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role of need for social approval, self-monitoring, and need for social power. Leadership Quarterly, 18, 134-
153.
• 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
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
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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,
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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,
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
120 121
Weighted
59.000
59.000
59.000
59.000
41.000
41.000
41.000
41.000
100.000
100.000
100.000
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
120 121
Weighted
59.000
59.000
59.000
59.000
41.000
41.000
41.000
41.000
100.000
100.000
100.000
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
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.
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
126 127
• Aaker, D. a. (2000). Brand Leadership. Ney York: The free press.
• Aaker, D. (1996). Building Strong Brands. New York: The Free Press.
• Agarwal, S., & Aggrawal, A. (2012). A critical analysis of impact of pricing on consumer buying behaviour in
apparel retail sector: a study of Mumbai city. International Journal of Multidisciplinary Educational Research , 1
(1), 34-44.
• Baohong, S., & Morwitz1, V. G. (2009). Predicting Purchase Behavior from Stated Intentions: A Unified Model.
Marketing Science Conference, University of Chicago , 1-41.
• Carpenter, J. M., & Moore, M. (2010). Product attributes and retail format choice among US Apparel
purchasers. Journal of Textile and Apparel Technology and Management , 6 (4), 1-11.
• Day, D., Gan, B., Gendall, P., & Essle, D. (1991). Predicting Purchase Behaviour. Marketing Bulletin , 3, 18 - 30.
• Devanathan, M. (2008). Strategic model for predicting customer’s intention to purchase apparel online.
Innovative Marketing , 4 (1), 29 - 36.
• Easwaran, S., Singh, & Sharmila, J. (2010). Marketing Research-Concepts, Practices and Cases (11th Edition ed.).
Oxford University Press.
• Forsythe, S. M. (1991). Effect of Private, Designer, and National Brand Names on Shoppers’ Perception of
Apparel Quality and Price. Clothing and Textiles Research Journal , 9 (1), 1-6.
• Ghodeswar, B. M. (2008). Building brand identity in competitive markets: A Conceptual Model. Journal of
product and brand management. , 17 (1).
• Hanzaee, K. H., & Adibifard, S. (2012). New Product Specification and Purchase Intention:Validating and
Developing a Native Scale (NPPI-I). Middle-East Journal of Scientific Research , 12 (4), 484-489.
• John, D. R. (1999). Consumer Socialization of Children:A Retrospective Look at Twenty-Five Years of Research.
Journal of Consumer Research , 26, 183-209.
• Knapp, D. (. (2000). The Brand Mindset. New York: McGraw-Hill.
• Kothari, C. R. (2004). Research Methodology-Methods & Techniques, (2nd Edition ed.). New Age International.
• Kotler, P., & Keller, K. L. (2006). Marketing Management (12 ed.). New Delhi: Pearson Education.
• Mann, P. W., Sharma, S., & Dhingra, N. (2012). Role and Influence of children in buying childrens apparel. Pacific
Business Review International , 4 (3), 45 — 50.
• Mazumdar, R. (1991). Marketing Research; Text, Applications and Case Studies. New Delhi: New Age
International (P) Ltd. Publishers.
• Nargundkar, R. (2004). Marketing Research-Text and Cases (Second Edition ed.). Tata McGraw Hill.
• Ngamkroeckjoti, C., Lou, X. B., & Kijboonchoo, T. (2011). Determinant Factors of Purchase Intention: A case
study of imported wine in city of Hangzhou. International Conference on Management, Economics and Social
Sciences , 591-595.
• Rajagopal. (2010). Consumer Culture and Purchase Intentions towards Fashion Apparel. Working Paper #MKT-
01-2010, EGADE Business School , 1-39.
• Rajput, N., & Kesharwani, S. (2012). Consumers’ Attitude towards Branded Apparels:Gender Perspective.
International Journal of Marketing Studies , 4 (2), 111-120.
• Sadasivan, K., Rajakumar, S., & Raijnikanth, R. (2011). Role of Involvement and Loyalty in Predicting Buyer’s
Purchase Intention towards Private Apparel Brand Extensions. International Journal of Innovation,
Management and Technology , 2 (6), 519-524.
• Schmitt, B. a. (1997). Marketing Aesthetics:The Strategic Management of Brands, Identity, and Image. New
York: The Free Press .
• Srivastava, M., & Ali, S. A. (2013). A study of perception and buying behaviour of customers in apparel market
segment with special reference to five major departmental stores in Pune city. International Journal of Sales &
Marketing Management Research and Development (IJSMMRD) , 3 (1), 21-34.
• Still, R. R., Cundiff, E. W., & Govoni, N. A. (1999). Sales Management: Decisions, Strategies and Cases (5 ed.).
New Delhi: Prentice Hall India.
• Wan Chik, R. L. (2006). Effect Of Product Customization On Consumers’ Shopping Enjoyment Towards The
Intention To. Proceedings of the International Conference on Business Information Technology 2006 (BIZIT ’06).
UPENA. ISBN: 983-3643-47-7.
• Ward, S. L. (1999, July-August). “What high-tech managers need to know about brands”. Harvard Business
Review , 85-95.
