Transcript
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A Study on the Product Designing of
Formal ApparelsBIRLA INSTITUTE OF MANAGEMENT TECHNOLOGY
Submitted By:
GROUP 2
Manmeet Walia (084)
Neha Aggarwal (099)
Neha Mittal (100)
Nikita Saraiwala (106)
Nisha Maurya (107)
Pratibha Tatia (117)
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ACKNOWLEDGEMENT
We are thankful to Prof. A.K. Dey, Distinguished professors of Research Methodology at
BIMTECH and Prof. Tuhin Chattopadhyay, Guest faculty at BIMTECH for giving us the
opportunity to work on this project. It was a great learning experience for us and we could
actually put in practice, the learning acquired in the classroom. Also the project helped us in
understanding the customer perspective for the apparel industry.
We are also thankful to the all the respondents of the survey who helped us throughout the
course of the project.
GROUP 2
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ABSTRACT
Objective: The objective of this research is to evaluate the factors influencing the buying
decision of consumers of the formal apparel industry.
Apparel is the second largest retail category in India. The major factors contributing to its
growth are greater purchasing power of the young generation, access to fashion trends outside
the country, and the superior quality of fabrics. Moreover, due to globalization there has been
tremendous rise in the foreign institutional investments which has led to a boom in the
corporate sector. Seeing the drastic demand for the western formal apparels marketers are
eager to know about the buying behaviour of consumers. Therefore, our research mainly
focuses on understanding the apparel industry by studying the various motivational factors
influencing the purchase or selection decisions for formal apparels. To achieve the objective a
descriptive research was conducted and the data was collected through surveys using
structured questionnaire. Factor analysis was performed through SPSS tool to analyse the
data and find the major factors influencing the buying behaviour of customers. After the
analysis we found six major factors which will help the marketers to understand the
consumer purchase intentions and accordingly position their products which in turn will
satisfy the consumer needs for western formals.
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INDEX
S. No. Title Page No.
1 Acknowledgement 2
2 Abstract 3
3 Introduction 5
4 Literature Review 8
5 Research Design 14
6 Findings & Discussions 31
7 Conclusion 33
8 Limitations & Future Research 34
9 List of Tables 35
10 List of Figures 35
11 Appendix 36
12 Bibliography 41
13 References 41
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INTRODUCTION
Introduction to Apparel Industry
Apparel is one of the basic necessities of human civilization along with food, water and
shelter. The Apparel Industry reflects people’s lifestyles and shows their social and economic
status. The Apparel and Textile industry is India’s second largest industry after IT Industry.
At present, it is amongst the fastest growing industry segment and is also the second largest
foreign exchange earner for the country.
The concept of readymade garments was relatively new for the Indians. Traditionally, Indians
preferred dresses stitched by local tailors, who had tailoring units in townships or cities and
catered exclusively to local demand. The growing fashion consciousness during the 1980s
and the convenience offered by ready-to-wear garments were largely responsible for the
development of the branded apparel industry in India. Over the years there have been
sweeping changes in the apparel industry. Once strictly a made-to-order market for clothing
is now transforming into a ready-to-wear market. The growth of the domestic demand for
clothing in India is also linked with the success of the retailing sector. Other factors which
contributed to its growth were: greater purchasing power in the hands of the youth, access to
fashion trends outside the country, and the superior quality of fabrics. Today most of the
international brands have found their way into some of the best malls in the country. Brands
like Mango, Armani and Diesel were unheard of in India till a few years back but today these
brands are found in almost all Indian cities.
Apart from providing one of the basic necessities of life, the textile industry also plays a
pivotal role through its contribution to industrial output, employment generation, and the
export earnings of the country. Currently, it contributes about 14 percent to industrial
production, 4 percent to the GDP, and 30 percent to the country’s export earnings. It provides
direct employment to over 35 million people.
It is said that in the last ten years the fashion industry in India has moved from a very nascent
stage to a full-fledged booming industry. The value of the apparel market in India is
estimated at around Rs.20, 000 crore. The branded apparel market's size is Rs.5, 000 crore
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which is a quarter of the total share. The apparel market is India is categorized into branded
and non-branded. The Top Apparel Brands in India are Madura Garments, Arvind Mills,
Provogue Zodiac Clothing, and Raymond. Giving a closer look it was found that Men’s
apparel market include 46% of the total apparel market in India followed by 17% of the
market size by women and 37% by kids.
Consumer spending on apparel in India has grown over the last five years, touching the
global benchmark of 5 per cent of the total income, according to Consultancy firm McKinsey.
It continues to stimulate consumer demand for apparels and is estimated to grow at the rate of
12-15 per cent annually in terms of growth in rupee value. The Indian government has
targeted the apparel and textiles industry segments to reach $50 billion by the year 2015. One
of the most interesting features of the apparel industry is that, it migrates from high cost
nations to the low cost nations.
TABLE 1: Indian Apparel Industry Value Forecast
YEAR $ BILLION INR BILLION % GROWTH
2005 18.3 806.0 12.10%
2006 20.2 891.3 10.60%
2007 22.3 982.5 10.20%
2008 24.4 1078.4 9.80%
2009 26.7 1179.3 9.40%
2010 29.2 1288.9 9.30%
CAGR: 2005-2010: 9.8%
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Formal Apparel Industry
Formal wear or formal dress is a general term used to describe men’s clothing suitable for
formal events, including weddings, debutante balls, etc. Since, the world is simultaneously
fashion oriented, every women desire to be donned in formal and stylish outfits. In this
commercial world, even women are contributing to the business sector which increases the
demand for women's formal dresses. The reason why it is important to be donned in
impressive formal outfits is that it does not only make you look good during office hours but
also creates a good impression at your workplace and gets you exposure. To meet this
requirement of the female population, the fashion industry has contributed numerous
magnificent formal attires to serve the ladies also along with the men.
