The modified UTAUT model accounts for 62% of variance in behavioral intention and 19% of variance in use behavior.¶
Claim · All claims
Evidence · 1 study · associationalr2·q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2023: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? The proposed model explained 62% of variance in behavioral intention and 19% in use behavior. → Vo 2020
Evidence¶
Vo 2020¶
Vo, H. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers and Education, 146, Article 103761. doi:10.1016/j.compedu.2019.103761
q2 · i? · associational · r2
The path model fitted to Rasch-calibrated measures of 293 learners explained "62% of the variances in the behavioral intention" and 19% of variance in use behavior, indicating weaker prediction of actual use.
"The proposed model accounted for 62% of the variances in the behavioral intention of Vietnamese learners to adopt MALL, and 19% of variances in their usage of MALL (see Figure 3)."
Discussion¶
Related Claims¶
- Behavioral intention translates only weakly into self-reported use behavior, and the extended UTAUT model explains intention formation better than enactment — related
- In a Vietnamese EFL sample, all six Meta-UTAUT hypotheses on MALL adoption were accepted with p-values below 0.05 — related
- The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit — related
- Behavioral intention has a statistically significant but weak positive effect on actual MALL usage behavior in EFL learning — related
- ARCS motivation did not significantly predict behavioral intention but exerted a strong direct effect on use behavior, dominating its explanation — related
- The structural model explains 71.6% of variance in GenAI-assisted learning procrastination and 58.5% in behavioral intention — related
- Facilitating condition has no direct effect on actual use of MALL, departing from prior literature. — related
- Performance expectancy significantly predicts both behavioral intention and actual use of MALL. — related
Page metadata
| Field | Value |
|---|---|
status |
review |
generated |
by process:wiki-ingest on 2026-10-30 |
trust |
unverified — no one has confirmed this page's content |
Frontmatter is the page's machine-readable record — see the Schema & Guide.