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Article

The Integration of the Technology Acceptance Model and Value-Based Adoption Model to Study the Adoption of E-Learning: The Moderating Role of e-WOM

by
Ying-Kai Liao
1,
Wann-Yih Wu
2,
Trang Quang Le
2,3,* and
Thuy Thi Thu Phung
3
1
Program of International Business, Nanhua University, Chiayi County 62249, Taiwan
2
Department of Business Administration, Nanhua University, Chiayi County 62249, Taiwan
3
Faculty of Sport Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(2), 815; https://doi.org/10.3390/su14020815
Submission received: 20 December 2021 / Revised: 7 January 2022 / Accepted: 9 January 2022 / Published: 12 January 2022
Browse Figure
Versions Notes

Abstract

:
The Technology Acceptance Model (TAM) has lately been utilized in a number of studies to investigate why people reject or adopt new technologies like mobile commerce or e-learning. However, several studies have found weaknesses in TAM’s ability to predict consumers’ purchase intention behavior. To compensate for TAM’s weaknesses, this study presents a model that integrates all of TAM’s components with the Value-Based Adoption Model (VAM). The perceived benefits and sacrifices were considered to provide a list of the implications for both researchers and e-learning service providers. Furthermore, the moderating role of e-word of mouth was utilized to examine the relationship between attitude, intention, perceived value, and intention toward e-learning, in order to match the current circumstances with the growing popularity of social networks. This study was conducted with a quantitative analysis by using data collected from survey 417 e-learning consumers. Except for perceived fee, which has a negative effect on perceived value, the results demonstrate that all hypotheses of latent correlations in TAM and VAM were strongly significant. Furthermore, attitude and perceived value have a significant role in determining consumer adoption of e-learning. Consumers’ perceived value will be driven by the high and low levels of the moderating influence of e-word of mouth, influencing their intention toward e-learning. Since e-learning is an effective sustainable education system, the result of this study can provide a good solution to facilitate e-learning in current and future conditions.

1. Introduction

E-learning is a virtual learning method using information communication technologies (ICT) such as a computer, cell phone, or tablet. E-learning has been shown to have many advantages over traditional learning, including making it possible for users to study anytime, anywhere, at a low cost. Recently, the important role of e-learning has been demonstrated through the COVID-19 pandemic [1]. It offers a unique approach that can replace traditional learning and minimize direct interaction between teachers, and students. Numerous studies have been conducted to determine the factors which influence consumers’ adoption of e-learning technologies using TAM to provide universities and educational institutions with a way to help users accept e-learning [2,3,4]. TAM is the most extensively utilized theoretical framework model for assessing new technology adoption. Especially in the context of adopting e-learning, many extensions of TAM have been developed to better suit the current context. [5] examined 107 previous studies before proposing a general extended TAM for e-learning. However, other studies have argued that TAM is only suitable for individual use and acceptance, not for organizations and companies [6]. According to [7], in the current context, TAM still has many flaws because it does not take into account how social, individual, and cultural factors affect consumers’ acceptance of technology. Furthermore, previous studies have applied extended-TAM to evaluate consumers’ attitude toward e-learning (ATE) [8,9], which attempted but failed to demonstrate that perceived ease of use (PEU) has a significant influence on consumers’ ATE. Furthermore, the meta-analysis research by [10] confirmed that PEU is the weakest factor in determining both the intention toward e-learning (IE) and the actual usage. Recently, the study of [11] presented TAM combined with Innovation Diffusion Theory to investigate students’ IE by adding initial factors acting on two extrinsic motivating factors, perceived usefulness (PU) and PEU, to complement the efforts of previous studies. The results of their study showed that PU and PEU positively affect IE and it also indicated that intrinsic motivation affects certain factors, such as perceived enjoyment (PE), which are often ignored or receive limited attention by researchers. Therefore, most studies focus on the TAM extension and external factors, ignoring perceived value (PV). Especially in the current context, when information is received quickly and easily through social networks, consumers can easily raise awareness about the value of adopting new technology. They can also consider new technology for themselves by accepting the fee and risks that are worth what they get in return.
This study considers the adoption of e-learning from not only a technology acceptance perspective but also a value perspective. It incorporates TAM [12], which has been used when testing consumers’ adoption behavior in new technology and the Value-Based Adoption Model (VAM) [13], which was developed to predict new technology adoption by using extrinsic factors, intrinsic factors, effort, and monetary and non-monetary factors. In addition, this study proposes to test the effect of e-word of mouth (eWOM) as a moderator on the relationship between ATE and IE, and the link between PV and IE as well. We replace the social influence variable often proposed by previous studies to better suit the context of social networks. Moreover, during the COVID-19 pandemic, a sustainable learning system is extremely needed for students and users [14], has pointed out that e-learning helps to resolve the problems we have faced in the pandemic, such as limited risk potential of direct interaction between instructors and users as well as social distancing policies. The findings of [15,16] demonstrate how an e-learning system can enhance students’ and users’ perception of sustainability learning in the COVID-19 crisis.
The research’s objectives are as follows: (a) to develop and evaluate an integration model of TAM and the VAM on consumers’ adoption of new technologies; (b) to explore the influence of each component of the perceived benefits and sacrifices on PV; (c) to compare the level impact between attitude and PV to find out the key factors that influence consumers’ intention toward new technologies; (d) to determine the moderating effects of eWOM on the relationship between ATE and PV in terms of the IE.

