Factors Influencing Consumer Behaviour towards Online Purchase
Intention on Popular Shopping Platforms in Malaysia
WONG CHEE HOO1, AW YOKE CHENG2, ALEX HOU HONG NG1,
SYARIFAH MASTURA BT SYED ABU BAKAR3
1Faculty of Business and Communication,
INTI International University,
MALAYSIA
2Office of the Registrar,
BERJAYA University College,
MALAYSIA
3Faculty of Business and Management,
University Technology MARA,
MALAYSIA
Abstract: - The number of individuals willing to make purchases of goods and services over the internet has
steadily increased over the past several years. Additionally, as a result of the pandemic, it has reshaped
consumer behavior, making people more hesitant to leave their homes to obtain the things they need. Therefore,
the safest method for them is to shop online. Consequently, we need to assess and investigate the quality of
Malaysian e-commerce that has the potential to influence a consumer's intention to make an online purchase.
This research topic represents a limited investigation into how the standard of e-commerce in Malaysia may
impact consumer preferences regarding online shopping. As a consequence of this, the primary goal of this
research is to conduct in-depth studies and future research on how e-commerce quality influences customers'
intentions to shift from traditional purchases to online ones. It is crucial to conduct further research and explore
the intentions of Malaysians to gain a better understanding of customers' motives for making purchases. To
obtain respondent samples for the study, researchers employed non-probability sampling strategies such as
convenience sampling and snowball sampling. In this study, we also used social media to distribute 384
different sets of online questionnaires to random respondents for data collection purposes. Approximately 350
valid responses were collected from Malaysians who had made online purchases and were under the age of 40
using an online Google form. Subsequently, the Statistical Package for Social Science (SPSS) software was
utilized to analyse the gathered data. In the final phase of this study, researchers will summarise and outline all
findings from each test. They will then use multiple regression analysis to determine whether each variable
(service quality, information quality, e-trust, and performance expectation) significantly affects online purchase
intentions.
Key-Words: - Consumer behavior, online shopping platforms, purchase intention, service quality, information
quality, performance expectation, Malaysia.
Received: August 15, 2023. Revised: December 3, 2023. Accepted: January 2, 2024. Published: January 12, 2024.
1 Introduction
In developed countries, electronic commerce (e-
commerce) has flourished and brought major
economic and social benefits, but in developing
countries, the situation is different. E-commerce in
these economies has faced many obstacles. E-
commerce and Internet use have changed the
lifestyles of industrialized consumers. E-
commerce—the electronic execution of business
transactions using cutting-edge technologies like
electronic data interchange (EDI) and electronic
funds transfer (EFT)—was first coined in the early
1990s, [1]. This simplifies business data exchange
and electronic transactions. Traditional commercial
markets and brick-and-mortar operations will
become primitive without technology. Upstream, a
company makes and sells products. The process was
simple, and the market was small. Electronic
commerce involves buying, selling, and sending
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.45
Wong Chee Hoo, Aw Yoke Cheng, Alex Hou Hong Ng,
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E-ISSN: 2224-2899
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money and data over an electronic network, usually
the Internet. Business-to-Business (B2B), Business-
to-Consumer (B2C), and Consumer-to-Business
(C2B) transactions are possible. E-commerce and e-
business are interchangeable. Online retail
transactions are called e-tail. In the traditional brick-
and-mortar market, transactions, sales, and services
were always limited by geography, [2]. Office
hours, marketing, the need for face-to-face sales and
purchases, and a heavy reliance on local client
referrals were other constraints. Sales and purchases
were restricted to the same local business district.
Business-to-consumer (B2C) online shopping
resembles shopping at a mall or store. Business-to-
business (B2B) online shopping involves businesses
buying from each other. The limitation study on
how Malaysia's e-commerce quality affects
consumer intention to buy online identified this
topic's research gaps. Thus, this study's main goal is
to investigate how e-commerce quality affects
customers' decision to buy online. To better
understand the customer's purchase intention,
Malaysian behavioral intention must also be studied.
