a high Brand resonance exists between

a consumer and a brand, his or her preference for that brand must be higher than for

other brands. At the same time, logically the amount of products bought from that

specific brand would go up as well.

50

Hypothesis 4: Brand resonance has a positive effect on Brand preference.

Hypothesis 5: Brand resonance has a positive effect on Share-of-wallet.

In addition, we will measure the predictive power of the NPS metric on Brand

preference and Share-of-wallet to make a comparison with Brand resonance as it

theoretically is expected to perform better in this analysis. When it does, it indicates to

be a better brand performance indicator than this currently well-established metric.

Hypothesis 6: Brand resonance has a higher predictive power on Brand preference

than NPS.

Hypothesis 7: Brand resonance has a higher predictive power on Share-of-wallet

than NPS.

Phase 4 – The predictive power on Brand preference

Brand preference is a widely used brand performance measure and thus qualifies to

assess the predictive power of Brand resonance. As the Brand preference variable is

dichotomous (answer options preferred/non-preferred), Binary logistic regression will

be used. Both Brand preference and Brand resonance were included in a study

regarding espresso machines over nine countries worldwide: Brazil, France, Germany,

Italy, Netherlands, Poland, Russia, South Korea and the US. In Appendix 3 an

overview is shown with the descriptive statistics regarding the age and gender

distribution over the countries. Usable cases for each country are about N=650 with

an overall base of N=6648. The mean age is 36,73 (SD=12,17) and 53% of the

respondents are male. The Brand resonance metric is integrated into one variable by

calculating the mean over the six items and all variables are thus equally weighted.

NPS was rescaled to fit the 7-point scales of the other variables before mean

calculation. Control variables Age and Gender are added to the regression. The mean

Brand resonance score is 4.83, which is a fairly neutral score on a 7-point scale.

51

First, a bivariate correlations analysis will be performed to get an indication if the

variables we want to use in the model correlate high enough to perform Binary

logistic regression.

Note. Pearson correlation. **Correlation is significant at the 0.01 level (2-tailed). **Correlation is

significant at the 0.05 level (2-tailed). N=6648

This preliminary analysis shows a significant correlation of Brand resonance on

Brand preference. Brand resonance appears to be a valid predictor and will thus be

included in the model. As expected the control variables Gender and Age do not

effect Brand preference.

Table 4.12

BLR – Block 0: Classification table

Observed

Predicted

Allocated brand is preferred

brand

%

Correct 0 1

Step 0

Allocated brand is

preferred brand

0 0 2382 .0

1 0 4266 100.0

Overall Percentage 64.2

Note. Constant is included in the model. The cut value is .500. N=6648

First results of the Binary logistic regression are shown in Table 4.12. Wherein we

look at the model before adding the predictor variables. Without any predictor

variables added, the chance that the brand is preferred by the respondent is 64.2%.

This outcome can be treated as the null hypotheses of the model.

Table 4.11

Bivariate correlations

1 2 3 4

1. Brand preference 1

2. Brand resonance .188** 1

3. Gender .008 .029* 1

4. Age .000 -.072** -.027** 1

52

Note. N=6648

Table 4.13 shows the results wherein all predictor variables are added simultaneously

to the model. A significant Chi-square shows that the predictor variables together

form a significant addition to the model in predicting Brand preference. We see that

the Chi-square of 246.103 is significant at 3 degrees of freedom (for the three

variables Brand resonance, Gender and Age) meaning a second indication is given

that Brand resonance indeed predicts Brand preference.

Table 4.14 shows the change in Chi-square compared to the base model and

significance tests Cox & Snell R Square and Nagelkerke R Square are performed on

this difference called the Log likelihood value (Hair et. al., 2010). Both measures

indicate to what extend the regression model accounts for the variation in the

dependent measure. Results show the logistic regression model accounts for less than

5% of the variation in the dependent measure, which is a fairly low and thus indicates

a weak prediction of Brand resonance on Brand preference.

Table 4.13

BLR – Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 246.103 3 .000

Block 246.103 3 .000

Model 246.103 3 .000

Table 4.14

BLR – Model Summary

Step -2 Log

likelyhood

Cox & Snell R

Square

Nagelkerke R Square

1 8428.682 .036 .050 Note. N=6648

53

Note. N=6648

The Hosmer and Lemeshow test (Table 4.15) is a measure of overall fit of the model

and needs to have a P-value higher than .05. With a result of .000 an indication is

given that the model lacks fit (Hosmer & Lemeshow, 1989).

