Logistics digitalization on import document activities: Electronic delivery orders and service quality in maritime ports
Bibliographic record
Abstract
Significant changes have occurred in the business world, with one of the most notable being the transformation of technology into digitalization. An example of this digitalization in the logistics sector is the implementation of electronic delivery orders (E-DO), aimed at enhancing convenience and efficiency in logistics activities. The objective of this research was to analyze the extent of influence that electronic delivery orders, logistics digitalization, and logistics service quality exerted on import document activities at Semarang port. Additionally, this study aimed to examine the role of logistics service quality as a mediating variable in the relationship between E-DO and logistics digitalization concerning import document activities. The research employed quantitative research methods, with a sample of 173 individuals selected through random sampling techniques, primarily comprising employees working in the logistics operations division and the export and import documentation division of shipping companies located in Semarang port. Data were collected through a questionnaire utilizing a Likert scale ranging from 1 to 7. Subsequently, the data were analyzed using SmartPLS 4 software to determine the relationships between variables. The findings of this research revealed that Electronic Delivery Orders (E-DO) had a significant influence on the quality of logistics services, although E-DO did not significantly impact import document activities. Furthermore, logistics digitalization was found to significantly enhance the quality of logistics services but did not exert a noteworthy influence on import document activities. The quality of logistics services was identified as having a significant effect on import document activities. Additionally, logistics service quality was recognized as a mediating factor in the relationship between electronic delivery orders and logistics digitalization concerning import document activities, with a substantial influence.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".