2. Literature Review and Hypotheses Development

2.1. Technology Acceptance Model (TAM)

Reference [12] developed the TAM model in predicting consumers’ attitudes toward new technology. PU and PEOU are the model’s core constructs for determining consumer attitudes toward new technologies. In the research subject of information technology, TAM has been thoroughly studied and recognized. Therefore, TAM is a powerful model widely used in assessing the consumer acceptability of new technology services in ITC [17]. However, given the current situation, the variables in TAM have become simple and have a limited ability to predict a customer’s psychology during decision-making in a practical context [18]. TAM has been extended many times to apply to different technologies. In the current context, e-learning is a topic that attracts a large number of researchers interested in the use of various new technologies. Reference [19] conducted a systematic review of the use of artificial intelligence (AI) in online education. Additionally, reference [20] emphasized the necessity of using AI in e-learning to better user experience and personalize learning material recommendations in order to increase learning efficiency and sustainable learning environments. Among them, TAM is still the most frequently used to predict adoption intentions for a variety of technologies, particularly in the application of technology to support learning performance. Reference [21] utilized an extended TAM to assess teachers’ intentions to incorporate augmented reality and virtual reality technologies into their classroom teaching. Additionally, reference [22] used the TAM-3 model to determine the desire of participants in e-learning to use cloud storage. On the topic of IE, Table 1 summarized some of the research that used the TAM model. The proposed variables in their extended TAM research model mainly focus on: subjective norms, PE, self-efficacy, and some quality-related factors such as service quality and content quality. The study results show that applying TAM to the research model in the field of e-learning is completely appropriate. This study proposes an extended TAM model by combining it with VAM to emphasize the role of PV in research into new technology applications.

2.2. Value-Based Adoption Model (VAM)

According to [13], adoption intention can be predicted through PV. PV was defined based on a balance of benefits and sacrifices and the classification of motivations into extrinsic and intrinsic subsystems. Consumers gain benefits that are not just useful but also thrilling and enjoyable. Sacrifices, which include both monetary and non-monetary aspects, are the prices users incur while utilizing new technology. These costs include money, time, and the intangible costs associated with attempting and implementing new technology. The VAM was designed in response to the limitations of TAM, considering factors affecting PV. PV is beyond the point of maximum value. More specifically, TAM was proposed based on PU and PEU variables to explain and predict customer intent, while the VAM is based on perceived benefits and sacrifices. This includes both positive, and negative influences to bypass the limitations of TAM. Customers can assess the value they receive, resulting in a more accurate intention to use new technology [29]. The VAM model has been merged with other models and applied in a range of studies based on consumer value perspectives. Reference [29] proposed TAM and VAM be integrated into applications of Internet of Things (IoT) smart home services; [30] devised the Acceptance and Use of Technology (UTAUT) and the VAM in the context of AI research; and [31] developed an integration model of VAM and transaction cost theories. Considering that, this research proposes a combination of TAM and the VAM to support each other. The illustration of the research framework for this study is shown in Figure 1.

2.3. Relationships among Variables in TAM

Many prior studies applied TAM to investigate which factors affect e-learning orientation. These studies have proven that PU and PEU are two critical variables for understanding customer behavior in the application of new technologies. PU is the customer’s perception of how using the technology will help them improve and gain more benefits [11]. Besides that, PEU is defined as “the degree to which a users feel that utilizing a e-learning system will be uncomplicated”. Rather than spending time learning how to utilize the system, users can begin using it immediately, reaping the benefits of learning such as time, money, and effort savings. This enhances their PU in association with the e-learning system; the more user-friendly the system, the greater the PU [32]. Additionally, the positive effects of PEU on PU have been demonstrated in e-learning systems [33]. According to [34] attitude is defined as a psychological emotion that is channeled through consumers’ assessments of the innovation. When consumers’ perceptions of these two constructs improve, their ATE is more aggressive. This could also boost a user’s receptivity to an e-learning system. In the context of e-learning, both PEU and PU have been proven to have a considerable positive effect on ATE [35,36]. In TAM, intention is essential in determining how new technology is actually used [11]. PU and PEU also have a key role in customer ATE. Besides, consumers’ views that adopting an e-learning system will result in positive results for their learning performance, and the positive influences of PU and PEU drive IE in this study [37]. Additionally, the influence of ATE on IE has been confirmed [38]. Therefore, the following hypotheses are proposed:
Hypothesis 1 (H1).
Perceived ease of use positively impacts perceived usefulness.
Hypothesis 2 (H2).
Perceived ease of use positively impacts attitude towards e-learning.
Hypothesis 3 (H3).
Perceived usefulness positively impacts attitude towards e-learning.
Hypothesis 4 (H4).
Attitude towards e-learning positively impacts intention toward e-learning.
Hypothesis 5 (H5).
Perceived usefulness positively impacts intention toward e-learning.

