AMB201: MARKETING & AUDIENCE RESEARCH Assessment 3: Predictors of Online Retail Shopping

Student name: Jenny Chan

Student number: n8738254

Tutor name: Jay Kim

Tutorial time: Friday 2pm-3pm

Due Date: 3 June 2016

Word count: 1993

Word count:

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Table of Contents Participant Reflection .............................................................................................................................. 2

Executive Summary ................................................................................................................................ 3

1.0 Introduction and Background............................................................................................................ 4

1.1 Importance of the research ............................................................................................................ 4

1.2 Scope of the report ........................................................................................................................ 4

1.3 Research problem/question ........................................................................................................... 4

1.4 Aim and objectives ....................................................................................................................... 4

2.0 Method .............................................................................................................................................. 5

2.1 Methodology considerations and assumptions ............................................................................. 5

2.2 Sample considerations .................................................................................................................. 5

2.3 Data collection and framework, and analytical considerations ..................................................... 6

3.0 Ethical considerations ....................................................................................................................... 6

4.0 Analysis............................................................................................................................................. 7

4.1 Data cleaning and editing .............................................................................................................. 7

4.2 Descriptive Data ............................................................................................................................ 7

4.3 Analysis for Objective 1 ............................................................................................................. 10

4.4 Analysis for Objective 2 ............................................................................................................. 12

4.4.1 Correlation ............................................................................................................................... 12

4.4.2 Regression ................................................................................................................................ 13

5.0 Discussion and Recommendations.................................................................................................. 14

5.1 Objective 1 .................................................................................................................................. 14

5.1 Objective 2 .................................................................................................................................. 14

6.0 Limitations ...................................................................................................................................... 15

7.0 References ....................................................................................................................................... 16

8.0 Appendices ...................................................................................................................................... 17

8.1 Objective 2 definition for two variables ..................................................................................... 17

8.2 Surveys ........................................................................................................................................ 17

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Participant Reflection

The project I chose to participate was Handbags! Showy and Blingy, or Subtle and confident

which it is an online study of quantitative survey based on the brand research on the

handbags. Reason why I took this participant as a quantitative survey project because it is an

good experience for me as a participant which I am interested in handbags fashion and I have

the knowledge about the handbags design include luxury handbags. Before I take this project

participant, I read the description on this project on the handbags research is to examining on

the consumer perceptions of different types of luxury handbags. The participation project is

based on the survey questions include the knowledge question about the luxury handbags

brands and there is a two picture of the luxury handbags product brands are Coach and Louis

Vuitton. While I did the survey, the Coach products with an equally price show the two

similar products include handbags size design but different colour and pattern compare to

Louis Vuitton. Louis Vuitton products has the equally price which it show the pictures of the

same bags design but the Louis Vuitton logo is different on the bags. The first bag of Louis

Vuitton logo is the middle of the bags and second bag is on the right hand corner. Overall

with this survey are negative and positive questions for people are willing to buy the luxury

bags design. Also there is a question in the survey is that if my friends were shopping for a

handbag and I would recommend to my friend which bag design of Coach and Louis Vuitton

between those two bags design. The use of this participate project experience would use my

own survey question as different question. Using with my own questions in the survey would

be easier because I can use my own questions by using the scale 1 to 7. 1 is disagree and 7 is

agree which it can be relate how people see the brand of the handbags products for example:

would you buy a handbags with a good quality products, I am motivated to buy luxury

products, did some research on luxury handbags products and have you own any luxury

handbags products. The researchers who are interested and have the knowledge in handbags

can also take part in research as a participant because it shows that the researchers can see

who take part of this project and they can have their own opinion because there is no right or

wrong answer in the survey.

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Executive Summary

This descriptive research report is to investigate the determinants of Australian consumers’

attitude toward online retail shopping purchase behaviour and it is builds on previous

qualitative research. The two objectives are based on the cognitive dimension of attitude will

be analysed as the dependent variables includes:

The segment is analysing on the relationship between age cohort and gender

The t-test used the two group is different to their attitudes toward online shopping

Measure the relationship between price consciousness and convenience seeking of

individual characteristics

The regression and correlation with the two variables characteristics of the result with

positive or negative significant.

