CHAPTER 3 RESEARCH METHODOLOGY

Research Methodology

75 Ph.D. Thesis

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Introduction

If I compare marketing to a long train with multiple compartments, then marketing research

would comprise the dual roles of the engine that powers the train and the links that connect

the individual compartments to form a cohesive functional unit. In other words, marketing

research is the backbone of the marketing function in any organisation. A critical part of the

marketing intelligence system, it helps to improve management decision making for product

promotion and increasing sales by providing relevant, accurate, and timely information, by

aiding the formulation of requisite strategies.

A review of literature in the marketing domain shows a considerable body of work

converging on the importance of the corporate brand. This has resulted in the need to manage

a corporate brand effectively (Lane Keller, 1999) and identification of the need to reveal the

processes involved in building and sustaining positive corporate reputations with wider

shareholder audiences (Balmer, 1998, Ind, 1998, Macrae, 1999).

This research thesis takes on the scientific method of exploring the concept of corporate

brand identity in the minds of the consumers, thus identifying the determinants of corporate

brand identity, the value of customer-centric branding and the need to improve consumer

brand knowledge. An analysis of brand functions further helps devise a process for consumer

segmentation. Design of an experiment helps formulate strategies for increasing consumer

emotion for a brand by increasing the level of the consumer’s knowledge about the brand.

3.2 Research questions

3.2.1. Pilot study to calculate Brand association scores to reflect the degree the consumers

relate the product brands to the corporate brand.

Chapter 3

Ph.D. Thesis 76

As stated in the introduction, the purpose of this dissertation is to open up the black-box of a

consumer’s perception of a brand which is done by exploring the consumer’s mind space and

improve product positioning in terms of the knowledge acquired about the brand. The study

began with the identification and exploration of the mental association constructs of

corporate-brand identity in the consumer mind space and identification of the factors

responsible for the development of the brand image in the minds of the consumers. This

involved an initial exhaustive study of the literature on corporate brand identity, thereby

signifying that corporate brand identity is a function of Brand Image and Brand personality.

Brand image is a function of perceived value by a consumer which results in brand

association (Aaker and Jennifer, 1997). In the above context, I try to link corporate brand

identity with the brand images of products in the corporate portfolio. Amongst other

parameters, Corporate Brand Identity is also a function of the ability of the consumers to

associate all other brands in the corporate portfolio with the corporate brand. This helped to

develop the conceptual framework for the theses.

3.2.1.1. Sampling

To illustrate the relationship between the brand association and the corporate brand identity, a

pool of 200 product brands was created. The pool created was a random selection of a set of

product brands across eight Corporates. The study was conducted by using the brand pool as

a research instrument. This was administered to respondents in urban Indian cities and only

those brands which could find recognition with the respondents were included in the pool.

The same was administered to a set of 100 respondents.


77 Ph.D. Thesis

The pool of 200 product brands (Table 3.1) is listed below:

Table 3.1: Pool of Brands

Dabur Promise, Kwality Wall's ice cream, Dabur Odomos, Lifebuoy, Real Active Fruit Juice, Amul Lite, Lux , Wheel, Jaguar, Sunsilk, Xenon, Mint-O, Amul Kool Café, Tata Indica, Bingo, Wills Life Style, Amul fresh Milk, John Player, Indigo Marina, Land Rover, Amul Gold Milk, ITC welcome Group, Breeze, Liril, Rexona, Aashirwad Aata, Hamam, Moti soaps, Pureit Water Purifier, Lipton tea, Tata Safari, Brooke Bond tea, Bru Coffee, Ultra Tech Cement, Tata Salt, Coorg Pure, Mysore Gold Coffee, Pepsodent, Close Up, Surf, Rin and Wheel, Dabur Odonil, Amul Kool, Vim, Sunfeast, Kitchen’s of India, Kissan squashes and jams, Annapurna salt and atta, Pond's talcs and creams, Vaseline lotions, Fair & Lovely creams, Vivel DiWills, Fiama Di Wills, Lakmé, Clinic Plus, Clinic All Clear, Sunsilk, Dove, Ala bleach, Domex, Rexona, Pears, Amul Mithai Mate, Amul Pure Ghee, Amul Shakti Toned Milk, Dabur Chyawanprash, Dabur Active Blood Purifier, Dabur Gulabari Rosewater, Hajmola, Dabur Pudinhara, Amul Lassi, Pond’s Chakra Gold, Tetley, Voltas, Westside, Amul shreekhand, Titan, Sagar Skimmed Milk Powder, Tanishq, Tata Tiscon, Masti Dahi, Lipton Tea, Amul Malai Paneer, Kwality Walls, Nutramul, Tata Sky, Star Bazaar, Virgin Mobile, Moti Soaps, Pure it, Vaseline, Sanifresh, Shilajit, Dabur Nature care, Modern Bread, Axe, Superia, Classmate, PaperKraft, AIM, Mangaldeep, Candy man, Amul Butter Milk, Amul Fresh Cream, Amul Shakti Toned milk, Amulya Dairy Whiteness, Amul Cheese Spread, Amul Pizza Mozzarella cheesse, Utterly Delicious Pizza, Amul Ice Cream, Amul Choclates, Amul Basundi, Dabur Amla Hair oil, Babool Toothpaste, Dabur Badam oil, Hingoli, Homemade, Dabur Lal Dant manjan, Dabur Lal Tail, Meswak, Dabur Shanka Pushpi, Sarbyana Strong, Satisabgol, Vatika Dandruff Control Shampoo, Vatika Fairness Face pack, Vatika Enriched Coconut Oil, Vatika Smooth and Silky Shampoo, Vatika Root strengthening Shampoo, Real Nature Fresh fruit juice, Dabur Red Tooth paste, Dabur Active Fruit juice, Sun Chips, Parle-G, Krackjack, Magix, Monaco, Kreams, Parle 20-20 cookies, Nimkin, Chox, Hide and Seek, Hide and Seek Milano, Digestive Marie, Parle Marie, Milk Shakti, Goldenarcs, Kreams Gold, Monaco Jeera, Melody, Mango Bite, Kaccha MangoBite, Poppins, Kisme Toffee, Kisme Toffee Bar, Mazelo, Kisme Gold, Orange Candy, Xhale, 2 in 1 Éclair, Golgappa, Melody Softy, Parle Lites, Musst Bites, Cheeslings, Sixer, Jeffs, Musst Stix & Musst Chips, Sixer Zeera, Aviance, Knorr, Olay, Oral B, Pampers, Pantene, Duracell, Gillette, Tide, Pringles Potato Chips, Old Spice, Clearasil, Whisper, Camay, Hugo, Lacoste, Naomi Campbell, Puma, Ariel, Vicks Healthcare, Braun, Dunkin’ Donuts, Rejoice, Ayush, Sunlight, Cadbury Dairy Milk, 5 Star, Perk, Eclairs, Celebrations, Temptations, Gems, Bournvita, Bytes, Halls, Bubbaloo, Head & Shoulders.

FILL YOUR RESPONSES BELOW

DABUR PARLE CADBURY HUL TATA ITC P&G

Gujarat Co-

Operative Milk

Marketing Federation (AMUL)

Total

Chapter 3

Ph.D. Thesis 78

From the set of above mentioned BRANDS identify the Corporate behind them. Put the

brand under the right corporate listed below (Columns).

The product brands in the brand pool were grouped on the basis of the brand name typology,

(Table 4.2 as indicated in the Result/Findings chapter).

For the purpose of this study, I define the following types of brand names:

(i)Family Brand Name-A family brand name comprises usage of the name of the corporate

brand which is used for all products produced or marketed by that corporate. By building

customer trust and loyalty for the family brand name, all products that use the brand can

benefit.

(ii)Individual Brand name-An individual brand name does not identify a brand with a

particular company.

(iii)Combination Brand Name-A combination brand name brings together a family brand

name and an individual brand name. The idea here is to provide some association for the

product with a strong family brand name but maintaining some distinctiveness so that

customers know what they are getting.

The above study thus details that the brand image is what is currently in the minds of

consumers, whereas brand identity is aspirational from the brand owners' point of view.

Further corporate brand identity is a function of Brand Image and Brand personality. Brand

image is a function of perceived value by a consumer which results in brand association. In

the above context, I try to link corporate brand identity with the brand images of products in

the corporate portfolio. Amongst other parameters, Corporate Brand Identity is also a

function of the ability of the consumers to associate all other brands in the corporate portfolio

with the corporate brand identity.

The inverse correlation figures illustrated in the chapter on Results and findings indicate a

significant gap between the corporate brand identity per se and the association of the

individual with the specific brand. This gap brings in the need to study the relevant literature

in the domain of corporate brand identity and its constructs.


79 Ph.D. Thesis

3.2.2. Identification of determinants for defining Customer Centric Brand Identity

The next step was to identify the determinants that make a brand customer centric. Brands are

so much more than a name, logo or image. They represent nothing less than a customer’s

complete experience with the product, service or company. As Kevin Keller said: The power

of a brand lies in the minds of the consumers and what they have experienced and learned

about the brand over time. Hence it becomes crucial to fix the determinants. There are many

projective (indirect) approaches to understand brand associations. The commonly used

methods are word association, picture completion, thematic appreciation tests, sentence

completion and story completion (Aaker, 1991; Kotler and Armstrong, 1996; Aaker, et al.,

1998). An exhaustive literature review and a mixed approach using word association and a

variant of Unique Corporate Association Valence approach (UCAV), (Spears, 2006) has been

used to identify the determinants.

Literature review in detail talks about the determinants and the six brand functions that a

brand performs in the minds of the consumer. An adaptation of the Unique Corporate

Association Valence approach was also used to extract the determinants and the functions

then were linked to the determinants.

3.2.2.1. Unique Corporate Association Valence (UCAV) approach

Spears (2006), integrates the quantitative and qualitative approaches with the specific intent

of capturing the primary benefit of the qualitative approaches-the ability to uncover what an

individual really knows about a company-while still offering quantitative assessment. The

measure is straight forward; it asks the respondents to write words or short phrases that

describe the focal company/brand as if they were telling someone else about the

company/brand. The goal is to capture the meaning of the company/brand for the individual.

I used a variant of the UCAV measure and asked a focus group of 25 respondents to identify

attributes associated with the above given set of 8 brands.

Chapter 3

Ph.D. Thesis 80

3.2.2.2. Brand Attributes for defining Customer Centric Brand Identity

An exhaustive review of literature in the domain of branding was conducted along with

related topics such as corporate strategy, brand identity and brand attributes to extract the

attributes of brand identity. The literature review revealed that although there are several

studies on brand identity, there was still a need for a more elaborate operational definition of

the concept with respect to the attributes that define brand identity. The research aimed to

ascertain from a consumer perspective, the core components (determinants) of brand identity.

The research methodology adopted was primarily exploratory and correlational in nature. 57

brand attributes were extracted from literature review, catering to various dimensions of a

brand. These were used to develop an evaluation grid to link the diverse brand attributes to

the functions they perform in a brand. This was done by administering the same to a set of

respondents, (as a pilot study, the premise was to include the users of the brand) identified by

the sampling strategy. The list of the attributes was personally administered to the

respondents.

This helped me to develop the research instrument for the purpose of the study. The list of 57

attributes (Table 3.2) which was developed on the basis of literature review and discussion

with a focus group of 25 respondents is as mentioned below. The attributes extracted

described the consumer’s perception of a brand.


81 Ph.D. Thesis

Table 3.2: List of Brand Identity Attributes

S. No. Brand Identity Attributes

1 Active Engagement 2 Admirable 3 Advertising and Jingles 4 Appealing 5 Approachable 6 Association of Celebrity or Endorsement 7 Attitudinal Attachment 8 Authenticity 9 Behavioral Loyalty

10 Believable 11 Brand Resonance 12 Captivating 13 Cheerful 14 Conscientious 15 Contribution of Corporate Values to Brand Identity 16 Delivery of Benefits 17 Dependability 18 Durability 19 Dynamic 20 Empathy 21 Engagement 22 Excitement 23 Global Image 24 Glorification of "MY" Personality 25 Honest 26 Innovative 27 Intelligent 28 Intense 29 Likeability 30 Liking of Brand 31 Meaningfulness 32 Mesmerizing 33 Popular 34 Price 35 Product performance better than competitor 36 Recognition 37 Recognition of Logo

Chapter 3

Ph.D. Thesis 82

38 Relevant 39 Reliability 40 Reliable 41 Sense of Community 42 Sensual Experience 43 Service Oriented 44 Serviceability 45 Social Approval/Social Respect 46 Social Responsibility of the organization 47 Sophistication of the product 48 Spirited 49 Stands for Something 50 Successful 51 Superiority 52 Sustainability 53 Trust 54 Unique 55 Visibility of Brand 56 Visual Appeal 57 Wholesome

3.2.2.3. Brand Customer Centricity Determinants

These attributes were used to develop the evaluation grid (Table 3.3). This was developed to

extract the corporate brand identity determinants as a result of the various attributes listed.

The study was oriented towards providing an operational definition, and so the grid aimed to

examine the perspectives of the consumers across six determinants which perform the

expected/probable six functions (Chapter 2 on Literature Review) a brand performs in the

minds of the consumers.


