Quantification of economic influences on technology adoption and diffusion in the construction industry
Bibliographic record
Abstract
The construction industry has been slow to adopt technologies like design automation and robotic fabrication due to high initial costs and long project cycles. However, demand for efficiency, sustainability and safety has spurred technological transformation. This study explores how technology spreads in construction by examining mimetic behaviour among enterprises of varying income levels using an agent-based modelling (ABM) approach. The model uses empirical data from literature to simulate interactions and economic impacts on adoption across high-, medium- and low-income enterprises. Findings show that high-income firms adopt technologies during economic growth to strengthen market position, while low-income firms invest during downturns to cut costs and enhance competitiveness. Medium-income firms adopt cautiously but steadily in stable growth scenarios. These results highlight economic conditions and income differences in shaping adoption strategies, offering insights for policymakers promoting technology diffusion in construction. As a premise work to explore quantifiable economic impact of technology adoption, this study has reviewed key economic drivers and barriers that may influence technology diffusion across different enterprise categories. Coupled this with the ABM findings, this study offers construction managers practical insights to align technology investments with economic trends, enabling them to reduce risks and enhance competitiveness under varying market conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".