The research report is the recommendation is that the two variables of attitudes toward online

retail shopping is that the frequency of age is that the younger and older cohort. The future

research is to improve in the market research by the respondent will conducted the survey

without error.

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1.0 Introduction and Background

1.1 Importance of the research

The importance of the research is to be part as the market research is to systematic and

objective process of generating the information by making in a marketing decision on the

Australian adults with their attitudes toward online shopping (Zikmund, D’Alessandro,

Winzar, Lowe & Babin, 2014, p.6). The research is important to be part of the market

research is to use the descriptive research with their behaviour on the Australian consumers

attitudes towards online shopping by their behaviour, as well as any potential market

segment.

This research is related to the findings from the previous qualitative research with their

experience online shopping. Quantitative research is important to allow the questioning of

facts necessary determine a course of action which it building the insight from the qualitative

research (Zikmund et al, 2014, p72). Unlike the exploratory research is conducted the initial

stage of the research process and the descriptive research being undertaken based on a

previous research to understanding of the research problem (Zikmund et al, 2014).

1.2 Scope of the report

The scope of this research report is based on the Australian adults consumers’ behaviour

concerns with their attitudes toward online shopping. The role of this research is stage 2 of 2

in the research report process. The participants must be over 18 to participate in surveys. The

participants must have one respondent aged of 18-40 which it is younger cohort and one

respondent aged of 41+ which it is older cohort. This report will cover on segmentations

variables and characteristics constructs based on the attitudes toward online retail shopping

cognitive.

1.3 Research problem/question

The research question is to show the behaviour on the determinants of Australian consumers’

attitudes toward online shopping.

1.4 Aim and objectives

The aim and objectives of this report are to explore the quantitative research on the

determinants of Australian consumers’ attitudes toward online retail shopping. The two

specific objectives used in the survey include:

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Objective 1: to examine if attitude toward online retail shopping differ across population

segments is based on the younger verse older, men verse women and married verse single of

the target population.

Objective 2: to determine the impact of individual characteristics on attitudes toward online

retail shopping is various each individual characteristics relate to the respondents attitude

toward online shopping.

2.0 Method

2.1 Methodology considerations and assumptions

The methodology in the report use the descriptive research is being is use quantitative

research techniques is the objective to conducted the survey are designed of online shopping

behaviour (Zikmund et al, 2014). The descriptive research is to describe the characteristics of

the target population and conducted to help to segment the market and often to reveal the

nature of shoppiung or other consumer behaviour (Zikmund et al, 2014, p.25). This research

study is cross-sectional are surveys to divide the sample by age into subgroups and the

respondents are only interviewed once (Zikmund, Ward, Lowe, Winza r & Babin, 2011,

p134). Cross-sectional studies are time and cost efficient, measure the behaviour and attitudes

at a point of time and the predictability of findings can be questionable (Zikmund et al, 2011,

p135). The primary data is that the researcher specific purpose of addressing the problem

(Malhotra, Hall, Shaw & Oppenheim, 2006, p138) to gather and assembled specifically for

the research at hand (Zikmund et al, 2014, p.23-4). It is assumed that the participants have

correct insight with their own behaviour and being honest with these as accuracy of

paramount in descriptive research ((Zikmund et al, 2014, p.25). The number of male and

female from each age have been selected as assumed the sample representative. However, the

random sample error and systematic errors is related to the sampling process can be

representative of the sample (Zikmund et al, 2011, p329-30).

2.2 Sample considerations

The sample is the target population of the survey is Australian adults who regularly use the

internet aged of 18 and above. The target population sample size is 748. The probability

sampling techniques use the population known as non-zero probability in the research

selection (Zikmund et al, 2014, p355). With these sampling is a quota sampling which the

Australian consumers’ attitude toward shopping which it is various subgroups in the target

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population to represent the sample with their characteristics result (Zikmund et al, 2011,

p338).

2.3 Data collection and framework, and analytical considerations

The research is generally involves with quantitative techniques in this report. Researchers

were divided two equal groups according to the family surname. One group gathered two

male respondents and other gathered two female respondents aged of 18-40 and aged 41+.

