segmentation of the car market in china

Segmentation of the car

market in China

2013

Authors: Adrien SAINT

Imran SYED

Tutor: Anders Pehrsson

Examiner: Sarah Philipson

Subject: Marketing

Level: Master

1

Table of Contents

Abstract ...................................................................................................................................... 5

1. Introduction ............................................................................................................................ 6

1.1 Background ...................................................................................................................... 6

1.1.1 The growing Chinese car market ............................................................................... 6

1.1.2 Government intervenes to control the car market ..................................................... 7

1.1.3 Changes in Chinese market segments ....................................................................... 7

1.2 Problematization ........................................................................................................... 7

1.3 Research questions ........................................................................................................... 8

1.4 Problem formulation ........................................................................................................ 9

1.5 Delimitations .................................................................................................................... 9

1.6 Outline of the thesis.......................................................................................................... 9

2. Theory .................................................................................................................................. 10

2.1 Market segmentation ...................................................................................................... 10

2.2 Bases for Market Segmentation ..................................................................................... 11

2.2.1 Geographic segmentation ........................................................................................ 11

2.2.2 Demographic segmentation ..................................................................................... 12

2.2.3 Psychographic segmentation ................................................................................... 13

2.2.4 Behavioural segmentation ....................................................................................... 13

2.3 Macro Segmentation ...................................................................................................... 14

2.4 Micro segmentation ........................................................................................................ 15

2.5 Hybrid segmentation: the mix of micro and macro segmentations ................................ 15

2.6 Dynamic segmentation: the predictive implication of segmentation ............................. 16

2.7 State-of-the-art ............................................................................................................... 16

2.8 Model, how the constructs relate.................................................................................... 19

3. Methodology ........................................................................................................................ 21

3.1 Introduction .................................................................................................................... 21

3.2 Population and sample ................................................................................................... 21

3.2.1 Data collection method ............................................................................................ 22

3.2.2 Questionnaire Design .............................................................................................. 22

3.3 Operationalization .......................................................................................................... 23

3.4 Data analysis method ..................................................................................................... 28

3.5 Validity ........................................................................................................................... 28

3.6 Reliability and Replicability........................................................................................... 29

2

4. Empirical Data ..................................................................................................................... 31

4.1 Geographic data: ............................................................................................................ 31

4.2 Demographic data: ......................................................................................................... 31

4.3 Behavioural data:............................................................................................................ 32

4.3.1 Customer behaviour explained by Geographic data ................................................ 33

4.3.2 Customer behaviour explained by Demographics: .................................................. 34

4.4 Psychographic data:........................................................................................................ 35

4.4.2 Respondent’s opinion on global preferences ........................................................... 36

4.4.3 Car owners’ current preferences .............................................................................. 37

4.4.4 Car owners’ preferences for their next purchase ..................................................... 40

4.4.5 Non-car owners’ preferences for their first purchase .............................................. 43

4.5 Macro data for the dynamic aspect ................................................................................ 45

5. Analysis................................................................................................................................ 49

5.1 Control Variables ........................................................................................................... 49

5.2 Hybrid Behavioural segmentation .................................................................................. 49

5.3 Hybrid Psychographic segments .................................................................................... 50

5.4 Dynamic Hybrid Psychographic segments .................................................................... 52

6. Conclusion and discussion ................................................................................................... 53

7. Managerial implications, reflections, further research ........................................................ 55

7.1 Managerial implications ................................................................................................. 55

7.2 Reflections ...................................................................................................................... 57

7.3 Limitations ..................................................................................................................... 58

7.4 Future Research .............................................................................................................. 58

References ................................................................................................................................ 60

Appendix A .............................................................................................................................. 64

The Survey ........................................................................................................................... 64

Survey emails ................................................................................................................... 68

Descriptive statistics ............................................................................................................. 71

Appendix B

Linear regression analysis and linear curve estimates ............................................................. 77

B.1. Customer behaviour explained by Geographic and Demographic data ....................... 77

3

B.1.1. Region of residence ................................................................................................ 77

B.1.2. Degree of urbanisation. .......................................................................................... 80

B.1.3. Behaviour by age ................................................................................................... 82

B.1.4. Behaviour by Gender ............................................................................................. 84

B.1.5. Behaviour according to annual income.................................................................. 87

B.2. Psychographic data by Demographic and Geographic ................................................. 89

B.2.1. Psychographs by age .............................................................................................. 89

B.2.2. Psychographs by gender. ..................................................................................... 103

B.2.3. Psychographics with Annual income (Yuan) ...................................................... 115

B.2.4. Psychographics by region of residence. ............................................................... 127

B.2.5. psychographics by degree of urbanisation ........................................................... 143

B.3. Customer psychographics by behaviours ................................................................... 156

B.3.1. Psychographics by car replacement rate .............................................................. 156

B.3.2. Psychographics by experience ............................................................................. 169

B.3.3. Psychographics with frequency of use ................................................................. 187

B.3.4. Psychographics by non-car owners who plan to have a car in the future. ........... 201

B.3.5. psychographics by style of use; long trips ........................................................... 216

B.3.6. Psychographics by style of use; short trips .......................................................... 229

B.3.7. Car owners preferences for the next purchase by loyalty. ................................... 242

4

ACKNOWLEDGMENT

This thesis was written between March 20th 2013 and May 25th 2013, at the Linnaeus

University, Campus Växjö. The authors have received support from several people during

this period. In this acknowledgment page they want to thank all these wonderful people, for

their assistance during this thesis.

First of all we would like to thank their examiner Doctor Sarah Philipson who has been

giving the authors support, feedback and inspiration. The authors would also like to thank

their tutor Professor Anders Pehrsson who has supported them by giving much needed

feedbacks and inspiration; he has also given inputs in developing the model used throughout

the thesis. The authors also want to send out their gratitude to the respondents of their survey.

Their contribution to the thesis has been of great value and the authors are very much

thankful for their precious time.

Växjö, May 25th, 2013

Imran Syed Adrien Saint

5

Abstract

The Chinese car market has, through the last decade evolved into the major market in the

world. Its car market from has become the world’s largest market from 2009 until today.

With the emerging market that is China, the demand for cars is supposed to grow even more

in the next decade.

The thesis starts by studying the theories of consumer market segmentation with a hybrid and

dynamic aspect. A quantitative investigation was conducted with the help of a survey. The

survey was sent out to car consumers and potential car consumers who are residing in China.

From this study the authors were able to anticipate possible preferential profiles.

Keywords: China, market segmentation, cars, hybrid

6

1. Introduction

1.1 Background

The history of the automobile is complicated and rich. It dates back to the 15th century, when

Leonardo da Vinci was designing and modelling transport vehicles (library of congress,

2013-3-22). According to the library of congress (2013-3-22) Nicolas Joseph Cugnot built the

first self-propelled vehicle for the French army in 1769. 1832-1839, Robert Anderson in

Scotland built an electric carriage. George Baldwin Selden integrated an internal combustion

engine in the United States in 1876/95.

The first companies to produce automobiles for the public market emerged in 1896,

manufacturing electric gas-powered vehicles (Bentley historical library, 2013-3-22). The love

of cars and their ever-growing necessity in our lives has increased the number of automobiles

to an estimate of over 1 billion vehicles in 2010, from 500 million in 1986 (Autos Cars, 2013-

3-22).

1.1.1 The growing Chinese car market

During the last two decades social and economic events have brought immense opportunities

in soaring emerging markets. Many multinational corporations have made substantial

investments in these countries, as a part of their global expansion strategies, creating more

and more jobs thus increasing the purchasing power of customers. Such an emerging market

is China (Cui, & Liu, 2000).

Half a century ago bicycles were considered a mark of family fortune in China (The guardian,

2013-3-22). Since then, China has become the world’s largest automobile producer and the

largest market with annual sales of some 14 million vehicles in 2009, continuing to expand in

2010 according to APCO (2013-3-22).

The growing car industry does not only give direct economic benefits and a growing number

of car dealerships. It also has powerful multiplier effects for other sectors, like fuelling steel

production and other industries (The guardian, 2013-3-22).

http://autoscars.sexyi.am/index/

7

1.1.2 Government intervenes to control the car market

The Chinese government has implemented a number of tax adjustments and subsidies for

hybrid electric vehicles, electric vehicles, and traditional vehicles with small engine

displacement, attempting to impact the national consumption (APCO, 2013-3-22).

1.1.3 Changes in Chinese market segments

Richard Cant of Dezan Shira & Associates at the HSBC International Exchange to China said

“For the first time ever there are more people living in China’s cities than in the countryside

and this trend will only continue,” (HSBC, 2013-3-22). There is an increasing middle class in

China (HSBC, 2013-3-22). The Chinese consumers are now spending their disposable

income rather than saving it, as they did before. They are also educated, skilled, price savvy

and internet savvy (HSBC, 2013-3-22). Foreign brands, principally for visible products, are

very popular and trendy among China’s young consumers, as they have status appeal (HSBC,

2013-3-22).

McKinsey (2013-3-22) points out that more of China’s middle class is shifting to premium

cars, notably foreign cars like VW, BMW, Volvo, Aston Martin, et cetera. McKinsey (2013-

3-22) also states in his report that on average Chinese families change car every six to eight

years on average. Premium car owners say that they would like to change their car two to

three years ahead of the average (McKinsey 2013-3-22).

1.2 Problematization

The Chinese customers have shown a strong interest in cars, making it a challenging

environment for marketers, and the industry now sees the market as the most important in the

world. The demand evolved radically and is prone to continue changing. As the Chinese car

market is emerging, the marketing processes are evolving with it as the market changes really

quickly. Segmentation, a key marketing process to reach customers, has to be very efficient

for car manufacturers that want to perform well in the Chinese car market.

The Chinese car market is the most important to consider nowadays for a constructor, and yet

remains really mysterious. Despite the quantities produced being easily sold, no qualitative

trend can be observed, and only absolutely attractive quantities trends are observable. But the

constructors cannot rely forever on the quantities and have to choose a strategy in order to

8

gain market shares on the promising market and be ahead of the competition in the heart of

Chinese people. This is where the importance of segmenting efficiently the market appears.

Segmentation can be made on basic macro dimensions: economic, geographic, culture & and

technology (Grundy, 2006). Specific segments can also be identified by carrying out

segmentation based on micro dimensions, such as life styles, behaviors etcetera (West, Ford

and Ibrahim, 2006). Those segmentation styles are chosen by car makers and have a static

dimension, and feature no flexibility or enough details to gain a clear understanding of the

demand.

The whole environment affecting the segments is bound to evolve (Grundy, 2006). This will

leave the company marketing their cars in an obsolete way, positioning themselves on

segments that they could not target as efficiently, as if they were adapting to them. The

surroundings of car manufacturers are influencing the way segmentation is carried out and in

its results. In this emerging market, with its quickly changing environment, “hybrid

segmentation”; mixing different styles of segmentations, is said to be necessary to have

detailed customer segments (Hassan, Craft and Kortam, 2003).

Micro and macro dimensions are connected and influencing each other continuously, causing

a challenge for companies as their segments evolves (Cannon and Yaprak, 2011). Marketers

and academics like Hasan, Craft and Kortam (2003); Amine and Smith (2009); Cannon and

Yaprak (2011); and Quinn, Hines, and Bennison (2007) have discussed the mix of macro and

macro to anticipate possible segmentation, these studies are rather recent. But there has been

little studies done on segmenting the Chinese car consumers. The study of consumer

preferences of the Chinese car consumers is there for a gap to be recognised. Finding the

different segments and build predictions of their evolution in the near future is a really

practical source of information to actually serve the challenges imposed to firms marketing

cars in China.

1.3 Research questions

What are the main profiles of customers on the Chinese car market?

Can any preferential profiles be anticipated for the near future?

9

1.4 Problem formulation

The purpose of this paper is to study customer segmentation in the Chinese new car market,

and the changes and movements of segmentation that could be predicted for the next

purchase.

1.5 Delimitations

The study is delimited in time and location. The research is made only on the Chinese market

of new cars. The focus is also time-based since the authors will study trends for the next

purchase.

1.6 Outline of the thesis

This thesis continues with a theory chapter containing an introduction about market segmentation

and continues explaining the four vital variables of market segmentation with the heading bases

for market segmentation and the four variables will be explained with the sub-headings

Geographic, Demographic, Psychographic and Behavioural. Then further in the theory chapter

the micro and macro segmentation are explained. Furthermore in the Hybrid segmentation the

mixing and links between macro and micro segmentation are explained. A state-of-the-art

subchapter also explains the acceptability and readability of the sources being used. Further are

the research questions presented and next chapter concerns the methodology part. This chapter

brings up for example the following subheadings; populations and sample, data collection

method, questionnaire design, operationalization, validity and reliability. After this follows the

empirical chapter which consists of summary presentation of the empirical data the authors have

gathered. Next is the analysis chapter which is created by interpreting the empirical data, with the

theory, connected to the background. The second-to-last chapter is the conclusion, which consists

of the findings and results that have been made in the thesis. Also theoretical and managerial

implications, limitations, proposed future research are brought up at the end of the thesis. The

authors’ own reflections are being presented at the last.

10

2. Theory

The four ‘Ps’ of strategic marketing is useful to analyse a marketplace according to Kotler

(1989).

He terms them as ‘probing’. The second step is ‘partitioning’ the market; this is the process

of segmenting clusters of the customers of a market. The third step is ‘prioritising’ the market

segments that a company has possibly greater advantage in pursuing. The fourth step is

‘positioning’; “pinpointing the competitive options in each segment that you're going to

target.” (Kotler, 1989). Partitioning is the term referring to customer segmentation, which is

the focus of the thesis.

2.1 Market segmentation

Market segmentation is an essential element of marketing for goods and services. The

recognition of heterogenic demand needs to be considered before a newly produced product

is being considered to be sold in a market (Wedel and Kamakura, 2000).

Markets as a whole are not entirely homogeneous (Beane and Ennis, 1987). Market segments

are homogeneous subsets of consumers in a particular market (West et al., 2006). Market

segmentation is a vital process to succeed, identifying suitable segments of consumers with

shared preferences for targeting purposes (West et al., 2006; Kotler and Keller, 2009).

Beane and Ennis (1987) gave the following major reasons for market segmentation:

(1) To look for new product opportunities or areas that may be accessible to current

product repositioning; and

(2) To create improved advertising messages by gaining a better understanding of

one’s customers.

In segmenting a market to sell a product it is not necessary to identify all segments, only the

groups that appear to be yearning for the product (Beane and Ennis, 1987).

There are many ways to assess market segmentation. To determine its effectiveness and

profitability some criteria have often been put forward: identifiability, measurability,

sustainability, accessibility, responsiveness and actionable (Frank, Massy, and Wind, 1972;

Wedel and Kamakura, 2000; Beane and Ennis, 1987).

11

Identifiability and measurability are the extent to which marketers are able to distinguish and

approximately measure the clusters of customers according to its size, location and market

from within a market (Wedel and Kamakura, 2000; Beane and Ennis, 1987).

Sustainability/ stability; the segment must be large enough to justify attention (Beane and

Ennis, 1987). Kotler, in Beane and Ennis (1987) says: "a segment should be the largest

possible homogeneous group of buyers that it pays to go after with a specially designed

marketing program". Wedel and Kamakura (2000) states market segments that are steady for

a long enough period can provide a foundation for the development of successful market

segmentation

Accessibility; Segments must be accessible to the managers to some degree. It can be

accessible for example through promotional efforts or through secondary data (Wedel and

Kamakura, 2000).

Actionable; “Market segments are actionable if their identification provides guidance for

decision on the effective specification of the marketing instruments”, (Wedel and Kamakura,

2000).

2.2 Bases for Market Segmentation

There are many ways to segment a market, amongst those, Beane and Ennis (1987)

acknowledge Kotler to divide market segmentation variables into four main parts;

Geographic;

Demographic;

Psychographic;

Behavioural;

2.2.1 Geographic segmentation

Consumer demand or ways to fill that demand differ geographically. When a market is

geographically segmented it simply means that consumer are sub-categorised according to

the consumer needs geographically, it can be by region of a country, population density or

climate (Beane and Ennis, 1987; Kotler and Armstrong, 2011).

12

“Consumers in the Southeast use more vegetable shortening than in any other part of the US.

Since Northeastern and Midwestern regions have more locally unique beer drinking

segments, they have more small beer breweries than any other region. The consumption of

menthol cigarettes is greater in the Southeast than in any other region of the country”. These

are few examples given by Beane and Ennis (1987).

2.2.2 Demographic segmentation

Beane and Ennis (1987) refer to demographic segmentation to be the most prevailing variable

of market segmentation. They think that’s probably because consumers are placed on certain

scales of measurement that can easily be understood; information is easily gathered,

interpreted, and convertible from one study to another. Satisfying the segmentation criteria of

identifiability, substantiality, accessibility, and actionability (Wedel and Kamakura, 2000),

three of the most common demographic variables employed in domestic and international

segmentation include: age, gender, income (Beane and Ennis, 1987; Cleveland et al, 2011).

Examples are Life Stage vitamins (four types depending on age and sex) and disposable

diapers according to the age of infant (Beane and Ennis, 1987). Younger techno-savvy

individuals are less committed to definite patterns and are more open to new perspectives and

products especially when it comes to high-technology products (Cleveland et al., 2011).

It is well recognised that the demand for categories of goods and services alters as they age

and pass through the various life cycle stages (Cleveland et al., 2011).

Income also has a strongly effect on individuals demand or choice of a product (Cleveland, et

al., 2011). Consumers with higher- income tend to buy better and expensive products, which

are also status- enhancing, examples are household appliances, consumer electronics, and

luxury products (Cleveland, et al., 2011).

The demographic variable of gender also has a vital differentiating effect. Males and females

have different characteristics of consumer behaviour; in their shopping patterns, information

processing, judgment, responses to advertising, and the products they tend to buy (Cleveland,

et al., 2011).

13

2.2.3 Psychographic segmentation

According to Beane and Ennis (1987) psychographic or life-style segmentation is tougher to

describe as it is no longer looking at clearly definable, quantitative measures. Psychographic

segmentation, unlike geographic and demographic segmentation, reflects consumers’ choices

depending on personality, values and lifestyles, though there has been no clear agreement

about the variables that should be included (Lancaster and Williams, 2002; Kim and Lee,

2011; Wells, 1975).

Wells (1975), while writing a review article about psychographics, stated: “Twenty-four

articles on psychographics contain no less than 32 definitions, all somewhat different”. While

reviewing a Product-Specific Psychographic Profile Wells (1975) argued that when a

psychographic study is devoted to a product category, it is not essential to rely on item

diversity to get useful relationships. The investigator can focus upon a limited set of relevant,

product-related dimensions that would make more sense regarding the psychographic

dispositions of the population investigated towards the product in question.

Product-specific psychographic profile is a useful tool used to get insights of the market

dispositions towards a product. Such a study is investigating profiles through series of

questions about product attributes (Wells, 1975; Chin-Feng, 2002).

According to Chin-Feng (2002), products attributes are reliable to study personalities and life

styles. He states that the psychographics regarding a product are driven by the brand equity of

the current actors. Due to the personality associated to brands, clusters can be found

investigating the preferences of the customers. The analysis of personality traits and life

styles through their linkage to product attributes is preparing ground to the response an

individual can have when confronted with a purchase decision on the market (Chin-Feng,

2002).

2.2.4 Behavioural segmentation

Originally, benefit segmentation was categorised as a psychographic method, then classified

as behavioural (Kim and Lee, 2011). Kim and Lee (2011) points out “benefits are a function

of consumers’ beliefs about product offerings and promises, which motivate their purchase

decisions, and are therefore the basis of behavioural rather than psychographic

14

segmentation”. Behavioural segmentation pays less attention to economic or age related

issues unlike psychographic segmentation, and concentrates more on consumer related

behaviour, concentrating on psychological attributes and product orientation (Lancaster and

Williams, 2002).

Behavioural segmentation, segments consumers based on knowledge of the product, attitude,

or response to the product. This includes such areas as purchase occasion, benefits sought,

user status, degree of usage, degree of loyalty, readiness stage, and marketing factor

sensitivity (Beane and Ennis, 1987).

Holbrook and Hirschman (1982) recognise that people buy products not only for its use, but

also for what they signify or mean. Consumer behaviour also relates to the multi-sensory,

fantasy and emotive aspects of one's experience with products. These facets of consumer

behaviour are defined as Hedonic consumption; Multi-sensory means several sensory

modalities including tastes, sounds, scents, tactile impressions and visual images. Emotions

represent motivational phenomena with characteristic neurophysiological, expressive and

experiential factors; they include feelings such as joy, jealousy, fear, rage and rapture.

Holbrook and Hirschman (1982) further proposed:

Differences in consumer emotional and imaginary response to products are closely tied to a

variety of subcultural differences, for example ethnicity or nationality.

Differences in consumer emotional and imaginary response to products appear closely tied to

a variety of subcultural differences.

Consumers of many hedonic ally-experienced products are characterized by very distinctive

social class profiles.

2.3 Macro Segmentation

Macro segmentation is a segmentation method relying on macro data. Economic, political,

geographic and demographic are the variables in macro segmentation. These variables are

widely available as secondary data. (Gaston-Breton and Martín, 2011). Studying this

information gives a good insight of the market size and potential (Douglas and Craig, 2011).

15

2.4 Micro segmentation

Gaston-Breton and Martin (2011) stress the need of micro segmentation at a consumer level.

They present three classes of micro-segmentation that have been used in most European

segmentation studies, from product-specific characteristics, such as attitudes toward

attributes, to domain-specific features such as lifestyles, and general characteristics such as

central values. It is important to note that a country's culture has also been recognized as a

significant environmental trait and essential to understand systematic variances in behaviour

(Steenkamp, 2001). For example, markets around the world are getting more and more

cosmopolitan, but the consumption of local foods and fashions are across most countries

dominated by products compliant with the specific country’s ethnic identity (Cleveland et al,

2011).

Holbrook and Hirschman (1982) pointed out in an article some years back, about the different

aspect of consumption. He states that all products, no matter how ordinary they are, may

carry a symbolic significance. This significance in some cases is rich and noticeable to the

users. Holbrook and Hirschman (1982) also mention that many products projects essential

nonverbal indications that must be seen, heard, tasted, felt, or smelled to be appreciated

properly.

