amb201 marketing & audience research€¦ · analysis that can further aid marketing decisions....

AMB201 MARKETING & AUDIENCE RESEARCH QUANTITATIVE PROJECT REPORT

SARAH PRAKASH [N9396543]

WORD COUNT: 2,192 TUTOR/TUTORIAL NO: JEREMY TAN, T3 TUES 1-2PM

DUE DATE: JUNE 2, 2017

Table of Contents

Participation Reflection............................................................................................. 1

Executive Summary .................................................................................................. 2

1.0 Introduction and Background ............................................................................ 3

1.1 Importance of the Research........................................................................................... 3

1.2 Scope of the Report ....................................................................................................... 3

1.3 Research Question ........................................................................................................ 3

1.4 Aims and Objectives ...................................................................................................... 4

2.0 Method .................................................................................................................. 4

2.1 Methodical Considerations and Assumptions ................................................................ 4

2.2 Sample Considerations .................................................................................................. 4

2.3 Data Collection, Framework and Analytical Considerations ........................................... 5

3.0 Ethical Considerations ........................................................................................ 5

Table 1: Examples of industry guidelines .................................................................... 6

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

4.1 Data Cleaning and Editing ............................................................................................. 7

Image 1: Example of a data entry error ....................................................................... 7

4.2 Descriptive Statistics ..................................................................................................... 7

Figure 1: Age Cohorts Frequencies ............................................................................ 7

Figure 2: Gender Frequencies .................................................................................... 8

Figure 3: Descriptive Statistics .................................................................................... 8

Figure 4: Age and Gender Crosstabulation ................................................................. 9

Graph 1: Spread of Age Cohorts ................................................................................. 9

4.3 Analysis for Objective 1 ................................................................................................. 9

4.3.1 Question 1) .............................................................................................................. 9

Figure 5: Age Cohort Descriptive Statistics (ATTA) ................................................... 10

Figure 6: Age Cohort T-Test Statistics ...................................................................... 10

4.3.2 Question 2) ............................................................................................................ 10

Figure 7: Communication Preference Descriptive Statistics (ATTA) .......................... 10

Figure 8: Communication Preference T-Test Statistics ............................................. 11

Figure 9: Age and Communication Preference Crosstabulation ................................ 11

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

Figure 10: Correlations ............................................................................................. 12

4.5 Analysis for Objective 3 ............................................................................................... 12

4.5.1 Question 1) ............................................................................................................ 12

Figure 11: Risk Aversion Model Summary ................................................................ 12

Figure 12: Risk Aversion ANOVA Table .................................................................... 13

Figure 10: Risk Aversion Coefficients Table .............................................................. 13

4.5.2 Question 2) ............................................................................................................ 14

Figure 14: Price Consciousness Model Summary ..................................................... 14

Figure 15: Price Consciousness ANOVA Table ........................................................ 14

Figure 16: Price Consciousness Coefficients Table .................................................. 14

5.0 Discussion and Recommendations ................................................................. 15

5.1 Objective 1: Interpretation and implications of the data ................................................ 15



5.4 Application to Business and Future Recommendations ............................................... 16

Table 2: Marketing Mix Recommendation for Online Business ................................. 16

6.0 Limitations ......................................................................................................... 17

Reference List .......................................................................................................... 18

Appendices .............................................................................................................. 20

Appendix 1: Survey 1 – Women 18-40 years ..................................................................... 20

Appendix 2: Survey 2 – Women 41+ years ........................................................................ 23

1

Participant Reflection

Many more online studies were available for this report, and due to the ease of access,

time slot availability and the subjects available, I chose to do two online studies. These

quantitative studies were:

Survey of attitudes toward television advertisements; and

Employee Gratitude Survey.

I chose these studies as they were of particular interest and relevance to me. As an

advertising student, the survey of attitude toward television advertisements piqued my

interest. The survey involved being presented a set of questions regarding attitudes

towards three brands, then watching a video advertisement from the company,

followed by the same questions initially asked, to see if our opinions had changed

since watching the videos. Near the end of the survey it was revealed how different

themes of advertisements can alter a consumers’ perspective on the entire brand. I

found this survey effective and interesting, as the use of videos helped to keep the

participant’s attention, and it was easily to discern what the purpose of the survey was.

