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Descriptive Research Report

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AMB201 Summer Semester, 2013
Descriptive Research Report

Fashion Clothing Purchases

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Table of Contents

Abstract……………………………………………………………………………….

1.0 Introduction and Background…………………………………………………… 2.1 Importance of the research…………………………………………………... 2.2 Scope of the report…………………………………………………………… 2.3 Research problem/question………………………………………………….. 2.4 Aims and objectives…………………………………………………………...

2.0 Method……………………………………………………………………………...
2.1 Methodological considerations and assumptions……………………………
2.2 Sample considerations…………………………………………………………
2.3 Data collection and framework, and analytical considerations……………

3.0 Ethical consideration………………………………………………………………

4.0 Analysis……………………………………………………………………………. 5.5 Data cleaning and coding…………………………………………………….. 5.6 Descriptives…………………………………………………………………………... 5.7 Exploring the Data (t-Tests)………………………………………………………… 5.8 Correlation…………………………………………………………………………… 5.9 Multiple regression…………………………………………………………… 5.10.1 Gender………………………………………………………………………. 5.10.2 Generational cohort………………………………………………………… 5.10.3 Relationship status………………………………………………………….. 5.10.4 Social Desirability…………………………………………………………... 5.0 Findings and Recommendations…………………………………………………. 6.10 Interpretation of the data …………………………………………………… 6.11 What this means for managers and for the next stage of the research……

6.0 Limitations…………………………………………………………………………

7.0 References………………………………………………………………………….

8.0 Appendices……………………………………………………………………….... 9.12 Three completed surveys……………………………………………………. 9.13 Any other relevant information…………………………………………….

Abstract

1.0 Introduction and Background

1.1 Importance of the research
Consumer spending is expected to improve in the short term, leading to revenue growth for the Clothing Retailing industry. According to IBISWorld (2013), the industry revenue will rise at an annualized 1.1% over the next five years, to reach $13.5 billion in 2018-19, and the industry revenue is forecast to grow by 2.6% in 2014-15. So, there will be a big opportunity clothing industry. With the rapid improvement of living standard, an increasing numbers of citizens pursuing fashion, especially in the fashion clothing. Therefore, fashion clothing purchasing will be one of be one of the biggest parts in clothing industry. Having a good and right marketing research will contribute to the successful of dominating competitive position in fashion clothing industry. That is why this research is important from a practical viewpoint.

Marketing research is defined as the systematic and objective process of generating information to aid in making marketing decision (Zikmund, Ward, Lowe, Winzar, & Babin, 2011, p.4). At the most basic level, when something needs to be measured, quantitative research is used. While, when a question needs to be described and investigated in some depth, qualitative research is used (Shields and Twycross, 2003). After qualitatively exploring (why) the drivers of fashion clothing purchase behavior, in order to have a “measure and forecast” of fashion clothing purchases, quantitatively examine (how many) the drivers of fashion purchase behaviors is necessary. Findings of this quantitative research will be beneficial to the clothing manufactures to make the right marketing decisions to maximize their profits. This research is also beneficial to the 4Ps theory. In the measure understanding of fashion clothing purchases, there will be clear evidences that how business owners manage their business in fashion clothing within the product, price, place and promotion. This research will provide useful information to achieve the marketing goals.

Overall, this research will contribute to the successful of achieving the marketing goals and dominating competitive position in fashion clothing industry.

1.2 Scope of the report
This report will quantitatively address 5 objectives including impact of individual characteristics, intrinsic and extrinsic motives, potential segments and social desirability bias of the drivers of fashion purchasing behaviors of consumers aged in 3 age groups (from 18-65).

1.3 Research problem/question
The traditional segmentation view of marketing and mass marketing, which is most suited to the marketing of fashion clothing? What are the drivers of purchasing behavior associated with fashion clothing? Do these drivers differ across market segments or are they generally similar across segments?

1.4 Aims and objectives
The aim of this report is to quantitatively examine the drivers of fashion purchase behaviors.
Specific objectives include:
i) To identify the impact of individual characteristics on fashion clothing purchase behavior. ii) To identify the impact of intrinsic motives on fashion clothing purchase behavior. iii) To identify the impact of extrinsic motives on fashion clothing purchase behavior. iv) To identify any meaningful market segments in the fashion clothing market.
v) Understand how social desirability bias may influence the results of the research.