2.5 Hybrid segmentation: the mix of micro and macro segmentations

The concept of hybrid segmentation is relying on the principle that segments can be made

more profoundly by combining different segmentation methods. Hybrid segmentation in

different areas of the world, standardising some marketing elements, and adapting others,

could be a good solution (Hassan, Craft, and Kortam, 2003).

Segmenting a market should start with the macro dimensions and refined by micro factors

(Amine and Smith, 2009) to adapt to local behaviours and thus getting better performance.

Successful marketing often depends on balancing product attributes with customer attitudes

and values; psycho-graphic segmentation is a compelling basis for categorizing consumers

internationally, complementing approaches primarily based on demographics, marketers need

to consider the culture variables and subsequent consumer behaviours (Cleveland et al.,

2011). Hybrid segmentation with the combination of all type of segmentation cited in the

16

previous sections is resulting in mass customization. If macro data is associated to micro data,

the segmentation allows the distinction of clear precise segments that can be targeted to

achieve greater customer satisfaction through coherent value delivery (Jiang, 2000).

2.6 Dynamic segmentation: the predictive implication of segmentation

Very few authors have emphasised the importance of dynamic segmentation. Among these

few is Weinberg (1972), who stressed its importance dynamic segmentation. He stressed, that

certain episodes can be predicted by the use of already available records. Weinberg (1972)

referred to Dr. Spock’s example of segmenting the baby product market: on the average,

three months after birth a baby begins to eat solid food, five months later the birth weaning

should begin, and a year later, a baby normally grows six teeth and can chew. Those different

periods are called time sequences. Episodes like these or the application of time sequence can

be beneficial to marketers and researchers in anticipating the future, while segmenting

international market and its trends. A similar approach can be applied to develop time

sequences for other areas such as automobiles (Weinberg, 1972).

According to Holbrook and Hirschman (1982), post purchase consumer behaviours are driven

by a learning effect, transforming the initial behaviour. This phenomenon results in a

feedback loop and depends on the interaction between the purchase experience and the

consumers’ inputs. The consumers’ inputs are defined by the macro and micro factors

influencing them. The customers’ behaviour is thus ever changing, due to the different

contexts the customers could be in. (Holbrook and Hirschman, 1982)

Quinn, Hines and Bension (2007) explained and made sense of the particular observed

phenomenon of marketing segmentation, ongoing. The aspect of ongoing, explained by

Weick (1995), is unending and continuous sense-making. In a milieu of dynamic

segmentation, in a world, which is becoming more and more cosmopolitan, cluster behaviour

is always ever hanging and ongoing.

2.7 State-of-the-art

The notion of market segmentation is explained with the help of Beane and Ennis (1987) and

Wedel and Kamakara (2000). Beane and Ennis (1987) review on market segmentation is well

17

accepted and it reviews Kotler (1980) theories. Wedel and Kamakara (2000) dominating

theory on market segmentation is also well accepted and validated.

Cleveland et al. (2011) Lancaster and Williams (2002), and Kim and Lee (2011) are the other

sources that have been used to explain bases and variables for market segmentation. The for

variables geographic segmentation, demographic segmentation, psychographic segmentation

and behavioural segmentation are the most common segmentation variables by Philip Kotler.

The sources are recent and do not have a low acceptance or validation and so are proposals to

fill a gap. These sources have been used to discuss the different segmentation variables.

Gaston-Breton and Martin (2011) and Douglas and Craig (2010) are again very recent articles

and has a low acceptance and validation. Douglas and Craig (2010) is a study that provides a

framework to understand contextual factors as a means to refine entry strategy and develop

effective segmentation strategies. Gaston-Breton and Martin (2011) proposes a two stage

model of international market segmentation. These sources have only been used in our thesis

to understand Macro segmentation.

Cleveland et al, 2011) and Steenkamp (2001) have been used for micro segmentation.

Steenkamp (2001) has moderate acceptance and is well validated. Steenkamp (2001) reviews

and discusses the role of national culture in international marketing research, the frameworks

presented by Hofstede and the Schwartz. Holbrook and Hirschman (1982) is another very

dominating article that has used to explain micro segmentation and to explain behavioural

segments. It has further been used to elaborate more on how knowledge that is gathered

through time effect segmentation. Holbrook and Hirschman (1982)’s article have been highly

accepted and is well validated.

Wells (1975) article review has also been very important in understanding psychographics.

Wells (1975) is well accepted and well validated.

Hasan, Craft and Kortam (2003) introduce hybrid approach to global market segmentation,

which is a vital theory in the thesis. Hasan, Craft and Kortam (2003) has low amount of

acceptance but it is well validated and so it is an emerging theory. Though Amine and Smith

(2009) has a very low acceptance the source has been used to rationalize the concept of

standardisation and adaptation in other words hybrid segmentation. Cannon and Yaprak

18

(2011) have also been used as source for the integration of macro and micro segmentation.

This source has very low amount of acceptance but is thoroughly validated.

Weinberg, C. B. (1972) brings up the issue of changing market segmentation through time

half a century ago but it does not have any acceptance or validation. Holbrook and Hirschman

(1982) have also been reused to explain the dynamic aspect of segmentation. Later Quinn,

Hines, and Bennison (2007) propose that past facts can be used to predict market

segmentation patterns. Quinn, Hines, and Bennison (2007) theories have very low acceptance

but the theories are validated so they are emerging theories. Chin-Feng, 2002 brings up how

products attributes are reliable to study personalities and life styles. Chin-Feng, 2002 is

validated but has low acceptability.

19

2.8 Model, how the constructs relate

Figure 1: The Analysis Model for dynamic segmentation

This model shows the micro and macro segmentation styles. The model features the influence

of the different segmentation styles: micro factors are influenced by macro ones.

Geographic and demographic criteria influence the behaviour of customers and their

preferences. For instance, according to where they live and factors such as their purchasing

power will have different behaviours towards a product offer. Moreover those criteria also

influence their way of thinking towards a product and why they are buying it, their tastes and

preferences. Here, the demographic and geographic data help predicting a behaviour and

psychographics of a person.

The second possible view included in this model is the cascade from macro data, to

behavioural data and then psychographics. Here, the customer profile, so to say, the “why” a

customer buys, is explained by the “how” he purchases, itself explained by the macro data

that are the demographic and geographic sets of variables.

20

The model of analysis therefore includes a diverse set of criteria aimed at a complete

understanding of segmentation, and offers the possibility to predict eventual changes in

segmentation needs with the help of purchase-intention-oriented questions. This last aspect is

particularly important in an uncertain environment, such as the Chinese car market.

This kind of hybrid segmentation includes a dynamic aspect of the interaction between

several criteria influencing the individual and his surroundings. A good understanding of

one’s environment enables to see how one behaves in a specific situation (purchase decision

of a car) and how he can participate into an ever changing, revolving environment. The

authors will use this model in order to build the main profiles of customers on the Chinese

new car market, in the present and what they could be in the future.

21

3. Methodology

3.1 Introduction

This chapter presents the different stages of the research and how it has been used in the

thesis. The data collected derives from quantitative research, an online survey, which was

sent out to China. The authors presented a survey based cross tabulation to study

segmentation of the Chinese car market with a time aspect; they also included secondary data

from different time period to capture a dynamic view of the Chinese car market.

3.2 Population and sample

A population is all the people belonging to a group or a geographical area. A segment of the

population is chosen for research, when sampling is done. Bryman and Bell (2005) states, a

sampling frame is the total list of units who are a share of the population from which a

sample is derived. A convenience sample means that the people in it happens to be available

to the researcher, making it easier, cheaper and less time consuming than a probability sample

which demands more preparations (Bryman & Bell, 2005).

The population consists of students and people from all walks from China. Survey questions

were sent to various companies. The authors found list of China-based companies and

manufacturers; there were no specific choice criteria only they had to be from China. From

the list a lot of emails were invalid so they could not be reached. A major number of peoples

contacted emailed back that the thought the survey link was a spam, and they refused to

answer the survey. The authors have also contacted friends who are residing in China to

answer the survey. The authors have also contacted various universities asking if they could

help the authors circulating the survey within their universities. Unfortunately no

confirmation that the email had been dispatched further to the students has been received.

The authors have also tried approached Chinese individuals with the age group of 65 and

above, by trying to contact various nursing homes for elderly in China via email, but

unfortunately the authors were not able to contact them.

The authors believed that as the study was mainly based on car consumer preferences it was

best if respondents were chosen with a variation in income group, age group. In total 512

individuals have been contacted with the survey. They were also sent five reminders with a

four to five day interval.

22

Due to time restrain the authors had to close the survey with 89 respondents with a response

rate of 17.4%. As the survey was answered by individuals with a varied age and income

group and also from a scattered geographical location, the number of answers is judged

important enough to carry out an analysis.

3.2.1 Data collection method

Surveys can be conducted on paper or electronically. The respondents can answer

individually. The electronic survey is the most common research method today. (Bryman &

Bell, 2007).

The primary quantitative data collection was made via the online survey provider called

Keysurvey. This service provider is convenient to use and to collect data from, since it is easy

to export SPSS files. The SPSS file was ready to use directly in SPSS, the statistical program

used. The authors have used an email–marketing service called MailChimp to send out mass

emails, with this tool the distribution of the surveys was made more efficient than it would

have been, if it had been send out to each email, manually.

3.2.2 Questionnaire Design

Surveys are cheap and quick to administer, and the interviewer does not affect the answers

with their presence, no variation of the questions occur and they can be made when the

respondent has the time for it Bryman and Bell (2007). Bryman and Bell (2007) also pointed

out few disadvantages with surveys; Misunderstandings may occur, regarding what the

researchers are asking about, since the participants cannot ask the researcher directly.

Participants cannot ask follow-up questions, and it is difficult to ask many questions since

researchers risk losing the respondents’ will of participating.

Bryman and Bell (2007) suggested for getting a higher response rate it is important to have a

good introduction letter, where it signifies why the answers are important. Since participants

tend to skip questions it is also good not to have open questions. Closed questions may be

easier to analyse since the coding is easier. It can also be necessary to remind the participants

to answer. (Bryman & Bell, 2005) For their survey the authors had an introduction letter,

which explained shortly the structure of the survey and its purpose. The survey was both in

English and Chinese as the authors were looking for participants only from China. The

23

Chinese translation of the survey was done with the help of a Chinese friend, later the

language and simplicity of the language was reassured by two other Chinese individuals who

were profound in the language. The survey consisted of 16 questions with the simplest

language possible. The survey had mostly closed question. To study the psychographic and

behavioural segmentation the authors used likert scales.

3.3 Operationalization

Questions Theoretical

Reference

Concept Motivation

1. Where do you

live?

- East Coast

- Central China

- North East China

- Western China

(Beane and Ennis 1987);

(Kotler and Armstrong

2011); (Gaston-Breton

and Martín, 2011);

(Douglas and Craig,

1982)

Geographic

segmentation

and Macro

Segmentation

(control

variable)

Helps segmenting the

sample geographically.

Controls the respondent

to be only from China.

2. What size is the

city you live in?

-Municipality of

China

- Sub provincial

level city

- Sub prefecture

level city

- Town, village

(Beane and Ennis 1987)

& (Kotler and Armstrong


and Martín, 2011);

(Douglas and Craig,

1982)

Geographic

segmentation

and Macro

Segmentation

Refine the first question

on geographical

segmentation

3. How old are you?

(age groups)

16-19

Beane and Ennis (1987) ;

(Cleveland, M. et al,

2011); (Wedel and

Demographic

segmentation

and Macro

Helps segmenting the

sample by age

This is also revealing a

24

20-34

35-49

50-64

65 and above

Kamakura, 1998);

(Gaston-Breton and

Martín, 2011); (Douglas

and Craig, 1982)

Segmentation

(control

variable)

control variable to base

the analysis on and make

sense of the other results.

4. Gender:

- Male

- Female



2011); (Wedel and

Kamakura, 1998);

(Gaston-Breton and

Martín, 2011); (Douglas

and Craig, 1982)

Demographic

segmentation

and Macro

Segmentation

Tool to segment the

sample by gender

5. Annual Income

- 30 000 to 46 000

- 46 000 to 87 000

- 87 000 to 120 000

- 120 000 to 163

000

- above



2011); (Wedel and

Kamakura, 1998)

Demographic

segmentation

and Macro

Segmentation

This question is giving

an insight on the

revenues available for

the sample

.

6. What factor do

you think is the most

important for most

of the people:

- Safety

- Brand image

- Design

- Fuel efficiency

- Quality

- Technology

- Affordability

- Comfort

- Chinese brand


(Holbrook and

Hirschman 1982);

(Wells, 1975); (Kim and

Lee, 2011); (Steenkamp,


and Martin, 2011);

(Laroche, 2011)

Psychographic

segmentation

and Micro

Segmentation

This question is divided

into a series asked in

order to know what

purchasing factor is

currently the most

important for a Chinese

person.

25

7. How many cars

have you bought?


(Holbrook and

Hirschman 1982);




and Martin, 2011);

(Laroche, 2011)

Psychographic

segmentation

and Micro

Segmentation

For the experience?

8. Do you have a car

now?

Micro and

behavioural

segmentation)

This question is more of

a preparatory question to

introduce the other

psychographic and

behavioural questions

8.1.1 How often do

you use your car?

- every day

-only on weekends

- less than once a

week


(Lancaster and Williams,

2002); (Kim and Lee,

2011); (Steenkamp,


and Martin, 2011);

(Laroche, 2011)

Behavioural

and Micro

segmentation

To determine the

frequency of use of the

car

8.1.2 You mainly

use your car for:

- long trips

- short trips




2011); (Steenkamp,


and Martin, 2011);

(Laroche, 2011)

Behavioural

and Micro

segmentation

To determine the sort,

type or nature of use of

the car

8.1.3 How often do

you change your



Behavioural

and Micro

To determine the

purchase frequency of

26

car?

- every year

- every 2 years

- in between 2 and 5

years

- less than once

every 5 years


2011); (Steenkamp,


and Martin, 2011);

(Laroche, 2011)

segmentation car consumers in China.

8.1.4 If you have

ever owned a car, to

what degree were

the following items

important when

purchasing it?

- Safety

- Brand image

- Design

- Fuel efficiency

- Quality

- Technology

- Affordability

- Comfort

- Chinese brand


(Holbrook and

Hirschman 1982);




and Martin, 2011);

(Laroche, 2011)

Psychographic

segmentation

and Micro

Segmentation

Understanding the

motivation for the last

car purchase

8.1.4.1 Would you

buy your next car

for the same reason?


(Holbrook and

Hirschman 1982);




and Martin, 2011);

(Laroche, 2011)

Behavioural

and Micro

segmentation

This questions the

loyalty of the respondent

and the possibility that a

purchasing factor lasts in

time. It gives an

indication for dynamic

segmentation.

27

8.1.4.1.1 If you

would buy a

different car, what

attribute would you

consider the most

important for the

next purchase?

- Safety

- Brand image

- Design

- Fuel efficiency

- Quality

- Technology

- Affordability

- Comfort

- Chinese brand


(Holbrook and

Hirschman 1982);




and Martin, 2011);

(Laroche, 2011)

Psychographic

segmentation

and Micro

Segmentation

The question investigates

on the possible change in

the purchasing reasons

for car owners. It gives

an indication for

dynamic segmentation.

8.2 If you do not

own a car, do you

plan to buy one in

the future?

Behavioural

segmentation

and Micro

Segmentation

This question prepares to

the next one and helps

assessing the potential of

the demand.

8.2.1 If you plan to

buy a car, to what

degree would the

following items be

the most important

to you?

- Safety

- Brand image

- Design

- Fuel efficiency

- Quality

- Technology


(Holbrook and

Hirschman 1982);




and Martin, 2011);

(Laroche, 2011)

Psychographic

segmentation

and Micro

Segmentation

With this question, the

current non owner of a

vehicle is taken in the

equation, and it

questions his/her future

purchase motivation.

It gives an indication for

dynamic segmentation.

28

- Affordability

- Comfort

- Chinese brand

3.4 Data analysis method

The survey’s data was analysed with the use of the statistical computer PASW Statistics 18

from SPSS Inc. According to Bryman and Bell (2005), the process of deciding how to

analyse the data should be done while gathering it and not after the collection. The choice of

the kind of analyse and the variable choice are crucial. The authors chose the linear

regression analysis, with curve estimates in order to predict sets of variables for behaviours

and psychographics relying on the macro factors included in Demographics and Geographics.

The statistical significance is fixed at 10%, and all results found over this level will be

rejected.

3.5 Validity

Validity could be seen as the most important research criteria, validity is concerned with the

integrity of the conclusions which is generated from performing a research (Bryman and Bell

2007). Bryman and Bell (2007) categorises different types of validity which are typically

used for quantitative research:

Measurement validity – is also referred to as construct validity. It essentially deals

with the fact, whether or not a measure that is formulated of a concept really does

reflect the concept that it is supposed to be representing. It is important to find

suitable measurements of terms which are stated in the research. Otherwise the

construct validity will be poor and the results questionable.

The authors used simple regression analysis to measure significant relations between

variables. Managerial decisions are often based on these (Anderson et al, 2002). The

questions that were constructed are linked to the theories, as shown in the

operationalization. The questions have been also translated in simple Chinese as the

focus is only on the Chinese car market.

Internal validity – relates to credibility. This form of validity mainly relates to

causality. Whether a conclusion containing a causal relation is sustainable or not. If it

29

can be seen that it is one specific variable that affects another and makes it to vary,

and that it is not a third variable causing the effect.

This study is based on a single survey, and the relations of the variables are based on

that survey. With time the variations in the independent variable cannot be predicted.

So the internal validity is low.

External validity – relates to transferability. It stresses whether the results of a study

can be generalized beyond the context at hand for the researcher.

The study is focussed only on China car market which gives it external validity.

Ecological validity - stresses whether the results of a study can be applied in people’s

everyday life and their natural social context. The ecological validity might become

poor when using surveys, because they disturb the natural situation that the

participants are in.

As this study was based on an email survey, and the presence of the surveyor was

absent, the natural social context was not disturbed so the authors believe the study

consists of ecological validity.

3.6 Reliability and Replicability

Reliability is defined as whether a research would provide the same results if the research

would be done again, with other researchers but with the same circumstances as the original

researchers; that there were no random or temporary conditions in the original research. If the

measurement of the study is not reliable enough this may influence the results differently and

the results may be not reliable. Replicability can be described as, if the investigation of the

study is reproduced by another researcher to study the reliability of the original study.

Detailed explanation of the procedure of the original study must be provided. (Bryman &

Bell, 2007).

The motives and reasoning of the survey questions and the measuring instrument used in the

thesis have all been pointed out in the methodology section. All the steps have been

elaborated in the paper that leads the authors to the results. The authors believe that this

strengthens the study’s reliability and replicability. The authors also believe that if the study

is conducted by another researcher there would not be any major variations. The results have

not been affected by temporary or random circumstances.

31

4. Empirical Data

In this chapter the authors have used both descriptive statistics and simple linear regression to

estimate relationship between the variables.

Managerial decisions are often based on this relation. In regression terminology the variables

being predicted are called dependent variable. The variable or variables being used to predict

the dependent variable is called independent variable. Only the relations with significance has

been used in the study; the significant value has been set to sig<0.10. (Anderson et al, 2002)

All the regression tables have been presented in appendix B.

4.1 Geographic data:

When asked their residence region in China, the respondents mainly indicated to be living in

Eastern China. The other regions had far less represented but nevertheless equally mentioned

at around 10% each, whereas the people living in Eastern China amount 72.4% of the

responses. The region of residence is also a control variable in the study. (See Appendix

A.1.1)

The urbanisation rate is really high since it can be observed that 63.2% live in a Municipality

of China. Sub provincial level city, sub prefecture of China and village inhabitants

respectively amount for 11.49%, 17.24% and 8.05%. (See appendix A.1.2)

4.2 Demographic data:

In this part of the questionnaire, people are asked basic information about themselves: their

gender, age and annual incomes.

The results show that the survey reached both male and female respondents in a balanced

way. Women are slightly less represented with 45.98% of the responses whereas men

represent 54.02% of the sample. (See appendix A.2.1)

The 20-34 years old is the main age category reached by the survey with 68.7% of responses,

and is followed by the 35-49 years old age category. The youngest category, from 16 to 19

years old, is represented at 8.05% and the 60 to 64 years old at 4.6%. The oldest age category

32

that is above 65 years old shows no entry. Age is also the second control variable in the

study.

Concerning the question about incomes, the majority of respondents are earning very low

incomes for more than half of them: 34.48% have no incomes at all and 20.69% are in the

first category (10 000 to 30 000 Yuan a year). The other income categories are dispatched at

around 7% of the responses, except for the highest earners who responded for11.49% as well

as the 3rd

highest earners (87 000 to 120 000 Yuan a year). (See appendix A.2.3.)

4.3 Behavioural data:

For the questions regarding consumer behaviour, the sample had to respond according to their

consumption habits, and answered questions in order to know if they currently own a car or

not, their level of experience with the purchase of cars, the frequency and style of use, their

replacement rate and their loyalty. For non-car owners, the respondents had to indicate if they

were planning to buy a car.

In the sample, most of the answers show that people do not have a car yet (64.37%), but that

they are very likely to buy one in the future: 93.59% of the respondents are thinking of

buying one. (See appendixes A.3.1. and A.3.2.)

The persons who answered the survey have not purchased many cars in their lives yet, and

can be considered as non-experienced as car purchasers with all responses showing that they

have bought less than four. Half of the sample has never purchased any car and 32.18% only

one. 10.34% have purchased two cars and 3.45% of respondents have bought three cars. (See

appendix A.3.3.)

For the car owners, the consumption style is clearly in favour of short daily trips. (See

appendixes A.3.4. and A.3.5.)

