EFFECTS OF PERCEIVED SERVICE QUALITY OF VESSEL TRAFFIC SERVICES ON THE CORPORATE IMAGE: A STUDY ON THE TURKISH STRAITS
Bibliographic record
Abstract
Vessel Traffic Services (VTS) is one of the most important aids for ships in the dangerous waters. Due to the ever-increasing sea transport and thus the number of ships in this service, the importance of the services offered by VTS has increased more in the course of time. Despite the advanced technological infrastructure and the improved VTS, however the safety of sea traffic is still mostly based on human factor. Although in the relevant literature there have been some studies revealing the effects of VTS on the safety, there has been no research on the service quality. The aim of this study is, considering the views of ship masters, to analyze the effects of the VTS service quality perceptions on the corporate image. The process of this study has been two-fold; developing a VTS service quality scale for shipmasters and the required through this scale measuring the perceptions of the service quality of the Turkish Straits Vessel Traffic Services (TSVTS) and the effects of these perceptions on the corporate image. Developing the model of study has been based on the relevant literature, and SERVQUAL model has been chosen. Firstly, in analyzing the perceptions, 20 experts from four diverse field have been interviewed and a pilot study has been carried out with shipmasters (n=72). The questionnaire, which has been conducted through shipmasters (n=192) who have used the Turkish Straits, has comprised 25 statements constituting two dimensions as “Perceived Service Quality (PSQ)” and “Corporate Image (CI)”. The results reveal that there is a strong interrelationship between the perceptions of the TSVTS’ service quality and corporate image for shipmasters. The VTS-PSQ scale develop through this research is expected to contribute to evaluating the VTS service quality and thus to the safe navigation through the relevant sea ways.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".