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    Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

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    Publication date
    2016
    Author
    Dwivedi, Y.K.
    Janssen, M.
    Slade, E.L.
    Rana, Nripendra P.
    Weerakkody, Vishanth J.P.
    Millard, J.
    Hidders, J.
    Snijders, D.
    Keyword
    Big data; Open data; Linked data; Innovation; Interpretive structural modelling
    Rights
    © 2016 The Authors. This article is published open access at Springerlink.com. Available at Springer via http://dx.doi.org/10.1007/s10796-016-9675-5
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    Innovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions.
    URI
    http://hdl.handle.net/10454/11680
    Version
    Accepted Manuscript
    Citation
    Dwivedi YK, Janssen M, Slade M, Rana NP, Weerakkody V, Millard J, Hidders J and Snijders D (2016) Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling. Information Systems Frontiers. 19(2): 197–212.
    Link to publisher’s version
    http://dx.doi.org/10.1007/s10796-016-9675-5
    Type
    Article
    Collections
    Management and Law Publications

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