Introduction
A brand is commonly known as a name and a symbol but it is a critical means for organizations to differentiate with rivals and help create positive images on customers
Managers identify the brand name as a crucial intangible asset that fosters a strong, positive identity in customers' minds (Aaker, 2009) Maintaining brand popularity and loyalty not only boosts sales revenue and market share but also drives profitability and supports business growth or survival amid intense market competition (Erdoğmuş).
Stimulating consumer interest and building and promoting the brand are complex strategic processes that require careful planning and execution by marketers To achieve this, many utilize a variety of methods to establish strong customer relationships and influence purchase intentions, including traditional advertising and innovative digital marketing approaches These modern tactics encompass social media campaigns, events, and sponsorships, helping brands effectively connect with their target audiences.
The evolution of social media has significantly transformed the online commerce landscape for both companies and consumers, especially through platforms like Facebook, Twitter, and YouTube, which have expanded their reach and enabled millions of users worldwide to build personal and professional relationships Notably, Facebook alone had 1.45 billion active daily users and 2.2 billion active monthly users in March 2018, highlighting its enormous influence Engagement on social media has become a crucial component of modern marketing strategies, allowing consumers to easily view, interact with posts, and connect with companies’ websites to express reviews and receive instant solutions The more consumers connect with a brand on social media, the higher the likelihood of repeat purchases, making social media an effective marketing channel that enables businesses to bypass traditional media and foster direct relationships with their audience (Holt, 2016) Additionally, social media provides excellent communication platforms that strengthen brand loyalty and customer engagement.
Companies that leverage social media to share compelling stories and engage with customers in real time can establish their brands as central hubs for consumer interaction within online social communities This active engagement fosters meaningful consumer interest online, strengthening brand loyalty and visibility (Holt, 2016).
In contrast, Zhu and Chen (2015) show a Gallup survey that advertisements in social media do not motivate the majority of consumers in USA to make buying decisions
Gallup warns that social media is not a highly effective marketing tool, as consumers often tune out brand content on platforms like Facebook and Twitter Users tend to ignore or dismiss advertisements by shifting their attention to other topics, reducing the impact of social media campaigns Furthermore, Fournier and Avery (2011) emphasize that social media primarily serves to foster personal connections rather than function as a new channel for branding communications.
Despite the rapid adoption of social media and its potential to create powerful new social networks, there is a notable lack of empirical research exploring how marketers leverage these platforms effectively for brand building According to Hsu and Tsou (2011), more studies are needed to understand these dynamics fully Existing research, such as Laroche et al (2013), primarily focuses on defining social media, outlining its characteristics, and discussing its advantages and disadvantages for marketing, often providing strategic recommendations for businesses Notable studies by Edelman et al (2010) further contribute to understanding how brands can utilize social media for effective marketing campaigns.
Kietzmann et al (2011) emphasize the importance for brand owners to focus on the outcomes of social media marketing to effectively promote their products and services, as well as strengthen customer relationships through online social channels In the context of Vietnam, a transitional economy that has achieved a 53% internet penetration rate, leveraging social media marketing becomes crucial for businesses seeking to expand their reach and engagement in this rapidly evolving digital landscape.
Internet penetration rate (Statista, 2018) More interestingly, Vietnam has almost half of
95 million citizens use social media and anticipates achieving 56 million Facebook users by 2021 (Bloomberg, 2017)
The firms likely use social media to design marketing programs through trust-building mechanisms and impact consumers’ intent to purchase online (M N Hajli, 2014)
Many Vietnamese companies have not yet recognized the critical role of branding and brand promotion programs, with limited research on branding conducted within Vietnam (Nguyen et al., 2011; Nguyen, 2003) Understanding the influence of social factors on trust is essential, as it can significantly enhance customers’ purchase intentions By leveraging these insights, marketers can develop effective marketing strategies and create engaging advertising campaigns that facilitate proactive and meaningful interactions with target consumers through social media platforms.
Importantly, gender is considered as a crucial variable on consumer’s behavior
Research by Bendall & Powers (2002) highlights key factors influencing technology adoption Venkatesh et al (2000) reveal gender differences in decision-making processes related to personal technology adoption, noting that men’s decisions are primarily influenced by their attitudes toward technology, whereas women base their choices on subjective norms and perceived behavioral control Additionally, younger users aged 18 to 34 frequently use social media for information exchange with friends and family more than older age groups, demonstrating age-related trends in social media usage.
In 2013, studies indicate that consumers in this age group tend to consider others’ experiences and feedback regarding products and services from specific brands While numerous research papers examine how demographic factors influence consumer behavior, the relationship remains unclear due to conflicting findings (Mai and Zhao, 2004; Mitchell and Walsh, 2004) To effectively develop marketing strategies in emerging markets, it is crucial to investigate how key demographic variables such as age and gender moderate consumer preferences and behaviors.
This research emphasizes the pivotal role of social media in differentiating a company's products, services, and values through effective marketing strategies It focuses on examining how social factors influence trust, which subsequently impacts consumers' buying intentions Understanding these dynamics can reveal the key elements that build trust and foster social commerce on social networking sites (SNSs) The study aims to achieve five specific objectives: to analyze how social media motivates trust in electronic commerce, to assess the moderating effects of age and gender on this impact, to explore the relationship between trust and consumers’ purchase intentions, to examine how trust and perceived usefulness influence buying decisions, and to evaluate both the direct and indirect effects of social media on trust and purchase intent.
This study investigates whether social media on social networking sites influences consumers’ trust and examines if perceived usefulness and trust directly impact users’ purchasing intentions It seeks to determine whether perceived usefulness or trust serves as the primary decision-making factor affecting buying behavior Additionally, the research explores how age and gender may moderate the relationship between social media, trust, and purchase intention Utilizing the Technology Acceptance Model’s perceived usefulness alongside variables such as social media engagement, trust, purchase intention, age, and gender, the study develops an interdisciplinary research framework The primary objectives are to evaluate the impact of social media on purchase intention in Vietnam and to assess how demographic factors like age and gender influence this relationship.
This research examines diverse groups in Ho Chi Minh City, Vietnam's largest city, including teachers, students, office staff, household members, managers, and workers The study focuses on evaluating how social media elements influence trust among these populations It highlights that increased trust generated through social media can significantly impact consumers' buying intentions The findings aim to understand the role of social media in shaping consumer behavior and driving purchasing decisions within the vibrant urban context of Ho Chi Minh City.
Comprehending of this issue can benefit in recognizing features that inaugurate trust and social commerce intent through social networking sites
This research validates that trust and perceived usefulness significantly influence consumers' buying intentions within social commerce environments It explores how trust can become a major challenge when utilizing social media networking sites, highlighting the importance of trust in electronic markets The study emphasizes the critical role of social media in shaping consumer trust and its overall impact on consumer behavior in digital marketplaces.
This article is structured into several key sections to provide a comprehensive understanding of the research The Introduction outlines the research background, problem statement, objectives, questions, contributions, and identifies the existing research gap The Literature Review and Hypotheses section examines previous studies on social media, trust, purchase intentions, and the influence of age and gender, leading to the development of a research model The Research Methodology section details the scale development, sample selection, data collection methods, and the statistical analysis techniques employed The subsequent section presents the data analysis results, offering insights into the study’s findings.
Literature review and hypothesis
Technology acceptance model (TAM) and theory of planned behavior (TPB)
The Technology Acceptance Model (TAM), developed by Davis et al (1989), is primarily designed to understand and predict user acceptance of information technology Grounded in established theoretical frameworks, TAM has become a foundational model in technology adoption research Numerous empirical studies, such as those by Lucas Jr and Spitler (1999), have validated its effectiveness and robustness in diverse contexts.
