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The study examines the impact of financial risk, convenience risk, non-delivery risk; return policy risk and product risk on online consumer behavior of Malaysian consumers. The research employed a self-administered survey to collect empirical data from 245 Malaysian online shoppers by using convenience sampling. Cronbach alpha was calculated to confirm the reliability of the data and then normality was assessed. Confirmatory Factor Analysis was then conducted to test the model using the goodness-of-fit tests. And finally, structural equation modeling is used to test the hypotheses and draw conclusions. IBM SPSS AMOS version 22.0 was utilized for data analysis. The research indicates that product risk, convenience risk, and return policy risk have a significant and positive impact on online shopping behavior. Financial risk is found to have insignificant and negative effects on consumer behavior. In addition, the non-delivery risk is found to have a significant and negative impact on online shopping behavior. The findings provide a useful model for measuring and managing perceived risk in online shopping which may result in an increase in participation of Malaysian consumers and reduce their cognitive deficiencies in the e-commerce environment. Several managerial implications are discussed along with the scope for future research.
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Kok Wai THAM, Omkar DASTANE, Zainudin JOHARI, Nurlida Binti ISMAIL / Journal of Asian Finance, Economics and Business Vol 6 No 4 (2019) 249-260
Print ISSN: 2288-4637 / Online ISSN 2288-4645
doi:10.13106/jafeb.2019.vol6.no4.249
Perceived Risk Factors Affecting Consumers Online Shopping Behaviour*
Kok Wai THAM
1
,
Omkar DASTANE
2
,
Zainudin JOHARI
3
,
Nurlida Binti ISMAIL
4
Received: July 27, 2019
Revised: September 16, 2019
Accepted: September 30, 2019
Abstract
The study examines the impact of financial risk, convenience risk, non-delivery risk; return policy risk and product risk on online
consumer behavior of Malaysian consumers. The research employed a self-administered survey to collect empirical data from 245
Malaysian online shoppers by using convenience sampling. Cronbach alpha was calculated to confirm the reliability of the data and
then normality was assessed. Confirmatory Factor Analysis was then conducted to test the model using the goodness-of-fit tests. And
finally, structural equation modeling is used to test the hypotheses and draw conclusions. IBM SPSS AMOS version 22.0 was utilized
for data analysis. The research indicates that product risk, convenience risk, and return policy risk have a significant and positive
impact on online shopping behavior. Financial risk is found to have insignificant and negative effects on consumer behavior. In
addition, the non-delivery risk is found to have a significant and negative impact on online shopping behavior. The findings provide a
useful model for measuring and managing perceived risk in online shopping which may result in an increase in participation of
Malaysian consumers and reduce their cognitive deficiencies in the e-commerce environment. Several managerial implications are
discussed along with the scope for future research.
Keywords : Perceived Risk, Online Shopping Behaviour, Malaysian Consumer, E-Commerce
JEL Classification Code : M31, L86
1. Introduction
12
The Internet is a common, collaborative and self-
sufficient setup that can be used by millions of people
around the world. The advancement of Internet technology
has made the growth of online shopping beyond legacy
methods. Thus, the assurance of secure transactions is
* All authors contributed equally to this manuscript.
1
First Author, Postgraduate Research Fellow, Lord Ashcroft
International Business School, Anglia Ruskin University,
Cambridge, United Kingdom. Email: kw_tham77@hotmail.com
2
Corresponding Author, Head of Postgraduate Centre Cum Senior
Lecturer, School of Accounting & Business Management, FTMS
College Malaysia, Malaysia. [Postal Address: Block 3420,
Persiaran Semarak Api, Cyber 4, Cyberjaya, 63000, Malaysia]
Email: omkar.dastane@gmail.com
3. Head of School, School of Engineering & Information Systems,
FTMS College Malaysia, Malaysia.
Email: Zainudin@ftms.edu.my
4. Senior Lecturer, Taylor’s Business School, Taylor’s University,
Subang Jaya, Malaysia. Email: nurlida.ismail@taylors.edu.my
Copyright: Korean Distribution Science Association (KODISA)
This is an Open Access article distributed under the terms of the Creative Commons
Attribution Non-Commercial License (http://Creativecommons.org/licenses/by-nc/4.0/) which
permits unrestricted noncommercial use, distribution, and reproduction in any medium,
provided the original work is properly cited.
important and must be accommodated by the new
technological advancement. In Malaysia, ever since the first
Internet Service Provider (ISP) JARING launched in 1990,
and later the TMNET in 1996, the usage of the Internet has
been growing with steady, up to today. From the provided
data for Malaysia between year 1990 and 2017 by World
Bank, the average value of Malaysia internet users within
this period was 36.36%, the lowest in 1990 was 0%, and the
highest in 2017 was 80.14%. While in quarter three of year
2017, penetration rate of Malaysia's mobile phone have
reached 131.8%, compare to penetration rate for smartphone
reached 70%, and, Malaysia's broadband penetration rate
reached to 84.5% as stated in the "3Q 2017 Communications
and Multimedia: Facts and Figures" report, it has been
shown that in the region, Malaysia have emerged as e-
commerce markets at the rapid growth rate (TheStarOnline,
2018).
