Human or virtual: How influencer type shapes brand attitudes
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 has grown, firms have increasingly sought endorsements from social media influencers rather than traditional celebrity endorsements. Technological advancements in computer-generated imagery have led to the emergence of a particular new type of social media influencer: virtual influencers. Virtual influencers offer advantages over human influencers because they have no physical limitations and their images are more easily controlled. It remains to be seen, however, whether virtual influencers can be as effective as human influencers in generating a positive brand attitude. Five experimental studies (N = 1,734) reveal that virtual influencers are not as effective as their human counterparts. The underlying process driving this effect is the perceived lack of credibility of virtual influencers compared to their human counterparts, which, in turn, leads to a less positive attitude toward the brands that they endorse. This research, however, identifies a boundary condition: when virtual influencers use rational language (rather than emotional language) in their endorsements, the effect of influencer type on credibility perceptions of the influencers and attitude toward brands is eliminated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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