The impact of diverse performance measurement on the customer-orientated selling behaviours of B2B salespeople
Bibliographic record
Abstract
The pervasive use of performance measurement frameworks, such as the balanced scorecard, coupled with the growing complexity of today’s B2B sales role is increasing the need for greater levels of measure diversity to evaluate the performance of the modern salesperson. Yet very little is known regarding the behavioural impacts of using more balanced and diverse measures to evaluate individual salesperson performance. This research investigates the relationship between the use of diverse measures of performance and the customer-oriented selling behavior of B2B salespeople. Based on data collected from 274 business-to-business salespeople from Canada, the United States and the United Kingdom and using partial-least squares, structural equation modelling, the author finds that measure diversity is positively associated with salesperson customer-oriented selling behaviour and that this behaviour is fully mediated through salesperson attitudes towards customer-oriented selling. Findings also suggest that measure diversity within a sales performance measurement system is positively associated with increased levels of supervisory sales coaching activity.
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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 | 0.006 | 0.023 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".