Impact of Colour

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Impact of ColourOn

Consumers Buying BehaviorBy Aurangzaib, Jun 28, 2007

Abstract:

Purpose The purpose of this research is to understand colour strategy. Colour strategy

has become part of todays marketing life. Trends for each year are no longer only

predicted by the fashion industry. Moreover, the psychological value of colour is in

marketing more important than in the fashion industry. Some colour associations and

reactions can be very dangerous for your product. Be aware however, not to lose track of

the origin of a product. On top, some colours can never work for a certain product

whereas they will be perfect for another one.

Introduction

If a marketer can identify consumer buying behavior, He or She will be in a better

position to target products and services at them. Buying behavior is focused upon the

needs of individuals, groups and organization.

The processes of decision and acts of final household consumers related with evaluating,

purchasing, consuming, and discarding products for personal consumption

Literature Review

According to Brown,(2005) Buying decisions involve many factors that most consumers

are not even aware of them. In every purchase five steps are involved: need recognition,

information search, evaluation of alternatives, purchase decision, and finally post

purchase behavior. Even the simplest purchases can include any or all of these steps.

Armstorng et al (2005) suggest that personal, psychological, and socialissues are other

variable influenced purchases. Demographics normallyplay a major role in the buyingprocess, since social, religious, and economic factors all influence a persons thought

processes. (OBrien).

Coloris one of the important attributes which acts as a driving force in cosmetics use

from a cross-cultural perspective. Use of color cosmetics (right color) satisfies the need to

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lookyoungwhich leads to confidence for the consumer in an individualistic society. Use

of color cosmetics involves PDI (power distance), UAI (uncertainty avoidance) and IDV.

Mooij further suggested that together with national wealth Hofstedes cultural dimensions

can explain more than half of the differences in consumption and consumer behavior.

By Spear, Study of behavior of consumers goods and services regarding their buying

patterns and reactions to advertising and marketing.

Consumer psychology seeks to explain human, or consumer behavior, in two basic ways:

what the consumer wants and what the consumer needs.

According to Krigjsman, Culture is the set of basic value, perception, wants and

behaviors learned by a member of society from family and other institution. According

to Pervin, The body of work considers the role of culture and its impact on consumer

behavior. The study attempts to provide an in-depth analysis into the way cultural

factors influence consumers decision-making processes.

Hofsteede (1980) defines culture as the interactive aggregate of common characteristics

that influence a groups response to its environment.

Social Class Almost every society has some form of social class structure. Social

classes are society's relatively permanent and ordered divisions whose members share

similar values, interest, and behaviors

Personal FactorA consumer's decision also are influenced by personal characteristics

such as the consumer's age and life cycle stage, occupation, economic situation, lifestyle,

personalityand self concept:

Psychological FactorA consumer's buying choices are further influenced by four major

psychological factors: Motivation, Perception, Learning, Beliefs and Attitudes

Schutte and Ciarlante (1998) suggest that Consumers form an attitude towards the

advertising of a product as well as in the act of buying the product.

Solomon (1996) says According to ABC attitude is divided into three components

Affect, Behaviors, and Cognition.

It is commonly accepted that occupation, age, and gender influence car-buying attitudes.

This study uses the Wheel of Consumer Analysis Model to explain how cultural

differences between U.S. and Chinese consumers affect car buying decisions.

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The Wheel of Consumer Analysis consists of three elements (a) environment, (b)

behavior, and (c) affect/cognition, and is a useful model for explaining buyer behavior in

general, and car buying behavior in particular.

Methodology:Following are the research methods which we will apply in our research process,

Face to face interviews

Questionnaire

Hierarchy of Research Design

Sample Selection and Size:

Sample Selection and Size will 600 people which select in different area of the

city and different markets. Some are the people will higher class and some people

will middle class. We will select the sample randomly not very specific

customers. Some will select outlets owners, employees and senior employees

who are working on that outlet. All the Sample Selection will select on the base of

observation last 3 to 4 months.

Data Collection Methods:

Create the questionnaire

Brief interviews

Data compilation

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Research Design

Sample Selection Data Collection Procedures

Surveys and interviews


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All Data will be collected via survey forms and consumers which use and

purchases product on the bases of its colours and package, some data will collect

on internet and different web sits.

