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

TLDR

Big data and predictive analytics are increasingly central to operations and manufacturing management, yet research has largely overlooked how external institutional pressures shape firms’ resource allocation for building big data capability. This paper seeks to develop and test a model that links resource availability, big data culture, and operational performance. The model, grounded in resource‑based view, institutional theory, and organizational culture, is empirically tested with 195 survey responses collected via a pre‑tested questionnaire. Results indicate that external pressures influence resource selection, the effect is moderated by big data culture, and the resulting capability enhances cost and operational performance.

Abstract

Abstract 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.