Impact of Big Data & Predictive Analytics Capability on Supply Chain Sustainability

Abstract

The main purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization. We have developed our theoretical model using resource based view (RBV) logic and contingency theory (CT). The model was further tested using PLS-SEM (partial least squares- Structural Equation Modelling) following Peng and Lai (2012) arguments. We gathered 205 responses using survey based instrument for PLS-SEM. The statistical results suggest that out of four research hypotheses, we find support for three hypotheses (H1-H3) and we did not found support for hypothesis H4. Although, we did not find support for H4 (moderating role of supply base complexity (SBC)). However, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies. This study makes some original contribution to the operations and supply chain management literature. We provide theory-driven and empirically-proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures we have attempted to answered some of the unresolved questions. We also offer numerous guidance to the practitioners and policy makers, based on empirical results.