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Effect of Gender and Left Handedness on Online Shopping
Behaviour Marketing Research Project
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CONTENTS
Executive Summary
2
Problem Statement
2
Introduction
2
Literature Review
3
Research Approach
6
Research Design
7
Regression Model
7
Data Description
8
Result
8
Discussion and Conclusion
9
References
10
Appendix A: Questionnaire
11
Appendix B: Linear Regression Model
13
About Authors
15
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EXECUTIVE SUMMARY
Previous ecommerce studies have shown that consumer
characteristics are important when
considering issues related to online shopping behaviour.
However, most studies have focused
on attitude and preferences of the buyers. The effects of
left-handedness and gender have
been relatively neglected. Previous researches have limited
their study to characteristics like
age and attitude. This study explores the effects of gender and
left-handedness on attitude of
people during online shopping.
Keywords: online shopping, shopping behaviour, dexterity,
perceived risks
PROBLEM STATEMENT
The marketing research problem asks specific questions, some of
which are pertaining to the
following topics in our case:
What factors affect online shopping behavior of men and
women.
Does left or right-handedness have an impact on online
shopping.
INTRODUCTION
In e-commerce, consumers use Internet for variety of reasons
such as: Searching for products
features, prices and customer reviews, selecting products and
services through Internet,
placing the order, making payments, which is then followed by
delivery of the products, after
sales service through Internet or other mean (Sinha, 2010).
Thus, understanding potential
markets is important for businesses investing in ecommerce.
Accordingly, it is important to
have a better understanding of online shopping attitude for
designing and maintaining
effective websites that can help online retailers attract and
retain online customers.
The considerable growth and consistent increase of online
shopping generates great interest
in understanding what affects peoples decisions to participate
in or abstain from shopping
online (Hassan, 2010). Among the variety of factors studied in
past affecting online shopping,
attitude toward online shopping revealed a significant impact on
online shopping behaviour.
While studies of online shopping behaviour are extensive in the
literature, studies on impact
of gender and dexterity on online shopping attitude are scarce
and reported findings are
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inconsistent and insufficient. Their importance in the online
shopping process has appeared to
be unquestionable, and essential to understanding user
behaviour. Personal factors have the
most influence on buyer behaviour. Previous studies found that
consumer characteristics are
important while considering online shopping acceptance-related
issues.
A survey of 5,500 women across Asias major urban areas conducted
by The Economist
Intelligence Unit finds that they are increasingly getting
financially independent and they are
driving the growth of e-commerce in the region. Yet, even though
the gender gap in terms of
the number of individuals online has reduced, gender differences
may possibly still exist in
terms of Internet-related attitudes and activities. One of the
explanations for the gender gap in
online purchasing behaviour is that women may be more concerned
than men with regards to
the risks of buying online.1 Another possible explanation might
be the difference in internet
usage might lead to differences in online purchasing behaviour.
Prior research has shown that
as Internet usage increases, online purchase risk decreases.
Without controlling for
differences in usage it is impossible to know if online
purchasing behaviour is truly a
function of gender or merely an artefact of gender differences
in Internet usage. Also, as most
of the website is designed with keeping in mind the right-handed
user, it is essential to know
the difficulties (if any) faced by left-hand users in navigating
the website and how this
translates into affecting their online shopping behaviour. Hence
our study aims at finding
how gender and left-handedness affects online shopping
behaviour.
LITERATURE REVIEW
There is no dearth of the number of studies that have been
conducted on gender differences in
various computer-related beliefs. Table 1 gives us a snapshot of
the studies carried out based
on gender differences (Hasan, 2010).
1 Women Driving Explosive Growth Of E-Commerce In Asia.
Afternoon.
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It is evident from the above table, that number of studies
carried out to study the gender
differences affect online shopping attitude are few. Despite the
scanty number of studies that
have been conducted in this area, following are the insights one
can gather form the existing
pool of data
1. A study by Cyr and Bonanni (2005) examined how perceptions
between the genders
differed in matters related to transaction security and website
design elements.
Additionally, it studies how the perceptions of website trust,
e-loyalty and satisfaction
were different between the genders. The findings of these
studies were inconsistent.
2. A paper by Chang, Cheung and Lai (2005) tried to identify
areas that would prove
helpful in understanding the dynamics of the decision of a
customer to shop online. It
showed that men shopped online more than what women did in some
studies and in
others there were no significant differences between the
two.
3. A study by Zhou, Dai and Zhang (2007) synthesised the
findings of related studies of
the impact of gender on online shopping activities into a
reference model called
Online Shopping Acceptance Model (OSAM).The paper demonstrated
conflicting
findings.
