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N9265503
YANG SHAN (TAK)
AMB201 Marketing and Audience Research
Quantitative Research Report
Determinants of Online Retail shopping
Student Name: YANG SHAN (TAK)
Student Number: N9265503
Tutor’s Name: Jennifer Doig
Tutorial Time: Thursday, 1:00pm-2:00pm
Words: 1759
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Table of Contents
Participation Reflection………………………………………………………….......2
Executive Summary.....................................................................................................3
1.0 Introduction and Background………………………………………………...
1.1 Importance of the research……………………………………………….…...4
1.2 Scope of the report……………………………………………………………4
1.3 Research question………………………………………………….................5
1.4 Aims and Objectives………………………………………………………..5-6
2.0 Method……………………………………………………………………….....6-8
2.1 Methodological considerations and assumptions………………………..……6
2.2 Sample considerations……………………………………………...…………7
2.3 Data collection and framework, and analytical considerations……………..7-8
3.0 Ethical considerations……………………………………………………………8
4.0 Analysis………………………………………………………………………..8-17
4.1 Cleaning and coding………………………………………………………….8-9
4.2 Descriptives………………………………………………………………….9-11
4.3 Exploring the data (t-tests)……………………………………………........11-13
4.4 Correlation…………………………………………………………………13-14
4.5 Multiple regressions………………………………………………………..14-16
4.6 Social desirability…………………………………………………………..16-17
5.0 Discussion and Recommendations…………………………………….……17-19
5.1 To examine how self-concept dimensions relate to online shopping attitudes……………....17-18
5.2 To determine the impact of individual characteristics on online retail shopping attitudes……...18
5.3 To evaluate how social desirability may influence the results of the research………………18-19
6.0 Limitations…………………………………………………………………........19
7.0 Reference List………………………………………………………………..20-21
8.0 Appendix: Two completed surveys
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Participation Reflection
The course name of AMB201 is marketing and audience research so the research is
strongly of importance to this course. Before writing this report, I handed two surveys
to two females who live in Australia. It is strongly important to mention that asking
two female to complete the surveys is significant for me to increase my confidence in
talking with others in English. This means that the research helps me to improve my
communication skills with others.
Before handing the survey to the first female, I forgot to remind her that the survey
may cost about half an hour. The female had an important appointment at that time so
she finished about 1/3 of the survey in a short time. Although the female participant
finally completed the survey, it still wasted the participant’s time. Besides, she did not
answer the questions in survey carefully and this can exert a negative impact on the
result of the research. Thus, if I will ask others to do the survey for the research in the
future, I will remember to remind participants of the length of the survey. This is
because it is important for the researcher to clarify some essential matters, such as the
length of the survey. This is also because participants have their right to know about
the length of the survey. Besides, during the process of completing the surveys, the
respondent asked me some questions about the survey because they could not
understand what some questions mean, but I did not answer respondents’ questions
well because I could not understand some question in the survey as well. This will
also negatively influence the result of the research because respondents cannot answer
some questions accurately. Thus, it is necessary for me to gain a deep understanding
of all questions in the survey. This is because the in-depth understanding of survey
questions can help the researcher to draw some useful conclusions and think out more
feasible recommendations.
It is worth mentioning that it is strongly valuable for researchers to also take part in
the research as a participant. This can be explained by the fact that researchers can
consider some questions from participants’ point of view if researchers can take part
in the research as participants. It is also important to mention that researchers can
correct some mistakes when they find out some faults or problems as a participant.
Therefore, it is necessary and valuable for researchers to also take part in the research
as a participant.
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Executive Summary
In today’s society, online market has become the main market. The purpose of this
report is to quantitatively examine the determinants of Australian consumers’ attitudes
towards online retail shopping.
Descriptive and quantitative research approaches will be applied to this report.
Surveys were used to collect Australian Internet users’ opinion about online retail
shopping. The answers to research questions are strongly important and beneficial to
develop the online market well.
The results of this report will provide some ideas and practical suggestions. This can
be helpful to implement some useful strategies of online shopping to improve its
continuance as effective means of market share and overall revenue of online
businesses. According to the findings and the three objectives, this report provides
some recommendations: marketers can also apply the advertising which contains
conventional and innovative elements to attract consumers’ attention and drive them
to purchase items online; online shopping marketers can strive to cooperate with some
sectors or companies which dedicate to maintain online security; it is necessary for
marketers to do further research or observational studies to determine what the actual
performance do respondents have associated with online purchasing behaviour.
