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AMB201 Quantitative Report

SEM 2 2016

LUCY APPLEGARTH N8320357

TUTOR: ALEX ANTHONESS | 2PM WED DUE DATE: 30TH OCT | WORD COUNT: 2182

N8320357 Lucy Applegarth

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CONTENTS PAGE

Participation reflection

Executive summary

1.0 Introduction and background

3

4

4

1.1 Importance of the research 4

1.2 Scope of the report

1.3 Research problem/question

1.4 Aims and objectives

5

5

5

2.0 Method

2.1 Methodological considerations and assumptions

2.2 Sample considerations

2.3 Data collection and framework, and analytical considerations

6

6

6

6

3.0 Ethical considerations 7

4.0 Analysis

4.1 Data Cleaning and editing

4.2 Descriptive statistics

4.3 Analysis for Objective 1



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10

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13

5.0 Discussion and recommendations

5.1 Interpretation of the data

5.1.1 Objective 1 – Population segments

5.1.2 Objective 2 & 3 – Individual characteristics

14

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14

14

6.0 Limitations

7.0 References

8.0 Appendix

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18


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QUANTITATIVE PARTICIPATION REFLECTION Lucy Applegarth | n8320357

STUDY 1: Digital influencers and Instagram posts (online study) STUDY 2: Brands & Marketing Study (lab study) For the quantitative participation component of the assignment, I took part in two different types of studies. The first was an online study on digital influencers and Instagram posts, where I was shown a series of Instagram posts from a digital influencer and asked some corresponding questions about each one. I was drawn to this study because I am a frequent Instagram user, and am already conscious of the fact that I will purchase an item solely based on the fact that a certain influencer is promoting it. The second was a lab study that aimed to evaluate brand information, where I was asked to view information about brands and answer questions about them. All the material was presented via computer. I was drawn to this study because I was yet to participate in a lab study, only online, and am quite interested in marketing branding effects and strategies. The studies adopted similar approaches, in that they both asked you to view an image or sentence before being asked a series of questions relating to the stimulus. I found the simplicity of the first study very effective, in that the questions were asked immediately after viewing the photo. This meant that the visual stimulant was fresh in my mind and enabled me to answer the questions as accurately and in-depth as possible. In the second study, memory played a large part in your answers. This would have worked well, but there wasn’t much indication at the start that that would be the case. Because of this, I recall most of my answers being pure guesses. I don’t know if this was intentional for the researcher, but I can’t imagine the data would have been very useful or reliable. In the past, I have only ever signed up for online studies purely for convenience, but reflecting back I do think that a lab study was more effective. The quiet, controlled environment put me in a studious and concentrated mindset, so I was able to answer the questions without any distractions. Even though this was the case, I know that I would almost always be more likely to participate in an online study that I could complete anywhere. Another insight I gained was that I would be more likely to be viewing Instagram posts at home or in a familiar environment anyway, so perhaps the researcher chose this method intentionally. Vice versa, I would consume most of my brand exposure in an unfamiliar environment. I may use this experience to inform my own research in that I will endeavor to make my participant feel as normal and comfortable as possible in order to receive the most reliable results. If I required a large quantity of data for my study, I would lean towards making it an online study, as in my experience a student will choose convenience over actual interest in the study. I think it is very valuable for researchers to also take part in research as a participant as it may give them further insight into what data collection methods are most effective in order to enhance the design of their study. This will ultimately gain the most reliable results for the researcher, as well as maximizing the benefit to their participant.


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EXECUTIVE SUMMARY

The purpose of this report is to investigate and understand the attitudes towards online retail

shopping of Australian consumers, in order to understand future motives and provide

recommendations for marketers and other industry professionals. Our sample for the study was

English speaking Australian’s over 18 years of age who have previously engaged in online retail

shopping. The data was collected through surveys administered to 702 male and female

respondents split into two different age cohorts, and results were analyzed using the SPSS software.

By quantitatively examining the determinants of attitudes toward online retail shopping, it was

found that although the attitudes do not differ significantly amongst men and women, there was a

significant difference between male and female attitudes. Further research discovered that people

who are more risk averse are more likely to have an unfavorable attitude toward online retail

shopping, and that those who seek convenience are more likely to have a favorable attitude towards

online retail shopping.