At the same time, attitude can be used as a multidimensional
tool consisting of cognitive,
affective and behavioural parts to examine the gender
differences across these 3
attitudinal parts. A book by Fishbein and Ajzen (1975) stated
that studying attitude as a
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multidimensional concept can help provide insights on
understanding gender differences
in shopping (online) attitudes in contrast to most of that
studied attitude as a unifactor
concept.
While we have only discussed individual studies based on gender
differences and attitude
affecting online shopping behaviour, there is sufficient
research on the combined effect of
both the mentioned factors on the same. Below are the key
findings of some papers that
have studied both the factors in conjugation.
1. A research paper by Long and Meek (2010) reports that the
attitude of men stays
more or less the same in both conventional and online shopping
whereas the attitude
of women changes substantially and becomes less favourable
towards online
shopping.
2. Slyke et all (2002) studied the impact of the characteristics
of the products available
online on the shopping preferences and attitudes of both the
genders. While the
products that were more male centric such as computers and
electronics were easily
available online and showed higher revenue in terms of
purchases, products such
house dcor and clothing which connected more with the female
audience were not
widely available online.
3. A paper by Jinsook Cho (2004) stated the preference of women
towards physical
evalutions such as touch and feel towards products before buying
them. While the e-
commerce sites provided clear images and animations of the
products on their website
pages, the female audience felt the absence of the touch and
feel aspect and hence
preferred the conventional mode of shopping.
4. A study by Zhou et all (2007) suggested explanations for
gender differences for
online shopping. One was the difference in shopping orientation
between men and
women. While men preferred convenience over social interaction,
women preferred
otherwise. Hence the lack of face to face communication in
online shopping would
deter women more than men.
Hence we can clearly state that even though a few researchers
have tried studying the
correlation among gender, attitudes and online shopping, the
studies have come out with
inconsistent results. Take the case of attitudes, studies that
the strength of the path coefficient
between online shopping and attitudes varies from .35 to .7 in
different studies.
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While some studies indicate that the variation in the strength
of coefficients can be attributed
to the difference in conceptualization and varied definitions of
the concept of attitudes, other
researchers such as Bruner and Kumar (2002) indicate that the
available scales are not
psychometrically equivalent and hence conclusions drawn from
different scales can vary
considerably.
RESEARCH APPROACH
Through this study we have tried to find the various factors
that affect the shopping behavior
of shopping online. To achieve this we have defined our
components, research question and
thus developed our hypothesis, which we have tested through
linear regression and ANOVA
test. We have defined them below:
Component 1: What factors affect the online shopping behavior of
men and women.
Research Question 1: Does secure mode of payment impact online
shopping behavior of
men?
H1: Shopping behavior of men is impacted by the secure mode of
payment
H2: Shopping behavior of women is impacted by the secure mode of
payment
Research Question 2: Does customer review and prior knowledge of
product impact
H3: Shopping behavior of men is impacted by customer
reviews.
H4: Shopping behavior of women is impacted by customer
reviews.
H5: Shopping behavior of men is impacted by prior knowledge of
product.
H6: Shopping behavior of women is impacted by prior knowledge of
product.
Research Question 3: Does the design of website impact online
shopping behavior
H7: Shopping behavior of men is not impacted by the design of
website.
H8: Shopping behavior of women is not impacted by the design of
website.
Research Question 4: Does different hand orientation affect
online shopping?
H9: Hand orientation has no impact on online shopping
behavior.
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RESEARCH DESIGN
We chose to perform direct research method to study the online
behavior by asking online
shoppers to rate how they felt about various factors that affect
online purchase behavior. To
achieve this, a survey was floated to selected sample from
various geographies from various
industries. They were asked to rate factors like ease of
navigation, prior knowledge of
product, perceived risk of buying online. In survey we used
likert scale to study the ratings.
The control factors chosen by us were gender and hand
orientation (left handedness and right
handedness). Through this we analyzed various factors impacting
online purchase and have
brought the conclusion about the behavior patterns that impact
online shopping. The model
used is shown below.
REGRESSION MODEL We split the database into male and female and
developed the mathematical model to see
how each gender is affected by the various factors.
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Male:
Impact on online purchasing behavior = a + b1*(design) +
b2*(reviews) + b3*(Knowledge)
+ b4*(online payment comfort) + b5*(left-right Handedness)
Female:
Impact on online purchasing behavior = a + b1*(design) +
b2*(reviews) + b3*(Knowledge)
+ b4*(online payment comfort) + b5*(left-right Handedness)
Questions were designed to check each of the constructs. Gender
and hand orientation were
the independent variables the rest constructs depended on these
independent variable.