However, there are some limitations in this report, such as the age group of the
respondents.
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1.0 Introduction and Background
1.1 Importance of the research
According to MENA (2013), online shopping has become increasingly popular in
Australia in recent years. It is worth firstly mentioning that the majority of Australian
Internet users are online shoppers (MENA, 2013). This means Australian online
shoppers can be helpful to promote the ongoing development of online transaction
economy. Besides, it is significant for marketers to use some information from the
marketing research to drive the business growth. Many mistakes may occur without
the marketing research and this can exert an adverse impact on a firm’s business
profits. It is useful to use the marketing research to learn more about targeting
audience, a product or the service’s market viability or a firm’s image or reputation
(Sontakki, 2010). Thus, it is necessary to examine the predictors of Australian
shoppers’ attitudes toward online retail shopping. In this report, the descriptive
research can be applied to gain a good understanding of the determinants of
Australian consumers’ attitude toward online retail shopping.
1.2 Scope of the report
This report is based on three provided surveys from QUT Blackboard and conducted
using two English-speaking adults from two different age cohorts which are younger
people (18-40 years) and older people (41+ years) who consistently use Internet in
Australia. In addition, the report is based on self-report study method. There are many
different types of methods of the self-repot study method, including questionnaires,
inventories, interviews and focus groups (Basch, DeCicco and Malfetti, 1989).
However, some potential problems may associate with applying the self-report
method. This can be explained by Curtis’s (2004) state that the data from self-report
are personal and it may not reality because people are not always truthful. It is
important to mention that there is no classification of specific consumer dataset in this
report.
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1.3 Research question
What are the determinants of Australian consumers’ attitudes towards online
retail shopping?
In recent years, online retail has experienced significant development in Australia
with an increasing number of Internet users purchasing products online. This means it
is useful for online retailers to understand what leads Australian consumers to shop
online because of the considerable growth of the online retail. If marketers intend to
expand their businesses and achieve their business outcomes, it is necessary for them
to gain a good understanding of the determinants of Australian consumers’ attitudes
toward online retail shopping. This is because online retailers can carry out effective
marketing strategies, sell products which consumers are likely to buy online, and
improve some aspects of online retail to attract more online consumers to purchase
products online according to the determinants of Australian consumers’ attitudes
toward online retail shopping. Thus, it is significant for online retailers to deeply
understand the determinants of Australian consumers’ attitudes toward online retail
shopping.
1.4 Aims and Objectives
The aim of this report is to quantitatively examine the determinants of Australian
consumers’ attitudes toward online retail shopping. Specific objectives include:
i)
To examine how self-concept dimensions relate to online retail shopping
attitudes;
Self-concept refers to a person’s self-image. It can be conceptualised as having
a variety of dimensions, some of which may relate to the dependent variable.
Correlation will be used to examine the relationship between self-concept
dimensions and attitude toward online retail shopping.
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ii) To determine the impact of individual characteristics on online retail
shopping attitudes;
Multiple regression will be used to examine the impact of five potential
predictors of attitude toward online retail shopping.
iii) To evaluate how social desirability may influence the results of the research.
Social desirability is a potential bias in data because people tend to present
themselves favourably. An analysis of the distribution of social desirability
scores will allow for an evaluation of how this might contribute to results.
2.0 Method
2.1 Methodological considerations and assumptions
This report will apply the descriptive research method and quantitative methodologies
rather than using the casual research method. According to Barker, Pistrang and
Elliott (2005), descriptive research is used to describe features of a population or
phenomenon instead of answering the reasons of specific questions. In addition, the
casual research focuses on the cause-effect relationships (Zikmund, Lowe, Babin,
D’Alessandro & Winzar, 2014). In this case, as this report is to examine the
determinants of online shopping behaviour instead of the cause-effect, the descriptive
research method can be more appropriate. It is also important to use an appropriate
method for investigation. Quantitative research is applied in this report and this kind
of research is to explain specific phenomenon by collecting numerical data (Watson,
2015). Additionally, quantitative data are collected by mathematical method such as
statistics and this type of data is more objective than qualitative research (Aliaga &
Gunderson, 2000). However, there are some limitations in quantitative research. This
is because the quality of the research is dependent on the researchers’ skill, can be
easily influenced by investigators’ personal biases, and is time-consuming (Claire,
2010). In this report, the researcher assumes all respondents can accurately answer the
questions which can stand for respondents’ real insight.