The respondents appear to be loyal to their car attribute since they would mainly make their

next purchase for the same reason. Only 22.58% would change their main car attribute for a

new purchase. (See appendix A.3.6)

33

67.92% of respondents mention that they would use their car a long time and buy a new one

after more than six years, and 22.58% in between 2 and 6 years. People replacing their car

within a short period of less than 2 years represent only 6.46% of the sample. (See appendix

A.3.7)

4.3.1 Customer behaviour explained by Geographic data

This part is dedicated to the following relation of the model:

Figure 2: Customer behaviour explained by Geographic data

Customer behaviour explained by region of residence (See appendix part B.1.1):

Experience is not significant (0.181), neither is frequency of use (0.173), long trips (0.558),

car replacement rate (0.709), and loyalty to attribute (0. 586).

The link between region of residence and short trips has an acceptable significance (0.093).

Customer behaviour explained by the degree of urbanization (See appendix part B.1.2):

None of the behaviours can apparently be explained by the degree of urbanization:

34

Experience is not significant (0.784) and neither frequency of use (0.293), long trips (0.97),

short trips (0.756), replacement rate (0.836) nor loyalty to attribute (0.776) has acceptable

significances.

4.3.2 Customer behaviour explained by Demographics:

Figure 3: Customer behaviour explained by Demographics

Consumer behaviour explained by age (see appendix part B.1.3.):

The statistical significances for the frequency of use (0.55), long trips (0.92), short trips

(0.615), replacement rate (0.937), and loyalty (0.442) are showing no clear relations.

Nevertheless, the experience of a driver appears significant (0.008).

Customer behaviour explained by the gender of the respondent (see appendix part

B.1.4.):

Experience is not significant (0.721), and nor are the frequency of use (0.945), long trips

(0.218), short trips (0.685) and replacement rate (0.101).

Loyalty towards the current car attribute is though significant (0.004).

35

Customer behaviour explained by incomes level (see appendix part B.1.5.):

Again, many behavioural variables cannot be explained by the income level due to

insufficient significance level: experience (0.195), long trips (0.29), short trips (0.14),

replacement rate (0.364), loyalty (0.391), are all over our maximum of 10%.

On another hand, the frequency of use is significant (0.039).

4.4 Psychographic data:

Figure 4: Customer psychographics explained by both macro data and customer behaviour

In this part, the psychographic side of the customer is explained by both the macro

dimensions that are demographics and geographic variables, and by the other micro

dimension that is customer behaviour.

In this type of questions, that is recurrent in the survey and filtered by behavioural answers,

the sample is investigated in terms of preferences for car attributes such as safety, brand

image, design, fuel efficiency, quality, technology, affordability, comfort, Chinese brand.

36

4.4.2 Respondent’s opinion on global preferences

The first question is about what the respondent thinks about the global preferences in China,

and the results show that safety (4.7/5 average) and quality (4.6/5 average) are rated as really

important by most of them. Then come fuel efficiency (4.1/5 average), comfort (4/5 average)

and technology (4/5 average) which are still very important but less than the two first

attributes. The design (3.8/5 average), affordability (3.7/5 average) and brand image (3.4/5

average) are rated averagely and finally, the fact that the car is a Chinese brand (2.1/5

average) is rated as non-important. (See appendix A4)

Respondents’ opinion on others’ preferences by age of the respondents (see appendixes

B.2.1.1 to B.2.1.9):

Seven out of nine psychographic variables cannot be explained by the age of the respondent:

safety (0.373), brand image neither (0.568), fuel efficiency neither (0.742), quality neither

(0.288), technology neither (0.833), affordability neither (0.628), Chinese brand (0.707).

Design and comfort’s importance perception for the global market are the only

significant relations (respectively 0.006 and 0.014).

Respondents’ opinion on others’ preferences by gender of the respondent (see appendix

part B.2.2.1 to B.2.2.9):

The results fail to give an acceptable explanation of what the respondents think of others’

preferences according to their gender due to statistical significances higher than the limit

fixed by the authors: safety (0.297), brand image (0.135), design (0.346), fuel efficiency

(0.564), quality (0.839), technology (0.731), affordability (0.208), comfort (0.313), Chinese

brand (0.898).

Respondents’ opinion on others’ preferences by annual incomes (see appendixes B.2.3.1

to B.2.3.9):

The statistical significance for each variables being too high, the results could not be

considered valid and the respondents’ opinion on others’ preferences cannot be correctly

explained relying on the annual incomes of the respondents:

Safety (0.664), as well as brand image (0.605), design (0.9), fuel efficiency (1), quality

(0.731), technology (0.516), affordability (0.899), comfort (0.674), and even Chinese brand

(0.975) do not have valid statistical significance that would highlight a valid relation.

37

Respondents’ opinion on others’ preferences by region of residence (see appendixes

B.2.4.1 to B.2.4.9):

The same happens with people’s opinion on others’ preferences according to where they live

in China:

Safety shows no acceptable significance (0.352), nor brand image (0.502), design (0.194),

fuel efficiency (0.297), quality (0.268), technology (0.453), affordability (0.952), comfort

(0.551), and Chinese brand (0.555).

Respondents’ opinion on others’ preferences by degree of Urbanisation (see appendixes

B.2.5.1 to B.2.5.9):

All the psychographic variables cannot be related to the degree of urbanization due to non-

acceptable statistical significances:

Safety (0.237), brand image (0.786), design (0.114), fuel efficiency (0.433), quality (0.131),

technology (0.149), affordability (0.918), comfort (0.103).

Only one psychographic variable, Chinese brand (significance = 0.060), can be

explained by the degree of urbanisation of the respondent.

4.4.3 Car owners’ current preferences

4.4.3.1 Car owners’ current preferences explained by demographics

For car owners, safety (4.7/5 average) and quality (4.6/5 average) are also the most important

reasons of purchase. Fuel efficiency (4.1/5 average) and comfort (3.9/5 average) appear to

have been important attribute for their last purchase as well. Technology (3.8/5 average),

design (3.7/5 average) and affordability (3.7/5 average) represent an average attribute for

them, which are rated as slightly more important than brand image. Chinese brand (2.4/5

average), once again, cannot be considered as a valid purchase reasons when looking at the

importance granted by the respondents. (See appendix A4)

Car owners’ preferences for their current car according to the age (see appendixes

B.2.1.10 to B.2.1.18):

The other variables are not exploitable:

Safety not significant (0.928), brand image (0.125), fuel efficiency (0.228), quality (0.586),

technology (0.830), affordability (0.277), comfort (0.193), Chinese brand (0.254).

38

Design is an exploitable variable with a significance of 0.082.

Car owners’ preferences for their current car according to the gender (see appendixes

B.2.2.10 to B.2.2.18):

Safety is not significant (0.457) and cannot be treated, the same happens with the other

psychographic variables: brand image (0.862), design (0.772), fuel efficiency (0.165), quality

(0.712), technology (0.375), comfort (0.171), Chinese brand (0.610).

The gender though, explains the perception of affordability for car owners

(significance for affordability is 0.084).

Car owners’ preferences for their current car according to their annual incomes (see

appendixes B.2.3.10 to B.2.3.18):

None of the psychographic variables can be explained with annual incomes for car owners:

Safety is not significant (0.253), and neither are brand image (0.233), design (0.972), fuel

efficiency (0.784), quality (0.253), technology (0.652), affordability (0.495), comfort (0.393),

and Chinese brand (0.966).

4.4.3.2 Car owners’ current preferences explained by geographic variables

Car owners’ preferences for their current car according to region of residence (see


The relation regarding car owners’ perception of safety and their region of residence is not

significant (0.250) and neither it is with their perception of brand image (0.347), the design

(0.148), the fuel efficiency (0.519), the technology (0.254), the affordability (0.667), the

comfort (0.281), or the fact a car is from a Chinese brand (0.394).

The relation regarding car owners’ perception of quality and the region of residence is

significant (0.020).

Car owners’ preferences for their current car according to degree of urbanisation(see


Safety is not significant (0.924) and the significance of brand image (0.431), design (0.639),

fuel efficiency (0.319), quality (0.689), technology (0.408), affordability (0.696), comfort

(0.687) are also non-acceptable.

39

Chinese brand is significant (0.090) and is the only significant relation with the degree

of urbanisation of car owners

4.4.3.3 Car owners’ current preferences explained by behaviours

Car owners’ preferences for their current car according to the frequency of use (see


Most of the variables cannot be used since they do not reach the 10% limit for statistical

significance that the authors imposed:

Safety is not significant (0.413), brand image (.568), design (0.897), fuel efficiency (0.393),

technology (.32), comfort (0.641), Chinese brand (0.641).

But a relation in between affordability importance and the frequency of use by a car

owner can be observed (significance is 0.096), as well as a relation with the

importance of quality (significance is 0.021).

Car owners’ preferences for their current car according to the style of use: Long trips

(see appendixes B.3.5.10 to B.3.5.18):


comfort (0.152), and Chinese brand (0.648) cannot be explained by the behaviour variable

“Style of use: long trips” since their significance does not meet the standard of 10%.

Nevertheless, technology is significant (0.01) and can be related to long trip drivers.

Affordability is significant too (0.012).

Car owners’ preferences for their current car according to the style of use: Short trips


Only one variable is having a statistical significance meeting the 10% standard, all the others

are higher:

Safety (0.521), brand image (0.481), design (0.5), fuel efficiency (0.823), technology (0.347),

affordability (0.125), comfort (0.547), Chinese brand (0.415).

This significant relation shows a link in between quality perception for short trips

drivers (significance=0.022).

Car owners’ preferences for their current car according to the replacement rate (see


40

The majority of variables having higher statistical significance are therefore not highlighting

any acceptable relation:

Safety (0.323), design (0.439), fuel efficiency (0.898), quality (0.807), technology (0.236),

affordability (0.258), comfort (0.372), Chinese brand (0.396).

Brand image is the only significant variable (0.039) enabling the observation of a

relation between its importance and the replacement rate of car owners.

4.4.4 Car owners’ preferences for their next purchase

When asked what importance they would grant to the different attributes for the next

purchase, car owners answered that, again, quality (4.5/5 average) and safety (4.7/5 average)

would be the most determining criteria. Then come comfort (4.1/5 average), fuel efficiency

(4/5 average) and technology (4.1/5 average). Affordability (3.8/5 average), design (3.8/5

average) and brand image (3.6/5 average) still appear important but less than the first

attributes. Chinese brand is not judged as important (2.6/5 average). (See appendix A4)

4.4.4.1 Car owners’ preferences for their next purchase explained by

Demographics

Car owners’ preferences for their next car according to their age (see appendixes

B.2.1.19 to B.2.1.27):

Most of the variables cannot be related to the age of the respondent because of the

significance level:

Safety is not significant (0.40), brand image (0.147), design (0.148), quality (0.191),


Age is a valid variable to explain the car owners’ preferences for the next purchase

(significance = 0.086).

Car owners’ preferences for their next car according to their gender (see appendixes

B.2.2.19 to B.2.2.27):

None of the psychographic variables could be effectively linked to gender for the next

purchase by a car owner, statistical significances not being satisfactory:



41

Car owners’ preferences for their next car according to their annual incomes (see


Eight variables could not be studied more since the significance is over the 10% rate:

Brand image (0.446), design (0.715), fuel efficiency (0.598), quality (0.36), technology

(0.868), affordability (0.808), comfort (0.83), Chinese brand (0.705).

Only safety is meeting the 10% significance rate chosen by the authors (0.082). A

relation can thus be observed.


Geographic variables

Car owners’ preferences for their next car according to their region of residence (see


There are still seven variables from which no relation can be drawn due to insufficient

statistical significance:

Safety (0.182), design (0.194), fuel efficiency (0.414), quality (0.502), technology (0.132),

affordability (0.929), comfort (0.464).

The results from the survey indicate that there is a relation in between the region of

residence of a Chinese person and what how he would rate Brand Image and Chinese

Brand attributes for his/her next purchase. Brand image significance is 0.06 and

Chinese brand significance is 0.01.

Car owners’ preferences for their next car according to their urbanization degree (see


Most of the variables do not meet the significance level of 10% and cannot be studied

meaningfully:

Safety (0.726), brand image (0.91), design (0.952), fuel efficiency (0.627), technology


The urbanization degree of a respondent influences his/her preference level for quality

(significance = 0.052) when thinking about their next car.


customer behaviour

42

Car owners’ preferences for their next car according to how often they replace their car


The frequency of replacement of a respondent’s car is not explaining any psychographic

variables:

Safety is not significant (0.363), and neither are brand image (0.158), design (0.35), fuel

efficiency (0.354), quality (0.535), technology (0.32), affordability (0.238), comfort (0.499),

and Chinese brand (0.114).

Car owners’ preferences for their next car according to their level of experience (see


Safety (0.63), fuel efficiency (0.285), quality (0.282), technology (0.764), affordability

(0.118), comfort (0.553), and Chinese brand (0.591) cannot be related due to their non-

acceptable level of statistical significance.

The level of experience of a driver who answered the survey is related to the

importance he grants to the brand image (significance = 0.008) and the design of their

next purchase (significance = 0.062).

Car owners’ preferences for their next car according to the frequency they use their car

at (see appendixes B.3.3.19 to B.3.3.27):

The majority of the variables have a level of significance too high to be studied:


technology (0.337), comfort (0.1), Chinese brand (0.828).

A relation in between the importance granted to affordability for the next purchase by

someone who has a car and the frequency to which the driver uses it can be built


Car owners’ preferences for their next car according to their style of use: long trips (see


All but one variable have a level of significance too high to be studied:

Safety (0.647), brand image (0.276), fuel efficiency (0.594), quality (1), technology (0.776),

affordability (0.104), comfort (1), Chinese brand (0.87) are not passing the 10% significance

rate.

43

One variable is surfacing as a preference that can be explained by the behaviour: long

trip style of use for the next purchase: design (significance = 0.098).

Car owners’ preferences for their next car according to their style of use: short trips


Here are the variables having a level of significance too high to be studied:


technology (0.807), comfort (0.407), Chinese brand (0.725).

A relation can be drawn in between a person using his/her car for short trips and

his/her preference for an affordable car (significance = 0.038).

Car owners’ preferences for their next car according to their loyalty towards car

attributes (see appendixes part B.3.7.):

Safety (significance = 0.83) having a statistical significance higher than the 10% fixed by the

others cannot be studied here. The other variables are in the same situation:

Brand image (0.385), design (0.414), fuel efficiency (0.975), quality (0.836), technology


4.4.5 Non-car owners’ preferences for their first purchase

Concerning respondents not owning yet a car, they will consider safety (4.8/5 average) and

quality (4.6/5 average) as the most relevant criteria when making their purchase decision.

Fuel efficiency (4.3/5 average), comfort (4.1/5 average), technology (4/5 average) and design

(3.9/5 average) would still be important but not as much as the safety and quality of the

vehicle they would chose. Affordability (3.8/5 average) and brand image (3.3/5 average)

would be of average concern and Chinese brand (2.4/5 average) would not be considered at

the time of purchase. (See appendix A4)

4.4.5.1 Non-car owners’ preferences for their first purchase explained by

Demographics

Non-car owners’ preferences for their next purchase explained with age (see appendixes

B.2.1.28 to B.2.1.36)

Most of the variables are not significant enough to be treated:

44

Safety (0.919), brand image (0.233), fuel efficiency (0.52), quality (0.215), technology


The importance of design (significance = 0.056) for the next purchase by a non-car

owner, can be explained by the age of the respondent.

Non-car owners’ preferences for their next purchase explained with gender (see

appendixes B.2.2.28 to B.2.2.36)

The importance granted to the different possible attributes of a car by non-car owners cannot

be related to their gender. Indeed, safety is not significant enough (1), as are not the brand

image (0.452), the design (0.309), the fuel efficiency (0.709), the quality (0.783), the

technology (0.691), the affordability (0.324), the comfort (0.144) and the fact that a car is

Chinese (0.122).

Non-car owners’ preferences for their next purchase explained with annual incomes

(see appendixes B.2.3.28 to B.2.3.36)

The importance granted to the different possible attributes of a car by non-car owners cannot

be related to their annual incomes. Indeed, safety is not significant enough (0.626), and

neither are the brand image (0.168), the design (0.615), the fuel efficiency (0.664), the quality

(0.23), the technology (0.724), the affordability (0.427), the comfort (0.603), or the Chinese

branded car (0.599).


Geographic variables

Non-car owners’ preferences for their next purchase explained according to the

respondents’ region of residence (see appendixes B.2.4.28 to B.2.4.36)

For their next purchase, a relation can be found according to which region Chinese

people live in and their preferences for brand image (significance = 0.02) design

(significance = 0.034), technology (significance = 0.016), and Chinese brand


Despite many variables being related with the region of residence, some cannot be explained

because of their significance level:

Safety (0.335), fuel efficiency (0.171), quality (0.944), affordability (0.804), comfort (0.207).

45


respondent’s degree of urbanization (see appendixes B.2.5.28 to B.2.5.36)

The relation with in between the degree of urbanization and the following preferences could

not be validated because of non-acceptable significance levels:


affordability (0.712), Chinese brand (0.469).

Results show that there is a relation in between comfort (significance = 0.014) and

technology (significance=0.01) importance perception regarding the degree of

urbanisation of the non-car owners.


Customer behaviour


respondent’s level of experience (see appendixes B.3.2.28 to B.3.2.36):

The relation with in between the level of experience and the following preferences could not

be validated because of non-acceptable significance levels:

Safety (0.836), design (0.126), quality (0.566), technology (0.987), affordability (0.369),

comfort (0.586), Chinese brand (0.179).

Nevertheless, the importance of brand image (significance = 0.013) and fuel

efficiency (significance = 0.07) for non-car owners is linked to their level of

experience.

4.5 Macro data for the dynamic aspect

The authors, in order to predict the dynamics of potential segments, chose to rely on macro

data used in previous researches and that theory suggests. The macro factors used are the

GDP per capita evolution in China, the age distribution, gender distribution, degree of

urbanization and region of residence trends. Those predictive elements will enable the

estimation of the size of the different segments found by the analysis of the empirical data

collected by the survey.

46

Figure 5: China GDP per capita from 1999 to 2020

The data from Index mundi (2013-05-18) have been gathered until 2011 and the authors drew

an exponential trend line from the data available to predict the Chinese GDP per capita until

2020. The purchasing power of Chinese people is predictably rising exponentially for the

coming years. The age of Chinese people, the authors’ second prediction tool, is available

below on the Figure 6.

The population in age of driving, composed of the working age population and the elderly

population, is increasing, but also getting older. According to Booz & co. (2013-05-20), the

shift in this driving able population is happening between the population 25-44y.o and 45 and

above. The first category is going to decrease while the other one is going to increase.

Figure 6: Evolution of Chinese population’s age (Nomura, 2013-05-18)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

1 3 5 7 9 11 13 15 17 19 21

China GDP per capita from 1999 to 2020

China

Expon. (China)

47

As the Figure 7 suggests it, there are currently more men than women in China, but the

balance should stay the same until 2020.

Figure 7: Chinese gender repartition in 2010 and 2020 (U.S Census Bureau after

NationMaster (2013-05-20))

The urbanization rate is still on the way up in and by 2020, a switch should be seen in

between urban and rural populations.

48

Figure 8: Evolution of the degree of urbanisation in China (Stanford university, 2013-5-22)

Concerning the dynamic view on intern Chinese migrations, Central China is a major

departure place and many people living there are going to Eastern China. To some extent,

Western China is a region of destination, and North East China sees as much arrivals as

departures.

Figure 9: Inter-regional migrations (China data centre, 2013-05-20)

49

5. Analysis

In this part, the authors will deal with the empirical data relating it to the model of analysis in

order to make sense out of all the data collected.

5.1 Control Variables

The authors chose their first control variable as region of residence as the study is only

focused on the Chinese car market and the respondents are currently residing in China. The

majority of the respondents are from the east coasts of China and the rest three regions have

fairly distributed respondents.

The second control variable in the study is the age of the respondents. According to the

library of congress (2013-05-20) and DSAL (2013-05-20) the minimum age of working in

China is 16 for private jobs and 18 for working in public services. The minimum age for

getting a driver’s license is 18 (Inter Nations 2013-05-20). The authors therefor set the

minimum age of respondents from 16, choosing the minimum working age. As shown in the

diagram in the empirical chapter the authors were able to reach major part of the age groups,

this gives the authors a wide variety of age group to study. Though the 65 and older group

could not be reached, the authors believe this will not affect the study as the authors therefor,

simply excluded this age group while finding relations through simple linear regression

analysis.

The control variable in the study controls the respondents by age and region of residence in

China, thus strengthening the findings.

5.2 Hybrid Behavioural segmentation

The first part of the model is how the behaviour is explained by demographics and

geographic data. It is shown by the black circles in the Figure 61 below:

50

Figure 10: Behaviour explained by Macro data

Looking at the results, the macro data indeed influences the behaviour of people and hybrid

segmentation can be made in between D, G and B. The following patterns can be observed:

Chinese people living west travel less for short distances than the Chinese living Eastern.

The older the car user is, the more experienced they are concerning car purchases

Women are more loyal than men and tend to change their mind less than men from one

purchase to the next.

The higher incomes Chinese people have the less the respondents use their car.

5.3 Hybrid Psychographic segments

In this other part, product-specific psychographics are explained by both macro data from

demographics and geographic profiles, and by behaviours, relying on the time aspect: current

opinion or predictive opinion.

51

Figure 11: Psychographics explained by Geographic data, Demographics and Behaviours

Global perceptions of importance are merely explained by macro data. The results show that

the older the respondents are, the more they think that others do not care about design and

comfort when purchasing their car. The respondents think that the less a Chinese person is

urbanised, the more importance this person would grant to the fact that a car is under a

Chinese brand. To summarize, the product specific psychographics here are not going in a

positive way and people are expressing a decreasing importance of attributes consideration by

the market.

Concerning the preferences expressed by car owners, many more relations can be deduced

from their behaviour, geographic and demographic situation. As the age of the respondents

grows, the less they care about the design of their car. Affordability seems to be a more

feminine than masculine concern. The western a respondent lives the less importance he

grants to quality. The less urbanized the more the importance for Chinese brand is granted.

