COVID-19 and Cost Stickiness: the Impact of the Pandemic on Resource Management Decisions
Bibliographic record
Abstract
This study examines the impact of the COVID-19 pandemic on corporate executives’ cost management decisions during revenue declines. Managers often cut costs in response to poor performance, but this approach during a downturn can lead to higher costs when sales recover. Such decisions depend on management’s expectations about the upcoming market. At the start of the pandemic, firms had to assess the potential severity and duration of COVID-19 and manage resources accordingly. This research explores (1) the management’s perception of the pandemic as a short- or long-term event and (2) changes in cost behaviour before and after COVID-19, especially regarding executives’ uncertainty perceptions. Pre-COVID-19 findings indicate that firms displayed cost stickiness, aligning with previous studies that demonstrate costs decrease less during sales declines than they increase during sales rises. Post-COVID-19, this stickiness weakened, with costs adjusting more symmetrically to revenue changes. These results highlight how executives’ expectations and perceptions of uncertainty during the global crisis affected firm resource management, leading to changes in asymmetric cost behaviours.
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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 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".