2.4. Perceived Benefits and Perceived Sacrifice in VAM

2.4.1. Perceived Benefits

Reference [13] proposed VAM with two antecedent factors: perceived benefits and perceived sacrifices to assess PV. Accordingly, in this study, we defined perceived benefits including PU and PE. In the e-learning context, the degree to which users feel that learning through an e-learning system will be able to enhance their knowledge and help them accomplish the goal was described as PU. For this reason, consumers may consider the benefits of using e-learning to be greater than what they have to pay. Reference [39] found that PU has been considered an important determinant of PV. Some recent research [40,41,42] has found that the relation of PU to PV is fully significant. We therefore propose this hypothesis:
Hypothesis 6 (H6).
Perceived usefulness positively impacts perceived value.
Many researchers have suggested that when a user experiences more enjoyment using an IT system, he/she has increasingly intense motivations to interact with IT [43]. In this research, PE is referred as to the consumer’s self-consciousness of fun, pleasure, and delight when participating and interacting in an e-learning system. Besides, while PU plays an important role in the utilitarian dimension of PV, PE is an essential dimension in e-learning users’ perception of hedonic PV [44]. E-learning systems should be designed to provide a pleasant learning experience, an interesting method of learning, and appealing technology because consumers do not want to use a system that causes them stress or fatigue [45]. According to [46,47,48], PE has a significant positive impact on PV. Therefore, the hypothesis has been presented:
Hypothesis 7 (H7).
Perceived enjoyment positively impacts perceived value.

2.4.2. Perceived Sacrifice

The intention of the behavior of consumers through new technology services is influenced by the value they received from the service, which is a perceived fee (PF) [49]. If the value they get from e-learning is higher than the costs they spend, then purchase intent will be formed. If the value is lower, they will refuse the service. In the context of e-learning, service providers have to take care of the balance between expenses and the value that consumers receive. The costs include not only money but also other factors such as time and effort [13,50]. According to [43,51] the degree of PF has a considerable negative impact on customers’ PV. Thus, the hypothesis was given:
Hypothesis 8 (H8).
Perceived fee has a negative influence on perceived value.
Additionally, the financial costs and the popular opinion of using a technological service like e-learning limits the spread of new technology [52]. The financial risks include the original purchase price and maintenance costs [53]. When consumers make purchase decisions, they are generally concerned about the product’s efficiency and the financial consequences of the purchase, especially with new technologies such as e-learning [47]. This concern includes the perceived risk (PR) [48,54]. PR often arises from system hackers targeting the poor security of the system in order to steal consumer information such as personal information, credit card details, etc. These risks have a strong negative impact on consumer IE. This is a problem service providers should prioritize [47,48]. Thus, the PR of adopting e-learning will affect PV. Therefore, we can hypothesize:
Hypothesis 9 (H9).
Perceived risk has a negative impact on perceived value.

2.5. Perceived Value on Intention toward E-Learning

The possibility of a consumer purchasing a specific product in the future is measured by purchase intention [55]. Similarly, when consumers sense the worth of a product or brand, they are more likely to acquire it [47]. Consumers attempt to achieve the maximum benefit. PV is reflected by comparing benefits and sacrifices and forming an intent based on those comparisons. Moreover, consumers can shift their attitudes and emotions from the benefits of the product and create PV. Thus, if consumers can receive trustworthy PV when they purchase e-learning services, the services will bring many benefits for an e-learning institution such as creating a good brand image, the loyalty of consumers, profit, and competitiveness. In the e-learning context, PV is proportional to the intention. The higher the value, the greater the intent [56,57]. Research has indicated that PV has a strongly significant influence on IE [58,59]. Therefore, we can hypothesize:
Hypothesis 10 (H10).
Perceived value positively impacts intention toward e-learning.