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2.2 Sample considerations
The targeted population of the research is English-speaking adults in Australia who
are used to surf the Internet. Target cohorts are divided into two age groups, which are
younger people (18-40 years) and older people (41+ years). According to Bownas
(1985), the objective of the quantitative sampling approach is to draw a representative
sample from the population so that results can easily be generalised from the
population. Regarding the sampling technique, non-probability sampling, such as
quota sampling, is applied in this research. This can be explained by Moser’s (1952)
state that it is suitable to choose the quota sampling technique when researchers know
about the distribution of population for certain characteristic.
2.3 Data collection and framework, and analytical considerations
The two surveys were handed to two English-speaking adults who are respectively
from the age groups of 18-40 years and 41+ years in Australia. These surveys were
pre-tested by the administer DR. Weeks. This is because the pre-test can provide
information on how the issues of reliability and validity were solved (Anonymous,
1992).
Researchers firstly downloaded the provided surveys from QUT Blackboard and then
presented hard copies to the targeted population. According to the requirement from
QUT, researchers with family names beginning A-L should collect two male
respondents, with one from 18-40 years and 41+ years age cohort. For researchers
with family names beginning M-Z should collect two female respondents, with one
from 18-40 years and 41+ years age cohort. It is also important to mention that
researchers are required to ask their target respondents to sign the consent form. The
next step is to upload the completed surveys on QUT Blackboard from researchers.
This can provide the SPSS System with the opportunity to sort out descriptive and
bivariate statistics, and predict outcomes (Hinton, 2004, p.2). In terms of analysis of
the collecting data, Descriptive, t-tests, Correlations and Multiple Regressions will be
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used.
3.0 Ethical Considerations
Before handing out the surveys to the respondents, it is necessary for researchers to
explain the survey consent form and require respondents to sign the consent form.
This can be explained by Kolb’s (2008) state that during the process of marketing
research, it is responsible for researchers to minimise the risks of harm to participants
and clarify both the potential benefits and risks of the survey. This means researchers
have the responsibility to treat participants in a fair way and to gain the accurate
information from the respondents (Kolb, 2008). It is worth mentioning that in the
quantitative non-experimental research, it is important for researchers to explain the
purpose of the research, respondent demographics and how the results of the research
will be used (Roe & Webb, 1998).
4.0 Analysis
4.1 Cleaning and Editing
In the analysis, it is necessary to clean some inappropriate data by data cleaning. The
processes of data cleaning include deleting respondents with uninterpretable
responses with non-existent postcode; changing suburb listing to postcode or within
range; converting birth year to age in years; altering mixed number/text responses to
number only; converting approximations to conservative estimate.
In this report, reverse coding, which can help to reverse any negatively phrased
survey items, is used by SPSS. For example, RA5 was reversed such that responses of
1 were recoded as 7, responses of 2 were recoded as 6, etc. Reversed items in this
report are:
 ATTA3, ATTC3
 RA5, IMP3, IMP4, VS6, VS7
 SocDes1, SocDes2, SocDes5, SocDes8
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In terms of construct calculations, the average of the relevant items is rearded as
construct value:
 ATTA = (ATTA1 + ATTA2 + ATTA3 + ATTA4)/4
 ATTC = (ATTC1 + ATTC2 + ATTC3 + ATTC4 + ATTC5)/5
 ATTBI = (ATTBI1 + ATTBI2 + ATTBI3)/3




RA = (RA1 + RA2 + RA3 +RA4 + RA5 + RA6)/6
IMP = (IMP1 + IMP2 + IMP3 + IMP4)/4
VS = (VS1 + VS2 + VS3 + VS4 + VS5 + VS6 + VS7)/7
CS = (CS1 + CS2 + CS3 + CS4 + CS5 + CS6 + CS7)/7
 PC = (PC1 + PC2 + PC3 + PC4)/4
 SocDes = (SocDes1 + SocDes2 + …+ SocDes8 + SocDes9 + SocDes10)/10
According to the survey and analysis, ATTA, ATTC and ATTBI were measured, but
only ATTBI will be analysed.
