Next Article in Journal
Spatio-Temporal Dynamics of Water Conservation Service of Ecosystems in the Zhejiang Greater Bay Area and Its Impact Factors Analysis
Previous Article in Journal
Study on Interface Interaction between Uniaxial Geogrid Reinforcement and Soil Based on Tensile and Pull-Out Tests
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 16
10.3390/su141610390
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy

by
Fuhai An
*,
Linjin Xi
*,
Jingyi Yu
and
Mohan Zhang
Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(16), 10390; https://doi.org/10.3390/su141610390
Submission received: 5 July 2022 / Revised: 11 August 2022 / Accepted: 18 August 2022 / Published: 20 August 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
With the deep integration of ICT into education and teaching, the effect of technology acceptance on students’ self-directed learning has been one of the key concerns in the education field. This study examines the relationship between technology acceptance and self-directed learning and the mediating role played by positive emotions and technological self-efficacy in a sample of 501 middle school students in eastern China. The results show that: (1) positive emotions mediate the relationship between technology acceptance and self-directed learning; (2) technological self-efficacy also mediates the relationship between technology acceptance and self-directed learning; (3) positive emotions and technological self-efficacy play a mediating role between technology acceptance and self-directed learning. The findings not only reveal the mediating role of positive emotions and technological self-efficacy between technology acceptance and self-directed learning but are also valuable for Chinese teachers to guide middle school students to engage in self-directed learning with the help of technology.

1. Introduction

Self-directed learning is a learning method in which individuals empower themselves and take responsibility for a variety of decisions related to their learning [1]. It is a necessary competency for lifelong learning [2] and has received extensive attention from researchers around the world [3,4]. Self-directed learning can be influenced by factors such as positive emotions [5] and self-efficacy [6]. With the deep integration of ICT into education and teaching, it is increasingly common for students to use technology to learn, and the effect of technology acceptance on students’ self-directed learning has also received widespread attention from researchers [7,8,9]. Although many studies have suggested that technology acceptance can affect students’ self-directed learning [10], studies in this field have mainly focused on university undergraduates, which cannot provide scientific guidance for middle school students to improve their self-directed learning ability in the information environment. Moreover, no research has explored whether positive emotions and technological self-efficacy mediates the relationship between technology acceptance and self-directed learning.

1.1. The Relationship between Technology Acceptance and Self-Directed Learning

It has been demonstrated that there is a correlation between technology acceptance and self-directed learning [11]. The TAM proposed by Davis [12] sets perceived usefulness and perceived ease of use as antecedent variables that affect users’ attitudes toward information technology, both of which directly affect individual’s attitudes to use technology and indirectly affect actual use behavior [13,14]. Reference [15] introduced a series of social influencing variables (i.e., subjective norm, voluntariness, image and experience) into the classical TAM model, and proposed TAM2. Reference [16] constructed TAM3 including perceived usefulness and perceived ease of use, and pointed out that community influence, system characteristics, individual differences and convenience conditions are important factors affecting perceived usefulness and perceived ease of use. The UTAUT theory also believes that the willingness and convenience to use technology determines the behaviors [17]. In summary, in the technology acceptance model (TAM) and its extended models TAM2 and TAM3 as well as UTAUT theory, perceived usefulness, perceived ease of use, attitude towards using and behavioral intention are still the core factors affecting technology acceptance [18]. In the education field, a prerequisite for students to adopt information technology to promote learning is technology acceptance [19]. When students learn with the help of information technology, technology acceptance has an effect on improving students’ ability to engage in self-directed learning [20]. In the learning environment of technology, when students have the right to plan and make decisions about their learning activities, they will be more inclined to apply technology as a tool to learning [21]. A study showed that technology acceptance has a positive impact on self-directed learning in a blended learning environment [22]. Another study reported that under the background of the integration of information technology into education and teaching, students in the offline learning environments are also more inclined to accept technology and apply it for self-directed learning [23]. The findings of these studies also imply that students may prefer to use technology to help them with self-directed learning [24].

1.2. The Mediating Role of Positive Emotions

Previous studies have shown a correlation between positive emotions and technology acceptance [25]. Positive emotions can positively influence learning behaviors [26,27,28,29], including positive high-arousal (e.g., enjoyment and pride) and positive low-arousal emotions (e.g., relief and satisfaction) [5,30]. According to Pekrun’s control-value theory [31], students’ adoption of self-directed learning forms will increase their levels of positive emotions [32]. A recent study also indicated that when middle school students generate higher levels of positive emotions, their ability to engage in self-directed learning increases accordingly [33]. There has been growing research on the impact of learning with the help of information technology on students’ emotions [34]. For example, when students are willing to accept a technology, their perceived ability to use it will increase, and they will also experience more positive emotions [35,36]. A qualitative study found that high technology acceptance facilitates learners in experiencing more positive emotions and having a more positive learning experience [37]. In addition, studies have shown that there is an interaction between technology acceptance and positive emotions [38,39]. Therefore, it is also valuable for future research to explore models with positive emotions as independent variable and self-directed learning as dependent variable.

1.3. The Mediating Role of Technological Self-Efficacy

It has been demonstrated that technological self-efficacy has an important influence on student learning with the help of technology [40]. Technology-rich environments have the potential to facilitate students’ self-directed learning [41]. Self-efficacy was defined by Bandura as “an individual’s confidence in their ability to organize and execute specific tasks to solve a problem or accomplish a task”, and he believed that it will be influenced by successful experience, alternative experience and positive emotions [42]. Different disciplines have their own corresponding types of self-efficacy, for example, technological discipline has technological self-efficacy [43]. Technological self-efficacy means a learner’s judgment of his ability to apply a computer to accomplish a task [44], which includes both self-efficacy related to online learning platforms and general computer self-efficacy [43]. In the present study, technological self-efficacy refers to general computer self-efficacy. A study showed a significant but weak correlation between technological self-efficacy and self-directed learning among high school graduates [45]. Another longitudinal study also showed that self-directed learning is influenced by technological self-efficacy [46]. Research reported that learners will show a higher degree of technological self-efficacy when they have some willingness to accept a technology. Learners will also show a higher degree of technological self-efficacy when they feel that a certain technology is easy to use [47]. In addition, the research also showed that technology acceptance and technology self-efficacy also have mutual influence [48]. Therefore, future research could also attempt to explore the interrelation between technological self-efficacy and self-directed learning.

