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

Aghimien, D; Aghimien, E I; Aigbavboa, C O; Aliu, J O; Oke, A E and Chan, D W M (2026) Strategies for enhancing construction digitalisation: A machine learning-based sensitivity analysis. Engineering, Construction and Architectural Management, 33(15), pp. 115-136. ISSN 0969-9988

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.

Item Type: Article
Uncontrolled Keywords: construction digitalisation; fourth industrial revolution; machine learning classification; mixed-method; sensitivity analysis
Index terms: construction market, future study, fourth industrial revolution, factor analysis, construction organization, digital construction, machine learning, digital technology, South Africa, methodology, questionnaire, strategy, survey, sensitivity analysis, digitalization
Subjects: digital technology, organization, market analysis, environmental hazards, research methods, Geography, technology adoption, artificial intelligence, research design and methodology, data collection methods, computing systems, information systems, statistical analysis, management
Topics: Geographical Context, Sustainability, Business Strategy, Research Practice, Digital Applications
Descriptive scope: 5 PCTEA

N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here