2.6. The Moderating Role of eWOM

Reference [60] have defined eWOM as the positive or negative reviews, and comments of potential users or actual users about a product or a company via social networks or online tools. Recently, with the rapid growth of social networks, consumers have had the tendency to look for information and suggestions from others. They initially find information from those who are in close relation to them such as family members, friends, and colleagues [61], then, they search for information more widely. Usually, they search for information from those who influence them or internet influencers. The research of [62] pointed out that consumers rely more upon user-generated eWOM than firm-generated communications. Moreover, in social media channels, both the quality and quantity of eWOM impact consumers’ purchase decisions [63]. Thus, this study only considered user-generated positive eWOM to explore the moderator role of eWOM. Previous studies had shown the positive influence of eWOM on IE [64,65,66], and positive eWOM will shape consumer ATE [67]. Furthermore, the study of [68] indicated that eWOM has a strong relationship with PV. According to [69], when customers have a favorable attitude toward online lecture websites, they are willing to recommend the e-learning course to others in their social networks. According to [70] students’ e-learning directly impacts eWOM. This proves that when the strong ties of eWOM give a high recommendation about an e-learning system or e-learning service the overall value will increase value perception among consumers. Thus, the following hypotheses are proposed:
Hypothesis 11 (H11).
eWOM positively moderates the relationship between attitude and intention, such that attitude toward e-learning influences the intention toward e-learning more strongly when eWOM is higher.
Hypothesis 12 (H12).
eWOM positively moderates the relationship between perceived value and intention, such that perceived value influences the intention toward e-learning more strongly when eWOM is higher.

3. Methodologies

3.1. Data Collection

The data collection was conducted at Ton Duc Thang University, Vietnam from August to November 2019. The respondents targeted are undergraduate students, masters students, and Ph.D. students as well. They had to meet the following criteria. First, they had to have at least 4 months of involvement in an e-learning service provided by Ton Duc Thang University; because all students are required to complete at least 4 h per week in a semester of self-learning at the library via an e-learning system, most of the exercises and learning materials were uploaded into the system. Therefore, each student had to get used to and have good experiences with e-learning. Second, they had to have completed at least one paid online course offered by another institution. The author designed an online questionnaire using Google Forms and sent it directly to target respondents. In order to keep the sample from being duplicated, the target respondents had to log into their university student email account to finish the questionnaire. A total of 417 valid samples were returned. Specifically, there were 417 respondents—236 males and 181 females—and most of them are bachelor students with an average age of 18 to 25 years old. 198 respondents (47.5%) had at least one year of experience in e-learning. The demographic profile of the respondents is detailed in Table 2.

3.2. Research Instrument

Nine latent constructs comprised of 35 items were developed based on previous research literature. The author adopted five items of PU, five items of PEU, four items of ATE, and four items of IE from [12]. Three items of PE were adopted by [71]. Three items of PR were gleaned from [34]. PF with three items were picked from [72]. Four items for PV were adopted from [13]. Four items of eWOM were based on [73,74]. Measurement for each item was done using a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.

3.3. Analytical Methods

The authors used partial least squares (PLS) and the Smart-PLS software version 3.0 application to analyze the data [75]. PLS-SEM is a technique that is well suited for predictive model research as well as exploratory studies. Its advantage is that it is capable of processing models with multiple independent and dependent variables as well as variables with multicollinearity. The bootstrapping algorithm was used to evaluate the path significance of the hypothesized model on a total of 5000 re-samples and 417 cases, as recommended by [76].
In addition, this study examined the common method bias using Harman’s single factor test [77]. The data indicated that a single factor extracted 43.96 percent of the total variance, well below the 50% threshold. Thus, it was established that there is no risk of methodological bias in this investigation.

4. Data Analysis and Results

4.1. Measurement Model

Table 3 shows the result of the assessment of the construct reliability and validity of the model by following the criteria of [76]. Factor loadings (0.739–0.940) were greater than the recommended value of 0.7 [78]. Furthermore, the CR of each structure (0.784–0.954) was greater than 0.7. This proved that the scale has good internal consistency reliability. To reach a convergent validity, the AVE must be greater than 0.5 [78,79]. Therefore, these findings show the AVE (0.643–0.833) met the required criteria.
Second, to evaluate the discriminant validity the author used both the Fornell–Larcker criterion [80] and the heterotrait–monotrait ratio [81]. Table 4 shows the square root of the AVE of each variable is higher than the other correlation values of other constructs. Moreover, Table 4 shows that the highest value of HTMT is 0.841, lower than 0.9, in line with the suggestion of [81]. Thus, all variables had discriminant validity. In sum, the results indicate adequate model fit, good reliability, and sufficient convergent and discriminant validity.
Finally, the author conducted a multicollinearity evaluation before running the structural model analysis. According to [82], multicollinearity can occur if the tolerance is less than 0.20 or if the coefficient of magnification variance (VIF) exceeds 5. Table 3 shows the VIF ranging from a minimum value of 1.426 to a maximum value of 4.207. Given that these values are lower than the threshold of 5, they confirm that multicollinearity was not a concern.

