IPD-inspired framework for measuring stakeholder integration in public-private partnerships
Bibliographic record
Abstract
Purpose In a public-private partnership (PPP), the private sector is represented by a company termed the special purpose vehicle (SPV), which combines different stakeholders including designers, contractors and service providers under one umbrella. Correct SPV team selection is critical to ensure PPP success as the SPV must act as an integrated entity. In fact, unless the SPV takes an active role in developing trust and promoting integration principles, segmentation of interests, highly adversarial atmospheres, loss of value and economic inefficiency will prevail. Absence of awareness of such principles among stakeholders and the scarcity of literature investigating SPV stakeholder integration create great risks that jeopardize project success. Accordingly, to mitigate the aforementioned risks and provide stakeholders with both the knowledge and the tools to instigate and maintain integration, this paper aims to develop and test a framework to measure SPV stakeholder integration, inspired by the correlation between integrated project delivery (IPD) and SPV operations. Design/methodology/approach Following a design science research approach, a structured review is conducted to develop the SPV integration metrics and framework. The framework is then validated through face validation by a panel of industry and academic experts to assess its applicability in measuring SPV integration. Finally, the framework is tested on a well-recognized international PPP project to measure the SPV integration level, and the outcomes are discussed and analyzed. Findings The framework was able to assess the integration level of the studied SPV highlighting several areas of low-integration settings and providing guidance for achieving better integration. Originality/value This research is the first that develops a model to investigate the SPV’s integration level, from a holistic IPD perspective, to enable successful relationship management and enhance collaboration success. This study inspires practical recommendations for PPP practitioners to reduce the risks of segregated SPVs and their contribution to PPP failure.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".