A meta-analytic review of the effectiveness of personnel selection procedures and training interventions in sales occupations
Bibliographic record
Abstract
Applied research on the effectiveness of personnel selection procedures and training interventions in sales occupations was examined by meta-analytic techniques. The literature review included 170 predictorcriterion effect sizes of selection procedures, and 12 effect sizes of training interventions. On average, composite-domain selection assessments predicted salesperson performance (multiple R = .59, p < .05), while the effects of single domain selection assessments and training interventions (r = .27, p > .05; r= .16, p > .05, respectively) were not statistically significant. Among the personnel selection procedures, subjective ratings of performance were predicted by personality assessments (r = .20, p < .05), biographical information inventories (r = .20, p < .05), and cognitive ability (r = .32, p < .05). While objective measures of performance were predicted by special purpose sales assessments (r = .49, p < .05). An analysis of the utility of selection procedures and training interventions based on the average effect sizes found in the present study revealed substantial dollar value gains to the company from the use of personnel selection procedures and training interventions in sales occupations.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".