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
2019-02-27
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
Recommended Citation
Dubey, R., Gunasekaran, A., Childe, S., Fosso, W., Roubaud, D., & Forupon, C. (2019) 'Empirical Investigation of Data Analytics Capability and Organizational Flexibility as Complements to Supply Chain Resilience', International Journal of Production Research, . Available at: https://doi.org/10.1080/00207543.2019.1582820
