Influencing Factors of Organizational Performance in Nepal Airlines Corporation
Bibliographic record
Abstract
The objective of the study is to evaluate performance influencing factors in Nepal Airlines Corporation (NAC) through mixed research method, qualitative and quantitative analysis. The primary data were obtained from in-depth interview with fifteen government and NAC executives. Secondary data were collected from Nepal Government, NAC publications and International Air Transport Association (IATA). Revenue generation and passenger movement rate is found with average performance. Motivated employee, entrepreneurial marketing, collective leadership, ownership feeling of government and environmental support were explored as key performance factors. Sophisticated technology, airworthiness, and international standard and recommended practice were found as unique features. Lack of aircraft, unfair political influence and alienation of staffs in unionism were identified the reasons of lacking the business growth. A performance framework is proposed which comprises entrepreneurial marketing (proactiveness, risk taking, innovativeness, opportunity focused, resource leveraging, customer intensity and value creation), collective leadership, sophisticated technology and sufficient number of modern aircrafts, service reliability and safety, and government support. The study recommends Nepal government to take ownership of NAC, and adopt fair and professional management practice rather than political quota distribution in its governing body, board of directors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".