Descriptive Research Report

Table of Contents Table of Contents ii List of Tables iii List of Figures iv Executive Summary v 1. Introduction and Background 1 1.1. Importance of the research 1 1.2. Scope of the report 1 1.3. Research problems/question 2 1.4. Aims and Objectives 2 2. Method 3 2.1. Methodological considerations and assumptions 3 2.2. Sample considerations 3 2.3. Data collection and framework, and analytical considerations 4 3. Ethical considerations 5 4. Analysis 5 4.1. Descriptives 5 4.2. Regression 7 4.3. T-Test 17 4.4. ANOVA 19 4.5. Correlation 19 5. Findings and Recommendations 21 5.1. Objective 1 21 5.2. Objective 2 & 3 22 5.3. Objective 4 22 5.4. Objective 5 23 6. Limitations 24
Reference List
Appendix

List of Tables

Table 1 Descriptive table 6 Table 2 Significant correlations self concept with purchase behaviour 20 Table 3 Correlations social desirability bias with purchase behaviour 21

List of Figures

Figure 1 Fashion purchasing model of all respondents 7 Figure 2 Fashion purchasing model of men 8 Figure 3 Fashion purchasing model of women 9 Figure 4 Fashion purchasing model of people in full time work 10 Figure 5 Fashion purchasing model of people in part time work 11 Figure 6 Fashion purchasing model of people not work 11 Figure 7 Fashion purchasing model of single people 12 Figure 8 Fashion purchasing model of people in a relationship 13 Figure 9 Fashion purchasing model of people who are younger than the mean 14 Figure 10 Fashion purchasing model of people who are older than the mean 14 Figure 11 Fashion purchasing model of people who are younger than the median 15 Figure 12 Fashion purchasing model of people who are older than the median 16 Figure 13 Fashion purchasing model of people who are younger than the minimum visual split 16 Figure 14 Fashion purchasing model of people who are older than the minimum visual split 17

Executive Summary
This research will assist managers to explore the drivers of consumer fashion purchasing behavior and there are five objectives of this research. The descriptive research was applied via questionnaire survey. Three respondents were chosen from two females and one male who are 20-25 years old in Australia. Ethical issues were also considered. From the statistic, consumers tend to purchase more fashion apparel when they have high innovativeness or live at urban areas, but usually purchase less if they are not confident. Both intrinsic and extrinsic motives can drive their purchasing behaviour although extrinsic factors are greater. Besides, age, work status, marital status and social desirability bias of consumers also more or less affect their fashion purchasing behaviour. However, because of the sample size, question subjectivity, imperfect layout of questionnaire, validity problem and errors in data, some limitations might be caused.

1. Introduction and Background 2.1. Importance of the research
Recently, markets have presented several obvious changes and characteristics, including fierce market competition, increasing distance between production and consumption, the dramatic rise of marketing investments and critical consumers who are difficult to satisfy. The development of the market economy requires enterprises that fully understand market information, which makes marketing and consumer research necessary to marketers. Through this research, marketers may get the relevant market information, thus, reducing the risk of both marketing decisions and other related business decision-making. This quantitative research focuses on representing the phenomena by assigning numbers in an ordered and meaningful way (Zikmund, Ward, Lowe, Winzar & Babin, 2011), which is based on the collective findings of the qualitative, exploratory research conducted earlier to help marketers investigate if the mass marketing approach is appropriate for the fashion apparel market. Thus, marketers may predict and influence consumers’ behavior by influencing Consumer Decision-Making Process, namely, problem recognition, information search, evaluation of alternatives, choice and post-choice evaluation (Mowen, 1988).

2.2. Scope of the report
The scope of the report is to identify the factors that may affect consumer fashion purchase behavior through quantitative research, so that marketers may make meaningful decisions based on the data collected. This research will investigate three English-speaking adults’ view about apparel products in Australia. Both males and females who are 18 years old or above and are responsible for making their clothing purchase decisions will be surveyed.

2.3. Research problems/question
The research question should be feasible, clear and worthy of investigation, and it should help in solving the problem. The question for this research is: “Is the mass marketing approach appropriate for fashion apparel markets in Australia?” and the following analysis will focus on resolving this question. However, these answers might be biased more or less because of the existence of some limitations.

