Introduction
A brand serves as a crucial differentiator for organizations, extending beyond just a name and symbol to foster positive customer perceptions According to Aaker (2009), a brand name is a vital intangible asset that helps establish a strong identity in the minds of consumers Products and services that cultivate brand loyalty and popularity are more likely to achieve higher sales revenue, capture additional market share, and enhance profitability, ultimately enabling firms to thrive or at least endure in a competitive marketplace (Erdoğmuş).
Stimulating consumer interest and promoting a brand are complex strategic processes that require careful planning and execution by marketers To build relationships with customers and influence their purchasing intentions, marketers utilize a variety of methods, including traditional advertising and innovative approaches through social media platforms, such as digital marketing, events, and sponsorships.
The evolution of social media has significantly transformed the online commerce landscape for both businesses and consumers Platforms like Facebook, Twitter, and YouTube have grown immensely, allowing millions of users to forge personal and professional connections As of March 2018, Facebook boasts 1.45 billion daily active users and 2.2 billion monthly active users, highlighting the importance of social media engagement in marketing strategies Consumers can easily interact with posts and connect with company websites to provide feedback, enabling firms to address customer issues promptly The stronger the connection consumers feel with a brand, the more likely they are to make repeat purchases Social media serves as a powerful marketing channel that enables companies to surpass traditional media and foster direct relationships with consumers, while also facilitating effective communication.
Leveraging social media to share compelling stories allows companies to engage with customers in real time, transforming their brands into vital hubs for consumer interaction within the social community This approach not only fosters meaningful online consumer interest but also enhances brand visibility and loyalty.
Zhu and Chen (2015) highlight a Gallup survey indicating that social media advertisements fail to motivate most U.S consumers in their purchasing decisions The survey warns companies that social media is not a powerful marketing tool, as consumers often tune out brands associated with platforms like Facebook and Twitter Furthermore, individuals tend to ignore social media ads, frequently diverting their attention to other topics Fournier and Avery (2011) emphasize that social media serves primarily as a platform for personal connections rather than a new avenue for brand communication.
The rapid adoption of social media presents new opportunities for building potent social networks; however, empirical studies on this phenomenon remain scarce (Hsu and Tsou, 2011) Consequently, there is a pressing need to investigate how marketers leverage social media for brand development According to Laroche et al (2013), existing research primarily focuses on defining social media, outlining its characteristics, and discussing its advantages and disadvantages, while providing strategic recommendations for marketers, as seen in studies by Edelman et al (2010) and Kietzmann et al (2011) Therefore, it is crucial for brand owners to emphasize the outcomes of social media marketing to enhance their products and services and strengthen customer relationships through online platforms This research is particularly relevant in a transitional country like Vietnam, which has achieved a 53% internet penetration rate (Statista, 2018), highlighting the significance of social media in this market.
95 million citizens use social media and anticipates achieving 56 million Facebook users by 2021 (Bloomberg, 2017)
Many firms leverage social media to create marketing programs that build trust and influence online purchase intent (M N Hajli, 2014) However, numerous Vietnamese companies have yet to recognize the significance of branding and brand promotion (Nguyen et al., 2011), and there is a lack of branding research in Vietnam (Nguyen, 2003) By understanding how social factors affect trust and motivate customer purchase intentions, marketers can develop effective marketing strategies and design engaging advertising campaigns that interact proactively with target consumers through social media.
Gender plays a significant role in consumer behavior, influencing decision-making processes in technology adoption Research by Venkatesh et al (2000) highlights that men's choices are primarily driven by their attitudes towards advanced technology, whereas women tend to be influenced by subjective norms and perceived behavioral control Furthermore, individuals aged 18 to 34 are more likely to use social media for information exchange with friends and family compared to older age groups (Bolton et al.).
In 2013, it was noted that users in a specific age group often rely on the experiences and feedback of others regarding products and services from particular brands While numerous studies have explored how demographic factors influence consumer behavior, the findings have been inconsistent (Mai and Zhao, 2004; Mitchell and Walsh, 2004) To understand the moderating effects of age and gender in an emerging market, it is essential to investigate these demographic variables to develop effective marketing strategies.
This research investigates the influence of social media on trust and its subsequent effect on consumer buying intentions It emphasizes the importance of effective marketing strategies in helping organizations distinguish their products and services from competitors The study aims to achieve five objectives: first, to explore how social media fosters trust in electronic commerce; second, to analyze how age and gender moderate the relationship between social media and trust; third, to examine the correlation between trust and consumer buying intentions; fourth, to discuss the connection between trust and perceived usefulness in purchasing decisions; and finally, to assess both the direct and indirect effects of social media on trust and purchase intent Through this framework, the thesis addresses key inquiries related to these dynamics.
This study investigates the influence of social media on consumers' trust and its subsequent effect on buying intentions, focusing on perceived usefulness (PU) as a key factor from the technology acceptance model It aims to determine whether PU or trust more significantly impacts users' purchasing decisions, while also examining how age and gender may moderate the relationship between social media and trust The research model incorporates variables such as social media engagement, trust levels, and buying intent, with a specific emphasis on the Vietnamese market Ultimately, the study seeks to evaluate the role of social media in shaping purchase intentions and the influence of demographic factors on this connection.
This research examines the impact of social media elements on trust and its influence on customer buying intent, focusing on a diverse group of individuals in Ho Chi Minh City, Vietnam's largest city Understanding these dynamics can help identify the key features that foster trust and enhance social commerce intentions on social networking platforms.
This research study examines the Technology Acceptance Model (TAM) constructs and presents a model that confirms the influence of trust and perceived usefulness on consumer buying intentions It also explores the significant challenges trust poses in social commerce, particularly through social media networking sites The key contribution of this study is to emphasize the critical role of social media in shaping trust within electronic markets.
This paper is structured into several key sections: Section 1 provides an introduction that outlines the research background, problem, objectives, questions, contributions, and identifies the research gap Section 2 offers a literature review and hypotheses, discussing the theoretical foundations of social media, trust, and purchase intentions, while also addressing age and gender factors, leading to the proposal of a research model In Section 3, the research methodology is detailed, including scale development, sample selection, data collection tools, and statistical analysis techniques Section 4 focuses on data analysis and results, interpreting the findings and discussing limitations, while drawing conclusions related to the proposed hypotheses Finally, Section 5 presents the discussion and implications, highlighting the main findings, contributions, limitations, and suggesting future research directions along with practical recommendations.
Literature review and hypothesis
Technology acceptance model (TAM) and theory of planned behavior (TPB)
The Technology Acceptance Model (TAM), developed by Davis et al in 1989, is a well-established framework for understanding user acceptance of information technology, supported by extensive empirical research (Lucas Jr and Spitler, 1999) Central to TAM are two key constructs: perceived usefulness and perceived ease of use, which are considered crucial factors influencing user acceptance and can be widely applied across various contexts (Adam et al., 1992).
