A theoretical framework for classifying project complexity at the preconstruction stage using cluster analysis techniques
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Related Research Unit(s)
|Journal / Publication||Engineering, Construction and Architectural Management|
|Online published||25 Aug 2021|
|Publication status||Online published - 25 Aug 2021|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85113806662&origin=recordpage|
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.
- Clustering analysis, Construction project complexity, Pre-construction stage, Staffing allocation
A theoretical framework for classifying project complexity at the preconstruction stage using cluster analysis techniques. / Sing, Michael C.P.; Edwards, David J.; Leung, Arthur W.T.; Liu, Henry; Roberts, Chris J.In: Engineering, Construction and Architectural Management, 25.08.2021.