Evidently, formal wear is something that is in demand throughout the year, irrespective of
season and climate. This is the reason why most trend setters include formal attires in their
designer collection for outfits. Many national and international brands have come up with
exclusive formal range of collections like Blackberrys, Allen Solly, Van Heusen, Wills
Lifestyle, Marks and Spencers, etc. and have captured a major market share in the industry.
And with the growth of western culture in the Indian corporate and businesses and the entry
of Foreign Institutional Investor (FII) the industry expected to grow in future as well.
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LITERATURE REVIEW ON FORMAL APPARELS
Apparel Industry
The global apparel industry is one of the most important sectors of the economy in terms of
investment, revenue, and trade and employment generation all over the world. Apparel
industry has short product life cycles, tremendous product variety, volatile and unpredictable
demand, long and inflexible supply processes.
The clothing and apparel industry produces finished clothing products made from both
natural and manmade fibbers like cotton, silk, wool, Lenin, polyester, rayon, Lycra and
denim. The important segments covered in apparel industry include kids clothing, men’s
clothing, clothing for women and intimate apparel.
Word Of Mouth' Biggest Influence on Apparel Buyers - Survey, USA
When it comes to buying apparel and electronics, shoppers are most interested in hearing
from their peers about products, retailers and past shopping experiences. In a recent survey,
conducted for the Retail Advertising and Marketing Association by BIG research, consumers
say that word of mouth is still the number one influencer in their apparel (34.3%) and
electronics (44.4%) purchases.
In addition to first-hand knowledge, product reviews (36.8%) and retail advertising inserts
(29.2%) – or circulars – will also resonate with consumers in their electronics purchases this
holiday season. Shoppers looking for the best deal on apparel items, from new jeans to winter
coats, will check out circulars (33.3%) and in-store promotions (30.4%).
“Retailers offering great deals will use many channels this holiday season to make sure their
customers aren’t left in the dark.” said Mike Gatti, Executive Director, Retail Advertising and
Marketing Association. “If retailers can’t get the word out to shoppers about their sales and
promotions this holiday season, the lowest prices in the world won’t bring customers into the
stores.”
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Product placement is another huge driver in adults’ purchase decisions. When it comes to
apparel purchases 11.5 percent say it influences their purchase decision. Blogging also
influences 3.3 percent of their apparel purchases.
Many apparel studies were found to investigate the relationship between an individual
stimulus cue and consumers’ perception of product quality. Many researchers found that
price is often interpreted as an important cue by consumers in perceiving apparel quality.
Davis (1987) used white blouse to investigate how consumers use label information in ratings
of clothing quality, and found that price was one of the five cues that most subjects selected
to assess the product quality. Hatch and Roberts (1985) used socks and sweaters, and Render
and O’Vonnor (1976) used shirts to investigate the influence of price on consumers’
perception of product quality. Both studies found that the higher the price, the higher the
perceived quality.
Author(s): Hye-Shin Kim, (Assistant Professor in the Department of Consumer Studies at the
University of Delaware (USA)), Mary Lynn Damhorst, (Associate Professor of
Textiles and Clothing at Iowa State University in Ames)
Citation: Hye-Shin Kim, Mary Lynn Damhorst, (1993) "Environmental attitude and
commitment in relation to ad message credibility", Journal of Fashion Marketing
and Management, Vol. 3 Iss: 1, pp.18 – 30
Publisher: MCB UP Ltd
The product Management in apparel Industry is done through following steps-
Design- Innovate new design by constantly iterating across your global product development
team.
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Development and pre-production Streamline your product development processes from idea
capture to specification development, sampling, lab dips, testing, approvals and ultimately,
product launch.
Material management Unify and communicate the material information that your global
team needs for production.
Sourcing and production Collaborate with vendors to provide the right compliant materials
and facilitate timely production, thereby ensuring that you stay on trend and meet committed
in-store dates.
Quality assurance Ensure quality soft lines, hard lines and footwear products.
Distribution and logistics Model fully landed costs and track delivery through seamless
supply chain system integration, thereby ensuring that your product assortment reaches the
right customers on a cost effective basis.
Sales and marketing Centrally manage information and images to facilitate optimal store.
Owner’s experience catalog merchandising incorporate customer feedback captured through
social media or store input into future seasonal development.
Seasonal and line planning Assign templates for go-to-market styles and product
assortments across multiple seasons, based on hot trends, past sales and optimal cost.
Finally, Goals of Apparel Industry are-
- Accelerate launch
- Increase profitable growth
- Re-use best practices
- Reduce design costs
Formal Apparel- Formal dressing means dressing well, to be presentable to others. A person
may want to give a little more attention to how he/she dress at work because what you wear
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may be substantially influencing your career path. Looking your professional best in the
workplace can give you a competitive advantage and is also a code of conduct in almost all
the corporate all over the world. It simply means dressing in a way that projects an image of
the sophisticated, successful working individual is or would like to become.
Company's objective in establishing a formal work dress code is to enable our employees to
project the professional image that is in keeping with the needs of our clients and customers
to trust us. Because our industry requires the appearance of trusted business professionals and
we serve clients at our site on a daily basis, a more formal dress code is necessary for our
employees. You must project the image of a trustworthy, knowledgeable business
professional for the clients who seek our guidance, input and professional services.
