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Fashion Clothing Purchase Behaviours AMB201 Assessment 3 – Descriptive Research Report
Colleen Dunne
Colleen Dunne n8614725
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Table of Contents III. EXECUTIVE SUMMARY ..................................................................................................................... 2 1.0 INTRODUCTION AND BACKGROUND .......................................................................................... 3
1.1 IMPORTANCE OF THE RESEARCH .......................................................................................................................... 3 1.2 SCOPE OF THE RESEARCH ....................................................................................................................................... 3 1.3 RESEARCH PROBLEM/QUESTION .......................................................................................................................... 4 1.4 AIMS AND OBJECTIVES ........................................................................................................................................... 4
2.0 METHOD .................................................................................................................................................... 4 2.1 METHODOLOGICAL CONSIDERATIONS AND ASSUMPTIONS ......................................................................... 4 2.2 SAMPLE CONSIDERATIONS .................................................................................................................................... 5 2.3 DATA COLLECTION AND FRAMEWORK, AND ANALYTICAL CONSIDERATIONS ..................................... 5
3.0 ETHICAL CONSIDERATIONS ........................................................................................................... 5 4.0 ANALYSIS .................................................................................................................................................. 6
4.1 DATA CLEANING AND CODING ............................................................................................................................ 6 4.2 DESCRIPTIVE DATA .................................................................................................................................................. 6 4.3 T-TESTS ..................................................................................................................................................................... 10 4.4 CORRELATION ......................................................................................................................................................... 11 4.5 MULTIPLE REGRESSION ....................................................................................................................................... 12
4.5.1 Gender ................................................................................................................................................................... 13 4.5.2 Age .......................................................................................................................................................................... 15 4.5.3 Relationship Status ........................................................................................................................................... 17 4.5.4 Social Desirability ............................................................................................................................................ 19
5.0 FINDINGS AND RECOMMENDATIONS ....................................................................................... 22 5.1 IDENTIFYING THE IMPACT OF INDIVIDUAL CHARACTERISTICS ON FASHION CLOTHING PURCHASE BEHAVIOUR. .................................................................................................................................................................... 22 5.2 IDENTIFYING THE IMPACT OF INTRINSIC MOTIVES ON FASHION CLOTHING PURCHASE BEHAVIOUR. .................................................................................................................................................................... 23 5.3 IDENTIFYING THE IMPACT OF EXTRINSIC MOTIVES ON FASHION CLOTHING PURCHASE BEHAVIOUR. .................................................................................................................................................................... 23 5.4 IDENTIFYING ANY MEANINGFUL MARKET SEGMENTS IN THE FASHION CLOTHING MARKET. ...... 23 5.5 UNDERSTANDING HOW SOCIAL DESIRABILITY BIAS MAY INFLUENCE THE RESULTS OF THE RESEARCH. ....................................................................................................................................................................... 25
6.0 LIMITATIONS ........................................................................................................................................ 25 7.0 REFERENCES ........................................................................................................................................ 27 8.0 APPENDICES .......................................................................................................................................... 29
8.1 APPENDIX A – CONSTRUCT DEFINITIONS ...................................................................................................... 29 8.2 APPENDIX B – HOW CONSTRUCTS RELATE TO OBJECTIVES ................................................................... 30 8.3 APPENDIX C – SURVEYS ...................................................................................................................................... 31
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iii. Executive Summary This descriptive research report investigates the drivers of Australian fashion clothing
purchase behaviours, and builds on previous qualitative research. Findings indicate the need
to segment across age and relationship status when marketing fashion clothing.
Findings include:
• Respondents identified they seek to own trends before the mass market does, and like
to be confident in what they are wearing.
• Respondents who were more delicate and excitable were more likely to engage in
fashion purchasing behaviours.
• Intrinsic motives were found to impact on fashion purchasing behaviours: respondents
indicated they sought to project a ‘good’ image and are motivated to engage in fashion
purchasing as a pastime.
• Extrinsic motives were found to impact fashion purchasing behaviours: respondents
identified they were motivated to gain social approval and attention when choosing
clothing.
• No meaningful market segment exists for gender.
• A meaningful market segment exists for generational cohorts and relationship status
• Social desirability impacted the results of this report.
Drivers of fashion clothing differ across age and relationship status, reinforcing them as
meaningful market segments. Gender is not a meaningful market segment and can be
approached using mass marketing.