First, the quality of the e-commerce experience is
the most important and influential factor that
requires further research because it can influence
consumers' online purchase behavior, [3]. Based on
previous studies, we found that e-commerce quality
positively affects customer purchase intention, [4].
We also examined how information technology and
the internet improve e-commerce quality, [5]. Thus,
this study must investigate how e-commerce
quality—service quality, information quality, e-
trust, and performance expectations—affects
Malaysian consumers' online purchase intentions.
Performance expectation also positively affects
online purchase intention, according to some
studies, [6]. Thus, performance expectation
influences product or service purchases. Trust also
affects e-commerce quality. Risk perception lowers
consumer trust and online purchase intention, [7].
However, lack of trust is the biggest long-term
barrier to e-full commerce's potential, [8], so this
study also wants to investigate important factors that
affect consumers' online purchase intention, such as
information quality and service quality. This study
focuses on e-commerce quality and how
independent variables like performance
expectations, information quality, service quality,
and e-trust affect Malaysian consumers' online
purchase intentions. The purpose of our research in
this study is to determine how Malaysia's E-
commerce quality affects the online purchase
intention of Malaysians. To be more explicit the
research objectives are as Figure 1.
Fig.1: Research objectives
2 Review of Literature
In this study, five variables required researchers to
review on published academic journals and articles
and also further research the definition, concepts as
well as the relationship between all variables.
Hence, the following are the related information to
support each independent variable and dependent
variable.
2.1 Online Purchase Intention
Consumers' willingness to make purchases from
online retailers is measured by their "online
purchase intention," which can be defined as the
extent to which they intend to do so, [9]. There are a
great many distinct types of research to back up the
definition of online purchase intention. According to
the findings of a study conducted by Rodrguez, the
purpose of conducting an analysis of online
purchase intention is to propose a model of the
process by which online purchase intention is
formed and investigate the factors that determine the
success of online fashion retailing. Perceived value,
trust, and fashion innovativeness are the primary
factors that influence an individual's intention to
make a purchase online, [10]. Time savings and
perceived security are the primary antecedents that
predict perceived value and trust, respectively.
Consequently, it is also stated that perceived value,
trust, and fashion innovativeness are the primary
factors that influence an individual's intention to
make a purchase online, [11], [12], [13].
2.2 Service Quality
Service quality refers to the company's assessment
of the extent to which the services provided meet
customer expectations. This assessment can
intuitively respond to the performance of the
company, which companies can evaluate to find
deficiencies and improve the quality of service
timely. The different kinds of literature to support
the definition of service quality. The quality of
website design and service quality in the E-
commerce industry will affect consumers' purchase
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intention. Customers tend to buy again when their
satisfaction with the design and quality of service is
high, [14]. The quality of express delivery services
will also have implications for customers and
companies, especially with the rapid growth of the
E-commerce industry and during the pandemic,
[15].
2.3 Information Quality
There are 5 different types of research journals to
support Information quality and it mainly refers to
the quality of the content of an information system.
From a pragmatic standpoint, it is generally
described as" the appropriateness of employing the
information supplied. "It also includes a framework
that offers a practical way for assessing and
measuring Information Quality. Actually,
Information Quality means that the information
provided is accurate, relevant, personalized,
formatted and easy to understand to encourage
purchase intentions. Previous study believed that for
organizations, information is a vital resource, [16].
As the corporate climate grows more competitive,
businesses are being forced to collect quality data
and information in order to remain competitive. The
worth of information given by websites as viewed
by users is referred to as information quality. Prior
research discovered that consumers desire the
information supplied to be fully easy to understand,
personalized, relevant and secure, so that their trust
in carrying out the transactions may be improved,
therefore increasing their happiness. There was also
study that stated individuals looking for information
online are often exposed to poor quality consumer
health information, [17]. Those who want to buy
products online are not provided with the key
information necessary to make an informed decision
about its use.
