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Article

Learners’ Continuous Use Intention of Blended Learning: TAM-SET Model

by
Xiulan Chen
1,
Xiaofei Xu
2,
Yenchun Jim Wu
3,4,* and
Wei Fong Pok
5
1
School of Foreign Language, Huaqiao University, Quanzhou 362021, China
2
School of Business Administration, Huaqiao University, Quanzhou 362021, China
3
MBA Program in Southeast Asia, National Taipei University of Education, Taipei 106, Taiwan
4
Department of Hospitality Management, Ming Chuan University, Taipei 111, Taiwan
5
Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(24), 16428; https://doi.org/10.3390/su142416428
Submission received: 8 November 2022 / Revised: 4 December 2022 / Accepted: 5 December 2022 / Published: 8 December 2022
(This article belongs to the Special Issue Sustainable E-learning and Education with Intelligence)
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Versions Notes

Abstract

:
Blended learning (BL) combines online and face-to-face teaching and learning and is thought to be an effective means to cultivate learners’ sustainability literacy. The success of BL relies on learners who take the initiative to participate in the learning process. Therefore, this study aims to examine learners’ acceptance of the BL system. The technology acceptance model (TAM) and the self-efficacy theory are combined to construct a systematic model to determine the learners’ continuous intention to adopt BL. Seven constructs are identified, i.e., course quality (CQ), technical support (TS), perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SE), self-efficacy (SE), and behavioral intentions (BI). A survey was conducted using a close-ended questionnaire, and 461 valid responses were collected from Huaqiao University’s undergraduate students. Covariance-based structural equation modelling was performed. The empirical findings show that except for the hypothesis regarding the connection between PU and PEOU, all the other hypotheses are verified. CQ stands out as having the greatest positive effect on PEOU, which highlights the importance of CQ for BL. The study also confirms that PU significantly impacts SA, SE, and BI, and both SA and SE significantly influence BI. Based on these results, some suggestions are provided for educators and administrators as to how to better design BL systems to strengthen sustainability education.

