Construction professionals’ perspectives of adaptive learning adoption: an SEM-machine learning approach

Abstract

Purpose

This study aims to examine the acceptance of adaptive learning (AL) amongst construction professionals in Singapore. It seeks to compare their perceptions and attitudes with those of professionals from other industries to assess the rate of AL adoption in the construction sector. Furthermore, the study aims to identify the factors influencing construction professionals’ intention to adopt AL technologies.

Design/methodology/approach

A questionnaire survey was conducted with 188 construction professionals and 153 non-construction professionals. By employing the extended unified theory of acceptance and use of technology (UTAUT2) and the general extended technology acceptance model for e-learning (GETAMEL), this study also explored factors influencing construction professionals’ behavioural intention (BI) towards AL adoption. An SEM-machine learning approach facilitated the evaluation of the factors’ influence on BI.

Findings

A comparative analysis of the data found that construction professionals’ intention to use AL surpassed 75%, which had no significant difference with professionals from other industries. The findings revealed that learning value (LV) and self-efficacy (SE) were statistically significant predictors of construction professionals’ intentions to use AL. Furthermore, a supervised machine learning analysis identified performance expectancy (PE) as a crucial factor in predicting these intentions.

Research limitations/implications

The study’s focus on self-reported intentions and a specific demographic limits its generalisability; further research should examine actual usage across diverse cultures.

Practical implications

The results offered insights into construction professionals’ perceptions and attitudes towards AL adoption, guiding the integration of AL into construction professional development.

Originality/value

This paper addresses a recognised gap by examining construction professionals’ perceptions and attitudes towards adopting AL.

Citation

Hu, X., Goh, Y.M. and Tay, J. (2024), "Construction professionals’ perspectives of adaptive learning adoption: an SEM-machine learning approach", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-07-2024-0896