Internet Shopping: How the Consumer Purchase Behaviour is Impacted by Risk Perception

Abstract

Online shopping is popular nowadays because consumers feel comfortable purchasing products from their home or office. One of the major reasons for the consumers internet-shopping excitement is when the peak season arrives; they are not required to wait in long queues in a physical store to purchase product and services since the consumer can directly purchase the product through online facilities. With technology advancement, past scholars have identified what consumers consider to be risk, their primary concern in the decision-making process when purchasing through internet shopping as in shopping online. The relationship between consumer risk perceptions and internet shopping intentions is not widely explored, particularly in the context of Malaysia, but current studies have suggested that there is a close link between them. This study aims to study the influence of consumer risk perception towards internet-shopping purchase behaviour to purchase products and services in Malaysia. A total of 138 valid responses from the samples have been obtained. The convenience sampling technique has been adapted for the young consumer in this study. The data collection instrument is a questionnaire that adopts a self-administered distribution technique. Structural Equation Model Partial Least Square (SEM-PLS) version 3.0 has been used to analyse the data. The findings of Partial Least Square have confirmed model fitness in the studied population. Similarly, the findings from path analysis have found that consumer risk perception of functional risk has influenced their internet shopping intention in purchasing the product and services.

Full text

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ISSN: 0193-4120 Page No. 1014 - 1021
1014
Published by: The Mattingley Publishing Co., Inc.
Internet Shopping: How the Consumer
Purchase Behaviour is Impacted by Risk
Perception
Nik Alif Amri Nik Hashim*1, Shah Iskandar Fahmie Ramlee2, Abdullah Muhammed Yusoff1, Normaizana
Mat Nawi1, Zaimatul Awang1, Siti Afiqah Zainuddin2, Tahirah Abdullah2, Ghazali Ahmad1 & Marlisa
Abdul Rahim1& Boyd Sun Fatt3
1Faculty of Hospitality, Tourism & Wellness, Universiti Malaysia Kelantan
*nikalifamri@gmail.com, abdullah.my@umk.edu.my,
maizana.mn@umk.edu.my,zaimatul@umk.edu.my, ghazali@umk.edu.my, marlisa@umk.edu.my
2Faculty of Entrepreneurship & Business, Universiti Malaysia Kelantan
shah@umk.edu.my, sitiafiqah@umk.edu.my, tahirah@umk.edu.my
3Faculty of Hotel & Tourism Management, Universiti Teknologi MARA
boyds156@uitm.edu.my
Article Info
Volume 59 Issue 6s
Page Number: 1014- 1021
Publication Issue:
November-December 2019
Article History
Article Received: 3 January 2019
Revised: 25 March 2019
Accepted: 28 July 2019
Publication: 25 November 2019
Abstract
Online shopping is popular nowadays because consumers feel
comfortable purchasing products from their home or office. One of the
major reasons for the consumers internet-shopping excitement is when
the peak season arrives; they are not required to wait in long queues in a
physical store to purchase product and services since the consumer can
directly purchase the product through online facilities. With technology
advancement, past scholars have identified what consumers consider to
be risk, their primary concern in the decision-making process when
purchasing through internet shopping as in shopping online. The
relationship between consumer risk perceptions and internet shopping
intentions is not widely explored, particularly in the context of Malaysia,
but current studies have suggested that there is a close link between
them. This study aims to study the influence of consumer risk perception
towards internet-shopping purchase behaviour to purchase products and
services in Malaysia. A total of 138 valid responses from the samples
have been obtained. The convenience sampling technique has been
adapted for the young consumer in this study. The data collection
instrument is a questionnaire that adopts a self-administered distribution
technique. Structural Equation Model Partial Least Square (SEM-PLS)
version 3.0 has been used to analyse the data. The findings of Partial
Least Square have confirmed model fitness in the studied population.
Similarly, the findings from path analysis have found that consumer risk
perception of functional risk has influenced their internet shopping
intention in purchasing the product and services.
Keywords:Young Consumer, Risk, Internet Shopping, Behaviour,
Malaysia

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1. INTRODUCTION
Online shopping is a priority for customers
as the internet has become an essential tool
for communication and business worldwide.
According to the Internet World Stats
(2018), in 2017, there were more than four
billion internet users and a growing of 57.7%
compared to internet users in 2000. The
Asian region captures 49.2 % of the total
internet users. In addition, approximately
1.66 billion people worldwide had purchased
online in 2017 which generated $2.3 million
in sales from the internet. As stated by
Statista (2018), the projections
demonstration a growing up to $4.48 million
by year 2021 which represents a huge surge
in online shopping (Paynter& Lim, 2001).
This shows that the internet has
revolutionized businesses for online
shopping (BourlakisPapagiannidis& Fox,
2008). With access to internet-connected
computers, as well as mobile computers and
tablets, whether at workplaces, home or
through facilities like cybercafes, cafes and
libraries, today this shopping trend has
become a regular style of transaction.
Despite the fast development in e-commerce
and online shopping, these exciting
developments has led to several new
problems and challenges involving major
internet users’ data protection, payments
security, and enforcement, information
disclosure, e-contract validity, acceptable
product enforcement and quality (Paynter&
Lim, 2001).
The risk perceived by consumers to purchase
online has become an important issue for
research as it will impact directly to the
attitude of customers to purchase online, and
their attitude will have a major impact on the
behaviour of online shopping (Ariff,
Sylvester, Zakuan, Ismail, & Ali, 2014). As
mentioned by Almousa (2011), the risk
perceived in shopping online negatively
affects the intention to buy the product
online. Consumers might possibly
experience a certain level of risk when they
intend to spend over the internet. Yet, the
perceived risks of online shopping are not
fully identified as a whole since there are
many online retailers that still pose a threat
to online business, and this may affect
retailers' transactions and performance. An
understanding on the broader perception of
young consumer risk will help marketers and
retailers to bring about a positive image for
consumers to participate in online shopping.
To address these gaps, this paper studies the
effects of consumer risk on online
purchasing behaviour. Such an approach can
assist to improve a more effective reduction
plan in response to potential threats.
2. LITERATURE REVIEW
2.1 Risk Perception
Risk perception or perceived risk is an
expectation of loss (Schierz, Schilke&Wirtz,
2010). The bigger the expected loss is, the
higher the level of risk a user will see.
Laroche, McDougall & Bergeron (2005)
have stated that risk could be known as a
negative impact on the expected and changed
outcome of the purchased product.
Meanwhile, Ko, Jung, Kim & Shim (2004)
have defined risk as the consumer’s
perception of outcomes that are variable and
contrary to the purchase of a product or
service. The concept is comprised of two
elements, namely uncertainty and
consequence. Uncertainty can be known as
the probability of an unfavourable outcome
and consequently is defined as the benefit of
the loss (Larocheet al., 2005). Kim, Kim &
Kumar (2003) add that the confidences of
customers on the changes are derived from
the transactions of online shopping. The

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perception of risk plays an important role in
determining the purchase intentions of
consumers. Perceptions of consumer risk are
important in determining their valuation and
purchasing behaviour (Koet al., 2004).
Previous studies have shown that consumer
perceived risk can negatively impact online
consumer purchase intentions for apparel
(Almousa, 2011;Meskaran, Ismail
&Shanmugam,2013; Zhang, Tan, Xu& Tan,
2012). The greater the risk perception among
consumers, the more it dissuades consumers
from their buying intentions. As mentioned
by Lee & Tan (2003), consumers at higher
risk are less likely to buy products or
services online. In conclusion, perceived
risks have a negative impact on consumers'
intention to buy over the internet (Liu &
Wei, 2003).As Kim & Lennon (2013) point
out, the higher the risk that online retailers
are seeing, the less likely they are to
persuade consumers towards online retailers.
On the other hand, Masoud’s (2013) study
on the impact of perceived risk on online
shopping intentions toward online shoppers
in Jordan pertained to time risk, financial
risk, information security risk, delivery risk
and product risk. Correspondingly, the
results of this study shown that product,
information security, financial and delivery
risk negatively affect online shopping
intentions. It can be concluded that online
sellers should be alert of the risk perception
of their customers, and strategies need to be
implemented to avoid these risks. In
addition, Akhlaq&Ahmed (2015) have found
that perceived risk has a negative effect on
consumer intentions to purchase online. This
indicates that the consumers’ intention to
buy online is suppressed when consumers
realize the transaction is at risk (Akhlaq&
Ahmed, 2015). In this regard, consumers will
have positive online shopping experience if
they have lower risk levels on the internet. In
the upcoming, the growth in purchase
intentions will happen if lower levels of risk
are perceived. Based on the literature, the
theoretical framework for this study will be
conceptualized based on the above
outcomes, using financial risk, physical risk,
time risk, social risk and functional risk. This
is because variables that are widely
recognized as consumer risk variables that
hinder the intent of online purchases and
experts in this field mostly study these
variables. Therefore, the variables are
suitable for this study.
2.2 Internet Shopping Behaviour
Internet shopping behaviour is a type of
attitude that involves the customers browsing
the web to find, select and purchase goods
and services, to meet their needs and wants.
It comprises the responses and choices of
consumer decisions. According to Close
&Kukar-Kinney (2010), online purchase
intent comes from the intention to purchase.
Online purchase intention is the willingness
of customers to buy over the internet
(Meskaran et al., 2013). The consumers'
willingness to purchase products or services
through the internet shop is defined as the
intention to purchase online (Li & Zhang,
2002). In general, the behaviour of online
shopping in a positive way will lead to the
success of e-commerce transactions. Online
shopping has become a new type of retail
shopping worldwide. This is because
customers will find it very easy, it has a
wider selection, a very competitive price,
better product information (including people
reviews) and it is very easy to find a product.
In the online store, consumers might develop
little confidence and consider online shopping

