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CHAPTER - 4

RESEARCH METHODOLOGY

4.1 Introduction

4.1.1 Consumer attitude

4.1.2 Purchase decision

4.1.3 Decision machining process

4.2 Statement of Research Problem

4.3 Rationale of the Study

4.4 Objectives of the Research Study

4.5 Universe of the Study

4.6 Sample Design

4.6.1 Sampling Units

4.6.2 Sampling Method

4.6.3 Sample Size

4.7 Sources of Data

4.7.1 Questionnaire Development

4.8 Research Hypothesis

4.9 Data Analysis and Statistical Tools

4.10 Supportive Technology

4.11 Limitations of the Study

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Research is a scientific and systematic search for pertinent information on a

specific topic. The formidable problem that follows the task of the defining the

research problem is the preparation of the design of the research project, popularly

known as the “research design”. Decision regarding what, where, when, how much,

by what means concerning an inquiry or a research study constitute a research design.

4.1 Introduction The interaction between technology and society has been studied in the

context of a technological revolution in industry: automated factories, massive

business computers, and so forth. Households eventually enter a similar technological

race. The technological revolution affects daily life within a household in time

allocation patterns, in the choice of social functions, in the transmittal of cultural

values, and in overall human behavior. When a given technology begins to affect the

life of a household, it is a safe conclusion that the technology is being integrated into

the social system and is accepted as a basis for future social behavior. Other

technologies popularized in the past two or three decades have introduced structural

changes and new ideologies within the household: washing machines and

refrigerators, entertainment oriented products such as radio, television, and stereo

equipment, architectural changes in the design of kitchens, bathrooms, and other units

of physical space, all give new meaning to child rearing, women's roles, family

interactions, shopping behavior, and value systems.

4.1.1 Consumer Attitude It is the attitude of the consumer as to why, when, how and where the

consumer intends to buy the product. It blends elements of psychology, sociology,

social psychology, anthropology and economics. It attempts to understand the buyers’

decision process. It studies characteristics of individual consumer such as

demographic and behavioral variables in an attempt to understand people’s want. It

also tries to assess the influence on the consumer from groups such as family, friends,

reference groups and society in general. Customer behavior study is based on

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consumer buying behavior, with the consumer playing three distinct roles of user,

payer, and buyer.

4.1.2 Purchase Decision In many purchase situations the consumer is confronted with a complex set of

alternatives. He has to choose among a variety of alternative products from a variety

of products. From a variety of products he makes selection, based on size, color,

models and brands. The consumer can make decisions about when and where to buy

the products. Some purchase decisions are routine and may not require much

attention. Some other purchase decisions include more cash outlays. The economic

concept of consumers’ sovereignty points out that the consumer is the king of the

market. According to this concept all the productive resources are deployed so as to

fulfill the needs of the consumer. Hence, it is important to understand in depth the

term purchase decision of the consumer.

4.1.3 Decision Making Process There are five stages in the purchase decision process7. They are:

a) Need/Want/Desire is recognized

b) Search for information

c) Evaluate options.

d) Purchase.

e) After-Purchase Evaluation.

Deciding what to buy is one of the consumer’s most basic tasks. No purchase takes

place unless this fundamental decision is made. The consumer has to make decisions

on brands, price and product features. The study broadly aims at examining

perceptions of the consumers mainly in terms of the information gathered and used

for the purpose of the ultimate purchase decision. This study attempts to determine the

sources and factors that influence the purchase of Car.

4.2 Statement of the Research Problem This research work focuses on the factors influencing the households to adapt

personal travel facility at the home and to assess the perceptions of the households,

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with the societal implications of traveling facility in his hand. Two fundamental

assumptions are focuses in this research. First, the household is assumed as a social

system with differentiated actors, behaviors and patterns of socialization. Second, the

act of acquiring a car is in itself significant as evidence of adoption by the household.

Households adopt car, and acquire the facility of traveling in his hand with a view to

realizing some goals. Over time, households manifest certain types of behavior in

relation to the automobile technology, such behaviors having been determined by the

needs and characteristics of the household and the nature of the available technology.

If these behaviors persist over time they are in turn likely to have an impact on the

future behavior of the household. If the impact affects a larger segment of the

population they can have broader societal implications.

To acquire and possess car the consumer faces problems relating to acquiring

authentic information about car, regarding sources of information, genuineness of car,

the economic price to be paid etc. After acquiring one, the consumer faces the

problem of adopting it at the household and its social implication on the household.

Through this research the researcher has addressed some of these issues.

4.3 Rationale of the Study In the study area researchers need to understand the extent to which policies aimed at

increasing access to purchase the car. Household surveys not only measure the impact

of policies and interventions affecting the purchase behavior, but can also provide

valuable information on the relative importance of different types of outlets in

providing computers from a consumer standpoint. Repeated Household Surveys

ultimately allow the impact of all consumer behavior. Household Surveys can also

facilitate an understanding of the determinants of appropriate purchasing behavior.

4.4 Objectives of the Research Study The overall objective of the present study is to analyze the consumer attitudes and

purchase decisions with reference to the consumers of car in Saurashtra Region.

The study is undertaken with the following objectives:

1. To study the conceptual background with focus on consumer behaviour

2. To find out the sources of information for purchase of car.

3. To assess the perceptions of the households regarding the economic and

social/psychological benefits.

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4. To investigate and explore the characteristics of personal traveling facility and its

implications.

5. To offer suggestions for effective marketing of Car.

4.5 Universe of the Study Universe of the study is finite. This primary research is conducted in the the

entire district of Saurashtra region i.e., Rajkot, Junagadh, Amreli, Jamnagar,

Surendranagar, and Porbandar. So, Saurashtra Region is the size of the population.

4.6 Sample Design

Research design constitutes the blueprint for the data collection, measurement

and analysis of data. This is a descriptive research study.

Research study describes the buying patterns of consumers. Descriptive

research includes surveys & fact-finding enquiries of sampled respondents. The main

characteristic of this method is that the researcher has no control over the variables; he

can only report what has happened or what is happening.

4.6.1 Sampling Units Sampling design will be imperative in every scientific study. Hence, the

researcher has planned to adopt non-probability sampling method. The sampling

units are Rajkot, Junagadh, Amreli, Jamnagar, Surendranagar, and Porbandar.

4.6.2 Sampling Method All the samples are selected haphazardly from the sub-geographical urban

areas of the Gujarat State. So, area sampling method was adopted to find the list of

respondents for the research study.

4.6.3 Sample Size The Total sample size of the study will be 600 samples on data will be

collected through 100 samples in each district of Saurashtra region.

4.7 Sources of Data Primary data will be collected from the respondents through questionnaire.

The variables will be measure using three points, five points scale with closed, open-

ended and multiple-choice questions. To test the hypothesis of the present study

primary as well as secondary data will be collected for the purpose. Researcher will

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visit various libraries of the colleges and car distributors. The secondary data will be

obtain from research publications, books, articles, journals, seminar papers and

magazines, other reports available in different libraries and from websites.

4.7.1 Questionnaire Development

A well-structured questionnaire was developed after an extensive review of

Internet commerce literatures. Close ended questions have been asked to each

respondent from the questionnaire. The respondents were requested to assess

dichotomous questions, multi-dichotomous questions, ranking scale, constant sum

scale questions, numerical scale questions and some of the scale items on a Likert

point scale used for each statement where 1 = strongly disagree (not important at each

and every one) and 5 = strongly agree (extremely important). Questionnaires were

administered in English to consumers near office premises, shopping mall, colleges

and Internet centers.

A pilot study and survey was conducted with a small one number of 25

respondents to arrive at the twelve factors that the consumer feels are significant and

also to understand the degree to which respondents understand the questions.

4.8 Research Hypothesis Research hypothesis provides the base to derive the research conclusions. It was

preferred to test hypothesis at significance level (α) of 5% and at confidence level (1-

α) of 95%. This allowed to fix the acceptance region is equal to 95% & the rejection

region is equal to 5% to accept or reject the null hypothesis H0 or alternative

hypothesis Ha. Following is the list of hypothesis used to verify in this research study.

Basing on the Planned Behavior Theory the following hypothesis is formed.

H0: Income does not affect to purchase either old or new car.

H0: Income does not affect to the purchasing price of a car.

H0: Occupation does not affect to the purchasing price of a car.

H0: Income does not affect to purchase payment mode of a car.

H0: Occupation does not affect the consideration of fuel to purchase a car.

H0: Gender is independent of owning a car.

H0: Income is independent of owning a car.

H0: Occupation is independent of owning a car.

H0: Employment of family member is independent of owning a car.

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H0: Occupation is independent of purchasing a car.

H0: Gender is independent of considering a brand of a car.

H0: Education is independent of considering a brand of a car.

4.9 Data Analysis and Statistical Tools

To make this research more scientific and systematic, the researcher will use

Master Sheet, formation of One Way Tables, Cross Tables, Chi-square Test,

Correlations, ANOVA and Factor Analysis Test will be use to find out the factors

contributing to the preferences for a particular brand of Car. The output of the

analysis of data will be present in tables, figures and charts for the better

understanding and presentation of findings. Data Analysis will be as done with the

help of SPSS package in computer. Variables and their relationship were analyzed

through Cross Tables.

4.10 Supportive Technology There could be the support of information technology and computer to speed

up calculations all the way with acceptable accuracy of research study. Researcher

used MS-Excel application software of MS-OFFICE package for sorting all the

collected data with the numerical codes. Researcher used Statistical Package of Social

Science (SPSS) software with version 19 to process the collected data and give

appropriate conclusions according the selected hypothesis of this research study.

4.11 Limitations of the Study

The main purpose of our research was to investigate Consumer Attitude and

Purchase Decision of car, consumer perceptions about the use of car and the attributes

they associate with use of car of districts of Saurashtra region. The sample will be

dividing into six clusters based on the sampling plan. As almost no research has so far

been implemented in Saurashtra Region with regard to attitudes, purchase decisions,

perceptions and use of car, substantial room exists for further research.

