Are Different Innovations More Challenging to Implement? A Comparison of Different Types of Changes in the AEC
Bibliographic record
Abstract
The architecture, engineering, and construction (AEC) industry is introduced to a lot of innovations and changes in various types such as technology (software and hardware informational systems), management process (alternative project delivery, alternative procurement methods, and process improvements), and business structure (mergers, acquisitions, reorganizations, prefabrication, etc.). The industry is rapidly adopting different types of changes. The objective of the study was to determine if certain types of change are harder than others to successfully adopt and implement. An industry-wide approach was taken using an online survey methodology to collect more than 500 cases of organization-wide changes from AEC firms across the United States and Canada. The method of analysis includes reliability testing, principal component analysis, and group differences. The results showed that successful adoption rates of different types of change were not significantly different for certain change types than the others. Further analysis was performed to determine if different demographical considerations of adopting organizations (type and size) had different rates of successful adoption of change. The overall successful adoption rates were generally consistent between different demographical considerations of adopting organizations, but there were minor differences. The discussion addresses those minor differences and provides possible explanations. For example, higher rates of successful adoption were found in specialized firms (roofing contractors, plumbing contractors, etc.) when compared to wide-focused firms (general contractors, EPC firms, etc.). This study contributes an industry-wide view of successful change adoption rates between different types of changes and different demographical considerations of adopting organizations in the AEC industry.
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".