Enterprise AI Analysis
Academic transformation in the era of artificial intelligence: drivers of university faculty adoption of GenAI based on the UTAUT model
This study investigates university faculty adoption of Generative Artificial Intelligence (GenAI) based on an extended UTAUT model. Key findings indicate that performance expectancy and usage attitudes positively impact adoption intention. GenAI literacy significantly affects attitudes, which fully mediate the effect of literacy on adoption intention. Practical implications include fostering GenAI literacy and positive attitudes to enhance adoption.
Executive Impact at a Glance
Key metrics from the study highlighting the drivers of GenAI adoption among university faculty.
0.731
Direct Impact of GenAIL on Usage Attitude (β)
0.425
Direct Impact of Usage Attitude on Behavioral Intention (β)
53.4%
Variance Explained in Usage Attitude (R²)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction & Background
GenAI Literacy
UTAUT Model
Research Design & Data
Generative Artificial Intelligence (GenAI) is rapidly transforming higher education, impacting research and teaching paradigms. University faculty adoption of GenAI is crucial for educational innovation. This study uses the Unified Theory of Acceptance and Use of Technology (UTAUT) model, extended with GenAI literacy and usage attitudes, to explore influencing factors.
Despite GenAI's expanding use, scholarly attention has primarily focused on learners. This study fills the gap by examining educators' perspectives, offering theoretical and practical guidance for promoting GenAI integration.
GenAI literacy refers to the multidimensional ability to master AI technology and adapt to future developments. It encompasses technical proficiency, critical evaluation, communication proficiency, creative application, and ethical competence. Enhancing GenAI literacy is crucial for forming positive attitudes towards GenAI technology.
The UTAUT model integrates eight theoretical models to explain technology acceptance, focusing on Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC). This study extends UTAUT by incorporating GenAI literacy and usage attitudes.
PE and Usage Attitudes (UA) significantly impact faculty members' usage intention. GenAI literacy positively affects attitudes, fully mediating its effect on usage intention.
This study employed Structural Equation Modeling (SEM) to investigate key drivers of GenAI adoption among university faculty. Data was collected from 238 university teachers through a questionnaire based on task-based experiential activities in AI for Science and AI-Enabled Education contexts.
The questionnaire included 10 latent variables (37 items total) and utilized a Likert seven-point scale. Reliability and validity were confirmed through Composite Reliability (CR) and Average Variance Extracted (AVE) analyses.
0.731
GenAIL’s direct positive impact on Usage Attitude (β)
Enterprise Process Flow
GenAI Literacy
→
Influences
→
Usage Attitude
→
Mediates
→
Behavioral Intention
| Supported Hypotheses | Unsupported Hypotheses |
|---|---|
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Driving GenAI Adoption in Academia: Key Levers
The study highlights critical pathways to successful GenAI integration in higher education. Focusing on enhancing GenAI literacy (especially Communication Proficiency and Creative Application) and cultivating positive Usage Attitudes (UA) are paramount. Practical strategies include practice-oriented training, case-sharing seminars, and technical support to build confidence and demonstrate tangible benefits, thereby reinforcing Performance Expectancy.
- Enhance GenAI Literacy through practice-oriented activities.
- Foster positive Usage Attitudes via seminars, technical support, and incentive mechanisms.
- Increase Performance Expectancy by demonstrating tangible improvements in research efficiency and teaching effectiveness.
61.3%
Total variance explained in Behavioral Intention (R²)
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