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1. On the partial fulfillment of 3rd Tri-semester of
POST GRADUATE DIPLOMA IN BUSINESS MANAGEMENT
AT
INSTITUTE OF MANAGEMENT STUDIES, Ghaziabad
We the following students submit our report entitled
:: FACTORS AFFECTING CAR BUYING BEHAVIOUR OF
CUSTOMERS::
Under the esteemed guidance of Prof. Manish Agarwal
ACADEMIC SESSION
2007-2009
:: Submitted To :: :: Submitted By ::
Dr. Manish Agarwal SHELLY DIXIT (138)
TAMONASH ADITYA (160)
TARUN KUMAR (165)
VIGYAN (178)

2. INSTITUTE OF MANAGEMENT STUDIES,
GHAZIABAD
CERTIFICATE
This is to certify that this report contains bonafide work of SHELLY DIXIT, TAMONASH
ADITYA, TARUN KUMAR, VIGYAN during Term III, session 2007-2009 for the subject
Research Method in Business
DATE: Signature of Faculty
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3. ACKNOWLEDGEMENT
This report bears the imprint of many people and without their support it would
not have existed. First of all we would like to express our sincere indebt ness and
profound sense of gratitude to our parents whose support in all manners had made
us capable to complete this project.
We acknowledge our deepest thanks to Prof. Manish Agarwal for all
her care and encouraging words and giving suggestion at different point of times.
At the outset we would like to put on record our sincere gratitude to all of our
friends for giving us valuable ideas throughout of our project.
Shelly Dixit(138)
Tamonash Aditya (151)
Tarun Kumar (153)
Vigyan (178)
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4. Introduction
According to the ninth annual Capgemini automotive study – Cars Online 07/08. Each year
they extend the scope and depth of their survey to explore new and evolving trends within the
retail side of the automotive industry, with a particular focus on consumer buying habits. Cars
Online 07/08 continues the detailed analysis of the changing patterns of consumer demand,
shopping trends, web usage and customer loyalty that we have uncovered over the past eight
years. This year, however, we have broadened the scope to explore in greater detail
environmental issues, including fuel-efficient and alternative-fuel vehicles, as well as consumer
use of new online tools, such as web logs, discussion forums and search engines. These
additional areas of focus reflect changes in today’s automotive landscape. The industry is
clearly in transition, with static sales in almost all developed markets; growing pressure from
Asian manufacturers; eroding customer loyalty; and increased emphasis on environmental and
regulatory compliance. Consumer behaviour will be a primary force in determining how this
transition will evolve. Getting closer to the customer in today’s highly competitive landscape is
essential for the entire industry and is no longer just a retail issue. It requires all organisations
across the supply chain to work as a single enterprise, sensing and responding rapidly to
consumer demand in a co-ordinated manner.
Capgemini’s annual Cars Online study is designed to give automotive companies information
that can help them get a better grasp on changing consumer trends, shopping patterns and
demands. This year’s research involved almost 2,600 consumers in five countries: China,
France, Germany, the United Kingdom and the United States. Interestingly, we found
significant commonalities among responses across the more mature markets, with differences
still quite apparent in the emerging Chinese automotive market. This report highlights these
results, as well as country-specific differences. The executive summary provides an overview of
key findings from the study, and the sections that follow offer more in-depth data and analysis
on consumer behaviour, environmental issues, web usage, lead management and customer
loyalty. The automotive world today is changing; consumers are changing. And the speed of
change is continuing to accelerate.
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5. Executive Summary
Competitive pressures and increasing complexity have led automotive companies to look for an
edge wherever they can find it. Improved consumer insight into vehicle shopping and buying
behaviour can provide that valuable advantage. Capgemini’s Cars Online report contains insight
that can help vehicle manufacturers and dealers develop and execute more effective strategies in
areas such as sales, marketing and advertising, after sales service, Customer Relationship
Management (CRM) and manufacturer/dealer collaboration.
AUTOMOBILE INDUSTRY IN INDIA
In India there are 100 people per vehicle, while this figure is 82 in China. It is expected that
Indian automobile industry will achieve mass motorization status by 2014.
Industry Overview
Since the first car rolled out on the streets of Mumbai (then Bombay) in 1898, the Automobile
Industry of India has come a long way. During its early stages the auto industry was overlooked
by the then Government and the policies were also not favorable. The liberalization policy and
various tax reliefs by the Govt. of India in recent years has made remarkable impacts on Indian
Automobile Industry. Indian auto industry, which is currently growing at the pace of around 18
% per annum, has become a hot destination for global auto players like Volvo, General Motors
and Ford.
A well developed transportation system plays a key role in the development of an economy, and
India is no exception to it. With the growth of transportation system the Automotive Industry of
India is also growing at rapid speed, occupying an important place on the 'canvas' of Indian
economy.
Today Indian automotive industry is fully capable of producing various kinds of vehicles and
can be divided into 03 broad categories: Cars, two-wheelers and heavy vehicles.
Snippets
• The first automobile in India rolled in 1897 in Bombay.
• India is being recognized as potential emerging auto market.
• Foreign players are adding to their investments in Indian auto industry.
• Within two-wheelers, motorcycles contribute 80% of the segment size.
• Unlike the USA, the Indian passenger vehicle market is dominated by cars (79%).
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6. • Tata Motors dominates over 60% of the Indian commercial vehicle market.
• 2/3rd of auto component production is consumed directly by OEMs.
• India is the largest three-wheeler market in the world.
• India is the largest two-wheeler manufacturer in the world.
• India is the second largest tractor manufacturer in the world.
• India is the fifth largest commercial vehicle manufacturer in the world.
• The number one global motorcycle manufacturer is in India.
• India is the fourth largest car market in Asia - recently crossed the 1 million mark.
Segment Know how
Among the two-wheeler segment, motorcycles have major share in the market. Hero Honda
contributes 50% motorcycles to the market. In it Honda holds 46% share in scooter and TVS
makes 82% of the mopeds in the country.
40% of the three-wheelers are used as goods transport purpose. Piaggio holds 40% of the
market share. Among the passenger transport, Bajaj is the leader by making 68% of the three-
wheelers.
