Evidence map›Paper›PMID 42792374›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Determinants of Learners' Continuance Intention in Informal Video Learning: Evidence from YouTube.

Tewarit Khanmolee, Somchai Lekcharoen, Sumaman Pankham

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 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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0cells of the map it votes in
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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

3 authors.

Tewarit KhanmoleeCollege of Digital Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand.ORCID 0009-0003-5660-2158
Somchai LekcharoenCollege of Digital Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand.ORCID 0009-0003-2207-6006
Sumaman PankhamCollege of Digital Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand.ORCID 0009-0002-6821-7049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Informal video platforms support self-directed learning, yet the determinants of learners' continuance intention to use YouTube for informal learning remain insufficiently integrated. This study addressed two questions: how the hypothesized structural relationships in an extended expectation-confirmation model explain Thai learners' continuance intention and which determinants are necessary for high continuance intention. A sequential exploratory mixed-methods design was employed. Fuzzy e-Delphi with 23 experts retained 33 measurement items; survey data from 797 Thai learners were analyzed using covariance-based structural equation modeling, bootstrapped specific indirect effects, and Necessary Condition Analysis (NCA). The model explained 74% of the variance in continuance intention. Satisfaction was the strongest direct predictor (β = 0.559), followed by perceived enjoyment and trust. All 12 hypothesized structural relationships and eight indirect effects were statistically significant, revealing interconnected roles for confirmation, content quality, subjective norm, perceived enjoyment, perceived usefulness, satisfaction, and trust. NCA identified satisfaction and content quality as the principal necessary conditions, with smaller supporting roles for perceived usefulness and perceived enjoyment. Theoretically, the findings extend the expectation-confirmation model to voluntary informal video learning and distinguish contributing relationships from necessary constraints. Practically, they support prioritizing high-quality content and satisfactory learning experiences while strengthening perceived usefulness and perceived enjoyment.

Indexed as

continuance intentionexpectation confirmation modelinformal learningnecessary condition analysissatisfactionstructural equation modelingYouTube

Identifiers

PMID42792374
PMCPMC13603986

What OpenQuestion holds

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Registered trials

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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.