• Zamri, N. B., & Idris, I. (2013). The effects of attitude, social influences and perceived behavioural. 3rd
international conference on management(3rd icm 2013) proceeding (pp. 124-144). Penang, Malaysia: ISBN:
978-967-5705-11-3. Website: www.internationalconference.com.my.
***(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
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
128 129
References
• Aaker, D. a. (2000). Brand Leadership. Ney York: The free press.
• Aaker, D. (1996). Building Strong Brands. New York: The Free Press.
• Agarwal, S., & Aggrawal, A. (2012). A critical analysis of impact of pricing on consumer buying behaviour in
apparel retail sector: a study of Mumbai city. International Journal of Multidisciplinary Educational Research , 1
(1), 34-44.
• Baohong, S., & Morwitz1, V. G. (2009). Predicting Purchase Behavior from Stated Intentions: A Unified Model.
Marketing Science Conference, University of Chicago , 1-41.
• Carpenter, J. M., & Moore, M. (2010). Product attributes and retail format choice among US Apparel
purchasers. Journal of Textile and Apparel Technology and Management , 6 (4), 1-11.
• Day, D., Gan, B., Gendall, P., & Essle, D. (1991). Predicting Purchase Behaviour. Marketing Bulletin , 3, 18 - 30.
• Devanathan, M. (2008). Strategic model for predicting customer’s intention to purchase apparel online.
Innovative Marketing , 4 (1), 29 - 36.
• Easwaran, S., Singh, & Sharmila, J. (2010). Marketing Research-Concepts, Practices and Cases (11th Edition ed.).
Oxford University Press.
• Forsythe, S. M. (1991). Effect of Private, Designer, and National Brand Names on Shoppers’ Perception of
Apparel Quality and Price. Clothing and Textiles Research Journal , 9 (1), 1-6.
• Ghodeswar, B. M. (2008). Building brand identity in competitive markets: A Conceptual Model. Journal of
product and brand management. , 17 (1).
• Hanzaee, K. H., & Adibifard, S. (2012). New Product Specification and Purchase Intention:Validating and
Developing a Native Scale (NPPI-I). Middle-East Journal of Scientific Research , 12 (4), 484-489.
• John, D. R. (1999). Consumer Socialization of Children:A Retrospective Look at Twenty-Five Years of Research.
Journal of Consumer Research , 26, 183-209.
• Knapp, D. (. (2000). The Brand Mindset. New York: McGraw-Hill.
• Kothari, C. R. (2004). Research Methodology-Methods & Techniques, (2nd Edition ed.). New Age International.
• Kotler, P., & Keller, K. L. (2006). Marketing Management (12 ed.). New Delhi: Pearson Education.
• Mann, P. W., Sharma, S., & Dhingra, N. (2012). Role and Influence of children in buying childrens apparel. Pacific
Business Review International , 4 (3), 45 — 50.
• Mazumdar, R. (1991). Marketing Research; Text, Applications and Case Studies. New Delhi: New Age
International (P) Ltd. Publishers.
• Nargundkar, R. (2004). Marketing Research-Text and Cases (Second Edition ed.). Tata McGraw Hill.
• Ngamkroeckjoti, C., Lou, X. B., & Kijboonchoo, T. (2011). Determinant Factors of Purchase Intention: A case
study of imported wine in city of Hangzhou. International Conference on Management, Economics and Social
Sciences , 591-595.
• Rajagopal. (2010). Consumer Culture and Purchase Intentions towards Fashion Apparel. Working Paper #MKT-
01-2010, EGADE Business School , 1-39.
• Rajput, N., & Kesharwani, S. (2012). Consumers’ Attitude towards Branded Apparels:Gender Perspective.
International Journal of Marketing Studies , 4 (2), 111-120.
• Sadasivan, K., Rajakumar, S., & Raijnikanth, R. (2011). Role of Involvement and Loyalty in Predicting Buyer’s
Purchase Intention towards Private Apparel Brand Extensions. International Journal of Innovation,
Management and Technology , 2 (6), 519-524.
• Schmitt, B. a. (1997). Marketing Aesthetics:The Strategic Management of Brands, Identity, and Image. New
York: The Free Press .
• Srivastava, M., & Ali, S. A. (2013). A study of perception and buying behaviour of customers in apparel market
segment with special reference to five major departmental stores in Pune city. International Journal of Sales &
Marketing Management Research and Development (IJSMMRD) , 3 (1), 21-34.
• Still, R. R., Cundiff, E. W., & Govoni, N. A. (1999). Sales Management: Decisions, Strategies and Cases (5 ed.).
New Delhi: Prentice Hall India.
• Wan Chik, R. L. (2006). Effect Of Product Customization On Consumers’ Shopping Enjoyment Towards The
Intention To. Proceedings of the International Conference on Business Information Technology 2006 (BIZIT ’06).
UPENA. ISBN: 983-3643-47-7.
• Ward, S. L. (1999, July-August). “What high-tech managers need to know about brands”. Harvard Business
Review , 85-95.
• Zamri, N. B., & Idris, I. (2013). The effects of attitude, social influences and perceived behavioural. 3rd
international conference on management(3rd icm 2013) proceeding (pp. 124-144). Penang, Malaysia: ISBN:
978-967-5705-11-3. Website: www.internationalconference.com.my.
***(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
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
ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014
128 129
References