The second survey was chosen as I felt it had relevance as I am currently employed.

The survey consisted of numerous questions regarding opinions and attitudes towards

and about our organisation and our roles. These questions asked the respondent to

choose a rating from strongly disagree to strongly agree, and finished with asking for

further feedback regarding our opinion on the relationship between employee gratitude

and organisations. I found this survey less easily discernible, as I did not finish the

survey feeling as if it had revealed or helped me understand something I had not

considered before. In the first survey, I had finished it with a better understanding of

how an advertisement can change a consumers’ mind. The second survey was also

somewhat tedious and uninteresting, as it was much less engaging.

Researchers should also participate in their experiments as it would help them to

gauge the type of audience they should be engaging with; an audience that can relate

to the topic. It also allows for them to understand the best way to engage with the

respondent, as a more interactive or less repetitive nature might increase interest and

reduce the likelihood of absent-minded responses.

2

Executive Summary

This report builds on the findings from the qualitative report, and aims to further

investigate into Australian consumer attitudes towards online retail shopping. A

quantitative study using surveys was conducted, where characteristic constructs were

determined and analysed in relation to affective dimensional attitude. The study found

that Risk Aversion is a significant determinant and predictor of attitudes, and an

increase in the construct will decrease a consumers’ attitude towards online shopping.

3

1.0 Introduction and Background

1.1 Importance of the Research

Australia’s annual growth in online retail sales reached a staggering 11.7% in January

this year, with an estimated $21.83 billion spent online in the previous twelve months

alone (McDonald, 2017). International trends have shown a growing preference

towards online shopping with the main motivators being lower pricing (61%) and

convenience (60%) (Stancombe Research and Planning, 2012, p.6). A NSW Fair

Trading study found that while there is extensive research into online shopping

behaviours, there is insubstantial research into attitudes towards it (p.3). To

successfully tap into the potential of online shopping and develop strategies that

effectively target these drivers, marketers need to develop an understanding into

consumer psychological traits (Leyiaro, 2015, p.4). Building on the in-depth consumer

understanding developed in the qualitative report, this report will provide a statistical

analysis that can further aid marketing decisions.

1.2 Scope of the Report

Where the previous report focussed on ‘why’ consumers shop online, this report

incorporates a descriptive research method of surveying, where the focus was instead

on the ‘how’, ‘what’, ‘where’, ‘when’ and ‘who’ (Hair & Lukas, 2014, p.12). The

structured questions revolved around three research objectives that aim to examine

determinants of attitudes towards online retail shopping. This report involves analysing

survey data collected from a sample of Australian men and women from two age

groups (18-40 years; 41+ years). To keep the topic focussed, the respondents must

regularly use the Internet, but do not need to have previously shopped online.

1.3 Research Question

In order to produce accurate research data and clearer overall results, the quantitative

research question must be similar to the qualitative research question. Therefore, the

research question of “What are the determinants of Australian consumers’ attitudes

towards online retail shopping?” is most suitable.

4

1.4 Aims and Objectives

The aim of this report is to examine the determinants of Australian consumers’

attitudes towards online retail shopping through quantitative methods. The research

objectives of this report are stated below, and were used to help structure the survey

questionnaire.

Objective 1) To examine if attitudes toward online retail shopping differ across

population segments;

Objective 2) To understand the relationship between individual characteristics

and attitudes toward online retail shopping;

Objective 3) To determine which individual characteristics can be used to

predict attitudes toward online retail shopping.

2.0 Method

2.1 Methodological Considerations and Assumptions

Quantitative research was the most suitable method for this report, as it allows for an

emphasis on the statistical analysis and comprises of predetermined responses (Hair

& Lukas, 2014, p.13). The framework of this survey allows for an accurate investigation

into relationships between characteristics and behavioural constructs. This report

utilised a descriptive research approach, with an aim to describe the characteristics of

a target population (p.12). Considerations and assumptions include the:

- Data Accuracy: Of the 889 surveys originally conducted, only 885 remained

after the data was cleaned, assuming these are accurate.