Procedures:

Survey forms will be utilized to capture the consumer psyche and behavior, when

they select different products. Statistical data on psychological impact,

environmental impact, fashion impact and locating impact will be gathered to

support the results.

Surveys and interviews

Surveys are way to systematically find information from a particular group of

people- particularly information that those people know better than anyone else.Interviews are a specific survey mechanism that tends to require more time from

the individuals responding to questions. Because all surveys take the time of the

people who respond (whether writing on paper, on the Internet, over the phone, or

face-to-face), it is important to limit these methods to information that cannot be

gathered in other ways.

For both interviews and surveys, the basic method involves:

1. Developing a question or set of questions that will measure change in an

indicator,

2. Selecting a group of people to question/survey, and

3. Asking those people to answer the same question or set of questions at different

times during the life of the project.

The nature of the interviewers' questions focused on the color, style or quality, and

price of the different products along with perceived image, consumer budget and

payment procedures. Appendix A contains a copy of the questions used in the

interview. The next section deals with the results of the in-depth interviews of

Outlets owner and buyers.

I have interviewed and send questionnaire form to more than 55 people from

Gujrnawala and Gujrat. I made these interviews through meeting personally and

send questionnaire form through email.

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Open ended interviews

"Open-ended interviews" permit the respondent (interviewee) to provide

additional information, ask broad questions without a fixed set of answers, andexplore paths of questioning which may occur to the interviewer spontaneously

during the interview. An open-ended approach allows for an exploratory approach

to uncover unexpected information, used especially when the exact issues of

interest haven't been identified yet.

Results & Findings:

EDA:Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Market Visit in Month 280 1 4 2.59 .980

