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THE S-COMMERCE USAGE AND ACCEPTANCE MODELLING IN
MALAYSIA
Samer Bamansoor
Ph.D. Candidate, Faculty of Informatics and Computing, UniSZA, (Malaysia).
E-mail: Si2398@putra.unisza.edu.my ORCID: https://orcid.org/0000-0001-8982-6674
Syarilla Iryani A. Saany
Associate Professor Dr., Faculty of Informatics and Computing, UniSZA, (Malaysia).
E-mail: syarilla@unisza.edu.my ORCID: https://orcid.org/0000-0003-0764-2754
Yousef A. Baker El-Ebiary
Associate Professor Ts. Dr., Faculty of Informatics and Computing, UniSZA University, (Malaysia).
E-mail: yousefelebiary@unisza.edu.my ORCID: https://orcid.org/0000-0002-4392-8015
Recepción:
06/01/2020
Aceptación:
27/02/2020
Publicación:
30/03/2020
Citación sugerida:
Bamansoor, S., Saany, S. I. A., y El-Ebiary, Y. A. B. (2020). The S-Commerce usage and acceptance modelling in
Malaysia. 3C TIC. Cuadernos de desarrollo aplicados a las TIC, 9(1), 99-115. http://doi.org/10.17993/3ctic.2020.91.99-115
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ABSTRACT
The evolution of technology acceptance theories and models have started since the beginning of the
20th century and it is still evolving. This evolution is happened in dierent theoretical perspectives, such
as: cognitive, aective, motivational, and behavioral intentions and reactions for individuals. Nowadays,
understanding the reason of accepting or rejecting any new technology by users has become one of
the most important areas in the IT eld. The social media applications are beneted and enhanced
the E-Commerce, Electronic Marketing (E-Marketing), and Electronic Shopping (E-Shopping) usage
behaviors to get any information of any oered commodity in the easiest, fastest, and most familiar
way, that will increase the retail prot as well. Social Commerce (S-Commerce) has become one of the
most important elds and one of the fastest growing areas of the high technology sector development,
especially in the trading and commercial environments. In this scope, it is presenting here the theories and
models which were developed to study the acceptance by users and their adoption for new technology.
this study adheres to the methodology of quantitative research, which oers a numerical measurement
and analysis of the factors that determine adoption for samples 30 as a pilot study in Malaysia as a limit
of this research specically among 2 Malaysian Universities, that will lead to distribute the updated
survey around 484 samples later. That results a high ratio of questionnaire validity and the eectiveness
of the research hypothesis also found that the new model identies the factors aecting S-Commerce
usage behavior and continued usage intention, nd the relationship between education and S-Commerce
usage behavior and found such relationship between age and S- Commerce usage behavior.
KEYWORDS
E-Commerce, S-Commerce, Information systems, Electronic Enterprise.
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1. INTRODUCTION
At the time being, it is clearly perceived the rapid changes and developments in technology. So, many
people name this generation as the speed generation. In these days, nobody denies the high prevalence
of the use of smart devices. For example, smart phones have become an integral part of the lives of
many people with all its creative facilities. The increasing number of people who own smart devices leads
to increasing in the growth of social networks over the world. The variation in people interests and their
ways to use the technology explains the current massive diversity of services, applications, and social
networks as well.
In this context, Electronic Commerce (E-Commerce) has become one of the most important elds and
one of the fastest growing areas of the high technology sector development, especially in the trading
and commercial environments (Mahajan & Agarwal, 2015). It is the newest way for companies and
individuals to make prot and meet their requirements. These requirements are met by E-Commerce.
Due to advancement of technologies in Malaysia, organizations have utilized the internet to manage the
information and conform E-Commerce into their business processes.
Herein, the social media tools are beneted and enhanced the E-Commerce, Electronic Marketing
(E-Marketing), and Electronic Shopping (E-Shopping) usage behaviors to get any information of any
oered commodity in the easiest, fastest, and most familiar way, that will increase the retail prot as
well (Foltz, Newkirk, & Schwager, 2016). Apart from that, Social Commerce (S-Commerce) is: “a term
that often used to describe new online retail models, or marketing strategies that incorporate established
social networks and/or peer-to-peer communication to drive sales”. Also S-Commerce as: “a technology-
enabled shopping experience where online consumer interactions while shopping provide the main
mechanism for conducting social shopping activities” (Yeng et al., 2015).