For Men Formal dressing (Western) includes-
- Suit (Well Tailored)
- Shirts ( Both full and half sleeved)
- TROUSERS
- Tie
For Women formal dressing (Western) includes-
- Formal pant suite
- Skirt
- Formal shirt or Blouse
- Scarf or a tie
Factors important in Apparel industry
(Source- research papers by Bonnie D. Belleau, (Professor at the School of Human Ecology,
Louisiana State University), Jacqueline Didier, (Instructor in the Department of Counselling,
Family Studies and Educational Leadership, at South Eastern Louisiana University), Lori
Broussard, (Based at Louisiana State University), Teresa A. Summers, sources-
OPpapers.com, Emerald Research Papers)
Promotion and Offers- Apparel advertising has evolved from selling a product to selling an
image. Various forms of media play a significant role in shaping attitudes towards apparel.
Two hundred and twenty nine men and women were surveyed about their attitudes
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concerning apparel and media. A reliability analysis of the instrument was conducted. A
discriminant analysis indicated that, of the 56 items on the apparel and media scales, seven
items significantly discriminated between the two groups. There were few differences
between age groups, which suggested that Men and women between ages 25 to 40 held
similar attitudes towards apparel, media and promotional offers. This age group was more
influenced by the Promotional offers, discounts, seasonal sales, brand name and image. Men
and women of age group 45years and above were found to be less influenced by the same
kinds of marketing strategies. They were found to be more tolerant to the ads and
promotional offers and tended to be loyal to the Brand of apparel that they have been buying
and switched rarely.
Comfort- The same survey also suggested that Men and women between ages 25 to 40 were
more experimental with fashions and fads and were less influenced by degree of comfort of
apparels .Older men and women who are 45+ were more resistant of social appearance errors
and more concerned with comfort versus fashion. Older generation also seemed to recognize
the magnitude and penetration of media and short fashion lifecycles in the apparel arena more
than younger women, and was less satisfied with apparel currently available. Results have
implications for apparel manufacturers and retailers, as well as advertising executives.
Brand Image- Today's global market witnesses a cut-throat competition. Many new products
enter the market, stay for a while, and then go obsolete. Fads come into existence and vanish
even quicker than they appear. Rapid changes in the consumers' choices, increase in their
disposable income, globalization, media exposure, and influence of global and psychological
trends attribute to this behaviour. In order to sustain them in the market, it is necessary for
every manufacturer to build a 'brand image' for his product in the market. This is more
important for apparel makers as garments have a short life cycle and trends keep changing
every now and then. Brands create the strongest competitive advantage for the manufacturer,
and the retailer.
Branded apparels not only add a stylish image to the apparel, but it also gives something
extra to the consumers. It enables them to create perceptions about the value of the apparel
and the brand itself. The value of the brand or the 'brand equity' is the difference of cash the
customer pays for a non-branded garment, and a branded one. The customer can buy similar
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apparel somewhere else; without the label and for a lesser price as well. But, branded apparel
with a label on it gives a status symbol to the customer thus satisfying his ego. The reputation
that the brand image carries helps in promoting the product among status savvy consumers.
Private Labels are also one of the category killers and many of the prominent apparel
industries like Ralph Lorren, GAP Inc., etc. are found to be struggling and more focused on
brand image building in order to counterattack.
Price- From the data collected and compiled from many countries across globe research
papers- “Exploring globe for differences between apparel purchasers, browsers and non-
purchasers & their attitudes” and “An investigation of competitive pricing among apparel
retailers and brands” were published. The study reveals that the concept of price tiers is
applicable to apparel retailers and brands. Price tiring is a vehicle for market positioning for
the retail apparel industry. Retailers are enacting a price tier strategy by branding their retail
store formats or engaging store brands as a vehicle of differentiation for a tier. Retailers and
brands can be successful with a price tier strategy, unless they fail to differentiate between
tiers on factors other than on price alone.
It was also found that consumers in south pacific and Asian parts of the world were more
price and discount sensitive than in the other parts of the world.
Apparel retailers operate in an intensely rivalries and highly saturated market environment,
with slow sales growth and high price competition. When many firms are competing for the
same consumer with homogeneous product offerings, price defines the competitive position,
and is as a powerful competitive weapon. However, if a firm is not accustomed to having to
compete on price, it is often hard for firms to adjust to that notion. Competing solely on price
requires a business model that allows for significant cost cutting measures below those of
their competitors. Notably, price alone can rarely build or sustain marketing strategy. Unless
committed to this strategy, through cost models and sourcing strategies, this model is
potentially highly unstable.
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RESEARCH DESIGN
A research design is a framework or blueprint for conducting the marketing research project.
It details the procedures necessary for obtaining the information needed to structure or solve
marketing research problems. Although a broad approach to the problem has already been
developed, the research design specifies the details - the nuts and bolts – of implementing that
approach. A research design lays the foundation for conducting the project. A good research
design will ensure that the marketing research project is conducted effectively and efficiently.
Figure 1
Descriptive Research was used to study the perceptions of product characteristics. As the
name implies, the major objective of descriptive research is to describe something- usually
market characteristics or functions. Descriptive research is conducted for the following
reasons:
1. To describe the characteristics of relevant groups, such as consumers, salespeople,
organizations, or market areas.
2. To estimate the percentage of units in a specified population exhibiting a certain
behaviour.
