What most attracts potential candidates? Innovative perks, training, or ethics?
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of two instrumental organizational attributes (innovative perks and training) and one symbolic organizational attribute (ethics) on applicant attraction. Design/methodology/approach Using a sample of business undergraduates in their final year ( n =339) and a policy-capturing approach, the authors tested a 2 (absence/presence of innovative perks) ×2 (few/many training opportunities) ×2 (ethics is not very important/is important) quasi-experimental design using ANCOVA. Findings In regard to main effects, results show that all attributes have a significant effect on applicant attraction, the “ethics” organizational attribute having the strongest direct effect followed by “training” and then “innovative perks.” In regard to all interaction effects, findings are only significant for two two-way interaction effects: “innovative perks×training” and “innovative perks×ethics.” Specifically, results indicate that offering innovative perks only had a positive and significant effect on applicant attraction when: a firm offered few training opportunities and ethics was important for the firm. Originality/value This study compared three key organizational attributes where most studies only tested one. Understanding which organizational attributes have the greatest influence on potential candidates’ attraction can help organizations optimize recruiting. The results suggest that developing an organizational brand that focuses particularly on ethics and training constitutes a winning recruitment strategy. This experiment is the first to provide causal conclusions on the relationship between innovative perks and attraction.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".