Enhancing Service Quality Through Effective Language and Fuzzy SERVQUAL in Occupational Safety Companies
Bibliographic record
Abstract
This study explores the application of the Fuzzy SERVQUAL method to evaluate and enhance service quality in Occupational Safety and Health (OSH) training, with a particular focus on effective communication.The research aims to identify gaps between participant expectations and perceptions, particularly regarding the clarity and effectiveness of language used during training sessions.Data were collected through questionnaires distributed to OSH Expert Training participants at various stages of their training, supplemented by direct observations to gain deeper insights into their interactions with service providers.The responses were then converted into fuzzy values and analyzed using a gap analysis approach to determine the extent to which service quality expectations were met.The results indicate that all gap values were negative, suggesting that the services provided have not yet achieved a satisfactory level for participants, especially in terms of communication effectiveness.The most significant gaps were found in the clarity of information, responsiveness of instructors, and overall service reliability.These findings highlight the need for improvements in language use and communication strategies to enhance participant satisfaction and service effectiveness.Based on the Fuzzy SERVQUAL analysis, specific recommendations are proposed to optimize service delivery through clearer and more structured communication approaches, ultimately improving the quality of OSH training and ensuring better engagement from participants.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".