Women in Management at the Port Sector of Maritime Industry in Malaysia - Is there a Gender Imbalance?
Bibliographic record
Abstract
The objective of this paper is to present an insight on the women at management level in the port sector of the maritime industry of Malaysia. While women in Malaysia form 54.7 percent out of 15 million total labour forces in 2017, the overall percentage of women in the top management level is only 15.4 percent in third quarter 2018 of top 100 Public Listed companies. These women at the top management level are mainly in the accounting and legal professions. Their involvement in the logistics and transport service sector are relatively small. The industry carries over 90 percent of the world goods need to access the entire talent pool, regardless of gender. Malaysia by virtue of its extended jurisdiction of the Economic Exclusive Zone has a maritime area which is larger than its land mass. It is the 17th largest trading nation in the world. Hence this paper discusses on whether there is gender imbalance in the sector and the challenges faced by women in the management of the port sector of the maritime industry in Malaysia. Based on the returned structured questionnaires, 57 percent male and 43 percent female of the major port operators illustrate that women represent 14.5 percent in the management level. In terms of perception, both male and female management treat female staff equally (75 percent) and agree that gender diversity should exist (75 percent). However, interestingly, management still have preference for men for port management (49 percent). We suggest reasons for the need to rise and argue that port organizations can capture the symbols of progressive social change and modernity by appointment of women in key positions at the ports. Based on measures in financial performance, Catalyst (2011) study, with 3 or more women in the decision-making level, companies significantly outperformed in return on sales (ROS), return on invested capital (ROIC) and return on equity (ROE). The future in the logistics profession is crucial for a country’s economic growth; and that professionals in the supply chain and logistics serve as the links connecting businesses, people and countries across the globe. Henceforth, the Eleventh Malaysia Plan (2016-2020) places strong emphasis on anchoring growth on people to further aggressively develop Malaysia towards a high-income nation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".