The theory of planned behavior, an extension of the theory of reasoned action (TRA), has been widely validated in research (Chang, 1998) TRA suggests that purchase intent serves as a precursor to actual purchasing behavior (Razak & Marimuthu, 2012) Van den Poel and Leunis (1999) highlight that consumers often perceive a higher risk when shopping online compared to offline, prompting them to seek information from experienced peers for normative recommendations (Hansen et al., 2004) Additionally, recent studies indicate that consumers encounter perceived behavioral control (PBC) obstacles in online shopping, leading them to rely on cognitive resources to shape their beliefs about relevant attributes, ultimately influencing their overall attitude toward the behavior (Rossiter & Percy, 1987).
Social media and trust
As social media continues to evolve and diversify across various platforms, a clear definition remains elusive Social media encompasses the activities, practices, and interactions of online communities that come together to exchange information, knowledge, and opinions through conversational media.
Social media, defined as internet-based applications rooted in Web 2.0 frameworks, empower consumers to create and exchange content, significantly shifting market power from manufacturers to consumers (Kaplan & Haenlein, 2010) This transformation allows today's online consumers unprecedented access to information and choices, enabling them to engage with brands actively (Constantinides & Fountain, 2008) Although there is no official classification of social media types, they can be categorized into five groups: blogs, social networking sites (like Facebook and Twitter), content communities (such as YouTube), e-forums, and content aggregators Social networking sites are the most prevalent, facilitating user interaction through sharing, recommending, and reviewing products (Mangold & Faulds, 2009) These interactions help businesses transition customers from awareness to engagement, enhancing brand recognition and loyalty (Gunelius, 2010) Unlike traditional push marketing, which seeks to inform and motivate customers, social media offers pull marketing opportunities, allowing consumers to engage with brands on their terms As highlighted by Gillin (2009), the reach of a dissatisfied customer has expanded dramatically in the social media era, making it crucial for firms to leverage these platforms as part of an integrated marketing strategy By sharing compelling stories and connecting with customers in real-time, companies can foster a vibrant social community and generate meaningful consumer interest (Holt, 2016).
Abeza et al (2013) highlight that engaging with social media presents several challenges, including difficulties in reaching target audiences, a lack of control, issues with resource allocation accuracy, and trust concerns Additionally, Fournier and Avery (2011) caution brands to approach social media carefully, as they risk being perceived as "uninvited crashers."
It is implying that create brand relationship via social media is more complicated than stimulate more interactions
Social media marketing presents unique challenges and opportunities compared to traditional marketing methods, as it fosters a more direct and transparent relationship between customers and brands Companies like Pepsi and Unilever are increasingly engaging consumers by involving them in advertising initiatives, which helps build emotional connections and enhances brand loyalty (Sashi, 2012) The convenience and low cost of social media empower users to interact freely, transcending geographical limitations (Lai & Turban, 2008) Consequently, businesses can communicate with their customers at unprecedented low costs while generating and disseminating online content effectively to enhance brand visibility (Hainla, 2017).
As social media marketing becomes a vital component of a company's strategy, managers are increasingly focused on identifying and tracking key performance indicators to assess project success One crucial metric is trust, which has gained significant attention from researchers in recent years, particularly with the rapid growth of online transactions and e-commerce.
Trust is essential for maintaining and developing long-term relationships, particularly in the online context where technological uncertainties abound (Pavlou, 2003) Extensive research by scholars has highlighted the significant impact of trust on individuals' intentions to use online applications for purchasing (Gefen et al., 2003; Hoffman et al., 1999; Kim, 2012; McKnight and Chervany, 2001) In social commerce, for instance, consumers often rely on recommendations from trusted online communities and experienced members who can provide reliable advice based on their purchasing experiences with specific products or services (Chen & Shen, 2015).
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)
A lack of trust poses a significant barrier to the adoption of electronic commerce, as it undermines the creation of an effective and interactive online environment Trust is essential for fostering the implementation of information and communication technologies across various boundaries, highlighting its critical role in the success of e-commerce.
To build trust with consumers, brands should provide comprehensive knowledge about their products (Chiu et al, 2010) Social media serves as a rich communication platform, allowing companies to foster and maintain relationships with key brand features This environment encourages consumers to actively share their experiences with favorite products and brands, enhancing engagement with both peers and marketers (Habibi, 2014) The interconnectedness of individuals on social media positively influences trust (Wu et al, 2010).
H1: There is a positive relationship between social media marketing and trust
Age and gender
Understanding the differing shopping behaviors of genders is crucial for retailers, as gender serves as a significant variable in sales and marketing strategies (Chou et al., 2015; Kuruvilla et al., 2009) Research indicates that women generally respond more favorably to advertisements on platforms like Facebook (Alalwan et al., 2017), highlighting the importance of tailoring marketing approaches to male and female consumers to enhance brand performance (Helgesen & Nesset, 2010) The psychological, emotional, and perceptual differences between genders have been extensively studied across various fields, including neurology and psychology (Khan & Rahman, 2016) In the technology sector, gender has been identified as a critical moderating factor (Venkatesh & Morris, 2000), while studies on social networking sites have revealed that although teenage boys and girls use the Internet similarly, their usage patterns differ Boys tend to focus on features and entertainment, whereas girls prioritize relational interactions, often discussing romantic relationships and personal feelings more frequently (Joiner et al., 2005; Rainie, 2003) Furthermore, recent findings suggest that girls primarily use social networking sites to maintain connections with peers, while boys are more inclined to seek new friendships (Barker, 2009).
Research by Barker highlights that females exhibit higher positive collective self-esteem and are more likely to use social networking sites (SNSs) for information exchange with friends, as well as for group identity and entertainment Conversely, negative collective self-esteem is associated with social compensation, where individuals feeling negatively about their social group turn to SNSs for alternative communication Males, on the other hand, are more likely to express negative collective self-esteem and utilize SNSs for social compensation, seeking validation through collective tasks when group performance falters Furthermore, male users tend to engage with the Internet for diversion and functional purposes, while females prioritize communication and interaction with friends Additionally, attitudes toward social networking advertising reveal that perceived informativeness and value have a greater influence on men's attitudes compared to women's.
Women tend to engage more deeply with specific message content compared to men, often exhibiting heightened sensitivity to relevant information when making judgments (Meyers-Levy and Maheswaran, 1991).
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 viewed as a feminine pastime, with studies indicating that women are more active in online purchasing compared to men (Chou et al., 2015) However, this trend may be shifting, as some researchers suggest that male engagement in shopping activities is on the rise (Buttle, 1992) Furthermore, women predominantly use social media platforms like Facebook and Twitter as their main sources of information influencing their purchasing decisions.
H2a: Relationship between social media and trust will be affected by age
Generations are classified by birth years: the Silent Generation (1925-1945), Baby Boomers (1946-1960), Generation X (1961-1981), and Generation Y (post-1981) (Brosdahl & Carpenter, 2011) Research shows that older adults often exhibit a negative attitude towards technology and are less inclined to adopt it (Gilly & Zeithaml, 1985) Furthermore, Madden and Savage (2000) found a negative correlation between age and Internet usage, highlighting the challenges older generations face in embracing new technologies.
Generation Y is characterized by their early exposure to international technology, which brings both cognitive and emotional benefits as well as social challenges (Brosdahl & Carpenter, 2011; Immordino-Yang et al., 2012) This generation frequently uses technology and social media for entertainment and social interaction, forming connections with others Additionally, Generation Y boasts a strong educational background (Wolburg & Pokrywczynski, 2001) and is more attuned to marketing strategies than previous generations who did not grow up with the internet (Lazarevic, 2012) They are adept at quickly researching and sharing information with their peers (Berkowitz & Schewe, 2011).
