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Rethinking construction cost overruns: cognition, learning and estimation.

Ahiaga-Dagbui, Dominic D.; Smith, Simon D.

Authors

Dominic D. Ahiaga-Dagbui

Simon D. Smith



Abstract

Purpose: Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost growth, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. Design: A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using Artificial Neural Networks (ANN). Findings: A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67% with 87% of the model predictions within a range of ±5% of the actual final cost. Practical implications: The model developed can be converted to a desktop package for quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the model could also greatly reduce the time and resources spent on estimation. Originality: A thorough discussion on the dynamics between cognitive dispositions, learning and cost estimation has been presented. It also presents a conceptual model for understanding two often conflated issues of cost overrun and under-estimation.

Journal Article Type Article
Publication Date Apr 30, 2014
Journal Journal of financial management of property and construction
Print ISSN 1366-4387
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 19
Issue 1
Pages 38-54
Institution Citation AHIAGA-DAGBUI, D.D. and SMITH, S.D. 2014. Rethinking construction cost overruns: cognition, learning and estimation. Journal of financial management of property and construction, 19(1), pp. 38-54. Available from: https://doi.org/10.1108/JFMPC-06-2013-0027
DOI https://doi.org/10.1108/JFMPC-06-2013-0027
Keywords Data mining; Prospect theory; Cost overruns; Dunning Kruger effects; Optimism bias; Referenced class forecasting

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