2.4. Aims and Objectives
This report aims to identify the drivers of consumer fashion purchasing behavior. The five objectives of the research are to:

1. Identify the impact of individual characteristics (specifically confidence in appearance, self concept, innovativeness and materialism) on fashion clothing purchase behavior. 2. Identify the impact of intrinsic motives on fashion clothing purchase behavior. 3. Identify the impact of extrinsic motives on fashion clothing purchase behavior. 4. Identify any meaningful market segments in the fashion clothing market. 5. Understand how social desirability bias may influence the results of the research. 2. Method 3.5. Methodological considerations and assumptions
For descriptive research, the main purpose is to describe characteristics of a population based on some previous understanding of the problem, which usually tries to verify the extent of differences in needs, attitudes and opinions among subgroups (Zikmund et al, 2011). Different from descriptive research, causal research generally focuses on identifying cause-and-effect relationships among variables, and certain types of evidence are often requested by researchers to help them understand and predict relationships.

In this study, the descriptive research was conducted based on the data gathered from an exploratory research which was carried out previously. The purpose of qualitative exploratory research is to obtain greater understanding of the scope and nature of the problem of fashion purchases in Australia (Problem definition, n.d.). Then, descriptive research here would help researchers understand the problem further and then transform an ambiguous problem into generally well-defined one and thereby identify the drivers of consumer fashion purchasing behavior. From the quantitative research, several variables were classified and analysed to identify how they influence the dependent variable of fashion purchasing behaviour.

3.6. Sample considerations
The target audiences of this research are both male and female English speaking adults who are 18 years old and above and are responsible for making their clothing purchase decisions. The data was collected from three English speaking adults (one male and two females) who are aged between 20 and 25years old and are responsible for making their clothing purchase decisions. Because of the small size and age skew of the sample, it cannot represent the whole target population and might have a bias.

3.7. Data collection and framework, and analytical considerations
Different from exploratory research conducted by face-to-face interview, this research was conducted by using a written questionnaire on a simple random sampling, which is one of the probability techniques and refers to sampling by “assuring each element in the population of an equal chance of being included in the sample” (Zikmund et al, 2011, p.334). Three respondents were randomly selected in the computer lab in QUT in a survey framework, then the three completed survey were uploaded to the Blackboard and are waiting to be analysed. After that, the analysis result was acquired by analysing all the uploaded data of surveys and the combination of past data and experiences, then a sample in the size of 711(282 males and 429 females) was obtained and the result was downloaded from the Blackboard. This report will explain and analyse the result via using Regressions, T-Tests, ANOVAs and Correlations respectively.

3. Ethical considerations
The importance of ethics to market research can never be ignored. Alsmadi (2008, p. 153) states that “ethical questions are often raised in marketing research due to varying behavioural expectations and moral responsibilities of researchers who interact with respondents to collect data, and report findings to their clients.”

QUT also asserts that it is important to consider the ethical implications to researchers and ensure they are aware of the responsibilities before they begin any research project (Ethics, n.d.); especially when the project involves human participations. This project adhered to specific QUT guidelines as well: firstly, QUT required this research, three questionnaires were handed out and completed by respondents, and then each of them signed a consent form to indicate their willingness to participate.

4. Analysis 5.8. Descriptives
The Table1 below illustrates the basic personal information about the respondents of the sample including gender, age, qualifications, marital status and work status, which can introduce a general situation of the whole sample to managers.