Brand Conscious 280 1 2 1.42 .495

Attractiv thing in shop 280 1 4 2.79 1.134

Relation b\w color &brand 280 1 2 1.36 .481

Quality of Product 280 1 5 4.14 .929

Colors of Product 280 1 5 3.94 .853

Design of Product 280 2 5 4.00 .783

Price of Product 280 1 5 3.60 .990

Trends 280 1 5 3.92 1.166

Attitude 280 1 5 3.69 .936

Emotion 280 1 5 3.66 .982

Personality 280 1 5 3.86 1.044

Fashion 280 1 5 3.72 .973

Product 280 1 5 3.29 1.132

Red 280 1 5 3.29 1.047

Black 280 1 5 3.97 1.097

Green 280 1 5 3.20 1.093

White 280 1 5 3.39 1.219

Blue 280 1 5 3.70 1.106

Orange 280 1 5 2.81 1.240

Yellow 280 1 5 2.57 1.291

Dark Colors 280 1 5 3.32 .964

Light Colors 280 1 5 3.62 .927

Bright Colors 280 1 5 3.50 1.064

Light Colors 280 1 5 3.40 1.046

Dark Bright Colors 280 1 5 2.84 1.221

Income 280 1 4 3.03 1.183

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Spending 280 1 4 3.45 .911

Profession 280 1 4 2.22 .807

Gender 280 1 2 1.42 .495

Age 280 1 6 3.92 1.540

Location 280 1 2 1.43 .496

Valid N (listwise) 280

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

Market Visit in Month 280 100.0% 0 .0% 280 100.0%

Brand Conscious 280 100.0% 0 .0% 280 100.0%

Attractiv thing in shop280 100.0% 0 .0% 280 100.0%

Relation b\w color &brand 280 100.0% 0 .0% 280 100.0%

Quality of Product 280 100.0% 0 .0% 280 100.0%

Colors of Product 280 100.0% 0 .0% 280 100.0%

Design of Product 280 100.0% 0 .0% 280 100.0%

Price of Product 280 100.0% 0 .0% 280 100.0%

Trends 280 100.0% 0 .0% 280 100.0%

Attitude 280 100.0% 0 .0% 280 100.0%

Emotion 280 100.0% 0 .0% 280 100.0%

Personality 280 100.0% 0 .0% 280 100.0%

Fashion 280 100.0% 0 .0% 280 100.0%

Product 280 100.0% 0 .0% 280 100.0%

Red 280 100.0% 0 .0% 280 100.0%

Black 280 100.0% 0 .0% 280 100.0%

Green 280 100.0% 0 .0% 280 100.0%

White 280 100.0% 0 .0% 280 100.0%

Blue 280 100.0% 0 .0% 280 100.0%

Orange 280 100.0% 0 .0% 280 100.0%

Yellow 280 100.0% 0 .0% 280 100.0%

Dark Colors 280 100.0% 0 .0% 280 100.0%

Light Colors 280 100.0% 0 .0% 280 100.0%

Bright Colors 280 100.0% 0 .0% 280 100.0%

Light Colors 280 100.0% 0 .0% 280 100.0%

Dark Bright Colors 280 100.0% 0 .0% 280 100.0%

Income 280 100.0% 0 .0% 280 100.0%

Spending 280 100.0% 0 .0% 280 100.0%

Profession 280 100.0% 0 .0% 280 100.0%

Gender 280 100.0% 0 .0% 280 100.0%

Age 280 100.0% 0 .0% 280 100.0%

Location 280 100.0% 0 .0% 280 100.0%

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Descriptives

Statistic Std. Error

Market Visit in Month

Mean 2.59 .05995% ConfidenceInterval for Mean

Lower Bound 2.47

Upper Bound2.70

5% Trimmed Mean 2.60

Median 3.00

Variance .960

Std. Deviation .980

Minimum 1

Maximum 4

Range 3

Interquartile Range 1Skewness -.068 .146

Kurtosis -1.001 .290

Brand Conscious

Mean 1.42 .030

95% ConfidenceInterval for Mean

Lower Bound 1.36

Upper Bound1.48


Median 1.00

Variance .245

Std. Deviation .495

Minimum 1

Maximum 2

Range 1

Interquartile Range 1

Skewness .320 .146


Attractiv thing in shop

Mean 2.79 .068


Lower Bound 2.66

Upper Bound2.92


Median 3.00

Variance 1.285

Std. Deviation 1.134

Minimum 1

Maximum 4

Range 3


Skewness -.307 .146


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Relation b\w color &brand

Mean 1.36 .029


Lower Bound 1.30

Upper Bound1.42


Median 1.00

Variance .231

Std. Deviation .481

Minimum 1

Maximum 2

Range 1


Skewness .583 .146


Quality of Product

Mean 4.14 .056


Lower Bound 4.03

Upper Bound

4.25


Median 4.00

Variance .863

Std. Deviation .929

Minimum 1

Maximum 5

Range 4


Skewness -.949 .146

Kurtosis .436 .290

Colors of Product


Lower Bound 3.84

Upper Bound4.04


Median 4.00

Variance .728

Std. Deviation .853

Minimum 1

Maximum 5

Range 4

Interquartile Range1

Skewness -.797 .146

Kurtosis .787 .290

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Design of Product

Mean 4.00 .047


Lower Bound 3.91

Upper Bound4.10


Median 4.00

Variance .613

Std. Deviation .783

Minimum 2

Maximum 5

Range 3


Skewness -.367 .146

Kurtosis -.423 .290

Price of Product

Mean 3.60 .059


Lower Bound 3.48

Upper Bound

3.71


Median 4.00

Variance .980

Std. Deviation .990

Minimum 1

Maximum 5

Range 4


Skewness -.147 .146

Kurtosis -.636 .290

Trends


Lower Bound 3.78

Upper Bound4.05


Median 4.00

Variance 1.359


Minimum 1

Maximum 5

Range 4


Skewness -.865 .146

Kurtosis -.246 .290

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Attitude

Mean 3.69 .056


Lower Bound 3.58

Upper Bound3.80


