Browsing by Author "Spanaki, Konstantina"

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    Data supply chain (DSC): research synthesis and future directions
    (Taylor and Francis, 2017-11-09) Spanaki, Konstantina; Gürgüç, Zeynep; Adams, Richard; Mulligan, Catherine
    In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the pre-existing SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies.
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    Data-driven digital transformation for emergency situations: the case of the UK retail sector
    (Elsevier, 2022-09-02) Papanagnou, Christos; Seiler, Andreas; Spanaki, Konstantina; Papadopoulos, Thanos; Bourlakis, Michael
    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.