- Data Representativeness: While there was a very large survey sample, any

hypotheses or recommendations are based off this sample.

2.2 Sample Considerations

The target audience for the study required respondents to be English-speaking

Australian adults who regularly use the Internet, but need not necessarily have

previously engaged in online retail shopping. The sample provided was large and fairly

evenly split between both genders (males: 53.2%; females: 46.8%) and age groups

(younger: 49.8%; older: 50.2%).

5

For this study, the most suitable sampling technique was a convenience sample, a

non-probability sampling method where respondents are chosen at the convenience

of the researcher (Hair & Lukas, 2014, p.260). This technique was used due to time

restraints, low cost and the convenience, but due to the lack of selection process, the

sample may not accurately represent the entire population (p.260).

2.3 Data Collection, Framework and Analytical Considerations

There were many stages in the collection of data for this study. The first stage required

researchers to distribute a hard copy of the pre-written survey to a suitable respondent

from a younger cohort (18-40 years) and an older cohort (41+ years). The survey

results were then uploaded onto a QUT survey response website, where the data was

cleaned then analysed using the SPSS software.

The survey responses were based on a Likert seven-point scale, allowing a researcher

to gain insight into the favourability of a persons’ attitude towards an object, rather

than a definitive ‘agree’ or ‘disagree’ (Hair & Lukas, 2014, p.294).

3.0 Ethical Considerations

Researchers are dependent on the voluntary assistance of respondents, which is

based on the participant’s assurance that the research is being executed in an honest

manner for the sole purpose of collecting and analysing information (Australian Market

& Social Research Society, 2016, p.3). With such a heavy reliance on the public for

potentially sensitive information, marketing researchers must adhere to codes of

conduct or risk violation consequences (Laczniak, 2012, p.78).

This study also followed the QUT Code of Conduct for Research, specifically sections:

- 2.6.1: Principles for the responsible conduct of research;

- 2.6.4: Research misconduct;

- 2.6.5: Management of research data; and

- 2.6.7: Publication and dissemination of research findings.

Researchers must adhere to these codes as they prohibit inappropriate and unethical

conduct, and offer confidentiality for the respondent and their responses (QUT, 2017).

Examples of some industry codes that have been adhered to during this study are

included in Table 1 below.

6

Table 1: Examples of industry guidelines adhered to in this study.

Industry Guideline Code Code Description QUT Consent Form

National Statement

on Ethical Conduct

in Human Research

(National Health

and Medical

Research Council,

2007, p.18)

2.2.20 Participants are

entitled to withdraw

from the research at

any stage.

Your participation in this project

is entirely voluntary. You may

withdraw your participation at any

time during the survey without

comment or penalty…

Code of

Professional

Behaviour

(Australian Market

& Social Research

Society, 2016,

p.10)

10 Participant’s

anonymity must be

strictly preserved.

All responses are anonymous

and will be treated confidentially.

The names of individual persons

are not required in any of the

responses.

The Market and

Social Research

Privacy Code

(Association of

Market and Social

Research

Organisations,

2007, p.13).

4.3 A research

organisation must

take reasonable

steps to protect any

identified information

that it holds from

misuse and loss and

from unauthorised

access, modification,

disclosure and

transfer.

Only the person conducting the

survey and the teaching team

involved in the project will be

able to link responses with the

identities of participants. Your

name will not be entered into the

class database. Any hardcopy

surveys will be kept in a secure

place and only the researcher

and the teaching team involved

will have access to them.

7

4.0 Analysis

For this report, the affective dimensional attitude (ATTA) will be analysed as the

dependent variable.

4.1 Data Cleaning and Editing

Of the 889 surveys conducted, only 885 will be analysed for this report due to data

cleaning and editing. Data cleaning and editing involves the identification and

correction of anomalies in a dataset in order to provide more accurate study results

(Hellerstein, 2008, p.1). The cleaning and editing process for this study was conducted

through the SPSS software. An example of an error is shown below, where respondent

#216 put the name of the suburb into the response box rather than the postcode.

