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A theoretical framework for classifying project complexity at the preconstruction stage using cluster analysis techniques

Michael C.P. Sing (School of Architecture and Built Environment, The University of Newcastle , Callaghan, Australia)
David J. Edwards ( School of Engineering and the Built Environment , Birmingham City University , Birmingham, UK)
Arthur W.T. Leung (Division of Building Science and Technology, City University of Hong Kong , Kowloon Tong, Hong Kong)
Henry Liu ( School of Design and the Built Environment , University of Canberra , Canberra, Australia)
Chris J. Roberts ( School of Engineering and the Built Environment , Birmingham City University , Birmingham, UK)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 25 August 2021

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Abstract

Purpose

The accuracy and reliability of subjectively assessing a construction project's complexity at the pre-construction stage is questionable and relies upon the project manager's tacit experiences, knowledge and background. The purpose of this paper is to develop a scientifically robust analytical approach by presenting a novel classification mechanism for defining the level of project complexity in terms of work contents (WCs), scope, building structures (BSs) and site conditions.

Design/methodology/approach

Empiricism is adopted to deductively analyze variables obtained from secondary data within extant literature and primary project data to develop project type classifications. Specifically, and from an operational perspective, a two-stage “waterfall process” was adopted. In stage one, the research identified 56 variables affecting project complexity from literature and utilized a structured questionnaire survey of 100 project managers to measure the relevance of these. A total of 27 variables were revealed to be significant and exploratory factor analysis (EFA) is adopted to cluster these variables into six-factor thematic groups. In stage two, data from 62 real-life projects (including the layout and structural plans) were utilized for computing the factor score using the six-factor groups. Finally, hierarchical cluster analysis (HCA) is adopted to classify the projects into collected distinctive groups and each of a similar nature and characteristics.

Findings

The developed theoretical framework (that includes a novel complex index) provides a robust “blueprint platform” for main contractors to compile their project complexity database. The research outputs enable project managers to generate a more accurate picture of complexity at the pre-construction stage.

Originality/value

While numerous research articles have provided a comprehensive framework to define project complexity, scant empirical works have assessed it at the pre-construction stage or utilized real-life project samples to classify it. This research addresses this knowledge gap within the prevailing body of knowledge.

Keywords

Citation

Sing, M.C.P., Edwards, D.J., Leung, A.W.T., Liu, H. and Roberts, C.J. (2021), "A theoretical framework for classifying project complexity at the preconstruction stage using cluster analysis techniques", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-09-2020-0726

Publisher

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Emerald Publishing Limited

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