Purpose Governments increasingly deploy artificial intelligence (AI)-based chatbots to improve accessibility, efficiency and consistency in citizen services. However, empirical evidence on how such systems contribute to public value and service process transformation remains limited. This study aims to examine how a government-operated chatbot shapes citizen interactions and public value through emotional, thematic and behavioral dynamics. Design/methodology/approach The study analyzes 1,846 real-world inquiries submitted to a Ministry of Labor chatbot. Sentiment analysis, computational content classification and longitudinal trend modeling were used to assess emotional tone, topic concentration, usage patterns and repeat-contact behavior. Findings Citizen inquiries were predominantly neutral or negative in tone, while chatbot responses remained largely neutral, indicating emotional stabilization rather than escalation. Employment security topics generated higher emotional intensity, although sentiment differences across topics were not statistically significant. Usage fluctuated over time, and repeated inquiries increased moderately, suggesting both informational friction and growing institutional reliance. Practical implications Effective public-sector chatbot deployment requires process-oriented governance, topic-sensitive response design, explainability mechanisms and continuous performance monitoring to align operational efficiency with citizen experience. Originality/value The study advances understanding of AI-enabled service transformation by operationalizing public value through interaction-level indicators and demonstrating how chatbot-mediated processes influence citizen experience in digital government.
Purpose This conceptual paper aims to examine information security policy (ISP) in Brazilian state legislative assemblies as a formal document that may operate as an institutional governance device and, under specific conditions, become a governance capability. Design/methodology/approach The paper integrates sociological institutionalism, network governance and state-capacity perspectives, in dialogue with governance-oriented information security scholarship, to theorize the conditions under which ISP may become institutionalized. Findings It develops the tridimensional model of legislative information governance (MTGIL), structured around institutional legitimacy, cross-functional coordination and political-administrative capacity and proposes three ideal-type configurations: symbolic compliance, institutional transition and substantive governance. Five testable propositions are derived. Research limitations/implications MTGIL is conceptual and untested. Its propositions, profile classifications and portability beyond Brazilian state legislative assemblies require comparative validation. Practical implications MTGIL offers legislative organizations a diagnostic lens for comparing formal ISP adoption with observable evidence of sponsorship, cross-functional routines, reporting channels, training uptake and continuity mechanisms, thereby informing policy redesign, coordination and continuity planning. Social implications By treating ISP as a governance capability, the paper suggests how legislative organizations may diagnose vulnerabilities affecting citizens’ data rights, democratic accountability and the transparency-confidentiality balance. Originality/value The paper distinguishes ISP as a formal document, an institutional governance device and a potential governance capability. MTGIL theorizes why similar policy documents, control frameworks or compliance scores may follow different institutionalization trajectories when legitimacy, coordination and capacity are configured differently.
Purpose This study aims to examine how public administrations perceive and govern collaboration with social-media influencers (SMIs), conceptualizing these partnerships as public values trade-offs between platform logics and administrative norms. Design/methodology/approach Drawing on 19 semistructured interviews with social-media managers in four countries, the study explores the challenges encountered in collaborating with SMI and the governance mechanisms developed to cope with them. It interprets this type of collaboration as involving potential conflict between platform logics (such as authenticity, personalization and immediacy) and core public values (such as transparency, accountability and integrity). Findings The findings reveal recurring dilemmas related to influencer selection and reputational risk, loss of message control, tone and institutional image, legitimacy of public spending, administrative rigidity and impact evaluation. Public administrations respond through governance mechanisms including vetting procedures, contractual formalization, content co-construction, disclosure practices, adapted approval chains and internal capacity-building. These mechanisms do not eliminate difficulties but seek to manage them. Originality/value By conceptualizing SMI collaborations as public values trade-offs, the study advances the theoretical understanding of digital public communication. It demonstrates how these collaborations compel public administrations to arbitrate between public values within platformized environments structured by commercial and algorithmic logics. In doing so, the study contributes to the literature on public values by empirically examining how such trade-offs are negotiated in practice and how governance mechanisms emerge as attempts to balance, rather than resolve, these tensions.