Image 1: Example of a data entry error

However, the problem of reliability of the cleaned datasets also arises. If a data entry

is edited to a presumed input, this may reduce the validity of the dataset results.

Removing entries that cannot be edited may produce an inaccurate dataset, resulting

in an unreliable recommendation. To address this the researcher should enter the data

themselves, but there is always a degree of human error (IBM Corporation, 2011).

4.2 Descriptive Statistics

After cleaning, the sample size was 885. Figures 1 and 2 below shows the group sizes

relating to age cohorts and gender. As shown, the groups are almost exactly evenly

balanced.

Figure 1: Age Cohorts Frequencies

Age Cohort Frequency Percent

Younger 441 49.8

Older 444 50.2

Total 885 100.0

8

Figure 2: Gender Frequencies

Gender Frequency Percent

Male 471 53.2

Female 414 46.8

Total 885 100.0

Figure 3 below shows the characteristic constructs relevant scores such as their

means and standard deviations. The means are similar, with the lowest being

Impulsiveness at 3.7155 and highest being Price Consciousness at 4.8912.

Figure 3: Descriptive Statistics

Constructs N Minimum Maximum Mean Std. Deviation

ATTA 885 1.00 7.00 4.3062 1,41776

Variety Seeking 885 1.43 7.00 4.5038 0.74129

Risk Aversion 885 2.17 7.00 4.6105 0.93299

Price Consciousness 885 1.00 7.00 4.8912 0.98553

Impulsiveness 885 1.00 6.75 3.7155 1.14605

Convenience Seeking 885 2.00 7.00 4.7430 0.78823

Materialism 885 1.17 7.00 4.3162 1.06435

The following crosstabulation shows the relationships between the age cohorts and

gender. Figure 4 shows a fairly even spread between the genders in both younger and

older cohorts. Graph 1 shows the spread of the two age cohorts, with a concentration

around the 18-30 years and 45-55 years. Therefore, while the age cohorts are

relatively even, the spread is not.

9

Figure 4: Age and Gender Crosstabulation

Gender Age Cohort

Total Younger Older

Male 237 234 471

Female 204 210 414

Total 441 444 885

Graph 1: Spread of Age Cohorts

4.3 Analysis for Objective 1

4.3.1 Question 1) Does attitude towards online retail shopping (ATTA) differ between

the younger and older cohorts?

To address the objective, t-tests will be analysed using the variables of age and

communication preference. The t-tests will allow for comparison between two groups

to answer a question. Figure 5 below displays the descriptive statistics of these

groups, and Figure 6 is the relevant t-test.

10

Figure 5: Age Cohort Descriptive Statistics (ATTA)

Age Cohort N Mean Std. Deviation Std. Error Mean

Younger 441 4.8764 1.28398 0.06114

Older 444 3.7399 1.31471 0.06239

Referring to the means in Figure 5, it appears that there may be a small difference in

the age cohorts’ attitudes towards online shopping, but it is uncertain if this is

statistically significant. The younger cohort holds a mean of 4.8764, with the older

cohort at 3.7399. To determine if there is a likely relationship between these two

variables, the t-test in Figure 6 will be analysed.

Figure 6: Age Cohort T-Test Statistics

Levene’s Test

for Equality of

Variances

T-Test for Equality of Means

F Sig. t df Sig. (2-tailed)

Mean Diff.

Std. Error Diff.

95% Confidence

Lower Upper

Equal Variances Assumed

0.118 0.732 13.009 883 0.000 1.13655 0.8736 0.96509 1.30802

Assuming equal variance, the t-value was 13.009 and the significance (2-tailed) was

0.000. As the significance value is less than 0.05, this shows there is a significant

difference, indicating that the younger cohort are more likely to engage in online

shopping than their older peers.

4.3.2 Question 2) Does attitude towards online retail shopping (ATTA) differ between

those who more frequently use email compared to online chat?

Figure 7: Communication Preference Descriptive Statistics (ATTA)

Preference N Mean Std. Deviation Std. Error Mean

Email 410 3.8396 1.28575 0.06350

Online Chat 475 4.7089 1.40407 0.06442

11

Figure 7 shows that the mean for communication preference of email lies at 3.8396,

and at 4.7089 for online chat. These figures are similar to those shown in Figure 5,

once again suggesting a small difference in attitudes towards online shopping.