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1.0 Introduction and Background
1.1 Importance of the research
This research will give insight into the impact of certain variables on fashion clothing
purchase behaviours, as well as any potential market segments. This is important for
marketers as it will allow for the systematic and objective generation of information to aid in
marketing decisions (Zikmund, Ward, Lowe, Winzar, & Babin, 2011, p. 4).
This research is important from a theoretical view as it will may add to current theoretical
models, or reinforce current marketing theories. Piamphongsant (2006) stipulates research has
shown conformity and individuality are important foundations in fashion clothing purchase
behaviour. Hourigan and Bougoure (2012) state materialism and gender are other drivers of
fashion clothing involvement. One potentially relevant theory is the theory of planned
behaviour: behaviours are governed by, and can be predicted, based on personal attitudes,
social pressures and a sense of control (Cooke & Sheeran, 2004). Another theory that may
apply is the theory of conspicuous consumption: people spend money on expensive items to
demonstrate their wealth and power (Trigg, 2001).
These theoretical and practical applications relate to findings in previous qualitative research.
It was found age, timeliness, self-perception, cost, media and context were all drivers of
fashion purchasing behaviours. Quantitative research is important as it allows the questioning
of facts to determine a course of action, building on the insight qualitative research provided
initially (Zikmund et al. 2011, p.68). Unlike the exploratory research conducted initially, the
descriptive research being undertaken is based on a previous understanding of the nature of
the research problem (Zikmund et al. 2011, p.23).
1.2 Scope of the research This research is based on reported behaviours (Smyth et al. 2013) concerning the drivers of
fashion purchasing behaviours of consumers aged between eighteen and sixty-five years in
Australia. The report will cover impact variables, intrinsic and extrinsic motives, potential
segments and social desirability bias.
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1.3 Research problem/question The research question is whether segmentation or mass marketing is most suited when
marketing fashion clothing to Australian consumers.
1.4 Aims and Objectives The aim of this report is to quantitatively examine the drivers of fashion purchase behaviours.
The table below outlines the objectives of this report.
Objective i. To identify the impact of individual characteristics on fashion
clothing purchase behaviour.
Objective ii. To identify the impact of intrinsic motives on fashion clothing
purchase behaviour.
Objective iii. To identify the impact of extrinsic motives on fashion clothing
purchase behaviour.
Objective iv. To identify any meaningful market segments in the fashion
clothing market.
Objective v. Understand how social desirability bias may influence the results
of the research.
2.0 Method
2.1 Methodological considerations and assumptions Descriptive research is being conducted as surveys are designed to describe the characteristics
of fashion purchase behaviours (see 1.4 Aims and Objectives) (Zikmund et al. 2011, p.23),
and not to identify a cause and effect relationship (i.e. causal research) (Zikmund et al. 2011,
p.25). Descriptive research is being conducted as it helps to segment and target markets and is
often used to reveal the nature of shopping or other consumer behaviours (Zikmund et al.
2011, p.23). The study is cross-sectional as the survey has been divided by age into sub-
groups and respondents are only interviewed once (Zikmund et al. 2011, p.134). Whilst cross-
sectional studies are time and cost efficient, measurements are made at a point of time and so
the predictability of findings can be questionable (Zikmund et al. 2011, p.135.).
Primary data is being collected as it has been gathered for the project at hand (Zikmund et al.
2011, p.22). It is assumed participants have correct insight into their own behaviours, and are
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being honest about these, as accuracy is imperative in descriptive research (Zikmund et al.
2011, p.23). An equal number of males and females from each age bracket have been
selected, and so it is assumed the sample is representative. However, random sampling errors
and systematic errors associated with the sampling process can affect the representativeness
of the sample (Zikmund et al. 2011, p.330).
2.2 Sample Considerations The target population of this survey is Australian purchasers of fashion clothing aged eighteen
and above. The sample consisted of males and females across three age brackets: 18-31 years,
32-48 years, and 49-65 years. Time and cost constraints dictated a sampling frame was not
feasible, so the selection of respondents was based on researcher judgement (Zikmund et al.
2011, p.326). Whilst this convenient sampling was more practical, it affects the
generalisability of results (Zikmund et al. 2011, p.328). The sample size was 1 174, with 571
males and 603 females.
