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Brand Equity for the Indian Telecom Market

INTRODUCTION

Background

Telecommunication may well be India's best told story and an apt indicator of its current economic potential. In around a decade, this booming industry has seen more growth and coverage in newspapers than any other sector. Aiding this superlative growth s of being one of the fastest growing markets in the world, is India's rising incomes, dropping tariff's, more options with newer entrants and more competition.

Also, external conditions like friendly government policies and a stable growing economy over the past decade has resulted in the Telecom sector becoming one of the key areas in India's growth story. India's wireless subscriber base as of December 2009 stood at ~ 525 million subscribers with new mobile connections for that month at ~ 19.1 mn which is a 8.5% growth M-o-M.(Edelweiss Monthly Telecom Tracker, 2010) With the markets saturating in most of the developed markets, the wide consumer base and potential in this market has attracted a number of new entrants with players such as Uninor that was launched in only 8 circles (out of the total of 23 Telecom circles present in the Indian market) adding an impressive 1.2 mn subscribers within the first month of its launch.

Rationale for this Study

There is steadily a paradigm shift that is happening in the way mobile devices are to be used in the future and it could well become the one-touch-point with the convergence of mobile and networking. Rural consumer base is a segment all providers are eyeing with the increasing saturation in the urban markets and growing competition.

Given the nature of this highly competitive sector and the rapidly changing needs of the Indian consumer it is highly relevant today for these service providing brands to look at means at targeting subscriber's beyond just a pricing or a product strategy. Tariff war's though common to this sector, would in silos prove to be unsustainable and a short term gain strategy.

Porter (1990) suggests that branding as a key means of ‘differentiation' and one of the most important ‘positioning' strategies. The significance of branding from the strategic perspective has been widely acknowledged across marketing literature (Kapferer, 1994, Keller 1999). Aaker (1989) argues that a brand provides a sustainable competitive advantage for firms. According to Farquhar (1989), brands with high equity show greater resilience towards competitor promotions as well as create high barriers to entry.

Although the literature identifies several dimensions of brand equity from other industries, specifically Consumer Products and Goods, existing literature on service firms and specifically with respect to the Telecom market is sparse. Despite the growing importance of this sector and its growing contribution to the Indian services market, the topic of how Telecom service providers build brand equity and their focus areas appears to be under researched

Expected Contribution

By applying the widely accepted Consumer based brand equity model for gauging the components, this study aims at empirically studying the inter relationships and impact of components to the overall equity within the context of the Indian Telecom market.

1. The identification of components of brand equity from the consumer's perspective in the context of the Indian Telecom Market

2. Understanding the relationship between the dimensions of brand equity and the overall equity for the top 4 brands in the Indian Market (Source: TRAI, Nov 2009)

3. Testing the relative importance of the dimensions of brand equity towards brand building for the 4 brands considered

A comparative framework in understanding the relative changes in perception and ideological differences between the four brands under consideration

Implication of Findings

Results would provide the relative significance of dimensions contributing to the overall brand equity and hence provide a direction for managers in their brand building in terms of the weight ages to be assigned to the indicators

The measurement of the brand equity would help in evaluation of the marketing mix elements. Gaining a knowledge of the relative importance of the dimensions would provide direction to the managers in terms of deciding the promotional support

Originality/value

The principal contribution of the present research is that it provides empirical evidence of building brand equity, supporting Aaker's and Keller's conceptualization of brand equity for the Indian Telecom market. Not only has the CBBE model not been widely adopted in India, the brand building of Telecom service providers has also not been widely explored. Also, it provides a comparative framework for understanding the dimensions across the four brands

Flow

To accomplish the above stated goals, this paper offers a brief introduction to India's Telecom market, an overview of the top service providers, their offerings, strategies and technological advances in the field. This is followed by a review of relevant theoretical literature to arrive at the research gap and the research objectives. Next, it describes the methodology and rationale for measuring customer-based brand equity. Analysis, Interpretation, conclusions and managerial implications would be arrived at the end of the study.

LITERATURE REVIEW

Brand Building in the Indian Telecom Market

Strong brands provide a means of competing beyond just functionality and price. Strong brands that connect with the customers provide a better path to growth and the added value to the customers in this case is beyond just features and pricing strategies (Ehrenberg, Goodhardt, & Barwise, 1990)

Although in the current Indian Telecom market, the aggressive competition has resulted in a virtual price war, empirical analysis states that competitive strategies based on pricing provide only short term and less effective measures (Tayebeh, Farahani & Manjappa, 2008)

In this context, the identification of dimensions of brand equity and its significance in building brands becomes highly relevant.

Indian Telecom Market - Overview

- Market Potential: Enormous business potential for entrants given the low tele- density which is around 42% as per QPAC- Indian Telecom Industry report.

- Role of Foreign players: The increase in the FDI (Foreign Direct Investment) limit from 49% to 74% in 2005 has further aided in this increasing number of players in the market bettering their offering in terms of functionalities and price.(Telecom Pulse- Enam Securities, 2009)

- Competitive Landscape:

o The landscape is highly competitive with aggressive entry of new players in the GSM market. The price wars have forced even the incumbents to join in, in order to arrest the fall in their market share

o Newer players in the market such as Tata Docomo (TTSL) topped the industry in terms of subscriber adds of upto 3.3 mn in December 2009 while the new entrant Uninor garnered up to ~ 1.2 mn subscribers in the first month of launch as per Edelweiss Telecom Tracker, Dec 2009.

o Players like Telenor and Elsihat DB are also set to launch their operations in India by June 2010 (Sector Review- India Infoline, 2009)

All this indicates that the incumbents no longer can afford to rely on short term measures to hold on to market shares

Company

Subscribers(mn)

Additions(mn)- Oct 09

Additions(mn) - Sep 09

% mom

Bharti Airtel

113.2

2.70

2.51

7.4

Reliance Commn

88.2

2.10

2.01

4.6

Vodaphone

85.8

2.98

1.97

51.2

BSNL

59.4

0.68

1.45

(53.5)

Idea Cellular

48.5

1.69

1.19

42.6

Tata Teleservices

50.7

3.87

4.01

(3.5)

Aircel

27.7

2.02

1.31

53.7

MTNL

4.7

0.06

0.02

228.9

Spice

4.9

0.137

0.21

(0.3)

Subscriber base of current players and monthly growth rates (as on Nov 2009)

Source: TRAI, November 2009

Changing Market Scenario:

Attractive Rural Markets: As per government statistics, the mobile penetration in rural regions is only around 13% as opposed to 73% in urban areas (Telecom and Technology Report- Economic Intelligence Unit, 2009)

Challenges Faced: Despite the strong growth s, there are issues the market is facing in terms of

- Increase in fragmentation in the urban markets

- Competitive nd Aggressive Pricing Strategies

Technological Updates:

- Atleast 60 to 80 million mobile subscribers will be 3-G enabled by 2012 which changes the market scenario. (QPAC- Indian Telecom Industry Report, 2009)

- The growing acceptance of Value added services (VAS), 3G would allow company's to increase their ARPU (Average Revenue per User) s by shifting from voice to non-voice segments

CUSTOMER BASED BRAND EQUITY (CBBE) CONSTRUCT

Given the context, companies have realized that investing in the right band building efforts will make brand equity one of their invaluable assets. Developing, maintaining and enhancing brand equity becomes the prerogative for brand building by any company

Brand equity theory as proposed by Aaker (1991) was further developed from the consumer's perspective by Keller (1993). According to Keller (2008), “customer-based brand equity is the differential effect that the brand has on consumer response to the marketing of the brand” (p. 70).

The brand equity concept is measured broadly from two perspectives

- Financial based measure

- Consumer Based measure

Various researchers have worked on developing a good model and constructs for its measurement as this is the basis for managing brand equity. In our paper we consider brand equity from the consumer perspective in terms of the value if provides to the consumer.

CONCEPTUAL MODEL

Aaker (1996) defines brand equity as a multidimensional concept and the components he associates with it include:

- Brand Awareness

- Perceived Quality

- Brand Associations and

- Proprietary Assets

Consumer based brand equity has also been used as a measure has been previously by several researchers such as Yoo and Donthu (2002) and Washman and Plank (2002)

Every company and sector looks at building and managing its equity as a means of gaining long term competitive advantage.

In the model developed by Yoo and Donthu (2001) based on consumer based equity model, the authors have adopted the following four dimensions for the brand equity construct;

- Brand Loyalty

- Brand Awareness

- Perceived Quality

- Brand Associations

An interesting fact contended by researchers by Srinivasan, Park and Chang (2005) was that apart from product related benefits, ‘non attributes' also form strong preferences in terms of building brand equity and associations and forming points of differentiation.

