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Understanding Sources of Brand Equity: A New Method to Represent Unbiased
Perceptions
ABSTRACT
In this study we propose a new method to represent unbiased perceptions of brands. Themethod is a likelihood-based model that simultaneously disentangles a major class of
psychological bias affecting attribute based perceptions, and represent the common structure
across multiple variable batteries, reducing the dimensionality of the problem. Our results
indicate that it is feasible to deepen brand equitys sources through a cognitive representation
that depicts the actual brand performance on attributes.
KEYWORDS: Brand ratings, perceptual mapping, dimensionality reduction, brand equity.
EMAC TRACK: Marketing Research and Research Methodology
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In brand positioning literature, researchers often deal with the necessity to collect
multiple batteries of measurements from the same set of respondents (DeSarbo and Wu,
2001). In this line, as previously pointed out in related literature, a promising research stream
is the one that jointly represents different types of data in a common dimensional space, that
is a single geometric representation (Carroll and Green, 1997). Following DeSarbo and Wu
(2001), our study proposes a MDS procedure that endeavours to analyze jointly variousdifferent variable batteries, such as brand-by-brand proximities (dissimilarities) and brand-by-
attribute ratings (attribute ratings), sorting out at the same time sources of bias in ratings.
The scale we chose is the brand personality measure, which has been developed in the
context of consumer behaviour research (Aaker, 1997). Previous research on this topic was
mainly focused - on the symbolic use of brands, since consumers often fill brands with human
personality traits (e.g. Gilmore, 1919; Aaker, 1997). The general tenet of this literature is that
the greater the correspondence between human characteristics and those that describe a brand,
the greater the preference for the brand (Malhotra, 1988; Sirgy, 1982). Relatedly, the brand
personality dimensions can be seen as a key way to differentiate a brand in a product category
(Halliday, 1996) and therefore to compare brands.
A specific contribution to the study is given by psychological literature that identifiesdifferent classes of bias affecting consumers evaluation of brands along attributes. As Dillon
et al. (2001) discuss, classical perceptual mapping may be not informative and difficult to
interpret in situations in which interattributes correlations are considerably high. See figures
1a, b (Appendix B) for a clear depiction of this effect within the two categories considered in
our study. This fact makes it impossible to evaluate the effect of a given attribute on the
purchasing behaviour for each brand. Marketing literature on brand equity (Aaker, 1991;
Keller, 1998; Dillon et al., 2001) recognizes that a brand rating contains something more than
a mere performance on specific attributes.
The process through which consumers obtain information likely depends on several
factors, such as a specific brand related experience, the level of brand awareness, context
effects that may affect information salience, earlier attribute ratings that affect later attribute
ratings, and so on. The general brand impressions bias is relevant when information on a
particular attribute are unavailable for a brand. In terms of brand rating variation this effect
could be not due to an attribute specific evaluation of the brand. In this study we do not
account for response style biases (Clemans, 1956; Rossi et al., 2000) while we drive our
attention to the possibility to disentangle brand holistic preference from the joint set of brand
ratings on specific attributes.
THE STUDY
The design of the study reflects our need to control for brands relevance for the target group
as well as for intellectual and emotional situations. After identifying the product categories tobe used1, we built a questionnaire using the scale developed in the literature on the FCB
matrix (McWilliam, 1997) to determine products positioning in the FCB matrixs quadrants.
We decided to assess brand ratings in high involvement conditions, where the analytic
valuation of the brand performance on one or more attributes is supposed to be high,
controlling for emotional and intellectual situations in which the overall brand impressions
(that is non-attribute information) may, or not, play a crucial role. We chose the 2 product
categories resulted as polarities among the factors generated as outcome of the factor analysis,
i.e.: notebook computer (high involvement, intellectual learning), cellular phone (high
1 This information was collected through a questionnaire in which we asked to rate, on a 7 point Likert scale, thelevel of familiarity and relevance for each of 12 product categories we found in the literature on the FCB matrix
(Weinberger and Spotts, 1989; Lambin, 2002). This procedure is aimed to obtain internal validity for the study.
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involvement, emotional learning). Then we assigned brand names to each product category
using subjects evoked set2.
As already mentioned in the above section, the measure we selected to map brand
perceptions is the brand personality scale (Aaker, 1997) which is composed of 15 items and
measured with 7-point Likert scales. To this scale an attribute-free section was added in
which subjects were asked to express dissimilarity judgments among brands on a 7-pointLikert scale. Appendix A shows the questionnaire structure. Acknowledging the cognitive
effort required of respondents to make multiple brand-by-attribute ratings, we wanted to avoid
any possible biases that may arise in responding to such a complex and large battery of
questions, exceeding human capabilities to remain concentrated (e.g. Brown and Melamed,
1990). Along these lines, we collected data from two samples of subjects, one for the
notebook category and the other for the cellular category.Afterwards, as we will describe in
detail in the next section, we develop a method which allows to ascertain the homogeneity of
perceptions through the two groups.
The questionnaires were distributed to university students. We gathered 130
questionnaires for each product category, however a total of 211 questionnaires were
completed and considered usable (109 cellular phone questionnaires, 102 notebookquestionnaires).