1.4. The Relationship of Positive Emotions and Technological Self-Efficacy

Emotion and self-efficacy have long been viewed as two variables that are highly correlated [49]. It has been verified that academic emotions can have a direct impact on self-efficacy [50] in that positive emotional experiences can increase self-efficacy [51] and negative emotions can affect self-efficacy in learning [52]. Another study demonstrated a certain degree of correlation between positive emotions and technological self-efficacy [53]. If students feel more positive emotions when using a certain technology or technological platform, they are likely to use it more often, resulting in a feeling of greater technological self-efficacy [54]. Both positive emotions and technological self-efficacy are associated with self-directed learning. This suggests that there may be an interaction between positive emotions and technological self-efficacy which, in turn, could affect self-directed learning.

1.5. Aims and Hypotheses

It has been shown that students’ technology acceptance, positive emotions, and technological self-efficacy are associated with self-directed learning. However, we found no research investigating whether positive emotions and technological self-efficacy can act simultaneously as mediators between technology acceptance and self-directed learning. More importantly, under the background of the deep integration of information technology into education and teaching, Chinese teachers have many dilemmas regarding how to improve middle school students’ ability to engage in self-directed learning, and Chinese middle school students also are still confused regarding how to improve their self-directed learning ability. This implies the necessity for systematic research and scientific guidance on the issue. This study aimed to examine the relationship between students’ technology acceptance and self-directed learning through mediation by positive emotions and technological self-efficacy. It is hoped that under the background of in-depth integration of information technology into education and teaching, it can provide a valuable reference for Chinese teachers and researchers to cultivate students’ self-directed learning ability and guide Chinese middle school students to better conduct self-directed learning. On the basis of the above literature review, we propose a hypothetical model (Figure 1). The hypotheses of the current study are as follows:
H1: 
Technology acceptance has a positive impact on self-directed learning.
H2: 
Positive emotions mediate the relationship between technology acceptance and self-directed learning.
H3: 
Technological self-efficacy plays a mediating role between technology acceptance and self-directed learning.
H4: 
Positive emotions and technological self-efficacy mediate the relationship simultaneously the relation between technology acceptance and self-directed learning.

2. Methods

2.1. Participants and Procedures

The survey was conducted in Hangzhou, Zhejiang Province, in eastern China. We surveyed 501 middle school students from a middle school. Table 1 showed the characteristics of the participants. Among them, 261 were male (52.1%) and 240 were female (47.9%). Considering the actual age distribution of Chinese students in the seventh and eighth grades, the age sampling distribution of the current study was as follows: 12 years old (n = 120, 24.0%), 13 years old (n = 135, 27.0%), 14 years old (n = 114, 22.7%) and 15 years old (n = 132, 26.3%). The grade distribution was as follows: Grade 7 (n = 255, 50.9%) and Grade 8 (n = 246, 49.1%). We did not assess middle school students in Grade 9 because they were occupied with preparations for the senior middle school entrance examination.
In the sample schools selected for this study, information technology courses are compulsory for students in such schools. The current study was conducted after the students had completed a semester of IT classes but before their grades were available. The data collection lasted 12 days in January 2022. The participants completed the paper questionnaires regarding technology acceptance, positive emotions, technological self-efficacy, and self-directed learning. The paper-based version of the questionnaire was distributed by the researcher to 501 middle school students at their schools. The students in the classroom answered and returned the questionnaires before the start of class. The participants needed approximately 20 min to complete the questionnaire. All 501 questionnaires collected were valid, with a 100% completion rate. The data collection for this study were anonymous to protect the privacy of the participants. Concerning the research ethics [55], this survey was approved by the Research Ethics Committee of the Jing Hengyi School of Education, Hangzhou Normal University, and the principals of the participating schools. All participants agreed to sign an informed consent form prior to the study.

2.2. Measurement Instruments

2.2.1. Technology Acceptance

The revised research questionnaire [56] for the Technology Acceptance Model [12] was used to measure technology acceptance. The TAM model has successfully demonstrated appropriate concurrent and construct validity in the Chinese environment. The questionnaire uses a self-report scale to assess students’ perceived technology acceptance from four perspectives: perceived usefulness (3 items, e.g., “Using computers will enhance my effectiveness”), perceived ease of use (3 items, e.g., “I think it’s easy to use a computer”), attitude towards using (3 items, e.g., ”I like using computers”) and behavioral intention (3 items, e.g., “I plan to use computers to study in the future”). The participants were asked to rate the assertions on a 5-point scale from 1 (totally disagree) to 5 (totally agree) based on the previous semester’s IT course. Thus, higher scores indicate higher levels of technology acceptance as a technology acceptance indicator. The Cronbach’s alpha value was 0.942 and the validation factor analysis fit was good (χ2/df = 2.788, CFI = 0.954, TLI = 0.939, GFI = 0.900, RMSEA = 0.0948, RMR = 0.048, and NFI = 0.930).

2.2.2. Positive Emotions

The Chinese version [30] of the Adolescent Academic Emotion Scale [32] was used to measure positive emotions. The AAEQ is an assessment of students’ generalized emotions about learning and is suitable for Chinese students. In this study, positive emotions were measured using two dimensions: positive high arousal (16 items, e.g., “Learning brings me a lot of joy”) and positive low arousal (14 items, e.g., “I think learning is fun”). A 5 point Likert scale with a range of 1 (totally disagree) to 5 (totally agree) was used to rate each item. Each response was based on generic feelings. Higher scores represent higher levels of positive emotions. Cronbach’s alpha for the current study was 0.891, and the validation factor analysis (CFA) showed the acceptable fitting index of the modified scale (χ2/df = 2.707, CFI = 0.935, TLI = 0.919, GFI = 0.911, RMSEA = 0.0926, RMR = 0.118 and NFI = 0.961).

