predicting consumer purchase intention: a discriminant ... · pdf filepredicting consumer...

role of need for social approval, self-monitoring, and need for social power. Leadership Quarterly, 18, 134-

• Stewart, G. L. (2006). A meta-analytic review of relationships between team design features and team

performance. Journal of Management, 32, 29-55.

• van Engen, M. L., & Willemsen, T. M. (2004). Sex and leadership styles: A meta-analysis of research published

in the 1990s. Psychological Reports, 94, 3-18.

• Walumbwa, F. O., Wang, P., Lawler, J. J., & Shi, K. (2004). The role of collective efficacy in the relations between

transformational leadership and work outcomes. ,

515-530.

• Zhu, W., Chew, I. K. H., & Spangler, W. D. (2005). CEO transformational leadership and organizational

outcomes: The mediating role of human-capital-enhancing human resource management. Leadership

Quarterly, 16, 39-52.

Sosik, J. J., & Dinger, S. L. (2007). Relationships between leadership style and vision content: The moderating

Journal of Occupational and Organizational Psychology 77

Venkat R. Krishnan has a Ph. D. in Business Administration from Temple University, Philadelphia. He is

Professor (Organizational Behavior) at Great Lakes Institute of Management since 2006. Before joining Great

Lakes, he had taught at XLRI Jamshedpur and Temple University, Philadelphia, and was a manager in State

Bank of India. He can be reached at [email protected]

Shikha Pahwa Verma has a post-graduate diploma in personnel management and industrial relations from

XLRI Jamshedpur. She is Partner of 'Produce to Perfection,' a Delhi-based firm that provides products and

solutions related to home furnishings to leading retailers in India.

Transformational Leadership and Follower'sOrganizational Commitment: Role of Leader's Gender

ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014

Predicting Consumer Purchase Intention:A Discriminant Analysis Approach

Sougata Banerjee

Sarwat Pawar

Abstract

The Indian retail space is seeing a clutter of

competitive retail brands today and the customers are

becoming more fragmented in their specifications and

choices. The marketing environment is becoming

more challenging to the retailers and the brand

owners. In this context, this study attempts to

formulate a discriminant equation on the basis of

some established predictors and separate the

segment into suspects and prospects. The research

attempts to forecast the purchase intentions of the

target segment where an optimal level of brand

awareness and brand knowledge is present. The

retailers can make a market assessment about their

brand through this approach which will in turn, help

the marketer to reduce the market risk.

Keywords: Kidswear, Brand Selection, Purchase

Intention, Discriminant Analysis, Prediction.

Predicting Consumer Purchase Intention: A Discriminant Analysis Approach ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014

112 113

Background of the Study:

Due to the market fragmentation and clutter of too

many brands in any segment, it is becoming tougher

for marketers to predict the consumer purchase

intention for their brands. To develop an appropriate

marketing plan, it has become extremely important for

marketers to assess the exact market potentiality.

Although some of the studies have tried to define the

intentions through various methods, with changing

market dimensions, further in-depth research is

always sought after. The Juster Scale is a purchase

probability scale used to predict the actual purchase

rate in a population from a sample of consumers from

that population. (Day, Gan, Gendall, & Essle, 1991)

concluded that there will always be an element of

unpredictability in consumer behaviour, and, like all

forecasting methods, the Juster Scale is an aid to

informed judgement, not a substitute for it.

(Mann, Sharma, & Dhingra, 2012) established in their

study that though the parents’ opinion dominates in

deciding when to buy, it is the child’s opinion which is

important while they choose the apparel; children

today are aware of fashion and the latest trends, and

are influenced by advertisements to a large extent.

(John, 1999) reviewed that children are doubtlessly

avid consumers and become socialized into this role

from an early age. Throughout childhood, children

develop the knowledge, skills, and values they will use

in making and influencing purchases now and in the

future.

(Rajput & Kesharwani, 2012) found in their study that

due to increased awareness and consciousness,

people are ready to spend any price for comfort and

quality. The study confirms that Indians have become

highly brand conscious presently. There are other

aspects like quality, comfort, expectations and

demographic characteristics that also influence the

purchasing decision of males and females. In this

study, we have tried to predict the purchase intention

of the target segment by discriminating the

respondents or the customers into suspects and

prospects so that a market assessment for a particular

brand may be done by the marketer and appropriate

marketing policies may be implemented to penetrate

the market.

Objectives of the Study:

There are two types of research problems – those that

relate to states of nature and those which relate to

relationships between variables (Kothari, 2004) to

formulate the research objectives. There were two

steps involved, i.e. understanding the problem

thoroughly and rephrasing the same into meaningful

terms from an analytical point of view. We tried to

define the objectives unambiguously to help to

discriminate relevant data and discard irrelevant data.

In the study, to define the problem, we took into

account the purpose of the study, the relevant

background information, what information is needed

and how it will be used in decision making

(Nargundkar, 2004).

The primary objective of the study is to identify the

potential customers within the target segment of a

brand which will help the marketer to assess the

market potentiality by identifying the consumer

purchase intention. The secondary objectives include

understanding the perception of the existing

customers about the brand on some given parameters

and their responses towards them. Secondly, we also

tried to identify the important predictors among the

mentioned factors in the study which will help

marketers to forecast the purchase intention. Thus,

the overall objective of the study is that taking the

response of the target segment on given parameters,

we are able to predict through a discriminant equation

whether that segment will be prospective customers

for the brand or not.

Literature Review:

The (Wan Chik, 2006) study indicated that the product

customization mechanism does affect online

consumers’ shopping enjoyment, which then leads to

the intention to purchase Batik fabric online.

Specifically, (Zamri & Idris, 2013) investigates on the

consumer’s purchasing intention towards Zalora using

six different independent variables of perceived ease

of use, perceived usefulness, information privacy and

security, product and service quality, social influences

and role of experiential online shopping motives.

However, the result supports only perceived ease of

use as being significant to the consumer’s intention to

purchase Zalora. (Ngamkroeckjoti , Lou, &

Kijboonchoo, 2011), in their study identified the

influences of attitude, prestige and purchase intention

of both genders. In addition, the causal relationship

among prestige, attitude, and purchase intention was

also confirmed.

