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1. Introduction
Since the 1950s, research has looked at the analytical treatment of information, but only in the last decade, its application as a driver of performance and as support for information flow was better understood (Chen et al., 2012). Today, it became almost mandatory that firms develop their capacity to collect information, analyze data and gather knowledge to support decision-making (Sanders, 2016). The large amount of data available has led to the term big data analytics (BDA) to represent the set of new techniques to manage large volumes of data. BDA could be defined as a field of information science that brings together how to capture, store, organize, process, analyze, disseminate and manage data and information in high volume and elevated variety and transacted in high speed (Chen et al., 2012; Sanders, 2016).
Among many applications of BDA, one of critical relevance and yet under-researched is supply chain risk management (SCRM). SCRM is “an inter-organisational collaborative endeavour utilising quantitative and qualitative risk management methodologies to identify, evaluate, mitigate and monitor unexpected macro and micro level events or conditions, which might adversely impact any part of a supply chain” (Ho et al., 2015, p. 6). Large amounts of data can be organized, structured and analyzed to support supply chain management (SCM) (Mcafee and Brynjolfsson, 2012). More importantly, BDA can help scanning the business competitive environment to minimize supply chain (SC) risks.
The International Data Corporation (Macgillivray and Reinsel, 2019) forecasts that by 2025 the number of devices connected to the Internet will reach 41.6bn, while generating 80 zettabytes of data per year. The recent rise of environmental complexity (Chen et al., 2016) and the availability of new technologies (Chen et al., 2012) have allowed firms to manage an exponential volume and variety of data sources and types (Zhan et al., 2017). In addition, technology has allowed handling such data in a much faster pace, as well as including unstructured information in data analysis (Chen et al., 2016; Sanders, 2016). Mastering the analytical capacity to analyze and to make sense of a large amount of data in a dynamic environment has become a valuable capability. Such capability is labelled by many authors as the biggest competitive differential...