ITcon Vol. 27, pg. 732-755, http://www.itcon.org/2022/36

Application of machine learning to limited datasets: prediction of project success

DOI:10.36680/j.itcon.2022.036
submitted:February 2022
revised:June 2022
published:July 2022
editor(s):Turk Ž
authors:Sofie Bang, PhD Candidate,
Norwegian University of Science and Technology (NTNU);
sofie.bang@ntnu.no (corresponding author)

Magnus O. Aarvold, Researcher,
Norwegian University of Science and Technology (NTNU)

Wilhelm J. Hartvig, Researcher,
Norwegian University of Science and Technology (NTNU)

Nils O. E. Olsson, Professor,
Norwegian University of Science and Technology (NTNU)

Antoine Rauzy, Professor,
Norwegian University of Science and Technology (NTNU)
summary:Much research is conducted on the importance of success factors. This study contributes to the body of knowledge by using artificial intelligence (AI), specifically machine learning (ML), to analyse success factors through data from construction projects. Previously conducted studies have explored the use of AI to predict project success and identify important success factors in projects; however, to the extent of the authors’ knowledge, no studies have implemented the same method as this study. This study conducts quantitative analysis on a sample of 160 Norwegian construction projects, with data obtained from a detailed questionnaire delivered to relevant project team members. The method utilises ML through a Random Forest Classifier (RFC). The findings obtained from the analysis show that it is possible to use AI and ML on a limited dataset. Furthermore, the findings show that it is possible to identify the most important success factors for the projects in question with the developed model. The findings suggest that a group of selected processes is more important than others to achieve success. The identified success factors support the theoretically acknowledged importance of thorough and early planning and analysis, complexity throughout the project, leadership involvement, and processes supporting project success.
keywords:artificial intelligence; limited datasets; machine learning; predictive modelling; project management success; project success; random forest classifier; success factors
full text: (PDF file, 1.254 MB)
citation:Bang S, Aarvold M O, Hartvig W J, Olsson N O E, Rauzy A (2022). Application of machine learning to limited datasets: prediction of project success, ITcon Vol. 27, pg. 732-755, https://doi.org/10.36680/j.itcon.2022.036