Olabode, OE, Boso, N, Hultman, M et al. (1 more author) (2022) Big data analytics capability and market performance: The roles of disruptive business models and competitive intensity. Journal of Business Research, 139. pp. 1218-1230. ISSN 0148-2963
Abstract
Research shows that big data analytics capability (BDAC) is a major determinant of firm performance. However, scant research has theoretically articulated and empirically tested the mechanisms and conditions under which BDAC influences performance. This study advances existing knowledge on the BDAC–performance relationship by drawing on the knowledge-based view and contingency theory to argue that how and when BDAC influences market performance is dependent on the intervening role of disruptive business models and the contingency role of competitive intensity. We empirically test this argument on primary data from 360 firms in the United Kingdom. The results show that disruptive business models partially mediate the positive effect of BDAC on market performance, and this indirect positive effect is strengthened when competitive intensity increases. These findings provide new perspectives on the business model processes and competitive conditions under which firms maximize marketplace value from investments in BDACs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Inc. This is an author produced version of an article published in Journal of Business Research. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Big data analytics capability, Market performance, Disruption, Disruptive business model, Competitive intensity, Knowledge-based view, Resource-based view, Contingency theory |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Oct 2021 13:25 |
Last Modified: | 02 May 2023 00:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jbusres.2021.10.042 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179092 |