This is of high significance in our research given that we are working with service brands.

For the purpose of our study, we adopt four dimensions to measure brand equity

Generic brand Equity Dimensions Adopted

1

Brand Loyalty

2

Brand Awareness

3

Perceived Quality

4

Brand Associations

The understanding of these generic dimensions in the context of the Indian Telecom market is done through the Pilot Qualitative research. This is further applied and tested onto the four brands using the Quantitative research

KNOWLEDGE GAP

The knowledge gaps identified are as follows:

- The existing literature points to the presence of various consumer based brand equity models and constructs, but there has been very few studies done in this field in terms of a particular sector but rather the focus is on development of a valid measurement model

- There are very few studies conducted in this field in the Indian context and specifically there are almost none that have been done from the perspective of identification of components for services

- There is hence paucity of literature about building service brands. Also, no previous research has examined the link dimensions of brand equity to the overall brand building for the Telecom market

- There is almost nil literature that is available that relates to branding and its impact on the Telecom market across the world. This would provide a whole scope of opportunities for future research in providing managers specific indicators and relative significance of factors that contribute to brand building. The research has further not been restricted to student samples only and is to be conducted on the actual consumers

This article focuses on the measurement and impact of the dimensions on overall rand building exercise which is of paramount importance to the managers specifically in the field of Telecom where there has been sparse research done to arrive at the sector specific factors that contribute to building a strong brand.

This research aims to address this need gap in both geography and sector (Indian Telecom market) through this study

RESEARCH PROBLEM DEFINITION

Research problem

To measure the components of brand equity and explore the impact of the different dimensions on the overall equity specifically for the top four service provider brands operating in the Indian Telecom Market. Also to find out which is the most important component of the branding that leads to success in the Indian Market by the application of Consumer based brand equity (CBBE) model.

Here, the category is a part of the design as we are specifically looking at how the components work in the case of service brands. Hence, the research findings would be applicable, if any, to other service categories than CPG or Durables.

Research Questions

§ To gauge the indicators of different components of Consumer based Brand Equity specifically in the context of the Indian Telecom Market*

§ To Investigate the causal relationship between the dimensions of brand equity and the overall equity for top four service provider brands operating in the Telecom market* in India

§ To use the Customer based Brand Equity model to test the relative importance of the dimensions of Brand Equity towards brand building for the Indian Telecom market*

§ To provide a comparative framework in understanding these dimensions from the perspective of the four brands under consideration.

* Here, the top 4 brands in the Indian Telecom Market (Airtel, Vodafone, Reliance and BSNL - as of November 2009) are considered as a part of the analysis

RESEARCH METHODOLOGY

1.1 Research Design: In order to achieve the objective as explained by the previous section, the following stages are proposed as a part of the research design.

RESEARCH DESIGN AND ANALYSIS FRAMEWORK - Storyboard

PILOT QUALITATIVE RESEARCH

The imperative behind this pilot Qualitative Analysis is to identify various parameters that are specific to the Indian Telecom market as derived on the basis of the CBBE model. These parameters are further taken as input for the Quantitative stage in the questionnaire.

Data Collection Techniques

Depth Interviews was used as the primary means of obtaining the qualitative data. Given the generic nature of the attributes to start with, the depth interviews provide flexibility in data collection and insights on pattern of usage. The purpose of these exploratory and unstructured interviews was to uncover the underlying motivation behind a person's behavior and actions.

A guideline/discussion guide (Please refer to Appendix<Refer> for a snapshot of this guideline used) was prepared for giving a direction and including the information areas to be probed in the discussion. The interview was free flowing on the basis of the responses obtained.

A total of 8 depth interviews were conducted to understand the nature of subscribers need satisfaction when it comes to communication. Also, it aims to understand the emotional and functional benefits that is derived

The discussion guide prepared broadly follows the below structure:

§ Perspective on the Indian Telecom current market scenario

§ The manner in which communication has changed over time

§ Factors influencing choice of service provider

§ Benefits sought in terms of functional and emotional attributes

§ Brand Associations and Image associated with current players

§ Association of Service Providers with instrumental and terminal values

§ Drivers and restraints in choosing provider

The tools and techniques used in order to probe included Projective Techniques such as Word Association, probing on attitudes and behavior with respect to their usage patterns and the emotional and functional benefits sought. Also, Projective and Enabling techniques such as Personification and Bubble drawing was used. The respondents were required to enter their thoughts associated with the provided brands. Data Elicitation techniques such as Sentence Completion and Clustering were used for the identification of instrumental and terminal values with the service providers apart from Brand Mapping

QUANTITATIVE RESEARCH

The various parameters that have been identified from the pilot qualitative questionnaire are to be tested to apply the CBBE model to the 4 top brands. The questionnaire is used for this purpose in order to identify the brand preferences and test the veracity of the parameters identified from the qualitative research.

For this study, for the purpose of data accuracy and constraints, the top 4 brands in the Indian telecom market (as of Nov 2009) are considered - Airtel, Vodafone, Reliance Communication and BSNL. Also, this selection allows us to compare and analyze the differences between diverse brands such as Airtel and BSNL. Also, it allows us to analyze the change in perceptions in the market towards brands such as BSNL over the years despite its strong head start in the market.

The various parameters that have been identified from the pilot qualitative questionnaire as being variables leading to brand equity interact with each other as well. The independent variables identified are the 16 variables from the factors given below:

a) Brand Knowledge

b) Brand Associations

c) Social Image

d) Brand Loyalty

e) Product Benefits

f) Brand Usage

i. These 16 variables have been expressed in form of attitudinal statements for each of the 4 brands.

ii. The respondents are required to rate them on a 5 point scale between Strongly Disagree to Strongly Agree on the basis of their usage/perceptions.

iii. Apart from this, the personal profiles of the respondent including the fundamental demographic details are collected.

iv. Also, the usage habits in terms of their brands and the services utilized are also collected for further analysis.

The questionnaire that is used is present in Appendix <> for reference. Following the data collection, the analysis is done using SPSS 15. This is further elaborated in the Data analysis section.

1.2 UNIVERSE SELECTION

Qualitative Stage:

In the first Qualitative Stage where we are looking at having Depth interviews to identify and assess the parameters specific to the Indian context, it is important to have a representation of the top 4 brands that is to be analysed. Hence the universe selection is as follows for the Qualitative Stage:

City

Gender

SEC

Age

Current Service Provider

DI's

Bangalore

M

A2

20- 35

Reliance

2

Bangalore

M

B2

26- 40

BSNL

2

Ahmedabad

F

A1

23-28

Airtel

2

Ahmedabad

M

B1

25-35

Vodafone

2

Quantitative Stage:

In the next stage of Quantitative analysis, we are looking at seeking responses and assessing the parameters identified to apply the CBBE model for the 4 brands. Hence, this should broadly meet the following criteria:

§ Born and Currently residing in India

§ Male or Female

§ Age group between 20 - 60

§ Must be a user/have used at least one of the following four brands - Airtel, Vodafone, Reliance or BSNL

As the questionnaire was to be primarily administered online, it also necessitated the presence of a internet connection and was geographically dispersed across Metros and Tier I cities pan India

SAMPLING DESIGN

The Pilot qualitative research required Depth interviews from the perspective of the 4 different brand users. A total of 8 depth interviews was conducted for this purpose across genders and SEC's. the sampling technique was stratified random with stratification on the basis of he brand

Quantitative Stage:

a. The questionnaire for the Quantitative stage was administered online. The targeted size was 130 to 150. This was arrived at considering the constraints given that each respondent was to provide responses for all the four brands thus providing rich data per response.

b. The current offerings offered by the service providers are not segment-specific. Covering the difference in attitudes depending on changes in age, gender or geographic dispersion is not within the scope of this study and is not statistically analyzed from the point of future research. Hence there is to be no age or gender restriction in the sample selection.

c. The cities chosen for sample selection include the metropolitans across the country and Tier I cities which would give a snapshot into the various geographic circles where the service is present within the constraints of administering the questionnaire online.