Purpose This study aims to critically examine how power structures and personal linkages influence governance systems, with a particular focus on elite dominance and informal networks in shaping institutional performance, accountability and transparency. It moves beyond formal institutional explanations by integrating informal governance dynamics into an integrative analytical framework that explains how governance operates in practice. Design/methodology/approach The study adopts a structured critical literature review using a narrative synthesis approach. Data were collected from peer-reviewed sources indexed in Scopus, covering publications from 2020 to 2025. A systematic screening process guided the application of inclusion and exclusion criteria, followed by thematic coding, comparative analysis and critical interpretation of 35 selected studies focusing on power structures, elite influence, patronage systems, informal networks and governance accountability. Findings The review demonstrates that governance outcomes are produced through the interaction of formal institutions and informal power structures. These interactions operate through four key mechanisms: complementarity, substitution, competition and overlap. While formal institutions emphasize transparency and rule-based governance, informal mechanisms such as elite networks and patronage systems shape how rules are implemented, modified or bypassed. These interaction patterns contribute to variation in governance performance, including accountability deficits, unequal resource distribution and reduced institutional legitimacy across contexts. Originality/value This study provides a structured integrative synthesis of formal and informal governance literature and develops an analytical typology of interaction mechanisms that explains how relational power structures systematically shape governance outcomes across diverse contexts, thereby offering a refined conceptual contribution to understanding governance complexity.
Purpose This study aims to examine how institutional legitimacy and governance conditions shape public acceptance of artificial intelligence (AI)-based threat detection systems, demonstrating that technical accuracy is necessary but normatively insufficient for sustainable policy-oriented support. Design/methodology/approach A survey-based research design was used, with data from 510 valid respondents in South Korea. The study applied a two-step structural equation modeling approach comprising confirmatory factor analysis and structural path analysis, alongside bias-corrected bootstrap mediation analysis with 5,000 resamples. Findings Trust in government significantly predicts institutional legitimacy (ß = 0.660, p < 0.001), which, in turn, shapes both performance expectancy and perceived social deterrence. Direct path analysis confirmed 11 of 12 hypotheses, and bootstrap analysis verified significant indirect effects for all major antecedents. The model explains 84.8% of variance in behavioral intention (R² = 0.848). Ethical concern did not directly undermine legitimacy, indicating conditional rather than automatic normative resistance. Research limitations/implications This research advances public-sector AI governance theory by positioning institutional legitimacy as a mediating filter and introducing perceived social deterrence as a policy-relevant cognitive mediator. It proves that governance readiness – not technical accuracy alone – determines durable policy support. Practically, it highlights the need for transparent oversight to secure public authorization. A limitation is its reliance on a South Korean general public sample, warranting future cross-national, multi-stakeholder comparative research. Practical implications Policymakers should complement technical development with governance mechanisms such as transparency, accountability and procedural safeguards. Social implications The study underscores the societal importance of legitimacy-based governance when deploying high-risk AI systems affecting public safety. Originality/value This research advances public-sector AI governance theory by structurally positioning institutional legitimacy as a mediating evaluative filter between normative antecedents and cognitive expectations. It introduces perceived social deterrence as a policy-relevant cognitive mediator and provides empirical evidence that governance readiness – not technical accuracy alone – determines whether AI systems receive durable policy-oriented authorization.
Purpose This study aims to examine the impact of Generative Artificial Intelligence (GenAI) integration on educational equity across diverse socioeconomic contexts in India through a digital inequality framework in context to socioeconomic stratification. This study adopted an integrated theoretical framework for understanding the importance of GenAI in achieving better results in education using new technologies. Design/methodology/approach The mixed methods research involves 27 focus groups (133 educators) conducted in the National Capital Region in Delhi, the interviews with school heads and the performance testing of 587 students from different socio-economic schools conducted in 2023–2024. Findings Through analysis of technological capital, engagement ecology and policy implementation, GenAI Educational Equity Matrix is obtained, which consists of three interrelated aspects of equity. The results prove the difference in the readiness of AI based on socioeconomic status. Practical implications The research provides empirically based, policy-related suggestions regarding responsible usage of GenAI in education, which may be helpful for policymakers, school administrators and educators in different socio-economic settings and is relevant to NEP 2020 of India. Social implications This study illustrates how technology may increase or decrease educational inequalities, emphasizing the necessity of proper technology integration to overcome any barriers. Originality/value GenAI Educational Equity Matrix gives a new multi-dimensional perspective on right technology integration in education, especially in developing countries with digital divide.
