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An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry

Mohamed T. Elnabwy (Coastal Research Institute, National Water Research Center, Alexandria, Egypt) (Department of Civil Engineering, Faculty of Engineering, Damietta University, New Damietta, Egypt)
Diaa Khalaf (Architecture and Built Environment (ABE)Department, Northumbria University Faculty of Engineering and Environment, Newcastle Upon Tyne, UK)
Ehab A. Mlybari (Department of Civil Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah, Saudi Arabia)
Emad Elbeltagi (Department of Civil Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 25 September 2024

155

Abstract

Purpose

In today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.

Design/methodology/approach

A questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.

Findings

The findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.

Originality/value

This study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

Keywords

Acknowledgements

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code (24UQU4260055DSR001).

Citation

Elnabwy, M.T., Khalaf, D., Mlybari, E.A. and Elbeltagi, E. (2024), "An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-05-2024-0537

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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