Each researcher interviewed one respondent from each age group using the same survey

document attached the consent form and the survey should takes approximately 10 to 15

minutes to answer all survey questions. Researcher uploaded the data onto online database

and leaving room for the systematic error. The survey use t-test, regression, correlation and

coefficient. The t-test is to choose the two segmentation variables of age cohort, gender,

relationship status and communication preference to statistically differ between with the two

groups by the variability these means. The correlation and regression is to choose the two

segmentation variables of risk aversion, price consciousness, variety seeking, convenience

seeking, impulsiveness and materialism. The correlation is the relationship between two

segment variables will be negative or positive correlation relationship (Zikmund et al, 2014,

p479). The regression is analysis to identifies the nature of the relationship using the equation

(Wilson, 2003, p215).

3.0 Ethical considerations

The ethics is the important part in the marketing research as research is depends on the

consumers willing be part of the research and willing to cooperate with their respondents

(AMSRS, 2015). The consumers can trust and rely on the assumption of the research must be

conducted honest, truthful and objectively with the respondents privacy (AMSRS, 2015). In

line with Queensland University of Technology (QUT) is that the data material will be

collected, maintained and retained in the ethics policy (QUT, 2016). The respondents are

entirely voluntary at all the research stages and were not misleading (AMSRS, 2015). The

Respondents are 18 and over, require to sign the consent form from the appendices section is

under QUT policy which the form explain the participants confirms and agrees to do the

surveys. The consent form describes the research, participant, expected benefits, risks and

privacy and confidentially (AMSRS, 2015).

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4.0 Analysis

4.1 Data cleaning and editing

The data was cleaned, issues with respondent data were resolved which some of the data sets

were removed and frequencies were checked to ensure that the data value were in range. Any

negative phrased items were reversed and the construct values were determined for each

respondent is to average the data across with their relevant items.

4.2 Descriptive Data

Table 1: Descriptive Statistics

* N Minimum Maximum Mean Std.

Deviation

ATTC 741 1.00 7.00 5.1109 1.18821

RA 741 1.50 7.00 4.6736 .94368

PC 741 1.50 7.00 4.9312 .96492

IMP 741 1.00 7.00 3.7038 1.16682

VS 741 1.86 6.43 4.5024 .74788

CS 741 2.43 7.00 4.7777 .79270

MAT 741 1.17 7.00 4.7665 .95731

Valid N

(listwise)

741

Table 1 show that the mean has the highest is PC at 4.9312 and the lowest is IMP at 3.7038.

However, the ATTC mean is higher at 5.1109 which the range is different to the other

variable constructs.

Figure 1: Age cohort

* Frequency Percent Valid Percent Cumulative

Percent

Valid Younger 370 49.9 49.9 49.9

Older 371 50.1 50.1 100.0

Total 741 100.0 100.0

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Figure 1 demonstrates there were similar numbers of respondents in each age cohort.

Figure 2: What is your gender?

* Frequency Percent Valid Percent Cumulative

Percent

Valid Male 419 56.5 56.5 56.5

Female 322 43.5 43.5 100.0

Total 741 100.0 100.0

The figure 2 explains the male is slightly more than female in the sample.

Figure 3: What is your relationship status?

* Frequency Percent Valid Percent Cumulative

Percent

Valid Single 263 35.5 35.5 35.5

Partnered 478 64.5 64.5 100.0

Total 741 100.0 100.0

Figure 3 shows that the number of partnered has more respondents than single who shop

online.

Figure 4: Which method of online communication do you use more frequently?

* Frequency Percent Valid Percent Cumulative

Percent

Valid Email 368 49.7 49.7 49.7

Instant

messaging

(online chat)

373 50.3 50.3 100.0

Total 741 100.0 100.0

Figure 4 explains that the two figures quite familiar, in figure 4 demonstrates respondents

prefer to instant messaging (online chat) than email.

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Figure 5: What is your gender? Age Cohort Crosstabulation

* Age Cohort Total

Younger Older

What is your gender Male 209 210 419

Female 161 161 322

Total 370 371 741

The figure 5 explains relatively the age is split between genders.

Figure 6: What is your relationship status? Age Cohort Crosstabulation

* Age Cohort Total

Younger Older

What is your relationship

status?