Cars dominate the passenger vehicle market by 79%. Maruti Suzuki has 52% share in passenger
cars and is a complete monopoly in multi purpose vehicles. In utility vehicles Mahindra holds
42% share.
In commercial vehicle, Tata Motors dominates the market with more than 60% share. Tata
Motors is also the world's fifth largest medium & heavy commercial vehicle manufacturer.
Miscellaneous
Hyderabad, the Hi-Tech City, is going to come up with the first automobile mall of the country
by the second half of 2008. It would be set up by city-based Prajay Engineers Syndicate in area
of more than 35 acres. This 'Autopolis' would have facilities for automobile financing
institutions and insurance services to create a complete range of services required for both auto
companies and customers. It will also have a multi-purpose convention centre for auto fairs and
product launches.
Cars by Price Range
Under Rs. 3 Lakhs • Maruti 800, Alto, Omni
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7. • Reva
• Ambassador
• Fiat Palio
• Hyundai Santro, Getz
• Chevrolet Opel Corsa
Rs. 3-5 Lakhs
• Maruti Zen, Wagon R, Versa, Esteem, Gypsy
• Ford Icon & Fiesta
• Tata Indica, Indigo XL, Indigo Marina
• Chevrolet Swing, Optra Magnum, Tavera
• Hyundai Accent, Elantra
• Mahindra Scorpio
• Maruti Baleno
• Toyota Innova
Rs. 5-10 Lakhs • Tata Safari
• Mitsubishi Lancer, Mitsubishi Cedia
• Honda City ZX
• Mahindra Bolero
• Hyundai Sonata Embera
• Toyota Corolla
• Ford Mondeo & Endeavour
• Chevrolet Forester
Rs. 10-15 Lakhs
• Skoda Octavia & Combi
• Honda Civic
• Honda CR-V
• Maruti Suzuki Grand Vitara
• Terracan & Tucson
• Mitsubishi Pajero
• Audi A4
Rs. 15-30 Lakh
• Opel Vectra
• Honda Accord
• Mercedes C Class
• Toyota Camry
• Audi A6, A8 & Audi TT
• BMW X5, 5 Series & 7 Series
• Mercedes E Class, S Class, SLK, SL & CLS-Class
Rs. 30-90 Lakhs
• Porsche Boxster, Cayenne, 911 Carrera & Cayman S
• Toyota Prado
Above Rs. 1 Crore • Bentley Arnage, Bentley Continental GT & Flying Spur
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8. • Rolls Royce Phantom
• Maybach
The following links gives the complete picture of Indian Auto Industry:
The first auto vehicle rolled out in India at the end of 19th century.
Today, India is the the 2nd largest tractor and 5th largest commercial
Automobile History vehicle manufacturer in the world. Hero Honda with 1.7M motorcycles
a year is now the largest motorcycle manufacturer in the world.
On the cost front, OEMs eyeing India in a big way to source products
and components at significant discounts to home market. On the revenue
Industry Investment
side, OEMs are active in the booming passenger car market in India.
The passenger car and motorcycle segment in Indian auto market is
growing by 8-9 per cent. The two-wheeler segment will clock 11.5%
Industry Growth
rise by 2007. Commercial vehicle to grow by 5.2 per cent.
India is the 11th largest Passenger Cars producing countries in the world
and 4th largest in Heavy Trucks. Maruti Udyog Ltd. is the leading 4-
Vehicle Production wheelers manufacturer. Hero Honda is the leading 2-wheelers
manufacturer.
Passenger vehicle exports have grown over five times and two-wheeler
exports have reached more than double. Exports of auto components,
Auto Export whose manufacturing costs are 30-40 per cent lower than in the West,
have grown at 25% a year between 2000 to 2005.
Hero Honda is the largest manufacturer of motorcycles. Hyundai Motors
India is the second largest player in passenger car market. Tata Motors
Auto Companies is the fifth largest medium & heavy commercial vehicle manufacturer in
the world.
Know about the number of vehicles registered as Transport or Non-
Vehicle Distribution Transport in the Indian states and Union Territories.
Get all the contact details of Automobile Association of Upper India
(AAUI), Automotive Research Association of India (ARAI),
Associations
Automobile Association of Southern India (AASI), Automotive
Component Manufacturers Association of India (ACMA) and more
Major Manufacturers in Automobile Industry
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9. • Maruti Udyog Ltd.
• General Motors India
• Ford India Ltd.
• Eicher Motors
• Bajaj Auto
• Daewoo Motors India
• Hero Motors
• Hindustan Motors
• Hyundai Motor India Ltd.
• Royal Enfield Motors
• Telco
• TVS Motors
• DC Designs
• Swaraj Mazda Ltd
Government has liberalized the norms for foreign investment and import of technology and that
appears to have benefited the automobile sector. The production of total vehicles increased from
4.2 million in 1998- 99 to 7.3 million in 2003-04. It is likely that the production of such vehicles
will exceed 10 million in the next couple of years.
The industry has adopted the global standards and this was manifested in the increasing exports
of the sector. After a temporary slump during 1998- 99 and 1999-00, such exports registered
robust growth rates of well over 50 per cent in 2002-03 and 2003-04 each to exceed two and- a-
half times the export figure for 2001-02.
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10. Anticipating Consumer Changes
What do these findings tell us? They make it clear that consumer behaviour is evolving and that
automotive companies need to anticipate this evolution in order to be part of, or even influence,
the changes. Is your company ready? What changes will you need to make? Companies will
need to take a look at their multi-channel approach as they consider the potential market for
online sales. Effective web strategies will be vitally important, as the online landscape evolves
rapidly with the emergence of powerful consumer-to-consumer tools like blogs, discussion
forums, social networking sites and virtual worlds. Automotive companies will need to stay
focussed on environmental developments and evolving consumer attitudes about fuel-efficient
and alternative-fuel vehicles. As with the web, green issues are dynamic and it’s still too early
to determine their ultimate impact on the automotive industry. Manufacturer/dealer
collaboration in the form of effective retail integration and integrated lead management will
become more important than ever to satisfy increasingly sophisticated and demanding
consumers and to retain loyalty. And companies will need to establish and maintain a true two-
way dialogue with individual customers through personalised communication. While this
topline review provides a summary of key findings from this year’s Cars Online study, the
sections that follow offer more in-depth data and analysis of consumer behaviour,
environmental issues, web usage, lead management and customer loyalty.
Consumer Behaviour: Turning to the Web and New C2C Tools
Consumers today have a multitude of sources from which to gather information during the
vehicle buying process, but the Internet tops the list. The web has become a standard resource in
the shopping process for eight out of 10 consumers when researching car purchases. However,
the way they use it is changing. As the web matures, vehicle buyers are visiting fewer sites and
focussing more on manufacturer and C2C websites and less on third-party information sites and
independent e-tailer sties.
Manufacturer Sites a Key Information Source
