A expansión dos sitios web de comercio social transformou gradualmente a vida da maioría das persoas en termos de interaccións sociais durante o tempo libre. A comunicación facilitouse debido ao avance da tecnoloxía da información e a comunicación debido ao feito de que as fontes de información poden axudar de maneira eficiente a proporcionar a información requirida. Con todo, existe a posibilidade de ter dificultades para comprender o contido en liña, xa que a información pode ter inexactitudes e rumbos. Ao combinar o modelo de aceptación de tecnoloxía (TAM) e a teoría de apoio social (SST), este estudo desenvolveu un modelo detallado de intención de compra para analizar as relacións relacionadas cos sitios web de comercio social. A confianza foi examinada como o mediador nesta investigación. O estudo empregou un enfoque cuantitativo en liña e realizou unha mostraxe non probabilístico (conveniencia) para recrutar a 392 enquisados. Os achados demostran que diferentes formas de SS (emocionais e informativas) conteñen relacións significativas co TAM, a confianza e a intención de compra. Con todo, a utilidade percibida e a intención de compra revelan relacións insignificantes. Mentres tanto, a confianza media significativamente na asociación entre as construcións SS e TAM neste modelo proposto. Os resultados tamén mostran que o modelo proposto ha logrado un bo axuste e explica claramente cada relación. Por tanto, a integración e extensión de constructos pode servir como camiños de investigación futuros. Os especialistas en márketing e os profesionais tamén poderían aplicar os achados ao desenvolver sitios web de comercio social.
Palabras chave
Apoio social, facilidade de uso percibida, utilidade percibida, confianza, intención de compra
Makmor, Shah Alam, Masukujjaman, Mohamed Makhbul, Helmi Ali, and Al Mamun: Integrating the social support theory and technology acceptance model of social commerce
websites
1. INTRODUCTION
The advancement of information technology has transformed communication via web connectivity,
allowing interaction with other internet users without time or space constraints (). Collaboration among internet users promotes social activities in different areas,
which offer more opportunities for companies to reach additional customers, promoting
ideas, products and services, mostly on their websites. The activities also provide
different insights into marketing and advertising events, which significantly transform
e-commerce into social commerce. defined social commerce as “the forms of internet-based social media that allow people
to actively participate in the marketing and selling of products and services in online
marketplaces and communities”.
Social commerce notably influences social interactions and supports website users,
assisting them to share their experience with others (; ). Social commerce has the potential to rapidly increase profitability by enhancing
sales. postulate that social commerce websites expand at significantly rapid rates. According
to a recent report by ), 96% of Americans occasionally make online purchases. Contrarily, Malaysia: Malaysia
recorded a 65.7% penetration of the online market in 2022, which was predicted to
rise to 76.8% by 2021. The country made US$ 10.12 billion in 2022 from the online
commerce industry, which is anticipated to rise by 18.48% to US$ 16.83 billion by
2025 ().
The popularity of social networking sites has allowed for substantial expansion in
social commerce in the last few decades (), which has transformed users’ thinking patterns from the individual mode to collaborative
thinking. Social commerce development is related to users’ communication methods and
their product review, where they give their own opinions or make suggestions, and
share their experiences (). Social commerce also provides opportunities for users to support and exchange information
(). Additionally, the benefit of adopting social media is improving information sharing,
enhancing the effectiveness of managing customers' relationship (). The effectiveness of website design strongly influences interaction, support and
many other activities.
Social interaction and integration on a website depend on network technology and accessibility
to product information and whereby trust on information sharing is important. (). The interaction process facilitates mainstream activities, such as marketing (), while influencing communities and societies via information sharing, which is a
valuable aspect of social support (). The initial stage of social commerce tends to impact social support behaviour in
the form of seeking advice from fellow users regarding their own experiences. If users
are deemed to have caring and responsible attitudes, they can make a fundamental contribution
to social support. affirms that emotional and informational support are crucial elements for helping
to reassure users. Meanwhile, online social support could also influence trust in
a website (). Previous studies have discovered that the most effective approach towards difficulties
encountered online is the provision of social support (). Nevertheless, only a limited number of studies have analysed social support in
social commerce (), implying a need for more in-depth research in this field ().
Although social commerce has been regarded as an area of technology undergoing constant
change and rapid expansion in recent years, inaccurate information is frequent (). For instance, fraudsters may post fake information sources and pictures to create
fake identity (), thus affecting users’ trust in the website. According to Azman (2018), customers
face potential challenges when making online purchases, namely differences between
the advertised and the actual products, the failure to deliver the items. posit that insufficient trust regularly prevents customers from regularly shopping
online. Prior researchers have also propounded that trust is a critical element online
(; Hajli, , ), although very few studies have been conducted on this topic in social commerce
(). Therefore, the impact of trust on purchasing trends should be investigated in the
context of social commerce while analysing other factors influencing trust, including
website design.
The literature has so far demonstrated that more research on trust is required to
determine the appropriate methods needed to establish social support. For example,
identify four dimensions affecting online trust, namely honesty, expertise, predictability
and reputation. Meanwhile, several other antecedents affect online trust, such as
information quality, service quality, perceived usefulness and design (; ). Furthermore, the reputation, scale, and offline existence of a firm significantly
influence consumer trust online (;). Generally, customers shop online when the system is considered valid and easy to
use (). Similarly, reveal that a useful, informative and straightforward website design influence consumer
trust and purchase intention.
Several researchers (; ) have discovered a positive relationship between trust and intention to purchase
organic food in a developing country. Nonetheless, little evidence exists regarding
methods of emotional and informational support that can shape trust and lead to higher
purchase intention. However, social support and relationship quality on social commerce
intentions, which lead to use behaviour of social networking sites for social commerce
(). Specifically, The effectiveness of websites is crucial for businesses seeking to
acquire the trust of consumers, who frequently and unexpectedly encounter technical
issues on them.. indicates that 30% of online users fail to complete purchases, while highlights that 21% of websites load sluggishly with another 20% perceiving the ordering
process to be confusing. Hence, more studies are required to bridge the literature
gap in social commerce website design ().
Several theories examine behavioural intention, such as the technology acceptance
model (TAM), the theory of planned behaviour (TPB), and the unified theory of acceptance
and use of technology (UTAUT). The TPB model is a self-interest theory generally employed
for individual decision-making (), especially concerning technology-driven behaviour (). Although the UTAUT model deriving from the TAM is more appropriate for technology
usage owing to the integration of seven other models, this model is comparatively
simplistic in its delineation of relevant relationships and its emphasis on moderating
relationships (). Nevertheless, both the TAM and the UTAUT model are criticised for having narrow
perspectives, the TAM having been developed earlier than the UTAUT model. As such,
the TAM might not be pertinent in terms of providing a comprehensive understanding
of social commerce, which demands an extension of the model. In contrast, the social
support theory (SST) recommends external variables that are compatible with the TAM's
external variable for an extension. No previous studies have incorporated the TAM
with the SST in social commerce research. Moreover, the integration of emotional support,
informational support, perceived usefulness, perceived ease of use, trust, and purchase
intention in a single framework is rare.