DATA DESCRIPTION
Survey form was floated after studying the already present
literature. The questions were
developed to test the hypothesis developed by us. We collected
data from over 93
respondents. The data was ordinal, as we had asked the ratings
to be filled on a likert scale.
By applying our regression model we were able to find conclusion
on the hypothesis.
RESULT
After performing the mathematical regression for both male and
female we obtained the following results: For male the R-value
obtained was 0.628 thus it explains 40% of the result. The
correlation was as follows: Impact on online shopping for male =
1.322 + 0.557*(online payment comfort) The male are not impacted by
the other independent variables studied. For female the R-value
obtained was 0.75 thus it explains 44% of the result. The
correlation was as follows: Impact on online shopping for female =
-0.29 + 0.43*(prior knowledge) + 0.288*(customer reviews) +
0.534*(design) After the result we can reject or accept the
hypothesis. After studying the significance of the various
independent variables in the impact of shopping online we accept
hypothesis: H1 (Shopping behavior of men is impacted by the secure
mode of payment), H4 (Shopping behavior of women is impacted by
customer reviews.) H6 (Shopping behavior of women is impacted by
prior knowledge of product) H8 (Shopping behavior of women is not
impacted by the design of website.) H9 (Hand orientation has no
impact on online shopping behavior.) We reject all other hypothesis
based on our regression result.
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DISCUSSION AND RECOMMENDATION
From the above study we conclude that men are more worried about
the online secure mode
of payment and rest other factors such as design, prior
knowledge of product and customer
reviews do not impact their online purchasing behavior. Thus
while targeting men more
emphasis must be made on the secure online payment. For women
our study states that their
online shopping behavior is impacted by customer reviews, prior
knowledge of product and
the design of the website. Thus while targeting women more
emphasis must be put on the
design and customer reviews for the product. Our study also
concludes that different had
orientation has no impact on the online shopping behavior.
CONCLUSION AND LIMITATIONS:
The major limitation of the research is that the sample of
respondents was similar, mostly
were students of IIM Kozhikode. Thus the sample under study is
not a good representation of
the entire population of online shoppers. Also the questionnaire
floated by us had one or two
questions judging the various factors. Thus we could not perform
the factor analysis and the
reliability test. Though we were able to find conclusive result
for the study done, the results
could have been better if the sample chosen was better and the
questionnaire had more
questions.
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REFERENCES
Bruner, G. C., & Kumar, A. (2002). Similarity analysis of
three attitude-toward-the-website scales. Quarterly Journal of
Electronic Commerce, 3, 163-172.
Chang, M. K., Cheung, W., & Lai, V. S. (2005). Literature
derived reference models for the adoption of online shopping.
Information & Management, 42(4), 543-559.
Cho, J. (2004). Likelihood to abort an online transaction:
influences from cognitive evaluations, attitudes, and behavioral
variables. Information & Management, 41(7), 827-838.
Constantinides, E. (2004). Influencing the online consumer's
behavior: the Web experience. Internet research, 14(2),
111-126.
Cyr, D., & Bonanni, C. (2005). Gender and website design in
e-business.International Journal of Electronic Business, 3(6),
565-582.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude,
intention and behavior: An introduction to theory and research.
Garbarino, E., & Strahilevitz, M. (2004). Gender differences
in the perceived risk of buying online and the effects of receiving
a site recommendation. Journal of Business Research, 57(7),
768-775.
Hasan, B. (2010). Exploring gender differences in online
shopping attitude. Computers in Human Behavior, 26(4), 597-601
Jen-Hung, H., & Yi-Chun, Y. (2010). Gender differences in
adolescents' online shopping motivations. African Journal of
Business Management, 4(6), 849-857.
Moshrefjavadi, M. H., Dolatabadi, H. R., Nourbakhsh, M.,
Poursaeedi, A., & Asadollahi, A. (2012). An analysis of factors
affecting on online shopping behavior of consumers. International
Journal of Marketing Studies, 4(5), p81.
Sinha, J. (2010). Factors affecting online shopping behavior of
Indian consumers. University of South Carolina.
Van Slyke, C., Comunale, C. L., & Belanger, F. (2002).
Gender differences in perceptions of web-based shopping.
Communications of the ACM, 45(8), 82-86.
Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping
acceptance model-A critical survey of consumer factors in online
shopping. Journal of Electronic Commerce Research, 8(1), 41-62.