Although the prospects for growth in e-commerce in
Malaysia are very promising, it is important to manage
consumer perceived risk to allow more buyers to attract
online shopping (Goi, 2016). Therefore, consumers' online
shopping behavior ought to study based on the attitude and
perceived risk, as this will result in more Malaysians trading
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Kok Wai THAM, Omkar DASTANE, Zainudin JOHARI, Nurlida Binti ISMAIL / Journal of Asian Finance, Economics and Business Vol 6 No 4 (2019) 249-260
online, which will have a major influence on the e-
commerce development in Malaysia. In the marketing
literature, it is realized that risk perception directly affects
purchase and purchase intent, that is, when consumers
perceive high risks, consumers are less likely to purchase or
buy back online. Obviously, the risk may be real, as long as
it is perceived; it will influence the consumer's buying
behavior. Thus, the study must constantly check the
consumer's perceived risk of online shopping to monitor
their degree of impact on consumers' online attitudes and
shopping behavior and to avoid disharmony after they
execute purchases (Hassan, Kunz, Pearson, & Mohamed,
2006). Therefore, the perceived risk of consumers should be
continuously researched so that they can be actively
managed and decrease the perceived risk of consumers, thus
helping to increase in online shopping.
Therefore, the purpose of this study is: To investigate the
effect of financial risk as a factor influencing consumers’
online shopping behavior and purchasing decisions in
Malaysia. To investigate the effect of product risk as a factor
influencing consumers’ online shopping behavior and
purchasing decisions in Malaysia. To investigate the effect
of convenience risk as a factor influencing consumers’
online shopping behaviors and purchasing decisions in
Malaysia. To investigate the effect of non-delivery risk as a
factor influencing consumers’ online shopping behaviors
and purchasing decisions in Malaysia. To investigate the
effect of return policy risk as a factor influencing consumers’
online shopping behaviors and purchasing decisions in
Malaysia.
2. Literature Review
Consumer Online Shopping Behaviour: The course of
online purchasing of goods or services through e-commerce
platforms can be referred to online shopping behavior. This
is a five-step process and is analogous to shopping
characteristics in conventional approaches (Liang & Lai,
2000). A study conducted by Shranck, Huang, and Dubinsky
(2006) indicates that people who shop online are less
technically at risk compared to those who shop directly. The
reliability of online resources relies on the receipt of orders,
timely response and delivery on time; as well as the security
of customer personal information (Janda, Trocchia, &
Gwinner, 2002; Kim, Lee, Han, & Lee, 2002; Parasuraman,
Zeithaml, & Berry, 1988). Online shopping involves users
online to search, select, purchase, use and process goods and
services to meet his or her needs. Individuals encounter a lot
of risks when visit and performing online purchases. As a
matter of fact, consumers experience high risks when
shopping through the Internet compared to traditional retail
transactions (Lee & Tan 2003).
Perceived Risks: The amount of risk perceived by the
consumer is a function of two main factors, namely, the
amount at stake in the purchase decision, and the
individual’s feeling of subjective certainty that he/she will
“win” or “lose” all or some of the amount at stake (Cox &
Rich, 1964). It is also defined as the uncertainty of bad
outcome that consumers may make when making
purchasing decisions (Naovarat & Juntongjin, 2015; Tsiakis,
2012), and, possibility of being dissatisfied when purchasing
a product compared to the buyer’s goal (Zheng, Favier,
Huang, & Coat, 2012). Perceived risk is a measurement of
unanticipated disaffection and disappointment with purchase
decisions based on the purchase target, and hence it is a
strong pointer of consumer behavior because consumers are
more likely to lessen possible failures rather than seek the
purchase accomplishment (Donni, Dastane, Haba, and
Selvaraj, 2018). In general, active online shopping behavior
will result in the success of e-commerce transactions (Safie,
Dastane, & Ma’arif, 2019). There are several factors of
perceived risk when it comes to online shopping.
Financial Risk: Consumers might be worried regarding
online safety and security in the use of their credit cards and
disclosure of personal information. Therefore, although if
customers make an order online, most customers prefer
other payment methods, for example, cash on delivery,
online/offline banking transfer and third party secured
payment method, like PayPal, rather than using credit cards.
Previous research has revealed that one of the most
instances worries when buying online is fear of credit card
deception (Adnan, 2014; Abrar, Naveed, & Ramay, 2017;
Saprikis, Chouliara, & Vlachopoulou, 2010). Financial risk
plays an important role for those who choose to shop online
whether to or not to go with the purchase. This is due to
financial risks raised a threat, leading to undesirable
opinions and affecting consumer behavior (Barnes, Bauer,
Neumann, & Huber, 2007; Haider & Nasir, 2016). Bitner
and Zeithaml (2003) assume that financial risk often occurs
in the first phase of online shopping; right after the customer
makes an online order.