Out of the total of 172 respondents, the number of complete valid responses obtained was 121. The demographics of this set are as follows:

The completed responses have been filtered as per the following criteria:

a. Location Constraint

b. Usage Constraint: User/have used at least one of the top four brands - Airtel, Vodafone, Reliance and BSNL

Stage 2: DATA ANALYSIS - Pilot Qualitative Study

The broad parameters arrived at from the in- depth interviews are as follows:

Performance of the Brand

With the evolution of the Indian Telecom Industry and the emergence of multiple players with competitive offerings, the Indian subscribers are at a stage where the minimum expectation from any new entrant is the presence of a good working model with uninterrupted service quality, responsive customer service and flexible tariff options.

“…. Having a clear connection cannot be a factor in choosing….everyone provides that….”

“ I would expect the provider to have good and responsive customer service to cater to complaints and resolve issues immediately”

“ …Apart from the basic services, I would also be interested in new offerings such as music and game downloads..”

“ …. I prefer lower recharge coupons and flexibility in payment plans..”

Loyalty towards the providers

Certain subscribers usually tend to stick with the current players unless there is a shift in either their needs or environment. Changes in provider are usually done when there is a shift in location geographically or a personal need. Also, there is the segment of consumers who do not really face an issue of number portability and are willing to switch to a different provider for want of a better offer or tariff

“ ..When I went to college, I found my friends with ‘X' connection and hence got one as well…”

“…This second connection was bought when I shifted from Hyderabad to Bangalore for my job..”

“I like the friends circle plan that is offered by brand ‘Y' and it suits my usage habits…..”

Trust Worthiness

Presence of a brand for a long duration in the market or with long term usage, subscribers develops a sense of attachment towards the brand that leads to the feeling of trust.

“… I think this brand is good and trust in subscribing to their offerings….”

“ I think they are the best in the market, being the leaders in this region..”

Brand Association/Image

Most urban subscribers are conscious about the fit of the brand with their personalities. The youthfulness of the brand or the positioning also dictates their preference towards it.

“ …I would like it to be a bit classy and not for everyone….”

“.. Trendy, with offers for the students is something I would look out for in my brand..”

“ ..The corporate connections are available only with these providers..which says a lot about these brands..”

Hence narrowing down from the Pilot qualitative research, the factors that are taken into consideration for Quantitative analysis are as follows:

Questionnaire - Data Collection- Parameters for Assessment

1) Personal Profile

a. Age

b. SEC (Data regarding Education and Occupation of the Chief Wage earner is collected and then coded to extract the SEC of the respondent)

c. Place of Residence

2) Brand Awareness

a. Brand Recall

i. Un-Aided

ii. Aided

b. Identification of Brand Elements

i. Color of Brand Logo

ii. Associated celebrity

iii. Associated Tag Line

3) Brand Knowledge

a. Brand Visibility across media

4) Brand Equity (Dependent variable)

a. Rating of brand as the ‘Most Preferred Service Provider'

5) Brand Associations

a. Sincere

b. Exciting

c. Competent

d. Rugged

e. Sophisticated

6) Social Image

a. I believe the brand is good and would subscribe to its offerings

b. Trust

7) Brand Loyalty

a. I believe this brand is worth the money I pay for its offerings

b. Recommend ability of the brand

c. Switching Likelihood to competitor's brand given better offerings

8) Product Benefits

a. Affordability

b. Good Connectivity

c. Clarity of Voice

d. Responsive Customer Service

e. Broad Set of Services and features

9) Brand Usage

a. Number of Providers used thus far

b. Current Service provider

c. Name of brands used so far

d. Choose type of services availed from the service provider

.

Stage 3: DATA ANALYSIS - Brand Awareness and Usage pattern study

Respondent Profile

a. Age Dispersion: 20-60 years

b. Locations considered: Metros, Tier I Cities pan India.

Ahmedabad, Bangalore, Chennai, Mumbai, Delhi and Hyderabad

c. By Gender:

Male

62.8%

Female

37.2%

d. By SEC Classification

From fig <>, it is seen that amongst the total valid respondent s, there is favorability towards SEC A. Given that most of the survey has been online in nature and that locations considered being metros and Tier I cities of India , this is justifiable.

Further fig <> , provides the split across the 4 brands on the basis of SEC. It is clearly seen that both Airtel and Vodafone are mostly similar in terms of their positioning and having a higher incidence towards SEC A1 and A2

Brand Awareness

It is interesting to note from fig <> that apart from Airtel that enjoys almost 95% unaided recall, the other brands are comparable in terms of their recall quotient. Specifically, BSNL as a brand has a higher recall on consumer's minds over Vodafone and Reliance.

In fig <>, when aided recall is considered, the disparity amongst brands reduces to a large extent and almost all brands fall between the 93 to 95% range except for new brands such as MTS which currently have a presence only across 11 out of 23 circles.

An interesting fact to note from fig <> above is that in spite of the time elapsed, the ‘Hutch pug' still holds a strong bond with the brand and subscribers compensate for the absence of celebrity's through these brand symbols

Usage Pattern

From the fig <> it can be seen that most of the respondents on an average cluster around the possession of 2 service providers till date. Also, the average period of usage for respondents is around 24 months as is seen from fig <>

STAGE 3: DATA ANALYSIS - QUANTITATIVE

Step 1: Identifying the important components of Brand Equity - brand wise - Using Exploratory Factor Analysis (EFA)

The various parameters that have been identified from the pilot qualitative questionnaire as being variables leading to brand equity interact with each other as well. These 16 variables have been expressed in form of attitudinal statements for each of the 4 brands and the subscribers are to rate them on a 5 point scale between Strongly Disagree to Strongly Agree on the basis of their usage/perceptions.

Exploratory factor analysis (EFA) is used to get this inter relationship or pattern between these variables and to reduce the number of variables. The resulting independent variables are termed ‘factors'.

The resulting factors and their variable groupings are observed to explain the nature of the factors and the resulting factors or components would be used in further analysis.

Further, this is performed for each of the four brands. As a heuristic, factors with close loading on two or more components are rejected as they are not explained uniquely by one component. Further Eigen values are used to identify the number of factors. Principal Component Analysis is the extraction method that is used.

Step2: Extracting the Scores of the components of Brand Equity - brand wise

The score of each of the resulting components of Brand Equity such as Perceived Quality, Brand Worthiness, Brand Loyalty, Brand Knowledge and Brand Personality have been computed using the mean of the weighted average of all the variables that are included within each component of the brand equity. This is calculated for each brand. For example: The component Airtel - Brand Worthiness consists of 7 variables. The score of the 7 variables is multiplied by their respective factor loadings. The mean of the ‘weighted' score of all the 7 variables is the score of the component Airtel-Brand Worthiness for Brand Airtel. These computed score of the factors and the scores of the dependent variable for Brand Equity are used to calculate the impact of components on the overall brand equity in the next stage

Step3: Impact of Components of Brand Equity on the overall Brand Equity - brand wise - Using Multiple Regression Model

On aggregating the variables on to different components brand wise, the next stage is to analyze the impact of these various components on the overall brand equity - brand wise.

Score on overall equity - The dependent variable has the following attitudinal statement to get a brand wise score on the following statement that is measured on a 5 point scale

“Your preference/liking levels for each of the 4 brands as the ‘Most Preferred Service Provider'”

It is seen that the variables that load onto the components vary brand wise. It is important to find the impact of these components on the overall brand equity for each brand in order to understand the significance each component plays on brand preference for each of the 4 brands. Hence, the weighted scores of the components are used as dependent variables to find their impact on the overall brand equity (as measured by the above rating) using Multiple regression analysis. This is calculated for each of the four brands.

The regression model is given as follows:

Y = a + b1X1 + b2X2 + b3X3 + b4X4 + …..bnXn + e

Y = Score on the overall brand equity as given by the dependent variable for each of the brands

X1 = Score on Component1 for each of the 4 brands

X2 = Score on Component2 for each of the 4 brands

X3 = Score on Component3 for each of the 4 brands

Xn = Score on Component'n for each of the 4 brands ….

b1, b2, b3…bn: Regression Coefficient of the independent variables

a = Constant

e = Error

AIRTEL - (Refer Appendix a1.1)

Step 1: Factor Analysis

A total of 16 variables that have been derived from the pilot are included in order to be extracted into factors of brand equity for the brand Airtel.

Data Validity:

The Kaiser-Meyer-Olkin (KMO) measure of Sampling Adequacy and Barlett's test of Sphericity are used as a test of the validity of data. The KMO index (between 0 and 1) has the minimum acceptable KMO measure as 0.7.

§ The KMO measure = 0.817 is very high and indicates the high degree of common variance. This is shared by the given group of variables and it is used to assess the extent to which it measures the common factor.

