P
Patrick Mikalef
Researcher at Norwegian University of Science and Technology
Publications - 137
Citations - 5193
Patrick Mikalef is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Big data & Dynamic capabilities. The author has an hindex of 24, co-authored 115 publications receiving 2500 citations. Previous affiliations of Patrick Mikalef include SINTEF & Ionian University.
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Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA
TL;DR: It is argued that the impact of IT-enabled dynamic capabilities on competitive performance is mediated by organizational agility, which in sequence enhance competitive performance.
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Big data analytics capabilities: a systematic literature review and research agenda
TL;DR: 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 and identifies gaps in the extant literature and proposes six future research themes.
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Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities
TL;DR: Results show that a strong BDAC can help firms build a competitive advantage, and suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how aBDAC can be leveraged to enable and support organizational capabilities.
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Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment
TL;DR: Examination of the indirect relationship between a big data analytics capability (BDAC) and two types of innovation capabilities finds that dynamic capabilities fully mediate the effect on both incremental and radical innovation capabilities.
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Big data analytics and firm performance: Findings from a mixed-method approach
TL;DR: This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments to show that depending on the context, big data Analytics resources differ in significance when considering performance gains.