BÁO CÁO TÓNG KÉTĐÈ TÀI NGHIÊN cứu KHOA HỌC THAM GIA XÉT GIẢI THƯỞNG “NHÀ NGHIÊN CỨU TRẺ UEH” NÀM 2024 THE EFFECTS OF SOCIAL SUPPORT, SOCIAL PRESENCE AND FLOW ON CUSTOMER ENGAGEMENT IN
INTRODUCTION
The demand/need of research
S-commerce, a new form of e-commerce, leverages social media to enhance user interactions and facilitate the buying and selling of products and services This shift has transformed customers into active participants in transactions, allowing them to engage in marketing, selling, comparing, managing, and purchasing within online marketplaces Consequently, understanding customer behavior in the realm of s-commerce is crucial for companies aiming to improve customer engagement and harness the power of social media Our research focuses on helping consumers comprehend the factors that influence customer engagement on social commerce platforms and the reciprocal effects of this engagement.
The research issue
As more individuals rely on social media for insights into products and services before making purchases, the importance of customer engagement in shaping buying decisions grows In the context of social commerce, customer engagement plays a crucial role in building relationships among consumers and between consumers and social commerce platforms.
This research paper explores the factors driving customer engagement on social commerce platforms, focusing on social support, social presence, flow, and trust transfer By measuring multidimensional customer interaction through vigor, absorption, and dedication, the study highlights the significant role of social presence in enhancing customer engagement via trust transfer The findings reveal the importance of understanding customers' willingness to invest effort in maintaining engagement and enjoying interactions within social media platforms This research contributes valuable insights into the theoretical framework of s-commerce marketing, laying the groundwork for future market surveys and strategies, while also encouraging further exploration in the field of social commerce.
Research objectives and research questions
This research paper explores the effects of Social Support and Presence Support on Customer Engagement, with Trust Transfer and Flow serving as mediating factors It examines how Social Support influences both Trust Transfer and Customer Engagement as a dual construct variable Additionally, the study assesses the impact of these elements on Customer Engagement measurement factors, specifically Vigor, Absorption, and Dedication.
The results of this paper also directly answer the following question:
- How does Social Presence affect Member Trust?
- How does Social Support affect Trust Transfer?
- How do factors (Social Support, Trust Transfer, Flow) impact Customer Engagement?
- How does Customer Engagement impact engagement measurement factors (Vigor, Absorption, Dedication)?
Subject and scope of research
The research subjects in this article:
Social Presence Theory highlights the significance of online interactions, emphasizing the social presence of the web, interactions, and others in fostering trust Additionally, social support plays a crucial role, encompassing both emotional and informational support, which further enhances trust transfer among users Understanding these dynamics is essential for building trust in digital environments.
- The influence of Trust Transfer (Member Trust, Community Trust), Social Support and Flow on Customer Engagement.
- The relationship between Trust Transfer, Social Presence Theory, Social Support and Customer Engagement.
Survey target: People who have been using social commerce - TikTok platform.Scope of research: Ho Chi Minh city.
The research method
The research was conducted in two phases:
- Phase 1: Secondary data research: Based on research related to the topic, the team proposed a research model and established questions and scales for it.
In Phase 2 of the study, quantitative research was conducted to evaluate the scale and appropriateness of the research model A questionnaire comprising 54 measurement statements was utilized as the primary data collection tool, with responses assessed on a 7-point Likert scale The research employed a convenience sampling method to gather the necessary data.
Scientific and practical significance
Our research introduces a novel model that integrates both social presence theory and social support theory to enhance understanding of trust in social commerce (s-commerce) This approach differs from prior studies, which typically focused on only one of these theories For instance, Lu, Fan, et al (2016) examined social presence theory and found that factors such as Web social presence and interaction with sellers positively influence trust in online sellers Similarly, Shanmugam et al (2016) explored various constructs of social commerce, further emphasizing the importance of these theories in predicting consumer behavior in s-commerce.
The study by Chao-Hsing Lee et al (2021) explores the impact of social support and customer engagement on stickiness and repurchase intention in social commerce, focusing on a trust transfer perspective It independently examines social support structures from informational and emotional viewpoints to establish trust on social commerce platforms By integrating these theories into a cohesive model, the research offers a novel understanding of trust-building in social commerce, highlighting social characteristics often overlooked in previous studies The findings provide valuable theoretical insights into the interplay of social presence theory and social support theory in fostering trust within the realm of social commerce.
Our research builds upon previous studies of online trust, which have typically focused on a singular aspect, such as confidence in a platform or brand Prior works, including "Social Commerce in Emerging Markets and its Impact on Online Community Engagement" by Algharabat & Rana (2021), primarily examined member trust's role in community engagement, while Molinillo et al (2019) concentrated on community trust's impact on customer engagement In contrast, our study distinguishes between member trust and community trust, demonstrating that both significantly affect customer involvement in social commerce platforms Additionally, we investigate how member trust influences consumer engagement through community trust, highlighting the trust transfer process.
This study builds on service-dominant logic theory and explores customer engagement in social commerce through insights from key research articles By analyzing works such as Brodie et al (2013), Dessart (2017), and Algharabat & Rana (2021), we offer a comprehensive conceptualization of customer engagement as a second-order construct comprising three components: dedication (affective), absorption (cognitive), and vigor (behavioral) In the realm of social commerce, heightened customer engagement is essential, as engaged customers are more inclined to remain active on social commerce platforms, contribute meaningfully, and support fellow community members.
Our study highlights key impact factors that enhance customer engagement on social commerce platforms, demonstrating how increased engagement influences customer interaction levels—specifically vigor, absorption, and dedication By fostering these aspects of engagement, businesses can attract more users to s-commerce, which is crucial for its success Additionally, this research serves as a valuable reference for future studies by examining the roles of social presence, social support, trust transfer, and flow in shaping customer engagement.
Social commerce platforms are rapidly gaining popularity as key shopping destinations for consumers in today's digital landscape Statista projects that global sales via social media will hit $1,298 billion in 2023, with forecasts suggesting a remarkable growth potential to nearly $3 trillion by 2026.