Just two years ago, information websites were identified as the number one information source
by web users responding to the Cars Online survey (tied with family and friends and
manufacturer specific dealer), named by 55% of consumers. This year, they dropped to the
number four source, named by 41% of web users. In comparison, manufacturer sites are now
the top source for consumers who use the web when researching vehicles, named by 70% of
respondents. Two years ago manufacturer sites held the number three position, named by 43%
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11. of web users. The use of dealer websites has remained steady, with about half of web users
turning to these sites.
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12. At the same time, the use of new online consumer-to-consumer tools such as blogs, RSS (Really
Simple Syndication) feeds, user-generated content, social networking sites and web forums is
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13. growing. In this year’s study, 29% of web users indicated that they use these kinds of tools
when researching during the vehicle shopping process, up from 21% a year ago. (For a more
detailed analysis of the use of these new online tools see separate section on “Web Usage.”)
Interestingly, it is not just the young generation who use the web to research vehicles. Almost
half of consumers 50 and older visit manufacturer sites, nearly the same number as those in the
18 to 34 age group. The numbers do fall off, however, when it comes to blogs and web forums.
About 30% of the youngest consumers rely on these new tools, compared with just 12% of
those 50 and older. As web usage rises, consumer reliance on other more traditional information
sources is on the decline. Take print advertising, for example, which has shown a steady
downward trend particularly among consumers who rely on the web during the vehicle
shopping process. This year, just 20% of web users said they use
print ads when researching vehicles, compared with 32% in 2005. The message for automotive
companies is clear: Consumers trust the information they receive from manufacturer and C2C
sites. Vehicle manufacturers and dealers need to be aware of how fast online changes are
occurring and continually adjust their marketing mix and resources accordingly to anticipate
tomorrow’s mix. Marketing funds directed toward more traditional media such as print
advertising should be regularly re-examined for ROI.
Key Factors in Vehicle Choice
When it comes to making their final decision about which vehicle to buy, consumers focus on
factors such as reliability, safety, price and fuel economy. At the bottom of the list are cash-back
incentives, named by fewer than half of consumers. The importance of incentives as a deciding
factor has declined for the past several years, indicating that consumers today seem less
interested in gimmicks when it comes to their car purchases. Where consumers are in the buying
cycle can make a difference in how they rank the factors that influence their vehicle choice. For
example, additional warranty coverage is important to consumers who are furthest away from
the point of purchase; it was named by 69% of respondents who were 13 to 18 months from
purchase. However, the number declines as consumers get closer to actually buying the car:
55% of respondents who were within three months of purchase said extra warranty coverage
was important. This reflects the fact that consumers will narrow down the factors that really
matter to them as they get closer to the point of purchase. Demographic factors such as age and
gender accounted for some variances. For example, older consumers tend to put more emphasis
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14. on reliability and safety than do younger respondents. Those in the 50-plus age group were also
more concerned with environmental issues and fuel economy. The youngest respondents were
most likely to rate the ability to research information on the Internet as an important factor in
their vehicle decision. Women tend to rate most of the factors as more important than do men.
The difference was most pronounced for cash-back incentives, low financing, safety,
environmental issues, fuel economy and additional warranty coverage.
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16. Going ‘Green’: Fuel Efficiency Takes Centre Stage
Fuel efficiency and environmental issues have moved to the forefront in consumers’ minds and
in automotive industry forums thanks to factors including global warming, fluctuating gasoline
prices, and proposed legislation to increase fuel efficiency and reduce CO2 emissions. This
growing interest in so-called green vehicles was evident in this year’s Cars Online research.
More than one-quarter of respondents said they currently own or lease a fuel-efficient vehicle
while almost half said they are planning to buy or thinking seriously about buying a fuel-
efficient vehicle. Not surprisingly, the numbers for alternative-fuel vehicles were lower. Just 2%
of respondents currently own an alternative-fuel vehicle and 11% are planning to buy or
thinking seriously about buying one. The most common type of alternative-fuel vehicle
represented in the survey were gas/ electric hybrids, named by about half of current alternative-
fuel car owners.
Biodiesel vehicles were the second most common, named by 15%. The alternative-fuel market
remains in transition and it’s still too early to tell how it will ultimately shake out, although
sales are expected to continue to grow. For example, J.D. Power and Associates predicts that
U.S. sales of hybrid vehicles will increase by 35% in 2007, compared with 2006.
Current ownership of fuel-efficient and alternative-fuel vehicles tended to be quite consistent
across gender and age groups, although the oldest consumers were somewhat more likely to be
seriously thinking about buying an alternative-fuel car.
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17. Why Buy a Green Vehicle?
Fuel economy is the number one factor driving consumer decisions about green vehicles
(named by 57% of respondents), followed by the impact on the environment (23%). Tax credits
and cost factors were less important. Some consumers pointed to less tangible reasons such as
“it makes me feel better.” This is in line with research conducted by CNW Marketing Research.
When asked why they bought a Toyota Prius, 57% of Prius owners said because it “makes a
statement about me.” However, the Cars Online research uncovered some differences in the
reasons behind consumer decisions about green vehicles. For example, European consumers
were more likely to cite environmental impact as a primary factor, while more respondents in
China and the U.S. pointed to fuel economy. Older consumers were somewhat more likely to
identify fuel economy as a primary factor, compared with the youngest respondents (18-34).
Men put more emphasis than did women on fuel economy, while a higher proportion of women
identified environmental impact as the primary reason driving their decisions about green
vehicles.
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18. 40

