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Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture

Dubey, Rameshwar, Gunasekaran, Angappa, Childe, Stephen J., Blome, Constantin, Papadopoulos, Thanos (2019) Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture. British Journal of Management, 30 (2). pp. 341-361. ISSN 1045-3172. (doi:10.1111/1467-8551.12355) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. Literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource-based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills, and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre-tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under moderating effect of big data culture and their utilisation for capability building, and how this capability affects cost and operational performance.

Item Type: Article
DOI/Identification number: 10.1111/1467-8551.12355
Uncontrolled keywords: Big Data, Predictive Analytics, Institutional Theory, Resource Based View, Manufacturing Performance, PLS SEM
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Thanos Papadopoulos
Date Deposited: 20 Feb 2019 09:23 UTC
Last Modified: 13 Jun 2019 09:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72639 (The current URI for this page, for reference purposes)
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