The Influence of Beauty Influencers, Electronic Word of Mouth, and Brand Image on Purchase Intention of Skintific Products in Bandar Lampung
Bibliographic record
Abstract
This study investigates the influence of Beauty Influencers, Electronic Word of Mouth, and Brand Image on the Purchase Intention of Skintific products in Bandar Lampung. Skintific, a Canadian skincare brand, has gained popularity in Indonesia, especially among younger consumers, through social media-based digital marketing. The research employs a quantitative method with a descriptive approach. The population includes individuals in Bandar Lampung who are aware of and intend to purchase Skintific products. A purposive sampling technique was used to obtain 126 respondents. Data were gathered using a Likert-scale questionnaire and analyzed through multiple linear regression with SPSS software. The findings reveal that Beauty Influencer, Electronic Word of, and Brand Image each have a positive and significant effect on Purchase Intention, both partially and simultaneously. Among these variables, Electronic Word of has the most dominant influence, followed by Beauty Influencer and Brand Image. The coefficient of determination (R²) is 0.512, indicating that the three variables explain 51.2% of the variance in purchase intention, with the remaining 48.8% influenced by other factors. These results highlight the effectiveness of digital marketing through credible influencers, positive online reviews, and a strong brand image in driving consumer purchase intention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".