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.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".