Purpose The growing deployment of generative artificial intelligence (GenAI) in urban governance has intensified global debates on trustworthy AI. However, existing research remains largely technology-centric and offers limited empirical insight into how citizens perceive trustworthy AI and how these perceptions are translated into policy legitimacy. Addressing this gap, this study aims to examine how and for whom trustworthy AI generates legitimacy by analysing the mechanisms through which perceived GenAI trustworthiness is associated with citizens’ trust in urban AI policy. Design/methodology/approach Drawing on public value theory, institutional trust scholarship and sociotechnical governance perspectives, this study proposes a mediation model in which perceived institutional transparency and perceived public value shape the relationship between perceived GenAI trustworthiness and citizen trust in AI-enabled urban policy. A cross-sectional survey (n = 527) was conducted among residents of Malaysia’s Klang Valley metropolitan region using stratified quota sampling. The data were analysed using structural equation modelling. Findings The findings indicate that technical trustworthiness alone is insufficient to foster citizen trust in GenAI policy decisions. Perceived public value emerged as a significant mediating mechanism, whereas institutional transparency did not exhibit a significant mediating effect. These results suggest that policy legitimacy is shaped less by procedural transparency in isolation and more by whether GenAI is perceived to generate tangible and socially meaningful outcomes. Research limitations/implications The study relies on cross-sectional, self-reported data from a single metropolitan region. Accordingly, the findings should be interpreted as associational rather than causal. Future research could use longitudinal, comparative or qualitative approaches to examine how citizens’ interpretations of AI governance, transparency and public value evolve across contexts. Practical implications The findings highlight the need to reframe trustworthy AI as a governance challenge rather than a purely technological achievement. For policymakers and urban administrators, prioritising public value creation, interpretable and citizen-oriented transparency and institutional accountability is critical to sustaining democratic legitimacy for GenAI-enabled policies. Originality/value This study advances research on AI governance by conceptualising trustworthy AI as a relational and institutionally embedded construct, shifting the debate beyond technical compliance toward the governance practices and public value considerations that shape AI legitimacy in urban contexts. By empirically demonstrating the differential roles of public value and transparency, the study provides a nuanced and context-sensitive contribution to understanding trust formation in AI-enabled governance.
Purpose Digital transformation intensifies pressures for experimentation and iterative innovation in public organizations. Yet many public-sector innovation processes remain structured around industrial governance models emphasizing problem-first entry, linear progression and deliverable-based evaluation. This study aims to examine how innovation governance shapes exploratory digital innovation trajectories, conceptualizing innovation entry as a governance filter.Design/methodology/approach This study adopts an interpretive case study of innovation practices in a large Swedish regional healthcare organization. Drawing on interviews with innovation intermediaries and formal governance documents, it traces how digitally oriented initiatives move through a stage-gated innovation process.Findings The findings show that innovation governance operates as a path-creating mechanism. Innovation entry requirements, stage-gated progression, evaluation criteria and legitimacy pressures cumulatively function as governance filters that privilege industrial logics. Problem-first entry conditions stabilize exploratory initiatives and create path dependencies that narrow experimentation. Rather than rejecting digital innovation, governance structures reshape exploratory trajectories to align with institutional expectations of predictability, deliverability and organizational fit.Originality/value This study contributes by conceptualizing innovation entry as a governance filter and demonstrating how governance mechanisms structure innovation trajectories from their point of origin. Integrating exploration-exploitation theory and innovation governance research, it advances a mechanism-based explanation of how digital transformation unfolds within institutionalized public-sector systems. The findings offer guidance for designing innovation processes that better accommodate experimentation while maintaining public accountability.
