brand resonance: a scale validation
TRANSCRIPT
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.
22
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
28
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
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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