Validating the General Extended Technology Acceptance Model for E-Learning: Evidence From an Online English as a Foreign Language Course Amid COVID-19

Jiang, Michael Yi‐Chao; Jong, Morris Siu‐Yung; Lau, Wilfred W. F.; Meng, Yanli; Chai, Ching Sing

doi:10.3389/fpsyg.2021.671615

Cited by 31 publications

(42 citation statements)

References 61 publications

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“…Research also indicated that students who assumed blended learning to be useful for their education would be more likely to adopt it (Tselios et al, 2011 ). Such a result indicates that it is important for instructors to understand students’ attitudes toward technologies in online or blended learning, especially when dealing with different situations, such as under the influence of COVID-19 (Jiang et al, 2021 ) and help students to build a positive belief in blended learning at the early stage of learning. Effective strategies include the use of lectures or social influences to help students understand the potential advantages of blended learning and the presentation of successful blended learning courses to students.…”

Section: Discussionmentioning

confidence: 99%

Examining key factors of beginner’s continuance intention in blended learning in higher education

et al. 2022

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With the outbreak of the COVID-19 pandemic, blended learning became exceptionally widespread, especially in higher education. As a result, many college students became beginners in this learning method. To identify key factors that impact beginners’ continuance intention in blended learning, this study surveyed 1845 first-year college students at a university in central China in the falls of 2020 and 2021 who used blended learning for the first time. Structural equation modeling was employed to verify a model that integrates intrinsic motivation and academic self-efficacy in the Expectation-Confirmation Model of Information System Continuance. The results show that performance expectancy, intrinsic motivation, and satisfaction significantly impact beginners’ continuance intention in blended learning. Moreover, performance expectancy, intrinsic motivation, and confirmation significantly impact beginners’ continuance intention through mediating variable satisfaction. Academic self-efficacy does not directly impact college students’ continuance intention but indirectly impacts their continuance intention through intrinsic motivation. Finally, this study provides suggestions for educators to improve beginners’ blended learning experience thus promoting their continuance intention in blended learning.

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Section: Discussionmentioning

confidence: 99%

Examining key factors of beginner’s continuance intention in blended learning in higher education

et al. 2022

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“…Two professors in the field of educational technology were consulted to ensure the surface validity of the three pairs. Following Jiang et al’s method [ 40 ], a filtering criterion was applied in the data preprocessing stage to exclude those whose responding performance was inconsistent. To be exact, if the sum of the absolute value of the averaged difference between all the three filtering pairs was greater than 1 unit per pair, then the responding performance of the participant was defined as inconsistent, and thus the corresponding data record was considered invalid for further analysis and should be removed from the sample.…”

Section: Methodsmentioning

confidence: 99%

“…PR of medical AI technologies measures how the AI technologies are able to enhance medical students’ productivity or performance. In literature pertaining to technology adoption, a plethora of primary studies have obtained positive evidence of the positive impact of attitude on behavioral intention [ 40 , 41 ]. Because it is considered a factor which behavioral studies commonly employ as an estimate of attitude toward behavior, PR is oftentimes considered as an antecedent of BI [ 42 ].…”

Section: Theory and Hypothesesmentioning

confidence: 99%

“…For example, Teo revealed that SN had significant but indirect effect on BI of technology adoption, and the effect was mediated by attitude toward the behavior [ 47 ]. Jiang et al validated an extended technology acceptance model against the background of COVID-19 and observed that SN was the only external factor that had a significant effect on BI to use technology through both perceived usefulness and perceived ease of use [ 40 ]. Based on the above, hypotheses H3a (in tandem with H2 aforesaid) and H3b are formed with regard to the indirect and direct effects of SN on BI, respectively.…”

Section: Theory and Hypothesesmentioning

confidence: 99%

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Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