1. Introduction

Blended learning (BL) is one of the emerging trends in education. According to Oxford Dictionary, BL can be defined as a style of education in which students learn via electronic and online media, as well as traditional face-to-face teaching. However, it is challenging to accurately define BL (or hybrid learning) due to dynamic combinations of online and face-to-face components. One scholar points out that “blended learning is a thoughtful combination of face-to-face learning experience in class and online learning experience” [1]. Web-based technologies, such as free or charging online courses, MOOCs, electronic textbooks, websites, and social media apps, are often adopted in blended learning. The mix of online and face-to-face components depends on the teaching objectives, curriculum, teachers’ teaching experience, students’ learning styles, etc. The key to blended teaching design is to cultivate students’ learning ability, with students as the main body and “learning as the center,” so that students can adapt to and develop the habit of active learning under the environment of deep integration of information technology and traditional teaching. Therefore, it is crucial to determine learners’ attitudes toward BL to help them build a firm belief in the adoption and continued usage of BL.
There is consensus among most universities that BL can be a source of sustainable education [2]. Currently, the world is encountering challenges in the protection of world sustainability. One of the key responsibilities of higher education is to develop sustainability literacy, i.e., the knowledge and skills that enable them to build a sustainable future for society, among students. Therefore, it is crucial for educational institutions to understand the students’ perceptions, attitudes, and continuous use intention related to blended learning.
Sustainability literacy (SL) has become a popular issue in education [3,4,5]. Critical thinking, self-study, and cooperative learning are needed to build one’s SL. BL has been advocated in higher education as an effective way to raise students’ awareness of environmental challenges and help them form the courage, confidence, and qualities to deal with environmental issues [4,5]. Accelerated environmental deterioration calls for environmental professionals who have mastered skills related to problem-solving, critical thinking, creative thinking, self-control, communication, and teamwork to solve environmental issues. Higher education shoulders the responsibility to help develop students’ life-long SL [6,7,8]. BL, which allows students to pursue their studies in a flexible, exploratory, collaborative way, is thought to be able to enhance learners’ SL.
As an innovative invention in education, online learning is drawing more and more attention due to its potential for self-enrichment. However, since learning is about communication and cooperation, traditional face-to-face instruction is regarded by most learners as indispensable. In addition, some technical problems regarding online communication are difficult to solve [9]. Researchers have found that learners still want offline learning to help them to improve and consolidate the knowledge acquired online [10]. Through online learning, students become familiar with course content in advance, discuss related topics in a virtual community with peers or teachers, complete assignments, review course materials, etc. When they meet face-to-face in the classroom, they are more confident that they are able to achieve the planned learning outcomes. Yet, no two BL modes are identical in design, due to variations in the characteristics of the courses and learners, as well as the goal of the learning. In such cases, only the learners can provide meaningful input to evaluate the effectiveness of the BL mode.
BL can be an effective way to solve the problem of large class size and increase learning outside the traditional face-to-face learning environment [11,12,13]. If properly adopted, BL can transform higher education into a more flexible and agile state, which allows for quick adaptation to the changes in the learning environment and eventually, improves its cost-effectiveness [14]. What is certain is that well-designed BL has the potential to achieve the best learning outcomes. However, BL design is a complex subject involving many factors that determine its effectiveness, and what motivates learners’ continuous use intention remains unclear. The learning experience between traditional face-to-face courses and online learning differs significantly, so a good implementation of BL is bound to encounter challenges, and these must be solved jointly by administrators, educators, and learners.
Since the late 1990s, discussions on BL have evolved from the application of technology to the concern of learners’ learning motivations and strategies [15,16]. The implementation of BL involves three parties—administrators, teachers, and students. Administrators need to provide reliable and accessible technology infrastructure for a smooth learning process, whereas educators take the responsibility of designing the blended course based on course features and learner backgrounds. Learners are the actual executors of BL. Their acceptance determines, to large extent, the achievement of expected learning outcomes. Therefore, it is necessary to identify the factors affecting learners’ intentions to adopt the BL mode. Currently, there are limited studies focusing on the factors influencing the acceptance of BL from the learners’ perspective [17]. Most of the studies focus on the administrators’ perspective or the educational management issues of BL [18,19].
As BL involves self-regulated learning, learners’ self-efficacy beliefs are an indispensable attribute in the BL system. So far, the literature concerning this issue has not received enough attention, and some scholars merely focus on relative factors, such as the function of collaborative learning, social presence, and self-regulating without combining them into a holistic system [20,21,22]. To identify the constructs that determine the continuous intention to adopt BL, the technology acceptance model (TAM) is adopted in this study because of its simplicity, strong explanatory power, and ease of operationalization. Due to the learner-centered nature of BL, learners’ self-efficacy determines the influence of their motivation, confidence, and satisfaction. To reveal learners’ acceptance of BL, the level of learners’ self-efficacy should be included.

2. Theoretical Model and Hypotheses Development

The technology acceptance model (TAM) was first proposed by Davis (1986) in his doctoral dissertation [23]. The underlying theory of this model is the rational behavior theory. The model also assimilates other theories, i.e., expectation theory, self-efficacy theory, input-output theory, and change adoption theory. It mainly consists of three independent constructs, namely perceived useful (PU), perceived ease of use (PEOU), and attitude to using technology (AT).
The self-efficacy theory (SET) sheds light on the development of TAM, as Davis noticed that self-efficacy determines the acceptance of technology-connected systems. SET is a subset of Bandura’s (1986) social cognitive theory [24]. According to this theory, the two key determinants of behavior are self-efficacy and outcome expectancy. Obviously, SET is often adopted when the behavior intention is concerned. In the context of BL, learners’ engagement plays an important role in achieving the expected outcome. Nonetheless, how the BL mode leads to the continuous behavior intention of the learner remains a question.
Various types of research have examined learner satisfaction with online learning environments [25,26]. However, few studies have focused on the blended learning context, despite the fact that more higher education institutions are adopting BL because of its flexibility and low cost, while maintaining the learner-teacher face-to-face interaction [17]. BL includes both online and offline learning, so it poses more challenges to educators and learners. There are studies investigating the factors that affect learner satisfaction in the context of BL, but these are limited to some exterior factors such as online resources, learning support services, etc. [11,18,27,28]. There is limited research exploring the attributing factors for learner satisfaction from a theoretical background. Despite the fact that the BL mode is widely implemented in many universities, the continuous use intention of learners is insufficiently studied. This paper aims to establish a TAM-SET model to examine learners’ continuous use intention regarding the BL mode. The findings will be used to propose solutions to boost their intentions.
In this study, seven constructs are identified in the research model: course quality (CQ), technological support (TS), perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SA), self-efficacy (SE), and behavior intention (BI). The hypothesis model is shown in Figure 1.

