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Record W4389918911 · doi:10.1080/09537287.2023.2294310

Variations in critical success factors of PPP-procured construction projects over lifecycle phases

2023· article· en· W4389918911 on OpenAlexaff
Udechukwu Ojiako, Maxwell Chipulu, Ahmad Meile Almeile, Lavagnon A. Ika, Hamdi Bashir, Alasdair Marshall, Eman Jasim Hussain AlRaeesi

Bibliographic record

VenueProduction Planning & Control · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCritical success factorBusinessSystem lifecycleComputer scienceProcess managementEngineeringProduct lifecycleMarketing

Abstract

fetched live from OpenAlex

We explore the variations in importance of critical success factors (CSFs) over project lifecycle stages of construction public–private partnership (PPP) projects. A two-staged study is employed involving a literature search and identification of factors supplemented with a two-staged Delphi exercise. Our findings point to the existence of 24 CSFs with varying levels of importance over the project lifecycle. Three CSF appeared important in three of the four phases. Most CSFs appeared important in two of the four phases while six CSFs appeared in only one phase. Findings suggest project stakeholders emphasized specific endogenous CSFs as being more important during the initial phases of the project. However, these CSFs then gave way to the prominence of more exogenous CSFs emphasizing change and transformation as the project moved towards the ‘Execution’ and ‘Benefits realization’ phases. Theoretical and practical implications of the findings are discussed.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.078
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.005
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.335
Teacher spread0.268 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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