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

Douglas Aghimien et al.

Engineering, Construction and Architectural Management2026https://doi.org/10.1108/ecam-04-2024-0427article
AJG 1ABDC A
Weight
0.50

What the paper says

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.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1108/ecam-04-2024-0427

Or copy a formatted citation

@article{douglas2026,
  title        = {{Strategies for enhancing construction digitalisation: a machine learning-based sensitivity analysis}},
  author       = {Douglas Aghimien et al.},
  journal      = {Engineering, Construction and Architectural Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ecam-04-2024-0427},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

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

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.