Table 4.16

BLR – Block 1: Classification table

Observed

Predicted

Allocated brand is preferred

brand

%

Correct 0 1

Step 0

Allocated brand is

preferred brand

0 354 2028 14.9

1 202 4064 95.3

Overall Percentage 66.5

Note. The cut value is .500. N=6648

When looking at the Classification table 4.16, we see how good the model is at

predicting the actual outcomes. The result of 66.5% is only 2.3% more than was

shown in Table 4.12.This tells us that the added variables have a doubtful predictive

ability and thus Brand resonance seems to be unable to make a real difference in

predicting Brand preference.

Table 4.15

BLR – Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 28.985 8 .000

Table 4.17

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step

1a

Brand resonance .300 .020 232.260 1 .000 1.351

Age .006 .002 9.483 1 .002 1.006

Gender .042 .052 .656 1 .418 1.043

Constant -1.150 .152 57.640 1 .002 .317 Note. a. Variable(s) entered on step 1: Brand resonance, Age, Gender. B=Logistic coefficient. S.E.=Standard error.

Wald=Wald statistic. df=Degrees of freedom. Exp(B)= Exponentiated coefficient. N=6648.

54

At last, other measures of overall fit will be assessed to see if the results reach

practical significance. Table 4.17 first shows the Logistic coefficient which is the

original Coefficient that shows the predicted probability and the magnitude of the

relationship of the independent on the dependent variable. The Wald statistic provides

a significance test for each estimated coefficient (Hair et. al., 2010). At last, the

Exponentiated coefficient is the logarithm of the original Logistic coefficient with 0.0

as starting point. An Exponentiated coefficient of 1.0 means that there is no direction

in the relationship.

Looking at the results in Table 4.17 we find a significant and positive Logistic

coefficient indicating a positive relationship and an Exponentiated coefficient of

1.351 which is above 1.0 and thus a positive relationship is observed (Hair et. al.,

2010). With a high score on Brand resonance for a brand, that respondent is 1.351

times more likely to prefer the brand. The control variables are not significant or have

a minimal effect on the constant variable. For Age, this seems logically because of its

continuous scale. Within the model Brand resonance clearly differentiates as a

predictor within the model.

Conclusion

Above results demonstrate a positive relationship between Brand resonance and

Brand preference which is in support of Hypothesis 4. Although the magnitude of its

predictive power is questioned by several tests of model fit, the significant

Exponentiated coefficient shows that when Brand resonance is high, there is a 35%

increase in the chance the respondent will prefer the brand.

Phase 5 – The predictive power on Share-of-wallet

In the last step of this scale validation, we will look at the predictive power of Brand

resonance on Share-of-wallet. As discussed earlier, Share-of-wallet measures the

amount of actual purchases from a specific brand done in the previous 12 months. As

a high Brand resonance should theoretically lead to more purchases, we are

performing a regression with both variables included. The same dataset is used as

55

selected for previous regression on Brand preference and linear regression is

performed. First, descriptives and a Pearson correlation is showed.

Note. N=664

Note. Pearson correlation. **Correlation is significant

at the 0.01 level (2-tailed). N=6648

The mean score for Share-of-wallet shown in Table 4.18 is 4.36 which means that

from all products the respondents bought the past 12 months, on average 44% was

from the brand they were assigned to in the questionnaire. The mean Brand resonance

score is 4.83. The Pearson correlation in Table 4.19 shows a fairly high and

significant correlation of Brand resonance on Share-of-wallet.

Table 4.18

Descriptive statistics

Mean Standard

Deviation

1. Share-of-wallet 4.360 3.342

2. Brand resonance 4.826 1.134

Table 4.19

Pearson correlation

1

1. Share-of-wallet 1

2. Brand resonance .447**

Table 4.20

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression 14837.829 1 14837.829 1659.497 .000b

Residual 59422.965 6646 8.941

Total 74260.794 6647

Note. a. Dependent Variable: Share-of-wallet. b. Predictors: (Constant), Brand resonance. N=6648

56

First, we look at the Table 4.20, which provides the statistical test for the overall

model fit in terms of F-ratio (Hair et. al., 2010). This test shows if the model that is

applied can significantly predict the outcome variable. Above results show that the

model is significant and thus Brand resonance is a valid predictor of Share-of-wallet.

