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.
Government virtual agents are increasingly used in digital government services to facilitate citizen access and interaction. A key challenge, however, is whether AI-mediated services meet citizens’ expectations. Drawing on expectation confirmation theory, this study examines how virtual-real service connection influences public expectation confirmation and investigates the mediating role of AI-driven civic interaction. Survey data were collected from 300 residents in Wuxi, China, who had used the government service digital human “Fubao.” Regression-based mediation analysis was employed to test the proposed relationships. The results show that virtual-real service connection has a significant positive effect on expectation confirmation and that AI-driven civic interaction partially mediates this relationship. These findings highlight the importance of integrating institutional service linkage and interaction quality in the design of AI-enabled government services and provide practical implications for improving expectation confirmation.
Document redaction is a critical but increasingly strained function within UK public authorities, where Freedom of Information (FOI) obligations coexist with stringent data protection duties. While AI is frequently positioned as a remedy to manual redaction's time and error risks, limited empirical evidence exists on whether public authorities are actually adopting such tools. This study uses FOI requests as an observational method to examine redaction governance and AI-readiness across 44 UK public authorities spanning healthcare, central government, and research-intensive higher education. We received 30 responses (68.2%) and analysed them using a mixed-methods approach. The findings reveal a pronounced implementation gap between AI's technical promise and organisational reality. Only one authority reported active AI tool use, describing a workflow where software-generated redactions are reviewed by staff. Half of responses indicated relevant policies were "not held," and only six authorities reported documented training. Most organisations relied on external guidance rather than formal internal standards, with respondents highlighting resource constraints, inconsistent decision-making, and difficulties with complex document formats. We contribute an FOI-based baseline of current practices and reframe AI-assisted redaction as a socio-technical capability dependent on recordkeeping, standardised guidance, and trained human oversight, proposing a staged hybrid model for safe automation experimentation.
The slow progress of digital transformation in public organizations has been a source of concern, not least due to a lack of digital government competences (DGCs). We approach the persisting issue by drawing on resource-based theory and human capital. We conceptualize DGCs as knowledge, skills, abilities, and other characteristics (KSAOs), which compose digital government-related human capital resources. We further posit that a lack of such resources on the individual level negatively impacts transformation capabilities on the organizational level. To substantiate our claims, we perform a competence assessment based on job advertisements. We analyzed 2,869 job ads from local governments in Germany, Australia, and New Zealand using a job-mining approach that combines large language models and topic modeling. Our findings reveal that DGCs are notably scarce in these job ads, which helps explain the slow progress of digital government pursuit. Our study contributes to research by providing a novel theoretical foundation for the literature stream on DGCs, framing them as KSAOs as the foundation for human capital resources. We also share our job-mining pipeline for replication and adaptation by other researchers. Finally, we propose four action points to address the challenges from a hiring perspective.
Digital government has expanded access to public services, but it has also redistributed substantial interpretive and procedural work to residents. Many citizens and residents must now navigate fragmented portals, decode administrative language, identify the appropriate service pathway, and complete complex forms with limited direct support. These demands often fall most heavily on people with lower administrative literacy, limited language proficiency, fewer resources, or less confidence in dealing with public institutions. This article advances a bounded conceptual proposition: governments should examine whether artificial intelligence can function as the primary digital front door for selected categories of public services. The argument is not that conversational AI can remove the underlying complexity of law, eligibility rules, or institutional fragmentation. Rather, it may help residents navigate that complexity more effectively in some domains while also introducing new risks and burdens. I argue that AI-first public services are most plausible in high-volume, rules-rich, documentation-heavy, and relatively low-discretion interactions, rather than as a universal model for all citizen-state encounters. The article positions conversational government as a research, design, and governance agenda requiring staged pilots, domain-specific evaluation, evidence on burden redistribution, privacy safeguards, meaningful human oversight, and retained traditional support channels for residents who most need them.
