Developing a logistic regression model to measure project complexity. (4th May 2022)

• Record Type: Journal Article
• Authors: Dao, Bac; Kermanshachi, Sharareh; Shane, Jennifer; Anderson, Stuart; Damnjanovic, Ivan
• Is Part Of: Architectural engineering and design management. Volume 18:Number 3(2022)
• Page Start: 226
• Page End: 240
• Publication Date: 2022-05-04
• Subjects: Project complexity -- complexity attribute -- complexity indicator -- logistic regression model; Building -- Periodicals; Architectural design -- Periodicals; Construction industry -- Management -- Periodicals; 720
• DOI: 10.1080/17452007.2020.1851166
• Languages: English
• ISSNs: 1745-2007

Abstract

ABSTRACT: The study develops a binary logistic regression model to assess and measure complexity levels of a project. The complexity measures were statistically verified to create a basis for the model. The variable reduction process called Principle Component Analysis was used to combine the significant complexity indicators into component variables. The study enriches the complexity theoretical basis in the field of project management by providing an innovative approach that aids scholars and practitioners in assessing complexity levels based on the applicability of identified complexity measures. The research results also help facilitate the management process and formulate an appropriate complexity management plan.