Next, the model summary is shown in Table 4.21. R Square indicates to what extend

the dependent variable Share-of-wallet can be explained by the independent variable

Brand resonance.

Results show an Adjusted R Square of .200 which means that 20% of the

variance of Share-of-wallet can be explained by Brand resonance, which is deemed

good taken into account only one variable is responsible for above results.

The standardized Coefficient (Beta) in Table 4.17 shows the magnitude of the

independent variable when added to the model. “Moreover it allows for an assessment

of practical significance in terms of relative predictive power of the added variable”

(Hair et. al., 2010, p.214). The statistical significance associated with the Beta is

Table 4.21

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .447a .200 .200 .990

Note. a. Predictors: (Constant), Brand resonance. b. Dependent Variable: Share-of-wallet. N=6648

Table 4.22

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t

Sig.

95,0% Confidence

Interval for B

B

Std.

Error

Beta

Lower Bound

Upper

Bound

1

(Constant) -1.023 .137 -7.466 .000 -1.292 -.754

Brand

resonance 1.115 .027 .447 40.737 .000 1.061 1.168

Note. a. Dependent Variable: Share-of-wallet. N=6648

57

significant and the unstandardized Beta shows to what extend a point increase of

Brand resonance the dependent variable Share-of-wallet increases.

Conclusion

Results show that the statistical significance associated with Beta is significant with a

factor 1.115, which is an acceptable outcome. This means that with one point increase

in Brand resonance, Share-of-wallet increases with 1.115. To conclude, previous

results show a significant predictive power of Brand resonance on Share-of-wallet

which is in support of Hypothesis 5.

Phase 6 - The predictive power of the Net promotor score

In this paragraph we will duplicate the analysis on Brand preference and Share-of-

wallet to be able to compare the predictive power of Brand resonance with the Net

promotor score. As already discussed, it is valuable to know how the newly developed

scale is performing in comparison to a widely used scale like the NPS.

For coming analysis, the same dataset is used as for the predictive power of

Brand resonance. The sample size is 6648 and the mean score for NPS is 7.60. This is

the likelihood on a scale from 0 to 10, that respondents would recommend their

product to a friend, relative or colleague.

Binary logistic regression on preference

Again the control variables Gender and Age are added. As the base model without the

predictor variables is still the same (64.2% chance the respondent prefer the brand),

we will continue to the model wherein all variables are added.

Note. N=6648

Table 4.23

BLR – Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1

Step 377.857 3 .000

Block 377.857 3 .000

Model 377.857 3 .000

58

The Chi-square in Table 4.23 is significant at 3 degrees of freedom which indicates

that also NPS predicts Brand preference.

The model summary in Table 4.24 shows that the logistic regression model accounts

for 7.6% of the variation is the dependent measure, which is a little higher than the

model that included Brand resonance (5%).

Note. N=6648

The model shows a slightly better fit in contrast to the Brand resonance model, as the

Hosmer and Lemeshow test is not significant (.121).

Table 4.26

BLR – Block 1: Classification table

Observed

Predicted

Allocated brand is preferred

brand

%

Correct 0 1

Step 0

Allocated brand is

preferred brand

0 486 1896 20.4

1 317 3949 92.6

Overall Percentage 66.7

Note. The cut value is .500. N=6648

Table 4.24

BLR – Model Summary

Step -2 Log

likelyhood

Cox & Snell R

Square

Nagelkerke R Square

1 8296.928 .055 .076 Note. N=6648

Table 4.25

BLR – Hosmer and Lemeshow Test

Step Chi-square df Sig.

1 12.744 8 .121

59

When looking at the Classification table 4.26 we see that this model predicts 66.7% of

the actual outcomes, which is a low result with an increase of only 2.5% compared to

the base model. Also NPS seems to be unable to make a real difference in predicting

Brand preference.

Looking at the results in Table 4.27, we find a positive logistic coefficient indicating a

positive relationship with an Exponentiated coefficient of 1.240. This shows that for a

high NPS score, the respondent is 1.240 times more likely to prefer the brand.

Compared to the brand resonance score of 1.351 this is lower.

Conclusion

Although the model fit seems to be slightly better with the NPS variable added than

with the Brand resonance variable added, the predictive power of Brand resonance

seems to be higher according to the Exponentiated coefficient.