83 Ph.D. Thesis

Table 3.3: Evaluation GRID

S.no. Attribute

Builds an

emotional

connection

with the

Brand

Contribution

of product to

my Lifestyle

and Image

Enhances

perception

of the

Brand

Drives

me to

buy a

product

because

i

perceive

greater

value in

the

brand

I buy

because

i Trust

in the

Brand

I feel

in sync

with

the

Brand

1 Active Engagement 27 21 21 4 61 62 2 Admirable 6 49 20 39 27 8 3 Advertising and Jingles 6 20 35 30 6 23 4 Appealing 46 23 20 19 47 7 5 Approachable 32 20 46 19 47 22 6 Association of Celebrity or Endorsement 6 21 28 19 61 23 7 Attitudinal Attachment 36 21 33 19 6 7 8 Authenticity 38 8 42 6 49 49 9 Behavioral Loyalty 38 21 42 46 22 7 10 Believable 36 46 36 45 36 12 11 Brand Resonance 6 47 36 33 21 7 12 Captivating 6 28 20 19 61 14 13 Cheerful 62 37 5 4 32 22 14 Conscientious 21 32 19 59 6 8 15 Contribution of Corporate Values to Brand 61 6 36 25 12 6

Chapter 3

Ph.D. Thesis 84

Identity

16 Delivery of Benefits 21 34 6 18 47 23 17 Dependability 8 20 22 45 36 6 18 Durability 21 21 62 21 21 6 19 Dynamic 6 38 20 18 32 23 20 Empathy 36 21 60 4 21 8 21 Engagement 21 22 26 30 21 62 22 Excitement 32 21 60 4 6 23 23 Global Image 21 8 22 59 21 23 24 Glorification of "MY" Personality 21 44 27 4 32 23 25 Honest 63 6 63 21 23 6 26 Innovative 46 21 27 59 6 14 27 Intelligent 6 6 47 33 21 21 28 Intense 21 47 35 18 6 23 29 Likability 6 42 34 60 36 6 30 Liking of Brand 6 23 31 48 36 23 31 Meaningfulness 7 27 27 4 63 62 32 Mesmerizing 6 78 26 19 27 22 33 Popular 21 13 47 19 42 13 34 Price 6 23 5 53 27 21 35 Product performance better than competitor 6 46 22 74 36 6 36 Recognition 6 14 37 19 61 7 37 Recognition of Logo 6 7 21 19 76 8 38 Relevant 20 13 12 45 21 43 39 Reliability 8 61 37 35 23 7 40 Reliable 7 6 47 20 47 7 41 Sense of Community 21 33 22 59 32 23


85 Ph.D. Thesis

42 Sensual Experience 47 41 45 4 26 49 43 Service Oriented 7 6 62 59 37 27 44 Serviceability 36 47 36 47 62 7 45 Social Approval/Social Respect 36 49 20 19 22 22 46 Social Responsibility of the organization 36 6 7 19 6 47 47 Sophistication of the product 46 8 26 25 6 49 48 Spirited 7 21 6 45 32 22 49 Stands for Something 36 6 22 45 36 22 50 Successful 32 22 36 34 27 34 51 Superiority 6 46 7 45 36 6 52 Sustainability 27 13 47 33 76 8 53 Trust 38 6 51 27 43 6 54 Unique 26 29 11 25 32 7 55 Visibility of Brand 20 21 32 25 35 7 56 Visual Appeal 6 23 62 45 21 8 57 Wholesome 6 61 7 4 42 6

*the numeric values in the column 3-8 are the number of respondents who linked the attribute to a specific brand function.

The grid was administered to respondents who were asked to link each brand attribute to the specific brand function that it performs as per

their perception. The numeric values in the columns 3 to 8 show the number of respondents who linked one specific brand function to one

specific brand attribute.

For example the numeric value of 27 in the second row and third column indicates the number of respondents who linked attribute “active

engagement” to the first brand function of “emotional connection”.

Chapter 3

Ph.D. Thesis 86

3.2.2.4. Factor analysis

Factor analysis is a statistical approach that can be used to analyse interrelationships among a

large number of variables and to explain these variables in terms of common underlying

dimensions (factors). The objective is to find a way of condensing the information contained

in a number of original variables into a smaller set of variables (factors) with a minimum loss

of information. Once these dimensions and the explanation of each variable are determined,

the two primary uses for factor analysis- summarization and data reduction can be achieved

(Hair et al., 1998).

In summarizing the data, factor analysis derives underlying dimensions that, when interrupted

and understood, describe the data in much smaller number of concepts than the original

individual variables. Data reduction can be achieved by calculating scores for each

underlying dimension and substituting them for the original variables.

The data was collected from 105 respondents. The factor analysis is being used to validate a

consumer perception extracted from a focus group of participants. Considering that it is being

used here more for validation rather than a classification perspective, thus limitation of a

small sample size was ignored. The respondents were asked to group these attributes across

the six major functions of the brand. This helped to fix the attributes under each function on

the basis of the perspectives of the respondents. The evaluation grid was further used to

conduct a factor analysis using Principal Components Analysis with Varimax rotation to

regroup these attributes under their functional roles with respect to a brand. The same was

used to extract brand identity determinants. This was done by using the highest loading as a

determinant of the factor a variable belonged to. This helped in the extraction of the Brand

Identity determinants (Table 3.2).The model (Figure 3.1), depicted below, shows the loading

of the attributes on the respective factors.

3.3. Brand Customer Centricity Calculator (BCCCS)

3.3.1. Brand Identity Attributes loaded on brand customer centricity functions

A focus group of participants were asked to rate the functions (Table 4.11) on a scale of (1-4)

and I subsequently identified the most important brand functions from a consumer’s

perspective. The consumer responses, with 1 being the highest and 4 being the lowest, were


87 Ph.D. Thesis

used to calculate factor wise frequencies. These, when compared with the sum total of the

composite frequencies across all factors, yielded the weights for the individual factors. The

weights were used to develop a relative scoring method, to eventually develop a brand

customer centricity calculator (discussed in detail under the chapter on findings and analysis).

3.3.2. Lexicographic Heuristic model Lexicography heuristics includes the selection of the best brand/product on the basis of its

most important attribute. I combined the lexicographic heuristic model to incorporate

consumer rating of brand attributes, along with the Weighted Linear Compensatory model

using weights of specific brand functions to develop what I term as the Brand Customer

Centricity Calculator (BCCC) (discussed in detail under the chapter on findings and analysis).

The objective here is to define brands on the basis of their attributes and use consumer

response to brands on these attributes as the premise for consumer behavioral and purchase

decisions. For specific products, consumers are asked to rate the product on a particular

attribute, based on their perception. This reflects the positioning of a specific brand on a

particular attribute in the consumer mind space.

3.3.3. Choice heuristics

The Heuristic-Systematic model (H-S) is conceptually similar to the Elaboration Likelihood

Model of persuasion (ELM, Petty and Cacioppo 1986), which more marketing scholars may

find familiar. Systematic and heuristic processing in the H-S are analogous to central and

peripheral routes in the ELM. Systematic processing is prototypically viewed as a

"comprehensive, analytic orientation in which perceivers access and scrutinize all

informational input for its relevance and importance to their judgment task"; alternatively,

individuals processing heuristically "focus on the subset of available information that enables

them to use simple inferential rules, schemata, or cognitive heuristics to formulate their

judgments and decisions" (Chaiken, Liberman and Eagly, 1989). Systematic processing

requires higher levels of cognitive effort and capacity than heuristic processing. Because

people prefer less effortful to more effortful modes of information processing, individuals

must be more highly motivated to process systematically. It is necessary to state a few

assumptions and define terms before applying the model to the brand choice context.

Chapter 3

Ph.D. Thesis 88

3.3.3.1. Assumptions and Definitions

As used here, the model assumes that the products under consideration are identical in

"objective" quality, differing only along extrinsic attributes (e.g., brand name, price, and

packaging). Objective quality refers to the inherent technical superiority of a product based in

its physical attributes, as opposed to "perceived" quality which is influenced by other,

peripheral aspects, such as "image" (Zeithaml, 1988).A second assumption is that consumers

generally seek to maximize the value of their purchases. "Value" here refers to the ratio of

objective quality to the price paid (Zeithaml, 1988). This definition of value, though

restrictive, follows from the first assumption of identical objective quality, thus allowing

hypothetical comparisons of consumers' ability to make quality judgments between product

choices (Pechmarim and Ratneshwar, 1992). It is central to the arguments made in this paper,

especially those concerning brand equity: sometimes consumers take "the easy way out" and

use a heuristic (which may or may not be reliable) to infer higher objective quality for name

brand products than for private labels. Several conditions under which a consumer is most

likely to do this are outlined below.

