ArticleScientific reports2025
An integrated approach using social support theory and technology acceptance model to investigate the sustainable use of digital learning technologies.
Article in Scientific reports, 2025. 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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7 citing papers in PubMed.
- Differences in Sports Learning by Digital Literacy Level Among Generation Z: An Application of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Media Richness Theory (MRT).Behavioral sciences (Basel, Switzerland) · 2026Article
- Does cultural identity catalyze behavior? A TAM-based study on museum cultural creative products consumption in China.PloS one · 2026Article
- Perceptions of Chinese higher-vocational nursing program directors on digital transformation: A qualitative study.PloS one · 2026Article
- Social influence and academic performance: a serial mediation model in AI-enhanced learning.Frontiers in psychology · 2026Article
- Latent obstacles in older adults' digital health participation: a community-based hybrid cluster analysis with natural language processing.BMC public health · 2025Article
- Factors influencing AI adoption by Chinese mathematics teachers in STEM education.Scientific reports · 2025Article
- Cancer patients' acceptance of virtual reality interventions for self-emotion regulation.Scientific reports · 2025Article
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Abstract
The present study explored the determinants influencing students' intentions towards utilizing digital learning technologies (DLTs). It proposes a holistic view model for students' utilization of digital learning technologies by integrating social support theory and the "Technology Acceptance Model" (TAM). Data were gathered from 262 students of the University of Ha'il through utilizing a questionnaire. Two steps in SEM were applied for data analysis: CFA was employed to develop the model measurement, and SEM was conducted to analyze the relationships between constructs. The results revealed that "Educational Support" (EDS) significantly affected students' "behavior intention" (BI) to utilize digital learning technologies through perceived usefulness (PU) (EDS→PU: β = 0.338, p < 0.05). Furthermore, "Emotional support" (EMS) significantly affected behavioral intention through both "perceived ease of use" (PEU) and PU (EMS→PEU: β = 0.635, p < 0.05; EMS→PU: β = 0.310, p < 0.05). Surprisingly, students' "perceived ease of use" (PEU) did not affect digital learning technologies' "perceived usefulness" (PU) (PEU→PU: β = 0.195, p > 0.05). The present study contributes to a refined understanding of how educational and emotional support influence students' receptivity towards and engagement with learning technologies. Thus, institutions and universities should enhance their students' educational and emotional support to facilitate the successful utilization of digital learning technologies. Furthermore, the study revealed that PU and PEU affected students' intentions to utilize DLTs. Thus, PU and PEU can enhance the utilization of digital learning technologies. This study provides practical implications that educational institutions and universities could apply to achieve successful usage of these digital learning technologies.
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