The Technology Acceptance Model (TAM) highlights two key constructs—perceived usefulness and perceived ease of use—which are essential factors influencing users' acceptance of new technologies These elements are widely recognized as vital determinants that determine whether users will adopt and embrace technological innovations (Adam et al., 1992) Understanding these constructs can significantly enhance the successful implementation and acceptance of technology in various settings.
The theory of planned behavior has been applied and validated in a greater number of researches (Chang, 1998) as this is an extension of the theory of reasoned action (TRA)
The TRA shows that behavior intention or purchase intent in this study is an antecedent to forecast actual volitional behavior (purchase completion) (Razak & Marimuthu, 2012)
Consumers often perceive higher risks when shopping online compared to offline experiences To mitigate these concerns, customers tend to exchange and verify information with experienced consumers to obtain trusted normative recommendations This behavior helps build confidence and reduces uncertainty in the online shopping process.
Moreover, recent research comes up that consumers perceive obstacles and difficulties
In online shopping behavior, perceived behavioral control (PBC) significantly influences consumers' intentions and actions Shoppers rely on cognitive sources to form beliefs about product attributes, which shape their overall attitude towards purchasing decisions (Rossiter & Percy, 1987) This process leads to a matured understanding and confidence in making online purchases, ultimately impacting their shopping behavior.
Social media and trust
As social media rapidly develop and diversify across multiple platforms, there is currently no official consensus on what defines social media Typically, social media encompasses activities, practices, and behaviors within online communities where people gather to share information, knowledge, and opinions through conversational media Understanding this evolving landscape is essential for effective digital marketing and online engagement strategies.
Social media are internet-based applications built on the frameworks of Web 2.0, enabling consumers to create, generate, and exchange content According to Kaplan and Haenlein (2010), these platforms facilitate interactive communication and user-generated content, revolutionizing how individuals share information online.
Economists and TIME magazine highlight that Web 2.0 has transformed individual and group behavior, shifting market power from manufacturers to consumers Today’s online users benefit from unprecedented access to vast information and unlimited choices, enabling them to make more informed decisions with just a click of a button This digital evolution fundamentally alters marketplace dynamics by empowering consumers and redistributing influence within the industry.
Constantinides and Fountain (2008) Actually, there is no official classification of
Internet-based applications types of social media Nevertheless, Constantinides and
Fountain (2008) classifies social media into five categories: blogs, social network sites (such as Facebook, Twitter, MySpace, and Google+), content communities (like YouTube and Wikipedia), e-forums, and content aggregators Among these, social network sites are currently the most widely used social media applications These platforms enable users to share information, recommend products or services, and review or rate offerings online with their peers (Mangold & Faulds, 2009).
Social media offers a powerful opportunity for brands to move customers from awareness to engagement, enhance brand recognition, consideration, and foster loyalty and advocacy (Gunelius, 2010) Unlike traditional push marketing tactics that require brands to actively push information, social media enables pull marketing, allowing customers to proactively engage with products and services and seek additional information that drives business growth Additionally, Gillin highlights that in the social media era, a single dissatisfied customer can now reach millions instantly, amplifying the importance of customer satisfaction and brand reputation.
According to Gillin (2009), integrating social media into marketing strategies is essential for creating engaging consumer experiences By leveraging social media platforms to share compelling stories and interact with customers in real-time, companies can position their brands as central hubs within active online communities This approach not only fosters meaningful consumer interest but also enhances brand loyalty and visibility in the digital landscape.
Implementing social media strategies can present several challenges, including difficulties in reaching target audiences, lack of control over content, and issues with accurately allocating company resources, which may lead to trust concerns Additionally, experts warn that brands should exercise caution with social media involvement, as they risk being perceived as "uninvited crashers," potentially damaging their reputation.
It is implying that create brand relationship via social media is more complicated than stimulate more interactions
Social media marketing fundamentally differs from traditional marketing methods by opening new opportunities and presenting unique challenges In today’s digital landscape, customers seek more authentic and direct interactions with brands To meet this demand, brand managers are emphasizing transparency and honesty to build stronger, more meaningful relationships with consumers.
Nowadays, there is a trendy that various well-known companies for instance Pepsi,
Unilever and other brands engage consumers by offering genuine opportunities to participate in their advertising campaigns, fostering meaningful interactions These interactions build emotional connections, strengthening brand loyalty and trust By involving customers directly, companies enhance commitment and create lasting relationships that benefit both the brand and consumers.
(Sashi, 2012) Interestingly, the essence is that online users control social media using, individuals can use these media with little or without cost at great convenience
Social media enables communication and collaboration beyond geographical barriers, allowing firms to connect with customers more effectively By leveraging social networks, businesses can significantly reduce communication costs while maintaining strong customer engagement Additionally, companies can generate widespread, cost-effective online content to enhance brand presence and visibility in the digital landscape.
In today's digital landscape, social media marketing plays a crucial role in shaping a company's strategic efforts, prompting managers to prioritize tracking key performance indicators (KPIs) to evaluate project success Among these KPIs, trust stands out as a vital measure, especially as online transactions and e-commerce continue to grow rapidly Consequently, online trust has garnered significant attention from researchers, highlighting its importance in fostering customer confidence and driving business growth (Hallikainen & Laukkanen, 2018).
Trust is essential for maintaining and developing long-term relationships, especially in the online environment where technological uncertainties pose significant challenges (Pavlou, 2003) Extensive research has focused on understanding how trust influences individuals' intentions to use online applications for purchasing purposes (Gefen et al., 2003; Hoffman et al., 1999; Kim, 2012; McKnight).
Social commerce exemplifies how consumers often rely on recommendations from online communities and trusted experienced members, who provide credible advice based on their purchasing experiences This trust-driven dynamic influences purchasing decisions and highlights the importance of community influence in online shopping.
Trust is defined is an attitude of confident expectation in an online environment that risk of individual’s vulnerabilities shall not be exploited (Corritoreet al., 2003)
Lack of trust is a significant barrier to the adoption of electronic commerce, as trust is essential for establishing a secure and reliable online environment (Chang et al., 2013) Without trust, creating an interactive and operational online platform becomes nearly impossible (Corritore et al., 2003) Additionally, trust serves as a crucial factor in encouraging the adoption of information and communication technologies across different boundaries, facilitating broader digital integration (Kirs and Bagchi, 2012).
Therefore, one effective method that brands can influence their consumers and make trust is to give the sufficient knowledge of the brand and products (Chiu et al, 2010)
Social media provides rich communication opportunities for consumers, allowing them to share their experiences with favorite products and brands It enables companies to build and maintain strong customer relationships by engaging directly with their target audience Consumers can proactively discuss and exchange feedback about brands, fostering a dynamic and interactive community This connectivity enhances brand loyalty and trust, making social media a vital tool for marketing and customer engagement.
(Habibi, 2014) The networks of individuals through social media lead to positive impact on trust (Wu et al, 2010) Thus, it is hypothesized that:
H1: There is a positive relationship between social media marketing and trust
Age and gender
Understanding gender differences in shopping behaviors is essential for retailers to develop effective marketing strategies (Chou et al., 2015) Gender remains a crucial segmentation variable in sales and marketing, influencing consumer responses and preferences (Kuruvilla et al., 2009) For example, females tend to respond more positively to Facebook advertisements, demonstrating more likable attitudes toward such campaigns (Alalwan et al., 2017) By analyzing male and female consumer behaviors separately, marketers can tailor their approaches to enhance brand performance and achieve better engagement (Helgesen & Nesset, 2010).