Linear regression on Share-of-wallet

As was reported before, the mean of Share-of-wallet is 4.36 with a standard deviation

of 3.342. Pearson correlation shows a significant correlation of .215 at the 0.01 level

(2-tailed). This score is a little less than half the correlation of Brand resonance on

Share-of-wallet (.447).

Table 4.27

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step

1a

NPS .215 .011 350.923 1 .000 1.240

Gender .109 .053 4.219 1 .040 1.115

Age .003 .002 2.178 1 .140 1.003

Constant -1.307 .147 79.350 1 .000 .271 Note. a. Variable(s) entered on step 1: NPS, Age, Gender. B=Logistic coefficient. S.E.=Standard error.

Wald=Wald statistic. df=Degrees of freedom. Exp(B)= Exponentiated coefficient. N=6648.

60

Table 4.28 shows an overall significant model fit but the regression accounts for only

3441.259 of the total Sum of squares of 74260.794, which is a low value compared to

14837.829 in the Brand resonance model.

The Adjusted R Square of .046 in Table 4.29 shows that only 4.6% of the variance of

Share-of-wallet can be explained by the independent variable NPS where this was

20% for Brand resonance.

Table 4.28

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression 3441.259 1 3441.259 322.942 .000b

Residual 70819.535 6646 10.656

Total 74260.794 6647

Note. a. Dependent Variable: Share-of-wallet. b. Predictors: (Constant), NPS. N=6648

Table 4.29

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .215a .046 .046 3.264

Note. a. Predictors: (Constant), NPS. b. Dependent Variable: Share-of-wallet. N=6648

Table 4.30

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t

Sig.

95,0% Confidence

Interval for B

B

Std.

Error

Beta

Lower Bound

Upper

Bound

1 (Constant) 1.993 .137 14.510 .000 1.724 2.263

NPS .311 .017 .215 17.971 .000 .277 .345

Note. a. Dependent Variable: Share-of-wallet. N=6648

61

In line with previous results in this analysis, results in Table 4.30 show that the

statistical significance associated with Beta is significant at 0.311. Thus, a one point

increase in NPS will increase Share-of-wallet with .311. Brand resonance showed a

Beta of 1.115 which is by far a better result.

Conclusion

In both the regression analysis in Phase 4 and 5, we see a significant predicting effect

of Brand resonance on Brand preference and Share-of-wallet. Although the effects on

Brand preference are weaker we find support for both Hypothesis 4 and 5 and the

predictive power of the construct can be deemed acceptable. The Nomological net

wherein Brand resonance is part of in this research was further developed and behaves

as expected within the system of related constructs as shown below in Figure 4.2.

Phase 6 shows the underperformance of the NPS metric in explaining Brand

preference and Share-of-wallet, which enables the support of both Hypothesis 6 and 7.

Although the Binary logistic regression model seemed to fit better with the NPS

metric added, the final conclusion is that Brand resonance explains Brand preference

slightly better. Results of the Linear regression on Share-of-wallet are more

convincing where Brand resonance clearly outperforms the Net promotor score.

62

5. Conclusion & Discussion

With the development of marketing as a more serious activity for companies in the

1960’s, the need to measure outcomes arose and more academics became interested in

the research area. Marketing metrics were developed to keep better track of results

from marketing investments. Nowadays, the importance of adequate brand building

and its measurement seems even more important. In an attempt to extend the work of

Keller (1993, 2009), a scale was developed aiming to compose a more complete brand

performance indicator by combining multiple behavioral and affective aspect of the

relationship between a customer and a brand in one metric. With this versatile

measure, a more accurate estimation of the depth and breadth of the relationship

between a brand and its customers is made possible, enabling companies to measure

and build their brands from a broader customer-based perspective.

In the process of validating Brand resonance, we took steps to establish

Content-, Construct-, and Criterion validity following the directives described by

Churchill Jr & Iacobucci (2009). First, the domain of the characteristic was defined

and items generated that would fit the scale. Face validity was covered by the

involvement of three experts on the topic of research. The diversity of the experts was

highly valuable during this research wherein both academic- and practical viewpoints

and experiences were taken into account.

