Does Demand Uncertainty Moderate the Relationship of Risk Attitude and Sticky Cost? Evidence From Egypt
Bibliographic record
Abstract
The current paper explores the relation between managers’ risk attitude and cost stickiness behavior and the role of demand uncertainty as a moderation variable in the Egyptian business environment. Managers’ risk attitudes are measured using Bo and Sterken (2007) proxy measure [Bo, H., & Sterken, E. (2007). Attitude towards risk, uncertainty, and fixed investment. The North American Journal of Economics and Finance, 18(1), 59–75]. Demand uncertainty is measured by the standard deviation of firms’ sales over the sample period. The study sample includes 114 Egyptian-listed firms over a 14-year period (2004 - 2017) which results in 1,419 firm-year observations. The study models are estimated using the ordinary least squares (OLS) with a fixed-effects model. Findings show that in the presence of high demand uncertainty, risk-averse managers respond to sales decrease by cutting resources which lowers cost stickiness. One of the limitations is that some factors like firms’ policies, corporate governance mechanisms, and board of directors’ characteristics could dilute the effect of manager’s risk attitude on cost stickiness. The current research emphasizes the importance of considering the firm’s operating environment when selecting a manager for the firm, and the role of directed training to align the manager’s personal characteristics with the firm's objectives. The current research contributes to the previous literature by documenting the effect of manager’s risk attitude on cost stickiness and the role of a firm’s demand uncertainty as a moderating variable between these two variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".