Salesperson coachability: what it is and why it matters
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce the concept of salesperson coachability and to propose potential relationships between it and sales coaching and sales performance. Design/methodology/approach This conceptual paper reviews the sales coaching and sales performance literature to highlight how the knowledge of each may be enhanced by the coachability construct. The concept of athletic coachability is then introduced to explain why it should be adapted and applied to salespeople in a personal selling context. Findings Adapting and applying the concept of athlete coachability to salespeople in a personal selling context may provide sales management practitioners and academics a better understanding of how certain salesperson personality traits combine and interact with certain situational influences to impact sales performance. Research limitations/implications Future studies need to test the propositions advanced. Practical implications Salesperson coachability may be used by sales managers as a screening criterion for sales force recruiting and retention. Social implications Salesperson coachability assessments for recruiting and training may result in lowering job turnover. Originality/value This paper introduces the concept of athletic coachability to the sales literature, argues why the concept should be adapted and applied to salespeople in a personal selling context, and advances testable propositions with respect to its expected relationship with sales coaching and sales performance.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".