The current study, due to the fact that it addresses the existing research gap, aims
to integrate the theoretical perspectives of the SST and the TAM to establish and
empirically assess the determining factors of purchase intention on social commerce
websites. In addition, it focuses on clarifying the relationships among the factors
which influence trust and purchase intention in the context of social commerce. Our
paper includes a literature review (Section 2) elucidating the theoretical background,
research model and research hypotheses. It goes on to discuss the methodology and
findings with the corresponding analyses. Lastly, it explicates the key findings,
study limitations and theoretical and practical implications.
2. LITERATURE REVIEW
2.1 Theoretical underpinning
2.1.1 The SST and the TAM for analysing consumer purchase intention
The SST expounds on the behaviour of social relations in a community when information
on well-being is accomplished (). The theory explicitly illustrates how social support effectively helps patients
deal with high-risk health conditions, reduces the impact of stress, and assists individuals
to become more self-confident. Social support can be measured when an individual is
offered assistance and is available for the social network. have demonstrated how internet-based social support helps groups of female breast
cancer patients, showing how feasible online networks are in encouraging bonds to
form and effectively boosting their confidence levels. Thus, social support significantly
impacts the technological behaviour of these internet users.
A social media study has discovered that social elements positively impact technology,
such as TAM constructs (). Social support can appear in a variety of socio-environmental categories that guide
users when they wish to actively participate with online information systems. The
procedures could serve as valuable tools for obtaining social support more easily,
which is closely connected to and integrated with information technology. The majority
of employees are expected to use online communities to encourage the adoption and
use of technology. Social support determinants of network technology are also vital
for identifying TAM elements deriving from consumer purchase intention (Bao, 2016).
Furthermore, conceptualise social media's role in the Internet banking system as a gauge of people's
technology adoption. Accordingly, Bao (2016) opines that social support is crucial
and should be integrated with the TAM for consumer purchase intention.
Social commerce encompasses technological advancement utilising social networking
sites to communicate with users via online social support platform. Pose the necessity of evaluating the degree to which technology usage supports social
connection and integration by fusing sociology, psychology, and technology online.
Alternative support systems to answer client inquiries regarding social support. In
particular, social networking sites allow users to share videos, pictures and personal
perspectives effortlessly twenty-four hours a day. Social factors have a big impact
on user behaviour in terms of how useful technology is (). Furthermore, users can skim and scan the content in the material provided by the
social network platform, which automatically compiles an encyclopaedia of priceless
knowledge not found on other networks or websites (Bao, 2016). High levels of usability
promote social integration and involvement on the social networking site, which is
thought to be lively and appealing. Information deemed commercially valuable and supportive
also significantly and positively impacts trust and purchase intention.
2.2 Conceptual framework
This study integrates the SST and the TAM as the theoretical base for developing and
empirically appraising the factors that determine purchase intention on social commerce
websites. The framework, which defines the ways in which social support improves social
care and assistance when a user seeks it via platforms, should make it easier to understand
the evolving social interactions among platform users. The relationships become active
and sought-after when a website is highly accessible for sharing information, in terms
of perceived ease of use and usefulness. The information supports recommendations
or discussions and the possible sharing of commercial data to further enhance trust
and maintain long-term relationships in the community. Besides this, it increases
commercial activity tendencies, such as purchase intention. Hence, the research framework
incorporates several constructs, which are illustrated in Figure 1:
Figure 1.Conceptual framework
2.3 Hypothesis development
2.3.1 Social support and the TAM
, the founder of social support, defines the term as “information leading the subject
to believe that he is cared for and loved, esteemed, and a member of a network of
mutual obligations” (p. 300). Social support specifically explains individuals’ perceptions
of situations where concern, respect and regard for others are involved, who feel
that they have responsibility on a website. Many researcher consider that social support
is a multidimensional construct (; ; ), which has been empirically supported and highlighted by other researchers (; ; ). Being a part of a social network and social integration are both established forms
of social support, and the terms are used interchangeably ().
Academics across various disciplines have explored the social support concept, including
psychologists, sociologists and health experts (). The TAM theory is described by as the individual perception of acceptance and intention towards alternative technology.
Users are more inclined to accept the online system and express personal feelings
on it, the more accessibility is provided, in which case they perceive it to be valuable.
Users show higher confidence levels by way of participation and the provision of support
(i.e., information and knowledge) if they are aware that the system is simple to use
and informative. Nevertheless, few studies have been conducted on the connection between
social support in emotional and informational forms, particularly for TAM constructs
of perceived ease of use and usefulness. Only Bao (2016) has recently indicated the
link between social support and the TAM.
have integrated social support factors and technology acceptance into a single model
to examine online users’ intentions on social commerce websites based on their attitudes.
Social interaction and integration on these websites considerably improve participation,
noted after scrutinising behaviour related to privacy concerns and video sharing.
Accordingly, those who use the internet to get emotional support are more at ease
and don't have many privacy concerns. Participation becomes prevalent when they are
willing to seek information and advice from friends and relatives or strangers. The
information received on these sites can subsequently be read by others, which allows
comprehensive and informative information to be gained, which may not be possible
to achieve elsewhere. Two relevant hypotheses are proposed:
HI: Emotional support significantly influences perceived ease of use.
H2: Informational support significantly influences perceived usefulness.
2.3.2 Social support and trust
Social support is a multidimensional construct () that originates from the SST. This study has utilised two main components, namely
emotional and informational support. The former can be defined as having personal
feelings, such as concern and understanding. The latter refers to individual perceptions,
including cognitive feelings, belief, suggestions and ideas. As outlined in the SST,
social relationships and the responses of specific individuals illustrate the priority
to resolve some of the difficulties experience by others by promoting other individuals’
self-esteem (). The primary components of social support are the ways that people encourage and
assist others in feeling more confident and at ease in social situations. This interaction
indirectly establishes trust between members within the same online community and
addresses the psychological needs of everybody involved (). Previous studies have revealed that social support is capable of developing circles
of friends and trust among online members (; ). Other researchers have also discovered that the direct relationship between informational
and emotional support significantly influences trust (Hajli, , ).
When medical information and contact details are provided based on medical conditions,
social support is viewed as a significant factor of trust (). According to , most users participate on online platforms for guidance and information from peers.
assert that individuals perceive calmness and warmth from messages on social networks
when receiving informational and emotional support online. have also demonstrated that these two forms of support can strengthen trust in the
seller on social commerce platforms. The following hypotheses are postulated:
H3: Emotional support significantly influences trust.
H4: Informational support significantly influences trust.
