ArticleEducation and information technologies2023
Understanding college students' continuous usage intention of asynchronous online courses through extended technology acceptance model.
Article in Education and information technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Polytechnic students' perceived satisfaction of using technology in the learning process: The context of Bangladesh TVET.Heliyon · 2024Article
- Exploring the transactive relationships of influence factors for online asynchronous learning transactive memory system.Heliyon · 2024Article
- Exploring Chinese Secondary School Students' Acceptance of Live Video-Streamed Teaching Platforms in EFL Class: An Application of the Technology Acceptance Model.Behavioral sciences (Basel, Switzerland) · 2024Article
- Modelling the effects of emotional engagement and peer interaction on the continuous intention to use asynchronous e-learning.PeerJ. Computer science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asynchronous online learning has gained great popularity in higher education, especially due to the recent COVID-19 pandemic. However, few studies have investigated how to maintain students' continuous usage intention of asynchronous online courses in the context of higher education. This study incorporated four key factors (intrinsic motivation, extrinsic motivation, perception of multiple sources, and cognitive engagement) associated with students' continuous usage intention of asynchronous online courses into technology acceptance model (TAM) to identify the influencing factors on students' continuous usage intention. A survey with 325 college students was conducted to explore their continuous usage intention of asynchronous online courses and structural equation modeling analysis was carried out to analyze the relationships between the key influencing factors and students' continuous usage intention. The results showed that cognitive engagement was the only factor that directly related to continuous usage intention. Intrinsic motivation, extrinsic motivation, and perception of multiple sources indirectly correlated with students' continuous usage intention through different pathways. The results of the study have several theoretical and practical implications. Theoretically, the study verified what key learning factors incorporated into TAM and in what way they relate to the continuous usage intention of asynchronous online courses. Practically, the present study indicated that it is required to take intrinsic motivation, extrinsic motivation, perception of multiple sources, cognitive engagement and TAM into consideration when designing and conducting asynchronous online learning courses to ensure college students' continuous usage intention of asynchronous online courses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.