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    Pathologies of Open Data Platforms and Desired Transparency-Related Affordances for Future Platforms


    Ojo, Adegboyega, Porwol, Lukasz, Waqar, Mohammad, Osagie, Edobor, Stasiewicz, Arkadiusz, Zeleti, Fatemeh Ahmadi, Hogan, Michael, Harney, Owen and Riordan, Pauline (2016) Pathologies of Open Data Platforms and Desired Transparency-Related Affordances for Future Platforms. In: Proceedings of the 17th International Digital Government Research Conference on Digital Government Research. ACM Digital Library, pp. 538-539. ISBN 978-1-4503-4339-8

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    Abstract

    The increasing volumes of datasets published on open data platforms have had little impact on the public use of open data and perceived transparency of respective governments. At the same time, the innovation potentials of these datasets are far from realized due to many factors including poor quality of datasets. While past studies have attempted to catalog barriers to open data exploitation and use; few studies have focused on the role of the available open data platforms in tackling this problem. In addressing this gap, this research work examines the problems (or pathologies) associated with the use of current generation of open data platforms and perspectives of stakeholders on desirable features and affordances. Results from our analysis of existing platforms and stakeholders’ views show several limiting factors on available platforms. Findings also provide insights into three categories of platform affordances that could spur greater use of open data published on these platforms and enhanced transparency of respective governments.
    Item Type: Book Section
    Keywords: Open Data Platforms; Open data and Transparency; Platform Affordances; Transparency Qualities; Future Open Data Platform;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15813
    Identification Number: 10.1145/2912160.2912237
    Depositing User: Adegboyega Ojo
    Date Deposited: 12 Apr 2022 13:44
    Publisher: ACM Digital Library
    Refereed: Yes
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/15813
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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