Data-driven digital transformation for emergency situations: The case of the UK retail sector

Christos Papanagnou, Andreas Seiler, Konstantina Spanaki, Thanos Papadopoulos, Michael Bourlakis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The study explores data-driven Digital Transformation (DT) for emergency situations. By adopting a dynamic capability view, we draw on the predictive practices and Big Data (BD) capabilities applied in the UK retail sector and how such capabilities support and align the supply chain resilience in emergency situations. We explore the views of major stakeholders on the proactive use of BD capabilities of UK grocery retail stores and the associated predictive analytics tools and practices. The contribution lies within the literature streams of data-driven DT by investigating the role of BD capabilities and analytical practices in preparing supply and demand for emergency situations. The study focuses on the predictive way retail firms, such as grocery stores, could proactively prepare for emergency situations (e.g., pandemic crises). The retail industry can adjust the risks of failure to the SC activities and prepare through the insight gained from well-designed predictive data-driven DT strategies. The paper also proposes and ends with future research directions.
Original languageEnglish
Article number108628
Number of pages11
JournalInternational Journal of Production Economics
Early online date2 Sept 2022
Publication statusPublished - 2 Sept 2022

Bibliographical note

© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (


  • Big data capability
  • Digital transformation
  • Emergency situations
  • Predictive analytics
  • Retail industry
  • Structural equation modelling


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