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Gabriella McSweeney Quantitative Project 2 N9422765

Queensland university of technology

AMB201 Marketing & Audience Research

Quantitative Report – Project 2 Topic: Predictors of Online Retail Shopping

Gabriella McSweeney – N 9422765

22nd of October 2015

Jay Kim – Tutorial 3pm-4pm Thursday’sWord Count: 1920

Table of Contents(i) Participation Reflection..................................................................................................................2

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http://www.google.com.au/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCOiD_u6bkccCFcQfpgodRgkBlA&url=http://misstango2.com/online-shopping/&ei=JaDBVajdC8S_mAXGkoSgCQ&bvm=bv.99261572,d.dGY&psig=AFQjCNFORQh3eZN5Nfx0OyQvpTZYr00r-g&ust=1438839185239589


(ii) Executive Summary/ abstract.....................................................................................................4

1.0 Introduction and Background.................................................................................................5

1.1 Importance of the research..................................................................................................5

1.2 Scope of the report..............................................................................................................5

1.3 Research problem/question.................................................................................................5

1.4 Aims and Objectives...........................................................................................................6

2.0 Method...................................................................................................................................7

2.1 Methodological considerations and assumptions................................................................7

2.2 Sample considerations........................................................................................................7

2.3 Data collection and framework, and analytical considerations...........................................9

3.0 Ethical considerations...........................................................................................................10

4.0 Analysis................................................................................................................................11

4.1 Cleaning and Editing........................................................................................................11

4.2 Descriptive Analysis.........................................................................................................11

4.3 T – Test Analysis..............................................................................................................12

4.4 Self-Concept.....................................................................................................................13

5.0 Discussion and Recommendations.......................................................................................18

5.1 Interpretation of the data based on the analysis you have undertaken...............................18

5.2 What this means for managers and for the next stage of the research...............................19

6.0 Limitations...........................................................................................................................20

6.1 Factors that affect the confidence that you have in your findings that impact your recommendations..........................................................................................................................20

7.0 References............................................................................................................................21

(i) Participation Reflection

Why you selected a particular project to participate in;

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As a part of AMB201 Quantitative Project 2, I participated in two (2) research projects on the

following topics:

- Evaluation of Media Announcements

- Consumer Ratings of Association: cities and tourism, brands and crisis, consumer

goods

I selected the above research topics as I found them the most interesting and appealing to me.

I believed that I would be able to give good insight to the researcher as I am confident and

aware of the topics.

What was involved in participating and what insights have you gained?

The initial stage of participating in these surveys was placing myself at a desk with a

computer. I read the consent form and signed the form prior to undertaking the research

questions. The first quantitative project on the evaluation of media announcements I read a

range of announcements on brands/labels and sponsoring certain events and then solved a

puzzle. After that was completed I was asked to try and remember the brands and companies

that sponsored/matched with the correct event. The puzzle was challenging as I was required

to remember which sponsors matched to each event. The second quantitative project on

consumer ratings of association was slightly similar. In the first part I had two words given to

me which I had to rate from weak, moderate or strong on how much they were associated

with each other. After completing a range of different ones on cites and tourism, brands and

crisis and consumer goods I was required to solve a puzzle. Similar to the other quantitative

project I was then required to try and remember which ones went together.

How you might use this experience to inform your own research;

After participating in this experience it has enable me to look at how researchers obtain data

from surveys. I believe that the survey projects were done creatively and effectively. I found

that the researcher used the puzzles as a distraction so the researcher would obtain the data

that stood out the most for the participant.

Whether you feel it is valuable for researchers to also take part in research as a participant.

After taking part in the whole experience I strongly believe it is essential for researchers to

take part in the research as a participant. Similar to the qualitative projects, I believe the

researchers will have a better understanding of where the participants are coming from when

they undergo the survey process and experience some of the pressure the participants may feel

towards answering certain questions. Similarly researchers will provide their own feedback at

the end and will relate more to the responses as they answered the same questions in the

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survey. Researchers can also recognise and make improvements to the questions in future

research to avoid bias.