19. PERSONAL SELLING:
CONSUMER BUYING BEHAVIOR
CONSUMER BUYING vs. ORGANIZATIONAL BUYING
Final (or ultimate) consumers purchase for:
• personal,
• family, or
• household use
Organizational consumers purchase for:
• further production,
• usage in operating the organization, and/or
• resale to other consumers
Consumer Buying Behavior
The decision processes and acts of final household consumers associated with evaluating,
buying, consuming, and discarding products for personal consumption
Consider the purchase an automobile. You generally will not consider different options until
some event triggers a need, such as a problem needing potentially expensive repair. Once this
need has put you "on the market", you begin to ask your friends for recommendations regarding
dealerships and car models. After visiting several dealerships, you test drive several models and
finally decide on a particular model. After picking up your new car, you have doubts on the way
home, wondering if you can afford the monthly payments, but then begin to wonder if instead
you should have purchased a more expensive but potentially more reliable model. Over the next
five years, the car has several unexpected breakdowns that lead you to want to purchase a
different brand, but you have been very happy with the services of the local dealership and
decide to again purchase your next car there.
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20. In this particular case, the following generic model of consumer decision making appears to
hold:
=====>need recognition
=====>information search
=====>evaluation of alternatives
=====>purchase decision
=====>postpurchase behavior
Now consider the purchase of a quart of orange juice. You purchase this product when you do
your grocery shopping once per week. You have a favorite brand of orange juice and usually do
your grocery shopping at the same store. When you buy orange juice, you always go to the
same place in the store to pick it up, and never notice what other brands are on the shelf or what
are the prices of other brands. How is it that the generic model above works differently in this
second scenario? Why does it work differently? Why would we generally need the ministrations
of a sales person in the sale of a car, but we generally do not need the help of a salesperson in
the purchase of orange juice?
How can the marketer of orange juice get a consumer like you to exert more effort into
information search or to consider alternative products? How is it that the marketer of your brand
got you to ignore alternative competing brands? What is the involvement of salespeople in sales
promotions that might be associated with products such as orange juice?
Consumer behavior researchers are not so interested in studying the validity of the above
generic model, but are more interested in various factors that influence how such a model might
work.
INFLUENCES ON THE GENERIC MODEL
• external
o group
-e.g., cultural, family, reference group influences
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21. o environmental/situational
-e.g., time of day, temperature and humidity, etc.
• inernal
o lifestyle, personality, decision making process, motivation, etc.
GROUP INFLUENCES ON CONSUMER BEHAVIOR
Culture
the set of basic values, beliefs, norms, and associated behaviors that are learned by a member of
society
Note that culture is something that is learned and that it has a relatively long lasting effect on
the behaviors of an individual. As an example of cultural influences, consider how the
salesperson in an appliance store in the U.S. must react to different couples who are considering
the purchase of a refrigerator. In some subcultures, the husband will play a dominant role in the
purchase decision; in others, the wife will play a more dominant role.
Social Class
A group of individuals with similar social rank, based on such factors as occupation, education,
and wealth
Reference Groups
Groups, often temporary, that affect a person's values, attitude, or behaviors
• E.g., your behaviors around colleagues at work or friends at school are probably
different from your behaviors around your parents, no matter your age or stage in the
family life cycle. If you were a used car salesperson, how might you respond differently
to a nineteen year old prospect accompanied by her boyfriend from one accompanied by
two girlfriends?
• Opinion leader
A person within a reference group who exerts influence on others because of special
skills, knowledge, personality, etc.
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22. o You might ask the webmaster at work for an opinion about a particular software
application. Software manufacturers often give away free beta copies of software
to potential opinion leaders with the hope that they will in turn influence many
others to purchase the product.
• Family
A group of people related by blood, marriage, or other socially approved relationship
ENVIRONMENTAL / SITUATIONAL INFLUENCES ON CONSUMER
BEHAVIOR
Circumstances, time, location, etc.
Do you like grapes? Do you like peas?
You might like grapes as a snack after lunch, but probably not as a dessert after a fancy meal in
a restaurant. You might like peas, but probably not as a topping on your pancakes. Everyday
situations cause an interaction between various factors which influence our behaviors. If you
work for tips (a form of incentive related to commission) as a waiter or waitress, you must
certainly be aware of such interactions which can increase or decrease your sales.
If you are doing your Saturday grocery shopping and are looking for orange juice, you are
probably much more sensitive to price than if you stop at the quick store late at night, when you
are tired and cranky, after a late meeting at the office. A prospect shopping for a new
automobile while debating the wisdom of a necessary expensive repair to his car might be more
interested in what cars are on the lot than in shopping for the best deal that might involve a
special order.
INTERNAL INFLUENCES ON CONSUMER BEHAVIOR
Personality
A person's distinguishing psychological characteristics that lead to relatively consistent and
lasting responses to stimuli in the environment
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23. We are each unique as individuals, and we each respond differently as consumers. For example,
some people are "optimizers" who will keep shopping until they are certain that they have found
the best price for a particular item, while other people are "satisficers" who will stop shopping
when they believe that they have found something that is "good enough." If you are a
salesperson in a retail shoe store, how might you work differently with these two personalities?
Lifestyle and Psychographics
• lifestyle is a pattern of living expressed through a person's activities, interests, and
opinions
• psychographics is a technique for measuring personality and lifestyles to developing
lifestyle classifications
Motivation: Multiple motives
Consumers usually have multiple motives for particular behaviors. These can be a combination
of:
• manifest
known to the person and freely admitted
• latent