2.1. Course Quality (CQ)

Based on Davis’s TAM (1986) [23], two external constructs are identified in the context of BL: course quality (CQ) and technology support (TS). CQ can be measured from five aspects: course characteristics, teaching and learning design, interaction platform, course content, and learning resources. The external constructs directly impact the two main constructs (PU and PEOU) that determine the attitudes and indirectly affect the use intention.

2.2. Technological Support (TS)

Blended learning requires the adoption of tools, e.g., learning management systems, network conferences, digital textbooks, simulations, and games. These pose great challenges to administrators. They need to provide resources and timely technological support (TS) in accordance with the functional characteristics of the teaching and learning tasks. A smooth network connection, system stability, compatibility, convenience, and friendly navigation settings are within their routine working obligations.

2.3. Perceived Usefulness (PU)

Perceived usefulness (PU) refers to the degree to which users subjectively believe that using a system can improve their work performance [29]. In most cases, PU is considered to play the most decisive role affecting users’ attitudes [30]. PU has been proven to be an essential construct to increase learners’ self-regulation in e-learning environments [31]. TAM proposes that both PU and PEOU have a significant impact on SA.

2.4. Perceived Ease of Use (PEOU)

Perceived ease of use (PEOU) can be defined as the degree to which users subjectively believe that using a particular system will require little effort [29]. PU and PEOU are often regarded as the two most important constructs in TAM. The extant literature proves that individuals are more inclined to adopt new technology if they think it is easy to use [29]. Studies concerning TAM have also suggested that PEOU positively influences PU [29,32].

2.5. Satisfaction (SA)

Attitude refers to the positive or negative feelings an individual has in the process of performing a certain behavior. Satisfaction (SA) is usually conceptualized as the aggregate of a person’s feelings or attitudes toward the various factors that affect a person’s decision. In this study, SA predicts a person’s willingness to continuously use a certain system when it involves some degree of self-motivation. Learner satisfaction is proven as one of the important factors contributing to the effectiveness of blended and purely online courses [33]. It has an important reference value for educators to improve course design and for administrators to improve service quality to ensure a satisfactory outcome.

2.6. Self-Efficacy (SE)

Self-efficacy is an individual’s judgment regarding his/her ability to engage in certain behavior, and it determines to what degree an individual will persist and commit efforts after he/she has made a choice. Self-efficacy, as an important component of emotion, plays a crucial role in the learning process because of its impact on learners’ motivation, self-regulation, and academic performance. The self-efficacy theory (SET) was first developed in 1977 by Albert Bandura, who proposed SET as the determining force for behavior change [34]. SET emphasizes the relative importance of personal factors, but acknowledges that behavioral and environmental factors have profound effects on outcomes as well.

2.7. Behavioral Intention (BI)

Behavioral intention describes an individual’s future intention to engage in certain behavior. It can be an immediate antecedent of actual behavior [35]. In the context of BL, it encompasses the likelihood that learners will again use the BL mode when it is made available to them. They may get involved in a wide variety of learning activities, such as self-regulated study, communication with teachers and friends, interaction online and offline, sharing information and materials, etc.

3. Research Methodology

This paper aims to establish a combined TAM-SET model to predict learners’ intentions to continuously use BL. After we identified the key contributing constructs and analyzed the relationships among them, we proposed how to enhance learners’ enthusiasm and interest in BL. A survey was adopted in this empirical study to test the hypotheses among these constructs. The subjects of this study were undergraduates from Huaqiao University who experienced using the BL mode for at least a total of one year. The courses offered in Huaqiao University can be divided into public courses (including compulsory courses and elective courses), professional basic courses, and professional elective courses. Most of the courses are available on MOOC platforms, which offer abundant online resources for blended teaching and learning practices. Students can directly use these existing MOOCs to complete online learning, and teachers can design classroom discussions and interactive exchanges based on MOOC resources. A total of 472 students responded to the online questionnaires, and 461 valid responses were included in the analysis. The data were then analyzed using IBM SPSS Statistics (Chicago, IL, USA) 24.0 and Amos (Chicago, IL, USA) 21.0. The reliability, the convergent and discriminant validity, the goodness-of-fit, and the truth of the hypothesis were tested.
The scale compilation involved two main processes. First, the contributing constructs of the model were sorted out based on the existing literature [36,37,38,39,40,41,42,43,44]. Then, 22 students who participated in BL for at least one year were selected as interviewees to explore possible constructs for the scale. By combining the findings of the two processes, seven factors were identified that might affect blended learning adoption from the learners’ perspective, namely course quality (CQ), technological support (TS), perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SA), self-efficacy (SE), and behavioral intentions (BI). Most of the items under each construct were adapted from well-tested scales, and some new items were designed according to the characteristics of BL used in the present study.
To further improve the validity of the questions, the draft of the questionnaire was sent to experts for their suggestions and improvement. After some amendments, the final questionnaire contained 7 constructs with 26 sub-items using a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree). CQ contains 5 sub-items; TS contains 3 sub-items; PU contains 3 sub-items; PEOU contains 4 sub-items; SA contains 3 sub-items; SE contains 5 sub-items, and BI contains 3 sub-items. The complete questionnaire consisted of two parts: a survey regarding the subjects’ demographic characteristics, including gender, major, grade, and scale. The data were analyzed to test their reliability and validity. Confirmative factor analysis (CFA) was performed to examine the construct validity and composite reliability of the model.