In the digital sphere, public organizations rely on commercial social media platforms to interact with their communities. In this commercial online space, public values such as privacy, inclusion, or user autonomy, are increasingly under pressure. This study focuses on safeguarding public values in the design of a social media alternative. We examine how public values can be negotiated and anchored in the value proposition that informs the design of public service social media (PSSM). A value proposition specifies how value can be created for platform users by solving particular problems that users experience. There is a lack of empirical studies on how public values can be concretized and anchored in PSSM. To explore this, we deploy a user-oriented design thinking methodology in three different organizations to develop this public value proposition. Our results indicate that there is a need for both a generic value proposition that serves all types of public organizations and a specific value proposition for their user communities. We contribute to theory and practice with a three-step process for the development of such a public value proposition and make a first sketch of what elements it could comprise. As the antidote to commercial social media platforms, a public alternative would offer human (as opposed to automated) moderation, decentralized and local (as opposed to centralized and global) data storage, and privacy friendly and secure identification that does not build profiles of users.
Citiverses offer virtual environments for regulatory learning via experimentation with governance, policy, and technology scenarios in safe, immersive settings. This paper proposes a science-for-policy agenda for regulatory learning in citiverses, developed through an expert consultation and informed by the literature and by international policy frameworks from the European Union and the OECD. It identifies four key research areas, including scalability, real-time feedback, complexity modelling, cross-border collaboration, risk reduction and citizen participation. In addition, the paper outlines a set of experimental topics, spanning transportation, urban planning and the environment/climate crisis, that could be tested in citiverse platforms to advance citiverse-enabled regulatory learning in these domains. The agenda emphasizes a responsible, human-centred approach, prioritizing ethical, social, environmental, and governance [ESG] considerations to inform future policy. To our knowledge, this is the first work to explicitly introduce regulatory learning as a citiverse use case, thereby contributing to ongoing debates on people-centric digital governance and the future of experimental policymaking in smart cities.
Clinical patient data must be transformed into financial claim formats for submission to state Medicaid agencies and the federal Centers for Medicare and Medicaid Services (CMS). This is achieved through a process called electronic data interchange (EDI), which is specified in regulation and has standards for data definition that are maintained by the American National Standards Institute/Accredited Standards Committee X12. This standard and the data exchange process is the financial gas and protocols that enable healthcare payment by the government. Claims payments made by the U.S. Government for Medicare, the Children’s Health Insurance Program, and Medicaid exceed $1T annually. Of this, and despite the validations enforced by EDI transmission, improper payments in Medicare and Medicaid exceeds $100B each year. We analyze the current state of the art in EDI, and provide a design-oriented technical brief of how blockchain and large language models could address improper payments.
Government social media accounts are regarded as authoritative sources of information, but are they also sources of misleading content? While there is ample research highlighting the benefits of government social media use, such as fostering trust in government, enhancing citizen engagement, and providing accurate information, less scholarly attention has been given to its potential negative effects on members of the public who engage with government social media accounts. This article examines types of information shared by users on government social media accounts from a comparative perspective. Additionally, we explore the presence and prevalence of misinformation on these accounts. The study draws on content analysis of tweets directed at the UK's Department for Environment, Food and Rural Affairs (DEFRA) and Environment and Climate Change Canada (ECCC) X accounts (formerly known as Twitter), collected between March 2022 and February 2023. Our findings demonstrate that both DEFRA and ECCC X users are spreading misinformation but in different ways, and to a different extent. ECCC X users predominantly post content denying the reality of climate change, while DEFRA X users critique government policies and services. Finally, while these tweets do not gain widespread traction, they remain accessible for users interacting on government accounts.
