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    Critical Factors for Dynamic Capabilities in Open Government Data Enabled Organizations


    Zeleti, Fatemeh Ahmadi and Ojo, Adegboyega (2016) Critical Factors for Dynamic Capabilities in Open Government Data Enabled Organizations. In: Proceedings of the 17th International Digital Government Research Conference on Digital Government Research. ACM Digital Library, pp. 86-96. ISBN 978-1-4503-4339-8

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    Abstract

    Open data (OD) is increasingly considered as a core resource for many organizations in the emerging data economy. Open data-driven organizations (ODDOs) like any other organizations must develop capabilities for competitiveness and agility in addition to processes for creating value from OD to survive. While questions about the extent to which OD could be used for competitive advantage has been raised in past studies, no previous study has investigated the salient factors for agility in a dynamic data ecosystem. This paper bridges the knowledge gap by developing an operationalization of the Dynamic Capability Theory for ODDOs. As a first step towards determining the critical factors for developing dynamic capabilities (DCs) in these organizations, we analyzed the information gathered from an expert interview on the saliency of the different aspects and stages of dynamic capability in developing the agility of an up-stream organization or OD supplier in the data ecosystem. Our findings suggest that critical factors for DCs differ for organizations in the upstream and downstream sectors, albeit some core elements are shared across sectors in data ecosystem.
    Item Type: Book Section
    Keywords: Open Data, Open Government Data; Dynamic Capabilities; Dynamic Capability Theory; Dynamic Capability Framework; Competitive Advantage of a Firm ;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15815
    Identification Number: 10.1145/2912160.2912164
    Depositing User: Adegboyega Ojo
    Date Deposited: 12 Apr 2022 13:47
    Publisher: ACM Digital Library
    Refereed: Yes
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/15815
    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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