2.0 Method

2.1 Methodological considerations and assumptions
According to Monroecollege (2011), causal research design in which the major emphasis is on determining cause-and-effect relationships. While, descriptive research design in which the major emphasis is on determining the frequency with which something occurs or the extent to which two variables covary. In other words, descriptive research is usually concerned with describing a population with respect to important variables. That is suitable for the current research, because the major purpose of this research is to describe characteristics of a population and provide evidence that answer questions of the drivers of fashion clothing purchase behaviors.

As it is mentioned in the 1.1 Importance of the research, this research is a quantitative research, which focus on “how many” aspect. It plays a measuring, monitoring, estimating and forecasting role. Moreover, it is a cross-sectional research, which a method frequently is used in developmental research that involves examining individuals of different ages all at the same time. Individuals are tested at a single point in time, and groups are compared at different ages on independent variables, such as the drivers of fashion clothing purchase behaviors (Larry, 2009). In other words, using this type of strategy, researchers can compare the drivers of fashion clothing purchase behaviors of different generations of individuals.

As for the assumptions aspect, it is assumed that participants own correct insight and are honest about their behaviors. It is believed of the accuracy. As for the representativeness, the number of males and females and even the people in each generational cohort are nearly the same. So, it is assumed the sample is representative.

2.2 Sample considerations
The target population is Australian consumers in fashion clothing aged 18-65. The sample includes 3 generational groups: Gen Y (18-31 years), Gen X (32-48 years) and Baby Boomer (49-65 years) of male and female. The sample size is 920, 420 male and 500 female. And the sampling technique, convenient sampling is used. Because of its convenience, researcher chooses sample based on ease of access (Zikmund, Ward, Lowe, Winzar, & Babin, 2011).

2.3 Data collection and framework, and analytical considerations
According to their surname, researchers are divided into two groups, one conducted survey from females, and the other males. Each researcher survey three respondents, with one from each age bracket. After it, researchers need to upload the data they collected. Unfortunately, the number of people in each group is different, but it is nearly the same.

The most popular type of scales in this survey is interval scale, which refers to the level of measurement in which the attributes composing variables are measured on specific numerical scores or values (Neil, 2010). From the survey is clearly to distinguish the comparisons between the respondents and segments.

3.0 Ethical Considerations
Market Research depends upon the willing co-operation of the public and the business community. This co-operation is based on market research is carried out honestly, objectively and without disadvantage to respondents (AMSRS, 2014). So, ethics are important in marketing research. In addition, in order to protect the privacy of the respondents, QUT expects that research data need to be properly collected, managed and retained in accordance with relevant legislation and University policies (QUT, 2014). Last but not least, consent forms are necessary to inform participants whether to take part in this survey.

4.0Analysis

4.1 Data cleaning and coding
The data was cleaned, issued with any items where respondents could freely enter data were identified, some poor data entry were deleted, such as the suspect responses, age out of range required for grouping, etc. In addition, frequencies were run to check values were all in range. And the negatively phrased items were reversed. As for the construct calculations, construct values were determined for each respondent by averaging across their relevant items. Social desirability was summed together to provide an index. It improves the accuracy.

4.2 Descriptives Figure 1.0 Descriptive Statistics | | N | Minimum | Maximum | Mean | Std. Deviation | PurchBehaviour | 920 | 1.00 | 6.60 | 2.9011 | 1.17885 | SocApproval | 920 | 1.00 | 6.29 | 3.5356 | 1.06345 | Recognition | 920 | 1.00 | 7.00 | 3.7796 | 1.24490 | ImageExpression | 920 | 1.00 | 7.00 | 4.0935 | 1.30008 | Recreation | 920 | 1.00 | 7.00 | 4.02670 | 1.46017 | Confidence | 920 | 1.00 | 7.00 | 4.5416 | 1.14768 | FashInnovativeness | 920 | 1.00 | 6.83 | 3.3962 | 1.09668 | Success | 920 | 1.00 | 6.83 | 3.5063 | 1.12384 | Centrality | 920 | 1.00 | 6.71 | 3.8398 | 0.97996 | Happiness | 920 | 1.00 | 7.00 | 3.8637 | 1.24062 | SocDesire | 920 | 10.00 | 20.00 | 1.48880 | 2.29051 | Valid N (listwise) | 920 | | | | |

Figure 1.0 indicates that Confidence is the highest mean among the data, at 4.5416, while PurchBehaviour is the lowest at 2.9011.