2.4 Trust
The term "trust," which also refers to "electronic
trust," refers to a qualified reliance on information
obtained by a customer from a website. This
provides the customer with the confidence necessary
to conduct business online. It is founded on the
conviction that a trusted company is one that is
dependable and highly honest, and it is linked to
characteristics such as consistency, competence,
honesty, fairness, and accountability. Trust is an
important factor in online shopping in general
because customers are less likely to make purchases
when they perceive a high level of risk and
uncertainty. In order to investigate how consumers,
evaluate the credibility of online reviews, this study
investigates the factors that determine the credibility
of online reviews and the effect that credibility has
on consumers' intentions to make purchases, [18].
Second, this makes it possible for customers located
in different parts of the country to give their honest
feedback on various products and services, which
enables them to make more educated and precise
purchasing decisions.
2.5 Performance Expectation
Danish explained that the purpose of the
performance expectation is to explore the individual
characteristics that motivate consumers to adopt
mobile commerce services such as personal
innovation. This research is mainly to understand
the relationship between performance expectations,
effort expectations, personal innovation and
behavioural intentions in the Pakistani consumer
market, and how individual variables called
personal innovation use the framework to adjust
performance expectations, effort expectations,
personal innovation and behaviour, [18]. A unified
theory of the relationship between intent and
acceptance and use of technology. Thus, the results
of performance expectations may differ from
perceived usefulness and explain that performance
expectations significantly influence mobile
commerce adoption behaviour. There is more
evidence in the literature that the same findings
regarding performance expectations are important
predictors of mobile payment adoption, [19].
2.6 Theoretical Framework
Apart from that, Theoretical framework mainly
applies a new research model to replace the past
research model which makes our research topic
more informative and suitable for the next
researchers. Therefore, this topic is to discuss how
the theories influence the people’s behaviour toward
the online purchase intention. The theories may
include Theory of Reasoned Action (TRA), as well
as the Theory of Planned Behaviour (TPB). So,
both theories can be applied to our research topic to
uncover every individual’s behavior based on a
particular step.
First, is reasoned action theory (TRA). It studies
how attitudes and behaviors affect human action and
predicts how people will react to pre-existing
attitudes and behavioral intentions, [20]. For
instance, a person's decision to perform a behavior
depends on the outcome they expect, which will
come from performing the behavior, [21]. The
1975-founded and developed the Theory of
Reasoned Action, [22]. Studies also found that the
Theory of Reasoned Action can help explain how
attitudes and beliefs affect individual intentions to
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act. The theory of planned behavior states that
intention—determined by attitude, subjective norms,
and perceived behavior control—affects behavior,
[23]. External factors can force or prevent behavior,
regardless of intention. Perceived behavioural
control matches actual behavioural control, [24].
2.7 Conceptual Framework
After an in-depth literature review and model study.
The following is the conceptual framework as
shown in Figure 2.
Fig. 2: Conceptual framework
Figure 2 shows the proposed conceptual
framework of this study, which was established after
reviewing and revising the original theoretical
models in previous studies. It contains a series of
interrelated concepts that guide the entire research
process. Figure 2 above illustrates the relationship
between four independent variables and one
dependent variable. The independent variables in
this study include service quality, information
quality, e-trust, and performance expectations, while
the dependent variable refers to the online purchase
intention. The purpose of this study is to determine
the relationship between independent variables and
dependent variables, [19].
3 Research Methodology
Participants in this research are Malaysians between
the ages of 18 and 40 who have purchased items
online. Convenient sampling was selected even
though there was only a limited amount of money
available for the project since it is the method that
can carry out probability sampling in the most cost-
effective manner, [24]. The information was
gathered via the use of online polls that were
conducted in a centralized manner. There was a total
of 350 completed questionnaires that were collected.
To check whether or not the hypothesis was correct,
the data that had been gathered were put through
statistical software and examined. A regression test
carried out using SPSS is one of the relevant tests
that were carried out to answer the hypothesis.
Before we carried out these tests, we examined the
data to ensure that they were normal and reliable,
and we also did some descriptive analysis of the
demographic information provided by the
respondents.
