Car Buying Behavior

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 :: Dr. Manish Agarwal

:: Submitted By :: SHELLY DIXIT (138) TAMONASH ADITYA (160) TARUN KUMAR (165) VIGYAN (178)

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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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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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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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.

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India is being recognized as potential emerging auto market.

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Foreign players are adding to their investments in Indian auto industry.

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Within two-wheelers, motorcycles contribute 80% of the segment size. 40

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Unlike the USA, the Indian passenger vehicle market is dominated by cars (79%).

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Tata Motors dominates over 60% of the Indian commercial vehicle market.

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2/3rd of auto component production is consumed directly by OEMs.

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India is the largest three-wheeler market in the world.

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India is the largest two-wheeler manufacturer in the world.

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India is the second largest tractor manufacturer in the world.

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India is the fifth largest commercial vehicle manufacturer in the world.

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The number one global motorcycle manufacturer is in India.

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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 threewheelers. 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

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•

Maruti 800, Alto, Omni

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Reva

• • • • • •

Ambassador Fiat Palio Hyundai Santro, Getz Chevrolet Opel Corsa Maruti Zen, Wagon R, Versa, Esteem, Gypsy Ford Icon & Fiesta

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Tata Indica, Indigo XL, Indigo Marina

• • • • • • • • •

Chevrolet Swing, Optra Magnum, Tavera Hyundai Accent, Elantra Mahindra Scorpio Maruti Baleno Toyota Innova Tata Safari Mitsubishi Lancer, Mitsubishi Cedia Honda City ZX Mahindra Bolero

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Hyundai Sonata Embera

• • • •

Toyota Corolla Ford Mondeo & Endeavour Chevrolet Forester Skoda Octavia & Combi

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Honda Civic

• • • • • • • •

Honda CR-V Maruti Suzuki Grand Vitara Terracan & Tucson Mitsubishi Pajero Audi A4 Opel Vectra Honda Accord Mercedes C Class

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Toyota Camry

• • • •

Audi A6, A8 & Audi TT BMW X5, 5 Series & 7 Series Mercedes E Class, S Class, SLK, SL & CLS-Class Porsche Boxster, Cayenne, 911 Carrera & Cayman S

•

Toyota Prado

Under Rs. 3 Lakhs

Rs. 3-5 Lakhs

Rs. 5-10 Lakhs

Rs. 10-15 Lakhs

Rs. 15-30 Lakh

Rs. 30-90 Lakhs

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Above Rs. 1 Crore

• •

Bentley Arnage, Bentley Continental GT & Flying Spur Rolls Royce Phantom

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Maybach

The following links gives the complete picture of Indian Auto Industry:

Automobile History

Industry Investment

Industry Growth

Vehicle Production

Auto Export

Auto Companies

Vehicle Distribution

Associations

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 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 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% 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 4wheelers 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, 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 is the fifth largest medium & heavy commercial vehicle manufacturer in the world. Know about the number of vehicles registered as Transport or NonTransport 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), Automobile Association of Southern India (AASI), Automotive Component Manufacturers Association of India (ACMA) and more

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Major Manufacturers in Automobile Industry •

Maruti Udyog Ltd.

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General Motors India

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Ford India Ltd.

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Eicher Motors

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Bajaj Auto

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Daewoo Motors India

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Hero Motors

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Hindustan Motors

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Hyundai Motor India Ltd.

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Royal Enfield Motors

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Telco

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TVS Motors

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DC Designs

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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- ahalf times the export figure for 2001-02.

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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 40

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% 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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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 40

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 40

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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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 fuelefficient 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 alternativefuel 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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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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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,

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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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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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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?

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Opinion leader A person within a reference group who exerts influence on others because of special skills, knowledge, personality, etc. 40

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 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 40

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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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 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

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used when little search/decision effort is needed 40

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e.g., buying a quart of orange juice once per week

Limited Problem Solving •

used when products are occasionally purchased

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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

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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: 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 40

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 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 40

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 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 40

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

60 40

0

Below18

18-25

26-35

36-50

Above 51

Percentage

20

40

Occupation

60 40 20 0

Percentage Service

Business

Student

House-Wife

Fig 3 Findings and Analysis Factor Analysis for factors affecting car purchasing decision 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

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Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity

.769

Approx. Chi-Square

1650.000

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] the

From the above table, we can interpret that there is no error in 76.9% of the sample and in 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. “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

S1 S2

Initial 1.000 1.000

Extraction .833 .692

S3

1.000

.760

40

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

40

Extraction Method: Principal Component Analysis.