Single 222 41 263

Partnered 148 330 478

Total 370 371 741

Figure 6 shows that the younger group is more single and older group is more partnered.

Figure 7: Which method of online communication do you more frequently use? Age Cohort

Crosstabulation

* Age Cohort Total

Younger Older

Which online communication

do you more frequently use?

Email 75 293 368

Instant

messaging

(online chat)

295 78 373

Total 370 371 741

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Figure 7 is explain that the older group prefer email and compare to younger group prefer

instant messaging (online chat).

In figure 8 the graph shows the highest frequency of ages from to 18 to 23 years and whilst

the frequency of ages is relatively similar.

4.3 Analysis for Objective 1

Figure 9: Age Cohort group

* Age Cohort N Mean Std.

Deviation

Std. Error

Mean

ATTC Younger 370 5.5319 1.00000 .05199

Older 371 4.6911 1.21344 .06300

In figure 9 is that the table for older cohort has the highest mean at 5.5319 attitudes toward

online shopping than younger cohort at 4.6911.

Figure 10: t-test on Age Cohort

0

10

20

30

40

50

60

70

Freq

uen

cy

Ages

Figure 8: Frequency of Ages

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Levene’s test

for equality

of variance

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

ATTC Equal

variances

assumed

Equal

variances

not

assumed

18.851 .000 10.291 739 .000 .84079 .08170 .68039 1.00118

10.294 713.663 .000 .84079 .08168 .68043 1.00115

In figure 10 is assuming equal variance, a t-test showed that the difference between younger

and older cohort is that the t-test is significant at .000 which it is below 0.05. This indicates

that the means difference for younger and older cohort.

Figure 11: Gender group

* What is your

gender?

N Mean Std.

Deviation

Std. Error

Mean

ATTC Male 419 5.1499 1.8736 .05801

Female 322 5.0602 1.8924 .06627

Figure 11 is that the male has the highest mean at 5.1499, while the mean attitude towards

online shopping for female was 5.0602.

Figure 12: t-test on Gender

Levene’s test

for equality

of variance

t-test for equality of means

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f Sig. t df Sig.

(2

tailed)

Mean

difference

Std error

difference

95% confidence

interval of the

difference

Lower Upper

ATTC Equal

variances

assumed

Equal

variances

not

assumed

.046 .831 1.018 739 .309 .08963 .08806 -

.08324

.26250

1.018 690.159 .309 .08963 .08806 -

.08329

.29256

The figure 12 assuming equal variance that the t-test showed that the difference the means for

male and female was not significant at .309. This indicates that the .309 is higher than 0.05

meaning there is no significant difference between male and female.

4.4 Analysis for Objective 2

4.4.1 Correlation

Table 2: Individual Characteristics

Price Consciousness Pearson Correlation

Sig. (2-tailed)

N

.024

.512

741

Convenience Seeking Pearson Correlation

Sig. (2-tailed)

N

.321**

.000

741

*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

Correlation is measure of the relationship between two variables showed the result that it is a

moderate positive significant of price consciousness and convenience seeking which it is

indicates as attitude toward online retail shopping.

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4.4.2 Regression

Figure 13: Price consciousness model summary

Model* R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .024ᵃ .001 -.001 1.18867

a. Predictors: (Constant), PC

Figure 13 shows that the R indicates correlation between predicted values and observed data

is .024². R square is the proportion of the variance in the dependent variable explained by the

regression equation which it is .001 and the adjusted R Square is the value at -.001 which

means it is 1% of the dependent variable by the model.

Figure 14: Price Consciousness Coefficients Regression

Model*

Unstandardized Coefficients Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 4.965 .228

0.24

21.818 .000

PC .030 0.45 .656 .512

a. Dependent Variables: ATTC

Figure 14 display that the individual characteristic of price consciousness is significant of

dependent variable of 0.24 and to predict the estimate of a person’s attitude toward online

shopping to give the good estimate for the ATTC. The predict estimate is 3 x 4.965+0.30=

15.195. The predict estimate is 15.195.

Figure 15: Convenience Seeking Model Summary

Model* R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .321ᵃ .103 .102 1.12604

a. Predictors: (Constant), CS

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Figure 15 for R is indicates that the correlation of the values and observed data is .321. The

proportion of R Square of the variance in the dependent variable regression equation is .103

and the adjusted R Square is the value at .102 which means it is 1.02%.