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CHAPTER 5

DATA ANALYSIS AND INTERPRETATION

5.1 Frequency Analysis: Gender

5.2 Frequency Analysis: Age

5.3 Frequency Analysis: Marital Status

5.4 Frequency Analysis: Income

5.5 Frequency Analysis: Education Level

5.6 Frequency Analysis: Occupation

5.7 Frequency Analysis: Owning a Car

5.8 Frequency Analysis: Purpose of a Car

5.9 Frequency Analysis: New/Old Car Purchase

5.10 Frequency Analysis: Brand of a Car

5.11 Frequency Analysis: Price of a Car

5.12 Frequency Analysis: Payment Mode

5.13 Frequency Analysis: Fuel Based Car

5.14 Frequency Analysis: Time to Purchase a Car

5.15 Frequency Analysis: Importance of Decision

5.16 Frequency Analysis: Discussion to Purchase a Car

5.17 Frequency Analysis: Dealer Visit

5.18 Frequency Analysis: Role in Purchasing a Car

5.19 Frequency Analysis: Main Car User

5.20 Frequency Analysis: Overall Satisfaction

5.21 Linear Regression: Income & Old/New Car

5.22 Linear Regression: Income & Price

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5.23 Linear Regression: Occupation & Price

5.24 Linear Regression: Income & Payment Mode

5.25 Linear Regression: Occupation & Fuel

5.26 Cross Tabulation and Chi-square Analysis: Gender & Owning a Car

5.27 Cross Tabulation and Chi-square Analysis: Income & Owning a Car

5.28 Cross Tabulation and Chi-square Analysis: Occupation & Owning a Car

5.29 Cross Tabulation and Chi-square Analysis: Employed Family Members &

Purpose of a Car

5.30 Cross Tabulation and Chi-square Analysis: Occupation & Purpose of a Car

5.31 Cross Tabulation and Chi-square Analysis: Gender & Brand of a Car

5.32 Cross Tabulation and Chi-square Analysis: Education & Brand of a Car

5.33 Factor Analysis

5.34 Factor Analysis: Information Gathering and Consumer Purchase Initiation

(IGCP)

5.35 Factor Analysis: Preference Based on Personal Needs (PPP)

5.36 Factor Analysis: Personal Preference Based on Convenience Factors

(PPC)

5.37 Factor Analysis: Personal Preference Based on Comfort Factors (PPCF)

5.38 Factor Analysis: Influence Factor Based on Car Dealer (IFD)

5.39 Factor Analysis: Influence Factor Based on Car Model (IFM)

5.40 Factor Analysis: External Influence (EI)

5.41 Factor Analysis: Satisfaction Level (SL)

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This chapter describes the analysis of collected statistics and facts and their

interpretation. Frequency distribution and cross tabulation is the way to examine the

relationship between two variables. Frequency distribution is one of the most primary

tools to bifurcate the data according the selected variables in a tabular format.

Cross tabulation is one of the vital tools to understand and to measure an

association between independent and dependent variable. From the developed

questionnaire, researcher have picked up several independent demographic variables

like age, gender, residential area, income, etc to be analyzed with one of the

dependent variable that’s the e-buying preference. researcher used SPSS 19 version to

analyze all these analysis. Along with the cross tabulation analysis, researcher

calculated their association in terms of numerical magnitudes by using the Chi-square

(Test of Independence), cross tabulation, factor analysis.

In the following section, initially researcher discussed frequency distribution,

cross tabulation and statistical analysis of the data.

5.1 Frequency Analysis: Gender

In the following Table 5.1 and Chart 5.1, it is comprehensible that 88.4% of the

respondents uses internet and 11.6% of the respondents do not use internet. It means

that 428 respondents are male and 56 respondents are female.

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Table 5.1 Gender

Frequency Percent Valid Percent Cumulative Percent

Valid Male 428 88.4 88.4 88.4

Female 56 11.6 11.6 100.0

Total 484 100.0 100.0

Chart 5.1 Gender

5.2 Frequency Analysis: Age In the following Table 5.2 and Chart 5.2, it is comprehensible that 9.3 %, 8.5%,

30.0%, 36.6% and 15.7% respondents belongs to below 20, 21-30, 31-40, 41-50, and

above 50 age groups respectively.

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Table 5.2 Age


Valid Below 20 45 9.3 9.3 9.3

21-30 41 8.5 8.5 17.8

31-40 145 30.0 30.0 47.7

41-50 177 36.6 36.6 84.3

Above

50

76 15.7 15.7 100.0

Total 484 100.0 100.0

Chart 5.2 Age

5.3 Frequency Analysis: Marital Status

In the following Table 5.3 and Chart 5.3, it is comprehensible that 84.3 % respondents

are married and 15.7% respondents are unmarried.

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Table 5.3 Marital Status


Valid Married 408 84.3 84.3 84.3

Unmarried 76 15.7 15.7 100.0

Total 484 100.0 100.0

Chart 5.3 Marital Status

5.4 Frequency Analysis: Income In the following Table 5.4 and Chart 5.4, it is comprehensible that 4.1 %, 15.5%,

24.2%, 32.2%, 8.3%, 5.2%, 6.2% and 4.3% respondents belongs to below 20,000,

20,001 to 50,000, 50,001 to 1,00,000, 10,0001 to 2,00,000, 2,00,0001 to 2,50,000,

2,50,001 to 3,00,000 and above 3,00,000 income respectively.

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Table 5.4 Monthly Incomes

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Below 20000 20 4.1 4.1 4.1

20001-50000 75 15.5 15.5 19.6

50001-100000 117 24.2 24.2 43.8

100001-

150000

156 32.2 32.2 76.0

150001-

200000

40 8.3 8.3 84.3

200001-

250000

25 5.2 5.2 89.5

250001-

300000

30 6.2 6.2 95.7

Above 300000 21 4.3 4.3 100.0

Total 484 100.0 100.0

Chart 5.4 Monthly Incomes

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5.5 Frequency Analysis: Education Level In the following Table 5.5 and Chart 5.5, it is comprehensible that 5.2%, 21.9%,

48.1%, and 24.8% respondents belongs to up to 12, graduate, post graduate, and

professional respectively.

Table 5.5 Educational Level


Valid Up to 12 25 5.2 5.2 5.2

Graduate 106 21.9 21.9 27.1

Post Graduate 233 48.1 48.1 75.2

Professional 120 24.8 24.8 100.0

Total 484 100.0 100.0

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Chart 5.5 Educational Level

5.6 Frequency Analysis: Occupation In the following Table 5.6 and Chart 5.6, it is comprises that 40.9%, 19.8%, 3.1%,

15.5%, 7.2%, 8.3%. 3.1% and 2.1% percentages respondents belongs to government

service, business, unemployment, private service, foreign company service, house

wife, student and agriculture occupation respectively.

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Table 5.6 Occupation


Valid Government Service 198 40.9 40.9 40.9

Business 96 19.8 19.8 60.7

Unemployment 15 3.1 3.1 63.8

Private Service 75 15.5 15.5 79.3

Foreign Company Service 35 7.2 7.2 86.6

House Wife 40 8.3 8.3 94.8

Student 15 3.1 3.1 97.9

Agriculture 10 2.1 2.1 100.0

Total 484 100.0 100.0

Chart 5.6 Occupation

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5.7 Frequency Analysis: Owning a Car In the following Table 5.7 and Chart 5.7, it is comprises that 95% respondents

do have a car and 5% respondents do not have a car.

Table 5.7 Owning a Car


Valid Yes 460 95.0 95.0 95.0

No 24 5.0 5.0 100.0

Total 484 100.0 100.0

Chart 5.7 Owning a Car

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5.8 Frequency Analysis: Purpose of a Car In the following Table 5.8 and Chart 5.8, it is comprises that 7.4%, 42.4%, and

50.2% respondents have their car for business purpose, personal/family purpose and

both purchases respectively.

Table 5.8 Purpose of a Car


Valid Business Purpose 36 7.4 7.4 7.4

Personal/Family Purpose 205 42.4 42.4 49.8

Both Purpose 243 50.2 50.2 100.0

Total 484 100.0 100.0

Chart 5.8 Purpose of a Car

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5.9 Frequency Analysis: New/Old Car Purchase In the following Table 5.9 and Chart 5.9, it is comprises that 91.7%

respondents have brand new car and 8.3% respondents have second hand car.

Table 5.9 New/Old Car Purchase


Valid Brand New 444 91.7 91.7 91.7

Second Hand 40 8.3 8.3 100.0

Total 484 100.0 100.0

Chart 5.9 New/Old Car Purchase

5.10 Frequency Analysis: Brand of a Car In the following Table 5.10 and Chart 5.10, , it is comprises that 17.8%, 11.6%, 9.3%,

16.5%, 4.1%, 8.5%, 10.3%,7.2%,6.4%, 5.2%, and 3.1% percentage respondents have

Maruti, Hyundai, Tata, Honda, Toyota, Chevrolet, Ford, Nissan, Volkswagon,

Renault and Skoda brand respectively.

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Table 5.10 Brand of a Car


Valid Maruti 86 17.8 17.8 17.8

Hyundai 56 11.6 11.6 29.3

Tata 45 9.3 9.3 38.6

Honda 80 16.5 16.5 55.2

Toyota 20 4.1 4.1 59.3

Chevrolet 41 8.5 8.5 67.8

Ford 50 10.3 10.3 78.1

Nissan 35 7.2 7.2 85.3

Volkswagon 31 6.4 6.4 91.7

Renault 25 5.2 5.2 96.9

Skoda 15 3.1 3.1 100.0

Total 484 100.0 100.0

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Chart 5.10 Brand of a Car

5.11 Frequency Analysis: Price of a Car In the following Table 5.11 and Chart 5.11, it is comprises 8.3%, 50.2%, 33.3% and

8.3% respondents who prefers prices below 3,00,000, 3,00,001 to 5,00,000, 5,00,001

to 10,00,000 and 10,00,001 to 15,00,000 respectively.

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Table 5.11 Price of a Car


Valid Below 300000 40 8.3 8.3 8.3

300001-500000 243 50.2 50.2 58.5

500001-1000000 161 33.3 33.3 91.7

1000001-1500000 40 8.3 8.3 100.0

Total 484 100.0 100.0

Chart 5.11 Price of a Car

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5.12 Frequency Analysis: Payment Mode In the following Table 5.12 and Chart 5.12, it comprises 35.5% respondents

who prefers cash payment and 64.5% respondents who prefers EMI payment mode.

Table 5.12 Payment Mode


Valid Cash 172 35.5 35.5 35.5

EMI 312 64.5 64.5 100.0

Total 484 100.0 100.0

Chart 5.12 Payment Mode

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5.13 Frequency Analysis: Fuel Based Car

In the following Table 5.13 and Chart 5.13, it comprises 37.2% respondents

prefer diesel, 29.3% respondents prefer petrol and 33.5% respondents prefer petrol as

well as gas based cars.

Table 5.13 Fuel based Car


Valid Diesel 180 37.2 37.2 37.2

Petrol 142 29.3 29.3 66.5

Petrol & Gas 162 33.5 33.5 100.0

Total 484 100.0 100.0

Chart 5.13 Fuel based Car

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5.14 Frequency Analysis: Time to Purchase a Car In the following Table 5.14 and Chart 5.14, it comprises 50.0% respondents

require 2 weeks to 1 month time to purchase a car, 33.3% respondents require 1

month to 3 months’ time to purchase a car and 16.7% respondents require 3 months to

6 months’ time to purchase a car.

Table 5.14 Decision Time to Purchase a Car


Valid 2 Week-1 Month 242 50.0 50.0 50.0

1 Month - 3 Month 161 33.3 33.3 83.3

3 Month - 6 Month 81 16.7 16.7 100.0

Total 484 100.0 100.0

Chart 5.14 Decision Time to Purchase a Car

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5.15 Frequency Analysis: Importance of Decision In the following Table 5.15 and Chart 5.15, it comprises 3.1%, 2.1%, 36.65, 25.2%,

and 33.1% respondents believes that decision regarding to purchase a car is very

unimportant, fairly important, neutral, fairly important and very important

respectively.

Table 5.15 Importance of the Decision of Purchasing a Car


Valid Very unimportant 15 3.1 3.1 3.1

Fairly important 10 2.1 2.1 5.2

Neutral 177 36.6 36.6 41.7

Fairly important 122 25.2 25.2 66.9

Very important 160 33.1 33.1 100.0

Total 484 100.0 100.0

Chart 5.15 Importance of the Decision of Purchasing a Car

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5.16 Frequency Analysis: Discussion to Purchase a Car In the following Table 5.16 and Chart 5.16, it comprises 16.7% respondents who

discuss car purchasing decision with their family members and friends, and 83.3%

respondents who did not discuss car purchasing decision with their family members.

