Sheik Project Final

Chapter-1

1.INTRODUCTION

The India Retail Industry is the largest among all the industries, accounting for over 10 per

cent of the country GDP and around 8 per cent of the employment. The Retail Industry in

India has come forth as one of the most dynamic and fast paced industries with several

players entering the market. But all of them have not yet tasted success because of the heavy

initial investments that are required to break even with other companies and compete with

them. The India Retail Industry is gradually inching its way towards becoming the next boom

industry. The total concept and idea of shopping has undergone an attention drawing change

in terms of format and consumer buying behavior, ushering in a revolution in shopping in

India. Modern retailing has entered into the Retail market in India as is observed in the form

of bustling shopping centers, multi-storied malls and the huge complexes that offer shopping,

entertainment and food all under one roof. A large young working population with median

age of 24 years, nuclear families in urban areas, along with increasing workingwomen

population and emerging opportunities in the services sector are going to be the key factors in

the growth of the organized Retail sector in India. The growth pattern in organized retailing

and in the consumption made by the Indian population will follow a rising graph helping the

newer businessmen to enter the India Retail Industry. In India the vast middle class and its

almost untapped retail industry are the key attractive forces for global retail giants wanting to

enter into newer markets, which in turn will help the India Retail Industry to grow faster.

Indian retail is expected to grow 25 per cent annually. Modern retail in India could be worth

US$ 175-200 billion by 2016. The Food Retail Industry in India dominates the shopping

basket. The Mobile phone Retail Industry in India is already a US$ 16.7 billion business,

growing at over 20 per cent per year. The future of the India Retail Industry looks promising

with the growing of the market, with the government policies becoming more favorable and

the emerging technologies facilitating operations.

1.1 OVERVIEW OF RETAIL MARKET

India is the country having the most unorganized retail market. Traditionally it is a family

livelihood, with their shop in the front and house at the back, while they run the retail business.

More than 99% retailer function in less than 500 square feet of shopping space. Global retail

consultants KSA Technopak have estimated that organized retailing in India is expected to touch

Rs 35,000 crore in the year 2005-06. The Indian retail sector is estimated at around Rs 900,000

crore, of which the organized sector accounts for a mere 2 per cent indicating a huge potential

market opportunity that is lying in the waiting for the consumer-savvy organized retailer.

Purchasing power of Indian urban consumer is growing and branded merchandise in categories

like Apparels, Cosmetics, Shoes, Watches, Beverages, Food and even Jewellery, are slowly

becoming lifestyle products that are widely accepted by the urban Indian consumer. Indian

retailers need to advantage of this growth and aiming to grow, diversify and introduce new

formats have to pay more attention to the brand building process. The emphasis here is on retail

as a brand rather than retailers selling brands. The focus should be on branding the retail business

itself. In their preparation to face fierce competitive pressure, Indian retailers must come to

recognize the value of building their own stores as brands to reinforce their marketing

positioning, to communicate quality as well as value for money. Sustainable competitive

advantage will be dependent on translating core values combining products, image and

reputation into a coherent retail brand strategy. There is no doubt that the Indian retail scene is

booming. Today the organized players have attacked every retail category. The Indian retail

scene has witnessed too many players in too short a time, crowding several categories without

looking at their core competencies, or having a well thought out branding strategy.

1.2 Problem Statement

India is experiencing dramatic changes in the retail industry over the decade with the emergence

of new retail market ,which has impacted ,affected the way consumer as they have more choice

to choose .hence ,in Chennai there is tremendous amount of potential on behalf of that that the

future group plans to start new store in royapettah .the marketing department of “Big Bazaar”

attempt to examine the consumer profile and consumer behavior pattern towards the

hypermarket industry

1.3 Research Objective

Primary objective:

Study about a “customer profile for new store launch”

Secondary Objective:

To analyze the Demographic profile of consumers

To analyze the psychographic profile of consumers

1.6 COMPANY PROFILE

COMPANY PROFILE – FUTURE GROUP

Pantaloon Retail (India) Limited, is India’s leading retailer

that operates multiple retail formats in both the value and lifestyle

segment of the Indian consumer market. Headquartered in Mumbai

(Bombay), the company operates over 7 million square feet of retail

space, has over 1000 stores across 53 cities in India and employs

over 25,000 people.

The company’s leading formats include Pantaloons, a chain of fashion outlets, Big

Bazaar, a uniquely Indian hypermarket chain, Food Bazaar, a supermarket chain, blends the

look, touch and feel of Indian bazaars with aspects of modern retail like choice, convenience

and quality and Central, a chain of seamless destination malls. Some of its other formats

include, Depot, Shoe Factory, Brand Factory, Blue Sky, Fashion Station, all, Top 10, MBazaar

and Star and Sitara. The company also operates an online portal, futurebazaar.com.

A subsidiary company, Home Solutions Retail (India) Limited, operates Home Town, a

large-format home solutions store, Collection i, selling home furniture products and E-Zone

focused on catering to the consumer electronics segment.

The US-based National Retail Federation (NRF) recently awarded the International

Retailer of the Year 2007 Pantaloon Retail and the Emerging Market Retailer of the Year 2007

at the World Retail Congress held in Barcelona. Pantaloon Retail is the flagship company of

Future Group, a business group catering to the entire Indian consumption space.

FUTURE GROUP

Future Group is one of the country’s leading business groups present in retail, asset

management, consumer finance, insurance, retail media, retail spaces and logistics. The group’s

flagship company, Pantaloon Retail (India) Limited operates over 7 million square feet of retail

space, has over 1000 stores across 53 cities in India and employs over 25,000 people. Some of

its leading retail formats include, Pantaloons, Big Bazaar, Central, Food Bazaar, Home Town,

eZone, Depot, Future Money and online retail format, futurebazaar.com.

Future Group companies includes, Future Capital Holdings,

Future Generali India Indus League Clothing and Galaxy Entertainment

that manages Sports Bar, Brew Bar and Bowling Co. Future Capital

Holdings, the group’s financial arm, focuses on asset management and

consumer credit. It manages assets worth over $1 billion that are being

invested in developing retail real estate and consumer-related brands

and hotels.

The group’s joint venture partners include Italian insurance major, Generali, French

retailer ETAM group, US-based stationary products retailer, Staples Inc and UK-based Lee

Cooper and India-based Talwalkar’s, Blue Foods and Liberty Shoes.

Future Group’s vision is to, “Deliver Everything, Everywhere, Every time to Every

Indian Consumer in the most profitable manner.”

CORPORATE STATEMENTS

FUTURE GROUP MANIFESTO

“Future” – the word that signifies optimism, growth, achievement, strength, beauty,

rewards and perfection. Future encourages us to explore areas yet unexplored, write rules yet

unwritten; create new opportunities and new successes. To strive for a glorious future brings to

us our strength, our ability to learn, unlearn and re-learn our ability to evolve.

