Negotiator style and influence in multi‐party negotiations: exploring the role of gender
Bibliographic record
Abstract
Purpose The extant literature suggests that men and women do not necessarily possess identical negotiating styles. However, unfortunately the literature has yet to clearly identify the role that gender plays in the negotiation context and in the behaviours of male and female negotiators. This paper aims to contribute to understanding of this topic. Design/methodology/approach Conceptual/theory paper (with relevant literature reviews). Findings Perceived power in a multi‐party negotiation can be affected by numerical status, as well as social status with the result that a minority female in a group dominated by males will act differently from a male in a female‐dominated group. Research limitations/implications This paper draws on theories of proportional representation, social roles and perceived status, in order to identify a number of factors that can affect the degree of influence exerted and the behavioural style adopted among male and female negotiators in mixed‐gender, multi‐party business negotiations. Practical implications This paper explores a very practical question – do men and women behave differently at the “bargaining table”? And how does gender play a role in multi‐party negotiations? Originality/value This study is highly original, given the lack of theory in this area.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".