Enhancing Technical Efficiency in the Oil and Gas Sector: The Role of CEO Characteristics and Board Composition
Bibliographic record
Abstract
This study investigates how CEO characteristics, board composition, and firm size influence the technical efficiency (TE) of energy firms. We aim to understand how these factors contribute to production inefficiencies, which may help explain fluctuations in oil prices. Using stochastic frontier analysis (SFA), we analyze data from 100 American energy firms over the period from 2006 to 2019. Our results show that inefficiencies in production are primarily driven by specific CEO traits, the size and structure of the board, and the overall size of the firm. Based on the findings of this study, we recommend focusing on the selection of executive managers with specific qualifications, particularly those with extensive experience in managing oil and gas companies. Leadership positions should prioritize seasoned managers with accumulated expertise in this sector, and preference should be given to candidates with advanced educational backgrounds. Encouraging CEOs to acquire equity stakes in the company can significantly boost the technical efficiency of oil and gas firms. Additionally, offering competitive salaries and performance-based bonuses may further enhance managerial effectiveness and drive technical improvements. In addition, expanding the size of boards of directors in oil and gas companies is also anticipated to positively influence their technical efficiency. Finally, pursuing mergers and acquisitions to grow the scale of oil and gas companies represents a strategic approach to improving operational efficiency while contributing to the stability of global energy prices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".