We, in Future Group, will not wait for the Future to unfold itself but create future

scenarios in the consumer space and facilitate consumption because consumption is

development. Thereby, we will effect socio-economic development for our customers,

employees, shareholders, associates and partners.

Our customers will not just get what they need, but also get them where, how and when

they need.

We will not just post satisfactory results. We will write success stories.

We will not just operate efficiently in the Indian economy. We will evolve it.

We will not just spot trends; we will set trends by marrying our understanding of the

Indian consumer to their needs of tomorrow.

It is this understanding that has helped us succeed. And it is this that will help us succeed

in the Future. We shall keep relearning. And in this process, do just one thing. “Rewrite

Rules. Retain Values.”

GROUP VISION

Future Group shall deliver Everything, Everywhere, Every time for Every Indian

Consumer in the most profitable manner.

GROUP MISSION

We share the vision and belief that our customers and stakeholders shall be served only

by creating and executing future scenarios in the consumption space leading to economic

development.

We will be the trendsetters in evolving delivery formats, creating retail realty, making

consumption affordable for all customer segments – for classes and for masses.

We shall infuse Indian brands with confidence and renewed ambition.

We shall be efficient, cost- conscious and committed to quality in whatever we do.

We shall ensure that our positive attitude, sincerity, humility and united determination

shall be the driving force to make us successful.

CORE VALUES

Indian ness: confidence in ourselves.

Leadership: to be a leader, both in thought and business.

Respect & Humility: to respect every individual and be humble in our conduct.

Introspection: leading to purposeful thinking.

Openness: to be open and receptive to new ideas, knowledge and information.

Valuing and Nurturing Relationships: to build long-term relationships.

Simplicity and Positivism in our thought, business and action.

Adaptability: to be flexible and adaptable, to meet challenges.

Flow: to respect and understand the universal laws of nature.

BOARD OF DIRECTORS

MR. KISHORE BIYANI, MANAGING DIRECTOR

Kishore Biyani is the Managing Director of Pantaloon Retail (India) Limited and the Group

Chief Executive Officer of Future Group. He has led Pantaloon Retail’s emergence as the

India’s leading retailer operating multiple retail formats that now cater to almost the

consumption basket of a large section of Indian consumers.

Kishore Biyani led the company’s foray into organized retail with the opening up of the

Pantaloons family store in 1997. This was followed in 2001 with the launch of Big Bazaar, a

uniquely Indian hypermarket format that democratized shopping in India. It blends the look,

touch and feel of Indian bazaars with aspects of modern retail like choice, convenience and

quality. This was followed by a number of other formats including Food Bazaar, Central and

Home Town.

The year, 2006 marked the evolution of Future Group, that brought together the multiple

initiatives taken by group companies in the areas of Retail, Brands, Space, Capital, Logistics

and Media.

Kishore Biyani advocates ‘Indian ness’ as the core value driving the group. The group’s

corporate credo is ‘Rewrite Rules, Retain Values.’

Prime Minister, Dr. Manmohan Singh in 2006, awarded the Ernst & Young Entrepreneur

of the Year 2006 in the Services Sector and the Lakshmipat Singhania - IIM Lucknow

Young Business Leader Award to Kishore Biyani. He was also awarded the CNBC First

Generation Entrepreneur of the Year 2006.

MR. GOPIKISHAN BIYANI, WHOLETIME DIRECTOR

Gopikishan Biyani, is a commerce graduate and has more than twenty years of experience in

the textile business.

MR. RAKESH BIYANI, WHOLETIME DIRECTOR

Rakesh Biyani, is a commerce graduate and has been actively involved in category

management; retail stores operations, IT and exports. He has been instrumental in the

implementation of the various new retail formats.

MR. VED PRAKASH ARYA, DIRECTOR

Ved Prakash Arya, is an engineer by training and is a graduate of the Indian Institute of

Management, Ahmedabad. Prior to joining Pantaloon Retail, he was the CEO of Globus.

MR. SHAILESH HARIBHAKTI, INDEPENDENT DIRECTOR

Shri Shailesh Haribhakti, is a Chartered Accountant, Cost Accountant, and a Certified

Internal Auditor. He is the Deputy Managing Partner of Haribhakti & Co., Chartered

Accountants and past president of Indian merchant Chambers. He is on the Board of several

Public Limited Companies, including Indian Petrochemicals Corporation Ltd., Ambuja

Cement Eastern Ltd. etc. He is on the Board of Company since June 1, 1999.

MR. S DORESWAMY, INDEPENDENT DIRECTOR

S. Doreswamy, is a former Chairman and Managing Director of Central Bank of India and

serves on the board of DSP Merrill Lynch Trustee Co and Ceat Limited among others.

DR. D O KOSHY, INDEPENDENT DIRECTOR

D. O. Koshy, holds a doctorate from IIT, Delhi and is the Director of National Institute of

Design (NID), Ahmedabad. He has over 24 years of rich experience in the textiles and

garment industry and was instrumental in the setting up of NIFT centers in Delhi, Chennai

and Bangalore. He is a renowned consultant specializing in international marketing and

apparel retail management.

MS. ANJU PODDAR, INDEPENDENT DIRECTOR

Anju Poddar, holds a Bachelor of Engineering from University of Oklahoma and is a

Director, NIFT, Hyderabad chapter. She also serves on the board of Maharishi Commerce

Ltd and Samay Books Ltd, among others.

MS. BALA DESHPANDE, INDEPENDENT DIRECTOR

Bala Deshpande, is Independent Director, Pantaloon Retail (India) Ltd. and also serves on the

boards of Deccan Aviation, Nagarjuna Construction, Welspun India and Indus League

Clothing Ltd, among others.

MR. ANIL HARISH, INDEPENDENT DIRECTOR

Anil Harish, is the partner of DM Harish & Co. Associates & Solicitors and an LLM from

University of Miami. He also serves on the board of Mahindra Gesco, Unitech, IndusInd

Bank and Hinduja TMT, among others.

AWARDS AND RECOGNITION

AT 2008

The Reid & Taylor Awards For Retail Excellence 2008

Retail Leadership Award: Kishore Biyani

Retail Best Employer of the Year: Future Group

Retailer of The Year: Home Products and Office Improvements: Home Town

The Reid & Taylor Awards for Retail Excellence are an important feature of the Asia Retail

Congress - Asia’s single most important global platform to promote world-class retail

practices - and are aimed at honouring the best, in Asian Retail scenario. India played host to

Asia Retail Congress 2008.

AT 2007

Images Retail Awards

Most Admired Retail Face of the Year: Kishore Biyani

Most admired retailer of the year: Large format, multi product store: Big Bazaar

Most admired retailer of the year: Food and Grocery: Food Bazaar

Most admired retailer of the year: Home & office improvement: HomeTown

Most admired Retail Company of the year: Pantaloon Retail (India) Ltd.

