68 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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68
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.
79
Table 5.2 Age
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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.
81
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
82
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
Frequency Percent Valid Percent Cumulative Percent
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.
84
Table 5.6 Occupation
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
89
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.
90
Table 5.11 Price of a Car
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
96
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
Frequency Percent Valid Percent Cumulative Percent
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
98
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
Frequency Percent Valid Percent Cumulative Percent
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.
100
Table 5.20 Overall Satisfaction of a Car
Frequency Percent Valid Percent Cumulative Percent
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
dependent variable or not. Here value of R Square indicates the measurement about
these phenomena. Histogram shows the illustrative relationship among these
variables. Table 5.22 provides the model summary. In this table, 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.
102
Table 5.22 Model Summaryb
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
a. Predictors: (Constant), Monthly Income
b. Dependent Variable: What is the purchase price of car?
Chart 5.22 Histogram
5.23 Linear Regression: Occupation & Price 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 5.23 provides the model summary. In this table, adjusted R square
value is 0.140 means occupation does affect price based consideration to purchase a
car. Chart 5.23 is the histogram for the same.
103
Table 5.23 Model Summaryb
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?
Chart 5.23 Histogram
5.24 Linear Regression: Income & Payment Mode 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 5.24 provides the model summary. In this table, 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.
104
Table 5.24 Model Summaryb
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
a. Predictors: (Constant), Monthly Income
b. Dependent Variable: What was your mode of payment of car?
Chart 5.24 Histogram
5.25 Linear Regression: Occupation & Fuel 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 5.25 provides the model summary. In this table, adjusted R square
value is -0.002 means occupation does affect fuel consideration to purchase a car.
Chart 5.25 is the histogram for the same.
105
Table 5.25 Model Summaryb
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?
Chart 5.25 Histogram
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
Case processing summary table 5.29 furnish the information regarding the size
of population which is 484 means 100%. Cross tabulation table 5.30 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 5.31 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.
Table 5.29 Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
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
Table 5.31 Chi-Square Tests
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
N of Valid Cases 484
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
Case processing summary table 5.32 furnish the information regarding the size
of population which is 484 means 100%. Cross tabulation table 5.33 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 5.34 the
calculated chi-square value is 11.960 which is more than tabulated 0.106. This mean
occupation does have any significant impact on owning a car factor.
Table 5.32 Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
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
What is your current
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
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 11.960a 7 .102
Likelihood Ratio 13.978 7 .052
Linear-by-Linear Association 1.187 1 .276
N of Valid Cases 484
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
relationship between two selected variables. Researcher has applied contingency chi
square test which is also known as test of independence. Here in the table 5.37 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.
Table 5.35 Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
How many employed people
are in your family? * Which
purpose you use your car?
484
100.0%
0
.0%
484
100.0%
Table 5.36 Cross tabulation
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
Table 5.37 Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 152.651a 4 .001
Likelihood Ratio 155.843 4 .000
Linear-by-Linear Association .004 1 .948
N of Valid Cases 484
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
Case processing summary table 5.38 furnish the information regarding the size
of population which is 484 means 100%. Cross tabulation table 5.39 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 5.40 the
calculated chi-square value is 141.281 which is more than tabulated 0.001. This mean
occupation does have any significant impact on purpose of a car.
Table 5.38 Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
What is your current
occupation? * Which purpose
you use your car?
484
100.0%
0
.0%
484
100.0%
112
Table 5.39 Cross tabulation
Which purpose you use your
car?
Total
Business
Purpose
Personal
/Family
Purpose
Both
Purpose
What is your current
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
Table 5.40 Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 141.281a 14 .001
Likelihood Ratio 172.614 14 .000
Linear-by-Linear
Association
17.091 1 .000
N of Valid Cases 484
a. 6 cells (25.0%) have expected count less than 5.
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
relationship between two selected variables. Researcher has applied contingency chi
square test which is also known as test of independence. Here in the table 5.43 the
calculated chi-square value is 230.582 which is more than tabulated 0.001. This mean
gender does have any significant impact on brand of a car.
Table 5.41 Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Gender * What is the
brand of your car?
484 100.0% 0 .0% 484 100.0%
Table 5.42 Cross tabulation
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
Table 5.43 Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 230.582a 10 .001
Likelihood Ratio 202.162 10 .000
Linear-by-Linear Association .103 1 .748
N of Valid Cases 484
a. 6 cells (27.3%) have expected count less than 5.
b. The minimum expected count is 1.74.
5.32 Cross Tabulation and Chi-square Analysis: Education & Brand of a Car
Case processing summary table 5.44 furnish the information regarding the size
of population which is 484 means 100%. Cross tabulation table 5.45 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 5.46 the
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.
Table 5.44 Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
What is your current
educational level? * What is
the brand of your car?
484 100.0% 0 .0% 484 100.0%
115
Table 5.45 Cross tabulation
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
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 557.780a 30 .003
Likelihood Ratio 617.858 30 .000
Linear-by-Linear
Association
1.315 1 .252
N of Valid Cases 484
a. 15 cells (34.1%) have expected count less than 5.
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
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
Table 5.70 (a) Component Matrix
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
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)