H1: Financial risk has a negative significant impact on
consumers’ online shopping behavior.
Product Risk: Product risk relates to the performance or
quality of goods and services that consumers choose through
online shopping. Alreck and Settle (2002) stated product risk
includes a series of categories among various customers.
Crespo, Bosque and Salmones Sánchez (2009) assumed that
financial loss also as part of product risks, as product risks
also cause consumers to believe that there may be fraudulent
activities which may result in the loss of their money due to
the Internet. Product risk outlined as the discrepancy
between the product risk obtained and the predicted risk in
illustration of the product. The description and the display of
the product quality led to the cause of product risk, which
remarkably affects the consumer's capability to comprehend
the product. Inability to examine the product, inadequate
product information display might raise consumer anxiety
(Dastane, Jalal, & Selvaraj, 2018; Wong, Dastane, Safie, &
Ma’arif, 2019). The product problems are more towards
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those goods that do not have after-sales service. Product risk
is usually regarding purchasing merchandise that may not
function as initially anticipated (Kim 2010). Some studies
indicate that there is a product or performance risk in the
online environment or concerns that products are not
functioning properly or performing poorly, are increasing
(Cunningham, Gerlach, Harper, & Young, 2005; Hsin &
Wen, 2008; Yeniçeri & Akin, 2013). In brief, the impossible
to touch, inspect or try prior purchasing a product, which is
also a key concern when buying online, and these concerns
raise in product or performance risk (Saprikis et al., 2010).
H2: Product risk has a negative significant impact on
consumers’ online shopping behavior.
Convenience Risk: Various online stores and richly
variety products online (Forsythe, Liu, Shannon, & Gardner,
2006). It is sometimes uneasy for consumers to search for
the correct product that can generate a lot of fear. If the
customer cannot wait until the goods and services arrive, the
customer usually feels what will happen. They also believe
that there is a risk of controversy and they will unable to
submit if the products or services received not fulfill the
criteria. The lack of trust in online shopping judgments is
veto in some cases where switching programs occur. This
may be due to online shopping delays in accepting products
(Liu et al., 2006). Convenience risk is described as
disappointment from online shopping. The simplicity of the
shopping process can impact consumers' perception of the
degree of convenience risk (Jarvenpa & Tractinsky, 1999;
Kim, 2010; Kim, Ferrin, & Rao, 2008). Besides,
convenience risk can also refer to the risk perception of
consumers who need to spend substantial times and the
efforts to fix and fine-tune the purchased product prior to its
usage (Chang & Chen, 2008; Lee & Tan, 2003). When the
customer's perception feels that the convenient is risky, they
perceived that execute several online purchases is quite
knotty for them.
H3: Convenience risk has a positive significant impact on
consumers’ online shopping behavior.
Non-Delivery Risk: While this is an unusual situation,
online shoppers are often concerned that they will not
receive the product after purchase. Loss or damage to the
goods is related to potential delivery losses and causes
customers to worry that their goods cannot be received on
time. A variety of factors may impact whether the goods are
received by the customer, for example like improper deal
with the goods while in the process of delivery. By
furnishing with correct updates on the shipment status,
consumers should look forward to the arrival schedule of the
product, allowing customers to reduce their thoughts on
transportation and undeliverable (Masoud, 2013). In the
wrong place, the goods are also mistakenly transferred to the
wrong individual. According to Dan and Kim (2007), they
indicated the non-delivery risk as a failure that could result
in the loss of the goods, the damage to the goods and being
shipped to incorrect place after confirming the order. While
according to Naiyi (2004), delivery processes are the
concerns to consumers, for example, the product damages
may occur during shipment, delivered to an incorrect
address, or in certain situations, it is prolonged. Worry about
items may be harmed during the delivery process. Its
packaging may not be suitable for it (Claudia, 2012; Masoud,
2013). Therefore, when customers decide to purchase
products online, the risk of non-delivering is one of the
biggest concerns.
H4: Non-Delivery risk has a negative significant impact on
consumers’ online shopping behavior.
Return Policy Risk: The exchange of return policies
implies that the easiest way to trade is to ensure that buyers
are unconditionally committed. This is to assure them that if
they are not satisfied with the items they buy, they can give
back their items without difficulty. A positive impact is
where customers are dissatisfied with the products they
request; they can give back to the seller. The negative result
is that when they need to get back the item, the entire
process may leave a long chance to complete. The simplicity
to return product always is one of the interest factor for
online shoppers (Teo, 2002). The concerns associated to
return policy include the result of product replacement
policy, the product return grace period and the transport
expenses on product return to the online merchants. The
simplest approach to process products online is to guarantee
a “cash return guarantee”. This policy has a significant or
opposite impact on the customer's choices. On the positive
side of this approach, is the buyers can do the shopping
without worry, and protected by return policy without fear,
and provided the chance for buyers to return the product if it
does not fulfill the demands. Conversely, the drawback side
is, the return process might take longer time to process or
involve minor costs on return items (Haider & Nasir, 2016).
