Big data analytics based enablers of supply chain capabilities and firm competitiveness: a fuzzy-TISM approach. Issue 1 (4th December 2020) - Bamel, Nisha; Bamel, Umesh
Abstract : Purpose: This paper aims to identify the big data analytics (BDAs) based enablers of supply chain capabilities (SCCs) and competitiveness of firms. This paper also models the interaction among identified enablers and thus projects the relationship strength of these enablers with SCC and a firm's competitiveness. Design/methodology/approach: In order to achieve the research objectives of this paper, we employed fuzzy total interpretive structural modeling (TISM), an integrated approach of an interpretive structural model and TISM. Findings: Results suggest that BDA-based enablers namely, IT infrastructure for BDA; leadership commitment; people skills for use of BDA and financial support for BDA significantly enable SCC and enhance firm competitiveness. Practical implications: Results of the present study have implications for researchers and practitioners; the results will enable them to design policies around identified enablers of BDA initiatives. Originality/value: The present paper is one of a few early efforts that address the role of BDA in augmenting SCC and subsequently a firm's competitiveness from a resource-dynamic capability perspective.