(Hanzaee & Adibifard, 2012) designed an experiment

consisting of 39 product performance parameters; 5

items of product specifications, 6 items of market

potentiality, 8 items of technological aspects, 7 items

of product novelty and its advantages, 6 items of R&D

and marketing interface, and 7 items of customer

behaviour and their purchasing intentions. The study

demonstrated that new product purchase intention

was highly related to the need and uniqueness

aspects, product prices, trust, commitment and

satisfaction. (Sadasivan, Rajakumar, & Raijnikanth,

2011) examined the relationship between

involvement, brand loyalty and the consumer’s

willingness to buy the extension products from private

stores that sell apparel. The results that emerged from

the study were (i) Involvement plays a significant role

in decision making for apparel and influences the

brand loyalty; (ii) The consumer’s evaluation towards

the extension from apparel store brands is influenced

by relevance and similarity. Further, the outcome also

indicates that the consumer’s reaction towards the

extension product category (non-durable or durable)

is influenced by brand association.

(Srivastava & Ali, 2013) found in their study that

goodwill, friendly staff, proximity and specific product

availability at the store have different mean from the

rest. Goodwill is the most important factor in selecting

the retail store followed by status, availability of fresh

stock, trendy stock, promotional scheme and shopping

environment whereas proximity and the availability of

a specific product at the store are less influential

factors in selecting a specific store. Selection of the

most preferred apparel store is dependent on the

marital status of respondents and is independent of

their age, gender, number of members in the family,

education, employment status and income, frequency

of shopping and annual spending on the purchase of

apparel.

(Devanathan, 2008) in his study found that the

variables “It is easy to place an order through web site”,

“Web sites enable you to touch/try merchandise”,

“Online shopping protects security and privacy”,

“Online shopping provides ease of price comparison”

are predicting the intention to purchase (ITP).

(Baohong & Morwitz1, 2009) in their study developed

a unified model of the relationship between intentions

and purchasing that (1) takes into account all possible

sources of discrepancies between intentions and

Predicting Consumer Purchase Intention: A Discriminant Analysis Approach Predicting Consumer Purchase Intention: A Discriminant Analysis Approach ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014

114 115

Background of the Study:

Due to the market fragmentation and clutter of too

many brands in any segment, it is becoming tougher

for marketers to predict the consumer purchase

intention for their brands. To develop an appropriate

marketing plan, it has become extremely important for

marketers to assess the exact market potentiality.

Although some of the studies have tried to define the

intentions through various methods, with changing

market dimensions, further in-depth research is

always sought after. The Juster Scale is a purchase

probability scale used to predict the actual purchase

rate in a population from a sample of consumers from

that population. (Day, Gan, Gendall, & Essle, 1991)

concluded that there will always be an element of

unpredictability in consumer behaviour, and, like all

forecasting methods, the Juster Scale is an aid to

informed judgement, not a substitute for it.

(Mann, Sharma, & Dhingra, 2012) established in their

study that though the parents’ opinion dominates in

deciding when to buy, it is the child’s opinion which is

important while they choose the apparel; children

today are aware of fashion and the latest trends, and

are influenced by advertisements to a large extent.

(John, 1999) reviewed that children are doubtlessly

avid consumers and become socialized into this role

from an early age. Throughout childhood, children

develop the knowledge, skills, and values they will use

in making and influencing purchases now and in the

future.

(Rajput & Kesharwani, 2012) found in their study that

due to increased awareness and consciousness,

people are ready to spend any price for comfort and

quality. The study confirms that Indians have become

highly brand conscious presently. There are other

aspects like quality, comfort, expectations and

demographic characteristics that also influence the

purchasing decision of males and females. In this

study, we have tried to predict the purchase intention

of the target segment by discriminating the

respondents or the customers into suspects and

prospects so that a market assessment for a particular

brand may be done by the marketer and appropriate

marketing policies may be implemented to penetrate

the market.

Objectives of the Study:

There are two types of research problems – those that

relate to states of nature and those which relate to

relationships between variables (Kothari, 2004) to

formulate the research objectives. There were two

steps involved, i.e. understanding the problem

thoroughly and rephrasing the same into meaningful

terms from an analytical point of view. We tried to

define the objectives unambiguously to help to

discriminate relevant data and discard irrelevant data.

In the study, to define the problem, we took into

account the purpose of the study, the relevant

background information, what information is needed

and how it will be used in decision making

(Nargundkar, 2004).

The primary objective of the study is to identify the

potential customers within the target segment of a

brand which will help the marketer to assess the

market potentiality by identifying the consumer

purchase intention. The secondary objectives include

understanding the perception of the existing

customers about the brand on some given parameters

and their responses towards them. Secondly, we also

tried to identify the important predictors among the

mentioned factors in the study which will help

marketers to forecast the purchase intention. Thus,

the overall objective of the study is that taking the

response of the target segment on given parameters,

we are able to predict through a discriminant equation

whether that segment will be prospective customers

for the brand or not.

Literature Review:

The (Wan Chik, 2006) study indicated that the product

customization mechanism does affect online

consumers’ shopping enjoyment, which then leads to

the intention to purchase Batik fabric online.

Specifically, (Zamri & Idris, 2013) investigates on the

consumer’s purchasing intention towards Zalora using

six different independent variables of perceived ease

of use, perceived usefulness, information privacy and

security, product and service quality, social influences

and role of experiential online shopping motives.

However, the result supports only perceived ease of

use as being significant to the consumer’s intention to

purchase Zalora. (Ngamkroeckjoti , Lou, &

Kijboonchoo, 2011), in their study identified the

influences of attitude, prestige and purchase intention

of both genders. In addition, the causal relationship

among prestige, attitude, and purchase intention was

also confirmed.

(Hanzaee & Adibifard, 2012) designed an experiment

consisting of 39 product performance parameters; 5

items of product specifications, 6 items of market

potentiality, 8 items of technological aspects, 7 items

of product novelty and its advantages, 6 items of R&D

and marketing interface, and 7 items of customer

behaviour and their purchasing intentions. The study

demonstrated that new product purchase intention

was highly related to the need and uniqueness

aspects, product prices, trust, commitment and

satisfaction. (Sadasivan, Rajakumar, & Raijnikanth,

2011) examined the relationship between

involvement, brand loyalty and the consumer’s

willingness to buy the extension products from private

stores that sell apparel. The results that emerged from

the study were (i) Involvement plays a significant role

in decision making for apparel and influences the

brand loyalty; (ii) The consumer’s evaluation towards

the extension from apparel store brands is influenced

by relevance and similarity. Further, the outcome also

indicates that the consumer’s reaction towards the

extension product category (non-durable or durable)

is influenced by brand association.

(Srivastava & Ali, 2013) found in their study that

goodwill, friendly staff, proximity and specific product

availability at the store have different mean from the

rest. Goodwill is the most important factor in selecting

the retail store followed by status, availability of fresh

stock, trendy stock, promotional scheme and shopping

environment whereas proximity and the availability of

a specific product at the store are less influential

factors in selecting a specific store. Selection of the

most preferred apparel store is dependent on the

marital status of respondents and is independent of

their age, gender, number of members in the family,

education, employment status and income, frequency

of shopping and annual spending on the purchase of

apparel.