According to Foscht, Ernstreiter, Maloles, Sinha, and
Swoboda (2013), when customers are less helpless and the
things in a particular online store are typical for them, they
basically return less, they will coordinate more things, and
this will definitely get itself brings more benefits.
H5: Return Policy risk has a positive significant impact on
consumers’ online shopping behavior
The perceived risk of experience is higher, and
consumers might shift to traditional physical retailers to
acquire products. Yet, the lesser the perceived risk, the trend
of online shopping is higher (Tan, 1999). This may be
because consumers are afraid of risks when shopping online.
Because of the high level of risk in online shopping,
consumers must consider the risks that may occur during the
purchase process (Adnan, 2014). The figure 1 depicts the
conceptual framework for the current study. Although there
are several researches in the past focussing on the factors of
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perceived risk in the context of online shopping, this study
selects factors which are suitable for Malaysian setting. This
selection is done after careful review of the related
researches carried out for Malaysian consumers. The factors
with strong impacts are selected to further analyze. It can be
said that online shopping is well adopted in Malaysia, the
warrents need to re-investigate the impact of such selected
risk factors. Thus the independent variables are financial risk,
product risk, convenience risk, non-delivery risk, and return
policy risk.
Figure 1: Conceptual Framework
3. Research Methodology
By understanding the nature of the study, positivism is
the favorable research paradigm for conducting quantitative
research, above the interpretive, transformative, and critical.
This research has conducted a cross-sectional explanatory
study via a self-administered questionnaire because the
model attempts to relate thoughts to perceived causality.
Data Collection Method: For this study, the researchers used
a self-administered questionnaire with a seven-point Likert
scale to collect respondents' perceptions of risk factors
affecting the online shopping behavior of Malaysian
consumers. Population & Sample: To achieve the purpose of
this research, and avoid unprejudiced data, this study is
targeted the right sample, that is, customers with online
shopping experience in Malaysia.
In this study, data is collected based on convenience
from online shopping consumers in Malaysia, mainly
shoppers who make online purchases from local and
international online marketplaces that have operations to
serve the Malaysian market. The data collection period is
from January 2019 to April 2019. The questionnaire
designed in divided into two parts and for the data collection
from participants. However, in order to facilitate quality data
collection, the questionnaire was made understandable using
English as a language, and description of survey statement
supplement in Mandarin for target respondent who not
understand the objective and the meaning of the research
survey. The sample size for this study targeted at 300
respondents through a convenient sampling method using
non-probability sampling techniques.
Data Collection Instrument: Use of structured
questionnaires is done to collect the necessary data. The
structured questionnaire with short questions was prepared
and respondents were asked to choose an answer from a
given list of responses. The questionnaire consists of 15
different parts, divided into two sections. The first section is
on factors assessment, each of which contains five questions
related to different parts of the study, and, second section is
on demographics of respondents. Given the time and cost
constraints and the large number of Internet users in the
country, convenience sampling is used to collect data on
current Internet users in Klang Valley and Penang, which are
the most concentrated places for Internet users. Although the
sampling method used has limitations in terms of
universality compared to other sampling methods, it is
assumed that the sample represents the entire Internet user
community in Malaysia. The survey was conducted through
Google's online survey form, which is then distributed via
social messaging groups, such as the WhatsApps Group, and
posted on social media, such as Facebook in relevant
communities.
The questionnaire starts with demographic and few
warm-up questions on: Gender; Age; Race; Education
Qualification; Marital Status; Occupation; Income Range;
Online Shopping Experience - Year; and Frequency of
Shopping Online. In this study, perceived risk is an
independent variable and online shopping behavior is the
dependent variable. A total of 30 items were generated,
including of the five questions for each of the variable: FI
(5), PR (5), CR (5), NDR (5), RP (5) and CB (5), together
with the nine demographics questions (Table 1). In order to
develop a questionnaire, the researcher used straightforward
and clear wording, making it easy for each survey to
understand and answer questions from the respondents. In
multivariate studies, the required sample size should be 5 to
10 times the variable, 10% and 5% marginal error (Hair,
Anderson, Tatham, & Black, 1998). In this study, the total
number of questions was 30, so at least 300 questionnaires
were required to obtain a 5% margin error, and 100
questionnaires were required to obtain a 10% margin error.
In 300 questionnaires, 245 respondents were completed and
useful questionnaires were collected. Each response
received is filtered against errors, incomplete or missing
responses. The final sample size of 245 surveys was used,
which did not have missing information for data analysis
(Table 1).
