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PLEASE NOTE




ALL THE EMBOLDEND LETTERS IN RED INDICATE INFORMATION WHICH IS NOT
PROVIDED IN THIS VERSION OF THE REPORT.

NPF IS “NON PRODUCT FACTOR” OR “EMOTIONAL ASSOCIATION”

PF IS “PRODUCT FACTOR”




MAINAK BAG
ABSTRACT




This study attempts to find the relationship of brand attitude and how it is comprised with inputs
from consumer perceptions about products attributes and non product attributes, which are largely
a felt response of emotional acknowledge of a brand. In essence it is peek into the making of
brand attitude through a two way interaction of product attributes and perceived emotional
benefits.




                                                                                                2
INTRODUCTION

The key to marketing, always, has been understanding the consumers. A greater realization is the
simple one of recognizing the consumer as a human being. This recognition can lead as all the
way to understanding the customer from a holistic perspective.
A human being is essentially a thinking – feeling being. The Stanford Encyclopedia of
Philosophy states: “No aspect of our mental life is more important to the quality and meaning of
our existing than emotion”.
Important studies have been done in marketing and psychology affirming this.
Brand attitude has been described by Keller as the “overall evaluation of the brand in terms of its
quality and the satisfaction it generates”. Richard Elliot and Larry Percy have said “People who
think about brands…often talk in terms of things like value, perceived quality and image. What
all this comes down to is a brand attitude, the association in memory linked to the brand.”

Brand awareness           Brand salience         Brand attitude              Brand equity


                  Learning        Associations build          Preference

Attitude is considered example of affect by some (Bagozzi). Cohen and Areni, 1991 described
affect as “valenced feeling states”. There are others who opine that emotions are evaluative
judgments or in other words cognitive appraisals (Eagly and Chaiken, 1993). Martin Fishbein and
Icek Agezen (1975) have arguably regarded affect “as isomorphic with evaluation itself” (Eagly
and Chaiken, 1993).
But there are studies that have worked on arguing that attitude is a function of two different and
highly correlated components: Affect and cognition (Bagozzi & Burnkrant 1997, Batra and Ahtol
1990). But it is also noted that the terms attitude, emotion, affect, mood have been used
inconsistently in marketing literature (Bagozzi). Below is described the relationship between
affect, emotions mood and attitude.

                               Attitude (Bagozzi & Burkrant, Batra & Ahtol)



         Affective                                               Cognitive
        (Affect) (“Valenced feeling state”        (“Evaluative judgments or a
                   - Cohen & Areni)                 Appraisals” – Eagly & Chaiken ’93)




      Emotion                                         Mood
(Intense, accompanied by physical             (Longer lasting, diffused, non-action
  expression, action oriented, a mental state   oriented – Frijda 1993)
  of readiness – (Bagozzi, Gopinath, Iyer)
Emotions are a mental state of readiness and often are a cognitive appraisal of events and
thoughts which act as referents prompting an evaluative judgment and thus give rise to a



                                                                                                 3
particular action/ reaction oriented response often accompanied with physical expression
     (Bagozzi, Gopinath, Iyer).


     The process is shown below.

                                         Events
“THE FULL HUMAN
IMPACT OF EMOTIONS
IS ONLY REALIZED
WHEN THEY ARE                    Cognitive Processing
SENSED, WHEN THEY
BECOME FEELING AND
WHEN THOSE FEELINGS            Emotion             Leraning
ARE FELT. THAT IS
WHEN THEY BECOME
KNOWN”                         Feeling
   (Damasio, 1999, Quoted in
Elliot and Percy.)                                Attitude


                                                  Action

     Importantly, there has been agreement that biological system dealing with emotions in the human
     brain can operate separately storing information without involving cognitive processing (Eliot
     and Percy). The ‘Prefrontal cortex’ and ‘Amygdala’ process emotion and they engage the
     declarative memory system (Eichendaun, 2002). The declarative memory system is responsible
     for cognitive processing (Eliot and Percy). What this implies is that although emotional
     processing is done by a separate system (‘Prefrontal cortex’ and ‘Amygdala’), it influences and
     overlaps with cognitive processing.

     A measure of emotional involvement is trust (Rempel et al, 1985), which moves in stages from
     predictability to dependability, to trust and then to faith, representing a hierarchy of emotional
     involvement.
      Trust requires a move from reliance on rational cognitions to reliance on emotion and sentiment
     (Rempel et al, 1985, P13).

     The scope of this study is limited to finding contribution and importance of non product attributes
     based and emotion oriented factors to brand attitude.

     Research Methodology:

     Sampling frame:    Students of a business school
     Sampling units:    Both male and female students,
     Sampling elements: Users of the three product used for the study

     Three products were arbitrarily chosen to find out their product level of involvement.* Seven
     variables were used to find the levels of involvement.




                                                                                                      4
Projective techniques and laddering techniques were used to find out the relevant variables of
brand attitude from ten subjects. The most occurring variables were used to framing the
questionnaire.

The resultant variables were used to find the brand attitude scores for each of the three brands,
using a slightly modified “Expectancy Value Model” developed by Martin Fishbein and Icek
Ajzen.
The formula used is Ai = Wi │Ii - bi│
When Ai = Attitude on variable i
       Wi = Importance or weight of variable i.
        I = Ideal performance on variable i.
       bi = Actual performance on variable i
Respondents were asked ideal level of performance on each variable

       Wi = {Mean of I for variable i/ ∑ Mean of I} *100, where I = Ideal performance

The variables were reduced to two factors by factor analysis.

A two dimensional brand trust scale, developed and validated by Ellena Delgado- Ballester,
Jose Luis Munuera –Aleman and Maria Jesus Yague-Guillen (published in “International
Journal of Marketing Research”, 2003, Vol. 45 Quarter1) was used to find the reliability trust,
intention trust and composite trust.