4.2. Hypothesis Testing Results

As shown in Table 5, the model explains 60% of the variance in intention, 63.8% of the variance in PV, 60.1% of the variance in ATE, and 61.2% in PU. Both PEU and PU had a significant positive effect on the ATE (β = 0.464, β = 0.356, p < 0.01, respectively). Thus, H2 and H3 were supported. Besides that, PEU had a positive effect on PU (β = 0.782, p < 0.01). Thus, H1 was supported. The results also show that PU and ATE have a positive impact on IE (β = 0.224, β = 0.203, p < 0.01, respectively). Therefore, H4 and H5 were supported. Additionally, PU (β = 0.468, p < 0.01) and PE (β = 0.224, p < 0.01) directly influenced PV. The negative influence of PF was unexpectedly not significant on PV (β = 0.040). Thus, H6 and H7 were supported but H8 was rejected. Conversely, PR (β = 0.096, p < 0.01) was found to have a significant negative effect on PV. It was found that PV (β = 0.381, p < 0.01) had a positive effect on IE, supporting H10. The moderating impact of eWOM on the relationship between ATE and IE had no statistical significance as the results related to H11 are shown in Table 5. In terms of H12, the moderating role of eWOM on the relationship between PV and IE (β = 0.118, p < 0.01) was supported and significant.

5. Discussion and Implications

The primary aims of this study are as follows: Firstly, we are investigating the feasibility of integrating TAM and VAM for forecasting consumer IE. Secondly, examine the effect of perceived benefits and sacrifices on PV. Thirdly, comparing the extent of influence of ATE and PV on IE. Finally, we identify the moderator role of eWOM on the association between ATE and PV in terms of e-learning intention. Several findings have been illustrated. In the relationships among constructs in TAM, the findings of this study complement earlier research, indicating that PEU has a considerable positive impact on PU [32,83,84] which indicates that e-learning systems designed with functions that are easier to interact with will generate more consumers’ PU than complex systems. The results also point out that PEU and PU also have a strong impact on ATE [85]. Meanwhile, the impact of PU on IE is greater than that of ATE, which means that in e-learning, PU is an extrinsic factor that should be prioritized to be considered. This outcome is also consistent with past research findings. [86,87].
Additionally, the findings of this study indicate the strong impact of PV on IE [88,89]. The significant role of PV in the new technology is also evident in this study. The findings will help e-learning service providers better understand how to maximize their customers’ PV. Based on the results of two benefit variables, the positive influence of PU and PE on PV, these results are consistent with previous studies [47] that show that the benefits acquired from using online services are very important when predicting consumers’ PV. Additionally, the impact of PU on PV is higher than PE [48]. Thus, by providing rich learning content and paying attention to the learners’ experiences, the interest will increase the learners’ PV. Besides that, the content of e-learning should be arranged in separate detailed sections and focus on the experience of consumers to bring a sense of comfort to the learning process of consumers in order to increase PE. Thereby, customers will have better ATE and PV will increase. Unsurprisingly, the results show that PV has a stronger positive impact on IE than ATE. This finding supports the results of [58]. It also proves that PV is the strongest factor influencing IE to explore consumers’ adoption of new technologies based on two opposing factors, including both positive and negative effects, which TAM is lacking in this respect. Simultaneously, the results show the negative influence of PR on PV [47,48,54]. On the contrary, PF had no significance on PV. Although these findings are inconsistent with those of previous studies [39,51,90], based on the consumers’ perspective, they indicate that e-learning institutions should give more consideration to PR than to PF when developing an e-learning system. Indeed, consumers want to be protected, and they usually conduct a risk assessment before they use a service rather than considering the fees. If learners truly appreciate learning through an e-learning system, they are willing to trade-off fees in order to obtain greater learning performance.
Finally, the findings of this study indicate that eWOM has a positive moderating effect on the link between PV and IE. Consumers who have been impacted by increased eWOM via social media will have a greater PV in terms of their willingness to accept e-learning. In contrast to the influence of eWOM on PV and IE, consumers rarely seek information through social networks to reinforce their attitudes towards the application of new technologies because their attitudes are made up of beliefs about various factors, including ease to use and usefulness. These two factors are based on individual self-efficacy because only individuals know their abilities and what is truly useful to them [5,91]. With the rapid growth of social networks in recent years, eWOM has become more powerful than ever because people can easily connect to each other. They talk, share, and discuss e-learning, and information can go viral. The PV factor can be positively influenced by the eWOM of previous users or social influencers because consumers tend to search for information to consider whether using e-learning is really worth what they spend in order to maximize the value they receive. So, this will strongly affect the decision making of other customers.
The findings of this research provide several theoretical implications for researchers. This study confirms the evidence and feasibility of integrating TAM and VAM in research on technology adoption in the e-learning context, as the study of [29] has demonstrated in IoT-based smart home services. In addition, this study also shows evidence of the important role of PV in influencing IE, which helps researchers in future studies consider the role of PV in the application of technology. In particular, applications that are conducted in areas that include hedonic and utilitarian value, such as research of intention to use gamification of e-learning, virtual reality in e-learning, or AI e-learning training assistant. Furthermore, the finding of the moderating role of eWOM strengthens the positive relationship between PV and IE. The significant role of eWOM has a great influence on digital marketing, increasing the PV of users and strengthening the intention to use the e-learning system.
Some managerial implications can be inferred from this study’s findings for universities or e-learning provider institutions. This study demonstrates how to design an e-learning system around intrinsic and extrinsic incentives, perceived benefits, and perceived sacrifices in order to attract consumers’ intention towards e-learning. Firstly, providers of e-learning services must prioritize developing learning content that is appropriate to learners’ needs. Additionally, the e-learning system must be user-friendly and adaptable to a broad range of consumers. Because learning is a long process, if the content is excessively tough or unappealing, it reduces learners’ PV. This research finding highlights how e-learning service providers can use the findings to gain a better understanding of how to optimize users’ PV. Secondly, in order to reduce the anxiety levels of consumers due to PR, e-learning service providers should provide high-security platforms that either protect consumers’ private information or payment information. Thirdly, the wide presence of the e-learning course on the homepage and social networks is essential in order to attract users to give reviews and feedback on the quality of the e-learning content. This brings great benefits to e-learning service providers based on the influence of eWOM. The affiliation programs are recommended to be implemented in the current context, especially for e-learning services. Finally, service providers should anticipate the impact of eWOM because it can reduce the PV of consumers towards e-learning adoption through negative comments or reviews. Therefore, there should be a support team for consumer care to answer questions as well as resolve the dissatisfaction of consumers who use the service. Positive comments or reviews can be collected from previous users at the same time to increase PV for potential users. Additionally, the combination of these two models has been shown to be an appropriate approach to increase students’ and users’ PV toward sustainability e-learning based on the lifetime learning process.

