Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience

Rameshwar Dubey, Angappa Gunasekaran, Stephen J. Childe, Samuel Fosso Wamba, David Roubaud, Cyril Foropon

Research output: Contribution to journalArticlepeer-review

103 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)110-128
Number of pages19
JournalInternational Journal of Production Research
Issue number1
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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