The definition of brand trust as proposed by Ballester, Aleman and Guillen reflects two distinct
components: (1) brand reliability, and (2) brand intentions. Brand reliability has a competence of
technical nature in other words, it concerns the perception that the brand fulfils or satisfies the
consumer's needs. Brand intention describes the consumer's belief that the brand's behaviour is
guided or motivated by favourable and positive intentions towards the consumer's welfare and
interests.

Then a Trust: Involvement ratio was calculated for each respondent. This ratio can show the trust
per unit of perceived risk as perceived risk is represented by product involvement level. The T/I
ratio will show us how much positive emotional association [represented by trust] the brand in
question has been able to generate for the perceived risk it carries. This concept is supported by
the following statement: “trust is required in situations of high perceived risks” (Elliot and Percy,
P 30)


                   T/I = Trust Score / Involvement Level

The scores were categorized as below:
0 to 1.32 – Low T/I.
1.32 to 3 --- medium T/I.
>= 3 – high T/I.

* Consumer involvement comes from three sources: i) Consumer, ii) Product
and iii) Situation. Product source of involvement was chosen because firstly consumer
profiling would have been an elaborate process which may not have been met with limited
time, money and access, secondly, to find the right consumer at their purchasing time is, in
more than one way, difficult, lengthy and, for the purpose of study, impractical.



                                                                                                   5
All the product features were analyzed to find out their Principal Components. The Principal
Components could be easily categorized into Product and Service Efficiency Related
Components and Non Product and Service Efficiency Related Components, henceforth referred to
as PF and NPF respectively.

Univariate analysis of covariance (ANCOVA) was done first with interaction between
involvement levels* NPF & involvement levels* PF and then with interactions between T/I* PF
and T/I *NPF.
The dependent variable was attitude scores, category factors were involvement levels and T/I
scores and covariates were NPF and PF.

To understand the degree and the direction of the influence of PF and NPF on Brand Attitude
regression analysis was executed.

The direction of the influence of PF and NPF on Brand Attitude found by the regression analysis
is again substantiated by Chao Test.

RESULTS:

Cadbury Mean Attitude: 44.05, Trust score (reliability): 4.12,
Trust score (Intention): 3.32 Trust score (all): 3.43

Axe Mean Attitude: 90.74, Trust score (reliability): 3.48,
Trust score (Intention): 3.13 Trust score (all): 3.30

Compaq Mean Attitude: 86.23, Trust score (reliability): 3.61,
Trust score (Intention): 3.37 Trust score (all): 3.49


Descriptive Statistics (COMPAQ)

                         Std.
         Mean            Deviation     N
 ATTITUD
 E    OF
         86.2318         52.76146      32
 EACH
 PERSON

         Descriptive Statistics (CADBURY)

                         Std.
             Mean        Deviation     N
 ATTITUD
 E    OF
         44.0550         31.83615      34
 EACH
 PERSON




                                                                                             6
Descriptive Statistics (AXE)

                      Std.
            Mean      Deviation    N
ATTITU
DE   OF
        90.7385       59.52077     34
EACH
PERSON

FACTOR ANALYSIS:

CADBURY                           Total Variance Explained

                                   Extraction Sums of        Rotation    Sums     of
          Initial Eigenvalues      Squared Loadings          Squared Loadings
                    % of Cumul              % of Cumul                % of Cumul
 Comp               Varian ative            Varian ative              Varian ative
 onent Total ce             %      Total ce        %         Total ce        %
 1        5.091 46.286 46.286 5.091 46.286 46.286            3.135 28.497 28.497
 2        1.190 10.821 57.108 1.190 10.821 57.108            2.517 22.885 51.382
 3        1.097 9.971 67.079 1.097 9.971 67.079              1.727 15.697 67.079
 4        .913      8.296 75.374
 5        .807      7.333 82.707
 6        .623      5.662 88.369
 7        .486      4.422 92.792
 8        .325      2.955 95.747
 9        .239      2.169 97.916
 10       .125      1.140 99.056
 11                         100.00
          .104      .944
                            0
Extraction Method: Principal Component Analysis.

Rotated Component Matrix (a)

             Component
             PF        NPF              3
MIKY                                    .700          Extraction   Method: Principal
TASTY        .881                                     Component Analysis.  Rotation
SMOOTH       .519                                     Method: Varimax with Kaiser
SWEET        .846                                     Normalization.
LOVE                                    .646          a Rotation converged in 6
CELEB        .677                                     iterations.
FRNDS                     .667
KIDS         .760
YOUNG                     .667
PAMP                      .831
COLR                                    .657



                                                                                       7
KMO and Bartlett's Test

 Kaiser-Meyer-Olkin        Measure   of    Sampling
 Adequacy.                                               .738

 Bartlett's    Test     of Approx. Chi-Square    185.364
 Sphericity                 df                   55
                            Sig.                 .000
Reliability Coefficients 5 items FOR FACTOR PF (ONE)

Alpha = .8878          Standardized item alpha = .8901

Reliability Coefficients   3 items FOR FACTOR NPF (TWO)

Alpha = .69          Standardized item alpha = .6921
Reliability Coefficients 2 items FOR FACTOR 3 (THREE)

Alpha = .3662          Standardized item alpha = . 3772


AXE
           Rotated Component Matrix (a)

              Component
              PF        NPF
 VAR          .807
 SKIN         .748
 STYL         .773
 YOUNG        .814
 SEX                    .713
 ELIT                   .868
 XFAC         .778
 MYSTRE
             .605
 Y
 TRND        .595
 FASHIO
             .846
 N
 FUN                     .808
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization.
a Rotation converged in 3 iterations.