Table 1 Descriptive table Numbers (N) | Categories | Whole sample size (711) | 282 | Males | 429 | Females | Average age (26)Median age (21)Visual minimum age (43) | 182 | ≥ 26 | 528 | < 26 | 419 | ≥ 21 | 291 | < 21 | 124 | ≥ 43 | 586 | <43 | Qualification | 65 | Post graduate degree or equivalent | 194 | Undergraduate degree or equivalent | 75 | Diploma | 32 | Certificate IV | 293 | Year 12 High School Certificate | 31 | Year 10 School Certificate | 21 | Other | Marital status | 404 | Single | 307 | Married/committed | Work status | 235 | Working FT | 325 | Working PT | 151 | Not working |

5.9. Regression
All respondents
The adjusted R² is 0.398, which means that 39.8% of fashion purchase behaviour is explained by the independent variables. In addition, the significance value of the F statistic on purchase behaviour is 0.000, which is lower than 0.05, so the relationship between independent variables and purchase behaviour is significant. However, only five independent variables are significant—centrality, fashion innovativeness, social approval, success and confidence, which means only the five constructs, are significant and influential to purchase behaviour, where confidence has a negative relationship with fashion purchase behaviour. These are seen in Figure1 below:

Figure 1 Fashion purchasing model of all respondents
Centrality

Happiness β=0.196
Image Expression β=NS β=NS
FashInnovativenesss
Fashion Purchase Behaviour β=0.452
Recognition
β=NS
Recreation
β=NS
SocApproval
β=0.103
Success
β=0.138
Confidence
β=-0.134

Men
The adjusted R² is 0.396 which means 39.6% of fashion purchase behaviour is explained by the independent variables in Figure2 below. The significant level of centrality, fashion innovativeness and social approval are less than 0.05 (sig=0.030, 0.000, 0.023). Therefore, only the three independent variables have significant impacts on males’ purchase behaviour and they can be seen as following:

Figure 2 Fashion purchasing model of men
Centrality

Happiness β=0.138
Image Expression β=NS β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.358
Recognition
β=NS
Recreation
β=NS
SocApproval
β=0.152
Success
β=NS
Confidence
β=NS

Women
The adjusted R² of purchase behaviour of women is 0.440, so the independent variables that affect their purchase behaviour occupy 44%, and this set of data is reliable as the significance level is less than 0.05 (0.000). For females, centrality, fashion innovativeness, success are positively related to their purchase behaviour (sig=0,000, 0.000 and 0.008) and confidence has a negative effect on it (sig level=0.010). As shown in Figure3 below:
Figure 3 Fashion purchasing model of women
Centrality

Happiness β=0.249
Image Expression β=NS
FashInnovativeness
β=NS
Recognition
Fashion Purchase Behaviour β=0.496
Recreation
β=NS β=NS
SocApproval
β=0.152
Success
β=0.148
Confidence
β=-0.122

Full time work
The adjusted R2 is 0.406 this means that 40.6% of fashion purchasing behaviour is explained by the independent variables in Figure4 below. The constructs that are significant in explaining fashion purchasing behaviour are centrality, fashion innovativeness, social approval and confidence.

Figure 4 Fashion purchasing model of people in full time work
Centrality

Happiness β=0.190
Image Expression β=NS β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.430
Recognition
β=NS
Recreation
β=NS β=0.199
SocApproval
β=NS
Success
β=-0.156

Confidence

Part time work
The adjusted R2 is 0.451 that means 45.1% of fashion purchasing behaviour is explained by the independent variables in the following Figure5. The constructs that are significant in explaining fashion purchasing behaviour are centrality, fashion innovativeness, success and confidence.

Figure 5 Fashion purchasing model of people in part time work
Centrality

Happiness β=0.191
Image Expression β=NS
FashInnovativeness
β=NS
Recognition
Fashion Purchase Behaviour β=0.488
Recreation
β=NS β=NS
SocApproval
β=NS
Success
β=0.215
Confidence
β=-0.127

Not working
The adjusted R2 is 0.207, which means that 20.7% of fashion purchasing behaviour is explained by a certain independent variables. The construct which are significant in explaining fashion purchasing behaviour is only fashion innovativeness and is shown below:

Figure 6 Fashion purchasing model of people not work
Centrality

Happiness β=NS
Image Expression β=NS
FashInnovativeness
β=NS
Recognition
Fashion Purchase Behaviour β=0.409
Recreation
β=NS
SocApproval
β=NS
Success
β=NS
Confidence
β=NS β=NS
Single
The adjusted R2 is 0.413 so there is 40.6% of fashion purchasing behaviour explained by the independent variables in Figure7 below. For single people, centrality, fashion innovativeness, success and confidence are significantly related to the purchasing behaviour.