Figure 8: Communication Preference T-Test Statistics

Levene’s Test

for Equality of

Variances

T-Test for Equality of Means

F Sig. t df Sig. (2-tailed)

Mean Diff.

Std. Error Diff.

95% Confidence

Lower Upper

Equal Variances Assumed

3.235 0.72 -9.548 883 0.000 -0.86931 0.09104 -1.0480 -0.6906

Assuming equal variance, the t-value was -9.548 and the significance (2-tailed) was

0.000. With the significance value below 0.05, this indicates a significant difference.

Figure 9: Age and Communication Preference Crosstabulation

Communication Preference

Age Cohort Total

Younger Older

Email 62 348 410

Online Chat 379 96 475

Total 441 444 885

The t-tests and descriptive statistics, along with the crosstabulation above (Figure 9),

indicate that there is a relationship between attitudes towards online retail shopping

(ATTA) and the variables, age cohorts and communication preference.


To address the objective, two individual constructs, Risk Aversion and Price

Consciousness, will be analysed using correlation analysis. The correlation analysis

will offer a measure on the relationship between the variable and attitude (ATTA).

12

Figure 10: Correlations

ATTA

Risk Aversion Pearson Correlation

Sig. (2-tailed)

N

-0.439

0.000

885

Price Consciousness Pearson Correlation

Sig. (2-tailed)

N

0.054

0.111

885

At -0.439, the strength of association for Risk Aversion is moderately strong, and as

the Pearson Correlation is above the 0.01 level and the significance value is below

0.05 (at 0.000), this suggests a significant correlation. Due to the negative direction of

the line, it is hypothesised that the greater Risk Aversion-driven a consumer is, the

more negative their ATTA will be.

Price Consciousness had a weak strength of association at 0.054, and a significance

value of 0.111, suggesting that the relationship is not significant, and may be due to

chance. Although weak, the positive direction of the line indicates that the more price

conscious a consumer is, the more positive their ATTA is.


4.5.1 Question 1) Is Risk Aversion a useful predictor of attitudes towards online

shopping?

A model summary, an ANOVA (Analysis of Variance) table and a coefficient table has

been produced to help investigate into which construct can be used to predict ATTA.

This objective will utilise bivariate regression analysis, which builds on concepts from

the correlations. It will provide an indication into the strength, direction and significance

of a relationship, but most importantly will allow one variable to be predicted. Once

again, Risk Aversion and Price Consciousness will be analysed.

Figure 11: Risk Aversion Model Summary

Model R R Squared Adj. R Squared Std. Error of the Estimate

1 0.439 1.93 0.192 0.06442

13

Figure 12: Risk Aversion ANOVA Table

Model Sum of Squares df Mean Square F Sig.

1

Regression

Residual

Total

342.949

1433.942

1776.891

1

883

884

342.949

1.624

211.183

0.000

Figure 13: Risk Aversion Coefficients Table

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

1

(Constant)

Risk Aversion

7.384

-0.668

0.216

0.046

-0.439

34.171

-14.532

0.000

0.000

Figure 11 displays key features, including the R, R Squared and the Adjusted R

Squared. The ANOVA table in Figure 12 will help test the overall significance of the

regression equation. Figure 13 shows the specific predictor variable and its’

significance level.

As shown in Figure 11, the Adjusted R Squared value is 0.192, indicating that 19.2%

of variation in attitudes is explained by this model. The standardised coefficient of the

predictor is -0.439 (Refer to Figure 13), indicating that the predictor has a strong yet

negative impact on ATTA. The significance value determined in Figure 13 (0.000)

indicates that Risk Aversion is a significant predictor of attitudes as it is under 0.05.

The low significance value in Figure 12 (0.000) is also lower than 0.05, signifying that

the equation is good at explaining the variation in the dependent variable. It would thus

be feasible to use the following regression equation to estimate ATTA prediction:

ATTA Prediction = -0.668*(Risk Aversion score) + 7.384

14

4.5.2 Question 2) Is price consciousness a useful predictor of attitudes toward online

shopping?