2.2.3. Technological Self-Efficacy

The revised Technological Self-Efficacy Scale [56] was used to measure the technological self-efficacy. The scale is single-dimensional and includes six questions (e.g., “I believe I will receive an excellent grade in this class”). A 5 point Likert scale was used, ranging from 1 (totally disagree) to 5 (totally agree). As an indicator of technology self-efficacy, higher score represents a stronger sense of technology self-efficacy. Cronbach’s alpha in the present study was 0.905 and the fitting of the validation factor analysis was good (χ2/df = 2.086, CFI = 0.989, TLI = 0.983, GFI = 0.971, RMSEA = 0.0703, RMR = 0.031 and NFI = 0.980).

2.2.4. Self-Directed Learning

An adapted version of the Self-Directed Learning Scale [57] was used to measure self-directed learning. It has high homogeneity and internal consistency among Chinese adolescents, with a Cronbach’s alpha of 0.929 [58]. The Self-Directed Learning Scale consists of 60 items reflecting students’ attitudes toward self-directed learning and includes six dimensions: autonomy in learning content (12 items, e.g., “I often make study plans for myself”); time management (8 items); learning strategies (18 items); learning processes (7 items); evaluation and reinforcement of learning outcomes (9 items); and control over the learning environment (6 items). A 5 point Likert scale with a range of 1 (totally disagree) to 5 (totally agree) was used to rate each item. Higher scores represent a greater ability to self-directed learning. Cronbach’s alpha value of this scale was 0.976 and the fitting of the validation factor analysis was good (χ2/df = 2.667, CFI = 0.908, TLI = 0.928, GFI = 0.926, RMSEA = 0.0829, RMR = 0.066 and NFI = 0.922).

2.3. Data Analysis

In the current study, we first used SPSS version 25.0 (IBM, Chicago, IL, USA) to calculate the descriptive statistics and correlations. Next, Mplus 8.3 software was used to examine the hypothetical model in the current study. The validity and reliability of the measurement model were validated by analyzing the data. Factor loadings were used to ensure construct validity, composite reliability, and validity [59]. Finally, the convergence validity for the model was verified by the Cronbach’s alpha and the goodness of fit. Several fitting indices were used to assess the model fit. Previous researchers [60] noted that χ2/df < 3, goodness-of-fit index (GFI) ≥ 0.90, Tucker–Lewis index (TLI) ≥ 0.95, comparative fit index (CFI) ≥ 0.95, root mean square error of approximation (RMSEA) < 0.06 and standardized root mean residual (SRMR) < 0.08 reflect a good fit. In addition, we used bootstrap methods with robust standard errors to examine the significance of mediating effect [61]. The bootstrap method produced 95% deviation-corrected confidence intervals (CIs) for these effects from a resample of 5000 data. The significance of the indirect effects was indicated if there were no zeros in the CIs.

3. Results

3.1. Descriptive Statistics and Correlations

The four questionnaires were tested for common method bias using Harman’s single-factor test. Through exploratory factor analysis (EFA), it was established that the first factor accounted for explain 22.05% of the total variance, which was far below the threshold (i.e., 40% of the explained variance). Thus, there was no significant common method bias.
Table 2 presents the average values, standard deviations, correlation coefficients of the main study variables and Cronbach’s alpha values. There was a significant positive relationship between technology acceptance and positive emotions (r = 0.210, p < 0.01). There were also significant positive correlations between technological self-efficacy and technology acceptance and positive emotions (r = 0.490, p < 0.01; r = 0.362, p < 0.01). Finally, there were significant positive correlations between students’ self-directed learning and technology acceptance, positive emotions and technological self-efficacy (r = 0.207, p < 0.01; r = 0.514, p < 0.01; r = 0.453, p < 0.01).

3.2. Measurement Model

We measured the quality of the model by CFA. In this study, convergence effectiveness [62] was established by measuring item reliability of each measure, composite reliability of each construct (CR) and the average variance extracted (AVE). As shown in Table 3. In the current study, the factor loadings of each question were all greater than 0.70, and the factor loadings of all items ranged from 0.714 to 0.932, which was considered appropriate for each question, thus proving the convergence effectiveness at the item level [59]. A value of 0.70 or higher is recommended for adequate compound reliability [63]. The reliability of all structures in this study ranged from 0.818 to 0.944, which was suitable. Convergence validity is judged to be sufficient when the extracted average variance is equal to or more than 0.50 [64]. The results are shown in Table 3. The AVE values of the measurement model are between 0.542 and 0.822, so the convergence effectiveness of the measurement model is sufficient.
In addition, to assess whether a single index is sufficient to distinguish between constructs, discriminant validity was assessed by comparing the square root of the mean variance of a construct with the correlation between that construct and all other constructs [65]. In this study, the average variance extracted by each construct exceeded the variance due to the measurement error of that construct (i.e., the mean value exceeded 0.50) in the range of 0.684 to 0.864, so the discriminant validity was well validated. The structures in the research model proposed in this study can be further analyzed.