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(ii) Executive Summary / Abstract

Descriptive Analysis Design is aimed to describe the characteristics of a target population and

is able to provide conclusive information. The aim of this Quantitative Research report is to

focus on the analysis of determinates that either influence or do not influence Australian

consumer’s attitudes towards online retail shopping.

Researchers are able to draw conclusions about the target population based on the sample. In

this report the target population is English speaking women from an age cohort of 18-40 years

and 41+ years and who regularly use the internet and uses quota, non-probability sampling.

The research method for collecting data was through surveys which collected large amounts

of data using a question and answer format. Due to the nature of the sampling techniques used

it is known that errors can arise. It is recognized that non response bias may be present so

researchers must ensure that the sample is representative of the target population. Though

sampling can be seen as an efficient form of data collection inaccuracy in survey data can

occur.

Due to the descriptive nature of this report the data needs to be accurate and considered in all

aspects of this report. In addition, ethics has to be considered in relation to confidentiality and

analysis of the data collection process.

It was revealed that convenience seeking, variety seeking and risk aversion were main

constructs that influenced consumers attitudes towards online retail shopping. Three (3) out of

the four P’s - place, promotion, and price can be adopted by markets to help fulfil consumers’

needs. It is recommended from this descriptive study that it may be of benefit for researchers

to conduct casual research to look and explain the cause and effect relationships between the

variables.

Non-probability sampling and social desirability bias are the limitations of this report.

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1.0 Introduction and Background

1.1 Importance of the Research

Descriptive analysis provides in-depth description of a target population in order to

draw meaningful conclusions from the research. This report is an extension to the

previously conducted exploratory and qualitative report, which explored the

characteristics of the target population rather than describing the characteristics of the

target population (Hair, 2014 , p. 12). From a theoretical point of view, researchers

must identify the concepts relevant to the research problem to obtain high quality data.

The goal of the construct development process is to precisely identify and define what

is to be measured (Hair, 2014 , p. 279). Literature reviews are also frequently used by

marketing researchers. Literature reviews are a comprehensive examination of

available information related to a topic of interest (Hair, 2014 , p. 59). From a

practical point of view marketing research is important as it is able to provide

background information for the current study, helps to inform researchers or managers

of consumer behaviour such as wants and needs that motivate them to buy, and helps

to advise the way marketers should engage with their consumers now and in the

future.

1.2 Scope of the Report

The scope of this report includes a descriptive analysis of survey data collected. This

report is based on self-reported behaviour and it aims to identify the determinants of

Australia consumer’s attitudes towards online retail shopping. This analysis includes

aspects of t-testing, correlation and regression all of which are comparative to

developed constructs. Surveys have been used to quantitatively examine the drivers of

online retail shopping behaviour. For anyone engaged in online retail, and for

marketers in general, it is important to know who these shoppers are, what motivates

them to buy, and how they might best be reached.

1.3 Research Problem/Question

Australians have embraced online retail shopping, highlighting the tangible benefits of

the digital economy (ACMA, 2011). Consequently, researchers want to understand

what the determinants of Australian Consumers’ attitudes towards online retail

shopping.

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1.4 Aims and Objectives

The aim of this report is to quantitatively explore determinates of the Australian

Consumers’ attitudes towards online retail shopping. To achieve this aim the

following objectives are key components of the research question:

i. To examine how self-concept dimensions relate to online retail shopping

attitudes

ii. To determine the impact of individual characteristics on online retail shopping

attitudes

iii. To evaluate how social desirability might influence the results of the research

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2.0 Method

2.1 Methodological Considerations and Assumptions

Descriptive research is used to describe the characteristics of given data in relation to a

research objective. Descriptive research does not address any ‘why’ questions

associated with a given research problem like exploratory research does, however it

does provide answers to the ‘how, what, where, when and who’ questions (Hair,

2014 , p. 12). Casual research is used to identify “cause and effect” relationships

