Critical Factors of Total Logistics Cost: A Survey of Vietnam-Based Logistics Service Providers
Bibliographic record
Abstract
Since 1995, the integration of Vietnam into regional and global trade agreements, for example ASEAN, WTO and recent free trade agreements (FTA) with South Korea, Japan, EU has boosted the country’s import export volume tremendously. The ASEAN Economic Community (AEC) formed in late 2015 creates not only opportunities but also challenges for the Vietnamese economy in general and Vietnam's logistics sector in particular enter internationally. In addition, CPTPP (CPTPP - Comprehensive and Progressive Agreement for Trans-Pacific Partnership) and EVFTA (EVFTA- EU-Vietnam Free Trade Agreement) will contribute to expanding markets and increasing exports to 11 member countries (CPTPP, also known as TPP11) as well as 27 European Union member countries (EU). Especially when joining in the global organizations, the price of goods is always a decisive factor in the issue of competition with members in the organizations and outside the organizations. Countries always want to have products with high value but competitive prices to survive and create profits with those advantages and challenges.Logistics costs are considered as one of the factors causing high product’s price, especially Vietnam's leading import-export products, which contributes to reduce the competitive advantage of Vietnamese products in the international market. Therefore, there have been some previous studies to find out the factors that increase logistics costs in order to find solutions to reduce Vietnam's logistics costs, increase product value and increase competitiveness advantages. Many concerns about improving the logistics efficiency and effectiveness of these commodity chains in Vietnam recently have urged for more in-depth studies and academic researches about this topic. In this paper, we are going to conduct an empirical research about the critical factors on logistics cost by sending out surveys to Vietnam-based logistics service providers to interview. The SPSS software version 20 was used to check the suitability of six critical factors and their 41 elements and apply the dataset to build up the Analysis of Variance (ANOVA) model. Besides that, the authors also used in-depth interview method on different research subjects including: cargo owners, logistics service providers and associations for listening their difficulties related to logistics factors and finding out the causes for increasing logistics cost. Base on analyzing critical factors and opinions of enterprises, the authors want to suggest some solutions for decreasing logistics cost in Vietnam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".