1.0 INTRODUCTION AND BACKGROUND

1.1 IMPORTANCE OF THE RESEARCH

According to Roy Morgan Research, Australian’s spent an estimated $37.8 billion purchasing goods

over the internet last year, with around 4 in 10 people buying at least one product online during an

average four-week period, (Roy Morgan Research, 2015) Reports show that this figure grew by

13.5% in the past year, and along with emerging technologies and the growing popularity of mobile

devices, the online retail landscape will continue to expand (NAB Group Economics, 2016). With

more shoppers moving to online purchasing, it is imperative that online retailers understand their

market in order to focus their resources accordingly. By gaining a comprehensive understanding of

consumer motivations and attitudes towards both online and offline shopping, we can provide

managers and marketing researchers with industry insight in order to attract and retain online

customers, whilst also predicting their future behaviors (Hasan, 2010). Previous qualitative research

on the topic found that generally that people were more motivated to purchase online due to the

convenience and the competitive pricing. The main deterrents outlined were financial risk, as well


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as the in-store experience generally being more personable. Quantitative research will allow us to

expand on these constructs in order to gain further insight and validation for our findings.

1.2 SCOPE OF THE REPORT

This study involves the analysis of English speaking Australian adults who regularly use the internet.

The respondents needn’t have previously engaged in online retail shopping, but they do need to be

Internet users. For the purposes of this AMB201 project, the scope the report is limited to tangible

products that can be purchased online and offline, and the respondents have been segmented into

two generational cohorts, a younger category aged 18 to 40, and an older category aged 41 and

above.

1.3 RESEARCH PROBLEM/QUESTION

This report will investigate the determinants of Australian consumer’s attitudes toward online

retail shopping.

1.4 AIMS AND OBJECTIVES

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

towards online retail shopping. The specific objectives of this project include:

i) To examine if attitudes toward online retail shopping differ across population segments;

ii) To understand the relationship between individual characteristics and attitudes toward

online retail shopping;

iii) To determine which individual characteristics can be used to predict attitudes toward

online retail shopping


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2.0 METHOD

2.1 METHODOLOGICAL CONSIDERATIONS AND ASSUMPTIONS

For this study, a quantitative descriptive research design was used, as it places heavy emphasis on

using standard questions and predetermined response options in surveys administered to large

numbers of respondents (Hair & Lukas, 2014). Quantitative research is also appropriate when the

main objective of the research is to make predictions about the relationship between market factors

and behaviors, which is the aim of this report (Hair & Lukas, 2014). A descriptive research design

was chosen in order to describe the characteristics of the consumers who online retail shop, and

unlike exploratory research, is based on some previous understand of the nature of the research

problem (Zikmund, 2003). A cross-sectional survey design was chosen as it collects data to make

inferences about the population at a certain point in time, which aligns with the purpose of our

report (Hall, 2008).

2.2 SAMPLE CONSIDERATIONS

The target population for this study is English speaking Australian’s that are internet users. The

sample consisted of 702 respondents; 388 male and 314 female, which were then characterized into

two different age cohorts. The younger cohort was those aged 18 to 40, the older cohort was those

aged 41 and older. The researchers were divided into two groups based on their surname and were

each assigned a gender, and to pick an individual from each age cohort to survey. A non-probability

sampling method was utilized as it is more efficient and cost-effective, and is arguably more suitable

in the developmental stages of survey research (Statistics Canada, 2015). Quota sampling was also

used, for the reason that the sample effectively represents the population (Melero, 2011).

2.3 DATA COLLECTION AND FRAMEWORK, & ANALYTICAL CONSIDERATIONS

The data for this study was collected through surveys, in person on a hard copy document. Each

participant was required to read and sign an ethical consent form prior to commencing the survey.

After completion, the data was upload online by the surveyor for analysis. The data was then

cleaned and coded, and results were obtained through the SPSS software program.


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The most predominant response format used in the survey was 7 point Likert scales, whereby the

respondent rates the degree to which they agree or disagree with a statement with a series of

mental belief or behavioral belief statements about a given object (Sullivan & Artino, 2013) Nominal

scales were also used, requiring the respondent to provide only some type of descriptor as the

response (Hair & Lukas, 2014). Because of the multidimensional nature of many of the individual

characteristic constructs, multiple scales were employed in order to increase the reliability of the

data obtained.