Table 5.16 Discussion for Purchasing a Car with Family and Friends


Valid Yes 81 16.7 16.7 16.7

No 403 83.3 83.3 100.0

Total 484 100.0 100.0

Chart 5.16 Discussions for Purchasing a Car with Family and

Friends

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5.17 Frequency Analysis: Dealer Visit In the following Table 5.17 and Chart 5.17, it comprises 77.1%, 16.7%, 4.1%

and 2.1% respondents who have contacted dealers under 3 times, 3 to 5 times, 5 to 7

times and more than 7 times respectively.

Table 5.17 Contacted/Visited the Dealers


Valid Under 3 times 373 77.1 77.1 77.1

3 to 5 times 81 16.7 16.7 93.8

5 to 7 times 20 4.1 4.1 97.9

More than 7 times 10 2.1 2.1 100.0

Total 484 100.0 100.0

Chart 5.17 Contacted/Visited the Dealers

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5.18 Frequency Analysis: Role in Purchasing a Car In the following Table 5.18 and Chart 5.18, it comprises 14.7%, 78.1% 6.2%

and 1.0% respondents who is only decision maker, is one of the decision makers and

play the decisive role, one of the decision makers but not play the decisive role and

totally decided by others respectively.

Table 5.18 Role in Purchasing a Car

Freq

uenc

y

Perc

ent

Val

id P

erce

nt

Cum

ulat

ive

Perc

ent

Valid I am the only decision maker 71 14.7 14.7 14.7

I am one of the decision makers,

and play the decisive role

378 78.1 78.1 92.8

I am one of the decision makers,

but not play the decisive role

30 6.2 6.2 99.0

Totally decided by others 5 1.0 1.0 100.0

Total 484 100.0 100.0

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Chart 5.18 Roles in Purchasing a Car

5.19 Frequency Analysis: Main Car User In the following Table 5.19 and Chart 5.19, it comprises 61.6% respondents

who are self main car users, 25.0% respondents are spouses who are main car users,

5.2% respondents are parents who are main car users and 8.3% who are other family

members who are main car users.

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Table 5.19 Main Car Users


Valid My self 298 61.6 61.6 61.6

My husband/wife 121 25.0 25.0 86.6

My parents 25 5.2 5.2 91.7

Other family members 40 8.3 8.3 100.0

Total 484 100.0 100.0

Chart 5.19 Main Car Users

5.20 Frequency Analysis: Overall Satisfaction In the following Table 5.20 and Chart 5.20, it comprises 3.1%, 6.2%, 7.2%, 66.7%,

and 16.7% respondents who are very dissatisfied, dissatisfied, and neutral, satisfied

and very satisfied respectively.

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Table 5.20 Overall Satisfaction of a Car


Valid Very dissatisfied 15 3.1 3.1 3.1

Dissatisfied 30 6.2 6.2 9.3

Neutral 35 7.2 7.2 16.5

Satisfied 323 66.7 66.7 83.3

Very Satisfied 81 16.7 16.7 100.0

Total 484 100.0 100.0

Chart 5.20 Overall Satisfaction of a Car

5.21 Linear Regression: Income & Old/New Car Linear regression indicates that whether independent factor is having effect on

dependent variable or not. Here value of R Square indicates the measurement about

these phenomena. Histogram shows the illustrative relationship among these

variables. Table 5.21 provides the model summary. In this table, adjusted R square

value is 0.182 means income does affect in deciding to purchase either old or new car.

Chart 5.22 is the histogram for the same.

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Table 5.21 Model Summaryb

Mode R R Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .428a .183 .182 .24932 .183 108.296 1 482 .000

a. Predictors: (Constant), Monthly Income

b. Dependent Variable: Is this car bought brand-new or second-hand?

Chart 5.21 Histogram

5.22 Linear Regression: Income & Price Linear regression indicates that whether independent factor is having effect on




value is 0.203 means income does affect price based consideration to purchase a car.


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Model R R Square

Adjusted R

Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change F Change df1 df2

Sig. F

Change

1 .452a .204 .203 .67683 .204 123.824 1 482 .000


b. Dependent Variable: What is the purchase price of car?


5.23 Linear Regression: Occupation & Price Linear regression indicates that whether independent factor is having effect on




value is 0.140 means occupation does affect price based consideration to purchase a

car. Chart 5.23 is the histogram for the same.

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Model R

R

Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .377a .142 .140 .70289 .142 79.734 1 482 .000

a. Predictors: (Constant), What is your current occupation?

b. Dependent Variable: What is the purchase price of car?


5.24 Linear Regression: Income & Payment Mode Linear regression indicates that whether independent factor is having effect on




value is 0.239 means income does affect payment mode to purchase a car. Chart 5.24

is the histogram for the same.

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Model R

R

Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .490a .240 .239 .41802 .240 152.521 1 482 .000


b. Dependent Variable: What was your mode of payment of car?


5.25 Linear Regression: Occupation & Fuel Linear regression indicates that whether independent factor is having effect on




value is -0.002 means occupation does affect fuel consideration to purchase a car.


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Model R

R

Square

Adjuste

d R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .010a .000 -.002 .84148 .000 .044 1 482 .834

a. Predictors: (Constant), What is your current occupation?

b. Dependent Variable: Which fuel based car you have?


5.26 Cross Tabulation and Chi-square Analysis: Gender & Owning a

Car

Case processing summary table 5.26 furnish the information regarding the size

of population which is 484 means 100%. Cross tabulation table 5.27 establishes the

relationship between two selected variables. Researcher has applied contingency chi

square test which is also known as test of independence. Here in the table 5.28, the

calculated chi-square value is 0.259 which is less than tabulated 0.611. This mean

gender doesn’t have any significant impact on owning a car factor.

106

Table 5.26 Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

Gender * Do you/your family

own a car? (SA)

484 100.0% 0 .0% 484 100.0%

Table 5.27 Crosstabulation

Do you/your family own a car? (SA)

Total Yes No

Gender Male 406 22 428

Female 54 2 56

Total 460 24 484

Table 5.28 Chi-Square Tests

Value df

Asymp. Sig.

(2-sided)

Exact Sig.

(2-sided)

Exact Sig.

(1-sided)

Pearson Chi-Square .259 1 .611

Continuity Correction .033 1 .856

Likelihood Ratio .282 1 .595

Fisher's Exact Test 1.000 .459

Linear-by-Linear Association .258 1 .611

N of Valid Cases 484

a. 1 cells (25.0%) have expected count less than 5. The minimum expected

count is 2.78.

b. Computed only for a 2x2 table

107

5.27 Cross Tabulation and Chi-square Analysis: Income & Owning

a Car




square test which is also known as test of independence. Here in the table 5.31 the

calculated chi-square value is 11.344 which is more than tabulated 0.124. This mean

income does have any significant impact on owning a car factor.


Cases

Valid Missing Total


Monthly Income * Do you/your

family own a car? (SA)

484 100.0% 0 .0% 484 100.0%

Table 5.30 Crosstabulation

Do you/your family own a car? (SA)

Total Yes No

Monthly

Income

Below 20000 19 1 20

20001-50000 73 2 75

50001-100000 112 5 117

100001-150000 149 7 156

150001-200000 35 5 40

200001-250000 24 1 25

250001-300000 30 0 30

Above 300000 18 3 21

Total 460 24 484

108


Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 11.344a 7 .124

Likelihood Ratio 10.388 7 .168

Linear-by-Linear

Association

1.626 1 .202


a. 6 cells (37.5%) have expected count less than 5. The minimum

expected count is .99.

5.28 Cross Tabulation and Chi-square Analysis: Occupation &

Owning a Car






occupation does have any significant impact on owning a car factor.


Cases

Valid Missing Total


What is your current

occupation? * Do you/your

family own a car? (SA)

484 100.0% 0 .0% 484 100.0%

109

Table 5.33 Cross tabulation

Do you/your family own a car?

(SA)

Total Yes No


occupation?

Government Service 187 11 198

Business 90 6 96

Unemployment 15 0 15

Private Service 73 2 75

Foreign Company

Service

30 5 35

House Wife 40 0 40

Student 15 0 15

Agriculture 10 0 10

Total 460 24 484

Table 5.34 Chi square Tests




Linear-by-Linear Association 1.187 1 .276


a. 7 cells (43.8%) have expected count less than 5.

b. The minimum expected count is .50.

110

5.29 Cross Tabulation and Chi-square Analysis: Employed Family Members & Purpose of a Car

Case processing summary table 5.35 furnish the information regarding the size of

population which is 484 means 100%. Cross tabulation table 5.36 establishes the




employed family member does have any significant impact on purpose of a car.


Cases

Valid Missing Total


How many employed people

are in your family? * Which

purpose you use your car?

484

100.0%

0

.0%

484

100.0%


Which purpose you use your car?

Total Business Purpose

Personal/Family

Purpose Both Purpose

How many employed

people are in your family?

1.00 21 60 41 122

2.00 10 30 161 201

3.00 5 115 41 161

Total 36 205 243 484

111







a. 0 cells (.0%) have expected count less than 5.

b. The minimum expected count is 9.07.

5.30 Cross Tabulation and Chi-square Analysis: Occupation & Purpose of a Car






occupation does have any significant impact on purpose of a car.


Cases

Valid Missing Total



occupation? * Which purpose

you use your car?

484

100.0%

0

.0%

484

100.0%

112


Which purpose you use your

car?

Total

Business

Purpose

Personal

/Family

Purpose

Both

Purpose


occupation?

Government Service 21 70 107 198

Business 0 35 61 96

Unemployment 0 5 10 15

Private Service 5 15 55 75

Foreign Company

Service

5 30 0 35

House Wife 0 40 0 40

Student 5 5 5 15

Agriculture 0 5 5 10

Total 36 205 243 484





Linear-by-Linear

Association

17.091 1 .000



b. The minimum expected count is .74.

113

5.31 Cross Tabulation and Chi-square Analysis: Gender & Brand

of a Car

Case processing summary table 5.41 furnish the information regarding the size of

population which is 484 means 100%. Cross tabulation table 5.42 establishes the




gender does have any significant impact on brand of a car.


Cases

Valid Missing Total


Gender * What is the

brand of your car?

484 100.0% 0 .0% 484 100.0%


What is the brand of your car?

Total

Mar

uti

Hyu

ndai

Tata

Hon

da

Toyo

ta

Che

vrol

et

Ford

Nis

san

Vol

ksw

agon

Ren

ault

Skod

a

Gender Male 86 30 45 80 20 41 20 35 31 25 15 428

Female 0 26 0 0 0 0 30 0 0 0 0 56

Total 86 56 45 80 20 41 50 35 31 25 15 484

114








b. The minimum expected count is 1.74.

5.32 Cross Tabulation and Chi-square Analysis: Education & Brand of a Car





calculated chi-square value is 557.780 which are more than tabulated 0.003. This

mean education does have any significant impact on brand of a car.


Cases

Valid Missing Total



educational level? * What is

the brand of your car?

484 100.0% 0 .0% 484 100.0%

115


What is the brand of your car?

Total Mar

uti

Hyu

ndai

Tata

Hon

da

Toyo

ta

Che

vrol

et

Ford

Nis

san

Vol

ksw

agon

Ren

ault

Skod

a

What is

your

current

educational

level?