Median 4.00

Variance .876

Std. Deviation .936

Minimum 1

Maximum 5

Range 4


Skewness -.815 .146

Kurtosis .462 .290

Emotion

Mean 3.66 .059


Lower Bound 3.54

Upper Bound

3.77


Median 4.00

Variance .964

Std. Deviation .982

Minimum 1

Maximum 5

Range 4


Skewness -.754 .146

Kurtosis .247 .290

Personality


Lower Bound 3.73

Upper Bound3.98


Median 4.00

Variance 1.091


Minimum 1

Maximum 5

Range 4


Skewness -.757 .146

Kurtosis -.138 .290

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Fashion

Mean 3.72 .058


Lower Bound 3.61

Upper Bound3.84


Median 4.00

Variance .947

Std. Deviation .973

Minimum 1

Maximum 5

Range 4


Skewness -.709 .146

Kurtosis -.014 .290

Product

Mean 3.29 .068


Lower Bound 3.16

Upper Bound

3.42


Median 3.00

Variance 1.282


Minimum 1

Maximum 5

Range 4


Skewness -.259 .146

Kurtosis -.607 .290

Red


Lower Bound 3.17

Upper Bound3.42


Median 3.00

Variance 1.097


Minimum 1

Maximum 5

Range 4


Skewness -.345 .146

Kurtosis -.368 .290

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Black

Mean 3.97 .066


Lower Bound 3.84

Upper Bound4.10


Median 4.00

Variance 1.203


Minimum 1

Maximum 5

Range 4


Skewness -.960 .146

Kurtosis .139 .290

Green

Mean 3.20 .065


Lower Bound 3.07

Upper Bound

3.33


Median 3.00

Variance 1.195


Minimum 1

Maximum 5

Range 4


Skewness -.379 .146

Kurtosis -.660 .290

White


Lower Bound 3.25

Upper Bound3.53


Median 4.00

Variance 1.486


Minimum 1

Maximum 5

Range 4


Skewness -.422 .146

Kurtosis -.851 .290

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Blue

Mean 3.70 .066


Lower Bound 3.57

Upper Bound3.83


Median 4.00

Variance 1.223


Minimum 1

Maximum 5

Range 4


Skewness -.818 .146

Kurtosis .013 .290

Orange

Mean 2.81 .074


Lower Bound 2.66

Upper Bound

2.96


Median 3.00

Variance 1.538


Minimum 1

Maximum 5

Range 4


Skewness .024 .146

Kurtosis -.994 .290

Yellow


Lower Bound 2.42

Upper Bound2.72


Median 3.00

Variance 1.666


Minimum 1

Maximum 5

Range 4


Skewness .262 .146


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Dark Colors

Mean 3.32 .058


Lower Bound 3.21

Upper Bound3.43


Median 3.00

Variance .929

Std. Deviation .964

Minimum 1

Maximum 5

Range 4


Skewness -.561 .146

Kurtosis .039 .290

Light Colors

Mean 3.63 .055


Lower Bound 3.52

Upper Bound

3.73


Median 4.00

Variance .859

Std. Deviation .927

Minimum 1

Maximum 5

Range 4


Skewness -.602 .146

Kurtosis .027 .290

Bright Colors


Lower Bound 3.37

Upper Bound3.62


Median 4.00

Variance 1.133


Minimum 1

Maximum 5

Range 4


Skewness -.395 .146

Kurtosis -.616 .290

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Light Colors

Mean 3.40 .063


Lower Bound 3.28

Upper Bound3.52


Median 4.00

Variance 1.094


Minimum 1

Maximum 5

Range 4


Skewness -.348 .146

Kurtosis -.615 .290

Dark Bright Colors

Mean 2.84 .073


Lower Bound 2.70

Upper Bound

2.98


Median 3.00

Variance 1.490


Minimum 1

Maximum 5

Range 4


Skewness .025 .146

Kurtosis -.964 .290

Income


Lower Bound 2.89

Upper Bound3.17


Median 4.00

Variance 1.400


Minimum 1

Maximum 4

Range 3


Skewness -.794 .146

Kurtosis -.964 .290

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Spending

Mean 3.45 .054


Lower Bound 3.35

Upper Bound3.56


Median 4.00

Variance .829

Std. Deviation .911

Minimum 1

Maximum 4

Range 3


Skewness -1.568 .146

Kurtosis 1.304 .290

Profession

Mean 2.22 .048


Lower Bound 2.12

Upper Bound

2.31


Median 2.00

Variance .651

Std. Deviation .807

Minimum 1

Maximum 4

Range 3


Skewness -.252 .146


Gender


Lower Bound 1.36

Upper Bound1.48


Median 1.00

Variance .245

Std. Deviation .495

Minimum 1

Maximum 2

Range 1


Skewness .320 .146


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Age

Mean 3.93 .092


Lower Bound 3.74

Upper Bound4.11


Median 4.00

Variance 2.371


Minimum 1

Maximum 6

Range 5


Skewness -.194 .146


Location

Mean 1.43 .030


Lower Bound 1.37

Upper Bound

1.49


Median 1.00

Variance .246

Std. Deviation .496

Minimum 1

Maximum 2

Range 1


Skewness .290 .146


KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of SamplingAdequacy. .637

Bartlett's Test ofSphericity

Approx. Chi-Square 2548.739

df 496

Sig. .000

Communalities

Initial Extraction

Market Visit in Month 1.000 .658

Brand Conscious 1.000 .728

Attractiv thing in shop 1.000 .623

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Relation b\w color &brand 1.000 .567

