Jul 2026· Frontiers in Public Management· Vol 1, pp. 82· 0 citations· 27 references
TL;DR
It is demonstrated that data-driven platforms function as institutional governance systems rather than merely as service delivery technologies, which clarifies how big data and AI reshape contemporary institutional decision-making.
Abstract
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.
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.
Shengyu Gu· Frontiers in Public Manageme...· 0 citations
Artificial intelligence (AI) has become a central driver of digital transformation across multiple industries, reshaping governance structures, service systems, and institutional decision-making processes. This study examines how AI applications in tourism and healthcare reflect a shared cross-industry transformation logic rather than isolated sector-specific innovations. By situating AI within the broader framework of digital transformation, the analysis highlights how datafication, automation, platformization, intelligence, and integration collectively redefine organizational practices and policy coordination mechanisms. Tourism is identified as an early innovator in smart platforms, experience analytics, and data-driven governance, while healthcare represents a high-stakes application context where similar transformation models are adapted for system planning, resource coordination, and institutional oversight. Despite differences in service content and regulatory environments, both sectors rely on comparable digital infrastructures, AI application models, and governance frameworks to modernize decision-making and improve operational efficiency. The study demonstrates that AI functions as a transformation engine that enables predictive governance, smart services, intelligent coordination, and data-driven policy integration. These outcomes contribute to governance modernization, service innovation, institutional alignment, and efficiency gains across industries. Public trust and institutional legitimacy are strengthened through transparent digital governance, cross-sector regulation, and accountable data stewardship. By adopting a cross-industry perspective, this research positions healthcare AI as part of a broader digital transformation process rather than a standalone medical innovation. Tourism serves as the methodological origin of many smart system practices, while healthcare illustrates their institutional application. The findings confirm that digital transformation logic is systemic, transferable, and fundamentally reshapes how modern organizations govern, coordinate, and innovate through AI-enabled infrastructures.
Data-driven governance has emerged as a critical paradigm for enhancing institutional performance, transparency, and accountability in both public and private sectors. This review synthesizes the existing literature on data-driven governance, examining conceptual frameworks, key principles, applications, and the relationship between data-driven approaches and institutional performance. The review identifies core components of data-driven governance including data infrastructure, analytics capabilities, governance structures, and organizational culture. Applications span diverse domains including policy formulation, service delivery, performance management, and stakeholder engagement. Findings indicate that data-driven governance can enhance decision quality, operational efficiency, and institutional accountability, but significant challenges remain including data quality issues, privacy concerns, digital divides, and organizational resistance. Success factors include strong leadership, investment in data infrastructure and capabilities, robust governance frameworks, and a culture of data-informed decision-making. This paper contributes to the literature by providing a comprehensive overview of data-driven governance frameworks and applications, and identifying critical success factors and future research directions.future research directions.
Unknown authors· Global Journal of Advanced R...· 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
Cities are investing heavily in data platforms, sensing infrastructure, and algorithmic tools, yet the officials who must govern these systems are rarely educated for the task. This article addresses how universities should design executive academic programs that build the governance and planning capabilities smart city transformation actually requires. It proceeds as an integrative review of two literatures that have developed in isolation: research on smart city governance, which documents the competencies and organisational capacities cities lack, and research on executive education for the public sector, which documents how experienced professionals learn. From the first literature the article derives five competency domains: socio-technical governance literacy, data governance and ethics, technology appraisal for planning, collaborative innovation, and organisational transformation. From the second it derives four design principles: problem-anchored action learning, cohort composition as curriculum, co-production with employing governments, and multi-level evaluation of effects. These elements are integrated into a program design framework that maps competency domains onto curricular structures and connects program outcomes to the organisational capacities documented in the smart city literature. The framework's central claim is that executive programs succeed when the city itself becomes the learning environment, with live governance problems serving as the primary curricular material. The article states the framework's conceptual status plainly, identifies evaluation of program effects at the organisational level as the weakest link in the evidence, and sets an agenda for empirical testing in diverse institutional contexts.
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
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