3.3. Testing for Mediation Effects

This study used Mplus8.3 (Muthen & Muthen, Los Angeles, CA, USA) to further assess the mediation role of positive emotions and technological self-efficacy between technology acceptance and self-directed learning, using the structural equation model method [66]. Table 4 shows the fitting indices of the hypothetical model, and it shows that the model fit well according to the judgment criteria.
As shown in Table 5, we conducted a regression analysis for all of the variables. The results for the total sample identified that: (1) although the total effect of technology acceptance on self-directed learning is significant (β = 0.207, p < 0.001), after the mediators entered the regression, the direct effect of technology acceptance on self-directed learning became insignificant (β = −0.116, p = 0.498); (2) technology acceptance had a significant positive impact on positive emotions (β = 0.210, p < 0.001) and technological self-efficacy (β = 0.490, p < 0.001); (3) positive emotions had a significant positive effect on technological self-efficacy (β = 0.362, p < 0.001); (4) positive emotions had a significant positive impact on self-directed learning (β = 0.514, p < 0.001); (5) technological self-efficacy had a significant positive effect on self-directed learning (β = 0.453, p < 0.001).
As shown in Table 6, we conducted a series mediation analysis using the Bootstrap method. The mediation effect included three paths. Firstly, the mediating effect of positive emotions on the link between technology acceptance and self-directed learning was 0.334. In addition, the 95% confidence interval did not include 0, and the mediating effect was significant, which supports H2. Secondly, the mediating effect of technological self-efficacy on the relationship between technology acceptance and self-directed learning was 0.551 with a 95% confidence interval excluding 0, and the mediating effect was significant, supporting H3. Thirdly, the mediating effect of positive emotions and technological self-efficacy between technology acceptance and self-directed learning was 0.072, the 95% confidence interval did not include 0 and the mediating effect was significant, supporting H4.
These findings indicate that positive emotions and technological self-efficacy play a mediating role between technology acceptance and self-directed learning. The final model was shown in Figure 2.

4. Discussion

Taking middle school students in eastern China as participants, this study examined the mediating roles of positive emotions and technological self-efficacy between technology acceptance and self-directed learning. The results indicate that technology acceptance can affect self-directed learning through positive emotions and can also affect self-directed learning through technological self-efficacy; technology acceptance can also affect self-directed learning through the combined effects of positive emotions and technological self-efficacy. We describe each one next.

4.1. Technology Acceptance and Self-Directed Learning

This study found that technology acceptance could have a positive impact on self-directed learning, which is supported by previous studies [11]. However, when two mediating variables, positive emotions and technological self-efficacy, were added between technology acceptance and self-directed learning, the results did not support the direct predictive effect between them (H1). This suggests that technology acceptance may not directly affect students’ self-directed learning, but rather affects self-directed learning through certain underlying factors. Based on self-directed learning theory [67], UTAUT theory [17] and long-term observation of middle school students in eastern China, the current study focused on and validated the mediating role of positive emotions and technological self-efficacy in technology acceptance and self-directed learning. However, according to TAM3 [16], perceived usefulness and perceived ease of use in technology acceptance are influenced by community factors, system characteristics, convenient conditions and individual differences. The self-directed learning theory [16] also pointed out that there may be other influencing factors between students’ technology acceptance and self-directed learning, such as learning motivation and teacher support, etc. Future research should further explore the impact of these factors on the relationship between technology acceptance and self-directed learning.

4.2. The Mediation Role of Positive Emotions

The findings suggest that positive emotions can mediate the relation between technology acceptance and self-directed learning. That is, the more receptive a student is to technology, the more likely they are to feel positive emotions when they use technology for learning [38]. This may be associated with the perceived usefulness and perceived ease of use in technology acceptance [68]. Students’ positive emotions may be more pronounced when they overcome difficulties or solve challenges in their learning with the help of technology, which has been validated [37]. According to Pekrun’s control-value theory [31], the presence of positive emotions will provide confidence and guidance for students’ attitudes and willingness to use technology. According to the UTAUT theory [17], positive emotions can help students improve their behaviors in learning and using technology. That is to say, individuals who feel a high level of positive emotions when they use a technology will be more inclined to accept the technology and use it to help them learn by themselves [69]. The findings suggest that technology acceptance is only a basic condition that affects self-directed learning and does not absolutely predict self-directed learning. Self-directed learning can only be promoted if students perceive the technology as having both usefulness and ease of use and as generating positive emotions. Therefore, teachers should not only focus on the effect of technology acceptance on self-directed learning but also on positive emotional experiences in the process of technology acceptance and use.

4.3. The Mediation Role of Technological Self-Efficacy

The findings show that technological self-efficacy mediates the relationship between technology acceptance and self-directed learning. Technological self-efficacy is a belief in its ability to complete specific technical tasks and challenges [69]. It plays an essential role in technology-based attitudes and self-directed learning [46], which is consistent with our findings. In this way, we can reasonably infer that when students perceive a technology as highly practical and easy to use, it will increase their positive perception of using the technology, generating technological self-efficacy, thereby improving their behavior with technology, enhancing their ability to use it for self-directed learning. That is to say, when middle school students feel that a technology is relatively useful and easy to use, they are more likely to actively learn how to use the technology for learning. This is consistent with previous studies [70]. However, in the field of education, we cannot provide students with learning equipment and systems that do not match their age in order to improve their technological self-efficacy. Our findings suggest that in the context of the deep integration of ICT into educational teaching, educators should not only provide students with convenient and age-appropriate educational devices, but also enhance students’ belief in the ability to use technology to solve problems.

4.4. The Mediation Role of Positive Emotions and Technological Self-Efficacy

The results show that positive emotions and technological self-efficacy can simultaneously mediate between technology acceptance and self-directed learning. Moreover, the indirect effects of technology acceptance on self-directed learning through positive emotions and technological self-efficacy are also statistically significant. Previous studies have also reported that positive emotions, as an important factor in students’ learning, have a direct effect on technology acceptance and self-efficacy [71]. Based on Bandura’s self-efficacy theory, an individual’s self-efficacy has an effect on the behaviors [42]. Technological self-efficacy is a special type of self-efficacy, which will have an impact on students’ attitudes and behaviors with respect to technology acceptance and use for learning [43]. Namely, when middle school students perceive a technology as having usefulness and ease of use, they will have a greater willingness to use it [72] and experience a higher level of positive emotions, and these positive emotions will enhance their sense of technological self-efficacy which, in turn, will help them further use technology for self-directed learning. The results show that under the background of the deep integration of information technology into education and teaching, when teachers use technology to promote students’ ability to engage in self-directed learning, first of all, they should encourage students to feel that the technology is easy to use and can solve the problems they encounter in learning; second, they should encourage students to use technology to solve problems to achieve a positive emotional experience, and enhance their belief in their ability to apply technology to solve problems. Perhaps only in this way can technology better serve self-directed learning.

