The Gender Negotiation Gap: Examining the Impact of Perceived Academic Supervisory Support
Bibliographic record
Abstract
The recruitment and retention of academic staff in business schools is a highly challenging and important process for institutions to maintain their standing in a competitive global environment. As a result, perspective candidates are often provided with a significant amount of latitude during compensation package negotiations. The present study investigated the prevalence of a negotiation wage gap between men and women and the influence of perceived academic supervisory support (PASS) among a sample of management professors from Association to Advance Collegiate Schools of Business (AACSB) accredited business schools located in both the United States and Canada. We found that women were less likely to negotiate and were overall less successful than their male counterparts when they elected to negotiate, particularly on bargaining elements revolving around money (e.g., salary, funding). Further, we noticed that PASS moderates the relationship between gender and negotiation success such that a highly supportive supervisor improved negotiation success for women, but had no impact on men. We discuss the implications of this work and provide institutions with several recommendations to improve negotiation and gender wage parity.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".