1. Introduction
The data governance of universities serves as a cornerstone for supporting their digital transformation. Formally, data governance is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets (
DAMA International, 2012). It represents a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. Currently, data governance in most Chinese universities remains confined to the stage of “data silos,” with only a few institutions having achieved unified and integrated management of internal data. Both in theoretical understanding and practical implementation, data openness within universities significantly lags behind the demands of institutional governance, thereby hindering the effective realization of data value (
Nielsen, 2017). The fundamental reason for this lag lies in the outdated and fragmented governance systems that characterize most institutions.
In the digital age, university data governance systems have undergone several evolutionary phases. Studying those few universities that have successfully established unified governance provides an opportunity to identify the institutional factors, path-dependent mechanisms, and strategic decisions that influence the development of data governance frameworks. This inquiry is crucial for informing future reforms aimed at aligning data governance systems with the broader goals of digital transformation and institutional modernization. To operationalize this concept for our analysis, this study defines university data governance not merely as a technical framework for data management, but as a dynamic socio-technical system. This study moves beyond a purely procedural view. We conceptualize university data governance not as a static set of technical rules, but as a dynamic socio-political arena where power relations and institutional roles are actively negotiated and redefined. This perspective recognizes that data governance frameworks are more than just descriptions of workflows; they are powerful mechanisms that reflect the evolving role of the state in governance and the shifting relationships between various actors (
Kitchin, 2014). Drawing on the theoretical framework of historical institutionalism, this article explores the institutional evolution of university data governance in China. Specifically, it addresses the following research questions:
What are the critical junctures in the institutional evolution of data governance in Chinese universities? How have these junctures facilitated the transition from functional departmental governance to cross-departmental collaboration, and ultimately to governance modes that emphasize data openness and application?
What are the underlying mechanisms that give rise to path dependence in the evolution of university data governance systems? In what ways do governmental policies and the rational choices of universities reinforce the continuity and persistence of existing governance models?
Facing contemporary demands for data openness and effective utilization, how can Chinese universities overcome institutional inertia and successfully transition to a data governance paradigm centered on openness and public value? What specific institutional reforms and adjustments to data power structures are required for this transformation?
By addressing these questions, this article aims to deepen the understanding of the internal logic and driving mechanisms of institutional change in university data governance. It also seeks to provide both theoretical insight and practical guidance for the future design and implementation of data governance systems in higher education institutions. For the purposes of this study, university data governance refers to the management and control of administrative, academic, student, and research data assets. This includes data generated by core university functions—such as student records, teaching analytics, research outputs, and resource allocation—as well as information used for planning, evaluation, and external reporting. The analysis considers both technical and organizational dimensions of how these data are governed within Chinese higher education institutions.
2. Literature Review
With the development and application of next-generation digital technologies, digital transformation has become a global trend, driving reform and innovation in organizational management across sectors. In higher education, this transformation fosters deep integration between digital technologies and institutional governance structures, catalyzing what has been termed a “digital revolution” in university administration and organization (
L. P. Zhao et al., 2024). As universities shift toward evidence-based decision-making models, the demand for robust data governance mechanisms continues to grow (
Hora et al., 2017).
Data governance, a defining feature of the digital age, refers to the set of institutional practices through which universities exert control over, and derive value from, their data assets (
DAMA International, 2012). As such, it constitutes a core foundation of the digital transformation of university governance. Effective data governance enhances the scientific rigor of institutional decision-making and improves the overall efficiency of university operations (
Xu et al., 2015;
Chang, 2022).
In early discussions on university data governance, scholars noted that conventional approaches often resulted in fragmented, department-based data management systems. These structures failed to address the strategic needs of educational management in increasingly competitive and complex environments. Such fragmentation limited the ability of institutions to conduct comprehensive analyses and engage in coordinated decision-making across functional units (
Meng & Huang, 2003). This problem—commonly referred to as the persistence of data silos—has been extensively studied in the context of digital government, Mergel emphasizing that breaking down these silos requires agile coordination, interdepartmental trust, and cultural change, not merely technical solutions (
Mergel et al., 2019). As universities experienced a diversification of power structures and a trend toward flatter administrative hierarchies, the importance of collaborative governance models became increasingly evident (
Ji, 2012). These developments gave rise to new paradigms such as “education management data warehouses” and cross-functional collaborative governance platforms.
