November 30, 2025Journal of Computer Assisted LearningOpen Access

Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors

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Implication

Mixed-methods analysis reveals digital engagement and self-efficacy predict generative AI adoption, suggesting targeted training and regulatory frameworks are needed.

Key Points

User behavior is significantly influenced by self-efficacy, driving generative AI usage in various contexts.
Findings from 897 survey respondents and 38 students indicate strong predictors of generative AI adoption include digital engagement and user perceptions.
Assessment through qualitative methodology and quantitative methodology provided a nuanced understanding of user experiences with generative AI.
The study emphasizes the need for regulatory frameworks to ensure responsible practices and enhance digital literacy among users.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/692b9d8d1d383f2b2a379beb https://doi.org/10.1002/jcal.70162

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Implication

Mixed-methods analysis reveals digital engagement and self-efficacy predict generative AI adoption, suggesting targeted training and regulatory frameworks are needed.

Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors

Discussion

Member takes

Implication

Key Points

Cite This Study

Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider