The adoption of Fourth Industrial Revolution (4IR) technologies in the construction industry presents significant opportunities to enhance efficiency, automation, and data-driven decision-making. This study seeks to identify the key drivers influencing 4IR adoption and evaluate their impact on industry-level benefits, with a particular emphasis on the residential construction sector.
A hybrid Structural Equation Modeling–Artificial Neural Network (SEM–ANN) approach was employed in this study. SEM was utilized to test the hypothesized relationships between 4IR adoption drivers and industry benefits, while ANN was applied to rank the most influential factors.
The results confirm a statistically significant relationship between the adoption of 4IR drivers and the associated benefits. Management- and human-related factors emerged as the most critical enablers of successful implementation. The findings highlight the need for comprehensive training programs, enhanced collaboration among stakeholders, and supportive regulatory frameworks to facilitate the seamless integration of 4IR technologies.
The hybrid SEM–ANN approach offers deeper insights into the interplay among adoption drivers, providing actionable recommendations for policymakers and industry practitioners seeking to optimize the implementation of 4IR technologies.