4. Analysis and Findings

4.1. Reliability and Construct Validity Analysis

The reliability and validity of the scale were evaluated using reliability and convergent validity criteria. Reliability was established by calculating Cronbach’s alpha to measure the internal consistency of the measurement. A pilot study was carried out in which 30 students who completed undergraduate courses conducted with the BL mode for at least one year were invited to complete the online questionnaire posted on a questionnaire-sharing website called “The Scale Star.” As Hair et al. pointed out, Cronbach’s alpha coefficient must be at least 0.7 [45]. As shown in Table 1, Cronbach’s alpha values for all constructs were above 0.7. The Cronbach’s alpha for CQ, PEOU, and BI are above 0.9, indicating a high degree of internal consistency of these constructs.
Next, a massive survey was carried out, and 483 students participated in the online survey. A total of 461 valid questionnaire results were collected after excluding invalid answers or those that were not completed within the time limit. The first part of the survey consisted of the demographic characteristics of the respondents. The results indicated the representativeness of the subjects. As is shown in Table 2, in terms of gender, there was nearly an equal number of male and female learners. As for the major allocation, there were more science majors than arts majors, which was within the acceptable level. The majority of the respondents were juniors, which was due to the fact that many freshmen and sophomores did not meet the sampling inclusion criteria. Students in their senior year were required to perform an internship; therefore, they were not as motivated as juniors to participate in this survey.
Instead of using an exploratory factor analysis (EFA), the study conducted a confirmative factor analysis (CFA) to test the convergent validity of each construct, since most of the items in each construct were adapted from mature and effective scales. To achieve a satisfactory construct validity, the values of standardized loading estimated for all the items should be higher than 0.7, while the composite reliability (CR) is recommended to be higher than 0.7, and the average variance extracted (AVE) should be higher than 0.5 [46,47,48]. As is shown in Table 3, the values for the standardized loading of all constructs were above 0.7, and AVE and CR were all higher than 0.5 and 0.7.
Next, the discriminant validity, which provides evidence of the external validity of the measurement instrument, was assessed. It is determined by comparing the squared correlation between two constructs and their AVE values. It is recommended that all of the squared correlations should be less than the AVE values, which indicates sufficient discriminant validity [47,48,49]. Table 4 presents the discriminant validity values for the constructs.

4.2. Model Fit Measurement

To assess how well the proposed structural equation model fits the data, measures of goodness-of-fit, such as chi-square testing, the goodness-of-fit index (GFI), root mean square error approximation (RMSEA), the residual root mean quarter residual (RMR), the comparative fit index (CFI), the normed fit index (NFI), and the non-normed fit index (NNFI) are examined [45,50]. Table 5 presents the rules of thumb indicating acceptable model fit and the analysis results. As is shown, all the goodness of fit indices fell within the recommended range, suggesting that the proposed research model provided a good fit to the data.