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Table 1: Questionnaires Development
Variables
Financial Risk (FR)
Product Risk (PR)
Convenience Risk
(CR)
Non-Delivery Risk
(NDR)
Return Policy Risk
(RPR)
Online Shopping
Behavior (OSB)
Data Analysis Plan: The collected data is automatically
saved in a Google spreadsheet and finally exported to cross-
examine by using Social Science Statistics Package (SPSS)
application for analysis. IBM SPSS AMOS version 22.0 was
selected for statistical analysis of this study. Researchers
normally make use of Cronbach's Alpha to evaluate the
reliability factor of the conformity of the entire scale. KMO
and Bartlett's sampling sufficiency tests and Bartlett’s
Sphericity tests were executed to affirm the applicability of
the data in factor analysis. Descriptive statistics is used to
describe the fundamental features of the data in research.
AMOS 22 is used to measure the validity of the
questionnaire and uses a confirmatory factor analysis (CFA)
to inspect the correlation among independent and dependent
variables, as well as the convergent validity and
discriminant validity tests to check the validity of the
construct to ensure that model adaptability. Correlation
analysis is used to examine the common connections of each
variable item to assess the strength or extent associated with
two or more variables (Dalgaard, 2008). Structural Equation
Modeling was then executed. Regression analysis is a
conceptual approach to studying the functional relationships
between variables (Chatterjee & Hadi, 2015).
4. Analysis and Findings
4.1. Demographics Analysis
A total of 300 questionnaires were distributed to online
shoppers in Malaysia, with only 245 valid and reliable
respondents replied, which equivalent to a total of 81.67%
data collected. From the 245 respondents, 49.4% are female
and 50.6% are male, this can show that the online shopper in
Malaysia are quite balance in term of online shopping.
While on the age group, 38% of respondents are in between
31 and 40, compared to 29.8% from age between 18 to 30,
and 32.8% from age at 41 and above; this can be interpreted
as age group at the highest percentage might be the working
adults with better income and less commitment, but busy
with their schedule, thus, encouraging them to purchase
more products online, however, it also shows that
purchasing power is not limited by age.
From the survey, most of the responses were received
from Chinese at 73.5%, followed by Malay at 13.5%, and
less responds from Indian, which is only 9%. In terms of
education level, the respondents mostly are having
Bachelor’s degree, representing 59.2%, while the diploma
and master’s or doctorate degree holders are at 16.3% and
13.5% respectively. Meanwhile, 53.9% of respondents are
married and 44.9% are still single. On the employment
status survey, 68.2% are employed personal, while 18.8%
are still college or university students. 40.8% of respondents
are from the higher monthly income group, compare to
others, which are 24.1% are within RM2,499, 19.6% within
RM4,999 and 15.5% within RM7,499. Most of the
respondents are having experience in online shopping
between 1 to 3 years, representing 31.4%, while 27.8% of
respondents already doing online shopping 3 to 5 years and
23.3% of respondents have done online shopping for more
than 5 years. From the 245 respondents, 41% are only
shopping online every once in a while, and, 29.4% will do
online shopping once in a month, the remaining which doing
online shopping twice a month and more than twice a month
are at 14.7% for both.
4.2. Reliability Analysis
In the Table 2 result, the summation of all the six
variables’ scale, it shows all the six variables are getting
good scores with the results are at the range between 0.806
and 0.942. It is considered highly reliable, and valid for this
research, where they are fulfilling the rule of thumb for good
and excellent rating, high reliable alpha value of > 0.70
(Glen, 2014). From the reliability test result, which shows in
this research, it can be concluded that it is an appropriate,
sufficient and acceptable score; therefore, the analysis is
trustworthy and rightful to use.
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Table 2: Reliability Test
Variables
Number
of Items
Cronbach's
Alpha
Cronbach's Alpha
Based on
Standardized Items
FR
5
.806
.810
PR
5
.881
.882
CR
5
.876
.880
NDR
5
.873
.875
RPR
5
.942
.942
OSB
5
.916
.923
4.3. Factor Analysis
Exploratory Factor Analysis: Analysis results show that
KMO value 0.939 represent very strong sample sufficiency,
which rated as “Marvellous” in the fundamental guideline
by Kaiser (1970). This result confirms the null hypothesis of
no correlation. According to the chi-square statistic, no
sampling error occurred. This test demonstrates that the
variables are really interrelated to each other in this study,
and is suitable for further advancement to run meaningful
factor analysis.
Communalities: Based on the results, as a whole, all
variables are firmed and reliable factors, with all values,
exceeded 0.40, recorded at range between 0.512 to 0.858.
When the commonality of the variables is high, the extracted
factors account for a huge amount of the variance of the
variables, meaning that the specific variables reflect well, so
the factor analysis is reliable (Field, 2000).
