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

Abstract : Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.
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https://hal.umontpellier.fr/hal-02061341
Contributeur : Odile Hennaut <>
Soumis le : vendredi 8 mars 2019 - 09:20:05
Dernière modification le : jeudi 6 juin 2019 - 14:45:41

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Shirish Jeble, Rameshwar Dubey, Stephen Childe, Thanos Papadopoulos, David Roubaud, et al.. Impact of big data and predictive analytics capability on supply chain sustainability. International Journal of Logistics Management, The, Emerald, 2018, 29 (2), pp.513-538. ⟨10.1108/IJLM-05-2017-0134⟩. ⟨hal-02061341⟩

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