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

TLDR

Supply chain resilience and data analytics capability have attracted growing academic and practitioner interest, yet most studies treat them separately. This study integrates these streams by modeling data analytics capability as a means to enhance information‑processing capacity and supply chain resilience as a means to mitigate ripple effects and accelerate recovery. Grounded in organisational information processing theory, the model is tested with 213 Indian manufacturing firms using a pre‑tested survey and variance‑based structural equation modelling (PLS‑SEM). All four hypotheses are supported, indicating that data analytics capability improves supply chain resilience and offering new insights for IS and OM literature and managerial practice. The authors note study limitations and propose future research directions.

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.