of 25
8/14/2019 Impact of Colour
1/25
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
- 1 -
http://www.associatedcontent.com/theme/1609/play.htmlhttp://www.associatedcontent.com/theme/1609/play.html8/14/2019 Impact of Colour
2/25
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.
- 2 -
http://www.bizcovering.com/Marketing-and-Advertising/Characteristic-Affecting-Consumer-Purchase-Behavior.34983http://www.bizcovering.com/Marketing-and-Advertising/Characteristic-Affecting-Consumer-Purchase-Behavior.34983http://www.bizcovering.com/Marketing-and-Advertising/Characteristic-Affecting-Consumer-Purchase-Behavior.34983/2http://www.bizcovering.com/Marketing-and-Advertising/Characteristic-Affecting-Consumer-Purchase-Behavior.34983/2http://www.bizcovering.com/Marketing-and-Advertising/Characteristic-Affecting-Consumer-Purchase-Behavior.34983http://www.bizcovering.com/Marketing-and-Advertising/Characteristic-Affecting-Consumer-Purchase-Behavior.34983/28/14/2019 Impact of Colour
3/25
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
- 3 -
Research Design
Sample Selection Data Collection Procedures
Surveys and interviews
8/14/2019 Impact of Colour
4/25
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.
- 4 -
8/14/2019 Impact of Colour
5/25
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
- 5 -
8/14/2019 Impact of Colour
6/25
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%
- 6 -
8/14/2019 Impact of Colour
7/25
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
5% Trimmed Mean 1.41
Median 1.00
Variance .245
Std. Deviation .495
Minimum 1
Maximum 2
Range 1
Interquartile Range 1
Skewness .320 .146
Kurtosis -1.911 .290
Attractiv thing in shop
Mean 2.79 .068
95% ConfidenceInterval for Mean
Lower Bound 2.66
Upper Bound2.92
5% Trimmed Mean 2.82
Median 3.00
Variance 1.285
Std. Deviation 1.134
Minimum 1
Maximum 4
Range 3
Interquartile Range 2
Skewness -.307 .146
Kurtosis -1.350 .290
- 7 -
8/14/2019 Impact of Colour
8/25
Relation b\w color &brand
Mean 1.36 .029
95% ConfidenceInterval for Mean
Lower Bound 1.30
Upper Bound1.42
5% Trimmed Mean 1.35
Median 1.00
Variance .231
Std. Deviation .481
Minimum 1
Maximum 2
Range 1
Interquartile Range 1
Skewness .583 .146
Kurtosis -1.672 .290
Quality of Product
Mean 4.14 .056
95% ConfidenceInterval for Mean
Lower Bound 4.03
Upper Bound
4.25
5% Trimmed Mean 4.22
Median 4.00
Variance .863
Std. Deviation .929
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.949 .146
Kurtosis .436 .290
Colors of Product
Mean 3.94 .05195% ConfidenceInterval for Mean
Lower Bound 3.84
Upper Bound4.04
5% Trimmed Mean 4.00
Median 4.00
Variance .728
Std. Deviation .853
Minimum 1
Maximum 5
Range 4
Interquartile Range1
Skewness -.797 .146
Kurtosis .787 .290
- 8 -
8/14/2019 Impact of Colour
9/25
Design of Product
Mean 4.00 .047
95% ConfidenceInterval for Mean
Lower Bound 3.91
Upper Bound4.10
5% Trimmed Mean 4.04
Median 4.00
Variance .613
Std. Deviation .783
Minimum 2
Maximum 5
Range 3
Interquartile Range 1
Skewness -.367 .146
Kurtosis -.423 .290
Price of Product
Mean 3.60 .059
95% ConfidenceInterval for Mean
Lower Bound 3.48
Upper Bound
3.71
5% Trimmed Mean 3.62
Median 4.00
Variance .980
Std. Deviation .990
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.147 .146
Kurtosis -.636 .290
Trends
Mean 3.92 .07095% ConfidenceInterval for Mean
Lower Bound 3.78
Upper Bound4.05
5% Trimmed Mean 4.01
Median 4.00
Variance 1.359
