Do candidate reactions relate to job performance or affect criterion-related validity? A multistudy investigation of relations among reactions, selection test scores, and job performance.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considerable evidence suggests that how candidates react to selection procedures can affect their test performance and their attitudes toward the hiring organization (e.g., recommending the firm to others). However, very few studies of candidate reactions have examined one of the outcomes organizations care most about: job performance. We attempt to address this gap by developing and testing a conceptual framework that delineates whether and how candidate reactions might influence job performance. We accomplish this objective using data from 4 studies (total N = 6,480), 6 selection procedures (personality tests, job knowledge tests, cognitive ability tests, work samples, situational judgment tests, and a selection inventory), 5 key candidate reactions (anxiety, motivation, belief in tests, self-efficacy, and procedural justice), 2 contexts (industry and education), 3 continents (North America, South America, and Europe), 2 study designs (predictive and concurrent), and 4 occupational areas (medical, sales, customer service, and technological). Consistent with previous research, candidate reactions were related to test scores, and test scores were related to job performance. Further, there was some evidence that reactions affected performance indirectly through their influence on test scores. Finally, in no cases did candidate reactions affect the prediction of job performance by increasing or decreasing the criterion-related validity of test scores. Implications of these findings and avenues for future research are discussed.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it