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Article
Publication date: 7 July 2020

Dominic D. Ahiaga-Dagbui, Olubukola Tokede, John Morrison and Anthony Chirnside

Effective inter-organisational relationships are key to engendering innovation and ensuring the successful delivery of infrastructure projects. Relationship-based…

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765

Abstract

Purpose

Effective inter-organisational relationships are key to engendering innovation and ensuring the successful delivery of infrastructure projects. Relationship-based contracts are thus widely used to stimulate best-for-project ideals and attenuate the otherwise adversarial relationship that often exists between clients and contractors. This study examines the effectiveness and limitations of a project facilitation model as coaching tool for developing conducive inter-organisational relationships for construction project delivery.

Design/methodology/approach

The study adopts a case-study approach using evidence from triangulated data sources of focus group workshops, semi-structured interviews and document analysis.

Findings

(1) The facilitation model enabled an environment for psychological safety to be developed, which engendered a platform for effective cooperation for problem-solving and achieving quasi best-for-project ideals. (2) The model provides the mechanism to develop team behaviours that support enhanced performance and create an environment less adversarial and more collaborative than traditional contracting.

Originality/value

The novelty of this research is that relationship-based principles have been utilised as part of a traditional design-bid-build contract with lump-sum payment arrangements.

Details

Engineering, Construction and Architectural Management, vol. 27 no. 10
Type: Research Article
ISSN: 0969-9988

Keywords

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Article
Publication date: 7 April 2014

Dominic D. Ahiaga-Dagbui and Simon D Smith

Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive…

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3011

Abstract

Purpose

Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, 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. The paper aims to discuss these issues.

Design/methodology/approach

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.

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 percent with 87 percent of the model predictions within a range of ±5 percent 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/value

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.

Details

Journal of Financial Management of Property and Construction, vol. 19 no. 1
Type: Research Article
ISSN: 1366-4387

Keywords

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Article
Publication date: 4 April 2016

Brendon Lim, Madhav P. Nepal, Martin Skitmore and Bo Xiong

Preliminary cost estimates for construction projects are often the basis of financial feasibility and budgeting decisions in the early stages of planning and for effective…

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1642

Abstract

Purpose

Preliminary cost estimates for construction projects are often the basis of financial feasibility and budgeting decisions in the early stages of planning and for effective project control, monitoring and execution. The purpose of this paper is to identify and better understand the cost drivers and factors that contribute to the accuracy of estimates in residential construction projects from the developers’ perspective.

Design/methodology/approach

The paper uses a literature review to determine the drivers that affect the accuracy of developers’ early stage cost estimates and the factors influencing the construction costs of residential construction projects. It used cost variance data and other supporting documentation collected from two case study projects in South East Queensland, Australia, along with semi-structured interviews conducted with the practitioners involved.

Findings

It is found that many cost drivers or factors of cost uncertainty identified in the literature for large-scale projects are not as apparent and relevant for developers’ small-scale residential construction projects. Specifically, the certainty and completeness of project-specific information, suitability of historical cost data, contingency allowances, methods of estimating and the estimator’s level of experience significantly affect the accuracy of cost estimates. Developers of small-scale residential projects use pre-established and suitably priced bills of quantities as the prime estimating method, which is considered to be the most efficient and accurate method for standard house designs. However, this method needs to be backed with the expertise and experience of the estimator.

Originality/value

There is a lack of research on the accuracy of developers’ early stage cost estimates and the relevance and applicability of cost drivers and factors in the residential construction projects. This research has practical significance for improving the accuracy of such preliminary cost estimates.

Details

Journal of Financial Management of Property and Construction, vol. 21 no. 1
Type: Research Article
ISSN: 1366-4387

Keywords

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