Exploratory analysis of natural cosmetic products purchase intention: Evidence from Jakarta, Indonesia
Bibliographic record
Abstract
In the COVID-19 era, green consumption has risen into a global trend, leading beauty products to be more environmentally friendly to satisfy these new consumers’ needs. However, not every natural beauty brand, especially natural cosmetic products, survived in the market. This study aimed to examine the cause and effect of a phenomenon of storytelling marketing strategies on the public's purchase intention for cosmetic products made from natural ingredients. The literature study showed that the relationship between storytelling and purchase intention is somewhat inconsistent. As a countermeasure and study gap, this study implemented product innovation as a mediating variable. This research was carried out quantitatively in Jakarta, Indonesia. Data used in this study was primarily obtained through questionnaires in 2021 and judgement sampling of 200 respondents. The data were tested using Structural Equation Model Partial Least Square (SEM-PLS) models’ technique on SmartPLS 3.0. The results showed that storytelling positively increases natural cosmetic purchase intention through product innovation.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".