This study highlights the crucial role of enhancing customer engagement on social commerce platforms By implementing social support and social presence, businesses can lower purchasing costs and build trust within the community, thereby facilitating a positive flow experience for customers The interconnectedness of trust transfer and flow experience significantly influences customer engagement Understanding these factors allows companies to refine their marketing strategies, improve product offerings, and elevate customer experiences, ultimately aiding in the effective execution of social commerce initiatives Additionally, this research equips businesses with insights to attract and retain customers, conduct market research, and boost competitiveness, making it a valuable resource for both academic understanding and practical business applications in the realm of social commerce.
Structure of the research
Chapter 1 Introduction: General introduction to the research article.
Chapter 2 Literature Review: Presents an overview of related research, fundamental theories while building a model and making research hypotheses.
Chapter 3 Research methods: Presents the research process and research methods in building and testing scales and models.
Chapter 4 Research results: Analyze and present research results to draw conclusions about research hypotheses.
Chapter 5 Conclusions and recommendations: Summary of the content and results of the study, thereby proposing a number of recommendations, limitations and future research directions.
LITERATURE REVIEW
Previous research
2.1.1 Customer Engagement Behaviour on Social Commerce Platforms: An Empirical Study (2020)
This study presents a comprehensive research model that elucidates Customer Engagement Behavior (CEB) within social commerce (s-commerce) platforms, aiming to identify the variables influencing CEB in this context It enhances our understanding of customer behavior in s-commerce by developing and validating a theoretical model of CEB By integrating the Information Systems (IS) success model, social presence, uses and gratifications theory, and social support, the study identifies the distinct components that drive engagement in s-commerce The analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the variance in dependent constructs and the interactions between the constructs and the model's measurement items.
Figure 2.1 Research model (Busalim, A H., Ghabban, F., & Hussin, A R c.)
2.1.2 Predicting the antecedents of trust in social commerce - A hybrid structural equation modeling with neural network approach (2020)
This study enhances our understanding of how social factors influence trust in s-commerce, addressing gaps in previous research It explores the mechanisms through which these social elements foster trust, positioning itself as one of the first to utilize a dual-theory, dual-stage SEMANN approach to analyze trust factors in s-commerce Following the PLS testing of the hypotheses, relevant predictors were employed as input nodes for the ANN analyses.
Figure 2.2 The Theoretical Framework (Leong, L.-Y., Hew, T.-S., Ooi, K.-B., &
2.1.3 Analyzing the effect of social support and community factors on customer engagement and its impact on loyalty behaviors toward social commerce websites (2019)
This study aims to answer the calls for an empirical inquiry into the reasons for and effects of consumer participation in social commerce made by Busalim and Hussin
This study aims to investigate the influence of community and social support factors on customer involvement in social commerce, while also examining the impact of both transactional and non-transactional elements of customer engagement on loyalty The research draws upon existing literature related to customer involvement, social support theory, social identity theory, and loyalty theory to provide a comprehensive understanding of these dynamics.
Figure 2.3 Research model of consumer response to social commerce (Molinillo, s., Anaya-Sanchez, R., & Liébana-Cabanillas, F.)
2.1.4 Analyzing the effect of social support and customer engagement on stickiness and repurchase intention in social commerce: a trust transfer perspective (2021)
This study invests further into the four antecedents of customer engagement in four dimensions: (1) emotional support, (2) information support, (3) member trust, and
This study explores the impact of community trust and consumer engagement on customer behaviors, specifically focusing on stickiness and repurchase intention Utilizing the Stimuli-Organism-Response model, it posits that customer engagement is a multidimensional construct influenced by social support and trust transfer Employing a structural equation model (SEM), the research assesses the empirical strength of the relationships within the proposed framework.
Figure 2.4 Research Framework (Chao-Hsing Lee, Chien-Wen Chen, Wen-Kuo
()rganism Response (customer engagement outcome)
Stimuli (drivers of customer engagement) factor
2.1.5 Social Commerce in Emerging Markets and its Impact on Online Community Engagement (2021)
This research investigates the relationships between social presence, dimensions of social support (including emotional and informational support), and components of social commerce (such as ratings, reviews, suggestions, and community forums) It explores how these factors influence trust among community members, which subsequently affects flow and the three dimensions of online community participation: cognitive, affective, and behavioral Additionally, the study examines the impact of flow on various aspects of online community interaction A conceptual model is proposed, integrating theories of social support, trust, social presence, flow, and service-dominant logic Data was collected from 400 respondents in Jordan through a Facebook online group and analyzed using AMOS-based structural equation modeling.
Figure 2.5 Proposed conceptual framework (Raed s Algharabat & Nripendra p
'\ First-order construct f ) Second-order construct
Theoretical background
Social support theory, as described by Leahy-Warren (2014), is a middle-range theory centered on relationships and interactions, significantly influencing users' cognition, emotion, and behavior (Lin et al., 2018; Raed S Algharabat & Nripendra P Rana, 2021; Park et al., 2023) It is defined by Rozzell et al (2014) as being connected to user information and behavior, fostering a sense of being loved, cared for, and appreciated (Hu et al., 2019; Kim & Park, 2023) Additionally, social support embodies the personal experience of receiving assistance from members of a specific social group.
In the age of social commerce (s-commerce), social support plays a crucial role in how individuals perceive care and responsiveness within their social networks (Tajvidi et al., 2017) This support can be quantified by the assistance offered and the availability of individuals within these networks Social support facilitates emotional expression and mutual understanding among users of social networking sites (SNS) When members of an online community provide help through shared knowledge, experiences, or emotional backing, it fosters a culture of reciprocity, encouraging others to extend their support as well (Raed S Algharabat & Nripendra P Rana, 2021).
