2.3 Institutional Innovation, Data Governance and Economic Coordination
The literature on institutional innovation and governance in innovation systems has extensively examined how regulatory reforms, policy instruments, and organizational restructuring shape technological development and sustainability transitions. Institutional change is commonly framed as the redesign of formal rules, policy regimes, and coordination structures that enable or constrain innovation trajectories (Solis-Navarrete et al., 2023). Within sustainability transitions research, institutional innovation is frequently associated with the creation of governance mechanisms that support carbon neutrality pathways, green finance, and systemic technological upgrading (Pan & Jiang, 2025; An & Di, 2024). In this perspective, institutions are not passive background conditions but active enablers of transformation within innovation systems.
Similarly, the innovation systems literature emphasizes that governance architectures—comprising regulatory frameworks, intermediary organizations, and public–private coordination mechanisms—determine the diffusion and scaling of new technologies (Solis-Navarrete et al., 2023). Institutional change is thus interpreted as systemic restructuring aimed at improving coordination across heterogeneous actors. Recent studies highlight the role of intermediaries, stakeholder engagement, and organizational redesign in facilitating innovation adoption and sustainability-oriented transformation (Ouerghemmi et al., 2024). These analyses collectively position governance reform as a central mechanism in shaping innovation ecosystems.
However, while the institutional innovation literature acknowledges the importance of governance in enabling innovation systems, it rarely engages explicitly with the economic microfoundations of coordination. In particular, the relationship between data governance structures and core economic concepts—such as transaction costs, information asymmetry, adverse selection, and moral hazard—remains under-theorized. The absence of results linking “data governance” to “transaction costs” or “information asymmetry” in business and economics databases suggests a significant conceptual gap.
The emerging literature on data governance and innovation primarily frames data governance as a managerial or strategic capability. Recent studies examine how data governance improves data quality, enhances AI system performance, supports digital transformation, or strengthens corporate competitiveness (Weinbaum & Kamp, 2026; Liu et al., 2026; Gao et al., 2026). In this strand, data governance is understood as a mechanism for managing privacy, ensuring compliance, enabling data sharing, and fostering digital innovation. Public data openness and data marketization are similarly analyzed as drivers of firm growth and innovation outcomes (Liu et al., 2026; Gao et al., 2026). While these contributions demonstrate that data governance positively influences innovation performance, they largely treat governance as an internal organizational function rather than as an economic coordination institution.
This managerial framing overlooks a crucial dimension: data governance fundamentally reshapes the information structure of markets. From a transaction cost perspective, institutions exist to reduce uncertainty and enable exchange under conditions of incomplete information. Transaction cost economics demonstrates that coordination mechanisms—whether markets, hierarchies, or hybrid governance forms—emerge to mitigate opportunism and reduce the costs of search, verification, monitoring, and enforcement. Similarly, the economics of asymmetric information highlights how hidden information and hidden action distort investment incentives and contract efficiency (Ni et al., 2021; Zhou et al., 2012). Monitoring mechanisms, including auditing, can partially alleviate moral hazard but remain costly and incomplete (Nikoofal & Gümüş, 2020).
Despite these well-established theoretical insights, the data governance literature has rarely conceptualized governance architectures as transaction-cost-reducing institutions. Yet data governance systems define who can access data, under what conditions, how data are standardized, how information is verified, and how accountability is enforced. These design choices directly influence search costs, verification costs, and monitoring intensity. In effect, data governance determines whether information remains a private credence attribute or becomes a verifiable and tradable economic signal.
This theoretical blind spot becomes particularly salient in sustainability-oriented innovation systems. Sustainability attributes—such as carbon footprints, recyclability rates, or durability—are typically credence characteristics that are costly to observe and verify. Institutional innovation in sustainability transitions has focused largely on policy instruments and regulatory mandates (Pan & Jiang, 2025; An & Di, 2024), but has paid comparatively less attention to how standardized data infrastructures reshape economic coordination at the micro level. Without credible data governance mechanisms, sustainability claims remain vulnerable to adverse selection and greenwashing, undermining investment incentives and market formation.
Reframing data governance as an institutional innovation allows a deeper integration with innovation economics. Rather than viewing data governance as merely a digital management practice, it can be conceptualized as a governance architecture that restructures information asymmetries across innovation systems. By standardizing data formats, defining interoperability protocols, embedding verification mechanisms, and allocating informational rights, data governance infrastructures reduce uncertainty and enable new forms of contractual and market coordination.
This perspective suggests that data governance plays at least three economic roles within innovation systems:
Asymmetry Mitigation: By transforming private, fragmented information into standardized and accessible data, governance infrastructures reduce adverse selection and moral hazard.
Transaction Cost Reduction: Standardized data lower search, negotiation, monitoring, and enforcement costs, enabling more complex inter-organizational arrangements.
Market Formation Enablement: By converting credence attributes into verifiable signals, data governance enables the emergence of new markets—particularly for sustainability-linked products, services, and financial instruments.
The absence of explicit integration between data governance and transaction cost economics in the existing literature therefore represents a significant gap. Addressing this gap is particularly important in the context of circular economy transitions, where lifecycle coordination among producers, repairers, remanufacturers, recyclers, and financiers depends critically on credible and interoperable information flows.
Digital Product Passports (DPPs) can be understood as a sector-specific instantiation of data governance as institutional innovation. Rather than functioning solely as compliance databases, DPPs embed standardized lifecycle information within a regulatory and technical governance architecture. In doing so, they restructure the informational foundations of circular markets. By reducing asymmetries across lifecycle actors and lowering coordination costs, DPP infrastructures have the potential to reshape innovation incentives and enable the formation of scalable circular business models.
In this light, data governance should be conceptualized not merely as a managerial enabler of digital transformation but as a foundational economic institution within innovation systems. The following section builds on this reconceptualization to articulate the mechanisms through which Digital Product Passports may reshape innovation dynamics and circular market formation.