Figure 7 Fashion purchasing model of single people
Centrality
β=0.192
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.457
Recognition
β=NS
Recreation
β=NS
SocApproval
β=NS
Success
β=0.174
Confidence
β=-0.142

In a relationship
The adjusted R2 is 0.351 that means 35.1% of fashion purchasing behaviour is explained by the independent variables in Figure8 below. For people who are in a relationship, centrality, fashion innovativeness, social approval and confidence are significantly related to the purchasing behaviour.

Figure 8 Fashion purchasing model of people in a relationship
Centrality
β=0.211
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.424
Recognition
β=NS
Recreation
β=NS
SocApproval
β=0.138
Success
β=NS
Confidence
β=-0.130

Younger than the mean age (≤ 26)
The adjusted R² is 0.392, which means that 39.2% of fashion purchase behaviour is explained by the independent variables. Five independent variables are significant—centrality, fashion innovativeness, social approval, success and confidence. These are seen in Figure9 below:

Figure 9 Fashion purchasing model of people who are younger than the mean
Centrality
β=0.222
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.458
Recognition
β=NS
Recreation
β=NS
SocApproval
β=0.109
Success
β=0.147
Confidence
β=-0.148

Older than the mean age (> 26)
The adjusted R² is 0.336, which means that 33.6% of fashion purchase behaviour is explained by the independent variables. Only two independent variables are significant—fashion innovativeness and recognition. These are seen in Figure10 below:

Figure 10 Fashion purchasing model of people who are older than the mean
Centrality
β=NS
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.421
Recognition
β=0.256
Recreation
β=NS
SocApproval
β=NS
Success
β=NS
Confidence
β=NS
Younger than the median (≤ 21)
The adjusted R² is 0.412, which means that 41.2% of fashion purchase behaviour is explained by the independent variables. There are five independent variables are significant—centrality, fashion innovativeness, social approval, success and confidence. These are seen in Figure11 below:

Figure 11 Fashion purchasing model of people who are younger than the median
Centrality
β=0.196
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.494
Recognition
β=NS
Recreation
β=NS
SocApproval
β=0.119
Success
β=0.151 β=-0.140
Confidence

Older than the median (> 21)
The adjusted R² is 0.374 this means that 37.4% of fashion purchase behaviour is explained by the independent variables. Three independent variables are significant—centrality, fashion innovativeness and confidence. These are displayed in Figure12 following:

Figure 12 Fashion purchasing model of people who are older than the median
Centrality
β=0.212
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.392
Recognition
β=NS
Recreation
β=NS
SocApproval
β=NS
Success
β=NS
Confidence
β=-0.120

Younger than the minimum visual spit (≤ 43)
The adjusted R² is 0.395 this means that 39.5% of fashion purchase behaviour is explained by the independent variables in Figure13 below. Five independent variables are significant—centrality, fashion innovativeness, social approval, success and confidence.

Figure 13 Fashion purchasing model of people who are younger than the minimum visual split
Centrality
β=0.211
Happiness
β=NS
Image Expression β=NS
FashInnovativeness
Fashion Purchase Behaviour β=0.435
Recognition
β=NS
Recreation
β=NS
SocApproval
β=0.120
Success
β=0.141
Confidence
β=-0.158
Older than the minimum visual spit (> 43)
The adjusted R² is 0.325, which means that 32.5% of fashion purchase behaviour is explained by the independent variables. Only one independent variable is significant, namely, fashion innovativeness. These are seen in Figure14 below:

Figure 14 Fashion purchasing model of people who are older than the minimum visual split
Centrality

Happiness β=NS
Image Expression β=NS
FashInnovativeness
β=NS
Recognition
Fashion Purchase Behaviour β=0.515
Recreation
β=NS β=NS
SocApproval
β=NS
Success
β=NS
Confidence
β=NS

5.10. T-Test
AMB201 students Vs non-AMB201 students
The average score of respondents studying AMB201 is 3.3122, higher than that of non-AMB201students that means respondents studying AMB201 have higher purchase intentions than respondents who are not. The Levene’s test is significant (sig=0.018) which equal variances are not assumed and the t-test is not significant (sig=0.076), so there is no significant difference between the two groups.