EIGEN Values: For the purposes of analysis and
interpretation, the researcher only focuses on the extraction
sum of squared loadings. With the general role of thumb of
Eigenvalue on selection of component with Eigenvalue at
least 1, out of the 30 questions from the 6 variables, it seems
that only the first 5 underlying or “meaningful” factors are
to be measured, because their Eigenvalue is at least 1 and
above. Other remaining components with low-quality scores,
which is Eigenvalue less than 1 are not considered to
represent the true characteristics of the 30 questions. These
components are considering "fragments". It should be noted
here that the first factor accounts for 44.734% of the
variance, the second factor accounts for 11.411%, then 5.273%
of the third factor, followed by the fourth-factor accounts for
4.092%, and the fifth factor accounts for 3.592%. The
accumulation percentage of 69.102% by these five factors
mean for about almost 70%, more than half of the variance
is accounted for by the first five factors. All the remaining
factors are not important.
Descriptive Statistics: The whole data has a normal
distribution with outstanding skewness value between -1.0
and +1.0, and most have an almost symmetric data
distribution. If the skewness of the normality test and the
value of the kurtosis between -1.0 and +1.0 are considered
excellent, the variable is rationally near to normal, and the
deviation value falls outer of the range to show a
significantly skewed distribution (Hair, Black, Babin,
Anderson, & Tatham, 2006). The kurtosis statistics in this
study showed generally satisfactory normal distribution,
except for 7 of the 30 variables: CR1, RPR1 to RPR5 and
OSB4 falling outside the range of scores between -1.0 and
+1.0. Although Kurtosis does not evaluate a small number of
variables, it is considered acceptable that the skewness of all
variables is symmetric. To demonstrate normal univariate
distribution, skew and kurtosis scores between -2.0 and +2.0
are considered acceptable.
However, Hair, Black, Babin, and Anderson (2010) and
Bryne (2010) argue that if Skewness is between -2 and +2
and Kurtosis is between -7 and +7, then the data is
considered normal. In addition, in the Kline (2011) study,
the skewness and kurtosis index were mentioned to identify
the normality of the data. The outcomes show that with the
skewness and kurtosis index below 3 and 10, respectively,
the data deviation from normality is not serious. According
to descriptive statistical analysis for the five variables of
perceived risks factors that affecting online shopping
behavior, the most influential factor is the Return Policy
Risk, with an average value of 5.78 and a standard deviation
of 1.213. This is followed by Non-Delivered Risk (5.48),
Convenience Risk (5.37), Financial Risk (4.94) and Product
Risk (4.62) based on the average value in the table.
Ultimately, the average value of Online Shopping Behavior
is 5.78.
4.4. Confirmatory Factor Analysis
In Table 3, the CFA results are summarized based on the
acceptance levels of the corresponding tests. The chi-
squared results of 0.000 indicate statistically significant
estimates of the overall model fit for this research (Barrett,
2007). Meanwhile, the RMSEA result of 0.069 shows that
the model of the research is a good fit, the confidence
interval around the value can be calculated, and the null
hypothesis (bad fit) can be tested more accurately (McQuitty,
2004). The Normed Chi-Square result of 2.148, which
fulfill the rule of thumb value < 3.0, shows an acceptable fit
between the collected sample data and a hypothetical model
(Kline, 2011). On the Comparative Fit Index (CFI) result of
0.919, the statistical range is between 0.0 and 1.0, and a
value close to 1.0 indicates a good fit (Hooper, Coughlan, &
Mullen, 2008). Whereby according to Bentler and Bonnet
(1980), the value indicates the good fit, and meet the rule of
thumb value > 0.90 based cut-off standard recommendation,
while keep CFI > 0.80 as the minimal condition for an
acceptable cutoff value. Since there is no variable to
eliminate out from the total 30 variables, they now describe
the overall variance and importance to be counted in the
model, to well examine the results of Malaysian online
shopping behavior (Figure 2).
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Table 3: Confirmatory Factor Analysis (CFA) Result
Indices
Category
Index
Initial
Cut-Off
Threshold
CFA Result
Absolute Fit
Chisq
P < 0.05
0.000
RMSEA
≤ 0.08
0.069
Incremental Fit
CFI
> 0.90
0.919
Parsimony Fit
Chisq/DF
< 3.0
2.148
Convergent Validity Measurement: The measurement
model’s factor loading for entirely 30 perceived variables is
now > 0.5, and significant at p < 0.05, presenting that
entirely items have acceptable level of convergence validity
when interpreting theoretical constructs. (Hair et al., 2006).
In addition, based on the data in the table, all constructs in
this study support convergence validity because the average
variance extraction (AVE) for all potential constructs is
between 0.683 and 0.876, which are larger than the
advocated value of .5 (Hair et al., 2010). The reliability of
these variables once again tested using Cronbach's Alpha,
the results showed that the variables ranged from 0.806 to
0.942 for all six variables, indicating high reliability with an
empirical value > 0.70 (Kline, 2011).