In the contemporary era, data is no longer viewed solely as a resource but increasingly as a strategic asset and a form of power. This transformation has elevated the significance of data within higher education institutions and sparked growing scholarly interest in understanding its institutional logic and influence. Recent research highlights that data’s value has expanded alongside the emergence of big data technologies, with increasing attention paid to its economic, organizational, and political dimensions (
Jim & Chang, 2018). The notion that “data is power” has gained traction in academic discourse (
Mei, 2023), prompting inquiries into how data reshapes power relationships and governance structures in universities.
Accordingly, scholars have sought to address a wide range of issues in university data governance, including the design and implementation of governance frameworks (
Dong et al., 2019), the operational mechanisms underlying data governance systems (
Wu & Chen, 2018), the persistent challenges faced by institutions (
Y. Zheng & Liang, 2020), and strategies for system-level construction (
Komljenovic et al., 2024;
H. Zhang et al., 2022). These studies collectively contribute to the broader project of modernizing university governance systems through data-driven reform.
Research into the factors influencing university data governance has further emphasized the importance of leadership commitment, institutional support structures, collaborative mechanisms, data standards, data quality, and data-sharing protocols. As the scale and complexity of data in universities continues to grow, scholars increasingly argue that the development of comprehensive governance frameworks is essential. Well-structured data governance not only improves internal efficiency but also supports more targeted and personalized services for students, faculty, and other stakeholders (
Picciano, 2012), thereby promoting more informed and accountable institutional decision-making (
Yu & Liu, 2019).
Despite the growing volume of research, two significant gaps remain in the existing literature. First, most studies focus on the design and functioning of data governance systems, while relatively little attention has been paid to their historical evolution. Second, existing research tends to focus on internal organizational dynamics within universities, paying insufficient attention to external institutional factors, such as state policy regimes and macro-level governance environments, that shape university behavior. In practice, only a handful of universities in China have managed to transcend siloed governance and achieve integrated data systems. Understanding the mechanisms that have allowed these universities to break out of path dependence is essential for both theory and policy.
To address these gaps, this study adopts historical institutionalism as its primary analytical lens and integrates the concept of institutional logics as a complementary perspective (
Y. Yang & Fan, 2024). As
Y. Cai and Mountford (
2022) note, institutional logics analysis provides a powerful framework for interpreting the normative complexity of higher education institutions and the multiple, often conflicting, value systems that guide organizational behavior. This dual-theoretical framework enables a more nuanced analysis of how institutional structures and logics interact to shape the trajectory of university data governance reform in China. From an institutional perspective, university data governance worldwide has also followed an evolutionary trajectory shaped by national policy logics, administrative traditions, and institutional reforms. In countries such as the United States, the United Kingdom, and Australia, reforms have emphasized centralized data infrastructures and performance-based accountability mechanisms, driven by funding models and regulatory pressures. Across these contexts, historical institutionalism has been widely applied to examine how policy continuity, path dependence, and governance norms influence the formation and transformation of university data governance systems It helps contextualize the Chinese case in a broader global framework.
3. Analytical Framework and Research Methodology: Theoretical Foundations and Normative Design
3.1. Historical Institutionalism: Core Dimensions for Analyzing Institutional Change
Historical institutionalism, one of the core strands of new institutionalism, emerged in the 1990s as a theoretical approach that integrates historical context into the analysis of institutional structures and change (
Pan & Zhu, 2019). It offers a value-neutral framework situated between macro-level political economy and meso-level organizational dynamics (
X. L. Zhang, 2021). The central insight of historical institutionalism is that institutional development follows a temporal logic: past decisions and structures exert a profound influence on current practices and future trajectories. Institutions, in this view, are not static; rather, they evolve through sequences of path-dependent processes and critical junctures.