3.0 ETHICAL CONSIDERATIONS

Ethical considerations in research are critical. They prevent against the fabrication of data to

promote truth in research, as well as protecting the confidentiality and anonymity of the subjects

(CIRT, 2015). The Code of Professional Behaviour was adhered to throughout the duration of this

study. This Code covers the standards of practice for research relating to the respondent’s rights,

the researcher’s professional responsibilities, and the researcher and client’s mutual rights and

responsibilities (Weeks, 2016). Before the respondent commenced the survey, they were required

to sign a consent form that confirmed the following ethical outlines:

Participant was over the age of 18 and participated voluntarily

Consent to upload answers to a database made available for student analysis

Consent to examine the survey questions prior to agreeing to participate

Consent to withdraw from the survey at any time without comment or penalty

Ensured confidentiality and anonymity


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4.0 ANALYSIS

4.1 DATA CLEANING AND EDITING

Data cleaning refers to checking the data for responses that might need to be deleted or altered to

be usable, and is crucial prior to our analysis (Weeks, 2016). Most of the issues were identified in

the section of the survey where respondents could freely enter data. Data was deleted where the

response was uninterpretable or non-existent, and responses were altered so that they were all in

a consistent format. Birth years were converted to ages, and approximations were converted to

conservative estimates. In order to prepare the data for the SPSS software, all negatively phrases

survey items were reverse coded, which also screens out respondents who were intentionally

providing false responses. Construct values were determined for each respondent by averaging

across the relevant items. Three dimensions of attitude were measured: Affective (ATTA), Cognitive

(ATTC) and Behavioral Intention (ATTBI). For the purpose of this AMB240 report, only the behavioral

dimension for attitude (ATTBI) will be analyzed as the dependent variable.

4.2 DESCRIPTIVE STATISTICS

The final overall sample size after cleaning was 702 respondents, 388 males and 314 females (see

table 1). Out of these respondents, 353 were in the younger cohort aged 18 to 40, and 349 were in

the older cohort aged 41 and older (see table 2).

Table 1: Gender of sample

Frequency Percent (%)

Male 388 55.3

Female 314 44.7

Total 702 100.0

Table 2: Age cohort of sample

Percent (%)

Younger 353 50.3

Older 349 49.7

Total 702 100.0


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Through crosstabulation between the variables (see table 3), we were able to see that there is a

fairly even number of older and younger people, however there are slightly fewer older females.

The age distribution graph (see figure 1) shows us that the largest age group measured was of 20

years of age. We can also see that most people surveyed were partnered (see Table 4), and that

significantly less older people were single. Older people use email most frequently as their form of

online communication, whereas younger people use instant messaging more frequently (table 5).

Table 3: Gender / Age Crosstabulation

Age Cohort Total

Younger Older

Male 192 196 388

Female 161 153 314

Total 353 349 702

Table 4: Relationship status / Age Crosstabulation

Age Cohort Total

Younger Older

Single 211 52 263

Partnered 142 297 439

Total 353 349 702

Table 5: Communication method / Age Cohort Crosstabulation

Age Cohort Total

Younger Older

Email 45 276 321

Instant messaging 308 73 381

Total 353 349 702


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Figure 1: Spread of respondents’ ages

Table 6 shows the overall mean for each construct relevant to our analysis. A high score indicates

a high level of construct, whereas a low score indicates a low level of construct.

Table 6: Overall mean for each construct

N Mean

ATTBI 702 4.9615

Risk Aversion 702 4.6617

Price Consciousness 702 4.9765

Impulsiveness 702 3.6955

Variety Seeking 702 4.4880

Convenience Seeking 702 4.7572

Materialism 702 4.7369

4.3 ANALYSIS FOR OBJECTIVE 1

The first objective aims to examine if attitudes towards online retail shopping differ across

population segments. To address this objective, t-tests have been conducted on the gender and age

cohort segmentation variables. T-tests can be used to assess whether, based on our sample, we can

be confident in our conclusions that these two segmentations are likely to differ on attitude within


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the wider population. For the purposes of this report, we only used the first row of results, “Equal

Variances Assumed”. Our results show that the mean attitude rating for younger people is slightly

higher than that of older people (table 7). After conducting a t-test, the significance level is 0.00, so

we can conclude that younger and older people’s attitudes towards online shopping differ

significantly.

Table 7: Mean attitude rating for Age Cohort

Mean

Younger 5.4788

Older 4.4384

Table 8: t-test & Sig. (2-tailed) scores

t Sig. (2-tailed)

Equal variances assumed 9.334 0.00

Table 9 shows that the mean attitude rating for men and women is relatively similar. After

conducting a t-test, the significant level is 0.436, so we can conclude that there is not a statistically

significant difference between men and women’s attitudes towards online shopping.

Table 9: Mean attitude rating for Gender

Mean

Male 4.9201

Female 5.0127

Table 10: t-test & Sig. (2-tailed) scores

t Sig. (2-tailed)

Equal variances assumed -.780 0.436


The second objective aims to understand the relationship between individual characteristics and

attitudes toward online retail shopping. To address this objective, we will use correlation analysis

to provide a measure of the relationship between attitudes towards online shopping and the ‘risk

aversion’ and ‘convenience seeking’ constructs. Table 11 outlines the descriptions of the constructs


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used in the AMB201 survey. Table 12 shows the correlation results for the aforementioned

variables. The results show that there is a relatively strong negative correlation between attitudes

and risk aversion. This negative relationship indicates that people who are more risk averse are not

likely to shop online. The results show that there is a moderately positive correlation between

attitudes and convenience seeking. This positive correlation indicates that people who favor

convenience are more likely to shop online.

