Technology Acceptance Model and Factors Affecting Acceptance of Social Media: An Empirical Study in Vietnam

Abstract

The main objective of this study is to investigate the consumers' attitude and intention toward using social media by adopting the Technology Acceptance Model (TAM). This study further develops a comprehensive framework by identifying Knowledge Sharing Factors and Social Influence Factors as moderating variables that influence the relationship of attitude and behavior intention toward using social media. Based on the literature review, a research framework questionnaire is developed and conducted to test the research hypothesis in this study. The questionnaire survey method is employed to collect data from relevant social media, whereby 309 valid responses are used in the analysis. The results reveal that three TAM factors, namely, the impact of perceived usefulness, perceived ease of use, and perceived enjoyment are indeed the antecedents of attitude and behavior intention toward social media adoption. Also, the results indicate that social influence factors (social networking, social norms, and social trusts) and knowledge sharing factors (altruism, expected reciprocal benefit, and expected relationships) have moderating effect on the relationship between attitude and behavior intention toward social media. This research provides a comprehensive framework as important reference for professionals to develop social media marketing plan as well for academicians to conduct further validation.

1. Introduction

Nowadays, with the advancement of information technology, all barriers about communications are almost gone thank to the arrival of modern communications technologies. Information now is no longer exclusive to the mainstream press; but social media allows netizens to create their own information channels. Social media is an environment on the Internet with strong spread and strong interactive where people can create, use ad and share content they care about and are more accessible than ever. Social media also communicates as the old media, but it does not attempt to be a “speaker” to convey the message, it allows people to become spontaneous transmitters to spread the information and interact with friends in that community (Sharma & Rehman, 2017). Social media also support the exchange of information and knowledge, turning content users into content producers, from passive (one-way) to positive (two-way). It is reported that in the first quarter of 2020, the number of worldwide Internet users has exceeded 3.8 billion Internet users and one in every two people uses social networks every day. It is easy to see that today’s age is a time of technological boom and especially a boom in social networking. Because of the dramatic changes and development of social networking, it has inspired not only the researchers, but also the professionals in researching social-networking topics. However, the comprehensive model to explain consumer adoption of social media is yet to be finalized. Previous studies have pointed out a lot of key factors that impact customer intention on using social media. Different authors tried to explain the phenomenon of social media adoption from different perspectives. Rare of them have integrated the factors of social media adoption into a more comprehensive framework. This study intends to fill this research gap and discuss social media adoption from holistic point of view.

First of all, various studies have investigated TAM (Davis, 1989) to explain the attitude and behavior intention toward social media usage. Brodie et al. (2013) identified perceived usefulness, perceived ease of use, and perceived enjoyment to illustrate their influences on social media adoption. However, there is still a lack in comprehensive social media adoption model. Secondly, Le et al. (2020) have applied social influence factors to understand their influence on social media adoption. They identified subjective network, social norms and social trust as the key factors for adoption intention. However, Zhao, Yin, and Song (2016) claimed that those factors only advocate user’s attitude and behavior to some extent. If the level of social influences becomes extremely high, people may become overconfident which result in the lack of social media adoption. Similarly, social trust researchers also revealed that the influence of trust on performance is also limited to certain extent (To et al., 2020). Extremely high level of trust will cause overconfident in business activity. Therefore, more work is needed to fully understand the role of social influence factors on social commerce model.

Thirdly, the role of knowledge sharing factors has been extensively discussed in many previous studies. Zhang et al. (2017) illustrated knowledge sharing as the mediator variable in the research model. Widyani et al. (2017) concluded that knowledge-sharing factors directly affect innovative behavior. In addition, Widyani et al. (2017) argued that knowledge-sharing factors depend on the impact of observation, listening, and the level of social interaction. However, there are few studies exploring moderating role of knowledge sharing factors. Bradshaw, Chebbi, and Oztel (2015) presented that empirical research before demonstrated the roles of knowledge sharing factor is diverse and complex. But the moderating role of knowledge sharing is still limited and unconvincing. In addition, Yu, Yu and Yu (2013) suggested that the influence of attitudes on behavioral intention should be contingent upon certain moderating variable (such as Altruism, Expected Relationship and Expected Reciprocal Benefit).

