Establishing Success Measurements of Joint Ventures in Mega Projects
Bibliographic record
Abstract
Joint ventures (JVs) in the oil and gas industry will not be disappearing anytime soon; the number of JVs is expected to increase in the near future as the global demand for energy resource increases. Joint ventures need to be measured correctly to properly asses their potential for success. This paper presented a study of 11 measures with respect to their contribution in measuring the degree of success of JVs. This research was supported by a variety of academic and private studies spanning several years; the aim was to develop a model that can be used in measuring the degree of success in JVs projects. Correlations and components analysis were used to examine the relations among measures and to discover underlying patterns. As a result, sustainability and financial measures were the two extracted success measures. Then, reliability test and structural equation modeling (SEM) were used to validate and findings and the relationships between measures and degree of success of JVs. The sustainability measure encompassed five factors including longevity, stability, community alignment, environmental influence, and dispute resolution. The financial measure consisted of five factors including profitability, market share, access to new markets, growth, and reputation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".