4.2 Descriptives
Figure 1: Descriptive Statistics
ATTA
ATTC
ATTBI
Risk Aversion
Impulsiveness
Variety Seeking
Convenience Seeking
Price Consciousness
Social Desirability
N
Mean
Std. Deviation
659
659
659
659
659
659
659
659
659
4.4256
5.0847
4.9727
4.6171
3.7075
4.4917
4.7657
4.9522
1.4980
1.38845
1.25722
1.49702
.94226
1.15526
.78855
.79547
.99876
.21652
Figure 2 & 3: Gender Distribution
Frequency
Male
Valid Female
Total
Percent
325
334
659
49.3
50.7
100.0
Valid
Cumulative Percent
Percent
49.3
49.3
50.7
50.7
100.0
Age Cohort* What is your gender?
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Younger
Age Cohort Older
Total
What is your gender?
Male
Female
166
167
159
167
325
334
Total
333
326
659
Figure 2 & 3 shows that the final sample population in the research is 659. Female
respondents (334) are few higher than male respondents (325). Specifically, there are
respectively 166 and 167 younger people in the male and female group, while there
are respectively 159 and 167 older people in the male and female group in the
research.
Figure 4-5: Relationship Status Distribution
Frequency
Single
Valid Partnered
Total
216
443
659
Percent
32.8
67.2
100.0
Valid
Cumulative
Percent
Percent
32.8
32.8
67.2
100.0
100.0
Age Cohort * What is your relationship status?
Age Cohort
Younger
Older
Total
What is your relationship
status?
Single
Partnered
177
156
39
287
216
443
Total
333
326
659
Figure 4 & 5 illustrates most of participants are partnered (67.2%), and the larger part
of partnered status is belonging to older age group which includes 287 respondents.
The larger part of single respondents is younger age group, including 177 participants.
Figure 6 & 7: Age Distribution
Valid
Younger
Older
Frequenc Percent
y
333
50.5
326
49.5
Valid
Cumulative
Percent
Percent
50.5
50.5
49.5
100.0
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Total
659
100.0
100.0
Figure 6 & 7 shows that two age groups are involved in the report, which include
younger age group and older age group. The population of younger age group is 333
(50.5%), which is slightly larger than the population of older age group (326; 49.5%).
The average age of all respondents in this research is 36.54; however; the majority of
respondents are 18-25 years old (Figure 7).
4.3 Exploring the data (t-tests)
Q: Does attitude toward online retail shopping (ATTBI) differ between younger
and older people?
Figure 8 & 9:
Group Statistics
ATTBI
Age cohort based
on age
Younger
Older
N
333
326
Mean
5.5335
4.3998
Std.
Deviation
1.22035
1.53768
Std. Error
Mean
.06687
.08516
Independent Samples Test
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Levene's Test
t-test for Equality of Means
for Equality of
Variances
F
Sig.
t
df
Sig.
Mean
(2-tailed) Difference
Std. Error
95% Confidence
Difference
Interval of the
Difference
Lower
Equal variances
27.071
.000 10.495
657
.000
1.13374
.10802
.000
1.13374
.10828
Upper
.92163 1.34585
assumed
ATTBI
Equal variances
10.470
618.983
.92109
1.34683
not assumed
Figure 8 & 9 shows that the mean of younger age group (5.5335) is a little higher than
that of older age group (4.3998), and the Sig. (2-tailed) value is less than 0.05 (0).
Thus, there is statistically significant difference between younger people and older
people.
Q: Does attitude toward online retail shopping differ between males and
females?
Figure 10 & 11:
Group Statistics
What
is
gender?
Male
ATTBI
Female
your N
Mean
325
334
4.9005
5.0429
Std. Deviation Std.
Error
Mean
1.49302
.08282
1.49980
.08207
Independent Samples Test
Levene's Test
for Equality
of Variances
F
Sig. t
Equal variances .270
assumed
ATTBI
Equal variances
not assumed
.603
t-test for Equality of Means
df
-1.221 657
Sig.
Mean
Std. Error 95% Confidence
(2-tailed Difference Difference Interval of the
)
Difference
Lower Upper
.222
-.14240
.11660
-.37135 .08655
-1.221 656.658 .222
-.14240
.11659
-.37134 .08654
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Figure 10 & 11 displays that female have more positive attitudes toward online retail
shopping because the mean of female (5.0429) is higher than that of male (4.9005).
The Sig. (2-tailed) value of them are both 0.222, which are higher than 0.05.
Therefore, there is not statistically significant difference between these two groups.
Q: Does attitude toward online retail shopping differ between those who more
frequently use email compared to online chat?
