Public organisations are undergoing rapid digital transformation aimed at enhancing service eficiency, transparency, and accountability. However, persistent challenges remain in managing and reusing data across agencies, as well as in ensuring trust and explainability within Artificial Intelligence (AI)-driven systems. This study examines how the principles of FAIR data management (Findable, Accessible, Interoperable, Reusable) and blockchain technologies can be operationalised within public sector enterprise models to enable transparent, sovereign, and trustworthy AI adoption. Guided by the Technology-Organisation-Environment (TOE) framework and Dynamic Capabilities theory, we conducted a qualitative analysis of interview data from multiple government organisations engaged in AI readiness and digital transformation initiatives. The findings indicate that FAIR and blockchain-related practices are emerging as key enablers of interoperability, provenance, auditability, and data sovereignty. Based on the result of a thematic analysis and theoretical synthesis, the study proposes an enterprise modelling framework that integrates FAIR principles and blockchain mechanisms across the TOE dimensions to support transparent and sovereign AI-driven transformation. The study contributes an actionable model for policymakers and practitioners seeking to align governance, data infrastructure, and technological innovation within public digital ecosystems.
The continuous adoption of emerging technologies has transformed public administration, enhancing
service eficiency, accessibility, and responsiveness [
The success of AI-driven digital transformation in government thus hinges on the ability to manage,
share, and reuse data across institutional and jurisdictional boundaries. However, despite
considerable progress in digitising administrative services, many agencies still function in fragmented data
ecosystems, limiting integration and reuse across departments [
Together, FAIR data management principles and blockchain integration ofer a pathway towards trustworthy and sovereign AI-driven digital transformation in the public sector. Embedding these paradigms within enterprise models supports a holistic form of digital transformation in which interoperability, auditability, and transparency are designed into the very architecture of public information systems.
While the individual merits of the FAIR data principles and blockchain technologies have each garnered
considerable scholarly attention, their combined potential within the unique context of public sector
digital transformation remains relatively underexplored. Prior studies extensively investigated the
benefits of FAIR for improving data stewardship and reusability, for instance, in scientific and research
domains [
This study aims to empirically examine how public organisations can operationalise the FAIR data principles and blockchain-enabled trust mechanisms within AI-driven digital transformation initiatives. The research is guided by the following questions: 1. How do FAIR data principles and blockchain-related practices manifest within the technological, organisational, and environmental contexts of public sector AI adoption? 2. How can these insights be represented within an enterprise modelling framework that supports transparent and sovereign digital transformation? 3. What dynamic capabilities enable public organisations to sense, seize, and reconfigure resources for FAIR- and blockchain-aligned AI adoption?
To address these questions, the study integrates the Technology–Organisation–Environment (TOE) framework [18, 19] and the Dynamic Capabilities theory [20, 21]. The TOE framework provides a basis for understanding the technological infrastructures, organisational readiness, and environmental pressures shaping AI adoption. In turn, Dynamic Capabilities theory elucidates how organisations sense opportunities, seize them through governance and investment, and reconfigure systems and processes to sustain innovation. By combining these perspectives, the study conceptualises FAIR and blockchain integration as dynamic enablers within enterprise modelling. Thus, the contribution of this paper is threefold. First, it empirically identifies how FAIR and blockchain-related practices are emerging within public sector AI initiatives. Second, it proposes a novel Enterprise Modelling Framework for FAIR–Blockchain–AI Integration, grounded in the TOE and Dynamic Capabilities perspectives. Third, it ofers actionable implications for policymakers and enterprise architects seeking to design transparent, interoperable, and sovereign digital systems.
The remainder of this paper is organised as follows. The next section reviews the extant literature, outlining the theoretical bases of the study—the TOE framework and the Dynamic Capabilities perspective—and examining how FAIR data management, blockchain, and AI adoption intersect in the public sector to enhance transparency, interoperability, and digital sovereignty. The subsequent section describes the research methodology, including data collection and analysis methods. The results section presents key findings from thematic analysis of the qualitative data. The discussion interprets these findings through the combined TOE–Dynamic Capabilities lens, leading to a FAIR–Blockchain enterprise modelling framework. The paper concludes by summarising main contributions, implications for research and practice, limitations, and future research directions.