2.3.3 Trust and the TAM
The TAM is highly appropriate for investigating user intentions when it comes to utilising
any systems (). Initially, the model was designed for predicting computer usage behaviour. Two
of its factors are deemed to impact individual intention and usage, namely perceived
ease of use and usefulness. The former is the extent to which an individual considers
that the use of a particular any system is effortless. Meanwhile, perceived usefulness
is the degree to which an individual perceives that the use of a particular any system
can enhance work performance (). The TAM theory is regarded as a unique theory with two salient beliefs, in which
higher trust levels in technology usage could be connected to more favourable user
responses to perceived ease of use and usefulness ().
have incorporated trust into the TAM through constructs, such as perceived ease of
use and usefulness. In addition, various studies have frequently reported the relationships
between trust, perceived ease of use and perceived usefulness (; ; ; ). have confirmed that trust significantly influences perceived usefulness for online
shopping, while assert that trust significantly influences TAM constructs. Moreover, and propose that perceived trust is positively correlated to perceived usefulness. argues that trust exerts positive effects on perceived ease of use and usefulness.
concur with the results by demonstrating that trust significantly influences perceived
ease of use and usefulness. Therefore, this study propounds the following hypotheses:
H5: Trust significantly influences perceived ease of use.
introduced the TAM to predict an individual’s usage intention and acceptance of information
technology. Consumer engagement is influenced by a user-friendly online system. (). Consumers exhibit higher favourability towards technology when the online system
can be navigated effortlessly, hence leading to higher purchase intention. Previous
scholars have discovered that TAM constructs significantly influence purchase intention
(), with multiple studies utilising the TAM to examine technology usage (; ; ; ; ; ; ; ; ). Previous researchers (; ) have also demonstrated that purchase intention is significantly influenced by perceived
ease of use and usefulness, including renewable technology purchase intention (; ), and augmented reality technology (; ). In summary, the usefulness and ease of use of an information system highly influences
user purchase intention on social commerce platforms. This study hypothesises that:
H7: Perceived ease of use significantly influences purchase intention.
Trust has gained considerable traction in e-commerce () and social commerce studies (; ; ), examined in various results, with multiple studies conceptualising it in different
terms. This study defines trust as a user’s beliefs about information delivered by
social commerce websites. defines it as “a broad sense, which is the confidence an individual has in his or
her favourable expectations of what other people will do, based in many cases, on
previous interactions”. Meanwhile, certain academics regard trust as a mediator of
electronic system transactions (; ). The mediating impact of trust on individuals’ relationships with purchase intention
has been thoroughly explored according to the bootstrapping results of the indirect
effect (mediating effect) developed by . Essentially, the indirect relationship between the constructs is significant in
assessing the mediating effect (). Based on the relevant literature review, trust functions as a significant mediator,
which has been hypothesised to mediate the associations between social support forms
(emotional and informational) and purchase intention.
Trust has been included in the proposed framework due to the possible risks involved
when engaging in Internet-based social commerce, including scams, security risks,
fraudulent activities and misleading information (). Information sharing and support between users on online platforms strengthen trust,
thus encouraging online purchases (). have revealed that emotional and informational social support significantly impact
trust, as social support is deemed a type of assistance employed by online users to
resolve relevant issues about products or services on online platforms. The support
establishes unique inter-individual trust, which reassures users wishing to purchase
a product or pay for a service. To date, there are few studies on trust as a mediator
in both relationships between emotional and informational support and purchase intention
despite its significance in the online social commerce context. In health studies,
the theory plays a key underlying role in offering encouragement and assistance for
developing trust when discussing medical conditions. In this study, social support
might reduce stress when self-esteem produces an indirect influence on trust and subsequently
on purchase intention. Social support creates an attachment between users and enhances
trust in online platforms ().
Existing studies have discovered the impact of informational and emotional support
on trust (; Hajli, , ). Furthermore, the relationship between both emotional and informational support
and purchase intention could facilitate trust-building, the latter being a positive
indication that social support increases user purchase intention on virtual platforms.
claim that trust is the factor that most significantly influences online transactions.
have demonstrated the positive impact of trust on purchase intention in social commerce.
Moreover, users are highly predisposed to engage more in social interactions on online
platforms or utilise a system with higher trust levels, which increases purchase probability
on social commerce websites. Recent studies (; ) have revealed a positive relationship between trust and intention to purchase an
organic food in a developing country. Correspondingly, this study seeks to test the
mediating impact of trust on the association between social support and purchase intention.
It also assesses the relationship between trust and purchase intention and its impact
on social commerce website activities. Consequently, it postulates the following:
H10: Trust mediates the relationship between emotional support and purchase intention.
H11: Trust mediates the relationship between informational support and purchase intention.
3. METHODOLOGY
3.1 Research design
This piece of research has quantitatively investigated the variables by distributing
online questionnaires to consumers to assess the factors influencing online shopping
decisions. To be exact, a cross-sectional survey was employed before drawing inferences
from a population in a specific period.
3.2 Data collection and sampling
86% of Malaysia's population is active on social media, making up social media users.
The research for this paper was conducted on the Facebook platform, a popular communication
website, to reach a wide range of online shoppers. Facebook is the leading social
media platform for sharing personal updates with friends and performing online transactions.
It is a typical social commerce platform, where online purchases are made simpler
thanks to social interactions and contact with Facebook friends. The sample frame
of this study was Malaysians who use Facebook. The Facebook users were given a link
to a survey where direct and anonymous feedback could be given.
This questionnaire enabled information to be collected and was an ideal way to reach
many consumers performing online purchases as well cover a wide geographical area.
The data was collected in November 2021. Due to the risk of subjects receiving the
link not answering the online survey, they were sent at least two reminders. All respondents
were informed that their participation was voluntary and would receive payment upon
completion. The academic purpose of the study was also explained along with reassurance
that any personal information given would be stored confidentially and not disclosed
to other parties. Each respondent signed a consent form for voluntary participation
before receiving the survey link from the researchers.
A test was done by using G*power, a software program, was run to determine the most
suitable sample size. Vidaver-Cohen (1998) recommends the sample size to be 146 for
six structures or predictors (F2=0.15 for effect size, 0.05 for type I errors, and 0.20 for type II errors). According
to , a general rule of thumb for sampling is to multiply 10 by the largest number of
formative indicators employed. As such, this study required 300 participants with
30 measurement items. A total of 392 questionnaires were selected to lessen potential
problems from a small sample size. All received responses were completed properly
because all questions were set as mandatory in Google Forms. The non-probability convenience
technique has been utilised in this study, which is the best option due to cost-effectiveness
and a high likelihood of acquiring a sufficient number of respondents.
3.3 Data analysis
Construct items were adapted from the existing literature. A pre-test was conducted
with three marketing and technology experts before the actual data was collected.
