Influencing Beauty Perceptions: Role of TikTok Influencer Information Adoption in Shaping Consumer Views of Cosmetic Product Quality
Bibliographic record
Abstract
This research examines how influencer information spreads and is accepted by consumers, focusing on a Tunisian sample of social media users, and how these effects percolate into consumers’ perception of the quality of cosmetic products. Drawing on the Information Adoption Model (IAM), this study develops a conceptual framework adapted to the social media landscape, particularly the TikTok platform. To test this framework, we conducted a survey targeting 285 consumers using a non-random sampling frame, primarily through Facebook and Instagram. The findings suggest that consumers perceive influencer information as useful when they believe it is credible and of high quality. Interestingly, while high-quality information tends to lead to influencer information adoption, credibility alone does not guarantee adoption. Additionally, our study emphasizes the role of influencer information usefulness in driving its adoption. One notable discovery is the link between influencer information adoption and consumers’ perceptions of the quality of cosmetic products. However, this correlation does not hold equally for both genders, thus suggesting a moderation effect between gender and influencer information processing in this context.
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 | 0.004 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".