Figure 14: Price Consciousness Model Summary

Model R R Squared Adj. R Squared Std. Error of the Estimate

1 0.54 0.003 0.002 1.41652

Figure 15: Price Consciousness ANOVA Table

Model Sum of Squares df Mean Square F Sig.

1

Regression

Residual

Total

5.117

1771.773

1776.891

1

883

884

5.117

2.007

2.550

0.111

Figure 16: Price Consciousness Coefficients Table

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

1

(Constant)

Price

Consciousness

3.929

0.077

0.241

0.048

0.054

16.288

1.597

0.000

0.111

Figure 14 shows an Adjusted R Squared value of 0.002, indicating that 0.2% of

variation in attitudes is explained by this model. Figure 16 displays a standardised

coefficient of the predictor at 0.054, indicating that the predictor has a positive, weak

impact on ATTA. The significance value determined in Figure 16 of 0.111 indicates

that Price Consciousness is a non-significant predictor of attitudes, as it is over 0.05.

The significance value in Figure 15 is also greater than 0.05 at 0.111, signifying that

the equation does not explain significant variation in the dependent variable. It would

therefore not be feasible to use the following regression equation to estimate ATTA

prediction:

ATTA Prediction = 0.077*(Risk Aversion score) + 3.929

15

5.0 Discussion and Recommendations

5.1 Objective 1: Interpretation and implications of the data

The findings from the analysis of Objective 1 found that the younger cohort and those

who preferred online chat have a stronger ATTA. Referring to Figure 9, the younger

cohort were more likely to report online chat as their preferred communication method

than the older cohort. This result is unsurprising as there has been previous research

findings that indicate older consumers are reluctant to shop online, largely due to a

lack of technology experience, resistance to change and insisting on trying products

prior to purchasing (Emerald Group Publishing, 2015, p.6).


The findings from this analysis found that ATTA and Risk Aversion had a significant

correlation, with a negative, moderately-strong strength of association. This

relationship indicated that as a customer’s Risk Aversion increased, their ATTA

decreases. Like the first objective, this finding is predictable, as studies have found

that trust towards online merchants and security problems are greatly important when

determining online purchasing behaviours (Leyiaro, 2015, p.10).

The relationship between ATTA and Price Consciousness was non-significant, with a

weak, positive strength of association. This indicated that as a consumer becomes

more price conscious, their ATTA increases slightly. Price conscious consumers are

more likely purchase online due to a deal or lower pricing (Cheah, Phau & Liang, 2015,

p.765). These findings are unlikely to have any implications as they are not

unexpected.


The analysis found that only the Risk Aversion variable could be used to predict ATTA.

Price Consciousness findings indicated the variable is a non-significant predictor of

attitudes. This study has significant implications for both practice and understanding,

as marketers are researchers are able to input a consumers’ Risk Aversion score into

the equation and predict their ATTA score.

16

5.4 Application to Business and Future Recommendations

Recommendations for business application are found in Table 2 below. It is also

recommended that marketers and researchers implement and utilise the equation

derived from the findings to gain a better understanding of their target markets and

consumer behaviour. This study has successfully identified Risk Aversion as a

determinant that influences consumer attitudes towards online retail shopping. Further

research may determine if any of the other four constructs are also determinants.

Table 2: Marketing Mix Recommendation for Online Businesses

Marketing

Element Research Finding Business Application

Price

Price-sensitive consumers like

finding good deals, will often

experience purchase

satisfaction, and may become

repeat purchasers (Cheah, Phau

& Liang, 2015, p.765).

Implement online only incentives

that are far greater than in-store

deals. After the initial purchase,

continue to have return

incentives to keep loyalty.

Product

Older consumers are reluctant to

shop online due to a lack of

technology experience,

resistance to change and

insisting on trying products first.

If marketing to the older cohort,

implement strategies to reduce

the uncertainty often felt in their

online experience, largely

regarding poor IT skills.

