Purchase Intention of Counterfeit Products: The Role of Subjective Norm
Bibliographic record
Abstract
The purpose of the research is to test the purchase intention difference based on the subjective norm role and relationship of subjective norms. The research was done by testing the purchase intention difference when the subjective norm role was high and when the subjective norm was low. The sample in this research was the executive women that had knowledge about counterfeit bags. The respondents who were successful to be collected in the research were 86 executive women respondents in Yogyakarta-Indonesia. The data analysis in this research uses the difference testing helped by Analysis of Variance. The data analysis results show that there is the purchase intention difference based on the highness and the lowness of the subjective norm role of the consumers to use the counterfeit products. There are positive corellation between subjective norm and purchase intention. Consumers with the high subjective norm role have the low intention to buy and consumers with the low subjective norm role have the high intention to buy.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".