Figure 12 & 13:
Group Statistics
ATTBI
Which method of online N Mean Std. Deviation Std.
communication do you more
Mean
frequently use?
Email
332 4.5402 1.52347
.08361
Online Chat
327 5.4118 1.33523
.07384
Error
Independent Samples Test
Levene's Test for
Equality
of
Variances
F
Sig. t
Equal
8.585
variances
assumed
ATTBI
Equal
variances
not assumed
.004
t-test for Equality of Means
df
-7.806 657
Sig.
Mean
Std. Error 95% Confidence
(2-tailed) Difference Difference Interval of the
Difference
Lower Upper
.000
-.87166 .11166
-1.09092 -.65241
-7.814 648.268 .000
-.87166
.11155
-1.09070 -.65262
Figure 12 & 13 indicates that the mean of online chat (5.4118) is higher than that of
email (4.5402), and the Sig. (2-tailed) value is less than 0.05 (0). Thus, there is a
statistically significant difference between two groups.
4.4 Correlation
Figure 14: Correlations of Self Concept Dimensions with ATTBI
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Conventional|||Unconventional
Innovative|||Routine
Uncomfortable|||Comfortable
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
ATTBI
-.150**
.000
659
-.238**
.000
659
.022
.565
659
As can be seen in Figure 14, three results from top to bottom are for negative
significant correlation, positive significant correlation and non-significant correlation
because their values of Pearson Correlation respectively less than, more than, and
equal to 0. Additionally, it can be concluded that people who are more conventional
and innovative are more likely to purchase items online than the unconventional and
routine people. However, there is invalid assumption about attitudes toward online
retail shopping between people who are uncomfortable and comfortable because its
person correlation value is 0.022, and the Sig. (2-tailed) value is more than 0.05
(0.565). Therefore, this group (uncomfortable and comfortable) is regarded as not
significant correlation.
4.5 Multiple regressions
In this section, Multiple Regression will be used to test overall model, which involves
testing multiple predictors of a dependent variable and is an extension of Bivariate
Regression. It aims to generate a linear equation fitted to the observed data, then use it
to create predictions associated with the dependent variable through plugging in
values for each independent variable. All constructs entering to the analysis are
indicated in Figure 15:
Figure 15: Variables Entered/Removed*
Variables Entered
Variables Removed
Method
Model
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Risk Aversion,
Impulsiveness,
Variety Seeking,
Convenience Seeking,
Price Consciousness,
.
Enter
a. Dependent Variable: ATTBI
b. All requested variables entered.
Figure 16: Model Summary
Model
R
.623a
1
R Square
Adjusted R
Square
.388
Std. Error of the
Estimate
.383
1.17564
a. Predictors: (Constant), SOC, PC, INF, RA, CS, VS, WOR, IMP, ENT
Figure 16 shows that “R” indicated that the strength of correlation between the
observed and predicted values of the dependent variable is 0.623, and the relationship
is positive. “R Square” shows the proportion of variance in the dependent variable
explained by the regression model is 0.388. “Adjusted R Square” indicates the model
occupies 38.3% of variance in the dependent variable, which can more closely reflect
the goodness of fit than “R Square”.
Figure 17: ANOVAa
Model
Regression
1
Residual
Total
Sum of
Squares
572.090
902.530
1474.619
df
Mean Square
5
114.418
653
658
1.382
F
82.784
Sig.
.000b
a. Dependent Variable: ATTBI
b. Predictors: (Constant), Price Consciousness, Risk Aversion, Convenience Seeking, Variety
Seeking, Impulsiveness
As can be seen in Figure 17, the significance value of F statistics is .000 (less than 0.5)
so the predictors do a good job explaining the variation in the dependent variable.
Figure 18: Coefficientsa
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Model
1
(Constant)
Risk Aversion
Impulsiveness
Variety Seeking
Convenience Seeking
Price Consciousness
Unstandardised
Coefficients
B
Std. Error
2.202
.509
-.581
.055
-.007
.046
.551
.062
.537
.061
.090
.049
Standardised t
Coefficients
Beta
4.382
-.366
-10.489
-.005
-.152
.290
8.888
.285
8.831
.060
1.842
Sig.