National Retail Federation Awards

International Retailer for the Year 2007 – Pantaloon Retail (India) Ltd

World Retail Congress Awards

Emerging Market Retailer of the Year 2007 – Pantaloon Retail (India) Ltd

Hewitt Best Employers 2007

PC World Indian Website Awards

Best Indian Website In The Shopping Category - Futurebazaar.com

Reader’s Digest Trusted Brands Platinum Awards

Trusted Brands Platinum Award (Supermarket Category) – Big Bazaar

At 2006

Retail Asia Pacific Top 500 Awards

Asia Pacific Best of the Best Retailers – Pantaloon Retail (India) Ltd

Best Retailer in India – Pantaloon Retail (India) Ltd

The Retail Asia publication in association with Euro Monitor and KPMG honors the best

retailers in 14 countries across the Asia Pacific region. The awards were presented in

Singapore in October 2006.

Images Retail Awards

Best Value Retail Store – Big Bazaar

Best Retail Destination – Big Bazaar

Best Food & Grocery Store – Food Bazaar

The Images Retail Awards are decided through a nationwide consumer & industry poll

and nominations followed by performance assessment by team of analysts and jury.

Readers’ Digest Awards

Platinum Trusted Brand Award - Big Bazaar

CNBC Awaaz Consumer Awards

Most Preferred Large Food & Grocery Supermarket – Big Bazaar

Reid & Taylor Awards for Retail Excellence

Retail Entrepreneur of the Year – Kishore Biyani

Chapter-2-Review of literature

2.1 Conceptual Review

Retail comes from the French word retailler, which refers to "cutting off, clip and divide" in

terms of tailoring (1365). It first was recorded as a noun with the meaning of a "sale in small

quantities" in 1433 (French). Its literal meaning for retail was to "cut off, shred, paring". Like the

French, the word retail in both Dutch and German (detailhandel and Einzelhandel respectively),

also refers to the sale of small quantities of items

Retailing consists of the sale of goods or merchandise from a very fixed location, such as a

department store, boutique or kiosk, or by mail, in small or individual lots for direct consumption

by the purchaser. Retailing may include subordinated services, such as delivery. Purchasers may

be individuals or businesses. In commerce, a "retailer" buys goods or products in large quantities

from manufacturers or importers, either directly or through a wholesaler, and then sells smaller

quantities to the end-user. Retail establishments are often called shops or stores. Retailers are at

the end of the supply chain. Manufacturing marketers see the process of retailing as a necessary

part of their overall distribution strategy. The term "retailer" is also applied where a service

provider services the needs of a large number of individuals, such as a public utility, like electric

power.Shops may be on residential streets, shopping streets with few or no houses or in a

shopping mall. Shopping streets may be for pedestrians only. Sometimes a shopping street has a

partial or full roof to protect customers from precipitation. Online retailing, a type of electronic

commerce used for business-to-consumer (B2C) transactions and mail order, are forms of non-

shop retailing.Shopping generally refers to the act of buying products. Sometimes this is done to

obtain necessities such as food and clothing; sometimes it is done as a recreational activity.

Recreational shopping often involves window shopping (just looking, not buying) and browsing

and does not always result in a purchase.

.

2.2 Research Findings review

To segment consumer good and service markets the food compaies use market information and

collect based on certain key customer- product- or situation-related criteria. Jobber-Fahy (2006)

These are classified as segmentation bases and include profile (e.g. who are my market and

where are they) such as behavioural (e.g. where, when, and how does my market behave) than

psychological criteria (e.g. why does my market behave that way).

Psychological criteria: They are used for segmenting consumer product and service markets

include using attitudes and perceptions (e.g. negative feelings about fast food); psychographics

or the lifestyles of customers (e.g. extrovert, fashion conscious, high achiever), and the types

of benefits sought by customers from products and brands and their consumption choices

Mentally visualizing a prototypical member of a market segment is critically important .Being

able to mentally “walk in their shoes” helps a marketer understand what articulated needs this

person might have , and how we can communicate most effectively to the segment they

represent (winstein 1986: 1987).it has been claimed that “the fifth ‘P’ of the marketing is the

personalization”(Schultz 1991).That is , the classic four P’s of marketing –Product ,price ,place

(distribution),and promotion are only effective to the extent that they are used make a

“personal” connection with the customer . “Every sale is personal sale”……although the

important point about persuasion is not new (Carnegie 1937),it does not seem understood by

most marketer. Many marketing decision,however,are becoming more driven by data bases than

by a personal understanding of the customer .when told the importance of “really knowing your

customer ,” its easy to imagine marketers and advertiser who would claim they already know

their customer Knowing the profile characteristics of target groups can help marketing strategists

to tailor the product or service and promote the product or service more effectively.Each group

can be targeted and reached with a distinct marketing mix (McDonald & Dunbar, 1995).

The purpose of market segmentation is to identify homogeneous groups of people with similar

characteristic of customer (Laws, 1997; Doole & Lowe, 2001). This will enable the marketer to

more closely match a product or service to the needs of the target market. It is known that

customers respond better to offerings that are tailored and aimed directly at them, rather than at

the broader public (Trigg, 1995).

(

Khan, Chang & Horridge, 1992) indicated the selection of media is based on the characteristics

of media, the demographics and psychographics of the target market, and the characteristics of

the product. Results indicated that self-consciousness and demographic Variables such as age,

education, occupation, marital status, ethnic group, and political outlook affected the usage of

newspapers, magazines, radio, television,

personality characteristic that is closely related to expertise is self – efficacy, which refers to

individuals’ beliefs that they have the ability and the resources to successfully perform a specific

task .Personality traits have been shown to influence consumer decision-making behavior. The

personality variables self-confidence and anxiety were reported to be related to consumer choice

behavior (Horton, 1979).

Gutman & Mills, 1982) investigated the effects of life-style and self-perception on consumers’

purchase intention or behavior toward clothing products Results concluded that consumers with

different self-perceptions have different attitudes or responses toward fashion/clothing products.

Morgan and Doran (2003) argue that psychographic segmentation, if used to design and

implement a communication strategy, can result in more effective and efficient campaigns, and

change the communicator into a strategist rather than a tactician, moving his or her work from

that of an inexact art to an exact science.

Clawson, and Vinson (1978) express the importance of values in predicting the consumer

behaviour that values can surpass the contributions of other major constructs including attitudes,

product attributes, degree of deliberating, product classification, and life style. The value

expressed in a consumption experience is the result of the emotions that accompany the

consumption experience.

