Reclaiming the contingent nature of the determinants of salesperson performance: an extended meta-analysis
Bibliographic record
Abstract
Professional selling has been transformed over the last decade and is in constant flux. Fundamental changes have occurred in how best to achieve salesperson performance and how to measure it. In this article, we present a meta-analysis of the determinants of salesperson performance using 150 studies, 936 raw effects, and ten moderating variables captured from 2009 to 2020. Our findings enable us to identify 19 key determinants of performance and to classify these within a sales determinant continuum, ranging from universal, to context-specific to necessary but insufficient predictors of performance. Our results show the significant influence the operationalization of salesperson performance has on the relationship between sales determinants and performance outcomes. Our study offers more nuanced guidelines for sales management practice than previous studies and suggests a greater alignment between research sampling and performance measurement methods to further our understanding of sales performance going forward.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".