2.3 Data collection and framework, and analytical considerations Researchers were divided into two equal groups according to surname. One group gathered
results from females, the other, males. Each researcher interviewed one respondent from each
age bracket using the same survey document with an attached consent form. Researchers
uploaded data onto an online database, leaving room for systematic error (see 6.0
Limitations).
The surveys consisted of interval and nominal scales, including likert scales, semantic
differential scales and dichotomous scales. These interval scales allow for averaging and
adding of data to make comparisons between respondents and segments. Demographical
information was collected at the end of the survey to identify any relevant market segments.
3.0 Ethical Considerations Ethics are important in marketing research as research depends on the continuous willing
cooperation of respondents (AMSRS, 2013). In addition, consumers trust and rely on the
assumption that research is being conducted honestly, objectively and with regard to
respondent’s privacy (AMSRS, 2013). In line with the Queensland University of
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Technology’s (QUT) ethics policy, data was properly collected, maintained and retained
(QUT, 2013).
Respondent’s cooperation was entirely voluntary at all stages and respondents were not
mislead (AMSRS, 2013). Technical details of the research carried out were given to
respondents (AMSRO, 2013). Respondents were aged 18 and over and were required to sign
a consent form which described the research, participation, expected benefits, risks and
confidentiality (AMSRS, 2013).
4.0 Analysis
4.1 Data Cleaning and Coding
The data was cleaned, issues with respondent data were resolved, some data sets removed and
frequencies checked to ensure values were in range. Each discrepancy was assessed
individually. Any negatively phrased items were reversed. Construct values were determined
for each respondent for each respondent by averaging across their relevant items. Social
desirability was determined by summing values together to provide an index.
4.2 Descriptive Data
Figure 1.0 Descriptive Statistics
N Minimum Maximum Mean Std.
Deviation
PurchBehaviour 1174 1.00 7.00 2.8985 1.16744
SocApproval 1174 1.00 6.86 3.4848 1.03994
Recognition 1174 1.00 7.00 3.7076 1.22290
ImageExpression 1174 1.00 7.00 4.0094 1.25162
Recreation 1174 1.00 7.00 4.2216 1.46690
Confidence 1174 1.00 7.00 4.4504 1.10037
FashInnovativeness 1174 1.00 7.00 3.4219 1.05335
Success 1174 1.00 7.00 3.5327 1.08547
Centrality 1174 1.14 7.00 3.9401 0.94630
Happiness 1174 1.00 7.00 3.8327 1.20055
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SocDesire 1174 10.00 20.00 15.0281 2.32083
Valid N (listwise) 1174
Figure 1.0 demonstrates the highest mean in the data is Confidence at 4.4504, whilst the
lowest is purchasing behaviour at 2.8985. Though social desirability’s mean is higher at
15.0281, its range is different to the other constructs.
Figure 1 What is your gender?
Frequency Percentage Valid Percent Cumulative
Percent
Val
id Male 571 48.6 48.6 48.6
Female 603 51.4 51.4 100.0
Total 1174 100.0 100.0
Figure 1 demonstrates there were slightly more females than males in the sample.
Figure 2 Generational Cohort? (Group)
Frequency Percentage Valid Percent Cumulative
Percent
Val
id
GenY 390 33.2 33.2 33.2
GenX 398 33.9 33.9 67.1
BBoomer 386 32.9 32.9 100
Total 1174 100.0 100.0
Figure 2 demonstrates there were similar numbers of respondents in each generational cohort.
Figure 3 When buying clothing, do you prefer to shop alone or with others?
Frequency Percentage Valid Percent Cumulative
Percent
Val
id
Alone 649 55.3 55.3 55.3
With
others
525 44.7 44.7 100.0
Total 1174 100.0 100.0
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While the two figures are quite similar, Figure 3 demonstrates more respondents prefer to
shop alone than with others.
This graph demonstrates there is a large grouping of respondents aged eighteen to twenty-two
years, whilst the frequency of the rest of the ages is relatively similar.
Figure 5 What is your gender? Crosstabulation
What is your gender? Total
Male Female
Group
GenY 190 200 390
GenX 195 203 398
BBoomer 186 200 386
Total 571 603 1174
Figure 5 demonstrates there is a relatively even split between genders.
Figure 6 When buying clothing, do you prefer to shop alone or with others?
Crosstabulation
When buying clothing, do you prefer to
shop alone or with others?
