The effect of social media influencers’ characteristics on consumer intention and attitude toward Keto products purchase intention
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".