(Devanathan, 2008) in his study found that the

variables “It is easy to place an order through web site”,

“Web sites enable you to touch/try merchandise”,

“Online shopping protects security and privacy”,

“Online shopping provides ease of price comparison”

are predicting the intention to purchase (ITP).

(Baohong & Morwitz1, 2009) in their study developed

a unified model of the relationship between intentions

and purchasing that (1) takes into account all possible

sources of discrepancies between intentions and

114 115

purchasing; (2) forecasts purchasing probability at the

individual level by linking explanatory variables (e.g.,

socio-demographics, product attributes and

promotion variables) and intentions with actual

purchasing; (3) considers multiple levels of purchase

decisions rather than the simple purchase / no-

purchase decision. So most of the variables considered

in this study go along well with the previous research

initiatives as we see in this section.

Research Methodology:

The research design of the study is partly exploratory

and partly descriptive in nature. The objective of

exploratory research is to explore or search through a

problem or situation to provide insight and understanding (Kothari, 2004). The major objective of

exploratory research is to identify and define the

problem and scope by helping to arrive at the best

research design, method of data collection and

sample, which is characterized by highly flexible,

unstructured and at times, informal research methods

(Easwaran, Singh, & Sharmila, 2010). Descriptive

studies attempt to determine the frequency with

which something occurs or the relationship between

two phenomena; here, emphasis would be on

obtaining the relative frequency of occurrence of the

given phenomenon (Mazumdar, 1991). For the study,

we have chosen the brand Cherokee, an Arvind Retail

brand of kidswear, and the primary data was collected

from Mega Mart Stores in Delhi.

Type of Data and Sample size: In the proposed study,

we used both primary and secondary data. Primary

Data is originated by the researcher for the specific

purpose of addressing the problem at hand and

Secondary Data (Kothari, 2004) has already been

collected for purposes other than the problem at

hand. Data was collected from 100 samples. Unequal

sample sizes are acceptable in DA. The sample size of

the smallest group needs to exceed the number of

predictor variables. As a “rule of thumb”, the smallest

sample size should be at least 20 for a few (4 or 5)

predictors.

Data Collection Tool: As a data collecting tool, we have

used a structured non-disguised questionnaire with

both open and close ended questions. A Questionnaire

is a scheduled interview form or measuring instrument

including a formalized set of questions for obtaining

information from respondents. (Kothari, 2004) The

reason for asking structured questions is to improve

the consistency of the wording used in doing the study

at different places which increases the reliability of the

study by ensuring that every respondent is asked the

same question. (Nargundkar, 2004)

Sampling Technique: In the proposed research study,

we have implemented Probability Sampling Technique

(Nargundkar, 2004), where each sampling unit has a

known probability of being included in the sample.

Systematic sampling technique has been used in the

study, where the sample frame is the list of loyal

customers in the kidswear market provided by a chain

store. It is been chosen since it facilitates low cost of

data collection, no need to enumerate all study objects

and operationally it is easier to control (Mazumdar,

Marketing Research; Text, Applications and Case

Studies, 1991).

Data Analysis: Statistical inferences were drawn from

the primary data collected by applying statistical tool

like SPSS 19 and statistical analysis like Discriminant

Analysis.

Findings and Analysis:

Discriminant Analysis: It is the appropriate statistical

technique when the dependent variable is categorical

and the independent variables are quantitative. The

basic purpose of discriminant analysis is to estimate

the relationship between a single categorical

dependent variable and a set of quantitative

independent variables. It involves deriving a variate,

the linear combination of the two (or more)

independent variables that will discriminate best

between defined groups. The linear combination for a

discriminant analysis, also known as the discriminant

function, is derived from an equation that takes the

following form:

The descriptive technique successively identifies the

linear combination of attributes known as canonical

discriminant functions (equations) which contribute

maximally to group separation. In a two-group

situation, the predicted membership is calculated by

first producing a score for D for each case using the

discriminate function. Then cases with D values

smaller than the cut-off value are classified as

belonging to one group while those with larger values

are classified into the other group. The group centroid

is the mean value of the discriminant score for a given

category of the dependent variable. There are as many

Centroids as there are groups or categories. The cut-off

is the mean of the two Centroids.

Here we have used DA with the objective that we

would like to establish a linear discriminant function of

purchase intention of shoppers to buy a brand as

grouping variable with four predictor or independent

variables namely Age, Self Concept Scores of Core

Product Attributes of Cherokee, Score of Brand Identity

of Cherokee and Cherokee as a Value for Money Brand

***. From the purchase intention, we separated the

types of customers into Prospect i.e. shoppers who

intend to buy Cherokee in future and Suspect i.e.

shoppers who don’t have any plan to buy Cherokee in

the near future. Prospective customers must have the

willingness, the financial capacity and the authority to

buy and they must be available to the salesperson

(Still, Cundiff, & Govoni, 1999). Suspects are people or

organisations who might conceivably have an interest

in buying the company’s product or service, but may

not have the means or real intention to buy (Kotler &

Keller, 2006). If we can establish the discriminant

equation, it will help a marketer to predict the group

behaviour of the shoppers from which they can make

an estimation of their prospective customers. Apart

from age, all the three other predictor variables were

extracted in exploratory factor analysis through SPSS,

which influences the buying behaviour of consumers

in the kidswear market ***.

1. Core product attributes: The core product

comprises the fundamental benefit that responds to

the customer’s problem of an unsatisfied need or

want. (Forsythe, 1991) show that actual garment

characteristics are more important than brand name in

evaluating garment quality and suggest that the

assumption that consumers associate quality in

apparel with brand name may be erroneous.

(Rajagopal, 2010) concluded that product attributes

influence consumer perceptions of the personal

relevance of a product or service to their needs. In

addition, consumer preferences for product attributes

are significantly linked to their lifestyle. The (Carpenter

& Moore, 2010) study indicates distinctive linkages

between product attributes related to price, physical

product attributes, brand attributes, usage attributes,

116 117

purchasing; (2) forecasts purchasing probability at the

individual level by linking explanatory variables (e.g.,

socio-demographics, product attributes and

promotion variables) and intentions with actual

purchasing; (3) considers multiple levels of purchase

decisions rather than the simple purchase / no-

purchase decision. So most of the variables considered

in this study go along well with the previous research

initiatives as we see in this section.

