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

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

International Journal of Production Economics, 2022, vol. 250, issue C

Abstract: 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.

Keywords: Digital transformation; Big data capability; Emergency situations; Predictive analytics; Retail industry; Structural equation modelling (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:250:y:2022:i:c:s0925527322002109

DOI: 10.1016/j.ijpe.2022.108628

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