Unpacking the presence of women as HR directors: organisational factors from MNCs subsidiaries operating in Canada
Bibliographic record
Abstract
Purpose This paper aims to examine the organisational factors responsible for the inclusion of women as Human Resource (HR) directors in Canadian-based subsidiaries of multinational companies (MNCs). Design/methodology/approach Based on the resource dependence theory, this study outlines the features of subsidiaries that appoint a woman HR director. Hypotheses were developed and assessed through analysis of a database obtained from a quantitative investigation. Analyses are based on 100 multinational subsidiaries operating in Canada. Findings Three primary findings arise from this study. Firstly, the larger the subsidiary, the less likely it is for a woman to hold the position of HR director. Secondly, there is a positive and significant correlation between the percentage of women employed in an MNC subsidiary and the presence of women in the HR director position. Finally, MNC subsidiaries with high executive career progression autonomy are more likely to have a woman HR director than those lacking in such autonomy. Practical implications This study proposes improving the representation of women in HR director positions by increasing the percentage of women employed in organisations and by granting greater decision-making autonomy to subsidiaries of MNCs. Originality/value This paper contributes to broader research on gender inequality in leadership. This paper responds specifically to the dearth of research into gender inequality in HR directorships, despite HR as a profession being female dominated. This study focuses upon HR in multinational corporations – again, an under-researched 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".