Theorizing the evolution of public data ecosystems: An empirically grounded multi-generational model and future research agenda

Abstract

Public Data Ecosystems (PDEs) are increasingly viewed as dynamic socio-technical systems shaped by evolving interactions among actors, infrastructures, data types, and governance mechanisms. Yet, most existing research remains static or domain-specific, offering limited insight into the temporal and co-evolutionary dynamics of PDEs. To address this gap, this study adopts a theory-building approach to examine how PDEs evolve over time and to define a forward-looking research agenda. Drawing on empirical insights from five European countries, we investigate how key meta-characteristics and attributes of PDEs manifest, shift, and co-evolve in practice. Leveraging a recent multi-generational model as an analytical lens, we assess its alignment with real-world trajectories, identify overlooked and emerging features, and revise its structure accordingly. In doing so, we theorize PDE evolution as a multi-generational process shaped by institutional, technological, and contextual dynamics. This results in a refined model that better captures the complexity and diversity of PDE development, particularly considering emerging technologies such as artificial intelligence (AI), generative AI, and large language models (LLMs) shaping the forward-looking PDE generation. Building on these insights, we propose a future research agenda comprising 17 directions organized around revised meta-characteristics. This agenda supports the development of sustainable, resilient, and intelligent PDEs. The study contributes to the theorization of PDEs by offering an empirically grounded, temporally aware, and actionable roadmap for future research and policy design.