Table 11: Individual Characteristics

CHARACTERISTIC

CONSTRUCT

DESCRIPTION CORRESPONDING SURVEY QUESTIONS

Risk Aversion Refers to the way

individuals seek to

avoid risk and

uncertainty.

1. When making a purchase, I would rather be

safe than sorry.

2. I like to be sure about a product before I

purchase it.

3. I avoid risky purchases.

4. I would avoid using credit cards online.

5. I would feel safe giving my personal details

over the Internet.

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 at 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.

Table 12: Risk aversion & Convenience seeking correlation statistics

Pearson Correlation Sig. (2-tailed)

ATTBI 1

Risk aversion -.454 .000

Convenience seeking .399 .000


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The third objective aims to determine which individual characteristics can be used to predict

attitudes toward online retail shopping. To address this objective, we will use bivariate regression

to indicate the strength, direction, and significance of the relationship between attitudes towards

online shopping and the ‘risk aversion’ and ‘convenience seeking’ constructs (the independent

variables), to see if they are a useful predictor for future attitudes. The regression equation allows

us to predict future scores without needing all of the data, saving time and resources in future

studies.

Table 13: Regression equation scores

Adj. R2 value Standard coefficient Sig. (2-tailed)

Risk aversion .205 -.454 0.00

Convenience seeking .158 .399 0.00

Risk aversion has an Adjusted R squared value of .205, indicating that 20.5% of variation in attitudes

is explained by our model. The relationship between the predictor and the dependent variable

(ATTBI) is strongly negative, as depicted by the standard coefficient, indicating that people who are

most risk averse are more likely to have a negative attitude towards online retail shopping.

Convenience seeking has an Adjusted R squared value of .158, indicating that 15.8% of variation in

attitudes is explained by our model. The relationship between the predictor and the dependent

variable (ATTBI) is moderately positive, as depicted by the standard coefficient, indicating that

people who seek convenience are more likely to have a positive attitude towards online retail

shopping.

The regression equation for both constructs produced a significance level of 0.00, suggesting that

the equation successfully explains the variation in attitudes toward online shopping and can be used

to predict future attitudes.


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5.0 DISCUSSIONS AND RECOMMENDATIONS

5.1 INTERPRETATION OF THE DATA

5.1.1 OBJECTIVE 1 – POPULATION SEGMENTS

T-tests on the age cohort and gender segmentation variables allowed us to examine if attitudes

towards online retail shopping differ across population segments. It was determined that there was

a significant difference between younger and older people’s attitudes, but that there was not a

significant difference between men and women attitudes. For industry professionals, our results

imply that different marketing approaches would be beneficial for different consumer age

segments. Older consumers need to be enticed to purchase online in the first place, whilst younger

consumers may require incentive to translate their browsing into actual purchasing (Sorce & Perotti,

2006). An area for future research that could extend from these results and to assist this

recommendation, would be to investigate the impact of age on searching for and purchasing

products online.

5.1.2 OBJECTIVE 2 & 3 – INDIVIDUAL CHARACTERISTICS

Correlation analysis and bivariate regression allowed us to understand the relationship between

attitudes towards online shopping and the ‘risk aversion’ and ‘convenience seeking’ constructs, and

to determine if we can predict future attitudes from these particular characteristics. It was evident

that people who are more risk averse are more likely to have an unfavorable attitude toward online

retail shopping. This is consistent with the results from the Qualitative report, and solidifies the

importance of online security. Online retailers should endeavor to simplify the purchasing

procedure to give a feeling friendliness or salesmanship, and employ financial security measures

such as PayPal to ensure the customer feels at ease during the transaction (Javadi & Dolatabadi,

2012).

It was also deciphered that people who seek convenience are more likely to have a favorable

attitude towards online retail shopping. Both of these characteristic constructs were found to be

significant predictors in determining future attitudes towards online retail shopping. An area for


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future research could investigate whether respondents who were found to be risk averse actually

behaved accordingly in an online shopping environment. What our results conclude, and what

current theory suggests, is that motivational factors are more powerful than demographic factors

in predicting online buying (Sorce & Perotti, 2006). As technology continues to progress, online

retail shopping is becoming more convenient, so retailers should aim to stay as up to date with the

technology as possible and ensure product information is accessible and in-depth (Akbar & James,

2012).

