Females at Strategic Level Affecting Logistics Firms’ Competitiveness: Qualitative Comparative Analysis of Contrasting Gender in Pakistan and Canada
Bibliographic record
Abstract
Post-World War II, a significant growth in the representation of females in the workforce emerged, reflecting the significance of the female workforce in the present era. This qualitative research explores distinctive factors associated with female representation at the top levels of cargo logistics firms affecting overall competitiveness and performance in Pakistan and Canada. Earlier research has been of a single dimension and quantitative to a large extent, whereas this study undertakes a multivariate stance by considering leadership style, economies, and gender diversity in a qualitative manner, using networking, connections, and snowball sampling semi-structured interviews conducted with employees at all three levels of management. After a combined 91 (31=strategic level, 28=middle level, and 32=operational level) interviews, we reached the saturation point from which to draw a logical conclusion. The findings revealed that higher female representation at the top levels enhances innovation and the competitiveness of the firms. Gender diversity improves operational efficiency and performance. Males showed a higher preference for structured leadership, while females preferred flexible leadership. Interestingly, females in emerging economies have a higher chance of career advancement. Males are task-oriented and therefore demonstrated a preference for autonomy, while females are people-oriented, and thus showed creativity and concern for others. The original contribution of this study is that it enhances the body of knowledge by offering a qualitative in-depth understanding of the relationship between variables from a multi-dimensional perspective, namely gender, management levels and economies of interest, within one research framework.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".