Prior studies, within the context of private firms, have widely employed the Technology–Organisation–Environment (TOE) framework to examine the determinants of technology adoption across organisations [18, 19, 22]. Within the public sector, researchers argue that it ofers a structured lens for analysing how technological infrastructures, organisational readiness, and environmental factors, including regulation, policy, and inter-agency coordination, jointly shape digital transformation [23, 24]. The TOE framework thus provides a valuable basis for understanding how emerging technologies such as AI, FAIR data infrastructures, and blockchain systems are introduced and assimilated within government settings. However, TOE primarily explains adoption drivers and contextual enablers; it does not suficiently account for the dynamic, path-dependent processes through which organisations evolve and continuously adapt to technological change [25].
To address this limitation, the Dynamic Capabilities (DC) perspective ofers a complementary lens by
emphasising the adaptive and evolutionary nature of organisational transformation [20, 21]. According
to Teece et al. [20], dynamic capabilities refer to an organisation’s ability to sense new opportunities
and threats, seize them through strategic decisions and resource mobilisation, and reconfigure existing
structures and competencies to sustain long-term performance in changing environments. This theory
has been increasingly applied to digital transformation research, where organisations must balance
technological innovation with institutional constraints [26, 27]. In the public sector, dynamic capabilities
have been linked to the ability to institutionalise new governance practices, develop data-driven
decisionmaking routines, and respond flexibly to regulatory and ethical demands [
Integrating TOE and DC perspectives enables a more comprehensive understanding of public sector digital transformation by linking the structural determinants of adoption (as captured by TOE) with the processual mechanisms of adaptation and renewal (as described by DC). In the context of this study, the combined framework is used to conceptualise how public organisations operationalise the FAIR data principles and blockchain-based trust mechanisms within AI-driven enterprise modelling. TOE provides insight into the technological, organisational, and environmental conditions shaping the adoption of these mechanisms, while DC illuminates how institutions cultivate sensing, seizing, and reconfiguring capabilities to embed them sustainably. Together, these perspectives support a dynamic and multi-level analysis of how interoperability, transparency, and digital sovereignty can be institutionalised through FAIR–Blockchain integration in public sector enterprise systems.
The FAIR data principles—Findable, Accessible, Interoperable, and Reusable—were initially formulated
to guide the management and stewardship of scientific research data [
Within the context of public sector and government data systems, FAIR adoption has been increasingly recognised as a prerequisite for AI readiness and data-driven innovation and transformation [28, 29]. The results from prior empirical studies demonstrate that FAIR-aligned data infrastructures enable eficient data integration and reuse across administrative domains, thereby enhancing the transparency, accountability, and reusability of public data resources [30]. The rationale is that when embedded into enterprise architectures, FAIR principles help establish common vocabularies, shared ontologies, and open standards that promote interoperability between disparate systems, improving both data quality and the traceability of algorithmic decisions [31].
However, despite their growing relevance, the findings of prior studies indicate that the
implementation of FAIR principles in the public sector remains uneven and fraught with institutional and
technical challenges [
Blockchain technology provides a distributed, tamper-evident ledger that enables secure, transparent,
and auditable record-keeping across organisational boundaries[35]. By design, blockchain systems
ensure that transactions are recorded chronologically and cryptographically verified, reducing the need
for central intermediaries and enhancing the integrity of shared information [36]. The application of
blockchain technology in public administration has been a topic of discussion among researchers and
practitioners [37]. Most recently, blockchain applications have become a reality in domains such as
identity management, public procurement, land registration, and data provenance, where transparency
and accountability are essential [
From a governance perspective, blockchain ofers mechanisms that can reinforce compliance,
traceability, and accountability across complex data ecosystems [
However, it is worth noting that significant challenges need to be tackled before blockchain can be widely institutionalised within public-sector ecosystems [37]. Technical limitations such as scalability, interoperability, and energy eficiency continue to constrain adoption [ 41]. Moreover, issues of legal interoperability, data protection compliance (e.g., GDPR), and governance accountability pose nontrivial policy challenges [42, 43]. To address these concerns, public organisations must adopt a sociotechnical approach that integrates blockchain deployment with regulatory adaptation, ethical oversight, and institutional learning. Within this study, blockchain is therefore conceptualised not solely as a technological artefact but as a governance infrastructure—one that operationalises transparency, accountability, and digital sovereignty within FAIR-aligned, AI-enabled enterprise systems.