Research Methodology:

The research design of the study is partly exploratory

and partly descriptive in nature. The objective of

exploratory research is to explore or search through a

problem or situation to provide insight and understanding (Kothari, 2004). The major objective of

exploratory research is to identify and define the

problem and scope by helping to arrive at the best

research design, method of data collection and

sample, which is characterized by highly flexible,

unstructured and at times, informal research methods

(Easwaran, Singh, & Sharmila, 2010). Descriptive

studies attempt to determine the frequency with

which something occurs or the relationship between

two phenomena; here, emphasis would be on

obtaining the relative frequency of occurrence of the

given phenomenon (Mazumdar, 1991). For the study,

we have chosen the brand Cherokee, an Arvind Retail

brand of kidswear, and the primary data was collected

from Mega Mart Stores in Delhi.

Type of Data and Sample size: In the proposed study,

we used both primary and secondary data. Primary

Data is originated by the researcher for the specific

purpose of addressing the problem at hand and

Secondary Data (Kothari, 2004) has already been

collected for purposes other than the problem at

hand. Data was collected from 100 samples. Unequal

sample sizes are acceptable in DA. The sample size of

the smallest group needs to exceed the number of

predictor variables. As a “rule of thumb”, the smallest

sample size should be at least 20 for a few (4 or 5)

predictors.

Data Collection Tool: As a data collecting tool, we have

used a structured non-disguised questionnaire with

both open and close ended questions. A Questionnaire

is a scheduled interview form or measuring instrument

including a formalized set of questions for obtaining

information from respondents. (Kothari, 2004) The

reason for asking structured questions is to improve

the consistency of the wording used in doing the study

at different places which increases the reliability of the

study by ensuring that every respondent is asked the

same question. (Nargundkar, 2004)

Sampling Technique: In the proposed research study,

we have implemented Probability Sampling Technique

(Nargundkar, 2004), where each sampling unit has a

known probability of being included in the sample.

Systematic sampling technique has been used in the

study, where the sample frame is the list of loyal

customers in the kidswear market provided by a chain

store. It is been chosen since it facilitates low cost of

data collection, no need to enumerate all study objects

and operationally it is easier to control (Mazumdar,

Marketing Research; Text, Applications and Case

Studies, 1991).

Data Analysis: Statistical inferences were drawn from

the primary data collected by applying statistical tool

like SPSS 19 and statistical analysis like Discriminant

Analysis.

Findings and Analysis:

Discriminant Analysis: It is the appropriate statistical

technique when the dependent variable is categorical

and the independent variables are quantitative. The

basic purpose of discriminant analysis is to estimate

the relationship between a single categorical

dependent variable and a set of quantitative

independent variables. It involves deriving a variate,

the linear combination of the two (or more)

independent variables that will discriminate best

between defined groups. The linear combination for a

discriminant analysis, also known as the discriminant

function, is derived from an equation that takes the

following form:

The descriptive technique successively identifies the

linear combination of attributes known as canonical

discriminant functions (equations) which contribute

maximally to group separation. In a two-group

situation, the predicted membership is calculated by

first producing a score for D for each case using the

discriminate function. Then cases with D values

smaller than the cut-off value are classified as

belonging to one group while those with larger values

are classified into the other group. The group centroid

is the mean value of the discriminant score for a given

category of the dependent variable. There are as many

Centroids as there are groups or categories. The cut-off

is the mean of the two Centroids.

Here we have used DA with the objective that we

would like to establish a linear discriminant function of

purchase intention of shoppers to buy a brand as

grouping variable with four predictor or independent

variables namely Age, Self Concept Scores of Core

Product Attributes of Cherokee, Score of Brand Identity

of Cherokee and Cherokee as a Value for Money Brand

***. From the purchase intention, we separated the

types of customers into Prospect i.e. shoppers who

intend to buy Cherokee in future and Suspect i.e.

shoppers who don’t have any plan to buy Cherokee in

the near future. Prospective customers must have the

willingness, the financial capacity and the authority to

buy and they must be available to the salesperson

(Still, Cundiff, & Govoni, 1999). Suspects are people or

organisations who might conceivably have an interest

in buying the company’s product or service, but may

not have the means or real intention to buy (Kotler &

Keller, 2006). If we can establish the discriminant

equation, it will help a marketer to predict the group

behaviour of the shoppers from which they can make

an estimation of their prospective customers. Apart

from age, all the three other predictor variables were

extracted in exploratory factor analysis through SPSS,

which influences the buying behaviour of consumers

in the kidswear market ***.

1. Core product attributes: The core product

comprises the fundamental benefit that responds to

the customer’s problem of an unsatisfied need or

want. (Forsythe, 1991) show that actual garment

characteristics are more important than brand name in

evaluating garment quality and suggest that the

assumption that consumers associate quality in

apparel with brand name may be erroneous.

(Rajagopal, 2010) concluded that product attributes

influence consumer perceptions of the personal

relevance of a product or service to their needs. In

addition, consumer preferences for product attributes

are significantly linked to their lifestyle. The (Carpenter

& Moore, 2010) study indicates distinctive linkages

between product attributes related to price, physical

product attributes, brand attributes, usage attributes,

116 117

fashion attributes and the ultimate retail format

choice for apparel.

2. Brand Identity: Brand identity is a unique set of

brand associations implying a promise to customers

and includes a core and extended identity. Core

identity is the central, timeless essence of the brand

that remains constant as the brand moves to new

markets and new products. Core identity broadly

focuses on product attributes, service, user prole,

store ambience and product performance. Extended

identity is woven around brand identity elements

organized into cohesive and meaningful groups that

provide brand texture and completeness, and focuses

on brand personality, relationship, and strong symbol

association (Ghodeswar, 2008). To be effective, a

brand identity needs to resonate with customers,

differentiate the brand from competitors, and

represent what the organization can and will do over

time (Aaker D. a., 2000). It is also stated that to excel, a

brand image must be well planned, nurtured,

supported, and vigilantly guarded (Knapp, 2000). One

key to successful brand-building is to understand how

to develop a brand identity – to know what the brand

stands for and to effectively express that identity

(Aaker D. , 1996). A brand is a distinctive identity that

differentiates a relevant, enduring, and credible

promise of value associated with a product, service, or

organization and indicates the source of that promise

(Ward, 1999). Companies that present a cohesive,

distinctive, and relevant brand identity can create a

preference in the marketplace, add value to their

products and services, and may command a price

premium (Schmitt, 1997). When a brand faces

aggressive competition in the marketplace, brand

personality and reputation of the brand help it

distinguish from competing offerings. This can result in

gaining customer loyalty and achieving growth.

(Ghodeswar, 2008) concluded that a strong brand

identity that is well understood and experienced by

the customers helps in developing trust which, in turn,

results in differentiating the brand from competition.

A company needs to establish a clear and consistent

brand identity by linking brand attributes with the way

they are communicated which can be easily

understood by the customers.

