Determinants of Artificial Intelligence Adoption in Construction Project Delivery: Evidence from Large Construction Firms in Nigeria Using the TOE Framework
DOI:
https://doi.org/10.15377/2409-9821.2026.13.12Keywords:
Construction digitalization, Construction project delivery, Artificial intelligence adoption, Technology–organization–environment (TOE) framework, Partial least squares structural equation modeling (PLS-SEM)Abstract
Artificial intelligence (AI) is increasingly recognized as a transformative technology for enhancing construction project delivery. However, empirical evidence explaining the factors associated with its adoption remains limited, particularly in developing-country construction contexts. This study investigated the determinants of AI adoption across construction project delivery functions among large-scale construction firms in Nigeria using the Technology–Organization–Environment (TOE) framework. A quantitative survey design was adopted, and data were collected through a structured questionnaire administered to construction professionals across 14 purposively selected large-scale construction firms. A total of 288 valid responses were analyzed using IBM SPSS Statistics and SmartPLS 4. Descriptive statistics were employed to examine respondent characteristics, AI application areas, and construct performance, while Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the hypothesized relationships. The findings indicate that AI adoption remains at a moderate stage, with implementation concentrated in design and BIM processes (50.7%) and planning and project control (38.9%). Organisational factors demonstrated the strongest statistical association with AI adoption (β = 0.404), followed by technological (β = 0.169) and environmental factors (β = 0.158), with the TOE model explaining 30.8% of the variance in adoption. The relatively high organisational readiness score alongside moderate AI adoption suggests that favourable internal conditions do not necessarily translate into extensive AI implementation. By providing TOE-based empirical evidence from these firms in Nigeria, the findings contribute to the growing literature on construction digitalization and offer practical insights for firms seeking to accelerate AI-enabled project delivery.
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