Table 3: Total Variance Explained

Component

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

Total 7.102 3.539 2.543 2.188 1.716 1.631 1.218 1.112 1.018 .948 .815 .683 .634 .567 .500 .489 .439 .421 .330 .297 .277 .271 .226 .209 .194 .183 .161 .129 .089 .070

Initial Eigenvalues % of Cumulative Variance % 23.672 23.672 11.798 35.470 8.477 43.947 7.292 51.239 5.721 56.960 5.435 62.396 4.059 66.455 3.706 70.161 3.394 73.555 3.160 76.715 2.717 79.432 2.278 81.710 2.113 83.823 1.889 85.712 1.667 87.379 1.631 89.010 1.464 90.475 1.403 91.878 1.099 92.976 .991 93.967 .924 94.891 .905 95.796 .752 96.547 .697 97.245 .647 97.892 .608 98.501 .537 99.038 .431 99.468 .297 99.766 .234 100.000

Extraction Sums of Squared Loadings % of Cumulative Total Variance % 7.102 23.672 23.672 3.539 11.798 35.470 2.543 8.477 43.947 2.188 7.292 51.239 1.716 5.721 56.960 1.631 5.435 62.396 1.218 4.059 66.455 1.112 3.706 70.161 1.018 3.394 73.555

Rotation Sums of Squared Loadings % of Cumulative Total Variance % 3.398 11.327 11.327 3.227 10.756 22.083 3.080 10.268 32.350 2.556 8.520 40.870 2.543 8.476 49.345 2.356 7.855 57.200 1.909 6.364 63.564 1.718 5.725 69.289 1.280 4.266 73.555

40

Extraction Method: Principal Component Analysis.

Table 4:

Component 1 Component 2 Component 3 Component 4 Component 5 Component 6 Component 7 Component 8 Component 9

Explain a variance of 3.398, which is 11.327 % of the total variance of 30 Explain a variance of 3.327, which is 10.756 % of the total variance of 30 Explain a variance of 3.080, which is 10.268 % of the total variance of 30 Explain a variance of 2.556, which is 8.520 % of the total variance of 30 Explain a variance of 2.543, which is 8.476 % of the total variance of 30 Explain a variance of 2.356, which is 7.855 % of the total variance of 30 Explain a variance of 1.909, which is 6.364 % of the total variance of 30 Explain a variance of 1.718, which is 5.725 % of the total variance of 30 Explain a variance of 1.280, which is 4.266 % of the total variance of 30

Cumulative Frequency 11.327% 22.083% 32.350% 40.870% 49.345% 57.200% 63.564% 69.289% 73.555%

40

Scree Plot

8 7.1

Eigenvalue

6

4

3.54

2.54 2.19

2

1.63 1.72

1.11 1.22

1.02

0 1

2

3

4

5

6

7

8

0.95 0.68 0.57 0.82

0.63

0.49

0.3

0.44 0.5 0.42

0.28 0.33

0.27

0.23 0.21

0.19 0.16 0.13 0.18

0.07

0.09

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

Component S1 S2

1 .377 -.166

2 .267 -.163

3 .541 -.228

4 -.333 .665

5 .217

S3

.649

-.382

.347

.119

.188

S4

-.551

-.191

.503

.338

-.271

S5

.599

-.244

.388

.141

.102

.115

.108

.751

-.265

-.164

-.156

.291

.131

.390

S6 S7

.223

.232

-.138

.223

-.344

S8

.430

.124

.128

.249

.581

S9

-.104

-.267

.229

.699

-.224

S10

-.170

.698

-.418

.363

.129

-.272

.132

S11

.232

.808

S12

-.542

-.211

S13

.177

.528

.462

S14

.627

-.139

-.171

S15

.689

.337

.100

S16

.569

S17

-.312

.398

S18

6

8 -.327 .180

-.271

-.106 -.166

.210

-.237

.498

.125

.306

-.147 .178 -.161

-.103

.157

.144

.135

.205

-.100

-.220

.279

-.227

.170

.273

-.119

.592

.161

-.109

.427

.117

-.343

.254

.107

.667

-.197 .357

-.346

.481

.343

S19

.718

-.106

S20

-.395

-.122

S21

.730

-.205

-.139

.116

-.367

.114

S22

.537

-.294

.154

-.108

.245

.470

S23

.484

S24

.368

S25

-.499

-.196

.168 .617

S26

.621

.112

.187

S27

-.503

.430

.516

.186

.663

S28

.652

S29

-.535

S30

-.154 -.183

-.273

-.354 .207

9 .176 .303

.121

.628 -.216

7 .171 -.119

.226

.115 -.252

.527

-.288

-.106

.256 .251

.311

.395

.104

-.341

-.145

.191

.323

.186

-.195

-.278

-.236

-.138

.247

.227

.112

.158

-.225

-.123

-.153

.459 .422 .117 Extraction Method: Principal Component Analysis. a 9 components extracted.