In political science and policy studies, historical institutionalism emphasizes how formal political institutions—such as bureaucratic arrangements, rule systems, and policy frameworks—condition the behavior of actors, the formulation of policy, and the allocation of resources (
Zhuang, 2008). This approach has been widely applied to analyze changes in governance regimes, welfare states, and public administration. Filgueiras applies it to Latin America’s digital governance reforms, highlighting how institutional legacies, regulatory fragmentation, and bureaucratic inertia shape the uneven implementation of data policies (
Filgueiras et al., 2025). In the context of higher education, historical institutionalism provides an effective framework to examine long-term policy shifts, governance transformations, and institutional responses to state-led reform agendas (
Amaral, 2010).
In this study, historical institutionalism is employed to analyze the evolution of university data governance systems in China during the digital era. Since the turn of the 21st century, universities have progressively adopted formalized data governance practices, often codified in the form of internal rules, policy documents, and platform-based management systems. These transformations have occurred against the backdrop of broader shifts in government data governance and digital strategy.
The analytical framework of historical institutionalism deployed in this study focuses on three dimensions:
Deep Structure Analysis: This dimension examines how macro-level political and administrative institutions—particularly national data governance policies and regulatory frameworks—influence the design and evolution of university data governance systems. Governmental ideologies, resource allocation strategies, and hierarchical control mechanisms serve as structural constraints and enablers.
Path Dependency Analysis: Once institutional arrangements are established, they tend to become self-reinforcing due to sunk costs, coordination effects, and learning effects. This dimension explores how legacy systems of university data governance create inertia, making institutional innovation difficult even when functional deficiencies are apparent.
Driving Mechanisms Analysis: Critical junctures—moments of rupture or realignment—offer opportunities for institutional change. This dimension identifies the mechanisms that drive universities to alter their data governance systems, focusing on how internal needs, external pressures, and perceived performance benefits converge to create reform momentum (
Peters, 1999).
By mapping institutional change along these dimensions, this framework enables a systematic analysis of how Chinese universities have moved from decentralized, fragmented data systems toward more integrated and potentially open governance models.
3.2. Institutional Logics and the Mechanism of Change in University Data Governance Systems
While historical institutionalism accounts for the temporal and structural dimensions of institutional change, it is relatively less equipped to explain how actors within institutions navigate conflicting norms, priorities, and pressures. For this reason, the study integrates institutional logics theory, which emphasizes the normative and cultural frameworks that shape organizational behavior.
Institutional logics, as defined by Thornton, Ocasio, and Lounsbury, refer to the socially constructed, historically contingent patterns of practices, assumptions, and values that provide meaning to the institutional order. In higher education, institutional logics analysis reveals how different actor groups (e.g., government officials, university leaders, faculty, and students) operate under distinct normative systems and pursue sometimes competing goals (
G. Zheng et al., 2024).
In the case of university data governance in China, three primary logics are particularly influential:
State Logic: Characterized by hierarchical authority, compliance orientation, and centralized planning. Under this logic, universities align their governance structures with national policy priorities and respond to top-down directives from ministries and administrative agencies.
Professional (Academic) Logic: Emphasizing scholarly autonomy, peer accountability, and discipline-based knowledge production. This logic manifests in the desire for decentralized decision-making, customization of data systems, and resistance to purely bureaucratic metrics.
Market Logic: Focused on outcomes, performance indicators, and external stakeholder expectations. This logic encourages the standardization of processes, benchmarking, and the use of data for managerial control and competitive advantage.
These logics do not operate in isolation; they coexist and interact within universities, shaping both the content and direction of institutional change. For instance, the shift toward open data governance represents a recalibration of legitimacy—from satisfying government mandates (state logic), to demonstrating accountability to broader publics (market logic), while still preserving academic integrity (professional logic).