Up to 12 0 0 0 10 0 10 0 5 0 0 0 25

Graduate 35 5 5 5 20 11 5 0 5 5 10 106

Post

Graduate

51 51 40 0 0 0 40 0 26 20 5 233

Professional 0 0 0 65 0 20 5 30 0 0 0 120

Total 86 56 45 80 20 41 50 35 31 25 15 484

Table 5.46 Chi square Tests




Linear-by-Linear

Association

1.315 1 .252



The minimum expected count is .77.

5.33 Factor Analysis Factor analysis attempts to identify underlying variables, or factors, that

explain the pattern of correlations within a set of observed variables. Factor

analysis is often used in data reduction to identify a small number of factors that

explain most of the variance that is observed in a much larger number of manifest

variables. Factor analysis can also be used to generate hypotheses regarding causal

116

mechanisms or to screen variables for subsequent analysis (for example, to identify

collinearity prior to performing a linear regression analysis).

5.34 Factor Analysis: Information Gathering and Consumer Purchase Initiation (IGCP)

With a view to studying about information gathering and consumer purchase

initiation, the responses of respondents have been examined with the help of factor

analytical approach using principal component method with varimax rotation.

Initially, test to check the adequacy of data for the application of factor analysis

(Stewert, 1981) were conducted.

Table 5.47 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .687

Bartlett's Test of

Sphericity

Approx. Chi-Square 1507.910

df 28

Sig. .000

The value of the Kaiser-Meyer-Okin (KMO) measure of sampling adequacy statistics

found to be 0.687, which is adequately large. Moreover, the correlation matrix reveals

that there is enough correlation for the application of factor analysis. Besides,

Bartlett’s test of sphericity value was found to be 1507.910, which is also significant

(p < 0.001). Communalities for each factor are presented in table – 5.48 and total

variable explain presented in table – 5.49. Result of component matrix is presented in

table – 5.50 (a). Eventually, the decision for arriving at the number of factors to be

retained was made on the basis of latent root criterion, i.e., variables having

eigenvalues greater than 1 and also on the basis of scree plot which reveals that there

are seven underlying factors. Moreover, factors having loading greater than or equal

to 0.40 (ignoring signs) have been retained (Dixon, 1997) which yielding three

interpretable factors. Varimax rotated factor analytic results for all respondents are

presented in Table – 5.50 (b).

117

Table 5.48 Communalities

Initial Extraction Search in Internet websites of the manufacturer (IGCP1) 1.000 .928

Information received from friends (IGCP2) 1.000 .701 Information received from office colleagues (IGCP3) 1.000 .852 Opinion from family members (IGCP4) 1.000 .866 Advertisement in newspapers / magazine (IGCP5) 1.000 .517 TV commercials on car models and brands (IGCP6) 1.000 .311 Visit to dealers / distributors (IGCP7) 1.000 .873 Dealer Sales Staff assurance (IGCP8) 1.000 .733

Extraction Method: Principal Component Analysis.

Table 5.49 Total Variance Explained

Com

pone

nt Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 3.045 38.063 38.063 3.045 38.063 38.063 2.861 35.764 35.764

2 1.728 21.595 59.658 1.728 21.595 59.658 1.767 22.092 57.856

3 1.008 12.600 72.258 1.008 12.600 72.258 1.152 14.402 72.258

4 .832 10.395 82.653

5 .550 6.873 89.526

6 .424 5.306 94.832

7 .236 2.954 97.786

8 .177 2.214 100.000


Table – 5.49 depicts three rotated factors which together explain 72.258% of the total

variance. The last column in the table shows the communalities which represent the

portion of variance that a variable shares with other variables. Eigenvalues for factors

F1 to F3 are 3.045, 1.728, and 1.008 respectively. Further, appropriate names have

been assigned to all the three dimensions extracted based on the various variables

representing each case. The names factors with constituting and their respective factor

118

loadings are summarized in Table . The respective factor loadings represent the

relationship between original variable and factor. Moreover, on each factor, ‘like

signs’ of factor loadings reflect positive correlation between factor loadings and the

factor and ‘opposite signs’ of factor loadings reveal negative correlation between

factor loadings and factor. But the sign of factor loading relates to only that factor on

which they appear, not to other factors as they are orthogonally rotated (Hair et al.,

2006).

Table 5.50 (a) Component Matrix

Component

1 2 3

Search in Internet websites of the manufacturer (IGCP1) .326 .151 .893

Information received from friends (IGCP2) .757 -.054 -.353

Information received from office colleagues (IGCP3) .915 .098 -.076

Opinion from family members (IGCP4) -.347 .853 -.131

Advertisement in newspapers / magazine (IGCP5) .693 -.003 .191

TV commercials on car models and brands (IGCP6) .534 .075 -.144

Visit to dealers / distributors (IGCP7) -.090 .930 -.002

Dealer Sales Staff assurance (IGCP8) .797 .306 -.070


a. 3 components extracted.

119

Table 5.50 (b) Rotated Component Matrixa

Component

1 2 3

Search in Internet websites of the manufacturer (IGCP1) .089 .016 .959

Information received from friends (IGCP2) .807 -.164 -.152

Information received from office colleagues (IGCP3) .905 -.065 .173

Opinion from family members (IGCP4) -.161 .910 -.106

Advertisement in newspapers / magazine (IGCP5) .605 -.146 .360

TV commercials on car models and brands (IGCP6) .558 -.013 .007

Visit to dealers / distributors (IGCP7) .060 .928 .093

Dealer Sales Staff assurance (IGCP8) .823 .159 .175


Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 4 iterations.

A scree plot is a plot is of the eigen values against the number of factors in order

of extraction. The point of interest is where the curve starts to flatten. It can be

seen that the curve begins to flatten between TV Commercial facto 6 & Dealer

visit factor 7.

Chart 5.26 Scree Plot

120

Information Gathering and Consumer Purchase Initiation (IGCP)

The broad dimensions Information Gathering and Consumer Purchase Initiation of

respondents have been detailed below.

Factor 1 Commercial Sources contained four attributes explained 38.063% of

variance in the data, with the eigenvalue of 3.045. The attributes associated with this

factor dealt with information received from friends, office colleagues, news paper

advertisements and TV commercials.

Factor 2: Personal Sources accounted for 21.595% variance in the data with

eigenvalue 1.728. This factor loaded with opinion from family members, visit to

dealers/distributors and dealer sales staff assurance.

Factor 3 Web Sources loaded with two attributes. This factor accounted for 12.600%

of the variance, with an eigenvalue of 1.008. These attribute was search in internet

websites of the manufacturer.

5.35 Factor Analysis: Preference Based on Personal Needs (PPP)






Table 5.51 KMO Value


Bartlett's Test of

Sphericity


df 36

Sig. .000

The value of the Kaiser-Meyer-Okin (KMO) measure of sampling adequacy

statistics found to be 0.650, which is adequately large. Moreover, the correlation

matrix reveals that there is enough correlation for the application of factor analysis.

121

Besides, Bartlett’s test of sphericity value was found to be 1241.089, which is also

significant (p < 0.001). Communalities for each factor are presented in table – 5.52

and total variable explain presented in table – 5.53. Result of component matrix is

presented in table – 5.54 (a). Eventually, the decision for arriving at the number of

factors to be retained was made on the basis of latent root criterion, i.e., variables

having eigenvalues greater than 1 and also on the basis of scree plot which reveals

that there are seven underlying factors. Moreover, factors having loading greater than

or equal to 0.40 (ignoring signs) have been retained (Dixon, 1997) which yielding

three interpretable factors. Varimax rotated factor analytic results for all respondents

are presented in Table – 5.54 (b).


Initial Extraction

Need to upgrade from two-wheeler to four-wheeler(PPP1) 1.000 .486

Need of your business firm(PPP2) 1.000 .243

Peer pressure from family members owning a car (PPP3) 1.000 .700

Need of Fuel Efficiency (PPP4) 1.000 .619

Upgraded the model to suit personal ambition (PPP5) 1.000 .723

Family wanted a car for functions, social gathering (PPP6) 1.000 .875

Need to travel long distance on Trips (PPP7) 1.000 .628

Need to suit social standings (PPP8) 1.000 .685

Social pressure from friends / neighbors (PPP9) 1.000 .733


122

Total 5.53 Variance Explained C

ompo

nent

Initial Eigenvalues


Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 2.804 31.153 31.153 2.804 31.153 31.153 2.369 26.320 26.320

2 1.654 18.379 49.531 1.654 18.379 49.531 2.054 22.823 49.143

3 1.233 13.697 63.229 1.233 13.697 63.229 1.268 14.085 63.229

4 .947 10.526 73.754

5 .764 8.494 82.248

6 .566 6.290 88.539

7 .438 4.863 93.402

8 .420 4.661 98.064

9 .174 1.936 100.000





F1 to F3 are 2.804, 1.654, and 1.233 respectively. Further, appropriate names have



loadings are summarized in Table 5.54. The respective factor loadings represent the






2006).

123


Component

1 2 3

Need to upgrade from two-wheeler to four-wheeler(PPP1) .543 -.256 -.354

Need of your business firm(PPP2) .063 .132 .471

Peer pressure from family members owning a car (PPP3) .703 -.392 .227

Need of Fuel Efficiency (PPP4) -.004 .288 .732

Upgraded the model to suit personal ambition (PPP5) .528 .666 .033

Family wanted a car for functions, social gathering (PPP6) .913 -.203 -.022

Need to travel long distance on Trips (PPP7) .544 .397 -.417

Need to suit social standings (PPP8) .524 .636 .077

Social pressure from friends / neighbors (PPP9) .572 -.537 .342




Component

1 2 3

Need to upgrade from two-wheeler to four-wheeler(PPP1) .507 .192 -.439

Need of your business firm(PPP2) .071 .054 .484

Peer pressure from family members owning a car (PPP3) .830 .075 .075

Need of Fuel Efficiency (PPP4) -.018 .088 .782

Upgraded the model to suit personal ambition (PPP5) .050 .829 .181

Family wanted a car for functions, social gathering (PPP6) .839 .394 -.126

Need to travel long distance on Trips (PPP7) .124 .712 -.325

Need to suit social standings (PPP8) .073 .796 .215

Social pressure from friends / neighbours (PPP9) .831 -.137 .153




124

A scree plot is a plot is of the eigen values against the number of factors in

order of extraction. The point of interest is where the curve starts to flatten. It can be

seen that the curve begins to flatten between family need factor 6 & long travelling

distance factor 7.


Preference Based on Personal Needs (PPP)

The broad dimensions Preference Based on Personal Needs of respondents have

been detailed below.

Factor 1 Social Status contained four attributes explained 31.153% of variance in the

data, with the eigenvalue of 2.804. The attributes associated with this factor dealt with

need to upgrade from two-wheeler to four-wheeler, peer pressure from family

members owning a car, family wanted a car for functions, social gathering and social

pressure from friends/neighbours.

Factor 2 Personal Ambition accounted for 18.379% variance in the data with

eigenvaluec1.654. This factor loaded with upgrade the model to suit personal

ambition, need to travel a long distance and need to suite social standings.

Factor 3 Business Use loaded with two attributes. This factor accounted for 13.697%

of the variance, with an eigenvalue of 1.233. These attribute were need of your

business firm and need of fuel efficiency.