People decide which product to buy in different ways. The influence that people exercise over

decisions, as well as their brand loyalty, variety seeking behavior, information search,

distribution channel used and decision making units (centralized versus decentralized) all impact

the brand that they will purchase. While this focus is more prevalent in business-to-business

settings, certain elements are perfectly applicable to consumer segmentation as well(Haley and

Russel,1984).

The mix of cultural, social, personal, psychological factors and previous experiences, all which

influence behaviour, is largely uncontrollable. Because of the influence exerted upon patterns of

buying, it is essential that as much effort as possible is put into understanding how these factors

interact and ultimately how they influence decisions (Lamb et al., 2002).

CHAPTER – III

RESEARCH METHODOLOGY

3.1 RESEARCH DESIGN

The research design phase deals with the detailing of procedure that will be adapted to

carry out the research study. The kind of research that is carried out, whether the study is carried

out in the field or in the laboratory, are decided .the details of data collection procedures and the

schedule of analytical procedures to be used in order to accomplish the research objective (set in

the earlier stage of research process) are also dealt with in research design.

3.3 SAMPLING

Sampling is an inescapable part of research, since populations are large and resources are limited

.sampling is aimed at obtaining representativeness and determining size of the sample.

Sampling is aimed at two major objectives.

The sample is representative of the population.

The size of the sample is adequate to get the desired accuracy.

3.6 Administration of Questionnaire

A descriptive research was used to analysis the customer profile. Primary data were

collected for the research. An undisguised structured questionnaire, was used for the research.

The respondents were asked to provide their personal profile,a nominal scaling technique were

used for the research.The sample size used was 500 respondents. The population from which our

sample was selected is from Royapetta, Mylapore, Manthaveli, Triplicane, Thousand lights and

Gopalapuram in Chennai, Tamilnadu ,India. The customer were selected in and around the area

in which the new retail hyper market is going to launch.

3.7 Statistical tools used:

frequency analysis

chi-square

Bivariate correlations

one-way ANOVA

In Post hoc test

Chapter -4

DATA ANALYSIS AND

INTERPRETATION

4.1 FREQUENCY

ANALYSIS

TABLE 4.1.1:

Table showing the Age wise classification of the respondents

The table displays the number and percentage of cases for each value of variables.

Frequency tables are useful for summarizing categorical variables – variables with limited

number of distinct categories. The information in a frequency table can be graphically displayed

in a Pie chart.

chart showing the Age wise classification of the respondents

Age Frequency Valid Percent20 -30 169 33.831-40 206 41.241-50 84 16.8above 50 41 10.0Total 500 100.0

Inference The above Chart visualizes the age profile of the customers of 500 respondents, 206

respondents (41.2%) were in the age group between 31 and 40 years, 169 respondents (33.8%)

were in the age group between 20 and 30 years and 84 respondents (16.8%) were in the age

group between 41 and 50 years

TABLE 4.1.2:

Table showing the Gender wise classification of the respondents

Gender Frequency Valid Percent

male250 50.0

female250 50.0

Total500 100.0

Chart showing the Gender wise classification of the respondents

Inference

The above Chart visualizes the gender profile of the customers of 500 respondents, 250

respondents (50%) of them are females and 250 respondents (50%) are male.

TABLE 4.1.3:

Table showing the Family size wise classification of the respondents

Family size Frequency Valid Percent

1 - 2 4 .8

3- 4 314 62.8

5 - 6 167 33.4

above 6 15 3.0

Total 500 100.0

Chart showing the Family size wise classification of the respondents

Inference

The above Chart visualizes the family size profile of the customers of 500 respondents, 314

respondents had 3 to 4 members in their family, 167 respondents had 5 to 6 members in their

family and 15 respondents had above 6 members in their family .

TABLE 4.1.4:

Table showing the Family income wise classification of respondents

Family income Frequency Valid Percent

below 10000 9 1.8

10001 - 15000 59 11.8

15001 - 25000 254 50.8

25001- 35000 150 30.0

above 35000 28 5.6

Total 500 100.0

Chart showing the Family income wise classification of respondents

Inference

The above Chart visualizes the family income profile of the customers of 500 respondents ,254

respondents of them earn 15000 – 25000, 150respondents of them earn 25000 – 35000 and 59

respondents of them earn 10001 – 15000.

TABLE 4.1.5:

Table showing the Education qualification wise classification of respondents

Education qualification Frequency Valid Percentilliterate 3 .6primary 4 .8SSLC 65 13.0HSC 113 22.6Graduate 277 55.4Post Gradute 38 7.6Total 500 100.0

Chart showing the Education qualification wise classification of respondents

InferenceThe above Chart visualizes the education profile of the customers of 500 respondents, 277

respondents of the customer had graduate qualification, 113 respondents of the customer had

HSC qualification,38 respondents of the customer had post graduate qualification.

TABLE 4.1.6:

Table showing the Occupation wise classification of respondents

Occupation Frequency Valid Percent

Govt employee 11 2.2

Private Employee 139 27.8

Professional 69 13.8

self employed 89 17.8

Home maker 192 38.4

Total 500 100.0

Chart showing the Occupation wise classification of respondents

Inference

The above Chart visualizes the Occupation profile of the customers of 500 respondents, 192

respondents of them are home maker, 139 respondents of them are Private employees, 89

respondents of them are self- employed and 69 respondents of them are professional.

TABLE 4.1.7:

Table showing the Religion wise classification of respondents

Religion Frequency Valid Percent

Hindu 306 61.2

Christian 40 8.0

Muslim 132 26.4

jain 22 4.4

Total 500 100.0

Chart showing the Religion wise classification of respondents

Inference

The above Chart visualizes the Religion profile of the customers of 500 respondents, 306

respondents of the customer from Hindu religion, 132 respondents of the customer from Muslim

religion and 40 respondents of the customer from Christian religion

TABLE 4.1.8:

Table showing the Lifestyle wise classification of respondents

Life style Frequency Valid Percent

culture-oriented306 61.2

sports-oriented72 14.4

outdoor-oriented 122 24.4

Total500 100.0

Chart showing the Lifestyle wise classification of respondents

Inference

The above Chart visualizes the lifestyle profile of the customers of 500 respondents, 306

respondents (61.2%) in culture oriented lifestyle, 122 respondents (24.4%) in Outdoor oriented

lifestyle and 72 respondents (14.4%) in Sports oriented lifestyle.

TABLE 4.1.9:

Table showing the Often Visit wise classification of respondents

Often visit Frequency Valid Percent

weekly 80 16.0

weekly twice 24 4.8

monthly 252 50.4

monthly twice 144 28.8

Total 500 100.0

Chart showing the Often Visit wise classification of respondents

Inference

The above chart visualizes the often visit behavior of the customers of 500 respondents, 252

respondents (50.4%)of them visit the Department /hypermarket/supermarket monthly144

respondents (28.8%)of them visit the Department /hypermarket/supermarket monthly twice and

80 respondents (50.4%)of them visit the Department /hypermarket/supermarket weekly.