§ The Barlett's test of Sphericity again with a high value of 915.68 with a zero percent significance of chi-square satisfies the data validity conditions for factor analysis

Rotation and Data Interpretation:

From the Total Variance Explained table we can see the extraction of 5 factors with eigen value > 1 and they together explain up to 71.43% of the total variance. This is a meritorious level of variance explained from the 5 factors.

Rotation is used in order to ease the interpretation of the output and make it more understandable. In this case ‘varimax' rotation technique is used. This is an orthogonal rotation technique used to maximize variance of square loadings of a factor.

An explanation of the 5 extracted factors with its factor loadings (correlation between a variable and the extracted factor) is as follows:

ð COMPONENT/FACTOR 1

There are a total of 5 variables loading on Component/Factor1. The statements corresponding to these variables are as follows:

1) I trust this brand[Airtel]

2) I am very likely to recommend the brand to a friend or relative [Airtel]

3) I believe that I get my money's worth for what I pay for the services of this brand [Airtel]

4) I have always found this brand to be good and therefore would subscribe to their new offerings[Airtel]

5) I consider this brand to be sincere (Sincere = Down to earth, Honest, Cheerful)[Airtel]

6) I consider this brand to be competent (Competent = Reliable, Intelligent, Successful)[Airtel]

7) I consider this brand to be exciting (Exciting = Daring, Imaginative, Up-to-date)[Airtel]

Analyzing the variables that are loading on to this component shows that this construct can be termed as

“Brand Worthiness”

ð COMPONENT/FACTOR 2

There are a total of 4 variables loading on Component/Factor2. The statements corresponding to these variables are as follows:

1) Broad set of features and services[Airtel]

2) Good Clarity of Voice[Airtel]

3) Good Connectivity[Airtel]

4) Highly Responsive Customer Service[Airtel]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Airtel - Percieved Quality” that includes service quality and product perception.

ð COMPONENT/FACTOR 3

There are a total of 3 variables loading on Component/Factor3. The statements corresponding to these variables are as follows:

1) I consider this brand to be Rugged (Ruggedness : Outdoorsy, Tough)[Airtel]

2) I consider this brand to be Sophisticated (Sophistication = Upper Class, Charming)[Airtel]

3) Affordable Tariff Plans[Airtel]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Airtel - Brand Personality”

ð COMPONENT/FACTOR 4

There is only one variable loading on Component/Factor4. The statement corresponding to this variable is as follows:

1) I am highly likely to switch from this brand given a better offer from a competitor brand [Airtel] {Negative}

This is a negative attitudinal statement that loads separately on to Component 4 and this construct can be termed as “Airtel - Switching Propensity”

ð COMPONENT/FACTOR 5

There is only one variable loading on Component/Factor5. The statement corresponding to this variable is as follows:

1) I quite often come across promotions for this brand across Media[Airtel]

This is a attitudinal statement that tests the knowledge of the brand and hence loads separately on to Component 5 and this construct can be termed as “Airtel - Brand Knowledge”

Factor Loadings for Brand Airtel - Summary Table 1.1

Rotated Component Matrix (a) - For Brand Airtel

Component

1

2

3

4

5

Trust

.894

Recommendation

.834

Money's Worth

.780

Good and Would Subscribe

.758

Sincere

.753

Competent

.585

Exciting- Up-to-Date

.519

Broad set of features

.864

Good Clarity of Voice

.816

Good Connectivity

.811

Responsive Customer Service

.580

Rugged - Outdoorsy

.809

Sophisticated - Charming

.580

Affordable Tariff Plans

.523

Switching Likelihood

.860

Visibility across media

.931

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Rotation converged in 6 iterations.

Component 1 - Reliability Statistics

Cronbach's Alpha

N of Items

.884

7

Component 2 - Reliability Statistics

Cronbach's Alpha

N of Items

.822

4

Component 3 - Reliability Statistics

Cronbach's Alpha

N of Items

.409

3

Airtel : COMPONENTS OF BRAND EQUITY

Sl No.

Components

Eigen Value

Percent of Variance Explained

Cumulative % of Variance Explained

1

Airtel - Brand Worthiness

5.738

27.63

27.63

2

Airtel - Perceived Quality

2.079

17.93

45.56

3

Airtel - Brand Personality

1.532

9.845

55.40

4

Airtel - Switching Propensity

1.076

8.695

64.10

5

Airtel - Brand Knowledge

1.005

7.329

71.43

Barlett's test of Sphericity:

Chi-square: 915.68*

KMO Measure of Sampling Adequacy: 0.817

* Significant at 5 percent level

Step2: Extracting the Scores of the components of Brand Equity - Airtel

The score of each of the resulting components of Brand Equity for Airtel such as

Brand Worthiness

Percieved Quality

Brand Personality

Switching Propensity and

Brand Knowledge

Is computed using the mean of the weighted average of all the variables that are included within each component of the brand equity. This is calculated for each brand. For example: The component Airtel - Brand Worthiness consists of 7 variables. The score of the 7 variables is multiplied by their respective factor loadings. The mean of the ‘weighted' score of all the 7 variables is the score of the component Airtel-Brand Worthiness for Brand Airtel. These computed score of the factors and the scores of the dependent variable for Brand Equity (from the attitudinal statement of ‘Most preferred Service provider') are used to calculate the impact of components on the overall brand equity in the next stage

Step3: Extracting the Scores of the components of Brand Equity - brand wise

The regression model is given as follows:

Y = a + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + e

Y = Overall score of Brand Equity from the dependent variable

X1 = Score on Airtel Brand Worthiness

X2 = Score on Airtel Perceived Quality

X3 = Score on Airtel Brand Personality

X4 = Score on Airtel Switching Propensity

X5 = Score on Airtel Brand Knowledge

b, b2.. b5 = Regression coefficient of independent variables

a = constant

e = error

The resulting regression coefficients and the regression model is as explained below <name tables>

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.723(a)

.523

.502

.748

a Predictors: (Constant), Brand Worthiness, Perceived Quality, Brand Personality, Switching Propensity, Brand Knowledge

Interpretation

R Square indicates the explanatory power of the model. It is the percentage of variance in the dependent variable explained by the collection of the independent variables. Here, the dependent variable of being the ‘Most Preferred Service Provider' is explained to up to 52.3% by the independent variables

ANOVA(b)

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

70.452

5

14.090

25.184

.000(a)

Residual

64.341

115

.559

Total

134.793

120

a Predictors: (Constant), Brand Worthiness, Perceived Quality, Brand Personality, Switching Propensity, Brand Knowledge

b Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the 'Most Preferred Service Provider

The term Sig. refers to the significance test. A value of p < 0.05(i.e Sig here is 0.00) is a very good measure and indicates that at least one independent variable is a significant predictor of the dependent

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

B

Std. Error

1

(Constant)

.983

.431

2.280

.024

Brand Worthiness

.793

.108

.604

7.321

.000

Perceived Quality

.065

.106

.051

.612

.542

Brand Personality

.106

.128

.059

.825

.411

Switching Propensity

-.094

.048

-.128

-1.967

.052

Brand Knowledge

.163

.075

.143

2.180

.031

a Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The Sig values are the p values of the independent variables. This provides the test of significance of each individual independent variable. If p<0.05 then we reject the hypothesis of non-significance and conclude that there exists a significant relationship between the independent and the dependent variable. Hence, in the given set above Switching Propensity, Perceived Quality and Brand Personality components are considered insignificant

The value under ‘B' represents the ‘Regression Coefficients'. It indicates that, say for Airtel Brand Worthiness, an unit increase in this component or in its perception results in an increase in Brand Equity to up to 0.793 unit. The negative value of the coefficient for Switching Propensity implies that an increase in the switching to competitor offerings results in a decrease in the equity to up to 0.094 units

Airtel: Insights/Interpretation - Key points

§ The grouping of the variables into factors indicate a clear demarcation in the minds of consumers between the product functionalities, perceptions, brand associations and brand knowledge

§ The most important factor was ‘Brand Worthiness' (Trust, Recommendation, Money's Worth, Good and Would Subscribe, Sincere, Competent, Exciting). It has an eigen value of 5.738 and percentage of variance explained as 27.63%

§ From the Regression model, it is seen that changes in the perception of the 5 components in brand equity explain the changes in overall brand equity to the extent of 52.3% as the R squared value is 0.523 which is a high value indeed. The model validity is proven by the significant ‘F' statistic

§ From the model it is seen that Brand Worthiness and Brand Knowledge explain the changes in Brand Equity to a large extent.

