Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors.
Objectives: This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method: Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to ca...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 1; pp. 1 - 20 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191181614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191181614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2026 vid: 42 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191181614 191181614 191181614 10.1002/jcal.70162 191181614 ppf: 1 ppct: 19 formats: tig: atl: Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors. aug: au: Gazit, Tali Eitan, Tal Gal, Lilach Gradovitch, Noah affil: Department of Information Science, Bar‐Ilan University, Ramat Gan, Israel sug: subj: Artificial Intelligence, Generative Adaptation, Psychological Personality Behavior Models, Theoretical Israel Human Male Female Adult Middle Age Students, College Psychosocial Factors Multimethod Studies Snowball Sample Quantitative Studies Qualitative Studies Coefficient alpha Summated Rating Scaling Pearson's Correlation Coefficient One-Way Analysis of Variance Analysis of Covariance Mediation Analysis Pretest-Posttest Design Power Analysis Regression P-Value Descriptive Statistics Questionnaires Personality Disorders Self-Efficacy Social Media Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objectives: This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method: Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open‐ended questions; additionally, 38 students participated in a semi‐experimental stage to assess the impact of an online course, "GAI: From Theory to Practice." Results: Key findings underscore the role of personality traits such as openness and self‐efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self‐reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion: This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools. Summery: What is currently known about the subject matter? ○Generative AI (GAI) has revolutionised work, learning, and communication globally.○GAI systems create text, images, or code, transforming diverse sectors like education.○Adoption varies by traits (e.g., openness, self‐efficacy) and digital engagement.○Ethical concerns and access challenges accompany GAI's rapid societal integration.What this paper adds to this knowledge? ○Examines GAI adoption across varied demographics, addressing prior research gaps.○Integrates personal, social, and technological factors for a nuanced analysis.○Highlights the impact of GAI training on perceptions, skills, and daily usage.○Provides a holistic view of GAI's benefits, risks, and regulatory implications.Implications of the study findings for practitioners ○Design training to enhance digital literacy and promote responsible GAI use.○Create inclusive tools and policies addressing ethical concerns and user needs.○Support GAI adoption in education and workplaces through tailored initiatives.○Inform regulations balancing innovation with equitable and ethical practices. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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