4 Data Analysis
In total, researchers sent 384 online questionnaires
to various respondents, which may include close
friends, family members, colleagues, and strangers
from social media. To collect accurate information,
we only allow respondents who are aged 40 years
old or below to do this online questionnaire, so
researchers can collect the real opinions and
thoughts from the respondent samples. As a
result,350 out of 384 questionnaires have been
collected and will be used for data analysis.
However, we also found that 20 respondents were
not answered and 14 were not fully answered. So,
we will use completed respondent samples (350 data
sets) for data analysis. Multiple regression analysis
will be used to test the study hypothesis for each
independent and dependent variable. Thus, Pearson
correlation analysis can measure the strength of
association between two continuous variables, [25].
Multiple regression analysis allows researchers to
predict the strength of multiple independent
variables for the dependent variable, [26]. The
correlation coefficient increases accuracy and
relationship with relevant variables, [27]. The
correlation matrix showed that all four variables are
significantly correlated with another variable (online
purchase intention), which is 0.40–0.70. However,
these four variables have a moderate relationship
with the dependent variable and no multicollinearity
because all variable correlation coefficients are less
than 0.9.
5 Findings
The researchers divided the interviewees into 4
different age groups, namely: under 20 years old, 21
to 25 years old, 26 to 30 years old, and 36 to 40
years old, of which the 26 to 30 years old age group
accounted for the highest proportion. There are a
total of 165 respondents, accounting for 47.3% of
the total. The second-highest age group is 21 to 25
years old, with a total of 129 respondents,
accounting for 37.0% of the total respondents. This
is followed by the 36–40 age group, with 54
respondents, accounting for 15.5% of the total
respondents. The minimum age group is under the
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age of 20, and there is one respondent, accounting
for 0.3% of the total. As for the academic
qualifications of 349 respondents, the most surveyed
are those with degrees, accounting for 179 people,
or 50.4%. Then there are the respondents who study
master's—57 people, or 16.3%. There are 67 people
with secondary education (Sijil Pelajaran Malaysia)
degrees, accounting for 19.2% of the total. Finally,
there were 49 respondents with a foundation or
diploma degree, accounting for only 14% of the
total. For the employment status of these 349
respondents, Investigators classified themselves
asself-employed, students, and employees (part- or
full-time). Employees (part-time or full-time) have
the largest number of respondents with 173
respondents, accounting for 49.6% of the total.
Students are followed by a total of 109 respondents,
accounting for 31.2% of the total number. Finally,
self-employed people accounted for the least, with
67 respondents, or 19.2%. From the data collected,
most of the respondents have income levels of
MYR1,000 and below, and there are 108
respondents, accounting for 30.9% of the total. The
second highest proportions among the respondents
were from MYR2001 to MYR3000, with a total of
130, or 37.2%. There are 57 respondents with
income levels ranging from MYR1001 to
MYR2000, accounting for 16.3% of the total. There
are 13 people, or 12.3% of respondents, with
income levels ranging from MYR3001 to
MYR4000. Finally, the group with the lowest
number of people has an income level of 4000 MYR
or above, with 11 people, or 3.2%. The frequency
with which people visit the online shopping website
In total, we got 349 responses. Most people visited
the online shopping website 1–3 times per month; it
took 45.3%. Then 107 out of 349 respondents, or
30.7%, visited the online shopping website 4-6
times per month.48 people visited an online
shopping website 7 to 9 times, accounting for 13.8%
of all visits.8.9% of people visited an online
shopping website less than once per month. Only 5
people visited the online shopping website more
than 10 times per week, which took 1.4%. The
frequency with which people decide to purchase a
product or service online Most people shop online
1–3 times a month, which occupies 53.9% of the
total. The second highest proportion is the
respondents who had shopped online 4-6 times; it
took 107 out of 349, or 30.7%. 49 people purchase
less than once a month, which represents 14%. Only
1.4% were purchased more than 10 times, which is
5 people. The device that was most commonly used
by respondents was a smartphone, with a total of
289 respondents, or 82.8%. Followed by laptops and
desktop, with 50 respondents, or 14.3%. There were
only 10 respondents who used tablets for online
shopping; it took 2.9%. According to the survey, the
most commonly used online shopping website is
Shopee, with a total of 154 respondents, or 43.6%.