6. Conclusions

This study integrated TAM and VAM in order to discover critical elements affecting a consumer’s adoption of e-learning. The findings answer the research questions and contribute to the evidence that confirms the relationship between variables in TAM and VAM. On the other hand, the results support the integration of TAM and VAM in practice and confirm the moderating role of eWOM on the link of PV and IE. The research also explored the moderator effects of eWOM on the relationship of ATE and IE, but the result had no statistical significance. As mentioned above, [29] have confirmed the combination of the TAM and the VAM models; thus, the results of this study support it. Therefore, in practice, e-learning service providers can rely on it to improve service quality, optimize customer experience, and provide information security. There were some limited points of this research. Firstly, because the data was collected only at a university in Vietnam, it cannot reflect the exact behavior of consumers across cultures. However, in terms of theory application, [92] suggested that it is motivated by a need for scientific information about events and interactions that occur in a range of real-world contexts. The objective of theory application is to determine the theory’s adequacy; hence, the sample’s representativeness of the population is insignificant. Future research should conduct a wider investigation across more countries. Secondly, this study only dissects the general perceived fee and risk. The inclusion of more specific factors like technicality fees and private risk would have provided clearer observations. Additionally, the next research should examine the role of control variables like age and income on IE. Because consumers of different ages or incomes will have a different perspective, especially in terms of new technologies adoption, older consumers might be much more hesitant than their younger counterparts. Finally, this study omits an important variable in reinforcing consumer ATE in the IE that is the e-servicescape environment, where consumers directly experience the quality of e-learning services and enhance perceived. Therefore, future research should examine the role of e-servicescape as an independent variable that directly affects ATE or as a moderator that strengthens the relationship of ATE and IE. Despite these limitations, the results of this study will have implications for further theoretical research and practical implications for future research.