Place

As a consumer becomes

increasingly distrustful in a site or

worried about security, they will

be less likely to shop online.

Ensure customers that the

business’ website is secure

through measures such as

HTTPS and PayPal.

Promotion

Findings show that while online

chat is the more preferred form

of communication, the younger

cohort make up almost 80% of

the group.

When marketing to the younger

cohort, offer online chat on the

business website. Offer email

communication on the website if

marketing to the older cohort,

17

6.0 Limitations

Confidence in the findings may be reduced due to factors such as the editing of the

data, as edited data entries may skew the sample results. Another issue is the

selecting of two characteristics and constructs to analyse, as they may also be

determinants of consumer attitudes. Improvements could be made in future research

studies, such as ensuring higher data credibility by interviewers entering survey

answers themselves, and broadening the scope of the study to investigate into all

characteristics and constructs.

18

Reference List

Association of Market and Social Research Organisations. (2007). The Market and

Social Research Privacy Code. Retrieved from

https://blackboard.qut.edu.au/bbcswebdav/pid-6676339-dt-content-rid-

7893422_1/courses/AMB201_17se1/AMSRO_Code%281%29.pdf

Australian Market & Social Research Society. (2016). Code of Professional

Behaviour. Retrieved from https://www.amsrs.com.au/documents/item/194

Cheah, I., Phau, I., & Liang, J. (2015). Factors influencing consumers’ attitudes and

purchase intentions of e-deals. Marketing Intelligence & Planning, 33(5), 763-

783. doi: 10.1108/ MIP-05-2014-0081

Chu, X., Morcos, J., Ilyas, I. F., Ouzzani, N., Papotti, P., Tang, N., & Ye, Y. (2015).

KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing.

Proceedings of the VLDB Endowment, 8(12). Retrieved from

https://cs.uwaterloo.ca/~x4chu/VLDB2015_demo.pdf

Emerald Group Publishing. (2015). A Focus on Consumer Behaviours and

Experiences in an Online Shopping Environment. Bradford, UK: Emerald Group

Publishing Ltd.

Hair, J.F. and Lukas, B. (2014). Marketing Research (4th Ed.), Sydney: McGraw Hill

Hellerstein, J. M. (2008). Quantitative Data Cleaning for Large Databases. Retrieved

from http://db.cs.berkeley.edu/jmh/papers/cleaning-unece.pdf

IBM Corporation. (2011). Data Cleaning. Retrieved from

https://www.ibm.com/support/knowledgecenter/SSLVQG_6.0.1/com.spss.ddl/d

mgr_cleaning.htm

Laczniak, G. (2012). Ethics of marketing. SAGE Brief Guide to Marketing Ethics, 72-

86. doi: 10.4135/9781452243962.n9

Leyiaro, L. (2015). Factors influencing consumer online buying behaviour in a project

based company. Retrieved from http://erepository.uonbi.ac.ke/

19

McDonald, B. (2017). NAB Online Retail Sales Index: January 2017. Retrieved from

http://business.nab.com.au/nab-online-retail-sales-index-monthly-update-

january-2017-22843/

National Health and Medical Research Council. (2007). National Statement on

Ethical Conduct in Human Research 2007. Retrieved from

https://www.nhmrc.gov.au/_files_nhmrc/publications/attachments/e72_national_

statement_may_2015_150514_a.pdf

QUT. (2017). MOPP - D/2.6 QUT Code of Conduct for Research. Retrieved from

http://www.mopp.qut.edu.au/D/D_02_06.jsp#D_02_06.02.mdoc

Stancombe Research and Planning. (2012). Quantitative research report: ‘Attitudes

towards online shopping and knowledge about shopping online securely’.

Retrieved from

http://www.fairtrading.nsw.gov.au/biz_res/ftweb/pdfs/Consumers/Quantitative_r

esearch_report_attitudes_towards_online_shopping_and_knowledge_about_sh

opping_online_securely.pdf

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Appendices

Appendix 1: Survey 1 – Women 18-40 years

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Appendix 2: Survey 2 – Women 41+ years

amb201 marketing & audience research€¦ · analysis that can further aid marketing decisions....

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