Quality of Product 1.000 .665

Colors of Product 1.000 .652

Design of Product 1.000 .632

Price of Product 1.000 .582

Trends 1.000 .507

Attitude 1.000 .765

Emotion 1.000 .697

Personality 1.000 .573

Fashion 1.000 .632

Product 1.000 .538

Red 1.000 .538

Black 1.000 .607

Green 1.000 .633

White 1.000 .467

Blue 1.000 .551

Orange 1.000 .809

Yellow 1.000 .739

Dark Colors 1.000 .704

Light Colors 1.000 .702

Bright Colors 1.000 .535

Light Colors 1.000 .738

Dark Bright Colors 1.000 .626

Income 1.000 .829

Spending 1.000 .516

Profession 1.000 .808

Gender 1.000 .715

Age 1.000 .755Location 1.000 .621

Extraction Method: Principal Component Analysis.

Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of SquaredLoadings

Rotation Sums of SquaredLoadings

Total% of

VarianceCumulative

% Total% of

VarianceCumulative

% Total% of

VarianceCumulative

%

1 3.745 11.703 11.703 3.745 11.703 11.703 3.109 9.714 9.714

2 3.242 10.132 21.835 3.242 10.132 21.835 2.222 6.944 16.658

3 2.299 7.184 29.018 2.299 7.184 29.018 2.167 6.771 23.430

4 1.972 6.162 35.180 1.972 6.162 35.180 1.990 6.217 29.647

5 1.692 5.288 40.468 1.692 5.288 40.468 1.859 5.810 35.457

6 1.491 4.660 45.128 1.491 4.660 45.128 1.817 5.677 41.134

7 1.466 4.582 49.709 1.466 4.582 49.709 1.782 5.570 46.703

8 1.404 4.387 54.096 1.404 4.387 54.096 1.638 5.119 51.823

9 1.233 3.854 57.950 1.233 3.854 57.950 1.510 4.719 56.541

10 1.147 3.584 61.534 1.147 3.584 61.534 1.377 4.303 60.845

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Market Visit inMonth

-.220

.233 .163 .076 -.060 .012 -.199 .074 -.389 .568 -.021

Brand Conscious -.235

-.100 .052 .215 -.017 .454 .319 .074 .330 .155 .408

Attractive thing inshop

.067 -.298 .203 .456 .163 .145 -.412 -.110 .217 -.066 -.011

Relation b\w color

& brand

-.10

1 -.018 .150 .424 .162 .369 -.207 .103 -.167 .186 .275Quality of Product .469 .166 -.080 .032 -.075 -.018 -.392 -.347 -.064 .237 .265