4.3. Hypothesis Testing

The above findings confirm that the research measurement instruments used in this study are reliable and can be used for hypothesis testing. The path analysis of the initial model was studied from the aspects of standardized path coefficient, standard error (S.E), and critical ratio (C.R.). The result is shown in Table 6.
Most of the path coefficients are significant in the expected direction. The results confirmed that external variables could include CQ and TS. Hypotheses 1 to 4 are supported. However, Hypothesis 5 is not supported. The path coefficient between PU and PEOU was negative and insignificant. This is inconsistent with many studies based on TAM [23,51,52,53,54,55,56,57]. Hypotheses 6 and 7 were supported, confirming the hypothetical effect of PU and PEOU on SA. Hypotheses 8 and 9 were supported, verifying again the effect of PU on BI, and SA can lead to BI. Both SE and SA were found to positively affect BI. Hypothesis 10, which proposed a positive relationship between PU and SE, was supported as well, as was proved in some previous studies [57,58,59,60,61,62]. Similarly, Hypothesis 11, which projected a positive relationship between PEOU and SE, was also supported, and this is consistent with the results of previous studies [63]. Hypothesis 12 was also supported, meaning that a higher level of SE will lead to greater BI, and this opinion has been proven to be true as well [62,64]. The path standardized coefficients of the structural model are depicted in Figure 2. The results indicate that TAM and SET can well be combined to predict learners’ continuous use intention of BL.

5. Discussion and Implications

The increasing popularity of BL indicates its vitality and sustainability. This study explores the comprehensive influencing factors regarding learners’ continuous use intention of the BL mode and the relationship among these factors. As shown in Figure 2, the impact of CQ on PEOU is far greater than that of PU, implying that learners pay more attention to the content a course can offer. If a course includes abundant resources, it will trigger more motivation and enthusiasm in learners, thus lowering the perceived difficulty level of using it. The richer and friendlier the interface and the communication tools, the more eager learners are to use the course. The more self-efficacy they have, the more belief they hold regarding their ability to overcome any technological problems in learning and focus only on the learning process. This implies that the most important determinant of BL is the usefulness of the learning content. The holistic design of the course will induce more confidence and curiosity in the study. As CQ is mainly presented through course resources and teaching methods, teachers must be very careful in designing the course syllabus to meet the needs of the learners.
The influence of TS on PU (path coefficient is 0.502, p < 0.05) is greater than its influence on PEOU (path coefficient is 0.153), which is also out of expectations. It can be seen that in BL, TS no longer focuses on solving technological problems such as equipment and system failures or network failures because currently, the advancement of technology has caused this to be rare, and even if there are such problems, they can be solved without much effort or delay. The new form of technological support that learners need is convenient instruction regarding the use of BL. Once they become familiar with it, TS seems to have done its work. All that remains is for learners to carry out the course requirements on schedule.
In this study, PU and PEOU continue to play a huge role in affecting use intention indirectly through SA and SE. PU has a significant positive impact on SA and SE (path coefficients are 0.458 and 0.374). PEOU also has a significant positive impact on SA and SE (path coefficients are 0.358 and 0.346). This shows that SA is more likely to be affected by PU. This result echoes the above findings that information technology is no longer a big barrier for learners. This conclusion can also be supported by the only false hypothesis in this model.
In TAM, PEOU has been proven to have a direct positive influence on PU [18]. However, in this study, PEOU has a negative but insignificant influence on PU. This is consistent with some studies [65,66,67,68]. In the past, when new technology was put into use, potential users tended to feel anxious about it. To quickly eliminate this anxiety, IT technicians made every effort to make users familiar with the new technology. As the practice went on, they came to realize that basic technological skills should not be barriers to the use of technology. New technological design should be more humanized and friendly to advocate the use of new technology. At the same time, learners’ technological skills are improving. In this case, IT technicians’ responsibilities are greatly reduced, and they just need to list all the possible problems in a guidebook for learners to refer to whenever a problem comes up.
In this model, there is still a strong positive relationship between SA and BI (path coefficient = 0.278, p < 0.05), and there is also a positive relationship between SE and BI (path coefficient = 0.32, p < 0.05). These two impacts are close, with SE having a little higher effect on BI. It can be seen from the study that TAM alone is not enough for predicting learners’ BI for BL. Their self-efficacy also plays a crucial role in the decision-making process. Although SE has been proven to have a significant impact on PU and PEOU [69,70], their relationship is actually two-way. In this study, to show the importance of the design of BL, PU and PEOU were tested to determine their impact on SE, and the result did show that there is a strong relationship between them. In the study, BI is mainly affected by learners’ self-efficacy, followed by learners’ satisfaction with blended learning. The affirmation of the effect of SA and SE on BI supports the necessity to build a TAM-SET model to predict learners’ continuous intention to use BL.
To help achieve the goal of the study, active exploration and reform can be carried out from the following five aspects. First, improve the teaching quality of online and offline instruction. Teachers must expand their teaching knowledge scope, optimize the teaching design and help learners to gain more personalized learning experience in a timely manner. At the same time, technical support staff should strengthen the service level of online learning support to ensure learners’ smooth implementation of BL. Second, enhance learners’ perception of the usefulness of BL. Since PU has a significant impact on the continuous use intention of the BL mode, teachers should enhance the course quality to form a valuable curriculum knowledge system for learners and to attract them to study actively. Third, improve the usability of the learning platform. Online learning platforms are essential media for BL. This study finds that the difficulty of using the platform should not be the barrier to adopting the BL mode. Platform designers (mostly teachers and technical support staff) should provide learners with more complex platforms to encourage more active learning. Fourth, improve learners’ self-efficacy. Self-efficacy plays a huge role in the learning process. Teachers should understand the needs of learners, and guide them to establish a systematic knowledge structure. At the same time, teachers can take advantage of online and offline Q&A opportunities to strengthen their supervision and guidance of the learners’ learning process, help them eliminate learning obstacles, and gain more learning achievements.

