Corporate Social Responsibility Practices of Selected Airline Companies: Inputs for a Sustainable Aviation Service
Bibliographic record
Abstract
This paper tries to identify the strategic corporate social responsibility of selected airline companies to ascertain inputs for the creation of a sustainable service delivery, by looking at the implementation level of its corporate social responsibility in the environmental, philanthropic, economic and ethical areas. The study shows that in terms of environmental responsibility, waste segregation has the highest level of practice, while carbon footprint reduction is the lowest. Philanthropic activities participated in by employees is the most frequently practiced among other philanthropic responsibility initiatives, while the provision of scholarship grants is the least practiced. For economic responsibility, the recruitment of diverse talent received the highest mean score indicating it as the most practiced activity, while financial management strategy practices is the most challenged. As far as ethical responsibility is concerned, the practice of honesty is prevalent, while direct stakeholder involvement in planning the manual of ethical operations is almost none. The study also explored whether there was a significant difference in the assessment of CSR strategies between the two groups of respondents. The results indicated that there was a significant difference in the assessments of employees and passengers in terms of environmental, philanthropic, economic, and ethical responsibilities in comparison to management administrators. Moreover, the challenges encountered in the implementation of CSR strategies were identified and ranked, providing insights into the perceived difficulties faced by airline companies in implementing CSR strategies. Based on the findings of the study, recommendations for responding to the challenges of implementing CSR strategies were proposed. These recommendations include additional signage for environmental awareness, ensuring the sustainability of philanthropic activities, crafting financial measures to address constraints, and ensuring sustainable implementation of its manual of operation. The findings and recommendations provides inputs to for a sustainable service delivery and promoting responsible business practices in the airline industry.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".