(Hair, 2014 ). Due to this report being a quantitative research method it allows the

researcher to infer certain characteristics of a population, given the sample (Hair, 2014

, p. 12). This research was conducted through surveys using cross –sectional research

with data collected at a single point in time (Hair, 2014 ). Assumptions can be made in

using survey’s such as, the sample size is sufficient to reflect the target population and

participant’s insights are accurately recorded to gain greater insight. Therefore it is a

descriptive methodology for gathering quantitative data from a large group of people.

2.2 Sample Considerations

We identified whether a sample or census was needed to be considered. Due to the

nature of this report, a sample was conducted as it is more practical regarding cost,

time and the research design. By using a sample we are able to have generalisations

across the whole population. The sample of this data aims to infer attitudes of online

retail shopping about a population. The following stages of sampling model have been

used as the framework in sampling considerations from Marketing Research, by

Joseph Hair (Hair, 2014 ).

The Target population for this report includes English-speaking adults, above the age

of 18 who regularly use the internet. Enabling to quantitatively explore determinates

of the Australian Consumers’ attitudes towards online retail shopping

There is no sample frame.

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1. Define the Target Population

2. Select a Sample Frame


Non-Probability Sampling was considered and some elements of the population have

zero probability of being selected for this study.

Quota Sampling was considered, which forces researchers recruit certain

demographics. It enables us to have an equal number of males and females and age

group. In this report for example, respondents must be above 18years old, internet

users and English speaking. Two groups were formed to conduct in the survey being

between 18-40 years of age and the other group being 41 + years of age.

After cleaning, coding and response checks of the data was conducted the sample size

was equal to 659. As the sample size is relevantly large it is more representative of the

population making it an appropriate sample size (Unite for Sight , 2009-20015).

Units used to determine sample size include men aged 18-40 years, men 41+ years,

women aged 18-40 years and women 41+ years.

Market researcher goes out to collect data and framework.

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3. Probability or Non-Probability Sampling

4. Plan procedure for selecting sampling units

5. Determine Sample Size

6. Select Actual Sampling Units

7. Conduct Fieldwork


2.3 Data Collection and Framework and Analytical Considerations

For this report data was collected through surveys, which seek to gather large

quantities of data using question-and-answer format (Hair, 2014 , p. 171). As this

study is using a larger sample size using surveys it is an appropriate research method.

The survey uses free response questions, scale item questions, simple dichotomy,

frequency determination questions and checklist questions. Participants collected two

surveys based on their family names being names beginning with A-L conducted 2

surveys from each ago cohort: Men (18-40 years) Men (41+years) and names

beginning M-Z conducted 2 surveys from each age cohort: Women (1-40 years)

Women (41+years). Surveys can be communicated through face to face conversation,

over the phone and internet (Hair, 2014 , p. 180). For this report the surveys were

communicated through email by sending the participants the surveys for them to

complete and send back for data collection.

Data was then cleaned and coded, and the results were obtained. SPSS data analysis

software was used to reverse response to negatively phrases survey items. Constructs,

concepts and operationalization were used. Constructs being concepts or generalised

ideas that stand for something of meaning regarding the research topic for marketing

research (Hair, 2014 , p. 279). Constructs were calculated to determine response

averages across the relevant items. Operationalization gives meaning to a concept by

specifying the activities or operations necessary to measure it (Hair, 2014 , p. 286).

Frequency tables and crosstabulations were formulated to initially describe the dataset.

T-test analysis, correlation and multiple regression were then used to more explicitly

explore the attitudes towards online retail shopping.