By integrating institutional logics into the historical institutionalist framework, this study captures both the structural constraints and agentic strategies that shape data governance reform. It enables a deeper understanding of how governance logics shift over time, how one logic may dominate in a given phase, and how hybrid configurations emerge to accommodate the complex demands of digital-era university management.
3.3. Methodology: A Normative-Analytical Approach to Institutional Change
In line with the theoretical perspectives of historical institutionalism and institutional logics, this study adopts a normative analytical methodology to examine the institutional transformation of university data governance in China.
Three interrelated methodological strategies structure this research design:
Normative Reasoning and Logical Deduction: Drawing on national policy texts, digital governance strategies, and administrative guidelines, the study engages in normative reasoning to construct an interpretive framework for institutional change. Through logical deduction, it infers a developmental trajectory in university data governance that moves from fragmented departmentalism toward integrated and open data systems. The identification of three governance systems—System 1.0, 2.0, and 3.0—emerges from this deductive logic grounded in policy direction and institutional response.
Periodized Analysis of Institutional Evolution: The study applies a stage-based analytical model to conceptualize institutional evolution as a sequential and cumulative process. Institutional phases are delineated based on shifts in governance mechanisms, organizational configurations, and strategic orientations. This periodization draws upon national informatization initiatives, platform architecture reforms, and the diffusion of digital governance models in higher education. While historical institutionalism provides the theoretical lens, the methodological focus is on deductive classification and normative synthesis rather than empirical historicism.
Illustrative Case-Based Exemplification: To support the theoretical propositions, the study employs purposively selected cases—such as Tongji University’s integrated data governance structure and Zhejiang University’s internal data exchange platform. These cases are used not as empirical generalizations, but as illustrative examples that embody specific governance logics and institutional responses at different phases. They serve to demonstrate how the proposed analytical framework aligns with real-world patterns of reform within leading Chinese universities.
It should be noted that this study primarily employs a normative analytical approach, relying on national policy documents, publicly accessible institutional reports, and representative cases from selected Chinese universities. The cases of Tongji University, Zhejiang University, and Peking University are used to exemplify the key phases of institutional change and governance logic, thereby offering a specific perspective on the evolution of digital governance in higher education.
4. The Evolutionary Process of University Data Governance Systems
From the perspective of institutional development over time, the evolution of university data governance in China during the digital era can be broadly divided into three phases: (1) function-based data governance centered on departmental roles, (2) cross-departmental collaborative governance through umbrella structures, and (3) open data governance oriented toward application and value creation. These phases reflect a general pattern of transformation rather than a strict chronological progression, and universities vary in their positions along this continuum depending on their institutional conditions, leadership priorities, and external policy environment.
While the three systems—function-based, collaborative, and open data governance—are presented in a sequential framework, this categorization does not imply a uniform or strictly linear progression across all institutions. Rather, it serves as an ideal-typical construct to analyze the dominant logic and governance orientation in different phases of institutional development. In reality, many universities exhibit hybrid configurations, with overlapping elements from multiple phases coexisting due to uneven organizational readiness, leadership priorities, or policy pressures. However, despite such hybridity, the phase-based model remains analytically useful for understanding the structural direction and normative evolution of university data governance in China.
4.1. University Data Governance System 1.0: Function-Based Data Governance Centered on Departmental Roles
In the pre-digital era and the early stages of digital development, data in Chinese universities was managed primarily through manual operations and paper-based documentation. Information systems, where they existed, were developed within individual departments, such as academic affairs, finance, or student services, to support local administrative functions. These systems were rarely interoperable, and the collection, processing, and storage of data were labor-intensive and inefficient.