6. Conclusions and Future Work

Based on TAM and SET theories, this study proposed a model to explore the relationship among constructs that contributed to learners’ continuous use of the BL mode. It identified seven constructs, i.e., course quality (CQ), technological support (TS), perceived usefulness (PU), perceived ease of use (PEOU), satisfaction (SA), self-efficacy (SE), and behavioral intention (BI). Twelve research hypotheses were proposed and tested, and eleven hypotheses are statistically significant. Among them, CQ had the greatest positive impact on PEOU, meaning that the richer the course contents are, the easier and exciting the BL system seems according to the perceptions of the learners. This reveals that students expect to derive more useful knowledge from BL, and this high desire lowers their perception of the level of difficulty regarding the operating the system. If the system is too easy to handle, it will make them feel the hollowness of the resource. Whereas, if the system is abundant, with more useful content, it will arouse their curiosity and interest, making them eager to explore more, regardless of the efforts it may require.
The study confirms again that PU has a significant impact on SA, SE, and BI, and both SA and SE significantly influence BI, with SE playing a more distinct role, which lays a solid foundation for combining TAM and SET theories for predicting learners’ continuous use intention.
The main contributions of the study are two-fold. One is that it determines that learners’ attitudes toward PEOU are complex. On the one hand, learners hope that the course learning system is easy and flexible to operate to give full play to their autonomous learning. On the other hand, learners want the BL system to be optimized and delicately designed to enable them to acquire more knowledge. The learners would prefer sufficiency to simplicity when using the system. The other contribution of this study is that it adds the construct of SE to TAM and makes it more predictable for acceptance intention tests. Although SE is not first recognized in this field, this is the first time it has been joined with TAM for better prediction in terms of learners’ behavior intention toward BI. This shows that SE plays an essential role in BL. A combined TAM-SET model seems to be more appropriate for this study. The findings have great value in helping administrators and educators to think out effective ways to boost learners’ SL [71,72]. Keeping pace with the goal of sustainability education is crucial for higher education, and this study has just set an example to help hit this target.
In the future, the study can be improved from two perspectives. First, the subjects of this study come only from Huaqiao University. Although the number of questionnaires collected meets the basic demand, such samples are not representative of the whole situation. In the future, the subjects can be expanded to more colleges and universities to obtain a larger picture of how BL is perceived among learners in higher education. Second, the model in this study is built by combining the theories of TAM and SAT. To further confirm the effectiveness of this effort, future studies should include the outcome of BL to more deeply consider how the model helps build learners’ confidence in BL, thus improving their SL.
BL can be an effective means for sustainability education, as it helps cultivate the necessary qualities, such as critical thinking, creative thinking, problem-solving, and cooperative spirit. The increasing popularity of BL makes traditional “teaching” and “learning” undergo profound changes. By merging the advantages of online learning and traditional face-to-face learning, BL has a great potential to arouse learners’ learning enthusiasm, strengthen their learning experience, and help them better prepare themselves for constructing a sustainable society for mankind in the future.

Author Contributions

Conceptualization, X.C. and X.X.; methodology, X.C. and X.X.; validation, Y.J.W.; formal analysis, X.C.; investigation, X.C. and X.X.; writing—original draft preparation, X.C., X.X., Y.J.W. and W.F.P.; writing—review and editing, X.C., X.X., Y.J.W. and W.F.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Huaqiao University’s first-class undergraduate online and offline blended curriculum construction projects and National Science and Technology Council, Taiwan (111-2410-H-003-072-MY3).

