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Classification of Open Government Data Solutions' Help: A Novel Taxonomy and Cluster Analysis
University of Borås, Faculty of Librarianship, Information, Education and IT.ORCID iD: 0000-0003-4740-1242
Namur Digital Institute, University of Namur, Namur, Belgium.ORCID iD: 0000-0003-3936-0532
2023 (English)In: Electronic Government. EGOV 2023.: Lecture Notes in Computer Science, vol 14130, Cham: Springer, 2023, p. 230-245Conference paper, Published paper (Refereed)
Abstract [en]

Open Government Data (OGD) pose that public organisations should freely share data for anyone to reuse without restrictions. However, the rawness of this data proves to be a challenge for data or information seekers. OGD-based solutions, such as interactive maps and dashboards, could help seekers overcome this difficulty and use OGD to satisfy needs, helping them to work effectively, solve problems, or pursue hobbies. However, there are several challenges that need to be considered when designing solutions, such as seekers wanting to solve problems rather than consuming information and aiming for quick wins over quality. Previous research has classified OGD solutions, focusing on general concepts. The next step is to reveal helpful patterns in OGD solutions, helping seekers. This paper presents a taxonomy with 24 criteria to classify these patterns. It was tested on 40 OGD solutions, and the resulting classifications were grouped in a cluster analysis, identifying 16 key criteria and 6 clusters. The clusters are (1) simple-personalised, (2) proactive multi-visual, (3) lightly-facilitated exploration, (4) facilitated data-management, (5) facilitated information exploration, and (6) horizon solutions. One unexpected finding is that helpful patterns do not cluster following themes, types, or purposes of solutions. Another finding is that the importance of key criteria varies between the clusters.

Place, publisher, year, edition, pages
Cham: Springer, 2023. p. 230-245
Series
Lecture Notes in Computer Science ; 14130
Keywords [en]
Open Government Data, solution, taxonomy, classification, cluster analysis, information behaviour
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hb:diva-31343DOI: 10.1007/978-3-031-41138-0_15Scopus ID: 2-s2.0-85172033461ISBN: 978-3-031-41137-3 (print)OAI: oai:DiVA.org:hb-31343DiVA, id: diva2:1828450
Conference
EGOV 2023
Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-09-11Bibliographically approved

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Crusoe, Jonathan

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CiteExportLink to record
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