The pre-test results demonstrated that the majority of measurement items represented
the intended constructs. The experts involved in this study recommended improvements
concerning the item wording, and so amendments were made accordingly. Subsequently,
the SmartPLS software based on partial least squares structural equation modelling
(PLS-SEM) was employed. As suggested by the experts, the software was optimised for
exploratory research by focusing on prediction and estimation (). The PLS-SEM is more appropriate than Covariance-based SEM for a relatively moderate
sample size (Lowry & Gaskin, 2014). To prevent significant challenges, this study
did not analyse any established model but instead proposed a comprehensive one. The
PLS-SEM was also chosen due to its ability to examine larger constructs featuring
complicated relationships. It did not contain bindings to fulfil the normal distribution
of research results. In addition, a seven-point Likert scale ranging from 1 as strongly
disagree to 7 as strongly agree was employed to measure the six variables. The items
of emotional and informational support were adapted from , the items of perceived ease of use and usefulness were modified from , trust items were adapted from and purchase intention items were modified from .
4. RESULTS
4.1 Measurement model
The SEM approach was employed to analyse the collected data and examine several dependent
variables (). A reliability analysis was conducted to fulfil the study objectives before hypothesis
testing. For the items fitting the respective constructs or the inter-correlation
between the items, reliability was ascertained according to Cronbach’s alpha, composite
reliability (CR), average variance extracted (AVE) and factor loading values. As recommended
by , the threshold values for AVE were 0.5, 0.7 for CR and 0.7 for factor loading. Meanwhile,
the threshold value for Cronbach’s alpha was 0.7 (), while loading exceeded 0.4 in accordance with the levels suggested by . The details of the construct reliability analysis are shown in Table 1.
Table 1Construct reliability analysis
Construct name
Item
Loading
Cronbach’s alpha
CR
AVE
Emotional support
ES1
.781
.868
0.910
0.716
ES2
.852
ES3
.871
ES4
.878
Informational support
IS1
.903
.883
0.928
0.810
IS2
.920
IS3
.876
Perceived ease of use
PEOU1
.866
.910
0.933
0.735
PEOU2
.886
PEOU3
.879
PEOU4
.826
PEOU5
.830
Perceived usefulness
PU1
.827
.926
0.942
0.730
PU2
.890
PU3
.896
PU4
.833
PU5
.834
PU6
.844
Trust
TRUST1
.791
.925
0.939
0.658
TRUST2
.853
TRUST3
.843
TRUST4
.862
TRUST5
.846
TRUST6
.805
TRUST7
.736
TRUST8
.744
Purchase intention
PI1
.902
.922
0.945
0.811
PI2
.908
PI3
.915
PI4
.877
4.2 Discriminant validity
This paper employed the Fornell-Larker criterion and heterotrait-monotrait (HTMT)
ratio to test discriminant validity. All constructs (see Table 3) attained an AVE
value higher than 0.5, which suggested acceptable convergent validity (Fornell & Larcker,
1981; ). The results also indicated that the square root of AVE values diagonally was higher
than other constructs not diagonally (Fornell & Larcker, 1981). recommend the HTMT ratio to assess discriminant validity owing to higher sensitivity
from 97% to 99%. The most optimal threshold value for the HTMT ratio is below 0.85
(). As a result, all constructs had HTMT ratios under 0.85, indicating that they fulfilled
the requirement with low cross-loadings in the reflective model (see Table 2).
Table 2Discriminant validity through the Fornell-Larker criterion and HTMT ratio
ES
IS
PEOU
PI
PU
TRUST
ES
0.846
0.783
0.467
0.510
0.484
0.491
IS
0.736
0.900
0.539
0.496
0.498
0.554
PEOU
0.397
0.487
0.857
0.566
0.715
0.397
PI
0.488
0.433
0.516
0.901
0.420
0.495
PU
0.458
0.438
0.677
0.389
0.854
0.417
TRUST
0.426
0.511
0.322
0.428
0.388
0.811
[i]Note: ES = Emotional support, IS = Informational support, PEOU = Perceived ease of use,
PU = Perceived usefulness and PI = Purchase intention. The bold off-diagonal values
are the square roots of AVE. The italic values to the right of the off-diagonal bold
values are HTMT values and the values to the left of the diagonal values are the correlations
between the constructs.
4.3 Non-response bias and common method bias (CMB)
The t-test was performed in the study to analyse the differences between early and
late survey respondents. The results revealed an insignificant difference in gender
and age between the two respondent groups. Therefore, the possibility of the sample
having non-response bias was discarded. By adhering to the recommendations of , common method bias (CMB) concerning self-reported cross-sectional data was addressed
through an operationalised Likert scale. Harman’s one-factor test, which accounted
for 50% of data variation, was employed to generate a static factor from all primary
structures (). The first component was responsible for 36.21% of the variation. A correlation
higher than 0.9 indicates CMB, although the strongest relationship between perceived
ease of use and purchase intention was actually 0.728. Hence, CMB was not an issue
in this study.
4.4 Hypothesis testing for direct relationships
Hypothesis testing is a critical step in quantitative research enabling research questions
to be answered and allowing the significance of the study to be ensured. The findings
indicate that emotional support significantly influences perceived ease of use (=0.258, t=5.562, p<0.001) while informational support significantly influences perceived usefulness
(=0.269, t=5.752, p<0.001), thus supporting H1 and H2. Furthermore, the results have revealed that trust
is significantly influenced by emotional support, informational support, perceived
ease of use and perceived usefulness. The significant relationships between emotional
support and trust (=0.121, t=2.404, p=0.008), informational support and trust (=0.209, t=5.107, p<0.001), trust and perceived ease of use (=0.180, t=3.894, p<0.001) and trust and perceived usefulness (=0.195, t=3.967, p<0.001), thus supporting H3, H4, H5 and H6. Perceived ease of use also significantly
and positively impacts purchase intention (=0.321, t=6.651, p<0.001). A strong relationship has been identified between trust and purchase intention
(=0.290, t=5.850, p<0.001). Thus, H7 and H9 are supported. Nonetheless, perceived usefulness significantly
influences purchase intention (=0.034, t = 0.089, p< 0.464), hence rejecting H8. The findings of the study suggest that certain consumers
are not influenced by the usefulness of social commerce platforms when it comes to
purchase intention. Nevertheless, the social commerce platform could be employed to
seek information sources for future reference. The hypothesis testing outcomes are
displayed in Table 3.
Note: ES = Emotional support, IS = Informational support, PEOU = Perceived ease of use,
PU = Perceived usefulness, PI = Purchase intention
* p<.05
** p<.01
*** p< .001
4.5 Hypothesis testing for mediation effects of trust
This study has appraised the mediating effect of trust as few studies have assessed
it as a mediator of the relationship between social support (emotional and informational
forms) and purchase intention on social commerce websites. While investigating the
mediating effects, this piece of research has also implemented the bootstrapping approach
suggested by The results demonstrate that trust significantly and indirectly impacts emotional
support and purchase intention, with a 95% bootstrap confidence interval (CI0.95=0.112,
0.021), which indicates a partially-mediation. A significant indirect mediating effect
of trust has also been reported on the relationship between informational support
and purchase intention, with a 95% bootstrap confidence interval (CI0.95=0.166, 0.071).
