ArticleFrontiers in psychology2023
Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic.
Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Acceptance and use of GenAI among medical and health sciences students in Saudi Arabia: an extended TAM study.BMC medical education · 2026Article
- Acceptance of Men Living With HIV Toward Treatment-Supportive Mobile Apps Using the Unified Theory of Acceptance and Use of Technology: Cross-Sectional Study.JMIR formative research · 2026Article
- Drivers and barriers of acceptance of eHealth interventions in postpartum mental health care: a cross-sectional study.BMC public health · 2025Article
- Acceptance, Drivers, and Barriers to Use of mHealth Apps to Improve Quality of Life in Female Patients Affected by Hypothyroidism: Cross-Sectional Study.JMIR formative research · 2025Article
- Videoconferencing psychotherapy: determining acceptance, drivers and barriers of use.Frontiers in digital health · 2025Article
- Acceptance, Needs, and Demands for Nutritional mHealth Support in Patients with Cardiovascular Disease.Nutrients · 2024Article
- Article
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Authors and funding
5 authors.
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Abstract
Online learning systems have become an applied solution for delivering educational content, especially in developing countries, since the start of the COVID-19 pandemic. The present study is designed to identify the factors influencing the behavioral intention of agricultural students at universities in Iran to use online learning systems in the future. This research uses an extended model in which the constructs of Internet self-efficacy, Internet anxiety, and output quality are integrated into the technology acceptance model (TAM). Data analysis was performed using the SmartPLS technique. The analyses showed the proposed model to be strong in terms of predicting the attitude to online learning and the intention to use it. The extended TAM model fit the data well and predicted 74% of the intention variance. Our findings show attitude and perceived usefulness to have directly affected intention. Output quality and Internet self-efficacy indirectly affected attitude and intention. Research findings can help with the design of educational policies and programs to facilitate education and improve student academic performance.
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