Evidence map›Paper›PMID 41981194›Full record

ArticleScientific reports2026

Explaining EFL students' continuance intention to use e-learning through technology acceptance, expectation confirmation, and flow theory.

Xuejie Xu, Chunhui Yang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xuejie XuSchool of Sports Humanities and Sociology, Harbin Sport University, Harbin, 150008, Heilongjiang, China. 13904801101@163.com.
Chunhui YangSchool of Sports Humanities and Sociology, Harbin Sport University, Harbin, 150008, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the widespread adoption of digital learning tools and the continuous upgrading of online learning technologies, e-learning has increasingly become a common way for English as Foreign Language (EFL) college students to obtain learning resources and practice English beyond the classroom. Drawing on the Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT), and Flow Theory, this study examines key factors associated with EFL college students' continuance intention toward e-learning. Using responses from 435 EFL college students, the study applied PLS-SEM (Partial Least Squares Structural Equation Modeling) using SmartPLS to examine the hypothesized relationships. The results indicate that perceived ease of use, perceived usefulness, confirmation, satisfaction, and perceived enjoyment are significantly related to continuance intention, with the model explaining 59.3% of the variance. Among these factors, perceived usefulness and satisfaction show the strongest associations with continuance intention, while perceived enjoyment is positively related to continuance intention and is also associated with perceived ease of use, perceived usefulness, and satisfaction. Furthermore, the multigroup analysis reveals significant differences between higher-grade and first-year students in specific paths: higher-grade students demonstrate stronger associations in the paths from perceived enjoyment to perceived ease of use and from confirmation to perceived enjoyment. These findings underscore the moderating role of learning experience in the associations underlying continuance intention toward e-learning. Overall, the findings deepen theoretical understanding by integrating TAM, ECT, and Flow Theory into the EFL e-learning setting and inform practical efforts aimed at refining platform design, elevating user satisfaction, and maintaining sustained usage.

Indexed as

Education, DistanceIntentionLearningMultilingualismStudentsFemaleHumansMaleUniversitiesYoung AdultContinuance intentionEFL studentsE-LearningExpectation confirmation theory (ECT)Perceived enjoymentTechnology acceptance model (TAM)

Identifiers

PMID41981194
PMCPMC13237203

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.