Purpose This study aims to examine the evolution of digital policies in the Global South, focusing on how global digital governance frameworks are translated into national digital strategies in middle-income countries, using Colombia as an analytical case.Design/methodology/approach The study adopts a qualitative documentary analysis of national policy instruments, CONPES reports and international frameworks. It traces the policy trajectory leading to the 2023-2026 National Digital Strategy (NDS), drawing on the OECD Digital Government Policy Framework and the multiple streams framework.Findings The findings identify four stages of policy evolution, revealing a shift from infrastructure-centred information and communication technology (ICT) interventions towards an integrated digital governance approach. While the NDS consolidates key priorities - connectivity, digital skills, data governance, cybersecurity and the digital economy - significant challenges persist, including limited inter-agency interoperability, uneven territorial implementation, rural digital exclusion and low MSME digital readiness. The analysis also highlights gaps in organizational readiness, public-sector IT coordination and institutional digital capabilities that constrain effective implementation.Practical implications The study provides actionable insights for policymakers to enhance inter-agency coordination, digital capability development and implementation governance in public-sector digital transformation.Originality/value This study contributes to the Global South debates by linking policy evolution with institutional capacity and implementation dynamics. It offers a global information technology management perspective, providing managerial and institutional insights to strengthen public-sector digital capabilities and improve the translation of national digital strategies into operational outcomes.
Purpose The rapid adoption of Artificial Intelligence (AI) into digital business platforms has led to growing concerns surrounding data privacy, algorithmic transparency and user trust. This study aims to explore how responsible data practices can be implemented within AI-powered innovation systems, where regulatory enforcement and jurisdictional frameworks remain inconsistent. Design/methodology/approach The study provides an overview based on a review of 79 journal articles published during 2011–2024. It examines how privacy, innovation and trust have been addressed in organisational contexts, and how these issues have been framed across geographic regions and thematic clusters. Privacy laws such as general data protection regulation (GDPR) and central consumer protection authority (CCPA) are considered in relation to how they influence organisational responses to data governance challenges. The review also identifies persistent gaps between formal compliance and meaningful consumer empowerment, and uses these patterns to inform framework development. Findings The review concludes that while compliance-based approaches are necessary, they are insufficient to sustain trustworthiness in data-driven and AI-mediated settings characterised by information asymmetries, trans-border data flows and shifting notions of accountability. Privacy-by-Design (PbD), explainable AI (XAI) and privacy-enhancing technologies (PETs) are highlighted as governance enablers that support more dynamic and trust-oriented approaches. Drawing on Regulatory Governance, Trust Theory and AI Ethics, the study develops an integrated framework that reinterprets ethical data governance as a trust-centred governance capability rather than a narrow compliance exercise. In doing so, it elevates trust as a central object of governance and reframes privacy as an organisational capability that helps firms navigate fragmented regulatory environments while sustaining legitimacy and innovation. Originality/value This research contributes by synthesising fragmented debates on governance, trust, privacy and AI ethics, and by reframing ethical data governance as a trust-centred governance capability. Its originality is therefore integrative and practical rather than based on wholly new theoretical mechanisms. The paper explains how regulatory diversity, organisational accountability, PbD and technical safeguards interact under conditions of cross-border data flows and technological complexity. In doing so, it provides a framework that can guide enterprises, regulators and public institutions in managing AI ethically across jurisdictions while strengthening trust as a core governance objective.
Purpose Despite millions of likes, comments and reposts, government microblogging may fail to achieve real communication impact during public events. This study aims to evaluate the effectiveness of information release by government microblogging and identify which public-response indicators drive success or failure across different events. Design/methodology/approach Drawing on 304,588 government microblog posts and 1,546,665 associated user responses from four major public events on Sina Weibo, the authors developed a multi-indicator evaluation system using topic modeling and qualitative analysis. A high-performing Robustly Optimized BERT Pretraining Approach (RoBERTa)-based classifier was then trained to automatically categorize user responses. To evaluate information release effectiveness, the super-efficiency slack-based measure data envelopment analysis model was used. Furthermore, key evaluation indicators functioning as sources of efficiency loss or drivers of success across different public events were identified using slack analysis and Cliff’s Delta. Findings Three key findings emerge. First, the evaluation indicators based on public responses include aligned appraisal of event, divergent appraisal of event, affirmation of government, criticism of government, affective reassurance, affective venting, prosocial behavior, organizational expected behavior, adverse behavior, liking behavior, favorable sharing behavior and unfavorable sharing behavior. Second, emergency responses led by government microblogging accounts with superior information release effectiveness exhibit a structural pattern of “function-situation” matching. Third, key evaluation indicators causing efficiency loss vary across public events while core drivers are highly consistent. Originality/value This study contributes to the existing knowledge of digital government communication and proposes practical approaches for governments, social media platforms and ordinary users during public events.