The respondents currently owning a car and using it often grant more importance to the fact

that a car was affordable when they purchased their car; they are also more concerned by the

52

quality of it. The more the respondents are having long trips, the less technology is

considered as important as attribute for his current vehicle. The more the respondents drive

for long trips, the less they have granted importance to affordability at the time they

purchased their car and when would prefer quality for short trips. The more often a

respondent replaces his/her car, the more he/her judges the brand image as an important

purchase factor.

Concerning the dynamic aspect included in the next purchase questions asked to car owners

the hybrid segmentation is giving a lot of relations: the older the respondent is, the more

importance he/she will grant to fuel efficiency for the next purchase. The higher income

he/she earns the more important safety is bound to be considered. The results from the survey

indicates that the western a respondent lives, the more he/she is going to take brand image

and brand image into consideration and the more urbanized, the more he/she would consider

quality as important. As a Chinese person is more and more experienced with car purchases,

he/she will bother less for brand image and design. The more he/she uses his/her car the more

important the more he/she will consider affordability. Concerning the type of use, long trips

are associated to an increased importance for design and short trips to an increased

importance of affordability.

The predictive market is also composed by the preferences of people who do not currently

own a car but plan to buy one in the future. The older someone who has no car is, the less

important design will be. The more Chinese people live in the West, the more they will grant

importance to the brand image, design, technology and Chinese brand. The more urbanized,

the more the non-car owner is going to judge technology and comfort as important for his/her

next purchase. People who do not currently have a car will tend to consider the brand image

as less important as they are experienced but would consider fuel efficiency as important.

5.4 Dynamic Hybrid Psychographic segments

If it is possible to put those hybrid psychographic segments in relation with available macro

data predictions, the authors should be able to show a dynamic aspect of the hybrid segments.

Chinese population is getting older, more urbanized, richer, and more concentrated in the

Eastern and Western parts of China, this is impacting the future importance of the product-

specific profiles found with the psychographic measurement above.

53

The profiles found can therefore be strengthened by those dynamics, their importance

growing up for the coming years.

6. Conclusion and discussion

Market segmentation has been very interesting area to study; especially as the focus was on

segmenting the emerging Chinese car market. China with its fast growing economy, and huge

population, made this study more interesting and challenging than if the authors focused on a

very predictable and well-known market.

With the help of hybrid segmentation; combining the macro and micro aspects of

segmentation, the authors were able to create behavioral and preferential profiles of the

Chinese car consumers. Only the relations with significant values, between the bases of

segmentation, were taken into account while all the relations with insignificant values were

discarded. The authors have been able to present the profiles according to its desirability.

In many cases, profiles identified by the study also anticipated the preferential demand of

potential car customers, who either have never bought a car or are planning to buy a car in the

near future. This helps having an insight of what the demand would look like in the near

future. The secondary data has also been used to find the way towards which the speech is

formulated: for example, as the Chinese population is getting older, the results are expressed

in this way and not towards a younger and younger Chinese population.

In terms of behaviour, Chinese people tend to have owned more and more cars but the rate of

usage gets lower. Western Chinese and Eastern Chinese differ in the fact that in the East

people use the car more for short trips and in the West for long trips.

In terms of preferences, here are the profiles found for each car attribute:

Safety:

Globally, safety is the most important criteria people consider and should stay the most

important. For their next purchase, car owners would consider it more important as they are

richer.

54

Quality:

Quality is the second most considered car attribute and is specially associated with current car

owners. The western car owners are from in China, the less they grant importance to quality.

The more often the use their car for short daily trips the more important they judge quality.

For car owners next purchase the more urbanised they are the more importance they will be

giving to quality.

Fuel efficiency:

Fuel efficiency is the third most considered car attribute and is a rising attribute for everyone

in China. Old car owners, experienced persons and non-car owners will consider this attribute

more important for their next car purchase.

Comfort:

Comfort is the fourth most considered car attribute and is associated with young urban

Chinese people. With age Chinese people think comfort is not a major public concern when

buying a car. For non-car owners next purchase the more they are urbanised the more comfort

is going to be important.

Technology:

Technology is the fifth most considered car attribute and seems attractive for western

urbanised people. Car owners using their cars mainly for long trips consider technology as

less important when driving a long time. For non-car owners’ next purchase the western they

live the more importance they give to technology and the more urbanised non-car owners are

the more importance they give to technology.

Design:

Design is the sixth most considered car attribute and its importance is globally decreasing as

it is related to the age of the Chinese people. Old Chinese individuals think people do not

consider design to be very important. They do think the same for their own car, so they

basically think that their judgement of the non-importance of design is shared by the market.

The more they are experienced the stronger this belief is for the next purchase.

For older non-car owners design is not going to be important for the coming purchase, and

design is not associated with long trips usage.

55

The western the Chinese people are situated the more they give importance to design.

Affordability:

Affordability is the seventh most considered car attribute and is supposed to grow for people

not using their car often in an urban environment. Women grant more importance to

affordability than men do when they have a car. The more often people use their car for short

daily trips the more importance they give to affordability, and the less they are using it the

less they will consider it for their next purchase.

Brand image:

Brand image is the eighth most considered car attribute and could be effective in Western

China for someone’s first car who intends to keep it. Chinese people who don’t change their

car often will grant less important to brand image. For the next purchase both car owners and

non-car owners from the west think that it is more important than in the east. The more

experienced people are the less important Brand Image will be for the non-car owners’ next

car.

Chinese brand:

Chinese brand is the least considered car attribute and is not globally important enough, but

would be effective for rural people of Western China. The more urbanised people are in

China, the less they are keen on Chinese brand when buying a car. And this belief is actually

observable in car owners’ preferences for their car. For both car owners’ and non-car owners’

next purchase, the western they live the more importance they give to Chinese brands.

7. Managerial implications, reflections, further research

7.1 Managerial implications

As the car market in China is literally booming, the preferences of car buyers change quickly,

and the market is really challenging to understand and anticipate.

The demand becoming higher, the Chinese car market is representing a crucial target for

manufacturers to make business on.

56

The profiles built are really precise and, depending on the positioning of the cars, the

manufacturer will be able to know how his business might evolve and might consider

marketing different types of cars if the demand is not showing any acceptable potential.

Since manufacturers build and market various types of cars, such as urban cars, SUVs or

sedan, the study can help them putting the highlight on special attributes of the type of car

according to the market preferences found.

The current demand is mainly oriented towards safety and quality as main attribute, and those

attributes are bound to stay a major preoccupation for Chinese people’s next car, and

constructors should adopt an attitude towards those attributes in order to reach the largest

current and potential targets. The Chinese people are also to use their car less often but the

float is still strongly growing.

Safety should absolutely be considered for luxury models.

Quality, technology and affordability should be emphasized, particularly for urban cars used

for short trips.

Fuel efficiency is a rising attribute for the next purchase and should be really considered for

any kind of behaviour.

Even though design is still important, it is going to be less and less important for Chinese

people, and reach the same levels as Brand image and Chinese brand, which are not major

concerns when buying a car, except in Western China.

From the profiles found, a typical car meeting the tastes of the demand can be imagined. This

ideal car would be built for short daily trips in an urban environment and prioritizing safety

and quality as major attributes. This car should also include a highlight on its fuel efficiency

since the future demand is going to consider it as important. The other aspects shall not be

neglected, but Chinese brand and Brand image seem not important to particularly emphasize.

Companies marketing cars on the market should have a car like this in their range. According

to trends, this type of car would be sold more and more.

57

The choice of positioning resulting after the segmentation is the car manufacturers’, and this

segmentation is helping them in the sense that, according to their strategy, they can have an

overview of what is realisable in China.

The managerial implications cited above can also help creating a specific strategy for the

Chinese market in order to reach a maximum of the demand. The segmentation process, if

made by the company itself, should follow the authors’ model in order to have the most

precise and valid profile possible. The preferences can be switched by the company’s core

competencies in order to have an insight of what it could achieve on the market with what it

mastered within the company. The profiles should nevertheless include demographic,

geographic, behavioural and psychographic data, as the model is built:

Figure 12: The hybrid segmentation model

7.2 Reflections

Working on the topic of car segmentation in China taught the authors a lot on segmentation

practices and gave them more experience in market research, this task eventually being a part

58

of their eminent career, after the master thesis. The study is thus a real plus and gives a

professional qualification.

The topic chosen also made them learn a lot about the car market in China.

Although the authors feel they could have had more answers from Chinese people, this is a

point that can be picked up for further research in order to obtain more validity from the

survey.

The study revealed itself very challenging and created strong links in the team carrying it out.

The team spirit has been submitted to a lot of pressure and the authors find a great

satisfaction in its resistance.

7.3 Limitations

Due to time restraints and in order to keep a precise focus, the authors did not take external

factors such as the influence of the Chinese government policies in the equation.

Also, the study is limited by the number of responses gathered, that could have been greater.

7.4 Future Research

As future research, authors should investigate points such as the influence of the Chinese

Government policies on the demand for cars, as it is assumed to be playing a major role and

could alter preferences.

60

References

Amine, L. S. and Smith, J. A. (2009), "Challenges to Modern Consumer Segmentation in a

Changing World: The Need for a Second Step", Multinational Business Review, Vol. -17,

Issue 3, pp. 71–100.

Anderson, D. Sweeney, D. and Williams, T. (2002), Statistics for business and economics 8e,

(pp 539-612). Ohio, South Western.

APCO, (2013-03-22) “Market Analysis Report: China’s Automotive Industry”. APCO

Worldwide, [Online]. Available at:

http://www.export.gov.il/uploadfiles/03_2012/chinasautomotiveindustry.pdf, Accessed:

2013-03-22.

Armstrong, G. and Kotler, P. (2011), Marketing: An Introduction, London, Pearson.

Autos Cars, (2013-03-22) “Automobile”. Autos Cars, [Online]. Available at:

http://autoscars.sexyi.am/index/automobile/?wiki-maping=[6], Accessed: 2013-03-22.

Beane, T.P. and Ennis, D.M. (1987), "Market Segmentation: A Review", European Journal

of Marketing, Vol. -21, Issue 5, pp. 20-42.

Bentley historical library, (2013-03-22) “Automotive History”. Bentley historical library

University of Michigan, [Online]. Available at:

http://bentley.umich.edu/research/guides/automotive/, Accessed: 2013-03-22.

Bryman, A. and Bell, E. (2007), Business research methods, Oxford: Oxford university press.

Cannon, H. M. and Yaprak, A. (2011),"A dynamic framework for understanding cross-

national segmentation", International Marketing Review, Vol. -28, Issue 3, pp. 229-243.

China data center, (2013-05-20), “The Inter-Regional Migration in China”, China data center

University of Michigan, [Online]. Available at:

http://chinadatacenter.org/presentation/ChinaMigration.pdf Accessed: 2013-05-20.

Chin-Feng, L. (2002), “Segmenting customer brand preference: demographic or

psychographic”, Journal of Product & Brand Management, Vol. -11, Issue 5, pp. 249-268.

Cui, G. and Liu, Q. (2000), "Regional market segments of China: opportunities and barriers

in a big emerging market", Journal of International Business Studies, Vol. -39, Issue 4, pp.

652-669.

Douglas, S. P and Craig, C. S. (2010), "The role of context in assessing international

marketing opportunities", International Marketing Review, Vol. -28, Issue 2, pp. 150-162.

http://chinadatacenter.org/presentation/ChinaMigration.pdf

61

DSAL (2013-05-20), “Convention concerning Minimum Age for Admission to

Employment”, Driving license school of Louisiana, [Online]. Available at:

http://www.dsal.gov.mo/law/E138.htm Accessed: 2013-05-20.

Frank, R., Massy, W. and Wind, Y. (1972), Market Segmentation, Englewood Cliffs,

Prentice-Hall.

Gaston-Breton, C. and Martin, O. M., (2011), "International market selection and

segmentation: a two-stage model", International Marketing Review, Vol. -28, Issue. 3, pp.

267-290.

Grundy, T. (2006), “Rethinking and reinventing Michael Porter's five forces model”,

Strategic Change, Vol. -15, Issue 5, pp. 213-229.

Hassan, S.S., Craft, S. and Kortam, W. (2003),"Understanding the new bases for global

market segmentation", Journal of Consumer Marketing, Vol. -20, Issue 5, pp. 446–462.

Holbrook, M. and Hirschman, E. (1982), “The Experiential Aspects of Consumption:

Consumer Fantasies, Feelings, and Fun”, Journal of Consumer Research, Vol. -9, Issue 2, pp.

132-140.

HSBC, (2013-03-22) “China: an ever-changing market”. HSBC global connection [Online],

Available at: http://globalconnections.hsbc.com/global/en/articles/china-ever-changing-

market, Accessed: 2013-03-22.

Indexmundi.com, (2013-05-18), “GDP – per capita (PPP) (US$)”, indexmundi.com, [Online].

Available at: http://www.indexmundi.com/g/g.aspx?c=ch&v=67, Accessed: 2013-05-18.

Inter Nations, (2013-05-20), “Traffic laws in China”, Driving in China, [Online]. Available

at: http://www.internations.org/china-expats/guide/driving-in-china-15632/traffic-laws-in-

china-2 Accessed: 2013-05-20.

Jiang, P. (2000), “Segment-based mass customization: an exploration of a new conceptual

marketing framework”, Internet Research, Vol-10, Issue 3, pp. 215-226.

Kotler, P. (1989), "From mass marketing to mass customization", Strategy & Leadership,

Vol. -17, Issue 5, pp.10–47.

Kotler, P. and Keller, K. (2009), Marketing Management, Upper Saddle River, Pearson

Prentice Hall.

Lancaster, G and Williams, I. (2002), "Consumer segmentation in the grey market relative to

rehabilitation products", Management Decision, Vol. -40, Issue 4, pp. 393-410.

Library of congress (2013-05-20), “Children's Rights: China”, Library of congress, [Online].

Available at: http://www.loc.gov/law/help/child-rights/china.php Accessed: 2013-05-20.

http://www.dsal.gov.mo/law/E138.htm

http://www.indexmundi.com/g/g.aspx?c=ch&v=67

http://www.internations.org/china-expats/guide/driving-in-china-15632/traffic-laws-in-china-2

http://www.internations.org/china-expats/guide/driving-in-china-15632/traffic-laws-in-china-2

http://www.loc.gov/law/help/child-rights/china.php

62

Library of congress (2013-03-22), “Everyday Mysteries”. Science Reference Services,

[Online]. Available at: http://www.loc.gov/rr/scitech/mysteries/auto.html, Accessed: 2013-

03-22.

McKinsey (2013-03-22), “Upward Mobility: The Future of China’s Premium Car Market”.

McKinsey & Company, [Online]. Available at: http://www.mckinseychina.com/wp-

content/uploads/2013/03/mckinsey-upward-mobility-the-future-of-chinas-auto-market.pdf,

Accessed: 2013-03-22.

NationMaster (2013-05-20), “Age distribution”, NationMaster [Online]. Available at:

http://www.nationmaster.com/country/ch/Age_distribution, Accessed: 2013-05-22.

Nomura (2013-05-18), Comments on China’s economic transition: Supply side,

Nomura Institute of Capital Markets Research [online].Available at:

http://www.nomurafoundation.or.jp/data/20121113_C_H_Kwan_Comment-PPT.pdf,

Accessed: 2013-05-18.

Pehrsson, A. (2006), “Business relatedness and performance: a study of managerial

perceptions”, Strategic Management Journal, Vol-27, Issue 3, pp. 265-282.

Quinn, L., Hines, T. and Bennison, D. (2007),"Making sense of market segmentation: a

fashion retailing case", European Journal of Marketing, Vol. -41, Issue 5, pp. 439–465.

Booz & co. (2013-05-20), “The eight overarching China automotive trends that are

revolutionizing the auto industry”, Booz & Company. [Online], Available at:

http://www.booz.com/media/file/The_Eight_Overarching_China_Automotive_Trends_en_pa

rt2.pdf, Accessed: 2013-05-22.

Steenkamp, J. B. E. M. (2001),"The role of national culture in international marketing

research", International Marketing Review, Vol-18, Issue 1, pp. 30–44.

Taehyun Kim, T. and Lee, H.Y. (2011), "External validity of market segmentation methods;

A study of buyers of prestige cosmetic brands", European Journal of Marketing, Vol. 45,

Issue 1/2, pp. 153-169.

The guardian (2013-03-22), “China and cars: a love story”. World news [Online]. Available

at: http://www.guardian.co.uk/world/2012/dec/14/china-worlds-biggest-new-car-market,

Accessed: 2013-03-22.

Wedel, M. A. and Kamakara, W.A.A. (2000), Market segmentation: conceptual and

methodological foundations, Massachusetts, Kluer Academic Publisher.

Weick, K. E. (1995), Sensemaking In Organizations, Thousand Oaks, Calif, Sage.

Weinberg, C. B. (1972),"The time sequence of events B a basis for market segmentation",

European Journal of Marketing, Vol. -6, Issue 1, pp. 60-63.

http://www.nationmaster.com/country/ch/Age_distribution

http://www.nomurafoundation.or.jp/data/20121113_C_H_Kwan_Comment-PPT.pdf

http://www.booz.com/media/file/The_Eight_Overarching_China_Automotive_Trends_en_part2.pdf

http://www.booz.com/media/file/The_Eight_Overarching_China_Automotive_Trends_en_part2.pdf

63

Wells, W.D. (1975), “Psychographics: A Critical Review”, Journal of Marketing Research,

Vol. -12, Issue 2, pp. 196-213.

West, D., Ford, J. and Ibrahim, E. (2006), Strategic Marketing: Creating Competitive

Advantage, Oxford, Oxford University Press

64

Appendix A

The Survey

Chinese Car Market Survey

Hello, we are two students living in Sweden and writing our master thesis. The purpose of this questionnaire is

to study the Chinese car market, and to determine its future in terms of preferences. We are going to ask you

basic information about yourself, your past experience with cars, what you would like now and in the future.

(您好，我们是两个住在瑞典的学生，在进行我们的硕士论文。这份问卷的目的，是为了研究中国汽车

市场，并尝试探讨以后市场的喜好。我们会问及到你的一些基本信息，你过去有关汽车的经验，和你对

现在和未来汽车需要或者想法。)

The survey takes about 5 minutes and is free of charge. The answers are anonymous.

(此调查卷大约需要5分钟。这个调查卷是以自愿和匿名的形式进行。)

If you have any question regarding our research, feel free to contact our tutor Anders Pehrsson

on [email protected] or ourselves on [email protected] or [email protected].

(如果您对我们的研究有任何问题，请随时联系我们的导师安德斯Pehrsson anders.pehrsson@ lnu.se

或者我们 [email protected]或[email protected]。)

Thank you in advance.

(致敬

感谢)

1. Where do you live? (您目前在中国的居住地属于哪个地区？)

o East coast (中国东海岸)

o Central China (中国中部)

o North East China (中国东北)

o Western China (中国西部)

2. What size is the city you live in? (您所生活的城市有多大？)

o Municipality of China (省级城市)

o Sub provincial level city (副省级城市)

o Sub prefecture level city (地级市)

o Town, village (镇，村)

3. How old are you? (您的年龄为？)

o 16-19

o 20-34

o 35-49

o 50—64

o Above 65(65岁及以上)

4. Gender: (您的性别为：)

mailto:[email protected]



65

o Male (男)

o Female (女)

5. Annual income (in Yuan)? (全年总收入（元）？)

o no incomes (没有收入)

o 10 000 to 30 000 (10 000至30000)

o 30 000 to 46 000 (30000至46 000)

o 46 000 to 87 000 (46 000至87000)

o 87 000 to 120 000 (87 000至120 000)

o 120 000 to 163 000 (120000 至 163000)

o above (163000以上)

YOUR OPINION (您个人的看法)

6. What factor do you think is the most important for most of the people? Please grade the following car

attributes from 1 (not important at all) to 5 (very important).

(对于大多数的人而言，你认为哪个因素是汽车最重要的？

请对汽车各个不同属性评分，从1-5分，（评分从低到高，1分为最不重要，5分为最重要）)

1 2 3 4 5

1 Safety (安全)

2 Brand image

(品牌形象/社会地位)

3 Design (设计)

4 Fuel

efficiency (燃油效率)

5 Quality (质量)

6 Technology (技术)

7

Affordability (负担能力)

8 Comfort (舒服性)

9 Chinese

Brand (中国品牌)

YOUR EXPERIENCE WITH CARS (您个人与汽车的经验)

7. How many cars have you bought? (你买过多少辆汽车？)

0, 1, 2, 3, 4, 5, 6 or more.

8. Do you have a car now? (你现在有汽车吗？)

Yes No

8.1.1. How often do you use your car?

66

o every day (每天)

o only on weekends (只在周末)

o less than once a week (每周少于一次)

8.1.2. You mainly use your car for : (您主要使用车的用途是：)

o long trips (长途旅行)

o short trips (短途旅行)

8.1.3. How often do you change your car? (你换新车的时间周期为多久？)

o Every year (每年)

o Every 2 years (每2年)

o in between 2 and 5 years (每2至5年)

o less than once every 5 years (每5年或者多于5年一次)

o less than once every 5 years (每5年或者多于5年一次)

8.1.4. If you have ever owned a car, to what degree were the following items important when

purchasing it? Please grade the following car attributes from 1 (not important at all) to 5 (very

important).

如果你曾经拥有一辆车，在购买的时候，根据不同项目对你个人的重要性不同，进行排列

和评分. 请对汽车各个不同项目评分，从1-

5分，（评分从低到高，1分为最不重要，5分为最重要).

1 2 3 4 5

1 Safety (安全)

2 Brand image


3 Design (设计)

4 Fuel


5 Quality (质量)


7



9 Chinese


8.1.4.1. Would you buy your next car for the same reason?

(你在购买你下一辆车的时候是会出于同样的原因吗？)

Yes No

67

8.1.4.1.1. If you would buy a different car, what attribute would you consider the most

important for the next purchase? Please grade the following car attributes from 1

(not important at all) to 5 (very important).