Total
Alone With others
0 10 20 30 40 50 60 70 80
18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64
Frequency
Age
Figure 4. Frequency of Ages
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Group
GenY 184 206 390
GenX 231 167 398
BBoomer 234 152 386
Total 649 525 1174
Figure 6 demonstrates most respondents from generation X and the baby boomers prefer
shopping alone, whilst generation Y prefers shopping with others.
Figure 7 Do you regularly use public transport? Crosstabulation
Do you regularly use public transport? Total
Yes No
Group
GenY 239 151 390
GenX 123 275 398
BBoomer 89 297 386
Total 451 723 1174
Figure 7 demonstrates the majority of respondents from generation Y rely on public transport,
whilst the majority of respondents from the two older age brackets do not.
Figure 8 What is your relationship status? Crosstabulation
What is your relationship status? Total
Single Partnered
Group
GenY 217 173 390
GenX 100 298 398
BBoomer 64 322 386
Total 381 793 1174
Figure 8 demonstrates most generation Yer’s are single and most people in generation X and
baby boomers are partnered.
Figure 9 What is your employment status? Crosstabulation
What is your employment status? Total
FT Work PT Work No Work
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Group
GenY 95 219 76 390
GenX 284 79 35 398
BBoomer 212 93 81 386
Total 591 391 192 1174
Figure 9 demonstrates most generation Y respondents are part time workers, whilst most
generation X respondents and baby boomers are in full time employment.
4.3 t-Tests
Does self-reported fashion purchasing differ between males and females?
Figure 10. Group Statistics
What is your
gender?
N Mean Std.
deviation
Std. Error
Mean
PurchBehaviour Male 571 2.9222 1.18564 0.4962
Female 603 2.8760 1.15047 0.04685
This table displays that the mean self-reported fashion purchasing behaviour for males
(2.9222) was higher than females (2.8760).
Assuming equal variances, a t-Test shows sig. is 0.497, which is higher than 0.005, meaning
there is no significant difference between the means for males and females.
Figure 11. Independent Samples Test Levene’s Test
for Equality of Variances
t-Test for Equality of means
f Sig. t df Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference Lower Upper
PurchBehaviour
Equal Variances assumed
.361 .548 .679 1172 .497 .04629 .06819 -.08749 .18007
Equal Variances not assumed
.678 1163.663 .498 .04629 .06824 -.08760 .18018
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Does self-reported fashion purchasing differ based on relationship status?
Figure 12. Group Statistics
What is your
relationship status?
N Mean Std.
deviation
Std. Error
Mean
PurchBehaviour Single 381 3.1207 1.14750 0.05879
Partnered 793 2.7917 1.16258 0.04128
Figure 12 shows the mean for singles (3.1207) is higher than partnered people (2.7917).
Figure 13 demonstrates the difference in means for singles and partnered people is significant.
4.4 Correlation Correlation was used to measure self concept.
Figure 14. Correlations Self Concept with Purchase Behaviour
PurchBehaviour
Rugged/Delicate Pearson Correlation
Sig. (2 tailed)
N
.157**
0.000
1174
Excitable/Calm Pearson Correlation
Sig. (2 tailed)
-.149
0.000
Figure 13. Independent Samples Test Levene’s Test
for Equality of Variances
t-Test for Equality of means
f Sig. t df Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference Lower Upper
PurchBehaviour
Equal Variances assumed
.052 .820 4.560 1172 .000 .32906 .07217 .18747 .47065
Equal Variances not assumed
4.581 758.694 .000 .32906 .07184 .18804 .47008
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N 1174
Tense/Relaxed Pearson Correlation
Sig. (2 tailed)
N
-.005
.872
1174
Results showed a significant positive correlation between rugged and delicate: fashion
purchasing behaviour is higher in people who consider themselves to be delicate. A
significant negative correlation between excitable and calm qualities is present: fashion
purchasing behaviour is higher in people who consider themselves to be excitable. A non-
significant correlation is present between tense and relaxed qualities: there is no impact.
4.5 Multiple Regression Other constructs in the model can be tested using multiple regression, allowing the impact of
independent variables on the dependent variable to be measured.
Figure 15. Model Summary
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .707a .500 .496 .82846
a. Predictors (constant): Recreation, Happiness, Confidence, Recognition,
SocApproval, Centrality, FashInnovativenss, Success, ImageExpression
R indicates the strength of the correlation between the predicted values and observed data and
is .7072. R square is the proportion of variance in the dependant variable explained by the
regression equation and is 0.50. The adjusted R Square value was 0.496 which means that
49.6% of variation in the dependent variable is accounted for by the model.