3. Value for Money: It is a very important factor which

determines the purchase decision of the consumer. In

today’s competitive market, the value conscious

consumer wants to purchase a brand which satisfies

him as per usage and also as per the money spent on it.

Thus, kidswear retailers should be conscious about

their pricing and offerings while doing their

assortment planning. (Forsythe, 1991) stated that

consumers appear to use both intrinsic and extrinsic

cues to differentiate among products and to form

impressions of such variables as quality and value.

(Sadasivan, Rajakumar, & Raijnikanth, 2011) also

concluded in their study that ever-rising aspirations of

customers have sent signals in the market that they are

looking for quality products, innovativeness, product

width, attractive promotional schemes and

competitive pricing from the retailers. (Agarwal &

Aggrawal, 2012) stated that the Indian consumer is not

beguiled by retail products which are high on price but

commensurately low on value or functionality. A scale

of 50 points is chosen for more accurate marking by

the respondents of their perceptions and opinions.

The survey was done in Mega Mart stores in Delhi. Sample questions are indicated below:

1. Please Mention your Age: ( 20 and above is eligible only ): ____________________

2. Please give your weightage within 50 points to the following factors according to your preference and

acceptance, which you believe will affect your purchase decision of Cherokee:

a. Core Product Attributes of Cherokee: ____________________

b. Brand Identity of Cherokee: ____________________

c. Cherokee as a Value for Brand: ____________________

3. Would you like to buy Cherokee in future? Yes No

Table no. 1: Analysis Case Processing Summary

Group statistics tables

In discriminant analysis, we are trying to predict a

group membership, so firstly, we examine whether

there are any significant differences between groups

on each of the independent variables using group

means and ANOVA results data. The Group Statistics

and Tests of Equality of Group Means tables provide

this information. If there are no significant group

differences, it is not worthwhile proceeding any

further with the analysis. A rough idea of variables that

may be important can be obtained by inspecting the

group means and standard deviations.

The Group Statistics Table clearly indicates that there is

a large separation among all the predictors within each

other apart from Age and Cherokee as a ‘Value for

Money’ Brand.

Apart from this, the same relation has been

established in Tests of equality of Group Means Table

where apart from Age, all the other three predictors

have a statistically significant difference. The F Value

for Value for Money is also very high (161.726), which

can act as a very good differentiator. The ‘Pooled

within group’ Matrix also establishes the fact that the

intercorrelations among all the variables are very low

and statistically insignificant.

118 119

fashion attributes and the ultimate retail format

choice for apparel.

2. Brand Identity: Brand identity is a unique set of

brand associations implying a promise to customers

and includes a core and extended identity. Core

identity is the central, timeless essence of the brand

that remains constant as the brand moves to new

markets and new products. Core identity broadly

focuses on product attributes, service, user prole,

store ambience and product performance. Extended

identity is woven around brand identity elements

organized into cohesive and meaningful groups that

provide brand texture and completeness, and focuses

on brand personality, relationship, and strong symbol

association (Ghodeswar, 2008). To be effective, a

brand identity needs to resonate with customers,

differentiate the brand from competitors, and

represent what the organization can and will do over

time (Aaker D. a., 2000). It is also stated that to excel, a

brand image must be well planned, nurtured,

supported, and vigilantly guarded (Knapp, 2000). One

key to successful brand-building is to understand how

to develop a brand identity – to know what the brand

stands for and to effectively express that identity

(Aaker D. , 1996). A brand is a distinctive identity that

differentiates a relevant, enduring, and credible

promise of value associated with a product, service, or

organization and indicates the source of that promise

(Ward, 1999). Companies that present a cohesive,

distinctive, and relevant brand identity can create a

preference in the marketplace, add value to their

products and services, and may command a price

premium (Schmitt, 1997). When a brand faces

aggressive competition in the marketplace, brand

personality and reputation of the brand help it

distinguish from competing offerings. This can result in

gaining customer loyalty and achieving growth.

(Ghodeswar, 2008) concluded that a strong brand

identity that is well understood and experienced by

the customers helps in developing trust which, in turn,

results in differentiating the brand from competition.

A company needs to establish a clear and consistent

brand identity by linking brand attributes with the way

they are communicated which can be easily

understood by the customers.

3. Value for Money: It is a very important factor which

determines the purchase decision of the consumer. In

today’s competitive market, the value conscious

consumer wants to purchase a brand which satisfies

him as per usage and also as per the money spent on it.

Thus, kidswear retailers should be conscious about

their pricing and offerings while doing their

assortment planning. (Forsythe, 1991) stated that

consumers appear to use both intrinsic and extrinsic

cues to differentiate among products and to form

impressions of such variables as quality and value.

(Sadasivan, Rajakumar, & Raijnikanth, 2011) also

concluded in their study that ever-rising aspirations of

customers have sent signals in the market that they are

looking for quality products, innovativeness, product

width, attractive promotional schemes and

competitive pricing from the retailers. (Agarwal &

Aggrawal, 2012) stated that the Indian consumer is not

beguiled by retail products which are high on price but

commensurately low on value or functionality. A scale

of 50 points is chosen for more accurate marking by

the respondents of their perceptions and opinions.

The survey was done in Mega Mart stores in Delhi. Sample questions are indicated below:

1. Please Mention your Age: ( 20 and above is eligible only ): ____________________

2. Please give your weightage within 50 points to the following factors according to your preference and

acceptance, which you believe will affect your purchase decision of Cherokee:

a. Core Product Attributes of Cherokee: ____________________

b. Brand Identity of Cherokee: ____________________

c. Cherokee as a Value for Brand: ____________________

3. Would you like to buy Cherokee in future? Yes No

Table no. 1: Analysis Case Processing Summary

Group statistics tables

In discriminant analysis, we are trying to predict a

group membership, so firstly, we examine whether

there are any significant differences between groups

on each of the independent variables using group

means and ANOVA results data. The Group Statistics

and Tests of Equality of Group Means tables provide

this information. If there are no significant group

differences, it is not worthwhile proceeding any

further with the analysis. A rough idea of variables that

may be important can be obtained by inspecting the

group means and standard deviations.

The Group Statistics Table clearly indicates that there is

a large separation among all the predictors within each

other apart from Age and Cherokee as a ‘Value for

Money’ Brand.

Apart from this, the same relation has been

established in Tests of equality of Group Means Table

where apart from Age, all the other three predictors

have a statistically significant difference. The F Value

for Value for Money is also very high (161.726), which

can act as a very good differentiator. The ‘Pooled

within group’ Matrix also establishes the fact that the

intercorrelations among all the variables are very low

and statistically insignificant.