With the advancement of information technologies in the 1990s and early 2000s, and under the influence of national informatization strategies, universities began investing in IT infrastructure to digitize their administrative operations. Government documents such as the 1989 Master Plan Outline for the Management Information System and the Tenth Five-Year Plan for Educational Informatization launched in 2002 urged institutions to modernize data systems. However, these initiatives generally followed a department-centric logic, where each administrative unit developed its own data tools based on its specific needs, resulting in data silos (
Huo & Huo, 2023). In many Chinese universities, early information systems were developed independently by individual departments—most commonly starting with student affairs or finance—without a unified institutional strategy. As a result, these systems operated in silos, lacking interoperability, data compatibility, and integration.
Notable cases include:
Nankai University (prior to 2017): In its early digitalization phase, Nankai University attempted to build a centralized public database to integrate administrative data. However, due to severe information silos caused by inconsistent platforms and departmental autonomy, the initiative failed to achieve cross-system interoperability, resulting in fragmented data ownership and limited usable sources
Zhang notes that this fragmented approach led to dispersed operational data across multiple systems, with no standardized definitions or centralized platforms for data cleaning. Consequently, issues such as data inconsistency, inaccuracy, and delays in accessibility became prevalent (
D. H. Zhang et al., 2017).
This phase, which we refer to as University Data Governance System 1.0, was characterized by:
Fragmented data ownership;
Independent departmental platforms;
Lack of standardized data definitions and formats;
Minimal data sharing across departments.
While these systems improved internal efficiency within departments, they also deepened organizational segmentation, making institution-wide data integration and governance virtually impossible. This challenge mirrored similar problems across the broader public sector, including government ministries and agencies, which faced issues of islanded systems and segmented data repositories. By 2017, national documents such as the Implementation Plan for the Integration and Sharing of Government Information Systems began explicitly acknowledging the systemic consequences of siloed architectures and called for more unified approaches to information governance.
4.2. University Data Governance System 2.0: Collaborative Data Governance in an Umbrella Structure
The shortcomings of system 1.0 created both internal pressures and external incentives to seek more integrated data solutions. By the mid-2010s, a growing number of universities began to recognize the strategic importance of data integration and the limitations of decentralized data management (
Liu, 2020). This was partly influenced by the diffusion of institutional research (IR) models from the United States, which emphasized the use of comprehensive, high-quality data to support strategic planning and institutional effectiveness (
Chang, 2009;
J. M. Zhao, 2007).
At the same time, government agencies also intensified efforts to improve public sector data sharing and advocated for platform-based coordination. Policies such as the Opinions on Establishing a Data Infrastructure System to Enhance the Role of Data Elements and the Notice on Strengthening Information Technology in Education Management in the New Era called for the “one source per entity” principle and the elimination of redundant data collection.
In this context, some universities initiated top-down reforms to build integrated data platforms and umbrella-style governance structures. These platforms consolidated data from across departments, standardized metadata, and enabled centralized management and analysis. Implementation typically relied on high-level leadership commitment, cross-functional coordination, and the establishment of dedicated IR or data governance teams.
Notable cases include:
Tongji University, which developed a “1 + N” data governance architecture encompassing a centralized data warehouse and data analytics interface, underpinned by institutional research principles (
S. F. Cai et al., 2016,
2023).
Zhejiang University, which launched a lightweight internal open data exchange platform to streamline data sharing and enhance decision-making (
Bai et al., 2023).
However, this reform was far from uniform across the sector. Many universities faced:
Resistance from departments reluctant to relinquish control over data;
Technical difficulties in integrating legacy systems;
Challenges in defining and enforcing data standards.
System 2.0 thus represents a hybrid phase, where partial integration is achieved, but where true interoperability and strategic data use remain uneven.
4.3. University Data Governance System 3.0: Data Governance Centered on Open Data Applications
The third phase marks a qualitative shift in the purpose and orientation of data governance—from internal integration to external openness and application (
Alexander, 2022;
L. Zheng, 2015). This reflects broader developments in public administration and digital government, where open data is increasingly seen not just as a tool for transparency, but as a resource for innovation, collaboration, and public value creation (
Okunleye, 2024;
L. Zheng & Gao, 2015).
In the context of higher education, open data facilitates:
Enhanced public accountability;
Evidence-based academic policy;
Cross-institutional benchmarking;
Support for research and civic engagement.
