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:
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:
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:
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:
2.3.4 The TAM and purchase intention
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:
2.3.5 Trust as a mediator
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:
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.
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).
[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.
Path | Hypothesis | Beta value | T-statistic | Level of significance |
---|---|---|---|---|
ES PEOU | H1 | 0.258 | 5.562*** | Significant |
IS PU | H2 | 0.269 | 5.752*** | Significant |
ES TRUST | H3 | 0.121 | 2.404** | Significant |
IS TRUST | H4 | 0.209 | 5.107*** | Significant |
TRUST PEOU | H5 | 0.180 | 3.894*** | Significant |
TRUST PU | H6 | 0.195 | 3.967*** | Significant |
PEOU PI | H7 | 0.321 | 6.651*** | Significant |
PU PI | H8 | 0.034 | 0.464 | Insignificant |
TRUST PI | H9 | 0.290 | 5.850*** | Significant |
ES TRUST PI | H10 | 0.112 | 2.224* | Partial mediation |
IS TRUST PI | H11 | 0.178 | 3.847* | Partial mediation |
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.
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
6
Allen, M. P. (1997). The multiple regression model. In: Understanding Regression Analysis. Springer, Boston, MA. https://doi.org/https://doi.org/10.1007/b102242
7
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
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
12
13
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
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
22
23
Cobb, S. (1976). Social support as a moderator of life stress. Psychosomatic Medicine, 38(5), 300–314. https://doi.org/10.1097/00006842-197609000-00003.
24
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
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
34
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), pp.725–737.https://doi.org/10.1016/S0305-0483(00)00021-9
35
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
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
44
Hallegatte, D., & Nantel, J. (2006). The intertwined effect of perceived usefulness, perceived ease of use and trust in a website on the intention to return. The E-Business Review, 6, 1–5. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=72dc997fb26fa9affaea7e23c82dab3bae6f5630
45
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
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
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
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
57
Li, L. (2010). A critical review of technology acceptance literature. Southwest Decision Sciences Institute, 22. http://www.swdsi.org/swdsi2010/SW2010_Preceedings/papers/PA104.pdf
58
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
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
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
78
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
81
Ridings, C. M., & Gefen, D. (2004). Virtual community attraction: why people hang out online. Journal of Computer-Mediated Communication, 10(1). https://doi.org/10.1111/j.1083-6101.2004.tb00229.x.
82
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
83
Shachak, A., Kuziemsky, C., & Petersen, C. (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research. Journal of Biomedical Informatics, 100, 1-5. https://doi.org/https://doi.org/10.1016/j.jbi.2019.103315.
84
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
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
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
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