Abstract

Supply chain resilience and data analytics capability have generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organisational information processing theory (OIPT). Four research hypotheses are tested using responses from 213 Indian manufacturing organisations collected via a pre-tested survey-based instrument. We further test our model using variance-based structural equation modelling, popularly known as PLS-SEM. All of the hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions.

DOI

10.1080/00207543.2019.1582820

Publication Date

2019-01-30

Publication Title

International Journal of Production Research

ISSN

0020-7543

Embargo Period

2020-02-27

Organisational Unit

Plymouth Business School

Keywords

Data analytics, ripple effect, disruption, supply chain resilience, competitive advantage, structural equation modelling, organisational information processing theory

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