Recent research highlights the growing convergence between the FAIR data principles and blockchain technologies as complementary approaches to achieving trustworthy, interoperable, and transparent data ecosystems [44, 45]. The FAIR principles provide a framework for ensuring that data is findable, accessible, interoperable, and reusable, while blockchain ofers distributed and immutable infrastructures that enhance data integrity, provenance, and verifiability [ 46]. When integrated, these two paradigms can mutually reinforce each other, bridging the gap between data management best practices and decentralised trust mechanisms.
From a technical perspective, blockchain can strengthen FAIR implementation by embedding
provenance metadata directly within distributed ledgers, thereby securing the authenticity and lineage of
datasets [47]. For instance, smart contracts enable automated access control and consent management,
ensuring that FAIR principles such as accessibility and reusability are operationalised in a verifiable and
auditable manner. Immutable ledger entries, on the other hand, support reproducibility and
accountability in AI pipelines by maintaining transparent records of model training data, algorithmic parameters,
and validation processes. Prior studies suggest that such capabilities are increasingly recognised as
essential for ethical AI governance and compliance with emerging regulatory frameworks such as the
EU AI Act [
In summary, FAIR principles provide the semantic and procedural foundations for efective data
management, while blockchain ofers the infrastructural assurances of integrity, provenance, and
sovereignty. When combined within enterprise modelling, these mechanisms enable governments to
design AI systems that are not only eficient and interoperable but also ethically robust and publicly
verifiable. Thus, the FAIR–Blockchain integration ofers a pathway towards achieving sustainable,
sovereign, and trustworthy AI ecosystems in the public sector.
Digital transformation in the public sector extends far beyond the adoption of new technologies; it
entails the comprehensive reconfiguration of organisational structures, service delivery processes,
and policy frameworks [
In this study, enterprise modelling is employed as a conceptual and analytical framework to integrate the Technology–Organisation–Environment (TOE) and Dynamic Capabilities (DC) perspectives, enabling the systematic representation of how FAIR and blockchain mechanisms can be institutionalised within AI-driven public sector transformation. Embedding these principles into enterprise models allows governments to represent not only organisational and technical systems but also the normative and ethical infrastructures that underpin trustworthy digital transformation.
This study adopts a qualitative, interpretivist research design to examine how the FAIR data principles and blockchain-related mechanisms are shaping AI adoption within public organisations. A qualitative approach is particularly appropriate for investigating emerging socio-technical phenomena where context, perception, and institutional dynamics are central to understanding complex processes [55]. Interpretivism assumes that social reality is constructed through the meanings and interactions of participants, making it suitable for studies seeking to capture the lived experiences and sense-making of public oficials involved in digital transformation [56].
The data collection is conducted through semi-structured interviews with senior oficials, data managers, and digital transformation oficers from multiple ministries and government agencies across Kenya. Participants were selected based on their involvement in national AI readiness, data governance, and policy coordination initiatives. Participants represented a range of public organisations, including ministries responsible for Information and Communication Technology (ICT), finance, planning, and regulatory oversight. Each interview lasted between 45 and 60 minutes and was conducted via Zoom. The interview guide covered topics including data management practices, AI strategy implementation, governance frameworks, and interoperability challenges. The cross-institutional focus enabled comparative insights into how technological, organisational, and environmental factors influence FAIR and blockchain integration within AI-related programmes. This design aligns with established qualitative methodologies in digital government and information systems research, which emphasise depth of insight over statistical generalisation [57].