5. Conclusions, Implications and Future Directions

The current study makes an innovative contribution to the understanding of the relationship between technology acceptance and self-directed learning, because it emphasizes the co-mediating roles of positive emotions and technological self-efficacy. Namely, technology acceptance can improve students’ ability to engage in self-directed learning by promoting their positive emotions and enhancing their technological self-efficacy. This study has the potential to improve middle school students’ ability to engage in self-directed learning against the context of the deep integration of information technology into education and teaching.
The current study has several limitations. First, a cross-sectional study design was used. The survey data in this study resulted in our inability to draw causal inferences about the associations between the variables. In the future, it is hoped that the current study can be validated by a follow-up or longitudinal experimental design that will extend its findings. Second, this study included only a sample of middle school students in eastern Chinese cities. Therefore, it is not representative of the reality of middle school students in other regions of China. In our future research, we aim to conduct as comprehensive a survey of middle students in different regions of China as possible. Third, only the TAM model was used for evaluation. It would be more reliable if other models, e.g., TAM2, TAM3, Unified Theory of Acceptance and Use of Technology (UTAUT), Innovation Diffusion Theory (IDT), Website Analysis and Measurement Inventory (WAMMI), could be used in conjunction with this research.

Author Contributions

Conceptualization, F.A. and L.X.; methodology, L.X.; software, L.X. and J.Y.; validation, F.A., L.X. and J.Y.; formal analysis, L.X.; investigation, F.A.; resources, F.A. and M.Z.; data curation, L.X. and J.Y.; writing—original draft preparation, F.A., L.X. and J.Y.; writing—review and editing, F.A. and M.Z.; visualization, F.A. and M.Z.; supervision, F.A. and M.Z.; project administration, F.A.; funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a general project of the National Social Science Fund of China, grant number BHA210138 and Cultivation Project for Provincial Advantageous and Characteristic Discipline of Hangzhou Normal University, grant number 20JYXK001.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Jing Hengyi School of Education, Hangzhou Normal University, and the Ethical Approval ID is 2022010.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

According to the data access policies, the data used to support the findings of this study are available from Hangzhou Normal University upon reasonable request made by email: [email protected].