125

5.36 Factor Analysis: Personal Preference Based on Convenience

Factors (PPC)








Bartlett's Test of Sphericity Approx. Chi-Square 156.555

df 28

Sig. .000














126


Initial Extraction

Inconvenience of public transport for family journeys (PPC1) 1.000 .395

Easy Availability of Bank Loans (PPC2) 1.000 .539

Easy car availability in the market (PPC3) 1.000 .694

Compact Car (PPC4) 1.000 .459

Good After- Sales Service (PPC5) 1.000 .771

Re-sale Value (PPC6) 1.000 .722

Safety & Security (PPC7) 1.000 .658

Engine Performance (PPC8) 1.000 .671


Total 5.57 Variance Explained

Com

pone



Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 1.480 18.496 18.496 1.480 18.496 18.496 1.301 16.260 16.260

2 1.293 16.157 34.653 1.293 16.157 34.653 1.283 16.034 32.294

3 1.101 13.762 48.415 1.101 13.762 48.415 1.252 15.645 47.939

4 1.035 12.938 61.354 1.035 12.938 61.354 1.073 13.414 61.354

5 .944 11.804 73.157

6 .811 10.133 83.290

7 .788 9.852 93.141

8 .549 6.859 100.000


Table – 5.57 depicts four rotated factors which together explain 61.354% of

the total variance. The last column in the table shows the communalities which

represent the portion of variance that a variable shares with other variables.

Eigenvalues for factors F1 to F4 are1.480, 1.293, 1.101 and 1.035 respectively.

Further, appropriate names have been assigned to all the three dimensions extracted

based on the various variables representing each case. The names factors with

127

constituting and their respective factor loadings are summarized in Table 5.58. The

respective factor loadings represent the relationship between original variable and

factor. Moreover, on each factor, ‘like signs’ of factor loadings reflect positive

correlation between factor loadings and the factor and ‘opposite signs’ of factor

loadings reveal negative correlation between factor loadings and factor. But the sign

of factor loading relates to only that factor on which they appear, not to other factors

as they are orthogonally rotated (Hair et al., 2006).


Component

1 2 3 4

Inconvenience of public transport for family

journeys (PPC1)

.411 .324 -.294 -.187

Easy Availability of Bank Loans (PPC2) -.238 .680 -.023 -.137

Easy car availability in the market (PPC3) -.386 .552 .391 -.296

Compact Car (PPC4) -.514 -.361 .244 .071

Good After- Sales Service (PPC5) -.233 .166 .378 .739

Re-sale Value (PPC6) .478 .173 .666 -.142

Safety & Security (PPC7) .732 -.078 .293 .173

Engine Performance (PPC8) .148 .476 -.360 .541



128


Component

1 2 3 4

Inconvenience of public transport for family

journeys (PPC1)

.621 .042 .052 -.069

Easy Availability of Bank Loans (PPC2) .231 .671 -.126 .141

Easy car availability in the market (PPC3) -.132 .816 .096 -.041

Compact Car (PPC4) -.657 .000 -.156 -.049

Good After- Sales Service (PPC5) -.402 .093 .146 .761

Re-sale Value (PPC6) .045 .167 .829 -.072

Safety & Security (PPC7) .232 -.359 .681 .107

Engine Performance (PPC8) .442 .008 -.166 .670




A scree plot is a plot is of the eigen values against the number of factors in order of

extraction. The point of interest is where the curve starts to flatten. It can be seen that

the curve begins to flatten between Good After- Sales Service factor 5 & Re-sale

value factor 6.


129

Personal Preference Based on Convenience Factors (PPC)

The broad dimensions Personal Preference Based on Convenience Factors (PPC) of


Factor 1 Inconvenient Travel contained four attributes explained 18.496% of


factor dealt with inconvenience of public transport for family journeys.

Factor 2 Personal Ambition accounted for 16.157% variance in the data with

eigenvalue 1.293. This factor loaded with easy availability of bank loans and easy car

availability in the market.

Factor 3 Safety loaded with two attributes. This factor accounted for 13.762% of the

variance, with an eigenvalue of 1.101. These attribute safety & security and re-sale

value.

Factor 4 Maintenance Use loaded with three attributes. This factor accounted for

12.938% of the variance, with an eigenvalue of 1.035. These attribute were compact

car, good afer-sales service, and engine performance.

5.37 Factor Analysis: Personal Preference Based on Comfort Factors

(PPCF)






130


Kaiser-Meyer-Olkin Measure of Sampling

Adequacy.

.530

Bartlett's Test of

Sphericity


df 15

Sig. .000















Initial

Extractio

n

Style & Looks of the car (PPCF1) 1.000 .775

Exterior Design (PPCF2) 1.000 .561

Interior Design (PPCF3) 1.000 .565

Comfort in driving (PPCF4) 1.000 .638

Brand Name (PPCF5) 1.000 .737

Value for Money (PPCF6) 1.000 .694


131


Com

pone



Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 1.714 28.562 28.562 1.714 28.562 28.562 1.704 28.400 28.400

2 1.186 19.768 48.329 1.186 19.768 48.329 1.147 19.123 47.523

3 1.071 17.856 66.185 1.071 17.856 66.185 1.120 18.662 66.185

4 .850 14.163 80.348

5 .710 11.841 92.189

6 .469 7.811 100.000





F1 to F3 are 1.714, 1.186 and 1.071 respectively. Further, appropriate names have



loadings are summarized in Table 5.62. The respective factor loadings represent the






2006).

132


Component

1 2 3

Style & Looks of the car (PPCF1) -.052 -.743 .469

Exterior Design (PPCF2) .744 .054 .068

Interior Design (PPCF3) -.010 .662 .356

Comfort in driving (PPCF4) -.660 .250 -.374

Brand Name (PPCF5) .825 .187 -.145

Value for Money (PPCF6) -.201 .308 .748




Component

1 2 3

Style & Looks of the car (PPCF1) -.039 .878 -.048

Exterior Design (PPCF2) .749 -.021 -.005

Interior Design (PPCF3) .066 -.330 .672

Comfort in driving (PPCF4) -.684 -.406 -.075

Brand Name (PPCF5) .813 -.255 -.110

Value for Money (PPCF6) -.098 .190 .805





extraction. The point of interest is where the curve starts to flatten. It can be seen in

the scree plot 5.28 that the curve begins to flatten between brand name factor 5 &

value for money factor 6.

133


Personal Preference Based on Comfort Factors (PPCF) The broad dimensions Personal Preference Based on Convenience Factors (PPC) of


Factor 1 Brand contained four attributes explained 28.562% of variance in the data,

with the eigenvalue of 1.714. The attributes associated with this factor dealt with

exterior design and brand name.

Factor 2 Style accounted for 19.768% variance in the data with eigenvalue 1.186.

This factor loaded with style & looks of the car.

Factor 3 Economic Return loaded with three attributes. This factor accounted for

17.856% of the variance, with an eigenvalue of 1.071. These attribute interior design,

comfort in driving and value for money

5.38 Factor Analysis: Influence Factor Based on Car Dealer (IFD) With a view to studying about information gathering and consumer purchase





134



Adequacy.

.498

Bartlett's Test of

Sphericity


df 28

Sig. .000















Initial Extraction

Dealer and show room experience (IFD1) 1.000 .675

Your car as a Status Symbol / Prestige Value (IFD2) 1.000 .653

Car served to project your image to the society (IFD3) 1.000 .334

Importance you attached to the Manufacturer (IjhFD4) 1.000 .530

Importance attached to the car Brand (IFD5) 1.000 .747

Dealer Offers of your specific car model (IFD6) 1.000 .651

After-sales service package (IFD7) 1.000 .492

Number of Service Station (IFD8) 1.000 .839


135


Com

pone



Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 1.469 18.358 18.358 1.469 18.358 18.358 1.283 16.043 16.043

2 1.299 16.242 34.599 1.299 16.242 34.599 1.277 15.961 32.004

3 1.099 13.733 48.332 1.099 13.733 48.332 1.217 15.216 47.220

4 1.055 13.188 61.520 1.055 13.188 61.520 1.144 14.299 61.520

5 .938 11.722 73.242

6 .814 10.173 83.415

7 .706 8.828 92.244

8 .621 7.756 100.000


Table – 5.65 depicts four rotated factors which together explain 61.520% of



Eigenvalues for factors F1 to F4 are 1.469, 1.299, 1.099 and 1.055 respectively.

Further, appropriate names have been assigned to all the three dimensions extracted

based on the various variables representing each case. The names factors with

constituting and their respective factor loadings are summarized in Table 5.66. The

respective factor loadings represent the relationship between original variable and

factor. Moreover, on each factor, ‘like signs’ of factor loadings reflect positive

correlation between factor loadings and the factor and ‘opposite signs’ of factor

loadings reveal negative correlation between factor loadings and factor. But the sign

of factor loading relates to only that factor on which they appear, not to other factors

as they are orthogonally rotated (Hair et al., 2006).

136

Table 5.66 (a) Component Matrixa

Component

1 2 3 4

Dealer and show room experience (IFD1) .493 -.017 -.378 -.538

Your car as a Status Symbol / Prestige Value (IFD2) -.621 .516 .032 -.007

Car served to project your image to the society

(IFD3)

.393 -.134 .389 .099

Importance you attached to the Manufacturer (IFD4) .367 .571 .245 .098

Importance attached to the car Brand (IFD5) .315 .563 -.126 .561

Dealer Offers of your specific car model (IFD6) .133 -.432 .624 .238

After-sales service package (IFD7) -.622 -.214 -.071 .233

Number of Service Station (IFD8) -.216 .373 .577 -.566




Component

1 2 3 4

Dealer and show room experience (IFD1) -.801 -.083 -.160 -.042

Your car as a Status Symbol / Prestige Value (IFD2) .469 .131 -.503 .404

Car served to project your image to the society

(IFD3)

-.120 .145 .546 .024

Importance you attached to the Manufacturer (IFD4) -.134 .664 .100 .247

Importance attached to the car Brand (IFD5) .082 .818 -.087 -.253

Dealer Offers of your specific car model (IFD6) .221 -.113 .767 .024

After-sales service package (IFD7) .578 -.331 -.185 -.120

Number of Service Station (IFD8) -.018 -.017 .021 .915




137



the scree plot 6.29 that the curve begins to flatten between after-sales service package

factor 7 & number of service station factor 8.


Factor 1 Social status contained two attributes explained 18.358% of variance in the

data, with the eigenvalue of 1.469. The attributes associated with this factor dealt with

your car as a status symbol/prestige value and dealer offers of your specific car

model.

Factor 2 Brand Value accounted for 34.599% variance in the data with eigenvalue

1.299. This factor loaded importance attached to the car brand and importance you

attached to the manufacturer.

Factor 3 Social Image loaded with two attributes. This factor accounted for 48.332%

of the variance, with an eigenvalue of 1.099. These attributes are car served to project

your image to the society and dealer offers of your specific car model.

138

Factor 4 Car Service loaded with two attributes. This factor accounted for 61.520%

of the variance, with eigenvalue of 1.055. These attributes are after-sales service

package and number of service station.

5.39 Factor Analysis: Influence Factor Based on Car Model (IFM)








Adequacy.