Generation Y, often perceived as more materialistic than previous generations, exhibits a heightened consumptive orientation (Bakewell & Mitchell, 2003; O’Donnell, 2006) Young adults aged 19 to 24 demonstrate distinct attitudes toward social media advertising (Durlak et al., 2011), actively engaging with, sharing, and adopting content across various platforms This demographic's social media usage has garnered significant attention from both practitioners and researchers, as it may signal future consumer behaviors (Bolton et al., 2013) Their shopping styles and behaviors differ markedly from those of earlier generations (Bakewell & Mitchell, 2003) Consequently, if brand marketing content lacks consistency or fails to convey a genuine brand identity, Generation Y consumers are likely to dismiss the brand as inauthentic (Merrill, 1999).
H2b: Relationship between social media and trust will be affected by gender
Effect of trust on intention to buy and perceived usefulness
Trust is defined as the willingness to rely on a partner in whom one has confidence, and it is established through perceptions of a brand's reliability and honesty When customers interact with a brand they trust, they experience a heightened sense of security, knowing that the brand is responsible for their interests and welfare In the context of social networking sites and online transactions, trust significantly influences consumer behavior, especially in risky situations where individuals cannot control others' actions This importance of trust is particularly evident in electronic commerce, where it plays a crucial role in risk assessment during buying and selling processes Furthermore, trust is vital for fostering strong relationships between retailers and consumers in the digital marketplace.
Positive customer feedback significantly enhances the trustworthiness of a brand or vendor (Ba & Pavlou, 2002) When reputable online users provide positive ratings for a vendor, it fosters a high level of trust among other members in the online shopping environment (Lu, 2010) According to the theory of reasoned action, trust cultivates favorable attitudes toward web retailers, alleviating concerns about seller opportunism and addressing infrastructure issues related to online transactions (Pavlou, 2003).
Trust is essential for branding across all industries, serving as a foundation for long-term business relationships It significantly influences the quality of social and business interactions (Gefen, 2000) Without trust, brands struggle to grow, as consumers prioritize their time, money, and resources, choosing to engage only with brands that demonstrate reliability In the realm of e-commerce, the absence of physical sellers and the inherent uncertainties of the online environment make trust even more crucial for attracting and retaining customers (Shiau & Luo, 2012) Therefore, building and maintaining trust is vital for a brand’s success in today’s competitive global market.
Building emotional trust is crucial for companies aiming to meet customer expectations and instill confidence in their reliability and integrity (Morgan & Rego, 2006) Sustainable growth hinges on retaining current customers and fostering brand loyalty (Dekimpe et al., 1997), which encourages repeat purchases Trust in commercial transactions significantly influences online consumers (McCole et al., 2010) and enhances their intention to buy (Shin, 2010) Additionally, as consumer trust in vendors increases, perceived risks associated with online shopping diminish (Van der Heijden et al., 2003).
Trust plays a crucial role in enhancing consumers' intention to purchase online (Lu et al., 2010; Shin, 2010) Building confidence and reducing perceived risk are essential factors when exploring new products and services in the digital marketplace (Hassanein & Head, 2007; Shin, 2010).
Research has established a strong connection between trust and perceived usefulness, indicating that higher levels of trust enhance the elements of perceived usefulness (Gefen et al., 2003) Therefore, this thesis proposes the following hypothesis:
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 crucial factor in the technology acceptance model (TAM), defined by Davis (1989) as the extent to which an individual believes that utilizing a specific system enhances their job performance This concept is a significant motivator for users to embrace advanced technologies In the context of electronic commerce, PU can be influenced by various factors, such as website quality, effective customer service, and standards for information sharing, all of which positively impact customer purchasing behavior (Ahn et al., 2007).
The intention to buy is a key element of the Technology Acceptance Model (TAM), a widely recognized framework that helps predict an individual's willingness to adopt a system (Pavlou, 2003) TAM serves as a foundational theory in the realm of e-commerce research.
The Technology Acceptance Model (TAM) has been extensively studied by researchers, including Martins et al (2014) and Hsiao and Yang (2011) This study focuses on the intention to buy, defined as a customer's willingness to engage in online purchases through social networking sites.
Research indicates that perceived usefulness significantly influences the intention to engage with electronic commerce (Gefen & Straub, 2000) Moreover, it serves as a predictor for both the intention to use and the actual usage of IT applications (Nguyen, 2007) Consequently, businesses should prioritize enhancing social media interactions and improving service and system quality to boost consumers' perceived usefulness (M N Hajli, 2014).
A study conducted in 2009 reveals that customers who find social networking sites (SNSs) to be helpful during their online shopping experience are more likely to intend to make purchases through these platforms This leads to the hypothesis that perceived helpfulness on SNSs positively influences purchase intention among consumers.
H5: Perceived usefulness relates positively to intention to buy
Hypotheses
This study presents five hypotheses aimed at empirically assessing the influence of social media on consumer behavior while also examining the moderating effects of demographic factors such as age and gender.
H1: There is a positive relationship between social media marketing and trust
H2a: Relationship between social media and trust will be affected by age
H2b: Relationship between social media and trust will be affected by gender
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
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 serves as Vietnam's primary business hub and a key opinion leader in the country This study will utilize a combination of qualitative and quantitative research methods, as outlined in the accompanying chart.
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
This research examines the influence of social media on trust and perceived usefulness in relation to purchasing intentions, drawing from existing literature It proposes five hypotheses in the literature review, exploring the connections between these factors and their antecedents Subsequently, the model was refined, and a preliminary scale for the study's questionnaire was established.
The preliminary survey aimed to ensure that the measurement scale items were aligned with customer perspectives While previous literature outlined many constructs, this step was essential for adapting them to the study's context and enhancing clarity Additionally, the survey enabled the identification of errors and necessary modifications to strengthen the scale's effectiveness It also facilitated the generation of new ideas and items to enrich the research model Participants from diverse age groups and professions were included to ensure a broad representation of opinions and validate the proposed model, aligning with the research's objective of understanding online users' viewpoints The final official survey was conducted after refining the questionnaires based on the preliminary survey results.
The study utilized both online and paper questionnaires, primarily targeting residents of Ho Chi Minh City, who comprised 90% of the participants Additionally, the research included five international respondents from England and Sweden, as well as some online users from the Mekong Delta region.
A qualitative pilot study was conducted using a survey link shared on Facebook, alongside in-depth interviews with 10 respondents to validate the constructs in the model The pilot findings informed modifications to the survey instrument, ensuring a more objective and comprehensive main survey Final questionnaires were reviewed with experts, Dr Doan Anh Tuan and Dr Nguyen Phong Nguyen from the University of Economics Ho Chi Minh City Within 12 hours of posting the survey link, 20 respondents provided feedback, highlighting the engaging topic and clarity of the questions Ultimately, 30 respondents completed the survey, contributing valuable insights for refining the final questionnaire, which was enhanced by adding an additional item, "Intention3," to the variable intention to buy.