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3. To determine the perceptions of product characteristics.
4. To determine the degree to which marketing variables are associated.
5. To make specific predictions.
Sampling Design Process
The sampling design process includes five steps. These steps are closely interrelated and
relevant to all aspects of the marketing research project, from problem definition to the
presentation of the results. Therefore, sample design decisions should be integrated with all
other decisions in a research project.
Defining the Target Population
Sampling design begins by specifying the target population. The target population is the
collection of elements or objects that possess the information sought by the researcher and
about which inferences are to be made.
The target population for our project was defined as follows:
Gender: Females
Age Group: 18-35 years
Geographic Area: Delhi/ NCR
Determining the Sampling Frame
A sampling frame is a representation of the elements of the target population. It consists of a
list or set of directions for identifying the target population.
The sampling frame for our research was defined as follows:
Target Sample: a) BIMTECH students
b) Young women at random in the street
c) Other friends and relatives
Problems: The selected sample may be unwilling, unable and biased.
Selecting a Sampling Technique
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Selecting a sampling technique involves several decisions of a broader nature. In probability
sampling, sampling units are selected by chance. It is possible to pre-specify every potential
sample of a given size that could be drawn from the population, as well as the probability of
selecting each sample. Every potential sample need not have the same probability of
selection, but it is possible to specify the probability of selecting any particular sample of a
given size.
Stratified Sampling is a two-step process in which the population is partitioned into sub-
populations, or strata. The strata should be mutually exclusive and collectively exhaustive in
that every population element should be assigned to one and only one stratum and no
population elements should be omitted.
For our research purpose, we chose the following sampling technique:
Probability Sampling – the probability of selection is nonzero and is known in
advance for each population unit
Stratified Sampling – Population is divided into mutually exclusive and exhaustive
strata based on gender, age and geographic area. Simple random samples are then
drawn from each stratum.
Determining the Sample Size
Sample size refers to the number of elements to be included in the study. Determining the
sample size is complex and involves several qualitative and quantitative considerations.
The sample size for our research problem was 158.
DATA COLLECTION DESIGN
1. Data Collection Method: The data was collected from two sources:
Secondary Data: Data from research papers, business magazines, websites of the
companies, etc.
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Primary Data: The survey method of obtaining information was used. Respondents
were asked a variety of questions regarding their behaviour, intentions, attitudes,
awareness, motivations and demographic and lifestyle characteristics. These questions
were asked verbally, in writing or via computer.
2. Data Collection Instrument: Questionnaire, a formalized set of questions for
obtaining information from respondents, was selected for collecting the data.
Structured questions specifying the set of response alternatives and the response
format was significantly used. Multiple choice questions, dichotomous questions and
Likert scale of 5 were used.
Unstructured questions, open-ended questions that respondents answer in their own
words, were used as well.
STATISTICAL DESIGN:
1. Statistical Techniques: Multivariate techniques which are suitable for analyzing
data when there are two or more measurements of each element and the variables are
analyzed simultaneously were used. Both dependence and interdependence
techniques were used.
Factor Analysis, variable interdependence technique, in which the interrelationships
among large number of variable (questionnaire responses) are analyzed and then they
are represented in terms of common underlying dimensions (factors). It brought out
the hidden or latent dimensions relevant in the relationships among product
preferences.
2. Statistical Software: SPSS is a comprehensive and flexible statistical analysis and
data management solution. SPSS can take data from almost any type of file and use
them to generate tabulated reports, charts, and plots of distributions and trends,
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descriptive statistics, and conduct complex statistical analyses. SPSS is available from
several platforms; Windows, Macintosh, and the UNIX systems.
FACTOR ANALYSIS
Factor analysis is a general name denoting a class of procedures primarily used for data
reduction and summarization. In marketing research, there may be a large number of
variance, most of which are correlated and which must be reduced to a manageable level.
Relationships among sets of many interrelated variables are examined and represented in
terms of a few underlying factors. For example, store image may be measured by asking
respondents to evaluate on a series of items on a semantic differential scale. These item
evaluations may then be analyzed to determine the factors underlying store image. It is an
independence technique in that an entire setoff interdependent relationship is examined.
Factor Analysis Model
Mathematically, factor analysis is somewhat similar to multiple regression analysis, in that
each variable is expressed as a linear combination of underlying factors. The amount of
variance a variable shares with all other variables included in the analysis is referred to as
communality. The co-variation among the variables is described in terms of a small number
of common factors plus a unique factor for each variable. These factors are not overtly
observed.
The unique factors are uncorrelated with each other and with the common factors. The
common factors themselves can be expressed as linear combinations of the observed
variables.
It is possible to select weights or factors score coefficients so that the first factor explains the
largest portion of the total variance. Then a second set of weights can be selected, so that the
second factor accounts for most of the residual variance, subject to being uncorrelated with
the first factor. This same principle could be applied to selecting additional weights for the
additional factors. Thus, the factors can be estimated so that their factor scores, unlike the
value of the original variables, are not correlated. Furthermore, the first factor accounts for
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the highest variance in the data, the second factor the second highest, and so on. Several
statistics are associated with factor analysis.
Statistics associated with factor analysis:
Bartlett’s test of sphericity: It is a test statistic used to examine the hypothesis that the
variables are uncorrelated in the population. In other words, the population correlation
matrix is an identity matrix, each variable correlates perfectly with itself (r=1) but has
no correlation with the other variables (r=0).
Correlation matrix: A correlation matrix is a lower triangle matrix showing the
simple correlations, r, between all possible pairs of variables included in the analysis.
