Understanding the development of public data ecosystems

From a conceptual model to a six-generation model of the evolution of public data ecosystems

Review (2024)
Author(s)

Martin Lnenicka (University of Hradec Kralov, University of Pardubice)

Anastasija Nikiforova (University of Tartu)

Mariusz Luterek (University of Warsaw)

Petar Milic (University of Pristina)

Daniel Rudmark (Swedish National Road and Transport Research Institute, University of Gothenburg)

Sebastian Neumaier (St. Pölten University of Applied Sciences)

Karlo Kević (University of Zagreb)

Anneke Zuiderwijk (TU Delft - Information and Communication Technology)

Manuel Pedro Rodríguez Bolívar (Universidad de Granada)

DOI related publication
https://doi.org/10.1016/j.tele.2024.102190 Final published version
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Publication Year
2024
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Journal title
Telematics and Informatics
Volume number
94
Article number
102190
Downloads counter
305
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Abstract

There is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems. The aim of the study is twofold: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems. Moreover, this study develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations that differ in terms of (a) components and relationships, (b) stakeholders, (c) actors and their roles, (d) data types, (e) processes and activities, and (f) data lifecycle phases. Finally, three avenues for a future research agenda are proposed. This study is relevant for practitioners suggesting what elements of public data ecosystems have the most potential to generate value and should thus be part of public data ecosystems. As a scientific contribution, this study integrates conceptual knowledge about the elements of public data ecosystems, the evolution of these ecosystems, defines a future research agenda, and thereby moves towards defining public data ecosystems of the new generation.

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