Importance-performance analysis of port’s services quality form perspective of containerized liner shipping
Bibliographic record
Abstract
One of the important issues in port services is to evaluate the performance of the services. Without evaluating port services and their related components, these services cannot be considered desirable and its quality cannot be enhanced. However, the evaluation and the quality assurance of port services should be accomplished based on a scientific framework and a coherent framework to have desirable results. The importance-performance analysis model is an appropriate framework where each component is evaluated in terms of two dimensions of importance and performance. This study performs performance-importance analysis of ports' services quality form perspective of containerized liner shipping in the Imam Khomeini port. In this exploratory study, 150 shipping lines experts are chosen, randomly in 2012. The study identifies 28 components of quality in port services and shipping lines' experts are requested to evaluate these components in terms of two dimensions of importance and performance. Results reveal that there is a gap between the importance and performance of all port services components except three components of 6, 19 and 24. In addition, the results indicate that Importance-Performance Analysis (IPA) model is capable of evaluating and assuring quality in port services and can precisely identify the strengths and weaknesses of the seaport system and provide guidance for strategy formulation for quality improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".