The diagonal elements, which are all 1, are usually omitted.
Communality: It is the amount of variance of variable shares with all the other
variables being considered. This is also the proportion of variance explained by the
common factors.
Eigen value: It represents the total variance explained by each factor.
Factor loadings: Factor loadings are simple correlations between the variables and
the factors.
Factor loading plot: It is a plot of the original variables using the factor loadings as
coordinates.
Factor matrix: It contains the factor loadings of all the variables on all the factors
extracted.
Factor scores: Factor scores are the composite scores estimated for each respondent
on the derived factors.
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Kaiser –Meyer-Olkin (KMO) measure of sampling adequacy: The Kaiser –Meyer-
Olkin (KMO) measure of sampling adequacy is an index used to examine the
appropriateness of factor analysis. High values (between 0.5 and 1.0) indicate factor
analysis is appropriate.
Percentage of variance: It is the total variance attributed to each factor.
Residuals: They are the differences between the observed correlations, as given in the
input correlation matrix, and the reproduced correlations, as estimated from the factor
matrix.
Scree plot: It is a plot of the Eigen values against the number of factors in order of
extraction.
Cronbach’s alpha test: One of the most commonly used indicators of internal
consistency is Cronbach’s alpha coefficient. Ideally, the Cronbach alpha coefficient of
a scale should be above .7. Cronbach alpha values are, however, quite sensitive to the
number of items in the scale. With short scales (e.g. scales with fewer than ten items),
it is common to find quite low Cronbach values (e.g. .5). In this case it may be more
appropriate to report the mean interterm correlation for the items. Briggs and Cheek
(1986) recommend an optimal range for the inter-item correlation of .2 to .4. The
cronbach’s alpha value is .717 (greater than 0.5) as shown in Table 2
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Case Processing Summary
Table 2
N %
Cases Valid 154 98.1
Excludeda 3 1.9
Total 157 100.0
a. List-wise deletion based on all variables
in the procedure.
Reliability Statistics
Table 3
Cronbach's
Alpha N of Items
.717 19
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CONDUCTING FACTOR ANALYSIS
The steps involved in conducting factor analysis are as follows:
1. Define the factor analysis problem and identify the variables to be factor analyzed.
2. Construct a correlation matrix of these variables and select a method of factor
analysis.
3. Decide on the number of factors to be extracted and the method of rotation.
4. Interpretation of rotated factors.
5. Depending upon the objectives, calculate the factor scores, or surrogate the selected
variables.
6. Finally, determine the fit of the factor analysis model.
FORMULATION OF THE PROBLEM
Problem formulation includes several tasks. First, the objectives of factor analysis should be
identified. The variables to be included in the factor analysis should be specified based on
past research, theory, and judgement of the researcher. It is important that the variables be
appropriately measured on an interval or ratio scale. An appropriate sample size should be
used.
To illustrate factor analysis, we wish to determine the underlined factors which affect
consumers while purchasing formal apparels. A sample of 158 respondents was surveyed
using structured questionnaire. The respondents were asked to indicate the degree of influence
with the following 20 factors.
1. Price 2. Comfort3. Fabric Quality 4. Novelty5. Sizes Available 6. Durability7. Brand image 8. Service Quality9. Accessibility of brand outlets 10. Brand Ambassador11. Colours 12. Stitch/Tailoring Component13. Promotions & offers 14. Customization15. Designs 16. Latest Trends17. Outlet type 18. Brand Logo19. Proper Fitting 20. Family & Friends opinions
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CONSTRUCTION OF THE CORRELATION MATRIX
The analysis process is based on a matrix of correlations between the variables. Valuable
insights can be gained from an examination of this matrix. For the factor analysis to be
appropriate, the variables must be correlated. If the correlations between all the variables are
small, factor analysis may not be appropriate.
Formal statistics are available for testing the appropriateness of the factor model. Bartlett’s
test for sphericity can be used to test the null hypothesis which is that the variables are not
correlated in the population; in other words, the population correlation matrix is an identity
matrix. The test statistic for sphericity is based on a chi-square transformation of the
determinant of the correlation matrix. A large value of the test statistic will favour the
rejection of the null hypothesis. If the hypothesis cannot be rejected, then the appropriateness
of the factor analysis should be questioned. Another useful statistic is the Kaiser-Meyer-
Olkin (KMO) measure of sampling adequacy. This index compares the magnitudes of the
observed correlation coefficients to the magnitudes of the partial correlation coefficients.
Small values of the KMO statistic indicate that the correlations between pairs of variables
cannot be explained by other variables and that factor analysis may not be appropriate.
Generally, a value greater than 0.5 is desirable.
The Null hypothesis, that the population correlation matrix is an identity matrix, is rejected
by the Bartlett’s test of sphericity. The approximate chi square statistics is 483.856 with 171
degrees of freedom, which is significant at the 0.05 level. The value of the KMO statistic
(0.677) is also large (greater than 0.5) as shown in Table 3. Thus, factor analysis will be
considered an appropriate technique for analysing the correlation matrix.
DETERMINATION OF THE METHOD OF FACTOR ANALYSIS
Once it has been determined that factor analysis is an appropriate technique for analyzing the
data, an appropriate method must be selected. The approach used to derive the weights or
factor score coefficients differentiates the various methods of factor analysis. The two basic
approaches are principals component analysis and common factor analysis. In principals
component analysis, the total variance in the data is considered. The diagonal of the
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correlation matrix consists of unities, and full variance is brought into the factor matrix.