Colors of Product .017 .377 -.251 -.217 .009 .040 -.235 -.475 .178 .279 -.089

Design of Product .298 .035 .295 .194 .012 -.421 -.074 .475 -.046 .073 .018

Price of Product .240 .099 .491 .106 -.209 -.026 .013 .447 .065 .115 -.018

Trends -.057

.430 .137 .090 .153 .208 .298 .071 -.012 .338 -.128

Attitude -.015

.343 -.180 -.284 -.210 .344 -.422 .406 .014 -.162 .043

Emotion -.173

.557 -.229 -.191 -.295 .293 -.232 .130 -.052 -.084 .123

Personality .524 .250 -.095 .358 -.087 -.185 -.041 .105 -.113 -.089 .153

Fashion .291 .296 -.065 .297 .200 -.233 .411 -.165 -.239 .123 .065

Product .448 .159 .065 .121 .070 .240 .145 .049 -.144 -.429 -.039

Red .389 .248 .332 -.089 -.088 .141 .068 -.301 -.086 -.230 .152

Black .161 .510 -.535 -.013 .034 -.054 -.067 .159 -.029 .016 .013

Green .026 .417 .090 -.154 -.092 -.152 .092 -.026 -.504 -.166 .323

White .237 .477 -.193 -.098 -.231 -.087 .159 -.033 .134 -.052 -.169

Blue .199 .370 -.039 -.179 -.003 -.354 .053 .007 .394 .230 .069

Orange .065 -.011 .693 -.546 -.066 .000 -.014 -.137 -.046 .002 .041

Yellow .044 .079 .707 -.464 .039 .051 -.062 -.001 .043 .064 -.050

Dark Colors .258 .214 .108 .299 -.606 .149 .178 .013 .132 .095 -.210

Light Colors .444 .185 .206 .280 -.398 .275 .155 -.235 .098 .002 -.164

Bright Colors .432 .273 .147 .142 .408 .142 -.083 -.127 .112 -.104 .012

Light Colors .340 .413 .097 -.021 .643 .087 -.079 .039 .082 -.071 -.033

Dark BrightColors

.180 .341 -.041 -.147 .409 .257 .048 .238 -.002 .071 -.396

Income -.729

.401 .141 .143 -.040 -.144 .055 -.114 .115 -.202 .053

Spending -.388

.479 .243 .116 .063 -.079 .008 -.180 .059 -.060 .115

Profession -.655

.480 .185 .218 .023 -.105 -.028 .014 .188 -.131 .047

Gender .171 -.009 -.186 -.387 .120 .086 .397 .216 .270 .104 .438

Age -.681

.436 .129 .202 .074 -.117 -.032 .056 .036 -.089 -.100

Location -.429

-.053 -.118 -.107 .060 .348 .318 -.095 -.387 .055 -.142

Extraction Method: Principal Component Analysis.11 components extracted.

Rotated Component Matrix (a)

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Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.A Rotation converged in 18 iterations.

Component Transformation Matrix

Component 1 2 3 4 5 6 7 8 9 10 11

1 -.686 .380 .004 .343 .100 .330 -.076 .267 .195 -.182 -.025

2 .537 .434 -.073 .243 .334 .072 .340 .254 .328 .234 .006

3 .238 .075 .794 .199 -.291 .331 -.226 -.121 .046 .065 -.045

4 .256 .033 -.576 .377 -.528 .255 -.324 -.033 -.044 .025 -.089

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Component

1 2 3 4 5 6 7 8 9 10 11

Market Visit inMonth

.138 -.098 .065 -.039 -.153 .075 .080 .133 .085 .738 -.137

Brand Conscious .152 -.084 -.051 .157 -.319 -.129 -.024 -.104 -.145 .027 .720

Attractive thing in

shop .005 .092 -.029 .066 -.606 .127 -.126 .161 -.335 -.224 -.149Relation b\w color& brand .078 .096 -.106 .014 -.665 .052 .063 .028 .048 .258 .145