The differences between males and females in terms of their thoughts, emotions, and emotional needs have been extensively studied across various disciplines, including neurology, physiology, and psychology (Khan & Rahman, 2016).
Research indicates that gender plays a significant role as a moderating variable in technology usage (Venkatesh & Morris, 2000), but studies on social networking sites reveal limited gender differences in manipulation While teenagers of all genders now utilize the Internet equally, their usage patterns differ—boys focus more on features and entertainment, whereas girls prioritize relational aspects of social media (Joiner et al., 2005) Girls are more likely than boys to discuss romantic relationships, secrets, and deep feelings online, and they tend to engage more frequently in conversations with peers about personal topics (Rainie, 2003) Additionally, recent research shows that girls mainly use SNSs to stay connected with existing friends, while boys often use these platforms to make new friends (Barker, 2009), highlighting distinct social motivations based on gender.
In addition, Barker emphasizes the result in his research that females are more probably to show high positive collective self-confident, higher overall usage, and utilize
Social networking sites (SNSs) are primarily used for exchanging information and connecting with friends Females tend to post more content related to group identity and entertainment Negative collective self-esteem is linked to social compensation, where individuals who feel negatively about their social group use SNSs as an alternative means of communication Males are more likely than females to comment on negative aspects of collective self-esteem and utilize SNSs for social compensation, which involves contributing to group efforts to receive recognition or remittance, especially when group performance is poor Additionally, male users often use SNSs to fulfill social identity needs and seek validation through group participation.
Internet for diversion, recreation and functional intents, while female users are more probably to browse Internet for facilitating communication and interacting with friends
(Weiser, 2000) It recommends that concerning attitude toward social networking advertising, perceived informativeness and pursuit value of advertising have more profound impacts on men’s attitudes than women’s (Taylor et al , 2011)
Research suggests that women are more engaged with the details of specific message content compared to men, often demonstrating heightened sensitivity to relevant information when forming judgments (Meyers-Levy & Maheswaran, 1991) This increased attentiveness can influence how women process and respond to information in various contexts.
Herter et al (2014) (Maurer Herter et al., 2014) highlight the differences in the shopping behavior of women and men customers According to Buttle (1992) (Buttle,
Shopping has traditionally been regarded as a typical feminine hobby, with women being more dynamic and likely to purchase goods online than men, according to Chou et al (2015) However, Buttle (1992) notes that this trend is gradually decreasing as males become increasingly engaged in shopping activities.
Facebook and Twitter are selected by women as the primary information source to obtain the purchasing behavior for such a group of females Therefore, it is hypothesized that:
H2a: Relationship between social media and trust will be affected by age
Regarding age factor, the generations are categorized by selecting the birth dates for every cohort as below: the Silent Generation whose birth dates from 1925 to 1945, the
Baby Boomers was born from 1946 to 1960, Generation X is the generation that people were born from 1961 to 1981 while Generation Y “born after 1981” (Brosdahl &
Research by Carpenter (2011) highlights that aging often correlates with a negative perception of technology, with older adults tending to express more negative viewpoints and show reluctance to adopt new technologies Previous studies, such as those by Gilly and Zeithaml (1985), confirm that older individuals generally demonstrate disinclination toward implementing technological innovations Additionally, Madden and Savage (2000) found that Internet usage is negatively associated with age, indicating that younger populations are more likely to engage with online technologies compared to older adults.
Generation Y is frequently exposed to international technology from an early age, which influences their cognition, emotions, and social behaviors, presenting both advantages and challenges (Brosdahl & Carpenter, 2011; Immordino-Yang et al., 2012) They commonly use technology and social media for entertainment, social interaction, and building connections with others Additionally, Generation Y consumers tend to have strong educational backgrounds and are more attentive to marketing strategies compared to previous generations who did not grow up with the internet (Wolburg & Pokrywczynski, 2001; Lazarevic, 2012) They actively research and share information quickly within their peer groups, demonstrating their digital proficiency and social connectivity.
Generation Y has been socialized to believe in a materialistic society, leading to a more consumptive orientation compared to previous generations (Bakewell & Mitchell, 2003; O’Donnell, 2006) Young adults aged 19 to 24 exhibit specific attitudes toward social media advertising, actively supporting, exchanging, and engaging with content on social platforms (Durlak et al., 2011) Their proactive social media usage makes them a key demographic for understanding future consumer trends, attracting the attention of both researchers and practitioners (Bolton et al., 2013) This generation adopts distinct shopping behaviors and styles compared to earlier cohorts, highlighting the importance of consistent and authentic marketing strategies; otherwise, they may perceive inconsistent branding or a lack of genuine identity as fake, diminishing brand recognition and trust (Bakewell & Mitchell, 2003).
(Merrill, 1999) Thus, it is proposed that:
Effect of trust on intention to buy and perceived usefulness
tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Trust is defined as "a willingness to rely on an exchange partner in whom one has confidence" (Moorman et al., 1993), and it is built through perceptions of a brand’s reliability and honesty during customer interactions When customers perceive a brand as responsible for their interests and welfare, they experience a high sense of security and trustworthiness (Delgado-Ballester et al., 2003; McCole et al., 2010) This concept of trust is especially relevant in online transactions, social networking sites (SNSs), and Internet-based applications, where trust significantly influences user behavior Experts agree that trust plays a critical role in decision-making, particularly when individuals face risky situations or are unable to predict others' actions (Shiau and).
Luo, 2012), particularly trust is an essence in electronic commerce development (M N
In the B2C e-commerce model, trust in the electronic vendor is crucial for risk assessment during every stage of the buying or selling process Additionally, trust plays a vital role in strengthening the relationship between retailers and consumers in an online environment, fostering customer loyalty and confidence (Hajli, 2014; Gefen, 2000).
Positive customer comments and feedback significantly enhance a brand's trustworthiness, leading to increased consumer confidence (Ba & Pavlou, 2002) When reputable users recommend vendors through positive ratings, other online shoppers tend to develop higher trust in the e-commerce platform (Lu & Pavlou, 2010) According to the theory of reasoned action, trust fosters positive attitudes toward web retailers, which can help alleviate fears of seller opportunism and ease concerns about online transaction security (Pavlou, 2003).
Trust is the foundation of effective branding, essential across all industries for long-term business success It positively influences business relationships and the overall quality of social and commercial interactions (Gefen, 2000) Without trust, brands struggle to grow, as consumers prioritize trustworthiness when investing their time, money, and resources In e-commerce, trust is especially crucial due to the absence of physical sellers and the uncertainties of the online environment, making it vital for attracting and retaining customers (Shiau & Luo, 2012) Building and maintaining trust with customers is essential for brands to thrive in today’s highly competitive global market.
Building emotional trust is crucial for companies, as demonstrating a focus on customer expectations fosters confidence in the brand’s reliability and integrity (Morgan & Rego, 2006) Sustainable growth depends on retaining current customers and cultivating their loyalty, encouraging repeat purchases and long-term commitment (Dekimpe et al., 1997) Trust in commercial transactions and connection systems significantly influences online consumers, increasing their purchase intentions and overall satisfaction (McCole et al., 2010; Shin, 2010).
Consumer trust in a vendor significantly reduces perceived risks associated with online shopping, fostering greater confidence in e-commerce transactions (Van der Heijden et al., 2003) Building trust between buyers and sellers is essential for enhancing customer satisfaction and encouraging repeat purchases, ultimately driving online sales growth.
