A Study on Factors of the E-Purchasing Product Intention toward a Dynamic Vietnamese Internet Shopping
Bibliographic record
Abstract
Vietnam in recently years is considered to be a country with rapid development of the e-commerce with a lot of online shopping sites e.g., Shoppee, Lazada, Tiki, and Adayroi. These sites provide a high variety of products from household devices to motorbike. However, Vietnamese people are still aware of risk in doing e-purchasing. They often go to tradition outlets to buy products. This study aims to investigate the antecedents of customers’ intentions for the case of using Internet as a new way to purchase products in Vietnam. A research model is developed relied on the integration of theory of Technology Accepted Model and Perceived risk theory to predict customer intentions to online shopping. Five factors are included in this research: Perceived Usefulness, Perceived Ease of use, Perceived Risk, Customer’s Attitude and Customer’s intention. A convenience sample of 336 respondents was collected through online and offline survey. The results of this study support that exist a positive relationship between Perceived ease of use and Perceived usefulness. Attitude, in turn is positive affected by Perceived ease of use and Perceived usefulness in which Perceived ease of use has stronger influences. While perceived risk negative impacts customers’ intention, perceived risk and Perceived usefulness, on the other hand, positive influences customers’ intention. All hypotheses are supported. It is evident that online shoppers intention are evaluated mostly based on their attitude, perceived usefulness, perceived ease of use and perceived risk.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".