Artificial intelligence (AI) is increasingly recognized as a transformative force in public administration, yet its adoption in local governments, particularly in developing countries, remains fragmented and uneven. This study examines the administrative readiness of Thai local governments to integrate AI into public management systems, addressing gaps in existing research that often emphasize technical dimensions while neglecting organizational, cultural, and behavioral factors. Employing a qualitative case study approach, data were collected through semi-structured interviews with 25 key informants and analyzed through thematic analysis supported by triangulation and member checking. Findings revealed that AI readiness extends beyond technological capacity to encompass eight interrelated dimensions organized according to the Technology-Organization-Environment (TOE) framework: one technological dimension (technology and infrastructure), five organizational dimensions (human resources, change management readiness, financial resources, leadership and vision, organizational culture), and two environmental dimensions (environmental context, and policy and governance). Of these eight dimensions, two are AI-specific (technology and infrastructure, policy and governance), three are partially AI-specific (human resources, organizational culture, environmental context), and three represent general digital transformation requirements adapted for AI contexts (financial resources, leadership and vision, change management). Analysis through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) demonstrated that hedonic motivation, price value, and habit formation serve as critical behavioral drivers translating structural readiness into sustained use. Strategic directions proposed through the McKinsey 7S Framework emphasize coherent alignment across organizational elements. This study contributes an integrated framework capturing contextual realities of local governments in developing settings, advancing theoretical discourse while offering practical guidance for fostering sustainable, citizen-centered AI transformation.
City governments increasingly rely on data and analytics to address complex policy problems, building responsive capacity through information collection and management. This paper examines how interoperability challenges compromise data analytics practices in city governments and how interoperability theory can inform improvements in analytics delivery. Drawing on interoperability research, we develop a conceptual framework linking data analytics enablement and delivery to semantic, technical, and organizational constraints. Empirical evidence comes from semi–structured interviews with 26 data analytics practitioners in two U.S. cities–Syracuse, New York, and Kansas City, Missouri. Findings show that gaps in data architecture and shared understanding, legacy systems that reinforce path-dependent processes, and organizational resource constraints and process rigidity significantly hinder analytics efforts. At the same time, collaborative arrangements that foster shared understanding emerge as a key mechanism for addressing knowledge gaps and improving interorganizational alignment. The paper contributes an expanded interoperability framework that integrates data analytics research and highlights practical leverage points for practitioners in city governments.
Understanding the payment behavior of sociodemographic groups is important for public institutions in designing inclusive policies. Thus, public institutions regularly conduct payment surveys to monitor the payment behavior of these groups. However, such surveys are costly, conducted infrequently, and limited in the number of participants. This paper presents a methodology that enables policy-makers to monitor the payment behavior of sociodemographic groups with card data while complying with privacy rights. Specifically, it provides a correlational analysis of payment behavior across sociodemographic groups, demonstrates the potential of payment data to infer sociodemographic information, and proposes a methodology for enriching card data with this information. This paper reveals that sociodemographic groups exhibit different payment behaviors, that groups can be inferred from payment data, and that anonymized card data can be enriched with sociodemographic information. The proposed methodology enables public institutions to complement surveys with timely sociodemographic insights from anonymized card data, reducing costs, easing participant burden, and allowing more frequent updates.
Artificial Intelligence (AI) is transforming public sector services by improving efficiency, accessibility, and decision-making. However, security and privacy remain key challenges to user adoption. Despite the global rise in AI-enabled public services, empirical research on public perceptions remains limited, particularly regarding the relationship between security, trust, and adoption intention. Additionally, research on the factors shaping security perception in AI-enabled public services remains scarce, leaving key antecedents unexplored. To bridge this gap, this study offers empirical insights into the determinants of security, trust, and adoption intention in AI-enabled public services. Informed by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), a theoretical model is developed and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey data from 506 respondents in Saudi Arabia. The findings show that information security awareness, cybersecurity law, and security culture significantly influence security. Furthermore, security, privacy, recommendation quality, and anthropomorphism positively affect trust. Among adoption factors, trust emerges as the second most influential after performance expectancy. The results contribute to AI adoption research by highlighting the role of security-related factors and trust in shaping citizen adoption of AI-enabled public services, offering practical implications for policymakers to enhance public confidence in AI-driven services.
