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


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering
Additional Information: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (
Uncontrolled Keywords: Big data capability,Digital transformation,Emergency situations,Predictive analytics,Retail industry,Structural equation modelling,Business, Management and Accounting(all),Economics and Econometrics,Management Science and Operations Research,Industrial and Manufacturing Engineering
Publication ISSN: 0925-5273
Full Text Link:
Related URLs: https://www.sci ... 925527322002109 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-09-02
Published Online Date: 2022-09-02
Accepted Date: 2022-08-27
Authors: Papanagnou, Christos (ORCID Profile 0000-0002-5889-4209)
Seiler, Andreas
Spanaki, Konstantina
Papadopoulos, Thanos
Bourlakis, Michael



Version: Published Version

License: Creative Commons Attribution

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