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to be high risk because of the less of face to
face communication. Researchers recognize
that the consumer’s confidence in the ability
of companies to meet their needs and wants is
more than just believing, it is also the goodwill
to influence the purchase intention of
consumers.
3. CONCEPTUAL FRAMEWORK
The conceptual framework in Figure 1 is
proposed according to past literature
reviews. Past studies have indicated that risk
perception plays a vital role in understanding
the consumer’s purchase intentions.
Although lots of studies have been
conducted in this context, many studies have
been solved in their approach. Therefore,
there is a need to understand the impact of
perception of risk on the intention of buying
through internet shopping. This study
examines the influence of perceptions of
young consumers risk perception on internet
shopping to buy products and services in
Malaysia. Based on the literature review of
research variables and conceptual
frameworks, the following hypotheses have
been developed:
H1: Financial risk perception affects the
internet shopping purchase behaviour of
young consumer.
H2: Physical risk perception affects the
internet shopping purchase behaviour of
young consumer.
H3: Time risk perception affects the internet
shopping purchase behaviour of young
consumer.
H4: Functional risk perception affects the
internet shopping purchase behaviour of
young consumer.
H5: Social risk perception affects the internet
shopping purchase behaviour of young
consumer.
Figure 1: A conceptual framework
4. RESEARCH METHODOLOGY
4.1 Methodology
A quantitative approach method was used for
this study. This study engaged with a young
consumer population aged 18-35 years old.
Samples had been selected to meet certain
criteria, for example, young users must be
skilled in using internet-shopping to buy
their products. The process of data collection
was conducted in Kota Bharu Kelantan over
the weekend. Young internet-shopping users
were approached, and a total of 138
responses had been obtained using GPower
sample size (Faul, Erdfelder, Lang, &
Buchner, 2007). After data was collected, the
data were analysed via partial least squares
structural equation model (SEM-PLS). The
detail of the data collection procedure is
listed in table 1.
Table 1: Procedure of Data Collection
Population
Young Consumer

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Size of Sampling
138 Young Consumer
Sampling Method
Convenience Sampling
Research
Approach
Quantitative Technique
Data collection
Self-Administered
distribution of a
questionnaire
Data Analysis
Smart-PLS Version 3
4.2 Instrumentation
The four sections of the questionnaire have
been developed to gather data from young
consumers. Part A of the questionnaire is the
screening questions, part B’s questions relate
to the perceived tourist risk of the
destination, and section C pertains to
questions that are related to online
purchasing behaviour. Last but not least
section D is about demographic questions.
The instrumentation for perceived risk and
intention are adopted and adapted from prior
studies, for instance Ariffin, Mohan&Goh
(2018);Lim, Osman, Salahuddin,
Romli&Abdullah (2016);and NikHashim,
Yusoff, Awang, Aziz, Ramlee, Bakar, Noor
andFatt (2019). A seven-point Likert scale
has been used for this item to ensure that the
research would gain in-depth info, ranging
from (1 = very strongly disagree to 7 = very
strongly agree).
4.3 Reliability and Validity
The study was testing the reliability and
validity prior to the actual data collection
process. The purpose of carrying out a
reliability analysis was to examine the
consistency and stability of the reactions
from the respondents. According to
Sekaran&Bougie (2016), there are four
criteria that researchers can follow in
improving reliability: (1) configuring all
construction, (2) refining measurement, (3)
using several indicators, and (4) conducting
pilot test.If the value exceeds0.7, it can be
assumed that the items in the questionnaire
are reliably measuring the constructs that are
needed. Hence, the higher the score, the
higher is the reliability of the scale that is
generated. In relation to the validity of the
content, the academic staff in technology
from Universiti Malaysia Kelantan (UMK)
was contacted to check the validity of the
content. Table 2showsthe rules of thumb of
the Cronbach’s Alpha Coefficient Size.
Table 2: Rules of Thumb of Cronbach’s
Alpha Coefficient Size
Coefficient Alpha Range
Strength of Association
>0.90
Excellent
0.80 to < 0.90
Very Good
0.70 to < 0.80
Good
0.60 to < 0.70
Moderate
< 0.60
Poor
(Hair, Hult, Ringle, &Sarstedt, 2017).
5. FINDING AND DISCUSSION
5.1 Profile of Respondent
The demographics of the respondents are
depicted in table 3. About 78 respondents
were female (56.5%), and 60 respondents
were male (44.4%). Pertaining to age, more
than half of respondents were between 26
and 30 years old (63.8%), approximately
23.9% were 21-25 years old, and 7.9% were
under 20 years old. Nearly more than 60% of
the respondents are bachelor degree holders,
followed byrespondents who hold a diploma
or STPM (23.9%). Furthermore, 8% of the
respondents are SPM holders and smaller
percentages (4.3%) of respondents have a
Bachelor's degree education background.
The majority of the respondents were
students (73.9%), followed by those who
were self-employed(13.8%), the

