Quantification of economic influences on technology adoption and diffusion in the construction industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it