Effects of the Institutional Environment on P3 Projects: Evidence from Canada
Bibliographic record
Abstract
Public and private sector actors are increasingly deploying public private partnerships (P3s) to develop and deliver critical infrastructure and services in Canada, and accounting researchers have noted the importance. Using the Neo-Institutionalism perspective and a case study approach, we investigate the effects of the institutional environment on project outcomes to contribute to the accounting literature on P3s. Based on an empirical study of the Alberta institutional environment using the Edmonton’s Anthony Henday Highway P3 projects, we provide a Canadian evidence that the institutional environment has significant effects on project performance, and program permanence and continuity. Our study suggests that P3 enabling environments present: 1) relevant P3 policy measures and committed political support by field actors; 2) a path-dependent response to project outcomes; and 3) institutional environment elements that are mutually re-enforcing and have synergistic effects. We document a strong political support for P3s, a favourable policy environment, and the organizational capacity to implement P3s in Canada, but we are unsure whether or not our findings are driven by “gaming” accounting treatment resulting from inconsistent accounting standards for P3s. Our study also provides evidence on P3 enabling attributes that would be beneficial to regulators, and to managers saddled with the tasks of developing and implementing P3 projects in various contextual settings.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".