Principal components analysis is recommended when the primary concern is to determine the
minimum number of factors that will account for maximum variance in the data for use in
subsequent multivariate analysis. The factors are called principal components.
In common factor analysis, the factors are estimated based only on the common variance.
Communalities are inserted in the diagonal of the correlation matrix. This method is
appropriate when the primary concern is to identify the underlying dimensions and the
common variance is of interest. This method is also known as principal axis factoring.
We have used principal component analysis. Under communalities, initial column, it can be
seen that the communality for each variable from 1 to 20 is 1 as unities were inserted in the
diagonal of the correlation matrix. The table labelled initial Eigen values gives the Eigen
values. The Eigen values for the factors are as expected, in decreasing order of magnitude as
we go from factor1 to factor 20.The Eigen value for a factor indicates the total variance
attributed to that factor. The total variance accounted for by all 20 factors is 20, which is
equal to the number of variables. Factor 1 account for a variance of 3.387 which is (3.287/20)
or 16.43% of the total variance. Likewise second factor accounts for 2.021 which is
(2.021/20) or 10.1% of the total variance. Several considerations are involved in determining
the number of factors that should be used in the analysis.
Table 4: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .671
Bartlett's Test of
Sphericity
Approx. Chi-Square 483.856
Df 171
Sig. .000
25
Table 5 : Communalities
Initial Extraction
Price 1.000 .615
Fabric quality 1.000 .539
Sizes available 1.000 .423
Brand image 1.000 .666
Accessibility 1.000 .632
Colours 1.000 .457
Promotions and offers 1.000 .582
Designs 1.000 .634
Fitting 1.000 .522
Comfort 1.000 .584
Novelty 1.000 .602
Durability 1.000 .558
Quality of service 1.000 .492
Brand ambassador 1.000 .617
Stitch or tailoring 1.000 .509
Customization 1.000 .486
Latest trends 1.000 .496
Brand logo 1.000 .616
Family and friends 1.000 .506
Extraction Method: Principal Component
Analysis.
26
Table 6: Total Variance Explained
Compo
nent
Initial Eigen values Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.287 17.300 17.300 3.287 17.300 17.300
2 2.021 10.639 27.939 2.021 10.639 27.939
3 1.688 8.886 36.825 1.688 8.886 36.825
4 1.253 6.595 43.420 1.253 6.595 43.420
5 1.208 6.356 49.776 1.208 6.356 49.776
6 1.080 5.687 55.462 1.080 5.687 55.462
7 .949 4.994 60.456
8 .882 4.644 65.100
9 .839 4.418 69.517
10 .796 4.192 73.709
11 .765 4.027 77.737
12 .714 3.756 81.492
13 .677 3.566 85.058
14 .595 3.131 88.189
15 .542 2.852 91.042
16 .482 2.539 93.581
17 .456 2.401 95.982
18 .420 2.211 98.192
19 .343 1.808 100.000
27
DETERMINATION OF THE NUMBER OF FACTORS
It is possible to compute as many principal components as there are variables, but in doing so,
no parsimony is gained. In order to summarize the information contained in the original
variables, a smaller number of factors should be extracted. The question is, how many?
Several procedures have been suggested for determining the number of factors. These include
approaches based on Eigen values, scree plot, percentage of variance accounted for, etc.
Determination Based on Eigen values: In this approach, only factors with Eigen values
greater than 1.0 are retained; the other factors are not included in the model. An Eigen value
represents the amount of variance associated with the factor. Hence, only factors with a
variance greater than 1.0 are included. Factors with variance less than 1.0 are no better than a
single variable, because, due to standardization, each variable has a variance of 1.0. If the
number of variables is less than 20, this approach will result in a conservative number of
factors.
Based on the Eigen value criterion first six factors (factor1 to factor 6) are selected.
Determination based on Scree Plot: A scree plot is a plot of the Eigen values against the
number of factors in order of extraction. The shape of the plot is used to determine the
number of factors. Typically, the plot has a distinct break between the steep slope of factors,
with large Eigen values and a gradual trailing off associated with the rest of the factors. This
gradual trailing off is referred to as the scree. Experimental evidence indicates that the point
at which the scree begins denotes the true number of factors. Generally, the number of factors
determined by a scree plot will be one or a few more than that determined by the Eigen value
criterion. From the scree plot shown in Figure 2, a distinct break occurs at 4 factors.
Determination Based on Percentage of Variance: In this approach, the number of factors
extracted is determined so that the cumulative percentage of variance extracted by the factor
reaches a satisfactory level. What level of variance is satisfactory depend upon the problem.
However, it is recommended that the factors extracted should account for at least 60 percent
of the variance.
28
Finally from the cumulative percentage of variance accounted for, we see that the first 6
factors account for 55.46% of the variance. So finally from the above criterion we have
selected 6 factors.
Rotate factors:
An important output from factor analysis is the factor matrix, also called the factor pattern
matrix. The factor matrix contains the coefficients used to express the standardized variables
in terms of the factors. These coefficients, the factor loadings, represent the correlations
between the factors and the variables. A component with a large absolute value indicates that
the factor and variable are closely related. The coefficients of the factor matrix can be used to
interpret the factors.
The most commonly used method for rotation is varimax procedure. This is an orthogonal
method of rotation that minimizes the number of variables with high loadings on a factor,
thereby enhancing the interpretability of the factors. Orthogonal rotation results in factors that
are uncorrelated.