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professionals and those who were in
management (7.2%). A minority of the
respondents were housewives(5.0%).
Table 3: Demographics of consumer (n =
138).
5.2 Hypotheses Testing and Path
Analysis
Table 4 demonstrations the β coefficients of
all the relationships between the model
variables. Using the Partial lease square, it is
shown that only the H4 hypothesis is
supported whereas the hypotheses H1, H2,
H3, and H5 has been rejected. Financial,
physical, time, functional and social risks are
independent variables and online shopping
purchase behaviour are dependent variables.
As depicted in figure 2, functional risk has a
major influence on online purchase
behaviour (β = −0.459, p <0.05), a P value
less than 0.05.As a result, hypothesis 4 is
supported. Furthermore, the results indicate
that the sum of the effects on financial risk
(H1), physical risk (H2), time risk (H3) and
social risk (H5) is insignificant for online
purchasing behaviour. The results of the
structural relationships and the importance of
the path, β-values and their importance, p-
values are presented in table 4.
Table 4: Hypothesis Testing Results
Hypot
hesis
Relatio
nship
Bet
a
Val
ue
(β)
p-
val
ue
Signif
icant
level
Resu
lt
H1
FR
=>IPB
-
0.0
054
0.5
32
ns
Not
Supp
orted
H2
PR
=>RI
0.1
41
0.0
98
ns
Not
Supp
orted
H3
TR
=>RI
-
0.1
42
0.0
97
ns
Not
Supp
orted
H4
FNR
=>RI
-
0.4
59
0.0
00
**
Supp
orted
H5
SR
=>RI
-
0.1
13
0.1
87
ns
Not
Supp
orted
Note: Significant level =** p<0.05; ns= not
significant; FR=Financial Risk; PR=
Physical Risk; TR=Time Risk;
FNR=Functional Risk; SR= Social Risk.
6. CONCLUSION
In conclusion, previous studies have shown
that perceptions of consumer risk are
complex and multidimensional. The results
from this study indicate that customers are at
risk when they want to buy online; a number
of samples- of financial, physical, time,
social and functional risks, in this study
confirm this. Meanwhile, the four risk
factors that are found to be insignificant are
financial, physical, time and social risk.
Amongst these factors, functional risk is a
Variables
Category
Frequency
Percentag
e (%)
Gender
Male
60
44.4
Female
78
56.5
Age
18- 20
11
7.9
21-25
33
23.9
26-30
88
63.8
31-35
6
4.3
Education
SPM
11
8.0
Diploma/STPM
33
23.9
Bachelor Degree
88
63.8
Mater Degree
6
4.3
Occupation
Student
102
73.9
Self-Employment
19
13.8
Professional &
Management
10
7.2
Housewife
7
5.0

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major contributor to consumers' cancellation
of their online purchases. The results
demonstrate that it is important to understand
the factors that influence risk perception
purchase intention of online users because
they provide valuable information to online
retailers in e-commerce activities. To
enhance the generalisability of the findings
replicated studies are proposed in future
studies, where this study can be carried out
with similar model studies between different
settings, such as those that are unique in
other countries and not just in the context of
Kelantan. It is more beneficial to have a
sufficient number of respondents per
respondent. This study’s emphasis is to
present the different perceptions of the
national group of consumers. In addition, the
existing study has not been precisely
designed to assess factors that are related to
moderators and mediators of the perceptions
of risk perception, and the intention to buy
online. Upcoming studies may consist of the
effects toward personality traits and previous
experience in the model to understanding
what way the variables can affect the
simplification of both independent and
dependent variables. The role of mediation,
such as trust is also recommended for
upcoming study. In short, it is hoped that the
results will provide info and knowledge to
Malaysian online retail participants, in
particular, in designing their marketing
strategies to attract consumers. For instance,
the results of a upcoming study may show
online stores where they can offer
comprehensive info about their business and
considerations of their safety policies, in
order to prevent cyber scam. Online vendors
may decrease the financial and other related
risks that are involved by acknowledging
those risks and offering a trading plan for
inappropriate products.
ACKNOWLEDGEMENTS
This study did not receive any funding from
the public, commercial, or not-for-profit
sectors.
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