As such, the relationships between emotional and informational support and purchase
intention have been partially mediated by trust, shown through the bootstrapping procedure,
with 5,000 non-parametric re-samplings (Preacher & Hayes, , ), as illustrated in Figure 2.
Figure 2.Bootstrapping results for mediation effects
5. Conclusion
The aim of this paper is to thoroughly research the determinants of trust and the
relevant impact that they have on purchase intention in the context of social commerce
websites. The study also makes theoretical and practical contributions by explicating
the factors that can influence consumer trust and purchase intention. The four examined
constructs were chosen because they were expected to elucidate the importance of trust
in social commerce, taking into account that social support significantly influences
the TAM. This study highlights social factors and technological networks as the key
attributes for establishing consumer trust and purchase intention, the SST and TAM
being integrated in particular to fulfil the study’s objectives. Indeed, multiple
dimensions of social support conceptualisation have been revealed to significantly
impact both TAM and trust. Network technology designs also expand related behaviour
and subsequently increase purchase intention on social commerce websites.
More research needs to be conducted to examine the relationships between social support
and the TAM constructs regarding purchase intention in social commerce. Nevertheless,
previous studies have suggested that the connections between social support and TAM
constructs significantly affect intention (Bao, 2016). Furthermore, have integrated social support with the TAM to measure the effectiveness of social
commerce buying intention. The recommendations outlined in previous studies have been
incorporated into this study by delineating the substantial effect of the associations
between emotion and perceived ease of use and between information and perceived usefulness
(H1 and H2). The relationship between emotional support and informational support
also significantly influences trust (H3 and H4), which is consistent with prior findings
discovering a significant relationship between social support and trust in social
commerce (; ; ). The availability of different information sources, including experience and knowledge,
influences trust when a tendency for social support exists (). Hence, supportive relationships and problem-solving mechanisms result in indirect
trust-building () . The combination of social commerce with technological and social factors is considered
effective for increasing purchase intention.
The results indicate that trust significantly influences perceived ease of use and
perceived usefulness (H5 and H6). Similarly, other studies reveal that trust significantly
impacts both constructs (; ). This mediator is crucial in online purchase behaviour as higher engagement is observed
when users consider the information to be trustworthy, useful and accessible on the
website. In addition, the results demonstrate that trust has a significant influence
on perceived ease of use and purchase intention (H7 and H9), which parallels previous
marketing literature on the positive impacts of perceived ease of use () and trust (; ) on purchase intention. Nevertheless, an insignificant influence has been revealed
for the correlation between perceived usefulness and purchase intention (H8) as has
also been the case in previous studies (; ).
The influence of perceived usefulness on purchase intention is highly significant
in this study, which contradicts past findings (; ). For a website, the former factor does not essentially drive the latter, although
a user’s perception might be beneficial as a valuable information source. Meanwhile,
trust significantly impacts the relationships between emotional and informational
support and purchase intention, which consistently reflects the principles of the
SST and the TAM. When users converse on online platforms in the context of effective
social support, those who are engaged are more inclined to place their trust on social
commerce platforms and increase their personal purchase intention on them. The study
further expounds that emotional support plays a crucial role in enhancing the relationship
between sellers and users on social networks.
5.1 Theoretical implications
This study makes several theoretical contributions to the existing literature. To
be specific, an alternative framework has been developed integrating the SST and the
TAM to determine the influence of social factors on the latter construct. have allowed social technology to be supported by integrating psychological, sociological
and technological considerations online, although the theory and model have previously
been integrated under social commerce and individual model contexts. Moreover, the
study contributes to existing social commerce literature by including three different
variables in the TAM, namely emotional support, informational support and trust. identify social factors such as social support while combine the TAM with social network features, such as informational, emotional, network,
tangible and esteem forms of support. Bao (2016) suggests integrating social support
with the TAM as the former is a key determinant of trust; by doing so, it could ascertain
the extent to which technology is accepted and help understand purchase intention
on social commerce websites. Furthermore, the integrated framework has been tested
empirically in the Malaysian setting to analyse the social commerce trend. The proposed
constructs in the framework have achieved high model fitness (R2= 0.67 for purchase intention). However, the study has not discovered a significant
relationship between perceived usefulness and purchase intention unlike past studies
have. Therefore, this study results might encourage future academics to retest the
same model in other settings to test all the hypothesised relationships.
5.2 Practical implications
This study provides several practical contributions. The proposed framework demonstrates
potential benefits to businesses in applying this research concept to social commerce
websites. Of particular importance is that the paper offers a greater understanding
of how different social support forms could be implemented and the corresponding impact
that they could have on users’ purchase intentions for Internet-based businesses.
It also reveals that a range of forms of social commerce support, such as emotional
and informational assistance, are pivotal to enhancing users’ trust and purchase intention.
This trust improves when companies introduce modern technological features, including
simplicity of usage and usefulness, to their respective social commerce websites.
Online platforms can include detailed descriptions of products or services, such as
prices, materials and features. As a consequence, integrating social support via a
thorough procedure with social and technological features can serve as the foundation
of users’ trust. By increasing the attractiveness and reliability of websites, online
companies can strengthen their corporate image and encourage their customers to purchase
relevant products. For instance, they can highlight products on special offer with
eye-catching artwork and engaging information. In addition, they can take a range
of actions to attract more customers to participate in social commerce website activities,
such as live streaming on Facebook with a charismatic presenter for live shopping.
Companies can also provide specific product information based on customer preferences
or launch tailor-made activities that facilitate interaction.
High-quality information that companies obtain from users enables them to perform
analyses and improve website content. The positive impact of information technology
on social commerce, especially in terms of how effortless navigation is on online
platforms and how useful the online system is, could enhance consumer purchase intention.
Nevertheless, this study demonstrates that perceived usefulness does not significantly
influence purchase intention, which suggests that respondents are not familiar about
platform usability. As such, marketeers should propose alternative uses of social
media or develop tutorials so that the perceived usability of their platform increases.
For example, marketers could offer alternative payments and delivery options. Furthermore,
social commerce platforms execute additional marketing strategies on a frequent basis
which are cost-effective to improve organisations’ prospects and profits. The findings
should encourage newly established companies to make extra efforts to maintain amicable
relationships with their customers via social commerce platforms in the hope of expanding
their consumer base. Enterprises could apply innovative business strategies to promote
their latest products or services with the aim of gaining higher revenue or making
profit while forming sustainable relationships with customers. In sum, increasing
purchase intention via innovative business strategies could add economic value to
newly established businesses during initial ventures on social commerce platforms.