Figure 16. ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1
Regression 799.793 9 88.866 129.477 0.000b
Residual 798.904 1164 .686
Total 1598.697 1173
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a. Dependent variable: PurchBehaviour
b. Predictors (constant): Recreation, Happiness, Confidence, Recognition,
SocApproval, Centrality, FashInnovativenss, Success, ImageExpression
Figure 16 displays the F statistic is less than 0.05 (0.00), meaning predictors do a good job
explaining the variation in the dependent variable.
Figure 17. Coefficients
Model Unstandardised
Coefficients
Standardised
Coefficients
t Sig.
B Std. Error Beta
(Constant) -.116 .129 -.900 .368
Confidence -.142 .027 -.134 -5.272 .000
FashInnovativenes .516 .034 .465 14.965 .000
Success .050 .035 .047 1.423 .155
Centrality .226 .037 .183 6.149 .000
Happiness -.080 .027 -.083 -2.970 .003
SocApproval .253 .033 .226 7.744 .000
Recognition .055 .026 .058 2.097 .036
ImageExpression .110 .031 .118 3.523 .000
Recreation -.096 .027 -.120 -3.590 .000
Figure 17 displays all individual predictors are significant except Success, which has a
significance level above 0.05 and is therefore not a useful predictor. Fashion Innovativeness
(.465), Social Approval (.226) and Centrality (.183) have the strongest impact on fashion
purchasing behaviours.
4.5.1 Gender
Figure 18. Model Summarya
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Male 1 .727b .528 .520 .82107
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Female 1 .710b .504 .497 .81633
a. What is your gender?
b. Predictors (constant): Recreation, Happiness, Confidence, Recognition,
SocApproval, Centrality, FashInnovativenss, Success, ImageExpression
Figure 18 displays that the strength of correlation (R) and proportion of variance (R Square) is
higher in males (.727; .528) then females (.710; .504). 52% of variation for males, and 49.7%
for females is accounted for by the model.
Figures 19 and 20 demonstrate Success, Recognition and Recreation are insignificant
constructs for both male and female fashion purchasing behaviours. Fashion Innovativeness,
Centrality and Social Approval have strongest correlation to fashion purchasing behaviours..
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4.5.2 Age
Figure 21. Model Summarya
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
GenY 1 .651b .423 .410 .85662
GenX 1 .755b .570 .560 .79522
BBoomer 1 .726b .527 .515 .78987
a. What is your age?
b. Predictors (constant): Recreation, Happiness, Confidence, Recognition,
SocApproval, Centrality, FashInnovativenss, Success, ImageExpression
Figure 21 shows the correlation between the predicted values and purchasing behaviour and
proportion of variance (R Square) is strongest for generation X (.755; .570), followed by baby
boomers (.726; .527) and generation Y is the lowest (.651; .570). 41% for of variance is
accounted for by the model in generation X, 56% for generation Y and 51.5% for baby
boomers.
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Figures 22, 23 and 24 demonstrate that drivers of fashion purchasing behaviour differ greatly
across age groups. Success and recognition were insignificant for all age groups. Fashion
innovativeness and social approval showed strong correlations across all age groups.
Centrality and showed strong correlations for generation X and baby boomers, but was an
insignificant factor for generation Y.
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4.5.3 Relationship Status
Figure 25. Model Summarya
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Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Single 1 .668b .446 .432 .86460
Partnered 1 .723b .523 .518 .80745
a. What is your relationship status?
b. Predictors (constant): Recreation, Happiness, Confidence, Recognition, SocApproval,
Centrality, FashInnovativenss, Success, ImageExpression
Figure 25 shows the correlation between the predicted values and purchasing behaviour is
stronger for partnered people (.723) than singles (.668). R Square was also higher in partnered
people (.523) than singles (.446). 43.2% of variance is accounted for by the model for single
people and 51.8% for partnered people.
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Figures 26 and 27 demonstrate a difference in purchasing behaviours based on relationship
status. Fashion innovativeness is similar for both groups and shows the strongest correlation.
Success is insignificant for both, whilst confidence, recognition and image expression are also
insignificant for singles and happiness is insignificant for partnered people.
