Bibliographic record
Abstract
Purpose – This paper aims to answer a prominent question that arises for the manager who wishes to recruit a salesperson to maintain and develop a portfolio–customer relationship: Under which condition is this decision profitable for the firm? Though several authors have underscored the importance of the salesperson's role in the creation of purchaser–salesperson relationships, in the author's knowledge, no study has focused on the salesperson's profitability in the relationship approach. This issue is significant for sales managers because the investment in sales force is greater, and the relationship profitability with customers is not guaranteed. Design/methodology/approach – Econometric model based on transaction cost economics theory and dynamic exchange between firm, salesperson and a customer. Specifically, this model links between customer life value, firm financial value, salesperson cost and relationship time. Findings – Three zones are identified that can characterize the dynamic salesperson profitability. It was shown that only one zone can be profitable to the firm. Research limitations/implications – This result is important because it can solve the equivocal posit between scholars with regard to the success or the failure of relationship marketing. This study also specifies the critical retention rate, the critical duration time in which a salesperson begins to be profitable. Originality/value – In the author's knowledge, this study is the first to use an exchange model to show in which conditions the salesperson will be profitable in relationship marketing.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".