Big Data Analytics for Crisis Management From an Information Processing Theory Perspective: A Multimethodological Study

Abstract

COVID-19 pandemic has created disruptions and risks in global supply chains. Big data analytics (BDA) has emerged in recent years as a potential solution for provisioning predictive and pre-emptive information to companies in order to preplan and mitigate the impacts of such risks. The focus of this article is to gain insights into how BDA can help companies combat a crisis like COVID-19 via a multimethodological scientific study. The advent of a crisis like COVID-19 brings with it uncertainties, and information processing theory (IPT) provides a perspective on the ways to deal with such uncertainties. We use IPT, in conjunction with the Crisis Management Theory, to lay the foundation of the article. After establishing the theoretical basis, we conduct two surveys towards supply chain managers, one before and one after the onset of the COVID-19 pandemic in India. We follow it up with qualitative interviews to gain further insights. The application of multiple methods helps ensure the triangulation of results and, hence, enhances the research rigor. Our research finds that although the current adoption of BDA in the Indian industry has not grown to a statistically significant level, there are serious future plans for the industry to adopt BDA for crisis management. The interviews also highlight the current status of adoption and the growth of BDA in the Indian industry. The article interestingly identifies that the traditional barriers to implementing new technologies (like BDA for crisis management) are no longer present in the current times. The COVID-19 pandemic has hence accelerated technology adoption and at the same time uncovered some BDA implementation challenges in practice (e.g., a lack of data scientists).