THE MODEL
Starting with subjects perceived brand ratings, the model implemented in the study
decomposes the holistic dimension, defined as general brand impressions, and the unbiased
perception of a brand on attributes, which represents the brand specific associations. This
measure is then projected on T-dimensional space (where T = 2) using a joint representation
of both attribute-free and attribute-based perceptions.
Let Jj ...1= be the brands within a given product category. Brands evaluated by I
consumers Ii ...1= along the attributes Mm ...1= . Leti
mjx , be the evaluation given by
individual i of brand j along attribute m . For each couple of brands let i kj , be the perceived dissimilarity between brand j and brand k rated by individual i . The aim ofthe paper is to develop a statistical procedure to simultaneously reduce the original
dimensionality of the problem, and disentangle brand preference effects and attribute specific
evaluations.
To reduce the dimensionality of the problem we use an approach that finds a projection
matrix ( )tmA ,= that, given the M -dimensions representation of brand j (namely thevector mjj xx ,1, ... of judgments along attributes), outputs a T -dimensional (usually
2=T for perceptual mapping purposes) representation
= ==
m
mTmmk
m
mmmkj xxAX
1
,,
1
1,, ,..., . The matrix A will be the projection that ensures
the best correspondence between empirical non attribute-based judgments jk, and distances
jkd , between projected brands in the final T space. Namely distances in the projected
spaces are defined by Euclidean norm:
= ==
=T
t
m
m
tm
i
mk
m
m
tm
i
mj
i
kj xxd1
2
1
,,
1
,,, (1)
2 The notebook category is composed by the followings 6 competing brands: Acer, Apple, Asus, Hewlett-Packard, Sony, Toshiba. The cellular phone category is composed by the following 6 competing brands: LG,
Motorola, Nokia, Samsung, Siemens, Sony-Ericsson.
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As in DeSarbo and Wu (2003) we can suppose that jkii
jk
i
jkd ,,,, += with
),0(,, Njki . This allows us to solve the problem of finding A maximizing the
associated likelihood =
=I
i
P
iLL1
where:
=J
kj
diiP
i
i
kj
i
kj
eXAL2
2
,,
2)(
22
1),|,(
(2)
is the individual likelihood for the projection problem.
The second problem requires a different approach. We suppose (see Dillon et al., 2001) that
attribute specific judgements mjx , for brand j can be decomposed into a brand preference
effect jand an attribute specific evaluation mj , . We can suppose that for each observed
individual i , it holds mjijmji
mjx ,,,, ++= where ),0(,, Nmji . This allows us a
likelihood representation of the problem, with individual likelihood given by:
= =
+
=J
j
M
m
x
ijmmj
Di
jmmjimj
eXBL1 1
2
))((
2,
2
2,,
21)|,,,(
(3)
Maximization of =
=I
i
D
iLL1
gives the best choice of matrix mj,= of genuine attribute
based representation of brands, and vector jB = of holistic evaluation of brands.Note that
P
iL and
DiL are defined by different psychological underlying processes. This
(Ramsay, 1980; MacKay et al., 1995) allows to consider )mji ,, and )jki ,, independentand to evaluate a joint likelihood
CL . The individual likelihoodC
iL is formally the productD
i
P
i
P
iLLL = but the definition of distances jkd , between projected brands must discount the
holistic terms. The new definition of jkd , is obtained modifying equation (1) and gives:
( ) ( ) = ==
=
T
t
m
m
tmk
i
mk
m
m
tmj
i
mj
i
kj xxd1
2
1
,,
1
,,, (4)
Consequently the complete likelihood of the problem is:
=
=I
i
iiP
i
iD
i BXALXBLXBAL1
),,|,()|,,(),|,,,,(
(5)
where ( )i mjxX ,= is the MJI matrix of attribute based data, ( )i
mj
i xX ,= is the i th
MJ block pertaining individual i , and with the same notation ( )i mj ,= and( )i mj
i
,= store the 2/)1( JJI dissimilarities judgements.
RESULTS
Figure 1a and Figure 1b (Appendix B) are the PCA maps obtained from attribute-based
ratings respectively in the cellular phone and notebook product categories. The first PCA
component accounts for about 90% of variance in the case of cellular phones and for more
than 95% in the case of notebooks. These results are consistent with Dillon et al. (2001) even
with a different measurement of perceptions. The two situations are non informative in terms
of attribute specific performance for each brand. As the maps show the attributes arrows have
all the same orientation. Therefore brand ratings dont indicate why some brands are mostliked, as the first dimension provides only a connotative meaning. Conversely, Figure 2a and
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2b (Appendix B) show a clear and informative configuration of brand positions along
attributes. In fact disentangling general brand impressions (i.e. brand image) the configuration
of brands and attributes spreads out to explain the specific impact of attributes on each brand.
The following two examples may clarify the implications of our result. Acer is a leader in the
national notebook market used in this study, however the PCA map (Figure 1b) fails to assign
a set of favourable brand associations. Figure 2b explains the origin of this lack of brandpersonality, as remarked by the holistic rating calculated in Table 2b. In the same line, Nokia
seems to be the favourite cellular phone brand in the PCA map (Figure 1a). However, table 2a
fails to show the highest holistic rating for this brand, and map 2a fails to show a well defined
positioning on most attributes.