Figure 16: Convenience Seeking Coefficients Regression

Model*

Unstandardized Coefficients Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 2.811 .253

.321

11.117 .000

CS .481 .052 9.217 .000

a. Dependent Variable: ATTC

Figure 16 show that the individual characteristic of convenience seeking is significant of .321

which it is below 0.05 to predict the estimate of a person’s attitude toward online shopping to

give the estimate for ATTC. The predict equation is 2 x .481+2.811= 3.773. The predict

estimate is 3.773.

5.0 Discussion and Recommendations

5.1 Objective 1

The objective 1 is to examine on the different population of the age cohort and gender

variables if attitudes toward online retail shopping differ across population segments on two

segmentation variables are age cohort and gender. In figure 9 and 11 shows that the males

and female have different behaviour attitudes toward online shopping, therefore that male in

older cohort have the most population. Figure 10 and 12 have the equal variance for gender

and age cohort. The recommendation is that the two segmentations variables is to have male

and female similar result with their behaviour towards shopping online.

5.1 Objective 2

The objective 2 is to determine the impact of individual characteristics on attitudes toward

online retail shopping based on two individual characteristics variables are price

consciousness and convenience seeking (appendix 8.1). The behaviour of the two variables

behaviour in table 2 for the correlation show that the attitude is moderate positive and the

regression in figure 14 and 16 is the coefficient with their attitude of how it is convenience

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and the price for online shopping. The recommendation for the two variables for correlation

and the regression to make strong positive instead of moderate positive with their attitudes

toward online shopping.

6.0 Limitations

In figure 8 Frequency of Ages demonstrates is that the 18 to 23 years has the highest

frequency and compare to 31 to 38 years old has the lowest frequency. Whilst the frequency

of ages is that the sample is not representative which it is impacts on the accuracy and

generalisability of the result. Random sampling error result may occur through statistical

fluctuation due of chance variation (Zikmund, et al, 2011, p329). To improve the random

sampling error by using the sample frame which relate to the target population to subset the

list of infeasible is highly of how representative the subset (Zikumnd et al, 2014, p349).

Interviewer cheating occurs when an interviewer falsifies the entire questionnaires or filled in

the answers to intentionally skipped (Zikumnd, et al, 2014, p133). To improve for the future

research by telling the interviewers with a small number of respondents will be called back to

confirm whether the survey was conducted (Zikumd et al. 2011, p 133).

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7.0 References

AMSRS. (2015) Code of Professional Behaviour. Retrieved from

http://www.amsrs.com.au/documents/item/194

Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2006). Marketing Research: An

applied orientation (3rd

ed). French Forest, New South Wales: Pearson Australia.

QUT. (2016). D/2.6 QUT Code of Conduct for Research. Retrieved from Queensland

University of Technology: http://www.mopp.qut.edu.au/D/D_02_06.jsp

Wilson, A. (2003). Marketing Research. An Integrated Approach. Harlow, England, Pearson.

Zikmund, W., D'Alessandro, S., Winzar, H., Lowe, B., & Babin, B. (2014). Marketing

Research (3rd

ed). South Melbourne, Victoria: Cengage Learning Australia.

Zikmund, W.G., Ward, S., Lowe, B., Winzar, H., & Babin, B. J. (2011). Marketing

Research (2nd

ed.). South Melbourne, Victoria: Cengage Learning.

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8.0 Appendices

8.1 Objective 2 definition for two variables

Convenience Seeking – refers to searching for ways of achieving tasks with minimal

difficulty

1. I hate to spend time gathering information on products

2. I do not like complicated things

3. It is convenient to shop from home

4. It is important to me that I can shop anytime I choose

5. It is important to me that I can shop no matter where I am

6. The ability to quickly compare products is important to me

7. When shopping, I like to find what I want quickly

Price consciousness – refers to having a vigilance for difference in price among available

options and avoiding those purchases that are too expensive.

1. I usually buy the cheapest product available

2. I usually purchase items on sale

3. I often find myself checking prices

4. A person can save a lot of money by shopping for bargains

8.2 Surveys