                                                                                   8
Correlations

                                                                   MYS           FAS
                SKI          STY    YOU                XFA         TRE TRN       HIO
           VAR N             L      NG  SEX       ELIT C           Y   D         N      FUN
       VAR 1.00
                .629         .673   .540   .292   .214      .575   .344   .404   .653   .236
           0
       SKI      1.00
           .629              .560   .600   .417   .280      .587   .318   .583   .666   .379
       N        0
Sig.   ATO
(1-    FEA
           .030 .028         .000   .001   .002   .000      .000   .002   .041   .019   .023
tailed CH
)
       VAR .    .000         .000   .000   .047   .112      .000   .023   .009   .000   .090
       SKI
           .000 .            .000   .000   .007   .054      .000   .033   .000   .000   .013
       N
                                    Total Variance Explained

                                       Extraction Sums of   Squared Rotation Sums of    Squared
           Initial Eigenvalues         Loadings                     Loadings
                      %     of                   %    of                     %    of
 Compo                Varianc Cumula             Varianc    Cumula           Varianc    Cumula
 nent      Total      e        tive %  Total     e          tive %  Total    e          tive %
 1         5.971      54.282 54.282 5.971        54.282     54.282 4.733     43.031     43.031
 2         1.359      12.356 66.638 1.359        12.356     66.638 2.597     23.607     66.638
 3         .948       8.620    75.258
 4         .581       5.279    80.537
 5         .498       4.523    85.060
 6         .430       3.905    88.966
 7         .361       3.279    92.245
 8         .272       2.473    94.718
 9         .225       2.042    96.760
 10        .209       1.896    98.656
 11        .148       1.344    100.000
Extraction Method: Principal Component Analysis.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.                              .881

Bartlett's Test   of Approx. Chi-Square    209.189
Sphericity           Df                    55
                     Sig.                  .000




                                                                                          9
R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) FOR FACTOR 1 (ONE)

Reliability Coefficients   8 items

Alpha = .9136          Standardized item alpha = .9145

Reliability Coefficients   FOR FACTOR 2(TWO) 3 items

Alpha = .7911          Standardized item alpha = .7910




COMPAQ

           Rotated Component Matrix(a)

               Component
               1         2             3
 BATTER
                         .428
 Y
 SPEAK                   .825
 SOUND                   .753
 LCD                     .523
 MOVI                                  .856
 MUSIC                                 .688
 ENTERT                  .645
 ATTRAC
              .799
 T
 STYL         .832
 FEELBET
              .878
 T
 LOGO         .736
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization.
a Rotation converged in 12 iterations.




                                                                                   10
Total Variance Explained
                                      Extraction    Sums    of Rotation Sums of   Squared
          Initial Eigenvalues         Squared Loadings          Loadings
                     % of Cumul                % of Cumul                % of     Cumul
 Compo               Varian ative              Varian ative              Varian   ative
 nent     Total      ce       %       Total    ce      %        Total    ce       %
 1        5.109      46.448 46.448    5.109    46.448 46.448 3.241       29.465   29.465
 2        1.486      13.511 59.959    1.486    13.511 59.959 2.867       26.062   55.528
 3        1.183      10.755 70.714    1.183    10.755 70.714 1.670       15.186   70.714
 4        .914       8.309    79.023
 5        .728       6.620    85.643
 6        .533       4.842    90.486
 7        .336       3.058    93.544
 8        .296       2.689    96.232
 9        .190       1.725    97.957
 10       .155       1.407    99.365
 11                           100.00
          .070       .635
                              0
Extraction Method: Principal Component Analysis.


KMO and Bartlett's Test

 Kaiser-Meyer-Olkin Measure of Sampling
 Adequacy.                              .778

 Bartlett's Test    of Approx. Chi-Square    194.454
 Sphericity            Df                    55
                       Sig.                  .000

R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) FOR FACTOR 1 (ONE)

Reliability Coefficients   4 items

Alpha = .8728          Standardized item alpha = .8812

Reliability Coefficients   FOR FACTOR 2 (TWO) 5 items

Alpha = .7715          Standardized item alpha = .7722

Reliability Coefficients   FOR FACTOR 3 (TWO) 2 items

Alpha = .7316          Standardized item alpha = .7372




                                                                                           11
INTERPRETATION:

In case of Cadbury factor one can be named ‘product feature’ factor as it contains variables taste,
smoothness and sweetness, all of which convey quality of the product.
Milky has low correlation with other three qualitative factors (.289, .406 and .287 for taste,
smoothness and sweetness respectively). Factor one has an alpha of .8878 which is great.
Factor two represents friendship, childhood and the feeling of being pampered. We call it the
‘childhood factor’. It has a relatively low alpha at .69, which borders the acceptance level of .70.
We accept the factor.
Factor three represents love and colour and we call it the ‘romance factor’. But it has a very low
alpha value of .3662 implying that this factor may be measuring multidimensionality.

In case of Axe factor one can be named the ‘product feature’ factor as it contains variables variety
and skin care both of which convey quality of the product. But it also contains variables like style,
youth, fashion, and x factor, which are non product attribute related. However correlation among
them is high and significant meaning these emotional qualities have been strongly associated and
identified with the two product factors. This factor has high alpha value of .9136.
The second factor, again, is measuring the emotional benefits like the feeling of being elite or a
class feeling, fashionable, and fun. This non-attribute factor can be called ‘fashion factor’. This
factor has an alpha value of .7911

Compaq also yields two factors the first of which relates to the emotional benefits like feeling
competent, attraction, and style. It has an alpha value of.8728.

The second factor is clearly a ‘product attribute factor’ measuring sound, battery, speaker and
LCD screen. It has an alpha score of .7715.
The third factor measures entertainment value and contains movie and music variables.
The third factor has an alpha value of .7316.


Bartlett's Test of Sphericity has been done to test for the reliability of the factor.
Bartlett's Test of Sphericity tests the null hypothesis that the intercorrelation matrix comes from a
population in which the variables form an identity matrix. If it is an identity matrix it would
extract as many factors as variables, since each variable would be its own factor. And Kaiser-
Meyer-Olkin Measure of Sampling Adequacy test has also been done.
If two variables share a common factor with other variables, their partial correlation (aij) will be
small, indicating the unique variance they share.

