The effect of social media influencers’ characteristics on consumer intention and attitude toward Keto products purchase intention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media influencers have become a more effective modern marketing approach used by businesses to influence consumers' intention and attitude. This study explores this influence by involving several factors of influencer’s characteristics on both consumers’ attitude and intention. Also, a moderation role of vloggers as a new emerging marketing tool is also examined in this research. To conduct this research and achieve its key objective, the study uses a quantitative research method to collect data from TikTok users which has also become a more worldwide favorable web device for short videos. PLS-SEM method is conducted in the phase of analysis and the results show a significant influence of the hypothesized research model except the influence of source relatability on consumer attitude and the moderating role of vloggers on consumer intention. The research findings provided unsurprisingly implications and supported the existing related literature in this field but contribute to cover the research knowledge gap through the integrated new model including numerous variables that have not been examined previously together in a unique framework.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it