Social media influencer endorsement: the conditional effects of product attribute description in sponsored influencer videos
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
As social media influencer endorsement gains significance in marketing communication, an increasing number of influencers have started to incorporate product information into their sponsored content. This study examines the effectiveness of product attribute description in the context of sponsored videos. Using a field dataset of 598 sponsored videos, we demonstrate that influencers’ use of product attribute description as an endorsement strategy has a negative impact on video engagement, and this effect is stronger for trial versus awareness campaigns. However, the negative impact is reversed to a positive one when product attribute description is employed for utilitarian products but not for hedonic products. These results reveal that the effectiveness of product attribute description depends on the nature of the product and the campaign objectives. Overall, this study contributes to the understanding of influencer marketing effectiveness and sheds light on the nuances of endorsement strategies. Practical implications on how to optimise endorsement effectiveness and video performance are discussed.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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