If aij ≅ 0.0
The variables are measuring a common factor, and KMO ≅ 1.0

If aij ≅ 1.0
The variables are not measuring a common factor, and KMO ≅ 0.0

For all three factor analysis done the Bartlett's Test of Sphericity is significant at .000.
And the KMO scores are .738, .881, and .778 for Cadbury, Axe and Compaq repectivly.


Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO)



                                                                                                  12
KMO Value                                 Degree of Common Variance

0.90 to 1.00                              Marvelous

0.80 to 0.89                              Meritorious

0.70 to 0.79                              Middling

0.60 to 0.69                              Mediocre

0.50 to 0.59                              Miserable

0.00 to 0.49                              Don't Factor


ANCOVA:

Univariate Analysis of Variance

MODEL 1

Between-Subjects Factors

                              Value Label    N
 INVOLVEM 1
                              Low
 ENT                                         34
                              involvement
 LEVELS
          2                   Medium
                                             34
                              involvement
               3              High
                                             32
                              involvement

                         Tests of Between-Subjects Effects

Dependent Variable: ATTITUDE
                     Type III Sum
 Source              of Squares      df           Mean Square   F         Sig.
 Corrected Model     133273.412(a) 8              16659.176     10.233    .000
 Intercept           542359.443      1            542359.443    333.143   .000
 INVOLVEM            44768.606       2            22384.303     13.750    .000
 NPF                 53447.047       1            53447.047     32.830    .000
 PF                  11874.539       1            11874.539     7.294     .008
 INVOLVEM          *
                     14657.975       2            7328.988      4.502     .014
 NPF
 INVOLVEM * PF 9322.060              2            4661.030      2.863     .062
 Error               148148.718      91           1628.008
 Total               820530.360      100
 Corrected Total     281422.130      99
a R Squared = .474 (Adjusted R Squared = .427)


                                                                                 13
MODEL 2


            Between-Subjects Factors

                Value
                Label     N
 Trus 1         Low
 t              trust     51
 ratio          ratio
       2        Medium
                n trust   23
                ratio
        3       High
                trust     26
                ratio

                                   Tests of Between-Subjects Effects

                                       Dependent Variable: Attitude
                    Type III Sum
 Source             of Squares     df         Mean Square        F            Sig.
 Corrected Model 125745.942(a) 8              15718.243          9.188        .000
 Intercept          328536.855     1          328536.855         192.045      .000
 TRUSTRAT           41607.309      2          20803.655          12.161       .000
 PF                 3873.284       1          3873.284           2.264        .136
 NPF                18441.985      1          18441.985          10.780       .001
 TRUSTRAT *
                    5272.548       2          2636.274           1.541        .220
 PF
 TRUSTRAT *
                    16792.134      2          8396.067           4.908        .009
 NPF
 Error              155676.187     91         1710.727
 Total              820530.360     100
 Corrected Total    281422.130     99
a R Squared = .447 (Adjusted R Squared = .398)


Model one shows that there is significant interaction between NPF and involvement level, which
means the influence of the covariate take into account the value or the category of the factor
(level of involvement). But there is no significant interaction between PF and involvement at 5
percent significance level.
However the main effects show NPF and involvement level to have the highest influence.
If we wanted to get a “semipartial R-square” for just the effects of NPF, after controlling for these
other two variables, we would look at the Type III SS for NPF, and compare it to the “corrected
total” Type III SS.




                                                                                                  14
Semipartial R^2 for “NPF” = Type III SS for NPF/“corrected total” Type III SS

                            = 53447.047/281422.130 = 0.1899177 or 19%

Semipartial R^2 for “involvement” = 44768.606/281422.130 = 15.907%

Semipartial R^2 for “PF” = 11874.539/281422.130 = .0421947 or 4.219%

Semipartial R^2 for “involvement*NPF” = 14657.975/281422.130 = 0.052085 or 5.208%

Model two, again, shows significant interaction between Trust and NPF. And Trust and NPF has
much higher main effects than PF. PF does not have significant main effect at 5% significance.

Semipartial R^2 for “NPF” = 18441.985/281422.130 = 6.553%

Semipartial R^2 for “Trust*NPF” = 16792.134/281422.130 = 5.966%

Semipartial R^2 for “Trust” = 41607.309/281422.130 = 14.784%


IMPLICATIONS:

From ANCOVA we find that the influence of emotion (NPF) make significant differences in
brand attitude and that the extent of this influence is mediated by both trust and involvement level.
But even without interactions the main effects of emotion (NPF) is quite palpable. In other words
the extent of influence of emotion on brand attitude depends on how involved the product
requires the consumer to be to consume it and how much the consumer trusts the band.
But, as has been mentioned, emotion has large individual influence on brand attitude as well,
regardless of trust and involvement level.

In conflict with some of the theories*, emotion has been found to be intruding where, by
common perception, rationality only should reign. For different products with different
involvement levels, emotion has been found to be making a significant difference in the
formation of brand attitude.
To understand the degree and the direction of the influence of emotion regression analysis is
helpful. The following are the information from that effort.

Beta:

For Cadbury (low involvement):

PF = -.086,   NPF = -.204

For Axe (medium involvement)

PF = -.415, NPF = -.484

For Compaq (high involvement)

PF = -.109, NPF= -.666



                                                                                                 15
Emotion is making continuously larger impact on brand attitude, as for higher involvement level
products the slope (beta) is continuously increasing. It has to be seen whether higher involvement
means larger impact of emotion on brand attitude or not. A Chow test has been done to test
whether this increase holds true for the population or not.
The Chow test basically sees if slope for two or more groups are similar or not or in other words
if two regression lines are different from one another. It was originally designed
to analyze the same variables obtained in two different data sets to determine if they were similar
enough to be pooled together.

The test is run in SPSS. As it cannot be done with GUI, Paste (command) was used.

          Tests of Between-Subjects Effects

Dependent Variable: ATTITUDE

 Source                  Type III Sum of Squares     DF Mean Square        F               Sig.

