Modelling the training strategies for reskilling UK construction workers in the Fourth Industrial Revolution era: a structural equation model approach

Purpose The United Kingdom (UK) construction industry is experiencing a significant skills shortage due to the advancements of the Fourth Industrial Revolution (4IR), leading to a high demand for reskilling. However, there is a lack of effective training frameworks for this purpose. This study aims to develop a framework to serve as a training strategy for reskilling construction professionals in the 4IR era, addressing the existing skills gap in the industry. Design/methodology/approach This study adopts random sampling in collecting data from stakeholders in the UK construction industry through the aid of a close-ended questionnaire. The data were subjected to principal component analysis (PCA) using SPVSS V24 and confirmatory factor analysis using AMOS Graphics V28 software. The PCA was used to group the training strategies extracted from literature into meaningful constructs, which helped inform the development of the conceptual framework. Covariance-based structural equation modelling was then used to validate the framework, with model validation carried out using fit indices such as discriminant validity, standardized root mean square residual, comparative fit index, normed fit index and others. Findings The model revealed that the key training strategies for reskilling construction workers in the 4IR era are immersive training, experiential learning, and collaborative workforce development. The findings indicate that immersive training, particularly through technologies like virtual reality and augmented reality, plays a critical role in equipping workers with both the technical and soft skills required for the future of construction. The study recommends that 4IR-driven technologies should also be used as reskilling tools for construction workers, as this approach would allow them to learn while working. Originality/value Many studies have advocated for the reskilling of construction workers. However, there is a notable gap in research on the training strategies for effective reskilling of construction workers in the 4IR era. This paper provides a framework for stakeholders to implement effective reskilling strategies, thereby enhancing the industry’s capability to thrive in the 4IR era.

Authors

Institutions

Publication Details

Journal
Smart and Sustainable Built Environment
Published
2025-03-24
DOI
https://doi.org/10.1108/sasbe-08-2024-0337
Citations
3
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
4.62
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modelling the training strategies for reskilling UK construction workers in the Fourth Industrial Revolution era: a structural equation model approach

Temidayo Oluwasola Osunsanmi, Abdullah Mazen Alhamoudi
3 citations
Smart and Sustainable Built Environment
Occupational Health and Safety Research
4.62
article

Modelling the training strategies for reskilling UK construction workers in the Fourth Industrial Revolution era: a structural equation model approach

Temidayo Oluwasola Osunsanmi, Abdullah Mazen Alhamoudi
article en
3 citations

Abstract

Purpose The United Kingdom (UK) construction industry is experiencing a significant skills shortage due to the advancements of the Fourth Industrial Revolution (4IR), leading to a high demand for reskilling. However, there is a lack of effective training frameworks for this purpose. This study aims to develop a framework to serve as a training strategy for reskilling construction professionals in the 4IR era, addressing the existing skills gap in the industry. Design/methodology/approach This study adopts random sampling in collecting data from stakeholders in the UK construction industry through the aid of a close-ended questionnaire. The data were subjected to principal component analysis (PCA) using SPVSS V24 and confirmatory factor analysis using AMOS Graphics V28 software. The PCA was used to group the training strategies extracted from literature into meaningful constructs, which helped inform the development of the conceptual framework. Covariance-based structural equation modelling was then used to validate the framework, with model validation carried out using fit indices such as discriminant validity, standardized root mean square residual, comparative fit index, normed fit index and others. Findings The model revealed that the key training strategies for reskilling construction workers in the 4IR era are immersive training, experiential learning, and collaborative workforce development. The findings indicate that immersive training, particularly through technologies like virtual reality and augmented reality, plays a critical role in equipping workers with both the technical and soft skills required for the future of construction. The study recommends that 4IR-driven technologies should also be used as reskilling tools for construction workers, as this approach would allow them to learn while working. Originality/value Many studies have advocated for the reskilling of construction workers. However, there is a notable gap in research on the training strategies for effective reskilling of construction workers in the 4IR era. This paper provides a framework for stakeholders to implement effective reskilling strategies, thereby enhancing the industry’s capability to thrive in the 4IR era.

Smart and Sustainable Built Environment
Edinburgh Napier University (GB), Imam Abdulrahman Bin Faisal University (SA)
Openalex Percentile: Top 7%
Occupational Health and Safety Research
4.62
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.