Purpose With the growing use of artificial intelligence (AI) in public governance, understanding public willingness to delegate decision-making authority to algorithmic systems has become a key issue. While prior research has examined the relationship between trust in public institutions and trust in AI, the role of institutional trust in shaping willingness to delegate high-stakes decisions to AI remains understudied. This study aims to address this gap using nationally representative survey data from Wave 152 of the Pew Research Center’s American Trends Panel (August 2024, n = 5,410). Design/methodology/approach The study uses weighted logistic regression to assess whether confidence in the US federal government’s ability to effectively regulate AI predicts citizens’ willingness to entrust AI with important decision-making responsibilities. The analysis is based on 2,940 valid responses after excluding non-substantive answers. Findings The findings demonstrate that institutional trust is a statistically significant predictor of support for algorithmic delegation. Higher levels of confidence in governmental AI regulation were associated with substantially higher odds of supporting the delegation of important decisions to AI systems (OR = 1.33; 95% CI [1.19, 1.50]; p < 0.001). Although utilitarian evaluations of personal benefit exert the strongest influence, institutional trust remains significant even after controlling for sociodemographic, informational, affective factors and political predispositions. Research limitations/implications The cross-sectional design and reliance on self-reported measures limit causal inference. The dependent variable captures normative willingness to delegate rather than the observed behavior, which is appropriate given that institutional-level AI use in higher domains is still emerging. Nevertheless, the use of national survey weights and extensive controls enhances the robustness of the findings. The results contribute to the literature on digital governance by identifying institutional trust as an independent legitimacy mechanism in the acceptance of algorithmic authority. Practical implications For policymakers, the findings suggest that public support for AI-driven governance depends not only on the performance or perceived benefits of AI systems but also on citizens’ confidence in governmental regulatory capacity. Given that AI awareness was independently associated with higher support for delegation (OR = 1.36), strengthening institutional transparency, regulatory credibility and public AI literacy may be essential for sustainable AI implementation. Social implications As governments increasingly rely on algorithmic systems in high-stakes domains, the findings suggest that institutional trust may be an important condition for the democratic legitimacy and public acceptance of digital transformations. Originality/value This study advances research on AI governance by empirically demonstrating that institutional trust in regulatory competence functions as an independent political condition for delegating authority to algorithmic systems. Unlike prior work that examines institutional trust as one predictor among many or that measures cross-national trust differences without testing the delegation pathway, this paper theorizes institutional regulatory trust as the central legitimacy mechanism and uses normative willingness to delegate, rather than abstract approval, as the outcome.
Purpose This paper aims to examine why digital public service reform remains uneven across European Union member states despite a shared supranational policy framework. It compares Germany and Romania to analyse how institutional structures, coordination patterns and reform logics shape different trajectories of digital government reform. Design/methodology/approach The study uses a comparative qualitative document analysis of EU country reports, national digital strategies and public sector reform documents. The analysis is structured around four dimensions: governance structure, coordination capacity, service orientation and reform bottlenecks. Findings The article argues that uneven reform trajectories cannot be explained by economic capacity alone. Instead, differences in institutional complexity, administrative coordination and reform sequencing play a decisive role. Germany represents a case of comparatively high administrative capacity combined with institutional complexity, whereas Romania illustrates a different reform path shaped by more selective but in some respects more focused digital reform dynamics. Research limitations/implications This study is based on comparative qualitative document analysis and therefore captures how reform is framed, organised and officially reported rather than how it is experienced in practice by all actors. The findings are analytically transferable, but not statistically generalisable beyond the two cases. Practical implications The findings highlight the importance of coordination capacity, reform prioritisation and institutionally adapted implementation strategies for digital public service reform in the EU. Originality/value The article contributes to digital government research by showing that digital public service reform in the EU is not linear but institutionally uneven. It offers a comparative explanation of reform divergence under a common European framework.
