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

Papanagnou, C ORCID:, Seiler, AC, Spanaki, K, Papadopoulos, T and Bourlakis, M ORCID: 2022, 'Data-driven digital transformation for emergency situations: the case of the UK retail sector' , International Journal of Production Economics, 250 , p. 108628.

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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.

Item Type: Article
Schools: Schools > Salford Business School
Journal or Publication Title: International Journal of Production Economics
Publisher: Elsevier
ISSN: 0925-5273
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 16 Dec 2022 09:19
Last Modified: 16 Dec 2022 09:30

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