Reducing airline accident risk and saving lives: financial health, corporate governance, and aviation safety
Bibliographic record
Abstract
Purpose This study investigates the impact of financial health and corporate governance on aviation safety, aiming to fill a critical gap in existing research. The purpose of this study is to identify how these factors influence the safety records of airlines and provide insights for regulators, airlines and stakeholders to enhance aviation safety. Design/methodology/approach Using a comprehensive international sample spanning 1950–2009 and later, this empirical analysis draws on diverse databases. The authors examine 372 airlines across 70 countries from 1990 to 2016. The research uses statistical models to analyze the relationship between financial indicators, corporate governance quality and aviation safety, addressing limitations of prior single-country studies. Findings The findings reveal a significant inverse relationship between financial health and accident propensity, with profitable airlines exhibiting lower accident rates. Additionally, airlines with higher corporate governance quality, characterized by qualified directors and stable leadership, experience fewer accidents. The study identifies key factors such as pilot errors, mechanical failures and adverse weather, contributing to approximately 75% of accidents, emphasizing the importance of organizational control. Practical implications This research has crucial implications for aviation safety policies and practices. Regulators and international organizations, such as International Civil Aviation Organization and International Air Transport Association, should allocate resources to supervise financially vulnerable airlines and those with lower governance quality. Governments might consider incentivizing safety practices through tax deductibility for relevant expenses. Shareholders are encouraged to prioritize qualified, younger and less busy directors, recognizing their impact on safety performance. Originality/value This study contributes to existing literature by addressing methodological biases and offering a comprehensive international perspective. The identification of a link between financial health, corporate governance and accident rates in the aviation industry provides valuable insights. The research informs policymakers, regulators and industry stakeholders on effective strategies to improve safety by considering financial and governance factors under their control.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".