Figure 1.1 What is your gender? | | Frequency | Percentage | Valid Percent | Cumulative Percent | Valid | Male | 420 | 45.7 | 45.7 | 45.7 | | Female | 500 | 54.3 | 54.3 | 100.0 | | Total | 920 | 100.0 | 100.0 | |

Figure 1.1 indicates that there are 120 more females than males in the sample.

Figure 1.2 Generational Cohort (Group) | | Frequency | Percentage | Valid Percent | Cumulative Percent | Valid | GenY | 315 | 34.2 | 34.2 | 34.2 | | GenX | 303 | 32.9 | 32.9 | 67.2 | | BBoomer | 302 | 32.8 | 32.8 | 100.0 | | Total | 920 | 100.0 | 100.0 | |

Figure 1.2 indicates that there are slightly more GenY than GenX and BBoomer, but the numbers of respondents in each generational cohort were similar.

Figure 1.3 Do you regularly use public transport? | | Frequency | Percentage | Valid Percent | Cumulative Percent | Valid | Yes | 313 | 34.0 | 34.0 | 34.0 | | No | 607 | 66.0 | 66.0 | 100.0 | | Total | 920 | 100.0 | 100.0 | |

Figure 1.3 indicates that nearly two thirds of respondents do not regularly use public transport.

Figure 1.4 Frequency of Ages

Figure 1.4 indicates that the most largest groupings of respondents is 18-21years, and the frequency of the GenX and BBoomer is correspondingly similar.

Figure 1.5 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 | 420 | 500 | 920 |

Figure 1.5 indicates that the number of people in each generation group is nearly the same between male and female.

4.3 Exploring the Data (t-Tests) Does self-reported fashion purchasing behavior differ between those people who prefer to shop alone versus those who prefer to shop with others?Figure 2.0 Group Statistics | | Do you prefer to shop alone or with others? | N | Mean | Std. Deviation | Std. Error Mean | PurchBehavior | Alone | 518 | 2.8660 | 1.22187 | 0.05369 | | With others | 402 | 2.943 | 1.12087 | 0.05590 |

Figure 2.0 indicates that people who like to shop with others have higher self-reported fashion purchasing behavior than those who like to shop alone.

Figure 2.1 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 | Purchbeh. | Equal variances assumed | 2.999 | .084 | -1.024 | 918 | .308 | -.08025 | .07835 | -.23402 | .07353 | | Equal variances not assumed | | | -1.035 | 892.750 | -.301 | -.08025 | .07751 | -.23236 | .07187 |

Assuming equal variances, the Sig. (2-tailed) value is greater than 0.05, (.308). Figure 2.1 indicates that there is no statistically significant difference between the two groups. Thus, people who prefer to shop alone and those who prefer to shop with others actually showed similar levels of self-reported fashion behavior.

Does self-reported fashion purchasing behavior differ between single and partnered people?Figure 2.2 Group Statistics | | What is your relationship status? | N | Mean | Std. Deviation | Std. Error Mean | PurchBehavior | Single | 293 | 3.0444 | 1.20095 | .07016 | | Partnered | 627 | 2.8341 | 1.16330 | .04646 |

Figure 2.2 indicates that single people show higher levels of fashion purchasing behavior than partnered people. Means for single and partnered, are 3.0444 and 2.8341 respectively.

Figure 2.3 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 | Purchbeh. | Equal variances assumed | .337 | .582 | 2.528 | 918 | .012 | .21024 | .08318 | .04899 | .37348 | | Equal variances not assumed | | | 2.498 | 554.480 | .013 | .21024 | .08415 | .04495 | .37552 |

Assuming equal variances, the Sig. (2-tailed) value is less than 0.05, (.012). Figure 2.3 indicates that there is statistically significant difference between the two groups. Thus, people who are single and those who are partnered actually showed differentr levels of self-reported fashion behavior.