Author Contributions

Conceptualization: Y.-K.L., W.-Y.W., T.Q.L., T.T.T.P.; Methodology: Y.-K.L., W.-Y.W., T.Q.L., T.T.T.P.; Data curation: Y.-K.L.; Formal analysis: Y.-K.L.; Project administration: Y.-K.L., W.-Y.W.; Resources: Y.-K.L., T.Q.L., T.T.T.P.; Supervision: Y.-K.L., W.-Y.W.; Validation: Y.-K.L., W.-Y.W., T.Q.L.; Software: T.Q.L.; Visualization: T.T.T.P.;Writing—original draft: Y.-K.L., W.-Y.W., T.Q.L., T.T.T.P.; Writing—review & editing: Y.-K.L., W.-Y.W., T.Q.L., T.T.T.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 00815 g001 550
Figure 1. Conceptual proposed model.
Figure 1. Conceptual proposed model.
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Table
Table 1. Previous studies review.
Table 1. Previous studies review.
StudyFactorsContext and Extension ModelResults and Findings
[23]SE, SN, PE, PEU, PU, IN, computer anxiety, experience,e-learning, General Extended TAM
  • SN positively affects PU
  • Experience, PE positively affects PEU, PU
  • Computer anxiety negatively affects PEU and PU
  • SE positively affects PEU
  • PU and PEU positively affects IN
[24]AT, perceived behavioral control, SN, INe-learning, theory of planned behavior with social identity and social bonds.
  • User’s greater IN invest more time using e-learning technologies.
  • AT positively impacts IN.
  • Perceived behavioral control positively impacts IN.
  • SN positively impacts IN.
[25]PEU, PU, AT, INe-learning, TAM, and Elearning User Interface
  • PEU positively impacts PU
  • PEU positively impacts AT.
  • PU positively impacts AT and IN.
  • AT positively impacts IN.
[26]Educational quality, SEQ, SQ, IQ, PEU, PU, satisfaction, INe-learning, TAM, and Information Systems success model
  • Educational quality, SEQ, SQ, and IQ positively affect satisfaction.
  • PEU positively affects PU
  • SEQ, IQ, SQ, satisfaction, and PU positively affect IN
[27]SE, PE, PEU, PU, INCloud e-learning application, TAM, and usability factors.
  • SE and PE positively affect IN to use Cloud e-learning applications.
[28]IQ, SQ, PEU, PU, user interface, INe-learning in the COVID-19 pandemic. E-TAM and Information Systems success model
  • IQ positively impacts PU
  • SQ and user interface affect PEU.
  • PEU positively affects PU.
  • PEU and PU directly affect IN.
Note: SE = self-efficacy; SN = subjective norm; AT = attitude; IN = intention; IQ = Information quality; SEQ = service quality; SQ = system quality.
Table
Table 2. Respondents’ characteristics.
Table 2. Respondents’ characteristics.
ClassificationRespondents
FrequencyPercentage (%)
Gender
Male23656.6%
Female18143.4%
Age
18–25 years old31675.8%
25–35 years old8821.1%
Above 35 years old133.1%
Education
Bachelor31877%
Master8821.3%
Doctoral71.7%
Income
300–500$34783.2%
500–1000$6214.9%
Higher than 1000$81.9%
E-learning experience
3–6 months9323.3%
7–12 months12630.2%
Higher than 1 year19847.5%
Table
Table 3. Reliability measures for the measurement model.
Table 3. Reliability measures for the measurement model.
Variables/ItemsFactor LoadingCRAVEVIFR2
Attitude toward e-learning (α = 0.917) 0.9410.800 0.601
ATE1: I believe it would be prudent to use e-learning for the purpose of improving my learning performance.0.890 3.470
ATE2: Studying via e-learning is a prudent choice0.905 3.757
ATE3: I am an advocate of e-learning.0.892 3.055
ATE4: I’m interested in taking courses that incorporate e-learning.0.889 2.949
Intention toward e-learning (α = 0.933) 0.9520.833 0.600
IE1: In general, I intend to make future use of e-learning.0.924 4.207
IE2: If the opportunity presents itself, I intend to utilize e-learning in the future.0.900 3.289
IE3: I anticipate utilizing e-learning in the future.0.913 3.481
IE4: I intend to use e-learning in the future to supplement my knowledge.0.914 3.865
Perceived enjoyment (α = 0.900) 0.9380.833 N/A
PE1: I have fun interacting with E-learning0.918 3.183
PE2: Using E-learning provides me with a lot of enjoyment0.940 3.846
PE3: I enjoy using E-learning0.880 2.328
Perceived ease of use (α = 0.940) 0.9540.807 N/A
PEU1: I have no difficulties comprehending how to use the e-learning system0.902 3.474
PEU2: I have no trouble getting the e-learning system to perform what I want0.882 3.078
PEU3: My interaction with the e-learning system is easy and straightforward0.899 3.465
PEU4: Learning how to use the e-learning system is simple for me0.904 3.758
PEU5: I find the e-learning system to be very user-friendly0.904 3.595
Perceived fee (α = 0.844) 0.8990.749 N/A
PF1: The fee for using e-learning is excessive0.872 2.160
PF2: The fee for using e-learning is reasonable0.922 1.955
PF3: I am satisfied with the cost of using e-learning0.798 1.965
Perceived risk (α = 0.790) 0.8740.700 N/A
PR1: I feel unsafe when using E-learning0.835 1.909
