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Behavioural Analytics: Exploring judgments and choices in large data sets

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  • Ian N. Durbach
  • Gilberto Montibeller

Abstract

The ever-increasing availability of large data-sets that store users’ judgements (such as forecasts and preferences) and choices (such as acquisitions of goods and services) provides a fertile ground for Behavioural Operational Research (BOR). In this paper, we review the streams of Behavioural Decision Research that might be useful for BOR researchers and practitioners to analyse such behavioural data-sets. We then suggest ways that concepts from these streams can be employed in exploring behavioural data-sets for (i) detecting behavioural patterns, (ii) exploiting behavioural findings and (iii) improving judgements and decisions of consumers and citizens. We also illustrate how this taxonomy for behavioural analytics might be utilised in practice, in three real-world studies with behavioural data-sets generated by websites and online user activity.

Suggested Citation

  • Ian N. Durbach & Gilberto Montibeller, 2019. "Behavioural Analytics: Exploring judgments and choices in large data sets," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(2), pages 255-268, February.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:2:p:255-268
    DOI: 10.1080/01605682.2018.1434400
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    Cited by:

    1. Vilkkumaa, Eeva & Liesiö, Juuso, 2022. "What causes post-decision disappointment? Estimating the contributions of systematic and selection biases," European Journal of Operational Research, Elsevier, vol. 296(2), pages 587-600.
    2. Käki, Anssi & Kemppainen, Katariina & Liesiö, Juuso, 2019. "What to do when decision-makers deviate from model recommendations? Empirical evidence from hydropower industry," European Journal of Operational Research, Elsevier, vol. 278(3), pages 869-882.

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