Men Vs women
Men and women tend to have a similar mean score towards purchase intentions (3.1766 and 3.1259). Levene’s test is not significant (sig=0.174) and the equal variances assumed (t-test) is found to be not significant (sig=0.558). Thus, the difference is not significant between men and women and their intentions to purchase fashion apparel are not different.

In relationship Vs single
The mean score of single people is 0.2409 greater than that of people who are in a relationship. Besides, the Levene’s test is not significant (sig=0.903), and the equal variances assumed (t-test) is found to be significant (sig=0.005). Hence, there is a significant difference between the two groups and single people usually buy more fashion items.

Age
≥26 Vs <26
The mean score of respondents who are younger than 26-year-old is 0.4678 higher than that of people aged over 26. There is a significant difference between these two groups and their intentions to purchase fashion apparel, with a non-significant Levene’s test (0.850) and a significant equal variances assumed t-test (0.000).

≥21 Vs <21
The mean score of respondents aged younger than 21 is 0.1251 larger than that of those older than 21. There is no significant difference between these two groups and their intentions to purchase fashion apparel, with a non-significant Levene’s test (0.525) and a non-significant equal variances assumed t-test (0.146).

≥43 Vs <43
The mean score of respondents aged younger than 43 is 0.4925 higher than that of respondents older than 43, which means that people aged younger than 43 have higher purchase intentions than others. There is a significant difference between these two groups and their intentions to purchase fashion apparel, with a non-significant Levene’s test (0.906) and a significant equal variances assumed t-test (0.000).

5.11. ANOVA
Different work status
The purchasing behaviour has significant data since its significance level is lower than 0.05 (0.002), which means that there is a significant difference in purchasing behaviours between people of full time, part time and unemployed work status.

Different qualifications
There is no significant difference between the fashion apparel purchasing intentions of people with different qualifications, with an ANOVA significance level of 0.077.

5.12. Correlation
Age
The significance level is less than 0.05 (sig=0.000) and age is negatively correlated with fashion purchasing behaviour (-0.185), so that people’s fashion purchase behaviour decreases with the increase of their age. Therefore, younger people usually have higher fashion purchase behaviours than older people.

Self concept
There are 16 significant correlations between respondents’ constructs and purchasing behaviour as demonstrated in Table2 below. The following is an analysis of this taking two of the strongest correlations: thrifty/ indulgent and urban/rural as examples.

Thrifty/ indulgent construct is positively correlated with fashion purchasing behaviour (0.393), so the more people associate with being thrifty, the lower their fashion purchasing behaviour, and the more they associate with being indulgent, the higher their fashion purchasing behaviour. In contrast, urban/rural construct is negatively related with fashion purchasing behaviour (-0.180), thus, the more people live in urban areas, the higher their fashion purchasing behaviour, and the more they live in rural areas, the lower their purchasing behaviour.

Table 2 Significant correlations self concept with purchase behaviour Self concept constructs | Correlations | Rugged / Delicate | 0.132 | Excitable / Calm | -0.092 | Changeable / Stable | -0.077 | Thrifty / Indulgent | 0.393 | Urban / Rural | -0.180 | Contemporary / non-contemporary | -0.142 | Lush / Austere | -0.099 | Imperfect / Perfect | 0.097 | Rational / Emotional | 0.137 | Youthful / Mature | -0.112 | Negative / Positive | -0.074 | Formal / Informal | -0.126 | Complex / Simple | -0.108 | Plain / Flamboyant | 0.220 | Colourless / Colourful | 0.106 | Modest / Vain | 0.197 |

Social desirability bias
The significance level of each construct is lower than 0.05 and each of the constructs are positively correlated with social desirability bias. The strongest influence is on success (0.329), and the weakest influence is on recreation (0.100). These are seen in Table3 below:

