Common biases in client involved decision-making in the AEC industry

Sujesh F. SUJAN, Arto KIVINIEMI, Steve W. JONES, Jacqueline M. WHEATHCROFT, Eilif HJELSETH

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Front. Eng ›› 2019, Vol. 6 ›› Issue (2) : 221-238. DOI: 10.1007/s42524-019-0026-3
RESEARCH ARTICLE

Common biases in client involved decision-making in the AEC industry

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Abstract

Understanding of the constitution of client involved decisions is important for future improvements of the processes. Significant decisions in construction projects are reliant on heuristic processes where assumptions are developed from past experience. The paper presents a methodology to collect empirical data in an unstructured manner utilizing participant intuition and experience regarding project level collaboration, a term easily understood by practitioners. Empirical data collected from 6 focus group discussions in Norway and 18 individual interviews in Finland is associated with biases in decision making aimed at bridging the gap of understanding and literature’s insufficient coverage. An analytic framework was developed to suit the diverse emergence of concepts to allow application of psychological principles in a structured manner to empirical data. The paper contributes by identifying types of cognitive and motivational biases in client involved decisions. The biases are found to be alleviated by one another depending on the particular application of the decision. Findings suggest that normative beliefs exist developed from past experience and habitual thinking. A number of emerged biases in this domain are alleviated from normative beliefs which are discussed in this paper.

Keywords

collaboration / construction industry / social science / decision-making / client / cognitive bias / motivational bias / holistic analysis / human factor

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Sujesh F. SUJAN, Arto KIVINIEMI, Steve W. JONES, Jacqueline M. WHEATHCROFT, Eilif HJELSETH. Common biases in client involved decision-making in the AEC industry. Front. Eng, 2019, 6(2): 221‒238 https://doi.org/10.1007/s42524-019-0026-3

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Acknowledgements

This study was supported by the open access funding provided by University of Liverpool.

Open Access

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the appropriate credit is given to the original author(s) and the source, and a link is provided to the Creative Commons license, indicating if changes were made.

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2019 The Author(s) 2019. This article is published with open access at link.springer.com and journal.hep.com.cn
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