Importantly, it has been accepted that trust has a vital role in boosting intention to buy
(Lu et al., 2010; Shin, 2010) Getting confidence and having less perceived risk are crucial elements when finding new products and services in an online environment
Previous research confirms a strong, definitive link between trust and perceived usefulness, with trust significantly enhancing the elements that contribute to perceived usefulness (Gefen et al., 2003) Building on this evidence, this study hypothesizes that increased trust will directly improve perceived usefulness, underscoring the importance of fostering trust to enhance user perceptions of usefulness in digital platforms.
H3: Individuals’ trust in social networking sites positively influence intention to buy H4: Trust influences profoundly to perceived usefulness.
Perceived usefulness and intention to buy
Perceived usefulness (PU) is a key factor in the Technology Acceptance Model (TAM), driving users' acceptance of new technology According to Davis (1989), perceived usefulness refers to a person's belief that using a specific system will enhance their job performance This perception significantly influences users' motivation and willingness to adopt advanced technology, highlighting its importance in technology adoption research.
In electronic commerce, perceived usefulness (PU) is influenced by factors such as website quality, including a well-built system, excellent customer service, and effective information sharing A high level of perceived usefulness enhances customers' confidence and motivation to make a purchase, highlighting the importance of optimizing these aspects to drive sales Ensuring a seamless and reliable online shopping experience directly contributes to increased perceived value, which can significantly stimulate consumer buying behavior.
The intention to buy is a core component of the Technology Acceptance Model (TAM), which is one of the most influential theories for understanding individuals' willingness to use new systems (Pavlou, 2003) TAM plays a crucial role in e-commerce research by explaining how perceived usefulness and ease of use influence consumers' purchase intentions Understanding these factors is essential for businesses aiming to enhance user engagement and boost online sales.
Many researchers, including Martins et al (2014), as well as scholars like Hsiao and Yang (2011), have extensively studied the Technology Acceptance Model (TAM) In this context, this study defines the intention to buy as a customer's willingness to engage in online purchasing through social networking sites, highlighting the growing importance of social commerce in digital consumer behavior.
Research indicates that perceived usefulness significantly influences consumers' intention to use electronic commerce platforms (Gefen & Straub, 2000) Additionally, perceived usefulness plays a crucial role in predicting both the intention to use IT applications and actual usage behavior (Nguyen, 2007) To boost perceived usefulness, companies should focus on enhancing social media interactions, as well as improving service and system quality, thereby increasing customer engagement and satisfaction (M N Hajli, 2014).
(2009) indicates in his study that the more customers perceive to be helpful when purchasing online via SNSs, the more purchase intention they have in SNSs
Accordingly, the hypothesis is offered as following:
H5: Perceived usefulness relates positively to intention to buy
Hypotheses
This study proposes five hypotheses aimed at empirically examining the impact of social media on consumers Additionally, it assesses the moderating effects of demographic factors such as age and gender on this relationship The research seeks to provide valuable insights into how social media influences consumer behavior across different demographic groups.
H1: There is a positive relationship between social media marketing and trust H2a: Relationship between social media and trust will be affected by age
The relationship between social media and trust is significantly influenced by gender, with individuals' trust in social networking sites (SNSs) playing a crucial role in shaping their online behaviors Higher levels of trust in SNSs positively impact users' intention to make purchases through these platforms, highlighting the importance of trust-building strategies for businesses aiming to enhance consumer engagement and sales on social media.
H4: Trust influences profoundly to perceived usefulness
H5: Perceived usefulness relates positively to intention to buy
According to these hypotheses above, the research model used in this study is presented in this figure:
Research methodology
Research procedure
In order to create a design for research, researchers carefully pondered the kind of model and measurement that were suited to the research subject The research was conducted in
Ho Chi Minh City is the prime business hub of Vietnam and serves as an influential opinion leader for the nation This study employs a mixed-method approach, combining both qualitative and quantitative research methods The research procedures are outlined in the following chart, ensuring a comprehensive understanding of the city's economic dynamics and leadership influence.
Main survey and data analysis:
Two phases of survey were undertaken in this study: a pilot study and a main survey to consolidate data for validating the proposed model
Multi regression analysis Conclusions tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Based on prior research, the study examines the impact of social media on consumer trust and perceived usefulness, which influence purchasing intent The research also explores the relationships between these factors and their antecedents, leading to the formulation of five hypotheses Following this, the model was refined, and a preliminary scale was selected to develop the questionnaire for the study, ensuring alignment with established literature and SEO best practices.
The preliminary survey aimed to validate that the measurement scale reflects the customer perspective, ensuring clarity and appropriateness for the study It helped identify and correct mistakes, thereby enhancing the scale’s reliability and validity Additionally, the survey generated new ideas and items to improve the research model and measurement tools Participants from diverse ages and careers were included to ensure the generalizability of results and validate the proposed model Following revisions based on preliminary findings, the final official survey was conducted to gather comprehensive data aligned with the research’s goal of understanding online user perspectives.
The study utilized both online and paper questionnaires to gather data, primarily involving residents of Ho Chi Minh City, who made up 90% of participants Additionally, the research included five international respondents from England and Sweden, as well as online users from the Mekong Delta region This diverse participant pool enhances the reliability and global relevance of the study findings.
A qualitative pilot study was conducted by distributing a survey link via Facebook to collect responses from individuals, complemented by in-depth interviews with 10 respondents to validate the measurement constructs The insights gained from the pilot survey were used to refine and improve the survey instrument, ensuring a more objective and comprehensive main study Additionally, the final questionnaires were reviewed and discussed with academic experts, including Dr Doan Anh Tuan and Dr Nguyen Phong Nguyen from the university, to enhance their validity and reliability.
Following a brief 12-hour Facebook survey campaign on economics in Ho Chi Minh City, 20 respondents provided valuable feedback due to the engaging topic, and the questionnaires were clearly understandable, enabling all participants to respond easily In total, 30 respondents completed the survey, and during the pilot study, their feedback contributed to significant improvements, leading to the addition of a new item, Intention3, to the final intention-to-buy variable (see Appendix 1), after ceasing further posting on Facebook.
After conducting qualitative research, the questionnaire was revised to better suit the Vietnamese environment, ensuring improved accuracy and clarity Subsequently, the main survey was widely conducted using a convenience sampling method to gather comprehensive data The research process followed a systematic approach, adhering to predefined steps to ensure reliability and validity of the results.
Step 1: Composing final questionnaire for the research: tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The questionnaires were prepared by bilingual language including English and
Vietnamese researchers ensure that respondents fully understand all questionnaire terms by providing clear instructions The instructions emphasize that the study prioritizes respondents' individual opinions to minimize response bias The first section of the questionnaire gathers demographic information, including age and gender (see Appendix 1).
Step 2: Defining the sample size of the research:
Designing a targeted sample is crucial for obtaining accurate research findings, emphasizing the importance of defining the appropriate sample size for surveys (Sekaran & Bougie, 2016) The sample size was determined based on the sample estimation method, with careful consideration of how it impacts statistical analysis According to Hair et al (2010), selecting an optimal sample size is essential, as too small a sample can undermine the validity of statistical tests, while an excessively large sample may lead to overly sensitive results.
Therefore, the sample size should be preferably 100 or greater Moreover, Hair et al suggested a general rule that the minimum sample is at least five times per each scale
The research model in this study consisted of 6 factors with 15 scales so that the minimum sample size should be at least 100 observations
According to Tabachnick and Fidell (2007), the recommended minimum sample size for standard multiple regression analysis is N > 50 + 8m, where N represents the sample size and m is the number of independent variables In this study, with two independent variables, the minimum sample size required is 66 observations (N > 50 + 8×2) Ensuring an adequate sample size is essential for reliable regression analysis results and valid conclusions.
Step 3: The questionnaire was issued to the interviewees
The author created an online questionnaire using Google Docs and actively invited participation via email, Zalo, Facebook, and other social networking platforms like LinkedIn To ensure the validity of the sample, the researcher took steps to verify the legitimacy of respondents and prevent sampling bias.
Google link directly to friends via Zalo, Facebook and colleagues, customers by email
The interviewer distributed questionnaires through various methods, providing clear instructions on how to complete them and addressing any questions from respondents Mail and online surveys were identified as the most cost-effective options for data collection This convenient approach facilitated efficient participation, enabling respondents to easily complete and return the questionnaires.
Internet broadcasting allows users to quickly complete surveys by clicking links, selecting their answers, and submitting responses effortlessly, thus maintaining privacy effectively (Mangione, 1995) The data collection process was efficiently conducted over a three-week period, ensuring timely and secure data collection.
Totally there were about 400 questionnaires sent from online channel both from email, networking site posts and messages returned more 223 respondents Thus, this study used
223 samples; this sample size was fitting for EFA and multiple regression analysis
Respondents ranged from 20 to 57 years old because the study directed mainly to investigate the impact of social media on intention to buy with demographic is age and gender
Step 4: The author received the questionnaire and checked again for suitable result
The researcher collected a total of 253 responses but identified 30 missing answers through frequency analysis, with specific omissions in Intention 3 (pilot study data from 30 interviewees) and Perceived Usefulness items PU2 and PU3 After removing all incomplete data, the final sample included 215 observations, which met the minimum requirements for multiple regression analysis The collected data was further refined using confirmatory factor analysis (CFA) to improve measurement validity and evaluated through structural equation modeling (SEM) to assess the structural relationships within the model. -Streamline your research insights with Wren AI's GenBI platform and AI-powered spreadsheets—perfect for refining data like your CFA and SEM analysis [Learn more](https://pollinations.ai/redirect/397623)
Measurement of the constructs
This study utilized a five-point Likert scale to assess all questions, ranging from (1) “Strongly Disagree” to (5) “Strongly Agree,” ensuring accurate measurement of participant responses The questions were carefully adapted from prior research to enhance the study's validity The research focused on four key constructs: social media, trust, perceived usefulness, and purchase intention Social media was specifically examined through three questions adapted from M N Hajli (2014), aligning with established frameworks in social commerce research.
SM1 I use online forums and communities for acquiring information about a product
SM2 I usually use people ratings and reviews about products on the internet
SM3 I usually use people’s recommendations to buy a product on the internet
I trust my friends and community members on online forums for advice and support Engaging with reputable online communities helps me gain valuable insights and build trustworthy relationships Connecting with like-minded individuals through forums enhances my knowledge and confidence in various topics.
Trust and perceived usefulness also adapted from Hajli (M N Hajli, 2014)
Trust1 Promises made by my favorite social networking sites are likely to be reliable
Trust2 I do not doubt the honesty of my favorite social networking site
Trust3 Based on my experience with my favorite social networking site, I know it is honest
Trust4 Based on my experience with my favorite social networking site, I know they care about
PU1 Searching and buying on my favorite social networking site is useful for me
PU2 Searching and buying on my favorite social networking site makes my life easier
PU3 The websites of my favorite social networking sites enable me to search and buy materials faster
Intention to buy also adapted from Hajli (M N Hajli, 2014) and (N Hajli, 2015)
Intention to buy (Intention) Content
Intention1 I am very likely to provide the online vendor with the information it needs to better serve my needs through my favorite social networking site
Intention2 I am happy to use my credit card to purchase from an online vendor through my favorite social networking site
Many users are willing to pay for memberships if social media platforms start charging fees, reflecting a growing expectation for premium features and exclusive content This shift indicates that users see value in investing financially to access enhanced experiences on SNSs As platforms explore monetization strategies, offering paid memberships could meet user demand while supporting ongoing platform development.
Data analysis and method
Following previous steps in collecting data activities, all the available data would be keyed into the SPSS (Statistical Package for Social Science) software version 23.0 and
AMOS software version 24 to analyze data
In order to evaluate the reliability of scales, this study used Exploratory Factor
Analysis (EFA), Cronbach’s Alpha and Confirmatory Factor Analysis (CFA) as this method helps improve internal consistency Continuously, writer used Structural
Equation Modeling (SEM) analysis to test research hypotheses and Multiple Regression to evaluate the relationship between independent variables and dependent variables
Finally, author used Multiple-Group Analysis to test whether the effect of age and gender
(moderating variables) had significant impact on the influence of the dependent variables towards the independent variable
Alpha is a fundamental concept in evaluating assessments and questionnaires, essential for testing the validity and reliability of interpretation data Academic researchers and practitioners are compelled to use this method to ensure the accuracy of their measurement instruments Validity and reliability are two critical elements in the evaluation of measurement tools, highlighting the importance of Alpha in establishing consistent and meaningful results (Tavakol & Dennick, 2011).
There were different studies about the acceptance level of alpha values, ranging from
0.70 to 0.95 A low value of alpha could be due to a low number of questions, low inter- relation between items or heterogeneous constructs If alpha value was too great it probably showed that some items were dispensable A maximum alpha value of 0.90 was tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg recommended and the acceptable value of Cronbach’s alpha for reliability was above 0.7
(N Leech et al, 2013; Tavakol & Dennick, 2011)
The Corrected Item-Total Correlation is a crucial factor in evaluating survey items According to Leech et al (2005), an item with a correlation of 0.40 or higher is likely to be well-related to other items, making it a valuable component of the summated rating scale Conversely, items with a negative or very low correlation (less than 0.30) should be carefully examined for wording issues or conceptual misalignment, and may need to be modified or removed to improve scale consistency and validity.
Exploratory Factor Analysis (EFA) is based on a testable model and can be evaluated for its fit to the hypothesized population model, with indices designed to aid in model interpretation (Norris & Lecavalier, 2010) Additionally, EFA is commonly used to identify which items cluster together or are most similarly responded to by respondents, helping to reveal underlying dimensions within a large set of data (N L Leech et al., 2014).
This research used EFA to test below requirements:
- KMO value should be from 0.5 to 1 to prove that factor analysis was sufficient (Hoang and Chu, 2005)
In factor analysis, rotated factor loadings should generally be greater than 0.5 to be considered practically significant With a sample size of around 100, a factor loading of at least 0.55 is recommended, while very high loadings (0.80 and above) are uncommon The practical significance of these loadings is a key criterion in evaluating the results (Hair, 2010).
Hair also showed the guideline as table below:
Table 1: Guidelines to identify significant factor loadings based on sample size
Factor loadings Sample size needed for significance
- Eigenvalue for extracted factors must be larger than 1 which is a popular criteria for a factor to be significant and regarded (N L Leech et al., 2014)
The purpose of multiple regression analysis was to analyze the effects in the dependent variable in response to the independent variables changes as well as test the hypotheses
In addition, multiple regressions also resourced the researcher a means to access the nature of the relationship between independent variables and dependent variable
Interestingly, multiple regression offered an insight into the relationships among independent variables in their prediction of the dependent measurement (Hair, 2010)
The multiple regression analysis requires many assumptions but it is better to focus on the major ones that are tested easily with SPSS The assumptions include:
1 A linear relationship between each of the predictor variables and the dependent variables tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
2 Residual which was the difference between the observed and predicted values for the dependent variable or the error was normally distributed
3 No multicollinearity It could be checked by VIF or Tolerance value According to
Hair (2010) the suggested cutoff for the tolerance value was 10 (or equivalent to VIF of
10.0), which corresponds to a multiple correlation of 95 with the other independent variables
Finally, according to (Hair, 2010) the model would demonstrate the goodness of fit across different model situation if the scales matched these criteria
Table 2: Characteristics of different fit indices demonstrating goodness-of-fit across different model situations
12 < m < 30 m ≥ 30 χ2 “Significant p-values even with good fit” “Significant p-values expected”
SRMR “.08 or less (with CFI of 95 or higher)”
“Less than 09 (with CFI above 92) CFI above 92)”
RMSEA “Values < 08 with CFI of 95 or higher” “Values < 08 with CFI above 92”
Note: “m = number of observed variables; N applied to number of observations for each group when testing CFA to multiple groups at the same time”
Data analysis and results
Sample characteristic
Total 253 questionnaires were gathered from online questionnaire and direct interview
The sample comprised 143 females (56.5%) and 110 males (43.5%) In that, 38 questionnaires were uncompleted or sloppy done The majority of samples were
Generation Y from under 37 years old (were born after 1981) (65%), and the second one
Generation X (35%) With high feedback ratio from younger generation (Generation Y) and female, tt can be predicted that age and gender could influence the initial decision regarding whether to purchase on the internet
The analysis was conducted on a sample of 215 observations, reflecting an 85% feedback response rate Descriptive statistics indicate a diverse respondent pool in terms of career backgrounds and ages Of the usable samples, 61% are female, while 39% are male, highlighting gender diversity within the dataset.
Before conducting structural equation modeling (SEM) to test the hypotheses, the researcher first performed an exploratory factor analysis to purify and validate the measurement instruments, ensuring the reliability and validity of the data for accurate model testing.
(EFA) and followed reliability analysis to check Cronbach’s alpha for the scale items to ensure internal consistency All the items ran properly on their intended scale.
The reliability test
To ensure the reliability of each construct in the research, it was essential to test Cronbach’s alpha for every scale This process verified that all items within a scale consistently measured the intended research concept, thereby confirming the reliability of the measurement instruments.
As mentioned in methodology above, Cronbach alpha should be above 0.7 Moreover, correlation of each specific item with total of the other items in the scale (Corrected Item-
Total correlation should ideally exceed 0.5 to ensure strong construct validity (Hair, 2010) It is important to review the questionnaire items carefully, assessing the clarity and conceptual relevance of each question Items with low or negative total correlation (less than 0.3) should be modified or removed to improve the questionnaire’s reliability and validity Adjusting the wording and ensuring conceptual fit are essential steps for optimizing the measurement instrument.
The study demonstrated strong internal consistency, as indicated by Cronbach’s alpha values all exceeding 0.70, meeting the recommended threshold for reliability These results confirm that the research constructs possess acceptable internal consistency, ensuring the overall reliability and validity of the findings.
The overview was shown in Appendix 2.
Exploratory factor analysis (EFA)
After conducting reliability testing with Cronbach’s alpha, it was essential to evaluate the measurement scales using exploratory factor analysis (EFA) The purpose of EFA was to identify which items grouped together logically based on respondents' answers The analysis utilized Principal Axis Factoring for extraction, with Promax rotation and an eigenvalue threshold of 1, to ensure accurate identification of underlying factors Promax rotation was chosen because it more effectively recovers simple structure, especially when factors are highly correlated and the test includes numerous items, compared to Varimax This approach enhances the clarity and validity of the factor structure, providing a more reliable measurement model for the study.
The KMO measure was 0.873, indicating that there were sufficient items for each construct and that the sample was appropriate for factor analysis Additionally, the Bartlett’s test was significant (p < 0.001), demonstrating strong inter-correlations among variables, which provides a solid foundation for conducting factor analysis and enhancing the validity of the results.
Table 4: KMO and Barllett’s test Result
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .873
Bartlett's Test of Sphericity Approx Chi-Square 1624.588 df 91
Table 5: Total Variance Explained tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Initial Eigenvalues Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings a Total % of Variance Cumulative % Total % of Variance Cumulative % Total
“Extraction Method: Principal Axis Factoring a When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.”
Pattern Matrix a tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
“Extraction Method: Principal Axis Factoring
Rotation Method: Promax with Kaiser Normalization a Rotation converged in 5 iterations.”
By doing EFA (Principal Axis Factoring with Promax rotation method), the result showed that four constructs were extracted from 14 items measuring: Social media, Trust,
Perceived usefulness significantly influences consumers' intention to buy, with four key constructs accounting for 63.48% of the total variance This indicates that over two-thirds of the variance in purchase intention can be explained by these four initial factors The eigenvalues for all four factors were greater than 1, confirming their effectiveness in explaining the underlying variance These findings highlight the importance of perceived usefulness and related constructs in predicting purchasing behavior.
The rotated factor matrix highlighted the items and their factor loadings post-rotation, with loadings above 0.5 considered significant The analysis revealed that the items clustered into four distinct groups, demonstrating meaningful underlying factors This results suggest a clear structure in the data, aligning with the importance of factor loadings for reliable factor identification.
Confirmatory Factor Analysis (CFA) result
tot nghiep do wn load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The CFA results for the research model's standardized estimates indicate an acceptable fit to the data, with a Chi-square/df ratio of 120.797 (p = 000) Fit indices such as NFI (.928), RFI (.907), IFI (.969), TLI (.960), and CFI (.968) support the model's adequacy, demonstrating that the scales are suitable for the data.
The scales demonstrated satisfactory convergent and discriminant validity, confirming their effectiveness Additionally, they achieved unidimensionality as correlations among item errors were eliminated, ensuring the scales reliably measure a single construct.
Research hypotheses test
This study utilized structural equation modeling (SEM), a widely used method in social science research, to analyze the data The model demonstrated an acceptable fit with key indices: Chi-square/df = 150.587 (p = 000), NFI = 91, RFI = 887, IFI = 0.951, TLI = 0.939, and CFI = 951, indicating strong model validity The results revealed statistically significant relationships among the concepts within the model, with p-values less than 001, confirming the robustness of the theoretical framework and its alignment with the observed data.
The results showed that Social Media had a substantial contribution to Trust (β = 31)
Thus, H1 was supported and illustrated that the interactive activities of online users through social media encouraged trust in electronic commerce
Trust also gave remarkable direct impact on Perceived Usefulness (β directly = 62)
Trust directly influences the Intention to Buy, highlighting its significant role in consumer decision-making Additionally, Perceived Usefulness has a notable direct effect on purchase intention, with a beta coefficient of 41, indicating strong impact The research findings support hypotheses H1, H3, H4, and H5, demonstrating that these factors are critical in understanding consumer behavior Overall, the data confirms that both Trust and Perceived Usefulness are key determinants in driving consumers' purchase intentions.
H1: There is a positive relationship between social media marketing and trust
H3: Individuals’ trust in SNSs positively influence intention to buy H4: Trust influences profoundly to perceived usefulness
H5: Perceived usefulness relates positively to intention to buy
The study found that perceived usefulness has a stronger influence on consumers' intention to buy than trust, with influence scores of 0.41 compared to 0.31 This highlights that users are more motivated to make purchases based on how useful they perceive a social website to be.
