Bibliographic record
Abstract
Purpose This paper aims to examine the roles of both aggregate and specific commission rates to control the sales force in relationship marketing with a customer portfolio. Design/methodology/approach Drawn on the concept of customer lifetime value and agency theory, the author calculated both specific and aggregate sales force commission rates in a relationship marketing perspective. Contrary to the prior researchers, the author assumes that, at any period, both the gross margins and retention rate of each customer are a stochastic function of the salesperson’s effort. Findings The results indicated that when there is symmetric information between a sales manager and salesperson, both aggregate and specific commissions can be used to monitor the sales force. Under asymmetric information, however, each type of commission rate can only be used under certain conditions. In addition, conditions in which the aggregate commission is equivalent to the specific commission for each customer were derived. Research limitations/implications Hypothetical data were used to explain the model. It would be more appropriate to use real data to see its managerial relevance. Originality/value In the author’s knowledge, this study is the first that specifically links scholastic customer’s retention and salesperson commission rate to monitor salesperson effort in relationship marketing. It is also the first that shows in which conditions aggregate and specific commission rates are equal for a salesperson’s customer portfolio management.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".