PR2: I am worried that private information would be leaked when using E-learning0.923 2.033
PR3: I am worried about personal information suffering from
unauthorized use when using E-learning		0.743 1.426
Perceived usefulness (α = 0.933)0.8840.9490.7902.9620.612
PU1: By using the e-learning system, I will be able to complete educational activities more quickly0.910 3.775
PU2: By using the e-learning system, I will be able to enhance my learning performance0.876 2.836
PU3: Using an e-learning system simplifies the process of learning course content0.870 2.811
PU4: By using an e-learning system, I will be able to maximize my learning productivity0.903 3.640
PU5: By using the e-learning system, I will increase my learning efficacy
Perceived value (α = 0.918) 0.9420.803 0.638
PV1: Considering the fee, I believe that using E-learning is a valuable idea.0.893 2.821
PV2: E-learning is advantageous to me due to the general amount of effort I need to put in0.900 3.009
PV3: E-learning is worthwhile for me based on the amount of time I need to spend0.897 3.064
PV4: E-learning provides me with good value in general0.896 3.063
e-word of mouth (α = 0.823) 0.8780.643 N/A
e-WOM1: I have recommended my e-learning course on social media to others without being asked.0.817 1.766
e-WOM2: I offer favorable comments and information about my e-learning experience on social media to those who ask for my advice.0.855 1.666
e-WOM3: I often persuade my contacts on social networks about the benefits of e-learning can improve their learning performance.0.739 1.775
e-WOM4: When others people talked about the benefits of e-learning on social media, I have made positive comments.0.792 1.782
Note: α = Cronbach’s α; CR = Composite reliabilities; AVE = average variance extracted; N/A = Not available.
Table
Table 4. The assessment of discriminant validity.
Table 4. The assessment of discriminant validity.
ConstructMeanSDATEIEPEPEUPFPRPUPVe-WOM
ATE3.880.9480.8940.6940.7860.7990.2530.1500.7770.7480.101
IE3.900.8860.6430.9130.7400.7660.1650.2140.7190.7610.221
PE3.830.9780.7140.6780.9130.8410.1560.1500.7890.7920.099
PEU3.880.9490.7420.7180.7740.8980.2230.1330.8340.8140.085
PF4.200.6930.2220.1660.1520.2150.8650.0910.1940.1770.103
PR3.500.6330.1350.1920.1290.122−0.0670.8370.1240.2130.206
PU3.890.9530.7190.6720.7240.7820.1800.1150.8890.8070.100
PV3.730.8820.6880.7060.7200.7580.1730.1940.7490.8960.079
e-WOM3.500.732−0.092−0.211−0.084−0.0800.045−0.065−0.096−0.0560.802
Note: The diagonal values represent the square roots of AVEs, above the diagonal are the values of HTMT values, and below the diagonal are the correlation coefficients between the construct values.
Table
Table 5. Summary of hypotheses testing results.
Table 5. Summary of hypotheses testing results.
HypothesesPath Coefficientt-Valuep Values
H1. Perceived ease of use → Perceived usefulness0.782 ***26.8880.000
H2. Perceived ease of use → Attitude toward e-learning0.464 ***7.8540.000
H3. Perceived usefulness → Attitude toward e-learning0.356 ***5.7070.000
H4. Attitude toward e-learning → Intention toward e-learning0.203 ***3.8220.000
H5. Perceived usefulness → Intention toward e-learning0.224 ***4.6220.000
H6. Perceived usefulness → Perceived value0.468 ***9.0670.000
H7. Perceived enjoyment → Perceived value0.363 ***7.0360.000
H8. Perceived fee → Perceived value0.040 n.s1.2550.210
H9. Perceived risk → Perceived value0.096 ***3.2130.001
H10. Perceived value → Intention toward e-learning0.381 ***8.5470.000
Moderating effect
H11. e-word of mouth → Attitude toward e-learning on Intention toward e-learning−0.015 n.s0.3380.736
H12. e-word of mouth → Perceived value on Intention toward e-learning0.118 ***2.5390.011
*** p < 0.01. Note: ns= non-significant
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Liao, Y.-K.; Wu, W.-Y.; Le, T.Q.; Phung, T.T.T. The Integration of the Technology Acceptance Model and Value-Based Adoption Model to Study the Adoption of E-Learning: The Moderating Role of e-WOM. Sustainability 2022, 14, 815. https://doi.org/10.3390/su14020815

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Liao Y-K, Wu W-Y, Le TQ, Phung TTT. The Integration of the Technology Acceptance Model and Value-Based Adoption Model to Study the Adoption of E-Learning: The Moderating Role of e-WOM. Sustainability. 2022; 14(2):815. https://doi.org/10.3390/su14020815

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Liao, Ying-Kai, Wann-Yih Wu, Trang Quang Le, and Thuy Thi Thu Phung. 2022. "The Integration of the Technology Acceptance Model and Value-Based Adoption Model to Study the Adoption of E-Learning: The Moderating Role of e-WOM" Sustainability 14, no. 2: 815. https://doi.org/10.3390/su14020815

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