TABLE 4.1.10:

Table showing the User Status wise classification of respondents

User status Frequency Valid Percent

Non-User 158 31.6

Ex-User 47 9.4

Potential 153 30.6

First time user 11 2.2

Regular user 131 26.2

Total 500 100.0

Chart showing the User Status wise classification of respondents

Inference

The above chart visualizes the User Status of Hypermarket of the customers of 500

respondents, 158 respondents of them non-user of hyper market, 153 respondents of them

potential user of hyper market and 131 respondents of them Regular user of hyper market

TABLE 4.1.11:

Table showing the Disposable Income wise classification of respondents

Disposable income Frequency Valid Percent

< 1000 10 2.0

1001 - 2500 146 29.2

2501 - 5000 276 55.2

5001 - 10000 67 13.4

> 10001 1 .2

Total 500 100.0

Chart showing the Disposable Income wise classification of respondents

Inference The above chart visualizes the often visit behavior of the customers of 500 respondents,

276 respondents disposable income is 2501 -5000 , 146 respondents disposable income is 1001-

2500 and 68 respondents disposable income is 5001-10000.

TABLE 4.1.12:

Table showing the Benefits Avail wise classification of respondents

Benefits avail Frequency Valid Percent

quality 208 41.6

service 95 19.0

economy 174 34.8

speed 23 4.6

Total 500 100.0

Chart showing the Benefits Avail wise classification of respondents

Inference

The above chart visualize the Benefits avail in hyper market for customer of 500

respondents, 208 respondents of them avail quality , 174 respondents of them avail Economy and

95 respondents of them avail service.

TABLE 4.1.13:

Table showing the Food Prefer wise classification of respondents

Food prefer Frequency Valid Percent

Veg83 16.6

Non-Veg194 38.8

Both223 44.6

Total500 100.0

Chart showing the Food Prefer wise classification of respondents

Inference The above table visualize the food profile of customer of 500 respondents, 222

respondents of them prefer both Veg and Non-Veg and also 194 respondents of them are prefer

Non-Vegetarian .

TABLE 4.1.14:

Table showing the Preferred food for Consume wise classification of respondents

Preferred food for consume Frequency Valid Percent

packaged foods 25 5.0

Home made foods 224 44.8

Both 251 50.2

Total 500 100.0

Chart showing the Preferred food for Consume wise classification of respondents

Inference

The above chart visualize the food consumption profile of customer of 500 respondents,

251 respondents of them are prefer to consume both Packaged and homemade foods and 224

respondents of them are prefer to consume homemade foods.

TABLE 4.1.15:

Table showing the Kind of Dress wise classification of respondents

Kind of dress Frequency Valid Percent

Readymade 298 59.6

Tailored 96 19.2

Both 106 21.2

Total 500 100.0

Chart showing the Kind of Dress wise classification of respondents

Inference

The above chart visualize the dress preference of customer of 500 respondents, more

than 298 respondents of them prefer the readymade dresses, and 106 respondents from them

prefer both readymade and tailored dresses

TABLE 4.1.16:

Table showing the Store for Groceries wise classification of respondents

Store for groceries Frequency Valid PercentBigBazar 27 5.4Reliance fresh 99 19.8Nilgiris 102 20.4Deapartment Store 124 24.8Provision store 78 15.6others 70 14.0Total 500 100.0

Chart showing the Store for Groceries wise classification of respondents

Inference The above chart visualize the preferred stores for groceries for customers of 500

respondents, 124 respondents of them are prefer the department stores , 102 respondents of them

are prefer Nilgiris and 99 respondents of them are prefer Reliance Fresh ,70 respondents of

them are prefer other stores (like nearby provision store ,spencers,Heritage Fresh..)

TABLE 4.1.17:

Table showing the Store for Apparel wise classification of respondents

Store for apparel Frequency Valid PercentPothys 110 22.0the chennai silks 72 14.4The Kumaran silks 37 7.4Jeychadra textiles 37 7.4Nalli silks 54 10.8Branded Showroom 90 18.0others 100 20.0

Total 500 100.0

Chart showing the Store for Apparel wise classification of respondents

Inference The above chart visualize the preferred stores for Apparel for customers of 500

respondents, 110 respondents of them are prefer Pothys, 99 respondents of them are prefer

Branded Showroom and 100 respondents of them are prefer others (shops in

T.Nagar ,Rmkv,Naidu Hall..etc)

TABLE 4.1.18:

Table showing the Store for Furniture wise classification of respondents

Store for furniture Frequency Valid Percent

Damro 47 9.4

saravana store 300 60.0

Bigbazar 14 2.8

Furn World 15 3.0

jayabharatham 38 7.6

Others 86 17.2

Total 500 100.0

Chart showing the Store for Furniture wise classification of respondents

Inference The above chart visualize the preferred stores for Furniture for customers of 500

respondents, 300 respondents of them are prefer Saravana Store,86 respondents of them are

prefer others(shops in royapettah) and 38 respondents of them are prefer Jayabharatham.

TABLE 4.1.19:

Table showing the Store for Electronics wise classification of respondents

Store for electronics Frequency Valid Percentviveks 234 46.8jainsons 52 10.4vasanth&co 68 13.6saravana store 103 20.6BigBazar 3 .6others 40 8.0Total 500 100.0

Chart showing the Store for Electronics wise classification of respondents

Inference

The above chart visualize the preferred stores for electronics for customers of 500

respondents,184 respondents of them are prefer Viveks,103 respondents of them are prefer

Saravana Store and 90 respondents of them are prefer others ( shops in paris, Rich Street )

TABLE 4.1.20:

Table showing the Store for Gifts wise classification of respondents

Store for Gifts Frequency Valid PercentArchies 255 51.0Ellora 17 3.4memori 28 5.6landmark 64 12.8spencers 44 8.8BigBazar 42 8.4Others 50 10.0Total 500 100.0

Chart showing the Store for Gifts wise classification of respondents

InferenceThe above chart visualize the preferred stores for Gifts for customers of 500 respondents,

255 respondents of them are prefer Archies ,64 respondents of them are prefer landmark and 80

respondents of them are prefer others ( shops in Royapettah..etc )

TABLE 4.1.21:

Table showing the Mode of Purchase wise classification of respondents

Mode of purchase Frequency Valid Percent

Cash 385 77.0Credit card 86 17.2Debit card 11 2.2All 18 3.6Total 500 100.0

Chart showing the Mode of Purchase wise classification of respondents

Inference

The above chart visualize the mode of purchase of customers of 500 respondents, 385

respondents of them made cash purchase and 86 respondents of them use credit card .