Purpose The purpose of this study is to contribute to research on democratic governance in AI regulation by answering two questions: how did participation demographics shift across the EU AI Act's consultation phases and what mechanisms explain the pattern; and how was asymmetric participation associated with the Act's regulatory provisions. Design/methodology/approach This study uses an explanatory sequential mixed-methods design combining quantitative demographic analysis across three consultation phases (2020 White Paper, n = 1,215; 2021 legislative proposal, n = 303; and 2024 implementation guidance, n = 383) with qualitative provision-tracing through co-production theory and a four-indicator structural capture rubric. Findings EU citizen representation collapsed to 3.96% during the decisive legislative phase, while industry consolidated as the dominant stakeholder group across Phases 2 and 3. Three reinforcing mechanisms - resource disparities, institutional power imbalances and definitional flexibility - link demographic asymmetry to regulatory architecture. The Article 6(3) provider self-assessment framework satisfies all four structural capture indicators. The 2025 Digital Omnibus amendments, targeting the same accountability provisions, are consistent with the structural vulnerability embedded in this process. Research limitations/implications The analysis establishes institutional conditioning rather than deterministic causation, and findings from a single provision should not be generalised across the Act's full architecture. Better Regulation consultation mechanisms appear structurally insufficient for technically complex domains and require reform toward deliberative mechanisms with binding democratic authority. Originality/value This study offers the first cross-phase longitudinal comparison of AI Act consultation demographics, introduces a replicable structural capture rubric for assessing participation quality and positions the AI Act as a diagnostic case for EU regulatory governance.
Purpose This paper aims to address the systemic issue of data deterioration caused by fragmented corporate data practices. It introduces the "data cold chain" as a novel regulatory framework for government-led standardization, designed to ensure data integrity and usability as information traverses organizational and sectoral boundaries.Design/methodology/approach This study uses a qualitative approach, drawing on semistructured interviews with 49 experts from 27 countries. These participants include government officials, corporate data officers and technology providers, ensuring a broad geographical and industrial perspective on data lifecycle management.Findings The research identifies four distinct features of data deterioration within corporate ecosystems and reveals how fragmented regulatory environments lead to failures in public service delivery. The findings demonstrate that internal governance is insufficient for cross-border interoperability, necessitating a shift toward binding, universal data integrity variables, such as provenance completeness and schema stability, enforced by central public authorities.Research limitations/implications This study shifts the locus of responsibility for data quality from individual firms to public institutions. It suggests that data usability and interoperability should be regulated with the same rigor as data privacy, framing the "data cold chain" as a necessary enabling infrastructure for digital governance.Practical implications This paper provides policy recommendations for regulatory bodies, such as National Data Offices, to mandate specific technical standards. It also highlights the use of public procurement rules as a nonregulatory lever to incentivize private-sector compliance and drive the adoption of harmonized data practices.Originality/value By applying the analogy of a "cold chain" from perishable supply chains to the digital economy, this paper offers a transformative perspective on the government's role in data governance. It moves beyond voluntary industry guidelines to propose a mandatory framework that supports both business innovation and public welfare.
Purpose As digital governance advances, digital inclusion increasingly depends not only on information and communication technology (ICT) access and skills but also on citizens' effective engagement with digital public services. This study aims to examine the level, inequality and dynamic evolution of digital inclusion in the context of accelerating public service digitalization, with particular attention to digital public services as a critical transition stage in the inclusion process.Design/methodology/approach This study develops a four-dimensional framework of digital inclusion and constructs composite and dimension-specific indices using annual panel data for 31 provincial-level administrative units in Mainland China. Dagum Gini decomposition, K-means clustering and s- and ss-convergence analyses are used to examine regional disparities, structural typologies and dynamic convergence patterns.Findings The results show that although overall digital inclusion has improved, interprovincial disparities remain large and persistent, driven mainly by interregional differences and distributional overlap. Digital public services constitute the most unequal and limiting dimension, with pronounced gaps between service provision and actual use. Clustering and convergence analyses further reveal divergent provincial trajectories, indicating limited convergence and risks of path dependence.Originality/value This study conceptualizes digital inclusion as a multidimensional governance process linking ICT conditions, digital capability and usage intensity and digital public services with effective use, thereby bridging the analytical divide between ICT and e-government research. By integrating multidimensional measurement, inequality decomposition and dynamic convergence analysis, it provides a novel empirical framework for examining regional disparities and offers new evidence on how public service digitalization shapes substantive inclusion outcomes.