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2. BACKGROUND AND LITERATURE REVIEW
Social commerce, which is known as social business, is considered as a subset of electronic commerce.
It refers to electronic commerce transaction that are delivered thru social media. In another word,
social commerce made from integration of electronic commerce, electronic marketing using Web
2.0, social media and supporting technologies. Social commerce is the evolution of the social Web
and social media. Customer service areas, online sales transactions, marketing communications, and
user-generated content networks are determined as the major useful activities of social commerce.
Furthermore, activities relevant to recruiting, collaborations, and problem solving are other important
activities of social commerce. Another benet of social commerce is to make the organizations closer to
their markets for collaboration by using social networks. This kind of collaborations give rise to product
innovation as well as greater customer value (Sharma & Crossler, 2014).
Social commerce has signicant monetary and strategic benets for industries and organizations. Social
commerce has benets for three parties including customers, enterprises, and retailors. Customer can get
recommendation easily for a particular product using product reviews, group discussion, etc. In addition,
their purchase can be matched with specic needs which increases satisfaction and reduce selection
decision time. Social commerce is easy for customer to use and it ts the mobile device lifestyle. It
encourages customers to help other customers and nally their trust will be increased. One of the benet
of social commerce for retailors is providing feedback by customers on market communication strategies
and the product itself. The website trac will be increased by using social commerce and therefore
the sales and revenue will be more. In addition, the vendors can get free word-of-mouth marketing.
Enterprises can conduct faster requirement using social commerce and foster their relationship with
partners locally and internationally (Turban, Strauss, & Lai, 2016).
Briey put, this study investigates Unied Theory Acceptances and Use Technology UTAUT and
UTAUT2 theory within an unstable environment by looking at the joint impact of multiple variables
in explaining Social Commerce usage behavior. To look at it in further detail, the eects of many
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variables, such as Performance Expectancy (PE), Social Inuence (SI), Facilitating Conditions (FC),
Eort Expectancy (EE), Hedonic Motivation (HM), Habit (H), Education (EDU), Age (AGE), Social
commerce Usage Behavioural (UB) and continued usage intention of (CUI) were examined in an
unstable environment to test UTAUT’s general applicability. Also, two external factors namely Trust
factor (TF), and cultures (C) were also measured (Nur Adlia, 2015).
This review shows the UTAUT and UTAUT2 model as a powerful and exible to enable studying the
adoption of any new technology. This research work is identied as being of importance to researchers
in technology acceptance eld in providing them with the necessary background for their studies. And
as a result, this review points out to the road which leads to this research for the purpose of extending
the model depending on its tested technology and the proposed research sample. The following Figure 1
depicts the illustration of UTAUT.
Performance
Expectancy
Effort
Expectancy
Social
Influence
Facilitating
Conditions
Gender Age Experience
Voluntariness
of Use
Use
Behavior
Behavioral
Intention
Figure 1. Unied Theory of Acceptance and Use of Technology.
As the focus of this study is on S-Commerce usage behavior, UTAUT is used to form a research framework;
however, UTAUT did not include costumer-related construct. Therefore, as customer-related constructs
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are important for S-Commerce, UTAUT2 is considered in this study. UTAUT2 is an adapted model,
which added three customer-related constructs into UTAUT, including (1) Hedonic Motivation (HM),
(2) Price Value (PV), and (3) Habit as shown in Figure 2. Hedonic Motivation (HM) refers to the fun
and pleasure that consumer experienced from use of technology whereas Price Value (PV) is related to
the monetary cost of using technology. Finally, Habit is related to the behavior that consumers behave
automatically for using technology (Nur Adlia, 2015).
