Schmooze or lose: Social friction and lubrication in e-mail negotiations.
Bibliographic record
Abstract
We explored how the process of e-mail negotiation differs from face-to-face negotiation and then tested hypotheses about how its liabilities can be minimized. In the first experiment, participants negotiated one-on-one, either face-to-face or via e-mail. Consistent with expectations, negotiators took advantage of e-mail by exchanging more complex, multiple-issue offers than they exchanged face-to-face. Yet, e-mail reduced rapport-building conversation about non-negotiable, contextual issues, and clarifying questions which prevent misunderstandings and facilitate rapport. E-mail negotiators compensated with more explicit statements about the relationship, but these were less effective in preventing mistrust and misunderstanding. In a second experiment, we tested the power of a minimal intervention designed to reduce the liabilities of e-mail. Half the negotiation dyads had a personalized telephone conversation (schmoozed) before engaging in e-mail negotiations, and the other half did not schmooze. Even though the telephone conversation was strictly non-business, schmoozing negotiators anticipated and planned a cooperative, positive negotiation experience from the outset, and they attained better economic and social outcomes. This was primarily true among mixed-gender dyads.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".