8/2/2019 20070426 Visentin

1/15

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

8/2/2019 20070426 Visentin

2/15

8/2/2019 20070426 Visentin

3/15

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.

3

8/2/2019 20070426 Visentin

4/15

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.

4

8/2/2019 20070426 Visentin

5/15

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

5

8/2/2019 20070426 Visentin

6/15

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.

6

8/2/2019 20070426 Visentin

7/15

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 .

7

8/2/2019 20070426 Visentin

8/15

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 .........

8

8/2/2019 20070426 Visentin

9/15

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

9

8/2/2019 20070426 Visentin

10/15

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

10

8/2/2019 20070426 Visentin

11/15

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

11

8/2/2019 20070426 Visentin

12/15

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

12

8/2/2019 20070426 Visentin

13/15

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

13

8/2/2019 20070426 Visentin

14/15

REFERENCES

Aaker, David A. (1991), Managing Brand Equity. New York: Free Press.

Aaker, Jennifer L. (1997), Dimensions of Brand Personality,Journal of MarketingResearch, 34 (August), 347-356.

Brown, Steven R. and Lawrence E. Melamed (1990),Experimental design and analysis. SageUniversity Papers.

Carroll, J. Douglas and Paul E. Green (1997), Psychometric Methods in Marketing Research:

Part II, Multidimensional Scaling,Journal of Marketing Research, 34 (May), 193-204.Church, Timothy A. and Peter J. Burke (1994), Exploratory and Confirmatory Tests of The

Big Five and Tellegens Three and Four-Dimensional Models,Journal of Personality

and Social Psychology, 66 (1), 93-114.

Clemans, William V.(1956), An Analytic and Experimental Examination of Some Properties

of Ipsative Measures, doctoral dissertation, University of Washington, Seattle.

DeSarbo, Wayne S. and Jianan Wu (2001), The Joint Spatial Representation of Multiple

Variable Batteries Collected in Marketing Research,Journal of Marketing Research,38 (May), 244-253.

Dillon, William R., Thomas J. Madden, Amna Kirmani, and Soumen Mukherjee (2001),

Understanding Whats in a Brand Rating: A Model for Assessing Brand and Attribute

Effects and Their Relationship to Brand Equity,Journal of Marketing Research, 38

(November), 415-429.

Gilmore, George W. (1919),Animism. Boston: Marshall Jones Company.

Green, Paul E., Frank J. Carnone, and Scott M. Smith (1988), Multidimensional Scaling:Concepts and Applications. Boston: Allyn and Bacon.

Halliday, Jean (1996), Chrysler Brings Out Brand Personalities with 97 Ads,AdvertisingAge, (September 30), 3.Keller, Kevin Lane (1993), Conceptualizing, Measuring and Managing Customer-Based

Brand EquityJournal of Marketing, 57 (January), 1-22.

Keller, Kevin Lane (1998), Strategic Brand Management: Building, Measuring and

Managing Brand Equity. Upper Saddle River, NJ: Prentice Hall.

Keller, Kevin Lane and Donald R. Lehmann (2005), Brands and Branding: Research

Findings and Future Priorities, Marketing Science Institute Special Report, n. 05-200.

MacKay, David B., Robert F. Easley, and Joseph L. Zinnes (1995), A Single Ideal Point

Model for Market Structure Analysis,Journal of Marketing Research, 32 (November),433-443.

Malhotra, Naresh K. (1988), Self Concept and Product Choice: An Integrated Perspective,Journal of Economic Psychology, 9, 1-28.

McWilliam, Gil (1997), Low Involvement Brands: Is the Manager to Blame?, MarketingIntelligence and Planning, 15/12, 60-70

Ramsay, J.O. (1980), The Joint Analysis of Direct Ratings, Pairwise Preferences, and

Dissimilarities,Psychometrika, 45 (June), 149-165.Roedder-John, Deborah, Barbara Loken, Kyeong-Heui Kim, and Alokparna Basu Monga

(2005), Brand Concept Maps: A Methodology for Identifying Brand Association

Networks, Marketing Science Institute Reports, Issue 3, 05-112, 41-65.Rossi, Peter E., Zvi Gilula, and Greg M. Allenby (2000), Overcoming Scale Usage

Heterogeneity: A Bayesian Hierarchical ApproachJournal of American Statistical

Association.

14

8/2/2019 20070426 Visentin

15/15

Shocker, Allan D. and V. Srinivas (1979), Multiattribute Approaches for Product Concept

Evaluation and Generation. A Critical Review,Journal of Marketing Research, 16

(May), 159-180.

Sirgy, Joseph (1982), Self-Concept in Consumer Behavior: A Critical Review,Journal of

Consumer Research, 9 (December), 287-300.

Steenkamp, Jan-Benedict E.M., Hans C.M. Van Trijp, and Jos M.F. Ten Berge (1994),Perceptual Mapping Based on Idiosyncratic Sets of Attributes,Journal of Marketing

Research, 31 (February), 15-27.Weinberger, Marc G. and Harlan E. Spotts (1989), A Situational View of Information

Content in TV Advertising in the U.S. and U.K.,Journal of Marketing, 53 (January),

89-94.

15