如果你曾经拥有一辆车，在购买的时候，根据不同项目对你个人的重要性不

同，进行排列和评分. 请对汽车各个不同项目评分，从1-

5分，（评分从低到高，1分为最不重要，5分为最重要)

1 2 3 4 5

1 Safety (安全)

2 Brand image


3 Design (设计)

4 Fuel


5 Quality (质量)


7



9 Chinese


8.2. If you do not own a car, do you plan to buy one in the future?

(如果你不拥有自己的汽车，你打算购买一台在未来？)

Yes NO

8.2.1. If you plan to buy a car, to what degree would the following items be the most important to you?

Please grade the following car attributes from 1 (not important at all) to 5 (very important).

如果你打算买一辆车，以下项目，请给予评分，评出他们在你购买的重要性占多少 . 请对汽车各个

不同项目评分，从1-5分，（评分从低到高，1分为最不重要，5分为最重要)

1 2 3 4 5

1 Safety (安全)

2 Brand image


3 Design (设计)

4 Fuel


5 Quality (质量)

68


7



9 Chinese


Survey emails

First email of the survey.

Dear Sir/Madam

亲爱的先生，女士

Hello, we are two students from Linnaeus university, Sweden,we are writing our master thesis. The purpose of

this questionnaire is to study the Chinese car market, and to determine its future in terms of preferences. We are

going to ask you basic information about yourself, your past experience with cars, what you would like now and

in the future.

(您好，我们是两个住在瑞典的学生，在进行我们的硕士论文。这份问卷的目的，是为了研究中国汽车

市场，并尝试探讨以后市场的喜好。我们会问及到你的一些基本信息，你过去有关汽车的经验，和你对

现在和未来汽车需要或者想法。)

The survey takes about 5 minutes and is free of charge.The answers are anonymous.

(此调查卷大约需要5分钟。这个调查卷是以自愿和匿名的形式进行。)

You can find our survey at this website: 您可以在这个网站上找到我们的调查问卷：

http://www.keysurvey.co.uk/votingmodule/s180/f/509482/9236/

If you have any question regarding our research, feel free to contact our tutor Anders Pehrsson

on [email protected] or ourselves on [email protected] or [email protected].

(如果您对我们的研究有任何问题，请随时联系我们的导师安德斯Pehrsson anders.pehrsson@ lnu.se


Thank you in advance.

(致敬

感谢)

Adrien SAINT, Imran SYED

(阿德里安·SAINT，赛义德伊姆兰)

The second survey email after an interval of 4 to 5 days

亲爱的先生，女士





69

您好，我们是两个住在瑞典的学生，在进行我们的硕士论文。这份问卷的目的，是为了研究中国汽车市

场，并尝试探讨以后市场的喜好。我们会问及到你的一些基本信息，你过去有关汽车的经验，和你对现

在和未来汽车需要或者想法。

此调查卷大约需要5分钟。这个调查卷是以自愿和匿名的形式进行。

您可以在这个网站上找到我们的调查问卷：


(如果您对我们的研究有任何问题，请随时联系我们的导师安德斯Pehrsson [email protected]


致敬

感谢

阿德里安•SAINT，赛义德伊姆兰

Dear Sir/Madam,

We are two marketing students from Linnaeus University, Sweden. We are at present writing our master thesis

about the car market segmentation in China. Therefore we would like to ask you few questions. The purpose of

this questionnaire is to study the Chinese car market, and to determine its future in terms of preferences. We are

going to ask you basic information about yourself, your past experience with cars, what you would like now and

in the future.

The survey takes about 5 minutes and is free of charge. The answers are anonymous. We would very much like

if you could kindly help us with our survey by answering the survey yourself and if could also please forward

this message to your respectable colleagues.

You can find our survey at this website:


If you have any question regarding our research, feel free to contact us at [email protected]. or

[email protected]. You can also verify us with our tutor, professor Anders Pehrsson at

[email protected].

Thanking you in advance.

With Best Regards

Imran SYED &

Adrien SAINT

70

The third survey email (This email was later sent out four more times with an interval of 4 to 5 days)

Respectable sir/madam,

We are two marketing students from Linnaeus University, Sweden. We are at present writing our master thesis

about the car market segmentation in China. Therefore with your permission we would like to ask you few

questions. The purpose of this questionnaire is to study the Chinese car market, and to determine its future in

terms of preferences. We are going to ask some basic information about your experience with cars; what you

would like now and in the future.

The survey takes about 5 minutes and is free of charge. The answers are anonymous. We would very grateful if

you could kindly help us with our survey by answering the survey. Also if you could forward this survey to your

respectable colleagues.

You can find our survey at this website:


If you have any question regarding our research, feel free to contact us [email protected].

or [email protected]. You can also verify us with our tutor, professor Anders Pehrsson

at [email protected].

Thanking you in advance.

With Best Regards.

Imran SYED &

Adrien Saint

--------------------------

可敬的先生/女士*,

我们是两个营销学生从瑞典林奈大学。目前,我们写我们的硕士论文关于汽车在中国市场分割。因此如

果你允许我们想问你几个问题。此问卷的目的是研究中国汽车市场,并确定其未来方面的偏好。我们要

问自己一些基本信息,你过去的经历与汽车;你想在现在和未来。

调查大约需要5分钟,是免费的。答案是匿名的。我们会很感激如果你可以请帮助我们与我们的调查回答

调查。而且如果你能把这个调查你的可敬的同事。

你可以找到我们的调查在这个网站用蓝色标示:


如果你有任何问题关于我们的研究,随时联系我们 [email protected] 或 [email protected].







71

你也可以验证我们导师,教授安德斯Pehrsson在 [email protected].

提前感谢你。

此致敬礼。

Imran SYED &

Adrien Saint

Descriptive statistics

A.1 Geographic data

A.1.1 Region of residence repartition of the sample

A.2.2 Urbanization of the sample

A.2 Demographic data

A.2.1 Gender repartition of the sample


72

A.2.2 Age repartition of the sample

A.2.3 Annual income repartition of the sample

A.3 Behavioural data

A.3.1. Car owners repartition

73

A.3.2. Potential from non-car owners

A.3.3. Experience of respondents

A.3.4 Frequency of use

A.3.5. Style of use

74

A.3.6. Loyalty to attribute

A.3.7 Replacement rate

A.4 Psychographic data : table of average attributes importances

Statistiques descriptives

N Minimum Maximum Moyenne Ecart type

6. Perception of Global

Importance of Safety 89 1.00 5.00 4.6854 .76270


Importance of Brand image 89 1.00 5.00 3.3820 1.07138


Importance of Design 89 1.00 5.00 3.7865 .89788

75


Importance of Fuel

efficiency

89 1.00 5.00 4.1236 .92712


Importance of Quality 89 1.00 5.00 4.6067 .71697


Importance of Technology 89 1.00 5.00 4.0449 .82448


Importance of Affordability 89 1.00 5.00 3.6517 .97818


Importance of Comfort 89 1.00 5.00 4.0225 .90425


Importance of Chinese

Brand

89 1.00 5.00 2.1461 1.05044

8.1.4 Importance of Safety

for car owners 32 2.00 5.00 4.6875 .64446

8.1.4 Importance of Brand

image for car owners 32 1.00 5.00 3.3438 .90195

8.1.4 Importance of Design

for car owners 32 1.00 5.00 3.6563 .93703

8.1.4 Importance of Fuel

Efficiency for car owners 32 2.00 5.00 4.0937 .85607

8.1.4 Importance of Quality

for car owners 32 1.00 5.00 4.5938 .83702

8.1.4 Importance of

Technology for car owners 32 1.00 5.00 3.8125 1.06066

8.1.4 Importance of

Affordability for car owners 32 1.00 5.00 3.5000 1.27000

8.1.4 Importance of

Comfort for car owners 32 1.00 5.00 3.9063 1.05828

8.1.4 Importance of

Chinese Brand for car

owners

30 1.00 5.00 2.4333 1.19434

8.1.4.1.1 Importance of

Safety for the next purchase

by car owners

32 2.00 5.00 4.6875 .64446


Brand Image for the next

purchase by car owners

32 1.00 5.00 3.5625 .91361

76


Design for the next


31 1.00 5.00 3.8387 .86011


Fuel Efficiency for the next


32 1.00 5.00 4.0313 .93272


Quality for the next


32 2.00 5.00 4.5312 .84183


Technology for the next


32 1.00 5.00 4.1250 .87067


Affordability for the next


32 1.00 5.00 3.7500 1.19137


Comfort for the next


31 1.00 5.00 4.1290 .95715


Chinese Brand for the next


32 1.00 5.00 2.5938 1.24069

8.2.1 Importance of Safety

for non car owners for their

next purchase

83 1.00 5.00 4.7831 .64483

8.2.1 Importance of Brand

Image for non car owners

for their next purchase

83 1.00 5.00 3.3253 1.11649

8.2.1 Importance of Design


next purchase

83 1.00 5.00 3.9277 .82319

8.2.1 Importance of Fuel

Efficiency for non car

owners for their next

purchase

83 2.00 5.00 4.3133 .76394

8.2.1 Importance of Quality


next purchase

83 2.00 5.00 4.6265 .63842

77

8.2.1 Importance of

Technology for non car


purchase

83 1.00 5.00 4.0000 .91064

8.2.1 Importance of

Affordability for non car


purchase

82 1.00 5.00 3.8049 1.02366

8.2.1 Importance of

Comfort for non car owners

for their next purchase

80 1.00 5.00 4.1375 .97752

8.2.1 Importance of

Chinese Car for non car


purchase

82 1.00 5.00 2.4146 1.11057

N valide (listwise) 21

Appendix B

Linear regression analysis and linear curve estimates

B.1. Customer behaviour explained by Geographic and Demographic data

B.1.1. Region of residence

B.1.1.1. Number of cars owned (Experience)

Model Summary

R R Square Adjusted R Square Std. Error of the

Estimate

.243 .059 .028 .751

The independent variable is 1. Region of Residence.

ANOVA

Sum of Squares df Mean Square F Sig.

Regression 1.061 1 1.061 1.880 .181

Residual 16.939 30 .565

Total 18.000 31


B.1.1.2. Frequency of Use

78

Model Summary


Estimate

.247 .061 .030 .650


ANOVA


Regression .824 1 .824 1.949 .173

Residual 12.676 30 .423

Total 13.500 31


B.1.1.3. Style of use: Long trips

Model Summary


Estimate

.108 .012 -.021 .424


ANOVA


Regression .063 1 .063 .351 .558

Residual 5.406 30 .180

Total 5.469 31


B.1.1.4. Style of Use: Short trips

Model Summary


Estimate

.302 .091 .061 .326


ANOVA


Regression .318 1 .318 3.001 .093

Residual 3.182 30 .106

Total 3.500 31


79

B.1.1.5. Car replacement rate

Model Summary


Estimate

.069 .005 -.028 .717


ANOVA


Regression .073 1 .073 .142 .709

Residual 15.427 30 .514

Total 15.500 31


B.1.1.6. Buying the next car for the same reason (Loyalty attributes)

Model Summary


Estimate

.100 .010 -.023 .445


ANOVA


Regression .060 1 .060 .304 .586

Residual 5.940 30 .198

Total 6.000 31


80

B.1.2. Degree of urbanisation.


Model Summary


Estimate

.050 .003 -.031 .774

The independent variable is 2. Degree of Urbanisation.

ANOVA


Regression .046 1 .046 .076 .784

Residual 17.954 30 .598

Total 18.000 31



Model Summary


Estimate

.192 .037 .005 .658


ANOVA


Regression .497 1 .497 1.147 .293

Residual 13.003 30 .433

Total 13.500 31



Model Summary


Estimate

.007 .000 -.033 .427


ANOVA


Regression .000 1 .000 .001 .970

81

Residual 5.468 30 .182

Total 5.469 31



Model Summary


Estimate

.057 .003 -.030 .341


ANOVA


Regression .011 1 .011 .098 .756

Residual 3.489 30 .116

Total 3.500 31



Model Summary


Estimate

.038 .001 -.032 .718


ANOVA


Regression .022 1 .022 .043 .836

Residual 15.478 30 .516

Total 15.500 31



Model Summary


Estimate

.052 .003 -.031 .447


ANOVA

82


Regression .016 1 .016 .082 .776

Residual 5.984 30 .199

Total 6.000 31


B.1.3. Behaviour by age


Model Summary


Estimate

.461 .213 .186 .687

The independent variable is 3. Age.

ANOVA


Regression 3.828 1 3.828 8.103 .008

Residual 14.172 30 .472

Total 18.000 31



Model Summary

83


Estimate

.110 .012 -.021 .667


ANOVA


Regression .162 1 .162 .365 .550

Residual 13.338 30 .445

Total 13.500 31



Model Summary


Estimate

.018 .000 -.033 .427


ANOVA


Regression .002 1 .002 .010 .920

Residual 5.467 30 .182

Total 5.469 31


B.1.3.4.Style of Use: Short trips

Model Summary


Estimate

.092 .009 -.025 .340


ANOVA


Regression .030 1 .030 .258 .615

Residual 3.470 30 .116

Total 3.500 31



Model Summary


Estimate

84

.015 .000 -.033 .719


ANOVA


Regression .003 1 .003 .006 .937

Residual 15.497 30 .517

Total 15.500 31


B.1.3.6. Buying the next car for the same reason (loyalty attributes)

Model Summary


Estimate

.141 .020 -.013 .443


ANOVA


Regression .119 1 .119 .608 .442

Residual 5.881 30 .196

Total 6.000 31


B.1.4. Behaviour by Gender


Model Summary


Estimate

.066 .004 -.029 .773

The independent variable is 4. Gender.

ANOVA


Regression .078 1 .078 .130 .721

Residual 17.922 30 .597

Total 18.000 31



Model Summary

85


Estimate

.013 .000 -.033 .671


ANOVA


Regression .002 1 .002 .005 .945

Residual 13.498 30 .450

Total 13.500 31



Model Summary


Estimate

.224 .050 .018 .416


ANOVA


Regression .274 1 .274 1.582 .218

Residual 5.195 30 .173

Total 5.469 31



Model Summary


Estimate

.075 .006 -.028 .341


ANOVA


Regression .019 1 .019 .168 .685

Residual 3.481 30 .116

Total 3.500 31



Model Summary


Estimate

86

.295 .087 .057 .687


ANOVA


Regression 1.353 1 1.353 2.869 .101

Residual 14.147 30 .472

Total 15.500 31


B.1.4.7. Buying the next car for the same reason (loyalty attributes)

Model Summary


Estimate

.494 .244 .219 .389


ANOVA


Regression 1.463 1 1.463 9.676 .004

Residual 4.537 30 .151

Total 6.000 31


Coefficients

Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

4. Gender .450 .145 .494 3.111 .004

(Constant) .645 .206 3.127 .004

87

B.1.5. Behaviour according to annual income


Model Summary


Estimate

.235 .055 .024 .753

The independent variable is 5. Annual income (in Yuan).

ANOVA


Regression .997 1 .997 1.759 .195

Residual 17.003 30 .567

Total 18.000 31



Model Summary


Estimate

.366 .134 .105 .624


ANOVA


Regression 1.807 1 1.807 4.635 .039

88

Residual 11.693 30 .390

Total 13.500 31



Model Summary


Estimate

.193 .037 .005 .419


ANOVA


Regression .204 1 .204 1.160 .290

Residual 5.265 30 .176

Total 5.469 31



Model Summary


Estimate

.267 .071 .040 .329


ANOVA


89

Regression .249 1 .249 2.300 .140

Residual 3.251 30 .108

Total 3.500 31



Model Summary


Estimate

.166 .028 -.005 .709


ANOVA


Regression .426 1 .426 .848 .364

Residual 15.074 30 .502

Total 15.500 31



Model Summary


Estimate

.157 .025 -.008 .442


ANOVA


Regression .147 1 .147 .756 .391

Residual 5.853 30 .195

Total 6.000 31


B.2. Psychographic data by Demographic and Geographic

B.2.1. Psychographs by age

B.2.1.1. Perception of Global Importance of Safety

Model Summary


Estimate

.205 .042 -.008 .580


90

ANOVA


Regression .280 1 .280 .833 .373

Residual 6.387 19 .336

Total 6.667 20


B.2.1.2. Perception of Global Importance of Brand image

Model Summary


Estimate

.132 .017 -.034 1.041


ANOVA


Regression .366 1 .366 .338 .568

Residual 20.587 19 1.084

Total 20.952 20


B.2.1.3. Perception of Global Importance of Design

Model Summary


Estimate

.575 .330 .295 .803


ANOVA


Regression 6.036 1 6.036 9.362 .006

Residual 12.250 19 .645

Total 18.286 20


91

B.2.1.4. Perception of Global Importance of Fuel efficiency

Model Summary


Estimate

.076 .006 -.046 .792


ANOVA


Regression .070 1 .070 .111 .742

Residual 11.930 19 .628

Total 12.000 20


B.2.1.5. Perception of Global Importance of Quality

Model Summary


Estimate

.243 .059 .010 .727


ANOVA


Regression .630 1 .630 1.193 .288

Residual 10.037 19 .528

Total 10.667 20

92


B.2.1.6. Perception of Global Importance of Technology

Model Summary


Estimate

.049 .002 -.050 .886


ANOVA


Regression .036 1 .036 .045 .833

Residual 14.917 19 .785

Total 14.952 20


B.2.1.7. Perception of Global Importance of Affordability

Model Summary


Estimate

.112 .013 -.039 1.227


ANOVA


Regression .366 1 .366 .243 .628

Residual 28.587 19 1.505

Total 28.952 20


B.2.1.8. Perception of Global Importance of Comfort

Model Summary


Estimate

.529 .280 .242 .779


ANOVA


Regression 4.480 1 4.480 7.389 .014

Residual 11.520 19 .606

Total 16.000 20


93

B.2.1.9. Perception of Global Importance of Chinese Brand

Model Summary


Estimate

.087 .008 -.045 1.186


ANOVA


Regression .206 1 .206 .146 .707

Residual 26.747 19 1.408

Total 26.952 20


B.2.1.10. Importance of Safety for car owners

Model Summary


Estimate

.021 .000 -.052 .413


ANOVA


Regression .001 1 .001 .008 .928

Residual 3.237 19 .170

Total 3.238 20

94


B.2.1.11. Importance of Brand image for car owners

Model Summary


Estimate

.345 .119 .073 .825


ANOVA


Regression 1.750 1 1.750 2.574 .125

Residual 12.917 19 .680

Total 14.667 20


B.2.1.12. Importance of Design for car owners

Model Summary


Estimate

.388 .151 .106 .968


ANOVA


Regression 3.156 1 3.156 3.369 .082

Residual 17.797 19 .937

Total 20.952 20


95

B.2.1.13. Importance of Fuel Efficiency for car owners

Model Summary


Estimate

.275 .076 .027 .758


ANOVA


Regression .893 1 .893 1.554 .228

Residual 10.917 19 .575

Total 11.810 20


B.2.1.14. Importance of Quality for car owners

Model Summary


Estimate

.126 .016 -.036 .410


ANOVA


Regression .051 1 .051 .307 .586

Residual 3.187 19 .168

Total 3.238 20

96


B.2.1.15. Importance of Technology for car owners

Model Summary


Estimate

.050 .002 -.050 1.039


ANOVA


Regression .051 1 .051 .048 .830

Residual 20.520 19 1.080

Total 20.571 20


B.2.1.16. Importance of Affordability for car owners

Model Summary


Estimate

.249 .062 .012 1.317


ANOVA


Regression 2.173 1 2.173 1.252 .277

Residual 32.970 19 1.735

Total 35.143 20


B.2.1.17. Importance of Comfort for car owners

Model Summary


Estimate

.296 .088 .039 .980


ANOVA


Regression 1.750 1 1.750 1.822 .193

Residual 18.250 19 .961

Total 20.000 20


B.2.1.18. Importance of Chinese Brand for car owners

97

Model Summary


Estimate

.261 .068 .019 1.156


ANOVA


Regression 1.851 1 1.851 1.386 .254

Residual 25.387 19 1.336

Total 27.238 20


B.2.1.19. Importance of Safety for the next purchase by car owners

Model Summary


Estimate

.194 .037 -.013 .439


ANOVA


Regression .143 1 .143 .740 .400

Residual 3.667 19 .193

Total 3.810 20


B.2.1.20. Importance of Brand Image for the next purchase by car owners

Model Summary


Estimate

.328 .107 .060 .900


ANOVA


Regression 1.851 1 1.851 2.286 .147

Residual 15.387 19 .810

Total 17.238 20


B.2.1.21. Importance of Design for the next purchase by car owners

Model Summary

98


Estimate

.327 .107 .060 .927


ANOVA


Regression 1.956 1 1.956 2.275 .148

Residual 16.330 19 .859

Total 18.286 20


B.2.1.22. Importance of Fuel Efficiency for the next purchase by car owners

Model Summary


Estimate

.383 .147 .102 .710


ANOVA


Regression 1.651 1 1.651 3.273 .086

Residual 9.587 19 .505

Total 11.238 20


99

B.2.1.23. Importance of Quality for the next purchase by car owners

Model Summary


Estimate

.297 .088 .040 .780


ANOVA


Regression 1.120 1 1.120 1.843 .191

Residual 11.547 19 .608

Total 12.667 20


B.2.1.24. Importance of Technology for the next purchase by car owners

Model Summary


Estimate

.048 .002 -.050 .718


ANOVA


Regression .023 1 .023 .044 .835

Residual 9.787 19 .515

Total 9.810 20


B.2.1.25. Importance of Affordability for the next purchase by car owners

Model Summary


Estimate

.153 .023 -.028 1.288


ANOVA


Regression .756 1 .756 .455 .508

Residual 31.530 19 1.659

Total 32.286 20


B.2.1.26. Importance of Comfort for the next purchase by car owners

100

Model Summary


Estimate

.162 .026 -.025 .739


ANOVA


Regression .280 1 .280 .512 .483

Residual 10.387 19 .547

Total 10.667 20


B.2.1.27. Importance of Chinese Brand for the next purchase by car owners

Model Summary


Estimate

.069 .005 -.048 1.250


ANOVA


Regression .143 1 .143 .091 .766

Residual 29.667 19 1.561

Total 29.810 20


B.2.1.28. Importance of Safety for non-car owners for their next purchase

Model Summary


Estimate

.024 .001 -.052 .368


ANOVA


Regression .001 1 .001 .011 .919

Residual 2.570 19 .135

Total 2.571 20


B.2.1.29. Importance of Brand Image for non-car owners for their next purchase

Model Summary

101


Estimate

.272 .074 .025 .983


ANOVA


Regression 1.463 1 1.463 1.515 .233

Residual 18.347 19 .966

Total 19.810 20


B.2.1.29. Importance of Design for non-car owners for their next purchase

Model Summary


Estimate

.423 .179 .135 .688


ANOVA


Regression 1.956 1 1.956 4.130 .056

Residual 8.997 19 .474

Total 10.952 20


B.2.1.30. Importance of Fuel Efficiency for non-car owners for their next purchase

102

Model Summary


Estimate

.149 .022 -.029 .807


ANOVA


Regression .280 1 .280 .429 .520

Residual 12.387 19 .652

Total 12.667 20


B.2.1.32. Importance of Quality for non-car owners for their next purchase

Model Summary


Estimate

.282 .080 .031 .530


ANOVA


Regression .463 1 .463 1.645 .215

Residual 5.347 19 .281

Total 5.810 20


B.2.1.33. Importance of Technology for non-car owners for their next purchase

Model Summary


Estimate

.248 .062 .012 .745


ANOVA


Regression .691 1 .691 1.246 .278

Residual 10.547 19 .555

Total 11.238 20


B.2.1.34. Importance of Affordability for non-car owners for their next purchase

Model Summary

103


Estimate

.270 .073 .024 1.206


ANOVA


Regression 2.173 1 2.173 1.494 .237

Residual 27.637 19 1.455

Total 29.810 20


B.2.1.35. Importance of Comfort for non-car owners for their next purchase

Model Summary


Estimate

.364 .133 .087 .998


ANOVA


Regression 2.893 1 2.893 2.906 .105

Residual 18.917 19 .996

Total 21.810 20


B.2.1.36. Importance of Chinese Car for non-car owners for their next purchase

Model Summary


Estimate

.294 .087 .039 1.070


ANOVA


Regression 2.063 1 2.063 1.802 .195

Residual 21.747 19 1.145

Total 23.810 20


B.2.2. Psychographs by gender.