The data analysis followed a thematic coding approach guided by the TOE framework and the Dynamic DC theory. This dual-theoretical lens provided a structured means of examining both the contextual determinants of adoption and the adaptive processes of organisational learning [21, 19]. The analysis focused specifically on segments related to FAIR data management and blockchain-aligned mechanisms such as data provenance, auditability, transparency, and digital sovereignty. A hybrid deductive–inductive coding strategy was employed. Deductive codes were derived from established TOE–DC constructs, while inductive codes emerged from the empirical data to capture novel insights. The final coding matrix (see Table 1 in the supplementary material) consisted of 14 major themes distributed across the technological, organisational, and environmental contexts. Each theme was subsequently mapped to corresponding dynamic capabilities—sensing, seizing, and reconfiguring—to elucidate how public organisations identify, mobilise, and adapt resources in response to governance challenges. Short, representative quotations were retained to preserve authenticity and contextual richness.
The analysis of interview data generated insights into how public organisations are engaging with FAIR data management and blockchain-related mechanisms as integral components of their wider AI adoption and digital transformation initiatives. Using the TOE framework as an interpretive lens, three core dimensions emerged, each reflecting the contextual forces shaping digital governance. Within each dimension, respondents described practices and constraints corresponding to the Dynamic Capabilities of sensing, seizing, and reconfiguring. View the thematic coding framework here.
Across all participating agencies, respondents acknowledged steady progress in data digitisation and improvements in data quality, but they also highlighted persistent fragmentation between systems and departments. One participant observed that “data availability is improving . . . but integration remains challenging due to diferent systems and standards across agencies.” This fragmentation limits the realisation of the FAIR principles—particularly interoperability and reusability—which are essential for developing comprehensive AI datasets and models. Another oficial commented that “data quality for our AI applications is generally good, but challenges remain in accessing comprehensive data across all government agencies.” While individual institutions have modernised their local data infrastructures, cross-agency integration remains weak, hindering the development of national-scale AI capabilities and collaborative analytics.
In response, several ministries have begun implementing the National Data Management Framework (NDMF), which introduces governance standards, metadata stewardship roles, and inter-agency coordination mechanisms. These eforts represent organisational seizing capabilities—concrete attempts to operationalise FAIR accessibility and interoperability through structured governance and dedicated stewardship functions. Metadata stewards were identified as pivotal actors in enforcing consistency and quality, translating abstract FAIR principles into daily data practices and institutional norms. However, legacy infrastructures remain a major obstacle. Respondents described incompatibilities between outdated databases and new interoperability standards, as well as limited automation in data cataloguing. These dificulties indicate the need for systematic reconfiguration involving both technical modernisation (e.g., adoption of APIs, deployment of metadata registries) and organisational adaptation (e.g., redefining custodianship roles and harmonising workflows).
Overall, the findings reveal incremental but uneven FAIR maturity across public organisations— notable advances in governance structures and stewardship capacity, yet incomplete interoperability between data ecosystems. The technological layer is evolving towards more structured, FAIR-aligned infrastructures, but the achievement of seamless, cross-ministerial data reusability remains a continuing challenge requiring sustained investment and institutional coordination. Within the organisational context, respondents emphasised that privacy and accountability are no longer peripheral concerns but central design imperatives in the development of AI systems. One senior oficial explained that “stringent security and privacy requirements limit AI design options, alongside the need for explainable AI systems that can justify compliance decisions.” These privacy-by-design and explainability expectations have transitioned from aspirational policy goals into enforceable architectural principles. Thus, the responses indicate that agencies are embedding transparency and traceability into data pipelines, ensuring that every stage of data collection, processing, and model inference is logged and reviewable. These practices align with FAIR’s emphasis on documentation, provenance, and traceability. Respondents also highlighted the potential of blockchain to complement such mechanisms through immutable ledgers and tamper-evident provenance tracking, ofering verifiable assurance of compliance and accountability.
