Skip to main content
Kent Academic Repository

Big Data Analytics and Business Failures in Data-Rich Environments: An Organizing Framework

Amankwah-Amoah, J., Adomako, S. (2018) Big Data Analytics and Business Failures in Data-Rich Environments: An Organizing Framework. Computers in Industry, 105 . pp. 204-212. ISSN 0166-3615. (doi:10.1016/j.compind.2018.12.015) (KAR id:71169)


In 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

categorisation of a number of fruitful avenues for research on data-driven approaches to business


Item Type: Article
DOI/Identification number: 10.1016/j.compind.2018.12.015
Uncontrolled keywords: big data analytics; technology; innovation management; big data; business failure
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Marketing, Entrepreneurship and International Business
Depositing User: Joseph Amankwah-Amoah
Date Deposited: 17 Dec 2018 09:08 UTC
Last Modified: 09 Dec 2022 08:21 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.