Factors influencing demand of air cargo: a case study on Malaysia Airlines Cargo Sdn Bhd (MASKARGO) / Nurul Uzma Ngah @ Omar
Bibliographic record
Abstract
This project is conducted to fulfill the requirement needed for Faculty of Business Management, UiTM. The researcher has selected a topic entitled “Factors Influencing Demand of Air Cargo” using Malaysia Airlines Cargo Sdn. Bhdas case study. In this study, the researcher uses the data from the first quarter of 2001 until the second quarter of 2013 which consists of 50 data. Data was collected from department of statistics and provided by the company. To estimate the function related to variables involved in independent variables (gross domestic product (GDP), jet fuel price and export), the researcher uses multiple linear regression analysis. After the researcher analysed the data, the researcher found that jet fuel price and export have significant influence toward demand of air cargo while gross domestic product (GDP) has no significant relationship to demand of air cargo. Furthermore, this study fulfills the researcher’s first objective which is to investigate the factors that influence demand of air cargo and the second objective which is to know the impact of air cargo demand towards Malaysia economy that is national income and unemployment rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".