unknown to the person or the person is very reluctant to admit
Note: different motives can lead to the same behavior; observing behavior is not sufficient to
determine motives.
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24. What are the thoughts of John's friend?
What is John's manifest motive?
What might be his latent motive?
How might a salesperson discover these motives? What features should a salesperson
emphasize?
Involvement
Has to do with an individual's
• intensity of interest in a product and the
• importance of the product for that person
The purchase of a car is much more risky than the purchase of a quart of orange juice, and
therefore presents a higher involvement situation. This modifies the way that the generic model
works.
As involvement increases, consumers have greater motivation to comprehend and elaborate on
information salient to the purchase. A life insurance agent, for example, would typically be
more interested in contacting a young couple who just had a baby than an eighteen year old
college student - even though the new parents might be struggling to make ends meet while the
student is living more comfortably. Although the annual investment into a policy is much lower
if started at a younger age, most young college students are not open to thinking about long term
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25. estate planning. A young couple with a new child, however, is much more open to thinking
about issues associated with planning for the child's future education, saving to buy a house, or
even saving to take an extended vacation upon retirement.
TYPES OF CONSUMER PROBLEM-SOLVING PROCESSES
Routinized
• used when buying frequently purchased, low cost items
• used when little search/decision effort is needed
• e.g., buying a quart of orange juice once per week
Limited Problem Solving
• used when products are occasionally purchased
• used when information is needed about an unfamiliar product in a familiar product
category
Extended problem solving
• used when product is unfamiliar, expensive, or infrequently purchased
• e.g., buying a new car once every five years
Under what sorts of conditions would the assistance of a salesperson be needed? Not needed?
POST-PURCHASE CONSUMER BEHAVIOR
Satisfaction
After the sale, the buyer will likely feel either satisfied or dissatisfied. If the buyer beleives that
s/he received more in the exchange than what was paid, s/he might feel satisfied. If s/he believes
that s/he received less in the exchange than what was paid, then s/he might feel dissatisfied.
Dissatisfied buyers are not likely to return as customers and are not likely to send friends,
relatives, and acquaintences. They are also more likely to be unhappy or even abusive when the
product requires post-sale servicing, as when an automobile needs warranty maintenance.
The above idea can be modeled as Homans' basic exchange equation:
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26. Profit = Rewards - Costs
Unfortunately, even a buyer who "got a good deal" with respect to price and other terms of the
sale might feel dissatisfied under the perception that the salesperson made out even better.
This idea is called equity theory, where we are concerned with:
Outcomes of A
Inputs of A
vs.
Outcomes of B
Inputs of B
Consider, for example, that you have purchased a used car for $14,000 after finding that the
"e;blue book" value is listed at $16,000. You are probably delighted with the purchase until you
accidentally meet the prior owner who had received a trade-in of $10,000 on the car just a few
days before. That the dealer appears to have received substantially greater benefit than you
could lead to extreme dissatisfaction, even though you received good value for the money spent.
(Note that the selling dealer might actually have paid $12,000 for the car at a statewide dealer's
auction, and then might have incurred another $1,000 in expenses associated with transporting
the car and preparing it for sale. Management of buyer perceptions is very important!)
An issue related to this is attribution theory. According to attribution theory, people tend to
assign cause to the behavior of others. Mary's life insurance agent advises her to purchase a
whole life policy, while her accountant advises her, "buy term insurance and invest the
difference.". The reason, explains the accountant, "is that insurance agents receive substantially
higher commission payments on sales of whole life policies."
If Mary believes that the insurance agent is recommending a product merely because he
receives a higher commission, she will likely be displeased with the relationship and will not
take his recommendation. If the agent is able to show Mary that the recommended product is the
best solution for her situation, then she will likely attribute his recommendation to having her
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27. best interests in mind and will not be concerned about how it is that he is compensated for his
services.
Cognitive dissonance
It has to do with the doubt that a person has about the wisdom of a recent purchase
It is very common for people to experience some anxiety after the purchase of a product that is
very expensive or that will require a long term commitment. Jane and Fred, for example, signed
a one year lease on an apartment, committing themselves to payments of $1500 per month. A
week later, they are wondering if they should have instead leased a smaller $900 apartment in a
more rough part of town; they are not sure if they really can afford this much of a monthly
obligation. Dick and Sally, on the other hand, ultimately rented the $900 apartment, and now
are wondering if the savings in rent will be offset by noisy and sometimes unsafe conditions in
this neighborhood.
Perhaps neither couple would be experiencing this anxiety if their landlords had given them just
the smallest of assurances that they had made a good decision. After a close on products that are
expensive or that require a long term commitment, the salesperson should provide the prospect
with some reasons to be happy with the decision. Allow the car buyer to reinforce her own
positive feelings by calling her a week after the purchase to ask how things are going. Call the
new life insurance policy holder after two months to see if there are any questions; a lack of
questions can only help the buyer to convince himself that he did the right thing.
Methodology
The study is based on primary data collection with a sample size of 100 respondents residing in
National Capital Region of New Delhi, India. The questionnaire used for the sample survey is a
structured and non-disguised questionnaire and consisted of two major sections. The first
section intended to collect the various demographic factors; the second section intended to
collect the various opinions containing questions about the various factors affecting the car
purchasing decision. A five point Likert scale was used to capture the consumers responses
ranging from strongly agree to strongly disagree. The different statements regarding the various
factors affecting the car buying behavior of customers were generated based on literature review
40