Finally, to fill in the research gaps, this study proposes a comprehensive research model that encompasses TAM, social influence factors, and knowledge-sharing factors, and investigate the moderating effects of social influence factors and knowledge sharing factors for the effects of and attitude on behavior intention of social media adoption.

2. Literature Review

2.1. Technology Acceptance Model (TAM)

Davis (1989) developed TAM to explain or predict people behavior intention of adopting IT systems/products. Davis originally suggested two important factors that influence users’ adoption of information system. First, people tend to use a system/product depending on how they believe a system/product will help them perform their job better (Tahar et al., 2020), which is referred as perceived usefulness and is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance”. Second, even if users believe that a particular system or product is useful, they may also believe that the system or product is too hard to use or adopt (Lee, Hsiao, & Purnomo, 2014). Hence, they will consider the performance benefits of using this system or product are outweighted by the effort of using the application. Davis (1989) referred this perception as perceived ease of use. Hsu and Lin (2008) further mentioned that enjoyment is the level of emotional satisfaction of social network users, which consists of two factors: the using time and the using performance. Therefore, it can be said that social networking users are involved in the process of interacting with the social network as it brings joy and excitement (Lee et al., 2014). Lin and Bhattacherjee (2010) further contended that perceived enjoyment can be defined as the excitement and happiness derived from IT use.

The ease of use in social media depends very much on the interface design of the site, the training of using program, the presentation language, and the installation software needed (Brodie et al., 2013). If the usage environment is too difficult and time-consuming, it will lead to a negative attitude and eventually abandoning the intention to participate in social media (Aprilia & Kusumawati, 2021). The nature of social media is the interaction between the users. Singaraju et al. (2016) claimed that social media users pay more attention to how fast and easy to connet with other users and it will impede the approach process to social media (Singaraju et al., 2016). In other words, it can be concluded that a social media which can be easily accessed, solve user’s problem flexibly, observation interface easily and information clearly will play an important role in shaping positive attitude of users. The attitude of users is reflected by the satisfaction in usefulness of social media (Al-Ghaith, 2015). Users will greatly appreciate the usefulness of social media when it brings satisfaction to users and helps them improve productivity in work and study.

Tahar et al. (2020) suggested that the usefulness of social media is one of the first determinants and important of user social media adoption. In addition, Kumar and Shenbagaraman (2017) argued that user attitudes become positive when awareness of the usefulness of social media is accepted and appreciated. Furthermore, according to Hsu and Lin (2008), individuals would like to participate in social media activities because the interacting process can create fun and enjoyment. Even though some previous studies have underestimated the importance of enjoyment, fun and curiosity motivate consumers to use social media, researchers further confirmed that perceived enjoyment has a significant effect on social media usage (Al-Ghaith, 2015; Lin & Bhattacherjee, 2010). TAM admitted that three cognitive factors include perceived usefulness, perceived ease of use, and perceived enjoyment are the basis for deciding attitude toward using social media. Therefore, the following hypotheses were developed:

H1a: Perceived usefulness will positively affect attitude toward social media adoption.

H1b: Perceived ease of use will positively affect attitude toward social media adoption.

H1c: Perceived enjoyment will positively affect attitude toward social media adoption.