A second theme within this dimension is related to organisational sovereignty. A regulatory manager noted, “External vendors assist us, but we maintain strict data sovereignty and security requirements.” This statement reflects a deliberate strategy to preserve control over data assets and to avoid vendor lock-in—a stance that resonates with blockchain’s principles of decentralised trust and distributed control. Sovereignty thus functions simultaneously as a protective mechanism and a strategic enabler, allowing ministries to collaborate across boundaries while retaining autonomy over their data and infrastructure.
Respondents also stressed the enduring importance of human oversight in regulatory AI contexts. As one participant remarked, “We must maintain strong human accountability mechanisms.” This awareness underscores a cautious approach to automation in public decision-making, ensuring that algorithmic recommendations remain subject to human judgment, particularly when rights and welfare are at stake. Collectively, these perspectives point to ongoing organisational reconfiguration, where accountability, sovereignty, and transparency are being embedded into internal structures and processes rather than imposed through external compliance mandates. The organisational layer, therefore, reflects both the seizing of new governance capabilities and the reconfiguration of legacy accountability systems to align with emerging digital ethics standards. At the environmental level, respondents perceived regulatory frameworks as both constraints and catalysts for digital transformation. One oficial observed that “the Data Protection Act emphasises privacy by design and algorithmic accountability.” Such legislation imposes external compliance pressures but simultaneously encourages organisations to adopt FAIR-aligned and auditable systems, thereby reinforcing ethical design and traceability norms across government.
Other respondents underscored the importance of harmonised AI governance frameworks and technical standards to promote consistency among ministries and agencies. Fragmented standards were said to inflate compliance costs and impede interoperability, whereas harmonisation was viewed as a mechanism for reducing redundancy and promoting cross-agency data exchange. By establishing shared reference architectures and legal clarity, harmonised frameworks create enabling conditions under which FAIR and blockchain mechanisms can scale nationally.
A further recurring theme was the importance of cross-border collaboration and regional data governance. As one participant explained, “...improved cross-border data-sharing agreements would enable more comprehensive AI applications across the public sector.” This sentiment reflects a growing awareness that AI-enabled public services depend increasingly on regional data ecosystems, requiring not only interoperability but also shared accountability mechanisms. In this regard, respondents viewed blockchain’s verifiable credentials and distributed ledgers as promising tools for cross-jurisdictional compliance, allowing machine-verifiable trust between governments and partner institutions.
Collectively, the findings related to the environmental context reveal a dynamic interplay between regulation and innovation. In other words, legal frameworks and policy directives do not merely constrain organisational behaviour. They also drive the institutionalisation of FAIR-aligned data stewardship and stimulate experimentation with blockchain-enabled transparency. The environmental layer thus exemplifies the sensing capability—where organisations interpret and respond to evolving legal, ethical, and geopolitical expectations that shape the governance of AI-driven transformation.
In summary, across the technological, organisational, and environmental dimensions, the findings demonstrate how FAIR data management and blockchain mechanisms are becoming embedded within the evolving digital architectures of public administration. These practices exemplify how sensing, seizing, and reconfiguring capabilities fuse to support ethical, interoperable, and sovereign AI ecosystems. While progress remains uneven, the convergence of FAIR and blockchain principles is fostering a new mode of digital governance—one characterised by verifiable transparency, institutional accountability, and adaptive learning across the public sector.
The analysis of our interview data reveals how FAIR data principles and blockchain-related mechanisms are being institutionalised within public sector digital transformation, and how these practices resonate with or extend prior research. The findings also suggest that public organisations are progressively embedding the FAIR and blockchain paradigms as dual enablers of transparency, interoperability, and sovereignty of an AI-driven digital transformation.
