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dc.contributor.authorAmankwah-Amoah, J.
dc.contributor.authorAdomako, Samuel
dc.date.accessioned2019-01-11T11:38:24Z
dc.date.available2019-01-11T11:38:24Z
dc.date.issued2019-02
dc.identifier.citationAmankwah-Amoah J and Adomako S (2019) Big Data Analytics and Business Failures in Data-Rich Environments: An Organizing Framework. Computers in Industry. 105: 204-212.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16746
dc.descriptionYesen_US
dc.description.abstractIn view of the burgeoning scholarly works on big data and big data analytical capabilities, there remains limited research on how different access to big data and different big data analytic capabilities possessed by firms can generate diverse conditions leading to business failure. To fill this gap in the existing literature, an integrated framework was developed that entailed two approaches to big data as an asset (i.e. threshold resource and distinctive resource) and two types of competences in big data analytics (i.e. threshold competence and distinctive/core competence). The analysis provides insights into how ordinary big data analytic capability and mere possession of big data are more likely to create conditions for business failure. The study extends the existing streams of research by shedding light on decisions and processes in facilitating or hampering firms’ ability to harness big data to mitigate the cause of business failures. The analysis led to the categorization of a number of fruitful avenues for research on data-driven approaches to business failure.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.compind.2018.12.015en_US
dc.rights© 2019 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.en_US
dc.subjectBig data analyticsen_US
dc.subjectTechnologyen_US
dc.subjectInnovation managementen_US
dc.subjectBusiness failureen_US
dc.titleBig Data Analytics and Business Failures in Data-Rich Environments: An Organizing Frameworken_US
dc.status.refereedYesen_US
dc.date.Accepted2018-12-17
dc.date.application2018-12-24
dc.typeArticleen_US
dc.date.EndofEmbargo2020-12-25
dc.type.versionAccepted Manuscripten_US
dc.description.publicnotesThis article will be released for public view at the end of the publisher embargo on 25 Dec 2020.en_US


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