Perspectives on Audit Opinions and Key Audit Matters in the Global Airline Industry and the COVID-19 Pandemic
Bibliographic record
Abstract
The present study investigates whether the COVID-19 pandemic had a negative effect on audit opinion and led to differences in the composition of key audit matters (KAMs) observed in the airline industry. This study uses a sample of 55 airlines whose financial statements are based on International Financial Reporting Standards (IFRSs) and whose financial statement audit follows National or International Standards on Auditing (ISAs) for audit opinion, as well as a sample of 42 airlines whose financial statements are based on IFRSs and whose financial statement audit follows ISAs for the composition of KAMs. A textual analysis, a content analysis, a frequency distribution, and a chi-square test were conducted for the periods before, during, and after the COVID-19 pandemic. The findings reveal that the COVID-19 pandemic had no significant effect on audit opinion, except for one airline whose audit report declared a disclaimer of opinion. In contrast, the impairment of goodwill and intangible assets (as an industry-specific KAM) and going concern (as a KAM specific to the COVID-19 pandemic) were the two KAMs that were typically observed during the COVID-19 pandemic due to increased uncertainty. This was found to be the case, even though the main KAMs in the airline industry are usually revenue recognition; lease accounting; property, plant, and equipment (PPE); and hedge accounting. This study contributes to the debate on the effect of the COVID-19 pandemic on audit opinions and KAMs by offering evidence from the underexplored airline industry.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".