In-House Engineering as a Catalyst for Sustainable Innovation in Construction
Bibliographic record
Abstract
In-house engineering can be a powerful driver of innovation and sustainability in the construction industry. The authors examine the strategic choice between internalizing engineering services and outsourcing them and how it influences a firm's ability to innovate and meet sustainability goals. Integrating the four established theoretical perspectives (Transaction Cost Economics, Resource-Based View of the Firm, Dynamic Capabilities, and Absorptive Capacity), they develop a decision framework providing insights into the contexts and situations when in-house engineering adds the most value, becoming the preferred choice for construction firms. They start with an overview of industry trends, showing a clear shift from traditional outsourcing toward more integrated models as firms seek long-term competitive advantage. Then, they integrate decision criteria (cost, risk, innovation potential, sustainability, knowledge retention, and governance considerations) into a unified framework and illustrate them with case studies of major construction and engineering firms.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".