4.5.4 Social Desirability
Social Desirability results were scored from 10-20 and were split into two groups (high and
low) using a median of 15, as is shown in Figure 28. Those with scores over 15 had low social
desirability, whilst 15 and under had high social desirability.
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Figure 29. Model Summarya
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
High 1 .667b .445 .437 .82646
Low 1 .743b .552 .544 .82836
a. Social desirability
b. Predictors (constant): Recreation, Happiness, Confidence, Recognition,
SocApproval, Centrality, FashInnovativenss, Success, ImageExpression
Figure 29 shows the correlation between the predicted values and purchasing behaviour and R
Square is significantly stronger for those with low social desirability (.743; .552) than those
with high levels of social desirability (.667; .445). 54.4% of variance is accounted for by the
model for people with low social desirability and 43.7% for high social desirability.
Figures 30 and 31 demonstrate a difference in reported purchasing behaviours based on social
desirability. Success was insignificant for both high and low groups, but happiness,
recognition and recreation were additionally insignificant for those with high social
desirability levels. Fashion innovativeness showed the strongest correlation for both groups.
0
50
100
150
200
10 11 12 13 14 15 16 17 18 19 20
Frequency
Social Desireability
Figure 28 Social Desirability
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5.0 Findings and Recommendations
See 8.1 Appendix A and 8.2 Appendix B for the definitions of constructs and their relationship
with objectives.
5.1 Identifying the impact of individual characteristics on fashion clothing
purchase behaviour.
All constructs of individual characteristics are relevant to fashion purchasing behaviour,
except success. This is surprising as Hourigan and Bougoure (2012) stipulated materialism is
a strong driver of fashion purchasing behaviours. However, centrality had the second
strongest correlation with fashion purchasing behaviour, and happiness the fourth (both
elements of materialism), which supports Hourigan and Bougoure’s (2012) stipulation. The
significance of these two constructs also reinforces the theory of conspicuous consumption:
people may make purchases because they believe possessions to be central t the individual
and are essential to satisfaction. Because the results conflict with secondary research, more
research may need to be conducted to determine the extent materialism actually does impact
fashion purchasing behaviour.
Fashion Innovativeness had the strongest impact of fashion clothing purchase behaviour, and
confidence had the second. This indicates fashion purchasers seek to own trends before the
mass market does, and that they like to be confident in what they are wearing. This reinforces
Piamphongsant’s (2006) stipulation that individuality is an important factor of fashion
purchasing behaviour. It also reinforces the theory of planned behaviour (Cooke & Sheeran
2004), as fashion purchasing behaviour are governed by and can be predicted based on
personal attitudes (such as innovativeness and confidence) and social pressures surrounding
fashion innovativeness.
Results from Figure 14. Correlations Self Concept with Purchase Behaviour demonstrates the
more delicate someone perceives himself or herself to be, the higher their fashion purchasing
behaviour is, and the more rugged, the lower it is. In addition, fashion purchasing behaviour is
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higher in people who consider themselves to be excitable rather than calm. Whether a person
is tense or relaxed was found to have no impact on fashion purchasing behaviour.
These findings are important as they can impact how a marketer selects a target audience: a
target audience that considers themselves to be delicate or excitable would be more likely to
purchase fashion clothing than someone who perceived themselves to be rugged or calm., and
a campaign would be more successful if it focussed around its clothing being fashion
innovative.
5.2 identifying the impact of intrinsic motives on fashion clothing purchase
behaviour.
Figure 17. Coefficients found image expression and recreation impact similarly on fashion
purchasing behaviour. This means respondent’s seek to project a ‘good’ image and are
motivated to engage in fashion purchasing as a past time. It is interesting to note these two
constructs are not consistently significant when data is broken into s gender, relationship
status and age segments (see 5.4 Identifying any meaningful segments in the fashion clothing
market). For marketers, these findings could impact how they market fashion clothing
campaigns.
5.3 Identifying the impact of extrinsic motives on fashion clothing purchase behaviour. Figure 17 Coefficients demonstrates social approval and recognition both impact on fashion
purchasing behaviours. The findings that people are motivated to gain social approval through
clothing and attention from being fashionable indicates the theory of planned behaviour
(Cooke & Sheeran 2004), as it demonstrates fashion purchasing behaviours are governed by
and can be predicted based on social pressures. This is important for marketers of fashion
clothing as extrinsic motives are important to be identified when creating a campaign.