104

Model Summary

R R Square Adjusted R

Square

Std. Error of the

Estimate

.239 .057 .008 .575


ANOVA


Regression .381 1 .381 1.152 .297

Residual 6.286 19 .331

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.337 .114 .067 .989


ANOVA


Regression 2.381 1 2.381 2.436 .135

Residual 18.571 19 .977

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.217 .047 -.003 .958


ANOVA


Regression .857 1 .857 .934 .346

Residual 17.429 19 .917

Total 18.286 20



Model Summary

105


Square

Std. Error of the

Estimate

.134 .018 -.034 .788


ANOVA


Regression .214 1 .214 .345 .564

Residual 11.786 19 .620

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.047 .002 -.050 .748


ANOVA


Regression .024 1 .024 .043 .839

Residual 10.643 19 .560

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.080 .006 -.046 .884


ANOVA


Regression .095 1 .095 .122 .731

Residual 14.857 19 .782

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

106

.287 .082 .034 1.183


ANOVA


Regression 2.381 1 2.381 1.703 .208

Residual 26.571 19 1.398

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.231 .054 .004 .893


ANOVA


Regression .857 1 .857 1.075 .313

Residual 15.143 19 .797

Total 16.000 20



Model Summary


Square

Std. Error of the

Estimate

.030 .001 -.052 1.191


ANOVA


Regression .024 1 .024 .017 .898

Residual 26.929 19 1.417

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.171 .029 -.022 .407


107

ANOVA


Regression .095 1 .095 .576 .457

Residual 3.143 19 .165

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.040 .002 -.051 .878


ANOVA


Regression .024 1 .024 .031 .862

Residual 14.643 19 .771

Total 14.667 20



Model Summary


Square

Std. Error of the

Estimate

.067 .005 -.048 1.048


ANOVA


Regression .095 1 .095 .087 .772

Residual 20.857 19 1.098

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.314 .099 .051 .748


ANOVA


108

Regression 1.167 1 1.167 2.083 .165

Residual 10.643 19 .560

Total 11.810 20



Model Summary


Square

Std. Error of the

Estimate

.086 .007 -.045 .411


ANOVA


Regression .024 1 .024 .141 .712

Residual 3.214 19 .169

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.204 .042 -.009 1.019


ANOVA


Regression .857 1 .857 .826 .375

Residual 19.714 19 1.038

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.390 .152 .108 1.252


ANOVA


Regression 5.357 1 5.357 3.417 .080

Residual 29.786 19 1.568

109

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

.311 .096 .049 .975


ANOVA


Regression 1.929 1 1.929 2.028 .171

Residual 18.071 19 .951

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.118 .014 -.038 1.189


ANOVA


Regression .381 1 .381 .270 .610

110

Residual 26.857 19 1.414

Total 27.238 20



Model Summary


Square

Std. Error of the

Estimate

.079 .006 -.046 .446


ANOVA


Regression .024 1 .024 .119 .733

Residual 3.786 19 .199

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.037 .001 -.051 .952


ANOVA


Regression .024 1 .024 .026 .873

Residual 17.214 19 .906

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.108 .012 -.040 .975


ANOVA


Regression .214 1 .214 .225 .640

Residual 18.071 19 .951

Total 18.286 20

111



Model Summary


Square

Std. Error of the

Estimate

.230 .053 .003 .748


ANOVA


Regression .595 1 .595 1.063 .316

Residual 10.643 19 .560

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.173 .030 -.021 .804


ANOVA


Regression .381 1 .381 .589 .452

Residual 12.286 19 .647

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.099 .010 -.042 .715


ANOVA


Regression .095 1 .095 .186 .671

Residual 9.714 19 .511

Total 9.810 20



112

Model Summary


Square

Std. Error of the

Estimate

.244 .060 .010 1.264


ANOVA


Regression 1.929 1 1.929 1.207 .286

Residual 30.357 19 1.598

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.094 .009 -.043 .746


ANOVA


Regression .095 1 .095 .171 .684

Residual 10.571 19 .556

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.311 .097 .049 1.191


ANOVA


Regression 2.881 1 2.881 2.033 .170

Residual 26.929 19 1.417

Total 29.810 20



Model Summary

113


Square

Std. Error of the

Estimate

.000 .000 -.053 .368


ANOVA


Regression .000 1 .000 .000 1.000

Residual 2.571 19 .135

Total 2.571 20



Model Summary


Square

Std. Error of the

Estimate

.173 .030 -.021 1.006


ANOVA


Regression .595 1 .595 .589 .452

Residual 19.214 19 1.011

Total 19.810 20



Model Summary


Square

Std. Error of the

Estimate

.233 .054 .005 .738


ANOVA


Regression .595 1 .595 1.092 .309

Residual 10.357 19 .545

Total 10.952 20



Model Summary


Square

Std. Error of the

Estimate

114

.087 .008 -.045 .813


ANOVA


Regression .095 1 .095 .144 .709

Residual 12.571 19 .662

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.064 .004 -.048 .552


ANOVA


Regression .024 1 .024 .078 .783

Residual 5.786 19 .305

Total 5.810 20



Model Summary


Square

Std. Error of the

Estimate

.092 .008 -.044 .766


ANOVA


Regression .095 1 .095 .162 .691

Residual 11.143 19 .586

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.226 .051 .001 1.220


115

ANOVA


Regression 1.524 1 1.524 1.024 .324

Residual 28.286 19 1.489

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.330 .109 .062 1.011


ANOVA


Regression 2.381 1 2.381 2.328 .144

Residual 19.429 19 1.023

Total 21.810 20



Model Summary


Square

Std. Error of the

Estimate

.348 .121 .075 1.050


ANOVA


Regression 2.881 1 2.881 2.615 .122

Residual 20.929 19 1.102

Total 23.810 20


B.2.3. Psychographics with Annual income (Yuan)


Model Summary


Square

Std. Error of the

Estimate

.101 .010 -.042 .589


116

ANOVA


Regression .068 1 .068 .195 .664

Residual 6.599 19 .347

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.120 .014 -.038 1.043


ANOVA


Regression .301 1 .301 .277 .605

Residual 20.652 19 1.087

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.029 .001 -.052 .981


ANOVA


Regression .016 1 .016 .016 .900

Residual 18.270 19 .962

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .795


ANOVA


117

Regression .000 1 .000 .000 1.000

Residual 12.000 19 .632

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.080 .006 -.046 .747


ANOVA


Regression .068 1 .068 .121 .731

Residual 10.599 19 .558

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.150 .023 -.029 .877


ANOVA


Regression .338 1 .338 .439 .516

Residual 14.615 19 .769

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.029 .001 -.052 1.234


ANOVA


Regression .025 1 .025 .016 .899

Residual 28.927 19 1.522

118

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.098 .010 -.043 .913


ANOVA


Regression .152 1 .152 .183 .674

Residual 15.848 19 .834

Total 16.000 20



Model Summary


Square

Std. Error of the

Estimate

.007 .000 -.053 1.191


ANOVA


Regression .001 1 .001 .001 .975

Residual 26.951 19 1.418

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.261 .068 .019 .399


ANOVA


Regression .220 1 .220 1.388 .253

Residual 3.018 19 .159

Total 3.238 20


119


Model Summary


Square

Std. Error of the

Estimate

.272 .074 .025 .846


ANOVA


Regression 1.084 1 1.084 1.516 .233

Residual 13.583 19 .715

Total 14.667 20



Model Summary


Square

Std. Error of the

Estimate

.008 .000 -.053 1.050


ANOVA


Regression .001 1 .001 .001 .972

Residual 20.951 19 1.103

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.064 .004 -.048 .787


ANOVA


Regression .048 1 .048 .077 .784

Residual 11.762 19 .619

Total 11.810 20



Model Summary

120


Square

Std. Error of the

Estimate

.261 .068 .019 .399


ANOVA


Regression .220 1 .220 1.388 .253

Residual 3.018 19 .159

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.105 .011 -.041 1.035


ANOVA


Regression .225 1 .225 .210 .652

Residual 20.347 19 1.071

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.158 .025 -.026 1.343


ANOVA


Regression .873 1 .873 .484 .495

Residual 34.270 19 1.804

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

121

.196 .039 -.012 1.006


ANOVA


Regression .772 1 .772 .763 .393

Residual 19.228 19 1.012

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.010 .000 -.053 1.197


ANOVA


Regression .003 1 .003 .002 .966

Residual 27.235 19 1.433

Total 27.238 20



Model Summary


Square

Std. Error of the

Estimate

.388 .151 .106 .413


ANOVA


Regression .574 1 .574 3.371 .082

Residual 3.235 19 .170

Total 3.810 20


122


Model Summary


Square

Std. Error of the

Estimate

.176 .031 -.020 .938


ANOVA


Regression .533 1 .533 .606 .446

Residual 16.706 19 .879

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.085 .007 -.045 .977


ANOVA


Regression .131 1 .131 .138 .715

Residual 18.154 19 .955

Total 18.286 20

123



Model Summary


Square

Std. Error of the

Estimate

.122 .015 -.037 .763


ANOVA


Regression .167 1 .167 .287 .598

Residual 11.071 19 .583

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.210 .044 -.006 .798


ANOVA


Regression .560 1 .560 .879 .360

Residual 12.107 19 .637

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.039 .001 -.051 .718


ANOVA


Regression .015 1 .015 .028 .868

Residual 9.795 19 .516

Total 9.810 20



124

Model Summary


Square

Std. Error of the

Estimate

.056 .003 -.049 1.301


ANOVA


Regression .103 1 .103 .061 .808

Residual 32.183 19 1.694

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.050 .002 -.050 .748


ANOVA


Regression .026 1 .026 .047 .830

Residual 10.640 19 .560

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.088 .008 -.045 1.248


ANOVA


Regression .229 1 .229 .147 .705

Residual 29.580 19 1.557

Total 29.810 20



Model Summary

125


Square

Std. Error of the

Estimate

.113 .013 -.039 .366


ANOVA


Regression .033 1 .033 .246 .626

Residual 2.539 19 .134

Total 2.571 20



Model Summary


Square

Std. Error of the

Estimate

.312 .098 .050 .970


ANOVA


Regression 1.932 1 1.932 2.053 .168

Residual 17.878 19 .941

Total 19.810 20



Model Summary


Square

Std. Error of the

Estimate

.117 .014 -.038 .754


ANOVA


Regression .149 1 .149 .262 .615

Residual 10.804 19 .569

Total 10.952 20



Model Summary


Square

Std. Error of the

Estimate

126

.101 .010 -.042 .812


ANOVA


Regression .128 1 .128 .194 .664

Residual 12.539 19 .660

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.274 .075 .026 .532


ANOVA


Regression .436 1 .436 1.540 .230

Residual 5.374 19 .283

Total 5.810 20



Model Summary


Square

Std. Error of the

Estimate

.082 .007 -.046 .766


ANOVA


Regression .075 1 .075 .128 .724

Residual 11.163 19 .588

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.183 .034 -.017 1.231

127


ANOVA


Regression .999 1 .999 .659 .427

Residual 28.811 19 1.516

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.120 .015 -.037 1.064


ANOVA


Regression .316 1 .316 .280 .603

Residual 21.493 19 1.131

Total 21.810 20



Model Summary


Square

Std. Error of the

Estimate

.122 .015 -.037 1.111


ANOVA


Regression .354 1 .354 .287 .599

Residual 23.456 19 1.235

Total 23.810 20


B.2.4. Psychographics by region of residence.


Model Summary


Square

Std. Error of the

Estimate

128

.214 .046 -.005 .579


ANOVA


Regression .304 1 .304 .909 .352

Residual 6.362 19 .335

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.155 .024 -.027 1.037


ANOVA


Regression .503 1 .503 .467 .502

Residual 20.449 19 1.076

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.295 .087 .039 .937


ANOVA


Regression 1.590 1 1.590 1.810 .194

Residual 16.696 19 .879

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.239 .057 .007 .772


129

ANOVA


Regression .685 1 .685 1.150 .297

Residual 11.315 19 .596

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.253 .064 .015 .725


ANOVA


Regression .685 1 .685 1.303 .268

Residual 9.982 19 .525

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.173 .030 -.021 .874


ANOVA


Regression .449 1 .449 .588 .453

Residual 14.504 19 .763

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.022 .000 -.052 1.234


ANOVA


130

Regression .014 1 .014 .009 .925

Residual 28.938 19 1.523

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.138 .019 -.033 .909


ANOVA


Regression .304 1 .304 .368 .551

Residual 15.696 19 .826

Total 16.000 20



Model Summary


Square

Std. Error of the

Estimate

.137 .019 -.033 1.180


ANOVA


Regression .503 1 .503 .361 .555

Residual 26.449 19 1.392

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.263 .069 .020 .398


ANOVA


Regression .224 1 .224 1.409 .250

Residual 3.014 19 .159

131

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.216 .047 -.003 .858


ANOVA


Regression .685 1 .685 .931 .347

Residual 13.982 19 .736

Total 14.667 20



Model Summary


Square

Std. Error of the

Estimate

.327 .107 .060 .992


ANOVA


Regression 2.242 1 2.242 2.277 .148

Residual 18.710 19 .985

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.149 .022 -.029 .780


ANOVA


Regression .262 1 .262 .432 .519

Residual 11.547 19 .608

Total 11.810 20


132


Model Summary


Square

Std. Error of the

Estimate

.504 .254 .214 .357


ANOVA


Regression .821 1 .821 6.458 .020

Residual 2.417 19 .127

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.261 .068 .019 1.005


ANOVA


Regression 1.398 1 1.398 1.385 .254

Residual 19.174 19 1.009

Total 20.571 20

133



Model Summary


Square

Std. Error of the

Estimate

.100 .010 -.042 1.353


ANOVA


Regression .349 1 .349 .191 .667

Residual 34.793 19 1.831

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

.247 .061 .011 .994


ANOVA


Regression 1.217 1 1.217 1.231 .281

Residual 18.783 19 .989

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.196 .039 -.012 1.174


ANOVA


Regression 1.050 1 1.050 .762 .394

Residual 26.188 19 1.378

Total 27.238 20

134



Model Summary


Square

Std. Error of the

Estimate

.303 .092 .044 .427


ANOVA


Regression .349 1 .349 1.918 .182

Residual 3.460 19 .182

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.418 .174 .131 .865


ANOVA


Regression 3.006 1 3.006 4.013 .060

Residual 14.232 19 .749

Total 17.238 20


135


Model Summary


Square

Std. Error of the

Estimate

.295 .087 .039 .937


ANOVA


Regression 1.590 1 1.590 1.810 .194

Residual 16.696 19 .879

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.188 .035 -.015 .755


ANOVA


Regression .398 1 .398 .697 .414

Residual 10.841 19 .571

Total 11.238 20

136



Model Summary


Square

Std. Error of the

Estimate

.155 .024 -.027 .807


ANOVA


Regression .304 1 .304 .468 .502

Residual 12.362 19 .651

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.340 .115 .069 .676


ANOVA


Regression 1.132 1 1.132 2.479 .132

Residual 8.678 19 .457

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.021 .000 -.052 1.303


ANOVA


Regression .014 1 .014 .008 .929

Residual 32.272 19 1.699

Total 32.286 20



137

Model Summary


Square

Std. Error of the

Estimate

.169 .029 -.023 .739


ANOVA


Regression .304 1 .304 .558 .464

Residual 10.362 19 .545

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.549 .301 .264 1.047


ANOVA


Regression 8.969 1 8.969 8.177 .010

Residual 20.841 19 1.097

Total 29.810 20


138


Model Summary


Square

Std. Error of the

Estimate

.221 .049 -.001 .359


ANOVA


Regression .126 1 .126 .977 .335

Residual 2.446 19 .129

Total 2.571 20



Model Summary


Square

Std. Error of the

Estimate

.505 .255 .215 .882


ANOVA


Regression 5.045 1 5.045 6.492 .020

Residual 14.764 19 .777

Total 19.810 20


139


Model Summary


Square

Std. Error of the

Estimate

.464 .216 .174 .672


ANOVA


Regression 2.362 1 2.362 5.224 .034

Residual 8.591 19 .452

Total 10.952 20


140


Model Summary


Square

Std. Error of the

Estimate

.310 .096 .049 .776


ANOVA


Regression 1.217 1 1.217 2.020 .171

Residual 11.449 19 .603

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.016 .000 -.052 .553


ANOVA


Regression .002 1 .002 .005 .944

Residual 5.808 19 .306

Total 5.810 20

141



Model Summary


Square

Std. Error of the

Estimate

.517 .268 .229 .658


ANOVA


Regression 3.006 1 3.006 6.939 .016

Residual 8.232 19 .433

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.058 .003 -.049 1.250


ANOVA


Regression .099 1 .099 .064 .804

Residual 29.710 19 1.564

142

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.287 .082 .034 1.026


ANOVA


Regression 1.795 1 1.795 1.704 .207

Residual 20.014 19 1.053

Total 21.810 20



Model Summary


Square

Std. Error of the

Estimate

.557 .311 .274 .930


ANOVA


Regression 7.393 1 7.393 8.556 .009

Residual 16.417 19 .864

Total 23.810 20


143

B.2.5. psychographics by degree of urbanisation


Model Summary


Square

Std. Error of the

Estimate

.269 .073 .024 .570


ANOVA


Regression .484 1 .484 1.488 .237

Residual 6.183 19 .325

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.063 .004 -.048 1.048


ANOVA


Regression .083 1 .083 .076 .786

144

Residual 20.869 19 1.098

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.356 .126 .080 .917


ANOVA


Regression 2.313 1 2.313 2.751 .114

Residual 15.973 19 .841

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.181 .033 -.018 .782


ANOVA


Regression .392 1 .392 .642 .433

Residual 11.608 19 .611

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.341 .116 .070 .704


ANOVA


Regression 1.239 1 1.239 2.498 .131

Residual 9.427 19 .496

Total 10.667 20

145



Model Summary


Square

Std. Error of the

Estimate

.326 .107 .060 .839


ANOVA


Regression 1.593 1 1.593 2.266 .149

Residual 13.359 19 .703

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.024 .001 -.052 1.234


ANOVA


Regression .017 1 .017 .011 .918

Residual 28.936 19 1.523

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.365 .133 .088 .854


ANOVA


Regression 2.135 1 2.135 2.925 .103

Residual 13.865 19 .730

Total 16.000 20



146

Model Summary


Square

Std. Error of the

Estimate

.417 .174 .131 1.082


ANOVA


Regression 4.695 1 4.695 4.008 .060

Residual 22.257 19 1.171

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.022 .000 -.052 .413


ANOVA


Regression .002 1 .002 .009 .924

Residual 3.237 19 .170

Total 3.238 20


147


Model Summary


Square

Std. Error of the

Estimate

.182 .033 -.018 .864


ANOVA


Regression .484 1 .484 .649 .431

Residual 14.183 19 .746

Total 14.667 20



Model Summary


Square

Std. Error of the

Estimate

.109 .012 -.040 1.044


ANOVA


Regression .247 1 .247 .227 .639

Residual 20.705 19 1.090

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.228 .052 .002 .768


ANOVA


Regression .617 1 .617 1.047 .319

Residual 11.193 19 .589

Total 11.810 20



Model Summary

148


Square

Std. Error of the

Estimate

.094 .009 -.043 .411


ANOVA


Regression .029 1 .029 .169 .686

Residual 3.210 19 .169

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.191 .036 -.014 1.021


ANOVA


Regression .748 1 .748 .717 .408

Residual 19.824 19 1.043

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.091 .008 -.044 1.354


ANOVA


Regression .288 1 .288 .157 .696

Residual 34.855 19 1.834

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

149

.093 .009 -.043 1.021


ANOVA


Regression .174 1 .174 .167 .687

Residual 19.826 19 1.043

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.379 .144 .099 1.108


ANOVA


Regression 3.912 1 3.912 3.187 .090

Residual 23.326 19 1.228

Total 27.238 20



Model Summary

150


Square

Std. Error of the

Estimate

.081 .007 -.046 .446


ANOVA


Regression .025 1 .025 .127 .726

Residual 3.784 19 .199

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.026 .001 -.052 .952


ANOVA


Regression .012 1 .012 .013 .910

Residual 17.226 19 .907

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.014 .000 -.052 .981


ANOVA


Regression .004 1 .004 .004 .952

Residual 18.282 19 .962

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

151

.113 .013 -.039 .764


ANOVA


Regression .143 1 .143 .244 .627

Residual 11.095 19 .584

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.430 .185 .142 .737


ANOVA


Regression 2.343 1 2.343 4.312 .052

Residual 10.324 19 .543

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.184 .034 -.017 .706


ANOVA


Regression .332 1 .332 .666 .424

Residual 9.477 19 .499

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.084 .007 -.045 1.299


152

ANOVA


Regression .228 1 .228 .135 .717

Residual 32.058 19 1.687

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.107 .011 -.041 .745


ANOVA


Regression .121 1 .121 .218 .646

Residual 10.546 19 .555

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.111 .012 -.040 1.245


ANOVA


Regression .368 1 .368 .237 .632

Residual 29.442 19 1.550

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.149 .022 -.029 .364


ANOVA


153

Regression .057 1 .057 .430 .520

Residual 2.515 19 .132

Total 2.571 20



Model Summary


Square

Std. Error of the

Estimate

.105 .011 -.041 1.015


ANOVA


Regression .218 1 .218 .212 .651

Residual 19.591 19 1.031

Total 19.810 20



Model Summary


Square

Std. Error of the

Estimate

.186 .035 -.016 .746


ANOVA


Regression .380 1 .380 .682 .419

Residual 10.573 19 .556

Total 10.952 20



Model Summary


Square

Std. Error of the

Estimate

.274 .075 .026 .785


ANOVA


Regression .949 1 .949 1.538 .230

Residual 11.718 19 .617

154

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.280 .079 .030 .531


ANOVA


Regression .457 1 .457 1.622 .218

Residual 5.353 19 .282

Total 5.810 20



Model Summary


Square

Std. Error of the

Estimate

.549 .301 .264 .643


ANOVA


Regression 3.381 1 3.381 8.177 .010

Residual 7.857 19 .414

Total 11.238 20


155


Model Summary


Square

Std. Error of the

Estimate

.086 .007 -.045 1.248


ANOVA


Regression .218 1 .218 .140 .712

Residual 29.591 19 1.557

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.526 .276 .238 .911


ANOVA


Regression 6.027 1 6.027 7.256 .014

Residual 15.782 19 .831

156

Total 21.810 20



Model Summary


Square

Std. Error of the

Estimate

.167 .028 -.023 1.104


ANOVA


Regression .664 1 .664 .545 .469

Residual 23.145 19 1.218

Total 23.810 20


B.3. Customer psychographics by behaviours

B.3.1. Psychographics by car replacement rate


Model Summary


Square

Std. Error of the

Estimate

.316 .100 .053 .562

157

The independent variable is 8.1.3 Car replacement rate.

