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

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.

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Publication Details

Journal
Engineering Construction & Architectural Management
Published
2026-02-13
DOI
https://doi.org/10.1108/ecam-04-2024-0427
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
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article

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

Emmanuel Imuetinyan Aghimien, John Ogbeleakhu Aliu, DWM Chan, Clinton Ohis Aigbavboa et al.
Engineering Construction & Architectural Management
BIM and Construction Integration
article

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

Emmanuel Imuetinyan Aghimien, John Ogbeleakhu Aliu, DWM Chan, Clinton Ohis Aigbavboa, Douglas Aghimien, Ayodeji Emmanuel Oke
article en

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.

Engineering Construction & Architectural ManagementVol. 33(15)
Georgia Department of Education (US), Hong Kong Polytechnic University (HK), Federal University of Technology (NG), University of Utah (US), University of Johannesburg (ZA)
Industry, innovation and infrastructure
Openalex Percentile: Top 53%
BIM and Construction Integration
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Strategies for enhancing construction digitalisation: a machine learning-based sensitivity analysis — Emmanuel Imuetinyan Aghimien, John Ogbeleakhu Aliu, et al. · Engineering Construction & Architectural Management (2026) | TGRS Research Map | TGRS