Explaining Differences in Men and Women's Use of Unethical Tactics in Negotiations
Bibliographic record
Abstract
Abstract Emerging evidence suggests that competitiveness and empathy explain men's greater willingness to use unethical tactics in negotiations. We tested whether and how robustly they do with three distinct studies, run with three distinct populations. Simultaneous mediation analyses generally, but not completely, confirmed our expectations. In Study 1, only competitiveness mediated sex differences in unethical negotiation tactics among Chilean business students. Although empathy also explained willingness to use unethical negotiation tactics, the Chilean men and women did not differ in this regard. In Study 2, competitiveness and empathy both mediated sex differences in American business students’ intentions to lie to a client, but competitiveness explained greater variance. In Study 3, both factors explained sex differences in lying to bargaining partners for real stakes by working‐age Americans. Our findings suggest that competitiveness and empathy each explain sex differences in willingness to use unethical tactics, but the former does so more consistently.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".