Nowadays, workflows in judiciary systems are undergoing rapid transformation, stimulated by the technological opportunities offered by Generative AI solutions, including Large Language Models (LLMs). These technologies offer promising tools for addressing the inefficiencies and accessibility challenges inherent in traditional judicial workflows, which have long resisted digital modernization. By automating repetitive and time-intensive tasks such as text summarization and document analysis, LLMs can assist humans, thus enhancing operational effectiveness. This paper presents an exploratory study conducted in the scope of a collaboration between researchers and IT experts from the University of Brescia and the Prosecutor General’s Office at the Court of Appeal of Brescia. Adopting a Design Science Research methodology, the paper describes the design and evaluation of a Proof-of-Concept Web application that leverages LLMs and prompt engineering to support text summarization and analysis tasks. The prototype is intended to assist legal professionals with domain expertise, but without advanced IT skills, in interacting effectively with Generative AI technologies. The study highlights the potential of LLMs to streamline human effort, reduce manual overhead, and support decision-making, while also pointing out key challenges for their adoption in judicial workflows.
This study employs bibliometric analysis, co-occurrence network analysis, and topic modelling techniques to support policymakers, practitioners, and researchers in making informed decisions about resource allocation and research priorities in public procurement and emerging technologies. The goal is to understand the research landscape and emphasise the importance of various themes, thereby guiding efforts to enhance knowledge, foster innovation, and optimise the benefits of emerging technologies in public procurement. The methodology involves collecting bibliographic data from Scopus and constructing a co-occurrence network based on identified themes or keywords. Additionally, topic modelling techniques are applied for a deeper analysis of the co-occurrence network, using tools such as Bibliometrix, LDAShiny, and ggplot2, aided by Biblioshiny and LDAshiny apps for visualisations. The findings emphasise the need to keep researchers updated with advancements in machine learning, data mining, and emerging technologies in public procurement. Subjectivity, the dynamic character of the field, and data quality and availability are among the limits of the approaches, though, which call for domain knowledge and qualitative techniques for validation. In this field, the insights of bibliometric analysis can inform future studies, policy-making, and decision-making. A notable gap identified is the lack of research on digital road mapping transition strategies and technological assessment of emerging technologies in public procurement.
Artificial intelligence (AI) is a rapidly growing sector within the African innovation ecosystem. Amidst rapid technological advancements in the development of AI solutions in African countries, it has been noted that AI technologies pose ethical quandaries. As a result, there is a need to identify these AI ethical policies in Africa and explore their benefits and detrimental in the AI ecosystem. Therefore, this study utilized both qualitative and quantitative methodologies using desk research and AI stakeholders’ engagement through key information interviews and focus group discussions, case studies, online surveys, and webinar sessions. A total of 300 publications and 165 participants contributed to the study. The result shows that about 12% of the collected responses indicate that the adoption, development, and use of AI ethical policies in African countries are in the initial stage. The AI ethical policies are beneficial; however, the following challenges hinder their adoption and development, which include limited understanding of AI, funding, lack of access to data, inadequate infrastructure, such as internet connectivity, and skills shortage. The study recommends the enhancement of collaborative networks for resource sharing within the AI stakeholders’ ecosystem and the development of adequate infrastructure that enhances the adoption of AI ethical policy.
The genre of municipal websites, as the most visible aspect of local e-government, has evolved rapidly worldwide. This study provides a comprehensive analysis of the similarities and differences in the content and services offered by Chinese and Western municipal websites. Using a functional analysis approach, the study first identifies five key functions of municipal websites: local identity promotion, community information dissemination, online service provision, organizational and procedural transparency, and intergovernmental alignment. Guided by this functional framework, a total of 40 websites, 20 from China and 20 from Western countries, was analyzed using a coding scheme with 111 content elements across 22 topics. The findings reveal a functional similarity in core functions between Chinese and Western websites, particularly in their roles in disseminating information, providing services, and promoting transparency. Differences in content elements are linked to varying administrative structures, governance responsibilities, and models of citizen participation across the two contexts. By employing the functional analysis framework, this study offers a systematic tool for identifying key functions and assessing the quality of municipal website content and services across different countries. Ultimately, the research provides valuable insights into how municipal websites have developed globally, enhancing understanding of their role in local e-government.