Figure 2
29
Table 7: Component Matrixa
Component
1 2 3 4 5 6
Price .122 .453 .417 .316 .239 -.253
Fabric quality .385 .289 -.425 .185 -.137 -.271
Sizes available .434 .030 -.279 .125 -.374 .025
Brand image .320 -.542 -.339 .377 -.084 .070
Accessibility .517 -.194 .202 .149 -.416 -.302
Colours .302 .176 .304 .155 -.323 .339
Promotions and offers .361 .156 .590 .166 -.162 -.156
Designs .417 .013 .267 .413 .279 .375
Fitting .452 .491 -.117 .134 -.146 .155
Comfort .356 .422 -.158 -.059 .101 .491
Novelty .452 -.034 .347 -.453 -.217 -.155
Durability .494 .245 -.149 -.302 .337 -.167
Quality of service .576 -.001 -.240 -.248 -.056 -.194
Brand ambassador .365 -.638 .161 -.057 -.065 .210
Stitch or tailoring .510 .108 -.391 -.204 .053 .200
Customization .526 -.068 .211 -.365 .165 .012
Latest trends .385 -.305 .294 -.128 .375 .109
Brand logo .366 -.574 -.151 .224 .250 -.128
Family and friends .328 .135 -.127 .306 .413 -.316
Extraction Method: Principal Component Analysis.
a. 6 components extracted.
30
Table 8: Rotated Component Matrixa
Component
1 2 3 4 5 6
Price -.264 -.024 -.111 .380 .112 .613
Fabric quality -.020 .026 .680 -.010 .040 .270
Sizes available .177 .023 .575 .150 .159 -.115
Brand image .745 -.138 .295 -.050 .015 -.039
Accessibility .295 .150 .356 .609 -.157 -.015
Colours -.011 -.034 .072 .474 .448 -.158
Promotions and offers -.044 .134 -.036 .718 .086 .195
Designs .347 .014 -.160 .241 .580 .306
Fitting -.166 .079 .452 .169 .485 .141
Comfort -.139 .189 .189 -.124 .691 .014
Novelty -.053 .602 .070 .433 -.085 -.193
Durability -.079 .603 .233 -.137 .118 .319
Quality of service .132 .502 .470 .020 -.011 .032
Brand ambassador .640 .259 -.124 .193 .038 -.295
Stitch or tailoring .114 .392 .413 -.221 .348 -.038
Customization .122 .654 -.033 .163 .127 .018
Latest trends .376 .463 -.311 .102 .137 .122
Brand logo .729 .144 .061 -.061 -.126 .203
Family and friends .156 .106 .186 -.046 .020 .658
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 13 iterations.
31
Table 9: Component Transformation Matrix
Component 1 2 3 4 5 6
1 .383 .596 .462 .343 .359 .194
2 -.821 -.036 .250 .051 .390 .327
3 -.126 .124 -.642 .744 .020 .049
4 .362 -.730 .113 .244 .191 .476
5 .129 .283 -.480 -.486 .125 .649
6 .122 -.121 -.260 -.175 .816 -.453
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Interpretation of Factors
Interpretation is facilitated by identifying the variables that have large loadings on the
same factor. That factor can then be interpreted in terms of the variables that load high on
it. Another useful aid in the interpretation is to plot the variables using the factor loadings
as coordinates. Variables at the end of an axis are those that have high loadings on only
that factor, and hence describe the factor. Variables near the origin have small loadings on
both the factors. Variables that are not near any of the axes are related to both the factors.
Now, the factors are being extracted according to the highest loading in each column. Like in
first column the highest loading is at brand image i.e. .745, second factor is customization
with highest loading of .654, third factor is fabric quality with highest loading of .680, fourth
factor is promotion and offers with highest loading of .718, fifth factor is comfort with
highest loading of .691 and the sixth factor is family and friends with highest loading of .658.
FINDINGS AND DISCUSSIONS
After conducting the consumer survey and performing factor analysis on data collected we
were able to come out with the six major factors by which the consumers were most
influenced while purchasing the western formal apparels.
32
These six factors are listed below:
1. Brand Image
2. Customization
3. Fabric Quality
4. Promotion and offers
5. Comfort
6. Family and friends
Now let’s discuss each of the factors in details as to how do they influence the purchase
decisions of the consumers.
First one is Brand Image. It is an impression in the mind of the consumer about a brand's
total personality (real and imaginary qualities and shortcomings). It is developed over time
through advertising campaigns with a consistent theme, and is authenticated through the
consumers' direct experience. In the case of formal apparels too it plays a major role.
Consumers do make a choice according to the image of the brand they are having in their
mind.
Second is Customization which means marketers can differentiate products by making them
customized to an individual. As companies have grown proficient at gathering information
about individual customers and business partners, and as their factories are being designed
more flexibly, they have increased their ability to individualize their market offerings,
messages and media. Western formal apparel industry is also not lagging behind. With
increasing trends, customers will demand customization for western formals as well.
Next is the Fabric Quality. Our results have shown that consumers do care about the fine
quality of the fabric they are buying. Fabric should be durable and of premium quality.
Therefore, brands should be concerned of the quality and standards of their clothing.
Another important factor found by our research is Promotion and offers. Promotion is
advancement of a product through publicity and/or advertising and offers are the special
incentives like discount, coupons, rebates, gifts, sweep stakes etc. given by the companies to
encourage buyers to purchase their products. Seeing this as the major factor influencing the
33
consumer buying behaviour the marketers should indulge in good amount of promotion and
offers to retain and enhance the customer base.
