Beyond simple structures: a bifactor-ESEM Approach to understanding student acceptance of generative AI in higher education
CURRENT PSYCHOLOGY, cilt.45, sa. 1432, ss.1, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 45 Sayı: 1432
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s12144-026-09959-w
- Dergi Adı: CURRENT PSYCHOLOGY
- Derginin Tarandığı İndeksler: Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Sociology Source Ultimate (EBSCO), Social Sciences Citation Index (SSCI), IBZ Online, BIOSIS, Psycinfo
- Sayfa Sayıları: ss.1
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
Generative artificial intelligence (GenAI) is rapidly transforming higher education, making it crucial to understand the factors that influence student acceptance. However, existing measurement tools are often limited by classical validation methods, such as Confirmatory Factor Analysis (CFA), which may fail to capture the complex, multidimensional nature of this phenomenon. The objective of this study was to rigorously validate the psychometric properties of the 10-factor, 46-item GenAI-TAM scale using a more advanced methodological framework. A systematic model-comparison approach was used to test seven competing models ranging from traditional confirmatory factor analysis (CFA) to exploratory structural equation modeling (ESEM) with data from 860 undergraduates. The final, best-fitting model was a bifactor ESEM (B-ESEM), and its measurement invariance was maintained across gender. The results demonstrated the better fit of the B-ESEM model, which fit the data excellently (CFI = .972, RMSEA = .046), unlike the inadequate CFA-based models. The analysis revealed a strong general ‘GenAI Acceptance’ factor (ωₕ = 0.87), which provides strong psychometric justification for using a single total score. Scalar invariance across gender supported the comparability of latent mean scores across male and female students. These findings provide robust evidence for the validity and reliability of the GenAI-TAM scale, conceptualizing GenAI acceptance as a hierarchical construct. In short, the study provides a methodologically sound basis for assessing undergraduate students’self-reported acceptance of generative AI, while further criterion and predictive validity evidence is needed before the scale can be used for diagnostic profiling or institutional decision-making