 INVOLVEM * NPF               14657.985                 2      7328.993      3.282        .042

 INVOLVEM * PF                 9322.044                 2      4661.022     1.721         .184

It shows that emotion (NPF) significantly differs in different involvement levels. And we can
infer that emotion contributes more for higher involvement level products as it shown by the
regression slopes.

Conclusion*:

Based on what we have found here, we can safely say that the brand and product manager has
more scope and elbow space to position the brand on emotional parameters and not restrict
themselves to exploring only the product attribute, price based positioning. Acceptance of the fact
that emotion is a supremely powerful gift of the mind, can lead us to yet unexplored facets of
marketing. Advertising can, for example, take a lot from this realisation and go for creatives that
address the emotions more.

The rational and thinking area of the FCB grid is invaded strongly by emotion from the emotional
and feeling area. When feeling becomes heavily important, then it has to be explored, tested and
proved how much of thinking really happens and when and for what exactly.



*THIS CONCLUSION IS NOT EXHAUSTIVE.




YOUR CONTRIBUTION IN COMMENTS AND CRITICISM IS HEARTILY WELCOM.




                                                                                                  16
REFERENCES:




              17

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Is It In The Product Or Is It In The Mind

  • 1. PLEASE NOTE ALL THE EMBOLDEND LETTERS IN RED INDICATE INFORMATION WHICH IS NOT PROVIDED IN THIS VERSION OF THE REPORT. NPF IS “NON PRODUCT FACTOR” OR “EMOTIONAL ASSOCIATION” PF IS “PRODUCT FACTOR” MAINAK BAG
  • 2. ABSTRACT This study attempts to find the relationship of brand attitude and how it is comprised with inputs from consumer perceptions about products attributes and non product attributes, which are largely a felt response of emotional acknowledge of a brand. In essence it is peek into the making of brand attitude through a two way interaction of product attributes and perceived emotional benefits. 2
  • 3. INTRODUCTION The key to marketing, always, has been understanding the consumers. A greater realization is the simple one of recognizing the consumer as a human being. This recognition can lead as all the way to understanding the customer from a holistic perspective. A human being is essentially a thinking – feeling being. The Stanford Encyclopedia of Philosophy states: “No aspect of our mental life is more important to the quality and meaning of our existing than emotion”. Important studies have been done in marketing and psychology affirming this. Brand attitude has been described by Keller as the “overall evaluation of the brand in terms of its quality and the satisfaction it generates”. Richard Elliot and Larry Percy have said “People who think about brands…often talk in terms of things like value, perceived quality and image. What all this comes down to is a brand attitude, the association in memory linked to the brand.” Brand awareness Brand salience Brand attitude Brand equity Learning Associations build Preference Attitude is considered example of affect by some (Bagozzi). Cohen and Areni, 1991 described affect as “valenced feeling states”. There are others who opine that emotions are evaluative judgments or in other words cognitive appraisals (Eagly and Chaiken, 1993). Martin Fishbein and Icek Agezen (1975) have arguably regarded affect “as isomorphic with evaluation itself” (Eagly and Chaiken, 1993). But there are studies that have worked on arguing that attitude is a function of two different and highly correlated components: Affect and cognition (Bagozzi & Burnkrant 1997, Batra and Ahtol 1990). But it is also noted that the terms attitude, emotion, affect, mood have been used inconsistently in marketing literature (Bagozzi). Below is described the relationship between affect, emotions mood and attitude. Attitude (Bagozzi & Burkrant, Batra & Ahtol) Affective Cognitive (Affect) (“Valenced feeling state” (“Evaluative judgments or a - Cohen & Areni) Appraisals” – Eagly & Chaiken ’93) Emotion Mood (Intense, accompanied by physical (Longer lasting, diffused, non-action expression, action oriented, a mental state oriented – Frijda 1993) of readiness – (Bagozzi, Gopinath, Iyer) Emotions are a mental state of readiness and often are a cognitive appraisal of events and thoughts which act as referents prompting an evaluative judgment and thus give rise to a 3
  • 4. particular action/ reaction oriented response often accompanied with physical expression (Bagozzi, Gopinath, Iyer). The process is shown below. Events “THE FULL HUMAN IMPACT OF EMOTIONS IS ONLY REALIZED WHEN THEY ARE Cognitive Processing SENSED, WHEN THEY BECOME FEELING AND WHEN THOSE FEELINGS Emotion Leraning ARE FELT. THAT IS WHEN THEY BECOME KNOWN” Feeling (Damasio, 1999, Quoted in Elliot and Percy.) Attitude Action Importantly, there has been agreement that biological system dealing with emotions in the human brain can operate separately storing information without involving cognitive processing (Eliot and Percy). The ‘Prefrontal cortex’ and ‘Amygdala’ process emotion and they engage the declarative memory system (Eichendaun, 2002). The declarative memory system is responsible for cognitive processing (Eliot and Percy). What this implies is that although emotional processing is done by a separate system (‘Prefrontal cortex’ and ‘Amygdala’), it influences and overlaps with cognitive processing. A measure of emotional involvement is trust (Rempel et al, 1985), which moves in stages from predictability to dependability, to trust and then to faith, representing a hierarchy of emotional involvement. Trust requires a move from reliance on rational cognitions to reliance on emotion and sentiment (Rempel et al, 1985, P13). The scope of this study is limited to finding contribution and importance of non product attributes based and emotion oriented factors to brand attitude. Research Methodology: Sampling frame: Students of a business school Sampling units: Both male and female students, Sampling elements: Users of the three product used for the study Three products were arbitrarily chosen to find out their product level of involvement.* Seven variables were used to find the levels of involvement. 4
