Factors impact on purchasing behavior of protective and marine coatings in Viet Nam market
Bibliographic record
Abstract
Vietnam's economy is developing rapidly, it leads to the strong development of industries, including the marine and manufacturing industry. This has caused an explosion of demand for the protective coatings industry, especially the industrial and marine coatings. However, in Vietnam, although there have been many studies on the purchasing behavior of customers in the decorative paint segment, there have not been many studies on the buying behavior of industrial and marine paints. Therefore, this quantitative study aims to clarify the factors affecting the purchasing behavior of customers in the industrial and marine coatings sectors. Data is gathered from a survey with the participation of 300 customers who have been using industrial and marine paint products. The analysis results show that there are four factors that affect the purchasing behavior of customers, which are product quality, discount promotion, selling price and availability. In which, product quality is the factor that has the strongest impact on the purchasing behavior of customers in this field, followed by discount promotion, availability and selling price. While brand image factor has no effect on customers’ purchasing behavior. From the research results, a number of implications are proposed to suppliers of industrial and marine paint companies make appropriate strategic adjustment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".