.558

Bartlett's Test of

Sphericity


df 78

Sig. .000

139


Initial Extraction

Advanced Technology of your model (IFM1) 1.000 .684

Willing to pay a higher price for Fuel Efficiency (Mileage) alone of your

specific model (IFM2)

1.000 .799

Market value of the brand of your car (IFM3) 1.000 .547

Market value of model of your specific car (IFM4) 1.000 .689

The Price of your specific model (IFM5) 1.000 .614

Interior Design (IFM6) 1.000 .755

Exterior Design (IFM7) 1.000 .724

Security features of the specific model (IFM8) 1.000 .842

Safety of your specific car (IFM9) 1.000 .710

Driving Comfort of your specific car (IFM10) 1.000 .672

Entertainment Features of your specific car (IFM11) 1.000 .538

Environmental Friendly (IFM12) 1.000 .753

Maintenance Cost (IFM13) 1.000 .684

Extraction method : Principal component analysis














140


Com

pone



Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 2.441 18.779 18.779 2.441 18.779 18.779 2.193 16.872 16.872 2 1.828 14.060 32.839 1.828 14.060 32.839 1.730 13.304 30.176 3 1.424 10.955 43.793 1.424 10.955 43.793 1.473 11.329 41.505 4 1.191 9.164 52.958 1.191 9.164 52.958 1.246 9.587 51.092 5 1.098 8.449 61.407 1.098 8.449 61.407 1.188 9.138 60.230 6 1.028 7.910 69.317 1.028 7.910 69.317 1.181 9.087 69.317 7 .871 6.703 76.020 8 .761 5.852 81.872 9 .611 4.702 86.574

10 .552 4.246 90.821 11 .490 3.769 94.590 12 .383 2.947 97.537 13 .320 2.463 100.000


Table – 5.69 depicts six rotated factors which together explain 69.317% of the

total variance. The last column in the table shows the communalities which represent

the portion of variance that a variable shares with other variables. Eigenvalues for

factors F1 to F6 are 2.441, 1.828, 1.424, 1.191, 1.098 and 1.028 respectively. Further,

appropriate names have been assigned to all the three dimensions extracted based on

the various variables representing each case. The names factors with constituting and

their respective factor loadings are summarized in Table 5.70. The respective factor

loadings represent the relationship between original variable and factor. Moreover, on

each factor, ‘like signs’ of factor loadings reflect positive correlation between factor

loadings and the factor and ‘opposite signs’ of factor loadings reveal negative

correlation between factor loadings and factor. But the sign of factor loading relates to

only that factor on which they appear, not to other factors as they are orthogonally

rotated (Hair et al., 2006).

141


Component

1 2 3 4 5 6

Advanced Technology of your model (IFM1) .782 -.200 -.093 .036 -.079 .124 Willing to pay a higher price for Fuel Efficiency (Mileage) alone of your specific model (IFM2)

.194 -.137 .034 .794 -.186 -.276

Market value of the brand of your car (IFM3) -.203 -.569 -.187 .280 -.190 .178 Market value of model of your specific car (IFM4) .434 .307 .447 .083 .038 -.445 The Price of your specific model (IFM5) -.329 .502 -.104 .418 -.251 -.075 Interior Design (IFM6) .406 .442 -.165 -.311 -.479 .204 Exterior Design (IFM7) .773 -.151 -.170 .042 .118 .245 Security features of the specific model (IFM8) -.189 .207 .519 .280 .149 .626 Safety of your specific car (IFM9) .670 .089 .055 .253 .397 .171 Driving Comfort of your specific car (IFM10) -.146 .737 -.071 .140 -.020 .287 Entertainment Features of your specific car (IFM11) .346 .484 -.365 .011 -.042 -.221

Environmental Friendly (IFM12) -.217 .225 -.318 .009 .729 -.152 Maintenance Cost (IFM13) .137 .039 .775 -.196 -.030 -.158




Component

1 2 3 4 5 6

Advanced Technology of your model (IFM1) .764 -.054 .036 -.165 .255 .063 Willing to pay a higher price for Fuel Efficiency (Mileage) alone of your specific model (IFM2)

.137 -.016 .026 -.029 .072 .879

Market value of the brand of your car (IFM3) -.046 -.366 -.548 .047 .176 .278 Market value of model of your specific car (IFM4) .168 .107 .769 -.094 .009 .221 The Price of your specific model (IFM5) -.384 .559 -.059 .100 -.017 .375 Interior Design (IFM6) .252 .574 .060 -.174 .484 -.306 Exterior Design (IFM7) .844 -.013 -.042 -.072 .073 -.016 Security features of the specific model (IFM8) -.017 .088 .055 .911 .016 .003 Safety of your specific car (IFM9) .752 .077 .205 .171 -.225 .129 Driving Comfort of your specific car (IFM10) -.117 .734 -.001 .323 -.106 -.058 Entertainment Features of your specific car (IFM11)

.227 .561 .118 -.380 -.110 .042

Environmental Friendly (IFM12) -.052 .109 -.061 -.059 .848 -.114 Maintenance Cost (IFM13) -.068 -.244 .712 .223 .234 -.093 Extraction Method: Principal Component Analysis.



142



the scree plot 6.30 that the curve begins to flatten between environmental friendly

factors 12 & maintenance cost factor 13.


Influence Factor Based on Car Model (IFM) The broad dimensions Influence Factor Based on Car Model (IFM) of


Factor 1 Additional Features contained four attributes explained 18.779% of


factor dealt with advanced technology of your model, exterior design, safety of yoru

specific car and entertainment features of your specific car.

Factor 2 Price Consideration accounted for 32.839% variance in the data with

eigenvalue 1.828. This factor loaded importance attached to the price of your specific

model, interior design and driving comfort of your specific car.

Factor 3 Market Value loaded with two attributes. This factor accounted for

43.793% of the variance, with an eigenvalue of 1.424. These attributes are market

value of model of your specific car, market value of the brand of your car.

143

Factor 4 Security feature loaded with one attribute. This factor accounted for

52.958% of the variance, with eigenvalue of 1.191. These attribute is security features

of the specific model.

Factor 5 Environment Consideration loaded with one attribute. This factor

accounted for 61.407% of the variance, with eigenvalue of 1.098. These attribute is

environmental friendly.

Factor 6 Fuel Efficiency loaded with one attribute. This factor accounted for

69.317% of the variance, with eigenvalue of 1.028. These attribute is willing to pay a

higher price for fuel efficie ncy (mileage) alone of your specific model.

5.40 Factor Analysis: External Influence (EI)








Adequacy.

.599

Bartlett's Test of

Sphericity


df 36

Sig. .000









144







Initial Extraction Family (Wife, Son / Daughter) (EI1) 1.000 .560 Parents (EI2) 1.000 .562 Relatives (EI3) 1.000 .577 Friends (EI4) 1.000 .575 Opinion of your colleagues (EI5) 1.000 .328 Market goodwill (EI6) 1.000 .447 Car Loan availability (EI7) 1.000 .507 Advertisement of Cars (EI8) 1.000 .560 Car Fairs / shows (EI9) 1.000 .389 Extraction Method: Principal Component Analysis.

Table 5.73 Total Variance Explained

Com

pone



Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 1.814 20.155 20.155 1.814 20.155 20.155 1.710 19.005 19.005

2 1.569 17.431 37.586 1.569 17.431 37.586 1.548 17.202 36.207

3 1.123 12.475 50.061 1.123 12.475 50.061 1.247 13.854 50.061

4 .954 10.601 60.662

5 .918 10.205 70.867

6 .770 8.557 79.424

7 .672 7.470 86.894

8 .633 7.035 93.929

9 .546 6.071 100.000


145

Table – 5.73 depicts three rotated factors which together explain 69.317% of



Eigenvalues for factors F1 to F3 are 1.814, 1.569 and 1.123 respectively. Further,

appropriate names have been assigned to all the three dimensions extracted based on

the various variables representing each case. The names factors with constituting and

their respective factor loadings are summarized in Table 5.74. The respective factor

loadings represent the relationship between original variable and factor. Moreover, on

each factor, ‘like signs’ of factor loadings reflect positive correlation between factor

loadings and the factor and ‘opposite signs’ of factor loadings reveal negative

correlation between factor loadings and factor. But the sign of factor loading relates to

only that factor on which they appear, not to other factors as they are orthogonally

rotated (Hair et al., 2006).


Component

1 2 3

Family (Wife, Son / Daughter) (EI1) .710 .057 -.230

Parents (EI2) -.492 .555 .109

Relatives (EI3) .083 .480 -.583

Friends (EI4) .364 .136 .651

Opinion of your colleagues (EI5) .281 .490 -.095

Market goodwill (EI6) .115 .659 .006

Car Loan availability (EI7) .683 .201 -.007

Advertisement of Cars (EI8) .547 -.491 -.140

Car Fairs / shows (EI9) .264 .231 .515



146


Component

1 2 3

Family (Wife, Son / Daughter) (EI1) .616 .418 .082

Parents (EI2) -.714 .228 .020

Relatives (EI3) -.031 .650 -.392

Friends (EI4) .074 .021 .754

Opinion of your colleagues (EI5) .009 .559 .125

Market goodwill (EI6) -.238 .597 .187

Car Loan availability (EI7) .467 .446 .301

Advertisement of Cars (EI8) .735 -.140 -.013

Car Fairs / shows (EI9) -.024 .111 .613




A scree plot is a plot is of the eigen values against the number of factors in

order of extraction. The point of interest is where the curve starts to flatten. It can be

seen that the curve begins to flatten between advertisement of cars factor 8 & car

fairs/shows factor 9.

147


External Influence (EI) The broad dimensions External Influence (EI) of respondents have been detailed

below.

Factor 1 FLA contained three attributes explained 20.155% of variance in the data,

with the eigenvalue of 1.8141. The attributes associated with this factor dealt with

Family, car loan availability and advertisements.

Factor 2 Opinion Leadership accounted for 37.586% variance in the data with

eigenvalue 1.569. This factor loaded with parents, relatives, opinion of your

colleagues and market goodwill.

148

Factor 3 Car Fair & Friends with two attributes. This factor accounted for 50.061%

of the variance, with an eigenvalue of 1.123. These attributes are friends and car

fairs/shows.

5.41 Factor Analysis: Satisfaction Level (SL) With a view to studying about information gathering and consumer purchase







Adequacy.

.439

Bartlett's Test of

Sphericity


df 120

Sig. .000














149


Initial Extraction

Fuel Efficiency (SL1) 1.000 .672

Value for Money (SL2) 1.000 .691

Power of the car (SL3) 1.000 .752

Brand (SL4) 1.000 .628

Model (SL5) 1.000 .669

Re-Sale Value (SL6) 1.000 .574

Technology (SL7) 1.000 .603

Safety (SL8) 1.000 .591

Security (SL9) 1.000 .503

Riding comfort (SL10) 1.000 .468

Convenience (SL11) 1.000 .669

Performance (SL12) 1.000 .455

Style of the car (SL13) 1.000 .557

Appearance (SL14) 1.000 .677

After-sale service experience of your car (SL15) 1.000 .631

Country of Origin of Car (SL16) 1.000 .632


150


Com

pone



Loadings


Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 1.782 11.134 11.134 1.782 11.134 11.134 1.537 9.608 9.608 2 1.604 10.027 21.162 1.604 10.027 21.162 1.530 9.564 19.172 3 1.486 9.285 30.447 1.486 9.285 30.447 1.417 8.856 28.028 4 1.368 8.548 38.995 1.368 8.548 38.995 1.410 8.815 36.843 5 1.292 8.072 47.068 1.292 8.072 47.068 1.318 8.240 45.083 6 1.173 7.329 54.397 1.173 7.329 54.397 1.279 7.996 53.079 7 1.068 6.673 61.070 1.068 6.673 61.070 1.279 7.991 61.070 8 .978 6.114 67.183 9 .896 5.603 72.787

10 .835 5.218 78.005 11 .750 4.690 82.695 12 .717 4.480 87.175 13 .672 4.198 91.373 14 .532 3.322 94.695 15 .468 2.927 97.622 16 .380 2.378 100.000


Table – 5.73 depicts seven rotated factors which together explain 61.070% of



Eigenvalues for factors F1 to F7 are 1.782, 1.604, 1.486, 1.368, 1.292, 1.173 and

1.068 respectively. Further, appropriate names have been assigned to all the three

dimensions extracted based on the various variables representing each case. The

names factors with constituting and their respective factor loadings are summarized in

Table 5.78. The respective factor loadings represent the relationship between original

variable and factor. Moreover, on each factor, ‘like signs’ of factor loadings reflect

positive correlation between factor loadings and the factor and ‘opposite signs’ of

factor loadings reveal negative correlation between factor loadings and factor. But the

sign of factor loading relates to only that factor on which they appear, not to other

factors as they are orthogonally rotated (Hair et al., 2006).

