Embedding Sustainability in Human Resource Systems: A Case Study of Green HRM at Philippine Airlines
Bibliographic record
Abstract
This study examines how Philippine Airlines integrates Green Human Resource Management (Green HRM) practices, focusing on perceived effectiveness, implementation challenges, and strategic relevance. Guided by the Ability–Motivation–Opportunity (AMO) framework and the Resource-Based View (RBV), the research employs a single-case descriptive design using quantitative and qualitative data derived from an employee survey. Quantitative responses assess the presence of green HRM practices, while qualitative insights are drawn from open-ended items. Results show that ability-enhancing practices, particularly environmentally conscious recruitment and targeted training, are the most institutionalized. In contrast, performance evaluations and participatory mechanisms related to environmental sustainability are inconsistently applied. Thematic responses point to the need for stronger communication, standardized appraisal criteria, and formal engagement structures. These findings emphasize the strategic potential of aligning HR practices with environmental goals, particularly in service-sector organizations. The study contributes empirical evidence to the Green HRM literature in aviation and highlights actionable pathways to strengthen organizational sustainability through human capital.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".