Abstract
Purpose: The accuracy and reliability of subjectively assessing a construction project’s complexity at the preconstruction stage is questionable and relies upon the project manager’s tacit experiences, knowledge and background. To develop a scientifically robust analytical approach, this paper presents a novel classification mechanism for defining the level of project complexity in terms of work contents, scope, building structures and site conditions.
Methodology: Empiricism is adopted to deductively analyse 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 fifty-six variables affecting project complexity from literature and utilized a structured questionnaire survey of 100 project managers to measure the relevance of these. 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 sixty-two 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 theoretical framework developed 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 preconstruction stage.
Originality: Whilst numerous research articles have provided a comprehensive framework to define project complexity, scant empirical works have assessed it at the preconstruction stage or utilized real-life project samples to classify it. This research addresses this knowledge gap within the prevailing body of knowledge.
Methodology: Empiricism is adopted to deductively analyse 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 fifty-six variables affecting project complexity from literature and utilized a structured questionnaire survey of 100 project managers to measure the relevance of these. 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 sixty-two 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 theoretical framework developed 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 preconstruction stage.
Originality: Whilst numerous research articles have provided a comprehensive framework to define project complexity, scant empirical works have assessed it at the preconstruction stage or utilized real-life project samples to classify it. This research addresses this knowledge gap within the prevailing body of knowledge.
| Original language | English |
|---|---|
| Pages (from-to) | 3754-3774 |
| Number of pages | 21 |
| Journal | Engineering, Construction and Architectural Management |
| Volume | 29 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 24 Nov 2022 |