151


Component

1 2 3 4 5 6 7

Fuel Efficiency (SL1) .400 -.117 .016 .278 .015 -.649 -.001 Value for Money (SL2) -.265 .278 .105 .103 .689 .198 .087 Power of the car (SL3) -.164 .336 -.071 .569 -.149 .458 .226 Brand (SL4) -.013 .383 .297 .504 -.204 -.048 -.307 Model (SL5) .014 -.298 -.232 .383 .500 -.234 .274 Re-Sale Value (SL6) .618 -.167 -.288 -.083 .199 .119 -.144 Technology (SL7) .411 -.259 .510 .247 .042 .148 .149 Safety (SL8) .455 -.043 .421 .044 .023 .185 .410 Security (SL9) .454 -.096 -.315 -.084 .237 .349 -.064 Riding comfort (SL10) .535 .209 .326 -.038 -.155 -.053 .058 Convenience (SL11) .319 .241 -.540 -.092 -.286 .260 .244 Performance (SL12) .053 .318 .433 -.305 .187 .137 -.129 Style of the car (SL13) .053 .403 .155 -.480 .348 -.124 .035 Appearance (SL14) .095 .623 -.244 .227 .241 -.266 .197 After-sale service experience of your car (SL15)

.396 .453 -.190 .104 -.002 -.064 -.467

Country of Origin of Car (SL16) -.028 .317 -.046 -.287 -.290 -.273 .536 Extraction Method: Principal Component Analysis.


152


Component

1 2 3 4 5 6 7

Fuel Efficiency (SL1) -.059 .232 .384 -.263 -.450 .434 .090

Value for Money (SL2) -.040 -.087 -.069 .629 .340 .355 -.199

Power of the car (SL3) -.076 .061 .141 -.130 .837 .041 .050

Brand (SL4) -.348 .126 .612 -.057 .264 -.097 -.183

Model (SL5) .085 -.001 -.134 -.087 -.002 .791 -.100

Re-Sale Value (SL6) .697 .137 .108 -.051 -.201 .085 -.086

Technology (SL7) .015 .731 -.034 -.067 .009 .065 -.241

Safety (SL8) .099 .737 -.107 .088 .075 .020 .111

Security (SL9) .696 .067 -.022 .039 .045 .034 -.092

Riding comfort (SL10) .088 .530 .306 .076 -.132 -.172 .182

Convenience (SL11) .519 -.076 .064 -.201 .264 -.147 .509

Performance (SL12) -.063 .188 .081 .566 -.047 -.286 -.071

Style of the car (SL13) .033 -.020 .046 .673 -.218 -.054 .225

Appearance (SL14) .023 -.116 .461 .268 .202 .405 .416

After-sale service

experience of your car

(SL15)

.312 -.102 .709 .089 -.033 -.108 -.011

Country of Origin of

Car (SL16)

-.194 .041 -.084 .050 -.053 -.054 .760




153


Satisfaction Level (SL) The broad dimensions Satisfaction Level (SL) of respondents have been detailed

below.

Factor 1 RCS contained three attributes explained 11.134% of variance in the data,

with the eigenvalue of 1.782. The attributes associated with this factor dealt with re-

sale value, convenience and security.

Factor 2 TSC accounted for 21.162% variance in the data with eigenvalue 1.604.

This factor loaded with technology, safety and riding comfort.

Factor 3 BAA with three attributes. This factor accounted for30.447% of the

variance, with an eigenvalue of 1.486. These attributes are brand, appearance and

after-sale service experience of your car.

Factor 4 MPS feature loaded with three attributes. This factor accounted for 38.995%

of the variance, with eigenvalue of 1.368. These attributes are money value,

performance and style of the car.

Factor 5 Power of Car loaded with one attribute. This factor accounted for 47.068%

of the variance, with eigenvalue of 1.292. These attribute is power of a car.

154

Factor 6 Model & Fuel loaded with two attributes. This factor accounted for

54.397% of the variance, with eigenvalue of 1.173. These attributes are model of a car

and fuel efficiency.

Factor 7 COO loaded with one attribute. This factor accounted for 61.070% of the

variance, with eigenvalue of 1.068. These attribute is country of origin of car.

155

CHAPTER - 6

RESEARCH FINDINGS, CONCLUSION & SUGGESTIONS

This chapter deals with findings, suggestions, as well as conclusion of the

study.

6.1 FINDINGS

In the Table 5.1 and Chart 5.1, it is comprehensible that 88.4% of the

respondents uses internet and 11.6% of the respondents do not use internet. It

means that 428 respondents are male and 56 respondents are female.

In the Table 5.2 and Chart 5.2, it is comprehensible that 9.3 %, 8.5%, 30.0%,

36.6% and 15.7% respondents belongs to below 20, 21-30, 31-40, 41-50, and

above 50 age groups respectively.

In the Table 5.3 and Chart 5.3, it is comprehensible that 84.3 % respondents

are married and 15.7% respondents are unmarried.

In the Table 5.4 and Chart 5.4, it is comprehensible that 4.1 %, 15.5%, 24.2%,

32.2%, 8.3%, 5.2%, 6.2% and 4.3% respondents belongs to below 20,000,

20,001 to 50,000, 50,001 to 1,00,000, 10,0001 to 2,00,000, 2,00,0001 to

2,50,000, 2,50,001 to 3,00,000 and above 3,00,000 income respectively.

In the Table 5.5 and Chart 5.5, it is comprehensible that 5.2%, 21.9%, 48.1%,

and 24.8% respondents belongs to up to 12, graduate, post graduate, and

professional respectively.

In the Table 5.6 and Chart 5.6, it is comprises that 40.9%, 19.8%, 3.1%,

15.5%, 7.2%, 8.3%. 3.1% and 2.1% percentages respondents belongs to

government service, business, unemployment, private service, foreign

company service, house wife, student and agriculture occupation respectively.

In the Table 5.7 and Chart 5.7, it is comprises that 95% respondents do have a

car and 5% respondents do not have a car.

In the Table 5.8 and Chart 5.8, it is comprises that 7.4%, 42.4%, and 50.2%

respondents have their car for business purpose, personal/family purpose and

both purchases respectively.

In the Table 5.9 and Chart 5.9, it is comprises that 91.7% respondents have

brand new car and 8.3% respondents have second hand car.

156

In the Table 5.10 and Chart 5.10, , it is comprises that 17.8%, 11.6%, 9.3%,

16.5%, 4.1%, 8.5%, 10.3%,7.2%,6.4%, 5.2%, and 3.1% percentage

respondents have Maruti, Hyundai, Tata, Honda, Toyota, Chevrolet, Ford,

Nissan, Volkswagon, Renault and Skoda brand respectively.

In the Table 5.11 and Chart 5.11, it is comprises 8.3%, 50.2%, 33.3% and

8.3% respondents who prefers prices below 3,00,000, 3,00,001 to 5,00,000,

5,00,001 to 10,00,000 and 10,00,001 to 15,00,000 respectively.

In the Table 5.12 and Chart 5.12, it comprises 35.5% respondents who prefers

cash payment and 64.5% respondents who prefers EMI payment mode.

In the Table 5.13 and Chart 5.13, it comprises 37.2% respondents prefer

diesel, 29.3% respondents prefer petrol and 33.5% respondents prefer petrol as

well as gas based cars.

In the Table 5.14 and Chart 5.14, it comprises 50.0% respondents require 2

weeks to 1 month time to purchase a car, 33.3% respondents require 1 month

to 3 months time to purchase a car and 16.7% respondents require 3 months to

6 months time to purchase a car.

In the Table 5.15 and Chart 5.15, it comprises 3.1%, 2.1%, 36.65, 25.2%, and

33.1% respondents believes that decision regarding to purchase a car is very

unimportant, fairly important, neutral, fairly important and very important

respectively.

In the Table 5.16 and Chart 5.16, it comprises 16.7% respondents who discuss

car purchasing decision with their family members and friends, and 83.3%

respondents who did not discuss car purchasing decision with their family

members.

In the Table 5.17 and Chart 5.17, it comprises 77.1%, 16.7%, 4.1% and 2.1%

respondents who have contacted dealers under 3 times, 3 to 5 times, 5 to 7

times and more than 7 times respectively.

In the Table 5.18 and Chart 5.18, it comprises 14.7%, 78.1% 6.2% and 1.0%

respondents who is only decision maker, is one of the decision makers and

play the decisive role, one of the decision makers but not play the decisive role

and totally decided by others respectively.

In the Table 5.19 and Chart 5.19, it comprises 61.6% respondents who are self

main car users, 25.0% respondents are spouses who are main car users, 5.2%

157

respondents are parents who are main car users and 8.3% who are other family

members who are main car users.

In the Table 5.20 and Chart 5.20, it comprises 3.1%, 6.2%, 7.2%, 66.7%, and

16.7% respondents who are very dissatisfied, dissatisfied, and neutral,

satisfied and very satisfied respectively.

Linear regression indicates that whether independent factor is having effect on

dependent variable or not. Here value of R Square indicates the measurement

about these phenomena. Histogram shows the illustrative relationship among

these variables. Table 5.21 provides the model summary. In this table,

adjusted R square value is 0.182 means income does affect in deciding to

purchase either old or new car. Chart 5.22 is the histogram for the same.





adjusted R square value is 0.203 means income does affect price based

consideration to purchase a car. Chart 5.22 is the histogram for the same.





adjusted R square value is 0.140 means occupation does affect price based






adjusted R square value is 0.239 means income does affect payment mode to

purchase a car. Chart 5.24 is the histogram for the same.





158

adjusted R square value is -0.002 means occupation does affect fuel



of population which is 484 means 100%. Cross tabulation table 5.27

establishes the relationship between two selected variables. Researcher has

applied contingency chi square test which is also known as test of

independence. Here in the table 5.28, the calculated chi-square value is 0.259

which is less than tabulated 0.611. This mean gender doesn’t have any

significant impact on owning a car factor.





independence. Here in the table 5.31 the calculated chi-square value is 11.344

which is more than tabulated 0.124. This mean income does have any






independence. Here in the table 5.34 the calculated chi-square value is 11.960

which is more than tabulated 0.106. This mean occupation does have any






independence. Here in the table 5.37 the calculated chi-square value is

152.651 which is more than tabulated 0.001. This mean employed family

member does have any significant impact on purpose of a car.