4.4 Correlation
We can examine the way the various Self Concept dimensions correlate with self-reported fashion purchasing behavior.

Figure 3.0 Correlations self concept with purchase behavior | | PurchBehavior | Rugged/ delicate | Pearson Correlation Sig. (2-tailed)N | .188**.000920 | Excitable/ calm | Pearson CorrelationSig. (2-tailed)N | -.190**.000920 | Uncomfortable/ comfortable | Pearson CorrelationSig. (2-tailed)N | .030.368920 |

Figure 3.0 indicates that there is a significant positive correlation between rugged and delicate, it means fashion purchasing behavior reaches highest point in people who are delicate. While, there is a significant negative correlation between excitable and calm, it means fashion purchasing behavior reaches highest point in people who are excitable. There is non-significant correlation between uncomfortable and comfortable.

4.5 Multiple Regression
We use Multiple Regression to test our overall model. Multiple Regression involves testing multiple predictors of a dependent variable. It works by producing a linear equation fitted to our observed data. We can then use this equation to make predictions about our dependent variable by plugging in values for each independent variable.

The constructs entered into the analysis are Happiness, Recreation, Confidence, Recognition, SocApproval, Centrality, FashInnovativeness, Success and ImageExpression. Each construct is to be tested as a predictor of purchasing behavior.

Figure 4.0 Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | 1.. | .716 | .512 | .508 | .82718 | a. Predictors:(Constant), Happiness, Recreation, Confidence, Recognition, SocApproval, Centrality, FashInnovativeness, Success, ImageExpression |

Figure 4.0 indicates that R is .716, which is the correlation between the observed and predicted values of purchasing behavior. R Square is .512, which is the proportion of variance in the dependent variable explained by the regression model. Adjusted R Square is .508, which means 50.8% of variation in the dependent variable is accounted for by the model.

Figure 4.1 ANOVA | Model | Sig. | 1.. | Regression | .000 |

Figure 4.1 indicates that F statistic is .000, which is less than 0.05. It means the predictors do a good job explaining the variation in the dependent variable.

Figure 4.2 Coefficients | Model | Standardized Coefficients | t | Sig. | | Beta | | | (Constant) | | .640 | .522 | SocApproval | .164 | 5.027 | .000 | Recognition | .011 | .358 | .720 | ImageExpression | .129 | 3.433 | .001 | Recreation | -.158 | -4.335 | .000 | Confidence | -.147 | -5.030 | .000 | FashInnovativeness | .520 | 14.804 | .000 | Success | .055 | 1.489 | .137 | Centrality | .249 | 7.450 | .000 | Happiness | -.101 | -3.250 | .001 |

Figure 4.2 indicates that Success and Recognition have no significant impact on purchase behavior, because the significance levels are greater 0.05. Therefore, they are not useful predictors. In other words, the other predictors are useful. Looking at the standardized coefficients, FashionInnovativeness has the largest impact on purchase behavior.

4.5.1 Gender Figure 5.0 Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Male | 1.. | .730 | .533 | .523 | .82694 | Female | 1 | .713 | .509 | .500 | .82027 | a. What is your gender? b. Predictors:(Constant), Happiness, Recreation, Confidence, Recognition, SocApproval, Centrality, FashInnovativeness, Success, ImageExpression |

Figure 5.0 indicates that the strength of correlation (R) and proportion of variance (R Square) of male (.730, .533) are higher than female (.713, .509). And the Adjusted R Square shows that 52.3% of variation for male and 50% of that for female is accounted for by the model.

Figure 5.1 ANOVA | Model | Sig. | Male | 1.. | Regression | .000 | Female | 1.. | Regression | .000 |

Figure 5.1 indicates that F statistic of male and female are both .000. It means the predictors do a good job explaining the variation in the dependent variable.