  • 5. Projective techniques and laddering techniques were used to find out the relevant variables of brand attitude from ten subjects. The most occurring variables were used to framing the questionnaire. The resultant variables were used to find the brand attitude scores for each of the three brands, using a slightly modified “Expectancy Value Model” developed by Martin Fishbein and Icek Ajzen. The formula used is Ai = Wi │Ii - bi│ When Ai = Attitude on variable i Wi = Importance or weight of variable i. I = Ideal performance on variable i. bi = Actual performance on variable i Respondents were asked ideal level of performance on each variable Wi = {Mean of I for variable i/ ∑ Mean of I} *100, where I = Ideal performance The variables were reduced to two factors by factor analysis. A two dimensional brand trust scale, developed and validated by Ellena Delgado- Ballester, Jose Luis Munuera –Aleman and Maria Jesus Yague-Guillen (published in “International Journal of Marketing Research”, 2003, Vol. 45 Quarter1) was used to find the reliability trust, intention trust and composite trust. The definition of brand trust as proposed by Ballester, Aleman and Guillen reflects two distinct components: (1) brand reliability, and (2) brand intentions. Brand reliability has a competence of technical nature in other words, it concerns the perception that the brand fulfils or satisfies the consumer's needs. Brand intention describes the consumer's belief that the brand's behaviour is guided or motivated by favourable and positive intentions towards the consumer's welfare and interests. Then a Trust: Involvement ratio was calculated for each respondent. This ratio can show the trust per unit of perceived risk as perceived risk is represented by product involvement level. The T/I ratio will show us how much positive emotional association [represented by trust] the brand in question has been able to generate for the perceived risk it carries. This concept is supported by the following statement: “trust is required in situations of high perceived risks” (Elliot and Percy, P 30) T/I = Trust Score / Involvement Level The scores were categorized as below: 0 to 1.32 – Low T/I. 1.32 to 3 --- medium T/I. >= 3 – high T/I. * Consumer involvement comes from three sources: i) Consumer, ii) Product and iii) Situation. Product source of involvement was chosen because firstly consumer profiling would have been an elaborate process which may not have been met with limited time, money and access, secondly, to find the right consumer at their purchasing time is, in more than one way, difficult, lengthy and, for the purpose of study, impractical. 5
  • 6. All the product features were analyzed to find out their Principal Components. The Principal Components could be easily categorized into Product and Service Efficiency Related Components and Non Product and Service Efficiency Related Components, henceforth referred to as PF and NPF respectively. Univariate analysis of covariance (ANCOVA) was done first with interaction between involvement levels* NPF & involvement levels* PF and then with interactions between T/I* PF and T/I *NPF. The dependent variable was attitude scores, category factors were involvement levels and T/I scores and covariates were NPF and PF. To understand the degree and the direction of the influence of PF and NPF on Brand Attitude regression analysis was executed. The direction of the influence of PF and NPF on Brand Attitude found by the regression analysis is again substantiated by Chao Test. RESULTS: Cadbury Mean Attitude: 44.05, Trust score (reliability): 4.12, Trust score (Intention): 3.32 Trust score (all): 3.43 Axe Mean Attitude: 90.74, Trust score (reliability): 3.48, Trust score (Intention): 3.13 Trust score (all): 3.30 Compaq Mean Attitude: 86.23, Trust score (reliability): 3.61, Trust score (Intention): 3.37 Trust score (all): 3.49 Descriptive Statistics (COMPAQ) Std. Mean Deviation N ATTITUD E OF 86.2318 52.76146 32 EACH PERSON Descriptive Statistics (CADBURY) Std. Mean Deviation N ATTITUD E OF 44.0550 31.83615 34 EACH PERSON 6
  • 7. Descriptive Statistics (AXE) Std. Mean Deviation N ATTITU DE OF 90.7385 59.52077 34 EACH PERSON FACTOR ANALYSIS: CADBURY Total Variance Explained Extraction Sums of Rotation Sums of Initial Eigenvalues Squared Loadings Squared Loadings % of Cumul % of Cumul % of Cumul Comp Varian ative Varian ative Varian ative onent Total ce % Total ce % Total ce % 1 5.091 46.286 46.286 5.091 46.286 46.286 3.135 28.497 28.497 2 1.190 10.821 57.108 1.190 10.821 57.108 2.517 22.885 51.382 3 1.097 9.971 67.079 1.097 9.971 67.079 1.727 15.697 67.079 4 .913 8.296 75.374 5 .807 7.333 82.707 6 .623 5.662 88.369 7 .486 4.422 92.792 8 .325 2.955 95.747 9 .239 2.169 97.916 10 .125 1.140 99.056 11 100.00 .104 .944 0 Extraction Method: Principal Component Analysis. Rotated Component Matrix (a) Component PF NPF 3 MIKY .700 Extraction Method: Principal TASTY .881 Component Analysis. Rotation SMOOTH .519 Method: Varimax with Kaiser SWEET .846 Normalization. LOVE .646 a Rotation converged in 6 CELEB .677 iterations. FRNDS .667 KIDS .760 YOUNG .667 PAMP .831 COLR .657 7
  • 8. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .738 Bartlett's Test of Approx. Chi-Square 185.364 Sphericity df 55 Sig. .000 Reliability Coefficients 5 items FOR FACTOR PF (ONE) Alpha = .8878 Standardized item alpha = .8901 Reliability Coefficients 3 items FOR FACTOR NPF (TWO) Alpha = .69 Standardized item alpha = .6921 Reliability Coefficients 2 items FOR FACTOR 3 (THREE) Alpha = .3662 Standardized item alpha = . 3772 AXE Rotated Component Matrix (a) Component PF NPF VAR .807 SKIN .748 STYL .773 YOUNG .814 SEX .713 ELIT .868 XFAC .778 MYSTRE .605 Y TRND .595 FASHIO .846 N FUN .808 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 3 iterations. 8
