Theorizing the evolution of public data ecosystems: An empirically grounded multi-generational model and future research agenda
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022616" target="_blank" >RIV/62690094:18450/25:50022616 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0740624X25000565" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0740624X25000565</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.giq.2025.102062" target="_blank" >10.1016/j.giq.2025.102062</a>
Alternative languages
Result language
angličtina
Original language name
Theorizing the evolution of public data ecosystems: An empirically grounded multi-generational model and future research agenda
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50803 - Information science (social aspects)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
GOVERNMENT INFORMATION QUARTERLY
ISSN
0740-624X
e-ISSN
1872-9517
Volume of the periodical
42
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
"Article Number: 102062"
UT code for WoS article
001555101400001
EID of the result in the Scopus database
2-s2.0-105013394800