Figure 2: Confirmatory Factor Analysis
Discriminant Validity Measurement: After completing
the CFA process for the fresh assessment model with all
affirmative factor loads and the fitness index reached the
desired level, the validity, and reliability of the hypotheses
in Table 6 are generated and generalized by discriminant
validity process. The discriminant validity measures of this
study show that neither methods point to the discriminant
validity problem at the level of mutual correlation between
the two constructs. Therefore, the discriminant validity is
supported, which indicates the correlation matrix of the
construct in this study. This is because all square root values
are greater than the correlation coefficients. The correlation
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Kok Wai THAM, Omkar DASTANE, Zainudin JOHARI, Nurlida Binti ISMAIL / Journal of Asian Finance, Economics and Business Vol 6 No 4 (2019) 249-260
between all measured constructs is less than 0.90, which has
very strong reliability and validity within the HTMT rule of
thumb threshold for further study of model correlation
measurements (Kline, 2011). Among the correlation
coefficients, the results show that all hypotheses are below
the threshold <1.00, as proposed by Hair et al. (2010), the
highest realized value is 0.549, indicating that all structures
are valid and acceptable. An HTMT value close to 1 or
above a predefined threshold is considered to be a lack of
discriminant validity, and the two constructs overlap very
heavily, and the same thing in similar possibly measured
(Hamid, Sami, & Sidek, 2017).
4.5. Correlation Analysis
Table 4 shows a correlation analysis based on Pearson
Correlation(r), which shows the degree of association among
independent and dependent variables. Based on the result
shown, the correlation coefficient (r) of each variable is as
follows: (FR r = .465 mean Strong positive relationship; PR
r = .392 mean Moderate positive relationship; CR r = .580
mean Strong positive relationship; NDR r = .562 mean
Strong positive relationship; RPR r = .690 mean Strong
positive relationship). On top of the significant value of
0.000 for all variables, the affiliation among the five
variables and online shopping behavior is significant. The
correlation coefficient of all variables is between the
minimum value of +0.392 and the maximum value of
+0.690, indicating that the strong point of the affiliation
among the independent variable and the dependent variable
is from moderate to strong, demonstrating the variables that
perceive risk have a positive and significant relationship
with online shopping behavior.
Table 4: Correlations Analysis
Financial
Risk
Product
Risk
Convenience
Risk
Non-
Delivery
Risk
Return
Policy
Risk
Online
Shopping
Behavior
Financial Risk
Pearson
Correlation
1
.674**
.595**
.650**
.418**
.465**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
245
245
245
245
245
245
Product Risk
Pearson
Correlation
.674**
1
.516**
.640**
.281**
.392**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
245
245
245
245
245
245
Convenience Risk
Pearson
Correlation
.595**
.516**
1
.758**
.608**
.580**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
245
245
245
245
245
245
Non-Delivery Risk
Pearson
Correlation
.650**
.640**
.758**
1
.618**
.562**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
245
245
245
245
245
245
Return Policy Risk
Pearson
Correlation
.418**
.281**
.608**
.618**
1
.690**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
245
245
245
245
245
245
Online Shopping
Behavior
Pearson
Correlation
.465**
.392**
.580**
.562**
.690**
1
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
245
245
245
245
245
245
**. Correlation is significant at the 0.01 level (2-tailed).
4.6. Structural Equation Modelling
Figure 3 is developed with AMOS version 22 for the
model testing and calculation of the structural model. The
model is deemed to be in the acceptable range of goodness-
of-fit with the model fit. The following results of CMIN/DF
value ≤0.080; GFI, TLI and CFI value≥0.90 indicates that
the model fit is acceptable. CMIN/DF (2.148), CFI (0.919),
and RMSEA (0.069) are the test result of the study. The
achievement of the threshold is suggested with the results
being in the acceptable range, it implicates that the model is
well converged and the SEM model being in an acceptable
level fitting to the data and data structure that is collected
and gathered in Malaysian setting. The investigation of the
construct exhibits the direct effects amongst the constructs
as can be seen in the parameter estimates of the structural
model. Significant relationships among the latent constructs
are shown based on the significant coefficients from the
output revealed.
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According to Table 5, R = 0.732, it is a scale for the
dependent variable which is Online Shopping Behaviour, R²
is 0.537 > 0.5, the relationship between variables is
significant. From the ANOVA results, the regression sum of
squares value is 165.019 and the total sum of squares value
is 307.583, which means that the regression model accounts
for 165.019 / 307.583 (about 54%) of all variations in the
dataset is explained. To answer whether this regression
model is or is not useless, and the hypotheses test will be
rejected or not, on the equation calculation by using mean
square, F5,239=33.004/0.597 = 55.283, which not allow to
reject the hypotheses at a value greater than the 5% level of
significance. In addition, the residual sum of squares value
is 142.564, normally, the smaller the error, the better the
regression model interprets the changes in the dataset, so the
researcher usually want to minimize this error.