The results reveal that technological progress in public organisations is characterised by incremental
but uneven FAIR maturity. Respondents reported improvements in data quality, metadata management,
and the implementation of national data frameworks, such as the NDMF; however, they also emphasised
enduring fragmentation and legacy infrastructure challenges. These findings are consistent with prior
studies, which demonstrate that technical interoperability remains one of the most persistent barriers
to digital transformation in the public sector [
FAIR-aligned infrastructures, particularly metadata catalogues, persistent identifiers, and open APIs,
were recognised as critical components for ensuring data findability and accessibility. Similar
observations are reported by researchers [
Blockchain emerged as a complementary technological enabler, particularly in terms of data
provenance, auditability, and immutability. Respondents’ discussions of blockchain-based verification and
logging mechanisms align with previous findings [
At the organisational level, the findings indicate that privacy, accountability, and digital sovereignty are
becoming central organising logics for AI governance. This mirrors global trends in digital government
research that emphasise transparency, explainability, and citizen trust as the ethical foundations of
algorithmic decision-making [
Blockchain’s perceived role as an accountability infrastructure also parallels findings in the extant
literature. For example,[
The emphasis on data sovereignty and the avoidance of vendor lock-in reflect a distinctive strategic
orientation in the public sector. Respondents’ insistence on retaining control over infrastructure and
data aligns with prior studies [
The continued emphasis on human oversight underscores the hybrid nature of AI-enabled public administration—one that combines automation with institutional accountability. Similar insights have been reflected in the findings of previous studies [ 54], where the authors note that sustaining public trust requires embedding human judgment into digital systems. This study contributes to the discourse by demonstrating how human accountability mechanisms co-evolve with technological innovations, thereby reinforcing the ethical underpinnings of algorithmic governance.
The environmental findings demonstrate that regulatory frameworks act as both external constraints and
dynamic catalysts for innovation. The role of data protection and algorithmic accountability legislation as
stimuli for FAIR adoption corroborates prior research emphasising that regulation can drive responsible
digital transformation [
Harmonised standards and cross-ministerial coordination were identified as essential for scaling FAIR and blockchain mechanisms in the public sector. This observation resonates with prior studies [24, 19], which found that inter-organisational alignment is a decisive factor in technology adoption within the public sector. The implementation of shared governance frameworks, such as the NDMF, illustrates the emergence of what authors, for instance [26], term dynamic alignment processes—structures that enable learning and adaptation across institutional boundaries. By formalising interoperability and metadata standards, these frameworks enable the translation of FAIR principles into enforceable policy instruments.
The emphasis on cross-border data-sharing and regional digital cooperation expands the
environmental perspective to include geopolitical and transnational considerations. Respondents’ calls for verifiable
cross-jurisdictional audit mechanisms reflect the literature on blockchain-enabled data sovereignty
[
Taken together, the findings provide empirical evidence that FAIR and blockchain mechanisms are being institutionalised through multi-level interactions across technological, organisational, and environmental contexts. This confirms the analytical value of integrating the TOE and DC perspectives in explaining AI-driven digital transformation within complex, multi-actor public systems. The TOE framework captures the structural determinants—technological infrastructures, organisational capacities, and environmental pressures—while DC theory illuminates the dynamic processes by which organisations sense opportunities, seize resources, and reconfigure systems for long-term sustainability [21, 27].
In technological terms, sensing and reconfiguring capabilities were evident in the recognition of interoperability challenges and the subsequent adoption of FAIR infrastructures. Within organisations, seizing and reconfiguring manifest through the creation of data stewardship roles and accountability structures that operationalise FAIR and blockchain principles. In terms of environment, sensing capabilities were displayed in the interpretation of regulatory signals and regional cooperation incentives that drive innovation. This dynamic interplay aligns with a study [26], which describes digital transformation as a process of continuous organisational renewal enabled by learning, coordination, and strategic adaptation.
By embedding FAIR and blockchain within this dual-theoretical frame, the study advances an understanding of how AI-driven digital transformation in the public sector is both constrained and enabled by institutional context. It demonstrates that technical interoperability, data sovereignty, and ethical governance are not isolated outcomes but interdependent capabilities that evolve collectively through sensing, seizing, and reconfiguring processes. In doing so, it contributes to the literature on enterprise modelling by ofering an empirically grounded model of how FAIR and blockchain can be represented as modular governance components within public data architectures [51, 52, 53].