  • 9. Correlations MYS FAS SKI STY YOU XFA TRE TRN HIO VAR N L NG SEX ELIT C Y D N FUN VAR 1.00 .629 .673 .540 .292 .214 .575 .344 .404 .653 .236 0 SKI 1.00 .629 .560 .600 .417 .280 .587 .318 .583 .666 .379 N 0 Sig. ATO (1- FEA .030 .028 .000 .001 .002 .000 .000 .002 .041 .019 .023 tailed CH ) VAR . .000 .000 .000 .047 .112 .000 .023 .009 .000 .090 SKI .000 . .000 .000 .007 .054 .000 .033 .000 .000 .013 N Total Variance Explained Extraction Sums of Squared Rotation Sums of Squared Initial Eigenvalues Loadings Loadings % of % of % of Compo Varianc Cumula Varianc Cumula Varianc Cumula nent Total e tive % Total e tive % Total e tive % 1 5.971 54.282 54.282 5.971 54.282 54.282 4.733 43.031 43.031 2 1.359 12.356 66.638 1.359 12.356 66.638 2.597 23.607 66.638 3 .948 8.620 75.258 4 .581 5.279 80.537 5 .498 4.523 85.060 6 .430 3.905 88.966 7 .361 3.279 92.245 8 .272 2.473 94.718 9 .225 2.042 96.760 10 .209 1.896 98.656 11 .148 1.344 100.000 Extraction Method: Principal Component Analysis. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .881 Bartlett's Test of Approx. Chi-Square 209.189 Sphericity Df 55 Sig. .000 9
  • 10. R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) FOR FACTOR 1 (ONE) Reliability Coefficients 8 items Alpha = .9136 Standardized item alpha = .9145 Reliability Coefficients FOR FACTOR 2(TWO) 3 items Alpha = .7911 Standardized item alpha = .7910 COMPAQ Rotated Component Matrix(a) Component 1 2 3 BATTER .428 Y SPEAK .825 SOUND .753 LCD .523 MOVI .856 MUSIC .688 ENTERT .645 ATTRAC .799 T STYL .832 FEELBET .878 T LOGO .736 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 12 iterations. 10
  • 11. Total Variance Explained Extraction Sums of Rotation Sums of Squared Initial Eigenvalues Squared Loadings Loadings % of Cumul % of Cumul % of Cumul Compo Varian ative Varian ative Varian ative nent Total ce % Total ce % Total ce % 1 5.109 46.448 46.448 5.109 46.448 46.448 3.241 29.465 29.465 2 1.486 13.511 59.959 1.486 13.511 59.959 2.867 26.062 55.528 3 1.183 10.755 70.714 1.183 10.755 70.714 1.670 15.186 70.714 4 .914 8.309 79.023 5 .728 6.620 85.643 6 .533 4.842 90.486 7 .336 3.058 93.544 8 .296 2.689 96.232 9 .190 1.725 97.957 10 .155 1.407 99.365 11 100.00 .070 .635 0 Extraction Method: Principal Component Analysis. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .778 Bartlett's Test of Approx. Chi-Square 194.454 Sphericity Df 55 Sig. .000 R E L I A B I L I T Y A N A L Y S I S - S C A L E (A L P H A) FOR FACTOR 1 (ONE) Reliability Coefficients 4 items Alpha = .8728 Standardized item alpha = .8812 Reliability Coefficients FOR FACTOR 2 (TWO) 5 items Alpha = .7715 Standardized item alpha = .7722 Reliability Coefficients FOR FACTOR 3 (TWO) 2 items Alpha = .7316 Standardized item alpha = .7372 11
  • 12. INTERPRETATION: In case of Cadbury factor one can be named ‘product feature’ factor as it contains variables taste, smoothness and sweetness, all of which convey quality of the product. Milky has low correlation with other three qualitative factors (.289, .406 and .287 for taste, smoothness and sweetness respectively). Factor one has an alpha of .8878 which is great. Factor two represents friendship, childhood and the feeling of being pampered. We call it the ‘childhood factor’. It has a relatively low alpha at .69, which borders the acceptance level of .70. We accept the factor. Factor three represents love and colour and we call it the ‘romance factor’. But it has a very low alpha value of .3662 implying that this factor may be measuring multidimensionality. In case of Axe factor one can be named the ‘product feature’ factor as it contains variables variety and skin care both of which convey quality of the product. But it also contains variables like style, youth, fashion, and x factor, which are non product attribute related. However correlation among them is high and significant meaning these emotional qualities have been strongly associated and identified with the two product factors. This factor has high alpha value of .9136. The second factor, again, is measuring the emotional benefits like the feeling of being elite or a class feeling, fashionable, and fun. This non-attribute factor can be called ‘fashion factor’. This factor has an alpha value of .7911 Compaq also yields two factors the first of which relates to the emotional benefits like feeling competent, attraction, and style. It has an alpha value of.8728. The second factor is clearly a ‘product attribute factor’ measuring sound, battery, speaker and LCD screen. It has an alpha score of .7715. The third factor measures entertainment value and contains movie and music variables. The third factor has an alpha value of .7316. Bartlett's Test of Sphericity has been done to test for the reliability of the factor. Bartlett's Test of Sphericity tests the null hypothesis that the intercorrelation matrix comes from a population in which the variables form an identity matrix. If it is an identity matrix it would extract as many factors as variables, since each variable would be its own factor. And Kaiser- Meyer-Olkin Measure of Sampling Adequacy test has also been done. If two variables share a common factor with other variables, their partial correlation (aij) will be small, indicating the unique variance they share. If aij ≅ 0.0 The variables are measuring a common factor, and KMO ≅ 1.0 If aij ≅ 1.0 The variables are not measuring a common factor, and KMO ≅ 0.0 For all three factor analysis done the Bartlett's Test of Sphericity is significant at .000. And the KMO scores are .738, .881, and .778 for Cadbury, Axe and Compaq repectivly. Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) 12