Jiang

Jong

et al. 2022

IJERPH

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Medical students learning to use artificial intelligence for medical practices is likely to enhance medical services. However, studies in this area have been lacking. The present study investigated medical students’ perceptions of and behavioral intentions toward learning artificial intelligence (AI) in clinical practice based on the theory of planned behavior (TPB). A sum of 274 Year-5 undergraduates and master’s and doctoral postgraduates participated in the online survey. Six constructs were measured, including (1) personal relevance (PR) of medical AI, (2) subjective norm (SN) related to learning medical AI, (3) perceived self-efficacy (PSE) of learning medical AI, (4) basic knowledge (BKn) of medical AI, (5) behavioral intention (BI) toward learning medical AI and (6) actual learning (AL) of medical AI. Confirmatory factor analysis and structural equation modelling were employed to analyze the data. The results showed that the proposed model had a good model fit and the theoretical hypotheses in relation to the TPB were mostly confirmed. Specifically, (a) BI had a significantly strong and positive impact on AL; (b) BI was significantly predicted by PR, SN and PSE, whilst BKn did not have a direct effect on BI; (c) PR was significantly and positively predicted by SN and PSE, but BKn failed to predict PR; (d) both SN and BKn had significant and positive impact on PSE, and BKn had a significantly positive effect on SN. Discussion was conducted regarding the proposed model, and new insights were provided for researchers and practitioners in medical education.

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“…Research that examined the potential relation between PEOU and PU in the domain of online learning has shown that PEOU of online learning positively affected the learners' PU of online learning (Almarabeh, 2014;Al-Okaily, Alqudah, Matar, Lutfi, & Taamneh, 2020;Farahat, 2012;Hsieh, Huang, & Wu, 2016;Jiang et al, 2021). While previous research has shown a significant relationship between learners' PEOU of online learning and their attitudes towards using online learning (Almarabeh, 2014;Farahat, 2012;Jiang et al, 2021), learners' PEOU of using online learning in the study of Natasia, Wiranti, & Parastika, (2022) did not significantly affect the learners' attitudes towards using online learning system.…”

Section: Literature Reviewmentioning

confidence: 99%

EFL Students’ Attitudes towards Using Online Learning during Covid-19: Applying Technology Acceptance Model

Mizher¹,

Amoush²,

Alwreikat³

2022

AWEJ

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This study examined the attitudes toward online learning of EFL students by applying technology acceptance model (TAM) constructs along with the characteristics of the online learning system and the experience of online learning as external independent variables. The study used a quantitative approach. The researchers collected data by administering a questionnaire using Microsoft’s online form to the tertiary students who were studying for their Bachelor of Arts degree (B.A.) in the English Language and Literature Department at Al-Balqa Applied University – Jordan. . One hundred eighty-five students out of 602 responded to the questionnaire. The researchers used SPSS and Structural Equational Modeling (SEM – Smart PLS) to analyze the data. Among the findings, the characteristics of an online learning system significantly affected perceived usefulness (PU), perceived ease of use (PEOU), attitudes, and the behavioral intentions (BIs) of EFL learners. In contrast, EFL learners’ PEOU negatively affected their attitudes towards online learning and BIs. Finally, the attitudes of students who studied online for four semesters or more were negative compared to those who took less than four semesters online. These findings could be attributed to the supposedly temporary and rapid shift to online learning due to Covid-19 lockdown measures. This study is significant because it develops an understanding of tertiary EFL attitudes towards online learning. It also reflects on their behavioral intentions to utilize online learning after the pandemic. The researchers believe that this is the first study to examine EFL learners’ attitudes and BIs on using online learning during/after the pandemic in Jordan using TAM.

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Validating the General Extended Technology Acceptance Model for E-Learning: Evidence From an Online English as a Foreign Language Course Amid COVID-19

Cited by 31 publications

References 61 publications

Examining key factors of beginner’s continuance intention in blended learning in higher education

Examining key factors of beginner’s continuance intention in blended learning in higher education

Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

EFL Students’ Attitudes towards Using Online Learning during Covid-19: Applying Technology Acceptance Model

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