Non-response bias is considered in this report and may be inclined to arise due to the

nature of the sampling techniques used. Though it has been said that using a sample

can be a sufficient form of collecting data it cannot always be the most accurate and

relevant form of information. Non – response error is a systematic bias that occurs

when the final sample differs from the planned sample (Hair, 2014 , p. 176). This is

likely to occur when a sufficient number of the prospective respondents in the sample

cannot be reached for participation (Hair, 2014 , p. 176). In this report non-response

bias has to be considered when analysing and discussing the data and for

recommendations.

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3.0 Ethical Considerations

Ethical and unethical behaviours manifest in the research process (Hair, 2014 ). The

Researcher must consider the ethical issues directly and indirectly that affect the development

and evaluation of the research report (Hair, 2014 ). Maintaining an ethical approach during

the research process is extremely important because consumers are more likely to contribute

during the research (Angelo, 2012). Marketing research relies on the systematic gathering and

interpretation of information from individuals, organisations and the general public (AMSRS,

2015). Consumers of research rely on research being carried out honestly, objectively and in a

manner that protects participants ‘rights’ (The Queensland University of Technology, 2015).

It is critical for the researcher and the participant to establish a strong relationship and it is

through this relationship that all data are collected and data validity is strengthened (Burkard,

2009). In addition, respect between the research and the person being surveyed will help

ensure that there are accurate and honest responses to the questions.

Ethics refers to the accepted rules and standards that govern the conduct of members of a

given group or profession (The Queensland University of Technology, 2015). The

Queensland University of Technology has clear principles for the responsible conduct of

research that all staff researchers and research students have to comply. The research policy

states that ‘Research at QUT is undertaken in an ethical and accountable environment, based

on shared commitment by the University and its researchers to maintain a strong culture of

integrity and fair conduct.’ (The Queensland University of Technology , 2015) . The research

conducted for this project does meet the above ethical policy as the researcher ensured that the

person being surveyed reviewed and read the interview consent form and signed it before

proceeding.

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4.0 Analysis

4.1 Cleaning and Editing

Quantitative data analysis requires that the data obtained is subject to data cleaning

and editing in order to remove invalid and inaccurate data. Data cleaning and editing

allows researchers to properly analyse data. In this analysis, data was cleaned but

issues identified with items where respondents could freely enter data and corrections

were then made. Doing this by deleting respondents with uninterpretable responses

and non-existent postcode, converting birth year to age in years and estimating

approximations. For example ‘21 years old’ can be cleaned and edit to ‘21’.

Frequencies were also run to check vales were all in range, for example all ages within

the range of no one less than 18 years. The data was then reverse coded and following

the constructs were then calculated.

4.2 Descriptive Analysis Descriptive data is very important because if we simply presented our raw data it

would be hard to visualize what it is showing; therefor it allows us to make

conclusions beyond data we have analysed to reach conclusions (aerd Statistics ,

2013). Table 1 indicates means and standard deviations for each developed construct.

The overall sample size after the cleaning of the data was 659. The lowest mean score

of 1.4980 and lowest standard deviation score of 0.21652 was social desirability. The

Constructs of risk aversion, variety seeking, convenience seeking and price

consciousness indicated fairly low standard of deviations indicating that the mean is a

good representation of the data. ATTBI as a mean of 4.9727 with a standard

deviation of 1.49702 which is the highest standard deviation in this construct set.

Therefore, the mean is not exactly a good representation of the whole data set.

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Table 1: Descriptive Statistics Constructs Mean Std.Deviation

ATTA 4.4256 1.38845

ATTC 5.0847 1.25722

ATTBI 4.9727 1.49702

Risk Aversion 4.6171 0.94226

Impulsiveness 3.7075 1.15526

Variety Seeking 4.4917 0.78855

Convenience seeking 4.7657 0.79547

Price Consciousness 4.9522 0.99876

Social Desirability 1.4980 0.21652

4.3 T – Test Analysis

A T – test analysis assists to understand and evaluate how likely it is that any apparent

difference has occurred between the two given variables relative to the research

problem. The significance level represents the probability that any difference that has

been observed is due to chance. The significance level was tested at 0.05%

Significance.

