Recruiting Through the Stages: A Meta‐Analytic Test of Predictors of Applicant Attraction at Different Stages of the Recruiting Process
Bibliographic record
Abstract
We used meta‐analysis and semipartial correlations to examine the relative strength and incremental variance accounted for by 7 categories of recruiting predictors across multiple recruitment stages on applicant attraction. Based on 232 studies (250 samples, 3,518 coefficients, n = 108,632), we found that characteristics of the job, organization, and recruitment process, recruiter behaviors, perceived fit, and hiring expectancies (but not perceived alternatives) accounted for unique variance in applicant attraction at multiple stages. Perceived fit was the strongest relative and unique variance predictor of applicant attraction albeit a nonsignificant predictor of job choice. Although not among the largest zero‐order predictors, recruiter behaviors accounted for substantial incremental variance at the first 2 stages. Organizational characteristics are more heavily weighed by applicants when maintaining applicant status as compared to the stage of application, and recruitment process characteristics are weighed progressively more as the recruitment stages advance. Job characteristics accounted for the greatest unique variance in job choice decisions. Job characteristics are more predictive in field studies, whereas recruiter behaviors, recruitment process characteristics, hiring expectancies, and perceived alternatives produced larger effect sizes in the laboratory. Results are discussed in terms of their theoretical and practical implications with future research suggestions.
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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 | 0.046 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".