A study of linkages among leadership gender diversity, pay equity practices and financial performance
Bibliographic record
Abstract
Purpose Pay equity practices may help address the gender pay gap and resulting inequalities, which have organizational, economic, and social implications. Little is known about the determinants and benefits of pay equity practices. The knowledge created through this research can help drive and justify the offering of pay equity practices. Using an institutional theory lens, we propose: a positive relationship between leadership gender diversity and financial performance, a positive relationship between leadership gender diversity and pay equity practices, a positive relationship between pay equity practices and financial performance, and a mediating role of pay equity practices between leadership gender diversity and financial performance. Design/methodology/approach Based on a time-lagged design, archival data from multiple sources were collected from up to 4,943 organizations in Australia. Findings The analyses supported all hypotheses. The findings support and extend institutional theory, addressing important knowledge gaps pertaining to determinants and outcomes of pay equity practices and the mediating role of pay equity practices. Practical implications Practical implications include the need for management of leadership gender diversity to drive pay equity practices and financial performance. Originality/value Using novel theorizing, we provide new evidence for relationships between leadership gender diversity and pay equity practices, pay equity practices and operating revenue, and a mediating role of pay equity practices. These findings set a new solution-focused direction for pay equity research, as past research focused on pay gaps and perceived pay inequity.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".