Following qualitative research, the questionnaire was refined to better align with the Vietnamese context and enhance clarity Subsequently, a comprehensive main survey was conducted using a convenience sampling method The main research procedure adhered to a structured series of steps.
Step 1: Composing final questionnaire for the research:
The questionnaires were designed in both English and Vietnamese to ensure clarity for all respondents Emphasizing the importance of individual opinions, the instructions aimed to reduce response bias Additionally, the initial section of the questionnaire gathered demographic information, including age and gender (refer to Appendix 1).
Step 2: Defining the sample size of the research:
Designing a targeted sample is crucial for accurately addressing research questions, making the definition of the right survey sample essential (Sekaran & Bougie, 2016) The sample size is determined by the estimating method, and it is important to note that sample size can significantly influence statistical tests; small samples may yield inconclusive results, while large samples can produce overly sensitive outcomes (Hair et al., 2010) Ideally, a sample size of 100 or more is recommended, with a guideline suggesting a minimum of five observations per scale In this study, the research model includes six factors with 15 scales, indicating that at least 100 observations are necessary for valid results.
To conduct a standard multiple regression analysis, a minimum sample size is recommended by Tabachnick and Fidell (2007) as N > 50 + 8m, where N represents the sample size and m denotes the number of independent variables In this research, there are two independent variables, which establishes a minimum requirement of n > 50 + 8*2 = 66 observations to perform the multiple regression test effectively.
Step 3: The questionnaire was issued to the interviewees
The writer created an online questionnaire using Google Docs and invited participants through various platforms, including email, Zalo, and Facebook, while also sharing it on social media sites like LinkedIn To ensure the authenticity of the sample, the author directly sent the Google link to friends and colleagues via Zalo and Facebook, as well as to customers through email.
The interviewer effectively distributed questionnaires through various methods, providing clear instructions to respondents on how to complete them and addressing any questions they had Mail and online surveys proved to be cost-effective options, allowing respondents to easily participate by clicking a link and submitting their answers quickly while maintaining their privacy (Mangione, 1995) The data collection process took place over three weeks, during which approximately 400 questionnaires were sent via online channels, including email and social media, resulting in 223 completed responses.
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 study gathered a total of 253 responses, but after identifying and addressing unanswered questions through descriptive statistics, it was found that 30 respondents did not answer Intention 3, while 8 missed responses for Perceived Usefulness 2 (PU2) and Perceived Usefulness 3 (PU3) As a result, all missing values were removed, leaving 215 usable observations, which met the minimum sample size requirements for standard multiple regression analysis The data from the main survey was then utilized to refine the measurement through confirmatory factor analysis (CFA) and to assess the structural model using structural equation modeling (SEM).
Measurement of the constructs
The study employs a five-point Likert scale to assess responses, ranging from "strongly disagree" (1) to "strongly agree" (5) Key questions were derived from previous research to enhance the study's validity Four primary constructs were examined: social media, trust, perceived usefulness, and intention to buy The social media construct includes three questions adapted from M N Hajli's 2014 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
SM4 I trust my friends on online forums and communities
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
I am pleased to use my credit card for purchases from online vendors via my preferred social networking site, and I am open to paying for membership if social networking sites begin charging fees.
Data analysis and method
After gathering data through various activities, all available information will be entered into SPSS (Statistical Package for Social Science) software version 23.0 and AMOS software version 24 for comprehensive data analysis.
This study employed Exploratory Factor Analysis (EFA), Cronbach’s Alpha, and Confirmatory Factor Analysis (CFA) to assess the reliability of scales and enhance internal consistency Additionally, Structural Equation Modeling (SEM) was utilized to test research hypotheses, while Multiple Regression analyzed the relationships between independent and dependent variables Lastly, Multiple-Group Analysis was conducted to determine the significant impact of age and gender as moderating variables on the influence of dependent variables towards independent variables.
Alpha is a crucial method for assessing the validity and reliability of data interpretations in academic research and practice Ensuring the validity and reliability of measurement instruments is essential for accurate evaluation (Tavakol & Dennick, 2011).
Various studies have examined the acceptable levels of Cronbach's alpha, typically ranging from 0.70 to 0.95 A low alpha value may indicate a limited number of questions, weak inter-item correlations, or the presence of heterogeneous constructs Conversely, an excessively high alpha value could suggest that some items are redundant It is generally recommended to maintain a maximum alpha value of 0.90, with an acceptable reliability threshold set above 0.70 (N Leech et al., 2013; Tavakol & Dennick, 2011).
The Corrected Item-Total Correlation is crucial for evaluating items in a summated rating scale Leech et al (2005) suggest that a correlation of 0.40 or higher indicates that the item is likely well-correlated with other items, contributing positively to the scale Conversely, if the item-total correlation is negative or below 0.30, it is essential to reassess the item for potential wording issues or conceptual misalignment, which may necessitate modification or deletion.
Exploratory Factor Analysis (EFA) is grounded in a testable model that can be evaluated for its alignment with the proposed population model, with indices developed to enhance model interpretation (Norris & Lecavalier, 2010) Additionally, the EFA method serves to determine which items within a large set are grouped together or exhibit similar response patterns among participants (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)
For practical significance, factor loadings greater than 0.5 are generally considered essential, while a sample size of around 100 necessitates loadings above 0.55 High loadings of 0.80 or more are uncommon, underscoring the importance of practical significance in evaluating loadings (Hair, 2010) Hair also provided guidelines in a referenced table.
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
Multiple regression analysis provides researchers with a valuable tool to explore the relationships between independent and dependent variables It also reveals how independent variables interact in predicting the dependent measurement (Hair, 2010) While multiple regression involves several assumptions, it is essential to concentrate on the primary assumptions that can be easily tested using SPSS.
1 A linear relationship between each of the predictor variables and the dependent variables
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
A total of 253 questionnaires were collected through online surveys and direct interviews, comprising 143 females (56.5%) and 110 males (43.5%) However, 38 questionnaires were incomplete or poorly filled out The majority of respondents were from Generation Y, aged under 37 (born after 1981), accounting for 65% of the sample, while Generation X represented 35% The high response rate from the younger generation and females suggests that age and gender may significantly influence initial online purchasing decisions.
A total of 215 observations were analyzed, resulting in an impressive 85% feedback ratio As detailed in Table 3, the sample exhibits significant diversity in terms of career backgrounds and age groups Among the usable samples, 61% of respondents are female, while 39% are male.
Before running structural equation modeling (SEM) to test the hypotheses, in order to purify and validate the measures, the writer first performed an exploratory factor analysis
(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
Testing Cronbach's alpha for each construct is essential to verify the reliability of all items within a scale, ensuring they accurately measure the intended research concept.
To ensure the reliability of a scale, Cronbach's alpha should be greater than 0.7, while the Corrected Item-Total correlation for each item must exceed 0.5 (Hair, 2010) Additionally, it's essential to review the wording of the questionnaire items, assessing their meanings and making modifications or deletions as needed to achieve conceptual fit Items with a total correlation that is negative or below 0.3 should be scrutinized and potentially removed.
The internal consistency of the results, measured by Cronbach’s alpha, met the required threshold, with all rates exceeding 0.70 This indicates a strong reliability of the research, as all constructs demonstrated reasonable internal consistency For a detailed overview, please refer to Appendix 2.