159


141.281 which is more than tabulated 0.001. This mean occupation does have

any significant impact on purpose of a car.






230.582 which is more than tabulated 0.001. This mean gender does have any

significant impact on brand of a car.






557.780 which are more than tabulated 0.003. This mean education does have

any significant impact on brand of a car.

Factor analysis attempts to identify underlying variables, or factors, that

explain the pattern of correlations within a set of observed variables. Factor

analysis is often used in data reduction to identify a small number of factors

that explain most of the variance that is observed in a much larger number of

manifest variables. Factor analysis can also be used to generate hypotheses

regarding causal mechanisms or to screen variables for subsequent analysis

(for example, to identify collinearity prior to performing a linear regression

analysis).

In the table 5.48 highest value of extraction is for search in internet websites

of the manufacturer and lowest value of extraction is for TV commercials on

car models and brands.

The eigen value represents the total variance explained by each factor. In the

table 5.49, notice that internet search factor accounts for 38.063% variance,

family member and dealer opinion factor accounts for 21.595% variance and

friend/colleague and TV Advertisement opinion factors means component 3

accounts for 12.600% variance. All other variances are not significant.

160

A component matrix contains the factor loading of all the variables on all the

factors extracted. The higher the absolute value of the loading, the more the

factor contributes to the variable. The gap on the Table 5.50 represent loadings

that are less than 0.5, this makes reading the Table easier. We suppressed all

loadings less than 0.5. Here internet search factor is having more loading value

than any other factors. Opinion from family members and dealer’s factor do

have less loading than internet search factors but more than residuals.

In the table 5.52 highest value of extraction is for Family wanted a car for

functions, social gathering and lowest value of extraction is for Need of your

business firm.

The eigen value represents the total variance explained by each factor. In the

table 5.53 notice that fuel efficiency need factor accounts for 31.153%

variance, social standing need factor accounts for 18.379% variance and

family need factor means component 3 accounts for 13.697% variance. All

other variances are not significant.

The gap on the Table 5.54 represent loadings that are less than 0.5, this makes

reading the Table easier. We suppressed all loadings less than 0.5. Need for

fuel efficiency is having more loading than any else. Social standing is having

less loading than need for fuel efficiency but more than residual factors.

In the table 5.56 highest value of extraction is for Re-sale Value and lowest

value of extraction is for Inconvenience of public transport for family

journeys.

In the table 5.57 notice that good after-sales service factor accounts for

18.496% variance, re-sale value factor accounts for 16.157% variance and

bank loan and safety & security factor accounts for 13.762% variance. All



reading the Table easier. We suppressed all loadings less than 0.5. Good after-

sales service factor is having more loading than anyone else. Resale value is

having less loading than good after-sales service but more than easy car

availability in the market and residual factors.

In the table 5.60 highest value of extraction is for Style & Looks of the car

factor and lowest value of extraction is for Exterior Design factor.

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In the table 5.61, notice that brand name factor accounts for 28.562% variance,

interior design factor accounts for 19.768% variance and styles & look of a car

accounts for 17.856% variance. All other variances are not significant.

The gap on the Table represent loadings that are less than 0.5, this makes

reading the Table easier. We suppressed all loadings less than 0.5. Brand name

is having more loading factor value than anyone else. Interior design is having

less loading than brand name but more than residuals.

In the table 5.64 highest value of extraction is for Importance attached to the

car Brand and lowest value of extraction is for Car served to project your

image to the society.

In the table 5.65 notice that number of service station accounts for 18.358%

variance, importance attached to a car brand accounts for 16.242% variance

and dealer and show room experience accounts for 13.733% variance. All


In the table 5.67 highest value of extraction is for Security features of the

specific model and lowest value of extraction is for Market value of the brand

of your car.

In the table 5.68 notice that Security features of the specific model accounts

for 18.779% variance, Willing to pay a higher price for Fuel Efficiency

(Mileage) alone of your specific model accounts for 14.060% variance and

Market value of model of your specific car accounts for 10.955% variance. All



reading the Table easier. We suppressed all loadings less than 0.5. Security

features of the specific model are having more loading than anyone else.

Willing to pay a higher price for Fuel Efficiency (Mileage) alone of your

specific model is having less loading than security features of the specific

model but more than residuals.

In the table 5.71 highest value of extraction is for Relatives and lowest value

of extraction is for Car Fairs / shows.

In the table 5.72 notice that family accounts for 20.155% variance, car loan

availability accounts for 17.431% variance and market goodwill accounts for

12.475% variance. All other variances are not significant.

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The gap on the Table represent loadings that are less than 0.5, this makes

reading the Table easier. We suppressed all loadings less than 0.5. Family

factor loading is having more than anyone else. Car loan availability factor

loading is having less loading than family factor but more than residuals.

In the table 5.75 highest value of extraction is for Power of the car and lowest

value of extraction is for Style of the car.

In the table 5.76 notice that Power of the car accounts for 11.134% variance,

Value for Money accounts for 10.027% variance and Fuel Efficiency accounts

for 9.285% variance. All other variances are not significant.


reading the Table easier. We suppressed all loadings less than 0.5. Power of

the car factor is having more loading than anyone else. Value for money is

having less loading than power of the car but less than residuals.

6.2 CONCLUSION In research study, researcher concludes on several facets regarding a car

preference. More number of male respondents prefer than females. More number of

respondents are coming from 31-40 age group than anyone else age groups. More

number of respondents are married who prefers car usage. More number of

respondents who likes car having income more than 1,00,000 per month. More

number of respondents who uses car more are post graduate in their study. More

number of respondents are doing government jobs who have car(s) than any else

commercials activities.

In the current scenario of car market indicates progressive usages of car

among the different societal groups. Generally people prefer car for family usages as

well as business purposes. More number of people prefer new branded car than the

second handed car. People likes to purchase Maruti cars more than anyone else.

Second numbered car company is TATA which has captured Indian market. And third

largest company is Hyundai which has captured Indian market. People are having

medium budget of 3 Lakhs to 5 Lakhs to have a car. Statistics indicates that people do

not prefer cash payment mode to purchase a car rather they prefer EMI payment

mode.

More number of people prefers diesel car models than petrol or gas model.

Generally car purchasers require the time of 2 weeks to 1 month in deciding about

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which car to be purchased by keeping several factors in mind. According to most of

car users purchasing a car is fairly important and wise decision to have it.

It is very surprising that majority of car purchaser do not have strong

interaction or discussion with friends or family members regarding to purchase a new

car. A car purchaser makes contact dealers less than three times to decide about in

selection of a car. In most of time car purchaser is one of the decision makers in

deciding about selection and purchasing a car. It is common to see that majority

people are self car users for their purposes. After the selection and purchasing a

particular car most of car purchasers and users are satisfied for their car performance

except few.

Income of car purchaser does affect in deciding to purchase either old or new

car but income does affect price based consideration to purchase a car. Occupation of

car purchaser does affect price based consideration to purchase a car. Level of income

does affect payment mode to purchase a car. Occupation of car purchaser does affect

fuel consideration to purchase a car.

Gender of car user doesn’t have any significant impact on owning a car factor.

Income as well as occupation of car purchaser does have significant impact on

owning a particular car. Employment of family members as well as occupation does

have significant impact on purpose of car usages. Gender of respondent does have

significant impact on brand of a car. Education of car purchaser does have an impact

on brand of a car.

Car users like to search regarding details about car in internet websites of the

car manufacturer but prefer less TV commercials on car models and brands. Most of

car users like to purchase car for family for social gatherings, meetings and functional

occasions rather than business and commercial use. They consider car more as a need

for social perspective than business perspective. Majority of car users considers re-

sale value of car before they select and purchase any car. Style & looks of the car is

the prime consideration than exterior design by the car users.

Security aspect is more needed than the market value of the branded car by the

car drivers. They prefer advises of relatives in terms of getting valuable information

than the details available from car fairs or shows. Power of the car is prime value in

application than the style value of the car.

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6.3 SUGGESTIONS

Research is providing some of the valuable suggestion based on statistical

findings which could be vital for implementing the same. Male are more dominating

so could be targeted accordingly to enhance the personality of car models

accordingly. Middle age people should be get accessed more by car manufactures to

increase the sales of their cars. Car marketers should highlight their car as the need of

family more as it is preferred more by married people than unmarried. If car dealers

impart more factual details, it could attract more literate people. Car marketers are

advised to facilitate car purchasing on equated monthly installment facilities to win

the government job employees as well as middle class families.

Car market is very bright. Car users are advised to compare the different car models

from different companies to have a reasonable as well as negotiable car deal.

Especially in Gujarat, car ownership is increasing day by day which attracts more car

companies to sell their new varieties into this emerging and booming market.

Gradually, is wiping out as a status except luxuries but becoming as a regular routine

need of transportation for their users. Now it is not an uncommon vehicle for even

middle class families. Simultaneously, second hand car market is becoming giant as

car purchasers do prefer brand new car and wants to sale after few years of

consumption only. Still car has not got a segregated identity to be called as a solely a

family car or a business car where is it preferred as a dual purpose. This none

demarcation has a big impact on having different wagon for different routes. Slowly

the market is losing its loyal nature regarding prejudice of having a specific car firm

merely. Current scenario is going to be get diluted very soon. All the main players are

in fierce completion and will be moving equally and parallel to each other with slight

ups & downs till the drastic innovative modes in their cars. All the manufacturers are

required to be stayed with low to medium budget models to be survived in the Gujarat

market otherwise it could be out of the game. Frequent price hikes in fuels motivate

people to choose cheaper and omni-available fuel based car models more. Normal car

purchaser wishes to have reasonable time to learn, to compare and to purchase a car.

Marketers should prove adequate consumer services in fulfilling their need to know

their decision to evaluate critically. This shows that car purchasers’ decision about the

selection of a car is fairly vital and valuable to them as well as for their family

members. In Indian car market, word-of-mouth plays an important impact over the

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buying preferences of car irrespective of any features, facility, brand, reputation, etc.

Till they found everything ok they won’t buy. Dealers could expect sales in the first

meeting with customers rather they should at least try to develop a good rapport with

their customers to take their follow ups regarding their car purchase matter. If

possible, main car user should involve family members in the selection process of a

car to have a better as well as suitable family choice.

As we know, India is made by most of middle class as well as lower class

social families, it is better to prefer any one of the car by keep their level of earnings

otherwise it easy to have luxuries unaffordable cars on car loans. But later on if it

could be seized because of default or irregular EMI payment to a car company.

Finally, car users are advised to avail latest car models reasonably by selecting it

wisely with help of adequate details to dealers, websites or other public sources to

match according to family as well as business needs. Now-a-days, its fierce

competition among car firms to launch innovative, effective and power saver wagons

to be purchased as quickly as possible by car thinkers. It is warning bell for purchaser

to have as we observed many fiascos in past from popular MNCs. Safely, fuel

consumption, mileage should be prime consideration than style, look, status etc. Car

journey should be not only comfortable to drivers but pedestrians also. Car should be

incorporated with air bags, auto drive, GPS, Sensors, speed limit, and powerful

surveillance to eliminate the hit & run crime, kidnapping and unwanted highway

accidents.

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