Performance expectancy significantly predicts both behavioral intention and actual use of MALL.¶

Claim · All claims
Evidence · 1 study · associational r2 · q2 · i2 medium
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i2 Performance expectancy directly predicted use behavior (β = .22), a departure from prior UTAUT literature. → 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 · i2 · associational · r2

In the path analysis of 293 learners' Rasch-calibrated measures, the direct path to use behavior was significant ("β = .22"), contrary to prior UTAUT studies where performance expectancy had no direct effect on use.

"The direct path from performance expectancy to use behavior was also significant at p < .001 level (β = .22)."

Discussion¶

Learner Variables¶

• Motivation — predictor: learners who differ on it differ in outcomes

Related Claims¶

• In a Vietnamese EFL sample, all six Meta-UTAUT hypotheses on MALL adoption were accepted with p-values below 0.05 — related
• Behavioral intention translates only weakly into self-reported use behavior, and the extended UTAUT model explains intention formation better than enactment — related
• The modified UTAUT model accounts for 62% of variance in behavioral intention and 19% of variance in use behavior. — related
• Performance expectancy, effort expectancy, and social influence positively predict behavioral intention to use ChatGPT among Lesotho higher education students — related
• Behavioral intention has a statistically significant but weak positive effect on actual MALL usage behavior in EFL learning — related
• MALL effect sizes show no visible correlation with grade level or sample size — related