Enterprise AI Analysis
Artificial-intelligence-driven governance: addressing emerging risks with a comprehensive risk-prevention-centred model for public health crisis management
This study proposes the Risk Prevention-centred and AI-enabled Anti-pandemic Technology Acceptance Model (RPAA-TAM) to understand public adoption of anti-pandemic digital tools. Integrating TAM, social influence, and risk perception theories, it identifies seven key factors influencing public acceptance of AI in pandemic prevention: external variables, public trust, perceived benefit, perceived risk, attitude toward use, behavioural intention to use, and system usage. The model provides insights for ethical and socially acceptable AI integration in public health crisis management, offering seven novel propositions based on a literature review.
Executive Impact & Key Findings
This research provides a foundational framework for understanding and enhancing AI adoption in public health crises.
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Key Factors Identified
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Propositions Formulated
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Theoretical Integration
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Theoretical Integration
Explores how social-influence dynamics and public risk-perception mechanisms deepen understanding of citizens' willingness to adopt AI-driven tools during public-health crises. It combines the Technology Acceptance Model (TAM), Social Influence Theory, and Risk Perception Theory to form RPAA-TAM.
Ethical & Societal Embedding
Addresses how an extended TAM can transparently embed and systematically evaluate the ethical and societal ramifications (privacy, equity, employment) of AI-infused public-health technologies, ensuring responsible AI deployment.
Empirical Validation
Validates the proposed RPAA-TAM framework through a real-world case study: the nationwide roll-out of AI-enabled medical-service robots in China's COVID-19 response, corroborating its predictive and explanatory power.
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Key Factors Influencing Public Acceptance of AI in Pandemic Prevention
RPAA-TAM Model Construction Flow
Integrate TAM
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Incorporate Social Influence Theory
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Add Risk Perception Theory
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Formulate RPAA-TAM
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Identify Influencing Factors & Propositions
| Theory | Focus in Crisis Management | RPAA-TAM Contribution |
|---|---|---|
| Technology Acceptance Model (TAM) | Perceived usefulness and ease of use of technology. Limited scope for crisis-specific factors. |
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| Social Influence Theory | Normative and informational pressure from peers, government, and media on behavior. Critical for public compliance. |
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| Risk Perception Theory | Individuals' awareness and perception of objective risks (privacy, financial, psychological). Highly relevant for new, risky technologies. |
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AI-Enabled Medical Robots in China's COVID-19 Response
During the COVID-19 pandemic, D company deployed AI-enabled medical service robots in China. These robots performed high-risk tasks like temperature measurement, medicine delivery, and disinfection, significantly reducing human intervention in contaminated areas. Voice-interactive chatbots disseminated pandemic prevention knowledge, further easing the workload of human staff. This case exemplifies how AI-driven technology acceptance was influenced by perceived benefits (P1), public trust (P3), and positive outcome demonstrations (P6c, P6d) from the observable efficiency and safety improvements.
Key Takeaways:
- Robots enhanced medical care quality and efficiency in temporary admission hospitals.
- Voice-interactive chatbots improved public perception of service usability and reduced virus contamination risk.
- Government policies and social influences (family, relatives, neighbors) positively shaped public perception and enhanced trust in these technologies (P5, P6a, P6b).
Projected ROI Calculator
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Your AI Implementation Roadmap
A strategic approach to integrating AI-driven governance for public health crises, inspired by the RPAA-TAM model.
Phase 1: AI Impact Assessment & Privacy Protocols
Mandate algorithmic-impact statements for AI tools and implement differential-privacy protocols and open-source data-handling audits to build trust and neutralize privacy fears.
Phase 2: Social Influence & Outcome Visibility
Launch 'positive-deviance' storytelling campaigns to amplify neighbor testimonials and real-time public outcome dashboards to map infection-rate reductions to specific AI interventions.
Phase 3: Infrastructure & Capacity Building
Establish pre-crisis Memoranda of Understanding (MOUs) with telecom and cloud providers to guarantee bandwidth and edge-compute capacity within 72 hours of outbreak declaration.
Phase 4: Continuous Monitoring & Ethical Review
Implement ongoing monitoring of AI system performance, user feedback, and ethical implications, with regular reviews and adjustments to ensure alignment with public values and safety.
Ready to Transform Your Governance with AI?
Leverage the insights from RPAA-TAM to ethically and effectively integrate AI into your public health crisis management strategies. Schedule a free consultation with our experts.
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