Joint determination of salesforce compensation, production, and pricing decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research on salesforce compensation contract has focused on contract design itself for a long time. However, how contract design influences both production and pricing decisions of a firm is still not explored. This paper attempts to fill this research gap and answer the following questions. If the market demand is not only controlled by salesperson's effort, but also by the firm's pricing decision, how does the private information possessed by the salesforce affect the firm's marketing and operational decisions? Specifically, under the environment of incomplete information, what are the joint optimal contract design, optimal quantity, and pricing decisions? How is the production/inventory quantity decision affected by other decisions? In this research, three scenarios (first best, pooling, and separating) are analysed and closed-form optimal solutions are obtained. Detailed numerical analyses are carried out to gain some further insights into the sensitivity of the optimal solution.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it