Various researches have confirmed the strong relationship between attitudes and behavior, that is, one’s attitude determines how one behave. This connection has been proved in many different contexts: web application usage (Hughes et al., 2012), visiting websites (Dixit & Prakash, 2018), social networking site usage (Al-Ghaith, 2015). Moreover, this relationship can be seen as evidence when observing and explaining human behavior that takes place daily. Guner and Acarturk (2018) showed evidence such as that many people only watch television performances they like; employees try to avoid jobs they do not like; students drop class on subjects they do not like, etc. Usually, attitudes and behaviors are united. This relationship is even more obvious when considering specific attitudes and behaviors. The more specific an attitude is, the easier it is to identify a related behavior, and the greater relationship between attitudes and behaviors is (Hsu & Lin, 2008). Therefore, the following hypothesis is developed:

H2: Attitude toward social media will positively affect intention toward using social media.

2.2. The Moderator Effects of Social Influence Factors

Udo, Bagchi, and Kirs (2012) argued that previous scholars have neglected the role of social influence in consumer adoption and usage, which represented a critical aspect for better understanding the real-world application. Al-Ghaith (2015) explained that psychological attachments, which are social influence factors are very critical in TAM.

Social networking is a network created to spread itself in the community through the interactions of its members (Hajli et al., 2017). Basically, social network is a place where members are connected and linked together to communicate a vast network of information with each other. Those can be blogs, message boards, forums and other social networking sites (SNS). “Social Trust” concept is a multi-faceted concept that can be defined differently based on the different field (Nicolaou, Ibrahim, & Van Heck, 2013). Hajli et al. (2017) concluded that “Trust” is the ultimate goal of other credible concepts in social science theory, such as in everyday life, consists of satisfaction and happiness, optimism, health, strong economy, gender equality, participation in education and social welfare, etc. Social trust is quite subjectively assessed by individual depending on the origin, the concern or the underlying belief of the evaluator (Nicolaou et al., 2013). Finally, social norm was studied, developed, and revised excessively in the marketing literature. Han and Kim (2010) referred social norms as an extension of subjectivity, which can be either clear, specific, or hidden rules in a community. The concept was further defined as the rules in a community that specify which behavior is appropriate or inappropriate for that community. Social norms are flexible, changeable over time, and can be transformed by culture, social class, and social groups. A skirt, a word or a behavior can be accepted to one group but may be rude or inappropriate to another group (Han & Kim, 2010).

Han and Kim (2010) explained that the online forum of an individual group can be considered as a miniature society where the forum rules are considered the norms in the miniature society. These standards make it easy for members to participate and in line with the general orientation of the whole community in the forum. Besides that, the credibility and accuracy of the postings or the high security of the identity will affect the trust of the users. In additional, SNS has acted as an inevitable communication medium used by individuals to express opinions and share ideas or information with others (Lee & Hong, 2016). As a matter of fact, social networking users mainly surf Internet to specify their relationship with others and check their followers on the sites or social networking. Thus, social media usages are more encouraged by connecting users with each other in creating or participating in the online communities or social networking. In brief, the experiences on social networking could enhance the positive effect of attitude on behavioral intention (Hughes et al., 2012).

Numerous studies support social influence factors as a moderator role on the relationship between attitude and behavioral intention. Yahia, Al-Neama, and Kerbache (2018) based on Theory Reasoned Action (TRA) model found that users with higher positive in social trust and social norm could be more interested to intend social media. To et al. (2020) stated that higher levels of experience will perform higher levels influences of attitude on behavioral intention. Further, Hughes et al. (2012) supposed that users tend to weigh and evaluate comments from others to form an intention to engage in social media. Users intend to use the value of using social media as they rely on comments from members of the virtual community, which stimulate the relationship between attitudes and behavioral intentions (Hughes et al., 2012). It means if the trust in social media is positive, it will make the influence of attitude to behavior become stronger. Therefore, based on the above discussions, the following hypothesis are developed:

H3a: Social networking has a moderating effect on the relationship between attitude and behavioral intention.

H3b: Social norms have a moderating effect on the relationship between attitude and behavioral intention.

