A Study on the Impact of Pricing on Cosmetic Products among Generation Z with special Reference to Faces Canada
Bibliographic record
Abstract
I. ABSTRACT Due to shifting consumer tastes, growing brand recognition, and the growing impact of digital media, the cosmetics sector has grown rapidly in recent years. Pricing is one of the key components of the marketing mix that influences customer purchasing decisions, especially for Generation Z, who are very value-oriented and price-conscious. The purpose of this study is to investigate how Generation Z is affected by the cost of cosmetics, with a focus on Faces Canada. The study focuses on the impact of important pricing factors on purchase intention, including affordability, price worthiness, price–quality balance, and promotional pricing. A standardized questionnaire based on a five-point Likert scale is used to gather primary data from Gen Z consumers using a descriptive research design. The data is analyzed using statistical techniques such chi-square analysis, mean score ranking, and percentage analysis. The study's conclusions could shed light on how price tactics affect Gen Z consumers' purchasing decisions and assist cosmetic companies, including Faces Canada, in creating sensible pricing strategies. By providing brand-specific insights into pricing and customer behavior in the Indian cosmetic sector, the study adds to the body of scholarly knowledge. Key Words – GenZ, Cosmetics, Faces Canada, Pricing
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".