Digitalization and logistics service quality: Evidence from Indonesia national shipping companies
Bibliographic record
Abstract
The research aimed to know the impact of electronic delivery orders and logistics digitalization on the import document activities mediated by logistics service quality in the national shipping lines, especially the members of Regional Container Lines. Several logistics companies in Indonesia have now begun to utilize digital technology supported by the national logistics ecosystem platform to advance logistics activities, to assist logistics processes in all sectors, including the industrial sector. Shipping, especially by sea shipping. One of the research problems, among others, is that the TSJ/T3 terminal still uses manual delivery orders. The research used the path analysis method and Sobel test with a random sample of 102 PT Bhum Mulia Prima customers. The result of the study showed that electronic delivery orders and logistics digitalization partially contribute to the import document activities through logistics service quality. Furthermore, logistics service quality as the intervening variable strengthened the contribution of an electronic delivery order to the import document activities. Testing is also needed by comparing digital systems between the shipping companies that still do not use manual documents and those that have used online processes in import activities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".