Performance
Expectancy
1
Effort
Expectancy
2
Social
Influence
3
Facilitating
Conditions
4
Gender Age Experience
Use
Behavior
Behavioural
Intention
Hedonic
Motivation
Price Value
Habit
Notes
1. Moderated by age and gender.
2. Moderated by age, gender and
experience.
3. Moderated by age, gender and
experience.
4. Effect on use behaviour is
moderated by age and
experience.
5. New relationships are shown as
darker lines.
Figure 2. UTAUT2.
3. PROBLEM STATEMENT
Despite all the promised benets of using Social Commerce services and their utilizations in Malaysia,
the citizens are still reluctant in using new methods of business such as S- Commerce as a subset of
E-Commerce services. Malaysians are still in conservative mindset that make the reluctant to change in
adapting new technologies in their business processes (Othman et al., 2019). The basic curiosity is about
why the customers and the public are reluctant to use S- Commerce to secure Social media through
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Social websites and about the linkage between such reluctance and the percentage of Malaysian society
adopting the Social Commerce (Lim, Lim, & Trakulmaykee, 2018).
From the practical perspective, the present study proves its signicance by presenting an understanding of
one of the most important concerns among Malaysian citizens, which is the acceptance of S-Commerce
services. Hence, the factors with the capacity to impact the citizen’s usage of S-Commerce services
in Malaysia are determined. Consequently, the factors will reduce the citizen’s resistance in utilizing
S- Commerce services. Knowledge gap and emphasized the importance of the clear utilization of S-
Commerce services for the potential users i.e. the citizens. This gap is the motivation behind this study
which focuses on using a new model for the purpose of tweaking the usage of S- Commerce services
among the citizens in Malaysia by giving encouragement to them in employing the S- Commerce services
at anytime and anywhere (Hashim, Nor, & Janor, 2017).
Based on the above, there is a need to come up with a new model that increases the usage of S- Commerce
services within developing countries, particularly in the context of Malaysia. This proposed model will
facilitate the understanding of how factors tend to give eect on the level of S- Commerce services usage
in Malaysia. In turn, this model will greatly contribute in boosting the level of usage of S- Commerce
services in developing countries, in particular, Malaysia (El-Ebiary et al., 2018).
4. RESEARCH METHODS
For the achievement of the objectives of the research, this study adheres to the methodology of
quantitative research, which oers a numerical measurement and analysis of the factors that determine
adoption. Further, this study according to pilot study concept for 30 samples. but survey questionnaires
will be distributed to 484 respondents, and these will be used in the results aggregation. The investigation
has the aim to identify whether or not the independent variables and control variables signicantly
impacted the utilization of S- Commerce services.
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5. PROPOSED MODEL AND HYPOTHESIS DEVELOPMENT
This study has the intention to examine the aspect of acceptance of S-Commerce services by looking
into the citizen’s intention to use besides the Continued Usage Intention of S-Commerce services. This
notion is selected because this study seeks to scrutinize the acceptance as well as its level, and to achieve
this, both data of Continued Usage Intention and Use Behavior will be assessed. Another reason for using
these data is that, the data of Continued Usage Intention gives good indication of use continuation in the
future, and this is crucial when concerning S-Commerce. Online service utilization implies continuance
in service adoption and for this reason, this study uses the construct of intention to use in addition to that
of behaviour usage for gauging citizens’ acceptance of S-Commerce.
The model used in this study extends the UTAUT2 concept of belief through the inclusion of eight
more constructs namely, Performance Expectancy (PE), Social Inuence (SI), Facilitating Conditions
(FC), Eort Expectancy (EE), Hedonic Motivation (HM), Habit (H), Trust Factor (TF), Culture (C), Use
Behavior (UB) and Continued Usage Intention (CUI) and Moderator Variables (MV) namely Education
“E” and Age “A”. As we shall see below, this study develops the following hypotheses:
H1: Performance Expectancy positively aect use behaviour of social commerce.
H2: Social Inuence positively aect use behaviour of social commerce.
H3: Facilitating Condition positively aect use behaviour of social commerce.
H4: Eort Expectancy positively aect use behaviour of social commerce.