Figure 3: Structural Equation Modelling
Table 5: Regression Statistics Table
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
Durbin-Watson
1
.732a
0.537
0.527
0.7723
1.927
a. Predictors: (Constant), Return Policy Risk, Product Risk, Convenience Risk, Financial Risk, Non-Delivery Risk
b. Dependent Variable: Online Shopping Behavior
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4.7. Hypotheses
The hypotheses testing result for this research as shown
in Table 6 is measured by Estimate, Standard Errors (S.E.),
Critical Ratios (C.R.) and P-Value (P), where acceptance or
rejection of hypotheses is determined by the P-value (Filho
et al., 2013).
From the result shown in Table 6, Financial Risk (FR)
variable is insignificant and negatively affecting the online
shopping behavior with a p-value greater than 0.05 at 0.992
and estimate value at -0.001. Whilst Non-Delivery Risk
(NDR), is the variable that highly significant negatively
affecting online shopping behavior, on its p-value 0.015 is
lesser than threshold value < 0.05, and estimate value at -
0.487. The P-Value is defined as an uninterrupted measure
of proof to show the implication of the assumption and with
a probability threshold of <0.05. Meanwhile, p-value that <
0.01 represents highly significance. The remaining variables
as per composed in conceptual framework, such as Product
Risk (PR), Convenience Risk (CR) and Return Policy Risk
(RPR), are found highly significant and positively affecting
the online shopping behavior with the p-value lesser or near
to 0.01.
Table 6: Hypotheses Testing Result
Hypotheses
Estimate
S.E.
C.R.
P
Decision
H1
Online Shopping
Behaviour (OSB)
<---
Financial Risk (FR)
-0.001
0.132
-0.01
0.992
Rejected
H2
Online Shopping
Behaviour (OSB)
<---
Product Risk (PR)
0.291
0.116
2.502
0.012
Accepted
H3
Online Shopping
Behaviour (OSB)
<---
Convenience Risk
(CR)
0.429
0.124
3.46
***
Accepted
H4
Online Shopping
Behaviour (OSB)
<---
Non Delivery Risk
(NDR)
-0.487
0.201
-2.422
0.015
Accepted
H5
Online Shopping
Behaviour (OSB)
<---
Return Policy Risk
(RPR)
0.646
0.084
7.651
***
Accepted
5. Conclusion
The study intended to measure impact on five types of
perceived risk factors namely financial risk, product risk,
convenience risk, non-delivery risk and return policy risk on
consumer behavior of Malaysian online consumers. It can be
thus concluded that the objectives of the study have been
achieved by testing the impact of selected five types of risk
on online shopping behavior. Financial risk is found to have
a negative but insignificant impact on online shopping
behaviors. This shows that although consumers prefer to
avoid possible financial risk, this factor is not significant in
Malaysian online shopping context. This research confirms
the positive significant impact of product risk, convenience
risk, and return policy risk on consumer behavior of online
shoppers. On another note, it was identified that non-
delivery risk has a negative impact on online consumer
behavior. The outcome of the study recommends online
businesses to minimize return policy risk by laying down
clear policy and procedures, furthermore, to adhere to such
stated policy standards. Product risk can be reduced by
displaying clear product information. The results also
warranted the need for providing convenience while
shopping online. The study has some limitations in terms of
representation of sample and sample composition because of
data collection is carried out at limited geographical
locations in Malaysia. Furthermore, the study has not
considered online shopping in specific context e.g. apparels,
etc. In the future, this research outcome can be a
contribution to scale development studies in the context of
online shopping risks perception. Researchers can also
explore various perceived risk factors when it comes to
particular industries such as travel or electronic retail thus
uncovering various risk dimensions.
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... Maciejewski revealed that the risk of a wrong purchase, which accompanies the consumer, is an essential aspect of purchasing decision-making [36]. Furthermore, that risk affects decisions, and the order of the main risks perceived by consumers is financial risk, performance risk, and service risk [37]. Priyono's findings say that the perception of risk hurts the adoption of electronic payment technology. ...
... When consumers have more knowledge, they will be better at making decisions, more efficient, more precise in processing information, and able to recall information better The results of research by Kim, Mirusmonov, and Lee [12] suggest that product knowledge is an important factor in facilitating users in making mobile payments. On the other hand, Parastiti and Mukhlis [37] found that product knowledge has no significant effect on use of electronic money. This could be due to the lack of information, thus causing a person's low interest in using a product, as well as the culture of the Indonesian people who still feel comfortable using cash instead of electronic money. ...
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... Convenience and time-saving variables loaded higher in the second factor which could be named as easiness factor and was preferred by consumers after the service factor. This finding is comparable with the findings of Hasan Online Purchase of Food Products in Bangalore City-Preferences and Constraints and Rahim, (2008) and Wai et al. (2019) which indicated convenience as a positive factor in the behaviour of online shoppers. Lower prices along with offers, discounts and lack of bargaining (pricing factor) were variables loaded higher in the third factor and hence preferred by consumers after the easiness factor. ...
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