Institutional Review Board Statement

Ethical review and approval was not required for this study on human participants, in accordance with the local legislation and institutional requirements.

Informed Consent Statement

Written informed consent from the participants was not required to participate in this study, in accordance with the national legislation and the institutional requirements.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researchers.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 16428 g001 550
Figure 1. Research model and hypotheses.
Figure 1. Research model and hypotheses.
Sustainability 14 16428 g001
Sustainability 14 16428 g002 550
Figure 2. Structural equation model.
Figure 2. Structural equation model.
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Table
Table 1. Pilot test of Cronbach’s alpha reliability.
Table 1. Pilot test of Cronbach’s alpha reliability.
CodePilot Test
CQ0.912
RT0.830
PU0.772
PEOU0.914
SE0.895
AT0.784
BI0.918
Table
Table 2. Demographic characteristics of the respondents.
Table 2. Demographic characteristics of the respondents.
ItemsDescriptionN%Cumulative %
GenderMale25154.454.4
Female21045.6100
GradeSophomore13429.129.1
Junior20243.872.9
Senior12527.1100
MajorArts18640.440.4
Science27559.4100
Table
Table 3. Construct reliability and convergent validity.
Table 3. Construct reliability and convergent validity.
FactorsItemsStandardized
Loading		Cronbach’s AlphaCRAVE
CQCQ10.7530.8610.8630.558
CQ20.716
CQ30.826
CQ40.701
CQ50.749
RTRT10.7390.7940.7990.571
RT20.793
RT30.735
PUPU10.7610.7660.7660.522
PU20.703
PU30.707
PEOUPEOU10.8700.8860.8870.662
PEOU20.772
PEOU30.772
PEOU40.841
SESE10.7580.8940.9010.651
SE20.761
SE30.893
SE40.893
SE50.706
SAAT10.7000.7680.7730.535
AT20.782
AT30.701
BIBI10.9000.9330.9340.825
BI20.914
BI30.910
Table
Table 4. Discriminant validity.
Table 4. Discriminant validity.
CQRTPEOUSEATBIPU
CQ0.747
RT0.4610.756
PEOU0.6490.4280.814
SE0.4180.4770.4180.807
AT0.4560.4480.4060.4360.731
BI0.5090.5110.4630.5340.4900.908
PU0.3600.4190.2920.3610.3990.4560.722
Table
Table 5. Goodness of fit indices for the measurement model.
Table 5. Goodness of fit indices for the measurement model.
Type of MeasureAcceptable Level of FitValues
Chi-square/degree of freedom<32.051
Goodness-of-fit index (GFI)>0.90.913
Root mean square residual (RMSEA)<0.100.054
Root-mean residual (RMR)<0.050.049
Comparative fit index (CFI)>0.90.948
Normed fit index (NFI)>0.90.913
Non-normed fit index (NNFI)>0.90.939
Table
Table 6. Results summary.
Table 6. Results summary.
EstimateS.E.C.R.p
PEOU<---CQ0.6570.0710.9760.000
PEOU<---TS0.1530.0642.9140.004
PU<---CQ0.3060.0973.4660.000
PU<---TS0.5020.0846.8190.000
PU<---PEOU−0.1180.078−1.420.156
SA<---PU0.4580.0587.050.000
SA<---PEOU0.3580.0476.3690.000
SE<---PU0.3740.0446.3520.000
SE<---PEOU0.3460.0376.5390.000
BI<---PU0.2810.0934.3750.000
BI<---SA0.2780.0974.690.000
BI<---SE0.320.0966.3920.000
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Chen, X.; Xu, X.; Wu, Y.J.; Pok, W.F. Learners’ Continuous Use Intention of Blended Learning: TAM-SET Model. Sustainability 2022, 14, 16428. https://doi.org/10.3390/su142416428

AMA Style

Chen X, Xu X, Wu YJ, Pok WF. Learners’ Continuous Use Intention of Blended Learning: TAM-SET Model. Sustainability. 2022; 14(24):16428. https://doi.org/10.3390/su142416428

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Chen, Xiulan, Xiaofei Xu, Yenchun Jim Wu, and Wei Fong Pok. 2022. "Learners’ Continuous Use Intention of Blended Learning: TAM-SET Model" Sustainability 14, no. 24: 16428. https://doi.org/10.3390/su142416428

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