Factors affecting consumer intentions and decisions to purchase OCOP products: Exploring the role of pride in local specialties
Bibliographic record
Abstract
The purpose of the study is to identify factors affecting consumers' intention and decision to purchase OCOP products as well as to test the impact of intention to purchase OCOP products on Vietnamese consumers' decision to purchase OCOP products through a case study in Thanh Hoa province. This study uses a combination of qualitative and quantitative research methods. Qualitative research aims to test the reasonableness of each scale and screen observed variables. Quantitative research methods are used through the collection and processing of data from 250 consumers who have purchased OCOP products. Data are collected, processed and analyzed using PLS-SEM software. Based on the use of TAM consumer behavior theory and the development of 4P marketing elements to build a research model. The study added the fifth P, Pride in Local Specialties, as a new factor to match the characteristics of OCOP products. The results of the study showed that the factors: (1) Pride in Local Specialties; (2) Product Awareness; (3) Price Perception; (4) Convenience in Shopping and (5) Product Communication all have a positive impact on consumers' intention to buy OCOP products. The results of the study also showed that the intention to choose OCOP products (INT) has a great impact on consumers' decision to choose OCOP products (DEC).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".