118 119

Table No. 2: Group Statistics

Group Statistics

Table no. 3: Tests of Equality of Group Means

Tests of Equality of Group Means

Table no. 4: Pooled Within-Group Matrices

Pooled Within-Groups Matrices

Log determinants and Box's M tables

In ANOVA, an assumption is that the variances were

equivalent for each group but in DA, the basic

assumption is that the variance-co-variance matrices

are equivalent. Box's M tests the null hypothesis that

the covariance matrices do not differ between groups

formed by the dependent. In our study, we conducted

this test for making it not to be significant so that the

null hypothesis that the groups do not differ can be

retained.

For this study to hold, the log determinants should be

more or less equal, and in our study we got the same,

which is a positive signal for the analysis. When tested

by Box's M, we are looking for a non-significant M to

show similarity and lack of significant differences. In

this case, the log determinants appear similar and

Box's M is 26.669 with F = 2.554 which is significant as p

= .005 and is less than .05. Thus, we reject the null

hypothesis which means the covariance matrices

differ between groups formed by the dependent.

However, it can be opposite if the sample size is being

increased (large sample size).

Table no.5: Log Determinants

Box's Test of Equality of Covariance Matrices

Log Determinants

The ranks and natural logarithms of determinants printed are those of the group covariance matrices.

120 121

Weighted

59.000

41.000

100.000

Table No. 2: Group Statistics

Group Statistics

Table no. 3: Tests of Equality of Group Means

Tests of Equality of Group Means

Table no. 4: Pooled Within-Group Matrices

Pooled Within-Groups Matrices

Log determinants and Box's M tables

In ANOVA, an assumption is that the variances were

equivalent for each group but in DA, the basic

assumption is that the variance-co-variance matrices

are equivalent. Box's M tests the null hypothesis that

the covariance matrices do not differ between groups

formed by the dependent. In our study, we conducted

this test for making it not to be significant so that the

null hypothesis that the groups do not differ can be

retained.

For this study to hold, the log determinants should be

more or less equal, and in our study we got the same,

which is a positive signal for the analysis. When tested

by Box's M, we are looking for a non-significant M to

show similarity and lack of significant differences. In

this case, the log determinants appear similar and

Box's M is 26.669 with F = 2.554 which is significant as p

= .005 and is less than .05. Thus, we reject the null

hypothesis which means the covariance matrices

differ between groups formed by the dependent.

However, it can be opposite if the sample size is being

increased (large sample size).

Table no.5: Log Determinants

Box's Test of Equality of Covariance Matrices

Log Determinants

The ranks and natural logarithms of determinants printed are those of the group covariance matrices.

120 121

Weighted

59.000

41.000

100.000

Table No. 6: Test Result

This provides information on each of the discriminate

functions (equations) produced. The maximum

number of discriminant functions produced is the

number of groups minus 1. We are only using two

groups here, namely 'Prospect' and 'Suspect', so only

one function is displayed. The canonical correlation is

the multiple correlations between the predictors and

the discriminant function. With only one function, it

Table no. 7: Eigen Values

Summary of Canonical Discriminant Functions

provides an index of overall model fit which is

interpreted as being the proportion of variance 2explained (R ). In our study, a canonical correlation of

.852 suggests the model explains 72.6% (1 - .274) of

the variation in the grouping variable, i.e. whether a

respondent is a prospective customer or not. High

canonical value describes the good overall fit of the

analysis, which in our findings is quite high at .852.

Tests null hypothesis of equal population covariance matrices.

Eigen values

a. First 1 canonical discriminant function was used in the analysis.

Wilks' lambda

Wilks' lambda indicates the significance of the discriminant function. The table below indicates a highly significant

function (p = .000, which is less than .05, and we accept the Null Hypothesis) and provides the proportion of total

variability not explained, i.e. it is the converse of the squared canonical correlation. So in our study, we have 27.4%

unexplained.

Table no. 8: Wilks' Lambda

The standardized canonical discriminant function

coefficients table

The interpretation of the discriminant coefficients (or

weights) is like that in multiple regressions. The table

below provides an index of the importance of each

predictor like the standardized regression coefficients

(beta's) did in multiple regression. The sign indicates

the direction of the relationship (here, all the

predictors have + ve sign). Score for Cherokee as a

'value for money' brand was the strongest predictor

while Brand Identity of Cherokee was next in

importance as a predictor. These two variables with

large coefficients stand out as those that strongly

predict allocation to the “prospect” or “suspect”

group. Age and core product attribute scores were less

successful as predictors.

Table no. 9: Standardized Canonical Discriminant Function Coefficients

The structure matrix table

The table below provides another way of indicating the

relative importance of the predictors and it can be

seen below that the same pattern holds. Many

researchers use the structure matrix correlations

because they are considered more accurate than the

Standardized Canonical Discriminant Function

Coefficients. The structure matrix table shows the

correlations of each variable with each discriminate

function. These Pearson coefficients are structure

coefficients or discriminant loadings. They serve like

factor loadings in factor analysis. By identifying the

largest loadings for each discriminate function, the

researcher gains insight into how to name each

function. In the study, we found out the value for

money and brand identity, and we suggest a label of

personal belief and perception as the function that

discriminates between Prospect and Suspect.

Generally, just like factor loadings, 0.30 or 0.50 is seen

as the cut-off between important and less important

variables. Age of the buyer is clearly not loaded on the

discriminant function, i.e. it is the weakest predictor

and suggests that the age of the customer is not

associated with brand decision but is a function of

other unassessed factors.

122 123

Table No. 6: Test Result

This provides information on each of the discriminate

functions (equations) produced. The maximum

number of discriminant functions produced is the

number of groups minus 1. We are only using two

groups here, namely 'Prospect' and 'Suspect', so only

one function is displayed. The canonical correlation is

the multiple correlations between the predictors and

the discriminant function. With only one function, it

Table no. 7: Eigen Values

Summary of Canonical Discriminant Functions

provides an index of overall model fit which is

interpreted as being the proportion of variance 2explained (R ). In our study, a canonical correlation of

.852 suggests the model explains 72.6% (1 - .274) of

the variation in the grouping variable, i.e. whether a

respondent is a prospective customer or not. High

canonical value describes the good overall fit of the

analysis, which in our findings is quite high at .852.

Tests null hypothesis of equal population covariance matrices.

Eigen values

a. First 1 canonical discriminant function was used in the analysis.

Wilks' lambda

Wilks' lambda indicates the significance of the discriminant function. The table below indicates a highly significant

function (p = .000, which is less than .05, and we accept the Null Hypothesis) and provides the proportion of total

variability not explained, i.e. it is the converse of the squared canonical correlation. So in our study, we have 27.4%

unexplained.

