Factors affecting consumer’s bargaining behavior: The case of fashionable clothing
Bibliographic record
Abstract
Shopping is one of the current trends of the Vietnamese. According to Nielsen’s research results about consumer confidence in the fourth quarter of 2017, more than half of Vietnamese people (51%) use their spare money to buy new clothes at modern business models such as supermarkets, shopping centers, or at very traditional models like street vendors and wet markets where haggling (also known as bargaining) is considered as a common habit for Vietnamese. This is due to business characteristics from a very long time ago in Viet Nam. The bargaining behavior is not so hard to recognize in shopping. Even now, a large part of foreign tourists is familiar with the bargaining culture. This study aimed to discover bargaining behavior, the factors affecting such a behavior in buying fashionable clothing of the consumers in Ho Chi Minh City, and to consider whether the differences in bargaining behavior exist among different groups of gender, age, and income. The research was conducted using mixed methods concluding qualitative research (in-depth interview and focus group) and quantitative one (survey). The results showed that Attitude towards bargaining, Perceived behavioral control, Interest in bargaining affect Consumer’s bargaining behavior when buying fashionable clothes. Results were validated in Ho Chi Minh City context, and some conclusions were also presented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".