  • 13. KMO Value Degree of Common Variance 0.90 to 1.00 Marvelous 0.80 to 0.89 Meritorious 0.70 to 0.79 Middling 0.60 to 0.69 Mediocre 0.50 to 0.59 Miserable 0.00 to 0.49 Don't Factor ANCOVA: Univariate Analysis of Variance MODEL 1 Between-Subjects Factors Value Label N INVOLVEM 1 Low ENT 34 involvement LEVELS 2 Medium 34 involvement 3 High 32 involvement Tests of Between-Subjects Effects Dependent Variable: ATTITUDE Type III Sum Source of Squares df Mean Square F Sig. Corrected Model 133273.412(a) 8 16659.176 10.233 .000 Intercept 542359.443 1 542359.443 333.143 .000 INVOLVEM 44768.606 2 22384.303 13.750 .000 NPF 53447.047 1 53447.047 32.830 .000 PF 11874.539 1 11874.539 7.294 .008 INVOLVEM * 14657.975 2 7328.988 4.502 .014 NPF INVOLVEM * PF 9322.060 2 4661.030 2.863 .062 Error 148148.718 91 1628.008 Total 820530.360 100 Corrected Total 281422.130 99 a R Squared = .474 (Adjusted R Squared = .427) 13
  • 14. MODEL 2 Between-Subjects Factors Value Label N Trus 1 Low t trust 51 ratio ratio 2 Medium n trust 23 ratio 3 High trust 26 ratio Tests of Between-Subjects Effects Dependent Variable: Attitude Type III Sum Source of Squares df Mean Square F Sig. Corrected Model 125745.942(a) 8 15718.243 9.188 .000 Intercept 328536.855 1 328536.855 192.045 .000 TRUSTRAT 41607.309 2 20803.655 12.161 .000 PF 3873.284 1 3873.284 2.264 .136 NPF 18441.985 1 18441.985 10.780 .001 TRUSTRAT * 5272.548 2 2636.274 1.541 .220 PF TRUSTRAT * 16792.134 2 8396.067 4.908 .009 NPF Error 155676.187 91 1710.727 Total 820530.360 100 Corrected Total 281422.130 99 a R Squared = .447 (Adjusted R Squared = .398) Model one shows that there is significant interaction between NPF and involvement level, which means the influence of the covariate take into account the value or the category of the factor (level of involvement). But there is no significant interaction between PF and involvement at 5 percent significance level. However the main effects show NPF and involvement level to have the highest influence. If we wanted to get a “semipartial R-square” for just the effects of NPF, after controlling for these other two variables, we would look at the Type III SS for NPF, and compare it to the “corrected total” Type III SS. 14
  • 15. Semipartial R^2 for “NPF” = Type III SS for NPF/“corrected total” Type III SS = 53447.047/281422.130 = 0.1899177 or 19% Semipartial R^2 for “involvement” = 44768.606/281422.130 = 15.907% Semipartial R^2 for “PF” = 11874.539/281422.130 = .0421947 or 4.219% Semipartial R^2 for “involvement*NPF” = 14657.975/281422.130 = 0.052085 or 5.208% Model two, again, shows significant interaction between Trust and NPF. And Trust and NPF has much higher main effects than PF. PF does not have significant main effect at 5% significance. Semipartial R^2 for “NPF” = 18441.985/281422.130 = 6.553% Semipartial R^2 for “Trust*NPF” = 16792.134/281422.130 = 5.966% Semipartial R^2 for “Trust” = 41607.309/281422.130 = 14.784% IMPLICATIONS: From ANCOVA we find that the influence of emotion (NPF) make significant differences in brand attitude and that the extent of this influence is mediated by both trust and involvement level. But even without interactions the main effects of emotion (NPF) is quite palpable. In other words the extent of influence of emotion on brand attitude depends on how involved the product requires the consumer to be to consume it and how much the consumer trusts the band. But, as has been mentioned, emotion has large individual influence on brand attitude as well, regardless of trust and involvement level. In conflict with some of the theories*, emotion has been found to be intruding where, by common perception, rationality only should reign. For different products with different involvement levels, emotion has been found to be making a significant difference in the formation of brand attitude. To understand the degree and the direction of the influence of emotion regression analysis is helpful. The following are the information from that effort. Beta: For Cadbury (low involvement): PF = -.086, NPF = -.204 For Axe (medium involvement) PF = -.415, NPF = -.484 For Compaq (high involvement) PF = -.109, NPF= -.666 15
  • 16. Emotion is making continuously larger impact on brand attitude, as for higher involvement level products the slope (beta) is continuously increasing. It has to be seen whether higher involvement means larger impact of emotion on brand attitude or not. A Chow test has been done to test whether this increase holds true for the population or not. The Chow test basically sees if slope for two or more groups are similar or not or in other words if two regression lines are different from one another. It was originally designed to analyze the same variables obtained in two different data sets to determine if they were similar enough to be pooled together. The test is run in SPSS. As it cannot be done with GUI, Paste (command) was used. Tests of Between-Subjects Effects Dependent Variable: ATTITUDE Source Type III Sum of Squares DF Mean Square F Sig. INVOLVEM * NPF 14657.985 2 7328.993 3.282 .042 INVOLVEM * PF 9322.044 2 4661.022 1.721 .184 It shows that emotion (NPF) significantly differs in different involvement levels. And we can infer that emotion contributes more for higher involvement level products as it shown by the regression slopes. Conclusion*: Based on what we have found here, we can safely say that the brand and product manager has more scope and elbow space to position the brand on emotional parameters and not restrict themselves to exploring only the product attribute, price based positioning. Acceptance of the fact that emotion is a supremely powerful gift of the mind, can lead us to yet unexplored facets of marketing. Advertising can, for example, take a lot from this realisation and go for creatives that address the emotions more. The rational and thinking area of the FCB grid is invaded strongly by emotion from the emotional and feeling area. When feeling becomes heavily important, then it has to be explored, tested and proved how much of thinking really happens and when and for what exactly. *THIS CONCLUSION IS NOT EXHAUSTIVE. YOUR CONTRIBUTION IN COMMENTS AND CRITICISM IS HEARTILY WELCOM. 16