Purpose The digitalisation of official statistics has accelerated globally, yet limited empirical evidence exists on its operational impact within national statistics offices in developing countries. This study aims to evaluate the transition to digital consumer price index data collection at Statistics South Africa, framing it as a socio-technical transition that enhances operational efficiency and transforms organisational workflows. Design/methodology/approach A pragmatic case study and mixed-methods approach was used. The study integrated semi-structured interviews with a System and Information Quality survey grounded in the updated DeLone and McLean information systems success model. Findings Results demonstrate substantial improvements in timeliness, accuracy and efficiency through “process compression” within the sampled organisational context. While digitalisation eliminated manual re-entry and enhanced oversight, findings reveal heightened individual accountability through stringent system validation and reduced procedural flexibility for field staff. Quantitative results confirmed high user satisfaction, though infrastructure-dependent reliability remained a critical moderator. Research limitations/implications Generalisability is limited by the single-domain focus and head-office sample, which likely underestimates the severity of infrastructural challenges in remote areas. Practical implications The findings offer guidance for national statistics offices pursuing digital transformation, emphasising the need for offline synchronisation, multi-layered device security and role redeployment aligned to new workflows. Social implications This study shows how digitalisation strengthens the reliability and timeliness of official statistics, supporting more effective public policy and service delivery. Originality/value This study provides empirical insight into a developing-country national statistics office. It extends the DeLone and McLean model to official statistics, offering actionable lessons on managing socio-technical trade-offs in mandatory public-sector digital transformations.
PurposeThis study aims to analyze the interconnections among governance, risk management, public integrity, and social and environmental sustainability within the Brazilian Federal Public Administration. It examines how these elements jointly support organizational resilience in complex crisis contexts, offering empirical and practical insights for professionals and policymakers.Design/methodology/approachA mixed-methods approach was adopted, combining a systematic literature review (2000-2025) conducted using the Methodi Ordinatio protocol, with covariance-based structural equation modeling applied to secondary data from 387 public organizations, as reported in the iESGo index technical report by the Federal Court of Accounts of Brazil (TCU).FindingsThe results indicate that the alignment among governance, integrity and risk management, in conjunction with the social and environmental dimensions of ESG, strengthens the legitimacy and adaptive capacity of public institutions. The proposed model demonstrated satisfactory fit indices and construct validity, supporting seven of the eight tested hypotheses. Integrity was found to mediate the relationship between governance and risk, while sustainability practices contributed to greater transparency and public trust.Practical implicationsThe findings inform strategies for improving governance and integrity systems and policies, reinforcing evidence-based, risk-informed and sustainability-driven management practices within the public sector.Social implicationsThis research supports the development of more ethical, transparent and resilient public institutions, fostering greater social trust and the advancement of robust public policies in alignment with practitioner objectives.Originality/valueThis study introduces and empirically tests a comprehensive model that integrates governance, risk, integrity and sustainability, providing new insights to strengthen institutional resilience and public value in complex administrative contexts.
Purpose This study aims to examine how emerging technologies shape the cost-efficiency and sustainability performance of public governance institutions. It explores the influence of digital transformation, technological complexity and bureaucratic inertia on environmental policy outcomes, accounting for departmental heterogeneity and interorganizational coordination. Design/methodology/approach Grounded in an adaptation of production network theory and public management literature, a multilevel framework conceptualizes government departments as interdependent production units. Using a panel dataset from 31 Chinese provinces (2010–2023), the study employs fixed-effects regression, spatial network models (to proxy for interdepartmental spillovers) and stochastic frontier analysis. Key constructs include a novel Digital Transformation Index, a Sustainable Governance Performance Score and a newly operationalized latent measure of bureaucratic inertia derived from administrative process indicators. Findings Digital transformation significantly enhances sustainable governance performance, particularly in technologically complex departments, while bureaucratic inertia constrains efficiency. Nonlinear effects reveal diminishing returns, and spatial analysis confirms province-level spillovers that amplify systemic sustainability gains. Originality/value This study advances public management theory by systematically adapting production network logic to the public sector and by quantifying bureaucratic inertia using a novel, empirically grounded latent construct. It offers evidence-based insights for optimizing institutional efficiency and accelerating progress toward sustainable development goals through coordinated digital transformation, while acknowledging limitations in measuring direct interdepartmental flows.