Quality of Product -.254 .063 -.025 .121 -.119 .062 .058 .713 .208 .043 -.081

Colors of Product .101 .132 -.011 .045 .261 -.345 .122 .616 -.120 .142 -.075

Design of Product -.064 .062 .021 -.025 .012 .764 -.094 -.085 .074 .087 -.103

Price of Product -.024 .040 .272 .292 -.058 .595 .087 -.162 -.017 .156 .075

Trends .224 .348 .018 .215 .097 -.040 -.052 -.072 .006 .475 .212

Attitude -.001 .082 .003 -.012 .023 .052 .869 .006 .007 .000 -.004

Emotion .246 -.003 -.083 .112 .114 -.140 .720 .127 .186 .113 .055

Personality -.140 .170 -.330 .262 -.012 .421 -.010 .189 .347 -.088 -.073

Fashion.020 .278 -.282 .156 .158 .082 -.450 .080 .418 .181 .048

Product -.176 .412 -.033 .315 -.075 .035 .071 -.191 .328 -.279 -.050

Red -.045 .219 .344 .351 -.028 -.034 -.016 .185 .410 -.203 .005

Black .019 .252 -.479 -.024 .336 .042 .351 .195 .168 .096 .008

Green .151 -.011 .094 -.048 .117 .040 .128 .025 .744 .101 -.047

White .064 .149 -.127 .337 .505 .031 .162 .132 .103 -.026 -.018

Blue .115 .123 .026 .010 .491 .291 -.029 .388 -.078 .023 .196

Orange -.023 -.015 .885 -.005 .070 .034 -.018 .034 .132 .006 -.014

Yellow .049 .136 .832 -.003 .042 .121 .038 -.001 -.006 .090 -.002

Dark Colors -.007 -.131 -.064 .794 .103 .162 .070 -.004 -.045 .094 .006

Light Colors -.108 .098 .061 .808 -.036 .016 -.057 .121 .052 -.037 -.015

Bright Colors -.032 .636 .037 .121 -.135 .090 -.079 .231 .085 -.142 .006

Light Colors .039 .821 .043 -.126 .002 .112 .004 .155 .080 -.028 .024

Dark BrightColors

-.072 .669 .005 -.004 .185 -.042 .182 -.165 -.134 .238 -.050

Income .883 -.141 .000 -.055 .034 -.123 .031 -.081 .038 -.014 -.004

Spending .674 .080 .104 .026 -.012 -.041 -.015 .121 .147 .072 .025

Profession .888 -.013 -.023 -.032 -.024 .024 .096 -.045 -.036 .047 .029

Gender -.235 .076 .034 -.183 .294 .018 .062 -.005 .084 -.085 .717

Age .819 .006 -.067 -.095 -.002 -.023 .083 -.146 -.045 .165 -.108

Location .067 -.064 -.016 -.062 -.042 -.577 -.009 -.395 .110 .322 .031


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5 .038 .717 -.042 -.588 -.165 -.082 -.307 -.027 -.067 .011 .061

6 -.173 .267 .093 .339 -.452 -.499 .420 -.167 -.118 .104 .304

7 .010 .027 -.054 .251 .391 -.171 -.481 -.485 .216 .053 .484

8 -.103 .058 -.135 -.182 .024 .616 .428 -.543 -.111 .170 .189

9 .212 .082 .036 .138 .189 .149 .002 .246 -.677 -.416 .421

10

-.174

-.10

8 .013 -.015 .031 .071 -.181 .354 -.277 .815 .22811

.045-.23

2-.011 -.254 -.306 .142 .065 .322 .487 -.162 .628

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

ReliabilityCase Processing Summary

N %

Cases

Valid 280 100.0

Excluded(a)

0 .0

Total 280 100.0

List wise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach'sAlpha

Cronbach'sAlpha Based

onStandardized

Items N of Items

.727 .713 43

Summary Item Statistics

Mean Minimum Maximum RangeMaximum /Minimum Variance N of Items

Item Variances 1.025 .231 2.371 2.139 10.244 .153 4

Inter-Item Correlations .055 -.665 .888 1.554 -1.335 .032 4

The covariance matrix is calculated and used in the analysis.

Cross tabsCase Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

Age * Personality 280 100.0% 0 .0% 280 100.0%

Age * Personality Crosstabulation

Count

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Personality

Not At AllImportant Unimportant Normal Important Very Important

Total

Age

Less then18

0 0 2 1 8 11

19 to 24 3 1 9 20 27 60

25 to 30 1 4 4 24 13 4631 to 35 0 9 4 15 7 35

36 to 40 2 13 16 28 19 78

More then41

0 4 11 23 12 50

Total 6 31 46 111 86 280

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 43.460(a) 20 .002

Likelihood Ratio 46.063 20 .001Linear-by-LinearAssociation

8.007 1 .005

N of Valid Cases280

11 cells (36.7%) have expected count less than 5. The minimum expected count is .24.

Symmetric Measures

Value

Asymp.Std.

Error(a)Approx.

T(b) Approx. Sig.

Interval by Interval Pearson's R -.169 .057 -2.866 .004(c)

Ordinal by Ordinal SpearmanCorrelation

-.186 .058 -3.160 .002(c)

N of Valid Cases 280

a Not assuming the null hypothesis.b Using the asymptotic standard error assuming the null hypothesis.c Based on normal approximation.

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Johnson, Chang, (Nova Southeastern University) A COMPARISON OF CAR BUYING

BEHAVIOR BETWEEN AMERICAN AND CHINESE PEOPLE LIVING IN NORTH

AMERICA: AN EXPLORATORY STUDY

Lisbet Berg, Competent Consumers? Consumer Competence profiles in Norway

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