5.3 Limitations and future lines of research
This paper has some limitations, which paves the way for an alternative strategy for
future research. As this is a pilot study, future researchers could increase the number
of respondents and conduct a comprehensive empirical study. Given that some of the
respondents were students, the findings might not be generalisable to the entire population.
Future academics could widen the scope of research by integrating other theoretical
elements of the TAM and social support to determine consumer purchase intention on
social commerce platforms. It has been observed that several existing theories underlying
the social support concept include social integration and social interaction theories
(). Thus, this study recommends that future researchers incorporate the theories with
the TAM, which might provide valuable insights into the theoretical implications of
raising consumer purchase intention on social commerce websites. Moreover, external
variables, such as subjective norms, are not considered to be comparable to the TAM.
Future lines of research on social commerce may wish to incorporate the theories for
a more comprehensive understanding while excluding the relationship between perceived
ease of use and perceived usefulness to create a simpler model.
Author contribution
Conceptualization, N.M.; data curation, S.S.A.; formal analysis, N.M. and M.M.; investigation,
M.H.A.; methodology, Z.K.M.M.; project administration, N.M..; software, M.M.; supervision,
S.S.A.; validation, M.H.A. and S.S.A.; writing—original draft, N.M. and S.S.A.; writing—review
& editing, N.M., M.H., and S.S.A. All authors have read and agreed to the published
version of the manuscript.
Acknowledgments
The researchers would like to express their gratitude to the anonymous reviewers and
to the Editor-in-chief for their efforts to improve the quality of this paper.
References
1
Akman, I., & Mishra, A. (2017). Factors influencing consumer intention in social commerce
adoption. Information Technology & People, 30(2), 356–370. https://doi.org/10.1108/ITP-01-2016-0006
2
Alam, S. S., Ahmad, M., Othman, A. S., Shaari, Z. B. H., & Masukujjaman, M. (2021).
Factors affecting photovoltaic solar technology usage intention among households in
Malaysia: Model integration and empirical validation. Sustainability, 13(4), 1773. https://doi.org/10.3390/su13041773
3
Alam, S. S., Susmit, S., Lin, C. Y., Masukujjaman, M., & Ho, Y. H. (2021). Factors
affecting augmented reality adoption in the retail industry. Journal of Open Innovation: Technology, Market, and Complexity, 7(2). https://doi.org/10.3390/joitmc7020142
4
Alam, S. S., Masukujjaman, M., Sayeed, M. S., Omar, N. A., Ayob, A. H., & Wan Hussain,
W. M. H. (2022). Modeling Consumers’ Usage Intention of Augmented Reality in Online
Buying Context: Empirical Setting with Measurement Development. Journal of Global Marketing, 36(1), 1–24. https://doi.org/10.1080/08911762.2022.2087580
5
Albayati, H., Kim, S. K., & Rho, J. J. (2020). Accepting financial transactions using
blockchain technology and cryptocurrency: A customer perspective approach. Technology in Society, 62, 101320. https://doi.org/10.1016/j.techsoc.2020.101320
Alsajjan, B., & Dennis, C. (2010). Internet banking acceptance model: Cross-market
examination. Journal of Business Research, 63(9–10), 957–963. https://doi.org/10.1016/j.jbusres.2008.12.014
8
Alshibly, H. H. (2015). Customer perceived value in social commerce: an exploration
of its antecedents and consequences. Journal of Management Research, 7(1), 17–37. https://doi.org/10.5296/jmr.v7i1.6800
9
Azman, N. H. (2018, April). 5 major concerns in online purchase. Utusan Malaysia, 17.
10
Bai, Y., Yao, Z., & Dou, Y. F. (2015). Effect of social commerce factors on user purchase
behavior: an empirical investigation from renren.com. International Journal of Information Management, 35(2015), 538–550. https://doi.org/10.1016/j.ijinfomgt.2015.04.011
11
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: personal computer adoption
and use as an Illustration.
12
Basak, S. K., Govender, D. W., & Govender, I. (2016). Examining the impact of privacy,
security, and trust on the TAM and TTF models for e-commerce consumers: a pilot study.
Proceeding of the 2016 14th Annual Conference on Privacy, Security and Trust (PST), 19–26.
13
Bertoldo, R., & Castro, P. (2016). The outer influence inside us: Exploring the relation
between social and personal norms. Resources, Conservation and Recycling, 112, 45–53.
14
Bhardwaj, M., & Aggarwal, D. R. (2016). Examining the impact of social media on Internet
banking. Arabian Journal of Business and Management Review, 6(4), 1-5. https://doi.org/10.4172/2223-5833.1000146.
15
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation
model. MIS Quarterly, 351–370.
16
BigCommerce. (2017). 2017 release notes.
17
Busalim, A. H., & Hussin, A. R. C. (2016). Understanding social commerce: A systematic
literature review and directions for further research. International Journal of Information Management, 36(6), 1075–1088. https://doi.org/10.1016/j.ijinfomgt.2016.06.005
18
Chen, J., & Shen, X. L. (2015). Consumers’ decisions in social commerce context: an
empirical investigation. Decision Support Systems, 79, 55–64. https://doi.org/10.1016/j.dss.2015.07.012
19
Chen, Y. H., & Barnes, S. (2007). Initial trust and online buyer behaviour. Industrial Management & Data Systems, 107(1), 21–36. https://doi.org/10.1108/02635570710719034
20
Cheung, C. M. K., & Lee, M. K. O. (2013). Full-Text Citation Analysis: A New Method
to Enhance. Journal of the American Society for Information Science and Technology, 64(July), 1852–1863. https://doi.org/10.1002/asi
21
Chircu, A. M., Davis, G. B., & Kauffman, R. J. (2000). Trust, expertise, and e-commerce
intermediary adoption. Proceeding of the Americas Conference on Information Systems (AMCIS) 2000, 710–716.
22
Cho, Y. C. (2015). Exploring factors that affect usefulness, ease of use, trust, and
purchase intention in the online environment. International Journal of Management Systems, 19(1), 21–35.
Corritore, C. L., Kracher, B., & Wiedenbeck, S. (2003). Online trust: Concepts, evolving
themes, a model. International Journal of Human Computer Studies, 58(6), 737–758. https://doi.org/10.1016/S1071-5819(03)00041-7.
25
Crocker, J., & Canevello, A. (2008). Creating and undermining social support in communal
relationships: the role of compassionate and self-image goals. Journal of Personality and Social Psychology, 95(3), 555–575. https://doi.org/10.1037/0022-3514.95.3.555
26
Dachyar, M., & Banjarnahor, L. (2017). Factors influencing purchase intention towards
consumer-to-consumer e-commerce. Intangible Capitals, 13(5), 946–966. https://doi.org/10.3926/ic.1119
27
Dashti, M., Sanayei, A., Dolatabadi, H. R., & Moshrefjavadi, M. H. (2016). An analysis
of factors affecting intention to purchase products and services in social commerce.