Exploratory factor analysis (EFA)
Following the assessment of Cronbach’s alpha, it was essential to conduct exploratory factor analysis (EFA) to evaluate the measurement scales The purpose of EFA was to identify groups of items that respondents answered similarly The analysis utilized the Principal Axis Factoring extraction method with Promax rotation, setting the eigenvalue at 1, and ensuring that total variance extraction was at least 0.5 Promax rotation was selected due to its superior ability to recover simple structures compared to Varimax, particularly when dealing with a larger number of items and high correlations between factors, as noted by Finch (2006).
The KMO value of 0.873 indicates that there are adequate items for each construct, confirming the suitability of the data for factor analysis Additionally, the significant Bartlett's test result (p < 0.005) suggests strong inter-correlations among the dependent variables, reinforcing the validity of conducting factor analysis.
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
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.”
“Extraction Method: Principal Axis Factoring
Rotation Method: Promax with Kaiser Normalization a Rotation converged in 5 iterations.”
The Exploratory Factor Analysis (EFA) using Principal Axis Factoring with Promax rotation revealed four key constructs from 14 items: Social Media, Trust, Perceived Usefulness, and Intention to Buy These constructs accounted for 63.48% of the total variance, indicating that over two-thirds of the variance could be explained by the initial items Additionally, the Eigenvalues for the four factors were all greater than 1, confirming their significance in explaining the variance.
The Rotated Factor Matrix revealed significant items and factor loadings, with values exceeding 0.5 considered essential The analysis identified four distinct groups based on these clustered items.
Confirmatory Factor Analysis (CFA) result
The CFA results for the research model indicated a standardized estimate with 71 degrees of freedom, revealing a Chi-square/df of 120.797 and a p-value of 000 The model also showed strong fit indices, including NFI = 928, RFI = 0.907, IFI = 0.969, TLI = 0.960, and CFI = 0.968, confirming that the scales achieved an acceptable fit to the data.
The scales demonstrated satisfactory levels of convergent and discriminant validity, confirming their effectiveness Additionally, the achievement of unidimensionality was evident as the correlations among item errors were eliminated.
Research hypotheses test
The current study utilized structural equation modeling (SEM), a widely accepted method in social science research The model estimation indicated a satisfactory fit, with a Chi-square/df ratio of 150.587 and a significance level of p = 000 Fit indices such as NFI = 91, RFI = 0.887, IFI = 0.951, TLI = 0.939, and CFI = 951 further supported this conclusion The relationships among the concepts within the model were statistically significant, achieving a high confidence interval (p < 001), thereby confirming that the theoretical model aligns well with the data.
The findings indicated a significant impact of social media on trust, with a coefficient of β = 31 This supports Hypothesis 1 (H1), demonstrating that the interactive engagement of online users via social media fosters trust in electronic commerce.
The research findings indicate that trust has a significant direct effect on perceived usefulness (β = 62) and also directly influences the intention to buy Additionally, perceived usefulness has a notable direct impact on the intention to buy (β = 41) Consequently, the data strongly supports hypotheses H1, H3, H4, and H5.
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 findings indicate that perceived usefulness significantly influences consumers' intention to buy, with a stronger impact (0.41) compared to trust (0.31) This suggests that the perceived benefits of a social website play a crucial role in driving purchasing decisions among consumers.
Figure 2: SEM for suggestion model
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)
The analysis of gender as a moderating variable involved dividing the data into two groups: males and females The multi-group analysis of variance revealed significant results, with χ2 = 255.178, degrees of freedom (df) = 146, and a p-value of 0.000 Additionally, the fit indices indicated strong model performance, with a Normed Fit Index (NFI) of 0.858, a Relative Fit Index (RFI) of 0.823, an Incremental Fit Index (IFI) of 0.934, a Tucker-Lewis Index (TLI) of 0.916, and a Comparative Fit Index (CFI) of 0.932.
Table 8: Assuming model unconstrained to be correct - gender
The study found that gender did not significantly impact the relationship between social media usage and trust, with a p-value of 54.2% This indicates that there was no notable difference between male and female participants, leading to the conclusion that hypothesis H2a was not supported.
H2a: Relationship between social media and trust will be affected by age
However, in terms of male, they highly concentrated on perceived usefulness of website to make buying decision
Figure 3: Multi-group result for female
Figure 4: Multi-group result for male
Testing the moderating effects of age variable
In the analysis of gender as a moderating variable, data was categorized into two groups: Male and Female The multi-group analysis of variance revealed significant findings, with a chi-square value of χ2 = 256.793, degrees of freedom (df) = 146, and a p-value of 0.000 The model fit indices indicated a Normed Fit Index (NFI) of 0.859, a Relative Fit Index (RFI) of 0.825, an Incremental Fit Index (IFI) of 0.934, a Tucker-Lewis Index (TLI) of 0.916, and a Comparative Fit Index (CFI) of 0.933.
Table 9: Assuming model unconstrained to be correct_age
The study found that age did not significantly affect the relationship between social media and trust, with a p-value of 35.3% There was no notable difference between Generation X and Generation Y, leading to the rejection of hypothesis H2b This suggests that once the initial barriers to electronic commerce are overcome, the age of online shoppers does not significantly influence their behavior.
H2b: Relationship between social media and trust will be affected by gender
Figure 5: Multi-group result for Generation X
Figure 6: Multi-group result for Generation Y
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 research indicates that while gender disparities in internet usage have been prominent, the gap is narrowing, with a growing number of women utilizing the internet (Eurostat, 2009) Additionally, a study by Zhang (2005) found no statistically significant differences between men and women in their internet usage patterns.
Al-Somali et al (2009) conducted a study on e-banking among experienced customers, revealing that age does not significantly influence attitudes or behaviors towards e-banking.
According to McCloskey (2006), age influences the initial decision to shop online, but it does not affect the subsequent behaviors of e-shoppers, including the frequency of transactions or the total expenditure.
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 significantly influence individuals' ability to navigate initial challenges in electronic commerce However, contrary to expectations, research found no moderating effects of age or gender on the relationship between social media and trust This may be attributed to the fact that experienced online shoppers exhibit similar behaviors regardless of their demographic characteristics (Hernández et al., 2011) Notably, older users, once familiar with e-shopping and online transactions, demonstrate attitudes and behaviors akin to younger individuals This demographic represents a valuable market segment due to their low debt, high disposable income, and diverse interests (McCloskey, 2006) As the digital-savvy generations age, the internet is increasingly becoming a viable marketplace for all age groups, highlighting the potential for organic growth among older consumers (Hernández et al., 2011).
For seasoned online shoppers, socioeconomic factors may not significantly influence their perceptions and behaviors Instead, trust and purchasing decisions are likely shaped by more complex variables such as individual characteristics, lifestyle choices, environmental influences, and global technology perceptions.
The main results
This research has made major contributions in both theoretical aspects and practical management
The rise of social networking sites has fostered a dynamic environment for social interactions, enabling online users to engage through various platforms such as forums, communities, ratings, reviews, and recommendations (M N Hajli, 2014).
This study integrates constructs from the Technology Acceptance Model and the Theory of Planned Behavior with trust and social media principles to develop a model assessing the significance of social media in the context of electronic commerce and social commerce adoption Utilizing Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), the findings indicate that social media enhances customer trust, which in turn indirectly boosts purchase intentions through social networking sites.
Actually, social interactions can foster interconnectivity as well as establish trust and increase intention to use social commerce
Research indicates that trust generated through social media significantly influences buying intent, as individuals are more likely to purchase from vendors they trust within their networks and social networking sites (SNSs) This finding supports the initial hypotheses and addresses the research questions posed earlier Additionally, perceived usefulness emerges as a crucial factor that impacts buying intention, demonstrating a stronger effect through SNSs compared to trust Therefore, enhancing website quality and performance is essential for improving perceived usefulness in the minds of customers.
Data analysis reveals that trust significantly enhances perceived usefulness in social commerce When individuals feel trust, their intention to purchase increases, along with their perception of the platform's usefulness This highlights trust's critical mediating role in adopting social commerce Consequently, trust is essential in electronic transactions, as it directly influences purchase intentions and indirectly affects perceived usefulness.
This thesis argues that advancements in the Internet and the emergence of Web 2.0, along with social media, empower consumers to create value through collaboration and social interactions online Consumers enhance business success by co-creating value through their engagement and interactions within social platforms.
In today's digital landscape, customers actively create content on social media, sharing knowledge and experiences with peers while gaining easy access to valuable information This engagement significantly enhances the adoption of electronic commerce and fosters intentions for social commerce Individual interactions on social platforms promote online social support, building trust and ultimately influencing users' purchasing intentions Furthermore, the positive atmosphere cultivated by social media encourages more individuals to explore online environments and engage in social interactions.
Result contributions to management practices
The evolution of social media has transformed the dynamics between sellers and customers, shifting the roles of each party Unlike traditional marketing, where sellers primarily dictate the marketing mix decisions related to product, price, promotion, and place, social media encourages customer participation and oversight in these processes This platform empowers consumers to influence value-adding decisions, impacting not only sellers but also other customers and non-customers Additionally, user-generated content significantly enhances customer satisfaction and loyalty, particularly as consumer demands continue to rise, as seen with applications for the Apple iPhone and Android.
Consumers naturally connect with others who share similar motivations, and companies can capitalize on this by creating social communities for like-minded individuals A notable example is Dove's "Campaign for Real Beauty," launched by Unilever in 2004, which fostered discussions among women about self-esteem and societal beauty standards Similarly, in 2009, Procter & Gamble partnered with Facebook to promote Old Spice through a playful campaign, "Turn up Your Man Smell," which successfully garnered nearly 175,000 new fans within a week.
This research emphasizes that B2C e-commerce managers must gain valuable insights into leveraging social media resources for effective product and service promotion To achieve this, organizations should develop engaging and visually appealing interaction platforms that encourage consumer participation and facilitate the sharing of feedback and reviews.
This study provides valuable insights for managers to develop effective strategies for utilizing social networking sites (SNSs) to improve value co-creation with consumers, based on a newly developed theoretical model Additionally, the findings suggest that electronic vendors should enhance their website quality to attract customers and promote repeat purchases of their products and services (M N Hajli, 2014).
Managers can gain valuable insights into leveraging the value co-creation concept on social networking sites to encourage customers to make repeat purchases This strategic approach not only enhances customer loyalty but also drives sales revenue and boosts profits for organizations.
Limitations and directions for future research
Like many studies, this research has limitations that should be recognized for future investigations Notably, it utilized a five-point Likert scale; thus, future research should consider employing a seven-point Likert scale to achieve more comprehensive results.
Secondly, the model was tested with a convenience sample of online users who live in
To enhance the generalizability of the study's findings, it is essential to include a diverse range of online users from various cities and provinces in Vietnam, such as Can Tho, Da Nang, and Hanoi, as well as rural areas, utilizing probability sampling methods.
This study, with only 215 usable samples, has limited statistical power, which may hinder the ability to accurately determine the true effects of social media This raises ethical concerns, as untrustworthy research can be both ineffective and wasteful (Button et al., 2013) Future research should aim for a more careful and larger sample size to enhance reliability and validity.
Fourthly, the writer primarily recruited respondents who were from 20 – 57 years old
To enhance the value of insights, it is essential to diversify perspectives across both older and younger generations Future research could also explore the moderating effect of income as a socioeconomic variable, providing a deeper understanding of its influence.
In this study, more than 75% of responses were gathered via social media platforms such as Facebook, Zalo, and LinkedIn However, these data collection methods could introduce selection bias or non-response bias, particularly if some respondents struggle to comprehend the questionnaire Therefore, future research should carefully consider the data collection process from the chosen sample.
Conclusion
This model assesses the influence of social media features on the adoption of electronic business, highlighting the connection between information systems, marketing, and social media studies The research confirms that social interactions significantly affect consumer attitudes towards products and services, aligning with previous findings on consumer socialization (Wang et al., 2012) Additionally, the thesis emphasizes the critical role of trust in online and social commerce, suggesting that fostering trust through social media is essential for online vendors This aligns with earlier research (McCole et al., 2010) that demonstrates trust's impact on consumer purchasing behavior Furthermore, networking on social media platforms facilitates trust-building mechanisms essential for the adoption of electronic and social commerce.
Aaker, D A (2009) Managing brand equity: simon and schuster
Abeza, G., O’Reilly, N., & Reid, I (2013) Relationship marketing and social media in sport International Journal of Sport Communication, 6(2), 120-142
In their 1992 study, Adams, Nelson, and Todd explored the relationship between perceived usefulness, ease of use, and the utilization of information technology, highlighting key factors that influence user engagement Similarly, Ahn, Ryu, and Han (2007) examined how web quality and playfulness affect user acceptance in online retailing, emphasizing the importance of these elements in enhancing the online shopping experience Together, these studies underscore the critical role of user perception and website attributes in driving technology adoption and online consumer behavior.
Al-Somali, S A., Gholami, R., & Clegg, B (2009) An investigation into the acceptance of online banking in Saudi Arabia Technovation, 29(2), 130-141
Alalwan, A A., Rana, N P., Dwivedi, Y K., & Algharabat, R (2017) Social media in marketing: A review and analysis of the existing literature Telematics and Informatics
Research indicates that trust-building technology significantly impacts buyer behavior and price premiums in electronic markets (Ba & Pavlou, 2002) Additionally, Generation Y female consumers exhibit distinct decision-making styles that influence their purchasing choices (Bakewell & Mitchell, 2003) Furthermore, older adolescents are motivated to use social networking sites based on factors such as gender, group identity, and collective self-esteem, highlighting the interplay between social dynamics and online engagement (Barker, 2009).
Bendall-Lyon, D., & Powers, T L (2002) The impact of gender differences on change in satisfaction over time Journal of Consumer Marketing, 19(1), 12-23
Berkowitz, E N., & Schewe, C D (2011) Generational cohorts hold the key to understanding patients and health care providers: Coming-of-age experiences influence health care behaviors for a lifetime Health Marketing Quarterly, 28(2), 190-204
Blair, E., & Zinkhan, G M (2006) Nonresponse and generalizability in academic research Journal of the Academy of marketing Science, 34(1), 4-7
Bolton, R N., Parasuraman, A., Hoefnagels, A., Migchels, N., Kabadayi, S., Gruber, T.,
The research by Solnet (2013) explores the characteristics and social media usage patterns of Generation Y, proposing a framework for future studies in this area Additionally, Brosdahl and Carpenter (2011) examine the shopping behaviors of American males across different generational cohorts, highlighting significant differences in their shopping orientations Together, these studies contribute valuable insights into the evolving dynamics of consumer behavior among younger generations in the digital age.
