Bibliographic record
Abstract
• Objectives/Research questionsThe salesperson role goes beyond the basic sales function, as they also need to build good relationship quality with professional customers to engender and maintain loyalty. This research aims to identify the levers of salespeople’s relational performance and reveal how those levers contribute to relationship quality.• MethodologyDrawing on a literature review, we provide a list of 21 elements of relational performance (ERPs) for salespeople. Using the tetraclass model (Llosa, 1997) with a sample of 202 artisan and shopkeeper customers, we then hierarchize the contribution of the 21 factors to the formation of relationship quality by classifying them into four categories: basic, key, plus, and secondary elements.• ResultsThe results show that customer-oriented behaviors are basic, customer culture elements are key, and elements linked to salespeople’s extra roles (such as acting as a friend and proactivity) are plus.• Managerial/societal implicationsThis research puts forward new findings on the the hierarchizing of HR actions (training, customer culture, and recruitment) with salespeople with a view to improving relationship quality with customers, depending on customer value and competition intensity.• OriginalityAlthough it is usually used to explain the construction of satisfaction in B2C services, in this research tetraclass is applied to the relationship quality between salespeople and their customers in the B2B domain, thus broadening its conceptual scope and external validity.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.914 | 0.892 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".