Social presence theory, as outlined by Algharabat and Rana (2021), emphasizes the varying capabilities of media to create the psychological impression of physical presence through visual and verbal signals This theory is particularly relevant in social commerce, as it effectively examines human interactions in online environments (Lowenthal, 2011) By enhancing communication and social interaction among users on social networking sites (SNS), social presence theory plays a crucial role in understanding user engagement in s-commerce (Cui et al., 2013; Tu, 2002; Xiao et al., 2019a) Our research integrates this theory to explore user interactions within SNSs, focusing on how social commerce elements—such as recommendations, ratings, and community forums—foster a sense of presence through personal communication (Huang and Benyoucef, 2013; Liang and Turban, 2011).
Trust transfer theory posits that trust can be shifted between various sources, particularly when a close relationship exists between a trusted source, such as social media, and a target, like e-commerce sites (Lin et al., 2019) This theory suggests that individuals may extend their trust from a reliable source to a less familiar target if a connection is perceived (Slewart, 2003) Research by Zhao, Huang, and Su (2019) further supports this notion, indicating that trust in one entity can be transferred to another when a relationship is established Numerous studies in the realm of social commerce have leveraged trust transfer theory to understand trust construction (Lim et al., 2006; Pavlou and Gefen, 2004; Sia et al.).
Members of social networking service (SNS) communities often exhibit high levels of trust, as they feel trusted by others and reciprocate that trust (Algharabat & Rana, 2021) This mutual trust among community members surpasses their trust in businesses utilizing social commerce on these platforms Consequently, we propose that trust is shared among users, aligning with the principles of trust transfer theory.
2.2.4 The Service-Dominant (S-D) Logic Theory
Service-dominant (S-D) logic theory serves as the foundation for understanding customer engagement, emphasizing collaborative interactions among consumers, firms, and service agents (Vargo & Lusch, 2008) Brodie et al (2013) define customer engagement as a psychological state arising from co-creative encounters with brands during service interactions This study applies S-D logic to explore a specific aspect of customer interaction, particularly in social media communities, where consumer participation reflects their engagement with others in online forums Furthermore, consumer attitudes towards these communities are evident through various affective, cognitive, and behavioral expressions (Brodie et al., 2013; Dessart, 2017; Algharabat & Rana, 2021).
D logic theory because it can be used to explain why customers interact and appreciate co-crealion as the primary drivers of SNS engagement.
Definition of concepts
New electronic commerce business models, referred to as social commerce, have gained traction due to the increasing popularity of social media platforms such as Facebook, Instagram, YouTube, and Twitter Social commerce leverages social media to facilitate e-commerce transactions and activities, positioning itself as a distinct subset of e-commerce.
(2019), social commerce or s-commerce is a business idea that blends commercial activities like marketing, advertising, and word-of-mouth with social media platforms like Facebook, Twitter, and YouTube,
Customer engagement, from a behavioral standpoint, refers to the degree of interaction and connection that a customer has with a brand or its offerings This engagement can be initiated by either the organization or the customer themselves.
This study defines customer engagement through a comprehensive lens, operationalizing it as a three-dimensional construct: absorption (cognitive), dedication (emotional), and vigor (behavioral), specifically tailored for online social platforms based on the framework by Cheung, Lee, and Jin (2011) In the context of social commerce, absorption signifies a deep focus and immersion in the platform, while dedication reflects enthusiasm, interest, and self-pride during usage Vigor encompasses the energy and mental resilience customers exhibit, along with their willingness to invest time and effort in utilizing social media effectively.
Research indicates that social commerce components, such as forums, communities, recommendations, and referrals, enhance sociability by fostering genuine social connections (Hajli, 2015) Recent findings by Kumar, Salo, and Li (2019) highlight the importance of social characteristics, including social identity and social contact, in supporting sociability within social commerce This study focuses on two key components of the social factors dimension: social support and social presence.
Social support plays a crucial role in enhancing an individual's well-being and health by addressing their psychological needs related to illness (Bao, 2016) It encompasses the experience of being cared for and assisted by one's social network, as defined by Liang et al (2011) Rozzell et al (2014) further explain that social support involves behaviors and knowledge that foster feelings of love, respect, and appreciation According to Gottlieb and Bergen (2010), this dynamic process varies based on interactions between the support giver and receiver Ultimately, individuals perceive social support when they feel cared for and aided by their community (Doha et al., 2019; Han et al., 2018).
Zhang et al (2014) describe social support as a multidimensional concept that underpins s-commerce attributes, which encompass both emotional and informational support (N Hajli, 2014c; T P Liang et al., 2011) Emotional support, akin to messages from friends on social networking sites, fosters a sense of belonging and care within a group, emphasizing feelings of love and connection (Bai, Yao, and Dou, 2015) This type of support includes elements such as caring, understanding, and empathy (Liang et al., 2011) Conversely, informational support involves providing guidance and useful information to assist other clients (Liang et al., 2011).
Social presence, derived from social presence theory, refers to a communication medium's capacity to convey social cues (Baozhou Lu, Fan, & Zhou, 2016) As outlined by Lim, Hwang, Kim, and Biocca (2015), it is primarily recognized as a quality of communication channels that influences how individuals interact and engage, as well as their level of awareness during communication.
Social presence on the web, as defined by Lu, Fan, et al (2016), refers to a website's ability to convey warmth and sociability This concept, similar to the findings of Gefen & Straub (2004), encompasses three key aspects: the social presence of the web, the social presence of interaction, and the social presence of others The social presence of the web highlights how effectively a site can create a welcoming atmosphere, while the social presence of interaction focuses on facilitating communication between users and vendors Finally, the social presence of others pertains to how individuals perceive the presence and responsiveness of other community members in virtual environments.
User interaction on social commerce (s-commerce) websites plays a crucial role in transferring trust, which significantly influences purchase intentions (Ng, 2013) Trust in social commerce not only fosters information sharing but also reduces costs, providing competitive advantages (Rodgers, 2010) Additionally, users can develop trust in social media businesses through the process of trust transfer (Lal, 2017; Liu et al.).