Globally, governments and universities have begun to institutionalize open data practices. Examples include:
The U.S. Integrated Postsecondary Education Data System (IPEDS), which offers public access to a wide range of institutional data;
The U.K. and France’s national education open data portals;
China’s gradual rollout of policies promoting educational data openness, such as the Educational Data Management Measures and the Education Informatization 2.0 strategy (
X. M. Yang et al., 2018). Notable cases include:
Peking University: Under its Campus Data Management Measures, Peking University institutionalized the “one source per entity” principle to enforce cross-departmental data sharing and established anonymization protocols for personal data queries. Supported by a three-dimensional data model (person-event-time), its integrated platform consolidated 800 million records to enable “zero-run” administrative reforms, reducing bureaucratic processes by 60% through data-driven service optimization
Despite these developments, most Chinese universities remain in the early stages of system 3.0. While some have opened access to research data and key statistics, few have developed the governance structures, cultural orientation, or technical safeguards necessary to fully embrace data openness.
Key barriers include:
Unclear data ownership and privacy regulations;
Low trust in external usage of institutional data;
Lack of incentives and support mechanisms for transparency.
Nonetheless, the logic of open data governance continues to gain ground. It challenges traditional views of data as proprietary departmental property and instead conceptualizes it as a public institutional asset—a shift that has profound implications for university governance, legitimacy, and societal relevance.
5. The Logical Framework of Institutional Change in University Data Governance Systems
Institutional change is a core concept in historical institutionalism. Rather than viewing institutions as static structures, this approach emphasizes how they evolve through dynamic interactions among structural constraints, strategic agency, and external shocks. In the context of Chinese university data governance, institutional transformation is shaped not only by macro-level mandates but also by internal organizational configurations, cost–benefit calculations, and path-dependent behaviors.
This section builds on the historical institutionalist framework by analyzing the logic of institutional change along three dimensions: deep structures, path dependence, and driving mechanisms.
5.1. Deep Structures
The evolution of government data governance systems functions as a key structural force influencing the configuration of university-level governance. In China’s centralized administrative system, universities operate under strong state supervision and are required to align their management practices with national development strategies, regulatory guidelines, and political values.
Historical institutionalism posits that macro-institutional environment—shaped by political, economic, and ideological forces—condition the behavior of subordinate organizations (
Ye & Yang, 2020). In this case, governmental documents and legislative norms provide the authoritative templates that universities must follow in designing their data governance frameworks. The influence of government manifests in several ways:
Through policy directives that define strategic goals (e.g., informatization, integration, openness);
Through budgetary and technical support mechanisms, which create incentives for alignment;
Through evaluative frameworks and audits, which link compliance to performance assessments.
Over time, government-driven reforms have led universities to adopt increasingly formalized governance arrangements. These often mirror developments in state data systems and follow similar patterns of platformization, centralization, and standardization.
These deep structures also influence the configuration of data power within universities. Initially, data power resided within decentralized operational units, which used data for task-specific management. As integration efforts gained traction, authority shifted to centralized governance units or lead departments tasked with standardizing data flows. In the emerging era of openness, however, a new model is gradually taking shape—one in which data rights are redistributed, and all stakeholders are seen as potential users, not just data custodians. This marks a normative shift from “data as power” to “data as a right.”
5.2. Path Dependence
While external pressures may open windows for reform, institutional change is rarely linear or automatic. One of the most enduring features of historical institutionalism is its emphasis on path dependence—the tendency of institutions to reproduce existing patterns due to self-reinforcing mechanisms, including sunk costs, legal inertia, and organizational routines.
In the case of university data governance, both government and institutional actors make rational choices that contribute to the continuation of legacy systems.