H3c: Social trust has a moderating effect on relationship between attitude and behavioral intention. the

2.3. Knowledge Sharing Factors

Knowledge sharing refers to the intention to deliver, obtain, and communicate knowledge of an individual. Ma and Chan (2014) defined knowledge sharing as the flow of knowledge from a source in such a way that it is learned and applied by the other. Nowadays, customers tend to rate or share information about a product or service they have experienced on social networks, or on the media of the organization where provide those product or service (Aprilia & Kusumawati, 2021). This sharing is not compulsory or costly. However, customers are still involved because they want other people to have beautiful experiences they already have or they want to warn other people about the potential issues or problems of the products (Brodie et al., 2013). Han et al. (2010) argued that sharing information or evaluating on a virtual community consists of three main elements as following.

Altruism is a variation in social research and is described as contribution to society since it derives entirely from the inside of an individual to bring the benefits to others without getting back anything (Ma & Chan, 2014). In the context of the sharing of knowledge of an altruistic behavior, individual sometime unconditionally assists others in their own community, they can self-identify and enhance their influence (Li, 2015). Furthermore, reciprocity is described as a behavior, which related to fairness, that is giving and receiving. Zhang et al. (2017) explained that individuals will perceive the fair benefits only if the process of the exchange and sharing is judged by two parties involved, that is the individual who shares and the other who receive. Wang and Noe (2010) argued that relationships in a virtual community are where individuals with similar interests or personal goals gather and share information, contributing benefits to common development of the virtual community, or may be their own benefits. The relationship in this virtual community is the core value of most social media (Khan et al., 2016). Li (2015) demonstrated that interactivity and effective interaction are key factors in building an online community relationship that social media that creates strong interactions and ties virtual relationships into real gains will success.

Lee and Hong (2016) argued that users will tend to use social media when their attitudes are positive in helping others through the use of social media. This means, with the higher level of altruism, the influence of attitude on behavioral intention toward using social media will be amplified. Users will automatically stops using if they get an error in social media connecting without any information to improve it or no one can help them to improve it, because this situation made them upset, it means the attitude with this situation was negative. Therefore, they will stop to using (Kwahk & Park, 2016).

Intention to use social networking also comes from user relationships. They want to connect and keep in touch with friends and family in a convenient way (Yahia et al., 2018). Therefore, if their relationship is positive, they will form a positive attitude. A positive attitude will have a positive impact on the intention of social networking (Yu et al., 2013). With this situation, Wang and Noe (2010) suggested that the relationship could be served as the moderator for the influence of attitude on behavioral intention. Mutual help on social media will influence the relationship between attitudes and behavioral intention (Moghavvemi et al., 2015). When an individual perceives the help and support from other community member while facing difficulties, he/she will feel reassured and excited to continue access to social media (Zhang et al., 2017). The influence of attitude and behavioral intention will also be much higher in high reciprocal interactive between users rather than low reciprocal interactive (Yahia et al., 2018). Based on these evaluations, this paper proposed the following hypotheses:

H4a: Altruism has a moderating effect on the relationship between attitude and behavioral intention.

H4b: Expected reciprocal benefit has a moderating effect on the relationship between attitude and behavioral intention.

H4c: Expected relationship has a moderating effect on the relationship between attitude and behavioral intention.

3. Research Methodology

To test the hypotheses, seven research constructs and respondents’ demographic information were operationalized. The measurement scales were adapted from literature review. All measurement items will be made on a seven-point Likert scales from 1 = strongly disagree to 7 = strongly agree. Technology acceptance factors were measure by six items of perceived usefulness, five items of perceived ease of use, and six items of perceived enjoyment adapted from Lee et al. (2014). Each attitude and behaviour intention construct consists of four items from Lee et al. (2014) and Hsu and Lin (2008). Five items of social networking (Ma & Yuen, 2009), six items of social norms (Zhao et al., 2016) and six items of social trust (Nicolaou et al., 2013) were used to measure social influence factors. Finally, knowledge-sharing factors were measured by altruism (five factors from Ma and Chan, 2014), expected reciprocal benefit (six factors from Moghavvemi et al., 2015) and expected relationships (five factors from Moghavvemi et al., 2016).