This research focuses on trust transfer within social commerce communities, as defined by Chen and Shen (2015) Trust transfer is the degree to which social commerce users are inclined to trust the reviews of other users (member trust) before placing their confidence in the overall transaction community (community trust) Understanding this interaction is crucial for comprehending how different levels of trust function within these platforms.
The level of trust that people have in the decisions, words, and deeds of other members of a social commerce community is known as member trust (Chen and Shen,
Research indicates that in environments characterized by trust, individuals are more inclined to support each other and engage in collaborative social activities (Shen et al., 2014; Chen and Shen, 2015) Moreover, information obtained from credible sources is often regarded as more valuable, serving as a crucial resource for decision-making (Sussman and Siegal, 2003).
Long-term relationship maintenance hinges on community trust, which is defined as the reliability and honesty perceived in interactions with exchange partners (Margahana, H., 2020) Community trust refers to an individual's belief in their community as a dependable space for social engagement (Chen and Shen, 2015) This trust is cultivated through a series of positive experiences, ultimately influencing consumers' intentions to participate actively within the community (Chen & Shen, 2015; Hajli, 2014).
Percussive experience in social commerce is closely linked to flow experience, a psychological state that enhances customer engagement by fostering complete absorption and emotional involvement Prior research emphasizes the importance of flow experience in social networking sites (SNSs), highlighting its role in facilitating user interactions In the context of social commerce, customers often achieve a state of flow through immersive connections with other users, which significantly contributes to their overall experience.
Research Model and Hypothesis Development
We propose a research model illustrated in Figure 2.6, grounded in established theories and research principles The model outlines a network of relationships and variable definitions, detailed in Figure 2.6 and Table 2.1 The rationale for the proposed relationships will be discussed in the following section.
Table 2.1 Summarized Definition of Constructs
“A dynamic, iterative psychological state, Hollebeek (2013);
Customer , r r ' derived from a satisfactory interactive Pansari & Kumar
Engagement relationship with the organization.” (2017); Van Doorn ct al (2010)
“The degree of users' enthusiasm to use online social platforms and platforms' willingness to Molinillo et al.
Vigor 7 invest time and effort in his/her role as a (2020) member.”
“The user concentrates fully, being happy, and being deeply engrossed in a social commerce website, whereby lime passes quickly.”
“The customer’s sense of significance, enthusiasm, inspiration, pride, and challenge
“An individual's experience of being cared for, being responded to and being helped by people in that individual’s social group.”
Liang, Ho, Li, and Turban (2011)
“The perception that the messages received from friends on a social networking site provide
Liang et al (2011) the needed assistance.”
“The perception that the messages received Emotional
Support from friends on a social networking site include emotional concerns such as caring, understanding and empathy.”
Social “A communicator's sense of awareness of the Kushnir et al.
Presence presence of an interaction partner.” (2001)
“The capability of a website to convey a sense of human warmth and sociability."
“The ability of a website to provide a platform to interact with sellers."
“The degree to which other social actors seem to present and respond to the members within Gefen & Straub
“An individual's willingness to trust on the words, actions, and decisions of other members in the social commerce community."
“An individual's perception of the community as a reliable and predictable place for social interaction."
“I consumers' involvement within a particular Flow stimulus, and as a result it reflects the Gao and Bai (2014) consumer's feelings when they are fully absorbed with the experience."
2.4.1 Social Presence of Web and Member Trust
Trust can develop within a social environment, as highlighted by Hassanein & Head (2007) When websites are perceived as social actors, user interactions online resemble interpersonal relationships (Pavlou, Liang, & Xue, 2007) Since communication is essential for building trust, interactions among web users can foster this trust (Blau, 2017) Moreover, a heightened presence on social media can enhance information sharing and social cues, increasing transparency and potentially mitigating suspicious behavior (Lu, Fan, et al.).
Social presence in online environments plays a crucial role in fostering trust among users Research indicates that a strong social presence can lower social distance and enhance trustworthiness (Pavlou et al., 2007; Lu, Fan, et al., 2016) Conversely, s-commerce websites that convey a lack of social presence can hinder trust establishment, as users may doubt the integrity of such platforms (Blau, 2017; Gefen & Straub, 2004) Studies have confirmed the significant impact of social presence on trust in s-commerce contexts (Hassanein, Head, & Ju, 2009; Casey & Poropat, 2014; Ogonowski et al., 2014) It is anticipated that consumers will be more inclined to trust s-commerce websites that effectively communicate warmth and friendliness, leading to the hypothesis that enhanced social presence correlates with increased consumer trust.
Hl: The Social Presence of Web positively affects Member Trust.
2.4.2 Social Presence of Interaction and Member Trust
In a virtual community, social presence is characterized by direct communication and the exchange of social information between buyers and sellers (Lu, Fan, et al., 2016) This interaction fosters a sense of connection and engagement, enhancing the overall online shopping experience (Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Chong, A Y.-L.).
Interaction is a crucial element of social presence in social commerce (s-commerce), significantly influencing trust among consumers (Kim & Park, 2013) Chat applications like Facebook Messenger and Instagram Inbox enhance buyer-seller interactions, providing customers with social information that fosters trust According to Qiu & Benbasat (2005), these chat tools create a sense of social presence, enabling customers to perceive the honesty and attitudes of retailers through computer-mediated communication Furthermore, research by Kim and Park (2013) and others supports the notion that increased interaction correlates positively with trust in s-commerce Therefore, it is hypothesized that enhanced interaction will lead to greater trust in online shopping environments.
H2: Social Presence of Interaction positively affects Member Trust.
2.4.3 Social Presence of Others and Member Trust
Customers increasingly rely on reviews and social cues to make informed purchasing decisions in online marketplaces Indicators such as transaction history, popularity lists, and positive electronic word-of-mouth (eWoM) enhance trust in the marketplace's integrity and competence As buyers observe the behavior of previous customers, their purchasing decisions are influenced more by these social signals than by their own experiences, leading them to emulate past buyers and contribute to the growth of larger consumer bases.