5.4 Identifying any meaningful market segments in the fashion clothing market.
T-tests and multiple regression found no meaningful market segment present in gender, which
conflicts with Hourigan and Bougoure’s (2012) stipulation gender is a driver of fashion
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clothing involvement. Whilst different clothing is marketed to males and females, campaigns
do not need to be segmented due to gender: a mass marketing approach applies.
A meaningful market segment was found to be present between generational cohorts. Fashion
innovativeness, confidence and social approval impacted all age groups, reinforcing that
people like to be confident in what they wear, ahead of trends, and gain social approval
through clothing (once again reinforcing the theory of planed behaviour (Cooke and Sheeran
2004)).
Based on the surveyed generation Y’s answers, recreation was the only additional value
showing a relationship for fashion purchasing behaviour. This implies generation Y is likely
to engage in fashion purchasing as a pastime. More research is needed, potentially
observational, to study what influences generation Y, as few factors were found to have
significance for this group.
The most values impacted on generation X: centrality, happiness, social approval, image
expression and recreation. These findings imply generation X seek to gain social approval
through clothing, be seen as projecting a ‘good’ image and engage in fashion purchasing as a
pastime, which indicates the theory of planned behaviour is relevant (Cooke & Sheeran
2004). Respondents in generation X also believed possessions and acquisitions are central to
the individual and are essential to satisfaction and wellbeing. This highlights that generation
X may be more materialistic than other age cohorts, implying the theory of conspicuous
consumption applies to this generation (Trigg 2001).
Multiple regression test indicated respondents from the baby boomers consider acquisitions to
be central to their self, seek to gain social approval through their clothing and want to be seen
as projecting a ‘good’ image.
T-tests and multiple regressions also found relationship status to be a meaningful market
segment. Once again, fashion innovativeness was a strong driver for both groups. Levels of
centrality were also similar, indicates both groups like to be ahead of the fashion pack and
acquisitions are important to them. Social approval and recreation had more impact on fashion
purchasing behaviour for singles than partnered people. This implies single respondents seek
to gain more social approval through clothing and engage in fashion purchasing as a pastime
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more than partnered people. In addition, single respondents indicated they based satisfaction
on their acquisitions, whereas this was an insignificant factor for partnered people. In
contrast, partnered respondents indicated their confidence in appearance, attention from being
fashionable and projection of a ‘good’ image impacted on their fashion purchasing
behaviours. This means that marketers could segment audiences based on relationship status.
5.5 Understanding how social desirability bias may influence the results of the research.
In order to gain an accurate understanding of results, it is important to gauge the extent social
desirability affects respondent’s answers, as well as the drivers (Zikmund et al. 2011, p.132).
Social desirability was found to affect the results of the research. Happiness, recognition and
recreation were insignificant for those with high social desirability levels, but not those with
low social desirability levels. This indicates findings surrounding these three values have been
impacted by social desirability and may not be accurate. Fashion innovativeness and
confidence weren’t affected by social desirability as they showed similar levels across both
groups, indicating findings around these values are accurate. Centrality and social approval
may also have been affected by social desirability bias as they were significantly stronger for
those with high levels of social desirability. Image expression had a far lower impact for
those with high levels of social desirability, meaning the impact of this value may have been
misrepresented to be perceived in a certain manner. Thus, it is recommended further
observational study be conducted into the drivers of fashion purchasing behaviours to
determine how respondents actually behave.
6.0 Limitations
Figure 4. Frequency of Ages demonstrates there was a large representation of respondents
aged eighteen to twenty-two years, and a small number of those aged twenty-seven to thirty-
one years. Whilst the frequency of the rest of the ages are relatively proportionate, this infers
the sample is not representative, which impacts on the accuracy and generalizability of results
(Zikmund et al. 2011, p.127). In addition to this, random sampling error may have occurred
through a statistical fluctuation due to chance variation (Zikmund et al. 2011. P.329). Issues
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with this could be improved by using a sampling frame, or by using probability sampling such
as simple random sampling (Zikmund et al. 2011, p.331-34).
Reliability of the data is also questionable because of the multiple researchers (Guest, 2012).