Followed by Taobao and Lazada, which took 64
respondents, or 18.3%, and 58 respondents, or
16.6%, respectively. 75 respondents (21.5%) chose
Amazon. What type of product or service do people
usually purchase online? The majority of people
(32.6% of 309 respondents) purchased fashion-
related products such as apparel and accessories.
Only 0.2% of people bought books, music, or
stationery; the lowest response rate was two. The
remaining products from high to low are IT &
Mobile (22.1%), Food Ordering (15.3%), Furniture
and Homeware (13.6%), Beauty and Health
Care(7.4%), Sports Equipment (5.5%), Travel
Products and Services (2.8%), and Game-Related
Products (0.5%). Of the shopping experience of
people in this survey,239 people thought they had
bad online shopping experiences, which
occupied68.5%, and 110 people thought they didn’t
have bad online shopping experiences, which
occupied 31.5%.
5.1 Inferential Analyses
As the p-values (sig) of all variables are as low as 0,
which means that the test is statistically significant.
As we mentioned earlier, there is a moderate
relationship between four variables and online
purchase intention, and these are the variable results
that indicate that all four variables are significantly
related to online purchase intention. For example,
Service Quality (r = 0.519), Information Quality (r =
0.526), E-Trust (r = 0.470), and Performance
Expectation (r = 0.641).
Apart from that, multiple regression analysis
will be used to analyze all variables in this study.
According to the proposed conceptual framework,
online purchase intention is the dependent variable
to identify the strength of online shopping intention,
and therefore H1, H2, H3, and H4 are the
hypotheses formulated to test how service quality,
information quality, e-trust, and performance
expectations would affect the strength of online
purchase intention. However, the following tables
will describe the results obtained from each
hypothesis test.
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Table 1. Model Summary
Model Summary
Model
R
R Square
Adjusted R
Square
Std.Error of the
Estimate
1
.672a
.452
.445
.50402
Predictors:(Constant), Performance Expectation, E-trust,
Service Quality, Information Quality
Dependent Variable: Online Purchase Intention
The statistical model summary table (Table 1)
has shown the regression model's ability to explain
the dependent variable's population variance and
each predictor variable's strength and direction.
Thus, multiple correlation coefficients using all
predictors simultaneously were 0.672 (R2 = 0.452),
and the adjusted R2 of this linear regression model is
0.445. explained that performance expectations, e-
trust, service quality, and information quality predict
44.5% of online purchase intention variation. Other
variables may explain 55.5% of the variance in
online purchase intention.
Table 2. ANOVA
ANOVA
Model
Sum of
Squares
df
Mean
Square
F
Sig.
1
Regression
71.761
4
17.940
70.620
.000b
Residual
87.135
343
.254
Total
158.895
347
a. Dependent Variable: Online Purchase Intention
b. Predictors:(Constant), Performance Expectation, E-trust,
Service Quality, Information Quality
According to Table 2, the statistical table for
ANOVA primarily checks the overall regression
model's suitability for the data. It also shows that the
multiple regression model with two predictors
yielded F (4,343) = 70.620, and the p-value (Sig)
was significant with a value of 0.000, which is less
than 0.05. Both of these results are by the fact that
the ANOVA table checks the suitability of the
overall regression model for the data. As a
consequence of this, one can assert that the
aforementioned predictors have a considerable
impact on the intention to make an online purchase.
Table 3. Coefficients
Coefficients
Model
Unstandardized
Coefficients
Standard
ized
Coeffici
ents
t
Sig
.
