Jul 2026· Frontiers in Public Management· Vol 1, pp. 102· 0 citations· 26 references
TL;DR
This study proposes a unified governance framework for high-dimensional data-driven intelligent platforms by integrating insights from platform ecosystem theory, algorithmic governance, and data governance research, and reframes intelligent platforms as socio-technical governance systems.
Abstract
This study proposes a unified governance framework for high-dimensional data-driven intelligent platforms by integrating insights from platform ecosystem theory, algorithmic governance, and data governance research. Rather than conceptualizing intelligent platforms as neutral technological tools, the paper positions them as decision infrastructures that structure authority, accountability, and regulation through data architectures and algorithmic control. Using tourism and healthcare platforms as comparative governance contexts, the analysis demonstrates that platforms perform equivalent institutional functions, including regulation, coordination, monitoring, accountability, and optimization, despite differences in service domains and data content. A central contribution of this research is the introduction of data efficiency as a governance performance criterion, emphasizing the capacity of platforms to transform complex, high-dimensional data into interpretable, actionable, and institutionally usable decisions. By shifting evaluation from predictive accuracy toward governance outcomes such as transparency, compliance, coordination, and trust, the study reframes intelligent platforms as socio-technical governance systems. The findings confirm that platform governance logic is institutional rather than sector-specific and that algorithms operate as operational governors within platform ecosystems. This framework advances management science by providing a transferable model for understanding how intelligent platforms govern through high-dimensional data in smart service environments.
Data-driven intelligent service platforms have evolved from simple service intermediaries into institutional governance infrastructures that shape decision-making, coordination, and accountability across sectors. This study examines how platform governance operates in tourism and healthcare systems, emphasizing the roles of data integration, algorithmic decision mechanisms, and institutional oversight. Rather than treating these platforms as domain-specific service tools, the analysis positions them as decision infrastructures that regulate participation, coordinate stakeholders, monitor performance, and enforce compliance through embedded governance mechanisms. Drawing on verifiable platform datasets and governance frameworks, the study demonstrates that tourism platforms historically pioneered large-scale data-driven governance through booking systems, mobility analytics, and review ecosystems. Healthcare platforms subsequently adopted similar governance architectures to support service coordination, regulatory compliance, and risk management in high-stakes institutional environments. Despite differences in sectoral context, both domains rely on the same core governance functions: regulation, coordination, monitoring, accountability, and optimization. The findings show that intelligent platforms enhance decision quality, institutional coordination, policy compliance, and risk governance by embedding regulatory logic into technical systems. Public trust is strengthened through transparent reporting, accountable platform operations, and responsible data stewardship. Healthcare is treated as a governance application case, while tourism represents the methodological origin of platform-based governance practices. Overall, this study contributes to platform governance and AI management research by demonstrating that data-driven platforms function as institutional governance systems rather than merely as service delivery technologies. It highlights the cross-domain transferability of platform governance logic and clarifies how big data and AI reshape contemporary institutional decision-making.
Shengyu Gu· Frontiers in Public Manageme...· 0 citations
The findings are that companies that implement organized governance systems record dramatic advancement in the quality of data, compliance rates, and the accuracy in analytics, and the need to incorporate governance frameworks in enterprise analytics strategies to promote sustainable data-driven change is highlighted.
Jessica Rachel Brown· International Journal of App...· 0 citations
Governance systems across sectors exhibit significant variability in their ability to integrate
artificial intelligence, real-time monitoring, and adaptive oversight. While some sectors
demonstrate advanced governance maturity—characterized by continuous sensing, predictive
analytics, and event-validated learning—others remain anchored in reactive, compliance-centric
oversight models. This manuscript presents a cross-domain comparative analysis of governance
capability across four major sectors: critical infrastructure, healthcare, finance, and public
administration. Using the Governance Maturity Model (GMM) as an evaluative framework, the
study identifies sector-specific patterns in governance readiness, oversight integration, and
adaptive capacity. Findings reveal that governance variability is shaped by environmental
complexity, regulatory intensity, technological integration, and organizational culture. The
analysis further demonstrates that governance variability reflects broader differences in
governance observability, operational intelligence integration, adaptive oversight capability,
institutional learning maturity, and resilience modernization across interconnected sociotechnical ecosystems. This manuscript extends the Adaptive Governance Systems Framework
(AGSF), the AI-Enabled Governance Oversight Model (AIGOM), and the Governance Maturity
Model (GMM) by providing a comparative foundation for cross-sector governance
transformation.
Dr. Robb Shawe· International journal of adv...· 0 citations
Unless the challenges of collective analytics in e-governance, including those related to data visualization, data quality, capacity building, capacity building, technological capabilities, and inter-agency communications, are recognized, the implementation of collective analytics can be challenging.
Ashutosh Prasad Maurya, Pradeep Kumar Suri· International Journal of Inf...· 0 citations
Smart governance represents a modern approach to public administration that uses digital technologies, data-driven decision-making, and citizen-centric service models to improve efficiency, transparency, and accountability. With the rapid growth of information and communication technologies (ICT), governments are shifting from traditional bureaucratic systems to digitally transformed governance models that emphasize interoperability, inclusiveness, and responsiveness. Technologies such as e-governance platforms, mobile government services, artificial intelligence, blockchain, and cloud computing are reshaping the government–citizen relationship. This study proposes an integrated policy framework for smart governance to ensure secure, accessible, and sustainable digital public service delivery. Using a multidisciplinary and mixed-method approach, including policy analysis, global case studies, and quantitative performance evaluation, the research examines factors such as interoperability, citizen engagement, institutional capacity, and regulatory compliance. The findings indicate that governments with integrated policy and strong technological governance achieve higher efficiency and greater public trust. However, challenges such as cybersecurity risks, data privacy concerns, digital illiteracy, and organizational resistance remain significant barriers. The study recommends strategic policy interventions and collaborative governance models to support sustainable digital transformation in public administration.
Jose Fernandez, M. González· International Journal of Eme...· 0 citations
This study aims to examine how artificial intelligence (AI) governance supports sustainable decision-making across organizational contexts in Europe, focusing on six Portuguese firms in energy, urban mobility and finance.
Adopting a sociotechnical perspective, this research uses a qualitative multiple case study design with semi-structured interviews of Chief Information Officers across diverse organizational contexts. It integrates technical and social dimensions to capture how digital infrastructures, governance practices and human factors interact in decision-making processes.
The findings reveal that governance increasingly aligns with formal frameworks through policies, dedicated structures, human oversight and Environmental, Social and Governance (ESG) oriented indicators, enhancing transparency and reliability. However, maturity varies by sector, resources and technology and challenges such as data limitations, organizational resistance and regulatory uncertainty persist. Furthermore, AI governance emerges as an adaptive, iterative capability for navigating sustainability complexities.
This study provides original insights by linking AI governance to sustainable decision-making through a sociotechnical lens, an area still underexplored in empirical research. It advances theory by integrating ESG considerations into AI governance and offers practical value by identifying mechanisms that enhance transparency, accountability and sustainability outcomes.
Fernando Almeida· Journal of Ethics in Entrepr...· 0 citations