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Record W4401541465 · doi:10.3390/jrfm17080354

A Structural Equation Model on Critical Risk and Success in Public–Private Partnership: Exploratory Study

2024· article· en· W4401541465 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingGeneral partnershipPublic–private partnershipCritical success factorBusinessExploratory factor analysisQuality (philosophy)Project risk managementSchedulePrivate sectorRisk managementActuarial scienceRisk analysis (engineering)Operations managementFinanceMarketingProject managementEconomicsComputer scienceProject management triangleEconomic growthManagement

Abstract

fetched live from OpenAlex

In construction, risk is inherent in each project, and success involves meeting defined objectives beyond budget and schedule. Factors vary for infrastructure projects, and their correlation with performance must be studied. In the case of public–private partnership (PPP) transportation, the level of complexity is higher due to more involved parties. Risks and success factors in PPP projects affect each other, which may lead to project failure. Recognizing the critical risk factors (CRFs) and critical success factors (CSFs) is indispensable to ensure the success of PPP infrastructure project implementation. However, the existing research on the PPP risk and success relationship has not gone into sufficient detail, and more support to address the existing gaps in the body of knowledge and literature is necessary. Therefore, in response to the missing area in the public–private partnership transportation industry, this paper analyzed the correlation between PPP risks and success factors. It identified, explored, and categorized various risk and success factors by combining a literature review, expert panel interviews, and a questionnaire survey among both the public and private sectors, a win–win principle. The data collected were analyzed using the structural equation modeling (SEM) approach and relative significance. Results show the relationship between risk and success factors, their influence on PPPs, and the most important factors, known as CRFs and CSFs, with high loading factors (LF > 0.5) and high relative importance (NMS > 0.5). The top five CRFs include “Contract quality (incomplete, conflicting)”, “Staff expertise and experience”, “Financial market risk”, “Conflicting objectives and expectations”, and “Inefficient feasibility study”. The top five CSFs were found as “Appropriate risk allocation and risk-sharing”, “Strong financial capacity and capability of the private sector”, “Government providing guarantees”, “Employment of professional advisors”, and “Realistic assessment of the cost and benefits”. This study advances the understanding of risk and success factors in PPPs and contributes to the theoretical foundations, which will benefit not only public management, policy consultants, and investors but also academics interested in studying PPP transportation projects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.289
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it