Acknowledgments

We thank all the participants of the survey and all the peer reviewers for their excellent suggestions to this work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Knowles, M. Self-directed learning: A guide for leaners and teachers. J. Contin. Educ. Nurs. 1975, 7, 60–71. [Google Scholar]
  2. Battelle for Kids (BFK). Partnership for 21st Century Skills. Available online: http://www.21stcenturyskills.org/documents/RTM2006.pdf (accessed on 22 May 2006).
  3. Bolhuis, S. Towards process-oriented teaching for self-directed lifelong learning: A multidimensional perspective. Learn Instr. 2003, 13, 327–347. [Google Scholar]
  4. Voogt, J.; Roblin, N.P. A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. J. Curric. Stud. 2012, 44, 299–321. [Google Scholar]
  5. Pekrun, R.; Goetz, T.; Titz, W.; Perry, R. Academic emotions in Students’ self-regulated learning and achievement: A program of qualitative and Quantitative research. Educ. Psychol. 2002, 37, 91–105. [Google Scholar]
  6. Sumuer, E. Factors related to college students’ self-directed learning with technology. Australas. J. Educ. Technol. 2018, 34, 29–43. [Google Scholar] [CrossRef]
  7. Song, L.; Hill, J.R. A conceptual model for understanding self-directed learning in online environments. JIOL 2007, 6, 27–42. [Google Scholar]
  8. Robertson, J. The educational affordances of blogs for self-directed learning. Comput. Educ. 2011, 57, 1628–1644. [Google Scholar] [CrossRef] [Green Version]
  9. Morris, T.H.; Rohs, M. The potential for digital technology to support self-directed learning in formal education of children: A scoping review. Interact. Learn. Environ. 2021, 2021, 1–14. [Google Scholar] [CrossRef]
  10. Curran, V.; Gustafson, D.L.; Simmons, K.; Lannon, H.; Wang, C.; Garmsiri, M.; Fleet, L.; Wetsch, L. Adult learners’ perceptions of self-directed learning and digital technology usage in continuing professional education: An update for the digital age. J. Adult Contin. Educ. 2019, 25, 74–93. [Google Scholar]
  11. Pan, X. Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator. Front. Psychol. 2020, 11, 68–78. [Google Scholar]
  12. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–339. [Google Scholar] [CrossRef] [Green Version]
  13. Teo, T.; van Schaik, P. Understanding the intention to use technology by Preservice teachers: An empirical test of competing theoretical models. IJHCI 2012, 28, 178–188. [Google Scholar] [CrossRef]
  14. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  15. Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  16. Venkatesh, V.; Bala, H. Technology Acceptance Model 3 and a Research Agenda on Interventions. Decis. Sci. 2008, 39, 273–315. [Google Scholar] [CrossRef] [Green Version]
  17. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q 2012, 36, 157–178. [Google Scholar] [CrossRef] [Green Version]
  18. Teo, T. Factors influencing teachers’ intention to use technology: Model development and test. Comp. Educ. 2011, 57, 2432–2440. [Google Scholar] [CrossRef]
  19. Hsieh, J.S.C.; Huang, Y.M.; Wu, W.C.V. Technological acceptance of LINE in flipped EFL oral training. Comput. Hum. Behav. 2017, 70, 178–190. [Google Scholar] [CrossRef]
  20. Lai, C.; Li, X.S.; Wang, Q. Students’ perceptions of teacher impact on their self-directed language learning with technology beyond the classroom: Cases of Hong Kong and US. ETR&D-Educ. Tech. Res. Dev. 2017, 65, 1105–1133. [Google Scholar]
  21. Zheng, B.B. Medical Students’ Technology Use for Self-Directed Learning: Contributing and Constraining Factors. Med. Sci. Educ. 2022, 32, 149–156. [Google Scholar] [CrossRef]
  22. Geng, S.; Law, K.M.Y.; Niu, B. Investigating self-directed learning and technology readiness in blending learning environment. Int. J. Educ. Technol. High. Educ. 2019, 16, 17–20. [Google Scholar] [CrossRef] [Green Version]
  23. Lai, C. A framework for developing self-directed technology use for language learning. Lang. Learn. Technol. 2013, 17, 100–122. [Google Scholar]
  24. Lee, K.; Tsai, P.S.; Chai, C.S.; Koh, J.H.L. Students’ perceptions of self-directed learning and collaborative learning with and without technology. J. Comput. Assist. Learn. 2014, 30, 425–437. [Google Scholar] [CrossRef]
  25. Sabine, S. Mastery goals, positive emotions and learning behavior in self-directed vs. teacher-directed learning. Eur. J. Psychol. Educ. 2020, 35, 205–223. [Google Scholar]
  26. Efklides, A.; Schwartz, B.L.; Brown, V. Motivation and Affect in Self-Regulated Learning: Does Metacognition Play a Role? In Handbook of Self-Regulation of Learning and Performance, 2nd ed.; Schunk, D.H., Greene, J.A., Eds.; Routledge: New York, NY, USA, 2017; pp. 64–83. [Google Scholar]
  27. Fredrickson, B.L. What good are positive emotions? Rev. Gen. Psychol. 1988, 2, 300–319. [Google Scholar] [CrossRef]
  28. Hascher, T.; Hagenauer, G. The Importance of Quality Factors of Teaching and Learning Emotions for Well-Being at School. In Emotions and Emotion Regulation in School and College; Hagenauer, G., Hascher, T., Eds.; Waxmann: Münster, Germany, 2018; pp. 103–121. [Google Scholar]
  29. Linnenbrink-Garcia, L.; Barger, M.M. Achievement Goals and Emotions. In International Handbook of Emotions in Education; Linnenbrink-Garcia, L., Pekrun, R., Eds.; Routledge: New York, NY, USA, 2014; pp. 142–162. [Google Scholar]
  30. Dong, Y.; Yu, G. The development and application of an academic emotion questionnaire. Acta Psycho. Sinica 2007, 39, 852–860. (In Chinese) [Google Scholar]
  31. Pekrun, R. The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 2006, 18, 315–341. [Google Scholar] [CrossRef]
  32. Pekrun, R.; Frenzel, A.C.; Goetz, T.; Perry, R.P. The control-value theory of achievement emotions: An integrative approach to emotions in education. Educ. Psychol. 2007, 2, 13–36. [Google Scholar]
  33. Schweder, S.; Raufelder, D. Positive Emotions, Learning Behavior and Teacher Support in Self-Directed Learning during Adolescence: Do Age and Gender Matter? J. Adolesc. 2019, 73, 73–84. [Google Scholar] [CrossRef]
  34. Loderer, K.; Pekrun, R.; Lester, J.C. Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learn. Instruct. 2018, 70, 2–12. [Google Scholar] [CrossRef]