Buttle, F (1992) Shopping motives constructionist perspective Service Industries
Button, K S., Ioannidis, J P., Mokrysz, C., Nosek, B A., Flint, J., Robinson, E S., &
Munafò, M R (2013) Power failure: why small sample size undermines the reliability of neuroscience Nature Reviews Neuroscience, 14(5), 365
Chang, M K (1998) Predicting unethical behavior: a comparison of the theory of reasoned action and the theory of planned behavior Journal of business ethics, 17(16), 1825-1834
Chang, M K., Cheung, W., & Tang, M (2013) Building trust online: Interactions among trust building mechanisms Information & Management, 50(7), 439-445
Chen, J., & Shen, X.-L (2015) Consumers' decisions in social commerce context: An empirical investigation Decision Support Systems, 79, 55-64
Chiu, C.-M., Huang, H.-Y., & Yen, C.-H (2010) Antecedents of trust in online auctions
Electronic Commerce Research and Applications, 9(2), 148-159
Chou, S., Chen, C.-W., & Lin, J.-Y (2015) Female online shoppers: Examining the mediating roles of e-satisfaction and e-trust on e-loyalty development Internet Research, 25(4), 542-561
Constantinides, E., & Fountain, S J (2008) Web 2.0: Conceptual foundations and marketing issues Journal of direct, data and digital marketing practice, 9(3), 231-
Corritore, C L., Kracher, B., & Wiedenbeck, S (2003) On-line trust: concepts, evolving themes, a model International journal of human-computer studies, 58(6), 737-
Davis, F D., Bagozzi, R P., & Warshaw, P R (1989) User acceptance of computer technology: a comparison of two theoretical models Management science, 35(8), 982-1003
Dekimpe, M G., Steenkamp, J.-B E., Mellens, M., & Abeele, P V (1997) Decline and variability in brand loyalty international Journal of Research in Marketing, 14(5), 405-420
Delgado-Ballester, E., Munuera-Aleman, J L., & Yague-Guillen, M J (2003)
Development and validation of a brand trust scale International Journal of Market
Durlak, J A., Weissberg, R P., Dymnicki, A B., Taylor, R D., & Schellinger, K B
(2011) The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions Child development, 82(1),
Eurostat (2009), “Internet usage in 2009 – households and individuals”, available at: http:// eeurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-QA-09-046/EN/KS-QA- 09-046-EN.PDF (accessed 15 October 2010)
Erdoğmuş, İ E., & Cicek, M (2012) The impact of social media marketing on brand loyalty Procedia-Social and Behavioral Sciences, 58, 1353-1360
Finch, H (2006) Comparison of the performance of varimax and promax rotations:
Factor structure recovery for dichotomous items Journal of Educational Measurement, 43(1), 39-52
Fournier, S., & Avery, J (2011) The uninvited brand Business horizons, 54(3), 193-207 Gefen, D (2000) E-commerce: the role of familiarity and trust Omega, 28(6), 725-737 Gefen, D., Karahanna, E., & Straub, D W (2003) Trust and TAM in online shopping:
An integrated model MIS quarterly, 27(1), 51-90
Gefen, D., & Straub, D W (2000) The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption Journal of the association for Information Systems, 1(1), 8
Gillin, P (2009) The new influencers: A marketer's guide to the new social media:
Gilly, M C., & Zeithaml, V A (1985) The elderly consumer and adoption of technologies Journal of Consumer Research, 12(3), 353-357
Gunelius, S (2010) 30-Minute Social Media Marketing: Step-by-step Techniques to
Spread the Word About Your Business: Social Media Marketing in 30 Minutes a Day: McGraw Hill Profesional
Habibi, M R., Laroche, M., & Richard, M.-O (2014) The roles of brand community and community engagement in building brand trust on social media Computers in Human Behavior, 37, 152-161
Hainla, L (2017) 21 Social Media Marketing Statistics You Need to Know in 2017
DreamGrow Np, 10 Mar 2017 Web https://www dreamgrow com/21-social- media-marketing-statistics
Hair, J F (2010) Black, WC, Babin, BJ, & Anderson, RE (2010) Multivariate data analysis, 7
Hajli, M N (2014) A study of the impact of social media on consumers International
Hajli, N (2015) Social commerce constructs and consumer's intention to buy
International Journal of Information Management, 35(2), 183-191
Research by Hallikainen and Laukkanen (2018) highlights the significant impact of national culture on consumer trust in e-commerce, emphasizing that cultural differences can influence online purchasing behaviors Similarly, Hansen, Jensen, and Solgaard (2004) explore the factors predicting online grocery buying intentions, comparing the theory of reasoned action with the theory of planned behavior Their findings suggest that understanding these psychological frameworks can enhance strategies for engaging consumers in the online grocery market.