Human decision making is a topic of great interest to marketers, psychologists, economists,

and others. People are often modeled as rational utility maximizers with unlimited mental

resources. However, due to the structure of the environment as well as cognitive limitations,

people frequently use simplifying heuristics for making quick yet accurate decisions. In this

research, we apply discrete optimization to infer from observed data if a person is behaving in

way consistent with a choice heuristic (e.g., a non-compensatory lexicographic decision rule).

3.3.4. Weighted linear compensatory model

I used the weighted linear compensatory model to incorporate weights of individual brand

functions, along with the consumer response on specific brand attributes to develop what I

term as the Brand Customer Centricity calculator (BCCC). For specific products, consumers

are asked to rate the product on a particular attribute, based on their perception. This reflects

the positioning of a specific brand on a particular attribute in the consumer mind space, which

eventually has significant impact on consumer behavioral and purchase decisions. Each

attribute performs a specific function for the brand. The weight associated with the brand

function is multiplied with the consumer rating on the attribute. The same is repeated for all


89 Ph.D. Thesis

attributes under the particular brand function and the Brand Customer Centricity Score is

calculated. Weighting and summing are processes used not only to define rational choices but

also rational inferences (Gigerenzer and Kurz, 2001). As already seen in literature review,

consumers will adopt brands that score high on Customer Centricity.

Chapter 3

Ph.D. Thesis 90

Figure 3.1: Determinants of Brand Identity on 6 Brand Functions

Intelligent

Active Engagement

Advertising and Jingle

Appealing

Attitudinal attachment

Likeable

Captivating

Excitement

Empathy

Cheerful

Believable

Behavioral Loyalty

Intense

Mesmerizing

Sensorial Experience

Spirited

Glorification of “MY” Personality

Visual Appeal

Wholesome

Brand Visibility

Dynamic

Imaginative

Meaningfulness

Price

Recognition

Recognition of Logo

Stands for something

Admirable

Association of celebrity

Delivery benefits

Dependability

Innovative

Popular

Product performance

Sophistication of the product

Successful

Superiority

Tough

Unique

Up to date

Approachable

Authentic

Durability

Global Image

Honest

Relevant

Trust

Corporate values

Sense of Community

Service oriented

Serviceability

Social Approval

Social Responsibility of the organization

Sustainability

Emotional

Connection

Consumer

Brand

Knowledge

Trust My Life

style and

Image

My

perception

Responsible

towards

Customer

Brand

Customer

Centricity


91 Ph.D. Thesis

3.3.5. Validity and reliability of the research instrument

To test the validity of the instrument, a study was done on 100 participants in the month of

January 2008. Based on their responses, validity tests were done to check for the validity and

usability of the instrument. Cronbach alpha, KMO measure of adequacy and Bartlett’s test of

sphericity were conducted. Cronbach alpha was calculated to measure the internal

consistency reliability of the instrument. The cronbach alpha for each of the dimensions came

as 0.9438 for Emotional connection (EC), 0.8987 for Lifestyle and Image (LSI), 0.9135 for

My Perception (MBP), 0.9335 for Consumer Brand Knowledge (CBK), 0.9032 for Trust (T)

and 0.9193 for Responsible Towards Consumer (RC) thus the instrument was considered

reliable for the study. Kaiser-Meyer-Olkin test was done to measure the homogeneity of

variables and Bartlett's test of sphericity was done to test for the correlation among the

variables used. The KMO value for the instrument was 0.867, which is acceptable as a

middling value. The Bartlett’s test showed significant results for the instrument hence the

instrument was accepted for further study. Table IV summarizes the entire result viz.

cronbach alpha, KMO test values, and Bartletts’s significance of the instrument, mean and

standard deviation values. On getting quite meritorious results of the validity, the instrument

was floated for data collection.

The validity and the reliability scores of the instrument thus developed are as under.

Table 3.4: Test of Validity of Research Instrument

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy Bartlett's Test of Sphericity

0.867

Approx. Chi-Square 6052.012

df 210.000

Sig. .000

Chapter 3

Ph.D. Thesis 92

Table 3.5: Test of Reliability of the Research Instrument

Dimension No. of items

per Dimension Cronbach’s

Alpha Mean Standard

Deviation

Emotional

Connection 15 .9438 3.5227

0.5042

Life Style

and Image 20 .8987 3.6078

0.3650

Enhances

Brand

Perception

8 .9135 3.6672 0.43042

Consumer

Brand

Knowledge

14 .9335 3.5178 0.3993

Trust 8 .9032 3.5072 0.3662

Responsible

Towards

Customer

7 .9193 3.5011 0.3022

3.4. Sampling of Brands for Data Collection

The Most Trusted Brands Survey identifies brands that bond with consumers on. These

brands are not just the familiar, but those that the consumers believe provide quality and

reassurance.

3.4.1. List of 50 Brands

For the purpose of this research study the 50 brands were chosen from the India’s Most

Trusted top 100 brands. The list had the representation form the various product and service

categories consisting of a range of fast moving consumer goods (FMCG) products, banking

sector, automobile, consumer durables etc. The sampling of the brands under various

categories has been explored in the subsequent paragraphs.

(Adopted from the PAN India survey of “India’s Most Trusted top 100 brands, published by

4Ps Business & Marketing, vol V, Issue 17, 27 Aug-23 Sept’10)


93 Ph.D. Thesis

Table 3.6: List of 50 Brands

The below listed 50 brands were chosen from the India’s Most Trusted top 100 brands.

S.No. Brand/Product Name

1 7 UP

2 Crocin

3 Dettol

4 Asian Paints

5 Tide

6 Titan

7 Sony Ericsson

8 Sony

9 Surf

10 Goodknight

11 Dabur

12 Whirlpool

13 Zandu Balm

14 Garnier

15 Ponds

16 Philips

17 Vodafone

18 Thums UP

19 Fair and Lovely

20 Sprite

21 Nescafe

22 Motorolla

23 Lays

24 Limca

25 Samsung Mobile Phones

S.No. Brand/Product Name

26 Rasna

27 Pepsodent

28 Pepsi

29 Pears

30 Maruti Suzuki

31 Lux

32 Iodex

33 Coca Cola

34 BSNL

35 Bajaj Motorcycle

36 Bournvita

37 Amul

38 Bata

39 Complan

40 Parle

41 Hero Honda

42 Lakme

43 H&S

44 Maggi

45 Nokia

46 Mirinda

47 Pears

48 Tata Salt

49 Sunsilk

50 Videocon

Chapter 3

Ph.D. Thesis 94

3.4.2. Methodology for Brand Selection

The Most Trusted Brands survey conducted by Brand Equity and Neilsen (YEAR 2010) was

used as basis for brand selection. 300 brands (217 consumer products and 83 service brands)

were finalized. Each brand was then evaluated on seven parameters and Top 100 brands were

extracted.

