Influence of Knowledge and Attitudes About Prohibited Cosmetic Substances on Generation Z's Online Purchases in Lampang
Bibliographic record
Abstract
Background and Aims: This study investigates how knowledge of prohibited substances in cosmetics and attitudes toward these substances influence the purchasing decisions of Generation Z consumers on online platforms in Lampang Province. Online platforms have become a dominant marketplace for cosmetics, but concerns about harmful ingredients such as mercury and hydroquinone have raised awareness among consumers. Understanding these factors can provide insights into consumer behavior and support safer purchasing practices. Lampang Province represents an emerging market with increasing online cosmetic purchases among Generation Z." Methodology: The study employed a cross-sectional analytical research design with 390 Generation Z participants residing in Lampang Province. Data were collected through a structured questionnaire covering demographic information, knowledge of prohibited substances in cosmetics, attitudes toward these substances, and purchasing decision behavior. The data were analyzed using descriptive statistics and multiple regression analysis. Results: The findings reveal that both knowledge and attitudes toward prohibited substances significantly influence purchasing decisions. Consumers with a higher knowledge of harmful substances tend to avoid products containing these ingredients. Similarly, negative attitudes toward prohibited substances encourage safer purchasing behavior. Regression analysis demonstrated a strong positive relationship between these variables and purchasing decisions, with attitudes having a slightly greater impact than knowledge. Conclusion: The study concludes that knowledge and attitudes about prohibited substances in cosmetics play crucial roles in shaping purchasing decisions among Generation Z consumers in Lampang Province. These findings highlight the importance of education and transparent product labeling in promoting safer online purchasing behaviors.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".