Achieving superior organizational performance via big data predictive analytics: A dynamic capability view
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2020-10Keyword
Big data predictive analysisBDPA
Market performance
Operational performance
Financial performance
Dynamic capability view theory
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© 2020 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.Peer-Reviewed
YesOpen Access status
openAccess
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The art of unwinding voluminous data expects the expertise in analyzing meaningful decisions out of the acquired information. To encounter new age challenges, practitioners are trying hard to shatter the constraints and work edge-to-edge to achieve higher performance (Market, Financial and Operational performance). It is evident that organizations desire to exploit maximum of their injected resources, but often fail to reap their actual potential. Developing resource-based capabilities stands out to be the most concerned aspect for the firms in recent times, and the same is studied by the previous scholars. In the dearth of literature, it is challenging to find out evidence which marks up the effect of strategic resources in the development of dynamic organizational capability. This study is a two-fold attempt to examine the relationship between organizational capabilities, i.e. big data predictive analytics while achieving superior organizational performance; also, examining the effect of control variables on superior organizational of performance. We tested our research hypotheses using cross-sectional data of 209 responses collected using pre-tested single-informant questionnaire. The results underpin criticality human factor while developing analytical capabilities dynamic in nature in the process of achieving superior performance.Version
Accepted manuscriptCitation
Gupta S, Drave VA, Dwivedi YK et al (2020) Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management. 90: 581-592.Link to Version of Record
https://doi.org/10.1016/j.indmarman.2019.11.009Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.indmarman.2019.11.009