§ It is also seen that the Perceived Quality (Connectivity, Clarity, Responsive service and Broad set of features) are looked at as hygiene factors that MUST be present with any service provider- specifically in the case of Brand Airtel. Hence it is not a differentiating factor that leads to overall increase in the equity of the brand and is hence insignificant.

VODAFONE (Refer Appendix a1.2)

A total of 16 variables that have been derived from the pilot are included in order to be extracted into factors of brand equity for the brand Vodafone.

Data Validity:

The Kaiser-Meyer-Olkin (KMO) measure of Sampling Adequacy and Barlett's test of Sphericity are used as a test of the validity of data. The KMO index (between 0 and 1) has the minimum acceptable KMO measure as 0.7.

§ The KMO measure = 0.832 is very high and indicates the high degree of common variance. This is shared by the given group of variables and it is used to assess the extent to which it measures the common factor.

§ The Barlett's test of Sphericity again with a high value of 893.89 with a zero percent significance of chi-square satisfies the data validity conditions for factor analysis

Rotation and Data Interpretation:

From the Total Variance Explained table we can see the extraction of 3 factors with eigen value > 1 and they together explain up to 58.94% of the total variance. This is a meritorious level of variance explained from the 3 factors.

Rotation is used in order to ease the interpretation of the output and make it more understandable. In this case ‘varimax' rotation technique is used. This is an orthogonal rotation technique used to maximize variance of square loadings of a factor.

An explanation of the 3 extracted factors with its factor loadings (correlation between a variable and the extracted factor) is as follows:

ð COMPONENT/FACTOR 1

There are a total of 9 variables loading on Component/Factor1. The statements corresponding to these variables are as follows:

1) I consider this brand to be competent (Competent = Reliable, Intelligent, Successful) [Vodafone]

2) I am very likely to recommend the brand to a friend or relative [Vodafone]

3) I believe that I get my money's worth for what I pay for the services of this brand [Vodafone]

4) I trust this brand[Vodafone]

5) I consider this brand to be sincere (Sincere = Down to earth, Honest, Cheerful)[Vodafone]

6) I have always found this brand to be good and therefore would subscribe to their new offerings[Vodafone]

7) I consider this brand to be Rugged (Ruggedness : Outdoorsy, Tough)[Vodafone]

8) I consider this brand to be Sophisticated (Sophistication = Upper Class, Charming) [Vodafone]

9) I consider this brand to be Exciting [Vodafone]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Vodafone - Brand Worthiness” for brand Vodafone

ð COMPONENT/FACTOR 2

There are a total of 6 variables loading on Component/Factor2. The statements corresponding to these variables are as follows:

1) Broad set of features and services[Vodafone]

2) Good Clarity of Voice[Vodafone]

3) Good Connectivity[Vodafone]

4) Highly Responsive Customer Service[Vodafone]

5) Affordable Tariff Plans[Vodafone]

6) I am highly likely to switch from this brand given a better offer from a competitor brand {Negative}

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Vodafone - Percieved Quality” that includes service quality and product perception.

ð COMPONENT/FACTOR 3

There is only one variable loading on Component/Factor3. The statement corresponding to this variable is as follows:

1) I quite often come across promotions for this brand across Media[Vodafone]

This is a attitudinal statement that tests the knowledge of the brand and hence loads separately on to Component 3 and this construct can be termed as “Vodafone -Brand Knowledge”

Factor Loadings for Brand Vodafone - Summary Table 1.2

Rotated Component Matrix (a) for Brand Vodafone

Component

1

2

3

Competent

.879

Recommend

.840

Money's Worth

.839

Trust

.810

Sincere

.760

Good and would subscribe

.740

Rugged

.581

Sophistication = Charming

.560

Exciting = Up-to-date

.469

Clarity of voice

.814

Responsive Customer Service

.774

Good Connectivity

.768

Affordable Tariff Plans

.715

Broad set of features

.679

Switching Likelihood

.454

Media Visibility

.818

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 4 iterations.

Component 1 - Reliability Statistics

Cronbach's Alpha

N of Items

.892

9

Component 2 - Reliability Statistics

Cronbach's Alpha

N of Items

.787

6

Vodafone: COMPONENTS OF BRAND EQUITY

Sl No.

Components

Eigen Value

Percent of Variance Explained

Cumulative % of Variance Explained

1

Vodafone- Brand Worthiness

5.308

33.175

33.175

2

Vodafone - Perceived Quality

2.878

17.99

51.165

5

Vodafone - Brand Knowledge

1.245

7.781

58.946

Barlett's test of Sphericity:

Chi-square: 893.89*

KMO Measure of Sampling Adequacy: 0.832

* Significant at 5 percent level

Step2: Extracting the Scores of the components of Brand Equity - Vodafone

The score of each of the resulting components of Brand Equity for Vodafone such as

Brand Worthiness

Perceived Quality

Brand Knowledge

Is computed using the mean of the weighted average of all the variables that are included within each component of the brand equity. This is calculated for each brand. For example: The component Vodafone - Brand Worthiness consists of 9 variables. The score of the 9 variables is multiplied by their respective factor loadings. The mean of the ‘weighted' score of all the 9 variables is the score of the component Vodafone-Brand Worthiness for Brand Vodafone. These computed score of the factors and the scores of the dependent variable for Brand Equity (from the attitudinal statement of ‘Most preferred Service provider') are used to calculate the impact of components on the overall brand equity in the next stage

Step3: Extracting the Scores of the components of Brand Equity - brand wise

The regression model is given as follows:

Y = a + b1X1 + b2X2 + b3X3 + e

Y = Overall score of Brand Equity from the dependent variable

X1 = Score on Vodafone Brand Worthiness

X2 = Score on Vodafone Perceived Quality

X3 = Score on Vodafone Brand Knowledge

b, b2, b3 = Regression coefficient of independent variables

a = constant

e = error

The resulting regression coefficients and the regression model is as explained below <name tables>

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.436(a)

.190

.169

.974

a Predictors: (Constant), Brand Worthiness, Perceived Quality, Brand Knowledge

Interpretation

R Square indicates the explanatory power of the model. It is the percentage of variance in the dependent variable explained by the collection of the independent variables. Here, the dependent variable of being the ‘Most Preferred Service Provider' is explained to up to 19 % by the independent variables

ANOVA(b)

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

25.991

3

8.664

9.133

.000(a)

Residual

110.984

117

.949

Total

136.975

120

a Predictors: (Constant), Brand Worthiness, Perceived Quality, Brand Knowledge

b Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The term Sig. refers to the significance test. A value of p < 0.05(here the Sig = 0.00 which is a very high value of significance) indicates that at least one independent variable is a significant predictor of the dependent

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

B

Std. Error

1

(Constant)

1.614

.483

3.342

.001

Brand Worthiness

.529

.159

.402

3.319

.001

Perceived Quality

-.060

.144

-.050

-.414

.679

Brand Knowledge

.246

.109

.191

2.259

.026

a Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The Sig values are the p values of the independent variables. This provides the test of significance of each individual independent variable. If p<0.05 then we reject the hypothesis of non-significance and conclude that there exists a significant relationship between the independent and the dependent variable. Hence, in the given set above Perceived Quality is the component that is insignificant

The value under ‘B' represents the ‘Regression Coefficients'. It indicates that, say for Vodafone Brand Worthiness, a unit increase in this component or in its perception results in an increase in Brand Equity to up to 0.529 units. The negative value of the coefficient for Perceived Quality arises from the presence of Switching Intention loading onto Component 2. Also, this component does not have a significant inter relationship with the brand equity of the provider (p> 0.05)

Vodafone: Insights/Interpretation - Key points

- The grouping of variables is into three distinct components - Brand Worthiness, Perception and Knowledge.

- The most important factor was ‘Brand Worthiness' (Competent, Recommendation, Money's Worth, Trust, Good and Would Subscribe, Rugged, Sophistication and Exciting). It has an eigen value of 5.308 and percentage of variance explained as 33.175%

- From the Regression model, it is seen that changes in the perception of the 3 components in brand equity explain the changes in overall brand equity to the extent of 19 % as the R squared value is 0.19. The model validity is proven by the significant ‘F' statistic

- From the model it is seen that Brand Worthiness and Brand Knowledge explain the changes in Brand Equity to a large extent.