  35. Wakefield, R.L. The Acceptance and Use of Innovative Technology: Do Positive and Negative Feelings Matter? Data Base Adv. Inf. Syst. 2015, 46, 48–67. [Google Scholar] [CrossRef]
  36. Butz, N.T.; Stupnisky, R.H.; Pekrun, R. Students’ emotions for achievement and technology use in synchronous hybrid graduate programmes: A control-value approach. Re. Learn. Technol. 2015, 23, 14–32. [Google Scholar] [CrossRef] [Green Version]
  37. Chiu, C.J.; Kuo, S.E.; Lin, D.C. Technology-embedded health education on nutrition for middle-aged and older adults living in the community. Glob. Health Promot. 2019, 26, 80–87. [Google Scholar] [CrossRef] [PubMed]
  38. Kock, N.; Moqbel, M. Social Networking Site Use, Positive Emotions, and Job Performance. J. Comput. Inf. Syst. 2021, 61, 163–173. [Google Scholar] [CrossRef]
  39. Stephan, M.; Markus, S.; Glaser-Zikuda, M. Students’ Achievement Emotions and Online Learning in Teacher Education. Front. Edu. 2021, 4, 109–111. [Google Scholar] [CrossRef]
  40. Moos, D.C.; Azevedo, R. Learning with computer-based learning environments: A literature review of computer self-efficacy. Rev. Educ. Res. 2009, 79, 576–600. [Google Scholar] [CrossRef] [Green Version]
  41. Fonseca, D.; Marti, N.; Redondo, E.; Navarro, I.; Sanchez, A. Relationship between student profile, tool use, participation, and academic performance with the use of Augmented Reality technology for visualized architecture models. Comput. Hum. Behav. 2014, 31, 434–445. [Google Scholar] [CrossRef]
  42. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977, 84, 191–215. [Google Scholar]
  43. Wang, C.H.; Shannon, D.M.; Ross, M.E. Students’ characteristics, self-regulated learning, technological self-efficacy, and course outcomes in online learning. Distance Educ. 2013, 34, 302–323. [Google Scholar] [CrossRef]
  44. Compeau, D.R.; Higgins, C.A. Computer self-efficacy: Development of a measure and initial test. MIS Q. 1995, 19, 189–211. [Google Scholar] [CrossRef] [Green Version]
  45. Holt, L.; Brockett, R.G. Self-direction and factors influencing technology use: Examining the relationships for the 21st century workplace. Comput. Hum. Behav. 2012, 28, 2075–2082. [Google Scholar] [CrossRef]
  46. An, Z.J.; Wang, C.; Li, S.Y.; Gan, Z.D.; Li, H. Technology-Assisted Self-Regulated English Language Learning: Associations with English Language Self-Efficacy, English Enjoyment, and Learning Outcomes. Front. Psychol. 2021, 11, 34–51. [Google Scholar] [CrossRef] [PubMed]
  47. Lavidas, K.; Achriani, A.; Athanassopoulos, S.; Messinis, I.; Kotsiantis, S. University students’ intention to use search engines for research purposes: A structural equation modeling approach. Educ. Inf. Technol. 2020, 25, 2463–2479. [Google Scholar] [CrossRef]
  48. Shiau, W.L.; Yuan, Y.; Pu, X.D.; Ray, S.; Chen, C.C. Understanding fintech continuance: Perspectives from self-efficacy and ECT-IS theories. Ind. Manag. Data Syst. 2020, 120, 1659–1689. [Google Scholar] [CrossRef]
  49. Pekrun, R. Motivation, Emotion and Personality [Emotion, Motivation und Persönlichkeit]; Psychologie Verlags Union: München, Germany, 1998. [Google Scholar]
  50. Putwain, D.; Sander, P.; Larkin, D. Academic self-efficacy in study-related skills and behaviours: Relations with learning-related emotions and academic success. Br. J. Educ. Psychol. 2013, 83, 633–650. [Google Scholar] [CrossRef] [PubMed]
  51. Pajares, F. Self-efficacy beliefs in academic settings. Rev. Educ. Res. 1996, 66, 78–96. [Google Scholar]
  52. Brígido, M.; Borrachero, A.B.; Bermejo, M.L.; Mellado, V. Prospective primary teachers’ self-efficacy and emotions in science teaching. Eur. J. Teach. Educ. 2013, 36, 200–217. [Google Scholar] [CrossRef]
  53. Heckel, C.; Ringeisen, T. Pride and anxiety in online learning environments: Achievement emotions as mediators between learners’ characteristics and learning outcomes. J. Comput. Assist. Learn. 2019, 35, 667–677. [Google Scholar] [CrossRef]
  54. Lim, S. How and Why Do College Students Use Wikipedia? J. Am. Soc. Inf. Sci. Technol. 2009, 60, 2189–2202. [Google Scholar] [CrossRef]
  55. Petousi, V.; Sifaki, E. Contextualizing harm in the framework of research misconduct. Findings from discourse analysis of scientific publications. Int. J. Sustain. Dev. 2020, 23, 149–174. [Google Scholar] [CrossRef]
  56. Teo, T. Modelling technology acceptance in education: A study of pre-service teachers. Comp. Educ. 2009, 52, 302–312. [Google Scholar] [CrossRef]
  57. Guglielmino, L.M. Development of the Self-Directed Learning Readiness Scale. Doctor’s Thesis, University of Georgia, Athens, GA, USA, 1977. [Google Scholar]
  58. Pang, W.G. Self-directed learning assessment methods. Chin. J. Psychol. Sci. 2003, 5, 882–884. (In Chinese) [Google Scholar]
  59. Hair, J.F., Jr.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 6th ed.; Prentice-Hall International: Hoboken, NJ, USA, 2006. [Google Scholar]
  60. Hu, L.; Bentler, P.M. Structural Equation Modeling: A Multidisciplinary Journal. Struct. Equ. Modeling 1999, 6, 1–55. [Google Scholar] [CrossRef]
  61. Hayes, A. Introduction to mediation, moderation, and conditional process analysis. J. Educ. Meas. 2013, 51, 335–337. [Google Scholar]
  62. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 48, 39–50. [Google Scholar] [CrossRef]
  63. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory; McGraw-Hill, Inc.: New York, NY, USA, 1994. [Google Scholar]
  64. Segars, A.H. Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research. Int. J. Manag. Sci. Eng. Manag. 1997, 25, 107–121. [Google Scholar]
  65. Fornell, C.; Tellis, G.J.; Zinkhan, G.M. Validity assessment: A structural equations approach using partial least squares. In Proceedings of the American Marketing Association Educators’ Conference; American Marketing Association: Chicago, IL, USA, 1982. [Google Scholar]
  66. Kline, B.R. Principles and Practice of Structural Equation Modeling, 3rd ed.; The Guılford Press: New York, NY, USA, 2005. [Google Scholar]
  67. Deci, E.L.; Ryan, R.M. The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychol. Inq. 2000, 11, 227–268. [Google Scholar] [CrossRef]
