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Big Data Analytics in turbulent contexts: towards organizational change for enhanced agility

Author

Listed:
  • Yves Barlette

    (MRM - Montpellier Research in Management - UPVM - Université Paul-Valéry - Montpellier 3 - UPVD - Université de Perpignan Via Domitia - Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School - UM - Université de Montpellier)

  • Paméla Baillette

    (IRGO - Institut de Recherche en Gestion des Organisations - UB - Université de Bordeaux - Institut d'Administration des Entreprises (IAE) - Bordeaux)

Abstract

This conceptual paper addresses the capabilities offered by big data analytics (BDA), critical in turbulent environments, in the context of the Industry 4.0 revolution. BDA enhances organizational agility, as it is highly valuable for the identification of opportunities and threats (i.e. the sensing process of agility); however, with the exception of aiding decision-making tools, seizing its benefits for reacting (i.e. responding) is more difficult without organizational adaptation. Hence, the changes required to exploit the full capabilities offered by BDA, particularly at the organizational level, are considerable. From a theoretical perspective, through a narrative literature review, this paper highlights several possibilities of organizational changes for enhancing a company's agility and leveraging BDA to achieve enhanced performance in turbulent contexts. The findings are then related to three organizational theories. This work ends in a conceptual model and several research paths for future studies. From a managerial perspective, this paper highlights, through several examples, the importance of top management's role in achieving the necessary organizational changes to enhance the companies' responding capabilities.

Suggested Citation

  • Yves Barlette & Paméla Baillette, 2021. "Big Data Analytics in turbulent contexts: towards organizational change for enhanced agility," Post-Print hal-03037251, HAL.
  • Handle: RePEc:hal:journl:hal-03037251
    DOI: 10.1080/09537287.2020.1810755
    as

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