Changing an unfavorable employer reputation: the roles of recruitment message‐type and familiarity with employer
Bibliographic record
Abstract
Abstract An unfavorable employer reputation can impair an organization's ability to recruit job seekers. The present research used a 4 week longitudinal experimental design to investigate whether recruitment messages can positively change an existing unfavorable employer reputation. Two hundred and twenty‐two job seekers rated their perceptions of an organization before and after being randomly assigned to receive a series of high‐ or low‐information recruitment messages. As expected, job seekers receiving high‐information messages changed their perceptions more than job seekers who were exposed to low‐information messages. In addition, job seekers' initial familiarity with the employer was negatively related to change in their perceptions of employer reputation. Finally, there was some evidence that job seekers' familiarity with the employer influenced the impact of different recruitment messages. Implications for research and practice are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".