Figure 5.2 Coefficients | Model | Standardized Coefficients | Sig. | | Beta | | | Male | Female | Male | Female | (Constant) | | | .171 | .136 | SocApproval | .146 | .128 | .004 | .005 | Recognition | -.001 | .013 | .981 | .746 | ImageExpression | .176 | .094 | .001 | .072 | Recreation | -.138 | -.089 | .015 | .069 | Confidence | -.080 | -.163 | .074 | .000 | FashInnovativeness | .495 | .529 | .000 | .000 | Success | .036 | .044 | .532 | .394 | Centrality | .257 | .241 | .000 | .000 | Happiness | -.087 | -.102 | .052 | .019 |

Figure 5.2 indicates that Success and Recognition have no significant impact on purchase behavior for both male and female. In addition, ImageExpression and Recreation have no significant impact on purchase behavior only for female. As only for male, is Confidence and Happiness. Therefore, they are not useful predictors. In other words, the other predictors are useful. Looking at the standardized coefficients, FashionInnovativeness has the largest impact on purchase behavior for both male and female.

4.5.2 Generational cohort Figure 6.0 Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | GenY | 1.. | .663 | .440 | .423 | .87103 | GenX | 1 | .755 | .570 | .557 | .79196 | BBoomer | 1.. | .750 | .563 | .549 | .78198 | a. What is your age? b. Predictors:(Constant), Happiness, Recreation, Confidence, Recognition, SocApproval, Centrality, FashInnovativeness, Success, ImageExpression |

Figure 6.0 indicates that the strength of correlation (R) and proportion of variance (R Square) of GenX (.755, .570) are highest, followed BBoomer (.750, .563). And the GenY is the lowest (.663, .440). Adjusted R Square shows that 55.7% of variation for GenX and 54.9% of that for BBoomer, and also 42.3% for GenY is accounted for by the model.

Figure 6.1 ANOVA | Model | Sig. | GenY | 1.. | Regression | .000 | GenX | 1.. | Regression | .000 | BBoomer | 1.. | Regression | .000 |

Figure 6.1 indicates that F statistic of male and female are both .000. It means the predictors do a good job explaining the variation in the dependent variable.

Figure 6.2 Coefficients | Model | Standardized Coefficients | Sig. | | Beta | | | GenY | GenX | BBoomer | GenY | GenX | BBommer | (Constant) | | | | .170 | .271 | .344 | SocApproval | .091 | .200 | .195 | .089 | .000 | .000 | Recognition | .007 | -.052 | .089 | .901 | .289 | .083 | ImageExpression | .245 | .132 | .006 | .000 | .030 | .924 | Recreation | -.216 | -.120 | -.107 | .002 | .039 | .080 | Confidence | -.140 | -.121 | -.194 | .006 | .016 | .000 | FashInnovativeness | .489 | .494 | .538 | .000 | .000 | .000 | Success | .111 | .100 | -.084 | .083 | .108 | .167 | Centrality | .193 | .238 | .323 | .002 | .000 | .000 | Happiness | -.129 | -062 | -.024 | .022 | .223 | .625 |

Figure 6.2 indicates that Success, Recognition and Happiness have no significant impact on purchase behavior for GenY, GenX and BBoomer. In addition, SocApproval has no significant impact on purchase behavior only for GenY. As only for BBommer, is ImageExpression and Recreation. Therefore, they are not useful predictors. In other words, the other predictors are useful. Looking at the standardized coefficients, FashionInnovativeness has the largest impact on purchase behavior for GenY, GenX and BBoomer.

4.5.3 Relationship status Figure 7.0 Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Single | 1.. | .716 | .512 | .496 | .85218 | Partnered | 1 | .719 | .516 | .509 | .81484 | a. What is your relationship status? b. Predictors:(Constant), Happiness, Recreation, Confidence, Recognition, SocApproval, Centrality, FashInnovativeness, Success, ImageExpression |

Figure 7.0 indicates that the strength of correlation (R) and proportion of variance (R Square) of partnered (.719, .516) are slightly higher than single (.716, .512). And the Adjusted R Square shows that 50.9% of variation for partnered and 49.6% of that for single is accounted for by the model.

Figure 7.1 ANOVA | Model | Sig. | Single | 1.. | Regression | .000 | Partnered | 1.. | Regression | .000 |

Figure 7.1 indicates that F statistic of single and partnered are both .000. It means the predictors do a good job explaining the variation in the dependent variable.