- It is also seen that in Perceived Quality - Switching intention loads with functional attributes such as connectivity, clarity, Responsive service etc which again implies that these are looked at as hygiene factors that MUST be present with any service provider. Also, the presence of switching intention in this component results in the negative coefficient. Overall this component does not have a significant relationship in determining the ‘most preferred service provider'

- Also to be noted is that the variable ‘Exciting' also loads onto the Brand Worthiness component and to a certain extent on Brand Knowledge component and hence it implies that for Brand Vodafone, ‘Exciting' is one of an important features that leads to Brand Equity.

RELIANCE COMMUNICATION (Refer Appendix a1.3)

A total of 16 variables that have been derived from the pilot are included in order to be extracted into factors of brand equity for the brand Reliance.

Data Validity:

The Kaiser-Meyer-Olkin (KMO) measure of Sampling Adequacy and Barlett's test of Sphericity are used as a test of the validity of data. The KMO index (between 0 and 1) has the minimum acceptable KMO measure as 0.7.

§ The KMO measure = 0.827 is very high and indicates the high degree of common variance. This is shared by the given group of variables and it is used to assess the extent to which it measures the common factor.

§ The Barlett's test of Sphericity again with a high value of 826.19 with a zero percent significance of chi-square satisfies the data validity conditions for factor analysis

Rotation and Data Interpretation:

From the Total Variance Explained table we can see the extraction of 4 factors with eigen value > 1 and they together explain up to 64.34% of the total variance. This is a meritorious level of variance explained from the 4 factors.

Rotation is used in order to ease the interpretation of the output and make it more understandable. In this case ‘varimax' rotation technique is used. This is an orthogonal rotation technique used to maximize variance of square loadings of a factor.

An explanation of the 4 extracted factors with its factor loadings (correlation between a variable and the extracted factor) is as follows:

ð COMPONENT/FACTOR 1

There are a total of 7 variables loading on Component/Factor1. The statements corresponding to these variables are as follows:

1) I consider this brand to be competent (Competent = Reliable, Intelligent, Successful) [Reliance]

2) I consider this brand to be exciting (Exciting = Daring, Imaginative, Up-to-date)[Reliance]

3) I consider this brand to be sophisticated (Sophistication = Upper Class, Charming) [Reliance]

4) I trust this brand[Reliance]

5) I have always found this brand to be good and therefore would subscribe to their new offerings[Reliance]

6) I consider this brand to be sincere (Sincere = Down to earth, Honest, Cheerful)[ Reliance]

7) I consider this brand to be rugged (Ruggedness : Outdoorsy, Tough)[ Reliance]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Reliance - Brand Worthiness” for brand Reliance Communication.

ð COMPONENT/FACTOR 2

There are a total of 6 variables loading on Component/Factor2. The statements corresponding to these variables are as follows:

1) Broad set of features and services[Reliance]

2) Good Clarity of Voice[Reliance]

3) Good Connectivity [Reliance]

4) Highly Responsive Customer Service [Reliance]

5) Affordable Tariff Plans [Reliance]

6) I am highly likely to switch from this brand given a better offer from a competitor brand {Negative}

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Reliance - Perceived Quality” that includes service quality and product perception.

ð COMPONENT/FACTOR 3

There are a total of 2 variables loading on Component/Factor2. The statements corresponding to these variables are as follows:

1) I believe that I get my money's worth for what I pay for the services of this brand [Reliance]

2) I am very likely to recommend the brand to a friend or relative [Reliance]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “Reliance - Brand Value” that includes Price premium and satisfaction

ð COMPONENT/FACTOR 4

There is only one variable loading on Component/Factor4. The statement corresponding to this variable is as follows:

1) I quite often come across promotions for this brand across Media[Reliance]

This is a attitudinal statement that tests the knowledge of the brand and hence loads separately on to Component 3 and this construct can be termed as “Reliance -Brand Knowledge”

Factor Loadings for Brand Reliance - Summary Table 1.2

Rotated Component Matrix (a)

Component

1

2

3

4

Competent

.807

Exciting = Up-to-date

.791

Sophistication = Charming

.742

Trust

.649

Good and therefore would subscribe

.638

Sincere

.581

Ruggedness : Outdoorsy

.524

Good Clarity of Voice

.866

Responsive Customer Service

.780

Broad set of features

.764

Good Connectivity

.728

Switching Likelihood

.617

Affordable Tariff Plans

.604

Recommendation

.774

Money's Worth

.701

Visibility across Media

.

.935

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 6 iterations.

Brand Worthiness - Reliability Statistics

Cronbach's Alpha

N of Items

.836

7

Perceived Quality - Reliability Statistics

Cronbach's Alpha

N of Items

.828

6

Brand Value - Reliability Statistics

Cronbach's Alpha

N of Items

.855

2

Reliance : COMPONENTS OF BRAND EQUITY

Sl No.

Components

Eigen Value

Percent of Variance Explained

Cumulative % of Variance Explained

1

Reliance- Brand Worthiness

5.059

33.621

31.621

2

Reliance - Perceived Quality

3.075

19.221

50.842

3

Reliance - Brand Value

1.140

7.126

57.968

4

Reliance - Brand Knowledge

1.021

6.381

64.348

Barlett's test of Sphericity:

Chi-square: 826.19*

KMO Measure of Sampling Adequacy: 0.827

* Significant at 5 percent level

Step2: Extracting the Scores of the components of Brand Equity - Reliance

The score of each of the resulting components of Brand Equity for Reliance such as

Brand Worthiness

Perceived Quality

Brand Value

Brand Knowledge

Is computed using the mean of the weighted average of all the variables that are included within each component of the brand equity. This is calculated for each brand. For example: The component Reliance - Brand Worthiness consists of 7 variables. The score of the 7 variables is multiplied by their respective factor loadings. The mean of the ‘weighted' score of all the 7 variables is the score of the component Reliance -Brand Worthiness for Brand Reliance. These computed score of the factors and the scores of the dependent variable for Brand Equity (from the attitudinal statement of ‘Most preferred Service provider') are used to calculate the impact of components on the overall brand equity in the next stage

Step3: Extracting the Scores of the components of Brand Equity - brand wise

The regression model is given as follows:

Y = a + b1X1 + b2X2 + b3X3 + b4X4 + e

Y = Overall score of Brand Equity from the dependent variable

X1 = Score on Reliance Brand Worthiness

X2 = Score on Reliance Perceived Quality

X3 = Score on Reliance Brand Value

X4 = Score on Reliance Brand Knowledge

b, b2, b3, b4 = Regression coefficient of independent variables

a = constant

e = error

The resulting regression coefficients and the regression model is as explained below <name tables>

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.515(a)

.265

.239

.867

a Predictors: (Constant), Brand Worthiness, Perceived Quality, Brand Value, Brand Knowledge

Interpretation

R Square indicates the explanatory power of the model. It is the percentage of variance in the dependent variable explained by the collection of the independent variables. Here, the dependent variable of being the ‘Most Preferred Service Provider' is explained to up to 26.5 % by the independent variables

ANOVA(b)

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

31.396

4

7.849

10.445

.000(a)

Residual

87.166

116

.751

Total

118.562

120

a Predictors: (Constant), Brand Worthiness, Perceived Quality, Brand Value, Brand Knowledge

b Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The term Sig. refers to the significance test. A value of p < 0.05(here Sig = 0.00 which is a very high level of significance) indicates that at least one independent variable is a significant predictor of the dependent

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

B

Std. Error

1

(Constant)

1.155

.359

3.218

.002

Brand Worthiness

.496

.149

.343

3.335

.001

Perceived Quality

-.036

.107

-.033

-.335

.738

Brand Value

.201

.107

.212

1.873

.064

Brand Knowledge

.099

.075

.108

1.316

.191

a Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The Sig values are the p values of the independent variables. This provides the test of significance of each individual independent variable. If p<0.05 then we reject the hypothesis of non-significance and conclude that there exists a significant relationship between the independent and the dependent variable. Hence, in the given set above the Brand Worthiness Component is Accepted

The value under ‘B' represents the ‘Regression Coefficients'. It indicates that, say for Reliance Brand Worthiness, a unit increase in this component or in its perception results in an increase in Brand Equity to up to 0.747 units. The negative value of the coefficient for Perceived Quality arises from the presence of Switching Intention loading onto Component 2. Also with p> 0.05 this component has a insignificant relationship

Reliance: Insights/Interpretation - Key points

§ The grouping of variables is into four distinct components - Brand Worthiness, Perception, Loyalty and Knowledge.