Modern Applied Science, 10(12), 98. https://doi.org/10.5539/mas.v10n12p98
28
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance
of information technology. MIS Quarterly, 319–340.
29
Dube, W. (2010). Deciphering the water cooler effect. Rochester Institute of Technology.
30
Eriksson, K., Kerem, K., & Nilsson, D. (2005). Customer acceptance of internet banking
in Estonia. International Journal of Bank Marketing, 23(2), 200–216. https://doi.org/10.1108/02652320510584412
31
Featherman, M. S., & Hajli, M. N. (2015). Self-service technologies and e-services
risks in social commerce era. Journal of Business Ethics, 139(2), 251–269. https://doi.org/10.1007/s10551-015-2614-4
32
Gao, Q., Dai, Y., Fan, Z., & Kang, R. (2010). Understanding factors affecting perceived
sociability of social software. Computers in Human Behavior, 26(6), 1846–1861. https://doi.org/10.1016/j.chb.2010.07.022
33
Gefen, D. (1997). Building users’ trust in freeware providers and the effects of this trust on users’
perceptions of usefulness, ease of use and intended use [Ph.D. Thesis, Georgia State University].
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping:
an integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
36
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modelling (PLS-SEM). SAGE, 2013.
37
Hajli, M. N., Lin, X., Featherman, M., & Wang, Y. (2014). Social word of mouth how
trust develops in the market. International Journal of Market Research, 56(5), 387–404. https://doi.org/10.2501/IJMR-2014-000
38
Hajli, N. (2013). A study of the impact of social media on consumers. International Journal of Market Research, 56(3), 387–404. https://doi.org/10.2501/UMR-2014-025
39
Hajli, N. (2014). The role of social support on relationship quality and social commerce.
Technological Forecasting and Social Change, 87, 17–27. https://doi.org/10.1016/j.techfore.2014.05.012
40
Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 35(2), 183–191. https://doi.org/10.1016/j.ijinfomgt.2014.12.005
41
Hajli, N., & Sims, J. (2015). Social commerce: the transfer of power from sellers
to buyers. Technological Forecasting and Social Change, 94, 350–358. https://doi.org/10.1016/j.techfore.2015.01.012.
42
Hajli, N., Sims, J., Zadeh, A. H., & Richard, M. O. (2016). A social commerce investigation
of the role of trust in a social networking site on purchase intentions. Journal of Business Research, 71, 133–141. https://doi.org/10.1016/j.jbusres.2016.10.004
43
Hajli, N., Wang, Y., Tajvidi, M., & Hajli, M. S. (2017). People, technologies, and
organizations interactions in a social commerce era. IEEE Transactions on Engineering Management, 64(4), 594–604. https://doi.org/10.1109/TEM.2017.2711042
Han, B., & Windsor, J. C. (2011). User’s willingness to pay on social network sites.
Journal of Computer Information Systems, 51(4), 31–40. https://doi.org/10.1080/08874417.2011.11645499
46
Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction
of perceived ease of use and behavioral control in predicting consumers’ use of social
media for transactions. Computers in Human Behavior, 80, 197–206.https://doi.org/10.1016/j.chb.2017.11.01
47
Harman, H. H. (1976). Modern Factor Analysis. University of Chicago Press.
48
Harris, L., & Rae, A. (2009). Social networks: the future of marketing for small business.
Journal of Business Strategy, 30(5), 24–31. https://doi.org/10.1108/0275666091098758
49
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing
discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.https://doi.org/10.1007/s11747-014-0403-8
50
House, J. S. (1987). Social support and social structure. Sociological Forum, 2(1), 135–146.
51
Islam, T., Syeikh, Z., Hameed, Z., Khan, I. U., & Azam, R. I. (2018). Social comparison,
materialism, and compulsive buying based on stimulus-response-model: a comparative
study among adolescents and young adults. Young Consumers, 19(1), 19–37. https://doi.org/10.1108/yc-07-2017-00713
52
Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Das Wiesenkalk- oder Seekreidelager
des Turloffer Sees. Consumer Trust in an Internet Store, 1(1/2), 45–71. https://doi.org/10.1023/A:1019104520776
53
Jones, K., & Leonard, L. N. K. (2008). Trust in consumer-to-consumer electronic commerce.
Information and Management, 45(2), 88–95. https://doi.org/10.1016/j.im.2007.12.002
54
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
55
Koufaris, M., & Hampton-Sosa, W. (2004). The development of initial trust in an online
company by new customers. Information and Management, 41(3), 377–397. https://doi.org/10.1016/j.im.2003.08.004
56
Lakey, B., & Cohen, S. (2000). Social support theory and measurement. In S. Cohen,
L. G. Underwood, & B. H. Gottlieb (Eds.), Support measurement and intervention: a guide for health and social scientists (pp. 29–46). Oxford University Press. https://doi.org/10.1093/med:psych/9780195126709.003.0002
Liang, T. P., Ho, Y. T., Li, Y. W., & Turban, E. (2011). What drives social commerce:
the role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69–90. https://doi.org/10.2753/JEC1086-4415160204
59
Liang, T. P., & Turban, E. (2011). Introduction to the special issue social commerce:
a research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5–13. https://doi.org/10.2753/JEC1086-4415160201
60
Liébana-Cabanillas, F., Villarejo-Ramos, Á. F., & Sánchez‐Franco, M. J. (2015). Mobile
social commerce acceptance model: factors and influences on intention to use s-commerce.
Proceedings of the XXVI Congreso Nacional de Marketing, 1–15.
61
Lin, X., Zhang, D., & Li, Y. (2016). Delineating the dimensions of social support
on social networking sites and their effects: a comparative model. Computers in Human Behavior, 58, 421–430. https://doi.org/10.1016/j.chb.2016.01.017
62
Liu, H., Chu, H., Huang, Q., & Chen, X. (2016). Enhancing the flow experience of consumers
in China through interpersonal interaction in social commerce. Computers in Human Behavior, 58, 306–314. https://doi.org/10.1016/j.chb.2016.01.012
63
Lu, Y., Zhao, L., & Wang, B. (2010). From virtual community members to C2C e-commerce
buyers: trust in virtual communities and its effect on consumers’ purchase intention.