ANOVA


Regression .667 1 .667 2.111 .163

Residual 6.000 19 .316

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.491 .241 .201 .915


ANOVA


Regression 5.042 1 5.042 6.021 .024

Residual 15.911 19 .837

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

158

.072 .005 -.047 .979


ANOVA


Regression .094 1 .094 .098 .758

Residual 18.192 19 .957

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.354 .125 .079 .743


ANOVA


Regression 1.500 1 1.500 2.714 .116

Residual 10.500 19 .553

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.156 .024 -.027 .740


ANOVA


Regression .260 1 .260 .475 .499

Residual 10.406 19 .548

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.185 .034 -.017 .872


159

ANOVA


Regression .510 1 .510 .672 .423

Residual 14.442 19 .760

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.095 .009 -.043 1.229


ANOVA


Regression .260 1 .260 .172 .683

Residual 28.692 19 1.510

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.153 .023 -.028 .907


ANOVA


Regression .375 1 .375 .456 .508

Residual 15.625 19 .822

Total 16.000 20



Model Summary


Square

Std. Error of the

Estimate

.157 .025 -.027 1.176


ANOVA


160

Regression .667 1 .667 .482 .496

Residual 26.286 19 1.383

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.227 .051 .002 .402


ANOVA


Regression .167 1 .167 1.031 .323

Residual 3.071 19 .162

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.453 .205 .163 .783


ANOVA


Regression 3.010 1 3.010 4.907 .039

Residual 11.656 19 .613

Total 14.667 20


161


Model Summary


Square

Std. Error of the

Estimate

.178 .032 -.019 1.033


ANOVA


Regression .667 1 .667 .624 .439

Residual 20.286 19 1.068

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.030 .001 -.052 .788


ANOVA


Regression .010 1 .010 .017 .898

Residual 11.799 19 .621

Total 11.810 20

162



Model Summary


Square

Std. Error of the

Estimate

.057 .003 -.049 .412


ANOVA


Regression .010 1 .010 .061 .807

Residual 3.228 19 .170

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.270 .073 .024 1.002


ANOVA


Regression 1.500 1 1.500 1.494 .236

Residual 19.071 19 1.004

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.258 .067 .018 1.314


ANOVA


Regression 2.344 1 2.344 1.358 .258

Residual 32.799 19 1.726

Total 35.143 20



163

Model Summary


Square

Std. Error of the

Estimate

.205 .042 -.008 1.004


ANOVA


Regression .844 1 .844 .837 .372

Residual 19.156 19 1.008

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.196 .038 -.012 1.174


ANOVA


Regression 1.042 1 1.042 .756 .396

Residual 26.196 19 1.379

Total 27.238 20



Model Summary


Square

Std. Error of the

Estimate

.209 .044 -.007 .438


ANOVA

Sum of Squares Df Mean Square F Sig.

Regression .167 1 .167 .869 .363

Residual 3.643 19 .192

Total 3.810 20



Model Summary

164


Square

Std. Error of the

Estimate

.320 .102 .055 .903


ANOVA


Regression 1.760 1 1.760 2.161 .158

Residual 15.478 19 .815

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.215 .046 -.004 .958


ANOVA


Regression .844 1 .844 .919 .350

Residual 17.442 19 .918

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.213 .045 -.005 .751


ANOVA


Regression .510 1 .510 .904 .354

Residual 10.728 19 .565

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

165

.143 .021 -.031 .808


ANOVA


Regression .260 1 .260 .399 .535

Residual 12.406 19 .653

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.228 .052 .002 .700


ANOVA


Regression .510 1 .510 1.043 .320

Residual 9.299 19 .489

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.269 .073 .024 1.255


ANOVA


Regression 2.344 1 2.344 1.487 .238

Residual 29.942 19 1.576

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.156 .024 -.027 .740


166

ANOVA


Regression .260 1 .260 .475 .499

Residual 10.406 19 .548

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.355 .126 .080 1.171


ANOVA


Regression 3.760 1 3.760 2.743 .114

Residual 26.049 19 1.371

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.191 .036 -.014 .361


ANOVA


Regression .094 1 .094 .719 .407

Residual 2.478 19 .130

Total 2.571 20


B.3.1.29. Importance of Brand Image for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.321 .103 .056 .967


ANOVA


167

Regression 2.042 1 2.042 2.183 .156

Residual 17.768 19 .935

Total 19.810 20



Model Summary


Square

Std. Error of the

Estimate

.247 .061 .011 .736


ANOVA


Regression .667 1 .667 1.231 .281

Residual 10.286 19 .541

Total 10.952 20



Model Summary


Square

Std. Error of the

Estimate

.315 .100 .052 .775


ANOVA


Regression 1.260 1 1.260 2.100 .164

Residual 11.406 19 .600

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.042 .002 -.051 .552


ANOVA


Regression .010 1 .010 .034 .855

Residual 5.799 19 .305

168

Total 5.810 20



Model Summary


Square

Std. Error of the

Estimate

.152 .023 -.028 .760


ANOVA


Regression .260 1 .260 .451 .510

Residual 10.978 19 .578

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.262 .068 .019 1.209


ANOVA


Regression 2.042 1 2.042 1.397 .252

Residual 27.768 19 1.461

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.087 .008 -.045 1.067


ANOVA


Regression .167 1 .167 .146 .706

Residual 21.643 19 1.139

Total 21.810 20


169


Model Summary


Square

Std. Error of the

Estimate

.460 .212 .170 .994


ANOVA


Regression 5.042 1 5.042 5.104 .036

Residual 18.768 19 .988

Total 23.810 20


B.3.2. Psychographics by experience


Model Summary


Square

Std. Error of the

Estimate

.243 .059 .010 .575

The independent variable is 7. Number of cars owned

(Experience).

ANOVA


170

Regression .394 1 .394 1.194 .288

Residual 6.272 19 .330

Total 6.667 20

The independent variable is 7. Number of cars owned (Experience).


Model Summary


Square

Std. Error of the

Estimate

.171 .029 -.022 1.035


(Experience).

ANOVA


Regression .611 1 .611 .571 .459

Residual 20.341 19 1.071

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.351 .123 .077 .919


(Experience).

ANOVA


Regression 2.248 1 2.248 2.663 .119

Residual 16.038 19 .844

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.078 .006 -.046 .792


(Experience).

ANOVA

171


Regression .072 1 .072 .115 .738

Residual 11.928 19 .628

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.055 .003 -.049 .748


(Experience).

ANOVA


Regression .032 1 .032 .058 .813

Residual 10.634 19 .560

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.156 .024 -.027 .876


(Experience).

ANOVA


Regression .363 1 .363 .472 .500

Residual 14.590 19 .768

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.305 .093 .045 1.176


(Experience).

172

ANOVA


Regression 2.690 1 2.690 1.946 .179

Residual 26.262 19 1.382

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.135 .018 -.034 .909


(Experience).

ANOVA


Regression .290 1 .290 .350 .561

Residual 15.710 19 .827

Total 16.000 20


Coefficients


Coefficients

t Sig.

B Std. Error Beta

7. Number of cars owned

(Experience) -.145 .245 -.135 -.592 .561

(Constant) 4.324 .582 7.424 .000


Model Summary


Square

Std. Error of the

Estimate

.212 .045 -.005 1.164


(Experience).

ANOVA


Regression 1.214 1 1.214 .897 .356

Residual 25.738 19 1.355

173

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.007 .000 -.053 .413


(Experience).

ANOVA


Regression .000 1 .000 .001 .976

Residual 3.238 19 .170

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.468 .219 .178 .776


(Experience).

ANOVA


Regression 3.218 1 3.218 5.341 .032

Residual 11.448 19 .603

Total 14.667 20


174


Model Summary


Square

Std. Error of the

Estimate

.476 .226 .186 .924


(Experience).

ANOVA


Regression 4.745 1 4.745 5.563 .029

Residual 16.207 19 .853

Total 20.952 20


175


Model Summary


Square

Std. Error of the

Estimate

.377 .142 .097 .730


(Experience).

ANOVA


Regression 1.675 1 1.675 3.140 .092

Residual 10.134 19 .533

Total 11.810 20


176


Model Summary


Square

Std. Error of the

Estimate

.292 .085 .037 .395


(Experience).

ANOVA


Regression .276 1 .276 1.771 .199

Residual 2.962 19 .156

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.195 .038 -.013 1.021


(Experience).

ANOVA


Regression .782 1 .782 .751 .397

177

Residual 19.790 19 1.042

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.052 .003 -.050 1.358


(Experience).

ANOVA


Regression .095 1 .095 .051 .823

Residual 35.048 19 1.845

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

.602 .362 .328 .819


(Experience).

ANOVA


Regression 7.241 1 7.241 10.784 .004

Residual 12.759 19 .672

Total 20.000 20


178


Model Summary


Square

Std. Error of the

Estimate

.071 .005 -.047 1.194


(Experience).

ANOVA


Regression .138 1 .138 .097 .759

Residual 27.100 19 1.426

Total 27.238 20


Coefficients


Coefficients

t Sig.

B Std. Error Beta

7. Number of cars owned

(Experience) -.100 .321 -.071 -.311 .759

(Constant) 2.700 .765 3.529 .002


Model Summary

179


Square

Std. Error of the

Estimate

.112 .012 -.040 .445


(Experience).

ANOVA


Regression .047 1 .047 .240 .630

Residual 3.762 19 .198

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.559 .312 .276 .790


(Experience).

ANOVA


Regression 5.379 1 5.379 8.619 .008

Residual 11.859 19 .624

Total 17.238 20


180


Model Summary


Square

Std. Error of the

Estimate

.414 .171 .127 .893


(Experience).

ANOVA


Regression 3.127 1 3.127 3.920 .062

Residual 15.159 19 .798

Total 18.286 20


181


Model Summary


Square

Std. Error of the

Estimate

.245 .060 .010 .746


(Experience).

ANOVA


Regression .673 1 .673 1.209 .285

Residual 10.566 19 .556

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.050 .003 -.050 .815


(Experience).

ANOVA


182

Regression .032 1 .032 .048 .828

Residual 12.634 19 .665

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.070 .005 -.048 .717


(Experience).

ANOVA


Regression .047 1 .047 .092 .764

Residual 9.762 19 .514

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.352 .124 .078 1.220


(Experience).

ANOVA


Regression 3.996 1 3.996 2.684 .118

Residual 28.290 19 1.489

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.137 .019 -.033 .742


(Experience).

ANOVA

183


Regression .201 1 .201 .365 .553

Residual 10.466 19 .551

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.124 .015 -.036 1.243


(Experience).

ANOVA


Regression .461 1 .461 .299 .591

Residual 29.348 19 1.545

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.048 .002 -.050 .367


(Experience).

ANOVA


Regression .006 1 .006 .044 .836

Residual 2.566 19 .135

Total 2.571 20



Model Summary


Square

Std. Error of the

Estimate

.533 .284 .246 .864


(Experience).

184

ANOVA


Regression 5.620 1 5.620 7.525 .013

Residual 14.190 19 .747

Total 19.810 20



Model Summary


Square

Std. Error of the

Estimate

.345 .119 .072 .713


(Experience).

ANOVA


Regression 1.301 1 1.301 2.560 .126

Residual 9.652 19 .508

Total 10.952 20



Model Summary

185


Square

Std. Error of the

Estimate

.403 .163 .119 .747


(Experience).

ANOVA


Regression 2.060 1 2.060 3.690 .070

Residual 10.607 19 .558

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.133 .018 -.034 .548


(Experience).

ANOVA


Regression .103 1 .103 .342 .566

Residual 5.707 19 .300

Total 5.810 20



Model Summary


Square

Std. Error of the

Estimate

.004 .000 -.053 .769


(Experience).

ANOVA


Regression .000 1 .000 .000 .987

Residual 11.238 19 .591

Total 11.238 20



186

Model Summary


Square

Std. Error of the

Estimate

.207 .043 -.008 1.226


(Experience).

ANOVA


Regression 1.272 1 1.272 .847 .369

Residual 28.538 19 1.502

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.126 .016 -.036 1.063


(Experience).

ANOVA


Regression .347 1 .347 .308 .586

Residual 21.462 19 1.130

Total 21.810 20



Model Summary


Square

Std. Error of the

Estimate

.305 .093 .045 1.066


(Experience).

ANOVA


Regression 2.210 1 2.210 1.944 .179

Residual 21.600 19 1.137

Total 23.810 20


187

B.3.3. Psychographics with frequency of use


Model Summary


Square

Std. Error of the

Estimate

.187 .035 -.016 .582

The independent variable is 8.1.1 Frequency of Use.

ANOVA


Regression .233 1 .233 .689 .417

Residual 6.433 19 .339

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.030 .001 -.052 1.050


ANOVA


Regression .019 1 .019 .017 .897

Residual 20.933 19 1.102

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.032 .001 -.052 .981


ANOVA


Regression .019 1 .019 .020 .890

Residual 18.267 19 .961

Total 18.286 20


188


Model Summary


Square

Std. Error of the

Estimate

.139 .019 -.032 .787


ANOVA


Regression .233 1 .233 .377 .547

Residual 11.767 19 .619

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .749


ANOVA


Regression .000 1 .000 .000 1.000

Residual 10.667 19 .561

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.089 .008 -.044 .884


ANOVA


Regression .119 1 .119 .152 .701

Residual 14.833 19 .781

Total 14.952 20



Model Summary

189


Square

Std. Error of the

Estimate

.475 .225 .184 1.087


ANOVA


Regression 6.519 1 6.519 5.521 .030

Residual 22.433 19 1.181

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.121 .015 -.037 .911


ANOVA


Regression .233 1 .233 .281 .602

Residual 15.767 19 .830

Total 16.000 20



190

Model Summary


Square

Std. Error of the

Estimate

.027 .001 -.052 1.191


ANOVA


Regression .019 1 .019 .013 .909

Residual 26.933 19 1.418

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.038 .001 -.051 .413


ANOVA


Regression .005 1 .005 .028 .869

Residual 3.233 19 .170

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.126 .016 -.036 .872


ANOVA


Regression .233 1 .233 .307 .586

Residual 14.433 19 .760

Total 14.667 20



Model Summary

191


Square

Std. Error of the

Estimate

.030 .001 -.052 1.050


ANOVA


Regression .019 1 .019 .017 .897

Residual 20.933 19 1.102

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.201 .040 -.010 .772


ANOVA


Regression .476 1 .476 .798 .383

Residual 11.333 19 .596

Total 11.810 20



Model Summary


Square

Std. Error of the

Estimate

.499 .249 .209 .358


ANOVA


Regression .805 1 .805 6.284 .021

Residual 2.433 19 .128

Total 3.238 20


192


Model Summary


Square

Std. Error of the

Estimate

.228 .052 .002 1.013


ANOVA


Regression 1.071 1 1.071 1.044 .320

Residual 19.500 19 1.026

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.372 .139 .093 1.262


ANOVA


Regression 4.876 1 4.876 3.061 .096

Residual 30.267 19 1.593

193

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

.108 .012 -.040 1.020


ANOVA


Regression .233 1 .233 .224 .641

Residual 19.767 19 1.040

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.383 .147 .102 1.106


ANOVA


Regression 4.005 1 4.005 3.275 .086

194

Residual 23.233 19 1.223

Total 27.238 20



Model Summary


Square

Std. Error of the

Estimate

.106 .011 -.041 .445


ANOVA


Regression .043 1 .043 .216 .647

Residual 3.767 19 .198

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.100 .010 -.042 .948


ANOVA

195


Regression .171 1 .171 .191 .667

Residual 17.067 19 .898

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.145 .021 -.030 .971


ANOVA


Regression .386 1 .386 .409 .530

Residual 17.900 19 .942

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.021 .000 -.052 .769


ANOVA


Regression .005 1 .005 .008 .929

Residual 11.233 19 .591

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.136 .018 -.033 .809


ANOVA


Regression .233 1 .233 .357 .557

196

Residual 12.433 19 .654

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.220 .049 -.002 .701


ANOVA


Regression .476 1 .476 .969 .337

Residual 9.333 19 .491

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.534 .286 .248 1.102


ANOVA


Regression 9.219 1 9.219 7.594 .013

Residual 23.067 19 1.214

Total 32.286 20


197


Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .749


ANOVA


Regression .000 1 .000 .000 1.000

Residual 10.667 19 .561

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.051 .003 -.050 1.251


ANOVA


Regression .076 1 .076 .049 .828

Residual 29.733 19 1.565

198

Total 29.810 20


B.3.3.28. Importance of Safety for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.043 .002 -.051 .368


ANOVA


Regression .005 1 .005 .035 .853

Residual 2.567 19 .135

Total 2.571 20



Model Summary


Square

Std. Error of the

Estimate

.155 .024 -.027 1.009


ANOVA


Regression .476 1 .476 .468 .502

Residual 19.333 19 1.018

Total 19.810 20


B.3.3.30. Importance of Design for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.042 .002 -.051 .759


ANOVA


Regression .019 1 .019 .033 .858

Residual 10.933 19 .575

Total 10.952 20


199

B.3.3.31. Importance of Fuel Efficiency for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.136 .018 -.033 .809


ANOVA


Regression .233 1 .233 .357 .557

Residual 12.433 19 .654

Total 12.667 20


B.3.3.32 Importance of Quality for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.315 .099 .052 .525


ANOVA


Regression .576 1 .576 2.092 .164

Residual 5.233 19 .275

Total 5.810 20


B.3.3.33. Importance of Technology for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.165 .027 -.024 .759


ANOVA


Regression .305 1 .305 .530 .476

Residual 10.933 19 .575

Total 11.238 20


B.3.3.34. Importance of Affordability for non car owners for their next purchase

Model Summary

200


Square

Std. Error of the

Estimate

.404 .164 .120 1.146


ANOVA


Regression 4.876 1 4.876 3.716 .069

Residual 24.933 19 1.312

Total 29.810 20


B.3.3.35. Importance of Comfort for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.163 .026 -.025 1.057


ANOVA


Regression .576 1 .576 .516 .481

Residual 21.233 19 1.118

Total 21.810 20


201


Model Summary


Square

Std. Error of the

Estimate

.057 .003 -.049 1.118


ANOVA


Regression .076 1 .076 .061 .808

Residual 23.733 19 1.249

Total 23.810 20


B.3.4. Psychographics by non-car owners who plan to have a car in the future.


Model Summary


Square

Std. Error of the

Estimate

.131 .017 -.041 .594

The independent variable is 8.2 Potential of demand for

people currently not owning a car.

ANOVA


Regression .105 1 .105 .298 .592

Residual 6.000 17 .353

Total 6.105 18

The independent variable is 8.2 Potential of demand for people currently not owning a

car.


Model Summary


Square

Std. Error of the

Estimate

.084 .007 -.051 1.092



ANOVA


Regression .143 1 .143 .120 .733

202

Residual 20.278 17 1.193

Total 20.421 18


car.


Model Summary


Square

Std. Error of the

Estimate

.165 .027 -.030 1.018



ANOVA


Regression .494 1 .494 .477 .499

Residual 17.611 17 1.036

Total 18.105 18


car.


Model Summary


Square

Std. Error of the

Estimate

.294 .087 .033 .767



ANOVA


Regression .947 1 .947 1.611 .222

Residual 10.000 17 .588

Total 10.947 18


car.


