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The social distancing due to the Covid-19 epidemic has disturbed all sectors of society, including education. To maintain normal operations, it is necessary to adapt quickly to this situation. Many technologies and platforms have rushed to offer their support to users. This article adopts a critical perspective to reflect on the factors that may cause the hasty adoption of 5G smart learning technology. To investigate students' intentions toward smart learning, this article provides a theoretical framework premised on the technology acceptance model (TAM) by adding components from the social practise theory (SPT). Based on data analysis through Structural equation Modeling (SEM) of a survey (n = 375) conducted in China, we found that the choice of 5G smart-learning technology depends on the combined effect of Material (MAA), Meanings (MEA), and Competency access (COA) factors. The results illustrate that these are the effective factors for student's intentions to adopt 5G smart-learning technology. These outcomes are intended to aid service providers and decision-makers in developing effective ways to increase smart learning use. These findings can also enable us to identify challenges affecting smart learning adoption and to contribute to the design and proper supply of smart learning programs in other countries.
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Stored only when both papers are in the corpus — 20 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.