Brands were rated on the following attributes-

Table 3.7: Brand Attributes

S.NO. ATTRIBUTES

1 Always maintains a high level of quality.

2 Is worth the price it commands.

3 Is a brand I would surely consider if I have to buy the product

4 Has been a popular brand for many years.

5 Has something that no brand has.

6 Evokes a feeling of confidence and pride among its users.

7 Is a very special brand with unique feelings associated to it.

After this the next crucial step was to determine the sample. The idea was to interview all the

possible consumers who use the brands-the Chief Wage Earners (CWE) who contribute

maximum to the household income, housewives, young adults (both males and females). The

survey has been restricted to urban India, (Delhi and NCR) with a view to focus on the prime

target audience for the consumer-branded products. It was also felt that if a rural consumer is

asked to rate various brands, his ratings would be driven mainly by familiarity or popularity,

i.e. brand with mass market appeal. Therefore if rural opinion is included, brands would be

rated on just two parameters of the seven that were set down and the other parameters would

get played down unconsciously. Given these difficulties, the most Trusted Brands survey

represents urban India in all town classes where a more balanced survey can be assured.

Target respondents were interviewed in five metros, four Class 2 towns (population between

5 and 10 lakh) and four Class 3 towns (population between 50,000 and one lakh) in each


95 Ph.D. Thesis

geographic zone. Each respondent was shown the brand list and was asked to rate on a 1-to-4

familiarity scale, 1 for ‘not heard of the brand’, 4 for ‘know the brand very well’. Then the

respondent would evaluate all brands rated above 1 by him/her on the familiarity scale. The

evaluation is done at an overall level about each brand by indicating his/her rating to a scale

where one end is ‘extremely poor’ and the other ‘perfect in every way’. Conducted by

research agency, The Nielsen Company, the survey is the largest of its kind in India, with a

sample of 8,160 distributed across socio-economic classifications, age, income and

geography. After considerable brainstorming by Brand Equity along with Nielsen, the list of

300 brands (212 consumer products and 88 service brands) is finalized. Each brand is then

evaluated on relatedness (does it evoke a feeling of warmth or friendliness); perceived

popularity (is it known, recognized and accepted by a wide array of consumers); quality

connotation (what does it stand for in the quality of its product); distinctiveness or uniqueness

of what it stands for; value for money that it offers (does it strike a chord with the consumer)

and repurchase intent (which would show how deeply is the brand ingrained).

3.4.3. Sampling Strategy for 50 brands

3.4.3.1. Sampling of 50 Brands

The most Trusted Brand Survey identified brands that bond with consumers. The Survey had

a significant representation of 100 brands across the 13 categories outlined as part of the

research. A table of percentage of brands across each category was created. Sample of 50

Brands which had an equivalent representation of each category was extracted for the

purpose of the research. The numbers of brands selected across each category were as listed

below:

Table 3.8: Brand Selection Category

CATEGORIES NO. OF BRANDS ACROSS

EACH CATEGORY

Apparels, Shoes and Accessories 1

Automobiles (4 Wheeler) 1

Automobiles (2 Wheeler) 2

Banks and Financial Services 1

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FMCG-Food Products 4

FMCG-Personal Care 12

FMCG-Home Care 4

FMCG-Bakery, Confectionery, Health and Hygiene 5

Beverages (Soft Drinks) 5

OTC (Over the Counter) 3

Consumer Durables 6

Communications-Service Providers 2

Mobile Phone Handsets 4

3.4.3.2. Sampling of Respondents

Constraints with regard to accessibility, time and money and also multiplicity of objectives

and research questions make limited allowance to collect or analyze all the data available.

Many have argued that sampling as a whole does provide overall accuracy. (Saunders et al.,

2000) have divided the techniques of collecting data in two- Probability and Non-probability

sampling.

3.4.3.2.1. Personal profiles of the respondents All the research instruments used for the purpose of this research study had a section on age

and gender of the respondents so as to gather respondent’s personal profile on these two

variables.

3.4.3.2.2. Sampling Premise

The sampling elements had both males and females in the various age groups as listed above.

These respondents were the households or head of the households responsible for most of the

shopping. Thus I have used Judgmental sampling. Exercising my own judgement and

expertise, the elements of this research study have been chosen to be included in the sample.

Care has been taken to have almost equal representation of both the genders, considering the

diversity of the brands used for the purpose of this study.


97 Ph.D. Thesis

3.4.3.2.3. Sampling Technique

(i) Population

Population is aggregate of all consumers using the brands or under consideration, which is

infinite. The population for the study is defined as under:

Element : Consumer

Sampling Unit : Consumers of the brands under consideration in the study.

(ii) Sampling Unit

It is the physical unit about which information is collected. For the study at hand the

consumers using the products of the brands are the elements. The sampling units form the

basis of the actual sampling procedure. It is that which is actually chosen by the sampling

process. In the present study, the age and gender of the respondents is considered to gather

respondent’s personal profile. The age is divided further into three categories. These are: Less

than 30, 30 – 35 and above 35.

(iii) Sampling Design/Procedure

Sampling Designs deal with specifications which include the method of selecting individual

sample members, and involves both theoretical & practical considerations. As the study

requires a survey of consumers using the brands under consideration in the study,

judgemental sampling is used in the study.

(iv) Sample Size

Sample size refers to the number of elements included in the study. After the population has

been defined, the sampling frame established and specific sampling type selected,

conceivably another important consideration is sample size determination. Appropriateness of

sample size is quite complex for large enough sample for any researcher’s objective may turn

out to be too large for the amount of time, money and personnel requirement. So a trade-off

has to be evolved between the required information and cost and resources have to strike off.

So while specifying sample size, the factors such as the number of units to be included in the

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Ph.D. Thesis 98

sample in which neither so few are selected as to render the risk of sampling error intolerably

large, nor too many units are included which may raise the cost of the study to make it

inefficient, must be weighed properly.

The sample size for the study is 1500 units. For the purpose of this research study sampling

with replacement technique (Malhotra and Dash, 2010) has been used. In this, an element is

selected from the sampling frame and appropriate data is obtained. Then the element is

placed back in the sampling frame. As a result it is possible for an element to be included in

the sample more than once. Therefore for this study, the respondents were used in groups of

30 for each of the 50 brands.

The number of responses for this study can hence be considered as, N=1500, which

represents number of filled up responses and does not represent individual number of

respondents.

3.4.3.2.4. Additional validation study of BCCC

To validate the Brand Customer Centricity Calculator, an additional study was carried out

The objective of this Validation process was to validate the Brand Customer Centricity

Calculator by comparing the Brand Customer Centricity Scores (BCCS) across different

product brands under the umbrella of one parent brand, HUL (Hindustan Unilever Limited)

and subsequently studying the brand’s performance from the consumer perspective across

the six brand functions.

3.4.3.2.5. Focus Group Details

The focus groups (comprising of 15 respondents) for the purpose of the validation process

were chosen in an intricate manner. Care was taken to choose consumers of the brand as

focus group members. This was maintained as a basic premise for all the five brands chosen

for the validation.

Three factors were the qualifying criteria for the respondents to be a part of the focus group.

1. The respondent should have been using the brand.

2. The frequency of the usage should have been at least one purchase a month.

3. Length of association with the brand should have been at least 1 year or more.


99 Ph.D. Thesis

The demographic factors like age and gender etc, were ignored for this validation process.

The objective of this Validation process was to validate the Brand Customer Centricity

Calculator by comparing the Brand Customer Centricity Scores (BCCS) across different

product brands under the umbrella of one parent brand and subsequently studying the brand’s

performance from the consumer perspective across the six brand functions.

The results of the same have been shown in the chapter on findings and results.

3.5. Consumer Segmentation using Cluster Analysis

3.5.1. Datamining

Analysis of large quantities of data require approaches that are very different from the

traditional data analysis approaches and this has given birth to the field of Knowledge

Discovery in Databases (KDD), more popularly known as Datamining. Knowledge

Discovery in Databases is a non-trivial process of identifying valid, novel, potentially useful

and ultimately understandable patterns in data (Fayyad et al., 2007). Others look at data

mining in terms of a set of tools and techniques that operate on and extract implicit patterns

from data. Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area

focusing upon methodologies for extracting useful knowledge from data for Business

Intelligence. The ongoing rapid growth of online data due to the Internet and the widespread

use of databases have created an immense need for KDD methodologies. The challenge of

extracting knowledge from data draws upon research in a wide variety of fields to draw upon

tools that can synthesize and organize knowledge on any given topic of interest from a corpus

of documents. There is an increasing realization that effective brand management can be done

only based on a true understanding of the needs and preferences of the customers. Under

these conditions, data mining tools can help uncover the hidden knowledge and understand

customer better, while a systematic knowledge management effort can channel the

knowledge into effective marketing strategies. This makes the study of knowledge extraction

and management particularly valuable for marketing (Shaw et al., 2001).

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Ph.D. Thesis 100

3.5.2. Data mining techniques

Data mining tools and Techniques operate on large databases and extract patterns that are

implicit in them, resulting in actionable information. Data mining can be directed or

undirected. Directed data mining attempts to explain or categorize some particular target field

such as income or response. Undirected data mining attempts to find patterns or similarities

among groups of records without the use of a particular target field or collection of

predefined classes (Berry et al., 2007).

Customer understanding is the core of Consumer Brand knowledge which in turn

encompasses customer segmentation and actions to maximize customer conversion, retention,

loyalty and profitability, through proper customer understanding and actionability. Incorrect

customer understanding can lead to hazardous actions. Similarly, unfocused actions, such as

unbounded attempts to access or retain all customers, can lead to Hence, emphasis should be

put on correct customer understanding and concerted actions derived from it.

In view of the above, corporates commenced accumulation of a wide spectrum of consumer

data viz. transaction data, customer databases based on consumer behavior and purchase

transactions and in this context, creation of data warehouses. But, due to lack of appropriate

tools and techniques to analyze these huge databases, a wealth of customer information and

buying patterns was permanently hidden and unutilized in such databases. But, memory is of

little use without intelligence. The central idea of data mining is that data from the past

contains information that will be useful in the future. It works because data pertaining to

consumer behavior captured in corporate data are not random, but reflect the differing

consumer needs and preferences. Knowledge-based marketing, which uses appropriate data

mining tools and knowledge management framework, addresses this need and helps leverage

knowledge hidden in databases. Customer profiling is one of the major areas of the

application of data mining for knowledge-based marketing. This is of relevance because

consumer behavioral data is a more valuable source of information than consumer

demographics data.

Data mining techniques are algorithms and methods used to carry out data mining tasks. They

differ from each other in type of data handled, assumptions about the data, scope and

interpretation of the output.


101 Ph.D. Thesis

3.6. Clustering

3.6.1. Hierarchical Clustering

Cluster Analysis, also called data segmentation, relates to grouping or segmenting a

collection of objects (also called observations, individuals, cases, or data rows) into subsets

or "clusters", such that those within each cluster are more closely related to one another than

objects assigned to different clusters. Hence, objects in a cluster are similar to each other.

They are also dissimilar to objects outside the cluster, particularly objects in other clusters.

Clustering algorithms function such that intracluster similarity is maximum and inter-cluster

similarity is minimum. Clustering also has applications in the field of marketing

segmentation.

There are two major methods of clustering-hierarchical clustering and k-means clustering.

For my study, I use the technique of Hierarchical Cluster Analysis. This is a statistical

method for finding relatively homogeneous clusters of cases based on measured

characteristics. It starts with each case in a separate cluster and then combines the clusters

sequentially, reducing the number of clusters at each step until only one cluster is left. When

there are N cases, this involves N-1 clustering steps, or fusions. This hierarchical clustering

process can be represented as a tree, or dendrogram, where each step in the clustering process

is illustrated by a join of the tree.

3.6.2. K-means Clustering

Considering that the BCCS uses the concept of weighting to encode the relative importance

of the brand function variables, I use the Brand Customer Centricity score to create different

consumer segments which can be targeted separately. This is done using the k-means

clustering algorithm, SPSS 17.0. The clustering algorithm is initiated by creating k-different

clusters and subsequently the distance measurement between each of the sample, within a

given cluster, to their respective cluster centroid is calculated. I use Euclidean distance

measure for my study. After obtaining initial cluster centers, the procedure, (i) Assigns cases

to clusters based on distance from the cluster centers and (ii) Updates the locations of cluster

centers based on the mean values of cases in each cluster. These steps are repeated until any

reassignment of cases would make the clusters more internally variable or externally similar.

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Ph.D. Thesis 102

The initial cluster centers are the variable values, whereby the final cluster centers are

computed as the mean for each variable within each final cluster. The final cluster centers

reflect the characteristics of the typical case for each cluster

3.7. Experiment Creation

In this chapter, an experimental design is developed to validate the proposed objectives.

Increasingly, researchers are attempting to improve the effectiveness of their qualitative

approaches as well as go beyond traditional quantitative and qualitative techniques to

research consumers in their natural environment but they are not well suited to making

definitive judgments about, which appeal, if any, will work. To determine the answer to these

more demanding causal questions, experimentation is employed.

Experiments are defined as studies in which conditions are controlled so that one or more

independent variable(s) can be manipulated to test a hypothesis about a dependent variable.

For the purpose of this research study I have used CBK to observe any change in the CBE of

the respondent at any given point of time.

3.7.1. Experimental Design

The purpose of the experimental design in this case is to examine the impact of variation in

brand information or brand knowledge on consumer’s brand emotion with the introduction of

moderating variables. In this context, brand knowledge in the form of online exposure of the

consumer to the corporate or brand blog, serves as a moderating variable. An experimental

approach is used because of the associated level of explicitness in data collection and

experimental control attempts to predict events that will occur in the experimental setting by

neutralizing the effects of other factors. I attempt to maintain control over all factors that may

affect the result of an experiment, and subsequently determine or predict what may occur.

Carefully focused instruments (tests, questionnaires, etc.) that generate precise quantitative

data are the norm in our experiments. These data were analyzed using statistical tests of

significance in order to accept or reject the hypothesis. The experimental stimuli were

constructed in such a way that the product attributes information in the advertisement varied

systematically in terms of information mode and presentation form, with real brand


103 Ph.D. Thesis

information setting. The information was provided and the subjects’ responses were collected

for recall and judgment measures (e.g., attitude towards the ad, and brand).

3.7.2. Experiment Research Design Methodologies

I based the experiment design on the following three methodologies-

(i) One-Group Pretest-Posttest Design

The one group pretest-posttest may be symbolized as

O1 X O2

In this design a test group is measured twice. There is no control group. First a pretreatment

measure is taken (O1), then the group is exposed to the treatment (X). Finally, a post

treatment measure is taken (O2). The treatment effect is computed as O2 - O1, but the validity

of this conclusion has a limitation as it has the internal validity, but scores low on external

validity.

(ii) Interactive Testing Effect.

Testing effects are caused by the process of experimentation. These are the effects on the

experiment, of taking a measure on the dependent variable, before and after the presentation

of the treatment (Malhotra and Dash, 2011).

(iii) Matching

The method of matching aids controlling extraneous variables that involve matching test units

on a set of key background variables before assigning them to the treatment conditions. The

matching was used for the purpose of this study to match the units of study on basis of their

higher scores on two functions of consumer brand knowledge and consumer brand emotion.

In light of the drawback that test units can be matched on only a fewer characteristics, thus

only two functions were considered.

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Ph.D. Thesis 104

3.7.3. The Experiment Procedure

The experiment was conducted using two key strategic variables namely, consumer brand

Knowledge (CBK) and Consumer Brand Emotion (CBE). It was conducted in two phases

using each of the variables separately with a focus group of 30 respondents. The variables

have been explained in detail in the previous chapters. (As defined earlier in the chapter on

literature review).

Phase I, of the experiment was conducted using CBK as the strategic variable. The focus was

on analyzing the improvement in Consumer brand knowledge by exposure of the consumer to

a corporate blog. For the purpose of this study I have used the level of interactivity of a

corporate blog for brand communication, (Ahuja, Medury, 2008) as the benchmark. As part

of the previous study, the level of interactivity scores were calculated for 33 corporate blogs

and the result showed that the level of interactivity has a direct impact on brand

communication.

The interactivity scores for the twenty (20) blogs were lifted from the previous research paper.

A higher level of interactivity signifies a higher global reach, as well as greater popularity of

a blog (Ahuja and Medury, 2008).

The interactivity scores in the previous study were calculated using the following equation

(Ahuja and Medury, 2008):

INT=f(FEED)*w(FEED)+f(CE)*w(CE)+f(INF)+f(BBR)*w(BBR)

Two way communication and incorporation of feedback (FEED), Customer Engagement

(CE), Ability to locate information a user is looking for (INF) and Building Brand

Relationships (BBR) are significant variables. The above equation was developed by taking

into account the consumer perspective on the purpose of interactivity and the structural

features of the online site which catered to the function of interactivity.

The scores calculated in the above mentioned study became the premise for the selection of

the blogs for the experimental study, to be included in my research work.


105 Ph.D. Thesis

The objective of the study is to demonstrate that Corporate Blogs can be used by

organizations for increasing the level of Consumer Brand Knowledge. While designing the

experiment, the level of Consumer Brand Knowledge of the respondent/consumer is mapped

using a RESEARCH INSTRUMENT (as shown below). The similar kind of tool was being

developed for each of the 20 product blogs. The experiment aimed to check for the consumer

brand knowledge (CBK) levels both pre and post, of a respondent for a given product blog.

Twenty corporate blogs from the previous study having the highest interactivity score for

brand communication were lifted from the paper. The corporate blogs used for the purpose of

the experiment are mentioned as under (Table 3.9). I calculated the pre and post-consumer

brand knowledge scores, with a focus group of 30 respondents for these blogs as shown in the

chapter on findings and analysis.

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Ph.D. Thesis 106

Table 3.9: Corporate Blogs: Interactivity Score

S.NO. PRODUCT BRAND BLOG INTERACTIVITY SCORE

1 Facebook 2.13

2 Google 2.661

3 Volkswagen 2.24

4 Cadbury 3.35

5 Frito Lay 1.8006

6 HP 2.2366

7 ICICI 3.533

8 Kingfisher Airlines 2.661

9 Levis 2.443

10 Maruti 2.8848

11 M&M 1.3704

12 McDonalds 2.4546

13 Nokia 3.097

14 Philips 2.879

15 Sony 3.097

16 Yahoo 2.879

17 Xperia 3.315

18 Yamaha 2.2366

19 LG 1.8064

20 Coca-Cola 2.01896

(Source: Ahuja and Medury, 2008)


107 Ph.D. Thesis

Figure 3.2. Research Instrument to measure CBK levels (pre and post) of participants.

CADBURY

1. Does Cadbury pay its Cocoa farmers in Ghana a fair price for its product, something the farmers weren’t receiving before?

Yes No 2. Has Cadbury won the prestigious BITC - Business in the community award for its

Cadbury Cocoa Partnership (CCP)?

Yes No 3. Is it true that your favorite Cadbury's product be discarded and remade, if the precise

method of production isn't followed?

Yes No 4. Are lucky Facebook fans of Cadbury rewarded with a 1Kg! Cadbury’s bar on their

wedding?

Yes No 5. Do Consumers get a chance to get involved in wrapper design for Cadbury’s?

Yes No 6. Would Cadbury be investing a sum of 45 Million British Pounds over a period of

10yrs for its CCP?

Yes No 7. Does Cadbury facilitate meetings between British Dairy farmers and Ghana Farmers

to help transfer expertise?

Yes No 8. Have you heard of the Cadbury's cocoa partnership program in Ghana?

Yes No 9. Have you heard of the Cadbury's Facebook initiative that allows and rewards fans for

recreating Cadbury's Advertisements?

Yes No 10. Do you know how a Cadbury's production facility looks like?

Yes No

Phase II, I now proceed to define another independent variable CBE which is- (as defined earlier in the chapter on literature review).

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Ph.D. Thesis 108

Figure 3.3. Research Instrument to measure CBE levels (pre and post) of participants.

Brand Blog: ……………………………..

Use one word to describe YOUR perception of the given Brand on the following attributes:

Active

Engagement

Superb Excitable Constructive Unnoticeable

Advertising

and Jingle

Excitable Full of Life Admired Disgustful

Appealing Smart Magnetic Excitable Unnoticeable

Attitudinal

Attachment

Graceful Well off Royal Disappointing

Behavioral

Loyalty

Sincere Genuine Responsible Disappointing

Believable Recognized Genuine Responsible Disappointing

Captivating Superb Mesmerizing Full of Life Unnoticeable

Cheerful Full of Life Happy Well off Disgustful

Empathy Understanding Compassion Responsiveness Disappointing

Excitement Terrific Fascinating Encouraging Disappointing

Intense Associable Genuine Intelligent Disappointing

Likeable Purposive Smart Magnetic Disgustful

Mesmerizing Captivating Purposive Superb Unnoticeable

Sensorial

Experience

Intensely Spirited Stunning Disappointing

Spirited Splendid Encouraging Amazing Disappointing


109 Ph.D. Thesis

Various viewpoints abound about the definition and scope of interactivity. One viewpoint

suggests that interactivity is a psychological user orientation viz. a customer’s choice to

interact, thus making it a characteristic of the people involved, (Schumann, Artis, and Rivera,

2001). As per another viewpoint, it is the characteristic of the medium being used

to communicate, to stimulate interpersonal communication between involved entities. Still

another view understands interactivity as a multidimensional concept, which is a combination

of user perception and features of the medium involved.

In the context of this research study, I define Interactivity as the ability of an online tool to

provide a 2 way interaction between the organization and the customer and use a

combination of user perception and structural features to calculate the level of interactivity of

a corporate blog. Interactive measures are added to capture and hold an audience's attention,

(McAdams, 1995) and are used to stimulate public discussions and draw thousands (or

millions) of people together in a virtual community. In the context of a marketing

environment, these web based interactions can be eventually used by organizations to refine

marketing efforts, educate the customer about their brand, develop new products and to some

extent, by responding to a customer comment, add an element of customization to improve

the customer relationship. Research has indicated that a sizeable number of customers leave

the patronage of a company product or service because of the perceived indifference of the

company. These web based interactions can aid in reducing the level of perceived

indifference of a company, and at the same time reinforce a customer purchase decision, by

offsetting the feeling of cognitive dissonance , (Mc Daniel, Lamb, Hair 2006).

i) User perception of interactivity of a corporate blog-

Psychology of users varies as I refer to their perception of what purpose the feature of

interactivity serves in a corporate blog. A corporate blog serves as a touch point between

organization and consumers where a bidirectional learning process can commence between

the two entities. Success of Customer Relationship Management (CRM) endeavors of

organizations depends on their ability to establish and manage interaction with their

customers. The greater the latitude of this interaction, the greater the organizational ability to

generate and manage knowledge about its customers. A corporate blog helps increase the

dimension of this interaction by helping the customer ask questions, get responses, look for

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Ph.D. Thesis 110

information, contact customer service, contact senior organizational executives, portray his

viewpoint, and at times access other forums related to the same organization/product/service,

while at the same time help the organization capture consumer information as also actionable

data to aid customization of offerings. (Sinha, Ahuja and Medury, 2011).

3.7.4. Validity in Experimentation

When conducting an experiment, a researcher has two goals

1) Draw valid conclusions about the effects of the independent variables on the study

group.

2) Make valid generalizations to a larger population of interest.

I will discuss the above stated experiment goals in the light of internal and external validity.

3.7.4.1. Internal validity

Internal validity refers to the manipulation of independent variables, which are responsible

for causing the observed effects on the dependent variable. In the light of internal validity, it

is important to examine the external variables, other than those already defined in the

experiment. One strategic variable, which I introduce here, is-level of internet savviness of

the participants. And the experiment was conducted on a control group where all the

participants display the same level of internet savviness.

3.7.4.2. External validity

The cause and effect relationships found in the experiment can be generalized. The results

cannot be generalized beyond the experimental situation. Hence, populations, settings, (The

pool of respondents was at the same level of internet savviness), independent variables

(CBK) and dependent variable (CBE) too which the result can be projected.

3.7.5. Sampling of respondent pool for the study

The sample for the experiment group was drawn from the original sample for the entire study.

In total, 40 respondents, 10 from each of the four different age groups (20-25, 25-30, 30-35

and 35-40) were made to participate in the study for calculating the internet savviness scores.


111 Ph.D. Thesis

This was done through an evaluation grid (Table 3.10). It was seen that the older age group of

35-40 had very minimal usage hours of the internet (less than 2 hrs) and their internet

savviness score was also less than 22. Considering the results, these respondents were not

included for the final experiment study.

For this, the respondent pool was selected by the process of matching. In the experiment

conducted, the test units (members of the focus group) were of similar age groups, the

internet usage hours of the test group were more than 2 hrs a day and the internet savviness

score of the test unit was more than 22 (Table 3.11).

Continuing with our research experiment, when respondents were asked to answer the

questions, for a particular corporate blog, before and after exposure to a blog, it was possible

to measure the variation in CBK, courtesy their sensitization to the blog. Sensitization would

imply degree of association/ exposure and ability to navigate the blog (Ahuja and Medury,

2010).

In the context of the Interactive Testing effect, the prior measurement of CBK levels impacts

the consumer emotion, and subsequent response to the independent variable. Hence consumer

brand emotion becomes a dependent variable (CBE) dependent on the independent variable,

CBK. Increased exposure to the blog (increased time) helps streamline the variation in the

CBK level.

3.7.6. Correlation between Delta Consumer Brand Knowledge and Delta Consumer Brand

Emotion Scores (∆ CBK and ∆ CBE)

The final step was to see the strength of association between the two metric variables, CBK

and CBE. It was done using correlation which aided in observing the degree to which

variation in CBK is related to the variation in CBE. This was done using the Karl Pearson

correlation coefficient (SPSS 17.0).

The results of the same have been clearly indicated under the chapter on Findings and Results.

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Ph.D. Thesis 112

Table 3.10: Evaluation Grid to evaluate Interactivity Scores

Answer the following questions. 1 What is your age?

2 Number of hours spent on the Internet per day?

0-2 hrs 2-4 hrs 4-6 hrs more than 6 hrs

3 Have you been using Internet for? Less than 1 Yr More than 1 Yr More than 2 Yrs more than 5 Yrs

4 Rate the following purposes for which you use the internet based on your usage and internet priorities. First rate these on a scale of 1-10, 1=L and 10=H and then rate these parameters individually on a scale of 1-4, 1=L and 4=H Rate (I) Rate (W)

1 Shopping 2 Chatting 3 Surfing for news 4 Searching or information gathering 5 Involvement in an online community or group 6 Downloading 7 Reservation 8 Banking Transaction 9 Gaming

10 Academic and Research purposes TOTAL


113 Ph.D. Thesis

Table 3.11: Interactivity Scores

Internet Savyyness Score Sheet

Respondent Age Group Usage Hrs/day Internet Savvyness Score

R1 20-25 more than 2 hrs 26






























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Ph.D. Thesis 114

3.7.7. Sentiword net

For the purpose of this research study I have used sentiword net version1.0. In this thesis I

describe SENTIWORDNET (version 1.0), a lexical resource in which each synset of

WORDNET(version 2.0) is associated to three numerical scores Obj(s), Pos(s) and Neg(s),

describing how Objective, Positive, and Negative the terms contained in the synset are. The

assumption that underlies our switch from terms to synsets is that different senses of the same

term may have different opinion-related properties. Each of the three scores ranges from 0.0

to 1.0, and their sum is 1.0 for each synset. This means that a synset may have nonzero scores

for all the three categories, which would indicate that the corresponding terms have, in the

sense indicated by the synset, each of the three opinion-related properties only to a certain

degree1. Opinion mining (OM – also known as “sentiment classification”) is a recent sub

discipline at the crossroads of information retrieval and computational linguistics which is

concerned not with the topic a text is about, but with the opinion it expresses. Opinion-driven

content management has several important applications, such as determining critics’ opinions

about a given product by classifying online product reviews, or tracking the shifting attitudes

of the general public towards a political candidate by mining online forums or blogs.

Documents

CHAPTER 3 RESEARCH METHODOLOGY