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  4. Building open government data platform ecosystems: A dynamic development approach that engages users from the start
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Building open government data platform ecosystems: A dynamic development approach that engages users from the start

Journal
Government Information Quarterly
ISSN
0740-624X
ISSN-Digital
1872-9517
Type
journal article
Date Issued
2023
Author(s)
Hein, Andreas  
;
Engert, Martin
;
Ryu, Sunghan
;
Schaffer, Norman
;
Hermes, Sebastian
;
Krcmar, Helmut
DOI
10.1016/j.giq.2023.101878
Abstract
Open government data (OGD) platform ecosystems hold immense potential for promoting transparency, civic engagement, economic growth, and improved governmental offerings. The prevailing strategy to building OGD platform ecosystems follows a sequential approach where the OGD platform is built first and the ecosystem is built second, resulting in low engagement. In this paper, we derive insights into an alternative approach to developing OGD platform ecosystems from TourismData, a state-owned tourism initiative in Germany. We report on the phases between 2018 and 2022 and derive four dynamic and incremental phases from which we derive three learnings: context specificity, continuous adaptation, and organic expansion. Our findings have theoretical and practical implications for developing high-engagement OGD platform ecosystems that include and engage ecosystem actors from the start and, hence, take advantage of the generative potential of OGD. This approach illustrates the importance of developing OGD platform ecosystems with high contextual relevance to ensure that data can be used to enable meaningful interactions between ecosystem actors and promote continuous adaptation and expansion.
Language
English (United States)
HSG Classification
contribution to scientific community
Refereed
Yes
Volume
40
Number
4
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/119421
Subject(s)

information managemen...

Division(s)

IWI - Institute of In...

File(s)
Thumbnail Image
Name

Hein et al._2023 GIQ.pdf

Size

2.85 MB

Format

Adobe PDF

Checksum (MD5)

b65890b1d92911ce59b361975c96302f

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