Strategies for enhancing construction digitalisation: a machine learning-based sensitivity analysis

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Authors: Douglas Omoregie Aghimien, Emmanuel Imuetinyan Aghimien, Clinton Ohis Aigbavboa, John Ogbeleakhu Aliu, Ayodeji Emmanuel Oke, Daniel W.M. Chan
Cited by: 1
References: 42

Abstract

Purpose To achieve digitalisation, construction organisations need strategies to help align existing business structures with emerging digital technologies. To this end, this paper presents the findings of an assessment of the strategies required for construction digitalisation using South Africa as a point of reference. Design/methodology/approach The study adopted a mixed-method design using a Delphi and questionnaire survey, while the critical strategies for digital construction were identified using three different machine learning (ML)-based sensitivity analyses. Findings The study, through factor analysis, found five major groups of strategies: (1) understanding the construction market, (2) creating a digital culture, (3) technology deployment and assessment, (4) communication management and (5) finance. However, the three ML-based sensitivity analyses all revealed that technology deployment and assessment, as well as understanding the construction market, are the two most important strategies for construction digitalisation in South Africa. Originality/value The paper offers practical guidelines for construction organisations to be digitalised. It also offers methodological contributions to using ML in survey studies within construction. Theoretically, the study provides a foundation for future studies on strategies for construction digitalisation – an aspect that has received less attention in the current construction digitalisation discourse.

Fields of Study & Subjects

BIM and Construction Integration Construction Project Management and Performance Digital Transformation in Industry

Publication details

Journal / source
Engineering Construction & Architectural Management
Publisher
Emerald
Volume
33
Issue
15
Pages
115–136
Publication date
13 February 2026
Language
EN