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
journal contribution
posted on 2019-01-24, 14:11 authored by Ian N Durbach, Gilberto MontibellerGilberto MontibellerThe 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 SocietyPages
1 - 14Citation
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 FrancisVersion
- 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
2018-03-05Publication date
2018Notes
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
0160-5682eISSN
1476-9360Publisher version
Language
- en