The role of service quality, customer perceived value, and trust in enhancing customer satisfaction of expedition service
Bibliographic record
Abstract
The swift growth of online commerce has significantly impacted the expedition service industry. As online shopping becomes increasingly prevalent, reliable shipping service has become crucial. Therefore, this study aims to determine the correlation between service quality, customer perceived value, and trust, as well as their impact on customer satisfaction within the expedition service industry. A survey was carried out through various messaging platforms, such as WhatsApp and Telegram, to reach users. Data were then obtained from 165 respondents, who were users of the 8 most popular expedition services in Indonesia (including J&T, JNE, SiCepat, Ninja Express, SAP, and others), followed by PLS-SEM analysis. The results showed that the three independent variables positively influenced customer satisfaction. In addition, trust acted as a mediator, indirectly affecting both service quality and customer perceived value. These results are expected to serve as a foundation for developing more efficient and contextually relevant strategies for expedition companies in the future.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".