Factors affecting marine economic development: evidence from central coastal provinces, Vietnam
Bibliographic record
Abstract
The Central Coast of Vietnam, spanning from Thanh Hoa province to Binh Thuan province, holds a highly significant political and economic position, with the marine economy making substantial contributions to the existence and development of the region. This study aims to identify the factors influencing marine economic development (MED), evaluate the extent of influence of each factor, and determine whether security and coastal defense (SCD) plays an intermediary role in the development of the marine economy in the Central Coast provinces of Vietnam. The research conducted a typical survey of 268 fishermen engaged in marine economic activities, including aquaculture and the exploitation of aquatic resources, across the coastal provinces of the Central Coast of Vietnam. The collected data was analyzed using the structural equation modeling method through PLS-SEM software. The results of the study indicate that six factors have a direct and positive impact on MED. These factors, ranked by decreasing levels of influence, are: SCD (β = 0.412), Policy and Management (PM) (β = 0.329), Human Resources (HR) and Marine Infrastructure (MI) (both β = 0.268), Marine Resources (MR) (β = 0.204), and the lowest, Technology and Protection of Marine Environment (TPME) (β = 0.152). Additionally, the findings reveal that the three factors are HR, PM, and MI positively influence the SCD variable, with the levels of influence ranked as follows: HR (β = 0.272), PM (β = 0.224), and MI (β = 0.166). Moreover, at a significance level of 5%, all independent variables (HR, PM, and MI) exhibit statistically significant indirect relationships with MED through the SCD variable, confirming that SCD serves as an intermediary in the relationship with MED. Based on these findings, the study proposes managerial implications to support the development of the marine economy in the Central Coast provinces of Vietnam.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".