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Impacts of big data analytics and absorptive capacity on sustainable supply chain innovation: a conceptual framework

Abstract : Background: Big data and predictive analytics could improve the ability to help with the sustainability of sourcing decisions. Sustainability has become a necessary goal for businesses and a powerful strategy for competitive advantage. There's a need for sustainable innovations along the supply chain to enable companies to have a strong market presence. Developing absorptive capacity both in firms and in supply chains are also integral to responding to dynamic markets and customer needs. The main objective of this paper is to identify the features of big data and predictive analytics applied to sustainable supply chain innovation, and to analyze the role of absorptive capacity. Methods: A literature review investigates how absorptive capacity affects the impact of the utilization of big data and predictive analytics on sustainable supply chain innovation. Results: This paper proposes a conceptual framework linking the different elements. It also proposes a synthesis of the existing definitions of the used concepts. In particular, the role of absorptive capacity as enabler on Big Data and Predictive Analytics on sustainable supply chain innovation is stressed. Conclusions: The paper investigates the emerging paradigm of big data and predictive analytics. The conceptual framework use theoretical foundation of absorptive capacity, and the extant literature on Big Data and predictive analytics. This framework will help us to build a research model for sustainable supply chain innovation applications. Further work is required to develop an action research methodology for validating the framework in depth within a company.
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Submitted on : Tuesday, April 3, 2018 - 1:54:30 PM
Last modification on : Friday, August 5, 2022 - 2:54:51 PM


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Lineth A. Rodriguez Pelliere, Catherine da Cunha. Impacts of big data analytics and absorptive capacity on sustainable supply chain innovation: a conceptual framework. LogForum, 2018, 14, pp.151 - 161. ⟨10.17270/J.LOG.2018.267⟩. ⟨hal-01757102⟩



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