.163

-.300

.335

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

Component 1

2

3

S1 S2

4

5

6

.447 .191

S3

-.122

-.414

S4

.189

-.123

.591

.282

.153 .158

.120

.256

.304

.114

.265

.295 .102

S7

.219

S8

.150

.821

-.308

.193

.388 .117

.826 .872

.134

.284

.158

.570 -.192

S11

.194

-.175

.755

.308

.205 .201

-.115 -.134

-.374

S10

9

-.232

S6

.191

8

-.799

S5

S9

7

.753 -.765

.111

.799 .131 -.466

-.213

-.344

-.152

-.253

-.293

.225

S12

.210

-.138

-.329

.177

-.570

-.208

S13

.319

.221

.122

.109

.224

.643

S14

-.337

.481

.152

S15

-.174

.385

.215

.156

S16

-.213

.438

.566

S17

.113

.429

.161

-.165

-.591

.311

.513

.233

.426

.136

-.499

.225

.152

.482

.269

.124

S21

-.422

.323

.280

.148

S22

-.151

.226

.413

.263

S23

-.187

.212

S25

.885

-.160

-.120

S26

.537

.391

S18 S19

.271

S20 -.205

S24

-.404

.108

.190

-.114

.371

.272

.180

.379

.302

.332

.235

-.267

.172

-.139

-.195

-.238

-.190

-.182

-.821

-.112

.543

.217

-.140

-.129

.551

-.178

.187

.670

.175

.178 .809

-.179

.105

S27

.851

.195

S28

-.289

S29

.860

S30

.141

.143 .167

.246

-.135

.215

.121

.148

-.103 .250

.636

.107

.274

.207

-.222

-.151 -.290

.413

.135

.461

-.157

.152

-.107 .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

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 40

Factor 8: • •

S22 : Mileage S23 : Power

Factor 9: •

S7 : Festival Season/Offers

Table 8: Component Score Coefficient Matrix Component 1

.179 .164

4 -.106 -.069

5 -.051 -.018

.347 -.386

7 -.042 -.001

-.113

.193

.009

-.010

.082

.079

-.025

-.058

.034

.119

-.033

-.386

-.084

.132

-.020

-.144

.202

-.120

-.001

-.010

.062

-.054

-.017

.226

-.031

.226

.010

.031

-.105

.060

-.035

.102

-.080

.101

.149

.009

-.035

.016

-.053

.008

.035

.627

.011 .119

.064

.400

-.185

-.120

-.091

-.054

.111

.065

-.112

.105

.005

.244

-.177

-.097

-.193

S10

-.055

.261

.122

.081

-.028

.029

-.074

-.163

-.101

S11

.035

.040

S12

.030

.282

.025

-.007

.003

-.087

.116

.059

-.023

-.011

-.075

.212

-.240

-.050

-.187

.164

S13

.124

.099

S14

-.022

.022

.000

.047

.097

.282

-.100

-.043

-.010

.010

-.099

.169

-.035

-.110

.175

.103

S15

.078

.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

S1 S2

.019 .062

S3

.051

S4

.026

S5

.022

S6 S7 S8 S9

2 -.096 .064

3

6

8 -.094 .100

9 .209 .174

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

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

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

(SA-Strongly Agree; A-Agree; N-Neutral; DA-Disagree; SDA-Strongly Disagree) Sl.No 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Statements

Strongly Agree

Agree

Neutral

Disagree

Strongly Disagree

Price Of The Car You Take Suggestions From Your Family Members Family Needs Status Symbol Brand Name Income Level Festival Season/Offers Special Family Programs/Events Like Anniversary, Birthday Family Members & Friend Circle Government Policies And Regulations

11.

Import Duties Imposed By Government

12.

Advertisements And Promotions

13.

After Sales Service

14.

Installment Payment Facility

15. 16.

Insurance Facility 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. 23. 24.

Mileage Power Looks

25.

Safety

26.

Car Accessories

27.

Easy Availability Of Spare Parts 40

28.

Availability Of Service Station

29.

Technology

30.

Overall, I Am Satisfied With My Car I Own

40