ŞEHİR İÇİ DENİZYOLU ULAŞIMINDA MÜŞTERİLERİN HİZMET ALGISI VE MEMNUNİYETİ: BİR UYGULAMA - CUSTOMER’S PERCEPTION OF SERVICE AND SATISFACTION IN MARITIME TRANSPORT IN THE CITY: AN APPLICATION
Bibliographic record
Abstract
Özet Taşımacılık ve ulaştırma sektörü lojistiğin önemli bir faaliyet alanıdır. Teknolojide ve hizmet sektöründe yaşanan gelişmeler bu sektörü de her yönüyle değiştirmiştir. Müşteri memnuniyetini sağlamak rekabet gücünü arttırmada en etkili faktörlerden biridir. Günümüz çağdaş rekabet dünyasında ulaştırma sektöründe de büyük bir çekişme mevcuttur. Bu gelişmeleri göz önünde bulunduran işletmeler, insanların hizmet algısı-memnuniyet ilişkisine önem vermeye başlamıştır. İşletmeler, müşteri memnuniyetini sağlamak için müşterilerinin alışkanlıklarını, beklentilerini, algılarını ve sosyo-demografik özelliklerini iyi tespit etmeye çalışmaktadırlar. Yapılan bu çalışmanın literatür bölümünde lojistik, taşımacılık, şehir içi deniz yolu taşımacılığı, hizmet kavramı ve müşteri memnuniyeti üzerinde durulmuş ve İstanbul Şehir Hatları hakkında genel bilgi verilmiştir. Araştırmanın uygulama kısmında ise İstanbul Şehir Hatlarını kullanan 396 müşterinin, sunulan hizmete ilişkin algısı ve memnuniyetleri incelenmiştir. Veri toplama yöntemi olarak anket kullanılmış, anket verileri frekans analizi, güvenilirlik analizi, faktör analizi, t-testi, Anova, korelasyon analizi ve regresyon analizine tabi tutularak araştırmadan elde edilen sonuçlar yorumlanmıştır. Abstract The transportation sector is one of the important activity field in logistics. Developments in technology and the service sector has changed every aspect of this industry. Customer satisfaction is one of the most important factor in increasing competitiveness. In today’s modern competitive world, there is also a great contention in the transportation sector. Considering these developments, businesses began to give attention to the relationship between the service perception and satisfaction of people. Business tries to identify customers’ habits, expectations, perceptions and socio-demographic characteristics to ensure customer satisfaction. In this study, we focused on logistics, transportation, maritime transportation in the city, the concept of service, and customer satisfaction in literature. Provided general information about Istanbul Sehir Hatlari. In the application part of the study, 396 customers who use Istanbul Sehir Hatları participated and the data collection method used is questionnaire. (After questionnaires recovered, frequence analysis, reliability analysis, factor analysis, t-tests, Anova, corelation analysis and regression analysis were used respectively). The results and findings of the research were examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".