Hassanein, K., & Head, M (2007) Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping International journal of human-computer studies, 65(8), 689-708
Helgesen, ỉ., & Nesset, E (2010) Gender, store satisfaction and antecedents: a case study of a grocery store Journal of Consumer Marketing, 27(2), 114-126
Hernández, B., Jiménez, J., & José Martín, M (2011) Age, gender and income: do they really moderate online shopping behaviour? Online information review, 35(1),
Hoang, T., Chu, N., M., N (2005) Data analysis in SPSS Statitisc publisher (in
Hoffman, D L., Novak, T P., & Peralta, M (1999) Building consumer trust online
Holt, D (2016) Branding in the age of social media Harvard business review, 94(3), 40-
Hsiao, C H., & Yang, C (2011) The intellectual development of the technology acceptance model: A co-citation analysis International Journal of Information Management, 31(2), 128-136
Immordino-Yang, M H., Christodoulou, J A., & Singh, V (2012) Rest is not idleness:
Implications of the brain’s default mode for human development and education
Joiner, R., Gavin, J., Duffield, J., Brosnan, M., Crook, C., Durndell, A., Lovatt, P
(2005) Gender, Internet identification, and Internet anxiety: Correlates of Internet use Cyberpsychology & behavior, 8(4), 371-378
Kaplan, A M., & Haenlein, M (2010) Users of the world, unite! The challenges and opportunities of Social Media Business horizons, 53(1), 59-68
Khan, I., & Rahman, Z (2016) E-tail brand experience’s influence on e-brand trust and e-brand loyalty: the moderating role of gender International Journal of Retail & Distribution Management, 44(6), 588-606
Kim, J B (2012) An empirical study on consumer first purchase intention in online shopping: integrating initial trust and TAM Electronic Commerce Research, 12(2), 125-150
Kirs, P., & Bagchi, K (2012) The impact of trust and changes in trust: A national comparison of individual adoptions of information and communication technologies and related phenomenon International Journal of Information Management, 32(5), 431-441
Kuruvilla, S J., Joshi, N., & Shah, N (2009) Do men and women really shop differently? An exploration of gender differences in mall shopping in India
International Journal of Consumer Studies, 33(6), 715-723
Lai, L S., & Turban, E (2008) Groups formation and operations in the Web 2.0 environment and social networks Group Decision and negotiation, 17(5), 387-
Laroche, M., Habibi, M R., & Richard, M.-O (2013) To be or not to be in social media:
How brand loyalty is affected by social media? International Journal of Information Management, 33(1), 76-82
Lazarevic, V (2012) Encouraging brand loyalty in fickle generation Y consumers
Leech, N., Barrett, K., & Morgan, G A (2013) SPSS for intermediate statistics: Use and interpretation: Routledge
Leech, N L., Barrett, K C., & Morgan, G A (2014) IBM SPSS for intermediate statistics: Use and interpretation: Routledge
Lu, Y., Zhao, L., & Wang, B (2010) From virtual community members to C2C e- commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention Electronic Commerce Research and Applications, 9(4), 346-
Lucas Jr, H C., & Spitler, V (1999) Technology use and performance: A field study of broker workstations Decision sciences, 30(2), 291-311
Mai, L W., & Zhao, H (2004) The characteristics of supermarket shoppers in
Beijing International journal of retail & distribution management, 32(1), 56-62 Mangione, T.W (1995) Mail surveys; Improving the quality (Vol 40) Thousand Oaks,
Mangold, W G., & Faulds, D J (2009) Social media: The new hybrid element of the promotion mix Business horizons, 52(4), 357-365
Martins, C., Oliveira, T., & Popovič, A (2014) Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application International Journal of Information Management, 34(1), 1-13
Maurer Herter, M., Pizzutti dos Santos, C., & Costa Pinto, D (2014) “Man, I shop like a woman!” The effects of gender and emotions on consumer shopping behaviour outcomes International Journal of Retail & Distribution Management, 42(9),
In the study by McCloskey (2006), the significance of ease of use, perceived usefulness, and trust is highlighted as critical factors influencing online consumer behavior, particularly among older customers The research examines the Technology Acceptance Model (TAM) and emphasizes that these elements are essential for enhancing user engagement and satisfaction in digital environments By understanding the needs of older consumers, businesses can improve their online platforms to foster greater acceptance and usage of technology.
McCole, P., Ramsey, E., & Williams, J (2010) Trust considerations on attitudes towards online purchasing: The moderating effect of privacy and security concerns
McKnight, D H., & Chervany, N L (2001) What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology International Journal of electronic commerce, 6(2), 35-59
Meyers-Levy, J., & Maheswaran, D (1991) Exploring differences in males' and females' processing strategies Journal of Consumer Research, 18(1), 63-70
Mitchell, V W., & Walsh, G (2004) Gender differences in German consumer decision making styles Journal of Consumer Behaviour: An International Research
Moorman, C., Deshpande, R., & Zaltman, G (1993) Factors affecting trust in market research relationships the Journal of Marketing, 81-101
Morgan, N A., & Rego, L L (2006) The value of different customer satisfaction and loyalty metrics in predicting business performance Marketing science, 25(5), 426-
Morrisey, B (2009) Brands seek fans on advertising Retrieved October, 12, 2009 Nguyen, T D (2007) Factors affecting the utilization of the internet by internationalizing firms in transition markets: Evidence from Vietnam Marketing Intelligence & Planning, 25(4), 360-376
Nguyen, T D., Barrett, N J., & Miller, K E (2011) Brand loyalty in emerging markets
Norris, M., & Lecavalier, L (2010) Evaluating the use of exploratory factor analysis in developmental disability psychological research Journal of autism and developmental disorders, 40(1), 8-20
O’Donnell, J (2006) Gen Y sits on top of consumer food chain; they’re savvy shoppers with money and influence USA Today, 11(2), 322-358
Pavlou, P A (2003) Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model International Journal of electronic commerce, 7(3), 101-134
Rainie, L (2003) Teenage life online: The rise of the networked generation Paper presented at the Youth Net Conference Singapore
Razak, N S A., & Marimuthu, M (2012) The relationship between co-creation value and Facebook shopping Procedia-Social and Behavioral Sciences, 65, 768-774 Rossiter, J R., & Percy, L (1987) Advertising and promotion management: McGraw-
Safko, L., & Brake, D (2009) The social media bible: ExecuGo media
Sashi, C (2012) Customer engagement, buyer-seller relationships, and social media
See-To, E W., & Ho, K K (2014) Value co-creation and purchase intention in social network sites: The role of electronic Word-of-Mouth and trust–A theoretical analysis Computers in Human Behavior, 31, 182-189
Sekaran, U., & Bougie, R (2016) Research methods for business: A skill building approach: John Wiley & Sons
Shiau, W.-L., & Luo, M M (2012) Factors affecting online group buying intention and satisfaction: A social exchange theory perspective Computers in Human Behavior, 28(6), 2431-2444
Shin, D.-H (2010) The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption Interacting with computers, 22(5), 428-438
Tabachnick, B G., & Fidell, L S (2007) Using multivariate statistics: Allyn &
Tavakol, M., & Dennick, R (2011) Making sense of Cronbach's alpha International journal of medical education, 2, 53
Taylor, D G., Lewin, J E., & Strutton, D (2011) Friends, fans, and followers: do ads work on social networks?: how gender and age shape receptivity Journal of advertising research, 51(1), 258-275
Van den Poel, D., & Leunis, J (1999) Consumer acceptance of the Internet as a channel of distribution Journal of Business Research, 45(3), 249-256
Van der Heijden, H., Verhagen, T., & Creemers, M (2003) Understanding online purchase intentions: contributions from technology and trust perspectives
European journal of information systems, 12(1), 41-48
Venkatesh, V., & Morris, M G (2000) Why don't men ever stop to ask for directions?
Gender, social influence, and their role in technology acceptance and usage behavior MIS quarterly, 115-139
Wang, X., Yu, C., & Wei, Y (2012) Social media peer communication and impacts on purchase intentions: A consumer socialization framework Journal of Interactive marketing, 26(4), 198-208
Weiser, E B (2000) Gender differences in Internet use patterns and Internet application preferences: A two-sample comparison Cyberpsychology and behavior, 3(2), 167-
Wolburg, J M., & Pokrywczynski, J (2001) A psychographic analysis of Generation Y college students Journal of advertising research, 41(5), 33-52
Wu, J.-J., Chen, Y.-H., & Chung, Y.-S (2010) Trust factors influencing virtual community members: A study of transaction communities Journal of Business Research, 63(9-10), 1025-1032
Zhang, Y (2005) Age, gender, and Internet attitudes among employees in the business world Computers in Human Behavior, 21(1), 1-10
Zhu, Y.-Q., & Chen, H.-G (2015) Social media and human need satisfaction:
The implications of social media marketing are significant for businesses, particularly in Vietnam, where platforms like Facebook play a crucial role in driving growth and engagement With the rise of internet users in the country, companies can leverage social media to enhance their marketing strategies and reach a broader audience The article discusses how businesses can utilize these platforms to overcome challenges and capitalize on opportunities in the digital landscape.