Figure 7.2 Coefficients | Model | Standardized Coefficients | Sig. | | Beta | | | Single | Partnered | Single | Partnered | (Constant) | | | .718 | .862 | SocApproval | .196 | .163 | .001 | .000 | Recognition | .030 | .011 | .572 | .784 | ImageExpression | .218 | .083 | .001 | .067 | Recreation | -.228 | -.133 | .000 | .003 | Confidence | -..171 | -.143 | .002 | .000 | FashInnovativeness | .450 | .554 | .000 | .000 | Success | .073 | .050 | .279 | .262 | Centrality | .277 | .227 | .000 | .000 | Happiness | -.125 | -.081 | .029 | .030 |

Figure 7.2 indicates that Success and Recognition have no significant impact on purchase behavior for both single and partnered. In addition, ImageExpression has no significant impact on purchase behavior only for partnered. In other words, the other predictors are useful. Looking at the standardized coefficients, FashionInnovativeness has the largest impact on purchase behavior for both single and partnered.

4.5.4 Social Desirability
One way to evaluate social desirability effects is to consider how the relationships differ for low vs high rating individuals. The results are scored 10-20, and the respondents are split into low and high SocDesire groups. The scores of 15 and under is the low group, while, that of over 15 is the high group, as is shown below in Figure 8.0. Figure 8.0 Social Desirability

Figure 8.1 Model Summary | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | High | 1.. | .733 | .537 | .525 | .86833 | Low | 1 | .708 | .502 | .494 | .79512 | a. Social desirability b. Predictors:(Constant), Happiness, Recreation, Confidence, Recognition, SocApproval, Centrality, FashInnovativeness, Success, ImageExpression |

Figure 8.1 indicates that the strength of correlation (R) and proportion of variance (R Square) of high social desirability (.733, .537) are higher than that of low (.708, .502). And the Adjusted R Square shows that 52.5% of variation for high one and 49.4% of that for low is accounted for by the model.

Figure 8.2 ANOVA | Model | Sig. | High | 1.. | Regression | .000 | Low | 1.. | Regression | .000 |

Figure 8.2 indicates that F statistics of high and low social desirability are both .000. It means the predictors do a good job explaining the variation in the dependent variable.

Figure 8.3 Coefficients | Model | Standardized Coefficients | Sig. | | Beta | | | High | Low | High | Low | (Constant) | | | .409 | .315 | SocApproval | .105 | .224 | .037 | .000 | Recognition | .044 | -.014 | .391 | .722 | ImageExpression | .175 | .089 | .006 | .055 | Recreation | -.156 | -.178 | .015 | .000 | Confidence | -.135 | -.140 | .003 | .000 | FashInnovativeness | .435 | .587 | .000 | .000 | Success | .132 | -.023 | .019 | .626 | Centrality | .289 | .216 | .000 | .000 | Happiness | -.124 | -.065 | -2.559 | .011 |

Figure 8.3 indicates that Recognition has no significant impact on purchase behavior for both high and low social desirability groups. In addition, ImageExpression and Success have no significant impact on purchase behavior only for low one. In other words, the other predictors are useful. Looking at the standardized coefficients, FashionInnovativeness has the largest impact on purchase behavior for both high and low one.

5.0 Findings and Recommendations

5.1 Interpretation of the data
5.1.1 Identifying the impact of individual characteristics on fashion clothing purchase behavior
All individual characteristics have impact on fashion purchasing behavior, except success. Fashion Innovativeness has the largest impact on fashion purchase behavior. This is not surprising because fashion clothing has been described as a code, and however, such a code is quite different from the other codes in other areas or languages (David, 1994). In other words, fashion varies from person to person. So, it is difficult to define fashion. Fashion Innovativeness makes them to create their own style, their own fashion which can be confident. However, that is surprising that Confidence is not the important impact on fashion purchase behavior. On the contrary, Centrality has the second strongest correlation with fashion purchasing behavior.