95.0%Confide
nce Interval for
B
B
Std.Error
Beta
Lower
Bound
Upper
Boun
d
1
(Consta
nt)
.015
.279
.053
.95
8
-.534
.564
Service
Quality
.389
.124
.351
3.138
.00
2
.145
.633
Informa
tion
Quality
-.323
.138
-.341
-
2.336
.02
0
-.596
-.051
E-trust
.198
.104
.171
1.915
.05
6
-.005
.402
Perform
ance
Expecta
tion
.779
.076
.573
10.27
4
.00
0
.630
.928
a. Dependent Variable: Online Purchase Intention
Based on Table 3, provides information to
establish multiple regression equations by using
service quality, information quality, e-trust, and
performance expectations to explain the online
purchase intention equation. So, the equation will be
expressed as follows:
Online Purchase Intention =
0.015 + 0.389 Service Quality + 0.198 E-
Trust + 0.779 Performance Expectation -
0.323 Information Quality
In addition, the table of coefficients enables
researchers to not only understand the positive and
negative relationships between the dependent
variables and each predictor variable based on the
unstandardized coefficients, but also to be aware of
the degree to which each predictor influences the
dependent variables if the effects of all other
predictors are held constant. This is because the
researchers can know the degree to which each
predictor influences the dependent variables. It is
also obvious from the equation that there is a
positive relationship between service quality
(0.389), e-trust (0.198), and performance
expectations (0.779) on the intention to make an
online purchase. On the other hand, there is an
inverse relationship between the quality of the
information and the likelihood of an individual
intending to make a purchase online. As a
consequence of this, the score of online purchase
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intention will also increase by 0.389, 0.198, and
0.779 points, respectively, if the scores for each
item of service quality, e-trust, and performance
expectations improve. If, on the other hand, the
score of each piece of information quality goes
down, this will lead to a fall of 0.323 in the intention
to make an online purchase.
The regression coefficient also shows the most
significant variable on the dependent variable.
Performance expectations have the largest
unstandardized coefficient value (0.779) of the four
factors; hence they influence online purchase
intention the most. Hence, performance expectations
may strongly influence online buying intention.
Performance expectations, service quality (0.389),
and e-trust also affect online purchase intention
(0.198).
5.2 Hypotheses Testing
Table 3 shows a service quality test p-value of 0.002
(p 0.05). It may reject H1 and accept H1A. This
study supported the alternative hypothesis with 95%
confidence. Service quality boosts online purchase
intent. Table 3 shows that the information quality p-
value is 0.020 (p 0.05). It may reject H2 and accept
H2A. Thus, the alternative hypothesis is supported
by a 95% confidence interval. Thus, information
quality boosts online purchase intent. Table 3
showed that E-p-value trust's is 0.056 (p > 0.05). It
may not reject H3. Thus, e-trust does not affect
online purchase intention at a 95% confidence
interval, and the alternative hypothesis is not
supported. E-Trust does not significantly improve
online purchase intention. Table 3 shows a
performance expectation p-value of 0.000 (p 0.05).
It may reject H4 and accept H4A. Thus, the
alternative hypothesis is strongly supported and the
relationship is supported at a 95% confidence
interval. Overall, performance expectations boost
online purchase intention.
6 Discussion
In this study, we have examined a total of four
proposed hypotheses to ascertain whether they can
indeed have a significant positive impact on Online
Purchase Intention. The outcomes of each of these
proposed hypotheses are detailed in Table 4.
Table 4. Summary of Hypotheses Testing Results
Thus, service quality affects online shopping
site use and consumer intent to buy online. Service
quality affects online purchase intention, [28].
Thus, this may support the theories of reasoned
action and planned behavior, which focus on a
person's intention to act. Table 4 shows that the
information quality p-value is 0.020, below 0.05.
Thus, information quality—a measure of data's
accuracy, integrity, consistency, reliability, and
freshness—can also influence online purchase
intention. Information quality strongly affects online
purchase intention. As information quality p-value
is 0.056, higher than 0.05. Trust has no significant
positive effect on online purchase intention, and the
dependent variables are negatively correlated. This
result could be due to customers' online shopping
experiences or the questionnaire design being too
complicated for respondents. The study's limitations
required us to improve journal research while giving
readers and business people a discussion topic on
how to improve e-impact trust on online purchase
intention. Thus, other researchers can explain why
e-trust does not positively affect online purchase
intention, and we can understand why. Performance
expectations are the most important factor affecting
online shopping site use and purchase intention.