To test the hypotheses, this study conducted an online questionnaire survey to students or employees currently employed by firms or other institutes. Respondents were invited through a convenient sampling. Respondents were asked to express their opinions about their social media adoption via email. After three months, this study received 309 valid responses (23 questionnaires were deleted due to incomplete and unrealizable answers). From 309 respondents, 177 were female (57.3%), and most of the respondents were aged between 18 and 25 years old (54.0%). More than half of respondents obtained a bachelor degree (57.6%) with month income of below US $500. Most of respondents are Vietnamese (54.0%) and Taiwanese (36.6%).

After verifying the description, this study conducted a test of the validity and reliability of the variables. This paper used factor loading analysis and reliability test by using SPSS software. The resulting values must satisfy the following criteria: factor loading >0.6, Eigenvalue >1, cumulative explained variance >50%, Item to total correlation >0.5, Cronbach’s Alpha >0.6 (Hair, Ringle, & Sarstedt, 2011). If any variable that does not meet the criteria mentioned above, it was deleted from further analysis. It shows that no variables were excluded from the further analysis.

4. Results and Finding

4.1. Evaluation of the Measurement Model

This study conducted an assessment of the measurement model, to test the reliability and fit of the theoretical model against the actual data reflected by the numbers. Measurement evaluation focuses on values such as: the coefficient of determination (R²), Cronbach’s Alpha, composite reliability (CR), and the average variance extracted (AVE) (Hair et al., 2011). The results of this study showed that the Cronbach’s Alpha reliability of all observable variables satisfied the condition, the values vary from 0.768 (ATT) to 0.895 (ST). All of observed variables were significant at >0.5 level, with the highest observation variable PE (0.613), the lowest being PU (0.544). R-square reflects the degree of impact of independent variables on the dependent variable, resulting table show that R-square ranges from 0.296 to 0.783. The strongest impact is in PU, the lowest on PE. Finally, CR is from 0.852 to 0.917. The highest CR is on PE and the lowest is on ATT, it means CR also meet the condition.

4.2. Evaluation of the Structural Equation Model

Using a sample of 309, a non-parametric bootstrapping procedure was performed with 5000 sub-samples to obtain the statistical significance of each path coefficient for hypothesis testing. In total, four hypotheses are accepted.

This research developed the hypothesis for illustrating that the factor of technology acceptance factors had positive influence as an antecedent on attitude toward social media adoption (H1a, H1b, H1c). As shown in Table 1, the technology acceptance factors were significant influence on attitude toward social media adoption included perceived usefulness (β = 0.445, t = 2.435), perceived ease of use (β = 0.419, t = 5.925) and perceived enjoyment (β = 0.524, t = 4.556). Based on the results the hypotheses H1a, H1b, H1c were supported.

Table 1: The Moderating Role of Social Influence Factors

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

This research developed the hypothesis for illustrating that attitude had positively influence to behavioral intention toward social media adoption. The empirical results show that attitude also was significant influence on behavioral intention toward social media adoption (β = 0.601, t = 7.243). Therefore, the hypothesis H2 was supported.

4.3. Moderating Effect

In order to test the moderating role of social influence factors and knowledge sharing factors, this research uses Hierarchy regression analysis on the SPSS tool to conduct data analysis. Furthermore, this research tested the role of moderator variables by adding independent variable, moderating variable, and interactive effect variable (independent × moderating variable).