§ The most important factor was ‘Brand Worthiness' (Competent, Exciting, Sophistication, , Trust, Good and Would Subscribe, Sincere, Rugged). It has an eigen value of 5.059 and percentage of variance explained as 33.621%

§ From the Regression model, it is seen that changes in the perception of the 4 components in brand equity explain the changes in overall brand equity to the extent of 49.6 % as the R squared value is 0.496. The model validity is proven by the significant ‘F' statistic

§ It is also seen that in Perceived Quality - Switching intention loads with functional attributes such as connectivity, clarity, Responsive service etc which again implies that these are looked at as hygiene factors that MUST be present with any service provider as its presence is not considered to be significant enough to have a significant relationship on Brand Equity

§ In the case of Brand Reliance, the two interesting point to note are that Brand Value and Brand Knowledge are both statistically insignificant that is p > 0.05. This implies that there is no strong inter relationship between these two components and the attitudinal statement of Reliance being the ‘Most Preferred Service Provider'.

BSNL (Refer Appendix a1.4)

A total of 16 variables that have been derived from the pilot are included in order to be extracted into factors of brand equity for the brand BSNL.

Data Validity:

The Kaiser-Meyer-Olkin (KMO) measure of Sampling Adequacy and Barlett's test of Sphericity are used as a test of the validity of data. The KMO index (between 0 and 1) has the minimum acceptable KMO measure as 0.7.

§ The KMO measure = 0.803 is very high and indicates the high degree of common variance. This is shared by the given group of variables and it is used to assess the extent to which it measures the common factor.

§ The Barlett's test of Sphericity again with a high value of 686.24 with a zero percent significance of chi-square satisfies the data validity conditions for factor analysis

Rotation and Data Interpretation:

From the Total Variance Explained table we can see the extraction of 4 factors with eigen value > 1 and they together explain up to 60.82% of the total variance. This is a meritorious level of variance explained from the 4 factors.

Rotation is used in order to ease the interpretation of the output and make it more understandable. In this case ‘varimax' rotation technique is used. This is an orthogonal rotation technique used to maximize variance of square loadings of a factor.

An explanation of the 4 extracted factors with its factor loadings (correlation between a variable and the extracted factor) is as follows:

ð COMPONENT/FACTOR 1

There are a total of 5 variables loading on Component/Factor2. The statements corresponding to these variables are as follows:

1) Broad set of features and services[BSNL]

2) Good Clarity of Voice[BSNL]

3) Good Connectivity [BSNL]

4) Highly Responsive Customer Service [BSNL]

5) Affordable Tariff Plans [BSNL]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “BSNL - Perceived Quality that includes service quality and product perception.

ð COMPONENT/FACTOR 2

There are a total of 5 variables loading on Component/Factor2. The statements corresponding to these variables are as follows:

1) I believe that I get my money's worth for what I pay for the services of this brand [BSNL]

2) I am very likely to recommend the brand to a friend or relative [BSNL]

3) I consider this brand to be sincere (Sincere = Down to earth, Honest, Cheerful)[ BSNL]

4) I am highly likely to switch from this brand given a better offer from a competitor brand {Negative}

5) I quite often come across promotions for this brand across Media- Visibility [BSNL]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “BSNL - Brand Worthiness” that includes Price premium and satisfaction

ð COMPONENT/FACTOR 3

There are a total of 3 variables loading on Component/Factor3. The statements corresponding to these variables are as follows:

1) I have always found this brand to be good and therefore would subscribe to their new offerings[BSNL]

2) I trust this brand[BSNL]

3) I consider this brand to be rugged (Ruggedness : Outdoorsy, Tough)[ BSNL]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “BSNL - Brand Image”

ð COMPONENT/FACTOR 4

1) I consider this brand to be competent (Competent = Reliable, Intelligent, Successful) [Reliance]

2) I consider this brand to be exciting (Exciting = Daring, Imaginative, Up-to-date)[Reliance]

Analyzing the variables that are loading on to this component shows that this construct can be termed as “BSNL - Brand Associations” for brand BSNL.

Factor Loadings for Brand BSNL - Summary Table 1.4

BSNL - Rotated Component Matrix(a)

Component

1

2

3

4

Good Clarity of Voice

.824

Good Connectivity

.805

Broad set of features

.779

Responsive Customer Service

.703

Affordable Tariff Plans

.602

Money's Worth

.756

Sincere

.718

Recommendation

.634

Switching Likelihood

.551

Media Visibility

.502

Good and Would Subscribe

.821

I trust this brand

.683

Ruggedness : Outdoorsy

.667

Exciting = Up-to-date

.862

Competent

.635

Sophistication = Charming *

.164

.029

.563

.582

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 5 iterations.

Component 1 - Reliability Statistics

Cronbach's Alpha

N of Items

.818

5

Component 2 - Reliability Statistics

Cronbach's Alpha

N of Items

.690

5

Component 3 - Reliability Statistics

Cronbach's Alpha

N of Items

.702

3

Component 4 - Reliability Statistics

Cronbach's Alpha

N of Items

.667

2

BSNL : COMPONENTS OF BRAND EQUITY

Sl No.

Components

Eigen Value

Percent of Variance Explained

Cumulative % of Variance Explained

1

BSNL - Perceived Quality

4.599

28.743

28.743

2

BSNL - Brand Worthiness

2.663

16.643

45.386

3

BSNL - Brand Image

1.391

8.696

54.082

4

BSNL - Brand Associations

1.079

6.745

60.827

Barlett's test of Sphericity:

Chi-square: 686.245*

KMO Measure of Sampling Adequacy: 0.803

* Significant at 5 percent level

Step2: Extracting the Scores of the components of Brand Equity - BSNL

The score of each of the resulting components of Brand Equity for BSNL such as

Perceived Quality

Brand Worthiness

Brand Image

Brand Associations

Is computed using the mean of the weighted average of all the variables that are included within each component of the brand equity. This is calculated for each brand. For example: The component BSNL - Perceived Quality consists of 5 variables. The score of the 5 variables is multiplied by their respective factor loadings. The mean of the ‘weighted' score of all the 5 variables is the score of the component BSNL - Perceived Quality for Brand BSNL. These computed score of the factors and the scores of the dependent variable for Brand Equity (from the attitudinal statement of ‘Most preferred Service provider') are used to calculate the impact of components on the overall brand equity in the next stage

Step3: Extracting the Scores of the components of Brand Equity - brand wise

The regression model is given as follows:

Y = a + b1X1 + b2X2 + b3X3 + b4X4 + e

Y = Overall score of Brand Equity from the dependent variable

X1 = Score on BSNL Perceived Quality

X2 = Score on BSNL Brand Worthiness

X3 = Score on BSNL Brand Image

X4 = Score on BSNL Brand Associations

b, b2, b3, b4 = Regression coefficient of independent variables

a = constant

e = error

The resulting regression coefficients and the regression model is as explained below <name tables>

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.437(a)

.191

.163

.965

a Predictors: (Constant), Perceived Quality, Brand Worthiness, Brand Image, Brand Associations

Interpretation

R Square indicates the explanatory power of the model. It is the percentage of variance in the dependent variable explained by the collection of the independent variables. Here, the dependent variable of being the ‘Most Preferred Service Provider' is explained to up to 19.1 % by the independent variables

ANOVA(b)

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

25.542

4

6.385

6.863

.000(a)

Residual

107.929

116

.930

Total

133.471

120

a Predictors: (Constant), Perceived Quality, Brand Worthiness, Brand Image, Brand Associations

b Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The term Sig. refers to the significance test. A value of p < 0.05 (here Sig = 0.000 which is a very high value of significance) indicates that at least one independent variable is a significant predictor of the dependent

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

B

Std. Error

1

(Constant)

1.548

.314

4.938

.000

Perceived Quality

-.029

.108

-.025

-.266

.791

Brand Worthiness

.518

.171

.323

3.036

.003

Brand Image

.076

.119

.063

.637

.525

Brand Associations

.197

.117

.157

1.685

.095

a Dependent Variable: Rate the following in terms of your preference/liking levels for the following brands as the \'Most Preferred Service Provider\

The Sig values are the p values of the independent variables. This provides the test of significance of each individual independent variable. If p<0.05 then we reject the hypothesis of non-significance and conclude that there exists a significant relationship between the independent and the dependent variable. Hence, in the given set above Brand Worthiness is accepted

The value under ‘B' represents the ‘Regression Coefficients'. It indicates that, say for BSNL Brand Worthiness, a unit increase in this component or in its perception results in an increase in Brand Equity to up to 0.518 units.

