4. Theoretical Contribution
This article advances the literature on innovation economics, institutional change, and CE by reconceptualizing DPPs as institutional innovation infrastructures that restructure the informational foundations of markets. While existing scholarship has examined CBMs, data governance, and supply chain transparency largely in isolation, this study integrates these strands through the lens of transaction cost economics and information asymmetry theory.
First, the paper extends transaction cost theory into the domain of digital data infrastructures. Traditional transaction cost economics has focused on governance forms—markets, hierarchies, and hybrids—as mechanisms for mitigating opportunism under conditions of incomplete information. However, it has paid comparatively limited attention to how standardized digital data architectures themselves function as coordination mechanisms. By conceptualizing DPPs as institutionalized lifecycle data infrastructures, this study demonstrates that data governance structures can operate as transaction-cost-reducing institutions. Standardized, interoperable lifecycle data reduce search, verification, monitoring, and enforcement costs across product ecosystems, thereby altering the feasible set of contractual arrangements available to market actors.
Second, the article bridges information asymmetry theory and innovation systems research. The economics of asymmetric information has long shown that hidden characteristics and hidden actions distort investment incentives and lead to adverse selection and moral hazard. Meanwhile, innovation systems literature emphasizes the importance of governance architectures and standards in shaping technological trajectories. Yet these literatures have rarely been integrated. This study connects them by showing that lifecycle data infrastructures influence incentive compatibility at the system level. By transforming sustainability attributes from credence characteristics into verifiable signals, DPPs restructure how quality differentiation, risk assessment, and residual value estimation occur within markets. In doing so, they affect not only individual transactions but the stability and scalability of emerging circular ecosystems.
Third, the paper reframes data governance as institutional innovation rather than merely managerial capability. Existing research typically treats data governance as an organizational function that enhances digital performance, AI quality, or regulatory compliance. In contrast, this study conceptualizes data governance as a systemic institutional architecture that defines data standards, interoperability protocols, disclosure rules, and verification procedures. These design choices shape the distribution of information rents, the allocation of risk, and the coordination costs of multi-actor exchange. By embedding data governance within institutional economics, the article elevates digital infrastructures to the status of market-shaping institutions.
Fourth, the study contributes to CE scholarship by shifting the analytical focus from firm-level adoption barriers to market-level formation conditions. CBMs such as product-as-a-service, authenticated resale, remanufacturing, and residual-value financing depend on credible information regarding durability, reparability, material composition, and environmental performance. Without institutionalized mechanisms for structuring and verifying such information, these models remain confined to niche applications. By identifying four interrelated mechanisms—asymmetry reduction, transaction cost mitigation, capability amplification, and circular market formation—the article provides a systemic explanation of how DPP infrastructures may enable the scaling of circular markets.
Finally, the paper introduces the concept of data governance infrastructures as coordination technologies within innovation systems. Rather than viewing regulation solely as a constraint or compliance requirement, this framework highlights how regulatory-technical architectures can create enabling infrastructures that reshape competitive dynamics and industrial transformation. In this respect, DPPs are interpreted not merely as transparency tools, but as institutional technologies capable of altering incentive structures, lowering systemic uncertainty, and stabilizing expectations in sustainability-oriented markets.
Taken together, these contributions position DPPs at the intersection of innovation economics, institutional theory, and digital governance. By integrating transaction cost reasoning, information asymmetry theory, and innovation systems perspectives, the study offers a novel conceptual framework for understanding how standardized lifecycle data infrastructures can catalyse CBM innovation and industrial restructuring. This reconceptualization advances both theoretical understanding and policy discourse by highlighting the central role of data governance in shaping the economic architecture of sustainable markets.