Std. Deviation 1.166
Minimum 1
Maximum 5
Range 4
Interquartile Range2
Skewness -.865 .146
Kurtosis -.246 .290
- 9 -
8/14/2019 Impact of Colour
10/25
Attitude
Mean 3.69 .056
95% ConfidenceInterval for Mean
Lower Bound 3.58
Upper Bound3.80
5% Trimmed Mean 3.73
Median 4.00
Variance .876
Std. Deviation .936
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.815 .146
Kurtosis .462 .290
Emotion
Mean 3.66 .059
95% ConfidenceInterval for Mean
Lower Bound 3.54
Upper Bound
3.77
5% Trimmed Mean 3.71
Median 4.00
Variance .964
Std. Deviation .982
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.754 .146
Kurtosis .247 .290
Personality
Mean 3.86 .06295% ConfidenceInterval for Mean
Lower Bound 3.73
Upper Bound3.98
5% Trimmed Mean 3.92
Median 4.00
Variance 1.091
Std. Deviation 1.044
Minimum 1
Maximum 5
Range 4
Interquartile Range2
Skewness -.757 .146
Kurtosis -.138 .290
- 10 -
8/14/2019 Impact of Colour
11/25
Fashion
Mean 3.72 .058
95% ConfidenceInterval for Mean
Lower Bound 3.61
Upper Bound3.84
5% Trimmed Mean 3.77
Median 4.00
Variance .947
Std. Deviation .973
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.709 .146
Kurtosis -.014 .290
Product
Mean 3.29 .068
95% ConfidenceInterval for Mean
Lower Bound 3.16
Upper Bound
3.42
5% Trimmed Mean 3.32
Median 3.00
Variance 1.282
Std. Deviation 1.132
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.259 .146
Kurtosis -.607 .290
Red
Mean 3.29 .06395% ConfidenceInterval for Mean
Lower Bound 3.17
Upper Bound3.42
5% Trimmed Mean 3.33
Median 3.00
Variance 1.097
Std. Deviation 1.047
Minimum 1
Maximum 5
Range 4
Interquartile Range1
Skewness -.345 .146
Kurtosis -.368 .290
- 11 -
8/14/2019 Impact of Colour
12/25
Black
Mean 3.97 .066
95% ConfidenceInterval for Mean
Lower Bound 3.84
Upper Bound4.10
5% Trimmed Mean 4.06
Median 4.00
Variance 1.203
Std. Deviation 1.097
Minimum 1
Maximum 5
Range 4
Interquartile Range 2
Skewness -.960 .146
Kurtosis .139 .290
Green
Mean 3.20 .065
95% ConfidenceInterval for Mean
Lower Bound 3.07
Upper Bound
3.33
5% Trimmed Mean 3.23
Median 3.00
Variance 1.195
Std. Deviation 1.093
Minimum 1
Maximum 5
Range 4
Interquartile Range 2
Skewness -.379 .146
Kurtosis -.660 .290
White
Mean 3.39 .07395% ConfidenceInterval for Mean
Lower Bound 3.25
Upper Bound3.53
5% Trimmed Mean 3.43
Median 4.00
Variance 1.486
Std. Deviation 1.219
Minimum 1
Maximum 5
Range 4
Interquartile Range2
Skewness -.422 .146
Kurtosis -.851 .290
- 12 -
8/14/2019 Impact of Colour
13/25
Blue
Mean 3.70 .066
95% ConfidenceInterval for Mean
Lower Bound 3.57
Upper Bound3.83
5% Trimmed Mean 3.77
Median 4.00
Variance 1.223
Std. Deviation 1.106
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.818 .146
Kurtosis .013 .290
Orange
Mean 2.81 .074
95% ConfidenceInterval for Mean
Lower Bound 2.66
Upper Bound
2.96
5% Trimmed Mean 2.79
Median 3.00
Variance 1.538
Std. Deviation 1.240
Minimum 1
Maximum 5
Range 4
Interquartile Range 2
Skewness .024 .146
Kurtosis -.994 .290
Yellow
Mean 2.57 .07795% ConfidenceInterval for Mean
Lower Bound 2.42
Upper Bound2.72
5% Trimmed Mean 2.52
Median 3.00
Variance 1.666
Std. Deviation 1.291
Minimum 1
Maximum 5
Range 4
Interquartile Range3
Skewness .262 .146
Kurtosis -1.079 .290
- 13 -
8/14/2019 Impact of Colour
14/25
Dark Colors
Mean 3.32 .058
95% ConfidenceInterval for Mean
Lower Bound 3.21
Upper Bound3.43
5% Trimmed Mean 3.36
Median 3.00
Variance .929
Std. Deviation .964