In addition, from Figure 3.0 Correlations Self Concept with purchase behavior, people who are more delicate, the higher fashion purchasing behavior they do. On the contrary, the more rugged, the lower it is. Similarly, people who are more excitable, the higher it is, and more calm they are, the lower it is. It is surprising that there is no significant correlation

5.1.2 Identifying the impact of intrinsic motives on fashion clothing purchase behavior
It can easily find that from the figure above, Image Expression and Recreation impact similarly on fashion purchase behavior, both have no significant impact on it, not only the gender, but also the Generational cohort, and even the Relationship status.

5.1.3 Identifying the impact of extrinsic motives on fashion clothing purchase behavior
As for the intrinsic motives, Social Approval and Recognition need to be considered. In order to gain social approval and recognition, people tend to pursue fashion clothing. In other words, social pressures may impact on fashion clothing purchase behavior.

5.1.4 Identifying any meaningful market segments in the fashion clothing market
Segmentation is the process of dividing a market into distinct subsets that behave in different ways or have dissimilar needs (Raulerson and Malraison, 2009). In the market segments, we will focus on the gender, generational cohort and relationship status. From the t-Test and multiple regression, there is no significant market segment in gender aspect.

As for the generational cohort, Fashion Innovativeness, Confidence and Centrality impact all generational groups. Just for the GenY, SocApproval has no significant impact on purchase behavior. In other words, unlike the other two generation GenY is not pursuing for the approval from the social, Figure 5.2 indicates that Success and Recognition have no significant impact on purchase behavior for both male and female. In addition, ImageExpression and Recreation have no significant impact on purchase behavior only for female. As only for male, is Confidence and Happiness. Therefore, they are not useful predictors. In other words, the other predictors are useful. Looking at the standardized coefficients, FashionInnovativeness has the largest impact on purchase behavior for both male and female.

5.1.5 Understanding how social desirability bias may influence the results of the research
Considering the social desirability is significant, because they will affects respondents’ answers and the drivers (Zikmund, Ward, Lowe, Winzar, & Babin, 2011). From Figure 8.3 Coefficients, Recognition is insignificant for both high and low social desirability levels. But,

6.0 Limitations
The respondents in each generational cohort are nonuniform, especially in the GenY, there is a huge number of respondents storing in the age of 18-20. Therefore, the sample may be not representative. In other words, it may impact the accuracy and generalizability of results. In addition, convenient sampling error may impact on the results. So, a good sampling frame is necessary.

Reliability of the results needs to be considered as well. There is a huge number of respondents for the survey, it is inevitable that some questions are not interested for some respondents. They may fill in answers in random ways. In other to improve this, an interesting questionnaire is necessary, in addition, it is one of the best ways to encourage respondents in a rewarding way. And inform them it is necessary to complete the survey carefully. Another factor may be influenced the reliability is the processing that enter the data, double check is a good approach to avoid it.

Raulerson, P., & Malraison, J. (2009). Market segmentation. (pp. 1-48). New York, NY: Springer New York. doi:10.1007/978-0-387-79951-3_3

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...items on the DISC Survey assess productive aspects of the styles; the other half focus on counterproductive aspects. This section of the feedback report summarizes your responses to the items measuring the productive aspects of the styles. After presenting your productive style results, targets for development are identified and an exercise for strengthening your least productive style is offered. Date: Sunday, November 02, 2014 Your DISC Profile (Productive Aspects) Note: The styles that are least extended on the profile represent opportunities for development. Copyright © 2000 by Center for Applied Research, Inc. All Rights Reserved. Page 2-1 DISC Results: Productive Aspects The profile graphically portrays the extent to which you reported on the survey that the productive aspects of the styles are descriptive of you. Thus, the styles with the greatest extensions (that is, the most shading) represent personal strengths. The styles with the weakest extensions are least descriptive of you and represent opportunities for development. These latter styles are likely, though are not necessarily, the same styles that were previously identified as least characteristic of you. Your percentile scores for the productive aspects of the DISC styles are listed immediately below, beginning with the style that is most descriptive of you. DISC Styles (Productive Aspects) 1. Directing 2. Supportive 3. Influencing 4. Contemplative Percentile Scores 100 99 98......