Electronic Commerce Research and Applications, 9(4), 346–360. https://doi.org/10.1016/j.elerap.2009.07.003
64
Luna-Nevarez, C., & Torres, I. M. (2015). Consumer attitudes toward social network
advertising. Journal of Current Issues & Research in Advertising, 36(1), 1–19. https://doi.org/10.1080/10641734.2014.912595
65
Madjar, N. (2008). Emotional and informational support from different sources and
employee creativity. Journal of Occupational and Organizational Psychology, 81(1), 83–100. https://doi.org/10.1348/096317907X202464
66
Masukujjaman, M., Alam, S. S., Siwar, C., & Halim, S. A. (2021). Purchase intention
of renewable energy technology in rural areas in Bangladesh: Empirical evidence. Renewable Energy, 170(January), 639–651. https://doi.org/10.1016/j.renene.2021.01.125
67
Moslehpour, M., Pham, V. K., Wong, W. K., & Bilgiçli, I. (2018). e-purchase intention
of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived
ease of use. Sustainability (Switzerland), 10(1), 1-17. https://doi.org/10.3390/su10010234.
68
Ng, C. S. P. (2013). Intention to purchase on social commerce websites across cultures:
a cross-regional study. Information and Management, 50(8), 609–620. https://doi.org/10.1016/j.im.2013.08.002
69
Norman, G. R., & Streiner, D. L. (1994). Biostatistics: The Bare Essentials. Mosby-Year Book.
70
Nunnally, J. C. (1978). Psychometric Theory. McGraw-Hill, New York, NY.
71
Park, D. H., Lee, J., & Han, I. (2007). The effect of online consumer reviews on consumer
purchasing intention: the moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148. https://doi.org/10.2753/JEC1086-4415110405
72
Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust
and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275
73
Pavlou, P. A., & Chai, L. (2002). What drives electronic commerce across cultures?
A cross-cultural empirical investigation of the theory of planned behavior. Journal of Electronic Commerce Research, 3(4), 240–253. https://doi.org/10.1.1.144.1549
74
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common
Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended
Remedies. Journal of Applied Psychology, 88(5),
879–903. https://doi.org/10.1037/0021-9010.88.5.879
75
Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect
effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/BF03206553
76
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing
and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
77
Rakuten. (2010). Malaysian shoppers tend to regret their online purchases. In Digital News Asia.
78
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0 (2nd ed.). Pearson Malaysia Sdn Bhd.
79
Ramayah, T., & May-Chiun, M. (2007). Impact of shared beliefs on “perceived usefulness”
and “ease of use” in the implementation of an enterprise resource planning system.
Management Research News, 30(6), 420–431. https://doi.org/10.1108/01409170710751917
80
Reychav, I., & Weisberg, J. (2010). Bridging intention and behavior of knowledge sharing.
Journal of Knowledge Management, 14(2), 285–300. https://doi.org/10.1108/13673271011032418
Roca, J. C., Garcia, J. jose, & Vega, J. J. de la. (2009). The importance of perceived
trust, security and privacy in online trading systems. Information Management & Computer Security, 17(2), 96–113. https://doi.org/10.1108/09685220910963983
Shanmugam, M., Sun, S., Amidi, A., Khani, F., & Khani, F. (2016). The applications
of social commerce constructs. International Journal of Information Management, 36(3), 425–432. https://doi.org/10.1016/j.ijinfomgt.2016.01.007
85
Sheikh, Z., Yezheng, L., Islam, T., Hameed, Z., & Khan, I. U. (2016). Impact of social
commerce constructs and social support on social commerce intentions. Information Technology & People, 32(1),68-93. https://doi.org/10.1108/ITP-04-2018-0195
86
Shen, J. (2013). Exploring Chinese users’ acceptance of social commerce sites. Proceedings of the International
Conference on E-Learning, e-Business, Enterprise Information Systems, and e-Government, 219–224.
87
Statista. (2022). The statistics portal.
88
Stephen, A. T., & Toubia, O. (2010). Deriving value from social commerce networks.
Journal of Marketing Research, 47(2), 215–228. https://doi.org/10.1509/jmkr.47.2.215
89
Tian, H., Siddik, A. B., & Masukujjaman, M. (2022). Factors Affecting the Repurchase
Intention of Organic Tea among Millennial Consumers: An Empirical Study. Behavioral Sciences, 12(2), 50.https://doi.org/10.3390/bs12020050
90
To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile
wallets in Vietnam: Extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1), 1-14. https://doi.org/10.1080/23311975.2021.1891661
91
Vidaver-Cohen, D. (1998). Moral climate in business firms: A conceptual framework
for analysis and change. Journal of Business Ethics, 17(11), 1211–1226. https://doi.org/10.1023/A:1005763713265
92
Walczuch, R., & Lundgren, H. (2004). Psychological antecedents of institution-based
consumer trust in e-retailing. Information and Management, 42(1), 159–177. https://doi.org/10.1016/j.im.2003.12.009
93
Wang, C., & Zhang, P. (2012). The evolution of social commerce: The people, management,
technology, and information dimensions. Communications of the Association for Information Systems, 31(1), 105–127. https://doi.org/10.17705/1cais.03105
94
Wang, Y., & Hajli, M. N. (2014). Co-creation in branding through social commerce:
the role of social support, relationship quality and privacy concerns. Proceedings of the 20th Americas Conference on Information Systems (AMCIS) 2014, 1–16.
95
Weber, K., Johnson, A., & Corrigan, M. (2004). Communicating emotional support and
its relationship to feelings of being understood, trust, and self‐disclosure. Communication Research Reports, 21(3), 316–323. https://doi.org/10.1080/08824090409359994
96
Whirty, H. (2017). 35 amazing online shopping and e-commerce statistics.
97
Winzelberg, A. J., Classen, C., Alpers, G. W., Roberts, H., Koopman, C., Adams, R.
E., Ernst, H., Dev, P., & Taylor, C. B. (2003). Evaluation of an internet support
group for women with primary breast cancer. Cancer, 97(5), 1164–1173. https://doi.org/10.1002/cncr.11174
98
Xie, B. (2008). Multimodal computer-mediated communication and social support among
older Chinese internet users. Journal of Computer-Mediated Communication, 13(3), 728–750. https://doi.org/10.1111/j.1083-6101.2008.00417.x
99
Zhao, J.-D., Huang, J.-S., & Su, S. (2019). The effects of trust on consumers’ continuous
purchase intentions in C2C social commerce: A trust transfer perspective. Journal of Retailing and Consumer Services, 50, 42–49.https://doi.org/10.1016/j.jretconser.2019.04.014
100
Zheng, G. W., Akter, N., Siddik, A. B., & Masukujjaman, M. (2021). Organic foods purchase
behavior among generation Y of Bangladesh: The moderation effect of trust and price
consciousness. Foods, 10(10). https://doi.org/10.3390/foods10102278
Alam, S., Makmor, N., Masukujjaman, M., Mohamed Makhbul, Z. K., Ali, M. H., & Al Mamun, A. (2023). Integración da teoría do apoio social e o modelo de aceptación da tecnoloxía de sitios web de comercio social. Revista Galega de Economía, 32(2), 1-24. https://doi.org/10.15304/rge.32.2.8558