For governments, policy continuity serves the purpose of maintaining macro-control over the higher education system. Once a governance model is promoted—such as centralized data platforms or “one source of truth” architecture—it becomes difficult to reverse course, especially when implementation is monitored through hierarchical performance systems.
For universities, the costs of reform are often high. Existing data governance systems involve multiple interdependent subsystems—such as academic affairs databases, finance reporting modules, and legacy performance dashboards—which are often built on incompatible logic. For example, several universities reported difficulties dismantling department-level systems due to personnel resistance and concerns over data loss, despite formal plans for centralization. Attempting to replace or significantly alter them requires substantial investment in both financial and political capital. Moreover, success is not guaranteed, especially in the absence of strong regulatory protection or shared infrastructure. Even when reform is initiated, many institutions adopt a “reform on the surface, inertia underneath” strategy—implementing new tools while preserving the underlying logic and authority relationships of previous systems.
These dynamics explain why many universities continue to operate hybrid systems, where localized data autonomy coexists with central mandates, and why fully open, integrated governance remains the exception rather than the norm.
5.3. Driving Mechanisms
Despite the persistent influence of institutional inertia, organizational change within Chinese universities remains achievable, particularly when driven by the dual forces of legitimacy and efficiency. Drawing from organizational sociology, institutions are understood to require both normative legitimacy—reflected in public and political endorsement—and instrumental efficiency—reflected in functional performance—in order to sustain their status and succeed in competitive environments (
Li, 2013;
Zhou, 2003).
In the Chinese context, higher education institutions are deeply embedded in a political system where ideological conformity and structural alignment are essential. To secure state endorsement, universities frequently undertake governance reforms that echo official rhetoric, even in the absence of full institutional readiness. For example, the widespread deployment of departmental information systems during the 2000s was motivated not only by technical considerations but also by the need to respond to political expectations. More recently, initiatives promoting open data governance have been framed around enhancing public accountability, resonating with broader discourses on transparency and digital modernization.
Concurrently, universities must respond to growing internal demands for improved operational efficiency. The rapid expansion of higher education has significantly increased the volume and complexity of data across institutional domains. Administrative departments now rely on faster, more integrated, and more precise information systems to manage critical functions such as admissions, financial operations, research administration, and student services. In this context, data governance platforms that demonstrably enhance institutional performance—ranging from decision-support dashboards to data warehouses and predictive analytics—tend to be adopted regardless of their alignment with political mandates.
When reforms succeed in satisfying both legitimacy and efficiency imperatives, the prospects for sustained institutional change are markedly improved. High-level strategic initiatives that simultaneously align with governmental priorities and deliver measurable performance gains often garner strong institutional support and secure necessary resources.
Amid the ongoing transformations driven by artificial intelligence and big data, universities now face intensifying pressure to demonstrate the productive value of data. This emerging expectation extends beyond mere data accumulation, emphasizing instead the generation of actionable insights and meaningful applications. As a result, a new efficiency-oriented logic is taking shape—one that accelerates the shift toward governance models characterized by openness, adaptability, and user-centered design.
6. Conclusions
This study investigates the institutional transformation of university data governance in China by integrating the analytical perspectives of historical institutionalism and institutional logics theory. It delineates three distinct developmental trajectories—function-based departmentalism, cross-departmental collaborative integration, and application-centered open data governance—each shaped by a combination of structural imperatives, policy directives, and organizational rationality.
The evolving framework of governmental data governance has functioned both as a strategic guidepost and a regulatory constraint, compelling universities to align their internal governance practices with broader national development agendas. Nevertheless, institutional responses have not been purely passive. Universities have exercised strategic agency, made cost–benefit-informed decisions and sought reputational gains, which, in turn, have reinforced path-dependent behaviors and contributed to the gradual pace of reform.
Despite such institutional inertia, the simultaneous intensification of legitimacy and efficiency demands—arising from the pursuit of data-informed decision-making, heightened external accountability, and digital responsiveness—has generated renewed momentum for governance innovation. Particularly, the global diffusion of open data governance norms has begun to disrupt established models of centralized control, introducing alternative logics grounded in transparency, inter-organizational collaboration, and the generation of public value.