The social influence factors included three components such as social networking, social norms, and social trust. First, social networking was considered to have moderating effect on the relationship between attitude and behavioral intention toward social media adoption. The result in Table 1 proved that attitude toward social media adoption (β = 0.681, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Model 2 showed that social networking (β = 0.770, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Model 3 displayed that social networking (β = 0.659, p < 0.001), attitude toward social media adoption (β = 0.669, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Model 4 indicated that the interaction effect of social networking and attitude toward social media adoption (β = 0.403, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Besides that, the values D.W is ranging from 1.5 to 2.5, while VIF is smaller than 2. Therefore, there is no autocorrelation and multi-collinearity phenomenon. Based on the results above, the moderating role of social networking was supported.

Using the same categorizing method for other social influence moderators, it could be concluded from Table 1 that the moderating role of social norms and social trust were supported and respondents perceived higher social norms and higher social trust tended to perform higher positive attitude, higher behavior intention. Thus, hypotheses H3a, H3b, and H3c are supported.

The knowledge-sharing factors included three components such as altruism, expected reciprocal benefit, and expected relationship. First, altruism was considered as the moderate affected on the relationship between attitude and behavioral intention toward using social media. The result in Table 2 proved that attitude toward social media adoption (β = 0.681, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Model 2 showed that altruism (β = 0.541, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Model 3 displayed that altruism (β = 0.578, p < 0.001), attitude toward social media adoption (β = 0.400, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Model 4 indicated that the interaction effect of altruism and attitude toward social media adoption (β = 0.279, p < 0.001) positively and significantly affected behavioral intention toward social media adoption. Besides that, the values D.W is ranging from 1.5 to 2.5, while VIF is smaller than 2. Therefore, there is no autocorrelation and multi-collinearity phenomenon. Based on the results above, the moderating role of altruism was supported.

Table 2: The Moderating Role of Knowledge Sharing Factors

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

The results in Table 2 also supported the moderating role of expected reciprocal benefit and expected relationship on the relationship of attitude and behavior intention toward social media adoption. Thus, hypotheses H₄a, H₄b, and H₄c are supported.

5. Conclusion

Several conclusions are drawn from the results of this study. First of all, this study reconfirmed the Technology Acceptance model in social media adoption. Dixit and Prakash (2018) concluded that perceived ease of use would create a powerful effect on attitude toward using social networking. Besides that, Moghavvemi et al. (2017) concluded that perceived enjoyment can create a stronger effect on users’ attitude toward hedonic system which would eventually promote consumers’ adoption intention toward using social media. It can be understood that attitudes of users be affected only when they were aware of the value that social media has to offer. Besides, it needs to meet users’ needs in terms of utility and entertainment aspect, looking for information. Attitude is also confirmed as the most important determinant of behavior (Celebi, 2015). These results are in agreement with the view of Al-Ghait (2015) that if social media users develop a positive attitude in understanding the SNS usage, they will be more likely to participate in the SNS. Similarly, Guner and Acarturk (2018) agreed that an attitude (negative or positive) can create an (negative or positive) impact to behavior.

Additionally, the social influence set of moderators includeing social networking, social trust and social norms also impacted on the relationship between attitude and behavioral intention toward using social media. Regarding the role of social norms, Lin (2006) argued that for the social forum online or social networking official and reputable should have the rules or the attitudes to behaviors properly to preserve the distinct culture of that space. In reality, social media requires specific standards that participants need to pay attention to follow, as they a contain culture. This process supported that limited the turmoil of the person who does not have the same goal. From this point of view, Celebi (2015) argued that the social norms of a particular virtual community would support the participant to be free to create, share information and knowledge freely without fear of information distortion and deviation from the original concept of the community.

Finally, the results of this study also prove that the knowledge-sharing factors including altruism, expected reciprocal benefit, and expected relationship moderate the relationship between attitude toward using social media and behavioral intention toward using social media. In fact, people who have many relationships on social media or many experiences with that relationships on a virtual community usually have very positive about their intentions of engaging in other social media. Wang and Noe (2010) argued that support from fellow participants needed to help a newcomer access cyberspace. From that, newcomers would feel more interested and the behavioral intention of reaching a social media outlet could be easier.