28. as well as expert opinion in an iterative manner. It could be therefore said that the itemized scale
in this case actually asks the respondents to rank their opinions in a decreasing order of
importance. Data analysis was done using SPSS software. The statistical analysis methods
employed was factor analysis. To study the impact most frequently indulged in weighted
average method was used.
Data collection
The study entailed data collection with the help of a questionnaire from the residents of
National Capital Region of New Delhi, India. Data was collected by personally contacting the
respondents and explaining in detail about the survey. A total of 120 customers from different
areas were contacted and 100 correctly completed questionnaires were obtained from all the
customers, the break-up of which is given in Figure 1,2 and 3
Descriptive profile of respondents (n=100)
Gender
Percentage
Female
Male
0 20 40 60 80 100
Fig 1
Fig 2
Age
40

29. 60
40
20
0
Percentage
Below 18 18-25 26-35 36-50 Above 51
Occupation
60
40
20 Percentage
0
Service Business Student House-Wife
Fig 3
Findings and Analysis
Factor Analysis for factors affecting car purchasing decision
40

30. Factor analysis was performed to identify the key dimensions affecting purchase of cars
provided by different car manufacturing companies. The respondent ratings were subject to
principal axis factoring with varimax rotation to reduce potential multicollinearity among the
items and to improve reliability on the data (see Table 6: Rotated Factor Matrix). Varimax
rotation (with Kaiser Normalization was converged in thirty-one iterations. Thirty items were
reduced to nine orthogonal factor dimensions which explained 73.555% of the overall variance
(Table 4) indicating that the variance of original values was well captured by these nine factors.
The nine factors and their components is given in table 7.
Reliability of Data
Table 1: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .769
Bartlett's Test of Approx. Chi-Square 1650.000
Sphericity df 435
Sig. .000
Kaiser-Meyer-Olkin
[Index for comparing the magnitudes of the observed co-relation coefficient to the
magnitude of the partial correlation coefficients]
From the above table, we can interpret that there is no error in 76.9% of the sample and in
the remaining 23.1%, there may occur some sort of error.
“Bartlett’s Test of Sphericity”
[Strength of relationship among variables is strong. It presents good idea to proceed to
factor analysis for the data.]
Ho : There is significant indifference of all the factors affecting car purchase decision
H1 : There is significant difference of all the factors affecting car purchase decision
The observe significance level is 0.0000 which is less than .05, which is small enough to
reject the hypothesis. It means there is a significant difference between the factors affecting
car purchasing decisions.
Communality”- Common Factor Variance
Communality of each statement refers to the variance being shared or common
by other statements. With reference to the first statement, the extraction is .833 which indicates
that 83.3% of the variance is being shared or common to other statements. Refer Table 2.
40

31. “Eigen Value”: Indicates the amount of variance in the original variables accounted or by each
component. The total initial variance in the new components will be 30.
Table 2: Communalities
Initial Extraction
S1 1.000 .833
S2 1.000 .692
S3 1.000 .760
S4 1.000 .800
S5 1.000 .695
S6 1.000 .795
S7 1.000 .746
S8 1.000 .731
S9 1.000 .783
S10 1.000 .875
S11 1.000 .851
S12 1.000 .782
S13 1.000 .642
S14 1.000 .628
S15 1.000 .674
S16 1.000 .715
S17 1.000 .662
S18 1.000 .707
S19 1.000 .653
S20 1.000 .728
S21 1.000 .762
S22 1.000 .710
S23 1.000 .642
S24 1.000 .687
S25 1.000 .835
S26 1.000 .684
S27 1.000 .803
S28 1.000 .683
S29 1.000 .857
S30 1.000 .650
Extraction Method: Principal Component Analysis.
40

32. Table 3: Total Variance Explained
Extraction Sums of Squared
Component Initial Eigenvalues Loadings Rotation Sums of Squared Loadings
% of Cumulative % of Cumulative % of Cumulative
Total Variance % Total Variance % Total Variance %
1 7.102 23.672 23.672 7.102 23.672 23.672 3.398 11.327 11.327
2 3.539 11.798 35.470 3.539 11.798 35.470 3.227 10.756 22.083
3 2.543 8.477 43.947 2.543 8.477 43.947 3.080 10.268 32.350
4 2.188 7.292 51.239 2.188 7.292 51.239 2.556 8.520 40.870
5 1.716 5.721 56.960 1.716 5.721 56.960 2.543 8.476 49.345
6 1.631 5.435 62.396 1.631 5.435 62.396 2.356 7.855 57.200
7 1.218 4.059 66.455 1.218 4.059 66.455 1.909 6.364 63.564
8 1.112 3.706 70.161 1.112 3.706 70.161 1.718 5.725 69.289
9 1.018 3.394 73.555 1.018 3.394 73.555 1.280 4.266 73.555
10 .948 3.160 76.715
11 .815 2.717 79.432
12 .683 2.278 81.710
13 .634 2.113 83.823
14 .567 1.889 85.712
15 .500 1.667 87.379
16 .489 1.631 89.010
17 .439 1.464 90.475
18 .421 1.403 91.878
19 .330 1.099 92.976
20 .297 .991 93.967
21 .277 .924 94.891
22 .271 .905 95.796
23 .226 .752 96.547
24 .209 .697 97.245
25 .194 .647 97.892
26 .183 .608 98.501
27 .161 .537 99.038
28 .129 .431 99.468
29 .089 .297 99.766
30 .070 .234 100.000
Extraction Method: Principal Component Analysis.
40

33. Table 4:
Cumulative Frequency
Component 1 Explain a variance of 3.398, which 11.327%
is 11.327 % of the total variance of
30
Component 2 Explain a variance of 3.327, which 22.083%
is 10.756 % of the total variance of
30
Component 3 Explain a variance of 3.080, which 32.350%
is 10.268 % of the total variance of
30
Component 4 Explain a variance of 2.556, which 40.870%
is 8.520 % of the total variance of
30
Component 5 Explain a variance of 2.543, which 49.345%
is 8.476 % of the total variance of
30
Component 6 Explain a variance of 2.356, which 57.200%
is 7.855 % of the total variance of
30
Component 7 Explain a variance of 1.909, which 63.564%
is 6.364 % of the total variance of
30
Component 8 Explain a variance of 1.718, which 69.289%
is 5.725 % of the total variance of
30
Component 9 Explain a variance of 1.280, which 73.555%
is 4.266 % of the total variance of
30
40