Reliance: Insights/Interpretation - Key points

§ The grouping of variables is into four distinct components - Perceived Quality, Brand Worthiness, Brand Image and Brand Associations.

§ The factor ‘Brand Worthiness' (Money's Worth, Sincere, Recommendation, Switching Likelihood and Media Visibility) has an eigen value of 2.663 and percentage of variance explained as 16.643%.

§ It is interesting to note that ONLY in BSNL does Brand Visibility load on to Brand Worthiness and not as a separate component as was in all other service providers. This could imply that the worth of the brand is intrinsically derived from promotions and media visibility does not form an additional factor aiding brand equity as it is in the case of the other service providers.

§ From the Regression model, it is seen that changes in the perception of the 4 components in brand equity explain the changes in overall brand equity to the extent of 19.1 % as the R squared value is 0.191. The model validity is proven by the significant ‘F' statistic

§ Also, here the last two components - Brand Image and Brand Association though loading separately do not in the final regression equation as they are statistically insignificant with p > 0.05. This implies that the Brand Associations and Image it currently possesses are not strong enough to create a direct inter relationship to the dependent statement of BSNL being the ‘ Most Preferred Service Provider'

§ Also to note is that Perceived Quality which includes the product functionalities is assumed as a hygiene factor. The functional attributes loading on this such as Clarity of voice, Connectivity, Broad set of features and Affordable Tariff Plans are the variables that cause a dip in BSNL being the most preferred Service Provider. But this cannot be extrapolated further as the p > 0.05 indicating that this relationship is not significant.

Note:

§ The attribute ‘Sophistication' has close loadings on both Component3 and Component 4 and is hence rejected (Refer * in the Summary table 1.4)

§ This also implies that in case of Brand BSNL, there is no clarity in terms of whether the Brand is sophisticated or not and also the source of such a variable.

INTERPRETATION OF FINDINGS

The stage- wise identification and application of the components of Brand equity for the 4 brands provides us with in- depth information of the following -

AIRTEL

Brand Worthiness

Perceived Quality

Brand Personality

Switching propensity

Brand Knowledge

% of Variance Explained

27.63

17.93

9.845

8.695

7.329

Regression Coeff (Sig/Not Sig)

0.793 (Sig)

0.065 (Not Sig)

0.105 (Not Sig)

-0.094(Not Sig)

0.163 (Sig)

Trust

Broad set of Features

Rugged

Switching Likelihood

Visibility across media

Recommendation

Clarity of Voice

Sophisticated

Money's Worth

Connectivity

Affordable

Good & would Subscribe

Responsive Customer Service

Sincere

Competent

Exciting

VODAFONE

Brand Worthiness

Perceived Quality

Brand Knowledge

% of Variance Explained

33.175

17.99

7.789

Regression Coeff (Sig/Not Sig)

0.529 (Sig)

-0.06 (Not Sig)

0.246 (Not Sig)

Trust

Broad set of Features

Media Visibility

Recommendation

Clarity of Voice

Money's Worth

Connectivity

Good & would Subscribe

Responsive Customer Service

Sincere

Broad set of Features

Competent

Affordable

Exciting

Switching Likelihood

Rugged

Sophisticated

RELIANCE

Brand Worthiness

Perceived Quality

Brand Value

Brand Knowledge

% of Variance Explained

33.621

19.221

7.126

6.381

Regression Coeff (Sig/Not Sig)

0.496 (Sig)

-0.036 (Not Sig)

0.201 (Not Sig)

0.099(Not Sig)

Trust

Broad set of Features

Recommendation

Media Visiblity

Good & would Subscribe

Clarity of Voice

Money's Worth

Sincere

Connectivity

Competent

Responsive Customer Service

Exciting

Broad set of Features

Rugged

Affordable

Sophisticated

Switching Likelihood

BSNL

Perceived Quality

Brand Worthiness

Brand Image

Brand Associations

% of Variance Explained

28.743

16.643

8.696

6.745

Regression Coeff (Sig/Not Sig)

-0.029 (Not Sig)

0.518 (Sig)

0.076 (Not Sig)

0.197 (Not Sig)

Clarity of Voice

Recommendation

Trust

Competent

Connectivity

Money's Worth

Good & would Subscribe

Exciting

Responsive Customer Service

Sincere

Rugged

Broad set of Features

Switching Likelihood

Affordable

Media Visibility

Insights/Interpretations

1) Grouping of Factors:

The grouping of the variables into factors indicate a clear demarcation in the minds of consumers between the product functionalities, perceptions, brand associations and brand knowledge

2) Most Important Component:

The most important factor was ‘Brand Worthiness'. It has a high Eigen value and high percentage of variance between 25 to 35% for all brands.

Also, it has the highest degree of significant relationship towards brand equity. The change in this component increases equity as measured in terms of ‘the most preferred subscriber' between 0.45 to 0.80 units. This is hence, quite a significant component to be considered by all telecom service providers.

The common factor loadings on to this component across the 4 brands include variables such as -

§ Trust

§ Worth the Money I pay

§ I believe the product is good and hence would subscribe to its new offerings

§ Sincere

§ Competent

This implies that as a service brand, the Telecom provider's in the current scenario stand to gain as the ‘Most Preferred Service Provider' by focusing on not just the product functionalities and features, but also fulfill the emotional need - in terms of earning the customer's trust about the product

Product Features as a Hygiene factor: <to Do>

It is also seen that the Perceived Quality (Connectivity, Clarity, Responsive service and Broad set of features) are looked at as hygiene factors that MUST be present with any service provider. Hence it is not a differentiating factor that leads to overall increase in the equity of the brand and is hence insignificant.

Product - hygiene

Worthiness' (Common variables - Trust, Money's Worth, Good and Would Subscribe, Sincere)

 From the Regression model, it is seen that changes in the perception of the 5 components in brand equity explain the changes in overall brand equity to the extent of 52.3% as the R squared value is 0.523 which is a high value indeed. The model validity is proven by the significant ‘F' statistic

 From the model it is seen that Brand Worthiness and Brand Knowledge explain the changes in Brand Equity to a large extent.

CONCLUSION

To add - Future outlook

Marketing Implications

Limitations of Study

Future Research

Bibliography

Aaker, D.A (1991), “Managing Brand equity”, The Free Press, Newyork

Aaker, D. A. (1996), “Measuring brand equity across products and markets” California Management Review

Cynus (2009, October), QPAC -Indian Telecom Industry

Ehremberg, Andrews S, Goodhart, Gerald J & Barwise (1990), “Double Jeopardy Revisited”, Journal of Marketing, Vol. 54, Issue 3

Farquhar, P.H (1989), “Managing brand equity”, Marketing Research, Vol. 1, Issue 3

Jhaveri, D & Patni R (2010, January) Edelweiss Monthly Telecom Tracker, p1-6

Keller, K. L. (1993), “Conceptualizing, Measuring, and Managing customer-based brand equity” Journal of Marketing

Keller Kevin (2008), Strategic Brand Management: Building Measuring and Managing Brand Equity, Prentice-Hall

Manjappa, D.H and Farahari, T, (2008), "Telecom Market Structure, Regulation and Pricing in India: An Empirical Study", The ICFAI university press, pg 8

Park, C.J and Srinivasan,V (2005), “ An Approach to the Measurement, Analysis, and Prediction of Brand Equity and Its Sources”, Management Science

Rajasekar, N and Nalina, K.G, (2008), “Measuring Customer-Based Brand Equity in Durable Goods Industry”, Journal of Marketing and Communication

Pappu, R., Quester, P and Cooksey, R.W (2005), “Consumer-based brand equity: improving the measurement - empirical evidence”, Journal of Product and Brand Management

Rohira,P & Desai, S, (2009, November), Enam Securities Telecom Pulse

Telecoms and Technology: In Focus (2005, June), Industry Forecast, The Economist Intelligence Unit

Washburn, J.H & Plank, R.E (2002), “Measuring brand equity- an evaluation of a consumer - based equity scale”, Journal of Marketing Theory and Practice

Tong, X & Hawley, J(2009), “Measuring customer-based brand equity: empirical evidence from the sportswear market in China”, Journal of Product and Brand Management

Yoo, Boonghee, & Donthu, N. (2001), “Developing and validating a multidimensional consumer-based brand equity scale”, Journal of Business Research

VITA

Notes

Test and Apply CBBE

Factors are available at a generic level

Smaller variables empirical and Measurable

Factor analyzed - > Why?

(Variables will load on some factor/component)

|| - > Variable which are loading is empirical indicator of the factors

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