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...1. What is business research? Why should there be any question about the definition of research?     Business research is described as a planned out inquiry that give results that guides managers in making decisions. In other words, “it is a process of planning, acquiring, analyzing and disseminating relevant data, information and insights to decision makers in ways that mobilize the organization to take appropriate actions that, in turn, maximize performance”. Basically, it is the process of finding information, analyzing it and finding a possible solution that can make things better. It is the process that business managers use to solve issues that may occur in day to day business procedures.     There should be questions on the definition of research because research is not a standard that is done the same for everyone. It all depends on the person that is doing the research. Some people like to take specific steps and planning to ensure they are getting all the information before they make a decision. Others like to go straight to where they know they can get some information and make a decision on what they find. Research has to be defined so that everyone working on the same research understands what is being referred to when the word research is being used. (Cited: Cooper & Schindler, 2011) 2. Distinguish between an explanatory and predictive research study.     Explanatory research study is research conducted for a problem that has not been clearly defined. It...

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Consumer Behavior

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...Module 1 The Research Process * Is a scholarly activity aimed at finding new truths about a specific discipline basically designed to find solution to a problem. * Done in various fields of studies. * May be done in natural sciences like Biology, Chemistry and Physics. And in the field of social sciences such as Sociology, Psychology, Education, Anthropology, Industry and the like. Definitions of Research * a systematic patient study and investigation in some field of knowledge undertaken to discover or establish facts or principles (Webster’s New World Dictionary, 1994) * a systematic investigation in order to establish facts and new conclusions (Oxford, 1996) Definitions of Research * a systematic and objective analysis and recording of controlled observations that may lead to the development of generalizations, principles or theories resulting in prediction and possibly ultimate control of events (Best & Kahn, 1998) * purposive, systematic and scientific process of gathering, analyzing, classifying, organizing, presenting and interpreting data for the solution of a problem, for prediction, for invention, for the discovery of truth, or for the expansion or verification of existing knowledge, all for the preservation and improvement of the quality of life (Calderon, 2000) * a process of systematically examining and explaining the observables. It seeks to generate answers to questions but it also generates further questions for study....

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...RUNNING HEAD: Descriptive Research Questions Shekubah Kamara 15 February 2013 RES 500 Research Methods for the Health Science Module 1 Trident University Newton, Bower, and Williams (2004) outline the process of “devising and revising” a research question, be it descriptive, relational, or comparative. After reading their paper, write a three-page paper that addresses the following: How do descriptive research questions differ from questions of relationship? From questions of comparison? When pursuing a descriptive research questions, what is being measure is aimed to describe the variables you are measuring. When the use of the word describe is used, simply means that these research questions are focused to quantify variables. To recognize a descriptive of to use these question you will often use questions that start with words such as "How much?", "How often?", "What percentage?", and "What proportion?” but also sometimes questions starting "What is?" and "What are?". Descriptive research questions focus on only one variable and one group, but they can include multiple variables and groups. In comparison to questions of relationship or comparison it is also common when using descriptive research questions to measure percentages and proportions. However, if you are using an experimental or quasi-experimental research design, or a more involved relationship-based research design, you are more likely to use just one or two descriptive research questions as a......

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...Business Research Applications Paper Individuals and companies use research on a daily basis and frequently throughout the day – with or without necessarily even realizing it. Research might be conducted simply to uncover the answer to a question or determine a possible outcome to a situation. I have a close friend who works for a bank that is focusing heavily on delivering a great customer experience. She frequently shares her professional experiences with me and I find it very interesting – the mechanisms the company puts into place to measure results. It may not seem to be research in the traditional sense however the bank is constantly conducting research. Before considering the types of research conducted at this financial institution, first it is important to understand what types of research exist. According to the text book, there are four types of research: reporting, descriptive, predictive, and explanatory (Cooper & Schindler, 2011). The reporting method entails information collection. This can be a difficult task – collecting the information necessary. An important source for information is found in the gatekeeper or an expert regarding the topic being researched. The reporting method does not require interpretation – the information is as it is presented. This method can be considered one of “fact finding” as it might be considered simply a collection of facts. The descriptive method provides answers to questions of who, what, where, and how. Generally,......

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A Critical Evaluation of Women in Management Breaking the Glass Ceiling

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