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Appendix A: Questionnaire On a scale of 1-5, how comfortable are
you in making payments online? (5 highest) 5 4 3 2 1
On a scale of 1-5, rate your prior knowledge of products while
shopping online? (5-highest)
5 4 3 2 1
On a scale of 1-5, how does the presence of online customer
reviews affect your purchase? (5-Highest)
5 4 3 2 1
On a scale of 1-5, rate your ease of navigating through a
website while shopping online? (5-Highest)
5 4 3 2 1
On a scale of 1-5 (5-affects most), how does the pro
right-handed design of websites (eg. Presence of purchase summary
and payment options on the right of page) impact your online
shopping experience?
5 4 3 2 1
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Are you a left-Handed or right-handed person?
Right-Handed Left-Handed
Please specify your age group?
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Appendix B: SPSS Output
Model Summary
Please Specify your gender? Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Male 1 .628a .395 .260 .686
Female 1 .750b .562 .452 .681
Coefficientsa
Please Specify your
gender? Model
Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig. B Std. Error Beta
Male 1 (Constant) 1.332 .702 1.897 .069
On a scale of 1-
5 how comfortable
are you in making
payments online? (5-
highest)
.557 .161 .554 3.451 .002
On a scale of 1-5
how does the
presence of online
customer reviews
affect your purchase?
(5-High...
-.232 .139 -.290 -1.663 .108
On a scale of 1-5 rate
your ease of
navigating through a
website while
shopping online? (5-
Highest)
.133 .161 .140 .826 .416
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On a scale of 1-5 (5-
affects most) how
does the pro right-
handed design of
websites (eg.
Presence...
-.103 .120 -.146 -.858 .398
(Constant) -.298 .700 -.426 .674
On a scale of 1-
5 how comfortable
are you in making
payments online? (5-
highest)
.089 .151 .092 .590 .560
Female 1 On a scale of 1-
5 rate your prior
knowledge of
products while
shopping online? (5-
highest)
.432 .174 .409 2.482 .020
On a scale of 1-5
how does the
presence of online
customer reviews
affect your purchase?
(5-High...
.288 .108 .374 2.674 .013
On a scale of 1-5 rate
your ease of
navigating through a
website while
shopping online? (5-
Highest)
.534 .242 .370 2.208 .037
On a scale of 1-5 (5-
affects most) how
does the pro right-
handed design of
websites (eg.
Presence...
-.132 .096 -.211 -1.368 .184
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About the Authors:
Ankita Qanungo She has completed her engineering in Information
Technology from Panjab University, Chandigarh. After graduation,
she joined Infosys Ltd. as a Test Engineer wherein she worked in
the Financial Services domain. After working in the Technology
domain for about two years, she went ahead to pursue her Masters in
Business Administration from Indian Institute of Management
Kozhikode.
Isha Chhabria She completed her Bachelors in Arts in Economics
from Lady Shri Ram College for Women, Delhi. Post-graduation, she
joined the Listed Derivatives team in Nomura Mumbai and worked in
the capacity of a Business Analyst for 2 years. In 2013-14, she was
a part of the support campaign management team for BJP as a lead up
to the upcoming elections. To expand her knowledge base and skill
set further, she joined Indian Institute of Management, Kozhikode
to pursue higher studies.
Manasi Meshram A B.E in Computer Technology, She has over 5
years of work experience in software development. She has worked in
two different domains in the span of these five years. Being expert
in her domain, she was given added responsibility to handle new and
old clients. She had an opportunity to interact with foreign as
well as Indian clients and gain technical as well as customer
relations knowledge. She joined Indian Institute of Management
Kozhikode in 2014 to pursue MBA.
Pranav Sondhi He did his engineering in Electronics and
Communication form NSIT Delhi. Being good with number and data
analysis, he joined Futures First as a derivatives trade in the
exotic futures contract of Brent crude oil. Working there for over
17 months, he switched his job and joined ZS Associates as a
Business Analytics Associate in the business consulting domain.
Working with Fortune 300 company in the pharmaceutical sector, he
was exposed to working with big data. Using oracle tool to fetch
data, to synthesise it and communicate the same to clients, he
gained proficiency in data analytics. In purview to increase
knowledge in this field, he joined Indian Institute of Management,
Kozhikode to pursue higher studies.
Rahul S. More He has competed his engineering in Electronics
& Telecommunications from University of Pune. After his
graduation he joined CSIR-National Chemical Laboratory as a
research fellow to work on a project in the domain of Wireless
Communication and Instrumentation. He has successfully published
and co-authored two research papers in this domain. He also has
interest in sports and follows cricket, table-tennis. After gaining
sufficient experience in technical field, he decided to pursue
Masters in business administration and is currently pursuing his
PGDM degree from Indian Institute of Management, Kozhikode.