Table 3 Correlations social desirability bias with purchase behaviour Social desirability bias constructs | Correlations | Centrality | 0.282 | Happiness | 0.308 | Image Expression | 0.201 | Fashion Innovativeness | 0.116 | Purchase Behaviour | 0.134 | Recognition | 0.113 | Recreation | 0.100 | Social Approval | 0.242 | Success | 0.329 | Confidence | 0.279 |

5. Findings and Recommendations 6.13. Objective 1
All respondents identified the impact of their individual characteristics on fashion clothing purchase behavior. Confidence in appearance has a negative relationship with fashion purchase behaviour, in other words, the more appearance confident a person is, he/she purchases less fashion apparels. In contrast, fashion innovativeness has a positive relationship with it, so if a person has high innovativeness, he/she is more likely to purchase fashion apparel. Faced with people of different self concept, there are different purchase behaviours. For instance, people usually buy more fashion apparel when they are indulgent or live at urban areas. Finally, materialism often stands for happiness, centrality and success (Zikmund et al, 2011); centrality and success are significant and influential to purchasing behavior. Based on this, marketers should grasp some consumers’ personal characteristic when they design and market their products such as centrality and success, which can help different consumers reflect their own unique advantage then increase their appearance confidence. Marketers also need to innovate their brand and products to keep pace with the fashion trend, and open shopping centers in prosperous areas in a city.

6.14. Objective 2 & 3
The fashion purchasing of people usually has a variety of motives, which might be a function of retail format, cultural, economic and social environment (Jin & Kim, 2003), namely, intrinsic and extrinsic motives. Generally, intrinsic motives include recreation and image expression, such that people purchase fashion apparel for fashion itself; extrinsic motives include recognition and social approval, such that people’s fashion purchasing behaviour is stimulated by external factors. In the above analysis, recreation and image expression which are intrinsic motives are not significant in the fashion purchasing behaviour, but social approval of the extrinsic motives are significant and influential so that social approval is the main reason of people’s fashion purchasing behavior. It is recommended that the fashion apparel products not only should satisfy people’s external pursuit and expression, but meet their spiritual needs and expectations; importantly, they need to explore the reason why the intrinsic motives cannot be satisfied by the purchasing behavior. In this way, the products can possess both external and internal implication.

6.15. Objective 4
As analysis showed, the gender and qualifications of consumers have few differences in fashion purchasing intentions. There are three age groups that split people’s different purchasing intentions, which are aged younger than 26, between 26 and 43 and older than 43; the older the people, the less intentions on purchasing fashion apparels. In addition, fashion purchasing often has a variety of meanings to people with full time or part time job and who generally have a higher income level than non-working people. Finally, single people also have higher purchasing intentions than people who are in a relationship and which maybe because they have more disposable time on their own. Faced with this condition, marketers should segment the market via giving priority to young and single people who have a stable job, and design and market for these groups dominantly. So consumer loyalty can be improved and the brand position can be consolidated and enhanced, and marketing efficiency can also be developed, thus the company can eventually improve the economic benefits.

6.16. Objective 5
Every construct is correlated with the social desirability bias, which success has a biggest bias so that the fashion purchasing behaviour does not have such an important effect on success. This kind of bias might make information untruthful and influence the final result. It can be recommended that managers should have less confidence in the results, instead, rationally analyse and judge the results in order to reduce the bias.

Overall, the sample of respondents in different gender, age, employment status, education and marital status have different understanding and preferences on fashion apparel purchasing, including intrinsic and extrinsic motives, individual characteristics, and so on.

6. Limitations
There are several limitations in this report, the questionnaire used only scale questions throughout, which respondents ranked to specify the level of agreement or disagreement. Scaling is simple to read and has low respondent burden (Martinez-Martin, 2010), but it is more or less subjective. Therefore, more question types are needed next time. Besides, the spacing of each scale question was too little which may lead to respondents skipping a line when answering the question, which might reduce the credibility of the research. In addition, there is no information about the respondents’ nationality in the analysis result, but which was asked in the questionnaire. It can be seen that some details might be wrong and there was a problem of validity. Finally, some data were not cleaned carefully and some errors were included when analyzing the data. Researchers should correct all of these limitations gradually in the future.

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