Table no. 8: Wilks' Lambda

The standardized canonical discriminant function

coefficients table

The interpretation of the discriminant coefficients (or

weights) is like that in multiple regressions. The table

below provides an index of the importance of each

predictor like the standardized regression coefficients

(beta's) did in multiple regression. The sign indicates

the direction of the relationship (here, all the

predictors have + ve sign). Score for Cherokee as a

'value for money' brand was the strongest predictor

while Brand Identity of Cherokee was next in

importance as a predictor. These two variables with

large coefficients stand out as those that strongly

predict allocation to the “prospect” or “suspect”

group. Age and core product attribute scores were less

successful as predictors.

Table no. 9: Standardized Canonical Discriminant Function Coefficients

The structure matrix table

The table below provides another way of indicating the

relative importance of the predictors and it can be

seen below that the same pattern holds. Many

researchers use the structure matrix correlations

because they are considered more accurate than the

Standardized Canonical Discriminant Function

Coefficients. The structure matrix table shows the

correlations of each variable with each discriminate

function. These Pearson coefficients are structure

coefficients or discriminant loadings. They serve like

factor loadings in factor analysis. By identifying the

largest loadings for each discriminate function, the

researcher gains insight into how to name each

function. In the study, we found out the value for

money and brand identity, and we suggest a label of

personal belief and perception as the function that

discriminates between Prospect and Suspect.

Generally, just like factor loadings, 0.30 or 0.50 is seen

as the cut-off between important and less important

variables. Age of the buyer is clearly not loaded on the

discriminant function, i.e. it is the weakest predictor

and suggests that the age of the customer is not

associated with brand decision but is a function of

other unassessed factors.

122 123

Table No. 10: Structured Matrix

Pooled within-groups correlations between

discriminating variables and standardized canonical

discriminant functions Variables ordered by absolute

size of correlation within function.

The canonical discriminant function coefficient

tableThese unstandardized coefficients (b) are used to

create the discriminant function (equation). It

operates just like a regression equation. In this study,

we have:D = (0.145 X Cherokee as a ‘Value for Money’

Brand) + (0.121 X Brand Identity of Cherokee) + (0.048

X Core Product Attribute of Cherokee) + (0.017 X Age of

the Shopper) +Constant

The discriminant function coefficients b or

standardized form beta both indicate the partial

contribution of each variable to the discriminate

function controlling for all other variables in the

equation. They can be used to assess each predictor’s

unique contribution to the discriminate function and

therefore provide information on the relative

importance of each variable.

Table No. 11: Canonical Discriminant Function Coefficients

Unstandardized coefficients

Group Centroids table

A further way of interpreting discriminant analysis

results is to describe each group in terms of its profile,

using the group means of the predictor variables.

These group means are called Centroids. These are

displayed in the Group Centroids table. In our study,

Prospects (who want to buy Cherokee in the near

future) have a mean of 1.342 while Suspects (who

don’t plan to buy Cherokee in the near future)

produce a mean of –1.931. Cases with scores close to

Centroids are predicted as belonging to that group.

That means a respondent whose score tends to 1.342

is a Prospect and if his score tends to -1.931 can be

segregated as a suspect.

Table No. 12: Functions at Group Centroids

Unstandardized canonical discriminant functions evaluated at group means

Classification table

In the classification Table below, the rows are the

observed categories of the dependent and the

columns are the predicted categories. When

prediction is perfect, all cases will lie on the diagonal.

The percentage of cases on the diagonal is the

percentage of correct classifications.

The cross validated set of data is a more honest

presentation of the power of the discriminant function

than that provided by the original classifications and

often produces a poorer outcome. The cross validation

is often termed a ‘jack-knife’ classification, in that it

successively classifies all cases but one to develop a

discriminant function and then categorizes the case

that was left out. This process is repeated with each

case left out in turn. This cross validation produces a

more reliable function.

The classification results reveal that 94.0% of

respondents were classified correctly into ‘Prospect’

or ‘Suspect’ groups. This overall predictive accuracy of

the discriminant function is called the ‘hit ratio’.

Prospects and Suspects were predicted in the same

accuracy in the study that is 94% which is on the higher

side as it tends to 100%.

Classification Statistics

Table No. 13: Classification Processing Summary

Table No. 14: Prior Probabilities for Groups

124 125

Table No. 10: Structured Matrix

Pooled within-groups correlations between

discriminating variables and standardized canonical

discriminant functions Variables ordered by absolute

size of correlation within function.

The canonical discriminant function coefficient

tableThese unstandardized coefficients (b) are used to

create the discriminant function (equation). It

operates just like a regression equation. In this study,

we have:D = (0.145 X Cherokee as a ‘Value for Money’

Brand) + (0.121 X Brand Identity of Cherokee) + (0.048

X Core Product Attribute of Cherokee) + (0.017 X Age of

the Shopper) +Constant

The discriminant function coefficients b or

standardized form beta both indicate the partial

contribution of each variable to the discriminate

function controlling for all other variables in the

equation. They can be used to assess each predictor’s

unique contribution to the discriminate function and

therefore provide information on the relative

importance of each variable.

Table No. 11: Canonical Discriminant Function Coefficients

Unstandardized coefficients

Group Centroids table

A further way of interpreting discriminant analysis

results is to describe each group in terms of its profile,

using the group means of the predictor variables.

These group means are called Centroids. These are

displayed in the Group Centroids table. In our study,

Prospects (who want to buy Cherokee in the near

future) have a mean of 1.342 while Suspects (who

don’t plan to buy Cherokee in the near future)

produce a mean of –1.931. Cases with scores close to

Centroids are predicted as belonging to that group.

That means a respondent whose score tends to 1.342

is a Prospect and if his score tends to -1.931 can be

segregated as a suspect.

Table No. 12: Functions at Group Centroids

Unstandardized canonical discriminant functions evaluated at group means

Classification table

In the classification Table below, the rows are the

observed categories of the dependent and the

columns are the predicted categories. When

prediction is perfect, all cases will lie on the diagonal.

The percentage of cases on the diagonal is the

percentage of correct classifications.

The cross validated set of data is a more honest

presentation of the power of the discriminant function

than that provided by the original classifications and

often produces a poorer outcome. The cross validation

is often termed a ‘jack-knife’ classification, in that it

successively classifies all cases but one to develop a

discriminant function and then categorizes the case

that was left out. This process is repeated with each

case left out in turn. This cross validation produces a

more reliable function.

The classification results reveal that 94.0% of

respondents were classified correctly into ‘Prospect’

or ‘Suspect’ groups. This overall predictive accuracy of

the discriminant function is called the ‘hit ratio’.

Prospects and Suspects were predicted in the same

accuracy in the study that is 94% which is on the higher

side as it tends to 100%.

