Authenticity in TikTok: How content creator popularity and brand size influence consumer engagement with sponsored user‐generated content
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
Abstract This research examines how sponsored user‐generated content influences consumer engagement on TikTok across three studies. In the first study, we demonstrate that when content creators endorse brands through sponsorship, they are perceived as less authentic. This perceived lack of authenticity, in turn, reduces consumer engagement with brands. In the second study, we show that the influence of sponsorship on consumer engagement is moderated by the content creator's popularity, as reflected by their follower count. Specifically, the negative effect of sponsorship on consumer engagement is observed only among popular creators with large followings, while less popular creators do not experience the same negative impact. In the third study, we show that for popular creators, sponsorship can enhance consumer engagement when the endorsed brand is perceived as small, compared to when it is perceived as large. Together, these findings extend our theoretical understanding of how sponsored user‐generated content shapes consumer engagement on TikTok. Additionally, our research provides valuable insights for brand managers aiming to develop effective digital marketing strategies and for content creators looking to optimize engagement with their audience.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it