Evidence-based modelling of strategic fit: an introduction to RCaRBS

Beynon, Malcolm James ORCID: https://orcid.org/0000-0002-5757-270X, Andrews, Rhys Williams ORCID: https://orcid.org/0000-0003-1904-9819 and Boyne, George Alexander 2010. Evidence-based modelling of strategic fit: an introduction to RCaRBS. European Journal of Operational Research 207 (2) , pp. 886-896. 10.1016/j.ejor.2010.05.024

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Abstract

This paper presents an important development of a novel non-parametric object classification technique, namely CaRBS (Classification and Ranking Belief Simplex), to enable regression-type analyses. Termed RCaRBS, it is, as with CaRBS, an evidence-based technique, with its mathematical operations based on the Dempster–Shafer theory of evidence. Its exposition is demonstrated here by modelling the strategic fit of a set of public organizations. In addition to the consideration of the predictive fit of a series of models, graphical exploration of the contribution of individual variables in the derived models is also undertaken when using RCaRBS. Comparison analyses, including through fivefold cross-validation, are carried out using multiple regression and neural networks models. The findings highlight that RCaRBS achieves parity of test set predictive fit with regression and better fit than neural networks. The RCaRBS technique can also enable researchers to explore non-linear relationships (contributions) between variables in greater detail than either regression or neural networks models.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HG Finance
Uncontrolled Keywords:	Decision analysis; Evidence theory; Neural networks; Strategic fit; Trigonometric differential evolution
Publisher:	Elsevier
ISSN:	0377-2217
Last Modified:	19 Oct 2022 08:35
URI:	https://orca.cardiff.ac.uk/id/eprint/18339

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