Customer Expectation, Satisfaction and Loyalty Relationship in Turkish Airline Industry
Bibliographic record
Abstract
In recent years, the development rate of aviation industry in Turkey has ascended faster than the rate of the worldaviation industry. The number of airlines has been increasing with the supports of Turkish government to vitalizethe aviation sector. Therefore, for airline companies, understanding passenger expectations, satisfaction andloyalty relationship has become very important because of tough competition in the industry.The main objective of this study is to find therelationship among the above mentioned variables in Turkishairline industry. As data collecting method in the study, 5-point Likert type self-report questionnaire includingdemographic variables was used. The survey was conducted in June and July 2012 with voluntary participationof the passengers waiting in both the domestic and international lines’ areas in four main Turkish airports. Thedata was collected from 421 domestic flight passengers and 400 for international flight passengers. To analyzethe data, structural equation modeling was applied.The results showed that reliability and facilities had a significant positive effecton customer satisfaction. Inaddition customer satisfaction was found to be a significant determinant of customer loyalty. Based on thefindings, some suggestions for airline management were made and also study limitations were discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".