How do data, project management maturity, and PMO support shape resilient oil and gas projects?
Bibliographic record
Abstract
The global disruption of 2020-2022 forced organizations to reassess their resilience strategies, with executives agreeing to prioritize project management, data analytics, and problem-solving among the top 10 skills for their workforce. In this context, the role of the Project Management Office (PMO) in building resilience and achieving project success has been critically examined. While PMOs are assumed to enhance governance and structured guidance, it remains unclear whether their presence significantly influences resilience-building during disruptions. This study investigates this issue by conducting multi-group analysis (MGA) using data from 170 practicing project managers worldwide who navigated the global disruption. The study examines how Data Analytics Capability (DACP) and Project Management Maturity (PMMM) impact Project Resilience (PRES) and Project Success (PSUC), employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for empirical validation. Results indicate that both DACP and PMMM significantly enhance resilience, with DACP demonstrating a comparatively stronger effect. Project resilience, in turn, has a robust positive impact on project success, highlighting its strategic value beyond mere survival. Notably, while multi-group analysis shows no statistically significant differences between PMO-supported and non-PMO-supported projects, this does not undermine the importance of PMMM and PMO support. Instead, it reinforces that structured governance and maturity practices are considered baseline expectations in high-reliability industries, rather than distinct differentiators of project resilience.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".