Project innovation - a function of procurement mode?
Bibliographic record
Abstract
The use of public-private partnerships (P3s) has increased in popularity with governments worldwide as a way of meeting ever-increasing demands for infrastructure, such as highways, water supply and wastewater treatment facilities, hospitals, and schools. One of several arguments forwarded by P3 advocates in support of one or more P3 variants as a procurement mode (in place of traditional design–bid–build (DBB) procurement for delivering such infrastructure) is the ability of a P3 to harness more of the innovative capability of the private sector. It is asserted that this capability results in lower capital or life-cycle costs, shorter delivery time, and enhanced long-term project performance. In this paper, we examine the notion that the innovation potential of the private sector is a function of delivery mode, and we describe findings from a study to identify evidence to support or contradict such a viewpoint. We have identified 22 factors or conditions that can act as drivers or inhibitors of innovation for infrastructure projects as a function of procurement mode and project context (e.g., project type, project scale, nature of competition, risk assignment). The product, process, organizational–contractual, and financial–revenue innovations achieved on a major transportation project are then reviewed, and innovation drivers that were present are discussed. The factors and conditions influencing the choice of procurement mode for a large-scale student housing facility are also discussed.Key words: infrastructure procurement, public–private partnerships, innovation drivers and inhibitors, case studies.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".