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Erschienen in: Information Systems and e-Business Management 3/2018

15.07.2017 | Original Article

Big data analytics capabilities: a systematic literature review and research agenda

verfasst von: Patrick Mikalef, Ilias O. Pappas, John Krogstie, Michail Giannakos

Erschienen in: Information Systems and e-Business Management | Ausgabe 3/2018

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Abstract

With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.

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Metadaten
Titel
Big data analytics capabilities: a systematic literature review and research agenda
verfasst von
Patrick Mikalef
Ilias O. Pappas
John Krogstie
Michail Giannakos
Publikationsdatum
15.07.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Information Systems and e-Business Management / Ausgabe 3/2018
Print ISSN: 1617-9846
Elektronische ISSN: 1617-9854
DOI
https://doi.org/10.1007/s10257-017-0362-y

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