The fifth factor is comfort. So the brands should keep in mind that the apparels should be
designed and styled in such a way that excels in providing the maximum comfort to the
consumers.
Finally, family and friends are also one of the major factors that influence the buying
behaviour of the costumers. So companies cannot ignore the social factors as their opinions
too matter in the decision making of the consumer. Therefore the brands should be
appropriately positioned to target the opinion leaders.
CONCLUSIONS
From the findings, we get a clear insight on the priority consumers give to various factors in
the process of decision making while shopping for formal apparels. If one excepts the
definition of brand image by Reynolds and Gutman (1984), they have defined brand image in
terms of the stored meaning that an individual has in memory, suggesting that what is called
up from memories provided meaning we attribute most basically to image, (Dawn and
George, 1990), then as founded by our research “brand image is in the eye of the consumer”.
Other factors found in the priority list of the consumer while deciding on the formal apparel
in decreasing order of priority are – Customization, fabric quality, promotion and offers,
comfort, and family and friends. From a theoretical point of view apparel products are seen as
having, in the first place, intrinsic physical properties (such as design, materials, construction
and finishes), specifying what the item is, and, second, behavioural properties (functional and
aesthetical), specifying what the product can achieve (Brown and Rice, 1998; Gersak, 2002).
34
Limitations and Future Research
Due to multi-dimensionality of the concept, a qualitative research design has been and
therefore the sample size is small. Conjoint analysis could not be carried out due to the
statistical constraint of having more than 3 factors. Findings in this study could be used to
direct future quantitative studies with a lager sample size that would ensure better
representation and further conjoint analysis can be carried out with less number of factors to
identify the most sort after attribute that might influence the decision making of the consumer
of apparel industry. Similar and more comprehensive research could be carried out for the
entire apparel industry.
35
LIST OF TABLES
Table No. Title Page No.
1 Indian Apparel Industry 6
2 Case Process Summary 21
3 Cronbach Alpha Results 21
4 KMO and Bartlett’s Test 24
5 Communalities 25
6 Total Variance 26
7 Component Matrix 29
8 Rotated Component Matrix 30
9 Component Transformation Matrix 31
LIST OF FIGURES
Figure No. Title Page No.
1 Research Design 14
2 Scree Plot 28
36
APPENDIX
Survey Form for Formal Apparel
* Required
1. Name
2. Age *
3. Marital Status *
Single
Married
4. Place *
5. Profession *
Student
Service
Self-Employed
Other:
6. How often do you shop for formal Apparels? *
Frequently (Once in a month)
Occasionally (Once in Six Month)
Seldom (Once in a Year)
7. Which brand comes to your Mind FIRST while shopping for Formals? *
37
8. How much importance do you give to price factor while going for formals? * 1.Least
influenced & 5.Highly influenced
1 2 3 4 5
9. To what extent do fabric quality matters? * 1.Least influenced & 5.Highly influenced
1 2 3 4 5
10. Rate the importance given to sizes available in brands? * 1.Least influenced & 5.Highly
influenced
1 2 3 4 5
11. How much role does the brand image play in making your purchase decisions? * 1.Least
influenced & 5.Highly influenced
1 2 3 4 5
12. To what extent does the accessibility of brand outlets influence your brand preferences *
1.Least influenced & 5.Highly influenced
1 2 3 4 5
13. How much are you influenced by colours while going for western formals? * 1.Least
influenced & 5.Highly influenced
38
1 2 3 4 5
14. How much are you influenced by the promotions and offers given by the brands? *
1.Least influenced & 5.Highly influenced
1 2 3 4 5
15. How much does the design provided by the brand influence your purchase decision? *
1.Least influenced & 5.Highly influenced
1 2 3 4 5
16. From where do you prefer to buy formals? *
Retail Outlets
Exclusive Showrooms
Factory Outlets
Other:
18. How much importance do you pay to the proper fitting of formal clothing? * 1.Least
influenced & 5.Highly influenced
1 2 3 4 5
19. What rank will you give to the comfort factor of formal clothing? * 1.Least influenced &
5.Highly influenced
39
1 2 3 4 5
20. How much are you influenced by the novelty in apparels provided? * 1.Least influenced
& 5.Highly influenced
1 2 3 4 5
21. To what extent do the durability matters? * 1.Least influenced & 5.Highly influenced
1 2 3 4 5
22. What priority will you give to the quality of service provided? * 1.Least influenced &
5.Highly influenced
1 2 3 4 5
23. To what extent does brand ambassador matters while purchasing the formals? * 1.Least
influenced & 5.Highly influenced
1 2 3 4 5
24. What importance will you give to the stitch/tailoring component of the formal apparels? *
1.Least influenced & 5.Highly influenced
1 2 3 4 5
40
25. What role does customization play while buying formals? 1. Least influenced & 5.Highly
influenced
1 2 3 4 5
26. to what extent does latest trends matters in case of formal clothing? * 1.Least influenced
& 5.Highly influenced
1 2 3 4 5
27. How much does brand logo attract you? * 1.Least influenced & 5.Highly influenced
1 2 3 4 5
28. How much does the opinion of your friends and family matter to you? * 1.Least
influenced & 5.Highly influenced
1 2 3 4 5
0
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BIBLIOGRAPHY
1. Kotler Keller Koshi Jha: Marketing Management-A south Asian perspective, 13th
edition by Pearson
2. Naresh K Malhotra, Satyabhushan Dash: Marketing Research – An applied
orientation, 5th edition by Pearson
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