Smart Cities & AI Governance
Artificial Intelligence Governance in Smart Cities: A Causal Model of Citizen Sustainability Co-Creation Through Acceptance, Trust, and Adaptability
Urban sustainability is a major governance challenge as smart cities increasingly integrate AI into public services. While AI offers efficiency, concerns about trust and citizen engagement highlight that technology alone doesn't guarantee sustainable outcomes. This study introduces the AI–Urban Citizen Sustainability Co-Creation Framework (AI–CSCF), linking AI acceptance, trust in AI, and citizen adaptability to sustainability co-creation. Empirical results from 1002 citizens in Thai smart cities show AI acceptance indirectly influences sustainability co-creation via trust in AI and citizen adaptability. Trust in AI emerges as a key mediating mechanism, connecting AI-enabled governance to participatory sustainability outcomes. These findings advocate for human-centered, trustworthy AI governance that strengthens citizen trust, enhances adaptive capacities, and positions citizens as active co-creators of sustainable urban development, aligning with SDG 11.
Quantifying Citizen Engagement in AI-Driven Smart Cities
The study reveals critical metrics on citizen perceptions and engagement with AI-enabled public services in smart cities, highlighting high acceptance, trust, and adaptability, crucial for sustainability co-creation.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Smart Cities & Urban Sustainability
AI in Smart City Governance
AI Acceptance, Trust & Adaptability
Sustainability Co-Creation
Smart Cities & Urban Sustainability
This category examines the evolution of smart city concepts from technology-driven to sustainability-oriented, highlighting the need for smart initiatives to align with broader urban sustainability objectives like environmental protection, social inclusion, and institutional effectiveness. It underscores that sustainability gains are contingent on institutional design, governance arrangements, and meaningful citizen involvement, rather than solely on technological outputs.
- Evolution from technology-driven to sustainability-oriented smart cities.
- Multidimensional nature of urban sustainability (environmental, social, economic, institutional).
- Importance of governance and citizen involvement over mere technological deployment.
AI in Smart City Governance
This section delves into how Artificial Intelligence is integrated into smart city governance, marking a shift towards algorithmically mediated public administration. It discusses AI's role in service allocation, policy analysis, and decision support, while also addressing challenges related to opacity, diffused responsibility, and citizen trust. It emphasizes that AI's sustainability impacts depend on its embedding within robust governance mechanisms that ensure transparency, accountability, and citizen adaptive capabilities.
- AI's role in public administration beyond efficiency to decision-making.
- Governance challenges: transparency, accountability, citizen trust.
- AI-enabled systems as socio-technical systems requiring governance mechanisms.
AI Acceptance, Trust & Adaptability
This category focuses on three interrelated governance mechanisms: AI acceptance, trust in AI, and citizen adaptability. AI acceptance is the initial willingness to engage with AI services. Trust in AI is a central mediating mechanism, encompassing perceptions of competence, reliability, transparency, and alignment with values. Citizen adaptability refers to the capacity to understand and adjust to AI-driven governance, crucial for sustained engagement. The literature highlights these as interdependent factors for effective AI-enabled smart city governance and sustainability.
- AI Acceptance as the initial condition for interaction.
- Trust in AI as a multidimensional, central mediating mechanism.
- Citizen Adaptability as a capability for sustained engagement and coping with uncertainty.
Sustainability Co-Creation
Sustainability co-creation is framed as a generative process where citizens actively contribute to defining goals, shaping implementation, and evaluating outcomes in smart cities. It moves beyond passive consumption, recognizing citizens as active stakeholders in public value creation. This approach emphasizes that participation must be meaningful, redistribute decision authority, and be embedded within normative frameworks that protect human rights and address long-term societal impacts, ensuring sustainability is an emergent outcome of quality citizen engagement.
- Citizens as active contributors to public value and urban sustainability.
- Distinction between mere participation and meaningful co-creation.
- Importance of transparency, institutional accountability, and human rights in co-creation.
Strong Overall Perceptions of AI
4.32 Avg. Citizen Adaptability (out of 5)Respondents reported high mean levels across all constructs (AI Acceptance, Trust in AI, Citizen Adaptability, Sustainability Co-Creation), indicating generally positive attitudes and willingness to engage with AI-enabled public services in Thai smart cities. For example, Citizen Adaptability scored highest.
