Impact of Project Complexity and Environmental Factors on Project Success: A Case of Oil and Gas Sector of Pakistan
Bibliographic record
Abstract
Oil and gas industry significantly contribute for economic development of countries enriched with petroleum resources. Mega projects of oil and gas sector usually face many challenges due to environmental issues, high level of risks, huge investments, tight schedules and interdependencies between project activities. Therefore keeping in view, the issues faced by oil and gas sector this study was made to analyze the impact of project complexity and environmental factors on success of oil and gas projects of Pakistan. Based upon hypothetical framework developed for this study, data collection was made from an oil and gas company of Pakistan. After which, data analysis was carried out by using a statistical technique known as structural equation modeling. Project complexity, environmental factors and project success were taken as constructs for model evaluation on AMOS. Analysis of data has concluded that project complexity has negative impact on project success whereas better control over environmental factors enhance the project success rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".