2020) Therefore, we pul up the following theory:
H3: Social Presence of Others positively affects Member Trust.
2.4.4 Social Support and Member Trust
Research indicates that social media users derive emotional and informational support from their interactions, fostering trust among members (Maier et al., 2015; Leong et al., 2020) The presence of empathetic and caring users enhances this support, which in turn strengthens trust within social networks (Sheikh et al., 2017) Lal (2017) emphasizes that both emotional and informational support significantly influence member trust in social commerce Furthermore, numerous studies affirm that social support, encompassing both emotional and informational aspects, positively affects member trust (Fan et al., 2018; Zhao et al., 2019; Chao-Hsing Lee et al., 2021) Thus, the following hypothesis is proposed:
H4: Social Support positively affects Member Trust.
2.4.5 Social Support and Community Trust
Previous research has established a strong positive correlation between social support and community trust (Liang et al., 2011) Trust is deeply intertwined with emotional support (Weber & Johnson, 2004), while the trustworthiness of intermediaries significantly impacts viewers' community trust (Xiao et al., 2019b) Additionally, community trust is directly linked to system quality, service quality, and information quality (Sõllner et al., 2016) Furthermore, trust is closely associated with the informational and emotional support components (Zhou, 2017; Alalwan et al., 2019) Based on these findings, we propose the following theory.
H5: Social Support positively affects Community Trust.
2.4.6 Member Trust and Community Trust
Social media serves as a vital intermediary in social commerce, providing users with an interactive platform to engage with one another and foster community connections (Parker et al., 2005) This connectivity enhances user experience and promotes collaborative interactions within the digital marketplace.
Trust in social media platforms is essential for individuals to complete transactions, as highlighted by Liu et al (2019) and Shi and Chow (2015), who explored the impact of transaction experiences on trust formation in social commerce Chen et al (2009) emphasized that trust within social communities is largely dependent on the trust among members Additionally, research by Chen and Shen (2015) and Lal (2017) demonstrates that member trust can be transferred within s-commerce communities, further influencing overall trust dynamics.
Multiple studies, such as those by Farivar et al (2017), Cao et al (2018), Fu et al (2018), Cheng et al (2019), Liu et al (2019), Wu et al (2019), and Chao-Hsing Lee et al (2021), have identified a positive correlation between member trust and community trust Therefore, this study suggests that
H6: Member Trust has a positive influence on Community Trust.
Wu and Chang (2005) highlight a significant link between flow and community trust, noting that community members often share relevant and helpful information, which fosters mutual trust and enhances member experiences (Kim and Li, 2009) This interaction not only helps members save money but also cultivates a sense of community Liu et al (2016) further emphasize that a strong relationship exists between flow experiences and trust within online communities, indicating that increased trust enhances the flow experience Additionally, Zhou (2017) and Raed s Algharabat & Nripendra p Rana (2021) corroborate the substantial correlation between trust and flow Thus, we propose the following hypothesis:
H7: Community Trust positively affects Flow.
Previous research has established a strong connection between flow experience and user engagement, with Algharabat and Rana (2021) highlighting that deep engagement stems from flow experiences Wu and Wang (2011) noted that users’ perception of time and place can be enhanced during social media interactions through flow experiences Social media platforms can foster sensations of flow, leading to increased immersion and absorption Banhawi et al (2012) found a significant relationship between flow experiences on Facebook and user participation on social networking sites Additionally, Zhang et al (2017) documented the impact of flow on online community involvement, while Triantafillidou and Siomkos (2018) demonstrated the positive effects of flow on behavioral engagement within Facebook environments Based on these findings, we propose the following hypothesis:
H8: Flow positively affects Customer Engagement.
2.4.9 Member Trust and Customer Engagement
In online communities, trust among individuals reflects their confidence in each other's integrity and skills, fostering a moral expectation of behavior (McKnight et al., 2002; Gefen et al., 2003) This trust not only enhances faith in the efforts and value of community members but also promotes reciprocal benefits, linking trust to increased involvement (Chan et al., 2014; Liu et al., 2018) Research by Molinillo et al (2019) and Vohra and Bhardwaj (2019) supports the positive correlation between community trust and member engagement Therefore, this study posits the following hypothesis.
H9: Member Trust positively affects Customer Engagement.
2.4.10 Community Trust and Customer Engagement
Community trust is essential in influencing the relationship between consumers and sellers, as well as in the successful marketing of products on social commerce platforms It enhances customer engagement on social media through positive experiences, highlighting the importance of fostering community trust for relationship-building with customers Research indicates that community trust significantly impacts customer engagement on social media websites, reinforcing the need for businesses to prioritize trust in their marketing strategies.
H10: Community Trust positively affects Customer Engagement.
2.4.11 Social Support and Customer Engagement
RESEARCH METHODS
Research Process
Research Methods
The research paper investigates how Social Presence and Social Support influence Customer Engagement in social commerce, with Trust Transfer serving as a mediating factor The study specifically targets users who have engaged in shopping activities on social networks, particularly on the TikTok platform.
To align with budget constraints and research needs, the team opted for a convenient non-probability sampling method, gathering responses from TikTok users Data was collected via an online questionnaire using Google Forms, with assistance from friends and students across various universities, ensuring the survey reached a diverse group of students as well as their families and alumni.
Formula 1: Since the number of questions in the group's research paper is 51, we have: n = 5 * 54 = 270
- n: sample size to be determined.
When calculating the sample size, it is common practice to set the probability of success (p) at 0.5 This approach maximizes the product of (1-p) and p, leading to a more accurate estimate of the required sample size.
To satisfy both formulas, the sample size of the study is n > 385.
This study employs qualitative research methods, drawing on relevant studies and theoretical foundations to develop a comprehensive research model The authors have refined and constructed a measurement scale to create a complete questionnaire for data collection in the quantitative research phase This questionnaire builds upon the group's previous research efforts.
“There is a sense of human contact in the web of the s-commerce seller." Gefen and
“There is a sense of personalness in the web of the s-commerce seller."