After selecting the items to be used in the scale, steps in Construct- and

Criterion validity were taken. In Phase 1, data from research on two different product

categories in Germany and the US was collected before starting Exploratory factor

analysis. With the removal of the moderately performing item Share-of-wallet, a clear

single factor solution holding six items was identified. Confirmatory factor analysis in

Phase 2 showed weaker results for Brand following, which could be explained by the

product category the data was collected in. Convergent- as well as Discriminant

validity were well established with a Brand trust and Brand affect scale that were

included in the study in Phase 3. Results showed that Brand resonance clearly

contributed to the model wherein Brand trust and Brand affect hardly differentiated

63

from each other. The good performance of Brand resonance within this model showed

that it clearly stands out compared to the other measures included. Criterion validity

was established in Phase 4 and 5 by testing the predictive power of Brand resonance

on Brand preference and Share-of-wallet. Although the regression model wherein

Brand resonance predicts Brand preference was significant, the magnitude of its

predictive power was moderate. The relationship between Brand resonance and

Share-of-wallet was strong. As the Net promotor score is a widely accepted

performance indicator in practice, the regression on Brand preference and Share-of-

wallet was duplicated in Phase 6 to be able to compare the results with those of the

Brand resonance metric. Results showed a slightly inferior performance on Brand

preference. In the regression on Share-of-wallet, Brand resonance clearly

outperformed the NPS metric. Overall, the construct behaved as expected within the

Nomological net and all Hypothesis were supported as shown in Figure 4.2

Implications

This attempt on scale validation resulted in a reliable and valid indicator of brand

performance which can be used by companies for whom the relationship with

customers is vital to establish financial performance. As the metric has six items, it is

highly usable in a practical environment wherein short scales are very welcome fitting

the pragmatic solutions companies often seek for. It explains the popularity of the

one-question metrics like NPS and Brand preference. The Brand resonance metric is

not developed to replace current brand performance indicators, but is intended to

enrich the perspective of brand managers in the process of building and maintaining

brands and enable them to better explain and understand the consequences of their

actions. As stated earlier, unilateral measures like NPS are unable to capture all

aspects of the relationship between customers and brands and in this research shown

that the metric is outperformed when it comes to explaining Brand preference and the

amount of products that will be bought from a brand. As Keiningham et. al. (2007)

could not replicate Reichheld’s findings (2003) about the superiority of the NPS as a

brand performance indicator, also this study is unable to do so. In this rapidly

64

changing marketing environment more communication about brands exists wherein

consumers know more about the company behind the brand and exchange information

with each other (Keller, 2009). This metric will facilitate tracking these

communication flows better and helps brand managers better understand consumer

brand knowledge structures. Brand resonance will be valuable in both building and

maintaining brands as it is able to identify healthy and deep relationships between

customers and brands. Repeat purchases could be very well existing due to a low

price or a short hype. Also potential problems could be identified wherein for example

a customer is ashamed for his or her brand choice. Apart from the absent

recommendation, also no preference for the brand will be communicated in market

research, which could lead to an internal devaluation of the brand and possibly wrong

decision making. At last, the appreciation of Brand equity can be done in a more

accurate and complete way.

This research contributes to current literate on Customer-based brand equity

and provides a base for the development of metrics wherein both behavioral- and

affective dimensions are integrated. Brand resonance seems to be better in explaining

the actual behavior (Share-of-wallet) of consumers, than a stated preference, which is

a big step forward in the development of brand performance indicators. Furthermore,

this metric can facilitate a better measurement of the strength of different marketing

activities as well as the valuation of the magnitude those activities have on a brand.

Limitations and future research

The following limitations of this research are worth noting. First, the Brand resonance

scale was only tested on high involvement products where Brand resonance was

expected to be higher. Directions for future research could be the examinations of the

performance of the metric in other (low involvement) product categories like food or

beverages or in a service environment. Second, no specific loyalty scales were

included in this research. To increase Nomological validity, the Nomological net

could be extended with other affiliated metrics.

65

References

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

Management Review, 38(3), 103.

Aaker, J. L. (1997). Dimensions of brand personality. Journal of Marketing Research,

, 347-356.

Aaker, J., Fournier, S., & Brasel, S. A. (2004). When good brands do bad. Journal of

Consumer Research, 31(1), 1-16.

Algesheimer, R., Dholakia, U., & Hermann, A. (2004). Interplay between brand and

brand community: Evidence from european car clubs. Available at SSRN 534542,

Anastasi, A., & Urbina, S. (1997). Psychological testing Prentice Hall Upper Saddle

River, NJ.

Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of

customer satisfaction for firms. Marketing Science, 12(2), 125-143.