To navigate this transition effectively, Chinese universities should prioritize three strategic shifts. First, they must overcome entrenched path dependencies by redesigning legacy information systems that impede integration and transparency. Second, institutional data power structures should be reconfigured to enable more inclusive, collaborative governance arrangements and to democratize access to institutional data resources. Third, data openness must be embedded not only as a normative commitment but also as a pragmatic strategy to enhance service delivery, research quality, and stakeholder engagement.
The urgency for these reforms is amplified by China’s current higher education policy landscape, particularly the national strategy for the digital transformation of education. This strategy explicitly positions data as a foundational element for modern institutional governance, emphasizing the need for integrated, interoperable, and open data systems to support real-time decision-making and educational modernization. In parallel, recent policies aimed at optimizing disciplinary and program structures—such as those guiding the strategic adjustment of academic offerings and talent pipelines—require universities to engage in continuous, data-driven analyses of societal needs, employment trends, and student development trajectories. These policy initiatives collectively accelerate the transition toward more open and application-oriented data governance by institutionalizing the demand for high-quality, dynamic, and transparent data infrastructures. Rather than treating data openness as a voluntary reform, these top-down imperatives transform it into a compliance-based institutional necessity, thereby embedding open data governance within the fabric of policy implementation.
Moreover, the success of these university-level transformations hinges on robust, multi-level governmental support. Policymakers should enact dedicated legislation and regulatory standards for data openness and privacy in higher education, while also promoting cross-institutional data sharing alliances and technical consortia through targeted funding and interoperability standards. Capacity-building and incentive programs are essential for strengthening data governance in less-resourced universities, ensuring more equitable participation in digital transformation. Such coordinated actions will empower universities, enhance transparency and public value, and position the government as both regulator and enabler in building a resilient, future-oriented higher education data governance system.
This reform aligns with a clear global trend. International organizations like UNESCO have long championed Open Educational Resources (OERs) and data sharing to promote educational equity and quality. Advanced economies have established mature systems for data transparency and application, such as the Integrated Postsecondary Education Data System (IPEDS) in the United States, which provides a public-facing portal for institutional data, and the European Union’s directives promoting open data across public sectors, including education. These international precedents underscore the strategic value of data openness in enhancing accountability and fostering innovation.
Building on global momentum, these internal university transformations require strong and multi-faceted external support from policymakers. This support should be structured around a multi-pronged policy approach. Policymakers should articulate a coherent and balanced regulatory framework that promotes data openness while safeguarding privacy and institutional security. Concurrently, the provision of technical and financial assistance for platform development, interoperability standards, and institutional capacity building is critical. Additionally, the establishment of sector-level coordination mechanisms—such as university consortia, open data alliances, and experimental policy zones—can foster collective action and reduce reform-associated risks.
Given the limited capacity of individual universities to spearhead systemic reform independently, these collaborative and policy-driven approaches are indispensable for fostering scalable innovation. In this process, institutional data should be redefined not merely as an operational resource but as a public asset whose strategic value increases through responsible sharing, contextualization, and applied use.
While this study provides a theoretically grounded interpretation of the institutional evolution of university data governance in China, several avenues merit further empirical exploration. First, future research should examine the interdependencies between data governance and other subsystems of university administration, such as budgeting processes, academic performance evaluation, and human resource management. Second, comparative studies—both across Chinese universities and in international contexts—are needed to identify exemplary practices and assess the transferability of reform strategies. Third, more attention should be given to the role of intra-organizational actors, including middle-level administrators, IT personnel, and academic staff, in interpreting, contesting, and enacting governance reforms from within.
By addressing these issues, future scholarship can build upon the theoretical foundations laid by this study to generate more fine-grained, actionable insights into the ongoing digital transformation of higher education governance.