Building upon the insights above, the study synthesises the empirical findings and theoretical perspectives into a conceptual enterprise model. This model integrates the TOE dimensions with dynamic capability processes to explain how FAIR and blockchain mechanisms can be institutionalised within AI-driven public sector transformation. The following section presents this model, illustrating its structural and functional components.
At its core, the model conceptualises AI-driven digital transformation as a cyclical and evolutionary process. The technological context encompasses infrastructures, standards, and data architectures that support FAIR-aligned interoperability and blockchain-enabled provenance. The organisational context captures governance structures, stewardship roles, and accountability mechanisms that operationalise these principles within public institutions. The environmental context encompasses the regulatory, institutional, and socio-political factors that shape pressures and incentives for the adoption of responsible AI.
Dynamic Capabilities—sensing, seizing, and reconfiguring—operate across these layers as the
mechanisms through which organisations interpret external stimuli, mobilise resources, and continuously
adapt infrastructures and governance routines [21, 26]. FAIR principles guide data stewardship and
interoperability, while blockchain provides technological assurances of provenance, auditability, and
digital sovereignty [
The framework, therefore, contributes a dynamic enterprise modelling approach, depicting how technical, organisational, and environmental forces interact with capability-building processes to embed trust and accountability into the design of public information systems. It also provides a diagnostic structure for assessing FAIR and blockchain maturity across government agencies, thereby linking conceptual understanding with practical implementation.
This study has explored how the FAIR data principles and blockchain-related practices materialise within the technological, organisational, and environmental contexts of public sector AI adoption. By situating these developments within the TOE and DC frameworks, the research demonstrates that the transition toward responsible and sovereign AI is not merely a technical undertaking but a multidimensional process of institutional adaptation and learning.
From a theoretical perspective, the study bridges previously distinct domains—data governance, digital sovereignty, and dynamic capability theory—through a unified conceptual lens. It demonstrates that FAIR principles and blockchain mechanisms operate as complementary governance instruments— FAIR ensures semantic and procedural transparency, while blockchain strengthens integrity, provenance, and sovereignty. Within the TOE–DC framework, these mechanisms illuminate how public organisations transform structural constraints into dynamic opportunities for innovation. The findings extend dynamic capability theory by evidencing how sensing, seizing, and reconfiguring processes underpin not only strategic adaptation but also ethical and institutional renewal. Furthermore, by situating FAIR and blockchain within enterprise modelling, the study contributes to digital governance scholarship by conceptualising how technical and organisational layers interact to enable transparent and accountable data ecosystems.
From a practical standpoint, the research provides actionable insights for policymakers, digital strategists, and public sector leaders seeking to embed responsible AI practices. It proposes that adopting FAIR and blockchain-aligned systems requires not only technological investment but also organisational capability development and regulatory alignment. The enterprise modelling framework introduced here ofers a practical tool for visualising interdependencies between data standards, governance roles, and technological infrastructures. Through such modelling, public organisations can anticipate governance bottlenecks, strengthen accountability structures, and enhance inter-agency collaboration. In doing so, the framework enables decision-makers to operationalise principles of transparency, interoperability, and sovereignty in concrete organisational settings.
The study also identifies key dynamic capabilities that enable public organisations to sense, seize, and reconfigure resources in response to evolving digital ecosystems. The capacity to sense emerging technologies and ethical imperatives allows organisations to interpret environmental signals efectively. Seizing involves mobilising investments and partnerships that align with FAIR and blockchain principles, while reconfiguring reflects the ongoing transformation of institutional routines, data infrastructures, and governance norms. Together, these processes highlight that sustainable AI adoption is as much about cultivating adaptive and learning-oriented institutions as it is about deploying advanced technologies.
In conclusion, this research provides a holistic framework for understanding and guiding FAIRand blockchain-aligned AI adoption in the public sector. It demonstrates that transparent, sovereign, and ethically grounded digital transformation depends on the interplay between technological infrastructures, organisational capabilities, and institutional values. By combining theoretical synthesis with practical modelling tools, the study contributes to both academic inquiry and public governance practice—ofering a roadmap for designing trustworthy, resilient, and future-ready public sector AI systems.
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