Classification Statistics

Table No. 13: Classification Processing Summary

Table No. 14: Prior Probabilities for Groups

124 125

Separate-Groups Graphs

Fig 1: Separate Group Graphs Fig 2: Separate Group Graphs

Table No. 13: Classification Results

a. cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case.

b. 94.0% of original grouped cases correctly classified.c. 94.0% of cross-validated grouped cases correctly classified.

Saved variables

As a result of asking the analysis to save the new

groupings, two new variables can now be found at the

end of the data file. dis_1 is the predicted grouping

based on the discriminant analysis coded 1 and 2,

while dis1_1 are the D scores by which the cases were

coded into their categories. The average D scores for

each group are of course the group Centroids reported

earlier. While these scores and groups can be used for

other analyses, they are useful as visual

demonstrations of the effectiveness of the

discriminant function. Histograms and box plots above

are alternative ways of illustrating the distribution of

the discriminant function scores for each group. By

reading the range of scores on the axes, noting (group

Centroids table) the means of both as well as the very

minimal overlap of the graphs and box plots, a

substantial discrimination is revealed. This suggests

that the function does discriminate well, as the

previous tables indicated.

Table No. 14: Reliability Statistics

Cronbach’s Alpha No. of Items

.710 3

Cronbach's Alpha is a major of internal consistency, i.e.

how closely related a set of items are as a group. The

alpha coefficient for the three items is .710 suggesting

that the items have relatively high internal consistency.

Reliability coefficient of .70 or higher is considered

acceptable in most social science research situations.

Discussion and Conclusion

A discriminant analysis was conducted to predict

whether a shopper is a prospective customer or a

suspect only on the basis of the purchase intention.

Predictor variables were age, brand Cherokee as value

for money, core product attributes of Cherokee and

brand identity of Cherokee. Significant mean

differences were observed for all the predictors. The

log determinants were quite similar and Box's M also

indicated that the assumption of equality of

covariance was also accepted. The discriminate

function revealed a significant association between

groups and all predictors, accounting for 72.6% of

between group variability, although closer analysis of

the structure matrix revealed only two significant

predictors, namely Cherokee as a 'value for money'

brand (0.790) and Brand Identity of Cherokee score

(0.513) with age and core product attributes as poor

predictors. The cross validated classification showed

that overall 94% were correctly classified.

This study can be implemented by marketers to assess

the real market positioning of a brand in terms of the

customers' purchase intention. Marketers can find the

market potentiality of their brand in a new market also

through this research apart from finding out problems

in the existing market in terms of the predictors so that

appropriate marketing policies can be implemented to

tap the market.

126 127

Separate-Groups Graphs

Fig 1: Separate Group Graphs Fig 2: Separate Group Graphs

Table No. 13: Classification Results

a. cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case.

b. 94.0% of original grouped cases correctly classified.c. 94.0% of cross-validated grouped cases correctly classified.

Saved variables

As a result of asking the analysis to save the new

groupings, two new variables can now be found at the

end of the data file. dis_1 is the predicted grouping

based on the discriminant analysis coded 1 and 2,

while dis1_1 are the D scores by which the cases were

coded into their categories. The average D scores for

each group are of course the group Centroids reported

earlier. While these scores and groups can be used for

other analyses, they are useful as visual

demonstrations of the effectiveness of the

discriminant function. Histograms and box plots above

are alternative ways of illustrating the distribution of

the discriminant function scores for each group. By

reading the range of scores on the axes, noting (group

Centroids table) the means of both as well as the very

minimal overlap of the graphs and box plots, a

substantial discrimination is revealed. This suggests

that the function does discriminate well, as the

previous tables indicated.

Table No. 14: Reliability Statistics

Cronbach’s Alpha No. of Items

.710 3

Cronbach's Alpha is a major of internal consistency, i.e.

how closely related a set of items are as a group. The

alpha coefficient for the three items is .710 suggesting

that the items have relatively high internal consistency.

Reliability coefficient of .70 or higher is considered

acceptable in most social science research situations.

Discussion and Conclusion

A discriminant analysis was conducted to predict

whether a shopper is a prospective customer or a

suspect only on the basis of the purchase intention.

Predictor variables were age, brand Cherokee as value

for money, core product attributes of Cherokee and

brand identity of Cherokee. Significant mean

differences were observed for all the predictors. The

log determinants were quite similar and Box's M also

indicated that the assumption of equality of

covariance was also accepted. The discriminate

function revealed a significant association between

groups and all predictors, accounting for 72.6% of

between group variability, although closer analysis of

the structure matrix revealed only two significant

predictors, namely Cherokee as a 'value for money'

brand (0.790) and Brand Identity of Cherokee score

(0.513) with age and core product attributes as poor

predictors. The cross validated classification showed

that overall 94% were correctly classified.

This study can be implemented by marketers to assess

the real market positioning of a brand in terms of the

customers' purchase intention. Marketers can find the

market potentiality of their brand in a new market also

through this research apart from finding out problems

in the existing market in terms of the predictors so that

appropriate marketing policies can be implemented to

tap the market.

126 127

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***(the same factors were extracted through exploratory factor analysis by the same authors in a research paper

named “Factors affecting the Consumer Buying Behaviour in Kidswear Market and Perceptual mapping of the

Kidswear Brands of Shopper’s Stop”. The Paper has been accepted by Journal of Management Research, Faculty of

Management Science, University of Delhi, for publication in October-December, 2013 Issue)

Dr. Sougata Banerjee presently works with National Institute of Fashion Technology, Kolkata under

Ministry of Textiles, Govt. of India as Assistant Professor in Department of Fashion Management Studies

and is taking care of the International & Domestic Linkages of NIFT, Kolkata. He is also a registered Ph.D.

Supervisor with NIFT and WBUT, and is guiding research scholars. After the completion of his MBA in

Marketing Management, he did his Ph.D. in Retail Marketing. Apart from his long years of experience in

academics, he has experience with corporates and is also involved with consultancies and market research.

Brand management, retail management, consumer behaviour, sales management are some of his interest

areas in research work. He has authored a number of research papers and is also a visiting faculty in reputed

institutions. He can be reached at [email protected]

Ms. Sarwat Pawar is presently working in Zolijns Design Pvt. Ltd. as Sales cum Design Executive. After

completion of her Bachelor of Science with honours in Resource Management in 2011 from Aligarh Muslim

University, she did her Masters in Fashion Management from National Institute of Fashion Technology,

Kolkata under Ministry of Textiles, Govt. of India in 2013. Her research interest lies in fashion retail

management, luxury branding, consumer behaviour etc. She can be contacted at

[email protected]

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References