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Durbach and Montibeller (2019) Exploring_Judgments_and_Choices_in_Behavioral_Data_Sets.pdf (436.37 kB)

Behavioural analytics: Exploring judgments and choices in large data sets

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journal contribution
posted on 2019-01-24, 14:11 authored by Ian N Durbach, Gilberto MontibellerGilberto Montibeller
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.

Funding

This work is based on the research supported in part by the National Research Foundation of South Africa [grant numbers 90782, 105782].

History

School

  • Business and Economics

Department

  • Business

Published in

Journal of the Operational Research Society

Pages

1 - 14

Citation

DURBACH, I.N. and MONTIBELLER, G., 2018. Behavioural analytics: Exploring judgments and choices in large data sets. Journal of the Operational Research Society, 70 (2), pp.255-268.

Publisher

© Operational Research Society 2018. Published by Taylor and Francis

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2018-03-05

Publication date

2018

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 5 March 2018, available online: http://www.tandfonline.com/10.1080/01605682.2018.1434400.

ISSN

0160-5682

eISSN

1476-9360

Language

  • en