6.0 LIMITATIONS

The limitations of the report are as follows. Firstly, time restrictions only enabled us to investigate

two of the six individual characteristics in our report, which means our conclusions and

recommendations were made solely off of two constructs. By investigating all six constructs would

render our results more in-depth and reliable. Secondly, Figure 1 shows that there was not a very

even distribution of ages with the majority of respondents being 20 years of age, reducing the

generalizability of the results. In the future, the researchers could be split in half according to

number of students rather than just by the first letter of their family name. During data cleaning,

respondents were deleted if their results were interpretable, rendering the male to female and age

cohort slightly uneven, resulting in potentially skewed results. In the future, the researcher could

take further precautions in explaining the correct way to fill out the form to ensure inclusion in data

cleaning.


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7.0 REFERENCES

Akbar, S., & James, P. (2012). Consumers’ attitude towards online shopping: Factors influencing employees of crazy domains to shop online. Journal of Management and Marketing Research.

Awad, N., & Ragowsky, A. (2008). Establishing Trust in Electronic Commerce Through Online Word

of Mouth: An Examination Across Gender. Journal of Management Information Systems, 24(4).

CIRT. (2015). Ethical Considerations. Retrieved Oct 2016, from Center For Innovation in Research

and Teaching: https://cirt.gcu.edu/research/developmentresources/tutorials/ethics Hair, J., & Lukas, B. (2014). Marketing Research (4th Edition ed.). North Ryde, NSW: McGraw Hill

Education. Hall, J. (2008). Cross-Sectional Survey Design. (P. J. Lavrakas, Ed.) Retrieved Oct 2016, from SAGE:

http://methods.sagepub.com/reference/encyclopedia-of-survey-research-methods/n120.xml

Hasan, B. (2010, July). Exploring gender differences in online shopping attitude. Computers in

Human Behavior, 26(4), 597-601. Javadi, M., & Dolatabadi, H. (2012). An Analysis of Factors Affecting on Online Shopping Behavior

of Consumers. International Journal of Marketing Studies, 4(5). Melero, J. (2011). Quota Samping. Retrieved Oct 2016, from Universitat Pompey Fabra:

https://www.upf.edu/survey/_pdf/Melero_seminar_3_2011.pdf NAB Group Economics. (2016, August). NAB Online Retail Sales Index: Indepth report – June 2016.

Retrieved from NAB Business Research and Insights: http://business.nab.com.au/nab-online-retail-sales-index-june-2016-17897/

Roy Morgan Research. (2015, Dec 10). The state of Australia’s $37.8b online shopping landscape.

Retrieved Oct 2016, from Roy Morgan Research: http://www.roymorgan.com/findings/6591-online-shopping-in-australia-june-2015-201512012314

Sorce, P., & Perotti, V. (2006). Attitude and age differences in online buying. International Journal

of Retail & Distribution Management, 33(2), 122-132. Statistics Canada. (2015). Non-probability sampling. Retrieved Oct 2016, from Statistics Canada:

http://www.statcan.gc.ca/edu/power-pouvoir/ch13/nonprob/5214898-eng.htm


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Sullivan, G., & Artino, A. (2013, Dec). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541-542.

Weeks, C. (2016). AMB201 Marketing and Audience Research [LECTURE 10] Data Analysis.

Retrieved Oct 2016, from QUT: https://blackboard.qut.edu.au/bbcswebdav/pid-6428535-dt-content-rid-7210601_1/courses/AMB201_16se2/Lecture_10_Data_Analysis_2016s2_multipage.pdf

Weeks, C. (2016). AMB201 Marketing and Audience Research [LECTURE 2] Research Process and

Code of Conduct. Retrieved Oct 2016, from QUT Blackboard: https://blackboard.qut.edu.au/bbcswebdav/pid-6428535- dt-content-rid- 6726730_1/courses/AMB201_16se2/Lecture_2_Process_and_Conduct_2016s2_student_ multipage.pdf

Weeks, C. (2016). AMB201 Marketing and Audience Research [TUTORIAL 10] Data Analysis.

Retrieved Oct 2016, from QUT: https://blackboard.qut.edu.au/bbcswebdav/pid-6428535-dt-content-rid-7262795_1/courses/AMB201_16se2/AMB201_Tutorial_10_2016s2_student_multipage.pdf

Zikmund, W. (2003). Essentials of Marketing Research (2nd Edition ed.). Mason, Ohio, USA: South-

Western.


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8.0 APPENDICES


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