Salesforce Compensation Scheme and Consumer Inferences
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
We investigate the salesforce compensation strategy of a firm selling products in a category that several consumers find technically sophisticated, such as electronics or financial products with legal fine print. Consumers are unable to judge the value difference between a baseline product and a product upgrade with add-on features. While the firm and the salespeople are informed of the value of these features, consumers are uncertain. Thus, consumers have to rely on sales assistance to evaluate alternatives. The salesperson decision variables include selling effort and whether to “oversell” the consumer by overclaiming the value of added features. Because sales revenue depends on both the salesperson's selling effort and consumers' valuation of the added features, the salesforce incentive scheme (which can consist of salary, sales commission, or consumer satisfaction-based commission) may induce the short-term oriented salesperson to misrepresent the value of the upgrade. Exaggeration of the value of the added features, however, results in reduced satisfaction levels leading to lower profits for the firm. We show that a salesperson selling products where the value of the upgrade is low prefers to make higher claims when the sales commission rate is sufficiently high. We conjecture that consumers aware of the incentive structure facing the salesperson expect the true value of the add-on feature to be lower than the claimed value. We study the optimal compensation scheme of a firm, which has to communicate her true type and retain its salesforce credibility. We identify the conditions under which a high-upgrade-type firm indicates its true value by altering sales commission rate and satisfaction-based commission rate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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