Bearden, W. O., & Netemeyer, R. G. (1999). Handbook of marketing scales: Multi-

item measures for marketing and consumer behavior research Sage.

Bhattacharya, C. B., & Sen, S. (2003). Consumer-company identification: A

framework for understanding consumers' relationships with companies. Journal

of Marketing, , 76-88.

Bohrnstedt, G. W. (1970). Reliability and validity assessment in attitude

measurement. Attitude Measurement, , 80-99.

Bower, G. H., Forgas, J. P., & Forgas, J. (2001). Mood and social memory. Handbook

of Affect and Social Cognition, , 95-120.

66

Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2011). Consumer engagement in a

virtual brand community: An exploratory analysis. Journal of Business Research,

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by

the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81.

Carlson, B. D., Suter, T. A., & Brown, T. J. (2008). Social versus psychological brand

community: The role of psychological sense of brand community. Journal of

Business Research, 61(4), 284-291.

Chandrashekaran, M., Rotte, K., Tax, S. S., & Grewal, R. (2007). Satisfaction

strength and customer loyalty. Journal of Marketing Research, , 153-163.

Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and

brand affect to brand performance: The role of brand loyalty. The Journal of

Marketing, , 81-93.

Churchill Jr, G. A., & Iacobucci, D. (2009). Marketing research: Methodological

foundations Cengage Learning.

Clark, B. H. (1999). Marketing performance measures: History and interrelationships.

Journal of Marketing Management, 15(8), 711-732.

Cronin Jr, J. J., & Taylor, S. A. (1992). Measuring service quality: A reexamination

and extension. The Journal of Marketing, , 55-68.

Curlo, E., & Chamblee, R. (1998). Ad processing and persuasion: The role of brand

identification. Psychology & Marketing, 15(3), 279-299.

Dick, A. S., & Basu, K. (1994). Customer loyalty: Toward an integrated conceptual

framework. Journal of the Academy of Marketing Science, 22(2), 99-113.

Fornell, C. (1992). A national customer satisfaction barometer: The swedish

experience. The Journal of Marketing, , 6-21.

67

Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale

development incorporating unidimensionality and its assessment. Journal of

Marketing Research, , 186-192.

Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis: A

global perspective: Pearson education.

Heeler, R. M., & Ray, M. L. (1972). Measure validation in marketing. Journal of

Marketing Research, 9(4), 361-370.

Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the

loyalty nexus. Journal of Marketing Management, 27(7-8), 785-807.

Hoyer, W. D., & Brown, S. P. (1990). Effects of brand awareness on choice for a

common, repeat-purchase product. Journal of Consumer Research, , 141-148.

Jacoby, J., & Chestnut, R. W. (1978). Brand loyalty: Measurement and management

Wiley New York.

Kapferer, J. -. (2005).

The new strategic brand management

. London: Kogan-Page.

Keiningham, T. L., Cooil, B., Andreassen, T. W., & Aksoy, L. (2007). A longitudinal

examination of net promoter and firm revenue growth. Journal of Marketing, ,

39-51.

Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based

brand equity. The Journal of Marketing, , 1-22.

Keller, K. L. (2001). Building customer-based brand equity: A blueprint for creating

strong brands Marketing Science Institute.

68

Keller, K. L. (2009). Building strong brands in a modern marketing communications

environment. Journal of Marketing Communications, 15(2-3), 139-155.

Keller, K. L., Apéria, T., & Georgson, M. (2012). Strategic brand management:

Building, measuring, and managing brand equity Pearson Education.

Lai, F., Griffin, M., & Babin, B. J. (2009). How quality, value, image, and satisfaction

create loyalty at a chinese telecom. Journal of Business Research, 62(10), 980-

986.

Lau, G. T., & Lee, S. H. (1999). Consumers' trust in a brand and the link to brand

loyalty. Journal of Market-Focused Management, 4(4), 341-370.

Leenheer, J., Van Heerde, H. J., Bijmolt, T. H., & Smidts, A. (2007). Do loyalty

programs really enhance behavioral loyalty? an empirical analysis accounting for

self-selecting members. International Journal of Research in Marketing, 24(1),

31-47.

Marzocchi, G., Morandin, G., & Bergami, M. (2013). Brand communities: Loyal to

the community or the brand? European Journal of Marketing, 47(1/2), 93-114.

McAlexander, J. H., Schouten, J. W., & Koenig, H. F. (2002). Building brand

community. The Journal of Marketing, , 38-54.