The SEM analysis for the suggestion model highlights key factors influencing user engagement and content relevance This study provides insights into optimizing suggestions to improve user experience and increase conversion rates Implementing these SEO strategies can enhance visibility and ensure that the recommendation system effectively meets user needs By focusing on relevant keywords and user intent, the model can boost search rankings and drive more targeted traffic Overall, this research emphasizes the importance of data-driven approaches in refining suggestion algorithms for better performance.
Results of Multi-group analysis
In order to examine the moderator effects of age and genders to the relationship between
Social media and Trust variables, the multi group analysis in SEM was deployed
Testing the moderating effects of gender variable (Male and female)
This study examined gender as a moderating variable by dividing the data into two groups: Male and Female The multi-group analysis of variance revealed a significant difference between these groups, with a chi-square value of χ2 = 255.178, degrees of freedom (df) = 146, and a p-value of 0.000, indicating notable gender-based variations in the analyzed variables.
NFI = 0.858, RFI = 0.823, IFI = 0.934, TLI = 0.916, CFI = 0.932
Minimum was achieved Chi-square = 255.178 Degrees of freedom = 146 Probability level = 000
Table 8: Assuming model unconstrained to be correct - gender
Results showed that gender did not significantly influence the relationship between social media and trust, with no notable differences observed between males and females (p = 54.2%) Consequently, hypothesis H2a was not supported.
H2a: Relationship between social media and trust will be affected by age
Men predominantly focus on the perceived usefulness of a website when making purchasing decisions, highlighting the importance of user-friendly and informative sites for successful conversions.
Figure 3: Multi-group result for female
The multi-group analysis presented in Figure 4 provides comprehensive insights into the patterns observed among male participants This data highlights significant trends and relationships that are crucial for understanding the overall findings of the study The results contribute valuable information for further research and practical applications in the related field.
Testing the moderating effects of age variable
The study examined gender as a moderating variable by dividing the data into two groups: male and female The multi-group analysis of variance revealed significant differences between the genders, with results indicating χ2 = 256.793, degrees of freedom (df) = 146, and a p-value of 0.000, highlighting the impact of gender on the studied variables.
NFI = 0.859, RFI = 0.825, IFI = 0.934, TLI = 0.916, CFI = 0.933
Table 9: Assuming model unconstrained to be correct_age
The study found that aging did not significantly influence the relationship between social media and trust, with no notable differences between Generation X and Generation Y (p = 35.3%) Consequently, hypothesis H2b was not supported and was rejected This suggests that once initial barriers to e-commerce are overcome, the age of electronic shoppers has little to no impact on their online behavior.
The relationship between social media and trust is influenced significantly by gender, with research indicating that men and women perceive and respond to social media interactions differently Understanding these gender-based differences is crucial for developing effective communication strategies and fostering trust across diverse audiences Tailoring social media content to account for these variations can enhance user engagement and improve overall trust in online platforms.
Figure 5: Multi-group result for Generation X
The multi-group analysis for Generation Y provides valuable insights into their behaviors and preferences This comprehensive dataset highlights key patterns in downloading activities and online engagement among Millennials Understanding these trends is essential for creating targeted marketing strategies and optimizing digital content for this demographic Leveraging such insights can improve user experience and drive better engagement with Generation Y audiences.
Discussion and implications, limitations and directions for future research
Discussion
In this research, the moderating roles of age and gender perceptions between social media and trust are not significant in the Vietnam context
Recent surveys, such as Eurostat (2009), indicate that while gender differences in internet usage are still visible, the number of women utilizing the internet has significantly increased, leading to a decline in the gender gap globally.
Recent studies, such as Zhang (2005), have found no statistically significant differences between women and men regarding internet usage, indicating similar online behavior across genders.
Al-Somali et al (2009) analyze the use of e-banking among experienced customers and find that age does not significantly influence their attitudes Consequently, age has minimal impact on customers' e-banking behavior, highlighting the importance of other factors in shaping user engagement with digital banking services.
According to McCloskey (2006), age influences the initial decision to purchase online, but it does not affect subsequent e-shopping behaviors such as the frequency of transactions or the total amount spent.
Importantly, when analyzing sample of experienced e-shoppers, the moderator effect of gender onto the relationships between previous internet usage and online shopping behavior evaporates (Hernández et al., 2011)
Demographic factors, such as age and gender, continuously influence an individual's ability to overcome initial obstacles in electronic commerce However, this study found no significant moderating effect of age and gender perceptions on the relationship between social media and trust This may be because experienced electronic shoppers exhibit similar behaviors regardless of their age, gender, or income level, indicating that familiarity with digital shopping diminishes demographic differences.
Older users, after becoming familiar with electronic shopping and gaining experience with online transactions, tend to have attitudes and behaviors similar to other consumers (Hernández et al., 2011) This demographic represents a lucrative market due to their low debt levels, high disposable income, and available leisure time (McCloskey, 2006) Although the older generation remains a niche market, their presence online is expected to grow organically as digital literacy among seniors increases Consequently, the internet has become a suitable marketplace for all age groups, including older consumers (Hernández et al., 2011).
Experienced online shoppers may find that socioeconomic variables do not significantly influence how their perceptions affect their online buying behavior Instead, factors such as individual characteristics, lifestyle, environmental influences, and perceptions of international technology play a more complex role in shaping trust and purchasing decisions Understanding these intricate variables is essential for businesses aiming to enhance online shopping experiences and build consumer trust.
The main results
This research has made major contributions in both theoretical aspects and practical management
The rapid development of social networking sites has fostered an innovative environment for social interactions online Users actively engage through various social media activities such as online forums, community groups, ratings, reviews, and personalized recommendations These digital interactions enhance connectivity and facilitate valuable social engagement across diverse platforms (M N Hajli, 2014).
This study integrates constructs from the Technology Acceptance Model and Theory of Planned Behavior, enhanced with trust and social media principles, to develop a comprehensive model for evaluating the role of social media in electronic commerce and social commerce adoption The model, validated through CFA and SEM analyses, demonstrates that social media significantly increases customer trust levels and indirectly boosts purchase intentions through social networking platforms.
Actually, social interactions can foster interconnectivity as well as establish trust and increase intention to use social commerce
Trust triggered by social media significantly influences consumers’ buying intentions, especially when individuals trust both their social networks and the platforms themselves, leading to increased purchasing behavior on social networking sites The study confirms this hypothesis and addresses key research questions regarding social media's role in consumer decision-making Additionally, perceived usefulness plays a crucial role, demonstrating a stronger impact on buying intention than trust alone To boost purchasing rates, enhancing website quality and performance to improve perceived usefulness is essential for shaping positive consumer perceptions.
Data analysis confirms that trust positively influences perceived usefulness in social commerce When individuals trust a platform, they are more likely to intend to make purchases and perceive the platform as more useful This highlights trust’s critical mediating role in the adoption of social commerce, as it directly enhances purchase intention and indirectly boosts perceived usefulness, ultimately fostering electronic transaction success.
Advancements in Internet technology and the emergence of Web 2.0 have revolutionized the way consumers engage online Social media platforms enable consumers to actively participate in value co-creation through their social interactions, supporting businesses by fostering collaboration This shift highlights the importance of consumer cooperation and social engagement in adding value and driving innovation in the digital economy.
In today's digital landscape, customers actively generate content on social media, exchanging knowledge and experiences with peers and gaining access to valuable personal information This increased social engagement significantly boosts electronic commerce adoption and social commerce intentions As social interactions foster online social support, they help build trust among users, which in turn encourages purchasing behavior Moreover, social media-driven social components create a positive environment that motivates more individuals to explore the online marketplace and engage in social interactions.
