Generative AI (GenAI) is reshaping public sector decision-making, yet empirical understanding of how it reconfigures governance remains limited. Drawing on 24 semi-structured interviews with public officials in Kenya, this interpretive qualitative study examines how GenAI reshapes decision-making authority, accountability, and trust in human-centred governance. The findings identify three interrelated mechanisms: the concentration of epistemic authority in generative outputs, strongest among officials lacking the technical or situated knowledge to interrogate them; a structured pattern of default compliance and selective override, where organisational norms place the burden of justification on deviation rather than compliance; and a diffusion of accountability through system-based attribution for co-produced outputs, reversible where organisations maintained clear review and documentation. GenAI’s fluent, narrative outputs, unlike the scores and flags of earlier systems, appear to lend a persuasive authority we interpret as theoretically significant rather than formally demonstrated. Officials perceived that resulting opacity and diffused responsibility could strain institutional trust even where performance improves, reflecting their accounts rather than direct evidence from citizens. The study contributes a process-oriented, GenAI-specific analytical framework, empirically grounds hybrid discretion in organisational practice, extends emerging accounts of GenAI-specific accountability entanglement, and offers a theoretically informed account of officials’ perceptions of GenAI’s implications for institutional trust.
Government chatbots are increasingly deployed to support digital public service delivery, yet their continued value depends on whether citizens with prior experience intend to use them again when a relevant public-service need arises. Drawing on Diffusion of Innovations (DOI) theory, this study examines factors associated with citizens’ continuance intention to use government chatbots by integrating five DOI constructs—relative advantage, compatibility, perceived complexity, trialability, and observability—with responsiveness and anthropomorphic design. Data were collected from 358 citizens with prior experience using government chatbots and analyzed using PLS-SEM. Responsiveness showed the strongest association with continuance intention, followed by anthropomorphic design and relative advantage, while perceived complexity was negatively associated. Trialability showed a weaker but statistically significant positive association, whereas compatibility and observability were not significantly associated. The model explains 62.4% of the variance in continuance intention. The findings suggest that reuse intention is more strongly associated with interaction quality and perceived functional benefits than with contextual fit or visibility of outcomes. Continuance intention refers to future-use intention reported by citizens with prior chatbot experience and does not represent observed continued use over time. Theoretically, the study extends DOI to a post-adoption government-chatbot context, while practically highlighting responsiveness and anthropomorphic design.
Recent advances in generative AI have renewed interest in whether large language models can simulate aspects of public deliberation. Current deliberation simulations remain methodologically fragile: fluent conversational outputs are often treated as evidence of deliberative quality despite limited reproducibility, weak safeguards for viewpoint diversity, and limited systematic evaluation. This article argues that AI-deliberation simulations can function as credible analytical instruments only if they incorporate infrastructures for reproducibility, plurality preservation, and deliberative quality assessment. The article develops a stage-structured method for deliberative simulation combining persona validation, plurality auditing, and discourse-quality assessment within a reproducible pipeline. The framework is presented as a methodological proof-of-concept and evaluated through a simulation study on the June 2023 Swiss federal referendum on the Climate and Innovation Act together with a batch replication study comparing direct and structured deliberation architectures. The findings show that, as an integrated theory-driven architecture, the structured pipeline generates more stable deliberative dynamics than unconstrained interaction, including clearer plurality preservation and more coherent opinion updating. At the same time, the simulations remain below human minipublics in terms of contextual grounding and justificatory depth. The article positions AI-deliberation simulations as methodological infrastructures for cumulative and empirically testable research on AI-supported deliberation in digital governance.
The metaverse is emerging as a transformative virtual environment that promises to reshape people's lives, introducing broad technological, social, economic, and cultural changes. Despite growing academic interest, existing literature remains dispersed, addressing isolated technological, economic, or ethical concerns without offering a general framework for understanding its broader implications. This article addresses this gap by conducting a systematic review of literature sourced from the scientific repositories ScienceDirect, Web of Science, Google Scholar, and Scopus. The review integrates existing knowledge and results in the creation of a taxonomy that categorizes the implications of the metaverse across multiple dimensions, including economic, technological, social, cultural, and environmental domains. The resulting taxonomy provides valuable information on both the opportunities and risks associated with the development of the metaverse and its implications, which also highlights its disruptive and transformative effect of smart city to AI-enabled citiverse. Finally, the identified implications are correlated with Key Performance Indicators (KPIs) to create a conceptual framework for assessing their impact.
Smart government research emphasizes people-centric, data-driven governance, yet conceptual fragmentation persists over how socio-technical systems, multi-level governance, and Citiverse environments interact. This article proposes a People-Centric Smart Government Ecosystem Framework that reconceptualizes smart government as a socio-technical system structured along two dimensions: (1) three horizontal pillars, People, Process, and Technology (PPT), and (2) a vertical, four-layer governance structure comprising strategic, tactical, operational, and service (citizen-facing) levels. Drawing on organizational systems theory and Activity Theory, the framework defines people-centricity as a property that spans internal government actors and external stakeholders. It differentiates digital intelligence capabilities across governance layers, including policy recommendation systems, decision support systems, operational information systems, and citizen-facing service interfaces. Methodologically, the study combines a structured literature review with iterative coding and thematic clustering of constructs, cross-layer mapping, and conceptual modeling. An application to Indonesia's digital government reform agenda demonstrates how the framework can be used to diagnose cross-layer alignment gaps and inform design choices in developing-country contexts. The Citiverse is positioned as a future-oriented extension rather than a structural requirement, enabling the framework to remain technology-neutral while extensible to immersive governance scenarios. The study contributes an integrative, people-centric, and ecosystem-oriented foundation for advancing smart government theory and practice.
