The Influence of the Trade-off between Profitability and Future Increases in Sales on Cost Stickiness: Evidence from Jordan
Bibliographic record
Abstract
This study aims at analyzing cost stickiness under the dilemma between current profitability and future sales increase. The study population consisted of all Jordanian industrial companies listed on the Amman Stock Exchange (ASE) during the period (2007-2017). The study sample consisted of (30) industrial companies, were used in the analysis. Panel data regression was used to test the relationship between the variables in the study. Results supported the Anderson et al. (2003) argument in that selling, general and administrative expense for Jordanian industrial firms listed in Amman stock exchange (ASE) follow the sticky cost behaviour, they increased by (0.34%) for 1% increase in sales, however, they didn’t change by any sales decrease. During sales decline results showed that future sales growth did not have a stressing effect on cost stickiness and didn’t drive greater cost stickiness, however, changes in profitability was proved to have a significant positive relation to cost stickiness when sales decrease, meaning that managers apply greater adjustments in SGA (greater cost stickiness) in the case of the attainment of unfavourable changes in profitability. The study recommended a number of recommendations, including Companies should know the factors that affect the cost behavior and take into consideration when analyzing costs and making administrative decisions in companies which will, in turn, improve the process of making administrative decisions and investment decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".