Result contributions to management practices
The evolution of social media transforms the relationship between sellers and customers, shifting the roles of each party Unlike traditional marketing, where sellers solely control the marketing mix—product, price, promotion, and place—social media enables customers to actively participate in and influence these decisions (Sashi, 2012) This platform empowers consumers to contribute to value creation and marketing strategies, impacting not only sellers but also other customers and non-customers Additionally, user-generated content significantly enhances customer satisfaction and loyalty, particularly as customer demands continue to grow over time, exemplified by popular applications like Apple iPhone and Android.
Consumers tend to connect with others who share similar motivations and desires, creating opportunities for companies to leverage this by establishing communities of like-minded individuals (Mangold & Faulds, 2009) For example, in 2004, Dove (Unilever) launched the “Campaign for Real Beauty,” a marketing initiative designed to foster a forum for women seeking to boost self-esteem and engage in conversations about societal beauty standards Additionally, in 2009, Procter & Gamble partnered with Facebook to promote the Old Spice fragrance brand by inviting male users to become fans through humorous and engaging campaigns, enhancing brand engagement among target audiences.
Man Smell” Within one week, Procter & Gamble attracted nearly 175,000 new fans
This research advises B2C electronic-commerce managers to gain valuable insights into effectively leveraging social media resources to promote their products and services Organizations should develop engaging and visually appealing interaction platforms that encourage consumers to actively participate, share feedback, and review products By doing so, businesses can strengthen customer engagement and enhance brand visibility through social media.
This study provides valuable insights for managers to develop effective strategies for leveraging social networking sites (SNSs) as platforms to enhance value co-creation with consumers Based on the developed theoretical model, the findings highlight the importance of improving website quality to attract and retain customers Electronic vendors can utilize these insights to encourage repeat purchases and strengthen customer loyalty, ultimately driving business growth (M N Hajli, 2014).
Managers can leverage the value co-creation concept on social networking sites to encourage customer engagement and repeat purchases By actively involving customers in the development and enhancement of products and services, organizations can foster brand loyalty and improve customer satisfaction This strategic approach ultimately leads to increased sales revenue and higher profit margins for businesses.
Limitations and directions for future research
This study, like others in the field, has certain limitations that should be addressed in future research One key limitation is the use of a five-point Likert scale, which may restrict the granularity of responses Future studies are recommended to utilize a seven-point Likert scale to achieve more nuanced and accurate results, thereby enhancing the reliability and depth of the findings.
Secondly, the model was tested with a convenience sample of online users who live in
To achieve comprehensive and accurate insights, the study should expand its testing to include online users from various cities and provinces across Vietnam, such as Ho Chi Minh City (90%), Hanoi, Da Nang, and Can Tho, as well as rural areas Utilizing probability sampling methods will enhance the generalizability of the results, ensuring they accurately reflect the diverse population of Vietnam.
This study analyzed 215 usable samples, which is notably smaller compared to other large-sample social media research, potentially limiting statistical power Low statistical power can reduce the ability to accurately detect true effects, raising concerns about the reliability of findings Additionally, conducting research with insufficient sample sizes can be considered ethically questionable, as it may lead to untrustworthy results, ultimately undermining the study's credibility and wasting resources (Button et al., 2013).
Therefore, in future research the sampling should collect carefully with larger size
Fourthly, the writer primarily recruited respondents who were from 20 – 57 years old
It must be diversified to older and younger generations to provide more useful insights
Future research should explore the moderating effect of income on the studied variables to better understand how socioeconomic status influences the outcomes Investigating this relationship can provide valuable insights into the role of income as a significant factor in shaping results Incorporating income as a moderator may enhance the robustness of findings and inform targeted interventions Further analysis is needed to assess how income impacts the dynamics within the studied context, contributing to a more comprehensive understanding of socioeconomic influences.
In this study, over 75% of responses were collected through social media platforms such as Facebook, Zalo, and LinkedIn While social media facilitates efficient data collection, it may introduce selection bias and non-response bias, particularly when respondents misunderstand the questionnaire Future research should carefully consider and diversify data collection methods to ensure more representative and accurate results.
Conclusion
This study evaluates the role of social media features in electronic business adoption, highlighting their impact on consumer behavior and attitudes It integrates key theories from information systems and marketing disciplines to better understand social media's influence on consumer socialization Consistent with previous research by Wang et al (2012) on social media's role in shaping consumer perceptions, the findings demonstrate that social interactions significantly influence consumer attitudes toward products and services Additionally, the study offers practical insights for practitioners looking to leverage social media features to enhance electronic business success.
This thesis highlights the critical importance of trust in online and social commerce, emphasizing that establishing and enhancing trust through social media is essential for online vendors Strengthening trust in digital platforms directly influences consumer confidence and purchasing decisions, aligning with prior research that underscores trust as a key determinant of success in e-commerce Therefore, the primary managerial implication of this study is that online vendors should prioritize building authentic relationships and credibility on social media to drive business growth and customer loyalty.
According to McCole et al (2010), trust significantly influences consumer attitudes and their purchasing decisions Effective networking on social media platforms enhances trust-building mechanisms, which are crucial for the successful adoption of both electronic commerce and social commerce Building trust through social media can lead to increased consumer confidence and improved purchase behavior in online shopping environments.
Certainly! Here's a rewritten paragraph that summarizes the core content with SEO considerations:David Aaker's "Managing Brand Equity" (2009) emphasizes the importance of building strong brand assets to create long-term competitive advantage Effective brand management involves understanding brand identity, establishing brand loyalty, and maintaining positive brand associations By focusing on these key elements, companies can enhance their overall brand equity, leading to increased customer retention and market success This comprehensive approach provides a strategic framework for businesses aiming to strengthen their brand in a competitive environment.
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The article by Gilly and Zeithaml (1985) explores the factors influencing elderly consumers' adoption of new technologies, emphasizing the importance of understanding their unique needs and barriers It highlights that tailored marketing strategies and user-friendly designs are crucial to encourage technology acceptance among older adults The study underlines that improving digital literacy and providing adequate support can significantly enhance the adoption process Overall, the research offers valuable insights into how companies can better serve elderly consumers by addressing their specific challenges with technology.
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The study by Hansen, T., Jensen, J M., and Solgaard, H S (2004) compares the effectiveness of the Theory of Reasoned Action and the Theory of Planned Behavior in predicting online grocery purchase intentions Their research provides valuable insights into consumer behavior, highlighting key factors that influence online shopping decisions Understanding these models helps businesses optimize their marketing strategies to increase online grocery sales This research is essential for anyone interested in e-commerce trends and consumer psychology.
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The study by Wu, J.-J., Chen, Y.-H., and Chung, Y.-S (2010) examines key trust factors that influence members of virtual transaction communities Their research highlights how trust plays a critical role in fostering successful online transactions and member engagement The findings suggest that perceived security, reputation, and prior experience significantly impact users' willingness to participate in virtual communities Building trust through effective communication and reliable platform features can enhance member retention and transaction success This research provides valuable insights for businesses aiming to optimize their online community strategies and strengthen user trust in digital environments.
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This article explores the significant implications of social media marketing for businesses, highlighting how platforms like Facebook are instrumental in driving brand awareness and customer engagement With the rapid increase in internet users in Vietnam, as reported by Statista, businesses have a vast digital audience to reach Despite previous bans, Vietnam's reliance on Facebook has revived opportunities for startups and enterprises to leverage social media for growth, as detailed by Bloomberg Overall, understanding these dynamics is essential for effective social media marketing strategies in emerging markets.