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.561 .146
Kurtosis .039 .290
Light Colors
Mean 3.63 .055
95% ConfidenceInterval for Mean
Lower Bound 3.52
Upper Bound
3.73
5% Trimmed Mean 3.66
Median 4.00
Variance .859
Std. Deviation .927
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.602 .146
Kurtosis .027 .290
Bright Colors
Mean 3.50 .06495% ConfidenceInterval for Mean
Lower Bound 3.37
Upper Bound3.62
5% Trimmed Mean 3.53
Median 4.00
Variance 1.133
Std. Deviation 1.064
Minimum 1
Maximum 5
Range 4
Interquartile Range1
Skewness -.395 .146
Kurtosis -.616 .290
- 14 -
8/14/2019 Impact of Colour
15/25
Light Colors
Mean 3.40 .063
95% ConfidenceInterval for Mean
Lower Bound 3.28
Upper Bound3.52
5% Trimmed Mean 3.43
Median 4.00
Variance 1.094
Std. Deviation 1.046
Minimum 1
Maximum 5
Range 4
Interquartile Range 1
Skewness -.348 .146
Kurtosis -.615 .290
Dark Bright Colors
Mean 2.84 .073
95% ConfidenceInterval for Mean
Lower Bound 2.70
Upper Bound
2.98
5% Trimmed Mean 2.82
Median 3.00
Variance 1.490
Std. Deviation 1.221
Minimum 1
Maximum 5
Range 4
Interquartile Range 2
Skewness .025 .146
Kurtosis -.964 .290
Income
Mean 3.03 .07195% ConfidenceInterval for Mean
Lower Bound 2.89
Upper Bound3.17
5% Trimmed Mean 3.09
Median 4.00
Variance 1.400
Std. Deviation 1.183
Minimum 1
Maximum 4
Range 3
Interquartile Range2
Skewness -.794 .146
Kurtosis -.964 .290
- 15 -
8/14/2019 Impact of Colour
16/25
Spending
Mean 3.45 .054
95% ConfidenceInterval for Mean
Lower Bound 3.35
Upper Bound3.56
5% Trimmed Mean 3.56
Median 4.00
Variance .829
Std. Deviation .911
Minimum 1
Maximum 4
Range 3
Interquartile Range 1
Skewness -1.568 .146
Kurtosis 1.304 .290
Profession
Mean 2.22 .048
95% ConfidenceInterval for Mean
Lower Bound 2.12
Upper Bound
2.31
5% Trimmed Mean 2.23
Median 2.00
Variance .651
Std. Deviation .807
Minimum 1
Maximum 4
Range 3
Interquartile Range 1
Skewness -.252 .146
Kurtosis -1.111 .290
Gender
Mean 1.42 .03095% ConfidenceInterval for Mean
Lower Bound 1.36
Upper Bound1.48
5% Trimmed Mean 1.41
Median 1.00
Variance .245
Std. Deviation .495
Minimum 1
Maximum 2
Range 1
Interquartile Range1
Skewness .320 .146
Kurtosis -1.911 .290
- 16 -
8/14/2019 Impact of Colour
17/25
Age
Mean 3.93 .092
95% ConfidenceInterval for Mean
Lower Bound 3.74
Upper Bound4.11
5% Trimmed Mean 3.96
Median 4.00
Variance 2.371
Std. Deviation 1.540
Minimum 1
Maximum 6
Range 5
Interquartile Range 3
Skewness -.194 .146
Kurtosis -1.277 .290
Location
Mean 1.43 .030
95% ConfidenceInterval for Mean
Lower Bound 1.37
Upper Bound
1.49
5% Trimmed Mean 1.42
Median 1.00
Variance .246
Std. Deviation .496
Minimum 1
Maximum 2
Range 1
Interquartile Range 1
Skewness .290 .146
Kurtosis -1.930 .290
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
- 17 -
8/14/2019 Impact of Colour
18/25
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
- 18 -
8/14/2019 Impact of Colour
19/25
8/14/2019 Impact of Colour
20/25
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)
- 20 -
8/14/2019 Impact of Colour
21/25
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
- 21 -
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
8/14/2019 Impact of Colour
22/25
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
- 22 -
8/14/2019 Impact of Colour
23/25
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.
- 23 -
8/14/2019 Impact of Colour
24/25
8/14/2019 Impact of Colour
25/25
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