Customers’ managerial expectations and suppliers’ asymmetric cost management
Bibliographic record
Abstract
This paper investigates how managers in the upstream firm (i.e., supplier) adjust their allocations of cost resources in response to managerial expectations of the downstream firms (i.e., customers) on the future demand and prospects. We conduct an empirical analysis to examine the impact of the tone of customers’ forward‐looking disclosures (FLDs) contained in the Management Discussion and Analysis section of 10‐K filings on suppliers’ asymmetric cost behaviors, characterizing costs decreasing less for sales fall than increasing for equivalent sales rise (i.e., “cost stickiness”). We show that the degree of suppliers’ asymmetric cost management is positively associated with their customers’ tone of FLDs. Moreover, such an association is stronger when the suppliers produce more unique products for their major customers. Our inferences remain robust after controlling for the strategic disclosure behavior of the customer firms, ruling out an alternative mechanism of suppliers’ own managerial expectations and managerial empire‐building incentives. Lastly, using a decision made by the U.S. Supreme Court in 2005 as a quasi‐natural experiment setting, we show that the effect of customers’ tone of FLDs on suppliers’ cost stickiness becomes stronger when FLDs are more informative. To the best of our knowledge, this paper is the first to introduce cost stickiness in the operations management context to capture management's operational decision intervention regarding resource allocation. We also contribute to information sharing literature by highlighting the importance of channels other than the traditional explicit information sharing channel in obtaining demand‐relevant information in supply chains.
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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 | 0.005 | 0.030 |
| 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 | 0.001 | 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".