Authenticity in TikTok: How content creator popularity and brand size influence consumer engagement with sponsored user‐generated content
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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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 | 0.015 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".