Expectations and perceived performance define
performance expectations. Thus, if the product or
service performs poorly, the customer will be
dissatisfied, but if it meets or exceeds expectations,
the customer will be thrilled, [29]. Thus, better
performance makes customers happier.
From a management perspective, we propose
and test a comprehensive model of electronic
reliability improvement measures, recognizing that
electronic reliability is influenced by
multidimensional factors such as electronic
fulfillment, electronic trust, and electronic retail
quality. To gain a holistic understanding of e-
commerce quality, we aim to encompass the entire
purchasing experience, going beyond website
usability or framework quality. We break down e-
commerce quality into four elements:
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satisfaction/reliability, network architecture,
security and protection, and responsiveness.
Our findings indicate that improvements in e-
commerce are influenced by both e-fulfillment and
e-Trust. Notably, there is a significant connection
between e-trust and e-fulfillment. Certain aspects of
e-commerce quality exert distinct effects on
electronic fulfillment and electronic trust. For
instance, satisfaction assessment and reliability
impact electronic performance, just as electronic
trust does. The emphasis on the web portfolio
contributes to electronic fulfillment, while security
and protection positively influence electronic trust.
Surprisingly, contrary to our initial assumptions,
responsiveness does not significantly impact either
electronic fulfillment or electronic trust, [30].
In this study, we've identified several flaws in
our previous research, any of which could
potentially compromise the overall quality of the
study. First and foremost, there's a concern that the
method we used to collect data might limit the
applicability of our results. Our survey gathered
responses from a total of 350 young consumers in
Malaysia, which is a relatively small sample size.
Consequently, our findings may not accurately
represent the online shopping intentions of the
broader Malaysian population. Moreover, a
significant portion of our study participants were
undergraduate students working towards bachelor's
degrees, with the majority being of Chinese-
Malaysian descent. This particular demographic
skew may further limit the generalizability of our
results, which, in turn, has a direct impact on how
we interpret and assess these findings.
Given these limitations, we suggest several
areas for improvement in future research to better
understand the impact of e-commerce on online
purchase intentions in Malaysia. Subsequent studies
should prioritize gathering larger sample sizes to
ensure more representative, accurate, and reliable
findings. This implies that researchers should be
afforded extended timeframes to effectively collect
larger samples.
The rise of e-commerce has fundamentally
altered consumers' shopping habits. They no longer
rely solely on physical cues when making purchase
decisions. Our study developed and tested a research
model that delves into the factors influencing online
purchase intentions in e-commerce and e-trade. This
conceptual framework encompasses various
elements related to online commerce, including
Service Quality, E-Trust, Information Quality, and
Performance Expectations. Specifically, our
research focused on examining how website
characteristics impact online purchase intentions,
with E-Trust serving as an intermediary factor.
To stay competitive, merchants should place
greater emphasis on improving website quality by
enhancing site usability, design, and information
quality. These factors are likely to significantly
affect consumers' intentions to make online
purchases. Importantly, merchants must also work
on building consumer trust in their services. In some
measure, the findings of our study may offer
valuable insights and relevance to e-commerce
professionals.
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Syarifah Mastura Bt Syed Abu Bakar
E-ISSN: 2224-2899
551
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DOI: 10.37394/23207.2024.21.45
Wong Chee Hoo, Aw Yoke Cheng, Alex Hou Hong Ng,
Syarifah Mastura Bt Syed Abu Bakar
E-ISSN: 2224-2899
552
Volume 21, 2024
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Each author contributed distinctively to this
research, from the initial problem identification to
the finalization of results and solutions. They also
participated in the internal editing and review of the
paper. Additionally, Aw Yoke Cheng served as the
primary contact in the role of corresponding author.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The authors would like to thank INTI International
University, Malaysia, for providing the financial
support necessary to publish this paper.
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.45
Wong Chee Hoo, Aw Yoke Cheng, Alex Hou Hong Ng,
Syarifah Mastura Bt Syed Abu Bakar
E-ISSN: 2224-2899
553
Volume 21, 2024