CONCLUSIONS
The objective of the study was to offer a perceptual mapping approach that depicts the real
perceived brand positioning along a set of attributes. In fact, perceived brand performance on
one or more attributes may lack revealing diagnostic power given the spurious intertwinement
among the actual brand-specific associations and the overall global brand impressions.
The main contribution of the study is the simultaneous decomposition of a brandrating into its two main components, and the projection of the unbiased rating in a two-
dimensional space, in which distances between objects reflect the observed dissimilarities, as
the map is obtained through the joint representation of two batteries of variables
(dissimilarities and attribute ratings). The model is a likelihood-based procedure which uses a
parsimonious set of parameters, as compared to other similar models in literature.
The implemented model has important implications for the analysis of brand equity.
By showing the actual brand ratings, our method provides a means to assess the uniqueness
and strength of brand associations (Dillon et al., 2001). The cognitive representation offered
by the map helps us to reach a better comprehension of both the specific impact of each
attribute on the brands, and the role played by the semantic structure underlying the
measurement scale used in this study.
LIMITATIONS AND FUTURE DIRECTIONS
The model presented in this paper focuses on the purification of perceptions with respect to
general brand impressions bias. Other sources of biases can be analyzed and their effect could
be disentangled as well. A major bias to take into account is given by the idiosyncratic
perceived importance of attributes assigned by consumers. This might be a strategic issue to
classify heterogeneity, operatively changing the expression mjijmji
mjx ,,,, ++= into the
expression mjiji
mmj
i
mjx ,,,, ++= . This formulation could be convenient but theestimation problem could be difficult to address.
Another direction suggested by the study is the use of other measurement scales. Ourstudy focuses only Aakers brand personality, but some other theoretical frameworks of brand
equity measurement should be taken into account.
Finally, this study focuses on high-involvement product categories while could be of
interest to consider routine or impulse products.
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APPENDIX A
QUESTIONNAIRES STRUCTURE
BRAND PERSONALITY QUESTIONNAIRE
Strongly Strongly
X is a brand: disagree agree
Down-to-earth.
Honest.....
Wholesome..
Cheerful
Daring .
Spirited. ..
Imaginative.
Up-to-date.......
Reliable...
Intelligent... Successful..
Upper class.....
Charming...
Outdoorsy..
Tough.
ATTRIBUTE-FREE QUESTIONNAIRES
a. CELLULAR PHONES
Very Very
dissimilar similar
1. Nokia and Motorola ...............
2. Sony-Ericsson and Samsung ..
3. Siemens and Nokia .
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4. LG and Motorola.....
5. Siemens and Sony-Ericsson
6. Samsung and Asus ..
7. Siemens and LG ..........
8. Apple and Nokia......................
9. Toshiba and Sony-Ericsson ....
10. Nokia and LG..................................
11. Sony-Ericsson and Motorola ...
12. Motorola and Siemens .....
13. Samsung and Motorola
14. Samsung and LG .
15. Nokia and Sony-Ericsson ............
b. NOTEBOOK COMPUTERS
Very Very
dissimilar similar
16. Acer and Sony .......................
17. Hewlett-Packard and Apple .. 18. Asus and Acer ...
19. Toshiba and Sony...
20. Asus and Hewlett-Packard
21. Apple and Asus .
22. Asus and Toshiba ......
23. Apple and Acer......................
24. Toshiba and Hewlett-Packard ..
25. Acer and Toshiba ..........................
26. Hewlett-Packard and Sony
27. Sony and Asus ...............
28. Apple and Sony .
29. Apple and Toshiba .
30. Acer and Hewlett-Packard .........
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APPENDIX B
THE MAPS
Figure 1a (cellular phones): Principal Component Analysis
Table 1a: Importance of components:PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 2.414 0.6893 0.3416 0.14513 0.14189 8.34e-16
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Proportion of
Variance
0.902 0.0735 0.0181 0.00326 0.00312 0.00e+00
Cumulative
Proportion
0.902 0.9756 0.9936 0.99688 1.00000 1.00e+00
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Figure 2a (cellular phones): Model map (joint unbiased perception map)
Table 2a: Brand image (holistic) ratingsNokia Motorola Sony-Ericsson Samsung Siemens LG
4.552316 3.324714 4.485089 5.182024 2.929133 5.749445
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Figure 1b (computer notebook): Principal Component Analysis
Table 1b: Importance of componentsPC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 2.355 0.451
3
0.18073 0.14834 0.10317 8.15e-16
Proportion of Variance 0.954 0.035
0
0.00562 0.00379 0.00183 0.00e+00
Cumulative Proportion 0.954 0.988
8
0.99438 0.99817 1.00000 1.00e+00
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Figure 2b (computer notebook): Model map (joint unbiased perception map)
Table 2b: Brand image (holistic) ratingsAcer Sony Hewlett-
Packard
Toshiba Asus Apple
3.487614 4.908848 4.100208 3.749849 3.678156 5.377646
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