Supplementary file 1_Beyond technology acceptance: AI-application readiness and continuance intention among Chinese university students—a measurement-aware secondary analysis.docx

Perceived practical usefulness refers to respondents’ perceived career, efficiency, competitiveness, and problem-solving value rather than verified learning, employment, or institutional outcomes.</p>Results<p>The five domains were internally reliable but not empirically distinct. HTMT estimates ranged from 0.983 to 1.007, the first unrotated component explained 58.75% of item variance, and the five-factor CFA provided negligible improvement over the one-factor model, with latent correlations ranging from 0.980 to 0.997.

A collapsed 16-item descriptive composite was strongly associated with continuance intention (β = 0.896, R² = 0.805). The regression coefficients therefore describe how shared variance is distributed across the questionnaire composites rather than independent acceptance mechanisms. fsQCA produced one high-readiness configuration and its symmetric low-readiness counterpart, with no evidence of equifinality or configurational asymmetry.</p>Contribution<p>The study provides a reproducible measurement critique of a public AI-acceptance dataset.

Its central finding is inadequate discriminant separation among the five affirmative questionnaire blocks. Their shared covariance can be summarized descriptively, but the present data cannot determine whether that common dimension is substantive, method-related, or both, and it should not be treated as a newly validated AI-readiness construct.</p>