H5: Trust Factor positively aect use behaviour of social commerce.
H6: Culture positively aect use behaviour of social commerce.
H7: Hedonic Motivation positively aect use behaviour of social commerce.
H8: Habit positively aect use behaviour of social commerce.
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H9: Use Behaviour positively aect Continued Usage Intention of social commerce.
All hypotheses are intertwined, and they are the building blocks of the model recommended in this
current work. The model suggested by the study is the S-Commerce Acceptance Model.
Performance
Expectancy
1
Effort
Expectancy
2
Social
Influence
3
Facilitating
Conditions
4
Education Age
Use
Behavior
Hedonic
Motivation
Price Value
Habit
H1
H2
H3
H4
H5
H6
H7
H10-H17 H18-H25
H9
Continued
Usage
Intention
Figure 3. The Proposed Conceptual model.
Education plays a role of moderator between independent variables and use behaviour in this study.
Therefore, eight hypotheses are proposed as followings based on the proposed model illustrated in Figure 3.
H10: Education moderates the positive relationship between Performance Expectancy and Use
Behaviour.
H11: Education moderates the positive relationship between Social Inuence and Use Behaviour.
H12: Education moderates the positive relationship between Facilitating Condition and Use
Behaviour.
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H13: Education moderates the positive relationship between Eort Expectancy and Use Behaviour.
H14: Education moderates the positive relationship between Trust and Use Behaviour.
H15: Education moderates the positive relationship between Culture and Use Behaviour.
H16: Education moderates the positive relationship between Hedonic Motivation and Use Behaviour.
H17: Education moderates the positive relationship between Habit and Use Behaviour.
Age is also considered as another moderator between independent variables and use behaviour in this
study. Another framework is proposed to show this moderating eect which is illustrated in Figure 3.
Eight hypotheses are also proposed to test age as the moderator in this framework.
H18: Age moderates the positive relationship between Performance Expectancy and Use Behaviour.
H19: Age moderates the positive relationship between Social Inuence and Use Behaviour.
H20: Age moderates the positive relationship between Facilitating Condition and Use Behaviour.
H21: Age moderates the positive relationship between Eort Expectancy and Use Behaviour.
H22: Age moderates the positive relationship between Trust and Use Behaviour.
H23: Age moderates the positive relationship between Culture and Use Behaviour.
H24: Age moderates the positive relationship between Hedonic Motivation and Use Behaviour.
H25: Age moderates the positive relationship between Habit and Use Behaviour.
6. RESULT AND DISCUSSION
Twenty-ve (25) hypotheses about S-Commerce services within the situation of citizens residing in
Malaysia have been formulated by various scholars, and since specic research methodology shall be
required in testing these formulated hypotheses, this part will shed light on 25 relevant research designs
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and methodologies. This will encompass the descriptions of the research design, population, sampling
methods and sample size, research instrument, pilot test, nal instrument, data collection procedure as
well as the technique of data analysis.
Convergent validity is the degree to which the perceived variables of certain construct demonstrates a
high percentage of the variance in common. As added by the authors, the constructs’ convergent validity
is assessed using factor loadings, Average Variance Extracted (AVE), as well as Construct Reliability
(CR) estimation. In particular, the authors proposed the value of 0.7 or greater as the best estimate of
standardized loading whereas for the estimation of AVE, the obtained value should be more than 0.5.
With respect to reliability estimate, the obtained value should be more than 0.7. If all the proposed
values are met, convergent validity is met. Accordingly, to measure the convergent validity, the least
possible cut-o criteria employed in this study are: loadings >0.7, AVE >0.5, and reliability >0.7.
Discriminant validity is viewed as the degree to which a latent construct is totally dierent when
compared with other latent constructs. The assessment of discriminant validity a method which entails
the comparison of the extracted average variance of each construct with the respective Squared Inter
Construct Correlations (SIC). Furthermore, the AVE estimate that is constantly bigger than those of SIC
denotes support for the construct’s discriminant validity. The current study employs this procedure in the
discriminant validity assessment for all constructs.
Table 1. Validity pilot Test (Factors Analysis Loadings for Model).