Model Summary


Square

Std. Error of the

Estimate

.117 .014 -.044 .778

203



ANOVA


Regression .143 1 .143 .237 .633

Residual 10.278 17 .605

Total 10.421 18


car.


Model Summary


Square

Std. Error of the

Estimate

.275 .075 .021 .873



ANOVA


Regression 1.056 1 1.056 1.386 .255

Residual 12.944 17 .761

Total 14.000 18


car.


Model Summary


Square

Std. Error of the

Estimate

.071 .005 -.053 1.290



ANOVA


Regression .143 1 .143 .086 .773

Residual 28.278 17 1.663

Total 28.421 18


car.


204

Model Summary


Square

Std. Error of the

Estimate

.252 .063 .008 .907



ANOVA


Regression .947 1 .947 1.150 .298

Residual 14.000 17 .824

Total 14.947 18


car.


Model Summary


Square

Std. Error of the

Estimate

.267 .071 .016 1.138



ANOVA


Regression 1.684 1 1.684 1.301 .270

Residual 22.000 17 1.294

Total 23.684 18


car.


Model Summary


Square

Std. Error of the

Estimate

.102 .010 -.048 .383



ANOVA


Regression .026 1 .026 .179 .678

Residual 2.500 17 .147

205

Total 2.526 18


car.


Model Summary


Square

Std. Error of the

Estimate

.086 .007 -.051 .907



ANOVA


Regression .105 1 .105 .128 .725

Residual 14.000 17 .824

Total 14.105 18


car.


Model Summary


Square

Std. Error of the

Estimate

.131 .017 -.041 1.092



ANOVA


Regression .354 1 .354 .297 .593

Residual 20.278 17 1.193

Total 20.632 18


car.


Model Summary


Square

Std. Error of the

Estimate

.372 .139 .088 .752



206

ANOVA


Regression 1.547 1 1.547 2.736 .116

Residual 9.611 17 .565

Total 11.158 18


car.


Model Summary


Square

Std. Error of the

Estimate

.122 .015 -.043 .428



ANOVA


Regression .047 1 .047 .256 .620

Residual 3.111 17 .183

Total 3.158 18


car.


Model Summary


Square

Std. Error of the

Estimate

.207 .043 -.014 1.056



ANOVA


Regression .845 1 .845 .758 .396

Residual 18.944 17 1.114

Total 19.789 18


car.


Model Summary

207


Square

Std. Error of the

Estimate

.074 .005 -.053 1.423



ANOVA


Regression .187 1 .187 .092 .765

Residual 34.444 17 2.026

Total 34.632 18


car.


Model Summary


Square

Std. Error of the

Estimate

.224 .050 -.006 1.029



ANOVA


Regression .947 1 .947 .895 .357

Residual 18.000 17 1.059

Total 18.947 18


car.


Model Summary


Square

Std. Error of the

Estimate

.131 .017 -.041 1.188



ANOVA


Regression .421 1 .421 .298 .592

Residual 24.000 17 1.412

Total 24.421 18

208


car.


Model Summary


Square

Std. Error of the

Estimate

.122 .015 -.043 .428



ANOVA


Regression .047 1 .047 .256 .620

Residual 3.111 17 .183

Total 3.158 18


car.


Model Summary


Square

Std. Error of the

Estimate

.132 .017 -.040 .984



ANOVA


Regression .292 1 .292 .302 .590

Residual 16.444 17 .967

Total 16.737 18


car.


Model Summary


Square

Std. Error of the

Estimate

.165 .027 -.030 1.018



ANOVA

209


Regression .494 1 .494 .477 .499

Residual 17.611 17 1.036

Total 18.105 18


car.


Model Summary


Square

Std. Error of the

Estimate

.367 .134 .084 .732



ANOVA


Regression 1.415 1 1.415 2.641 .123

Residual 9.111 17 .536

Total 10.526 18


car.


Model Summary


Square

Std. Error of the

Estimate

.107 .012 -.047 .850



ANOVA


Regression .143 1 .143 .198 .662

Residual 12.278 17 .722

Total 12.421 18


car.


Model Summary


Square

Std. Error of the

Estimate

210

.475 .225 .180 .608



ANOVA


Regression 1.827 1 1.827 4.949 .040

Residual 6.278 17 .369

Total 8.105 18


car.


Model Summary


Square

Std. Error of the

Estimate

.134 .018 -.040 1.353



ANOVA


Regression .573 1 .573 .313 .583

Residual 31.111 17 1.830

Total 31.684 18

211


car.


Model Summary


Square

Std. Error of the

Estimate

.425 .181 .133 .698



ANOVA


Regression 1.827 1 1.827 3.753 .070

Residual 8.278 17 .487

Total 10.105 18


car.


Model Summary


Square

Std. Error of the

Estimate

.030 .001 -.058 1.295

212



ANOVA


Regression .026 1 .026 .016 .902

Residual 28.500 17 1.676

Total 28.526 18


car.


Model Summary


Square

Std. Error of the

Estimate

.081 .007 -.052 .323



ANOVA


Regression .012 1 .012 .112 .742

Residual 1.778 17 .105

Total 1.789 18


car.

B.3.4.29 Importance of Brand Image for non-car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.185 .034 -.022 1.043



ANOVA


Regression .658 1 .658 .605 .448

Residual 18.500 17 1.088

Total 19.158 18


car.


213

Model Summary


Square

Std. Error of the

Estimate

.327 .107 .054 .758



ANOVA


Regression 1.170 1 1.170 2.033 .172

Residual 9.778 17 .575

Total 10.947 18


car.


Model Summary


Square

Std. Error of the

Estimate

.389 .151 .101 .778



ANOVA


Regression 1.827 1 1.827 3.023 .100

Residual 10.278 17 .605

Total 12.105 18


car.

214


Model Summary


Square

Std. Error of the

Estimate

.113 .013 -.045 .575



ANOVA


Regression .073 1 .073 .221 .644

Residual 5.611 17 .330

Total 5.684 18


car.


Model Summary


Square

Std. Error of the

Estimate

.417 .174 .125 .686



ANOVA

215


Regression 1.684 1 1.684 3.579 .076

Residual 8.000 17 .471

Total 9.684 18


car.


Model Summary


Square

Std. Error of the

Estimate

.150 .023 -.035 1.295



ANOVA


Regression .658 1 .658 .392 .539

Residual 28.500 17 1.676

Total 29.158 18


car.


Model Summary


Square

Std. Error of the

Estimate

.270 .073 .019 1.074



ANOVA


Regression 1.547 1 1.547 1.341 .263

Residual 19.611 17 1.154

Total 21.158 18


car.


Model Summary


Square

Std. Error of the

Estimate

216

.034 .001 -.058 1.150



ANOVA


Regression .026 1 .026 .020 .890

Residual 22.500 17 1.324

Total 22.526 18


car.

B.3.5. psychographics by style of use; long trips


Model Summary


Square

Std. Error of the

Estimate

.187 .035 -.016 .582

The independent variable is 8.1.2 Style of use: Long trips.

ANOVA


Regression .233 1 .233 .689 .417

Residual 6.433 19 .339

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.347 .120 .074 .985


ANOVA


Regression 2.519 1 2.519 2.596 .124

Residual 18.433 19 .970

Total 20.952 20



Model Summary

217


Square

Std. Error of the

Estimate

.258 .067 .018 .948


ANOVA


Regression 1.219 1 1.219 1.357 .258

Residual 17.067 19 .898

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.279 .078 .029 .763


ANOVA


Regression .933 1 .933 1.602 .221

Residual 11.067 19 .582

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.148 .022 -.030 .741


ANOVA


Regression .233 1 .233 .425 .522

Residual 10.433 19 .549

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

218

.089 .008 -.044 .884


ANOVA


Regression .119 1 .119 .152 .701

Residual 14.833 19 .781

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.295 .087 .039 1.180


ANOVA


Regression 2.519 1 2.519 1.811 .194

Residual 26.433 19 1.391

Total 28.952 20



Model Summary


Square

Std. Error of the

Estimate

.121 .015 -.037 .911


ANOVA


Regression .233 1 .233 .281 .602

Residual 15.767 19 .830

Total 16.000 20



Model Summary


Square

Std. Error of the

Estimate

.120 .014 -.038 1.182


219

ANOVA


Regression .386 1 .386 .276 .606

Residual 26.567 19 1.398

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.038 .001 -.051 .413


ANOVA


Regression .005 1 .005 .028 .869

Residual 3.233 19 .170

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.252 .064 .014 .850


ANOVA


Regression .933 1 .933 1.291 .270

Residual 13.733 19 .723

Total 14.667 20



Model Summary


Square

Std. Error of the

Estimate

.286 .082 .034 1.006


ANOVA


220

Regression 1.719 1 1.719 1.698 .208

Residual 19.233 19 1.012

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.060 .004 -.049 .787


ANOVA


Regression .043 1 .043 .069 .795

Residual 11.767 19 .619

Total 11.810 20



Model Summary


Square

Std. Error of the

Estimate

.230 .053 .003 .402


ANOVA


Regression .171 1 .171 1.062 .316

Residual 3.067 19 .161

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.548 .300 .263 .871


ANOVA


Regression 6.171 1 6.171 8.143 .010

Residual 14.400 19 .758

221

Total 20.571 20



Model Summary


Square

Std. Error of the

Estimate

.535 .287 .249 1.149


ANOVA


Regression 10.076 1 10.076 7.638 .012

Residual 25.067 19 1.319

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

.324 .105 .058 .971


ANOVA


222

Regression 2.100 1 2.100 2.229 .152

Residual 17.900 19 .942

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.106 .011 -.041 1.191


ANOVA


Regression .305 1 .305 .215 .648

Residual 26.933 19 1.418

Total 27.238 20



Model Summary


Square

Std. Error of the

Estimate

.106 .011 -.041 .445


ANOVA


Regression .043 1 .043 .216 .647

Residual 3.767 19 .198

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.249 .062 .013 .922


ANOVA


Regression 1.071 1 1.071 1.259 .276

Residual 16.167 19 .851

223

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.371 .138 .092 .911


ANOVA


Regression 2.519 1 2.519 3.036 .098

Residual 15.767 19 .830

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.124 .015 -.037 .763


ANOVA


224

Regression .171 1 .171 .294 .594

Residual 11.067 19 .582

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .816


ANOVA


Regression .000 1 .000 .000 1.000

Residual 12.667 19 .667

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.066 .004 -.048 .717


ANOVA


Regression .043 1 .043 .083 .776

Residual 9.767 19 .514

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.364 .133 .087 1.214


ANOVA


Regression 4.286 1 4.286 2.908 .104

Residual 28.000 19 1.474

225

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .749


ANOVA


Regression .000 1 .000 .000 1.000

Residual 10.667 19 .561

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.038 .001 -.051 1.252


ANOVA


Regression .043 1 .043 .027 .870

Residual 29.767 19 1.567

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.043 .002 -.051 .368


ANOVA


Regression .005 1 .005 .035 .853

Residual 2.567 19 .135

Total 2.571 20


226


Model Summary


Square

Std. Error of the

Estimate

.388 .150 .106 .941


ANOVA


Regression 2.976 1 2.976 3.359 .083

Residual 16.833 19 .886

Total 19.810 20



Model Summary


Square

Std. Error of the

Estimate

.334 .111 .065 .716


ANOVA


Regression 1.219 1 1.219 2.380 .139

Residual 9.733 19 .512

227

Total 10.952 20



Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .816


ANOVA


Regression .000 1 .000 .000 1.000

Residual 12.667 19 .667

Total 12.667 20


B.3.5.32. Importance of Quality for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.115 .013 -.039 .549


ANOVA


Regression .076 1 .076 .252 .621

Residual 5.733 19 .302

Total 5.810 20



Model Summary


Square

Std. Error of the

Estimate

.165 .027 -.024 .759


ANOVA


Regression .305 1 .305 .530 .476

Residual 10.933 19 .575

Total 11.238 20


228


Model Summary


Square

Std. Error of the

Estimate

.404 .164 .120 1.146


ANOVA


Regression 4.876 1 4.876 3.716 .069

Residual 24.933 19 1.312

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.059 .003 -.049 1.070


ANOVA


Regression .076 1 .076 .067 .799

Residual 21.733 19 1.144

229

Total 21.810 20


B.3.5.36. Importance of Chinese Car for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.042 .002 -.051 1.118


ANOVA


Regression .043 1 .043 .034 .855

Residual 23.767 19 1.251

Total 23.810 20


B.3.6. Psychographics by style of use; short trips


Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .592

The independent variable is 8.1.2 Style of Use: Short trips.

ANOVA


Regression .000 1 .000 .000 1.000

Residual 6.667 19 .351

Total 6.667 20



Model Summary


Square

Std. Error of the

Estimate

.019 .000 -.052 1.050


ANOVA


Regression .008 1 .008 .007 .933

Residual 20.944 19 1.102

230

Total 20.952 20



Model Summary


Square

Std. Error of the

Estimate

.021 .000 -.052 .981


ANOVA


Regression .008 1 .008 .008 .929

Residual 18.278 19 .962

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.360 .130 .084 .741


ANOVA


Regression 1.556 1 1.556 2.830 .109

Residual 10.444 19 .550

Total 12.000 20



Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .749


ANOVA


Regression .000 1 .000 .000 1.000

Residual 10.667 19 .561

Total 10.667 20


231


Model Summary


Square

Std. Error of the

Estimate

.138 .019 -.033 .879


ANOVA


Regression .286 1 .286 .370 .550

Residual 14.667 19 .772

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.480 .231 .190 1.083


ANOVA


Regression 6.675 1 6.675 5.693 .028

Residual 22.278 19 1.173

Total 28.952 20


232


Model Summary


Square

Std. Error of the

Estimate

.156 .024 -.027 .906


ANOVA


Regression .389 1 .389 .473 .500

Residual 15.611 19 .822

Total 16.000 20



Model Summary


Square

Std. Error of the

Estimate

.137 .019 -.033 1.180


ANOVA


Regression .508 1 .508 .365 .553

Residual 26.444 19 1.392

233

Total 26.952 20



Model Summary


Square

Std. Error of the

Estimate

.149 .022 -.029 .408


ANOVA


Regression .071 1 .071 .429 .521

Residual 3.167 19 .167

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.163 .027 -.025 .867


ANOVA


Regression .389 1 .389 .518 .481

Residual 14.278 19 .751

Total 14.667 20



Model Summary


Square

Std. Error of the

Estimate

.156 .024 -.027 1.037


ANOVA


Regression .508 1 .508 .472 .500

Residual 20.444 19 1.076

Total 20.952 20


234


Model Summary


Square

Std. Error of the

Estimate

.052 .003 -.050 .787


ANOVA


Regression .032 1 .032 .051 .823

Residual 11.778 19 .620

Total 11.810 20



Model Summary


Square

Std. Error of the

Estimate

.495 .245 .205 .359


ANOVA


Regression .794 1 .794 6.169 .022

Residual 2.444 19 .129

Total 3.238 20



Model Summary


Square

Std. Error of the

Estimate

.216 .047 -.003 1.016


ANOVA


Regression .960 1 .960 .930 .347

Residual 19.611 19 1.032

Total 20.571 20



Model Summary

235


Square

Std. Error of the

Estimate

.346 .119 .073 1.276


ANOVA


Regression 4.198 1 4.198 2.578 .125

Residual 30.944 19 1.629

Total 35.143 20



Model Summary


Square

Std. Error of the

Estimate

.139 .019 -.032 1.016


ANOVA


Regression .389 1 .389 .377 .547

Residual 19.611 19 1.032

Total 20.000 20



Model Summary


Square

Std. Error of the

Estimate

.188 .035 -.016 1.176


ANOVA


Regression .960 1 .960 .694 .415

Residual 26.278 19 1.383

Total 27.238 20



Model Summary


Square

Std. Error of the

Estimate

236

.091 .008 -.044 .446


ANOVA


Regression .032 1 .032 .160 .694

Residual 3.778 19 .199

Total 3.810 20



Model Summary


Square

Std. Error of the

Estimate

.215 .046 -.004 .930


ANOVA


Regression .794 1 .794 .917 .350

Residual 16.444 19 .865

Total 17.238 20



Model Summary


Square

Std. Error of the

Estimate

.021 .000 -.052 .981


ANOVA


Regression .008 1 .008 .008 .929

Residual 18.278 19 .962

Total 18.286 20



Model Summary


Square

Std. Error of the

Estimate

.266 .071 .022 .741


237

ANOVA


Regression .794 1 .794 1.444 .244

Residual 10.444 19 .550

Total 11.238 20



Model Summary


Square

Std. Error of the

Estimate

.175 .031 -.020 .804


ANOVA


Regression .389 1 .389 .602 .447

Residual 12.278 19 .646

Total 12.667 20



Model Summary


Square

Std. Error of the

Estimate

.057 .003 -.049 .717


ANOVA


Regression .032 1 .032 .062 .807

Residual 9.778 19 .515

Total 9.810 20



Model Summary


Square

Std. Error of the

Estimate

.455 .207 .165 1.161


ANOVA


238

Regression 6.675 1 6.675 4.952 .038

Residual 25.611 19 1.348

Total 32.286 20



Model Summary


Square

Std. Error of the

Estimate

.191 .036 -.014 .735


ANOVA


Regression .389 1 .389 .719 .407

Residual 10.278 19 .541

Total 10.667 20



Model Summary


Square

Std. Error of the

Estimate

.082 .007 -.046 1.248


239

ANOVA


Regression .198 1 .198 .127 .725

Residual 29.611 19 1.558

Total 29.810 20



Model Summary


Square

Std. Error of the

Estimate

.222 .049 -.001 .359


ANOVA


Regression .127 1 .127 .987 .333

Residual 2.444 19 .129

Total 2.571 20



Model Summary


Square

Std. Error of the Estimate

.100 .010 -.042 1.016


ANOVA


Regression .198 1 .198 .192 .666

Residual 19.611 19 1.032

Total 19.810 20


B.3.6.30. Importance of Design for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.027 .001 -.052 .759


ANOVA


240

Regression .008 1 .008 .014 .908

Residual 10.944 19 .576

Total 10.952 20



Model Summary


Square

Std. Error of the

Estimate

.175 .031 -.020 .804


ANOVA


Regression .389 1 .389 .602 .447

Residual 12.278 19 .646

Total 12.667 20


B.3.6.32. Importance of Quality for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.333 .111 .064 .521


ANOVA


Regression .643 1 .643 2.364 .141

Residual 5.167 19 .272

Total 5.810 20


B.3.6.33. Importance of Technology for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.106 .011 -.041 .765


ANOVA


Regression .127 1 .127 .217 .647

Residual 11.111 19 .585

241

Total 11.238 20


B.3.6.34. Importance of Affordability for non car owners for their next purchase

Model Summary


Square

Std. Error of the Estimate

.490 .240 .200 1.092


ANOVA


Regression 7.143 1 7.143 5.987 .024

Residual 22.667 19 1.193

Total 29.810 20


B.3.6.35. Importance of Comfort for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.172 .029 -.022 1.055


ANOVA


Regression .643 1 .643 .577 .457

Residual 21.167 19 1.114

Total 21.810 20


B.3.6.36. Importance of Chinese Car for non car owners for their next purchase

Model Summary


Square

Std. Error of the

Estimate

.091 .008 -.044 1.115


ANOVA


Regression .198 1 .198 .160 .694

Residual 23.611 19 1.243

Total 23.810 20


242

B.3.7. Car owners preferences for the next purchase by loyalty.


Model Summary


Square

Std. Error of the

Estimate

.132 .017 -.034 .587

The independent variable is 8.1.4.1 Would you buy your next

car for the same reason? .

ANOVA


Regression .117 1 .117 .338 .568

Residual 6.550 19 .345

Total 6.667 20

The independent variable is 8.1.4.1 Would you buy your next car for the same reason?

.


Model Summary


Square

Std. Error of the

Estimate

.011 .000 -.053 1.050



ANOVA


Regression .002 1 .002 .002 .963

Residual 20.950 19 1.103

Total 20.952 20


.


Model Summary


Square

Std. Error of the

Estimate

.068 .005 -.048 .979



ANOVA

243


Regression .086 1 .086 .089 .768

Residual 18.200 19 .958

Total 18.286 20


.


Model Summary


Square

Std. Error of the

Estimate

.000 .000 -.053 .795



ANOVA


Regression .000 1 .000 .000 1.000

Residual 12.000 19 .632

Total 12.000 20


.



Square

Std. Error of the

Estimate

.105 .011 -.041 .745



ANOVA


Regression .117 1 .117 .210 .652

Residual 10.550 19 .555

Total 10.667 20


.


Model Summary


Square

Std. Error of the

Estimate

.101 .010 -.042 .883

244



ANOVA


Regression .152 1 .152 .196 .663

Residual 14.800 19 .779

Total 14.952 20



Model Summary


Square

Std. Error of the

Estimate

.086 .007 -.045 1.230



ANOVA


Regression .215 1 .215 .142 .710

Residual 28.738 19 1.513

Total 28.952 20


.


Model Summary


Square

Std. Error of the

Estimate

.128 .016 -.035 .910



ANOVA


Regression .262 1 .262 .317 .580

Residual 15.738 19 .828

Total 16.000 20


.


245

Model Summary


Square

Std. Error of the

Estimate

.108 .012 -.040 1.184



ANOVA


Regression .315 1 .315 .225 .641

Residual 26.638 19 1.402

Total 26.952 20


246

Linnaeus University – a firm focus on quality and competence On 1 January 2010 Växjö University and the University of Kalmar merged to form Linnaeus University. This

new university is the product of a will to improve the quality, enhance the appeal and boost the development potential of

teaching and research, at the same time as it plays a prominent role in working closely together with local society.

Linnaeus University offers an attractive knowledge environment characterised by high quality and

a competitive portfolio of skills.

Linnaeus University is a modern, international university with the emphasis on the desire for knowledge, creative

thinking and practical innovations. For us, the focus is on proximity to our students, but also on the world around us and

the future ahead.

Linnæus University

SE-391 82 Kalmar/SE-351 95 Växjö

Telephone +46 772-28 80 00

segmentation of the car market in china

Documents