Digitalization Opportunities Road Mapping Tool (DORMT©): A framework to assess digitalization opportunities in construction organizations
Bibliographic record
Abstract
The construction industry is entering the digital age, which offers innovative digitalization opportunities (DOs) regarding cost efficiency, project management, and improved client experience. In their early efforts to implement DOs, construction organizations have had varying degrees of success, and the results prompted organizations to question whether they have the appropriate digital strategy and capabilities. Hence, construction organizations need a framework to systematically evaluate the potential benefits of implementing DOs and factors influencing their successful implementation. This paper presents a framework that supports decision makers in construction organizations to assess DOs based on experts’ judgement of the factors influencing their successful implementation. The framework incorporates fuzzy arithmetic and linguistic evaluation to capture experts’ subjective assessments and is implemented in the Digitalization Opportunities Road Mapping Tool (DORMT©). DORMT©, which allows organizations to evaluate individual DOs, rank multiple DOs, and identify the best options for implementing digitalization within their organization.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".