Potential application areas and benefits of blockchain-enabled smart contracts adoption in infrastructure Public-private partnership (PPP) projects
Bibliographic record
Abstract
The traditional, paper-centric infrastructure public-private partnership (PPP) contracts have experienced record numbers of failures and terminations due to contract compliance issues, lack of trust and transparency, and information distortions. While studies on the adoption of blockchain and smart contracts in PPP are still growing, a quantitative survey of global experts on the application areas and potential benefits of Blockchain-enabled smart contracts (BSC) in the context of PPP is lacking. This study comprehensively examined the potential application areas and benefits of BSC adoption in infrastructure PPP projects to understand their impact on the decision to digitalise PPP and ensure sustainable PPP project performance. The snowball sampling technique and questionnaire were used to gather data from experts across countries. Data analysis was done using means analysis, normalisation value, coefficient of variation , Kendall's coefficient of concordance, Kruskal-Wallis test and partial least square-structural equation modelling (PLS-SEM). The study found high awareness and knowledge of the potential benefits of smart contract adoption in infrastructure PPP projects. The leading benefits of BSC adoption in PPP are (1) decentralisation of payments and other transactions, (2) enhancing supply chain visibility and integration, (3) the autonomy in contract administration, (4) prevent misapplication of contractual provisions, and (5) enhances alternative dispute resolution (ADR). The PLS-SEM revealed that six of the eight hypothetical paths were significant. This study advocated for promoting the digitalisation of infrastructure PPP projects. It could serve as an essential resource to policymakers and industry professionals in their quest to improve PPP project performance and minimise failures.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".