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Impact of big data and predictive analytics capability on supply chain sustainability

Shirish Jeble (Symbiosis Centre for Research and Innovation, Symbiosis International University, Pune, India)
Rameshwar Dubey (Montpellier Business School, Montpellier, France)
Stephen J. Childe (Plymouth Business School, Plymouth University, Plymouth, UK)
Thanos Papadopoulos (Kent Business School, University of Kent, Kent, UK)
David Roubaud (Montpellier Research in Management, Montpellier Business School, Montpellier, France)
Anand Prakash (School of General Management, National Institute of Construction Management and Research, Pune, India)

The International Journal of Logistics Management

ISSN: 0957-4093

Article publication date: 14 May 2018




The 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.


The authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.


The statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support for H4. Although the authors 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. The authors 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 the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.



Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Roubaud, D. and Prakash, A. (2018), "Impact of big data and predictive analytics capability on supply chain sustainability", The International Journal of Logistics Management, Vol. 29 No. 2, pp. 513-538.



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