  68. Pan, X.Q.; Chen, W. Modeling Teacher Supports Toward Self-Directed Language Learning Beyond the Classroom: Technology Acceptance and Technological Self-Efficacy as Mediators. Front. Psychol. 2021, 12, 751017. [Google Scholar] [CrossRef]
  69. Celik, V.; Yesilyurt, E. Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Comput. Educ. 2013, 60, 148–158. [Google Scholar] [CrossRef]
  70. Oriol-Granado, X.; Mendoza-Lira, M.; Covarrubias-Apablaza, C.G.; Molina-Lopez, V.M. Positive Emotions, Autonomy Support and Academic Performance of University Students: The Mediating Role of Academic Engagement and Self-efficacy. Rev. Psicodidact. 2017, 22, 45–53. [Google Scholar] [CrossRef]
  71. Buonomo, I.; Fiorilli, C.; Benevene, P. The Impact of Emotions and Hedonic Balance on Teachers’ Self-Efficacy: Testing the Bouncing Back Effect of Positive Emotions. Front. Psychol. 2019, 10, 1670. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Chatzopoulos, A.; Kalogiannakis, M.; Papadakis, S.; Papoutsidakis, M. A Novel, Modular Robot for Educational Robotics Developed Using Action Research Evaluated on Technology Acceptance Model. Educ. Sci. 2022, 12, 274–287. [Google Scholar] [CrossRef]
Sustainability 14 10390 g001 550
Figure 1. Hypothesized research model.
Figure 1. Hypothesized research model.
Sustainability 14 10390 g001
Sustainability 14 10390 g002 550
Figure 2. Path coefficients for the research model. *** p < 0.001. Notes. PU = perceived usefulness; PEU = perceived ease of use; ATT = attitude towards using; BI = behavioral intention; PHA = positive high-approval; PLA = positive low- approval; TSE = technological self-efficacy; ALC = autonomy in learning content; TM = time management; LS = learning strategies; LP = learning processes; LO = evaluation and reinforcement of learning outcomes; LE = control over the learning environment.
Figure 2. Path coefficients for the research model. *** p < 0.001. Notes. PU = perceived usefulness; PEU = perceived ease of use; ATT = attitude towards using; BI = behavioral intention; PHA = positive high-approval; PLA = positive low- approval; TSE = technological self-efficacy; ALC = autonomy in learning content; TM = time management; LS = learning strategies; LP = learning processes; LO = evaluation and reinforcement of learning outcomes; LE = control over the learning environment.
Sustainability 14 10390 g002
Table
Table 1. Demographic statistics (N = 501).
Table 1. Demographic statistics (N = 501).
VariablesFrequencyPercentage (%)
Gender
Male26152.1
Female24047.9
Age group
12 years old12024.0
13 years old13527.0
14 years old11422.7
15 years old13226.3
Grade
Grade 725550.9
Grade 824649.1
Table
Table 2. Descriptive statistics, correlations and Cronbach’s alpha.
Table 2. Descriptive statistics, correlations and Cronbach’s alpha.
VariablesCronbach’s Alpha1234
1. Technology Acceptance0.9421
2. Positive Emotions0.8910.211 **1
3. Technological Self-Efficacy0.9050.446 **0.356 **1
4. Self-Directed Learning0.9760.207 **0.506 **0.446 **1
Range-1–51–51–51–5
Mean-3.7103.7003.6513.792
Standard Deviation-1.0691.0271.0311.021
Note. ** p < 0.01.
Table
Table 3. The reliability and convergent validity of the model.
Table 3. The reliability and convergent validity of the model.
Factors (Items)Factor Loading (>0.70)CR (>0.70)AVE (>0.50)Cronbach’s AlphaR Square
PU (3)0.736~0.8420.8440.6450.8380.41~0.68
PEU (3)0.730~0.8400.8370.6330.8330.57~0.65
ATT (3)0.822~0.8960.8980.7460.8960.60~0.71
BI (3)0.884~0.9320.9320.8220.9320.60~0.67
PHA (16)0.714~0.7850.8180.5820.8240.55~0.63
PLA (14)0.749~0.7940.8840.6590.8530.62~0.68
TSE (6)0.765~0.9230.9250.6740.9050.58~0.69
LC (12) 0.723~0.8200.9310.5420.7300.64~0.71
TM (8)0.749~0.7970.8860.6940.7860.62~0.71
LS (18)0.716~0.8030.9440.5870.7480.57~0.69
LP (7)0.721~0.7830.8270.6150.8130.72~0.79
LO (9)0.792~0.8670.8850.6670.8580.71~0.82
LE (6)0.854~0.9310.8640.7140.8660.74~0.84
PU = perceived usefulness; PEU = perceived ease of use; ATT = attitude towards using; BI = behavioral intention; PHA = positive high-approval; PLA = positive low- approval; TSE = technological self-efficacy; ALC = autonomy in learning content; TM = time management; LS = learning strategies; LP = learning processes; LO = evaluation and reinforcement of learning outcomes; LE = control over the learning environment.
Table
Table 4. Fitting indices of the hypothetical model.
Table 4. Fitting indices of the hypothetical model.
Modelχ2/dfGFITLICFIRMSEASRMR
The hypothetical model2.4800.9530.9740.9800.0540.041
The suggested guidelines were based on Hu and Bentler (1999) [60].
Table
Table 5. Regression analysis results.
Table 5. Regression analysis results.
RegressionModel IndexCoefficients
Outcome VariablesIndependent VariablesR2Fβt
PETA0.0523.06 ***0.2114.80 ***
TSETA0.24157.62 ***0.44612.56 ***
PE 0.3568.67 ***
SDLTA0.0422.41 ***−0.042−0.424
PE 0.50613.39 ***
TSE 0.28811.43 ***
*** p < 0.001. All variables were standardized. TA = technology acceptance; PEs = positive emotions; TSE = technological self-efficacy; SDL = self-directed learning.
Table
Table 6. Total, direct and indirect effects.
Table 6. Total, direct and indirect effects.
PathFull
EffectBoot SEBoot LLCIBoot ULCI
Total effect0.2140.1720.1070.322
Direct effects−0.0420.163−0.1440.060
Indirect effects
Total indirect effects0.2570.1490.1570.360
TA→PE→SDL0.1070.0910.0380.187
TA→TSE→SDL0.1280.1090.0570.210
TA→PE→TSE→SDL0.0220.0270.0080.046
TA = technology acceptance; PEs = positive emotions; TSE = technological self-efficacy; SDL = self-directed learning.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

An, F.; Xi, L.; Yu, J.; Zhang, M. Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy. Sustainability 2022, 14, 10390. https://doi.org/10.3390/su141610390

AMA Style

An F, Xi L, Yu J, Zhang M. Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy. Sustainability. 2022; 14(16):10390. https://doi.org/10.3390/su141610390

Chicago/Turabian Style

An, Fuhai, Linjin Xi, Jingyi Yu, and Mohan Zhang. 2022. "Relationship between Technology Acceptance and Self-Directed Learning: Mediation Role of Positive Emotions and Technological Self-Efficacy" Sustainability 14, no. 16: 10390. https://doi.org/10.3390/su141610390

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop