THE INFLUENCE OF MEANING ON CAREER COMPETENCY AMONG AIRCRAFT LINE MECHANICS IN JAPANESE LCCS: MEDIATING ROLE OF EXPERIENTIAL LEARNING
Bibliographic record
Abstract
The Japanese aircraft industry faces serious issues with competent aircraft mechanics due to business changes, reduced tacit knowledge learning, increased aircraft reliability, diversity of work values, and an unstructured development system. Therefore, this study examines the influence of meaning on career competency among the aircraft line mechanics in Japanese Low-Cost-Carriers (LCCs), using experiential learning as a mediator. Based on the expertisation and adult learning theories, this study attempts to test 12 hypotheses concerning the relationships of meaning, experiential learning, and career competency, emphasising the mediating effects of experiential learning. Data were collected using an online survey of 284 respondents, and 220 usable responses were obtained (77%). In this regard, validity, reliability, and empirical accuracy were assessed using the Partial Least Squares Structural Equation Modelling (PLS-SEM). eight hypotheses out of 12 were supported. The test result showed that meaning positively influence on experiential learning, some dimensions of experiential learning positively influence on career competency, and some dimensions of experiential learning mediate between meaning and career competency. The findings of this study have significant implications across methodology, theoretical, and practical implications. However, several limitations should be acknowledged, and these form the basis for future research directions.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".