TABLE 4.1.22:

Table showing the Know Information wise classification of respondents

Know information from Frequency Valid Percent

TV ads 382 76.4News paper 25 5.0Bills 12 2.4Hoardings 3 .6Reference 66 13.2Radio 10 2.0others 2 .4Total 500 100.0

Chart showing the Know Information wise classification of respondents

Inference

The above chart visualize the information gathered by customers of 500 respondents,

382 respondents of them gather information from TV ads ,66 respondents of them gather

information from reference and 25 respondents of them gather information from News Paper.

4.2 CHI-SQUARE ANALYSIS

The chi-square measures test the hypothesis that the row and column variables in a cross

tabulation are interdependent. A low significance value typically below 0.05 indicates that there

may be some relationship between two variables. While the chi-square measures may indicate

that there is a relationship between two variables, they do not indicate the strength or direction of

relationship.

Table – 4.2.1

Age Vs Attitude

Null Hypothesis (Ho) : There is no significant association between age

and Attitude

Alternate Hypothesis (H1) : There is a significant association between age and

. Attitude

AgeAttitude

Totalenthusiastic positive indifferent hostile

Below 20 0 1 0 0 1

20 -30 68 97 4 0 169

31-40 48 144 13 1 206

41-50 22 60 1 1 84

above 50 3 29 8 0 40

Total 141 331 26 2 500

Asymptotic value (P) = 0.000 Expected value = 0.05

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 44.866(a) 12 .000

Likelihood Ratio 41.944 12 .000

Linear-by-Linear Association 22.028 1 .000

Number of Valid Cases 500

Inference

From the above table, it is inferred that the calculated P value (.000) which is less

than .05 (level of significance). Hence Null hypothesis is rejected and the alternate hypothesis is

accepted. Hence, the age of the respondents is having significant association with attitude of the

respondents

Table - 4.2.2

Age Vs Spend Holiday

Null Hypothesis (Ho) : There is no significant association between age

and spend Holiday

Alternate Hypothesis (H1) : There is a significant association between age and

. spend Holiday

Agespend holiday

Totalbeach temple go to movies tour go with friends visit relatives shopping others

Below 20 0 0 0 1 0 0 0 0 1

20 -30 54 14 23 2 55 5 16 0 169

31-40 91 33 21 9 14 23 14 1 206

41-50 37 16 1 4 1 18 7 0 84

above 50 13 7 1 2 1 11 3 2 40

Total 195 70 46 18 71 57 40 3 500


Chi-Square Tests




Linear-by-Linear Association .209 1 .648

N of Valid Cases 500

Inference:



accepted. Hence, the age of the respondents is having significant association with Spend Holiday

Table - 4.2.3

Family income Vs Often visit to hyper market

Null Hypothesis (Ho) : There is no significant association between family

Income and Often visit to hyper market.

Alternate Hypothesis (H1): There is a significant association between family

income and Often Visit to hypermarket.


Chi-Square Tests

Inference:



accepted. Hence, Family income having significant association with Often visit

Table - 4.2.4

Family income Vs User Status of Hypermarket


Income and User Status of Hypermarket.


Family incomeOften visit

Totaldaily weekly weekly twice monthly monthly twice

below 10000 0 1 0 8 0 9

10001 - 15000 0 3 1 43 12 59

15001 - 25000 1 29 13 146 65 254

25001- 35000 0 34 8 53 55 150

above 35000 0 12 2 2 12 28

Total 1 79 24 252 144 500






. income and User Status of Hypermarket.

Family incomeUser Status

TotalNon-User Ex-User Potential First time user Regular user

below 10000 6 1 2 0 0 9

10001 - 15000 38 2 17 1 1 59

15001 - 25000 93 34 84 3 40 254

25001- 35000 20 9 49 7 65 150

above 35000 1 1 1 0 25 28

Total 158 47 153 11 131 500


Chi-Square Tests


Pearson Chi-Square 157.571(a

)16 .000


Linear-by-Linear

Association120.675 1 .000


Inference:



accepted. Hence, Family income having significant association with User Status

Table - 4.2.5

Family income Vs Disposable income


Income and Disposable income


. income and Disposable income.

Family Income Disposable income Total

< 1000 1001 - 2500 2501 - 5000 5001 - 10000 > 10001

below 10000 4 4 1 0 0 9

10001 - 15000 0 47 12 0 0 59

15001 - 25000 1 68 180 5 0 254

25001- 35000 5 23 78 44 0 150

above 35000 0 4 5 18 1 28

Total 10 146 276 67 1 500


Chi-Square Tests


Pearson Chi-Square 320.176(a

)16 .000


Linear-by-Linear



Inference:



accepted. Hence, Family income having significant association with Disposable income

Table - 4.2.6

Family income Vs Store for groceries


Income and Store For groceries


. income and Store For groceries.

Family income Store For Groceries Total

BigBazar Reliance fresh Nilgiris Deapartment Store Provision store others

below 10000 0 0 2 3 3 1 9

10001 - 15000 4 9 8 10 24 4 59

15001 - 25000 9 34 61 83 44 23 254

25001- 35000 14 41 27 28 6 34 150

above 35000 0 15 4 0 1 8 28

Total 27 99 102 124 78 70 500


Chi-Square Tests






Inference:

From the above table, it is inferred that the calculated P value (.000) which is less than .05 (level

of significance). Hence Null hypothesis is rejected and the alternate hypothesis is accepted.

Hence, Family income having significant association with Store for groceries.

Table -4.2.7

Family income Vs Store for apparel


Income and Store For apparel.


. income and Store For apparel.

Family

Income

store for apparelTota

lpothysthe chennai

silks

The Kumaran

silks

Jeychadra

textiles

Nalli

silks

Branded

Showroomothers

below 10000 2 1 0 2 0 0 4 9

10001 - 15000 8 7 8 3 1 4 28 59

15001 - 25000 72 50 17 20 20 20 55 254

25001- 35000 26 14 9 9 29 52 11 150

above 35000 2 0 3 3 4 14 2 28

Total 110 72 37 37 54 90 100 500


Chi-Square Tests






Inference:

From the above table, it is inferred that the calculated P value (.000) which is less than .05

(level of significance). Hence Null hypothesis is rejected and the alternate hypothesis is

accepted. Hence, Family income having significant association with Store for apparel.

Table -4.2.8

Family income Vs Mode of purchase


income and Mode of Purchase.


. income and Mode of Purchase.


Chi-Square Tests

Inference:



accepted. Hence, Family income having significant association with Mode of purchase

Table -4.2.9

Family size Vs Disposable income


size and Disposable income.


. size and Disposable income

Family IncomeMode of Purchase

Totalcash credit Debit All

below 10000 8 0 0 1 9

10001 – 15000 51 8 0 0 59

15001 – 25000 200 43 6 5 254

25001- 35000 108 28 4 10 150

above 35000 18 7 1 2 28

Total 385 86 11 18 500




Linear-by-Linear




Chi-Square Tests

.