34. Scree Plot
8
7.1
6
Eigenvalue
4
3.54
2.54
2.19
2 1.63
1.72 1.11
0.95
1.22 0.68 0.57 0.49
1.02 0.44 0.3
0.82 0.28 0.23
0.63 0.19 0.16 0.13
0.5 0.07
0.42
0 0.33 0.27 0.21 0.18 0.09
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Component Number
Fig 4
With the help of table 3 and 4, we can interpret that 30 statements are now reduced to 9
components contributing 73.555% of the total variance. With the help of Fig1. Scree plot, we
can just visualize that nine factors are reduced with eigen value greater than 1.0000
Table 5. Component Matrix:
This table reports the factor loadings for each variable on the unrotated components or factors.
Component Matrix
40

35. Component
1 2 3 4 5 6 7 8 9
S1 .377 .267 .541 -.333 .217 .171 -.327 .176
S2 -.166 -.163 -.228 .665 -.119 .180 .303
S3 .649 -.382 .347 .119 .188 .121
S4 -.551 -.191 .503 .338 -.271 -.106
S5 .599 -.244 .388 .141 .102 .115 .108 -.166 .210
S6 .751 -.265 -.164 -.156 .291 .131
S7 .223 .232 -.138 .223 -.344 .390 -.237 .498
S8 .430 .124 .128 .249 .581 .125 .306
S9 -.104 -.267 .229 .699 -.224 -.271 -.147
S10 -.170 .698 -.418 .363 .129 .178
S11 .232 .808 -.272 .132 -.161 .157
S12 -.542 -.211 .628 .144 .135
S13 .177 .528 .462 -.216 -.103 .205 -.100
S14 .627 -.139 -.171 -.220 .279 -.227
S15 .689 .337 .100 -.197
S16 .569 .357 .170 .273 -.119 -.346 -.154
S17 -.312 .398 .592 .161 -.109
S18 .481 .343 .427 .117 -.343 -.183
S19 .718 -.106 -.196 -.273
S20 -.395 -.122 .254 .107 .667 .163
S21 .730 -.205 -.139 .116 -.367 .114
S22 .537 -.294 .154 -.108 .245 .470
S23 .484 -.354 .115 .311 .395
S24 .368 .168 .226 -.252 .527 .104 -.341
S25 -.499 .207 .617 -.288 -.106 -.145 .191
S26 .621 .112 .187 .256 .323 .186 -.195
S27 -.503 .430 .516 .251 -.138
S28 .652 .247 .227 -.278 -.236
S29 -.535 .186 .663 .112 .158 -.225
S30 .459 .422 .117 -.300 -.123 -.153 .335
Extraction Method: Principal Component Analysis.
a 9 components extracted.
Each number represents the correlation between the item and the unrotated factor. This
correlation helps to formulate an interpretation of the factors or components. This is done by
looking for a common thread among the variables that have large loadings for a particular factor
or component. It is possible to see items with large loadings on several of the unrotated factors,
which makes interpretation difficult. In these cases, it can be helpful to examine a rotated
solution.
Table 6: Rotated Component Matrix
40

36. Component
1 2 3 4 5 6 7 8 9
S1 .447 .753 .205
S2 .191 -.765 -.115 .201
S3 -.122 -.414 .591 .282 .153 .194 .284
S4 .189 -.123 -.799 -.232 -.134 -.175
S5 -.374 .570 .295 .158 .158 .120 .256
S6 .755 -.192 .102 .304 .114 .265
S7 .219 .191 .111 .799
S8 .150 .821 .131
S9 .308 -.308 .193 .388 -.466 -.213 -.344 .135
S10 .826 -.152 -.253 -.293
S11 .872 .134 .117 .225
S12 .210 -.138 -.329 .177 -.570 -.208 -.404 .108 .190
S13 .319 .221 .122 .109 .224 .643
S14 -.337 .481 .152 -.114 .371 .272 .180
S15 -.174 .271 .385 .215 .156 .379 .302 .332
S16 -.213 .438 .566 .235 -.267 .172
S17 .113 .429 .161 -.165 -.591 -.139 -.195
S18 .311 .513 .233 .426 .136 -.238 -.190
S19 -.499 .225 .152 .482 .269 .124
S20 -.182 -.821
S21 -.422 .323 .280 .148 -.112 .543 .217 -.140
S22 -.151 -.205 .226 .413 .263 -.129 .551 -.178
S23 -.187 .212 .178 .187 .670 .175
S24 .809 .143
S25 .885 -.160 -.120
S26 -.179 .105 .537 .391 .167 .246 -.135 .207 -.222
S27 .851 .195 -.103 -.151
S28 -.289 .250 .636 .215 .121 .148 -.157 .152
S29 .860 -.290 -.107
S30 .141 .413 .107 .274 .461 .276 .281
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 31 iterations.
With the help of table 6, we can categorize each statements depending upon the factor loadings
and shown in table7.
Table 7: Factors
Factor 1:
• S19 : Information Provided By Salesperson
• S25 :Safety
• S27 : Easy Availability Of Spare Parts
• S29 : Technology
40

37. Factor 2:
• S10 : Government Policies And Regulations
• S11 : Import Duties Imposed By Government
Factor 3:
• S3 : Family Needs
• S5 : Brand Image
• S6 : Income Level
• S8 : Special Family Programs/Events Like Anniversary, Birthday
• S15 : Insurance Facility
• S18 : Credit Card Acceptance
• S26 : Car Accessories
Factor 4:
• S14 : Installment Payment Facility
• S16 : Location Of The Car Dealer Shop
• S24 : Looks
• S28 : Availability Of Service Station
Factor 5:
• S4 : Status Symbol
• S12 : Advertisements And Promotions
• S17 : Home Delivery Facility
• S30 : Overall, I Am Satisfied With My Car I Own
Factor 6:
• S1 : Price Of The Car
• S2 : You Take Suggestions Of Your Family Members
• S9 : Family Members & Friend Circle
• S13 : After Sales Service
Factor 7:
• S20 : Availability Of Variety Of Cars Under One Roof
• S21 : Information Provided By Various Car Related Magazines
Factor 8:
40