4.3.1 Significant Difference

After looking at the results of analysis based on which method of online

communication do you more frequently use, the mean for email

communication was 4.540, while the mean for online chat was 5.4118.

Assuming equal variances, the T-test displayed that the difference between the

means was significantly different with the T-test score being -7.806 with a

significance of 0.000. This reflects that there is a difference between the mean

of email and online chat. Therefore it shows that the attitude towards online

retail shopping does differ between these two forms of communication.

Table 2: Group Statistics - Email and Online Chat

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Which method of online communication do you more frequently use?

N Mean Std. Deviation Std. Error Mean

Email 332 4.5402 1.52347 .08361Online Chat 327 5.4118 1.33523 .07384


Table 3: T-Test - Email and Online Chat T – Test of Equality of Means

ATTBI

Equal variance assumed

t df Sig. (2-tailed)

-7.806 657 .000

4.3.2 No significant Difference

After looking at the results of analysis based on gender, male or female, the

mean of male was 4.9005, while the mean of female was 5.0429. Assuming

equal variances, a t-test displayed that the difference between the means was

not significantly different, with the t-test score being -1.221 and a significance

of 0.222. This reflects that there is no significant difference between the mean

of gender, male and female. Therefore it shows that the attitude toward online

retail shopping does not differ between male and female.

Table 4: Group Statistics – Gender, Male or Female

Table 5: T-test – Gender, Male or Female

T – Test of Equality of Means

ATTBI

Equal variance assumed

T df Sig. (2-tailed)

-1.221 657 .222

4.4 Self-Concept Semantic differential scales were used to measure a range of self – concept

dimensions. Rather than averaging across items, it was conducted by examining how

the various self – concept dimensions correlate with attitudes towards online retail

shopping. The significance level was tested at 0.05% significance.

4.4.1 Significant Positive Correlation

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What is your Gender N Mean Std. Deviation Std. Error Mean

Male 325 4.9005 1.49302 .08282Female 334 5.0429 1.49980 .08207


The data chosen for this example includes the dimension of conventional to

unconventional. Results showed a significant correlation between conventional

to unconventional and attitudes towards online retail shopping, with a

correlation of 0.150 and a significance of 0.000. This significant positive

correlation indicates that as unconventional towards online retail shopping

increases so does the overall attitude.

Table 6: Significant Positive Correlation

Self-Concept Dimension ATTBI

Conventional|||

UnconventionalPerson Correlation 0.150

Sig (2-tailed) 0.000

N 659

4.4.2 Significant Negative Correlation

A significant negative correlation includes a sematic dimension of innovative

to routine and attitudes towards online shopping, with a correlation of -0.238

and a significance level of 0.000. This significant negative correlation indicates

that as routine grows attitudes towards online shopping decreases.

Table 7: Significant Negative Correlation


Innovative|||Routine Person Correlation -0.238


N 659

4.4.3 Non-Significant Correlation

The dimension of uncomfortable to comfortable and attitudes towards online

shopping represent a non-significant correlation, with a correlation of 0.022

and a significance level of 0.565. As this is an insignificant correlation it

indicates that there is no relationship to indicate that as someone becomes

more comfortable so do attitudes towards online shopping.

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Table 8: Non-Significant Correlation


Uncomfortable|||Comfortable Person Correlation 0.022


N 659

4.5 Multiple Regression

Multiple regression tests determine how a set of independent variables impact on

dependent variable. All variables were tested at 0.05 significance.

4.5.1 All Respondents

Multiple regression was used to analysis the data. Based on the results this

showed an R value of 0.623 and an adjusted R squared value of 0.383. This

indicates that there is a 38.3% of variation of attitudes towards online retail

shopping can be explained by the predictors of the model. The two more

important predictors in explaining variation in attitudes towards online retail

shopping are variety seeking with a coefficient 0.290 and a significance level

of 0.000 and convenience seeking with a coefficient of 0.285 and a

significance level of 0.000. Figure 1 below displays the construct model for the

analysis of variety seeking and convenience seeking indicating a greater

impact on the construct of this model. It is noticed that impulsiveness showed

no level of significance and impact towards attitudes of online retail shopping,

with a coefficient of -0.005 and a significance level of 0.879. Risk Aversion

and Impulsiveness are in inverse relationships. For example, when Risk

Aversion increases attitudes towards online retail shopping decreases.

