Determinants of Artificial Intelligence Adoption among Small and Medium-Sized Construction Businesses (SMEs) in Nigeria

Abstract

Artificial Intelligence (AI) is increasingly recognized as a transformative tool for improving efficiency, accuracy, and decision-making in construction project management. Despite its potential, the level of AI adoption among small and medium-sized construction businesses (SMEs) in developing economies remains uneven and poorly understood. This study investigates the key determinants influencing AI adoption among construction SMEs in Nigeria. A quantitative research design was employed, informed by Innovation Diffusion Theory and underpinned by the Technology-Organisation-Environment (TOE) framework and the Technology Acceptance Model (TAM). Data were collected through structured questionnaires administered to 382 randomly selected registered construction SMEs, of which 360 valid responses were analysed. Exploratory factor analysis reduced 18 AI adoption variables into three core dimensions: AI functions, AI utilisation, and perceived AI effectiveness. Multiple regression analysis revealed that Technological Infrastructure Readiness was the strongest positive predictor of AI adoption (β = 0.471, p < 0.001). In contrast, Workforce Training and Skills, Industry Collaboration, Regulatory/Institutional Support, and Top Management Support did not significantly influence AI adoption. High implementation costs, resistance to cultural change, and lack of skilled expertise emerged as significant barriers to AI adoption among Nigeria's construction SMEs. The study contributes empirical evidence from a developing-country construction context. It provides practical insights for policymakers, industry regulators, and SME managers seeking to accelerate digital transformation in Nigeria's construction sector.