Data-processing error in the form of a mistake in the entering of data into the database may
have occurred (Zikmund et al. 2011, p.132). Interviewer cheating may also have occurred
where interviewers falsified questionnaires or filled in answers where they had been skipped
(Zikmund et al. 2011, p.133). Issues with this could be improved for future research by telling
interviewers a small number of respondents will be called back to check whether the survey
was conducted (Zikmund et al. 2011, p.133).
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7.0 References
AMSRO. (2013). The Market and Social Research Privacy Code. Retrieved April 6, 2013
from Association of Market and Social Research Organisation:
http://www.amsro.com.au/privacy/the-market-and-social-research-privacy-code
AMSRS. (2013). AMSRS Code of Professional Behaviour. Retrieved April 6, 2013 from
Australian Market and Social Research Society:
http://www.amsrs.com.au/documents/item/194
Cooke, R., & Sheeran, P. (2004). Moderation of Cognition Intention and Cognition-
Behaviour relations: A meta-analysis of properties of variables from the theory of
planned behaviour. British Journal of Social Psychology (43), 159-186.
Guest, G. (2012). Applied Thematic Analysis. Thousand Oaks, California: Sage
Publications.
Hourigan, Sally & Bougoure, Ursula-Sigrid (2012). Towards a Better Understanding of
Fashion Clothing Involvement. Australasian Marketing Journal. 20(2):127-135.
Retrieved from
http://gateway.library.qut.edu.au/login?url=http://search.proquest.com.ezp01.library.q
ut.edu.au/docview/1027770401?accountid=13380
Piamphonsant, T. (2006, July). A Cross-Cultural Study of Fashion Clothing Behaviours:
Specific Situations and In-Group Differences Among Career Women in Cosmopolitan
City Contexts. Retrieved April 6, 2013 from ABI/Inform Global:
http://search.proquest.com.ezp01.library.qut.edu.au/abiglobal/docview/304914326/13
D43299E7056CB8B74/3?accountid=13380
QUT. (2013). D/2.6 QUT Code of Conduct for Research. Retrieved April 5, 2013 from
Queensland University of Technology:
http://www.mopp.qut.edu.au/D/D_02_06.jsp#D_02_06.01.mdoc
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Smyth , J., Webb, M., & Oikawa, M. (2013). Self-report of cancer related behaviours .
Retrieved June 1, 2013 from National Cancer Institute :
http://dccps.cancer.gov/brp/constructs/self-report/
Trigg, A. (2001). Veblen, Bourdieu, and conspicuous consumption. Journal of Economic
Issues , 35 (1), 99-115.
Zikmund, W. G., Ward, S., Lowe, B., Winzar, H., & Babin, B. J. (2011). Marketing Reserach
(2nd ed.). Sydney: Cengage Learning.
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8.0 Appendices
8.1 Appendix A – Construct Definitions
Construct Definition
Social Approval This is how motivated the respondent is to gain social
approval through their clothing.
Recognition This is how much the person believes that they will gain
attention from being fashionable.
Image Expression This is how important it is to the respondent to be seen as
projecting a ‘good’ image.
Recreation This is how motivated the person is to engage in fashion
purchasing as a pastime.
Confidence in
Appearance
This is how confident an individual is in their general
appearance. It is possible that people with low confidence will
compensate by being more fashionable.
Fashion
Innovativeness
Innovativeness is the extent to which a person is more in tune
with fashion and more likely to adopt trends before the mass
market.
Materialism
(success, centrality,
happiness)
Success: the extent to which people tend to judge themselves
and others by the number and quality of possessions
accumulated.
Centrality: the extent to which possessions and acquisitions
are central to the individual.
Happiness: the belief that possessions and acquisitions are
essential to ones satisfaction and well-being in life.
Self Concept These are descriptions of a person’s image. It is possible that
some people have a self image that leads them to be more
fashion consuming than other images.
Social Desirability A potential bias in data.
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8.2 Appendix B – How Constructs Relate to Objectives
Objective Constructs
Objective (i) individual characteristics • Confidence in appearance
• Innovativeness
• Materialism
• Self-concept
Objective (ii) intrinsic motives • Image expression
• Recreation
Objective (iii) extrinsic motives • Social approval
• Recognition
Objective (iv) segmentation • Gender
• Generational cohort
• Relationship status
• Employment status
• Residential location
• Nationality
• Education
• Shopping preference
• Living situation
• Transport usage
Objective (v) social desirability • Social desirability
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8.3 Appendix C – Surveys