Table 9: Model Summary

Model R R Square Adjusted R

Square

Std. Error of

Estimate

1 0.623 0.388 0.383 1.17564

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Table 10: ANOVA Analysis of Variance

Model Sum of

Squares

df Mean

Square

F Sig

1 Regression 572.090 5 114.418 82.784 0.000

Figure 1: Model Predictors of Online Retail Shopping Attitudes

B=-.581

B=ns

B=.551

B= .537

B=ns

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Individual Characteristics Risk Aversion

Impulsiveness

Variety Seeking

Convenience Seeking

Price Consciousness

Attitudes towards online retail shopping


4.6 Social Desirability Bias

An analysis of social desirability scores within our sample has been undertaken. Social

desirability can be spilt into high and low desirability to differ people’s responses. The

social desirability was measured by answering ten (10) yes or no questions to be able

to engage how they present themselves more or less favourably then what is actually

the case. Figure 2 below of the histogram represents a visual inspection of the data set.

Most people were getting the social desirability score around the average mark of 1.50

indicting that most people have some social desirability and want to positively present

themselves. It is important to be more cautious with interpreting data when social

desirability is present as over interpreting the data can cause the data not to be

accurate.

Figure 2: Social Desirability

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5.0 Discussion and Recommendations

5.1 Interpretation of the data based on the analysis you have undertaken

i. To examine how self-concept dimensions relate to online retail shopping attitudes

After analysis of the data, self-concept dimensions displayed characteristics of

attitudes towards online retail shopping. It has been understood that as a person’s

level of unconventional grows as does their attitudes towards online retail

shopping positively increase. It is notice that online retail shoppers are more

unusual consumers rather than conservative consumers. There is no relationship to

indicate that as someone becomes more comfortable so do their attitude towards

online shopping.

ii. To determine the impact of individual characteristics on online retail shopping

attitudes

In the previous exploratory report it was discovered that price, convenience and

being a time poor society were determinates of online retail shopping. From the

descriptive analysis it has be understood that convenience seeking and variety

seeking are the main characteristics that influence attitudes towards online retail

shopping. In summary, convenience seeking, variety seeking and risk aversion are

the individual characteristics that have the greatest impact on attitudes towards

online retail shopping.

iii. To evaluate how social desirability might influence the results of the research

Social desirability bias is when a person acts or changes their personal view to suit

a socially acceptable response (DeMaio). For this study, social desirability bias

may affect the accuracy of the sample data. It is important to be more cautions

when looking at social desirability as then the data is considered more accurate and

reliable.

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5.2 What this means for managers and for the next stage of the research

Managers are going to have to adapt to the ongoing changes in the market and look at

trends in order to survive. Managers will need to re-assess their marketing plans by

looking at the four P’s of marketing. Product, Price, Place and Promotion need to be

reviewed to ensure that they satisfy consumer’s needs with variety, convenience and

risk aversion. Developing websites that allow customers to conveniently browse and

purchase items will fulfil consumers’ needs of convenience seeking. The development

of return policies to enable consumers to return goods for any reason will greatly

improve consumers online purchasing as it will reduce the risks associated with the

transaction. In addition, consumers will be greatly influenced by the website having a

large variety and range of products available for purchase. Overall, businesses must

adopt elements of the marketing mix so it can enhance convenience and variety so that

it can capture the attention of consumers.