There is a sense of sociability in the web of s-commcrcc seller.”
“There is a sense of human warmth in the web of the s-commerce seller.
“There is a sense of human sensitivity in the web of the s-commerce seller.”
“I can make sense of the attitude of sellers by interacting with them via s-commcrcc.”
“1 can imagine how they may look like by interacting with them via s- commerce.”
There is a sense of human touch to communicate with sellers via s- commerce.
“Communication via s-commerce was warm.”
“There are many other buyers feel interested in the product in s- commerce.
“There are many other buyers sharing information regarding the product in s-commerce.”
“There are many others who have bought the product through s- commcrcc.”
“When I encountered a problem, some people on the social commerce websites would give me information to help me overcome the problem.”
“Some “Friends” on the social commerce websites would offer suggestions when I needed help.”
“When faced with difficulties, some people on the social commerce websites would help me discover the cause and provide me with suggestions.”
“When faced with difficulties, people on the social commerce websites will tell me where to solve the problems.”
I obtained sufficient assistance from my “Friends” on the SNS.”
“When faced with difficulties, some “Friends” on the social commerce websites are on my side.”
“When faced with difficulties, some “Friends” on the social commerce websites comforted and encouraged me.”
“When faced with difficulties, some people on the social commerce websites expressed interest in and concern for my well-being.”
“When faced with difficulties, some people on the social commerce websites listened to me talk about my private feelings.”
“There is someone (on the social commerce websites) 1 can get emotional support from.”
“Members in the social commerce websites will always try and help me out if I get into difficulties.”
“Members in the social commerce websites will always keep the promises that they make to one another.”
“Members in the social commerce websites arc truthful in dealing with one another.”
“Members of this social commerce websites are in general trustworthy.”
“I trust the information provided by member of social commerce websites.”
Huang (2016); Lin et al (2016); Zhang et al
Chen and Shen (2015); Cheng et al (2019);
“The performance of this social commerce websites always meets my expectations.”
Chen and Shen (2015); Cheng et al (2019); Fuet al (2018)
“This platform I often use can be counted on as a good social commerce websites.”
“Social commerce websites community is reliable.”
“1 believe that the social commerce websites have the skills and expertise to provide quality service to buyers and sellers.”
“Social commerce websites is trustworthy.”
“Interaction with community members in this SNS community is fun.”
“Interaction with community members in this SNS community is interesting.”
“Interaction with community members in this SNS community makes me feel the excitement of exploring.”
“Interaction with community members in this SNS community makes me feel absorbed.”
“I can continue using this social commerce websites for very long periods at a time.” Cheung el al
(2015); Huang et al (2017); Molinillo et al (2020);
“I feel strong and vigorous when using the social commerce websites.”
“I feel very resilient, mentally, as far as this social commerce websites is concerned.”
“In this social commerce websites, I always persevere, even when things do not go well.”
“I devote a lot of energy to this social commerce websites.”
“I try my hardest to perform well on this social commerce site.”
“Time flies when 1 am participating in this social commerce websites.”
Huang et al (2017); Molinillo et al.
“Using this social commerce websites is so absorbing that I forget about everything else.”
“I am rarely distracted when using this social commerce websites.”
“I am immersed in this social commerce websites.”
“I pay a lol of attention to this social commerce websites.”
“My mind is focused when using this social commerce site.”
“I am enthusiastic in this social commerce websites.”
“I am excited when using this social commerce websites.”
“This social commerce websites inspires me.”
“I am passionate about this social commerce websites.”
“I am proud of the social commerce websites I use.”
“I found this social commerce site full of meaning and purpose.”
The quantitative research method was employed to assess the scale and significance of the research model This study involved surveying participants who have engaged with social commerce, utilizing an online questionnaire developed from previously analyzed secondary data.
3.2.2.1 Analytical process in quantitative research
All collected data will be analyzed with the help of Excel, SPSS, and Smart PLS software Data is encrypted and cleaned before being processed through the analysis steps:
Researchers utilized exploratory factor analysis (EFA) to assess the convergent and discriminant validity of a set of observed variables For effective EFA, it is essential to meet specific criteria that ensure the reliability and interpretability of the results.
3.2.2 Ỉ 2 Assess the reliability of the scale (Cronbach's Alpha)
The reliability of the scale is evaluated through the internal consistency method, specifically utilizing Cronbach's Alpha coefficient (Nguyen Dinh Tho & Nguyen Thi Mai Trang, 2009) To identify the most relevant variables, the Corrected Item-Total Correlation is calculated, enabling the elimination of any observed variables that do not significantly contribute to the overall understanding of the measured concept (Hoang Trong & Chu Nguyen Mong Ngoc, 2008) Based on this analysis, the research assesses the scale according to established standards.
To assess the relationship between group efficiency and its influencing factors, the correlation coefficient r is utilized This statistical measure helps in evaluating the strength of the correlation between the two variables Additionally, the significance value associated with the correlation coefficient is crucial in determining the closeness of this relationship.
Adjusted R Square evaluates the model's fit, while the F-test assesses the model's generalizability to the broader population Additionally, the T-test is employed to test the hypothesis that the regression coefficients are collectively equal to zero.
Variance inflation factor (VIF) is indicative of collinear/multicollinearity As the VIF gets smaller, multicollinearity becomes less common.
The hypothesis test and partial regression coefficient p indicate how a one-unit change in an independent variable affects the mean of the dependent variable, while controlling for the influence of other independent variables.
3.2.2 J.4 Tested by Structural Equation Modeling (SEM)
Effect size (f²) measures the influence of independent variables on a dependent variable According to Cohen (1988), an index table for f² is used to assess the significance of these independent variables.
- f2 < 0,02: extremely small or no impact.
Q2: Hair et al (2019) define the values of Q2 that correlate to the model's predictive capability as follows:
3.2.2.2 Survey questionnaire design and Data collection
3.2.2.2.1 The structure of the questionnaire: The questionnaire consists of four parts
(1) Introduction: State the purpose and meaning of the survey to the surveyor This helps survey participants complete surveys with more objective data quality and value.
