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Wise Data-Driven Decision-Making

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Responsible AI and Analytics for an Ethical and Inclusive Digitized Society (I3E 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12896))

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Abstract

This article reports on the preliminary findings of research in progress. In one of the first empirical studies in the information systems and organisations literature, we investigate the role of wisdom as a decision-making capacity in the use of analytics. To address the research question of how decision-makers can use analytics to make wise decisions, we interviewed six decision-makers and four data analytics in a diverse range of industries. Based on the findings, we introduce a process model of wise data-driven decision-making (WD3M). This study offers significant theoretical and practical implications as it extends our understanding of how wisdom can be defined and used in the analytics context. For practitioners, this study offers important guidelines as to how to make wise and more effective data-driven decisions.

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Author information

Authors and Affiliations

  1. The University of Queensland, St Lucia, QLD, 4067, Australia

    Morteza Namvar & Ali Intezari

Authors
  1. Morteza Namvar
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  2. Ali Intezari
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Corresponding author

Correspondence to Morteza Namvar .

Editor information

Editors and Affiliations

  1. National University of Ireland, Galway, Galway, Ireland

    Denis Dennehy

  2. National University of Ireland, Galway, Galway, Ireland

    Anastasia Griva

  3. Athens University of Economics and Business, Athens, Greece

    Nancy Pouloudi

  4. Swansea University, Swansea, UK

    Yogesh K. Dwivedi

  5. University of Agder, Kristiansand, Norway

    Ilias Pappas

  6. University of Turku, Turku, Finland

    Matti Mäntymäki

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Namvar, M., Intezari, A. (2021). Wise Data-Driven Decision-Making. In: Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y.K., Pappas, I., Mäntymäki, M. (eds) Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. I3E 2021. Lecture Notes in Computer Science(), vol 12896. Springer, Cham. https://doi.org/10.1007/978-3-030-85447-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-85447-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85446-1

  • Online ISBN: 978-3-030-85447-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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