It may be useful for managers and researchers to conduct a causal study. A casual

research design seeks to understand the cause – and – effect relationships among two

or more decision variables (Hair, 2014 , p. 12). In addition, understanding the cause-

effect relationships among market performance allows the decision-maker to make if-

then statements about the variables (Hair, 2014 , p. 12).

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6.0 Limitations

6.1 Factors that affect the confidence that you have in your findings that impact

your recommendations

The major limitations of this research that impact confidence in the recommendations

provided are non-probability sampling and social desirability bias. Due to having no sample

frame, non – probability sampling was used. Non-probability sampling can be a limitation

that affects the sample of the data that is provided. In non-probability sampling it is known

that participants are not randomly selected to be a part of this survey. Therefore, when making

recommendations it is not entirely certain that information is reflected of the population.

However non-probability sampling is fast to do and doesn’t require much skill. Social bias

could affect some of the respondent’s answers on surveys which then cause recommendations

to not be accurate. It is important to apply reliability and validity to your research and data

collection.

It is important when implementing surveys to ensure accuracy, researchers must resolve a

variety of issues associated with construct development, scaling and questionnaire design

(Hair, 2014 , p. 173). A limitation of this being that issues can produce many kinds of errors

in survey findings. As the possibility of systemic error increases, so does the likelihood of

collecting irrelevant or inaccurate data (Hair, 2014 , p. 173). Non- response bias may also be a

limitation due to the nature of the sampling techniques used.

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7.0 References

Abeyasekera, S. (n.d.). Quantitative Analysis Approaches to Qualitative data - why, when and how . Retrieved from http://www.reading.ac.uk/ssc/resources/Docs/Quantitative_analysis_approaches_to_qualitative_data.pdf

ACMA. (2011). Australian Government . Retrieved September 22, 2015, from Communications 2010-2011 Report 1 - E-comerce marketplace in Australia: Online shopping : http://www.acma.gov.au/webwr/_assets/main/lib410148/CR_comp_report1-E-commerce_Marketplace_in_Australia.pdf

aerd Statistics . (2013). Descriptive and Inferential Statistics . Retrieved from aerd Statistics : https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics-faqs.php

AMSRS. (2015, May). Code of professional behaviour . Retrieved September 22 , 2015, from Australian Market & Social Research History : http://www.amsrs.com.au/documents/item/194

Angelo. (2012, June 8). Consideration on ethics in marketing research. Retrieved September 22, 2015, from Gulp: http://www.busandman.com/?p=213

DeMaio, T. J. (n.d.). Social Desirability and Survey Measurement: A Review . Retrieved from Social Desirability and Survey Measurement: A Review : https://books.google.com.au/books?hl=en&lr=&id=cQi5BgAAQBAJ&oi=fnd&pg=PA257&dq=social+desirability+definition&ots=HTzIf7EPkH&sig=pFDU__jvr3ePMI31jX1AlULdTeA#v=onepage&q=social%20desirability%20definition&f=false

Dictionary . (2015). Ethics . Retrieved September 22, 2015, from Dictionary: http://dictionary.reference.com/browse/ethics

Explorable. (2009, November 3). Quantitative and Qualitative Research - Objective or Subjective . Retrieved from Explorable: https://explorable.com/quantitative-and-qualitative-research

Hair, J. (2014 ). Marketing Research (4th ed) . North Ryde NSW, Australia: McGraw-Hill Education .

Purely Branded. (n.d.). The Four Ps of Marketing . Retrieved from News and insights - Purely Branded : http://www.purelybranded.com/insights/the-four-ps-of-marketing/

The Queensland University of Technology . (2015, July 23). D/2.6 QUT Code of Conduct for Research . Retrieved August 19, 2015, from Queensland University of Technology - Manual of Policies and Procedures : http://www.mopp.qut.edu.au/D/D_02_06.jsp

Unite for Sight . (2009-20015). The importance of Quality Sample Size . Retrieved from Unite for Sight : http://www.uniteforsight.org/global-health-university/importance-of-quality-sample-size

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