Change management practices for adopting new technologies in the design and construction industry
Bibliographic record
Abstract
The architecture, engineering, and construction (AEC) industry has often been accused of being slow to adopt change. Yet the breadth of available technology solutions in the modern AEC industry continues to grow. Companies therefore must be adept at organizational change management; otherwise, the full benefits of technology solutions may never be realized when a company fails to achieve successful change adoption. The objective of this study was to identify the relationships between specific change management practices and organizational adoption of new technology solutions. An industry-wide approach was taken, wherein an online survey methodology was used to collect 167 cases of organization-wide change from AEC firms across the United States and Canada. The method of analysis included a correlation analysis between change management practices and change adoption. Reliability testing and principal components analysis were used to extract a single construct measure of change adoption. Rank-based nonparametric testing investigated if there are statistically significant differences between different groups of participants and technologies. Results include a rank-order of specific change management practices most associated with successful technology adoption. Change-agent effectiveness, measured benchmarks, realistic timeframe, and communicated benefits are the four change management practices that had the strongest association strength with successful change adoption. The discussion addresses how these leading change management practices compare with previous literature. Also, it was found that organization type and job position were correlated with the levels of change-adoption success compared to other listed factors. This study contributes an industry-wide view of change management practices within the context of technology-based change adoption and may assist practitioners to better manage technology adoptions in their organizations.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".