Causal Pathways to Sustainability Co-Creation
AI Acceptance
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Trust in AI
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Citizen Adaptability
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Sustainability Co-Creation
The study's structural model reveals how AI Acceptance indirectly influences Sustainability Co-Creation through a sequence involving Trust in AI and Citizen Adaptability. AI Acceptance is a foundational step, but its impact on sustainability is mediated by citizens' trust and their capacity to adapt to AI-enabled governance.
Trust in AI: The Most Influential Mediator
β 0.835 Trust in AI's Total Effect on Sustainability Co-CreationTrust in AI emerged as the most significant mediating construct, with a substantial total effect on Sustainability Co-Creation (β = 0.835). This highlights that merely accepting AI is insufficient; building and maintaining citizen trust in algorithmic systems is paramount for achieving sustainable outcomes and fostering participatory behavior.
| AI Governance Domain | Illustrative AI-Enabled Public Services |
|---|---|
| Smart Mobility & Traffic |
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| Public Health & Public Safety |
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| Smart Environment & Utilities |
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| Citizen Engagement & Government Communication |
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Context of Thai Smart Cities: Digital Engagement & Policy
The study was conducted in certified Thai smart cities like Samyan, Khon Kaen, and Chiang Mai University, representing advanced stages of smart city development with active AI-enabled public services. The sample, dominated by digitally engaged younger citizens, reflects a population accustomed to interacting with digital governance environments.
Client: Thai Smart Cities
Challenge:
Translating AI adoption into sustainable urban outcomes amidst concerns about trust and citizen engagement, especially in Global South contexts with uneven digital inclusion.
Solution:
Implementing human-centered and trustworthy AI governance strategies that prioritize building citizen trust, enhancing adaptive capacities, and fostering active co-creation in line with SDG 11.
Results:
Empirical evidence supports the AI–Urban Citizen Sustainability Co-Creation Framework, demonstrating that AI acceptance indirectly drives sustainability through trust and adaptability, validating the importance of governance mechanisms over mere technological deployment.
Full Mediation of AI Acceptance
β 0.770 AI Acceptance's Total Indirect Effect on Sustainability Co-CreationAI Acceptance does not directly influence Sustainability Co-Creation; its impact is fully mediated by Trust in AI and Citizen Adaptability. The total indirect effect of AI Acceptance on Sustainability Co-Creation is substantial (β = 0.770), underscoring the multi-stage governance process required.
Quantify Your AI Governance ROI
Estimate the potential cost savings and efficiency gains by implementing robust, citizen-centered AI governance frameworks in your smart city initiatives.
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Phased Implementation for AI Governance
A strategic roadmap to integrate human-centered AI governance, foster trust, and enable citizen adaptability for sustainable smart city development.
Phase 1: Assessment & Trust Framework Development (Months 1-3)
Conduct a comprehensive audit of existing AI systems and citizen interaction points. Develop a transparent AI Trust Framework outlining ethical principles, data privacy safeguards, and accountability mechanisms. Focus on clear communication and establishing foundational reliability.
Phase 2: Citizen Adaptability & Digital Literacy Programs (Months 4-9)
Launch targeted digital literacy and AI awareness programs for diverse citizen segments. Develop intuitive user interfaces and provide accessible resources to enhance citizens' capacity to understand and engage with AI-enabled public services. Implement feedback loops to refine adaptability support.
Phase 3: Participatory Governance & Co-Creation Pilots (Months 10-18)
Initiate pilot projects for citizen co-creation in specific smart city initiatives, leveraging AI-enabled platforms for input gathering and collaborative decision-making. Ensure mechanisms for shared responsibility and transparent integration of citizen contributions into policy outcomes. Evaluate impact on sustainability goals.
Phase 4: Scaling & Continuous Improvement (Months 19+)
Scale successful co-creation models across more domains, informed by pilot learnings. Establish continuous monitoring and evaluation of AI governance effectiveness, trust levels, and citizen adaptability. Implement mechanisms for adaptive learning and institutionalized feedback to ensure long-term sustainability and legitimacy.
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