The filtering section targets individuals who have experience using and shopping on S-commerce platforms, ensuring that only relevant participants continue with the survey Those who have never engaged with S-commerce will be excluded from participation.
(3) Main question section: Statements are measured using a Likert scale of 7.
(4) Sub-questions: Include general and personal information.
Once created online through the Google Forms platform, the questionnaire will be sent to individuals participating in a convenient sampling method tailored to real- world conditions.
The study titled "Analyzing the Effects of Social Support and Social Presence Factors on Customer Engagement Mediated by Trust Transfer" introduces a comprehensive scale comprising 11 components and 54 observed variables, including 5 for Social Presence of the Web (SPW), 4 for Social Presence of Interaction (SPI), and additional variables for Social Presence of Organization (SPO).
(3), IS (5), ES (5), MT (5), CT (5), FL (4), VI (6), AB (6) and DE (6).
RESEARCH RESULTS
Descriptive Statistics
The research team conducted an online survey using Google Forms and distributed it through social networking platforms, resulting in a total of 501 responses After filtering the data, 441 valid responses were retained, leading to a final sample size of 441 for the study.
The study analyzed data from 441 valid samples using SPSS 26.0 and SMART PLS 4.0 This section aims to present an overview of the insights gathered from the respondents.
Among the 441 valid responses, 302 are from women (68.5%) and 139 from men (31.5%) The predominant age group is 18 to 25, representing 91.8% of the total with 405 responses Responses from individuals under 18 account for 3.4% (15 samples), while those over 25 make up 4.8% (21 samples).
The survey revealed that 85.2% of respondents hold a university degree, comprising 376 out of 441 samples In contrast, those with high school, intermediate, college, and postgraduate education represent less than 10% of the total This educational distribution is logical, as the majority of participants, aged 18-25, are predominantly students, recent graduates, or early in their careers.
The research team categorized the monthly income levels of Vietnamese citizens in Ho Chi Minh City into five common ranges The survey revealed that 75.5% of participants earn below 10,000,000 VND, while 18.4% reported incomes between 10,000,000 VND and less than 20,000,000 VND Additionally, 4.5% of respondents fall within the 20,000,000 VND to less than 30,000,000 VND range, totaling 20 responses Lastly, only 1.6% of the survey participants have a monthly income exceeding 30,000,000 VND.
A study analyzing 441 samples revealed that the majority of TikTok users spend between 60 to 180 minutes on the platform daily, comprising 49.6% of respondents Meanwhile, 32.9% of users access TikTok for less than 60 minutes, and only 17.5% exceed 180 minutes of usage These findings indicate a significant interest among young people in engaging with TikTok.
Duration of using TikTok per day
The results of Exploratory Factor Analysis (EFA) and Cronbach’s Alpha
Table 4.2 The results of EFA factors of the scales after eliminating AB I, VII
KMO Factor loading Communalities Eigenvalue
In this study, the group establishes a minimum satisfactory condition of 0.5 to eliminate items with unsatisfactory indicators, including factor loadings below 0.5, communalities under 0.5, or cumulative explained variance below 60%, as seen with items VII and ABI However, in the case of the Competitive Advantage variable, item AB3, which has a communality of 0.498, is retained despite not meeting the 0.5 threshold, as other criteria are satisfied and the difference is deemed insignificant.
After running data on SPSS, the group has the figures shown in table 4.2 below.
I'able 4.3 I'he results of Cronbach’s Alpha and Item to total correlation of the scales after eliminating ABI, VII
Cronbach’s Alpha Item to total correlation
After eliminating 2 items, the data has satisfied the research group’s demand of Cronbach Alpha indicator higher than 0,7 and Item to Corrected Item - Total Correlation higher than 0,5.
The results of Pearson correlation analysis
The research paper examines the second-order variables of Social Support and Customer Engagement, necessitating the combination of first-order variables Emotional Support and Information Support were merged into Social Support, while Vigor, Absorption, and Dedication were integrated into Customer Engagement Following the theoretical framework outlined in Chapter 3, data analysis was conducted using SPSS, resulting in the figures presented in Table 4.3.
Table 4.4 The results of the Pearson correlation
CE SPW SPI SPO SP MT CT FL
** Correlation is significant at the 0,01 level (2-tailed).
In this research, we consider two types of correlation relationships: correlation between independent variables and dependent variables and correlation between independent variables.
The analysis reveals a significant correlation between independent and dependent variables, with all correlation relationships showing a significance level (Sig.) of less than 0.05 This confirms the presence of meaningful relationships between the independent and dependent variables.
The analysis reveals that the correlation between the independent variables SPO and MT is not significant, with a p-value greater than 0.05, indicating no correlation and minimal risk of collinearity In contrast, the other independent variables show significant correlations, with p-values less than 0.05 Additionally, all Pearson correlation coefficients are below 0.7, suggesting a low likelihood of collinearity among these variables Further investigation into potential collinearity will be conducted using VIF indicators in the regression analysis.
The results of Regression analysis
4.4.1 Social Presence of Web’s impact on Member Trust
Table 4.5 Hl result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient
The regression analysis results indicate an Adjusted R-squared value of 0.165, meaning that the independent variables explain 16.5% of the variation in the dependent variable, while the remaining 83.5% is attributed to out-of-model variables and random errors The use of Adjusted R-squared is favored for providing a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is less than 0,05 Therefore, the regression model is statistically significant.
The results indicate that the significance value from the t-test is below 0.05, demonstrating that the Social Presence of the Web has a statistically significant impact on Member Trust.
As the Beta index of Hl is 0,408 > 0, we can conclude that Social Presence of Web has a positive effect on Member Trust.
In conclusion, since all the index is satisfied, we accept the hypothesis that Social Presence of Web positively affects Member Trust.
4.4.2 Social Presence of Interaction’s impact on Member Trust
Table 4.6 H2 result of Regression analysis
The regression analysis summary indicates an Adjusted R-squared value of 0.155, meaning that the independent variables explain 15.5% of the variation in the dependent variable, while 84.5% is attributed to unaccounted variables and random errors The use of Adjusted R-squared is favored as it offers a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- tesl is less than 0,05 Therefore, the regression model is statistically significant.
The t-test results indicate a statistically significant relationship, as the Sig value is less than 0.05, demonstrating that Social Presence of Interaction positively influences Member Trust.
As the Beta index of H2 is 0,397 > 0, we can conclude that Social Presence of Interaction has a positive effect on Member Trust.
In conclusion, since all the index is satisfied, we accept the hypothesis that Social Presence of Interaction positively affects Member Trust.
4.4.3 Social Presence of Other’s impact on Member Trust
Table 4.7 H3 result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient B t
The regression analysis results indicate an Adjusted R-squared value of 0.005, suggesting that the independent variables explain only 0.5% of the variation in the dependent variable This implies that a significant 99.5% of the variation is attributed to external factors and random errors The use of Adjusted R-squared is preferred as it provides a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is more than 0,05 Therefore, the regression model is not statistically significant.
The t-test results indicate that the significance value (Sig.) is greater than 0.05, suggesting that the Social Presence of Others does not have a statistically significant impact on Member Trust.
In conclusion, since the ANOVA(Sig.) and the Coefficient(Sig.) are not satisfied, we reject the hypothesis that Social Presence of Other impact on Member Trust.
4.4.4 Social Support’s impact on Member Trust
Table 4.8 H4 result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient
The model summary indicates an Adjusted R-squared value of 0.363, revealing that the independent variables in the regression analysis explain 36.3% of the variation in the dependent variable The remaining 63.7% of the variation is attributed to unmodeled factors and random errors The Adjusted R-squared is favored over the standard R-squared as it offers a more accurate representation of the model's fit.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is less than 0,05 Therefore, the regression model is statistically significant.
The results indicate that the significance value (Sig.) of the t-test is less than 0.05, demonstrating that Social Support has a statistically significant effect on Member Trust.
As the Beta index of H4 is 0,604 > 0, we can conclude that Social Support has a positive effect on Member Trust.
In conclusion, since all the index is satisfied, we accept the hypothesis that Social Support positively affects Member Trust.
4.4.5 Social Support’s impact on Community Trust
Table 4.9 H5 result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient _ z z ' Beta
The model summary analysis reveals an Adjusted R-squared value of 0.292, indicating that the independent variables in the regression analysis explain 29.2% of the variation in the dependent variable The remaining 70.8% of the variation is attributed to variables not included in the model and random errors The Adjusted R-squared is favored for its ability to provide a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is less than 0,05 Therefore, the regression model is statistically significant.
The t-test results indicate that the significance value (Sig.) is less than 0.05, demonstrating that Social Support has a statistically significant impact on Community Trust.
As the Beta index of H5 is 0,542 > 0, we can conclude that Social Support has a positive effect on Community Trust.
In conclusion, since all the index is satisfied, we accept the hypothesis that Social Support positively affects Community Trust.
4.4.6 Member Trust’s impact on Community Trust
Table 4.10 H6 result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient
The analysis results in the model summary reveal an Adjusted R-squared value of 0.335, indicating that the independent variables in the regression analysis explain 33.5% of the variation in the dependent variable The remaining 66.5% is attributed to out-of-model variables and random errors The Adjusted R-squared is favored as it provides a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is less than 0,05 Therefore, the regression model is statistically significant.
The results indicate that the significance value (Sig.) of the t-test is less than 0.05, demonstrating that Member Trust has a statistically significant impact on Community Trust.
As the Beta index of H6 is 0,580 > 0, we can conclude that Member Trust has a positive effect on Community Trust.
In conclusion, since all the indexes are satisfied, we accept the hypothesis that Member Trust positively affects Community Trust.
4.4.7 Community Trust’s impact on Flow
Table 4.11 H7 result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient
The analysis results, as shown in the table, indicate an Adjusted R-squared value of 0.314, meaning that the independent variables in the regression analysis explain 31.4% of the variation in the dependent variable The remaining 68.6% of the variation is attributed to out-of-model variables and random errors The Adjusted R-squared is favored for its ability to provide a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is less than 0,05 Therefore, the regression model is statistically significant.
The t-test results indicate that the significance value (Sig.) is below 0.05, demonstrating that Community Trust has a statistically significant impact on Flow.
As the Beta index of H7 is 0,562 > 0, we can conclude that Community Trust has a positive effect on Flow.
In conclusion, since all the index are satisfied, we accept the hypothesis that Community Trust positively affects Flow.
4.4.8 Member Trust’s impact on Customer Engagement
Table 4.12 H8 result of Regression analysis
ANOVA ANOV R Adjusted Coefficient Coefficient B t
The model summary analysis reveals an Adjusted R-squared value of 0.391, indicating that the independent variables in the regression analysis explain 39.1% of the variation in the dependent variable The remaining 60.9% is attributed to variables not included in the model and random errors The Adjusted R-squared is favored for its ability to provide a more accurate representation of the model's fit compared to the standard R-squared coefficient.
Based on the ANOVA (Sig.), we obtain the outcome that the Sig value of the F- test is less than 0,05 Therefore, the regression model is statistically significant.
Result and discussion
Relationship Hypothesis Beta Sig VIF Conclusion
SPW —MT Hl The Social Presence of Web positively affects Member Trust 0,408 pCT H5 Social Support positively affects
MT >CT H6 Member Trust has a positive influence on Community Trust 0,580 p 0,35 From there, it is concluded that the impact level between the above two variables is large.
Table 4.18 The results of Q2 (PLS Predict)
The Q2 results for Q2, analyzed using SMART PLS, indicate that the component models of the dependent variables exhibit an average predictive accuracy, with values ranging from 0.25 to 0.5 Specifically, the predictive accuracy for CE is 0.339.