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Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture
Author(s) -
Dubey Rameshwar,
Gunasekaran Angappa,
Childe Stephen J.,
Blome Constantin,
Papadopoulos Thanos
Publication year - 2019
Publication title -
british journal of management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.407
H-Index - 108
eISSN - 1467-8551
pISSN - 1045-3172
DOI - 10.1111/1467-8551.12355
Subject(s) - big data , predictive analytics , resource based view , analytics , knowledge management , resource (disambiguation) , computer science , organizational culture , dynamic capabilities , data science , test (biology) , supply chain , business , marketing , competitive advantage , management , economics , data mining , computer network , paleontology , biology
The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The 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 the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.