38. • S22 : Mileage
• S23 : Power
Factor 9:
• S7 : Festival Season/Offers
Table 8: Component Score Coefficient Matrix
Component
1 2 3 4 5 6 7 8 9
S1 .019 -.096 .179 -.106 -.051 .347 -.042 -.094 .209
S2 .062 .064 .164 -.069 -.018 -.386 -.001 .100 .174
S3 .051 -.113 .193 .009 -.025 -.058 .034 .119 -.033
S4 .026 -.010 .082 .079 -.386 -.084 .132 -.020 -.120
S5 .022 -.144 .202 -.001 -.010 .062 -.054 -.017 .226
S6 -.031 .226 -.105 .060 -.035 .102 -.080 .101 .149
S7 .010 .031 .009 -.035 .016 -.053 .008 .035 .627
S8 .011 .064 .400 -.185 -.120 -.091 -.054 .111 .065
S9 .119 -.112 .105 .005 .244 -.177 -.097 -.193 .122
S10 -.055 .261 .081 -.028 .029 -.074 -.163 -.101 .035
S11 .040 .282 .025 -.007 .003 -.087 .116 .059 -.023
S12 .030 -.011 -.075 .212 -.240 -.050 -.187 .164 .124
S13 .099 .022 .000 .047 .097 .282 -.100 -.043 -.010
S14 -.022 .010 -.099 .169 -.035 -.110 .175 .103 .078
S15 .007 .080 .084 -.017 -.037 .084 .068 .150 .041
S16 -.048 -.037 .092 .240 -.129 .031 .099 -.311 .084
S17 -.013 .149 .148 -.012 -.305 .020 .046 -.028 -.147
S18 .048 .087 .145 .057 .150 -.049 .019 -.200 -.191
S19 -.111 -.007 -.011 -.033 .164 -.004 .054 -.026 -.087
S20 -.080 .034 .081 .039 .056 -.001 -.563 .075 -.067
S21 -.040 .001 .028 .042 -.054 -.130 .289 .050 -.139
S22 .037 -.002 .006 .158 .071 -.077 -.219 .358 -.196
S23 .021 .001 .076 -.179 -.028 -.022 .021 .444 .171
S24 .073 .026 -.146 .470 -.076 .010 -.068 .035 -.079
S25 .329 -.023 -.029 -.008 .116 -.040 .076 .130 -.021
S26 -.027 .055 .148 .147 -.006 .085 -.248 .079 -.225
S27 .291 .047 .067 .001 .027 -.041 .061 .013 .003
S28 -.071 -.018 -.032 .289 .000 .079 -.004 -.250 .039
S29 .295 -.042 .018 .112 -.102 .018 .125 .001 -.044
S30 .150 .153 -.061 .078 .170 -.117 .109 .201 -.132
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
40

39. From the table 8 of component score coefficient matrix, we can obtain the quantifiable data of each
factor. The coefficients between the statements and the factors are taken according to the statement
affecting the factor ( on the basis of Table 7)
Conclusions and Recommendations
Since Indian Automobile market is continuously in the prowl of surging as a major car
manufacturer, people are purchasing car as there is increase of income of common people as
well as change in tastes and preferences of consumers. It is important for the car manufacturers
and car dealers to be able to understand the different factors affecting the extent in car
purchasing behaviour. The factor analysis results indicate that factor 1 (table 7) which consists
of Information provided by salesperson; safety; easy availability of spare parts; technology are
affecting the car purchasing behavior. People are more conscious about the on spot information
provided about various cars who serves according to the needs and wants of the customer. The
type of technology used and the wider reach of the service stations also affect the most on car
purchasing decision. While government obligations and various policies like import duties,
custom exemptions is seen as second most affecting driver (factor 2, table 7) of purchase of
cars. Factor 3 includes family needs; brand image; income level; special family programs/events
like Anniversary, Birthday; insurance facility; credit card acceptance; car accessories affects
customers car purchase decision causing a variance of 3.080.This shows that importance of
family decisions, special occasions in family and the various services provided by car dealers.
Factor 4, Table 7 includes installment payment facility; location of the car dealer shop; looks;
availability of service station showing customers accessibility to the service provided. Factor 5
shows the impact of various promotional activities and extra care taken by car dealers. Factors 6
shows the impact of suggestion provided by family members and peers as well as price and after
sales service provided. Factor 7 includes the infrastructural benefits of the shop and the variety
of cars it stores .The last but not the least ones shows the impact of factors of technical
specifications of the car and the festive season offers
Overall, various internal and external factors like extra care facilities, location of the shops,
various information provided by car dealers, advertisement and print media promotions,
features of the car in all are contributing in making car purchasing behaviour of customers.
40

40. ANNEXURE
Opinion Survey
Section I
Demographic Factors
Name……………………………………………………………Gender…………..
Age
Below18 18-25 26-35 36-50 51 and above
Occupation
Service Business Student Housewife
Section II
According to you which of these Factors are Affecting Car Purchasing Decision in India.
Please fill according to instruction in bracket given below
40

41. (SA-Strongly Agree; A-Agree; N-Neutral; DA-Disagree; SDA-Strongly Disagree)
Sl.No Statements Strongly Agree Neutral Disagree Strongly
Agree Disagree
1. Price Of The Car
2. You Take Suggestions From
Your Family Members
3. Family Needs
4. Status Symbol
5. Brand Name
6. Income Level
7. Festival Season/Offers
8. Special Family Programs/Events
Like Anniversary, Birthday
9. Family Members & Friend Circle
10. Government Policies And
Regulations
11. Import Duties Imposed By
Government
12. Advertisements And Promotions
13. After Sales Service
14. Installment Payment Facility
15. Insurance Facility
16. Location Of The Car Dealer
Shop
17. Home Delivery Facility
18. Credit Card Acceptance
19. Information Provided By
Salesperson
20. Availability Of Variety Of Cars
Under One Roof
21. Information Provided By Various
Car Related Magazines
22. Mileage
23. Power
24. Looks
25. Safety
26. Car Accessories
27. Easy Availability Of Spare Parts
40

42. 28. Availability Of Service Station
29. Technology
30. Overall, I Am Satisfied With My
Car I Own
40