McMahan, H. W. (1980). TV loses the'name game'but wins big in personality.

Advertising Age, 54

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship

marketing. Journal of Marketing, 58(3)

Muniz Jr, A. M., & O’guinn, T. C. (2001). Brand community. Journal of Consumer

Research, 27(4), 412-432.

69

Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues

and applications Sage.

Netemeyer, R. G., Krishnan, B., Pullig, C., Wang, G., Yagci, M., Dean, D., . . . Wirth,

F. (2004). Developing and validating measures of facets of customer-based brand

equity. Journal of Business Research, 57(2), 209-224.

Odin, Y., Odin, N., & Valette-Florence, P. (2001). Conceptual and operational aspects

of brand loyalty: An empirical investigation. Journal of Business Research,

53(2), 75-84.

Park, C. W., Jaworski, B. J., & Maclnnis, D. J. (1986). Strategic brand concept-image

management. The Journal of Marketing, , 135-145.

Park, C. W., MacInnis, D. J., Priester, J. R., Eisingerich, A. B., & Iacobucci, D.

(2010). Brand attachment and brand attitude strength: Conceptual and empirical

differentiation of two critical brand equity drivers. Journal of Marketing,

Forthcoming, , 16-10.

Pessemier, E. A. (1959). A new way to determine buying decisions. The Journal of

Marketing, , 41-46.

Petrecca, L. (2006). Product Placement—You Can’t escape it. USA Today, 10

Punj, G. N., & Hillyer, C. L. (2004). A cognitive model of customer-based brand

equity for frequently purchased products: Conceptual framework and empirical

results. Journal of Consumer Psychology, 14(1), 124-131.

Rossiter, J. R., & Percy, L. (1987). Advertising and promotion management.

McGraw-Hill Book Company.

Schau, H. J., Muniz Jr, A. M., & Arnould, E. J. (2009). How brand community

practices create value. Journal of Marketing, 73(5), 30-51.

70

Schmitt, B., Zarantonello, L., & Brakus, J. (2009). Brand experience: What is it? how

is it measured? does it affect loyalty? Journal of Marketing, 73(3), 52-68.

Söderlund, M. (2006). Measuring customer loyalty with multi-item scales: A case for

caution. International Journal of Service Industry Management, 17(1), 76-98.

Solomon, M. R. (1983). The role of products as social stimuli: A symbolic

interactionism perspective. Journal of Consumer Research, , 319-329.

Suh, J., & Youjae, Y. (2006). When brand attitudes affect the customer satisfaction-

loyalty relation: The moderating role of product involvement. Journal of

Consumer Psychology, 16(2), 145-155.

Weiner, I. B., & Craighead, W. E. (2010). The corsini encyclopedia of psychology

Wiley. com.

Wilkie, W. L. (1994). Consumer behavior. John Willey and Sons, New York,

Yoo, B., & Donthu, N. (2001). Developing and validating a multidimensional

consumer-based brand equity scale. Journal of Business Research, 52(1), 1-14.

71

Appendix 1 – Brand trust and Brand affect scales

Brand trust

Brand trust was measured as a four-item index based on seven-point ratings of

agreement (1 = very strongly dis-agree, 7 = very strongly agree) with the following

four statements:

1. "I trust this brand"

2. "I rely on this brand"

3. "This is an honest brand"

4. "This brand is safe"

Coefficient alpha for this four-item index of brand trust was .81.

Brand affect

Brand affect was measured as a three-item index based on seven-point ratings of

agreement (1 = very strongly dis-agree, 7 = very strongly agree) with the following

three statements

1. "I feel good when I use this brand"

2. "This brand makes me happy"

3. "This brand gives me pleasure"

Coefficient alpha for brand affect was .96.

72

Appendix 2 – Structural equation model CFA - standardized

estimates

73

Appendix 3 – Countries and gender/age distributions Phase 4

Comparison samples over base, gender and age

Base Male Mean age SD Age

Brazil 1238 67% 28.58 7.903

France 1077 51% 40.61 12.451

Germany 1299 51% 41.03 12.319

Italy 1261 50% 36.39 10.934

Netherlands 1263 47% 45.36 13.109

Poland 1261 53% 34.45 11.930

Russia 1275 51% 31.52 8.556

South korea 1218 55% 35.11 10.189

US 1241 54% 35.16 11.980

Total 11128 53% 36.43 12.169