.000
.000
.879
.000
.000
.066
a. Dependent Variable: ATTBI
Figure 18 shows that impulsiveness and price consciousness have no significant
impact on attitudes toward online retail shopping because the significant levels of
them are more than 0.05 (0.879 and 0.066 respectively). In contrast, other models,
such as risk aversion, significantly affect the attitudes of online shopping. Importantly,
risk aversion has the most degree influence on the purchasing attitudes because it got
Beta -0.366, which is the biggest numeric in Standardised Coefficients. Besides,
variety seeking (0.29) has the strongest positive influence on online shopping
attitudes.
4.6 Social Desirability
Considering how the relationships differ for low vs high rating individuals can assist
to evaluate social desirability effects on the results. The results were scored from 1.00
to 2.00, and they should be separated to two groups, including low and high social
desirability groups. Based on the median (1.50), scores of and under 1.50 are
considered as low group, and scores of over 1.50 are considered as high group (Figure
19 below).
Figure 19: Social Desirability
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5.0 Discussion and Recommendations
5.1 To examine how self-concept dimensions relate to online retail shopping
attitudes
According to the analysis of correlations of self-concept dimension with ATTBI
(Figure 14), people who are more conventional and innovative have strongly positive
attitudes toward online retail shopping. Conversely unconventional and routine people
have weak desire to purchase items online. However, there is no impact on online
retail shopping no matter consumers who are comfortable or uncomfortable. These
findings can be helpful for marketers to position target market. Marketers can target
the audiences who consider themselves are more conventional and innovative.
Additionally, marketers can also apply the advertising which contains conventional
and innovative elements to attract consumers’ attention and drive them to purchase
items online. This can be explained by Rodgers and Thorson’s (2012) state that
advertising is an effective marketing strategy, which can be beneficial for marketers to
target consumers and attract consumers’ attention. Thus, it is an effective way for
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marketers to implement the advertising strategy which contains conventional and
innovative elements.
5.2 To determine the impact of individual characteristics on online retail
shopping attitudes
As Figure 18 shows, apart from impulsiveness and price consciousness, all constructs
of individual characteristics have a significant impact on online retail shopping. The
figure also indicates that risk aversion has the strongest negative impact on
consumers’ attitude toward online retail shopping, and variety seeking has the most
positive impact on online retail shopping. This means people think variety seeking
information of products is the most appealing factor for online retail shopping but
they are worried about the risks at purchasing items online. Thus, marketers should
pay more attention to designing briefer website, and try to show different kinds of
information about the products to attract more consumers’ attention. Moreover, online
shopping marketers can strive to cooperate with some sectors or companies which
dedicate to maintain online security. This can help to improve the security system of
online retail shopping and be beneficial for consumers to reduce the risks at buying
items online (Miyazaki & Fernandez, 2001). This can finally be helpful for marketers
to raise consumers’ confidence in the security of online shopping, thereby improving
the sales volume of products sold online.
5.3 To evaluate how social desirability might influence the results of the research
According to Zikmund et al (2014), gauging the effects of social desirability from
respondents’ responses can contribute to gain an accurate understanding of results.
Considering the relationships differ from low and high rating individuals can assist to
evaluate the social desirability effects on the results. The impact of social desirability
has been exposed. As Figure 19 shows more respondents belong to the low social
desirability group but there are inadequate resources about the social desirability in
this research. However, it can be concluded that it probably has social desirability bias,
which will influence the result of the research. For marketers, they need to examine
and get rid of the influence of social desirability bias, and try to find out the causes
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and effects of the social desirability. To prevent inaccuracy of the research and report,
it is necessary for marketers to do further research or observational studies to
determine what the actual performance do respondents have associated with online
purchasing behaviour.
6.0 Limitations
The form of questions in the survey is scale items which respondents indicate a rating
or extent of attribute. For scale items, respondents can just circle the number to
indicate their agreements. Thus, the respondents’ responses cannot accurately express
their thinking. Besides, too many scale items can cause the respondents losing their
patience about answering questions. This can lead to the fact that the responses
probably come out by skipping many information of the questions. To relieve these
problems, researchers can use many kinds of questions to generate a survey.
Unevenly distributing is the second main limitation. According to Figure 7, the age of
most respondents is between 18 and 25; besides, the average age of respondents is
36.54. This means the age group of young people occupies a large proportion of all
respondents and this can lead to the overbalance of the results. Thus, researchers
should avoid this situation by distributing appropriate respondents.
The error of sampling method will be unavoidably occurred by statistical fluctuation
because of the chance variation (Zikmund, et al, 2014). It is necessary for researchers
to use a better sampling frame to solve this issue.
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