Inference:



accepted. Hence, Family size having significant association with Disposable Income

Table -4.2.10

Occupation Vs Life style

Null Hypothesis (Ho) : There is no significant association between Occupation and

. Life Style.

Alternate Hypothesis (H1): There is a significant association between Occupation and

. Life Style

Family SizeDisposable income

Total< 1000

1001 -

2500

2501 -

5000

5001 -

10000 > 10001

1 - 2 0 0 4 0 0 4

3- 4 6 105 172 31 0 314

5 - 6 4 35 97 30 1 167

above 6 0 6 3 6 0 15

Total 10 146 276 67 1 500






Occupation Lifestyle Total

culture-

oriented

sports-

oriented

outdoor-

oriented

Govt employee 8 1 2 11

Private Employee 71 31 37 139

Professional 32 6 31 69

self employed 59 7 23 89

Home maker 136 27 29 192

Total 306 72 122 500


Chi-Square Tests




Linear-by-Linear



Inference:



accepted. Hence, Occupation having significant association with Life Style.

4.3 Bivariate Correlation

The correlations table displays Pearson correlation coefficients, significance values, and the

number of cases with non-missing values. Pearson correlation coefficients assume the data are

normally distributed. The Pearson correlation coefficient is a measure of linear association

between two variables. The values of the correlation coefficient range from -1 to 1. The sign of

the correlation coefficient indicates the direction of the relationship, The absolute value of the

correlation coefficient indicates the strength, with larger absolute values indicating stronger

relationships. The correlation coefficients on the main diagonal are always 1.0.

Table- 4.3.1

Correlations between Family income and Disposable income

Family income Disposable income

Family income

Pearson

Correlation1 .501(**)

Sig. (2-tailed) .000

N 500 500

Disposable income

Pearson

Correlation.501(**) 1


N 500 500

** Correlation is significant at the 0.01 level (2-tailed).

Inference:

Here we have positive relation between Disposable income and Family income . because

disposable income and family income has a perfect positive linear relationship with itself.

Correlations above the main diagonal are a mirror image of those below.

Table- 4.3.2

Correlations between Age and Value

Age Values

Age Pearson



N 500 500

Values

Pearson



N 500 500


Inference:

Here we have positive relation between Age and values. Because Age and values has a perfect

positive linear relationship with itself. Correlations above the main diagonal are a mirror image

of those below.

Table- 4.3.3

Correlations between Family Income and User Status of Hyper Market

Family income User status

Family income Pearson



N 500 500

User status Pearson



N 500 500


Inference:

Here we have positive relation between Family income and User Status. Because Family

income and User Status has a perfect positive linear relationship with itself. Correlations above

the main diagonal are a mirror image of those below.

4.4 ANOVA & POST-HOC

In one-way ANOVA, the total variation is partitioned into two components. Between Groups

represents variation of the group means around the overall mean. Within Groups represents

variation of the individual scores around their respective group means.

TABLE-4.4.1

Family Income and Spend Holiday

Null Hypothesis : There is no significant difference between Family income and

Spend holiday

Alternate Hypothesis : There is significant difference between Family income and

Spend holiday

Sum of

Squares Df Mean Square F Sig.

Between

Groups61.803 4 15.451 3.305 .011

Within Groups 2313.965 495 4.675

Total 2375.768 499

Inference

Significance indicates the significance level of the F-test in the above table the

significance level is observed to be .011 which is lesser than 0.05. Hence there is significance

difference between the group’s namely Spend holiday and Family income. Small significance

values (<0.05) indicate the group difference. The calculated F value is 3.305 and its P value is

0.011 which is lesser than the accepted significance level of .05. Therefore we reject Null

hypothesis and accept alternative hypothesis. Hence, we conclude there is significant difference

between the family income and spend holiday .

Post Hoc Test – Multiple Comparisons

Post hoc test

Planned contrasts or post hoc comparisons are methods used to determine which groups

differ. Planned contrasts are one method for comparing means in a one way ANOVA.

Dependent Variable: Spend holiday

(I) family income (J) family income Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval

Lower Bound Upper Bound

below 10000 10001 - 15000 -.849 .774 1.000 -3.03 1.33

15001 - 25000 -1.317 .733 .732 -3.38 .75

25001- 35000 -1.369 .742 .657 -3.46 .72

above 35000 -2.401(*) .828 .039 -4.74 -.06

10001 - 15000 below 10000 .849 .774 1.000 -1.33 3.03

15001 - 25000 -.467 .312 1.000 -1.35 .41

25001- 35000 -.520 .332 1.000 -1.46 .42

above 35000 -1.551(*) .496 .019 -2.95 -.15

15001 - 25000 below 10000 1.317 .733 .732 -.75 3.38

10001 - 15000 .467 .312 1.000 -.41 1.35

25001- 35000 -.052 .223 1.000 -.68 .58

above 35000 -1.084 .431 .121 -2.30 .13

25001- 35000 below 10000 1.369 .742 .657 -.72 3.46

10001 - 15000 .520 .332 1.000 -.42 1.46

15001 - 25000 .052 .223 1.000 -.58 .68

above 35000 -1.032 .445 .208 -2.29 .22

above 35000 below 10000 2.401(*) .828 .039 .06 4.74

10001 - 15000 1.551(*) .496 .019 .15 2.95

15001 - 25000 1.084 .431 .121 -.13 2.30

25001- 35000 1.032 .445 .208 -.22 2.29

Inference

This table lists the pair wise comparisons of the group means for all selected post hoc

procedures. Mean differences lists the differences between the sample means. Significant lists

the probability that the population mean difference is zero.

A 95% confidence interval is constructed for each difference. If this interval contains zero, the

two groups do not differ.

TABLE-4.4.2

Family Income and Disposable Income

Null Hypothesis : There is no significant difference between Family income

. and Disposable income

Alternate Hypothesis : There is significant difference between Family income and

. Disposable income.

Sum of


Between Groups 61.206 4 15.302 42.798 .000

Within Groups 176.976 495 .358

Total 238.182 499

Inference

From the above table the significance level is less than 0.05. Therefore we reject null

hypothesis and accept alternate hypothesis. Hence there is a significance difference between the

family income with that of Disposable income and further planned contrasts or post hoc

comparisons or methods used to determine which groups differ.

