Perceived Value and Purchase Intention Among Young Adults Perfume Consumers: A Mixed-Method Exploration
Bibliographic record
Abstract
Background: The cosmetics industry including perfume was one of the industries that contributed to the economy in Indonesia (3.83% in the first quarter of 2023). This made the perfume industry a worthy field to discuss. Purpose: The objective of this study is to see the influence of dimensions of perceived value on the intention to purchase perfume X in young adults. Design/methodology/approach: The research design used was a mixed method. The online survey was conducted with 217 early adulthood (18-25 years). Quantitative data analysis used PLS SEM. Qualitative data was obtained by interviewing 24 respondentsFinding/Result: In study 1, it was found that social and emotional perceived value have an influence on the intention to buy product X. Based on study 2, it was found that the emergence of certain memories or nostalgia and the influence of friends and praise from people closest to them were important. Conclusion: In the context of teenagers and non-luxury perfumes, emotional and social perceived value are important because they will be related to purchase intentions. Originality/value (State of the art): In this study, it was found that social and emotional perceived value are important things related to purchase intention, while quality and price have no influence. Keywords: perceived value, intention to purchase, perfume industry, early adulthood, mixed method
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".