Cod
#
Variables
Performance
expectancy
Social
inuence
Facilitating
conditions
Effort
expectancy
Trust
factor
Culture
Hedonic
motivation
Habit
Use
behaviour
Continued
usage
intention
PE1 .644
PE2 .574
PE3 .954
PE4 .928
PE5 .901
SI1 .941
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Cod
#
Variables
Performance
expectancy
Social
inuence
Facilitating
conditions
Effort
expectancy
Trust
factor
Culture
Hedonic
motivation
Habit
Use
behaviour
Continued
usage
intention
SI2 .896
SI3 .894
SI4 .675
SI5 .710
FC1 .529
FC2 .876
FC3 .862
FC4 .840
FC5 .606
EE1 .949
EE2 .945
EE3 .874
EE4 .948
EE5 .895
T1 .937
T2 .879
T3 .883
T4 .724
C1 .787
C2 .949
C3 .871
C4 .969
HM1 .949
HM2 .724
HM3 .928
HM4 .896
H1 .675
H2 .949
H3 .879
H4 .883
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Cod
#
Variables
Performance
expectancy
Social
inuence
Facilitating
conditions
Effort
expectancy
Trust
factor
Culture
Hedonic
motivation
Habit
Use
behaviour
Continued
usage
intention
UB1 .846
UB2 .985
UB3 .971
UB4 .967
CUI1 .979
CUI2 .981
CUI3 .978
CUI4 .670
Pilot test enables the researcher to determine the reliability of the measurement instruments before
the main empirical work is carried out. Accordingly, the notion of reliability has been described as the
degree to which a test unfailingly measures what is being measured. Additionally, Cronbach’s alpha
is employed in the measurement of the initial reliability of internal consistency concerning the data
generated by the pilot study carried out prior to the actual study. With respect to the value of Cronbach
alpha, it is increasable within either the average correlation or the number of items.
The context of survey test, as to allow its inclusion, the value obtained for the “item-to-total-test”
correlation should fall in the range of 0.3-0.7. This will be followed by a comprehensive analysis of
reliability executed the complete dataset. For reliability coecient with a high value, it denotes highly
reliable instrument. Consequently, mentioned that for a start, the minimum satisfactory reliability
coecients should fall in the range of 0.70-0.80. In particular, the value must be higher than 0.7. For
pilot test, concerning the sample size, it should be at least 30. Following this recommendation, a total of
30 randomly chosen citizens were solicited to partake in this study’s pilot test. The citizens were all from
Al- Madinah International University and University Utara Malaysia in Kuala Lumpur. Accordingly,
Table 2 shows the results generated from the test. The prerequisites discussed in this paragraph guide
this study in its execution of reliability and factor analysis test.
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Table 2. Scale Reliability Alpha – Pilot Test of Model’s Questionnaire (N=30).
Variable N. of Items Alpha (a)
Performance Expectancy 5 .802
Social Inuence 5 .808
Facilitating Conditions 5 .890
Effort Expectancy 5 .815
Trust Factor 4 .702
Culture 4 .877
Hedonic Motivation 4 .743
Habit 4 .704
Use Behavior 4 .724
Continued Usage Intention 4 .707
7. CONCLUSION
The research design and methodology are the highlighted and present the research model that applies the
quantitative method using an organized questionnaire. The methodical (and random) sampling process
is used for this study with a sample comprised 484 respondents whom were citizens from four regions of
west Malaysia. Then, the subject of pilot study as a procedure to rene the research instrument, based
on the reliability analysis results, and on the content validity of the construct. Finally, the description of
the data collection procedure and the data analysis techniques which will be applied in this study.
ACKNOWLEDGEMENT
This research was supported by foundation from Universiti Sultan Zainal Abidin (UniSZA), therefore
we thank our Universiti Sultan Zainal Abidin (UniSZA) that provided insight and expertise that greatly
assisted the research.
Main Author: Samer Bamansoor.
Corresponding Author: Yousef A. Baker El-Ebiary.
Co-Authors: Syarilla Iryani A. Saany.
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