The Influence of Price Perception and Product Quality on Potential Consumers' Purchase Interest in Skintific Products in the Tiktok Shop Marketplace
Bibliographic record
Abstract
In Indonesia, there are many online stores. TikTok shop is one of these online stores. One of the features in the TikTok application that allows users to sell and buy goods from TikTok is TikTok shop. TikTok shop has become one of the e-commerce platforms that is widely used lately because the prices are cheaper than other e-commerce.One of the beauty brands that has caught the attention of TikTok users is Skintific. Skintific is a beauty product from Canada and is also one of the new products to enter the Indonesian market, from the Skintific product itself, it prioritizes skin health and is able to overcome skin problems. Skintific products are traded online such as Instagram, Shopee, and most recently through the TikTok shop feature.Perception of price and product quality has a significant effect on the buying interest of potential consumers of Skintific products on TikTok Shop. Consumers tend to be more interested in buying when they consider the price offered to be reasonable and the product quality is high. Therefore, a marketing strategy that focuses on increasing positive perceptions of price and quality is very important to attract buying interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".