Dependent Variable: Disposable income

(I) family income (J) family income Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval


below 10000 10001 - 15000 -.537 .214 .124 -1.14 .07

15001 - 25000 -1.077(*) .203 .000 -1.65 -.51

25001- 35000 -1.407(*) .205 .000 -1.99 -.83

above 35000 -1.905(*) .229 .000 -2.55 -1.26

10001 - 15000 below 10000 .537 .214 .124 -.07 1.14

15001 - 25000 -.541(*) .086 .000 -.78 -.30

25001- 35000 -.870(*) .092 .000 -1.13 -.61

above 35000 -1.368(*) .137 .000 -1.75 -.98

15001 - 25000 below 10000 1.077(*) .203 .000 .51 1.65

10001 - 15000 .541(*) .086 .000 .30 .78

25001- 35000 -.329(*) .062 .000 -.50 -.16

above 35000 -.827(*) .119 .000 -1.16 -.49

25001- 35000 below 10000 1.407(*) .205 .000 .83 1.99

10001 - 15000 .870(*) .092 .000 .61 1.13

15001 - 25000 .329(*) .062 .000 .16 .50

above 35000 -.498(*) .123 .001 -.85 -.15

above 35000 below 10000 1.905(*) .229 .000 1.26 2.55

10001 - 15000 1.368(*) .137 .000 .98 1.75

15001 - 25000 .827(*) .119 .000 .49 1.16

25001- 35000 .498(*) .123 .001 .15 .85

Inference

From the post hoc test by inferring the significance value, if the significance

value is less than 0.05 which will be denoted in the mean difference column with the * mark.

Hence there is a significant difference between Disposable income and Family income

TABLE-4.4.3

Family Income and User Status of Hyper market

Null Hypothesis : There is no significant difference between Family income and User

. Status of Hypermarket

Alternate Hypothesis : There is significant difference between Family income and User Status

Of Hypermarket

ANOVA

Sum of


Between Groups 302.330 4 75.582 41.781 .000

Within Groups 895.470 495 1.809

Total 1197.800 499

Inference



family income with that of User Status and further planned contrasts or post hoc comparisons or

methods used to determine which groups differ.

Dependent Variable: user status

(I) family income (J) family income

Mean

Difference (I-

J) Std. Error Sig.

95% Confidence Interval


below 10000 10001 - 15000 -.173 .481 1.000 -1.53 1.18

15001 - 25000 -.905 .456 .478 -2.19 .38

25001- 35000 -2.031(*) .462 .000 -3.33 -.73

above 35000 -3.123(*) .515 .000 -4.58 -1.67

10001 - 15000 below 10000 .173 .481 1.000 -1.18 1.53

15001 - 25000 -.732(*) .194 .002 -1.28 -.18

25001- 35000 -1.858(*) .207 .000 -2.44 -1.28

above 35000 -2.950(*) .309 .000 -3.82 -2.08

15001 - 25000 below 10000 .905 .456 .478 -.38 2.19

10001 - 15000 .732(*) .194 .002 .18 1.28

25001- 35000 -1.126(*) .139 .000 -1.52 -.74

above 35000 -2.218(*) .268 .000 -2.97 -1.46

25001- 35000 below 10000 2.031(*) .462 .000 .73 3.33

10001 - 15000 1.858(*) .207 .000 1.28 2.44

15001 - 25000 1.126(*) .139 .000 .74 1.52

above 35000 -1.092(*) .277 .001 -1.87 -.31

above 35000 below 10000 3.123(*) .515 .000 1.67 4.58

10001 - 15000 2.950(*) .309 .000 2.08 3.82

15001 - 25000 2.218(*) .268 .000 1.46 2.97

25001- 35000 1.092(*) .277 .001 .31 1.87

Inference



Hence there is a significant difference between Family income.and User Status of hyper market

TABLE-4.4.4

Values and Educational Qualification

Null Hypothesis : There is no significant difference between Values and Educational . .

. Qualification

Alternate Hypothesis : There is significant difference between Values and Educational . . . . . .

Qualification

Sum of


Between Groups 26.476 5 5.295 5.110 .000

Within Groups 511.946 494 1.036

Total 538.422 499

Inference



Educational Qualification with that of Values and further planned contrasts or post hoc

comparisons or methods used to determine which groups differ.

Dependent Variable: values

(I) education qualification (J) education qualification

Mean

Difference (I-

J) Std. Error Sig.

95% Confidence Interval


illiterate Primary .500 .778 1.000 -1.79 2.79

SSLC 1.262 .601 .545 -.51 3.03

HSC 1.389 .595 .301 -.37 3.15

Graduate 1.700 .591 .063 -.04 3.44

Post Gradute 1.447 .611 .272 -.35 3.25

primary Illiterate -.500 .778 1.000 -2.79 1.79

SSLC .762 .524 1.000 -.79 2.31

HSC .889 .518 1.000 -.64 2.42

Graduate 1.200 .513 .294 -.31 2.71

Post Gradute .947 .535 1.000 -.63 2.53

SSLC Illiterate -1.262 .601 .545 -3.03 .51

Primary -.762 .524 1.000 -2.31 .79

HSC .128 .158 1.000 -.34 .60

Graduate .439(*) .140 .028 .02 .85

Post Gradute .186 .208 1.000 -.43 .80

Illiterate -1.389 .595 .301 -3.15 .37

Primary -.889 .518 1.000 -2.42 .64

SSLC -.128 .158 1.000 -.60 .34

Graduate .311 .114 .096 -.02 .65

Post Gradute .058 .191 1.000 -.51 .62

Graduate Illiterate -1.700 .591 .063 -3.44 .04

Primary -1.200 .513 .294 -2.71 .31

SSLC -.439(*) .140 .028 -.85 -.02

HSC -.311 .114 .096 -.65 .02

Post Gradute -.253 .176 1.000 -.77 .27

Post Gradute Illiterate -1.447 .611 .272 -3.25 .35

Primary -.947 .535 1.000 -2.53 .63

SSLC -.186 .208 1.000 -.80 .43

HSC -.058 .191 1.000 -.62 .51

Graduate .253 .176 1.000 -.27 .77

Inference



Hence there is a significant difference between Values and Educational Qualification

4.5 Findings

Frequency Analysis

41 % respondents were in the age group between 31 and 40 years

50 % respondents of them are male

63 % respondents had 3 to 4 members in their family

51 % respondents of them earn 15000 – 25000

55 % respondents of the customer had graduate qualification

38 % respondents of them home maker

61 % respondents of the customer from Hindu religion

61 % respondents in culture oriented lifestyle

50.4 % respondents of them visit the Department /hypermarket/supermarket monthly

32 % respondents of them non-user of hyper market

Chi-Square Analysis

The age of the respondents is having significant association with attitude of the respondent

The age of the respondents is having significant association with Spending Holiday

Family income of the respondents is having significant association with Store for apparel.

Family size of the respondents is having significant association with Disposable Income

Occupation of the respondents is having significant association with Life Style.

Bivariate Correlation

Family income and Disposable income has a perfect positive linear relationship

Age and values has a perfect positive linear relationship

Family income and User Status has a perfect positive linear relationship

Conclusion

Sheik Project Final

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