Enterprise AI Analysis
Research on the Influencing Factors of University Teachers' Acceptance of Generative Artificial Intelligence Technology
This research investigates the factors influencing university teachers' acceptance of generative AI, integrating the TAM and TOE models. Based on a survey of 439 teachers, it analyzes both independent and synergistic effects. Perceived usefulness and ease of use positively impact acceptance. Self-efficacy, technological efficiency, organizational AI readiness, and environmental support positively influence perceived usefulness and ease of use. Technological complexity negatively impacts perceived ease of use. The study identifies three synergistic configurations: individual initiative, system incubation, and bilateral engagement, providing practical suggestions for enhancing generative AI acceptance in higher education.
Key Performance Indicators
Based on comprehensive research, here are the critical metrics driving successful generative AI adoption in higher education.
73.2%
Survey Response Rate
439
Valid Survey Responses
3
Synergistic Configuration Types Identified
0.82
Overall Solution Consistency (fsQCA)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
0.754
Path Coefficient: Perceived Usefulness to Technology Acceptance
TAM Fundamentals
The Technology Acceptance Model (TAM) identifies perceived usefulness and perceived ease of use as the primary determinants of technology acceptance. This study confirms these factors significantly influence university teachers' acceptance of generative AI. Perceived usefulness is defined as the degree to which an individual believes using a technology will improve work efficiency, while perceived ease of use refers to the perceived difficulty in using it. The higher these perceptions, the greater the acceptance.
| Factor | Influence on Acceptance | Key Implications |
|---|---|---|
| Perceived Usefulness |
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| Perceived Ease of Use |
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TOE Framework & AI Readiness
The TOE framework extends TAM by considering broader influences: Technology, Organization, and Environment. This research applies TOE to generative AI acceptance, finding that these external factors play a crucial role alongside individual perceptions. Organizational AI readiness, encompassing top management support, resource allocation, and infrastructure, significantly impacts both perceived usefulness and ease of use. Environmental support (policy, economic, cultural, and market factors) also positively influences these perceptions.
0.496
Path Coefficient: Organizational AI Readiness to Perceived Ease of Use
| Factor | Influence on Usefulness/Ease of Use | Implications for Universities |
|---|---|---|
| Technological Efficiency |
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| Technological Complexity |
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| Organizational AI Readiness |
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| Environmental Support |
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Synergistic Acceptance Pathways
Self-efficacy
→
Technological Efficiency
→
Organizational AI Readiness
→
Environmental Support
→
Perceived Usefulness/Ease of Use
→
Generative AI Acceptance
Beyond Independent Effects: Synergies
The study reveals that antecedents do not act in isolation but synergistically influence AI acceptance through various combinations. Three distinct configurations for high acceptance were identified: Individual Initiative Type, System Incubation Type, and Bilateral Engagement Type. This highlights the multi-faceted nature of adoption and suggests that different pathways can lead to high acceptance, reflecting 'diverse paths to the same goal'.
Configuration Type: Individual Initiative
Teachers with strong self-efficacy and a comprehensive understanding of generative AI's value and risks, coupled with high perceived technological efficiency, are more likely to adopt. This pathway emphasizes the importance of individual agency and competence in driving AI acceptance, especially when the AI demonstrates significant efficacy in enhancing educational processes or student growth. High organizational AI readiness or environmental support can further amplify this effect.
Configuration Type: System Incubation
Even teachers less versed in digital intelligence and generative AI tools show high acceptance if there is strong organizational AI readiness and robust environmental support. This pathway suggests that institutional and external factors can 'incubate' acceptance, compensating for individual gaps in AI literacy. It underscores the critical role of university infrastructure, training, and a supportive policy/cultural environment in fostering widespread adoption.
Configuration Type: Bilateral Engagement
This type represents a collaborative process. Teachers with significant AI knowledge and experience (high self-efficacy) are willing to adopt when combined with strong organizational AI readiness. This reflects a partnership where informed individual initiative is met with robust institutional support, leading to mutual reinforcement and high acceptance. It's the synthesis of individual readiness and systemic enablement.
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Estimated Annual Cost Savings
$0
Estimated Annual Hours Reclaimed
0
Strategic AI Implementation Roadmap
A structured approach ensures successful integration and maximum benefit from generative AI initiatives.
Phase 1: Assessment & Strategy Alignment
Evaluate current faculty AI literacy, identify key pain points, and align AI integration strategy with institutional goals. Secure top management buy-in and allocate initial resources. This phase includes pilot programs and feedback collection.
Phase 2: Infrastructure & Training Development
Develop or acquire necessary AI infrastructure, tools, and platforms. Design and roll out comprehensive training programs for faculty, focusing on both technical proficiency and pedagogical application of generative AI. Establish support channels.
Phase 3: Integration & Culture Building
Integrate generative AI tools into existing learning management systems and teaching workflows. Foster a university-wide culture that embraces innovation, ethical AI use, and continuous learning. Monitor adoption rates and gather ongoing feedback.
Phase 4: Optimization & Scaling
Continuously optimize AI tools based on performance data and user feedback. Expand successful pilot programs across departments. Develop advanced AI-driven pedagogical models and share best practices across the institution.
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