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Data-driven pathways for sustainable AI assimilation in Ghana's built environment: towards an innovation diffusion theory (IDT) perspective

Joseph Teye Ignatius Buertey et al.

Property Management2026https://doi.org/10.1108/pm-04-2025-0041article
AJG 1ABDC B
Weight
0.50

What the paper says

Purpose The incorporation of artificial intelligence (AI) into the built environment industry has the potential to transform the way infrastructure is designed, constructed, and managed. With the world inching towards Industry 5.0, this research examines the potential drivers of AI and provides a context-specific approach to “sustainable assimilation” using the innovation diffusion theory (IDT). Design/methodology/approach This study employs a quantitative approach, collecting data from 182 built-environment professionals, and analyses the data using regression analysis. Findings Analysis of data revealed that 81% of built environment professionals had never utilised AI tools in their professional practice, suggesting that the built environment in Ghana is struggling to assimilate and apply AI technologies. Regression models revealed that, at p = 0.001, perceived impact and usefulness (ß = 0.399), institutional support (ß = 0.367), reliability (ß = 0.278), ease of use (ß = 0.283) and funding requirement (ß = 4.10) were identified as the key drivers for AI adoption in the built and real estate industries. Practical implications Modelling the adoption of AI in the Ghanaian construction industry using IDT would require piloting and proving the relative advantage of AI to professionals. This could be achieved by providing institutional support, technical capacity, simplifying the perceived complexity through the integration of industry-specific solutions, ensuring job impact compatibility, and de-risking the use of AI through policy innovations. Originality/value The study suggests that by effectively modelling the factors influencing AI and promoting effective communication and understanding, built environment professionals can significantly accelerate their adoption of AI, transitioning from early innovators to widespread use across the sector.

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https://doi.org/https://doi.org/10.1108/pm-04-2025-0041

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@article{joseph2026,
  title        = {{Data-driven pathways for sustainable AI assimilation in Ghana's built environment: towards an innovation diffusion theory (IDT) perspective}},
  author       = {Joseph Teye Ignatius Buertey et al.},
  journal      = {Property Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/pm-04-2025-0041},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.